Methods in Enzymology 496 - Research on Nitrification and Related Processes, Part 2

METHODS IN ENZYMOLOGY Editors-in-Chief JOHN N. ABELSON AND MELVIN I. SIMON Division of Biology California Institute of ...

Author: Martin G. Klotz | Lisa Y. Stein

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METHODS IN ENZYMOLOGY Editors-in-Chief

JOHN N. ABELSON AND MELVIN I. SIMON Division of Biology California Institute of Technology Pasadena, California Founding Editors

SIDNEY P. COLOWICK AND NATHAN O. KAPLAN

Academic Press is an imprint of Elsevier 525 B Street, Suite 1900, San Diego, CA 92101-4495, USA 225 Wyman Street, Waltham, MA 02451, USA 32 Jamestown Road, London NW1 7BY, UK First edition 2011 Copyright # 2011, Elsevier Inc. All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: permissions@ elsevier.com. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made For information on all Academic Press publications visit our website at elsevierdirect.com ISBN: 978-0-12-386489-5 ISSN: 0076-6879 Printed and bound in United States of America 11 12 13 14 10 9 8 7 6 5 4 3 2 1

CONTRIBUTORS

Rita Bartossek Centre for Geobiology, Department of Biology, University of Bergen, Bergen, Norway Nicholas Beckloff Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Micol Bellucci School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, United Kingdom Peter J. Bottomley Department of Crop and Soil Science, and Department of Microbiology, Oregon State University, Corvallis, Oregon, USA N. J. Bouskill Department of Geosciences, Princeton University, Princeton, New Jersey, USA Martin E. Brummell Department of Soil Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada Emily O. Burton Department of Soil Science, University of Wisconsin-Madison, Madison, Wisconsin, USA Mark Campbell Department of Biology, University of Louisville, Louisville, Kentucky, USA Huiluo Cao School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, PR China Patrick S. G. Chain Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA

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Contributors

Thomas P. Curtis School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, United Kingdom Holger Daims Department of Microbial Ecology, Ecology Center, University of Vienna, Vienna, Austria Hajnalka E. Daligault Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Hongyue Dang State Key Laboratory of Heavy Oil Processing and Centre for Bioengineering and Biotechnology, China University of Petroleum (East China), Qingdao, PR China Karen W. Davenport Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA M. J. Dempsey Division of Biology and Conservation Ecology School of Science and the Environment, Faculty of Science and Engineering, Manchester Metropolitan University, and Advanced Bioprocess Development Ltd., c/o MMU, Manchester, UK J. Chris Detter Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Oliver Einsle Lehrstuhl fu¨r Biochemie, Institut fu¨r organische Chemie und Biochemie, Albert-Ludwigs-Universita¨t Freiburg, Freiburg, Germany Tracey A. K. Freitas Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Cheryl D. Gleasner Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA James E. Graham Department of Biology, and Department of Microbiology and Immunology, University of Louisville, Louisville, Kentucky, USA

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Contributors

Lance D. Green Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Ji-Dong Gu School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, PR China Cliff S. Han Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA William J. Hickey Department of Soil Science, and Molecular and Environmental Toxicology Program, University of Wisconsin-Madison, Madison, Wisconsin, USA Tomonori Kindaichi Graduate School of Engineering, Higashihiroshima, Japan

Hiroshima

University,

Kagamiyama,

Martin G. Klotz Department of Biology, and Department of Microbiology and Immunology, University of Louisville, Louisville, Kentucky, USA Dorien M. Kool Department of Soil Quality, Wageningen University and Research Centre, Wageningen, The Netherlands Ellen G. Lauchnor School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, Oregon, USA Thomas J. Lawton Departments of Molecular Biosciences and of Chemistry, Northwestern University, Evanston, Illinois, USA Meng Li School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, PR China Chien-Chi Lo Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Willm Martens-Habbena Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA

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Contributors

Kim K. McMurry Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Linda J. Meincke Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA David D. Myrold Department of Crop and Soil Science, Oregon State University, Corvallis, Oregon, USA Graeme W. Nicol Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom Satoshi Okabe Division of Environmental Engineering, Graduate School of Engineering, Hokkaido University, Sapporo, Hokkaido, Japan Jennifer Pett-Ridge Chemical Sciences Division, Lawrence Livermore National Laboratory, California, USA James I. Prosser Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom Tyler S. Radniecki School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, Oregon, USA Laila J. Reigstad Centre for Geobiology, Department of Biology, University of Bergen, Bergen, Norway Krista G. Reitenga Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Amy C. Rosenzweig Departments of Molecular Biosciences and of Chemistry, Northwestern University, Evanston, Illinois, USA Hisashi Satoh Division of Environmental Engineering, Graduate School of Engineering, Hokkaido University, Sapporo, Hokkaido, Japan

Contributors

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Christa Schleper Centre for Geobiology, Department of Biology, University of Bergen, Bergen, Norway, and Vienna Ecology Centre, Department of Genetics in Ecology, University of Vienna, Vienna, Austria Matthew B. Scholz Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Xiaohong Shen Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Steven D. Siciliano Department of Soil Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada Bongkeun Song Department of Biology and Marine Biology, University of North Carolina Wilmington, North Carolina, USA David A. Stahl Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA Shawn R. Starkenburg Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Craig R. Tobias Department of Marine Sciences, University of Connecticut, Groton, Connecticut, USA Jan Willem Van Groenigen Department of Soil Quality, Wageningen University and Research Centre, Wageningen, The Netherlands Michael Wagner Department of Microbial Ecology, Ecology Center, University of Vienna, Vienna, Austria Nicholas B. Wantland Department of Microbiology and Immunology, University of Louisville, Louisville, Kentucky, USA

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Contributors

B. B. Ward Department of Geosciences, Princeton University, Princeton, New Jersey, USA, and Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA Nicole Wrage Institute of Grassland Science, University of Go¨ttingen, Go¨ttingen, Germany Gary Xie Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA Ahmet Zeytun Genome Biology Group, Los Alamos National Laboratory, Los Alamos, New Mexico, and Microbial and Metagenomics Programs, Joint Genome Institute, Walnut Creek, California, USA

PREFACE

For well over 100 years, humans have learned how to alter the biogeochemistry of the Earth toward our own benefit. Nowhere is this trend clearer than in the nitrogen cycle where humans are now responsible for greater than 50% of the reactive-N (nitrogen) input to the biosphere, mostly in the form of chemically produced ammonium-based fertilizer for agricultural production. Humans have also harnessed the power of the N cycle to clean wastewater in more efficient ways and are using N-cycle microorganisms and their enzymes to monitor toxicity and contribute to pollutant remediation in a variety of industrial processes and ecosystems. Research on the transformation of reactive N in an environmental context has lagged behind similar research in the biomedical field. Until recently, this stagnation was largely due to missing instrumentation, tools, and molecular methods to describe and discriminate among the organisms and enzymes involved in transforming reactive N within a vast diversity of habitats. Due to several important breakthroughs over the past decade, the momentum has shifted and serious gains have been made in our understanding of how to measure and monitor reactive N transformations in nature. Through the application of improved methodologies and molecular tools, new groups of organisms including the anerobic ammonia-oxidizing bacteria (anammox Process: 1999; molecular verification: 2006) and the ammonia-oxidizing Thaumarchaea have been added to the growing list of microorganisms that carry out important, and perhaps ancient (anammox) to very recent (ammonia-oxidizing Thaumarchaea), steps in the conversion of reactive N. In large part, our discovery and understanding of environmentally relevant N-cycle organisms has accelerated due to a convergence with high-throughput genome sequencing and proteomics technologies. We now have an ever-increasing number of complete genome sequences to apply postgenomic tools for transcriptomic and proteomic analysis and were early adopters of metagenomics tools to better describe enriched or uncultivated microorganisms. The idea for developing a volume on “Research on Nitrification and Related Processes” came after a productive international meeting on the Nitrogen Cycle organized by the Agouron Institute in Scottsdale, Arizona, in October 2009 (http://agi.org/pdf/nmtg-abstracts/NMtgAbstractsforWeb. pdf), where we presented work on N-cycle evolution and the function of aerobic ammonia-oxidizing microorganisms. The Agouron Nitrogen meeting succeeded another significant event in the N-cycle community: the first xix

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International Conference on Nitrification (ICoN1) organized by the Nitrification Network (http://nitrificationnetwork.org/news%20and%20events. php#icon1) in Louisville, Kentucky, July 2009. We are now swiftly approaching the second meeting, ICoN2, which will be held in Nijmegen, the Netherlands, July 3–7, 2011. Both ICoN1 and ICoN2 were made possible by support from the U.S. National Science Foundation and the Gordon and Betty Moore Foundation. In addition, the University of Louisville supported ICoN1. Because of these international gatherings, we have become better informed as a community about the existing wealth of new approaches to gather and analyze data in N-cycle research, which led Martin to take on guest editorship for these two volumes 486 (Part A) and 496 (Part B) of Methods in Enzymology and Lisa to join as a guest editor of Part B to round out coverage on research areas on reactive N in environmental and application-based contexts. The chapters span the range of organisms and environments from basic to highly applied research areas. We believe that the readership will greatly benefit from both parts of this series, whether new or old to the field, as these chapters represent state-of-the-art techniques developed specifically to study reactive N transformations. These two parts would not have been possible without the generous sharing and enthusiasm of so many colleagues and friends inside and outside the Nitrification Network. We thank them all, and also Delsy Retchagar and Sujatha Thirugnanasambandam (Elsevier, Chennai, India) and Zoe Kruze (Elsevier, Oxford, UK), for their help and advice in steering both parts to a successful and timely outcome. We also gratefully acknowledge the external reviewers of chapters in volumes 486 and 496 for sharing their valuable time and skill to refine the words and ideas of the authors: Margarida Archer, Sharon Avrahami, Elizabeth Baggs, Do¨rte Becher, Dirk de Beer, J. Michael Beman, Anne Bernhard, Victoria Bertics, Annette Bollmann, Peter J. Bottomley, Karen L. Casciotti, Kartik Chandran, Ludmila Chistoserdova, Nabin Chowdhury, Hongyue Dang, Alan A. DiSpirito, Christopher A. Francis, Michael Y. Galperin, Peter R. Girguis, Ramesh Goel, Steven Hallam, Stephen C. Hart, Zhili He, James Hemp, Gerhard Herndl, Richard Higashi, Moritz Holtappels, Christopher K. Junium, Marlies Kampschreur, Boran Kartal, Jan Keltjens, Martin Ko¨nneke, Martin G. Klotz, Jessica Kozlowski, Gaute Lavik, Thomas Lawton, Ines Mandic-Mulec, Veronica Molina, J. Colin Murrell, Graeme Nicol, Jeanette M. Norton, Victoria J. Orphan, Andrew Pacheco, Ines Pereira, Tyler Radniecki, Andreas Schramm, Holly M. Simon, Markus Schmid, Martin Schmidt, Steven Siciliano, Bongkeun Song, David A. Stahl, Lisa Y. Stein, Marc Strous, Yuichi Suwa, Anne Taylor, Tamara Tikhonova, Hidetoshi Urakawa, Ste´phane Vuilleumier, Bess B. Ward, Chuanlun Zhang, and Xiang Zhang. LISA YAEL STEIN AND MARTIN GU¨NTER KLOTZ

METHODS IN ENZYMOLOGY

VOLUME I. Preparation and Assay of Enzymes Edited by SIDNEY P. COLOWICK AND NATHAN O. KAPLAN VOLUME II. Preparation and Assay of Enzymes Edited by SIDNEY P. COLOWICK AND NATHAN O. KAPLAN VOLUME III. Preparation and Assay of Substrates Edited by SIDNEY P. COLOWICK AND NATHAN O. KAPLAN VOLUME IV. Special Techniques for the Enzymologist Edited by SIDNEY P. COLOWICK AND NATHAN O. KAPLAN VOLUME V. Preparation and Assay of Enzymes Edited by SIDNEY P. COLOWICK AND NATHAN O. KAPLAN VOLUME VI. Preparation and Assay of Enzymes (Continued) Preparation and Assay of Substrates Special Techniques Edited by SIDNEY P. COLOWICK AND NATHAN O. KAPLAN VOLUME VII. Cumulative Subject Index Edited by SIDNEY P. COLOWICK AND NATHAN O. KAPLAN VOLUME VIII. Complex Carbohydrates Edited by ELIZABETH F. NEUFELD AND VICTOR GINSBURG VOLUME IX. Carbohydrate Metabolism Edited by WILLIS A. WOOD VOLUME X. Oxidation and Phosphorylation Edited by RONALD W. ESTABROOK AND MAYNARD E. PULLMAN VOLUME XI. Enzyme Structure Edited by C. H. W. HIRS VOLUME XII. Nucleic Acids (Parts A and B) Edited by LAWRENCE GROSSMAN AND KIVIE MOLDAVE VOLUME XIII. Citric Acid Cycle Edited by J. M. LOWENSTEIN VOLUME XIV. Lipids Edited by J. M. LOWENSTEIN VOLUME XV. Steroids and Terpenoids Edited by RAYMOND B. CLAYTON xxi

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VOLUME XVI. Fast Reactions Edited by KENNETH KUSTIN VOLUME XVII. Metabolism of Amino Acids and Amines (Parts A and B) Edited by HERBERT TABOR AND CELIA WHITE TABOR VOLUME XVIII. Vitamins and Coenzymes (Parts A, B, and C) Edited by DONALD B. MCCORMICK AND LEMUEL D. WRIGHT VOLUME XIX. Proteolytic Enzymes Edited by GERTRUDE E. PERLMANN AND LASZLO LORAND VOLUME XX. Nucleic Acids and Protein Synthesis (Part C) Edited by KIVIE MOLDAVE AND LAWRENCE GROSSMAN VOLUME XXI. Nucleic Acids (Part D) Edited by LAWRENCE GROSSMAN AND KIVIE MOLDAVE VOLUME XXII. Enzyme Purification and Related Techniques Edited by WILLIAM B. JAKOBY VOLUME XXIII. Photosynthesis (Part A) Edited by ANTHONY SAN PIETRO VOLUME XXIV. Photosynthesis and Nitrogen Fixation (Part B) Edited by ANTHONY SAN PIETRO VOLUME XXV. Enzyme Structure (Part B) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME XXVI. Enzyme Structure (Part C) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME XXVII. Enzyme Structure (Part D) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME XXVIII. Complex Carbohydrates (Part B) Edited by VICTOR GINSBURG VOLUME XXIX. Nucleic Acids and Protein Synthesis (Part E) Edited by LAWRENCE GROSSMAN AND KIVIE MOLDAVE VOLUME XXX. Nucleic Acids and Protein Synthesis (Part F) Edited by KIVIE MOLDAVE AND LAWRENCE GROSSMAN VOLUME XXXI. Biomembranes (Part A) Edited by SIDNEY FLEISCHER AND LESTER PACKER VOLUME XXXII. Biomembranes (Part B) Edited by SIDNEY FLEISCHER AND LESTER PACKER VOLUME XXXIII. Cumulative Subject Index Volumes I-XXX Edited by MARTHA G. DENNIS AND EDWARD A. DENNIS VOLUME XXXIV. Affinity Techniques (Enzyme Purification: Part B) Edited by WILLIAM B. JAKOBY AND MEIR WILCHEK


VOLUME XXXV. Lipids (Part B) Edited by JOHN M. LOWENSTEIN VOLUME XXXVI. Hormone Action (Part A: Steroid Hormones) Edited by BERT W. O’MALLEY AND JOEL G. HARDMAN VOLUME XXXVII. Hormone Action (Part B: Peptide Hormones) Edited by BERT W. O’MALLEY AND JOEL G. HARDMAN VOLUME XXXVIII. Hormone Action (Part C: Cyclic Nucleotides) Edited by JOEL G. HARDMAN AND BERT W. O’MALLEY VOLUME XXXIX. Hormone Action (Part D: Isolated Cells, Tissues, and Organ Systems) Edited by JOEL G. HARDMAN AND BERT W. O’MALLEY VOLUME XL. Hormone Action (Part E: Nuclear Structure and Function) Edited by BERT W. O’MALLEY AND JOEL G. HARDMAN VOLUME XLI. Carbohydrate Metabolism (Part B) Edited by W. A. WOOD VOLUME XLII. Carbohydrate Metabolism (Part C) Edited by W. A. WOOD VOLUME XLIII. Antibiotics Edited by JOHN H. HASH VOLUME XLIV. Immobilized Enzymes Edited by KLAUS MOSBACH VOLUME XLV. Proteolytic Enzymes (Part B) Edited by LASZLO LORAND VOLUME XLVI. Affinity Labeling Edited by WILLIAM B. JAKOBY AND MEIR WILCHEK VOLUME XLVII. Enzyme Structure (Part E) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME XLVIII. Enzyme Structure (Part F) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME XLIX. Enzyme Structure (Part G) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME L. Complex Carbohydrates (Part C) Edited by VICTOR GINSBURG VOLUME LI. Purine and Pyrimidine Nucleotide Metabolism Edited by PATRICIA A. HOFFEE AND MARY ELLEN JONES VOLUME LII. Biomembranes (Part C: Biological Oxidations) Edited by SIDNEY FLEISCHER AND LESTER PACKER

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VOLUME LIII. Biomembranes (Part D: Biological Oxidations) Edited by SIDNEY FLEISCHER AND LESTER PACKER VOLUME LIV. Biomembranes (Part E: Biological Oxidations) Edited by SIDNEY FLEISCHER AND LESTER PACKER VOLUME LV. Biomembranes (Part F: Bioenergetics) Edited by SIDNEY FLEISCHER AND LESTER PACKER VOLUME LVI. Biomembranes (Part G: Bioenergetics) Edited by SIDNEY FLEISCHER AND LESTER PACKER VOLUME LVII. Bioluminescence and Chemiluminescence Edited by MARLENE A. DELUCA VOLUME LVIII. Cell Culture Edited by WILLIAM B. JAKOBY AND IRA PASTAN VOLUME LIX. Nucleic Acids and Protein Synthesis (Part G) Edited by KIVIE MOLDAVE AND LAWRENCE GROSSMAN VOLUME LX. Nucleic Acids and Protein Synthesis (Part H) Edited by KIVIE MOLDAVE AND LAWRENCE GROSSMAN VOLUME 61. Enzyme Structure (Part H) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME 62. Vitamins and Coenzymes (Part D) Edited by DONALD B. MCCORMICK AND LEMUEL D. WRIGHT VOLUME 63. Enzyme Kinetics and Mechanism (Part A: Initial Rate and Inhibitor Methods) Edited by DANIEL L. PURICH VOLUME 64. Enzyme Kinetics and Mechanism (Part B: Isotopic Probes and Complex Enzyme Systems) Edited by DANIEL L. PURICH VOLUME 65. Nucleic Acids (Part I) Edited by LAWRENCE GROSSMAN AND KIVIE MOLDAVE VOLUME 66. Vitamins and Coenzymes (Part E) Edited by DONALD B. MCCORMICK AND LEMUEL D. WRIGHT VOLUME 67. Vitamins and Coenzymes (Part F) Edited by DONALD B. MCCORMICK AND LEMUEL D. WRIGHT VOLUME 68. Recombinant DNA Edited by RAY WU VOLUME 69. Photosynthesis and Nitrogen Fixation (Part C) Edited by ANTHONY SAN PIETRO VOLUME 70. Immunochemical Techniques (Part A) Edited by HELEN VAN VUNAKIS AND JOHN J. LANGONE


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VOLUME 71. Lipids (Part C) Edited by JOHN M. LOWENSTEIN VOLUME 72. Lipids (Part D) Edited by JOHN M. LOWENSTEIN VOLUME 73. Immunochemical Techniques (Part B) Edited by JOHN J. LANGONE AND HELEN VAN VUNAKIS VOLUME 74. Immunochemical Techniques (Part C) Edited by JOHN J. LANGONE AND HELEN VAN VUNAKIS VOLUME 75. Cumulative Subject Index Volumes XXXI, XXXII, XXXIV–LX Edited by EDWARD A. DENNIS AND MARTHA G. DENNIS VOLUME 76. Hemoglobins Edited by ERALDO ANTONINI, LUIGI ROSSI-BERNARDI, AND EMILIA CHIANCONE VOLUME 77. Detoxication and Drug Metabolism Edited by WILLIAM B. JAKOBY VOLUME 78. Interferons (Part A) Edited by SIDNEY PESTKA VOLUME 79. Interferons (Part B) Edited by SIDNEY PESTKA VOLUME 80. Proteolytic Enzymes (Part C) Edited by LASZLO LORAND VOLUME 81. Biomembranes (Part H: Visual Pigments and Purple Membranes, I) Edited by LESTER PACKER VOLUME 82. Structural and Contractile Proteins (Part A: Extracellular Matrix) Edited by LEON W. CUNNINGHAM AND DIXIE W. FREDERIKSEN VOLUME 83. Complex Carbohydrates (Part D) Edited by VICTOR GINSBURG VOLUME 84. Immunochemical Techniques (Part D: Selected Immunoassays) Edited by JOHN J. LANGONE AND HELEN VAN VUNAKIS VOLUME 85. Structural and Contractile Proteins (Part B: The Contractile Apparatus and the Cytoskeleton) Edited by DIXIE W. FREDERIKSEN AND LEON W. CUNNINGHAM VOLUME 86. Prostaglandins and Arachidonate Metabolites Edited by WILLIAM E. M. LANDS AND WILLIAM L. SMITH VOLUME 87. Enzyme Kinetics and Mechanism (Part C: Intermediates, Stereo-chemistry, and Rate Studies) Edited by DANIEL L. PURICH VOLUME 88. Biomembranes (Part I: Visual Pigments and Purple Membranes, II) Edited by LESTER PACKER

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VOLUME 89. Carbohydrate Metabolism (Part D) Edited by WILLIS A. WOOD VOLUME 90. Carbohydrate Metabolism (Part E) Edited by WILLIS A. WOOD VOLUME 91. Enzyme Structure (Part I) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME 92. Immunochemical Techniques (Part E: Monoclonal Antibodies and General Immunoassay Methods) Edited by JOHN J. LANGONE AND HELEN VAN VUNAKIS VOLUME 93. Immunochemical Techniques (Part F: Conventional Antibodies, Fc Receptors, and Cytotoxicity) Edited by JOHN J. LANGONE AND HELEN VAN VUNAKIS VOLUME 94. Polyamines Edited by HERBERT TABOR AND CELIA WHITE TABOR VOLUME 95. Cumulative Subject Index Volumes 61–74, 76–80 Edited by EDWARD A. DENNIS AND MARTHA G. DENNIS VOLUME 96. Biomembranes [Part J: Membrane Biogenesis: Assembly and Targeting (General Methods; Eukaryotes)] Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 97. Biomembranes [Part K: Membrane Biogenesis: Assembly and Targeting (Prokaryotes, Mitochondria, and Chloroplasts)] Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 98. Biomembranes (Part L: Membrane Biogenesis: Processing and Recycling) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 99. Hormone Action (Part F: Protein Kinases) Edited by JACKIE D. CORBIN AND JOEL G. HARDMAN VOLUME 100. Recombinant DNA (Part B) Edited by RAY WU, LAWRENCE GROSSMAN, AND KIVIE MOLDAVE VOLUME 101. Recombinant DNA (Part C) Edited by RAY WU, LAWRENCE GROSSMAN, AND KIVIE MOLDAVE VOLUME 102. Hormone Action (Part G: Calmodulin and Calcium-Binding Proteins) Edited by ANTHONY R. MEANS AND BERT W. O’MALLEY VOLUME 103. Hormone Action (Part H: Neuroendocrine Peptides) Edited by P. MICHAEL CONN VOLUME 104. Enzyme Purification and Related Techniques (Part C) Edited by WILLIAM B. JAKOBY


VOLUME 105. Oxygen Radicals in Biological Systems Edited by LESTER PACKER VOLUME 106. Posttranslational Modifications (Part A) Edited by FINN WOLD AND KIVIE MOLDAVE VOLUME 107. Posttranslational Modifications (Part B) Edited by FINN WOLD AND KIVIE MOLDAVE VOLUME 108. Immunochemical Techniques (Part G: Separation and Characterization of Lymphoid Cells) Edited by GIOVANNI DI SABATO, JOHN J. LANGONE, AND HELEN VAN VUNAKIS VOLUME 109. Hormone Action (Part I: Peptide Hormones) Edited by LUTZ BIRNBAUMER AND BERT W. O’MALLEY VOLUME 110. Steroids and Isoprenoids (Part A) Edited by JOHN H. LAW AND HANS C. RILLING VOLUME 111. Steroids and Isoprenoids (Part B) Edited by JOHN H. LAW AND HANS C. RILLING VOLUME 112. Drug and Enzyme Targeting (Part A) Edited by KENNETH J. WIDDER AND RALPH GREEN VOLUME 113. Glutamate, Glutamine, Glutathione, and Related Compounds Edited by ALTON MEISTER VOLUME 114. Diffraction Methods for Biological Macromolecules (Part A) Edited by HAROLD W. WYCKOFF, C. H. W. HIRS, AND SERGE N. TIMASHEFF VOLUME 115. Diffraction Methods for Biological Macromolecules (Part B) Edited by HAROLD W. WYCKOFF, C. H. W. HIRS, AND SERGE N. TIMASHEFF VOLUME 116. Immunochemical Techniques (Part H: Effectors and Mediators of Lymphoid Cell Functions) Edited by GIOVANNI DI SABATO, JOHN J. LANGONE, AND HELEN VAN VUNAKIS VOLUME 117. Enzyme Structure (Part J) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME 118. Plant Molecular Biology Edited by ARTHUR WEISSBACH AND HERBERT WEISSBACH VOLUME 119. Interferons (Part C) Edited by SIDNEY PESTKA VOLUME 120. Cumulative Subject Index Volumes 81–94, 96–101 VOLUME 121. Immunochemical Techniques (Part I: Hybridoma Technology and Monoclonal Antibodies) Edited by JOHN J. LANGONE AND HELEN VAN VUNAKIS VOLUME 122. Vitamins and Coenzymes (Part G) Edited by FRANK CHYTIL AND DONALD B. MCCORMICK

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VOLUME 123. Vitamins and Coenzymes (Part H) Edited by FRANK CHYTIL AND DONALD B. MCCORMICK VOLUME 124. Hormone Action (Part J: Neuroendocrine Peptides) Edited by P. MICHAEL CONN VOLUME 125. Biomembranes (Part M: Transport in Bacteria, Mitochondria, and Chloroplasts: General Approaches and Transport Systems) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 126. Biomembranes (Part N: Transport in Bacteria, Mitochondria, and Chloroplasts: Protonmotive Force) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 127. Biomembranes (Part O: Protons and Water: Structure and Translocation) Edited by LESTER PACKER VOLUME 128. Plasma Lipoproteins (Part A: Preparation, Structure, and Molecular Biology) Edited by JERE P. SEGREST AND JOHN J. ALBERS VOLUME 129. Plasma Lipoproteins (Part B: Characterization, Cell Biology, and Metabolism) Edited by JOHN J. ALBERS AND JERE P. SEGREST VOLUME 130. Enzyme Structure (Part K) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME 131. Enzyme Structure (Part L) Edited by C. H. W. HIRS AND SERGE N. TIMASHEFF VOLUME 132. Immunochemical Techniques (Part J: Phagocytosis and Cell-Mediated Cytotoxicity) Edited by GIOVANNI DI SABATO AND JOHANNES EVERSE VOLUME 133. Bioluminescence and Chemiluminescence (Part B) Edited by MARLENE DELUCA AND WILLIAM D. MCELROY VOLUME 134. Structural and Contractile Proteins (Part C: The Contractile Apparatus and the Cytoskeleton) Edited by RICHARD B. VALLEE VOLUME 135. Immobilized Enzymes and Cells (Part B) Edited by KLAUS MOSBACH VOLUME 136. Immobilized Enzymes and Cells (Part C) Edited by KLAUS MOSBACH VOLUME 137. Immobilized Enzymes and Cells (Part D) Edited by KLAUS MOSBACH VOLUME 138. Complex Carbohydrates (Part E) Edited by VICTOR GINSBURG


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VOLUME 139. Cellular Regulators (Part A: Calcium- and Calmodulin-Binding Proteins) Edited by ANTHONY R. MEANS AND P. MICHAEL CONN VOLUME 140. Cumulative Subject Index Volumes 102–119, 121–134 VOLUME 141. Cellular Regulators (Part B: Calcium and Lipids) Edited by P. MICHAEL CONN AND ANTHONY R. MEANS VOLUME 142. Metabolism of Aromatic Amino Acids and Amines Edited by SEYMOUR KAUFMAN VOLUME 143. Sulfur and Sulfur Amino Acids Edited by WILLIAM B. JAKOBY AND OWEN GRIFFITH VOLUME 144. Structural and Contractile Proteins (Part D: Extracellular Matrix) Edited by LEON W. CUNNINGHAM VOLUME 145. Structural and Contractile Proteins (Part E: Extracellular Matrix) Edited by LEON W. CUNNINGHAM VOLUME 146. Peptide Growth Factors (Part A) Edited by DAVID BARNES AND DAVID A. SIRBASKU VOLUME 147. Peptide Growth Factors (Part B) Edited by DAVID BARNES AND DAVID A. SIRBASKU VOLUME 148. Plant Cell Membranes Edited by LESTER PACKER AND ROLAND DOUCE VOLUME 149. Drug and Enzyme Targeting (Part B) Edited by RALPH GREEN AND KENNETH J. WIDDER VOLUME 150. Immunochemical Techniques (Part K: In Vitro Models of B and T Cell Functions and Lymphoid Cell Receptors) Edited by GIOVANNI DI SABATO VOLUME 151. Molecular Genetics of Mammalian Cells Edited by MICHAEL M. GOTTESMAN VOLUME 152. Guide to Molecular Cloning Techniques Edited by SHELBY L. BERGER AND ALAN R. KIMMEL VOLUME 153. Recombinant DNA (Part D) Edited by RAY WU AND LAWRENCE GROSSMAN VOLUME 154. Recombinant DNA (Part E) Edited by RAY WU AND LAWRENCE GROSSMAN VOLUME 155. Recombinant DNA (Part F) Edited by RAY WU VOLUME 156. Biomembranes (Part P: ATP-Driven Pumps and Related Transport: The Na, K-Pump) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER

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VOLUME 157. Biomembranes (Part Q: ATP-Driven Pumps and Related Transport: Calcium, Proton, and Potassium Pumps) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 158. Metalloproteins (Part A) Edited by JAMES F. RIORDAN AND BERT L. VALLEE VOLUME 159. Initiation and Termination of Cyclic Nucleotide Action Edited by JACKIE D. CORBIN AND ROGER A. JOHNSON VOLUME 160. Biomass (Part A: Cellulose and Hemicellulose) Edited by WILLIS A. WOOD AND SCOTT T. KELLOGG VOLUME 161. Biomass (Part B: Lignin, Pectin, and Chitin) Edited by WILLIS A. WOOD AND SCOTT T. KELLOGG VOLUME 162. Immunochemical Techniques (Part L: Chemotaxis and Inflammation) Edited by GIOVANNI DI SABATO VOLUME 163. Immunochemical Techniques (Part M: Chemotaxis and Inflammation) Edited by GIOVANNI DI SABATO VOLUME 164. Ribosomes Edited by HARRY F. NOLLER, JR., AND KIVIE MOLDAVE VOLUME 165. Microbial Toxins: Tools for Enzymology Edited by SIDNEY HARSHMAN VOLUME 166. Branched-Chain Amino Acids Edited by ROBERT HARRIS AND JOHN R. SOKATCH VOLUME 167. Cyanobacteria Edited by LESTER PACKER AND ALEXANDER N. GLAZER VOLUME 168. Hormone Action (Part K: Neuroendocrine Peptides) Edited by P. MICHAEL CONN VOLUME 169. Platelets: Receptors, Adhesion, Secretion (Part A) Edited by JACEK HAWIGER VOLUME 170. Nucleosomes Edited by PAUL M. WASSARMAN AND ROGER D. KORNBERG VOLUME 171. Biomembranes (Part R: Transport Theory: Cells and Model Membranes) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 172. Biomembranes (Part S: Transport: Membrane Isolation and Characterization) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER


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VOLUME 173. Biomembranes [Part T: Cellular and Subcellular Transport: Eukaryotic (Nonepithelial) Cells] Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 174. Biomembranes [Part U: Cellular and Subcellular Transport: Eukaryotic (Nonepithelial) Cells] Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 175. Cumulative Subject Index Volumes 135–139, 141–167 VOLUME 176. Nuclear Magnetic Resonance (Part A: Spectral Techniques and Dynamics) Edited by NORMAN J. OPPENHEIMER AND THOMAS L. JAMES VOLUME 177. Nuclear Magnetic Resonance (Part B: Structure and Mechanism) Edited by NORMAN J. OPPENHEIMER AND THOMAS L. JAMES VOLUME 178. Antibodies, Antigens, and Molecular Mimicry Edited by JOHN J. LANGONE VOLUME 179. Complex Carbohydrates (Part F) Edited by VICTOR GINSBURG VOLUME 180. RNA Processing (Part A: General Methods) Edited by JAMES E. DAHLBERG AND JOHN N. ABELSON VOLUME 181. RNA Processing (Part B: Specific Methods) Edited by JAMES E. DAHLBERG AND JOHN N. ABELSON VOLUME 182. Guide to Protein Purification Edited by MURRAY P. DEUTSCHER VOLUME 183. Molecular Evolution: Computer Analysis of Protein and Nucleic Acid Sequences Edited by RUSSELL F. DOOLITTLE VOLUME 184. Avidin-Biotin Technology Edited by MEIR WILCHEK AND EDWARD A. BAYER VOLUME 185. Gene Expression Technology Edited by DAVID V. GOEDDEL VOLUME 186. Oxygen Radicals in Biological Systems (Part B: Oxygen Radicals and Antioxidants) Edited by LESTER PACKER AND ALEXANDER N. GLAZER VOLUME 187. Arachidonate Related Lipid Mediators Edited by ROBERT C. MURPHY AND FRANK A. FITZPATRICK VOLUME 188. Hydrocarbons and Methylotrophy Edited by MARY E. LIDSTROM VOLUME 189. Retinoids (Part A: Molecular and Metabolic Aspects) Edited by LESTER PACKER

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VOLUME 190. Retinoids (Part B: Cell Differentiation and Clinical Applications) Edited by LESTER PACKER VOLUME 191. Biomembranes (Part V: Cellular and Subcellular Transport: Epithelial Cells) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 192. Biomembranes (Part W: Cellular and Subcellular Transport: Epithelial Cells) Edited by SIDNEY FLEISCHER AND BECCA FLEISCHER VOLUME 193. Mass Spectrometry Edited by JAMES A. MCCLOSKEY VOLUME 194. Guide to Yeast Genetics and Molecular Biology Edited by CHRISTINE GUTHRIE AND GERALD R. FINK VOLUME 195. Adenylyl Cyclase, G Proteins, and Guanylyl Cyclase Edited by ROGER A. JOHNSON AND JACKIE D. CORBIN VOLUME 196. Molecular Motors and the Cytoskeleton Edited by RICHARD B. VALLEE VOLUME 197. Phospholipases Edited by EDWARD A. DENNIS VOLUME 198. Peptide Growth Factors (Part C) Edited by DAVID BARNES, J. P. MATHER, AND GORDON H. SATO VOLUME 199. Cumulative Subject Index Volumes 168–174, 176–194 VOLUME 200. Protein Phosphorylation (Part A: Protein Kinases: Assays, Purification, Antibodies, Functional Analysis, Cloning, and Expression) Edited by TONY HUNTER AND BARTHOLOMEW M. SEFTON VOLUME 201. Protein Phosphorylation (Part B: Analysis of Protein Phosphorylation, Protein Kinase Inhibitors, and Protein Phosphatases) Edited by TONY HUNTER AND BARTHOLOMEW M. SEFTON VOLUME 202. Molecular Design and Modeling: Concepts and Applications (Part A: Proteins, Peptides, and Enzymes) Edited by JOHN J. LANGONE VOLUME 203. Molecular Design and Modeling: Concepts and Applications (Part B: Antibodies and Antigens, Nucleic Acids, Polysaccharides, and Drugs) Edited by JOHN J. LANGONE VOLUME 204. Bacterial Genetic Systems Edited by JEFFREY H. MILLER VOLUME 205. Metallobiochemistry (Part B: Metallothionein and Related Molecules) Edited by JAMES F. RIORDAN AND BERT L. VALLEE


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VOLUME 206. Cytochrome P450 Edited by MICHAEL R. WATERMAN AND ERIC F. JOHNSON VOLUME 207. Ion Channels Edited by BERNARDO RUDY AND LINDA E. IVERSON VOLUME 208. Protein–DNA Interactions Edited by ROBERT T. SAUER VOLUME 209. Phospholipid Biosynthesis Edited by EDWARD A. DENNIS AND DENNIS E. VANCE VOLUME 210. Numerical Computer Methods Edited by LUDWIG BRAND AND MICHAEL L. JOHNSON VOLUME 211. DNA Structures (Part A: Synthesis and Physical Analysis of DNA) Edited by DAVID M. J. LILLEY AND JAMES E. DAHLBERG VOLUME 212. DNA Structures (Part B: Chemical and Electrophoretic Analysis of DNA) Edited by DAVID M. J. LILLEY AND JAMES E. DAHLBERG VOLUME 213. Carotenoids (Part A: Chemistry, Separation, Quantitation, and Antioxidation) Edited by LESTER PACKER VOLUME 214. Carotenoids (Part B: Metabolism, Genetics, and Biosynthesis) Edited by LESTER PACKER VOLUME 215. Platelets: Receptors, Adhesion, Secretion (Part B) Edited by JACEK J. HAWIGER VOLUME 216. Recombinant DNA (Part G) Edited by RAY WU VOLUME 217. Recombinant DNA (Part H) Edited by RAY WU VOLUME 218. Recombinant DNA (Part I) Edited by RAY WU VOLUME 219. Reconstitution of Intracellular Transport Edited by JAMES E. ROTHMAN VOLUME 220. Membrane Fusion Techniques (Part A) Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 221. Membrane Fusion Techniques (Part B) Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 222. Proteolytic Enzymes in Coagulation, Fibrinolysis, and Complement Activation (Part A: Mammalian Blood Coagulation Factors and Inhibitors) Edited by LASZLO LORAND AND KENNETH G. MANN

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VOLUME 223. Proteolytic Enzymes in Coagulation, Fibrinolysis, and Complement Activation (Part B: Complement Activation, Fibrinolysis, and Nonmammalian Blood Coagulation Factors) Edited by LASZLO LORAND AND KENNETH G. MANN VOLUME 224. Molecular Evolution: Producing the Biochemical Data Edited by ELIZABETH ANNE ZIMMER, THOMAS J. WHITE, REBECCA L. CANN, AND ALLAN C. WILSON VOLUME 225. Guide to Techniques in Mouse Development Edited by PAUL M. WASSARMAN AND MELVIN L. DEPAMPHILIS VOLUME 226. Metallobiochemistry (Part C: Spectroscopic and Physical Methods for Probing Metal Ion Environments in Metalloenzymes and Metalloproteins) Edited by JAMES F. RIORDAN AND BERT L. VALLEE VOLUME 227. Metallobiochemistry (Part D: Physical and Spectroscopic Methods for Probing Metal Ion Environments in Metalloproteins) Edited by JAMES F. RIORDAN AND BERT L. VALLEE VOLUME 228. Aqueous Two-Phase Systems Edited by HARRY WALTER AND GO¨TE JOHANSSON VOLUME 229. Cumulative Subject Index Volumes 195–198, 200–227 VOLUME 230. Guide to Techniques in Glycobiology Edited by WILLIAM J. LENNARZ AND GERALD W. HART VOLUME 231. Hemoglobins (Part B: Biochemical and Analytical Methods) Edited by JOHANNES EVERSE, KIM D. VANDEGRIFF, AND ROBERT M. WINSLOW VOLUME 232. Hemoglobins (Part C: Biophysical Methods) Edited by JOHANNES EVERSE, KIM D. VANDEGRIFF, AND ROBERT M. WINSLOW VOLUME 233. Oxygen Radicals in Biological Systems (Part C) Edited by LESTER PACKER VOLUME 234. Oxygen Radicals in Biological Systems (Part D) Edited by LESTER PACKER VOLUME 235. Bacterial Pathogenesis (Part A: Identification and Regulation of Virulence Factors) Edited by VIRGINIA L. CLARK AND PATRIK M. BAVOIL VOLUME 236. Bacterial Pathogenesis (Part B: Integration of Pathogenic Bacteria with Host Cells) Edited by VIRGINIA L. CLARK AND PATRIK M. BAVOIL VOLUME 237. Heterotrimeric G Proteins Edited by RAVI IYENGAR VOLUME 238. Heterotrimeric G-Protein Effectors Edited by RAVI IYENGAR


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VOLUME 239. Nuclear Magnetic Resonance (Part C) Edited by THOMAS L. JAMES AND NORMAN J. OPPENHEIMER VOLUME 240. Numerical Computer Methods (Part B) Edited by MICHAEL L. JOHNSON AND LUDWIG BRAND VOLUME 241. Retroviral Proteases Edited by LAWRENCE C. KUO AND JULES A. SHAFER VOLUME 242. Neoglycoconjugates (Part A) Edited by Y. C. LEE AND REIKO T. LEE VOLUME 243. Inorganic Microbial Sulfur Metabolism Edited by HARRY D. PECK, JR., AND JEAN LEGALL VOLUME 244. Proteolytic Enzymes: Serine and Cysteine Peptidases Edited by ALAN J. BARRETT VOLUME 245. Extracellular Matrix Components Edited by E. RUOSLAHTI AND E. ENGVALL VOLUME 246. Biochemical Spectroscopy Edited by KENNETH SAUER VOLUME 247. Neoglycoconjugates (Part B: Biomedical Applications) Edited by Y. C. LEE AND REIKO T. LEE VOLUME 248. Proteolytic Enzymes: Aspartic and Metallo Peptidases Edited by ALAN J. BARRETT VOLUME 249. Enzyme Kinetics and Mechanism (Part D: Developments in Enzyme Dynamics) Edited by DANIEL L. PURICH VOLUME 250. Lipid Modifications of Proteins Edited by PATRICK J. CASEY AND JANICE E. BUSS VOLUME 251. Biothiols (Part A: Monothiols and Dithiols, Protein Thiols, and Thiyl Radicals) Edited by LESTER PACKER VOLUME 252. Biothiols (Part B: Glutathione and Thioredoxin; Thiols in Signal Transduction and Gene Regulation) Edited by LESTER PACKER VOLUME 253. Adhesion of Microbial Pathogens Edited by RON J. DOYLE AND ITZHAK OFEK VOLUME 254. Oncogene Techniques Edited by PETER K. VOGT AND INDER M. VERMA VOLUME 255. Small GTPases and Their Regulators (Part A: Ras Family) Edited by W. E. BALCH, CHANNING J. DER, AND ALAN HALL

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VOLUME 256. Small GTPases and Their Regulators (Part B: Rho Family) Edited by W. E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 257. Small GTPases and Their Regulators (Part C: Proteins Involved in Transport) Edited by W. E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 258. Redox-Active Amino Acids in Biology Edited by JUDITH P. KLINMAN VOLUME 259. Energetics of Biological Macromolecules Edited by MICHAEL L. JOHNSON AND GARY K. ACKERS VOLUME 260. Mitochondrial Biogenesis and Genetics (Part A) Edited by GIUSEPPE M. ATTARDI AND ANNE CHOMYN VOLUME 261. Nuclear Magnetic Resonance and Nucleic Acids Edited by THOMAS L. JAMES VOLUME 262. DNA Replication Edited by JUDITH L. CAMPBELL VOLUME 263. Plasma Lipoproteins (Part C: Quantitation) Edited by WILLIAM A. BRADLEY, SANDRA H. GIANTURCO, AND JERE P. SEGREST VOLUME 264. Mitochondrial Biogenesis and Genetics (Part B) Edited by GIUSEPPE M. ATTARDI AND ANNE CHOMYN VOLUME 265. Cumulative Subject Index Volumes 228, 230–262 VOLUME 266. Computer Methods for Macromolecular Sequence Analysis Edited by RUSSELL F. DOOLITTLE VOLUME 267. Combinatorial Chemistry Edited by JOHN N. ABELSON VOLUME 268. Nitric Oxide (Part A: Sources and Detection of NO; NO Synthase) Edited by LESTER PACKER VOLUME 269. Nitric Oxide (Part B: Physiological and Pathological Processes) Edited by LESTER PACKER VOLUME 270. High Resolution Separation and Analysis of Biological Macromolecules (Part A: Fundamentals) Edited by BARRY L. KARGER AND WILLIAM S. HANCOCK VOLUME 271. High Resolution Separation and Analysis of Biological Macromolecules (Part B: Applications) Edited by BARRY L. KARGER AND WILLIAM S. HANCOCK VOLUME 272. Cytochrome P450 (Part B) Edited by ERIC F. JOHNSON AND MICHAEL R. WATERMAN VOLUME 273. RNA Polymerase and Associated Factors (Part A) Edited by SANKAR ADHYA


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VOLUME 274. RNA Polymerase and Associated Factors (Part B) Edited by SANKAR ADHYA VOLUME 275. Viral Polymerases and Related Proteins Edited by LAWRENCE C. KUO, DAVID B. OLSEN, AND STEVEN S. CARROLL VOLUME 276. Macromolecular Crystallography (Part A) Edited by CHARLES W. CARTER, JR., AND ROBERT M. SWEET VOLUME 277. Macromolecular Crystallography (Part B) Edited by CHARLES W. CARTER, JR., AND ROBERT M. SWEET VOLUME 278. Fluorescence Spectroscopy Edited by LUDWIG BRAND AND MICHAEL L. JOHNSON VOLUME 279. Vitamins and Coenzymes (Part I) Edited by DONALD B. MCCORMICK, JOHN W. SUTTIE, AND CONRAD WAGNER VOLUME 280. Vitamins and Coenzymes (Part J) Edited by DONALD B. MCCORMICK, JOHN W. SUTTIE, AND CONRAD WAGNER VOLUME 281. Vitamins and Coenzymes (Part K) Edited by DONALD B. MCCORMICK, JOHN W. SUTTIE, AND CONRAD WAGNER VOLUME 282. Vitamins and Coenzymes (Part L) Edited by DONALD B. MCCORMICK, JOHN W. SUTTIE, AND CONRAD WAGNER VOLUME 283. Cell Cycle Control Edited by WILLIAM G. DUNPHY VOLUME 284. Lipases (Part A: Biotechnology) Edited by BYRON RUBIN AND EDWARD A. DENNIS VOLUME 285. Cumulative Subject Index Volumes 263, 264, 266–284, 286–289 VOLUME 286. Lipases (Part B: Enzyme Characterization and Utilization) Edited by BYRON RUBIN AND EDWARD A. DENNIS VOLUME 287. Chemokines Edited by RICHARD HORUK VOLUME 288. Chemokine Receptors Edited by RICHARD HORUK VOLUME 289. Solid Phase Peptide Synthesis Edited by GREGG B. FIELDS VOLUME 290. Molecular Chaperones Edited by GEORGE H. LORIMER AND THOMAS BALDWIN VOLUME 291. Caged Compounds Edited by GERARD MARRIOTT VOLUME 292. ABC Transporters: Biochemical, Cellular, and Molecular Aspects Edited by SURESH V. AMBUDKAR AND MICHAEL M. GOTTESMAN

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VOLUME 293. Ion Channels (Part B) Edited by P. MICHAEL CONN VOLUME 294. Ion Channels (Part C) Edited by P. MICHAEL CONN VOLUME 295. Energetics of Biological Macromolecules (Part B) Edited by GARY K. ACKERS AND MICHAEL L. JOHNSON VOLUME 296. Neurotransmitter Transporters Edited by SUSAN G. AMARA VOLUME 297. Photosynthesis: Molecular Biology of Energy Capture Edited by LEE MCINTOSH VOLUME 298. Molecular Motors and the Cytoskeleton (Part B) Edited by RICHARD B. VALLEE VOLUME 299. Oxidants and Antioxidants (Part A) Edited by LESTER PACKER VOLUME 300. Oxidants and Antioxidants (Part B) Edited by LESTER PACKER VOLUME 301. Nitric Oxide: Biological and Antioxidant Activities (Part C) Edited by LESTER PACKER VOLUME 302. Green Fluorescent Protein Edited by P. MICHAEL CONN VOLUME 303. cDNA Preparation and Display Edited by SHERMAN M. WEISSMAN VOLUME 304. Chromatin Edited by PAUL M. WASSARMAN AND ALAN P. WOLFFE VOLUME 305. Bioluminescence and Chemiluminescence (Part C) Edited by THOMAS O. BALDWIN AND MIRIAM M. ZIEGLER VOLUME 306. Expression of Recombinant Genes in Eukaryotic Systems Edited by JOSEPH C. GLORIOSO AND MARTIN C. SCHMIDT VOLUME 307. Confocal Microscopy Edited by P. MICHAEL CONN VOLUME 308. Enzyme Kinetics and Mechanism (Part E: Energetics of Enzyme Catalysis) Edited by DANIEL L. PURICH AND VERN L. SCHRAMM VOLUME 309. Amyloid, Prions, and Other Protein Aggregates Edited by RONALD WETZEL VOLUME 310. Biofilms Edited by RON J. DOYLE


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VOLUME 311. Sphingolipid Metabolism and Cell Signaling (Part A) Edited by ALFRED H. MERRILL, JR., AND YUSUF A. HANNUN VOLUME 312. Sphingolipid Metabolism and Cell Signaling (Part B) Edited by ALFRED H. MERRILL, JR., AND YUSUF A. HANNUN VOLUME 313. Antisense Technology (Part A: General Methods, Methods of Delivery, and RNA Studies) Edited by M. IAN PHILLIPS VOLUME 314. Antisense Technology (Part B: Applications) Edited by M. IAN PHILLIPS VOLUME 315. Vertebrate Phototransduction and the Visual Cycle (Part A) Edited by KRZYSZTOF PALCZEWSKI VOLUME 316. Vertebrate Phototransduction and the Visual Cycle (Part B) Edited by KRZYSZTOF PALCZEWSKI VOLUME 317. RNA–Ligand Interactions (Part A: Structural Biology Methods) Edited by DANIEL W. CELANDER AND JOHN N. ABELSON VOLUME 318. RNA–Ligand Interactions (Part B: Molecular Biology Methods) Edited by DANIEL W. CELANDER AND JOHN N. ABELSON VOLUME 319. Singlet Oxygen, UV-A, and Ozone Edited by LESTER PACKER AND HELMUT SIES VOLUME 320. Cumulative Subject Index Volumes 290–319 VOLUME 321. Numerical Computer Methods (Part C) Edited by MICHAEL L. JOHNSON AND LUDWIG BRAND VOLUME 322. Apoptosis Edited by JOHN C. REED VOLUME 323. Energetics of Biological Macromolecules (Part C) Edited by MICHAEL L. JOHNSON AND GARY K. ACKERS VOLUME 324. Branched-Chain Amino Acids (Part B) Edited by ROBERT A. HARRIS AND JOHN R. SOKATCH VOLUME 325. Regulators and Effectors of Small GTPases (Part D: Rho Family) Edited by W. E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 326. Applications of Chimeric Genes and Hybrid Proteins (Part A: Gene Expression and Protein Purification) Edited by JEREMY THORNER, SCOTT D. EMR, AND JOHN N. ABELSON VOLUME 327. Applications of Chimeric Genes and Hybrid Proteins (Part B: Cell Biology and Physiology) Edited by JEREMY THORNER, SCOTT D. EMR, AND JOHN N. ABELSON

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VOLUME 328. Applications of Chimeric Genes and Hybrid Proteins (Part C: Protein–Protein Interactions and Genomics) Edited by JEREMY THORNER, SCOTT D. EMR, AND JOHN N. ABELSON VOLUME 329. Regulators and Effectors of Small GTPases (Part E: GTPases Involved in Vesicular Traffic) Edited by W. E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 330. Hyperthermophilic Enzymes (Part A) Edited by MICHAEL W. W. ADAMS AND ROBERT M. KELLY VOLUME 331. Hyperthermophilic Enzymes (Part B) Edited by MICHAEL W. W. ADAMS AND ROBERT M. KELLY VOLUME 332. Regulators and Effectors of Small GTPases (Part F: Ras Family I) Edited by W. E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 333. Regulators and Effectors of Small GTPases (Part G: Ras Family II) Edited by W. E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 334. Hyperthermophilic Enzymes (Part C) Edited by MICHAEL W. W. ADAMS AND ROBERT M. KELLY VOLUME 335. Flavonoids and Other Polyphenols Edited by LESTER PACKER VOLUME 336. Microbial Growth in Biofilms (Part A: Developmental and Molecular Biological Aspects) Edited by RON J. DOYLE VOLUME 337. Microbial Growth in Biofilms (Part B: Special Environments and Physicochemical Aspects) Edited by RON J. DOYLE VOLUME 338. Nuclear Magnetic Resonance of Biological Macromolecules (Part A) Edited by THOMAS L. JAMES, VOLKER DO¨TSCH, AND ULI SCHMITZ VOLUME 339. Nuclear Magnetic Resonance of Biological Macromolecules (Part B) Edited by THOMAS L. JAMES, VOLKER DO¨TSCH, AND ULI SCHMITZ VOLUME 340. Drug–Nucleic Acid Interactions Edited by JONATHAN B. CHAIRES AND MICHAEL J. WARING VOLUME 341. Ribonucleases (Part A) Edited by ALLEN W. NICHOLSON VOLUME 342. Ribonucleases (Part B) Edited by ALLEN W. NICHOLSON VOLUME 343. G Protein Pathways (Part A: Receptors) Edited by RAVI IYENGAR AND JOHN D. HILDEBRANDT VOLUME 344. G Protein Pathways (Part B: G Proteins and Their Regulators) Edited by RAVI IYENGAR AND JOHN D. HILDEBRANDT


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VOLUME 345. G Protein Pathways (Part C: Effector Mechanisms) Edited by RAVI IYENGAR AND JOHN D. HILDEBRANDT VOLUME 346. Gene Therapy Methods Edited by M. IAN PHILLIPS VOLUME 347. Protein Sensors and Reactive Oxygen Species (Part A: Selenoproteins and Thioredoxin) Edited by HELMUT SIES AND LESTER PACKER VOLUME 348. Protein Sensors and Reactive Oxygen Species (Part B: Thiol Enzymes and Proteins) Edited by HELMUT SIES AND LESTER PACKER VOLUME 349. Superoxide Dismutase Edited by LESTER PACKER VOLUME 350. Guide to Yeast Genetics and Molecular and Cell Biology (Part B) Edited by CHRISTINE GUTHRIE AND GERALD R. FINK VOLUME 351. Guide to Yeast Genetics and Molecular and Cell Biology (Part C) Edited by CHRISTINE GUTHRIE AND GERALD R. FINK VOLUME 352. Redox Cell Biology and Genetics (Part A) Edited by CHANDAN K. SEN AND LESTER PACKER VOLUME 353. Redox Cell Biology and Genetics (Part B) Edited by CHANDAN K. SEN AND LESTER PACKER VOLUME 354. Enzyme Kinetics and Mechanisms (Part F: Detection and Characterization of Enzyme Reaction Intermediates) Edited by DANIEL L. PURICH VOLUME 355. Cumulative Subject Index Volumes 321–354 VOLUME 356. Laser Capture Microscopy and Microdissection Edited by P. MICHAEL CONN VOLUME 357. Cytochrome P450, Part C Edited by ERIC F. JOHNSON AND MICHAEL R. WATERMAN VOLUME 358. Bacterial Pathogenesis (Part C: Identification, Regulation, and Function of Virulence Factors) Edited by VIRGINIA L. CLARK AND PATRIK M. BAVOIL VOLUME 359. Nitric Oxide (Part D) Edited by ENRIQUE CADENAS AND LESTER PACKER VOLUME 360. Biophotonics (Part A) Edited by GERARD MARRIOTT AND IAN PARKER VOLUME 361. Biophotonics (Part B) Edited by GERARD MARRIOTT AND IAN PARKER

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VOLUME 362. Recognition of Carbohydrates in Biological Systems (Part A) Edited by YUAN C. LEE AND REIKO T. LEE VOLUME 363. Recognition of Carbohydrates in Biological Systems (Part B) Edited by YUAN C. LEE AND REIKO T. LEE VOLUME 364. Nuclear Receptors Edited by DAVID W. RUSSELL AND DAVID J. MANGELSDORF VOLUME 365. Differentiation of Embryonic Stem Cells Edited by PAUL M. WASSAUMAN AND GORDON M. KELLER VOLUME 366. Protein Phosphatases Edited by SUSANNE KLUMPP AND JOSEF KRIEGLSTEIN VOLUME 367. Liposomes (Part A) Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 368. Macromolecular Crystallography (Part C) Edited by CHARLES W. CARTER, JR., AND ROBERT M. SWEET VOLUME 369. Combinational Chemistry (Part B) Edited by GUILLERMO A. MORALES AND BARRY A. BUNIN VOLUME 370. RNA Polymerases and Associated Factors (Part C) Edited by SANKAR L. ADHYA AND SUSAN GARGES VOLUME 371. RNA Polymerases and Associated Factors (Part D) Edited by SANKAR L. ADHYA AND SUSAN GARGES VOLUME 372. Liposomes (Part B) Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 373. Liposomes (Part C) Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 374. Macromolecular Crystallography (Part D) Edited by CHARLES W. CARTER, JR., AND ROBERT W. SWEET VOLUME 375. Chromatin and Chromatin Remodeling Enzymes (Part A) Edited by C. DAVID ALLIS AND CARL WU VOLUME 376. Chromatin and Chromatin Remodeling Enzymes (Part B) Edited by C. DAVID ALLIS AND CARL WU VOLUME 377. Chromatin and Chromatin Remodeling Enzymes (Part C) Edited by C. DAVID ALLIS AND CARL WU VOLUME 378. Quinones and Quinone Enzymes (Part A) Edited by HELMUT SIES AND LESTER PACKER VOLUME 379. Energetics of Biological Macromolecules (Part D) Edited by JO M. HOLT, MICHAEL L. JOHNSON, AND GARY K. ACKERS VOLUME 380. Energetics of Biological Macromolecules (Part E) Edited by JO M. HOLT, MICHAEL L. JOHNSON, AND GARY K. ACKERS


VOLUME 381. Oxygen Sensing Edited by CHANDAN K. SEN AND GREGG L. SEMENZA VOLUME 382. Quinones and Quinone Enzymes (Part B) Edited by HELMUT SIES AND LESTER PACKER VOLUME 383. Numerical Computer Methods (Part D) Edited by LUDWIG BRAND AND MICHAEL L. JOHNSON VOLUME 384. Numerical Computer Methods (Part E) Edited by LUDWIG BRAND AND MICHAEL L. JOHNSON VOLUME 385. Imaging in Biological Research (Part A) Edited by P. MICHAEL CONN VOLUME 386. Imaging in Biological Research (Part B) Edited by P. MICHAEL CONN VOLUME 387. Liposomes (Part D) Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 388. Protein Engineering Edited by DAN E. ROBERTSON AND JOSEPH P. NOEL VOLUME 389. Regulators of G-Protein Signaling (Part A) Edited by DAVID P. SIDEROVSKI VOLUME 390. Regulators of G-Protein Signaling (Part B) Edited by DAVID P. SIDEROVSKI VOLUME 391. Liposomes (Part E) Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 392. RNA Interference Edited by ENGELKE ROSSI VOLUME 393. Circadian Rhythms Edited by MICHAEL W. YOUNG VOLUME 394. Nuclear Magnetic Resonance of Biological Macromolecules (Part C) Edited by THOMAS L. JAMES VOLUME 395. Producing the Biochemical Data (Part B) Edited by ELIZABETH A. ZIMMER AND ERIC H. ROALSON VOLUME 396. Nitric Oxide (Part E) Edited by LESTER PACKER AND ENRIQUE CADENAS VOLUME 397. Environmental Microbiology Edited by JARED R. LEADBETTER VOLUME 398. Ubiquitin and Protein Degradation (Part A) Edited by RAYMOND J. DESHAIES

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VOLUME 399. Ubiquitin and Protein Degradation (Part B) Edited by RAYMOND J. DESHAIES VOLUME 400. Phase II Conjugation Enzymes and Transport Systems Edited by HELMUT SIES AND LESTER PACKER VOLUME 401. Glutathione Transferases and Gamma Glutamyl Transpeptidases Edited by HELMUT SIES AND LESTER PACKER VOLUME 402. Biological Mass Spectrometry Edited by A. L. BURLINGAME VOLUME 403. GTPases Regulating Membrane Targeting and Fusion Edited by WILLIAM E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 404. GTPases Regulating Membrane Dynamics Edited by WILLIAM E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 405. Mass Spectrometry: Modified Proteins and Glycoconjugates Edited by A. L. BURLINGAME VOLUME 406. Regulators and Effectors of Small GTPases: Rho Family Edited by WILLIAM E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 407. Regulators and Effectors of Small GTPases: Ras Family Edited by WILLIAM E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 408. DNA Repair (Part A) Edited by JUDITH L. CAMPBELL AND PAUL MODRICH VOLUME 409. DNA Repair (Part B) Edited by JUDITH L. CAMPBELL AND PAUL MODRICH VOLUME 410. DNA Microarrays (Part A: Array Platforms and Web-Bench Protocols) Edited by ALAN KIMMEL AND BRIAN OLIVER VOLUME 411. DNA Microarrays (Part B: Databases and Statistics) Edited by ALAN KIMMEL AND BRIAN OLIVER VOLUME 412. Amyloid, Prions, and Other Protein Aggregates (Part B) Edited by INDU KHETERPAL AND RONALD WETZEL VOLUME 413. Amyloid, Prions, and Other Protein Aggregates (Part C) Edited by INDU KHETERPAL AND RONALD WETZEL VOLUME 414. Measuring Biological Responses with Automated Microscopy Edited by JAMES INGLESE VOLUME 415. Glycobiology Edited by MINORU FUKUDA VOLUME 416. Glycomics Edited by MINORU FUKUDA


VOLUME 417. Functional Glycomics Edited by MINORU FUKUDA VOLUME 418. Embryonic Stem Cells Edited by IRINA KLIMANSKAYA AND ROBERT LANZA VOLUME 419. Adult Stem Cells Edited by IRINA KLIMANSKAYA AND ROBERT LANZA VOLUME 420. Stem Cell Tools and Other Experimental Protocols Edited by IRINA KLIMANSKAYA AND ROBERT LANZA VOLUME 421. Advanced Bacterial Genetics: Use of Transposons and Phage for Genomic Engineering Edited by KELLY T. HUGHES VOLUME 422. Two-Component Signaling Systems, Part A Edited by MELVIN I. SIMON, BRIAN R. CRANE, AND ALEXANDRINE CRANE VOLUME 423. Two-Component Signaling Systems, Part B Edited by MELVIN I. SIMON, BRIAN R. CRANE, AND ALEXANDRINE CRANE VOLUME 424. RNA Editing Edited by JONATHA M. GOTT VOLUME 425. RNA Modification Edited by JONATHA M. GOTT VOLUME 426. Integrins Edited by DAVID CHERESH VOLUME 427. MicroRNA Methods Edited by JOHN J. ROSSI VOLUME 428. Osmosensing and Osmosignaling Edited by HELMUT SIES AND DIETER HAUSSINGER VOLUME 429. Translation Initiation: Extract Systems and Molecular Genetics Edited by JON LORSCH VOLUME 430. Translation Initiation: Reconstituted Systems and Biophysical Methods Edited by JON LORSCH VOLUME 431. Translation Initiation: Cell Biology, High-Throughput and Chemical-Based Approaches Edited by JON LORSCH VOLUME 432. Lipidomics and Bioactive Lipids: Mass-Spectrometry–Based Lipid Analysis Edited by H. ALEX BROWN

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VOLUME 433. Lipidomics and Bioactive Lipids: Specialized Analytical Methods and Lipids in Disease Edited by H. ALEX BROWN VOLUME 434. Lipidomics and Bioactive Lipids: Lipids and Cell Signaling Edited by H. ALEX BROWN VOLUME 435. Oxygen Biology and Hypoxia Edited by HELMUT SIES AND BERNHARD BRU¨NE VOLUME 436. Globins and Other Nitric Oxide-Reactive Protiens (Part A) Edited by ROBERT K. POOLE VOLUME 437. Globins and Other Nitric Oxide-Reactive Protiens (Part B) Edited by ROBERT K. POOLE VOLUME 438. Small GTPases in Disease (Part A) Edited by WILLIAM E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 439. Small GTPases in Disease (Part B) Edited by WILLIAM E. BALCH, CHANNING J. DER, AND ALAN HALL VOLUME 440. Nitric Oxide, Part F Oxidative and Nitrosative Stress in Redox Regulation of Cell Signaling Edited by ENRIQUE CADENAS AND LESTER PACKER VOLUME 441. Nitric Oxide, Part G Oxidative and Nitrosative Stress in Redox Regulation of Cell Signaling Edited by ENRIQUE CADENAS AND LESTER PACKER VOLUME 442. Programmed Cell Death, General Principles for Studying Cell Death (Part A) Edited by ROYA KHOSRAVI-FAR, ZAHRA ZAKERI, RICHARD A. LOCKSHIN, AND MAURO PIACENTINI VOLUME 443. Angiogenesis: In Vitro Systems Edited by DAVID A. CHERESH VOLUME 444. Angiogenesis: In Vivo Systems (Part A) Edited by DAVID A. CHERESH VOLUME 445. Angiogenesis: In Vivo Systems (Part B) Edited by DAVID A. CHERESH VOLUME 446. Programmed Cell Death, The Biology and Therapeutic Implications of Cell Death (Part B) Edited by ROYA KHOSRAVI-FAR, ZAHRA ZAKERI, RICHARD A. LOCKSHIN, AND MAURO PIACENTINI VOLUME 447. RNA Turnover in Bacteria, Archaea and Organelles Edited by LYNNE E. MAQUAT AND CECILIA M. ARRAIANO


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VOLUME 448. RNA Turnover in Eukaryotes: Nucleases, Pathways and Analysis of mRNA Decay Edited by LYNNE E. MAQUAT AND MEGERDITCH KILEDJIAN VOLUME 449. RNA Turnover in Eukaryotes: Analysis of Specialized and Quality Control RNA Decay Pathways Edited by LYNNE E. MAQUAT AND MEGERDITCH KILEDJIAN VOLUME 450. Fluorescence Spectroscopy Edited by LUDWIG BRAND AND MICHAEL L. JOHNSON VOLUME 451. Autophagy: Lower Eukaryotes and Non-Mammalian Systems (Part A) Edited by DANIEL J. KLIONSKY VOLUME 452. Autophagy in Mammalian Systems (Part B) Edited by DANIEL J. KLIONSKY VOLUME 453. Autophagy in Disease and Clinical Applications (Part C) Edited by DANIEL J. KLIONSKY VOLUME 454. Computer Methods (Part A) Edited by MICHAEL L. JOHNSON AND LUDWIG BRAND VOLUME 455. Biothermodynamics (Part A) Edited by MICHAEL L. JOHNSON, JO M. HOLT, AND GARY K. ACKERS (RETIRED) VOLUME 456. Mitochondrial Function, Part A: Mitochondrial Electron Transport Complexes and Reactive Oxygen Species Edited by WILLIAM S. ALLISON AND IMMO E. SCHEFFLER VOLUME 457. Mitochondrial Function, Part B: Mitochondrial Protein Kinases, Protein Phosphatases and Mitochondrial Diseases Edited by WILLIAM S. ALLISON AND ANNE N. MURPHY VOLUME 458. Complex Enzymes in Microbial Natural Product Biosynthesis, Part A: Overview Articles and Peptides Edited by DAVID A. HOPWOOD VOLUME 459. Complex Enzymes in Microbial Natural Product Biosynthesis, Part B: Polyketides, Aminocoumarins and Carbohydrates Edited by DAVID A. HOPWOOD VOLUME 460. Chemokines, Part A Edited by TRACY M. HANDEL AND DAMON J. HAMEL VOLUME 461. Chemokines, Part B Edited by TRACY M. HANDEL AND DAMON J. HAMEL VOLUME 462. Non-Natural Amino Acids Edited by TOM W. MUIR AND JOHN N. ABELSON VOLUME 463. Guide to Protein Purification, 2nd Edition Edited by RICHARD R. BURGESS AND MURRAY P. DEUTSCHER

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VOLUME 464. Liposomes, Part F Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 465. Liposomes, Part G Edited by NEJAT DU¨ZGU¨NES¸ VOLUME 466. Biothermodynamics, Part B Edited by MICHAEL L. JOHNSON, GARY K. ACKERS, AND JO M. HOLT VOLUME 467. Computer Methods Part B Edited by MICHAEL L. JOHNSON AND LUDWIG BRAND VOLUME 468. Biophysical, Chemical, and Functional Probes of RNA Structure, Interactions and Folding: Part A Edited by DANIEL HERSCHLAG VOLUME 469. Biophysical, Chemical, and Functional Probes of RNA Structure, Interactions and Folding: Part B Edited by DANIEL HERSCHLAG VOLUME 470. Guide to Yeast Genetics: Functional Genomics, Proteomics, and Other Systems Analysis, 2nd Edition Edited by GERALD FINK, JONATHAN WEISSMAN, AND CHRISTINE GUTHRIE VOLUME 471. Two-Component Signaling Systems, Part C Edited by MELVIN I. SIMON, BRIAN R. CRANE, AND ALEXANDRINE CRANE VOLUME 472. Single Molecule Tools, Part A: Fluorescence Based Approaches Edited by NILS G. WALTER VOLUME 473. Thiol Redox Transitions in Cell Signaling, Part A Chemistry and Biochemistry of Low Molecular Weight and Protein Thiols Edited by ENRIQUE CADENAS AND LESTER PACKER VOLUME 474. Thiol Redox Transitions in Cell Signaling, Part B Cellular Localization and Signaling Edited by ENRIQUE CADENAS AND LESTER PACKER VOLUME 475. Single Molecule Tools, Part B: Super-Resolution, Particle Tracking, Multiparameter, and Force Based Methods Edited by NILS G. WALTER VOLUME 476. Guide to Techniques in Mouse Development, Part A Mice, Embryos, and Cells, 2nd Edition Edited by PAUL M. WASSARMAN AND PHILIPPE M. SORIANO VOLUME 477. Guide to Techniques in Mouse Development, Part B Mouse Molecular Genetics, 2nd Edition Edited by PAUL M. WASSARMAN AND PHILIPPE M. SORIANO VOLUME 478. Glycomics Edited by MINORU FUKUDA


VOLUME 479. Functional Glycomics Edited by MINORU FUKUDA VOLUME 480. Glycobiology Edited by MINORU FUKUDA VOLUME 481. Cryo-EM, Part A: Sample Preparation and Data Collection Edited by GRANT J. JENSEN VOLUME 482. Cryo-EM, Part B: 3-D Reconstruction Edited by GRANT J. JENSEN VOLUME 483. Cryo-EM, Part C: Analyses, Interpretation, and Case Studies Edited by GRANT J. JENSEN VOLUME 484. Constitutive Activity in Receptors and Other Proteins, Part A Edited by P. MICHAEL CONN VOLUME 485. Constitutive Activity in Receptors and Other Proteins, Part B Edited by P. MICHAEL CONN VOLUME 486. Research on Nitrification and Related Processes, Part A Edited by MARTIN G. KLOTZ VOLUME 487. Computer Methods, Part C Edited by MICHAEL L. JOHNSON AND LUDWIG BRAND VOLUME 488. Biothermodynamics, Part C Edited by MICHAEL L. JOHNSON, JO M. HOLT, AND GARY K. ACKERS VOLUME 489. The Unfolded Protein Response and Cellular Stress, Part A Edited by P. MICHAEL CONN VOLUME 490. The Unfolded Protein Response and Cellular Stress, Part B Edited by P. MICHAEL CONN VOLUME 491. The Unfolded Protein Response and Cellular Stress, Part C Edited by P. MICHAEL CONN VOLUME 492. Biothermodynamics, Part D Edited by MICHAEL L. JOHNSON, JO M. HOLT, AND GARY K. ACKERS VOLUME 493. Fragment-Based Drug Design Tools, Practical Approaches, and Examples Edited by LAWRENCE C. KUO VOLUME 494. Methods in Methane Metabolism, Part A Methanogenesis Edited by AMY C. ROSENZWEIG AND STEPHEN W. RAGSDALE VOLUME 495. Methods in Methane Metabolism, Part B Edited by AMY C. ROSENZWEIG AND STEPHEN W. RAGSDALE VOLUME 496. Research on Nitrification and Related Processes, Part B Edited by MARTIN G. KLOTZ AND LISA Y. STEIN

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C H A P T E R

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Strategies to Determine Diversity, Growth, and Activity of AmmoniaOxidizing Archaea in Soil Graeme W. Nicol and James I. Prosser Contents 1. Introduction 2. Common Methods 2.1. Soil sampling and experimental design 2.2. Nucleic acid extraction 2.3. Reverse transcription and cDNA production 2.4. PCR of 16S rRNA genes 2.5. PCR amplification of amoA genes 2.6. Genes involved in CO2 fixation in the 3-hydroxypropionate/ 4-hydroxybutyrate cycle 3. Community Composition and Diversity 3.1. Nucleic acid fingerprinting 3.2. Analysis of clone libraries 3.3. High-throughput sequencing methods 4. Determining Growth and Abundance 4.1. Changes in relative abundance 4.2. Quantification of gene abundance 5. Activity 5.1. Estimating activity from gene abundance data 5.2. Quantifying transcriptional activity 5.3. Stable isotope probing 6. Conclusions References

4 6 6 7 9 10 13 14 15 17 18 20 21 21 22 23 25 26 26 29 31

Abstract Ecological studies of soil microorganisms require reliable techniques for assessment of microbial community composition, abundance, growth, and activity. Soil structure and physicochemical properties seriously limit the

Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom Methods in Enzymology, Volume 496 ISSN 0076-6879, DOI: 10.1016/B978-0-12-386489-5.00001-4

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2011 Elsevier Inc. All rights reserved.

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Graeme W. Nicol and James I. Prosser

applicability and value of methods involving direct observation, and ecological studies have focused on communities and populations, rather than single cells or microcolonies. Although ammonia-oxidizing archaea were discovered 5 years ago, there are still no cultured representatives from soil and there remains a lack of knowledge regarding their genomic composition, physiology, or functional diversity. Despite these limitations, however, significant insights into their distribution, growth characteristics, and metabolism have been made through the use of a range of molecular methodologies. As well as the analysis of taxonomic markers such as 16S rRNA genes, the development of PCR primers based on a limited number of (mostly marine) sequences has enabled the analysis of homologues encoding proteins involved in energy and carbon metabolism. This chapter will highlight the range of molecular methodologies available for examining the diversity, growth, and activity of ammonia-oxidizing archaea in the soil environment.

1. Introduction The composition of soil microbial communities was traditionally determined by enrichment, isolation, and identification of organisms grown on laboratory media, with cell abundances determined by enumeration of culturable organisms or total cell counts following staining. These cultivation-based methods were supplemented with quantitative chemical analysis of cell components, such as phospholipid fatty acids, peptidoglycan, and nucleic acids. Growth was determined by changes in abundance or was inferred from activity measurements of broad-scale functional groups, for example, as respiration, denitrification, and nitrification. Many of these methods have been supplanted by molecular, nucleicacid-based techniques, which remove the major biases associated with cultivation on laboratory media. The advantages of molecular methods are most evident for determination of community composition and abundance, but they also enable the targeting of specific functional groups, providing finer scale analysis of growth and activity. The composition of microbial communities is now typically determined by analysis of 16S rRNA or functional gene sequences and abundance and growth increasingly by quantitative PCR. Nucleic-acid-based methods are also being developed for assessment of physiological activity of specific groups and their contribution to soil ecosystem processes. Some involve molecular assessment of changes in absolute and relative abundance in combination with traditional activity measures, while others, such as stable isotope probing (SIP) and measurement of transcriptional activity, attempt more direct activity measurements. Although eliminating cultivation-bias, molecular techniques are by no means perfect and potentially suffer from a range of biases which, following initial development, are rarely assessed or quantified (Prosser et al., 2010).

Analysis of Soil Thaumarchaeota

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Caution should therefore be used when applying these techniques and when interpreting data, particularly as analysis techniques become more affordable and routine. The limitations of cultivation-based approaches for analysis of soil microbial communities are exacerbated for ammonia oxidizers, because of major difficulties in isolating, enumerating, and identifying these organisms. Specific growth rates of soil ammonia oxidizers are low, with typical doubling times measured in days. Cultivated organisms are unlikely to reflect natural communities because of bias introduced by laboratory conditions (Smith et al., 2001) and traditional identification and classification are based on a limited number of phenotypic characteristics. These technical limitations prevented meaningful studies of soil ammonia oxidizer ecology until the 1990s, when development was initiated of molecular techniques to determine community composition, to quantify ammonia oxidizer abundance, and to distinguish the growth and activities of different populations within ammonia oxidizer communities. Initial molecular investigations focused on betaproteobacterial ammonia oxidizers, to which all known soil ammonia oxidizer isolates belong. These organisms are found in all soils with nitrifying activity, which are dominated by nitrosospiras, rather than more readily enriched nitrosomonads. Differences in ammonia oxidizer communities were associated with a range of environmental factors and soil management strategies and new phylogenetic groups were discovered and characterized, some of which have no cultivated representative, but are presumed to be ammonia oxidizers. Analysis of communities of soil bacterial ammonia oxidizers was facilitated by their apparent exclusive membership of a single phylogenetic group within the betaproteobacteria, enabling the use of specific 16S rRNA gene primers (Kowalchuk et al., 1997; Stephen et al., 1998). In addition, this approach was complemented by the design of primers for amoA genes encoding subunit A of ammonia monooxygenase (Rotthauwe et al., 1997), the enzyme which catalyzes the first step in oxidation of ammonia to hydroxylamine (Arp and Stein, 2003). These major advances in our understanding of soil ammonia oxidizer population and community ecology were extended by discovery of ammonia-oxidizing archaea. Initial evidence for putative soil archaeal ammonia oxidizers was the presence of a crenarchaeal 16S rRNA gene and a homologue of bacterial amoA and amoB genes on a 43 kb fosmid clone recovered from soil (Treusch et al., 2005). Isolation of the first archaeal ammonia oxidizer, Nitrosopumilus maritimus, from a marine aquarium conclusively demonstrated autotrophic growth on ammonia as a sole energy source (Ko¨nneke et al., 2005). This organism possessed highly similar ammonia monooxygenase subunit homologues to those found on the soil fosmid, providing further support that soil archaea are also capable of oxidizing ammonia. These discoveries led to widespread surveys of archaeal amoA genes in a broad range of habitats (Francis et al., 2005) demonstrating their ubiquity

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and frequent higher abundance than bacterial amoA genes in most soils (Leininger et al., 2006) and other environments such as marine waters (Wuchter et al., 2006). Although these surveys continue, attention has moved to assessment of the environmental factors that determine the relative abundance of putative bacterial and archaeal ammonia oxidizers, their relative contributions to soil nitrification and the potential to control and manipulate their activities (Prosser and Nicol, 2008). These studies are dependent on molecular techniques. There is currently no known inhibitor that clearly distinguishes bacterial and archaeal ammonia oxidation, and cultivation of archaeal ammonia oxidizers is currently even more difficult than that of bacterial ammonia oxidizers, particularly from soil. Current evidence indicates that archaeal ammonia monooxygenase is found only in a specific lineage of archaea which have been described as mesophilic crenarchaea due to a distinct but specific phylogenetic association with thermophilic crenarchaea based on the comparison of 16S rRNA gene sequences. However, recent phylogenetic analyses of genomic data indicate that these organisms belong to a phylum which is distinct from both the established Crenarchaeota and Euryarchaeota lineages (Brochier-Armanet et al., 2008; Kelly et al., 2010; Spang et al., 2010) and which has been placed in a novel phylum described as the Thaumarchaeota (Brochier-Armanet et al., 2008). This term will therefore be used throughout this article. Molecular analysis of thaumarchaeal ammonia oxidizers has been used in analysis of field samples, usually for surveys or to assess the influence of soil characteristics and management practices on community structure, and in soil microcosm systems designed to investigate mechanisms controlling community composition, growth, and activity. Many of these studies have also involved parallel analysis of bacterial ammonia oxidizers. Strategies and techniques for the two groups are very similar, often differing only in the nature of primers for target genes, but a major current distinction is the difficulty in enriching and isolating soil thaumarchaeal ammonia oxidizers, while bacterial ammonia oxidizers grow readily in culture, although they are difficult to isolate. This chapter describes the methods used to study the abundance, diversity, growth, and activity of putative soil archaeal ammonia oxidizers.

2. Common Methods 2.1. Soil sampling and experimental design Analysis of thaumarchaeal soil ammonia oxidizers is subject to the same sampling and experimental design considerations as other microbial groups. Soil sampling procedures will depend on the nature of the study, but there is some evidence that thaumarchaeal ammonia oxidizer relative abundance


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increases with depth (Ho¨fferle et al., 2010; Jia and Conrad, 2009; Leininger et al., 2006) which may influence the choice of sampling procedures. For molecular analyses of communities present under “field conditions,” samples must be stored at 20 C or, preferably, 80 C, particularly when analyzing gene transcripts. Ammonia, nitrite, and nitrate concentrations are best determined as soon as possible after sampling, as concentrations (particularly of nitrate) can change with storage, even in refrigerated samples. If soil is to be used for microcosm experiments, short-to medium-term storage (at 5 C) of soil must be considered. For example, sieving and homogenizing soil cores may be required to produce a consistent starting soil substrate for future experimentation, but sieving also changes the soil structure drastically and will release nutrients. Although this may not have large effects on slowgrowing ammonia-oxidizing populations, other members of the community may be affected. Therefore, the storage of soil in original soil cores may be desirable.

2.2. Nucleic acid extraction Nucleic acids can be extracted from suspended cells or directly from soil. The latter approach is more common and most studies extract DNA by bead-beating soil at high speed in the presence of a buffer, to dissolve nucleic acids in combination with other solutions that remove proteins and reduce coextraction of humic acids, which can inhibit downstream processes. A number of manufacturers produce kits for extraction of both DNA and RNA from soil (e.g., MPBio, MoBio), but several “home-made” methods are used which produce highly reproducible results. A widely used protocol (cited 297 times; November 2010) is that based on the method published by Griffiths et al. (2000). It is relatively inexpensive ($5 per sample) and uses a phosphate buffer with cetyl trimethyl ammonium bromide (CTAB) to reduce coextraction of humic acids. Generally, it extracts nucleic acids efficiently from a wide range of soil types, although soils with a high clay content can be problematic. Importantly, the method allows recovery of both DNA and RNA. The following solutions and reagents are required: 1. 5% CTAB in 120 mM phosphate buffer (2.58 g K2HPO43H2O, 0.10 g KH2PO4, 5.0 g CTAB, 2.05 g NaCl, water to 100 ml). Sterilize by autoclaving and store at room temperature. [Note: CTAB may precipitate from the solution. The buffer should therefore be warmed to redissolve if necessary.] 2. 30% polyethylene glycol (PEG) 6000 solution in 1.6 mM NaCl (30 g PEG6000, 9.35 g NaCl, water to 100 ml). Sterilize by autoclaving and store at room temperature. 3. 70% ice-cold ethanol. Store at 20 C.

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4. Liquified phenol 5. 24:1 Chloroform:isoamyl alcohol 6. Optionally, prepare a 25:24:1 phenol:chloroform:isoamyl alcohol solution. 7. 2 ml screw-cap tubes containing glass beads (e.g., Roche Lysing Matrix B) Method 1. Add 0.50 g soil, 0.5 ml CTAB phosphate buffer, 0.25 ml phenol, 0.25 ml chloroform:isoamyl alcohol (25:24:1) to a 2 ml screw-cap tube with glass beads. For RNA work, these steps should be performed on ice. 2. Lyse cells by shaking in a bead-beater (e.g., a FastPrep instrument) for 30 s, speed 4 m s 1. [Note: different speeds and/or duration of beadbeating may influence the amount of recovered nucleic acids from a target group, and optimal conditions can be determined prior to analysis of experimental samples, for example, see Leininger et al., 2006.] 3. Centrifuge at top-speed (e.g., 16,000g) in a bench-top centrifuge for 5 min at 5 C, remove top aqueous layer, and place in fresh 1.5 ml microcentrifuge tube containing 0.5 ml chloroform:isoamyl alcohol (this is to remove any residual phenol). 4. Invert the aqueous/organic mixture several times and centrifuge for 5 min at 16,000g at 5 C. 5. Extract the top aqueous layer and place in a new microcentrifuge tube. It is important not to remove any chloroform, and leaving behind a known volume of some aqueous solution (e.g., 50 ml) is preferable to carry-over of organic solvent. 6. Precipitate DNA by adding two volumes of 30% PEG/NaCl solution and mix well. 7. Leave on the bench on ice for 2 h (or overnight at 5 C) and centrifuge at 16,000g for 20 min at 5 C. A pellet may be visible at the bottom of the tube. 8. Slowly pour off the supernatant and add 1 ml of ice-cold 70% ethanol. Invert slowly two to three times and centrifuge for 20 min at 16,000g at 5 C. A pellet should be visible at the bottom of the tube. 9. Slowly pour off ethanol wash (take care not to lose pellet) and place tube back in centrifuge and spin for 5 s to collect residual ethanol. Remove the remaining ethanol using a pipette. [Note: this decreases the time required to dry the pellet significantly.] Leave the pellet to air-dry or warm in hot-block at 55 C for 1–2 min. Be careful not to overdry the pellet. 10. Resuspend the pellet in 30–50 ml of sterile molecular grade H2O. 11. Examine yield and quality (size range/shearing) of DNA by standard agarose gel electrophoresis. Accurate quantification of DNA and RNA


9

(in particular) may be achieved by spectrophotometric analysis (e.g., Nanodrop, Labtech, UK) after subsequent removal and purification of RNA and DNA, respectively. Although this method is a relatively vigorous extraction protocol, bias could potentially be introduced in the cell-extraction step as cells and nucleic acids may be adsorbed to soil organic matter or clay minerals and lysis of different cell types may vary. Studies comparing extraction methods using spiked soils samples (DNA or cells) have reported significant differences in recovery of nucleic acids with different methods (e.g., Mumy and Findlay, 2004). Adequate replication and experimental design should enable accurate determination of changes in relative amounts of specific target groups. However, absolute gene abundances determined for soil samples should be interpreted with some caution. Cell lysis bias may be important when comparing bacterial and archaeal ammonia oxidizers, due to significant differences in cell structure. The lack of cultivated representatives of both bacterial and thaumarchaeal ammonia oxidizers makes it difficult to assess the significance and extent of cell lysis and nucleic acid extraction biases at present.

2.3. Reverse transcription and cDNA production RNA, rather than DNA, is often used to study which organisms are potentially metabolically active in an environmental sample. In particular, mRNA transcripts of functional genes (e.g., amoA) potentially have a very short-half life and are therefore more likely to be indicative of in situ activity in an environmental sample. The production of cDNA from extracted RNA is required for such analyses. RNA extracted from soil, like DNA, is often contaminated with coextracted humic acids. Depending on the level of contamination, extracts can simply be diluted before DNase treatment and reverse transcription, or further purification steps (e.g., using a Qiagen RNeasy purification kit) can be used if required. As initial template concentrations can affect the efficiency of cDNA production significantly (Chandler et al., 1998), comparison of different samples requires all extracts to be treated in a consistent manner. For example, if analyses are performed on a series of microcosm experiments using the same soil with RNA extracts exhibiting little variability in concentration and quality between treatments, then a specific dilution of extracted soil RNA applied to all samples can be appropriate. However, if different soil types or different RNA yields are being compared, it may be more appropriate to purify RNA extracts further and use identical, quantified amounts. The first step in generating cDNA is removal of contaminating DNA. For most soils, this can be successfully achieved using a simple dilution of a DNA/RNA nucleic acid extract and DNase (e.g., RQ1 DNase (Promega)). However, additional steps can be used prior to DNase treatment

10


such as using an RNeasy kit (Qiagen) with gDNA columns to remove most DNA and humic acids, though these add considerable expense to the procedure. Control reactions should always be performed to ensure that all DNA is digested, as humic acids, high salt contents, and other factors can reduce the efficiency of the reaction. RNA can then be purified from the DNase reaction, but it can also be used directly in reverse transcription reactions. cDNA can be generated by using a gene-specific reverse primer, if only one target gene is analyzed. Alternatively, random hexamer primers can be used to produce a cDNA library for all RNA molecules and resultant cDNA can be used for any target gene (e.g., 16S rRNA and amoA of both ammonia-oxidizing bacteria and archaea). In addition, the use of random hexamer primers (e.g., Invitrogen) has been observed to be more successful in generating archaeal amoA cDNA than a specific amoA reverse primer (G. W. Nicol, unpublished data). Briefly, a relatively simple protocol used by Nicol et al. (2008) is as follows: 1. In a microcentrifuge tube (0.2 or 0.6 ml allows the use of a thermocycler for all incubation temperatures), add 1 ml RQ1 DNase buffer, 2 ml RQ1 DNase, and 7 ml of DNA/RNA nucleic acid extract (equivalent to around 100 ng RNA, but much lower should also be successful depending on the relative level of transcription). Incubate this 10 ml reaction for 60 min at 37 C before adding 1 ml stop solution (supplied) and incubating for 10 min at 65 C. 2. Briefly centrifuge tubes to collect contents, add 1 ml of 150 mM random hexamer primers (e.g., Invitrogen), 1 ml of 40 mM (i.e., 10 mM each) dNTPs, and optionally 1 ml of an RNase inhibitor (e.g., RNaseOUT (Invitrogen)). Incubate this 13 ml reaction mixture at 65 C for 2 min and then quickly move to an ice water bath to rapidly chill the contents and prevent secondary structure formation. 3. Add 4 ml 5 Superscript II buffer, 2 ml 0.1 M DTT (Invitrogen), and incubate at 25 C for 2 min. Add 1 ml Superscript II RT enzyme and incubate at 25 C for 10 min and then 42 C for 50 min. Inactivate the reaction by heating at 70 C for 15 min. Negative controls must always be performed and should include the treatment of RNA samples with all reagents except the addition of RT enzyme to ensure that positive PCR products are derived from cDNA. In addition, a water-only control ensures that all reagents are contaminant free (including the RT enzyme).

2.4. PCR of 16S rRNA genes Putative soil thaumarchaeal ammonia oxidizers have been investigated using both 16S rRNA genes and functional genes. The former are of great value for investigation of bacterial ammonia oxidizers, all of which appear to fall


11

within a single, monophyletic 16S rRNA-gene-defined group within the betaproteobacteria. All cultivated members of this group oxidize ammonia, and 16S rRNA genes have arguably been used more than amoA genes to examine ammonia-oxidizing bacterial populations. For thaumarchaea, although there is evidence of some congruency between 16S rRNA- and amoA-phylogenies (e.g., Nicol et al., 2008), few cultures are available and no soil thaumarchaeal ammonia oxidizers have been cultivated. All known ammonia-oxidizing archaea belong to the Thaumarchaeota, but it is not yet known whether all organisms within this lineage are capable of ammonia oxidation. As the known diversity of thaumarchaeal lineages (based on 16S rRNA gene sequences) is greater than that of amoA-defined lineages, there is a possibility that current amoA gene primer sets target a restricted diversity of sequences. The use of thaumarchaeal 16S rRNA gene primers may therefore be useful in assessing thaumarchaeal growth during nitrification activity (or some other functional process) in situations where amoA genes are either divergent and uncharacterized or simply absent in some thaumarchaeal populations. They will also be useful in addressing the potential for different metabolisms (autotrophy, mixotrophy, heterotrophy) within soil thaumarchaeal communities. In addition, RNA-targeted analysis of 16S rRNA genes could potentially provide information on the more “active” members of a community, rather than the total community of a particular phylotype. This is based on the assumption that active cells will contain greater numbers of ribosomes than inactive cells and that cellular RNA levels will increase prior to cell division, following stimulation of activity. However, it should be noted that high and low ribosome contents are not always indicative of high and low activity, respectively (Wagner et al., 2003). In many soil environments, thaumarchaea represent the dominant component of archaeal communities (Bates et al., 2010), that is, they are much more abundant than methanogenic euryarchaea, and therefore the use of “general” archaeal primers sets results in the recovery of mainly thaumarchaeal sequences. However, thaumarchaeal biased primers may be required where methanogens represent a significant component of the microbial community (e.g., high water contents soils such as rice paddies or peat bogs). A brief selection of widely used archaeal and thaumarchaeal 16S rRNA gene primers is presented in Table 1.1 and can be generally paired in any combination with little requirement for optimization. For phylogenetic analysis, longer sequences are of course more informative, but analyses commonly use the first 900 nucleotides or so of the 16S rRNA gene and this is suitable for discrimination and robust phylogenetic analysis of major thaumarchaeal lineages. It should be noted that while certain primers or primer sets may be purported to cover “archaea” or subgroups, there are no individual primers that exhibit 100% identity across all archaeal lineages and some level of mismatch and bias should be expected. If a particular lineage is

Table 1.1 A selection of primers used to amplify thaumarchaeal gene sequences in environmental samples Primer

16S rRNA 20F A109f PARCH519r 771f Ar9r 957r 1492r amoA Arch-amoAF CrenAmo1F CrenamoA23f Arch-amoAR CrenAmo1R CrenamoA616r accA Crena_529F Crena_981R AccAF573 AccAR279 AccTaq183 hcd hcd-465F hcd-911F hcd-1267R a b c

Specificity

Sequence

Positionc

Reference

Archaea Archaea Archaeaa Thaumarchaeac Archaea Thaumarchaeac Universalb

TTCCGGTTGATCCYGCCRG ACKGCTCAGTAACACGT TTACCGCGGCKGCTG ACGGTGAGGGATGAAAGCT CCCGCCAATTCCTTTAAGTTTC CGGCGTTGACTCCAATTG GYYACCTTGTTACGACTT

2–20 80–96 451–465 685–703 842–863 894–911 1420–1437

Massana et al. (1997) Grosskopf et al. (1998) vrea˚s et al. (1997) Ochsenreiter et al. (2003) Jurgens et al. (1997) Ochsenreiter et al. (2003) Nicol et al. (2008)

Thaumarchaea Thaumarchaea Thaumarchaea Thaumarchaea Thaumarchaea Thaumarchaea

STAATGGTCTGGCTTAGACG AATGGTCTGGCTWAGACGC ATGGTCTGGCTWAGACG GCGGCCATCCATCTGTATGT GACCARGCGGCCATCCA GCCATCCATCTGTATGTCCA

4–23 6–24 7–23 619–638 628–644 616–635

Francis et al. (2005) Ko¨nneke et al. (2005) Tourna et al. (2008) Francis et al. (2005) Ko¨nneke et al. (2005) Tourna et al. (2008)

Thaumarchaea Thaumarchaea Thaumarchaea Thaumarchaea Thaumarchaea

GCWATGACWGAYTTTGTYRTAATG TGGWTKRYTTGCAAYTATWCC GTTYGTYACDGGDCCYGAYG TGATRTRRTCCATRCAHTCRTA TTTCRWTBGAYGAWYTDGGTGGAGCWA

529–552 961–981 573–592 697–718 620–646

Yakimov et al. (2009) Yakimov et al. (2009) Yakimov et al. (2009) Yakimov et al. (2009) Yakimov et al. (2009)

Thaumarchaea Thaumarchaea Thaumarchaea

GGHGGTGCWATGACTGAT AGCTATGTBTGCAARACAGG CTCATTCTGTTTTCHACATC

448–465 892–911 1267–1286

Offre et al. (2010) Offre et al. (2010) Offre et al. (2010)

This primer is identical to several “bacterial-specific” primers over this region and can be considered suitable for PCR amplification of bacterial 16S rRNA genes sequences also. This is a modified version of the standard 1492r which has several mismatches to thaumarchaeal 16S rRNA gene sequences. According to 54d9 numbering for 16S rRNA and amoA primers, Nitrosopumilus maritimus for acc and hcd sequences.


13

of interest, it is often possible to use specific, biased primers (Nicol et al., 2006). Therefore, for most soil environments, primers A109f and 1492r (Table 1.1) are suitable for amplifying archaeal 16S rRNA genes. For specifically thaumarchaeal sequences, a suitable primer combination would be A109f and 957r (Table 1.1). For both these sets, a standard PCR cycling protocol with a 55 C annealing temperature is suitable (95 C 5 min; 95 C 30 s, 55 C 30 s, 72 C for 1 min per kb for 35 cycles; 72 C for 10 min). When soil DNA is used as a PCR template, it is generally recommended to add BSA (final concentration 0.2 mg ml 1).

2.5. PCR amplification of amoA genes Analysis of amoA genes enables specific detection of putative ammoniaoxidizing archaea. The presence of the amoA gene does not, however, provide direct evidence of ammonia-oxidizing activity by the host organism. Cells may be dormant, amoA gene products potentially have alternative functions and genes may not be expressed under prevailing environmental conditions. However, such reservations apply equally to bacterial ammonia oxidizers and comparison of bacterial and archaeal amoA genes is the most common approach to assessment of the potential relative importance of these groups in soil nitrification. Thaumarchaeal amoA gene sequences are distinct from those of bacteria, with only 25% identity and 40% similarity shared between AOA and AOB sequences at the amino acid level. Therefore, different primer sets are used to specifically amplify amoA gene sequences from either AOA or AOB. Initial primers sets for studying AOA amoA diversity and abundance (e.g., Francis et al., 2005; Ko¨nneke et al., 2005; Treusch et al., 2005) were designed on a limited number of sequences deposited in GenBank, specifically those from the soil fosmid 54d9 (Treusch et al., 2005) and sequences recovered in the Sargasso Sea metagenome sequencing project (Venter et al., 2004). Consequently, these primers are designed to hybridize at similar positions, producing PCR products of 600 bp. A number of other primers and probes were also designed which hybridize toward the center of the gene (e.g., Treusch et al., 2005) but increasing amounts of sequence data highlight that there are no suitable conserved primer sequences which cover all sequences toward the middle of the archaeal amoA gene. Therefore, primers which hybridize at the ends of the amoA gene remain the best choice for general assays. However, assays for specific AOA lineages can be designed using other positions (e.g., Beman et al., 2008; Offre et al., 2009) and their comparatively short length may increase the efficiency of amplification which may be particularly important in qPCR assays. Table 1.1 highlights a selection of commonly used thaumarchaeal amoA primers.

14


2.6. Genes involved in CO2 fixation in the 3-hydroxypropionate/4-hydroxybutyrate cycle There is currently a large amount of uncertainty regarding the mode(s) of carbon metabolism possessed by soil thaumarchaea, particularly as they are represented by a relatively wide amount of phylogenetic diversity. SIP studies provide evidence for both heterotrophic ( Jia and Conrad, 2009) and autotrophic (Zhang et al., 2010) metabolism in soil and available pure (but nonsoil) cultures all grow autotrophically. Genomes and cultures of soil thaumarchaea are not currently available but the genomes of N. maritimus (Walker et al., 2010) and C. symbiosum (Hallam et al., 2006), both of marine origin, possess genes indicative of both heterotrophic and autotrophic metabolism. The latter indicate assimilation of inorganic carbon using the 3-hydroxypropionate/4-hydroxybutyrate cycle (Berg et al., 2007). A number of studies have therefore developed primers targeting genes encoding key enzymes of this pathway. 2.6.1. Analysis of acetyl-CoA carboxylase genes Acetyl-CoA carboxylase (ACCase) catalyzes the transformation of acetylCoA and one bicarbonate molecule into malonyl-CoA. This enzyme is involved in fatty acid synthesis in many organisms, but as archaea do not possess fatty acids this enzyme could be indicative of the 3-hydroxypropionate/4-hydroxybutyrate cycle in autotrophic archaea. Yakimov et al. (2009) developed assays for accA, encoding the a-subunit of ACCase. Several primer sets were developed (Table 1.1) based on analysis of N. maritimus and C. symbiosum genomes and six marine environmental sequences of assumed thaumarchaeal origin. Primers PcB_388F and PcB_1271R were used to amplify genes from DNA extracted from a deep-sea sample and amplified both bacterial and archaeal accA sequences which were derived from organisms placed in distinct phylogenetic clusters. Specific primers Crena_529F and Crena_981R (Table 1.1) were then designed to target crenarchaeal accA genes which were subsequently sequenced and used to develop a TaqMan qPCR protocol using primers AccAF573, AccAR279, and probe AccTaq183 (Table 1.1). qPCR analysis indicated similar abundance of amoA and accA genes. The study therefore demonstrates the potential for analysis of putative autotrophic thaumarchaea using these primer sets, though primer design and environmental analyses were restricted to marine environments and analysis of soil communities may require further development. Similarly, Auguet et al. (2008) designed primer sets targeting genes encoding the c-subunit (accC). While these retrieved sequences are of assumed archaeal origin, they also amplified a large proportion of bacterial accC gene sequences and may not be suitable for retrieving exclusively autotrophic thaumarchaeal sequences.


15

2.6.2. Analysis of hcd genes 4-Hydroxybutyryl-CoA dehydratase (4HCD) catalyzes the transformation of 4-hydroxybutyryl-CoA to crotonyl-CoA and has been reported in crenarchaeal orders Sulfolobales, Desulfurococcales, and Thermoproteales, the euryarchaeal orders Archaeoglobales and Thermoplasmatales (Berg et al., 2007), thaumarchaea (Hallam et al., 2006; Walker et al., 2010), and some bacteria. Importantly, however, unlike ACCase encoding genes, 4HCD has not been found in obligately organotrophic archaea and therefore may be a more appropriate marker for detecting autotrophic populations. Offre et al. (2010) designed three sets of hcd primers, for construction of clone libraries, community analysis by DGGE, and qPCR analysis of putative autotrophic thaumarchaea in both terrestrial and aquatic environments. Details of hcd primers are given in Table 1.1. hcd analyses were performed on DNA extracted from N. maritimus and from a variety of habitats including soils and sediments. Clone libraries were constructed using 850-bp hcd PCR products amplified using primers hcd-465F and hcd-1267R before performing phylogenetic analysis (Fig. 1.1). Distinct phylogenetic clusters associated with the different habitats were observed demonstrating that this gene is a suitable marker for phylogenetic analysis of distinct autotrophic thaumarchaeal communities. DGGE profiles from environmental samples also indicated potential environment-specific communities, equivalent to those obtained from phylogenetic analysis providing confidence in the reliability of the primers. Primers hcd-911F and hcd-1267R were used for qPCR amplification (see Section 4), generating a 400-bp fragment of hcd genes and suitable for SybrGreenI qPCR assays with amplification efficiencies greater than 98.4% and r2 values > 0.999. Design of hcd primers is currently limited by the lack of cultured thaumarchaea and environmental genomes. The primers employed by Offre et al. (2010) showed good specificity, but were based on only seven sequences of marine origin. They were efficient for analysis of community structure and abundance in environmental samples, but reproducibility was lower for acid soil samples. This suggests that coverage of soil sequences may be low and that further development of hcd primers will be required for application to a broad range of environments, as additional sequences from cultivated organisms and from environmental surveys become available.

3. Community Composition and Diversity Soil thaumarchaeal ammonia oxidizer communities are generally characterized by amplification of archaeal amoA genes from nucleic acids extracted from soil and subsequent analysis by fingerprinting techniques (e.g., denaturing gradient gel electrophoresis (DGGE; Nicol et al., 2008) or

to Crenarchaea type-2 and Marine cluster 2

16


Ignicoccus hospitalis (ABU81777) Pyrobaculum aerophilum (NP_560189) Pyrobaculum islandicum (ABL87428) 1 Pyrobaculum calidifontis (ABO08816) 100 Sulfolobus solfataricus (NP_344059) 1 Sulfolobus tokodaii (BAB66740) 100 Metallosphaera sedula (ABP95479) Sulfolobus acidocaldarius (AAY81436) Archaeoglobus fulgidus (AAB90900) Fusobacterium nucleatum (ZP_00144047) 1 Clostridium kluyveri (EDK35028) 99 Clostridium aminobutyricum (CAB60035) Porphyromonas gingivalis (AAQ65866) Plesiocystis pacifica (ZP_01911327) 0.74 GOS_2467004 (ECX83722) 1 100 GOS_3005373 (ECU86878) 100 GOS_1581051 (EDB85322) GOS_2881847 (ECV53061) GOS_2593340 (ECX13204) HF4000_APKG3H9 (ABZ08580) 0.97 GOS_2981125 (ECU99544) 100 1 GOS_2974840 (ECV02783) 100 Craib20 Craib15 Craib4 Craib12 Craib17 Craib11 1 Craib3 Soil cluster 0.61 100 Craib8 81 Craib1 1 100

1 100

0.10

1 100

Craib7 Craib18 Craib16 Craib19 Craib14 1 GOS_2900658 (ECV42905) 100 GOS_1125765 (EDE46652) Craib5 1 Ythan1 100 Ythan18 1 Ythan11 100 Ythan13 Estuarine 1 Ythan19 sediment cluster 100 Ythan20 1 Ythan2 100 Ythan12 Ythan16 Ythan5 Cenarchaeum symbiosum (YP_874977) 1 Ythan15 95 Nitrosopumilus maritimus (ABX12103) Ythan6 Craib2 Craib9 1 77 Craib10 Soil cluster Craib13 Craib6 Ythan14 1 Ythan4 Estuarine 1 100 Ythan17 sediment Ythan9 86 cluster Ythan3 Ythan7 Ythan10

Crenarchaea type-1

Anaerobe cluster Marine bacteria

1 100

Thaumarchaea

Figure 1.1 Bayesian phylogenetic analysis of derived HCD protein sequences from Craibstone soil (pH 7) and Ythan river intertidal estuarine sediment. This analysis was performed on 260 unambiguously aligned positions and clustering of sequences associated with different habitats indicates that the hcd gene is a highly suitable marker for discriminating different autotrophic thaumarchaeal populations. Posterior probabilities and the most conservative value from three bootstrapping methods (ML, parsimony, and distance) are shown above and below nodes of major sequence groups, respectively. (Adapted from Offre et al., 2010, with permission.)

terminal restriction fragment length polymorphism (T-RFLP; Ho¨fferle et al., 2010) or by sequencing, comparison of sequences with those in databases and phylogenetic analysis (e.g., Leininger et al., 2006). The limitations of these approaches to characterize ammonia oxidizer communities, and microbial communities in general, are both conceptual (see Section 2.5)


17

and technical. Technical issues involve the possibility of preservation of amoA genes in extracellular nucleic acids, variation in cell lysis and cellextraction efficiency, PCR and primer biases, and other general limitations of molecular methods (Wintzingerode et al., 1997). Many controls and modifications are available to test for and limit potential technical problems. Nevertheless, many studies represent “true” molecular ecology, in studying the ecology of molecules, rather than the ecology of organisms that contain those molecules. In this respect, studies characterizing thaumarchaeal amoA genes must be aware of studying “communities” of amoA genes, rather than communities of thaumarchaeal ammonia oxidizers. Although frequently purporting to investigate diversity, many studies do not determine diversity in a meaningful way. Coverage is rarely determined and quantitative estimates of diversity (richness, evenness, diversity indices) are rarely calculated. Estimation of coverage is generally not possible using fingerprinting techniques, which also provide little information on richness (Bent and Forney, 2008). These techniques detect differences in dominant phylotypes only, and therefore much of the richness will be below the level of detection or beyond the discriminatory power of DGGE gels or T-RFLP traces. amoA richness is not equivalent to the number of DGGE bands or T-RFLP peaks. Coverage and diversity indices can be determined and quantified from sequence data and provide one of the major advantages of high-throughput sequencing, which can generate sufficient numbers of sequences to make meaningful estimates.

3.1. Nucleic acid fingerprinting Commonly used fingerprinting techniques to investigate thaumarchaeal ammonia oxidizers in soil are DGGE and T-RFLP. While not directly providing any information of the primary sequence of profiled organisms, these techniques allow the rapid and relatively inexpensive comparison of community compositions. Both approaches aim to distinguish PCRamplified 16S rRNA gene or amoA gene fragments with identical length but different sequences. DGGE separates PCR products of the same length but differing in primary sequence. Different sequences possess different melting domains resulting in contrasting denaturing and migration characteristics in an increasing gradient of urea and formamide (Muyzer et al., 1993). Similarly, T-RFLP involves the separation of amplicons amplified using a primer (s) with an attached fluorophore. After digestion with selected restriction enzymes, fragments containing the labeled primer (the terminal restriction fragment or T-RF) are separated and detected using a standard Sanger sequencer, with different sequences potentially having different restriction sites and therefore producing different T-RFs. The resolution of the two techniques is similar. T-RFLP has advantages in that greater numbers of samples can be analyzed more quickly and with greater consistency, but

18


provides less information on identity, which can be obtained by excision, purification, and amplification of DGGE bands. (For a detailed description of T-RFLP, see Marsh, 2005.) For DGGE analysis of thaumarchaeal communities in soil, both 16S rRNA and amoA genes have been successfully used to profile community structures (e.g., Nicol et al., 2008; Fig. 1.2). DGGE analysis often (but not always) requires the use of primers with a 40 bp GC-clamp to improve resolution. A suitable primer set for profiling thaumarchaeal 16S rRNA genes is 771f and 957r (Table 1.2) but with the GC-clamp of Muyzer et al. (1993) added to the 50 end of 957r. For amoA genes, primers CrenamoA23f and CrenamoA616r (Table 1.2) produce excellent results with these primers not requiring a GC-clamp.

3.2. Analysis of clone libraries Thaumarchaeal putative ammonia oxidizer communities in soil can also be characterized by sequencing amplified archaeal amoA gene sequences (e.g., Francis et al., 2005). This therefore provides direct information on amoA sequences, rather than indirect analysis by fingerprinting techniques, but traditional analysis by construction of clone libraries and sequencing of 16S rRNA pH 4.9

pH 7.5

amoA pH 4.9

pH 7.5

Figure 1.2 DGGE analysis demonstrating differences in thaumarchaeal community structures in soils at pH 4.9 or 7.5 using PCR amplicons of 16S rRNA and amoA genes. For 16S rRNA genes, a nested PCR approach was used with archaea-specific primers A109f and 1492r in the first round, and thaumarchaea-specific primers 771f and 957r (with GC-clamp) in the second round (see Table 1.2) and a denaturing gradient of 35–70% denaturant. For amoA genes, a single round of amplification was used with primers CrenamoA23f and CrenamoA616r (Table 1.2) with a gradient of 15–55%. Each lane represents an individual field sample. (Adapted from Nicol et al., 2008, with permission.)

Table 1.2 A selection of primers used for DGGE analysis of thaumarchaeal communities Primer

16S rRNA 771f 957r-GC

Specificity

Sequence

Gradient (%)a Positionb Reference

Thaumarchaea ACGGTGAGGGATGAAAGCT 35–70 Thaumarchaea CGCCCGCCGCGCGCGGCGGG CGGGGCGGGGGCACGGGGG GCGGCGTTGACTCCAATTG

amoA CrenamoA23f Thaumarchaea ATGGTCTGGCTWAGACG 15–55 CrenamoA616r Thaumarchaea GCCATCCATCTGTATGTCCA hcd hcd-681F-SCM1-GC Thaumarchaea CGCCCGCCGCGCGCGGCGGG 20–50 CGGGGCGGGGGCACGGGG GGGCAATTCCTGCAGATGCA hcd-892R-SCM1 Thaumarchaea CCTGTTTTGCAAACATAGCT a b

100% denaturant defined as 40% formamide (v/v) and 7 M urea (42%, w/v). According to 54d9 numbering for 16S rRNA and amoA primers, Nitrosopumilus maritimus for hcd sequences.

685–703 Ochsenreiter et al. (2003) 894–911 Ochsenreiter et al. (2003)

7–23 Tourna et al. (2008) 616–635 Tourna et al. (2008) 664–681 Offre et al. (2010)

892–911 Offre et al. (2010)

20


representatives from libraries is significantly more time consuming and expensive, particularly if a reasonable level of replicated coverage is desired. In addition, 16S rRNA gene analysis can also be useful, particularly for profiling thaumarchaeal lineages that are not yet known to possess ammonia monooxygenase genes (e.g., Groups 1.1c and 1.3 thaumarchaea which are abundant in acidic forest and flooded soils, respectively; Prosser and Nicol, 2008). Phylogenetic analysis of sequences places communities within the context of database sequences from cultivated organisms and other environmental studies and relative abundances of different phylotypes can be determined by quantification of relative proportions of phylotypes in clone libraries. Identity of individual sequences of phylotypes can be determined by phylogenetic analysis or by direct comparison with database sequences. For the analysis of amoA genes (or any coding gene), phylogenetic analysis can be performed on both the DNA and translated protein sequence. Selection pressure during evolution is on the resulting amino acid and functioning protein, and it could be considered most appropriate to use protein sequences for reconstructing phylogenies. However, due to the relatively high level of conservation of amoA sequences, discrimination of closely related sequences may be better achieved by performing analyses on DNA sequences. As with any phylogenetic analysis, it is recommended that careful consideration is given to the correction models used in analysis. Programs such as JModelTest (Posada, 2008) and ProtTest (Abascal et al., 2005) can be used for selecting suitable correction models in phylogenetic analysis, and comparison of different methods (e.g., maximum likelihood, Bayesian, distance, parsimony) will give confidence of particular tree topologies.

3.3. High-throughput sequencing methods High-throughput sequencing methods (typically 454 pyrosequencing in microbial ecology) are now becoming less expensive and are likely to replace traditional clone library analysis and potentially even fingerprinting techniques in the near future (for a detailed description of this approach, see Huse et al., 2008). Due to the large number of sequences obtained from a single plate (or a small portion of a plate such as one quarter or one eighth) and the increasing read-length of sequences obtained, high-throughput sequencing approaches offer an increase in the depth of coverage and the ability to detect particularly rare phylotypes, potentially providing more reliable estimates of relative abundance and assessment of diversity indices, such as richness and evenness, compared to traditional clone libraries. For simply determining similarities or differences in ammonia-oxidizing archaeal communities, the use of DGGE and T-RFLP analyses is likely to continue for some time as both are rapid and inexpensive. However, additional costs required to determine which community members contribute to changes (i.e., identification of band/peak sequences) are significant and relatively time consuming


21

and the cumulative costs may soon exceed those of high-throughput sequencing. A significant issue with pyrosequencing data is the level of sequence “noise,” that is, identification and removal or treatment of sequences which have errors and which can artificially increase diversity estimates (Kunin et al., 2010). The treatment of sequence data is therefore a critical issue and approaches are being developed which aim to remove sequencing noise from pyrosequencing data (e.g., Quince et al., 2009).

4. Determining Growth and Abundance Ammonia oxidizer abundance is traditionally determined using the most probable number (MPN) method (e.g., McCaig et al., 1999). This assesses viable cell abundance only, suffers from the limitations of all cultivation-based techniques (Head et al., 1998), and does not distinguish bacterial and archaeal ammonia oxidizers. Indeed, the difficulties experienced in enriching archaeal ammonia oxidizers in standard ammonia oxidizer inorganic media suggest that MPN counts were dominated by bacterial ammonia oxidizers. Abundance, or biomass concentration, can also be estimated through measurement of potential nitrification, in which nitrate production rates are measured under “optimal” growth conditions. This approach assumes constant and maximal growth and activity of all ammonia oxidizers, which is unlikely, and therefore also suffers many of the problems associated with cultivation-based methods. Quantification of marker genes potentially provides a better estimate of ammonia oxidizer abundance and qPCR of specific 16S rRNA genes (bacterial ammonia oxidizers) and amoA genes (bacterial and thaumarchaeal ammonia oxidizers) has been employed. Use of specific amoA gene primers distinguishes bacterial and archaeal ammonia oxidizers, and subgroups, and has been used to quantify the relative gene abundances of these two groups in attempts to assess their relative contributions to soil nitrification (e.g., Leininger et al., 2006; Offre et al., 2009). These methods quantify the abundance of genes and not cells, and the number of gene copies will vary between different populations. For example, the thaumarchaea N. maritimus and C. symbiosum each possess one copy (Hallam et al., 2006; Walker et al., 2010), whereas Nitrosomonas and Nitrosospira species can possess between one and three copies (Norton et al., 2002).

4.1. Changes in relative abundance Changes in community composition in environmental samples can be followed using fingerprinting techniques described previously, with relative abundance of dominant thaumarchaea ammonia oxidizer phylotypes potentially extrapolated by calculation of the relative area of T-RFLP traces or

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relative band intensities of DGGE profiles. Although assessment of relative abundance from fingerprint data has limitations, it has proved reliable for analysis of relatively simple profiles generated by ammonia oxidizers. In itself, however, information on changes in relative abundance does not provide evidence of growth, as increase in relative abundance of a phylotype could be due to its growth or decrease in abundance of other phylotypes. These data do, however, indicate whether communities are responding to different conditions, and this approach has been used to provide evidence of response of thaumarchaeal ammonia oxidizers to temperature (Tourna et al., 2008), pH (Nicol et al., 2008), and inhibition by particular compounds such as acetylene (Offre et al., 2009).

4.2. Quantification of gene abundance The most commonly used approach to measure the abundance of individual populations or communities of thaumarchaeal ammonia oxidizers in soil is quantitative PCR of amoA genes (see Smith and Osborn (2009) for a review of quantitative PCR methodologies). This enables not only quantification of population sizes in a soil sample, but allows relatively accurate assessment of changes in abundance during incubation studies. A significant issue in the analysis of ammonia-oxidizing archaea is the design of primers (and potentially probes) used in qPCR assays. Studies involving single organisms/populations (e.g., monitoring the expression of a particular gene in response to a particular stimulus) have the option of designing primers using standard optimality criteria (i.e., lack of dimerization and complementarity, primers possessing similar Tm, relatively and within relatively close proximity (100–200 bp apart), etc.). However, in most microbial ecology studies, researchers often do not have the luxury of designing primers based on thermodynamic considerations, but only those which enable selection for particular phylogenetic groups. The number of suitable discriminatory primer positions (i.e., those which are conserved between all members of a monophyletic lineage but exclude nontarget sequences) is often limited, primers hybridizing to suitably conserved regions may not possess optimal or comparable thermodynamic properties, and there may be relatively large distances between suitable primer sites. Therefore, substantial optimization may be required to produce a qPCR assay with acceptable amplification efficiencies. As with any qPCR assay, the choice of standard is critical. Primers must exhibit complete identity to the standard, as mismatches reduce the efficiency of amplification and potential overestimation of gene abundance in an environmental sample (if the primers match perfectly to environmental DNA templates). For example, the first primers designed for AOA amoA generally exhibit at least one mismatch with the amoA gene of N. maritimus, including those presented in Table 1.1, indicating that there should be a revision of primer sets which are currently used.


23

A common approach is to generate PCR products using the primers that will be used in the qPCR followed by subsequent cloning and preparation of a plasmid containing an amoA insert. While this will ensure that there is an exact match between the standard template DNA and primer, it must be noted that the primer sites used are “artificial” (i.e., they represent the sequence of synthesized primers and not native DNA sequences). It is good practice to linearize the plasmid, as this can affect the efficiency of amplification (Suzuki et al., 2000). While these approaches will enable comparison of gene abundance between different samples, caution must always be employed in interpreting data as absolute numbers. No extraction method will recover all nucleic acids, and different protocols will most probably vary in the quantities recovered. An example of the application of both fingerprinting and qPCR in assessing growth of thaumarchaeal putative ammonia oxidizers is illustrated in Fig. 1.3 (Offre et al., 2009). Soil microcosms were incubated in the presence and absence of acetylene (a nitrification inhibitor) and were destructively sampled at 10-day intervals for analysis of ammonia, nitrite, and nitrate. In microcosms containing acetylene, ammonia oxidation was inhibited (with ammonia concentrations increasing due to continued ammonification) while nitrite þ nitrate concentrations did not increase. Nitrification was accompanied by changes in DGGE profiles of thaumarchaeal amoA genes, with a number of bands appearing, indicative of growth. To demonstrate actual growth, thaumarchaeal ammonia oxidizers were quantified by qPCR of amoA genes, using primers targeting a specific subpopulation. These data therefore demonstrate the detection of growth of soil thaumarchaeal ammonia oxidizers directly associated with nitrification.

5. Activity The previous section describes analysis of growth of putative thaumarchaeal ammonia oxidizers by estimating changes in abundance or relative abundance of thaumarchaeal amoA genes. These methods target DNA amplified from environmental samples and there is often strong correlation between changes in gene abundance and activity in soils (e.g., Offre et al., 2009) and other environments (e.g., Beman et al., 2008). The high abundances of ammonia oxidizers in some soils, and high rates of cell activity, could, however, lead to high rates of ammonia oxidation without increases in actual cell numbers and detectable growth. This is particularly important where ammonia concentration or ammonia flux is low and insufficient to give significant increases in biomass or cell abundance. Soil nitrification activity is measured, in the simplest situations, as decreases in ammonia concentration or increases in concentrations of nitrite

24


A

Day 0 Control (no C2H2)

Day 30

B

C amoA gene copies (g–1 soil)

Nitrite + Nitrate (mg N g–1 soil)

Control (no C2H2)

10 Pa C2H2

50 40 30 20 10 0

0

10

20 Time (days)

30

Control

3 × 106 2 × 106 1 × 106 0

0

10

20 Time (days)

30

10 Pa acetylene

Figure 1.3 Inhibition of growth of ammonia-oxidizing archaea by acetylene. (A) DGGE analysis of amoA genes from AOA communities. Arrow indicates the growth of a specific population for which a specific qPCR assay was developed. (B) Inhibition of ammonia-oxidizing activity in microcosms with a 0.01% acetylene headspace partial pressure. (C) qPCR analysis demonstrating growth of AOA only in microcosms with active nitrification. (Adapted from data obtained by Offre et al., 2009, with permission.)

plus nitrate. This approach assumes negligible rates of other nitrogen cycling processes influencing ammonia, nitrite, and nitrate (e.g., mineralization of organic nitrogen, assimilation of ammonia and nitrate, and denitrification of nitrate). However, in other situations where these processes are significant, more sophisticated methods are required. These include utilizing specific inhibitors of ammonia oxidation and/or 15N-based techniques (see Norton and Stark, 2011). Demonstration and measurement of ammonia oxidation is frequently ignored in molecular analyses. Nevertheless, demonstration of ammonia oxidation is essential when interpreting molecular data on abundance, relative abundance, and community structure of ammonia oxidizers. The current lack of reliable specific inhibitors of archaeal or bacterial ammonia oxidation prevents discrimination of activity of these two groups, but they can be distinguished using specific amoA primers, enabling


25

exploitation of molecular techniques for in situ, cultivation-independent analysis of microbial activity. Two approaches, determination of transcriptional activity and SIP, are applicable to soil thaumarchaeal ammonia oxidizers. In addition, estimates of maximal activity have been made on the basis of amoA abundance.

5.1. Estimating activity from gene abundance data Ammonia-oxidizing activity can theoretically be determined as the product of specific cell activity (i.e., ammonia-oxidizing activity per cell) and the abundance of active cells. In practice, this approach is severely limited by a number of factors. Reliable quantitative estimates of specific activity are available only for a limited number of cultivated ammonia oxidizers and activity will vary between and within communities and with physicochemical conditions. Specific cell activities of cultivated organisms under optimal conditions may differ significantly from those of organisms that have never been characterized in laboratory culture under suboptimal conditions in the soil. In addition, abundance estimates based on traditional MPN methods suffer from the disadvantages of all cultivation-based methods and do not distinguish bacterial and archaeal ammonia oxidizers, while quantification of amoA gene abundance does not distinguish active and inactive cells. Nevertheless, this approach has been used to estimate maximal activity of archaeal ammonia oxidizers by extrapolation from cell activity of the marine archaeal ammonia oxidizer, N. maritimus. Data from BoyleYarwood et al. (2008) are used in the example below: Estimated specific activity of N. maritimus ¼ 0.3 1015 mol cell1 h 1 Number of amoA genes per cell ¼ 1 Approximate cell (amoA gene) abundance ¼ 107 archaeal amoA g 1 soil Maximum ammonia-oxidizing activity ¼ 0.3 10 2 mmol g 1 soil h 1 5. Unit conversion

1. 2. 3. 4.

¼ 7:2 102 mmol g1 soil d1 ¼ 1:08 mg N g1 soil d1 ¼ 1:08 mg N kg1 soil d1 6. Measured nitrification potential ¼ 1.68–3.89 mg N kg 1 soil d 1 On the basis of these estimates and calculations, thaumarchaeal ammonia oxidizers could have contributed a maximum of 27–64% of the nitrification potential in this soil.

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5.2. Quantifying transcriptional activity As RNA is more labile than DNA, it is assumed that RNA (rRNA and mRNA) is a better indicator of activity than DNA. While this may be the case, it should be recognized that ribosomes can be relatively stable structures and ribosome content in metabolically inactive cells can be maintained at relatively high levels (e.g., Flardh et al., 1992). Also, changes in the abundance of gene transcripts may not necessarily relate directly to actual measured process activity (Freitag and Prosser, 2009), and knowledge of RNA turnover, mRNA stability, and rates of translation are difficult to determine for microbial communities in soil. Ratios of gene transcript to gene abundance can, however, show better correlation with process measurements (Freitag and Prosser, 2009). Therefore, quantitative PCR analysis of mRNA transcripts and gene abundance can be a useful approach in determining changes in potential activity. In terms of measuring absolute abundance of mRNA in an extracted RNA sample (rather than relative changes in response to an amendment or perturbation), there are a number of issues which must be considered and recognized. First, a significant issue is the efficiency of cDNA production from mRNA. For absolute amounts, standard curves should be generated from cDNA which is derived from known quantities of RNA copies, produced from a standard by in vitro transcription. Also, depending on the purification steps used before cDNA synthesis, there may be significant losses of RNA. Quantitative analysis of transcripts (or for that matter, gene copies) is perhaps more robust and informative when analyzing changes in transcript abundance (rather than absolute abundance) during an experiment, be it a microcosm experiment or response to perturbation.

5.3. Stable isotope probing SIP determines which members of a microbial community actively assimilate a substrate during incubation with a stable isotope of the substrate. The majority of SIP studies have involved incubation with 13C-labeled compounds and, for ammonia oxidation, have been used to investigate autotrophic activity, through incubation with 13C-labeled carbon dioxide or bicarbonate. SIP therefore measures assimilation of inorganic carbon by putative ammonia oxidizers which are autotrophs, and is not a direct measure of ammonia oxidation per se. Following incubation, extracted 13 C- and 12C-labeled DNA or RNA are fractionated and PCR/RTPCR (analysis of 16S rRNA or functional genes) is used to determine which organisms have incorporated inorganic carbon. Only autotrophic organisms will assimilate inorganic carbon and, in many cases, soil ammonia oxidizers will be detected by analysis of bacterial or archaeal 16S rRNA


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gene sequences present in labeled nucleic acids. In the presence of other active autotrophs, analysis of amoA gene sequences provides greater specificity and more direct evidence for links between ammonia oxidation and carbon fixation. During extended incubation, cross-feeding may occur, through secondary utilization of organic carbon derived from autotrophs through death or excretion. This problem is much less important than when applying SIP to heterotrophs, as 13CO2 produced during respiration by both primary and secondary utilizers will be assimilated only by autotrophs, and analysis of functional genes will detect activity of ammonia oxidizers only. It is possible, however, that organisms with amoA genes may be mixotrophic or heterotrophic and secondary utilization of organic carbon, fixed by autotrophs, could lead to amoA genes in 13C-labeled nucleic acids. Problems of cross-feeding are therefore best solved by SIP analysis of samples taken over a time course during incubation. RNA-SIP may provide greater sensitivity than DNA-SIP as 13C can be incorporated into new RNA transcripts without the cell going through cell division. Sequence analysis of functional genes in 13C-labeled RNA provides information on which organisms are actively transcribing specific genes, rather than those that are growing, potentially using other metabolisms. SIP analysis has been used to investigate ammonia oxidizers in microcosms containing soil or sediment incubated with inorganic carbon in the form of 13CO2 in the headspace (e.g., Jia and Conrad, 2009; Zhang et al., 2010) or 13C-bicarbonate in solution (e.g., Freitag et al., 2006). For soil incubations, a typical protocol is the following: 1. Sample and prepare soil to be used in microcosm experiment. Soil should be sieved (e.g., using a 3.35 mm sieve) to remove stones and plant material and the water content determined (105 C for 24 ) so that the soil can be adjusted to a particular percentage of the water-holding capacity or water-filled pore space. 2. Aliquot soil into serum vial bottles, maintaining a large headspace:soil volume ratio to enable maintenance of aerobic conditions (e.g., 10 g soil (dry weight equivalent) in a 144-ml serum vial bottle). Apply amendment (if any, e.g., ammonium) and close with butyl rubber stoppers and crimp to seal. 3. Supply carbon dioxide from a gas cylinder by injection through a rubber suba-seal into a gas bag or “reservoir” syringe (Fig. 1.4A). Consideration should be given to any air that may have diffused into the regulator, and it is advisable to expel a volume of gas equal to or greater than that present in the regulator, to avoid sampling air. 4. Using a smaller syringe and needle, remove gas from the reservoir syringe/gas bag (Fig. 1.4B) and inject into a microcosm through the butyl rubber seal (Fig. 1.4C). Depending on the volume being added, a slight overpressure will be produced in the microcosm bottle. Although not always necessary, after allowing an adequate time for gas diffusion, a

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A

B

C MS MS

RS

RS

Microcosm

13CO 2

D

Peristaltic pump

E

Sampling assembly

Fraction collector

Water in

CsCl out

Figure 1.4 Establishing stable isotope probing experiments with 13CO2 gas and sampling isotopically enriched DNA after ultracentrifugation. (A) To ensure only small volumes of expensive 13CO2 gas are sampled, gas should be taken from a cylinder by slowly injecting gas through a suba-seal into a reservoir syringe (RS) (e.g., 50 ml in volume). (B) Small volumes can be removed from the large reservoir syringe using a smaller microcosm syringe (MS). (C) Gas is added to an individual microcosm by injection through a rubber septum. (D) After incubation, DNA extraction and ultracentrifugation (see text), genomic DNA of different buoyant densities is collected by fractionating CsCl gradients by water displacement. Water is slowly added to the top of a polyallomer tube using a peristaltic pump and displaced through a needle which pierces the bottom of the tube (E). To ensure equal volumes are collected, displaced CsCl is dropped into microcentrifuge tubes positioned in a fraction collector.

syringe and needle can be used to remove the same volume of gas added to maintain the original pressure. If the seal has been punctured several times, a very thin smear of liquid silicone rubber can be applied to the top of the seal to prevent leakage. 5. If it is important to maintain aerobic conditions, microcosms should be opened regularly and flushed with air for a few minutes. If respiration kinetic data are available for a particular soil, this can be used as a guide to determine oxygen consumption rates. However, replenishment at 3–4-day intervals is adequate for most soils with a 10:1 headspace:soil ratio. Microcosms are then resealed and any gas treatments reestablished immediately. 6. Upon sampling, soils should be removed and placed in a zip-lock bag and frozen immediately (20 C is adequate for non-mRNA work. Otherwise 80 C is recommended).


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7. Extract and quantify DNA as described previously (see Section 2.2). 8. Prepare a CsCl solution with a buoyant density of 1.696 g ml 1 (refractive index 1.399) by adding 1.27 g CsCl ml 1 TE and adjusting the density by adding small amounts of CsCl or TE buffer. The buoyant density is (indirectly) determined accurately by measuring the refractive index using a refractometer (e.g., a Reichert AR200 digital refractometer) and converting to buoyant density. 9. Polyallomer tubes (typically 2 or 5 ml, with larger volumes easier to handle) are filled with CsCl solution and a small volume (e.g., 200 ml) of CsCl solution mixed with 0.5–1.0 mg DNA and 1.5 ml ethidium bromide (1 mg ml 1). [Depending on the choice of rotors available, a series of preliminary runs should be performed to achieve a density gradient of 1.64–1.74 g ml 1.] After careful balancing and sealing, suspensions are then centrifuged (e.g., in a Beckman Coulter VTI65.2 vertical rotor at 184,388g (45,000 rpm) for 24 h at 20 C). 10. CsCl density gradients are fractionated into equal volumes (e.g., 200 ml) by displacement with water and a fraction recovery system (Fig. 1.4D). The refractive index (buoyant density) is determined for each individual fraction (using 20 ml) and DNA recovered by overnight precipitation in PEG solution and ethanol washing as described previously (Section 2.2). 11. The distribution of genomic DNA of a target group (e.g., thaumarchaea) through the gradient is then determined by qPCR of an appropriate target gene (e.g., amoA or hcd). DNA moves within the CsCl gradient by diffusion and not all DNA migrates to the correct buoyant density. It is therefore important that both 12 C and 13C incubations are performed in parallel to remove ambiguities if levels of carbon incorporation are small. This approach has been used to demonstrate incorporation of inorganic carbon into actively growing thaumarchaeal populations in soil using a variety of genes encoding proteins putatively involved in carbon (hcd) and energy (amoA) metabolism as well as 16S rRNA genes (Zhang et al., 2010; Fig. 1.5). However, DNA-SIP does require that cells are actively growing in order to be detected and therefore other biomarkers may potentially be more sensitive for SIP of autotrophic populations in soil. These include analysis of RNA (rRNA and mRNA; Whiteley et al., 2007), analysis of thaumarchaeal-specific lipids such as crenarchaeol (Wuchter et al., 2003), and potentially even the analysis of individual proteins ( Jehmlich et al., 2009).

6. Conclusions Thaumarchaeal ammonia oxidizers were discovered only 5 years ago, few laboratory cultures have been obtained, and no soil isolate has yet been reported. Although soil isolates are likely to be obtained, molecular analysis

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0.5

Proportion of total copies in CsCl gradient

0.4

0.5 Archaeal hcd Day 0 0.4

Archaeal amoA Day 0

0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0 1.66

1.68

1.70

1.72

1.74

1.66

1.68

0.5 Archaeal amoA Day 14 0.4


0.3

0.3

0.2

0.2

0.1

0.1

0.0

1.70

1.72

1.74

1.70

1.72

1.74

1.70

1.72

1.74

0.0 1.66

1.68

1.70

1.72

1.74

1.66

1.68

0.5 Archaeal amoA Day 28 0.4


0.3

0.3

0.2

0.2

0.1

0.1

0.0

0.0 1.66

1.68

1.70

1.72

1.74

1.66

1.68

–1

Buoyant density (g ml ) 12C

13C

Figure 1.5 Buoyant density distribution of thaumarchaeal genomic DNA extracted from soil after incubation with a headspace concentration of 5% (v/v) 12C- or 13CO2. Replicate CsCl gradients were fractionated and represented a density range from 1.66 to 1.74 g ml 1. DNA was precipitated from each individual fraction and the abundance of thaumarchaeal amoA and hcd and bacterial amoA genes was determined in each fraction by quantitative PCR and plotted on a proportional scale. Vertical error bars represent the standard error of proportional abundance from triplicate CsCl spins (representing triplicate microcosms). Horizontal error bars represent the standard error of the buoyant density of fractions collected from the same point in replicate CsCl tubes. (Adapted from data obtained by Zhang et al., 2010, with permission.)

is limited by a lack of information on both metabolic and sequence diversity. For example, there is evidence for autotrophic, mixotrophic, and heterotrophic growth of thaumarchaea containing amoA genes, and the breadth of


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coverage of primers for amoA and other functional genes is uncertain. Within these limitations, the diversity, abundance, growth, and activity of thaumarchaea can be, and have been, investigated using a range of molecular microbial ecology techniques. In general, the only modification required for application of these techniques has been the use of specific primers. Importantly, however, the molecular analysis of thaumarchaeal gene and gene transcript diversity and abundance must be performed alongside appropriate process activity measurements.

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Spang, A., Hatzenpichler, R., Brochier-Armanet, C., Rattei, T., Tischler, P., Spieck, E., et al. (2010). Distinct gene set in two different lineages of ammonia-oxidizing archaea supports the phylum Thaumarchaeota. Trends Microbiol. 18, 331–340. Stephen, J. R., Kowalchuk, G. A., Bruns,, M.-.V., McCaig, A. E., Phillips, C. J., Embley, T. M., and Prosser, J. I. (1998). Analysis of b-subgroup proteobacterial ammonia oxidizer populations in soil by denaturing gradient gel electrophoresis analysis and hierarchical phylogenetic probing. Appl. Environ. Microbiol. 64, 2958–2965. Suzuki, M. T., Taylor, L. T., and DeLong, E. F. (2000). Quantitative analysis of smallsubunit rRNA genes in mixed microbial populations via 5’-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614. Treusch, A. H., Leininger, S., Kietzin, A., Schuster, S. C., Klenk, H.-P., and Schleper, C. (2005). Novel genes for nitrite reductase and Amo-related proteins indicate a role of uncultivated mesophilic crenarchaeota in nitrogen cycling. Environ. Microbiol. 7, 1985–1995. Tourna, M., Freitag, T. E., Nicol, G. W., and Prosser, J. I. (2008). Growth, activity and temperature responses of ammonia-oxidizing archaea and bacteria in soil microcosms. Environ. Microbiol. 10, 1357–1364. Venter, J. C., Remington, K., Heidelberg, J. F., Halpern, A. L., Rusch, D., Eisen, J. A., et al. (2004). Environmental genome shotgun sequencing of the sargasso sea. Science 304, 66–74. Wagner, M., Horn, M., and Daims, H. (2003). Fluorescence in situ hybridisation for the identification and characterisation of prokaryotes. Curr. Opin. Microbiol. 6, 302–309. Walker, C. B., de la Torre, J. R., Klotz, M. G., Urakawa, H., Pinel, N., Arp, D. J., et al. (2010). Nitrosopumilus maritimus genome reveals unique mechanisms for nitrification and autotrophy in globally distributed marine crenarchaea. Proc. Natl. Acad. Sci. USA 107, 8818–8823. Whiteley, A. S., Thomson, B., Lueders, T., and Manefield, M. (2007). RNA stable-isotope probing. Nat. Protoc. 2, 838–844. Wintzingerode, F. V., Go¨bel, U. B., and Stackebrandt, E. (1997). Determination of microbial diversity in environmental samples: Pitfalls of PCR-based rRNA analysis. FEMS Microbiol. Rev. 21, 213–229. Wuchter, C., Schouten, S., Boschker, H. T. S., and Sinninghe Damste´, J. S. (2003). Bicarbonate uptake by marine Crenarchaeota. FEMS Microbiol. Lett. 219, 203–207. Wuchter, C., Abbas, B., Coolen, M. J. L., Herfort, L., Van Bleijswijk, J., Timmers, P., et al. (2006). Archaeal nitrification in the ocean. Proc. Natl. Acad. Sci. USA 103, 12317–12322. Yakimov, M. M., Cono, V. L., and Denaro, R. (2009). A first insight into the occurrence and expression of functional amoA and accA genes of autotrophic and ammonia-oxidizing bathypelagic Crenarchaeota of Tyrrhenian Sea. Deep Sea Res. II 56, 748–754. Zhang, L., Offre, P. O., He, J.-Z., Verhamme, D. T., Nicol, G. W., and Prosser, J. I. (2010). Autotrophic ammonia oxidation by soil thaumarchaea. Proc. Natl. Acad. Sci. USA 107, 17240–17245.

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Responses of Aerobic and Anaerobic Ammonia/Ammonium-Oxidizing Microorganisms to Anthropogenic Pollution in Coastal Marine Environments Huiluo Cao,* Meng Li,* Hongyue Dang,† and Ji-Dong Gu* Contents 1. Introduction 2. Selection of Sampling Sites and Physicochemical Characterization 3. Molecular Ecological Characterization 3.1. Genetic markers 3.2. Extraction methods of total genomic DNA and clone library constructions 3.3. The abundance of aerobic and anaerobic ammonia/ ammonium-oxidizing microbes as determined by real-time fluorescent PCR (qPCR) 4. Community Structure Analyses 4.1. The diversity and richness of microbial community 4.2. Phylogenetic lineages 4.3. Community classification 5. Relationship Between the Community Structure and Physicochemical Parameters 5.1. Preparation of the input files 5.2. CCA analysis 5.3. Explanation of CCA statistical results 6. Relationships Between the Community Change and the Environments 7. Conclusions Acknowledgments References

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* School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, PR China State Key Laboratory of Heavy Oil Processing and Centre for Bioengineering and Biotechnology, China University of Petroleum (East China), Qingdao, PR China

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Methods in Enzymology, Volume 496 ISSN 0076-6879, DOI: 10.1016/B978-0-12-386489-5.00002-6

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Abstract Up to date, numerous studies have shown that the community structure of aerobic ammonia oxidizers including ammonia-oxidizing Betaproteobacteria (Beta-AOB) and ammonia-oxidizing archaea (AOA) and, more recently, the anaerobic ammonium-oxidizing (anammox) bacteria is responsive to environmental conditions including salinity, pH, selected metal ions, concentrations of inorganic nitrogen, total phosphorus, the ratio of organic carbon and nitrogen, and sedimentological factors such as the sediment median grain size. Identification of these responses to known anthropogenic pollution is of particular interest to better understand the growth dynamics and activities of nitrogen transforming microorganisms in marine environments. This chapter discusses currently available methods including molecular ecological analysis using clone library constructions with specific molecular genetic markers for delineating community changes of Beta-AOB, AOA, and anammox bacteria. Using data on ammonia-oxidizing microbial community structures from Jiaozhou Bay in North China and three marine environments with anthropogenic pollution gradients in South China from coastal Mai Po Nature Reserve of Hong Kong to the South China Sea as examples, statistical analyses packages (DOTUR, UniFrac, and Canoco) are presented as useful tools to illustrate the relationship between changes in nitrogen metabolizing microbial communities and established environmental variables.

1. Introduction Autotrophic oxidation of ammonia/ammonium plays a significant role in the global nitrogen cycle. Ammonia/ammonium is being oxidized by various types of microorganisms: in the presence of oxygen by obligate aerobic ammonia-oxidizing Gamma- and Betaproteobacteria (Gamma- and Beta-AOB) (Head et al., 1993; Kowalchuk and Stephen, 2001) and a newly discovered cohort of archaea, the ammonia-oxidizing archaea (AOA) (Ko¨nneke et al., 2005; Treusch et al., 2005; Venter et al., 2004), and in the absence of oxygen by anaerobic ammonium-oxidizing (anammox) bacteria identified as members of the Planctomycetes (Strous et al., 1999; van de Graaf et al., 1995). Aerobic ammonia-oxidizing microorganisms are known to catalyze the biochemical reaction from ammonia to nitrite, which is the first and rate-limiting step in nitrification (Kowalchuk and Stephen, 2001), while anammox bacteria oxidize ammonium to N2 using nitric oxide as oxidant under anoxic conditions ( Jetten et al., 2009). Earlier studies identified nitrite as electron acceptor (van de Graaf et al., 1995), but recent genome-informed studies showed that nitrite is first reduced to NO, which enables the oxidation of ammonium in the anammox process ( Jetten et al., 2009, and references therein).

Ammonia-Oxidizing Community and Anammox to Pollution

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Recent comprehensive molecular ecological studies have provided substantial information in that aerobic ammonia oxidizers respond to a wide range of environmental factors (Erguder et al., 2009; Kowalchuk and Stephen, 2001) indicating the possibility to utilize them as bioindicators for environmental changes (Dang et al., 2009a,b) and anthropogenic pollution (Cao et al., 2011; Li et al., 2010b, 2011b). The distinct AOB assemblages identified in these reports in correlation with particular environmental factors such as salinity, pH, concentrations of ammonium and oxygen may be the result of their differential ecophysiological adaptivity (Arp et al., 2007; Purkhold et al., 2003). Moreover, because the Beta-AOB are monophyletic, represent a greater phylotypic and ecophysiological diversity than GammaAOB, and contribute essential ecosystem functions to their diverse habitats, determination of their community structure (diversity and abundance) has been proposed to serve as a model system for the study of fundamental aspects of molecular microbial ecology (Kowalchuk and Stephen, 2001; Ward, 2005). Dang et al. (2010b) reported recently on the diversity, abundance, and spatial distribution of sediment Beta-AOB in response to environmental gradients and coastal eutrophication in Jiaozhou Bay, China, in which particular Beta-AOB community structures correlated with degrees of pollution in marine sediments, which prompted the proposal to use them as bioindicators for pollution. Other studies showed that spatial distribution and structure of sediment AOA communities were also influenced by a variety of environmental factors (Beman and Francis, 2006; Cao et al., 2011; Dang et al., 2008c, 2009a; Erguder et al., 2009; Li et al., 2011a,b; Mosier and Francis, 2008; Sahan and Muyzer, 2008; Santoro et al., 2008). The relative contributions by archaea and bacteria to aerobic ammonia oxidation in the environment have been recently reviewed (Prosser and Nicol, 2008; Schleper and Nicol, 2010). All of these reports indicated in particular, that estuarine and continental margin systems were significantly affected by continental input and these authors hypothesized that the sediment AOA community may serve as useful biotracers and bioindicators for specific environmental disturbances, especially land related wastewater pollution (Dang et al., 2008c, 2009a). For instance, the spatial distribution of putative soil-related AOA in the Changjiang (Yangtze) Estuary and the adjacent East China Sea indicated a strong influence of the Changjiang freshwater discharge on the microbial community in the coastal areas (Dang et al., 2008c). Likewise, the abundances of AOA and Beta-AOB correlated with the levels of environmental perturbation, and they may thus “bioindicate” feedback responses to environmental changes (Dang et al., 2010b; Mosier and Francis, 2008; Santoro et al., 2008). For example, AOA and Beta-AOB appear to respond to the availability of inorganic nitrogen species in different depths of the sediment and at different distances from the mangrove trees at Mai Po Nature Reserve in Hong Kong where anthropogenic pollution has been persistent for several decades (Li et al., 2011b). Collectively, sediment Beta-AOB or AOA community profiles

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appear to provide important and characteristic information about coastal marine environments under various degrees of pollution pressure including especially anthropogenic wastewater. The diversity and distribution of anammox bacteria indicate also niche specificity among various ecosystems (Dale et al., 2009). Whereas the Candidatus “Scalindua” genus dominates in marine environments (Schmid et al., 2007; Woebken et al., 2008), the Candidatus genera “Brocadia” and “Kuenenia” are usually found in engineered systems such as wastewater treatment plants (WWTP) though a few reports indicated their presence also in freshwater and marine ecosystems (Amano et al., 2007; Nakajima et al., 2008; Zhang et al., 2007). Several recent reports showed that the structures of anammox bacterial communities might be strongly affected by anthropogenic pollution input (Amano et al., 2011; Dang et al., 2010a; Li et al., 2010b). These results suggest that specific anammox bacteria closely associated with wastewater and anthropogenic contamination may serve as bioindicators for environmental quality, and they may thus be also suited for forensic detection of past environmental pollution (Li et al., 2010b).

2. Selection of Sampling Sites and Physicochemical Characterization Beta-AOB abundance and activity is higher in environments with high input of nitrogenous compounds, for example, sewage and agricultural runoff pollution, and the surface sediments beneath the influx-rich water column may be a site of choice for detecting evidence for past and present nitrification activity (Satoh et al., 2007). In addition, existing records of water and sediment physicochemical analyses are helpful in the selection of the specific sampling sites, because the available information may provide necessary and important background information with regard to polluted versus nonpolluted areas or existing gradients of pollution. Such strategy guided the research conducted at Jiaozhou Bay of China in that sampling sites of known different degrees of pollution were selected to detect differences in BetaAOB community structures as a function of pollution thereby constituting bioindicators (Dang et al., 2010b). In that study, the sampling stations of the Eastern Bay formed a group of “hyper-eutrophied” habitats with river runoffs and WWTP discharges from the nearby area (Dang et al., 2010b), while all other sampling sites grouped together representing the “non-eutrophied” type with lower nutrient concentrations. This study was complemented by a structure analysis of the anammox bacterial community in the same Jiaozhou Bay sediments in northern China (Dang et al., 2010a). Similarly, surface sediments from both the long-term marine aquaculture zones, the Mai Po Nature Reserve in Hong Kong, and the South China Sea have been studied,


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because these three adjacent environments form a strong gradient of anthropogenic pollution from the coastal shelf of the Pearl River Delta to the open ocean, thereby providing an ideal set of samples to elucidate the relationship between ammonia oxidizers and anthropogenic pollution (Li et al., 2010b). Considering historical events, stability, and higher biomass, surface sediments are generally better suited for analysis of the aerobic and anaerobic ammonia/ammonium oxidizer communities than the water column. Water samples are most problematic due to the hydrodynamics and, in particular, the high mobility due to tidal and current activity in coastal areas and circulation patterns in the open sea. This is different for sediment. While coastal sediment samples may exhibit significant site heterogeneity and should be sampled at least in triplicate, the open ocean sea floor is fairly homogenous. Sediment samples are usually collected from the surface layer (1–5 cm depth) and/or from sediment cores at different depths in regular intervals using a core sampler. It is highly recommended to collect overlying water simultaneously from each site for analysis of water chemistry, especially in coastal areas. Further, it is necessary to measure a range of physical and chemical parameters in situ, such as temperature, dissolved oxygen (DO), pH, salinity, turbidity, chlorophyll a content, etc., using portable instruments for accurate and quick measurement. All samples must be stored in ice-cooled containers and transported back to laboratory quickly. Water samples can be stored at 4 C prior to chemical analysis, while sediment samples should be treated quickly for pore water chemistry analysis. For molecular analysis, sediment samples can be stored frozen at 80 C before extraction of nucleic acids, but freeze-and-thaw cycles should be minimized as much as possible to avoid alteration of the sample characteristics. Among the physical and chemical characteristics of water, temperature, pH, and DO of the overlying seawater and sediments can be easily measured in situ. All other analyses of environmental parameters such as salinity and the concentrations of ammonium, nitrite, and nitrate are best carried out in the laboratory. These parameters could be determined from the pore water obtained by centrifugation of sediments before analysis of nutrients as mentioned above (Dang et al., 2008c, 2009a). Other sediment chemical parameters including organic carbon (OrgC), organic nitrogen (OrgN), and total phosphorus (TP) contents can be measured (Dang et al., 2009c), and the quantification of metals and other pollutants of known importance in the area is desirable.

3. Molecular Ecological Characterization 3.1. Genetic markers The 16S rRNA gene is still the most widely used phylogenetic marker gene for studying microbial communities and AOB have been successfully studied through amplification and analysis of the 16S rRNA gene for

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almost 2 decades. This lead to the early recognition of two monophyletic lineages, the Beta- and Gamma-AOB (Head et al., 1993; Norton, 2011; Purkhold et al., 2003), and the phylogenetic coherence of AOB has prompted to develop several specific 16S rRNA gene PCR primers (reviewed in Junier et al., 2010; Sekido et al., 2008). However, the 16S rRNA gene sequence identity is usually very high (for instance, >99% within the genus Nitrosospira), thereby providing a low resolution, which leads to proposals to employ functional marker genes (Klotz and Norton, 1995; Rotthauwe et al., 1997) and comparisons with the 16S rRNA gene considering their functionality and conserved features ( Junier et al., 2010). Functional molecular marker genes suited for studying aerobic and anaerobic ammonia oxidizers have been reviewed recently ( Junier et al., 2010; Kartal et al., 2011; Li et al., 2010b; Schmid et al., 2008; Sekido et al., 2008). The community structure of aerobic ammonia oxidizers in polluted coastal marine environments can be detected through amplifying functional genes such as the amoA gene, which encodes Subunit A of ammonia monooxygenase (AMO). AMO facilitates the first and rate-limiting step of the ammonia-oxidizing process (Kowalchuk and Stephen, 2001). The amoA gene has been successfully used to unravel the relationship between sediment aerobic obligate ammonia-oxidizing Beta-AOB and their environment in Jiaozhou Bay of China (Dang et al., 2010b) and the Mai Po Nature Reserve in Hong Kong (Cao et al., 2011; Li et al., 2011b). It was demonstrated already in the pregenomic era that cells of Beta-AOB encode multiple homologous copies of amoA gene (McTavish et al., 1993) with nearly identical nucleic acid sequence (Norton et al., 1996) and the amoA gene orthologs in cells of different strains differ significantly enough in sequence to make them suitable for use as highly efficient genetic biomarkers (Rotthauwe et al., 1997). Following genomic studies confirmed that the genomes of Beta-AOB encode multiple homologous copies of amoCAB gene clusters and singletons of the amoC gene (Arp et al., 2007; Chain et al., 2003; Norton et al., 2008; Stein et al., 2007). Several published bacterial amoA PCR primers have been successfully used to amplify a portion DNA templates of the amoA gene in environmental samples (Hoshino et al., 2001; Juretschko et al., 1998; Nicolaisen and Ramsing, 2002; Nold et al., 2000; Norton et al., 2002; Okano et al., 2004; Purkhold et al., 2000; Rotthauwe et al., 1997; Webster et al., 2002), and a recent comparison of these primers indicated considerable differences in their performance and specificity ( Junier et al., 2008). When considering its unique features, for instance, the functionality and available conserved sites, the amoA gene remains still the best genetic biomarker for determination of AOB community structures. Interestingly, the relatively short length of the amoA amplicon (450 bp) generated by the most used PCR primer pair (Rotthauwe et al., 1997) and its highly conserved


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sequence may generate less resolution than the 16S rRNA gene sequences (Purkhold et al., 2003); however, the high specificity provided by the fact that the amoA gene is restricted to aerobic ammonia oxidizers may overcome this shortcoming for environmental studies. Ammonia-oxidizing archaea are monophyletic and form a separate clade delineated from the Crenarchaeota (Spang et al., 2010); therefore, the primer sets originally used for their detection (targeting the 16S rRNA gene of group “MGI” of the Crenarchaeota) will generate too much sequence noise. As for AOB and anammox bacteria mentioned above, the use of specific 16S rRNA gene primers is also not a promising approach for detailed molecular ecological studies of AOA and several primer sets have been developed to amplify archaeal amoA genes from diverse environments (Francis et al., 2005; Ko¨nneke et al., 2005; Park et al., 2008; Tourna et al., 2008; Treusch et al., 2005; Urakawa et al., 2008). A comparison of diversity studies based on the 16S rRNA and archaeal amoA genes indicates a substantial congruence in the phylogeny of crenarchaeal ribosomal and amo genes (Prosser and Nicol, 2008) even though the detected diversity as defined by 16S rRNA gene diversity is significantly greater than the diversity associated with amoA gene detection (Nicol and Schleper, 2006; Prosser and Nicol, 2008). Still, the number of archaeal amoA sequences submitted to GenBank is increasing quickly indicating that the archaeal amoA gene remains the most preferred genetic biomarker. Other potential genetic markers are those genes that encode dissimilatory NO-forming nitrite reductase (copper-NirK, cytochrome cd1-NirS). nirK nitrite reductase genes associated with AOB and AOA have been successfully retrieved from many environments and could serve as an alternative genetic biomarker to define the community structure of ammonia-oxidizing microorganisms; however, the nirK gene was subjected to numerous horizontal gene transfers (Bartossek et al., 2010; Cantera and Stein, 2007). A key function of the nirK gene in ammonia catabolism of AOA has been proposed only recently (Schleper and Nicol, 2010). The hydrazine oxidoreductase (hzo) gene has been used with high specificity for the detection of anammox bacteria from the environment (Schmid et al., 2008). Interestingly, the distribution of hzo gene sequences recovered from sediment anammox bacteria correlated with identified environmental pollution along a pollution gradient from a coastal mangrove to the pristine South China Sea (Li et al., 2010b). Other recent studies identified the hcd (encoding putative 4-hydroxybutyrate-CoA dehydratase) gene as an additional potential genetic marker (Offre et al., 2010; Zhang et al., 2010). Hence, it can be expected that the combined simultaneous use of multiple genetic markers may provide solid and more convincing information about the abundance and distribution as well as changes in the community structure of microbes responsible for initial nitrogen transformations in the environment.

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3.2. Extraction methods of total genomic DNA and clone library constructions The method of extracting genomic DNA from sediment and water samples has fundamental impact on the gene amplification process and concomitantly the results of the molecular ecological analysis. Molecular characterization of environmental microbial populations is generally based on the assumption that the extraction of DNA from different samples and environmental matrices is equally efficient or unbiased; however, this assumption is not necessarily justified (Urakawa et al., 2010). Available commercial kits for extracting genomic DNA (especially from sediment samples) are significantly variable due to varying efficiency of extraction and selectivity associated with each commercial product. Genomic DNA from biological replicates with nonsignificant site heterogeneity may be pooled together so as to decrease the variability from the different methods of extraction and inherited sample errors, but such approach may also have subsequently statistical implication (Prosser, 2010). Total genomic DNA may be extracted from 0.5 g of fresh sediments using FastDNA spin kit for soil (Qbiogene) according to the manufacturer’s instructions or other kits, such as the Power SoilTM DNA kit (Mo Bio Laboratories, Solana Beach, CA, USA), which is effective at removing PCR inhibitors from even the most difficult soil types. Known local water and sediment chemistries may justify modifications to the application of these kits (Abell et al., 2010). For instance, the application of purification kits (such as the GeneClean Spin Kit by Qbiogene) is helpful to eliminate polymerase inhibitors and improve the efficiency of PCR. Following several independent extractions of DNA from each sediment sample (biological replicates), the DNA samples from each site may be pooled together or processed individually for further molecular analysis. Community DNA concentrations can be measured by spectrophotometrically. When compared with other approaches, the Nanodrop (Thermo Fisher Scientific Inc., Wilmington, DE, USA) has the advantageous ability to measure highly concentrated samples without dilution. Determination of DNA concentration in all samples will allow adjustment and amplification from nearly equally concentrated DNA samples. Great care is needed to avoid contamination by exogenous DNA. Pooling of DNA template, concentrating PCR products, and using nested PCR protocols may increase the sensitivity, particularly if the target DNAs are suspected to be rare in the environment. Moreover, the use of the degenerate primers may reduce stochastic PCR biases and also improve the detection of rare templates in environmental samples. The success of the amplification process (length and intensity of amplicons) can be verified quickly by agarose gel electrophoresis. The addition of certain reagents such as bovine serum albumin will increase the specificity of reaction by eliminating polymerase inhibitors. The PCR amplification product may be used directly or after purification using dedicated purification kits for cloning into linearized


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plasmids with TA-overhangs at the 50 and 30 ends (e.g., Takara or Invitrogen TA cloning kits). The plasmids in transformed hosts such as Escherichia coli DH5a will usually be screened for insert residence by colony PCR or bluewhite screening on agar plate surfaces, isolated when insert positive and subsequently sequenced.

3.3. The abundance of aerobic and anaerobic ammonia/ ammonium-oxidizing microbes as determined by real-time fluorescent PCR (qPCR) The abundance of AOA and Beta-AOB as well as the ratio of their abundances is likely specific to individual environments, and local and seasonal changes might be a response to environmental changes indicating that abundance and abundance ratios are informative bioindicators of the extent of environmental change (Cao et al., 2011; Dang et al., 2010b; Li et al., 2011a,b; Mosier and Francis, 2008; Santoro et al., 2008). Presently, two types of qPCR protocols are in use, the SYBR green and the Taqman protocols, of which the SYBR green protocol is much more popular for quantification of ammonia oxidizer abundance, which is likely due to the higher specificity of primers used. This chapter will discuss the application of the qPCR assay based on SYBR green. Beta-AOB amoA genes can be detected using the general primers amoA-1F and amoA-2R (Mosier and Francis, 2008; Rotthauwe et al., 1997; Santoro et al., 2008), but other primers have been designed to quantify individual lineages with higher specificity such as the amoA genes of sediment Nitrosomonads (e.g., P128r and P365r in Dang et al., 2010b). Because of the still limited information on the molecular diversity of AOA, the 16S rRNA gene is the predominant target in environmental studies, even though detected crenarchaeal 16S rRNA gene abundance only roughly correlates with the abundance of the amoA gene from AOA (Park et al., 2008). Further, present crenarchaeal 16S rRNA gene targeting primer sets (Wuchter et al., 2006) are not specific for all AOA and most primers used for detection of crenarchaeal amoA genes have high specificity in quantification. Primers Arch-amoAF and Arch-amoAR (Francis et al., 2005; Mosier and Francis, 2008; Santoro et al., 2008) can be used for quantification of archaeal amoA genes associated with phylotype group MGI using the SYBR green qPCR method. Additional modified amoA gene primers such as primer CrenAmoAQ-F (Mincer et al., 2007; Moin et al., 2009) have been used to quantify and target new archaeal amoA genes recovered from estuaries and salt marshes, and primers CG I.1b-amoAF and CG I.1b-amoAR were suited to target amoA genes residing in phylotype group CG I.1b (Park et al., 2008). Forward primers Arch-amoAFA and Arch-amoAFB specifically target two AOA groups in the Gulf of California (Beman et al., 2008). For the determination of anammox bacterial abundance, both the 16S rRNA and hzo genes are suitable targets for quantification by qPCR (Dang et al., 2010a;

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Li et al., 2010b), and the PCR primers and reaction conditions for such analyses have been summarized in several previous studies (Kartal et al., 2011; Li et al., 2010b; Penton et al., 2006; Schmid et al., 2005, 2008). Information about reaction volumes can be found in the manuals affiliated with the SYBR green mixture kit. Addition of BSA to the PCR assay improves the efficiency of reactions significantly. Plasmids containing 16S rRNA, amoA, or hzo gene amplicons as inserts can be extracted with the Miniprep Spin Kit (Qiagen) for generation of amplicon concentration standard curves. The standard curves are obtained using serial dilutions of plasmids carrying the target genes. In all experiments, negative controls containing insertless plasmids and no template DNA should be included and subjected to the same qPCR procedures as sample DNA to detect and exclude any possible contamination or carryover. Agarose gel electrophoresis and melting curve analysis should be routinely employed to confirm the specificity of the qPCR primers. The specificity of the primers should be confirmed by sequence analysis of amplicons generated with the qPCR primers for both the respective functional and the16S rRNA gene. Different Beta-AOB contain different copy numbers of amo gene clusters (Norton et al., 2002); however, a general assignment of gene copy number to individual lineages among the Beta-AOB needs more genome sequence information. However, all Beta-AOB so far investigated contain only one copy of the ribosomal RNA gene cluster. This permits a rough estimate of Beta-AOB cell numbers and the finding that the abundance ratios of 16S rRNA genes (number of cells) and of amoA genes are affected by environmental changes (Beman and Francis, 2006; Dang et al., 2010b; Mosier and Francis, 2008; Sahan and Muyzer, 2008; Santoro et al., 2008). For instance, the copy numbers of the sediment Beta-AOB amoA genes showed a different abundance in different areas, and the eastern and northern areas showed higher amoA gene abundance than the western and bay mouth areas in Jiaozhou Bay of China (Dang et al., 2010b). Quantitative analyses by qPCR of Beta-AOB amoA genes indicated that continental input from the nearby WWTPs and polluted rivers has significant impact on the abundance of the sediment Beta-AOB assemblages in Jiaozhou Bay, China (Dang et al., 2010b). For anammox bacteria, hzo gene abundances also showed a significant correlation with anthropogenic pollution at both of these two sites (Dang et al., 2010a; Li et al., 2010b, 2011c).

4. Community Structure Analyses 4.1. The diversity and richness of microbial community Microbial species diversity and richness can be represented by indices such as the Shannon-Wiener H and Simpson D indices and two nonparametric richness estimators, the abundance-based coverage estimator and the bias-corrected


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index Chao 1 (SChao1). Before calculating diversity and richness of a microbial community, it is critical to evaluate the size of the ammonia-oxidizing microbial community in the environment. One component of this evaluation is to determine whether the isolated/amplified clone number is adequate to reflect the natural community. This coverage can be calculated. Library coverage is normally calculated by the formula C ¼ [1 (n1/N )] 100, where n1 is the number of unique operational taxonomic units (OTUs) and N represents the total number of clones in a library (Mullins et al., 1995). Changes in diversity and richness can be calculated using respective software such as DOTUR (Schloss and Handelsman, 2005). Indices of diversity and richness are calculated for each library. Generally, 5% for amoA gene sequence and 3% for 16S rRNA gene (Schloss and Handelsman, 2005) are deemed acceptable cutoff distances (which is the genetic distance between two sequences based on nucleic acid sequence) for the calculation of amoA diversity AOA or AOB. A genetic distance cutoff of 5% has been widely used in most studies based on the amoA gene nucleic acid sequence (Beman and Francis, 2006; Dang et al., 2008c, 2009a, 2010b; Mosier and Francis, 2008; Sahan and Muyzer, 2008; Santoro et al., 2008) except for a few studies in which the deduced AmoA protein sequences have been employed to conduct this analysis. In contrast, a 3% or less than 3% cutoff for 16S rRNA and hzo genes have been used in a number of previous studies on anammox bacteria (Dang et al., 2010a; Li et al., 2010a,b, 2011c). The following procedure may be followed for diversity and richness analyses using DOTUR: (1) Produce an gene sequence alignment and create and export a distance matrix file using alignment software such as ARB (Ludwig et al., 2004) and PHYLIP (Felsenstein, 1989). (2) Import the distance matrix file and execute it using the pertinent command in DOTUR. (3) Generate the desired outfiles, most useful one with the number of the sequences (*.otu) and one with their identities (*.list) for each OTU as a function of distance. The rarefaction curve outfile is needed for plotting the curves. In general, the rarefaction analyses should also provide some information about whether or not enough clones have been selected for the analysis. The number of OTU should be saturated if adequate clones have been selected and the OTUs will not increase again with the clone numbers increasing, which reflects itself in the saturation profile of the rarefaction curves. Simultaneously, other richness estimates and diversity index may also be obtained. These parameters will represent different aspects of the analysis and indicate whether the current data reflect the natural community. In addition to above rarefaction analysis, calculation of two nonparametric richness estimators, the abundance-based coverage estimator (Shannon-Weiner H and Simpson D)

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and the bias-corrected Chao1, can provide additional information about the studied community. A comparison between these estimators calculated for the community in each sample could indicate that the community structure variation could be followed along a gradient of pollution or other stressors. Above analyses have been employed to show that the dynamics in communities of ammonia-oxidizing microorganisms correlate with changes in their environments (Dang et al., 2010b; Moin et al., 2009; Mosier and Francis, 2008). For instance, two study sites (A3 and Dx) exhibited the least diversity in Beta-AOB amoA gene sequences with regard to the majority of the calculated indices when compared with other sites in the unevenly hypernutrified quadrants of Jiaozhou Bay of China (Dang et al., 2010b).

4.2. Phylogenetic lineages The phylogenetic lineages retrieved from environmental samples can provide important information about the nature of resident microbial communities. Phylogenetic analysis of experimental sequences in context with related sequences retrieved from GenBank allows a comparison of the resident microbial community with communities of similar cohorts in other environments. Generally, phylogenetic trees constructed after subjecting alignments of related sequences to inference of phylogeny using suitable object functions (maximum parsimony, maximum likelihood, etc.) and analysis software such as ARB (Ludwig et al., 2004), MEGA 4.0 (Tamura et al., 2007), Phylip (Felsenstein, 1989), or MrBayes (Ronquist and Huelsenbeck, 2003). In particular, relevant and related sequences can be obtained from the GenBank using the BLAST program (Altschul et al., 1997) and imported together with the experimental data set into software programs useful for alignment such as Clustal X (Larkin et al., 2007). The aligned sequences can be exported in the corresponding format readable by phylogeny inference software programs such as Phylip, MEGA, or MrBayes. The output of these phylogenetic inferences will produce the information necessary to construct majority consensus trees qualified by resampling confidence values (bootstrap values) or posterior probability value, which support the clade structure of the tree. The overall tree structure is helpful for comparing the experimental sequences with one another or with those from other studies. For instance, based on the 16S rRNA gene phylogenetic tree, Beta-AOB group into approximately 10 clusters (Purkhold et al., 2000) congruent with corresponding amoA gene lineages (Avrahami et al., 2002, 2003; Freitag and Prosser, 2003; Stephen et al., 1996). For AOA, two larger clades, namely water column/sediment and soil/sediment clades, have been defined (Francis et al., 2005; Park et al., 2006; Treusch et al., 2005) and these clusters provide the basis for the comparison of experimental results with those from other studies. Some lineages separated from others in the phylogenetic trees may


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also be present only in some environments with specific habitat characteristics. For example, in the research of Jiaozhou Bay in China, some sequences were commonly present at every sampling site in the surveyed area (10 of the 43 OTUs) or they represented a unique cluster such as OTU A5-a-16, which was unique to only one sampling site and the sequence represented a novel single sequence clade at a basal position in phylogenetic tree (Dang et al., 2010b). This phylogenetic information collectively describes the differential distribution of Beta-AOB in the studied research area and, together with diversity and richness information, provides the opportunity to distinguish different microenvironments (Dang et al., 2010b).

4.3. Community classification A fundamental goal of ecological studies is to compare microbial communities from different environments or in response to different treatments. Various statistical tools are available for this type of analysis (Ramette, 2007; Schloss, 2008). Because the majority of environmental nitrifying archaea and bacteria have not yet been cultured, ecology studies of nitrifying microorganisms are mostly carried out using molecular approaches that include statistical tools developed to evaluate nucleic acid sequence data. The UniFrac statistical software package was specifically designed for this purpose (Lozupone and Knight, 2005; Lozupone et al., 2006, 2007). Due to its easy access (online), ease of use (intuitive, user-friendly interface, online tutorial and help, graphical presentation and output), and high performance (parallel computing), UniFrac is gaining more and more popularity in the field of microbial ecology. Originally, this statistical program was successfully applied to microbial community classification based on 16S rRNA gene sequences. Recently, it has also been applied in community classification of marine N cycling microbiota (Dang et al., 2009b,c), including nitrifying archaea and bacteria (Cao et al., 2011; Dang et al., 2008c, 2009a, 2010b,c) and anammox bacteria (Dang et al., 2010a; Li et al., 2010a) based on functional gene sequence data. With its expansion to a new version of Fast UniFrac (Hamady et al., 2010), more features and computing power were added to this valuable statistical tool. The basic procedure for microbial community analyses using UniFrac is outlined below. 4.3.1. Preparation of the input files Before starting the UniFrac analyses, input files to UniFrac have to be prepared. The UniFrac program requires two input files, a rooted phylogenetic tree file and an environment file (Lozupone et al., 2006). The tree file can be in either Nexus or Newick format and can be built based on alignments of gene sequences subjected to phylogenetic inference with programs such as Phylip or MrBayes. It has been shown that the UniFrac results are not sensitive to the method used to infer phylogeny, thus most of the phylogenetic analysis

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programs can be used for producing the phylogenetic tree input file (Lozupone et al., 2007). The environment file contains a list of entries, each of which contains a sequence name, an environment name, and optionally the number of times the sequence was observed in each environment. 4.3.2. Log on to UniFrac UniFrac was developed by Rob Knight and colleagues from the University of Colorado (Lozupone and Knight, 2005; Lozupone et al., 2006, 2007). The internet Web site for online UniFrac is http://128.138.212.43/unifrac/ index.psp. This Web site also contains a suite of tutorials, help, and FAQ facilities that are very valuable for learning and working on UniFrac analyses. New users of UniFrac need to register and log in to fully take advantage of the features of this statistical program. 4.3.3. Check data accuracy within UniFrac The UniFrac interface includes an option termed “environment counts,” with which the accuracy of the input data can be checked before carrying our various analyses. 4.3.4. Microbial community classification using UniFrac Two multivariate statistical tools, Jackknife environment clustering and principal coordinates analysis (PCoA), are provided within UniFrac for microbial community classification using gene sequence data. Both statistics can work on qualitative data (unweighted, sequences alone) and quantitative data (weighted with sequence abundance) (Lozupone et al., 2007). It has been demonstrated that the qualitative analyses may detect the microbial community long-term evolution or adaptation with strong genetic background, while the quantitative analyses may detect the microbial community adaptation to transient characteristics of the environment (Lozupone et al., 2007). Thus, the unweighted and weighted UniFrac analyses may give different results about the microbial community classification, depending on the specific environmental condition and/or environmental history. 4.3.5. Explanation of UniFrac statistical results Figures 2.1 and 2.2 show example UniFrac Jackknife environment clustering and PCoA analysis results using the molecular data of Beta-AOB collected from the coastal sediments of Jiaozhou Bay (Dang et al., 2010b). Both figures show that the deduced AmoA protein sequences clearly distinguished two groups of the sediment Beta-AOB assemblages in Jiaozhou Bay. The assemblages of stations A3, A5, C4, and Y1 at the Eastern and Northern areas of Jiaozhou Bay constituted Class 1, whereas those of stations B2, D1, D5, and Dx at the Western and bay mouth areas constituted Class 2. This clustering pattern of the Beta-AOB microbial assemblages correlated with the classification of the Jiaozhou Bay environments (Dang et al., 2010b): the Eastern

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A5 100

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Figure 2.1 Dendrogram of the hierarchical clustering analysis of the Jiaozhou Bay sedimentary AmoA genotype assemblages using the UniFrac normalized and weighted Jackknife environment clusters statistical method. The percentage supports of the classification tested with sequence jackknifing resamplings are shown near the corresponding nodes (modified from Dang et al., 2010b).

P2—percent variation explained 4.18%

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Figure 2.2 Ordination diagram of the UniFrac weighted and normalized PCoA analysis of the sediment Beta-AOB communities using the AmoA sequences recovered from the Jiaozhou Bay. Shown are the plot of the first two principal coordinate axes (P1 and P2) of PCoA and the distribution of the AmoA genotype assemblages (designated with the sampling station names) in response to these axes (modified from Dang et al., 2010b).

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and Northern areas are usually the most polluted and eutrophied areas of Jiaozhou Bay (Dang et al., 2008a,b,c, 2009c; Liu et al., 2005). Figures 2.3 and 2.4 also show that anammox bacteria from three marine environments clearly distinguished from each other following a strong gradient of known anthropogenic pollution in South China (Li et al., 2010b).

5. Relationship Between the Community Structure and Physicochemical Parameters Canonical correspondence analysis (CCA) is a simple yet powerful statistical tool that is often used to detect the relationship between microbial community structure and environmental variables. CCA is based on unimodal species–environment relationships to model microbial community response to the environmental variation. This model is thought to be robust even when some unimodal species–environment relationships are not PCA-P1 versus P2 0.06 South China Sea sediments


0.04

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Figure 2.3 Principal coordinate plot by UniFrac analyses of the 16S rRNA gene sequences from three marine environmental sediments with a known gradient of anthropogenic pollution. Aquaculture: Hong Kong Deep Bay aquaculture sediment; Mai Po: Hong Kong Mai Po Nature Reserve coastal sediment; SCS: the South China Sea sediment (modified from Li et al., 2010b).

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PCA-P1 versus P2 0.5


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Figure 2.4 Principal coordinate plot by UniFrac analyses of the Hzo protein sequences from three marine environmental sediments with a known gradient of anthropogenic pollution. Aquaculture: Hong Kong Deep Bay aquaculture sediment; Mai Po: Hong Kong Mai Po Nature Reserve coastal sediment; SCS: the South China Sea sediment (modified from Li et al., 2010b).

satisfied (Ramette, 2007; ter Braak and Smilauer, 2002). CCA is suitable for sparse community data with a large proportion of zero entries in the species or OTU data matrix (Lepsˇ and Sˇmilauer, 2003). CCA statistics may detect specific species or OTUs that respond to specific environmental variables, helping identify candidate indicator species or OTUs (Ramette, 2007). Thus, CCA is gaining its popularity in modern microbial ecology studies. Canoco is an implementation of CCA that is widely used in ecological studies (ter Braak and Smilauer, 2002). The basic procedure for microbial community-environment CCA analyses using Canoco is outlined below.

5.1. Preparation of the input files To remove the influence of the difference in scales and units for environmental variables, data standardization is necessary for CCA analyses. Usually, the z-score transformation is adequate for this purpose (Ramette, 2007). For analysis of sequence data, the relative abundance (percentage) of

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each OTU is often used. The WCanoImp program in the Canoco software package is used for formatting the input species (or OTU) and environment files from spreadsheets (Lepsˇ and Sˇmilauer, 2003).

5.2. CCA analysis In the Canoco software package, the Canoco for Windows 4.5 software module is used for running CCA, along with several other statistical tools. Because CCA is used to explain OTU (species) data in the context of environmental data, both the input files of the OTU data (the primary data) and environment data (the explanatory data) are needed for a CCA analysis. The Canoco for Windows program has a user-friendly interface and “help” facility, making its use for statistical analyses simpler than the console implementation. The user can follow the submenus to select desired options to carry out data processing and statistical analyses. CCA in Canoco uses the Monte Carlo permutation test for the evaluation of significance. Resulting P-values and F-ratios for the most significant variables are stored in the Log file. The data for intermediate steps and results are also stored in this Log file. Colinearity is tested and can be checked from the weighted correlation matrix stored in the Log file. Graphical presentations of the ordination diagrams (OTU-environment-sample correlation) can be viewed from the CanoDraw subprogram in Canoco for Windows.

5.3. Explanation of CCA statistical results Figure 2.5 shows example CCA analysis results using the molecular data of Beta-AOB collected from the coastal sediments of Jiaozhou Bay (Dang et al., 2010b). Figure 2.5A shows that the environmental variables in the first two CCA dimensions (CCA1 and CCA2) explained almost half (46.9%) of the total variance in the Beta-AOB (represented by the AmoA OTUs) composition and 48.7% of the cumulative variance of the BetaAOB-environment relationship. Two classes of Beta-AOB assemblages could be distinguished by CCA1, with the assemblages in Class 1 comprising sampling sites A3, A5, C4, and Y1, and the assemblages in Class 2 comprising sampling sites B2, D1, D5, and Dx. This classification pattern is highly consistent with the UniFrac community classification results (Figs. 2.1 and 2.2) (Dang et al., 2010b). The CCA analysis result also indicates that only the sediment pore water NO2–N concentration contributed significantly (P ¼ 0.028, 1000 Monte Carlo permutations) to the Beta-AOB–environment relationship, and this factor alone provided 22.5% of the total CCA explanatory power. Although the contribution of all other environmental factors was not statistically significant (P > 0.150), the combination of these variables provided additionally 73.0% of the total CCA explanatory power (Dang et al., 2010b). This CCA result indicates that the

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A

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CCA2—percent variation explained 22.4 %

Environmental factor

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NO3-N

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1.0 CCA1—percent variation explained 64.5 %

Figure 2.5 CCA ordination plots for the first two principal dimensions of the relationship between the distribution of AmoA OTUs (A) or clusters (B) and the sediment and pore water environmental parameters in Jiaozhou Bay. Abbreviation: Nm143, Cluster 143; Nm-O, Cluster 6a; Nm-M, Cluster 6b; C13, Cluster 13; C14, Cluster 14; C15, Cluster 15 (modified from Dang et al., 2010b).

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Jiaozhou Bay sediment Beta-AOB composition and spatial distribution may be influenced by or related to a variety of environmental factors, and the sediment pore water NO2–N concentration may be the most important environmental variable. Figure 2.5B shows the CCA correlation of the sediment Beta-AOB clusters with environmental variables in Jiaozhou Bay (Dang et al., 2010b). The first two CCA axes (CCA1 and CCA2) explained 84.7% of the total variance in Beta-AOB cluster composition and 86.1% of the cumulative variance of the Beta-AOB cluster–environment relationship. CCA1 also clearly distinguished the Beta-AOB assemblages of Class 1 from Class 2, as depicted in Fig. 2.3A. Both sediment pore water NO2–N concentration (P ¼ 0.001, 1000 Monte Carlo permutations) and sediment sand content (P ¼ 0.028, 1000 Monte Carlo permutations) contributed significantly to the Beta-AOB cluster–environment relationship, and they provided 69.6% of the total CCA explanatory power (Dang et al., 2010b). It appears that a direct link between a single sedimentological parameter and the composition and distribution of the sediment Beta-AOB community may exist in Jiaozhou Bay. The correlation of the sediment Beta-AOB assemblages with sediment pore water NO2–N (Fig. 2.5) indicates that the sediment BetaAOB may contribute to active nitrification in Jiaozhou Bay (Dang et al., 2010b). This example also demonstrates the power and flexibility of the CCA statistics that can target at different taxonomic levels (or different sequence evolutionary distances) of microbial functional group to present a more complete ecological explanation. Similar examples using CCA to analyze anammox bacterial communities could also be found in other reports (Dang et al., 2010a; Li et al., 2010a).

6. Relationships Between the Community Change and the Environments By combining all of the data and analyses mentioned above, it is possible to comprehensively explain the relationships between the community structure of aerobic and anaerobic ammonia/ammonium oxidizers and select environmental parameters. The richness, diversity of the genetic markers, the abundance of ammonia/ammonium oxidizers, and the phylogenetic lineages unique to the sampling sites may have a strong relationship with the environments. For example, the individual components of the study of Beta-AOB in Jiaozhou Bay simultaneously support that the Nitrosomonas amoA gene in the sediments of four class 1 stations was higher than that of the four class 2 stations indicating a differential distribution of BetaAOB amoA genes in different environments (Dang et al., 2010b). The community classification of ammonia oxidizers also shows both common


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and unique features of some sampling sites. Further, the correlation analysis indicates the potential relationships between the microbial community composition and environmental factors. As sedimentological conditions could be related to the hydrological regime such as the currents, tides, waves, upwelling, lateral transport, water mixing, and the intensity and dynamics of these activities, these factors could shed light on why the community of ammonia oxidizers differed at the different sites. Moreover, the hydrological activities affected sediment source, composition, organic matter content, pore water redox, nutrient composition and concentration, and other physicochemical, sedimentological, or geochemical factors, which could in turn affect the relationships between community structure of aerobic ammonia oxidizer and environmental parameters. In addition, other environmental features, especially the input of freshwater or some wastewater from the nearby WWTP, could also be responsible for the observed relationships. For example, the shift in the sediment Beta-AOB community structure in the Jiaozhou Bay of China might be the result of exogenous input of microorganisms and nutrients from the surrounding rivers and WWTPs (Dang et al., 2010b). Similarly, amplicons of 16S rRNA and hzo genes from the pristine South China Sea floor sediments show clearly differences between the areas affected by mariculture and polluted coastal mangrove (Li et al., 2010b), indicating that land, pollution, and wastewater-associated species of microorganisms key to nitrogen transformation processes may be important indicators for evaluating environmental health and its history. The study of all sites produced identical clusters of ammonia oxidizer genetic marker genes along a gradient of pollution from anthropogenically polluted coastal areas to the pristine South China Sea (Figs. 2.3 and 2.4). In addition, the described approach may provide also forensic evidence of past pollution even though the current chemical analysis of the sites may indicate that present environmental conditions are acceptable.

7. Conclusions Through decoding the community structure of aerobic and anaerobic ammonia/ammonium oxidizers including AOA, Beta-AOB, and anammox bacteria via constructing clone libraries of genetic markers, calculating microbial richness and diversity analysis using DOTUR, relationships between the community structure and the environmental parameters can be analyzed using the Canoco. Available information sheds light on the relationship between changes in community profiles of aerobic and anaerobic ammonia/ammonium oxidizers and the health of coastal marine environments, especially anthropogenic pollution. It may prove feasible that

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community and composition information of aerobic and anaerobic ammonia/ammonium oxidizers are a useful bioindicator for environmental health and forensic analysis of prior pollution history.

ACKNOWLEDGMENTS This work was supported in part by grants from Agriculture, Fisheries and Conservation Department of the Hong Kong SAR Government (J.-D. G), a PhD studentship from the University of Hong Kong to Huiluo Cao and Meng Li; China National Natural Science Foundation grants 91028011, 41076091, and 40576069, China Ocean Mineral Resources R&D Association grants DYXM-115-02-2-20 and DYXM-115-02-2-6, Hi-Tech Research and Development Program of China grant 2007AA091903, Fundamental Research Funds for the Central Universities of China grant 09CX05005A, the National Qingdao Economic and Technological Development Area Science and Technology Development Project grant (2009-2-34), and Foundation of the State Key Laboratory of Heavy Oil Processing, China University of Petroleum grant SKL2010-02 to Hongyue Dang. We would like to thank Martin G. Klotz for his vision, patience, and guidance throughout this project.

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C H A P T E R

T H R E E

Molecular and Stable Isotope Methods to Detect and Measure Anaerobic Ammonium Oxidation (Anammox) in Aquatic Ecosystems Bongkeun Song* and Craig R. Tobias† Contents 1. Introduction 2. Molecular Methods to Detect and Quantify Anammox Bacteria in Environmental Samples 2.1. PCR protocols of 16S rRNA gene detection 2.2. PCR protocol of hzo gene detection 2.3. Quantitative PCR of anammox bacteria 3. Stable Isotope Methods to Measure Anammox Rates in Environmental Samples 3.1. Sediment slurries and whole water incubations 3.2. Working toward in situ rates—Whole cores and ambient O2 Acknowledgments References

64 66 68 71 74 77 78 82 84 84

Abstract Numerous microbial processes transform nitrogen (N) but three anaerobic respiratory pathways remove fixed N from the environment: denitrification (nitrate conversion to N2), anaerobic ammonium oxidation (anammox; ammonium plus nitrite conversion to N2), and nitrite dependent methane oxidation (nitrite conversion to N2). Nitrification becomes a part of N removal processes as a supplier of nitrite (NO2) and nitrate (NO3) to anammox and denitrifying bacteria in anoxic water and sediments. It is important to detect and measure anammox and denitrification to understand biogeochemical N cycle and to estimate N removal potential in aquatic ecosystems. Denitrification has been extensively studied in many ecosystems to examine diversity and spatial and temporal dynamics of denitrifying communities as well as to understand its * Department of Biology and Marine Biology, University of North Carolina Wilmington, North Carolina, USA Department of Marine Sciences, University of Connecticut, Groton, Connecticut, USA

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Bongkeun Song and Craig R. Tobias

importance in regional and global N cycles. Nitrite dependent methane oxidation was recently discovered as a new pathway of removing fixed N and just started to examine its importance in different ecosystems. Anammox has undergone limited examination, although the number of studies is continuously increasing. There are many questions remaining in order to understand the factors controlling activities and community structures of anammox bacteria in different ecosystems. This chapter reviews both molecular and stable isotope methods to detect and measure anammox in anoxic sediments and water.

1. Introduction Microbial processes obviously have a great impact on biogeochemical N cycling in aquatic ecosystems. Organic N is decomposed by microbes to NH4þ (ammonification), which can be taken up by primary producers or converted to NO2 and then NO3 by nitrification. Nitrate can be assimilated or dissimilated in denitrification, anammox, and dissimilatory NO3 reduction to NH4þ (DNRA) depending on the prevailing conditions (Fig. 3.1). Recently, denitrifying anaerobic methane oxidation (DAMO) was discovered as a new pathway of converting NO2 to N2 (Ettwig et al., 2010). Among these processes, only denitrification, anammox, and DAMO remove fixed N from the environment. Denitrification typically removes fixed N by reducing NO3 to N2 or N2O while consuming available organic carbon. These N gases produced during denitrification are emitted

Atmosphere N2 / N2O River input ON, NO3–, NH+ 4

To coastal Ocean

NH4+

ON

NH4+

Mineralization

NO3– ANAMMOX Denitrification NO2–

flux

flux

Water

NO–3

Nitrification Sediment

Figure 3.1 Biogeochemical N cycle in rivers and estuaries.

Molecular and Stable Isotope Analysis of ANAMMOX

65

to the atmosphere (Fig. 3.1). Denitrification rates are influenced by labile organic carbon and nitrate availability, both of which can vary spatially within an estuary and/or through the sediment profile (Cornwell et al., 1999; Tobias et al., 2001). Direct denitrification (fueled by water column NO3) and “coupled denitrification” (fueled by NO3 produced by nitrification) constitute major pathways for N removal. High rates of coupled denitrification often accompany high mineralization rates, if sufficient oxygen is available for nitrification (Seitzinger, 1994; Seitzinger and Giblin, 1996). Increased NO3 loading to estuaries (i.e., increased NO3 concentration) influences total denitrification (direct plus coupled) rates, and the partitioning between direct and coupled pathways responds primarily to changes in water column NO3 concentrations (Nedwell et al., 1999; Seitzinger et al., 2006). Changing patterns of salinity can also exert influences on denitrification through nitrification as well as by sulfide inhibition through direct impacts on the denitrifying community composition (Srenson, 1987). High salinities are coincident with decreased diversity in denitrifying communities (Yoshie et al., 2004). Abundance of denitrifiers decreased along the salinity gradient at the Colne River estuary and had a significant correlation with denitrification rates (Dong et al., 2009). Denitrifying bacteria have been extensively studied to understand the correlation between structures and functions of the communities under various environmental perturbations. Anammox bacteria, however, have not been examined to determine these types of correlations in these environments even though they have a significant role in the global N cycle. In addition, DAMO was recently discovered as a new pathway in N cycle and just started to explore in freshwater ecosystems (Ettwig et al., 2009, 2010; Hu et al., 2009; Raghoebarsing et al., 2006). Anammox is a recently identified microbial process involved in N removal by producing N2 while oxidizing NH4þ coupled to NO2 reduction under anoxic conditions (van de Graaf et al., 1995; Fig. 3.1). It has been shown to be an important N remover in marine environments (e.g., Dale et al., 2009; Engstro¨m et al., 2005; Hietanen and Kuparinen, 2008; Rich et al., 2008; Risgaard-Petersen et al., 2004a; Thamdrup and Dalsgaard, 2002). Anammox contribution to total N2 production is varied depending on the ecosystem. Trimmer and Nicholls (2009) reported 33–65% of N2 produced by anammox in the continental shelf and slope sediments in the North Atlantic. Although it is not yet a clear controlling factor of anammox in sediments, increased water depth offshore related to organic carbon availability is one factor that supports higher anammox contribution to total N2 production as denitrification becomes limited (Dalsgaard et al., 2005; Engstro¨m et al., 2005; Thamdrup and Dalsgaard, 2002). Meyer et al. (2005) reported strong correlations between anammox rates and the production of NO2 by nitrification and denitrification in the Logan and Albert River sediments. Ammonia oxidation and dissimilatory nitrate

66


reduction were proposed to provide NO2 to anammox in the Peruvian oxygen minimum zone where denitrification was not detectable (Lam et al., 2009). The microbial interactions among anammox, denitrification, and nitrification appear to be either mutualistic or competitive. In addition, the reduction of manganese (Mn) and iron (Fe) oxides by oxidizing organic matters and high amounts of H2S are considered to create favorable conditions for anammox because both metal reduction and sulfide toxicity limit denitrification (Engstro¨m et al., 2005; Hanning et al., 2007). Thus, the environmental parameters such as the availabilities of DOC, NH4þ, NO2, Fe, and Mn are considered to have impacts on anammox rates in anoxic sediments. Since microbial N removal processes are the combined result of microbial interactions among anammox and denitrifying bacteria, it is important to measure the rates of each process as well as to detect and quantify bacteria responsible for each pathway to understand biogeochemical N cycle and the fate of N in aquatic ecosystems. Anammox bacteria and their activities without cultivation can be examined using 15N stable isotope technique, fluorescent in situ hybridization (FISH), ladderane lipid detection, and 16S rRNA or functional gene targeted polymerase chain reaction (PCR) studies. This chapter focuses on the methods of PCR and stable isotope techniques to detect and measure anammox in anoxic sediments and water.

2. Molecular Methods to Detect and Quantify Anammox Bacteria in Environmental Samples The anammox process is mediated by bacteria belonging to the new family Brocadiaceae of the phylum Planctomycetes. The first anammox bacteria were cultured as an enrichment from an effluent of a fluidized bed methanogenic reactor (Mulder et al., 1995) and named as “Candidatus Brocadia anammoxidans” (Strous et al., 1998). Additional bacteria have since been discovered from wastewater treatment systems and named “Ca. Kuenenia stuttgartiensis” (Schmid et al., 2000), “Ca. Scalindua brodae,” “Ca. Scalindua wagneri,” (Schmid et al., 2003) “Ca. Brocadia fulgida” (Kartal et al., 2008), “Ca. Anammoxoglobus propionicus” (Kartal et al., 2007b) and “Ca. Jettenia asiatica” (Quan et al., 2008). Kuypers et al. (2003) first reported marine anammox bacteria, “Ca. Scalindua sorokinii,” from the Black Sea. Since then, “Ca. Scalindua spp.” were detected from various aquatic ecosystems including marine sediments (Amano et al., 2007; Dale et al., 2009; Dang et al., 2010; Li et al., 2010b; Penton et al., 2006; Rich et al., 2008; Risgaard-Petersen et al., 2004a; Schmid et al., 2007) and anoxic water (Galań et al., 2009; Hamersley et al., 2007, 2009; Hanning et al., 2007; Kirkpatrick et al., 2006; Kuypers et al., 2003, 2005; Lam et al., 2007; Li et al., 2010a,b; Schubert et al., 2006; Ward et al., 2009; Woebken et al., 2007).


67

Detection of anammox bacteria in various ecosystems showed two common features of anammox bacterial diversity and distribution. First, all the anammox bacteria are affiliated within 85% sequence similarity of 16S rRNA genes in the new family Brocadiaceae. Second, the bacteria assigned to the genera “Ca. Brocadia” and “Ca. Kuenenia” were mostly found in wastewater treatment systems and terrestrial environments while “Ca. Scalindua spp.” were mostly present in marine ecosystems (Dale et al., 2009; Humbert et al., 2010; Penton et al., 2006; Schmid et al., 2007). Detection and identification of anammox bacteria have been commonly conducted using two different molecular techniques; fluorescent in situ hybridization (FISH) and PCR amplification of anammox bacterial 16S rRNA genes. FISH is one of the standard molecular methods to detect and quantify anammox bacteria. Based on 16S rRNA sequences obtained from anammox bacteria (“Anammoxoglobus, Brocadia, Jettenia, Kuenenia, and Scalindua”), fluorescent oligo-nucleotide probes were developed with different levels of specificities (genus and species) to detect and quantify anammox bacteria in environmental samples (Pavlekovic et al., 2009; Schmid et al., 2005). PCR amplification of anammox bacterial 16S rRNA genes is an alternative molecular detection method. Several PCR methods with various primer combinations were used to detect anammox bacteria in environmental samples (e.g., Amano et al., 2007; Dale et al., 2009; Dang et al., 2010; Humbert et al., 2010; Kirkpatrick et al., 2006; Li et al., 2010b; Penton et al., 2006; Rich et al., 2008; Schmid et al., 2000, 2005; Schubert et al., 2006). PCR methods enhanced the capability of detecting anammox bacteria and examining their diversity based on the 16S rRNA gene sequences. However, the amplified PCR products must be verified with subsequent cloning and sequence analyses since non-anammox bacterial 16S rRNA genes were also amplified with the current PCR protocols. In order to overcome this limitation, the functional genes involved in anammox pathway have started to use for the detection and quantification of anammox bacteria in environmental samples. Putative cytochrome cd1-contating nitrite reductase gene (nirS) was initially targeted to detect and quantify Scalindua like organisms in the Peruvian oxygen minimum zone (Lam et al., 2009). Since the nirS genes are present in both anammox and denitrifying bacteria, it may become ambiguous to differentiate the nirS sequences of anammox bacteria from denitrifiers. Alternatively, the gene encoding hydrazine oxidation (hzo), the key step for gaining energy in the anammox pathway, was targeted to develop PCR primers and protocols for the detection of anammox (Hirsch et al., 2011; Li et al., 2010a,b; Quan et al., 2008; Schmid et al., 2008). Diversity of anammox bacteria has been examined in various environmental samples using the developed primers and protocols of hzo genes (Dang et al., 2010; Hirsch et al., 2010; Li et al., 2010a,b). Further, quantitative PCR (Q-PCR) method for the hzo gene was recently reported by Dang et al. (2010).

68


2.1. PCR protocols of 16S rRNA gene detection PCR of 16S rRNA genes has widely used to detect anammox bacteria with many different combinations of primers. We selected the most specific primers and PCR conditions (Tables 3.1 and 3.2) for the detection of anammox 16S rRNA genes although sequence analysis of the amplicons is still required for verification. Direct PCR of anammox bacterial 16S rRNA genes can be conducted by following the protocols of Amx16S1 and Amx16S2 (Table 3.2). The Amx16S1 protocol with the primer combinations of Amx368F and Amx820R or Amx368F and BS820R primers was successfully used to examine anammox communities in Yodo River estuarine sediments (Amano et al., 2007). The Amx820R primer is specific for “Ca. Brocadia” and “Ca. Kuenenia” while the BS820R is for “Ca. Scalindua.” However, PCR of either primer combined with the Amx368F detected all the 16S rRNA sequences closely related to those in known 5 genera of anammox bacteria (Amano et al., 2007; Dale et al., 2009; Li et al., 2010a). Penton et al. (2006) designed the Brod541F and Brod1260R primers specific for “Ca. Scalindua” (Table 3.1) and found the presence of anammox bacteria in various freshwater and marine sediments and permafrost soils using the Amx16S2 PCR protocol (Table 3.2). This protocol was also used to examine diversity of anammox bacteria in coastal marine sediments of China (Dang et al., 2010; Li et al., 2010b). The primers and PCR conditions specific for the Planctomyctes (Tables 3.1 and 3.2) were used for anammox bacterial detection. The Pla16S1 PCR protocol utilizing the Planctomycetes specific primer Pla46F and eubaterial universal primer (1037R) targeting 23S rRNA genes was used to examine anammox communities in the oxygen minimum zones off Namibia, Peru, and in the Arabian Sea (Woebken et al., 2008). This protocol yielded partial sequences of 16S and 23S rRNA genes as well as complete sequences of the 16S–23S rRNA internal transcribed spacer (ITS), which can be used to examine microdiversity of closely related anammox bacterial phylotypes. However, specificity and sensitivity of 16S rRNA gene detection are highly dependent on the abundance of anammox bacteria in environmental samples. Among the amplicons of the Pla16S1 PCR, 0.2–48% of sequenced clones were closely related to “Ca. Scalindua” (Woebken et al., 2008). Li et al. (2010b) also showed 77% of detection specificity of anammox bacterial 16S rRNA genes using the Amx16S PCR2 protocol with costal wetland sediment. Although detection specificity of the Amx16S PCR1 protocol is not reported, this method sometimes does not yield the expected amplicons due to low abundance of anammox bacteria in environmental samples. In order to overcome these limitations, nested PCR protocols were developed by Dale et al. (2009) and Humbert et al. (2010). The Pla16S2 protocol was used for the initial PCR (Schmid et al., 2000) and the second PCR was conducted following the Amx16S1 protocol with a

Table 3.1 PCR primers for anammox bacterial 16S rRNA gene detection and quantification Primer name

Pla46F 1390R 1037R Amx368F

Target organism and gene

Planctomycetes 16S rRNA Eubacteria 16S rRNA Eubacteria 23S rRNA Anammox bacteria 16S rRNA Amx820R “Ca. Brocadia” and “Ca. Kuenenia” 16S rRNA BS820R “Ca. Scalinuda wagneri” 16S rRNA Brod541F “Ca. Scalinuda brodae” 16S rRNA Brod1260R “Ca. Scalinuda brodae” 16S rRNA AMX-808-F “Ca. Scalinuda” 16S rRNA AMX-1040-R “Ca. Scalinuda” 16S rRNA AMX-931 “Ca. Scalinuda” 16S rRNA

Orientation Sequence (50 –30 )

Reference

Forward Reverse Reverse Forward

GACTTGCATGCCTAATCC GACCGGCGGTGTGTACAA CGACAAGGAATTTCGCTAC TTCGCAATGCCCGAAAGG

Neef et al. (1998) Olsen et al. (1986) Ludwig et al. (1992) Schmid et al. (2005)

Reverse

AAAACCCCTCTACTTAGTGCCC

Schmid et al. (2005)

Reverse

TAATCCTCTATTAGT


Forward

50 -GAGCACGTAGGTGGGTTTGT-30

Penton et al. (2006)

Reverse

50 -ARCYGTAAACGATGGGCACTAA-30


Forward

50 -CAGCCATGCAAACACCTGTRATA-30

Hamersley et al. (2007)

Reverse

50 -TCGCACAAGCGGTGGAGCATGTGGCTT-30 Hamersley et al. (2007)

Forward

50 -TCGCACAAGCGGTGGAGCATGTGGCTT-30 Hamersley et al. (2007)

70


Table 3.2 PCR protocols for anammox bacterial 16S rRNA gene detection

Protocol

Primer combination

PCR condition

Amx16S1 Amx368F and (94 C/30 s ! 56 C/ Amx820R/ 30 s ! 72 C/ BS820R 1 min) 30 Amx16S2 Brod541F and (95 C/30 s ! 60 C/ Brod1260R 1 min ! 72 C/ 1 min) 30 Pla16S1 Pla46F and (94 C/45 s ! 58 C/ 1037R 50 s ! 72 C/ 3 min) 30 Pla16S2 Pla46F and (94 C/45 s ! 62 C/ 1390R 50 s ! 72 C/1 min 22 s) 30

PCR product length (bp) Reference

450

Amano et al. (2007)

720


> 3300


1344


modification of reannealing temperature to 62 C (Humbert et al., 2010). Although this nested PCR method enhances the detection of 16S rRNA genes, specificity of anammox bacterial detection is still low. Dale et al. (2009) combined the Pla16S1 and Amx16S1 protocols to detect anammox bacteria in estuarine sediments. The initial PCR amplification was performed following the Pla16S1 PCR and the second PCR was conducted with the Amx368F and Amx820R using the Amx16S1 PCR protocol. This nested PCR method enhances both detection sensitivity and specificity of anammox 16S rRNA genes as 100% of cloned sequences were found to be closely related to the known anammox bacterial genera. This protocol was also successfully used to examine anammox communities in subterranean oil reservoirs (Li et al., 2010a) and various sediments collected from estuary, river, and deep sea (Hirsch et al., 2011). This PCR protocol was based on a distinct genomic structure of anammox bacteria from other groups in Planctomycetales (Schmid et al., 2001). Anammox bacteria possess linked 16S and 23S rRNA genes by an ITS of 450 bp. The initial PCR with the primers Pla46F and 1037R selected the bacteria carrying the linked rRNA operon, and the nested PCR reaction amplified the 16S rRNA genes from anammox bacteria. The requirement of the nested PCR reaction might be related to the abundance of anammox bacteria, the complexity of the overall microbial assemblage in the sediments, or possible reduction of PCR amplification efficiency from inhibitory substances in sediment extracts. The nested PCR method provided higher specificity and sensitivity for the detection of anammox bacteria and can be thus used for a simple and rapid detection of anammox in the environments.


71

2.2. PCR protocol of hzo gene detection The metagenome study of “Ca. Kuenenia stuttgartiensis” enrichment cultures inferred a putative anammox pathway that has four enzymes and genes; dissimilatory nitrite reductases (NirS), hydrazine hydrolase (Hh), and hydroxylamine oxidoreductase (Hao)/hydrazine oxidase (Hzo) (Strous et al., 2006). Nitrite is reduced to nitric oxide (NO) by NirS which then reacts with NH4þ to convert hydrazine (N2H4) by Hh. Finally, hydrazine is oxidized to N2 by either Hao or Hzo (Shimamura et al., 2007). The hydroxylamine oxidoreductase (Hao) has similar enzymatic activities to the Hao found in the aerobic ammonia oxidizing bacterium Nitrosomonas europaea. Both Hao enzymes are capable of oxidizing hydroxylamine and hydrazine (Schalk et al., 2000). The sequence comparison of the hao genes between anammox bacteria and N. europaea showed very low similarity, however. At least eight genes encoding Hao-like proteins were found and proposed for hydrazine oxidation in “Ca. Kuenenia stuttgartiensis” (Strous et al., 2006). Shimamura et al. (2007) purified hydrazine oxidase (Hzo) from the anammox enrichment culture KSU-1 and showed that the enzyme catalyzed the oxidation of hydrazine but not hydroxylamine. Two copies of nearly identical genes (hzoA and hzoB) coding for Hzo were detected in the genome of KSU-1. The hzoA and hzoB genes share 77% of nucleotide sequence similarities with two putative hao genes in the genome of “Ca. Kuenenia stuttgartiensis” (GenBank accession numbers: CT573073 and CT573072). Klotz et al. (2008) reannotated eight putative hao genes to hzo genes in “Ca. Kuenenia stuttgartiensis” and differentiated them into three different clusters (hzo clusters 1–3) based on phylogenetic relationships and biochemical characteristics. The hzoA and hzoB genes in KSU-1 and two homologous genes (CT573073 and CT573072) in “Ca. Kuenenia stuttgartiensis” were grouped within hzo cluster 1 (Klotz et al., 2008; Schmid et al., 2008). In addition, several putative hzo genes associated with hzo cluster 1 are detected from bioreactors enriched with “Ca. Jettenia asiatica” (Quan et al., 2008) and “Ca. Kuenenia sp.” (Li et al., 2009). Detection of hzo genes in environmental samples was initially accomplished with many degenerate PCR primers and various PCR conditions (Schmid et al., 2008). Although the hzo genes in cluster 1 (hereafter hzo1) were detected only in anammox enrichment cultures and landfill leachate, the hzo1 genes were suggested as a proper genetic marker of anammox bacterial detection due to the ubiquitous presence in all the examined anammox bacterial enrichment cultures (Schmid et al., 2008). Li et al. (2010b) recently compared different degenerate primers (Table 3.3) and three PCR conditions (hzocl1, Ana-hzo, and hzoFR in Table 3.4) for hzo1 gene detection. The hzocl1 PCR protocol was reported to have the highest efficiency of detecting hzo1 genes. This method was successfully used to detect hzo1 genes in groundwater samples collected from subterranean oil

Table 3.3

PCR primers for hzo1 gene detection and quantification

Forward primer

hzoAB1F

Sequence 0

5 -GAAGCNAAGGCNGTAGAAAT TATCAC-30 0 hzoAB4F 5 -TTGARTGTGCATGGTCTAW TGAAAG-30 Hzocl1F1 50 -TGYAAGACYTGYCAYTGG-3’ Ana-hzo1F 50 -TGTGCATGGTCAATTGAAAG-30 hzoF1 50 -TGTGCATGGTCAATTGAAAG-30

Reverse primer

hzoAB1R hzoAB4R

Sequence 0

5 -CTCTTCNGCAGGTGCA TGATG-30 0 5 -GCTGACCTGACCARTCAGG-30

References

Hirsch et al. (2011) Hirsch et al. (2011)

hzocllR2 50 -ACTCCAGATRTGCTGACC-30 Schmid et al. (2008) Ana-hzo2R 50 -ACCTCTTCWGCAGGTGCAT-30 Quan et al. (2008) hzoR1 50 -CAACCTCTTCWGCAGG Li et al. (2010b) TGCATG-30

Table 3.4 PCR protocols for hzo1 gene detection

PCR setup

Primers

Template

PCR condition

hzocl1

hzocl1F1 and hzocl1R2

DNA

Ana-hzo

DNA

hzoFR

Ana-hzo 1 F and Ana-hzo2R hzo1F and hzo 2R

hzoAB1

hzo AB1F and hzo AB1R

DNA

nested hzoAB4

hzoAB4F and hzoAB4R

hzoAB1P reaction

(94 C/1 min ! 50 C/1 min 72 C/1 min 30 s) 30 (95 C/1 min ! 53 C/1 min 72 C/2 min 30 s) 30 (95 C/1 min ! 53 C/1 min 72 C/2 min 30 s) 30 (94 C/1 min ! 53 C/1 min 72 C/2 min) 35 (94 C/1 min ! 53 C/1 min 72 C/1 min) 30

DNA


!

470


!

1033

Quan et al. (2008)

!

1034

Li et al. (2010b)

!

1550

Hirsch et al. (2011)

!

600

Hirsch et al. (2011)

74


reservoirs in China (Li et al., 2010a). Hirsch et al. (2011) also evaluated different primers (Table 3.3) and PCR conditions of hzo1 gene detection in various environmental samples (Table 3.4). The hzocl1 PCR protocol with the primers hzocl1F1 and hzocl1R2 generated the expected size fragments (450 bp) with multiple amplicons of different sizes. The PCR protocol Anahzo and hzoFR (Table 3.4) yielded the expected amplicons with 1000 bp in most of environmental samples. However, the hzo1 genes in some environmental samples were not amplified, although the presence of anammox bacteria was confirmed based on 16S rRNA gene analysis (Hirsch et al., 2011). In order to enhance the hzo1 gene detection, two different sets of primers and nested PCR protocols (Tables 3.3 and 3.4) were developed by Hirsch et al. (2011). Specific amino acid sequence regions of hzo1 genes targeted by the primers in Table 3.3 are illustrated in Fig. 3.2. The hzoAB4F primer targets a region 150 bp upstream from the hzocl1F1 primer, and has a 17 bp overlap with the Ana-hzo1F primer. The hzoAB4R primer has an 8 bp overlap with the hzocl1R2 primer while hzoAB1R primer has 16 bp overlapping with Ana-hzo2R and hzoR1 primers (Fig. 3.2). The nested hzoAB4 PCR generated a single amplicon with 600 bp from all the environmental DNA samples. Cloning and sequence analysis of the amplicons confirmed 100% detection specificity of the hzo1 gene as expected. The nested PCR approach increases the sensitivity of hzo1 gene detection in different environmental samples. Conventional PCR with different primers did not amplify hzo1 genes from some of environmental samples, probably due to lower abundance of anammox bacteria than other sites. Thus, the nested PCR of hzo1 genes can increase the detection limit of anammox bacteria in variety of environments.

2.3. Quantitative PCR of anammox bacteria Quantification of anammox bacteria was mostly conducted with the FISH method as described above; however, it is limited when enumerating anammox cells in aggregates and sediments. Q-PCR of 16S rRNA genes was alternatively used to enumerate anammox bacteria in anoxic water and sediments (Table 3.5). Hamersley et al. (2007) developed a Taqman probe and primers to quantify anammox bacterial abundance in the Peruvian oxygen minimum zone. This Q-PCR method (Q-amx16S4 in Table 3.5) was used to measure anammox bacterial abundance in estuarine sediments (Dale et al., 2009). The primers were also used for SYBR green assay (Q-amx16S3 in Table 3.5) to measure anammox populations in petroleum reservoirs (Li et al., 2010b). The Amx16S1 PCR protocol was modified for SYBR green assay (Q-amx16S1 in Table 3.5) to quantify anammox cells in the Arabian oxygen minimum zone (Ward et al., 2009). The Amx16S2 protocol was also modified for Q-PCR assay (Q-amx16S2 in Table 3.5) of anammox bacteria in coastal marine sediments (Dang et al., 2010).


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

75

HZO_cluster_1_BAF98481 HZO_cluster_1_BAF36963 HZO_cluster_1_CAJ71439 HZO_cluster_1_CAJ72085 HZO_cluster_2_CAJ70788

hzoAB1F GAMGAASSLMLKEAKAVEIITHWVPHEVYGQIGEPDNNGKVFFSGLGAKY GAMGAASSLMLKEAKAVEIITHWVPHEVYGQIGEPDNNGKVFFSGLGAKY GVIGTVSSLMVKEAKAVEIITHWVPHEVYGMPGEPDNSGKVFFSGLKAKY GVIGTVSSLMVKEAKAVEIITHWVPHEVYGMPGEPDNSGKVFFSGLKAKY KHAPVVKEDMADTHPKDARTIQQIISELTGEKIGPDNSGDVYHLGLTATY

66 66 140 66 150


MGYPKHENPPPYPGKYSKFWRTLPAYRYYIPDFMYNRDEVRPSNPIKGTF MGYPKHENPPPYPGKYSKFWRTLPAYRYYIPDFMYNRDEVRPSNPIKGTF MGYPKDAQRSPYPGKYSKFWKTLPAYRYYIPDYMYNRDEVRPSNPIKGTF MGYPKDAQRSPYPGKYSKFWKTLPAYRYYIPDYMYNRDEVRPSNPIKGTF T-PPKE--LLPGEGKFGKLFSFLPLMRWYDPDHYYT-----PNQAIGGEF

116 116 190 116 192


QLEQCIACHSVMTPGIVRDYKKSAHSRAEPNPTGCDTCHGNNHQKLTMPS QLEQCIACHSVMTPGIVRDYKKSAHSRAEPNPTGCDTCHGNNHQKLTMPS KLEQCVACHSVMTPGIVRDYNKSAHSKAEPAPTGCDTCHGNNHQKLTMPS KLEQCVACHSVMTPGIVRDYNKSAHSKAEPAPTGCDTCHGNNHQKLTMPS THGECLMCHTIQTPGIVAQWKKSKHAAVEQ-VVGCDKCHGNNHQQLYMPS

166 166 240 166 241


hzoAB4F/Ana-hzo1F SKACGTSECHETQYSEQGQGGIGSHASCSSFAQIECAWSIERPPGDTAGC 216 SKACGTSECHETQYSEQGQGGIGSHASCSSFAQIECAWSIERPPGDTAGC 216 SKACGTAECHETQYNEQGQGGIGSHASCSSFAQVECAWSIERPPGDTAGC 290 SKACGTAECHETQYNEQGQGGIGSHASCSSFAQVECAWSIERPPGDTAGC 216 WQHCG--ECHPEQKEGHRGGAMASHTYAFHVSTIEAPWQIAKPAAEVTAC 289


hzocl1F1 TFCHTSSEERCSTCHQRHQFDPKVARRAEQCKTCHWGKDHRDWEAYDIGL TFCHTSSEERCSTCHQRHQFDPKVARRAEQCKTCHWGKDHRDWEAYDIGL TFCHTSPEERCSTCHQRHQFDPAVARRSEQCKTCHWGKDHRDWEAYDIGL TFCHTSPEERCSTCHQRHQFDPAVARRSEQCKTCHWGKDHRDWEAYDIGL ATCHGIAENRCDGCHTRHDFSLAEARKPNNCGICHTGLDHYEYEMYRESY

266 266 340 266 339


HGVVYQVNKWDPKQFDWDKKLADADYVGPTCQYCHMRGGHHNVQRFSTVY HGVVYQVNKWDPKQFDWDKKLADADYVGPTCQYCHMRGGHHNVQRFSTVY HGTVYQVNKWDTEQFDFSKKLSDADYVGPTCQYCHMRGGHHNVQRASIVY HGTVYQVNKWDTEQFDFSKKLSDADYVGPTCQYCHMRGGHHNVQRASIVY HGMIYES---EQHTWDWTKPMKPENYKTPTCAYCHMRDGEHNAQKFSTVN

316 316 390 316 386


TSMGMSMADRGAPIWKEKRDRWASVCDDCHSPRFAKENLQALDESVKDAG TSMGMSMADRGAPIWKEKRDRWASVCDDCHSPRFAKENLQALDESVKDAG TSMGMSMADRGAPLWKEKRDRWVSICDDCHSPRFARENLQAMDESVKDAS TSMGMSMADRGAPLWKEKRDRWVSICDDCHSPRFARENLQAMDESVKDAS SHMGTSLVDRGAPKYKEARQSWINTCKGCHSPRFAADQLEAMDEAIKVSF

366 366 440 366 436


hzoAB4R/hzocl1R2 LKYRETFKVAEDLLKDGVADPMPKDLAPDWSGQHIWSLKIGAYHDGPEYG LKYRETFKVAEDLLKDGVADPMPKDLAPDWSGQHIWSLKIGAYHDGPEYG LKYRETFKVAEDLLIDGVLDPMPKDLCPDWSGQHIWSLKIGAYHDGEAYG LKYRETFKVAEDLLIDGVLDPMPKDLCPDWSGQHIWSLKIGAYHDGEAYG TKWREAMKIVVDLYNDGMLDPMPKDLAPDYAGHYTFSL-IG---------

416 416 490 416 476


GKTGESGEFRMSNCSDIERLCFESVGYFQTYIYKGMAHGSWNDATYSDGS GKTGESGEFRMSNCSDIERLCFESVGYFQTYIYKGMAHGSWNDATYSDGS GTTGESGEFRMSNCTDVERLCFESVGYFQTYIYKGMAHGSWNDATYSDGS GKTGESGEFRMSNVTDVERLCFESVGYFQTYIYKGMAHGSWNDATYSDGS ------GEGRMYNVSDIERTAFEMLVYITNAVYKAMAHGAMYGATYGKGA

466 466 540 466 520


FGMDRWLVNVKQNASQARRLAALEKKVGISWVPESFWKTGEWLDQLTGPY FGMDRWLVNVKQNASQARRLAALEKKVGISWVPESFWKTGEWLDQLTGPY FGMDRWLVNVKQNASRARRLAALEKKVGISWQPEQFWKTGEWLDQLTGPY FGMDRWLVNVKQNASRARRLAALEKKVGISWQPEQFWKTGEWLDQLTGPY FLQDRWLIQVKAEASKLRRIRALEERVGIKHKAYDFWKHGEYTDLLLG--

516 516 590 516 568


hzoAB1R/Ana-hzo2R/hzoR1 IVKNHPGKTIFDLCPDPGWLDTHHAPAEEVEYINRKLEELGMRHESHGSA IVKNHPGKTIFDLCPDPGWLDTHHAPAEEVEYINRKLEELGMRHESHGSA IVKNHPGKTIFDLCPDPGWLDTHHAPAEEVEYIERKLKELGITAGSHSAH IVKNHPGKTIFDLCPDPGWLDTHHAPAEEVEYIERKLKELGMEAGTHDVK -WKRKPGD-----------VDKAACKHEGADCLVE---------------

566 566 640 566 591

Figure 3.2 Primers targeting specific amino acid sequence regions of hzo1 genes. The primer sequences are listed in Table 3.3.

The number of anammox bacteria measured with these Q-PCR assays is a cautious estimate since non-anammox 16S rRNA genes can be amplified and counted. The primers used in the four Q-PCR protocols of 16S rRNA genes (Table 3.5) amplified not only anammox bacteria but also detected

Table 3.5

Quantitative PCR protocols for 16S rRNA and hzo1 genes

Protocol

Fluorescent Target gene dye

Q-amx16S1 16S rRNA

SYBR

Q-amx16S2 16S rRNA

SYBR

Q-amx16S3 16S rRNA

SYBR

Q-amx16S4 16S rRNA

Taqman

Q-hzoFR

hzo1 gene

SYBR

Primer combination

Probe

Amx368F and None Amx 820R Brod541F and None Brod1260R AMX-808-F and AMX1040-R AMX-808-F and AMX1040-R hzo1F and hzo2R

None

PCR condition

(94 C/15 s ! 62 C/ 1 min) 40 (95 C/5 s ! 61 C/ 20 s ! 72 C/54 s) 40 (95 C/30 s ! 55 C/ 30 s ! 72 C/ 30 s) 45 (95 C/15 s ! 60 C/ 2 min) 50

[6-FAM]AMX-931[TAMURA] None (95 C/5 s ! 56 C/ 20 s ! 72 C/ 60 s) 40

Fluorescent read temperature ( C)


62

450

Ward et al. (2009)

72

720

Dang et al. (2010)

55

232

Li et al. (2010a)

60

232

Hamersley et al. (2007)

80

1034

Dang et al. (2010)


77

non-anammox bacterial 16S rRNA genes, as confirmed by cloning and sequence analysis. Therefore, the Q-PCR of 16S rRNA genes may overestimate anammox bacterial abundance due to non-specific detection of both anammox and other bacteria in environmental samples. The amplified products should be cloned and sequenced to verify the detection of anammox 16S rRNA genes. In order to circumvent the issues of detection specificity, two functional genes (nirS and hzo1) were targeted to develop Q-PCR protocols. Lam et al. (2009) developed Q-PCR method of the nirS genes based on the genome sequences of “Ca. Scalindua sp. T23” and examined abundance and expression of the putative Scalindua-nirS genes in the Peruvian oxygen minimum zone. The nirS gene PCR method was tested with the DNA samples extracted from various marine sediments where “Ca. Scalindua spp.” were detected previously (Hirsch et al., 2011). But we were not able to detect the putative Scalindua-nirS genes from the tested samples. This might show some limitation of this method to detect and quantify the Scalindua-nirS genes in environmental samples. Q-PCR protocol to quantify the hzo1 genes (Q-hzoFR in Table 3.5) were recently developed by modifying the hzoFR method (Dang et al., 2010). Although this method enhances the specificity of anammox bacterial detection, the estimation of anammox cell number in sediments is still questionable. There are at least two copies of hzo1 genes found in anammox bacterial cultures, which hinder proper estimation of anammox bacterial cells in environmental samples. Dang et al. (2010) compared the 16S rRNA gene and hzo1 gene numbers measured in the same samples, but the ratio of hzo1 over 16S rRNA genes ranged from 3.7 to 45.4. This showed some issues of anammox bacterial quantification using both Q-PCR assays. Further development of Q-PCR assays targeting hzo1 gene should be conducted. In addition, the gene encoding hydrazine hydrolase (hh) can be use as an alternative genetic marker for anammox bacteria since it is a unique gene found in the genome of “Ca. Kuenenia stuttgartiensis” and Plancktomycetes KSU-1 (Shimamura et al., 2007; Strous et al., 2006).

3. Stable Isotope Methods to Measure Anammox Rates in Environmental Samples The in situ distribution of anammox substrates and products (DIN and N2) in select marine and groundwater environments in conjunction with the natural abundance d15N distribution of those fractions has been used to infer the presence of anammox (Clark et al., 2008; Fuchsman et al., 2008; Konovalov et al., 2008; Propenko et al., 2006). However, the coutilization/ production of NH4þ and NO2 in other N cycling reactions and currently insufficient information regarding isotopic fractionation factors unique to

78


anammox preclude in situ estimates of actual anammox rates. Anammox rates are determined in the laboratory using 15N tracers.

3.1. Sediment slurries and whole water incubations The overwhelming majority of anammox rates reported in the literature to date are based on anaerobic incubations of water or sediment slurries spiked with 15N tracer (Table 3.6). The incubation conditions and substrate availability may deviate from in situ conditions, so the approach does not necessarily yield in situ rates. Rates using this approach should be considered “potentials” in some cases; a measure of metabolic readiness for activity under favorable conditions. Samples collected from environments that possess dissolved O2 < 10 mM but ample NH4þ and NO3/NO2 (e.g., waters or sediments collected from redox boundaries) will be most similar to the incubation conditions such that laboratory-derived rates more closely approximate in situ rates. The labeled substrate can either be 15NO3, 15NO2, or 15NH4þ. Depending on site characteristics, the labeled substrate is either added solely or in conjunction with unlabelled (14N) complimentary substrate for anammox (e.g., 15NO3 or 15NO2 þ 14NH4þ; 14NO3 or 14NO2 þ 15 NH4þ). The majority of published anammox rates have been based on the application of this tracer methodology as outlined in Thamdrup and Dalsgaard (2002) using 15NO3 tracer and in Trimmer et al. (2003) using 15 NH4þ tracer. Regardless of the choice of 15N tracer, the incubation setup is identical. Sediments or water is collected in containers and stored with zero headspace. The zone of nitrate reduction is targeted for collections or alternatively, the zone of anammox can be largely captured by retaining the top 2–3 cm of the sediment profile. In the laboratory, sediment slurries or water is transferred to a sealed incubation vessel (e.g., Exetainers, Hungate tubes, serum bottles) in an O2-free glove bag. The headspace of the incubation vessel is flushed with ultra high purity helium. In the case of soils or large amounts of sediment, the incubation vessels are evacuated prior to helium flushing. The 15N-labeled substrate is injected and the headspace is either repeatedly sampled in the single incubation vessel or more commonly, sampled over a time series in replicate incubation vessels. The N2 evolved is isotopically analyzed using an isotope ratio mass spectrometer (IRMS) or a quadrupole mass spectrometer if enrichments are sufficiently high, for the distribution of 29N2 and 30N2, depending on the labeled substrate used. 15 NO3 or 15NO2 (15NOx) labeling: Either 15NO3 or 15NO2 þ 14 NH4þ is added to the incubation vessel as a small-volume concentrated stock solution to attain post injection concentrations ranging from 0.95), that is, when there is negligible ambient NO3þNO2 in the incubating sample relative to the amount of the 15N spike. Preincubating sediments under anaerobic conditions for 4–24 h prior to adding the 15 NO3 tracer can be effective in lowering or completely removing residual NOx in sediments that contain ample electron donor (Dale et al., 2009; Nicholls and Trimmer, 2009; Rich et al., 2008). In organic poor sediments, however, or soils with very high NO3, the preincubation step is largely ineffective at reducing the NOx inventory. This residual NO3 isotopically dilutes the added 15NO3 or 15NO2 spike. Unless sediments are highly organic rich, it should not be assumed that preincubation will remove ambient NOx to a level where FN can be assumed to be equivalent to FN-spike. A secondary issue of using 15NO3 as the tracer source is the dependence of initial NO3 reduction to NO2 used in anammox. Although anammox bacteria are capable of NO3 reduction to NO2 (Kartal et al., 2007a,b), there is likely a strong dependence of NO2 supply for anammox from denitrification (Trimmer et al., 2003) under the imposed incubation conditions. This dependence on denitrification may contribute to observed covariance between anammox and denitrification rates measured using 15NO3 additions (Fig. 3.3). With all substrate amendment techniques there is potential for enhancement (or inhibition) of rates. Trimmer et al. (2005) reports no substrate concentration effect on anammox rates with NO2 at concentrations above 10 mM. Nevertheless, when using 15 NO3 spikes, we have found it necessary to either match in situ NO3 concentrations or conduct parallel incubations at several substrate concentrations that can be used to derive and extrapolate ambient DIN concentrations. Anammox rates expressed as relative anammox importance (ra) to total N2 production (i.e., % anammox) is a metric that is less sensitive to the incubation substrate concentration than are the absolute rates.

81


1.8 1.6

Anammox

1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

0

2

4

6

8 10 12 Denitrification

14

16

18

Figure 3.3 Correlation between denitrification and anammox rates in the sediments of the Cape Fear River Estuary determined in anaerobic slurries spiked with 15NO3 þ 14 NH4þ.

NH4þ labeling: Tracer additions to sediments under anaerobic conditions (Koop-Jakobsen and Giblin, 2009; Trimmer et al., 2003) can also be used to estimate anammox rates, although this approach does not yield estimates of denitrification. The use of 15NH4þ may be supplemented with 14NO3 or 14NO2 if sufficiently high residual NOx does not exist. It is the tracer of choice in sediments with high NOx and low NH4þ, although it can also be used in NH4þ-rich environments provided some measure of the effective 15NH4þ enrichment (FA) is done. The anammox rate (in units of N per time) is derived from the production of 29N2 according to 15

dN =dt ¼ 2d29 N2 =dtFA 1

ð3:3Þ

where dN/dt is the anammox rate, FA is the fractional effective 15N enrichment of the ammonium pool over the duration of the incubation. If not directly measured using various isotopic techniques (e.g., Holmes et al., 1997; Risgaard-Petersen et al., 1995), FA is calculated from known amounts of added tracer and ambient NH4þ. These can be derived from Eq. (3.2) by substituting NH4þ for the NO3 terms. An additional consideration is the mineralization of organic nitrogen to NH4þ during the incubation, particularly in organic rich samples. This may lead to a large source of isotopic dilution that should be accounted for by postincubation measurements of the soluble and/or extractable NH4þ pool. The mechanics of setting up and executing the incubations are the same as that described for the 15NO3 or 15NO2 tracer experiments.

82


Rates calculated from 15NH4þ to 29N2 under anaerobic conditions should provide a more direct measurement of anammox. Theoretically, using either 15NOx or 15NH4þ approaches should yield similar rates, however, comparability is mixed among studies (Table 3.6). For some studies where rates and substrate concentrations are high, there is good agreement between the approaches (Trimmer et al., 2003). In other instances, the use of 15 NH4þ yielded lower or nondetectable rates relative to the use of 15NOx (Dale et al., 2009; Nicholls and Trimmer, 2009; Rich et al., 2008; Thamdrup and Dalsgaard, 2002). Lower and/or nondetectable rates using 15NH4þ may be attributable to unaccounted for isotopic dilution of the added 15NH4þ tracer with soluble and extractable ambient NH4þ pools. Regardless of the labeled substrate choice used in the incubations, the shutdown of coupled nitrification/denitrification should be verified, otherwise anammox will be overestimated. For sediments that are NOx free or can be made NOx free through preincubation, parallel anaerobic incubations using only 15NH4þ can be done to detect 29N2 production not attributable to anammox (Dale et al., 2009; Engstro¨m et al., 2005, 2009; Rich et al., 2008; Thamdrup and Dalsgaard, 2002).

3.2. Working toward in situ rates—Whole cores and ambient O2 The potential rate measurements described above using sediment slurries are constrained by: (1) the destruction of natural redox gradients during the slurry preparation; (2) the need to maintain anaerobic conditions which may decouple anammox from oxidative sources of substrate. Attempts to simultaneously derive anammox and denitrification (as well as nitrification) in sediment slurry incubations using parallel incubations of 15NH4þ and 15 NO3 additions are described in Minjeaud et al. (2008). This approach addresses potential oxidative sources of anammox substrate with sediment slurries. Recent approaches (Risgaard-Petersen et al., 2003, 2004a; Trimmer and Nicholls, 2009; Trimmer et al., 2006) to measuring anammox under quasi in situ conditions using an 15N tracer in intact sediment cores with aerated overlying water represent advancements toward generating anammox rate estimates that are closer to in situ rates. This “revised isotope pairing” (r-IPT) approach uses the addition of 15NO3 labeled substrate to the aerated overlying water of intact cores incubated in the dark. The anammox rate is calculated from the production of 29N2 and 30N2 (P29 and P30, respectively), and the ratio between 14NO3 and 15NO3 in the NOx reduction zone (r14) according to Anammox ¼ 2r14 ðP29 2r14 P30 Þ The value of r14 can be estimated in one of three ways.

ð3:4Þ


83

(1) If parallel 15NO3 or 15NO2 slurry incubations are performed and the relative contribution of anammox to total N2 production (ra) and the P29 and P30 are known, r14 is estimated from:

r14 ¼

ð1 ra ÞðP29 =P30 Þ ra ð2 ra Þ

ð3:5Þ

This approach, of course, shares some of the constraints associated with the potential rate measurements described in the previous section. (2) A second approach to derive an estimate of r14 requires two sets of 15 NO3-labeled core incubations (Eqs. (3.1) and (3.2)) that use different 15NO3 concentrations. P29ð1Þ VP29ð2Þ r14 ð1Þ ¼ 2 P30ð1Þ V 2 P30ð2Þ

ð3:6Þ

V is the ratio between the 15NO3 concentrations in incubation (Eqs. (3.1) and (3.2)). It can be measured directly or estimated from the P29 and P30 in the two incubations: V ¼

P29ð1Þ þ 2P30ð1Þ P29ð2Þ þ 2P30ð2Þ

ð3:7Þ

Derivations of the equations for approaches 1 and 2 to estimate r14 are presented in Risgaard-Petersen et al. (2003, 2004a,b). (3) The third approach found in Trimmer et al. (2006) and Trimmer and Nicholls (2009) does not require additional incubations and is based on the isotopic analysis of N2O following the addition of 15NO3 tracer. It is assumed that the distribution of 15N and 14N in N2O (produced by denitrification) is a reasonable proxy for the 15N and 14N distribution in the NO3 pool in the NOx reduction zone that contains both anammox and denitrification. This assumption may be complicated in cases where there is a substantial N2O contribution from nitrification. The value of r14 is calculated from the production rate of 15N14N2O (P45) and 15N15N2O (P46). r14 ¼

P45 2P46

ð3:8Þ

84


Collection/analysis of N2O samples from the incubations for isotopic analysis of N2O is described in Trimmer et al. (2006, 2009). Recent analytical methods for N2O isotope analysis using IRMS are detailed in McIlvin and Casciotti (2010). Quadrupole mass spectrometry methods for isotope N2O analysis can be found in Minjeaud et al. (2008). Relatively few anammox measurements have been performed to date using the r-IPT approach. Results so far suggest that r-IPT yields somewhat higher anammox rates relative to slurry incubations in environments where the ra is one-third or more of the total N2.

ACKNOWLEDGMENTS The data presented in this chapter were from the research projects supported by NC Sea Grant and NSF (OCE-0851435). We acknowledge Matthew D. Hirsch for his contribution of anammox and denitrification measurements presented in Fig. 3.3.

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C H A P T E R

F O U R

Nitrogen Mineralization and Assimilation at Millimeter Scales David D. Myrold,* Jennifer Pett-Ridge,† and Peter J. Bottomley*,‡ Contents 92 92 93 93 94 94 95 96 101 109 109

1. Introduction 2. Microbial Habitats in Soil 2.1. Rhizosphere 2.2. Plant detritus 2.3. Soil aggregates 3. Methodological Approaches 3.1. General isotope pool dilution principles and procedures 3.2. Applications of IRMS analysis to soil microhabitats 3.3. Applications of SIMS analysis to soil microhabitats 4. Conclusions References

Abstract The assimilation (uptake or immobilization) of inorganic nitrogen (N) and the production of ammonium (NH4þ) from organic N compounds are universal functions of microorganisms, and the balance between these two processes is tightly regulated by the relative demands of microbes for N and carbon (C). In a heterogeneous environment, such as soils, bulk measurements of N mineralization or immobilization do not reflect the variation of these two processes in different microhabitats (1 mm–1 mm). Our purpose is to review the approaches that can be applied to measure N mineralization and immobilization within soil microhabitats, at scales of millimeter (using adaptations of 15N isotope pool dilution and IRMS—isotope ratio mass spectrometry) to micrometer (using SIMS—secondary ion mass spectrometry).

* Department of Crop and Soil Science, Oregon State University, Corvallis, Oregon, USA Chemical Sciences Division, Lawrence Livermore National Laboratory, California, USA Department of Microbiology, Oregon State University, Corvallis, Oregon, USA

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Methods in Enzymology, Volume 496 ISSN 0076-6879, DOI: 10.1016/B978-0-12-386489-5.00004-X

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David D. Myrold et al.

1. Introduction Microorganisms need nitrogen (N) to carry out cellular functions and to grow. Ammonium (NH4þ) is generally acknowledged as their preferred source of inorganic N; however, microorganisms may also use organic N compounds, such as amino acids, or nitrate (NO3). When microorganisms encounter organic N in excess of the growth needs for N, they engage in a combination of enzymatic processes that depolymerize and deaminate organic forms of N into NH4þ. This process is commonly termed N mineralization or ammonification. Through nitrification, NH4þ can be oxidized to NO3. The opposing, anabolic process is the transformation of NH4þ or NO3 into organic N compounds for assimilation into cellular constituents, a process also known as N immobilization. In soil microbiology, it is conventional to think of mineralization of organic N occurring under carbon (C)-limited conditions and assimilation of inorganic N occurring under C-sufficient (i.e., N-limited) conditions. In the environment, N mineralization and immobilization can occur simultaneously, but at a given time the two processes are likely to be spatially partitioned into different microhabitats (Chen and Stark, 2000; Myrold and Bottomley, 2008; Schimel and Bennett, 2004). To gain a more sophisticated understanding of the factors that control the balance between N mineralizing and immobilizing activities, there is a need to move beyond bulk measurements that average across microhabitats to measuring N mineralization and assimilation at scales relevant to microorganisms. In this chapter, we review methods that can be used to measure N mineralization and assimilation at several spatial scales. In doing so, we will focus primarily on soils because of their variability over a wide range of spatial scales.

2. Microbial Habitats in Soil Soil is a complex, spatially and temporally heterogeneous, threedimensional habitat in which microorganisms function to process C, N, and other nutrients. Consequently, soil can be considered a mosaic of niches, or microhabitats, with unique characteristics that structure their associated microbial communities and their functions (Dechesne et al., 2007; Jastrow and Miller, 1998; Nunan et al., 2007). In soil science, these microhabitats have commonly been classified and named based on their sphere of influence (Beare et al., 1995; Fig. 4.1). The rhizosphere, decaying plant material, and soil aggregates are soil microhabitats that have been examined for sub-centimeter scale measurements of N mineralization and immobilization.

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Nitrogen Cycling at Millimeter Scales

Mycorrhizal hyphae

Clay microstructures

Plant root

Pore space Microbial debris

Macroaggregates

Microaggregate

Detritus with saprotrophic fungi

Figure 4.1 Examples of microhabitats found in soil. Nitrogen mineralization and immobilization have been investigated in soil associated with roots and mycorrhizal hyphae (rhizosphere), plant detritus, and aggregates of various sizes. Adapted from Jastrow and Miller (1998).

2.1. Rhizosphere Plants alter the physical and chemical environment around their roots, generating the microhabitat known as the rhizosphere (e.g., Dessaux et al., 2010), thereby influencing the microorganisms living there and their function (Hawkes et al., 2007; Philippot et al., 2009). Most directly, this is through symbiotic relationships of plant roots with mycorrhizal fungi or with root-nodulating, N2-fixing bacteria. Indeed, the ubiquity and influence of mycorrhizae has led to finer microhabitat distinctions (e.g., “mycorrhizosphere” or “hyphosphere”). More generally plants put substantial amounts of C into the soil through root exudation ( Jones et al., 2009), alter soil water and gas relationships (Philippot et al., 2009), and compete with rhizosphere microorganisms for nutrients (Inselsbacher et al., 2010; Kaye and Hart, 1997; Ma˚nsson et al., 2009). Although N mineralization and immobilization are elevated in rhizosphere compared to bulk soil (Breland and Bakken, 1991; Herman et al., 2006; Norton and Firestone, 1996), within the rhizosphere there is also likely to be significant heterogeneity and the development of smaller microhabitats (Philippot et al., 2009).

2.2. Plant detritus Dead plant parts are diverse, ranging from succulent, fresh leaves or fine roots to highly lignified, coarse woody materials. Whether incorporated into soil through agricultural activities or entering through natural litter fall

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or root turnover, plant detritus represents a major input of C, N, and other nutrients into soil systems and is a hot spot for microbial activity. In general, the tipping point between N mineralization and immobilization is a function of the quality of the residue (e.g., its C:N ratio, lignin content) and the composition and physiology of the microbial community (Myrold and Bottomley, 2008). Microbial communities directly associated with decaying residue are known to be different than bulk soil (McMahon et al., 2005) and vary with residue characteristics (Baumann et al., 2009; Rousk and Ba˚a˚th, 2007; Williams et al., 2007). As decomposition proceeds, the chemical composition of the residue changes (e.g., C:N ratio narrows, percentage of lignin increases); these changes are accompanied by shifts in the relative magnitudes of N immobilization and mineralization (Gaillard et al., 1999; Magid et al., 2006) and sometimes by concomitant shifts in the microbial community (Baumann et al., 2009; Williams et al., 2007). It is not clear if this is a cause or an effect of the latter.

2.3. Soil aggregates The structure of the soil—soil aggregation—imparts many of the key properties to soil in terms of the transport of water, solutes, and gases, and it shapes the habitats available to soil microorganisms (Or et al., 2007). Soil aggregates vary in size and are dynamic in terms of their formation and disintegration; these characteristics are partly a function of microbial activities, such as the production of extracellular polymeric materials that help stabilize aggregates (Six et al., 1999; Tisdall and Oades, 1982). Consequently, microbial communities and their activities can vary among different sizes of soil aggregates (Mendes et al., 1999; Muruganandam et al., 2009). For example, microbial N mineralization and immobilization were greatest in intermediate to larger water-stable soil aggregates (Angers et al., 1997; Muruganandam et al., 2010). Variation in microbial processes can also occur within individual soil aggregates. For example, the interplay between oxygen diffusion and microbial respiration results in a gradient of oxygen concentrations within a soil aggregate, which in turn influence denitrification (Sexstone et al., 1985). Gradients of nutrients and C are also likely to exist within aggregates, and thereby have the potential to influence the balance between N mineralization and immobilization processes.

3. Methodological Approaches Measurements of N mineralization and immobilization in soils are typically made on bulk samples, such as undisturbed cores ( 10–1000 cm3) or sieved soils (10 g–1 kg), and most often changes in the pool sizes of NH4þ

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and/or NO3 are measured at two or more times to determine a net rate of N mineralization (or immobilization if a decrease in inorganic N is observed). In order to separate N mineralization from N immobilization, it is most common to use compounds labeled with the stable isotope 15N as part of the isotope pool dilution technique (Hart and Myrold, 1996; Hart et al., 1994; Murphy et al., 2003). Assimilation of N can also be measured by the incorporation of an 15 N label directly into the microbial biomass (Hatch et al., 2000; Ledgard et al., 1998; Myrold and Tiedje, 1986) or by determination of residual 15N in soils after removal of the added tracer (Andersen and Jensen, 2001; Mary et al., 1998; Recous et al., 1999). Because the processes occur simultaneously, the use of 15N is necessary when measuring N mineralization or immobilization at small spatial scales, regardless of the approach used.

3.1. General isotope pool dilution principles and procedures

NH4+ p

5

14N

5

15N

Initial time

NH4+ c

p

8

14N

2

15N

Final time

c

NH4+ 15N fraction

The conceptual model of isotope dilution and its associated mathematical calculations were developed by Kirkham and Bartholomew (1954). In brief, an 15N-labeled compound is added to a soil pool (15NH4þ in the case of gross N mineralization or immobilization) and as unlabeled organic N is mineralized to NH4þ, the 15N abundance of the NH4þ is diluted and decreases exponentially (Fig. 4.2). By measuring the size and 15N abundance of the NH4þ pool at two (or more) times, the rates of NH4þ production (gross N mineralization) and consumption (the sum of NH4þ assimilation and nitrification) can be calculated using the equations derived by Kirkham and Bartholomew (1954) or more recent modeling approaches (Mary et al., 1998; Mu¨ller et al., 2007; Myrold and Tiedje, 1986). The isotope dilution model has several assumptions, including: (i) no isotopic discrimination, (ii) uniform distribution of label, (iii) equilibrium

0.5 0.4 0.3 0.2 0.1 0.0 Initial

Final Time

Figure 4.2 The principle of 15N isotope dilution as applied to the production (p, gross N mineralization) and consumption (c) of NH4þ. 15N-labeled NH4þ is added to a soil (in this case, in equal amounts to the unlabeled, native soil NH4þ) and measured shortly thereafter (initial time). The sample is incubated (often for 1–2 days) and measured again (final time). At the right is the expected change in the 15N abundance of the NH4þ pool through time as it is diluted with unlabeled NH4þ produced from organic N through mineralization.

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between added label and indigenous, unlabeled pools, and (iv) no remineralization of the label, although this can be taken into account (Kirkham and Bartholomew, 1955; Mary et al., 1998; Mu¨ller et al., 2007; Myrold and Tiedje, 1986). The assumptions of homogeneity of labeling and equilibrium are influenced by spatial heterogeneity of indigenous soil inorganic N, as well as the spatial distribution of the soil microbial community, and can result in inaccurate calculations of gross rates of N mineralization and immobilization (Davidson et al., 1991; Manzoni et al., 2008). Consequently, there has been interest in scaling down the 15N isotope dilution approach to study N mineralization and immobilization in soil microhabitats. In principle, 15N isotope dilution can be applied to soil microhabitats by specifically labeling particular microhabitats (often experimentally isolated with physical barriers), by labeling the whole soil and later separating the desired fraction, or with a combination of these two approaches. The most significant practical challenge when working with microhabitats is whether the sample is large enough so that the concentration and 15N abundance of the pool of interest (e.g., NH4þ) can be measured with existing analytical instruments. For example, it is difficult to accurately measure the 15N abundance of a sample with < 10 mg of N using modern isotope ratio mass spectrometers (IRMS; Barrie and Prosser, 1996). To use isotope dilution at the scale of a single soil aggregate, an aggregate with a 1 cm3 volume would need to have an NH4þ concentration of 10 mg N kg 1 soil, which is several-fold higher than typical soil NH4þ concentrations (Fig. 4.3). The volume of soil can be reduced when highly enriched 15N solutions are added if a “spike” of NH4þ of known mass and 15N abundance is added; however, this reduces the sensitivity and precision of the measurement. In general, sensitivity declines in proportion to the amount of spike added relative to amount of N from the sample. Thus, if the size of the spike was 100-fold, the sensitivity would decline by a factor of 100.

3.2. Applications of IRMS analysis to soil microhabitats Applying 15N-labeling methods to soil microhabitats requires samples large enough (>10 mg or >10 mm3; Fig. 4.3) to be analyzed by IRMS techniques. In general, the methods can be placed into two categories based on the order of isolating the microhabitat of interest and adding the label. 3.2.1. Physical separation followed by labeling The primary requisite for isolating a microhabitat is that doing so will not compromise its ability to function in its natural state. This is unlikely to be the case for rhizosphere soil because by definition it is dependent upon the functions of living roots, and this dependency is disrupted once the roots are removed. Physical fractionation methods are more appropriate to isolating detritusphere (e.g., particulate organic matter, light fraction) or water-stable

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NanoSIMS ToF-SIMS MALDI-ToF 15

N isotope dilution + spike 15

N isotope dilution 10–12 10–11 10–10 10–9 10–8 10–7 10–6 10–5 10–4 10–3 10–2 10–1 100 101

Mass (g) 0.001

0.010 0.100 1.000 Linear dimension (mm)

10.000

Figure 4.3 Recommended method for measuring nitrogen (N) assimilation (gray bar) as a function of sample size. Linear dimensions based on cubic geometry and a soil bulk density of 1.25 g cm 3. Nitrogen assimilation assumes a microbial biomass of 40 mg N kg 1 soil. Isotope dilution measurements of N mineralization (white bar) require samples 10 times larger, and are based on a pool size of 2 mg N kg 1 soil following label addition. Note that there is an intermediate range in which N assimilation cannot be conveniently measured by existing methods. SIMS—secondary ion mass spectrometry; ToF—time-of-flight; MALDI—matrix-assisted laser desorption/ionization.

aggregates, although the separation methods used have the potential to alter subsequent microbial activity. An example of this approach is the study of gross N-cycling rates in soil aggregates of different sizes by Muruganandam et al. (2010). They chose three size classes (2–4, 0.5–1.0, and 1.0 mm diameter) than in smaller aggregates, at least during the first 200 days of incubation. The greater N immobilization activity is consistent with gross N immobilization rates measured by 15N isotope dilution (Muruganandam et al., 2010). All the physical dissection methods described so far depend upon collecting enough soil to use IRMS approaches without the addition of a “spike” of N (Fig. 4.3). It is tempting to think that one could physically


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separate smaller samples, for example, by sampling a transect through a soil aggregate (Fig. 4.4C), as has been done for ammonia- and nitrite-oxidizing bacteria or the mineralization of a 14C-labeled herbicide (Gonod et al., 2006; Grundmann and Debouzie, 2000). Calculations suggest that, with current technology, this could be scaled down to a sample of 2 mm in linear dimension (Fig. 4.3). Other methods, such as secondary ion mass spectrometry (SIMS), are necessary to examine finer-scale resolution of N mineralization or assimilation.

3.3. Applications of SIMS analysis to soil microhabitats SIMS has the potential to provide quantitative measures of N assimilation at the single-cell scale; however, relatively few SIMS studies have been carried out in microbiological systems, with only a handful of applications in soil (Blair et al., 2006; Clode et al., 2009; Cliff et al., 2002, 2007; DeRito et al., 2005; Herrmann et al., 2007a,b; Pumphrey et al., 2009). In SIMS imaging, a highly charged ion beam is used to sputter a sample surface, causing secondary ions that are derived from the sample’s upper layers to be emitted (Fig. 4.5). When these ions are separated by their mass/charge ratio and detected, a quantitative ion map of the sputtered area is created. There are two broad classes of SIMS instruments used to study N dynamics in soils: time-of-flight (ToF) and dynamic (magnetic sector) SIMS. In ToF-SIMS, a primary ion beam is pulsed and resulting secondary ions are detected based on their mass-to-charge ratio and the time an ion takes to reach the detector. Although ToF-SIMS has the capacity to measure the full mass spectrum and detect molecular species, its design can require the user to compromise between high spatial resolution and mass resolving power (see reviews by Jacoby, 2006; Lockyer and Vickerman, 2004). With dynamic SIMS (e.g., the Cameca 3f, 5f, and NanoSIMS), N assimilation can be quantitatively measured with up to 50-nm resolution, typically after a cell culture or environmental sample has been exposed to a continuous or pulsechase 15N-labeling experiment. The highest spatial resolution is accomplished by visualizing 15N distribution with either the NanoSIMS 50 or 50 L ion microprobe (Cameca, Gennevilliers, France). Because N ionizes poorly alone, N must be detected as part of the cyanide (12C14N or 12C15N) polyatomic ion. In a ratio image of the 12C15N:12C14N ion maps, an 15N enrichment greater than the natural 15N abundance ratio of 0.37 atom% suggests that the cell, particle, or subcellular region assimilated “new” N during the labeling period. MALDI-ToF (matrix assisted laser desorption/ ionization ToF mass spectrometry) is one additional non-SIMS imaging technique that may also be useful for fine-scale imaging of N uptake. However, this type of analysis, where a laser is used to desorb surface molecules that are then detected by a ToF spectrometer, has so far been used primarily for biomolecule imaging in tissue and organs (reviewed by Burnum et al., 2008; McDonnell and Heeren, 2007) as opposed to soils.

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Figure 4.5 NanoSIMS instrument schematic. The left panel depicts the NanoSIMS sputtering process where primary ions (Csþ in this case) impact the sample surface, and secondary ions derived from the sample’s upper layers are extracted coaxial to the primary ion beam. The right panel shows an overview of the entire instrument, following the ion path from the primary ion source, to sample chamber, through the magnetic sector and ultimately secondary ion detection by a series of five electron multiplier detectors. Courtesy of Cameca (Gennevilliers, France), as modified by Dr. Peter Weber (Lawrence Livermore National Laboratory, USA).

ToF-SIMS and MALDI-ToF may be particularly useful for questions where the spatial scale of interest ranges between 100 mm and 1 cm (Fig. 4.4; e.g., in micro-and some macroaggregates), or where the molecular fate of assimilated 15N is of interest. For finer scale applications (e.g., single cells or colonies, bacteria–mineral interactions, 2 mm. At smaller spatial scales, it is necessary to use SIMS, which can measure the uptake and assimilation of 15N into microorganisms and allow visualization of cell–cell interactions, but cannot measure N mineralization. Clearly, there are trade-offs between the use of the 15N isotope pool dilution approach (which yields bulk-scale gross N immobilization, but little insight into microscale controlling factors) and 15N stable isotope probing with SIMS (which can measure gross N assimilation at high spatial resolution, though at a significant cost). Given the right experimental design, this method may provide new insights into the primary factors controlling N assimilation at the microscale (e.g., moisture content and temperature), as well as biotic factors ranging from spatial distribution of active microorganisms, association of microorganisms with particular minerals, roles of filamentous fungi in mediating transport of N and other elements, and distribution/impact of N2-fixing soil microbes. To date, studies have demonstrated that N mineralization and assimilation can vary at small scales and that the balance between these opposing processes can be influenced by C inputs from roots or decaying plant materials. It is also likely that synergistic or competitive interactions among microorganisms (e.g., bacteria growing in association with fungal hyphae) affect the balance between N mineralization and assimilation, but demonstration of this possibility awaits future applications of SIMS imaging methods coupled with well-designed 15N labeling experiments. 15

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C H A P T E R

F I V E

Measurement of Carbon Dioxide, Methane, Nitrous oxide, and Water Potential in Soil Ecosystems Martin E. Brummell and Steven D. Siciliano Contents 1. 2. 3. 4. 5. 6.

Introduction Soil Gas Probes Flux Estimates to the Atmosphere Using Recirculating Chambers Data Analysis Soil Profile Analysis FTIR Calibration 6.1. Recirculating versus static chambers 6.2. Absolute calibrations 7. Correlations Between Profile Concentrations and Surface Flux in Field Measurements 8. Correlations Between FTIR and Traditional Water Activity Measures 9. Common Issues During FTIR Measurement 9.1. Other sources of difficulty 10. Conclusions Acknowledgments References

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Abstract New technologies in trace gas detection are revolutionizing our ability to study soil microbiological ecosystems. Field-deployable infrared-spectroscopy detectors capable of rapidly measuring multiple analyte gases simultaneously allow estimates of soil:atmosphere gas exchange and below-ground gas concentrations, and production dynamics across divergent ecosystems, creating opportunities to study interactions between microorganisms, soils, atmospheres, and global cycling, as well as interactions between different gases. The greenhouse gases CO2, CH4, and N2O can be measured in the field and compared to each other to uncover links between the biochemical pathways responsible for the Department of Soil Science, University of Saskatchewan, Saskatoon, Saskatchewan, Canada Methods in Enzymology, Volume 496 ISSN 0076-6879, DOI: 10.1016/B978-0-12-386489-5.00005-1

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Martin E. Brummell and Steven D. Siciliano

production and consumption of these gases. We have developed techniques using a nondestructive, Fourier-transform infrared detector under remote field conditions in three campaigns in the Canadian High Arctic to measure highly variable gas processes in soils.

1. Introduction Greenhouse gas (GHG) emissions from soils, primarily CO2, CH4, and N2O, are an important component of global emissions. Soils are responsible for a large fraction of CH4 and N2O emissions, and while CO2 is produced by plant roots as well as microorganisms and abiotic processes (Kuzyakov, 2006), CH4 and N2O are overwhelmingly the result of prokaryote and fungal metabolic pathways (Firestone and Davidson, 1989; Laughlin and Stevens, 2002). Methane has a global warming potential 40 times that of CO2 per mole, and N2O has a potential 300 times that of CO2. Measurement of the distribution and production of these gases is a critical step in understanding both how soil ecosystems contribute to and may react to climate change. The three major GHG emitted from soils are the end products, or in some cases by-products, of different biochemical pathways. Besides the weathering of carbonate minerals, CO2 production is the result of soil respiration, the sum total CO2 production by all soil organisms. Soil respiration is related to biomass, soil organic matter, nutrient limitation, temperature, and other factors (Davidson et al., 2000; Kuzyakov, 2006; Risk et al., 2002; Rustad et al., 2000; Smith et al., 2003). Much of the CO2 produced by soils may be consumed by photosynthesis, and highly productive ecosystems are net sinks for CO2 despite high levels of soil respiration (Gilmanov et al., 2010). While CO2 is not consumed below ground by soil organisms in significant quantities in most ecosystems (Kellman and Kavanaugh, 2008; Risk et al., 2002), both CH4 and N2O may be oxidized or reduced, respectively, by some functional groups of soil microorganisms depending on local and sometimes transient physical and chemical conditions (Smith et al., 2003). Methane is produced almost exclusively under very wet or saturated conditions, by methanogenic bacteria and archaea that may be either facultative or obligate anaerobes. Like CO2, CH4 production is linked to temperature but is more strongly related to soil water content (Conrad, 1989). Under more aerobic conditions, as are found near the surface in many soil ecosystems, populations of methanotrophs intercept CH4 diffusing upward from deeper, wetter layers (Galchenko et al., 1989; Whalen and Reeburgh, 2000).

Measurement of CO2, CH4, N2O, H2O Potential in Soil

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Nitrous oxide is produced through several distinct biochemical pathways in soils. Nitrifying organisms release N2O during the reduction of NO2 when O2 is limiting (Firestone and Davidson, 1989; Firestone et al., 1980). Denitrifying organisms, in contrast, release N2O depending on the availability of electron donors (primarily organic carbon) and electron acceptors, often a range of N-oxides (Firestone and Davidson, 1989). Denitrification is the major source of N2O in soils ( Johnson et al., 2005), but due to the multiple contributing pathways and diverse organisms, rates of production may be high under wet conditions conducive to denitrification (e.g., Bedard-Haughn et al., 2006), or under drier conditions where both denitrification and nitrification may occur simultaneously (Davidson et al., 2000; Stevens et al., 1997); the composition of the microbial community may be as important to N2O emissions as environmental parameters such as water content or temperature (Bedard-Haughn et al., 2006). Nitrous oxide is reduced by denitrifiers under more strictly anaerobic conditions, when they use it as an electron acceptor in the absence of other electron acceptors such as O2 (McBride, 1994). Diffusion of N2O from regions of production to regions of consumption can occur in soils, especially where adjacent soil layers differ in water and oxygen content (Kellman and Kavanaugh, 2008). Surface flux estimates of N2O are particularly vulnerable to underestimation of N2O production due to transient or microhabitatassociated zones of consumption (Chapuis-Lardy et al., 2007). Soil GHG processes are highly variable and dynamic, and respond to physical and chemical conditions that may change rapidly. Measurement of GHG in situ is therefore critical for understanding actual dynamics under natural conditions and estimating landscape-scale emissions. There are two major approaches to measuring soil GHG in the field: collect samples and return them to the laboratory for analysis, or bring field-portable analytical instruments to the field site. Neither approach to field work is fundamentally superior, and different research programs will require different approaches depending on the scale of investigation, time available in the field, logistical constraints, and budget. Many studies of soil atmospheres have been conducted using the first approach, collecting gas samples in the field, and analyzing using sophisticated and powerful laboratory-based techniques. The most widely used technique includes the use of a gas chromatograph (GC) and electron capture detector. While some field-portable designs exist, most GC units never leave the lab. Gas samples collected from the field or from laboratory microcosm experiments are injected into the column and concentrations of CO2, CH4, N2O, and other gases are measured individually (Kabwe et al., 2005; Pennok et al., 2010; Siciliano et al., 2009). Sample collection in the field is most often conducted using syringes; a volume of gas is extracted from a chamber or soil probe and injected into a previously evacuated airtight container.

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The alternate approach, of bringing a field-portable analyzer to the field, introduces several additional sources of potentially disastrous error such as loss or failure of the instrument. Given the cost and delicacy of highly sensitive instruments, damage to the equipment is usually a primary concern of the research team. However, field-based analysis provides several advantages over a GCbased field study, most importantly that of the ability to examine and interpret results as soon as they are collected. This near-real-time feature of field-based analytical instruments allows very flexible study designs, and the researchers can react to unusual and unexpected situations and modify the study design accordingly; with the results unknown until after the end of the field campaign with a laboratory GC-based approach, such flexibility is not possible. Soil gas processes measured in the field fall into two broad categories: static measurement of in situ gas concentrations, often via a probe buried or inserted into the soil to a set depth, and flux measurements of the emission of gases from the soil surface to the atmosphere, either by chambers enclosing an area of soil, or by eddy-covariance towers. Both probes and chambers start with a measurement of concentration of the gases of interest, this measurement is compared directly between probes and ambient surface conditions to generate a soil gas profile, while a chamber can be used to estimate flux by observing the accumulation of gases inside the chamber over a period of time. Recent reviews of the use of chamber techniques for estimating fluxes such as (Davidson et al., 2002) and (Grndahl et al., 2008) provide excellent overviews of the contrasting requirements and constraints of a range of methodologies. Each probe, and each chamber, represents an experimental unit. An incubation is a more general term covering a set of probes and/or chambers installed in an ecosystem; both probes and chambers require some time after installation for the disturbance associated with their installation to dissipate. A volume of gas removed from a probe or chamber constitutes a single sample; in the case of nondestructive, recirculating analytical systems, a sample is taken when the experimental unit is connected to the instrument, the gas circuit is closed to outside air, and headspace gas mixes with the gas already present inside the measurement cell and associated tubing. Repeated sampling is used to generate a flux estimate from a chamber, as gas concentrations rise continuously during the measurement period. This continuous increase is recorded as a series of contiguous instantaneous measures based on integration of the large number of rapid measurements taken by the analytical instrument; in the case of the flux estimates described in this chapter, 600,100 ms measurements are integrated into a single 1-min gas concentration estimate, and 10 such measurements are combined to generate an estimate of flux. New commercially available detector technologies, namely, Fourier Transformed Infrared (FTIR) spectroscopy, represent a way to use the field-based approach to estimate gaseous compound concentrations in soil


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or the flux of these compounds at the soil surface. This chapter will focus on field proven methods to estimate four key soil parameters either at the surface of the soil or throughout the soil profile when using FTIR detectors. The methods outlined in the chapter will likely work for any detector which uses a flow through, recirculating system, although pressurized detection systems would likely not work because of the difference in pressure inside the detection cell and the recirculating systems described here. Similarly, systems such as the Tekran group of analyzers that accumulate an analyte, such as gaseous mercury, will also not work because they alter the amount of analyte in the recirculating gas. The use of nondestructive detectors is revolutionizing our understanding of how microbial ecosystem processes influence biogeochemical cycling. This is largely due to two key advantages: (1) The ability to measure multiple analytes simultaneously allows investigators to explore links between the C and N cycles in a fashion not previously feasible. (2) The ability to monitor gaseous release from a system without altering the gaseous headspace, and to do so in near-real-time fashion, allows investigators to explore transient processes and investigate how transient fluxes of carbon and nitrogen are intertwined. For example, in the past, one would typically set up an incubation for a defined period of time, remove samples along a time course, hoping to hit key time steps, and then analyze the results. Often, the experiment would be completed before the results were known (e.g., Ma et al., 2007). With recirculating detectors, this is no longer a concern; results are available the instant a sample is taken. However, there is one drawback: these recirculating detection systems are not well suited to measuring many different experimental units simultaneously. This is because of the time required for analysis and purging each sample from the measurement cell; realistically it takes between 2 and 5 min to switch between experimental units. Hence, if, for example, one has a collection of chambers arranged along an environmental gradient such as a hillslope or across a soil discontinuity, each chamber can provide one flux estimate in 12–15 min, and the entire collection of chambers could be measured, one at a time, over the course of a few hours. In contrast, one can rapidly take samples for GC analysis, up to one every 30 s, allowing nearly simultaneous estimation of flux at all chambers; such systems are likely better suited to repeated measurement throughout a day, or large batch experiments than an FTIR-based system (Corre et al., 1996 Yates et al., 2006). Typically, CO2, CH4, and N2O are not measured simultaneously because of the additional vacutainers and GC runs required. Additional vacutainers require that investigators remove more samples out of their flux chamber or soil probe with resultant worries that this will result in a pressure

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differential within the chamber or probe and thereby “pull” gas out of the surrounding soil matrix. To combat this, investigators will inject an inert gas or a known atmosphere back into the system, with attendant worries that this no longer represents what is occurring in those ecosystems. In addition, multiple GC runs are required because of the different detectors used for CO2 and CH4 compared to N2O and each GC analysis is a destructive form of analysis which uses up a vacutainer. The principle advantages of FTIR detectors are that they can scan for multiple analytes and are nondestructive. Thus, analyzing for additional analytes does not incur additional time or cost. Further, because the sampling procedure is nondestructive, FTIR sampling typically involves setting up the chamber or probe in a sampling loop that includes the FTIR cell. Hence, no samples are removed from the flux chamber or soil probe and as long as the FTIR system is not too far from the sample chamber, then no vacuum will be created. Thus, an FTIR detector provides an investigator the capability to measure multiple gases, in real time, and nondestructively in the field. The FTIR can only detect gases with an infrared absorbance band between 900 and 4200 cm 1. Diatomic gases such as O2, H2, and N2 as well as noble gases and some airborne solid particles do not absorb IR in that region, and cannot be detected. Water content and CO2 concentration are often used as a proxy for O2 concentration, especially to estimate the status of a soil system as anaerobic versus aerobic; H2 and N2 are important gases in the processes of CH4 production and N2O consumption, respectively (Conrad, 1999; Firestone and Davidson, 1989). Some gas-specific detectors such as the Vaisala OMT-355 O2 detector can be connected to the FTIR measurement circuit in series, as they are also nondestructive samplers. Measurement of the small proportional changes in O2 concentration associated with soil respiration requires great sensitivity in the O2 detector, and we are currently evaluating the use of this technique to supply additional data in real time. The nondestructive and real-time analysis of the data under field conditions allows one to rapidly move between different soil ecosystems in the field and be assured that one is collecting high quality data. For example, in traditional flux measurement approaches, one takes a sample after a set period of time. In soil ecosystems that differ dramatically, this approach has many difficulties (Christensen et al., 1990a,b). In order to obtain high quality flux data, investigators need to sample gaseous concentrations through the period of near linear increase in headspace concentration of gases (Davidson et al., 2002). Because analytes increase in concentration in the headspace being analyzed due to concentration differentials between the soil pore space and the atmosphere, analyte concentration in headspace versus time is a sigmoidal curve (Farrell and Elliott, 2008). Thus, the key step for good quality flux data is to analyze at the appropriate time step for the ecosystem of interest. For example, should one sample every minute, every 5 min, or every 15 min in a


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productive upland system compared to a nearby slough, or as is often encountered by our research group, how often should a polar desert be sampled compared to an arctic wetland? Using flux chambers that recirculate and are tied to nondestructive samplers allows one to avoid this problem and rapidly analyzes soil ecosystems that differ fundamentally in their flux rates. The reason for this is that a typical FTIR detector can be set to repeatedly analyze samples between 20 and 180 s which will provide the analyte versus time relationship that one can estimate flux from. Flux estimates by this method are calculated from a series of consecutive measurements from a single chamber, with no interruption in the flow of gas through the closed, recirculating circuit. When this circuit is opened, as when moving between sampling positions, the system must be allowed to reach equilibrium by mixing the gas already in the measurement circuit (primarily the gas volume of the measurement cell and associated tubing) with the sample atmosphere to be measured. This takes up to 3 min under the configuration we have used, though this time would be longer or shorter depending on the length and diameter of the tubing and the flow rate of the pump. Excessively high flow rates create large errors of measurement, while excessively low flow rates or very long tubes prevent adequate mixing of sample atmospheres. For these reasons, we recommend leaving a chamber open while in-circuit with the FTIR and pump at 5 L min 1, at the end of tubes no longer than 10 m, for at least 2 min between flux estimates or between gas probe samples. One exciting application of multiple gas analysis is in the investigation of soil microbial ecosystems, both in manipulated and in native ecosystems. The concentrations of CO2, CH4, and N2O in the soil atmosphere vary with depth and with the communities of organisms in the soil (Blume et al., 2002; Yu et al., 2008). These concentration differences, when coupled with diffusivity estimates, can highlight regions of production and consumption of gases. By analyzing multiple gases simultaneously, we can investigate if microbial communities cycling N2O are also influencing the CH4 cycle directly through gas transformation or indirectly through competition. To investigate subsurface processes, many researchers have investigated belowground atmospheric conditions using a range of pit and probe techniques (e.g., Davidson and Trumbore, 1995; Lee et al., 2010; Mastepanov and Christensen, 2008). Whereas the majority of these techniques involve digging a soil pit and inserting probes horizontally into the profile (e.g., Fang and Moncrieff, 1998; Kamman et al., 2001), a few studies have used probes inserted directly into undisturbed soil to survey subsurface gas concentrations (e.g., Czimczik and Welker, 2010; Elberling et al., 2004). In such surveys, gases were sampled via syringe, introducing a pressure difference at the probe:soil interface and potentially sampling from an unknown volume of soil. The probes used in conjunction with the FTIR are inserted into undisturbed soil and then allowed to equilibrate by diffusion for 24 h

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before sampling the internal gases using a recirculating system that applies negligible pressure differential and turbulence at the probe:soil interface. We would note that the probes described below for field assessments of microbial communities could also be readily transferred to laboratory microcosms. In this scenario, the reservoirs attached to the probes here are attached to the laboratory microcosms. Then, the FTIR is attached to the reservoir/microcosm to estimate gas production. The internal volume of the measurement cell necessitates reservoirs attached to microcosms or probes to ameliorate the dilution effect and consequent loss of sensitivity when a small sample volume is mixed with the in-circuit gas.

2. Soil Gas Probes Each probe consisted of a hollow steel cylinder (56 1.9 cm i.d.) with a conical point welded to one end and flanges extending 5 mm beyond the outside diameter of the steel tube (Fig. 5.1). The steel point allowed the probe to penetrate hard soil matrices and push aside small stones; the top end of the tube was open during installation, but was sealed with a black butyl rubber stopper (VWR, West Chester PA, USA) during equilibration and measurement. A handle near the top of the probe allowed for an embedded probe to be pulled free of the soil at the end of the season. Each probe was constructed with a ring of 12 holes (2 mm dia.) around the circumference of the tube at depths ranging from 4 cm to a maximum of 50 cm below the zero mark (i.e., below-ground surface, bgs). A 0–26 cm scale was engraved on the side of each probe, allowing for an accurate measurement of the depth at which gases were collected. Concentrations of CO2, CH4, and N2O were measured using a Gasmet DX-4015 Fourier Transform Infrared trace gas analyzer (FTIR-TGA; Gasmet Technologies Oy, Helsinki, Finland). The measurement cell of the FTIR-TGA had an internal volume of 500 mL, whereas each soil probe had an internal volume of 112.5 mL. Thus, to increase the effective sample volume—and minimize dilution effects from ambient air in the gas measurement cell—each probe was connected to a 1.0-L polyethylene bottle (Thermo Fisher Scientific, Waltham MA, USA) via the opening at the top of the probe during equilibration and measurement. A paired, 30 cm 3 mm (i.d.) Teflon sample hose (Zeus Inc., Orangeburg SC, USA) connected the bottle in-line with the probe. A quick-disconnect fitting (Li-Cor Inc., Lincoln, USA) in the return line was used to attach the probe to the FTIR-TGA (via a 10 m 3 mm i.d. Teflon sample hose) without contaminating the interior volume of the probe or reservoir bottle with atmospheric air. The butyl stopper is best attached to the probe using duct tape to avoid the stopper accidentally coming off during the equilibration period.


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Open top Handle

Central joint, unscrews for storage

Diffusion holes at 46 cm in this example Flange Pointed tip

Figure 5.1 Soil gas probes were constructed from tube steel stock by the University of Saskatchewan Physics Machine Shop. Each probe consists of a hollow steel tube with a point welded to the downward end and a handle near the upper end. A scale at 1 cm intervals is etched down the side, extending 26 cm from the zero mark 4 cm below the upper end. A set of holes penetrates the steel tube at a position that varies between probes; the deepest probes can access soil air 50 cm below the surface. Probes are installed in clusters of six, spaced 10 cm apart and arranged to allow measurement of gases throughout the soil profile.

Gases were sampled from depth by diffusion. Probes were inserted into the soil to a set depth using a dead blow hammer. Initially a 2 lb hammer is used to start the probe followed by a 8 lb hammer to drive the probe to its full depth. The reservoir bottles are then attached to the probes, thus isolating the probe’s interior volume from the above-ground atmosphere. The time required to reach equilibrium between the probe-plus-reservoir internal atmosphere and the soil atmosphere was investigated by sampling from a series of probes over a 24-h period at the University of Saskatchewan field research site (Saskatoon, SK, Canada). Under ambient conditions, concentrations of CO2, CH4, and N2O ceased accumulating after 7 h (Fig. 5.2). To obtain an estimate of repeatability, measurements were repeated on consecutive days, after flushing the probes with ambient surface air and re-equilibrating with the soil atmosphere for 24 h (Fig. 5.3).

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25 20 15 10

160

16

140

14

120

100

80

N2O concentration (nmol L–1)

30

CH4 concentration (nmol L–1)

CO2 concentration (mmol L–1)

35

12

10

8

CO2 CH4

6 5

N2O

60 0

10 Time (h)

20

Figure 5.2 Gas concentrations ceased accumulation in probes installed at the University of Saskatchewan after 7 h. The line shown fits the CO2 concentrations, which showed the clearest signal of accumulation, reaching equilibration with soil gases at concentrations close to double CO2 concentrations at the surface.

35

% Deviation of duplicates

30 25 20 15 10 5 0 B3b

P1

B1 W1 Ecosystem

G3

P2

Figure 5.3 Percent deviation between consecutive days varied between ecosystems and between measured gases. CO2 (black bars) was the least variable between days at all ecosystems except P2, whereas CH4 (white bars) was less variable than either CO2 or N2O (hatched bars). The two most variable ecosystems, P2 and G3, also showed the greatest CO2 flux and AUC values, as well as the most variable N2O flux (see Fig. 5.5).


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After a probe (with reservoir bottle) was connected to the FTIR-TGA, the soil air was cycled through the closed loop for 3 min at a rate of 5 L min 1. During this time, the FTIR-TGA collected one spectral sample every 100 ms, with the on-board software (CalcmetTM ver. 2005.1) recording gas concentrations averaged over the 3-min interval. The probe was then disconnected and the FTIR-TGA system flushed with ambient air for 2 min. Preliminary studies under ambient conditions demonstrated that this sampling/flushing scheme resulted in negligible carry over from one probe to the next, even at high GHG concentrations.

3. Flux Estimates to the Atmosphere Using Recirculating Chambers The key difference between the method described here and the traditional recirculating chamber design commonly used for CO2 fluxes is the use of the FTIR detector. Measurements of the GHG flux at the soil: atmosphere interface were measured by connecting the FTIR-TGA to a Li-Cor long-term monitoring chamber (Model 8100-104; Li-Cor). The chamber (with an internal volume of 4.5 L) was used to monitor GHG emissions from a 0.0314 m2 area enclosed by a collar (20 cm i.d.) that had been inserted into the soil surface to a depth of 7 cm. Collars were placed in an area representative of the vegetation community, and in close proximity to the soil probes, 1 day prior to chamber deployment. Flux measurements were obtained by closing the chamber and monitoring the change in gas concentration over a 10 min period. The FTIR-TGA collected one spectral sample every 100 ms, with the on-board software recording gas concentrations averaged over 60 s intervals. GHG fluxes were calculated by plotting the change in concentration versus time and using standard curve fitting techniques to determine the slope of the curve between 2 and 8 min of the accumulation plot.

4. Data Analysis For a given soil probe, the gas concentration measured by the FTIRTGA (CT) includes a contribution from the ambient air in the measurement system (Ca) as well as the soil atmosphere (Cs), that is, VS VFTIR CT ¼ CS þ Ca ð5:1Þ VT VT

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where VS is the volume of the sample probe, including the gas reservoir bottle (1.1125 L); VFTIR is the volume of the gas measurement cell (0.5000 L) and associated tubing (0.1455 L); and VT is the total volume of the closed sample loop (1.7580 L). The gas concentration in the soil atmosphere was calculated by rearranging Eq. (5.1) to solve for CS: Cs ¼

CT VT Ca VFTIR VS

ð5:2Þ

Gas concentrations in ppmv (i.e., mL L 1) were converted to molar concentrations by multiplying the measured concentration (CT and Ca) by the molar volume of a gas at standard temperature and pressure (STP; 0.04462 mmol L 1). It should be noted that the cell in the Gasmet DX-4015 is maintained at ambient pressure and 323 K, and thus, one does not need to adjust for temperature and pressure differences to convert ppmv to molar equivalents. Water potential (C) was calculated from measures of the relative humidity in the probes (based on FTIR-TGA measurements of water vapor) using Eq. (5.4) (Rawlins, 1972): RT e C¼ ln m e

ð5:3Þ

where R ¼ 8.31 J mol 1 K 1; T is temperature (K), m is the molar vapor pressure of pure water (18 g mol 1), and e/e* is the relative humidity. All values are reported as the mean standard error of the mean for each depth increment. In addition to gas fluxes, the FTIR system can also provide estimates of evapotranspiration by monitoring changes in relative humidity over time in the chambers (Brown et al., 2010; McLeod et al., 2004). Evapotranspiration is calculated (Stannard, 1988) by: ET ¼ 3:6

MVC A

ð5:4Þ

where ET is the instantaneous rate of evapotranspiration (mm h 1), 3.6 is a conversion factor to convert g m 2 to mm h 1, V is the volume inside the chamber (m3), C is a calibration factor (see below for how to calculate this), A is the area of land covered by the chamber (m2), and M is the slope of the constant slope section of the graph that plots vapor density (pv) versus time. The vapor density (pv) is calculated (Rosenberg et al., 1983) by: pv ¼

0:622e 1000 RT

ð5:5Þ


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where R is the gas constant (287.04 J kg 1 K 1), T is the temperature in Kelvin, and the factor 0.622 is the ratio of the molecular weights of water to dry air. The parameter e is the partial vapor pressure which was calculated using relative humidity which is calculated by: e¼

RHes 100

ð5:6Þ

Where RH is relative humidity provided by the instrument and es is the saturation vapor pressure which is calculated by (CSIRO DWR, 1994): 17:5T eS ¼ 6:11 f ðP Þ ð5:7Þ 241:2 þ T where f(P) is assumed to be constant at 100.47 and T is in C. The calibration factor (C) used in Eq. (5.4) can be calculated by boiling water at different rates and measuring accumulation of vapor in the chamber. Calibration factors range from 2.22 (Raz-Yaseef et al., 2010), 1.534 (McLeod et al., 2004) to 1.298 (McJannet et al., 1996) and investigators need to calibrate each specific chamber used for their study.

5. Soil Profile Analysis Because it is not possible to insert the probes into the soil to exactly the same depth, the samples are grouped into 5 cm depth increments for statistical analysis. Ambient concentrations of each gas are determined by measuring the gas concentration in the air above the soil surface (n ¼ 5) on each sampling day, by opening the FTIR-TGA gas circuit to the atmosphere and placing the intake hose at 2 cm above the soil surface within the array of clusters at each vegetation community, and measuring for 3 min at 5 L min 1. Area-under-the-curve (AUC) analysis is used to integrate gas concentrations over the depth of the entire soil gas profile and provide an integrated estimate of the profile gas balance for CO2, CH4, and N2O. The AUC analysis calculated the area under the gas concentration depth curve using the trapezoidal rule and was conducted using NCSS Statistical and Power Analysis Software (Hintze, 2009). The baseline was set at the mean gas concentration measured in ambient air; thus, positive numbers (mol cm L 1) indicate that, on a profile-scale, gas production exceeded consumption. Conversely, negative numbers indicate that gas consumption exceeded production.

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It should be noted that individual peaks and troughs in the profile may not indicate production or consumption of the gas but may instead be linked to diffusivity differences with the soil profile. In other words, imagine a constant source at the bottom of the profile diffusing to the surface. If there are significant differences in diffusivity in certain soil segments, for example, there is a region of low diffusivity connected to an area of high diffusivity, then the area of low diffusivity will have a greater gaseous concentration of the analyte compared to the area of high diffusivity. Diffusivity of trace gases in soil is largely a function of the porosity of the soil, and the degree to which the pores are filled with water or air (De Jong and Schappert, 1972; Jury and Horton, 2004; Risk et al., 2002). All else being equal, wetter conditions slow diffusion of gases and allow large accumulations of gases at particular soil layers. For gases typically produced under wet conditions, such as CH4 and N2O, wet soils will appear in soil profiles as regions of high concentration. In contrast, consumption of CH4 occurs mainly under aerobic conditions typical of drier soils, where gases are freer to diffuse and may therefore appear as low concentrations both because of oxidation and because of rapid movement to other areas.

6. FTIR Calibration 6.1. Recirculating versus static chambers GHG fluxes were determined using nonsteady state, vented aluminum chambers (Livingston and Hutchinson, 1995) wrapped with insulation and reflective foil to minimize heating effects (Hutchinson and Mosier, 1981). Chamber collars (15 20 cm i.d.) were manually inserted into the soil to a depth of 5 cm; chamber lids were fastened to the bases by means of two snap locks. To alleviate disturbance effects, chamber collars were inserted into the soil 1 day prior to the first gas sampling date (Tiedje et al., 1989). The first gas sample was used as time-zero (t0) measurement for the flux calculation and represented ambient CO2 and N2O concentrations. Three 20-cm3 gas samples were subsequently collected from the enclosed headspace every 5 min using 25-cm3 syringes equipped with 25 gauge, 1.6-cm long needles (Monoject, Kendall LT, USA) and injected into pre-evacuated ( 40 Pa), 12-mL ExetainerÒ vials (uncoated soda glass vials, Labco Limited, United Kingdom). Concentrations of N2O and CO2 were determined using gas chromatography (Kabwe et al., 2005; Yates et al., 2006). The results with this traditional static chamber system indicated that the FTIR-TGA with LI-COR recirculating chambers corresponded well (r2 between 0.69 and 0.88) with the static chamber systems.


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6.2. Absolute calibrations Calibrations were conducted in an acrylic open-top box with ports for gas injection and sampling. The box was fitted with a wood and mesh top and play sand was spread across the mesh to a depth of 5.5 cm. Pure gas samples of N2O and CH4 were injected into the box through a rubber septum, while pure CO2 was introduced through a copper line fixed to the box. Much larger volumes of CO2 were introduced because ambient CO2 concentrations inside the box were up to 1000 times higher than either N2O or CH4. Total volume of injected gas ranged from 70 to 150 mL, into a box internal volume of 300 L. Soil collars were inserted 2 cm into the sand in the box and an LI-COR long-term monitoring chamber positioned above the collar. Each calibration trial consisted of three phases. First, gases were injected into the box and a single FTIR estimate of gas concentrations in the footspace of the apparatus was taken. This was followed by 15 cycles of LI-COR chamber closing and measurement over a 10-min period, and finally by a second static measurement of the footspace concentrations. The two footspace measurements were used to calculate the “Box flux” or the expected gas flux over the sampling period from the sand to the atmosphere through the collar. This measure corresponds to a static chamber estimate. The Box flux values and the mean of the 15 active chamber measures were compared over a range of gas concentrations. Regressions between the box and chamber fluxes (Fig. 5.4) were used to examine the relationship between static and active chamber estimates. A perfectly calibrated system would result in regressions of box against chamber fluxes with a slope and an r2 of 1.

7. Correlations Between Profile Concentrations and Surface Flux in Field Measurements Gas profile net production or consumption inferred from AUC analysis of gas profiles agreed well with observed patterns of gas flux as measured by accumulation or depletion of gases in surface chambers for CO2 but not for CH4 or N2O (Fig. 5.5). The variability associated with the AUC analysis was similar to that observed in the surface flux measurements with a standard error of 25% in the AUC and 88% in the surface flux measurements. Correlation, as measured by Pearson’s r, was positive and significant for CO2 (r = 0.842, p = 0.035), but not significant for CH4 ( p = 0.186) or for N2O ( p = 0.649).

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Chamber flux (mol m–2 s–1)

4.0 × 10–6 Chamber = 0.85 * flux from box r2 = 0.97 3.0 × 10–6

2.0 × 10–6

1.0 × 10–6 CO2 0.0 0.0


4.0 × 10–9

1.0 × 10–6 2.0 × 10–6 3.0 × 10–6 4.0 × 10–6 Chamber = 0.91 * flux from box r2 = 0.77

3.0 × 10–9

2.0 × 10–9

1.0 × 10–9 CH4 0.0 0.0

1.0 × 10–9 2.0 × 10–9 3.0 × 10–9 4.0 × 10–9


4.0 × 10–9 Chamber = 1.16 * flux from box r2 = 0.95 3.0 × 10–9

2.0 × 10–9

1.0 × 10–9 N2O 0.0 0.0

1.0 × 10–9 2.0 × 10–9 3.0 × 10–9 4.0 × 10–9 Box flux (mol m–2 s–1)

Figure 5.4 Relationships between box flux (static chamber) and active chamber estimates of CO2, CH4, and N2O fluxes. The line indicates the regression line between the calibration box and the measured chamber flux. Gases injected into the footspace of the calibration box diffused upward through the layer of dry sand and into the flux chamber on top at rates proportional to their concentrations and the calculated diffusivity of the sand.

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A

2.1 r = 0.842

CO2 flux (mmol m–2 s–1)

1.8 1.5

Sedge/dwarf-shrub

1.2 0.9

Hemiprostrate Prostrate

0.6

Wetland 0.3 Mountain

0.0

Barrens

–20

–10

0

10

20

30

40

50

CO2 AUC (mmol L–1 cm)

B

5 r = 0.624

CH4 flux (nmol m–2 s–1)

4 Wetland

3 2 1 0 –1

Barrens

Prostrate

Mountain Sedge/dwarf-shrub

–2 –400

–200

0

200

Hemiprostrate

400

600

800

1000

CH4 AUC (mmol L–1 cm)

C

2.5

r = 0.239

N2O flux (nmol m–2 s–1)

2.0 Sedge/ dwarf-shrub

1.5

Mountain

1.0

Hemiprostrate

0.5 0.0

Barrens

Prostrate

–0.5 Wetland

–1.0 –1.5

–20

0

20

40

60

N2O AUC (mmol L–1 cm)

Figure 5.5 Greenhouse gas flux as a function of the profile-integrated gas concentration. Surface flux of CO2 (A) was significantly (p ¼ 0.035) and positively correlated with subsurface profiles as AUC calculated values, but there was a weak association between surface flux and subsurface profiles for CH4 (B) (p ¼ 0.186) and none for N2O (C) (p ¼ 0.649). Values reported are the mean standard error (n ¼ 6). Profileintegrated gas concentrations were calculated using area-under-the-curve (AUC) analysis of the gas concentration versus depth profiles.

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The presence of sinks for CH4 and N2O may explain the weaker or absent correlation between profile and surface flux for these gases. A gas concentration profile represents a snapshot of conditions underground, and the role of soil diffusivity in structuring gas concentrations across depths provides a time-dependent factor. Nevertheless, the strong correlation for CO2, a gas that is not consumed in significant quantities by soil organisms, suggests the CO2 produced at depth is reaching the surface in a manner proportional to its rate of production as well as its rate of passage through the soil matrix. In contrast, CH4 and N2O may be consumed (oxidized and reduced, respectively) by soil microorganisms, with soil water content strongly determining the degree of such activity.

8. Correlations Between FTIR and Traditional Water Activity Measures Where water has displaced the majority of gaseous oxygen from soils, CH4 production will be strong while N2O may be consumed. In permafrost-affected soils, such conditions are more commonly found at the bottom of a soil profile in the active layer than near the surface, and CH4 produced at depth may be oxidized by methanotrophs in upper, more aerated layers. Similarly, N2O produced under unsaturated conditions may be consumed in deeper layers. When the net direction of diffusion of a gas in soil is not upward to the surface, a profile showing high concentrations of the gas will not release correspondingly large amounts at the surface. Water availability as well as its displacement of atmospheric oxygen is also important in structuring soil microbial communities. At equilibrium, water vapor in soil air will reflect the water potential in water-filled pores. Under very dry conditions, water potential may be limiting to microbial activity, preventing CH4 oxidation as well as CO2 production. Under natural conditions, soils may range in water contents from extremely dry to saturated, and some ecosystems may experience the full range in a single season. The current water status of a soil is thus of greater importance in structuring soil GHG dynamics than average or prior conditions. Simultaneous measurement of the water potential of a soil with gas concentrations provides a clear snapshot of both microbial community composition and current activity.

9. Common Issues During FTIR Measurement Use of an FTIR and recirculating chamber or probe design in field measurements of soil gas patterns and processes provides some advantages over other methods, but also some disadvantages. These are primarily the


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use of sensitive and expensive analytical equipment under field conditions, the time required for sampling and flushing the circuit between samples, and the presence of surface-ambient air inside the measurement cell. An FTIR system may cost approximately the same as a GC analyzer, and requires a similar level of training to operate effectively. In addition, use of delicate instruments under field conditions carries significant risks of damage to the equipment and possible loss of data. The unit used in the experiments described here had a weather-sealed case and required the use of a 1000-W generator in the field; data were collected on a field-hardened laptop computer, and regularly backed up to different storage media. Additional costs associated with field conditions depend on logistics; in the extreme case of polar research, rental or purchase of a generator is a fraction of the costs of air transport that may be sensitive to the weight of the generator as well as the analytical equipment itself. These weights and associated costs may be several times higher than the cost and weight of a collection of evacuated vacutainers and lightweight, aluminum chambers. Running an FTIR detector on sometimes-unreliable generator power can present its own issues. While the FTIR unit itself suffered no ill effects from frequent power outages and movements across uneven terrain, a computer controlled valve/gas manifold system failed under these conditions, representing a waste of time and several thousand dollars. Moving the FTIR, the generator, the associated pump and hoses, and the chambers consumed a significant fraction of our field work time each day, though this cost clearly depends strongly on the available infrastructure at a field site. Unlike syringe-based sampling of headspace gases in GC-based designs that may require only a few seconds, sampling with the FTIR requires up to 12 min per sampling position. Moving between samples requires a flush cycle with the measurement circuit open to either local atmosphere or a carried supply of pure N2 gas; pure N2 is anyways required in small amounts to re-zero the device each day. This flush interval between samples can add considerable time to a measurement cycle; in the case of the soil gas probes used to examine soil gas profiles, the 2 min flush between each 3 min measurement effectively extended measurement times from 12 to 30 min per cluster. Measuring flux by continuously monitoring gas concentrations in a closed chamber requires flushing only between separate flux estimates, but each estimate is based on up to 10, 1 min measurements during which the FTIR must not be moved or turned off. These restrictions prevent the rapid measurement of many, widely spaced sampling positions within a narrow time window, such as at a particular time of day. The FTIR gas circuit can be purged with a supply of N2 gas or other pure atmosphere to improve sensitivity to extremely low concentrations of gases of interest. Unlike purging with ambient atmosphere, which requires 2 min and simply opening the measurement circuit, the re-zeroing procedure used each day, in which pure N2 is forced into the measurement

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cell to displace all CO2, CH4, and N2O, requires up to 5 min of N2 injection and, under the procedure used here, does not purge ambient gas from the rest of the gas circuit (pump, hoses, chamber, or probe). Replacing the atmosphere inside the measurement circuit with a pure gas between each sample would increase the time required to collect data, creating a trade-off between accuracy for very low concentration measurements and sample size, which is not usually present using GC-based sampling designs.

9.1. Other sources of difficulty The quick-disconnect systems used to rapidly disengage and reengage the hoses and chambers and probes are subject to failure, either as spontaneous disconnection (possibly in the middle of a measurement cycle) or as a leak, usually due to either improper connection or solid particles accumulating inside moving parts. Other connection points, such as the black butyl rubber stoppers used to seal the tops of the probes, may also fail and allow accumulated gases to escape. While the FTIR may be weather sealed, exposure to severe weather can result in liquid water inside the measurement cell, preventing operation and requiring an expensive service call to repair. We have found the advantages of the FTIR for studying soil gas processes in situ—real-time data production, flexible sample design, nondestructive sampling, and simultaneous measurement of multiple gases of interest—to outweigh the disadvantages, many of which are not unique to the FTIR system or are a matter of degree rather than kind. In particular, the nondestructive, real-time, simultaneous multigas measurements allow rapid response to changing conditions in the field and the implementation of improved experimental design to take advantage of unexpected discoveries including pockets of exceptional gas concentration driven by local organic matter accumulation or sudden changes in soil water content driven by rapid snowmelt or heavy precipitation.

10. Conclusions The new recirculating, nondestructive analyzers provide new opportunities for investigators to link how water, CO2, CH4, and N2O cycles are connected in a variety of settings. Our description above has focused on field portable, and proven techniques accumulated over three High Arctic expeditions. However, there is no reason why these approaches will not also work in a laboratory setting. We would caution investigators that the use of these steel probes in climates in which there is a large temperature


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differential between the soil depths and the air temperature may require additional insulation to avoid thermal transfer into the soil profile.

ACKNOWLEDGMENTS We would like to thank Dr. Rich Farrell for the initial design of the FTIR-TGA system along with the many conversations of the adaptation of this equipment to field conditions. The input of Dr. Ian Snape into the use and abuse of these systems under Arctic and Antarctic conditions is also deeply appreciated. We would also like to thank Dr. Lisa Stein and one anonymous reviewer for critical comments that improved the manuscript.

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Ma, W. K., Schautz, A., Fishback, L.-A.E., Bedard-Haughn, A., Farrell, R. E., and Siciliano, S. D. (2007). Assessing the potential of ammonia oxidizing bacteria to produce nitrous oxide in soils of a High Arctic lowland ecosystem on Devon Island, Canada. Soil Biol. Biochem. 39, 2001–2013. Mastepanov, M., and Christensen, T. R. (2008). Bimembrane diffusion probe for continuous recording of dissolved and entrapped bubble gas concentrations in peat. Soil Biol. Biochem. 40, 2992–3003. McBride, M. B. (1994). Environmental Chemistry of Soils. Oxford University Press, New York, USA. McJannet, D. L., Vertessy, R. A., Tapper, N. J., O’Sullivan, S. K., Beringer, J., and Cleugh, H. (1996). Soil and Litter Evaporation Beneath Re-growth and Old-growth Mountain Ash Forest. Cooperative Research Centre for Catchment Hydrology, Clayton, Victoria. McLeod, M. K., Daniel, H., Faulkner, R., and Murison, R. (2004). Evaluation of an enclosed portable chamber to measure crop and pasture actual evapotranspiration at small scale. Agric. Water Manag. 67, 15–34. Pennok, D., Yates, T., Bedard-Haughn, A., Phipps, K., Farrell, R., and McDougal, R. (2010). Landscape controls on N2O and CH4 emissions from freshwater mineral soil wetlands of the Canadian prairie pothole region. Geoderma 155, 308–319. Rawlins, S. L. (1972). Theory of thermocouple psychrometers for measuring plant and soil water. In “Psychrometry in Water Relations Research,” (R. W. Brown and B. P. van Haveren, eds.), pp. 43–50. Utah Agricultural Experiment Station, Logan, UT. Raz-Yaseef, N., Rotenberg, E., and Yakir, D. (2010). Effects of spatial variations in soil evaporation caused by tree shading on water flux partitioning in a semi-arid pine forest. Agric. For. Meteorol. 150, 454–462. Risk, D., Kellman, L., and Beltrami, H. (2002). Carbon dioxide in soil profiles: Production and temperature dependence. Geophys. Res. Lett. 29, doi: 10.1029/2001GL014002. Rosenberg, N. J., Blad, B. L., and Verma, S. H. (1983). Microclimate: The Biological Environment. 2nd edn. John Wiley, New York. Rustad, L. E., Huntington, T. G., and Boone, R. D. (2000). Controls on soil respiration: Implications for climate change. Biogeochemistry 48, 1–6. Siciliano, S. D., Ma, W. K., Ferguson, S., and Farrell, R. E. (2009). Nitrifier dominance of arctic soil nitrous oxide emissions arises due to fungal competition with denitrifiers for nitrate. Soil Biol. Biochem. 41, 1104–1110. Smith, K. A., Ball, T., Conen, F., Dobbie, K. E., Massheder, J., and Rey, A. (2003). Exchange of greenhouse gases between soil and atmosphere: Interactions of soil physical factors and biological processes. Eur. J. Soil Sci. 54, 779–791. Stannard, D. I. (1988). Use of a Hemispherical Chamber for Measurement of Evapotranspiration. United States Geological Survey, Denver, CO, Open-File Report 88-452. Stevens, R. J., Laughlin, R. J., Burns, L. C., Arah, J. R. M., and Hood, R. C. (1997). Measuring the contributions of nitrification and denitrification to the flux of nitrous oxide from soil. Soil Biol. Biochem. 29, 139–151. Tiedje, J. M., Simkins, S., and Groffman, P. M. (1989). Perspectives on measurement of denitrification in the field including protocols for acetylene based methods. Plant Soil 115, 261–284. Whalen, S. C., and Reeburgh, W. S. (2000). Methane oxidation, production, and emission at contrasting sites in a boreal bog. Geomicrobiol. J. 17, 237–251. Yates, T. T., Si, B. C., Farrell, R. E., and Pennock, D. J. (2006). Probability distribution and spatial dependence of nitrous oxide emission: Temporal change in hummocky terrain. Soil Sci. Soc. Am. J. 70, 753–762. Yu, K., Faulkner, S. P., and Baldwin, M. J. (2008). Effect of hydrological conditions on nitrous oxide, methane, and carbon dioxide dynamics in a bottomland hardwood forest and its implications for soil carbon sequestration. Glob. Change Biol. 14, 798–812.

C H A P T E R

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Source Determination of Nitrous Oxide Based on Nitrogen and Oxygen Isotope Tracing: Dealing with Oxygen Exchange Dorien M. Kool,*,1 Jan Willem Van Groenigen,* and Nicole Wrage† Contents 1. Introduction 2. Experimental Approach 2.1. Experimental setup 2.2. Laboratory analyses 3. Data Evaluation for Nitrous Oxide Source Determination 3.1. Main assumptions 3.2. Quantifying O exchange: The ERR approach 3.3. Distinguishing N2O production pathways: 15N data analyses 3.4. Distinguishing N2O production pathways: 18O data analyses 4. Application of the ERR Principle in Nitrate Source Determination 5. Discussion, Applications, and Future Directions References

140 143 143 144 145 145 146 147 148 150 152 156

Abstract Source determination of nitrous oxide (N2O) from soils has so far been complicated by methodological constraints: the frequently used 15N tracer method could not differentiate between pathways related to nitrification, that is, nitrifier nitrification (NN), nitrifier denitrification (ND), and nitrification-coupled denitrification (NCD). To overcome this problem, a dual isotope method using both 15 N and 18O was proposed. However, O exchange between nitrogen oxides and water has been found to disturb such a method. We here explain in detail a novel dual isotope method that allows to quantify O exchange in denitrification and to differentiate N2O production from NN, ND, NCD, and fertilizer denitrification (FD). The method has already been applied to a range of soils with good success. Potential of and scope for further improvement of the method are discussed. * Department of Soil Quality, Wageningen University and Research Centre, Wageningen, The Netherlands Institute of Grassland Science, University of Go¨ttingen, Go¨ttingen, Germany Current address: Radboud University Nijmegen, Nijmegen, The Netherlands

{ 1


#


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Dorien M. Kool et al.

1. Introduction To effectively mitigate emissions of the greenhouse gas nitrous oxide (N2O), it is essential to understand the biochemical pathways by which it is produced. As different pathways of N2O production respond differently to environmental factors, understanding and distinguishing these pathways is essential to develop accurate N2O emission inventories and effective mitigation strategies for N2O emissions. Globally, the majority of the N2O in our atmosphere is derived from soil (IPCC, 2007). In soils, N2O has long been assumed to be mainly produced through the pathways of nitrification and denitrification. It is by now clear that many more processes are capable of producing N2O, but their relative significance to total production is generally still thought to be minor. One particular pathway, however, has increasingly gained attention as it may in fact constitute a substantial contribution to N2O emissions from soil: nitrifier denitrification (ND) (Fig. 6.1). ND is the reduction of nitrite (NO2) to N2O and/or N2, alike denitrification, with the significant distinction that it is performed by autotrophic ammonia oxidizers. Soil-based experimental studies increasingly suggest that ND could contribute significantly to N2O production in soil (Granli and Bockman, 1994; Hu¨tsch et al., 1999; Ma et al., 2007; McLain and Martens, 2005; Sańchez-Martıń et al., 2008; Venterea, 2007; Webster and Hopkins, 1996; Wrage et al., 2004a), However, until

O2

NH4+

NH2OH

N2O

(NN)

N2O

(ND)

H2O NO2–

NO

H2O NO3–

NO2–

NO

N2O

(NCD)

NO3–

NO2–

NO

N2O

(FD)

Figure 6.1 Depiction of the major pathways of N2O formation. We distinguish N2O production from nitrifiers as by-product of ammonia oxidation (nitrifier nitrification: NN) as well as through nitrifier denitrification (ND), and N2O production from denitrifiers through reduction of NO3 produced from nitrification (nitrification-coupled denitrification: NCD) as well as through reduction of applied NO3 (fertilizer denitrification: FD).

Source Determination of Nitrous Oxide

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recently available methodology did not enable to experimentally distinguish and conclusively proof the presence of ND in soil. Here we describe our recently developed nitrogen (N) and oxygen (O) stable isotope tracing approach to distinguish nitrification, denitrification, and ND as N2O production pathways in soil. Conventional N isotope labeling techniques differentiate N2O production from nitrification and denitrification in soil by applying and tracing 15N enrichment from NH4þ and NO3. However, 15N labeling alone cannot distinguish the N2O that results from nitrifiers’ NO2 reduction (i.e., ND) from the N2O generated as by-product from ammonia oxidation (i.e., the first step of “conventional” nitrification), as in both pathways the N originates from NH3 (Hayatsu et al., 2008; Wrage et al., 2005). Based on the idea that the origin of the O atom in N2O differs for different biochemical pathways, an additional 18O tracing approach could help to further distinguish between these pathways than 15N tracing alone. In the N2O produced through the different pathways, the O atom originates from H2O, O2, and NO3, in ratios reflecting reaction stoichiometry (Table 6.1). However, reaction stoichiometry only partially determines the origin of the O atom and resultant isotopic composition of N2O. In addition, O exchange between H2O and nitrogen oxides during N2O production alters the 18O signature of N2O (Kool et al., 2007). Only recently the significance of O exchange has been properly acknowledged, and it is vital that it is taken into account when interpreting the O isotopic data of N2O (Kool et al., 2009a). Our novel dual isotope approach takes O exchange into account and therewith provides a more accurate approach to distinguish N2O sources based on combined O and N isotope tracing. Our methodology aims to distinguish N2O production (a) from nitrifiers as by-product of ammonia oxidation, that is, nitrifier nitrification (NN); (b) from nitrifiers through ND; (c) from denitrifiers by reduction of NO3 produced from nitrification, that is, nitrification-coupled denitrification (NCD); and (d) from denitrifiers by reduction of applied NO3, that is, fertilizer denitrification (FD) (Fig. 6.1). The method is based on the 15N and 18O tracing of applied labeled compounds into newly produced N2O from soil. As part of our method, the effect of O exchange during NO3 reduction to N2O is quantified with the so-called enrichment ratio retention (ERR) approach. Reaction stoichiometry and the quantified O exchange together are the determining factors of the isotopic signature of N2O (fractionation effects are negligible because of the use of enriched compounds). Based on this, the 15N and 18 O isotopic signature of N2O is evaluated to provide improved insight in the relative contribution of the different pathways to total N2O production from soil.

Table 6.1 The table shows how the O isotopic signature of N2O from the different N2O-forming pathways is determined by the O isotopic signal of O2, H2O, or NO3, in case no oxygen exchange between H2O and intermediate compounds takes place

N2O–O isotopic signature reflects that of

Preceding compound O2 H2O Fertilizer NO3

Nitrification

Nitrifier denitrification

Nitrification-coupled denitrification

Fertilizer denitrification

NH2OH 100% 0% 0%

NO2 50% 50% 0%

NO3 33% 67% 0%

NO3 0% 0% 100%

Furthermore, the preceding compounds indicate the source of N in N2O.


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2. Experimental Approach 2.1. Experimental setup Soil samples are collected, oven dried at 40 C, sieved over 2 mm, and stored cool until further use. It is also possible to use fresh soil. However, one has to keep in mind that water has to be added to enable the isotope tracer applications. Subsamples of 75–100 g dry soil are preincubated for 7 days, at the incubation temperature (e.g., 16 C). Moisture content during preincubation is lower than during the incubation, allowing for the tracer applications at the start of the incubation. The moisture content used for the incubations may be adjusted depending on the objective of the experiment. Samples are incubated in glass jars of 300 mL for 24–28 h. Such a headspace volume and incubation period was tested to allow for sufficient N2O production without undesirable side effects like headspace saturation and nonlinear N2O increase, possibility for dissimilatory nitrate reduction to ammonia (DNRA), oxygen deficiency, and/or significant changes in moisture content. The presence (absence) of DNRA can be checked afterward by examining the 15N signature of NH4þ and NO2 in soils treated with 15 N–NO3, which should not show significant enrichments after incubation; the O2 concentration in the headspace is verified to have remained sufficient by the end of the incubation by GC measurement; and the moisture content of the soil is determined gravimetrically. We used ambient O2 concentrations. However, it is also possible to work with experimental atmospheres for certain questions. At the start of the incubation, all units receive equal amounts of mineral N, that is, 50 mg NH4þ–N kg 1 and 50 mg NO3–N kg 1 soil. Four different treatments are included, which involve the application of compounds enriched in 18O or 15N. The established treatments (TR) are as follows: 18O-enriched H2O (TR1), 18O-enriched NO3 (TR2), 15N-enriched NO3 (TR3), and 15 N-enriched NH4þ (TR4). All treatments should be replicated properly, preferably (at least) five times. In previous work, the respective compounds in the treatment solutions were enriched in 18O at 1.0 at% excess and at 40.0 atom% excess for 15N. Higher levels of enrichment could very well be used, as discussed below. The NH4þ (15N-enriched and nonenriched) was applied as NH4Cl; NO3 as Ca(NO3)24H2O (15N-enriched and nonenriched) and partially as NaNO3 in TR2 (18O-enriched NO3). Demineralized water was used in all treatments to establish the correct moisture content. Immediately after treatment application, the jars are closed with septumequipped air-tight lids for the duration of the incubation. At the end of incubation, gas samples are taken from the headspace and transferred to (helium-flushed and evacuated) 12 mL exetainer vials, to be analyzed on N2O content and its isotopic signatures.

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Soil samples are taken after gas sampling. Subsamples of 20 g moist soil are taken for analyses of mineral N (NH4þ–N and NO3–N) and its 15N isotopic signature. Additional subsamples of each replicate (40 g) are taken to determine the soil dry weight and moisture content to calculate the exact 18 O enrichment of the soil water. Preliminary incubations should verify that only minor changes in the moisture content occur over the chosen incubation period.

2.2. Laboratory analyses 2.2.1. N2O analyses The gas samples are analyzed on the N2O concentration and 15N and 18O signatures. In our previous work (Kool et al., 2009a, 2010a,b), these are determined using a Sercon Cryoprep trace gas concentration system interfaced to a Sercon 20/20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Isotope ratios are compared with N2 and N2O reference gases. No international certified isotope standards are available for N2O; therefore, the d15N of the N2O reference gas is calibrated by comparison with N2 with known isotopic content (i.e., d15N ¼ 3.1% vs. atmospheric N2) after reduction of N2O to N2 over copper at 600 C. We derived a d18O value for the N2O reference gas by comparison with CO2 of known isotopic content (d18O ¼ 10.41% VSMOW) after conversion of both gases to CO over carbon at 1400 C. For reliable analysis, N2O concentrations of 800 and 5000 mL m 3 are considered to be the lower threshold values for the analysis of 15N and 18O signatures, respectively, corresponding to 0.4 and 2.5 nmol N2O in our gas samples (12 mL exetainer vials). At these amounts of N2O, the typical standard deviation of isotope measurements is 3%. For further calculations, measured values were corrected for background levels and isotopic compositions of N2O. 2.2.2. Soil mineral N analyses Soil mineral N content and isotopic signature of the NH4þ and NO3 pool need to be determined. We adopted the following procedure, as is also largely described in Kool et al. (2010a,b). Soil mineral N content is determined by extraction with 1 M KCl (50 mL 20 g 1 soil) followed by segmented flow analysis (SFA) (Skalar Analytical, Breda, The Netherlands) (Kool et al., 2006). The 15N enrichments of the mineral N in treatments TR3 and TR4 are derived using a microdiffusion method based on Brooks et al. (1989) and van Groenigen et al. (2005). Subsamples of the KCl extracts are taken, aiming for a volume that would contain an absolute amount of 80–120 mg N (in the relevant form, i.e., NH4þ or NO3). Depending on the soil mineral N content, this may entail that two subsamples are needed for separate NH4þ and NO3 analysis, otherwise both compounds can be isolated from the same sample subsequently. Demineralized water is added


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to the subsamples to a standard volume to minimize the headspace in the flasks for the microdiffusion isolation procedure. For the NH4þ isolation a microfilter spiked with KHSO4 (2 M) and packed in Teflon is added to the sample, together with ashed MgO to raise the pH to 10, and the sample containers are closed for (at least) 6 days. After this period all NH4þ is assumed to be transferred from the solution onto the microfilter. The filter is removed before the addition of Devarda’s alloy, and a new filter for the NO3 isolation is added. The containers are again left standing for at least 6 days to allow all NO3 to be converted to NH4þ (catalyzed by Devarda’s alloy) and transferred onto the new filter. The samples are left at room temperature (20 C) for both microdiffusion steps. The 15N isotopic analyses on the filters are carried out on an elemental analyzer interfaced to a continuous flow isotope ratio mass spectrometer (EA-IRMS) (Sercon 20/20, Sercon Ltd.). Preferably two laboratory standards, calibrated against NIST standard reference materials, are analyzed with every 12 samples.

3. Data Evaluation for Nitrous Oxide Source Determination 3.1. Main assumptions It is commonly understood and also here assumed that N2O derived as by-product from ammonia oxidation (NN) does not contain any O from H2O. Nitrous oxide derived from ammonia oxidation is thought to be a by-product of (incomplete) oxidation of hydroxylamine (Arp and Stein, 2003; Hooper and Terry, 1979). As the O in hydroxylamine has been shown to originate from O2 and not from H2O (Dua et al., 1979; Hollocher et al., 1981), O2 is assumed to be the sole source of the O in N2O resulting as by-product from ammonia oxidation. Frame and Casciotti (2010) report additional support for this, as their experimental data could not be satisfactorily described by a mixing model that incorporated that some of the O in N2O from oxidation of hydroxylamine (NN) comes from H2O. A second assumption for this approach is that NO3 is the substrate and an obligatory intermediate for “FD” and NCD (i.e., total “conventional” denitrification). However, denitrifiers might also directly take up and reduce NO2 formed in the first steps of nitrification, although denitrification of NO3 is energetically more profitable. In this experimental set-up, NO3 is abundant (applied) and readily available to be denitrified. It can therefore be assumed that NO3 will be intermediate for the large majority of N2O produced through NCD in such soil incubation studies. Thirdly, across the pathways where O exchange is considered to occur, it is assumed that it takes place at the same rate as quantified for denitrification of NO3 to N2O. From the literature it is clear that the rate of O exchange

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may differ significantly among different nitrifiers and denitrifiers (Casciotti et al., 2010; Kool et al., 2007). O exchange associated with nitrifiers is reported less often and may overall seem to be less profound than found for denitrifiers. Casciotti et al. (2010) measured in pure culture studies of ammonia oxidizers exchanges of less than 25% for four different species. Less than 3% of O in NO2 was exchanged with water in pure culture studies with nitrite oxidizers (Buchwald and Casciotti, 2010). Exchange rates for denitrifiers have usually been found to be higher (Kool et al., 2007 and references therein). Accordingly, assuming denitrification exchange rates would rather over- than underestimate the rate of exchange for the nitrifier pathways, and consequently rather under- than overestimate the occurrence and contribution of ND (and NCD) to total N2O production. Although there is some uncertainty related to this assumption, we argue that this is the best estimate of O exchange to be used.

3.2. Quantifying O exchange: The ERR approach Prior to evaluating the relative pathway contributions to total N2O production, the effect of O exchange is quantified with the “ERR” approach. This approach integrates the 18O and 15N isotope enrichment data. Application of both 18O and 15N enriched NO3 enables the quantification of O exchange during denitrification of NO3 based on the following principle. If no 18O from NO3 is exchanged with (nonenriched) H2O–O during denitrification, the 18O:15N enrichment ratio of NO3 should be retained in N2O, and all intermediates. Note that a dilution of the (intermediate) compounds would affect both enrichments equally, and therefore would not change their ratio. The 18O:15N ERR in the N2O compared to NO3 should therefore be 100% in the absence of O exchange: O N2 OðTR 2Þ ERRð%Þ ¼ 100 15 N N2 OðTR 3Þ 18

=

O NO3 ðTR 2Þ 15 N NO 3 ðTR 3Þ

18

where 18O(N2O(TR2)) denotes the 18O enrichment of the N2O produced in treatment TR2, and 18O(NO3(TR2)) and 15N(NO3(TR3)) the 18O and 15 N enrichment of the NO3 applied in treatment TR2 and TR3, respectively. The loss of the 18O enrichment relative to the 15N from NO3 into N2O consequently quantifies the percentage of O that has been exchanged (XERR): XERR ¼ 100 ERR


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3.3. Distinguishing N2O production pathways: 15 N data analyses First, the proportions of total N2O derived from NH4þ and NO3, N2O(NH4) and N2O(NO3), are derived. These are calculated from the 15N–N2O enrichment in treatment TR3 and TR4, 15N(N2O(TR3)) and 15N(N2O(TR4)), respectively: 15 N N2 OðTR4Þ N2 OðNH4 þ Þ ¼ 100 15 N N2 OðTR3Þ þ15 N N2 OðTR4Þ 15 N N2 OðTR3Þ N2 OðNO3 Þ ¼ 100 15 N N2 OðTR3Þ þ15 N N2 OðTR4Þ Next, the relative contribution of “Fertilizer Denitrification”, to N2O production, FD, is defined as N2O(NO3): FD ¼ N2 OðNO3 Þ Further, the NH4þ-derived N2O (N2O(NH4)) comprises the contributions of NN, ND, and NCD. The maximum proportion of N2O that may have been derived from NCD, NCDmax, is calculated from the 15Nenrichment of the N2O and NO3 resulting from treatment TR4, 15N (N2O(TR4)), and 15N(NO3(TR4)). It is based on the assumption that nitrate is an obligatory intermediate for NCD. Therefore, where the 15Nenrichment in the total N2O did not exceed the 15N-enrichment of the NO3 (from TR4), the N2O(NH4) could have exclusively originated from NCD. The NCDmax then equaled the total N2O(NH4). When 15N– N2O exceeded the 15N-enrichment in the NO3 in TR4, part of the N2O(NH4) is produced through NN and ND. In that case, the NCDmax is a fraction of the N2O(NH4) that can be calculated from the 15N-enrichment data (from TR4). In summary, the NCDmax is derived as follows: If 15 N N2 OðTR4Þ 15 N NO3 ðTR4Þ ; then NCDmax ¼ N2 OðNH4 Þ ; If 15 N N2 OðTR4Þ > 15 NðNO3 ðTR4Þ Þ; then NCDmax ¼ N2 OðNH4 Þ 15 NðNO3 ðTR4Þ Þ= 15 NðNO3 ðTR4 Þ Þþ15 NðNO3 ðTR3Þ Þ

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3.4. Distinguishing N2O production pathways: 18 O data analyses Primarily, the “actual O incorporation from H2O into N2O” (AOI) is determined from the application of 18O-enriched H2O (TR1). This AOI (as percentage) is calculated from the measured 18O-enrichment of N2O relative to the applied enrichment of the 18O–H2O: 18 O N2 OðTR1Þ AOI ¼ 100 18 O H2 OðTR1Þ The evaluation of the pathway contributions, and specifically the identification and contribution of ND, is subsequently based on explaining the measured AOI. The pathway contributions are calculated based on the notion that their combined contributions to total N2O production should explain the O isotopic signature of N2O, that is, the AOI. The O incorporation differs per pathway, depending on (i) reaction stoichiometry and (ii) the effect of O exchange. As depicted in Figure 6.1, reaction stoichiometry shows that in ammonia oxidation to hydroxylamine, the O incorporated into hydroxylamine originates from O2. Accordingly, the N2O derived as by-product of nitrification (NN) will have obtained its O solely from H2O. Further oxidation of hydroxylamine to NO2 incorporates O from H2O. The NO2 will thus have obtained one O from O2, and the second O from H2O. Without O exchange, the O in N2O from subsequent ND from the reduction of NO2 would thus also originate for 50% from O2, and 50% from H2O. NO3 produced from NO2 will have obtained its additional O form H2O, that is, two-third of the O in NO3 from nitrification and consequently the N2O from NCD comes from H2O (in the absence of O exchange). N2O from denitrification of fertilizer NO3 (FD) will contain O from the applied NO3, and no O at all from H2O without a contribution of O exchange. Oxygen exchange is defined as XERR, and is experimentally quantified for denitrification of NO3 to N2O (see below). Although experimental O isotopic data suggest that O exchange will also affect nitrifier pathways (Kool et al., 2009b), we have no quantified proof of its effect on N2O produced through the nitrifier pathways from NH4þ. We therefore need to make assumptions on the presence (or absence) of O exchange. As such, for the pathways of which we are uncertain about the exact O exchange effect, we identify a minimum and maximum contribution to N2O production depending on the assumed presence or absence of O exchange. In those cases, where we assume that O exchange affects nitrifier pathways as well, we assume that it will take place at the same rate as quantified with the XERR. A higher AOI could thus be explained by a larger contribution from the pathways that allow for (relatively) more H2O–O incorporation through


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reaction stoichiometry, as well as by increased O exchange. In our evaluation, O exchange is either maximized under (A), that is, assumed to take place during nitrifier pathways as well, or minimized under (B) when it is taken to only affect the N2O from denitrification of NO3. Next to the defined FD (which equals N2O(NO3)) and NCDmax, we put forward that the total N2O(NH4) is the sum of either (A) the maximum contribution of NN (NNmax), the minimum of ND (NDmin), and the maximum of NCD, or (B) the minimum contribution of NN (NNmin), the maximum of ND (NDmax), and the minimum of NCD (NCDmin). Under (A), a theoretical oxygen incorporation from H2O into N2O (TOI) is calculated that maximizes the O incorporation (assuming overall presence of O exchange) while minimizing the contribution of ND to N2O production (TOI1). In other words, this TOI tries to explain the AOI without any contribution of ND to N2O production and its resulting O isotopic signature. TOI1 ¼ N2 OðNO3 Þ XERR þ NCDmax 2=3 þ 2=3XERR 1=3ðXERR Þ2

When this TOI1 is lower than the actual oxygen incorporation AOI, this implies that without a contribution of ND the O incorporation from H2O cannot be explained. In other words, this model calculation signifies that ND will have had a minimum contribution (i.e., larger than zero) when the TOI1 is lower than the actual oxygen incorporation AOI. Under (B), the TOI2 is calculated, which maximizes the contribution of ND by assigning the total N2O(NH4) to ND and assuming the minimal contribution of this pathway to the O incorporation from H2O, that is, through reaction stoichiometry only without a contribution of O exchange during ND: TOI2 ¼ N2 OðNO3 Þ XERR þ N2 OðNH4 Þ 0:5 When the AOI is smaller than TOI2, not all of the N2O(NH4) can have originated from ND and part of the N2O(NH4) needs to be ascribed to NN (to obtain a lower TOI that better explains the AOI). On the other hand, when AOI is not smaller than TOI2 all N2O(NH4) may be assigned to NDmax. When the AOI is larger than TOI2, this may be explained by a minimal contribution of NCD, NCDmin. However, an effect of O exchange during nitrifier pathways may also cause the AOI to be higher than the TOI2 (under which O exchange is minimized for FD only). When additional O exchange is encountered, that is, affecting the intermediate NO2 as well, the O incorporation through ND can become just as high as through NCD. Therefore, assigning a minimum contribution to NCD would not necessarily be needed to explain the AOI better, as additional O exchange

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could just as well be the cause of a higher AOI. In other words, the minimum contribution of NCD, NCDmin, will be set to zero. After this evaluation of the TOI in comparison with the AOI, the minima and maxima of the pathway contributions are derived as below (based on explaining the measured AOI): (A) N2 OðNH4 Þ ¼ NNmax þ NDmin þ NCDmax ðNCD þ NDÞmin N2 OðNH4 Þ NNmax TOI1 ¼ N2 OðNO3 Þ XERR þ NCDmax 2=3 þ ð2=3ÞXERR 1=3ðXERR Þ2

If AOI TOI1 ; then NDmin ¼ 0; and NNmax ¼ 0 ðNCD þ NDÞmin ¼ N2 OðNH4 Þ If AOI > TOI1 ; then NDmin > 0; and ðNCD þ NDÞmin ¼ ðAOI FDXERR Þ= 2=3 þ ð2=3ÞXERR 1=3ðXERR Þ2 NDmin ¼ ðNCD þ NDÞmin NCDmax NNmax ¼ N2 OðNH4 Þ ðNCD þ NDÞmin (B) N2 OðNH4 Þ ¼ NNmin þ NDmax ðNCDmin ¼ 0Þ TOI2 ¼ N2 OðNO3 Þ XERR þ N2 OðNH4 Þ 0:5 If AOI TOI2 ; then NDmax ¼ N2 OðNH4 Þ ; and NNmin ¼ 0 If AOI < TOI2 ; then NDmax < N2 OðNH4 Þ ; and NNmin > 0 NDmax ¼ ðAOI FDXERR Þ=0:5 NNmin ¼ N2 OðNH4 Þ NDmax

4. Application of the ERR Principle in Nitrate Source Determination Isotopic analysis of the O isotopic signature of NO3 is a commonly used tool to distinguish different sources of NO3 in ecosystems catchments (e.g., Amberger and Schmidt, 1987; Burns et al., 2009; Durka et al., 1994;


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Kendall et al., 2007). However, O exchange is in such studies hardly considered to be a determining factor of the O isotopic signature. If O exchange between H2O and NO3 would take place and alter the NO3–O isotopic signature as well as the N2O–O, this would bias the discrimination between the relevant sources of NO3 (with supposed distinct/different O isotopic signatures). Therefore, studying the effect of O exchange will also be of interest with respect to NO3 source determination in studies on ecosystem functioning. The study of Snider et al. (2010) indicates that assuming the d18O of NO3 to reflect the isotopic composition of H2O and O2 in a 2:1 ratio leads to discrepencies with measured results. Here, it seemed that the 18O incorporation from 18O labeled H2O into NO3 exceeded the incorporation that could be ascribed to nitrification according to reaction stoichiometry. However, this does not quantify an effect of O exchange if the relative contribution of multiple NO3 sources is not known. Following the ERR principle a similar experimental approach can be applied to study the effect of O exchange on the O isotopic signature of NO3 in a more direct way (Kool et al., under review). The experimental set-up remains largely the same. What is new is that in addition, analyses of the O isotopic enrichment (18O) of the NO3 are included. The relevant samples to be analyzed are those that received 18O–NO3, that is, in treatment TR2. Additional samples are incubated for both TR2 and TR3 (15N–NO3), for destructive sampling immediately after treatment application (or as soon as possible, within 4 h after application at the latest). Samples for 18O and 15N analyses are thus collected both at the start and end of the incubation. The NO3 in the soil samples is extracted using KCl as described above. After KCl extraction and mineral N analyses, one series of subsamples is taken for 15N analyses (using, e.g., microdiffusion as above). An additional series of subsamples of the KCl extracts is taken to determine the 18O isotopic signature of the NO3, using the denitrifier method (Casciotti et al., 2002; Xue et al., 2010), of which Rock and Ellert (2007) showed that it could also be used for KCl-extractable NO3 in soil samples. To assess whether O exchange affects the O isotopic signature of NO3, we examine whether the 18O and 15N enrichment change relatively to each other during the incubation. In other words, we again look at the ERR, but this time in NO3 itself over time (instead of in N2O). We thus define the 18 O:15N enrichment ratio of the NO3 (ER(NO3)): ERðNO3 Þ ¼18 O NO3 ðTR1 Þ =15 N NO3 ðTR2 Þ where 18O(NO3(TR1)) and 15N(NO3(TR2)) are the 18O and 15N isotopic enrichments of the NO3 in TR1 and TR2, respectively. The enrichment ratio at the end of the incubation (ER(NO3)end) is compared to the ratio at the start (ER(NO3)start), defining the enrichment ratio retention ERR(NO3): ERRðNO3 Þ ð%Þ ¼ 100ERðNO3 Þend =ERðNO3 Þstart

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As above when studying O exchange during N2O production, in the absence of O exchange the ER(NO3) should not change over the course of the incubation leaving the ERR(NO3) at 100%. O exchange would cause a decrease in the ER(NO3) at the end of the incubation relative to the start, presented by a loss in the ERR(NO3).

5. Discussion, Applications, and Future Directions Above, we have described a novel 18O and 15N dual isotope method that—accounting for O exchange—enables to distinguish between four N2O sources: from nitrifiers as by-product of ammonia oxidation (NN), from nitrifiers through ND, from denitrifiers by NCD, and from denitrification of fertilizer NO3 (FD). We demonstrated the applicability of the method by studying a range of 12 soils from agricultural fields, grasslands, and forests across Europe. Through tracing the O incorporation from applied 18O enriched water into N2O, oxygen exchange was identified to play a major role in determining the O isotopic signature of N2O in all soils (Kool et al., 2009a). Oxygen exchange during denitrification of NO3 was quantified, and further data evaluation disclosed that O exchange also occurred in nitrifier pathways in at least nine and potentially in all soils (Kool et al., 2009b). With respect to the N2O production, our approach allowed to quantify the contribution of FD and identify ranges for N2O production by NCD, ND, and NN (Kool et al., 2010a). This showed for the first time that ND can indeed contribute significantly to N2O production in soils: ND must have occurred (had an N2O production significantly larger than zero) in at least four of the soils. It remained difficult to link variation in soil properties to variation in O exchange rates or relative contributions of the different N2O producing pathways. Under moist experimental conditions (WFPS of 80%), O exchange was very high (80–100%) in the majority of soils, and FD was the major N2O producing pathway. Soil C quality and content and pH were suggested to affect the relative pathway contributions, but further testing remains necessary. In a study with a 13th soil at three different moisture contents, it could be shown that ND may be regulated distinctly from denitrification (Kool et al., 2010b). At lower WFPS both FD and ND declined, but there was only a slight decline in ND where a strong decline in the contribution of FD was observed (Kool et al., 2010b). This latter study was performed on one soil only, and further studies are needed to better understand the relationships between the relative pathway contributions and soil moisture content, and other soil properties and land


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use. However, the results show that the dual isotope method helps to increase our understanding of N2O production in different environments. As described above, a similar approach as used to quantify the effect of O exchange on N2O could be employed to study the effect of O exchange on the O isotopic signature of NO3. We applied this approach in a study on two grassland soils and an arable soil. O exchange was found and could be quantified for both grassland soils (Kool et al., 2011). In the arable soil, ND may have occurred but could not be conclusively proven. Possibly this was related to differences in microbial activity, as the latter soil also previously showed lower N2O production rates. This stresses moreover the importance of measuring mineral N dynamics separately from N2O production: although the processes affecting both are the same, the net production of, for example, NO3 and N2O can differ largely (Wrage et al., 2004b). Despite the need for broader application, the above presented methods provide tools to improve our understanding of O exchange between nitrogen oxides and water, and the use of O isotopic signatures in N cycling studies. The dual isotope method for N2O production processes is the only method so far that allows a distinction between ND and the other processes regarded. However, there are also some shortcomings, which will be discussed below, together with suggestions for improvements of the method. First of all, there are other processes that may lead to N2O production in soils. However, potential nitrification by heterotrophs (both bacteria and fungi) (e.g., Papen et al., 1989; Robertson and Groffman, 2007) and archaea (Leininger et al., 2006), as well as fungal denitrification (Shoun et al., 1992) would not need to interfere with our approach when these processes would proceed via mainly the same reaction paths as assumed for autotrophic nitrification and heterotrophic bacterial denitrification, respectively. On the other hand, DNRA (e.g., Stevens et al., 1998) and codenitrification (e.g., Shoun et al., 1992) should be considered to affect the isotopic signatures in alternative ways. DNRA forms a distinct pathway in the N cycle. Although it is not yet well understood, it has been shown that N2O can be produced during ammonification of NO3 (Smith and Zimmerman, 1981; Stevens et al., 1998). Some studies speculate that DNRA could account for a significant part of NO3 reduction, also in soils (Bonin et al., 1998; Caskey and Tiedje, 1979; Huygens et al., 2007; Stevens et al., 1998; Wan et al., 2009). Disregarding N2O production by DNRA would overestimate the contribution of denitrification in isotope tracing studies, including the one in this thesis. Two types of DNRA are recognized: the first is coupled to fermentation, the second to sulfur oxidation (Burgin and Hamilton, 2007). Nitrate reduction through fermentative DNRA rather than denitrification is thought to be relatively favored in NO3-limited systems (Nijburg et al., 1997; Tiedje, 1988) and DNRA coupled to sulfur oxidation is found mainly in aquatic environments

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(Brettar and Rheinheimer, 1991; Brunet and Garcia-Gil, 1996). In our studies, DNRA was therefore unlikely to be significant. This is checked by measuring the 15N-enrichment of the NH4þ after application of enriched NO3. As DNRA can also lead to the production of enriched NO2, it would be desirable to also measure 15N–NO2 in incubations with added 15 N–NO3. However, this could also have been produced by FD, thus not presenting conclusive proof of DNRA. In general, understanding the pathway and role of DNRA in nitrogen cycling remains a challenge and it is necessary to check for DNRA in future N2O source determination studies. Another distinguished pathway of N2O production is codenitrification, where NO3 or NO2 is combined with other nitrogenous compounds to produce N2O or N2. This process is most commonly recognized in denitrifying fungi (Laughlin and Stevens, 2002; Morozkina and Kurakov, 2007; Shoun et al., 1992; Tanimoto et al., 1992), but some studies have also identified bacteria (including actinomycetes) able to carry out codenitrification (Garber and Hollocher, 1982; Kumon et al., 2002). Isotope (15N) labeling studies are suggested to enable the distinction between denitrification and codenitrification. However, in ecosystems the evident complexity of N-transformations complicates the isolation and discrimination of those two processes from the wide spectrum of other N2O and/or N2 producing processes. Furthermore, several Archaea have also been shown to carry out dissimilatory reduction of NO3 via NO2, NO and N2O to N2 (Cabello et al., 2004; Volkl et al., 1993; Werber and Mevarech, 1978). This pathway appears similar to the bacterial one (Hayatsu et al., 2008; Zumft and Kroneck, 2007), but genome sequencing has revealed differences in the genetic organization, structure, and regulation of the genes (Philippot, 2002). Recently, genes encoding for potential homologues of nitrite reductases (NirK) have also been found in ammonia oxidizing Archaea from various environments, including soils (Bartossek et al., 2010). Accounting for codenitrification and other so far not considered sources of N2O in soils remains a challenge. So far, the dual isotope method depends on the application of isotopically enriched compounds, which is related to a fertilization of the experimental units. The advantage of the use of enriched compounds with stable isotope tracing approaches is that the effect of fractionation, that is, the preferential use of the lighter isotope and residual enrichment of the heavier isotope, becomes negligible. This implies that with the inevitable fertilizer addition of NH4þ and NO3 this approach does not yet allow to determine the in situ relative contribution of N2O production pathways. Instead, it is designed to provide an assessment of the potential significance of the different pathways relative to each other, and to enable a comparison across, for example, soil types and environmental conditions. For the use in natural systems, it may be desirable to add less NH4þ and NO3, at potentially higher enrichments. Here, care has to be taken, however, as another


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assumption of the dual isotope method is that NO3 is the substrate and obligatory intermediate for NCD. This is justified as long as NO3 is abundant, as the use of NO3 is energetically more profitable than that of NO2. Should NO3 be less abundant, the direct use of NO2 by heterotrophic denitrifiers in NCD (without prior oxidation to NO3 by nitrifiers) might not be negligible anymore. This would lead to an underestimation of the contribution of NCD to total N2O production and an overestimation of ND. A potential solution and improvement of this method might be to also include isotopic analyses of NO2. Although the determination of 15N and 18 O in NO2 is not simple, methods exist (Bo¨hlke et al., 2007; Stevens and Laughlin, 1994) and could be implemented. Some assumptions are also required regarding O exchange. It is quantified for the reduction of NO3 to N2O and considered to occur in other processes with the same exchange rate. Uncertainties regarding the extent of exchange are in our approach addressed by formulating different scenarios, leading to the ranges of N2O production for nitrifier pathways. Investigations on O exchange in pure culture studies may enable to adjust assumptions for both N2O and NO3 studies (e.g., Casciotti et al., 2010; Frame and Casciotti, 2010). Moreover, also here the addition of isotopic analyses of NO2 could provide further insight. What is not considered so far is the speed of O exchange. As a biochemical process, we expect it may proceed progressively with the N transformation processes, and/or be affected by substrate availability. This will be of key interest in future investigation. Our method constitutes a framework in which novel insights with respect to nature and rate of O exchange during the various pathways can be relatively simply incorporated. As seen above, potential exists for further improving the method. Including the analysis of NO2 isotopic signatures is already suggested to be valuable to both better define the NCD and the presence of O exchange in nitrifier pathways. Additional promising steps would also be to combine the dual isotope method with isotopomer measurements for further information about N2O sources. Analyzing the isotopomer composition is increasingly suggested as a promising tool in source determination of N2O (Baggs, 2008; Ostrom et al., 2010; Schmidt et al., 2004; Sutka et al., 2006; Toyoda et al., 2005). Such an approach evaluates the intramolecular site preference (SP) of the 15N in N2O, at natural abundance. Where isotope tracing studies need to apply enriched compounds to discount the effect of isotopic fractionation, studying the isotopomer composition can be done without the need to disturb ecosystems with fertilizing compounds. However, ambiguity about the SP for different pathways and microbial communities currently limits the use of isotopomer ratios to assess the contributions of distinct pathways to N2O production (Ostrom et al., 2007, 2010; Schmidt et al., 2004; Well et al., 2006). Future studies could aim at further characterizing distinct SP values and combining this tool with other stable

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isotope techniques. While recognizing the need for future investigation, recent studies have already suggested the potential of the d18O/SP fingerprint of N2O as a tool to identify the dominant production process of N2O in soil (Well and Flessa, 2009; Well et al., 2008). Also a combination with molecular techniques as, for example, in stable isotope probing might offer potential despite some remaining challenges (Baggs, 2008). In conclusion, the proposed dual isotope method gives the option of distinguishing the potential contribution of NN, ND, NCD, and FD to N2O production from soils. Although scope remains for further improving the method, it already offers a valuable tool for quantifying O exchange and improving our understanding of N2O production.

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C H A P T E R

S E V E N

A Polyphasic Approach to Study Ecophysiology of Complex Multispecies Nitrifying Biofilms Satoshi Okabe,* Hisashi Satoh,* and Tomonori Kindaichi† Contents 1. Introduction 2. Microsensors 2.1. Amperometric microsensors 2.2. Potentiometric microsensors 3. Microsensor Measurements 4. Estimation of Microbial Activities 5. Limitations of Microsensor Measurements 6. MAR–FISH 7. Methodology of MAR–FISH 7.1. Sample incubation with radioactive compound 7.2. Sample fixation, washing, and preparation of slides 7.3. FISH 7.4. MAR 7.5. Microscopic observation 8. Application of Microsensors and MAR–FISH to Nitrifying Biofilms 9. Ecophysiological Interaction Among Community Members 10. Conclusions References

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Abstract This chapter aims to highlight the great potential of the combined use of microautoradiography (MAR) combined with fluorescent in situ hybridization (FISH) and microsensor technology in studies of complex multispecies nitrifying biofilms. The combination of FISH and microsensor technology is a powerful and reliable tool to link the spatial organization of microbial communities and their in situ function at community levels. MAR-FISH can be used to * Division of Environmental Engineering, Graduate School of Engineering, Hokkaido University, Sapporo, Hokkaido, Japan Graduate School of Engineering, Hiroshima University, Kagamiyama, Higashihiroshima, Japan

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simultaneously examine the 16S rRNA-based phylogenetic identity and specific metabolic activity of cultivable or uncultivable microorganisms within complex microbial communities at a single-cell level. Information obtained at both resolution levels must be combined to draw a clear picture of a complex multispecies biofilm ecosystem. In addition, ecophysiological interactions among community members in complex multispecies biofilms can be investigated by tracing the fate of radiolabeled [14C] atom incorporated in nitrifying bacteria with MAR-FISH. The structure, function, and ecophysiological interactions among community members in complex multispecies nitrifying biofilms will be illustrated as an example of the combined use of MAR–FISH and microsensor technology.

1. Introduction Most microorganisms in the natural environment and engineering systems are present in the form of complex multispecies biofilms or aggregates rather than as planktonic (free swimming) cells (Okabe et al., 2003, 2004a; Satoh et al., 2007; Schramm et al., 1996; Yawata et al., 2010). Such complex microbial communities are often characterized by phylogenetic identities, abundance, spatial distribution, activities, interaction among community members, and interspecies competition for space and substrate. Thus, understanding the in situ abundance, spatial distribution, and activity of target microorganisms in complex microbial community is a central theme in microbial ecology. Recent development of ribosomal RNA (rRNA) sequencebased molecular techniques provided new insights into the composition and structure of microbial communities and revealed a remarkably vast microbial diversity including many hitherto-recognized and yet uncultured species in various microbial habitats. There are a vast number of new groups of bacteria and archaea known only from molecular sequences, but their ecophysiological roles in the environment are largely unknown (Narihiro et al., 2009). The significance of this microbial diversity and its relation to function is not fully understood. It is difficult to understand a relationship between the microbial community structure and in situ function without knowing local microenvironments. Change in local microenvironments undoubtedly affects in situ microbial activity and consequently changes microbial composition and spatial distribution (Okabe et al., 2004a; Satoh et al., 2000). Even when microorganisms can be cultivated, physiological properties determined in the laboratory may not necessarily reflect the activity and physiology in the environments, where resource competition, environmental heterogeneity, and other interactions occur. Therefore, appropriate techniques and tools with sufficiently high spatial and temporal resolution are required to link microbial community structure with in situ function (activity).

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Combination of FISH and microsensor technology has been proven to be powerful and reliable tool to link microbial community structure with in situ function (Okabe et al., 1999a,b; Schramm, 2003). The microsensors can detect, directly and with minimum disturbance, the concentration profiles across biofilms, from which lateral average of in situ activity (net rates of production and consumption at a certain depth) can be calculated. The spatial resolution of microsensors is 10–100 mm, depending on the tip diameter. This resolution is at the community level, which is, however, inadequate to characterize substrate uptakes of both cultivated and uncultivated microorganisms at a single-cell level. Further, when the substrates used by unidentified microorganisms are not known, or their abundance is low, the substrate profiles cannot be correlated with the spatial distribution of specific microbial populations. To directly link specific microbial populations or cells to the specific metabolic function (specific substrate uptake) within complex microbial communities, microautoradiography (MAR) combined with fluorescent in situ hybridization (FISH) can be used (Lee et al., 1999; Okabe et al., 2004b; Ouverney and Fuhrman, 1999). In this combination, the activity or function of interest can be determined by microautoradiography, and the phylogenetic identity of microorganisms can be determined with FISH. If one can combine microsensor technology and MAR–FISH, (1) the determination of the physicochemical microenvironment, (2) in situ identification, localization, and quantification of specific microbial populations, groups, or species, and (3) in situ activity (i.e., substrate uptake) of target microorganisms or groups at single-cell level can be simultaneously examined within complex microbial communities such as biofilms. This chapter aims to highlight the principle, experimental protocol, and application of the MAR–FISH and microsensor technology to complex multispecies nitrifying biofilms to address structure, function, and ecophysiological interaction among community members from a single-cell level to a community level, with emphasis on MAR–FISH. The great potential of the combined use of MAR–FISH and microsensors will be addressed. The structure, function, and ecophysiological interactions among community members in complex nitrification biofilms will be illustrated below as an example of the combined use of MAR–FISH and microsensors.

2. Microsensors The microsensors are needle-shaped biochemical sensors with a tip diameter of 1–100 mm which allow for the measurements of the concentrations of specific chemical compounds in microbial communities. Net specific consumption and production rates (i.e., activity) at a certain depth can be estimated from the measured concentration profiles. However,

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microsensors may disrupt the physicochemical structure of the microbial communities by consumption of the solute measured, by compression of the local matrix, and by disturbing the flow regime. Although microsensors have inherently such disadvantages, the use of microsensors is at present the best way for direct in situ measurement of chemical concentrations inside microbial communities. Three types of electrochemical microsensors are most often used in studies of microbial ecology: (1) amperometric microsensors, (2) potentiometric microsensors, and (3) microbiosensors that are actually amperometric microsensors including a biological or an enzymatic reaction in the sensor tip. As the principle and construction of all the sensors have been well described elsewhere (de Beer et al., 1997; Gieseke and de Beer, 2004; Revsbech, 2005), they will be described briefly here.

2.1. Amperometric microsensors The typical examples of amperometric microsensors are O2, N2O, and NO microsensors (Andersen et al., 2001; Revsbech, 1989; Schreiber et al., 2008). The construction of amperometric microsensors is well established. As it is not possible to outline all the details for the construction of amperometric microsensors here, only the construction of Clark-type O2 microsensor is described (Revsbech, 1989). The Clark-type O2 microsensor consists of a tapered platinum wire situated behind a silicone membrane and immersed in an electrolyte solution, which works as a sensing cathode (Fig. 7.1). The platinum wire is finely tapered by etching in a saturated

Guard cathode Ag/AgCl anode as reference electrode

Platinum wire 8533 glass Electrolyte

Sensing gold cathode

Silicone membrane

Figure 7.1 Schematic drawing of a Clark-type oxygen microsensor.


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KCN solution, while 2–7 V AC is applied. For etching, a graphite rod (e.g., pencil lead) is immersed in the saturated KCN solution as the other electrode to complete the circuit. Then the etched platinum wire is coated with a glass capillary (e.g., Schott 8533 glass, Schott, Maim, Germany) that has good insulating characteristics and stability under alkaline conditions. The tip of the platinum wire should be exposed by heating excess glass with a small nichrome wire and electroplated with gold in the tip of the Pasteur pipette under a microscope. The glass-covered and gold-plated platinum wire is inserted in an outer casing made from a tapered Pasteur pipet. The opening of the tip of the outer casing is closed by silicone membrane. A guard cathode is made from a silver wire with a 50–200 mm of diameter, which is electrochemically coated with AgCl. The guard cathode is used to prevent diffusion of oxygen to the cathode from the electrolyte reservoir and thus reduce the background signal. The outer casing is filled with the electrolyte (1 M KCl) through an opening at the end of the casing, a chlorinated silver (Ag/AgCl) wire as a reference electrode and a guard cathode are inserted into the electrolyte, and then the microsensor is ready for use. As described above, the construction of amperometric microsensors is complicated and laborious, and requires special skills. However, the amperometric microsensors are now commercially available (www.unisense.com, Unisense A/S, Aarhus, Denmark), making the use of them easier.

2.2. Potentiometric microsensors Liquid ion exchange (LIX)-based microsensors are the examples of potentiometric sensors, and there are microsensors for NH4þ, NO2, NO3, and pH, which have been used often for the studies of nitrogen cycle (de Beer et al., 1997; Gieseke and de Beer, 2004; Gieseke et al., 2003; Kindaichi et al., 2004a; Okabe et al., 1999a, 2004a). The ion-selective membrane is in this case liquid ion exchangers, some of which are commercially available (e.g., from Fluka). The construction of the LIX-based microsensors is easier than that of the amperometric microsensors. A green glass capillary (e.g., 3.5 mm outer diameter, No. 8516; Schott, Maim, Germany) with good insulating characteristics is heated in the flame and pulled into a microcapillary with a thickness of about 200 mm. The shoulder at thick part of the green glass is cut and then fused together with a slightly tapered white glass (e.g., 5 mm outer diameter, AR glass, Schott, Maim, Germany) in a small flame (Fig. 7.2). Both glasses must be completely sealed. Further, the thin glass capillary is hanged, heated by a nichrome wire, and pulled by gravity to a thickness of ca. 15 mm. The tip of the green glass capillary is broken to open the hole with the desired dimension with a cutter knife. The opening is shaped to a sphere by heating it with the small nichrome wire. To seal the tip of the capillary with the hydrophobic LIX membrane, the glass must be

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Outer casing Ag/AgCl wire

White glass Electrolyte Green glass Electrolyte Ag/AgCl wire

LIX

Figure 7.2

Schematic drawing of a LIX microsensor.

silanized. The glass capillary is baked at 150 C for 3 h to dry completely and then is silanized with N-dimethyltrimethylsilylamine overnight at 200 C in a desiccator. It should be noted that the gaseous silane is toxic so that one must be careful not to inhale the vapor. The silanized glass capillary is completely filled from the backside with the electrolyte corresponding to the LIX used. The filling electrolyte should be degassed under vacuum. To complete the electric circuit the air bubbles must be removed. The tip of the glass capillary is inserted into the LIX at the tip of a Pasteur pipette by carefully adjusting the position of the tip with a micromanipulator under the microscope. The LIX is introduced into the tip of the green glass capillary by applying suction with the syringe until a length of the LIX becomes about 300 mm. Subsequently, the LIX membrane should be solidified in the tip with LIX containing PVC. A silver wire coated with AgCl (50–200 mm in diameter) is inserted into the glass capillary. Finally, an outer casing is made from a Pasteur pipet, and the silanized glass capillary is inserted into an outer casing containing the electrolyte (1 M KCl) to reduce electrical noise. LIX microsensors can be constructed with a tip diameter as small as 1 mm. Although the LIX microsensors are not commercially available due to their short lifetime, construction of them is relatively easy and well established (de Beer et al., 1997; Gieseke and de Beer, 2004). Due to interferences, none of the mentioned LIX sensors can be applied in brackish or marine waters. The signal stability is also often a problem, but a coating of the sensor tips with a protein layer may improve the stability (de Beer,


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2000). When constructing the LIX microsensors in your own laboratory, the estimation of selectivity factors is recommended.

3. Microsensor Measurements Intact biofilm samples are taken and fixed in a flow cell reactor that is filled with a synthetic medium. As LIX-based microsensors suffer from interference of other ions, the medium composition and concentrations should be considered carefully to minimize the interference. Temperature, pH, and other physicochemical parameters (e.g., a liquid flow rate) should be also adjusted and controlled to mimic the environment where the biofilm samples were taken from. Biofilm samples should then be acclimated in a medium a few hours before measurements to ensure that steadystate profiles are obtained. The profiles can be measured by advancing the microsensors at depth steps of 50–100 mm through the biofilm using a motor-driven micromanipulator. Signal readings are monitored with an ammeter for amperometric microsensors or a voltmeter for LIX-based microsensors. The motor-driven micromanipulator, ammeter, voltmeter, as well as other equipments for microsensor measurements are provided by Unisense A/S (www.unisense.com, Aarhus, Denmark). Due to fragility of microsensors and interference of other chemicals especially with LIX-based microsensors, it is difficult to measure the concentration profiles of the biofilms living in biological reactors in situ, although deep sea landers for in situ profiling has been successfully developed (www.unisense.com). Therefore, it is desirable that the concentration profiles should be measured under realistic conditions (i.e., water flow, water chemistry, temperature, etc.). To provide sufficient water flow (e.g., 2–3 cm s 1) above the biofilm, air and/or N2 gas is blown onto the water surface from a Pasteur pipette, the medium is mixed with a magnetic stirrer, or a flow cell (continuous feeding) system can be used. Concentration profiles should be measured several times at different positions or each chemical and condition because of biofilm heterogeneity. The experimenter must determine biofilm–liquid interface by viewing through a dissection microscope with special care.

4. Estimation of Microbial Activities Estimation of microbial activities in biofilm based on the concentration profiles measured with microsensors is one of the good examples of microsensor application. Due to the combined effect of microbial conversion and mass transfer resistance, substrate and product gradients develop inside biofilms. Assuming that no liquid flow occurs inside the biofilm,

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diffusion is the only transport mechanism inside the biofilm. The bulk liquid is usually well mixed by advective transport. In the boundary layer at the biofilm–liquid interface, the liquid flow velocity gradually decreases in direction of the biofilm. Consequently, the mode of transport changes gradually from advectional in the bulk liquid to diffusional in the boundary layer. Diffusional transport is driven by concentration differences as expressed in Fick’s law. The diffusional transport (i.e., total consumption rate or flux; Ji) of a type of solute i in biofilm can be calculated from the steady-state concentration profiles by using Fick’s first law of diffusion: dCi Ji ¼ Di dz where Di is the molecular diffusion coefficient in the liquid phase and dCi/ dz is the measured concentration gradient of the solute i in the boundary layer between the biofilm and bulk liquid. Moreover, net volumetric consumption rates of solute i in the biofilm are calculated from the steady-state concentration profiles by using Fick’s second law of diffusion as previously described by Lorenzen et al. (1998). Assuming steady state, the equation of Fick’s second law of diffusion including a consumption term (R(z)) can be reduced to dCðzÞ Di ¼ RðzÞ dz2 Defining A(z) ¼ R(z)/Di and using Euler’s formula for numeric integration, we find dC dC ¼ þ hAn dznþ1 dzn where h determines the step size used for numerical integration. After further integration, we have dC Cnþ1 ¼ Cn þ h dzn Substituting dC/dzn with the above equation, we find dC Cnþ1 ¼ Cn þ h þ hAn1 dzn1


171

As a boundary condition, we introduce the same concentration point below the deepest measuring point where the microbial activity is zero. From this point, concentration profiles were calculated stepwise toward the biofilm surface by altering net activities by using Microsoft Excel to minimize the sum of squared deviations of the calculated profile from the measured profile and the sum of squared first derivatives of the guessed activities. More details of this calculation method were described elsewhere (Lorenzen et al., 1998).

5. Limitations of Microsensor Measurements One of the advantages of microsensor measurements is to determine in situ microbial activities with biofilms alive. As the 90% response time of gas-sensitive amperometric microsensors is typically less than 0.5 s, a time series measurement of chemicals is possible in a biofilm in real time (Nakamura et al., 2004; Schreiber et al., 2009; von Ohle et al., 2010). However, it should be noted that true in situ measurements of microbial activity in the biofilms living in a biological reactor could be difficult, because profiles and rates measured in laboratory setups must show “pseudo in situ” profiles, and in general, they do not necessarily reflect the true in situ results. The actual spatial resolution of a microsensor should be at least two times of the tip diameter (Schramm, 2003). Some microsensors (e.g., NO2 microsensor) having too small tip diameter ( 1

20 10

Random distribution g(r) » 1

0 0

20

40

60 80 100 Distance, r (mm)

120

140

C x

y

Figure 8.1 Principle of the Linear Dipole Algorithm. (A) Artificial image of two microbial populations. Linear dipole probes of the same length, which are randomly oriented, have been placed at random locations in the image (refer to the main text for a detailed explanation). In practice, the algorithm would evaluate a much larger number of linear dipole probes than shown here for illustrative purposes. (B) Plot of the pair cross-correlation function, g(r), against the corresponding distances, r. This example shows an analysis of nitrifying bacteria (AOB, Nitrosomonas sp. and NOB, Nitrospira sp.) in activated sludge. A strong coaggregation signal with g(r) > 1 was obtained for distances below 50 mm, whereas the nitrifiers were randomly distributed at longer distances [g(r) 1]. The solid curve depicts the mean g(r) determined from 48 images, which were recorded at random positions in the sludge sample. The stippled curves show 95% confidence limits. The horizontal stippled line indicates the threshold of g(r) ¼ 1 that separates coaggregation from mutual avoidance [g(r) < 1]. Modified from Daims et al. (2006). (C) The scanning-based implementation of the Linear Dipole Algorithm. The analyzed image is scanned, pixel by pixel, along the x and y axes. The stippled frame depicts the image borders. Linear dipole probes are virtually extended from each scanned pixel as a semicircle (right sketch). Dipole probes that hit the two populations with their ends are counted as hits (solid lines). Probes that do not hit the two populations are misses (stippled lines). Probes that extend the image borders are not counted (dotted lines). Modified from Daims et al. (2006).

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Quantification of Spatial Localization Patterns

the image. In this case, P is constant for all distances and can directly be determined from the population densities: P ðrandomÞ ¼ 2D1 D2

ð8:2Þ

where D1 is the density of population 1 and D2 the density of population 2. The density of each population is defined as Dn ¼

An Ai

ð8:3Þ

with Dn as one of the densities (n ¼ 1 or n ¼ 2), An as the pixel area of the respective population in the image, and Ai as the pixel area of the whole image. These densities can easily be measured. Now we can normalize P(r), which was calculated in Eq. (8.1) from the numbers of hits and misses, with the probability for a hit if the two populations were randomly distributed. This step eliminates the dependence on the population densities: gðr Þ ¼

P ðr Þ P ðr Þ ¼ P ðrandomÞ 2D1 D2

ð8:4Þ

where g(r) is an estimate of the pair cross-correlation function at distance r for the two populations. Because of the normalization applied in Eq. (8.4), g(r) ¼ 1 if the two populations are randomly distributed at distance r. If g(r) > 1, then P(r) > P(random) and the two populations coaggregate at distance r. Consequently, if g(r) < 1, the two populations show mutual avoidance at distance r. To visualize the meaning of g(r), imagine a circle of radius r that is drawn around an arbitrary pixel of one population in the image. Then, g(r) is a measure of the likelihood to encounter a pixel of the other population on the edge of the circle (at distance r). The function g(r) can also be estimated for cells or cell clusters that belong to one and the same population; in this case, it is called the pair correlation function. For this purpose, Eq. (8.2) must be replaced by Eq. (8.5): P ðrandomÞ ¼ D2

ð8:5Þ

with D being the density of the analyzed population in the image. By using linear dipole probes of different lengths r, g(r) is determined for a range of distances. The values obtained for g(r) are then plotted against the distances r. The resulting graph (Fig. 8.1B) informs on the spatial localization pattern of the two populations at the analyzed distances. The analysis should be carried out for many images in order to allow statistical verification of the results (Fig. 8.1B). Further, a very large number of linear dipole

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Holger Daims and Michael Wagner

probes should be evaluated for each image and distance. The latter condition is easily met, because 100,000 or more randomly oriented linear dipole probes can be evaluated within a few seconds on current PCs. However, for most precise results the entire images are scanned pixel by pixel. During the scan, a semicircle of linear dipole probes extending from each pixel is evaluated (Fig. 8.1C). This scanning-approach examines every pixel, taking into account all information stored in each image, but requires more computation time than the evaluation of randomly thrown linear dipole probes (Fig. 8.1A). The Linear Dipole Algorithm does not make assumptions about the morphology of the analyzed populations, and thus it is not compromised by any of the issues mentioned at the beginning of this section. The obtained graphs do not only indicate whether coaggregation occurs, but they also show the distances that separate regularly arranged cells or cell clusters (Fig. 8.2). The careful reader may have noticed that this algorithm uses 2-D images, although spatial localization patterns in biofilms are a 3-D feature. This apparent contradiction resolves if one additional condition is met: All analyzed images must be recorded at random positions in the x, y, and z dimensions in the sample. In this case, the results obtained with (many) 2-D images reflect a 3-D localization pattern with sufficient accuracy (Reed and Howard, 1999). Thus, the method works best with a confocal microscope because only sharp high-quality images should be analyzed, but it does not require the acquisition of confocal image stacks (z-stacks). We consider this an advantage, because recording z-stacks is a time-consuming task, and the image quality may suffer from photobleaching of the fluorochromes during prolonged exposition of a sample to the laser. One drawback of the Linear Dipole Algorithm is that not more than two populations can be analyzed simultaneously. Thus, several pairwise analyses are needed to infer the spatial arrangement patterns of three or more populations. Another caveat is the relatively high sensitivity of the algorithm to noise in the images, which could cause false-positive hits. Importantly, the biomass of different populations must not overlap in the analyzed images. Overlapping pixels can occur if biomass objects appear too large in the images due to nonoptimal detector settings during image acquisition. Such overlap causes many false hits for short linear dipole probes, leading to a strongly exaggerated coaggregation signal at short distances r. However, the most critical problem arises if the entire biomass covers only small regions of an image. In this case, the analyzed populations will always appear to coaggregate, because the algorithm analyzes the whole image and not only those regions that contain the biomass. Imagine that the linear dipole probes are thrown onto the entire image, including the empty parts. The resulting bias is illustrated in Fig. 8.3A: Although the objects are randomly distributed within the biomass-containing region of the image, the plot

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A

16 d

d

Pair correlation function, g(r)

14 12 10 8 2d

6 4

3d

2 0 0

2

4

6

8

10

12

Distance, r (mm)

B Pair correlation function, g(r)

6 5 4 3 2 1 0 0

2

4

6

8

10

12

Distance, r (mm)

Figure 8.2 Selected spatial arrangement patterns and g(r) plots. (A) The left image shows a simple localization pattern of four artificial objects, which are separated by the same distance d. The three peaks in the right plot represent coaggregation at the most frequent distance between the objects, d (highest peak), the second frequent distance, 2d, and the least frequent distance, 3d (smallest peak). Between the peaks g(r) ¼ 0, indicating absence of object pixels (biomass) at these distances. (B) Three clusters, which consist of six objects each, are separated by approximately the same distance from each other. The higher peak in the plot represents coaggregation at the most frequent distance in the image, which is the short distance between objects in the same cluster. The smaller peak represents coaggregation at the longer distance between the clusters. As the Linear Dipole Algorithm takes all pixels of each object into account, especially the right peak is broad: There is no discrete distance that would separate the clusters, but a range of distances between pixels belonging to different clusters.

indicates coaggregation over a wide range of distances. Indeed, the objects are clustered when the whole image is considered (Fig. 8.3A). Fortunately, this problem is solved by a modification of the algorithm that uses an

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4 Pair correlation function, g(r)

A

3

2

1

0 0

2

4

6 8 Distance, r (mm)

10

12

B

Pair correlation function, g(r)

4

3

2

1

0 0

2

4

6 8 Distance, r (mm)

10

12

Figure 8.3 Use of a mask image with the Linear Dipole Algorithm. (A) The left image shows randomly distributed small objects, which are restricted to a particular region of the image. The corresponding g(r) plot suggests coaggregation of the objects, although they are randomly distributed within this region. However, if the whole image is considered, the objects are indeed clustered. Ten artificial images were analyzed, which contained randomly distributed objects in the same region. See the legend of Fig. 8.1B for a description of the g(r) plot. (B) The lower left image is a mask defining the region that contains the objects in the upper left image. When the randomly distributed objects are analyzed in combination with this mask image, the g(r) plot correctly reflects the random distribution of the objects within their region of the image [g(r) 1]. The confidence intervals for larger distances are broader, because long linear dipole probes often extend beyond the borders of the mask and are ignored by the algorithm. Thus, only few of the long dipole probes can be evaluated. This effect increases the statistical uncertainty.


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additional “mask image,” which indicates the location of the biomass. Linear dipole probes are evaluated only if both ends fall into those regions, which are flagged as “biomass” in the mask image, whereas all other dipole probes are ignored (Fig. 8.3B). In practice, the mask images can be obtained by labeling the entire biomass with a general nucleic acid stain and by recording this signal in addition to the fluorescence conferred by population-specific probes. Alternatively, one could capture images showing the autofluorescence of the biomass and the biofilm matrix. Even if this obstacle can be overcome one limitation remains, which indeed restricts the use of the Linear Dipole Algorithm. The underlying stereological approach requires that the analyzed structures are not strongly anisotropic (i.e., that they do not change unequally in one of the three spatial dimensions). This condition is often met by floccular structures or nonstratified biofilms, and small deviations from isotropy are tolerated. However, strongly anisotropic samples such as strongly stratified biofilms may not be suitable for this approach, unless the analysis can be restricted to a single biofilm layer whose microstructure is less anisotropic. The following section describes an alternative approach, which overcomes this limitation and complements the Linear Dipole Algorithm.

2.2. The “Inflate Algorithm” The Linear Dipole Algorithm should not be used with strongly anisotropic samples, such as stratified biofilms. Stratification of biomass may reflect concentration gradients of substrates or gases and has commonly been observed in nitrifying biofilms (e.g., Lydmark et al., 2006; Okabe et al., 1999). Therefore, we developed an alternative approach that can analyze spatial arrangement patterns in such structures. Like the Linear Dipole Algorithm, this approach uses 2-D (confocal) images and does not make assumptions about the morphology of the organisms. Consider an image that contains two different microbial populations (1 and 2) that are labeled and clearly identified by FISH probes or other specific markers. From now on, we distinguish the “analyzed” from the “reference” population. For example, let population 1 be the analyzed population. The goal is to determine whether it coaggregates with population 2, which is the reference population. First, the total number of image pixels that belong to the analyzed population is counted. Then all biomass objects (cells, clusters, etc.) of the reference population are virtually enlarged, by a defined micrometer range, in each spatial direction (Fig. 8.4A). Depending on the localization of the two populations in the image, the enlarged biomass may now overlap with biomass of the analyzed population (population 1; Fig. 8.4A). The number of overlapping pixels is counted and stored in computer memory. In the next iteration, the reference population is further enlarged, by the same range as before, and all

198


Inflated population 2 (one or two iterations)

B

Population 2 (reference) Population 1 (analyzed)

100

Cumulative fraction (%)

A

Overlapping pixels (after one or two iterations)

80 60 40 Nitrospira (analyzed), AOB (reference) Random distribution (control curve) 95 % confidence limits

20 0 0

10

20

30

40

50

Distance (mm)

D

6

Positional fraction (normalized)

Cumulative fraction (normalized)

C

Nitrospira (analyzed), AOB (reference) 95 % confidence limits

5 4 3 2 1 0

6 Nitrospira (analyzed), AOB (reference) 95 % confidence limits

5 4 3 2 1 0

0

10

20

30

Distance (mm)

40

50

0

10

20

30

40

50

Distance (mm)

Figure 8.4 Principle of the Inflate Algorithm. (A) Schematic illustration of two biomass objects that belong to different microbial populations. When the biomass of population 2 (reference) is iteratively enlarged, this object grows and overlaps with the population 1 (analyzed) biomass. The overlapping pixels are counted in each iteration of the algorithm. (B) Cumulative fractions of overlapping pixels plotted against the corresponding distances. This example shows an analysis of nitrifying bacteria (AOB, Nitrosomonas sp. and NOB, Nitrospira sp.) in activated sludge (see also Fig. 8.1B). Nitrospira were the analyzed and AOB the reference population. The curve obtained with the real populations is above the curve obtained with an artificial random analyzed population, indicating coaggregation of the nitrifiers. The solid curves show the mean fractions determined from 48 images. (C) and (D) Same analysis as in (B), but here the normalized fractions were plotted against the distances. Panel (C) shows normalized cumulative fractions and panel (D) normalized positional fractions. The horizontal stippled lines represent the null hypothesis of a random analyzed population. Both plots indicate strong coaggregation of the nitrifiers at short distances (< 10 mm), where the curves are high above the horizontal lines. The positional fraction plot (D) also shows that the nitrifiers avoid growing at far distances from each other.

pixels that overlap now with the analyzed population are counted again (Fig. 8.4A). Note that this number includes those pixels which overlapped in the previous iteration. This procedure is repeated until a stop condition is fulfilled, which may be a maximal size of the virtually enlarged biomass. All obtained numbers of overlapping pixels are finally converted to fractions of


199

the known total pixel number of the analyzed population. Thus, for each iteration, the algorithm stores the number of micrometers, by which the reference population was enlarged, and the overlapping fraction of the analyzed population. Note that the micrometer ranges equal the distances to the surface of the original (not enlarged) biomass of the reference population (Fig. 8.4A). Now the further evaluation is straightforward. The closer the analyzed population coaggregates with the reference population, the higher is the fraction of overlapping biomass soon after the algorithm has started (i.e., when the reference population has been enlarged by a few micrometers only). This can be visualized by plotting the overlapping pixel fractions against the micrometers (Fig. 8.4B). Note that this curve becomes saturated at larger micrometer distances because the overlapping fractions are cumulative: The more the reference population is enlarged, the more pixels overlap with the analyzed population. Sooner or later, the enlarged biomass of the reference population overlaps with the entire biomass of the analyzed population, even if these populations do not actually coaggregate. Therefore, an additional criterion is needed to decide whether the two populations coaggregate, are randomly distributed, or avoid each other. To get this criterion, we declare the null hypothesis that the analyzed population would be randomly distributed in space. To test this hypothesis, the algorithm creates a second artificial image, which contains only the (not yet enlarged) biomass of the reference population. In addition, it places pixels in this image at random locations. These pixels represent a virtual and randomly distributed analyzed population. The number of these random pixels equals the number of biomass pixels of the real analyzed population, which was determined at the beginning (see above). Thus, the random pixels replace the real analyzed population and represent the same density of biomass. Here we refer to these pixels as the “random analyzed” population. Now the entire procedure is repeated with this artificial image (i.e., with the reference and the random analyzed population). The resulting curve is plotted in the same graph as the curve for the two real populations (Fig. 8.4B). A comparison of the two curves reveals whether the enlarged reference population overlapped earlier and more pronouncedly, with the real analyzed population than with the random analyzed population (Fig. 8.4B). In this case, the null hypothesis is rejected and the real analyzed population coaggregates with the reference population in the sample. A simpler graph is obtained by normalizing the cumulative fractions. The fractions obtained with the two real populations are divided by the fractions obtained with the random analyzed population at the same micrometer distances. This leads to a graph where the null hypothesis is represented by a horizontal line at value 1, and the normalized cumulative fractions obtained with the two real populations are plotted against the micrometers (Fig. 8.4C). A curve located above the horizontal line indicates coaggregation of the real analyzed with the reference

200


population (Fig. 8.4C). Additional information on the spatial structure is obtained by determining “positional fractions.” This is the fraction of the analyzed population that overlaps in an iteration of the algorithm minus the sum of all fractions that overlapped in the previous iterations. Thus, the positional fraction reflects the actual increase in overlapping pixels when the reference population is enlarged. In each iteration, this increase depends on the spatial distribution of the analyzed population. For example, if the analyzed closely coaggregates with the reference population, the increase will be largest during the first few iterations (i.e., at positions close to the reference population). Like the cumulative fractions, the positional fractions can be normalized with the values obtained for the random analyzed population. The resulting graph shows, for each distance, whether the increase in overlapping pixels was larger (or smaller) with the real populations than with the random analyzed population (horizontal line at value 1; Fig. 8.4D). Notably, either graph (showing the “cumulative” or the “positional” fractions) can reveal not only coaggregation, but also random distribution or mutual avoidance of the analyzed relative to the reference population. In the case of random distribution, the curve obtained with the real populations is on the horizontal line (value 1) that represents the null hypothesis of a randomly distributed analyzed population. In the case of mutual avoidance, the curve is below that line. Because the method described in this section virtually enlarges one population, we named it the “Inflate Algorithm” (imagine that the objects, which belong to the reference population, are virtually inflated). Importantly, it quantifies only the spatial arrangement of the noninflated (analyzed) population relative to the inflated (reference) population, and not vice versa. Thus, if the results indicate coaggregation of the analyzed with the reference population it is possible that, nevertheless, much biomass of the reference does not coaggregate with the analyzed population. A biological example might be a heterotrophic commensal that closely coaggregates with an autotrophic nitrifier and uses organic carbon secreted by this autotroph. In contrast, much biomass of the autotrophic nitrifier may well grow far away from the heterotroph if the relationship is not mutualistic. The ability of the Inflate Algorithm to detect such differences in localization patterns is one fundamental difference to the Linear Dipole Algorithm, which treats both examined populations equally. Therefore, if more than two populations in the same sample must be investigated, it can make sense to choose always the same reference population and do the quantification with different analyzed populations. The Inflate Algorithm is less sensitive to strongly anisotropic structures than the Linear Dipole Algorithm and can be used with images of stratified biofilms, granules, or similar kinds of samples. For representative results, the algorithm should be applied to several images taken at random positions in the sample. If enough images have been analyzed, the means of the


201

overlapping pixel fractions are reported along with confidence intervals (Fig. 8.4B–D). The algorithm is generally sensitive to noise in the images, but especially artifacts labeled by the same (fluorescence) marker as the reference population can cause dramatic biases, because they are inflated just like the biomass itself. If large parts of the images are empty, mask images should be used to define the regions containing biomass. Otherwise, the Inflate Algorithm may report false coaggregation just like the Linear Dipole Algorithm (Fig. 8.3). If a mask image is provided, the pixels of the random analyzed population are placed in the area of that mask only (but not on top of the reference population). When applied to the same images, the Inflate and the Linear Dipole Algorithm may report similar, but not identical, localization patterns. As mentioned in Section 2.1, the Linear Dipole Algorithm measures the likelihood to encounter an arbitrary pixel of one population at distance r from an arbitrary pixel of the other population. This analysis includes all biomass pixels of the two populations, regardless of their location. In contrast, the Inflate Algorithm measures the pixel overlap at defined distances away from the surface of the reference biomass. Internal pixels in cell aggregates formed by the reference population do not affect the results. Understanding this difference can be important for comparing the results of the two approaches.

3. Protocols for the Spatial Arrangement Quantification of Nitrifiers This section provides a basic protocol for analyzing the spatial localization patterns of nitrifying microorganisms in biofilm samples. The protocol uses FISH with rRNA-targeted oligonucleotide probes for the in situ detection of the target populations, confocal microscopy, and our digital image analysis software DAIME (Daims et al., 2006). The DAIME program can be downloaded free of charge at the website (www.microbial-ecology. net/daime). The currently published version (1.3.1) contains an implementation of the Linear Dipole Algorithm. The Inflate Algorithm will be included in the next version (1.4) to be released in the first quarter of the year 2011.

3.1. In situ detection of nitrifiers by FISH An encompassing set of well-evaluated FISH probes is available for the detection and visualization of ammonia and nitrite oxidizers in environmental samples. Table 8.1 lists a selection of probes targeting most known lineages of nitrifiers that usually occur in nitrifying biofilms, together with

Table 8.1 rRNA-targeted oligonucleotide probes commonly used to detect nitrifying microorganisms in biofilm samples

Reference

CGC CAT TGT ATT ACG TGT GA 35

None

Mobarry et al. (1996)

CGA TCC CCT GCT TTT CTC C

55

None


TAT TAG CAC ATC TTT CGA T

5

None


0

Probe name

Target

Sequence (5 –3 )

Nso1225

Betaproteobacterial ammonia-oxidizing bacteria Betaproteobacterial ammonia-oxidizing bacteria Genus Nitrosomonas, Nitrosococcus mobilis Genus Nitrosospira Most halophilic and halotolerant Nitrosomonas Nitrosococcus mobilis Nitrosomonas oligotropha lineage (Cluster 6a) Most Archaea

Nso190

Nsm156 Nsv443 NEU

NmV Cluster 6a192 Arch915 Cren512 Cren537 Ntspa712

Most Crenarchaeota Marine group I.1a Crenarchaeota Phylum Nitrospirae

FAa (%)

Competitor oligonucleotideb

0

CCG TGA CCG TTT CGT TCC G 30 CCC CTC TGC TGC ACT CTA 40c

None Mobarry et al. (1996) TTC CAT CCC Wagner et al. (1995) CCT CTG CCG

None Juretschko et al. (1998) CTT TCG ATC C Adamczyk et al. (2003) CC TGC TTC C GTG CTC CCC CGC CAA TTC CT 10–35 None Stahl and Amann (1991) CGG CGG CTG ACA CCA G 5 None Jurgens et al. (2000) TGA CCA CTT GAG GTG CTG 20 None Teira et al. (2004)

TCC TCA GAG ACT ACG CGG 35 CTT TCG ATC CCC TAC TTT CC 35

CGC CTT CGC CAC CGG CCT TCC

50d

CGC CTT CGC CAC CGG TGT TCC

Daims et al. (2001)

Ntspa662

Genus Nitrospira

GGA ATT CCG CGC TCC TCT

35

Ntspa1026

Nitrospira sublineages I and IIe Nitrospira sublineage I Nitrospira sublineage II

AGC ACG CTG GTA TTG CTA

NIT3

TTG GCT TGG GCG ACT TCA TTC TCC TGG GCA GTC TCT CC Nitrospira sublineage II CCC GTT CTC CTG GGC AGT Nitrospira marina-related GCC CCG GAT TCT CGT TCG Nitrospira Genus Nitrobacter CCT GTG CTC CAT GCT CCG

NTG840

Nitrotoga arcticaf

Ntspa1431 Ntspa1151 Nsr1156 Nspmar62

CTA AGG AAG TCT CCT CCC

Daims et al. (2001)

20

GGA ATT CCG CTC TCC TCT None

35 35

None None

Maixner et al. (2006) Maixner et al. (2006)

30 40

None None

Schramm et al. (1998) Foesel et al. (2008)

Juretschko et al. (1998)

CCT GTG CTC Wagner et al. (1996) CAG GCT CCG 10–20 None Alawi et al. (2007)

40

For more details about the probes please refer to the respective publications or to probeBase (Loy et al., 2003). a FA, formamide. b To ensure probe specificity, the unlabeled competitor must be used in equimolar amounts together with the fluorescently labeled probe. c NEU can also be used with 35% formamide. d Ntspa712 can also be used with 35% formamide, especially if combined with Ntspa662. e Ntspa1026 does not cover all members of these Nitrospira sublineages. f NTG840 is not fully specific as some nontarget bacteria have no 16S rRNA sequence mismatches at the probe binding site.

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the formamide concentrations that are needed in the hybridization buffers to ensure probe specificity. A detailed description of the entire FISH procedure including a list of required equipment, a hands-on protocol, and the recipes of all buffer solutions is provided by Daims et al. (2005). However, for quantitative spatial analyses the normal FISH protocol needs modification. Usually, the environmental samples are first dried on a glass slide and then dehydrated in a series of increasing ethanol concentrations (e.g., Daims et al., 2005). During this procedure, biofilm specimens tend to shrink and their original 3-D architecture is distorted. Therefore, we have developed a modified FISH protocol (3-D FISH) that preserves the 3-D structures by embedding the samples in polyacrylamide (PAA) gel pads (Daims et al., 2006). As PAA shows no or only little autofluorescence, the probe-labeled biomass is clearly visible in PAA gel pads of about 250 mm thickness. However, care is needed to ensure that excess oligonucleotide probes are removed from the gel pads during the washing step of the FISH protocol, as remaining probes would cause very strong background fluorescence. In our protocol, the samples are directly applied to silanized microscope cover slips because the silane covalently binds PAA (Rehman et al., 1999) and thus prevents detachment of the embedded samples during the hybridization and washing steps. We recommend that the embedded samples are observed with a confocal microscope and a long-distance objective. The following paragraphs describe the steps, which are required for PAAembedding of biofilm, and the differences to the normal FISH protocol. 1. Wash paraformaldehyde-fixed biofilm samples in phosphate-buffered saline (1 PBS) and store them at 20 C in a 1:1 mixture of 1 PBS and glycerol instead of the normally used PBS:ethanol mixture. This is important because ethanol can inhibit the polymerization of PAA. 2. Prepare the Bind-Silane working solution [1 ml Bind-Silane (Amersham Biosciences, Uppsala, Sweden), 3 ml 10% (v/v) glacial acetic acid, 296 ml double-distilled H2O]. Mix until the solution becomes clear. 3. Wash microscope cover slips (24 50 mm) in acidic ethanol (1% HCl, 70% ethanol) and dry. Dip the cover slips in the Bind-Silane working solution for 60 min at room temperature, then wash the cover slips once with double-distilled H2O and once with 96% (v/v) ethanol. After drying, the coated cover slips can be stored for several months. 4. Wash standard microscope slides in acidic ethanol and dry. Apply 50 ml of Repel-Silane (Amersham Biosciences, Uppsala, Sweden) to the slide surface and spread evenly using tissue paper. Incubate for 5 min at room temperature, then rinse the slides with 96% (v/v) ethanol and subsequently with double-distilled H2O. After drying, coated slides can be stored for several months. 5. Freshly prepare the PAA solution [20% (w/v) PAA (37.5:1 acrylamide: methylenebisacrylamide), 0.1% (w/v) ammonium persulfate, 1% (v/v)


6. 7.

8.

9.

10.

11. 12. 13.

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tetramethylethylenediamine]. Mix the solution by vortexing and use it immediately. Apply 10 ml of sample (suspended in PBS:glycerol) to a Bind-Silanecoated cover slip, then add 10 ml of PAA solution, and mix with the sample. Put a rectangular Teflon spacer of 0.25 mm thickness around the biomass onto the cover slip. Suitable Teflon spacers are those which have been used, for example, to separate the glass plates covering DNA sequencing gels. Now cover the assembly with a Repel-Silanecoated microscope slide. To obtain a flat gel pad, carefully put a small weight onto the top of the microscope slide. Let the PAA polymerize for 10–15 min at room temperature. Important: Air inhibits the polymerization of PAA. Therefore, be careful not to enclose any air bubbles when applying the microscope slide on top of the sample–PAA mixture. After polymerization, carefully remove the microscope slide. Due to the pretreatment with Bind-Silane, the gel pad will stick to the cover slip. The pretreatment with Repel-Silane ensures that the gel pad does not stick to the microscope slide. Let the gel pad dry for a few minutes at room temperature. Dehydrate the sample by dipping the cover slip in 50%, 80%, and 96% (v/v) ethanol for 5 min each. Dry the gel pad completely after the last dehydration step. The gel pad will become opaque during dehydration in the ethanol series, but it will again become transparent after rehydration in hybridization buffer. Carefully cover the gel pad with 50 ml of standard FISH hybridization buffer that contains the right amount of formamide for the FISH probes to be used (see Table 8.1). Ideally the buffer should stay on the top side of the gel pad. Add 2 ml of each oligonucleotide probe (probe concentrations in the working solutions are 30 ng/ml for Cy3and Cy5-labeled probes and 50 ng/ml for FLUOS-labeled probes). Carefully mix probes and hybridization buffer by pipetting up and down. Put the cover slip into a moist hybridization chamber (see Daims et al., 2005) and incubate at 46 C for 2–3 h. Transfer the cover slip into 50 ml of prewarmed standard FISH washing buffer and wash for 35–40 min in a water bath at 48 C. To remove remaining buffer salts from the gel pad, dip the cover slip for eight times in ice-cold double-distilled H2O and then dry it immediately for 1 min using pressurized air. Subsequently, air-dry the gel pad for additional 10 min at room temperature in the dark. Important: Do not overdry the gel pad or it may crack. Dried cover slips with gel pads can be stored at 4 C for at least 2 days until observation by confocal microscopy.

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3.2. Microscopy and image acquisition Prior to microscopy, the PAA gel pad (see 3-D FISH, Section 3.1) is covered with antifadent (Citifluor AF1, Citifluor, London, UK). In order to prevent bending of the cover slip during microscopy, the cover slip with the gel pad is laid upside down (i.e., gel pad on the bottom side) onto a microscope slide with a rectangular Teflon spacer of 0.25 mm thickness surrounding the gel pad. This assembly is suitable for observation with an inverse or upright confocal laser scanning microscope. In any case, the embedded sample is observed through the cover slip. For both algorithms described in Section 2, at least 30 images of each probe signal should be acquired at randomly chosen positions. For this purpose, move the object holder of the microscope to a random location and also change the focal plane. However, all recorded images should contain at least one of the populations to be analyzed. Therefore, we recommend to visually confirm that one of the probe signals is present in the randomly chosen field of view. However, one should not intentionally select fields of view that contain all target populations (this would bias the analysis). Prior to recording the images, the detector settings should be adjusted to ensure that each probe signal is within the dynamic range of the instrument. The confocal pinhole should be adjusted for a thickness of 1–2 mm of the optical sections to ensure that each image contains nearly one cell layer and no out-of-focus fluorescence. Subsequently, all images must be taken with the same detector and pinhole settings. The images can be recorded in multicolor mode (i.e., one image contains all probe signals) or as separate images for each probe signal. If mask images showing the entire biomass are needed (see Section 2 and Fig. 8.3), they should be taken separately for each field of view. All images should be stored in the TIFF format (8 bits per pixel, monochrome or 24 bits per pixel, RGB).

3.3. Digital image analysis As mentioned above, the algorithms for spatial arrangement analysis (see Section 2) are implemented in the image analysis program DAIME (Daims et al., 2006). The image analysis workflow consists of three main steps: (1) image import, (2) image segmentation, and (3) the actual quantification. Prior to image import into DAIME, the names of the image files must be adapted to indicate which of the images belong to the same field of view. For example, if separate images were taken for each probe signal, the software must know that these images represent the same spot in the biofilm specimen. This is achieved by appending index numbers to the file names. For instance, images containing AOB could be named AOB_1.tif, AOB_2. tif, and so forth, whereas images containing NOB could then be named NOB_1.tif, NOB_2.tif, and so on. The running index is the number of the


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field of view, so that AOB_1.tif and NOB_1.tif show the respective population at the same location. If all image files are stored in the same directory on disk, DAIME can import all “AOB” and all “NOB” images as two batches or “image series.” These images will be analyzed together in all subsequent steps. Alternatively, one can store multicolor images where one image contains the probe signals of both populations in a field of view. In this case, however, a running index at the end of the file name is also needed by the software to determine which images should be imported as one batch. DAIME understands several different file name indexing schemes that are described in detail in the user manual, which can be downloaded together with the program. To import the images into DAIME, follow the instructions in the user manual and on screen. Multicolor images can be imported in this format and will then be suitable for color-based image segmentation in the following step. Alternatively, they can be split into the individual color channels during image import and will then be suitable for segmentation based on intensity thresholding or edge detection (see below). The next step is the identification of the biomass (cells, microcolonies, etc.) to be analyzed. This process is called “image segmentation,” because it segments an image first into background and biomass and then into relevant objects (microbes) and irrelevant objects (e.g., autofluorescent debris). Image segmentation is a critical step, because it strongly influences the results of all downstream analyses. Therefore, one should carefully check the segmented images for problems such as noise that has been segmented as biomass or dark objects that have been overlooked by the segmentation algorithms. On the other hand, the quality of the source images determines the success of segmentation: Weak fluorescence signals in front of a strong autofluorescent background, or the presence of many artifacts, hamper image segmentation. Consequently, the preceding wet-lab labeling and microscopy procedures must be optimized in order to facilitate image analysis and achieve the best possible results. DAIME offers automatic image segmentation by intensity thresholding or edge detection. Thresholding identifies biomass based on the brightness of the fluorescence signal, whereas edge detection finds the borders of objects (this may work even if the background is relatively bright due to autofluorescence of the biofilm matrix). For thresholding, the program offers a selection of different algorithms which have their own (dis)advantages, so that the user should try which algorithm performs best on a particular set of images. For example, “local thresholding” (Daims and Wagner, 2007) is suitable for segmenting images that contain very bright and also dark fluorescence signals. The procedures for automatic segmentation are described in the DAIME user manual. Importantly, the images must be 2-D segmented for use with the algorithms described in this chapter. The software also offers 3-D segmentation, which is intended for confocal

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z-stacks only. After automatic segmentation an “object editor” appears on screen, which allows the user to check the results and to reject, for example, any artifacts that are not biomass but were identified as “objects” by the program. A special case is multicolor images that contain all probe-labeled populations. They cannot be segmented fully automatically by DAIME. However, the object editor has tools for semiautomated color-based segmentation that can achieve complete and correct segmentation of such images. Like all other features of the object editor these tools are described in the user manual. Note that all segmentation approaches, fully automatic or color based and semiautomatic, can process all images in the same image series at once. This batch processing minimizes the time needed for segmenting even large sets of more than 100 images. Once the images have been segmented, they are ready for analysis by the Linear Dipole or the Inflate Algorithm. In either case, the user can select two populations (for the Inflate Algorithm the analyzed and reference populations must also be identified). Subsequently, the program asks for the range of distances to be analyzed and whether mask images (Fig. 8.3) should be used. For the Linear Dipole Algorithm the user can also choose between two implementations that either evaluate randomly thrown dipole probes (faster but less precise) or scan all pixels of the images (slower but more precise; see also Section 2.1). When all settings are made, the software checks the input images for common sources of bias such as noise or overlapping pixels of different populations. If such problems are identified the user is warned. When the analyses are finished, the results are reported on screen as tables of values and graphs. At this stage, the results can be saved as text files for later import into third-party plotting software or other programs. Please refer to the DAIME user manual for a detailed description of all steps with screenshots of the program.

4. An Application Example: Spatial Analysis of Three Nitrifying Biofilm Populations NOB of the genus Nitrospira are the key nitrite oxidizers in wastewater treatment plants and are widely distributed in nature, but most Nitrospira are still uncultured. Interestingly, this genus can be subdivided into stable phylogenetic lineages that occur in specific habitats (Daims et al., 2001). For example, lineage 1 seems to be highly adapted to life in wastewater treatment systems, whereas lineage 2 occurs in a wide range of natural ecosystems and also in wastewater treatment plants (Daims et al., 2001). In a previous study (Maixner et al., 2006), we used FISH probes specific for either lineage 1 or 2 to detect these NOB in biofilm from a nitrifying reactor. Surprisingly, we found that these two lineages coexisted in the


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reactor although they should compete for the same substrates nitrite and oxygen. Visual inspection, by epifluorescence microscopy, left the impression that lineage 1 Nitrospira generally lived closer to the cell aggregates of AOB than lineage 2 Nitrospira (Fig. 8.5A). We assumed that such a difference in spatial arrangement, if true, might point at an important biological difference between the two Nitrospira populations. Therefore, we quantified the localization patterns of the two Nitrospira lineages and AOB by using the Linear Dipole Algorithm (see Section 2.1) and the software DAIME (see Section 3.3). For Nitrospira lineage 1, this analysis confirmed the close coaggregation with AOB (Fig.8.5B). The maximal coaggregation signal was obtained for very short distances below 10 mm (Fig.8.5B). The results also supported our previous notion that lineage 2 Nitrospira seemed to avoid the close neighborhood of AOB (Fig. 8.5C). Indeed, the curve indicated mutual avoidance of lineage 2 and AOB at distances up to 10 mm, whereas it suggested coaggregation at 20–50 mm away from AOB (Fig. 8.5C). For longer distances, no significant coaggregation signal was obtained for either Nitrospira lineage and AOB (Fig. 8.5B and C), indicating that biological interactions among these nitrifiers did not have long-range spatial effects. Meanwhile we have analyzed the same image data by using the Inflate Algorithm, which had not been available yet when the study (Maixner et al., 2006) was published. For this purpose, either Nitrospira lineage 1 or 2 was the analyzed and the AOB were the reference population. The cumulative fractions strongly suggested close coaggregation of lineage 1 Nitrospira with AOB at short distances, but indicated mutual avoidance of lineage 2 Nitrospira and AOB at the same spatial scale (Fig. 8.5D). Like the Linear Dipole Algorithm (Fig. 8.5B and C), the Inflate Algorithm showed coaggregation of lineage 2 Nitrospira with AOB at longer distances only (Fig. 8.5D). The positional fractions also showed that significantly more lineage 1 Nitrospira lived at short distances (< 20 mm) from AOB than one would expect for a randomly distributed population (Fig. 8.5E). Beyond 20 mm, however, the positional fractions of lineage 1 were below the level of the null hypothesis (Fig. 8.5E). This result indicates that, compared to a randomly distributed population, lineage 1 Nitrospira were rare at such long distances away from AOB. The positional fractions confirmed that lineage 2 Nitrospira avoided to grow very closely to AOB and coaggregated with AOB at longer distances than lineage 1 (Fig. 8.5E). However, also lineage 2 was rarer than a randomly distributed population far away from the AOB (Fig. 8.5E). In summary, the analysis by the Inflate Algorithm was generally consistent with the Linear Dipole Algorithm (Fig. 8.5B–E). Based on the different approach, however, the Inflate Algorithm provided more insight into the localization pattern at long distances away from AOB (Fig. 8.5E). When the visually observed localization patterns had been confirmed by image analysis using the Linear Dipole Algorithm (Maixner et al., 2006), we hypothesized that the biological difference might be an adaptation of

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A Nitrospira lineage 2

Ammonia oxidizers

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Figure 8.5 Spatial analysis of three nitrifying biofilm populations. (A) Representative monochrome FISH images showing the localization of ammonia oxidizers and lineage 1 Nitrospira (left image) or lineage 2 Nitrospira (right image) in the same microscopic field of view. (B) and (C) Analysis of AOB and the two Nitrospira populations by the Linear Dipole Algorithm. For details please refer to the main text (Section 4). (D) and (E) Analysis of AOB and the two Nitrospira populations by the Inflate Algorithm. AOB were the reference and Nitrospira the analyzed populations. Panel (D) shows normalized cumulative fractions and panel (E) normalized positional fractions plotted against the distances. The horizontal stippled lines represent the null hypothesis of a random analyzed population. For details please refer to the main text (Section 4).


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Nitrospira lineage 1 to relatively high nitrite concentrations, whereas lineage 2 might prefer lower nitrite concentrations. In this scenario, the very close coaggregation of lineage 1 and AOB would ensure that these Nitrospira get access to most of the produced nitrite, whose local concentration should be highest directly at the AOB cell clusters. The remaining nitrite would then diffuse into the biofilm and could be consumed by lineage 2 Nitrospira living farther away from the AOB. This hypothesis was first tested by modeling the fluxes of nitrite in the biofilm. The model considered nitrite production, diffusion, and consumption and was based on known physiological parameters of nitrifiers (for details, see Maixner et al., 2006). Interestingly, the assumed small-scale nitrite concentration gradients were strongly supported by this biofilm model, which suggested high nitrite concentrations close to the AOB and much lower nitrite levels at the distances where lineage 2 Nitrospira were mainly found. Finally, we conducted a long-term competition experiment where a sample containing both Nitrospira populations was incubated with higher or lower nitrite concentrations. Indeed, this experiment confirmed that lineage 1 Nitrospira were better adapted to the higher nitrite concentration (Maixner et al., 2006). This biological difference between the two Nitrospira lineages was not previously known and was discovered because of the spatial localization patterns. We think that this example illustrates the high potential of spatial arrangement analyses for the discovery of novel ecophysiological features and interactions of poorly characterized and uncultured microorganisms.

ACKNOWLEDGMENTS We thank Sebastian Lu¨cker for contributing the 3-D FISH protocol and Robert Almstrand for fruitful discussions on the analysis of stratified nitrifying biofilms. The development and evaluation of the image analysis methods, which are described in this chapter, were partly funded by Grant I44-B06 of the Austrian Science Fund (FWF) and Grant LS09-40 of the Vienna Science and Technology Fund (WWTF).

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Stein, L. Y., and Arp, D. J. (1998). Loss of ammonia monooxygenase activity in Nitrosomonas europaea upon exposure to nitrite. Appl. Environ. Microbiol. 64, 4098–4102. Teira, E., Reinthaler, T., Pernthaler, A., Pernthaler, J., and Herndl, G. J. (2004). Combining catalyzed reporter deposition-fluorescence in situ hybridization and microautoradiography to detect substrate utilization by bacteria and Archaea in the deep ocean. Appl. Environ. Microbiol. 70, 4411–4414. Teissier, S., and Torre, M. (2002). Simultaneous assessment of nitrification and denitrification on freshwater epilithic biofilms by acetylene block method. Water Res. 36, 3803–3811. Wagner, M. (2009). Single-cell ecophysiology of microbes as revealed by Raman microspectroscopy or secondary ion mass spectrometry imaging. Annu. Rev. Microbiol. 63, 411–429. Wagner, M., Aßmuss, B., Hartmann, A., Hutzler, P., and Amann, R. (1994). In situ analysis of microbial consortia in activated sludge using fluorescently labelled, rRNA-targeted oligonucleotide probes and confocal laser scanning microscopy. J. Microsc. 176, 181–187. Wagner, M., Rath, G., Amann, R., Koops, H.-P., and Schleifer, K.-H. (1995). In situ identification of ammonia-oxidizing bacteria. Syst. Appl. Microbiol. 18, 251–264. Wagner, M., Rath, G., Koops, H.-P., Flood, J., and Amann, R. (1996). In situ analysis of nitrifying bacteria in sewage treatment plants. Water Sci. Tech. 34, 237–244. Wagner, M., Horn, M., and Daims, H. (2003). Fluorescence in situ hybridization for the identification of prokaryotes. Curr. Opin. Microbiol. 6, 302–309. Walker, C. B., de la Torre, J. R., Klotz, M. G., Urakawa, H., Pinel, N., Arp, D. J., BrochierArmanet, C., Chain, P. S., Chan, P. P., Gollabgir, A., Hemp, J., Hugler, M., et al. (2010). Nitrosopumilus maritimus genome reveals unique mechanisms for nitrification and autotrophy in globally distributed marine crenarchaea. Proc. Natl. Acad. Sci. USA 107, 8818–8823. Weidler, G. W., Gerbl, F. W., and Stan-Lotter, H. (2008). Crenarchaeota and their role in the nitrogen cycle in a subsurface radioactive thermal spring in the Austrian Central Alps. Appl. Environ. Microbiol. 74, 5934–5942. Winogradsky, S. (1892). Contributions a la morphologie des organisms de la nitrification. Arch. Sci. Biol. (St. Petersb.) 1, 88–137.

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Investigating Nitrosomonas europaea Stress Biomarkers in Batch, Continuous Culture, and Biofilm Reactors Tyler S. Radniecki and Ellen G. Lauchnor Contents 1. Introduction 2. Identifying Stress Responses in Batch Bioreactors 2.1. Preventing O2 and NH3 limitations 2.2. Experimental protocol and physiological measurements 2.3. Transcriptional assays and sentinel gene expression 3. Identifying Stress Responses in Continuous Growth Systems 3.1. Batch growth assays 3.2. Fill and draw reactors 3.3. Continuous growth reactors 4. Identifying Stress Responses in Biofilms 5. Conclusions References

219 222 222 223 227 233 233 234 237 239 242 244

Abstract The understanding of nitrification inhibition in ammonia oxidizing bacteria (AOB) by priority pollutants and emerging contaminants is critical in managing the nitrogen cycle to preserve current water supplies, one of the National Academy of Engineers Grand Challenges in Engineering for the twenty-first century. Nitrosomonas europaea is an excellent model AOB for nitrification inhibition experimentation due to its well-defined NH3 metabolism and the availability of a wide range of physiological and transcriptional tools that can characterize the mechanism of nitrification inhibition and probe N. europaea’s response to the inhibitor. This chapter is a compilation of the physiological and transcriptional methods that have been used to characterize nitrification inhibition of N. europaea under a wide variety of growth conditions including batch, School of Chemical, Biological and Environmental Engineering, Oregon State University, Corvallis, Oregon, USA Methods in Enzymology, Volume 496 ISSN 0076-6879, DOI: 10.1016/B978-0-12-386489-5.00009-9

#


217

218

Tyler S. Radniecki and Ellen G. Lauchnor

continuously cultured, and in biofilms. The protocols presented here can be applied to other AOB, and may be readily adapted for other autotrophic bacteria (e.g., nitrite oxidizing bacteria).

Abbreviations 2 DDCt AMO AMO-SOUR AOB ATU b BLAST bp cDNA CT D DART-PCR DFR Dmax DNA DNRA dO2 EDTA FDR GC GFP HAO HAO-SOUR HEPES ICP-OES Ka Ks pKa Q qRT-PCR R0 con

delta–delta Ct method ammonia monooxygenase ammonia monooxygenase specific oxygen uptake rate ammonia oxidizing bacteria allylthiourea specific decay coefficient basic local alignment search tool base pairs complementary DNA critical threshold dilution ratio data analysis of real-time polymerase chain reactions drip flow biofilm reactor the theoretical maximum dilution ratio that will not cause washout within a continuous growth reactor deoxyribonucleic acid dissimilarly nitrite reduction to ammonium dissolved oxygen ethylenediaminetetraacetic acid fill and draw reactors guanine and cytosine base pairs green fluorescent protein hydroxylamine oxidoreductase hydroxylamine oxidoreductase specific oxygen uptake rate 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid inductively coupled plasma optical emission spectroscopy acid dissociation constant half-saturation constant log10Ka media volumetric flow rate quantitative real-time polymerase chain reaction the calculated expression value of the gene of interest in the control cells

N. europaea, Inhibition and Bioreactors

R0 trt R16S con R16S trt RNA S TE Tm v V vmax WWTP yH m mmax

219

the calculated expression value of the gene of interest in the treated cells the calculated expression value of the 16S rRNA gene in the control cells the calculated expression value of the 16S rRNA gene in the treated cells ribonucleic acid substrate concentration tris-EDTA melting temperature specific NH3 oxidation rate cell culture volume maximum specific NH3 oxidation rate wastewater treatment plant hydraulic retention time specific growth rate maximum specific growth rate

1. Introduction Ammonia oxidizing bacteria (AOB) play a critical role in the global nitrogen cycle and the removal of nitrogen from wastewater treatment plants (WWTPs) through their oxidization of ammonia (NH3) to nitrite (NO2) (Fig. 9.1). The oxidation of NH3 is a two-step process in which NH3 is oxidized, via the ammonia monooxygenase (AMO) enzyme, to hydroxylamine (NH2OH), which is further oxidized to NO2, via the hydroxylamine oxidoreductase (HAO) enzyme. The oxidation of NH3 to NO2 yields a net gain of two electrons (Fig. 9.2) and is the sole source of energy for the growth and cell maintenance of AOB (Arp et al., 2002). Due to the low net energy gain from NH3 oxidation, the nonspecificity of the AMO enzyme and their requirement to fix CO2 for biosynthesis, AOB are widely considered to be the most sensitive microorganisms in the natural environment and WWTPs (U.S.EPA, 1993). Their inhibition in WWTP may result in weeks to months of nitrogen removal downtime, due to their slow growth rates, and can lead to fines from regulation agencies and cause the eutrophication of the body of water receiving the WWTP effluent. AOB are vulnerable to disturbance by a wide variety of contaminants including heavy metals (Park and Ely, 2008a), pH shifts (Stein et al., 1997), salts (Moussa et al., 2006), organic solvents (Radniecki et al., 2008), chlorinated hydrocarbons (Gvakharia et al., 2007), copper chelators, such as cyanide (Park and Ely, 2009), and emerging contaminants, including silver nanoparticles (Choi and Hu, 2008).

220


NO3– Nitrate respiration NO2– Nitrification

Denitrification

N2O

DNRA

Ana mm ox

NO NH2OH

Atmosphere N2

NH3

NH4+ Assimilation

Ammonification

Wastewater influent (organic N)

Figure 9.1 The biological removal of nitrogen from WWTPs.

H2O

O2

H2O

AMO NH3

HAO NO2–

NH2OH 2e– + 2H+

2 e– + 3 H+

Terminal electron acceptor

Figure 9.2 The oxidation of NH3 to NO2 by N. europaea.

Understanding how these contaminants inhibit ammonia oxidation at the physiological and transcriptional levels is critical toward developing a greater understanding of their impact on the global nitrogen cycle, creating


221

gene expression-based biosensors to detect these inhibitors in complex natural and engineered environments, and in developing better WWTP operational protocols upon the detection of a nitrification inhibitors (e.g., increasing the aeration upon detection of aromatic hydrocarbons or the addition of Ca2þ/Mg2þ upon the detection of heavy metals). Nitrosomonas europaea, a model AOB, has been found to be a key AOB in terrestrial and freshwater habitats, especially those containing high levels of NH4þ, including WWTPs (Kowalchuk and Stephen, 2001; Mobarry et al., 1996), and has been used in pure culture studies to gain a greater understanding of how various chemicals inhibit NH3 oxidation. In general, NH3 oxidation is most commonly disrupted in one of the following ways: (1) energy drain through the cometabolism of non-electron generating substrates (e.g., hydrocarbons), (2) direct interference with the AMO or HAO enzymes, (3) interference with cytochromes in the electron transport chain, (4) disruption of outer membrane stability, or (5) general oxidative stress damage. The method by which a chemical inhibits NH3 oxidation can either be directly measured (e.g., measuring AMO activity or cell membrane integrity), indirectly measured (e.g., detecting cometabolized products), or inferred (e.g., identifying the upregulation of oxidative stress genes). Nitrification inhibition tests have traditionally been conducted in shortterm batch assays lasting minutes to several hours. The advantages of this method are that it is fast, requires relatively small volumes (2–50 mL), and allows a large range of conditions to be tested simultaneously. However, this method does not lend itself well to examining the effects of long-term exposure to nitrification inhibitors, including the ability of AOB to recover growth and activity after the initial inhibition. To examine the effects of long-term exposure (days to weeks) to nitrification inhibitors, batch growth assays, fill and draw reactors, and continuous growth reactors are more suitable test methods. While conducting nitrification inhibition assays on planktonic cells is the most common method used, AOB are generally found in biofilms in natural and engineered systems. Drip flow biofilm reactors used to grow pure culture N. europaea biofilms allow for the determination of how the unique properties and conditions of biofilm cells (e.g., heterogeneity of cell age, the existence of pH, O2, and NH3 gradients across the thickness of the biofilm, etc.) influences nitrification inhibition. This chapter describes the methodology of various batch, continuous growth, and biofilm reactors used to characterize the physiological and transcriptional responses of pure cultures of N. europaea to nitrification inhibitors. The methods in this chapter aim to help researchers overcome some of the most common difficulties encountered in cultivating pure N. europaea cultures in these reactors and they may be directly applicable to cultivating other AOB as well as other aerobic autolithotrophs, including nitrite oxidizing bacteria.

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2. Identifying Stress Responses in Batch Bioreactors Batch bioreactors are the most common method employed for evaluating the stress responses of N. europaea to various nitrification inhibitors due to their relative ease of use, short test time spans, moderate volumes, and ability to test a wide range of conditions simultaneously. In general, a batch bioreactor used for nitrification inhibition studies consists of a sealed vessel, partially filled with test media and cells, leaving ample headspace to provide O2 for the cells, being shaken or stirred vigorously to eliminate mass transfer limitations. Using this configuration, researchers can easily measure NO2 production, O2 consumption, and the production of cometabolic products, and can harvest cells for further microscopy, proteomic, and genomic analyses. An advantage of batch bioreactor experiments is the short-time duration of the tests. Due to the slow growth of N. europaea (8–12 h doubling time), batch bioreactors allow for measurement of ammonia oxidation rates without significant cell growth during the length of the experiment. With this in mind, however, cell density measurements should be checked at the beginning and end of the batch inhibition tests to verify that cell growth is negligible.

2.1. Preventing O2 and NH3 limitations While the size of the batch bioreactor (2 mL to 2 L) and the length of the inhibition test (2 min to 4 h) can vary, certain parameters must be accounted for in every batch bioreactor used to examine nitrification inhibition. Typical maximum observed rates of NH3 oxidation to NO2 by N. europaea in batch bioreactors range from 1.8 to 2.2 mmol NO2 mg protein-min 1 (Radniecki et al., 2011) with N. europaea protein concentrations, measured using the biuret assay (Gornall et al., 1949), typically ranging from 5 to 8 mg protein L 1. With 1.5 mmol of O2 being consumed with every mmol of NO2 produced (Fig. 9.2), these rates and concentrations lead to an O2 utilization rate of 22–26 mM O2 min 1. The solubility of O2 at 25 C, the typical temperature for nitrification inhibition assays, is 253 mM (Lide, 1996). Thus, O2 would become limiting within 10 min. For this reason, it is vital to have adequate headspace, typically one to two times the batch bioreactor’s liquid volume, and mixing, typically either shaking at 250 rpm or mixed via stir bar and stir plate at 700 rpm, to ensure an adequate O2 supply for the cells during the experiment. In addition to O2, NH3 may also become limiting during batch bioreactor experiments. It has been well documented that NH3, not NH4þ, is the

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sole energy producing substrate for N. europaea (Suzuki et al., 1974). The amount of NH3 present in any given aquatic system is in equilibrium with NH4þ and is dependent on the pH of the system. With a pKa of 9.3, it is advantageous to keep the system as alkaline as possible without causing harm to the cells to maximize the concentration of NH3. For this reason, a pH of 7.8 is often chosen for nitrification inhibition experiments. However, to maintain this pH throughout the experiment, it is vital to properly buffer the system, either with 40 mM KH2PO4 or 30 mM HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid—a nonchelating buffer used when examining heavy metal toxicity), as N. europaea acidifies its environment by generating 3 net moles of Hþ for every mole of NH3 oxidized to NO2 (Fig. 9.2). In batch bioreactor experiments lasting 3 h, with the typical cell densities and NH3 oxidation rates listed above, around 2 mM NH3 is consumed. To minimize decreases in NH3-dependent oxidation rates over the course of the experiment, excess NH3 should be provided to the batch reactors in the form of at least 2.5 mM (NH4)2SO4 (173 mM NH3 at pH 7.8). At the end of the 3 h experiment, roughly 3 mM NH4þ (104 mM NH3) will remain, resulting in only a mild decrease in NH3 oxidation rates (Fig. 9.3).

2.2. Experimental protocol and physiological measurements The general protocol for testing the inhibitory affects of any given chemical in batch bioreactors uses the following format. A 1% (v/v) inoculum of pure N. europaea culture is grown in batch using a minimal growth media containing 25 mM (NH4)2SO4, 40 mM KH2PO4, 3.8 mM Na2CO3,

NH3 oxidation rate (mmol NH3 mg protein-min–1)

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0

Ks

125

250 NH3 (mM)

375

500

Figure 9.3 The Monod curve of NH3 oxidation to NO2 by N. europaea as defined by the following equation: v ¼ (vmaxS)/(Ks þ S). v, specific NH3 oxidation rate; vmax, maximum specific NH3 oxidation rate (3.27 mmol NH3 (mg protein-min) 1); Ks, halfsaturation constant (40 mM NH3); and S, substrate concentration (mM NH3).

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730 mM MgSO4, 200 mM CaCl2, 9.9 mM FeSO4 (with 16.5 mM EDTA (ethylenediaminetetraacetic) free acid, pH 9), and 0.65 mM CuSO4. The cultures are shaken in the dark at 100 rpm at 30 C until they are in their late-exponential/early-stationary phase (0.07–0.1 OD600 nm) after about 3 days. The cells are harvested via centrifugation, washed once with either 40 mM KH2PO4 (pH 7.8) or 30 mM HEPES (pH 7.8) to remove residual NH4þ, NO2, and trace metals, pelleted again via centrifugation, and suspended a final time in 30 mL of either the KH2PO4 or HEPES buffer. The cells are kept on ice until they are ready to be used in the batch bioreactors. The batch bioreactors are filled with the test media of choice containing 2.5 mM (NH4)2SO4 and either 40 mM KH2PO4 (pH 7.8), suitable for all nonmetal contaminants, or 30 mM HEPES (pH 7.8), suitable for all contaminants including metals, being mindful to leave adequate headspace for O2 transfer as discussed above. The addition of the trace metals found in the growth media to the test media is optional as it does not alter the nitrification activity of N. europaea during the 3 h test due to the limited growth of the cells over this time frame. However, the presence or absence of these trace metals has been shown to have a large influence on the sensitivity of N. europaea to heavy metals contaminants (Radniecki et al., 2009a,b). The batch bioreactors are sealed with either bottle caps containing tert-butyl rubber septa caps, best if the bottles are going to be shaken, or a multiport bottle cap containing a sampling tube, best if the bottles are going to be stirred. Once sealed, the inhibitor being tested is added and the batch bioreactors are either shaken or stirred for 1 h to allow the partitioning of the inhibitor to reach equilibrium. After 1 h, the washed and concentrated N. europaea cells are injected into the batch bioreactors to their desired concentration (5–8 mg protein L 1) and are immediately shaken (250 rpm) or stirred (700 rpm) to begin the experiment. All conditions, controls and treatments, should be run in triplicate batch bioreactors. Measuring the accumulation of NO2 within the batch bioreactor over time is one of the most common methods used to determine N. europaea nitrification activity. The concentrations of NO2 within the bioreactors are quantified using a colorimetric assay (Lide, 1996) in which 10 mL of sample from the reactor is added to 890 mL of 1% (w/v) sulphanilamide in 1 M HCl and 100 mL of 0.2% (w/v) N-(1-naphthyl)ethylenediamine dihydrochloride. The presence of NO2 will cause a pink hue to form and can be quantified by measuring its absorbance at 540 nm. The creation of a standard curve from stock NO2 solutions is necessary to complete this assay. This colorimetric assay is extremely sensitive, (0.01 mM lower detection limit), robust, and linear from 0 to 3 mM NO2. While measuring the accumulation of NO2 is a reliable and rapid measure of nitrification activity and inhibition, it provides limited information as to how or why the nitrification activity may be inhibited. However,


225

measuring the activities of the AMO and HAO enzymes through their AMO and HAO specific oxygen uptake rates (AMO-SOUR and HAO-SOUR, respectively) will provide further clues as to how and why nitrification inhibition is occurring. AMO-SOURs measure the total O2 consumption by N. europaea, not just by AMO as the name may imply. Theoretically, the AMO-SOURs should match the NO2 production rates by a 1.5 ratio. To measure AMOSOURs, N. europaea cells are removed from the batch bioreactor and are placed immediately into a sealed 1.8 mL O2 electrode chamber. This chamber contains a micro-stir bar, to prevent mass transfer issues, and a Clark O2 electrode probe (Yellow Springs Instrument Co., Model # 5331, Yellow Springs, OH) and is water-jacketed at 30 C. The cells continue to oxidize the residual NH3 in their test media and the rate at which they consume O2 during this process is recorded using an YSI model 5300 biological oxygen monitor (Yellow Springs Instrument Co.) and a strip chart recorder or other data acquisition device. The assay typically lasts from 2 to 5 min depending on the level of the cells’ nitrification activity. Once the AMO-SOUR has been completed, 40 mL of 20 mM allylthiourea (ATU) is added (444 mM final concentration) to the microelectrode chamber. ATU is a copper-specific chelator and will inhibit all AMO-SOUR activity. Once AMO activity has ceased, 100 mL of 75 mM NH2OH (4.2 mM final concentration) is added. The addition of NH2OH to the AMO-blocked cells will result in O2 consumption by HAO and cellular processes independent of AMO, although generally at a slower rate. The HAO-SOUR assay generally takes 5–8 min to complete. The comparison of the AMO-SOURs and HAO-SOURs of control and inhibited cells can begin to determine if the inhibitor is affecting the AMO enzyme directly or process further downstream, such as the electron transport chain or membrane stability (Fig. 9.4). If a nitrification inhibitor affects only the AMO-SOURs and not the HAO-SOURs, it can be concluded that the AMO enzyme is being inhibited directly, possibly through either competition for its active site or through interactions with AMO that changes its redox state or morphology, and that is what is blocking the flow of electrons to the terminal acceptor. If the inhibitor affects both AMO-SOURs and HAO-SOURs, it can be concluded that further downstream process are being affected, thus blocking the flow of electrons to the terminal acceptor, and that the AMO enzyme may or may not be affected. Once it has been determined if the inhibitor is affecting AMO alone or other down-stream processes, other assays may be employed to further detail the mechanisms of inhibition. For instance, if the inhibitor is AMO-specific and energy drain is suspected due to cometabolism, the inhibitor can be removed through a series of washes in which cells are harvested via centrifugation and suspended into fresh buffer a total of five

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Control Treatment

HAO-SOUR

AMO-SOUR

A

Time

Control and Treatment

Time

Control

Treatment

Time

HAO-SOUR

AMO-SOUR

B Control Treatment

Time

Figure 9.4 (A) Only AMO-SOURs are decreased, suggesting that the nitrification inhibitor affects only the AMO enzyme. (B) Both AMO-SOURs and HAO-SOURs decrease suggesting that the nitrification inhibitor affects processes down-stream from AMO (e.g., membrane integrity) and may or may not be affecting the AMO enzyme directly.

times before being placed into fresh test media. If full activity is restored, energy drain due to cometabolism is a likely culprit for the inhibition (e.g., aromatic hydrocarbons) (Radniecki et al., 2008). If activity is not restored, it is likely that the inhibitor is either permanently bound to the AMO enzyme (e.g., acetylene) (Hyman and Arp, 1992) or has displaced AMO’s metal core (e.g., Zn2þ) (Radniecki et al., 2009a). A reduction in HAO-SOURs suggests that the inhibitor has disrupted the electron transport chain through either direct interactions with the cytochromes (e.g., CN) (Park and Ely, 2009), general damage from oxidative stress (e.g., Cd2þ) (Radniecki et al., 2009b), or the compromise of outer-membrane integrity (e.g., Cu2þ and Agþ) (Park and Ely, 2008a). The integrity of N. europaea’s outer-membrane can be measured by the release of intracellular Kþ. All prokaryotic cells maintain a high intercellular Kþ gradient through the active transport of Kþ via Naþ/Kþ pumps found in the outer-membrane. The release of this intracellular Kþ is a sign of outer-membrane instability and can be measured in the supernatant of pelleted cells through inductively coupled plasma optical emission spectroscopy (ICP-OES) (Radniecki and Ely, 2011). Additionally, the disappearance of intracellular Kþ can be measured from the cell pellet by first dissolving the cell pellet in 3 N HNO3 overnight before using ICP-OES to measure the Kþ concentration. It should be noted that to use the


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intracellular Kþ assay, it is necessary to use a non-potassium containing buffer (e.g., Na–HEPES) and to increase the N. europaea cell concentration to at least 25 mg protein L 1 to have Kþ levels significantly higher than background levels.

2.3. Transcriptional assays and sentinel gene expression The N. europaea genome is fully sequenced and annotated (Chain et al., 2003) making genomic tools, including microarrays and quantitative realtime polymerase chain reaction (qRT-PCR), powerful approaches toward understanding how certain chemicals inhibit NH3 oxidation and how N. europaea cells respond to the inhibition. Additionally, these molecular tools allow for the identification and quantification of “sentinel genes”, genes that are only up-regulated in the presence of a specific inhibitor or class of inhibitors (e.g., NE1545—phenol, mbla—chloroform, moeZ— CN, and merA—heavy metals) (Gvakharia et al., 2007; Park and Ely, 2008a,b, 2009; Radniecki et al., 2008), which may prove useful in biosensors applications to detect these contaminants in complex environmental samples. Table 9.1 summarizes some of the recent research conducted on identifying N. europaea sentinel genes for various inhibitors. Microarrays are a useful tool to monitor global gene expression patterns but require large quantities of Total RNA, usually around 10 mg. With a typical cell density of 5–8 mg protein L 1, this requires the harvesting of Total RNA from 120 mL of cell culture. Due to the large volume required to harvest enough Total RNA, batch bioreactors containing at least 1 L of test media are recommended. To harvest high quality Total RNA that has not undergone degradation, it is necessary to use an RNase inhibitor, such as Trizol (Invitrogen, Co., Carlsbad, CA), to stabilize and preserve the Total RNA. To isolate Total RNA, N. europaea cell are harvested via centrifugation and washed in 1 mL of either 40 mM KH2PO4 or 30 mM HEPES buffer (pH 7.8). This wash step removes any trace metals, NH4þ or NO2 that may inhibit downstream extraction processes and increases Total RNA yields up to 10-fold. The cells are once again harvested via centrifugation for 1 min, decanted, and suspended in 500 mL of Trizol reagent and are aggressively mixed for 1 min using a vortexer. The cells and Trizol mixture is then stored at 80 C for further processing. For Total RNA extraction, the cell and Trizol mixture is thawed at room temperature and an additional 500 mL of Trizol reagent is added and mixed. The cells are lysed through shearing by rapidly passing the Trizol mixture through a 20-gauge needle 20 times, which we found to be the optimal number of passes to increase Total RNA extraction yield. At this point, 200 mL of chloroform is added and the Trizol–chloroform mixture is shaken by hand for 15 s. The mixture is incubated at room temperature for

Table 9.1 Summary of N. europaea stress responses to selected inhibitors

Inhibitor

Sentinel gene

Primer sequences 0

Changes in sentinel gene expressiona d,e

16-fold

AMOspecific inhibition onlyb

Yes

Mode of inhibition

Changes in amoA expressionc

Changes in hao expressionc

Energy drain

None

None

Batch/ Biofilm

Radniecki et al. (2008, 2011), Lauchnor et al. (2011)

Energy drain None (both)/ irreversible Inactivation of AMO (CF only)/ General cell cytotoxicity

None

Batch

Gvakharia et al. (2007, 2009)

8-fold (Cd2þ) 6-fold (Zn2þ)

3-fold (Cd2þ) None (Zn2þ)

Batch/ Park and Ely Chemostat (2008a,b), Radniecki et al. (2009a,b)

2-fold

2.5-fold

Batch

Phenol/Aniline/ p-Cresol

NE 1545 For 5 -GGATGATCTG (DUF209) ACGCAAGTGA-30 Rev 50 -CTGCGACAAA GTCGAAAGTG-30

Chloroform (CF)/ Chloromethane (CM)

NE2402 (clpB)

For 50 -TGACGCAAAG CCTCAAACTTCTG-30 Rev 50 -AGCACGTGTC GCTCCATATTGT-30

4-foldd (CF) No 8-foldd (CM)

Cd2þ/Zn2þ

NE0839 (merA)

For 50 -GCTTTATCAAG CTGGTCATC-30 Rev 50 -ACATCCTTGTT GAAGGTCTG-30

25-foldf to 277-foldd (Cd2þ) 48-foldd,f (Zn2þ)

Yes AMO core (Zn2þ) interation (both)/ No (Cd2þ) Oxidative Stress (Cd2þ only)

CN

NE2353 (moeZ)

For 50 -AGATCGGCAGC GATTGGTCG-30 Rev 50 -TTCACGTGATG TGCTGCTCG-30

35-foldd

Yes

Cu2þ-chelation of AMO

Culturing conditions

References

Park and Ely (2009)

a b c d e f

Housekeeping gene

RNA_45 (16S rRNA)

For 50 -GGCTTCACACG TAATACAATGG-30 Rev 50 -CCTCACCCCAGT CATGACC-30

Radniecki et al. (2008, 2011), Lauchnor et al. (2011), Radniecki et al. (2009a,b)

Metabolic enzyme (AMO)

NE0944 (amoA)

For 50 -TGGCGACATAC CTGTCACAT-30 Rev 50 -ACAATGCATCTT TGGCTTCC-30


Metabolic enzyme (HAO)

NE2044 (hao) For 50 -CAAACTTGCCGA AATGAACC-30 Rev 50 -GCTGGTGATGTT CTCTGCAA-30


Maximum relative gene expression observed. Yes, only AMO-SOURs were affected by the inhibitor; No, both AMO and HAO-SOURs were affected by the inhibitor. Maximum relative gene expression observed. Observed in batch reactors. Observed in biofilm reactors. Observed in continuous growth reactors.

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2–3 min to allow for the formation of a clear aqueous phase top layer. This top clear aqueous layer is removed and placed into an RNase-free 1.5 mL microcentrifuge tube, taking care to avoid transferring proteins from the thin layer directly underneath. An equal volume of RNase-free 70% ethanol is added to the 1.5 mL microcentrifuge tube and mixed via pipetting. This mixture is placed directly onto a Qiagen RNeasy Mini-Kit column (Qiagen, Valencia, CA) and the Total RNA is purified following the manufacturer’s instructions. Once purified, the purity and quantity of the isolated Total RNA can be measured using a cuvette-free spectrophotometer, such as a NanodropÒ ND-1000 UV/vis spectrophotometer (Thermo Fischer Scientific, Waltham, MA), in which 1 mL of sample is scanned from wavelengths ranging from 220 to 350 nm. The quantity of Total RNA is calculated by the absorbance at 260 nm using the following equation: Total RNA concentration (mg/mL) ¼ Abs260 40 (Ausubel et al., 2002). The purity of the Total RNA is determined by examining the Abs260/Abs280 and the Abs260/Abs230 ratios. Pure Total RNA will have an Abs260/Abs280 ratio between 1.8 and 2 and Abs260/ Abs230 above 1.8. Total RNA with Abs260/Abs280 ratios below 1.8 indicates the presence of contaminating DNA, proteins, phenol, or chaotropic salts and must be repurified before further use. For additional verification of RNA quality, an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) can be used determine if RNA degradation is present. Once the Total RNA has been quantified and determined to be of high quality, it should be kept at 80 C or on dry ice until the samples can be handled by either an in-house microarray user facility, if available, or shipped to a commercial microarray facility. These facilities will process the Total RNA further by creating fluorescently labeled cDNA, hybridizing this labeled cDNA to the microarrays, in which the labeled cDNA will bind to probes representing the gene that encodes the cDNA, and determine the level of gene expression for each probe on the microarray by quantifying the observed fluorescence of the hybridized cDNA when the microarray is scanned with a laser. By comparing the fluorescence of a given probe between control and treatment samples, it is possible to determine if the gene that probe represents changed its expression, either up-regulated or down-regulated, upon exposure to the inhibitor (Schulze and Downward, 2001). After a gene of interest has been identified using microarrays, it is important to verify that gene’s expression with a second method (e.g., qRT-PCR) due to the many inherent problems that may occur within a protocol as complex as microarrays which may lead to false-positive results (e.g., poor labeling of cDNA, improper hybridization conditions, mismatched probe-target binding, poor quality Total RNA, fluorescence bleaching, etc.) (Murphy, 2002). qRT-PCR uses PCR to amplify genes of interest and quantifies their abundance through the use of a double stranded DNA-specific fluorophore


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(e.g., SYBR green). The level of fluorescence, which indicates the relative abundance of each gene of interest, is measured after each PCR cycle. By determining how long it takes for a given gene of interest from both control and treatment samples to reach an arbitrarily set critical threshold of fluorescence (CT), selected to intersect the fluorescent curves during their linear exponential phase (usually between cycle 15 and 25), it is possible to determine if the gene of interest was upregulated or downregulated in response to the treatment condition. There are two main methods for analyzing qRT-PCR data. The delta– delta Ct (2 DDCt) method assumes an amplification efficiency of 1 and requires that all amplification efficiencies be equal and very close to 1, which may require additional optimization of amplification conditions (Livak and Schmittgen, 2001). The data analysis of real-time polymerase chain reaction method (DART-PCR) accounts for amplification efficiencies that are less than 1 and uses a correction factor based on the amplification efficiency to adjust the raw data accordingly (Peirson et al., 2003). While this method requires less optimization of amplification conditions, it may cause large errors if the amplification efficiencies are low (e.g., 1, the gene of interest is upregulated, if fold change ¼ 1, there is no change in gene expression and if fold change is 10 kb) which require minimal degradation and large amounts of starting material (10–20 mg of DNA). Degraded DNA is preferentially incorporated into cloning vectors and can ruin a library (Birren et al., 1997). The new requirements for constructing cloneless libraries for NGS platforms, such as Roche 454 and Illumina Genome Analyzer (and more recently HiSeq2000), are also quite substantial. Even though library fragment size is somewhat less important on these platforms (or rather, the requirement is to have smaller fragments amenable to the library construction and sequencing protocols), degraded samples often result in very poor-quality libraries (Roche, 2009b). The quantity requirement for starting material varies with the platform being used as well as with the type of sequencing being performed. In addition, the target library type (e.g., paired-end libraries) also dictates the sample quantity requirements up front. New methods for shearing DNA to smaller sizes, such as acoustic wave systems, have reduced sample requirements from 10 to 1–3 mg of starting material, due in part to the retention of most of the DNA within the sample (Kozarewa et al., 2009; Quail et al., 2008). Because such instruments are not capable of shearing genomic DNA in large size ranges (i.e., 10–20 kb), the advantages of these methods are lost in shearing large fragments for paired-end (“jumping”) libraries for current platforms, which thus require up to 10–15 mg. Recent advances by researchers and/or the availability of novel sample preparation kits have enabled NGS with as little as 5 ng of DNA or RNA (Wood et al., 2010). Another way to overcome low amounts of starting material is through whole genome amplification prior to library preparation. Though biases in coverage may arise during this amplification process (e.g., some regions may be preferentially amplified over others; Pinard et al., 2006), the added throughput of NGS technologies can offset this issue, and allow the recovery of the entire genome nonetheless. Thus, the coupling of such amplification strategies with NGS technologies is opening new avenues of genomic research for organisms where genomic DNA recovery is difficult and can help alleviate the challenges associated with nitrifying enrichment cultures.

2.2. Creating libraries for different platforms With sufficient high-molecular weight purified genomic DNA in hand, a number of protocols exist to create a library of sequenceable fragments that are highly dependent on the sequencing platform used (Fig. 12.1). Library preparation for traditional Sanger sequencing can take around 1 week to complete and consists of size selection (3, 8, and 40 kb are typical) of sheared fragments, followed by end repair and ligation into a sequencing vector. Transformation into Escherichia coli allows for selection

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of clones for storage of these random fragments for future use, which is a significant difference from NGS libraries. A 454 shotgun library can be completed in 1 day following a slightly modified Roche protocol (Roche, 2009a). Fragmentation of genomic DNA occurs with all NGS procedures and can be achieved by any number of methods (nebulization, hydroshearing, sonication), although some new methods such as acoustic shearing may be advantageous for 100 kb in size (Monaco and Larin, 1994). Of these, bacterial artificial chromosome (BAC) vectors have been used successfully in initial metagenomic analyses (Beja et al., 2000b; Rondon et al., 2000). They are based on the F-(fertility) factor of Escherichia coli, which ensures maintenance of recombinant plasmids at only one to two copies per cell, thus avoiding intermolecular recombination events (Shizuya and Kouros-Mehr, 2001; Shizuya et al., 1992). In order to facilitate the cloning of large inserts with an increase in cloning efficiencies, hmw environmental DNA is now more often ligated into a fosmid vector, a derivative of the BAC vector, that contains cos sites like cosmids, for subsequent packaging of the ligated DNA into phage heads of the bacteriophage lambda (Kim et al., 1992). Compared to BAC cloning, the hybrid fosmid vector allows the generation of genomic libraries with a relatively even distribution of insert sizes (between 38 and 45 kb; Collins and Hohn, 1978) and a high transformation (¼infection) efficiency. There are many protocols for the isolation of hmw DNA for metagenomic studies as there are a range of challenges dictated by the nature and complexity of different samples. The first is to isolate hmw DNA of high purity and quality directly from an environmental sample. Second, it is the aim in most approaches that the resulting DNA is representative for the range of organisms present in the community (although this goal is difficult to achieve due to the relatively mild lysis procedures used). Third, one needs high concentrations of the high-purity hmw DNA so that the resulting library is large enough to cover a sufficient amount of the microbial diversity. The detailed protocols presented here summarize results from different scientists in our laboratory who have used metagenomic libraries to characterize the genomic potential of archaea and acidobacteria from moderate and hot environments (Bartossek et al., 2010; Quaiser et al., 2002; Schleper et al., 1998; Treusch and Schleper, 2005; Treusch et al., 2004, 2005; Reigstad and Schleper, unpublished data). They are modified from protocols developed earlier in other laboratories (DeLong, 2005; Stein et al., 1996) and represent

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the result of years of struggle with complex samples from soils and terrestrial hot springs. This chapter will focus on these environments. However, the protocols have also been successfully applied to other samples, including marine sponges, freshwater liquids, microbial mats, and sediments. We also recommend to consult the protocols for the isolation of hmw DNA from environmental samples and the preparation of large-insert genomic libraries that have been published as online videos by Lee and Hallam (2009) and Taupp et al. (2009), respectively.

1.1. Soil as habitat There is not only a large diversity in the physicochemical properties of soils (e.g., from a dry, alkaline, low organic sandy soil to a wet, acidic, organicrich soil), but different habitat types and land-management regimens also have a profound effect on the microbial communities present (e.g., natural vs. managed soils, different fertilizer treatments, pristine vs. heavy metal or pesticide contaminated soils). Terrestrial ecosystems are likely to harbor the most complex prokaryotic and eukaryotic microbial communities on Earth (Gans et al., 2005; Torsvik et al., 2002; Treusch and Schleper, 2006; Urich et al., 2008). However, not only does the high complexity of soil communities need to be considered when planning a metagenomic study, but also soil contains a range of complex organic components such as humic and fulvic acids and other substances which can be coextracted with nucleic acids and potentially affect downstream molecular applications. In order to obtain clean hmw DNA, these contaminants need to be removed early in the procedure.

1.2. Terrestrial hot springs as habitat Similar to soils, terrestrial hot springs also represent a wide diversity of physical properties. These include differences in size, temperature, pH, mud viscosity, visible biological growth forms (mats, biofilms, filaments), rates of gas diffusion and visible bubbling, and colors associated with different bacterial pigments. In addition to these clearly visible differences, the chemistry of two apparently similar springs may be very different, even if they are separated by only a few meters (Reigstad et al., 2008, 2010). In general, the major challenge associated with hot spring samples is the extraction of DNA from microbial cells that tend to attach to particles of mud and clay. In particular, many acidic hot springs contain very finegrained clay minerals that need to be removed as they inhibit downstream molecular analysis by enzymatic inhibition and clogging of filters and pipette tips.

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Metagenomic Libraries from Soils and Hot Springs

1.3. Construction of a metagenomic fosmid library: An overview The construction of a metagenomic fosmid library, from the isolation of hmw DNA to the picking of the library, can be completed by one person in a couple of weeks. The bottleneck is to get high concentrations of highquality hmw DNA to make a fosmid library. By following our detailed step-by-step protocol, developed on the basis of studying complex and challenging environmental samples, the majority of pitfalls will hopefully be avoided. The different steps in the preparation of a metagenomic fosmid library are presented in Fig. 13.1. Concerning the isolation of hmw DNA, Isolation of environmental hmw DNA

In agarose plugs

In solution

Purification and size selection two-phase gel

Modification of DNA ends

Packaging of fosmids into phage heads

Infection of E. coli with phages

First plating of clones on agar plates

Quality testing

Large scale plating and archiving of fosmid clones in 384-well plates

Copying 384-well plates on agar plates and producing pooled DNA for screening

Storage at –80 °C Ready for screening

Figure 13.1 Flow diagram highlighting the different steps of metagenomic fosmid library preparation.

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one should always try to perform the procedures carefully but as quickly as possible, avoid the introduction of contaminants, and carefully consider the use of specific buffers and other extraction reagents to avoid unwanted or preferential biases towards particular cell types. The hmw DNA generated by the following protocol is normally above 50 kb in size (and sometimes >200 kb) and needs to be randomly sheared into shorter pieces of 40– 50 kb for cloning. The careful shearing of DNA here is performed mechanically rather than by enzymes to avoid introducing bias. After mechanical shearing and size analysis of the resulting genomic fragments, they are blunt ended and ligated to a fosmid vector. The fosmids are then packed into lambda bacteriophages, which infect the E. coli cells. Since the replication and partitioning functions of a fosmid are derived from the conjugative Ffactor of E. coli, it will replicate as a single-copy vector in the host cell (Kim et al., 1992; Shizuya et al., 1992). The phage-infected E. coli clones are then plated on Luria–Bertani (LB) plates with chloramphenicol (Cam) and incubated overnight at 37 C to allow the colonies of single-fosmid-bearing E. coli to grow. An initial quality check on some of the fosmids is recommended by restriction analysis before the extensive plating and picking phase starts. During colony picking (either manually or using a colony picking robot), single colonies are transferred into single wells of a microtiter plate containing LB medium/Cam/glycerol mixture. Glycerol is included for cryopreservation, enabling safe thawing and refreezing of the E. coli clones. The cultures are grown overnight at 37 C, and before storing the library at 80 C, an additional replicate plate is produced and one replicate plate print (on solid LB agar) using an aluminum 384-pin stamp tool.

2. Protocols 2.1. Sampling and preservation of samples Hot spring samples were not preserved in the field, but were rather processed as soon as possible. We routinely took extra material for chemical analyses and microscopy, and stored or treated these immediately in the required way. If the aim of the analysis is to target a specific group or taxon, then preliminary studies involving quantitative techniques (q-PCR) or lowresolution community profiling techniques (DGGE, t-RFLP) may be used to gain initial insights into the size and community structure of that specific group. Likewise, treatment of soil after the sampling is a critical step in the construction of metagenomic libraries. Once sampled, soils can be stored at low temperature (e.g., 4 C) but should not be stored at temperatures below 0 C, as this could result in a preliminary lysis and thus loss of specific cell


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types. For soil samples not undergoing immediate DNA extraction, we preferred not to process the soil by homogenization (e.g., by sieving) as this disturbs the soil matrix which may result in the release of nutrients or change other fundamental characteristics of the soil. This can influence the community structure of the soil.

2.2. Isolation of hmw DNA from environmental samples 2.2.1. Isolation of cells from hot spring filaments, mats, biofilms, and mud Samples taken from different hot springs require different approaches to obtain good-quality hmw DNA, based on the amount of clay minerals (pH-related) and prokaryotic cells present. Springs with pH < 5 usually contain a lot of clay minerals as the acidic conditions lead to weathering of the rocks along the spring edges. Hot springs with a pH > 5 are normally clear, with organisms appearing as filaments, biofilms, or mats. In these sites, clay minerals do not influence the DNA isolation. 2.2.1.1. Extraction of cells from acidic hot spring mud samples As the clay minerals of acidic hot springs (pH < 5) inhibit isolation of high-quality hmw DNA and other downstream molecular analysis, it is necessary to remove most of them before starting the DNA extraction. Use approximately 100–200 ml of original hot spring slurry and mix the sample by hand to ensure it is homogenous before starting.

Pipette the homogenized sample into standard 1.7 ml microcentrifuge tubes with a tapered, narrow bottom. Do not use microcentrifuge tubes with round bottom as firm pellets are not formed and usually detach in subsequent steps. “Scaling up” to larger tubes (e.g., 15 ml) has also resulted in losses of microbial cells. Spin the tubes briefly (5–10 s only) at full speed in a standard benchtop microcentrifuge. Most of the clay minerals are now pelleted. Carefully and immediately, transfer the supernatant to fresh tubes (1.7 ml microcentrifuge tube) and centrifuge for 20 min at 13,000g to harvest the cells. Resuspend the pellet carefully in 500 ml 1 TE buffer and immobilize the cells in agarose plugs before cell lysis (see Section 2.2.3). Please note that using this cell fractionation procedure, many of the cells attached to the clay minerals are lost. Therefore, in some cases (when samples were processed directly after sampling), we have included the following extra step that enabled us to extract more of the clay-attached cells. After mixing the acidic mud sample by hand, aliquot into 1.7 ml microcentrifuge tubes before placing in a shaking hot block (e.g., an

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Eppendorf Thermomixer). Shake the sample for 30–60 min at 600 rpm using the same temperature of the spring. This detaches more of the cells from the clay minerals. Immediately centrifuge the samples for 5–10 s and continue the cell harvest and agarose immobilization as described above.

2.2.1.2. Extraction of cells from filamentous hot spring samples, mats, and biofilms (pH > 5)

Carefully transfer the sample into a sterilized or disposable mortar and grind. Two to five g wet weight is usually sufficient as cell density is quite high (107–108 cells/g) in this type of material. Usually the original sample contains enough liquid for grinding. Avoid adding any buffer as this might introduce bias. Carefully pipette the resulting cell suspension into standard 1.7 ml microcentrifuge tubes. Spin for 20 min at minimum speed of 13,000g to harvest the cells. Resuspend the cell pellet carefully in 500 ml 1 TE buffer (or cell lysis buffer, see below) for subsequent DNA extraction (see section 2.2.3).

2.2.2. Isolation of cells from soil samples The following procedure has been used successfully in our laboratory to isolate cells from various soils suitable for different subsequent DNA isolation procedures (see below). Aliquot 900 g of soil into six sterile 500 ml bottles and add 300 ml 0.1 M Tris–HCl (pH 7.2). Shake the bottles at 200 rpm overnight at 4 C to detach the cells from the soil particles. Add the slurry from three bottles into a Waring blender and homogenize for 2 5 min. The sediment is left to settle for 30 s before the supernatant is transferred into clean 500 ml GS3 centrifugation bottles (Nalgene). Repeat the procedure for the remaining three bottles. To separate the cells from the remaining soil particles, the mixture can be further centrifuged at 60g (400–600 rpm) rpm at 4 C for 15 min before transferring the supernatant into GS3 bottles and repeating the procedure. At this low centrifugation speed, soil particles are sedimented while the microbial cells remain suspended in the supernatant. Centrifuge the soil-free supernatant in a GS3 rotor (Sorvall) at 9950g (7500 rpm) at 4 C for 2–3 h to pellet the cells. Resuspend cell pellets in TE or lysis buffer (see next section).


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2.2.3. Isolation of hmw DNA 2.2.3.1. Isolation of hmw DNA—Procedure 1 The isolation of hmw DNA from soil samples described here is based on a method by Zhou et al. (1996). The modifications by A. Treusch and R. Bartossek (partly published in Treusch et al., 2005) have led to both an increase in the DNA yield and reduced processing time. Cells are resuspended in 20 ml extraction buffer (100 mM Tris–HCl [pH 8.0], 100 mM EDTA [pH 8.0], 100 mM sodium phosphate [pH 8.0], 1.5 M NaCl, 1% cetyltrimethylammonium bromide [CTAB]; Zhou et al., 1996) and pooled.

Lyse the cells with the addition of lysozyme (2 mg/ml final concentration) and/or proteinase K (250 mg/ml, final concentration) and incubate (with shaking at 30 rpm): Lysozyme only: 37 C for 60 min. Lysozyme and proteinase K: 37 C for 60 min followed by 40 C for 60 min. Proteinase K only: 40 C for 120 min. [Note: If a specific prokaryotic group in the soil is of interest, the lysis procedure can be adjusted to achieve an optimal enrichment of this group. For example, if archaea are the group of interest, the addition of lysozyme can be omitted to avoid hydrolysis of peptidoglycan, thereby reducing the efficiency of bacterial cell lysis and thus passively enriching for archaea.] Add 1/6 volume of 10% SDS and incubate for 2 h at 65 C, inverting every 15 min. Centrifuge the mixture at 2700g (4000 rpm) for 30 min in the GS3 rotor to separate proteins (which appear on the top as a solid white layer) from the DNA which is in the supernatant. Carefully remove the lower (DNA-containing) liquid and place in a sterile 50 ml polypropylene centrifuge tube. If this DNA solution is not clear, add more extraction buffer and incubate for a further 1–3 h and repeat the last centrifugation step. The DNA solution can be coloured but should be clear for the organic extraction which is performed by adding 1 volume of chloroform:isoamyl alcohol (24:1) and centrifuging at 4400g for 30 min at 4 C. Transfer the aqueous phase into two sterile 250 ml GSA tubes (Nalgene). Precipitate the DNA by adding 0.7 volume of isopropanol (room temperature) and centrifuging at 16,000g for 30 min at 4 C. Wash the pellet with ice-cold 70% ethanol and centrifuge (16,000g, 15 min, 4 C). Remove the supernatant and air-dry the DNA pellets before slowly resuspending in 4–6 ml 1 TE (pH 7.85) using pipette tips with a

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broad aperture to avoid shearing the DNA (these are produced by trimming the ends off standard tips using a pair of scissors or a scalpel prior to autoclaving). Store the DNA at 4 C (and not 20 C). As the DNA solution contains a high concentration of salt, a dialysis step must be performed. Dialysis tubing (Spectra/POR 2, 25 mm) is prepared by placing in deionized water and heating in a microwave oven until the water almost reaches the boiling point. The water is discarded and the procedure is repeated a further 10 times. The DNA solution is then transferred into dialysis tubing and placed into 4.5 l of 1 TE (pH 7.2) and incubated overnight at room temperature. The dialysis tubing should be fixed vertically so it does not float on the surface and potentially dry out. Transfer the DNA solution into 30 ml Corex tubes (DuPont Instruments) and precipitate by adding 1 volume of isopropanol, 1/10 volume of 3 M sodium acetate, and centrifuging at 14,000g at 4 C for 30 min in a SS-34 rotor (Sorvall). Wash the DNA pellet with ice-cold 70% ethanol and carefully resuspend the pellet in 300 ml 1 TE using wide-aperture tips. Store the hmw DNA at 4 C until further processing. 2.2.3.2. Isolation of hmw DNA—Procedure 2 In general, hmw DNA isolation procedures result in either hmw DNA in solution or hmw DNA immobilized in a supportive matrix. The method chosen is dependent on the sample material and on the size of DNA needed. The use of agarose plugs for immobilizing cells for DNA preparation is particularly necessary when producing BAC libraries which can sustain much larger inserts than fosmids (Beja et al., 2000b). However, we have also used this technique for the preparation of DNA for fosmid libraries as this procedure offers additional advantages during hmw DNA isolation. When lysing cells in a matrix, the DNA will not be subjected to mechanical shearing by, for example, centrifugation steps or pipetting. Another advantage is that embedded hmw DNA can be easily placed into an agarose gel without the requirement for pipetting, by simply cutting a short piece of the plug and inserting into a hole in the gel. As pipetting can shear hmw DNA, the agarose immobilization effectively avoids this. The procedure has essentially been adopted and modified from a protocol developed at California Institute of Technology (CALTECH) and has been slightly modified for the use of environmental samples (Stein et al., 1996).

Prepare 1% agarose solution in ddH2O using Ultra Pure Low Melt Agarose (Sigma) and transfer it to a 50 C water bath and mix gently by hand intermittently. After cell extraction directly from an environmental sample (see Sections 2.2.1 and 2.2.2), the cell pellet is resuspended in 1 TE. The cell concentration should be around 0.5–1 109 cells/ml. Preheat this to 50 C.


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Carefully mix the 500 ml cell suspension with 500 ml 1% agarose (the final agarose concentration will be 0.5%, resulting in fairly fragile plugs). Immediately pipette 1 ml of the mixture into the open end of a sterile 1 ml syringe with the plunger drawn completely back. [Note: Prior to commencing the procedure, prepare 1 ml syringes by slicing off the tip of the syringe barrel by using a hot sterile scalpel, resulting in a large aperture and a uniform diameter from the opening.] Cover the opening of the syringe with parafilm and let it stand vertically (opening at the top, plunger at the bottom) in crushed ice for 1 h to completely solidify the agarose and prevent any potential DNAse activity. Carefully push the plunger and expel the agarose plug into a 15 ml polypropylene centrifuge tube containing 12 ml lysozyme buffer (100 mM EDTA in 10 mM Tris–HCl pH 8.0, and lysozyme added (to 1 mg/ml) just before incubation). Up to two plugs can be added in one tube. [Note: the agarose plugs in the 15 ml tubes should be placed horizontally during all incubation steps to support the plug and prevent it from breaking.] Lyse the immobilized cells in a two-step process: first, incubate the plug in 12 ml lysozyme buffer for 1 h at 37 C with 70 rpm horizontal shaking. Carefully pour off the lysozyme buffer and add 12 ml EPS buffer (0.5 M EDTA, 1% Lauroylsarcosin, 2 mg/ml proteinase K added just prior to incubation). The proteinase K not only digests proteins attached to the immobilized DNA but also inactivates nucleases that might otherwise degrade the DNA during purification. Incubate the tube at 50 C for 24 h with 70 rpm horizontal shaking. After 24 h, replace with fresh EPS buffer and incubate for another 24 h. Inactivate the proteinase K by removing the EPS buffer and adding 14 ml of 1 mM phenylmethanesulfonylfluoride (PMSF) in 1 TE buffer. Incubate for 1 h with 70 rpm horizontal shaking with the tube covered in foil as PMSF is light sensitive. Repeat this twice with fresh PMSF solution. PMSF inhibits serine proteases (trypsin, chymotrypsin). Therefore, in order to remove the PMSF afterward, dialyse the plugs by incubating in 1 TE for 1 h three times. The agarose plugs are then ready for storage at 4 C. Recommended storage buffer is 1 TE buffer with 200 mM EDTA. Before running the pulsed-field gel electrophoresis (PFGE), the storage buffer needs to be removed. Therefore, equilibrate the agarose plugs by incubating three times in 1 TAE for 30 min with 70 rpm horizontal shaking. To analyze the DNA content of the agarose plugs, cut a small piece of the plug (a few millimeters) and place horizontally in a well of the gel (Section 2.3.1). Seal the plug by adding 1% 50-60 C agarose solution and allow the agarose to solidify before running the PFGE.

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2.3. DNA purification and size analysis using PFGE Before starting the production of a metagenomic library, the size range of the isolated environmental DNA should be determined (Section 2.3.1), gel purified (Section 2.3.2), and sheared into smaller sizes for cloning (Section 2.3.3). All three steps are performed and evaluated using PFGE.

2.3.1. Initial size analysis of the isolated DNA After isolation of environmental hmw DNA, the size of the DNA should be estimated by running the DNA with size markers in a PFGE (Fig. 13.2; for PFGE theory see Herschleb et al., 2007). If the hmw DNA is embedded in agarose plugs, a small piece (e.g., 0.5 cm) of the plug can be placed in the gel tray before the 1% agarose (A-2929, Sigma) solution is poured into the casting chamber. Cooling the agarose solution to about 50 C before pouring prevents the low melt agarose plugs from melting. Hmw DNA in a solution and size markers should be added to wells of the PFGE gel after casting the gel. The PFGE is performed using 0.5 TAE as running buffer. Gels in PFGE are never prestained with ethidium bromide (EtBr) or SybrGold, but are stained after the PFGE run. For size markers and running conditions, see Section 2.3.2.

A

B

1 2

3

4

5

6

7

>680 kb

267 kb

21 kb

Figure 13.2 High-molecular weight DNA from environmental samples. (A) Precipitating high-molecular weight DNA isolated from soil after the addition of isopropanol and 1/10 volume 3 M sodium acetate to 8 ml of 1 TE in 30 ml Corex tubes before centrifugation. (B) Pulsed-field gel electrophoresis of DNA from hot spring filaments. Lane 1: Mid-Range marker (New England Biolabs). Lane 2: Yeast Chromosomal marker (New England Biolabs). Lanes 3–6: hmw DNA from four different hot springs, all > 80 C and pH 5.5. Lane 7: l-DNA/EcoRI þ HindIII marker (Fermentas). The kilobase sizes of the longest marker bands are given. This pulsed-field gel was run for 20 h at 200 V, 5–50 s pulses, and cooling at 10 C. Under these conditions, DNA fragments of 680 kb in size were compressed forming a single band.


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2.3.2. Purification of hmw DNA using a two-phased agarose gel in PFGE DNA isolated from soil and other habitats that are rich in organics is typically coextracted with contaminating polyphenolic compounds such as humic and fulvic acids. Those compounds are severely inhibitory to downstream enzymatic molecular procedures such as PCR and ligation and should be removed prior to cloning procedures. Protocols involving the use of CTAB or polyvinyl pyrrolidone (PVP) in extraction buffers have been developed, as these compounds are able to complex polyphenolics (CTAB) or retard electrophoretic mobility (PVP). Unfortunately, these compounds also inhibit enzymatic downstream procedures and therefore also have to be removed from DNA extractions. For these reasons, we have developed a two-phased agarose PFGE in our laboratory (Quaiser et al., 2002) that allows the removal of humic and fulvic acids from the hmw DNA and to subsequently purify the DNA from PVP. The first phase of the agarose gel contains 1% agarose (Sigma, A-2929) including 2% PVP (P5288, Sigma), in 0.5 TAE buffer. The second phase is only the 1% agarose in 0.5 TAE. During migration through the PVP phase, being approximately one-third of the total gel size (total gel dimensions: 14 13 cm), the phenolic compounds are separated from the hmw DNA. The continuing migration into the second gel phase separates the PVP from the hmw DNA. If the hmw DNA is embedded in agarose plugs, the plug can be placed in the gel tray before the agarose/PVP solution (cooled to 50 C) is poured into the casting chamber. Make sure to position the plugs such that the hmw DNA migrates through 2–3 cm of the agarose/PVP phase before entering the agarose phase. To ensure that PVP is removed from the hmw DNA afterwards, the hmw DNA should also migrate 2–3 cm in the agarose gel. Note that the type of agarose (low melt or standard) used in the second phase of the gel depends on the method chosen for recovering the hmw DNA after the electrophoresis (see Section 2.4). The running buffer in PFGE is 0.5 TAE. As in regular (one-dimensional) electrophoresis, markers are required to determine the size of the DNA using PFGE. Several hmw DNA PFGE size markers typically come pre-prepared as an agarose plug in a syringe tube. To load these PFGE size markers, a thin disc (1 mm thick) is cut off from the plug and placed into a well on the gel parallel to the environmental hmw DNA agarose plug, and is sealed by adding 1% low melt agarose, taking care that no air bubbles are trapped. Recommended agarose plug markers are Yeast Chromosomal marker (N0345S), Low Range marker (Cat no. 350S), and High Range marker (Cat no. 3551S; all New England Biolabs). In addition to the agarose plug markers, regular size markers prepared in solution can also be used in PFGE. The marker solutions are added into the wells after the gel is placed into the electrophoresis chamber and submerged in running buffer.

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Before running PFGE, the electrophoresis system needs careful precleaning with running buffer. Additionally, it is recommended to perform a pre-run with fresh 0.5 TAE buffer for 20–30 min. This pre-run buffer is then replaced with fresh 0.5 TAE before the agarose gel is placed in the chamber. The program used for PFGE depends on the purpose of the run. For analytical purposes, a good starting point is a program with 200 V (6 V/cm) electrical pulses increasing from 1 to 12 s for 14 h at 14 C (based on a distance between electrodes of 33 cm as found in the CHEF-DR system; Biorad). For analytical runs with DNA > 500 kb, it might be necessary to use longer pulses as well as a longer running time, such as 200 V (6 V/cm) 5–50 s pulses, 20 h at 14 C (as in Fig. 13.2). For the subsequent staining of the hmw DNA in the PFGE gel, both standard EtBr and SybrGold (Invitrogen) staining procedures have been used successfully. 2.3.3. Adjusting DNA size by shearing For a metagenomic fosmid library, DNA inserts of 40–50 kb are needed in order to be successfully packaged into lambda bacteriophage heads. It is therefore sometimes necessary to carefully shear the purified hmw DNA into pieces of a specific size, that is, approximately 50 kb. However, the DNA may already be sheared due to pipetting, handling, and electrophoresis and therefore may not need further processing. If shearing is necessary, careful pipetting up and down using a narrow pipette tip might be sufficient. Passing the DNA solution two or three times through a clean Hamilton syringe can also be used. To collect the resulting 40–50 kb fragments, the following PFGE program is used: 200 V (6 V/cm), 0.1–1 s pulses for 16 h at 14 C, using a one-phased preparative PFGE gel (Section 2.3.1). Under these conditions, DNA of this size will be compressed in the gel. The DNA should then be cut out without UV exposure to avoid damage. Therefore, the gel containing the hmw DNA is cut out and removed and only the borders of the gel containing the size markers (and a small sample plug of the hmw DNA) are stained for localization (Fig. 13.3). The gel borders are stained and placed on the UV table, and the location of the hmw DNA bands marked by cutting thin lines in the gel on each side of the hmw DNA. The location of the hmw DNA in the nonstained lanes can then be estimated by carefully reassembling the gel pieces together on the lab bench. This enables the hmw DNA to be size selected and cut out without exposure to UV.

2.4. Isolation of hmw DNA from agarose matrix There are two methods which can be used for the isolation of the hmw DNA from slices of agarose obtained after gel electrophoresis: digestion of agarose using an agarase or using electroelution. Both methods can

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Figure 13.3 Preparative two-phase pulsed-field gel electrophoresis of hmw DNA for purification and size selection. Prestained parts of the gel are used for the marking of the position of the hmw DNA and flank the middle poststained part of the gel which contained the hmw DNA to be isolated. The gel consists of 1% agarose with the top layer containing additionally 2% PVP. Lane A: Mid-Range marker I (New England Biolabs), lane B: l-DNA/EcoRI þ HindIII marker (Fermentas), lane C: Low Range marker (New England Biolabs), lane 1: 1.5 ml hmw DNA, and lane 2: 12 ml hmw DNA. The conditions for the pulsed-field gel were: 18 h, 200 V (6 V/cm), 0.1–1 s pulses, and cooling to 14 C. Under these conditions, DNA fragments of >40 kb are compressed forming a single band.

potentially damage hmw DNA by mechanical shearing. A comparison of both methods using hmw DNA from Sulfolobus solfataricus showed that the amounts of DNA recovered were comparable. However, the size of the DNA isolated with agarase was smaller, albeit still sufficiently large for cloning into fosmids (R. Bartossek, data not shown). 2.4.1. Isolation of hmw DNA from agarose matrix using agarase A prerequisite for the isolation of hmw DNA using digestion by agarase (such as GELase, Epicentre Biotechnologies) is the use of low melting agarose in the second phase of the pulsed-field gel (see Section 2.3.2). After excision of the gel pieces containing the hmw DNA, the gel is divided into several smaller blocks and transferred into sterile 2 ml vials.

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The gel is melted in agarase buffer (supplied by manufacturer) by incubation for 15 min at 68 C. After a 15 min equilibration step at 45 C, agarase is added and incubated for 2 h at 45 C. Ensure all agarose is dissolved. To stop the digestion reaction, the tubes are placed on ice. A subsequent centrifugation step at 9300g (10,000 rpm) at 4 C for 3 min is used to pellet possible traces of undigested gel. As the original gel slice is typically split among several microcentrifuge tubes, the DNA solution can be pooled and concentrated into a volume of 200 ml using a 2-ml microconcentrator (e.g., Vivaspin 100,000 MWCO column; Vivascience), at 4500g (7000 rpm) at 18 C. The DNA is washed by diluting in 2 ml 1 TE and concentrated again down to 200 ml before it can be stored at 4 C (but not frozen) until cloning (Section 2.5).

2.4.2. Isolation of hmw DNA from agarose matrix via electroelution Low melting agarose is not recommended for the electroelution procedure as it disintegrates easier than standard agarose and may be carried over, potentially inhibiting downstream procedures. Carefully transfer the excised gel pieces into dialysis tubes (preparation; Section 2.2.2) filled with 0.5 TAE (1–3 ml depending on gel size). Avoid introducing air bubbles. Close the dialysis tubes either by using dialysis membrane clamps or by tying knots. Place the dialysis tubes in a gel electrophoresis chamber with 0.5 TAE and fix them in a stable floating position, parallel to the electrodes. Carry out the electroelution at 200 V (6 V/cm) for 1–2 h with the whole electrophoresis chamber mounted in crushed ice to avoid the buffer heating using 60–70 V (1.5 V/cm) at 4 C. In some cases, most of the DNA is still in the gel piece after the 1–2 h run, so after harvesting this eluted DNA, new buffer is added to the dialysis tube and the elution is run for another 2 h. This two-step elution has proven successful for several DNA samples, minimizing the time the DNA is in the electric field. After the initial electrophoresis, swap the power cables to reverse the polarity of the direct current and perform electrophoresis for 1 min to detach the DNA from the dialysis tubing. Remove the DNA solution from the dialysis tube using a broad pipette tip. The volume of the DNA solution can then be reduced to 200 ml using a microconcentrator (as described above). After the elution, it is advisable to stain the gel piece to verify that the DNA was removed.


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2.5. Cloning of hmw DNA The purified DNA is now ready for ligation to a fosmid vector. We have successfully used two different fosmid vectors: the pEpiFOS-5 (7.5 kb) and the CopyControl pCC1FOSTM (8.1 kb) (both from Epicentre Biotechnologies). Both fosmid vectors are maintained as a single copy in the E. coli host cell in the resulting library, but the CopyControl pCC1FOSTM vector has the advantage that the plasmid of recombinant clones of interest can be induced by up to 50 copies per cell immediately before DNA extraction and purification. This step greatly increases DNA yields, while maintaining the stability of the plasmid. The vectors contain E. coli F-factor-based partitioning, the chloramphenicol-resistance antibiotic selection marker and cos sites for the packaging of the DNA into phage heads. The DNA needs to be blunt ended before cloning, often referred to as end repair. The cloning procedure described here follows essentially that recommended by the manufacturer (Epicentre Biotechnologies). Therefore, we only highlight in detail those steps that have been modified by us. 2.5.1. End repair of DNA The fosmid vector is provided linearized at the unique Eco72I (CAC/GTG) site, blunt ended, and dephosphorylated. For the ligation to the fosmid vector, the environmental hmw DNA has to be blunt ended and 50 phosphorylated. This end modification of the DNA is performed using the end repair enzyme mix provided with the fosmid vector containing T4 DNA polymerase and T4 polynucleotide kinase. All reactions are prepared on ice. Due to possible DNA loss in downstream procedures, it is recommended that high concentrations of at least 3 mg of starting DNA are used, preferably even more. Incubations of the end repair reaction are performed at room temperature for 45 min, followed by 10 min at 70 C to stop the reaction. A subsequent standard phenol:chloroform:isoamyl alcohol extraction is performed to remove the enzymes, followed by DNA precipitation with isopropanol to concentrate the DNA. Dissolve the resulting DNA pellet in approximately 30 ml of 1 TE and check DNA for yield and size by running an aliquot in a regular agarose gel. The DNA is now ready for ligation. 2.5.2. Ligation of insert DNA to fosmid vector A 10:1 molar ratio of vector to insert DNA is recommended to obtain a maximum yield of transformants. The ratio is somewhat critical, as an excess of phosphorylated insert DNA can potentially result in ligation of more than one insert to the vector, yielding chimeric genome fragments. It is recommended to mix different vector:insert ratios and subsequently use the lowest insert:vector that still yields enough clones.

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Perform the ligation in a 10 ml volume with 10 fmol insert DNA (265 ng DNA of ca. 40 kb size) and 100 fmol fosmid vector (0.5 mg) for 2 h at room temperature. Test two or three different ratios in the ligation step. Inactivate the ligase by incubating at 70 C for 10 min. The fosmid DNA can now be frozen at 80 C. After these first ligations and subsequent cloning (Section 2.8), the optimal insert:vector ratio can be defined and the remaining DNA ligated according to this optimal ratio.

2.6. In vitro packaging of DNA into lambda bacteriophage heads For the in vitro packaging of fosmid DNA into phage heads, the MaxPlax lambda packaging extracts (Epicentre Biotechnologies) are used. The procedure is performed according to the manufacturer. Based on our experience, we recommend a change in the phage dilution buffer: Stop the fosmid packaging into the phage heads by adding 500 ml of phage dilution buffer (50 mM Tris–HCl, pH 7.5; 10 mM MgSO4; 100 mM NaCl; 0.01% gelatine) and 25 ml chloroform. Mix carefully by inverting the tubes by hand. Centrifuge at 13,000 rpm for 30 s to separate the chloroform from the phage supernatant. It is not necessary to remove the chloroform, but it is recommended to perform a short spin prior to each use of the phage extracts. Store at 4 C. Storage for up to 1 week sometimes results in an increased number of packed phage heads but longer storage can result in lower efficiencies. An increase of efficiency can sometimes be reached by modifying the ratio of ligation extract to packaging extract.

2.7. Infection of the E. coli host cells with lambda bacteriophages Grow E. coli cells (EPI100-T1R Plating Strain, Epicentre Biotechnologies) in 1000 ml Erlenmeyer flasks with 100 ml LB medium supplemented with 10 mM MgSO4 (final concentration) and 0.2% maltose. It is recommended to prepare and store sterile 20% maltose stock solution in 1 ml aliquots at 4 C. Grow the culture under vigorous shaking (220 rpm) at 37 C until an optimal OD (600 nm) of 0.8–1.0 is reached. Transfer the culture to 250 ml tubes and harvest the cells by centrifugation at 4000g for 10 min at 4 C.


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Resuspend the cell pellet in one of the 50 ml tubes with an appropriate amount (7–8 ml) of 10 mM MgSO4 to gain a final OD of 3. The pellet of the second 50 ml tube can be kept at 4 C overnight for later use. Store the culture for up to several hours on ice until the infection starts.

2.8. Titration of the fosmid library An initial infection test is carried out to estimate the efficiency of the fosmid-containing phage extracts. This will determine the optimal plating conditions for subsequent plating of all material. Fill three 1.7 ml microcentrifuge tubes with 200 ml EPI100-T1R cell culture stored in MgSO4 (see previous section). Add three different amounts of packaged phage particles to the three tubes; for example, try 10, 30, and 60 ml. Mix carefully by inverting the tubes two to three times. Incubate at room temperature for 30 min. Add 1 ml of LB broth to each tube to regenerate the infected EPI100T1R cells. Incubate at 37 C for 45 min and invert the tubes every 15 min. Harvest the cells at 10,000g at 4 C for 5 min in a tabletop centrifuge and resuspend the pellet in 100 ml LB broth. Plate the cells on prewarmed LB/Cam agar plates (12.5 mg/ml final) and incubate for 16–20 h at 37 C. Count the fosmid-containing E. coli colonies appearing on the LB/Cam plates (between 40 and >500). Based on this efficiency test, use large prewarmed Petri dishes (150 mm diameter) with LB/Cam and plate out the amount of packaged phage particles that will result in 200–250 clones per plate. Before plating the whole library, it is strongly recommended to quality test 10–20 fosmids (see Section 2.9).

2.9. Quality testing of fosmid clones Before starting to pick and store a large metagenomic fosmid library, it is highly recommended to characterize at least 10–20 fosmids by restriction analysis in order to check if the correct vector is present, to estimate the sizes of the inserts, and to estimate the range of their G þ C content (i.e., diversity). Transfer a small part of a colony (from the efficiency test plating described in previous section) to 10 ml of LB/Cam (12.5 mg/ml final). Incubate on a shaking rotor (200 rpm) at 37 C overnight (14–16 h).

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Isolate the fosmid from 8 ml of the E. coli culture (to the last 2 ml, add sterile glycerol, mix, and freeze at 80 C for backup) by using a plasmid purification kit, such as the Qiagen Mini Prep Kit (Invitrogen; see Section 3.2). Remember to adjust the isolation protocol to low copy plasmids/cosmids/fosmids as stated by the manufacturer for the fosmid vector without copy control. Verify the fosmid output on a standard 1% agarose gel by analyzing 2 ml eluate. As a quality test for the vector and insert sizes, perform a restriction enzyme digest using the enzyme NotI that cuts the region flanking the insert, but hydrolyzes only infrequently chromosomal DNA, because it has an 8-bp recognition site with 100% G þ C content. Electrophorese the digestion mixture on an agarose gel, preferably a 1% pulsed-field gel (PFGE settings: 10 h at 180 V, using 0.1–2.0 s pulses at 14 C) or a 20 cm long regular 1% agarose gel, running at low power (30 V) over night. The vector should be seen in all of the fosmid preparations (Fig. 13.4A) as a DNA band of 7.5 or 8.1 kb for the EpiFOSTM-5 fosmid vector or the CopyControl pCC1FOSTM vector, respectively. In addition to the vector size, the approximate length of the fosmid insert can be estimated. In parallel to NotI digestion, it is helpful to cut with another restriction endonuclease with lower G þ C content. Figure 13.4B shows examples of EcoRI-digested recombinant plasmids. Please note that we sometimes had problems to clone AT-rich DNA (35–40% G þ C content) efficiently into the CopyControl pCC1FOS fosmid, while cloning of the same DNA into the pEpiFOS-5 fosmid was unproblematic. We are not aware of the underlying reasons for this, but have repeatedly observed very low cloning efficiency and/or fosmids of unusual nature (e.g., those which are not cut with NotI) when cloning A þ T-rich DNA.

3. Growing, Picking, Replicating, and Storage of the Fosmid Library The picking of colonies can be done either manually or by the use of a robot (like Qpix2, Genetix). Either choice, it is important that all steps handling the metagenomic libraries are performed in a sterile laminar flow. Plate the optimal amount of the packaged phage particles (see Section 2.8) on large prewarmed LB/agar/Cam dishes (150 mm diameter), estimated to give 200–250 colonies/plate. During picking, single colonies are transferred to individual wells on a 384-well microtiter plates (X7001, Genetix) containing 60 ml LB media with 12.5 mg/ml Cam and

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Figure 13.4 Quality control of randomly chosen fosmids from a soil metagenomic library. (A) Nine fosmids digested with restriction enzyme NotI to separate vector from insert and to control insert sizes. Arrow indicates band of EpiFOSTM-5 fosmid vector of 7.5 kb (Epicentre Biotechnologies). Lane 1: GeneRuler marker (Fermentas). Lanes 2–10: fosmid clone patterns after 3 h NotI digestion. Lane 11: GeneRuler marker (Fermentas). Lane 12: l-DNA/EcoRI þ HindIII marker (Fermentas). (B) Eleven fosmids digested with the 6-bp recognition-cutter EcoRI for 3 h. EcoRI cuts only once in the fosmid vector. The digestion patterns thereby highlight fosmid inserts differences. Both PFGE gels were run for 10 h at 180 V, using 0.1–2.0 s pulses at 14 C.

7.5% glycerol. If picking is performed manually, it is recommended to use an eight-channel dispensing pipette for filling the wells of the 384-well microtiter plates. Work in a hood with sterile air flow, and fill the amount of plates that will be picked during this day. The colony transfer can be done using sterile wooden tooth picks. One well on each plate is left inoculated as a control for contamination. As soon as a plate (referred to as the master plate) is picked, immediately cover it with the lid, wrap it in clingfilm, and incubate it at 37 C for 18–20 h (without shaking). For future screening, backups should be produced from each master plate, before it is stored at 80 C. Beside producing replicate/backup plates in further 384-well microtiter plates, we also inoculate all grown cultures onto a solid LB/agar/Cam plate. For both of these direct replications, a flame-proof 384-pin replicator is needed.

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Dip the 384-pin replicator into 70% ethanol for sterilization and afterward flame off the alcohol. Repeat twice. Let the replicator pins allow to cool before carefully placing the replicator in the master plate and leave it for a few seconds. Then carefully transfer the replicator into a fresh microtiter plate (referred to as backup plate) with wells filled with 60 ml LB/Cam/glycerol as described previously. Leave the replicator in the backup plate for a few seconds before removing it and placing it directly onto the plate with solid LB/agar/ Cam. It is necessary to carefully hold the replicator to prevent a deep puncturing of the agar. Keep the replicator on the LB/agar/Cam plate for about 30 s to enhance the chance that all 383 colonies will grow. Incubate the freshly inoculated backup plate and the large LB/agar/Cam plate at 37 C for 14–16 h. The master plate is immediately stored at 80 C. After overnight incubation, the backup plate is stored at 80 C, while the LB/agar/Cam plate is stored at 4 C until all 383 colonies are being collected for DNA preparation (next section).

3.1. Screening of specific genes in the fosmid library There are two different approaches to screen for specific genes or clones in the fosmid library. The screening can either be based on expressed protein activity, or based on DNA sequence. We present here the method for screening for a particular DNA target sequence. This can be done quite efficiently by generation of a clone pool of 383 fosmids of each 384microtiter plate in the fosmid library. The resulting DNA pools are then used as templates in PCRs with specific primers to target the desired gene. In this way, only 70 PCRs are needed to screen a library of ca. 27,000 clones (representing 1 Gb of DNA from the environment). Such 383-fosmid pools are made as following. A replica of all 383 colonies of one master microtiter plate is transferred onto a LB/agar/Cam plate and incubated for 18–20 h at 37 C as described in Section 3.0. The clones are then washed from the plate with 5 ml LB media using a plate spreader (e.g., a bent and sterilized Pasteur pipette). The cells are collected and pelleted at 4000 g at 4 C for 10 min. From the resulting cell pellet, the pooled fosmid DNA can be isolated using a plasmid preparation kit. (Remember to follow the manufacturer’s instructions for low copy number plasmids/fosmids/cosmids.)


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If a pooled template DNA is positive, the individual clones on this microtiter plate can be identified by colony hybridization or by PCR screens of pooled rows and columns. A second, faster but more expensive way of screening a 384-well plate by PCR is to run 384 reactions using a sterilized 384-pin replicator to transfer a small amount of each culture directly into the freshly prepared and aliquoted PCR mixture in a 384-well PCR plate (10 ml final PCR volume per reaction).

3.2. Fosmid sequencing Besides screening for specific DNA sequences, automated sequencing of insert ends is often performed to analyze the metagenomic libraries in large scale (Treusch et al., 2004). For this purpose, it is advisable to use a fosmid vector with inducible copy number, in order to reduce the costs for DNA preparation. The same principle also holds for manual sequencing of single fosmids. If the copy of the plasmid can be induced, preparation of pure DNA is easy. If a single-copy vector has been employed, we found that using the Qiagen midi prep kit containing the Top10 gravity flow columns works best for getting high-quality DNA, provided that the few protocol changes are followed with regard to those for low copy plasmids. Purifying fosmid DNA from a 60–80 ml E. coli culture was sufficient using the single-copy vector (see Section 2.9). Sequencing of a complete single-fosmid DNA can be either done by producing a subclone library of smaller fragments in regular plasmids for Sanger sequencing or by employing direct sequencing (e.g., pyrosequencing (454) technology) on the recombinant fosmid DNA.

3.3. General conclusions Metagenomics is a rapidly evolving field as it is dependent on efficient sequencing technologies that are currently developing quickly with respect to increasing capacity and to lowering costs. Because of these developments, high-throughput sequencing technologies are increasingly used directly on environmental DNA obviating the need of the time consuming and costly library constructions. However, one has to note that large-insert metagenomic libraries once constructed and characterized represent a sustainable and storable biological resource that can be reused and employed for various questions, which might not even been asked at the time of sampling or library construction. They also offer the opportunity to isolate a full gene, a gene cluster, or operon, of which only sequence tags might have been found in direct high-throughput sequencing (Fig. 13.5). Further, large-insert libraries are particularly useful, when the isolation of gene clusters or

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Soil fosmid 54d9 nirK

amo amo B A 16S

23S

C Sargasso sea plankton

Cenarchaeum symbiosum

Figure 13.5 Schematic representation of genome fragments of archaea with amoA, amoB, and/or amoC genes (encoding subunits of ammonia monooxygenase) indicating their potential capacity to oxidize ammonia. From the analysis of a large-insert fosmid from soil (54d9, upper panel), it became clear that amo genes are directly linked to the ribosomal rRNA genes of archaea (Treusch et al., 2005). The finding of homologous genes in a metagenomic shotgun sequencing project of marine plankton (middle panel; Venter et al., 2004) and on fosmids of the marine symbiont Cenarchaeum symbiosum (Hallam et al., 2006) indicated that the same metabolism might be widespread in nature.

other large contiguous genomic regions are desired for subsequent expression studies, as is the case, for example, in biotechnological screening procedures (Kakirde et al., 2010; Lorenz and Eck, 2005) or when the physiological pathways of microorganisms are studied that reside in low abundance in the microbial community (e.g., in Bartossek et al., 2010; Beja et al., 2000a).

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C H A P T E R

F O U R T E E N

Characterizing Bacterial Gene Expression in Nitrogen Cycle Metabolism with RT-qPCR James E. Graham,*,† Nicholas B. Wantland,† Mark Campbell,* and Martin G. Klotz*,† Contents 1. Why Study Bacterial Transformation of Reactive Nitrogen in the Environment? 2. Nucleic Acids as Markers of N-Metabolic Activity 2.1. RNA analyses have distinct advantages in determining microbial contributions to the nitrogen cycle 2.2. Quality RNA is key 2.3. cDNA synthesis is both necessary and problematic 2.4. PCR primers must be chosen (and chosen again) carefully 2.5. Determining relative mRNA expression levels requires a reliable internal standard 2.6. Protocol: Bacterial RNA isolation 3. Gene Expression in Bacteria That Facilitate Reactive Nitrogen Transformations 3.1. N-cycle organisms and reactive nitrogen transformation pathways 3.2. Laboratory culture of aerobic N-cycle bacteria with emphasis on expression studies 3.3. Using genome-informed metabolic reconstruction of catabolic pathways to select target genes 4. Future Directions of the Approach Acknowledgments References

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* Department of Biology, University of Louisville, Louisville, Kentucky, USA Department of Microbiology and Immunology, University of Louisville, Louisville, Kentucky, USA

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James E. Graham et al.

Abstract Recent advances in DNA sequencing have greatly accelerated our ability to obtain the raw information needed to recognize both known and potential novel modular microbial genomic capacity for nitrogen metabolism. With PCRbased approaches to quantifying microbial mRNA expression now mainstream in most laboratories, researchers can now more efficiently propose and test hypotheses on the contributions of individual microbes to the biological accessibility of nitrogen upon which all other life depends. We review known microbial roles in these key nitrogen transformations, and describe the necessary steps in carrying out relevant gene expression studies. An example experimental design is then provided characterizing Nitrosococcus oceani mRNA expression in cultures responding to ammonia. The approach described, that of assessing microbial genome inventory and testing putative modular gene expression by mRNA quantification, is likely to remain an important tool in understanding individual microbial contributions within microbial community activities that maintain the Earth’s nitrogen balance.

1. Why Study Bacterial Transformation of Reactive Nitrogen in the Environment? Nitrogen is a key element of life and the competition for its acquisition in natural environments is fierce. In its most common gaseous state, dinitrogen gas (N2) comprises 80% of our atmosphere but it is nonreactive, and therefore not directly assimilated by most life forms. Nitrogen availability was severely limited in the primordial environments where life is thought to have first existed in the form of precell nucleic acids and polypeptides, and evolved into the simple metabolic systems of self-replicating RNAs and cells (Glansdorff et al., 2008; Lane et al., 2010; Martin et al., 2008; Nitschke and Russell, 2009; and references therein). With the stabilization of the three lineages (domains) of cellular organisms, nitrogen has become an essential part of all information polymers (nucleic acids and protein) and the universal storage molecules for energy (ATP) and reductant (NADH, NADPH, FADH2). Before the invention of industrial ammonia production by Haber and Bosch in 1906, fixation of atmospheric dinitrogen occurred only via a limited group of microorganisms and abiotically by lightning. Discovery of the industrial conversion of dinitrogen into reactive inorganic “fixed” forms of N was a blessing for agriculture, and likely the most significant contribution to date toward meeting the growing nutritional needs of the world’s population. However, the extensive application of reactive fixed N in the form of fertilizer and manure has also led to a dangerous redistribution of environmental nitrogen, creating levels of fixed N estimated to be

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3.5-fold above natural levels (Rockstro¨m et al., 2009). Together with increasing combustion of coal and fossil fuels (which also produce highly reactive N oxides or NOx), excessive input of ammonium fertilizer has left a massive human ecological “footprint” on the natural N-cycle. Nitrous oxide (N2O) is 300 times more persistent than CO2 in the atmosphere, and is the major factor in ozone destruction. The continued expanding application of reactive N is now linked to a growing number of environmental problems, for example, harmful algal blooms and global warming (Duce et al., 2008; Galloway et al., 2008; Gruber and Galloway, 2008). Recent global assessments of N2O production identify the Mediterranean, Baltic and North Seas, the Caribbean, and the Southeast Asian coast line spanning from China to India as the most productive regions of reactive N in the world (Galloway et al., 2008). Understanding the specific contributions of different microbes and their metabolic capacities to the global N-cycle is therefore of critical importance to our continued existence and the well being of planet Earth. Recent advances in our ability to obtain sequences corresponding to entire microbial genomes now allows for relatively efficient identification of potential contributions in the N-cycle by comparative bioinformatics. Methods to assess relevant changes in gene expression by analysis of RNA have also been greatly simplified in the last half-decade, providing similarly powerful tools capable of identifying novel metabolic contributions to the global nitrogen cycle. Here we describe one of these approaches, and how they have allowed the authors to advance our knowledge of the potential contributions of individual microbes in individual biological nitrogen transformations upon which all life forms depend.

2. Nucleic Acids as Markers of N-Metabolic Activity 2.1. RNA analyses have distinct advantages in determining microbial contributions to the nitrogen cycle Complex microbial communities contribute combined nitrogen transformation activities constituting the N-cycle. It was recognized early on that it is important to identify individual microorganisms contributing to this process (“Who is there?”), and recent key findings confirm that many of the relevant microbes are still poorly described, or even unknown. More importantly perhaps, individual microbes are now known to contribute to multiple processes, for example, ammonification, nitrification, and denitrification by ammonia-oxidizing bacteria (Klotz and Stein, 2010; Wrage et al., 2001, 2004). The N-cycle research community has therefore now come to realize that investigation of the molecular basis for these

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transformation processes must go beyond identification of only the functional cohorts involved. Answering this second main question “What is everybody doing?” then requires assessment of the individual molecular inventories that facilitate all the steps of relevant transformation processes. A central feature of this new understanding and corresponding approach is the view of microbial metabolism in terms of functional modules rather than key enzymatic activities, which must then have clear genetic boundaries. Whereas the unity of biosynthesis (confirmed by genome-informed analyses of microbial metabolism) makes the discovery and study of anabolic processes comparatively simple, the identification and concomitant study of catabolic processes remains more difficult due to both a vast diversity and high redundancy in participating inventory, and the multitude of ways to regulate their availability (expression) and function (activity). Universal approaches to characterizing and evaluating individual contributions at the molecular level are limited, but one that can be applied to all microbes (including those that are still elusive to culture in laboratories) is the examination of relevant gene expression at the RNA level. While protein-encoding RNA is an intermediate in gene expression, coupled transcription and translation of gene products in bacteria and archaea increases the probability that steady-state mRNA levels will reflect protein expression levels. Regulation of prokaryotic gene expression has traditionally been viewed as taking place primarily at the RNA level. A few recent studies have followed global changes in both cellular protein and mRNA levels in bacteria growing in different environments (Hamilton et al., 2009; Taniguchi et al., 2010; Xia et al., 2006). Useful correlations indicating likely relevant metabolism were found, particularly among those genes whose expression changed most. Posttranscriptional regulatory mechanisms in bacteria obviously can play dominant roles, and protein abundance per mRNA also varies widely among different E. coli transcripts (Lu et al., 2007). While increased steady-state mRNA levels then do not alone guarantee an increase in any associated metabolic activity, it is also difficult to hypothesize why regulated mRNA levels in bacterial cells would rise if not to increase production of the encoded proteins. RNA like DNA (but not protein) has the unique property of allowing very accurate quantification by amplification with the polymerase chain reaction (PCR), extending to the very low levels typically present in natural sources.

2.2. Quality RNA is key All RNA-based analyses of gene expression are entirely dependent on the extent to which RNA can be obtained directly and without alteration from microbes in environments of interest. After making every effort to collect microbes while minimizing changes to their environments (and corresponding RNA expression patterns, individual transcripts having


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half-lives (on the order of minutes) we can prevent further changes by blocking the enzymatic activities that normally determine relative mRNA levels in bacterial cells. Both reduced temperature and chemical inhibition of cellular RNases have been found to be effective. Although mesophilic bacteria are known to respond to reduced temperature in the 10–15 C range by reducing turnover of a limited number of cold shock mRNAs (Gualerzi et al., 2003)), further reduced transcriptional and translational elongation rates at temperatures near freezing is expected to minimize this potential issue for microbes not adapted to life at low temperatures. Alternately, chemical reagents that quickly penetrate bacterial cells (often without lysis) including ethanol (Williams et al., 2003) and chaotropic salts (Chirgwin et al., 1979; Chomczynski and Sacchi, 1987) are able to quickly stop changes in cellular mRNA levels associated with removing bacteria from environments of interest. However, these must also be applied in a very timely way. While this is easy enough to appreciate, the relevant timescale being seconds often remains a real issue, particularly for those collaborating in these kinds of studies. Successful isolation of intact RNA is then dependent on effective disruption of bacterial cells and purification away from contaminating DNA and proteins, while maintaining inhibition of cellular ribonucleases. Fortunately even the most difficult to lyse bacteria are typically sensitive to the grinding action of dense zirconium beads when used in a high speed (e.g., 6.5 m/s) reciprocal shaker like a Savant Forma FP-120 or BioSpec Mini-BeadBeater. It is of course unlikely that relative transcript abundances will be maintained with any enzymatic treatment (e.g., lysozyme or protease) that requires incubation at 37 C in a compatible buffer. Commercial formulations of lysis buffers often containing guanidine thiocyanate typically contain proprietary detergents that may improve RNA yield and quality relative to the original N-lauroyl sarcosine (sarkosyl) (Chomczynski and Sacchi, 1987) for some organisms, but not others. Similarly, additional cold organic extractions with acidic phenol and chloroform may increase (and less often decrease) the desired removal of proteins, carbohydrates, and glycolipids, and do influence subsequent spectrometric and electrophoretic analyses (Fig. 14.1). A systematic approach to different inhibitors and purification regimes including increasing lysis buffer to cell pellet ratio is often the best approach, and may become necessary to work with less well-described organisms (Luo and Stevens, 1997). Addition of a LiCl RNA precipitation step (Cathala et al., 1983) will often help reduce carryover of bacterial genomic DNA to levels allowing effective deoxynuclease treatment, as commercial silica-based column resins may show very little discrimination for binding RNA relative to DNA in different lysates. Inclusion of fresh beta-mercaptoethanol (Chomczynski and Sacchi, 1987) in the working lysis buffer, or better adding dithiothreitol (DTT) powder directly to any lysis buffer, can also usually improve results by inhibiting refolding of some RNases.

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A 1

2 3a 3b 4a 4b 5a 5b 6a 6b 23 S 16 S

B

1

2 3a 3b 4a 4b 5a 5b

6a 6b

Figure 14.1 RNA extraction and analysis on nondenaturing agarose gels. Mycobacterium tuberculosis H37Rv, a difficult to lyse bacterium, was grown in standard laboratory broth to mid-logarithmic phase (2 108 bacteria/mL). Bacterial RNA was extracted by Savant FP120 “bead beating” with silica–zirconium beads in quadruplicate from 4 108 bacteria pelleted in 2 mL microcentrifuge tubes. All parallel extractions, as described in the text, involved precipitation of RNA with 2.5 M LiCl, while panel (B) shows corresponding RNAs obtained after a second ethanol–ammonium acetate precipitation. Lanes contain (1) standard DNA markers, (2) bacteriophage MS2 RNA loading control, (3) H37Rv RNA obtained in duplicate phenol–chloroform extractions, (4) by organic extraction followed by DNase I treatment, (5) by silica column binding, and (6) by silica column binding with DNase treatment. Variable yields with organic extraction as well as changes in gel migration from residual contaminants including nucleases, or additional ethanol precipitation (B6), can be seen following different purification methods. Samples were loaded in standard urea loading buffer on a nondenaturing gel in a buffer after heating for 1 min at 65 C.

Although RNA quality can be easily assessed by visualization of rRNA bands on a conventional agarose gel (Fig. 14.1), somewhat larger amounts of material (1–5 mg) are needed for electrophoresis than for most experimental RT-qPCR assays. Access to an Aligent capillary electrophoresis apparatus will allow laser-induced fluorescent dye binding relative to standards, allowing analyses of nanogram RNA quantities. Care must be taken in interpreting these data as mechanical disruption of hard-to-lyse bacterial cells can lead to variations in the apparent 28S/16S rRNA ratios determined as a proxy for integrity of sample relevant mRNAs. rRNAs are also likely less sensitive to degradation by the nucleases involved in normal mRNA turnover that are released during RNA isolation. Alternately, the RTqPCR assays described below once developed are a very sensitive approach


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to verifying mRNA integrity (e.g., comparison of relative reference mRNA levels in replicate and independent RNA preparations).

2.3. cDNA synthesis is both necessary and problematic Perhaps the “weakest link” in current RNA analysis methods is the conversion of RNA obtained from bacteria growing in environments of interest into cDNA. Although having the advantage of greatly stabilizing the research material (from enzymatic and chemical decay), in vitro synthesis of cDNA with viral reverse transcriptases is typically inefficient, giving at best about 30–80% conversion to first-strand cDNA (as incorporated radio nucleotide) with enzyme manufacturer’s protocols (and often less, in our experience) (Sieber et al., 2010; Stahlberg et al., 2004). Various suggestions including using longer random primers (9–15 mers) (Stangegaard et al., 2006) and reverse transcriptase primers targeting only specific transcripts of interest (Diercks et al., 2009) have been shown to increase efficiency. However, this area remains problematic and is not routinely assessed by experimenters, particularly with regard to potential differences in efficiency with different individual mRNA templates at different absolute and relative levels. Ruling out relevant difference in reverse transcriptase template efficiency would require relatively difficult comparative efforts with in vitro synthesized relevant RNAs. However, as described below, these issues are likely minimized when comparing steadystate transcript levels by RT-qPCR targeting the identical region of relevant transcripts in a microorganism residing in varying environments.

2.4. PCR primers must be chosen (and chosen again) carefully While current efforts typically choose “any region” of RNA for quantitative determinations, it’s quite clear that RT-qPCR results are highly dependent on the specific region targeted by amplification primers (PoretPeterson et al., 2008). Primers for quantitative PCR are frequently selected using popular software (e.g., Primer3, from M.I.T., Primer Express, from Applied Biosystems) to identify 200-bp target regions of average GC content lacking homopolymeric tracts and corresponding primers unlikely to form secondary structures or capable or pairing with one another. However, many other experiments require manual selection of primers based on specific residue polymorphisms to discern different messages and sources. Because bacterial and archaeal functional genes generally lack introns selection of exon boundaries as target sites is usually not an option. Therefore “no-RT” controls need to be included with every new RNA template to determine residual genomic DNA levels. Differences in detection of different transcript regions by RT-qPCR result from a combination of both “artificial” differences in templating efficiency (for both reverse transcriptase and amplifying DNA polymerase)

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in addition to the real differences in steady-state levels for different regions along an mRNA among transcripts present in bacterial cells. For example, among the few mRNAs whose decay pathways have been characterized in E. coli, there is often an initial endonucleotyic cut made in the upstream region, followed by exonucleolytic decay from both original and new 30 ends. Steady-state levels for different regions within transcripts then are determined by both their rate of synthesis and rate of degradation. Eubacterial RNA synthesis proceeds at an average rate of about 40–80 nucleotide per second (Proshkin et al., 2010), and is also highly variable within different template regions (Dennis et al., 2009). For transcripts with short half-lives and longer polycistronic messages, normal RNA decay pathways are already altering measured steady-state levels even before synthesis of the mRNA is complete. These activities are then strongly influenced by ribosome occupancy in terms of which regions along the RNA are more resistant to normal degradation. In terms of copies of mRNA per coding region, direct pyrosequencing of different bacterial transcriptomes by approaches such as RNA-seq indicate that levels of most mRNAs are normally distributed at very low level or stochastic levels of less than one per cell, and that these levels then can easily change by 100-fold or more (Passalacqua et al., 2009; Taniguchi et al., 2010; Wurtzel et al., 2010). The authors’ experiences would suggest that more modest changes in the range of 6- to 10-fold as determined by RT-qPCR are more typical when assaying levels of relevant individual mRNAs (Poret-Peterson et al., 2008; Price et al., 2008). Even when we are able to chose primers whose efficiency in PCR we can show to be similar on dilutions of corresponding bacterial genomic DNA, without direct determinations we are not sure that target regions are equally efficient as template for reverse transcriptase (e.g., containing relevant secondary structure) nor that they are representative of the coding region of the transcript on the whole. If this is a concern, then multiple transcript regions may need to be taken into account in estimating relative mRNA abundance levels, with the understanding that the least abundant region is likely to be limiting in terms of producing the encoded peptide. In experiments designed to determine potential bacterial metabolic contributions to the N-cycle, RNA will often be obtained from bacteria collected from or cultured in different environments. These issues and sources of errors in quantification will be reduced in these experiments in comparing detection of the same specific transcript region by RT-qPCR in different bacteria.

2.5. Determining relative mRNA expression levels requires a reliable internal standard Although addition of an external RNA standard to a fixed amount of extracted total RNA prior to RT-qPCR might be used to determine absolute mRNA expression levels in highly similar specimens (Liu et al.,


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2009; Smith et al., 2003) typically different microbial cultures or samples will contain large differences in numbers of cells. Bacteria grown under different conditions also often differ in the efficiency of extracting nucleic acids. While measuring total RNA (i.e., rRNA) or genomic DNA or enumerating microbes would provide a means of correcting for this variability, these measurements raise additional issues minimized if an mRNA whose steadystate levels remains constant under relevant conditions can be identified. Better even average mRNA levels for a group of reference genes (Vandesompele et al., 2002) or so-called “housekeeping” transcripts (Pfaffl et al., 2004) can be used to compare levels of mRNAs of interest in bacteria from different environments. Previously, rpoD, rpoB, gyrB, sigA, rssA, recA, and microbial cohort-specific functional genes have served as reliable internal references in different studies comparing mRNA expression levels of interest. The process of detecting PCR reaction products as the reaction proceeds, or “real-time” PCR (Higuchi et al., 1993) has greatly improved the efficiency of gene expression studies. Combining a preliminary reverse transcriptase activity with florescence-monitored PCR, or as suggested as a standard nomenclature “RT-qPCR” (Bustin et al., 2009) also provides the extraordinary sensitivity needed to compare typical baseline stoichiometric transcripts levels of less than one mRNA per cell in small number of bacteria (Palmer et al., 2003). RT-qPCR can also allow discrimination between nearly identical molecules, for example transcripts from paralogous genes within a single genome differing by a single nucleotide, or those from highly similar species contributing to N-cycle metabolism as a consortium. Methods to detect amplification of targeted regions in RT-qPCR include both intercalating dyes binding double stranded nucleic acid (i.e., ethidium bromide and SYBR green) as well as target-hybridizing oligonucleotides bearing dual fluorescent markers (e.g., TaqMan chemistry). Initial detection of signals above background at discrete PCR cycle numbers then provides a cycle number or threshold (or Ct value) reflecting the number of cDNA copies of the transcript region of interest present in the sample. Among the issues potentially impacting these determinations are the previously described RNA quality, target selection, and template efficiencies, and include selecting appropriate experimental design, quantification method, and data and statistical analyses, as well as lack of standard nomenclature in describing results (Bustin et al., 2009). Among the most widely used approaches is one suited to determining potential microbial N-cycle contributions, that of determining relative expression levels (rather than absolute levels indicated by constructing a standard curve). A reference transcript or group of transcripts can be identified or validated using existing or pilot studies, and used as an internal standard for determining changes in steady-state mRNA levels for genes of interest from samples taken from microbes in two different environments. A comparative Ct method can then be used to compare difference in detection threshold (Ct) for the

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transcript of interest and for the reference transcript among microbes in two different environments (as one would compare a treated and untreated sample). A specific approach developed by Pfaffl and colleagues (Pfaffl et al., 2002) allows for comparison of multiple reference and experimental transcripts levels with both PCR replicates and treatment groups. With a statistical approach called REST (Relative Expression Software Tool), relative expression levels in microbes can be compared if serial dilutions of each RNA or cDNA are first compared for efficiency of amplification (Bustin and Nolan, 2004), and limited differences corrected for when comparing targeted regions in each sample. REST then uses a pairwise randomization test to calculate a measure of the statistical significance of the reported fold-change in steady-state mRNA level based on these data. Planning a successful RT-qPCR experiment will need to take into consideration the specific type and amount of available sample, and will require choosing among available cDNA priming methods, reverse transcriptases, and PCR instrumentation and monitoring approaches. In the authors experience, good results have been obtained with random-priming total RNA extracted from bacterial pellets with either 9-mer or 15-mer deoxyoligonucleotides (Stangegaard et al., 2006), using RNAse H MMLV reverse transcriptases (Superscript II and III, Invitrogen, Carlsbad, CA) and 5 mg of RNA templates standard 20 mL reactions as described by the enzyme manufacturer. Care is taken to denature template with primers in 0.1 mM EDTA containing Tris–HCl buffer, and not exceeding 70 C. We then dilute the reaction products at least threefold for single-tube triplicate SYBR Green qPCR reactions for each primer pair (i.e., reference mRNA or transcript of interest), avoiding any possibility of competing PCR reactions. A strategy of using master and submaster mixtures divided across all similar reactions proves critical in our experience, as does the using a lightweight benchtop microcentrifuge to bring reactants to the bottom of tubes. We also find that although some oligodeoxnucleotide primers are very stable, other sequences are not at all, requiring even monthly synthesis, even when routinely resuspending new primers at high concentration (e.g., 3 mg/mL and storing in single-use aliquots at 70 C). Nolan et al. (2006) have previously described very detailed step-by-step protocols for qPCR, including important optimizations that will be appropriate and important in all RTqPCR analyses.

2.6. Protocol: Bacterial RNA isolation Preferred approach (most Gram-negative and readily lysed species, based on Emory and Belasco, 1990) 1. Grow 100 mL culture to mid-log phase


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2. Pour directly into four prechilled Oak Ridge centrifuge tubes (50 mL) on wet ice 3. Centrifuge at 4 C and 9000 rpm for 5 min, return to ice 4. Decant and resuspend each in 10 mL cold 300 mM glucose/50 mM Tris–HCl (pH 7.0) 5. Centrifuge at 4 C and 9000 rpm for 5 min, return to ice 6. Resuspend pellets in 500 mL cold 300 mM glucose/100 mM NaOAc (pH 4.0) 7. Transfer 125 mL to four cold microfuge tubes 8. Add 50 mL of 20% SDS, vortex and place in 65 C block for 3 min (should visibly clear on lysis) 9a. Add 500 mL of water equilibrated distilled phenol, vortex, 65 C for 3 min 9b. Alternately, proceed with Qiagen RNA-Easy as in 2b below without bead beating 10. Return to ice and add 100 mL of chloroform:isoamyl alcohol (CIA 24:1), vortex 11. Ice 3 min, spin 2 min, and remove aqueous phase 12. Add 500 mL phenol, vortex, add 100 mL CIA, vortex, ice 13. Spin, collect aqueous phase, extract with CIA 14. Precipitate with 0.6 vol of isopropanol, ice 10 min, spin 20 min at room temperature 15. Resuspend in 100 mL of “D” solution (see below) and reprecipitate with ethanol 16. Wash pellet by resuspending in 70% ethanol, spin, and dry briefly at 37 C 17. Check 1/20 of prep for rRNA on regular TAE agarose gel as for DNA. Load in urea buffer after heating to 55 C for 1 min 18. DNAse according to enzyme manufacturer’s instructions and recheck on gel 19. Store RNA in 5 mg aliquots as ethanol–amonium acetate precipitates For tougher bacteria including mycobacteria (based on Chomczynski and Sacchi, 1987) 1. Proceed through step 5 above 2a. Immerse Oak Ridge tube with pellet in 70 C water beaker and add 70 C “D” solution (4 m guanidinium thiocyanate, 25 mM Na-citrate (7.0), 0.5% sarcosyl, 0.1 M BME). 2b. Alternately, keep on ice and instead add Qiagen RNA-Easy lysis buffer after adding fresh DTT powder to the working aliquot. (Increase the ratio of lysis buffer to cell pellet until consistent results are obtained.) 3. Pipette to resuspend bacteria, and transfer to multiple 2 mL screw-cap microfuge tubes with 0.5 mL of zirconium beads (BioSpec Bead

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4. 5. 6. 7.


Beater). (For RNA-Easy, now heat tubes in block to 65 C for 1 min and return to ice.) Shake at maximum speed 5 1 min in FastA or Bead-Beater, alternating to ice as heat develops. Spin at maximum 1 min, and collect aqueous as above, adding NaOAc (pH 4.0) to 0.1 M prior to phenol extraction as in steps 12 and 13 above. DNAse according to enzyme manufacturer’s instructions setting after setting aside an aliquot for analytical gel comparison Aliquot RNA obtained to multiple tubes and precipitate with ethanolamonium-acetate for subsequent analytical and experimental analyses. Notes: 1% SDS seems to be a better RNase-inhibiting lysis buffer than chaotropic salts with some Gram-negative bacteria. The ratio of lysis buffer to cell pellet is critical, and must first be determined for each species with by performing small-scale extractions with decreasing buffer. The use of water equilibrated phenol, 5:1 ratio of phenol to CIA, and extracting on ice are often able to remove the bulk of the DNA. If not, better results may be obtained by first precipitating RNA with 2.5 M LiCl (final), and then removing remaining contaminants by washing RNA bound to silica-glass matrix columns available from several manufacturers. Check RNA obtained by resuspending a precipitate in urea gel loading buffer and electrophoresis on a nondenaturing agarose gel (as shown in Fig. 14.1). If the first procedure above gives good appearance, avoid the bead beating which shears contaminating DNA (otherwise seen as a single MW single band), and may not be as good as hot SDS in preserving mRNAs. If the first procedure fails or gives very low yields, use the second method. Bacteria typically have 5–20 mg of RNA per 109 cells. RNA prepared by any approach MUST be stored as multiple precipitates in ethanol, and a glycogen carrier will greatly help verify precipitation and successful ethanol washes.

3. Gene Expression in Bacteria That Facilitate Reactive Nitrogen Transformations 3.1. N-cycle organisms and reactive nitrogen transformation pathways The nitrogen cycle includes a wide array of intermediates that are classified as either fixed (solid or liquid) or volatile (gaseous), or as inert (N2) or reactive (all other intermediates). The major fixed reactive nitrogen species

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include ammonium (NH4þ), nitrite (NO2), and nitrate (NO3) and their interconversion through intermediates such as hydroxylamine (NH2OH) or nitroxyl (nitrosyl hydride, HNO) and their removal via fixed (hydrazine, N2H4) or volatile (nitric oxide, NO; nitrous oxide, N2O) intermediates to dinitrogen (N2) is facilitated by a diverse set of molecular inventory employed by numerous microorganisms. Since the interconversions represent redox processes, most of them are either coupled to dissimilatory catabolic electron flow tied to oxidative phosphorylation and anaerobic respiration or part of anabolic electron sink reactions during nitrogen assimilation (see Fig. 14.2). Dissimilatory nitrate reduction to dinitrogen, the major microbial nitrogen removal process in marine ecosystems, occurs in form of two major pathways under hypoxic and anoxic conditions, respectively: classical denitrification with nitrous oxide as a mandatory intermediate and denitrifying ammonia oxidation also known as anammox (Fig. 14.2; Klotz and Stein, 2010). In classical denitrification, the transformation of nitrate to nitrite to 0 N2

4

1

5 +1

N2O

–2

N2H4

–3

4

NH4+

5

NH3

7 +2

NO

5

4

– +3 NO2

4

7 +5

3

Mineralization

–3 R~NH2

2

6 2

Assimilation

NH2OH

8

–1 8

HNO +1

NO3–

Figure 14.2 Processes in the microbial nitrogen cycle reproduced from Klotz and Stein (2010). The oxidation state of each intermediate is indicated. The pathway for archaeal ammonia oxidation is putative as based on genomic inference (Walker et al., 2010). (1) Dinitrogen fixation. (2) Aerobic oxidation of ammonia to nitrite by bacteria. (3) Aerobic oxidation of nitrite to nitrate by bacteria. (4) Classical denitrification. (5) Denitrifying anaerobic ammonia oxidation (Anammox). (6) Respiratory ammonification. (7) Assimilatory ammonification. (8) Aerobic oxidation of ammonia to nitrite by archaea. The dots indicate aerobic hydroxylamine oxidation and dissimilatory nitrite reduction pathways of aerobic nitrifier denitrification.

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nitric oxide to nitrous oxide and often also to dinitrogen, nitrite reduction to nitric oxide is the rate-limiting step that is catalyzed by either coppercontaining NirK or cytochrome cd-1 NirS nitrite reductase enzymes (Zumft, 1997; Zumft and Kroneck, 2006). The genes encoding their catalytic subunits (nirK and nirS) have been used extensively as targets to study the structure of nitrite reducer communities in aquatic and terrestrial environments, and these studies were often amended with probing for genes encoding nitric oxide (i.e., norB, norZ) and/or nitrous oxide (nosZ, nosW) reductases to identify the classical denitrifier community (Avrahami et al., 2002; Braker and Tiedje, 2003; Braker et al., 1998, 2000, 2001; Falk et al., 2010; Oakley et al., 2007; Prieme et al., 2002; Santoro et al., 2006; Simon et al., 2004). Classical denitrification constitutes one of the main forms of anaerobic respiration and is performed by a great diversity of heterotrophic bacteria including many pathogens of humans and other animals. Classical denitrification has long been considered the only pathway for nitrogen loss from marine and terrestrial ecosystems (Zumft, 1997; Zumft and Kroneck, 2006). However, since 1999 evidence for denitrification during anaerobic oxidization of ammonia (anammox) as another major pathway for nitrogen removal has emerged (Fig. 14.2; Dalsgaard et al., 2005; Francis et al., 2007; Jetten et al., 2005; Kuenen, 2008; Schmidt et al., 2003; Strous et al., 1999). NO-forming nitrite reductase-encoding genes have been identified in the genomes of anammox bacteria; however, nitrous oxide-forming nitric oxide reductase genes are absent from anammox bacterial genomes ( Jetten et al., 2009; Strous et al., 2006). Because nitrite-derived nitric oxide is the sole oxidant of ammonia in the anammox process ( Jetten et al., 2009), both divergent denitrification pathways compete fiercely for nitrite (Kartal et al., 2007). Likewise, the molecular inventory responsible for denitrifying N-removal competes for the same nitrite pool with the inventory involved in ammonification, which retains the transformed nitrogen in the system. Both assimilatory nitrate reduction to ammonia (ANRA) and dissimilatory (respiratory) nitrate reduction to ammonia (DNRA) interchangeably employ one of the molybdopterine guanine dinucleotide-based enzymes for nitrate reduction: periplasmic dissimilatory (napA) and the cytoplasmic soluble (nasA) and membrane-bound respiratory (narG) nitrate reductases; any of which may also participate in the nitrate reduction step of denitrification (Fig. 14.2). Detection of nitrate reduction inventory per se is thus not useful for identifying or discriminating between either form of denitrification as well as ammonification. On the other hand, ammonifiers use dedicated sets of enzymes and reductant shuttles for the reduction of nitrite to ammonia. For ANRA, microbes employ assimilatory cytoplasmic ferredoxin-dependent (the nirA gene product in Cyanobacteria and Epsilonproteobacteria) or NADH-dependent (the nirB/nasB gene product in Beta- and Gammaproteobacteria) nitrite reductases, both of which contain a single siroheme and a [4Fe–4S]


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center (Malm et al., 2009; Martiny et al., 2009; Moreno-Vivian et al., 1999). For DNRA, the major enzyme is the respiratory pentaheme cytochrome c nitrite reductase (nrfA; Pittman et al., 2007; Simon, 2002; Smith et al., 2007). A novel dual N assimilation and respiratory mechanism employing the reverse hydroxylamine-ubiquinone redox module (HURM; Klotz and Stein, 2008) pathway (haoA’þcycB) has been reported recently (Campbell et al., 2009). Interestingly, the nrfAH and haoA’þcycB inventories are homologues (Bergman et al., 2005; Kim et al., 2008; Klotz et al., 2008). Ammonia, whether available in the environment, obtained by nitrogen fixation or by ammonification from NOx, is another important pool of reactive nitrogen. When not assimilated into biomass by respective pathways employing glutamate dehydrogenase, GS-GOGAT or alanine dehydrogenase or removed from the system by the anammox process, the major transformation pathway is nitrification (Fig. 14.2). Nitrification is defined as the aerobic oxidation of ammonia to nitrite followed by the aerobic oxidation of nitrite to nitrate. Together with DNRA, ANRA, assimilatory, and respiratory ammonification, nitrification represents one of the key transformation processes between different fixed nitrogen intermediates (Fig. 14.2; Allen et al., 2001; Brandes et al., 2007; Butler and Richardson, 2005; Ferguson and Richardson, 2005; Jepson et al., 2006; Klotz and Stein, 2008; Lin and Stewart, 1998; Moreno-Vivian et al., 1999; Potter et al., 2001; Simon, 2002; Smith et al., 2007; Tavares et al., 2006; and references therein). Although nitrifying bacteria and the nitrification process have been studied for more than 100 years (Arp and Bottomley, 2006; Bock et al., 1991; Prosser, 1989; Winogradsky, 1892), our knowledge of the molecular underpinnings of both was restricted to sequences of genes encoding rRNA and the key enzymes involved in nitrogen transformations; this has changed dramatically the genomic era (Arp et al., 2007; Klotz and Stein, 2010; and references therein). In addition, the discovery of broadly distributed ammonia-oxidizing archaea (de la Torre et al., 2008; Hallam et al., 2006; Hatzenpichler et al., 2008; Ko¨nneke et al., 2005; Leininger et al., 2006; Martens-Habbena et al., 2009; Nicol and Schleper, 2006; Prosser and Nicol, 2008; Walker et al., 2010) and cohorts of taxonomically diverse methanotrophic bacteria with the ability to nitrify (Nyerges and Stein, 2009; PoretPeterson et al., 2008) has extended the list of nitrifying microorganisms significantly. While the ammonia-oxidizing archaea also aerobically denitrify without a nitrous oxide intermediate (Bartossek et al., 2010; Klotz and Stein, 2010; Schleper and Nicol, 2010; Walker et al., 2010), AOB and nitrifying methanotrophs are also capable of (aerobic) nitrifier denitrification (Sutka et al., 2003, 2006; Wrage et al., 2001, 2004) because they produce and release NO and N2O in the presence of oxygen. The recently described anaerobic methane-oxidizing bacterium, Methylomirabilis oxyfera in the deep-branching phylum NC10, also has the genetic potential to nitrify (oxidize ammonia to nitrite via hydroxylamine using particulate

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methane monooxygenase and hydroxylamine oxidoreductase) in an anoxic environment by using the dioxygen produced by dismutation of NO (Ettwig et al., 2010). Because the dismutation of NO also produces dinitrogen (Ettwig et al., 2010), the anaerobic oxidation methane by M. oxyfera is thus coupled to denitrification (Ettwig et al., 2008, 2009) without a nitrous oxide intermediate (Ettwig et al., 2010). It thus appears that M. oxyfera harbors the necessary inventory to facilitate a closed nitrogen cycle within the cell (nitrification, ammonification, denitrification), which may allow this bacterium to thrive in environments with varying external ammonia and nitrite concentrations. The regulation of expression of relevant genetic inventory is likely very complex and presents itself as a likely target for testing hypotheses by applying the described methodology for the detection and quantification of steady-state mRNA levels using RT-qPCR.

3.2. Laboratory culture of aerobic N-cycle bacteria with emphasis on expression studies To illustrate the workflow for the investigation of gene expression in nitrogen cycle microorganism, we will describe one for the marine Gammaproteobacterium, Nitrososoccus oceani, a microbe with the ability to nitrify, denitrify, and ammonify. In particular, we chose to present the transcriptional state of a subset of genes involved in nitrification and denitrification that exhibited differential expression in response to treatment with the reactive nitrogen compounds ammonia, and hydroxylamine. Growth conditions. N. oceani ATCC19707 was grown in artificial seawater medium composed of 29.2 g NaCl, 1.7 g (NH4)SO4, 0.75 g KCl, 0.25 g NaHCO3, 4.1 g MgCl26H2O, 1.5 g CaCl22H2O, 0.02 g NaCO3 H2O, 7.5 g MgSO4 7H2O, 0.016 g K2HPO4, 0.011 K2HPO4 3H2O, 0.001 g Fe EDTA, 0.1 mg Na2MoO4 2H2O, 0.2 mg MnCl2 4H2O, 0.1 mg ZnSO47H2O, 0.002 mg CoCl2 6H2O, and 0.02 mg CuSO45H2O per L distilled H2O. Phenol red (0.001 g per L distilled H2O) was added as a pH indicator and the pH of medium adjusted to 6.9 with HCl prior to autoclaving (final pH 8.0). The final concentration of ammonium in this maintenance medium is 12.5 mM, which amounts to an availability of ammonia at approximately 0.144 mM (Table 14.1). Ammonia but not ammonium is the sole source of energy and reductant for obligate lithotrophic AOB (Hyman and Arp, 1992, 1995). N. oceani cultures were grown in the dark at 30 C in volumes of 0.2 L (in 0.5 L flasks) or 0.6 L (in 2 L flasks) and were monitored daily for pH change due to nitrite (NO2) production (pink to yellow) and adjusted to an approximate pH 8 with addition of 0.25 M K2CO3. To obtain sufficient biomass for RNA isolation, the cultures were harvested in the late exponential or early stationary growth phase by centrifugation (8000 g, 15 min, 4 C), resuspended in fresh artificial

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Table 14.1 pH dependent availability of ammonia in growth media

pH 6.9

pH 7.6

pH 8.0

(NH4)2SO4 (mM)

Equivalent NH3 (mM)

0.05 mM 5.0 mM 12.5 mM 0.05 mM 5.0 mM 12.5 mM 0.05 mM 5.0 mM 12.5 mM

0.00005 0.005 0.012 0.00024 0.0237 0.0593 0.00058 0.058 0.144

seawater medium and this step was repeated two or three times every 4 weeks until sufficient biomass was obtained. Isolation of nucleic acids at this point usually yields 2–3 mg total DNA or 15–30 mg total RNA. Prior to the 24-h experimental treatment, N. oceani cells were harvested at mid-exponential growth phase and washed twice with NH3-free artificial seawater medium. The washed cells were resuspended in 0.2 L artificial seawater medium containing 0.05 mM (NH4)2SO4 amended with 2.4 g HEPES per L to prevent acidification of medium upon NO2 production during the experiments (pH buffered at 7.6). For the experiments investigating gene expression at different concentrations of ammonia, cultures were maintained at either 0.05 mM (NH4)2SO4 or 5 mM (NH4)2SO4 for 24 h or at 0.05 mM (NH4)2SO4 for 20 h, followed by a 4 h exposure to 5 mM (NH4)2SO4. Cultures treated with hydroxylamine (NH2OH) were maintained at 0.05 mM (NH4)2SO4, for 23.5 h followed by exposure to NH2OH at a final concentration of 0.2 mM for 0.5 h. Stock solutions of NH2OH were prepared with autoclaved distilled H2O, filter sterilized, and used immediately. After each of the incubations, the cultures were harvested at 8000g for 10 min at 4 C and used immediately for RNA extraction as described above in Section 1.6.

3.3. Using genome-informed metabolic reconstruction of catabolic pathways to select target genes N. oceani encodes a greater than average diversity in the oxidative branch of its catabolic electron transport flow (Klotz and Stein, 2010; Klotz et al., 2006) suggesting the need for complex regulation. This diversity might be responsible for the bacterium’s ability to thrive in oligotrophic marine environments with varying salt and oxygen concentrations. Also, while AOB found in freshwater and soil environments can physically associate with a diverse array of NOB to form nitrification aggregates, the marine

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AOB cannot as to prevent the highly likely sinking out of the photic zone. In particular, while the inventory in the reductive branch of catabolic electron flow that facilitates nitrification is nonredundant and encoded single copies of respective genes (amoCABD, haoAB-cycAB), the oxidative branch contains several redundant players implementing energy conservation (e.g., complexes CIII and CIV), electron disposal (e.g., cytochrome c peroxidases), and electron-flow-coupled reactive nitrogen removal (e.g., NIR and NOR) (Arp et al., 2007; Klotz and Stein, 2010; Klotz et al., 2006). To selectively incorporate particular inventory into its respirasome complement at any given time, the transcription of their encoding genes needs to be differentially regulated. For the purpose of this example, we chose the genes listed in Table 14.2. Cells were grown into late exponential growth phase as described above, denied ammonia for 24 h and divided into equal batches. One batch culture was provided 5 mM (NH4)2SO4 for 4 h or 0.2 mM NH2OH for 30 min before total RNA was isolated from both cultures and further processed

Table 14.2 Selected genes targeted for analysis of transcript levels Gene

Enzyme complex

Process involved

amoA

Ammonia monooxygenase Hydroxylamine oxidoreductase [2Fe–2S]-protein of Complex III Monoheme cytochrome c552 Monoheme cytochrome c552 CyoA subunit of Complex IV CyoA subunit of Complex IV Soluble cytochrome c peroxidase Soluble cytochrome c peroxidase Multi-copper nitrite reductase Cyt c subunit of cNOR Soluble Cyt c 0 -beta putative NOR

Oxidation of NH3 to NH2OH

haoA Noc_0299 Noc_0751 Noc_3050 Noc_3047 Noc_1767 Noc_1263 Noc_2697 nirK norC cytS

Oxidation of NH2OH to NO2 Electron flow to periplasmic cyt c Electron shuttle between CIII and CIV Electron shuttle between CIII and CIV Cyt c oxidation and electron flow to O2 as the terminal e acceptor Cyt c oxidation and electron flow to peroxide as the terminal e acceptor Accept e and reduce of NO2 to NO Accept e for reduction of NO to N2O Accept e for reduction of NO to N2O

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for cDNA synthesis. Real-time fluorescent PCR was performed using a Bio-Rad iCycler (Bio-Rad, Hercules, CA, USA), designed primers (designed as described in Section 1.4), Superscript III reverse transcriptase (Invitrogen), and cDNA as the template (prepared as described in Section 1.6) to determine steady-state mRNA levels, which was normalized using the 16S rRNA gene and statistical analysis (REST; see Section 1.5) to calculate the normalized expression ratio. Even though not all of the pertinent electron flow inventory has been included in this example, Fig. 14.3 indicates that N. oceani has a diverse complement of quinol-oxidizing inventory. The results in Figure 14.3 suggest that this inventory is differentially expressed in response to availability of ammonia as well as to exposure to hydroxylamine. Ammonia differentially induces expression of both oxygen-dependent (Noc_1767, Noc_3047), peroxide-dependent (Noc_1263, Noc_2697), and N-oxide-dependent (nirK, norC, cytS) electron sink inventory thereby providing simultaneously for nitrification, aerobic denitrification, and peroxide detox capacity. Hydroxylamine, the useable reductant and thus a direct link to the cell’s redox state, and ammonia, the source of nitrogen for assimilation and energy that requires the activation of oxygen by recycled electrons before it can be 50 45 40

Expression ratio

35 30 25

5 mM (NH4)2SO4

20

0.2 mM NH2OH

15 10 5 0

amoA haoA 0299 (AMO) (HAO) (CIII)

cytS 0751 3050 3047 1767 1263 2697 nirK norC (c552) (c552) (CIV) (CIV) (CCP) (CCP) (NirK) (cNOR) (Cyt c⬘-b)

Figure 14.3 Expression ratios for selected genes in the ETC of N. oceani. Pertinent genes are indicated by their designated name or by locus tag (Noc_xxxx). The triangle and dotted line indicates an expression ratio of 2, determine by REST as described in the text.

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dehydrogenated to NH2OH, are causing different transcriptional responses and thus different steady-state transcript levels when supplied after cells were denied any source of energy and reductant for 20 h.

4. Future Directions of the Approach Improvements in DNA sequencing that are now replacing the Sanger dideoxy termination approach are allowing sequencing of genetic elements representing entire microbial genomes in a single day. These “next generation” approaches (Ansorge, 2009) are based on amplifying individual template molecules attached to beads in an emulsion that allows for physical separation, and then trapping beads in an etched chip capable of transmitting light produced by pyrophosphate hydrolysis to a detector by fiber optics (see Chapter 12). While full sequence assembly and annotation remains a formidable obstacle, the approach at a minimum allows identification of relevant inventory and specific alleles potentially encoding contributors to relevant nitrogen metabolism. Pyrosequencing now makes it feasible to compare both different strains and closely related isolates where whole-genome sequencing has previously been prohibitive. Somewhat simpler plasmid cloning approaches known as selective capture of transcribed sequences (SCOTS) and genome fragment enrichment (GFE) developed by one of the authors may also be useful in future studies by identifying target cDNA sequences for RT-qPCR assays (Graham and Clark-Curtiss, 1999; Shanks et al., 2006). Both of these techniques have the advantage of requiring only small number of cells, including those present in larger mixed natural populations, determining either relevant genetic capacity (GFE) or relevant active gene expression (SCOTS). Such directed studies assessing individual microbial or gene family expression have so far for the most part been limited to laboratory monocultures, although the concept and approaches now possible are not. Going from gene expression studies to appreciation of the full complexity of microbial community metabolism remains a formidable challenge, but any overall progress in that direction will likely involve first defining individual potential microbial contributions. Similarly, at least two approaches may hold promise for improving the least reliable and efficient step in RT-qPCR, that of faithfully representing RNA as cDNA. While subtractive hybridization is not new (Straus and Ausubel, 1990), its application to cDNA has always been problematic relative to identifying differences between genomic DNAs (e.g., Plum et al., 1997). Given the typical ratio of rRNA to medium bacterial mRNA abundance of about a million to one, physical removal of rRNA from total RNA by subtractive hybridization of bacterial cDNAs has great potential to “throw the baby out with the bathwater.” Recent quantitative analyses are showing


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that subtraction by hybridizing oligonucleotides as used in available commercial kits is partially successful, but can only make modest improvements in the ratios of rRNAs to mRNA (He et al., 2010). Additional rounds of subtraction did not improve results, an aspect the authors have also seen. Results of ongoing studies in other laboratories will next be needed to determine if the fidelity of mRNA ratios remain intact in the rRNA subtraction process. Secondly, efforts to raise the amount of available starting material for RT-qPCR linear preamplification of RNA could be helpful when culture is limiting or species of interest are not abundant in the environment. Transcribing cDNA copies with phage RNA polymerase has the potential to increase RNA template for RT-qPCR studies. By including phage promoter sequences in the 50 regions of deoxynucleotide primers, known biases in amplifying complex mixture of DNA sequences simultaneously by exponential PCR are potentially avoided, at least theoretically. Of course such approaches will still require an initial relatively inefficient cDNA synthesis, which is a concern because differences in efficiency are well known for RNA polymerase templates. However, improvements in primer design and approaches to linear amplification by RNA synthesis do remain an area with promise in efforts to study microbial gene expression and contributions to nitrogen metabolism. N-cycle research has only recently revealed several major new microbial players and processes, and it is highly likely that our present state of knowledge is still quite far from “knowing it all.” Detailed investigation of the molecular underpinnings of pathways in the organisms that facilitate these new and previously described N-cycle processes suddenly appears as a somewhat less daunting task given recent progress in instrumentation and investigative strategies. Next generation sequencing technology, for example, will also allow high fidelity sequencing of multiple genomes and transcriptomes at the levels of populations and communities (metagenomics, metatranscriptomics; see Chapters 12 and 13). Other approaches in development are aimed at characterization of both cellular and community proteomes (see Wessels et al., 2011, and chapter 18 by Burton and Hickey, pp. 435–463). Nevertheless, investigating responses of individual organisms to particular environments and signals relevant to nitrogen will likely remain a fundamental tool with which we increase our understanding of microbial contributions to the N-cycle.

ACKNOWLEDGMENTS The authors thank Christopher T. Price for his comments on the manuscript. M. G. K. was supported by NSF grant EF-0412129 and incentive funds from the University of Louisville Office of the Executive Vice President for Research (EVPR). J. E. G. received support from NIH NIAID, U.S. CRDF, and the University of Louisville.

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The Utility of Functional Gene Arrays for Assessing Community Composition, Relative Abundance, and Distribution of AmmoniaOxidizing Bacteria and Archaea B. B. Ward1 and N. J. Bouskill Contents 374 375 375 379 380 381 384 389

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Introduction DNA Microarrays: Introduction to Microarrays Probe Selection Target Preparation Array Printing, Hybridization, and Scanning Factors That Influence Hybridization Results Array Applications Possibilities and Limitations Detailed Protocol for Functional Gene Microarrays Using Oligonucleotide Probes References

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Abstract Ammonia-oxidizing bacteria (AOB) and archaea (AOA) transform ammonium to nitrite, an essential step in the complete mineralization of organic matter, leading to the accumulation of nitrate in oxic environments. The diversity and community composition of both groups have been extensively explored by sequence analysis of both 16S rRNA and amoA (encoding the critical enzyme, ammonia monooxygenase subunit A) genes. In this chapter, the power of the amoA gene as a phylogenetic marker for both AOB and AOA is extended to the development and application of DNA microarrays. Functional gene microarrays provide high throughput, relatively high resolution data on community

Department of Geosciences, Princeton University, Princeton, New Jersey, USA Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA

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composition and relative abundance, which is especially useful for comparisons among environments, and between samples in time and space, targeting the microbial group that is responsible for a biogeochemical transformation of interest, such as nitrification. In this chapter, the basic approaches to the design of probes to represent the target groups AOB and AOA are described, and the protocols for preparing hybridization targets from environmental samples are provided. Factors that influence the hybridization results and determine the sensitivity and specificity of the assays are discussed. A few examples of recent applications of amoA microarrays to explore temporal and spatial patterns in AOB and AOA community composition in estuaries and the ocean are presented. Array data are lower resolution than sequencing, but much higher throughput, thus allowing robust statistics and reproducibility that are not possible with large clone libraries. For specific functional groups, arrays provide more direct information in a more economical format than is possible with next generation sequencing.

1. Introduction Both ammonia-oxidizing bacteria (AOB) and the more recently discovered ammonia-oxidizing archaea (AOA) perform the critical step of oxidation of ammonia to nitrite in the nitrogen cycle of aquatic and terrestrial environments. Although many AOB are available in culture, and much has been learned from their genomes, extensive sequencing of both 16S rRNA and ammonia monooxygenase subunit a (amoA) genes from environmental samples demonstrates that the most abundant AOB in the environment are not represented in the culture collection. Similarly, many more operational taxonomic units (OTUs) corresponding to sequences of amoA genes from AOA are known than are represented in the very limited culture collection of AOA. Therefore, molecular methods are crucial for investigations of the distribution and ecology of ammoniaoxidizing microorganisms. The very existence of AOA was first discovered on the basis of amoA gene sequence information (Treusch et al., 2005; Venter et al., 2004) and initial clone library studies confirmed that AOA are widespread in the environment (Francis et al., 2005). Clone libraries targeting the functional genes of nitrifying bacteria continue to be the most common tool for these investigations. In this chapter, we describe a methodology building on sequence information obtained from clone libraries that provides a high-throughput approach for investigating the distribution and community composition of AOB and AOA assemblages without additional sequencing.

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2. DNA Microarrays: Introduction to Microarrays Functional gene microarrays provide a method for rapid, high resolution, high-throughput analysis of microbial community composition. They are applicable to DNA or RNA samples from any environment and for any target organism or gene for which a suitable sequence database exists or can be obtained. Hybridization data provide relative quantification of abundance and distribution of different archetypes or OTUs, such that robust comparisons can be made between samples, even though absolute quantification (i.e., number of organisms possessing each gene type) is not yet possible. Here we describe the development and application of DNA microarrays for the study of AOB and AOA. The rationale for focusing on these organisms is their essential role in the nitrogen cycle of aquatic and terrestrial environments, including wastewater treatment and agricultural systems. AOB and AOA are both relatively constrained taxonomically, as far as is presently known, so these groups can be comprehensively targeted and studied using the functional gene approach. A microarrays is a small solid support, usually a glass slide, plastic, or silicon thin film, on which a large number of discrete samples of biological materials (e.g., DNA) are fixed in an orderly arrangement. Those described here are based on glass microscope slide substrates. The slides are commercially coated (e.g., Corning, Agilent) with materials that allow DNA to be precisely bound to the substrate, but do not produce innate fluorescence or allow nonspecific binding of DNA, RNA, or other biological materials. The probes (DNA fragments representing the genes to be detected, see below) are printed robotically (DeRisi et al., 1997) and bound to the slide in a precise pattern. The targets (complementary DNA derived from DNA or RNA from the unknown sample, see below) are prepared by coupling a fluorescent tag to the sample DNA. The fluorescent target is then hybridized to the array, and the target molecules bind to the specific probes with complementary sequences. The amount of fluorescence associated with each probe is then quantified by laser scanning. The overall process is represented in the flow chart in Fig. 15.1 for reference throughout the steps described in the following sections.

3. Probe Selection Both AOB and AOA arrays use the amoA (ammonia monooxygenase subunit A) gene as the basis of detection of the two microbial groups. At present, the public databases contain thousands of sequences for both AOA

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Figure 15.1 Flow chart of main steps in target and probe preparation and array analysis. The steps in the protocol for target preparation and hybridization are described more fully in the following detailed protocol.

and AOB amoA genes. Depending on the hybridization conditions and array format, the amount of sequence diversity between probe and target that allows hybridization can vary, and is not always predictable from


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sequence or thermodynamic data alone (Pozhitkov et al., 2006). Perfect match (PM) (i.e., 100% sequence identity between probe and target sequence) probe–target pairs usually hybridize well, and the stringency of hybridization conditions constrains the binding of mismatch (MM) targets. The degree of mismatch that still allows binding depends on the length of the probe and target fragments, the GC content and melting temperature, the location of mismatch basepairs (the stretch of matching or mismatched regions), and in some cases, the secondary structure (e.g., presence of hairpin structures). The empirical testing of probe design criteria and the behavior of target–probe interactions is well described by He et al. (2005). For oligonucleotide probes, the intensity of hybridization signal varies with the degree of probe–target sequence identity (He et al., 2005; Taroncher-Oldenburg et al., 2003). That is, under normal hybridization conditions with targets prepared from complex environmental samples, most probes are not 100% specific. A small amount of PM target can yield as strong a signal as a much larger amount of MM target. Thus, the challenge is to devise a probe set with appropriate specificity to distinguish between ecologically relevant sequence types. In most applications, 50 and 70 bp oligonucleotide probes bind well with targets within 85% sequence identity (He et al., 2005; Taroncher-Oldenburg et al., 2003). Thus for the AOA and AOB arrays, we have designated the probes as archetype probes, implying that the signal represents the relative abundance of an archetype, or group of sequences with 85–87% identity. This level of resolution is appropriate for AOB, for which the culture collection can inform the question of ecologically relevant distinctions. For cultivated AOB, 16S rRNA sequence divergence of 2–8% defines the genus level (Koops et al., 2003). amoA sequence varies by less than 80% within species, and by much more between species in the same genus (Purkhold et al., 2000). Using 85% as the cutoff (15% divergence), we have developed an algorithm (Bulow et al., 2008) that identifies an optimal probe set from the set of all homologous sequences. The optimal probe set contains the minimal number of probes required to allow all known sequences to hybridize with at least one (and preferably only one) of the probes. The algorithm performs essentially the same function as OTU definition according to DOTUR (Schloss and Handlesman, 2005) and other groups have developed similar criteria for probe selection (He et al., 2005, 2007). The archetype probe suite that should allow detection of all known AOB amoA sequences contains about 30 probes (Bouskill et al., 2010), and a similar number suffices to cover the entire known sequence database for AOA amoA sequences. The oligonucleotide probes are synthesized commercially. Even with robotic printing and high quality substrates, the intensity of hybridization can vary across the surface of a single array. Therefore, some

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form of internal standardization is desirable. The standard approach for expression arrays in disease diagnostics is to employ a two-color competitive hybridization method. Two competing targets are prepared with different fluorescent tags (e.g., red and green), typically one representing the “control” condition and one prepared from “treatment” conditions. The two targets are then competitively hybridized to the same array, and the relative abundance for each target is determined from the fluorescence ratio (FR) between the two colors. The most commonly used fluorescent markers are Cy3 and Cy5, which are reactive water-soluble fluorescent dyes of the cyanine dye family: Cy3 (green: 550 nm excitation, 570 nm emission) and Cy5 (red: 650 nm excitation, 670 nm emission). In environmental samples, it is not obvious how to find or construct a “control” or “normal” sample. In theory, one could produce a mixture of every known sequence represented on the array and use that as the control. We have decided that this approach is not useful because the array hybridizes to all sequences within the 15% limit, not just the PM targets, and it is impossible to manufacture targets of every possible sequence variant for every probe. This has led some investigators to forego the two-color ratio approach altogether and to rely on single color, scaling the fluorescence intensity of each feature to that of a PM control (e.g., Bodrossy and Sessitsch, 2004). If hybridization is not perfectly even across the array, then the single color method may be prone to experimental artifacts, because it would be difficult to include sufficient PM probes to allow calibration of every feature. We have devised an alternative approach that includes controls and an internal standard two-color method to allow standardization at multiple levels. Firstly, the probe oligonucleotide (70 bp) is synthesized as a 90-mer with the additional 20 bp representing a nonsense sequence that is identical for every probe. The experimental target is labeled with one fluorescent tag, typically Cy3. In each hybridization experiment, a Cy5-labeled reference oligonucleotide, complementary to the nonsense 20-mer, is included in the hybridization mixture, and this 20mer binds to the complementary part of the 90-mer. Thus, each feature produces a Cy5 signal, regardless of the presence or absence of specific targets. When the specific target binds, each feature then yields a Cy3/Cy5 ratio. Since each probe includes both 70-mer and 20-mer, the ratio represents the ratio of unknown to standard binding and this ratio is not dependent upon the absolute amount of probe or target. Secondly, every block of features on the array includes a probe called “Mixall,” which is a mixture of all probes on the array. Any variation of hybridization quality across the array will be reflected in the intensity of Mixall signal, allowing for normalization of feature signal within each block. In practice, we usually find that data quality is excellent using the internal standard ratio without Mixall normalization, but the Mixalls provide a quality control check.


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4. Target Preparation The crucial step in target preparation is the incorporation of a fluorescent molecule into the DNA or cDNA to be hybridized to the array (see protocol; Fig. 15.1). The cyanine dyes are usually synthesized with Nhydroxysuccinimidyl (NHS) esters on the nitrogen side chains. These reactive groups bind to aliphatic amine groups. Therefore, in order to bind to DNA, the DNA must first be modified to incorporate aminomodified nucleotides, which can then be chemically linked to the esterified dye. The DNA modification is obtained by incorporating a modified nucleotide into the target DNA, and this can be done in the following three ways. (a) PCR targets: Use of specific PCR products as hybridization targets has the advantage of specificity and sensitivity. Because all the DNA in the target preparation comprises the specific gene fragment of interest, small mass of target is sufficient for a strong reaction, and nonspecific hybridization is minimized. Depending on the array format, 50–200 ng of PCR target suffices for a single array hybridization experiment. (i) One-step direct labeling: Incorporate the Cy3-modified nucleotides into the target molecule during the PCR step. Instead of an equimolar mixture of the four dNTPs, some of the dCTP is replaced with Cy3- or Cy5-modified dCTP (in a 1:1 or 2:1 ratio) (Taroncher-Oldenburg et al., 2003). Perform three replicate PCRs and combine the products before hybridization. (ii) Two-step labeling: Instead of an equimolar mixture of the four dNTPs, part of the dTTP is replaced with amino-allyl modified dUTP (dUaa; in a ratio of up to 1:10 dTTP:dUaa). After PCR, the purified fragment is chemically linked to Cy3- or Cy5-NHS ester. (iii) Three-step indirect labeling of PCR products: Perform the PCR as usual with an equimolar mixture of dNTPs. Then perform a nonlinear amplification of the PCR product using random primers and the Klenow polymerase. During the Klenow reaction, replace the equimolar dNTP mixture with the mixture containing dUaa (previous protocol). After the Klenow reaction, the purified product is chemically linked to Cy3- or Cy5-NHS ester. The indirect method involves the most steps but is often preferable because substitution of modified nucleotides often reduces the efficiency and yield of the PCR reaction in the one- and two-step labeling protocols. The Klenow reaction can amplify 25 ng of PCR product into 1000 ng of randomly labeled product and is therefore very useful for preparing targets from difficult PCR products. This 40fold amplification is the level cited in the guidelines for a typical

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Klenow reaction use in labeling kits. We find that the main variable in determining the yield is the dUaa concentration. Under the conditions prescribed here, a yield even better than 40-fold is often obtained. (b) Whole DNA (WDNA) targets: Although specific and sensitive, PCR targets have all the problems of bias and selectivity of standard PCR. Therefore, it may be preferable to avoid PCR in target preparation. Instead of labeling a PCR product, as in (iii) above, simply perform the Klenow reaction with 25 ng of total DNA or total cDNA extract. Total genomic extracts are usually first digested with a restriction enzyme to allow better access to the DNA during the Klenow reaction. If low sample DNA concentration is a problem, the DNA or cDNA can first be amplified using whole genome amplification (Wu et al., 2006), in which case as little as 1 ng of original DNA or cDNA extract is sufficient. For WDNA targets, it is necessary to use 500–1000 ng or more of target DNA in each hybridization because most of the target preparation is not specific for any of the probes on the array. An advantage of WDNA targets, however, is that many different unrelated genes can be spotted on the same slide and all detected with the same target preparation.

5. Array Printing, Hybridization, and Scanning The probe oligonucleotides are printed onto Corning Ultragaps or Erie Scientific Epoxy Silane slides using a DeRisi style arrayer (DiRisi et al., 1997) and pins from Parallel Synthesis. The printing step can be performed by a local array facility or a number of commercial companies to your specifications. Dried slides are stored in a desiccator until ready to use. Slides are cross-linked at 70 mJ using a Stratagene Stratalinker just prior to hybridization, or baked at 80 C for 2 h prior to storage. If it is necessary to minimize excessive background hybridization, slides can be prehybridized using Blocking Agent. Prepare the hybridization mixture immediately before use. The mixture typically contains the labeled target, a control target (if using the two-color competitive approach) or control oligonucleotide (for the internal standard approach), and a hybridization buffer containing blocking agents. Quickly add the mixture to the slide or coverslip and seal. Incubate with gentle mixing at the desired temperature for 16 h. After hybridization, the slides are washed to remove excess target, dried, and scanned. The scanning procedures are platform specific and cannot be addressed generally here. After scanning, the arrays are analyzed using commercial software to quantify fluorescence of each dye in each feature.


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The fluorescence data are subjected to various filters to identify significant signals using the following steps: (1) Calculate the signal intensities for each feature by subtracting the background fluorescence for each channel (i.e., the wavelengths 532 nm (Cy3) and 635 nm (Cy5)). (2) Calculate the mean background fluorescence across both channels and for all features (i.e., amoA archetypes, controls) and identify significant signals as those having a signal intensity two standard deviations above the mean background fluorescence. (3) Calculate the ratio of Cy3:Cy5 fluorescence for each feature. For methods using a ratio of two dyes, remove from further analysis any features that do not have significant signal for both wavelengths. For single color analysis, features with no significant signal above background are considered zero. Compute the average of replicate features for each probe for the subsequent analysis. To subject the array data to subsequent statistical analysis, the absolute signal strength, the FR, or a relative fluorescence ratio (RFR) can be used, depending on the approach. For the internal standard approach used for the AOA and AOB arrays described here, RFR is the most robust measure. RFR is calculated as the contribution of each probe to the sum of FR for all probes in the set (e.g., all AOA amoA probes on the array).

6. Factors That Influence Hybridization Results For arrays containing hundreds or thousands of probes, it is not realistic to perform empirical validation tests for each probe. Such testing of some of the earlier applications with simpler arrays has been described (Bodrossy et al., 2003; He et al., 2005; Taroncher-Oldenburg et al., 2003). For large arrays, the predicted specificity is usually evaluated by computing various physical parameters for the probes: free energy of binding (http:// frontend.bioinfo.rpi.edu/applications/mfold/cgi-bin/dna-form1.cgi), percent identity with high identity MM targets, etc. (e.g., He et al., 2005, 2010). The principles derived from these characterizations are generally applied and a posteriori tests rely on reproducibility and internal correlations for validation of results. It is useful to illustrate a few general principles of array behavior, however, so that protocols can be optimized within the constraints of the array format. (a) Hybridization conditions: As with any hybridization based assay, the stringency of the hybridization and wash conditions are critical to the sensitivity and specificity of the assay. When using a small array containing a small number of relatively similar probes (e.g., all representing the same gene fragment), the GC ratio of all the probes is similar and one hybridization temperature can be optimal for all the probes. In complex

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arrays, in which many different genes are represented, a single temperature may not be optimal for all probes because some genes have higher GC content than others, and thus their hybrids will be stable at higher temperatures. In selecting the probe region for multiple genes (see above for probe selection criteria), melting temperature is one of several features to be optimized. Similarly, the stringency of wash conditions after hybridization can influence specificity and cross reactivity. Longer low temperature washes tend to produce the best results, but stringency and signal strength must be balanced to obtain the best sensitivity for targets of interest. This will always require optimization with the targets and probe sets for each study. (b) Cross reactions: The characteristics of oligonucleotide probes were discussed above. Using optimized probe selection and hybridization conditions can yield highly specific and reproducible results, but some degree of nonspecific hybridization, that is, cross reactions, is probably unavoidable and cannot always be detected experimentally. A good way to address this problem is to include multiple probes for the same target organism. Multiple positive reactions are thus robust proof of target presence, while a mixture of positive and negative reactions would imply nonspecific reactions for some of the probes. This approach dramatically increases the number of probes required and the bioinformatics requirements, but is currently possible through commercial applications such as Nimblegen (http://www.nimblegen.com). (c) Length of incubation: Hybridizations are conveniently carried out “overnight,” a time period that ranges from 8 to 16 h. Target binding kinetics follow a Langmuir function (Dai et al., 2002), so that PM targets require longer to reach equilibrium than do MM targets. Thus, longer hybridization times should yield higher specificity of binding. (d) Concentration of target: The intensity of hybridization signal should increase with increasing target concentration. For an AOB amoA array, which was spotted with 25 pmol of probe in each feature, the relationship between concentration of PM target and fluorescence is shown in Fig. 15.2. It follows the generally expected linear increase in fluorescence at lower target concentrations, but appears to saturate, as all probe hybridization sites are filled (Held et al., 2003). Thus, it would be desirable for unknown targets to be present in the linear range of the response curve: if all targets are present at saturating concentrations, then no differences in signal will be discerned. In practice of course, it is not possible to optimize the concentration of every unknown target. The least amount of target that yields a strong signal is perhaps the best approach. The unknown sample, however, contains an undefined mixture of target and nontarget molecules, such that targets of similar total DNA concentration may contain quite variable amounts and mixtures of potential target molecules. There is essentially no way to

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Figure 15.2 Fluorescence ratio (Cy3/Cy5 ¼ archetype fluorescence/internal standard oligo fluorescence) as a function of amount of target in hybridization mixture. The target was prepared using the three-step PCR protocol (amoA gene amplified with amoA-1F and amoA-2R primers (Rotthauwe et al., 1997)) from a culture of Nitrosospira multiformis, which is a member of AOB archetype A3.

account for this variability, which is a good reason to use relative abundance (percent of total signal) data in comparing microarray results for different samples. (e) Sensitivity: It would be useful to know the detection limit for individual target sequences in unknown samples, but this is not simple to determine. Figure 15.2 shows that 1 ng of labeled PM target produces a statistically significant signal. How much sediment must be extracted, or water must be filtered, in order to end up with a target that contains 1 ng of an individual archetype target? For mixed unknown samples, a large volume of water, often 4 L or more, is usually filtered and extracted. From this DNA extract a minimum of 25 ng of total DNA can generally produce 1000 ng of labeled target, the standard amount of mixed WDNA target recommended for the AOA array. Most of the 1000 ng in the target does not represent specific target molecules, however, and it’s not possible to determine how much of the mixed target represents a single sequence. If one could design quantitative PCR primers with the exact specificity of the archetype probes, this comparison could be made. But it is not generally possible to obtain

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such specificity with qPCR. All of the archetype probe sequences can be obtained with one or two sets of amoA primers, and those are the primers most often used for qPCR of total AOA (Francis et al., 2005; Wuchter et al., 2006). As mentioned above in the protocols for WDNA target preparation, 1000 ng of target can be obtained from as little as 1 ng of total DNA extract using whole genome amplification, so sensitivity is not a great limitation to the array technology. (f) Probe/target capacity: Although all probe/target pairs follow the general relationship of increasing hybridization with increasing target concentration, the ratio of hybridization strength to target concentration (i.e., the slope of the linear portion of the curve in Fig. 15.2) varies among probe/target pairs. This is referred to as the probe capacity and cannot always be predicted from the thermodynamic properties of the PM or MM molecules (Held et al., 2003; Pozhitkov et al., 2006). For an AOB array, we showed that even among PM probe/target pairs, this capacity varies greatly (Ward et al., 2007). This means that the absolute hybridization signal cannot be interpreted in terms of absolute target concentration. In addition, the capacity varies between PM and MM probe/ target pairs for the same probe. In an unknown sample mixture, it is not known what portion of the signal is due to PM versus MM hybridizations. In an attempt to optimize specificity for highly similar probes, Marcelino et al. (2006) developed a mathematical approach for analysis of microarray data. Although not generally applicable to all array formats, this paper (Marcelino et al., 2006) is a useful resource for discussion of the hybridization behavior of oligonucleotide probes and targets. In practice, protocols are usually optimized and then performed consistently in order to remove variability from factors such as hybridization time and total DNA concentration.

7. Array Applications To illustrate the application of the functional gene microarray approach to study of AOB and AOA, we present a few recent examples. Both AOA and AOB are present in most environments, and their relative abundance often varies in correlation with environmental variables (e.g., Santoro et al., 2008), particularly in estuarine environments. AOB are more abundant in the upper freshwater reaches of Chesapeake Bay, where they exhibit an annually repeating pattern of community assembly (Bouskill et al., 2010). Using an AOB amoA array containing 26 betaproteobacterialAOB archetype probes representing all published sequences, 4 years of sampling showed that the assemblage varied consistently with season

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(Bouskill et al., 2010). The water column was sampled three to four times each year over a 4-year period and the relative abundance of all known AOB lineages examined simultaneously. The AOB assemblages varied predictably with season. While assemblages were often dominated by estuarine archetypes (typical of estuaries that experience wide salinity ranges), seasonally reoccurring dynamics were also observed for “rare” archetypes (i.e., those that were rarely a major component of the RFR). An example of AOB assemblage variation over time is shown for a station in the upper Chesapeake Bay at 1 m above the bottom (Fig. 15.3,

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Figure 15.3 Temporal classification of covarying archetypes based on K-means discrimination analysis at 1 m above the bottom in upper Chesapeake Bay. The left panel represents the RFR of individual archetypes in each temporal pattern (TP) over time. The identification of the archetype is not relevant for the overall analysis, which is to determine the seasonal behavior of the archetype as a whole. The right panel shows a centroid compiled from the cumulative behavior of all the archetypes in each cluster over time. This centroid is without magnitude and incorporates the trend of the RFR signals in each cluster through time.

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left panel). Applying a discrimination analysis to the array dataset yielded a time series analysis in which different archetypes were classified by their temporal RFR patterns. Here the 26 AOB amoA archetypes grouped into three significant clusters, with three distinctly different temporal patterns (TPs), regardless of the absolute magnitude of the hybridization signal. The three TPs identified at the deep depth showed strong seasonal reoccurrence, as illustrated in the centroid for each group (Fig. 15.3, right panel). The profile of TP1-D appears to be an October signal (except for Oct. 2002), TP2-D was a repeating August pattern and TP3-D appeared as a spring pattern with peaks in all four April samples. The array was also sensitive enough to demonstrate community resilience following a significant perturbation event to the surface water (Bouskill et al., 2010). The assemblage changed dramatically in response to flooding caused by a hurricane, but the “normal” assemblage returned within a few months. Following the hurricane flooding, the community was dramatically altered; the estuarine archetypes disappeared and the assemblage was streamlined down to two archetypes. Physiological characteristics for the two archetypes could explain their success under the flood conditions caused by the hurricane. Resilience was demonstrated by the return to a “normal” assemblage following removal of the perturbation. For reasons of cost and labor, it would have been impractical to attempt to detect the patterns found in this dataset using clone libraries or either Sanger or next generation sequencing. The current AOA array (Bouskill et al., submitted) contains 31 archetype probes, and was developed from the public database of amoA sequences in 2009. amoA sequences continue to accumulate in the public database rapidly and most of them are the result of clone library studies using the same primer sets. Recent checks of newly published sequences shows that the current probe set should cover the vast majority if not all of them. The AOA array has been used to characterize community composition in samples from diverse environments (estuaries to oceans, sediments, and water column) around the world (Chesapeake Bay, Pacific, Atlantic, and Indian Oceans) (Bouskill et al., submitted). For two samples, the community composition derived from targets prepared by PCR and WDNA are compared in Fig. 15.4. In both cases, the RFR values derived from both target methods are very similar; the RFRs estimated from the two targets are highly correlated. The same dominant archetypes were identified by both methods and greater variability is associated with detection of archetypes that are responsible for less than 5% of the total signal. Nevertheless, both targets contained some signal from most of the archetypes, and the array analysis yields greater diversity estimates than obtained from a typical clone library. This suggests that use of PCR to obtain amoA sequences for AOA does not dramatically bias the estimates of community composition for targets derived from the same DNA extraction. This may not be true for every probe set and probably depends on the PCR primers and the divergence of the target genes.

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Figure 15.4 Comparison of RFR signals from targets prepared from the same sample by the three-step protocol for amoA PCR fragments and whole DNA (WDNA). (A) Eastern Tropical South Pacific Station 10 80 m and (B) Sargasso Sea 100 m, April 2004.

Using WDNA targets, we compared the relative abundance of AOA archetypes (RFR) among three samples (Fig. 15.5). Each colored bar represents the percent of total fluorescence (RFR) attributed to a particular archetype as the average of duplicate arrays prepared from the same target preparation. In the deep water (1 m above the sediment) of the seaward end of Chesapeake Bay (CB_300D) and 100 m depth of the open ocean of the

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AOA32_CN8C_20_EF382433 0. AOA31_EF500_19O12_EF106947 AOA29_DS2_1_EF382468 AOA28_DS2_6_EF382473 0. AOA27_HB_29_EU022786

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Figure 15.5 Stacked bar plots comparing AOA community composition in three samples using targets prepared by the three-step protocol using WDNA.

Sargasso Sea (SS_M), archetype AOA12 is a major component. The clone library sequences that define this archetype were all derived from soils or estuarine sediments. The relative dominance of this archetype in the Sargasso Sea is not consistent with the robust biogeography of clone library sequences (Francis et al., 2005; Prosser and Nicol, 2008), which would suggest we should expect quite different assemblages in Chesapeake Bay and the Sargasso Sea. The Chesapeake Bay and Sargasso Sea samples were also similar in the relative contribution of archetypes AOA4 (sequences derived from various sediment environments, and including the hot spring enrichment culture, Nitrososphaera gargensis) and AOA26 (representing soil phylotypes). The appearance of sediment clades in the Sargasso Sea sample is unexpected, but unlikely due to artifacts of the method, such as cross reactions between probes. The level of identity between these probes representing sediment clades and those representing oceanic clades ( 0.05, Fig. 18.3). The range in variability of spot volumes could increase the probability that proteins with real expression differences between treatments would be excluded from analysis due to poor match quality. Highly variable proteins could also provide false-positive presence/absence differences if spots are not matched between treatments. Error for highly variable, matched spots could also occur if these spots have large random expression differences. Finally, spots with low variability could escape detection if their genuine expression differences

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Figure 18.3 Distribution of CVs for spots matched between base state and heatshocked gels. (A) Distribution histogram: CV values are along the y-axis; the x-axis corresponds to the number of observations. (B) Box plot. The ends of the box are the 25th and 75th quantiles, and the line across the box is the median. The diamond is centered on the mean and extends to the 95% confidence interval. Tick marks at left identify the most dense 50% of the observations. Whiskers extend from the quartile edge to 1.5 the interquartile range. (C) Normal quantile plot. CV values are along the y-axis; the x-axis shows expected normal scores for each value. Normally distributed data would align along the straight line and within the confidence bounds. At top is a probability scale.

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Emily O. Burton and William J. Hickey

were below the significance cutoff selected based on pooled CV values. A nonnormal distribution of variability among protein spots may not be unusual. Although normality was not tested in studies by Zhan and Desiderio (2003) or Mahon et al. (Mahon and Dupree, 2001), in both cases, the CV distribution appears to be skewed; Zhan and Desiderio’s (2003) changed-fold expression data have a distribution similar to the one found in the present study, and in the Mahon et al. (Mahon and Dupree, 2001) study using colloidal Coomassie-stained gels, the mean CV in gel-togel experiments was 32%, but the a median of the data was 18%. Due to the unequal variance observed for spots in the present study, the sample size necessary for significance with 80% power and p-value 0.05 was calculated for each spot. If a threefold expression cutoff is selected, 75% of the protein spots could be analyzed with three replicates; however, more than six replicates would be needed to reduce the error rate of the entire analysis to below 10%. Running large numbers of gels to achieve significance for every spot analyzed is not practical. Instead, it may be more useful to run experiments with three to four replicates and then attempt to exclude highly variable proteins from analysis. For example, in an experiment with two replicates, spots with a CV of 0.24 would be significantly different (power 80%, p-value < 0.05) if expression differed by twofold between treatments. To look for significant differences in spots with low variability, 30 spots were selected that had CVs < 0.3 in both treatments. Of these, eight were eliminated after a visual check. One had already been flagged as significantly different using a threefold expression cutoff, and none of the rest had significant expression differences. The 50-spot data set used by Asirvatham et al. (2002) was not selected randomly but by automated spot matching. Matching software can only be expected to match spots that were present and well detected in all 10 gels, which would tend to eliminate more variable spots from the analysis. Therefore, although the investigators observed that this sampling had a normal distribution and included spots from a range of pI and MW, the dataset may not be representative of the larger population. In the present study, the CVs for all matched protein spots (using automatic and manual matching) are reported. More than 50% of the visualized spots from each gel could be included in the analysis. The skewed distribution of spot volume CVs observed in the present study may therefore reflect the use of a broader, more unbiased spot sample than Asirvatham et al. (2002). Finally, while Asirvatham et al. (2002) used Coomassie to stain their gels, silver staining was used in the present study. Silver staining has a linear response to most proteins, but 39–46% of proteins have a significantly nonlinear staining profile (p < 0.01; Mahon and Dupree, 2001). Silver staining can also introduce different levels of variability to spots depending on their composition and volume. Smales et al. (2003) found that the CV of silver-stained spots decreased slightly with increasing spot volume, but

Assessing Variability in Gel-Based Proteomic Analysis of Nitrosomonas europaea

453

protein-specific staining differences led to considerable deviations from this relationship. At least part of the volume dependence of variability is likely due to spot finding and image analysis rather than staining; Mahon et al. (Mahon and Dupree, 2001) observed the same volume–CV relationship in a study using Coomassie-stained gels. To compensate for unequal spot variance, Smales et al. (2003) recommend that individual spot CVs be used to evaluate expression differences rather than an average value. Silver staining may also introduce more generalized variability. In contrast with Coomassie or fluorescent stains like SYPRO Ruby, which can run to completion without overstaining the gel, silver staining must be terminated before high-volume spots become oversaturated, obscuring faint spots. Silver staining relies on freshly made reagents and has many timesensitive steps, each of which can affect the final result. In the present study, even when gels were stained simultaneously in the same vessel, differences in background and spot intensity were observed (data not shown). Finally, in this chapter, terminating the development step at the same time for every gel did not result in equal stain intensity (data not shown); optimal development was instead gauged visually, as has been reported elsewhere (Dutt and Lee, 2001). Differences in the physical properties and cellular location of proteins are important to help explain the variance distribution observed. For example, while certain proteins may be solubilized readily (and therefore exhibit little variation), the solubilization of hydrophobic or membrane-associated proteins may be quite dependent on the success of the sonication step. Small variations during sonication could therefore affect just a subset of the proteins analyzed. Similarly, hydrophobic proteins are uniquely susceptible to solubilization problems during IEF due to interactions with IPG strip materials (Molloy et al., 2001). Independent of hydrophobicity, some proteins may be more reliably reduced and/or alkylated than others. Other problematic proteins may exist in complexes that can be separated and solubilized with different levels of effectiveness. Finally, different proteins have different staining profiles with silver staining (Giometti et al., 1991), and certain protein types, such as glycoproteins, are not sensitively stained by silver (Westbrook et al., 2001). Given the range of physical properties of proteins and the many steps involved in preparing samples for 2-DE, it would be surprising if the CVs of all proteins analyzed were distributed normally around their mean.

3.4. Protein identification Protein spots to be identified by mass spectrometry were chosen based on expression differences observed between treatments but within a single experiment. Of 16 spots analyzed, 10 had significant identifications (Mascot scores > 67, p-value < 0.05) and another had nonsignificant but possible

454


matches to two different N. europaea proteins (Table 18.5). Six clustered spots were identified as a single protein, the general diffusion Gram-negative porin encoded by NE2563 (Fig. 18.4). Under conditions used in 2-DE, single proteins, particularly those with stable secondary structures, can exist in an equilibria between several conformational isoforms and produce multiple spots (Berven et al., 2003). The porin cluster was a major feature of some 2-DE gels, but intensity of its component spots varied independently of treatment. Besides the porin, two other identified proteins were also predicted to be localized in the outer membrane, Pal and a member of the OmpA family. A heat shock protein and a hypothetical protein were predicted to be cytoplasmic. Of the identified proteins, only the heat shock protein had a significant increase in expression in the heat shock treatment. A second, unidentified protein had a significant decrease in expression following heat shock (spot “A,” Fig. 18.4). One of the two spots with significantly different expression between treatments is Hsp20, a heat shock protein in the alpha-crystallin protein family. Related proteins have been detected in other proteomic studies of stress response (Betts et al., 2002). Identification of this spot as a heat shock protein helps verify that the methods used in this study are capable of detecting biological differences between treatments.

3.5. Recommendations for dealing with variability in proteomic studies Choe et al. (Choe and Lee, 2003) developed a method by which to quantify between-gel variability (Fig. 18.5). They suggested that this experimental design be used for preliminary studies of between-gel variability. Preliminary work of the type they suggest could be invaluable; however, based on findings from the present study, it may not be sufficient to quantify all the variability that may be encountered. For example, the Choe et al.’s (Choe and Lee, 2003) experimental design does not provide information about gel reproducibility between SDS-PAGE batches or the effects of staining or image analysis on variability. They also did not take into account betweensample variation that is not due to treatment. In the present study, greater variability was observed between gels from samples from the same treatment but different cultures than gels from the same sample. This variability could have been introduced during sample preparation or attributed to biological differences between cultures, factors that are not tested by Choe et al. (Choe and Lee, 2003). In addition, in the Choe et al. (Choe and Lee, 2003) study, variability due to differences in sample load (as tested by their “Var Load” experiment) was quite similar to variability observed between gels separated in the same IEF run; therefore, it seems likely that the experiment could be streamlined by omitting the “Var Load” test.

Table 18.5 Proteins identified by mass spectrometry Mass (kDa)a

pIa

Average normalized volume (arbitrary units), CV

Mascot score, sequence coverage

420, 54% base 654, 54% heat

73, 61%

19.0 16.60

19.1

4.87 4.760

4.47

16, 118% base 113, 129% heat

73, 17%

24.9 21.80

17.8

5.23 4.830

4.3

227, 133% base 396, 91% heat; 360, 66% base 522, 60% heat; 575, 59% base 822, 81% heat; 473, 76% base 632, 52% heat; 36, 82% base 120, 62% heat; 67, 119% base 138, 63% heat; 83, 51% base NE2074, hsp20b Heat shock hsp20 (alpha-crystallin) 1758, 1% heat proteins family Cytosolic

69 31% 91 29% 70 31% 68 25% 71 23% 72 17%

42.9 40.60

31.134.5

5.29 5.180

4.5-4.7

68, 43%

15.9

14.7

5.5

5.8

Locus, gene name, description

NE0219, pal Bacterial outer membrane protein OmpA family NE2548 Bacterial outer membrane protein OmpA family NE2563 General diffusion Gram-negative porins General bacterial porin family (GBP): OmpF, OmpC, PhoP

Nominal Observed Nominal Observed

(continued)

Table 18.5 (continued)

Locus, gene name, description

NE0122 hypothetical protein CRISPR/Cas-associated protein Predicted cytosolic NE0642, hisI Phosphoribosyl-AMP cyclohydrolase Cytosolic or extracellular; likely cytosolic or NE0028, groEL Tailless complex polypeptide-1/ cpn60 chaperonin family Cytoplasmic a b

Mass (kDa)a

pIa

Average normalized volume (arbitrary units), CV

Mascot score, sequence coverage

Nominal Observed Nominal Observed

36, 121% base 142, 65% heat

68, 39%

14.8

16.5

9.1

4.7

343, 68% base 373, 30% heat

40, 48% or 36, 10%

14.7 or 57.4

16.7

7.74 or 5.15

4.9

Values are predicted from the genome. When a predicted cleaved signal sequence was present, the pI and mass for the signalless peptide were predicted and indicated with a prime symbol. Increase in expression in heat-shocked gels was significant (p < 0.05).


457

NE2563 A*

NE0219 NE2548

NE0642 or NE0028 NE0122 NE2563*

Figure 18.4 Locations of proteins identified by MALDI-TOF MS. Starred proteins had significant (p < 0.05) expression differences between base state and heat-shocked treatments. Spot A had a significant decrease in expression in heat-shocked gels but was not identified. Gene descriptions are provided in Table 18.5.

While studies of within-sample variation like that proposed by Choe et al. (Choe and Lee, 2003) can be used to find sources of error, they can underestimate the variation observed in experiments comparing a number of treatments. An alternative experimental design to samples that are processed independently at every level of the 2-DE method (Fig. 18.6) can be used to simulate the maximum between-sample variability likely to be encountered in an experiment where samples are processed completely independently. For the present study, statistically significant expression differences could be detected for 90% of the spots matched if six replicate experiments were done. This information was critical as it highlighted a potential problem with the experimental system; namely, that the relatively long period required for culture growth and inherent limitations of image analysis may have been impractical to attain the targeted level of replication. For experiments where all samples to be compared can be processed in a single IEF or SDS-PAGE batch, a corresponding adjustment in the design of this preliminary experiment would be appropriate. The objective is to simulate the maximum between-sample variability likely to be encountered in the final experiment. This is important because distinguishing

458


lysate aliquots

sample preparation

IEF

4 Prep

cells

1 Prep

130% normal sample load 70% normal sample load

Var Load

Figure 18.5 Experimental design to test for gel-to-gel variability proposed by Choe and Lee (2003). A single sample is divided into aliquots and then to IPG strips, which are run in parallel within each of the three experiments (4 Prep, 1 Prep, and Var Load). Batch information for SDS-PAGE is not available. Figure adapted from Choe and Lee (2003).

scientifically interesting differences in protein expression between treatments depends on the levels of between-sample variability, not gel–gel variability. The proposed experimental design (Fig. 18.6) points out a potential issue regarding the cultivation method. In this regard, a preliminary study following that design and relied on a single chemostat to generate steady state cultures for analysis could take more than 6 months to complete. A potential solution would be to run multiple chemostats; such replication could potentially compensate for increases in biological variability encountered by compressing the time scale of culture preparation. The overall variability of a proteomic experiment depends not only on the biological samples used and the skill of the operator but also on the methods chosen as well. Methods with low inherent variability can contribute to the success of a proteomic experiment, but they include tradeoffs in cost, labor, sensitivity, and the amount of sample needed. For example, although silver staining is more variable than Coomassie staining, it is also


Independent sample preparations

IEF

459

SDS-PAGE

Figure 18.6 Experimental design to determine between-sample variability in preparation for a proteomic study. Independent samples are separated in different IEF and SDSPAGE runs. Each sample could easily be duplicated; however, depending on gel-to-gel reproducibility, image analysis constraints could limit the number of replicate gels included in the analysis.

much more sensitive. It uses relatively inexpensive reagents, and the stained images can be captured by flatbed scanners. Fluorescent dyes like SYPRO Ruby (Molecular Bioprobes, Eugene, OR) can be as sensitive as silver nitrate, with a greater linear range and better reproducibility. As a progressive dye (which, like colloidal Coomassie, does not require a destaining step), stain intensity is easier to control and reproduce. However, fluorescent dyes and the specialized image capture equipment required are expensive. In some cases, the reduction in variability may be worth the price; in others, compensating for high variability by increasing sample size may be the most economical approach. The limitations of image analysis may constrain the number of replicates that can be effectively matched and analyzed. As the Asirvatham et al. (2002) study illustrated, automated spot matching alone is ineffective over large numbers of gels. Even user-guided matching becomes problematic when

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the slight differences between gels accumulate over many replicates. In the present study, matching was much better between gels run in the same SDS-PAGE batch than gels run independently (data not shown). An averaged gel composed of gels from four independent SDS-PAGE batches or from two independent treatments (six to eight gels, total) is probably the practical limit of matching under the conditions used. If more replicates are needed, both variability and matching efficiency could be increased through the use of a multiplex gel box capable of running up to 10 gels simultaneously. Gels run in parallel can be matched more effectively, thereby allowing more replicates to be averaged and analyzed in a single experiment.

ACKNOWLEDGMENTS These studies were funded by grants to W. J. H. (USDA-NRI; 2001-35107-11046) and E. O. B. (University of Wisconsin, NIH Biotechnology Training Program and Wisconsin Alumni Research Foundation University Fellowship).

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Nitrogen Metabolism and Kinetics of Ammonia-Oxidizing Archaea Willm Martens-Habbena and David A. Stahl Contents 466 468 468

1. Introduction 2. Strain Cultivation and Analytical Methods 2.1. Culture media and growth conditions 2.2. Nutrient measurements, cell counts, and protein quantification 2.3. Calculations and conversion factors 3. Microrespirometry Setup 4. Stoichiometry and Kinetics of Ammonia Oxidation of N. maritimus and AOB 5. Variability of Kinetic Constants in AOB and AOA 6. Summary and Conclusions Acknowledgments References

469 470 470 474 480 482 483 483

Abstract The discovery of ammonia-oxidizing mesophilic and thermophilic Group I archaea changed the century-old paradigm that aerobic ammonia oxidation is solely mediated by two small clades of Beta- and Gammaproteobacteria. Group I archaea are extremely diverse and ubiquitous in marine and terrestrial environments, accounting for 20–30% of the microbial plankton in the global oceans. Recent studies indicated that many of these organisms carry putative ammonia monooxygenase genes and are more abundant than ammonia-oxidizing bacteria in most natural environments suggesting a potentially significant role in the nitrogen cycle. The isolation of Nitrosopumilus maritimus strain SCM1 provided the first direct evidence that Group I archaea indeed gain energy from ammonia oxidation. To characterize the physiology of this archaeal nitrifier, we developed a respirometry setup particularly suited for activity measurements in dilute microbial cultures with extremely low oxygen uptake rates. Here, we describe the setup and review the kinetic experiments conducted with N. maritimus and other nitrifying microorganisms. These experiments Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA Methods in Enzymology, Volume 496 ISSN 0076-6879, DOI: 10.1016/B978-0-12-386489-5.00019-1

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Willm Martens-Habbena and David A. Stahl

demonstrated that N. maritimus is adapted to grow on ammonia concentrations found in oligotrophic open ocean environments, far below the survival threshold of ammonia-oxidizing bacteria. The described setup and experimental procedures should facilitate physiological studies on other nitrifying archaea and oligotrophic microorganisms in general.

1. Introduction The nitrogen cycle has changed significantly within the past two decades. New processes and microorganisms have been discovered that contribute to this still insufficiently resolved nutrient cycle (Francis et al., 2007; Prosser and Nicol, 2008). Among the most important findings was the discovery of aerobic ammonia oxidation within the domain Archaea. Ammonia oxidation, the first step of nitrification, has been known for over a century and it was thought to be restricted to three genera of Beta- and Gammaproteobacteria (Koops et al., 2000; Kowalchuk and Stephen, 2001). Putative ammonia monooxygenase (AMO) genes linked to archaeal ribosomal RNA genes were then found on fosmid clones from soil, suggesting a possible role of mesophilic archaea in the nitrogen cycle (Schleper et al., 2005; Treusch et al., 2005). This hypothesis was confirmed by the isolation of the ammoniaoxidizing archaea ammonia-oxidizing archaeon (AOA) Nitrosopumilus maritimus strain SCM1 (Ko¨nneke et al., 2005), and the description of additional mesophilic and thermophilic archaeal enrichment cultures that stoichiometrically oxidize ammonia to nitrite (de la Torre et al., 2008; Hatzenpichler et al., 2008; Schleper and Nicol, 2010; Wuchter et al., 2006). Detailed molecular surveys demonstrated that AOA are ubiquitous in marine and terrestrial environments and frequently outnumber AOB, especially in nutrient-poor environments (Beman et al., 2010; Herrmann et al., 2009; Leininger et al., 2006; Mincer et al., 2007; Prosser and Nicol, 2008; Zhang et al., 2010). AOA account for up to 30% of the microbial plankton in the oligotrophic ocean gyres (Agogue´ et al., 2008; Karner et al., 2001) and between 1% and 3% of the total microbial count in soils and sediments, together indicating that these poorly understood microorganisms belong to the most abundant microbial clades on Earth (Karner et al., 2001; Prosser and Nicol, 2008). Metagenomic studies on marine and soil fosmid clones, the uncultured sponge-associated archaeon, Cand. Cenarchaeum symbiosum, and the genome sequence of N. maritimus have offered new insights into the metabolism and phylogeny of these organisms (Brochier-Armanet et al., 2008; Hallam et al., 2006a,b; Spang et al., 2010; Treusch et al., 2005; Walker et al., 2010). Phylogenetic studies on ribosomal proteins and core genes indicated that AOA represent a novel phylum, Thaumarchaeota, within the Archaea

Nitrogen Metabolism and Kinetics of AOA

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(Brochier-Armanet et al., 2008; Spang et al., 2010). Thus far, ammonia oxidation is the only plausible pathway of generation of metabolic energy by these organisms, although the investigated (meta-) genomes do not share the ammonia oxidation pathway found in AOB. AOB possess an AMO, which derives electrons from the ubiquinone pool to oxidize ammonia to hydroxylamine. Hydroxylamine is subsequently oxidized to nitrite by a hydroxylamine oxidoreductase (HAO), which delivers electrons back into the ubiquinone pool for respiration and further activation of AMO (Arp et al., 2007; Hooper, 1989; Walker et al., 2010). Whereas a canonical AMO is consistently found in AOA (meta-) genomes, a putative HAO has not been identified (Hallam et al., 2006a,b; Treusch et al., 2005; Walker et al., 2010). Thus, energy generation in these archaea likely follows a novel pathway. Following the most parsimonious hypothesis, the archaeal AMO oxidizes ammonia to hydroxylamine similar to the bacterial pathway. Hydroxylamine would subsequently be oxidized to nitrite by one or multiple novel enzymes, for example, putative multicopper oxidases, which could function analogously to the bacterial HAO. Alternatively, the archaeal AMO could yield a more oxidized product than hydroxylamine (e.g., H2N2O2 or HNO), that may subsequently be oxidized to nitrite via one of the putative multicopper oxidases, and channel electrons via plastoand sulfocyanins into the mostly Cu-based respiratory chain typically found in AOA (meta-)genomes (Schleper and Nicol, 2010; Walker et al., 2010). Along with a different ammonia oxidation pathway and greater involvement of Cu-dependent enzymes, AOA possess strikingly different cellular characteristics than the AOB. The size of N. maritimus cells and their genome is close to the estimated lower limits of free-living organisms (Button, 2000) and comparable to those of Pelagibacter ubique (Rappe´ et al., 2002). Similar to P. ubique, SCM1 cells are only between 0.5–0.9 mm long and 0.25 mm wide and contain approximately 10 fg protein (16–20 fg dry weight cell per cell) (Martens-Habbena et al., 2009). Thus, even a single copy of the 1.645 Mbp Nitrosopumilus genome accounts for almost 10% of cellular dry weight. In contrast, cells of AOB strains in culture have at least 10-fold higher biomass per cell, equivalent to between 120 and up to 1000 fg protein per cell (Keen and Prosser, 1987; Martens-Habbena et al., 2009), and their genome size ranges from 2.8 to 3.5 Mbp (Arp et al., 2007). Small cell size, and associated increase in surface-to-volume ratio, as well as small genome size have previously been considered important evolutionary adaptations by oligotrophic microorganisms to life under resource limitation in nutrient-depleted environments (Button, 2000; Giovannoni et al., 2005; Harder and Dijkhuizen, 1983; Roszak and Colwell, 1987). In conjunction with the abundance patterns revealed by molecular surveys, these distinct characteristics suggested that AOA and AOB could occupy different ecological niches and that AOA could be particularly adapted to live in oligotrophic environments (Martens-Habbena et al., 2009).

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Under nutrient-limited conditions, growth and survival of microorganisms depend critically on their ability to scavenge nutrients from the surrounding environment, reflected by kinetics of nutrient uptake and energy source oxidation (Button, 1985; Button et al., 2004). We therefore sought to determine and compare the kinetic characteristics of ammonia uptake and oxidation of N. maritimus and known AOB strains. Slow growth rates and low biomass yields obtained in laboratory cultures of AOB and especially of N. maritimus make such kinetic studies and physiological experiments in general difficult. Further, growth of N. maritimus is strongly impaired by agitation, rendering nutrient-limited chemostat experiments challenging. We therefore elected to use microrespirometry to determine the stoichiometry and kinetics of ammonia oxidation via its associated oxygen consumption. In this chapter, we describe this microrespirometry setup and review kinetic experiments conducted with N. maritimus, as well as ammonia- and nitrite-oxidizing bacteria. The setup was particularly tuned to monitor activity of microorganisms in very dilute cultures. The high resolution and low detection limit of approximately 0.2 mM O2 uptake per hour permitted us to continuously monitor ammonia oxidation activity in undisturbed N. maritimus cultures without harvesting to concentrate cell material. Using this technique, we were able to accurately determine the stoichiometry and kinetics of ammonia oxidation by N. maritimus, demonstrating that this organism has one of the highest substrate affinities found among microorganisms. This physiology strongly supports the hypothesis that N. maritimus is among the most oligotrophic organisms known to date. Altogether, the available data indicate that oligophilic AOA may have significant impact on the nitrogen and carbon cycles of the global oceans (Martens-Habbena et al., 2009).

2. Strain Cultivation and Analytical Methods 2.1. Culture media and growth conditions N. maritimus strain SCM1 was grown and maintained in marine synthetic Crenarchaeota medium (SCM). All glassware used for media preparation or cultivation was acid washed (1% HCl) and rinsed with MilliQ water. The basal artificial seawater contained (in g l 1) NaCl, 26.0, MgSO47H2O, 5.0 g, MgCl26H2O, 5.0 g, CaCl22H2O, 1.5 g, KBr, 0.1 g. The basal salt solution was prepared freshly, autoclaved, cooled to room temperature, and supplemented with sterile stock solutions as follows (per liter): 10 ml HEPES buffer (1 M HEPES, 0.6 M NaOH, pH 7.8), 2 ml sodium bicarbonate (1 M), 5 ml KH2PO4 (0.4 g l 1), 1 ml FeNaEDTA (7.5 mM), 1 ml Nitrosopumilus trace element solution. The trace element solution


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contained (per liter) 8 ml conc. HCl ( 12.5 M), 30 mg H3BO3, 100 mg MnCl24H2O, 190 mg CoCl26H2O, 24 mg NiCl26H2O, 2 mg CuCl22H2O, 144 mg ZnSO47H2O, 36 mg Na2MoO42H2O. The medium was finally supplemented with the desired volume of NH4Cl (1 M). The pH of this medium at 30 C was 7.5. SCM1 grew in HEPES-buffered medium after acclimatization with incrementally increasing HEPES concentrations. Cultures were maintained in HEPES-buffered SCM medium at 25 or 30 C in the dark without agitation and transferred (0.1–1% inoculum size) to fresh medium when most of the ammonium was oxidized. Nitrosococcus oceani strain ATCC 19707 was grown in HEPES-buffered SCM medium as described above containing 10 mM NH4Cl. N. oceani was cultured at 30 C on a rotary shaker (150 rpm). Nitrosomonas europaea strain ATCC 19718 and Nitrobacter winogradsky strain Nb-255 (ATCC 25391) were grown in liquid freshwater medium containing 20 mM (NH4)2SO4, 50 mM HEPES, 150 mM CaCl2, 150 mM MgSO4, and 0.5 ml Phenol Red (0.4%). The pH of the medium was adjusted to 7.8 and autoclaved. After cooling 1 ml FeNaEDTA (5 mM), 0.25 ml KH2PO4 (1 M), 1.5 ml NaHCO3 (1 M), and 1 ml N. europaea trace metal solution (10 mg NaMoO42H2O, 20 mg MnCl22H2O, 0.2 mg CoCl26H2O, 10 mg ZnSO47H2O, 2 mg CuSO4 5 H2O per liter) were added from sterile stock solutions. For N. winogradsky, the medium was supplemented with up to 10 ml of sodium nitrite (1 M). N. europaea and N. winogradsky were cultured on a rotary shaker (150 rpm) at 30 C and 25 C, respectively.

2.2. Nutrient measurements, cell counts, and protein quantification Nitrite was determined spectrophotometrically using the Griess assay (Stickland and Parsons, 1972). Ammonium was determined by fluorescence measurement after o-phthaldialdehyde derivatization in a Trilogy Laboratory flourometer (Turner Designs, Sunnyvale, CA) or fluorescence microplate reader (Tecan Inc., Durham, NC) (Holmes et al., 1999). The detection limits were 5–10 and 100 nM, respectively. Cell counts were performed by fluorescence microscopy in a Zeiss Axioplan microscope after Sybr Green I staining as described (Lunau et al., 2005). Protein content of bacterial and archaeal cultures was determined using the Nano Orange kit (Invitrogen, Carlsbad, CA) according to manufacturer’s instructions. Cells of N. europaea and N. winogradsky were harvested by 15 min centrifugation at 10,000g in a bench top microcentrifuge. N. maritimus and N. oceani cells were collected by filtration using Centricon YM-100 units (Amicon Inc.) according to manufacturer’s instructions, rinsed with MilliQ, and resuspended in assay buffer. Nano Orange fluorescence was quantified in black 96-well plates (Corning, 9691) using a fluorescence microplate reader.

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2.3. Calculations and conversion factors It remains unknown whether ammonia or ammonium is the actual substrate encountered by N. maritimus cells. Therefore, all calculations presented are based on total reduced inorganic nitrogen (NH3 þ NH4þ). If necessary, the equilibrium concentrations of NH3 and NH4þ were calculated based on salinity, temperature, and pH and the respective stoichiometric dissociation constants of NH3 and NH4þ given in the literature (Clegg and Whitfield, 1995). Kinetic data of AOB, diatoms, organotrophic microorganisms, soil, and ocean water used for the calculations were compiled from the following references: Bollmann et al. (2005), Button (1985, 1998), Button et al. (1998), Eppley and Renger (1974), Glover (1985), Hashimoto et al. (1983), Jiang and Bakken (1999), Keen and Prosser (1987), Loureiro et al. (2009), Prosser (1989), Reay et al. (1999), Senn et al. (1994), Stark and Firestone (1996), Stehr et al. (1995), Suwa et al. (1994), Suzuki et al. (1974), Ward (1987, 1990), and Watson (1965). For the estimation of specific affinities (a0) growth rates, m (h–1), metabolic coefficients, q (g substrate g wet cells–1 h–1), and cell yields, Y (g wet cells g substrate–1) were calculated as previously described (Button, 1985, 1998; Button et al., 1998). The following conversion factors were used: 3 g cell wet weight per g cell dry weight, 5.7 g cell wet weight per g cell protein (Button, 1998), and 0.55 g cell carbon per g cell dry weight (Simon and Azam, 1989).

3. Microrespirometry Setup Continuous monitoring of metabolic activity provides an important method for laboratory studies of microorganisms. Specific and nondestructive detection systems are available only for a limited number of microbial substrates or products. One of the most widely used and broadly applicable techniques is therefore to monitor oxygen consumption associated with metabolic activity using Clark-type oxygen electrodes (Clark et al., 1953). A variety of such electrode systems are commercially available and the working principle has previously been reviewed in detail (Ku¨hl and Revsbech, 2001; Pouvreau et al., 2008; Renger and Hanssum, 2009; Revsbech and Jrgensen, 1986). The resolution of these systems is usually too low to monitor activity in dilute cultures and therefore most experimental protocols call for significant preconcentration of cells by harvesting and resuspension in smaller volumes of buffer. The sensitivity of the oxygen electrodes is limited mainly by several sources of noise and internal oxygen consumption by the electrode. In principle, both limitations can be overcome by use of microscale oxygen sensors (Revsbech, 1989; Revsbech and


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Jrgensen, 1986). The construction of microscale electrochemical sensors (i.e., combination electrodes of sensing cathodes or anodes, and reference electrodes) has several advantages over macroscopic electrodes, namely faster response times, better signal stability, and lower stirring sensitivity (Ku¨hl and Revsbech, 2001; Revsbech, 1989; Revsbech and Jrgensen, 1986). Further, the small electrode size significantly reduces internal oxygen consumption. We therefore used a commercially available microsensorbased respiration system, which after customization to reduce background noise and sensor drift ultimately permitted highly sensitive oxygen measurements in dilute cultures without prior harvesting or manipulation. The basic commercially available respiration system (Unisense AS, Aarhus, Denmark) consists of glass chambers (2 or 35 ml volume) that hold the cell suspensions. The chambers are covered with ground glass lids and are stirred with glass-coated stir bars (see below). The oxygen microsensors (OX-MR type, 500 mm membrane tip diameter) are connected to a picoammeter (PA 2000) and continuously polarized at 0.6 to 0.8 V. After sufficient initial polarization (>7 days), the sensors are placed in a rack and the sensor tip is inserted into the cell suspension through a capillary inlet in the center of the glass lid. The amperometric signal of the microsensor is amplified and converted to mV by a picoammeter (PA 2000), fed through a 16bit A/D converter, and is recorded by a data acquisition software (e.g., SensorTrace Basic, MicOx 2.6; Unisense AS). In general, four major sources of noise can be distinguished that influence the signal of electrochemical microsensors: vibrations, temperature fluctuations, electronic and electromagnetic noise, as well as turbulence at the sensor tip caused by stirring. Those should be minimized to achieve high sensitivity and signal stability. A schematic overview of the final optimized setup is depicted in Fig. 19.1. The entire system should be placed on a solid stone bench-top surface with minimal exposure to mechanical vibrations by general laboratory operation such as opening and closing of doors, operation of elevators, or shakers, centrifuges, etc. This is particularly important when measurements are conducted over long periods of time, that is, several hours to days. Temperature oscillations at the microsensor tip can effectively be limited by placing the sensor rack including glass chambers and microsensor in a Styrofoam-insulated water bath, which is connected to a circulating thermostatic water bath (Fig. 19.1). While in the thermostatic water bath the temperature will oscillate up to 0.2 C depending on the specifications of the thermostat, this oscillation is reduced to less than 0.02 C through the Styrofoam isolation and water recirculation in the insulated water bath. In addition, the recirculation eliminates transmission of mechanical vibrations from the circulation pump to the microsensor. Several sources of electronic and electromagnetic noise exist—mainly caused by potential differences between water bath, picoammeter, and

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To power grid

Stabilized power source Resistor 1000 µF

Computer

USB

USB optical isolator

A/D converter

Picoammeter

Oxygen microsensor in guard

Thermostated water bath (± 0.2 °C)

Insulated water bath (± 0.02 °C)

Stir plate

Figure 19.1 Schematic overview of the optimized microrespirometry setup used for the described experiments.

power grid. Although it is impossible to eliminate potential differences completely, these are strongly reduced by a “floating” operation of the necessary electrical components with a star-shaped ground connection. To avoid potential differences within the setup, the entire setup can only have one ground connection. This is by default the ground of the thermostatic water bath. The picoammeter is subsequently grounded to the water bath. The only remaining electrical and ground connection to other electronic components is eliminated using an USB optical isolator between the A/D converter and the USB port of the PC computer. Thus, the picoammeter, microsensor, and A/D converter should not experience potential differences. In addition, a stabilized power source may be used to power the picoammeter and the USB port of the optical isolator (Fig. 19.1). The remaining source of signal noise is owed to the turbulence at the sensor tip caused by stirring of the cell suspension. Only slight fluctuations

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of flow velocity at the sensor tip change the diffusive boundary layer at the sensor tip and thus influence the rate of oxygen diffusion through the membrane and, hence, the sensor signal. Stirring is required to maintain homogeneity of the cell suspension. Therefore, the best reproducibility of the sensor signal is obtained by using an external stir plate with very steady stirring motion in combination with well-balanced glass micro-stir bars and low stirring speeds of 100–150 rpm. Nonetheless, stirring noise remains the dominant source of noise and cannot be eliminated completely (Fig. 19.2). Due to their small tip and electrode size, microsensors consume very little oxygen. When monitored continuously for 24 h with a OxMR-type microsensor, the oxygen concentration in a 2-ml glass chamber filled with sterile artificial seawater changes less than 1 mM, even though the 90% response time is

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