Protein Microarray for Disease Analysis: Methods and Protocols (Methods in Molecular Biology, 723) 9781617790423, 1617790427

Citation preview

Methods in Molecular Biology 723

Catherine J. Wu Editor

Protein Microarray for Disease Analysis Methods and Protocols

Methods 

in

Molecular Biology™

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



For other titles published in this series, go to www.springer.com/series/7651

wwwwwwwwwwwwwwww

Protein Microarray for Disease Analysis Methods and Protocols Edited by

Catherine J. Wu Division of Hematologic Neoplasia, Department of Medical Oncology, Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA, USA

Editor Catherine J. Wu Division of Hematologic Neoplasia Department of Medical Oncology Cancer Vaccine Center Dana-Farber Cancer Institute Boston, MA 02115 USA [email protected]

ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-61779-042-3 e-ISBN 978-1-61779-043-0 DOI 10.1007/978-1-61779-043-0 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011921931 © Springer Science+Business Media, LLC 2011 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. While the advice and information in this book are believed to be true and accurate at the date of going to press, ­neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Humana Press is part of Springer Science+Business Media (www.springer.com)

Preface Protein microarrays are a rapidly growing segment of proteomics that enable highthroughput discovery-driven research through direct measurement of the molecular endpoints of various physiological and pathological states. The human genome has some 30,000 protein-coding genes, while the human proteome is estimated to have at least 90,000 proteins. By now, protein microarrays have been used for identifying protein– protein interactions, discovering disease biomarkers, identifying DNA-binding specificity by protein variants, and for characterization of the humoral immune response. In this volume, we provide concise descriptions of the methodologies to fabricate microarrays for comprehensive analysis of proteins or the response to proteins that can be used to dissect human disease. These methodologies are the toolbox for revolutionizing drug development and cell-level biochemical understanding of human disease processes. Three general categories of arrays have been developed, which we describe in detail in this volume. The first and most commonly used are the protein-detecting analytical microarrays, described in Part I. Conventionally, the design of these arrays is based on the principle of a sandwich immunoassay. Thus, these capture protein on an array surface from biologic samples and quantify presence of those specific analytes using a detection reagent. Arrays may be coated with antigen-specific antibodies to detect specific proteins from body fluids (Chap. 1), whose identity can be confirmed using label-free detection based on mass spectrometry (Chap. 2). An alternative to detection on solid phase uses newly available bead-based strategies (Chap. 3). Antibody-based detection can be also implemented in a high-throughput fashion on reverse-phase protein arrays. Here, cell lysates are printed to a solid support, followed by quantitative immunodetection, as described in Chap. 4. These general designs have been further modified by other investigators to optimize exploration of specific biologic problems. For example, aptamer (Chap. 5) and recombinant lectin (Chap. 6) arrays have been successfully developed. A second category of protein microarray is antigen microarrays that seek to detect antigen-specific antibody from biologic samples (primarily serum and plasma), covered in Part II. Here, arrays are coated with tens to thousands of proteins in order to detect specific reactive antibodies. These have proven valuable for biomarker discovery and detection. Many possible formats of antigen expression on microarrays are now available. Both commercial high-density protein microarrays that express recombinant protein for serum profiling, as well as technology for custom production of arrays to express a tailored collection of proteins, are now available (Chap. 7). Technology to synthesize comprehensive arrays of peptides has also been established (Chap. 8). Finally, high-throughput protein fractionation strategies have been developed that enable array spotting of antigens in their native format (Chap. 9). Production and isolation of proteins can be cost- and laborintensive. As an alternative, programmable arrays, in which cDNA-containing plasmids are spotted on solid support and protein is freshly translated in situ, offer a versatile solution to the problem of recombinant protein production (Chap. 10).

v

vi

Preface

The final category of protein microarray is protein function microarrays to interrogate direct biochemical and physical interactions among biomolecules (Part III). These include profiling of protein–protein, protein–lipid, protein–DNA/RNA, and small molecule interactions. In Chap. 11, we provide protocols for high-throughput mammalian-based detection of protein–protein interactions, operating on the principle of two-hybrid screening techniques. Programmable arrays have been also developed for this purpose (Chap. 12). Among the many specific applications of protein function arrays are the detection of kinase– substrates interactions (Chap. 13) and the characterization of posttranslational modifications that can serve important regulatory functions in eukaryotic cells (Chap. 14). In most cases, discovery by protein microarray screening requires validation of candidate targets, in order to focus subsequent biologic studies. Part IV of this volume offers two separate approaches to candidate target validation. Both require independent production of the protein analyte to confirm specific reactivity. Both the generation of protein microarrays and the implementation of validation steps have been greatly accelerated by the recent availability of large insect and mammalian proteome libraries. Within these libraries, numerous open reading frames have been cloned and deposited in vector formats that are amenable to protein expression (Part V). The two final sections of the volume are devoted to signal detection strategies (Part VI) as well as data analysis techniques (Part VII). The most conventional and widely used methods are based on fluorometric or colorimetric methods (Chap. 18), while newer label-free detection systems, such as using FRET (Chap. 19) or surface plasmon resonance (SPR) (Chap. 20), will likely be increasingly employed in the future. Validated software for analysis of protein microarrays is only developing now and is obviously critically important for data analysis (Chap. 21). Finally, knowledge of the publicly available databases that are relevant to proteomics studies can enable more efficient data analysis (Chap. 22). We hope that this volume provides a solid framework for understanding how protein microarray technology is developing and how it can be applied to transform our analysis of human disease. I am grateful to all the authors for their outstanding contributions to this edition. Boston, MA

Catherine J. Wu

Acknowledgments I want to thank my family for their support for all my academic endeavors. I want to also acknowledge the excellent assistance from Diana Ng in preparing this volume.

vii

wwwwwwwwwwwwwwww

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

v xi

Part I  Protein-Detecting Analytical Microarrays   1 Detecting and Quantifying Multiple Proteins in Clinical Samples in High-Throughput Using Antibody Microarrays . . . . . . . . . . . . . . . . . . . . . . . . Tanya Knickerbocker and Gavin MacBeath   2 Analysis of Serum Protein Glycosylation with Antibody–Lectin Microarray for High-Throughput Biomarker Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chen Li and David M. Lubman   3 Antibody Suspension Bead Arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jochen M. Schwenk and Peter Nilsson   4 Reverse Protein Arrays Applied to Host–Pathogen Interaction Studies . . . . . . . . . Víctor J. Cid, Ekkehard Kauffmann, and María Molina   5 Identification and Optimization of DNA Aptamer Binding Regions Using DNA Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nicholas O. Fischer and Theodore M. Tarasow   6 Recombinant Lectin Microarrays for Glycomic Analysis . . . . . . . . . . . . . . . . . . . . Daniel C. Propheter, Ku-Lung Hsu, and Lara K. Mahal

3

15 29 37

57 67

Part II Antigen Microarrays for Immunoprofiling   7 Recombinant Antigen Microarrays for Serum/Plasma Antibody Detection . . . . . 81 Persis P. Wadia, Bita Sahaf, and David B. Miklos   8 SPOT Synthesis as a Tool to Study Protein–Protein Interactions . . . . . . . . . . . . . 105 Dirk F.H. Winkler, Heiko Andresen, and Kai Hilpert   9 Native Antigen Fractionation Protein Microarrays for Biomarker Discovery . . . . . 129 Robert J. Caiazzo, Jr., Dennis J. O’Rourke, Timothy J. Barder, Bryce P. Nelson, and Brian C.-S. Liu 10 Immunoprofiling Using NAPPA Protein Microarrays . . . . . . . . . . . . . . . . . . . . . . 149 Sahar Sibani and Joshua LaBaer

Part III  Protein Function Microarrays 11 High-Throughput Mammalian Two-Hybrid Screening for Protein–Protein Interactions Using Transfected Cell Arrays (CAPPIA) . . . . . . . . . . . . . . . . . . . . . 165 Andrea Fiebitz and Dominique Vanhecke 12 Protein–Protein Interactions: An Application of Tus-Ter Mediated Protein Microarray System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Kalavathy Sitaraman and Deb K. Chatterjee 13 Kinase Substrate Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Michael G. Smith, Jason Ptacek, and Michael Snyder

ix

x

Contents

14 A Functional Protein Microarray Approach to Characterizing Posttranslational Modifications on Lysine Residues . . . . . . . . . . . . . . . . . . . . . . . 213 Jun Seop Jeong, Hee-Sool Rho, and Heng Zhu

Part IV Strategies for Validation of Candidate Targets 15 Multiplexed Detection of Antibodies Using Programmable Bead Arrays . . . . . . . . 227 Karen S. Anderson 16 A Coprecipitation-Based Validation Methodology for Interactions Identified Using Protein Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Ovidiu Marina, Jonathan S. Duke-Cohan, and Catherine J. Wu

Part V Generation of Proteomic Libraries 17 Development of Expression-Ready Constructs for Generation of Proteomic Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Charles Yu, Kenneth H. Wan, Ann S. Hammonds, Mark Stapleton, Joseph W. Carlson, and Susan E. Celniker

Part VI  Detection Methods 18 Reverse Phase Protein Microarrays: Fluorometric and Colorimetric Detection . . . 275 Rosa I. Gallagher, Alessandra Silvestri, Emanuel F. Petricoin III, Lance A. Liotta, and Virginia Espina 19 Förster Resonance Energy Transfer Methods for Quantification of Protein–Protein Interactions on Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . 303 Michael Schäferling and Stefan Nagl 20 Label-Free Detection with Surface Plasmon Resonance Imaging . . . . . . . . . . . . . 321 Christopher Lausted, Zhiyuan Hu, and Leroy Hood

Part VII  Data Analysis Techniques for Protein Function Microarrays 21 Data Processing and Analysis for Protein Microarrays . . . . . . . . . . . . . . . . . . . . . . 337 David S. DeLuca, Ovidiu Marina, Surajit Ray, Guang Lan Zhang, Catherine J. Wu, and Vladimir Brusic 22 Database Resources for Proteomics-Based Analysis of Cancer . . . . . . . . . . . . . . . . 349 Guang Lan Zhang, David S. DeLuca, and Vladimir Brusic Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365

Contributors Karen S. Anderson  •  Cancer Vaccine Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA Heiko Andresen  •  Karlsruhe Institute of Technology, Karlsruhe, Germany Timothy J. Barder  •  Eprogen, Darien, IL, USA Vladimir Brusic  •  Cancer Vaccine Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA Robert J. Caiazzo, Jr.  •  Molecular Urology Laboratory, Division of Urology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Joseph W. Carlson  •  Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Susan E. Celniker  •  Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Deb K. Chatterjee  •  Protein Expression Laboratory, Advanced Technology Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD, USA Víctor J. Cid  •  Departamento de Microbiología II, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain David S. DeLuca  •  Cancer Vaccine Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA Jonathan S. Duke-Cohan  •  Immunobiology Laboratory, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA Virginia Espina  •  Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA Andrea Fiebitz  •  Campus Benjamin Franklin, Charité, Berlin, Germany Nicholas O. Fischer  •  Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA Rosa I. Gallagher  •  George Mason University, Manassas, VA, USA Ann S. Hammonds  •  Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Kai Hilpert  •  Karlsruhe Institute of Technology, Karlsruhe, Germany Leroy Hood  •  Institute for Systems Biology, Seattle, WA, USA Ku-Lung Hsu  •  Department of Chemistry and Biochemistry, University of Texas at Austin, Austin, TX, USA Zhiyuan Hu  •  Institute for Systems Biology, Seattle, WA, USA Jun Seop Jeong  •  Department of Pharmacology and Molecular Sciences, High Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD, USA Ekkehard Kauffmann  •  Zeptosens – A Division of Bayer (Schweiz) AG-, Witterswil, Switzerland Tanya Knickerbocker  •  Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA Joshua LaBaer  •  Virginia G. Piper Center for Personalized Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, USA Lance A. Liotta  •  George Mason University, Manassas, VA, USA xi

xii

Contributors

Christopher Lausted  •  Institute for Systems Biology, Seattle, WA, USA Chen Li  •  Department of Chemistry, The University of Michigan, Ann Arbor, MI, USA Brian C.-S. Liu  •  Molecular Urology Laboratory, Division of Urology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA David M. Lubman  •  Department of Chemistry, Comprehensive Cancer Center, The University of Michigan, Ann Arbor, MI, USA; Department of Surgery, The University of Michigan Medical Center, Ann Arbor, MI, USA Gavin MacBeath  •  Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA Lara K. Mahal  •  Department of Chemistry and Biochemistry, University of Texas at Austin, Austin, TX, USA; Department of Chemistry, New York University, New York, NY, USA Ovidiu Marina  •  Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI, USA David B. Miklos  •  Department of Medicine, Blood and Marrow Transplantation Division, Stanford University, Stanford, CA, USA María Molina  •  Departamento de Microbiología II, Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Spain Stefan Nagl  •  Institute of Analytical Chemistry, University of Leipzig, Leipzig, Germany Bryce P. Nelson  •  Gentel Biosciences, Inc., Madison, WI, USA Peter Nilsson  •  Science for Life Laboratory, Department of Proteomics, School of Biotechnology, KTH – Royal Institute of Technology, 10691 Stockholm, Sweden Dennis J. O’Rourke  •  Molecular Urology Laboratory, Division of Urology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA Emanuel F. Petricoin III  •  George Mason University, Manassas, VA, USA Daniel C. Propheter  •  Department of Chemistry and Biochemistry, University of Texas at Austin, Austin, TX, USA Jason Ptacek  •  The Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA Surajit Ray  •  Department of Mathematics and Statistics, Boston University, Boston, MA, USA Hee-Sool Rho  •  Department of Pharmacology and Molecular Sciences, High Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD, USA Bita Sahaf  •  Department of Medicine, Blood and Marrow Transplantation Division, Stanford University, Stanford, CA, USA Michael Schäferling  •  Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, Regensburg, Germany Jochen M. Schwenk  •  Science for Life Laboratory, Department of Proteomics, School of Biotechnology, KTH – Royal Institute of Technology, 10691 Stockholm, Sweden Sahar Sibani  •  Virginia G. Piper Center for Personalized Medicine, Biodesign Institute, Arizona State University, Tempe, AZ, USA

Contributors

xiii

Alessandra Silvestri  •  George Mason University, Manassas, VA, USA; CRO-IRCCS, National Cancer Institute, Aviano, Italy Kalavathy Sitaraman  •  Protein Expression Laboratory, Advanced Technology Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD, USA Michael G. Smith  •  Illumina, Inc., San Diego, CA, USA Michael Snyder  •  Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA Mark Stapleton  •  NuGEN Technologies, Inc., San Carlos, CA, USA Theodore M. Tarasow  •  Tethys Bioscience, Inc., Emeryville, CA, USA Dominique Vanhecke  •  Center for Biomedicine, University Basel, Basel, Switzerland Persis P. Wadia  •  Department of Medicine, Blood and Marrow Transplantation Division, Stanford University, Stanford, CA, USA Kenneth H. Wan  •  Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Dirk F.H. Winkler  •  Peptide Facility, Kinexus Bioinformatics Corporation, Vancouver, BC, Canada Catherine J. Wu  •  Division of Hematologic Neoplasia, Department of Medical Oncology, Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA, USA Charles Yu  •  Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Guang Lan Zhang  •  Cancer Vaccine Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA Heng Zhu  •  Departments of Pharmacology and Molecular Sciences and Oncology, High Throughput Biology Center, Johns Hopkins School of Medicine, Baltimore, MD, USA

wwwwwwwww

Part I Protein-Detecting Analytical Microarrays

Chapter 1 Detecting and Quantifying Multiple Proteins in Clinical Samples in High-Throughput Using Antibody Microarrays Tanya Knickerbocker and Gavin MacBeath Abstract Many diagnostic and prognostic tests performed in the clinic today rely on the sensitive detection and quantification of a single protein, usually by means of an immunoassay. Even in the case of monogenic diseases, however, single markers are often insufficient to provide highly reliable predictions of disease onset, and the accuracy of these predictions only decreases for polygenic diseases and for very early detection or prediction. Recent studies have shown that predictive reliability increases dramatically when multiple markers are analyzed simultaneously. Antibody microarrays provide a powerful way to quantify the abundance of many different proteins simultaneously in a variety of sample types, including serum, urine, and tissue explants. Because the assay is highly miniaturized, very little sample is required and the assay can be performed in high-throughput. Using antibody microarrays, we have been able to identify prognostic markers of early mortality in patients with end-stage renal disease and have built multivariate models based on these markers. We anticipate that antibody microarrays will prove similarly useful in other discovery-based efforts and may ultimately enjoy routine use in clinical labs. Key words: Antibody microarray, Prognosis, Diagnosis, ELISA, Sandwich immunoassay, Highthroughput

1. Introduction Although some diseases can be accurately diagnosed by detecting a single mutation in a gene or by observing elevated serum levels of a single protein marker, most disease states are much more complex. For example, conditions such as high blood pressure, heart disease, or renal failure have both a genetic and environmental component and even diseases such as cancer, which are largely genetic in origin, are often difficult to diagnose using a simple, univariate test. Several recent studies have shown that the accuracy of cancer diagnoses can be enhanced substantially using ­multivariate approaches based on gene expression profiles (1–3). In addition, Catherine J. Wu (ed.), Protein Microarray for Disease Analysis: Methods and Protocols, Methods in Molecular Biology, vol. 723, DOI 10.1007/978-1-61779-043-0_1, © Springer Science+Business Media, LLC 2011

3

4

Knickerbocker and MacBeath

multivariate signatures based on DNA polymorphisms (4) or ­protein levels (5, 6) are proving useful in predicting how patients respond to targeted therapies. To usher in this era of personalized medicine, we need tools that can accurately, sensitively, and simultaneously measure the levels of many different proteins in a variety of clinical samples (serum, urine, and tissue explants). In addition, to enable the discovery of new diagnostic or prognostic signatures, we need methods that are relatively inexpensive and are compatible with high-throughput investigations. Antibody microarrays offer all of these features. They mimic an enzyme-linked immunosorbant assay (ELISA), but in a miniaturized and multiplexed format (Fig.  1). In a typical antibody microarray experiment, a panel of “capture antibodies” is spotted at high spatial density onto a solid support, typically a chemically derivatized glass substrate (Fig. 1a). A clinical sample (e.g., serum) is then applied to the array, and the immobilized antibodies capture

a

b

Cocktail of detection antibodies

Clinical sample (e.g., serum) Cytokines

1. Incubate 2. Wash 3. Add detection antibodies

Glass substrate

Capture antibody

Antibody microarray

d

c

4. Incubate 5. Wash 6. Add labeled secondary antibody

7. Incubate 8. Wash 9. Scan for fluorescence

Fig. 1. Detecting and quantifying multiple proteins in clinical samples using antibody microarrays. (a) Capture antibodies are spotted at high spatial density onto a chemically derivatized glass substrate, where they become immobilized. When a clinical sample (e.g., serum) is applied to the array, each immobilized antibody captures its cognate antigen. (b) After a brief washing step, a cocktail of detection antibodies is applied to the array. Each detection antibody recognizes and binds to its cognate antigen. (c) After a brief washing step, the arrays are incubated with a labeled secondary antibody, which recognizes and binds to all of the detection antibodies. For convenience, the secondary antibody is best labeled with a bright fluorophore, such as PBLX-3. (d) After a final washing step, the arrays are dried and scanned for fluorescence.

Detecting and Quantifying Multiple Proteins

5

their cognate antigens. After a brief washing step, the ­captured proteins are detected by applying a cocktail of “detection antibodies­” (Fig. 1b). To visualize and quantify the detection antibodies, the arrays are again washed and probed with a labeled secondary antibody (Fig. 1c). In a standard ELISA, highly sensitive detection is achieved using an enzyme label, such as horseradish peroxidase, which amplifies the signal by catalytically converting a soluble substrate into a chromophoric product. In an antibody microarray experiment, the final signal must be localized to each spot. A variety of strategies have been developed to achieve highly sensitive detection in a spatially localized fashion. For example, the process of rolling circle replication has been exploited to achieve enzymemediated signal amplification (7, 8). This method enables the detection of many proteins at concentrations as low as 1 pg/mL. We have found, however, that equally sensitive detection can be achieved in a more straightforward fashion without enzymemediated signal amplification using a secondary antibody that has been coupled directly to an extremely bright fluorophore (9). (PBXL-3, a phycobilisome protein complex isolated from red algae and cyanobacteria.) The biggest limitation of antibody microarrays, as well as other multiplexed technologies such as the Luminex® bead-based immunoassay, is the availability of suitable antibodies. Sandwichstyle immunoassays require two highly specific antibodies that recognize distinct, nonoverlapping epitopes on their target proteins. For this reason, most studies using antibody microarray technology have focused on cytokines, chemokines, and other frequently studied serum protein for which high quality, matched pairs of antibodies are commercially available (10). To date, antibody microarrays have been used to discover multivariate signatures for diagnostic purposes. For example, antibody microarrays were recently used to detect differential glycosylation patterns on a variety of serum proteins, which may prove useful for the early detection of pancreatic cancer (11). Similarly, antibody microarrays directed at a large panel of cluster of differentiation (CD) antigens on leukemias and lymphomas from peripheral blood and bone marrow aspirates showed high levels of consistency with diagnoses obtained using conventional clinical and laboratory ­criteria (12). In our own lab, we have used antibody microarrays to identify prognostic markers of early mortality in patients with endstage renal disease (ESRD) (9). This study serves as an example for how antibody microarrays can be used for discovery purposes. Approximately, 10% of patients with ESRD die within the first 3–4 months of initiating hemodialysis and, to date, no single marker has been found that accurately predicts outcome. We set out to develop a multivariate model that predicts which patients

6

Knickerbocker and MacBeath

are most at risk of dying within the first 15 weeks of initiating treatment. To do this, we collected serum samples from 468 patients initiating dialysis (13). We then assembled a panel of 14 matched pairs of antibodies directed at cytokines and other serum proteins that had previously been associated with ESRD, hypertension, or diabetes (14). To facilitate the rapid and accurate measurement of all 14 proteins in all 468 patient samples, we developed a high-throughput assay in which the capture antibodies were microarrayed in individual wells of 96-well microtiter plates (Fig. 2a). Serum samples were applied to each array and the captured cytokines were detected using a cocktail of biotinylated detection antibodies. The detection antibodies were subsequently visualized and quantified using PBXL-3-labeled streptavidin. Using this simple procedure, we were able to achieve exquisite sensitivity: most cytokines could be detected at a concentration of 1 pg/mL (Fig. 2b). The absolute concentration of each cytokine in each sample was determined by relating the fluorescence intensity of the microarray spots to a standard curve, generated for each cytokine in a multiplexed fashion using one column of each microtiter plate (Fig. 2a, b). For redundancy, each array contained five replicate spots of the capture antibodies and every sample was analyzed on two arrays. Overall, the average coefficient of variation was 6.6% for replicate spots within an array and 11% for replicate samples on separate arrays. Using these microarrays, cytokine levels were measured in all 468 patient samples (Fig. 2c). To develop a multivariate prognostic test, we started by building linear, additive models using logistic regression (9). To avoid overfitting and to construct a model that incorporates only as many variables as are necessary, we adopted the following strategy. If n is the number of variables in the model, we started with n = 1 and, in an incremental fashion, performed an exhaustive search for the best n-variable model. We continued to increment n until no n-variable model could be found in which all of the parameters were statistically significant (P  5), the lectin can be utilized in the microarray. In brief, the recombinant lectin microarray is fabricated by spotting the bacterial lectins onto an N-hydoxysuccinimide-, (NHS-), activated glass slide, and immobilization is achieved through amine-coupling of side-chain lysines.

Fig. 1. Schematic representation of the cloning, expression, and use of bacteria-derived recombinant lectins. The desired lectin is cloned out of the microbial genome and amplified by PCR. The gene is then annealed into the pET-41Ek/LIC vector and expressed into E. coli. The expressed protein is purified, and analyzed for activity using ELISA and microarray techniques. Adapted from Hsu et al. (7).

72

Propheter, Hsu, and Mahal

Fig.  2. Recombinant lectin microarray screening of tumor cell lines (ACHN, and Sk-MEL-5). Samples were prepared and analyzed as previously described (8). In brief, cell membranes were sonicated, and the resulting micellae isolated and labeled with NHS-Cy5. The samples were then incubated with the recombinant microarray (10 mg in 100 mL of buffer) and the arrays processed as previously described. A clear differential pattern can be observed between ACHN and Sk-Mel-5, which is described in the text.

A list of the recombinant bacterial lectins cloned, purified, and added to the lectin microarrays to date is given in Table 1. These lectins come from a variety of bacterial sources and include both adhesins from pili (GafD, PapGII, and PapGIII) and secreted ­lectins (PA-IL, PA-IIL, and RS-IIL). Incubation of the printed slides with fluorescently labeled samples provides a discernable pattern that gives insight into the extent of glycosylation of a given sample (9). Using this technology, we have shown that even the small panel of recombinant bacterial lectins utilized to date (Table 1) can distinguish tumor cell lines in the NCI-60 panel (Fig. 2). Clear differences can be observed between the renal cell carcinoma ACHN, which shows fucosylation (PA-IIL in the absence of RS-IIL), the presence of terminal b-N-acetyl-d-glucosamine (GafD) and galactosylation (PA-IL), and Sk-Mel-5, which shows an absence of both the terminal GlcNAc and galactose epitopes. Although one can obtain differences with this small of a lectin panel you cannot obtain a comprehensive snapshot of the glycome. However, the inclusion of these lectins in a larger lectin microarray format allows for a far more detailed analysis than is presented herein. 3.1. Cloning and Purification of Recombinant DNA

1. Identify microbial lectin via BLAST, the literature or other sources. 2. Prepare primers flanking the lectin encoding region (see Note 1). 3. Prepare PCRs as follows: 1× reaction buffer, 400 mM dNTP solution, 1 mM 5′ primer, 1 mM 3′ primer, 2.5 units of Taq

Recombinant Lectin Microarrays for Glycomic Analysis

73

Polymerase, and template DNA. Dilute to final volume of 50 mL with ddi H2O. (see Note 2). 4. Place the PCR tubes into the thermocycler and run on the following conditions: 95°C for 10 min, 95°C for 30 s, 45°C for 30 s (Tm), and 72°C for 1 min. Let the reaction go for 40 cycles, and cool PCRs to 4°C (see Note 3). 5. Prepare a 2% w/v agarose gel solution in 1× TAE buffer and heat until mixture is miscible (see Note 4). Add EtBr to a final concentration of 0.5  mg/mL. Pour gel into cast and allow it to solidify. Next, add 5 mL of DNA ladders and 2 mL of PCR mixture. Run gel on 90 V for 45 min, then visualize under UV irradiation. 6. To anneal the PCR insert, first determine the amount of PCR product required for the T4 treatment by using the following formula: (number of base pairs in insert) × 650 × 0.2 pmol =  n pg PCR insert. 7. In a sterile 1.5 mL microfuge tube, add the amount of purified PCR product calculated in step 6 (n pg), 2 mL 10× T4 DNA polymerase buffer, 2 mL 25 mM dATP, 1 mL 100 mM DTT, and 0.4 uL 2.5 units/uL T4 DNA polymerase. Add enough ddi H2O to have 20 mL of total volume (see Note 5). 8. Mix the components by flicking the tube and then incubate at room temperature for 30 min. 9. Inactivate the enzyme by incubating at 75°C for 20 min. 10. To anneal into pET-41 Ek/LIC vector, mix 1 mL of the vector with 2  mL of the treated PCR insert in a sterile 1.5  mL microfuge tube and incubate for 5 min at room temperature. 11. Add 1 mL of 25 mM EDTA to the reaction mix, and incubate at room temperature for 5 min (see Note 6). 12. Transform competent DH5a with 1  mL of the annealing reaction, add 1 mL SOC media and allow cells to recover for 1 h, shaking at 250 rpm at 37°C (see Note 7). 13. After 1 h, plate the transformed cells on LB-Agar plates (see Note 8). Allow the cells to grow overnight (~12 h) at 37°C. 14. Pick single colonies and grow in 5 mL of LB (~15 h) with 30  mg/mL kanamycin on a rotary shaker and incubator at 250 rpm at 37 C. 15. After 15 h, take the optical density of the colonies at 600 nm (OD600). Pick the best growing colony and inoculate in 25 mL of LB with kanamycin (30 mg/mL) and place on rotary shaker at 250 rpm, 37°C, for ~15 h. 16. After overnight culture, take OD600, and purify plasmid DNA via Qiagen Miniprep Kit and Instructions. Once DNA is isolated, check the DNA sequence.

74

Propheter, Hsu, and Mahal

3.2. Protein Expression and Purification

1. Transform electrocompetent BL21(DE3) cells with recombinant DNA. Transform 1–5 mg of DNA into a 50 mL aliquot of BL21 cells (see Note 9). 2. Upon electroporation, promptly add 1 mL of LB and grow on a rotary shaker for 1 h at 250 rpm and 37°C. Next, plate cells onto LB-Agar plates and incubate at ~15 h at 37°C. 3. Next, pick single colonies and grow each in 5 mL of LB with kanamycin (30 mg/mL) for 15 h at 250 rpm and 37°C. 4. Take OD600 of colonies, choose a colony with an average rate of growth, and inoculate 5  mL culture into 25  mL culture (see Note 10). Grow the culture to an OD600 of 0.7–1.0, then induce the culture with 1% w/v lactose and grow for 3 h at 250 rpm and 37°C (see Note 11). 5. After 3  h, transfer culture into centrifuge tubes and pellet cells at 6,000 × g, 4°C, 15  min (see Note 12). Discard the supernatant. 6. Resuspend pellet in 1  mL of lysis buffer and dilute 1,000× DMSO and aqueous protease inhibitor cocktails to 1× in lysis buffer (see Note 13). Then add approximately 1  mg/mL lysate of chicken egg white lysozyme and mix at 4°C for 30 min (see Note 14). 7. Next, immediately add DNAse, 5 mg/mL of lysate final concentration, and incubate further at 4°C for 10 min (see Note 15). 8. Centrifuge the samples in the appropriate tubes at 30,000 × g for 30 min at 4°C. Keep the supernatant. 9. Purify the lysate using the BioLogic LP low-pressure gradient chromatography system (or similar system). Load the supernatant onto an equilibrated glutathione column at a flow rate of 0.5 mL/min (see Note 16). Wash the column with ~10 column volumes of PBS at a rate of 1 mL/min. Elute lectin with 10 mM of reduced, free acid glutathione in PBS collecting 1 mL fractions at a rate of 1 mL/min (see Note 17). 10. Monitor lectin purification by 10% SDS-PAGE analysis (see Note 18). Pool fractions containing lectin and dialyze against PBS at 4°C. Aliquot, flash freeze, and store at −80°C (see Note 19).

3.3. ELISA Activity Assay

1. Dilute glycoprotein to a final concentration of 1–10 mg/mL in PBS containing 0.1% NaN3. Take a 96-well plate and coat each well with 100 mL of glycoprotein and incubate for ~12 h at 4°C. 2. Wash each well with wash buffer 5×. Next, add ELISA blocking buffer to each well and incubate at room temperature for 1 h (see Note 20). 3. After blocking, wash each well with wash buffer 5×. Next, dilute lectin into PBST++ (see Note 21). Add 50 mL of each

Recombinant Lectin Microarrays for Glycomic Analysis

75

dilution to each well (see Note 22). Incubate samples at room temperature for 1 h. 4. After incubation, wash each well with wash buffer 5×, then dilute anti-His6-HRP to an optimized dilution in the wash buffer + 1% BSA (see Note 23). Next, add 50 mL of anti-His6HRP solution into each well and incubate at room temperature for 1 h. 5. Wash each well with wash buffer 5×, and freshly prepare OPD reagent buffer. Add 100  mL to each well immediately after the last wash. Incubate at room temperature for 30 min, add 50 mL of stopping reagent to each well, and read on BioTek Plate Reader at 492 nm wavelength (see Note 24). 3.4. Recombinant Lectin Microarray

1. Prepare samples as previously described (8). 2. Dilute lectins to 1 mg/mL in print buffer and 1 mM monosaccharide as specified (see Table 1). 3. Print lectins onto Nexterion H slides using the SpotBot personal microarray with an SMP3 pin. Maintain cold plate at 8°C and internal humidity at 50–60%. 4. Print 5 spots per lectin, to ensure spot quality, on a 16-subarray format (see Note 25). 5. Upon completion of the print, slides are allowed to warm to room temperature in the SpotBot arrayer for 1 h while maintaining humidity control. Slides are then placed into blocking buffer inside a Coplin jar for 1 h at room temperature. 6. After blocking, wash slides with PBST 3× for 3  min, rinse once with PBS, and dry using the slide spinner. 7. Affix a 16-well subarray FAST frame to the slide and incubate with appropriate fluorescent sample for 2 h at room temperature (see Note 26). 8. After incubation, aspirate sample from the subarrays and wash 5× with PBST, once with PBS, and then dry using a slide spinner (see Note 27). 9. Scan slides using the Genepix 4100A scanner at the appropriate wavelength. Extract data using Genepix Pro 5.1 software and analyze using Microsoft Excel and/or Graphpad Prism 4.0.

4. Notes 1. The designed primers must have the overlapping LIC extensions. The forward primer must begin with 5¢ GAC GAC GAC AAG A 3¢ and the reverse primer must begin with 5¢ GAG GAG AAG CCC GG 3¢.

76

Propheter, Hsu, and Mahal

2. You will have to titrate the amounts of template DNA used in the first PCR to obtain the desired results. 3. The melting temperature (Tm) and number of cycles may need to be changed in order to obtain the desired results. 4. Depending on the size of your DNA, you may need to augment the percent of agarose used. 5. pET-41 Ek/LIC vector kit has a control vector which should be used to gauge efficiency of system. Also, it is important to include as a negative control plasmid with no insert to evaluate the selection. T4 DNA polymerase from Novagen is specifically designed for these ligation-independent cloning reactions. Nuclease-free or ddi H2O (i.e., from a purification system) may be used. 6. The T4 treated insert can be stored at −20°C for up to 3 months. 7. SOC may be substituted for LB in this recovery although efficiency may be reduced. Transform the DNA using a Micropulser (Bio-Rad), following the Bio-Rad electroporation protocol (found at http://www.bio-rad.com). 8. For the best results, plate two dilutions of sample. Also, the negative control should be plated to ensure the integrity of the kanamycin. 9. Following the Bio-Rad electroporation protocol referred to earlier (see Note 4). 10. Colony-dependent variations in protein expression arise, so be sure to test ~3 to 5 colonies. Take the best expressing colony and move on to next step. 11. The cultures can be easily scaled up to a 4 L culture 12. This pellet can be stored at −80°C for an indefinite amount of time. 13. Typically we add 4 mL of lysis buffer per 100 mL of culture. If using a 1,000× protease inhibitor cocktail, simply add 1 mL/mL of culture. 14. Be sure to keep all reagents and solutions on ice. 15. If lysate is very viscous, the DNA can be sheared by drawing the suspension through an 18-gauge needle several times. Keep a small aliquot (~50 mL) of the crude lysate for SDS-PAGE gel analysis. 16. We have found that taking the following steps ensures that the column maintains integrity through multiple experiments: First, flush the system with ddi H2O and inspect to make sure no clogs or air are in the system. Second, flow filtered PBS for 5 min at a rate of 1 mL/min. Then load supernatant and follow the protocol. After eluting the column, wash the column

Recombinant Lectin Microarrays for Glycomic Analysis

77

with 5 column volumes of 70% ethanol, followed by 5 column volumes of PBS, and then 1 column volume of 20% ethanol. Store column at 4°C indefinitely. 17. Be sure to keep an aliquot (~50 mL) of the lysate after purification for SDS-PAGE gel analysis. 18. You may need to use a different % acrylamide gel depending on the molecular weight of the desired lectin. 19. Aliquots may be stored for up to 6 months. 20. Be sure to keep the 96-well plate covered to prevent contamination. 21. We recommend serial dilutions for the first ELISA to obtain a larger, more informative dataset. 22. For negative control wells, simply add PBST++ with no lectin. 23. We recommend testing serial dilutions of anti-His6-HRP on initial work to optimize the working dilution. 24. Read ELISA plates also at 620  nm as the reference wavelength. For data analysis, subtract the readings at 620  nm from the 492  nm data set to obtain the true values. The 620 nm value is a background value taken to correct for any imperfections in the sample plate. 25. You can print in a 24-well format and/or limit the number of spots to three, based on previous protocols. Print spots are typically 100 mm. 26. Be sure to keep slides unexposed to light, which affects fluorescence. 27. Be sure to keep slides in the dark. Slides can be kept for long term storage at −20°C. References 1. Mahal LK (2008) Glycomics: towards bioinformatic approaches to understanding glycosylation. Anticancer Agents Med Chem 8: 37–51 2. Hirabayashi J (2008) Concept, strategy and realization of lectin-based glycan profiling. J Biochem 144(2):139–147 3. Pilobello KT, Krishnamoorthy L, Slawek D, Mahal LK (2005) Development of a lectin microarray for the rapid analysis of protein glycopatterns. Chembiochem 6:985–9 4. Hirabayashi J (2004) Lectin-based structural glycomics: glycoproteomics and glycan profiling. Glycoconj J 21(1):35–40 5. Sharon N (2006) Carbohydrates as future anti-adhesion drugs for infectious diseases. Biochim Biophys Acta 1760:527–37

6. Dodson KW, Pinker JS, Rose T, Magnusson G, Hultgren SJ, Waksman G (2001) Structural basis on the interaction of the pyelonephritic E. coli adhesion to its human kidney receptor. Cell 105:733–43 7. Hsu KL, Gildersleeve JC, Mahal LK (2008) A simple strategy for the creation of a recombinant lectin microarray. Mol Biosyst 4:654–62 8. Pilobello KT, Slawek DE, Mahal LK (2007) A ratiometric lectin microarray approach to analysis of the dynamic mammalian glycome. Proc Natl Acad Sci USA 104:11534–9 9. Krishnamoorthy L, Bess JW Jr, Preston AB, Nagashima K, Mahal LK (2009) HIV-1 and microvesicles from T-cells share a common glycome, arguing for a common origin. Nat Chem Biol 5(4):244–250

Part II Antigen Microarrays for Immunoprofiling

Chapter 7 Recombinant Antigen Microarrays for Serum/Plasma Antibody Detection Persis P. Wadia, Bita Sahaf, and David B. Miklos Abstract Recombinant antigen arrays represent a new frontier in parallel analysis of multiple immune response profiles requiring only minute blood samples. In this article, we review the benefits and pitfalls of recombinant antigen microarrays developed for multiplexed antibody quantification. In particular, we describe the development of antigen arrays presenting a set of Y chromosome-encoded antigens, called H-Y antigens. These H-Y antigens are immunologically recognized as minor histocompatibility antigens (mHA) following allogeneic blood and organ transplantation. Clinically relevant B-cell responses against H-Y antigens have been demonstrated in male patients receiving female hematopoietic stem cell grafts and are associated with chronic graft versus host development. This chapter discusses our recombinant antigen microarray methods to measure these clinically relevant allo-antibodies. Key words: H-Y proteins, Antibodies, Plasma, Recombinant antigen microarrays, Minor ­histocompatibility antigens

1. Introduction Identifying, understanding, and confirming complex multicellular processes, such as immunity, require a systems biology approach to integrate each component’s function and regulation within the network. Traditionally, genes and proteins were discovered and characterized in isolation as individual molecules. However, the development of DNA microarrays facilitated multiplexed gene expression pattern analysis in a variety of genomes spanning bacteria (1–3) to human (4, 5). In immunology, gene expression profiling has determined important lymphocyte gene regulation pathways and their linked biological functions (6–9). However, a more complete understanding of adaptive immune responses requires systematic target screening of proteomes isolated from Catherine J. Wu (ed.), Protein Microarray for Disease Analysis: Methods and Protocols, Methods in Molecular Biology, vol. 723, DOI 10.1007/978-1-61779-043-0_7, © Springer Science+Business Media, LLC 2011

81

82

Wadia, Sahaf, and Miklos

bacteria, viruses, or humans. Antibody secretion marks effective B lymphocyte immune responses, and historically, these antibodies have identified specific targets ex vivo via western blot, immunoprecipitation, or Enzyme-Linked Immunosorbant Assay (ELISA). While Western blot and immunoprecipitation provides qualitative antigen identification from complex lysates, determining the specific protein or responsible gene often requires numerous subsequent biochemical fractionations and sequencing reactions. ELISA quantifies antibody against specific antigens, but their single antigen design consumes precious samples and resources. In contrast, protein microarrays enable high-density presentation of thousands of spatially isolated candidate antigens. Following antibody incubation, specific antigen binding is detected with fluorochrome conjugation. In fact, differential flurochrome conjugation of multiple samples enables multiplexed detection using the same antigen microarray. Ideally, these protein microarrays contain highly-purified antigens (see Note 1) that maintain native protein structure and include posttranslational modifications (see Note 2). In this chapter, we discuss two critical considerations for the generation of recombinant antigen microarrays: (1) the ­format of the antigens to be printed (Subheading 1.1) and (2) optimization of printing the recombinant protein on printing substrates (Subheading 1.2). We will discuss commercially available microarrays followed by a detailed description of our approach to ­optimizing the generation of microarrays to express custom ­antigens (H-Y antigens) for the detection of allo-antibodies (Subheadings 1.3 and 1.4). 1.1. Considerations in Expression of Recombinant Antigens for Protein Microarrays

Posttranslational modifications vary by organisms used for recombinant protein expression. The various organisms used to produce proteins include: Escherichia coli, yeast (10), CHO cells (11), or baculovirus in insect cells (12, 13), and are listed in Fig. 1. The scientific need to preserve posttranslational modifications determines expression system requirements and is also offset by expression efficiency. Modifications such as phosphorylations, acylations, glycosylations, and carboxylations demand a eukaryotic expression system since prokaryotic expression, such as through E. coli, lacks the necessary posttranslational machinery. However, the disadvantage of decreased protein yield through eukaryotic expression is overcome by the decreased antigen requirements for the protein microarray. Nonetheless, bacterial expression will suffice for many recombinant antigen expression needs and remains ubiquitously available, inexpensive, and fast. A significant disadvantage of bacterial expression is the frequent development of inclusion bodies necessitating protein denaturation with subsequent renaturation attempts. Yeast systems and baculovirus-infected insect cells represent reasonable compromises providing proteins in large amounts with eukaryotic modifications.

Recombinant Antigen Microarrays for Serum/Plasma Antibody Detection Bacterial cells

Yeast

Insect cells

83

Mammalian cells

Transform cells with gene (with/without tag) Induce expression of protein Small scale purification of expressed protein: select the most robust protein expression colony/culture Large scale purification of expressed protein Secreted protein obtained in the supernatant of cells Native Nickel affinity chromatography

Nonsecreted protein obtained as inclusion bodies Denatured Nickel affinity chromatography Renaturation of purified protein

Quantify and concentrate expressed protein

Print expressed protein of interest

Fig. 1. Flow-sheet for protein expression and purification. A schematic flow-sheet of choosing an expression system and purifying the proteins is detailed in the figure.

However, these systems are more laborious and expensive than prokaryotic systems. Figure  1 presents a schema for the steps involved in antigen purification for recombinant antigen microarrays after the appropriate expression system is chosen (to be discussed in detail in the Subheading 3). The incorporation of epitope tags (GST, V5 or 6xHis tags) for detection and/or isolation of expressed antigens provides a major advantage for recombinant microarray development. Expression plasmids inserting open reading frames (ORF) in frame following N-terminal tags usually provide high-yield protein expression and an affinity tag for protein purification. C-terminal epitope tag recognition indicates the entire ORF has been expressed (see Note 3). One example of a commercially available high-density protein microarray that prints proteins expressed in the baculoviral expression system are Protoarrays™ marketed by Invitrogen (Carlsbad, CA). More than 9,000 human proteins with N-terminal GST epitopes expressed in baculovirus-infected insect cells are affinitypurified and printed in duplicate on nitrocellulose-coated slides. An advantage of using commercial microarrays is that there are

84

Wadia, Sahaf, and Miklos

Fig. 2. Representative figure of a subarray from a commercially available protein microarray with controls and antigen hits. Commercially available protein microarrays contain 48 subarrays with 9,000 proteins printed in duplicate (Protoarray, version 5.0). A representative subarray is shown with negative controls such as Buffer, GST tags in different concentrations, and empty spots. The subarray also contains positive controls, such as anti-human IgG and human IgG, each printed in four concentrations. We use human IgG3 (second highest concentration) and we aim to obtain an MFI of 55,000–60,000 while scanning to normalize our arrays. Alexa 647 is printed in various positions, but fixed positions, across subarrays to help distinguish subarrays while gridding the spotted antigens.

numerous controls printed on each subarray, and once a target has been identified, the protein can be purchased for further analysis or ELISA development analysis. A representative subarray with negative and positive controls is shown in Fig. 2. Negative controls include buffer, empty spots, and GST tags printed in different concentrations and positive controls include human IgG as well as anti-human IgG printed in four different concentrations. Currently, cost prevents wide use of proteome microarrays, but increased content and decreased cost are expected. Our laboratory has extensive experience in using the commercially available protein microarrays from Invitrogen. We used

Recombinant Antigen Microarrays for Serum/Plasma Antibody Detection

85

Fig. 3. Donor, Pretransplant, and 12-month Posttransplant proteins detected serologically using commercially available protein microarrays. The same representative subarray (Protoarray version 3.0) is shown across three different slides which were processed using donor serum, pre, and posttransplant plasma. One of the two targets identified statistically is shown in the figure (CHAF1b). This was chosen because CHAF1b was absent in the donor and pretransplant plasma sample, but was recognized by antibodies in the posttransplant plasma sample.

protein microarrays to serologically identify Nucleolar and Spindle Associated Protein 1 (NuSAP1) and Chromatin Assembly Factor 1, subunit B (p60) (CHAF1b) as targets of new antibody responses that developed after allogeneic hematopoietic cell transplantation (HCT; Fig.  3). Western blots and ELISA validated their postHCT recognition and enabled ELISA testing of 120 other alloHCT patients with various malignancies. CHAF1b-specific antibodies were predominantly detected in AML patients, whereas NuSAP1-specific antibodies were exclusively detected in AML patients 1 year posttransplant (p 200 bp of surrounding annotated sequence (including nearby SNPs). Furthermore, frequency information and per subpopulation as well as calculation of Hardy-Weinberg equilibrium for each subpopulation are also provided. The dbSNP was set up at NCBI to serve as a central repository for genetic variation (33). MedRefSNP provides integrated information about SNPs collected from the PubMed and OMIM databases (34).

2.2.3. Databases Cataloguing other Genetic Abnormalities

Chromosomal abnormalities can be caused by mutations which change the number of chromosomes (numerical abnormalities) or change the structure of the chromosome (structural abnormalities). One of the important causes of cancer is gene translocations and gross gene deletions. The Chromosomal Abnormalities in Cancer website, hosted by the Wisconsin State Laboratory of Hygiene, provides information on several human cancers associated with chromosome aberrations.

2.3. Tumor Antigen Databases

Since the identification of MAGEA1 as a tumor antigen recognized by cytolytic T lymphocyte on human melanoma, the number of characterized tumor antigens has exponentially increased (35). The identification of tumor antigens remains a high priority in cancer research and is an essential component in developing immune-based strategies to combat cancer. The databases listed in Table 3 are useful resources for the study of immune responses against tumors. The term cancer-testis (CT) antigen was proposed by Scanlan and colleagues to encompass a heterogeneous groups of antigens, which show restricted expression in cancer and testis and restricted immunogenicity in cancer patients (36). CT antigens are ideal targets for cancer immunotherapy due to their restricted expression pattern.

Database Resources for Proteomics-Based Analysis of Cancer

357

Table 3 Online databases on human tumor antigens Database

Description

URL

CTdatabase

A repository of cancer-testis antigen data

http://www.cta.lncc.br/

CAPD

An analysis system for cancerrelated data

www.bioinf.uni-sb.de/CAP/

Cancer immunome database

A repertoire of antigens eliciting antibody responses in cancer patients

http://ludwig-sun5.unil.ch/ CancerImmunomeDB/

Cancer immunity peptide Four data tables containing 129 database tumor antigens with defined T-cell epitopes

http://www.cancerimmunity.org/ peptidedatabase/Tcellepitopes.htm

TANTIGEN

http://cvc.dfci.harvard.edu/tadb/

A human tumor T-cell antigen database

CT database provides information on CT antigens, including gene names and aliases, RefSeq accession numbers, genomic location, known splicing variants, gene duplications, and additional family members. It also provides gene expression at the mRNA level in normal and tumor tissues, manually curated data related to mRNA and protein expression, antigen-specific immune responses in cancer patients, and links to PubMed for relevant CT antigen articles (37). The Cancer-Associated Protein Database (CAPD) was built upon SEREX database, in which the sequences were obtained by screening cDNA expression libraries using serum from cancer patients as probes and sequencing individual reactive clones (38). The database also contains microarray, epigenetic, and immunostaining data. It aims to provide information covering all the gene products against which an immune response has been documented in cancer patients. The Cancer Immunome database is a continuation of the SEREX database in a more organized form. It is an access point of information about all of the gene products against which an immune response has been documented in cancer patients (39). The development of T-cell immunity against cancer has the potential to effective rejection and elimination of tumor cells, and hence, T cell-defined tumor antigens are a particular focus of several databases. The Cancer Immunity Peptide database provides four static data tables, containing 129 human tumor antigens with defined T-cell epitopes (40). Among them, 45 entries are tumor antigens resulting from mutations, 29 are shared tumorspecific antigens, 12 are differentiation antigens, and 43 are antigens overexpressed in tumors. For each tumor antigen, a link to

358

Zhang, DeLuca, and Brusic

GeneCards (14) and literature reference are provided. The database is relatively simple without any query function or analysis tool. A list of mouse and human tumor T-cell antigens were reported in (41). The Tumor T-Cell Antigen Database (TANTIGEN) is a data source and analysis platform for cancer vaccine target discovery focusing on human tumor-derived HLA ligands and T-cell epitopes. It contains 4,006 curated antigen entries representing 251 unique proteins. TANTIGEN also provides information on experimentally validated T-cell epitopes and HLA ligands, antigen isoforms, antigen sequence mutations, and tumor antigen classification. Analysis tools integrated in the database include search tool for querying the dataset, multiple sequence alignment of antigen isoforms, sequence similarity search using BLAST, visual display of T-cell epitopes/HLA ligands, and prediction of  binding peptides of 15 HLA Class I and Class II alleles. TANTIGEN is the most comprehensive database on Tumor T-cell antigens so far. 2.4. Databases of Cancer-Associated Genes

This section provides descriptions of two databases that integrate multiple heterogeneous datasets, including molecular data, clinical data, and experimental data, together with computational analysis tools to advance the cancer research. The Cancer Genome Anatomy Project (CGAP) aims to improve detection, diagnosis, and treatment for cancer patients through the analysis of the gene expression profiles of normal, precancer, and cancer cells (42). Its website provides genomic data for humans and mice, including transcript sequence, gene expression patterns, SNPs, clone resources, and cytogenetic information. The Mitelman Database of Chromosome Aberrations in Cancer (http://cgap.nci.nih.gov/Chromosomes/Mitelman) is part of CGAP. It is one of the largest online catalogs of cytogenetic aberrations in cancer, containing 56,694 cases as of 2009. The database relates chromosomal abnormalities to tumor characteristics (43). The Mouse Tumor Biology Database (MTBD) supports the use of the mouse as a model system of hereditary and induced cancers (44). The database provides access to tumor names and classifications, tumor incidence and latency data in different strains of mice, tumor pathology reports and images, information on genetic factors that are associated with tumor biology, and the references associated with these data (Table 4).

2.5. Protein Interaction and Pathway Databases

Biological pathways are the blueprints of cellular actions and they describe the roles of genomic entities in various cellular mechanisms. Human PPI data are important for understanding molecular signaling networks and the functional roles of biomolecules. Approaches involving pathway and PPI data are useful for analyzing microarray data and for generating testable hypotheses. Much effort has been put into pathway studies and many pathway

Database Resources for Proteomics-Based Analysis of Cancer

359

Table 4 Databases of cancer-associated genes Database

Description

URL

Cancer genome anatomy project

Containing genomic data for humans and mice, including transcript sequence, gene expression patterns, SNPs, clone resources, and cytogenetic information

http://cgap.nci.nih.gov.ezp-prod1.hul. harvard.edu/

MTBD

Supports the use of the mouse as a model system of hereditary cancer

http://tumor.informatics.jax.org/ mtbwi/

databases have been developed and made available online. Table 5 contains a list of databases providing information on PPI and biological pathways. Pathguide is a meta-database which contains information about 302 biological pathway resources (45). They include databases on metabolic pathways, signaling pathways, transcription factor targets, gene regulatory networks, genetic interactions, protein–compound interactions, and PPIs. Pathguide serves as a starting point for biological pathway analysis. A recent paper reviewed the major databases of human pathways and discussed how to use the information for the reconstruction of signaling pathways (46). The KEGG pathway database is a collection of manually drawn pathway maps representing our knowledge on the molecular interaction and reaction networks involved in metabolism, genetic information processing, environmental information processing, cellular processes, and pathogenesis (47). The BioCarta website catalogs and summarizes classical pathways as well as newly suggested pathways information on more than 120,000 genes from multiple species, including human and mouse. Reactome is an expert-curated knowledgebase of human reactions and pathways. As of 2009, it hosts 2,975 human proteins, 2,907 reactions, and 4,455 literature citations (48). The Pathway Interaction Database (PID) hosts 100 human Pathways containing 6,298 interactions curated by domain experts from US National Cancer Institute and Nature Publishing Group. It also encompasses 329 human pathways containing 7,418 interactions imported from BioCarta and Reactome (49). The Human Pathway Database (HPD) combines heterogeneous human pathway data from PID, Reactome, BioCarta, KEGG, or indexed from the Protein Lounge Web sites (50). So far, HPD contains

360

Zhang, DeLuca, and Brusic

Table 5 Protein interaction and pathway database Database

Description

URL

Pathguide

A metadatabase providing an overview of web-accessible biological pathway and network databases

http://www.pathguide.org/

KEGG pathway database

A collection of manually drawn pathway maps

www.genome.jp/kegg/ pathway.html

BioCarta pathway

A collection of pathways for multiple species

http://www.biocarta.com/ genes/index.asp

Reactome

A curated resource for human pathway data

http://www.reactome.org/

PID

A collection of curated pathways related to human molecular signaling, regulatory events, and key cellular processes

http://pid.nci.nih.gov/

HPD

Providing combined view connecting human proteins, genes, RNAs, enzymes, signaling, metabolic reactions, and gene regulatory events

http://bio.informatics.iupui. edu/HPD

NetPath

A catalog of annotations for cancer and immune signaling pathways

www.netpath.org/

HAPPI

One of the most comprehensive public compilation of human protein interaction information

http://bio.informatics.iupui. edu/HAPPI/

HomoMINT

An inferred human network based on orthology mapping of protein interactions discovered in model organisms

http://mint.bio.uniroma2. it/HomoMINT

IntAct

An open-source, open data molecular interaction database and toolkit

www.ebi.ac.uk/intact

999 human pathways and more than 59,341 human molecular entities. A set of analysis tools is also provided in HPD to allow searching, managing, and studying human biological pathways. NetPath is a component of Human Protein Reference Database (HPRD), which is a centralized platform to visually depict and integrate information pertaining to domain architecture, posttranslational modifications, interaction networks, and disease association for each protein in the human proteome (51). NetPath has 20 annotated immune and cancer signaling pathways involving 1,682 molecules and 1,800 interactions. The HAPPI database integrates protein interaction data from multiple public databases, including HPRD, BIND, MINT, STRING, and OPHID. A measure of reliability (rank levels from 1 to 5) has been given to each entry in the database. As of 2008,

Database Resources for Proteomics-Based Analysis of Cancer

361

the database contains 142,956 nonredundant, medium to high-confidence level human protein interaction pairs among 10,592 human proteins (52). HomoMINT extends PPIs experimentally verified in models organisms to the orthologous ­proteins in human (53). As of 2009, the database has 2,4439 interactions of 8,041 proteins. The curated data can be analyzed in the context of the high-throughput data and displayed in graphics by a tool named MINT Viewer. Data in IntAct are obtained from the literature or from direct data depositions by expert curators. As of September 2009, it contains over 200,000 curated binary interaction entries. IntAct provides a two-tiered view of the interaction data: a simplified and tabular view and a specialized view providing the full annotation of interactions, interactors and their properties (54). Detailed review and evaluation of public human PPI databases can be found in (55, 56).

3. Discussion Here we have reviewed and summarized five groups of databases related to proteomics studies in cancer research. We have included databases containing gene/protein expression data produced by microarray studies, next-generation sequencing, and other high-throughput experiments, gene mutation and SNP databases, tumor antigen databases, databases of cancer-associated genes, and protein interaction and pathway databases. For more cancerrelated databases, refer to the 2009 database special issue of Nucleic Acids Research (www.oxfordjournals.org/nar/database/ subcat/8/33) (57). The relevant databases can be found in subheading “Cancer gene databases” under heading “Human Genes and Diseases.” An increasing use of databases is data mining (58). These applications involve systems that combine data from multiple specialized databases and analytic tools enabling detailed analysis. For example, BiomarkerDigger (http://biomarkerdigger.org) performs data analysis, searching, and metadata-gathering (59). When gathering metadata, it searches proteome DBs for PPI, Gene Ontology annotations, protein domains, human genetic disorders, and tissue expression profile information. These diverse sources are integrated into protein data sets that are accessed through a search function in BiomarkerDigger. The identification of a serological biomarker for hepatocellular carcinoma by comparison of plasma and tissue proteomic data sets from healthy volunteers and cancer patients was demonstrated by using this resource (59). The vast amount of information generated from cancer research has presented both a challenge and an opportunity for

362

Zhang, DeLuca, and Brusic

researchers worldwide. Researchers have invested lots of effort and time in cleaning, annotating, and organizing the data produced from proteomics studies and put them into specialized biological databases. Information retrieval from proteomics databases is the starting point in performing downstream bioinformatics analyses. Making use of these data enhances understanding of the disease and advance anticancer treatments.

Acknowledgments We thank Dr. Catherine J. Wu for thoughtful reviews of this manuscript. References 1. James P (1997) Protein identification in the post-genome era: the rapid rise of proteomics. Q Rev Biophys 30:279–331 2. Koomen JM, Haura EB, Bepler G, Sutphen R, Remily-Wood ER, Benson K, Hussein M, Hazlehurst LA, Yeatman TJ, Hildreth LT, Sellers TA, Jacobsen PB, Fenstermacher DA, Dalton WS (2008) Proteomic contributions to personalized cancer care. Mol Cell Proteomics 7:1780–1794 3. Gygi SP, Rochon Y, Franza BR, Aebersold R (1999) Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 19:1720–1730 4. Ivanov SS, Chung AS, Yuan ZL, Guan YJ, Sachs KV, Reichner JS, Chin YE (2004) Antibodies immobilized as arrays to profile protein post-translational modifications in mammalian cells. Mol Cell Proteomics 3:788–795 5. Bamford S, Dawson E, Forbes S, Clements J, Pettett R, Dogan A, Flanagan A, Teague J, Futreal PA, Stratton MR, Wooster R (2004) The COSMIC (catalogue of somatic mutations in cancer) database and website. Br J Cancer 91:355–358 6. Kopf E, Zharhary D (2007) Antibody arrays – an emerging tool in cancer proteomics. Int J Biochem Cell Biol 39:1305–1317 7. Tao SC, Chen CS, Zhu H (2007) Applications of protein microarray technology. Comb Chem High Throughput Screen 10:706–718 8. Sherman BT, da Huang W, Tan Q, Guo Y, Bour S, Liu D, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) DAVID knowledgebase: a gene-centered database integrating

9.

10.

11. 12.

13. 14.

15.

heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics 8:426 da Huang W, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 35:W169–W175 Rajcevic U, Niclou SP, Jimenez CR (2009) Proteomics strategies for target identification and biomarker discovery in cancer. Front Biosci 14:3292–3303 Grantzdorffer I, Carl-McGrath S, Ebert MP, Rocken C (2008) Proteomics of pancreatic cancer. Pancreas 36:329–336 Sahin U, Tureci O, Schmitt H, Cochlovius B, Johannes T, Schmits R, Stenner F, Luo G, Schobert I, Pfreundschuh M (1995) Human neoplasms elicit multiple specific immune responses in the autologous host. Proc Natl Acad Sci USA 92:11810–11813 Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A (2007) UniProtKB/Swiss-Prot. Methods Mol Biol 406:89–112 Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D (1998) GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics 14:656–664 Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Muertter RN, Edgar R (2009) NCBI GEO: archive for high-throughput

Database Resources for Proteomics-Based Analysis of Cancer

16.

17.

18.

19.

20. 21.

22.

23.

24. 25.

functional genomic data. Nucleic Acids Res 37:D885–D890 Ikeo K, Ishi-i J, Tamura T, Gojobori T, Tateno Y (2003) CIBEX: center for information biology gene expression database. C R Biol 326:1079–1082 Rocca-Serra P, Brazma A, Parkinson H, Sarkans U, Shojatalab M, Contrino S, Vilo J, Abeygunawardena N, Mukherjee G, Holloway E, Kapushesky M, Kemmeren P, Lara GG, Oezcimen A, Sansone SA (2003) ArrayExpress: a public database of gene expression data at EBI. C R Biol 326:1075–1078 Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, Abeygunawardena N, Berube H, Dylag M, Emam I, Farne A, Holloway E, Lukk M, Malone J, Mani R, Pilicheva E, Rayner TF, Rezwan F, Sharma A, Williams E, Bradley XZ, Adamusiak T, Brandizi M, Burdett T, Coulson R, Krestyaninova M, Kurnosov P, Maguire E, Neogi SG, Rocca-Serra P, Sansone SA, Sklyar N, Zhao M, Sarkans U, Brazma A (2009) ArrayExpress update – from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res 37:D868–D872 Kato K, Yamashita R, Matoba R, Monden M, Noguchi S, Takagi T, Nakai K (2005) Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues. Nucleic Acids Res 33:D533–D536 Penkett CJ, Bahler J (2004) Navigating public microarray databases. Comp Funct Genomics 5:471–479 Zeng ZY, Xiong W, Zhou YH, Li XL, Li GY (2006) [Advances in high-density whole genome-wide single nucleotide polymorphism array in cancer research]. Ai Zheng 25:1454–1458 Forbes SA, Bhamra G, Bamford S, Dawson E, Kok C, Clements J, Menzies A, Teague JW, Futreal PA, Stratton MR (2008) The catalogue of somatic mutations in cancer (COSMIC). Curr Protoc Hum Genet. Chapter 10, Unit 10 11 Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Thomas NS, Cooper DN (2009) The human gene mutation database: 2008 update. Genome Med 1:13 Shimizu N, Ohtsubo M, Minoshima S (2007) MutationView/KMcancerDB: a database for cancer gene mutations. Cancer Sci 98:259–267 Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, Rahman N, Stratton MR (2004) A census of human cancer genes. Nat Rev Cancer 4:177–183

363

26. Hernandez-Boussard T, Rodriguez-Tome P, Montesano R, Hainaut P (1999) IARC p53 mutation database: a relational database to compile and analyze p53 mutations in human tumors and cell lines. International Agency for Research on Cancer. Hum Mutat 14:1–8 27. Murphy JA, Barrantes-Reynolds R, Kocherlakota R, Bond JP, Greenblatt MS (2004) The CDKN2A database: integrating allelic variants with evolution, structure, function, and disease association. Hum Mutat 24:296–304 28. Gottlieb B, Trifiro M, Lumbroso R, Pinsky L (1997) The androgen receptor gene mutations database. Nucleic Acids Res 25:158–162 29. Gottlieb B, Beitel LK, Wu JH, Trifiro M (2004) The androgen receptor gene mutations database (ARDB): 2004 update. Hum Mutat 23:527–533 30. Jackson MA, Lea I, Rashid A, Peddada SD, Dunnick JK (2006) Genetic alterations in cancer knowledge system: analysis of gene mutations in mouse and human liver and lung tumors. Toxicol Sci 90:400–418 31. Hu Y, McDermott MP, Ahrendt SA (2005) The p53 codon 72 proline allele is associated with p53 gene mutations in non-small cell lung cancer. Clin Cancer Res 11:2502–2509 32. Packer BR, Yeager M, Burdett L, Welch R, Beerman M, Qi L, Sicotte H, Staats B, Acharya M, Crenshaw A, Eckert A, Puri V, Gerhard DS, Chanock SJ (2006) SNP500Cancer: a public resource for sequence validation, assay development, and frequency analysis for genetic variation in candidate genes. Nucleic Acids Res 34:D617–D621 33. Smigielski EM, Sirotkin K, Ward M, Sherry ST (2000) dbSNP: a database of single nucleotide polymorphisms. Nucleic Acids Res 28:352–355 34. Rhee H, Lee JS (2009) MedRefSNP: a database of medically investigated SNPs. Hum Mutat 30:E460–E466 35. van der Bruggen P, Traversari C, Chomez P, Lurquin C, De Plaen E, Van den Eynde B, Knuth A, Boon T (1991) A gene encoding an antigen recognized by cytolytic T lymphocytes on a human melanoma. Science 254:1643–1647 36. Scanlan MJ, Gure AO, Jungbluth AA, Old LJ, Chen YT (2002) Cancer/testis antigens: an expanding family of targets for cancer immunotherapy. Immunol Rev 188:22–32 37. Almeida LG, Sakabe NJ, deOliveira AR, Silva MC, Mundstein AS, Cohen T, Chen YT, Chua R, Gurung S, Gnjatic S, Jungbluth AA, Caballero OL, Bairoch A, Kiesler E, White SL, Simpson AJ, Old LJ, Camargo AA,

364

38.

39. 40.

41. 42.

43.

44.

45. 46.

47.

48.

49.

50.

Zhang, DeLuca, and Brusic Vasconcelos AT (2009) CTdatabase: a knowledge-base of high-throughput and curated data on cancer-testis antigens. Nucleic Acids Res 37:D816–D819 Donnes P, Hoglund A, Sturm M, Comtesse N, Backes C, Meese E, Kohlbacher O, Lenhof HP (2004) Integrative analysis of cancer-related data using CAP. FASEB J 18:1465–1467 Jongeneel V (2001) Towards a cancer immunome database. Cancer Immun 1:3 Novellino L, Castelli C, Parmiani G (2005) A listing of human tumor antigens recognized by T cells. Cancer Immunol Immunother 54:187–207 Van den Eynde BJ, van der Bruggen P (1997) T cell defined tumor antigens. Curr Opin Immunol 9:684–693 Strausberg RL, Buetow KH, Greenhut SF, Grouse LH, Schaefer CF (2002) The cancer genome anatomy project: online resources to reveal the molecular signatures of cancer. Cancer Investig 20:1038–1050 Schaefer C, Grouse L, Buetow K, Strausberg RL (2001) A new cancer genome anatomy project web resource for the community. Cancer J 7:52–60 Krupke D, Naf D, Vincent M, Allio T, Mikaelian I, Sundberg J, Bult C, Eppig J (2005) The Mouse Tumor Biology Database: integrated access to mouse cancer biology data. Exp Lung Res 31:259–270 Bader GD, Cary MP, Sander C (2006) Pathguide: a pathway resource list. Nucleic Acids Res 34:D504–D506 Bauer-Mehren A, Furlong LI, Sanz F (2009) Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol Syst Biol 5:290 Kanehisa M, Goto S, Furumichi M, Tanabe M Hirakawa, M (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs, Nucleic Acids Res 38:D355–D360 Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, Garapati P, Hemish J, Hermjakob H, Jassal B, Kanapin A, Lewis S, Mahajan S, May B, Schmidt E, Vastrik I, Wu G, Birney E, Stein L, D’Eustachio P (2009) Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 37:D619–D622 Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH (2009) PID: the Pathway Interaction Database. Nucleic Acids Res 37:D674–D679 Chowbina SR, Wu X, Zhang F, Li PM, Pandey R, Kasamsetty HN, Chen JY (2009) HPD: an

51.

52.

53.

54.

55. 56.

57.

58. 59.

online integrated human pathway database enabling systems biology studies. BMC Bioinformatics 10(Suppl 11):S5 Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A (2009) Human Protein Reference Database – 2009 update. Nucleic Acids Res 37:D767–D772 Chen JY, Mamidipalli S, Huan T (2009) HAPPI: an online database of comprehensive human annotated and predicted protein interactions. BMC Genomics 10(Suppl 1):S16 Persico M, Ceol A, Gavrila C, Hoffmann R, Florio A, Cesareni G (2005) HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms. BMC Bioinformatics 6(Suppl 4):S21 Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A, Derow C, Feuermann M, Ghanbarian AT, Kerrien S, Khadake J, Kerssemakers J, Leroy C, Menden M, Michaut M, Montecchi-Palazzi L, Neuhauser SN, Orchard S, Perreau V, Roechert B, van Eijk K, Hermjakob H (2009) The IntAct molecular interaction database in 2010. Nucleic Acids Res 38(Database issue):D525–31 Lehne B, Schlitt T (2009) Protein-protein interaction databases: keeping up with growing interactomes. Hum Genomics 3:291–297 Mathivanan S, Periaswamy B, Gandhi TK, Kandasamy K, Suresh S, Mohmood R, Ramachandra YL, Pandey A (2006) An evaluation of human protein–protein interaction data in the public domain. BMC Bioinformatics 7(Suppl 5):S19 Galperin MY, Cochrane GR (2009) Nucleic acids research annual database issue and the NAR online molecular biology database collection in 2009. Nucleic Acids Res 37:D1–D4 Brusic V, Zeleznikow J (1999) Knowledge discovery and data mining in biological databases. Knowledge Eng Rev 14:257–277 Jeong SK, Kwon MS, Lee EY, Lee HJ, Cho SY, Kim H, Yoo JS, Omenn GS, Aebersold R, Hanash S, Paik YK (2009) BiomarkerDigger: a versatile disease proteome database and analysis platform for the identification of plasma cancer biomarkers. Proteomics 9:3729–3740

Index A Absorption/covalent printing........................................... 86 Accutase.............................................................171, 175, 178 Acetone......................................................................171, 173 Acetylation..............................................214–217, 220–222 Acuity 4.0..........................................................144, 146, 147 Adherent cells............................ 53, 167, 171, 175, 178, 182 Affinity......................17, 29, 57, 58, 71, 83, 86, 98, 108, 109, 116, 120, 122, 146, 149, 155–157, 159, 186, 190, 213, 250, 259, 276, 297, 322, 351 printing................................................................. 83, 86 reagent................................. 57, 149, 186, 250, 276, 322 Allo-antibodies..................................................... 82, 87–88 Amino acid activation...................................................112, 123, 124 coupling.............................................110, 113, 123, 124 pentafluorophenyl esters................................... 107, 112 Ampicillin............. 91, 95, 102, 187, 258, 260, 261, 265, 270 Analytical methods............................ 38, 316–317, 338, 341 Androgen receptor (AR)................................168–170, 176, 177, 354, 355 Angiogenin (Ang).......................................................... 6, 7 Anti-beta galactosidase................................................... 179 Antibodies...............3–12, 15–27, 29–39, 42, 43, 47–52, 69, 81–102, 108–110, 118, 119, 121, 122, 130, 138, 140–141, 143, 145, 146, 149, 150, 153, 155–157, 159, 179, 186, 189–190, 194–199, 202, 217, 219, 220, 222, 227–242, 245, 250, 275–277, 279–285, 293, 295–297, 299, 307, 308, 311, 312, 317, 321–327, 329–332, 338, 340–342, 344–345, 350, 357 autoantibody.......130, 131, 142–143, 150, 151, 155–156 profiling.............................................130, 155, 157–158 screening....................................................155, 240, 245 validation....................................... 51–52, 240, 250, 276 Antibody-based detection...........4–6, 10, 11, 16, 30, 32, 33, 81–102, 118, 119, 186, 202, 222, 227–238 Antigens......................4–5, 9, 11, 12, 16, 20, 23, 24, 81–102, 129–147, 149–153, 155, 156, 158–160, 181, 186, 191, 196, 227, 228, 230, 234–237, 241, 243, 244, 281, 337, 350–352, 356–358, 361 isoform............................................................. 101, 359 Anti human IgG Alexa647 conjugate........................ 91, 93

Anti-phosphoprotein antibodies................................ 43, 51 Aptamer characterization.................................................... 57–59 high-throughput optimization.............................. 57–65 length minimization................................................... 63 microarray............................................................. 57–65 AR. See Androgen receptor Array format........................................................17, 75, 166 Autoantibodies..................130, 131, 142–143, 149–151, 153, 155–156, 159, 351 immunoglobulin G labeling of........................... 133, 138–140, 143, 146, 189, 195, 198, 229–230, 235, 238 purification of..................... 132–133, 137–138, 144, 145, 243, 245, 324 profiling.............................................130, 142–143, 155 Auto-fluorescence.................... 123, 171, 172, 222, 277, 297 Autoimmune disease.............................................. 130, 227 Autoimmunity.........................................130, 131, 227, 239 Automation...............................98, 106, 114, 142, 147, 171, 282–283, 286, 288, 291, 296, 342, 344 AxioVision LE....................................................... 172, 178

B Bacterial adhesins............................................................. 72 Bacterial invasion.............................................................. 44 Bait...................130, 166–170, 172–174, 177–180, 182–183, 240, 250, 275, 276 BCL2.............................................................................. 150 Bead array...................................................... 29–36, 227–238 BIOCCD image reader...........................167, 169, 171, 178 Bioinformatics.................................... 85, 87, 144, 146, 346, 350–352, 362 Biological pathway..........................................239, 358–360 Bioluminescence............................................................. 304 Biomarker................................ 8, 15–27, 129–147, 152, 158, 228, 311, 337, 349–351, 361 Biomolecular interaction.........................202, 304, 311, 316 Biotin.............................................. 16, 31, 34, 36, 116, 146, 186, 229, 235, 241–244, 250–252, 277, 281, 282, 314, 315 Biotinylation................................6, 10, 11, 18, 22, 186, 236, 277, 279, 282, 313, 314

Catherine J. Wu (ed.), Protein Microarray for Disease Analysis: Methods and Protocols, Methods in Molecular Biology, vol. 723, DOI 10.1007/978-1-61779-043-0, © Springer Science+Business Media, LLC 2011

365

Protein Microarray for Disease Analysis 366  Index

  

Bovine serum albumin (BSA)............... 8, 12, 17, 41, 43–46, 49, 59, 61, 69, 70, 75, 88, 90, 93, 122, 133, 140, 145, 204, 216, 219, 222, 229, 234, 235, 266, 279, 283, 284, 313–315, 332 Buffer blocking................................12, 17, 19, 41, 47, 48, 52, 69, 70, 74, 75, 93, 99, 108, 109, 118, 119, 122, 132, 133, 139, 193, 196, 204, 205, 217, 228, 244, 248, 252 crossdown......................................................... 109, 119 incubation.....................75, 109, 120, 121, 133, 140, 145 regeneration.......................................109, 120, 323, 328 spotting.......................................... 41, 47, 108, 118, 323 transfer..............................10, 11, 18, 118, 140, 244, 248 tris-buffered saline (TBS)...........................91, 122, 204, 217, 244, 252 tween-tris buffered saline (T-TBS)...................108, 122, 220, 244, 248, 279, 282, 283, 297

C Cancer breast................................................... 150, 158, 353–355 melanoma..................................................150, 355, 356 prostate......................................................130, 150, 355 vaccine target............................................................ 358 Candida........................................................................... 40 CAPPIA. See Cell array protein–protein interaction assay Capping........................................... 30, 107, 113, 123, 124 Carbohydrates.......................................................15, 67, 71 Catalyzed signal amplification (CSA).................... 277, 281 CDA. See Concentration dependent analysis cDNAs............... 94, 150, 154–156, 230, 258, 262, 270, 357 Cell array protein–protein interaction assay (CAPPIA)................................... 165–183 Cell arrays............................................................... 165–183 Cell culture................................9, 38, 42–43, 52, 87, 91–93, 95–98, 108, 118, 120, 131, 134–135, 145, 168, 171, 176, 178, 181, 260, 262, 284, 323 293 Cell line.......................94, 169, 171, 175, 176, 181, 182 Cell lysate arrays..............................................130, 143, 322 Cell monolayer........................................166, 167, 181, 182 Cell signaling...........................37, 38, 40, 44, 155, 228, 229, 244, 279, 280, 284 Cellulose membrane b-alanine................................................................... 107 amino-alkyl linked membranes.................107, 110–112, 117, 123–124 amino functionalization.............................110, 123–124 CAPE membrane............................................. 110, 111 cleavage......................................................108, 115–118 esterified membranes.........................110–111, 123–124 preparation.........................................107–108, 110–118 TFA-soluble membrane........................................... 109 TOTD membrane............................................ 110, 111

Charge-coupled device (CCD) camera..............50, 53, 178, 284, 285, 299, 311, 314, 316 Chemiluminescence....................... 109, 119, 122, 125, 229, 232, 244, 249, 252, 304, 321 Chemokine................................................................... 5, 11 Chip blocking................................................41, 47–48, 219 Chlamydia................................................................................40 Chronic lymphocytic leukemia............................... 241, 242 CLAMP kinetic analysis software.................................. 330 Cloning....................................68, 71, 72, 76, 170, 172, 179, 187, 190–192, 231, 250, 251, 257, 258, 259, 261–263, 266, 270 Colorimetric assay.......................................................... 285 Competitive assay............................................311, 314–317 Concentration dependent analysis (CDA)................ 85–86, 338–343, 345, 346 Conjugation................................................ 8, 10, 18, 24, 41, 43, 46, 59, 69, 82, 91, 93, 110, 124, 155, 157, 179, 188, 199, 214, 230, 232, 244, 281, 285, 308, 309, 312, 313 Contact printing............................................................. 181 Contact protein printer..................................................... 93 Co-precipitation..................................................... 239–254 COS7.................................................................171, 175, 176 Coupling cycle................................. 105, 113–115, 117, 123 Cre. See Cre/loxp Cre/loxp..................................................................258, 269 CSA. See Catalyzed signal amplification Cy3......................................8, 35, 59, 70, 134, 141, 157, 158, 188, 189, 193, 195–199, 222, 277, 310–312 Cy5....................................41, 42, 70, 72, 110, 121, 134, 141, 146, 190, 195–199, 222, 277, 310, 312, 313 Cytokine................................................................... 4–7, 10, 11

D DAPI. See 4’,6-Diamidino–2-phenylindole dihydrochloride Dark quencher........................................................ 309, 314 Data analysis........................... 10–11, 22–23, 39, 42, 49–50, 62–63, 77, 93, 100–101, 134, 137, 144, 147, 153, 204, 207–209, 219, 279, 281, 288–296, 298, 324, 330–331, 337–346, 350, 351 Database...............................................................62, 349–362 DBD. See DNA binding domain Denatured nickel affinity chromatography............83, 92, 97 Deprotection................................... 108, 111, 115–117, 124 Detection alkaline phosphatase (AP)................................ 109, 118 antibody.......................3–13, 16–23, 30, 33, 43, 81–102, 118, 119, 195, 202, 222, 227–238, 277, 308, 322, 350 chemiluminescence...........................................109, 119, 122, 125, 232 fluorescence.......................... 17–23, 109–110, 120–121, 171–172, 179, 220, 277, 285, 309, 311, 313

Protein Microarray for Disease Analysis 367 Index     

horse-radish peroxidase (HRP, POD)................... 5, 69, 75, 77, 91, 96, 98, 109, 118, 119, 155, 157, 188, 228–230, 232, 244, 248, 251, 252, 276–277, 281, 283, 285 methods.........................5, 108–110, 118–121, 146, 276, 279–281, 285, 297, 313 staining...................................... 109, 119, 168, 284, 322 X-ray film......................................................... 119, 216 DGC. See Drosophila gene collection Diabetes...............................................................6, 150, 158 Diagnosis.....................................................8, 311, 350, 358 4’,6-Diamidino–2-phenylindole dihydrochloride (DAPI)..................................171, 177, 178, 182 Diastolic blood pressure...................................................... 7 Differential expression analysis............................... 340–342 DLI. See Donor lymphocyte infusion D-MEM. See Dulbecco’s Modified Eagle’s Medium DNA binding domain (DBD)......... 166, 170, 172, 175, 179 DNA method..................................................172, 173, 186 DNA microarray reader.......................................... 171, 178 DNA microarrays......................... 57–65, 81, 154–156, 166, 167, 186, 198, 213–214, 217, 276, 311, 312, 321, 322, 338 DNA polymerase................................... 68, 73, 76, 260, 270 Donor-acceptor pairs.............................................. 308–310 Donor lymphocyte infusion (DLI)..................241, 242, 244 Dose-dependence............................................168, 176, 177 Drosophila gene collection (DGC)........................ 258, 263 Dulbecco’s Modified Eagle’s Medium (D-MEM).............................171, 175, 323, 325

E EBNA. See Epstein–Barr virus nuclear antigen EC-buffer............................................................... 171, 173 ECL. See Enhanced chemiluminiscence E.coli clones.................................................................... 259 Effectene transfection reagent................166, 167, 170–173, 180, 182 EGFP. See Enhanced green fluorescent protein ELISA. See Enzyme-linked immunosorbent assay Endo-Free Plasmid Maxi Kit................................. 169, 172 End-stage renal disease (ESRD).................................... 5–6 Energy transfer....................................................... 303–317 Enhanced chemiluminiscence (ECL)......................96, 109, 119, 122, 125, 229, 232, 244, 249, 321 Enhanced green fluorescent protein (EGFP)................ 167, 171–175, 178, 179, 310 Enteropathogenic Escherichia coli (EPEC)....................... 40 Enzyme-linked immunosorbent assay (ELISA)............ 4, 5, 69–71, 74–75, 77, 82, 84, 85, 88, 151–152, 155, 228–237 EPEC. See Enteropathogenic Escherichia coli Epithelial cells........................................................... 38–40, 52, 130

Epitope tags GST...................................................................... 83, 84 V5........................................................ 83, 88, 89, 94, 95 6xHis................................... 83, 86, 88, 94–95, 269–270 Epoxide coating...................................................... 108, 122 Epstein–Barr virus nuclear antigen (EBNA)..........153, 158, 236, 237, 242 ESRD. See End-stage renal disease Excited state........................................................... 304–306 Expression clone collection............................................ 259 Expression profiling...........................................85, 330, 350 Expression-ready clone collection.......................... 257–271 Expression system coupled transcription-translation in vitro................. 156

F FAST slides...............................................93, 188, 194, 217 FBS. See Fetal bovine serum Fetal bovine serum (FBS).............. 9, 12, 131, 171, 175, 323 Filter paper.......107, 108, 110–112, 114, 118, 120–122, 177 FLAG.............. 186, 188, 191–192, 198, 217, 220, 229, 230, 231, 233, 237, 250, 291 FLAG-HA..................................................................... 222 Flow cell .................................. 136, 322, 324, 327–329, 332 Fluidic cells................................................................. 42, 48 Fluorescence...................... 4, 6, 7, 10, 17–23, 30, 35, 42, 46, 49–50, 53, 61–63, 65, 77, 87, 89, 101, 109–110, 120–121, 123, 141, 147, 169, 171–172, 174, 175, 178–182, 197, 220, 277, 278, 285, 297, 304–307, 309, 311, 321, 322, 326, 344–345 lifetime......................................................312, 314–316 Fluorescent label.................................. 48, 59, 134, 199, 313 Fluorochrome............................................................. 82, 88 Fluorometric assay.................................................. 275–299 Fluoromount-G...................................................... 171, 178 Fmoc building block............................................112, 116, 117 chemistry.................................................................. 216 removal of protecting-group............................. 113, 114 Formaldehyde......................................................... 171, 176 Förster distance..................................................... 306–307, 309, 310 Förster resonance energy transfer........................... 303–317 FPLC chromatographic system............................ 92, 94–95 Free peptides..................................................106, 110, 115, 117–118, 124 Fusion proteins (amino and carboxy terminal).............. 258, 259, 269

G GAD65......................................................................150, 158 GAL4..............................................................172, 175, 179 GAL (file format). See Gene array list Gal4-pZsGreen...............................................172, 175, 179

Protein Microarray for Disease Analysis 368  Index

  

GAPS II coated slides............................................ 180, 181 Gateway system/technology................... 179, 187, 190, 231, 250, 257, 259, 270 Gelatin..................................... 170, 172, 173, 178, 180, 332 Gel electrophoresis.................. 244, 259–261, 264, 278, 351 Gene array list (GAL).................... 142, 207, 209, 221, 222, 261, 327, 330, 339, 341, 342 Gene expression..............3, 81, 278, 310, 351–353, 357–359 profile....................................................3, 352, 353, 358 GenePix.................................70, 75, 93, 101, 102, 141, 142, 189, 190, 193, 195, 197, 204, 207–210, 216, 339, 341–344 GenePix Pro 6.0........................................22, 146, 221, 222 GenePix results (GPR)........................... 101, 142, 144, 147, 209, 210, 342, 344 Gentamycin protection assay............................................ 53 Glass slides........................16, 71, 86, 88, 93, 108, 115, 118, 121, 150, 159, 166, 167, 173, 174, 176, 178, 180, 181, 201–202, 240, 290, 297, 315, 324, 327 aldehyde surface.........................................108, 122, 124 epoxide coating......................................................... 108 Glutathione-S-transferase (GST)................ 71, 83, 84, 155, 157, 186, 217, 219, 228, 230–237, 344–345 Glycan......................... 15–16, 19, 22–24, 27, 67, 68, 71, 322 Glycerol...........8, 26, 69, 91, 92, 98, 101, 108, 121, 131, 132, 137, 215, 217, 218, 244, 267, 312, 323 Glycomics................................................................... 67–77 Glycoprofiling................................................................ 350 Glycoprotein........................................ 15–17, 19, 20, 71, 74 Glycosylation..................5, 15–27, 68, 72, 82, 240, 244–245 GPR (file format). See GenePix results Graft versus host disease (GVHD)................................... 87 Graft versus leukemia (GVL)........................................... 87 GST. See Glutathione-S-transferase GTPases.........................................................................38, 40 GVHD. See Graft versus host disease GVL. See Graft versus leukemia

H HEK 293.................................................171, 175, 176, 181 HEK 293 T............................. 169, 171, 175, 176, 181, 182 HeLa cells 38, 40, 42–44, 51, 52, 171, 175, 176 HepG2.......................................................171, 175, 176, 325 HepG2 cell line...................................................... 323, 324 High-throughput.............................. 3–12, 37, 67, 129, 201, 202, 204, 228, 231, 276, 321, 337, 350–353, 361 High-throughput screening (HTS)........................... 15–27, 165–183, 229, 240, 244, 308, 312 HIV-p24................................................................88, 94, 97 HLA ligand.................................................................... 358 Hormone-dependence............................................ 169, 172 Host cell signalling................................................38, 40, 44 Host-pathogen interaction................................... 37–53, 68

HTS. See High-throughput screening Human..............................16, 24, 29, 40, 59, 81–83, 88, 130, 131, 134, 145, 191, 201, 202, 215, 259, 278, 349, 351, 354, 355– 361 Human blood........................16, 24, 99–100, 150, 230, 232, 235, 245 Human IgG.................................. 84, 88, 91, 145, 153, 230, 235, 238 H-Y antigens/H-Y proteins DDX3X.......................................................... 87–88, 94 DDX3Y.......................................................... 87–90, 94 EIF1AX.....................................................87–88, 94, 97 EIF1AY.....................................................87–88, 94, 97 RPS4X............................................................ 87–88, 94 RPS4Y............................................................ 87–88, 94 UTX............................................................... 87–88, 94 UTY...................................................................87–90, 94 ZFX................................................................ 87–88, 94 ZFY....................................................................87–90, 94 Hybridization..........................17, 20, 24, 25, 38, 39, 59, 65, 70, 146, 155, 157, 159, 166, 168, 175, 188, 189, 193–197, 308, 311, 312, 314, 353 Hydroxyflutamide (OH-Flu)................................. 176, 177

I IA2, 150 IC/PBS. See Interstitial cystitis/painful bladder syndrome IgG purification......................................132–133, 137–138, 145, 243, 245 IL–12. See Interleukin–12 Image acquisition..................................... 50, 209, 279, 280, 285–288, 298, 315 ImageJ......................................................242, 249–250, 252 Imidazole..................................... 86, 88, 91–93, 95–98, 107 Immunoassay.................................... 5, 30, 50, 51, 202, 275, 308, 316–317, 322 Immunoblotting........................................38, 249, 250, 278 Immunoglobulin E........................................................... 59 Immunoprecipitation................................82, 240, 241–243, 245–247, 249–250 Immunoprofile....................................................... 149–159 Immunostaining......................................279–284, 312, 357 Individualized therapy.................................................... 278 In situ activation..............................................112, 123, 156 InstrumentONE high-performance....................... 171, 174 Interleukin–12 (IL–12).................................................. 6–7 Interstitial cystitis/painful bladder syndrome (IC/PBS)...............................130, 131, 143, 144 Intracellular parasites.................................................. 37–38 Invasive bacteria................................................... 37–38, 40 In vitro infection..............................................38–44, 52–53 IPTG. See Isopropyl-b-D-thiogalactoside IRB approval.......................................................... 132, 151 Isopropyl-b-D-thiogalactoside (IPTG)................91, 95, 96



K Kinase...................................38, 40, 199, 214, 308, 313, 355 Kinase-substrate interaction............................201–212, 337 Kinetics....................................................115, 243, 321, 322

L Label-free.............................................................24, 321–332 Lab-Tek™ Chamber Slide™, 181 LacZ. See b-galactosidase Lanthanide..............................................309, 310, 314, 317 Large T antigen of SV40................................................ 181 LB-Ampicillin (LB-Amp) plates..................................... 95 LB-Amp plates. See LB-Ampicillin (LB-Amp) plates LBD. See Ligand binding domain Lectin........................................................ 15–27, 67–77, 322 microarray..................................................15–27, 67–77 l-glutamine................................................40, 171, 175, 323 Library............................................... 168, 178, 270, 283, 312 Ligand binding domain (LBD)....... 168, 169, 170, 176, 177 Linker biotin............................................................................116 coupling.....................................................116–117, 122 hydrazinobenzoic acid (HBA).......................... 116, 117 Lipid bilayer array.......................................................... 312 Lipid-DNA method............................................... 172–173 Listeria................................................................................40 Loxp. See Cre/loxp Luminex............................ 5, 30, 35, 228–230, 232–235, 237 Lysates............. 29, 37–41, 43–45, 47, 49, 51, 74, 76, 82, 95, 98, 130–137, 140, 142–145, 149, 156, 190, 192, 194, 228, 230, 231, 237, 242, 243, 245–247, 251, 278, 279, 284, 289, 322, 332, 350 Lysis buffer..............................41, 44, 52, 53, 69, 74, 76, 92, 96–98, 102, 131, 134, 345 Lysozyme...............................................................69, 74, 97

M Macroarrays.....................................106, 107–108, 110–115 MALDI. See Matrix-assisted laser desorption/ionization Mammalian cells........................ 52, 83, 166–168, 175, 179, 180, 222, 239, 241, 242 Mammalian protein-protein interaction trap (MAPPIT)................................................... 179 Mammalian two hybrid.......................................... 165–183 MAPK. See Mitogen-activated protein kinase MAPPIT. See Mammalian protein-protein interaction trap Mass spectrometry (MS).............................. 16, 17, 24, 145, 147, 213, 351 Mastoparan.................................................................... 116 Matrix-assisted laser desorption/ionization (MALDI)................................................. 24–26 Mean fluorescence intensity (MFI)......................10, 32, 84, 87, 101, 102, 236

Protein Microarray for Disease Analysis 369 Index      Medroxyprogesterone acetate (MPA)..................... 176, 177 Melanoma inhibitor of apoptosis (ML-IAP)................. 150 Membrane b-alanine.................................... 107, 110, 113, 121, 123 amino-alkyl linked............. 107, 110–112, 117, 123–124 amino functionalization.................................... 110, 121 CAPE............................................................... 110, 111 cleavage......................................108, 115–118, 121–122 esterification..................................................... 110, 121 PEG.....................................................................115, 124 TFA-soluble..............................................108, 109, 124 TOTD.............................................................. 110, 111 Methallothionein inducible promoter............................ 257 MFI. See Mean fluorescence intensity Mfold.....................................................................62, 63, 65 mHA. See Minor histocompatibility antigens Microarrays.............................4, 16, 29, 41, 58, 67, 81, 106, 129, 149, 166, 185, 201, 213, 227, 239, 275, 310, 321, 337, 350 analysis..........................10, 15–27, 30, 67–77, 106, 115, 134, 144, 147, 155, 199, 276, 280, 288–295, 337–346 forward phase microarrays.....................29–30, 129–130 printer 4.................................................. 70, 93, 98, 118, 132, 216, 218, 324 printing..............................9, 12, 17, 86–87, 93, 98, 132, 137, 150, 152, 153, 188–189, 192, 193, 197, 216–218, 277, 326, 344 printing and blocking of............................137, 188, 193 quality control....................................188–189, 193, 322 reverse phase microarrays.............. 29–30, 129–130, 227, 275–299 scanner...................................8, 10, 12, 62, 93, 100, 121, 134, 141, 216, 316 Micro flow system............................................................ 48 Microtiter plates (MTP)............................6–12, 30, 32–34, 41, 45, 48, 69, 118 Minimal binding domain................................................. 63 Minor histocompatibility antigens (mHA)...................... 87 Mitogen-activated protein kinase (MAPK)..................... 40 pathways..................................................................... 40 ML-IAP. See Melanoma inhibitor of apoptosis MOI. See Multiplicity of infection Molecular interaction analysis................................ 359, 360 Monolayer........................................ 166, 167, 178, 181, 182 MPA. See Medroxyprogesterone acetate MS. See Mass spectrometry MTP. See Microtiter plates Multiplex........................... 4–6, 18, 22, 29–30, 58, 81, 82, 86, 88, 129, 201, 227–238, 276, 280, 287, 312, 316 Multiplexed assay..................................................... 35, 228 Multiplicity of infection (MOI)........................... 44, 52–53 Multivariate approach........................................................ 3 Mycobacterium........................................................40, 42–43

Protein Microarray for Disease Analysis 370  Index

  

N Nanoparticle............................................309, 312–313, 317 NAPPA. See Nucleic acid programmable protein array Native nickel affinity chromatography............83, 92, 97, 98 Nebulization............................................................... 41, 48 NF-kB.................................................................... 172, 175 Non-contact microarray spotter........................................ 41 Non-contact piezo-dispensing system............................ 171 Nonradiative decay......................................................... 305 N-terminal domain (NTD).............................168–170, 176 Nucleic acid programmable protein array (NAPPA).............................................. 149–159

O Open reading frames (ORFs).............................83, 94, 101, 172, 191, 192, 194, 239–240, 250, 257–259, 262, 263, 269, 270 Organic compounds............................................... 105–106

P p53.......................................................... 150, 158, 175, 191, 195, 197, 237, 355 pAD-SV40T...................................................169, 172, 175 pAD-TRAF....................................................169, 172, 175 Pathogen..........................................37–53, 67–68, 310–312 Pathogenesis........................................................... 239, 359 pBD-NF-kB...................................................169, 172, 175 pBD-p53............................................................169, 172, 175 PBXL–3...................................................................5, 6, 8, 10 PC–3................................................................. 171, 175–176 pcDNA4-EGFP.............................................172–174, 178 pcDNA4/HisMax TOPO.............................................. 172 pCMV-AD............................................................ 170, 172 pCMV-BD..................................................................... 172 PCR. See Polymerase chain reaction PCR primer.............................................259, 262, 269, 270 Pellet................ 32, 34, 53, 64, 74, 76, 95–97, 102, 134, 145, 191, 192, 231, 235, 247, 268 Penicillin/streptomycin...................... 40, 131, 171, 175, 323 Peptide array macroarray.................................................. 106, 114 microarray...................................106, 109–110, 115, 118, 120–121 free peptide........................ 106, 110, 115, 117–118, 124 immobilization................................................. 116, 124 reconstitution............................................................ 118 solution......................................................115, 120, 124 synthesis........................................................... 105–125 transfer...................................................................... 118 unprotected peptides................................................. 116 pGAL/lacZ.................................................................... 179

Phosphoprotein/protein ratio........................................... 51 Phosphorylation................................. 38–40, 43, 51, 52, 82, 214, 240, 278, 312, 313 status......................................................................... 312 Photo multiplier tubes (PMT)................... 12, 22, 100, 141, 142, 146–147, 197, 286–287, 298, 316, 343 pIRES2-EGFP.............................................................. 172 Planar waveguide excitation....................................... 50, 53 Plasma.................................30, 32, 34, 35, 59, 81–102, 108, 157, 159, 228, 241, 243, 361 Plasmid..............................40, 68, 73, 76, 83, 101, 150, 155, 166, 167, 169–170, 172–175, 179–181, 186, 187, 189–195, 198, 228, 229, 231, 241, 243, 246, 250–251, 265 PMT. See Photo multiplier tubes Poly-l-lysine............................................171, 173, 180, 181 Polymerase chain reaction (PCR)...................34, 68, 71–73, 76, 85, 94, 168, 172, 186, 187, 190, 192, 245, 246, 250, 251, 258–267, 269, 270, 276, 309 Post-translational modifications glycosylation....................................15–27, 82, 244–245 phosphorylations..........38–40, 43, 51, 82, 214, 240, 278 Pre-activated derivatives......................................... 107, 112 Prey...........................166–170, 172–174, 177–180, 182, 183 Prey-reporter- (PR-) slides......................168, 169, 182–183 Primer design......................................................... 260, 263 Printing buffer....................8, 9, 11, 17, 18, 88, 90, 197, 215 Printing substrates glycosylation......................................................... 16, 72 nitrocellulose....................................................... 86, 278 Probing solution............................................................. 108 ProCAT.................................................................. 210, 211 Prognosis............................................................................ 8 Prospector................................................207, 341–342, 344 Protein.....................................3, 15, 29, 38, 57, 67, 81, 106, 129, 149, 165, 185, 201, 213, 227, 239, 257, 275, 304, 321, 337, 349 arrays................................37–53, 94, 140, 150, 166, 207, 227, 241, 311, 344, 350 binding............................64, 67, 86, 115, 119, 120, 132, 156, 179, 201, 214, 218, 235, 245, 321, 322 detection........................................................... 186, 276 expression................................20, 53, 69, 74, 76, 82, 83, 91–93, 95–98, 171, 192, 193, 195, 198, 228, 231, 232, 237, 241–242, 250, 257–259, 270, 311, 322, 337, 340, 350–352, 357, 361 gel quantification........................................ 44, 303–317 kinase................................... 40, 199, 202, 209, 308, 337 kinase B...................................................................... 40 labeled protein..............61, 109, 119, 122, 251, 252, 307 microarray..............29, 30, 82–86, 94, 98–100, 152–155, 185–199, 201–203, 208, 213–222, 215, 239, 241, 275–299, 311, 337–346, 350

Protein Microarray for Disease Analysis 371 Index     

antigen........................................................ 129–147 blocking................................... 12, 99, 108, 205, 324 DNA..........................57–65, 81, 166, 167, 171, 178, 186, 190, 191, 193, 194, 197, 213–214, 217, 276, 311, 312, 338 nucleic acid................................................. 150, 155 printing......................................86–87, 93, 216–218 replicates....................................................6, 10, 152 reproducibility........................................47, 156, 346 scanning....................... 134, 141–144, 146, 207, 311 zone variation......................................152–153, 155 phosphorylation................... 38, 202, 207, 208, 278, 284 profiling............ 30, 35, 53, 130, 185, 214, 322, 323, 350 protein interaction (PPI)..................105–125, 165–183, 185–199, 215, 240, 244, 276, 303–317, 312, 317, 337, 352, 358, 359, 361 slide............................................................216, 293–295 solution........................................ 95, 108, 109, 297, 332 synthesis................................... 156, 186, 192, 195, 198, 241, 243, 245–246 translation................................................................. 231 Protein kinase B (Akt)...........................................40, 51, 52 Proteomics...........................29, 37, 129, 227, 228, 257–271, 278, 279, 349–362 ProtMAT.........................................................341–343, 345 Protoarray™........... 83, 84, 85, 202, 207, 338, 341, 342, 344 PR-stable-bait assay....................................................... 182 PR-trans-bait assay......................................................... 182

Q QuadriPERM®..........................93, 108, 118, 120, 145, 171, 176, 181, 182, 189, 205 Quantum yield....................................................... 306, 307

R R1881...................................................... 168, 169, 176, 177 Rabbit reticulocyte lysate........................156, 190, 192–194, 228, 230, 237, 243 Radiolabel........................................ 202, 204, 208, 209, 220 Recombinant antigen arrays............................. 79–102, 130 Recombinant lectins................................................... 67–77 Recombinant protein expression...........................82, 91–93, 95–98, 228, 230 Recombination................................................179, 257, 259 Referenced fluorescence intensity (RFI)..................... 49–51 Referencing...........................21, 41, 43, 46, 47, 49, 77, 153, 309, 316, 330, 346 Regeneration............109, 120, 125, 322, 323, 328, 330, 331 Regions of interest (ROI)................................291, 329–331 Reporter........ 30, 35, 166–169, 172–176, 179, 180, 182, 277 Reverse-phase protein arrays.......................................... 227 Reverse phase protein microarray........................... 275–299 Reverse protein arrays (RPA)......................37–53, 129–130

Reverse transfection........................................169, 171, 172, 175–176, 178–181 RFI. See Referenced fluorescence intensity ROI. See Regions of interest RPA. See Reverse protein arrays

S Salmonella.............................................38, 40, 42–43, 51–53 Salmonella-containing vacuole (SCV).............................. 40 SAM. See Significance analysis of microarrays Sandwich style immunoassay.............................................. 5 ScanArray Express.......................................................... 146 SciFlexArrayer........................................................ 171, 174 sciFlexArrayer piezo–dispensing system S5................... 174 Screening.......................................................................15–27 SCV. See Salmonella-containing vacuole SDS-PAGE. See Sodium dodecyl sulfate polyacrylamide gel electrophoresis Selection marker ampicillin.......................................................... 258, 270 carbenicillin...................................................... 260, 261 chloramphenicol..........................................40, 187, 260 SELEX. See Systematic evolution of ligands by exponential enrichment Sensorgram......................................................330, 331, 332 Sequencing..........................18, 58, 60, 62–65, 82, 179, 186, 190–192, 239, 241, 250–252, 257–260, 262, 263, 267–270, 350–353, 356–359, 361 Serum............. 3–9, 15–27, 29–30, 34, 40, 81–102, 121, 130, 132, 134, 138, 144, 150, 154–159, 227, 228, 230, 232, 235, 240–243, 246, 279, 297, 322, 325, 332, 350, 357 screening............................ 153, 155, 159, 240, 244, 245 screening study..........................................151, 158–159 Shigella.............................................................................. 40 Signal corrections................................................... 178, 339 Signaling.................. 15, 37–38, 40, 42–44, 49, 51, 155, 214, 228, 229, 244, 278–280, 284, 337, 358–360 Significance analysis of microarrays (SAM)...................................338, 340, 342, 346 Single nucleotide polymorphisms (SNPs)......101, 310, 311, 351–356, 354, 356, 358, 359, 361 Single-stranded DNA (ssDNA)............................58, 59, 62 SNPs. See Single nucleotide polymorphisms Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE)......................... 74, 76, 77, 95, 96, 98, 244, 246, 247, 252 Software..................... 10, 26, 42, 60–62, 70, 75, 87, 93, 101, 102, 121, 134, 137, 142, 144, 146, 147, 172, 197, 204, 207, 209, 249, 260, 262, 280, 285, 286, 288–296, 324, 325, 327, 328, 330, 331, 338, 339, 341–342, 344, 346 Solid phase peptide synthesis..........................105, 112, 116

Protein Microarray for Disease Analysis 372  Index

  

Solvents dichloromethane (DCM)..................107, 108, 114, 115 dimethylformamide (DMF).............106, 107, 109–114, 123, 125 dimethylsulphoxide (DMSO).........................31, 69, 74, 107, 113, 178, 323, 325 ethanol (EtOH)............................ 76–77, 106, 107, 109, 111–113, 115, 119, 123, 173, 231, 262, 268, 270, 297 methanol (MeOH)................... 106, 107, 109, 111–113, 115, 119, 120, 123, 244, 248, 280 N-methylpyrrolidone (NMP)...................106, 112, 113, 115–117 SopB........................................................................... 40, 51 SopE........................................................................... 40, 51 SopE2......................................................................... 40, 51 SPOT synthesis cellulose membranes..........................105–108, 110–115 coupling solutions............................................. 105, 112 SPOT macroarray..............................106–108, 110–115 SPOT robot.....................................................41, 44, 46 SPOT technology..............................105–106, 114, 115 Spotting................. 17, 19, 43–47, 53, 71, 93, 108, 110, 113, 116–118, 124, 125, 153, 173, 174, 192, 201–202, 213, 240, 323–325 microplates................................................41, 44, 46–47 SPR. See Surface plasmon resonance ssDNA. See Single-stranded DNA Stable transfection.......................................................... 182 Staining bromophenol blue (BPB).......................................... 107 Statistical analysis false discovery rate............................................ 158, 341 overfitting data..................................................... 6, 158 Steady-state fluorescence................................................ 309 Stealth microarray printhead............................................ 93 Stealth micro spotting prints............................................ 93 Stock solution..............................33, 46, 109, 112, 178, 246, 247, 260 Storage amino-acid solutions................................................. 112 membranes............................................................... 123 microarray slides....................................................... 118 Streptavidin....................... 6, 8, 10, 16, 18, 32, 35, 116, 143, 146, 186, 229, 235, 238, 242, 252, 277, 279–283, 285, 313–316 Streptavidin-HRP........................... 244, 248, 252, 281, 283 Stripping.......................................... 109, 120, 260, 279, 298 Sucrose.............................108, 171, 173, 176, 244, 258, 260 Supernatant................. 9, 44–46, 53, 74, 76, 83, 95–97, 102, 123, 134, 233, 235, 240, 247, 268, 328, 330 Surface plasmon resonance (SPR) angle scan................................................................. 329 applications............................................................... 322 binding......................................................321, 327–330 blocking.....................................................323, 324, 327

data analysis...............................................324, 330–331 equipment................................................................. 324 imaging instruments......................................... 325, 327 materials....................................................325, 326, 329 printing arrays................................................... 324–327 region of interest (ROI).................................... 329–330 sample preparation............................................ 322, 325 Suspension bead array................................................ 29–36 SV40............................................................................... 181 Systematic evolution of ligands by exponential enrichment (SELEX)......................................................... 58

T T-cell epitope.....................................................87, 357, 358 T7 expression..........................................154, 156, 231, 250 T24 human bladder cancer cell line.........130, 131, 134, 145 Time-resolved fluorescence.....................309, 311, 313, 316 Tissue culture...........................9, 40, 43, 134, 240, 259, 324 Transcription.................................. 154, 156, 166, 175, 179, 186, 187, 189, 191, 228, 231, 242–245, 250, 251, 278, 322, 359 Transcriptional activating domain (AD)................ 166, 179 Transfection..................... 166, 167, 170–176, 178–182, 241 Transformation........................73, 74, 76, 83, 144, 147, 190, 259, 261, 262, 266–267, 345–346 Translocated bacterial effectors................................... 38, 40 Transplantation...............................................84–85, 87–88 Triple-transfection.......................................................... 180 Troglitazone (TGZ)................................323, 325, 328, 331 TSA. See Tyramide signal amplification Tumor antigen....................85, 237, 351, 352, 356–358, 361 Tumor-specific mutation........................................ 353–357 Two hybrid............................................................. 165–183 Type III secretion............................................................. 38 Type III secretion effector................................................ 38 Type IV secretion............................................................. 38 Tyramide signal amplification (TSA)..............155, 157, 217

U Ubiquitin (Ub)................................................214, 217–219 E3 ligase..............................................38, 214, 217, 219 Universal cloning system................................................ 258 Urea..................................................91, 92, 98, 109, 123, 131

V Vascular cell adhesion molecule–1 (VCAM–1).............. 6, 7 VCAM–1. See Vascular cell adhesion molecule–1 VECTABOND™ reagent......................171, 173, 180, 181 Vectors....................... 68, 71, 73, 76, 95, 169, 170, 172, 178, 179, 186, 187, 191, 192, 228, 229, 231, 234, 236, 237, 241–242, 250, 251, 257–259, 261–263, 266, 269, 270 Viral antigens................................................... 90, 235–237 Virulence factor.....................................................38, 39, 42 VPL slides.............................................................. 173, 180

Protein Microarray for Disease Analysis 373 Index     



W

Z

Western blotting....................................... 11, 52, 82, 85, 95, 96, 98, 106, 240, 242, 244, 246–250, 252, 297

Z-factor.......................................................................... 211 Z-score............................................................211, 338–342 ZsGreen...........................................................171, 178, 179