Cytokine Protocols (Methods in Molecular Biology)

METHODS

IN

MOLECULAR BIOLOGY™

Series Editor John M. Walker School of Life Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK

For further volumes: http://www.springer.com/series/7651

Cytokine Protocols Edited by

Marc De Ley Katholieke Universiteit Leuven, Heverlee, Belgium

Editor Marc De Ley Katholieke Universiteit Leuven Afd. Biochemie Celestijnenlaan 200 G 3001 Leuven Belgium [email protected]

ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-61779-438-4 e-ISBN 978-1-61779-439-1 DOI 10.1007/978-1-61779-439-1 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011940833 © Springer Science+Business Media, LLC 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Humana Press is part of Springer Science+Business Media (www.springer.com)

Preface Seven years have passed since the first volume of “Cytokine Protocols” was published in the series of “Methods in Molecular Biology” (Volume 249, 2004) of Humana Press. Since then, not only the number of known/characterized cytokines has drastically increased (e.g., interleukins up to IL-35) but also assays for gene expression have become more sensitive and sophisticated, allowing the simultaneous processing of higher numbers and/or smaller samples. In recent years we have witnessed the far-advanced miniaturization of microanalytical methods as well as the extensive development of bioinformatics and nanotechnology. Together these allow performing methods such as genomics, transcriptomics, and proteomics. At the same time a substantial reduction in sample size was achieved, allowing accurate determinations that were previously impossible. Single-cell based assays are expected to further extend this broad range of assays. The first chapter written by my colleague professor A. Billiau is not only of historical importance but also brings a message of general importance to researchers using bioassays in general. Careful observation and interpretation of results obtained in two different (biological) assays with respect to possible differences may reveal the presence of other hitherto unknown cytokines to be discovered and further characterized. The next three chapters deal with the quantification and characterization of cytokinerelated RNAs. These range from the cytokine mRNAs themselves over cytokine-induced genes until miRNAs. Real-time quantitative PCR (RT-qPCR), now widely established as a standard molecular biological technique, yields accurate determinations of single cytokine mRNA transcript levels (Chapter 2). Simultaneous measurement of gene expression profiles after cytokine stimulation is made possible through application of DNA microarray techniques (Chapter 3). The eventual level of mature active mRNA depends on multiple regulatory factors and processes, among which miRNAs. Their accurate quantitative determination (as well as that of their precursors) can also be executed by RT-qPCR (Chapter 4). The next seven chapters deal with the posttranscriptional modifications of RNA, taking place either naturally or artificially. One of the most decisive factors in determining cytokine levels and the response to it are mRNA levels, themselves being regulated by two opposite mechanisms: generation and decay, in turn regulated by cis-elements as well as trans-acting proteins. Both their characterization and evaluation yields further insight in the signal transduction processes (Chapter 5). One of the well-known mechanisms acts through the interaction of proteins with AU-rich elements in the 3¢UTR of mRNAs, the involvement of which can be demonstrated using a cell-based GFP assay (Chapter 6). Although the highly selective and efficient silencing of genes by siRNAs is known already for a long time, the delivery of these siRNAs to some kinds of cells restricts its broader application. A neat way to overcome this obstacle is through their inclusion in (integrin) targeted stabilized nanoparticles (Chapter 7). Another proven method for gene silencing is through the application of carefully designed and validated hammerhead ribozymes. These can either be introduced in the cell as chemically modified ribozymes (in order to increase their half-life) or be constitutively generated in situ by appropriate plasmids (Chapter 8). A well-known and often

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undesirable side effect of RNAi methodology is the induction of interferon response, either by the production of the cytokine itself or by the induction of interferon-related gene transcription. Hence, it is often difficult to distinguish between the pursued RNAi effect and the confusing interferon effects (Chapters 9 and 10). RNAi technology allows very specific targeting to a particular gene transcript and hence to a specific member in a signal transduction pathway. This very powerful approach is, however, often hampered by difficulties encountered at the introduction of the foreign DNA in the recipient cell (“hard-to-transfect cells,” e.g., primary cells) and by its possible toxicity. Therefore, different protocols and reagents should be carefully compared (Chapter 11). The last three chapters are devoted to observations at the protein level. Following the identification of a novel cytokine biological activity, the next big challenge is the isolation, purification, and characterization of its first contact with the cell, i.e., its membrane receptor. Ligand affinity chromatography is the method of choice, allowing in most cases the isolation of sufficient amounts of intact receptor for partial sequence determination followed by full sequence prediction from data banks. Moreover, this method may also lead to the discovery of unexpected, unpredicted (non-receptor), interacting proteins (Chapter 12). Accurate and sensitive detection of cytokine levels is of prime importance in the evaluation of their biological activity, both in situ (intracellular) and in vitro (solution) methods are needed. Application of fluorescently labeled monoclonal antibodies in combination with flow cytometry on permeabilized cells allows sensitive detection even in individual cells (Chapter 13). As already explained in the first chapter, sensitive and specific detection of the biological activity of cytokines is of utmost importance. It is well known that each cytokine is quantified most specifically, accurately, and with the lowest detection limit on a different cell type, thus obliging researchers that work with different cytokines to maintain a whole series of cultures of various cells, each with their own detection system). This problem can be partly circumvented by constructing cell lines with chimeric receptors, the extracellular part of them being specific for each cytokine, the intracellular part being the same for all and thus requiring only one kind of signal detection (Chapter 14). Heverlee, Belgium

Marc De Ley

Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 A Tale of Two Interferon Bioassays: How Frustration with Discrepant Results from Slightly Dissimilar Methods Can Engender Discovery. . . . . . . . . . . . . . . . . . . 1 Alfons Billiau 2 The Use of Real-Time Quantitative PCR for the Analysis of Cytokine mRNA Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Maria Forlenza, Thomas Kaiser, Huub F.J. Savelkoul, and Geert F. Wiegertjes 3 Interleukin-27 Induces Interferon-Inducible Genes: Analysis of Gene Expression Profiles Using Affymetrix Microarray and DAVID . . . . . . . . . . . . . . . . . 25 Tomozumi Imamichi, Jun Yang, Da Wei Huang, Brad Sherman, and Richard A. Lempicki 4 Quantitative Analysis of miRNA Expression in Epithelial Cells and Tissues . . . . . . . 55 Markus Bitzer, Wenjun Ju, Xiaohong Jing, and Jiri Zavadil 5 Evaluating Posttranscriptional Regulation of Cytokine Genes . . . . . . . . . . . . . . . . . 71 Bernd Rattenbacher and Paul R. Bohjanen 6 Cloning of Cytokine 3¢ Untranslated Regions and Posttranscriptional Assessment Using Cell-Based GFP Assay. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Latifa Al-Haj and Khalid S.A. Khabar 7 Integrin-Targeted Stabilized Nanoparticles for an Efficient Delivery of siRNAs In Vitro and In Vivo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Charudharshini Srinivasan, Dan Peer, and Motomu Shimaoka 8 Hammerhead Ribozyme-Mediated Knockdown of mRNA for Fibrotic Growth Factors: Transforming Growth Factor-Beta 1 and Connective Tissue Growth Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Paulette M. Robinson, Timothy D. Blalock, Rong Yuan, Alfred S. Lewin, and Gregory S. Schultz 9 Control of the Interferon Response in RNAi Experiments. . . . . . . . . . . . . . . . . . . . 133 Jana Nejepinska, Matyas Flemr, and Petr Svoboda

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10 shRNA-Induced Interferon-Stimulated Gene Analysis. . . . . . . . . . . . . . . . . . . . . . . 163 Núria Morral and Scott R. Witting 11 Use of RNA Interference to Investigate Cytokine Signal Transduction in Pancreatic Beta Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 Fabrice Moore, Daniel A. Cunha, Hindrik Mulder, and Decio L. Eizirik 12 Ligand Affinity Chromatography, an Indispensable Method for the Purification of Soluble Cytokine Receptors and Binding Proteins . . . . . . . . . 195 Daniela Novick and Menachem Rubinstein 13 In Vitro Stimulation and Detection of IFNa Production in Human Plasmacytoid Dendritic Cells. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 William C. Adams and Karin Loré 14 A Sensitive and Versatile Cytokine Bioassay Based on Type I Interferon Signaling in 2fTGH Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Lennart Zabeau, José Van der Heyden, and Jan Tavernier Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

Contributors WILLIAM C. ADAMS • Department of Medicine, Center for Infectious Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden LATIFA AL-HAJ • Program in Biomolecular Research, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia ALFONS BILLIAU • Rega Institute for Medical Research, University of Leuven (Katholieke Universiteit Leuven), Leuven, Belgium MARKUS BITZER • Internal Medicine, Nephrology, Michigan Diabetes Research and Training Center, University of Michigan, Ann Arbor, MI 48109, USA TIMOTHY D. BLALOCK • Department of Obstetrics and Gynecology, College of Medicine, Institute for Wound Research, University of Florida, Gainesville, FL 32610-0294, USA PAUL R. BOHJANEN • Department of Microbiology, Center for Infectious Diseases and Microbiology Translational Research, University of Minnesota, Minneapolis, MN 55455, USA DANIEL A. CUNHA • Laboratory of Experimental Medicine, Université Libre de Bruxelles, Brussels BE-1070, Belgium DECIO L. EIZIRIK • Laboratory of Experimental Medicine, Université Libre de Bruxelles, Brussels BE-1070, Belgium MATYAS FLEMR • Institute of Molecular Genetics, Academy of Sciences of the Czech Republic, 142 20 Prague 4, Czech Republic MARIA FORLENZA • Cell Biology and Immunology group, Department of Animal Sciences, Wageningen University, Wageningen PG 6709, Netherlands DA WEI HUANG • Laboratory of Immunopathogenesis and Bioinformatics, CSP, ADD, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA TOMOZUMI IMAMICHI • Laboratory of Human Retrovirology, Clinical Services Programs (CSP), Applied Developmental Directorate (ADD), Science Applications International Corporation (SAIC)-Frederick, Inc., National Cancer Institute (NCI)-Frederick, Frederick, MD 21702, USA XIAOHONG JING • Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA WENJUN JU • Internal Medicine, Nephrology, Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA THOMAS KAISER • Cell Biology and Immunology group, Department of Animal Sciences, Wageningen University, Wageningen, PG 6709, Netherlands KHALID S.A. KHABAR • Program in Biomolecular Research, King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia RICHARD A. LEMPICKI • Laboratory of Immunopathogenesis and Bioinformatics, CSP, ADD, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA ALFRED S. LEWIN • Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, USA ix

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KARIN LORÉ • Department of Medicine, Center for Infectious Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden FABRICE MOORE • Laboratory of Experimental Medicine, Université Libre de Bruxelles, Brussels, BE 1070, Belgium NÚRIA MORRAL • Department of Medical and Molecular Genetics, and Center for Diabetes Research, Indiana University School of Medicine, Indianapolis, IN 46202, USA HINDRIK MULDER • Unit of Molecular Metabolism, Department of Clinical Sciences in Malmö, Lund University Diabetes Center, Clinical Research Center 91:12, Malmö, SE 205 02, Sweden JANA NEJEPINSKA • Institute of Molecular Genetics, Academy of Sciences of the Czech Republic, 142 20 Prague 4, Czech Republic DANIELA NOVICK • Department of Molecular Genetics, The Weizmann Institute of Science, Rehovot 76100, Israel DAN PEER • Laboratory of Nanomedicine, Department of Cell Research & Immunology, George S. Wise Faculty of Life Sciences, and the Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv 69978, Israel BERND RATTENBACHER • Department of Microbiology, Center for Infectious Diseases and Microbiology Translational Research, University of Minnesota, Minneapolis, MN 55455, USA PAULETTE M. ROBINSON • Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, FL, USA MENACHEM RUBINSTEIN • Department of Molecular Genetics, The Weizmann Institute of Science, Rehovot 76100, Israel HUUB F.J. SAVELKOUL • Cell Biology & Immunology group, Department of Animal Sciences, Wageningen University,Wageningen PG 6709, Netherlands GREGORY S. SCHULTZ • Department of Obstetrics and Gynecology, College of Medicine, Institute for Wound Research, University of Florida, Gainesville, FL 32610-0294, USA BRAD SHERMAN • Laboratory of Immunopathogenesis and Bioinformatics, CSP, ADD, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA MOTOMU SHIMAOKA •Immune Disease Institute, Boston, MA, USA; Program in Cellular and Molecular Medicine, Children’s Hospital Boston, Boston, MA, USA; Department of Anesthesia, Harvard Medical School,Boston, MA 02115, USA CHARUDHARSHINI SRINIVASAN • Immune Disease Institute, Boston, MA, USA; Program in Cellular and Molecular Medicine, Children’s Hospital Boston, Boston, MA, USA; Department of Anesthesia, Harvard Medical School, Boston, MA 02115, USA PETR SVOBODA • Institute of Molecular Genetics, Academy of Sciences of the Czech Republic, 142 20 Prague 4, Czech Republic JAN TAVERNIER • Department of Medical Protein Research, Flanders Institute for Biotechnology, Ghent University, Faculty of Medicine and Health Sciences, Ghent BE-9000 , Belgium

Contributors

JOSÉ VAN DER HEYDEN • Department of Medical Protein Research, Flanders Institute for Biotechnology, Ghent University, Faculty of Medicine and Health Sciences, Ghent BE-9000, Belgium GEERT F. WIEGERTJES • Department of Animal Sciences, Cell Biology & Immunology group, Wageningen University, Wageningen PG 6709, Netherlands SCOTT R. WITTING • Department of Medical and Molecular Genetics, and Center for Diabetes Research, Indiana University School of Medicine, Indianapolis, IN 46202, USA JUN YANG • Laboratory of Immunopathogenesis and Bioinformatics, CSP, ADD, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA RONG YUAN • Department of Obstetrics and Gynecology, College of Medicine, Institute for Wound Research, University of Florida, Gainesville, FL 32610-0294, USA LENNART ZABEAU • Department of Medical Protein Research, Flanders Institute for Biotechnology, Ghent University, Faculty of Medicine and Health Sciences, Ghent BE-9000, Belgium JIRI ZAVADIL • Department of Pathology, Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY 10016, USA

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Chapter 1 A Tale of Two Interferon Bioassays: How Frustration with Discrepant Results from Slightly Dissimilar Methods Can Engender Discovery Alfons Billiau Abstract This introductory article describes an episode that took place in the mid-1980s when the first wave of cytokine discoveries took place. During studies aimed at complete purification of human interferon-γ from crude mitogen-stimulated lymphokine preparations, the use of two different antiviral bioassays for the cytokine yielded disparate results. Analysis revealed the presence of a “contaminant” IFN-like cytokine that was detectable with only one of the two assays. Superficially, the contaminant resembled IFN-β. However, further analysis showed that it was not an IFN at all but an IFN-inducing cytokine identifiable as interleukin-1. Key words: Bioassay, Interferon, Interleukin, Antiviral activity

1. “Die Methode ist Alles” This aphorism, attributed to Karl Friedrich Wilhelm Ludwig (1816–1895), seems like an appropriate thought to reflect upon in the introduction to this book. Witnessing, as we currently do, the bewildering progress in the biological sciences, mainly enabled by the booming of molecular gene technology, bioinformatics, and imaging techniques, we hardly need the influential German physiologist’s statement to be reminded of the key role of methodology in science in general. However, also in a more parochial context, each one of us, at a certain point in his/her scientific career, experiences instances when a methodological detail, trivial as it may seem at first, happens to provide the key to a scientific discovery.

Marc De Ley (ed.), Cytokine Protocols, Methods in Molecular Biology, vol. 820, DOI 10.1007/978-1-61779-439-1_1, © Springer Science+Business Media, LLC 2012

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This is exactly what happened to me and my coworkers (including the Editor of this book, Marc De Ley) when, in the early 1980s, we were working on the characterization of human interferons (IFNs) in the Rega Institute of the University of Leuven, Belgium. In a nutshell, what happened was that we became frustrated with a discrepancy in the results from two only slightly different bioassays for IFN-γ. Perseverance in trying to resolve these discrepancies led us to the discovery of the now generally accepted “cascade” principle underlying the cytokine network, i.e., the fact that one cytokine uses to induce production of several other ones. As it happened, what we were able to show was that interleukin-1 induces production of IFN-β and thereby can itself mimic IFN (Fig. 1) (1). I already described this episode in great detail (2). However, on request of the Editor, I here recall the main events. IFNs became known mainly by their ability to reprogram cells such that they mount a state of resistance to virus infection. Several molecular types of IFN have been identified, some with subtypes. The main types are IFN-α, IFN-β, and IFN-γ. IFN-α and IFN-β share sequence homology and use the same cellular receptor; IFN-γ is unrelated to all others and uses a different receptor. Besides acting as inhibitors of viral replication in cells, IFNs have other effects by which they contribute to inflammation and immune

Fig. 1. After stimulation with an appropriate mitogen, human peripheral blood mononuclear cells produce various cytokines, including a 22-kDa protein possessing antiviral activity that resembles HuIFN-β in being neutralized by specific antiserum against this interferon. In reality, 22 K is interleukin-1β endowed with the ability to induce IFN-β production by the diploid fibroblasts used in the interferon bioassay (reprinted with permission from Immunol. Today, ref. 1).

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responses to infections and tumors. In the 1970s, our laboratory had become strongly committed to the development of a system for production of human IFN to be used for molecular characterization and clinical trials. As the cellular source of the IFN, we had chosen to use serially subcultured human diploid skin fibroblasts induced with synthetic, double-stranded RNA poly-rI:rC. This system turned out to deliver the β-type of human IFN, as opposed to the α-type which was produced by others who used human leukocytes (collected from the buffy coats of blood donations) and stimulated these by viral infection. In the process, we had acquired not only the know-how for mass production of human IFN-β, but we had also developed various methodologies and reagents for bioassay, purification, and characterization of the IFN. One reagent in particular, a potent antiserum against IFN-β that did not cross-react with other known IFNs, would become important in the events that I wish to recall. Another important technicality was that, having at our disposal lots of diploid human cell cultures, we used these cells also for the bioassay of our IFN samples. Of note, today, most laboratories working with IFNs use ELISAs to quantify their samples. However, at the time, such assays were insufficiently developed. In a classical IFN bioassay, cell cultures in microtiter plates are treated with serial dilutions of the samples, and then infected with a challenge virus. The highest dilution that still provides protection against the virus gives a measure of the IFN content in a sample. Diploid cells are reputed to be highly sensitive to the antiviral effect of all IFNs, so they are a good choice in any case. Nevertheless, any other laboratory would tend to use a continuous cell line, such as Hep-2, as these are much easier to keep in culture and to manipulate. Around 1980, Marc De Ley (3) developed an interest in the third major type of human IFN, IFN-γ, then still widely called “immune IFN,” as it appeared to be produced only by lymphocytes and possessed strong immunoregulatory activities, e.g., macrophage activation. Today, IFN-γ, available as a laboratory reagent from commercial suppliers, is usually a product of recombinant DNA technology. At the time of my narrative, however, there was no other choice than to produce IFN-γ by treating suspensions of fresh leukocytes from blood donations with an appropriate mitogen, mostly concanavalin A. De Ley’s primary objective was to obtain sufficient IFN-γ in pure form to study its physical, chemical, and biological properties. Two difficulties had to be overcome: low yields in the production and a host of contaminants present in the crude starting product. Batches of crude product were submitted to a first rough concentration and purification step consisting of adsorption to silicic acid and desorption by ethylene glycol. Pools of this partially purified IFN were then further processed through several additional steps, the first of which was gel filtration. As soon as concentration and purification of the material began to run more or less smoothly, I convinced another collaborator, Jo Van Damme, who had acquired experience in mass production of

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human IFNs, to set up a routine of semi-mass production of IFN-γ so as to provide De Ley with ample crude product for concentration and purification. Soon, a minor dispute arose as De Ley found that samples seemed to contain less activity than expected from the initial values communicated by Van Damme. It so happened, however, that my two collaborators were using different titration methods to determine the potency of the samples. Van Damme used diploid fibroblasts as these were amply available in his laboratory; De Ley used the easy-to-cultivate continuous human cell line Hep-2 (alias HeLa or CCL-23). At one point, fractions of human immune IFN separated by molecular mass were titrated on both human cell types. It appeared that most of the antiviral activity migrated in fractions of approximately 45 kDa (natural IFN-γ proved later indeed to consist of a homodimer of 22 kDa glycoproteins). However, titration on diploid cells detected an additional minor peak at 22 kDa, not revealed by titration on Hep-2 cells (Fig. 2) (4). We became intrigued by this “22-K” fraction and to our surprise found that its antiviral activity was neutralized in the

Fig. 2. Elution profile on gel filtration of interferon produced by human peripheral blood mononuclear cells induced with concanavalin A, prepurified by adsorption to silicic acid, and elution with ethylene glycol in 1.4 M NaCl. Solid circles: titration by inhibition of viral infection using HEp-2 cells; empty circles: titration using human diploid cells (reprinted with permission from Eur. J. Immunol., ref. 4).

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presence of antibodies against IFN-β. We first speculated that 22 K was an IFN-γ monomer that exposed internal epitopes shared with IFN-β. However, in experiments set up to purify 22 K by affinity chromatography on the same anti-IFN-β antibody, Van Damme found that the activity percolated through the column without binding. This led us to the hypothesis, ultimately found to be correct, that 22 K was not an interferon but a different lymphokine-like factor that exerted antiviral activity by inducing IFN-β when titrated on diploid fibroblasts but not on Hep-2 cells (5). Proving this hypothesis turned out to be difficult as the amount of IFN induced by 22 K was so small as to just suffice to act in a paracrine fashion. It would take studies on mRNA expression using dot-blot technology to obtain reasonably convincing evidence (6). Only as late as 1989, with the advent of PCR technology, would induction of IFN-β (and other cytokines) by 22 K (meanwhile known to be IL-1β) became generally accepted (7). However, independently from testing the IFN-β induction hypothesis, we wanted to know whether 22 K was a novel cytokine or one that had already been described by others as being responsible for another biological activity. Solving this question involved developing new bioassays and submitting our purified 22-K material to various laboratories with technological expertise in bioassays for immunoregulatory or inflammatory “factors,” such as skinreactive factor (SRF), colony-stimulating factor (CSF), lymphotoxin, lymphocyte-activating factor (LAF), monocytic cell factor (MCF), and endogenous pyrogen (EP). These investigations revealed that our highly pure 22-K factor had activities assigned to MCF (induction of collagenase and prostaglandin in synovial cell cultures) as well as LAF activity, but not lymphotoxin activity. In addition, we found that 22 K induced production of CSF in the same way as it induced IFN-β and thereby mimicked CSF in some assays (8). These findings came at a time point when immunologists decided to rename LAF and henceforth call it interleukin-1. When it became evident that this cytokine was the active principle in various other “factors,” including MCF, it dawned to us that 22 K was probably identical to IL-1. We eventually succeeded in purifying the 22-K factor to homogeneity and obtain sufficient material for determination of the amino-terminal amino acid sequence. It then appeared that 22 K was nothing short of the mature form of interleukin-1β, as the sequence corresponded to part of an interleukin-1 cDNA clone isolated at exactly the same time by molecular biologists in the USA (9, 10). The significance of these findings was twofold. They represented the first complete purification of natural interleukin-1β and, by revealing the amino-terminal sequence, permitted to identify the site of proteolytic cleavage of the precursor into the mature interleukin-1β protein (11). Secondly, demonstration of IFN-β induction by an interleukin-1 represented one of the very first

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examples of a cytokine cascade. Induction of one cytokine by another is now generally accepted as key to the regulatory function of cytokine networks. Today, thanks to gene and genome technology, most novel bioactive proteins are first defined chemically before their biological activity becomes evident. However, at the time of my narrative, the reverse pathway was the usual one: one would first detect a peculiar activity in a biological fluid (serum, urine, cell culture fluid, …) using a particular bioassay. Next, one would apply classical protein-biochemical methods to concentrate, purify, and characterize the protein(s) responsible for the activity. Under these constraints on the process of discovery, the properties of the bioassay were of key importance. What was true in Ludwig’s time and in the 1980 when we were in the midst of the incipient wave of cytokine discoveries remains true today. Today, as in the past, investigators need to ascertain that their assays are highly sensitive and specific. However, having a good understanding of the mechanistic chain of events taking place during the assay is equally important. One would hope that this book makes a substantial contribution to this goal. References 1. Billiau, A., Opdenakker, G., Van Damme, J., De Ley, M., Volckaert, G., Van Beeumen, J. (1985) Interleukin-1: amino acid sequencing reveals microheterogeneity and relationship with an interferon-inducing monokine. Immunol. Today 6: 235–236. 2. Billiau, A. (1987) The interferon-interleukin 1 connection. In Interferon, Academic Press Inc., London, Vol. 9 pp.91–111. 3. De Ley, M., Van Damme, J., Claeys, H., Weening, H., Heine, J.W., Billiau, A., Vermylen, C., De Somer, P. (1980) Interferon induced in human leukocytes by mitogens: production, partial purification and characterization. Eur. J. Immunol. 10: 877–883. 4. Van Damme, J., De Ley, M., Claeys, H., Billiau, A., Vermylen, C., De Somer, P. (1981) Interferon induced in human leukocytes by concanavalin A: isolation and characterization of gamma and β-type components. Eur. J. Immunol. 11: 937–942. 5. Van Damme, J., Billiau, A., De Ley, M., De Somer, P. (1983) An interferon-β-like or interferon-inducing protein released by mitogenstimulated human leukocytes. J. Gen. Virol. 64: 1819–1822. 6. Van Damme, J., Opdenakker, G., Billiau, A., De Somer, P., De Wit, L., Poupart, P., Content, J. (1985) Stimulation of fibroblast interferon

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production by a 22 K protein from human leukocytes. J. Gen. Virol. 66: 693–700. Fujita, T., Reis, L.F., Watanabe, N., Kimura, Y., Taniguchi, T., Vilcek, J. (1989) Induction of the transcription factor IRF-1 and interferonbeta mRNAs by cytokines and activators of second-messenger pathways. Proc. Natl. Acad. Sci. USA 86: 9936–9940. Fibbe, W.E., Van Damme, J., Billiau, A., Voogt, P.J., Duinkerken, N., Kluck, P.M.C., Falkenburg, J.H.F. (1986) Interleukin-1 (22-K factor) induces release of granulocyte-macrophage colony-stimulating activity from human mononuclear phagocytes. Blood 68: 1316–1321. Auron, P.E., Webb, A.C., Rosenwasser, L.J., Mucci, S.F., Rich, A., Wolff, S.M., Dinarello, C.A. (1984) Nucleotide sequence of human monocyte interleukin 1 precursor cDNA. Proc. Natl. Acad. Sci. USA 81: 7907–7911. Van Damme, J., De Ley, M., Opdenakker, G., Billiau, A., De Somer, P., Van Beeumen, J. (1985) Homogeneous interferon-inducing 22 K factor is related to endogenous pyrogen and interleukin-1. Nature 314: 266–268. Auron, P.E., Rosenwasser, L.J., Matsushima, K., Copeland, T., Dinarello, C.A., Oppenheim, J.J., Webb, A.C. (1985) Human and murine interleukin 1 possess sequence and structural similarities. J. Mol. Cell. Immunol. 2: 169–177.

Chapter 2 The Use of Real-Time Quantitative PCR for the Analysis of Cytokine mRNA Levels Maria Forlenza, Thomas Kaiser, Huub F.J. Savelkoul, and Geert F. Wiegertjes Abstract Over the last decade, real-time-quantitative PCR (RT-qPCR) analysis has become the method of choice not only for quantitative and accurate measurement of mRNA expression levels, but also for sensitive detection of rare or mutated DNA species in diagnostic research. RT-qPCR is based on the standard principles of PCR amplification in addition to the use of specific probes or intercalating fluorescence dyes. At the end of every cycle, the intercalating dye binds to all double-stranded DNA. There is a quantitative relationship between the amount of starting DNA and the amount of amplification product during the exponential phase. However, to obtain meaningful RT-qPCR data, the quality of the starting material (RNA, DNA) and the analysis method of choice are of crucial importance. In this chapter, we focus on the details of RNA isolation and cDNA synthesis methods, on the application of RT-qPCR for measurements of cytokine mRNA levels using Sybr-Green I as detection chemistry, and finally, we discuss the pros and contras of the absolute quantification versus relative quantification analysis. RT-qPCR is a powerful tool, but it should be “handled” with care. Key words: Real-time-quantitative PCR, Absolute quantification, Relative quantification, Primer efficiency, Housekeeping gene

1. Introduction Over the last decade, real-time-quantitative PCR (RT-qPCR) analysis has become the method of choice not only for quantitative and accurate measurement of mRNA expression levels, but also for sensitive detection of rare or mutated DNA species in diagnostic research (1, 2). RT-qPCR is based on the standard principles of PCR amplification in addition to the use of specific probes or intercalating dyes. Various probe systems are available among which

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Fig. 1. A typical RT-qPCR profile can be divided in the initial, exponential, and plateau phases.

TaqMan probes, Molecular Beacons, MGB probes, and others increasing specificity and sensitivity of the real-time assays. RT-qPCR, using intercalating dyes that become fluorescent upon binding to double-stranded (ds) DNA, has the advantage of running melting curve analysis after each run in order to check specificity. In most cases, Sybr-Green I is used, but other dyes are available, including Eva-Green, Syto9, etc. Under optimal conditions, every PCR cycle should result in a doubling of the amplification product. At the end of every cycle, the intercalating dye binds to all double-stranded DNA. Ideally, the increase in amount of template is directly proportional to the increase in fluorescence. Fluorescence data are collected during each cycle allowing for real-time monitoring of amplification. A typical RT-qPCR profile is shown in Fig. 1: it can be divided in the initial, exponential, and plateau phases. The exponential phase of the amplification provides the most useful and reproducible data. There is a quantitative relationship between the amount of starting DNA and the amount of amplification product during the exponential phase. The number of cycles required for a sample to rise above the background fluorescence and reach the threshold level is called Ct-value (threshold cycle). The threshold is set at a level, where the rate of amplification is greatest during the exponential phase, allowing for the most accurate and reproducible results. An advantage of RT-qPCR over conventional PCR is the possibility to assess the amplification efficiency (E). Particularly, when the expression profile of more genes needs to be compared, it is important to take the efficiency into account and adjust for differences between different genes to be compared. In addition, at the end of every run, a melting curve analysis can be performed to assess amplification specificity. Taken together, this leads to increased sensitivity, specificity, and efficiency of the PCR analysis. To obtain meaningful RT-qPCR data, the quality of the starting material (RNA, DNA) and the analysis method of choice are of crucial importance. In this chapter,

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we focus on the details of RNA isolation and cDNA synthesis methods, the application of RT-qPCR for measurements of cytokine mRNA levels using Sybr-Green I as detection chemistry, and finally, we discuss the pro and contras of the absolute quantification versus relative quantification analysis. 1.1. Absolute Quantification

Absolute quantification analysis ideally determines the absolute copy number of a gene of interest (GOI) in an unknown sample. The unknown sample is compared to a standard curve with known concentrations of template. In most cases, recombinant plasmid DNA (recDNA), cDNA, recRNA, pooled samples, or PCR products are used for this purpose. Therefore, the accuracy of the absolute quantification assay entirely depends on the accuracy of the standard (3). No matter how accurate the concentration of the standard material is the final result is always expressed relatively to a defined unit of interest: e.g. copies per ng of total RNA, copies per cell, copies per gram of tissue, copies per mL blood. When absolute changes in copy numbers are important, the denominator has to be shown to be absolutely stable across the comparison. Although the word “absolute” suggests an exact measurement, one has to be aware that absolute quantification is relative to the standards used.

1.2. Relative Quantification

Relative quantification analysis determines the levels of expression of a GOI and expresses it relative to the levels of an internal control or reference gene (RG). Results are given as ratio of GOI versus one or more RGs (4). In this type of analysis, the function of the RG is to normalize the data for differences in RNA (DNA) quantification and template input. Therefore, expression of the RG has to be analyzed in the same sample as the GOI and can be coamplified in the same tube as a multiplex assay (probes) or the same sample should be used in separate tubes as a simplex assay (Sybr-Green I). Reference genes are genes that are not affected by the treatment in any way and are constant under the tested conditions. Hence, the reliability of the relative quantification analysis is strongly dependent on the stability of the RG. Several tools are available for the determination of the best RG: TATA Biocenter AB: http:// www.tataa.com/Products/Human-Endogenous-Control-Panel. html; geNorm (5): http://medgen.ugent.be/~jvdesomp/ genorm/; BestKeeper (6): http://www.gene-quantification.info. We have extensive experience with the BestKeeper software.

1.3. Real-Time PCR Cyclers

Most RT-qPCR cyclers make use of a solid block (96 or 384 wells) for thermal cycling while others use hot and cooled air. Most of the solid block-based real-time instruments are affected by thermal variation across the block and by differences in illumination and

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optical signals detected from each sample. Both aspects greatly contribute to well-to-well variability. Two air-based cyclers employ a rotary design using capillaries or plastic tubes and one of them uses a centrifuge, which guarantees optimal thermal and optical uniformity. Samples are continuously rotating in the thermal chamber, guaranteeing minimal temperature variation between tubes in contrast to positional effects, such as the recognized “edge effect” observed in block-based designs. In addition, every tube moves past the identical excitation light source and detection pathway, which guarantees optical uniformity. In our laboratory, we have extensive experience with the Rotor-Gene 6000™.

2. Materials 2.1. RNA Isolation and cDNA Synthesis

1. RNA isolation, including on-column DNase treatment: RNeasy Mini Kit and RNase-free DNase set (QIAgen). 2. cDNA synthesis, including DNase treatment: DNase I, Amplification Grade; Superscript™ III First Strand Synthesis Systems for RT-PCR Systems (Invitrogen). 3. Nuclease-free water (Promega). 4. RT-qPCR Master mix: ABsolute™ QPCR SYBR® Green Mix (ABgene). 5. NanoDrop spectrophotometer (Thermo Scientific). 6. Thermal cycler: Rotor-Gene 6000™ (Corbett Research). More detailed information to any RT-qPCR topic can be found on the following Web site: http://www.gene-quantification.info

2.2. Plasmid Construction and Isolation

1. Luria Bertani (LB) medium (1 L). 2. LB plates. 3. E. coli JM109 High Efficiency Competent Cells (Promega). 4. pGEM-T easy Ligation Kit (Promega). 5. QIA prep Spin Miniprep kit (QIAgen). 6. Gel Extraction Kit (QIAgen). 7. Ampicillin. 8. X-gal. 9. IPTG. 10. SOC medium. 11. 100% glycerol. 12. Agarose.

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3. Methods 3.1. RNA Isolation and Quantification

Isolation and quantification of good-quality RNA (see Note 1) are of extreme importance to obtain meaningful gene expression data by RT-qPCR. Several commercial kits are available; for RNA isolation, from small (30 mg), fresh-frozen or RNA-later stored tissue samples and from primary cells or cell lines (107 cells), we obtained high-quality results with the RNeasy mini kit from Qiagen. 1. Isolate RNA according to the manufacturer’s instructions. Work fast, clean, wear gloves, and use RNase-free tubes and tips. To reduce genomic DNA (gDNA) contaminations, include an on-column DNase digestion step. Elute RNA in 30–50 μL RNase-free water. 2. Use 1–2 μL of the eluted sample to determine RNA concentration (OD measurement at 260 nm) and RNA quality (OD 260/280 ratio) with the NanoDrop spectrophotometer. An OD 260/280 ratio greater than 1.8 is usually considered an acceptable indicator of good RNA quality. The presence of gDNA in the sample leads to an overestimation of the RNA concentration. 3. RNA integrity and the absence of gDNA can be assessed by loading 1–2 μL of RNA sample on a 1% agarose gel. Two major bands corresponding to the 28S and 18S rRNA should be clearly visible. In case of gDNA contaminations, an additional band of higher molecular weight than the two rRNA bands can be observed.

3.2. cDNA Synthesis

Several kits are available for cDNA synthesis. We routinely use the SuperScript™ III First strand cDNA synthesis kit with random primers from Invitrogen. 1. Prior to cDNA synthesis from 1 μg of total RNA (see Note 2), perform a second DNase digestion step using the DNase I Amplification Grade Kit (Invitrogen). 2. Proceed with the cDNA synthesis protocol according to the manufacturer’s instructions. For each sample, always include a control for gDNA contaminations: in this sample, the same amount of RNA is used but no reverse transcriptase is added to the mix (−RT control). 3. After cDNA synthesis, the final volume for each sample is 20 μL. We routinely bring the volume up to 100 μL and consider this our stock sample solution. Depending on the organ or cell type, we further dilute the stock five to ten times. This allows performing up to 200 reactions for each sample when using 5 μL of template in each PCR reaction.

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3.3. Construction of Recombinant Plasmid DNA

The calibration curves used in absolute quantification can be based on known concentrations of DNA standard molecules, e.g. recDNA, gDNA, RT-PCR product, commercially synthesized, big oligonucleotide (see Note 3). In this section, we describe how to construct a recombinant plasmid DNA containing the sequence of any GOI. 1. Design primers to amplify a large (500–1,000 bp) fragment of the gene. The region should of course contain the sequence to which the primers designed for RT-qPCR anneal. Amplify the large product by conventional PCR or reverse transcriptasePCR. 2. Gel purify the product using the QIAgen Gel Extraction Kit and elute in 30 μL of water. 3. Ligate the product into the vector by combining 3.5 μL of the gel-purified product to 5 μL of 2× ligation buffer, 0.5 μL (25 ng) of pGEM-T easy, and 1 μL (3 UI) of T4 DNA ligase (all reagents in the easy ligation kit). Mix by pipetting, and incubate for 1 h at room temperature or overnight at 4°C for the maximum number of transformant.

3.4. Amplification and Quantification of recDNA

1. Prepare LB agar plates containing ampicillin, X-Gal, and IPTG. 2. Centrifuge the ligation reactions briefly. Add 2–5 μL of each ligation reaction to a sterile 10-mL tube on ice. 3. Thaw one vial (200 μL) of JM109 High Efficiency Competent Cells on ice. When just thawed, mix the cells by gently flicking the tube. Carefully transfer 50 μL of cells to the ligation tube from step 2. Gently flick the tube and incubate on ice for 20 min. 4. Heat shock the cells for 45–50 s in water bath at exactly 42°C. DO NOT SHAKE. Immediately return the tube to ice for 2 min. 5. Add 950 μL room temperature SOC medium to each reaction tube. Incubate for 1.5 h at 37°C with shaking (~150 rpm). 6. Transfer the total volume of the transformation reaction to an Eppendorf tube, and centrifuge for 10 min at 350 g. Remove 900 μL of medium and resuspend the bacterial pellet in the remaining 100 μL. Spread 90 and 10 μL of cell suspension onto two LB agar plates containing ampicillin, X-Gal, and IPTG and incubate overnight at 37°C. 7. With a sterile pipette tip, tick pick 5–8 white colonies and transfer each of them in 4 mL LB medium containing ampicillin (50 μg/mL). Grow overnight with shaking at 300 rpm. 8. Isolate plasmid from 3 mL of the overnight culture using the QIAgen QIA prep Spin Miniprep kit. Elute plasmid in 50 μL of water.

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9. Make glycerol stocks by combining the remaining 1-mL overnight culture to 200 μL 100% glycerol. 10. Load 1–2 μL of isolated plasmid on a 1% agarose gel. Three bands of high molecular weight corresponding to the linear, circular, and supercoiled forms of the plasmid should be visible. 11. Linearize the plasmid by combining 30 μL of purified plasmid to 3 μL of restriction enzyme of choice, 5 μL of the appropriate 10× reaction buffer, and water up to a final volume of 50 μL. 12. Gel purify the linearized plasmid using the QIAgen Gel Extraction Kit and elute in 30 μL of water. 13. Determine plasmid concentration using the NanoDrop. Take an average out of at least five measurements (better ten) and perform the measurement at multiple template dilutions. The concentration of the plasmid has to be calculated very accurately because this measurement determines the outcome of the absolute quantification analysis. For use in RT-qPCR, prepare the plasmid as described below. 3.5. Calculation of Plasmid Copy Number and Preparation of the Standard Curve

Once the size of the plasmid containing the GOI is known, it is possible to calculate the number of grams/molecule, also known as copy number, as in the following example: Weight in Daltons (g/mol) = (bp size of plasmid + insert)(330 Da × 2 nucleotides/bp). Ex. g/mol = (5,950 bp)(330 Da × 2 nucleotides/bp) = 3,927,000 g/mol. Hence, (g/mol)/Avogadro’s g/molecule = copy number.

number

6.02214199 × 1023 =

Ex. 3,927,000 (g/mol)/6.02214199 × 1023 = 6.52 × 10−18 g/molecule. The precise number of molecules can be determined as follows: Concentration of plasmid (g/μL)/copy number. Ex.

(3 × 10−7 molecules/μL.

g/μL)/(6.52 × 10−18g/molecule) = 4.6 × 1010

Once the number of molecules in 1 μL of linearized plasmid solution is calculated, prepare standard dilutions to obtain an X plasmid copy number in 5 μL of water. Accurate pipetting is essential because the standards must be diluted over several orders of magnitude. It is recommended to divide standards into small aliquots, store at −80°C, and thaw only once before use. 3.6. Primer Design

The design of specific primers that work at a good efficiency is of crucial importance in RT-qPCR. Use the Primer3 program (http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi) to design primers of a length of 18–22 bp, with an annealing temperature of 60°C and a minimum self and 3¢ complementarily. To accomplish rapid quantification, short PCR cycling (45–75 s),

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Fig. 2. Melting curve profile of PCR products amplified with three different primer sets. Light grey: Four PCR products each showing the same specific melting peak. Dark grey : Four PCR products of which three showing a specific melting peak and a fourth one being a non-specific amplification product with a different melting temperature. In black: Amplification with the third set of primers resulted only in primer dimer formation.

and efficient PCR conditions, the optimal length of the PCR product is 100–200 bp. Use the OligoAnalyzer program (http://eu.idtdna. com/analyzer/Applications/OligoAnalyzer/Default.aspx?c=EU) to verify that the primers have low self- and hetero-complementarity. To increase the annealing temperature of primers, to improve the specificity of allele-specific primers, or for single-nucleotide polymorphism (SNP) analysis, the incorporation of locked nucleic acid (LNA) modifications can be of great advantage (7, 8). Software program to estimate melt behaviours of a template is POLAND MELTSIM (http://www.bioinformatics.org/meltsim/wiki/). 3.7. PCR Profile and Melting Curve Analysis

A typical PCR profile includes an initial denaturation step of 10–15 min at 95°C, depending on the Taq-Polymerase (see Note 4), followed by 35–40 cycles, including 95°C for 5–15 s (denaturation), 60°C for 15–30 s (annealing), and 72°C for 15–30 s (elongation). This profile is a general suggestion and the annealing temperature has to be verified. At the end of the run, a melting step needs to be performed to assess amplification specificity (see Note 5, Fig. 2). Each PCR product has a specific melting temperature, resulting in a single melting peak with no additional peaks at lower melting temperatures. Additional peaks can be primer dimers or unspecific products due to excessive amount of primers in the reaction, low annealing temperature, too high MgCl2 concentration, or too long hold times. Primer dimers’ formation can be reduced or eliminated by accurate design of the primers and optimization of primer concentration. When using a primer set for the first time, despite the presence of only one amplification peak, it is advised to sequence at least once the amplification product to confirm sequence specificity.

3.8. Optimization of Primer Concentration

Select a cDNA template or recDNA containing the sequence of the GOI. Prepare a master mix containing 7 μL of 2× Sybr-Green I Mix and 5 μL of DNA. Aliquot 12 μL of the master mix into reaction

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tubes and add 1 μL of each primer to give final concentrations as outlined in the table below. The final reaction volume is 14 μL (see Note 6). The primer stock concentrations are 1.4, 4.2, and 7 μM and give final concentrations of 100, 300, and 500 nM, respectively. Forward primer (nM) Reverse primer (nM)

100

300

500

100

100/100

300/100

500/100

300

100/300

300/300

500/300

500

100/500

300/500

500/500

We usually find 300 nM the optimal concentration for both forward and reverse primers. As a general guideline, choose the primer combination which gives the lowest Ct value for the same amount of template and does not lead to primer dimer formation. In every run, always include a non-template control (NTC), where the template is replaced by the same amount of water, in order to test for primer specificity and contaminations. 3.9. Determination of Primer Amplification Efficiency

Depending on the subsequent method of analysis, there are several ways to determine primer amplification efficiencies. The most commonly used is the standard curve method: a dilution series of a reference template or pooled samples of unknown concentration is generated. The reference sample can be cDNA or recDNA (of unknown concentration) that contains the target gene. The units used to describe the concentration of the dilution series are relative, as long as they reflect the dilution factor of the standard curve (Fig. 3). Set the threshold just above the take-off point of the reactions (if the result for more genes over different experiments need to be compared, set the threshold at the same level for all genes, for example 0.1). Record the Ct values and plot them against the log template concentration. Use the slope of the regression line to calculate the amplification efficiency for each primer according to the following formula: E = 10 (−1/slope). The optimal amplification efficiency of a reaction is 2, but we consider E values between 1.7 and 2 as acceptable, as long as the reproducibility over several runs as well as the replicates is good. Usually, all RT-qPCR software provide this type of calculations (see example in Table 1 and Fig. 4). In general, it is important that the amplification efficiency of the reference template reflects the amplification efficiency of the unknown sample.

3.10. Relative Quantification Analysis

Relative quantification is the method of choice for RT-qPCR analysis when investigating physiological changes in gene expression levels. It does not require standard curves with known concentration of templates and results are given as the ratio (R) of GOI versus

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Fig. 3. Standard curve of a tenfold dilution series of a reference cDNA sample used to calculate the amplification efficiency of the primer sets for the RG and GOI.

Table 1 Results obtained from the RG standard curve described in Fig. 3. By plotting the averaged Ct values from duplicate samples against the log of the given concentration, the corresponding standard curve will be obtained as shown in this table.

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Fig. 4. Possible RT-qPCR set up for relative quantification of IL-10 mRNA expression levels. A first run, where a standard curve for each of the analyzed genes is amplified has to be performed when analyzing the data using DDCt and Pfaffl method (see text). Particularly, for the validation experiment required for the DDCt method, it is important the template to be the same for each gene which needs to be compared. In a second run (the experimental run), amplify the RG (40S) and the GOI (IL-10) in each of the samples under investigation. Always include a non-template control (NTC), where water substitutes the template, and a control for genomic contamination (−RT). When a standard curve should be imported from a previous run, include a triplicate sample of one dilution point of the same standard curve in the current run (see Note 7).

one or more RGs. To date, several mathematical models have been developed and can be generally divided into two major categories: without and with primer efficiency correction. In this section, we provide examples on how to analyze an experiment applying both of those methods. For example, we want to determine the fold change in interleukin-10 (IL-10) mRNA expression at various time points after treatment. The 40S ribosomal protein S11 is used as RG. Set up the first run of the day by amplifying a standard curve for each target gene using a template cDNA or recDNA of unknown concentration (Fig. 4; it might not be necessary to run a standard curve every time depending on the chosen method of analysis). Set up a second run, where in separate tubes the 40S and IL-10 genes are amplified for each of the samples under investigation. Include a triplicate sample of one dilution point of the same standard from the first run. Analyze the results according to one of the methods outlined below. 3.10.1. Relative Quantification Without Efficiency Correction: DDCt Method

The ΔΔCt method (9) is based on the assumption that the primers of the GOI will have the same amplification efficiency as the primers for the RG. This assumption needs to be validated at least once before proceeding with the analysis of the experiment. See Note 8 for instructions on the validation experiment. In case of positive results from the validation experiment, proceed as follows. ●

Set the threshold to 0.1 for all genes.

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Export the Ct values to Excel.

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Select the sample at time point 0 (zero) as the calibrator (the calibrator is usually an untreated, unhandled sample).

●

Apply the following formula: DDCt = (Ct IL -10 - Ct 40S )(sample) - (Ct IL -10 - Ct 40S )(calibrator) , R(IL - 10) = 2- DDCt.

This method has the advantage that standard curves are required only once for the validation experiment and allows for normalization relative to an internal reference gene. However, the assumption that different primer sets will perform with the same amplification efficiency over different runs and over different templates might not always be valid. Therefore, the efficiency of all RG and GOI should be checked regularly, as changes in reagents, concentrations, calibrator, etc. could influence the efficiency of one or various genes differently. 3.10.2. Relative Quantification with Efficiency Correction: The Pfaffl Method

This method does not require the amplification efficiency of different primer sets to be similar; it rather takes into account the possibility that the efficiencies can be different and offers a way to correct for such differences (4, 10). Optimally, a standard curve for each of the target genes is amplified in the same run together with the unknown samples. However, when a large number of samples and numerous genes need to be analyzed, standard curves for several genes can be amplified in the first run of the day or even on a different day (Fig. 4). ●

Set the same threshold for all genes to be analyzed (i.e. 0.1) and record the amplification efficiency for each primer set as described in the previous paragraph.

●

In the experimental run, it is possible to either import the standard curve from the previous run and ask the software to adjust it to the standard in the current run (Run 2 in Fig. 4) or the threshold can be directly set manually to 0.1.

●

Export the Ct values to Excel.

●

Select the sample at time point 0 (zero) as calibrator and apply the following formula: (Ct

RIL -10 =

-10(calibrator) E (IL -IL10)

(Ct

40 S(calibrator) E E (40S)

- Ct IL -10(sample1-7) ) - Ct 40 S(sample1-7) )

.

The Pfaffl method is a modification of the ΔΔCt method with the obvious advantage that it does take into account differences in amplification efficiencies between primer sets. In order to obtain direct and valuable statistical information, it is possible to import the above-mentioned data in the gene

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quantification software called Relative Expression Software Tool (REST, freely available at http://rest.gene-quantification.info). This software uses the Pfaffl formula and generates statistical data, including the standard error and the confidence interval, by using randomisation tests via hypothesis testing P(H1) = difference between sample and control is due only to chance. 3.10.3. Relative Quantification with Efficiency Correction: “Sigmoidal” or “Logistic” Curve Fitting Models

To date, several methods have been developed to calculate the amplification efficiency of each primer set in each single sample (11–13). The great advantage of all these methods is that they do not require the preparation of standard curves or validation experiments and no assumption has to be made regarding the amplification efficiency of each primer set over different runs, templates, or master mixes. The method developed by Corbett Research has been incorporated in the Rotor-Gene 6000 software under the “Comparative Quantitation” analysis option and we routinely use it for our relative quantification of gene expression. We directly apply the set up of Run 2 in Fig. 4 (without the need of the standard samples). ●

The Ct values and the amplification efficiency for each sample are directly obtained from the software and exported to Excel.

●

The average amplification efficiency (EA) for each primer in each run is calculated and the relative fold change for each GOI is calculated according to the Pfaffl formula as above.

●

It often happens that the analysis of one large experiment cannot be completed within one run. In that case, we calculate the EA of each primer set over the whole experiment (two, three, or more runs). To reduce variation between runs, we usually prepare one master mix for each primer set which is enough for all runs of the day and not one master mix for each run. By doing so, we observe only a ±0.02 variation in EA for each primer set between two, three, or more runs on a single day.

Before using a new primer set for the first time, we perform a dilution series of a cDNA sample containing the target gene. This provides us with an estimation of the amplification efficiency and the melting curve analysis provides us the specificity of the assay. 3.11. Absolute Quantification Analysis: External Standard Curve Model

Absolute quantification refers to an analysis, where unknown samples are compared to a standard curve of cDNA, recDNA, or recRNA, where the absolute concentration is known. Especially for absolute quantification analysis, the standard curve for the target gene should be amplified in the same run together with the unknown samples. However, when a large number of samples and numerous genes need to be analyzed, it is possible to import a standard curve from a previous run.

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Standard curves for several GOI can be amplified in the first run of the day and in every subsequent run, together with the unknown samples. A triplicate of one dilution point of the standard curve should be included.

●

At the end of the run, ask the software to import the standard curve for the GOI from a previous run and adjust it to the standard in the current run (see Note 7). Read the absolute copy number given by the software.

●

Alternatively, it is possible to export data to excel and perform the quantification analysis by plotting the Ct values of the unknown sample against the standard line obtained by plotting the Ct values and the log concentration of the recDNA as described before.

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Express data as GOI (copy number)/x ng total RNA.

●

To normalize data and correct for variations in template input, a normalizer (RG) is used. In this way, the absolute copy number of an RG and GOI in an unknown sample is determined from the standard curve. The absolute value obtained for the GOI is divided by the absolute value obtained for the RG in the same sample. Obtained are the normalized data of the GOI in the unknown sample. The quality of the gene quantification data cannot be better than the quality of the denominator. Any variation in the denominator obscures real changes, produces artificial changes, and wrongs quantification results.

When optimized, standard curves are highly reproducible and allow the generation of highly specific, sensitive, and reproducible data. However, the external standard curve model has to be thoroughly validated as the accuracy of absolute quantification in realtime reverse transcriptase-PCR depends entirely on the accuracy of the standards. Standard design, production, determination of the exact standard concentration, and stability over long storage time are not straightforward and can be problematic. 3.12. Technical or Biological Replicates?

Depending on the applications, the use of technical and biological replicates or both has to be considered. A technical replicate refers to a sample, for example a piece of tissue, from which the RNA isolation and cDNA synthesis has been performed more than one time under the same identical conditions. This type of replicate tells us something about the variation in the chemistry we are using. Often, the same cDNA sample is analyzed in triplicate in one RT-qPCR run. This type of technical replicate only tells something about the pipetting skills of the operator and the accuracy of the PCR instrument (see also Subheading 1.3), but should absolutely NOT be considered for statistical analysis. Biological replicates refer to the application of the same treatment to two or more samples. From each of the samples, the RNA isolation and cDNA

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synthesis are performed independently but under identical conditions. Each of the obtained cDNA samples can be analyzed once by RT-qPCR. Both types of replicates (technical or biological) provide information about the experimental variation and allow statistics to be applied to identify differences in expression levels between samples. Being a beginner, it is a good practice to include technical replicates to test for pipetting skills. When testing the amplification efficiency of a new primer set, it is advisable to include at least a triplicate of each dilution point. When investigating the effects of a treatment, the use of biological replicates we think is of greater value (14). For example, in an in vitro experiment, cells are incubated in the presence or absence of a stimulus. The treatment is repeated in at least three replicate wells. Each of the three wells is a biological replicate; however, the cells are derived from a single individual. More relevant would be to repeat the same in vitro experiment on cells isolated from three different individuals, each of them being a biological replicate.

4. Notes 1. The extraction and purification procedure of total RNA must fulfill the following criteria: free of protein (absorbance 260/280 nm); free of genomic DNA; should be non-degraded (28S:18S ratio should be roughly between 1.8 and 2.0, with low amount of short fragments); free of enzymatic inhibitors for RT and PCR reaction, which is strongly dependent on the purification and clean-up methods; free of any substances which complex essential reaction cofactors, like Mg2+ or Mn2+; free of nucleases for extended storage (15). 2. From 0.1 ng up to 5 μg, total RNA can be transcribed into cDNA using this kit. Optimally, 1 μg of total RNA is used. In general, it is important to use the same amount of starting RNA material for each sample within the same experiment. This greatly reduces the sample-to-sample variation due to differences in cDNA synthesis efficiency and simplifies the subsequent analysis, particularly when absolute quantification is used. In some cases, not all samples (within the same experiment) would yield RNA amounts sufficient to use 1 μg of RNA/sample; it is possible to lower the amounts down to 0.1 μg, but again this amount should be used for all samples within the same experiment. 3. Cloned recDNA and gDNA are very stable and generate highly reproducible standard curves even after a long storage time.

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Furthermore, the longer templates derived from recDNA and gDNA mimic the average native mRNA length of about 2 kb better than shorter templates derived from RT-PCR product or oligonucleotides. A problem with DNA-based calibration curves is that they are subject to the PCR step only, unlike the unknown mRNA samples that must first be reverse transcribed. This increases the potential for variability of the RT-PCR results and the amplification results may not be strictly comparable with the results from the unknown samples (3). 4. The initial denaturation time depends on the type of TaqPolymerase present in the master mix. We strongly advise HotStart Taq-Polymerases that require 2 to 15 min at 95°C, depending on the Taq-Polymerase. This allows performing the preparation and aliquoting of the master mix on the bench at room temperature. 5. At the end of a run, after the last annealing step, all amplification products are present as double-stranded DNA and Sybr Green I is bound to it. During the melting step, the decrease in fluorescence is measured due to melting of dsDNA products and consequent release of the fluorescent dye. Each product melts at a specific temperature. Primer dimers usually have a lower melting temperature than PCR products ranging between 80 and 200 bp. 6. Usually, companies advise a final volume of 50 μL, but the reaction can easily be scaled down to save costs. We always try to add at least 5 μL of template. Lower volumes might increase the chance of pipetting errors. 7. The slope of the calibration curve is more reproducible than the intercept, and the slope directly correlates with PCR efficiency. Hence, only a single standard point is required to “reregister” a previously performed calibration curve level for the new unknown samples. However, this assumes that the efficiency in a given run is the same as in a previous run. 8. Amplify a standard curve as described in the Subheading 3.9. In this case, the reference template has to be the same for both primer sets, and preferably one of the cDNA samples which is going to be used for the subsequent experiment. ●

After having set the threshold (0.1), export the Ct values to EXCEL and average the Ct of replicate samples.

●

Calculate the LOG10 of the given arbitrary concentration (LOGconc).

●

Obtain the DCt: for each dilution point, calculate the difference between the Ct(RG) and Ct(GOI). Plot the LOGconc vs. ΔCt and obtain the equation of the curve.

2

The Use of Real-Time Quantitative PCR for the Analysis of Cytokine mRNA Levels

23

If the efficiencies of the two primer sets are approximately equal, the obtained curve should be a nearly horizontal line with a slope 8,000 × g at room temperature. Discard the flow-through. 4. Transfer the spin column into a new 2 mL collection tube, and add 700 μL of buffer RW1 to the column. Close the tube gently, and then centrifuge for 15 s at >8,000 × g to wash the column. Discard the flow-through and the collection tube. 5. Transfer the column into a new 2 mL collection tube. Pipet 500 μL of Buffer RE onto the RNeasy column. Close the tube gently, and centrifuge for 15 s at >8,000 × g to wash the column. Discard the flow-through. 6. Add another 500 μL of Buffer RE to the RNeasy column. Close the tube gently, and then centrifuge for 8 × g to dry the column. 7. Transfer the RNeasy column to a new 1 mL collection tube, and then centrifuge at full speed for 1 min to eliminate any chance of possible Buffer RE carryover. 8. To elute RNA from the column, transfer the column to an RNase-free 1.5 mL tube. Pipet 50 μL of RNase-free water directly onto the column. Close the tube gently, and centrifuge for 1 min at >8,000 × g. RNA amounts are quantitated by OD reading.

3.7. RNA Expression Analysis Using Affymetrix Gene Chip

The isolated RNA was labeled and hybridized on Affymetrix Hu133 plus 2.0 arrays following the Affymetrix one cycle target labeling protocol in the expression manual (https://www.affymetrix.com/support/downloads/manuals/expression_analysis_ technical_manual.pdf).

3.7.1. Affymetrix One Cycle Target Labeling

(a) First strand cDNA synthesis. 1. Place 5 μg of the total RNA in a PCR tube. 2. Add 2 μL of the appropriately diluted poly-A RNA. (The eukaryotic Poly-A RNA spike-in control could provide the exogenous control for monitoring the eukaryotic target labeling process. The table below shows the serial dilution according to the amount of starting total RNA).

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Serial dilutions Starting amount total RNA (mg)

First

Second

Third

Spike-in amount (mL)

1

1:20

1:50

1:50

2

5

1:20

1:50

1:10

2

10

1:20

1:50

1:5

2

3. Add 2 μL of 50 μΜ T7-Oligo(dT) Primer. 4. Add RNase-free H2O to a final volume of 12 μL. 5. Flick tube a few times to mix well and centrifuge briefly. 6. Incubate the mixture for 10 min at 70°C and cool to 4°C for 2 min. 7. Transfer 7 μL of First Strand mix (4 μL of 5× first Strand Reaction Mix; 2 μL of 0.1 M DTT; and 1 μL of 10 mM dNTP) into the sample RNA/T7 mixture and flick to mix and centrifuge briefly. 8. Incubate for 2 min at 42°C. 9. Add 1 μL of Superscript II (if the starting total RNA is 1–8 μg) or 2 μL of Superscript II (if the starting total RNA is 8.1–15 μg) into each tube. 10. Incubate for 1 h at 42°C and cool to 4°C for 2 min. 11. Centrifuge briefly to collect samples at the bottom. (b) Second strand cDNA synthesis. 1. Assemble Second-Strand Master Mix as shown below per sample. The second strand master mix is recommended to use immediately after making. Add additional material to compensate for loss during the process when making master mix. Reagent

Volume (mL)

RNase-free H2O

91

5× second Strand Reaction Mix

30

dNTP (10 mM)

3

E. coli DNA Ligase

1

E. coli DNA Polymerase I

4

RNase H

1

Total volume

130

2. Transfer 130 μL of Second-Strand Master Mix to each first-strand synthesis sample (total volume 150 μL). 3. Incubate for 2 h at 16°C. 4. Add 2 μL of T4 DNA Polymerase to each reaction.

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5. Incubate for 5 min at 16°C. 6. Add 10 μL of EDTA (0.5 M) and mix well to terminate reaction. (c) Cleanup double-stranded cDNA. 1. Transfer the 150 μL ds cDNA reaction to a new 1.5 mL tube for each sample. 2. Add 600 μL of cDNA Binding Buffer to the cDNA synthesis reaction. Mix by vortexing for about 3 s. 3. Add 500 μL of the sample to the cDNA Spin column and centrifuge 1 min at 8,000 × g. Discard flow-through. 4. Add remaining mixture onto the same spin column and centrifuge 1 min at 8,000 × g. Discard flow-through. 5. Add 750 μL of cDNA Wash Buffer (ensure ethanol has been added to Wash Buffer) to the spin column and centrifuge 1 min at 8,000 × g. Discard flow-through. 6. Open the cap of the spin column and centrifuge (place columns in every second well of rotor) for 5 min at max speed (60

NA 1.0 >0.6

Hypothesis Treated cell lines generating Treated primary cells In vivo isolated cells

3–5 5–10 >10

>75 >60 >50

0.4

Hypothesis testing

>10 >20 >30

>90 >90 >90

(p value x # of genes) 0.6 (p value x # of genes) 0.6 (p value x # of genes) 0.6

Treated cell lines Treated primary cells In vivo isolated cells

General guideline for determining the number of samples to use for each condition under study and for statistical thresholds that designing and analyzing a microarray study. % Present refers to the percent of samples for which a given gene is called “Present” by an analysis package such as Affymetrix’s GCOS and Gene Expression Console

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Guidelines for selecting statistical thresholds). The caveat to such a post hoc data mining approach is that the results should be followed up with confirmation studies. Such a mindset has greatly decreased our false-positive confirmation rate and has more rapidly led to new findings and publications than if we had used a standard of methods and statistics that were set a prior to the data analysis. 3.9. Gene Selection

To compare the gene expression profile between IL-27 and IFN-α treated CD4 T cells and macrophages, as mentioned above, a pilot study was initiated. We performed two experiments on CD4 T cells and macrophages treated with 100 ng/mL of IL-27 or 1,000 units/ mL of IFN-α, respectively. The samples were hybridized on Affymetrix Human genome U133plus 2.0 arrays (see Note 3). Data were collected in the Affymetrix GCOS system after scanning the array. Data analysis followed the steps as below: 1. The normalization and background correction were done in the Affymetrix GCOS system using the standard MAS5 algorithm (14). 2. Compared IL-27 or IFN-α treatment samples in the same cell type as its mock control sample, such as IL-27 treated vs. control and IFN-α treated vs. control within both CD4 cells and macrophages. 3. The selection focused on a number of Affymetrix calls. They are (a) Signal; as the intensity for the expression of each gene. (b) Detection Call; indicating if the transcript was detected (“A” representing Absent, “M” representing Marginal, and “P” representing Present). (c) Change; indicating up or down regulation in the comparison of two samples (“D” representing Decrease, “MD” representing Marginal Decrease, “NC” representing No Change, “MI” representing Marginal Increase, and “I” representing Increase). (d) Signal Log Ratio; the log2 fold change indicating up or down regulation in the comparison of two samples. In this pilot experiment, after comparing the treatment sample to its control within the same cell type, the selection criteria are (1) detection call is not “A,” (2) change is not “NC,” and (3) signal log ratio is larger than 1 or less than −1. 4. There were 132 transcripts up-regulated and 78 transcripts down-regulated by IFN-α, 37 transcripts up-regulated and 137 transcripts down-regulated by IL-27 in CD4 cells, 438 transcripts up-regulated and 683 transcripts down-regulated by IFN-α, and 662 transcripts up-regulated and 935 transcripts down-regulated by IL-27 in macrophages (a gene list is available at http://david.abcc.ncifcrf.gov/manuscripts/cp/). 5. All selected transcripts above were classified by the Hierarchical clustering using Partek Pro statistical software (www.partek. com) (Fig. 1). 6. The common genes among the lists derived from step 4 can be compared by Venn diagram (Fig. 2).

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Fig. 1. Heap map from microarray analysis. CD4 T cells or macrophages were treated with mock, 100 ng/mL IL-27, or 1,000 units/mL IFN-α for 24 h. Differentially expressed genes were selected between mock and cytokine-treated cells and were greater than a twofold change. The resulting retention of 1,868 genes out of approximately 54,000 AffyID was subjected to subsequent analysis. An increase or decrease of gene transcription by more than twofold is represented in red or green, respectively. Genes shown in black indicate no change in transcriptional activity.

Fig. 2. Venn Diagram of upregulated genes between IFN-α and IL-27 treated macrophages. MDM cells were treated with IFN-α and IL-27, respectively. Approximately 438 and 662 up-regulated genes were selected according to the microarray experiments.

3.10. Gene Functional Annotation with DAVID

In contrast to the traditional approach of studying one or a few genes at a time, the advantage of the high-throughput genomic microarray technology is to allow investigators to simultaneously measure the changes and regulation of genome-wide genes under certain biological conditions. After “interesting” gene lists are obtained from microarray experiments, DAVID systematically maps a large number of genes in a given list to the associated biological annotation terms (e.g., GO Terms or Pathways), and then iteratively examines each of the annotation terms by the Fisher Exact test. Thereafter, the annotation terms with enriched gene members can be identified from tens of thousands of other annotation terms in a high-throughput manner. The enriched annotation terms associated with the given gene list will give important insights for investigators to understand the biological themes under the study (see Note 4 for a hypothetical example of an enrichment analysis).

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Importantly, given that the high-throughput enrichment data-mining environment is extremely complicated (12), the analysis of large gene lists is indeed more of an exploratory procedure rather than a purely statistical solution. The analysts themselves still play critical roles in making the final decisions in terms of which enriched biology (terms) make more sense to a given study, and thereafter which enriched terms to follow up and focus on. Even though annotation terms may be associated with very significant enrichment p values, it is common that analysts ignore some of the enriched annotation terms based on whether or not the results make sense, biologically. The analogous example of this type of situation is like that of a Google search, which returns some results that are not relevant to the user’s original query. Users therefore can make the final judgment to ignore some of the results based on his or her knowledge of the situation. Given that high-throughput gene functional annotation analysis is an exploratory procedure, rather than a strictly defined protocol, the following section describes major steps and procedures using DAVID in order for readers to grasp the idea of the overall data analysis environment that is available, as well as the key spirit of high-throughput gene functional annotation analysis. As for the finer level of DAVID functions that are available, they may be explored in a logical manner throughout the course of the analysis by the reader. 1. Get gene list(s) ready for functional analysis with DAVID. A typical gene list is usually in a size ranging from hundreds to thousands of genes, which correspond to certain biological themes under study (see Note 5 for details of IFN_Up_List and IL27_Up_List which will be used throughout descriptions and discussions below). 2. Submit list(s) of gene IDs to DAVID at http://david.abcc. ncifcrf.gov or http://david.niaid.nih.gov. After clicking on “Start Analysis” on the header, a gene list manager panel will appear on the left side of the web page (Fig. 3). Then, perform the following steps to submit a gene list to the DAVID system: Step 1. Copy and paste a list of gene IDs into box A (i.e., Affymetrx_ID). Step 2. Indicate the list to be submitted as a gene list (i.e., genes to be analyzed). Step 3. Click the “Submit List” button (see Note 6). 3. Use the Tool Menu Page as the central page to access various DAVID analytic tools (Fig. 4) (see Note 7). 4. Invoke “Gene Name Batch Viewer” to explore the names of all genes, particularly for highly expected and related genes in the list (see Note 8). 5. Invoke “Gene Functional Classification” to classify individual genes into gene functional groups based on the overlap of

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Fig. 3. The gene list submission page of DAVID. The right side of panel lists a format example of Affymetrix gene list. The left side is the gene list manager. To submit a gene list to DAVID, the gene list can be copied/pasted to the box followed by steps 1, 2, 3, and 4 as labeled.

similar biological function. Users can treat this step as a clustering view of step 4, that is, highly functionally related genes are grouped together for the ease of exploration. To do this, return to the Tool Menu Page as described in step 3. Click on “Gene Functional Classification Tool” to classify the input gene list into gene groups (see Note 9 and Fig. 5). 6. Invoke “Functional Annotation Chart” to understand the fine details of enriched annotation terms associated with the large gene list (see Note 10 and Fig. 6).

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Fig. 4. The tool menu page of DAVID. After the gene list(s) are submitted to DAVID, the gene list manager on the left side displays appropriate information. The hyperlinks of four major DAVID tools (pointed by arrow icons) are listed on the right side. The strengths and indications of DAVID tools are various for different analytic goals as discussed in original papers (10), as well as in this protocol. By clinking on the hyperlink of DAVID tools, users can invoke the according DAVID tool to analyze the current gene list that is being highlighted (in blue) in the left gene list manager. Importantly, by clicking on “Start Analysis” on the menu, users can get to this page to access/switch DAVID tools at any time during analytic course.

7. Invoke “Functional Annotation Clustering” (Fig. 7) to explore enriched annotation terms in a clustered view instead of a linear term view as in step 6 (see Note 11). 8. Compare the annotation profiles across relevant gene lists (see Note 12, Table 2 and Fig. 9).

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Fig. 5. The layout of the result page of DAVID Gene Functional Classification Tool. The 443 genes in IFN_Up_List were classified into multiple gene functional classes that are separated by the blue rows. The genes in each functional class should share significant amount of biological functions (terms). Variety of hyperlinks is provided for each functional class and its gene members. Such clustering view make genes in users’ list much organized for the ease of exploration and focus.

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Fig. 6. The annotation summary page. According to research interests, the wide range of annotation categories, offered by DAVID, can be selected/deselected through the expandable tree structure by clicking on + icons. Then, three analytic modules (three buttons on the bottom) can be respectively invoked to analyze gene list (e.g., IFN_Up_List) against the selected annotations above.

Table 2 Comparison of the top enriched terms for IFN-a and IL-27 gene lists Top enriched biology from DAVID chart report

IFN_Up_List

IL27_list

Data source

Annotation term

%

%

SP_PIR_KEYWORDS

Interferon induction

GOTERM_BP_3

p value

p value

7.57

1.91E-43

6.26

7.37E-43

Defense response

23.24

8.86E-29

24.19

2.35E-39

GOTERM_BP_3

Immune response

22.16

9.02E-29

22.68

7.32E-38

GOTERM_BP_3

Response to pest, pathogen, or parasite

13.78

1.70E-21

13.39

2.04E-25

GOTERM_BP_3

Response to other organism

14.05

3.98E-21

13.39

5.32E-24 (continued)

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Table 2 (continued) Top enriched biology from DAVID chart report

IFN_Up_List

IL27_list

Data source

Annotation term

%

p value

%

p value

SP_PIR_KEYWORDS

Alternative splicing

25.95

2.03E-10

27.65

1.45E-15

SP_PIR_KEYWORDS

Direct protein sequencing

16.22

4.56E-08

16.85

8.13E-11

SP_PIR_KEYWORDS

Membrane

22.16

1.42E-05

25.27

7.52E-11

SP_PIR_KEYWORDS

Signal

16.22

2.39E-05

17.93

8.78E-09

SP_PIR_KEYWORDS

Metal-binding

14.59

9.34E-05

NA

NA

GOTERM_BP_3

Response to wounding

6.49

7.30E-07

SP_PIR_KEYWORDS

Zinc

12.16

0.000173

SP_PIR_KEYWORDS

Chelation

1.08

0.000184

1.08

9.21E-06

SP_PIR_KEYWORDS

Transmembrane

20.54

0.000215

23.33

1.77E-08

SP_PIR_KEYWORDS

Hydrolase

10.81

8.65E-06

9.94

1.93E-05

SP_PIR_KEYWORDS

Innate immunity

1.62

0.000255

2.16

3.26E-08

GOTERM_BP_3

Positive regulation of cellular process

6.76

3.67E-05

6.05

7.85E-05

SP_PIR_KEYWORDS

Transmembrane protein

6.49

0.000295

7.34

1.02E-06

SP_PIR_KEYWORDS

Immune response

2.70

0.000531

4.75

1.60E-12

SP_PIR_KEYWORDS

Antiviral defense

0.81

0.003541

1.94

1.17E-07

SP_PIR_KEYWORDS

Glycoprotein

17.57

0.003609

22.03

8.08E-09

GOTERM_BP_3

Positive regulation of physiological process

5.95

0.000119

4.75

0.002229

SP_PIR_KEYWORDS

Surface antigen

1.08

0.020628

1.73

7.95E-06

SP_PIR_KEYWORDS

sh2 Domain

1.35

0.032123

2.16

2.04E-05

SP_PIR_KEYWORDS

ubl Conjugation

2.70

0.000215

1.73

0.014942

GOTERM_BP_3

Hemopoietic or lymphoid organ development

2.43

0.000221

1.51

0.016281

5.83 NA

9.77E-07 NA

The top 20 enriched terms from each of DAVID Chart Reports for IFN_Up_List and IL27_Up_List are selected and combined. The according enrichment p values and gene hit percentages are listed side-by-side. There are large agreements on the very relevant immune-related terms between the two lists. Two terms of “metal-binding” and “zinc” are missing from IL-27 study. Considering the lists from immunology studies, the missing terms may not be very interesting. In such case, analysts should make the final judgment based on overall situations instead of solely relying on statistical values

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4. Notes 1. Pilot Study. The goal of a pilot study can be several fold but is most often performed in preparation for a larger study in order to optimize workflow and experimental conditions so that time and funds are most efficiently used to generate high-quality data. Hypothesis Generating Study. This type of study focuses on the discovery of genes and/or pathways not known to be involved in the current biological phenomena under study with the idea that the genes and pathways will lead to a new hypothesis(es) that can be confirmed via follow-up laboratory experiments. Hypothesis Testing Study. Hypothesis testing studies are designed in such a manner as to have very tight control over Type I Error, even at the cost of a high Type II Error, i.e., attempt to eliminate any false-positive error even if it means throwing out many true positives. Table 1 summarizes the number of recommended samples and statistical thresholds to use based on the scientific approach and sample type. 2. Visible precipitate may form after the addition of ethanol when preparing RNA from certain cell lines, but this will not affect the RNA extraction. 3. Affymetrix Hu133 plus 2.0 arrays contain from eleven to twenty-one 25-mer oligonucleotide probes that are specific for each gene being interrogated and are Perfect Match (PM) probes. Additionally, a second set of probes identical to the first, except for a single nucleotide change at the center position, is included to eliminate nonspecific binding signals and are termed Mis-Matched (MM) probes. A group of such probes specific for a given gene is called a probe set. There are numerous published methods (such as MAS5, RMA, GCRMA, etc. as reviewed by Harr et al. (13, 14)) available for Affymetrix data preprocessing, probe set summarization, and statistical analysis. There are several free software packages commonly used for Affymetrix GeneChip preprocessing including Affymetrix’s Gene Expression Console and Affymetrix Power Tools (for advanced users) which can be downloaded from the Affymetrix website (www.affymetrix.com), and various R statistic packages from Bioconductor (www.bioconductor.org). 4. In the previous gene statistical selection in Subheading 3.9, gene expression was compared between untreated and cytokine-treated cells. The gene selection statistical analysis identified over a 1,000 genes regulated in one or both cytokine treated MDM and CD4 T cells. Heat map analysis further categorized them into multiple subgroups/gene clusters with distinct up/down gene regulation patterns (Fig. 1).

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For functional analysis of the gene lists, we now illustrate the idea of DAVID enrichment analysis with a hypothetical example. For example, 1,000 regulated genes are selected via microarray study on IFN-α treated cells, of which, 50 out of 1,000 genes (5%) are IFIGs. As compared to the global background of the microarray chip, on which there are 20,000 genes in total, of which, 100 out of the 20,000 genes (only 0.5%) are IFIGs. It is obvious that IFIGs are much more strongly selected (5%) by the microarray experiment, than by random chance (0.5%). The significance of the enrichment p value can be mathematically measured by well-known statistical methods, such as the Fisher’s exact test (i.e., 2.5E-38). A conclusion can then be obtained for the particular example, that is, IFIGs are significantly enriched in the user’s gene list and therefore should be relevant to the study. 5. The following example gene lists will be used throughout the procedure. Approximately, 438 (IFN_Up_List) and 662 (IL27_Up_List) genes were identified as two subgene lists from Subheading 3.9, as up-regulated genes for IFN-α and IL-27-treated MDMs, respectively. The genes in the two lists are represented by Affymetrix probe set IDs. The list of gene IDs should be in an acceptable format (i.e., comma delimitation, space delimitation, or one ID per line). The detailed information and examples of supported common ID types and input ID formats can be found on DAVID web site. 6. After the list of 438 genes has been successfully submitted to DAVID, a gene list, named Uploaded_List_1, should appear in the gene list manager panel. It can be manually renamed to a more meaningful name such as “IFN_Up_List” as shown in Fig. 4. Multiple gene lists can be uploaded to DAVID one after another. Once gene lists are in the gene list manager, they can be accessed by any of the DAVID tools. Users can also manually switch back and forth between gene lists during the course of the analysis. 7. The Tool Menu Page provides a set of hyperlinks for four sets of available analytic tools (Fig. 4). Clicking on each link will lead to the corresponding DAVID tool for analysis of the current working gene list (IFN_Up_List), which is highlighted in the gene list manager. Importantly, by clicking on “Start Analysis” on the header menu, users can always go back to this page for choosing or switching to other analytic tools during the analysis course. 8. The purpose of this step is to give users a rough idea about the contents of the gene list. This way, questions like “Does my gene list contain important marker genes expected for the study?” can be answered. To do this, click on the “Gene Name Batch Viewer” link on the Tool Menu Page. All the gene names

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will be listed in a linear format. For a gene of interest, various hyperlinks are provided for more detailed annotation information (e.g., other gene names, more gene symbols, other important gene IDs, etc.). For IFN_Up_List, there are many immunology-related genes as expected for the study, such as chemokine ligand genes, interleukin genes, and interferon inducible genes. For example, the Affymetrix ID, “202086_at,” corresponds to the gene, “myxovirus (influenza virus) resistance 1, interferoninducible protein p78 (mouse)” (MxA). In addition, the hyperlink provided for the gene offers more summarized information about MxA’s IFN inducibility, antiviral ability, and OMIM phenotype association, as well as other ID types and annotation resources specific to this gene. After exploring through the details of many interesting genes in the list as above, analysts not only have a rough idea about the contents of the gene list, but also know more details about particular genes of interest. 9. For IFN_Up_List, genes can be classified into multiple groups, such as, an interferon-inducible gene group (group 2), and a chemokine gene group (group 4). Various hyperlinks are also provided for more detailed information for each gene/gene group corresponding to users’ interests. Tools in step 4 and 5 in Subheading 3.10 give us a similar global view of gene contents in the IFN_Up_List. The advantage of “Gene Functional Classification” is that it provides an organized clustering view of input genes so that analysts can quickly grasp the key spirit of the gene list by going through group-level information as opposed to a gene-by-gene level, and thereafter easily focus on genes of relevance. Analysts should keep in mind that it is not recommended to replace step 4 with step 5 in Subheading 3.10 because some important genes may be left out from the clustering view if they do not have a strong network context with other genes (Fig. 5). 10. This tool implements gene-term enrichment analysis as described in the DAVID introduction section. This is a key step among all others during the analytic course. To do this, return to the Tool Menu paged as described in step 3 in Subheading 3.10. Click on “Functional Annotation Chart” to show the “Summary Page.” There are seven annotation categories (e.g., Go Terms, BioCarta Pathways, Protein–protein interactions, etc.), which will be subjected to enrichment analysis, and are preselected by default (Fig. 6). However, users can always manually select and deselect any annotation categories through the expandable annotation category trees, according to their research interests. Next, click on the “Functional Annotation Chart” button on the bottom of the page leading to a Chart Report. For IFN_Up_List, the Chart Report lists enriched annotation terms ordered by their enrichment p values.

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Fig. 7. The layout of Chart Report. The enriched terms and their associated statistical values are listed in a linear tabular format. They are ordered by the enrichment p values. The top ranked terms such as “interferon induction,” “immune response,” are exactly what users expect for the study regarding IFN_Up_List. The hyperlinks on the terms lead to more detailed explanation of the terms. Clicking on blue horizontal bars, the genes in user’s list that belong to the corresponding terms will be listed.

For example, the top five reported terms in 1–5 are interferon induction, response to biotic stimulus, immune response, defense response, and response to pest (Fig. 7). Given IFN_Up_List was obtained from an IFN stimulation experiment; these terms are exactly what we expected. Thereafter, analysts can focus on particular annotation terms of interest and further ask questions like, “What are the genes in my list that are associated with this term?” For example, by clicking on the blue bar beside the term, interferon induction, ~28 genes associated with the term can be explored (i.e., AIM2, CXCL10, DDX58, EIF2AK2, G1P2, G1P3, GBP1, IFI16, IFI27, IFI35, IFI44, IFIT1, IFIT2, IFIT3, IFIT5, IFITM1, IFITM2, IFITM3, INDO, IRF1, ISGF3G,MX1, MX2, OAS1, OAS2, OAS3, OASL, PSME1, RNF31, etc.). For another example, the genes associated with the term, antiviral defense, include APOBEC3G, DDX58, IFIH1, ISGF3G, RNF31, MX1, MX2, PLSCR1, STAT1, and STAT2. Comparing the two sets of genes, analysts may focus more on genes having both antiviral and IFN inducible characters. Moreover, the KEGG and BioCarta pathways are other important sources that allow analysts to view their genes in a network context. The enrichment p value for IFN alpha signaling pathway is calculated and is found to be very

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Fig. 8. The genes in user’s list on biological pathway. For IFN_Up_List, BioCarta pathway of IFN-α Signaling Pathway is identified in the enrichment analysis with significant enrichment p value. The genes of STAT1, STAT2, p48 (indicated with red stars), regulated in this study, are shown on the map. Thus, users can examine genes of interests in a biological network context.

significant (i.e., 3.5E-5) in the Chart Report. A graphical representation of the pathway can be invoked for analysts to further explore regulated genes (e.g., STAT1 and p48) on the pathway (Fig. 8). At this point, it is shown that STAT1 plays an important role in IFN signal transduction in this study, which is expected based on a priori knowledge. DAVID tools provide an integrated and enriched data mining environment for analysts to identify the most relevant and important annotation terms associated with large gene list(s) under study. Analysts can extend the above example procedure and logic to many other relevant and enriched annotation terms, in order to obtain more comprehensive analytic results. Importantly, analysts themselves play the final decision-making role to judge which terms are more interesting and relevant for further focus based on a priori biological expectation and knowledge for the given study. 11. The advantage of this function is to organize/cluster redundant annotation terms with similar meaning (e.g., programmed cell

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death, induction of apoptosis, apoptosis, and regulation of apoptosis) into a clustered view for the ease of interpretation and focus. To do this, go back to the “Summary Page” as described in the beginning of step 6 in Subheading 3.10. Then click on the “Functional Annotation Clustering” button on the bottom of the page. Analysts can go through the important enriched annotation terms in a clustered view in a similar manner as that of step 6 in Subheading 3.10. We will not repeat the exploration procedure here again. In addition, as discussed in step 5 in Subheading 3.10, the clustered view cannot totally replace the linear view. It is recommended to explore both in order to obtain maximum satisfactory results from the various analyses. 12. The 662 up-regulated genes (IL27_Up_list) obtained from IL-27 treated MDM in Subheading 3.9 can also be submitted and explored through DAVID from step 1 to 7 as outlined for IFN_Up_List. Then, the two sets of annotation results for the IFN genes and IL-27 genes can be cross-compared. This comparison is particularly interesting because it may answer a key question asked by the study “Does IL-27, a novel anti-HIV cytokine, share similar mechanisms with IFN-α?” For other studies, with a time series design, such comparisons could be very important too. Unfortunately, there is no standard way to perform such comparisons. Thus, DAVID only provides annotation results for each of the gene lists, but does not currently offer any comparison functionality. Statistical tools, such as MS Excel, R, could be very useful for analysts to conduct these comparisons on the DAVID results. The comparison may be much customized based on the analyst’s statistical knowledge, specific research goals, and data situation. Several ways to implement the comparisons are recommended, but are not limited to (1) Directly comparing the overlapping genes between IL27_Up_List and IFN_Up_ List (Fig. 2). A large number of genes (i.e., 185) are common between the two independent lists. (2) Particularly, compare the overlapped genes of the very important and relevant terms (e.g., interferon induction, antiviral defense, immune response) that are obtained from step 6 of Chart Reports for IL27_Up_ List and IFN_Up_List, respectively. Interestingly, interferon inducible genes show over an 80% overlap between the two lists. (3) Specifically compare the key pathways (e.g., IFN alpha signaling pathway). The genes of STAT1, STAT2, and p48 are commonly regulated in both the IL-27 and IFN lists (Fig. 8). (4) Globally compare the enrichment p values, percentages of gene hits, and/or enrichment fold changes of the top significantly enriched terms obtained from the DAVID Chart Report for each of the lists. Analysts should keep in

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Fig. 9. The correlations of enrichment statistical values obtained from DAVID Chart Reports The correlation plots to measure the annotation agreement between IFN_Up_List and IL27_Up_List are drawn using MS Excel on the data obtained from Table 1. Regardless two exclusive terms (in yellow circle; more discussion in Table 1), the gene hit percentages and enrichment p values of top enriched terms between the two lists show very strong correlation in overall. It indicates that IFN-α and IL-27 may share, in general, many common mechanisms in MDM treatment experiments.

mind that the statistical values listed in the Chart Reports are influenced by biology within the gene lists, as well as by other factors, such as the size of the gene lists (12). Thus, caution should be used when comparing those statistical values across gene lists. For IFN_Up_List and IL27_Up_List, a positive indication for implementing the comparison is that the sizes of the two lists are fairly consistent. The top enriched terms obtained from the DAVID Chart Reports largely agree with each other, which suggests that the two gene lists not only share common mechanisms for particular terms as discussed in comparison NO. 2 and 3, but also share many other mechanisms in a global scope (Table 2 and Fig. 9). In contrast, a similar strong annotation agreement is not observed when comparing annotation results of two unregulated gene lists obtained from IL-27 and IFN-α treated CD4 T cells (data not shown). All together, a conclusion could be made that IL-27 may function through similar mechanisms to that of IFN-α in MDM, but not in CD4 T cells.

References 1. Pflanz, S., Timans, J.C., Cheung, J., Rosales, R., Kanzler, H., Gilbert, J., Hibbert, L., Churakova, T., Travis, M., Vaisberg, E., Blumenschein, W.M , Mattson, J.D., Wagner, J. L., To, W., Zurawski, S., McClanahan, T.K., Gorman, D.M., Bazan J.F., de Waal Malefyt, R., Rennick, D., Kastelein, R.A. (2002) IL-27,

a heterodimeric cytokine composed of EBI3 and p28 protein, induces proliferation of naive CD4 (+) T cells. Immunity 16: 779–790. 2. Hunter, C.A. (2005) New IL-12-family members: IL-23 and IL-27, cytokines with divergent functions. Nat. Rev. Immunol. 5: 521–531. 3. Foli, A., Saville, M.W., Baseler, M.W., Yarchoan, R. (1995) Effects of the Th1 and Th2 stimulatory cytokines interleukin-12 and interleukin-4

3

4.

5.

6.

7.

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on human immunodeficiency virus replication. Blood 85: 2114–2123. Fakruddin, J. M., Lempicki, R.A., Gorelick, R.J., Yang, J., Adelsberger, J.W., GarciaPineres, A.J., Pinto, L.A., Lane, H.C., Imamichi, T. (2007) Noninfectious papilloma virus-like particles inhibit HIV-1 replication: implications for immune control of HIV-1 infection by IL-27. Blood 109: 1841–1849. Imamichi, T., Yang, J., Huang, D.W., Brann, T.W., Fullmer, B.A., Adelsberger, J.W., Lempicki, R.A., Baseler, M.W., Lane, H.C. (2008) IL-27, a novel anti-HIV cytokine, activates multiple interferon-inducible genes in macrophages. AIDS 22: 39–45. Langer, J.A., Cutrone, E.C., Kotenko, S. (2004) The class II cytokine receptor (CRF2) family: overview and patterns of receptor-ligand interactions. Cytokine Growth Factor Rev. 15: 33–48. Pestka, S., Langer, J.A., Zoon, K.C., Samuel, C.E. (1987) Interferons and their actions. Annu. Rev. Biochem. 56: 727–777. Galligan, C.L., Murooka, T.T., Rahbar, R., Baig, E., Majchrzak-Kita, B., Fish, E.N. (2006) Interferons and viruses: signaling for supremacy. Immunol. Res. 35: 27–40.

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9. Samuel, C.E. (2001) Antiviral actions of interferons. Clin. Microbiol. Rev. 14: 778–809. 10. Dennis, G. Jr., Sherman, B.T., Hosack, D.A., Yang, J., Gao, W., Lane, H.C., Lempicki, R.A. (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4: R60. 11. Huang, D.W., Sherman, B.T., Tan, Q., Kir, J., Liu, D., Bryant, D., Guo, Y., Stephens, R., Baseler, M.W., Lane, H.C., Lempicki, R.A. (2007) DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35: W169–W175. 12. Huang, D.W., Sherman, BT., Lempicki, R.A. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37: 1–13. 13. Lim, W.K., Wang, K., Lefebvre, C., Califano, A. (2007) Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks. Bioinformatics 23: i282–i288. 14. Harr, B., Schlötterer, C. (2006) Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons. Nucleic Acids Res. 34: e8.

Chapter 4 Quantitative Analysis of miRNA Expression in Epithelial Cells and Tissues Markus Bitzer, Wenjun Ju, Xiaohong Jing, and Jiri Zavadil Abstract Reliable detection of the microRNA (miRNA) precursor and mature form expression levels is a fundamental starting block for more focused studies of the biogenesis and functional roles of these important posttranscriptional modulators of gene expression. Building on our expertise with miRNA expression programs downstream of TGF-b/Smad signaling in homeostasis as well as in pathological conditions associated with epithelial tissues, we present a series of detailed and broadly applicable protocols for expression profiling of the mature miRNA forms using quantitative real-time PCR TaqMan, both single assays or low-density arrays. We next highlight key steps necessary for the detection of primary precursors of miRNAs (pri-miRNAs) to address the initial steps of miRNA biogenesis, and we finally review some most widely used computational algorithms for miRNA target prediction used to complement experimental identification of the target mRNAs and proteins. Key words: microRNA, miRNA, miRNA expression, Pri-miRNA expression, Quantitative real-time PCR, TaqMan Array MicroRNA Card, TaqMan MicroRNA Assay, Primer3, SYBR Green PCR, miRNA target prediction, TGF-b, Epithelial cell, Epithelial tissue

Abbreviations FFPE LCM miRNA pri-miRNA qrt-PCR RNAi RT TLDA

Formalin-fixed, paraffin-embedded Laser capture microdissection microRNA Primary precursor of miRNA Quantitative real-time polymerase chain reaction RNA interference Reverse transcription/transcriptase TaqMan low-density array(s)

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1. Introduction Noncoding regulatory miRNAs are important pleiotropic posttranscriptional modulators of gene expression and function that act through the RNA interference (RNAi) machinery to inhibit protein translation initiation and to negatively regulate the mRNA stability (1). By computational predictions and functional experiments, miRNAs have been shown to directly regulate at least one half of all human genes (2, 3). As important modulators of gene expression programs (4), they play important roles in a variety of fundamental cellular processes such as the control of cell proliferation, differentiation, and cell death, in the contexts of development and homeostasis (1, 5). Deregulated miRNAs are also directly involved in the development and progression of a variety of human diseases including cancers. Recent miRNA-related research thus focuses not only on understanding of their physiological and pathological roles, but also on their potential to be used as therapeutical targets or agents. The pleiotropic cytokine TGF-b is a major player in development, in adult tissue homeostasis, and has been implicated in numerous human diseases. In epithelial cells, TGF-b elicits downstream transcriptional responses including the expression of miRNAs that in turn direct target genes with roles in epithelial homeostasis and can also become deregulated in disease states such as fibrogenesis and carcinogenesis (6, 7). It is thus important to study the miRNA expression patterns to understand their contribution to the control of physiological states or to aberrant disease programs. Using our expertise derived from long-term studies of the mammalian epithelial cell and tissue systems with active TGF-b signaling component, we present a methodological overview of expression profiling of mature miRNA forms of 20–22 nt using quantitative single TaqMan assays or TaqMan low-density arrays (TLDA), and we also focus on very important detection of primary precursors of miRNAs (pri-miRNAs) by quantitative real-time PCR. The latter methodological approach provides insight into the first steps of transcriptional control of miRNA expression and biogenesis. Finally, we also review selected computational tools for miRNA target mRNA prediction. Importantly, all techniques overviewed here can be also applied broadly to the examination of miRNA roles in any mammalian (mainly human and rodent) tissue of interest.

2. Materials 2.1. Equipment

1. FlashPAGE™ Fractionator. 2. Centrifuge with a swing out rotor, Sorvall or Heraeus.

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3. Four centrifuge buckets and ABI card holders (specific to the Sorvall or Heraeus centrifuge). 4. PCR cycler accommodating 96-well plates, 0.2 mL tubes or strips. 5. Applied Biosystems 7900HT Fast Real-Time PCR System with the 96-well, 384-well, and Micro Fluidic Card Upgrade. 6. TaqMan® Array Micro Fluidic Card Sealer. 2.2. Reagents

1. Applied Biosystems: TaqMan MicroRNA Reverse Transcription Kit, 200 reactions. Multiscribe RT. dNTPs with dTTP (100 mM) MultiScribe Reverse Transcriptase (50 U/mL). 10 ´ RT Buffer. MgCl2 (25 mM). RNase Inhibitor (20 U/mL). Megaplex RT Primers (10 ´). Megaplex™ PreAmp Primers. TaqMan® PreAmp Master Mix. TaqMan Universal PCR Master Mix No AmpErase® UNG. TaqMan (TLDA) microRNA arrays, human A v2 B v3, or rodent A and B v2. 2. Nuclease-free water. 3. Oligonucleotide primers (see Subheading 4.1). 4. 96- or 384-Well plates and optical cover (Applied Biosystems). 5. SuperScript First-Strand Synthesis System for RT-PCR (Invitrogen). 6. Power SYBR Green PCR master mix (Applied Biosystems).

2.3. Software

7900 SDS Software v2.1 or later. Note: SDS Software v2.1 through v2.2.2 includes the DDCT Study program. SDS Software v2.3 includes the RQ Manager program. Both programs are provided for relative quantification analysis. Version 2.4 was recently released but has not been tested by our groups at the preparation of this chapter.

3. Methods 3.1. Isolation of Total RNA for miRNA Analysis

The individual and Megaplex RT TaqMan miRNA assays are designed to work with total RNA isolated by any method which retains the small RNA contents (1 mg) are used. 8. Store the first-strand cDNA at −20°C until use for real-time PCR. 4.3. Real-Time PCR

1. Normalize the primer concentrations and mix gene-specific forward and reverse primer pair. Each primer (forward or reverse) concentration in the mixture is 2.5 pmol/mL (2.5 mM). 2. The total volume of the real-time PCR mixture can be modified depending on the plate (96- or 384-well plate). Minimum volume is preferred due to economic considerations. In our hands, minimal volume of 20 mL for 96-well plate and 10 mL for 384-well plate yield reliable results. Larger total volumes can be considered to increase robustness (evaporation or pipetting contributes relative more imprecision in small volumes than in lager volumes). For 10 mL: 5 mL SYBR Green Mix (2×). 2 mL diluted cDNA (cDNA can be diluted 20× to 40 using DNase- and RNase-free H2O). 2.5 mL primer pair mix (2.5 pmol/mL each primer). 0.5 mL H2O. (Double each volume for 20 mL reaction volume) 3. Standard PCR program includes: Hold 50°C

2 min

Hold 95°C

10 min

40 Cycles of: 95°C

15 s

60°C

60 s

Followed by: Hold 72°C

10 min

4. After PCR is finished, remove the tubes from the machine. The PCR specificity is examined by either 3% agarose gel using 5 mL from each reaction or by dissociation curve analysis.

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5. Analyze the real-time PCR result with the SDS 7900 software (see Subheading 3.3.7.) or in Excel spreadsheet using the using the 2−DDCt formula (8). Always inspect the amplification traces to see if there is any bimodal dissociation curve or abnormal amplification plot.

5. Algorithms for Prediction of miRNA Target Genes

As the function of miRNAs is determined by the regulatory effects on the function of target genes, many different computational algorithms have been generated that predict target genes based on different parameters, and essentially all include sequence homology to the “seed sequence” (usually base 2–8 at the 5¢ end) as one of their parameters. Only a very limited number experiments have been published so far that attempt to determine the accuracy of these predictions. These are largely based on unbiased transcriptomic and proteomic methods of changes in protein expression after manipulation of individual miRNAs in cell culture systems (10, 11). More recently, experimental approaches using sequencing of miRNAs and mRNA fragments co-immunoprecipitating with Argonaut proteins of the RISC complex have been used. But none of these methods identifies direct miRNA targets independent of certain sequence homologies. Of those algorithms with different provenance yet comparable performance, a relatively stringent and regularly updated algorithm is provided by the TargetScan tool (2) (http://www.targetscan.org) that also ranks the targets for each miRNA by a context score, considering the sequence contents in the vicinity of the target site. Similarly performing algorithm is PicTar (http://pictar. mdc-berlin.de/) (12). Experimental data have validated the ranking order of the scores as a measure of prediction reliability (11). A less stringent prediction program is miRanda (13, 14) that provides a much larger but likely less reliable number of predicted targets for each miRNA than TargetScan, similarly to MicroCosm Targets (formerly miRBase Targets) developed by the Enright group as part of the miRBase database (9, 15). RNA22 is an algorithm that allows the user to modify the settings used to search for target genes (16). It has to be emphasized that the overlap is limited between predictions from different algorithms, with an exception of PicTar and TargetScan (10, 11). Therefore, the obtained results have to be used with caution. Currently, probably no single computational system is available to predict target genes robustly and without errors and thereby infer the functions of upstream miRNA regulators. The concordance and the validation of performance between TargetScan and PicTar make them a widely used and recommended set of tools for target prediction, while experimental functional validations of the target predictions are usually required.

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References 1. Bartel, D.P. (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116: 281–297. 2. Friedman, R.C., Farh, K.K., Burge, C.B., Bartel, D.P. (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19: 92–105. 3. Lim, L.P., Lau, N.C., Garrett-Engele, P., Grimson, A., Schelter, J.M., Castle, J., Bartel, D.P., Linsley, P.S., Johnson, J.M. (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433: 769–773. 4. Bartel, D.P. (2009) MicroRNAs: target recognition and regulatory functions. Cell 136: 215–233. 5. Croce, C.M. (2009) Causes and consequences of microRNA dysregulation in cancer. Nat. Rev. Genet. 10: 704–714. 6. Massagué, J. (2008) TGFbeta in Cancer. Cell 134: 215–230. 7. Zavadil, J., Böttinger, E.P. (2005) TGF-beta and epithelial-to-mesenchymal transitions. Oncogene 24: 5764–5774. 8. Livak, K.J., Schmittgen, T.D. (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) Method. Methods 25: 402–408. 9. Griffiths-Jones, S., Grocock, R.J., van Dongen, S., Bateman, A., Enright, A.J. (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34: D140–144.

10. Baek, D., Villén, J., Shin, C., Camargo, F.D., Gygi, S.P., Bartel, D.P. (2008) The impact of microRNAs on protein output. Nature 455: 64–71. 11. Selbach, M., Schwanhäusser, B., Thierfelder, N., Fang, Z., Khanin, R., Rajewsky, N. (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455: 58–63. 12. Krek, A., Grün, D., Poy, M.N., Wolf, R., Rosenberg, L., Epstein, E.J., MacMenamin, P., da Piedade, I., Gunsalus, K.C., Stoffel, M., Rajewsky, N. (2005) Combinatorial microRNA target predictions. Nature Genet. 37: 495–500. 13. Betel, D., Wilson, M., Gabow, A., Marks, D.S., Sander, C. (2008) The microRNA.org resource: targets and expression. Nucleic Acids Res. 36: D149–153. 14. John, B., Enright, A.J., Aravin, A., Tuschl, T., Sander, C., Marks, D.S. (2004) Human MicroRNA targets. PLoS Biol. 2: e363. 15. Griffiths-Jones, S., Saini, H.K., van Dongen, S., Enright, A.J. (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res. 36: D154–158. 16. Miranda, K.C., Huynh, T., Tay, Y., Ang, Y.S., Tam, W.L., Thomson, A.M., Lim, B., Rigoutsos, I. (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126: 1203–1217.

Chapter 5 Evaluating Posttranscriptional Regulation of Cytokine Genes Bernd Rattenbacher and Paul R. Bohjanen Abstract A wide variety of cytokines are necessary for cell–cell communication in multicellular organisms, and cytokine dysregulation has detrimental effects, leading to disease states. Thus, it is a necessity that the expression of cytokines is tightly controlled. Regulation of cytokine gene expression takes place at different levels, including transcriptional and posttranscriptional levels. Ultimately, the steady-state levels of cytokine transcripts are determined by the equilibrium of transcription and degradation of this mRNA. Degradation rates of cytokine mRNAs can be measured in cells by blocking transcription with actinomycin D, harvesting RNA after different time points, and evaluating mRNA levels over time by northern blot. Cis-acting elements that mediate the rapid decay of numerous cytokine transcripts, including AU-rich elements (AREs), are found in the 3¢ untranslated region (UTR) of these transcripts. Putative regulatory cis-elements can be cloned into the 3¢ UTR of a reporter transcript in order to assess their function in regulating mRNA decay. Cis-elements, such as AREs, regulate cytokine mRNA decay by binding to trans-acting proteins, such as tristetraprolin or HuR. These RNA-binding proteins can be visualized using electromobility shift assays or UV crosslinking assays based on their binding to radioactively labeled RNA sequences. RNA-binding proteins that regulate cytokine mRNA decay can be purified using an RNA affinity method, using their target RNA sequence as the bait. In this chapter, we review the methods for measuring cytokine mRNA decay and methods for characterizing the cis-acting elements and trans-acting factors that regulate cytokine mRNA decay. Key words: mRNA decay, Actinomycin D chase, Northern blot, RNA–protein interaction, EMSA, UV crosslinking, One-step affinity purification, AU-rich element, Tristetraprolin, HuR

1. Introduction Cytokines regulate a variety of different events in the human body. They provide signals to cells, telling them when to divide, what proteins to produce, what other cytokines to secrete, and how they should differentiate. Because of the importance of cytokines in maintaining homeostasis and the dangers involved when cytokines

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become dysregulated, it is crucial that the expression of cytokines is tightly regulated at multiple levels, including transcriptional and posttranscriptional levels. Steady-state mRNA levels are determined by the balance between transcription and mRNA degradation. The biochemical mechanisms that regulate the degradation of cytokine transcripts is not well understood, although there is evidence that failure to degrade pro-inflammatory cytokine transcripts such as TNFα, INFγ, IL2, IL6, IL8, or IL10 transcripts leads to chronic inflammation (1–5). The degradation of cytokine transcripts is regulated through cis-elements in their 3¢ untranslated regions (3¢ UTRs) and trans-acting factors that bind to them. One important and well-known destabilizing element is the AU-rich element (ARE), which is found in the 3¢ UTR of many unstable cytokine mRNAs (6–9). AREs function by binding to trans-acting proteins that regulate the stability of the transcript. Some ARE-binding proteins, such as tristetraprolin and butyrate response factor 1, are responsible for rapid degradation of ARE-containing transcripts (10–12), whereas other proteins, such as HuR, have the potential to stabilize the same message (9, 13–18). Whether an AREcontaining cytokine transcript undergoes degradation or stabilization is dependent upon the activation status of the cells. Activation of a cell, for example, will lead to an inactivation of TTP or BRF1 (3, 19) and the recruitment of HuR to the ARE, which will result in increased stability of the ARE-containing message (9, 13–18). In this way, a cell can respond rapidly to changes in the outside environment and produce the required cytokine. This chapter introduces the techniques used to measure degradation of cytokine transcripts and to characterize the RNA-binding proteins involved in their regulation. We have used the techniques described below to characterize the role of mRNA decay in regulating the expression of cytokine genes and other early response genes after activation of primary human T cells (7, 8, 20, 21), and to identify and characterize the function of AREs and ARE-binding proteins in the regulation of cytokine mRNA decay (1, 10, 13, 14, 17). Although we have used the techniques to study posttranscriptional cytokine gene regulation in human T cells, the techniques can be broadly applied to a variety of cell types and experimental systems.

2. Materials 2.1. Measuring mRNA Decay Rates 2.1.1. Isolation of Primary Human T Cells

1. 40 mL Buffy coat white blood cell packs of 109 cells or more (American Red Cross). Buffy coat cells must be used immediately. 2. 2 mL RosetteSep human T cell enrichment cocktail (StemCell Technologies, Inc.).

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3. Dulbecco’s Phosphate-buffered saline (PBS, Gibco) supplemented with 2% (v/v) FBS. Store at 4°C. 4. Ficoll-Paque™ Plus (GE-Healthcare Amersham). 5. Whole blood erythrocyte lysing kit (R&D Systems). 6. RPMI medium 1640 (Gibco) supplemented with 10% (v/v) FBS, 1% (v/v) penicillin/streptomycin (Gibco), and 1% (v/v) L-glutamine (Gibco). 2.1.2. T Cell Stimulation and Measurement of mRNA Decay After Addition of Actinomycin D

1. Tissue culture dish with 20 mm grid 150 × 25 (Falcon) for T cell stimulation. 2. Anti-hCD3ε Antibody (R&D Systems) and anti-hCD28 Antibody (R&D Systems) resuspended in 500 μL sterile water. Aliquot 50 μL and freeze at −20°C. Make coating solution fresh and use immediately; add 5 μg anti-hCD3ε antibody and 5 μg anti-hCD28-antibody into 10 mL PBS per plate to be coated. Antibody solutions will be stable for several months at −20°C. 3. Actinomycin D-mannitol (Sigma) is resuspended in 1 mL sterile PBS. Actinomycin D should not be used after 1 month in solution. Store at −20°C. 4. 70% (v/v) Ethanol. 5. 2-Mercaptoethanol. 6. Qiashredder (Qiagen). 7. RNeasy mini kit (Qiagen).

2.1.3. b-Globin Reporter Based Assays to Measure mRNA Decay Half-Lives

1. HeLa Tet-Off cells (Clontech). 2. OptiMEM (Gibco). 3. Lipofectamine 2000 Reagent (Invitrogen). 4. BBB-plasmid (9). 5. pTracer™-EF C vector (Invitrogen). 6. TrypLE™ Express (Gibco). 7. Minimum essential medium alpha (MEMα, Gibco) supplemented with 10% (v/v) Tet system approved FBS (Clontech), 1% (v/v) penicillin/streptomycin (Gibco), and 1% (v/v) L-glutamine (Gibco). 8. Dissolve doxycycline (Clontech) to a concentration of 300 μg/μL in DMSO. This will result in a 1,000× stock solution. Store at −20°C. 9. 2-Mercaptoethanol. 10. Qiashredder (Qiagen). 11. RNeasy mini kit (Qiagen).

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2.1.4. Northern Blotting

1. NorthernMax®-Gly Sample Loading Dye (Ambion). 2. NorthernMax®-Gly 10× Gel Prep/Running Buffer (Ambion). 3. BrightStar®-Plus (Ambion).

Positively

Charged

Nylon

Membrane

4. NorthernMax® One-Hour Transfer Buffer (Ambion). 5. ULTRAhyb® Ultrasensitive Hybridization Buffer (Ambion). 6. NorthernMax® Low Stringency Wash Buffer (Ambion). 7. NorthernMax® High Stringency Wash Buffer (Ambion). 8. 3 mm Chr Chromatography paper (Whatman). 2.1.5. Probe Preparation for Northern Blotting

1. Taq DNA-Polymerase set (Qiagen). 2. The low dATP dNTP Mix contains 10 mM dGTP, 10 mM dTTP, 10 mM dCTP, and 2 mM dATP final concentrations in RNase-free water (Roche). 3. Primers for β-globin probe: β-globin+ (5¢-GTC TAC CCA TGG ACC CAG AGG-3¢), β-globin− (5¢-AGG ATC CAC GTG CAG C-3¢). 4. Primers for GFP probe: GFP+ (5¢-CCA TGG CTA GCA AAG GAG-3¢), GFP− (5¢-CCA TGT GTA ATC CCA GCA GCA G-3¢). 5. α32P-dATP (MP Biochemicals). 6. Microspin™ G-25 Columns (GE-Healthcare).

2.2. Characterization of RNA–Protein Interactions 2.2.1. Preparation of Cytoplasmic Extracts

1. NP40 Lysis buffer: 10 mM HEPES/KOH, pH 7.9, 40 mM KCl, 3 mM MgCl2, 5% (v/v) glycerol, 0.2% (v/v) NP40. NP40 Lysis buffer can be stored at 4°C without DTT and inhibitors. Before cell lysis add 1 mM DTT, 2 ng/mL leupeptin, 2 ng/mL pepstatin, 8 ng/mL aprotinin, 0.1 mg/mL phenylmethylsulfonyl fluoride (PMSF). 2. Biorad protein assay reagent (Biorad).

2.2.2. Electrophoretic Mobility Shift Assay

1. RBB buffer: 25 mM HEPES/KOH, pH 7.9, 40 mM KCl, 3 mM MgCl2, 5% (v/v) glycerol, 1 mM DTT, can be stored at 4°C or aliquoted and frozen at −20°C. 2. Heparan sulfate (Sigma) is reconstituted in 100 μL RNase-free water to obtain a 50-mg/mL solution and should be aliquoted and stored at −20°C. 3. 5× TB-buffer: 54 g Tris–base and 27.5 g boric acid in 1 L of water. 1× TB-buffer is used for gel preparation and running buffer. Store at room temperature. 4. 30% (w/v) acrylamide/bis solution, 19:1 (Bio-Rad).

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5. 6× DNA loading dye: 30% (v/v) glycerol and 0.25% (w/v) bromophenol blue in RNase-free water. Store at room temperature. 2.2.3. End-Labeling of Synthetic RNA Oligomer Probes

1. Custom-synthesized RNA (Dharmacon) is dissolved as a 100 μM solution and is stored at −20°C. 2. T4 Polynucleotide Kinase (Invitrogen) which includes 5× forward reaction buffer. 3. γ32P-ATP (MP Biochemicals). 4. Phenol/chloroform/isoamyl alcohol (Invitrogen). 5. Prepare a 3-M sodium acetate solution in RNase-free water. Store at room temperature. 6. Microspin™ G-25 Columns (GE-Healthcare).

2.2.4. UV Crosslinking Assays

1. Heparan sulfate (Sigma) is reconstituted in 100 μL RNase-free water to obtain a 50-mg/mL solution and should be aliquoted and stored at −20°C. 2. RNase T1 (Ambion). 3. RBB buffer: 25 mM HEPES/KOH pH 7.9, 40 mM KCl, 3 mM MgCl2, 5% (v/v) glycerol, 1 mM DTT, can be stored at 4°C or aliquoted and frozen at −20°C. 4. 5× Sample buffer: 25 mM Tris–HCl pH 6.8, 500 mM dithiothreithol (DTT), 0.1% (w/v) bromophenol blue, 50% (v/v) glycerol. Aliquot and store at −20°C.

2.2.5. Probes for UV Crosslinking Assays

1. Promega kit (Promega). 2. rNTP-mix: combine 10 mM rATP, 10 mM rCTP, and 10 mM rGTP final concentration in RNase-free water. Store at −20°C. 3. α32P-UTP (MP Biochemicals). 4. RNA stop solution mix: 10 mL Tris/EDTA (10 mM/1 mM), pH 8.0 with 10 μL 0.5 M EDTA, pH 8.0, and 100 μL 10% (w/v) SDS. Store at room temperature. 5. Phenol/chloroform/isoamyl alcohol (Invitrogen). 6. Prepare a 3-M sodium acetate solution in RNase-free water. Store at room temperature.

2.2.6. Isolation of RNA-Binding Proteins by One-Step Affinity Chromatography

1. Custom 5¢ biotinylated RNA and unbiotinylated competitor RNA (synthesized by Dharmacon). 2. Dissolve heparan (Sigma) as 100 mg/mL solution in RNasefree water. Aliquot and store at −20°C. 3. M-280 Streptavidin beads (Dynal Biotech).

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4. RBB buffer: 25 mM HEPES/KOH, pH 7.9, 40 mM KCl, 3 mM MgCl2, 5% (v/v) glycerol, 1 mM DTT, can be stored at 4°C or aliquoted and frozen at −20°C.

3. Methods This methods section is divided into two parts. The first part describes methods to measure decay rates of cytokine mRNAs, and the second part details methods to characterize RNA-binding proteins that bind to specific decay elements, such as the ARE. The methods described below all involve RNA in one way or another. Since RNA is prone to degradation by RNases, which can be found in dust and sweat, it is crucial that gloves are worn at all times and that the workspace is clean. To clean the workbench, we use RNA-ZAP (Ambion). Another source for RNase contamination is water used to make solutions and buffers; therefore, RNasefree water should be used for preparing every buffer and reaction. RNase-free water can be prepared relatively inexpensively using DEPC, which will destroy enzymes (reaction with histidines), and its preparation is described (see Note 1). DEPC needs to be inactivated by thoroughly autoclaving this solution. Failure to inactivate DEPC will result in nonreproducible results. 3.1. Measuring mRNA Decay Rates

The half-lives of different transcripts expressed in T cells vary greatly (8). Also, the activation status of T cells can greatly influence the rate of mRNA decay (8). This section focuses on the isolation of T cells, on how to stimulate them and how to prepare total RNA in order to measure transcript half-lives of the endogenous cytokine mRNAs. We describe methods for measuring cytokine mRNA decay rates by northern blot following transcriptional arrest with the RNA polymerase II inhibitor, actinomycin D. After it is ascertained that a certain transcript is regulated at the level of mRNA decay, the cis-elements that are responsible for this regulation can be identified using a reporter based assay. The most widely used reporter in the field of mRNA degradation is the β-globin based reporter plasmid, BBB, which produces the β-globin transcript under control of a tetracycline-regulated promoter (9). The putative degradation sequence can be cloned into the BBB plasmid via a unique BglII site in the 3¢ UTR. This plasmid can then be transfected into HeLa cells expressing a tet-repressor, and β-globin mRNA decay can be measured by northern blot after turning off transcription by the addition of doxycycline. The advantage of measuring mRNA decay in this system compared to the actinomycin D system is that doxycycline has minimal effects on the cells, whereas actinomycin D is extremely toxic. Another advantage is, that the function of each putative decay element can

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be singled out and studied individually. The disadvantages of this system are that reporter transcripts are studied, rather than the endogenous native transcripts, and the experiments cannot be performed in primary cells. 3.1.1. Isolation of Primary Human T Cells

1. Separate 40 mL of buffy coat into two 50 mL tubes equally (see Note 2). 2. Add 1 mL of RosetteSep Human T Cell Enrichment Cocktail (StemCell Technologies Inc) to each tube. 3. Mix by gently shaking the tubes. 4. Incubate the reaction for 20 min at room temperature to allow the antibodies to agglutinate everything except CD4-positive cells. 5. Bring each sample up to 35 mL with PBS supplemented with 2% (v/v) FBS (PBS/FBS) after the incubation is finished. 6. Prepare two new tubes containing 15 mL Ficoll-Paque™ Plus. 7. Layer the blood carefully over the Ficoll. Avoid mixing of the layers at all times (see Note 3). 8. Spin the samples at 1,700 × g for 20 min with the brake turned off. 9. Remove the top layer (serum) carefully to avoid disturbing the white, cloudy interphase which contains CD4 positive T cells. 10. Transfer the cells in the interphase to two new 50 mL tubes. 11. Add PBS/FBS solution up to 50 mL and centrifuge at 230 × g for 8 min. 12. Remove the supernatant carefully and as completely as possible without disturbing the cell pellet. 13. Remaining red blood cells are removed with the whole blood erythrocyte lysing kit (R&D Systems). Resuspend both pellets in 5 mL 1× lysis buffer and pool them in a 50 mL tube and incubate for 10 min at room temperature to allow erythrocyte lysis. 14. Add 40 mL of 1× wash buffer and spin tube at 230 × g for 8 min. 15. Remove the supernatant; the pellet should appear white now. 16. Resuspend cells in 50 mL RPMI medium 1640 supplemented (Gibco, 10% FBS, 1% penicillin/streptomycin (Gibco), 1% L-glutamine (Gibco)). 17. Count cells using a hemocytometer (use a dilution of 1:100 cell suspension to PBS). 18. Cells are incubated overnight in an incubator at 37°C, 5% (v/v) CO2.

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3.1.2. T Cell Stimulation and Measurement of mRNA Decay after Addition of Actinomycin D

1. Coat four 15 cm petri dishes overnight at 4°C with coating solution (10 mL PBS containing 5 μg anti-hCD3ε antibody and 5 μg anti-hCD28 antibody) and four 15 cm dishes with PBS only. Note: Use one plate for each time point of the actinomycin D chase experiment (see Note 4). 2. Remove coating solution or PBS. 3. Wash plates with 10 mL PBS. Remove PBS. 4. Equilibrate plates with 5 mL RPMI 1640 medium with supplements. 5. Remove medium. 6. Add 3 × 107 cells in 15 mL RPMI-medium with supplements to each plate. 7. Incubate cells for 3 h at 37°C and 5% (v/v) CO2. 8. Add actinomycin D at a final concentration of 5 μg/mL to each plate (see Note 5). 9. Incubate the plates at 37°C and 5% (v/v) CO2 for 0, 1, 2, or 3 h. 10. Remove cells by scraping. Note: Do not remove medium before scraping because the medium may contain floating cells. 11. Transfer cells and medium to a 50 mL tube. 12. Collect cells by spinning at 340 × g for 5 min. 13. Remove medium as completely as possible without disturbing the pellet. 14. Extract total RNA with the RNeasy Mini kit following manufacturer’s recommendations (see Note 6). 15. Elute total RNA in 50 μL RNase-free water. 16. Estimate RNA concentration using a spectrophotometer. The RNA concentration should be between 0.5 and 3 μg/μL (see Note 7). 17. Analyze the stability of the cytokine mRNA by northern blot; probes for endogenous RNA can be prepared (see Note 8).

3.1.3. b-Globin Reporter Based Assays to Measure mRNA Decay Half-Lives

1. HeLa Tet-Off cells are propagated in HeLa Tet-Off medium (see Note 9). 2. Seed HeLa Tet-Off cells into 15 cm dishes with HeLa Tet-Off medium; the cells should be about 90% confluent the next day. 3. Prepare a Lipofectamine master mix by adding 4 mL of OptiMEM and 100 μL of Lipofectamine 2000 per plate to be transfected. Use 5% more of each reagent to allow for pipetting error. Incubate the Lipofectamine master mix for 5 min at room temperature.

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4. Meanwhile, prepare the DNA master mixes: mix 15 μg of BBB decay reporter plasmid and 8 μg pTracer plasmid in 4 mL OptiMEM. Vortex. 5. Mix 4 mL of the Lipofectamine master mix slowly using stirring motions into 4 mL of the DNA master mix and incubate at room temperature for 20 min. 6. Remove the medium from the HeLa Tet-Off cells and replace with 8 mL of OptiMEM. 7. Add the transfection mixes from step 4 to each plate. 8. Incubate the plates with the transfection mixes for 4–5 h at 37°C, 5% (v/v) CO2 (see Note 10). 9. Remove the transfection mix from the cells and add 18 mL of HeLa Tet-Off medium to each plate. Let the cells recover overnight. 10. The next day, remove the medium completely, wash cells with PBS and remove cells from the plate with 2 mL TrypLE express. 11. Split the cells 1:4 on p10 plates in a total of 8 mL medium the next day. And incubate again over night at 37°C, 5% (v/v) CO2. 12. The next day, add doxycycline to a final concentration of 300 ng/mL (8 μL) to each plate. 13. Incubate the plates at 37°C and 5% (v/v) CO2 for 0, 1, 2, or 3 h. 14. Remove medium as completely as possible. 15. Extract total RNA with the RNeasy Mini kit following manufacturer’s recommendations. 16. Elute total RNA in 50 μL RNase-free water. 17. Estimate RNA concentration using a spectrophotometer. The RNA concentration should be between 1 and 3 μg/μL. 3.1.4. Northern Blotting

1. Dilute 10 μg of total RNA into 10 μL of RNase-free water. 2. Mix 10 μL of NorthernMax® Gly Sample Loading Dye with the sample. 3. Spin sample for 30 s to collect all liquid at the bottom. 4. Denature the samples at 50°C for 30–60 min in a heat block. 5. Meanwhile, melt 1 g of agarose in 90 mL of RNase-free water; be sure that the agarose melted completely. 6. After the agarose has cooled down to about 60°C add 10 mL of 10× NorthernMax®Gly 10× Gel Prep/Running Buffer and swirl.

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7. Pour the gel solution into a level tray to a thickness of about 6 mm and let the gel solidify for about 30 min; the comb should be about 1 cm from the end. 8. Prerun the gel at 85 V for about 1 min in 1× NorthernMax®Gly Gel Prep/Running Buffer prior to loading the samples. 9. Spin samples briefly to collect all liquid at the bottom prior to loading. 10. Run the gel at 5 V/cm of distance between anode and cathode until the blue dye front reaches the bottom of the gel. 11. Take a picture of the gel under UV-light (see Note 11). 12. Remove excessive gel by cutting the sides and top below the slots. 13. Assemble blotting apparatus: place the gel upside down on a mirror. 14. Prewet the membrane in NorthernMax® One-Hour Transfer Buffer and place on top of the gel and roll out trapped air bubbles. 15. Construct a filter paper stack and revert the gel (membrane down) onto the stack. 16. Prewet a long filter paper and put it one end on top of the gel and the other end into a chamber containing NorthernMax® One-Hour Transfer Buffer; remove air-bubbles. 17. Blot gel for 15–20 min per mm gel thickness. 18. Remove the membrane from the blotting apparatus and rinse with 1× NorthernMax®Gly Gel Prep/Running Buffer to remove salt. 19. UV crosslink RNA to the membrane at 500 mJ/cm2 in a UV stratalinker. 20. Wrap the membrane in plastic foil. 21. View the membrane under UV light and mark the prominent 18S and 28S rRNA bands. 22. The membrane can be frozen at this point at −20°C. 23. Preheat the ULTRAhyb® Ultrasensitive Hybridization Buffer at 42°C. 24. Place blot without the plastic wrap facing inward into a glass hybridization tube and add 10 mL of preheated ULTRAhyb buffer. 25. Prehybridize blot for at least 30 min prior to adding the probe. 26. Add the radioactive probe (preparation see Subheading 3.1.4) to the blot and hybridize the membrane at 42°C for 3–4 h.

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27. Discard the radioactive hybridization solution into the liquid radioactive waste. 28. Wash blot twice with 15 mL NorthernMax® Low Stringency Wash Buffer for 10 min at 42°C. (If precipitate has formed in the buffer prewarm at 37°C). Discard all washes into radioactive waste container. 29. Perform a high stringency wash with 15 mL NorthernMax® High Stringency Wash Buffer at 50°C for 15 min. Discard wash into radioactive waste container. 30. Wrap blot into clear plastic wrap and expose to a phosphorimager plate for at least 4 h. 31. Visualize Northern blot by scanning the plate in a phosphorimager. 3.1.5. Probe Preparation for Northern Blotting

Here, we describe the preparation of the GFP and β-globin probes. Other probes for detection of endogenous mRNAs can be prepared (see Note 8). 1. Mix 20–40 ng of β-globin or GFP PCR template with 5 μL 10× PCR-buffer, 1 μL 10 mM low dATP dNTP-mix, 25 pmol of primer β-globin+ or GFP+, 25 pmol of primer β-globin- or GFP-, and 5 μL α32P-dATP in a final volume of 50 μL water in a PCR-tube. 2. Add 0.3 μL of Taq-Polymerase and transfer the tubes into a PCR machine. 3. Amplify the probe with the following cycles: 94°C 5 min, (94°C 30 s, 48°C 30 s, 72°C 1 min) for 37 cycles, 72°C 10 min, 4°C indefinite (see Note 12). 4. The probe is purified afterward over a Microspin™ G-25 column following the manufacturer’s instruction. 5. Measure specific activity of the probe in scintillation counter. (Add 1 μL of probe to 5 mL of scintillation fluid, mix and count). 6. Denature probe at 95°C for 5 min and immediately place on ice. 7. Add 1 × 106 cpm per mL hybridization buffer per probe into the hybridization solution of the northern blot.

3.2. Characterization of RNA–Protein Interactions

As mRNA half-lives of cytokine transcripts vary with the activation status of a T cell, so does the composition of the proteins bound to mRNA decay elements. After stimulation of primary human T cells, the cells can be lysed to generated cytoplasmic extracts. We perform gentle lysis in a NP40-buffer which is compatible with electrophoretic mobility shift assay (EMSA), UV crosslinking assays, and the one-step affinity purification described below.

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EMSA is an easy way to see protein–RNA interactions in cytoplasmic lysates and is a good tool to identify a RNA-binding protein associated with a cis-element of interest. If candidate RNAbinding proteins are known, this assay can be used to confirm binding by simply adding a specific antibody against the protein of interest to the reaction to determine if the antibody supershifts the RNA-binding complex. If the proteins that associate with the cytokine mRNA are not yet known, UV-crosslinking experiments are able to give some information about the sizes of bound proteins and thus might lead to the identification by comparing to the sizes of known candidate RNA-binding proteins. Another method to identify proteins bound to the RNA decay elements is RNA affinity purification followed by mass spectrometry. We describe a one-step affinity purification in which biotinylated bait RNA is incubated with the T cell lysate to allow association of the RNAbinding protein to the RNA. The protein–RNA complex is captured by streptavidin beads, washed several times and eluted. The proteins in this complex can be analyzed by mass spectrometry. 3.2.1. Preparation of Cytoplasmic Extracts

1. T cells are purified and stimulated as described in Subheading 3.1.1. 2. Transfer the cells into 50 mL conical tubes and collect by centrifugation at 230 × g for 8 min. 3. Pool cells of unstimulated or stimulated cells into one 50 mL tube each (Never mix T cells from different buffy coats). 4. Bring the volume in each tube up to 50 mL with PBS. 5. Spin cells at 230 × g for 8 min and remove the supernatant. 6. Repeat steps 6 and 7 once more. Remove the supernatant as completely as possible without disturbing the pellet. 7. Estimate the pellet volume. 8. Resuspend cell pellets in 3 volumes of NP40 lysis buffer by pipetting up and down a few times and transfer suspension to a 1.5 mL precooled centrifugation tube. 9. Incubate on ice for 10 min. 10. Homogenize lysates with 50 strokes in a precooled dounce homogenizer. 11. Remove nuclei and unbroken cells by centrifugation at 850 × g for 5 min at 4°C. 12. Transfer supernatant to a new precooled 1.5 mL centrifugation tube. The lysates of the different buffy coats can now be pooled. 13. Measure protein concentration at OD595 using the Biorad protein assay, following manufacturer’s recommendations.

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14. Snap freeze the protein samples in liquid nitrogen and store at −80°C. 3.2.2. Electrophoretic Mobility Shift Assay

1. To prepare cytoplasmic extract from stimulated and unstimulated T cells follow the instructions of Subheading 3.2.1. 2. Prepare probe following the instructions of Subheading 3.2.3. 3. Prepare a native 5% polyacrylamide gel in 1× TB buffer. 4. Mix on ice in a test tube 10 μg of cytoplasmic extract, 100 μg heparan sulfate, cold competitor probe at a final concentration of 10–100 molar excess over probe, and 20,000–100,000 cpm of specific probe. For supershift assays add 2 μL of specific or control antibody. Bring reaction to a total of 20 μL with buffer RBB. Probes and competitor RNA can be diluted in RBB buffer. 5. Incubate the reaction on ice for 30 min. 6. Add DNA loading dye and immediately load the entire sample on a 5% native polyacrylamide gel (see Note 13). 7. Run the gel in 1× TB buffer at 100 V until the blue dye is 1 cm from the end of the gel. Do not let the dye run out because the probe is running in the front. 8. After the gel is finished remove one of the glass plates and cover the gel with a Whatman filter paper. The gel will stick to it and can be easily removed from the glass plate. Cover the gel with plastic wrap. Do not wrap the plastic around the gel, otherwise it will not dry. 9. Dry the gel in the gel dryer at 80°C for 2 h. 10. Expose the dried gel to film for approximately 2–48 h depending on signal strength.

3.2.3. End-Labeling of Synthetic RNA Oligomer Probes

1. Resuspend deprotected RNA Oligos in RNase-free water at a concentration of 100 μM. Dilute RNA oligos to a working concentration of 1 μM with RNase-free water. 2. Heat oligos to 95°C for 3 min and put immediately on ice to linearize the oligos. Linearized oligos can be stored at −20°C. 3. Mix 23 μL RNase-free water, 8 μL 5× forward reaction buffer, 4 μL 1 μM linearized RNA-oligomer, 4 μL γ32P-ATP (4,500 Ci/ mmol), and 1 μL of T4 PNK on ice. 4. Incubate the mixture at 37°C for 30 min. 5. Purify sample over a Microspin™ G-25 column into a 1.5 mL screw cap tube. 6. Add RNase-free water to a final volume of 200 μL. 7. Extract protein and free nucleotides by adding 200 μL of phenol/chloroform/isoamyl alcohol (24:25:1, v/v/v) (see Note 14).

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8. Vortex for 30 s. 9. Spin sample at maximum speed for 5 min in a microfuge. 10. Transfer aqueous phase to a new tube. 11. Precipitate RNA by adding 1/10 volume of 3 M sodium acetate and 2.5 volumes of 100% ethanol followed by an incubation of 2 h at −20°C. 12. Pellet the radiolabeled RNA by centrifugation at 14,000 × g for 20 min. 13. Remove the supernatant and dry the pellet in a vacufuge for 3 min. 14. Resuspend the probe in 20 μL of RNase-free water. 15. Measure specific activity of the probe in scintillation counter. (Add 1 μL of probe to 5 mL of scintillation fluid, mix and count). 16. Calculate the specific activity of the probe (see Note 15). 3.2.4. UV Crosslinking Assays

1. To prepare cytoplasmic extract from stimulated and unstimulated T cells follow the instructions of Subheading 3.2.1. 2. Prepare probe following the instruction of Subheading 3.2.5. Dilute the probe to 50,000 cpm/μL with buffer RBB before adding to reaction. 3. Mix 100 μg heparan sulfate, 2 μL RNase T1, and 50,000 cpm of radiolabeled probe. Add RBB buffer to a final volume of exactly 24 μL including the amount of cytoplasmic extract. 4. Add 8–10 μL of cytoplasmic protein to each tube, mix the tubes, and then quick spin in a shielded microcentrifuge. 5. Incubate tubes at room temperature for 30 min behind a shield. 6. Place the tubes on ice with the caps open and make sure that water or ice does not get inside the tubes. Crosslink the protein to the RNA at 250 mJ/cm2 in a UV-Stratalinker. 7. Remove the samples from the stratalinker and add 24 μL of 2× protein loading buffer. 8. Boil the samples for 5 min at 95°C on a heat block. 9. Spin down the contents of the tube in a microfuge. 10. Subject samples to PAGE on a 12% SDS-gel; do not forget to load a protein size marker. Do not let the dye run out of the gel. 11. After the gel is finished open the glass plates and carefully cut of the lower part containing the blue dye. 12. Cover the gel with a Whatman filter paper. The gel will stick to it and can be easily removed from the glass plate. Cover the gel with plastic wrap. Do not wrap the plastic around the gel, otherwise it will not dry.

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13. Dry the gel in the gel dryer at 80°C for 2 h. 14. Expose the dried gel to film for approximately 2–48 h depending on signal strength. 3.2.5. Probes for UV Crosslinking Assays

1. Mix 6 μL 5× buffer, 3 μL 100 mM DTT, 6 μL rNTP-mix, 1.2 μL rUTP (100 mM), 2 pg template, 1 μL RNAsin, 10 μL α32P-UTP, 1 μL T7-polymerase in a total of 30 μL RNase-free water in a PCR-tube. 2. Incubate reaction in a PCR machine for 1 h at 37°C. 3. Add 1 μL of DNase RQ1 to each sample and incubate for another 15 min at 37°C in a PCR machine. 4. Stop the reaction by adding 20 μL RNA stop solution. 5. Purify samples over a Microspin™ G-25 column following manufacturers’ instructions. 6. Bring volume up to 200 μL with RNase-free water and transfer to a screw cap tube. 7. Extract samples with 200 μL of phenol/chloroform/isoamyl alcohol (see Note 14). 8. Vortex sample for 1 min. 9. Spin sample at max speed for 10 min in a microfuge. 10. Transfer the aqueous phase to a new screw cap tube. 11. Add 200 μL chloroform and repeat steps 8–10. 12. Add 1/10 volume of 3 M sodium acetate and 2.5 volumes of 100% ethanol. 13. Precipitate probes for at least 2 h at −20°C. 14. Spin samples at max speed for 20 min in a microfuge. 15. Remove the supernatant and discard in radioactive waste. 16. Dry the pellet in a speedvac for 5 min. 17. Resuspend the RNA-probe in 20 μL of RNase-free water. 18. Measure specific activity of the probe in scintillation counter. (Add 1 μL of probe to 5 mL of scintillation fluid, mix, and count).

3.2.6. Isolation of RNA-Binding Proteins by One-Step Affinity Chromatography

Note: All steps are performed at 4°C or on ice. 1. Add 300 pmol of biotinylated RNA (bait), 750 pmol competitor RNA, and 1.5 mg heparan to 10 mg of T cell lysate (total volume about 1 mL) prepared as indicated under Subheading 3.2.1. 2. Incubate reaction at 4°C for 4 h tumbling on a wheel to assure protein–RNA interaction. 3. Meanwhile transfer 150 μL of M-280 streptavidin beads per pull down to a tube.

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4. Wash the beads two times with 1 mL RNase-free water, once with 1 mL RBB buffer and finally add the 50 μL of RBB buffer per reaction to the beads. 5. Stir up beads by inverting the tube a couple of times and add 50 μL of M-280 streptavidin bead slurry to each tube. 6. Tumble reaction at 4°C for 1 h on a wheel to allow RNA-bead binding. 7. Collect beads on the side of the tube with a magnet. 8. Remove the supernatant. 9. Add 1 mL of cold RBB buffer to wash unbound protein off the beads. 10. Collect beads on the side of the tube with a magnet. 11. Remove the supernatant. 12. Repeat steps 7–9 twice more. 13. Resuspend the beads in 20 μL of RBB buffer supplemented with 0.05% (w/v) sodium dodecylsulfate (SDS). 14. Incubate sample at 65°C for 15 min to elute the protein from the beads. 15. Collect beads using a magnet. 16. Transfer eluate to a new tube. 17. Spin sample at 7,600 × g for 2 min to remove residual beads. 18. Transfer the supernatant to a new tube. 19. Freeze samples in liquid nitrogen. 20. Samples can be store at −80°C prior to mass spectrometric analysis.

4. Notes 1. In all experiments RNase-free water is required. To prepare RNase-free water add 0.9 mL of diethylpyrocarbonate (DEPC) to 1 L of water. Stirr overnight and autoclave for 1 h to inactivate the DEPC. 2. When using more than one buffy coat for an experiment be sure to never mix T cells from different donors. This will lead to activation of these cells and will compromise the experiment. 3. Be very careful when overlaying the Ficoll-paque with blood. Mixing of the phases results in dramatically reduced T cell yields. Also, keep the brake of the centrifuge of. Reducing the speed of the rotor too fast will result in mixing the phases and an increased loss of T cells.

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4. To stimulate T cells with anti-hCD3ε antibody and antihCD28 antibody use no other plates than the ones indicated in Subheading 2.1.1, item 2. We tested different plates and these were the best in immobilizing the antibodies on their surface. 5. Swirl the plates well to equally distribute the Actinomycin D. Do not add Actinomycin D to the 0 h timepoint. 6. Be sure to dry the membranes of the RNeasy column extensively. Contamination of ethanol in the RNA samples will make the sample leak out of the pockets when northern blot is performed. Residual ethanol also has negative effect on reverse transcription reactions. 7. T cells do not have much RNA, do not expect high yields. 8. To test the stability of specific cytokine mRNAs, specific probes have to be generated for each transcript. For this reverse transcribe mRNA into cDNA following the instructions of the Superscript II reverse transcriptase protocol (Invitrogen). Generate specific primers for a 300–500 nucleotide long portion of the desired cytokine. Run a standard PCR to amplify that fragment, which needs to be gel purified subsequently. The purified fragment can now be used to generate a radiolabeled probe as indicated in Subheading 3.1.4. 9. When using the β-globin reporter based decay assay in HeLa Tet-Off cells be sure to use Tet system approved FBS at all times. Other types of FBS may contain tetracycline as a contaminant which will lead to irreproducible results. If the cells are cultured with nonapproved FBS you can recover them by growing them on a plate for a week with frequent changes of Tet system approved FBS. 10. Do not exceed 5 h of transfection due to cytotoxicity of the Lipofectamine 2000 reagent. 11. When performing northern blot, the gel as well as the membrane should show the two rRNA bands. If these bands are absent it hints either at the loss of a sample (probably due to ethanol contamination), or the degradation of the RNA sample. 12. The cycling of different PCR machines will be different which may result in a poor radiolabeled probe. Adjust the annealing temperatures in a nonradioactive PCR to solve that problem. 13. The DNA loading dye may interfere with protein–RNA interaction. Alternatively, the dye can be loaded into a free well or mixed with the probe-only control. 14. When phenol extracting the probe radioactive aerosols may develop. Always perform phenol extractions in a chemical fume hood. 15. Calculations of the specific activity of the radiolabeled probes for EMSA and UV-crosslinking: 4 pmol of synthetic RNA

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were added for radiolabeling and it is anticipated that 75% of RNA remains after purification and reconstitution. This results in a total of (4 pmol RNA/20 μL reconstitution volume) × 0.75 = 0.15 pmol/μL of purified RNA probe. After counting the probe the amount of radiolabeled per picomol of RNA can be determined. Count (cpm/μL)/0.15 pmol/μL RNA = cpm/pmol. For example, the specific activity of the probe is 200,000 cpm/pmol as measured in the scintillation counter and 50,000 cpm will be used in the experiment: 50,0 00 × 0.15 pmol/μL/200,000 cpm/μL = 0.0375 pmol/reaction.

Acknowledgments We thank K. Rattenbacher and I.A. Vlasova for their helpful comments and A-B Shyu for providing β-globin reporter plasmids. This work was supported by NIH grant AI072068. References 1. Ogilvie, R.L., Abelson, M., Hau, H.H., Vlasova, I., Blackshear, P.J., Bohjanen, P.R. (2005) Tristetraprolin down-regulates IL-2 gene expression through AU-rich elementmediated mRNA decay. J. Immunol. 174: 953–961. 2. Hamilton, T.A., Novotny, M., Datta, S., Mandal, P., Hartupee, J., Tebo, J., Li, X. (2007) Chemokine and chemoattractant receptor expression: post-transcriptional regulation. J. Leukoc. Biol. 82: 213–219. 3. Sandler, H., Stoecklin, G. (2008) Control of mRNA decay by phosphorylation of tristetraprolin. Biochem. Soc. Trans. 36: 491–496. 4. Seko, Y., Cole, S., Kasprzak, W., Shapiro, B.A., Ragheb, J.A. (2006) The role of cytokine mRNA stability in the pathogenesis of autoimmune disease. Autoimmun. Rev. 5: 299–305. 5. Stoecklin, G., Tenenbaum, S.A., Mayo, T., Chittur, S.V., George, A.D., Baroni, T.E., Blackshear, P.J., Anderson, P. (2008) Genomewide analysis identifies interleukin-10 mRNA as target of tristetraprolin. J. Biol. Chem. 283: 11689–11699. 6. Lam, L.T., Pickeral, O.K., Peng, A. C., Rosenwald, A., Hurt, E.M., Giltnane, J.M., Averett, L.M., Zhao, H., Davis, R.E., Sathyamoorthy, M., Wahl, L.M., Harris, E.D., Mikovits, J.A., Monks, A.P., Hollingshead, M.G., Sausville, E.A., Staudt, L.M. (2001) Genomic-scale measurement of mRNA turnover and the mechanisms of action of the anti-

7.

8.

9.

10.

11.

12.

cancer drug flavopiridol. Genome Biol. 2: RESEARCH0041. Raghavan, A., Bohjanen, P.R. (2004) Microarray-based analyses of mRNA decay in the regulation of mammalian gene expression. Brief. Funct. Genomic Proteomic 3: 112–124. Raghavan, A., Ogilvie, R.L., Reilly, C., Abelson, M.L., Raghavan, S., Vasdewani, J., Krathwohl, M., Bohjanen, P.R. (2002) Genome-wide analysis of mRNA decay in resting and activated primary human T lymphocytes. Nucleic Acids Res. 30: 5529–5538. Xu, N., Chen, C.Y., Shyu, A.B. (2001) Versatile role for hnRNP D isoforms in the differential regulation of cytoplasmic mRNA turnover. Mol. Cell. Biol. 21: 6960–6971. Hau, H.H., Walsh, R.J., Ogilvie, R.L., Williams, D.A., Reilly, C.S., Bohjanen, P.R. (2007) Tristetraprolin recruits functional mRNA decay complexes to ARE sequences. J. Cell. Biochem. 100: 1477–1492. Lykke-Andersen, J., Wagner, E. (2005) Recruitment and activation of mRNA decay enzymes by two ARE-mediated decay activation domains in the proteins TTP and BRF-1. Genes Dev. 19: 351–361. Stoecklin, G., Colombi, M., Raineri, I., Leuenberger, S., Mallaun, M., Schmidlin, M., Gross, B., Lu, M., Kitamura, T., Moroni, C. (2002) Functional cloning of BRF1, a regulator of ARE-dependent mRNA turnover. Embo J. 21: 4709–4718.

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13. Bohjanen, P.R., Petryniak, B., June, C.H., Thompson, C.B., Lindsten, T. (1991) An inducible cytoplasmic factor (AU-B) binds selectively to AUUUA multimers in the 3’ untranslated region of lymphokine mRNA. Mol. Cell. Biol. 11: 3288–3295. 14. Bohjanen, P.R., Petryniak, B., June, C.H., Thompson, C.B., Lindsten, T. (1992) AU RNA-binding factors differ in their binding specificities and affinities. J. Biol. Chem. 267: 6302–6309. 15. Fan, X.C., Steitz, J.A. (1998) HNS, a nuclearcytoplasmic shuttling sequence in HuR. Proc. Natl. Acad. Sci. USA 95: 15293–15298. 16. Ford, L.P., Wilusz, J. (1999) An in vitro system using HeLa cytoplasmic extracts that reproduces regulated mRNA stability. Methods 17: 21–27. 17. Raghavan, A., Robison, R.L., McNabb, J., Miller, C.R., Williams, D.A., Bohjanen, P.R. (2001) HuA and tristetraprolin are induced following T cell activation and display distinct

18.

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but overlapping RNA binding specificities. J. Biol. Chem. 276: 47958–47965. Shim, J., Karin, M. (2002) The control of mRNA stability in response to extracellular stimuli. Mol. Cells 14: 323–331. Schmidlin, M., Lu, M., Leuenberger, S.A., Stoecklin, G., Mallaun, M., Gross, B., Gherzi, R., Hess, D., Hemmings, B.A., Moroni, C. (2004) The ARE-dependent mRNA-destabilizing activity of BRF1 is regulated by protein kinase B. Embo J. 23: 4760–4769. Raghavan, A., Dhalla, M., Bakheet, T., Ogilvie, R.L., Vlasova, I.A., Khabar, K.S., Williams, B.R., Bohjanen, P.R. (2004) Patterns of coordinate down-regulation of ARE-containing transcripts following immune cell activation. Genomics 84: 1002–1013. Vlasova, I.A., McNabb, J., Raghavan, A., Reilly, C., Williams, D.A., Bohjanen, K.A., Bohjanen, P.R. (2005) Coordinate stabilization of growth-regulatory transcripts in T cell malignancies. Genomics 86: 159–171.

Chapter 6 Cloning of Cytokine 3¢ Untranslated Regions and Posttranscriptional Assessment Using Cell-Based GFP Assay Latifa Al-Haj and Khalid S.A. Khabar Abstract Cytokine biosynthesis is tightly regulated by a number of processes, including gene expression control. Posttranscriptional control of cytokine gene expression offers a fine-tuning mechanism that contributes not only to transient biosynthesis of cytokines, but also helps in rapid and early initiation of the cytokine response. Deregulation of cytokine biosynthesis has been associated with a number of disease conditions, including autoimmune diseases, cancer, and others. Thus, there is a need for accurate measurement of posttranscriptional gene expression events in cytokine research. The method described here is a cell-based GFP assay that quantitatively measures posttranscriptional effects. This method is used for assessing the effects of modulators and conditions that lead to changes in posttranscriptional gene expression during cytokine production or for assessment of cytokine action on posttranscriptional events of gene expression. Key words: Posttranscriptional regulation, 3¢ untranslated regions, AU-rich elements, mRNA stability, GFP

1. Introduction Posttranscriptional gene regulation of cytokine biosynthesis is controlled at multiple steps, including RNA splicing, transport, mRNA stability, and translation. The 3¢ untranslated region (UTR) is a major regulatory sequence hub for posttranscriptional control, particularly mRNA stability and miRNA-mediated regulation. Both cis-acting and trans-acting factors can affect mRNA stability. Several cis-acting sequence elements exist in the 3¢ UTR, notably, the adenylate uridylate-rich elements (ARE) which are mRNA instability

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determinants; about 13% of human and mouse genes harbor AREs in their mRNAs (1, 2). Many of the cytokine mRNAs harbor ARE in their 3¢ UTR, particularly those of proinflammatory nature. Table 1 lists examples of common cytokine ARE genes and the sequence classes of AREs. The AREs are classified as Class I or

Table 1 Nonexhaustive list of cytokines that harbor AREs-containing 3¢ UTR Definition

RefSeq

Amphiregulin

Gene

ARE

C

NM_001657 AREG

TAATTTATTTAAT

4

B-cell lymphoma/leukemia 10

NM_003921 BCL10

TTTTATTTAAATT

5

Bone morphogenetic protein 2

NM_001200 BMP2

ATATATTTAAAAT

5

Bone morphogenetic protein 4

NM_001202 BMP4

ATATATTTATAAC

5

Bone morphogenetic protein 5

NM_021073 BMP5

TTTTATTTATTTA

3

Small inducible cytokine A13

NM_005408 CCL13

TTTTATTTAAAAT

5

Small inducible cytokine A16

NM_004590 CCL16

TTATATTTATATT

5

Small inducible cytokine A28

NM_148672 CCL28

ATTTATTTATTTT

3

Small inducible cytokine A3

NM_002983 CCL3

TAATTTATTTATA

4

Small inducible cytokine A3-like 1

NM_021006 CCL3L1 TAATTTATTTATA

4

Small inducible cytokine A4

NM_002984 CCL4

CATTATTTATATT

4

Small inducible cytokine A4

NM_002984 CCL4

CATTATTTATATT

4

Cardiotrophin-like cytokine factor 1

NM_013246 CLCF1

ATTTATTTATTTG

4

Granulocyte-M colony-stimulating factor

NM_000758 CSF2

ATTTATTTATTTATTTATTTA

1

Cardiotrophin-1

NM_001330 CTF1

TTTTATTTAATTT

4

Fractalkine

NM_002996 CX3CL1 AATTATTTATTAA

5

Macrophage inflammatory protein 2-a

NM_002089 CXCL2

ATTTATTTATTTATTTATTTA

1

Macrophage inflammatory protein 2b

NM_002090 CXCL3

ATTTATGTATTTA

4

Small inducible cytokine B5

NM_002994 CXCL5

ATATATTTATATA

5

Endothelin-2

NM_001956 EDN2

AATTATTTATTTT

4

Epiregulin

NM_001432 EREG

TTTTATTTATTTT

5

Growth/differentiation factor 15

NM_004864 GDF15

GTATTTATTTAAA

4

Growth/differentiation factor 8

NM_005259 GDF8

TTTTATTTACTTT

4 (continued)

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Table 1 (continued) Definition

RefSeq

Heparin-binding EGF-like growth factor

Gene

ARE

C

NM_001945 HBEGF

TAATTTATTTAGT

4

Interferon alpha-14

NM_021057 IFNA14

ATTTATTTATTTA

3

Interferon alpha-16

NM_002173 IFNA16

ATTTATTTATTTA

4

Interferon alpha-2

NM_000605 IFNA2

TTATTTATTTAAC

4

Interferon alpha-21

NM_002175 IFNA21

ATTTATTTATTTA

3

Interferon alpha-5

NM_002169 IFNA5

ATTTATTTATTTA

3

Interferon alpha-6

NM_021002 IFNA6

ATTTATCTATTTA

3

Interferon alpha-8

NM_002170 IFNA8

ATCTATTTATTTA

3

Interferon beta

NM_002176 IFNB1

TTTTATTTATTTA

3

Interferon gamma

NM_000619 IFNG

TATTATTTATAAT

5

Interferon omega-1

NM_002177 IFNW1

TCATTTATTTATT

4

Insulin-like growth fact 2 mRNA bind protein

NM_006546 IGF2BP1 ATTTATTAATTTA

4

Interleukin-10

NM_000572 IL10

CAATATTTATTAT

5

Interleukin-11

NM_000641 IL11

ATTTATTTATTTATTTC

2

Interleukin-12 subunit alpha

NM_000882 IL12A

ATTTATTTATATA

3

Interleukin-12 subunit beta

NM_002187 IL12B

GTTTATTTATTTATTTA

2

Interleukin-13

NM_002188 IL13

TCTTATTTATTAT

5

Interleukin-15

NM_000585 IL15

TTTAATTTATTAT

5

Interleukin-17A

NM_002190 IL17A

ATTTATGTATTTA

3

Interleukin-1 beta

NM_000576 IL1B

ATTTATTTATTTATTTG

2

Interleukin-1 family member 8

NM_014438 IL1F8

AATTATTTACATA

4

Interleukin-2

NM_000586 IL2

CTATTTATTTAAA

4

Interleukin-20

NM_018724 IL20

ATTTATTTTTTTA

4

Interleukin-22

NM_020525 IL22

ATTTATTTATAGA

4

Interleukin-23 subunit alpha

NM_016584 IL23A

TTGTATTTATATT

5

Interleukin 27

NM_145659 IL27

ATATTTATTTATT

4

Interleukin-28B

NM_172138 IL28B

TTTTATTTATAAA

5

Interleukin-3

NM_000588 IL3

ATGTATTTATTTATTTA

2

Interleukin-4

NM_000589 IL4

ATTTATATATTTA

3 (continued)

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Table 1 (continued) Definition

RefSeq

Interleukin-4

Gene

ARE

C

NM_000589 IL4

ATTTATATATTTA

4

Interleukin-5

NM_000879 IL5

GTATTTATTTAAT

4

Interleukin-6

NM_000600 IL6

AAATATTTATATT

5

Interleukin-8

NM_000584 IL8

ATTTATGTATTTATTTA

2

Leukemia inhibitory factor

NM_002309 LIF

AAATATTTATTTT

5

Lymphotoxin-alpha

NM_000595 LTA

AATTATTTATTTA

3

Collagenase 3

NM_002427 MMP13

ATATATTTATAAG

4

Oncostatin-M

NM_020530 OSM

TTATATTTATAAG

5

Platelet-derived growth factor B chain

NM_002608 PDGFB

AAATTTATTTATA

4

Secretogranin-2

NM_003469 SCG2

TTTTATTTATAAG

5

TGF-beta receptor type III

NM_003243 TGFBR3 ATATATTTAATAT

4

Tumor necrosis factor

NM_000594 TNF

ATTTATTTATTTA

3

Thymic stromal lymphopoietin isoform 1

NM_138551 TSLP

TATAATTTATATA

5

Vascular endothelial growth factor A

NM_003376 VEGFA

TCATTTATTTATT

4

Lymphotactin

NM_002995 XCL1

AATTATTTATTAT

5

C: Clusters according to the number of repeats (Cluster I: five or more pentamer repeats; Cluster II: three repeats; Cluster III: four repeats; Cluster IV: two repeats, and Cluster V: one repeat in U-rich context). Clusters I–IV belong to Class II AREs while Cluster V belongs to Class I AREs

Class II based on the existence of either dispersed AUUUA pentamers in U-rich context or repeated overlapping pentamers, respectively (3). A bioinformatics-derived 13-nucleotide core sequence (WWWUUAUUUAUWWW) was used to classify ARE genes into five categories based on the number of continuous repeats of AUUUA pentamers (Cluster I–V; (4)). The Class II cytokine mRNAs, especially those of Clusters I–III, are the most unstable mRNAs according to this classification (5, 6). The ARE-mRNAs are regulated by an array of RNA-binding proteins that can selectively bind to the ARE and promote their mRNA stability or degradation (reviewed in refs. 7–11). A major RNA-binding protein that promotes mRNA stabilization is the human antigen R (HuR) which stabilizes many cytokine AREmRNA, such as IL-8, GM-CSF, and IFN-g. Whereas, tristetraproline (TTP), KSRP, and AUF1 tend to promote the decay of ARE-mRNA both in vitro and in vivo for cytokines, such as IL-1,

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TNF-a, IL-3, and other Class II ARE-mRNAs (12–14). Several signaling pathways regulate mobilization and activity of the RNAbinding proteins and subsequently cytokine ARE-mRNA stability and translation. The most notable signaling pathway that regulates ARE-mRNA stability is mitogen-activated protein kinase p38, and its downstream target kinase, MAPK-activated protein kinase 2 (5, 12, 15, 16). This chapter focuses on live cell-based assay which assesses the posttranscriptional gene regulation in cytokine studies. The assay is GFP based and thus it can be utilized to measure the total posttranscriptional outcome, e.g., mRNA stability and translation, using fluorescence as endpoint measurements. In this protocol, the GFP coding region is fused with the cytokine 3¢ UTR that contains mRNA turnover sequence elements, such as the AREs, and thus the fused GFP–3¢ UTR acts as a posttranscriptional reporter. When compared to control stable 3¢ UTR that lacks the specific mRNA sequence element in question, changes due to posttranscriptional regulation can be monitored.

2. Materials 2.1. Cell Culture

1. Human embryonic kidney epithelial cell line HEK293 (ATCC, Manassas, VA) and THP-1 monocytic cell line (ATCC, Manassas, VA). 2. Dulbecco’s Modified Eagle’s Medium (DMEM) (Invitrogen, Carlsbad, CA) or RPMI 1640 medium (Invitrogen) supplemented with 10% (v/v) heat-inactivated (56°C for 1 h) fetal bovine serum (FBS; Invitrogen), penicillin and streptomycin (100 units/mL penicillin, and 100 mg/mL streptomycin, Invitrogen), and 2 mM l-Glutamine (200 mM in 0.85% (w/v) NaCl solution). 3. Trypsin–EDTA solution, sterile (200 mg/L versene EDTA).

2.2. RNA Isolation, cDNA Synthesis, and RT-PCR

1. TRI Reagent (Sigma–Aldrich). 2. Chloroform. 3. Isopropanol. 4. 70% (v/v) ethanol. 5. Deionized and autoclaved water. 6. SuperScript II RNase H− reverse transcriptase enzyme (Invitrogen), 5× first-strand buffer (250 mM Tris–HCl, pH 8.3; 375 mM KCl; 15 mM MgCl2), and DTT 0.1 M (Invitrogen). 7. DNTPs mix: 100 mM dCTP, dATP, dGTP, and dTTP (Invitrogen).

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8. RNaseOUT (40 units/mL, Invitrogen). 9. Phosphorylated Oligo(dT) 12–18 sodium salt (Amersham). 10. Custom-made (ProOligo, France) HPLC-purified oligonucleotide primers for PCR. 11. Hot start Taq DNA polymerase (Qiagen). 12. QIAquick PCR purification Kit (Qiagen). 13. QIAquick gel extraction kit (Qiagen). 2.3. Agarose Gel Electrophoresis

1. Electrophoresis running buffer: Tris–borate–EDTA (5× TBE) (Sigma), dissolve one pouch 5× concentrate (0.445 M Tris– borate, 10 mM EDTA, pH 8.3) in 1 L of deionized water. 2. Electrophoresis running buffer: Tris–Acetate–EDTA (10× TAE) (Sigma), dissolve one pouch 10× concentrate (0.4 M Tris–acetate, 10 mM EDTA, pH 8.3) in 1 L of deionized water. 3. Agarose gel (Invitrogen). 4. Ethidium bromide stock solution, 10 mg/mL (Sigma). 5. 10× blue juice loading buffer: 65% (w/v) sucrose, 10 mM Tris– HCl (pH 7.5), 10 mM EDTA, 0.3% (w/v) bromophenol blue (Invitrogen). 6. 100 bp DNA ladder (Invitrogen): Ready-to-use stock is prepared at 0.2 mg/mL in 10 mM Tris–HCl (pH 7.5), 10 mM EDTA, 0.05% (w/v) bromophenol blue, and 5% (v/v) glycerol. 7. 1 kb DNA ladder (Invitrogen): Ready-to-use stock is prepared at 0.2 mg/mL in 10 mM Tris–HCl (pH 7.5), 10 mM EDTA, 0.05% (w/v) bromophenol blue, and 5% (v/v) glycerol.

2.4. Cloning, Purification of 3 ¢ UTR Reporter Constructs

1. Luria-Bertani (LB) medium (Qbiogene): One pouch that contains 10 g bacto-tryptone, 5 g bacto-yeast extract, 5 g NaCl, pH 7.0, is dissolved in 1 L of deionized water. 2. LB agar (Qbiogene): One pouch is added to 1 L of doubledistilled water (deionized water) and sterilized by autoclaving. Allow the medium to cool to 50°C, then add ampicillin to a final concentration of 100 mg/mL, and pour plates. 3. SOC medium (100 mL): 2.0 g bacto-tryptone, 0.5 g bactoyeast extract, 1 mL 1 M NaCl, 0.25 mL 1 M KCl, 1 mL 2 M Mg2+ stock (1 M MgCl2; 1 M MgSO4), 1 mL 2 M glucose are brought to 100 mL with distilled water, pH 7.0. 4. Competent Escherichia coli (DH5a or other strains). 5. BamHI and XbaI (Promega). 6. Restriction enzymes (XbaI and BamHI) and enzyme reaction buffers (Promega).

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7. Ampicillin. 8. T4 DNA Ligase 4 U/mL supplied with T4 DNA Ligase 5× buffer containing 250 mM Tris–HCl (pH 7.6), 50 mM MgCl2, 5 mM ATP, 5 mM DTT, and 25% (w/v) polyethylene glycol-8000 (Invitrogen). 9. QIA prep Spin plasmid miniprep Kit (Qiagen). 10. EGFP mammalian expression vector (Gene Therapy Systems, Inc., San Diego, CA). 2.5. Transfection

1. Lipofectamine 2000 transfection reagent (Invitrogen). 2. Opti-MEM reduced serum medium (Invitrogen).

2.6. Additional Materials, Equipment

1. Black opaque-walled, clear-bottom 96 well plate (Matrix). 2. ZENYTH 3100 Multimode Detectors (Anthos labtec, Eugendorf, Austria). 3. Zeiss fluorescence microscope. 4. NanoDrop UV-Vis Spectrophotometer (Labtech International).

3. Methods Figure 1 shows a flowchart of the main steps of cytokine 3¢ UTRlinked posttranscriptional assessment protocol and the scheme of the cloning approach of cytokine 3¢ UTR region of interest. An example is given with IL-8 3¢ UTR. 3.1. Bioinformatics Extraction of Cytokine 3 ¢ UTR

Cytokine 3¢ UTR is the main regulatory region for posttranscriptional control, particularly mRNA stability. The region of the 3¢ UTR that harbors the cytokine mRNA destability sequence elements, such as AU rich, can be obtained from GenBank sequence files (e.g., RefSeq mRNA database) from NCBI site (http://www. ncbi.nlm.nih.gov). Extraction of the 3¢ UTR sequence can be performed manually using bioinformatics programs packages, such as Vector NTI or LaserGen software. Use of databases, such as UTRdb, can facilitate multiple queries search. Batch 3¢ UTRs can also be executed using biomart bioinformatics data management system (http://www.biomart.org). The identity of ARE-mRNAs and the location of AREs can be obtained from ARED-Organism (http://brp.kfshrc.edu.sa/ARED).

3.2. RT-PCR of Cytokine 3 ¢ UTR: Total RNA Extraction and cDNA Synthesis

Complete media are prepared for each cell line. Cells are maintained in 5% (v/v) CO2 atmosphere at 37°C incubator. The source of the cytokine 3¢ UTR can be either genomic DNA or cDNA synthesized from total RNA extracted from cells of interest (see Note 1).

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Bioinformatics

ARE-Genes/Region

Primers/PCR

3′UTR RT-PCR

IL8

102 401 IL8 CD

ARE

972

1209 pA cDNA

BamH IXba I

Cloning

Promoter

EGFP Reporter

pA vector

BGH 3'UTR Control 3'UTR

BamH I Promoter

EGFP Reporter

Xba I

pA

3'UTR

vector

Cytokine ARE 3′ UTR Cellular Models/Transfection

Endpoint Measurements Fluorescence, RT-PCR

Fig. 1. Schematic diagram representation of the sequential steps used for reporter 3¢ UTR cloning. An example is given with IL-8 3¢ UTR.

1. Total RNA from THP-1 monocytic cell line (see Note 2) is extracted using TRI reagent. Cells are isolated by centrifugation and then lysed by adding 1 mL of TRI reagent per 5 × 106 cells. Cells are pipetted up and down to homogenize the cell lysate, and then 200 mL of chloroform is added per 1 mL of TRI reagent used. Samples are mixed very well and centrifuged at 12,000 × g for 15 min at 2–8°C. After centrifugation, the aqueous phase is transferred to a clean tube – making sure not to aspirate from the organic phase – and 500 mL of isopropanol is added per mL of TRI reagent used. Samples are allowed to

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stand for 10 min at room temperature, and then centrifuged at 12,000 × g for 15 min at 2–8°C. The supernatant is removed and washed once with 75% (v/v) cold ethanol, and then centrifuged at 7,500 × g for 5 min at 2–8°C. The RNA pellet is dried for 10 min at room temperature and then appropriate volume of nuclease-free water is added to each sample. 2. RNA concentration is calculated by measuring OD at 260 nm. The 260/280 nm ratio is calculated to estimate the purity of the RNA preparation; a ratio of 1.6–2.1 indicates pure RNA preparation. 3. RNA samples (1 mL) are analyzed on a 1% agarose gel to confirm the quality of the RNA. 4. The cDNA synthesis is performed by standard reverse transcriptase technique. Briefly, 5 mg total RNA in total volume of 11 mL in a 1.5 mL microcentrifuge tube is incubated with 500 ng oligo(dT) at 65°C for 10 min and then quick chilled on ice for 2 min. Eight microliters of reaction mix is added to each tube; reaction mix is prepared by adding 4 mL 5× firststrand buffer, 2 mL 0.1 M DTT, 1 mL RNaseOUT (40 units), and 1 mL SuperScript II RT (200 units). Tubes are incubated at 42°C for 1 h, and then the reaction is inactivated by heating at 70°C for 15 min. 3.3. Primer Design and PCR

1. The present method utilized IL-8 3¢ UTR as an example of cytokine 3¢ UTR (see Fig. 2). A 237-base pair region (nucleotides 972–1,209 of accession number NM_000584) from the 1,250-base pair IL-8 3¢ 3¢ UTR is amplified by RT-PCR (see Note 3). The forward primer with BamHI site (underlined) is 5¢ CAGCAGGATCC GATGTTGTGAGGACATGTG 3¢ and the reverse primer with the XbaI site (underlined) is 5¢ CGACCTCTAGAACCC TGATTGAAATTTAT 3¢. The additional 5¢ sequences are for thermal stability of the oligonucleotides and facilitation of restriction reaction. As a control, the eukaryotic translation elongation factor 1 alpha 1 (EEF1A1) 3¢ UTR is amplified with the following primers: the forward primer with BamHI site (underlined) is 5¢ GCACCGGATCCAATATTATCCC TAATACCTG, 3¢ and the reverse primer with XbaI site (underlined) is 5¢ GCCAGTCTAGAAATAACTTAAAACTGCCA 3¢. A 205-base pair region (nucleotides 1,451–1,655 accession number NM_001402) is amplified by RT-PCR (see Note 4). 2. PCRs are carried out in a 100 mL reaction by adding 1 mL cDNA containing 250 ng, 10 mL 10× PCR buffer, 2 mL dNTPs, 2 mL of the forward primer, 2 mL of the reverse primer, and two units hot-start Taq DNA polymerase.

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Fig. 2. Example of the assay performance. (a) Schematic diagram of reporter vector harboring IL-8 3¢ UTR containing AU-rich element (ARE) region. The AREs are underlined. Sequence in bold is Cluster II ARE. (b) Cells were transfected with 25 ng of control (EEFA1A) and IL-8 3¢ UTR-fused GFP vectors. After approximately 48 h, the cells were visualized using fluorescence microscope. Captured images were analyzed using algorithm that quantitates total fluorescence intensities in pixels. Data are presented as mean ± SEM of four replicate readings (c) *** denote p values < 0.001.

3. Samples are amplified by standard PCR: template denaturation at 94°C for 45 s, primer annealing at 50°C for 45 s, template elongation at 72°C for 2 min, for 35 cycles. Ten-microliters aliquot of the PCR product is ran on 1% agarose gel in 1× TBE buffer to check the quality of the PCR product. 3.4. Cloning into EGFP Mammalian Expression System

1. The PCR products (IL-8 and EEF1A1 3’ UTR) are purified by a spin column (QIAquick PCR purification Kit) and eluted in 40 mL deionized water. 2. The purified PCR products are cut by BamHI and XbaI sequentially (see Notes 5 and 6) and followed by phenol extraction and ethanol precipitation. 3. EGFP expression vector is digested with BamHI and XbaI sequentially (see Note 7) and purified by phenol extraction and ethanol precipitation (see Note 8). 4. The concentrations of the insert (IL-8 or EEF1A1 3’ UTR) and the vector are determined using NanoDrop UV-Vis Spectrophotometer.

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5. Ten microliters ligation reactions are set up as follows: The digested and purified inserts and EGFP plasmid are mixed in 3:1 access molar ratio (see Note 9). One hundred nanogram cut EGFP plasmid is added to 10 mL digested and purified PCR product, 2 mL of 5× T4 DNA ligase buffer, and 1 mL T4 DNA ligase. Ligation reaction is incubated at 16°C overnight. 6. For transformation preparation: One vial containing 100 mL of competent cells (DH5a cells) is thawed for each ligation reaction on ice and then 3 mL of ligation reaction is added (~50– 150 ng). Transformation reaction is incubated on ice for 20 min and then heat shocked for 45 s at 42°C and placed on ice for 2 min. 7. SOC medium (350 mL) is added and sample is incubated at 37°C for 1 h in shaking incubator. 8. Transformation reaction (100 or 200 mL) is applied onto LB agar plate containing 100 mg/mL ampicillin. The plates are incubated inverted at 37°C incubator overnight. 9. Several white colonies are inoculated into 2 mL LB medium and incubated at 37°C overnight in a shaking incubator (see Note 10). 10. Recombinant colonies are verified by PCR using a vector-specific forward primer and the EEF1A1 3¢ UTR or IL-8 3¢ UTR reverse primer. 3.5. Posttranscriptional Assessment of Cytokine 3 ¢ UTR Effect

1. HEK293 cells (3 × 104 cells per well) are grown overnight to 70% confluence in black, clear-bottomed 96-well plates. 2. The cells are transfected with 25 ng of the GFP plasmids (see Notes 11 and 12) diluted from the stock plasmid in OptiMEM-reduced serum medium in 10 mL volume. Lipofectamine 2000 (0.5 mL of lipofectin in 10 mL Opti-MEM) is mixed gently with the DNA solution for 20 min before adding the complex onto the cells. 3. Transfection is continued for 5 h and then medium is replaced with fresh complete medium (see Note 13). 4. All transfections are performed in quadruplicate. The variance in GFP fluorescence among replicate microwells was 7%. Transfection efficiency in HEK293 was always 80% ± 10%. 5. Day 1 and day 2 after transfection, the plates are read using bottom-read fluorescence reader. The ZENYTH 3100 is used with the following parameters: excitation filter (nm), 485 SL 1; emission filter (nm), 535 SL 1; integration time (s), 1; and bottom fluorescence read setting (see Note 14). 6. An example of the performance of the assay is given in Fig. 2.

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4. Notes 1. The source for amplifying the cytokine 3¢ UTR sequence can be the genomic DNA in case that the 3¢ UTR is not residing in two exons, i.e., is not within spliceable region. If the 3¢ UTR occurs in two exons, i.e., in spliced region, then cDNA is used from total RNA that is extracted from cells that express the cytokine of interest. Many of the cytokine 3¢ UTR appear to have the 3¢ UTR within the whole last exon. Examples are GM-CSF, CXCL-2, IL-1b, IL-8, and TNF-a 3¢ UTR. However, the protocol that is given here is from cDNA which is applicable for both spliced and unspliced 3¢ UTR. 2. The monocytic cell line (THP-1) is an excellent source of cytokine mRNA expression when stimulated with LPS (10 mg/ mL) and cycloheximide (5 mg/mL) for 2–4 h (5, 6, 17). 3. The length and the region of the cytokine 3¢ UTR to be cloned are subject to the experimental system in question. The whole 3¢ UTR can be very long and in many instances the mRNA destability elements are clustered in specific regions. Thus, a region of 200–300 bases can be sufficient; smaller regions have also been shown to harbor significant decay-promoting activity. In case of ARE, there may be a core ARE, usually of Class II, and other accessory ARE. For example, 60-base region of IL-8 contains both core and accessory ARE (18) that can mediate the decay activity. 4. The EEF1A1 3¢ UTR has two polyadenylation sites giving rise to potential alternative polyadenylation transcripts, a short 3¢ UTR (295 bases and transcript of 1.7 kb), and a long 3¢ UTR (2121 bases and transcript of 3.5 kb). The most abundant form is the transcript with the short 3¢ UTR in which 205 bases control 3¢ UTR was amplified (19). 5. First, digest (10 mg) PCR product with restriction enzyme that needs the buffer with the lowest salt concentration, in this case XbaI, and then add BamHI. If needed, the salt is removed and the DNA is subjected to an ethanol precipitation before the addition of the second enzyme. 6. Although in general digestion of PCR product is less efficient than plasmids, the combination of the selected restriction enzymes, PCR primer design, and digestion/purification protocol described here results in efficient digestion of the PCR product. 7. We routinely use this EGFP expression vector which is under the control of CMV IE promoter and also contains CMV intron A. It gives strong expression which is important when using cytokine 3¢ UTR that tends to destabilize the GFP mRNA and subsequently the fluorescent protein levels. The advantage

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of using EGFP as opposed to luciferase system is that it can be performed on live cells with repeated measurements and does not require cell lysis and reagent reaction leading to low intrareplicate variance. 8. If needed to reduce clone background, dephosphorylate the vector, and use 0.01 unit of calf intestinal alkaline phosphatase (CIP) per each pmol of DNA ends. Molarity of ends = [(mg/mL)/ (base pairs × 650 Da)] × 2 ends. 9. Calculate a 1:1 molar ratio: (bp insert)/(bp vector) = (ng insert)/ (ng vector). Then, multiply by three, and use 100 ng vector. 10. Do not incubate plates for longer than 20 h to avoid the formation of satellite colonies. 11. The amount of EGFP expression vector is important to be optimal – not saturated – so that the effects of reduction of the reporter due to the cytokine 3¢ UTR decay-promoting action can be appreciated. This is dependent on the cell line used, and thus various doses of the expression vector may need to be tested. Also, CMV promoter can be subject to transcriptional induction in certain experimental settings, thus the control 3¢ UTR is always needed. 12. Prepare pure plasmid DNA for transfection; several manufacturers’ kits provide transfection-quality plasmid isolation kits. 13. In HEK293 and other robust cell lines, the medium containing the lipofectin may not be changed. The described method works quite well with HEK293, Huh7, and HeLa. In the latter case, 50 ng of the plasmid is required. 14. Fluorescence readings of lower EGFP-emitting cells, e.g., especially with certain cell lines that are hard to transfect, can be problematic because of the low sensitivity of certain bottomread fluorimeters. An excellent alternative is the use of the modern imaging apparatus that utilize automated image focus and capture followed by segmentation and processing algorithms. Alternatively, pictures can be taken using standard fluorescence microscope (optimum excitation wavelength: 488 nm and emission wavelength: 503 nm). In all cases, exposure times and other settings are kept constant to allow equal comparison of experiments. The total cell and well area (pixels) and total green intensity within the cell regions are calculated using automated thresholding algorithms. The software tool is implemented in MATLAB (The Mathworks, Inc., Natick, MA, USA) programming environment, and images are processed in batch mode. 15. The described assay measures the combined outcome of posttranscriptional gene expression, including mRNA stability and translation. The mRNA stability effects can be monitored by GFP mRNA measurements, e.g., Northern and RT-PCR, and use of transcription inhibitors.

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References 1. Halees, A.S., El-Badrawi, R., Khabar, K.S. (2008) ARED Organism: expansion of ARED reveals AU-rich element cluster variations between human and mouse. Nucleic Acids Res. 36: D137–140. 2. Bakheet, T., Williams, B.R., Khabar, K.S. (2006) ARED 3.0: the large and diverse AU-rich transcriptome. Nucleic Acids Res. 34: D111–114. 3. Chen, C.Y., Shyu, A.B. (1995) AU-rich elements: characterization and importance in mRNA degradation. Trends Biochem. Sci. 20: 465–470. 4. Bakheet, T., Frevel, M., Williams, B.R.G., Greer, W., Khabar, K.S.A. (2001) ARED: Human AU-rich element-containing mRNA database reveals an unexpectedly diverse functional reportiore of encoded proteins. Nucleic Acids Res. 29: 246–254. 5. Frevel, M.A., Bakheet, T., Silva, A.M., Hissong, J.G., Khabar, K.S., Williams, B.R. (2003) p38 Mitogen-activated protein kinase-dependent and -independent signaling of mRNA stability of AU-rich element-containing transcripts. Mol. Cell. Biol. 23: 425–436. 6. Raghavan, A., Dhalla, M., Bakheet, T., Ogilvie, R.L., Vlasova, I.A., Khabar, K.S., Williams, B.R., Bohjanen, P.R. (2004) Patterns of coordinate down-regulation of ARE-containing transcripts following immune cell activation. Genomics 84: 1002–1013. 7. Khabar, K.S. (2007) Rapid transit in the immune cells: the role of mRNA turnover regulation. J. Leukoc. Biol. 81: 1335–1344. 8. Eberhardt, W., Doller, A., Akool el, S., Pfeilschifter, J. (2007) Modulation of mRNA stability as a novel therapeutic approach. Pharmacol. Ther. 114: 56–73. 9. Seko, Y., Cole, S., Kasprzak, W., Shapiro, B.A., Ragheb, J.A. (2006) The role of cytokine mRNA stability in the pathogenesis of autoimmune disease. Autoimmun. Rev. 5: 299–305. 10. Khabar, K.S. (2005) The AU-rich transcriptome: more than interferons and cytokines, and its role in disease. J. Interferon Cytokine Res. 25: 1–10. 11. Asirvatham, A.J., Magner, W.J., Tomasi, T.B. miRNA regulation of cytokine genes. Cytokine 45: 58–69. 12. Hitti, E., Iakovleva, T., Brook, M., Deppenmeier, S., Gruber, A.D., Radzioch, D., Clark, A.R., Blackshear, P.J., Kotlyarov, A.,

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Gaestel, M. (2006) Mitogen-activated protein kinase-activated protein kinase 2 regulates tumor necrosis factor mRNA stability and translation mainly by altering tristetraprolin expression, stability, and binding to adenine/ uridine-rich element. Mol. Cell. Biol. 26: 2399–2407. Carballo, E., Lai, W.S., Blackshear, P.J. (2000) Evidence that tristetraprolin is a physiological regulator of granulocyte-macrophage colonystimulating factor messenger RNA deadenylation and stability. Blood 95: 1891–1899. Winzen, R., Thakur, B.K., Dittrich-Breiholz, O., Shah, M., Redich, N., Dhamija, S., Kracht, M., Holtmann, H. (2007) Functional analysis of KSRP interaction with the AU-rich element of interleukin-8 and identification of inflammatory mRNA targets. Mol. Cell. Biol. 27: 8388–8400. Winzen, R., Kracht, M., Ritter, B., Wilhelm, A., Chen, C.Y., Shyu, A.B., Müller, M., Gaestel, M., Resch, K., Holtmann, H. (1999) The p38 MAP kinase pathway signals for cytokineinduced mRNA stabilization via MAP kinaseactivated protein kinase 2 and an AU-rich region-targeted mechanism. EMBO J. 18: 4969–4980. Mahtani, K.R., Brook, M., Dean, J.L., Sully, G., Saklatvala, J., Clark, A.R. (2001) Mitogenactivated protein kinase p38 controls the expression and posttranslational modification of tristetraprolin, a regulator of tumor necrosis factor alpha mRNA stability. Mol. Cell. Biol. 21: 6461–6469. Khabar, K.S., Dhalla, M., Al-Haj, L., Bakheet, T., Sy, C., Naemmuddin, M. (2004) Selection of AU-rich transiently expressed sequences: reversal of cDNA abundance. RNA 10: 747–753. Winzen, R., Gowrishankar, G., Bollig, F., Redich, N., Resch, K., Holtmann, H. (2004) Distinct domains of AU-rich elements exert different functions in mRNA destabilization and stabilization by p38 mitogen-activated protein kinase or HuR. Mol. Cell. Biol. 24: 4835–4847. Al-Zoghaibi, F., Ashour, T., Al-Ahmadi, W., Abulleef, H., Demirkaya, O., Khabar, K.S. (2007) Bioinformatics and experimental derivation of an efficient hybrid 3¢ untranslated region and use in expression active linear DNA with minimum poly(A) region. Gene 391: 130–139.

Chapter 7 Integrin-Targeted Stabilized Nanoparticles for an Efficient Delivery of siRNAs In Vitro and In Vivo Charudharshini Srinivasan, Dan Peer, and Motomu Shimaoka Abstract Utilizing small interfering RNAs (siRNAs) to silence disease-associated genes holds promise as a potential therapeutic strategy. However, the greatest challenge for RNAi remains the delivery of siRNA to target tissues or cells. Specifically lymphocytes are difficult to transduce by conventional methods but represent good targets for anti-inflammatory therapeutics. Integrins are an important class of cell adhesion receptors on leukocytes. Antibodies to integrins have been used to inhibit inflammatory reactions in patients. Here, we describe a strategy to deliver the siRNA cargo to leukocytes by stabilized nanoparticles surface-decorated with antibodies to integrin as targeting moieties. A detailed methodology for preparation of the integrintargeted stabilized nanoparticles (I-tsNPs) and their delivery in vitro and in vivo is discussed. Key words: Liposomes, RNAi, Leukocytes, Inflammation, Hyaluronan, Antibody, Transfection, Systemic delivery

1. Introduction Post-transcriptional gene silencing by RNA interference (RNAi) has shown great potential as a therapeutic tool in targeted suppression of disease causing genes. Several siRNA delivery vectors have been investigated for their efficiency via systemic delivery in animal models. To mention a few, non-targeted delivery vectors such as stable nucleic acid-lipid particles (SNALP) (1, 2), lipidoids (3), poly D,L-lactide-co-glycolide (PLGA) microspheres (4) have been developed for RNAi. However, cell- or tissue-specific targeted siRNA delivery is highly desirable due to improved gene silencing and lower undesirable side effects than those compared to nontargeted delivery (5–7). Some targeted siRNA delivery vectors that are recently developed are; transferrin antibody targeted cyclodextrin

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(CDP) for Ewing sarcoma in mice (8), HIV-targeted antibody protamine conjugate for mice melanoma xenografts (9), and transferrin antibody targeted cationic liposome for pancreatic tumor xenografts in mice (10). Most of these systems have focused on delivering siRNA to liver and tumors in animal models. Efforts to investigate targeted siRNA delivery to inflammatory leukocyte are vital and hold promise for future RNAi-based therapeutics in autoimmune disease, allergy, viral infections (HIV, ebola, dengue); and blood cancers (lymphoma, leukemia, and myeloma) (6, 11–14). But the greatest challenge is the delivery of siRNA to primary leukocytes that are resistant to conventional methods of transfection based on cationic lipid and polymers. Although electroporation method in in vitro conditions and hydrodynamic injection that force siRNA into cells in vivo have shown some success (15), this may not be feasible to use systemically due to disperse distribution of the leukocytes within the human body. To effectively deliver siRNAs in leukocytes, we have exploited the cell surface adhesion molecules, integrins that mediate adhesive interactions critical for leukocyte migration to sites of inflammation (16). In particular, the integrins β2 and β7 are exclusively expressed on leukocytes (17, 18). With this approach, integrin-targeted stabilized nanoparticles (I-tsNPs) was developed in our laboratory by encapsulating siRNAs within nano-sized neutral liposomes that are selectively targeted to leukocytes via surface-attached antibodies to leukocyte integrins (19–22). In this contribution, we describe the technology for developing I-tsNPs: NPs production, surface modifications, and purification. Characterizations of the NPs for their particle size, zeta potential, antibody-binding efficiency and siRNA entrapment is also discussed. In vitro transfection in TK-1 cells and in vivo delivery in mice have been demonstrated to show the efficacy of the NPs. A model siRNA Ku70, a ubiquitously expressed gene (17) is utilized to show the gene knockdown following delivery via β7 I-tsNPs (9).

2. Materials 2.1. I-tsNP Production

1. Multilamellar liposomes (MLL): L α-Phosphatidylcholine (PC Egg, Chicken), 1,2-Dipalmitoyl-sn-Glycero-3-Phosphoethanolamine (DPPE), cholesterol (Chol) (Avanti polar lipids, Inc., Alabaster, AL). Rotary evaporator (Buchi Corporation, Switzerland), Thermobarrel Lipex extruderTM (Lipex biomembranes Inc., Vancover, British Columbia, Canada) and nucleopore membranes 0.1–1 μm pore size (Nucleopore, Whatman). 20 mM Hepes buffered saline, pH 7.2 (Fluka, SigmaAldrich, Saint Louis, MO), and 1× phosphate buffered saline (PBS), pH 7.4 (Cellgro, Mediatech Inc., Manassas, VA).

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2. Hyaluronan (HA) coated nanoliposomes: Hyaluronan (HA, 751 kDa or 850 kDa, intrinsic viscosity: 14–16 dL/g, Genzyme Corp, Cambridge, MA); 400 mM 1-(3-dimethylaminopropyl)3-ethylcarbodimide hydrochloride (EDAC, Sigma-Aldrich, Saint Louis, MO); 100 mM N hydroxysuccinimide (NHS, Fluka, Sigma-Aldrich, Saint Louis, MO); 0.1 M sodium acetate buffer and 0.1 M borate buffer, pH 8.6. 3. Targeting antibody at 10 mg/mL concentration (FIB504 Ratanti mouse IgG2a against β7 integrin). 1 M ethanolamine hydrochloride, pH 8.5. 4. Purification of I-tsNP: Size exclusion column, sepharose CL-4B beads (Sigma-Aldrich, Saint Louis, MO). 5. Freeze drying of I-tsNPs: Alpha 1–2 LDplus lyophilizer (Christ, Osterode, Germany). 2.2. Characterization of NPs 2.2.1. Particle and Zeta Potential Analysis

Particle size and zeta potential analysis: Malvern Zetasizer nano ZSTM (Malvern Instruments Ltd., Southborough, MA), PBS 1× buffer, pH 6.7 (with 10 mM NaCl) at 20°C.

2.2.2. Binding Efficiency

FACScan flow cytometer/FACSCalibur (BD biosciences), FACS buffer (1% (v/v) FBS and 0.01% (w/v) sodium azide). TK-1 cells (ATCC, Manassas, VA); purified β7-I-tsNP fractions from the size exclusion column; positive control, FIB504 Rat- anti mouse IgG2a against β7 (10 μg/mL); isotype control, purified rat IgG2a (10 μg/ mL), secondary antibody FITC-Anti-Rat Ab IgG2a (1 μg/mL) (BD Pharmingen).

2.2.3. siRNA Entrapment Efficiency in NPs

1. Ku70 siRNAs from Dharmacon (Boulder, CO). The following four used in equimolar ratios siRNA#1: sense 5¢-GCUCUGCUCAUCAAGUGUCUGdTdT-3¢, antisense 5¢-CAGACACUUGAUGAGCAGAGCdTdT-3¢ siRNA#2: sense 5¢-UCCUUGACUUGAUGCACCUGAdTdT-3¢, antisense 5¢-UCAGGUGCAUCAAGUCAAGGAdTdT-3¢ siRNA#3: sense 5¢-ACGGAUCUGACUACUCACUCAdTdT-3¢, antisense 5¢-UGAGUGAGUAGUCAGAUCCGUdTdT-3¢ siRNA#4: sense 5¢-ACGAAUUCUAGAGCUUGACCAdTdT-3¢ antisense 5¢-UGGUCAAGCUCUAGAAUUCGUdTdT-3¢. Alternately, pre-designed ON-TARGETplus siRNA SMARTpool, Gene ID 14375 for mouse Ku70 (Dharmacon Inc., Boulder, CO) can also be used.

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2. Nuclease free water (Ambion Inc., Austin, TX). 3. Human recombinant Protamine (7,500 molecular weight, Abnova, TaipeiCity, Taiwan), or (Spermine, 348.18 molecular weight, Spermidine 255, Biosynth International, Inc., Itasca, IL). 4. Quant-iT RiboGreen RNA assay kit for percent entrapment efficiency (Molecular Probes, Invitrogen, Carlsbad, CA). 2.3. In Vitro Transfection of siRNA Using I-tsNPs 2.4. Ku70-siRNA Delivery In Vivo

T cell lymphoma cell line, TK-1 cells (ATCC, Manassas, VA).

1. Mice: Wild type and β7-integrin knockout mice with C57BL/6 background (Charles River Laboratories, Wilmington, MA). Mice should be maintained in a specific pathogen-free animal facility. 2. 27-gauge needle with a tuberculin syringe to inject to the tail vein of the mice. 3. Bath sonicator (Fisher Scientific) to briefly sonicate liposome suspension before injection. 4. Isolation of splenocytes: K10 medium: RPMI + 10% (v/v) FCS + supplements

70 μm sieves: Nylon sieves, BD Falcon 352350

2% (v/v) FCS: HBSS + 2% (v/v) FCS

Frosted glass slides: VWR 48312–002

RBC (red blood cell) lysis buffer: 8.3 g/L NaCl 0.001 M Tris–HCl, pH 7.5

Small Petri dishes: BD Falcon 35 3002

3. Methods 3.1. I-tsNP Production and Purification

I-tsNPs are nanometer sized hyaluronan coated neutral liposomes possessing targeting moieties on their surface (antibodies to integrin molecules on leukocytes). The preparation involves two critical processes (1) preparation of stabilized NPs by chemical conjugation of hyaluronan that coats the surface of liposomes and (2) introduction of targeting molecules (mAbs) on the surface of the stabilized NPs (Refer to Fig. 1, Schematic). 1. Prepare multilamellar liposomes (MLL), composed of phosphatidylcholine (PC), dipalmitoylphosphatidylethanolamine (DPPE), and cholesterol (Chol) at molar ratios of 3:1:1 (PC:DPPE:Chol), using conventional lipid-film hydration method (17, 18).

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Fig. 1. The schematic showing steps involved in the production of I-tsNPs. Multilamellar vesicle (MLV) prepared by rotary evaporation method is extruded to form a unilamellar vesicle (ULV). Hyaluronan is coated onto surface of liposomes by covalently binding to DPPE part of the lipids in the ULV. A monoclonal antibody (mAb against integrin) is then covalently attached to hyaluronan via an amide bond linkage forming I-tsNPs (e.g., β7 I-tsNPs). Protamine condensed siRNAs are then entrapped within the lyophilized NPs by rehydration to form a transfection complex.

Dissolve the lipids at final concentration of 40 mg/mL in ethanol (96%) by stirring for 30–45 min at 60°C. This is followed by rotary evaporation for 1–2 h at 65°C (see Note 1). 2. Hydrate the lipid film with 20 mL of 20 mM Hepes-buffered saline pH 7.4 or 1× PBS, pH 7.4 to create MLL. Thoroughly Vortex until a thin milky liposome suspension is formed. 3. Incubate the liposome suspension in a shaker (~200 rpm) at 37°C for 2 h to ensure complete mixing and homogeneity (see Note 2). 4. Extrude the resulting MLL into unilamellar nano-liposomes (ULNL) with a Thermobarrel Lipex extruder™ at 65°C under nitrogen pressures of 300–550 psi. 5. Carry out the extrusion in a stepwise manner using progressively decreasing pore-sized membranes (from 1, 0.8, 0.6, 0.4, 0.2, to 0.1 μm), with 10 cycles per pore-size. 6. ULNL are surface-modified with high molecular weight hyaluronan (HA) (751 kDa or 850 kDa intrinsic viscosity: 14–16 dL/g) as described below. 7. Dissolve 20 mg HA in 0.1 M sodium acetate buffer. Stir at 37°C for 30 min to fully dissolve HA. Pre-activate with 400 mg of EDAC, at pH 4.0 and stir for 2 h at 37°C. Centrifuge the extruded liposome suspension (ULNL) for 1–3 h in an ultracentrifuge and resuspend the pellet in 0.1 M borate buffer, pH 8.6. Combine the activated HA with the liposome suspension (ULNL) in a 1:1 volume ratio and incubate overnight at 37°C, with gentle stirring. Separate the resulting HA-ULNL from free HA by washing three times by ultra-centrifugation (1.3 × 105 g, 4°C, for 1–3 h for each wash). 8. Perform the coupling reaction of HA-modified liposomes to mAbs using an amine-coupling method. Incubate 50 μL HA-modified liposomes with 200 μL of 400 mM EDAC and 200 μL of 100 mM NHS for 20 min at room temperature with gentle stirring.

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9. Mix the EDAC-NHS-activated HA-nano-liposomes with 50 μL mAb (~500 μg of FIB504 Rat-anti mouse IgG2a against β7 integrin, 10 mg/mL in HBS or PBS, pH 7.4) and incubate for 150 min at room temperature with gentle stirring. Add 20 μL 1 M ethanolamine HCl (pH 8.5) to block the reactive residues (see Note 3). 10. Purify I-tsNPs (e.g., β7I-tsNPs) to remove uncoupled mAbs using a size exclusion column packed with sepharose CL-4B beads (Sigma-Aldrich, Saint Louis, MO) and equilibrated with HBS, pH 7.4. 11. Prepare the purified particle suspensions for lyophilization: Snap freeze 0.2 mL aliquots in a mixture of 100% (v/v) ethanol and dry ice for ~20–30 min; freeze the aliquots for 2–4 h at −80°C and lyophilize for 48 h using an alpha 1–2 LDplus lyophilizer (see Note 4). 12. Store the lyophilized particles at −80°C until further use. 3.2. I-tsNP Characterization 3.2.1. Particle and Zeta Potential Analysis 3.2.2. Binding Efficiency of NPs

Measure the nanoparticle diameter and surface charge (zeta potential) using a Malvern Zetasizer nano ZS™. Examples of particle size and surface charge of the NPs are given in Table 1.

Flow cytometry is used to confirm intact binding ability of the surface-attached antibodies. 1. Add cells (TK-1) at 0.5 × 106 cells/FACS tube. 2. Wash cells in 1 mL FACS buffer and centrifuge at 200 × g, 5 min, 4°C. 3. Resuspend the pellet with following controls and samples to a final antibody concentration of 10 μg/mL in respective tubes in 50 μL FACS buffer: (a) Cells alone as a mock control; (b) isotype control, purified rat IgG2a; (c) Positive Control, FIB504 Rat-anti mouse IgG2a against β7; and (d) Samples, purified β7-I-tsNP fractions. 4. Incubate on ice for 30 min.

Table 1 Particle size and zeta potential measurements Particle

Diameter

Zeta potential

IgG sNP

127 ± 13 nm

−18.5 ± 1.2 mV

β7 I-tsNP

139 ± 21 nm

−23.7 ± 2.6 mV

All measurements were done in 1× PBS, pH 6.7 (with 10 mM NaCl) at 20°C in a Zetasizer nano ZS, Malvern. Data presented as an average ± SD from n = 4 independent experiments

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β7 I-tsNP

Cell Number

IgG sNP

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102

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β7 expression Fig. 2. Binding efficiency of NPs by FACS. Comparison of control nanoparticles (isotype IgG NPs) and integrin-targeted NPs (β7 I-tsNPs) shows higher binding of β7 I-tsNPs in TK-1 cells that express high levels of β7 integrin. This demonstrates the specificity of the I-tsNPs to integrins expressed on leukocytes for targeted delivery.

5. Wash cells with 1 mL FACS buffer, centrifuge at 200 × g, 5 min. 6. Resuspend the cell pellets and stain with secondary antibody FITC-Anti-Rat Ab IgG2a (1 μg/mL) in 50 μL FACS buffer. 7. Incubate on ice for 20–30 min. 8. Wash with 1 mL FACS buffer, centrifuge at 200 × g, 5 min, 4°C. 9. Resuspend the cell pellets in appropriate volume of FACS buffer and analyze by FACS. An example of FACS analysis is shown in Fig. 2. 3.2.3. Entrapment Efficiency of NPs

Ku70 siRNAs entrapment in I-tsNPs is described as follows: 1. Mix siRNAs with full-length recombinant protamine (1:5, siRNA:protamine molar ratio) or spermine (1:22, siRNA: spermine molar ratio) or spermidine (1:30, siRNA:spermidine molar ratio), in nuclease free water and incubate for 20 min at RT to form a complex. 2. For siRNA entrapment in I-tsNPs, rehydrate the lyophilized nanoparticles (i.e., β7 I-tsNP, IgG-sNP, or sNP; 1–2.5 mg total lipids for in vivo experiments and 10–100 μg total lipids for in vitro experiments) by adding 0.2 mL nuclease free water containing protamine- (or spermine) condensed siRNAs (1,000–3,500 pmol for in vivo experiments and 50–750 pmol for in vitro experiments).

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Table 2 Number of mAb immobilized on the surface and entrapment of siRNAs molecules siRNAs entrapmenta (# of molecules)

Encapsulation efficiency of condensed siRNA

Nanoparticles type

Mean ± SEM

Mean ± SEM

IgG sNP

3,750 ± 1,300

78 ± 10

β7 I-tsNP

4,000 ± 1,200

80 ± 12

The amount of siRNAs that was used for encapsulation was known. Upon encapsulation, a RiboGreen assay (molecular probes) was preformed to assess the amount of siRNAs that was entrapped a

3. Perform the entrapment procedure immediately before use in in vitro transfection or in vivo injection. 4. The concentrations of siRNAs and percent entrapment are determined by a Quant-iT™ RiboGreen™ RNA assay (Molecular Probes, Invitrogen, Carlsbad, CA). An example is given in Table 2. 3.3. Ku70 siRNA Delivery In Vitro in TK-1 Cells (Studied by Flow Cytometry)

1. Plate TK-1 cells in microtiter plates (24 well plate) and culture them overnight at 37°C, 5% (v/v) CO2 (2.5 × 105 cells in 400 μl media/well) without serum or antibiotics. 2. Add 50 μl/well of β7 I-tsNP entrapping siRNAs (e.g., Ku70siRNA) dropwise and shake gently. Spin down the plate at 300 × g for 5 min. Appropriate controls should be included: cells with no treatment; cells with Ku70-siRNA alone; cells with negative control siRNA (e.g., silencer firefly Luciferase siRNA or scrambled siRNA). Culture the cells for 5 h. 3. Add 50 μL of serum containing culture media (10% (v/v) FBS in RPMI) and shake gently by rocking the plate from side to side. 4. Culture cells for further 60–72 h at 37°C, 5% (v/v) CO2 and perform intracellular staining (as described below) for detection of Ku70 to confirm the effect of siRNA delivered using I-tsNP.

3.3.1. Intracellular Staining and Flow Cytometry

1. Transfer TK-1 cells to 96 well V bottom plates. 2. For intracellular staining cells, fix and permeabilize cells with Fix-and-Perm KitTM (Caltag Laboratories, Burlingame, CA).

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Isotype control

KD1

NE

Counts

KD2

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100

101

102 Ku70

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NE -Ku70 expression inTK-1 cells - mock treated cells. KD1 - Knockdown of Ku70 using 100pmol-siRNA delivered viaβ7 I-tsNP inTK-1 cells KD2 - Knockdown of Ku70 using750pmol-siRNA delivered viaβ7 I-tsNP inTK-1 cells

Fig. 3. β7 I-tsNP entrapping Ku70 siRNAs induces silencing in TK-1 cells. FACS histograms show gene silencing effect of β7 I-tsNPs entrapping different concentrations of Ku70 siRNAs (100 and 750 pmol, KD1 and KD2, respectively) in comparison to isotype NPs (gray area under the curve) and mock treated cells (NE).

3. Detection of Ku70 expression performed by adding antibody to Ku70 (purified mouse anti-Ku70, Santa Cruz Biotechnology, Santa Cruz, CA) at a final concentration of 10 μg/mL, 50 μL in FACS buffer. 4. Incubate on ice for 30 min and counter stain with FITCconjugated Goat anti-mouse IgG (BD Pharmingen). 5. Wash with FACS buffer and perform FACS analysis for Ku70 expression. An example of the intracellular stain is given in Fig. 3. 3.4. Delivery of Ku70-siRNA In Vivo

1. Make groups of mice (at least 3 mice/group, preferentially 5–8 mice/group). Make sure to include a mock treated group. 2. Pre-heat mice with a lamp in order to expose their tail veins. 3. Prior to injection – sonicate the suspension for 10 min in a bath sonicator to dissolve any potential aggregates. 4. Use a 30-gauge needle with a tuberculin syringe to inject to the tail vein of the mice. 100–200 μL/mouse with 2.5 mg/kg (50 μg) siRNA entrapped in 250 μg liposomes. 5. 48 or 72 h post injection sacrifice the mice and isolate the spleen (see Note 5). 6. Make a single cell suspension from the spleen as reported in Note 5, and perform an intracellular staining with anti-Ku70 mAb (Santa Cruz) as detailed above.

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4. Notes 1. Liposome preparation: Following the evaporation of organic solvents, it is advisable to pass any inert gas like argon for 2–3 min to completely remove traces of organic solvent and prevent oxidation of lipids. 2. The liposome suspension following 2 h incubation can be stored at 4°C until extrusion procedure. But prior to extrusion, the liposomes should be pre-warmed to 37°C to enable easy extrusion process. 3. Coupling reaction: EDAC/NHS-activated HA-nanoliposomes with antibody reaction mixture can be incubated overnight at room temperature and then blocked with 20 μL of 1 M ethanolamine, pH 8.5. 4. Lyophilization of NPs: Prior to lyophilization of purified liposome fractions, the particle suspensions should be tested for antibody-binding efficiency by FACS analysis as described in Subheading 3.2.2. Select the fractions that give high binding efficiency and pool all the fractions. Aliquots (0.2 mL) of the I-tsNP suspension are added into amber glass vials prior to lyophilization. Depending on the cells that are transfected, I-tsNP fractions can be diluted before aliquots are prepared for lyophilization to give an optimal transfection or gene-silencing efficiency. 5. Isolation of splenocytes: One spleen yields approximately 108 splenocytes, of which ~10% are CD8+ and ~20% are CD4+. (a) Harvest spleens into K10 media, removing as much connective tissue as possible. (b) Place ~3 mL K10 media and the splenocytes in a small petri dish. Homogenize into a single cell suspension: ●

Using the flat top of a 5 mL syringe shaft, homogenize the splenocytes (or)

●

Using the frosted ends of two glass sides, homogenize the splenocytes.

(c) Rinse the sieve or slides with K10 media or 2% (v/v) FCS, then transfer splenocytes into a 15-mL conical tube. (d) Spin down cells for 5 min at 320 × g. (e) Aspirate off supernatant and flick cell pellet to loosen. (f) Resuspend cells in 2 mL RBC lysis buffer. (g) Incubate at 37°C for 5 min. (h) Add 10 mL 2% (v/v) FCS and spin 5 min, 320 × g.

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(i) Aspirate off supernatant and flick cell pellet to loosen. Resuspend cells in 1 mL 2% (v/v) FCS. (j) Place 70-μm sieve onto a 50-mL conical tube. Rinse the sieve with 2% (v/v) FCS. Pass splenocytes through sieve, rinsing with 30 mL 2% (v/v) FCS. Take an aliquot to count cells, taking note of final volume. (k) Spin 5 min at 200 × g, aspirate off supernatant and flick cell pellet to loosen. Resuspend cells at desired concentration. (l) Keep cells on ice or at 4°C. Cells can be kept overnight at 4°C for uses such as feeder cells. References 1. Morrissey, D. V., Lockridge, J. A., Shaw, L., Blanchard, K., Jensen, K., Breen, W., Hartsough, K., Machemer, L., Radka, S., Jadhav, V., Vaish, N., Zinnen, S., Vargeese, C., Bowman, K., Shaffer, C. S., Jeffs, L. B., Judge, A., MacLachlan, I., Polisky, B. (2005) Potent and persistent in vivo anti-HBV activity of chemically modified siRNAs. Nat. Biotechnol. 23: 1002–1007. 2. Zimmermann, T. S., Lee, A. C., Akinc, A., Bramlage, B., Bumcrot, D., Fedoruk, M. N., Harborth, J., Heyes, J. A., Jeffs, L. B., John, M., Judge, A. D., Lam, K., McClintock, K., Nechev, L. V., Palmer, L. R., Racie, T., Rohl, I., Seiffert, S., Shanmugam, S., Sood, V., Soutschek, J., Toudjarska, I., Wheat, A. J., Yaworski, E., Zedalis, W., Koteliansky, V., Manoharan, M., Vornlocher, H. P., MacLachlan, I. (2006) RNAi-mediated gene silencing in non-human primates. Nature 441: 111–114. 3. Akinc, A., Zumbuehl, A., Goldberg, M., Leshchiner, E. S., Busini, V., Hossain, N., Bacallado, S. A., Nguyen, D. N., Fuller, J., Alvarez, R., Borodovsky, A., Borland, T., Constien, R., de Fougerolles, A., Dorkin, J. R., Narayanannair Jayaprakash, K., Jayaraman, M., John, M., Koteliansky, V., Manoharan, M., Nechev, L., Qin, J., Racie, T., Raitcheva, D., Rajeev, K. G., Sah, D. W., Soutschek, J., Toudjarska, I., Vornlocher, H. P., Zimmermann, T. S., Langer, R., Anderson, D. G. (2008) A combinatorial library of lipid-like materials for delivery of RNAi therapeutics. Nat. Biotechnol. 26: 561–569. 4. Khan, A., Benboubetra, M., Sayyed, P. Z., Ng, K. W., Fox, S., Beck, G., Benter, I. F., Akhtar, S. (2004) Sustained polymeric delivery of gene silencing antisense ODNs, siRNA, DNAzymes and ribozymes: in vitro and in vivo studies. J. Drug Target. 12: 393–404.

5. Kawakami, S., Hashida, M. (2007) Targeted delivery systems of small interfering RNA by systemic administration. Drug Metab. Pharmacokinet. 22: 142–151. 6. Dykxhoorn, D. M., Lieberman, J. (2005) The silent revolution: RNA interference as basic biology, research tool, and therapeutic. Annu. Rev. Med. 56: 401–423. 7. Akhtar, S., Benter, I. F. (2007) Nonviral delivery of synthetic siRNAs in vivo. J. Clin. Invest. 117: 3623–3632. 8. Hu-Lieskovan, S., Heidel, J. D., Bartlett, D. W., Davis, M. E., Triche, T. J. (2005) Sequencespecific knockdown of EWS-FLI1 by targeted, nonviral delivery of small interfering RNA inhibits tumor growth in a murine model of metastatic Ewing’s sarcoma. Cancer Res. 65: 8984–8992. 9. Song, E., Zhu, P., Lee, S. K., Chowdhury, D., Kussman, S., Dykxhoorn, D. M., Feng, Y., Palliser, D., Weiner, D. B., Shankar, P., Marasco, W. A., Lieberman, J. (2005) Antibody mediated in vivo delivery of small interfering RNAs via cell-surface receptors. Nat. Biotechnol. 23: 709–717. 10. Pirollo, K. F., Rait, A., Zhou, Q., Hwang, S. H., Dagata, J. A., Zon, G., Hogrefe, R. I., Palchik, G., Chang, E. H. (2007) Materializing the potential of small interfering RNA via a tumor-targeting nanodelivery system. Cancer Res. 67: 2938–2943. 11. Behlke, M. A. (2006) Progress towards in vivo use of siRNAs. Mol. Ther. 13: 644–670. 12. Goffinet, C., Keppler, O. T. (2006) Efficient nonviral gene delivery into primary lymphocytes from rats and mice. Faseb J. 20: 500–502. 13. Marodon, G., Mouly, E., Blair, E. J., Frisen, C., Lemoine, F. M., Klatzmann, D. (2003) Specific transgene expression in human and

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C. Srinivasan et al. mouse CD4+ cells using lentiviral vectors with regulatory sequences from the CD4 gene. Blood 101: 3416–3423. Zhang, Y., Lu, H., LiWang, P., Sili, U., Templeton, N. S. (2003) Optimization of gene expression in nonactivated circulating lymphocytes. Mol. Ther. 8: 629–636. Lai, W., Chang, C. H., Farber, D. L. (2003) Gene transfection and expression in resting and activated murine CD4 T cell subsets. J. Immunol. Methods 282: 93–102. Song, E., Lee, S. K., Wang, J., Ince, N., Ouyang, N., Min, J., Chen, J., Shankar, P., Lieberman, J. (2003) RNA interference targeting Fas protects mice from fulminant hepatitis. Nat. Med. 9: 347–351. Peer, D., Park, E. J., Morishita, Y., Carman, C. V., Shimaoka, M. (2008) Systemic leukocytedirected siRNA delivery revealing cyclin D1 as an anti-inflammatory target. Science 319: 627–630. Peer, D., Zhu, P., Carman, C. V., Lieberman, J., Shimaoka, M. (2007) Selective gene silencing

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in activated leukocytes by targeting siRNAs to the integrin lymphocyte function-associated antigen-1. Proc. Natl. Acad. Sci. USA 104: 4095–4100. Luo, B. H., Carman, C. V., Springer, T. A. (2007) Structural basis of integrin regulation and signaling. Annu. Rev. Immunol. 25: 619–647. Zhu, J., Carman, C. V., Kim, M., Shimaoka, M., Springer, T. A., Luo, B. H. (2007) Requirement of alpha and beta subunit transmembrane helix separation for integrin outsidein signaling. Blood 110: 2475–2483. Peer, D., Shimaoka, M. (2009) Systemic siRNA delivery to leukocyte-implicated diseases. Cell Cycle 8: 853–859. Kim, S. S., Peer, D., Kumar, P., Subramanya, S., Wu, H., Asthana, D., Habiro, K., Yang, Y. G., Manjunath, N., Shimaoka, M., Shankar, P. (2010) RNAi-mediated CCR5 silencing by LFA-1-targeted nanoparticles prevents HIV infection in BLT mice Mol. Ther. 18: 370–376.

Chapter 8 Hammerhead Ribozyme-Mediated Knockdown of mRNA for Fibrotic Growth Factors: Transforming Growth Factor-Beta 1 and Connective Tissue Growth Factor Paulette M. Robinson, Timothy D. Blalock, Rong Yuan, Alfred S. Lewin, and Gregory S. Schultz Abstract Excessive scarring (fibrosis) is a major cause of pathologies in multiple tissues, including lung, liver, kidney, heart, cornea, and skin. The transforming growth factor-b (TGF-b) system has been shown to play a key role in regulating the formation of scar tissue throughout the body. Furthermore, connective tissue growth factor (CTGF) has been shown to mediate most of the fibrotic actions of TGF-b, including stimulation of synthesis of extracellular matrix and differentiation of fibroblasts into myofibroblasts. Currently, no approved drugs selectively and specifically regulate scar formation. Thus, there is a need for a drug that selectively targets the TGF-b cascade at the molecular level and has minimal off-target side effects. This chapter focuses on the design of hammerhead ribozymes, measurement of kinetic activity, and assessment of knockdown mRNAs of TGF-b and CTGF in cell cultures. Key words: Ribozymes, TGF-b, CTGF, Scar formation, Transduction, Oligonucleotides

1. Introduction Transforming growth factor-beta (TGF-b) and connective tissue growth factor (CTGF) play key roles in regulating scar formation in normal wound healing in tissues throughout the body (1, 2). Molecular analyses of pathological scars have found prolonged elevated levels of TGF-b and CTGF mRNAs and proteins, which has led to the hypothesis that fibrotic scars are a result of excessive activities of these two growth factor systems. In addition, CTGF has recently been shown to mediate most of the fibrotic activity of TGF-b, including stimulation of synthesis of extracellular matrix and differentiation of fibroblasts into myofibroblasts (3). Marc De Ley (ed.), Cytokine Protocols, Methods in Molecular Biology, vol. 820, DOI 10.1007/978-1-61779-439-1_8, © Springer Science+Business Media, LLC 2012

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Currently, no approved drugs selectively and specifically regulate synthesis and/or action of TGF-b and CTGF systems. Neutralizing antibodies to TGF-b and inhibitors of TGF-b receptor kinase have been developed and evaluated in clinical trials, but none have received clearance from the Food and Drug Administration. Thus, there is a need for a drug that selectively targets the TGF-b cascade at the molecular level that produces minimal off-target side effects. Our approach has been to develop gene-specific, oligonucleotidebased drugs, specifically hammerhead ribozymes that cleave TGF-b and CTGF mRNA molecules. This chapter gives a start to finish description of how to design, validate, and test in cell culture hammerhead ribozymes that target specific genes and could potentially be used as a therapeutic agent. The first method describes the general steps to design a hammerhead ribozyme. This method is the same for the design of all hammerhead ribozymes. The second method describes how to validate a hammerhead ribozyme’s ability to cleave its targeted mRNA substrate. The ribozyme cleavage time course study and the ribozyme multiturnover study can be done with minimal variation to assess the activity of different ribozymes. Two general approaches are typically used to deliver ribozymes to cells in culture: direct delivery of a chemically modified preformed ribozyme that resists enzymatic degradation by RNases and stable transfection with a plasmid expressing the ribozyme (4). Unprotected, nonchemically modified ribozymes have a half-life of seconds in the body and cell culture. Chemically protected ribozymes can be stabilized with a half-life that can be measured for several hours (5). To circumvent the short half-life of a ribozyme, construction of a plasmid that expresses the ribozymes allows for constitutive expression of the ribozyme. The third method describes the techniques used to make the plasmids containing either TGF-b1 ribozyme or the CTGF ribozyme. The last three sections describe methods to test the efficiency of ribozymes in cell culture using different transfection techniques and four different ways to determine knockdown of the selected protein.

2. Materials 2.1. Ribozyme Design and Synthesis 2.2. Ribozyme Time-Course and Multiturnover Kinetics

Mfold program: mfold.html.

http://bioweb.pasteur.fr/seqanal/interfaces/

1. Oligo deprotection and labeling with g-[32P]-dATP: OligoRNA (10 pmol/mL), 1 mL RNasin (Promega; Madison, WI, USA), 1 mL 0.1 M dithiothreitol (DTT), 3 mL double-distilled water (ddH2O), 1 mL [g32P]-dATP, 1 mL 10× PNK buffer, and 1 mL T4 polynucleotide kinase (Roche Molecular Biochemicals; Indianapolis, IN, USA).

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2. Phenol/chloroform/isoamyl alcohol extraction: A solution of the ratio of 25:24:1, respectively. 3. Sephadex G25 fine spin column (Roche Applied Science). 4. Ribozyme cleavage buffer: 40 mM Tris/HCl, pH 7.5, and 20 mM MgCl2. 5. Ribozyme cleavage stop solution: 6 mL of 90% formamide, 50 mM EDTA (pH 8.0), 0.05% xylene cyanol, and 0.05% bromophenol blue. 6. Polyacrylamide urea running buffer: 1× Tris/borate/EDTA (TBE) (obtained by adding 200 mL of 5× Novex® TBE Running Buffer to 800 mL of deionized water). 2.3. Plasmid Construction

1. Single-stranded synthetic DNA oligonucleotides encoding complementary sequences. 2. Restriction enzymes: NsiI and HindIII. 3. Initial plasmid construct: pTR-UF21HP.

2.4. Analysis of Endogenous Target mRNA Knockdown by a Ribozyme 2.4.1. CTGF Ribozyme Analysis

1. Dulbecco’s modified Eagle’s medium (DMEM), Medium 199, Ham’s F12 nutrient mixture containing 1 mM NaHCO3, and buffered with 25 mM HEPES at pH 7.4. The medium is supplemented with 10% heat-inactivated normal calf serum and 1× antibiotic-antimycotic (Gibco BRL). 2. 200 mg/mL geneticin (G418 Sulfate) dissolved in cell culture media.

Human Cell Culture and Transfection Quantitative Reverse Transcription-Polymerase Chain Reaction

1. TRIzol reagent (Invitrogen, Gaithersburg, MD). 2. 1× TaqMan One-step reverse transcription-polymerase chain reaction (RT-PCR) Master Mix, 900 nM forward, 900 nM reverse primer, 2 mM fluorescent TaqMan probe, and RNA sample (CTGF mRNA standard or 500 ng of sample RNA) to a final volume of 25 mL per reaction. 3. TaqMan glyceraldehyde phosphate dehydrogenase (GAPDH) Control Kit (Applied Biosytems, Foster City, CA, USA).

CTGF Enzyme-Linked Immunosorbent Assay

1. Biotinylated and nonbiotinylated, affinity-purified goat polyclonal antibodies. 2. Blocking buffer: Phosphate-buffered saline (PBS)/0.02% sodium azide/1% bovine serum albumin. 3. Alkaline phosphatase-conjugated streptavidin (1.5 mg/mL, Zymed, South San Francisco, CA, USA). 4. Alkaline phosphatase substrate solution (1 mg/mL p-nitrophenyl phosphate).

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5. Sodium carbonate/bicarbonate buffer/0.02% sodium azide, pH 9.6. 2.4.2. TGF-b1 Ribozyme Analysis Mouse-Immortalized Cell Culture and Transfection

1. Equal parts Ham’s F-12, Medium 199, and DMEM media, containing 20 mM HEPES, 1 mM NaHCO3, 100 U/mL penicillin, and 100 mg/mL streptomycin, supplemented with 10% normal goat serum. 2. Hypoosmolar electroporation buffer (Eppendorf Scientific, Inc., Germany). 3. 200 mg/mL geneticin (G418 Sulfate) dissolved in cell culture media.

RNA Extraction and Reverse TranscriptionPolymerase Chain Reaction

1. TRIzol reagent.

2.5. Analysis of Exogenous Synthetic Target Knockdown by a Ribozyme (TGF-b1): Human Embryonic Kidney298 Cell Culture and Dual Transfection

1. DMEM, with 4.5 g/L glucose and 1 g/L L-glutamine.

2. Superscript TM First-strand synthesis system for RT-PCR (GIBCO BRL).

2. Turbofect reagent (Fermentas Inc.; Glen Burnie, MD, USA). 3. Quanti-BlueTM (InvivoGen, San Diego, CA, USA).

3. Methods 3.1. Ribozyme Design and Synthesis

All potential ribozyme cleavage sites within the human CTGF or TGF-b1 cDNA sequences were initially identified based on the optimal G-U-C nucleotide sequence for hammerhead ribozymes (6). Potential cleavage sites were then evaluated for secondary folding structures around the G-U-C sequence using the theoretical lowest energy conformations calculated using the Mfold program (http://bioweb.pasteur.fr/seqanal/interfaces/mfold.html) (7). Only those sites for which the G-U-C sequence was in a single-stranded, nonbase-paired region were considered further. In addition, the nucleotide sequence of the 20mer centered on the G-U-C site were examined for the relative content of A and U bases, since previous studies had shown that flanking sequences with higher numbers of A and U bases tend to have more rapid release rate constants compared to flanking sequences that are rich in G-C content. The lengths of the 5¢ and 3¢ hybridization arms, which comprise the helical stems I and III of the ribozymes, were five and six nucleotides, respectively. The catalytic core structure was formed by the 21mer with the nucleotide sequence of CUGAUGAGGUCCUUCGGGACGAA (Fig. 1). Taken together,

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Fig.1. Sequence and secondary structure of the synthetic RNAs and their targets. The uppercase letters represent the ribozyme RNA sequences, and the lowercase letters represent the target RNA sequences. Roman numerals label the helices. Arrows indicate the site of c1eavage. (a) CTGF hammerhead ribozymes targeting nucleotide sequences at positions CHR 745 and CHR 859, (b) TGF-b1 hammerhead ribozyme targeting nucleotide sequences at positions THR 576 and THR 1429.

the hammerhead ribozyme sequence formed the 6-4-6-type helical stem structure which was shown to provide optimal in vitro kinetic values (8). Corresponding 33mer RNA hammerhead ribozymes and 12mer RNA targets were chemically synthesized with 2¢-ACE protection. (Dharmacon Research Inc, USA). 3.2. Ribozyme Time-Course and Multiturnover Kinetics

1. Target oligo RNAs are deprotected according to the Dharmacon’s deprotection protocol. Centrifuge tubes briefly. Add 400 mL of 2¢-deprotection buffer to each tube of RNA. Add 800 mL of 2¢-deprotection buffer to oligos with homopolymer stretches of rA longer than 12 bases. Completely dissolve RNA pellet by pipetting up and down. Vortex for 10 s and centrifuge for 10 s. Incubate at 60°C for 30 min. Incubate at 60°C for 2 h for oligos with biotin modifications or homopolymer stretches of rA longer than 12 bases. Lyophilize or SpeedVac to dryness before use. 2. Label with g-[32P]-dATP using the following reaction: 2 mL oligo-RNA (10 pmol/mL), 1 mL RNasin (Promega; Madison, WI, USA), 1 mL 0.1 M DTT, 3 mL ddH2O, 1 mL [g32P]-dATP, 1 mL 10× PNK buffer, and 1 mL T4 polynucleotide kinase (Roche Molecular Biochemicals; Indianapolis, IN, USA).

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3. Incubate at 37°C for 30 min, and then dilute to 100 mL with ddH2O followed by extraction with phenol/chloroform/ isoamyl alcohol (25:24:1). 4. Free nucleotides are removed by passing the aqueous layer on a Sephadex G25 fine spin column. 5. RNA is ethanol precipitated and resuspended in 100 mL ddH2O to a final concentration of 0.2 pmol/mL. 6. Ribozyme cleavage reactions are performed in the presence or absence of various concentrations of ribozyme and target RNA in a reaction mix (20 mL) containing 40 mM Tris/HCl, pH 7.5, and 20 mM MgCl2. Samples are incubated at 37°C and the reaction is initiated by addition of ribozyme to target RNA. 7. At the appropriate times, the reactions are arrested with the addition of a 6 mL of 90% formamide, 50 mM EDTA (pH 8.0), 0.05% xylene cyanol, and 0.05% bromophenol blue. 8. Reaction products are separated on a 15–19% polyacrylamide gel containing 8 M urea which can resolve RNA from 20 to 800 bases. The gel is run according to Invitrogen specifications using 1× TBE running buffer. The gel is run at a constant voltage of 180 V for 50–85 min. The samples are quantitated by radioanalytic scanning (PhosphorImager; Molecular Dynamics, Durham, NC, USA). 9. In the time-course study, reaction mixtures include 10 pmol ribozyme and 100 pmol target RNA (containing 0.2 pmol g-[32P]-target). Reactions are stopped at 0.5, 1, 2, 3, 4, 5, 10, and 30 min, 1, 2, 3, and 15 h. In the multiturnover study, reactions are stopped at 1 min. Reactions include 0.015 pmol/mL of ribozyme and increasing concentrations of target RNA (0.15– 15 pmol/mL) as shown in Table 1 (see Note 1) (Fig. 2). 10. Michaelis–Menton constant (Km) and reaction rate at saturating substrate concentration (kcat) are obtained using doublereciprocal plots of velocity versus substrate concentration. The concentrations of target RNA range from 0.15 to 15 pmol/mL with a constant ribozyme RNA concentration of 0.015 pmol/mL (Fig. 3). 3.3. Plasmid Construction

1. Ribozymes with the better kinetic properties for TGF-b1 or CTGF are selected to test their efficiency in cells. The first step is to synthesize a plasmid that expresses the ribozyme. Singlestranded synthetic DNA oligonucleotides encoding complementary sequences of the ribozyme are chemically synthesized. A second pair of oligonucleotides is constructed which contains a single-nucleotide replacement, shown as the underlined nucleotides (C ® G, G ® C), to create an inactive ribozyme that would assess the general toxicity and antisense effect of the ribozyme.

0

0.15

Ribozyme

Target

1

0.15

0.015

2

Table 1 Multiturnover kinetics analysis

0.3

0.015

3

0.6

0.015

4

0.9

0.015

5

1.2

0.015

6

1.5

0.015

7

3.0

0.015

8

6.0

0.015

9

9.0

0.015

10

12

0.015

11

15

0.015

12

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Fig. 2. Time-course studies of TGF-b ribozymes (Rz) cleaving target RNAs. Cleavage reactions were carried out at constant concentrations of 10 pmol ribozyme and 100 pmol target, and were stopped and analyzed at 0.5, 1, 2, 3, 4, 5, 10, and 30 min, 1, 2, 3, and 15 h. Both of TGF-b1 Rz 1 and TGF-b1 Rz 2 could cut their targets. Prolonged incubations caused a significant increase in cleaved product, although the reaction rates slowed markedly after 30 min. The TGF-b1 Rz 1 was slightly more active than TGF-b1 Rz 2, cleaving 62% compared with 43% at 10 min, and 94% compared with 82% at the end of incubation. Data was collected from three individual tests.

Fig. 3. Multiturnover studies of TGF-b1 Rz 1 and TGF-b1 Rz 2. The concentration of ribozymes and targets is indicated in Table 1. The enzymatic reaction displayed reaction kinetics amenable to Michaelis–Menten analysis. TGF-b1 Rz 1: Km = 2.78 mM, kcat = 74.1/min. TGF-b1 Rz 2: Km = 12.50 mM, kcat = 92.2/min. Although TGF-b1 Rz 1 had lower Vmax and kcat than that of TGF-b1 Rz 2, the lower kcat of this rïbozyme is compensated by its lower Km value. The kcat/Km of TGF-b1 Rz 1 and TGF-b1 Rz 2 were separate, 2.7 × l07/M/min and 7.4 × 106/M/min. Thus, TGF-b1 Rz 1 is 3.6 times more efficient.

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2. The complementary oligonucleotides are annealed, producing NsiI and HindIII restriction sites. The fragments are inserted into the pTR-UF-21HP vector, which has been linearized with HindIII and NsiI restriction enzymes. Synthesis of the ribozyme is driven by the chicken b-actin promoter and CMV enhancer in this vector. The presence and correct orientation of the insert are verified by DNA sequencing. The pTRUF21HP vector contains a hairpin ribozyme following the insert site that self-cleaves the mRNA when transcribed in the cell, yielding a relatively short 3¢ arm of hammerhead ribozyme that improves cleavage efficiency (7). 3.4. Analysis of Endogenous Target mRNA Knockdown by a Ribozyme 3.4.1. CTGF Ribozyme Analysis Human Cell Culture and Transfection

1. Cultures of human newborn foreskin fibroblasts (ATCC; Manassas, VA, USA) are cultured in equal parts DMEM, Medium 199, Ham’s F12 nutrient mixture containing 1 mM NaHCO3, and buffered with 25 mM HEPES at pH 7.4. The medium is supplemented with 10% heat-inactivated normal calf serum and 1× antibiotic–antimycotic (Gibco BRL). Exponentially growing cells are transfected with vector (pTRUF21), inactive ribozyme plasmid (pTR-UF21-In), or active CTGF ribozyme plasmid (pTR-UF21-CHR745) using Lipofecatime reagent (Invitrogen Life Technologies; Carlsbad, CA, USA). 2. Since pTR-UF-21 contains a neomycin-resistance gene; cells that are stably transfected are selected with 200 mg/mL geneticin (G418 Sulfate) added to the culture medium 48 h after transfection. After 7 days, selected cells are transferred to 48-well plates for evaluation of ribozyme effects.

Quantitative Reverse Transcription-Polymerase Chain Reaction

1. Confluent cultures of stably transfected fibroblasts in 48-well plates are held in serum-free medium for 48 h before RNA extraction (Qiagen RNeasy Kit; Valencia, CA, USA). Cells are stimulated for 24 h with 5 ng/mL human TGF-b1 to stimulate CTGF expression. Total RNA is extracted using TRIzol reagent according to the manufacturer’s protocol. 2. CTGF mRNA transcripts are detected using the TaqMan realtime quantitative RT-PCR procedure (9). A standard curve is generated using CTGF mRNA transcripts that are transcribed in vitro using T7 RNA polymerase from a plasmid containing CTGF cDNA. CTGF transcript is precipitated with ethanol and dissolved in diethylpyrocarbonate (DEPC)-treated water. 3. Reactions are assembled in a 96-well optical reaction plate. Each reaction contains 1× TaqMan One-step RT-PCR Master Mix, 900 nM forward primer (5¢-AGCCGCCTCTGCAT GGT-3¢), 900 nM reverse primer (5¢-CACTTCTTGCCCTTC TTAATGGTTCT-3¢), 2 mM fluorescent TaqMan probe ( 5 ¢ - 6 F A M - T T C C A G G T C A G C T T C G C A A G G C C TTAMRA-3¢), and RNA sample (CTGF mRNA standard or

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500 ng of sample RNA) to a final volume of 25 mL per reaction (see Note 2). The plate is analyzed on the ABI Prism 5700 Sequence Detection System (Applied Biosystem, Foster City, CA, USA), which simultaneously performs the RT-PCR and detects a fluorescence signal. A standard curve is generated using the transcribed CTGF mRNA samples (2.3 × 10−2 to 2.3 × 10−6 pmol). The level of GAPDH mRNA is also measured in each sample using the TaqMan GAPDH Control Kit (Applied Biosytems, Foster City, CA, USA), and the number of CTGF mRNA molecules in samples is expressed as pmol CTGF mRNA per pmol of GAPDH mRNA. 4. Levels of mRNA are expressed as mean ± standard error of six replicate samples for each condition, and ANOVA and Tukey’s HSD post hoc test are used to assess statistical significance between times and groups (Fig. 4). CTGF Enzyme-Linked Immunosorbent Assay

1. CTGF is measured in conditioned medium and in cytoplasmic extracts of serum-starved, cultured cells following stimulation for 24 h with 5 ng/mL human TGF-b1 using a capture sandwich enzyme-linked immunosorbent assay (ELISA) with biotinylated and nonbiotinylated, affinity-purified goat polyclonal antibodies to human CTGF (10). A flat-bottom ELISA plate (Costar 96-well) is coated with 50 mL of goat antihuman CTGF antibody (which recognizes predominately epitopes in the N-terminal half of the CTGF molecule) at a concentration of 10 mg/mL in PBS/0.02% sodium azide for 1 h at 37°C. 2. Wells are washed four times and incubated with 300 mL of blocking buffer (PBS/0.02% sodium azide/1% bovine serum albumin) for 1 h at room temperature (see Note 3). 3. The wells are washed four times and 50 mL of recombinant human CTGF protein (from 0.1 to 100 ng/mL) or sample is added and incubated at room temperature for 1 h. 4. After washing, 50 mL of biotinylated goat antihuman CTGF (2 mg/mL) is added and incubated at room temperature in the dark for 1 h, then washed, and 50 mL of alkaline phosphataseconjugated streptavidin is added and incubated at room temperature for 1 h. 5. The wells are washed again and incubated with 100 mL of alkaline phosphatase substrate solution (1 mg/mL p-nitrophenyl phosphate in sodium carbonate/bicarbonate buffer/0.02% sodium azide, pH 9.6). Absorbance at 405 nm is measured using a microplate reader (Molecular Devices, Sunnyvale, CA). 6. CTGF levels are normalized for total protein content of samples using bicinchoninic acid (BCA) protein assay reagent (Pierce Chemical, Rockford, IL, USA) and are expressed as ng/mg

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Fig. 4. Effect of TGF ribozyme on expression in cell culture. (a) Effect of CTGF Rz 1 on CTGF mRNA expression in human dermal fibroblast cultures. CTGF mRNA was then measured using TaqMan quantitative RT-PCR and results were normalized to GAPDH mRNA. The level of CTGF mRNA expression in fibroblasts that were stably transfected with the plasmid expressing CTGF Rz1 was decreased by 55% (p < 0.01, n = 6) compared with nontransfected control fibroblasts. In contrast, transfection of fibroblasts with the empty expression vector pTR-UF21 or with a plasmid expressing the catalytically inactive ribozyme did not significantly alter the levels of CTGF mRNA from control cells. (b) Effect of CTGF Rz1 on CTGF protein expression in human dermal fibroblast cultures. CTGF protein was measured in cytoplasmic extracts and conditioned medium samples using CTGF “sandwich” ELISA and results were normalized for total protein concentration. The levels of CTGF protein measured in conditioned medium of detergent extracts of fibroblasts expressing CTGF Rz1 were reduced by 72 and 71%, respectively, compared with nontransfected control fibroblasts control groups (p < 0.01, n = 6).

protein for six replicate samples for each condition. Sensitivity of the ELISA is 0.1 ng/mL with an intra-assay variability of 3%, which is similar to a previously published ELISA for CTGF (11). 7. Levels of protein are expressed as mean ± standard error of six replicate samples for each condition, and ANOVA and Tukey’s HSD post hoc test are used to assess statistical significance between times and groups.

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3.4.2. TGF-b1 Ribozyme Analysis Mouse-Immortalized Cell Culture and Transfection

1. CCL-1 cells (NCTC clone 929, ATCC, USA) are cultured with equal parts Ham’s F-12, Medium 199, and DMEM media, containing 20 mM HEPES, 1 mM NaHCO3, 100 U/mL penicillin, and 100 mg/mL streptomycin, supplemented with 10% normal goat serum at 37°C. 2. Exponentially growing cells are trypsinized and resuspended in hypoosmolar electroporation buffer (Eppendorf Scientific, Inc., Germany). Cell density is adjusted to 106 cells/mL. Cell suspensions are combined with pTR-UF-21-THRC576 or pTR-UF-21-THR576 (10 mg/mL final concentration, in ddH2O). 400 ml of the mixture is placed in electroporation cuvettes (2-mm-gap width, Eppendorf). Following incubation on ice for 10 min, the suspensions are electroporated in a Multiporator (Eppendorf) at 1 pulse of 400 V for 50 ms. Subsequently, the suspension is incubated in the cuvette for 5–10 min at room temperature. The suspension is then transferred from the cuvette into the culture medium and distributed into a 12-well plate. 3. Because pTR-UF-21 includes a gene for neomycin resistance, cells that are stably transfected are selected using Geneticin (G-418 Sulfate). 48 h after electroporation, G418 is added to the culture medium at a concentration of 600 mg/mL, as determined by a series of concentration tests. After 7 days, the culture medium is changed and the G418 concentration is decreased to 200 mg/mL to maintain selection. Cell monoclones are trypsinized and transferred to another plate to continue culturing in selecting culture medium (G418 200 mg/mL). Nontransfected CCL-1 cells are used as a negative control. Before examining RNA and protein expression, confluent cells in 6-well plates are cultured with 2 mL serum-free medium containing 200 mg/mL G418 for 48 h.

RNA Extraction and Reverse TranscriptionPolymerase Chain Reaction

1. Total RNA is extracted using TRIzol reagent according to the manufacturer’s protocol. Cells are washed with PBS three times followed by addition of 1 mL TRIzol to each well to lyse the cells. 2. Concentration and purity of RNA are measured using spectrophotometry at 260 nm (GeneQuant; Amersham Pharmacia Biotech, Uppsala, Sweden). 260/280 nm ratios of all the samples should be 1.90 or greater. 3. Reverse transcription is performed using a first-strand synthesis kit (Superscript TM First-strand synthesis system for RT-PCR, GIBCO BRL) using oligodeoxythymidine primers. Relative cDNA levels are quantitated by co-amplification of the

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housekeeping gene beta-actin and TGFb1 using PCR Master Mix (Promega). The primer sequences for beta-actin are as follows: forward, 5¢-TGCGTGACATTAAGGAGAAG-3¢ and reverse 5¢-GAAGGTAGTTTCGTGGATGC-3¢. The primer sequences for TGFb1 are forward primer 5¢-GAAGCGCATCGAAGC ATCC-3¢ and reverse primer 5¢-TTGGACAACTGCTCCA CCTT-3¢. 4. Since oligo-dT is used to perform the reverse transcription, the PCR products indicated intact RNA levels. Amplification is performed in a 50 mL reaction mixture using the following conditions: 1 mL of each primer (10 pmol/mL), 2 mL RT reaction product samples, 25 mL PCR master mix, and 19 mL ddH2O. PCR amplifications are initiated at 94°C for 2 min followed by 28 sequential cycles of denaturation at 94°C for 30 s, annealing at 56°C for 1 min, and extension at 72°C for 1.5 min. A final extension cycle at 72°C for 10 min is performed in a thermocycler (Twin blockTH System, San Diego, CA). 5. A video imaging and densitometry software system (Kodak digital science, Eastman Kodak Company) is used to quantify the relative band intensities of beta-actin and TGFb1. TGFb1 mRNA levels are then expressed relative to betaactin (Fig. 5). TGFb-1 Enzyme-Linked Immunosorbent Assay

The protocol for the TGF-b1 ELISA is same as that previously described from the CTGF, except the antibodies used are specific for TGF-b1.

3.5. Analysis of Exogenous Synthetic Target Knockdown by a Ribozyme (TGF-b1)

1. A secreted alkaline phosphatase (sAP) reporter gene driven by an hEF1–HTLV promoter is cloned into pBluescript and contains a multiple cloning site located upstream of the sAP reporter gene.

3.5.1. Production of Secreted Alkaline Phosphatase Target Expression Plasmid 3.5.2. Human Embryonic Kidney298 Cell Culture and Dual Transfection

2. A target sequence, approximately 300 bp, containing the ribozyme target sequence is cloned into the multiple cloning site upstream of the sAP reporter gene.

1. DMEM, with 4.5 g/L glucose and 1 g/L L-glutamine: Medium is supplemented with 10% heat-inactivated normal calf serum and 1× antibiotic–antimycotic (Gibco BRL). 2. Exponentially growing cells are transfected with two plasmids, the first being the psAP Bluescript TGF-b1 target plasmid and the second being a plasmid that expresses one of the following: green fluorescent protein (GFP), inactive TGF-b1 ribozyme,

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Fig. 5. (a) The effect of TGF-b1 Rz 1 reducing TGF-b1 expression was tested by intracontrol RT-PCR. Bands of 213 bps were the housekeeping gene beta-actin and bands at 1,014 bps were TGF-b1 expression in cells. Results showed that TGF-b1 expression in TGF-b1 Rz 1 transfected cells was significantly depressed by comparing with negative and inactive control groups; the decreasing rates are 16.2 and 12.1%, respectively. Semiquantitative study shows that in TGF-b1 Rz 1 transfected cells the TGF-b1 expression was significantly depressed compared with the control groups (p < 0.01, n = 4). (b) Testing the efficiency of TGF-b1 Rz 1 depressed the TGF-b1 protein expression in cytoplasm and culture medium supernatant. Protein expression was reduced by 59 and 37% in cytoplasm and conditioned medium, respectively. Compared with control groups, TGF-b1 Rz 1 depressed the TGF-b1 expression significantly, both in cytoplasm and culture medium supernatant (p < 0.01, n = 4).

and active TGF-b1 ribozyme. Turbofect reagent (Fermentas Inc.; Glen Burnie, MD, USA) is used for the dual transfection following the manufacturer’s protocol (see Note 4). 3. Forty-eight hours after transfection, concentration of sAP protein is assessed using Quanti-BlueTM (InvivoGen, San Diego, CA, USA). Ribozyme activity levels are expressed as relative sAP of GFP expression vector (Fig. 6).

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Fig. 6. The effect of TGF-b1 Rz 1 on exogenous synthetic target as reported by a secreted alkaline phosphatase gene. HEK293 cells were simultaneously transfected with two plasmids, one expressing the TGF-b1 ribozyme target sequence with secreted alkaline phosphatase reporter and the other a plasmid that expresses one of the following: GFP, inactive TGF-b1 ribozyme, or active TGF-b1 ribozyme. A statistically significant difference of 27% was found when comparing the GFP plasmid with the active TGF-b1 ribozyme plasmid.

4. Notes 1. When setting up kinetic experiments, be sure to make reaction mix with the substrate, but wait to add the ribozyme. Also, bring the reaction mix containing the substrate to 37°C and then add the ribozyme. If you do not do this, your reaction will need to be heated up to 37°C and the catalytic activity will be reduced. 2. When performing PCR, always mix the reagents by lightly flicking and quickly centrifuge to bring the reagents to the bottom of the tube. 3. When performing ELISA, cover the 96-well plate during the incubations. Also, when washing, gently tap the 96-well plate on a paper towel to remove all of the wash solution. 4. From our experience, when doing a dual transfection using Turbofect, a ratio of 1:1 of plasmids gave the greatest expression of both plasmids.

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References 1. Border, W.A., Noble, N.A., Yamamoto, T., Harper, J.R., Yamaguchi, Y., Pierschbacher, M.D. Ruoslahti, E. (1992) Natural inhibitor of transforming growth factor-beta protects against scarring in experimental kidney disease. Nature 360: 361–364. 2. Border, W. A., Noble, N. A. (1994) Transforming Growth Factor B in Tissue Fibrosis. N. Engl. J. Med. 331: 1286–1292. 3. Grotendorst, G.R., Duncan, M.R. (2005) Individual domains of connective tissue growth factor regulate fibroblast proliferation and myofibroblast differentiation. FASEB J. 19: 729–738. 4. Lewin, A., Hauswirth, W. (2007) Ribozyme gene therapy: applications for molecular medicine. Trends Mol. Med. 7: 221–228. 5. Lee, P.A., Blatt, L.M., Blanchard, K.S., Bouhana, K.S., Pavco, P.A., Bellon, L., Sandberg, J.A. (2000) Pharmacokinetics and tissue distribution of a ribozyme directed against hepatitis C virus RNA following subcutaneous or intravenous administration in mice. Hepatology 32: 640–646. 6. Shimayama, T., Nishikawa, S., Taira, K. (1995) Generality of the NUX rule: kinetic analysis of the results of systematic mutations in the trinucleotide at the cleavage site of hammerhead ribozymes. Biochemistry 34: 3649–3654.

7. Fritz, J.J., Lewin, A., Hauswirth, W., Agarwal, A., Grant, M., Shaw, L. (2002) Development of hammerhead ribozymes to modulate endogenous gene expression for functional studies. Methods 28: 276–285. 8. Drenser, K.A., Timmers, A.M., Hauswirth, W.W., Lewin, A.S. (1998) Ribozyme-targeted destruction of RNA associated with autosomaldominant retinitis pigmentosa. Invest. Ophthalmol. Vis. Sci. 39: 681–689. 9. Heid, C.A., Stevens, J., Livak, K.J., Williams, D.M. (1996) Real time quantitative PCR. Genome Res. 10: 984–986. 10. Blalock, T.D., Duncan, M.R., Varela, J.C., Goldstein, M.H., Tuli, S.S., Grotendorst, G.R., Schultz, G.S. (2003) Connective tissue growth factor expression and action in human corneal fibroblast cultures and rat corneas after photorefractive keratectomy. Invest. Ophthalmol. Vis. Sci. 44: 1879–1887. 11. Tamatani, T., Kobayashi, H., Tezuka, K., Sakamoto, S., Suzuki, K., Nakanishi, T., Takigawa, M., Miyano, T. (1998) Establishment of the enzyme-linked immunosorbent assay for connective tissue growth factor (CTGF) and its detection in the sera of biliary atresia. Biochem. Biophys. Res. Commun. 251: 748–752.

Chapter 9 Control of the Interferon Response in RNAi Experiments Jana Nejepinska, Matyas Flemr, and Petr Svoboda Abstract The RNA interference (RNAi) and interferons have been an uneasy marriage. Ever since the discovery of RNAi in mammals, the interferon response has been a feared problem. While RNAi became an efficient and widespread method for gene silencing in mammals, numerous studies recognized several obstacles, including undesirable activation of the interferon response, which need to be overcome to achieve a specific and robust RNAi effect. The aim of this text is to provide theoretical and practical information for scientists who want to control interferon response and other adverse effects in their RNAi experiments. Key words: RNA interference, Small interfering RNA, Short hairpin RNA, Double-stranded RNA, Interferon

1. Introduction RNAi is an excellent tool for selective inhibition of gene expression and studies of gene function(s) – when properly used. Otherwise, it is an excellent tool to generate confusing results. While RNAi became a standard tool, the lack of appropriate controls and/or ignorance of nonspecific effects undermined its efficient use in various cases. This text gives a brief overview of adverse effects found in RNAi experiments in mammalian cells and provides guidelines for designing RNAi experiments and identifying one of the frequently encountered undesirable effects – the interferon response. 1.1. RNA Silencing in Mammals and Its Experimental Use

RNA interference (RNAi) and the microRNA (miRNA) pathways regulate gene expression by inducing sequence-specific degradation and/or translational repression of target mRNAs (reviewed for example in refs. 1–4). A common feature of both pathways is 21–22 nucleotide-long RNA molecules serving as sequence-specific

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Fig. 1. RNA silencing in mammalian cells and its experimental use. The miRNA and RNAi pathways and two mechanisms of post-transcriptional silencing are depicted. Note that following Dicer cleavage, the miRNAs and siRNAs share a common pathway. Thus, the final silencing effect is dependent on the degree of homology with a cognate mRNA and the nature of an AGO protein rather than on the origin of the short RNA. The common entry points for experimental activation of RNAi are indicated in blue.

guides for silencing (Fig. 1). These short RNAs are released by Dicer, an RNase III family endonuclease, from various forms of double-stranded RNA (dsRNA). Mammals have only one Dicer protein, common for both RNAi and miRNA pathways. The classical RNAi is initiated by long perfect dsRNA, which is processed by Dicer into double-stranded short interfering RNAs (siRNAs). siRNAs perfectly base-pair with a cognate RNA and guide its cleavage in the middle of the base-pairing sequence. Delivery of chemically synthesized siRNAs into mammalian cells also induces sequence-specific knockdown (5). However, long dsRNA is likely not a natural substrate of Dicer in mammalian somatic cells as dsRNA >30 bp is known to trigger sequence-independent pathways such as the protein kinase R (PKR) pathway (6). Long dsRNA induces RNAi only in oocytes, early embryos, embryonic stem cells, and possibly a few other mammalian cell types (7). Small RNA cloning experiments discovered that virtually all endogenous short RNAs linked to RNA silencing in somatic mammalian cells

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are miRNAs (e.g., refs. 8, 9). miRNAs are transcribed as long primary transcripts (pre-miRNAs) with local hairpin structures that are processed by a nuclear RNase III Drosha-containing complex into short hairpin intermediates (pre-miRNAs). Pre-miRNAs are transported to the cytoplasm where Dicer releases a duplex containing a miRNA. A typical mammalian miRNA imperfectly base-pairs with a cognate 3¢UTR and inhibits protein translation. Despite certain distinctions, mammalian RNAi and miRNA pathways can be seen as one biochemical RNA silencing pathway because the effector complexes loaded by Dicer products appear functionally similar if not identical. Both siRNAs and miRNAs are loaded onto an Argonaute-containing effector ribonucleoprotein (RNP) complex, referred to as miRNP or RISC (RNA-induced Silencing Complex), which executes silencing. Four mammalian AGO proteins (AGO1 through AGO4) associate with miRNAs and are implicated in translational repression (10–12). In addition, AGO2 can mediate endonucleolytic cleavage of a target mRNA in the middle of the base-paired sequence (10, 11, 13). The AGO2mediated cleavage requires formation of a perfect RNA duplex, while imperfect base-pairing, typical for most miRNAs, generally results in translational repression (14, 15). However, examples of miRNAs inducing RNAi-like cleavage also exist (16). Therefore, whether a short RNA will cause RNAi-like endonucleolytic cleavage or will induce the translational repression as a miRNA depends on the degree of complementarity and the AGO protein present, rather than on the origin of the short RNA. Despite this overlap between miRNA pathway and RNAi in mammals, we use the term RNAi for any experimental induction of sequence-specific cleavage, even if triggered by miRNA-like RNAs. Structurally, there are three categories of short RNAs inducing RNAi, commonly referred here as RNAi triggers (Fig. 2): (1) siRNA – Duplexes of 21-22-mers with two nucleotide 3 ¢ overhangs. (2) Class I short hairpin – Based on covalent linking of strands carrying functional siRNA sequences. The minimal class I hairpin contains a 19-bp dsRNA stem and 4–9 nucleotide loop and it is probably not processed like a classical miRNA (17–20). (3) Class II hairpin – Directly modeled after pre-miRNA (18, 21, 22). RNA triggers can be prepared in vitro or expressed from DNA. The two most common experimental designs are (1) transient transfection of commercially obtained siRNAs and (2) transient or stable transfection of vectors expressing class I short hairpins from a pol III promoter. Class II hairpins were used less frequently but they have recently come into focus because they can be expressed together with a reporter from a single pol II promoter, thus providing more versatility than pol III-driven systems. It is important to mention these strategies because some of the nonspecific effects in RNAi experiments can be attributed to the carrying vector or delivery method.

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a

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lack of 3’ overhangs induces IFN via Rig-I

Fig. 2. Structural features of RNAi triggers and their relationship to interferon activation. (a) Three different types of RNAi triggers targeting firefly luciferase. siRNA and class II shRNAs were found in the literature (5, 56). Class I hairpin was modeled according to Brummelkamp et al. (17). Note that class I shRNA sequence represents the whole transcript produced by pol III from a vector while the class II shRNA corresponds to the part of the pol II transcript processed by Drosha and Dicer. Sequence within the gray box indicates the RNA fragment incorporated into the RISC complex. (b) Schematic display of structural features of an siRNA, which can trigger interferon response. See text for original references.

1.2. Nonspecific Effects in RNAi Experiments

Nonspecific effects can be divided into three categories. The first category includes the effects caused by activating interferon-related pathways. These effects are usually independent of siRNA sequence and they involve transcriptional activation of interferon-stimulated genes (ISGs). Although we generally refer to these effects as an interferon response, it should be kept in mind that an interferon response is broader and includes other pathways and effects not discussed here. The second category, referred to as off-targeting (see Subheading 1.4), includes sequence-dependent effects caused by targeting unintended transcripts. Finally, excessive use of RNAi

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triggers may cause unintended effects by saturating the endogenous miRNA pathway resulting in relief of repression of genes repressed by miRNAs. 1.3. Interferon Response

Mammals have a complex system for responding to dsRNA in the cytoplasm. Various forms of cytosolic dsRNA can interact with several proteins that take part in the innate immune response. The interferon pathway is the most ubiquitous sequence-independent pathway induced by dsRNA in mammalian cells (reviewed in detail for example in ref. 23). One of the best-characterized effects of dsRNA is activation of PKR, which phosphorylates translation initiation factor eIF2a and causes general repression of translation. PKR is also involved in the regulation of NF-kB, which plays a key role in interferon induction. Interferon and dsRNA also activate 2¢,5¢-oligoadenylate synthetase (2¢,5¢-OAS) that produces 2¢,5¢-oligoadenylates with 5¢-terminal triphosphate residues that subsequently induce activation of RNase L, a protein responsible for general RNA degradation (23). Experimental induction of RNAi may result in activation of interferon response through some of the aforementioned proteins but there are also mechanisms activating interferons in dsRNAindependent manner. Different stimuli can lead to activation of overlapping but distinct sets of ISGs (24), and in specific cell types, particularly immune cells, the interferon response can be elicited by additional pathways (reviewed in ref. 25). There are diverse features of RNAi triggers that can lead to the interferon activation through some of the proteins recognizing dsRNA. In 2003, two groups reported that transfection of siRNAs as well as pol III-driven shRNA expression can activate the interferon response (26, 27). Sledz et al. reported that transfection of siRNAs using Oligofectamine into different mammalian cells induced an RNAi effect as well as activation of PKR and expression of numerous ISGs (26). Microarray profiling of cells transfected with different concentrations (10, 25, 50 and 100 nM) of siRNAs revealed approximately 50 ISGs induced more than twofold at 48 h post-transfection. Some ISGs were induced at all siRNA concentrations while others only at higher ones (26). However, even mock transfection alone can sometimes have a stimulatory effect on ISGs (28). A detailed analysis of these effects revealed that siRNAs lacking 2-nt 3¢ overhangs activate the interferon system via RNA helicase RIG-I (29). Thus, transfection of higher siRNA amounts may result in appearance of interferon-stimulating small RNAs lacking 2-nt 3¢ overhangs, which seem to be a structural basis for discriminating between Dicer products and other short dsRNAs. Another mechanism of activating interferons in RNAi experiments was described for short hairpins or siRNAs generated by in vitro transcription with phage polymerases (30). In this case, the interferon response is induced by the 5¢ triphosphate GTP created by the

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phage polymerase. Interferon induction by the triphosphate RNA ends is also mediated by RIG-I (31, 32). Therefore, whenever T7 or other phage polymerases are used to produce siRNAs or shRNAs, it is advisable to appropriately process their 5¢ termini and include controls for measuring the interferon induction. The interferon response was also induced by type I shRNAs expressed from H1 or U6 promoters (27, 33). Analysis of U6-promoter vectors, which induced strong ISG activation, identified a critical AA dinucleotide motif near the transcription start site suggesting a design flaw in U6-promoter based vectors (33). The exact mechanism of how the presence of the AA motif induced ISGs is unknown but these results provide a rationale for a better design of U6-driven shRNA vectors. Plasmids with H1 promoter too is not immune to ISG induction, but its exact cause remains unknown (33). Notably, interferon activation may not be caused only by shRNA expression only as lentiviral vectors seem to be less likely to induce OAS (one of the ISGs) than plasmid vectors (27). Finally, interferon induction can be siRNA-specific as several motifs within single-stranded RNAs (ssRNA) from siRNAs, such as UGUGU and GUCCUUCAA, stimulate the interferon response in immune cells (34, 35). In addition, there are also immunostimulatory siRNAs without defined sequence motifs (36). Activation of the interferon response in these cases was likely mediated by TLR receptors and linked to endosomes (reviewed in detail in ref. 37). Most researchers aim to avoid interferon activation while achieving an RNAi effect. Their interest in mechanisms of interferon activation ends when the interferon response is absent in their experiments. One of the common approaches to detect induction of the interferon pathways in cell lysates or even culture media is to employ antibodies recognizing ISGs (see Subheading 3.5.2). However, this approach is less sensitive than the analysis of transcripts of the interferon pathway and it may become costly. Therefore, RT-PCR analysis of some of the ISGs is the most accessible approach to detect if the interferon response was elicited in an experiment (see Subheading 3.5.1). Finally, if one is using microarrays to analyze results of an RNAi experiment, examination of probes detecting ISGs will provide a good indication whether or not the interferon response occurred (see Subheading 3.5.3). 1.4. Off-Targeting

Off-targeting occurs because short RNAs intended to induce Ago2-mediated cleavage of perfectly base-pairing targets imperfectly hybridize to other transcripts. One can view a short RNA introduced into a cell as a novel, abundant miRNA for which the cellular transcriptome is not adapted. This implies that off-targeting is common and can be found in most if not all RNAi experiments. Even worse, off-targeting cannot be effectively predicted as the reliability of computational miRNA target prediction is poor and

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heavily dependent on conservation of miRNA binding sites. But, as we discuss later, off-targeting can be reduced by certain modifications of siRNAs and experimental design and its potentially destructive effect can be reduced by appropriate controls and careful interpretation of results. The extent of off-targeting was not recognized in initial RNAi experiments. The early studies suggested that RNAi silencing requires perfect base-pairing. A sufficient control for RNAi specificity seemed to be an unrelated gene, typically a well-expressed housekeeping gene. Such control would indicate a global repression of gene expression caused for example by global repression of translation and/or nonspecific mRNA degradation: the hallmarks of PKR and 2¢,5¢-OAS activation. However, such controls are unlikely to detect off-targeting. Even if off-targeting would affect expression levels of hundreds of genes in a cell, the chance that one selected marker gene would be affected is slim. The first strong evidence of off-targeting effects was demonstrated when mammalian cells transfected with siRNAs were systematically analyzed using microarrays (38). Jackson et al. found siRNA-specific expression patterns in transfected cells with only a few genes regulated in common by different siRNAs against the same gene. Although the effect was decreased when siRNA concentrations were lowered, the off-target regulation could not be eliminated completely and many of the off-targeted genes showed similar kinetics of targeting as the intended target. A similar effect has been observed with an siRNA targeting a luciferase sequence that has no homology in human genome. Moreover, off-target effects directed by the passive siRNA strand have also been detected. Although off-target regulation could not be completely explained, a portion of it appeared to be caused by partial complementarity between an siRNA and its target, reminiscent of the 5¢ seeding regions of miRNAs (39). Off-targeting with various forms of RNAi triggers has been repeatedly demonstrated (38, 40) and some degree of off-targeting is likely widespread in RNAi experiments. However, improved understanding of siRNA and miRNA target recognition, better siRNA chemistry and pooling provide measures allowing for elimination of off-target effects in future experiments (41). 1.5. Controlling Nonspecific Effects in RNAi Experiments

The nonspecific effects in RNAi experiments are common and the best way to deal with them is to proper experimental design (42, 43). It should become a common policy to accept only those RNAi experiments that include controls truly decreasing the probability of misinterpretation due to nonspecific effects. An ideal RNAi experiment should (1) include one or more sensitive markers for the interferon response, (2) use two or more different siRNAs

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targeting the same gene, (3) contain a rescue control by expressing an RNAi resistant version of the targeted gene, and (4) use phenotypic analysis designed to yield as uncommon phenotype(s) as possible.

2. Materials 2.1. Cell Lines and Culture Media

Selection of cell lines depends on individual needs or preferences. Common cell lines can be obtained from the American Type Culture Collection or other commercial sources. The following protocols are based on experience with the commonly used HeLa and HEK293 cell lines. These cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM, Invitrogen) containing 10% fetal calf serum (FCS, Invitrogen), penicillin (100 U/mL, Invitrogen), and streptomycin (100 mg/mL, Invitrogen).

2.2. siRNAs

There are a number of commercial sources offering siRNA synthesis. They also provide predesigned, and in some cases even verified siRNAs. Among the most common providers are Thermo Fisher Scientific (former Dharmacon), Ambion, Qiagen, and Sigma. We recommend searching Web sites of these providers for information concerning predesigned and validated siRNAs targeting gene(s) of interest. One of the attractive options for RNAi knockdown is to use pools of siRNAs. These can be prepared in vitro from long dsRNA substrates (esiRNA) using own protocol or some of the commercial kits. Or, one can purchase a pool directly from a vendor. Particularly attractive option is to use ON-TARGET plus SMART pool siRNAs (Thermo Fisher Scientific), which reduce off-targeting by pooling siRNAs (see Note 3), which are in addition modified at their 5¢end to reduce miRNA-like behavior (44).

2.3. DNA Oligonucleotides

These should be obtained from a local provider. As mutations in in vitro synthesized oligonucleotides are a common problem during cloning, we highly recommend using purified oligonucleotides from a provider with a good record of producing long DNA oligonucleotides.

2.4. shRNA Expressing Vectors

There are a number of different commercial and noncommercial plasmid vectors for RNAi that are accessible to individual researchers, and it is certainly a worthwhile investment to test several different vectors before committing resources to a specific one. The choice of the vector depends on whether one plans to make transient or stable transfections or to have an inducible or tissue-specific knockdown. Thus, refer to the recent literature and information at manufacturer’s Web sites to choose a suitable experimental setup. The following protocols are designed for inserting oligonucleotides to produce Type I small hairpin from pSuper (OligoEngine) and

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its derivates, such as pTer (20) (Bgl II/Hind III cloning sites) or Type II miRNA-like small hairpins from pTMP or pLMP plasmids (Open Biosystems) (EcoRI/XhoI cloning sites). However, we want to point out that, while we and our collaborators routinely use these vectors, other vectors are not necessarily inferior. To verify the sequence of inserted oligonucleotides, pSuper derivates can be sequenced with T3, T7 and M13 primers. Refer to the exact map of a pSuper derivate to select a suitable primer. For sequencing inserts in pTMP/pLMP vectors, we use the following primers, which are localized upstream of the hairpin insertion site: pTMP: 5¢-TTGACCTCCATAGAAGACACCG-3¢, pLMP: 5¢-CCTCATCACCCAGGTTAAGAT-3¢. 2.5. Reagents

1. Restriction enzymes (Fermentas, New England Biolabs, 10 U/mL) with buffers: BglII and HindIII for pSuper derivates or XhoI and EcoRI for pTMP and pLMP. 2. Agarose (e.g., Invitrogen), and electrophoresis running buffer. We use SB buffer: 10 mM NaOH, 36 mM boric acid. 3. T4 DNA ligase with 10× ligation buffer (Fermentas). 4. Chemically competent Escherichia coli cells (e.g., DH5a strain). shRNA-expressing vectors are usually readily propagated in normal lab strains. However, inverted repeats in plasmids occasionally cause complications. In such a case, one can use strains, which are able to maintain DNA with potentially highly structured sequences, such as Sure (Stratagene) or Stbl4 cells (Invitrogen). 5. Luria–Bertani (LB) medium [possibly terrific broth (TB) medium for more yield). 6. LB agar plates: 1.5% Agar in LB medium with 100 mg/mL ampicillin. 7. TE buffer: 10 mM Tris–HCl (pH 7.5), 1 mM EDTA. 8. Ampicillin: Stock solution 100 mg/mL in water, working concentration 100 mg/mL. 9. 60% Glycerol, sterile. 10. Gel extraction kit (e.g., QIAquick Gel Extraction Kit). 11. Miniprep kit (e.g., QIAprep Spin Miniprep kit). 12. MIDI or MAXIprep kit (e.g., Qiagen HiSpeed Plasmid Midi or Maxi kit). 13. Transfection reagents for plasmid transfection: Turbofect (Fermentas). 14. Transfection reagent for siRNA: Turbofect (Fermentas) or Oligofectamine + OptiMEM (Invitrogen). 15. 30% Fetal calf serum (FCS): A stock made of 50 mL FCS, 1.7 mL 200 mM glutamine, 1.7 mL penicilin (10,000 U/mL)/ streptomycin (10 mg/mL), and 113.6 mL DMEM.

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16. PolyI:C (Sigma), 5 mg/mL stock in water. 17. Plasmid expressing immunostimulatory shRNA, 200 ng/mL.

3. Methods The following protocol is a basic protocol we use for RNAi experiments. Analysis of the interferon response is a compilation of data found in the literature, our experience with occasional appearance of interferon response in RNAi experiments (28), and ongoing analysis of effects of long dsRNA expression in mammalian somatic cells. 3.1. siRNA Sequence Design

Time consideration: 2 h Proper siRNA design is crucial for conducting a successful RNAi experiment. Incorrect siRNA sequence will result in decreased siRNA efficiency and/or specificity of the silencing effect. Own siRNA design carries a risk of a failure and it is not unusual when only one of three, or even four siRNAs induces good repression of the cognate gene. Ideally, one should obtain two siRNAs of different sequences targeting the same gene, which sometimes makes siRNA design a painstaking process. If only a single siRNA is available, one of the controls should include a rescue experiment (see also Subheading 1.5 and Note 2). Therefore, prior to making own siRNA design, it is very useful to search PubMed and WWW (particularly siRNA vendor sites) to find whether suitable functional siRNA sequences are available already. Several important criteria for siRNA design have been identified and they were built into a number of freely available Web-based design tools (summarized for example in ref. 45). Preference of one siRNA design tool over another is to some extent a matter of personal choice (reviewed in ref. 46). We combine two design tools: BIOPREDsi (47) and RNAxs (48) and we subsequently verify the specificity of siRNAs using the Specificity Server (49). BIOPREDsi is a neural network-based algorithm that has been trained on a large set of siRNAs and has been used for a genomewide design of siRNA. BIOPREDsi was a top-scoring approach in a comparative study of several common siRNA design tools (50) and it is routinely used by us or our collaborators. BIOPREDsi is a representative of tools that design siRNAs according to optimized parameters of an siRNA sequence but not taking into an account the interaction of an siRNA with its cognate mRNA. Therefore, we complement the BIOPREDsi siRNA prediction with RNAxs tool, which introduces the analysis of the cognate sequence accessibility. RNAxs predicts secondary structures within the siRNA binding site and evaluates probability of efficient recognition of the binding site by the RISC complex, as it has been shown in biochemical studies (48, 51).

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1. Obtain the target mRNA sequence from NCBI (http://www. ncbi.nlm.nih.gov) or ENSEMBL (http://www.ensembl.org). For a simple knockdown, it is often recommended to use the coding region (CDS) of target mRNA to design siRNAs because the unique coding sequence reduces off-target risk. The downside of using CDS for siRNA design is that a rescue expression construct must carry specifically positioned mutations in the cognate sequence (see the section controls). When targeting a 3¢ UTR, one can just use a different 3¢ UTR to make a rescue expression plasmid. 2. Search for suitable siRNAs at the BIOPREDsi page (http://www. BIOPREDsi.org). Paste the target mRNA sequence in FASTA format (use Readseq to convert the sequence into FASTA format. Readseq online can be found at different sites, we typically use the one available in Sequence utilities at the BCM Search Launcher site (http://searchlauncher.bcm.tmc.edu/)). Make sure to have the “Input Type” set to RNA Sequence. Set “# of predicted siRNAs” to 20, click “Design siRNA sequences” and save the results. (Note: the siRNA design using BIOPREDsi might be a little more time-consuming). If the Web site is not available, you can use another similar siRNA designer Web site, e.g., http://www.dharmacon.com/designcenter/. 3. Repeat the same procedure with the RNAxs (http://rna.tbi.univie.ac.at/cgi-bin/RNAxs). Set “Maximal number of siRNAs” in the Output option to 20 again and click “REPRESS IT”. 4. Pick the sequences ranked from best to worst in RNAxs that also have high scores from the BIOPREDsi prediction. Ideally, at least four siRNAs with high scores should be selected. Verify that these sequences are specific using the Specificity Server (http:// informatics-eskitis.griffith.edu.au/SpecificityServer (49)). Paste the RefSeq code (NM_XXXXXX) of the target mRNA and the siRNA sequence to the Option B “Database search for matches” window and press “Search”. The server returns Automatic recommendation “OK” when the inspected siRNA passes specificity criteria. Similarly, test the specificity of all selected sequences. 5. Optional: To test if siRNAs will be designed with high scores in other siRNA design tools, one can use, for example DSIR (http://cbio.ensmp.fr/dsir/) (52). An extensive list of other siRNA design tools can be found elsewhere (45). 3.2. Production of Vectors Expressing Desired shRNAs 3.2.1. pTer/pSuper shRNA Vector Cloning

Time consideration: 1–2 weeks

1. Design and obtain the sense and antisense oligonucleotides as described in Fig. 3a. 2. Oligonucleotide annealing: Mix 5 mg of sense oligonucleotide with 5 mg of antisense oligonucleotide in TE buffer in a total volume of 100 mL. Place the tube with oligonucleotide mixture

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a

Designed 19 nt siRNA antisense strand (complementary to target mRNA sequence)

Sense oligo Antisense oligo

BglII

H1 promoter

HindIII BGH pA

TRE

SV40 ori

pTER 4500bp Zeocin

Ampicillin

b

Designed 19 nt siRNA sense strand (identical with target mRNA sequence)

Designed 19 nt siRNA antisense strand (complementary to target mRNA sequence)

Sense oligo Antisense oligo

XhoI

EcoRI

5’ miR30 min CMV

3’ miR30 pgk

E TR

5’LTR and ψ

Puromycin

TMP 8238bp

IRES

Ampicillin GFP

3’LTR ORI

Fig. 3. Schematic overview of pTer (a) and TMP (b) vectors. Restriction sites for insertion of oligonucleotides carrying shRNA sequences are visualized. The oligonucleotides should carry sense and antisense strands of the in silico designed 19-nt siRNA as indicated in (a) for pTer and in (b) for TMP.

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in a beaker with a large volume of boiling water (500–1,000 mL) and incubate for 2 min. Turn heating off and leave the oligonucleotide mix in hot water to cool down slowly. Dilute annealed oligonucleotide 100× with TE buffer. 3. pTer vector digestion: Mix 2 mg of pTer plasmid DNA with 2 mL of 10× restriction buffer and add water to a final volume of 18 mL. Add 1 mL of BglII enzyme and 1 mL HindIII enzyme and incubate for 2 h at 37°C. 4. Resolve the digested plasmid in a 1.0% agarose gel in SB buffer and extract DNA from the gel using a commercial gel extraction kit according to manufacturer’s recommendations (e.g., QIAquick Gel Extraction Kit). Elute the extracted DNA in 30 mL of elution buffer. 5. Ligation: Mix 2 mL of digested pTer DNA with 5 mL of annealed diluted oligonucleotide (1 ng/mL); add 2 mL of 10× ligation buffer and nuclease-free water to a final volume of 19 mL. Add 1 mL of T4 DNA ligase and incubate for 2 h at room temperature (RT) or overnight at 16°C. 6. Transformation: Add 2 mL of the ligation mixture to 50 mL of chemically competent E. coli cells (e.g., DH5a strain) and incubate for 20 min on ice. Submit cells to a 30 s heat-shock at 42°C and immediately add 1 mL of LB medium. Incubate for 1 h at 37°C with shaking. 7. Selection: Plate 100 mL of transformed cells on LB agar plate supplemented with 100 mg/mL ampicillin and incubate overnight at 37°C. 8. Vector preparation: Pick several colonies from the selection plate and incubate for 8 h to overnight at 37°C in 5 mL LB media (containing 100 mg/mL ampicillin) each. Isolate the vector DNA using a commercial Miniprep kit (e.g., QIAprep Miniprep kit). Make a glycerol stock of the vector by mixing 750 mL of the remaining culture and 250 mL of sterile 60% glycerol in a cryotube and store in −80°C for later use. Verify the insert sequence by sequencing (it is an important step because oligonucleotides often carry mutations). Once the vector sequence is verified, prepare sufficient amount of DNA for intended experiments. Note: Incompatible ends in the vector allow for direct insertion of annealed oligonucleotides without a need for dephosphorylation of the vector and phosphorylation of oligonucleotides. However, an incomplete restriction digest can produce a high background of empty vectors, so we recommend verifying the presence of the insert by restriction digest prior to sequencing. A typical cloning procedure yields up to a hundred of colonies. If several hundred colonies and more are obtained, this usually indicates high background of empty clones.

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3.2.2. TMP/LMP shRNA Vector Cloning

1. Design and obtain the sense and antisense oligonucleotides as described in Fig. 3b. 2. Oligonucleotide annealing: Mix 5 mg of sense oligonucleotide with 5 mg of antisense oligonucleotide in TE buffer in a total volume of 100 mL. Place the tube with oligonucleotide mixture in a beaker with boiling water and incubate for 2 min. Turn heating off and leave the oligonucleotide mix in hot water to cool down slowly. Dilute annealed oligonucleotide 100× with TE buffer. 3. TMP vector digestion: Mix 1 mg of TMP plasmid DNA with 2 mL of 10× restriction buffer and add water to a final volume of 18 mL. Add 1 mL of XhoI enzyme and 1 mL EcoRI enzyme and incubate for 2 h at 37°C. 4. Separate the digested plasmid in a 1.0% agarose gel in SB buffer (sodium borate buffer: 10 mM sodium hydroxide, pH adjusted to 8.5 with boric acid.) and extract from gel using commercial gel extraction kit (e.g., QIAquick Gel Extraction Kit). Elute the extracted DNA in 30 mL of elution buffer. 5. Ligation: Mix 2 mL of digested TMP DNA with 5 mL of annealed oligonucleotide (1 ng/mL), add 2 mL of 10× ligation buffer and nuclease-free water to a final volume of 19 mL. Add 1 mL of T4 DNA ligase and incubate for 2 h at RT or overnight at 16°C. 6. Transformation: Add 2 mL of the ligation mixture to 50 mL of chemically competent E. coli cells (e.g., DH5a strain) and incubate for 20 min on ice. Submit cells to a 30 s heat-shock at 42°C and immediately add 1 mL of LB medium. Incubate for 1 h at 37°C with shaking. 7. Selection: Plate 100 mL of transformed cells on LB agar supplemented with 100 mg/mL ampicillin and incubate overnight at 37°C. 8. Vector preparation: Pick several colonies from the selection plate and incubate for 8 h to overnight at 37°C in 5 mL LB media (containing 100 mg/mL ampicillin) each. Isolate the vector DNA using a commercial Miniprep kit (e.g., QIAprep Miniprep kit). Make a glycerol stock of the vector by mixing 750 mL of the remaining culture and 250 mL of sterile 60% glycerol in a cryotube and store in −80°C for later use. Verify the insert sequence by sequencing (it is an important step because oligonucleotides often carry mutations). Once verified the vector sequence is verified, prepare sufficient amount of DNA for intended experiments.

3.3. RNAi with Transient Transfection of siRNA with Oligofectamine

Time consideration: 4 days Before performing RNAi experiments, we recommend to test different concentrations of each siRNA (ideally 5, 10, 20, 50, and 100 nM) and subsequently use the lowest concentration required for

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efficient silencing (see Note 1). The following protocol is based on transfection of 50 nM siRNA into HEK293 and HeLa cells grown in 6-well plates. Values/volumes indicate amounts per well, values in parentheses indicate amounts per well for 24-well plates. 1. One day before transfection, plate cells to achieve optimal confluency. Suggested confluency of HeLa cells for transfection with Oligofectamine is ~50%. For HEK293 cells, we recommend a little bit higher confluency (60–70%) as they easily detach. For other cell types, optimization of ideal confluency may be needed. When using media with antibiotics, remove the medium with antibiotics and replace it with a medium without antibiotics before transfection. 2. Prepare siRNA-oligofectamine complexes. For each well, mix in a 1.5-mL tube (Tube A) 2.5 mL (1.25 mL) of siRNA (20 mM stock) and 180 mL (190 mL) of Opti-MEM media, mix gently by tapping on the tube, and incubate for 7 min at RT. To another tube (tube B), add 2 mL (1 mL) of Oligofectamine and 16 mL (8 mL) of Opti-MEM, mix gently by tapping on the tube, and incubate for 7 min at RT. After 7-min incubation, add content of tube B into tube A, mix gently by tapping, and incubate for 20 min at RT. 3. Transfect cells. Wash cells with Opti-MEM, add 800 mL (400 mL) of Opti-MEM and transfection mixture from above, and incubate for 4 h (37°C). Then add 500 mL (250 mL) of 30% FCS, and incubate for 48 h. It is also possible to replace the transfection medium with DMEM containing 10% FCS instead of adding 30% FCS to Opti-MEM. Cells can be cultured from 24 to 72 h after transfection but it should be kept in mind that shorter incubation may not be sufficient for detecting an RNAi effect and longer incubation increases the risk of interferon response. We also use Turbofect (Fermentas) for siRNA transfection with good results. siRNA transfection with Turbofect is performed as described in Subheading 3.4, except siRNA is used instead of DNA to achieve final concentration of 40 nM during transfection. 3.4. RNAi with Transient Transfection of shRNA-Expressing Vector with Turbofect

Time consideration: 1 week Plate cells to achieve density required for transfection. Knowing optimal density is critical for efficient transfection and may differ from one transfection reagent to another. The following protocol is for transfection of HEK293 and HeLa cells grown in 6-well plates, values in parentheses indicate amounts for 24-well plates. 1. For transfection of HeLa or HEK293 cells, dilute cells in culture medium to a density 60,000 cell/mL and plate 3 mL (0.5 mL) of suspension per well, 24 h prior to transfection. For optimal transfection conditions, cells should be 50–70% confluent.

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2. Change culture media just before transfection and add 1 mL (0.5 mL) of fresh media per well. 3. Transfect cells using Turbofect transfection reagent according to the manufacturer’s protocol. Briefly, combine the desired amount of shRNA-containing plasmid (usually 1.5 mg (0.5 mg)) and pBluescript plasmid up to the total DNA amount of 2.5 mg (1.0 mg) for each well and mix DNA with 500 mL (100 mL) DMEM. 4. Mix by pipetting and incubate for 30 min at RT. 5. Add the mixture dropwise into each well. 6. 6 h after transfection, add 1.5 mL (0.4 mL) of fresh DMEM medium supplemented with 10% FCS and antibiotics. 7. Harvest the cells 24–72 h (typically 48 h) after transfection and assay for RNAi effects. Transfection efficiency of pTMP or pLMP can be easily monitored because these plasmids carry a EGFP reporter. To monitor transfection efficiency in pSuper or pTer, one can use an EGFPexpressing plasmid for transfections instead of pBluescript. 3.5. Detection of the Interferon Response in RNAi Experiment 3.5.1. By RT-PCR or Quantitative Real-Time PCR

Table 1 lists several genes transcriptionally activated by the inteferon response found in the literature, which are suitable markers. Particularly IFIT1 (also known as p56) seems to be a suitable, sensitive marker for ISG activation (27, 29). If an analysis of knockdown effects does not include RT-PCR already, isolate total RNA from cells in 6-well plates 48 h after transfection using Trizol (Invitrogen) according to the manufacturer’s protocol. Remove DNA contamination by DNAse I treatment (Fermentas), followed by phenol/chloroform extraction (53). Prepare cDNA using Superscript III Reverse Transcriptase (Invitrogen) primed with random hexamer primers according to the manufacturer’s instructions. Perform quantitative or semi-quantitative PCR using the primers in Table 2.

3.5.2. In Culture

For the detection of interferon IFN-a or b by a conventional sandwich enzyme immunoassay (ELISA), some of the commercially available kits can be used (see Table 3). The IFN concentration in cell supernatants is measured 24–48 h after transfection.

3.5.3. On Arrays

Induction of the interferon pathway in RNAi experiments is generally monitored using RT-PCR or western blotting on interferon response marker genes or proteins, respectively. However, in experiments where effects of RNAi are studied on transcriptome level, the interferon stimulation can be assessed directly from microarray data. In such a case, the overall interferon stimulated gene (ISG) profile may vary between different cell types. We explored publicly available microarray data dealing with interferon induction in

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Table 1 Primers for RT-PCR detection of the interferon activation Primers for human samples

Gene name

Primer

Quantification

References

IFIT1 (p56, ISG56-K)

F R

CTAAGCAAAACCCTGCAGAACG GGAATTCAATCTGATCCAAGACTC

Real-time

This work

IFIT2

F R

GCCACAAAAAATCACAAGCCA CCATTGTCTGGATTTAAGCGG

Real-time

(57)

RIG-I (DDX58)

F R

CATGTCCACCTTCAGAAGTGTCTG GGTTTTTCCACAACCTGTAGGAGC

Real-time

This work

OAS1

F R

CTTTGATGCCCTGGGTCAGTTG CTCTGTAGTTCTGTGAAGCAGGTG

Real-time

This work

STAT1

F R

TGGGTTTGACAAGGTTCTT TATGCAGTGCCACGGAAAG

Semiquantitative

(58)

IFN-a2

F R

GGATGAGACCCTCCTAGACAAAT ATGATTTCTGCTCTGACAACCTC

Real-time

(59)

IFN-b

F R

ATGAGTGGTGGTTGCAGGC AAGCATCAGAGGCGGACTCTGGGA

Real-time

(60)

Primers for murine samples

Gene name

Primer

Quantification

References

Ifit1 (Isg56)

F R

AGAGAGTCAAGGCAGGTTTCTGAG TCTCACTTCCAAATCAGGTATGTCA

Real-time

This work

Ifit2 (Isg54)

F R

ATGAAGCAGGTGCTGAATACTAGTGA TGGTGAGGGCTTTCTTTTTCC

Real-time

(61)

Rig-I (Ddx58)

F R

AGCTTACTCGGAGGTTTGAAGAAA CAGTCAGTATGCCAGGCTTTAGAA

Real-time

This work

Oas1b

F R

AGACGTTGTGGAGTGAAGTTTGAG T CCCAGCTTCTCCTTACACAGTTG

Semiquantitative

This work

Stat1

F R

CACATTCACATGGGTGGAAC TCTGGTGCTTCCTTTGGTCT

Real-time

(62)

IFN-a2

F R

TCTGTGCTTTCCTCGTGATG TTGAGCCTTCTGGATCTGCT

Real-time

(62)

IFN-b

F R

GGAGATGACGGAGAAGATGC CCCAGTGCTGGAGAAATTGT

Real-time

(63)

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Table 2 TaqMan probes for markers for interferon activation in human and mouse cells Gene name

TaqMan probe

Ifit1 (ISG56)

Hs Mm

Hs03027069_s1 Mm00515153_m1

Ifit2 (ISG54)

Hs Mm

Hs01922738_s1 Mm00492606_m1

Rig-I (Ddx58)

Hs Mm

Hs01061434_m1 Mm01216860_m1

Oas1

Hs Mm

Hs00973635_m1 Mm01198570_m1

Stat1

Hs Mm

Hs01014000_m1 Mm01257291_m1

IFN-a2

Hs Mm

Hs00999940_s1 Mm00833961_s1

IFN-b

Hs Mm

Hs01077958_s1 Mm00439552_s1

Table 3 Selection of ELISA detection kits for assaying interferons Product

Manufacturer

Reference

Human IFN-a ELISA kit

R&D Systems, USA

(64)

Human IFN-b ELISA kit

R&D Systems, USA

(64)

Mouse IFN-a ELISA kit

PBL Biomedical Laboratories, USA

(65)

Human (mouse) IFN-b ELISA kit

BioSource International, USA

(66)

RNAi experiments in various human cell types and generated a list of ISGs that were induced (with one exception – OAS2) in at least two studies. These human ISGs with corresponding probe IDs for 3 widely used microarray systems are summarized in Table 4 and their mouse counterparts in Table 5. The expression of the listed genes should be verified in all microarray data obtained from RNAi experiment to exclude nonspecific RNAi mediated interferon stimulation. Eight particularly important known ISG marker genes are highlighted in bold.

RefSeq ID

NM_001111

NM_002983

NM_005195

NM_001565

NM_005409

NM_005101

NM_022873

NM_002053

NM_006877

NM_005531

NM_005532

NM_005533

NM_006417

NM_001548

NM_001547

NM_001549

NM_012420

NM_003641

NM_006435

NM_002199

NM_004029

Gene name

ADAR

CCL3

CEBPD

CXCL10

CXCL11

G1P2

G1P3

GBP1

GMPR

IFI16

IFI27

IFI35

IFI44

IFIT1

IFIT2

IFIT3

IFIT5

IFITM1

IFITM2

IRF2

IRF7

208436_s_at

203275_at

201315_x_at

214022_s_at

203595_s_at

204747_at

217502_at

203153_at

214453_s_at

209417_s_at

202411_at

206332_s_at

204187_at

231577_s_at

204415_at

205483_s_at

210163_at

204533_at

203973_s_at

205114_s_at

201786_s_at

Affymetrix HU133 ID

ILMN_1798181

ILMN_2090607

ILMN_1673352

ILMN_1801246

ILMN_1696654

ILMN_1701789

ILMN_1739428

ILMN_1707695

ILMN_1760062

ILMN_1745374

ILMN_2058782

ILMN_1710937

ILMN_1729487

ILMN_2148785

ILMN_1687384

ILMN_2054019

ILMN_2067895

ILMN_1791759

ILMN_1782050

ILMN_1671509

ILMN_1776777

A_24_P378019

A_23_P136478

A_24_P287043

A_23_P72737

A_24_P30194

A_23_P35412

A_23_P24004

A_23_P52266

A_23_P23074

A_23_P152782

A_23_P48513

A_23_P217866

A_24_P277657

A_32_P107372

A_23_P201459

A_23_P819

A_24_P20607

A_24_P303091

A_23_P31810

A_23_P373017

A_23_P200439

(continued)

(27, 28, 67)

(24, 67)

(26, 54)

(26–28, 54, 67)

(27, 54, 67)

(28, 54, 67)

(26, 28, 54)

(24, 26–28, 54, 67)

(27, 54)

(27, 67)

(27, 28, 54)

(54, 67)

(27, 68)

(24, 27, 28)

(26, 28, 54)

(26, 28, 54, 67)

(27, 68)

(27, 67)

(54, 68)

(67, 68)

(24, 28, 67)

Illumina Human_WG-6v3 ID Agilent Human Genome ID References

Control of the Interferon Response in RNAi Experiments

ENSG00000185507

ENSG00000168310

ENSG00000185201

ENSG00000185885

ENSG00000152778

ENSG00000119917

ENSG00000119922

ENSG00000185745

ENSG00000137965

ENSG00000068079

ENSG00000165949

ENSG00000163565

ENSG00000137198

ENSG00000117228

ENSG00000126709

ENSG00000187608

ENSG00000169248

ENSG00000169245

ENSG00000180733

ENSG00000006075

ENSG00000160710

Ensembl ID

Table 4 List of human ISGs induced by dsRNA or in RNAi experiments inferred from published microarray data

9 151

NM_002201

NM_006084

NM_022168

NM_002462

NM_002463

NM_004688

NM_016816

NM_016817

NM_006187

NM_002759

NM_021105

NM_014314

NM_003113

NM_004509

NM_007315

NM_005419

NM_000593

NM_003265

NM_198183

NM_017414

ISG20

ISGF3G

MDA5

MX1

MX2

NMI

OAS1

OAS2

OAS3

PKR

PLSCR1

RIG-I

SP100

SP110

STAT1

STAT2

TAP1

TLR3

UBE2L6

USP18

ENSG00000184979

ENSG00000156587

ENSG00000164342

ENSG00000168394

ENSG00000170581

ENSG00000115415

ENSG00000135899

ENSG00000067066

ENSG00000107201

ENSG00000188313

ENSG00000055332

ENSG00000111331

ENSG00000111335

ENSG00000089127

ENSG00000123609

ENSG00000183486

ENSG00000157601

ENSG00000115267

ENSG00000213928

ENSG00000172183

Ensembl ID

219211_at

201649_at

206271_at

202307_s_at

205170_at

200887_s_at

209761_s_at

210218_s_at

218943_s_at

202446_s_at

204211_x_at

218400_at

204972_at

205552_s_at

203964_at

204994_at

202086_at

219209_at

203882_at

33304_at

Affymetrix HU133 ID

ILMN_1740200

ILMN_1769520

ILMN_2155708

ILMN_1751079

ILMN_1690921

ILMN_1777325

ILMN_2415144

ILMN_2284998

ILMN_1797001

ILMN_1745242

ILMN_1706502

ILMN_2184262

ILMN_1674063

ILMN_1672606

ILMN_1739541

ILMN_2231928

ILMN_1662358

ILMN_1781373

ILMN_1745471

ILMN_1659913

A_23_P132159

A_23_P75741

A_23_P29922

A_23_P59005

A_23_P76090

A_24_P274270

A_23_P120002

A_23_P349928

A_23_P20814

A_23_P69109

A_23_P142750

A_23_P47955

A_24_P343929

A_23_P64828

A_23_P154235

A_23_P6263

A_23_P17663

A_23_P68155

A_23_P65442

A_23_P32404

(54, 67)

(26, 27)

(28, 54)

(24, 54)

(54, 67)

(26, 27, 54, 67)

(27, 54)

(27, 67)

(54, 67)

(27, 54, 67)

(54, 67)

(26, 27, 54, 67)

(26)

(26–28, 67)

(24, 54, 67)

(27, 67)

(27, 28, 54)

(54, 67, 68)

(27, 54, 67)

(27, 28, 54, 67, 68)

Illumina Human_WG-6v3 ID Agilent Human Genome ID References

There are probe IDs of 3 commonly used microarray systems from Affymetrix, Illumina, and Agilent included. Eight well known ISG marker genes are highlighted in bold

RefSeq ID

Gene name

Table 4 (continued)

152 J. Nejepinska et al.

RefSeq

NM_001038587

NM_011337

NM_007679

NM_021274

NM_019494

NM_015783

NM_010259

NM_025508

NM_008329

NM_029803

NM_027320

NM_133871

NM_008331

NM_008332

NM_010501

NM_026820

NM_030694

NM_008391

NM_016850

Gene name

Adar

Ccl3

Cebpd

Cxcl10

Cxcl11

G1p2

Gbp1

Gmpr

Ifi16

Ifi27

Ifi35

Ifi44

Ifit1

Ifit2

Ifit3

Ifitm1

Ifitm2

Irf2

Irf7

1417244_a_at

1418265_s_at

1417460_at

1424254_at

1449025_at

1418293_at

ILMN_1227573

ILMN_1251696

ILMN_1232667

ILMN_2640765

ILMN_2944666

ILMN_2981167

ILMN_2774340

ILMN_2680136

ILMN_2625290

ILMN_2762944

ILMN_3010089

ILMN_2602581

ILMN_1233293

ILMN_1256257

ILMN_1247446

ILMN_1214419

ILMN_2588570

ILMN_1253919

ILMN_2489167

Illumina MouseWG-6_v2 ID

A_51_P421876

A_51_P316523

A_51_P168459

A_52_P541802

A_51_P359570

A_52_P542388

A_51_P327751

A_51_P487690

A_51_P414889

A_52_P90363

A_51_P408343

A_51_P495986

A_51_P398766

A_52_P463936

A_52_P676403

A_51_P432641

A_51_P444447

A_51_P140710

A_52_P183181

(continued)

Agilent Mouse Genome ID

Control of the Interferon Response in RNAi Experiments

ENSMUSG00000025498

ENSMUSG00000031627

ENSMUSG00000060591

ENSMUSG00000025491

ENSMUSG00000074896

ENSMUSG00000045932

1450783_at

1423555_a_at

ENSMUSG00000028037

ENSMUSG00000034459

1445897_s_at

1426278_at

1452348_s_at

1448530_at

1420549_at

1431591_s_at

1419698_at

1418930_at

1423233_at

1419561_at

1434268_at

Affymetrix 430_v2 ID

ENSMUSG00000010358

ENSMUSG00000079017

ENSMUSG00000073489

ENSMUSG00000000253

ENSMUSG00000028269

ENSMUSG00000035692

ENSMUSG00000060183

ENSMUSG00000034855

ENSMUSG00000071637

ENSMUSG00000000982

ENSMUSG00000027951

Ensembl ID

Table 5 List of mouse counterparts to human ISGs induced in microarrays from RNAi experiments with probe IDs of 3 commonly used microarray systems from Affymetrix, Illumina, and Agilent

9 153

NM_020583

NM_008394

NM_027835

NM_010846

NM_013606

NM_019401

NM_001083925

NM_145227

NM_145226

NM_011163

NM_011636

NM_172689

NM_013673

NM_175397

NM_009283

NM_019963

NM_013683

NM_126166

NM_019949

NM_011909

Isg20

Isgf3g

Mda5

Mx1

Mx2

Nmi

Oas1

Oas2

Oas3

Pkr

Plscr1

Rig-i

Sp100

Sp110

Stat1

Stat2

Tap1

Tlr3

Ube2l6

Usp18

ENSMUSG00000030107

ENSMUSG00000027078

ENSMUSG00000031639

ENSMUSG00000037321

ENSMUSG00000040033

ENSMUSG00000026104

ENSMUSG00000070034

ENSMUSG00000026222

ENSMUSG00000040296

ENSMUSG00000032369

ENSMUSG00000024079

ENSMUSG00000032661

ENSMUSG00000032690

ENSMUSG00000029605

ENSMUSG00000026946

ENSMUSG00000023341

ENSMUSG00000000386

ENSMUSG00000026896

ENSMUSG00000002325

ENSMUSG00000039236

Ensembl ID

Eight well-known ISG marker genes are highlighted in bold

RefSeq

Gene name

Table 5 (continued)

1418191_at

1417172_at

1422782_s_at

1416016_at

1421911_at

1420915_at

1456493_at

1451821_a_at

1456890_at

1429527_a_at

1440866_at

1425374_at

1425065_at

1425119_at

1425719_a_at

1419676_at

1451905_a_at

1426276_at

1421322_a_at

1419569_a_at

Affymetrix 430_v2 ID

ILMN_2433990

ILMN_2431619

ILMN_2697002

ILMN_1250409

ILMN_2657822

ILMN_2510233

ILMN_1214911

ILMN_2846812

ILMN_2717127

ILMN_2911344

ILMN_1250410

ILMN_1216020

ILMN_2670150

ILMN_2613140

ILMN_2755958

ILMN_1239219

ILMN_2707870

ILMN_2648913

ILMN_1233461

ILMN_2735615

Illumina MouseWG-6_v2 ID

A_51_P164219

A_52_P214740

A_52_P85174

A_51_P100327

A_51_P225808

A_52_P496503

A_52_P512201

A_52_P127720

A_52_P523946

A_52_P654841

A_52_P559919

A_51_P472867

A_52_P587071

A_52_P110877

A_51_P125067

A_51_P514085

A_52_P446431

A_52_P121468

A_52_P176013

A_51_P510713

Agilent Mouse Genome ID

154 J. Nejepinska et al.

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Table 6 siRNA sequences and oligos for shRNA expression for deliberate induction of interferon response by small RNAs

3.6. Deliberate Induction of Interferon Response 3.6.1. With Poly I:C

3.6.2. With InterferonInducing shRNA/siRNAs

siRNA (sense strand)

Reference

GUCCGGGCAGGUCUACUUUTT

(36)

AGCUUAACCUGUCCUUCAAdTdT

(35)

UGUCCUUCAAUGUCCUUCAA

(35)

CUACACAAAUCAGCGAUUU

(34)

shRNA (designed for TMP vector)

Reference

TGCTGTTGACAGTGAGCGCTACACAAATCAGCG ATTTTTTTAGTGAAGCCACAGATGTAAAAAAAT CGCTGATTTGTGTAGTGCCTACTGCCTCGGA

This work

TGCTGTTGACAGTGAGCGTGTCCTTCAATGTCCT TCAATTTAGTGAAGCCACAGATGTAAATTGAAGG ACATTGAAGGACATGCCTACTGCCTCGGA

This work

The production of interferons in a positive control sample can be induced by direct addition of the polyinosinic:polycytidylic acid (polyI:C) to the cultured cells. The suggested final concentration in culture media is 50 mg/mL of polyI:C. Add polyI:C to cells 6 h after transfection. As discussed above, some siRNAs may stimulate the interferon response in immune cells (see Table 6). Some siRNAs contain specific immunostimulatory motifs within single-stranded RNAs (ssRNA) from siRNAs, such as UGUGU and GUCCUUCAA, (34, 35), while others do not have defined sequence motifs (36). As positive control for interferon induction, we use two of these siRNAs expressed from pTMP vector (Fig. 4). Oligonucleotides used for production of pTMP vectors are listed in Table 6. For transfection, use the same protocol as above.

4. Notes A good experiment should include appropriate controls, which would help to pinpoint the cause of a problem. The most common problems and their solutions are discussed below. 1. Troubleshooting poor RNAi knockdown. It can be caused by problems with the vector, its delivery, or inefficient siRNA.

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Fig. 4. Interferon response to immunostimulatory shRNA expressed from pTMP vectors. (a) EGFP expression from pTMP EGFP reporter in HeLa cells transfected with pTMP vectors expressing immunostimulatory shRNA (0.5 mg per well in a 6-well plate). (b) Induction of interferon response markers by expressing immunostimulatory shRNAs. HeLa cells were transfected with 0.5 mg of each plasmid pTMP plasmid derivate per well in a 6-well plate and relative interferon response marker expression was estimated by quantitative Real-Time RT-PCR. Expression of the markers in cells transfected with empty pTMP vector was set to 1.

●

Check the quality of the vector DNA (electrophoresis and sequencing).

●

Include a positive transfection control.

●

Increase the amount of the shRNA vector in transfection.

●

Assay at different time points (protein is downregulated later than mRNA).

●

Try another siRNA sequence. 2. Handling interferon response. If the interferon response is detected in an RNAi experiment, the following troubleshooting procedure may help. Verify that the RNAi trigger and not the delivery or induction method cause the effect since delivery methods themselves may induce the interferon response.

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A typical experiment should include appropriate controls, which would help to identify the cause. Changing the amount of the transfection reagent, using another transfection reagent, producing stable inducible cell lines, and titration of inducing and selecting agents to lower doses may help if the interferon response is caused by other factors than the RNAi trigger itself. If the interferon response is detected in an RNAi experiment, the following troubleshooting procedure may help. Verify that the RNAi trigger and not the delivery or induction method cause the effect since delivery methods themselves may induce the interferon response. A typical experiment should include appropriate controls, which would help to identify the cause. Changing the amount of the transfection reagent, using another transfection reagent, producing stable inducible cell lines, and titration of inducing and selecting agents to lower doses may help if the interferon response is caused by other factors than the RNAi trigger itself. If the RNAi trigger seems to be causing the effect and it is an siRNA, the following solutions may work. If the siRNA is from a commercial source, titrate the amount of the siRNA to the lowest effective dose first and/or use an earlier time-point for analysis. For example, the interferon response may be observed at 72 h after transfection but not at 24 or 48 h posttransfection (28). If one needs rather late time-points, it is worth considering the production of inducible stable cell lines. The purity of the siRNA is also very important and changing the manufacturer may help in some cases. Poor quality of chemically synthesized siRNAs may induce the interferon response (29). Also, avoid motifs that stimulate the interferon response discussed above (see Subheading 1.3). Sometimes, switching to another cell line can help, as different cell lines have different sensitivity to the interferon activation (54). As mentioned above, if the siRNAs are home-made by T7 polymerase, treatment of siRNAs with RNAse T1 and alkaline phosphatase to remove 5¢ triphosphate GTP should help (30). If the problem still persists, it is probably more economical to purchase siRNAs from one of the established sources rather than spending time and financial resources to solve the problem. If the problem appears to be linked to an shRNA expressed from a vector controlled by the U6 or H1 promoter, it is advisable to verify that the vector sequence does not contain the critical AA dinucleotide motif near the transcription start site (33). If that is not the case, one should try using another siRNA sequence and/or expression vector (e.g., switching from the type I to the type II hairpin). 3. Dealing with off-targeting. A commentary by Echeverri et al. (42) summarizing the guidelines for appropriate controls in RNAi experiments offers two ways to address off-target effects in RNAi experiments named “the two R’s”: rescue or redundancy.

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Rescue experiments are performed by delivering expression of a functional version of a targeted gene, which is mutated such that the base-pairing with a short RNA is disrupted. If the short RNA is targeting the 3¢UTR, it should not be a problem to appropriately replace the 3¢UTR. If the short RNA is targeting the coding sequence, the situation is little bit more complicated because one has to mutate/degenerate numerous positions within codons of the target sequence. It is important to consider codon usage so as not to introduce more rare codons than necessary. One or two nucleotides should be mutated in the middle of the sequence to impair Ago2mediated cleavage. In addition, it is useful to mutate one nucleotide in a position complementary to nucleotides 2–4 of the binding siRNA sequence to interfere with recognition of the sequence by RISC. Redundancy is another way to address off-target effects. Two or more RNA triggers with distinct sequences producing the same phenotype decrease the probability that the phenotype is caused by off-targeting. However, some phenotypes are fairly common (e.g., slower growth, apoptosis, developmental arrest), so using two or even more different RNAi inducers may not be enough to decipher if the phenotype is specific to the downregulated gene or not. In such a case, a rescue experiment is highly advisable. When microarray analysis is being performed on knockdown cells, one can estimate the extent of off-targeting on the cellular transcriptome for each short RNA used. Similarly to analysis of hexamer and heptamer seed enrichment among upregulated transcripts upon repression of RNA silencing (55), one can test if the seeding motif corresponding to the 5¢ end of an active siRNA strand is significantly enriched in 3¢UTRs of transcripts downregulated in knockdown cells. It must be pointed out that “nontargeting” controls (scrambled siRNAs or shRNAs, or short RNAs against nonexpressed genes such as EGFP, RL-luciferase, etc.) are not appropriate controls for off-targeting for reasons mentioned above. It is a common misconception that ignores the fact that offtargeting is individual to each RNAi trigger because it is sequence-specific. “Nontargeting” RNAi triggers rather serve as controls for the sequence-independent effects, such as interferon response and saturation of RNA silencing with an excess of exogenous short RNAs.

Acknowledgements We thank Witold Filipowicz group at the FMI for sharing their experience and protocols and Daniela Schmitter, Radek Malik, and Lenka Sarnova for help with preparation of the manuscript.

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Chapter 10 shRNA-Induced Interferon-Stimulated Gene Analysis Núria Morral and Scott R. Witting Abstract RNA interference (RNAi) is a cellular mechanism to inhibit the expression of gene products in a highly specific manner. In recent years, RNAi has become the cornerstone of gene function studies, shortening the otherwise long process of target identification and validation. In addition, small interfering RNA (siRNA) and short-hairpin RNA (shRNA) therapies are being developed for the treatment of a variety of human diseases. Despite its huge potential for gene silencing, a hurdle to safe and effective RNAi is the activation of innate immune responses. Induction of innate immunity is dose- and sequence-dependent, and is also influenced by target tissue and delivery vehicle. Research on the molecular mechanisms mediating this response is helping to improve the design of the RNAi molecules. Nevertheless, appropriate testing for the presence of this undesired effect is needed prior to making conclusions on the outcome of the silencing treatment. Key words: RNA interference, Short-hairpin RNA, Gene transfer, Animal models, Interferon response, Interferon-stimulated gene

1. Introduction RNA interference (RNAi) is the phenomenon by which noncoding double-stranded RNA (dsRNA) suppresses the expression of a gene. Although initially discovered in the nematode Caenorhabditis elegans, it was later realized that this is a widespread system to regulate gene expression, and an important mechanism to coordinate complex developmental as well as physiological processes in higher eukaryotes. RNAi is mediated by short RNA molecules of 21–28 nucleotides (nt) that bind to target mRNA, triggering protein translation inhibition or mRNA degradation, based on the degree of homology between the dsRNA molecule and the mRNA. RNAi has become an exceptional tool in molecular biology

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research, shortening the time-consuming process of studying gene function and generating novel drug treatments. In addition, this technology is being used as a therapeutic drug to suppress gene expression. Small interfering RNA (siRNA) are chemically synthesized dsRNA molecules that upon delivery into cells or tissues, block gene expression for a few days. Re-administration is necessary for prolonged silencing. Alternatively, short-hairpin RNA (shRNA) can be used to provide a continuous source of silencing molecules. shRNA expression cassettes are engineered using RNA polymerase II or RNA polymerase III promoters, and can be delivered to cells using plasmids or viral vectors such as retrovirus, lentivirus, adenovirus, or adeno-associated virus (AAV). An undesirable aspect of RNAi that limits its use is the activation of innate immunity. Mammalian cells have evolved to recognize single or double-stranded RNA as part of the innate immune response to pathogen infection, and siRNA/shRNA have the capacity to activate similar responses. Cells respond to the presence of foreign RNA by activating RNA binding proteins such as 2¢,5¢-oligoadenylate synthetases (OASs), dsRNA-dependent protein kinase (PKR), cell surface and endosome-expressed Tolllike receptors (TLRs) 3, 7, and 8, and the cytosolic ssRNA/dsRNA sensors retinoic acid-inducible gene I (RIG-I) and melanoma differentiation-associated gene 5 (MDA5) (1, 2). The RNA sensor 2¢,5¢-oligoadenylate synthetase promotes the conversion of ATP into short 2¢,5¢-oligoadenylates that bind to and activate RNaseL, resulting in cleavage of cellular and viral RNA, and inhibition of protein translation (3, 4). PKR, RIG-I, MDA5, and TLR activation initiate signal transduction events that lead to the production of interferons (IFNs) (5–8), and consequent up-regulation of interferonstimulated genes (ISG). Interferons represent part of the innate immune system and can induce proapoptotic events in response to bacteria, virus, and fungi infections to protect other cells from infection (9). While TLR activation is triggered predominantly in immune cells, cytoplasmic RNA sensors such as PKR and RIG-I are present in nonimmune cells and can induce type I IFN (IFNa/b) and inflammatory cytokine production (10, 11). Activation of the IFN response is sequence-dependent. For example, the GUCCUUCAA motif activates TLR7 (12), and the UGUGU motif is a potent inducer of the interferon response (13). More recently, GU-rich 4-mer RNA sequences such as UUGU, GUUC, GUUU, UUUC, UGUU, or UCUC, have been shown to stimulate TLR7 and TLR8 activity and produce IFN-a (14, 15). Soon after the discovery of RNAi, siRNA of 21–23 nt were found to bypass PKR activation and subsequent IFN production (16), triggering a major interest in using the system to silence gene expression in vitro (16–19) and in vivo (20–27). However, it has become clear that siRNA and shRNA molecules have the capacity to induce these unspecific effects (7, 13, 26, 28–31) through

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ssRNA or dsRNA recognition by RIG-1, MDA-5, and TLRs (28, 32, 33). siRNA activate IFN production in a sequence and dosedependent manner (12, 13, 26). Interestingly, incorporating 2¢-O-methyl (2¢OMe) uridine or guanosine nucleosides into the antisense strand of siRNA prevented an interferon response (34). Other structures that have been found associated with interferon production are blunt-ended dsRNA and ssRNA with 5¢-triphosphates (35–37). We have found that adenovirus-mediated expression of shRNA in liver results in similar responses to those observed with siRNA, i.e., activation of IFN production is sequence and dosedependent (26, 27). An important aspect of in vivo studies is the fact that expression of the shRNA is likely to occur in immune cells present in the target tissue. For example, the liver is a complex organ with multiple cell types (38). Hepatocytes comprise approximately 60% of cells, while sinusoidal endothelial cells (SECs), Kupffer cells, and hepatic stellate cells represent 20, 15, and 5% of cells, respectively (38). Adenoviral vectors transduce hepatocytes very efficiently (39, 40), and can infect hepatic stellate cells (41), and macrophages (42, 43). Kupffer cells are specialized macrophages located in the walls of the sinusoids and form part of the mononuclear phagocyte system. Thus, these cells have high potential for recognizing shRNA hairpin structures as foreign RNAs and stimulate interferon production. Based on these observations, it has become essential that investigators conduct rigorous studies to determine whether innate immunity is induced in their specific gene silencing application. One of the most sensitive ways to measure the presence of immunostimulation is quantification of mRNA levels of known interferonstimulated genes, such as OAS (1), interferon-stimulated gene 56 (ISG56, also known as IFIT1), and interferon-stimulated exonuclease gene 20 kDa (ISG20) (44). The measurement of secreted cytokines is not the most reliable method, as cytokine levels increase for a short time and may not be detectable systemically. Thus, a negative result does not necessarily mean absence of induction. Gene expression quantification in the tissue of interest remains the most reliable and sensitive assay. In addition, tissue sections can be evaluated histologically to look for signs of inflammation and cell death, to confirm the gene expression data.

2. Materials 2.1. shRNA Expression Cassette

1. BLOCK-iT™ U6 RNAi Entry Vector Kit (Invitrogen, Carlsbad, CA). 2. Top and bottom strand oligonucleotide primers. 3. E. coli XL1-Blue Subcloning-Grade (Stratagene, La Jolla, CA).

Competent

Cells

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4. LB powder (Sigma, St. Louis, MO). Dissolve 25 g in 1 L deionized water. Autoclave for 15 min at 121°C. Store at room temperature. 5. Ampicillin: 100 mg/mL. Store at −20°C. 6. LB agar EZmix (Sigma) – ampicillin (50 mg/mL) plates. Suspend 35.6 g in 1 L of deionized water. Autoclave for 15 min at 121°C. Transfer to a water bath at 50°C for 30 min. Add 0.5 mL 100 mg/mL ampicillin. Mix well and distribute 30–40 mL per plate. Store at 4°C. 7. QIAprep Spin Miniprep Kit (Qiagen, Valencia, CA). 8. SeaKem LE Agarose (Lonza, Rockland, ME). 9. 10× DNA gel loading buffer (5 Prime, Gaithersburg, MD). 10. TBE solution (5×): 270 g Tris base, 137.5 g boric acid, 100 mL 0.5 M EDTA, pH 8. Store at room temperature. Working 1× solutions are prepared by dilution in deionized water. 11. Molecular weight DNA marker: 1 kb ladder (New England Biolabs, Ipswich, MA). 2.2. Cell Culture

1. HEK293 cells (Microbix Biosystems, Toronto, Ontario). 2. Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco/BRL, Bethesda, MD) supplemented with 10% (v/v) fetal bovine serum (FBS, HyClone, Ogden, UT) and 100 U/mL penicillin, 100 mg/mL streptomycin (Gibco/BRL). 3. Trypsin/EDTA: 0.05% trypsin, 0.53 mM EDTA (Mediatech Inc., Manassas, VA). 4. Metafectene® Pro (Biontex-USA, San Diego, CA).

2.3. RNA Isolation

1. RNeasy Maxi kit (Qiagen). 2. Nuclease-free water (Ambion, Austin, TX).

2.4. Real-Time RT-PCR

1. QuantiTect SYBR Green RT-PCR kit, real-time One-Step RT-PCR (Qiagen, Valencia, CA). 2. ABI 7500 real-time PCR System (Applied Biosystems, Foster City, CA). 3. MicroAmp® Optical 96-Well Reaction Plate with Barcode (Applied Biosystems). 4. MicroAmp® Optical 8-cap strip (Applied Biosystems). 5. Primers: forward and reverse (50 nmol scale). Prepare a stock solution of 100 pmol/mL, in 10 mM Tris–HCl, pH 7.5 (Ambion). Store at −20°C.

2.5. Liver Enzyme Function Markers

Alanine Aminotransferase (ALT) kit (Pointe Scientific, Canton, MI).

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3. Methods 3.1. Hairpin Design

Two features are important to assemble an shRNA expression cassette: (1) promoter and (2) sequence of the hairpin. RNA polymerase II (45, 46) and RNA polymerase III (17, 18, 47) promoters have been used to drive expression of shRNA. The fact that cellular microRNA are largely transcribed from RNA polymerase II promoters, opened the possibility to use this type of promoter for expression of shRNA. However, transcripts generated by RNA polymerase II include 5¢ end caps and 3¢ polyadenylated [poly(A)] tails, which could potentially interfere with processing of the hairpin. One successful approach has been to design a minimal poly(A) signal (22). The cytomegalovirus (CMV) promoter has been used to drive expression of an shRNA embedded in the well-characterized miR30 transcript followed by a minimal polyA signal (48–50). The RNA polymerase III promoters of the small nuclear RNA genes U6 and H1 are commonly used, as these promoters are ubiquitously expressed, resulting in high level shRNA expression in most cell types, and have well-defined transcriptional start sites and stop signals (four to six consecutive thymidines) that yield a 2-nucleotide, 3¢ overhang. The U6 and H1 promoters have been used successfully to express shRNA in vivo (26, 51–55) and the first one will be used in the protocol described here. Selection of the hairpin sequence is the most important aspect to prevent interferon responses. As mentioned above, there are motifs that are known to stimulate interferon production (12–15) and these motifs should be avoided in the construct. Nevertheless, even if these are avoided, it is still possible that an interferon response will be induced from unknown motifs. Most constructs are designed to target a 19–21-nucleotide sequence of the mRNA. A couple of approaches can be used to express shRNA. The first one consists of generating a structure that has a stem with complete homology to the target sequence (17, 47). This is the more straightforward and the most commonly used approach. The second involves a longer structure, with the features of a microRNA (45, 56, 57). The target sequence is embedded within the microRNA, and after processing, the final siRNA is the same as a processed “naked” shRNA. We describe the design of “naked” shRNA constructs. 1. The target transcript sequence can be obtained from the Entrez database provided by the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/Entrez). Use the open reading frame (ORF) to select for potential sequences to be targeted. If the ORF is short, the 5¢ and 3¢ untranslated regions can also be included. For the beginner, several companies (Ambion, Dharmacon, Invitrogen) offer shRNA libraries and provide premade constructs. In the majority of cases, these constructs have not been validated, though, and the investigator is expected to test them. Alternatively, one can design the hairpin using algorithms currently available on the websites

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of the same companies. To use the BLOCK-iT™ U6 RNAi Entry Vector Kit, go to http://rnaidesigner.invitrogen.com/ rnaiexpress/setOption.do?designOption=shrna&pid=2095707 992663696800 (BLOCK-iT™ RNAi Designer). Introduce the nucleotide sequence or accession number and the species, and select pENTR™/U6 and sense/loop/antisense (choosing antisense/loop/sense will work equally well). The system includes a Basic Local Alignment Search Tool (BLAST) search for potential homologies to mRNAs other than the target. Click on “RNAi design.” Once target sequences are obtained, choose the top four on the list. A web-based algorithm to identify mRNAs with “seed” matches (nucleotides 2–8 starting the 5¢ of the guide strand) to the mature shRNA sequence can also be used to discard sequences that can potentially induce off-target effects (http://www.dharmacon.com/seedlocator/seedlocatortemplate. aspx). Sequences with the least number of matching nucleotides in genes are potentially less susceptible to give rise to these effects. Once four appropriate target sequences have been selected, click “Design shRNA oligos.” Choose one of the default loop sequences, and select “Design.” The sequence of the top and bottom sequences will appear. Oligonucleotides should be ordered at the 50 nmol scale, desalted, and unmodified. 2. Annealing and cloning into the plasmid is done following the kit instructions. It is a straightforward sticky-ends cloning step. 3. Transform E. coli subcloning grade bacteria, following the manufacturer’s recommendations. Use 2–4 mL of the ligation product and 50 mL of bacteria solution per construct. Include pUC18 as control for efficacy of the transformation, and a negative control without plasmid. Incubate the plates overnight at 37°C. 4. For each construct, test four to five colonies. Grow each colony in 3 mL LB broth containing 100 mg/mL ampicillin for 16 h at 37°C. 5. Prepare minipreps using the QIAprep Spin Miniprep Kit. The DNA should be sequenced to determine the correct insert that has been incorporated. 6. Testing the constructs for silencing efficacy can be done by transfection of a cell line known to express the target gene. An important point to keep in mind is that the level of transfection should be at least 50%, otherwise it will be difficult to observe silencing. Alternatively, a construct expressing the target gene can be generated and co-transfection experiments in HEK293 cells can be designed. We have successfully knock down genes following this approach. Plate 4 × 105 HEK293 cells in six-well plates, using DMEM medium, supplemented with 10% (v/v) FBS. The next day transfect cells using 0.5 mg plasmid expressing the target gene plus 1 mg plasmid expressing the shRNA.

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Metafectene Pro is used at a 4:1 ratio. As control, use the plasmid expressing a scrambled sequence. The level of knock-down is compared to the scrambled construct. 7. Simultaneously to testing for efficacy to silence the target gene, constructs can be tested for interferon response induction in the same cell line (28, 33). Nevertheless, one has to keep in mind that even if a negative result is observed, there is a possibility that the shRNA triggers interferon production in vivo, as the response can differ among cell types, means of delivery of the shRNA, and timing (58). In particular, when targeting tissues that are rich in dendritic cells, macrophages, or other immune cells, there is a possibility for activating RNA-binding receptors present in these cells. 3.2. Viral Vector Selection

For delivery of the shRNA expression cassette in vivo, a viral vector is typically used. Several viral systems have been reported for the use of shRNA, including retroviral and lentiviral vectors, adenovirus vectors, and adeno-associated virus (AAV) vectors. Retroviral and lentiviral vectors are useful tools for transducing cells in studies that require integration of the shRNA-expression cassette in the host genome (59, 60). Lentiviruses, in particular, are optimal delivery vectors because they can transduce quiescent as well as dividing cells. This vector system has been used for in vivo (61–64) and ex vivo (55, 65) approaches. Lentiviral vector systems are available from Dharmacon, GenScript, and Invitrogen. Adenovirus vectors can transduce a large variety of cell types and have the advantage over other viral systems of being produced at high titers. A few in vivo studies have shown it is possible to obtain a significant level of gene silencing using first generation (E1-deleted) vectors (22, 66–68). However, a limitation of this type of vector is that it is only suitable for short-term expression and it expresses VA1 noncoding RNA, known to interfere with the RNAi processing pathway (69). The latest generation of adenoviral vectors has all viral genes deleted, leaving only the ITRs and packaging sequence [known as helper-dependent (HDAd), high-capacity or “gutless” adenovirus]. This more advanced vector system yields long-term transgene expression in liver upon intravenous administration (70–72). Localized applications in the brain (73–75), muscle (76, 77), and lung (78) also resulted in persistent expression. We have used this system in liver to silence genes for several weeks (26, 27). Adenoviral vectors can be generated using commercially available kits from Invitrogen, GenScript, Agilent Technologies, and Microbix Biosystems. AAVs are single-stranded DNA viruses highly used in the field of gene therapy (79). AAV2 transduces a wide range of cell types, and several other serotypes have been shown to transduce specific tissues with greater efficiency. As described above for adenoviral vectors, local injection of AAV in tissues results in long-term expression.

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Disadvantages of this vector in the context of shRNA expression include a significant delay in transgene expression on the order of weeks – depending on serotype (80). Also, AAV is known to form high molecular weight DNA concatemers (81). This property could make it difficult to determine optimal vector doses, and result in toxicity as extremely high amounts of shRNA are expressed. AAV shRNA-expressing vectors have been used in a number of in vivo studies (53, 82, 83) and can be generated using commercially available kits from Cell Biolabs, Inc. 3.3. RNA Isolation and qPCR

For accurate mRNA quantification of interferon-responsive genes, forward and reverse primers are designed to generate an amplicon of approximately 150–250 bp and to bind different exons of the gene. This prevents amplification from any potential DNA contaminating the RNA solution. Primer sequences for mouse OAS1b, ISG56, and ISG20 are provided in Table 1, and are synthesized at the 50 nmol scale. If setting up a new primer pair, use a primer design application such as MacVector (Accelrys Inc., San Diego, CA) to determine whether the sequences chosen are free from hairpin structures, 3¢ self-dimers or duplex formation. As a normalizer, the b-actin, U6, or L32 ribosomal protein genes can be used. We have found that b-actin and other cytoskeleton-related genes are often upregulated in livers of animals that developed the IFN response (probably as the result of increased cell death and subsequent hepatocyte repopulation), thereby impairing using these genes as controls for RNA loading. The small nuclear gene U6 or the L32 ribosomal proteins are alternative genes. However, a Student’s t-test should be done to confirm that the gene used as normalizer is not differentially expressed between groups. When working with RNA, use gloves at all times and use solutions prepared with nuclease-free water to prevent RNA degradation. 1. Administer the viral vector in vivo, including the following groups: vehicle, viral vector expressing a scrambled shRNA sequence (shSCR), viral vector expressing the target shRNA sequence (shTG), viral vector without an expression cassette (NEC). 2. The timing for sacrificing the animals is an important aspect. Consider looking at multiple time points. The interferon response is short-lived and absence of positive data does not mean absence of the response at other time points. Using adenoviral vectors expressing shRNA in liver, we have detected interferon responses 1 week after vector administration. 3. Total RNA is isolated from approximately 100 mg tissue. Treat with DNase to remove contaminating DNA. 4. Using total RNA from a vehicle-treated sample, prepare a standard curve with 100, 50, 25, 12.5, and 6.25 ng/mL, by doing serial 1:2 dilutions (e.g., transfer 20 mL of first dilution to a clean tube with 20 mL of water, mix well, and discard tip.

AGAGATCACGGACTACAGAA

AGAGAACAGCTACCACCTTT

ISG20

ISG56

TGGACCTGCTCTGAGATTCT

TCTGTGGACGTGTCATAGAT

TGAGGCGCTTCAGCTTGGTT

TTGATGTGCTGCCAGCCTAT

OAS1b

ATGGCTGGGGTGTTGAAGGTC ATCCTCTTGCCCTGATCCTT

CTACAATGAGCTGCGTGTGGC

b-Actin

Reverse primer (5¢→3¢)

L32 ribosomal ACATTTGCCCTGAATGTGGT protein

Forward primer (5¢→3¢)

Gene

Table 1 Primer sequences

56

52

56

54

62

Annealing (°C)

75

78

77

72

78

160

197

186

199

125

Data Product collection (°C) size (bp)

(26)

(26)

(26)

(84)

(26)

References

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Table 2 Reaction setup Reagent

Volume

2× QuantiTect SYBR Green RT-PCR master mix

25 mL

Primer forward (25 pmol/mL)

1 mL

Primer reverse (25 pmol/mL)

1 mL

QuantiTect RT mix

0.5 mL

Template RNA (25 ng/mL)

2 mL

Nuclease-free water

20.5 mL

Final volume

50 mL

Repeat four times. Generating accurate standards is critical, as the quality of the standard curve determines the success of the assay). Prior to testing the samples, run the standards as described below and confirm that only one fragment is obtained by running the PCR product in a 1% agarose gel in 1× TBE. 5. Samples are diluted to 25 ng/mL and analysis is done using 2 mL of this solution. 6. Working solutions of primers are made by dilution to 25 pmol/mL in nuclease-free water. Use 0.5 mM of each primer in a 50 mL reaction. 7. Standards, samples, and blank (nuclease-free water) are tested in duplicate or triplicate (see Table 2). Prepare a second set of samples without RT mix, to confirm amplification is from RNA, instead of contaminating DNA. Program the ABI 7500 real-time PCR machine as follows: 30 min 50°C (RT reaction); 15 min 95°C (HotStarTaq DNA polymerase activation); 40 cycles of: 15 s 94°C (denaturation), 30 s 50–60°C (annealing), 30 s 72°C (extension), 15 s 75–78°C (data collection); add a melting curve analysis step. If using a different instrument, adjust these conditions as recommended by the manufacturer. 8. Results are expressed as amount of RNA relative to that present in the sample used as standard. For normalization, divide the value of the ISG gene by the value obtained for the gene used as normalizer. All groups are tested for statistical significance using a t-test and the vehicle-treated group as reference. The virus not expressing an shRNA is a good control for the impact of the virus capsid and genome on the innate immune response. Optimally, this group should not display differences with the vehicle-treated group. If it does, interpretation of the data becomes more complicated, as it is not possible to discern between interferon induction from the virus itself or the shRNA. The presence of an interferon response

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Fig. 1. Interferon-stimulated gene (ISG) expression. (a) Mice were administered 1 × 1011 viral particles of helper-dependent adenoviral vectors expressing shRNA against Sterol Regulatory Element Binding Protein 1 (gAd.shSREBP1), expressing a scrambled sequence (gAd.shSCR), or without expression cassette (gAd.NEC). A group of mice received vehicle. Mice were sacrificed 1, 3, and 6 weeks later. No activation of the interferon response was observed. (b) RNA was isolated from livers of mice that received the doses shown at the bottom, and sacrificed 1 week later. Mice that received the gAd.sh242 vector (expressing an shRNA against fatty acid binding protein 5, Fabp5), developed an interferon response. Reproduced with permission from The Journal of Biological Chemistry (26). Values indicate the -fold level of expression relative to the vehicle-treated group. Asterisk p < 0.05; n = 3.

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can be detected as a fold increase in gene expression compared to vehicle and NEC-treated group (Fig. 1). 9. In addition, tissue sections can be evaluated histologically to look for signs of inflammation and cell death. We have found a 100% correlation between presence of a strong interferon response and inflammatory foci in the liver of mice (26). 10. The presence of an interferon response can be further confirmed by analysis of liver function markers, such as ALT, from serum. If studying a different tissue and reliable markers of cellular damage are available, those should be included in the analysis as well.

Acknowledgments This research was supported by grants from the NIDDK (DK069432-01 and DK078595), American Diabetes Foundation (1-08-RA-135), and INGEN (Indiana Genomics Initiative of Indiana University supported in part by Lilly Endowment Inc.). References 1. Sledz, C.A., Williams, B.R. (2004) RNA interference and double-stranded-RNA-activated pathways. Biochem. Soc. Trans. 32: 952–956. 2. Sledz, C.A., Williams, B.R. (2005) RNA interference in biology and disease. Blood 106: 787–794. 3. Stark, G.R., Kerr, I.M., Williams, B.R., Silverman, R.H., Schreiber, R.D. (1998) How cells respond to interferons. Annu. Rev. Biochem. 67: 227–264. 4. Li, G., Xiang, Y., Sabapathy, K., Silverman, R.H. (2004) An apoptotic signaling pathway in the interferon antiviral response mediated by RNase L and c-Jun NH2-terminal kinase. J. Biol. Chem. 279: 1123–1131. 5. Alexopoulou, L., Holt, A.C., Medzhitov, R., Flavell, R.A. (2001) Recognition of doublestranded RNA and activation of NF-kappaB by Toll-like receptor 3. Nature 413: 732–738. 6. Diebold, S.S., Kaisho, T., Hemmi, H., Akira, S., Reis e Sousa, C. (2004) Innate antiviral responses by means of TLR7-mediated recognition of single-stranded RNA. Science 303: 1529–1531. 7. Kariko, K., Bhuyan, P., Capodici, J., Weissman, D. (2004) Small interfering RNAs mediate sequence-independent gene suppression and induce immune activation by signaling through toll-like receptor 3. J. Immunol. 172: 6545–6549.

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Chapter 11 Use of RNA Interference to Investigate Cytokine Signal Transduction in Pancreatic Beta Cells Fabrice Moore, Daniel A. Cunha, Hindrik Mulder, and Decio L. Eizirik Abstract Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by immune infiltration of the pancreatic islets resulting in an inflammatory reaction named insulitis and subsequent beta cell apoptosis. During the course of insulitis beta cell death is probably caused by direct contact with activated macrophages and T-cells, and/or exposure to soluble mediators secreted by these cells, including cytokines, nitric oxide, and free oxygen radicals. In vitro exposure of beta cells to the cytokines interleukin(IL)1β + interferon(IFN)-γ or to tumor necrosis factor(TNF)-α + IFN-γ induces beta cell dysfunction and ultimately apoptosis. The transcription factors NF-κB and STAT1 are key regulators of cytokine-induced beta cell death. However, little is known about the gene networks regulated by these (or other) transcription factors that trigger beta cell apoptosis. The recent development of RNA interference (RNAi) technology offers a unique opportunity to decipher the cytokine-activated molecular pathways responsible for beta cell death. Use of RNAi has been hampered by technical difficulties in transfecting primary beta cells, but in recent years we have succeeded in developing reliable and reproducible protocols for RNAi in beta cells. This chapter details the methods and settings used to achieve efficient and nontoxic transfection of small interfering RNA in immortal and primary beta cells. Key words: Small interfering RNA, siRNA, Pancreatic beta cells, Apoptosis, Gene knockdown, Inducible nitric oxide synthase, Interleukin-1β, Interferon-γ, Tumor necrosis factor-α

1. Introduction Research on RNA interference (RNAi) gained momentum in 1998, when Fire and Mello reported that double-stranded (ds) RNA induced gene silencing in Caenorhabditis elegans (1). Since then, there has been a growing understanding of the complexity of RNA-based gene silencing, including the discovery of several subclasses of small interfering (si)RNAs in plants, fungi, and mammals

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(reviewed in ref. 2, 3). SiRNA-based gene knockdown is mediated via the enzymatic generation of short RNA sequences from a larger dsRNA precursor that specifically recognize and degrade sequencematched target mRNAs (2). Typically, dsRNAs are processed by the RNAse Dicer (or sometimes by another RNAse) into 21–24 nucleotide duplexes with two nucleotides overhanging at 3¢ends. The guide strand of the siRNA duplex is then loaded into a multiprotein complex, or RNA-induced silencing complex (RISC), which identifies the complementary mRNA to be targeted for degradation, ultimately resulting in the decreased expression of the target protein (reviewed in ref. 2, 3). The precursor dsRNA may be endogenously generated, but it can also be derived from exogenous sources; this opened novel possibilities for the experimental modulation of gene expression. Indeed, synthetic siRNAs have been shown to potently inhibit gene expression in mammalian cells (4), and recent advances in systemic and/or local siRNA delivery opened new perspectives for their future use in human therapy (5, 6). Furthermore, synthetic siRNAs have proved very useful under in vitro conditions to decipher the role(s) of different genes in diverse cellular processes (7–9). Our laboratory has been studying for many years the gene networks and molecular pathways involved in cytokine-induced pancreatic beta cell apoptosis (10), but these studies were often hampered by the lack of fast and efficient methods to knockdown gene and protein expression in beta cells. Indeed, beta cells are hard-to-transfect cells, and do not tolerate well infection by adenoviral vectors at MOIs above 10–20. To obviate this problem, we have tested different approaches to efficiently introduce synthetic siRNAs in rat and human beta cells. We describe below the materials, reagents, and methods presently used in our laboratory to achieve efficient and nontoxic siRNA-mediated gene knockdown in both primary rat and human beta cells, and in insulin-producing cell lines.

2. Materials 2.1. Synthetic siRNAs and Transfection Reagents

1. siGLO Green Transfection Indicator (referred to as FITCconjugated siRNA – Thermo Scientific, Lafayette, CO, USA), used to assess the efficiency of transfection, is reconstituted with double distilled sterile water (ddH2O) at a concentration of 20 μM, aliquoted, and snap frozen at −80°C (see Note 1). 2. Allstars Negative Control siRNA (siCtrl – Qiagen, Venlo, The Netherlands) is reconstituted with the provided siRNA dilution buffer at 20 μM, aliquoted, and stored at −80°C.

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3. HP GenomeWide siRNA rat inducible nitric oxide synthase (referred to as si iNOS – Qiagen) is reconstituted at 20 μM in siRNA dilution buffer, aliquoted, and stored at −80°C. 4. The lipid-carrier DharmaFECT 1 Transfection Reagent was purchased from Thermo Scientific and stored at 4°C. This transfection reagent was selected as the best one after testing several other methods for siRNA transfection, including several other lipid reagents and electroporation. 5. OPTI-MEM medium (GIBCO, Bethesda, MD, USA) is used without additives (see Note 2). 2.2. Cell Culture and Transfection

1. PBS 1×: 137 mM NaCl, 2.68 mM KCl, 4.29 mM Na2HPO4, 1.47 mM KH2PO4, pH 7.4. 2. Solution of trypsin at 0.5 mg/mL (from Cambrex, East Rutherford, NJ, USA). 3. The rat insulin-producing INS-1E cell line (a kind gift from Dr. C. Wollheim, Centre Medical Universitaire, Geneva, Switzerland) is cultured in RPMI 1640 GlutaMAX-I, 5% (v/v) fetal bovine serum, 10 mM HEPES, 1 mM Na-pyruvate, 50 μM 2-mercaptoethanol (all from GIBCO), and 100 U/mL penicillin + 100 μg/mL streptomycin (Cambrex). For transfection, the same medium is used but without antibiotics (see Note 3). 4. Male Wistar rats (Charles River Laboratories, Brussels, Belgium) are housed and used according to the guidelines of the Belgian Regulations for Animal Care. Islets are isolated by collagenase digestion and hand picked under a stereomicroscope (11, 12). 5. Rat pancreatic islets are washed with Solution I (124 mM NaCl, 5.4 mM KCl, 0.8 mM H2SO4, 1 mM NaH2PO4, 0.71 mM NaHCO3, 5 mM glucose, and 1 mM EGTA), and then dispersed by incubation for 1–2 min in a solution of dispase (5 U/mL in Solution I, Roche Diagnostics, Vilvoorde, Belgium). Beta cells are purified by autofluorescence-activated cell sorting (FACSAria, BD Bioscience., San Jose, CA, USA) (11, 12). Purified cells are cultured attached to polylysinecoated 96-well plates (BD Falcon, Franklin Lakes, NJ, USA) in Ham’s F-10 medium containing 10 mM glucose, 50 μM 3-isobutyl-1-methylxanthine (both from Sigma, Bornem, Belgium), 2 mM glutaMAX, 5% (v/v) FBS (both from GIBCO), 0.5% charcoal-absorbed BSA (Fraction V; Boehringer, Indianapolis, IN, USA), 50 U/mL penicillin, and 50 μg/mL streptomycin (12, 13). For transfection, the same medium is used but without antibiotics (see Note 3) or BSA (see Note 4). Of note, in case FACS sorting facilities are not available, a similar

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transfection and culture procedure can be used with dispersed islet cells. 6. Human islets are isolated as previously described (14) and dispersed by incubation for 2–3 min in a solution of dispase (2.5 U/mL in Solution I – Roche). Dispersed human islets are cultured attached to polylysine-coated 96-well plates (BD Falcon) in Ham’s F-10 containing 6.1 mM glucose, 10% (v/v) FBS, 2 mM GlutaMAX, 50 μM 3-isobutyl-1-methylxanthine, 1% (w/v) BSA, 50 U/mL penicillin, and 50 μg/mL streptomycin (15). For transfection, the same medium described above for rat beta cell transfection is used. 7. Recombinant human IL-1β (specific activity 1.8 × 107 U/mg; a kind gift from C. W. Reinolds, National Cancer Institute, Bethesda, MD, USA) is used at 100 U/mL. 2.3. Assessment of Cell Viability and Efficiency of Transfection

1. Propidium iodide (PI, Sigma) is diluted in PBS at 1 mg/mL for the stock solution, filtered through a 0.22 μm filter, and used at a final concentration of 5 μg/mL. 2. The stock solution of Hoechst 33342 (HO, Sigma) is diluted at 1 mg/mL in PBS, filtered, and used at a final concentration of 5 μg/mL. 3. siGLO Green Transfection indicator (see Subheading 2.1). 4. An inverted microscope (Zeiss, Zaventem, Belgium) with filters for excitation at 358 nm (HO), 538 nm (PI), and 495 nm (FITC) is used.

2.4. Immunostaining for Insulin

1. Sterile glass chamber slides and microscope coverslips (Thermo Fisher Scientific, Rochester, NY, USA). 2. Formaldehyde solution: 4% buffered, pH 6.9, at room temperature. 3. Washing buffer: PBS 1×, pH 7.4 (see Subheading 2.2). 4. Permeabilization solution: 0.3% (v/v) Triton X-100 (Sigma) in PBS. 5. Blocking solution: PBS with 10% (v/v) normal goat serum (Gibco) and 0.8% (w/v) BSA (Fraction V; Boehringer). 6. Primary antibody: mouse monoclonal anti-insulin (1:1,000) (Sigma). 7. Rhodamine-conjugated anti-mouse IgG (1:200) (Lucron Bioproducts). 8. Hoechst 33342 solution (see Subheading 2.3). 9. Fluorescence mounting medium (Dakocytomation, Glostrup, Denmark). 10. Microscope (see above).

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3. Methods 3.1. Designing Efficient siRNAs and Selecting the Adequate Control Conditions

In recent years, major advances have been made in understanding how to design efficient and specific siRNAs (reviewed in ref. 16, 17). Important points to keep in mind when designing siRNAs include avoiding palindromic sequences and high GC-containing duplexes, and favoring a poorly stable 5 antisense end of the siRNA to ease the unwinding of the duplex and promote the loading of the guide strand into the RISC complex (17). In order to ensure the specificity of the gene knockdown (and thus decrease the risk of “off-target” effects), it is recommended to both respect a perfect match between the siRNA and the targeted mRNA sequence, and also to BLAST the siRNA sequence against the whole transcriptome to ensure that only one gene is targeted (17, 18). SiRNA duplexes over 23 nucleotides have been shown to exhibit RNAiindependent off-target effects in some cell lines through activation of the dsRNA recognition pathway (19). Various chemical modifications of the siRNA duplexes have been reported to improve their stability and specificity (20–22) and there are proprietary and nondisclosed modifications that are used by different industrial manufacturers. Many of these companies now offer siRNA designing software available online. The algorithms used by such softwares take into account the different criteria detailed above, and together with the chemical modifications added to the siRNA duplexes allow the design of highly effective and specific siRNAs (see Note 5). Importantly, it is recommended to perform siRNA-based experiments using two (or more) siRNAs recognizing different regions of the target mRNA sequence (23). This simple rule decreases the risk of drawing inappropriate conclusions due to off-target effects of a single siRNA. Moreover, siRNA experiments should include both nontransfected cells and cells transfected with a nonbiologically active control siRNA. In early RNAi development, it was assumed that the best control was a “scramble” sequence of the original siRNA, including two or three nucleotide mismatches. These scrambled controls, however, may pose serious problems, since (1) the 2–3 nucleotide change in the middle of the siRNA sequence may convert the siRNA into a miRNA that still inhibits the translation of the targeted gene; (2) the changes in the original siRNA sequence may generate an siRNA that inhibits other genes in an unexpected way (23). Nowadays, many “sequence-independent” control siRNAs are commercially available. The sequences of these control duplexes have been BLAST tested to minimize sequence homology to any known gene in a given species. It is generally recommended, however, that this control siRNA matches the GC content of the original siRNA (see Note 6).

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3.2. Preparation of the Cells for the Transfection 3.2.1. INS-1E Cells

1. The rat insulinoma INS-1E cells are cultured in an incubator at 37°C and 5% (v/v) CO2 until reaching ~80% confluence. The cells are then washed once with PBS, detached with trypsin and washed once with antibiotic-free INS-1E medium to remove the trypsin (see Note 3). 2. After counting, INS-1E cells are plated at 104 cells/well in a 96-well plate for viability experiments and 105 cells/well in a 24-well plate for RNA extraction or Western blot experiments. 3. The cells are cultured for a recovery period of 48 h in antibioticfree medium prior to transfection.

3.2.2. Rat Primary Beta Cells and Human Dispersed Islets

1. FACS-purified beta cells or dispersed human islets are obtained as described in, respectively (11, 12) and (8), and plated at 2 × 104 cells/well in 96-well plates with the appropriate medium (see Subheading 2.2). 2. After 24 h, the medium is removed and replaced by medium without antibiotic and BSA, which is suitable for transfection of both primary rat beta cells and dispersed human islets (see Note 7).

3.3. Transfection of siRNAs

This protocol is adapted for the transfection of 30 nM siRNA, a concentration which we found in dose–response experiments using several siRNAs to provide potent RNAi without toxicity (Moore et al., unpublished data). For certain targets, higher concentrations (75% inhibition in PTPN2, IKK, and MDA5 proteins (24). 3.5. Evaluation of siRNA Transfection Efficiency and Toxicity

A simple approach to evaluate siRNA transfection efficiency and toxicity is to use as probe a fluorescence-labeled siRNA and to evaluate cell viability with nuclear dyes after the transfection. 1. Cells are plated in two separate plates and prepared for transfection as described in Methods (Subheading 3.2). The cells are then left untransfected (control), or transfected with 30 nM of FITC-conjugated siRNA as described in Subheading 3.3. 2. After overnight incubation, the transfection medium is replaced by fresh medium (see Subheading 3.3) in one of the two plates, and the plate is incubated at 37°C for a recovery period. For the second plate, half of the transfection medium is carefully removed and replaced by the same volume of culture medium containing 10 μg/mL of the nuclear dyes HO and PI (final concentration of 5 μg/mL cf. Subheading 2.3). 3. After 15 min incubation, half of the staining medium is carefully removed and replaced by the same volume of control medium. 4. Cells are visualized and counted under a microscope with excitation wavelengths at: (a) 358 nm (HO – blue emission), for viable cells and early apoptotic cells (25). (b) 538 nm (PI – red (apoptotic + necrotic).

emission),

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(c) 495 nm (FITC-conjugated siRNA – green emission), for transfected cells. 5. The efficiency of transfection is calculated as number of transfected cells/total number of cells (living + dead cells). 6. The toxicity of transfection is calculated as number of dead cells/total number of cells (living + dead cells). 7. After 48 h of recovery, the second plate is removed from the incubator and stained with HO/PI as described in above. The viability of the cell is then evaluated under the microscope and calculated as described in step 6.

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The efficiency of transfection using fluorescent siRNAs should be evaluated immediately after the overnight transfection period, since there is a rapid decay in fluorescence. It is recommended, however, to evaluate cell viability in a second plate after a longer recovery period (24–48 h), in order to exclude later-stage toxicity of the transfection. We have used this approach to define the best conditions to transfect INS-1E cells, primary rat beta cells and dispersed human islets. Representative pictures of living (HO – blue), dead (PI – red), and transfected (FITC – green) INS-1E cells are shown in Fig. 1a. The efficiency of transfection (calculated as described above) was 95 ± 3% for INS-1E cells and 81 ± 3% for primary rat beta cells (Fig. 1b, c). No significant cell toxicity was observed with this transfection method (Fig. 1b, c). Of note, comparable results were obtained in MIN6 mouse insulin-producing cells, in which the efficiency of transfection was 86 ± 2%, with negligible toxicity (6 ± 1% of dead cells after transfection vs. 4 ± 1% in the control). For dispersed human islets, which contain a mixture of beta cells (40–60%) and other endocrine- and nonendocrine cells, we performed immunostaining for insulin to evaluate the specific efficiency of transfection in the insulin-positive beta cell population. The insulin staining for dispersed human islets is performed as follows: 1. 2 × 104 cells/well are plated in sterile glass chamber slides and transfected according to the procedure described above (see Subheading 3.3). 2. After the transfection, cells are fixed with 100 μL of 4% formaldehyde for 10 min at room temperature (RT). 3. Cells are washed 3× with PBS and permeabilized for 5 min with 100 μL/well of PBS/0.3% Triton-X 100 at RT. 4. Cells are washed 3× with PBS at RT and permeabilized for 5 min with 100 μL/well of PBS/0.3% Triton-X 100. 5. After three washes with PBS at RT, cells are blocked with 100 μL/well of blocking solution for 30 min at RT to decrease nonspecific staining. 6. Diluted primary anti-insulin antibodies (100 μL/well) are applied for 1 h at RT. 7. After three washes with PBS, cells are incubated with 100 μL/well of the diluted secondary antibody for 60 min at room temperature and in the dark. 8. The cells are washed three times with PBS and nuclei are counterstained with 100 μL/well of diluted HO (5 μg/mL) for 15 min at room temperature and protected from light. 9. Cells are washed 3× with PBS, coverslips are mounted in mounting medium, and immunofluorescence is visualized under a microscope.

Fig. 1. Evaluation of siRNA transfection efficiency and toxicity in INS-1E cells, rat and human beta cells. (a–c) INS-1E cells or primary rat beta-cells were left untransfected or transfected with 30 nM of FITC-conjugated siRNA using the DharmaFECT lipid reagent. Cells were labeled with the DNA-binding dyes Hoechst (5 μg/mL) and PI (5 μg/mL) immediately after transfection (plate 1 – for evaluation of transfection efficiency) and after 48 h of recovery (plate 2 – for evaluation of later stage viability) and counted under a fluorescence microscope; (a) Representative pictures of the results observed in INS-1E cells are shown; (b, c) The percentage of transfected fluorescence-positive cells (gray bars) or dead cells (black bars) for INS-1E cells (b) or primary beta-cells (c) are shown. Results are mean ± SEM of five experiments. (d–e) Dispersed human islets were left untransfected or transfected with 30 nM of FITC-conjugated siRNA using the DharmaFECT lipid reagent. Cells were labeled with the DNA-binding dyes Hoechst (5 μg/mL) and immunostained for insulin as described in Subheading 3.5. Cells were then counted under a fluorescence microscope. The viability was evaluated in a separate plate using HO and PI staining. (d) Representative pictures of the results are shown (the arrows indicate insulin- and FITC-positive cells). (e) The percentage of transfected and insulin-positive cells (gray bars) or dead cells (black bars) are shown. Results are mean ± SEM of three experiments.

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Representative pictures of the insulin and FITC staining in FITC-siRNA-transfected dispersed human islets are shown in Fig. 1d. The efficiency of transfection for insulin-positive human beta cells as assessed by this method was 87 ± 2%, with no toxicity (13 ± 1% dead cells after transfection vs. 14.4 ± 4% for untransfected controls – Fig. 1d, e). Of note, efficiency of siRNA transfection in the human islet nonbeta cells was only 21 ± 2%, indicating preferential transfection of beta cells under the present experimental conditions. 3.6. Evaluation of siRNA-Induced Target Gene Knockdown

Target gene knockdown mediated by transfection with siRNAs is best evaluated by combining the determination of the target gene mRNA and protein expression by respectively real-time quantitative RT-PCR and Western blot or other protein assays, such as ELISA in the case of secreted proteins (e.g., chemokines). In the case of transcription factors, inhibition of the target gene can be additionally evaluated through the decrease of downstream genes. In some cases, the absence of good antibodies for Western blot poses a problem for validation; in these cases, we rely on mRNA expression and determination of downstream genes/biological effects (e.g., prevention of apoptosis in the case of a known proapoptotic gene). An example of validation is shown in Fig. 2. INS-1E cells were transfected with increasing amounts of either a siCtrl or a siRNA targeting the iNOS as described above (Subheading 3.3). After 24 h of recovery, the cells were left untreated or challenged for 24 h with 100 U/mL interleukin-1β, which induces iNOS expression and NO formation (26). SiRNA-mediated iNOS inhibition was assessed at the mRNA and protein levels by respectively realtime RT-PCR and Western blot (Fig. 2a, b). Nitric oxide formation was assessed as nitrite accumulation in the cell culture supernatant (Fig. 2c). The three methods confirmed a >75% inhibition of iNOS expression and nitrite in si iNOS-transfected INS-1E cells, while cells transfected with the siCtrl showed similar iNOS expression and nitrite formation as the nontransfected cells exposed to IL-1β.

3.7. Combining the Use of siRNAs with Microarray Analyses and High-Throughput Screening Methods

Due to its potency and ease of use, siRNA-mediated gene knockdown is a valuable tool to study in vitro different intracellular mechanisms. We are presently combining the use of siRNA with microarray analysis to investigate the gene networks responsible for cytokine-induced beta cell apoptosis (10, 26). Thus, we have recently performed microarray analyses of cells transfected with control or siSTAT1 to identify the molecular pathways downstream of the transcription factor STAT1 (27), a well-known regulator of cytokine-induced beta cell apoptosis (28, 29). Since siRNA-mediated gene knockdown induces a rapid and efficient inhibition of most target genes in adult beta cells, it allows us to avoid putative

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Fig. 2. Evaluation of siRNA-induced target gene knockdown. INS-1E cells were left untransfected (NT) or transfected with 10, 30, or 50 nM of either control siRNA (siCtrl), or a siRNAs targeting iNOS (si iNOS). After 24 h of recovery, the cells were challenged for 24 h with IL-1β (100 U/mL). (a) iNOS mRNA expression was assayed by RT-PCR and normalized for the housekeeping gene GAPDH; (b) iNOS and α-tubulin expression were evaluated by Western blot; (c) Nitrite concentrations (nitrite is a stable product of NO oxidation) were evaluated in the in-culture supernatants using the Griess method. Results are mean ± SEM of four experiments.

compensatory responses that are often observed with systemic knockout models in which the target proteins are absent since fetal life (these compensatory responses have been a particularly relevant problem when dealing with KO mice targeting pro-inflammatory cytokines). Another promising application of RNAi technology is their use in high-throughput genome-scale loss-of-function screens

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in cell culture systems. In this approach, hundreds to thousands of siRNAs, constituting a siRNA library, are transfected individually (one siRNA per well) into cells. The function of the target cells is then analyzed in response to a given stimulus (e.g., interferons) by the use of high-throughput evaluation methods. This approach has been used for screens in both Drosophila melanogaster and mammalian cells (30), and genome-wide RNAi analysis have identified novel components of the JAK/STAT (31) and p53 (32) pathways in human cells. A present limitation of this approach is the elevated cost of siRNA libraries, but it is expected that these costs will decrease in the near future.

4. Notes 1. All siRNA should be reconstituted accordingly to the manufacturer’s instructions (usually in sterile ddH2O or siRNA reconstitution buffer), aliquoted in sterile microtubes and snap frozen at −80°C. Once frozen, siRNA vials should not be thawed/frozen more than two times to avoid degradation of the duplexes and loss of activity. 2. Alternatively, OPTI-MEM may be replaced by any culture medium in which the cells are usually cultured in, but the medium should be free of any additives (serum, BSA, etc.). 3. In order to avoid toxicity, DharmaFECT’s manufacturer (Thermo Scientific) strongly recommends the use of antibioticfree medium before and during transfection. Respecting this simple rule is crucial to obtain an efficient and nontoxic transfection. 4. When setting the presently described experimental conditions, we observed that BSA induces the aggregation of the lipid reagent, nearly completely preventing transfection. Thus, the medium used for transfection should be BSA-free. 5. We have tested up to now more than 40 siRNAs provided by different manufacturers. Most of them were highly effective at low concentrations (30 nM or less) and did not exhibit obvious off-target effects. A nonexhaustive list of web tools provided by suppliers for designing siRNA can be find in ref. 16. 6. When starting siRNA experiments, it is useful to test several different sequence-independent control siRNAs to make sure that it does not affect cellular function, viability, and/or metabolism. For example, we found that some commercially available control siRNAs alter insulin secretion in beta cells. Moreover, significant toxicity may be observed with some control siRNAs. We assume that this depends on the cell type.

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Of note, the control siRNA used in our experiments (see Subheading 2.1, item 2) neither affects beta cell viability nor modifies to major extent gene expression as evaluated by microarray analyses (27, and Fig. 2). 7. This step is very important and has a double goal: (1) To remove the antibiotics from the medium to avoid toxicity associated with the transfection; (2) To remove the BSA that is present in the medium and which interferes with the transfection. Caution should be taken when changing the medium, because the cells have been attached for only 24 h and may easily detach. It is not recommended to plate primary cells directly in antibiotic-free medium to minimize the risk of infection. 8. When transfecting siRNAs, the concentration stated corresponds to the final siRNA concentration in the transfection medium. According to the presently described protocol, this means that the initial concentration of the siRNA will be diluted 2× when adding the diluted DharmaFECT, and 5× more when medium is added to the siRNA/lipid complexes. The initial dilution of the siRNA should be then 10× more concentrated than the final desired siRNA concentration (e.g., transfection of 30 nM siRNA requires that the initial siRNA dilution is 300 nM). 9. In our experimental settings, we observed that the ratio between the concentration of DharmaFECT and the number of cells to be transfected is not always linear. This may be also affected by the cell type to be transfected. Thus, primary beta cells require a higher concentration of DharmaFECT than INS-1E cells for adequate transfection. When starting siRNA experiments in a given cell type, it is recommended to establish the best transfection conditions by testing different siRNA and DharmaFECT concentrations. 10. We have tested transfection periods of 14–18 h without noticing significant changes in the efficiency of transfection or toxicity. 11. At this step, no toxicity was observed with medium containing antibiotics; thus, antibiotic-free medium is not absolutely required from this stage on. 12. When starting siRNA-based experiments to inhibit the expression of a given gene, we recommend to first establish a time course of gene/protein expression 24–96 h after siRNA transfection, in order to determine the required time to reach a significant knockdown of the targeted protein. Of note, and when dealing with fast proliferating cells, dilution of the siRNA due to cell division may pose a problem after extended periods of time.

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Acknowledgments This work has been supported by grants from the Fonds National de la Recherche Scientifique (FNRS – FRSM) Belgium, the Communauté Française de Belgique – Actions de Recherche Concertées (ARC), the European Union (STREP Savebeta, contract no. 036903; in the Framework Programme 6 of the European Community) and the Belgium Program on Interuniversity Poles of Attraction initiated by the Belgium State (IUAP P6/40). F.M. is the recipient of a Post-Doctoral Fellowship from FNRS, Belgium. The authors have no duality of interest associated with this manuscript. We thank M.A. Neef, G. Vandenbroeck, M. Urbain, J. Schoonheydt, R. Leeman, A. M. Musuaya, and S. Mertens from the Laboratory of Experimental Medicine, ULB, for excellent technical support, Dr. Fernanda Ortis (Laboratory of Experimental Medicine) for helpful comments and Dr. Piero Marchetti (Department of Endocrinology and Metabolism, Metabolic Unit, University of Pisa, Pisa, Italy) for providing the human islets used for siRNA testing. References 1. Fire, A., Xu, S., Montgomery, M.K., Kostas, S.A., Driver, S.E., Mello, C.C. (1998) Potent and specific genetic interference by doublestranded RNA in Caenorhabditis elegans. Nature 391: 806–811. 2. Ghildiyal, M., Zamore, P.D. (2009) Small silencing RNAs: an expanding universe. Nat. Rev. Genet. 10: 94–108. 3. Naqvi, A.R., Islam, M.N., Choudhury, N.R., Haq, Q.M. (2009) The fascinating world of RNA interference. Int. J. Biol. Sci. 5: 97–117. 4. Elbashir, S.M., Harborth, J., Lendeckel, W., Yalcin, A., Weber, K., Tuschl, T. (2001) Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature 411: 494–498. 5. Wall, N.R., Shi, Y. (2003) Small RNA: can RNA interference be exploited for therapy? Lancet 362: 1401–1403. 6. Whitehead, K.A., Langer, R., Anderson, D.G. (2009) Knocking down barriers: advances in siRNA delivery. Nat. Rev. Drug Discov. 8: 129–138. 7. Chen, Y., Stamatoyannopoulos, G., Song, C.Z. (2003) Down-regulation of CXCR4 by inducible small interfering RNA inhibits breast cancer cell invasion in vitro. Cancer Res. 63: 4801–4804.

8. Cunha, D.A., Hekerman, P., Ladriere, L., Bazarra-Castro, A., Ortis, F., Wakeham, M.C. Moore, F., Rasschaert, J., Cardozo, A.K., Bellomo, E., Overbergh, L., Mathieu, C., Lupi, R., Hai, T., Herchuelz, A., Marchetti, P., Rutter, G.A., Eizirik, D.L., Cnop, M. (2008) Initiation and execution of lipotoxic ER stress in pancreatic beta-cells. J. Cell Sci. 121: 2308–2318. 9. Xia, H., Mao, Q., Paulson, H.L., Davidson, B.L. (2002) siRNA-mediated gene silencing in vitro and in vivo. Nat. Biotechnol. 20: 1006–1010. 10. Eizirik, D.L., Moore, F., Flamez, D., Ortis, F. (2008) Use of a systems biology approach to understand pancreatic beta-cell death in Type 1 diabetes. Biochem. Soc. Trans. 36: 321–327. 11. Pipeleers, D.G., in’t Veld, P.A., Van de Winkel, M., Maes, E., Schuit, F.C., Gepts, W. (1985) A new in vitro model for the study of pancreatic A and B cells. Endocrinology 117: 806–816. 12. Rasschaert, J., Ladriere, L., Urbain, M., Dogusan, Z., Katabua, B., Sato, S., Akira, S, Gysemans, C., Mathieu, C., Eizirik, D.L. (2005) Toll-like receptor 3 and STAT-1 contribute to double-stranded RNA + interferongamma-induced apoptosis in primary pancreatic beta-cells. J. Biol. Chem. 280: 33984–33991.

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13. Ling, Z., Hannaert, J.C., Pipeleers, D. (1994) Effect of nutrients, hormones and serum on survival of rat islet beta cells in culture. Diabetologia 37: 15–21. 14. Lupi, R., Dotta, F., Marselli, L., Del Guerra, S., Masini, M., Santangelo, C. Patané, G., Boggi, U., Piro, S., Anello, M., Bergamini, E., Mosca, F., Di Mario, U., Del Prato, S., Marchetti, P. (2002) Prolonged exposure to free fatty acids has cytostatic and pro-apoptotic effects on human pancreatic islets: evidence that beta-cell death is caspase mediated, partially dependent on ceramide pathway, and Bcl-2 regulated. Diabetes 51: 1437–1442. 15. Delaney, C.A., Pavlovic, D., Hoorens, A., Pipeleers, D.G., Eizirik, D.L. (1997) Cytokines induce deoxyribonucleic acid strand breaks and apoptosis in human pancreatic islet cells. Endocrinology 138: 2610–2614. 16. Pei, Y., Tuschl, T. (2006) On the art of identifying effective and specific siRNAs. Nat. Methods 3: 670–676. 17. Birmingham, A., Anderson, E., Sullivan, K., Reynolds, A., Boese, Q., Leake, D., Karpilow, J., Khvorova, A. (2007) A protocol for designing siRNAs with high functionality and specificity. Nat. Protoc. 2: 2068–2078. 18. Du, Q., Thonberg, H., Wang, J., Wahlestedt, C., Liang, Z. (2005) A systematic analysis of the silencing effects of an active siRNA at all singlenucleotide mismatched target sites. Nucleic Acids Res. 33: 1671–1677. 19. Reynolds, A., Anderson, E.M., Vermeulen, A., Fedorov, Y., Robinson, K., Leake, D., Karpilow, J., Marshall, W.S., Khvorova, A. (2006) Induction of the interferon response by siRNA is cell type- and duplex length-dependent. RNA 12: 988–993. 20. Dande, P., Prakash, T.P., Sioufi, N., Gaus, H., Jarres, R., Berdeja, A., Swayze, E.E., Griffey, R.H., Bhat, B. (2006) Improving RNA interference in mammalian cells by 4¢-thio-modified small interfering RNA (siRNA): effect on siRNA activity and nuclease stability when used in combination with 2¢-O-alkyl modifications. J. Med. Chem. 49: 1624–1634. 21. Hall, A.H., Wan, J., Shaughnessy, E.E., Ramsay, S.B., Alexander, K.A. (2004) RNA interference using boranophosphate siRNAs: structureactivity relationships. Nucleic Acids Res. 32: 5991–6000. 22. Zhang, N., Tan, C., Cai, P., Zhang, P., Zhao, Y., Jiang, Y. (2009) RNA interference in mammalian cells by siRNAs modified with morpholino nucleoside analogues. Bioorg. Med. Chem. 17: 2441–2446.

23. Editorial Comment (2003) Whither RNAi? Nat. Cell Biol. 5: 489–490. 24. Moore, F., Colli, M.L., Cnop, M., Esteve, M.I., Cardozo, A.K., Cunha, D.A., Buglian, M., Marchetti, P., Eizirik, D.L. (2009) PTPN2, a candidate gene for type 1 diabetes, modulates interferon-gamma-induced pancreatic beta-cell apoptosis. Diabetes 58: 1283–1291. 25. Hoorens, A., Van de Casteele, M., Kloppel, G., Pipeleers, D. (1996) Glucose promotes survival of rat pancreatic beta cells by activating synthesis of proteins which suppress a constitutive apoptotic program. J. Clin. Invest. 98: 1568–1574. 26. Eizirik, D.L., Mandrup-Poulsen, T. (2001) A choice of death – the signal-transduction of immune-mediated beta-cell apoptosis. Diabetologia 44: 2115–2133. 27. Moore, F., Naamane, N., Colli, M.L., Bouckenooghe, T., Ortis, F., Gurzov, E.N., Igoillo-Esteve, M., Mathieu, C., Bontempi, G., Thykjaer, T., Ørntoft, T.F., Eizirik, D.L. (2011) STAT1 is a master regulator of pancreatic beta cell apoptosis and islet inflammation. J. Biol. Chem. 286: 929–941. 28. Callewaert, H.I., Gysemans, C.A., Ladriere, L., D’Hertog, W., Hagenbrock, J., Overbergh, L. Eizirik, D.L., Mathieu C. (2007) Deletion of STAT-1 pancreatic islets protects against streptozotocin-induced diabetes and early graft failure but not against late rejection. Diabetes 56: 2169–2173. 29. Gysemans, C.A., Ladriere, L., Callewaert, H., Rasschaert, J., Flamez, D., Levy, D.E., Matthys, P., Eizirik, D.L., Mathieu, C. (2005) Disruption of the gamma-interferon signaling pathway at the level of signal transducer and activator of transcription-1 prevents immune destruction of beta-cells. Diabetes 54: 2396–2403. 30. Echeverri, C.J., Perrimon, N. (2006) Highthroughput RNAi screening in cultured cells: a user’s guide. Nat. Rev. Genet. 7: 373–384. 31. Muller, P., Kuttenkeuler, D., Gesellchen, V., Zeidler, M.P., Boutros, M. (2005) Identification of JAK/STAT signalling components by genome-wide RNA interference. Nature 436: 871–875. 32. Berns, K., Hijmans, E.M., Mullenders, J., Brummelkamp, T.R., Velds, A., Heimerikx, M. Kerkhoven, R.M., Madiredjo, M., Nijkamp, W., Weigelt, B., Agami, R., Ge, W., Cavet, G., Linsley, P.S., Beijersbergen, R.L., Bernards, R. (2004) A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428: 431–437.

Chapter 12 Ligand Affinity Chromatography, an Indispensable Method for the Purification of Soluble Cytokine Receptors and Binding Proteins Daniela Novick and Menachem Rubinstein Abstract Ligand affinity chromatography separation is based on unique interaction between the target analyte and a ligand, which is coupled covalently to a resin. It is a simple, rapid, selective, and efficient purification procedure of proteins providing tens of thousands fold purification in one step. The biological activity of the isolated proteins is retained in most cases thus function is revealed concomitantly with the isolation. Prior to the completion of the genome project this method facilitated rapid and reliable cloning of the corresponding gene. Upon completion of this project, a partial protein sequence is enough for retrieving its complete mRNA and hence its complete protein sequence. This method is indispensable for the isolation of both expected (e.g. receptors) but mainly unexpected, unpredicted and very much surprising binding proteins. No other approach would yield the latter. This chapter provides examples for both the expected target proteins, isolated from rich sources of human proteins, as well as the unexpected binding proteins, found by serendipity. Key words: TBPII, IFNAR2, Soluble LDLR, IL-18BP, IL-32BP, PR3, Mass spectrometry, Biomarkers, Interleukins, Interferons, Serendipity

1. Introduction In 1968, Cuatracasas, Wilchek, and Anfinsen (1) coined the term “affinity chromatography” in its form known today (2, 3). This rapid and selective single-step purification procedure of proteins exploits the immense power of bio-recognition between the covalently immobilized ligand to an insoluble matrix and the complementary target bio-molecule. Almost any given biomolecule has an inherent recognition site through which it recognizes a partner molecule. If one of these partners is immobilized on a polymeric

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carrier, it can be used to selectively capture the biomolecule of interest. Isolation of a protein by affinity chromatography is a very effective technique. It provides the protein in a fairly pure state and enables its identification by partial sequencing, either by mass spectrometry or by N-terminal microsequencing. Upon completion of the human genome project, a partial protein sequence is enough for retrieving its complete mRNA and hence its complete protein sequence. Ligand affinity chromatography provides a comprehensive solution for isolation and characterization of most receptors and binding proteins. Any receptor or binding protein can be captured on its immobilized ligand, provided that there is sufficient affinity and a high specificity of interaction between the two partners. In many cases, there is more than one type of receptor or binding protein for a given ligand. Examples include the two membraneassociated TNF receptors (4) and the two membrane-associated IL-1 receptors (5), each derived from a different gene. In other cases, a single receptor gene may generate more than one mRNA splice variant, yielding several receptor species (e.g., IFNAR2c, the ligand-binding chain of Type I interferons and its short version counterpart, the decoy receptor, IFNAR2b (5–7)). In addition to membrane-associated receptors, circulating ligand-binding proteins are quite common. One group of binding proteins consists of soluble forms of cell surface receptors. Such soluble receptors were identified for several hormones and growth factors, including insulin, somatotropin (growth hormone), IGF, and EGF. Among cytokine receptors, a soluble form of the IL-2 receptor (soluble Tac) was identified in cell culture supernatants and then reported to be present in body fluids of normal individuals (8). These soluble receptors correspond structurally to the extracellular ligand-binding domain of the cell surface receptor, hence retaining their ligand-binding properties. Some of these soluble receptors are produced by proteolytic cleavage of their cell surface receptor counterparts, some from alternatively spliced mRNA and some by both mechanisms (9). Studies performed in our laboratory and in other laboratories revealed the presence of many soluble cytokine receptors in body fluids, including the receptors for IL-6, IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IFN-α/β, IL-13, IL-18, IL-22, IL-33, and others (10–19). Based on these studies, it became apparent that there are soluble receptors for most if not all cytokines in the circulation, and that these receptors play key roles in regulating cytokine-mediated biological activities either in an antagonistic or an agonistic fashion. Another group of circulating binding proteins is derived from different genes and therefore is not homologous to the cell surface receptors with which they share ligands. Examples of such proteins include osteoprotegerin (20), Cytokine-like factor-1 (21), IL-18 binding protein isolated by us 10 years ago, (22) and the enzyme

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proteinase 3 (PR3), recently described by us as an IL-32 binding protein (23). The membrane bound receptor for IL-32 (24) is not known till today. In an attempt to isolate this receptor via its soluble counterpart, we rather isolated what we named IL-32 binding protein (IL-32BP). IL-32BP was isolated from normal human urine and identified by a combination of ligand affinity chromatography and mass spectrometry. Eluted proteins from the affinity column were subjected to SDS-PAGE and the gel was stained with mass spectrometry-compatible silver staining. Candidate bands that were observed exclusively in the elution fractions and not in the “load” or “wash” fractions were excised from the gel, the proteins were electro-eluted and digested with trypsin. The resulting tryptic digest was subjected to liquid chromatography in tandem with mass spectrometry (Smoler Protein Center, Technion, Haifa, Israel). IL-32BP is not a soluble receptor but the well-characterized proteolytic enzyme proteinase 3 (PR3) on the one hand and an IL-32 binding protein on the other. PR3, also known as Wegener’s auto-antigen, is a neutrophil granule serine proteinase, existing both in a soluble and in a membrane-bound form. Autoantibodies to PR3, known as ANCA (anti neutrophil cytoplasmic autoantibodies) are the hallmark of Wegener’s granulomatosis (25), and are present in systemic and small vessel autoimmune vasculitic diseases. No other technique, but ligand affinity chromatography, would come across this protein and its functions, which are deduced from the method used to purify it. First we demonstrated the enzymatic activity of the isolated PR3/IL-32BP. We thus showed that limited proteolysis of IL-32 by this enzyme, resulted in the formation of two peptides, a mixture of which exhibited enhanced biological activity compared to the activity of the intact cytokine. Employing Surface Plasmon Resonance (Biacore, see Subheading 3.2.4) we demonstrated a high binding affinity between PR3 and IL-32 (Kd in the nM range). In an attempt to dissect the binding ability of PR3 from its proteolytic activity, we treated PR3 with PMSF, a serine protease inhibitor, prior to the binding studies. We showed that inactivation of PR3 with PMSF did not significantly reduce its binding affinity for IL-32. This observation suggested that the binding of PR3 to the immobilized IL-32 represents the non-enzymatic interaction of enzyme to substrate and supports the concept that binding of IL-32 and the processing of IL-32 are two separate properties of PR3. Ligand affinity chromatography has been successfully employed for isolating and identifying the above mentioned soluble receptors and binding proteins. In some cases, isolation of these binding proteins and their N-terminal amino acid sequencing enabled cloning or identification of their unknown genes at that time (6, 11, 22). This chapter describes the methods used in our laboratory for the purification, characterization, and molecular cloning of soluble

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cytokine receptors, binding proteins, and cell surface receptors. The immense power of ligand affinity chromatography in protein purification is demonstrated when 1,000-fold purification is achieved in one step. For example, for the isolation of cell surface receptors, we used 1011 cells per batch, or two term placentas per batch as rich sources of proteins. For the isolation of soluble receptors and binding proteins we used highly concentrated (500–1,000-fold) urinary proteins (500 mL/batch equivalent to up to 500 L of human urine; ca. 100 mg protein/L). In the case of the latter, the affinity column “load” fraction contained 25–50 g of protein mixture while the “elution” fraction contained ca. 0.25 mg of proteins, including the target soluble receptor or binding protein. Thus ca. 100,000-fold purification of the target protein was achieved in one step. Specific ELISAs confirmed no big loss of specific protein in this purification procedure. Our approach of isolating proteins by ligand-affinity-chromatography enabled rapid and efficient isolation of seven soluble receptors corresponding to cell-associated receptors (examples in Fig. 1a–d). Moreover, this method also enabled the isolation of non-receptor binding proteins and associated enzymes (Fig. 1e, f). No other approach would yield the latter. Thus with the availability of a pure and active ligand on the one hand, and a rich source of proteins on the other, ligand affinity chromatography virtually guarantees a successful target-protein isolation (42).

2. Materials 2.1. Receptor Isolation

1. Affi-Gel 10, or any other suitable pre-activated carrier for ligand immobilization (Bio-Rad, Hercules, CA). 2. Phosphate-buffered saline (PBS): 137 mM NaCl, 2.7 mM KCl, 1.5 mM KH2PO4, 6.5 mM Na2HPO4, pH 7.4, if necessary, 0.02% (w/v) NaN3. 3. Benzamidine. 4. 0.5 μm membrane (e.g., Pellicon Cassette, Millipore, Bedford, MA). 5. 10 kDa cutoff membrane (PTGC 10, Millipore). 6. Hypotonic buffer: 10 mM Tris–HCl, pH 7.5, 1 mM MgCl2, 1 mM CaCl2, 1 mM phenylmethylsulfonyl fluoride (PMSF), 22 TIU/mL aprotinin. Additional protease inhibitors such as pepstatin and leupeptin may be included. 7. Vortex shaker. 8. Tris-sucrose buffer: 20 mM Tris–HCl, pH 7.4, 0.25 M sucrose, 1 mM PMSF. 9. Ultratorax homogenizer.

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Fig. 1. Various soluble receptors purified from concentrated human urinary proteins by ligand affinity chromatography. (a). Lane 1: Molecular mass (kDa) markers. Lane 2: Urinary proteins purified by ligand (IL-6) affinity chromatography followed by HPLC and analyzed by SDS-PAGE and silver staining (10). (b) Lane 1: Molecular mass (kDa) markers. Lane 2: Ligand (IFN-γ) affinity purified urinary proteins (300 ng) analyzed by SDS-PAGE and silver staining (10). (c) Lane 1: Urinary proteins and Lane 2: ligand (IFN-α2) affinity purified urinary proteins analyzed by SDS-PAGE and silver staining (6). Molecular mass markers (kDa) are indicated on the left side. (d) Immunoblot analysis of cellular IFNAR2 with anti IFNAR2 antibodies. Lane 1: A detergent-extract of Daudi cells (108), purified on monoclonal antibodies to soluble IFNα/β receptor. Lane 2: Crude detergent extract of Daudi cells (5 × 106). Lane 3: Crude detergent cell extract of mouse NIH 3T3 cells (5 × 106) (6). (e, f) Two binding proteins purified from concentrated human urinary proteins by ligand affinity chromatography and analyzed by SDS-PAGE and silver staining. (e) Lane 1: Crude urinary proteins. Lane 2–9: Elution fractions (1–8) from the IL-18 column. Lane m: Molecular mass markers (kDa). The arrow indicates the IL-18 binding protein (22). (f) Lane 1: Wash fraction representing crude urinary proteins. Lanes 2–6: Elution fractions (1–5 respectively) from the IL-32α column. Lane 7: Molecular mass (kDa) markers indicated on the right. The arrow indicates the IL-32-binding protein (23).

10. Gauze. 11. HEPES buffer: 50 mM HEPES, pH 7.5, 1 mM PMSF, 20 TIU/mL aprotinin and other anti-proteases, and 0.02% (w/v) NaN3. 12. Liquid nitrogen. 13. Solubilization buffer: 1% Triton X-100, 10 mM HEPES, pH 7.5, 150 mM NaCl, 1 mM PMSF, 20 TIU/mL aprotinin, and other anti-proteases. 14. Low pH buffer: 25 mM citric acid, pH approximately 2.5, 1 mM benzamidine, 0.01% (w/v) NaN3.

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15. High pH buffer: 25 mM Na2CO3, pH approximately 11.0, 0.5–3 M NaCl, 1 mM benzamidine, 0.01% (w/v) NaN3. 16. 1 M Na2CO3. 17. 1 M Tris–HCl, pH 9.5. 18. Acetic acid (3 M and 5%, v/v). 2.2. Receptor Characterization

1. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) equipment. 2. Fixing solution: 50% (v/v) methanol, 10% (v/v) acetic acid. 3. 10% (v/v) glutaraldehyde. 4. Ethanol. 5. 30% (w/v) NaOH. 6. Ammonia. 7. Silver nitrate. 8. 1% (w/v) citric acid. 9. 37% (v/v) formaldehyde. 10. Acetone. 11. PD-10 columns (Amersham-Pharmacia, Piscataway, NJ). 12. Gelatin. 13. 0.5 M phosphate buffer, pH 7.5. 14. Chloramine-T. 15. Na[125I]. 16. NaHSO3. 17. KI. 18. Bovine serum albumin (BSA), 1% (w/v) in water. 19. 20% (w/v) trichloroacetic acid (TCA). 20. Disuccinimidyl suberate (DSS). 21. Dimethyl sulfoxide (DMSO). 22. Stop solution (cross-linking): 1 M Tris–HCl, pH 7.5, 1 M NaCl.

3. Methods 3.1. Receptor Isolation Procedures 3.1.1. Preparation of a Ligand Affinity Column

Successful receptor isolation depends on availability of a pure ligand (see Note 1), which retains its binding capability upon immobilization to an affinity resin. Affinity resins suitable for chromatography of proteins usually consist of derivatized agarose. There are several types of chemistries and spacers of various lengths that may be useful to avoid steric hindrance. Pre-activated carriers are more convenient to work with. Several suppliers provide such carriers and also

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include detailed procedures for ligand immobilization and elaborate on performance of the chromatographic procedure (AmershamPharmacia, Bio-Rad, Sigma, Pierce and others). The following procedure uses Affi-Gel 10 (Bio-Rad, Hercules, CA, USA). This carrier is based on the active N-hydroxysuccinimide ester, which is relatively stable and reacts rapidly with primary (and secondary) amines of proteins, such as N-terminal sites and ε-amino groups of Lysine. Affi-Gel 10 is suitable for coupling proteins having isoelectric points from 6.5 to 11. Proteins with isoelectric points below 6.5 are better coupled to Affi-Gel 15. 1. Couple 2–5 mg (at a concentration of at least 1 mg/mL) of highly purified (preferably homogenous) ligand (see Note 2) to Affi-Gel 10 (0.5–1 mL wet beads) according to manufacturer’s instructions. Make sure that the protein solution does not contain primary amines such as TRIS, glycine, cell culture media, or ammonium salts. Usually, about 80–90% of the ligand is immobilized. 2. Store the resulting gel at 4°C in phosphate-buffered saline (PBS) containing 0.02% NaN3. Stability of the immobilized ligand upon repeated use of the column is not predictable. In most cases it may be used for chromatography numerous times. 3. Test the stability of the ligand by a bioassay following treatment with high and low pH. This will enable choice of the most suitable elution conditions. Elution at a pH value that inactivates the ligand may still be employed for small-scale procedures, provided that the gel is rapidly neutralized following elution. 3.1.2. Preparation of Crude Receptor Extracts

Successful receptor isolation depends not only on the availability of a pure ligand but also on a suitable and sufficient protein source (see Note 3) of the receptor to be isolated. Sufficient amounts will ensure a final yield of the isolated receptor, enabling its characterization (by e.g. N-terminal amino acid sequencing, mass spectrometry, SDS-PAGE, evaluation of biological activity, etc.) and antibody generation (see Note 4). Isolation of membrane-bound receptors requires a preliminary step of membrane isolation, followed by solubilization with a detergent. A number of mild detergents may be employed in the solubilization of cytokine receptors. Triton-X-100 is an example for a non-ionic detergent; CHAPS, which is more effective for membrane solubilization, is an example of a zwitterionic detergent. Isolation of soluble receptors does not require the use of detergents and hence is simpler to perform. Soluble receptors may be isolated from cell culture supernatants, from plasma, serum, or urine. Urinary proteins are derived from those plasma proteins that pass through the kidney. Because the kidney retains high molecular

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weight proteins more effectively, the urine of healthy individuals is enriched with proteins 90%) (6, 7, 9, 11, 17). The contaminating cells are mostly monocytes. Although the kits were developed for isolation of DCs directly from unfractioned PBMCs, using PBMCs rather than elutriated monocytes decreases the purity significantly. For that purpose, we refined the isolation procedure of PDCs from buffy coats by adding initial enrichment of monocytes and DCs using RosetteSep monocyte enrichment cocktail (18). Sorting of PDCs from buffy coats results in much lower PDC yields (on average £1 × 106 isolated PDCs per buffy coat). However, the purity is similar to that achieved using elutriated monocytes. Moreover, it has to be taken into consideration that BDCA-2 and BDCA-4 are also expressed on subpopulations of other cells. BDCA-4 is expressed on a subpopulation of monocytes and is also upregulated on cultured MDCs, which is why isolation of PDCs based on BDCA-4 expression should only be performed on freshly isolated cells. AntiBDCA-2 binds a subpopulation of PDCs, and has been shown to inhibit the levels of IFNα production by these cells, and thus should be avoided for this application (15). 5. The frequency of spontaneously IFNα producing PDCs or other cells within blood mononuclear cells from healthy blood donors or even patients with ongoing illnesses is very low and usually difficult to detect. In vivo stimulated IFNα producing cells are usually only detectable at sites of locally inflamed or virus infected tissue. Also, IFNα production in mock stimulated cell cultures is usually below detection limit and hence the cells need in vitro stimulation to induce production and enable detection. 6. PDCs, which express TLR7 and TLR9, respond to TLR7/ 8-binding imidazoquinolines (imiquimod and R-848) and ssRNA viruses such as HIV-1, as well as to TLR9-binding CpG ODN (classes A and C) and DNA viruses such as Adenovirus stimulation, resulting in upregulation of IFNα (6, 7, 9, 13, 19–21). In contrast, other DC subsets such as CD11c+ myeloid DCs express a different repertoire of TLRs (i.e. TLR3, TLR4, and TLR7/8) and respond to the corresponding TLR-ligands accordingly. However, in contrast to TLR-ligation of PDCs, stimulation of MDCs leads to no or very low levels of IFNα (13, 17, 21).

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7. Acidification of endosomal compartments is required for efficient recognition of ligands by TLR7/8 and TLR9, and for initiation of signaling cascades leading to IFNα production in PDCs. Chloroquine, NH4Cl, and Bafilomycin A1 are welldescribed inhibitors of endosomal acidification and are therefore useful tools for studying recognition of TLR ligands. In Fig. 3a, we show that chloroquine inhibits TLR7/8-Ligand and Adenovirus serotype 35 induced IFNα in PDCs. A range of concentrations for each inhibitor has been provided, but it is critical to determine the optimal concentration for each experiment. Also, it is recommended to determine the timing of when the inhibitors are added relative to stimulation, as they can, for example, interfere with viral lifecycle. DOTAP is a type of cationic liposome that forms complexes with negatively charged nucleic acids such as DNA. Such stabilization of certain CpG ODNs, for example, leads to retention in early endosomes and ultimately enhanced IFNα production (Fig. 3b). DOTAP and CpG ODNs may be added simultaneously to the cells in vitro, or pre-incubated together for a short period of time and then added to the cells. Reagents like DOTAP may be useful experimental tools for studies of intracellular trafficking of TLR-ligands. 8. The kinetics for IFNα production in PDCs are normally very rapid and declines rapidly thereafter. As such, initial experiments should aim to study the time-course of production for each stimulus and/or particular cell culture system to avoid false-negative results. We have found that PDCs have a very rapid and strong induction of IFNα in response to cognate TLR-ligands, especially TLR7/8-Ligands. By quantitativePCR of IFNα mRNA, we found that levels peaked at 4 h and rapidly declined thereafter (13). CpG ODNs also induced IFNα mRNA transcripts albeit to much lower levels and during a prolonged time span (4–12 h). Furthermore, detection of IFNα protein production by ICS showed a peak at 7 h for TLR7/8-ligand stimulation. In fact, we found that TLR7/ 8-ligand induced the most rapid and robust response with high frequencies of IFNα producing PDCs at 7 h while much lower frequencies were found in response to CpG ODNs. High levels of IFNα were also found in supernatants of PDCs stimulated with any of the classes of CpG ODN or TLR7/8-Ligand. Despite the highest detectable production of IFNα by TLR7/8-Ligand stimulation by qPCR and ICS (Fig. 2a–b), measurement of secreted IFNα in the supernatants showed that CpG ODN class C induced rather similar accumulative secretion after 24 h (Fig. 2c). Compared to single TLR-ligands, induction of IFNα in response to wild-type, inactivated or replication-incompetent whole virus particles usually has delayed

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Fig. 2. Kinetics of IFNα production in PDCs. Freshly isolated PDCs were exposed to either 1 μg/mL TLR7/8-Ligand or 10 μg/ mL CpG ODN class C for various durations, as indicated, between 0 and 24 h in the presence or absence of brefeldin A and monensin. (a) Representative pseudo-color flow plots of intracellular staining of IFNα in PDCs after 8 h stimulation, of which brefeldin A and monensin were present for the final 6 h. (b) PDCs were stimulated as in (a) and the duration of monensin and brefeldin A treatment is depicted in the line graph. These cells were then fixed, permeabilized, and stained with mAbs for IFNα. The frequency (%) of IFNα-producing PDCs at each time-point was determined by flow cytometry. (c) The concentration of secreted IFNα in collected supernatants was then measured by ELISA, at time points indicated in the bar graph. The data from the two complementary assays for measuring IFNα demonstrate the kinetic patterns for the induction of IFNα by two TLR ligands. TLR7/8-Ligand more rapidly induced IFNα in a greater frequency of PDCs, as compared to CpG C, yet the cumulative amount of IFNα in the supernatants is equivalent for both the TLR7/8-Ligand and CpG C ODN after 24 h. Data points show mean ± SEM and n ³ 3.

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kinetics (9). This likely relates to the time required for virus attachment, uptake or entry, and uncoating steps which are necessary to reveal nucleic acids detectable by the cells. 9. A cytokine transport inhibitor is required for the detection of IFNα expression by flow cytometry in order to increase the positive staining signal. Monensin and brefeldin A act to inhibit the cellular secretory pathway, causing newly synthesized proteins to accumulate diffusely within the cytoplasm, and thus promoting increased staining intensity readily detectable by flow cytometry. One should be aware that monensin can also interfere with endosomal acidification, and thus TLR signaling (22). Therefore, it is critical to determine the time point at which the cytokine transport inhibitors are added to cells poststimulation. Additionally, the protein transport inhibitors may interfere with the paracrine/autocrine signaling feedback loops required for sustained IFNα production. Both monensin and brefeldin A are toxic to the cells after longer exposure (>12 h). Brefeldin A is soluble in DMSO, so a DMSO stimulation control is strongly recommended. Finally, it is important to consider that monensin and brefeldin A may impede mobilization to the surface of co-stimulatory markers used for monitoring phenotypic maturation of DCs. 10. It is possible to add both cell surface and IFNα-specific antibodies simultaneously, but the system should be optimized. The general guideline is that the more highly expressed antigens should be stained with the weaker fluorescent dyes and vice versa. The antibodies may require different concentrations than when they are applied separately, and some antibody clones will not bind following fixation. Determining optimal combinations of fluorescent dyes will also maximize separation between populations. 11. The lower detection limit of the assay is governed by the frequency of IFNα positive events in mock stimulation (e.g., background). Because circulating PDCs have low to no production of IFNα, the background values should approach zero. To this end, one should take the following considerations in order to obtain optimal results: (1) titrate all antibodies because excessively high mAb concentrations can generate nonspecific background staining, (2) use viability dyes to remove dead cells during the flow analysis, as mAbs bind dead cells nonspecifically, (3) use isotype matched mAb controls, (4) use mAbs directly conjugated to bright fluorescent dyes such as, Alexa Fluor or Phycoerythrin (PE), and (5) remove all unbound fluorescent dye molecules from the antibody stock. 12. In order to confirm specificity of the antibody against IFNα one can abolish the signal of the immuno-reactivity by preabsorption of the specific antibody with recombinant human

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IFNα. To do this, we recommend a 4°C overnight incubation of the antibody and corresponding recombinant protein at a 1:10 molar ratio and then addition of this complex to the cells instead of primary antibody alone. Cytokine transfected cell lines may also be used for the assessment of antibody specificity (23, 24). 13. We stress that analysis of PDCs by flow cytometry should immediately follow the procedure for staining of intracellular IFNα. PDC morphology is somewhat susceptible to disruption by the fixation/permeabilization treatments. Over time the cells decrease in size such that they become difficult to differentiate from dead cells or debris. The absolute majority of IFN producing cells should be PDCs, so if that is not the case one has to consider problems with staining procedure (i.e., too much mAb, unspecific binding of mAb to dead cells, autofluorescence, lack of good positive control, incorrect compensation of the flow cytometer, etc.). Also, it is important that high event numbers are collected on the flow cytometer because PDCs are rare cells and IFNα-producing cells are normally rarer yet. For pure isolated PDCs, this means >5 × 104 total events, and for whole PBMCs >1 × 106 total events. Although most cells in the body may produce IFNα in response to specific stress stimulus, PDCs are well documented to be unique in their ability to produce very high levels of IFNα, far exceeding what has been found in any other cell type. The detection threshold for IFNα expression using ICS and flow cytometry usually does not allow for distinct appreciation of production from other cell types than PDCs. Therefore, it is unlikely that in a PBMC culture that significant numbers of producer cells would be represented among other cell types. However, confirmation of a PDC phenotype should be performed using specific markers such as CD123 and BDCA-4 (Fig. 1a). 14. Flow analysis is useful for determining the frequency (%) of IFNα producing PDCs. Data should preferably be presented as pseudocolor or contour type flow plots, rather than histograms (Figs. 2 and 3). This is mainly because IFNα producing PDCs are normally a population distinct from the nonproducing cells, rather than a slight fluorescence shift, as may be observed using histograms. In addition to using flow analysis software to quantify the frequency of IFNα producing cells, it may also be used to determine median fluorescence intensity (MFI). MFI is a measure of how much relative IFNα is produced on a per-cell-basis. We recommend using the median statistic as opposed to mean, because it better estimates the central tendency of the population.

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Fig. 3. Effect of Chloroquine and DOTAP on IFNα-induction in PDCs. (a) Freshly isolated PDCs were exposed to either TLR7/8-Ligand or recombinant Adenovirus serotype-35 for 8 h total, with brefeldin A and monensin present for the last 6 h. PDCs were also cultured in the presence or absence of chloroquine, an inhibitor of endosomal acidification, for the entire duration of the culture. These cells were then fixed, permeabilized, and stained with mAbs for IFNα. The frequency of IFNα-producing PDCs was determined by flow cytometry. Representative flow plots shows that chloroquine treatment completely blocked IFNα production in PDCs. (b) Experiments were completed as in (a), except carried out in the presence or absence of the cationic liposomal transfectant reagent, DOTAP, as indicated. While DOTAP increases the frequency of PDCs which respond to TL7/8-Ligand, the increase is more dramatic for CpG ODN class C. One representative experiment is shown for (a) and (b).

15. It is recommended to minimize freeze–thaw cycles of collected PDC supernatants in order to reduce possible IFNα protein degradation. Also, note that the lower detection threshold in most commercially available ELISA kits is ~5–10 pg/mL, which may not be sufficient for detection of IFNα in most cells

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or cell lines. In turn, supernatants from TLR-ligand or virus stimulated PDCs may need to be serially diluted, because the concentration often exceeds the upper detection limit of commercially available ELISA kits.

5. Conclusion We have outlined three complementary protocols in the preceding pages, which cover; the isolation of PDCs from human blood or identification of the cells in PBMCs, subsequent in vitro stimulation by cognate TLR-ligands or viruses, and finally detection of IFNα by ICS and analysis by flow cytometry. While we have provided information on the optimization of these protocols carried out in our laboratory, further fine tuning on the methods may be required depending on the particular aim of the investigation, and the equipment, reagents, and experience in one’s own laboratory.

Acknowledgments This work was supported by grants from the Swedish Research Council (Ventenskapsrådet), the Swedish Society for Medicine, and the Swedish International Development Agency (SIDA). References 1. Akira, S., Takeda, K., Kaisho, T. (2001) Tolllike receptors: critical proteins linking innate and acquired immunity. Nat. Immunol. 2: 675–680. 2. Takeuchi, O., Akira, S. (2009) Innate immunity to virus infection. Immunol. Rev. 227: 75–86. 3. Cao, W., Liu, Y.J. (2007) Innate immune functions of plasmacytoid dendritic cells. Curr. Opin. Immunol. 19: 24–30. 4. Siegal, F.P., Kadowaki, N., Shodell, M., Fitzgerald-Bocarsly, P.A., Shah, K., Ho, S., Antonenko, S., Liu, Y.J. (1999) The nature of the principal type 1 interferon-producing cells in human blood. Science 284: 1835–1837. 5. Vollmer, J., Weeratna, R., Payette, P., Jurk, M., Schetter, C., Laucht, M., Wader, T., Tluk, S., Liu, M., Davis, H. L., Krieg, A.M. (2004) Characterization of three CpG oligodeoxynucleotide classes with distinct immunostimulatory activities. Eur. J. Immunol. 34: 251–262.

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Chapter 14 A Sensitive and Versatile Cytokine Bioassay Based on Type I Interferon Signaling in 2fTGH Cells Lennart Zabeau, José Van der Heyden, and Jan Tavernier Abstract We have designed a sensitive and versatile bioassay for quantification of series of cytokines. The assay makes use of chimeric receptors composed of the extracellular, ligand-binding part of the cognate cytokine receptor and the transmembrane and cytosolic part of the type I interferon receptor. Receptors can be homo(e.g. erythropoietin), di- (e.g. interleukin-5), or even trimeric (e.g. interleukin-2). Stable expression of these chimeras in the 2fTGH cell line allows an interferon-type signaling, which makes a positive selection in conditioned medium possible or a negative selection using a toxic guanine analog. The cytokine of interest is quantified by the extent of cell survival or cell toxicity respectively, which can be measured by easy and cheap crystal violet staining. This bioassay is sensitive in the lower picogram per milliliter range and, in contrast to ELISA methods, only measures the concentration of biologically active cytokines. Using this approach, hypersensitive 2fTGH cell lines have been developed for type I and II interferons, erythropoietin, interleukin-2, and interleukin-5. Key words: Bioassay, Chimeric receptors, Interferon, 2fTGH, 6-Thioguanine, Toxicity

1. Introduction Accurate and sensitive methods for detection and quantification of small quantities of cytokines have become crucial in clinical diagnostics. Current techniques can be roughly divided in bioassays and immunoassays. Antibody-based techniques can allow detection of multiple cytokines simultaneously and can be adapted for high-throughput screening. On the other hand, biological active levels of a certain cytokine can only be measured using a bioassay. These latter often require specific cell lines and growth media depending on the cytokine. There is therefore a growing need for a unified system that makes sensitive, easy, cheap, and accurate measurements of large series of bioactive cytokines possible. Marc De Ley (ed.), Cytokine Protocols, Methods in Molecular Biology, vol. 820, DOI 10.1007/978-1-61779-439-1_14, © Springer Science+Business Media, LLC 2012

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The fibrosarcoma cell line 2fTGH is deficient in the gene encoding for hypoxanthine-guanine phosphoribosyl transferase (HGPRT), an enzyme involved in the generation of purine nucleotides through the so-called purine salvage pathway. The deficiency can be complemented by the stable integration of the bacterial gpt gene, coding for xanthine-guanine phosphoribosyl transferase (XGPRT). Induced expression of this gene allows both positive selection in hypoxanthine-aminopterine-thymidine (HAT) medium or negative selection using the toxic guanine analog 6-thioguanine (6-TG). A cell line with the gpt gene under control of the type I interferon (IFN) inducible 6–16 promotor (1) has been proven a very powerful tool for cloning and characterization of components involved in IFN-α/β signal transduction pathways (2, 3). This latter takes advantage of the endogenous expression of the type I IFN receptors on the surface of 2fTGH cells. The class II cytokine receptor IFNAR is composed of two subunits, IFNAR1 and IFNAR2-2, an IFNAR2 isoform with a complete intracellular domain (4). Like all members of the class II cytokine receptor family, the IFNAR lacks intrinsic kinase activity and uses the receptor-associated kinases Jak1 and Tyk2. In a generally accepted model, IFN stimulation clusters INFAR1 and INFAR2 receptors in such a way that the kinases are brought in close and correct proximity, allowing activation by cross-phosphorylation. Activated kinases phosphorylate tyrosine residues in the cytoplasmic tails, thereby providing docking sites for downstream signaling molecules, such as the signal transducers and activators of transcription 1 (STAT1) and STAT2. Once recruited, these STATs become a substrate of the kinase activity, and together with IRF9 (interferon regulatory factor 9) translocate to the nucleus where they regulate gene-transcription (see Fig. 1). In this bioassay we use chimeric receptors to combine specific cytokine capturing by the ectodomains of their cognate receptors with activation of the intracellular IFN signaling pathways and IFN-induced 6-TG sensitivity in the 2fTGH cell line. In a first step, the extracellular domains of the tested cytokine are coupled to the transmembrane and cytoplasmic domains of IFNAR1 and IFNAR2-2. To increase efficiency of signaling by these chimeric receptors, additional leucine residues can be inserted in the transmembrane domain. This latter allows rotations of the cytoplasmic tails in the activated chimeric receptor complex. Combinations of the resulting receptors are initially tested for 6–16 promotor activation in transient transfection experiments, which allows selecting the most signaling efficient combination. This combination is then transfected in 2fTGH cells and selected for survival in HAT medium supplemented with the tested cytokine. Clones are analyzed and finally used to measure the cytokine-induced 6-TG toxicity. Sensitivities of different bioassays are summarized in Table 1 (5).

STAT2

STAT1

trimeric receptors

IFNaR1

IFNaR1

-Y P

P Y-

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IFNaR2-2

heterodimeric receptors

IFNaR2-2

homodimeric receptors

IFNaR1

IFNaR1

IFNaR2-2

INFα/β

A Sensitive and Versatile Cytokine Bioassay Based on Type I…

IFNaR2-2

14

IRF9

Survival in HAT or Cell death in 6-TG

E. coli gpt

6-16 promotor

Fig. 1. Type I interferon and chimeric receptors and their downstream signal transduction pathway leading to activation of E. coli gpt transcription.

Table 1 6-TG cell survival assay Cytokine

Type receptor

EC50

Detection limit

IFN-α/β

Heterodimeric

~5 pg/mL

~2 pg/mL

IFN-γ

Heterodimeric

~20 pg/mL

~5 pg/mL

Erythropoietin

Homodimeric

~3 pg/mL

~1 pg/mL

Interleukin-2

Heterotrimeric

~6 pg/mL

~2 pg/mL

Interleukin-5

Heterodimeric

~15 pg/mL

~4 pg/mL

2. Materials 2.1. Construction of Chimeric Receptors

Restriction enzymes (5 U per reaction; Bioloabs) and T4 DNA ligase (1 U per reaction; Invitrogen) are used according to the manufacturer’s guidelines. Synthetic oligonucleotides are ordered from Eurogentec.

2.2. Transient Evaluation of Receptor Combinations

1. Culture medium: Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and antibiotics (penicillin/streptomycin; Invitrogen). FCS is inactivated (20 min at 56°C) for 2fTGH cell culture.

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2. CaCl2 buffer: prepare a 2.5 M CaCl2 stock and filter-sterilize (store at −20°C). 3. 2× HeBS (Hepes-buffered saline) buffer: 280 mM NaCl, 1.5 mM Na2HPO4 and 50 mM Hepes in double distilled water, adjust pH to 7.05 with 1 M NaOH (see Note 3), filter-sterilize and store in aliquots at −20°C (avoid repeated freeze-thaw cycles). 4. SEAP activity is measured with the Phospha-Light kit (Tropix) using the luminogenic CSPD (disodium 3-(4-methoxyspiro (1,2-dioxetane-3, 2¢-(5¢-chloro) tricyclo[3.3.1.1]decan)-4-yl) phenyl 1 phosphate) substrate. 5. The IFN used in this assay is human IFN-α2b (Pepro Tech). 2.3. Generation of Stable Cell Lines

2.4. Bioassay

Selection medium: HAT concentrate (containing 5 mM sodium hypoxanthine, 20 μM aminopterin, 0.8 mM thymidine; Invitrogen) 1/50 diluted in culture medium supplemented with penicillin/ streptomycin (Invitrogen). 1. Selection medium for cell survival: HAT in culture medium. Selection medium for cell toxicity: 50 μg/mL 6-TG (SigmaAldrich) in culture medium. 2. Crystal violet staining solution: 0.5% crystal violet (w/v) in 3% formaldehyde, 30% ethanol, and 0.17% NaCl (w/v). 3. Solubilization buffer: 33% acetic acid; OD reading at 595 nm.

3. Methods The protocol is organized in four sections, with the first three parts being optimization, the fourth the actual bioassay. The optimization includes the construction of chimeric receptors (see Subheading 3.1) and evaluation of efficiency of signaling using transient transfection experiments (see Subheading 3.2) (see Note 5). Once an optimal receptor combination is selected, these are stably transfected in the 2fTGH cell line (see Subheading 3.3). The last section (see Subheading 3.4) deals with the cytokine-induced 6-TG toxicity bioassay. 3.1. Construction of Chimeric Receptors

In this procedure, the extracellular (EC) parts of the receptor for the tested cytokine are fused to the transmembrane (TM) and intracellular (IC) domains of IFNAR1 or IFNAR2-2. In case of a homomeric receptor, this results in two chimeric receptors. When the receptor is composed of two different chains, each receptor subunit is fused to IFNAR1 or IFNAR2-2, leading to four different chimeras. To further enhance signaling, additional leucine residues (one, two or three) can be inserted in the TM domain of each

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chimeric receptor by site-directed mutagenesis (see Note 6). In the case of a heterodimeric receptor this involves 16 combinations to evaluate transiently (see Note 1). 1. Amplify the EC part of the receptor(s) of interest, as well as the TM + IC domains of IFNAR1 and INFaR2-2 using standard PCR techniques. 2. Cut fragments and ligate in an opened pcDNA3 vector (Invitrogen). 3. Transform in E. coli. 4. Purify plasmid using a method yielding DNA suitable for transfection. 5. Insert one, two, or three extra leucines in the TM domain using the QuikChange Site Directed Mutagenesis Kit (Stratagene). 3.2. Transient Evaluation of Receptor Combinations

In what follows, combinations of the different chimeric receptors are tested for efficiency of type I interferon signaling by transient transfection experiments. A reporter construct with the secreted alkaline phosphatase (SEAP) under control of the 6–16 promotor is thereby used to quantify receptor activation (6) (see Note 5). Day 0: Seed 400,000 2fTGH cells per 10 cm2 well. Day 1: Transfect chimeric receptors (or a combination thereof) together with 6–16 SEAP reporter (2 μg DNA in total) using standard calcium phosphate transfection techniques. Day 2: Wash cells with PBS without calcium and magnesium. Day 3: Trypsinize cells with 200 μL trypsin (Invitrogen), seed in a 96-well plate and stimulate overnight with a serial dilution of the cytokine or IFN-α as a positive control. Day 4: Lyse the cells in 10 μL lysis buffer for 10 min and measure alkaline phosphatase activity using 50 μL of a 1/20 dilution of the CSPD substrate in a chemiluminescence reader for 10 s (TopCount, Perkin Elmer).

3.3. Generation of Stable Cell Lines

Next, the most efficient chimeric receptor combination is stably expressed in 2fTGH cells and selected for cytokine-dependent growth in HAT medium. After 2 weeks of selection, individual clones are isolated and screened for highest sensitivity. 1. Day 1: seed 7 × 106 cells in a 175 cm2 flask and culture overnight. 2. Day 2: transfect 35 μg chimeric receptor(s) with calcium phosphate overnight. 3. Day 3: wash cells with PBS and culture overnight. 4. Day 4: trypsinize and subculture 1/10. 5. Replace with culture medium supplemented with HAT and suboptimal concentrations of the appropriate cytokine. Eventually different cytokine concentrations can be applied.

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6. Replace medium every 4 days. 7. Colony picking: remove medium and cover each colony with a small paper disk (3MM, Whatman) that has been soaked in trypsin. After a couple of minutes wipe off the colony with the paper disk using sterile forceps. 8. Transfer to a 24- or 48-well plate. 9. Upscale cultures. 3.4. Bioassay

The expanded clonal cell lines are finally screened for highest sensitivity in the cytokine-dependent 6-TG bioassay. The read-out is the cheap and easy crystal violet staining method for living cells quantified using a colorimeter (see Note 4). 1. Seed 3,000 to 5,000 cells per 96-well in medium containing a serial dilution of cytokine. 2. After 24 h, add 6-TG to a final concentration of 50 μg/mL. 3. Further incubate for 4 days. 4. Remove medium and stain for 10 min with 50 μL crystal violet staining solution. 5. Wash cells gently but extensively with tap water. 6. Add 100 μL solubilization buffer and measure in a colorimeter at 595 nm.

4. Notes 1. This protocol can also be adapted for trimeric receptors. In this case, the cytokine-capturing receptor subunit is first stably expressed in 2fTGH cells. IFNAR1 and IFNAR2-2 chimeras of the remaining two components are then transfected in these cells. Transient evaluation, selection, and the bioassay are essentially the same as for homo- and dimeric receptors. Using a 2fTGH cell line stably expressing the interleukin-2 receptor α chain, we have been able to set up a bioassay for this cytokine (see Table 1). 2. Since type I IFN receptors are endogenously expressed on the surface of the 2fTGH cells, a parallel assay using neutralizing anti-IFNAR2 antibody should be used to check the presence of type I IFNs when biological samples (like blood, urine, sputum, …) are tested. 3. An exact pH of the 2× HeBS buffer is critical for transfection efficiency. The optimal range is very narrow: from 7.05 to 7.12.

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4. As an alternative for the crystal violet staining, the use of ATPlite (Perkin Elmer) can be considered. 5. For transient evaluation Hek293 or Hek293T can be used because of their high transfection efficiency resulting in a robust luminescence signal. 6. Position of the extra leucine residues in the transmembrane region of the IFN receptors. As an example a Pac1 restriction site is added at the N-terminal site for the fusion with the extracellular part of the cytokine receptor of interest. IFNAR1 TM Pac1

LLL

TTAATTAAAATTTGGCTTATAGTTGGAATT---------TGT ATTGCATTATTTGCTCTCCCGTTTGTC TTAATTAAAATTTGGCTTATAGTTGGAATTCTC------TG TATTGCATTATTTGCTCTCCCGTTTGTC TTAATTAAAATTTGGCTTATAGTTGGAATTCTCCTC--TGTATTGCATTATTTGCTCTCCCGTTTGTC TTAATTAAAATTTGGCTTATAGTTGGAATTCTCCTCC TCTGTATTGCATTATTTGCTCTCCCGTTTGTC IFNAR2 TM Pac1

LLL

TTAATTAAAATTTGGCTTATAGTTGGAATT---------TGT ATTGCATTATTTGCTCTC TTAATTAAAATTTGGCTTATAGTTGGAATTCTC------TG TATTGCATTATTTGCTCTC TTAATTAAAATTTGGCTTATAGTTGGAATTCTCCTC--TGTATTGCATTATTTGCTCTC TTAATTAAAATTTGGCTTATAGTTGGAATTCTCCTCCT CTGTATTGCATTATTTGCTCTC References 1. Pellegrini, S., John, J., Shearer, M., Kerr, I.M., Stark, G.R. (1989) Use of a selectable marker regulated by alpha interferon to obtain mutations in the signaling pathway. Mol. Cell. Biol. 9: 4605–4612. 2. Velazquez, L., Fellous, M., Stark, G.R., Pellegrini, S. (1992) A protein tyrosine kinase in the interferon alpha/beta signaling pathway. Cell 70: 313–322. 3. Darnell, J.E., Jr., Kerr, I.M., Stark, G.R. (1994) Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. Science 264: 1415–1421. 4. Novick, D., Cohen, B., Rubinstein, M. (1994) The human interferon alpha/beta receptor:

characterization and molecular cloning. Cell 77: 391–400. 5. Van Ostade, X., Schauvliege, L., Pattyn, E., Verhee, A., Vandekerckhove, J., Tavernier, J. (2000) A sensitive and versatile bioassay for ligands that signal through receptor clustering. J. Interferon Cytokine Res. 20: 79–87. 6. Pattyn, E., Van Ostade, X., Schauvliege, L., Verhee, A., Kalai, M., Vandekerckhove, J., Tavernier, J. (1999) Dimerization of the interferon type I receptor IFNaR2-2 is sufficient for induction of interferon effector genes but not for full antiviral activity. J. Biol. Chem. 274: 34838–34845.

INDEX A

E

Actinomycin D .................................................73, 76, 78, 87 Affinity chromatography ..............5, 75–76, 85–86, 195–212 Affymetrix ................................................... 25–52, 151–154 AGO protein ........................................................... 134, 135 Antiviral activity .............................................. 2–5, 209, 215 Apoptosis..............................................50–51, 159, 180, 189 ARE. See AU-rich element AU-rich element (ARE) .................................72, 76, 91–95, 97, 98, 100, 102 Autoimmune disease ....................................................... 197

Electrophoretic mobility shift assay (EMSA)............. 74–75, 81–83, 87 EMSA. See Electrophoretic mobility shift assay End-labeling of synthetic RNA............................. 75, 83–84 Epithelial cells and tissues ........................................... 55–69 Epstein-Barr virus ............................................................. 25

B Basic local alignment search tool (BLAST) ............................................. 168, 183, 210 Bioassay .................................... 1–6, 201, 209, 212, 231–237 Biomarkers ...................................................................... 212 BLAST. See Basic local alignment search tool

C CD4........................................ 26–31, 38, 39, 46, 52, 77, 114 Chicken β-actin promoter ............................................... 125 Chimeric receptors .................................................. 232–235 Chloramine-T.......................................................... 200, 206 Concanavalin A ............................................................... 3, 4 Ct-value ........................................................8, 15–20, 22, 66 Cytoplasmic extracts ...............................74, 81–84, 126, 127

D Database for annotation, visualization, and integrated discovery (DAVID) ......................................... 25–52 DAVID. See Database for annotation, visualization, and integrated discovery DEPC. See Diethylpyrocarbonate Diabetes........................................................................... 179 Dicer..... .............................................................134–137, 180 Diethylpyrocarbonate (DEPC) ........................... 76, 86, 125 Doxycycline ........................................................... 73, 76, 79 Drosha. .................................................................... 135, 136 dsRNA-dependent protein kinase .......................... 134, 136, 137, 139, 152, 155, 164

G Geneticin ......................................................... 119, 120, 128 GFP probes ................................................................. 74, 81 β-Globin probes .......................................................... 74, 81

H HA. See Hyaluronan Hammerhead ribozyme ........................................... 117–131 HGPRT. See Hypoxanthine-guanine phosphoribosyl transferase Hierarchical clustering ...................................................... 38 HIV. See Human immunodeficiency virus House keeping gene .................................128–130, 139, 190 Human antigen R (HuR) ............................................ 72, 94 Human immunodeficiency virus (HIV) ............... 26–28, 51, 106, 218, 222, 223 HuR. See Human antigen R Hyaluronan (HA) .................................................... 107–109 Hypoxanthine-guanine phosphoribosyl transferase (HGPRT) ............................................................ 232

I ICS. See Intracellular cytokine staining IFN-α.................................. 30–32, 44–45, 49, 51, 148–150, 196, 199, 211, 215–229, 232, 233, 235 IFNAR2 ....................196, 199, 210, 211, 232, 234, 236, 237 IL–32 binding protein ..............................196–197, 199, 211 Inducible nitric oxide synthase ........................................ 181 Inflammation .......................... 2–3, 5, 72, 106, 164, 165, 174 Insulinoma....................................................................... 184 Insulitis ............................................................................ 179 Integrin.................................................................... 105–115 Interferon-β....................................................................... 93

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CYTOKINE PROTOCOLS 240 Index Interferon-γ ....................................................................... 93 Interferon-stimulated genes (ISGs) ........................ 136, 138, 148, 150, 153, 155, 163–174 Interleukin–1 ............................................................... 2, 5–6 Interleukin–27 ....................................................... 25–52, 93 Intracellular cytokine staining (ICS) ...................... 216, 221, 224, 227, 229 ISGs. See Interferon-stimulated genes

K Key pathways ..................................................................... 51 Knockout model ...................................................... 189–190

L Lentivirus ................................................................ 164, 169 Leukocyte .............................................3, 106, 108, 111, 223 Ligand affinity chromatography .............................. 195–212 Lipopolysaccharide (LPS) ............................................... 102 Liposome .................................. 106, 108–110, 113, 114, 224 LNA. See Locked nucleic acids Locked nucleic acids (LNA) ............................................. 14 LPS. See Lipopolysaccharide

M Macrophage .......................... 26–27, 29–32, 38, 39, 165, 169 MACS. See Magnetic-activated cell sorting Magnetic-activated cell sorting (MACS) ................... 27, 29, 30, 217, 220 MCF. See Monocytic cell factor Melting curve analysis ......................................8, 14, 19, 172 Messenger RNA (mRNA) decay .......................................................... 72–74, 76–81 knockdown ........................................ 119–120, 125–129 stability ......................................... 56, 91, 94, 95, 97, 103 MGB probes.................................................................... 7–8 Microarray .............................................25–52, 59, 137–139, 148–155, 159, 189–191 MicroRNA (miRNA)............. 55–69, 91, 133–141, 167, 183 miRBase ...................................................................... 58, 69 miRNA. See MicroRNA Mitogen ..................................................................... 2, 3, 95 Molecular beacons ........................................................... 7–8 Monocytic cell factor (MCF) ..............................................5 mRNA. See Messenger RNA

N NanoDrop .......................................... 10, 11, 13, 35, 97, 100 Nanoparticle ............................................................ 105–115 Northern blotting .................................................. 74, 79–81

O 2’,5’-Oligoadenylate ................................................ 137, 164 Oligofectamine .........................................137, 141, 146–147

P Pancreatic islets................................................................ 181 Pfaffl method............................................................... 17–19 PKR. See Protein kinase R Plasmacytoid dendritic cells .................................... 215–229 Polyadenylation ............................................................... 102 Polyinosinic:polycytidylic acid (PolyI:C) ......................... 156 Poly-rI:rC ............................................................................3 Posttranscriptional regulation ................................ 71–88, 95 Primer efficiency................................................................ 17 Protein kinase R (PKR) .................... 134, 137, 139, 152, 164

R Real-time-quantitative PCR (RT-qPCR) ......7–23, 125, 189 Relative expression software tool (REST) ................... 18–19 REST. See Relative expression software tool RISC complex ............................ 69, 135, 136, 142, 180, 183 RNA-binding protein........72, 75–76, 82, 85–86, 94, 95, 164 RNAi. See RNA interference RNA interference (RNAi) ................. 56, 105, 106, 133–160, 163–165, 168, 169, 179–193 RNase L .................................................................. 137, 164 RT-qPCR. See Real-time-quantitative PCR

S Short hairpin ................................................... 135, 137, 164 Silicic acid........................................................................ 3, 4 siRNA. See Small interfering RNA Small interfering RNA (siRNA) .............105–115, 134–144, 146–147, 156–159, 164–165, 167, 180–181, 183–192 Spectrometry ................................ 82, 86, 197, 209, 210, 212 Supershift assay ................................................................. 83 Surface plasmon resonance ...................................... 197, 208 Sybr-Green ................................8, 9, 14, 22, 57, 68, 166, 172

T TaqMan ................................ 7–8, 56–65, 119, 125–127, 150 TaqMan low-density array (TLDA)........................... 56, 57, 59, 60, 62–65 T cell 25–26, 72, 73, 77, 78, 81, 82, 85, 86, 108 TGF-β ......................................... 56, 94, 120, 121, 128–131 TLDA. See TaqMan low-density array TLR. See Toll-like receptor Toll-like receptor (TLR) .........................138, 164, 216–218, 220, 223–226, 228–229 Toxicity bioassay .............................................................. 234 Transfection ......................................... 76, 97, 106, 118, 135, 168, 180, 218, 232 Tristetraprolin (TTP) ............................................ 72, 94–95 TTP. See Tristetraprolin Turbofect .................................. 120, 129–131, 141, 147–148 Type I interferon ...................... 196, 210, 211, 215, 231–237

CYTOKINE PROTOCOLS 241 Index U

X

Urinary proteins .......................................198, 199, 201, 202 UV crosslinking .................................................... 75, 80–82, 84, 85, 87

Xanthine-guanine phosphoribosyl transferase (XGPRT) ............................................................ 232 XGPRT. See Xanthine-guanine phosphoribosyl transferase

W

Z

Wegener’s auto-antigen ................................................... 197

Zeta potential .................................................. 106, 107, 110