Advances in Microbial Physiology, Volume 53

Contents CONTRIBUTORS TO VOLUME 53 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix The Bacterial Resp...

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Contents

CONTRIBUTORS

TO

VOLUME 53 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Bacterial Response to the Chalcogen Metalloids Se and Te Davide Zannoni, Francesca Borsetti, Joe J. Harrison and Raymond J. Turner

1. 2. 3. 4. 5. 6. 7. 8.

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . Biological Uses of Se and Te . . . . . . . . . . . . . Resistance Towards Se and Te Oxyanions . . . . Microbial Processing of Metalloid Chalcogens . Chalcogens and Bacterial Physiology. . . . . . . . Other Chalcogens and Metalloids . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . .

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Gaining Insight into Microbial Physiology in the Large Intestine: A Special Role for Stable Isotopes Albert A. de Graaf and Koen Venema

1. 2. 3. 4. 5. 6.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . The Gut Microbial Ecosystem . . . . . . . . . . . . Stable Isotopes . . . . . . . . . . . . . . . . . . . . . . . Genomic Inventories of Intestinal Bacteria . . . Proteomic Aspects of Intestinal Microbial Life. Metabolomics . . . . . . . . . . . . . . . . . . . . . . . .

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CONTENTS

7. Metabolic Flux Analysis Applied to the Gut . . . . . . . . . . . . 8. Emerging Picture of the Role of Microorganisms Integrated in Man . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. New Aspects in the Study of Intestinal Bacterial Physiology . 10. Conclusions and Future Prospects . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Iron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Copper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Bacterial Physiology, Regulation and Mutational Adaptation in a Chemostat Environment Thomas Ferenci

1. General Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The Chemostat Environment and Its Applications to Studies of Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. The Physiological Changes in an Organism Inoculated into a Chemostat: The Example of Glucose-Limited Escherichia coli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Variations in Responses Within and Between Species . . . . . . . 5. Steady State or Constant Change in a Chemostat Population? 6. Mutation Rates and Mutators in Chemostat Populations . . . . 7. Mutational Takeovers and Population Changes . . . . . . . . . . . 8. A Mutational Sweep in Detail: The Physiological Advantage and Spread of mgl Mutations in Glucose-Limited E.coli . . . . . 9. Other Mutations in Chemostat Populations and Their Physiological Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10. Emerging Diversity in Chemostat Populations . . . . . . . . . . . . 11. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Metallosensors, The Ups and Downs of Gene Regulation Amanda J. Bird

CONTENTS

4. Zinc . . . . . . . . . . . 5. Cadmium . . . . . . . 6. Conclusions . . . . . Acknowledgements References . . . . . .

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AUTHOR INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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SUBJECT INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Colour Plate Section to be found in the back of this book

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Contributors to Volume 53

AMANDA J. BIRD, Division of Hematology, Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT 84132, USA FRANCESCA BORSETTI, Department of Biology, Unit of General Microbiology, Faculty of Sciences, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy ALBERT A. de GRAAF, Wageningen Center for Food Sciences, P.O. Box 557, 6700 AN Wageningen, The Netherlands; Department of Surgery, University of Maastricht, Maastricht, The Netherlands THOMAS FERENCI, School of Molecular and Microbial Biosciences G08, The University of Sydney, NSW 2006, Australia JOE J. HARRISON, Department of Biological Sciences, University of Calgary, Calgary, Alta., Canada RAYMOND J. TURNER, Department of Biological Sciences, University of Calgary, Calgary, Alta., Canada KOEN VENEMA, Wageningen Center for Food Sciences, P.O. Box 557, 6700 AN Wageningen, The Netherlands; TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands DAVIDE ZANNONI, Department of Biology, Unit of General Microbiology, Faculty of Sciences, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy

The Bacterial Response to the Chalcogen Metalloids Se and Te Davide Zannoni1, Francesca Borsetti1, Joe J. Harrison2 and Raymond J. Turner2 1

Department of Biology, Unit of General Microbiology, Faculty of Sciences, University of Bologna, Via Irnerio 42, 40126 Bologna, Italy 2 Department of Biological Sciences, University of Calgary, Calgary, Alta., Canada

ABSTRACT Microbial metabolism of inorganics has been the subject of interest since the 1970s when it was recognized that bacteria are involved in the transformation of metal compounds in the environment. This area of research is generally referred to as bioinorganic chemistry or microbial biogeochemistry. Here, we overview the way the chalcogen metalloids Se and Te interact with bacteria. As a topic of considerable interest for basic and applied research, bacterial processing of tellurium and selenium oxyanions has been reviewed a few times over the past 15 years. Oddly, this is the first time these compounds have been considered together and their similarities and differences highlighted. Another aspect touched on for the first time by this review is the bacterial response in cell–cell or cell–surface aggregates (biofilms) against the metalloid oxyanions. Finally, in this review we have attempted to rationalize the considerable amount of literature available on bacterial resistance to the toxic metalloids tellurite and selenite.

ADVANCES IN MICROBIAL PHYSIOLOGY, VOL. 53 ISBN 978-0-12-373713-7 DOI: 10.1016/S0065-2911(07)53001-8

Copyright r 2008 by Elsevier Ltd. All rights reserved

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DAVIDE ZANNONI ET AL.

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Tellurium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Selenium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Biological uses of Se and Te . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Use in Medicine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Use in Structural Biochemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Use in Selective Bacterial Growth Media . . . . . . . . . . . . . . . . . . . . 3.4. Isolates from the Environment with Te and Se Oxyanions . . . . . . . . 3.5. Applications of Te and Se in Biotechnology/Industry/Bioremediation. 4. Resistance toward Se and Te oxyanions . . . . . . . . . . . . . . . . . . . . . . . 4.1. Tellurium and TeR Determinants . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Tellurate Resistance Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Selenite/Selenate Resistance Genes . . . . . . . . . . . . . . . . . . . . . . . 5. Microbial processing of metalloid chalcogens . . . . . . . . . . . . . . . . . . . . 5.1. Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Biofilms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Chalcogens and bacterial physiology . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Selenium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Tellurium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Mechanism(s) of Chalcogen Toxicity . . . . . . . . . . . . . . . . . . . . . . . 7. Other chalcogens and metalloids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Polonium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Other Metalloids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ABBREVIATIONS Ch COX DPA GSH NMR MBC MBEC MIC PDTC QS R ROS

chalcogen cytochrome c oxidase dipicolinic acid reduced glutathione nuclear magnetic resonance minimum bactericidal concentration biofilms eradication concentration minimum inhibitory concentration pyridine-2,6-bis-thiocarboxylic acid quorum sensing organic constituent reactive oxygen species

. .2 . .3 . .4 . .5 . .6 . .7 . .7 . .8 . .9 . 10 . 11 . 13 . 14 . 21 . 21 . 22 . 22 . 29 . 30 . 38 . 38 . 42 . 45 . 49 . 49 . 50 . 50 . 51 . 52

THE BACTERIAL RESPONSE TO CHALCOGEN METALLOIDS

SAM SCV Se SEM SEM-EDS SRB TA Te

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S-adenosylmethionine small colony variant Selenium scanning electron microscopy scanning electron microscopy energy dispersive spectroscopy sulfate reducing bacteria toxin-antitoxin tellurium

1. INTRODUCTION Considering that heavy metals have been reasonably abundant throughout the majority of the Earth’s history, one needs to acknowledge that bacteria have had to deal with their toxic forms since the beginning. This view, pointed out by Silver and Phung (2005a), implies that metal resistance in bacteria is not a recent evolutionary event. Although levels of metals in localized environments become higher from time to time due to geological events, human activities have provided unique metal combinations and levels from industrial and pollution events. Regardless of the explanation of tolerance and biogeochemical interaction between heavy metals and bacteria, there is an amazingly wide occurrence of bacterial genetic elements with defined metal resistances. Thus, bacteria have found ways to eke out a life with such metals and the chalcogens Se and Te. Metal metabolism and resistance in bacteria has been of interest since the 1970s when it was recognized that microorganisms are involved in the transformation of metal compounds in the environment (Jernelov and Martin, 1975; Saxena and Howard, 1977; Summers and Silver, 1978). This area of research is beginning to be referred to as environmental bioinorganic chemistry or microbial biogeochemistry. Bacterial processing of selenium and tellurium oxyanions has been explored since these early years and remains a topic of interest. Here we overview the ways in which the chalcogen metalloids Se and Te interact with bacteria. Tellurite toxicity and resistance in bacteria has been reviewed a few times (Walter and Taylor, 1992; Taylor, 1999; Turner, 2001). While the focus of the literature on selenium in bacteria has been primarily on its incorporation into the amino acid selenocysteine (the 21st amino acid) (Bo¨ck et al., 1991), an overview of selenium processing in bacteria has been published (Turner et al., 1998). However, this is the first time these compounds have been considered together and their similarities and differences highlighted.

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2. CHEMISTRY Wilhelm Blitz of the Institute of Inorganic Chemistry at the University of Hannover, Germany, coined the term ‘‘chalcogen’’ sometime around 1930. Whereas other groups of elements had names, the Group 16 elements (formally Group VI A in USA labeling and VI B in European labeling) O, S, Se, and Te lacked a good collective term. The result was the term ‘‘chalcogens’’ (‘‘ore formers’’ from chalcos old Greek for ‘‘ore’’) for these elements and ‘‘chalcogenides’’ for their compounds (Fischer, 2001). The chalcogen elements (pronounced with a hard ‘‘C’’ as in chemistry) other than oxygen can be generally referred to in chemical structures by ‘‘Ch’’ and this abbreviation will also be used here. The most common compounds of the non-oxygen chalcogens are chalcogenide glasses. The most abundant materials in the earth’s crust are silicates (various compounds of silicon dioxide). ‘‘Chalcogenide glasses’’ are distinguished from these as non-silicate glasses. Se and Te generate compounds that are structurally related to their sulfur analogues, but that exhibit different properties and reactivities and are thus considerably more toxic. As one descends the column, the chalcogens become larger and more polarizable than sulfur. Selenium has a lower electronegativity and forms weaker bonds than sulfur (Whitham, 1995). The chemists find that selenium can be easily introduced into molecules as a radical, a nucleophile, or an electrophile. Tellurium has even greater metallike properties and is a true metalloid. In part, due to its polarizability, the C–Se bond is weaker than C–S bonds; C–Te bonds are weaker yet and tend to decompose in aqueous environments. This difference in bond energies may explain why telluromethionine and tellurocysteine amino acids have not been naturally found while selenocysteine (the 21st amino acid) has been found in all but a few organisms. Although a considerable amount of selenium chemistry has been studied, tellurium chemistry is still somewhat in the dark ages. Se and Te can exist in a number of redox states, namely: Ch2 or ChðIIÞ ! Ch0 or Chð0Þ 2 ! ChO2 3 or ChðIVÞ ! ChO4 or ChðVIÞ

Selenide ðSe2 Þ ! elemental ðSe0 Þ 2 ! selenite ðSeO2 3 Þ ! selenate ðSeO4 Þ


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Telluride ðTe2 Þ ! elemental ðTe0 Þ 2 ! tellurite ðTeO2 3 Þ ! tellurate ðTeO4 Þ

Bacteria are exposed to these elements mainly as their oxidized ions in the form of the oxyanions, as well as in organometalloid forms (RCh). However, the exact ionic form of the chalcogen to which microorganisms are exposed is unknown. For example, in solution at physiological pH, As(III) is primarily in the form of the undissociated acid arsenic trioxide [As(OH)3] and not the oxyanion arsenite (Ramirez-Solis et al., 2004). At physiological 1 2 pH, Se(IV) is predominantly HSeO 3 (pKa ¼ 2.6 and pKa ¼ 7.3) and Se(VI) 2 is SeO4 . Although selenide is a key metabolic intermediate, its ionic form is probably not Se2 but HSe. Te(IV) at pH 7.0 exists at a ratio of HTeO 3/ TeO2 of 104/1. Te(VI) would likely be TeO2 3 4 . Thus, the standard reduction potential of the Te/TeO2 3 couple (0.42 V) at basic pH would be 2 4+ raised to 0.12 V for the couple HTeO 3 /TeO3 at pH 7.0, with no Te present due to its instability in water (Di Tomaso et al., 2002).

2.1. Tellurium Tellurium was named from the Latin ‘‘tellus’’, meaning ‘‘earth’’, and was discovered by F.J. Mueller von Reichenstein in 1782 from ores mined in the gold districts of Transylvania (Bragnall, 1966; Cooper, 1971). Tellurium is occasionally found native, but is more often found as the telluride of gold (calaverite) or combined with other metals. It is recovered commercially from anode muds produced during the electrolytic refining of blister copper. The U.S., Canada, Peru, and Japan are its main producers. The concentration of total Te in the earth’s crust is estimated to be 0.002 ppm ranking Te as approximately 75th in abundance of earth’s elements (Bragnall, 1966; Cooper, 1971). Crystalline tellurium has a silvery-white appearance and when pure it exhibits a metallic luster. Amorphous tellurium is found by precipitating tellurium from a solution of tellurous acid. Tellurium is a p-type semiconductor, and shows greater conductivity in certain directions, depending on the alignment of the atoms. Tellurium has been used in blasting caps, and is added to cast iron for chill control and to steel for toughness. It is increasingly being used in ceramics and photovoltaic cells (Lide, 2005) and is presently very popular as a coloring and property-modifying agent in various types of glasses. It is also used as a reagent (tellurium chloride and tellurium dioxide) in producing the black finish on silverware. The addition of Te0 and Te diethyldithiocarbamate as primary vulcanizing agents to rubber allows it to withstand temperature fluctuations and

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enhance the overall lifetime of natural and synthetic rubber. Usually in combination with Pt, Te is also used as an accelerant/catalyst in a variety of reactions. In metallurgy, Te is used to modify and improve the properties and machinability of cast iron, lead, and copper alloys; its addition to lead decreases the corrosive action of acids and improves its strength and hardness. In the environment, Te exists in its elemental (Te0), inorganic – (telluride 2 (Te2), tellurite (TeO2 3 ), and tellurate (TeO4 )), and organic (dimethyl telluride (CH3TeCH3)) forms (Cooper, 1971). Of these, its oxyanion forms are more common than its non-toxic, elemental state (Summers and Jacoby, 1977). Presently, sparse research into anthropogenic emissions of Te-based compounds has been conducted and the implications of Te in the air have yet to be investigated.

2.2. Selenium Berzelius discovered selenium in 1818. Its name is derived from the Greek word selene, meaning ‘‘moon’’. Selenium is found in a few rare minerals such as crooksite and clausthalite. Previously, it has been obtained from flue dusts remaining from processing copper sulfide ores, but the anode metal from electrolytic copper refineries now provides the main source, as for tellurium. Elemental selenium has been said to be practically non-toxic and is considered to be an essential trace element; however, hydrogen selenide and other selenium compounds are extremely toxic, and resemble arsenic in their physiological reactions. Selenium exists in several allotropic forms, although three are generally recognized. Selenium can be prepared with either an amorphous or a crystalline structure. Amorphous selenium is either red (in powder form) or black (in vitreous form). Crystalline monoclinic selenium is deep red; crystalline hexagonal selenium, which is the most stable variety, is a metallic gray. Selenium exhibits both photovoltaic action and photoconductive action; therefore, it finds use in photocells. Selenium is also able to convert a.c. to d.c. electricity and is used in rectifiers. As a p-type semiconductor, selenium has many uses in electronic and solid-state applications. Selenium is used in Xerography for copying documents. Like tellurium, it is used by the glass industry as an additive to stainless steel (Lide, 2005). As opposed to tellurium, selenium is a very important, essential element for most organisms, including humans. This requirement stems from its incorporation into proteins as part of the 21st amino acid as selenocysteine


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(Bo¨ck et al., 1991) and many selenoproteins have now been identified (Gromer et al., 2005). Although considered a key trace element, Se can be highly toxic depending on its concentration and speciation. In the environment, Se occurs in a variety of oxidation states from the water-soluble oxyanions selenite 2 (SeO2 3 ) and selenate (SeO4 ). Under anoxic conditions, it is found in its insoluble elemental form of Se(0) and mineralized selenides. Redox transformations can occur in natural systems to increase or decrease the mobility and bioavailability of the element. Although such transformations can occur abiotically (Myneni et al., 1997; Zhang et al., 2004), the reduction of selenate and selenite to elemental Se clearly involves microorganisms.

3. BIOLOGICAL USES OF SE AND TE 3.1. Use in Medicine Tellurium found applications in the treatment of microbial infections prior to the discovery of antibiotics. Early documentation in 1926 reports its use in the treatment of syphilis. Its oxyanion tellurite, TeO2 3 , has been used in microbiology since the 1930s when Alexander Fleming reported its antibacterial properties (Fleming, 1932; Fleming and Young, 1940). In 1984, it was suggested that TeO2 could be a potential antisickling agent of red 3 blood cells in the treatment of sickle cell anemia (Asakura et al., 1984). In 1988, tellurium-containing immuno-modulating drugs were proposed as treatment agents for AIDs; however, little has been done on it since (Jacobs, 1989). This compound, AS-101, inhibits the production of IL-10, IFNgamma, IL-2R, and IL-5 (Shohat et al., 2005). A new use of tellurium compounds is in bone marrow stem cell protection during chemotherapy. Trichloro[dioxoethylene-O,O0 ]tellurite shows promise compared with other compounds (Guest and Uetrecht, 2001). In another recent example, organoselenium and organotellurium compounds are being explored as pharmaceuticals for defense against oxidative and nitrosative stress (Klotz et al., 2003). Selenium is intrinsically useful through its role in selenocysteine. Selenium has undergone a revolution since the days when it was only considered to be a toxin. Now, Se is not only recognized as an essential trace element, but has started to be considered the champion of antioxidants (Tapiero et al., 2003) and in cancer prevention (Fleming et al., 2001). It would be impossible in the context of this review to highlight all the eukaryotic biology of selenium.

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The reader is directed toward some other reviews for such purposes (Neve, 1991; Whanger et al., 1996; Burk, 2002; Hatfield, 2002; Klein, 2004). However, it is worth pointing out here that selenium can modify the toxicity of other heavy metals including mercury (Watanabe, 2002) and arsenate (Gailer et al., 2002; Manley et al., 2006) that occur through glutathione–Se–As/Hg complexes (Gailer et al., 2000, 2002).

3.2. Use in Structural Biochemistry The functional and structural properties of chalcogen analogues of sulfurand oxygen-containing amino acids in peptides and proteins is now possible with new synthetic and recombinant technologies. Applications are being increasingly explored with both natural and synthetic proteins (reviewed by Moroder, 2005). Selenocysteine has been recognized as a tool for the production of selenoenzymes with new catalytic activities. By exploiting the highly negative redox potential of selenols, disulfide replacement with diselenide is well suited to increase the robustness of cysteine frameworks in cystine-rich peptides and proteins and can even be used in the de novo design of non-native cysteine frameworks. The isomorphous character of seleniumand tellurium-containing amino acids can be easily exploited for the production of metalloid mutants of proteins. Such modified proteins have been shown to be useful in protein spectroscopy. Both selenomethionine and telluromethionine have been incorporated into proteins as heavy metal derivatives of proteins in protein crystallography and nuclear magnetic resonance (Boles et al., 1995; Budisa et al., 1995). Tellurium acts as a phasing vehicle for solving X-ray diffraction patterns and in NMR as an internal probe to examine structure/function biochemistry following the 125 Te signal. The first use of telluromethionine as a tool for phasing X-ray diffraction data was its incorporation in dihydrofolate reductase (Boles et al., 1994) and was also used in solving the structures of the phage P22 tailspike protein (Steinbacher et al., 1997) and pyrrolidone carboxypeptidase (Boles et al., 1997). To carry out these experiments, the protein is expressed in a bacterial methionine auxotroph host, typically Escherichia coli, in minimal media supplemented with selenomethionine or telluromethionine. Bioincorporation of telluromethionine is difficult due to its toxicity, presumably because of isotope effects on enzyme activities. Additionally, telluromethionine in aqueous solution is unstable and degrades to produce the toxic TeO2 3 .


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3.3. Use in Selective Bacterial Growth Media Tellurite has been extensively explored as an additive to growth media for the selection and identification of various microorganisms, particularly those resistant to tellurite, for almost 90 years. It is often employed in selective media to isolate a wide range of pathogens including: Corynebacterium diphtheriae, Vibrio cholerae (Shimada et al., 1990), Shigella spp. (Rahaman et al., 1986), and verocytotoxigenic E. coli O157:H7 (the ‘‘hamburger disease’’ bacterium) (Zadic et al., 1993; Kormutakova et al., 2000). Considerable work has been focused on the pathogenic E. coli O157:H7. This E. coli strain contains the terABCDEF TeR determinant on its chromosome as part of the O pathogenicity island (Taylor, 1999; Taylor et al., 2002). Because of the high level of resistance, several groups have explored the use of tellurite-enriched media for its identification and isolation. Tellurite is reduced in these strains resulting in a dark black colony that led to the adage ‘‘Beware the Black E. coli’’ (see Fig. 1). Although the resistance from this toxin-producing E. coli originates from the ter resistance determinant (see below), there is diversity in the number of gene copies present and there are even examples without the ter genes (Taylor et al., 2002). Tellurite is highlighted as a key selection ingredient (De Boer and Heuvelink, 2000) and is also used in media to select Shiga toxin-producing E. coli (STEC) O26 (Hiramatsu et al., 2002). However, a study on E. coli O46 and O15:H7 suggests that there is no correlation between the TeR and the ability to produce Shiga toxin (Taylor et al., 2002). In addition to E. coli strains, tellurite has been used in selection media for other organisms, including Mycobacterium avium complex (Afghani and Fujiyama, 2001), which also give black colonies, and in the selective media for methicillin-resistant Staphylococcus aureus (MSRA) (Zadic et al., 2001). Furthermore, tellurite is also used as an additive to culture media for the isolation of pathogeneic Vibrio spp. (Donovan and van Netten, 1995). Cefixime-tellurite media has been used for isolating organisms from minced beef (Dogan et al., 2003), rectal swabs of cattle (Yilmaz et al., 2002), raw vegetables (Fujisawa et al., 2002), and sprouts (Fujisawa et al., 2000). Tellurite and tellurate have also been proposed for use in selective media for fecal Streptococci (Saleh, 1980). It is clear that tellurite has proven to be a useful amendment for selection media in clinical laboratory settings and will continue to do so. However, this approach should be used with caution since non-pathogenic strains can acquire tellurite resistance determinants, for example the ter genes present in the pathogenic E. coli O157:H7, thereby appearing in many clinical assays as false positives. Conversely, as the biochemistry of Te is far from understood, it needs to be recognized that as

10


Figure 1 Biogeochemical transformation of tellurite and selenite by bacteria. The coloration of black cells (tellurite) and red-orange (selenite) is due to the reduction to Ch(0) product within the cells. (A) Pseudomonas aeruginosa grown in microtitre plate planktonically with tellurite. (B) P. aeruginosa grown on Calgary Biofilm Device pegs with tellurite. (C) P. aeruginosa grown in microtitre plate planktonically with selenite. (D) P. aeruginosa grown on Calgary Biofilm Device pegs with selenite. (E) E. coli grown on solid Luria Bertani broth showing the black colonies. (F) Thin section electron micrograph of E. coli grown in the presence of tellurite. The figure shows the precipitation of black crystals along the membrane. (G) E. coli harboring various tellurite resistance determinants. The non-colored culture of the ars is reflective of the resistance being an efflux system. (See plate 1 in the color plate section.)

yet unidentified physiological responses to this chalcogen may give rise to false negatives.

3.4. Isolates from the Environment with Te and Se Oxyanions Apart from the isolation and selection of infectious organisms described above by the augmentation of growth media with potassium tellurite, other bacteria have also been selected through the use of chalcogen oxyanions. Tellurite was found to be an excellent selective agent for Agrobacterium spp.


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(Mougel et al., 2001). It was also utilized to characterize a gene segment in an unculturable rove beetle symbiont that was found to have a functional terZABCDEF tellurite resistance operon (Piel et al., 2004). This organism is thought to be closely related to Pseudomonas aeruginosa. Recently, a proposal to use tellurite in a bioassay for quantification of cell viability in environmental samples has been put forward (Lloyd-Jones et al., 2006). The assay is based on the assumption that the tellurite reduction to the black precipitate only occurs in metabolically competent bacteria. Yurkov’s group has been involved in isolating bacteria from various unique and extreme environments. His group has concentrated investigations on the microorganisms located in close proximity to the hydrothermal vent of the Juan de Fuca Ridge in the Pacific Ocean (Rathgeber et al., 2002). Ocean hydrothermal vents emit an array of heavy metal/metalloid compounds into the aquatic environment, including TeO2 3 . Tellurite- and selenite-reducing strains were isolated in large numbers from the bacterial biofilms and sulfide-rich rocks near the hydrothermal vents. The isolates were found to be from the genus Pseudoalteromonas, were salt-, pH-, and heat-tolerant, and gave rise to very high MICs (1500–2500 mg K2TeO3) (Rathgeber et al., 2002). Some of these organisms were found to utilize 2 SeO2 3 or TeO3 as terminal electron acceptors. Recently, a strain performing anaerobic respiration on tellurate (TeO2 4 ) was isolated from the hydrothermal vent sulfide worm Paralvinella sulfincola (Csotonyi et al., 2006).

3.5. Applications of Te and Se in Biotechnology/Industry/ Bioremediation There are unique challenges in following fates of genetically modified bacteria released into the environment. The exploitation of microorganisms for the bioremediation of contaminated areas is of particular interest. The use of antibiotic resistance markers for following released organisms has deleterious ramifications in the spread of multi-drug resistance. Sanchez-Romero et al. (1998) have shown that the kilAtelAB tellurite resistance determinant can be used to trace Pseudomonas putida following environmental release for organic degradation. Tellurite has also been used to detect and quantify the release of Pseudomonas pseudoalcaligenes KF707 in soils for polychlorinated biphenyl (PCB) degradation (Zanaroli et al., 2002). Bacteria can mediate bioremediation of Se and Te either through direct sequestration, bioreduction, or biomethylation. In sequestration, bacteria do not biotransform the chalcogen oxyanions into a less toxic compound; the accumulation may occur either through uptake or interaction with

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surface biomolecules acting in the form of an ion-exchange matrix. Bioreduction instead reduces the more toxic oxyanion forms to the ‘‘non-toxic’’ Ch(0) form. This process usually occurs intracellularly, leading to the precipitation of the metallic form within the cell. Finally, biomethylation leads to volatile methyl derivatives that disperse into the atmosphere. The dimethyl chalcogens can undergo reactions with OHd , NO3 radicals, and ozone. The methylated products and the reactive products can interact with atmospheric particles leading to atmospheric residence times from hours to days (Atkinson et al., 1990). Thus, the chalcogen can travel considerable distances providing detoxification of local contamination sites through dilution by dispersal. Below, we explore some examples of Se and Te bioremediation studies. Bioremediation of selenium-contaminated environments has been reviewed by Frankenberger and Arshad (2001). The tellurite resistance determinants kilAtelAB, ter, tehAB, and arsABC were investigated for use in tellurite remediation. The use of the plasmidborne tellurite resistance determinant tehAB was found to facilitate the largest amount of uptake of tellurite from the external media (Turner et al., 1994a). Highly resistant microbes could also potentially be used for Te bioremediation. Strains of marine purple non-sulfur bacteria with resistance to 5 mM tellurite were found to decrease the concentration of tellurite in the external media 100-fold and led to accumulation of Te(0) deposits in the cells (Yamada et al., 1997). Similar levels of activity have been reported for strains of obligate anaerobes (Yurkov et al., 1996). This bioreduction, leading to sequestration and chemical transformation of chalcogen oxyanions, could have promise for aquifer contamination sites. Tellurium oxyanions could also be remediated through biotransformation via volatilization through production of methylated derivatives. Methylation and reduction processes are discussed further below. Both aerobic and anaerobic reduction processes of selenium oxyanions are considered to be useful for removing toxic forms of Se from Se-contaminated water. In certain aquatic systems, the effective bioremediation must include the physical removal of the precipitated Se(0) to prevent its re-oxidation to Se(IV) and Se(VI) and subsequent remobilization (Zhang et al., 2004). An interesting approach to the bioremediation of metals is to combine phytoremediation with microbial remediation. This would involve metal processing through plant rhizofiltration and is based on plant rhizobacteria interactions. An example is the isolation of Bacillus mycoides and Stenotrophomonas maltophilia from the rhizosphere of Astragalus bisulcatus, a plant that is able to hyper-accumulate selenium. These organisms were highly efficient in reducing selenite to Se(0) (Vallini et al., 2005).


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Microbial biofilms show potential for various industrial processes. The ability of metalloids to adsorb to and/or react with microbial biomass has been exploited as a means for detecting industrial pollutants in rivers (Mages et al., 2004). Moreover, biofilms grown on membrane biofilm reactors are now being explored as a means to extract SeO2 4 from industrial waste-water and mine tailings, either chalcogens alone or in the presence of other metals such as chromate or arsenate (Chung et al., 2006b,c). Bioremediation of selenium-contaminated water sources is promising. Early experiments have explored bioremediation of chalcogens using algal–bacterial mixtures (Gerhardt et al., 1991). Drainage water treatment using the selenate-respiring bacterium Thauera selenatis has been explored (Macy et al., 1993). In a system at the Panoche Water District in California, USA, a medium-packed biological reactor amended with acetate as the carbon source demonstrated a 98% reduction in selenium oxyanions levels. The Se was bioprocessed to Se(0) and then removed using Nalmet 8072, a Se precipitant coagulant (Cantafio et al., 1996). Another example using this organism utilized wheyamended fermentor to removal of up to 98% selenium oxyanions in the contaminated drainage water (Bledsoe et al., 1999).

4. RESISTANCE TOWARD SE AND TE OXYANIONS The field of toxic metal resistance microbiology has been frequently reviewed in the past 25 years. Notably, a number of extensive reviews have been written by Simon Silver and others (Trevors et al., 1985; Silver, 1996, 1998; Silver and Phung, 1996; Summers, 2005). However, with the exception of As, the metal oxyanions have not received much attention. In fact, in the recent review of Silver and Phung (2005a), the authors dedicate barely a paragraph to tellurium and do not discuss selenium. In general, the so-called heavy metals (although it would be more correct to refer to them as ‘‘toxic metals’’ as some heavy metals are not very toxic; Mo, for example) are toxic as they form stable long-lived complexes with sulfur, thus disrupting the thiol chemistry within the cell. The chalcogens Se and Te are no different and demonstrate interesting thiol chemistry within bacteria as described in the sections below. A lack of understanding of the toxicity of the Ch oxyanions has hampered investigations of the mechanism of several of the cloned resistance determinants. Although oxidative damage has been suggested as a mode of toxicity, generally speaking, tellurite resistance determinants generally do not provide global protection to other oxidants and tend to be very specific to

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tellurite. Recruitment of genes from the metabolic operons responsible for managing the toxicity of normal metabolites or from the genes targeted by the toxic agent are common themes in resistance determinants (Summers, 2005). This can be seen in the resistance mechanisms displayed for other metals. However, this is less evident in tellurite resistance to date. Furthermore, it is interesting to note that there is no cross-resistance observed for any of the defined resistance determinants, suggesting a specific evolution of Ter genes.

4.1. Tellurium and TeR Determinants Genes responsible for tellurite resistance in various organisms have been isolated and characterized by a number of groups. The Ter genes first appeared associated with plasmids; however, several determinants and plasmid homologues have now been found associated with the chromosome. Tellurium resistance mediated by plasmids was first described by Anne Summer and Diane Taylor in the 1970s (Summers and Jacoby, 1977; Taylor and Summers, 1979; Taylor et al., 1988). Taylor reviewed bacterial tellurite resistance in 1999, and the mechanisms of toxicity in E. coli were explored in 2001 by Turner. It has been recognized for some time that metal resistance determinants are found on conjugative plasmids (Summers and Jacoby, 1977; Izaki, 1978). Reviews focusing on plasmid-mediated tellurite resistance (Ter) include: Walter and Taylor (1992), Taylor (1999), and Turner (2001). Plasmid-encoded tellurite resistance determinants are generally associated with plasmids of the H and P incompatibility groups (Hou and Taylor, 1994). Additionally, a number of chromosomal genes have been found to be associated with tellurite resistance or to directly mediate tellurite resistance. To date, five genetically distinct chromosomal and plasmid-borne bacterial tellurite resistance systems have been described (Taylor, 1999; Turner et al., 1999; Turner, 2001; Taylor et al., 2002). However, there are also several unrelated Ter determinants emerging from various bacterial families, suggesting that these determinants provide some selective advantage in natural environments. The nature of this advantage may be unrelated to the Ter phenotype, as the levels of resistance demonstrated in the laboratory do not correlate with the levels of tellurium ion species present in the ecological or pathogenic environment. An interesting characteristic of the genes encoding Ter is that many confer other phenotypes as well. Thus, it is highly likely that the genes identified to be associated with tellurite resistance may act by


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encoding ‘‘moonlighting’’ enzymes. Our present understanding of the Ter determinants is summarized below. 4.1.1. ter The majority of plasmids within the incompatibility Groups HI-2 and HII confer protection against colicins and resistance to potassium tellurite (Taylor and Summers, 1979; Taylor, 1999). The isolated tellurite resistance determinants mediated by these plasmids convey a very high MIC (1024 mg/ml) and has been primarily studied in the plasmids pMER610 and R478 (Jobling and Ritchie, 1987, 1988; Whelan et al., 1995). The phenotypes of resistance to tellurite, bacteriophage (Phi), and pore-forming colicins (PacB) are associated with a large cluster of genes (terZABCDEF) referred to as the ter Ter determinant (Walter and Taylor, 1992; Whelan et al., 1995, 1997). This determinant was later identified to be on the chromosome of E. coli H157:O7, associated with the pathogenicity island (Tarr et al., 2000; Taylor et al., 2002). A few studies have been performed on the regulation of the ter operon. The terABCDE operon from the plasmid pMER610 was initially considered to be inducible (Jobling and Ritchie, 1987) but later was shown to be constitutively expressed (Hill et al., 1993). A study examining the ter determinant in pathogenicity islands of pathogens using reverse transcriptase-PCR analysis demonstrated that the majority of ter genes showed constitutive expression. However, a few isolates were recently found to be telluriteregulated and involved induction of the terB and terC genes (Taylor et al., 2002). Transposon mutagenesis suggests that only the terB, -C, -D, and -E genes are required for Ter (Kormutakova et al., 2000) and the data from Taylor et al. (2002) suggest a common transcriptional region for strains with high MIC. However, those with intermediate levels of resistance probably have separate tellurite-regulated promoters before terCDE and terZ as well as before terB. The regulatory sensor protein has not yet been identified. A terZABCDE operon was identified in Proteus mirabilis and was also found to be inducible as a single transcript (Toptchieva et al., 2003). This ter operon was inducible by tellurite and to a lesser extent by oxidative stress inducers, such as hydrogen peroxide and methyl viologen. The promoter resembled the OxyR-consensus sequence. From this work, it appeared that this determinant was common in the Proteus genus. Overall, the ter operon may be differentially regulated in different organisms. Other examples of the ter determinant include the E. coli KL53 conjugative plasmid pTE53, which contains homologous terBCDEF genes responsible for the TeR (Kormutakova et al., 2000) as well as the so-called

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protective region of genes terXYW (Vavrova et al., 2006). The terF gene was not required in this case to mediate full resistance. Burian et al. (1998) noted that this plasmid gives twice the tellurite uptake, compared with the cured strain. There is a considerable degree of homology between the ter genes on IncHI2 plasmid R478, which originated in Serratia marcescens, and pTE53 from the E. coli clinical isolate. The biochemical mechanism of resistance toward tellurite by the ter determinant remains unknown. However, it is clear that reduced uptake or efflux is not involved (Lloyd-Jones et al., 1991, 1994; Turner et al., 1995a). Additionally, there was no increased accumulation of tellurite from the media (Turner et al., 1994a). From the accumulation of Te(0) crystals in E. coli expressing this Ter, it has been inferred that this determinant facilitates the reduction (Lloyd-Jones et al., 1994). No in vitro reduction using cell extracts has been demonstrated (Lloyd-Jones et al., 1991). However, as the Te(0) deposits are closely associated with the membrane and a required protein, TerC, an integral membrane protein, it remains a viable hypothesis that ter components tap into the electron pool in the membrane for functionality (Lloyd-Jones et al., 1994). Additionally, the ter operon is able to protect against tellurite-mediated thiol oxidation (Turner et al., 1999). The resistance to channel-forming colicins has been reviewed by Alonso et al. (2000b). No clues arise from our present understanding of colicins and other cholicin resistance mechanisms on how the ter determinants might mediate Pac and Phi resistance. Bioinformatic analysis suggests a weak homology between TerC and some transporters. TerD is homologous to the cAMP binding protein. TerA, TerD, and TerE show some homology and are related to a stress response protein in a variety of organisms. Overall, bioinformatic analysis does not give any clues to biochemical activity other than the fact that ter operon homologues are found on the chromosomes of a wide range of bacteria. The observation of co-resistance to phage, colicins, and tellurite remains an unresolved biochemical and physiological link. 4.1.2. tehAB The tehAB genes were first described as a Ter determinant that was believed to have originated from the IncHII plasmid pHH1508a (Walter and Taylor, 1989; Walter et al., 1991). Later, these genes were localized on the E. coli genome (Taylor et al., 1994). The genes were thought to be specific to E. coli at that time based on hybridization and PCR approaches. However, through genome sequencing projects, homologues have been clearly shown to be present on many other bacterial genomes. In E. coli, cloning the tehAB genes into a multicopy plasmid or over-expressing them behind an inducible


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promoter leads to a tellurite MIC of 128 mg/ml. These genes do not appear to mediate resistance when encoded on the chromosome and no difference in the basal resistance is observed in a deletion mutant. No work to date has explored the expression and regulation of these genes on the chromosome. TehA is a polytopic integral membrane protein of 36 kDa with a putative topology of 10 transmembrane helices. This protein shows homology to C4-dicarboxylate transporter/malic acid transport proteins. Intriguingly, it was observed that four of its transmembrane helices are homologous to the small multidrug resistance (SMR) protein family (Turner et al., 1997). It was shown that both full length TehA and a truncated construct in which helices were removed except for the SMR homologous region could transport quaternary ammonium compounds, which are substrates of the SMR proteins. The SMR proteins have a conserved Glu-14 that is crucial to their activity as a proton drug antiporter and is thought to play a role in binding both ligands (Gutman et al., 2003). In fact, TehA contains a glutamic acid in the transmembrane region that could play a similar role. TehB is a 23-kDa cytoplasmic protein that associates weakly with the membrane. TehB contains three conserved motifs found in S-adenosyl-methionine (SAM)-dependent non-nucleic acid methyltransferase (Liu et al., 2000). Mutagenesis of key residues of these motifs eliminated the resistance mediated by the tehAB determinant. It was also shown that TehB undergoes a conformational change upon SAM and tellurite binding (Liu et al., 2000) and SAM can be photochemically reacted with TehB (R.J. Turner, unpublished results). Although a SAM-dependent depletion of tellurite in cultures is observed, tellurium methylation has not been directly observed with this determinant. In fact, expression of this determinant on a plasmid decreases the presence of methylated tellurides in the head gas of cultures and actually decreased the methylsulfide levels (van Fleet-Stalder and T.G. Chasteen, personal communication). A study of Liu and Taylor (1999) suggests that TehB has the ability to mediate resistance on its own and that it is partially responsible for the natural resistance of Streptococcus (Liu and Taylor, 1999). Additionally, overexpression of tehB from Streptococcus pneumoniae in E. coli causes a filamentous morphology in E. coli (Liu and Taylor, 1999). Morphological changes upon the over-expression of Ter determinants are a common theme. The tehAB determinant is very relevant to the physiological state of the cell. In order to mediate full resistance, the cell must have a functioning cysteine biosynthetic pathway, ubiquinone biosynthesis, nicotinamide metabolism, and a thioredoxin/glutathione/glutaredoxin system (Turner et al., 1995a). This suggests that the oxidoreductases and thiol-redox balance are important (Turner et al., 1995a, 1999). Additionally, cysteine residues

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were found to be functionally important for both TehA and TehB (DyllickBrenzinger et al., 2000). Each protein contains three cysteines and can withstand the loss of single cysteine residues; however, TehA and TehB mutants lacking more than one of these cysteines had a decreased tellurite resistance level. The work also demonstrated that TehB is a dimer and at least one Cys is involved in tellurite binding. These results, taken together, make clear that thiol biochemistry is fundamental to the mechanism of these genes. Although the biochemical mechanism of TehAB-mediated tellurite resistance is unknown, one can hypothesize a mechanism based on the observations to date. TehB is clearly a methylase but does not lead to (CH3)nTe products. As TehAB requires glutathione to mediate resistance, it is possible that glutathione participates in the TehB reaction leading to a GSTeCH3. This may follow a similar mechanism that is displayed in eukaryotic multiresistance proteins that utilize glutathione conjugation of drugs and efflux of the product (Deeley and Cole, 2006). TehA could then transport the GSTeCH3 compound via a proton antiport mechanism. Preliminary metabolomic experiments examining small molecular weight compounds in the media support such an idea (R.J. Turner, unpublished results). A direct efflux mechanism, in which there is no change in molecular form has been ruled out, giving this hypothesis further support (Turner et al., 1995a). 4.1.3. kilAtelAB/klaABtelB IncP plasmids do not normally express the Ter phenotype. However, a cryptic determinant was identified on the IncP plasmid RP4 or RK2 (Taylor and Bradley, 1987). The RK2 plasmids have a complex network of coregulated genes known as the kil-kor operon. A normally cryptic Ter was identified on some isolates as RK2TeR was mapped to the kilA locus, giving MICs of 256 mg/ml (Walter and Taylor, 1989). The operon comprises three genes, klaA, -B, -C, which in the Ter versions are referred to as kilAtelAB (Walter et al., 1991; Turner et al., 1994b,c). All three of the genes are required for resistance (Turner et al., 1994b). Furthermore, a single mutation – Ser125 to Cys125 in TelB – was identified as being responsible for the appearance of the resistance (Turner et al., 1994c). Due to nomenclature changes of the kil-kor region, and in order to clearly identify the Ter version, this determinant is also referred to as klaABtelB. The kil-kor region of the RK2 plasmid is responsible for plasmid maintenance and was named for cell killing or killing override (Goncharoff et al., 1991). The KilA (KlaA) was observed to have a strong lethality phenotype and was also found to inhibit assembly of lambda phage tails (Saltman et al.,


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1992). The growth inhibition phenotype of the operon was examined and it was demonstrated that all three genes provide some level of retarded growth (Turner et al., 1994b). In this study, cells expressing KilA were found to form non-septated filaments with distinctive evaginations or blebs on the membranes. A working hypothesis for this phenotype is that KilA inhibits the cell chaperone GroEL, as suggested by in vitro experiments (Rochet and Turner, unpublished results). Though the mutation is in TelB, all three genes are required for resistance (Turner et al., 1994c). KlaA (28 kDa) and KlaB (42 kDa) are cytoplasmic proteins while TelB is an integral membrane protein of 32 kDa. There is a cysteine pair (Cys125/Cys132) in a putative loop in TelB and both cysteines are required for resistance (Turner et al., 1994c). This suggests that thiol chemistry is also involved for this Ter. However, as opposed to the tehAB determinant, kilAtelAB is much less dependent on the physiological state of the cell to mediate full resistance (Turner et al., 1995a). Additionally, this determinant is able to protect against the tellurite-dependent glutathione oxidation in a cell (Turner et al., 2001). Reduced uptake or efflux of tellurite has been ruled out for this determinant as the resistance mechanism (Turner et al., 1995a). The operon appears to be unique to the IncP plasmid. KlaA is found on the chromosome of very few organisms such as Burkholderia spp., Proteus vulgaris, Paracoccus denitrificans, Roseobacter spp., and Acinetobacter spp. However, there is some annotation confusion in that KlaA in these organisms is referred to as TelA and designated as a putative toxic anion resistance protein. KlaB (TelA) is found on the chromosomes of many organisms and annotated as a hypothetical oxyanion resistance. TelB is found in only a few organisms again showing a strong conserved domain to the TrbC conjugal transfer protein. A telAB version is beginning to be identified on other plasmids such as pADPTel in P. putida CR30RNS (Hirkala and Germida, 2004). Overall, sequence analysis does not lead to any further clues as to the biochemical mechanism of resistance. Due to the lethality phenotype associated with this determinant, fewer microbial and biochemical studies have been performed. At this time, the biochemical mechanism of this determinant remains elusive. 4.1.4. tpmT The tpm gene was cloned from the tellurite resistant Pseudomonas syringae pathovar pisi (Cournoyer et al., 1998). This gene encodes a SAM-dependent thiopurine methyltransferase enzyme, which led the authors to propose that the resistance probably occurs through a volatilization of tellurite/selenite

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into dimethyl telluride/selenide, a biochemical mechanism that is also involved in the detoxification of thiopurine drugs and their analogues. Analysis of the genome of P. putida KT2440, a strain that has a high metal tolerance (Canovas et al., 2003), was also found to have tpmT and an arsRBCH, both of which likely contribute to the tellurite resistance in this organism.

4.1.5. cysM/cysK Chromosomally encoded genes, homologous to those involved in cysteine biosynthesis, have been isolated and are inferred to be involved in tellurite resistance. The cysM gene from S. aureus SH1000 was found to be functionally homologous to the O-acetyl serine (thiol)-lyase B family of cysteine synthase proteins. A deletion in this gene gives increased sensitivity to tellurite and could mediate TeR when transformed into E. coli (Lithgow et al., 2004). A clone of a single reading frame from pMip233, an IncHI3 plasmid, confirmed resistance against both tellurite and pore-forming colicin B. The sequence of this clone is also homologous with O-acetyl serine sulfhydrylase (Alonso et al., 2000a). This group designated this gene cysK and mediated resistance to 41000 mg/ml. This enzyme is a pyridoxal 50 -phosphatedependent enzyme and catalyzes the transformation of O-acetyl-L-serine and S2 to L-cysteine and acetate. The cysK gene was cloned and characterized in Azospirillum brasilense, where its deletion led to an eightfold decrease in tellurite resistance (Ramirez et al., 2006). Somehow this reductase-like enzyme mediates resistance to pore-forming colicins and towards tellurite. This dual phenotype is similar to that of the ter determinant above, yet no homology exists between them. Experiments using E. coli ton and tol mutants harboring pB22 (the cysK clone from plasmid Mip233 Inc HI3) indicate that the product of tolC, but not that of tonB, is required for both the PacB and Ter phenotypes (Vilchez et al., 1997). A homologue of cysK was also identified in Geobacillus stearothermophilus V (formerly Bacillus sterothermophilus) that is naturally resistant to tellurite (Vasquez et al., 2001). Vasquez’s group has explored this organism’s tellurite resistance (Vasquez et al., 1999) and has isolated different fractions from cell lysates that demonstrate a NADH-dependent reduction of tellurite (Moscoso et al., 1998). Additionally, the gene iscS (cysteine disulfurase) was cloned and shown to be responsible for some of the resistance in this organism and it could confer resistance in E. coli (Tantalean et al., 2003). This work suggests that there may be several genes that are involved in tellurite metabolism in this organism.


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It is tempting to suggest from the above findings that tellurite resistance in many organisms is due to genes involved in cysteine biosynthesis. However, if this were the case, then one must question why all organisms do not display high levels of resistance. Furthermore, a cysK mutant of E. coli shows no change in its basic level of sensitivity to tellurite (Turner et al., 1995b).

4.2. Tellurate Resistance Genes Tellurate (Te(VI), TeO2 4 ) toxicity is of the same order of that of tellurite. However, this oxyanion has been far less studied probably due to its markedly lower solubility in aqueous buffers, further indicating that our understanding of the electronic forms of tellurium metalloids remains poor. For the most part, the tellurite resistance determinants ter, klaABtelB, teh, and ars do not mediate resistance to tellurate (R.J. Turner, unpublished results). Few studies have been performed exploring the effects of tellurate on microbes. To our knowledge, no electron microscopy or other tools have been used to investigate tellurate exposure to microbes. E. coli cultures, both planktonic and biofilm, exposed to tellurate just below their MIC turn a gray color, compared with the black seen with tellurite. It is not clear if the difference in color and darkness is due to different levels of Te(0) accumulation or to a different metalloid product. E. coli nitrate reductase, which contributes to basal levels of tellurite resistance, may reduce selenate, Se(VI), to selenite, Se(IV), and tellurite, Te(IV), to Te(0), but does not show any tellurate, Te(VI), reduction activity (Avazeri et al., 1997). The ubiE gene of G. stearothermophilus V encodes a methyltransferase, which upon cloning into E. coli produced dimethyl telluride in the head gas of cultures amended with tellurate, but not tellurite (Araya et al., 2004). Although the biochemistry of UbiE is not completely worked out, the process is likely to be SAMdependent.

4.3. Selenite/Selenate Resistance Genes Selenite is 100- to 1000-fold less toxic than tellurite and thus specific resistance determinants have not evolved. Likewise, selenate is much less toxic than selenite (Frankenberger and Engberg, 1998). Nonetheless, a few studies have now been done that describe genes involved in resistance and/or bioconversion of Se oxyanions. The tpmT gene is proposed to mediate selenite methylation in addition to tellurite methylation (Cournoyer et al., 1998).

22


The ubiE gene of G. stearothermophilus described above, which mediates tellurate resistance through methylation, was found to also volatilize selenite and selenate (Swearingen et al., 2006). The methylated compounds were dimethyl selenide and dimethyl diselenide.

5. MICROBIAL PROCESSING OF METALLOID CHALCOGENS 5.1. Reduction The biochemical role of reduction and its tenuous correlation to susceptibility is an important unresolved factor in the study of microbial tolerance to Se and Te oxyanions. When cultures of microorganisms are exposed to Se or Te oxyanions, a reaction occurs that leads to the formation of crystals or nanoparticles of the metalloid in a reduced form (see also Fig. 2). There are several examples that suggest that the chemistry leading to reduction may provide a base level of resistance to an organism (Avazeri et al., 1997); however, many of the genetic resistance determinants described above act independently of this. For instance, while working to clone the ter operon from pTE53, Burian et al. (1998) discovered ‘‘white-colony’’ variants with a level of tellurite resistance comparable to ‘‘black-colony’’ variants harboring the same plasmid. In this case, a 3.5-fold relative decrease in TeO2 3 uptake was noted and the authors concluded that an insertion mutation had

Figure 2 There are a number of outcomes for a chalcogen oxyanion within the cells. The primary process dictates how toxic the oxyanion will be to the microorganism and the related damage as well as any transformation of the oxyanion.


23

occurred in an unknown chromosomal gene likely to be responsible for membrane transport of this anion. In another example, cultures of bacteria harboring kilAtelAB were observed to reduce TeO2 3 at a slower rate that growth controls lacking this determinant (Turner et al., 1994a). An attractive explanation for this was the hypothesis that kilAtelAB may encode a 2 TeO2 3 efflux system. However, the difference in the overall TeO3 reduction rate of the kilAtelAB transformed culture was the result of slower metabolism in the transformant (Turner et al., 1994a) and not due to reduced uptake (Turner et al., 1995a). This section is focused on the bioreduction of Se and Te oxyanions by bacteria and fungi. This biological process has a history of application to clinical microbiology as well as to electron microscopy and may also be important in the biogeochemical cycling of minerals. A frustrating limitation of the data presented here is that it cannot resolve whether there is a true correlation between metalloid reduction and the mechanism(s) of resistance. 5.1.1. Selenium Selenium is found in four inorganic oxidation states. Comparative biological toxicity of several selenium compounds representing the different oxidation states of this element were originally evaluated in rats by Franke and Painter (1938) and in humans by Vinceti et al. (2001). The soluble oxyanions selenate and selenite were poisonous in concentrations of ppm. In contrast, elemental selenium Se0 (0) is highly insoluble and relatively non-toxic and occurs as a prevalent chemical species under anoxic conditions (Barceloux, 1999). Selenide, S2 (-II), is both highly reactive and highly toxic, but is readily oxidized to Se0 through several possible, energetically favorable inorganic and/or biochemical reactions (Turner et al., 1998). A variety of bacteria from soil and aquatic environments have the ability to reduce Se(VI) and Se(IV) oxyanions to insoluble Se(0). Representative genera include Wolinella, Pseudomonas, Sulfurospirillum, Enterobacter, Thaurea, Bacillus, and Citrobacter (Zhang and Frankenberger, 2005; Sidique et al., 2006). Reduction of selenium oxyanions leading to bioaccumulation may also be mediated by plants (Hurd-Karrer, 1937). There is a great deal of interest in using resistant rhizosphere bacteria in conjunction with plant life as low cost treatments to manage contamination in selenium-laden effluents (Di Gregorio et al., 2005; Vallini et al., 2005). Deposition of selenium particles may occur in the extracellular milieu for some microorganisms (Klonowska et al., 2005). For others, bioaccumulation of reduced selenium is intracellular, frequently in association with the cell wall or membrane (Gerrard et al., 1974). Four different types of

24


biochemical mechanisms have been proposed that can account for the formation of nanoparticles of elemental selenium in cultures amended with Se(VI) or Se(IV). These are: (1) the Painter-type reactions of SeO2 or 4 SeO2 with reduced thiols, in particular with reduced glutathione, (2) the 3 enzymatic reduction of selenium oxyanions by periplasmic as well as cytosolic oxidoreductases, (3) inorganic reactions with bacterial metabolites, and (4) the reduction–oxidation reactions of Se oxyanions involving the siderophore pyridine-2,6-bisthiocarboxylic acid (PDTC). Some of these pathways have been described previously in E. coli (Turner et al., 1998). Below, we describe these four putative mechanisms focusing on the reduction to Se(0) (see also Fig. 3 for a general scheme). Painter (1941) was the first to observe the high reactivity of selenium oxyanions with thiol groups, particularly in proteins in toxic cereal grains during chemical analysis of poisonous plants growing in seleniferous soils. He discovered that selenium forms selenotrisulfides (RS-Se-SR), which may be produced according to the following reaction: 4RSH þ H2 SeO3 ! RS-Se-SR þ RSSR þ 3H2 O

(1)

Figure 3 Biochemical pathways for the biological reduction of selenium and tellurium. The chalcogen (Ch, denoting Se or Te) oxyanions may be reduced by bacteria to form elemental precipitates through four generalized routes via: (1) enzymatic reduction, (2) methylation, (3) dissimilatory reduction concomitant with sulfate reduction, (4) Painter-type reactions with the thiols of proteins as well as glutathione, and (5) a chemical reaction with the siderophore PDTC and the products of PDTC hydrolysis. The italicized numbers refer to reactions listed and detailed in the text.


25

It is important to note that the first probable step in metabolic processing of SeO2 4 is an enzymatic (discussed below) or abiotic reduction–oxidation reaction to form SeO2 3 . The latter is a slow but energetically favorable reaction with glutathione (Shamberger, 1985). In this manner, some cells 2 may process SeO2 4 by the same pathways as SeO3 . As an interesting aside, the formation of selenotrisulfides by E. coli has since been confirmed in vivo using 77Se NMR to examine bacterial cultures amended with SeO2 3 (Rabenstein and Tan, 1988). Ganther (1968) identified that an analogous, Painter-type reaction occurs between SeO2 and the tripeptide glutathione. Recent evidence suggests 3 that when glutathione functions as an electron donor, the reduction of SeO2 also leads to the formation of superoxide anions (O 3 2 ) (Kessi and Hanselmann, 2004). The proposed equation for this reaction is: 6GSH þ 3H2 SeO3 ! 3GS-Se-SG þ O 2

(2)

O 2

may be removed by the combined enzymatic In biological systems, activity of superoxide dismutase and catalase. Generation of reactive oxygen species (ROS) such as O 2 and H2O2 in this process may account for the observed oxidative stress response of bacterial cells exposed to selenium oxyanions (Bebien et al., 2002). Regardless of these later findings, Ganther (1971) also demonstrated that the biological reduction of selenodiglutathione (GS-Se-SG) was mediated by the cellular enzyme glutathione reductase (GR): GS-Se-SG þ NADPH ! GSH þ GS-Se þ NADPþ

(3)

As a terminal step in this biochemical pathway, elemental selenium may be produced by an inorganic reaction between the unstable glutathione selenopersulfide (GS-Se) and a proton (H+), regenerating a single molecule of glutathione in the process: GS-Se þ Hþ ! GSH þ Se0

(4)

Thioredoxin is a ubiquitous protein with a redox-active dithiol/disulfide in the active site. Work with thioredoxin reductase (TR) extracts from E. coli suggest that thioredoxin (Trx) may reduce selenodiglutathione (Bjornstedt et al., 1992). Oxidized Trx can in turn be reduced by TR in a NADPHdependent manner to regenerate reduced thioredoxin (Bjornstedt et al., 1992; Kumar et al., 1992). This may be represented by the following two reactions: Trx-ðSHÞ2 þ GS-Se-SG ! Trx-S2 þ GSH þ GS-Se

(5)

Trx-S2 þ NADPH þ Hþ ! Trx-ðSHÞ2 þ NADPþ

(6)

26


The selenopersulfide product from (5) may then undergo the spontaneous dismutation reaction (4) that generates Se0. It is important to note that the mechanism of biological reduction of SeO2 differs from the inorganic reduction–oxidation reaction that 3 produces Se0, particularly with respect to the generation of ROS (Kessi and Hanselmann, 2004). In summary, the likely first step in the biochemical mechanism for generating elemental selenium in bacterial cultures involves the reaction between Se oxyanions and reduced thiols, followed by the subsequent action of glutathione reductase and/or thioredoxin reductase. Other biomolecules that contribute to the biological process of metalloid reduction are redox active enzymes, many of which are components of bacterial electron transport chains. Various enzymatic systems, such as nitrate 2 (NO 3 ) and nitrite (NO2 ) reductases as well as sulfate (SO4 ) and sulfite (SO3 ) reductases, are suspected to be involved in the overall reduction of 0 2 2 2 SeO2 4 and SeO3 to Se . For example, the reduction of SeO4 to SeO3 may be carried out by the E. coli periplasmic nitrate reductase NapA, or through the action of the cytoplasmic nitrate reductases NarGHIJ or NarZUWV (Avazeri et al., 1997). Selenite generated in this fashion can undergo further reduction via reactions (1) through (6). In another example, De Moll-Decker and Macy (1993) have suggested that reduction of SeO2 to Se0 in T. selenatis may be catalyzed by a 3 periplasmic dissimilatory nitrite reductase. Similarly, a dissimilatory sulfite reductase from Clostridium pasteurianum has shown a high selenite reductase activity (Harrison et al., 1984). More recently, the work of Kessi (2006) has demonstrated that there is metabolic interference between selenite and sulfite as well as selenite and nitrite metabolism in logarithmic-growing Rhodobacter capsulatus. However, R. capsulatus stationary phase cells that can no longer reduce nitrite or sulfite still may metabolize selenite, suggesting that nitrite, sulfite, and selenite reduction may be catalyzed by independent pathways in this microorganism (Kessi, 2006). Overall, these studies suggest that the catalytic specificity of oxidoreductases for SeO2 4 and SeO2 may be different or even absent from certain classes and/or 3 families of these enzymes. It is also interesting to note that in sulfate-reducing bacteria (SRB), SO2 4 reduction is linked to the concomitant precipitation of sulfur and selenium in SeO2 3 amended cultures. It is likely that this is not due to the direct action of redox active enzymes; rather Hockin and Gadd (2003) postulated that this was due to the following inorganic reaction: þ SeO2 ! Se0 þ 2S0 þ 3H2 O 3 þ 2HS þ 4H

(7)


27

A final mechanism for metalloid reduction involves siderophores – ironspecific (Fe3+) chelators produced by microorganisms under nutrientlimited conditions as part of an iron acquisition system. The chelator PDTC, which is produced by Pseudomonas stutzeri and P. putida, has an ability to bind a broad range of metals, including many transition metals, lanthanides and actinides (Cortese et al., 2002). Many toxic metals form insoluble precipitates with PDTC, including toxic selenium and tellurium oxyanions (Zawadzka et al., 2006). These workers proposed that SeO2 may be 3 reduced and bound by PDTC or its hydrolysis product, dipicolinic acid [pyridine-2,6-bis(carboxylic acid)] (DPA). The authors represented this reaction qualitatively, but not stoichiometrically, as the following two linked reactions: PDTC þ H2 O ! DPA þ Hþ þ H2 S þ e

(8)

þ SeO2 ! Se0 þ S0 þ H2 O þ DPA 3 þ PDTC þ H2 S þ H þ e

(9)

SeO2 4

SeO2 3

and may be reduced through several mechTo summarize, anisms within bacteria, encompassing energetically favorable reactions with thiols, reduction–oxidation reactions mediated by enzymes, inorganic precipitation with bioenergetically produced sulfide, and precipitation via reactions with siderophores and their hydrolysis products. 5.1.2. Tellurium Similar to selenium, tellurium has four inorganic oxidation states: the II, 0, IV, and VI valence states. Te(II) is chemically reactive and is naturally incorporated into organic tellurides. In fact, reduction to dimethyl telluride is responsible for the hallmark garlic breath of acute tellurium toxicity in animals (Hollins, 1969; Taylor, 1996) as well as in humans (Blackadder and Manderson, 1975; Yarema and Curry, 2005). Biomethylation of Te is further discussed below. The tellurium oxyanions, tellurite and tellurate, have been considered in the literature as strong oxidizers and this chemical attribute is considered to be the explanation for their toxicity in vivo (Taylor, 1999). Gram-negative bacteria are especially sensitive to tellurium oxyanions; hence, there is a history of using potassium tellurite (K2TeO3) as a selective agent in microbiological growth medium for the isolation of pathogenic bacterial species from food, clinical, and environmental samples (Zadic et al., 1993; Donovan and van Netten, 1995). Klett (1900) was the first to note the biochemical transformation of TeO2 3 into a black, insoluble precipitate that, at the time, was presumed to be metallic tellurium. This chalcogen is considered to be

28


relatively non-toxic in its elemental state (Te0), although there is no published data that explicitly addresses this assumption. In support of this notion, it was observed that E. coli growth on agar plates amended with black Te0 precipitates from spent cultures is similar to growth on agar plates without the reduced metalloid (R.J. Turner, unpublished data). The capacity to reduce tellurium is not restricted to resistant microorganisms, nor is it unique to pathogens (Harrison et al., 2005c). A variety of bacterial aerobic and anaerobic phototrophs (Moore and Kaplan, 1992), hydrothermal vent heterotrophs (Rathgeber et al., 2002), eukaryotes, such as fungi (Kuhn and Jerchel, 1941) and plants (Schreiner and Sullivan, 1911), and the mitochondria in animal tissues (Barrnett and Palade, 1957) may carry out various reactions leading to black precipitates. In contrast to selenium, bacterial deposition of tellurium crystallites is almost exclusively intracellular (van Iterson and Leene, 1964a,b; LloydJones et al., 1994; Klonowska et al., 2005). Transmission electron microscopy (TEM) indicates that metalloid precipitation usually occurs in close physical proximity to the cell wall and/or lipid membranes. Reduction of tellurium oxyanions may also occur through four documented processes: (1) a Painter-type reaction with glutathione (Turner et al., 2001); (2) catalytic reduction by periplasmic and cytoplasmic oxidoreductases (Avazeri et al., 1997); (3) a reduction–oxidation reaction involving the iron siderophore PDTC (Zawadzka et al., 2006); and (4) direct or indirect reduction by electrons siphoned from the membrane-bound respiratory chain (Trutko et al., 2000). Tellurium and selenium chemistry are similar in many regards; this is exemplified again as the first three mechanisms of tellurite reduction are similar to those presented previously for selenium. However, tellurium oxyanions may differ in their site-specific interaction(s) with components of the bacterial respiratory chain; it has recently been shown that the redox state of several electron transport redox components can be affected by tellurite (Borsetti et al., 2007). In membrane fragments isolated from cells of the facultative phototroph R. capsulatus, addition of tellurite induces an acceleration of the QH2:cyt c oxidoreductase activity, an effect which is both specifically inhibited by antimycin A and dependent on the presence of the membrane-associated thiol:disulfide oxidoreductase DsbB. These results not only blur the proposal by Trutko et al. (2000) that membrane-bound oxidases are involved in tellurite reduction but also exclude the possibility that the oxyanion has a general oxidizing effect on the membrane redox components. Microbiologists generally accept that the crystalline precipitates produced by microorganisms growing in the presence of K2TeO3 are metallic tellurium. This is founded on two sets of observations: (1) chemical data,


29

including the observation that these precipitates rapidly dissolve in the presence of a strong oxidizing agent, such as bromine (Morton and Anderson, 1941); and (2) X-ray diffraction analysis of tellurium precipitates from C. diphtheriae and Group D Streptococci (Tucker et al., 1962, 1966). It is interesting to note that pure, metallic tellurium ore is a lustrous silverwhite, which starkly contrasts the black, reduced tellurium precipitates recovered from bacterial cultures. To date, this discrepancy has neither been sufficiently addressed nor adequately resolved in the literature. It is unclear whether any other organic material may be associated with the tellurium precipitates found in vivo. For instance, scanning electron microscopy energy dispersive spectroscopy (SEM-EDS) suggests that tellurium precipitates may be in proximal association with organosulfur compounds (Zawadzka et al., 2006).

5.2. Methylation A common biological response to Se and Te exposure is methylation. Over a hundred years ago, it was observed that upon Se or Te exposure, a distinct and unpleasant garlic-like odor emanated from their biological acquisition. We now recognize that this odor originates from methylated derivatives of the chalcogens. Such methylated forms in microbes include: dimethyl selenide, CH3SeCH3; dimethyl selenenyl sulfide, CH3SeSCH3; dimethyl diselenide, CH3SeSeCH3; dimethyl telluride, CH3TeCH3; and dimethyl ditelluride, CH3TeTeCH3 (Chasteen and Bentley, 2003). Of these compounds, the methylated tellurides are considered to have the more ‘‘disagreeable character’’. An interesting anecdote is that garlic as a nutriceutical appears to have the ability to reduce cholesterol levels, and garlic tends to accumulate Te; incidentally, tellurite has been shown to have hypocholesterolaemic effects (Larner, 1995). Methylation of selenium and tellurium in microorganisms has been extensively reviewed by Chasteen and Bentley (2003) and the reader is directed there for an extensive overview. Chalcogen methylation in bacteria appears to be reasonably common, and examples of methylated products have been reported from the IV and VI, as well as 0, redox states of Te and Se. Similarly, some organisms will convert organochalcogen compounds such as selenomethionine or telluromethionine to methylated derivatives (Chasteen and Bentley, 2003). The biochemical mechanism of the methylation has been explored in only a few organisms; nonetheless, such work has led to the assumption that the methyl group originates from S-adenosylmethione (SAM). Reports also suggest the possibility that methyl

30


cobalamin contributes to Se methylation as well (Thompson-Eagle et al., 1989). Overall, the methylation of Se is thought to occur via a form of the Challenger mechanism. This includes a series of reduction methylation steps alternating the redox state of the Se from VI to IV and finally a dual reduction of dimethylselenone through a Se(III) to Se(II) to dimethylselenide (Challenger, 1945). This mechanism was later modified to account for dimethyl diselenide via a methyl selenide intermediate (Reamer and Zoller, 1980). The majority of studies have explored mixed microbial populations in soils, waters, sediments, and effluents from metal-contaminated areas as well as in sewage sludge that has not changed much from such early reports (Chau et al., 1976; Cooke and Bruland, 1987). Methylated metals and metalloids are commonly observed in gases released from anaerobic wastewater treatment facilities, presumably due to the microbial activity (Michalke et al., 2000). Chasteen and Bentley (2003) provide a list of organisms that have been identified with the biomethylation of selenium. The surprise in the list is that Rhodobacter sphaeroides, Rhodocyclus tenuis, and Rhodospirillum rubrum have the ability to use Se(0) and Te(0) as a substrate (Van FleetStalder and Chasteen, 1998). This provides the possibility of biomining of minerals of these chalcogens. A point to consider is that methylation and reduction are likely to be mutually exclusive activities. A study that alludes to this examines P. fluorescens K27 where although, considerable dimethyl telluride is produced from methylation, reduction is still the fate of one third of the amended tellurite (Basnayake et al., 2001).

5.3. Biofilms Microbial biofilms are cell–cell or solid–surface attached assemblies of bacteria that are surrounded by an extracellular matrix of polymers. Growth in a biofilm is part of the natural ecological cycle for the vast majority of microbes (Kolter and Greenberg, 2006) and is regarded as a developmental process likened to differentiation in multicellular organisms (Hall-Stoodley et al., 2004; Harrison et al., 2005f). A typical biofilm forms when bacteria stick to a surface and become permanently attached, triggering a change in physiology. The bacteria then grow and divide to form layers, clumps or stalk, and mushroom-shaped microcolonies, all under the control of specific biofilm genes (Stoodley et al., 2002). At every stage of growth, biofilm bacteria are generally more resilient to antimicrobials than their planktonic counterparts. For example, E. coli biofilms are up to 100 times more tolerant


31

to antibiotics and disinfectants than the corresponding logarithmic-growing planktonic cells (Spoering and Lewis, 2001; Harrison et al., 2005b). The exploration of biofilm susceptibility to metal toxicity has only just begun (Teitzel and Parsek, 2003; Harrison et al., 2004a). Recently, Teitzel and Parsek (2003) demonstrated that P. aeruginosa biofilms are up to 600 times more tolerant to heavy metal cations than logarithmic-growing planktonic cells. Harrison and coworkers have shown a comparable trend in E. coli biofilms exposed to the selenium and tellurium 2 oxyanions, selenite (SeO2 3 ) and tellurite (TeO3 ) (Harrison et al., 2005b,c,d). For bacterial biofilms, this tolerance is time dependent. In fact, biofilms can be eradicated under certain test conditions by a number of 2 metal ions including SeO2 and TeO2 (Harrison et al., 2004a,b, 3 3 /TeO4 2005a,b).A 24-h challenge period using potassium tellurite gives an MIC1 of 0.006 mM, an MBC2 of 0.016 mM, and an MBEC3 of 0.014 mM for E. coli; MIC of 0.073 mM, MBC of 3.1 mM, and an MBEC of 4.4 mM for P. aeruginosa; and MIC of 0.18 mM, MBC of 40.73 mM, and an MBEC of 0.73 mM for S. aureus. For sodium selenite, there was no difference between the MIC, MBC and MBEC, which were: 8.1 mM for E. coli, 28 mM for P. aeruginosa, and 16 mM for S. aureus (Harrison et al., 2004a). Although the physiology of biofilms is considerably different from planktonic growth, the metabolism and thus the degree of protection conferred to microbes by biofilm formation varies from organism to organism. In terms of application, understanding the molecular mechanisms that contribute to metal tolerance is a logical first step in utilizing biofilms for bioremediation of metal(loid) contaminated soils and wastewaters. This section explicitly focuses on a multifactorial model of metal tolerance that may be used to explain the reduced susceptibility of biofilms to watersoluble oxyanions of selenium and tellurium. It is important to emphasize that the altered susceptibility of biofilms to these compounds occurs in the absence of specific selenium and tellurium genetic resistance determinants; therefore, microbial biofilm formation is an innate strategy for microorganisms to survive exposure to metal toxicity. Bacterial biofilms derive their astonishing tolerance to metal toxicity from at least six contributing factors (Harrison et al., 2005e,f), many of which also contribute to antibiotic tolerance (Lewis, 2001, 2005). This includes: (1) structure-dependent metabolic gradients arising from restricted penetration of nutrients and oxygen into the biofilm; (2) a distinct biofilm physiology controlled by a set of 1

MIC: minimum inhibitory concentration – inhibition of planktonic growth. MBC: minimum bactericidal concentration – killing of planktonic bacteria. 3 MBEC: minimum biofilm eradication concentration – killing of biofilm bacteria. 2

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biofilm-specific genes; (3) sequestration of ions in the biofilm matrix; (4) an adaptive physiological response to metal toxicity; (5) self-generated genetic diversity within the community that gives rise to variant cell phenotypes; and (6) a small population of specialized survivor cells termed ‘‘persisters’’. These factors and their potential contribution to the reduced susceptibility of bacterial biofilms to chalcogen toxicity are outlined in the sections below. 5.3.1. Structure and Susceptibility The architecture of mature biofilms is irregular but complex, and communities are intermingled with networks of fluid-filled channels (Lawrence et al., 1991). Biofilm structure is mechanically elastic and the constituent cells have metabolic plasticity, which together allow the bacteria to be malleable in the face of environmental factors, such as shear, chemical, and nutritive stresses (O’Toole and Kolter, 1998; Stoodley et al., 1999). There are many elegant studies showing metabolic gradients within solid surfaceattached biofilms, a stratification that is correlated to the restricted penetration of oxygen and nutrients from the liquid phase to the microbial community (Huang et al., 1998; Xu et al., 1998; Werner et al., 2004). As a result, bacteria growing in a biofilm can possess very different and distinct physiological states, even when separated by as little as 10 mm (Xu et al., 2000). In general, the bacteria nearest the substratum are in an anoxic zone and are thus slow-growing, which leads to an intrinsic resistance to killing by antibiotics relative to the fast-growers in the outer layers of the biofilm (Walters et al., 2003; Borriello et al., 2004). This structure-dependent metabolic heterogeneity may also explain, in part, the tolerance of the bacterial biofilms to metal(loid) ions. For example, since the reduction of TeO2 3 to Te0 is suggested to be correlated with specific electron transport activity of the respiratory chain (Trutko et al., 2000), differential expression of electron transport chain components in aerobic, anaerobic, and microaerophillic regions of a biofilm may play a role in biofilm tolerance to TeO2 3 . This structure–function relationship has recently been examined by comparing the biofilm susceptibility of a parental E. coli strain to its isogenic twin-arginine translocase (tat) mutant (Harrison et al., 2005c). E. coli strains lacking this membrane-associated, protein transport apparatus have a variety of cell envelope-related defects, including abnormal cell division as well as hypersensitivity to detergents and hydrophobic drugs (Stanley et al., 2001). E. coli DtatABC mutants also have an impaired ability to form biofilms, in particular under nutrient-restricted conditions (Ize et al., 2004; Harrison et al., 2005c). Biofilms of E. coli DSS640 (DtatABC) that


33

lack the highly organized microcolony structure of the isogenic, parental strain (E. coli TG1) still possess elevated tolerance to antimicrobials, including a 10-fold increased tolerance to TeO2 3 in comparison to the corresponding planktonic cells. However, this tolerance is still diminished relative to the wild type E. coli biofilms (Harrison et al., 2005c). This indicates that biofilm structure (and the interdependent metabolic stratification in the community) is only one amongst several contributing factors to chalcogen tolerance. 5.3.2. Biofilm Physiology A comparative study has shown that, with respect to the different forms of microbial growth, biofilms and planktonic cultures of the same bacterial 2 2 strain may process SeO2 3 , TeO4 , and TeO3 in different ways (Harrison et al., 2004b). P. aeruginosa ATCC 27853 biofilms and planktonic cultures reduce selenium and tellurium oxyanions to orange and black end-products, respectively. In all cases, P. aeruginosa is highly tolerant to killing by these metalloid oxyanions. Similarly, planktonic cultures of S. aureus ATCC 29213 are able to process these compounds to produce elemental precipitates and are resilient to their toxicity. However, the corresponding S. aureus biofilm cultures do not produce colored end-products typical of metalloid reduction and are two- and fivefold more susceptible to killing by 2 TeO2 4 and TeO3 , respectively. Although the change in biofilm cell physiology decreases tolerance to tellurium oxyanions, in this case it also demonstrates that chalcogen biochemistry and chemistry may be altered in a biofilm. An additional factor in biofilm physiology is the process of cell–cell signaling by quorum-sensing (QS). Many groups have examined QS control of biofilm formation. Although, in some cases, QS does not appear to be involved, there are many bacterial species in which QS does influence biofilm development (Parsek and Greenberg, 2005). In relationship to defense against chalcogen toxicity, some bacterial species, such as P. aeruginosa, upregulate expression of cellular defense machinery, for instance genes against ROS including superoxide dismutase (sodAB) and catalase (katA) (Hassett et al., 1999). This is important as oxidative stress is involved in tellurite toxicity (Rojas and Vasquez, 2005). Sublethal amounts of both selenite and tellurite increase superoxide dismutase activity and this effect mimics the early cellular response to oxidative stress (Bebien et al., 2002; Borsetti et al., 2005). In this fashion, the change in physiology innate to the biofilm lifestyle may afford an additional level of protection for bacteria against the oxidative toxicity of metalloid oxyanions.

34


5.3.3. Sequestration in the Biofilm Matrix The precise composition of a biofilm extracellular matrix varies with the environment as well as the genotype(s) of the constituent microorganism(s) (J.J. Harrison, H. Ceri, and R.J. Turner, submitted work). In general, the biofilm matrix is a highly charged, viscoelastic hydrogel that comprises oligonucleotides (Whitchurch et al., 2002), species-specific proteins (Branda et al., 2006), amino acids (Sutherland, 2001b), and assorted polysaccharides (Sutherland, 2001a; Wozniak et al., 2003; Branda et al., 2005). Selenium and tellurium oxyanions may equilibrate across the biofilm matrix at a slowed rate due to steric and/or ionic hindrance, similar to other charged molecules (Stewart, 2003). The biofilm itself has pH and reduction–oxidation gradients (Pringault et al., 1999) that can affect anion speciation; in these microenvironments, certain constituents of the extracellular matrix may bind, as well as react with, these oxyanions. Sequestration of chalcogens by the biofilm matrix is thus considered here as a potential contributor to metal tolerance. The extracellular polymeric substances produced by E. coli have been well studied. Colanic acid is the major extracellular polysaccharide for many E. coli strains (Potrykus and Wegrzyn, 2004) and is important for the ability of this microorganism to form biofilms (Whitfield and Roberts, 1999). Colanic acid is anionic and would thus have a low affinity for binding both 2 SeO2 3 and TeO3 . This is evidenced by the ability of these ions to eradicate biofilms, which necessitates that the oxyanions completely penetrate the matrix (Harrison et al., 2005b). Furthermore, the organic chelator sodium diethyldithiocarbamate can be used to coordinate and precipitate transition metals and metalloid oxyanions in vitro. Its use produces visible metal–chelator complexes in Cu2+-treated biofilms; however, when used against Se or Te oxyanion-treated E. coli biofilms, no precipitate is seen (Harrison et al., 2005b). Rather, E. coli in biofilms have the propensity and 2 2 2 capacity to reduce SeO2 to their elemental 4 , SeO3 , TeO4 , and TeO3 forms, which is predominantly an intracellular phenomenon in this microorganism (Harrison et al., 2005c). Overall, this indicates that the extracellular matrix of E. coli biofilms may sequester only small quantities of selenium and tellurium. In contrast, some microorganisms may produce chemically reactive metabolites that cause the precipitation of metalloid oxyanions in the extracellular matrix. For instance, sulfate-reducing bacteria (SRB) produce sulfide (S2) through dissimilatory sulfide biogenesis. Under low redox conditions and in the dark, precipitation of elemental selenium in SRB biofilms may occur via an abiotic reaction with bacterially generated S2 (Hockin and Gadd, 2003). Boils of Shewanella oneidensis growing in anaerobic conditions


35

may also reduce SeO2 3 and accumulate elemental selenium in the extracellular milieu (Klonowska et al., 2005). However, the amount of SeO2 3 reduced is highly dependent on the nature of the electron donor and terminal electron acceptor. It is important to note that S. oneidensis retains the capacity to reduce TeO2 3 and accumulate elemental tellurium within the cell (Klonowska et al., 2005). The coordination and/or reaction of chalcogens with components of the biofilm matrix could sequester toxic anions away from the bacterial cells. This may provide a level of protection commensurate with the kinetics of the reaction equilibrium, which may restrict diffusion as well as alter biological availability of the chalcogen oxyanions. 5.3.4. Adaptive Stress Responses Szomolay et al. (2005) have proposed that reaction-diffusion limited penetration of biofilms may result in low levels of antimicrobial exposure to bacterial cells in deep regions of the community. Cells sheltered in this fashion may be able to enter an adapted physiological state that is resistant to the antimicrobial. To date, there have been no studies examining the global changes to the bacterial transcriptome that accompany chalcogen exposure. However, proteomic fingerprinting of biofilm and planktonic E. coli exposed to TeO2 indicate that the proteome undergoes global 3 changes after only a few hours of exposure to this toxic compound. By contrast, similarities in protein profiles obtained for biofilms and planktonic cultures suggest that certain features of this adaptive response may be shared by both modes of bacterial growth (N.J. Roper, personal communication). Although the understanding of this physiological adaptation is still superficial, proteomic fingerprinting suggests that TeO2 3 elicits a complex biological response from bacterial biofilm populations. Accordingly, an adaptive stress response of biofilms (vs. the innate physiological difference of biofilms compared with planktonic cells) may further contribute to chalcogen tolerance. 5.3.5. Genetic Diversity and Colony Morphology Variants In many instances, biofilm growth leads to the formation of colony morphology variants that may display altered phenotypic traits relative to the parental, colonizing strain. Small colony variant (SCV) cells, which are frequently recovered from biofilms of clinical and/or rhizosphere Pseudomonas spp., are an example of this (HaüXler, 2004; Kirisits et al., 2005; van den Broek et al., 2005). Typically less motile, these SCV isolates are superior at

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forming biofilms compared with their progenitors and occur at a frequency in the population that is increased by exposure to certain antibiotics, metal ions, and H2O2 (Drenkard and Ausubel, 2002; Davies et al., 2007). It was recently discovered that TeO2 3 may trigger the formation of SCV cells in biofilms of P. fluorescens (J.J. Harrison, M.L. Workentine, H. Ceri, and R.J. Turner, unpublished data). Variant formation may be an important contributor to chalcogen tolerance, as the switch to the SCV phenotype is correlated with the emergence of multidrug and metal resistance (Drenkard and Ausubel, 2002; Davies et al., 2007). SCV cells of S. aureus are frequently auxotrophic for menadione or hemin, two compounds that are required for the biosynthesis of menaquinone and cytochromes, respectively (McNamara and Proctor, 2000). S. aureus strains bearing inactivating mutations in menD or hemB, which are required for the synthesis of menadione and hemin, produce stable SCV cells (von Eiff et al., 2006). S. aureus menD and hemB mutants with a stable SCV 2 phenotype are hypersensitive to SeO2 3 and/or TeO3 (von Eiff et al., 2006). Similar to P. aeruginosa SCV cells, S. aureus SCV cells are hyperadherent (Vandaux et al., 2002), providing an additional link between biofilm formation and altered susceptibility to chalcogen toxicity. Although the molecular mechanism that triggers the formation of SCV cells is unknown, it was shown that the reversion of P. aeruginosa PA14 and P. chlororaphis O6 SCV cells to a normal colony morphotype requires the sensor kinase GacS (Davies et al., 2007). In many rhizosphere and laboratory strains of Pseudomonas spp., gacS is naturally prone to inactivating mutations (Duffy and Defago, 2000; Sańchez-Contreras et al., 2002; van den Broek et al., 2005). Moreover, phenotypic variation in P. fluorescens is mediated by two site-specific recombinases, XerD and Sss, which appear to introduce mutations into gacA and/or gacS (Martinez-Granero et al., 2005). The stability of many types of biological systems is increased by genetic diversity, and Boles et al. (2004) have recently reported that P. aeruginosa may introduce genetic diversity into the biofilm population in a recAdependent manner. In this manner, genetic diversity may act as ‘‘insurance’’ for microbial survival in a diverse range of environmental stresses. In the analogous case of SCV cells, genetic diversity introduced to the biofilm community by XerD and Sss may lead to the variation in cell phenotype, that appears to be linked to metal(loid) resistance. 5.3.6. Persister Cells The tolerance of bacterial biofilms to antimicrobials may be explained, in part, by the presence of a large number of specialized survivor cells termed


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‘‘persisters’’ within the adherent community. This subpopulation of cells is estimated to account for 0.0001 to 0.001% of the logarithmic-growing bacteria in planktonic culture (Moyed and Bertrand, 1983), but may represent as much as 1–10% of the cells in a biofilm (Spoering and Lewis, 2001). These slow-growing phenotypic variants are able to withstand exposure to chemically and structurally unrelated bactericidal agents (Lewis, 2005). A hallmark of the persistent phenotype is biphasic population killing kinetics by antibiotics and disinfectants that are either time-dependent (Sufya et al., 2003; Balaban et al., 2004; Keren et al., 2004a) or concentration-dependent (Brooun et al., 2000; Spoering and Lewis, 2001). Both of these characteristics are common to cell death kinetics in biofilms exposed to bactericidal 2 2 concentrations of metal ions, including SeO2 3 , TeO4 , and TeO3 (Harrison et al., 2005a,b). Bacterial persistence is best understood in E. coli and has a wellestablished genetic basis linked to the expression of chromosomal toxin– antitoxin (TA) modules (Moyed and Bertrand, 1983; Moyed and Broderick, 1986; Scherrer and Moyed, 1988; Black et al., 1991, 1994; Korch et al., 2003). The fraction of persisters in the E. coli population is controlled, in part by the TA module high persistence (hipBA) operon (Keren et al., 2004b), which encodes a toxin (HipA) and an antitoxin (HipB). The mechanism of persister formation is only partially understood, but involves the interaction of HipA with a downstream target to arrest macromolecular synthesis (Keren et al., 2004b; Korch and Hill, 2006). Mutants bearing inactivating mutations in hipBA produce a smaller proportion of persisters in stationary-phase cultures and in biofilms than wild type E. coli (Keren et al., 2004b). In fact, certain alleles of hipA increase the frequency of persisters in the bacterial population. The hipA7 allele is a gain-of-function mutation known to mediate a 20-fold increase in relative size of the persister cell population produced by E. coli approaching stationary phase (Korch et al., 2003). Stationary phase cultures of the E. coli hipA7 mutant produce up to an 80-fold increase in the relative size of the bacterial population 2 surviving exposure to TeO2 4 and TeO3 (Harrison et al., 2005b). These data suggest that persister cells, which occur at increased frequency in biofilm bacterial populations, may mediate time-dependent tolerance to metalloid oxyanions. 5.3.7. Fungal Biofilms Up to this point, this review has focused on bacterial biofilms. Similarly, fungi may also form surface-adherent biofilms that are innately multidrug and metal resistant. Biofilm formation by fungi is best characterized for

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Candida spp. This genus of polymorphic yeast produces biofilms through a stepwise developmental process involving cellular differentiation (Parahitiyawa et al., 2006). For instance, C. albicans produces exopolymer entrenched, mature biofilms that are composed of a basal layer of yeast cells from which hyphae extend into the liquid medium (Chandra et al., 2001). To date, the influence of chalcogens on fungal biofilms has been examined only in C. tropicalis. Biofilms of this microorganism continue growing in twice the concentration of SeO2 3 required to sterilize a planktonic culture of equivalent cell density (Harrison et al., 2006). C. tropicalis biofilms are also highly resistant to TeO2 (Harrison et al., 2006). Surprisingly, low 3 concentrations of SeO2 affect the pattern of cellular differentiation in the 3 biofilm and thus change the structure and organization of the surfaceadherent community. In particular, SeO2 3 inhibits the transition from yeast to hyphal cell morphotypes; thus exposed biofilms consist of only a flat layer of yeast cells that lack both microcolony structure as well as hyphae (J.J. Harrison, R.J. Turner, and H. Ceri, unpublished data). Similarly, microscopic investigation of Aspergillus parasiticus Var. globosus revealed morphological changes to the fungi which were more marked with increased concentration of either selenite or tellurite (Zohri et al., 1997). Since the emergence of drug resistance coincides with multiple cell morphotypes in biofilm maturation (Chandra et al., 2001), metal(loid) ions may alter biofilm susceptibility to natural or synthetic antimicrobial agents, including the chalcogens. These studies suggest that biofilm formation may also be a strategy for fungi to survive exposure to metalloid toxicity.

6. CHALCOGENS AND BACTERIAL PHYSIOLOGY 6.1. Selenium The biochemistry of selenium in microbes has been discussed extensively (Heider and Bo¨ck, 1993; Turner et al., 1998; Stolz and Oremland, 1999; Birringer et al., 2002; Stolz et al., 2002, 2006) (see Fig. 2 for a general scheme). As mentioned above, Se is a trace element incorporated into several proteins in bacteria, archaea, and eukaryotes as selenocysteine (Sec) and selenomethionine. To date, hundreds of microbial selenoproteins have been identified. They belong to 10 principal selenoprotein families, although this number is destined to increase thanks to the development of innovative bioinformatic tools that allow the identification of new classes of selenoproteins (Zhang et al., 2005). Analysis of the composition of


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selenoproteomes revealed that most are redox proteins, which use selenocysteine to coordinate redox-active metals (Mo, Ni, or W) or are involved in Sec-thiol redox catalysis (Kryukov and Gladyshev, 2004; Zhang et al., 2005). At present, very little information exists concerning the metabolic processes responsible for assimilation of inorganic selenium (such as selenate and selenite) into these selenoproteins. It was noted in the selenate-resistant E. coli strains, that sulfate uptake was inhibited by selenate. This occurs to a lesser extent in wild type strains, suggesting a connection between sulfate and selenate transport (Springer and Huber, 1973). Due to the similarities in the chemical properties of selenium and sulfur, it was proposed that the two elements were assimilated through the same pathway (Shrift, 1969; Stadtman, 1974). Selenate uptake in the model system of E. coli is considered to follow the sulfate uptake system for the most part, for incorporation into selenocysteine. However, early studies suggest that there may be an alternative uptake pathway as inhibitors of the pathway of sulfur incorporation do not completely stop selenite assimilation (Brown and Shrift, 1982). Studies with Salmonella typhimurium have demonstrated that, whereas selenate (SeO2 4 ) is transported by the sulfate permease (CysTWA system), selenite (SeO2 3 ) is taken up by a different process, involving neither the sulfate nor the sulfite assimilatory pathways (Brown and Shrift, 1980). Analogous studies in E. coli have demonstrated that transport of selenate is repressed by the presence of cysteine in the medium as well as sulfate uptake. Selenite transport, in contrast, is not repressed in the presence of this amino acid, suggesting that a distinct carrier for this oxyanion could exist (Brown and Shrift, 1982). Selenite seems to be accumulated by the sulfate carrier only when it is present at high concentrations, reflecting the low affinity of the sulfate permease for the oxyanion compared with sulfate anion (Lindblow-Kull et al., 1985). Selenite uptake in C. pasteurianum was reported to occur via both a unidirectional ATPase (probably the sulfite uptake system) as well as via the DpH component of the proton motive force (Bryant and Laishley, 1989). The existence of an alternative carrier for selenite was also suggested for Selenomonas ruminatum, a species that cannot metabolize sulfate and selenate (Hudman and Glenn, 1984) as well as the phototrophic bacterium R. sphaeroides. In R. sphaeroides, a polyol ABC transporter has been indicated as the possible carrier of selenite into the cytoplasm (Bebien et al., 2001). It is worth noting that the uptake of the oxyanions arsenite (As(III)) and antimonite (Sb(III)) in E. coli AW3110 is facilitated by the glycerol facilitator GlpF, an aquaglyceroporin that helps the movement of neutral substrates but not ions (Sanders et al., 1997; Meng et al., 2004).

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In prokaryotes, selenium also participates in dissimilatory processes in which transformations result in the transduction of energy and/or detoxification. Microbes that can use selenium oxyanions as terminal electron acceptors are widespread amongst prokaryotes and this ability is often used to distinguish between closely related species (Stolz and Oremland, 1999). The majority of these microorganisms are able to reduce selenate to selenite and to elemental selenium that is finally accumulated inside the cells as dense orange-red precipitates. The ability to use selenate as an alternative acceptor is often associated with the ability also to use arsenate (Stolz et al., 2002). By contrast, only a few species have been isolated for their ability to use selenite as an electron acceptor, namely the haloalkaliphilic Bacillus selenitireducens and three strains of an Aquificales sp. (Switzer-Blum et al., 1998; Takai et al., 2002). Reduction of selenate and selenite to elemental selenium, which is insoluble and less toxic, may influence the mobility and hence the bioavailability of the element in the environment (Oremland et al., 1990, 1991; Steinberg and Oremland, 1990). Although the remobilization of selenium by oxidation is possible, this biotic process is very slow compared with dissimilatory reduction (Dowdle and Oremland, 1998). Overall, it appears that microbial metabolism is predominately responsible for the biogeotransformation of selenium oxyanions in the environment. Once inside the cell, selenium derived from selenate or selenite may be incorporated into polypeptides as selenocysteine and selenomethionine. In order for this to occur, selenium oxyanions must be reduced to selenide. Selenite is reduced to selenide by the Painter reaction with GSH, the most abundant reduced thiol in the cytoplasm of the cells (Painter, 1941; Fahey et al., 1978). Although several Se-glutathione intermediates may be produced (such as GSSeO 2 and GSOSeSR), the principal adduct formed and shown by 77SeNMR is selenodiglutathione GS-Se-SG (Milne et al., 1994). Selenate may also react with GSH, albeit slowly (Shamberger, 1985), although selenate reduction to selenite catalyzed by periplasmic or membrane-associated nitrate reductases may be the first step for the further incorporation of selenium. In the form of selenide, selenium can be incorporated into the free-amino acid selenocysteine by the enzyme O-acetylserine (thiol)-lyase (coded by cysK gene) or modified by the Sel system to give the specific aminoacyltRNASec. The free amino acid is not directly ligated to tRNA but can be esterified to tRNACys and subsequently inserted randomly into proteins in place of cysteine (Kaiser and Young, 1975). The effect of this replacement is deleterious for the cells as it can alter enzyme activities. The Sel system comprises four gene products (SelA, SelB, SelC, and SelD) and is directly involved in the biogenesis of Sec-proteins. SelC is the Sec-specific-tRNA


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(tRNASec), SelA and SelD are enzymes essential for the formation of the tRNASec from seryl-tRNA and SelB is an elongation factor that specifically recognizes the tRNASec (Stadtman, 1996). Importantly, tRNASec is not directly derived from ligation of selenocysteine to tRNA, but is derived from modification of an L-serine residue previously ligated to tRNASec by L-serine ligase. SelD is a selenophosphate synthase, which produces an activated form of selenium, selenophosphate. SelA is the selenocysteine synthase that converts seryl-tRNASec to aminoacrylyl-tRNASec. Selenocysteine is incorporated into the polypeptide chains in response to the UGA codon. Though UGA is universally recognized as a stop codon, it can cue selenocysteine incorporation into polypeptide chains. This occurs through an RNA stemloop structure designated Sec insertion sequence (SECIS). SECIS elements are present immediately downstream of UGA codons in bacteria (Zinoni et al., 1990; Liu et al., 1998) and in 30 untranslated regions in archea (Rother et al., 2001). SelB is the elongation factor that recognizes the mRNA context of selenocysteine codons and, additionally, it can discriminate tRNASec from other tRNA species (Heider and Bo¨ck, 1993). This recognition of selenocysteine has come a long way from early experiments exploring the physiology of selenium on bacterial growth, which include the work of Huber et al. (1967) in which the effect of selenate was evaluated by its ability to replace sulfur. It was noted that the majority of added selenate was incorporated into proteins. Phototrophic bacteria have been shown to grow in the presence of 0.1–10 mM selenate or selenite. When phototrophic bacteria are grown in the presence of selenium oxyanions, small amounts of the oxyanions are reduced and/or methylated. A study in R. sphaeroides indicated that volatilization of selenite or selenate occurs only at low levels and is an insignificant fate for the selenium oxyanions taken up by this organism (Van Fleet-Stalder et al., 2000). Selenite was processed more efficiently than selenate in this organism. Although volatilization is not significant, R. sphaeroides grown in the light produced more reduced volatile selenium than cultures kept in the dark (Van Fleet-Stalder et al., 1997). The selenium uptake system in R. sphaeroides operates at very low concentrations of selenium oxyanions. Its poor initial affinity for selenite and even lower affinity for selenate seems to be compensated for by a very effective subsequent reduction to trap any selenium that enters the cell (Van Fleet-Stalder et al., 2000). The physiology of the organism appears to initially put any excess Se into a form very similar to selenomethionine, or even selenomethionine itself. Any further Se excess appears to be completely reduced to the detoxified red elemental form Se(0), which has a very low bioavailability (Combs et al., 1996).

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It has been suggested that members of the Rhodospirillaceae family utilize oxidized compounds, including Te and Se oxyanions, to get rid of the excess electrons produced in anaerobic photosynthesis (Moore and Kaplan, 1992, 1994). This working hypothesis has recently garnered support by data indicating that tellurite reduction is likely to be mediated by the thiol:disulfide oxidoreductase DsbB by extracting reducing equivalents from the ubiquinone-pool (Borsetti et al., 2007). Selenite reduction is observed in R. sphaeroides f. sp. denitrificans under photosynthetic conditions after approximately 100 h lag time, the result of the induction of a molybdenumdependent enzyme (Pierru et al., 2006). This group concluded that there are several pathways of selenite reduction in this organism, at least one involving such an enzyme. Another example of Se oxyanion utilization in respiration is a novel, strictly anaerobic, hyperthermophilic, facultative organotrophic archaeon that utilizes carbon dioxide as the carbon source and can use hydrogen as an electron donor and arsenate, thiosulfate, or selenate as electron acceptors (Huber et al., 2000). This organism is related to Pyrobaculum aerophilum which can utilize arsenate, selenate, and selenite for electron acceptors. An organism of key importance in selenium microbial physiology is T. selenatis, which contains a respiratory selenate reductase that allows growth on selenate by utilizing it as a terminal electron acceptor (Schro¨der et al., 1997).

6.2. Tellurium Unlike selenite and selenate, no microorganism has been isolated for its ability to use tellurite as a terminal electron acceptor for growth. Tellurate, which is less toxic than tellurite, has recently been shown to sustain the anaerobic growth of a strain, EC-Te-48, isolated from hyperthermophilic vents (Csotonyi et al., 2006). Furthermore, it has been observed that nitrate reductases (NRs A and Z) from E. coli present tellurite and selenate reduction activities, leading to the deposition of Te0 and Se0. A soluble nitrate reductase is also able to reduce tellurite in anaerobically grown cells, though this activity does not allow the growth of the microorganism under anaerobic conditions without nitrate as a terminal electron acceptor. Interestingly, E. coli is able to utilize selenate and tellurite for anaerobic respiration when the NRA is induced in large amounts (Avazeri et al., 1997). Additionally, tellurite was found to negate the induction effect of nitrate on NRA, while Se oxyanions decrease the nitrate reduction by 50% (R.J. Turner and G. Giordano, unpublished results). This suggests that


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NR activity on Se and Te oxyanions is an unfavorable reaction for both the enzyme and the cell. Periplasmic and membrane-bound nitrate reductases from Ralstonia eutropha, P. denitrificans, Paracoccus pantotrophus, and the phototrophic bacterium R. sphaeroides have shown the ability to reduce tellurite and selenite in vitro as well, suggesting that tellurite- and selenate-reducing activities are a general feature of different denitrifying species. However, the catalytic activity of the isolated Nap enzyme from R. sphaeroides is too low to justify the high level of resistance of the microorganism, suggesting that other mechanisms may contribute to the resistance phenotype (Sabaty et al., 2001). Indeed, tellurite resistance without metal accumulation has been observed in some obligately aerobic photosynthetic bacteria, showing that tellurite resistance does not strictly depend on reduction to Te(0) (Yurkov et al., 1996). Trutko et al. (2000) have proposed that components of the respiratory chain of Gram-negative bacteria are involved in the reductive process. They postulate that the location of tellurium deposits is dependent on the plasma membrane position of the active site of terminal membrane-bound oxidases. However, they have also shown that rate of tellurite reduction does not always correlate with the intensity of respiration. Indeed, in cells of P. aeruginosa PAO ML 4262, the stimulation of cytochrome c oxidase (COX) activity by addition of ascorbate-dichlorophenolindophenol drastically lowers the Te0 deposition in cells (Trutko et al., 2000). This finding compromises the hypothesis that COX plays a direct role in reducing tellurite but is in line with other reports indicating that COX activities in membranes from P. pseudoalcaligenes KF707 and R. capsulatus cells grown in the presence of tellurite, drop concurrently with a drastic decrease of the soluble c-type heme content (Di Tomaso et al., 2002; Borsetti et al., 2003a). Additionally, despite the polarity of the respiratory COX in the membrane, R. capsulatus and P. pseudoalcaligenes KF707 accumulate elementary tellurium in the cytosol only (Di Tomaso et al., 2002; Borghese et al., 2004), suggesting that reduction of tellurite to Te0 is unlikely to be performed by respiratory cytochrome c oxidases. By contrast, whether the modifications observed in the plasma membrane redox chains of P. pseudoalcaligenes KF707 and R. capsulatus are specifically required to survive in the presence of tellurite or simply reflect toxic effects of the anion on the electron transport system, remains a matter for debate. This problem has recently been challenged in isolated plasma membrane vesicles of R. capsulatus. Borsetti et al. (2007) showed that tellurite (0.25–2.5 mM) alters the redox equilibrium of the Q/QH2-bc1-c2/cy segment of the redox chain. This effect is blocked by the bc1 complex-specific inhibitor antimycin A and it is absent from membranes

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of R. capsulatus MD22, a mutant lacking the thiol:disulfide oxidoreductase DsbB. The latter finding is particularly important because it suggests for the first time a possible molecular mechanism by which tellurite can perturb the plasma membrane redox components facing the periplasmic space. Little is known about the entry of tellurium oxyanions into bacterial cells. The observation of a ‘‘white’’ tellurite resistance variant (Burian et al., 1998) is an important factor to consider when analyzing the location of reduction of Te(IV) to Te(0) in the cell and the existence of specific transporters. It also leads to the question of how uptake is related to toxicity and resistance. A first report suggests that tellurite may be transported into E. coli cells by the phosphate transporter (Tomas and Kay, 1986). This conclusion was derived from two observations: first, TeO2 3 is a strong competitive inhibitor of the transport of phosphate in wild type strain and second, some mutants defective in phosphate transport are collaterally resistant to high levels of tellurite. Indeed, sensitivity to the anion is restored by a plasmid carrying the phoB region, which is involved in phosphate transport. Likewise, tellurite uptake in R. capsulatus cells is a DpH-dependent process strongly repressed by the K+/H+ exchanger nigericin and by the sulfhydryl reagent NEM (Borsetti et al., 2003b). These observations support the idea that R. capsulatus imports tellurite by a phosphate transporter belonging to the Pit 2 family, which catalyze the transport of H2PO across the inner 4 /HPO4 + membrane in an electro-neutral way, working as a H /solute symport system (Van Veen, 1997; Harris et al., 2001). However, these data do not exclude the existence of additional mechanisms for the uptake of the oxyanion; indeed, recent results in aerobically grown cells of R. capsulatus suggest that tellurite may enter the cells by exploiting other carriers, such as an as yet uncharacterized monocarboxylate transporter (R. Borghese, personal communication) in a DpH-dependent manner as previously shown by Borsetti et al. (2003b). These tellurite transport experiments are challenging as ‘‘friendly’’ radio-isotopes of tellurite do not exist. Even so, a few studies have utilized this approach to demonstrate levels of uptake (Lloyd-Jones et al., 1991, 1994). With the advent of a spectroscopic method to examine free tellurite concentration via the chelator diethyldithiocarbamate, tellurite transport can be more easily explored (Newman et al., 1989; Turner et al., 1992b). Using this assay, the Ter determinants, ter, kilAtelAB, and tehAB were shown not to mediate any change in the uptake rate (Turner et al., 1995a). However, it was observed that the arsenite/arsenate/antimonite resistance determinant arsABC, an ATP-dependent efflux pump, does in fact give rise to reduced tellurite accumulation suggesting that the ars is a general chalcogen efflux transporter (Turner et al., 1992a). Finally, it cannot be


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ruled out that, along with other oxyanions, GlpF (discussed above regarding Se oxyanion transport) may also facilitate movement of telluro compounds.

6.3. Mechanism(s) of Chalcogen Toxicity While different elements have different toxicity levels toward different bacterial groups, in general the toxicity levels of the different chalcogen oxyanions, from most toxic to least toxic, is: 2 2 2 2 TeO2 Tellurite is 3 4TeO4 AsO2 4AsO4 ; SbO2 4SeO3 SeO4 . toxic at MIC’s on the order of 0.006–0.8 mM, whereas selenite ranges from 8.1 to 28 mM for organisms such as E. coli, S. aureus, and P. aeruginosa (Harrison et al., 2004a). The study of the toxicity of these oxyanions in eukaryotes is far more developed. Information within these eukaryotic studies could provide clues to toxicity in prokaryotic systems. We briefly overview this research below. Selenium exemplifies Paracelsus’ statement: ‘‘It is the dose that makes the poison’’. Indeed, selenium is essential for animals and humans to guarantee growth and reproductive functions (Fan, 1990). Deficiency in humans results in a condition known as Keshan’s disease, a cardiomyopathy found in some areas of China, where the selenium concentration in soils is low (Chen et al., 1980). Another Se-responsive disease, also reported in regions of China, Siberia, and Korea, is an osteoarthropathy called Kaschin-Beck disease (Ge and Yang, 1993). Conversely, excess selenium in a diet leads to a pathological status defined as ‘‘selenosis’’ and acute selenium overexposure can cause several characteristic symptoms. For more detailed information on nutritional and toxicological aspects, see Fan (1990) and Goldhaber (2003). There are many proposed mechanisms by which selenium and its derivatives cause toxicity in eukaryotic cells. Metalloid Se can undergo redox reactions with thiols (Klassen et al., 1985), which can compromise the function of structural, enzymatic, and regulatory proteins (Park et al., 2000a,b; Gupta and Porter, 2002; Hartwig et al., 2002; Chung et al., 2006a). By reacting with thiols, and glutathione in particular, selenium initiates a ROS production, involving the formation of selenide (RSe). This intermediate may enter a redox cycle and generate the superoxide anion and oxidative stress, or may form free radicals that could inhibit further enzymes or cause damage to cell membranes and DNA (Chaudier et al., 1992; El-Demerdash, 2001; Abul-Hassan et al., 2004; Garcia et al., 2005). Another mechanism by which selenium and its derivatives may exert their toxicity in eukaryotic cells is through selenium substitution for sulfur in methionine, forming

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selenomethionine, which may be mis-incorporated into proteins. This interaction could explain the teratogenic action of these compounds (Combs and Combs, 1986) and the damage to keratin-containing proteins in adults exposed to high levels of selenium in their diet (Fan, 1990). It is important to note that the toxicity of Se depends on the chemical form of the element, as this determines its bioavailability and ability to enter the organism and/or cells. In addition, the negative action of the metalloid can be altered by its interactions with other substances, such as sulfate, methionine, cysteine, heavy metals (As, Cd, Cu, Pb, Hg, Ag, Zn), and vitamins C and E (Fan, 1990). Hence, a selenium effect is a result of a balance between antioxidant and pro-oxidant abilities in the cells. Superoxide production may be the major mechanism of selenium toxicity under aerobic conditions in prokaryotic cells as well, as suggested by several studies (Turner et al., 1998; Bebien et al., 2002; Kessi and Hanselmann, 2004). Selenite is the only known compound that induces both iron and manganese superoxide dismutases (SodB and SodA, respectively) in E. coli. The effect of the oxyanion on the proteomic response of the microorganism strengthens the hypothesis that selenium toxicity involves several molecular circuits and it is not directed to a specific and single target. 6.3.1. Tellurite Tellurium biochemistry in the context of animal and human toxicology was last reviewed by Taylor (1996). Despite many chemical homologies between selenium and tellurium, a nutritional role has never been identified for tellurium; moreover, tellurium at low concentrations induces both acute and chronic toxicity in a variety of organisms. Nevertheless, several studies have shown that trace amounts of tellurium are present in body fluids, such as blood and urine (Goulle´ et al., 2005). Tellurocysteine and telluromethionine can be found in bacteria (Boles et al., 1995; Budisa et al., 1995, 1997), yeast (Yu et al., 1993), and fungi (Ramadan et al., 1989) as a result of misincorporation of tellurium in place of sulfur or selenium, thus allowing the expression of protein analogs useful for protein structural studies. One possibility is that tellurium may act as a metabolic antagonist of selenium, inhibiting the catalytic activity of certain enzymes. This could be the case for the cytosolic glutathione peroxidase (GSHpx) in hepatocytes in which (121Te)-tellurite was shown to form adducts on the protein, with resulting inhibition of the catalytic reduction of hydrogen peroxide by the enzyme (Garberg et al., 1999). A contrasting situation is the one in which tellurium and/or its oxyanion forms act on the enzyme squalene monooxygenase, the second enzyme in the committed pathway for cholesterol biosynthesis.


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Tellurium blocks cholesterol synthesis, causing a transient demyelination of peripheral nerves (Wagner-Recio et al., 1991; Wagner et al., 1995). The same effect has been observed with selenite and other methylselenium compounds (Gupta and Porter, 2002). The sensitivity of squalene monooxygenase to tellurium and selenium compounds is due to the binding of these compounds to vicinal cysteines; the methylation of tellurium in vivo may enhance the toxicity of tellurium for this enzyme (Laden and Porter, 2001). It has also been observed that tellurite (TeO2 3 ) ions induce the alteration of the erythrocyte membrane and this activity is thiol-dependent as well (Deuticke et al., 1992). Finally, most of the Te(IV) derivatives are also able to inactivate cysteine proteases, but not other families of proteases. This seems to be related to the ability of Te(IV) compounds to react with the thiolcatalytic site of cysteine proteases. Te (VI) compounds do not exhibit any such inhibitory activity as they are inert towards thiol moieties (Albeck et al., 1998). Overall, the above work suggests that tellurium compounds interact with biological systems by specific chemical interaction with endogenous thiols. The precise biochemical explanation for the toxicity of oxyanions of the different chalcogens remains largely unknown in bacteria. In general, it has been assumed that the toxicity of tellurite is a consequence of the strong oxidizing properties. From various experiments examining tellurite resistance mechanisms, one can infer possible toxic pathways, which involve normal bacterial physiology. For example, one recognizes that the redox chemistry of the reaction of tellurite with nitrate reductase is unbalanced, as nitrate reductase would undergo two electron reductions and the reduction of TeO2 to Te0 would require four electrons. The two electron reduced 3 2 TeO3 would likely result in the formation of a radical species or radical oxygen ions being formed. Furthermore, the reaction of tellurite with glutathione would sequester glutathione and change the glutathione/ glutaredoxin/ thioredoxin redox balance as well as produce H2O2 and O2d , as discussed above. This latter observation corresponds with the evidence that the induction of the cambialistic superoxide dismutase (i.e. functional with Mn or Fe) of R. capsulatus leads to a significant increase in tellurite resistance. Interestingly, it has also been reported that SOD activity is increased by the addition of a sublethal amount of K2TeO3 (Borsetti et al., 2005). If tellurite is allowed to enter the metabolism of the cell, it can be incorporated into enzymes as telluromethionine and tellurocysteine as well replacing both sulfate/sulfite and/or phosphate in various biochemical events. Tellurite toxicity in cells of the obligate aerobe and PCB degrader P. pseudoalcaligenes KF707, has been linked to the production of ROS (Tremaroli et al., 2006). This study also indicates that although Od 2

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generation is clearly linked to tellurite reduction by reduced thiol (RSH) oxidation, the time courses of the two processes are different. Experiments with the iscS gene from G. stearothermophilus V complementing E. coli iscS and sodA sodB mutants, support this evolving hypothesis of tellurite toxicity. The reduction of tellurite generates superoxide and other ROS and the primary targets of the superoxide damage in E. coli may be [Fe-S] clusters (Tantalean et al., 2003). The interaction of tellurite with the electron transport chain via the DsbB link to the quinone pool leads to a short circuit in the electron transfer pathways of R. capsulatus (Borsetti et al., 2007) and also avoids overreduction of the quinone pool with a consequent stimulation of lightdependent electron transport under highly reducing conditions. Thus, under unfavorable growth conditions, i.e. phototrophic growth under anaerobiosis, sub-inhibitory amounts of tellurite might exert a positive effect on the redox state of the electron transport components of the facultative phototroph R. capsulatus. E. coli exposed to tellurite displays a rapid loss in free thiol content (Turner et al., 1999). In addition to this key observation, the transmembrane DpH gradient is dissipated and intracellular ATP levels are rapidly depleted (Lohmeier-Vogel et al., 2004). Tellurite exposure also causes a large change in the proteome fingerprint whether grown planktonically or as a biofilm (N.J. Roper, J.J. Harrison, J.M. Howell, H. Ceri and R.J. Turner, unpublished data).

6.3.2. Tellurate Tellurate (TeO2 4 ) is about 2- to 10-fold less toxic than tellurite in most organisms studied (Harrison et al., 2004a). However, due to its poor solubility in aqueous conditions, very little has been done with this form of tellurium. Basnayake et al. (2001) observed that adding tellurite and tellurate to cultures of P. fluorescens was more toxic than added individually. This work suggests a synergistic toxic effect of tellurate and tellurite on bacterium. At this time, our understanding of Te physiology does not provide any clues for this observation. As the observations of dark cells are also seen in cultures exposed to tellurite, it is likely at least some similar biochemistry is occurring or that tellurate is being reduced to tellurite. However, at this point it is not clear what enzyme or process would carry this reaction out, as it is clear that nitrate reductase does not have this capacity (Avazeri et al., 1997).


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6.3.3. Selenite/Selenate Very few studies have looked at the biochemical mechanism behind the selenite toxicity in bacteria because, at most concentrations, there is little effect on the growth rate or accumulated biomass (Hudman and Glenn, 1984). Lohmeier-Vogel et al. (2004), using 31P-NMR on E. coli, did not see any effect on ATP levels or on the transmembrane DpH gradient, in contrast to the case of tellurite exposure. Examining the free thiol oxidation in E. coli upon selenite exposure, one observes an initial loss of RSH content but this oxidation recovers over a short time (Turner, unpublished results). This is probably because the glutathione reductase can accept the GSSeSG as a substrate. Similarly, selenate is essentially non-toxic to most bacteria (Huber et al., 1967, 2000). It is unknown if this is due to simply a lack of uptake or the inability to reduce selenate to selenite. However, it is clear that nitrate reductases can perform this task (Avazeri et al., 1997; Sabaty et al., 2001). Overall, the toxicity of tellurite can be considered to be a combination of specific targeted thiol chemistry and the resulting production of oxygen radical species that contributes to poisoning of the cell’s electrochemistry. The difference in toxicity between the oxyanions of the different chalcogens may lie in the rate or ability to repair thiol oxidation and/or process RS Ch(II) species (such as GSSe vs. GSTe) and the rate of ROS production, all of which leads to subsequent damage to respiratory and biosynthetic pathways. On a final note, it has been observed that addition of selenite with tellurite will increase the tellurite MIC; i.e. selenite protects against the toxic affects of tellurite (R. Turner, unpublished results). This suggests that the preferential, and less toxic, biochemical paths and kinetics of selenite can dominate over that of tellurite. Thus, tellurite has a less noxious relationship with the cell.

7. OTHER CHALCOGENS AND METALLOIDS 7.1. Polonium Nothing has been done exploring polonium (Po) specifically due to its extremely low natural abundance and its high radioactivity. The chemistry of soluble forms of Po are expected to be similar to that of the other chalcogens; however, the toxicity level would be greater due to the radiation.

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Although no specific investigations exist, studies by Momoshima et al. (2001, 2002) on aquatic samples suggest that the microorganisms are able to generate volatile Po species most likely through methylation. Additionally, they may have the ability to reduce oxyanion forms of Po, leading to precipitates, as the authors indicate there was some accumulation of Po in some form as well. This observation is supported by a previous study that demonstrated that phytoplankton and bacteria accumulate 210Po (Wildgust et al., 1998). Other early work from the 1960s by various Russian groups simply used Po as a source of radiation to explore radiation tolerance of microbes.

7.2. Other Metalloids By definition, the metalloids include the elements Bo, Al, Si, Ge, As, Sb, Te, and Po. Of these, considerable research has been focused on the resistance of arsenite, arsenate, and antimonite. The microbiology of these will not be discussed here and the reader is referred to the following reviews: Rosen et al. (1999), Rosen (2002c), Bhattacharjee et al. (2000), Rosen (2002a,b), Mukhopadhyay et al. (2002), Silver and Phung (2005a,b). However, the arsenate resistance determinant arsABC is worth bringing up here. ArsAB is an ATP-dependent efflux pump and ArsC is an arsenate reductase, reducing arsenate to arsenite. Turner et al. (1992a) observed that this operon also provides tellurite resistance and effectively effluxes tellurite, keeping tellurite accumulation low and thus preventing the ‘‘blackening’’ of the cells through tellurite reduction. Surprisingly, ArsC, the thiol-dependent arsenate to arsenite reductase, was required for full resistance. Along the same lines, arsenate reductase activity was found in the plasmid pI258 from S. aureus. This reductase demonstrated selenate reduction and was inhibited by tellurite (Ji et al., 1994). The overlap of the biochemistry of arsenate with the chalcogen oxyanions remains mostly under-appreciated and under-investigated.

8. CONCLUDING REMARKS An interesting biological puzzle of tellurite resistance arises from the observation that levels of resistance frequently observed are higher than the concentrations typically experienced in the environment. Levels of selenite


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resistance are closer to environmental levels and the biochemistry of the cells may have naturally evolved in order to coexist with this metal. Perhaps, the striking difference between the cellular response against tellurite and the cellular response against selenite results from the evolutionary pressure of specific intracellular, periplasmic, and electron transfer redox components. The recent observation that the thiol:disulfide oxidoreductase DsbB allows the transfer of oxidizing equivalents from tellurite to membrane-embedded quinols opens new perspectives for both microbial physiologists and biochemists. The possibility that membrane-bound disulfide proteins might act as ‘‘electron conduits’’ between periplasmically localized metalloids and redox complexes raises the question of whether metalloids can be considered toxic per se or whether they might also act as ‘‘electron sinks’’ under unfavorable reducing conditions. Another biological puzzle concerns how metalloids get into cells and their subsequent cytosolic fate. No specific carriers have been identified although several non-specific mechanisms are known. Further, the reduction mechanisms are still uncertain for both tellurite and selenite to their less toxic elemental forms, while it is clear they are linked to generation of toxic ROS. Thus, in the case of tellurite and probably also that of selenite, the cellular response is more likely to be a suicide mechanism than a rescue mechanism. In conclusion, tellurite resistance has proved to be the greatest challenge in the metal and metalloid resistance field. The level of frustration was clearly illustrated in a recent review by Silver and Phung (2005a) where the area of tellurite resistance was given as a single paragraph indicating that the research had not progressed much in 10 years in comparison to the research on other metals. However, as seen here, advances have indeed been made in both understanding the mechanism of toxicity of this metalloid and the various biochemical processes it interacts or interferes with. We are hopeful that this review helps to bring ‘‘tellurium microbiology’’ out of the dark age.

ACKNOWLEDGMENTS This work was supported by MIUR (PRIN 2005) to D.Z. and NSERC to R.J.T. R.J.T. is grateful for discussions with Andrew J. Percy and discussions on the biofilm research with Howard Ceri. J.J.H. was supported by an AHFMR studentship and a NSERC CGSD award. We also thank Bronwyn Hasslam for the careful reading of the manuscript.

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Plate 1 Biogeochemical transformation of tellurite and selenite by bacteria. The coloration of black cells (tellurite) and redorange (selenite) is due to the reduction to Ch(0) product within the cells. (A) Pseudomonas aeruginosa grown in microtitre plate planktonically with tellurite. (B) P. aeruginosa grown on Calgary Biofilm Device pegs with tellurite. (C) P. aeruginosa grown in microtitre plate planktonically with selenite. (D) P. aeruginosa grown on Calgary Biofilm Device pegs with selenite. (E) E. coli grown on solid Luria Bertani broth showing the black colonies. (F) Thin section electron micrograph of E. coli grown in the presence of tellurite. The figure shows the precipitation of black crystals along the membrane. (G) E. coli harboring various tellurite resistance determinants. The non-colored culture of the ars is reflective of the resistance being an efflux system. (For b/w version, see page 10 in the volume)

Gaining Insight into Microbial Physiology in the Large Intestine: A Special Role for Stable Isotopes Albert A. de Graaf1,2,3 and Koen Venema1,3 1

Wageningen Center for Food Sciences, P.O. Box 557, 6700 AN Wageningen, The Netherlands 2 Department of Surgery, University of Maastricht, Maastricht, The Netherlands 3 TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands

ABSTRACT The importance of the human large intestine for nutrition, health, and disease, is becoming increasingly realized. There are numerous indications of a distinct role for the gut in such important issues as immune disorders and obesity-linked diseases. Research on this longneglected organ, which is colonized by a myriad of bacteria, is a rapidly growing field that is currently providing fascinating new insights into the processes going on in the colon, and their relevance for the human host. This review aims to give an overview of studies dealing with the physiology of the intestinal microbiota as it functions within and in interaction with the host, with a special focus on approaches involving stable isotopes. We have included general aspects of gut microbial life as well as aspects specifically relating to genomic, proteomic, and metabolomic studies. A special emphasis is further laid on reviewing relevant methods and applications of stable isotope-aided metabolic flux analysis (MFA). We argue that linking MFA with the ‘-omics’ technologies using innovative modeling approaches is the way to go to 3

Present address: TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands.

ADVANCES IN MICROBIAL PHYSIOLOGY, VOL. 53 ISBN 978-0-12-373713-7 DOI: 10.1016/S0065-2911(07)53002-X


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establish a truly integrative and interdisciplinary approach. Systems biology thus actualized will provide key insights into the metabolic regulations involved in microbe–host mutualism and their relevance for health and disease.

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Why Study Intestinal Microbial Physiology? . . . . . . . . . . . . . . 1.2. Purpose of This Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The gut microbial ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Butyrate is Important . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Gut pH Matters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Microbes Extend Our Genome . . . . . . . . . . . . . . . . . . . . . . . 2.4. Microbes Keep Our Immune System on Standby . . . . . . . . . . 2.5. Methods to Study Bacterial Physiology: Many Fields of Science Come Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6. Stable Isotopes Offer Unique Insights . . . . . . . . . . . . . . . . . . 3. Stable isotopes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. What are Stable Isotopes? . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. How are Stable Isotopes Detected? . . . . . . . . . . . . . . . . . . . 3.3. What Information can be Retrieved from Stable Isotope Experiments? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Important Fields of Applications of Stable Isotopes in Biomedicine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Basics of Metabolic Flux Analysis . . . . . . . . . . . . . . . . . . . . . 3.6. MFA in Detecting Microbial Metabolic Stress . . . . . . . . . . . . . 4. Genomic inventories of intestinal bacteria . . . . . . . . . . . . . . . . . . . . 4.1. General Aspects: Cataloguing Intestinal Microbial Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. The Microbiome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Stable Isotope Probing: Clues to Metabolic Function from Genomics Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Proteomic aspects of intestinal microbial life . . . . . . . . . . . . . . . . . . 5.1. Functions of Intestinal Bacterial Enzymes . . . . . . . . . . . . . . . 5.2. Proteomic Studies of the Gut Microbiota: A Largely Unprobed Area? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Can Stable Isotopes Help in Proteomics? . . . . . . . . . . . . . . . 6. Metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Microbial Products and What They Can Mean to Us. . . . . . . . 6.2. Tracing the Fate of Prebiotics: In Vitro Models and Stable Isotopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Evidence of Cross-Feeding. . . . . . . . . . . . . . . . . . . . . . . . . . 7. Metabolic flux analysis applied to the gut . . . . . . . . . . . . . . . . . . . . 7.1. Insights into Bacterial Metabolic Routes. . . . . . . . . . . . . . . . . 7.2. Get Quantitative: Mass Balances Reveal a Lot. . . . . . . . . . . . 7.3. Stable Isotope-Aided Quantification of Pathways: Functional Genomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Emerging picture of the role of microorganisms integrated in man . .

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8.1. Energy Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2. Innate Immune System . . . . . . . . . . . . . . . . . . . . . 8.3. Intestinal Microbiota: Is There a Link With Obesity? . 8.4. Role of Stable Isotopes . . . . . . . . . . . . . . . . . . . . . 9. New aspects in the study of intestinal bacterial physiology . 9.1. Microbes at War: Population Competition Models . . . 10. Conclusions and future prospects . . . . . . . . . . . . . . . . . . 10.1. Toward a Systems Biology of the Gut . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. INTRODUCTION 1.1. Why Study Intestinal Microbial Physiology? What happens in our intestine? The human gastro-intestinal (GI) tract is the primary site of food intake, food perception, and food conversion. In the first part, the small intestine, highly active enzymatic hydrolysis of carbohydrates, fats, and proteins takes place and the resultant digestive products are absorbed. Thus, the bulk of our food intake is processed by the small intestine. What then is the function of the second part of the intestine, the large intestine (or colon)? Until recently, the large intestine was considered just a storage place for undigested food components. However, the past 5–10 years have changed this view drastically. Nowadays, the large intestine is called the ‘forgotten organ’. The cells of microorganisms (totaling approximately 1014 cells) present in the colon outnumber the cells of the host by a factor of 10 and all these bacteria contribute to nutrient processing. The biochemical (metabolic) potential of this complex assemblage of different microorganisms is considered equal to that of the liver. This community of mostly anaerobic bacteria influences human gut physiology and health by performing a number of activities including fermentation of dietary compounds which escape digestion in the small intestine, processing of mucosal cells shed in the small intestine, and of intestinally secreted mucus. Thus, polymers of sugars are degraded by the colonic microbes into gases such as hydrogen, carbon dioxide, and methane as well as short-chain fatty acids (SCFAs) (Bergman, 1990; Cummings, 1991), notably butyrate, propionate, and acetate (Fig. 1). These SCFA are taken up by the host and contribute to its energy and health status. In addition, the microbial community produces a variety of other health-related compounds including vitamins (Hill, 1995) and other growth-promoting compounds. However, toxic, mutagenic, and carcinogenic substances (Cummings and Macfarlane, 1997) may be formed that negatively affect the host. It is also known that the colon plays a role

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Propionate

Carbohydrates

PropionylCoA Diacetyl Glycerol CO2

Succinate

Malate

Pyr

Acetolactate Lactate Acetate

Acetic Aldehyde

Formate CO2

Ethanol

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AcAcetate CrotonylCoA

3-Hydroxybutyrate

n-Butanol

Butyrate

Figure 1 Schematic overview of anaerobic bacterial metabolic pathways involved in carbohydrate metabolism. The dashed reaction represents the Wood–Ljungdahl pathway of acetate formation from CO2. Boxes show the short-chain fatty acids (SCFAs) from C1 (formate) up to C4 (butyrate), and lactate.


2,3-Butanediol

CO2

AcetylCoA

Acetoin


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in the modulation of the immune system (Chadwick and Anderson, 1995), the transformation of bile acids (Aries and Hill, 1970), and the provision of a barrier against pathogenic bacteria (Hill, 1995). The above facts already indicate that the processes going on inside the colon are important for us. There is indeed evidence that the bacterial metabolic processes in the colon are becoming an increasingly important issue for man. Numerous indications of a correlation between intestinal health status and the occurrence of various intestinal diseases, such as colon cancer, inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS) (Chadwick and Anderson, 1995) have been reported. The general hypothesis is that there is a strong correlation between proteolytic fermentation in the (distal) colon and the occurrence of colon cancer and IBD (Macfarlane et al., 1992a; Levine et al., 1998). Protein fermentation leads to the production of microbial metabolites that can be toxic to the host (Macfarlane et al., 1992b; Macfarlane and Macfarlane, 1995). In particular, these may include sulfur-containing metabolites (Hill et al., 1995; Rowland, 1995). Carbohydrate fermentation, on the other hand, leads amongst others to the production of SCFA, which are considered to be health promoting (Cummings, 1991, 1995). During carbohydrate fermentation, protein is incorporated into microbial biomass (Birkett et al., 1996), preventing fermentation of protein. However, most carbohydrates are completely fermented in the proximal colon, which leads to the depletion of these substrates in the distal colon. Here, the microorganisms switch to fermenting protein. It is hypothesized that it would therefore be of importance to prolong the fermentation of carbohydrates. This can be accomplished for instance by including more slowly fermentable carbohydrates in the diet (e.g. certain types of resistant starch). More generally, the idea developed that changing the diet in such a way that harmful bacteria (or their harmful activities) are suppressed and beneficial bacteria (or their beneficial activities) are stimulated, may contribute to improving gut health. However, the microbial processes occurring in the colon are hitherto largely unknown, because the accessibility of the lumen of the human colon is severely limited in practice.

1.2. Purpose of This Review The studies discussed above all served to demonstrate that there is a strong yet intricate link between intestinal bacterial metabolism and gut health. There is also little doubt that nutrition has an impact on the composition and activity of the intestinal microbiota and thereby influences human health and well-being. However, the mechanisms behind these processes are

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still largely unknown. If we want to be able to improve human health by optimizing nutrition, we must know what are the regulatory mechanisms that govern how our intestinal bacteria react to various food ingredients, and also what will be the determinant processes for the reaction of the host (i.e. our own body) in return. While this may seem a distant goal, a key step in this research, i.e. the characterization and analysis of the bacterial physiological behavior, has become possible using the powerful analytical methods available today. While genomics, proteomics, metabolomics, and bioinformatics immediately come to mind, it is becoming increasingly clear that the final endpoint of the genome, proteome, and metabolome, i.e. the fluxome, is perhaps the most closely linked with physiology. Indeed, it is argued that fully assembled metabolic pathways in living systems, rather than genes or proteins, are the true units of function in biology and biochemistry. A corollary is that measurement of metabolic fluxes (biochemical kinetics) is thereby required to understand biochemical control and gene function (Hellerstein, 2004). Studying the dynamics of an organism’s metabolic fluxes in response to various stimuli (such as different nutritional components) provides the unique and key insights to understand physiological regulation. As pointed out above, it is precisely this regulation which needs to be discovered so as to finally enable a targeted modulation of intestinal bacterial metabolism to achieve beneficial effects on human health. Therefore, the present review will put a special focus on the application and perspectives of metabolic flux analysis (MFA) and its main enabling technology, stable isotope labeling, to study intestinal microbial physiology.

2. THE GUT MICROBIAL ECOSYSTEM 2.1. Butyrate is Important As already eluded to above, SCFAs (primarily acetate, propionate, and butyrate) are the major end products of bacterial fermentation in the intestine and affect key functions of the colonic epithelium in vivo. These compounds are probably key participants in gut maintenance (Bergman, 1990; Kruh et al., 1991; Mariadason et al., 2000) and may also be beneficial contributors to the peripheral metabolism in humans (Macfarlane and Cummings, 1991). Therefore, SCFA have been the subject of numerous investigations. Butyrate is considered the most important of the SCFA. In the large intestine, butyrate is present in millimolar concentrations. Butyrate is metabolized by epithelial cells and is responsible for 70% of their


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energy needs (Roediger, 1982; Scheppach, 1994). In addition, it acts as a signaling metabolite, affecting epithelial cell proliferation and differentiation (Gamet et al., 1992; Gibson et al., 1992). In addition to its established role in regulating viability, differentiation, and proliferation (Velazquez et al., 1997), butyrate was reported to be effective in cancer suppression (McIntyre et al., 1993; Singh et al., 1997). Furthermore, butyrate might be beneficial in preventing mucosal inflammation, because decreased availability of butyrate has been associated with distinct forms of colitis (Harig et al., 1989; Chapman et al., 1994; Ahmad et al., 2000). Moreover, butyrate enemas have been shown to be an effective treatment for mucosal inflammation in both humans and animal models of colitis in some studies (Scheppach et al., 1992; Gibson and Rosella, 1995; Butzner et al., 1996; D’Argenio et al., 1996; Fernandez-Banares et al., 1999; Kanauchi et al., 1999; Okamoto et al., 2000; Andoh et al., 2003; Cherbut et al., 2003). Experimental work to explain the molecular mechanisms of this anti-inflammatory property of butyrate largely focused on cultured intestinal epithelial cells, where butyrate was shown to modulate IL-8, and macrophage inhibitory protein 2. In addition, butyrate has been shown to inhibit activation of the transcription factor NF-kB in cultured epithelial cells (Wu et al., 1999; Inan et al., 2000). Furthermore, butyrate has been shown to have an anti-inflammatory effect on human monocytes by the potent inhibition of IL-12 and upregulation of IL-10 production (Saemann et al., 2000). Moreover, butyrate resulted in an increase of IgA-producing cells and mucosal IgA concentrations (Morita et al., 2004; Roller et al., 2004), the secretion of anti-inflammatory cytokines (Fusunyan et al., 1998, 1999; Saemann et al., 2002; Bocker et al., 2003), and decreased activity of myeloperoxidase (Butzner et al., 1996; Cherbut et al., 2003), an enzyme which aids in the defensive properties of phagocytic cells of the human immune response. The (regulation of the) flux of butyrate (and the other SCFA for that matter) by (specific) intestinal microorganisms in the colon and the use of the SCFA in the rest of the body, however, is still largely unknown. It is important therefore to know which bacterial metabolic routes exist in vivo for the synthesis of SCFA. Knowledge of the regulation of these metabolic routes will allow the development of dietary strategies to influence SCFA metabolism, possibly even for therapeutic purposes.

2.2. Gut pH Matters Carbohydrate fermentation results in lowered cecal pH concomitant with the production of SCFA (Cummings and Bingham, 1987). In sudden death individuals, a significant trend from high to low concentrations of SCFA

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has been found on passing distally from cecum to descending colon, while pH changed from 5.670.2 in the cecum to 6.670.1 in the descending colon (Cummings et al., 1987). Interestingly, there seems to be an inverse effect of pH on SCFA metabolism as well; the response of human fecal microbial communities in anaerobic continuous culture showed markedly higher final butyrate concentrations at pH 5.5 compared with pH 6.5, whereas acetate and propionate were higher at pH 6.5 (Walker et al., 2005). Changes in colon pH are also reported to alter the metabolism of protein, bile acids, nitrate, sulfate, and other substances (Cummings and Bingham, 1987). In contrast to SCFA, products of protein fermentation, such as ammonia, branched chain fatty acids, and phenolic compounds, progressively increase from the proximal (right) to the distal (left) colon, as does the pH of gut contents (Macfarlane et al., 1992a). Epidemiological studies found a marked correlation between pH and the incidence of colon cancer (e.g. Levy et al., 1994), which in a subsequent study appeared to be associated with higher animal protein and fat consumption (O’Keefe et al., 1999). However, a direct linkage between colon cancer and alkaline colonic pH was questioned in other experimental studies (Hove et al., 1993). This demonstrates that it is difficult to establish unequivocally the cause–effect relationships between intestinal processes and human health due to the many interactions present. Clearly, extensive additional systematic research is necessary to elucidate the regulation of intestinal bacterial metabolism, the reaction of the host, and the interaction between both.

2.3. Microbes Extend Our Genome The colonic microbiota represents an enormous and largely unexplored potential of metabolic pathways for synthesis and degradation of compounds (Sekirov and Finlay, 2006). This has, for instance, been described for fatty acids (Juste, 2005). Microbial activity may be relevant here in various ways; bacteria may detoxify food components (Humblot et al., 2005), but also produce toxins themselves. Due to the multitude of chemical reactions they can carry out, intestinal bacteria may contribute significantly to drug metabolism (Shu et al., 1991; van de Kerkhof et al., 2005). This may be exploited by having a drug activated by intestinal bacteria that is otherwise absorbed before it can exert its action, or by applying bacteria themselves to act directly on gut epithelial cells (O’Hara et al., 2006). Clearly, while metabolic activation of drugs by intestinal host cells has already been demonstrated (Gharat et al., 2001), bacteria must also be considered able to carry out specific desired biotransformations of food (glucosinolates, flavonoids, etc.) and drugs. However, the establishment of


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the highly specific biotransformations needed for drug efficiency may prove difficult in such a complex microbiota as that found in the colon.

2.4. Microbes Keep Our Immune System on Standby Gut bacteria also play an important role in the development and modulation of our immune system. A healthy mucosa shows a certain chronic, basal inflammatory activity in the lamina propria. This is essential in relation to its barrier function and is closely related to the intestinal microbiota. It is assumed that a number of important health issues, such as IBS, are associated with an aberration of this inflammatory activity (Collins, 2001). In other words, it seems that we need our intestinal bacteria to keep up a certain basal level of activity of our intestinal immune system. The interaction between bacteria, their metabolic products, and the colonic epithelium is of pivotal importance for the inflammatory reaction. Recently, the importance of mast cell activity for intestinal function in relation to intestinal comfort has been described (O’Sullivan et al., 2000; Barbara et al., 2004; Siddiqui and Miner, 2004), and may well be modified by nutritional intervention (Rydzynski and Dalen, 1994; Ju et al., 1996; Ganessunker et al., 1999; Larauche et al., 2003). Unfortunately, a single biomarker for epithelial health is lacking. One has to rely on markers of permeability, e.g. production of certain tight junction proteins (Nusrat et al., 2000; Ma et al., 2004), Paneth cell defensins (Bevins, 2006), transport of paracellular and transcellular inert permeability markers (Baumgart and Dignass, 2002), inflammation (Saemann et al., 2000), mucus composition (Szentkuti et al., 1990), cell turnover and apoptosis (Andoh et al., 2003), mast cell activation (O’Sullivan et al., 2000), and others. Healthy individuals can be subjected to investigating these biomarkers as many of these can be determined by non- or minimally invasive, but sophisticated, techniques. Furthermore, in patients with established increased inflammatory activity, such as ulcerative colitis (UC) and pouchitis, these techniques can also be implemented. The intestinal immune system and the mucus layer are both important for human host defence and can be affected by microbial metabolites (amongst others butyrate).

2.5. Methods to Study Bacterial Physiology: Many Fields of Science Come Together Not surprisingly, recent years have seen the development of specially adapted experimental techniques to study the colon and its inhabitants. These include in vitro model systems, cell culture models, animal models, microdialysis, and breath tests (Table 1).

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Table 1 Some characteristics

References

In vitro model systems

-From simple anaerobic cultivation tubes to fully fledged sophisticated computer-controlled in vitro model systems -Enable quantitative studies under defined conditions using an inoculum isolated either from feces or, in an invasive manner, from specific sites along the GI tract -The effect of e.g. prebiotics on the composition and activity of the microbiota has been studied in such a model -From CaCO-2 or HT29 cell lines to intact intestinal mucosal strips -Enable the selective study of the properties of different types of (transformed) colon cells in isolation -The advantage of mucosal strips over cell cultures is that conditions probably better approach the in vivo situation because the strips include an intact basolateral lining of cells, as e.g. reflected in Km values for butyrate uptake that differ markedly from isolated colonocytes -Especially dogs and pigs, with several methods of sampling: either (i) dissection after sacrificing the animals, (ii) use of stomas implanted in the living animal to probe specific luminal sites in the GI tract, or (iii) multicatheterization of blood vessels so as to gain access to arterial, portal, and hepatic venous blood all at the same time in the living, conscious animal

Minekus et al. (1999), Jensen and Jorgensen (1994), van Nuenen et al. (2003), Venema et al. (2003)

Cell culture models

(Monogastric) animal models

Boren et al. (2003), Jorgensen and Mortensen (2000), Jorgensen and Mortensen (2001) Deutz et al. (1998), Rerat (1985), Wunsche et al. (1979)


Experimental technique

Breath tests and gas analyses

Rooyackers et al. (2004), Jansson et al. (2004)

Christian et al. (2002), Jensen and Jorgensen (1994), Slater et al. (2006)


Microdialysis

-Upon chemical analysis of blood samples, the techniques described in the line above allow for detailed quantitative studies of net splanchnic absorption and/ or intestinal production of metabolites, but such studies generally bear a (strong) invasive character -Can be used safely with low-grade invasiveness in humans with small catheters placed in specific tissue beds of interest -Allowing continuous sampling of the interstitial space over prolonged periods of time without taking any biopsies -Quantitative measures for metabolic activity are not easily obtained because the exact amount of tissue involved in the dialysis is not known. Results of intraperitoneal microdialyses were shown to strongly depend on catheter position -Enable non-invasive assessments of especially intestinal (carbohydrate) metabolism -Information is limited and quantitative aspects are not trivial since measured values represent overall metabolism of the complete organism

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The challenge for the coming years is to establish a truly integrative research approach that will enable the analysis and correlation in a meaningful manner of the many diverse aspects involved in intestinal microbial metabolism and its interaction with the human host. This will require the cooperation between scientists from various different scientific disciplines such as microbial physiology, human physiology, and gastroenterology, as well as highly technical disciplines including genomics, proteomics, metabolomics, analytical sciences, and bioinformatics and possibly nano-technology.

2.6. Stable Isotopes Offer Unique Insights Perhaps one of the greatest difficulties in the research of intestinal metabolism and function is the requirement for adequate, minimally invasive experimental techniques that allow for research in humans in vivo. Such techniques will have to cope with severely limited possibilities for manipulation of experimental conditions as well as for material sampling. The use of stable isotopes may prove to be a key factor to success here. Stable isotope-labeled molecules follow the same metabolic routes, and function identically in physiological processes as their natural unlabeled counterparts. The isotopic label, however, allows for their specific detection at any desired stage after their administration, allowing indirect monitoring of the processes in which they are involved. Over the past 20 years, stable isotopelabeling techniques have proven to be powerful tools to get quantitative as well as qualitative information about the metabolic processes in living organisms in general, including microorganisms, plants, animals, and humans, and also in the colon in vivo (Moran and Jackson, 1990; Wolfe, 1992; de Graaf, 2000; Shulman and Rothman, 2001; Pouteau et al., 2003; Kelleher, 2004; McCabe and Previs, 2004; Dolnikowski et al., 2005; Ratcliffe and Shachar-Hill, 2006). The use of isotopically labeled compounds enables the selective study of that part of the metabolism in which the isotopic tracer is involved, offering ample possibilities to probe microbial as well as host metabolism, or both. Isotopic labeling in compounds can be conveniently and specifically detected by mass spectrometry and/or nuclear magnetic resonance (NMR)-based analytical techniques. One indirect yet important feature of stable isotopes is that their application involves and bridges many disciplines at the same time, providing scientists with ample opportunities to come across different fields than their own, and stimulating cross-fertilization of ideas. Scientists from the life sciences working with stable isotopes will acquire a sense for analytical issues, and they will necessarily have to interact with physicists and


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mathematicians to undertake their modeling work successfully. The complexity of human metabolism may make researchers in the medical field refrain from in-depth mechanistic analyses of metabolic regulation, yet the impressive results obtained from stable isotope studies in microbial applications in recent years may stimulate the design of studies to tackle more complex issues. In return, analytical scientists will broaden their views because they need to find solutions that suit both the experimental constraints associated with biological material, as well as the requirements of the data modelers to reach accurate and precise parameter estimates and statistically significant results. Mathematicians and physicists are forced to make sense out of data that are often imprecise and show limited reproducibility, yet the inherent ability of such investigations to detect structure and logic even in complex multivariate data adds to the level and quality of conclusions that can ultimately be derived from experimental work. Put differently, working with stable isotopes conveys a natural inclination toward systems biology thinking (Kelleher, 2004).

3. STABLE ISOTOPES 3.1. What are Stable Isotopes? Isotopes were discovered in the 1910s after experiments conducted by F. Soddy gave the first demonstration that most of the elements in nature are composed of atoms identical from the chemical point of view but slightly different in weight. Very soon after the discovery of deuterium, for which H.C. Urey was awarded the Nobel Prize in 1934, researchers launched the idea of using stable isotopes in kinetic/dynamic investigations. Thus, early studies on fat metabolism in mice with deuterium were done by R. Schoenheimer and D. Rittenberg (Ratner et al., 1987), and studies in nutrition by using 15N, 13C, and 18O soon followed. Tracer methods find applications in nearly every field of science, be it typical life science fields (medicine, biology, physiology, nutrition, toxicology, biotechnology), or more technical areas (physics, chemistry, agriculture, geoscience, engineering), which have now become an integral part of everyday life. The common issues for all these isotope labeling applications concern the possibility of tracing the entity of interest, called tracee, which may be a substance, or a component of a substance, like a radical, a molecule, or an atom. An ideal tracer has the same physical, chemical, or biological properties as the tracee, but it presents some unique characteristic that enables

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its detection in the system where the tracee is also present. The production of an isotopic tracer involves the substitution of one or more naturally occurring atoms in specific positions in the tracee molecule with an isotope of that atom with a less common abundance. Either stable or radioactive isotopes can be used as tracers. Natural abundancies of a number of stable isotopes relevant for life science research are displayed in Table 2. Mass differences of isotopes are due to different numbers of nuclear neutrons, so that the chemical properties are not affected. Both stable and radioactive isotopes of an element take part in the same chemical reactions of the element. The use of a labeled tracer requires the assumption that the labeled molecule, or atom, will not be discriminated from the unlabeled in, for example, chemical or enzymatic reactions, and will trace the position or movement of the unlabeled molecules. Some isotopic effects (like evaporation processes or root uptake into plants) can be observed, especially for light elements or molecules, and should be taken into account. Radioactive tracers have been used very intensively in the earlier years of life sciences research (Table 2). However, their use has diminished much in favor of stable isotopes after the health risks of radioactivity became apparent (and radioactive waste processing costs rose significantly).

Table 2 Stable and radioactive isotopes used in life science research Element

Isotope

Stable or radioactive

% Natural abundance

H

1

Stable Stable Radioactive Stable Stable Radioactive Stable Stable Stable Stable Stable Stable Stable Stable Radioactive Stable Stable Radioactive

99.985 0.015 –a 98.89 1.108 – 99.63 0.37 99.76 0.037 0.204 95.00 0.76 4.22 – 0.014 100 –

C N O S

P a

H H 3 H 12 C 13 C 14 C 14 N 15 N 16 O 17 O 18 O 32 S 33 S 34 S 35 S 36 S 31 P 32 P 2

Abundance listed for the stable isotopes only.


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3.2. How are Stable Isotopes Detected? Detection of stable isotopes is based either on their specific masses, or on nuclear properties such as spin. Consequently, mass spectrometry (MS) and NMR are the main detection methods for stable isotopes used today. Mass spectroscopy is by far the more sensitive of both techniques. A great variety of mass spectrometer instruments and applications dedicated to the detection of compounds with different characteristics exists. Newest time-of-flight (TOF) and Fourier transform (FT) mass spectrometers reach such high mass accuracies that, from the measured mass of a molecule, its unique composition in terms of number of carbons, nitrogens, oxygens, and protons can be derived unequivocally, allowing for easy identification. Especially for MS instruments that do not have such high mass resolution, the spectrometer is often run ‘hyphenated’ with a chromatographic separation technique (either liquid chromatography (LC) or gas chromatography (GC)) to allow for better separation of signals, namely in both the (retention) time and the mass dimension. Illustrative examples of such two-dimensional approaches used for isotopic studies of metabolism include LC-MS of glycerol and glucose (McIntosh et al., 2002), LC-MS of amino acids (van Eijk and Deutz, 2004), GC-MS of amino acids (Christensen and Nielsen, 1999) in protein hydrolysates, volatile fatty acid detection by GC-MS, and GC-combustion-isotope ratio mass spectrometry (GC-C-IRMS) (Morrison et al., 2004), as well as IRMS approaches for breath test analysis (Stellaard and Elzinga, 2005). The latter authors describe also the use of infrared (IR) spectroscopy for gas isotopic analysis. One important difficulty in determination of isotopic enrichments by mass spectrometry is the presence, especially with larger molecules, of background isotope signals that stem from the natural abundance of stable isotopes, 13C in most cases making the largest contribution. Powerful matrix calculation protocols that allow for easy correction of those mass isotopomer peaks have recently been developed (Wahl et al., 2004) (isotopomers are molecules having an identical chemical composition but different masses due to the presence of one or more isotopes). NMR spectroscopy is far less sensitive than MS and can detect only nuclei that possess a nuclear spin. Fortunately, these include such important isotopes as 2H, 13C, 15N, and 17O. Moreover, NMR in contrast to MS offers the advantage that it also allows one to determine the position of an isotopic label within a molecule. This has proved such an enormous asset in studies of biosynthetic pathways that the number of isotope-aided NMR studies in the life science field is seemingly endless, and growing every day. The interested reader may find useful information and references in de Graaf (2000).

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3.3. What Information can be Retrieved from Stable Isotope Experiments? Basically, stable isotopes can convey two sorts of information: how fast a specific metabolic process is running, and what the products of the processes are. That is, the speed of incorporation of an isotopic label in a molecule gives information on the synthesis rate of that metabolite, and the position of the label in the molecule gives information on the biosynthetic pathway of the molecule. In special cases, namely experimental protocols where a steady state in time of the degree of isotopic labeling of the concerned molecules is established, it is the steady-state degree of isotopic enrichment at different positions in the concerned molecules that gives information on the synthesis rate (or, equivalently, the biosynthetic pathway flux) of the molecule (Fig. 2). The principles of kinetic analysis from isotopic labeling experiments have been described in detail (Wolfe, 1992). Recent interesting examples of ‘classical’ kinetic analysis and biosynthetic pathway elucidation can be found in papers by Teusink et al. (2003) and Bacher et al. (1999), respectively.

endogenous tracee synthesis

Ra tracer infusion Inf

whole body compartment

TTR

Rd

disposal

Figure 2 Principle of whole body synthesis rate determination using stable isotopes. Ra, rate of appearance of unlabeled, newly synthesized metabolite; Rd, rate of disappearance (due to metabolism, excretion, etc.) of metabolite; and Inf, rate of infusion of stable isotope. The tracer–tracee ratio (TTR) is determined experimentally by NMR or MS from a sample of the traced compartment. Because Inf is a parameter that is set by the researchers and TTR can be measured, Ra and Rd can be determined.


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A great variety of dedicated experimental protocols for isotope-aided studies have been developed to suit special needs, some of which are relevant here. Incubation of microorganisms with [U-13C]glucose for multiple doubling times followed by biomass hydrolysis and isotope labeling NMR analysis of the proteinaceous amino acids allows for overall analysis of the bacterial intermediary metabolism (Szyperski, 1995). Similarly, incubation of mammalian cells with [1,2-13C2]glucose and subsequent mass isotopomer analysis of cellular metabolites allows the characterization of cellular intermediary metabolism (Boren et al., 2003). Primed, continuous infusion of 13C-labeled essential amino acids combined with LC-MS detection of amino acid labeling allowed the study of protein turnover in man (Engelen et al., 2000). An alternative technique needs only a single tracer dose to be injected but is mathematically more involved and requires also biopsies to be taken (Zhang et al., 2002). Deuterium labeling of non-essential amino acids combined with mass isotopomer distribution analysis (MIDA) (Hellerstein and Neese, 1999) of body proteins allow the determination of their synthesis rates (Busch et al., 2006). The indicator amino acid oxidation method, which consists of infusion of [1-13C]phenylalanine and monitoring its oxidation product 13CO2 in exhaled air at different supplementation rates of another essential amino acid, may be used to determine dietary amino acid requirements such as for L-lysine (Zello et al., 1993). The use of multiple tracers in a single experiment may offer specific advantages. The aim of tracer studies is to gather quantitative information about a specific metabolic function. In case the measured isotope enrichment may be affected by other metabolic events, the necessary correction can be performed when a second tracer, which is known to be metabolized by all interfering metabolic events but not by the function of interest, is added simultaneously (Stellaard, 2005). A special case of this principle is the simultaneous administration of two tracers through both the intravenous and oral routes of administration, which permits the understanding of dynamic pictures of relevant processes such as first-pass splanchnic bed retention of nutrients in humans (Matthews et al., 1993). Smartly designed multiple tracer techniques may also be used to resolve multiple biosynthetic pathways leading to the same metabolite, as in the case of, for example, arginine metabolism (Lau et al., 2000), homocysteine remethylation metabolism (Davis et al., 2004), and gluconeogenesis in humans (Ekberg et al., 1999). Application of multiple substrates has also led to impressive results in quantification of complex microbial metabolic pathway networks (Petersen et al., 2000) and theoretical frameworks have been established that allow for optimal experimental design of stable isotope labeling experiments (Wiechert et al., 2001).

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3.4. Important Fields of Applications of Stable Isotopes in Biomedicine As mentioned previously, stable isotopes have been applied to almost every imaginable field of the life sciences. In the following, a number of references are given to literature on biomedical applications of stable isotopes that are relevant to the study of microbial physiology, mammalian metabolism, and their interaction. Stable isotope studies have been highly instrumental in the elucidation of (microbial) biosynthetic pathways (Bacher et al., 1999). Relatively recent examples that may bear relevance to investigating gut microbial pathways are, for example, a study on pathways of methanol conversion in anaerobic bacteria (Paulo et al., 2003), a study of propionate metabolism by sludges from bioreactors treating sulfate- and sulfide-rich wastewaters (Lens et al., 1998) and a study demonstrating how the presence of the Bifidobacterium pathway of acetate formation (Wolin et al., 1998) can be inferred from isotope labeling data. Stable isotopes have found widespread application in MFA of microbes (see following paragraphs); a recent review is Wiechert (2001). The technique can now be applied on a large scale for screening of metabolic flux distributions in microorganisms. Illustrative applications on Bacillus subtilis are described (Fischer and Sauer, 2005). Many examples of stable isotope work in mammalian systems are available. The reader looking for an overview of recent work may find reviews on the application of isotope tracers to the study of metabolism in mouse models (McCabe and Previs, 2004), on the estimation of fluxes in mammalian metabolic physiology (Kelleher, 2004), and on the application of stable isotopes in obesity research (Dolnikowski et al., 2005) useful. Organs that have been very intensively studied with stable isotopes include the heart (Des Rosiers et al., 2004), the liver (especially hepatic gluconeogenesis) (Previs and Brunengraber, 1998), muscle, and brain (Shulman and Rothman, 2001). Interesting areas where significant progress in recent years has been reported include lipogenesis (Bederman et al., 2004), nitric oxide metabolism in disease (Luiking and Deutz, 2003), intestinal and renal metabolism of L-citrulline and L-arginine (Boelens et al., 2005), and interorgan protein metabolism (Engelen et al., 2005). Stable isotopes have also been applied to the study of colonic metabolism (Pouteau et al., 2003); these studies will be reviewed in the section on MFA below. Stable isotopes are also beginning to be applied to the study of the interaction of human and gut microbial metabolism. For instance, knowing that SCFA are important products of gut microbial metabolism, and that they are taken up by the host, a further question is what exactly they are


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used for in our bodies. SCFA are known to be oxidized to CO2 in colonic tissue, contributing to colonocyte energy metabolism (Jorgensen and Mortensen, 2000). The fate of butyrate carbons in metabolism of HT29 colon cells was probed with [1,2-13C2]butyrate (Boren et al., 2003) and demonstrated their utilization in the citric acid cycle. The study of amino acid metabolism in man is another example where stable isotopes (in this case 15N) have been instrumental in demonstrating man–microbe interactions. Amino acids from circulating blood may exchange via enterocytes even with the colonic lumen, causing intermingling of endogenous and microbial amino acid metabolism. A number of studies employing 15N-labeled urea have been performed to assess this issue. Urea diffuses into the colon where it is hydrolyzed by bacterial urease to ammonia before being assimilated. Thus, 15N labeling of amino acids upon 15 N urea administration is a clear indication of the activity of microbial nitrogen metabolism (Fig. 3). The microbial origin of a significant fraction in body protein of several amino acids that cannot be transaminated in mammalian tissue (e.g. lysine and threonine) has thus been shown (Lien et al., 1997; Metges, 2000). This implies that our intestinal bacteria have the potential to supply us with essential amino acids! While the examples mentioned mostly pertained to studies focusing on metabolites of intermediary metabolism, recent applications with high relevance to the study of intestinal microbial physiology also target the synthesis of macromolecules, notably proteins and nucleic acids. Only a few stable isotope-aided studies of proteins targeting the intestine have appeared in the literature. These studies are focused on the synthesis of mucins and mucoproteins (Faure et al., 2002), mainly looking at the effect of food components (Coeffier et al., 2003; Faure et al., 2005). Studies on nucleic acids are especially relevant for the study of intestinal microbiota (see next chapter). Stable isotope probing (SIP) approaches, as they are called, involve incubation of microorganisms with a stable isotopelabeled substrate under conditions resembling the environmental situation. After a sufficiently long incubation, microbial nucleic acids are isolated and the heavier fractions (i.e. those that show incorporation of the stable isotope) are separated and analyzed by, for example, PCR and fingerprinting techniques. Hereby, in situ isotope tracking techniques allow the unraveling of the substrate utilization of microbes in their natural habitat by linking the isotopic signature of biomarker molecules to their inherent phylogenetic information (Manefield et al., 2004). These techniques are useful for a broad and unprejudiced activity-screen in complex communities, and also to verify whether selected groups of microbes utilize a certain substrate or not. Recent reviews have been written (Dumont and Murrell, 2005; Egert et al.,

enterocytes

serosa (blood)

from arterial

92

intestinal lumen

diet urea

microbial compartment

NH3

urea

His, Lys, Thr

AA

AA

microbial

Protein

endogenous

Protein NH3

AAdisp

Protein NH3 to portal vein

Figure 3 Schematic representation of gut-associated nitrogen metabolism, compiled from information in Metges (2000) and references therein. Colored circles symbolize 15N isotope label originating from urea (red) or ammonia (blue), respectively, with fading color intensity indicating isotope dilution. Urea diffuses from the blood through the enterocytes into the intestinal lumen, where it is hydrolyzed by bacterial urease into ammonia and carbon dioxide. Ammonium is the preferred non-specific microbial nitrogen substrate for synthesis of e.g. amino acids. Microbially synthesized amino acids may partially be released into the gut lumen and taken up by ileal enterocytes (in the colon, bacterial cell densities are so high that microbially synthesized amino acids probably never reach colonocytes). Therefore, bacteria may supply a significant portion of the body’s requirement for indispensable amino acids. 15N label appearance in histidine, lysine, and threonine upon 15N-urea or 15N-ammonia administration is proof of microbial activity since these amino acids cannot be endogenously transaminated. However, after administration of e.g. the 15N-labeled indispensable amino acid leucine, 15N label will appear in other branched-chain indispensable amino acids as well as in dispensable amino acids (AAdisp) since the body is able to transaminate leucine, valine, and isoleucine. Due to extensive amino acid exchange between blood, enterocytes, and intestinal microbiota, interpretation of 15N labeling experiments is often ambiguous. Combining nitrogen-15 labeling with carbon-13 or carbon-14 labeling as done e.g. in Torrallardona et al. (2003), therefore, may constitute a useful approach to arrive at unequivocal conclusions. (See plate 2 in the color plate section.)


Val, Ile, Leu


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2006). Since little is known about the actual in situ functions of human colonic microorganisms, SIP techniques appear particularly promising in investigating these. Ideally, to get good insight into the role of the intestinal microbiota, one would like to be able to probe bacterial and host metabolism simultaneously using stable isotopes, in a single approach. However, an integrative study of gut microbial metabolism of a relevant substrate (e.g. carbohydrate) that covers all the microbial products, and also monitors their metabolism in the host, is still lacking today. The setup of such a study would need to include the basic features of MFA. First, a careful mass balancing helps to ensure that all metabolites are accounted for. Second, tracing the routes where the microbially produced metabolites go using stable isotopes gives an insight into the host processes that receive input from gut microbial activities. Finally, determining the velocities of the various pathways involved allows us to address the (relative) importance of the pathways for the host. Likewise, the reverse routes (man to microbe) can be probed using stable isotopes. It is interesting to note that, whereas historically the fields of stable isotope studies in microorganisms and in mammalian cells and organisms seem to have developed much on their own, MFA is now starting to integrate both fields (Ramakrishna et al., 2001; Lee et al., 2003; Antoniewicz et al., 2006). Because of the key role MFA can play in integrating different fields and disciplines of science, the following sections will give a brief introduction to MFA with references to important literature.

3.5. Basics of Metabolic Flux Analysis At the heart of MFA as it is being used today to characterize microbial metabolism under steady-state conditions, stands the concept of metabolite balancing (FBA, flux balance analysis). Introduced to its full functionality by Stephanopoulos and Vallino (1991), this simple principle has proved very powerful in analyzing metabolic networks, and even in predicting their behavior under various environmental and genetic conditions (Schuster et al., 1999; Edwards and Palsson, 2000; Burgard et al., 2004). Special powerful computational methods like minimization of metabolic adjustment (MOMA) (Segre et al., 2002; Holzhutter, 2004), regulatory on/off minimization (ROOM) of metabolic flux changes (Shlomi et al., 2005), and flux coupling analysis (FCA; Burgard et al., 2004) have been developed especially for the latter purpose. Metabolite balancing applies the principle of material conservation for each and every metabolite pool in the metabolic

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network. At steady state, metabolite pools are constant, hence the total of all metabolite fluxes entering a specific pool must equal the total of fluxes leaving that same pool. This yields, for every metabolite pool, one linear equation relating all fluxes connecting with that pool. Available measurement data on fluxes (such as substrate uptake rates and product excretion rates) provide additional equations. Setting up the equations for every pool in the network then yields a high-dimensional system of linear equations, with the fluxes as unknowns, that can be solved mathematically using matrix procedures, provided the system is (over)determined (i.e. there are more independent equations than there are unknowns). Here comes the problem with FBA: it turns out that in practice, there are insufficient data to fully determine the equation system. Therefore, a solution can be obtained only when additional assumptions (such as on the stoichiometry of the electron transport chain, or a closed balance of cofactors such as NAD(P), etc.) are made. This has the drawback that the calculated fluxes depend more or less strongly on the assumptions made, with the associated risk of introducing important systematic errors. At this point, stable isotopes came in to help, providing additional measurement data to solve the flux analysis problem without having to rely on assumptions (Wiechert and de Graaf, 1996; de Graaf, 2000) (Fig. 4). The reason why isotopic labeling data allow this is that now a material conservation balance for each and every single carbon (in case of 13C labeling) for every metabolite in the network can be drawn up, resulting in a greatly increased number of equations. Although the number of unknown fluxes also increases in the procedure, for the reason that isotopic labeling patterns also depend on backward fluxes (Wiechert and De Graaf, 1997; Wiechert et al., 1997), the vastly increased amount of experimental data coming with the additional labeling information generally allows the solving of the new equations without having to rely on assumptions. Soon after that, it became obvious that a further increase in information could be obtained by including isotopomer measurement data. Isotopomer information holds knowledge as to whether a molecule is labeled in a single position, or two or more positions at the same time, and also which position(s) is/are labeled. Consequently, considering only natural carbon-12 and its stable isotope 13C, a molecule with a backbone of N carbons can exist as 2N different isotopomers. Extending the mass balancing principle up to the level of isotopomers proved to be difficult at first because the equations were no longer linear. However, an analytical solution was developed in due time (Mollney et al., 1999; Wiechert et al., 1999), making the full potential of MFA available. Theoretical properties of isotopomer labeling equation systems along with their associated consequences for experimental


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isotope-labeled substrate

pathway A

pathway B metabolism

FA 13 C 12 C

FB

FB

product metabolite = F A/ F B

Figure 4 Principle of pathway flux determination at (pseudo)steady state using stable isotopes. Via pathway A, carbons 1 and 2 of the substrate end up in carbons 1 and 2 of the product P, respectively, leading to an [1-13C]P isotopomer. In contrast, via pathway B, these carbons end up in carbons 2 and 1, respectively, leading to the [2-13C]P isotopomer (the fate of carbons 3 and 4 of the substrate is ignored here). When the labeling of pool P has reached steady state, the ratio of the [1-13C]P and [2-13C]P isotopomers present in the total pool equals the ratio FA/FB of the fluxes in the two pathways A and B. Once the total rate of synthesis of P is measured independently, the absolute fluxes FA and FB are known.

design have been analyzed (Wiechert and Wurzel, 2001; Isermann and Wiechert, 2003). The interested reader may find a number of tutorial reviews on MFA useful (de Graaf, 2000; Wiechert, 2001, 2002). Recent advances concentrate on providing mathematical frameworks that also allow metabolic fluxes from full isotopomer data sets under nonsteady-state conditions to be derived (Wiechert and Noh, 2005). This may provide a much hoped-for basis for the integration of the full potential of isotopomer labeling with ‘classical’ kinetic tracer approaches, both with regard to theoretical and experimental considerations.

3.6. MFA in Detecting Microbial Metabolic Stress One of the main advantages of MFA is that it visualizes the final effect of genetic, proteomic, and metabolomic responses to the changing environment

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of an organism. In other words, it reveals how the organism adapts itself at the metabolic level to changing requirements. This offers unique insights into physiological regulation that cannot usually be obtained from genetic, proteomic, or metabolomic analyses alone. To illustrate this point, the reader is referred to work by Petersen and colleagues (Petersen et al., 2000, 2001, 2003), on the amino acid-producing bacterium Corynebacterium glutamicum. This microorganism shows astonishingly little phenotypic change upon deletion/inactivation of important genes for anaplerosis (pyruvate carboxylase, phosphoenolpyruvate (PEP) carboxylase) or gluconeogenesis (PEPcarboxykinase) (de Graaf et al., 2001). This has severely hampered a targeted metabolic engineering for increased L-lysine production by genetic modification (Sahm et al., 2000). Whereas the organism does not respond at the genetic and proteomic level to inactivation of the aforementioned genes, metabolite levels changed up and down only to relatively small extents, with no clear picture discernible. Carbon-13 isotopomer-aided MFA experiments in contrast showed very clear-cut responses of the metabolic fluxes concerned (Petersen et al., 2001). Taking into account key kinetic data of the enzymes involved, a mathematical model of the relevant pathways was constructed of which the unknown parameters were subsequently tuned using the available experimental data on enzyme activities (‘genome’/‘proteome’), metabolite concentrations (‘metabolome’), and fluxes (‘fluxome’). Amazingly, this model could quite accurately predict the effects of genetic manipulations on L-lysine production (Petersen et al., 2003), making it the first working small-scale systems biology model to our knowledge. As of today, research in the medical field aiming at elucidation of disease mechanisms largely proceeds by way of making statistical correlations of observable physiological parameters (‘phenotype’) with measured metabolite concentrations (‘metabolome’), enzyme activities (‘proteome’), or gene transcription rates (‘genomics’/‘transcriptomics’). In our view, the advent of ‘-omics’ technologies in recent years has changed the scale of this approach, but not the paradigm. The example with C. glutamicum demonstrates that much can be gained from including MFA results, and from developing appropriate modeling frameworks as opposed to purely statistical analysis procedures. There is nowadays a growing consensus that, before a certain disease actually appears, there is a long preceding time period during which the metabolism is continuously stressed, until the point where normal regulatory mechanisms can no longer compensate (van der Greef et al., 2004), and the disease becomes manifest. Although changes in gene expression, enzyme activities, and/or metabolite levels may be apparent during the pre-disease period, pathway regulation often is so complex that a clear picture is very


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hard to obtain from statistical analysis of these data alone. From the example of C. glutamicum, we can expect that metabolic fluxes may offer a significantly improved view. A first example pertains to protein turnover measured with stable isotopes in chronic obstructive pulmonary disease patients (Engelen et al., 2000), where no apparent net unbalance in protein metabolism was found, but whole body protein synthesis and breakdown were significantly increased in patients (i.e. the turnover, or flux, was increased). Summarizing, it is predicted that stable isotope-aided methods, especially MFA, will significantly improve our understanding in the near future of metabolic regulation in response to the dynamic environment. In the special case of the intestinal microbiota interacting with our own metabolism, expectations are also high that stable isotopes will permit key insights into the metabolic regulations both on the microbial, and the host side in due course. The following sections will give an overview of important knowledge gained in recent years in the field of intestinal microbial metabolism on the genomic, proteomic, and metabolomic level as well as on the level of metabolic fluxes, with a special focus on studies that employed stable isotopes.

4. GENOMIC INVENTORIES OF INTESTINAL BACTERIA The human GI tract is colonized by a microbial community that develops in complexity during life, resulting in a climax community of microbial cells in adults which outnumber the host cells by an order of magnitude (Blaut, 2003; Zoetendal et al., 2006). In addition to this temporal development, the GI tract community is characterized by a distinct spatial variation of microbial communities that progressively develops in size and diversity distally from the stomach (Blaut, 2003; Zoetendal et al., 2006), culminating in a staggering 1012 microorganisms per gram of colonic content. The microorganisms in our large intestine contribute significantly to nutrient processing and are important for health and disease. While the enumeration of bacteria by conventional culture techniques has been imprecise and time consuming, analysis of the ecology of the intestinal microbiota has been greatly improved by designing 16S-rRNA-targeted oligonucleotide probes. Nowadays, many tools and techniques are available to characterize comprehensively the microbial diversity in the human gut (Wilson and Blitchington, 1996; Zoetendal et al., 1998; Suau et al., 1999; Rigottier-Gois et al., 2003). Use of these in molecular studies (Hugenholtz et al., 1998; Zoetendal et al., 2004a, 2004b) have shown that the majority of the

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microorganisms in our gut have not yet been cultivated as pure cultures in the laboratory, either because we do not know the nutritional requirements or growth conditions of these microorganisms (Finegold et al., 1983; Rigottier-Gois et al., 2003), or because they are damaged or dead (Ben-Amor et al., 2005). Despite the fact that, over the past few years, the use of these molecular techniques has given important insight into structure and spatial organization of the human intestinal microbiota (Hugenholtz et al., 1998; Zoetendal et al., 2004a, 2004b), only a limited number of tools are to hand to investigate the activity of the microbiota at the level of individual species. Several recent developments, discussed below, that aimed to characterize the activity of (particular species within) a microbial community have allowed a more detailed picture of the link between its structure and function. Frequently, these methods and tools have been developed for other ecosystems, but they have recently found their way to the anaerobic system of the GI tract. Food passes relatively quickly through the stomach and the small intestine. That, in combination with a hostile environment in the upper GI-tract (gastric acid, bile, pancreatic enzymes, immune system) precludes a dense colonization of this region of the gut, although up to 107 cells per gram of content may be present. Transit of undigested and indigestible food components through the colon is much slower. This allows for the development of the diverse microbiota present in the large intestine. Here, the microbes thrive on a variety of substrates, including some originating from the host, such as mucin. It appears that we have established an intimate relationship with the microbial world, a relationship of a largely symbiotic nature. Specific host–microbial interactions develop that are now starting to be understood and are essential for maintaining intestinal health (Hooper et al., 2002; Freitas et al., 2003). In an elegant paper (Backhed et al., 2005), the term ‘mutualism’ has recently been introduced as a more proper way to account for the fact that both host and microbes profit from their coexistence. In the following sections, we will discuss the principal findings of studies on diversity of the intestinal microbiota, look in some detail into the results of stable isotope-aided studies, and review the conclusions drawn from such studies on gut microbial functionality.

4.1. General Aspects: Cataloguing Intestinal Microbial Communities Microbial functionality represents perhaps the greatest unexplored realm of gastrointestinal biology with respect to our understanding of the effects


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of microbial activity on health and disease. The introduction of molecular biological techniques into intestinal microbial ecology in recent years has uncovered the vastness of microbial diversity in the GI tract. Considerable attention has been given to determine the spatial and temporal microbial diversity by high-throughput genetic approaches that are mainly based on analysis of the microbial signatures in 16S ribosomal RNA (rRNA) sequences (Amann and Ludwig, 2000; Backhed et al., 2005). All three domains of life have been detected in the GI tract, but the Bacteria are highly dominant. A total number of 1014 cells and 41000 species have been reported (Egert et al., 2006). Of the more than 200,000 rRNA gene sequences currently present in databases, only approximately 1% are annotated as being derived from the human intestinal bacteria, of which approximately 80–90% represent uncultured bacteria (Backhed et al., 2005). The bacterial divisions that dominate are the Cytophaga, Flavobacterium, Bacteroides, and the Firmicutes, each estimated to make up about 30% of the bacteria. Only 6 additional divisions (of a total of 55 discovered to date) have been reported to occur in the human large intestine, which make the diversity in the GI tract at the division level among the lowest (Hugenholtz et al., 1998; Backhed et al., 2005). Diversity present in the GI tract is hypothesized to be the result of strong host selection and coevolution and reflects natural selection at the microbial level and at the host level. At the microbial level, lifestyle strategies affect the competitiveness of individual species in a complex mixture. These strategies include, for instance, growth rate, substrate use (part of which is host derived, such as mucus), and ability to cope with the hostile environment (such as the intestinal immune system). At the host level, deleterious effects of bacteria can reduce host fitness, resulting in fewer hosts and therefore, less habitat for the microorganisms to grow in. On the other hand, an activity that promotes host fitness will create more habitats. One such positive interaction alluded to already earlier is for instance the production of butyrate, which is used as a source of fuel by the colonocytes (Roediger, 1982). Although this mutualistic coexistence between microbiota and host is generally accepted to be correct, it is also believed that the intestinal microbiota is responsible for numerous intestinal diseases, such as colon cancer and IBD. And even though there generally is a symbiotic relation between microbiota and host, the individual microorganisms live in constant battle with each other. For instance, they compete for substrates for growth (from dietary origin, but also mucus and exfoliated epithelial cells) and adherence sites (receptors), and they produce metabolites that may kill or slow down growth of other microorganisms when present in high concentrations (e.g. SCFA, lactate, bacteriocins). By contrast, however, these microorganisms also live in symbiosis, where one

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species may produce a nutrient/metabolite that is required for growth of another species, e.g. in the case of acetogenesis or methanogenesis, where H2 produced by some members of the microbiota is used to produce acetate or methane, respectively, by other members of the community. Normal colonization by the human intestinal commensal microbes stimulates a range of important functions, such as postnatal intestinal maturation, maintenance of the mucosal barrier, protection against pathogens, and the development and maturation of the immune system (Cummings and Macfarlane, 1997; Falk et al., 1998; Elson et al., 2005). Over adulthood, the composition of the microbiota is rather stable, but specific for each individual. This is in part determined by genetic factors (Zoetendal et al., 1998). Diet has the potential to influence the activity and composition of the microbiota, although that is generally believed to be only a temporary effect, unless the dietary components responsible for the change in composition and/or activity are taken on a frequent basis. Although increasing insight has been obtained into the microbial diversity, there is very limited knowledge of the metabolic function of the human intestinal microbes, the way the diet affects metabolic fluxes, and how the produced metabolites affect the health of the host. Even though it has been possible to determine production of microbial metabolites (even in vivo using stable isotope-labeled substrates, see below) by the collective microbiota, it has until recently not been possible to determine which microorganisms are primarily responsible for the production of these metabolites in situ.

4.2. The Microbiome Our gut microbiota can be pictured as a microbial organ placed within a host organ. It is believed that the collective microbiota can carry out more biochemical conversions than the liver, our most metabolically active organ with respect to the multitude of different biochemical reactions it can effect. The collective microbial genome, termed metagenome or microbiome, which contains more than 100 times the number of genes in the human genome, encodes biochemical pathways that we have not had to evolve ourselves. In a recent review in Nature Medicine, Sekirov and Finlay (2006) concluded that ‘Together with our microbes we are a human–bacterial superorganism with immense metabolic diversity and capacity’ . In September 2006, the full genome sequence of 279 bacterial and 23 archeal genomes was sequenced (resource: Comprehensive Microbial Resource Home Page: http://cmr.tigr.org/tigr-scripts/CMR/CmrHomePage.cgi), and another 17 genomes were in progress. Also, a large number of genomes of bacteria that


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can be found in the human gastrointestinal tract or have been associated with human disease have been sequenced (Table 3). Most of these microorganisms have been chosen due to either their pathogenic potential or their potential probiotic (i.e. health beneficial) activity. These sequencing activities already provide an enormous wealth of data with respect to the (potential) metabolic activity of these individual microorganisms. However, metagenomics provides insight into the genetic potential of complex microbial communities. Assuming that each bacterial species within the GI tract has an average genome size of 3 Mb, the human intestinal microbiome probably comprises several thousand Mb, and thus in size equals that of the human genome. However, due to a much higher gene density, it vastly exceeds the human genome’s coding capacity (Relman and Falkow, 2001; Backhed et al., 2005). In addition, an estimation of the total microbial genomic content in an individual should consider the genetic redundancy within these communities. Also, the total microbiome in a human population should, in addition to this redundancy, consider the individual composition of the microbiota (Egert et al., 2006). Compelling evidence suggests that disruption of the intestinal microbial ecosystem contributes to a number of diseases. However, without understanding the interactions between the human and microbial genomes, it is impossible to obtain a complete picture of the effects of the intestinal microbiota on health and disease. Elegant studies (Hooper et al., 1999; Freitas et al., 2001) have indicated a cross-talk between the members of the microbiota and the host. They have shown that soluble factors of Bacteroides thetaiotaomicron, a prominent member of the microbiota, results amongst others, in changes in the expression of glycosyl residues on host membrane-associated glycosylated proteins. In particular, the upregulation of fucosylated glycans (Hooper et al., 1999) by B. thetaiotaomicron revealed a novel signaling collaboration between host and microbe to produce nutrients for growth for the microbe. In a follow-up study, the genome-wide response of the host was carried out (Hooper et al., 2001; Stappenbeck et al., 2002). These studies used single cultivable microbial species and focused mostly on host-related genes and functionalities. The microbiome of the complex gut microbiota has only recently been identified as a target for exploration. Novel hydrolase genes were discovered in uncultured rumen bacteria (Ferrer et al., 2005) and beta-glucanase genes were identified from uncultured bacteria that colonize the large bowel of mice (Walter et al., 2005). Two metagenomic libraries constructed from DNA from fecal samples of healthy individuals and patients with Crohn’s disease (CD) (Manichanh et al., 2006) and screened for 16S rRNA genes revealed a greatly reduced diversity in the Firmicutes in CD patients, which

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Table 3 Fully sequenced genomes and those in progress for human gastrointestinal bacteria Completely sequenced genomes Actinomyces naeslundii Anaplasma phagocytophilum Bacillus anthracis Bacillus subtilis Bacteroides fragilisb B. fragilisb Bacteroides thetaiotaomicron Bartonella henselae Bartonella quintana Bifidobacterium longum Bordetella bronchiseptica Bordetella parapertussis Bordetella pertussis Borrelia burgdorferi Borrelia garinii Brucella abortus Brucella melitensis Brucella suis Burkholderia mallei Burkholderia pseudomallei Burkholderia thailandensis Campylobacter jejunib C. jejunib Chlamydia abortus Chlamydia pneumoniae Clostridium acetobutylicum Clostridium perfringens Clostridium tetani Corynebacterium diphtheriae Corynebacterium jeikeium Coxiella burnetii Desulfovibrio desulfuricans Ehrlichia chaffeensis Enterococcus faecalis Escherichia coli Francisella tularensis Fusobacterium nucleatum Haemophilus ducreyi Haemophilus influenzae Helicobacter hepaticus Helicobacter pylori Lactobacillus acidophilus Lactobacillus helveticus Lactobacillus johnsonii

Genome sizea (Mb) 3.0 1.5 5.2 4.2 5.2 5.3 6.7 1.9 1.6 2.3 5.3 4.8 4.1 1.5 1.0 3.3 3.3 3.3 5.8 7.3 6.7 1.6 1.8 1.1 1.2 4.1 3.1 2.8 2.5 2.5 2.0 3.7 1.2 3.4 4.6 1.9 2.2 1.7 1.9 1.8 1.7 2.0 2.0 2.0 (Continued )


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Table 3 (continued ) Completely sequenced genomes Lactobacillus plantarum Lactobacillus sakei Lactobacillus salivarius Lactococcus lactis subsp. lactis Legionella pneumophila Leptospira interrogans Listeria innocua Listeria monocytogenes Mycobacterium avium paratuberculosis Mycobacterium bovis subsp. bovis Mycobacterium leprae Mycobacterium tuberculosis Mycoplasma pneumoniae Mycoplasma pulmonis UAB Neisseria gonorrhoeae Neisseria meningitidis Nocardia farcinica Pasteurella multocida Porphyromonas gingivalis Prevotella intermedia Propionibacterium acnes Pseudomonas aeruginosa Rickettsia conorii Rickettsia felis Rickettsia prowazekii Rickettsia typhi Salmonella enterica S. enterica serovar Typhi Salmonella typhimurium Shigella boydii Shigella dysenteriae Shigella flexneri Shigella sonnei Staphylococcus aureus Staphylococcus epidermidis Staphylococcus haemolyticus Staphylococcus saprophyticus Streptococcus agalactiae Streptococcus mutans Streptococcus pneumoniae Streptococcus pyogenes Streptococcus thermophilus Treponema denticola ATCC Treponema pallidum Tropheryma whipplei

Genome sizea (Mb) 3.3 1.9 2.1 2.4 3.4 4.6 3.1 3.0 4.8 4.3 3.3 4.4 0.8 1.0 2.2 2.3 6.3 2.3 2.3 2.7 2.6 6.3 1.3 1.6 1.1 1.1 4.6 4.8 5.0 4.6 4.6 4.6 5.0 2.8 2.5 2.7 2.6 2.2 2.0 2.1 1.8 1.8 2.8 1.1 0.9 (Continued )

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Table 3 (continued ) Completely sequenced genomes Ureaplasma urealyticum parvum Vibrio cholerae Vibrio parahaemolyticus Vibrio vulnificus Wolinella succinogenes Yersinia pestis Yersinia pseudotuberculosis Genomes in progressc Lactobacillus gasseri Lactobacillus casei Lactobacillus rhamnosus Lactobacillus helveticus Lactobacillus delbrueckii Lactobacillus reuteri Lactobacillus brevis Leuconostoc mesenteroides Pediococcus pentosaceus Propionibacterium freundereichii Streptococcus mitis

Genome sizea (Mb) 0.8 4.0 5.2 5.1 2.1 4.8 4.8 Approximate genome size 1.8 2.6 2.4 2.4 2.3 2.5 2.0 2.0 2.0 2.6 2.0

a

Rounded to the nearest decimal; average of genome size of all sequenced strains where applicable. b Two strains of this species have been sequenced, with different genome sizes. c GI tract species listed are those for which we have information that they are being sequenced.

is in agreement with the hypothesis that the intestinal microbiota has an important role in CD development. The human gut microbiome initiative (HGMI) has been proposed as an extension of the human genome project. New and cost-effective approaches now allow fast and reliable highthroughput sequencing of millions of basepairs. The first published results from such a sequencing effort analyzed 78 million bases (Gill et al., 2006) in 140,000 sequence reads from DNA libraries from two healthy human adults. The study revealed that metabolic function analyses of identified genes of the sequenced microbiome has identified enrichment of genes encoding metabolism of carbohydrates, amino acids, and xenobiotics and methanogenesis compared with other sequenced microbial genomes. At least 81 different glycosyl hydrolases have been found in the microbiome, indicating its capacity to cleave a vast array of different (mostly food derived) carbohydrate linkages. Also, an overrepresentation of butyrate kinase (statistically increased with a factor of 9.30 (odds ratio) compared with other microbial genomes) was found. It was speculated that this corroborated the important commitment of the gut microbiota to generating this biologically


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important compound, which serves as the principal energy source for colonocytes. About 50% of the total of 50,164 open reading frames (ORFs) predicted matched to the database, of which 5700 were present in both subjects. Of the total number of ORFs, only 25% could be assigned unambiguously to members of the Archeae or Bacteria. The remainder could not be assigned unambiguously to either of the two, or did not match any known ORFs. The 78 million bases sequenced would represent approximately 1–3% of the total microbiome. Approximately 40% of the sequence reads could not be assembled into contigs, most likely because of low abundance of the microorganisms, from which the sequence originated, within the specimens studied. It should be mentioned that, even though the colon is colonized by a myriad of different microorganisms, only a limited number of species/genera make up the majority of the microbiota. Only approximately 15 16S rRNA-targeted probes for bacterial genera/phyla are required to measure approximately 90% of the bacterial cells in fecal samples from human adults (Harmsen et al., 2002). Therefore, any clone library obtained from an actual sample from the gut will be dominated by genomic DNA from these dominant species, even without the bias generated by the cloning procedure itself. Given the extensive sequencing efforts it would take to sequence the full complement of the intestinal microbiome, the true metabolic potential of the microbiota will not be unraveled in the near future. Since the major species probably make up the major activities, this may not have to be our major goal for the moment. Also, similarities and differences between the microbiota of different individuals (Gill et al., 2006; two subjects were studied) need to be studied in more detail to be able to decipher the major activities carried out by the human intestinal microbiota. Current screening approaches of intestinal metagenomic libraries are not fully established. Screens can be based either on nucleotide sequences or on enzyme activities but both strategies have limitations. PCR and hybridization techniques require primers or probes based on previously cloned (i.e. known) genes. Functional analysis enables the discovery of new classes of genes, but this requires the expression from the cloned inserts of active enzymes in heterologous hosts (usually Escherichia coli). In addition, appropriate assays must be available, and most phenotypes of interest, e.g. butyrate production, might not be suitable for high-throughput selection. Recently, an elegant screen has been developed that enables the rapid identification of clones with a desired inducible metabolic activity within large clone libraries (Uchiyama et al., 2005). The method is based on the commonly observed substrate induction of genes encoding biodegradative pathways, and relies on a promoter-trap system to trap genes that encode catabolic pathways in front of a gene that encodes green fluorescent protein

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(GFP). GFP-positive, induced clones are selected by fluorescence-activated cell sorting. However, one of the constraints of this method, as with activity screening in a heterologous host, is that it requires the regulatory machinery of the expression host to recognize both the promoter and the substrate (Handelsman, 2005), and therefore the method may have limited applicability. Thus, although these studies begin to define the functional activities of the human gut microbiome, future in-depth metagenomics studies are needed to provide deeper coverage of the microbiome, which has been termed the second human genome, and to study the relationship between microbiota and health and disease.

4.3. Stable Isotope Probing: Clues to Metabolic Function from Genomics Data Even though the picture of the complete microbiome may be incomplete, the first studies toward the metabolic function of individual members within the collective microbiota are being undertaken, and the first clues are known. These concern studies where stable isotope-labeled substrates are used to investigate the role and activity of certain members within the microbiota on specific substrates. In Section 5, we will describe the metabolomics approaches to this. Here, we focus on the contribution of specific microorganisms to the fermentation of the substrates. We have discussed that molecular DNA technologies allow for a comprehensive and integrated approach to assessing the structure of microbial communities, providing a perspective in GI tract microbiology. Although the application of these tools has significantly advanced our understanding of the gut microbial diversity, it does not provide functional insight on which microbes are relevant for specific dietary conversions (de Vos, 2001; Egert et al., 2006). The real challenge here is to develop and apply methodologies for analyzing the functionality of the microbiome. For this, it is important to know which microorganisms are responsible for the observed activities, elucidating dominant microbial functionalities in the human GI tract, the impact of specific dietary components on these functionalities, and ultimately the effect on gut health. Stable isotopes can play an important role in answering these questions. To couple the microbial diversity to metabolic function, in situ SIP approaches appear very promising (Egert et al., 2006). Typically, in nucleic acid-based SIP studies, 13C-labeled compounds that act as substrates in the food chain are delivered to cultures of (intestinal) bacteria (Fig. 5). Subsequently, the ribosomal DNA or RNA of the microbial community is


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isolated and subjected to density gradient centrifugation to isolate the heavy, labeled fraction of nucleic acids. These heavier fractions stem from bacteria that have consumed the substrate and incorporated the isotopic label in their nucleic acids. Either general or group-specific PCR amplification allows 16S rDNA fragments to be amplified (Satokari et al., 2001; Heilig et al., 2002). These can then be characterized by high-throughput rDNA sequence analysis providing insight into the microbial diversity of these fractions. By following the development of the rDNA sequence diversity in time, the specific groups of microbes involved in the food chain from, for example, carbohydrates to SCFA can be reconstructed. In addition, the spatial diversity can be probed. This approach has been shown to be useful to determine the substrate utilization in a variety of microbial communities (Radajewski et al., 2000; MacGregor et al., 2002). The approach has also been used in the gut, although until recently restricted to earthworms and larvae of the cockchafer and the cetoniid beetle (Egert et al., 2003, 2004, 2005; Lemke et al., 2003). Recently, we have taken this SIP strategy and applied it to a human gut microbial community (Egert et al., 2007). In this study, 16S rRNA-based SIP and NMR spectroscopy-based metabolic profiling were used to identify bacteria fermenting glucose (as a model substrate) under conditions simulating the human intestine. An in vitro model of the human intestine was inoculated with a GI tract microbiota resembling that of the small intestine and subsequently 40 mM of uniformly labeled 13 C-glucose was added. RNA was extracted from lumen samples after 0 (control), 1, 2, and 4 h of incubation and fractionated by density gradient ultracentrifugation. Phylogenetic analysis of the 16S rRNA revealed a microbial community dominated by microorganisms closely resembling lactic acid bacteria and Clostridium perfringens, not unlike the microbiota in the terminal ileum. Fingerprints of the most-labeled rRNA fraction identified Streptococcus bovis and C. perfringens as the most active glucose fermenters in the model. Accordingly, NMR analysis identified lactate, acetate, butyrate, and formate as the principal fermentation products, constituting up to 96% of the 13C-carbon balance. Thus, RNA-SIP combined with metabolic profiling allowed the detection of differential utilization of the general model carbohydrate glucose, indicating that this approach holds great potential to identify bacteria involved in the fermentation of relevant dietary oligo- and polymeric carbohydrates in the human large intestine as well. RNA is the most responsive (sensitive) biomarker for SIP analyses because it occurs in greater cellular copy numbers, has a higher turnover rate than does DNA and is produced more or less independent of cellular replication (Manefield et al., 2002). Owing to fewer variations in its GC content compared with DNA, ribosomal 16S-based RNA-SIP might also be less

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susceptible to GC effects that interfere with the separation of labeled 16S rRNA. It should be realized though that it is exactly this difference in GC content between different members of the microbiota which results in the fact that a density gradient of unlabeled RNA already contains RNA in the heavier fractions. For instance, bifidobacteria can be found in these fractions (our own unpublished results). Therefore, to study properly which microorganisms contribute to fermentation of certain substrates, molecular fingerprinting techniques, such as T-RFLP or DGGE, are required to assess the enrichment of the heavy fractions for those microorganisms actually using the substrate (Fig. 5; Egert et al., 2007). Human intestinal samples seem particularly suited to an RNA-SIP approach because these samples contain large numbers of highly active cells, resulting in quick and sufficient labeling of RNA. However, in view of these same large numbers of cells in the human GI tract, together with the nutrient-rich environment and the broad range of potential substrates (e.g. carbohydrates, proteins, hostderived substrates) that can be fermented, human intestinal samples necessitate a sensitive RNA-analysis approach to cope with label dilution. The use of in vitro gut models that closely mimic the environmental conditions in the GI tract and that are easy to sample enables detailed analyses of successive label incorporation into the RNA of different community members over time. Such cross-feeding effects (i.e. the use by one member of the microbiota of labeled metabolites derived from the initially added substrate produced by a different member) will help to identify food chains in intestinal systems. This may lead to the generation of hypotheses that need to be tested in vivo. Yet, application of SIP in human trials is challenging. It remains to be shown (i) whether a labeled substrate can be effectively delivered through the intestinal tract into the target region and homogenously distributed there, and (ii) whether the (singly or pulsed) applied substrate concentrations can be adjusted in a way that prevents dilution within the colon, while still enabling sufficient labeling of microbial (16S r)RNA. Figure 5 Principle of RNA-based stable isotope probing (SIP) for detection and characterization of microbes that actively metabolize the labeled substrate. 13 C-labeled substrates are incubated in (a) simple in vitro models (test tube or flask), (b) sophisticated in vitro systems, or (c) in vivo. Samples obtained from these experiments [in the figure only shown for samples from (a)] are subjected to RNA isolation and density gradient centrifugation. After separation of the gradient in fractions, molecular fingerprinting techniques, such as DGGE (Zoetendal et al., 2004a) or T-RFLP (Egert et al., 2003) can be used to determine the presence (usually enrichment) in the heavier fractions of those microorganisms that specifically fermented the substrate and this can be compared with the diversity present in an unlabeled, control sample. (See plate 3 in the color plate section.)

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5. PROTEOMIC ASPECTS OF INTESTINAL MICROBIAL LIFE 5.1. Functions of Intestinal Bacterial Enzymes Intestinal bacteria produce large amounts of extracellular enzymes, especially for degradation of mucins and dietary carbohydrates. These enzymes may be freely soluble or remain membrane bound; the latter are generally found to be more active. Mucin oligosaccharide chain-degrading bacteria have been isolated from feces of healthy subjects (e.g. Salyers et al., 1977; Hoskins et al., 1985; Derrien et al., 2004) and their enzymes studied. It was concluded that certain Bacteroides, Bifidobacterium, and Ruminococcus strains are numerically dominant populations degrading mucin oligosaccharides in the human colon due to their constitutive production of the requisite extracellular glycosidases including blood group antigen-specific alpha-glycosidases, sialidase, beta-glycosidases, alpha-galactosidase, and beta-N-acetyl-hexosaminidases. Enterococcus faecalis produced predominantly cell bound glycosidases (Salyers et al., 1977; Hoskins et al., 1985). Oligosaccharide side chains of human colonic mucins contain O-acetylated sialic acids and glycosulfate esters. Although these substituents are considered to protect the chains against degradation by bacterial glycosidases, sialate O-acetylesterase, N-acetylneuraminate lyase, arylesterase, and glycosulfatase activities have been found in fecal extracts (Corfield et al., 1992). Thus, mucin oligosaccharide chains terminating in O-acetylated sialic acids are unlikely to be protected from degradation by enteric bacteria. High levels (2–565 U/g) of amylase activity have been observed in human feces, with over 92% of amylase activity being of extracellular origin, whereas only about 9% of activity was associated with particulate material and washed cells (Macfarlane and Englyst, 1986). Bacterial cell-bound amylases were considerably more efficient in breaking down starch, however, than were the soluble enzymes which occurred in cell-free fecal supernatant fluids. Other hydrolytic and reductive bacterial enzymes measured in human colonic contents include beta-glucuronidase (GN), beta-glucosidase (GS), arylsulfatase (AS), azoreductase (AR), and nitroreductase (NR). These enzymes can be involved in production of mutagenic or genotoxic metabolites (McBain and Macfarlane, 1998). Cell-associated AS and extracellular GS were found to be approximately twice as high in the distal colon compared with the proximal bowel, while AR changed little throughout the


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gut. Upon studying 20 pure cultures of intestinal bacteria these authors found that various bacterial strains were active producers of GS, GN, NR, and AR. However, only few of the isolated bacteria produced AS in small amounts. In recent years, research on bacterial intestinal proteins seems to have focused more on the dark sides of our commensals, with a particular interest in the roles of enzymes and toxins in gut diseases. Infectious enteric Helicobacter pylori have been shown to produce matrix metalloproteinases (MMPs) that participate in degradation of the extracellular matrix also on HT29 colon epithelial cells, allowing bacteria to invade (Yanagisawa et al., 2005). Bacterial flagellin, a specific microbial ligand of Toll-like receptor-5 (TLR-5), is released by commensal and enteroinvasive microbes. Flagellin exposure to an in vivo mouse model of injured colon, but not to intact colon, was found to significantly aggravate colonic inflammation, increase mouse mortality, enhance histopathological damage in the colonic mucosa, and to cause severe apoptosis in colonic epithelium (Rhee et al., 2005). These results demonstrated that bacterial flagellin plays an important role in the development and progress of colitis, via TLR-5 engagement. Chitinase 3-like-1 (CHI3L1) is a putative key molecule involved in the dysregulation of host/microbial interactions that appears to play a central role in the development of IBD. A very recent study (Mizoguchi, 2006) demonstrated that CHI3L1 is required for the enhancement of adhesion and internalization of infectious bacteria in colonic epithelial cells. The expression of CHI3L1 protein was found to be clearly detectable in lamina propria and colonic epithelial cells in several murine colitis models and UC and Crohn’s disease patients but absent in normal controls (Mizoguchi, 2006). It was concluded that CHI3L1 contributes to the facilitation of bacterial invasion into the intestinal mucosa and the development of acute colitis, presumably by enhancing the adhesion onto and invasion of bacteria into colonic epithelial cells.

5.2. Proteomic Studies of the Gut Microbiota: A Largely Unprobed Area? Proteomic studies of the intestinal microbiota, in principle, could be useful to study expression patterns of proteins and enzymes in response to dietary components and thereby provide a rationale for the development of new active ingredients (e.g. pre- and probiotics). Yet, the full power of proteomic analysis remains to be demonstrated in this area also.

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The number of studies reported in this field is strikingly small and most studies concentrate on cultivated bacteria. This may have to do with the difficulty of accessing the colon; but more likely, the complex fecal nature of the samples causes severe problems especially with the widely used two-dimensional gel techniques the performance of which is susceptible to sample impurities and difficulties in reproducibility. It can be anticipated that less susceptible techniques such as surface-enhanced laser desorption/ ionization (SELDI)-TOF analysis (Barzaghi et al., 2004), a protocol that has been successfully applied to a wide range of Gram-positive and -negative bacteria, will accelerate progress of the study of proteomics of the gut microbiota in the near future. Nevertheless, technically impressive methodologies have been developed that allow the characterization of hundreds of microbial proteins from bacteria relevant to the colon in a single experiment. For instance, a nano-high-performance liquid chromatography/mass spectrometry (nano-HPLC/MS) system was established to separate proteins of E. coli in a two-dimensional manner by combining strong cation exchange (SCX) and reversed phased (RP) chromatography (Vollmer et al., 2003). Peptides were eluted online to an iontrap MS instrument and further analyzed by tandem MS fragmentation for identification using the Swiss Prot Database. Differentially expressed proteins on glucose and lactose were identified. Similarly, lactic acid bacteria that are widely used in the agro-food industry have been characterized by proteomic techniques as reviewed in Champomier-Verges et al. (2002). More recently, the proteome of bifidobacteria has received considerable attention. Adaptation to and tolerance of bile stress are among the main limiting factors to ensure survival of bifidobacteria in the intestinal environment of humans. Comparing protein patterns of strains grown with or without bile showed 34 different proteins whose expression was regulated (Sanchez et al., 2005). These proteins included general stress response chaperones, proteins involved in transcription and translation and in the metabolism of amino acids and nucleotides, and several enzymes of glycolysis and pyruvate catabolism, indicating that bile salts induce a complex physiological response rather than a single event in bifidobacteria. In a second study, a strong cation exchange-reversed phase-tandem mass spectrometry strategy was used to catalogue the most abundantly expressed proteins of a probiotic Bifidobacterium infantis strain (Vitali et al., 2005). These authors were able to obtain a global view of the B. infantis proteome with 136 proteins identified by multidimensional protein identification technology (MudPIT) analysis that were subsequently compared to available genomic information. Yuan et al. (2006)


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very recently published an even more comprehensive proteomic study on Bifidobacterium longum NCC2705 in which they succeeded in identifying 369 protein entries by MALDI-TOF-MS and/or ESI-MS/MS. The identified proteins represent 21.4% of the predicted 1727 ORFs in the genome and correspond to 30% of the predicted proteome. Interestingly, this study also aimed to characterize cellular pathways related to important physiological processes. Comparing proteome maps during growth on glucose and fructose suggested the presence of a specific transport system for fructose in B. longum. Interestingly, the proteome of bifidobacteria in the GI tract of the human infant is being studied (Te Biesebeke et al., 2004). Over a period of 9 weeks, fecal samples were collected from infants and studied with two-dimensional gel electrophoresis. A change in protein expression over time was observed. Detailed analyses of these changes using MS-analyses are in progress (Te Biesebeke et al., 2004). As was already apparent from enzymatic analysis discussed above, intestinal bacteria express many proteins that deploy their activities outside the cell, be it freely soluble or, in many cases preferably, attached to the cell wall. This makes sense because such essential factors as both the substrate and the opportunities to attach to the gut wall are located on the outside of the cell. A well-known intestinal bacterium, Clostridium difficile, has been analyzed for its cell wall-associated proteome recently (Wright et al., 2005). This bacterium causes disease of the large intestine, particularly after treatment with antibiotics, due to production of two toxins (A and B). In addition to these toxins, C. difficile expresses cell wall-associated virulence factors including cell wall protein Cwp66, highmolecular weight surface layer protein (HMW-SLP), and the flagella. However, the genome sequence predicted many more cell wall-associated proteins that could play a role as virulence factors, and indeed the study found, among 49 different identified cell wall proteins, a number of paralogs of HMW-SLP that present interesting targets for further research (Wright et al., 2005). The application of proteomics to complex microbial assemblages (metaproteomics) still presents considerable challenges (Wilmes and Bond, 2006). The most extensive metaproteomic study to date combined proteomics with metagenomics to study a low-complexity natural biofilm (Ram et al., 2005). 2033 individual proteins of the 12,148 predicted proteins (from the metagenome sequence) were identified. Summarizing, proteomic studies of gut microbiota are still very few which is a pity because they provide fascinating views on how the intestinal bacteria forage for food, attach to the host, and send out toxins to defend themselves.

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5.3. Can Stable Isotopes Help in Proteomics? The reason for the limited number of proteomic studies on the gut microbiota primarily lies in experimental problems especially related to sample acquisition and preparation. Also, proteomic techniques are less amenable to large-scale rapid screening protocols than transcriptome and metabolome technologies, due to required slow separation technology as well as a complex mass spectrometry-based identification with associated need for tryptic digestion of the sample. Faster, chip-based technologies (such as SELDI (Issaq et al., 2002) and antibody-based protein chips (Binder et al., 2006)) are in continuous development (see Ramachandran et al., 2005 for a recent review) but their application to map the complete proteome of the intestinal microbiota, containing many unknown proteins, and many important proteins attached to the bacterial cell walls, is still to be awaited. Stable isotopes do not seem to have properties that could change that situation. Nevertheless, stable isotopes play a significant role in proteomics as a means to provide standards for quantification. Isotope-coded affinity tags (ICAT) (Gygi et al., 1999) is probably the best example; this technique employs isotopic reagents for labeling two different populations of proteins that can subsequently be compared against each other quantitatively using mass spectrometry, allowing e.g. the determination of organelle location of proteins (Dunkley et al., 2004). SILAC (Stable Isotope-Labeling with Amino acids in cell Culture) (Ong et al., 2002) provides another, inexpensive and accurate procedure that can be used as a quantitative proteomic approach in any cell culture system simply by comparing the protein profiles measured by mass spectrometry from cell cultures grown in unlabeled culture medium vs. those grown in deuterium-labeled medium. Stable isotopes can be used to monitor protein synthesis and determine protein fractional synthesis rates (FSRs), i.e. protein metabolic fluxes, using the MIDA approach introduced by Hellerstein and Neese (1999). This technique, because it uses mass spectrometry analysis of peptide protein fragments, is possibly relatively easy to combine with existing mass spectrometry-based protein profiling approaches. The technique as originally published is technically involved and therefore requires close attention to potentially confounding factors and analytic performance for optimal application. However, a new development of MIDA was recently described (Busch et al., 2006) that employs 2H2O labeling to permit sensitive, quantitative, and operationally simple measurements of protein turnover in vivo, especially for proteins with slow constitutive turnover. While this technique appears best suited to slowly turning-over proteins, it does bring the prospect of dynamic protein profiling closer. It is to be


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awaited whether this prospect will turn into reality in the near future, and whether future developments will be suited also to probe the proteome of the colonic microbiota.

6. METABOLOMICS Of the functional genomics toolbox, metabolomics is the most recent addition. The technique involves the non-targeted, holistic analysis of changes in the total set of metabolites in a sample (the metabolome) in response to environmental or cellular changes. Only now is the metabolomics approach technically feasible, due to the enormous improvements made in the past few years in analytical chemistry and bioinformatics. There have been enormous improvements in the separation and detection of metabolites. Moreover, progress in bioinformatics makes it now possible to process and interpret large sets of biochemical data generated through this non-biased holistic approach. Metabolites are low molecular weight organic compounds (o1000 Da) that participate in general metabolic reactions or are required for the maintenance, growth, and normal functioning of a cell (Beecher, 2003). Metabolites mostly play a role in cellular metabolism and as carriers of energy and reducing equivalents. The total number of different metabolites that are present in any given cell is as yet unknown. In total, almost 20,000 microbial metabolites have been described so far (Vicente et al., 2003). However, many of these metabolites are only present in relatively few microorganisms. From the recent annotation of microbial genome sequences, between 241 and 794 metabolites were deduced to be present in microorganisms (Vaidyanathan and Goodacre, 2003). Since around 40% of the genes present in the microbial genomes have an unknown function, the actual number of metabolites may be approximately three times more (van der Werf et al., 2005). There are several reasons why metabolomics is the functional genomics technology of choice. First of all, the information that can be derived from the metabolome corresponds to a very different perspective on cellular functioning than those of the genome, transcriptome, and proteome. While genomic studies are highly instrumental in uncovering the genetic potential of the gut microbiome, and the transcriptome reflecting the functional response, the proteome and the metabolome together determine the actual functionality of a cell. The biochemical level of the metabolome is closest to that of the function of a cell (the phenotype), and thus the study of the metabolome (together with the fluxome) is the most relevant in order to

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comprehend biological functioning. This is especially so since changes in the levels of individual enzymes have, in general, little effect on metabolic fluxes, but do have an effect on the concentrations of the metabolites (Sanford et al., 2002; Goodacre and Kell, 2003) produced in the microbial pathway containing that enzyme. However, although metabolomics is thus ‘the preferred choice’ of the currently established ‘-omics’ technologies, from a more technical point of view, there are still several challenges. Many metabolites, especially signal molecules, are present only transiently and in very low concentrations. The sensitivity and dynamic range of analytical instrumentation, when applied in non-target mode, is still not as high as it should be, and thus these metabolites may not be measured. In addition, since the intestinal microbiota is composed of 41000 different species, it may be impossible to relate production of certain metabolites to specific members of the microbiota. By contrast, interaction of microbial metabolites, from the intestinal microbiota, with the host is basically restricted to the extracellular metabolites, simplifying matters again. In addition, whereas the microbial composition may be very different between subjects and even vary considerably with time (Barcenilla et al., 2000), we must assume that the microbiota as a whole performs a stable set of activities within a population, given the enormous functional overlap (redundancy) between microorganisms. Also, the use of stable isotopes may help to shed light on what microbes are doing, both on the level of identification of microbes that are actively fermenting a given substrate (using SIP, Section 4), and on the level of metabolite production, as is discussed below.

6.1. Microbial Products and What They Can Mean to Us The proximal colon receives food residues and other substrates from the small intestine and is therefore rich in carbohydrate and protein. It is generally accepted that carbohydrates are the preferred substrate for most members of the colonic microbiota. The carbohydrates are used to obtain energy, while any available protein is incorporated into biomass. However, fermentable carbohydrates may become depleted more distally along the colon, leading to decreased activity of saccharolytic bacteria. Conversely, the proteins and peptides that are present throughout the colon can be utilized by protein or amino acid fermenting bacteria when the carbohydrate is depleted. Numerically important proteolytic species identified in the large bowel include species belonging to the genera Bacteroides, Propionibacterium, Clostridium, Fusobacterium, Streptococcus,


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and Lactobacillus (Macfarlane and Cummings, 1991). Studies in sudden death individuals have shown that concentrations of metabolites from proteolytic fermentation are higher in the distal colon compared with the proximal colon (Cummings et al., 1987; Macfarlane et al., 1992a; Smith and MacFarlane, 1996, 1997): e.g. distal concentrations of phenolic compounds were four times that detected in proximal regions. Therefore, the presence of fermentable carbohydrates influences proteolytic fermentation in the colon, as also recently shown using stable isotopes (De Preter et al., 2004). Although carbohydrate fermentation predominates in the large intestine as a whole, the fermentation of proteins becomes quantitatively more important distally. It is also interesting to note that the majority of colorectal cancers occur in the distal side of the colon (Hughes et al., 2000; Hope et al., 2005) where the SCFA concentration is at its lowest, the concentrations of proteolytic metabolites is at its highest, and contact of the intestinal epithelium with luminal contents is increased due to the more solid nature of luminal contents and also due to the slower transit through this segment of the bowel. Therefore, it is speculated that protein degradation in the colon is relevant for colon cancer (Hughes et al., 2000). Similarly, it is speculated that there is a correlation between the occurrence of protein fermentation in the distal colon and the onset of UC (Roediger et al., 1997; Levine et al., 1998). This is, however, all circumstantial evidence, and hard proof is lacking. And therefore, little is known about the biological role in vivo of these potentially toxic metabolites derived from proteolytic fermentation. It is believed that carbohydrate fermentation results in the production of beneficial microbial metabolites such as the SCFAs (primarily acetate, propionate, and butyrate), while protein metabolism may lead to what are generally considered toxic metabolites, such as hydrogen sulfide, ammonia, and phenolic and indolic compounds. Hydrogen sulfide is also produced by the sulfate-reducing bacteria (SRB), which characteristically couple oxidative phosphorylation with the reduction of sulfate to sulfide. Butyrate is the principal energy source of colonic epithelial cells. Up to 70% of the energy used by these cells comes from microbially produced butyrate (Roediger, 1980). Butyrate has been implicated in colorectal tumorigenesis since it exerts a multitude of anti-tumor effects in transformed cells in vitro, such as modulation of cell proliferation, differentiation, and apoptosis (Young and Gibson, 1994). In vivo, luminal butyrate concentrations are inversely correlated with tumor size in experimental colorectal tumorigenesis, and direct rectal or cecal installation of butyrate reduced the size and number of tumors in experimental carcinogenesis. It is, therefore, no surprise that considerable experimental effort is being expended in order

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to define the effects of butyrate and the mechanisms by which it acts. However, the cellular effects of butyrate are complex, especially since those in one cell system may be the complete opposite of those in a different but related cell system. This so-called ‘butyrate paradox’ has been observed in relation to cell proliferation, differentiation, and apoptosis (Gibson et al., 1999b). The biological basis for these contrasting effects has not been deciphered. However, it is hypothesized that the responses of cells to butyrate may depend on the cells’ state of activation, independent of butyrate oxidation (Gibson et al., 1999b). The formation of toxic hydrogen sulfide (H2S) by human commensal bacteria, either from protein fermentation or sulfate reduction by SRB, is assumed to promote the development of inflammatory intestinal diseases (Levine et al., 1998; Ohge et al., 2005), particularly in the distal part of the human colon. Here, as discussed above, carbohydrate availability is limited because the quickly fermentable carbohydrates have already been fermented in the proximal colon. Therefore, the microbiota switches to protein fermentation, with concurrent production of putrefactive metabolites. It is unknown where SRB display the highest activity, but the presence of electron acceptors throughout the colon (e.g. acetate, H2) suggests that SRB may be active throughout the whole large intestine, although they have to compete for H2 with acetogens and methanogens. As touched upon above, there is speculation that there is a correlation between putrefaction and the occurrence or start of onset of UC and colon cancer. Although this is circumstantial evidence, the current belief is that H2S, in particular, may be responsible for this (Levine et al., 1998; Ohge et al., 2005) as it blocks oxidation of butyrate in colonic epithelial cells. Roediger et al. (1993) showed inhibition of butyrate oxidation by H2S in vitro in both rat and human colonocytes at a concentration of 2 mmol/L. Using human colon tissue, Christl et al. (1994) showed that 1 mmol sulfide/L significantly increased cell proliferation rates and other changes normally seen in UC. Studies have shown that dietary protein does contribute to sulfide production in the large intestine (Magee et al., 2000). In general, the higher the intake of protein, the higher is the production of sulfide in the colon, with a production of 3 mmol after a dietary protein intake of 200 g/day. Other sources of sulfur present in the colon are from inorganic sulfate and mucin. Daily intake of inorganic sulfate is estimated to range from 1.5 to 16 mmol/kg. Estimated amounts of mucin (an unknown part of which is sulfated) excreted in the lumen of the GI tract are 4100 g/day (Lichtenberger, 1995). Sulfate from mucin can be liberated by numerous members of the microbiota that contain sulfatases (e.g. Bacteroides), after which the liberated sulfate may become available for SRB. It is estimated that SRB derive 1.5 to 2.6 mmol


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sulfate/day from sulfated mucin (McGarr et al., 2005). Therefore, it is possible that a combination of dietary protein, sulfated mucins, and inorganic sulfur additives in food could result in fecal sulfide concentrations that may lead to pathological processes. Hydrogen sulfide also functions as a neuromodulator, but whether it modulates visceral perception and pain in humans is currently unknown. A recent study in rats (Distrutti et al., 2006) investigated the role of H2S in modulating the nociception (ability to feel pain) to colorectal distension (CRD), a model that mimics some features of IBS. Treating rats with NaHS resulted in a dose-dependent attenuation of CRD-induced nociception. It was concluded that H2S release in the colon might actually be beneficial in treating painful intestinal disorders. This contrasts with the current belief that H2S is deleterious to health. Equivalent with the ‘butyrate paradox’, there seems to be a ‘H2S paradox’ as well. How much of this paradox is based on differences in dose–response is presently unknown. The other proteolytic toxic metabolites are also deleterious for health. The branched chain fatty acids (BCFAs), which are produced by fermentation of the branched chain amino acids valine, leucine, and isoleucine, can cause liver problems (Mortensen and Clausen, 1996). Ammonia is toxic to the colonic epithelium and promotes colon cancer in rats. In addition, it is a potential liver toxin and has been implicated in the onset of neoplastic growth (Clinton, 1992; Macfarlane and Macfarlane, 1995). The production of phenolic and indolic compounds by intestinal bacteria has been associated with a variety of disease states in humans, including schizophrenia (Macfarlane and Macfarlane, 1995). In addition, they appear to act as co-promoter in the development of colorectal cancer (Rowland, 1995). Other metabolites that are produced by the intestinal microbiota but have not been discussed so far included gases, primarily H2, CO2, CH4, but 4250 other vapors can be detected in expired breath and are assumed to be produced partly in the colon (Brydon et al., 1986; Levitt et al., 1995; Suarez et al., 1998). Depending on the speed of production and accumulation (possibly up to 25 L/day), these gases may cause intestinal symptoms such as abdominal cramps and urge of defecation, while impaired transit and tolerance to gas has been implicated in IBS (Suarez, 2000; Serra et al., 2001). Also, certain minor food components may be fermented into bioactives, such as the conversion of sinigrin (a glucosinolate) into allyl-isothiocyanate (Krul et al., 2002), which has been shown to inhibit metastasis of human hepatoma cells (Hwang and Lee, 2006), or the breakdown of flavonoids into several different phenolic compounds, such as 3-methoxy-4-hydroxyphenyl acetic acid, 4-hydroxyphenyl acetic acid, 3,4-dihydroxyphenyl acetic acid, 3-(3-hydroxyphenyl) propionic acid, 2,4,6-trihydroxybenzoic acid, 3-(4-hydroxy-3-methoxyphenyl)

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propionic acid, and 3-hydroxyphenyl acetic acid (Gao et al., 2006). The biological activity of most of these is unknown, but 3,4-dihydroxyphenyl acetic acid has been shown to have an anti-proliferative activity (Gao et al., 2006). In addition, compounds such as nitrosamines may be formed by the reaction of secondary amines with nitrite at low pH. It goes beyond the scope of this review to discuss these in any detail. Microbial metabolites may influence the metabolic integrity of intestinal epithelial cells and induce mucosal immune responses. In recent experiments, the effects of the microbial metabolites butyrate, iso-valerate, and ammonium on CaCO-2 cells was investigated (van Nuenen et al., 2005). Barrier function was determined by measuring transepithelial electrical resistance. The barrier function of CaCO-2 cells remained intact in this study after exposure with the type and concentrations of metabolites used. However, addition of phenolic compounds, above a certain threshold value, had a dramatic effect on the transepithelial electrical resistance (Venema et al., unpublished data). These experiments need to be confirmed and extended. This can be done in in vitro experiments, but also in other models more closely simulating the real situation. In the same set of experiments (van Nuenen et al., 2005), the effect of microbial metabolites on cytokine production by macrophages was tested as well. In these experiments, the macrophage cell line U937 was cultured alone, or in combination with CaCO-2 cells. Production of TNF-a and IL-10 was measured. These experiments showed that CaCO-2 monoculture cells did not secrete detectable levels of TNF-a or IL-10 after metabolite exposure (in the presence or absence of stimulation with LPS) (van Nuenen et al., 2005). In the U937 monoculture experiments, addition of 50 and 100 mM butyrate or iso-valerate, or of 20 and 40 mM ammonia resulted in a dose-dependent inhibition of TNF-a secretion compared with LPS (positive control), while lower, more physiological concentrations (4–20 mM for butyrate and iso-valerate; 2 and 4 mM for ammonia) stimulated TNF-a secretion (dose independently). IL-10 secretion by these macrophages in monoculture was suppressed by all metabolites in all concentrations compared to LPS (dose dependently), except for the lowest concentration of ammonia, for which IL-10 secretion by macrophages was almost twice as high as with LPS. This shows that the immune system underlying the colonic epithelium may be differentially influenced by the different metabolites, which in turn may also be present in fluctuating concentrations, and therefore metabolites may have stimulating or suppressing effects on production of cytokines depending on concentration. An experiment that still needs to be performed would include a mixture of these metabolites, to determine whether the effects of one of the metabolites dominates over that of the others.


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The absorption of SCFA and butyrate from the colonic lumen is a very efficient process as only 5–10% is excreted in the feces. At least 60% of the SCFA uptake is effected by simple diffusion of protonated SCFA involving hydration of luminal CO2 (Topping and Clifton, 2001). The remainder occurs by active transport through two transporters, the monocarboxylate transporter isoform 1 (MCT1), which is coupled to a transmembrane proton gradient, and the sodium-coupled monocarboxylate transporter (SMCT1) (Gupta et al., 2006). How does butyrate exert such a wide array of effects? The ability of butyrate to regulate gene expression is often attributed to its induction of histone hyperacetylation through the inhibition of histone deacetylase (HDAC). Hyperacetylation of histones disrupts their association with DNA, resulting in more ‘open’ chromatin structure that facilitates access of transcription factors to specific genes. This has been demonstrated by the fact that trichostatin A, which specifically inhibits HDAC, mimics many of the effects of butyrate (Gibson, 2000). However, it is likely that butyrate has other intracellular targets. These include the hyperacetylation of non-histone proteins, alteration of DNA methylation, selective inhibition of histone phosphorylation, and the modulation of intracellular kinase signaling (Daly and Shirazi-Beechey, 2006). This multiplicity of effects may underlie the ability of butyrate to modulate gene expression at several levels including transcription, mRNA stability, and elongation. The response to butyrate is complex, involving multiple distinct mechanisms/pathways (Daly and Shirazi-Beechey, 2006), with different pathways operative in different cells. As said before, butyrate is transported across the membrane by MCT1. The expression of MCT1 is significantly downregulated in the human colon during the transition from normality to malignancy. This leads to a reduction of butyrate uptake and may contribute to the development of colonic neoplasia (Daly and Shirazi-Beechey, 2006). Currently, the mechanism of the effects observed is unknown. In addition to the observed effect on the inhibition of histone deacetylase, the promoters of several genes contain a highly conserved sequence, the butyrate response element. It is likely that effects of butyrate are realized through one of these mechanisms. However, it is currently unknown what the mechanism of action is for other microbial metabolites.

6.2. Tracing the Fate of Prebiotics: In Vitro Models and Stable Isotopes Prebiotics are defined as non-digestible food ingredients that beneficially affect the host by selectively stimulating the growth or activity of one or a

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limited number of bacterial species already resident in the colon and presumed to be health promoting (Gibson and Roberfroid, 1995), for example, by increasing numbers of indigenous bifidobacteria (Finegold et al., 1983; Gibson and Roberfroid, 1995; Gibson et al., 1999a). Many carbohydrates are reported to be prebiotic, including fructooligosaccharides, galactooligosaccharides, isomaltooligosaccharides, and lactulose (Gibson and Roberfroid, 1995; Gibson et al., 1999a). Apart from their activity on the composition of the large intestinal microbiota, prebiotics also affect the microbial metabolite pool in the colon. Most measurements on microbiota and metabolites in humans are performed in feces, which is basically the only non-invasive material that can be obtained from healthy volunteers. A drawback of analysing fecal samples is the fact that they do not represent quantitatively what happens in the proximal part of the colon where fermentation of most prebiotics takes place. SCFA from prebiotic fermentation are predominantly produced in the proximal part of the colon and will be absorbed by the body to a considerable extent during subsequent transit of the chyme to the distal colon and rectum, which may take anywhere from 24 to 472 h depending on the individual. Consequently, the amount and ratio of SCFA recovered in the feces will not reflect those resulting from fermentation of the prebiotics in the proximal colon, but rather that of local production in the distal colon. To be able to properly study the microbial processes occurring in the proximal colon, various in vitro models simulating the fermentation processes occurring in the lumen of the colon have been developed (McBain and Macfarlane, 1997; Minekus et al., 1999; De Boever et al., 2000). Although we acknowledge the existence of other in vitro models, it goes beyond the scope of this review to discuss these here. Here, we would like to exemplify the advantages of these models in studying the activity of the intestinal microbiota on the basis of some of our own results in a dynamic, computer-controlled in vitro model of the large intestine. This model features, amongst others, peristaltic movements and removal of microbial metabolites (Minekus et al., 1999). The model allows frequent sampling in time, such that time series can be prepared for MFA (see below) and SIP (Egert et al., 2007). In this manner, better insight is obtained in the chronological order of the processes that underlie fermentation of undigested dietary components, and the microbial pathways involved in the fermentation of these substrates. The model closely simulates the in vivo conditions of the GI tract of humans during the passage of food under average conditions of a population. Validation of the colon model was done with regard to the composition of the microbiota, the enzymatic activity of the microbiota, and the production and concentration of


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SCFA using data from sudden death individuals (Cummings et al., 1987; Macfarlane et al., 1992a). Since the in vitro system offers the possibility of separating the test substrate from other compounds, the effects of substrates and especially the mechanisms behind this effect can be properly studied. A mass balance can be made in this system, because in principle all metabolites that are produced are collected. This is a major advantage over in vivo studies. Even if in vivo samples from the lumen of the colon and the blood would be taken, not all produced SCFA would be measured (Cummings and Macfarlane, 1991). This is because butyrate especially is used as a substrate by colonocytes (Roediger, 1982) and therefore only low amounts of this microbial metabolite are found in the bloodstream (Cummings and Macfarlane, 1991). In the in vitro model all SCFA are detected, either in the lumen of the model, or in the collected dialysis fluid. The potential of this system as a tool to study fermentation of dietary components was demonstrated in experiments with a variety of substrates, including pectin (Minekus et al., 1999), fructooligosaccharides (Minekus et al., 1999), inulin (van Nuenen et al., 2003), lactulose (Venema et al., 2003), tagatose (Venema et al., 2005), resistant starch (Venema et al., 2004), and lactitol (Minekus et al., 1999). Parameters such as total SCFA production and the SCFA ratio were determined in time to characterize the fermentation. This allowed the mechanistic study of the effects of food components on microbial metabolite production at its most active site, the proximal colon. For instance, when lactulose was added, a bifidogenic effect, and thus prebiotic effect, was shown in vitro (Venema et al., 2003), as reported previously in the literature (Terada et al., 1991; Mizota, 1996). This study showed that, after in vitro addition of lactulose, the microbiota hardly produced any butyrate any more. Apparently, lactulose changed the activity and/or composition of the microbiota such that butyrate is no longer produced. The effects of lactulose on butyrate production would not have been evident in vivo, because in vivo a multitude of other substrates are available to the microbiota, such as resistant starch, mucin, desquamated epithelial cells, etc. Here, the value of being able to separate, in vitro, the mixture of in vivo substrates (amongst others the test compound, other dietary components, desquamated cells, and mucin) from the test substrate (in this case lactulose), was great and made it possible to investigate the underlying mechanism of specific stimulation of microorganisms by lactulose. It should be mentioned though that these models have limitations and cannot mimic all conditions prevailing in vivo, and also may not be able to allow growth of all microorganisms present in vivo. There are indications that the administration of prebiotics suppresses the generation and accumulation of toxic metabolites from protein

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fermentation, and through a suppression of toxic metabolites, the incidence of colon cancer may decrease. However, substantial evidence supporting these beneficial effects of prebiotics is currently lacking, mainly due to the inaccessibility of the colon and the unavailability of reliable tracers. In vitro, the addition of 10 g/day of inulin, the best studied prebiotic to date, resulted in a twofold reduction in the production of ammonia and an undetectable production of BCFA (Van Nuenen et al., 2003). Interestingly, the addition of a protein fermentative microorganism, C. difficile, increased the production of these protein-fermentative metabolites. Addition of inulin to this situation reduced ammonia, BCFA, and production of phenolics (van Nuenen et al., 2003). A recent study investigated in vivo whether the administration of a selected prebiotic (lactulose) would result in a reduced concentration of one or more protein-fermentative metabolites in the colon (De Preter et al., 2004). Ten grams of lactulose were given at breakfast and at supper for 2 weeks. Before and after these 2 weeks, a test meal containing [2H4]tyrosine and lactose-[15N]ureide was consumed. Urinary p-[2H4]cresol and total 15N were measured. This study showed a significant reduction in both urinary biomarkers, and provides direct evidence that in vivo also, colonic protein degradation is reduced by the administration of lactulose as a fermentable carbohydrate, resulting in a lower concentration of potentially toxic metabolites. The full metabolome upon addition of prebiotics can be studied, either from fecal material or from in vitro derived samples, of which the latter are more clean, but do not necessarily contain all metabolites observed in real samples. As far as we are aware, initiatives to measure a full metabolome of fecal matter have not been undertaken. In studies in in vitro models of the large intestine, a first attempt to measure as many metabolites as possible has been made (our own unpublished results). Here, the holistic approach of metabolomics was taken. Apart from metabolites that are usually studied and measured in intestinal microbiology, such as the SCFA, lactate, ammonia, etc., several other extracellular microbial metabolites have been identified using this metabolomics approach. These include ethanol, acetaldehyde, methanethiol, and dimethylsulfide. None of these should raise surprise, as all are known to be microbial metabolites, but they are not generally measured or detected in samples related to the (lumen of the) GI tract. In these types of experiments, however, it is unclear which metabolite is produced from which substrate, emphasizing once more that our knowledge about the activities of the intestinal microbiota is woefully inadequate. Use of stable isotopes may fill some of the gaps in this knowledge, as exemplified in the studies described above (De Preter et al., 2004).


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6.3. Evidence of Cross-Feeding An interesting aspect of the bifidogenic nature of certain prebiotics, is that upon feeding these carbohydrates, the butyrate production is also increased. Yet, bifidobacteria are uncapable of producing butyrate. This indicates that there is more occurring than just bifidobacteria fermenting the prebiotics, and this is either an indication of other species fermenting the prebiotics as well, or an indication of cross-feeding. Probably both occur. The gut microbiota, as we have seen, is a very complex community that comprises hundreds of different species of microbes from different genera. One of the great puzzling questions is, how these microbes can work together so well to perform the functions that the microbiota generally does, and how it is possible that the microbiota composition can adapt itself to such rapidly and strongly changing conditions as those in the colon, without significant disturbance of its overall function. Advancements of science in this area of gut research are among the most interesting today. Some striking results are discussed here. Oxalobacter formigenes, a strictly anaerobic bacterium found in the human colon, presents a beautiful example of how the benefits of cometabolism extend beyond the gut wall, and intestinal microbial metabolism really is integrated with host metabolism (Stewart et al., 2004). Oxalate is ingested in a wide range of foods and beverages and is formed endogenously as a waste product of metabolism. Bacterial, rather than host, enzymes are required for the intestinal degradation of oxalate in man and mammals. The bacterium primarily responsible is O. formigenes (Stewart et al., 2004). Oxalate is excreted in urine and the loss of O. formigenes may be accompanied by elevated concentrations of urinary oxalate, increasing the risk of recurrent calcium oxalate kidney stone formation. The interesting points here are that O. formigenes has an obligate requirement for oxalate (produced by the host) as a source of energy and cell carbon. In return, the host is saved from kidney stone formation. But colleague microbes would also benefit. In O. formigenes, the proton motive force needed for energy conservation is generated by the electrogenic antiport of oxalate and formate by the oxalate–formate exchanger. Thus, O. formigenes produces formic acid which in turn, as a cross-feeding substrate, may serve as a one-carbon donor in the metabolism of many other intestinal microbes. Even when considering a single aspect of the colonic microbiota’s function, butyrate production, the situation is complex and confusing. Many different species of butyrate-producing bacteria are present. In a study of seven of those, namely strains of Roseburia sp., Faecalibacterium prausnitzii, and Coprococcus sp. from the human gut that produce high levels of butyric

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acid in vitro, distinct patterns of available butyrate pathway enzymes and fermentation patterns were discovered (Duncan et al., 2002). Strains of Roseburia sp. and F. prausnitzii possessed butyryl coenzyme A (CoA): acetate-CoA transferase and acetate kinase activities, but no butyrate kinase activity. Although unable to use acetate as a sole source of energy, these strains showed net utilization of acetate during growth on glucose, indicating that in the gut also they need a co-substrate for growth. In contrast, Coprococcus sp. strain L2-50 possessed a complete set of detectable butyrate synthetic enzyme activities: butyrate kinase, acetate kinase, as well as butyryl-CoA:acetate-CoA transferase. Yet, this strain was found to be a net producer of acetate instead of butyrate! The use of lactate by intestinal bacteria also presents some puzzling questions. The microbial community of the human colon contains many bacteria that produce lactic acid, but lactate is normally detected only at low concentrations (o5 mM) in feces from healthy individuals. To study which microorganisms are mainly responsible for lactate utilization in the human colon, bacteria able to utilize lactate and produce butyrate were isolated from fecal samples (Duncan et al., 2004). Out of nine such strains identified, four were found to be related to Eubacterium hallii and two to Anaerostipes caccae, while the remaining three represented a new species within a clostridial cluster. Interesting in view of the results discussed above, no significant ability to utilize lactate was detected in the butyrate-producing species Roseburia intestinalis, Eubacterium rectale, or F. prausnitzii (raising the question of which co-substrate of acetate these bacteria employ in the gut). Whereas E. hallii and A. caccae strains used both D- and L-lactate, the remaining strains used only the D isomer. Lactate utilization was prevented by the presence of glucose. However, when grown on starch in separate co-cultures with a starch-utilizing Bifidobacterium adolescentis isolate, two E. hallii strains and one A. caccae strain formed butyrate and the lactate produced by B. adolescentis became undetectable (Duncan et al., 2004). The effects of changes in the gut environment upon the human colonic microbiota are poorly understood. Studies of the response of human fecal microbial communities to alterations in pH (5.5 or 6.5) and peptides (0.6 or 0.1%) yielded surprising results (Walker et al., 2005). SCFA profiles differed markedly between conditions. Moreover, very substantial changes in the levels of the bacterial groups Bacteroides and Roseburia were monitored by using fluorescence in situ hybridization with a panel of specific 16S rRNA probes. These findings suggested that a lowering of pH resulting from substrate fermentation in the colon may boost populations of butyrateproducing bacteria, while at the same time curtailing the growth of Bacteroides sp. (Walker et al., 2005).


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In a recent study, cross-feeding of microorganisms on acetate and lactate to form butyrate was investigated using stable isotopes (Morrison et al., 2006). [U-13C6]Glucose was used to show that M+2 and M+4 isotopomers were the principal butyrate species produced from glucose fermentation, via [13C2]acetyl CoA as intermediate (Fig. 6). Lactate conversion to acetate, propionate, and butyrate were also observed. Conversion of propionate or butyrate into other SCFA was negligible. The degree of interconversion was dependent on which individual provided the fecal sample, indicating some degree of host specificity in microbial activity between different individuals. For instance, in only two of the five fecal samples, lactate to propionate conversion was found (Morrison et al., 2006). In addition, the authors studied butyrate production from fructooligosaccharides, a prebiotic stimulating bifidobacteria. Bifidobacteria produce primarily acetate and lactate, but not butyrate. Yet, addition of fructooligosaccharides to fecal batch cultures significantly increased butyrate production, and the stable isotope data allowed the conclusion that as much as 80% of this butyrate was derived from interconversion of extracellular acetate and lactate, with acetate being quantitatively more significant (Morrison et al., 2006).

7. METABOLIC FLUX ANALYSIS APPLIED TO THE GUT What are the best parameters to characterize physiology? The end function of gene expression, protein synthesis, and establishment of metabolite pools is to maintain organism life. From life in its simplest form, prokaryotes, we learn that cell physiology foremost serves to maintain maximal growth rate under the prevailing environmental conditions in the organism’s habitat. This primarily implicates the directing of appropriate material in the various biosynthetic pathways, and the supply of sufficient metabolic energy to drive these processes. Furthermore, transport processes of metabolites and ions are the basic means by which the cell ensures a closed material balance, and homeostasis of its inner environment. Therefore, metabolic fluxes of the primary metabolism, together with membrane transport fluxes, can be considered parameters that are very closely linked with cellular physiology. Thus, it seems worthwhile to measure and monitor these fluxes in addition to the genomic, proteomic, and metabolomic characterizations addressed above. MFA, as this activity is called, has seen a tremendous development in the past few decades and stable isotopes have played a key role in this progress, as indicated previously.

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Figure 6 Probing colonic SCFA metabolism with stable isotopes. Using the principle of isotope dilution, the colonic bacterial synthesis rate of butyrate can be determined after continuous infusion of [1-13C]butyrate in the cecum by measuring the [1-13C]butyrate tracer–tracee ratio (TTR) in the portal blood (relevant carbon labeling patterns are indicated by squares). In this case, the bacterial metabolism will be producing unlabeled butyrate. Cross-feeding of butyrate-producing gut bacteria on acetate may be evidenced by infusing [U-13C2]acetate in the cecum, and measuring the abundance of [1,2-13C2]-, [3,4-13C2]-, and [U-13C4]isotopomers in portal butyrate (relevant 12C/13C carbon isotopomer patterns are indicated by circles). In this case, if butyrateproducing bacteria take up acetate, part of the acetyl-CoA pool will get labeled with [1,2-13C2]acetyl units which leads to the mentioned butyrate isotopomers.


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The aim of MFA applied to the gut is to identify and quantify significant metabolic fluxes in vitro and in vivo mediated by the microbial conversion of substrates in the colonic intraluminal environment. The fermentation of complex carbohydrates as well as of proteins deserves special attention here, as SCFA primarily result from carbohydrate fermentation while protein fermentation yields amongst others sulfur-containing compounds that compromise health (see previous section). The human in vivo approach necessitates non- or minimally invasive investigation techniques. Hence, the primary investigation tool is that of the use of stable isotopes, which is increasingly used in dietary interventions (Labayen et al., 2004). One may anticipate testing 13C-labeled carbohydrate substrates with a varying degree of polymerization, as well as isotopically labeled proteins. The metabolic fate of the isotopically labeled atoms can be followed by high-throughput mass spectroscopic analysis of the various metabolites in body samples including blood, urine, feces, and epithelial biopsies. These analyses can be coupled to dedicated gas chromatography-TOF-mass spectrometry (GC-TOF-MS) for the analysis of volatile organic compounds in the exhaled air, and their isotopic labeling. In addition, metabolite concentrations can be determined in these samples, and genomic and proteomic expression profiles may be recorded from biopsy samples. The resulting data set provides a basis for the correlation of gut microbial metabolic activity with host responses, and ultimately human health. Newest developments in experimentation technology that can be applied in this area include the use of targeted administration of isotopically labeled substrates to the colon using e.g. pH-sensitive coated capsules (Tuleu et al., 2002; Oo et al., 2003). In the following sections, illustrative results pertinent to MFA of the gut microbiota as well as the host metabolic response will be discussed.

7.1. Insights into Bacterial Metabolic Routes The first important step in developing MFA of the colonic microbiota is the definition of the metabolic network that is operative. This would seem a task of unprecedented difficulty given that we are dealing with a highly complex and diverse symbiotic community of microbes that altogether form a microbiome with a genome coding capacity vastly exceeding that of the human genome as referred to previously. Moreover, it is well documented that considerable variations in both intensity and type of microbial metabolic activity occur along the GI tract (Jensen and Jorgensen, 1994; Metges, 2000). The cecum and proximal colon are the metabolically most active parts. As has been elegantly demonstrated to be the case for termites

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(see Brune and Friedrich, 2000 and references therein), there very likely will be distinct structure–localization–function relationships of bacterial metabolism also in the human gut. One genus of bacteria may supply the substrate for another, thus leading to a diverse and dynamic yet functionally stable microbial ecosystem. For instance, lactate produced by lactic acid bacteria and bifidobacteria is rapidly converted to butyric acid by clostridia and Eubacterium sp. (Bourriaud et al., 2005). Bacteria and Archeae performing saturation of unsaturated fatty acids, reduction of nitrite to ammonia, reduction of sulfate to sulfide, reduction of CO2 to methane, and reduction of CO2 to acetate provide possible hydrogen sinks (Jensen and Jorgensen, 1994) and by their action lower the partial pressure of intestinal hydrogen gas, thus creating thermodynamic conditions that allow for increased overall fermentation rates (Backhed et al., 2005). The different bacterial genera present in the colon play distinct roles in the metabolic chain of polysaccharide processing (Backhed et al., 2005; Bourriaud et al., 2005; McGarr et al., 2005), from depolymerization, sugar utilization, and production of intermediate metabolites such as hydrogen, lactate or ethanol, and conversion of these intermediates into end products (SCFA, methane). How then, given all this complexity, can one think of setting up a metabolic network to perform flux analysis? The approach should be to consider the microbiota as a whole rather than concentrating on all individual members. One can then proceed in a meaningful manner knowing that the microbiota performs only a limited number of major metabolic functions (Backhed et al., 2005; McGarr et al., 2005), including:

breakdown of polysaccharides producing lactate, volatile SCFAs (formic acid, acetate, propionate, butyrate, valerate) and related metabolites, as well as gases (carbon dioxide, hydrogen, and methane); breakdown of dietary peptides to amino acids for incorporation into biomass or for subsequent fermentation, with concomitant production of putrefactive metabolites; breakdown of endogenous (i.e. produced by the host) proteins especially mucins and other mucosal proteins.

Thus, one may start by first accounting for the quantitatively major processes, after which the network can be refined more and more, so as to include also the quantitatively minor (but possibly at least as interesting) pathways such as bile acid metabolism and production of vitamins. Following this approach, the strategy is now to build the pathway network by putting together all available pieces of information on gut microbial


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pathways and their approximate metabolic fluxes from the literature. In the following, relevant available literature data is reviewed. To quantify carbohydrate digestion in the small intestine or fermentation in the large intestine in vivo, 13C-labeled carbohydrate substrates such as starch are administered orally and the appearance of products such as glucose in blood (Korach-Andre et al., 2004) or carbon dioxide in exhaled breath (Christian et al., 2002) has been monitored. Using mathematical modeling the relative amounts of the substrate digested in the small and large intestine can be determined (Christian et al., 2002). Small intestinal and oro-cecal transit time (OCTT) can be measured with the lactose-[13C]ureide breath test (Priebe et al., 2004). Considerable efforts have been made to quantify accurately the microbial SCFA production. This is not an easy task due to the very active metabolism of the colonocytes and the liver which interferes as soon as the SCFA are released in the gut lumen (Fig. 7). In an elegant protocol, Kien et al. (1996) used [2-2H3]acetate and [1-13C] sugars infused into the colonic lumen of pigs to determine the rate of microbial acetate synthesis as well as the fraction of the sugars metabolized to acetate in a single experiment. Using [1-13C]butyrate infusion in the colon and sampling of portal blood, these authors later determined microbial butyric acid production in pigs (Kien et al., 2002) and showed that butyrate is also produced endogenously by these animals (Kien et al., 2000). Pouteau et al. (2003) have developed and applied protocols to determine SCFA production in humans, including intragastric infusion to evaluate first-pass splanchnic retention of SCFA. Isotopic tracers have been highly instrumental for the clarification of metabolic pathways involved in biosynthesis of compounds, as described in an exquisite review by Bacher et al. (1999). By detecting doubly-13C-labeled acetate produced from [3-13C]glucose, Wolin et al. (1998) could establish that in a fecal suspension isolated from an infant, the Bifidobacterium pathway was the major glucose fermentation pathway used. These authors also demonstrated the operation of the Embden–Meyerhof–Parnas as the major glycolytic pathway leading to SCFA in fecal suspensions of adults (Wolin et al., 1999). In these experiments, they found that a considerable portion of microbially produced acetate was formed via the Wood–Ljungdahl pathway of CO2 reduction. The analysis of amino acid metabolism by isotope labeling is complicated by the fact that these compounds are very actively turned over in each and every organ of the body, and most of them are rapidly de- and reaminated in transaminase reactions. Thus, all indispensable branched-chain amino acids become 15N labeled after intravenous application of only 15N-labeled leucine (Lien et al., 1997), because they are in rapid transaminase

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Figure 7 Assessing colonic SCFA production in vivo is not a straightforward task. SCFA are mainly produced by bacterial fermentation in the colonic lumen, but endogenous production may also take place in liver and peripheral organs (as in the case of acetate). Part of the microbially produced SCFA may ‘disappear’ in the lumen itself due to uptake and metabolization by other microorganisms (cross-feeding). A large part of SCFA produced in the colonic lumen may be disposed of in colonocytes and never even reach the bloodstream (as in the case of butyrate). The liver also has an active metabolism of SCFA, and almost completely prevents butyrate from reaching the peripheral bloodstream. Using the principle of isotope dilution in the experimental configuration depicted in the figure, the measured TTR of arterial SCFA reflects the sum of SCFA coming from the liver and SCFA produced in peripheral organs, rather than the true colonic production. To probe the latter correctly, intraluminal infusion is required. Ra and Rd signify rates of appearance and disappearance, respectively, due to active metabolism in the various organs.

equilibrium with their respective precursor keto acids. Amino acids from circulating blood may exchange via enterocytes with the colonic lumen, causing mixing of endogenous and microbial amino acids. As much of 20–30% of liver-produced ureum may be used by intestinal bacteria for amino acid and protein synthesis (Moran and Jackson, 1990) (cf. Fig. 3). A number of studies employing 15N-labeled urea have been performed to assess this issue. Urea diffuses into the colon where it is hydrolyzed by


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bacterial urease to ammonia before being assimilated. The microbial origin of a significant fraction of lysine and threonine in body protein has been established from 15N urea labeling experiments (Lien et al., 1997; Metges, 2000). Studies with combined 15NH4Cl and 14C-polyglucose in pigs (Torrallardona et al., 2003) confirmed this fact and, following sampling at different locations along the GI tract, gave strong indications that absorption of amino acids from microbial origin mainly occurs in the ileum and not in the colon. An important implication of these findings is that, as with vitamins, metabolic requirement cannot be equated with dietary requirement. Closely related to this issue is the determination of the daily requirement for essential amino acids (for a recent overview on this subject see Kurpad and Young (2003), which is currently done via tracer-based protocols such as the indicator amino acid oxidation technique (for example, threonine (Wilson et al., 2000) and lysine (Kriengsinyos et al., 2002)). Dietary fat apparently is a minor substrate for the colon due to the high efficiency of the fat uptake. Also, the bile acid cycle is highly efficient. Nevertheless, a minor (1–5%) fraction of bile salts reach the colon where anaerobic bacteria from, for example, the genus Clostridium metabolize them to secondary bile acids, especially lithocholic acid and deoxycholic acid (DCA) (McGarr et al., 2005). The latter is partly absorbed but cannot be reconverted to cholic acid by the liver. Serum levels of DCA in patients with colon cancer have been shown to be consistently higher than in healthy subjects (McGarr et al., 2005). The sulfated amino acid taurine is an important substrate for bile acid conjugation in the liver and a more highly preferred sulfur source for fecal microbial metabolism (McGarr et al., 2005). Since taurine conjugation is increased in individuals on a high animal protein diet, investigation of the colonic metabolism of this amino acid in relation to colon cancer may be relevant. A first taurine turnover study, on whole body level, employing [1,2-13C2]taurine has recently appeared (Rakotoambinina et al., 2004), showing very low turnover in healthy adults. Toxic nitrogen containing and/or aromatic end products of bacterial fermentation may present a health risk especially on animal protein-rich diets. Pre- and/or probiotic intakes have various claimed beneficial effects which generally are difficult to prove. De Preter et al. (2004) and Geboes et al. (2005) introduced the use of lactose-15N2-ureide and [2H4]tyrosine as useful quantitative indicators for pre-/probiotics efficiency, namely from the capacity of a product to suppress the generation and accumulation in urine of (i) 15N label derived from toxic bacterial ammonium, and (ii) toxic p-[2H4]cresol, a bacterial degradation product from tyrosine fermentation.

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With the help of the information presented above, an overall metabolic network of gut microbial metabolism can be constructed. To complete the model, transport routes that account for uptake of substrate and removal of products (e.g. via epithelial absorption and feces) have to be included.

7.2. Get Quantitative: Mass Balances Reveal a Lot In a consistent metabolic network, considered at (quasi)steady state, the possible fluxes are constrained by stoichiometry relations that reflect the mass balances (Schilling et al., 2000). As a consequence, the ranges within which intracellular metabolic fluxes can vary can be predicted and conclusions can be drawn on how the metabolic network responds under different conditions of, for example, substrate availability. While this approach has been extensively and successfully applied with microorganisms (Reed and Palsson, 2003), it may equally well be used to determine and even predict metabolic fluxes in mitochondria (Ramakrishna et al., 2001). The next step in setting up MFA of the colonic microbiota therefore is to gather as much quantitative experimental data as possible on fluxes that represent the inputs and outputs of that metabolic network in order to reduce the available flux solution space (Wiback et al., 2004). In other words, measured rates of, for example, carbohydrate intake by the gut, SCFA use by the colon, etc., contribute to determine the bacterial intracellular metabolite fluxes. Careful balancing of SCFA production in anaerobic cultures of fecal bacteria for instance has already provided important insights in their regulation by carbohydrate availability and growth rate (Macfarlane and Macfarlane, 2003). The potential of this procedure to derive conclusions on important fluxes of intermediary metabolism such as the activity of the citric acid cycle (i.e. mitochondrial function) even in a complex organ, has recently been demonstrated with isolated perfused livers (Arai et al., 2001; Lee et al., 2003; Yokoyama et al., 2005). For the colon, experimental procedures as discussed in the previous section can be employed to obtain relevant data. Of course, to be really successful, MFA of the colonic microbiota has to be performed in vivo with the colon functionally operative in the entirety of the functioning body. Indeed there is a true perspective that this can actually be accomplished. Namely, the flux balancing approach is also possible in vivo using multi-catheterized blood sampling approaches (Ten Have et al., 1996). Basically, this method employs measurement of blood flow and sampling of blood upstream and downstream of organs, allowing the set up of a net material balance across each organ. This approach has been used to study interorgan amino acid metabolism during acute liver failure in pigs


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and humans, where it led to a much improved insight in the role played by different organs (muscle, kidney, spleen, liver, portally drained viscera) during pathogenesis of the disease (Olde Damink et al., 2002; Ytrebo et al., 2006). When the method is combined with isotopic tracers, the total disposal rate of a traced compound (e.g. an amino acid) in an organ can be derived from the loss of tracer across that organ. This allows the calculation of the true uptake or production of this amino acid in that organ as net balance plus disposal (Bruins et al., 2002, 2003). The use of suitably chosen isotopically labeled colon substrate tracers will further allow an increase in the analyzing power of flux analysis, much the same as discussed previously for single microorganisms. Therefore, once stable isotopes are included, there is genuine reason to be optimistic about the prospect of MFA of the gut microbiota also in vivo in the near future.

7.3. Stable Isotope-Aided Quantification of Pathways: Functional Genomics What is the practical route for MFA of the colonic microbiota? Once the network is defined, the first step as we have seen is to construct material and metabolite balances over the colon. On the experimental side, this will involve the measurement of the input of non-digested material from the small intestine into the colon, the measurement of differential metabolite appearance rates in portal vs. arterial blood, and correcting the results for material lost in feces as well as for products of digestion in the small intestine (cf. Fig. 7). This analysis yields the basic input–output analysis of the colon, which however still has to be completed by taking into account the material metabolized by the gut wall. Stable isotopes may be employed at this stage in addition to the net balancing to determine the total metabolite disposal and true production rates as explained previously. This is an enterprise that may involve elaborate and strongly invasive experiments. Next comes the probing of the actual bacterial intracellular metabolic network. While this needs finally to be done for the in vivo situation in the intact functioning colon, useful a priori information on the regulation of gut bacterial metabolism may be derived from carefully planned in vitro experiments such as those on SCFA production discussed above (Macfarlane and Macfarlane, 2003). These experiments should include product profiles, cross-feeding effects, influence of thermodynamic constraints and pH, among others. This work could be performed in validated in vitro colon model systems such as described in a previous section (Minekus et al., 1999).

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Of course, in vitro conditions will most certainly differ considerably from those in vivo, which will in turn affect the microbiota composition and hence the overall metabolic activity pattern. Nevertheless, such in vitro models may be highly instrumental in model development, i.e. setting up the basic metabolic network of involved pathways and transport routes that one expects. Then, this overall network might be decomposed into a limited set of sub-networks each characteristic for a certain genus of bacteria, much in the same way as elementary mode balancing (Schuster et al., 2002; Cakir et al., 2004). Changes in microbiota composition then will only affect the relative contributions of those modes, and not the pathway network model as a whole. FBA will subsequently reveal to which extent the resulting equation system is under-determined from a mathematical point of view, given the measured material balance data. Subsequently, intelligent strategies may be employed (Mollney et al., 1999; Isermann and Wiechert, 2003) to design stable isotope labeling experiments that will produce the additional data necessary to completely solve the metabolic flux network thus constructed, in vitro and also in vivo. Subsequently, after the experiments using the required isotopically labeled substrates are actually conducted, the data resulting from MS and/or NMR analyses will be used in a non-linear least squares fitting procedure to yield the full set of fluxes in the metabolic network model. The final perspective of MFA developed along these lines is a map of metabolic pathway activities in the colonic microbiota, that can be decomposed into sub-maps of methanogenic, mixed acid, and other typical microbial fermentation modes, each eventually attributable to a different genus of microorganisms. One may then proceed to investigate how these flux maps differ between subjects with differences in microbiota composition, or how these flux maps change upon feeding different prebiotic substrates, or how these flux maps relate to host disease parameters, etc. Ideally, this information is correlated with data from the various ‘-omics’ platforms, leading to a true functional genomics application (Hellerstein, 2004).

8. EMERGING PICTURE OF THE ROLE OF MICROORGANISMS INTEGRATED IN MAN In recent years, the picture of the role played by the gut microorganisms within us has become increasingly clear. All evidence points at a truly mutualistic relationship. Our guts provide a habitat for incredible numbers


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of mostly anaerobic bacteria in an otherwise deadly, oxygen-rich environment. What we are getting in return has been largely derived from studies that compared Germ-Free (GF) rodents with those that have acquired a microbiota since birth (conventionally raised CONV-R). A recent overview can be found (Backhed et al., 2005). Important findings from these studies show that GF rodents show disturbed bile acid balances which affect cholesterol homeostasis, they show reduced cardiac weight and output, they are more vulnerable to vitamin deficiencies (including vitamin K, B6, B12, biotin, folic acid, and pantothenate), they extract less energy from their diet, their immune system development is different (e.g. strongly reduced serum IgM and IgG levels), and they are unable to metabolize dietary oxalates, leading to kidney stone formation. Interestingly, GF mice are resistant to IBD and are less susceptible to arthritis and colitis, indicating that there is also a risk involved with carrying around our microbiota.

8.1. Energy Balance Large intestinal fermentation can account for 10% of our daily energy supply (Bergman, 1990). Thus, the colonic microbiota plays a very significant role in whole body energy supply. Studies with GF and CONV-R rodents have shown that CONV-R animals were able to extract significantly more energy from their diets than GF counterparts, as judged from the fact that they had 40% more total body fat while consuming less food per day (Backhed et al., 2004). This corroborates findings from other studies (Scheppach et al., 1991; Pouteau et al., 2005) that evidenced increased serum acetate concentrations and turnover, correlating with colonic carbohydrate fermentation. Microbially produced butyrate is the preferred and most important energy source for colonocytes (Csordas, 1996). These points all more or less reflect a direct effect, i.e. additional energy produced by microbial fermentation of substrates entering the colon that would otherwise be useless to the host. There is however reason to believe that indirect effects may be at least as important. The colon functions within the whole of the intestine and associated visceral organs in controlling body energy balance (Badman and Flier, 2005). Gut and organs together play a key sensing and signaling role in the physiology of energy homeostasis. The gut, the pancreatic islets of Langerhans, elements in the portal vasculature, and even visceral adipose tissue communicate via neural and endocrine pathways with the controllers of energy balance in the brain. Signals reflecting energy stores, recent nutritional state, and other parameters are integrated in the central nervous system, particularly in the hypothalamus, to coordinate

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energy intake and expenditure (Badman and Flier, 2005). We are only beginning to uncover all the different features of this complex regulatory network, and expectations are high that understanding of the mechanisms that control energy balance will provide clues for therapies to fight the metabolic syndrome.

8.2. Innate Immune System Only a thin layer of epithelial cells known as enterocytes separates the host from the intestinal lumen. These cells must form an effective barrier against incursions and introgressions by the intestinal microbiota. Interestingly, part of this barrier function appears to be carried out by intestinal bacteria themselves: the purified adhesin of a B. adolescentis strain was found to inhibit the adhesion of enteropathogenic E. coli and C. difficile to an intestinal epithelial cell line (Zhong et al., 2004). However, to offer protection in case that the barrier function becomes impaired, the bulk of cells aligned below the layer of enterocytes are immune cells (Chin, 2004). The intestinal immune response and the mucosal layer therefore are both very important for human host defence and can be affected by the gut microbial fermentation products of carbohydrates and proteins, of which notably SCFA and sulfur-containing compounds have been studied in most detail. Of the SCFA, butyrate has best been studied. Butyrate was found to decrease colitis in animal models. Moreover, butyrate resulted in an increase of IgA-producing cells and mucosal IgA concentrations, the secretion of anti-inflammatory cytokines and decreased myeloperoxidase (MPO) activity. Most of these parameters have been studied using cell lines or animal models. However, in patients with UC, sodium butyrate enemas are found to improve inflammatory scores, clinical symptoms, and intestinal permeability. Apart from possible anti-inflammatory effects, butyrate also influences the intestinal mucus production (Cassidy et al., 1982; Finnie et al., 1995; Barcelo et al., 2000). In vivo, the number of goblet cells was found to increase and dose-dependent increases in mucus secretion were observed (Barcelo et al., 2000) upon addition of butyrate. However, intestinal mucus also serves as substrate for bacterial fermentation (degradation of proteins and saccharide-side chains). In addition, intestinal mucus is a source of sulfur for SRB, which are able to use the sulfate liberated from mucins for the production of hydrogen sulfide. This sulfide can inhibit butyrate oxidation by the epithelial cell (Roediger et al., 1993) and is associated with apoptosis, loss of goblet cells, and distortion of the crypt cell architecture. Limited information is available about the effects of sulfur-rich diets on


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SRB and their effect on intestinal inflammatory parameters and mucus production in humans. Furthermore, the interactive effects of sulfur-rich diets (i.e. increased sulfide production) and carbohydrate consumption in humans are still largely unknown. In view of the rising number of clinical cases with intestinal complaints, there is a need for increased research efforts in this area. For example, IBS, a functional bowel disorder, affects a large number of subjects. Prevalence data show a frequency ranging from 3 to 20% in the general population of which a large part does not seek medical attention but has complaints of pain, diarrhea, and/or constipation (Verne, 2004). Moreover, the prevalence of IBS is found to increase. A disturbed inflammatory response, role of mast cells, and an abnormal colonic fermentation are all observed in IBS and beneficial effects have been reported using pre- and probiotics. Because of the magnitude of the population affected by this disorder, it is worthwhile to study the mechanisms behind this. Needless to point out, further insight into the health effects of SCFAs and sulfide with regard to major intestinal mucosal functions is important also for healthy subjects.

8.3. Intestinal Microbiota: Is There a Link With Obesity? As pointed out above, the colon functions within the whole of the intestine and associated visceral organs in maintaining the host energy balance. An important aspect is the role that intestinal microorganisms play in cholesterol and bile acid metabolism, performing deconjugation and metabolism of bile salts. Disturbed bile acid metabolism was observed in GF rodents, but it has also been found that the SCFA acetate can interfere directly with lipid metabolism. By contrast, acetate production (presumably microbial) after lactulose ingestion in overweight subjects was recently shown to result in short-term decrease in free fatty acid level and glycerol turnover related to a decrease of lipolysis (Ferchaud-Roucher et al., 2005), both factors believed to help in preventing insulin resistance. By contrast, however, acetate may also stimulate lipid synthesis (Wolever et al., 1995), and it remains to be settled whether acetate has a long-term beneficial effect. Interestingly, the latter study also showed that another SCFA, propionate, inhibited lipid synthesis from acetate. Backhed et al. (2004) found that conventionalization of GF mice with a microbiota harvested from cecum of CONV animals produced a 60% increase in body fat content and insulin resistance despite reduced food intake. An increased absorption of monosaccharides from the intestine was detected in conventionalized mice, resulting in de novo lipogenesis in the liver. In addition, they showed that Fiaf, a lipase inhibitor,

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was suppressed in the intestinal epithelium of conventionalized mice and that this suppression was essential for the microbiota-induced deposition of fat in adipose tissue. These findings suggest that, at least in the mouse, the gut microbiota affects energy harvest from the diet and energy storage in adipose tissue. Whether this is also the case in humans remains to be determined. Thus, the effects of colonic SCFA production or microbial activity in general on lipid metabolism is difficult to predict. The true role of the colon in regulation of lipid metabolism is very likely to be an even more complex one, involving multiple neural and endocrine pathways. The recent finding that ingestion of dietary fat stimulates cholecystokinin (CCK) receptors, but at the same time leads to attenuation of the inflammatory response by way of the efferent vagus nerve and nicotinic receptors, may be an interesting foretaste of the type of regulatory hardwiring that can be expected. Here, we have a novel neuro-immunologic pathway, controlled by nutrition, that may help to explain the intestinal hyporesponsiveness to dietary antigens. Our intestine developed during evolution for optimal survival on natural diets. The recent rise to epidemic dimensions of obesity-linked diseases correlates, in a timely manner, with a shift in dietary habits toward a reduced intake of dietary fiber, an increased intake of simple sugars, a high intake of refined grain products, an altered fat composition of the diet, and a dietary pattern characterized by a high glycemic load (Suter, 2005). Recent epidemiological research (Maskarinec et al., 2006) of a large ethnically diverse population showed that on an individual level, fat and protein consumption predicted a higher BMI, and dietary fiber intake predicted a lower BMI. Similarly, a higher consumption of meat, poultry, and fish was related to excess weight, whereas fruit and vegetable intake were inversely associated with excess weight. There is growing evidence of the high impact of dietary fiber and foods with a low glycemic index on single risk factors (e.g. lipid pattern, diabetes, inflammation, endothelial function, etc.) as well as the development of the endpoints of atherosclerosis (especially coronary heart disease; Suter, 2005). A recent review (Hyman, 2006) pointed out that it is the glycemic load, rather than the glycemic index, that affects the neuroendocrine–immune signaling. Dietary fiber is one of the main factors lowering the glycemic load, whence its beneficial effects in reducing weight. This author also points out that bacterially produced fatty acids lower cholesterol production in the liver. In line with this observation, it is tempting to hypothesize that obesity may, at least in part, be associated with deprivation of proper substrate for colonic microbial fermentation. Obviously, in obesity, the normal regulatory response to high leptin levels is blunted by another, as yet unknown, regulatory component. If this blunting is associated


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with substrate limitation of the microbiota, this would hint at a colon-linked regulator. There is an interesting parallel with polyphosphate metabolism in bacteria that could support this view: many bacteria in carbon-rich media, when confronted with certain dietary component limitations, shift to accumulation of large amounts of polyphosphate, probably for energy storage (Ault-Riche et al., 1998). Physiological studies could be instrumental in finding which aberrations in metabolic, proteomic, and/or genomic profiles linked to the colon can be found in obesity, after which subsequent research may uncover the relevant regulatory consequences of these aberrations. Such studies may profit considerably from the integration of enteric neurobiological approaches (Grundy, 2004; Grundy and Schemann, 2005), a fascinating and rapidly developing field.

8.4. Role of Stable Isotopes Can stable isotopes contribute to knowledge in the field of host–microbe interaction? Yes, they can. The key advantage of stable isotope methods is that they are very potent in tracing the fate of substrates entering the colon on the metabolic level, and therefore allow for a specific correlation of host responses to colon-derived metabolic events. This helps in discriminating colonic microbiota-related effects against those having an endogenous origin, and therefore allow a clearer picture of host–microbe interaction to emerge. For instance, supplementation with resistant starch (RS) has been shown to improve colonic lesions in a dextran sulfate sodium (DSS)-induced colitis model in rats. To find out whether it is the increased colonic butyrate production that accelerates the healing process, Moreau et al. (2004) measured the ceco-colonic uptake of butyrate and its oxidation into CO2 and ketone bodies in control and DSS-treated rats fed a fiber-free basal diet or a RS-supplemented diet. After cecal infusion of [1-13C]butyrate, concentrations and 13C-enrichment of butyrate, ketone bodies, and CO2 were quantified in the abdominal aorta and portal vein, and portal blood flow was measured. These measurements allowed the authors to determine the utilization of butyrate specifically by the colonic mucosa, and to conclude that increased utilization of butyrate by the mucosa is subsequent to evidence of healing, and appears to be a consequence rather than a cause of the RS healing effect (Moreau et al., 2004). In another study (Pouteau et al., 2005), it was tested whether acetate from colonic fermentation of inulin would stimulate peripheral acetate turnover in dogs. Dogs were administered with simultaneous infusions of

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[1-13C]acetate i.v. and [1,2-13C2]acetate intrarectally. After switching from a control diet to a 3% inulin-enriched diet, initially no changes in whole body acetate concentration and turnover were seen after 4 days, but after 21 days the whole body acetate turnover had increased significantly by 31%. While it was determined that a significant acetate production occurred in the colon, no [1,2-13C2]acetate tracer was recovered in the peripheral circulation. This led to the conclusion that the occurring colonic fermentation of inulin indirectly stimulated whole body acetate turnover (Pouteau et al., 2005). While these examples concern the effect of microbial processes on the host, the effects of host metabolism on microbial processes may also be probed by stable isotopes. One example obviously is the measurement of SCFA synthesis on various diets (a direct effect of host behavior), a nice example of a more indirect interaction concerns mucin. Mucus and mucosal proteins represent an important substrate for intestinal bacteria. Faure et al. (2002) have developed a method to measure intestinal mucoprotein FSR (%/day) in vivo by using the flooding dose method with the stable isotope L-[1-13C]valine. Free L-[1-13C]valine enrichments in the intracellular pool were determined by GC-MS, whereas L-[1-13C]valine enrichments in purified mucoproteins or intestinal mucosal proteins were measured by gas chromatography-combustion-isotope ratio mass spectrometry. Using this method, Faure et al. (2005) compared rats fed isonitrogenous diets (12.5% protein) containing 30% (group 30) and 100% (control group) of the theoretical threonine requirement for growth. The mucin FSR was significantly lower in the duodenum, ileum, and colon of group 30 compared with controls. Because mucin mRNA levels did not differ between these two groups, mucin production in group 30 probably was impaired at the translational level. These results clearly indicated that restriction of dietary threonine significantly and specifically impairs intestinal mucin synthesis. In clinical situations associated with increased systemic threonine utilization, threonine availability may limit intestinal mucin synthesis and consequently reduce gut barrier function in the absence of adequate dietary threonine intake. In another study, glutamine was found to stimulate gut mucosal protein synthesis (Coeffier et al., 2003). While the above examples pertain to in vivo studies, stable isotope-based methods are equally potent in sorting out regulatory effects at the cellular level, as e.g. evidenced in HT29 cells where it was found that, upon butyrate supplementation, these colon cells replace glucose for butyrate as an energy substrate (Boren et al., 2003). Therefore, stable isotope methods offer prospects for rapid screening protocols such as stable isotope-based dynamic metabolic profiling (SIDMAP) (Boros et al., 2003).


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9. NEW ASPECTS IN THE STUDY OF INTESTINAL BACTERIAL PHYSIOLOGY 9.1. Microbes at War: Population Competition Models Some of the species of adherent intestinal microorganisms in the intestine have exploited and adapted to particular microniches in different compartments of the colon with its extremely large surface area created by the complex involution of crypts and villi. These bacteria are continuously competing for survival. The ability to persist and propagate or be ultimately eliminated, is dependent to a large extent upon the armory of each combatant (Chin, 2004). Susceptibility or immunity of each strain to the arsenal of bacteriocins or quorum sensing factors produced by another constitutes a community at war. Yet, seemingly in stark contradiction, this scenario may be vital for their very existence, as we will see shortly. The weaponry of intestinal microbes is diverse and sometimes ingenious. In addition to ‘fair’ weapons such as bacteriocins, they can use less direct but potentially even more powerful tricks. For instance, very active excretion of acetate may induce growth limitation of a competitor who is susceptible to acetate uncoupling, as, for example, described for a Clostridium sp. (Baronofsky et al., 1984). As another example, some Bifidobacterium strains produce adhesins that competitively inhibit adherence of E. coli and C. difficile to intestinal epithelial cells, providing themselves with an increased resistance against being washed out of the colon (Zhong et al., 2004), while stimulating washout of their competitors. Similarly, Lactobacillus plantarum is able to produce a protein that may prevent adhesion of E. coli carrying type-1 fimbriae to bind to mannose-containing glycans (Pretzer et al., 2005). Competition experiments of gut microbial strains (Kato et al., 2005) have shed light on the mechanisms that allow stable coexistence of enemies in bacterial cultures. A cellulose-degrading defined mixed culture consisting of five intestinal bacterial strains was established that showed no change in cellulose-degrading efficiency, while all members stably coexisted through 20 sub-cultures. The mechanisms responsible for the observed stability were investigated by constructing ‘knockout communities’ in which one of the members was eliminated. Thereafter, the roles played by each eliminated member and its impact on the other members of the community were evaluated from measured dynamics of the community structure and the cellulose degradation profiles of these mixed cultures. Integration of the results showed different synergistic and detrimental relationships between different

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sets of the five bacterial strains. An obvious synergistic effect was that the aerobic bacteria introduced anaerobic conditions, which permitted anaerobic Clostridium to supply metabolites (acetate and glucose) for their growth in return. A detrimental effect was the inhibition of cellulose degradation due to excessive acetate production by another Clostridium sp. As an important conclusion, the balance of the various types of relationships (both positive and detrimental) is apparently essential for the stable coexistence of the members of this mixed culture (Kato et al., 2005). In this type of investigation, stable isotope analysis may be helpful. They may offer powerful tools to interpret the results of cross-feeding experiments in mixed cultures by their ability to trace back specific metabolic transitions (cf. Fig. 6). This has, for instance, allowed the unequivocal detection of the presence of bifidobacteria in a human colonic microbiota (Wolin et al., 1998) by establishing 13C-labeling patterns that are specific for the unique Bifidobacterium pathway of hexose catabolism. To better understand the mechanisms governing the stable coexistence of different competing bacterial strains, the approach of building a predictive theoretical model is worth serious consideration. This requires us to be philosophical to a certain extent. The outcome of the struggle for life of bacteria in the human colon to some level will reflect the results of coevolution. At first, one might be inclined to expect that one or another single species (let’s take us, humans, for a moment) may have such enormous competitional advantages that it will outgrow all others. However, this is obviously never the case in reality. If there is one thing that research on the colon shows, it is that even we as humans cannot live a healthy life without the help of those seemingly insignificant microbes inside us. Apparently, there is much evolutionary advantage in sharing resources and surviving as a consortium, rather than alone (Pfeiffer et al., 2001). The fact that the colonic microbiota is able to vary considerably in composition and time, while remaining able to perform a rather stable overall metabolic function, must reflect intrinsic principles or ‘laws’ governing their dynamic yet persistent coexistence. It is the task of biological modelers to sort out those principles. Indeed, they are already on the job and interesting parallels with such seemingly far-off fields as game theory have already been discovered. The rationale here is that by evolving toward optimal properties, organisms change their environment, which in turn alters the optimum. Evolutionary game theory provides an appropriate framework for analyzing evolution in such ‘dynamic fitness landscapes’ (Pfeiffer and Schuster, 2005). Indeed, theoretical simulations correctly predicted that an ensemble of toxinproducing, toxin-sensitive, and toxin-resistant strains of E. coli is able to coexist when living in spatially structured, non-transitive interaction


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(Kerr et al., 2002; Kirkup and Riley, 2004). A corollary of this observation is that bacteriocins promote, rather than eliminate, microbial diversity in the gut too. This in fact corroborates the experimental findings (Kato et al., 2005) discussed above. Further interesting progress in this area is certain to be awaited in coming years.

10. CONCLUSIONS AND FUTURE PROSPECTS 10.1. Toward a Systems Biology of the Gut The combination of high-throughput methods of molecular biology with advanced mathematical and computational techniques has propelled the emergent field of systems biology into a position of prominence. Unthinkable a decade ago, it has now become possible to screen and analyze the expression of entire genomes, simultaneously assess large numbers of proteins and their prevalence, and characterize in detail the metabolic state of a cell (population). Because of these general advances in life sciences, research on the physiology of the intestinal microbiota as it functions within and in interaction with the host is rapidly growing in intensity also. The literature covered in this review bears testimony to the fact that our knowledge on a wide range of issues related to gut microbes and the role they play in the mutualistic relationship with their host has expanded enormously in recent years. Having said this, it must however be admitted that all this knowledge in fact is still fragmentary and that a fully integrated picture of host–microbe interactions has yet to be established. Notably, the mechanisms by which the metabolic activity of our intestinal microbiota influence processes leading to disease, are still very far from being understood. Their elucidation requires an understanding of metabolic regulation that so far has been limited by a failure to consider regulation within the context of the whole network (Sweetlove and Fernie, 2005), in this case of microbial and host metabolic and signaling pathways. Several approaches that provide tools for the required integration of data, models, and thinking (Davis and Hord, 2005) are now appearing in the literature. Lee et al. (2005) discuss how, for the design of cells that have improved metabolic properties for industrial applications, informative high-throughput analysis and predictive computational modeling or simulation must be combined to generate new knowledge through iterative modification of an in silico model and experimental design. Such new modeling approaches should aim to take full advantage of genome sequence data, transcription profiling, proteomics and

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metabolite profiling, and integrate global metabolic models with genetic and regulatory models for strain improvement (Smid et al., 2005). The applicability of this approach has already been demonstrated in a study that describes the in-depth analysis of the intracellular metabolite concentrations, metabolic fluxes, and gene expression (metabolome, fluxome, and transcriptome, respectively) of lysine-producing C. glutamicum at different stages of batch culture revealing distinct phases of growth and lysine production (Kromer et al., 2004). The integrated approach was valuable for the identification of correlations between gene expression and in vivo activity for numerous enzymes, and allowed, for the first time, an integrated overview of the regulation of C. glutamicum intermediary metabolism when this organism switches to L-lysine production. The way to go that is emerging from the literature (Hellerstein, 2004; Boros, 2005) is obviously to link up stable isotope-aided MFA with the existing ‘-omics’ technologies. Innovative modeling frameworks that are able to integrate data from all these four platforms are required for this purpose. Such models should allow the determination of regulatory properties of the studied organism from the experimental data by incorporating such diverse information as pathway structures, flux balance constraints, isotopic labeling routes, thermodynamic constraints, enzyme kinetic properties, statistical correlations, and employing suitable minimization criteria. This challenge is formidable but methods are in very rapid development, and are rapidly gaining predictive power for metabolic regulation (Wiback et al., 2004). Recent developments in this field include the introduction of ‘scale-free’ networks (Barabasi and Albert, 1999), the implementation of constraints imposed by kinetic and equilibrium constants in the isotopomer distribution analysis (Selivanov et al., 2005), and hybrid cooperations between kinetics-based dynamic models and FBA-based static models (Yugi et al., 2005; Kitayama et al., 2006), while the importance of including new levels of the metabolic regulatory hierarchy (such as protein–protein interaction) has also been pointed out (Sweetlove and Fernie, 2005). With these recent advances in theoretical aspects of network thinking and a postgenomic landscape in which our ability to quantify molecular changes at a systems level is unsurpassed, the time is ripe for the development of this new level of understanding of metabolic network regulation in the world of our intestinal microbiota, with its immensely complex network of intricate microbe–microbe and microbe–host interactions. One of the more simple but invaluable lessons to be learned pertaining to this field is that, together with a fiber-rich diet, a good breakfast is the best guarantee to make that network do what is was designed for – keep us lean and healthy (Hyman, 2006).


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metabolism in a D-galactosamine-induced rat liver failure model. Metab. Eng. 7, 88–103. Young, G.P. and Gibson, P. (1994) Butyrate and the colorectal cancer cell. In: Short-Chain Fatty Acids (H.J. Binder, J. Cummings and K.H. Soergel, eds), pp. 148–160. Kluwer, Boston. Ytrebo, L.M., Sen, S., Rose, C., Ten Have, G.A., Davies, N.A., Hodges, S., Nedredal, G.I., Romero-Gomez, M., Williams, R., Revhaug, A., Jalan, R. and Deutz, N.E. (2006) Interorgan ammonia, glutamate, and glutamine trafficking in pigs with acute liver failure. Am. J. Physiol. Gastrointest. Liver Physiol. 291, G373–G381. Yuan, J., Zhu, L., Liu, X., Li, T., Zhang, Y., Ying, T., Wang, B., Wang, J., Dong, H., Feng, E., Li, Q., Wang, H., Wei, K., Zhang, X., Huang, C., Huang, P., Huang, L. and Zeng, M. (2006) A proteome reference map and proteomic analysis of Bifidobacterium longum NCC2705. Mol. Cell Proteomics 5, 1105–1118. Yugi, K., Nakayama, Y., Kinoshita, A. and Tomita, M. (2005) Hybrid dynamic/ static method for large-scale simulation of metabolism. Theor. Biol. Med. Model. 2, 42. Zello, G.A., Pencharz, P.B. and Ball, R.O. (1993) Dietary lysine requirement of young adult males determined by oxidation of L-[1-13C]phenylalanine. Am. J. Physiol. 264, E677–E685. Zhang, X.J., Chinkes, D.L. and Wolfe, R.R. (2002) Measurement of muscle protein fractional synthesis and breakdown rates from a pulse tracer injection. Am. J. Physiol. Endocrinol. Metab. 283, E753–E764. Zhong, S.S., Zhang, Z.S., Wang, J.D., Lai, Z.S., Wang, Q.Y., Pan, L.J. and Ren, Y.X. (2004) Competitive inhibition of adherence of enterotoxigenic Escherichia coli, enteropathogenic Escherichia coli and Clostridium difficile to intestinal epithelial cell line Lovo by purified adhesin of Bifidobacterium adolescentis 1027. World J. Gastroenterol. 10, 1630–1633. Zoetendal, E.G., Akkermans, A.D. and De Vos, W.M. (1998) Temperature gradient gel electrophoresis analysis of 16S rRNA from human fecal samples reveals stable and host-specific communities of active bacteria. Appl. Environ. Microbiol. 64, 3854–3859. Zoetendal, E.G., Cheng, B., Koike, S. and Mackie, R.I. (2004a) Molecular microbial ecology of the gastrointestinal tract: from phylogeny to function. Curr. Issues Intest. Microbiol. 5, 31–47. Zoetendal, E.G., Collier, C.T., Koike, S., Mackie, R.I. and Gaskins, H.R. (2004b) Molecular ecological analysis of the gastrointestinal microbiota: a review. J. Nutr. 134, 465–472. Zoetendal, E.G., Vaughan, E.E. and De Vos, W.M. (2006) A microbial world within us. Mol. Microbiol. 59, 1639–1650.

intestinal lumen

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Plate 2 Schematic representation of gut-associated nitrogen metabolism, compiled from information in Metges (2000) and references therein. Colored circles symbolize 15 N isotope label originating from urea (red) or ammonia (blue), respectively, with fading color intensity indicating isotope dilution. Urea diffuses from the blood through the enterocytes into the intestinal lumen, where it is hydrolyzed by bacterial urease into ammonia and carbon dioxide. Ammonium is the preferred non-specific microbial nitrogen substrate for synthesis of e.g. amino acids. Microbially synthesized amino acids may partially be released into the gut lumen and taken up by ileal enterocytes (in the colon, bacterial cell densities are so high that microbially synthesized amino acids probably never reach colonocytes). Therefore, bacteria may supply a significant portion of the body’s requirement for indispensable amino acids. 15N label appearance in histidine, lysine, and threonine upon 15N-urea or 15N-ammonia administration is proof of microbial activity since these amino acids cannot be endogenously transaminated. However, after administration of e.g. the 15N-labeled indispensable amino acid leucine, 15 N label will appear in other branched-chain indispensable amino acids as well as in dispensable amino acids (AAdisp) since the body is able to transaminate leucine, valine, and isoleucine. Due to extensive amino acid exchange between blood, enterocytes, and intestinal microbiota, interpretation of 15N labeling experiments is often ambiguous. Combining nitrogen-15 labeling with carbon-13 or carbon-14 labeling as done e.g. in Torrallardona et al. (2003), therefore, may constitute a useful approach to arrive at unequivocal conclusions (For b/w version, see page 92 in this volume).

Plate 3 Principle of RNA-based stable isotope probing (SIP) for detection and characterization of microbes that actively metabolize the labeled substrate. 13C-labeled substrates are incubated in (a) simple in vitro models (test tube or flask), (b) sophisticated in vitro systems, or (c) in vivo. Samples obtained from these experiments [in the figure only shown for samples from (a)] are subjected to RNA isolation and density gradient centrifugation. After separation of the gradient in fractions, molecular fingerprinting techniques, such as DGGE (Zoetendal et al., 2004a) or T-RFLP (Egert et al., 2003) can be used to determine the presence (usually enrichment) in the heavier fractions of those microorganisms that specifically fermented the substrate and this can be compared with the diversity present in an unlabeled, control sample (For b/w version, see page 108 in this volume).

13

C-labeled substrate

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Bacterial Physiology, Regulation and Mutational Adaptation in a Chemostat Environment Thomas Ferenci School of Molecular and Microbial Biosciences G08, The University of Sydney, NSW 2006, Australia

ABSTRACT The chemostat was devised over 50 years ago and rapidly adopted for studies of bacterial physiology and mutation. Despite the long history and earlier analyses, the complexity of events in continuous cultures is only now beginning to be resolved. The application of techniques for following regulatory and mutational changes and the identification of mutated genes in chemostat populations has provided new insights into bacterial behaviour. Inoculation of bacteria into a chemostat culture results in a population competing for a limiting amount of a particular resource. Any utilizable carbon source or ion can be a limiting nutrient and bacteria respond to limitation through a regulated nutrient-specific hunger response. In addition to transcriptional responses to nutrient limitation, a second regulatory influence in a chemostat culture is the reduced growth rate fixed by the dilution rate in individual experiments. Sub-maximal growth rates and hunger result in regulation involving sigma factors and alarmones like cAMP and ppGpp. Reduced growth rate also results in increased mutation frequencies. The combination of a strongly selective environment (where mutants able to compete for limiting nutrient have a major fitness advantage) and elevated mutation

ADVANCES IN MICROBIAL PHYSIOLOGY, VOL.53 ISBN 978-0-12-373713-7 DOI: 10.1016/S0065-2911(07)53003-1


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rates (both endogenous and through the secondary enrichment of mutators) results in a population that changes rapidly and persistently over many generations. Contrary to common belief, the chemostat environment is never in ‘‘steady state’’ with fixed bacterial characteristics usable for clean comparisons of physiological or regulatory states. Adding to the complexity, chemostat populations do not simply exhibit a succession of mutational sweeps leading to a dominant winner clone. Instead, within 100 generations large populations become heterogeneous and evolving bacteria adopt alternative, parallel fitness strategies. Transport physiology, metabolism and respiration, as well as growth yields, are highly diverse in chemostat-evolved bacteria. The rich assortment of changes in an evolving chemostat provides an excellent experimental system for understanding bacterial evolution. The adaptive radiation or divergence of populations into a collection of individuals with alternative solutions to the challenge of chemostat existence provides an ideal model system for testing evolutionary and ecological theories on adaptive radiations and the generation of bacterial diversity.

1. General introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The chemostat environment and its applications to studies of bacteria . 3. The physiological changes in an organism inoculated into a chemostat: The example of glucose-limited Escherichia coli . . . . . . . . . . . . . . . . . 3.1. Transport and Membrane Permeability. . . . . . . . . . . . . . . . . . . . . 3.2. Metabolism and Energetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Stress Regulation and Gene Expression . . . . . . . . . . . . . . . . . . . 3.4. Antibiotic Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Quorum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Variations in responses within and between species . . . . . . . . . . . . . . 5. Steady state or constant change in a chemostat population? . . . . . . . . 6. Mutation rates and mutators in chemostat populations . . . . . . . . . . . . . 7. Mutational takeovers and population changes . . . . . . . . . . . . . . . . . . . 8. A mutational sweep in detail: The physiological advantage and spread of mgl mutations in glucose-limited E. coli . . . . . . . . . . . . . . . . 9. Other mutations in chemostat populations and their physiological effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1. Changes in the lac System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2. Outer Membrane Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3. rpoS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4. mlc and malT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5. ptsG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6. Metabolic Changes and Cross-Feeding . . . . . . . . . . . . . . . . . . . . 9.7. Amplification and Other Genomic Rearrangements . . . . . . . . . . . . 10. Emerging diversity in chemostat populations . . . . . . . . . . . . . . . . . . . . 10.1. Diversity in Regulatory Strategies . . . . . . . . . . . . . . . . . . . . . . .

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10.2. Diversity in Transport Strategies . . . . . . . . . . . . . 10.3. Diversity in Metabolic and Bioenergetic Strategies. 11. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. GENERAL INTRODUCTION Unusually for microbiology, a science in which empirical observations predominate, continuous culture methods originated from conceptual and formal analyses of the growth properties of bacteria. In 1950, two milestone papers proposed and demonstrated that bacterial cultures can be grown indefinitely by pumping fresh medium at a fixed rate into a culture vessel with a constant volume (Monod, 1950; Novick and Szilard, 1950a). The medium controls growth in that one component becomes limiting, preventing growth beyond a particular density. In synthetic media containing a single component such as glucose, amino acid or ion at a concentration lower than generally used in batch culture, growth is capped by a single nutrient and is characteristically called a nutrient-limited chemostat. The behaviour of bacteria in a chemostat more or less follows the kinetic formulations derived and refined in several studies (Monod, 1950; Novick and Szilard, 1950a; Herbert et al., 1956; Pirt, 1975; Dykhuizen and Hartl, 1983; Panikov, 1995). Nevertheless, derived growth equations rely on empirical observations describing bacterial growth with sub-saturating concentrations of individual nutrients (Monod, 1949). Inherent assumptions in the description of chemostat cultures are saturable growth kinetics and a stable halfsaturation constant for the given substrate/organism pairing. Another assumption is the constancy of biomass yield from substrate for the studied organism. This review will not deal with growth kinetics in detail, but the concept of absolute constants in bacterial growth relevant to chemostat culture has been questioned and discussed elsewhere (Ferenci, 1999a). At several points in this review, entrenched views on the properties of chemostats will be reassessed in view of the plasticity in bacterial characteristics due to physiological and mutational adaptations in chemostat-grown populations. My approach will be more empirical than formal and I will mainly focus on describing the complexities of events in chemostats rather than forcing bacterial behaviour into equations or models. There are more than 5000 papers dealing with chemostat cultures so some added focus was needed to keep this review to manageable proportions.

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Several reviews and books deal with some aspects of chemostat culture (Kubitschek, 1970; Pirt, 1975; Dykhuizen, 1993; Kovarova-Kovar and Egli, 1998). This discussion will concentrate on basic principles in bacterial physiology and mutational adaptation arising from studies of chemostats limited by a single nutrient. Many lessons can be learned from single organismsingle limitation experiments so I will not consider multi-stage chemostats (Novick, 1959; Lovitt and Wimpenny, 1981) or other ingenious-stat variations. Neither chemostats inoculated with more than one type of organism (e.g. phage and bacteria, Horne, 1970; or inter-species competitions, Veldkamp and Jannasch, 1972) nor continuous cultures with more than one limiting nutrient (e.g. multiple sugars; Egli et al., 1993; Dykhuizen and Dean, 2004) will be discussed. Also not considered are numerous studies involving chemostats for the improvement of strains, directed gains in function, processes or plasmid stability. The motivation for this review is actually no different to that in a 50-year-old paper on a similar topic (Moser, 1957). It is sobering to re-quote some of the opening words of Moser: ‘‘population dynamics has been studied in bacteria for many years, but today this field has become an attractive and promising subject of experimental and theoretical investigation because of two factors. First, the astounding advances made in our knowledge of the genetic mechanisms of bacteria. Second, the development of devices for the continuous growth of large bacterial populations y’’. These ideas can be re-stated after 50 years but there is certainly a resurgence of interest in the area of chemostat research for three reasons. These are, firstly, the revival in the use of chemostats for gene expression comparisons under stable physiological conditions (Hoskisson and Hobbs, 2005 and references therein); secondly, the mushrooming interest in experimental evolution using a variety of selection environments including chemostats (Dykhuizen, 1990; Watt and Dean, 2000; Elena and Lenski, 2003; Adams, 2004); and thirdly the better understanding of the chemostat environment in eliciting physiological responses (Ferenci, 1999b, 2001). The last point impacts on the other two, because a better understanding of the physiological state of nutrient-limited bacteria is half the battle in interpreting gene expression studies and predicting the kind of beneficial mutations that occur in evolving populations. Regulatory and mutational changes giving rise to physiological and metabolic adaptations go hand-in-hand. Hence, the first aim of the review is to describe the rapid regulatory transitions in one well-studied system (glucose limitation in E. coli chemostat cultures) and then to proceed to discussion of ‘‘steady states’’, mutational processes and what mysterious assemblages constitute a long-term chemostat population.


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2. THE CHEMOSTAT ENVIRONMENT AND ITS APPLICATIONS TO STUDIES OF BACTERIA Not all chemostats are equal. There are numerous chemostat designs adopted in research besides those produced commercially. Two major hurdles in adopting chemostats for the usual microbiology laboratory are cost and space considerations. These costs are particularly problematic when multiple parallel cultures are needed as in most physiological and evolutionary studies; comparisons of gene expression under replicate or manipulated conditions, replicate parallel evolution experiments or testing competitive fitness of several strains all need more than one culture. In the author’s laboratory, simplified chemostats with less rigorous control shown in Fig. 1 are used, made of modified Schott bottles. Four such cheap chemostats with reduced working volumes can be run simultaneously in a small area. The level of control of environmental parameters such as pH, dissolved O2, biomass is less than in the most sophisticated commercial equipment, but a well-buffered medium and relatively low biomass levels prevent secondary limitations. The dangers of secondary limitations have been documented (Ihssen and Egli, 2004) so an awareness of the carrying capacity of home-made chemostats is needed. Further miniaturization of chemostat cultures to 10 mL working volumes has been reported recently for metabolic comparisons (Nanchen et al., 2006). Even more exciting developments are on the horizon with the description of continuous flow microfluidic devices in which the properties of individual cells can be observed (Balagadde et al., 2005; Groisman et al., 2005; Zhang et al., 2006). Once generally available, these will provide a new impetus for chemostat studies with the added advantage of on-line monitoring of expression and visualization of individual microbes under controlled conditions. Other recent technical advances for particular applications are in the tricky control of oxygen availability in studying transitions between aerobiosis and anaerobiosis (Alexeeva et al., 2002) and in the prevention of wall growth (change of cells from planktonic to biofilm forms adhering to the chemostat vessel) (de Crecy-Lagard et al., 2001; Kashiwagi et al., 2001). Whatever the design of the chemostat used, the experimental choices available to the researcher are: (a)

the choice of organism. The inoculum is of course determined by the interests of the experimenter, but when a choice of strain within a species or between laboratory strains is available, several factors can influence the course of experiments. The strain variations and polymorphisms that influence the behaviour in chemostats are considered in Section 4.

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medium inflow controlled by a Watson-Marlow 302S pump air inlet from a Hy-Flo air pump air inlet through medium break

overflow outlet

sampling port Screw-cap 100 ml Schott bottle sealed with silicone

Water bath

80 ml working volume

sparger stirrer

Figure 1 A simple positive-pressure chemostat. Four such chemostats can be concurrently operated on a multi-place stirrer base at the same dilution rate using the multi-channel peristaltic pump and individual air pumps attached to each inlet. The working volume is set by the positioning of the overflow outlet. The positive-pressure medium break prevents back-contamination of medium.

(b)

the choice of limiting nutrient. Limitation with carbon source, nitrogen source, phosphorus source, etc. can all give the same growth rate in a chemostat, but the gene expression patterns are very different (Hua et al., 2004). The hunger responses to specific limitations are considered in Section 3. The cell volumes of E. coli also differ under different forms of limitation (Shehata and Marr, 1971), reinforcing differences


(c)

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in growth and physiology. Of course, different limitations also result in very different selection pressures on cultures (Section 9.4) and were reported to give different mutation rates (Novick and Szilard, 1951). the choice of medium and concentration of limiting nutrient. The concentration of nutrient pumped into a chemostat determines the cell density achieved. The density of a culture not only influences quorum sensing aspects (Liu et al., 2000), but high densities also can bring on secondary limitations such as low oxygen availability or trace metal requirement. Unless high cell yields are absolutely needed, cultures at around 108 cells/mL will avoid most of these secondary effects of high density. the dilution rate for running the chemostat. The dilution rate, D, theoretically sets the growth rate in continuous culture (Monod, 1950). Any dilution rate that does not result in washout of bacteria results in nutrient limitation, but the growth rate and concentration of regulatory molecules (s factors, cAMP, ppGpp) are highly dilution rate dependent (Notley and Ferenci, 1996; Notley-McRobb et al., 1997; Teich et al., 1999). The production of particular enzymes and pattern of gene expression is thus very sensitive to dilution rate (Matin, 1979; Ferenci, 1999b). The regulatory differences brought on by differences in D are discussed in Section 3. Mutation frequency and fitness effects of mutations are also dependent on the dilution rate (Notley-McRobb et al., 2003) and are discussed in Sections 6 and 9. It also follows that no arbitrary dilution rate represents a state ‘‘characteristic’’ of a nutrient-limited condition. sampling times for particular tests of the culture, be they for gene expression or sampling for mutations. The time-course of events in chemostats after inoculation with a batch-cultured inoculum is more complex than simply a transition between two states. The incorrect but widespread belief that chemostats reach a steady state with constant properties is discussed in Section 5. The implications for reproducible sampling are also described in Section 5 and the time-scale of mutational take-overs and evolution experiments is discussed in Section 7.

So what can and cannot be studied with chemostats? Broadly speaking, two research applications were foreseen by the inventors of chemostat culture and these are still the most widely studied. Firstly, chemostats permit bacteria to be grown in one growth phase, with a fixed doubling time and with better control of the environment and cell density. In chemostats, studies of physiological and regulatory phenomena should be less affected by growth transitions, metabolic shifts or quorum sensing. The medium and

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atmosphere can also be controlled to look at the effect of individual environmental changes without modifying doubling times. An excellent recent example of this is the study of the effects of temperature on cellular composition without changes in growth rate (Cotner et al., 2006). Many other such studies are in the literature and Section 3 will include examples in various areas of bacterial physiology. Nevertheless, as noted above, the properties of cells in a nutrient-limited state are a function of D and several other factors noted above, so it is important not to lose sight of the effect of limitation and growth rate on gene expression, metabolism and physiology in the interpretation of chemostat experiments. The second fundamental area of application of chemostats is in the study of mutational adaptation in populations grown continuously under a more or less constant selection pressure. The strong competition between members of chemostat populations under nutrient limitation is itself a strong selection condition where mutants with better ability to utilize nutrients have a fitness advantage (Harder et al., 1977; Dykhuizen and Hartl, 1983). As shown in Sections 6–9, recent data suggest that mutational changes are evident very shortly after the start of chemostat cultivation. The ability to propagate chemostat populations for extended periods (weeks to months) further allows the study of successions of mutations and the gains in new characteristics. Results of the past 10 years also suggest that chemostat populations become highly heterogeneous in remarkably short time periods. This also makes chemostats good systems for the study of the generation of bacterial diversity, as discussed in Section 10. The nature of alternative beneficial mutations permitting fitness under nutrient limitation also reveals much about the redundancy of metabolic and physiological responses to the same environment. The emergence of diversity in chemostats further sees the appearance of intra-population interactions aside from competition, such as cross-feeding (Treves et al., 1998). Section 10 describes how the chemostat environment itself evolves over time, as a consequence of the changes in populations. In reality, there are still very limited examples of studies with chemostats in which the total complexity of changes in gene expression, physiology, metabolism, as well as mutational adaptations are well analysed. As far as I am aware, lactose- and glucose-limited cultures of E. coli are the closest to this breadth of coverage. The lactose studies have been reviewed recently (Watt and Dean, 2000) so much of the subsequent discussion will focus on glucose-limited chemostats. With E. coli, there is also the possibility of comparisons between different types of limitation, permitting identification of responses that are nutrient specific. Most importantly, E. coli is the only bacterium in which the regulatory and mutational adaptations can be


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considered in enough detail as two parts of the same story, namely the acquisition of fitness in a new environment, as considered in the later sections.

3. THE PHYSIOLOGICAL CHANGES IN AN ORGANISM INOCULATED INTO A CHEMOSTAT: THE EXAMPLE OF GLUCOSE-LIMITED ESCHERICHIA COLI So what happens when batch-cultured bacteria are inoculated into a chemostat? The initial transitions are similar in many ways to the set of changes in bacteria approaching stationary phase. The regulatory signals emanating from reduced growth rates (effects on ppGpp and RpoS levels) and reduced availability of carbon source in the case of glucose limitation (effects on cAMP levels) determine the global shifts in gene expression and physiological responses (Ferenci, 1999b). A major difference is that stationary phase bacteria rapidly deplete remaining carbon source on the way to starvation whereas the chemostat environment maintains a low but biologically significant level of nutrition and low growth rates. Batchcultured bacteria go through transient peaks of alarmones like cAMP in approaching starvation, but as shown in Fig. 2, chemostat cultures continue to maintain higher levels of cAMP when the bacteria reduce glucose levels to the micromolar range and begin to grow at the rate determined by the dilution rate. It is worth noting that the residual glucose level is different with particular D values; the higher the D and growth rate, the higher the residual glucose (Senn et al., 1994). In turn, this means that cAMP, RpoS and ppGpp levels are also distinct at different dilution rates (see Section 3.3), which is why the choice of D is significant in studies of gene expression. Metabolic regulation is also a function of D and the balance of glucose converted to CO2 or acetate is different at low and high dilution rates. The detailed effects of D on transport, regulation, metabolism and physiology are discussed below.

3.1. Transport and Membrane Permeability The residual glucose concentration in a chemostat is a function of D, but is of the order of micromolar or below (Senn et al., 1994). The most obvious physiological response in bacteria to such low nutrient levels is to improve scavenging ability for limiting nutrient (Harder and Dijkhuizen, 1983). Bacterial affinity for nutrients is far from constant. Affinity differs between

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Growth (A=580 nm)

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Figure 2 Transitions in E. coli upon acclimatization to a glucose-limited environment. In a chemostat inoculated with E. coli and established at a dilution rate of 0.3 h1 and 1 mM glucose in the feed medium, most of the major transitions occur within the first 8 h. Bacterial density stabilizes at the carrying capacity determined by the limiting medium, glucose levels drop markedly to the mM range, global regulatory circuits involving cAMP are activated and gene expression responsive to nutrient limitation (mgl, mal genes) are highly expressed. The curves are based on data in Notley-McRobb et al. (1997).

oligotrophic bacteria and bacteria like E. coli which needs to adapt to more than one habitat (Button, 1985), but neither is the affinity for limiting nutrient constant in the same bacterium under different environmental conditions (Ferenci, 1999a). The importance of transport to chemostat behaviour was recognized long ago (Hansen and Hubbell, 1980; Harder and Dijkhuizen, 1983) and several regulatory adaptations ensure the expression of multiple sets of genes responsible for high-affinity transport systems under nutrient limitation. Depending on the substrate, the expression of particular cytoplasmic membrane transporters as well as outer membrane porins in Gram-negatives is modified by nutrient limitation. This is true not just for glucose limitation; for example, phosphate limitation induces PhoE (involved in outer membrane permeability of anions; Overbeeke and Lugtenberg, 1980) and Pst proteins (involved in high-affinity, binding protein-dependent transport of phosphate; Medveczky and Rosenberg, 1971). Typical of many such limitational adaptations, PhoE complements the general porins and the Pst system complements a lower affinity system (Pit; Rosenberg et al., 1977) that bacteria use when phosphate is not at low concentration.


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A common theme in all kinds of nutrient limitation is that outer membrane composition is modified, including in organisms other than E. coli (Hancock et al., 1982; Sterkenburg et al., 1984). The compositional changes mainly affect the porin proteins responsible for outer membrane permeability (Nikaido and Vaara, 1985). In E. coli, the relative proportion of the less selective porins OmpF and OmpC is controlled by nutrient availability; the larger-channel, more permeable OmpF protein is preferentially expressed at low nutrient levels (sub-micromolar glucose at D ¼ 0.3–0.4 h1), whereas OmpC is more prevalent with excess nutrient (Liu and Ferenci, 1998). Interestingly, OmpC, which imparts reduced permeability and greater levels of antibiotic and detergent resistance on E. coli, is present in preference to OmpF at even lower dilution rates (D ¼ 0.1 h1) involving further reduced residual glucose levels. In other words, closer to starvation, the response of E. coli is towards self-protection and reduced permeability (Ferenci, 1999b). The complex relationship between dilution rate and porin expression involves control by several global transcriptional regulators (Liu and Ferenci, 2001). Another outer membrane adaptation peaks at intermediate dilution rates in glucose-limited chemostats. The level of LamB protein is 20-fold elevated at D ¼ 0.5–0.6 h1. As for OmpF, LamB is also lowered in amounts with nutrient excess or starvation conditions (Death et al., 1993). LamB is a sugar-selective porin (Death et al., 1993) produced from the lamB gene regulated as part of the mal regulon (Boos and Shuman, 1998). LamB and the mal regulon are rapidly induced on transition to glucose limitation (Fig. 2). The porin encoded by lamB improves glucose scavenging at micromolar glucose levels and is induced by the combined elevation of intracellular cAMP and endogenous inducers under glucose limitation (Notley and Ferenci, 1995). The maltotriose produced under glucose limitation is the result of multiple intracellular metabolite pool changes in chemostat-grown E. coli (Tweeddale et al., 1998). The combined effect of OmpF and LamB changes under glucose limitation at intermediate dilution rates is to increase permeability to limiting nutrients. The importance of these regulatory adaptations is apparent from the results that mutants lacking one or both of these porins are less fit in chemostat culture (Death et al., 1993; Liu and Ferenci, 1998). As well as the outer membrane changes described above, periplasmic and cytoplasmic membrane components also change under glucose limitation. In most forms of carbon source limitation, the concentration of cAMP is a major factor in the control of high-affinity cytoplasmic membrane transporters. cAMP levels in carbon-limited chemostats are much higher than in exponential batch culture, as discussed in Section 3.3. The cAMP effect is

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very broad and contributes to the elevated expression of many binding protein-dependent transport systems in E. coli, as shown in proteomic analyses (Wick et al., 2001; Ihssen and Egli, 2005). Transport systems involving periplasmic binding proteins generally have higher substrate affinities than facilitators, symporters or phosphotransferase-type transporters (Furlong, 1986; Ferenci, 1996). The high-affinity systems provide scavenging ability for amino acids, inorganic ions and sugars and allow bacteria to utilize micromolar concentrations of substrates. Specifically for glucose, limitation results in the induction of the high-affinity MglBAC system, involving the periplasmic glucose–galactose binding protein, which is half-saturated at sub-micromolar glucose levels (Death and Ferenci, 1993). The rapid increase in mgl expression on entry into glucose limitation is shown in Fig. 2. The induction of the Mgl system is in stark contrast to nutrient-excess situations, where glucose down-regulates transporters for other substrates and represses systems like the mal regulon and mglBAC by cAMP-mediated catabolite repression (Death and Ferenci, 1994; see Section 3.3). The usage of binding protein-dependent transport systems entails an affinity advantage but also a cost in terms of energetics. Binding protein-dependent systems are more expensive in ATP input than alternative transporters with lower affinity such as proton-coupled symporters or the phosphotransferase system (Muir et al., 1985; Driessen et al., 1987). As noted in Fig. 3, glucose can be recognized by several alternative cytoplasmic membrane transport systems besides Mgl (Lengeler, 1993), but these are of lesser importance under glucose-limiting conditions (Ferenci, 1996). In particular, the affinity advantage of transport at low substrate concentrations makes binding protein-dependent transport a fitness benefit in chemostats; with limiting glucose, mutants lacking the glucose–galactose binding protein-dependent Mgl system are much less competitive than wild-type bacteria or those lacking the glucose phosphotransferase system (Death and Ferenci, 1993). Despite the cost, active transport also contributes to the fitness of yeast in sugar-limited chemostats compared with transport dependent on facilitated diffusion (Weusthuis et al., 1994). A broad generalization in microbial physiology based on the examples above is that there is an in-built redundancy and energetic cost–benefit trade-off inherent in adapting nutrient transport to different nutrient levels. The importance of transport to growth in chemostats is also supported by theoretical studies (Hansen and Hubbell, 1980; Shoemaker et al., 2003). Additional strong evidence that elevated scavenging ability for limiting nutrients in chemostats is important in fitness comes from analysis of longer term, mutational adaptations that occur in continuous cultures. As noted from the first studies onwards, chemostat-evolved mutant bacteria are better


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O2

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Glucose

Slow growth rate Nutrient limitation

1 2 3

4 5 6 7

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19 20 21

8 9 10

cAMP RpoS ppGpp

Pyruvate

11 12

13

[H++e]

15 16 17

Anabolism/ Biomass

18

H2O CO2 Acetate

Figure 3 Redundancies in glucose transport and metabolism in E. coli. Through the outer membrane, glucose can pass through porins OmpC (1), OmpF (2) and LamB (3). Glucose is a substrate of the PEP:glucose phosphotransferase system (4), PEP:mannose phosphotransferase system (5), the glucose–galactose binding proteindependent Mgl system (6) and the GalP symporter (7) in the cytoplasmic membrane. When phosphorylated either with the PTS or glucokinase, glucose-6-phosphate can be converted to pyruvate using three alternative reactions sequences (glycolysis (8), the Entner–Doudoroff pathway (9) or the pentose phosphate pathway (10)). Reduced cofactors in glucose oxidation are oxidized by alternative enzymes (NDH-1 and NDH-2 (11 and 12)) which in turn can feed into alternative respiratory chains (cytO (13) or cytD (14)). Pyruvate can be oxidized to CO2 via the tricarboxylic acid cycle (15) or a variation using glyoxylate cycle enzymes (16). Carbon for biosynthesis is taken from both carbohydrate metabolism and pyruvate metabolism (17). Acetate production from pyruvate (18) can also take place depending on environmental conditions. Nutritional and growth rate-dependent factors in chemostats also influence at least three global regulatory circuits involving cAMP (19), RpoS (20) and ppGpp (21) under glucose-limited conditions. See text for discussion and references.

at uptake of limiting nutrient (Novick and Szilard, 1951), as described in more detail in Sections 8–10.

3.2. Metabolism and Energetics The range of metabolic flexibility of E. coli is even more extensive than seen with transport adaptations. Growth on glucose can involve alternative metabolic pathways and result in non-identical outputs (yields, metabolic products) in different situations. As summarized in Fig. 3, three known pathways can convert hexose phosphate to pyruvate, alternative pathways take pyruvate to CO2; there are alternative respiratory chain components, as well as non-constant fermentation balances or products in E. coli (see references

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in Neidhardt, 1996). The choice between alternatives is highly sensitive to the environment, including the chemostat environment. Recent studies have confirmed that the dilution rate setting under glucose limitation can strongly influence metabolic processes (Kayser et al., 2005; Nanchen et al., 2006). The proportion of glucose metabolized via the pentose phosphate pathway, the glyoxylate pathway and indeed almost all fluxes were non-linearly dependent on the dilution rate setting (Nanchen et al., 2006). Transcription of metabolic genes is also differentially regulated at different growth rates and also with different forms of limitation (Hua et al., 2004). Consistent with likely shifts in metabolism, metabolite pools are also strongly affected by dilution rate, as shown by metabolome analysis and comparisons between bacteria grown at different dilution rates. Slow dilution rates (at 0.1 h1) induced changes characteristic of stressed bacteria, such as trehalose accumulation (Tweeddale et al., 1998). An unexpected recent finding was that the glyoxylate cycle, normally associated with acetate incorporation and not with glucose utilization, was expressed in glucose-limited bacteria (Fischer and Sauer, 2003; Maharjan et al., 2005). The switch to the glyoxylate cycle can also be partly observed at the transcriptional level (Franchini and Egli, 2006). There is also increased production of isocitrate lyase (AceA) after prolonged glucose limitation (Wick et al., 2001). There are alternative views on why the glyoxylate cycle may be beneficial under glucose limitation (Wick et al., 2001; Fischer and Sauer, 2003). Also, the presence of the pathway is not universal in glucoselimited cultures; some E. coli laboratory strains such as the W3110 lineage does not use the glyoxylate cycle under glucose limitation (Fischer and Sauer, 2003). This is one of many strain-variable properties discussed in Section 4 that fog a unified view of even a single species. Respiration rates are also dilution rate dependent (Kayser et al., 2005). Although it is outside the scope of this review to consider the control of the respiration/fermentation balance and the related field of aerobiosis/ anerobiosis regulation, these are active fields of research using chemostats. Amongst the topics addressed, the regulation of acetate production due to NADH/NAD ratio (Vemuri et al., 2006), the role of global transcriptional regulators (Alexeeva et al., 2003) and the effect of alternative electron acceptors (Wang and Gunsalus, 2003) have all been investigated using chemostats. On the energetics side, it was also shown that transcription of ATPase genes decreases moderately with increasing growth rate (Kasimoglu et al., 1996) and the metabolic balance changes due to mutations in ATPase (Noda et al., 2006). Chemostats also allow analysis of a fundamental question in bioenergetics, namely whether there is evolutionary selection for optimal efficiency or


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rate in bacterial metabolism. There is an assumption that in general, metabolic efficiency is dependent on the trade-off between the rate and yield of energy metabolism (Pfeiffer et al., 2001). Relevant to the above question is that, perhaps paradoxically, a characteristic of bacteria is that microbial growth yields are often 50% less than optimal (Westerhoff et al., 1983). Energetic efficiency has been tested in chemostat culture and it was concluded that rate and not efficiency is optimized under nutrient limitation (Demattos and Neijssel, 1997). Bacteria in populations may not have the luxury of evolving in an energy-efficient way and in situations where there is kinetic growth competition amongst members of the same culture, perhaps there is more of a selection for rate than yield (Fong et al., 2003). A recent chemostat study has also looked at competition between energetically efficient and inefficient yeasts with temporal and spatial variation (MacLean and Gudelj, 2006). How prolonged nutrient limitation in an unstructured environment with a single resource or niche influences metabolic strategies shifts the yield-rate trade-off is discussed in Section 11. A long-observed feature of chemostat growth that still cries out for a metabolic explanation is the concept of maintenance energy (Pirt, 1965; Tempest and Neijssel, 1984). Numerous studies have documented the decreasing efficiency of biomass production at slow dilution rates and extrapolating energy demands to zero growth rates suggested that bacteria need some energy for maintaining viability at slow or zero growth rates. A comparison of measured maintenance coefficients was assembled recently and also shows inconsistencies between different strains (Nanchen et al., 2006). A molecular explanation of maintenance energy is still lacking however, and it needs testing whether elevated stress metabolism such as the synthesis of trehalose and many stress resistance proteins by starvation is responsible for the maintenance energy effect in starving or close-to-starving cells.

3.3. Stress Regulation and Gene Expression Several features of the chemostat environment contribute to altered gene expression relative to nutrient-excess bacteria and the extent of the transcriptional changes is evident from microarray comparisons (Hua et al., 2004; Franchini and Egli, 2006). Undoubtedly many regulators are involved in the adaptation to the chemostat environment and the sections below focus on the role of obviously important global controllers. This is not to say that the ones considered are totally responsible for chemostat adaptation. Many other transcriptional regulators change significantly in concentration in the chemostat environment, but have not yet been fully

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investigated in this context. For example, hns transcription and H-NS protein level, with major influences on DNA structure and transcription (McLeod and Johnson, 2001), both increase significantly with increasing D in glucose- and ammonia-limited chemostats (X. Liu and T. Ferenci, unpublished results) (Liu et al., 2000). In contrast, himA gene transcription and IHF, also involved in the regulation of many genes (Goosen and Van De Putte, 1995), increase greatly with decreasing D (Liu, 2001). The full implications of these changes have not been studied yet and it needs to be kept in mind that the overall patterns of regulation will be even more complicated than is apparent from the examples described below. 3.3.1. Starvation and Stress Signals and RpoS E. coli senses reduced growth rate as an indicator of stress and elevates RpoS protein levels (Hengge-Aronis, 2000) even when nutrient limitation is the sole problem in the environment. Consequently, the general stress response controlled by RpoS, with its hundreds of coregulated genes (Patten et al., 2004; Weber et al., 2005), is highly expressed in chemostats (Notley and Ferenci, 1996). The level of RpoS rises with decreasing dilution rate and especially sharply at slow growth rates near or below D ¼ 0.1 h1. This trend is seen both with glucose- as well as ammonia-limitation, and RpoS levels are if anything even higher under N-limitation (Liu and Ferenci, 2001). Paradoxically, almost none of the genes controlled by RpoS are of physiological benefit in directly overcoming nutrient limitation. Is the seemingly unnecessary induction of RpoS in chemostats at low dilution rates a failure of regulation or an artefact of the chemostat system? Several lines of argument could be used to support the RpoS response in chemostats as a sensible reaction to environmental signals. Chemostats are sometimes criticized as unnatural environments and it may be argued that low levels of nutrient without other stresses is not commonly met in the habitats of bacteria like E. coli. In more natural situations, a mix of simultaneous stresses such as osmotic, pH or oxidative stress is present. An alternative explanation is that even when temporarily present on its own, nutrient limitation is ecologically sensed as a useful signal for hard times to come. Despite some merit in these arguments, there is accumulating evidence that the induction of the general stress response in chemostats may be an indication of a fault in the sensing and processing of stress responses. The observation that rpoS polymorphisms and rpoS null mutants are common in natural populations does suggest that E. coli and Salmonella in nature can benefit from losing RpoS function (Ferenci, 2003). This indicates that expression of the general stress response is sometimes a burden even in


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natural habitats, as it is in chemostat culture. Consistent with this view, rpoS mutants are selected in continuous culture under glucose limitation to avoid the RpoS cost (Notley-McRobb et al., 2002a). Recent studies suggest that mutational loss of RpoS is a consequence of the trade-off between stress responses and nutritional responses to hunger in general (Ferenci, 2005). This balancing of stress/nutrition capabilities occurs not just in chemostats, but also influences growth with poorer carbon sources like acetate that occur in natural habitats (King et al., 2004). The sigma factor-dependent competition in transcription documented in other studies (Farewell et al., 1998; Nystrom, 2004) imposes two alternative and mutually exclusive choices on E. coli, namely either to optimize stress responses based on RpoS, or to utilize nutrients dependent on RpoD (Ferenci, 2005). Because of s factor competition, differences in RpoS levels affect both stress responses (controlled by RpoS) as well as vegetative gene expression due to RpoD (or s70) (Jishage et al., 1996; Jishage and Ishihama, 1997; Nystrom, 2004). The latter controls genes involved in nutrient utilization so fitness in chemostats is inversely proportional to RpoS protein level within a strain (King et al., 2004). Hence many polymorphisms affecting stress response and nutritional properties are seen within the species E. coli. As noted in Section 6, polymorphisms and strain variation in RpoS protein levels found in natural isolates can strongly influence regulation and fitness properties in a chemostat culture. 3.3.2. Nutritional Status and cAMP The extracellular levels of all essential nutrients are signals sensed by bacteria like E. coli. Depleting levels of phosphorus, nitrogen or sulphur sources have their own sensing and response mechanisms (Wanner, 1993; Ikeda et al., 1996; Gyaneshwar et al., 2005). In considering glucose limitation, here too, major shifts in gene expression occur with reduced levels of extracellular carbon source (Hua et al., 2004; Franchini and Egli, 2006). Indeed, the scale of the changes in switching from glucose-excess to glucose-limited environments is extensive because many of the changes are controlled by cAMP and Crp protein, which represent truly ‘‘global’’ regulators of E. coli (Saier, 1996). cAMP and Crp together control expression of about 200 operons and many more genes (Gosset et al., 2004; Zheng et al., 2004). As demonstrated in chemostats, cAMP levels (both intracellular and excreted) are much elevated under glucose but not nitrogen limitation (Harman and Botsford, 1979; Matin and Matin, 1982; Notley-McRobb et al., 1997). The control of cAMP levels occurs through regulation of adenylate cyclase, which is in turn is regulated by glucose availability

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through components of the PEP:glucose phosphotransferase system and Crp (Takahashi et al., 1998; Park et al., 2006). Reduced extracellular glucose levels trigger an increase in cAMP synthesis (Fig. 2). After a major jump in cAMP concentration when glucose drops below about 0.3 mM (NotleyMcRobb et al., 1997), cAMP levels continue to rise more moderately with decreasing dilution rates and further reduction of extracellular glucose levels. The precise shape of the dilution rate–cAMP response curve is strainspecific however, particularly at low dilution rates, due to unidentified variations even between E. coli K-12 strains (Notley-McRobb et al., 1997). Relevant to the chemostat environment, the physiological response to high cAMP levels is the much-elevated expression of a large number of nutrient transport proteins (Section 3.2; Wick et al., 2001; Hua et al., 2004; Franchini and Egli, 2006). These include many binding protein-dependent transporters besides the mglBAC and the mal-lamB gene products specifically useful in scavenging glucose. Extrapolating to natural environments, the expression of systems specific for other carbon sources has been interpreted as a response that can broaden the possibilities and prepare E. coli to utilize any available substrate in a nutrient-limiting environment (Ihssen and Egli, 2005).

3.3.3. Growth Rate and ppGpp The growth rate of E. coli has a major effect on the rate of macromolecular synthesis (Maaloe and Kjeldgaard, 1966). The ribosomal content of E. coli in glucose-limited chemostats rises linearly with dilution rate in the measured interval between D ¼ 0.2 and 0.7 h1 (Yun et al., 1996). The concentration of the alarmone ppGpp, produced by RelA and SpoT, controls many of the growth rate-related changes in bacteria (Cashel et al., 1996) although changes to nucleotide concentrations (Schneider and Gourse, 2003) and DNA supercoiling may also contribute (Travers and Muskhelishvili, 2005). The intracellular concentration of ppGpp increases with decreasing dilution rate in glucose-limited chemostats, particularly sharply at Do0.1 h1 (Teich et al., 1999). The multitude of ppGpp-dependent phenotypes in bacteria has been reviewed recently (Braeken et al., 2006). Significant to regulation in chemostats are the interaction of ppGpp with RNA polymerase (Artsimovitch et al., 2004) and the role of ppGpp in diverting transcription to promoters differentially regulated by sigma factors (Jishage et al., 2002). Both ppGpp and RpoS are elevated and function synergistically to provide a general repositioning of transcription in stressed cells (Nystrom, 2004).


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Table 1 Examples of physiological responses to environmental stresses studied with chemostat cultures Organism E. coli E. coli E. coli E. coli E. coli Bacillus cereus Streptococcus mutans Streptococcus mutans Xanthomonas campestris Lactococcus lactis

Stress Oxidative stress: paraquat effects on metabolomes Nitric oxide stress: transcriptome Water activity UV, sunlight killing effects Heavy metal effects (Zn): transcriptome Acid tolerance: effects of growth rate Acid effects on biofilms Acid effect on membrane lipids Acid stress on enzyme activities Acid stress and energetics

Reference Tweeddale et al. (1999) Flatley et al. (2005) Roller and Anagnostopoulos (1982) Berney et al. (2006) Lee et al. (2005) Thomassin et al. (2006) Li et al. (2001) Quivey et al. (2000) Esgalhado and Roseiro (1998) O’Sullivan and Condon (1999)

3.3.4. Other Studies of Regulation in Chemostats Chemostats can also be used to impose controlled changes to the environment and to study transcriptional and physiological responses aside from the general stress response. As summarized in Table 1, virtually any stress can be imposed on bacterial chemostat cultures by manipulating culture conditions or modifications such as illumination of the culture. As also included in this non-exhaustive list, Table 1 shows environmental conditions such as the effect of low pH that can be applied to studies of many different types of bacteria.

3.4. Antibiotic Sensitivity A number of chemostat studies have reached the conclusion that growth rate is an important factor in antibiotic susceptibility and bacteria growing at slow dilution rates are not as sensitive as fast-growing organisms (Brown et al., 1990 and references therein). This generalization does not hold for all forms of limitation, and interestingly different limitations affected

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chlorhexidine susceptibility in opposite ways; susceptibility increased with growth rate for carbon-limited chemostats but decreased with phosphorus limitation (Brown et al., 1990). In some cases, as with polymyxin sensitivity, the difference in susceptibility was ascribed to changes in lipopolysaccharide content at different dilution rates (Wright and Gilbert, 1987). Undoubtedly, changes in porin content from OmpF to OmpC described in Section 3.1 and responses associated with the general stress response in Section 3.3 also contribute to altered access and effects of antibiotics at slow growth rates. There is also some evidence that antibiotic resistance through efflux is also a function of growth rate. The expression of the E. coli acrAB efflux system was increased at low D and was higher under glucose limitation (Rand et al., 2002), possibly contributing to resistance at slow growth rates. Furthermore, decreased susceptibility to ciprofloxacin and tetracycline at slow dilution rates was associated with a higher proportion of persister cells at low D (Sufya et al., 2003). Persister cells, that are not killed by antibiotics as readily as the rest of a population, have been recently implicated in the emergence of treatment-resistant organisms in clinical settings (Balaban et al., 2004; Kussell et al., 2005). Chemostats potentially offer an excellent system to study the factors governing the generation and population distribution of persisters in much more detail.

3.5. Quorum Sensing Population density and the circulation of extracellular signal molecules (autoinducers) are important factors in bacterial behaviour (Reading and Sperandio, 2006). Several classes of autoinducer molecules are produced by bacteria and regulate multiple physiological responses (Camilli and Bassler, 2006). Chemostats offer an ideal way of studying the production and effects of autoinducers because population density in chemostats is directly controlled by the medium composition (Section 2). Early studies of autoinduction of bioluminescence indeed used chemostats to demonstrate density effects (Rosson and Nealson, 1981). The effect of growth rates at constant densities can also be studied and the production of one autoinducer, AI-2, has been studied in this way (DeLisa et al., 2001). AI-2 production is strongly elevated at high growth rates and was subject to stress-induced perturbations (DeLisa et al., 2001). Other physiological effects of density on gene expression and metabolism can also be measured in chemostats by changing the population density (Liu et al., 2000). For example, the expression of porin genes is controlled by E. coli population characteristics and ompF transcription is strongly repressed


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at high density (Liu et al., 2000). The regulation of the stress regulator RpoS by density is more controversial, and different results were obtained in different laboratories (Liu et al., 2000; Ihssen and Egli, 2004). It remains to be established whether a secondary limitation or strain differences (see Section 4) explain the different population density results obtained. Quorum sensing is an important factor in biofilm formation by different types of bacteria (Parsek and Greenberg, 2005). To remove variables from biofilm formation, chemostats have been modified to study the colonization of surfaces like glass rods inserted into continuous cultures (Keevil, 2001; Li et al., 2001). Of course, the bacteria in biofilms are not subject to the same thoroughly mixed local environment that planktonic bacteria enjoy, but at least some environmental aspects can be controlled in this way. Indeed recent evidence suggests the biofilm bacteria in a continuous culture grow faster than planktonic bacteria and can survive washout at fast dilution rates (Bester et al., 2005). The effect of dilution rate on biofilm buildup and the effect of antibiotics can be effectively studied in such systems (Molin et al., 1982; Anwar et al., 1992).

4. VARIATIONS IN RESPONSES WITHIN AND BETWEEN SPECIES The choice of organism in chemostat studies is of course determined by the aims of the experiment, but several strain characteristics have been shown to affect behaviour in continuous culture. A comparison of more than 70 isolates of E. coli taken straight from natural habitats resulted in an approximately threefold range of maximal growth rates in glucose minimal medium (Mikkola and Kurland, 1991). Chemostat cultivation resulted in the slower isolates rapidly adapting to the laboratory environment with altered growth kinetics. Ribosomal function was one property that changes after chemostat culture; translational kinetics is highly variable in natural isolates but converges on that of laboratory K-12 strains after growth in glucose-limited chemostats (Mikkola and Kurland, 1992; Rang et al., 1997). The maximal growth rate of such isolates changed within 200 generations and other more subtle changes possibly occurred even earlier. Hence care is needed in studying the growth and ecological behaviour of natural isolates in chemostats to avoid pitfalls due to acclimatization in laboratory culture. Even when strains well adapted to the laboratory, such as E. coli K-12, are available, at least three strain characteristics can change the course of

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chemostat experiments. Firstly, the propensity for wall growth (biofilm formation) and the rapidity of appearance of adherent mutants is species- and strain-variable (Larsen and Dimmick, 1964). The formation of a biofilm in a culture vessel alters the environment of a chemostat sub-population and can locally affect gene expression and conditions for selecting further mutations. Fortuitously rather than by design, the strain used in my laboratory for long-term evolution experiments does not appear to readily mutate to wall growth. Other strains of E. coli readily produce curli adhesins (Vidal et al., 1998; Prigent-Combaret et al., 2000) and rapidly change the characteristics of the chemostat culture away from a homogeneous environment. The genetic basis of the differences in adhesiveness between strains is not always known, so some empirical testing is advisable. There could well be a range of genetic pathogenicity elements that affect adhesion and it may be relevant that pathogenic O157:H7 bacteria show high adhesion in chemostat culture (James and Keevil, 1999). A second source of variation in chemostat behaviour between strains is subtle but with significant consequences. As introduced in Section 3.3, strain differences affecting the RpoS (or sS) s factor can influence gene expression, stress responses, competitive behaviour and the selection of beneficial mutations in long-term cultures (Ferenci, 2005). Such global differences are common and many surveyed strains within the species E. coli contain characteristic and distinct levels of RpoS protein even when grown under identical conditions (King et al., 2004). As discussed in Section 7, the level of RpoS influences which mutations are enriched and the evolutionary pathway with prolonged continuous culture. The third area of strain variation is in metabolism, as introduced in Section 3.2. Even in the well-studied utilization of glucose by E. coli, many discrepancies are present in the literature. To give one example of differences due to metabolic strain variation, acetate is produced from glucose in most E. coli strains under aerobic conditions when glucose is in excess (or in chemostats at high dilution rates; el-Mansi and Holms, 1989). Still, for unknown reasons, the amount of glucose converted to acetate by different E. coli lab strains is highly uneven (Luli and Strohl, 1990). A more comprehensive view of metabolic variation is available from metabolome analysis and comparative profiling of strains across the species E. coli. Most strikingly, the metabolome profiles of separate E. coli isolates are highly distinct and less than 30% of metabolite pools are conserved in all strains (Maharjan and Ferenci, 2005). There were even metabolome differences between laboratory strains of E. coli K-12. Metabolomic divergence into A, B1, B2 and D subgroups resembles the divergence seen in taxonomic trees within the species derived from gene sequence comparisons (Pupo et al.,


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1997; Maharjan and Ferenci, 2005). Hence, chemostat studies of metabolism with a particular strain need to be interpreted in this wider context. Finally and more broadly problematic in microbiological studies, the possibility of non-constancy of properties does exist even with the same labelled strain of the same species (O’Keefe et al., 2006). Differences in storage conditions, errors in handling and subculturing can readily create divergence in stocks (Johnson et al., 2001) and hence differences in strain characteristics. A good example came from comparison of the variations in stocks of the E. coli W3110 strain used in different laboratories in Japan (Jishage and Ishihama, 1997).

5. STEADY STATE OR CONSTANT CHANGE IN A CHEMOSTAT POPULATION? For applications needing stable and reproducible levels of gene expression, an important consideration is the timing of samples after chemostats are inoculated. When is the most reproducible time for stable comparisons? As shown in Fig. 2, within 24 h of inoculation, bacterial cell densities approach a constant value and the initial hunger responses also plateau. Of course, this is true only if the inoculum does not contain high levels of limiting nutrient which need to be depleted or washed out to attain limitation. With high volumes of inocula, an overshoot of bacterial density is observed before limitation sets in. An attainment of a constant bacterial density or yield is not however a clear-cut indicator of a steady state. As shown in independent studies with different organisms, the concentration of limiting nutrient continues to drop for many generations in chemostats even after a steady biomass level is reached, indicating a lack of a genuine steady state (Rutgers et al., 1987; Kovarova-Kovar and Egli, 1998). Nevertheless, classic chemostat texts recommend, and recent studies frequently use, 5–8 vol. of medium passing through a chemostat vessel before a ‘‘steady state’’ is reached (Hua et al., 2004; Nanchen et al., 2006). In one study with Saccharomyces cerevisiae, even 10–14 vol. changes were used to ‘‘avoid strain adaptation’’ for expression studies (Boer et al., 2003). It is highly unlikely that a steady residual nutrient level is reached in most of these studies. Moreover, at D ¼ 0.1 h1, or 0.1 vol./h, 5 vol. is equivalent to 50 h. With E. coli at least, this length of time is sufficient to see almost a complete replacement of a population by mutants and is even sufficient to see the initiation of a second round of mutational sweeps (Notley-McRobb et al., 2003; Section 7).

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So what is more problematic, the lack of attainment of a steady state or the danger of mutational changes? My personal view is that the latter is more likely to lead to misleading results in measuring expression or other physiological properties of chemostat-inoculated bacteria. Sampling for expression studies is therefore advisable soon after the initial rapid depletion of limiting nutrient and the attainment of a constant cell density. As shown in Fig. 2, the time-course of expression changes in genes susceptible to glucose limitation reach a fairly stable, elevated level within 24 h. The threshold concentration for glucose limitation in eliciting the hunger response is below approximately 0.3 mM, which occurs (with E. coli and glucose limitation) within the first 6–10 h (Notley-McRobb et al., 1997). The entrenched idea of steady states and chemostats as constant environments is probably due to two factors. The first is the easy but misleading description of chemostats in terms of simple equations. Starting with Monod but continuing to this day in many text-books, a basic assumption is that bacteria have fixed properties (Pirt, 1975; Panikov, 1995) with definable enzyme-like characteristics. This assumption is untenable except when a gross simplification is sufficient. The maximal growth rate, or mmax, of bacteria in chemostats changes with prolonged culture (Mikkola and Kurland, 1992) and the affinity, or Ks is also non-constant. Indeed, this has been demonstrated directly as a change of Ks at different dilution rates (Wick et al., 2002). The molecular explanation is probably distinct for different limiting substrates, but for E. coli/glucose, changes in apparent Ks are due to shifting expression of transporter genes at different dilution rates (Ferenci, 1996, 2001). These molecular differences probably explain the deviations from simple Monod saturation kinetics noted in numerous studies (e.g. Shehata and Marr, 1971). The second contributing factor in historically viewing chemostat populations as being in steady state was the unrecognized rapidity and extent of mutational changes. In the absence of gross morphological differences and lack of techniques for the analysis of genetic changes in populations, it was easy to overlook the impact of mutations on chemostat behaviour. Given the mutational adaptations considered below, there is no truly safe extended phase in which cultures exhibit constant properties.

6. MUTATION RATES AND MUTATORS IN CHEMOSTAT POPULATIONS In all life-forms, evolution is dependent on the availability of mutations in a population. Mutation supply and the exploration of a wide range of


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mutational variations is dependent on both the population size and the mutation rates within populations (de Visser et al., 1999). Most chemostat populations involve high numbers (usually1010–1012) of bacteria, so the generation of a large number of spontaneous mutations is inevitable. Given that loss-of-function mutations occur in 1 in 105 or 106 newly divided cells, and even rarer gain-of-function mutations occur in 1 in 109–1010 bacteria, the possibility of beneficial mutations leading to organisms fitter than the inoculated strain is ever-present. The first chemostat studies already demonstrated that in tryptophan-limited chemostats, E. coli mutants with better tryptophan transport take over the population although the mutations were not characterized (Novick and Szilard, 1950b). Similar trends in transport improvement occur in glucose-limited populations of E. coli or yeast (Dykhuizen and Hartl, 1981; Helling et al., 1987; Dunham et al., 2002) and probably all other chemostat populations. Detailed studies with E. coli populations of different sizes indicated that mutation availability is not a problem with commonly used (41010) populations, but does affect the rate and shape of mutational take-overs in small populations (Wick et al., 2002). The effect of limiting population size was demonstrated in the progression of glucose-limited E. coli cultures towards better glucose scavenging ability. By measuring residual glucose levels in chemostats over 500 h (Wick et al., 2002), a small population (107 bacteria) was found to exhibit a step-wise improvement in affinity whereas a continuum of increased affinities was observed when 1011 cells were evolving. Evolution towards low residual glucose levels was also more reproducible between parallel cultures with large populations, consistent with the expectation that the availability of beneficial mutations was more subject to chance in small populations. The constancy of mutation rates was not experimentally measured in Wick et al. (2002), but several studies measured mutation rates in chemostats (Novick and Szilard, 1951; Kubitschek and Bendigkeit, 1964). Mutation rates vary with the type of limiting nutrient (Novick and Szilard, 1950b). Different dilution rates also change mutation rates, with low dilution rates giving a 30-fold increase in the rate of accumulation of mutations with no selective advantage (e.g. resistance to phage T5 causes no benefit or loss of fitness under glucose limitation; Notley-McRobb et al., 2003). The mutational differences can be rationalized as a manifestation of the broader phenomenon of stress-induced mutagenesis, which is more often studied in stationary phase cultures and in colonies on plates (Bjedov et al., 2003). It should be emphasized that there has been no detailed study on the mechanism of mutation rate regulation in chemostats, although continuous cultures would be ideal for controlling the level of stress and analysis of the cellular systems that regulate mutation rates (Tenaillon et al., 2004; Foster,

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2005). For example, the study of the expression of the SOS response and error-prone DNA polymerases would add greatly to our understanding of mutation supply under nutrient limitation and the regulation of stressinduced mutagenesis (Tippin et al., 2004). Mutation rates in chemostats could also increase if DNA repair processes such as mismatch repair were less highly expressed under nutrient stress. This kind of regulation has been proposed for stationary phase changes in mutation rates (Feng et al., 1996). There has been one study on the effect of chemostat culture on the regulation of DNA repair involving mutY (Notley-McRobb et al., 2002b), but chemostat culture could also add much to understanding the contribution of other pathways of DNA repair in controlling mutation supply. For example, mutS expression is affected in stationary phase in many E. coli strains (Li et al., 2003), so is also likely to be altered in chemostat culture. The availability of mutations is also strongly influenced by the occurrence of mutator mutations in natural and chemostat populations of E. coli. Amongst environmental and especially clinical isolates of E. coli, individual strains are present with orders of magnitude higher mutation rates than seen in ‘‘normal’’ E. coli (Leclerc et al., 1996). Actually, surveys of mutation frequencies in large strain collections show that even ‘‘normal’’ mutation rates are subject to a wide range of variation (Bjedov et al., 2003) as well as are subject to different levels of regulation (Li et al., 2003). The isolates with greatly elevated mutation rates often contain mutator mutations, such as in mutS with over 100-fold increase in mutation rates (Li et al., 2003) or other defects in DNA repair (Cox, 1976; Denamur and Matic, 2006). Mutators were also present in some long-term experimental lineages (Sniegowski et al., 1997) as well as in significant numbers in 6 of 11 chemostat populations under prolonged glucose limitation (Notley-McRobb et al., 2002c). As shown in pioneering studies by Cox and others, mutator strains are associated with a fitness advantage in chemostat populations and outcompete bacteria with normal mutation rates (Gibson et al., 1970; Cox and Gibson, 1974; Trobner and Piechocki, 1984). The fitness benefit comes not from the mutator mutation itself but the ability to hitchhike with a greater number of beneficial mutations that arise in the mutator sub-population (Miller et al., 2000; Tenaillon et al., 2001; Denamur and Matic, 2006). This process of secondary selection has been demonstrated in evolving chemostat populations and both the mutator mutation and the linked beneficial mutation identified (Notley-McRobb and Ferenci, 2000a; Notley-McRobb et al., 2002b, c). In Fig. 4, the mutS mutation hitchhiked with beneficial mgl mutations. For reasons not yet explained, mutY and mutS mutations were the predominant mutators and occurred in over 30% abundance in the chemostat populations


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>99% mgl mutS+

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