ME T H O D S
IN
MO L E C U L A R BI O L O G Y
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
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Peptidomics Methods and Protocols
Edited by
Mikhail Soloviev Royal Holloway, University of London, Egham, UK
Editor Mikhail Soloviev Royal Holloway University of London School of Biological Sciences Egham Hill Egham, Surrey United Kingdom TW20 0EX [email protected]
ISSN 1064-3745 e-ISSN 1940-6029 ISBN 978-1-60761-534-7 e-ISBN 978-1-60761-535-4 DOI 10.1007/978-1-60761-535-4 Library of Congress Control Number: 2009940608 © Humana Press, a part of Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Humana Press, c/o Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com
Preface Despite being known and studied for years, peptides have never before attracted enough attention to necessitate the invention of the term “Peptidomics” in order to specify the study of the complement of peptides from a cell, organelle, tissue or organism. This volume presents a comprehensive range of analytical techniques for analysis of the peptide contents of complex biological samples. The emphasis is often on higher throughput techniques, suitable for the analysis of large numbers of peptides typically present in the peptidomes or other complex biological samples. A wide range of methods is presented, covering all stages of peptidomic research including, where applicable, organism handling, tissue and organ dissection, cellular and subcellular fractionation, peptide extraction, fractionation and purification, structural characterisation, molecular cloning and sequence analysis. In addition to this, a selection of methods suitable for quantification, display, immunochemical and functional analysis of peptides and proteins are presented. The methods and techniques covered in this volume encompass a number of species ranging from bacteria to man and include model organisms such as Caenorhabditis elegans, Drosophila melanogaster and Mus musculus. Strong emphasis is placed on data analysis, including mass spectra interpretation and in silico peptide prediction algorithms. Where relevant, the peptidomic approaches are compared to the proteomic methods. Here is a snapshot of the practical information, peptidomic methods and other related protocols included in this volume: Target organisms and samples covered: Bacteria (Chapter 2), hydra (Chapter 21), nematode (Chapter 3), mollusc (Chapter 4), crab (Chapter 5), spider venoms (Chapters 6 and 7), insects (Chapters 8, 9, 10, 11, 25), amphibians (Chapters 12, 13, 14), rodents (Chapters 15, 16, 17, 18), samples of human origin (Chapters 19, 20, 22, 23) and plants (Chapter 26). Peptide extraction and Liquid chromatography fractionation methods (mostly size exclusion, ion exchange, reverse-phase modes or their combinations) can be found in Chapters 2, 3, 4, 5, 6, 7, 8, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22). These include OFFline and ON-line techniques. The former are often used with MALDI-MS detection (e.g. Chapters 3, 4, 5, 6, 7, 8, 12, 15, 16, 19) whilst the latter more generally with single or multidimensional hyphenated LCN -MSN techniques (e.g. Chapters 2, 3, 15, 17, 18, 21). Other separation and fractionation methods covered include microdialysis of live animals (Chapter 5), SDS-PAGE (Chapters 6, 18), magnetic bead based purification (Chapter 20) and solid-phase extraction (Chapters 2, 6, 12, 19, 22). Affinity peptide detection including anti-peptide antibody development and characterisation, Affinity peptidomics, ELISA and microarray affinity assays are covered in Chapters 22, 23 and 24. Mass spectrometry techniques include MALDI-TOF MS (e.g. Chapters 3, 6, 7, 8, 12, 16, 19, 20), MALDI-TOF with PSD (Chapter 8), MALDI-TOF MS/MS (e.g. Chapters 4, 6, 15, 21); ESI-MS/MS techniques (Chapters 3, 6, 16, 17, 18) or high-resolution FTMS (Chapter 2). Direct MALDI-MS peptide profiling from cells and tissues is described in Chapters 9, 10 and 11.
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The description of functional assays can be found in Chapters 7, 14 and 21. Of particular interest in this respect is Chapter 21, where functional activity of the peptides is assessed through the analysis of mRNA transcription levels changes in response to the peptide application. That chapter contains a selection of protocols for peptide extraction, fractionation and functional testing using a combination of molecular biology techniques, cellular and morphological assays. Molecular cloning of peptide cDNAs and the associated techniques are described in Chapters 13 and 14. Issues related to peptide sequence analysis are addressed in many chapters dealing with MS spectra interpretation, but of special interest in this respect are Chapters 25 and 26, dealing with in silico peptide prediction techniques and Chapter 20 which includes a section on bioinformatics analysis of peptide expression profiling data. Differential peptide expression issues are also covered in Chapter 2. Peptidomics is 10-years old. My congratulations go to all scientists who have created and developed the science of Peptidomics through their research and especially those who found time to contribute their invaluable know-how in the form of methods and protocols for inclusion in this volume. Peptidomics: Methods and Protocols is designed to complement previously published titles in the Methods in Molecular BiologyTM series, which focused on protein analysis. This volume will help the beginner to become familiar with this fascinating field of research and will provide scientists at all levels of expertise with easy-to-follow practical advice needed to set up and carry out analysis of the peptide contents of complex biological samples. Royal Holloway University of London December 2009
Mikhail Soloviev
Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
Peptidomics: Divide et Impera . . . . . . . . . . . . . . . . . . . . . . . . . . Mikhail Soloviev
3
SECTION II FROM BACTERIA TO MEN . . . . . . . . . . . . . . . . . . . . . . . .
11
SECTION I 1.
2.
3.
Performing Comparative Peptidomics Analyses of Salmonella from Different Growth Conditions . . . . . . . . . . . . . . . . . Joshua N. Adkins, Heather Mottaz, Thomas O. Metz, Charles Ansong, Nathan P. Manes, Richard D. Smith, and Fred Heffron Approaches to Identify Endogenous Peptides in the Soil Nematode Caenorhabditis elegans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Steven J. Husson, Elke Clynen, Kurt Boonen, Tom Janssen, Marleen Lindemans, Geert Baggerman, and Liliane Schoofs
13
29
4.
Mass Spectrometric Analysis of Molluscan Neuropeptides Ka Wan Li and August B. Smit
. . . . . . . . . . . .
49
5.
Monitoring Neuropeptides In Vivo via Microdialysis and Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heidi L. Behrens and Lingjun Li
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6.
Protocols for Peptidomic Analysis of Spider Venoms . . . . . . . . . . . . . . . Liang Songping
7.
Purification and Characterization of Biologically Active Peptides from Spider Venoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander A. Vassilevski, Sergey A. Kozlov, Tsezi A. Egorov, and Eugene V. Grishin
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8.
MALDI-TOF Mass Spectrometry Approaches to the Characterisation of Insect Neuropeptides . . . . . . . . . . . . . . . . . 101 Robert J. Weaver and Neil Audsley
9.
Direct MALDI-TOF Mass Spectrometric Peptide Profiling of Neuroendocrine Tissue of Drosophila . . . . . . . . . . . . . . . . . . . . . 117 Christian Wegener, Susanne Neupert, and Reinhard Predel
10.
Direct Peptide Profiling of Brain Tissue by MALDI-TOF Mass Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Joachim Schachtner, Christian Wegener, Susanne Neupert, and Reinhard Predel
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11.
Peptidomic Analysis of Single Identified Neurons . . . . . . . . . . . . . . . . . 137 Susanne Neupert and Reinhard Predel
12.
Identification and Analysis of Bioactive Peptides in Amphibian Skin Secretions . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 J. Michael Conlon and J´erˆome Leprince
13.
An Efficient Protocol for DNA Amplification of Multiple Amphibian Skin Antimicrobial Peptide cDNAs . . . . . . . . . . . . . . . . . . . . . . . . 159 Shawichi Iwamuro and Tetsuya Kobayashi
14.
Combined Peptidomics and Genomics Approach to the Isolation of Amphibian Antimicrobial Peptides . . . . . . . . . . . . . . . . . . . . . . . 177 Ren Lai
15.
Identification and Relative Quantification of Neuropeptides from the Endocrine Tissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Kurt Boonen, Steven J. Husson, Bart Landuyt, Geert Baggerman, Eisuke Hayakawa, Walter H.M.L. Luyten, and Liliane Schoofs
16.
Peptidome Analysis of Mouse Liver Tissue by Size Exclusion Chromatography Prefractionation . . . . . . . . . . . . . . . . . . . . . . . . . 207 Lianghai Hu, Mingliang Ye, and Hanfa Zou
17.
Rat Brain Neuropeptidomics: Tissue Collection, Protease Inhibition, Neuropeptide Extraction, and Mass Spectrometric Analysis . . . . . . . . . . . . 217 Robert M. Sturm, James A. Dowell, and Lingjun Li
18.
Quantitative Neuroproteomics of the Synapse . . . . . . . . . . . . . . . . . . 227 Dinah Lee Ramos-Ortolaza, Ittai Bushlin, Noura Abul-Husn, Suresh P. Annangudi, Jonathan Sweedler, and Lakshmi A. Devi
19.
Peptidomics Analysis of Lymphoblastoid Cell Lines Anne Fogli and Philippe Bulet
20.
Peptidomics: Identification of Pathogenic and Marker Peptides . . . . . . . . . . 259 Yang Xiang, Manae S. Kurokawa, Mie Kanke, Yukiko Takakuwa, and Tomohiro Kato
. . . . . . . . . . . . . . . 247
SECTION III TOOLS AND APPROACHES . . . . . . . . . . . . . . . . . . . . . . . . 273 21.
Peptidomic Approaches to the Identification and Characterization of Functional Peptides in Hydra . . . . . . . . . . . . . . . . . . . . . . . . . 275 Toshio Takahashi and Toshitaka Fujisawa
22.
Immunochemical Methods for the Peptidomic Analysis of Tachykinin Peptides and Their Precursors . . . . . . . . . . . . . . . . . . . 293 Nigel M. Page and Nicola J. Weston-Bell
23.
Affinity Peptidomics: Peptide Selection and Affinity Capture on Hydrogels and Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Fan Zhang, Anna Dulneva, Julian Bailes, and Mikhail Soloviev
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24.
In Situ Biosynthesis of Peptide Arrays . . . . . . . . . . . . . . . . . . . . . . . 345 Mingyue He and Oda Stoevesandt
25.
Bioinformatic Approaches to the Identification of Novel Neuropeptide Precursors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357 Elke Clynen, Feng Liu, Steven J. Husson, Bart Landuyt, Eisuke Hayakawa, Geert Baggerman, Geert Wets, and Liliane Schoofs
26.
Bioinformatic Identification of Plant Peptides . . . . . . . . . . . . . . . . . . . 375 Kevin A. Lease and John C. Walker
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
Contributors NOURA ABUL-HUSN • Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY, USA JOSHUA N. ADKINS • Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA SURESH P. ANNAGUDI • Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA CHARLES ANSONG • Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA NEIL AUDSLEY
•
The Food and Environment Research Agency, Sand Hutton, York, UK
GEERT BAGGERMAN • ProMeta, Interfacultary Center for Proteomics and Metabolomics, K.U. Leuven, Leuven, Belgium JULIAN BAILES • School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK HEIDI L. BEHRENS Madison, WI, USA
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Department of Chemistry, University of Wisconsin-Madison,
KURT BOONEN • Functional Genomics and Proteomics Research Unit, Department of Biology, K.U. Leuven, Leuven, Belgium PHILIPPE BULET
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TIMC-IMAG, UMR 5525, Domaine de Chosal, Archamps, France
ITTAI BUSHLIN • Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY, USA ELKE CLYNEN • Functional Genomics and Proteomics, Department of Biology, K.U. Leuven, Leuven, Belgium J. MICHAEL CONLON • Department of Biochemistry, Faculty of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, UAE LAKSHMI A. DEVI • Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY, USA JAMES A. DOWELL • Department of Chemistry, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA ANNA DULNEVA • School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK TSEZI A. EGOROV • Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
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ANNE FOGLI • GreD UMR INSERM 931 CNRS 6142, Facult´e de M´edecine, Clermont-Ferrand, France TOSHITAKA FUJISAWA Germany
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Institute of Zoology, University of Heidelberg, Heidelberg,
EUGENE V. GRISHIN • Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia EISUKE HAYAKAWA • Functional Genomics and Proteomics Research Unit, Department of Biology, K.U. Leuven, Leuven, Belgium MINGYUE HE
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The Babraham Institute, Cambridge, UK
FRED HEFFRON • Department of Molecular Microbiology and Immunology, Oregon Health and Sciences University, Portland, OR, USA LIANGHAI HU • Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R&A Centre, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China STEVEN J. HUSSON • Functional Genomics and Proteomics, Department of Biology, K.U. Leuven, Leuven, Belgium SHAWICHI IWAMURO • Department of Biology, Faculty of Science, Toho University, Funabashi, Chiba, Japan TOM JANSSEN • Functional Genomics and Proteomics, Department of Biology, K.U. Leuven, Leuven, Belgium MIE KANKE • Clinical Proteomics and Molecular Medicine, St. Marianna University Graduate School of Medicine, Kawasaki, Japan TOMOHIRO KATO • Clinical Proteomics and Molecular Medicine, St. Marianna University Graduate School of Medicine, Kawasaki, Japan TETSUYA KOBAYASHI • Department of Regulation Biology, Faculty of Science, Saitama University, Saitama, Japan SERGEY A. KOZLOV • Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia MANAE S. KUROKAWA • Clinical Proteomics and Molecular Medicine, St. Marianna University Graduate School of Medicine, Kawasaki, Japan REN LAI • Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China BART LANDUYT • Functional Genomics and Proteomics Research Unit, Department of Biology, K.U. Leuven, Leuven, Belgium KEVIN A. LEASE • Division of Biological Sciences and Bond Life Sciences Centre, University of Missouri, Columbia, MO, USA ˆ ´ OME LEPRINCE • European Institute for Peptide Research (IFRMP 23), INSERM JER U-413, UA CNRS, University of Rouen, Mont-Saint-Aignan, France
Contributors
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KA WAN LI • Department of Molecular and Cellular Neurobiology, Centre for Neurogenomics and Cognitive Research, Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, The Netherlands LINGJUN LI • Department of Chemistry, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA MARLEEN LINDEMANS • Functional Genomics and Proteomics, Department of Biology, K.U. Leuven, Leuven, Belgium FENG LIU • Data Analysis and Modeling Group, Transportation Research Institute, Hasselt University, Diepenbeek, Belgium WALTER H.M.L. LUYTEN Leuven, Leuven, Belgium
•
Department Woman and Child, Faculty of Medicine, K.U.
NATHAN P. MANES • Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA THOMAS O. METZ • Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA HEATHER MOTTAZ • Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA SUSANNE NEUPERT • Institute of General Zoology and Animal Physiology, Friedrich-Schiller-University, Jena, Germany NIGEL M. PAGE • School of Life Sciences, Kingston University London, Kingston-upon-Thames, Surrey, UK REINHARD PREDEL • Institute of General Zoology and Animal Physiology, Friedrich-Schiller-University, Jena, Germany DINAH LEE RAMOS-ORTOLAZA • Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY, USA JOACHIM SCHACHTNER • Department of Biology, Animal Physiology, Philipps-University, Marburg, Germany LILIANE SCHOOFS • Functional Genomics and Proteomics Research Unit, Department of Biology, K.U. Leuven, Leuven, Belgium AUGUST B. SMIT • Department of Molecular and Cellular Neurobiology, Centre for Neurogenomics and Cognitive Research, Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, The Netherlands RICHARD D. SMITH • Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA MIKHAIL SOLOVIEV • School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, UK LIANG SONGPING China
•
ODA STOEVESANDT
College of Life Sciences, Hunan Normal University, Changsha, •
Babraham Bioscience Technologies, Cambridge, UK
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Contributors
ROBERT M. STURM • Department of Chemistry, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA JONATHAN SWEEDLER • Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA TOSHIO TAKAHASHI
•
Suntory Institute for Bioorganic Research, Osaka, Japan
YUKIKO TAKAKUWA • Clinical Proteomics and Molecular Medicine, St. Marianna University Graduate School of Medicine, Kawasaki, Japan ALEXANDER A. VASSILEVSKI • Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia JOHN C. WALKER • Division of Biological Sciences and Bond Life Sciences Center, University of Missouri, Columbia, MO, USA ROBERT J. WEAVER UK
•
The Food and Environment Research Agency, Sand Hutton, York,
CHRISTIAN WEGENER • Emmy Noether Neuropeptide Group, Animal Physiology, Philipps-University, Marburg, Germany NICOLA J. WESTON-BELL • Genetic Vaccine Group, Cancer Sciences Division, Southampton General Hospital, University of Southampton School of Medicine, Southampton, Hampshire, UK GEERT WETS • Data Analysis and Modeling Group, Transportation Research Institute, Hasselt University, Diepenbeek, Belgium YANG XIANG • Clinical Proteomics and Molecular Medicine, St. Marianna University Graduate School of Medicine, Kawasaki, Japan MINGLIANG YE • Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R&A Centre, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China FAN ZHANG Surrey, UK
•
School of Biological Sciences, Royal Holloway University of London, Egham,
HANFA ZOU • Key Laboratory of Separation Science for Analytical Chemistry, National Chromatographic R&A Centre, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
Section I Introduction
Chapter 1 Peptidomics: Divide et Impera Mikhail Soloviev Abstract The term “peptidomics” can be defined as the systematic analysis of the peptide content within a cell, organelle, tissue or organism. The science of peptidomics usually refers to the studies of naturally occurring peptides. Another meaning refers to the peptidomics approach to protein analysis. An ancient Roman strategy divide et impera (divide and conquer) reflects the essence of peptidomics. Most effort in this field is spent purifying and dividing the peptidomes, which consist of tens, hundreds or sometimes thousands of functional peptides, followed by their structural and functional characterisation. This chapter introduces the concept of peptidomics, outlines the range of methodologies employed and describes key targets – the peptide groups which are often sought after in such studies. Key words: Peptidomic, peptidome, peptide, functional peptide, methods.
1. Introduction Polypeptides, being short stretches of amino acids or small proteins, occupy a strategic position between proteins and amino acids and play, for the most part, fundamental roles by regulating the vast majority of biological processes in the animal kingdom. Whilst perhaps sometimes overlooked, the importance of the regulatory role of peptides is truly great and hard to overestimate. Peptides have in fact been the focus of much research for decades with the first successful attempts to analyse peptide content of various biological samples (including from urine, blood and brain tissues) having been reported over 60 years ago. These relied mostly on chromatography, including two-dimensional liquid chromatography (LC), or mass spectrometry (MS) techniques. Recent advances in MS and further developments in liquid chromatography, including nano-LC (1) and associated “omics” M. Soloviev (ed.), Peptidomics, Methods in Molecular Biology 615, DOI 10.1007/978-1-60761-535-4 1, © Humana Press, a part of Springer Science+Business Media, LLC 2010
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techniques, resulted in dramatic improvements in the sensitivity and high throughput of protein and peptide analyses (2) and generated unprecedented growth in the number of relevant publications (Fig. 1.1). 0.8%
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Fig. 1.1. Peptidomics publications since 1999. The bars represent the number of publications in PubMed containing “peptidomics OR peptidomic OR peptidome” normalised to the total number of publications added to PubMed each year. Proteomics publications in PubMed (found similarly) follow the same trend (solid line). Vertical axes show normalised data (in %) for proteomics papers (left) and peptidomics papers (right). Total number of publications on 1 January 2009 was 28,273 (proteomics) and 246 (peptidomics).
2. Peptidomics of Naturally Occurring Peptides and Peptide Pools
Similarly to “proteomics”, the term “peptidomics” can be defined as the systematic analysis of the peptide content within an organism, tissue or cell (3) in order to determine peptides’ identity, quantity, structure and function. Such interpretation made its public debut at the 2nd International Seminar on the Enabling Role of MS in 1999 (4), before finally appearing in press in 2001 in research papers by Peter D.E.M. Verhaert et al. (5), Peter Schulz-Knappe et al. (6) and Elke Clynen et al. (7). The discipline of peptidomics focuses on peptides that often display biological activity such as hormones, cytokines, toxins, neuropeptides and alike, which are generated from larger precursors, as well as biomarker-type peptides that may not have any bioactivity but are indicative of a particular pathology, for example the up/downregulation of many serum peptides that result from proteolysis.
Peptidomics: Divide et Impera
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Peptidomics is in its infancy relative to other “omics” (28,273 papers on PubMed for a “proteomics” search compared to just 246 for “peptidomics” as of January 1st, 2009) but is expanding rapidly (Fig. 1.1).
3. Peptidomics Approach to Proteomics
4. Peptidomics: The Methodologies
In parallel, and independently of the peptidomics definition given in (5–7), another meaning was introduced by Barry et al. (8) in relation to the analysis of peptide pools (of biological fluids, tissues or cells) obtained by means of proteolytic digestion of these samples and in particular using affinity-based analysis (hence “Affinity peptidomics”) e.g. in the form of protein arrays (9, 10). Since biologically occurring peptides (whether biologically active or not) are strictly speaking also the products of proteolysis (e.g. insulin pre-pro-insulin, or biologically active peptides obtained through “non-specific” proteolysis of e.g. haemoglobin), both definitions of “peptidomics” are therefore very similar in that they refer to the analysis of partially or fully proteolytically digested proteins, i.e. peptides. And finally, to acknowledge everyone involved in the birth of the “peptidomics” as a separate field of chemical biology, we should mention a Germany-based company “BioVisioN AG” which filed a trademark “peptidomics” in 1999 to cover “Chemicals used in science, in particular for analysis, other than for medical or veterinary purposes”; “Medical, veterinary and pharmaceutical products” as well as the “Scientific and industrial research; conducting medical and non-medical analyses; services in the field of diagnostics; development of pharmaceutical active substances; purchasing, licensing and exploitation of intellectual property” (11).
In the past, the majority of separation techniques used in protein and peptide analysis relied on their physical properties, such as protein or peptide size, shape, polarity, pI, the distribution of ionisable, polar and non-polar groups on the molecule surface, and their affinity towards specific or non-specific affinity capture reagents. Modern separation techniques rely on a combination of isoelectric focusing, electrophoretic separation and a great variety of liquid chromatography techniques, often linked together to yield two- or three-dimensional separation approaches, and
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frequently backed up by serious automation. Highly parallel analysis is often attempted through miniaturisation (12) and the use of chip-based techniques (13–16) or the Agilent 2100 Bioanalyzer (www.chem.agilent.com). The inherent heterogeneity of the proteins’ and to a lesser degree peptides’ physical properties which underlies all of the above separation options is, at the same time, the inherent problem of any highly parallel protein analysis. A single universal system suitable for extraction and separation (let alone functional analysis) of all classes of proteins is yet to be reported. Unlike proteins, the peptides are often less heterogeneous in their physico-chemical properties and therefore the complete peptidomic analysis of samples, tissues and in some cases whole organisms is more straightforward than proteomic analysis. In addition to physical methods of analysis and separation, chemical biology offers a number of other approaches, which rely directly or indirectly on chemical modifications and separation principles based on chemical properties of proteins and peptides. Chemical modification of the side chains of proteins and peptides was first reported many decades ago (see 17 for a review) and has been used widely since for protein modifications, labelling and cross-linking, but not so widely for protein separations – the latter because of the issues related to the availability and surface exposure of the reactive groups. Unlike proteins, peptides offer a unique chance to apply chemical selection techniques because of the lack of complex secondary structure and virtually complete exposure to solvent of all of the reactive groups. A number of reports utilising chemical biology approach to peptide separation and analysis have been published more recently. In most cases these describe various group-specific labelling procedures, often linked to peptide quantification (18, 19) as well as chemical depletion approaches (17, 20). Among the other “omics” technologies and approaches, “peptidomics” is the most comparable to “Proteomics” and although the terms are not synonymous, the underlying techniques and approaches are almost identical. For example, MS, a cornerstone of modern proteomics, in most cases actually analyses peptides (obtained through proteolytic digestion of proteins) or their fragments (obtained through e.g. CID), not proteins. The difference between the terms “peptidomics” and the “proteomics” is therefore blurred, especially if “methods” are being considered.
5. Peptidomics: The Targets Target-wise, the peptidomics research is often focused, although not always, on studying peptides formed in vivo by proteolysis of specialised or non-specialised precursor proteins (often bioactive
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peptides), rather than “artificially” or in vitro-produced peptides. The range of biological activities displayed by naturally occurring peptides is truly remarkable; it ranges from toxins that can paralyse or kill to peptides that have the ability to heal. The venoms of arthropods such as spiders and scorpions, as well as other species such as cone snails, comprise a vast number of neuromodulatory peptides that are capable of serious harm, but also serve as a highly useful point to discover new drugs such as painkillers (21). The identification and functional characterisation of peptides from all species including humans is crucial in the discovery of novel biomarkers and drug targets, and may yield novel therapeutic agents such as peptide-based vaccine Glatiramer acetate (GA) (Copaxone) used for the treatment of relapsing and remitting cases of multiple sclerosis (22). The suitability of peptides as biomarkers stems from the fact that they are present in all body fluids, cells and tissues (23), and many approaches focus on identifying them from such samples (24, 25). Peptides also play crucial roles in innate and adaptive immune responses by forming complexes with MHC-I, MHC-II and T cells where they stimulate defensive immune responses (26–28). The importance of peptides in cell-to-cell communication underpins the importance of peptidomics in understanding multiple pathologies that result from these communication processes going wrong. The peptide content of biological fluids, such as urine for example, can be used to produce a complete peptidomic fingerprint of an individual’s health (29). The evolutionary evidence to support the importance of peptides in such widespread biological roles is evident when one examines the conservation of peptide families across species. The Tachykinin peptides for example, the largest known neuropeptide family, are found in vertebrates, protochordates and invertebrates (30). On the other end of the scale, even primitive microorganisms rely on peptide signalling, such as for example bacterial quorum sensing (31, 32) and yeast mating factors (33). The following chapters provide a comprehensive guide to peptidomics methods and applications, spanning a range of species from bacteria to man and covering a wide range of relevant methods from basic biochemistry techniques to in silico tools and protocols. References 1. Chervet, J.P., Ursem, M., and Salzmann, J.B. (1996) Instrumental requirements for nanoscale liquid chromatography. Anal. Chem. 68, 1507–1512. 2. Quadroni, M. and James, P. (1999) Proteomics and automation. Electrophoresis 20, 664–677.
3. Schrader, M. and Schulz-Knappe, P. (2001) Peptidomics technologies for human body fluids. Trends Biotechnol. 19, S55–S60. 4. Verhaert, P., Vandesande, F., and De Loof, A. (1999) Automated analysis of the peptidome. No longer science fiction. In: 2nd
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5.
6.
7.
8.
9.
10. 11. 12.
13.
14.
15.
16.
International Seminar on the Enabling Role of MS in Manchester. Verhaert, P., Uttenweiler-Joseph, S., de Vries, M., Loboda, A., Ens, W., and Standing, K.G. (2001) Matrix-assisted laser desorption/ionization quadrupole time-of-flight mass spectrometry: an elegant tool for peptidomics. Proteomics 1, 118–131. Schulz-Knappe, P., Zucht, H.D., Heine, G., J¨urgens, M., Hess, R., and Schrader, M. (2001) Peptidomics: the comprehensive analysis of peptides in complex biological mixtures. Comb. Chem. High Throughput Screen 4, 207–217. Clynen, E., Baggerman, G., Veelaert, D., Cerstiaens, A., Van der Horst, D., Harthoorn, L., Derua, R., Waelkens, E., De Loof, A., and Schoofs, L. (2001) Peptidomics of the pars intercerebralis–corpus cardiacum complex of the migratory locust, Locusta migratoria. Eur. J. Biochem. 268, 1929–1939. Scrivener, E., Barry, R., Platt, A., Calvert, R., Masih, G., Hextall, P., Soloviev, M., and Terrett, J. (2003) Peptidomics: a new approach to affinity protein microarrays. Proteomics 3, 122–128. Barry, R., Diggle, T., Terrett, J., and Soloviev, M. (2003) Competitive assay formats for high-throughput affinity arrays. J. Biomol. Screen. 8, 257–263. Barry, R. and Soloviev, M. (2004) Quantitative protein profiling using antibody arrays. Proteomics 4, 3717–3726. Community Trade Mark No. 001274646; http://oami.europa.eu Marko-Varga, G., Nilsson, J., and Laurell, T. (2003) New directions of miniaturization within the proteomics research area. Electrophoresis 24, 3521–3532. Hoa, X.D., Kirk, A.G., and Tabrizian, M. (2007) Towards integrated and sensitive surface plasmon resonance biosensors: a review of recent progress. Biosens. Bioelectron. 23, 151–160. Kurosawa, S., Aizawa, H., Tozuka, M., Nakamura, M., and Park, J.W. (2003) Immunosensors using a quartz crystal microbalance. Meas. Sci. Technol. 14, 1882–1887. Lion, N., Rohner, T.C., Dayon, L., Arnaud, I.L., Damoc, E., Youhnovski, N., Wu, Z.Y., Roussel, C., Josserand, J., Jensen, H., Rossier, J.S., Przybylski, M., and Girault, H.H. (2003) Microfluidic systems in proteomics. Electrophoresis 24, 3533–3562. Lion, N., Reymond, F., Girault, H.H., and Rossier, J.S. (2004) Why the move
17.
18.
19.
20.
21. 22.
23.
24.
25.
26.
27.
28.
to microfluidics for protein analysis?. Curr. Opin. Biotechnol. 15, 31–37. Soloviev, M. and Finch, P. (2005) Peptidomics, current status. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 815, 11–24. Gygi, S.P., Rist, B., Gerber, S.A., Turecek, F., Gelb, M.H., and Aebersold, R. (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994–999. DeSouza, L., Diehl, G., Rodrigues, M.J., Guo, J., Romaschin, A.D., Colgan, T.J., and Siu, K.W. (2005) Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cICAT with multidimensional liquid chromatography and tandem mass spectrometry. J. Proteome Res. 4, 377–386. Soloviev, M., Barry, R., Scrivener, E., and Terrett, J. (2003) Combinatorial peptidomics: a generic approach for protein expression profiling. J. Nanobiotechnol. 1, 4. Rash, L.D. and Hodgson, W.C. (2002) Pharmacology and biochemistry of spider venoms. Toxicon 40, 225–254. Perumal, J., Filippi, M., Ford, C., Johnson, K., Lisak, R., Metz, L., Tselis, A., Tullman, M., and Khan, O. (2006) Glatiramer acetate therapy for multiple sclerosis: a review. Expert Opin. Drug Metab. Toxicol. 2, 1019–1029. Adermann, K., John, H., St¨andker, L., and Forssmann, W.G. (2004) Exploiting natural peptide diversity: novel research tools and drug leads. Curr. Opin. Biotechnol. 15, 599–606. Zimmerman, L.J., Wernke, G.R., Caprioli, R.M., and Liebler, D.C. (2005) Identification of protein fragments as pattern features in MALDI-MS analyses of serum. J. Proteome Res. 4, 1672–1680. Vidal, B.C., Bonventre, J.V., and I-Hong Hsu, S. (2005) Towards the application of proteomics in renal disease diagnosis. Clin. Sci. (Lond). 109, 421–430. Desjardins, M., Houde, M., and Gagnon, E. (2005) Phagocytosis: the convoluted way from nutrition to adaptive immunity. Immunol. Rev. 207, 158–165. Cresswell, P., Ackerman, A.L., Giodini, A., Peaper, D.R., and Wearsch, P.A. (2005) Mechanisms of MHC class I-restricted antigen processing and cross-presentation. Immunol. Rev. 207, 145–157. Van der Merwe, P.A. and Davis, S.J. (2003) Molecular interactions mediating T cell antigen recognition. Annu. Rev. Immunol. 21, 659–684.
Peptidomics: Divide et Impera 29. Metzger, J., Schanstra, J.P., and Mischak, H. (2009) Capillary electrophoresismass spectrometry in urinary proteome analysis: current applications and future developments. Anal. Bioanal. Chem. 393, 1431–1442. 30. Severini, C., Improta, G., FalconieriErspamer, G., Salvadori, S., and Erspamer, V. (2002) The tachykinin peptide family. Pharmacol. Rev. 54, 285–322.
31. Miller, M.B. and Bassler, B.L. (2001) Quorum sensing in bacteria. Annu. Rev. Microbiol. 55, 165–199. 32. Gibbs, R.A. (2005) Trp modification signals a quorum. Nat. Chem. Biol. 1, 7–8. 33. Kalkum, M., Lyon, G.J., and Chait, B.T. (2003) Detection of secreted peptides by using hypothesis-driven multistage mass spectrometry. Proc. Natl. Acad. Sci. USA. 100, 2795–2800.
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Section II From Bacteria to Men
Chapter 2 Performing Comparative Peptidomics Analyses of Salmonella from Different Growth Conditions Joshua N. Adkins, Heather Mottaz, Thomas O. Metz, Charles Ansong, Nathan P. Manes, Richard D. Smith, and Fred Heffron Abstract Host–pathogen interactions are complex competitions during which both the host and the pathogen adapt rapidly to each other in order for one or the other to survive. Salmonella enterica serovar Typhimurium is a pathogen with a broad host range that causes a typhoid fever-like disease in mice and severe food poisoning in humans. The murine typhoid fever is a systemic infection in which S. typhimurium evades part of the immune system by replicating inside macrophages and other cells. The transition from a foodborne contaminant to an intracellular pathogen must occur rapidly in multiple, ordered steps in order for S. typhimurium to thrive within its host environment. Using S. typhimurium isolated from rich culture conditions and from conditions that mimic the hostile intracellular environment of the host cell, a native low molecular weight protein fraction, or peptidome, was enriched from cell lysates by precipitation of intact proteins with organic solvents. The enriched peptidome was analyzed by both LC–MS/MS and LC–MS-based methods, although several other methods are possible. Pre-fractionation of peptides allowed identification of small proteins and protein degradation products that would normally be overlooked. Comparison of peptides present in lysates prepared from Salmonella grown under different conditions provided a unique insight into cellular degradation processes as well as identification of novel peptides encoded in the genome but not annotated. The overall approach is detailed here as applied to Salmonella and is adaptable to a broad range of biological systems. Key words: Comparative proteomics, Salmonella, mass spectrometry, peptide extraction, native proteases, accurate mass.
1. Introduction Controlled and coordinated protein degradation is critical for biological systems to function properly. The processes of protein degradation have roles in the cell-cycle (e.g., cyclins), signaling cascades (e.g., receptor shedding), protein maturation (e.g., M. Soloviev (ed.), Peptidomics, Methods in Molecular Biology 615, DOI 10.1007/978-1-60761-535-4 2, © Humana Press, a part of Springer Science+Business Media, LLC 2010
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plasminogen), and nutrient cycling. Despite the critical roles of protein degradation in biological processes, there have been surprisingly few systematic global analyses of protein degradation; the majority of studies that have been performed focus on eukaryotic systems. Specific protein degradation processes are very highly regulated in bacteria and determined by environmental conditions. Selective degradation of proteins followed by cannibalization of the released amino acids is the most efficient process for bacterial adaptation to changing metabolic requirements (1, 2). Indeed, the ability of a pathogen to survive in the host and exploit new resources is an essential virulence trait. The development of novel antibiotics against bacterial pathogens represents just a single discipline that can benefit from the elucidation of selective protein degradation processes. Recently our group developed an LC–MS/MS-based approach to globally profile a sub-set of peptides in a biological sample. Peptides, defined here, are short chains of amino acids linked via peptide bonds and are typically composed of fewer than 100 amino acids. The source of peptides in a biological system may result from short genes or through targeted degradation of proteins. Most of the peptides observed in this recent study were found to be the products of protein degradation (3); regardless of source we refer here to this naturally occurring peptide fraction as the “peptidome”. Interestingly, nearly 2% of the 4550 predicted proteins in S. typhimurium are annotated as being involved in protein degradation. Importantly, nearly all of these proteolytic proteins were identified in an early analysis of the S. typhimurium proteome, indicating that there is an upregulation of these functions under some of the growth conditions studied (4). The following is a step-by-step description of the sample preparation and analytical procedures that were used in determining the Salmonella peptidome. In addition, a discussion of the data analysis concerns that are unique to analyzing peptidomics samples is included.
2. Materials Unless stated otherwise, Materials were obtained from Sigma Aldrich, St. Louis, MO. 2.1. Cell Growth and Isolation
Cellgro Dulbecco’s Phosphate Buffered Saline (Mediatech, Mannasas, VA).
2.2. Lysis/Peptide Extraction Reagents
R 1. Water purified using a NANOpure0002 or equivalent system (≥ 18 M0002×cm, Barnstead International, Dubuque, Iowa).
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2. Ammonium bicarbonate, isopropanol, and methanol (Sigma Aldrich). 3. Protease inhibitor cocktail formulated for use with bacterial cell extracts (Cat. No. P8465, Sigma) 4. 0.1 mm zirconia/silica beads (BioSpec Bartlesville, OK) were used for cell lysis.
Products,
R (Bio-Rad, Hercules, CA) 5. 10–20% Tris-Tricine Ready Gels0002 R 0002 and GelCode Blue reagent from Pierce for SDS-PAGE analyses. R C-18 tips (100 ␮L) (Varian, Inc, Palo Alto, CA) for 6. OMIX0002 sample solid-phase extraction (SPE) clean-up prior to MS analysis.
7. SpeedVac (Thermo Fisher Scientific, Waltham, MA) to concentrate samples. 2.3. Liquid Chromatography– Mass Spectrometry/Mass Spectrometry
1. Ion trap mass spectrometers (LTQ, Thermo Fisher Scientific, San Jose, CA) R or equivalent system 2. Water purified using a NANOpure0002 (≥ 18 M0002×cm)
3. Mobile phase A: Degassed 0.2% acetic acid, 0.05% trifluoroacetic acid in water (Sigma Aldrich) 4. Mobile phase B: Degassed 0.1% trifluoroacetic acid in 90% acetonitrile (ACN), 10% water (Sigma Aldrich) 5. 5-␮m Jupiter C18 stationary phase (Phenomenex, Torrence, CA) packed into 60-cm (360 ␮m o.d. X 150 ␮m i.d.) fused silica capillary tubing (Polymicro Technologies Inc., Phoenix, AZ) 6. Liquid chromatography system is described elsewhere by Livesay et al. (15)
2.4. Liquid Chromatography– High-Resolution Mass Spectrometry
2.5. LC–MS/MS Data Analyses
2.6. Proteomics
1. Fourier transform ion cyclotron resonance (FTICR) mass spectrometer, either a custom-built 11 T instrument or 9.4 T instrument (Bruker Daltonics, Billerica, MA). 2. See Section 2.3 for details on mobile and stationary-phase materials. R R 1. SEQUEST0002 version [TurboSEQUEST0002 (cluster) v.27 (rev. 12), Thermo Fisher Corp.] .
1. RapiGestTM (Waters, Milford, MA) is a surfactant to aid in the solubilization and trypsin digestion of proteins. 2. Trypsin for protein digestion (Promega, Madison, WI)
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3. Bicinchoninic Acid (BCA) Protein Assay kit (Pierce, Rockford, IL) for quantitation of peptides 2.7. Data Visualization and Cluster Analysis
1. DAnTE, freely available software for comparative analysis of proteomics data available at http://omics.pnl. gov/software/ 2. MultiExperiment Viewer (MEV) is also freely available and designed for use in microarray experiments, but can be particularly useful for proteomics data visualization and clustering and is available at http://www.tm4.org/mev.html.
3. Methods 3.1. Culturing Conditions
The culturing conditions of the bacteria are not the focus of this review but are summarized here. The primary difference between the culture conditions used is that various forms of stresses, some relevant to pathogenesis, were compared relative to a rich growth medium at middle logarithmic growth phase. Wild-type S. typhimurium strains 14028 and LT2 were grown to mid-logarithmic (Log) and stationary (Stat) phases in LuriaBertani (LB) broth and harvested for analysis. Two other cell growth conditions were used that differed only in the pre-growth conditions. In one, the bacteria were grown to stationary phase in LB, the bacteria were isolated, washed, and then grown in magnesium-minimal acidic medium (Shock); in the other, the bacteria were diluted 1:100 and grown in acidic minimal media overnight (Dilu). All cultures were harvested following standard batch culture techniques as outlined (see references (3–5) for more detail of culture methods). Aliquots of cell cultures (corresponding to 0.15 g cell pellets) were pelleted, washed in PBS, flash frozen with liquid N2 , and used as needed to prepare samples.
3.2. Sampling Preparation and Peptide Extraction
The procedures outlined here are specific to samples that require Biosafety Level 2 (BSL2) containment and treatment. Many of the precautions (e.g., O-ring sealed cryovials, cooling following vortexing) are to prevent aerosolization of unlysed pathogenic organisms. When developing these protocols, we lysed cells in the presence of a protease inhibitor cocktail formulated for use with bacterial cell extracts. However, we did not evaluate corresponding analyses without the protease inhibitor cocktail (see Note 1). The listing of class-specific chemical inhibitors of proteases found in the excellent review by Overall and Blobel (6) may be consulted if protease inhibition is desired. In our previous work, we
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performed tests to mimic poor sample handling (incubation at 22◦ C for 20 min without inhibitors) and compared these results to those obtained when the samples were prepared at ∼7◦ C with a cooling block (normal handling temperatures with inhibitors) (see Note 2). We found no significant variation in the peptides identified. The procedure below is based on an isopropanol extraction that causes larger proteins to precipitate while endogenous peptides are maintained in solution. Different concentrations of isopropanol were tested and it was determined that a ratio of 3:2 resulted in the best recovery of endogenous peptides from S. typhimurium. This may not hold true for all biological samples. 1. Lysis of bacterial cells is accomplished by first resuspending the cell pellet in an equivalent volume of 100 mM NH4 HCO3 , followed by transfer of the sample to a 2.0-mL O-ring sealed cryovial. Next, 0.1-mm zirconia/silica beads are added to half of the volume in the tube, and the tube is then vortexed for 30 s, followed by cooling for 1 min in a cold-block. Six cycles of vortexing and cooling are performed. The lysate is then removed from the top of the settled beads, and the beads are rinsed five times with buffer. The lysates and rinses are then pooled separately in microcentrifuge tubes (see Note 3). 2. The pooled lysate is centrifuged at 16,000×g for 10 min at room temperature to pellet insoluble and precipitated proteins. Transfer the supernatant to a new microcentrifuge tube, and ensure that the entire pellet is left behind. The supernatant is now considered a cleared lysate. An aliquot of the cleared lysate can be saved for SDS-PAGE as a reference. 3. Isopropanol is then added to the cleared lysate in an appropriate ratio (we used 1:1, 3:2, 2:1, or 5:2 (v/v, isopropanol:lysate)), and the samples were mixed by vortexing. Pre-cooling the isopropanol to 4◦ C before adding to the lysate can assist with precipitation of proteins. The samples are then incubated at 4◦ C for 15 min, then microcentrifuged at 16,000×g for 10 min at 4◦ C to remove precipitated proteins. The resulting supernatants are transferred to new microcentrifuge tubes and concentrated in a SpeedVac to ∼75 ␮L. Ten-microliter aliquots can be removed at this time for SDS-PAGE. 4. Peptide concentrations are determined by BCA protein assay, and SDS-PAGE analyses are performed using 10–20% R . The Tris-Tricine gels are used Tris-Tricine Ready Gels0002 because they are specific for the separation of extremely small proteins and peptides. Gels are fixed for 30 min in 40% methanol/10% acetic acid and then stained for 60 min using R Blue reagent. GelCode0002
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5. Prior to MS analysis, the concentrated isopropanol extracts are cleaned via solid-phase extraction using OMIX C18 pipette tips. These tips are monolithic, rather than particulate, and are therefore much easier to use without clogging, while providing better recovery and reproducibility. Thirty micrograms of peptide mass from each sample is applied to a 100-␮L tip. The directions provided by the manufacturer are used to condition, wash, and load the samples. Peptides are eluted from the tips with 80:20 ACN:H2 O containing 0.1% TFA. Eluted peptides are concentrated to ∼15 ␮L in a SpeedVac. 6. Alternatively, samples can be fractionated using strong cation exchange (SCX) HPLC to minimize sample complexity prior to each LC–MS/MS analysis, as described previously (7). Each fractionation is performed using approximately 150 ␮g (peptide mass) of concentrated isopropanol extract, resulting in 25 fractions that are concentrated in a SpeedVac to dryness. The samples are then reconstituted in 25 mM NH4 HCO3 to a volume appropriate for LC–MS/MS analysis. 3.3. Liquid Chromatography– Mass Spectrometry/Mass Spectrometry
Our analytical instrumentation consists of commercially available platforms [e.g., ion traps (LTQ from ThermoFisher) and FTICR–MS (BrukerDaltonics)] that are in-house modified to increase the sensitivity and throughput of the analyses. However, the below LC–MS(/MS) approaches can be applied at a reasonable level of quality with more generally available off-theshelf instrumentation. LC–MS/MS analyses are useful for making identifications and for semi-quantitation based on “spectrum counting” techniques (4, 8–10). These analyses are also used to build a database of identified peptides annotated with determined reversed-phase elution times (11) and calculated masses. This database (also referred to as a mass and time tag lookup table) is used with results from the high-resolution MS analyses (Section 3.4) to increase throughput, perform label-free quantitation, and improve peptide-sampling methods in the MS experiment. This is a simplified description of the accurate mass and time (AMT)-tag process developed in our laboratory, which has been extensively discussed elsewhere (12–14). 1. The concentrated C18 SPE eluents from the peptide cleanup procedure and the SCX fractions are then analyzed by reversed-phase microcapillary HPLC (15) interfaced through nanoelectrospray ionization (nanoESI) to an ion trap mass spectrometer, as described previously (4). Briefly, the technique used in our laboratory entails gradient elution of peptides over 100 min using a 360 ␮m OD × 150 ␮m ID × 65 cm long capillary column packed with 5 ␮m Jupiter C18 particles.
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2. For typical “bottom-up” proteomics experiments, in which the proteins are digested with trypsin, the charge states of peptides detected during LC–MS/MS are typically +2 and +3. Detected peptides are then fragmented using collisioninduced dissociation. It should be noted that the peptides detected from the S. typhimurium endogenous peptidome include more +4 and +5 charge states than typically observed for other sample types. Due to the larger number of higher charged species, electron transfer dissociation may be considered for future analyses of the endogenous peptidome. 3.4. LC–MS Analyses
1. Concentrated C18 SPE eluents are also analyzed in our laboratory by reversed-phase microcapillary HPLC–nanoESI– FTICR–MS (11.5 T) (16). The same chromatographic platforms are used for LC–MS/MS analyses as is used with the FTICR–MS, and during analysis of multiple samples to be compared, the same chromatography column and electrospray emitter is preferred. This reduces the number of confounding variables during an experiment for downstream data analysis. 2. The analysis order for an experiment such as this needs to be addressed to minimize the effects of analysis time and possibility of carryover from highly abundant peptides. This is referred to as “randomized block design” and is meant to remove experimental nuisance factors that can obscure true differences between samples (see Note 4). These blocks typically contain one replicate for each experiment and the order of the analyses within a block is randomized. 3. Peptides from the LC–MS spectra are identified using the AMT tag approach (14), including any peptides with +4 and +5 charge states. The necessary software tools are publicly available (http://omics.pnl.gov). This approach uses the calculated mass and the observed normalized elution time (NET) of each filter-passing peptide identification (see Section 3.5) from the previous LC–MS/MS analyses to construct a reference database of AMT tags. Features from LC–MS analyses (i.e., m/z peaks deconvoluted of isotopic and charge state effects and then annotated by mass and NET) are matched (13) to AMT tags to identify peptides in a manner that results in roughly 5% false-positive identifications. For each protein, the sum of its peptide peak areas (NET vs. peak height) is used as a measure of the abundance of its fragments within the peptidome.
3.5. LC–MS/MS Data Analyses
Peptides can be identified using a number of different publicly available software packages.
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Adkins et al. R 1. In this example, we utilize SEQUEST0002 to search the resulting MS/MS spectra against the annotated S. typhimurium FASTA data file of proteins translated from genetic code provided by the J. Craig Venter Institute – formerly TIGR (4550 protein sequences, http://www.jcvi.org/) (17). These analyses used a standard parameter file with a peptide mass tolerance = 3, fragment ion tolerance = 0, and no amino acid modifications. Also, these analyses search for all possible peptide termini (i.e., not limited to only tryptic R searches that use the above termini). Separate SEQUEST0002 FASTA data file but with scrambled amino acid sequences are performed in parallel to estimate the false discovery rate. R generally returns multiple peptide identifica2. SEQUEST0002 tions for each MS/MS spectrum and for each parent ion charge state. Therefore, for each MS/MS spectrum and for each parent ion charge state, only the peptide identification with the highest XCorr value (i.e., the “top ranked hit”) is retained here.
3. Limiting false identification of peptides is an especially challenging issue for natively produced peptides because cleavage state (i.e., trypsin cleavage sites) is often used in making confident identifications. PeptideProphet (18) values are also not applicable because of a strong bias for “tryptic” peptides. The estimated percentage of falsepositive peptide identifications can be defined as %FPest. = 100% × (number of scrambled peptide identifications) / (number of normal peptide identifications) (19). %FPest. should be calculated for each charge state, XCorr Cutoff value (the minimum XCorr value requirement, which ranged from 1.5 to 5 in units of 0.02), and 0003Cn Cutoff value (i.e., the minimum 0003Cn value requirement, which ranged from 0 to 0.4 in units of 0.005). In an effort to maximize identifications, a two-dimensional analysis of the XCorr Cutoff and 0003Cn Cutoff is used for each parent ion charge state. This method is different from typical proteomics analyses in that it does not use a single 0003Cn Cutoff value. 4. The optimal XCorr Cutoff and 0003Cn Cutoff values for each parent ion charge state (+1 to +5) was determined in our previous work to be 1.84 and 0.21 (+1), 2.1 and 0.21 (+2), 2.8 and 0.23 (+3), 3.56 and 0.265 (+4), and 4.16 and 0.22 (+5), respectively. 5. A rough measure of the abundance of each parent protein and its fragments within the peptidome can be attained using a spectrum counting (i.e., tallying of filter-passing peptide identifications) approach (20).
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3.6. Comparison to Proteomics
Peptidomics data (samples acquired without digestion) should ideally be compared to proteomics data (samples acquired using typical bottom-up proteomics approaches including the use of trypsin) from the same source material. This comparison ensures that the peptidomics results are interpreted and can be compared with peptides resulting from abundant proteins being nonspecifically degraded. We performed a proteomics analysis with the same starting sample material to that used in the peptidomics experiment (4). Briefly, proteins are isolated and digested as described in the protocol provided by Waters with the modification of 2.0% TFA rather than using concentrated HCL to adjust to a pH of 3.0. Acid incubation occurred at 37◦ C for 1 h to fully precipitate the RapiGestTM surfactant. The samples are centrifuged in a microcentrifuge at full speed to pellet the RapiGestTM and the supernatant is returned to neutral pH with NH4 OH to allow for digested peptide concentration determination by BCA protein assay. The resultant peptides are then fractionated using strong SCX HPLC (7) into 25 fractions. A single unfractionated sample and the full set of 25 SCX fractions are then analyzed by reversed-phase LC–MS/MS. MS/MS spectra are searched using R and filtered to reduce false-positive peptide identifiSEQUEST0002 cations (3, 4, 20).
3.7. Data Visualization and Cluster Analysis
The comparative interpretation of the identified proteins and peptides can present unique challenges. In the case of comparing environmentally induced changes in the S. typhimurium proteome and peptidome, one challenge is that many proteins are not commonly observed across all conditions. If one generates a matrix of protein/peptides (rows) by experimental conditions (column) populated with values of spectral observations or peak abundance measurements, the unobserved proteins/peptides are sometimes referred to as “missing data”. The source of an unobserved species can be the result of either of the following: (1) its actual absence in a sample, (2) it is present, but below the detection limit of the mass spectrometer, or (3) the identification did not pass various quality thresholds used for confident peptide identifications. This results in a less than ideal direct application of statistical methods typically used for comparisons of high-throughput data (microarrays) such as an analysis of variance (ANOVA). For this reason, we typically try to combine the abundance values for all peptides from a source protein into a single representative protein abundance for comparison across conditions. This collapsing of peptide abundance to protein abundance is often referred to by us as “protein roll-up” (see Note 5). These protein values are then grouped by similar abundance profile
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changes using methods such as a hierarchical clustering, which are common for microarray analysis comparisons. The comparative analyses of the peptide and protein abundances are enabled with the use of data mining tools that offer clustering and heatmap visualization of the matrix form of the experimental results, e.g., R (22), or MeV (23). Some considerations DAnTE (21), OmniViz0002 that must be made when analyzing the data are listed below:
Fig. 2.1. Example heat map showing endogenous peptidomics results compared to global proteomics results. Observations across conditions were scaled using the Z-score across protein (with black representing a Z-score of 2.5 and white a Z-score of –1.0). Two selected regions were taken from data found elsewhere. “Stress response factors”, in this case endogenously occurring peptides, correspond well with the abundance of the proteins in the proteomics experiments. The “stress turn-over” peptides appear to be scavenged in the “Dilu” stress condition, and these proteins appear to only be overly abundant in the rich logarithmic growth condition.
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1. One of the first decisions is whether to fill arbitrary values into the unobserved peptide/protein abundances to make the analysis more amenable to various downstream data analysis methods typically applied in transcriptional microarray data analysis, such as ANOVA, principle component analysis, and/or clustering methods. If the number of spectra observed in a protein are used as a surrogate for an abundance measurement, filling might include applying the
Fig. 2.2. A demonstration of proteomics results in the context of endogenous peptidomics. Although tryptic digestion was use in this example, the disappearance of a number of peptides between the stationary (stat) and shock conditions indicates that the protein is being differentially acted upon by proteases in the cell between the two conditions. This is especially true when this protein YciF was observed to be particularly abundant in the peptidome in the shock condition previously (3). As a secondary confirmation, new peptides that were not observed in the stationary condition appear in the shock condition. New “partially tryptic peptides” also appear and are highlighted with ‘<’ in the figure. The numbers under the conditions represent the biological replicate of that growth condition.
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minimum number of required peptides for protein identification (see Note 6). 2. In both the peptide-centric and protein-centric (using a single abundance value for the protein) analysis, the difference in abundance between the most abundant peptide/protein versus the least abundant species may range several orders of magnitude. This large dynamic range of measurements may lead to difficulty comparing proteins with similar trends in a set of experiments. To use clustering tools, this dynamic range must be compensated for by scaling to similar magnitudes for comparison (i.e., a trend that is varied across 2 orders of magnitude should be grouped with other similar trends varying across 2 orders of magnitude even if the most abundant value to least abundant value between protein is across 6 orders of magnitude). Depending on the nature of quantitation (spectrum count versus peak area) and the number of experiments being compared (fewer than six versus thirty or more), different scaling approaches are preferred (see Note 7). 3. Once these steps are performed, comparisons between the experimental samples (both from the undigested native peptidome and the digested proteome) can be performed using heat maps of the clustered results (Fig. 2.1). 4. Once an endogenous peptidome analysis has been performed, and knowledge of proteins that are subject to native proteolysis is obtained, it is then possible to extract some additional information utilizing only a proteomics (i.e., trypsin was used) analysis by looking for non-tryptic cleavage sites (for an example Fig. 2.2 ).
4. Notes 1. It is reasonable to consider the goals of the experiment, there may be a specific desire to leave a class of proteases active to amplify the abundance of the cleaved products. 2. Set cooling block between 6 and 8◦ C, leaving the cooling blocks in the refrigerator 1 day prior to the experiment. Be sure to confirm that freezing will not occur by using microcentrifuge tubes of ∼100 ␮L of water in the block during cooling. 3. All microcentrifuge tubes from this point forward should be siliconized (Fisher 02-681-332) to prevent polymer contamination, which is detrimental to downstream LC– MS(/MS) analyses.
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4. The USA National Institute of Standards and Technology maintains an electronic Engineering Statistics Handbook (http://www.itl.nist.gov/div898/handbook) with a useful discussion of “Randomized block designs” for experiments. 5. “Protein roll-up” refers to methods that attempt to give a single value for each protein for quantitative purposes, even though each protein identification in a bottom-up proteomics experiment typically is based on more than one peptide identification. As of this writing, DAnTE offers multiple methods for protein roll-up (21). 6. Typically, an identification of a specific protein based on its tryptic cleavage products requires identification of three separate tryptic peptides. For native peptidomics this is not realistic because there is a high likelihood that only a single species will be present. Biological conclusions based on single peptide identifications should be based on methods with better relative abundance measurements such as the spectral peak abundance. 7. For large experiments, a Z-score (24) analysis can be helpful to visualize significant trends that are further than expected by a normal distribution. This is also better suited for peak area-based quantitation where the values are non-integers. For smaller experiments, dividing each value in a peptide or protein row by the associated sum, mean, or median of that entire row can be a useful method to scale the results.
Acknowledgments This work was supported by the National Institute of Allergy and Infectious Diseases (NIH/DHHS through interagency agreement Y1-AI-4894-01 and Y1-AI-8401-01). The authors also acknowledge the US Department of Energy Office of Biological and Environmental Research and National Center for Research Resources (RR18522) for the development of the instrumental capabilities used for the research. Significant portions of this research were performed in the Environmental Molecular Sciences Laboratory, a US Department of Energy (DOE) national scientific user facility located at the Pacific Northwest National Laboratory (PNNL) in Richland, Washington. PNNL is a multiprogram national laboratory operated by Battelle Memorial Institute for the DOE under Contract No. DE-AC05-76RLO-1830.
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References 1. El-Sharoud, W.M. and Niven, G.W. (2007) The influence of ribosome modulation factor on the survival of stationary-phase Escherichia coli during acid stress. Microbiology 153, 247–253. 2. Wada, A., Mikkola, R., Kurland, C.G., and Ishihama, A. (2000) Growth phase-coupled changes of the ribosome profile in natural isolates and laboratory strains of Escherichia coli. J. Bacteriol. 182, 2893–2899. 3. Manes, N.P., Gustin, J.K., Rue, J., Mottaz, H.M., Purvine, S.O., Norbeck, A.D., Monroe, M.E., Zimmer, J.S., Metz, T.O., Adkins, J.N., Smith, R.D., and Heffron, F. (2007) Targeted protein degradation by Salmonella under phagosome-mimicking culture conditions investigated using comparative peptidomics. Mol. Cell Proteomics 6, 717–727. 4. Adkins, J.N., Mottaz, H.M., Norbeck, A.D., Gustin, J.K., Rue, J., Clauss, T.R., Purvine, S.O., Rodland, K., Heffron, F., and Smith, R.D. (2006) Analysis of the Salmonella typhimurium proteome through environmental response toward infectious conditions. Mol. Cell Proteomics 5, 1450–1461. 5. Beuzon, C.R., Meresse, S., Unsworth, K.E., Ruiz-Albert, J., Garvis, S., Waterman, S.R., Ryder, T.A., Boucrot, E., and Holden, D.W. (2000) Salmonella maintains the integrity of its intracellular vacuole through the action of SifA. EMBO J. 19, 3235–3249. 6. Overall, C.M. and Blobel, C.P. (2007) In search of partners: linking extracellular proteases to substrates. Nat. Rev. Mol. Cell Biol. 8, 245–257. 7. Adkins, J.N., Varnum, S.M., Auberry, K.J., Moore, R.J., Angell, N.H., Smith, R.D., Springer, D.L., and Pounds, J.G. (2002) Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry. Mol. Cell Proteomics 1, 947–955. 8. Gao, J., Friedrichs, M.S., Dongre, A.R., and Opiteck, G.J. (2005) Guidelines for the routine application of the peptide hits technique. J. Am. Soc. Mass Spectrom. 16, 1231–1238. 9. Gao, J., Opiteck, G.J., Friedrichs, M.S., Dongre, A.R., and Hefta, S.A. (2003) Changes in the protein expression of yeast as a function of carbon source. J. Proteome Res. 2, 643–649. 10. Ishihama, Y., Oda, Y., Tabata, T., Sato, T., Nagasu, T., Rappsilber, J., and Mann, M. (2005) Exponentially modified protein abundance index (emPAI) for estimation of abso-
11.
12.
13.
14.
15.
16.
17.
18.
19.
lute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell Proteomics 4, 1265–1272. Monroe, M.E., Shaw, J.L., Daly, D.S., Adkins, J.N., and Smith, R.D. (2008) MASIC: A software program for fast quantitation and flexible visualization of chromatographic profiles from detected LC–MS(/MS) features. Comput. Biol. Chem. 32(3), 215–217. Norbeck, A.D., Monroe, M.E., Adkins, J.N., Anderson, K.K., Daly, D.S., and Smith, R.D. (2005) The utility of accurate mass and LC elution time information in the analysis of complex proteomes. J. Am. Soc. Mass Spectrom. 16, 1239–1249. Monroe, M.E., Tolic, N., Jaitly, N., Shaw, J.L., Adkins, J.N., and Smith, R.D. (2007) VIPER: an advanced software package to support high-throughput LC–MS peptide identification. Bioinformatics 23, 2021–2023. Zimmer, J.S., Monroe, M.E., Qian, W.J., and Smith, R.D. (2006) Advances in proteomics data analysis and display using an accurate mass and time tag approach. Mass Spectrom. Rev. 25, 450–482. Livesay, E.A., Tang, K., Taylor, B.K., Buschbach, M.A., Hopkins, D.F., LaMarche, B.L., Zhao, R., Shen, Y., Orton, D.J., Moore, R.J., Kelly, R.T., Udseth, H.R., and Smith, R.D. (2008) Fully automated fourcolumn capillary LC–MS system for maximizing throughput in proteomic analyses. Anal. Chem. 80, 294–302. Gorshkov, M.V., Pasa Tolic, L., Udseth, H.R., Anderson, G.A., Huang, B.M., Bruce, J.E., Prior, D.C., Hofstadler, S.A., Tang, L., Chen, L.Z., Willett, J.A., Rockwood, A.L., Sherman, M.S., and Smith, R.D. (1998) Electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry at 11.5 tesla: instrument design and initial results. J. Am. Soc. Mass Spectrom. 9, 692–700. Peterson, J.D., Umayam, L.A., Dickinson, T., Hickey, E.K., and White, O. (2001) The comprehensive microbial resource. Nucleic Acids Res. 29, 123–125. Keller, A., Nesvizhskii, A.I., Kolker, E., and Aebersold, R. (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392. Qian, W.J., Liu, T., Monroe, M.E., Strittmatter, E.F., Jacobs, J.M., Kangas, L.J., Petritis, K., Camp, D.G., 2nd, and Smith,
Comparative Peptidomics of Salmonella R.D. (2005) Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome. J. Proteome Res. 4, 53–62. 20. Qian, W.J., Jacobs, J.M., Camp, D.G., 2nd, Monroe, M.E., Moore, R.J., Gritsenko, M.A., Calvano, S.E., Lowry, S.F., Xiao, W., Moldawer, L.L., Davis, R.W., Tompkins, R.G., and Smith, R.D. (2005) Comparative proteome analyses of human plasma following in vivo lipopolysaccharide administration using multidimensional separations coupled with tandem mass spectrometry. Proteomics 5, 572–584. 21. Polpitiya, A.D., Qian, W.J., Jaitly, N., Petyuk, V.A., Adkins, J.N., Camp, D.G., 2nd, Anderson, G.A., and Smith, R.D. (2008) DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 24, 1556–1558.
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22. Saffer, J.D., Burnett, V.L., Chen, G., and van der Spek, P. (2004) Visual analytics in the pharmaceutical industry. IEEE Comput. Graph Appl. 24, 10–15. 23. Saeed, A.I., Sharov, V., White, J., Li, J., Liang, W., Bhagabati, N., Braisted, J., Klapa, M., Currier, T., Thiagarajan, M., Sturn, A., Snuffin, M., Rezantsev, A., Popov, D., Ryltsov, A., Kostukovich, E., Borisovsky, I., Liu, Z., Vinsavich, A., Trush, V., and Quackenbush, J. (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34, 374–378. 24. Adkins, J.N., Monroe, M.E., Auberry, K.J., Shen, Y., Jacobs, J.M., Camp, D.G., 2nd, Vitzthum, F., Rodland, K.D., Zangar, R.C., Smith, R.D., and Pounds, J.G. (2005) A proteomic study of the HUPO Plasma Proteome Project’s pilot samples using an accurate mass and time tag strategy. Proteomics 5, 3454–3466.
Chapter 3 Approaches to Identify Endogenous Peptides in the Soil Nematode Caenorhabditis elegans Steven J. Husson, Elke Clynen, Kurt Boonen, Tom Janssen, Marleen Lindemans, Geert Baggerman, and Liliane Schoofs Abstract The transparent soil nematode Caenorhabditis elegans can be considered an important model organism due to its ease of cultivation, suitability for high-throughput genetic screens, and extremely well-defined anatomy. C. elegans contains exactly 959 cells that are ordered in defined differentiated tissues. Although C. elegans only possesses 302 neurons, a large number of similarities among the neuropeptidergic signaling pathways can be observed with other metazoans. Neuropeptides are important messenger molecules that regulate a wide variety of physiological processes. These peptidergic signaling molecules can therefore be considered important drug targets or biomarkers. Neuropeptide signaling is in the nanomolar range, and biochemical elucidation of individual peptide sequences in the past without the genomic information was challenging. Since the rise of many genome-sequencing projects and the significant boost of mass spectrometry instrumentation, many hyphenated techniques can be used to explore the “peptidome” of individual species, organs, or even cell cultures. The peptidomic approach aims to identify endogenously present (neuro)peptides by using liquid chromatography and mass spectrometry in a high-throughput way. Here we outline the basic procedures for the maintenance of C. elegans nematodes and describe in detail the peptide extraction procedures. Two peptidomics strategies (off-line HPLC–MALDI-TOF MS and on-line 2D-nanoLC–Q-TOF MS/MS) and the necessary instrumentation are described. Key words: Nematode, Caenorhabditis elegans , neuropeptide, insulin, FMRFamide-like peptide, flp , neuropeptide-like protein, G-protein-coupled receptor, mass spectrometry.
1. Introduction 1.1. Caenorhabditis elegans Is an Ideal Model Organism
The transparent, free-living, non-parasitic soil nematode Caenorhabditis elegans (Caeno, recent; rhabditis, rod; elegans, nice) of only 1 mm in length can be safely handled and is easy to
M. Soloviev (ed.), Peptidomics, Methods in Molecular Biology 615, DOI 10.1007/978-1-60761-535-4 3, © Humana Press, a part of Springer Science+Business Media, LLC 2010
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grow and maintain. Since its introduction as a model system in the 1960s, C. elegans was used widely in many research laboratories due to the ease of handling and the well-defined anatomy. C. elegans contains exactly 959 cells that are ordered in sets of fully differentiated tissues. There are two sexes. Hermaphrodites can self-fertilize or mate with males in order to produce over 300 offspring. Although hermaphrodites are the most common sex in nature, mating with males will yield a 50% male progeny. In the laboratory, self-fertilization of the hermaphrodites or crossing with males can easily be manipulated for genetic studies. In addition, C. elegans has a short life cycle. It takes about 3–4 days from egg to egg and it goes through four larval stages (L1–L4) until reaching adulthood. A developmentally arrested “dauer” larva can be formed under conditions of starvation or overcrowding. These thinner dauers have a relative impermeable cuticle, are non-feeding, and can survive for months, in contrast to the average life span of around 2–3 weeks under standard conditions. A sophisticated knowledge infrastructure has been developed, with many research methods and protocols that are widely shared in the “worm-community.” Most information can be found in the easily accessible database “WormBase” at (http://www.wormbase.org). The “WormBook” (http:// www.wormbook.org) can be considered as the open-access collection of peer-reviewed chapters that covers all kinds of different topics and protocols related to C. elegans. This nematode is also perfectly suited for light microscopy due to its transparency. For high-end visual analysis of C. elegans, the microscope has to be equipped with differential interference contrast (DIC; Nomarski) optics for obtaining 3D-like view of the tissues. This way, individual neurons can be observed and recognized. As an example, a DIC image of an L1 larva is shown in Fig. 3.1. Detailed DIC and electron microscopic images are available on “WormAtlas” (http://www.wormatlas.org), together with a plethora of detailed schematic representations. C. elegans was the first multicellular organism to have its genome fully sequenced (1). Its genome (about 100 Mb) encodes for over 20,000 proteins and its size is about 1/30th of that of a human. The awarding of the Nobel Prize to the three “worm-pioneers” Sydney Brenner, Robert Horvitz, and John Sulston in 2002 for their discoveries concerning genetic regulation of organ development and programmed cell death, to Andrew Fire and Craig Mello in 2006 for their discovery of RNA interference in this nematode, and to Martin Chalfie in 2008 for the discovery and development of the green fluorescent protein (GFP), emphasizes the great potential of this tiny nematode of being a model organism, just like the fruit fly Drosophila melanogaster, the marine snail Aplysia californica, and the mouse Mus musculus.
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Fig. 3.1. Differential interference contrast image of a C. elegans L1 larva. The first larval stage of the nematode C. elegans is shown. This picture was taken using an Axio Observer Z1 instrument (Zeiss) equipped with differential interference contrast (DIC) or Nomarski optics to allow a clear 3D-like structure of individual neurons.
1.2. Peptidomics of C. elegans
(Neuro)peptides are small messenger molecules that are derived from larger precursor proteins by the highly controlled action of processing enzymes. These biologically active peptides can be found in all metazoan species where they orchestrate a wide variety of physiological processes. The knowledge of the primary amino acid sequence of the neuropeptidergic signaling molecules is absolutely necessary to understand their function and interactions with G-protein-coupled receptors. Three classes of neuropeptide-encoding genes have been predicted from the genomic data of C. elegans. Initially, 24 FMRFamide-like peptide (flp) genes have been found by searching cDNA libraries and genomic sequences (2–4); more flp genes were identified by mining the EST data (5) (see Table 3.1). By searching the C. elegans genome for predicted proteins with the structural hallmarks of neuropeptide precursors, 32 so-called neuropeptide-like protein (nlp) genes have been identified (6) (see Table 3.2). These neuropeptide preproproteins all contain peptides without the RFamide motif, but display sequence homology with other
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Table 3.1 FLP neuropeptides of C. elegans Peptide sequencea
Gene -LRFa family flp-1
flp-14
-MRFa family SADPNFLRFa
4×
flp-15
Peptide sequencea
Gene
flp-3
SPLGTMRFa
SQPNFLRFa
TPLGTMRFa
ASGDPNFLRFa
EAEEPLGTMRFa
SDPNFLRFa
NPLGTMRFa
AAADPNFLRFa
ASEDALFGTMRFa
(K)PNFLRFa
EDGNAPFGTMRFa
AGSDPNFLRFa
SAEPFGTMRFa
KHEYLRFa
SADDSAPFGTMRFa
GGPQGPLRFa
flp-18
3×
NPENDTPFGTMRFa
RGPSGPLRFa
flp-6
6×
KSAYMRFa
(DFD)GAMPGV LRFa
flp-20
2×
AMMRFa
EMPGVLRFa
flp-22
3×
SPSAKWMRFa
(SYFDEKK)SVP GVLRFa
flp-27
(EASAFGDIIGELKGK) GLGGRMRFa
EIPGVLRFa
flp-28
APNRVLMRFa
SEVPGVLRFa
-VRFa family
DVPGVLRFa
flp-7
3×
flp-21
GLGPRPLRFa
flp-23
TKFQDFLRFa
SPMERSAMVRFa
flp-26
(E)FNADDLTLRFa
SPMDRSKMVRFa
GGAGEPLAFSPD MLSLRFa -IRFa family flp-2 flp-4
2×
SPMQRSSMVRFa
flp-9
2×
flp-11
AMRNALVRFa ASGGMRNALVRFa
LRGEPIRFa
NGAPQPFVRFa flp-16
2×
ASPSFIRFa
3×
AQTFVRFa GQTFVRFa
GAKFIRFa
flp-17
AGAKFIRFa
flp-19
APKPKFIRFa flp-8
KPSFVRFa
SPREPIRFa (GLRSSNGK) PTFIRFa
flp-5
TPMQRSSMVRFa
2×
KSAFVRFa WANQVRFa ASWASSVRFa
KNEFIRFa
flp-24
VPSAGDM(ox)M(ox)VRFa
flp-10
pQPKARSGYIRFa
flp-25
DYDFVRFa
flp-12
RNKFEFIRFa
flp-32
AMRNSLVRFa
flp-13
(SDRPTR)AMD SPFIRFa
-PRFa family
(continued)
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Table 3.1 (continued) Gene
Peptide sequencea
Gene
AADGAPFIRFa
flp-33
Peptide sequencea APLEGFEDMSGFLRTIDGI QKPRFa
APEASPFIRFa ASPSAPFIRFa SPSAVPFIRFa ASSAPFIRFa SAAAPLIRFa flp-17
KSQYIRFa
flp-25
ASYDYIRFa
a Sequences
shown in bold have been confirmed by Edman degradation, MALDI-TOF MS, or Q-TOF mass spectrometry.
Table 3.2 NLP neuropeptides of C. elegans Gene nlp-1
×3
nlp-2 ×3 nlp-3
nlp-4
nlp-5
Peptide sequencea
Gene
Peptide sequencea
MDANAFRMSFa
nlp-21
GGARAMLH
MDPNAFRMSFa
GGARAFSADVGDDY
VNLDPNSFRMSFa
GGARAFYDE
SIALGRSGFRPa
GGARAFLTEM
SMAMGRLGLRPa
GGARVFQGFEDE
SMAYGRQGFRPa
GGARAFMMD
AINPFLDSMa
GGGRAFGDMM
AVNPFLDSIa
GGARAFVENS
YFDSLAGQSLa
GGGRSFPVKP GRLDD
SLILFVILLVAFA AARPVSEEVDRV
pQYTSELEEDE
DYDPRTEAPRRLPA DDDEVDGEDRV
nlp-22
SIAIGRAGFRPa
DYDPRTDAPIRVPV DPEAEGEDRV
nlp-23
LYISRQGFRPA
SVSQLNQYAGFD TLGGMGLa
SMAIGRAGMRPa
ALSTFDSLGGMGLa
AFAAGWNRa
ALQHFSSLDTL GGMGFa nlp-6
nlp-7
nlp-24
pQWGGGPYGGYGP
(MA)APKQMVFGFa
GYGGGYGGa
YKPRSFAMGFa
YGGYGa
AAMRSFNMGFa
FTGPYGGYGa
LIMGLa
GPYGYGa
pQADFDDPRMFTSSFa
GPYGGGGLVGALLa (continued)
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Table 3.2 (continued) Gene
Peptide sequencea
Gene
SMDDLDDPRL MTMSFa
nlp-25
Peptide sequencea IGTEVAEGVLVA EEVSEAIa GGGYGGGYGGGFGA QQAYNVQNAA
MILPSLADLH RYTMYD LYLKQADFDDP RMFTSSFa nlp-8
nlp-26
GGQFGGMQ
AFDRMDNSDFFGA
GGFNGN
SFDRMGGT EFGLM
GGFGQQSQFGa
YPYLIFPASPSS GDSRRLV nlp-9
nlp-10
pQFGFGGQQSFGa
AFDRFDNSGV FSFGA
×2
GGSQFNa
GGGRAFNHN ANLFRFD
GGFGFa
GGGRAFAGSWSPYLE nlp-27
pQWGYGGMPYGGYGGM GGYGMGGYGMGY
TPIAEAQGAPE DVDDRRELE
MWGSPYGGYGGY GGYGGWa
AIPFNGGMYa
nlp-28
GYGGYa
STMPFSGGMYa
GYGGYGGYa ×2
AAIPFSGGMYa GAMPFSGGMYa HISPSYDVEIDAG NMRNLLDIa
nlp-11
nlp-29
pQWGYGGYa GYGGYGGYa
SPAISPAYQFENA FGLSEALERAa ×2
nlp-13
GMYGGYa GMYGGWa
nlp-30
pQWGYGGYa
NDFSRDIMSFa
GYGGYGGYa
SGNTADLYDR RIMAFa
GYGGYa
pQPSYDRDIMSFa
GMWa
SAPSDFSRD IMSFa
PYGGYGWa
SSSMYDRDIMSFa SPVDYDR PIMAFa nlp-14
×3
DYRPLQFa DGYRPLQFa
GYGGYGGYa GMYGGWa
SAPMASDYGN QFQMYNRLIDAa
nlp-12
GGNQFGa
GGARAFYGF YNAGNS
nlp-31
pQWGYGGYa ×2
GYGGYGGYa
AEDYERQIMAFa
GYGGYa
×2
ALDGLDGSGFGFD
GMYGGYa
×5
ALNSLDGAGFGFE
PYGGYGWa (continued)
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Table 3.2 (continued) Gene ×3
Gene
Peptide sequencea
ALDGLDGAGFGFD
nlp-32
YGGWGa
ALNSLDGQGFGFE
GGWa
×3
ALNSLDGNGFGFD
GGa
AFDSLAGSGFDNGFN
GYGa
×2
AFDSLAGSGFGAFN
GGGWGa
AFDSLAGSGFSGFD
GGGWa
AFDSLAGQGFTGFE
GGGa
AFDTVSTSGFDDFKL
FGYGGa
STEHHRV
GWa
nlp-15
nlp-16
Peptide sequencea
SEGHPHE
pQWGYGGPYGGYG GGYGGGPWGYGGGW
nlp-33
ATHSPEGHIVA KDDHHGHE SSDSHHGHQ
HWGGYGGGPWGG YGGGPWGGYY nlp-34
PYGYGGYGGW
NAEDHHEHQ
nlp-35
AVVSGYDNIYQVLAPRF
SEHVEHQAEM HEHQ
nlp-36
PYGYGWa
SVDEHHGHQ
nlp-17
nlp-18
nlp-19
nlp-20
DDDVTALERWGY
STQEVSGHP EHHLV
NIDMKLGPH
GSLSNMMRIa
SMVARQIPQT VVADH
pQQEYVQFPNEGVV PCESCNLGTLMRIa nlp-37
NNAEVVNHILK NFGALDRLGDVa
SPYRAFAFA
nlp-38
(ASDDR)VLGWNKAHGLWa
ARYGFA
TPQNWNKLNSLWa
SPYRTFAFA
SPAQWQRANGLWa
ASPYGFAFA
nlp-39
EVPNFQADNV PEAGGRV
SDEENLDFLE
nlp-40
APSAPAGLEEKL(R)
IGLRLPNFLRF
MVAWQPM
IGLRLPNML
nlp-41
APGLFELPSRSV(RLI)
MGMRLPNIFLRNE
nlp-42
SALLQPENNPEWNQLGWAWa
FAFAFA
NPDWQDLGFAWa
SGPQAHEGA GMRFAFA
nlp-43
APKEFARFARASFA
nlp-44
×2
KQFYAWAa APHPSSALLVPYPRVa LYMARVa AFFYTPRIa
a Sequences
nlp-45
RNLLVGRYGFRIa
nlp-46
NIAIGRGDGLRPa
nlp-47
PQMTFTDQWT
shown in bold have been confirmed by Edman degradation, MALDI-TOF MS, or Q-TOF mass spectrometry.
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invertebrate neuropeptides. Finally, a systematic search for genes encoding members of the insulin superfamily revealed the presence of 40 insulin-like genes (7). Neuropeptidergic signaling in the nematode C. elegans has recently been reviewed (8). Based on such sequence information alone, one cannot deduce whether all the predicted peptides are actually expressed and properly processed. Therefore, each such neuropeptide needs to be purified and characterized biochemically. In the past, biochemical purification and elucidation of neuropeptide sequences required multiple chromatographic separation steps to purify an individual biologically active peptide. This approach appeared to be problematic, especially for small-sized animals, such as C. elegans. Previously, only 12 neuropeptides of C. elegans could be biochemically isolated and identified using Edman degradation analysis or gas-phase sequencing (9–14). Recently we set out to systematically search for and characterize neuropeptides of C. elegans using high-throughput peptidomics techniques. A peptidomics approach aims to identify endogenous (neuro)peptides using liquid chromatography and mass spectrometry. We aimed to elucidate which peptides were actually present in the nematode and to identify any post-translational modifications, which are often required for the peptide’s bioactivity. We successfully analyzed the peptidome of C. elegans (15, 16), and C. briggsae (17), while the Ascaris suum peptidome has been explored by others (18, 19). Differential peptidomics techniques allowed us to characterize the neuropeptide precursor processing enzymes EGL-3 (20, 21) and EGL-21 (22) and the neuroendocrine chaperone protein 7B2 (23). In this chapter we mainly focus on the basic techniques and methods required to culture the nematodes and to perform the sample preparation. Then, different technologies that can be used in peptidomic research are described and a short overview is provided of the instrumentation needed.
2. Materials 2.1. C. elegans Culture
1. C. elegans strains can be ordered from the Caenorhabditis Genetics Center (CGC, http://www.cbs.umn.edu/ CGC/), which is supported by the National Institutes of Health–National Center for Research Resources. This center collects, maintains, and distributes all kinds of C. elegans strains at a $7 fee per strain in the case of academic/nonprofit organizations or a $100 fee per strain for commercial organizations, in addition to the annual fee of $25. C. elegans N2 (Bristol) is referred to as the wild-type
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reference strain. The nematodes are sent by regular post as starved cultures on small Petri dishes. 2. Escherichia coli OP50 bacteria are also available at the CGC. 3. Nematode Growth Medium (NGM): Dissolve 3 g NaCl, 17 g agar, and 2.5 g peptone in 1 L H2 O. Sterilize by autoclaving, add 1 mL of 1 M CaCl2 , 1 mM of 5 mg/mL cholesterol in ethanol, 1 mL of 1 M MgSO4 , and 25 mL of 1 M KPO4 . Pour NGM medium in Petri dishes under sterile conditions (see Note 1). 4. Incubators (15–22◦ C) (see Note 2). 5. Drigalski spatula. 2.2. Sample Preparation
1. 60% sucrose solution; sugar can also be used. This solution can be stored at 4◦ C for a couple of weeks. 2. 0.1 M NaCl solution. Make this solution fresh each time. 3. Extraction solvent: methanol:water:acetic acid (90:9:1), used ice-cold. 4. 50% acetonitrile containing 0.1% trifluoroacetic acid (TFA). 5. Sample reconstitution buffers: 2–5% acetonitrile and 0.1% TFA (for HPLC analysis); 2–5% acetonitrile and 0.1% formic acid (FA) (for nano LC-ESI-Q-TOF MS). 6. n-hexane, ethyl acetate. 7. Solid-phase extraction cartridges, such as SepPak C18 cartridge (Waters, Milford, MA). 8. Glass homogenizator, sonicator (Sanyo MSE Soniprep 150 ultrasonic disintegrator or Branson 5510 ultrasonic cleaner). 9. SpeedVac vacuum centrifuge (Savant and Flexi-Dry MP, FTS systems). 10. 22-␮m spin filter (Ultrafree-MC, Millipore Corporation, Bedford, MA).
2.3. Peptidomics Analyses
1. High-performance liquid chromatograph (Beckmann, Fullerton, CA) equipped with a programmable solvent module 126 and a Diode Array Detector Module 168 (Gold System). 2. Symmetry C18 column (5 ␮m, 4.6 × 250 mm, Waters) for use with solvent flow rates of ∼1 mL/min. Symmetry C18 column (2.1 × 150 mm, 3.5 ␮m, Waters) for use with flow rates of ∼300 ␮L/min. 3. Matrix-assisted laser desorption ionization mass spectrometer (MALDI-TOF MS) Reflex IV (Bruker Daltonic
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GmbH, Germany); UltraflexII MALDI-TOF MS (Bruker Daltonic GmbH, Germany). Both mass spectrometers are operated using FlexControl software. The FlexAnalysis program is used to process mass readouts. 4. Standard calibration peptide mixture: Angiotensin 2 (1045.54 Da), angiotensin 1 (1295.68 Da), substance P (1346.73 Da), bombesin (1618.82 Da), ACTH clip 1–17 (2092.08 Da), and ACTH clip 19–39 (2464.19 Da) (Bruker Daltonic GmbH, Germany). 5. Mascot search engine (http://www.matrixscience.com). 6. Miniaturized LC system (nanoLC) comprising Ultimate HPLC pump, a Switchos column-switching device, and a Famos autosampler (LC Packings, Amsterdam, the Netherlands). 7. Electrospray quadrupole time-of-flight mass spectrometer (ESI-Q-TOF MS) (Waters-Micromass, Manchester, UK) (see Note 3). 8. Stainless steel emitter (Proxeon, Odense, Denmark). 9. C18 pre-column (␮-guard column MGU-30 C18, LCPackings). 10. Strong cation exchange column (Bio-SCX, 500 ␮m × 15 mm, LC-Packings). 11. Symmetry C18 column (3.5 ␮m, 75 ␮m × 100 mm, Waters); PepMap C18 column (3 ␮m, 75 ␮m × 150 mm, LC Packings). 12. ProteinLynx software (Waters-Micromass). 13. Solvents: Water, CH3 CN, TFA, H2 O. All solvents have to be HPLC grade. 14. Saturated ␣-cyano-4-hydroxycinnamic acid in acetone. 15. Pre-spotted anchorchip targets (Bruker Daltonics GmbH, Germany).
3. Methods 3.1. Maintenance of C. elegans Cultures
Here, we shortly describe how to get the nematode culture started. 1. C. elegans is normally grown using the E. coli OP50 as a food source (see Note 4). 2. OP50 bacteria can be grown using conventional microbiological methods and LB broth at 37◦ C.
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3. Apply 50–100 ␮L of an overnight grown culture of bacteria on medium-sized NGM plates. 4. Spread bacteria, let them dry, and allow them to grow overnight on the bench (20◦ C) or in a 37◦ C incubator to form a nice OP50 lawn (see Notes 5–7). 5. Equilibrate plate at 20◦ C before using them for culturing the nematodes. 6. Several methods can be used to transfer the worms from an old plate to a new one in order to expand the mass of nematodes for a peptidomics analysis, or to keep the nematodes in culture. We cut out a small piece of agar from the old plate, containing the worms, and transfer it to a new NGM plate using a sterile scalpel or spatula (see Note 8). Alternatively, individual animals can also be picked up using a home-made “worm-picker”, which is a small platinum wire with a flattened end that is melted into a glass Pasteur pipette. 7. To maintain the worm lines, the worms should be transferred to new plates weekly (see Notes 9 and 10). 3.2. Sample Preparation
1. Collect the mixed-stage worms from 10–15 fully grown Petri dishes by rinsing the plates with a 0.1 M NaCl solution (see Note 11). 2. Living animals shall be separated from the E. coli bacteria and dead animals by flotation on 30% sucrose or sugar. Add an equal volume of a 60% sucrose or sugar solution to the 0.1 M NaCl solution containing the worms. Centrifuge for 4 min at 500×g; the living animals will float on top of the sugar gradient. Harvest the nematodes and wash four times with 0.1 M NaCl (see Note 12). 3. Transfer the nematodes to 15 mL of an ice-cold extraction solvent (see Note 13). 4. Homogenize the worms using a glass stick homogenizator and sonicate the solution prior to centrifugation. 5. Discard the pellet, evaporate the methanol using a SpeedVac concentrator. 6. The remaining aqueous solution, containing the peptides, has to be delipidated by re-extraction with ethyl acetate or n-hexane (see Note 14). Add equal volume of organic solvent to the aqueous solution that contains the peptides. Mix by vigorous inversion of the sample, and centrifuge briefly (1 min at 13,000 rpm using a benchtop centrifuge) to separate the phases. Carefully remove and discard the top (organic) layer. 7. Desalt the aqueous solution using solid-phase extraction with a SepPak C18 cartridge (see Note 15). Activate the
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cartridge using 50–100% of CH3 CN, rinse the column using water containing 0.1% TFA, add the aqueous peptide sample. Wash the cartridge with 0.1% TFA in water. Elute the peptides with 50% (or higher) acetonitrile containing 0.1% TFA. 8. The desalted peptide sample shall be stored at 4◦ C prior to analysis. Alternatively, samples can be lyophilized by using a SpeedVac concentrator and stored at –20◦ C. 9. Immediately prior to the analysis by HPLC and MALDITOF MS, reconstitute the samples in water containing 2–5% acetonitrile and 0.1% TFA and filter them using 22-␮m spin filters. For the analysis by nano LC-ESI-Q-TOF MS, samples should be reconstituted in water containing 2–5% acetonitrile and 0.1% FA. 3.3. Peptidomics Analyses
3.3.1. Off-Line HPLC–MALDI-TOF MS
3.3.1.1. High-Performance Liquid Chromatography (HPLC) (see Note 16) 3.3.1.2. Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-TOF MS)
Here we describe two general strategies for the peptidomics analysis of C. elegans. The first method is an off-line strategy, in which the generated HPLC fractions are characterized using a MALDI-TOF instrument (summarized in Fig. 3.2). This strategy allows an easy comparison of different fractions from various mutant strains and is therefore preferred for differential peptidomics analysis. Peptides of interest can be sequenced later using, for example, MALDI-TOF/TOF MS. The other approach relies on a high-throughput two-dimensional separation of the peptide extract and the automated MS and MS/MS measurements using an ESI-Q-TOF instrument (summarized in Fig. 3.3). Using that on-line approach, the peptidomes of the fruitfly D. melanogaster (24) and the nematode C. elegans (15) have been successfully characterized in our lab. 1. Inject the peptide extract and wash the column for 10 min using 4% acetonitrile in 0.1% TFA (see Note 16). 2. Start a linear gradient of 4% acetonitrile in 0.1% TFA to 50% CH3 CN in 0.1% TFA (60 min). Endogenous peptides tend to elute between 22 and 37% of acetonitrile (see Note 17). 3. Collect fractions eluted from HPLC once every minute (see Note 18). This off-line HPLC–MALDI-TOF MS approach allows fast screening of the peptide content of different C. elegans strains as the mass readouts can be compared easily. We found 75 peptides using this robust peptidomics protocol (17, 20–23). 1. Vacuum dry one-fifth to one-half of each of the generated HPLC fractions and reconstitute each in 1 ␮L of 50% acetonitrile in 0.1% TFA prior to applying them to the ground steel target MALDI plate.
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Fig. 3.2. Overview of the off-line HPLC–MALDI-TOF MS workflow. (a) The peptide extract was separated using a reversedphase C18 column to generate a chromatogram as shown. Absorbance was monitored at 214 nm. Each HPLC fraction was then analyzed by MALDI-TOF mass spectrometry to generate a peptide profile. Only fractions 30–34 are shown. (b) Schematic representation of a typical MALDI-TOF instrument. All samples are deposited on a stainless steel target plate, together with an UV-absorbing matrix like ␣-cyano-4-hydroxycinnamic acid. When a pulsed laser beam hits the target plate, an ion plume is generated. Next, the ions are accelerated by an electrostatic field that is applied on the acceleration plates (Acc), and guided through the deflectors (Df) before entering the field-free flight tube. This time-offlight (TOF) analyzer measures the time an accelerated ion needs to reach the detector at the end of the flight tube. These data can be converted into m/z units as the kinetic energies of all ions in the flight tube are equal. When measuring in “reflectron mode”, an electrostatic mirror lengthens the flight path to increase the resolution and mass accuracy.
2. Mix the droplets with the saturated solution of ␣-cyano-4hydroxycinnamic acid in acetone (see Note 19). Dry the target plate and insert it into the mass spectrometer.
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Fig. 3.3. Overview of the on-line 2D-nanoLC–Q-TOF MS/MS workflow. (a) Schematic representation of the hardware used: an Ultimate high pressure LC pump, a Switchos column-switching device, a Famos autosampler (all LC Packings) and a quadrupole – time-of-flight mass spectrometer (Q-TOF) (Micromass-Waters). Two nanoscale columns (a strong cation exchange (SCX) column and a reversed-phase C18 column) are placed in line. Each fraction that elutes from the first SCX column will undergo a subsequent separation on the second reversed-phase column. This way, ten successive separations are performed. The eluent is directly connected to the Q-TOF mass spectrometer. Individual ions are formed in the electrospray source (Z-spray ESI source), which are guided through the hexapole (six parallel rods) to enter the quadrupole (four parallel rods) mass filter. This Q-TOF instrument allows a selection of particular ions in the first quadrupole (narrow bandpass mode), while the other non-resonant ions get lost. After fragmentation of the selected ion by collision with an inert gas in the collision cell, the generated fragments are measured in the time-of-flight (TOF) analyzer to generate the fragmentation or MS/MS spectrum. This TOF analyzer is equipped with a reflectron (to lengthen the flight path) and a multi-channel plate (MCP) detector. (b) Visualization of the data obtained. All spectra are converted into typical peak list files which can be submitted to a bioinformatics program that matches the experimental data against any protein database. For our work, we used a home-made database containing the predicted neuropeptide precursors of C. elegans.
3. Calibrate the instrument using a standard peptide mixture containing angiotensin 2, angiotensin 1, substance P, bombesin, ACTH clip 1–17, and ACTH clip 19–39. 4. Record spectra using the reflectron mode within a mass range of 500–3000 Da. Adjust the laser intensity to obtain optimal resolution and sensitivity. 5. Mass readouts can automatically be processed in the FlexAnalysis program to obtain peak list files. Experimental m/z values can then be compared with the theoretical masses of the predicted peptides (see Note 20).
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The main advantage of this approach is that the peptides are automatically sequenced in a high-throughput manner. Using this method we sequenced ∼60 endogenous peptides (15, 16); these peptides are indicated in bold font in Tables 3.1 and 3.2. 1. Load 20 ␮L of the peptide sample (corresponding to two fully grown NGM plates) onto a strong cation exchange column (Bio-SCX, 500 ␮m × 15 mm) using 2% acetonitrile in 0.1% FA and the flow rate of 30 ␮L/min. This cation exchange column was placed on-line with a C18 pre-column or trapping column (␮-guard column MGU-30 C18, LCPackings). 2. After loading the sample, the SCX column should be switched off-line, and the reversed-phase pre-column should be rinsed for 5 min. 3. Switch the reversed-phase trapping column on-line with the nanoscale Symmetry C18 column (3.5 ␮m, 75 ␮m × 100 mm) or a PepMap C18 column (3 ␮m, 75 ␮m × 150 mm). Separate the peptides using a linear gradient from 2% to 50% acetonitrile containing 0.1% FA at a flow rate of 200 nL/min for 50 min. 4. Elute the second fraction of peptides from the SCX column by injecting 20 ␮L of a 20 mM ammonium acetate solution. Concentrate and desalt these peptides again on the C18 precolumn prior to the nanoscale HPLC and MS analysis. 5. Repeat this elution procedure ten times using different salt plugs of ammonium acetate (0, 20, 50, 100, 200, 400, 600, 800, 1000, and 2000 mM). 6. The 2D-LC system should be connected directly to the electrospray interface of the Q-TOF mass spectrometer through a stainless steel emitter. 7. The mass spectrometer should be set to automatic datadependent MS to MS/MS switching when the intensity of the doubly and triply charged parent ions increases above 15 counts/s. The applied collision energy of the argon gas should be chosen automatically (between 25 and 40 eV) depending on the number of charges and the mass range of the selected parention. 8. Transform the MS/MS data of all ten SCX fractions into pkl (peak list) files using the ProteinLynx software. 9. Submit these text files to a Mascot search to identify the peptides (see Note 21).
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4. Notes 1. We prefer to use Petri dishes that allow air to float under the lid as the nematodes need oxygen to survive. Depending on the amount of plates needed, a peristaltic pump can be used to pour the NGM. 2. C. elegans is normally cultured at 20◦ C. Depending on the planning of the extractions, temperature can be lowered or increased to slow down or speed up the growth. Nematode cultures can also be stored on the bench when a constant room temperature of about 20◦ C is maintained. 3. The nanoLC column was directly coupled to the ESI-QTOF MS. 4. This bacterial strain is uracil auxotroph and thus has a limited growth on NGM plates. 5. It is very important not to damage the NGM surface as the worm will tend to crawl into the agar. Also, when spreading the bacteria, try not to cover the total surface of the plate as the nematodes will crawl up the sides of the plate and die when the bacterial lawn reaches the edges of the Petri dish. 6. Depending on the experiments planned, the bacteria can be grown for longer or shorter periods. In order to get more nematodes, we prefer to extend the incubation time to produce a thicker bacterial lawn. Also, conventional LB agar (35 g/L) can be used instead of 3 g NaCl, 17 g agar, and 2.5 g peptone in 1 L H2 O as described in Section 2.1. 7. (Seeded) plates may be stored at 4◦ C for a couple of weeks, although it is better to use fresh plates. 8. This technique is referred to as “chunking.” Worms will crawl out of the chunk and a typical sinusoidal “footprint” is generated by the worms. The worms can easily be visualized using a dissecting microscope or a stereo microscope. This method is preferred when a large numbers of nematodes are required, e.g., when starting a new peptidomics experiment. 9. This frequency will depend on the size of the chunks, the dimensions of the Petri dishes, and the growth temperature. 10. For a typical off-line HPLC–MALDI-TOF MS experiment, we use 10–20 fully grown Petri dishes (90 mm diameter) of C. elegans. Two plates of the starting material should be sufficient for an on-line 2D-nanoLC ESI Q-TOF MS/MS setup.
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11. Be careful not to damage the NGM surface when collecting the nematodes. 12. Ten to fifteen fully grown Petri dishes will yield a pellet containing ∼500 ␮L of living nematodes. 13. This extraction solvent is specially designed to extract small endogenous peptides, while larger proteins precipitate. When interested in larger peptides (5–15 kDa) such as the insulin-like peptides, diluted acids might be a better extraction solvent. All steps have to be performed on ice to avoid degradation of the proteins. Active peptidases result in degradation of proteins and might result in shortened and/or fragmented peptides, which are obviously not of interest. 14. Both solvents for re-extraction of the peptide extract perform equally well in our hands, but may have ramifications with other peptidomics experiments. If lots of lipids appear to be present, extraction with both organic solvents can be performed. 15. Other solid-phase cartridges may be used, e.g., Oasis HLB extraction cartridges (10 mg, Waters, Milford, MA). These are a good alternative to the SepPak C18 solid-phase extraction cartridges. The HLB column is equilibrated with methanol and then with water. After loading the aqueous solution of peptides, the cartridge is washed with water containing 5% methanol. Finally, peptides are eluted with 100% methanol. 16. Many different HPLC columns are available, we prefer a Symmetry C18 (5 ␮m, 4.6 × 250 mm) column that operates at a solvent flow-rate of 1 mL/min. Depending on the amount of starting material, a smaller Symmetry C18 column (2.1 × 150 mm, 3.5 ␮m) with a flow rate of 300 ␮L/min might be used (15, 17, 20, 22, 23). 17. Three-step gradient may be used at this step. For example: from 2% to 22% acetonitrile (in 0.1% TFA) for 20 min, followed by 22–37% acetonitrile (in 0.1% TFA) for 30 min, followed by 37–50% acetonitrile (in 0.1% TFA) for 10 min. 18. We prefer to collect the generated HPLC fractions automatically from the beginning of the (three-step) gradient. 19. We prefer to use ␣-cyano-4-hydroxycinnamic acid as matrix, because it is ideally suited for use with small peptides. If higher sensitivity is required, pre-spotted anchorchip targets can be used. 20. When using a LIFT/TOF or TOF/TOF instrument (like the Ultraflex II), fragmentation of ion peaks of interest can yield sequence information. MS/MS spectra can be
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analyzed by de novo sequencing. However, because a good protein database of C. elegans is available, we prefer to use search engines such as “Mascot.” 21. Our in-house Mascot server matches the fragmentation data from the peak list files against our home-made database containing all known FLP and NLP precursors. Individual ions with Probability Based Mowse Scores above the threshold (P<0.05) are further analyzed and annotated to gain sequence information.
Acknowledgments Research in the authors’ lab was sponsored by the Research Foundation Flanders (FWO-Vlaanderen grant G.0434.07 and 1.5.137.06). The authors strongly acknowledge the Interfacultary Centre for Proteomics and Metabolomics “Prometa”, K.U. Leuven, and wish to thank the Caenorhabditis Genetics Center for providing all the C. elegans strains. S.J. Husson, T. Janssen, M. Lindemans and E. Clynen are postdoctoral fellows of the Research Foundation Flanders (FWO-Vlaanderen). References 1. The C. elegans Sequencing Consortium (1998) Genome sequence of the nematode C. elegans: a platform for investigating biology. Science 282, 2012–2018. 2. Kim, K. and Li, C. (2004) Expression and regulation of an FMRFamide-related neuropeptide gene family in Caenorhabditis elegans. J. Comp. Biol. 475, 540–550. 3. Li, C., Nelson, L.S., Kim, K., Nathoo, A., and Hart, A.C. (1999) Neuropeptide gene families in the nematode Caenorhabditis elegans. Ann. N.Y. Acad. Sci. 897, 239–252. 4. Li, C., Kim, K., and Nelson, L.S. (1999) FMRFamide-related neuropeptide gene family in Caenorhabditis elegans. Brain Res. 848, 26–34. 5. McVeigh, P., Leech, S., Mair, G.R., Marks, N.J., Geary, T.G., and Maule, A.G. (2005) Analysis of FMRFamide-like peptide (FLP) diversity in phylum Nematoda. Int. J. Parasitol. 35, 1043–1060. 6. Nathoo, A.N., Moeller, R.A., Westlund, B.A., and Hart, A.C. (2001) Identification of neuropeptide-like protein gene families in Caenorhabditis elegans and other species. Proc. Natl. Acad. Sci. USA. 98, 14000–14005.
7. Pierce, S.B., Costa, M., Wisotzkey, R., Devadhar, S., Homburger, S.A., Buchman, A.R., Ferguson, K.C., Heller, J., Platt, D.M., Pasquinelli, A.A., Liu, L.X., Doberstein, S.K., and Ruvkun, G. (2001) Regulation of DAF2 receptor signaling by human insulin and ins-1, a member of the unusually large and diverse C. elegans insulin gene family. Genes Dev. 15, 672–686. 8. Husson, S.J., Mertens, I., Janssen, T., Lindemans, M., and Schoofs, L. (2007) Neuropeptidergic signaling in the nematode Caenorhabditis elegans. Prog. Neurobiol. 82, 33–55. 9. Marks, N.J., Shaw, C., Maule, A.G., Davis, J.P., Halton, D.W., Verhaert, P., Geary, T.G., and Thompson, D.P. (1995) Isolation of AF2 (KHEYLRFamide) from Caenorhabditis elegans: evidence for the presence of more than one FMRFamide-related peptide-encoding gene. Biochem. Biophys. Res. Commun. 217, 845–851. 10. Marks, N.J., Maule, A.G., Geary, T.G., Thompson, D.P., Davis, J.P., Halton, D.W., Verhaert, P., and Shaw, C. (1997) APEASPFIRFamide, a novel FMRFamide-related decapeptide from Caenorhabditis elegans:
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11.
12.
13.
14.
15.
16.
17.
structure and myoactivity. Biochem. Biophys. Res. Commun. 231, 591–595. Marks, N.J., Maule, A.G., Geary, T.G., Thompson, D.P., Li, C., Halton, D.W., and Shaw, C. (1998) KSAYMRFamide (PF3/AF8) is present in the freeliving nematode, Caenorhabditis elegans. Biochem. Biophys. Res. Commun. 248, 422–425. Marks, N.J., Maule, A.G., Li, C., Nelson, L.S., Thompson, D.P., Alexander-Bowman, S., Geary, T.G., Halton, D.W., Verhaert, P., and Shaw, C. (1999) Isolation, pharmacology and gene organization of KPSFVRFamide: a neuropeptide from Caenorhabditis elegans. Biochem. Biophys. Res. Commun. 254, 222–230. Marks, N.J., Shaw, C., Halton, D.W., Thompson, D.P., Geary, T.G., Li, C., and Maule, A.G. (2001) Isolation and preliminary biological assessment of AADGAPLIRFamide and SVPGVLRFamide from Caenorhabditis elegans. Biochem. Biophys. Res. Commun. 286, 1170–1176. Rosoff, M.L., Doble, K.E., Price, D.A., and Li, C. (1993) The flp-1 propeptide is processed into multiple, highly similar FMRFamide-like peptides in Caenorhabditis elegans. Peptides 14, 331–338. Husson, S.J., Clynen, E., Baggerman, G., De Loof, A., and Schoofs, L. (2005) Discovering neuropeptides in Caenorhabditis elegans by two dimensional liquid chromatography and mass spectrometry. Biochem. Biophys. Res. Commun. 335, 76–86. Husson, S.J., Clynen, E., Baggerman, G., De Loof, A., and Schoofs, L. (2005) Peptidomics of Caenorhabditis elegans: in search of neuropeptides. Commun. Agric. Appl. Biol. Sci. 70, 153–156. Husson, S.J., Landuyt, B., Thomas, N., Baggerman, G., Boonen, K., Clynen, E., Lindemans, M., Janssen, T., and Schoofs, L. (2008) Comparative peptidomics of Caenorhabditis elegans versus C. briggsae
18.
19.
20.
21.
22.
23.
24.
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by LC–MALDI-TOF MS. Peptides, 30, 449–457. Yew, J.Y., Dikler, S., and Stretton, A.O. (2003) De novo sequencing of novel neuropeptides directly from Ascaris suum tissue using matrix-assisted laser desorption/ionization time-of-flight/time-offlight. Rapid Commun. Mass Spectrom. 17, 2693–2698. Yew, J.Y., Kutz, K.K., Dikler, S., Messinger, L., Li, L., and Stretton, A.O. (2005) Mass spectrometric map of neuropeptide expression in Ascaris suum. J. Comp. Neurol. 488, 396–413. Husson, S.J., Clynen, E., Baggerman, G., Janssen, T., and Schoofs, L. (2006) Defective processing of neuropeptide precursors in Caenorhabditis elegans lacking proprotein convertase 2 (KPC-2/EGL-3): mutant analysis by mass spectrometry. J. Neurochem. 98, 1999–2012. Husson, S.J. and Schoofs, L. (2006) Characterization of a key neuropeptide processing enzyme in C. elegans by mass spectrometry. Commun. Agric. Appl. Biol. Sci. 71, 171–174. Husson, S.J., Janssen, T., Baggerman, G., Bogert, B., Kahn-Kirby, A.H., Ashrafi, K., and Schoofs, L. (2007) Impaired processing of FLP and NLP peptides in carboxypeptidase E (EGL-21)-deficient Caenorhabditis elegans as analysed by mass spectrometry. J. Neurochem. 102, 246–260. Husson, S.J. and Schoofs, L. (2007) Altered neuropeptide profile of Caenorhabditis elegans lacking the chaperone protein 7B2 as analyzed by mass spectrometry. FEBS Lett. 581, 4288–4292. Baggerman, G., Boonen, K., Verleyen, P., De Loof, A., and Schoofs, L. (2005) Peptidomic analysis of the larval Drosophila melanogaster central nervous system by two-dimensional capillary liquid chromatography quadrupole time-of-flight mass spectrometry. J. Mass Spectrom. 40, 250–260.
Chapter 4 Mass Spectrometric Analysis of Molluscan Neuropeptides Ka Wan Li and August B. Smit Abstract The central nervous systems of molluscan species contain high levels of structurally diverse peptides that function as neurotransmitters, neuromodulators or neurohormones. Peptide diversity is believed to be a way to increase the information handling capacity of neurons in the context of a brain with low cell numbers and neuronal connectivity. Accordingly, much effort has been made to identify peptides from single neurons and tissues of interest. In the past decade a mass spectrometry-based approach has been applied to detect and characterize peptides from single neurons, nerves and tissues of the molluscan brain. Peptides from single neurons are often analysed directly by mass spectrometry without prior sample preparation. Single neurons from the molluscan brain may be identified based on their position, cell morphology and colour. Neurons that cannot be readily identified can be tagged functionally or chemically. For the analysis of peptides from tissues, special extraction methods in conjunction with peptide separation by liquid chromatography coupled to mass spectrometry have been developed. Tens to hundreds of peptides from the tissue extract can be detected and characterized in a single analysis. Key words: Neuropeptides, MALDI mass spectrometry, single-cell analysis, tissue extraction, retrograde labelling.
1. Introduction Peptidergic neurons constitute the major class of nerve cells in the molluscan brain. Some of the neuropeptides are released from neurohemal areas as hormones. More often, neuropeptides are released from axon endings that closely appose the target cells and function as neuromodulators and/or neurotransmitters and are involved in fast cell–cell communication. The diversity of molluscan peptides is large (1) and estimated to be in the order of several hundreds within a given species. In contrast, peptide diversity M. Soloviev (ed.), Peptidomics, Methods in Molecular Biology 615, DOI 10.1007/978-1-60761-535-4 4, © Humana Press, a part of Springer Science+Business Media, LLC 2010
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in the vertebrate nervous system is low and classical transmitters, such as glutamate and GABA, are preferentially used for neurotransmission. Peptides are differentially expressed in distinct populations of neurons. While some neurons express a single peptide, other neurons may express a large number of structurally diverse peptides (2). Recent studies revealed the molecular mechanisms that are used to generate peptide diversity in single cells, including differential expression of multiple peptide precursors, alternative splicing of a single precursor, differential processing of peptide domains from a precursor, and different types of posttranslational modifications of peptides such as phosphorylation, glycosylation and hydroxylation (2). Peptide diversity is believed to be a way to increase the information handling capacity of the cells (3). This might be the evolutionary outcome of selecting complex behaviours, such as feeding or reproduction, whilst using a brain with low number of cells and limited neuronal connectivity. Much effort has been made to identify peptides from single neurons and tissues of interest. Mass spectrometry-based techniques play a major role in the detection and characterization of peptides in molluscan nervous systems (1, 4–6). In cases where peptide diversity is low, for example in a single cell that contains few tens of peptides, sample can be analysed directly by mass spectrometry without prior sample preparation (2). When nerves, organelles or the released peptides from nervous system need to be analysed (1, 6–8), special extraction methods in conjunction with peptide separation by liquid chromatography should be used. This (pre-) fractionation step is aimed to remove the interfering molecules and reduce sample complexity. This should increase the sensitivity and capacity of the mass spectrometric analysis of the peptides. Peptides may be analysed from different sources ranging from single neurons to nerves and whole tissues such as the reproductive organs (1, 2, 9). As these samples differ greatly in complexity and the ease of extraction, optimized methodologies for each sample type have been developed. In molluscs many giant neurons can be individually identified based on their position, colour and size. These neurons may be picked individually and analysed by mass spectrometry without extra treatment (2). Neurons that cannot be readily identified visually should be tagged functionally or chemically. To our advantage is the fact that neurons, functionally connected to the same target, often share a common nerve, and therefore they can be retrograde-labelled from this nerve (4). These back-filled cells can then be isolated for subsequent analysis. When extracting neuropeptides from tissues, one should avoid conventional homogenization-based extraction methods that extract large number of proteins in addition to neuropeptides. This would complicate the subsequent peptide
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fractionation and mass spectrometric analysis. We routinely use acetone extraction (10, 11). Acetone causes partial dehydration of the tissue resulting in the extraction of the small molecules including neuropeptides into the acetone solvent. The majority of proteins remain within the organ.
2. Materials 1. Solvents: 0.1% TFA in water; 60% acetonitrile in 0.1% TFA; acetone/HCl/H2 O solvent (40:1:6). 2. Reversed–phase solid-phase extraction column Supeclean (Supelco). 3. Saline buffer: 4 mM CaCl2 , 1.7 mM KCl, 1.5 mM MgCl2 , 30 mM NaCl, 5 mM NaHCO3 , 10 mM NaCH3 SO4 , and buffered with 10 mM HEPES to pH 7.8. All the chemicals are reagent-grade. 4. Saturated dithiooxamide: Add dithiooxamide (SigmaAldrich) to ethanol until it is saturated. Keep the supernatant. 5. Nickel-lysine solution: Add 1.7 g NiCl2 × 6H2 O and 3.5 g L-lysine to 20 mL H2 O. 6. Sylgard dish (Dow Corning) 7. Vaseline 8. Matrix solution (except for single-cell analysis): Dissolve 7 mg of ␣-cyano-4-hydroxycinnamic acid (ultra-pure grade, Sigma-Aldrich) in 1 mL of 50% acetonitrile/50% 10 mM ammonium monobasic phosphate (see Notes 1 and 2). 9. Matrix solution for single-cell analysis: 10 mg/mL 2,5-dihydroxybenzoic acid (Sigma-Aldrich) in acetonitrile (HPLC grade)/water/TFA (50%/50%/0.1%). 10. Solvents for high-performance liquid chromatography (HPLC). Solvent A: 5% acetonitrile in 0.05% TFA; Solvent B: 80% acetonitrile in 0.04% TFA. 11. Nano HPLC system complete with Ultimate LC system (LC-Packing) 12. MALDI target spotter Probot (Dionex) 13. Pipette capable of handling ∼0.5 ␮L volumes. 14. MALDI mass spectrometry: Large variety of MALDI mass spectrometers are available from different vendors. Our protocols have been optimized for use with the 4800 Proteomics Analyzer (Applied Biosystems).
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3. Methods 3.1. Single-Cell Analysis
1. Dissect the brain of the mollusc and pin it down on a Sylgard disc containing saline buffer. 2. Carefully remove the connective tissue with a pair of forceps under a stereo microscope. 3. Loosen the neuron of interest from the brain with tiny hooks. 4. Use a glass pipette to pierce through the neuron. Aspirate the cell content into the pipette and transfer to mix with 1 ␮L drop of matrix solution on a MALDI-metal plate (see Notes 1 and 3). 5. Let the matrix to dry at room temperature for a few minutes before inserting the sample plate into the mass spectrometer for analysis.
3.2. Retrograde Labelling of Neurons
1. Carefully cut open the skin of the head region to expose the brain. 2. Cut off all the nerves from the brain except the nerve of interest, which should be cut as far away as possible from the brain. 3. Transfer the brain to a dry Sylgard dish. 4. Use several pins to pierce through the connective tissues of the brain onto the Sylgard disc to fix the brain in position. 5. Cut the nerve of interest tens of centimetres away from the brain. 6. Apply a ring of Vaseline around the cut end of the nerve, and within a minute add 1–2 drop of nickel-lysine solution to the ring of Vaseline. The nickel-lysine solution should completely immerse the cut end of the nerve. 7. Seal the Vaseline ring with additional layer of Vaseline to cover the nickel-lysine solution. 8. Add enough saline buffer to the Sylgard disk to submerge the brain and leave it at room temperature overnight. 9. Transfer the brain to another Sylgard dish containing saline buffer. 10. Wash the brain once in fresh saline buffer. 11. Add the saturated dithiooxamide solution to the Sylgard dish containing the brain. Use 1 drop of dithiooxamide solution per 1 mL of saline buffer. 12. When the retrograde-labelled neurons appear brownish black in colour, they can be removed as shown in Section 3.1 for analysis.
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1. Dissect the nerve of interest under stereo microscope and transfer the nerve into a 2-␮L drop of matrix solution spotted on the MALDI-metal plate (see Note 3). 2. Break and tear apart the nerve with a pair of forceps while maintaining the nerve in the matrix solution. Neuropeptides diffuse from the damaged nerve into the matrix solution. 3. Remove the nerve debris from the matrix solution with a pair of forceps. The whole procedure should be finished within 2–3 min after applying the matrix on the stainless steel target. 4. Let the matrix to dry at room temperature for a few minutes before inserting the sample plate into the mass spectrometer for analysis.
3.4. Analysis of Neuropeptides from Molluscan Tissues and Organs
1. Dissect tissue and store at –80◦ C until used. We usually collect 20–50 samples for a single experiment, depending on the weight of the tissue collected. 2. Add 5–10 volumes of extraction solvent, acetone/HCl/H2 O solvent (40:1:6), in a glass beaker and stir overnight at 4◦ C (see Note 4). 3. Dilute the solvent containing the neuropeptide extract tenfold with water. 4. Prepare the C18 solid-phase extraction column. Condition the column with 2 volumes of 100% methanol and then wash with 5 volumes of 0.1% TFA. 5. Aspirate the diluted solvent containing the neuropeptides into a 10-mL plastic syringe and slowly inject into the conditioned C18 solid-phase extraction column. The flow rate should not be too high; we usually add the solvent to the column at around 2–4 mL per min. Do not dry the column. 6. Apply the rest of the solvent to the column in 10-mL aliquots. 7. Wash the column with 5 volumes of 0.1% TFA. 8. Elute the neuropeptides from the column with 2–3 volumes of 60% acetonitrile in 0.1% TFA, and collect in a 1.5-mL eppendorf tube (see Note 5). 9. Dry the neuropeptides in a SpeedVac. 10. Re-suspend the dried sample in 0.1% TFA and fractionate the neuropeptides with HPLC using a nano-C18 column. 11. Collect the fractions on a MALDI-metal plate for mass spectrometric analysis.
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3.5. HPLC Separation of Neuropeptides
1. Redissolve the peptides in 20 ␮L 0.1% TFA. 2. Inject the peptides into a 3-␮m nano-C18 LC column. 3. Separate the peptides using a linearly increasing concentration of acetonitrile from 5 to 50% in 30 min and to 100% in 5 min. Set the flow rate to 400 nL/min. 4. Mix the eluent from LC column with matrix (␣-cyano4-hydroxycinnamic acid) delivered at a flow rate of 1.5 ␮L/min, and deposit off-line to the MALDI-metal plate every 15 s for a total of 192 spots, using an automatic robot, such as Probot or similar others. 5. Analyse the peptides with MALDI MS/MS.
3.6. Maldi Ms/Ms
1. Analyse peptides on an MALDI MS/MS, such as ABI 4800 or similar proteomics analyzer. 2. Perform MS analysis. We usually acquire 1250 MS spectra per fraction/sample (see Note 6). 3. Select peptides with signal-to-noise ratio above 50 at the MS mode for MS/MS experiment; a maximum of 25 MS/MS is allowed per spot. Set the precursor mass window to 200 (see Note 7). 4. Perform collision-induced dissociation on each of the peptides at 1 kV with air as the collision gas. 5. Collect MS/MS spectra from 2500 laser shots per peptide.
3.7. Recrystallization of MALDI Matrix
1. Add ␣-cyano-4-hydroxycinnamic acid to 100 mL ethanol; heat in a boiling water bath until saturation (see Note 8). 2. Pour the solution into a beaker, and store it at –20◦ C for 2 days. 3. Matrix appears as yellow precipitate in the solution. Collect the matrix and air dry on a Whatman paper. 4. Break the matrix on the Whatman paper, and transfer to a Buchner funnel. 5. Wash the matrix with a few volumes of ethanol. 6. Weigh the air-dried matrix and put 7 mg into a 1.5-mL Eppendorf tube. Store the matrix at –20◦ C. The matrix is stable for years. 7. Dissolve matrix in 1 mL solvent for off-line LC analysis.
4. Notes 1. We use two types of matrix for different applications. The preferred matrix for the off-line LC analysis is ␣-cyano-4hydroxycinnamic acid. It forms a homogeneous layer of fine
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crystals on the MALDI-metal plate, which facilitates automatic MALDI MS and MS/MS analysis. 2. Alternatively, matrix can be purified from analytical grade to high purity by recrystallization (as described in Section 3.7). A considerable amount of matrix will be used for LC analysis. It is more economical to re-crystallize reagent-grade matrix rather than to purchase the expensive ultra-pure-grade matrix. This option is especially attractive when larger quantity is required, for example in the case of LC-MALDI MS. 3. For the direct single-cell or nerve MS analysis both 2,5-dihydroxybenzoic acid and ␣-cyano-4-hydroxycinnamic acid can be used. The advantage of using the 2,5dihydroxybenzoic acid is that it does not crystallize as fast as ␣-cyano-4-hydroxycinnamic acid. Therefore, more time will be available to break and mix the cell content in the matrix. However, 2,5-dihydroxybenzoic acid forms inhomogeneous spear-shaped crystals with sweet spots. These sweet spots represent the site where neuropeptides are co-crystallized with the matrix. They are usually formed around the rim of the matrix crystals. The analysis requires manual searching of sweet spots in the spear-shaped crystal. The laser beam is targeted on the crystal and the peptide peak intensity is continuously monitored. If a sweet spot is found, multiple mass spectra can be generated with higher sensitivity. 4. Neuropeptides and other small molecules will be preferentially extracted into the solvent. 5. The amount of neuropeptides is generally low. The low peptide concentration increases the risk of their loss during the sample handling and storage. We use Eppendorf safe-lock 1.5-mL microcentrifuge tubes because they have low peptide absorption. Furthermore, these tubes do not contain low molecular weight contaminants that may interfere with subsequent MALDI MS analysis. 6. A typical MALDI spot can withstand thousands of laser shoots before it is depleted of material. So multiple analyses can be performed on a single spot. To get high MS1 sensitivity it is possible to increase the laser energy until the gain of peak intensity reaches the plateau. A higher number of MS1 spectra can also be summed together to reduce background noise. Whereas a routine MS1 analysis is about 1250 shoots, we occasionally use up to 7000 laser shoots per analysis. 7. It is possible to select peptides with signal-to-noise ratio below 50 at the MS mode for MS/ MS experiment. However, the signal intensity of the MS/MS spectra may be low, and often only a few fragment ions are detected.
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Nevertheless, the low number of fragment ions detected may still be useful for some analyses – for example, for the confirmation of peptide identities in samples from, e.g. a single cell, often containing less than ten different peptide sequences (12) 8. Keep adding ␣-cyano-4-hydroxycinnamic acid until no more can be dissolved. We use about 10 g ␣-cyano-4hydroxycinnamic acid per 100 mL ethanol. References 1. El Filali, Z., Van Minnen, J., Liu, W.K., Smit, A.B., and Li, K.W. (2006) Peptidomics analysis of neuropeptides involved in copulatory behavior of the mollusk Lymnaea stagnalis. J. Proteome Res. 5, 1611–1617. 2. Jimenez, C.R., Spijker, S., de Schipper, S., Lodder, J.C., Janse, C.K., Geraerts, W.P., van Minnen, J., Syed, N.I., Burlingame, A.L., Smit, A.B., and Li, K.W. (2006) Peptidomics of a single identified neuron reveals diversity of multiple neuropeptides with convergent actions on cellular excitability. J. Neurosci. 26, 518–529. 3. Brezina, V., Orekhova, I.V., and Weiss, K.R. (1996) Functional uncoupling of linked neurotransmitter effects by combinatorial convergence. Science 273, 806–810. 4. El Filali, Z., Hornshaw, M., Smit, A.B., and Li, K.W. (2003) Retrograde labeling of single neurons in conjunction with MALDI high-energy collision-induced dissociation MS/MS analysis for peptide profiling and structural characterization. Anal. Chem. 75, 2996–3000. 5. Hummon, A.B., Amare, A., and Sweedler, J.V. (2006) Discovering new invertebrate neuropeptides using mass spectrometry. Mass Spectrom. Rev. 25, 77–98. 6. Jimenez, C.R., Li, K.W., Smit, A.B., and Janse, C. (2006) Auto-inhibitory control of peptidergic molluscan neurons and reproductive senescence. Neurobiol. Aging 27, 763–769. 7. Jakubowski, J.A., Hatcher, N.G., and Sweedler, J.V. (2005) Online microdialysis– dynamic nanoelectrospray ionization–mass
8.
9.
10.
11.
12.
spectrometry for monitoring neuropeptide secretion. J. Mass Spectrom. 40, 924–931. Jimenez, C.R., ter Maat, A., Pieneman, A., Burlingame, A.L., Smit, A.B., and Li, K.W. (2004) Spatio-temporal dynamics of the egg-laying-inducing peptides during an egg-laying cycle: a semiquantitative matrixassisted laser desorption/ionization mass spectrometry approach. J. Neurochem. 89, 865–875. Smit, A.B., van Kesteren, R.E., Spijker, S., Van Minnen, J., van Golen, F.A., Jimenez, C.R., and Li, K.W. (2003) Peptidergic modulation of male sexual behavior in Lymnaea stagnalis: structural and functional characterization of -FVamide neuropeptides. J. Neurochem. 87, 1245–1254. Van Golen, F.A., Li, K.W., Chen, S., Jimenez, C.R., and Geraerts, W.P. (1996) Various isoforms of myomodulin identified from the male copulatory organ of Lymnaea show overlapping yet distinct modulatory effects on the penis muscle. J. Neurochem. 66, 321–329. Van Golen, F.A., Li, K.W., De Lange, R.P., Van Kesteren, R.E., Van Der Schors, R.C., and Geraerts, W.P. (1995) Co-localized neuropeptides conopressin and ALA-PRO-GLYTRP-NH2 have antagonistic effects on the vas deferens of Lymnaea. Neuroscience 69, 1275–1287. Li, K.W., Miller, S., Klychnikov, O., Loos, M., Stahl-Zeng, J., Spijker, S., Mayford, M., and Smit, A.B. (2007) Quantitative proteomics and protein network analysis of hippocampal synapses of CaMKIIalpha mutant mice. J. Proteome Res. 6, 3127–3133.
Chapter 5 Monitoring Neuropeptides In Vivo via Microdialysis and Mass Spectrometry Heidi L. Behrens and Lingjun Li Abstract Neuropeptides are important signaling molecules that regulate many essential physiological processes. Microdialysis offers a way to sample neuropeptides in vivo. When combined with liquid chromatography– mass spectrometry detection, many known and unknown neuropeptides can be identified from a live organism. This chapter describes sample preparation techniques and general strategies for the mass spectral analysis of neuropeptides collected via microdialysis sampling. Methods for the in vitro microdialysis of a neuropeptide standard as well as the in vivo microdialysis sampling of neuropeptides from a live crab are described. Key words: Neuropeptides, mass spectrometry, microdialysis, crustacean, hemolymph.
1. Introduction Microdialysis is an in vivo sampling technique that allows the collection of molecules in real time with minimal disturbance to the organism; it produces relatively clean samples that do not require extensive preparation before analysis. Microdialysis is a well-established technique for sampling low molecular weight molecules from the brain, blood, and peripheral tissues (1). More recently, microdialysis has been applied to study larger molecules, such as neuropeptides. Neuropeptides are endogenous signaling molecules that are known to regulate many physiological processes. Most of the microdialysis studies of neuropeptides use radioimmunoassay (RIA) detection. While it has superb sensitivity, RIA cannot distinguish among the members of a neuropeptide family due to the similarity of their amino acid sequences. M. Soloviev (ed.), Peptidomics, Methods in Molecular Biology 615, DOI 10.1007/978-1-60761-535-4 5, © Humana Press, a part of Springer Science+Business Media, LLC 2010
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To overcome this lack of specificity, many researchers employ mass spectrometry (MS) to detect neuropeptides because it can sequence both known and unknown neuropeptides. This chapter describes a method to sample neuropeptides from the hemolymph (blood) of the crab, Cancer borealis, and detect them using liquid chromatography–mass spectrometry (LC–MS). Decapod crustaceans are important model organisms for studying the neuromodulatory and hormonal control of physiological processes (2, 3) and numerous studies have been published identifying the peptides present in the hemolymph and various tissues of these animals (4–9). This chapter contains protocols for performing in vitro microdialysis with LC–MS quantification of recovery as well as in vivo microdialysis, dialysate sample preparation, and the MS analysis of neuropeptides from microdialysates. In the in vitro microdialysis experiment a microdialysis probe is used to recover a standard peptide from artificial crab saline. This kind of experiment is often done to test the viability of the probe and the microdialysis setup. While it does not completely resemble in vivo conditions, the in vitro microdialysis experiment can provide a rough idea of the recovery of the particular analyte in vivo. If a higher recovery is desired, one can increase the surface area or pore size of the probe membrane, decrease the flow rate, or add molecules to the perfusion fluid that bind the analyte (10). Once the experiment is complete, the probe can be stored for later use. The sample preparation of in vitro and in vivo dialysates is fairly straightforward and the quantification of in vitro dialysates is accomplished via integration of the chromatographic peak using LC–MS software. In vivo microdialysis of the hemolymph in the pericardial sinus of a live crab is described, with details on probe preparation, implantation, and post-experiment probe visualization. Finally, we include some general suggestions for the MS analysis of neuropeptides from microdialysates. While this chapter focuses on a microdialysis and LC–MS method for the crab nervous system, many of the techniques described here can be readily applied to other systems. The principles of the in vitro microdialysis recovery experiment are the same regardless of the analyte or the organism. The section on quantification using LC–MS can also be used quite generally. Fewer desalting steps will be required for the LC–MS detection of neuropeptides from non-marine organisms.
2. Materials 2.1. In Vitro Microdialysis of 2 ␮M Arg Vasopressin in Saline
1. Crab saline: 440 mM NaCl, 11 mM KCl, 26 mM MgCl2 , 13 mM CaCl2 , 11 mM Trizma base, 5 mM maleic acid, pH 7.45. Store at 4◦ C.
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2. Arg vasopressin (AVP, CYFQNCPRGa with Cys1 –Cys6 forming disulfide bridge, MW 1083.45, American Peptide Company, Sunnyvale, CA). 3. 2.5 mL glass syringe and attached needle (CMA Microdialysis, North Chelmsford, MA). 4. 50% ethanol in water. 5. Water, double distilled by filtration system (Millipore, Bedford, MA). 6. Syringe pump, CMA/102. 7. Fluorinated ethylene propylene (FEP) tubing connectors (CMA), stored in ethanol at room temperature. 8. Microdialysis probe: CMA/20 Elite with 20 kDa molecular weight cut-off and 4 mm polyarylethersulfone membrane. 9. 20 gauge, 100060006 long PrecisionGlide hypodermic needles (BD, Franklin Lakes, NJ). 10. Total recovery collection vials (Waters, Milford, MA). 2.2. Sample Preparation of In Vitro Microdialysates
1. 0.1% formic acid (FA) in water
2.3. Analysis of In Vitro Microdialysates Using LC–MS to Determine AVP Recovery
1. LC–MS software with peak integration capabilities, such as Mass Lynx, version 4.0 (Waters)
2.4. In Vivo Microdialysis of Live Crab
In addition to the supplies listed in Section 2.1 , the following items will also be required: 1. FEP tubing, 0.12 mm inner diameter (CMA)
2. ZipTipsC18 (Millipore) 3. Solvent A, aqueous solvent for LC gradient: 95% water, 5% (v/v) acetonitrile (ACN), 0.1% (v/v) FA. Store at room temperature.
2. Hot glue and glue gun 3. 1–2 sheets of plexiglass, 3/800060006 thick, sized to fit your saltwater tank 4. Plumber’s epoxy (Poxy Plus, Inc., Sussex, WI) 5. Rotary tool, such as Dremel 7.2 V MultiPro Cordless (Dremel, Racine, WI) 6. Rotary tool drill bit, 1/3200060006 (0.8 mm) (Dremel) 7. Super epoxy (Poxy Plus, Inc.) 8. Green food dye 9. Dissecting tools: Side cutters or rongeurs, spatula, small scissors
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2.5. Sample Preparation of In Vivo Microdialysates
In addition to the supplies listed in Section 2.2 , the following items will also be required: 1. Arg vasopressin (AVP, CYFQNCPRGa with Cys1 –Cys6 forming disulfide bridge, MW 1083.45, American Peptide Company, Sunnyvale, CA). 2. Vivapure C18 micro spin columns (Vivaproducts, Inc., Littleton, MA).
3. Methods 3.1. In Vitro Microdialysis of 2 ␮M AVP in Saline (see Note 1)
1. Degas about 15 mL of crab saline to use as perfusate (see Note 2). 2. Prepare about 6–8 mL of peptide solution by dissolving AVP in crab saline (final concentration 2 ␮M AVP). Vortex to mix. Transfer to a 10 mL beaker before the in vitro experiment. Keep cold (see Note 3). 3. Wash syringe by rinsing with 50% ethanol three times, with water three times, and with the perfusate three times. Fill syringe with perfusate and push all air bubbles out of the syringe needle. 4. Set filled syringe in syringe pump. Attach three-prong clamp in clamp holder to burette stand. Attach hosecock clamp to three-prong clamp and hang probe from hosecock clamp by tightening on the plastic tab of the probe where the inlet and outlet tubing come together (see Note 4). 5. Fill a 10 mL beaker with about 6 mL of crab saline. Lower the probe into the beaker so that the membrane is completely immersed but does not touch the walls of the beaker. 6. Remove the FEP tubing connectors from ethanol and use them to make connections between the syringe, inlet tubing, probe, and outlet tubing. Blot dry with a kimwipe or a similar cleaning tissue. Let air dry for 10 min (see Note 5). 7. Set the outlet in a waste vial and start the pump at 10 ␮L/min for 5 min to push a few internal volumes of perfusate through the system (see Notes 6 and 7). 8. Use a ×10 magnifier to check that there are no bubbles in the membrane or shaft of the probe (see Note 8). 9. Stop the pump and slowly remove the probe from the crab saline solution and lower it into a 10 mL beaker filled with about 6 mL of the AVP solution. Remove 50 ␮L of
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the AVP solution now and save for LC–MS analysis (see Notes 9 and 10). 10. Push the perfusate out of the outlet tubing by starting the pump at 5 ␮L/min for 2 min (see Note 11). 11. Stop the pump and set the outlet tubing in a collection vial. Start the pump at 0.5 ␮L/min for the experiment and collect for 30 min. Transfer the outlet tubing to a new vial and collect fractions every 60 min for 3 h (three fractions of 30 ␮L each). Collect the fractions on ice and store at –20◦ C immediately upon collection (see Notes 12–15). 12. To reuse the probe and/or tubing, stop the pump and suspend the probe in a beaker of water. Replace the perfusate in the syringe with water (rinse with water three times). Reapply the tubing connectors and let air dry. Flush the system with several internal volumes of water (10 ␮L/min, 5 min). 13. To store the probe for later reuse, fill a 50 mL centrifuge tube with enough water to immerse the membrane (about 10–15 mL). Use a 20 G needle to poke two holes in the cap and feed the inlet and outlet tubes through these holes until the probe can be suspended in the water without touching the bottom or sides of the tube. Screw the cap tight. Place a square of parafilm around each tubing end to prevent dust from getting inside the tubing. Store in the refrigerator (see Notes 16 and 17). 3.2. Sample Preparation of In Vitro Microdialysates
1. Prepare the fractions for analysis by vacuum drying and redissolving in 10 ␮L of 0.1% FA. Centrifuge for 5 min at 10,000×g (see Notes 18–20). 2. Desalt with ZipTips. In the elution step, elute in 2–3 ␮L of elution solution, then dilute up to 30 ␮L with Solvent A. Elute into the LC–MS total recovery vial (see Note 21). 3. Analyze samples using LC–MS, preferably injecting full loop to increase injection reproducibility. Run all the fractions and the medium (see Notes 22 and 23).
3.3. Analysis of In Vitro Microdialysates Using LC–MS to Determine AVP Recovery
1. Determine the experimental m/z value (± 0.1) of each charge state of AVP. AVP displays (M+H)+ at around m/z 1084.5 and (M+2H)2+ at around m/z 542.7 (see Notes 24 and 25). 2. Create an extracted ion chromatogram (EIC) for the experimental m/z values of singly and doubly charged AVP. For each peak in the EIC, check the MS to determine if AVP is present. From this, determine the retention time of AVP for the specific LC–MS gradient used.
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3. Smooth and integrate the AVP peak for each charge state (see Notes 26 and 27). 4. Repeat Steps 1–3 for each LC–MS file. 5. Add together the peak areas for both AVP charge states for each microdialysis fraction you collected as well as for the 2 ␮M AVP medium. To determine the recovery for each fraction, divide the total AVP peak area for the fraction by the total AVP peak area for the medium. This percentage is the recovery of AVP in that fraction. To determine the recovery of AVP for the microdialysis experiment, average together the recoveries from the fractions after the 30 min equilibration period (average the recoveries of the 90, 150, and 210 min fractions). This averaged value is the final recovery value (see Notes 28 and 29). 3.4. In Vivo Microdialysis of a Live Crab (see Note 1)
1. Degas about 15 mL of crab saline to use as perfusate (see Notes 2 and 30). 2. Cut desired lengths of FEP tubing to extend the inlet and outlet tubing of the probe (see Notes 31 and 32). 3. Clean syringe by rinsing with 50% ethanol three times, with water three times, and then perfusate three times. Fill syringe with perfusate and push all air bubbles out of the syringe needle. 4. Set filled syringe in syringe pump. Attach three-prong clamp in clamp holder to burette stand. Attach hosecock clamp to three-prong clamp and hang probe from hosecock clamp by tightening on the plastic tab of the probe where the inlet and outlet tubing come together (see Note 4). 5. With the hot glue gun, apply a small ball of hot glue to the probe shaft about 1 cm from the probe tip, being careful not to get any glue on the membrane. Let dry. 6. Fill a 10 mL beaker with about 6 mL of crab saline. Lower the probe into the beaker so that the membrane is completely immersed but does not touch the walls of the beaker. 7. Remove the FEP tubing connectors from ethanol and use them to make connections between the syringe, FEP tubing, inlet tubing, probe, and outlet tubing. Blot dry with a kimwipe. Leave to air dry for 10 min (see Note 5). 8. Set the outlet in a waste vial and start the pump at 10 ␮L/min for 10 min to push a few internal volumes of perfusate through the system (see Notes 7 and 33). 9. Use a ×10 magnifier to check that there are no bubbles in the membrane or shaft of the probe (see Note 8).
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10. Set up a couple of plexiglass sheets in the tank to section off one side/corner in which to confine the crab during the microdialysis experiment (see Note 34). 11. Remove the crab from the tank and place it in a bucket filled with ice; note the time when the crab was first placed on ice. Dry the shell above the pericardial sinus and use a marker to mark where you will drill a hole later. Mix up the plumber’s epoxy on a plate and roll into a long cylinder. Press this in a circle around the pericardial region, so that the drilling site is in the center (Fig. 5.1 ). Move the ice up around the crab to cover the shell, but leave the area around the plumber’s epoxy dry. Leave the crab on ice about 20–25 min (see Notes 35 and 36). 12. While the crab is on ice, set up the surgery area in a cold room, preferably in the same room as the crab tank. Set out a dissection pan and fill it with a thin layer of ice. Set out the rotary tool with drill bit attached, have the super epoxy ready to mix on a plate with a plastic knife, and make sure the hot glue gun is heating. Set out wipes to blot dry hemolymph during surgery. 13. The probe should be rinsed by now (see Step 8 above), the syringe pump should be switched off, and both the probe and the pump should be transferred close to the surgery setup. Just before surgery, remove the probe from the beaker and unscrew the hosecock clamp slightly so the probe is sitting loosely in the clamp (see Notes 37 and 38). 14. Once the plumber’s epoxy is set and the crab is anesthetized, remove the crab from the bucket and place it dorsal side up in the dissection pan for surgery. Have someone else hold the crab while you dry the area inside the plumber’s epoxy with a kimwipe and mix the super epoxy on a plate. 15. Use the rotary tool to drill a hole in the shell over your mark. Quickly grab the probe and place it inside the hole, pushing down until the glue ball meets the shell (the probe tip should now be about 1 cm deep inside the crab). Hold the probe in the crab (see Note 39). 16. While holding the probe, scoop the super epoxy around the probe in the well created by the plumber’s epoxy. Fill this well with super epoxy. Hold the probe at an angle so that its tip faces the heart and keep it steady for about 10 min while you wait for the super epoxy to dry (see Notes 40–42). 17. Once the super epoxy has formed a gel, add hot glue around the base of the probe shaft to glue the probe shaft
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to the super epoxy. Let dry for a few minutes. Check that the probe is secure and let go off the probe (see Note 43). 18. Pick up the crab in the pan and the syringe pump (unplugged) and move it over to the tank. Carefully place the crab in the tank and expel gas from the stomach. Place the lid on the tank and place the syringe pump and a small bucket filled with ice on the lid. Set the outlet tubing in a collection vial in the ice bucket (see Note 44). 19. Plug in the syringe pump and begin the flow at 10 ␮L/min. Watch for fluid to come out of the outlet tubing (see Note 45). 20. Once you see fluid flowing out, change the flow to 0.5 ␮L/min for the microdialysis experiment. Set up the outlet tubing in the collection vial and tape the outlet tubing to the ice bucket so that it does not fall out of the collection vial. Begin collecting individual fractions. Immediately store collected fractions at –20◦ C (see Notes 46–49). 21. Once the experiment is completed, stop the syringe pump and remove the FEP tubing connector between the syringe needle and inlet tubing. Fill the syringe with 0.5 mL of green dye. Re-attach the inlet tubing to the syringe needle with a new FEP tubing connector, blot dry with a kimwipe, and let air dry for 10 min. 22. Start the pump at 5 ␮L/min. You should see green dye flow out the outlet tubing after a few minutes. Place the outlet tubing in a vial, wait a few minutes more, and then stop the pump (see Note 50). 23. Disconnect the inlet tubing from the syringe pump and remove the syringe pump and ice bucket from the lid of the tank. Remove the crab from the tank and place in a large bucket of ice for 20 min to anesthetize. Be careful not to touch the probe (see Note 51). 24. While the crab is on ice, set out the dissection pan, spatula, side cutters, and small scissors. 25. Once the crab is anesthetized, place the crab in the dissecting pan and begin by using the side cutters to remove the claws and legs at the base where they meet the body. 26. Use the side cutters to crunch around the outer rim of the crab shell. Then use the spatula to reach in between the top and bottom shells and separate the hypodermis from the upper shell. 27. Use the side cutters to remove the upper shell back to the pericardial region, saving the area behind the pericardial ridges. Use the scissors to cut the connective tissue between
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Fig. 5.1. Schematic representation of the microdialysis setup, the microdialysis probe placement in the pericardial sinus of a crab, and of the glues used to stabilize the probe.
the upper shell and the pericardial region so that you can pull the shell off. 28. Pull the pericardial shell off, leaving the part next to the tail attached if possible. Upon removing the shell, you should be able to see where the probe tip was located. Look for a small amount of green dye under the probe tip, probably on the surface of the heart. 29. Finish dissecting the crab and dispose it off according to the rules and procedures. 3.5. Sample Preparation of In Vivo Microdialysates
1. Defrost fractions and spike them with AVP to 1 nM. Vacuum dry the spiked fractions and redissolve in 10–200 ␮L of 0.1% FA, depending on the initial fraction volume and the desired desalting method. Centrifuge for 5 min at 10,000×g; a small white pellet may be visible (see Notes 19, 20, 52, and 53). 2. Desalt the supernatant of all fractions using ZipTips for smaller volumes (less than 50 ␮L) or micro spin columns for larger volumes (50–200 ␮L). For LC–MS analysis, elute in a small volume (2–10 ␮L) then dilute up to the desired volume with Solvent A. For MALDI MS analysis, elute in 2–3 ␮L elution solution. If necessary, store samples at –20◦ C (see Notes 20, 21, and 54). 3. Analyze desalted microdialysis fractions by LC–MS or MALDI MS (see Notes 55 and 56).
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4. Notes 1. All solutions could be chilled and the AVP solution kept on ice or, alternatively, perform all steps in a cold room. 2. Should be done on the day of the experiment. 3. Note that micromolar concentrations of AVP will degrade within a few months. 4. Many microdialysis suppliers offer probe clips that may simplify the suspension of the probe in the analyte solution. 5. Some probes (including the CMA/20) come with tubing connected to them and you can just use this or you can extend the attached tubing with FEP tubing; extending the tubing length is usually necessary for in vivo experiments. Other probes do not have tubing connected and you will have to attach your own FEP tubing to the probe. 6. The total internal volume of this setup is about 12 ␮L, so about four internal volumes were rinsed through this system. For other setups, one will need to measure the internal volume of the probe-tubing system. This can be calculated from the probe and tubing internal volumes (usually given in the accompanying product manuals). Typical probe internal volumes are 3–5 ␮L; 0.12 mm ID FEP tubing is 1.2 ␮L/100 mm. A good estimate for rinsing is 5 min at 10 ␮L/min. 7. Once the pump has started, check the system for leaks by looking for small beads of fluid, usually at the tubing connectors. If you find a leak, stop the pump and replace the tubing connector with one that has soaked in ethanol for 5–10 min. Blot dry with kimwipe and let air dry for 10–15 min. Gently test that the connection is secure and start the pump again. 8. If there are any bubbles, these need to be removed before continuing because they will decrease the recovery of your probe and alter the results. The easiest way to remove bubbles is to try a higher flow rate (20 ␮L/min or more) for a few minutes. If this does not work, remove the probe and clamp and carefully tap the clamp against a metal surface. You can also try forcefully swinging the probe and clamp in the air. If none of these options work, use a new probe. 9. The analyte solution can be stirred with a magnetic stirrer on a stirring plate if desired; this may increase recovery (11).
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10. The volume of the analyte solution should be no less than 5–10 mL; this will depend on the flow rate, expected recovery of the specific analyte, and the total time of the experiment. 11. To determine the void time, one needs to know the internal volume from the probe tip out through the outlet tubing to the vial. Typically, this is 5–10 ␮L which can be flushed out by running at 5 ␮L/min for 1–2 min. 12. The duration of the fractions will vary depending on the volume requirement and sensitivity of the analysis method as well as the number of analysis replicates desired. 13. For LC–MS, fractions could be injected without further sample preparation. In such a case the samples should be collected in the vials used for sample injection. This will reduce sample handling and decrease sample losses. 14. You may want to start by collecting fractions of larger volumes than needed to aid sample preparation and handling and allow for extra MS replicates. 15. The first 30 min are required to equilibrate the analyte solution and the perfusate in the membrane; this fraction will have a lower recovery than later fractions. Collect at least 2–3 fractions after the equilibration period. 16. Used probes can be stored for several months. When storing a probe, it is important to keep the membrane wet and free from contact with the storage container. Tubing should be free of salts to avoid clogging. 17. Alternatively, the same procedure can be done with allpurpose contact lens solution instead of water. The probe can be stored at room temperature, but the contact solution will need to be replaced every 2 weeks to minimize bacterial growth. 18. If water was used as the perfusate, it is not necessary to vacuum dry or desalt; the microdialyzed fractions can be analyzed by LC–MS directly. 19. Fractions with larger initial volumes will need more liquid to dissolve all the salt that precipitates out upon vacuum drying. Use just enough FA to dissolve the precipitates. 20. For MALDI MS analysis, 0.1% trifluoroacetic acid (TFA) in water can be substituted with the 0.1% FA. 21. Final ACN concentration in the samples for LC–MS analysis is 5–25%. Whilst using ZipTips, elute samples in small volumes of 50% ACN, then dilute the samples by adding aqueous LC–MS solvent. Alternatively, the samples can be
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vacuum-dried after ZipTip purification and redissolved in the required LC–MS solvent. 22. The volume of the fractions may need to be adjusted based on the requirements of the specific LC–MS system. For a 5 ␮L sample loop, full-loop mode with an overfill factor of 2 would require 10 ␮L, whilst only 5 ␮L would be injected. This means that 2–3 LC–MS analyses can be performed on each of the 30 ␮L fractions. 23. Run multiple replicates of fractions and of 2 ␮M AVP solution. Run each set of replicates on the same day, starting with the lowest analyte concentration. Wash the injectors with water to avoid carryover of analyte between runs. 24. The lower charge states of the analyte would require less signal integration. 25. Failure to determine the correct experimental m/z values can affect the extraction ion chromatogram and thus the integration, which could change the final recovery value. 26. Smoothing is not usually necessary, but it can help with noisy chromatograms. 27. Make sure that the integrated area includes the AVP charge state desired but not much of anything else. For instance, if there is an intense contaminant eluting shortly after and overlapping with AVP, one can modify the tail of the integrated peak to stop before the MS signal from the contaminant overwhelms that of AVP. Regardless of the integration method chosen, it is important to be consistent for all of the LC–MS files. 28. Expected recovery for AVP at this flow rate and with this probe is approximately 15–20%. 29. If you inject 5 ␮L of 2 ␮M AVP, you would be loading roughly 1–10 ng of AVP onto the instrument, depending on the microdialysis recovery. Make sure that the instrument can accurately quantify AVP over the specific concentration range chosen. 30. If the desired neuropeptide is particularly hydrophobic, it may adsorb to the microdialysis probe and the walls of the tubing, making its detection difficult. This sticking can be minimized by adding a small amount (0.5%, w/v) of bovine serum albumin (BSA) to the perfusate (12, 13). The BSA can be precipitated out of the dialysed samples with the addition of methanol or acetonitrile followed by centrifugation. 31. Use a commercial tube cutter to achieve straight edges and prevent leaks.
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32. Minimize total tubing length because peptides can stick to the walls of tubing. For our tank setup, we use about 45 cm of FEP to extend the probe inlet tubing and about 65 cm of FEP to extend the probe outlet tubing. 33. The total internal volume of this setup with the FEP tubing extensions is about 25 ␮L, so about four internal volumes were rinsed through this system. For other setups, one will need to know the internal volume of the probetubing system. This can be calculated from the probe and tubing internal volumes (usually given in the accompanying product manuals). Typical probe internal volumes are 3–5 ␮L; 0.12 mm ID FEP tubing is 1.2 ␮L/100 mm. A good estimate for rinsing is 10 min at 10 ␮L/min. 34. You should allow the crab to move, but excessive movements would require longer tube lengths. Typically we confine crabs to an area of about 11 in2 . 35. Leaving the crab on ice for too long (45 min or more) may kill the animal. 36. The plumber’s epoxy needs to be placed on the crab before transferring the animal to ice because it takes about 20–25 min to harden. Building the plumber’s epoxy circle a little higher on the side next to the tail will help to prevent the super epoxy from flowing down the tail during surgery. 37. Do not remove the probe from the beaker far in advance of surgery to prevent drying out of the membrane (it will become unusable). 38. The probe is still attached to the syringe through the inlet tubing, so the pump should be close enough that it can be connected to the crab without straining the tubing. 39. Keep drilling the shell until hemolymph flows out, indicating that you penetrated the pericardial cavity. This will happen shortly after the drill penetrates the shell (felt through the reduced mechanical resistance to drilling). 40. The hemolymph will keep flowing out of the crab during this step, but the super epoxy will gradually begin to harden, slowing the flow of hemolymph. 41. Try not to move the probe drastically, as you could run into some tissue on the sides of the pericardial cavity and clog the probe membrane. 42. After 8–10 min, you can tap the super epoxy to see if it is dry. The super epoxy will not be completely hard, but it will resemble a gel, which would provide sufficient adhesion. The probe may still be slightly moveable; the hot glue (added next) will set quickly and fix the probe in place.
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43. The hot glue will set very quickly, especially if the surgery is performed in a cold room. If the probe is not fixed after the first applicaiton, more hot glue could be added around the probe. 44. When placing the crab in the tank, be careful not to touch the walls of the tank with the probe. Try to keep the end of the outlet tubing out of the water to keep it clean and prevent saltwater from getting inside. This tube can be taped to the top of the tank to keep it out of the way whilst the crab is transferred into the tank. Try to keep the tubing away from the crab and tangle-free. 45. If no fluid is flowing out of the outlet tubing after a few minutes, the flow rate could be increased and check for leaks. If there are no leaks, it is likely that the system is blocked. Try cutting the outlet tubing by a few inches in case the clog is near the outlet. If this does not work, the clog is probably near the probe and a new probe would have to be used and the experiment re-started. 46. Using a tube holder prevents the collection tube from moving as the ice melts. A simple holder can be made by cutting a hole in a 1-inch-wide strip of cardboard and taping this across the ice bucket. 47. The first 30 min is an equilibration period and is not representative of basal conditions. The first 10–12 h may contain stress-induced neuropeptides. 48. Once basal samples have been collected, the in vivo analyte concentration can be estimated using the zero net flux microdialysis method (14). 49. Change the ice and collection tubes at least 2–3 times a day to minimize temperature-induced neuropeptide degradation. 50. Alternatively, you could use a disposable plastic syringe and needle for the green dye, as it can be quite messy; the injection rate is not critical for this step. The FEP tubing connectors fit well over a 21 G needle. 51. The tubing may be cut near the probe to simplify disassembly. 52. The spiked standard serves as a way to monitor sample loss and neuropeptide concentration as well as being an internal standard for MALDI MS. More standards can be added to cover the mass range of analysis. 53. Centrifugation helps to prevent large molecules or precipitated salts from clogging the membrane of the desalting devices.
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54. Neuropeptides exist at very low concentrations in the hemolymph, so increase the concentration factor until you can see neuropeptides with your instrument. Concentrating dialysed samples 6- to 10-fold should allow observation of neuropeptides at basal levels in sensitive MS instruments. The samples may need to be concentrated further (100- to 200-fold), depending on the concentration of the desired neuropeptide and the physiological state of the organism. 55. For MALDI, alpha-cyano-4-hydroxycinnamic acid (CHCA) works better for microdialysates, although some neuropeptides are detected easier with different matrices such as 2,5-dihydroxybenzoic acid (DHB). 56. When analyzing neuropeptides from in vivo microdialysates, it is important to adequately desalt the dialysates and employ a sensitive and selective detection scheme. Biological fluids contain high concentrations of various salts, which can be problematic for MS detection. Desalting of dialysates can be done offline using solid support (15), ZipTips, or reversed-phase capillary columns (16). Online desalting methods such as reversed-phase trap columns (17) and microdialysis-based devices (18) decrease sample handling and increase automation. In addition to desalting, it is essential to maximize the sensitivity of the MS detection. Neuropeptides exist at micromolar to picomolar levels in vivo so the MS instrument must be able to detect attomoles of neuropeptides in microliter samples. Triple quadrupole (16, 19), quadrupole ion trap (20, 21), time-of-flight (21–23), and quadrupole time-of-flight mass spectrometers have all been successfully employed to detect neuropeptides from microdialysates. For ion trap and quadrupolebased instruments, the sensitivity can be improved by detecting a narrow m/z range (24) or by performing single reaction monitoring experiments (16). Front-end separation of dialysates by reversed-phase LC or capillary electrophoresis (16, 25–27) can further enhance MS sensitivity by simplifying the sample that enters the mass spectrometer. In LC, a small inner diameter (ID) column (≤ 75 ␮m) is essential to neuropeptide detection, with very small ID columns providing the best sensitivity (24). Several microliters of dialysate should be injected and subjected to a slow elution gradient, ideally increasing by less than 1% organic solvent per minute. Finally, the enormous variety in neuropeptide sequences requires the selectivity of tandem MS detection to discern the correct amino acid sequence. Using MS/MS, one can sequence numerous neuropeptides from microdialysates. However, if the instrument is capable, MS3 can give better signal-to-noise ratios, resulting in more confident peptide identifications (20). Chemical derivatization is another strategy to improve peptide identification
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from tandem MS data and can be particularly helpful in determining the identity of the C- and N-terminal amino acids as well as the presence of certain internal amino acids (28, 29).
Acknowledgments The authors wish to thank Professor Craig Berridge (Department of Psychology, University of Wisconsin-Madison) for helpful discussions about microdialysis. This work was supported in part by the School of Pharmacy and the Wisconsin Alumni Research Foundation at the University of Wisconsin-Madison, a National Science Foundation CAREER Award (CHE-0449991), and the National Institutes of Health through Grant 1R01DK071801. L.L. acknowledges an Alfred P. Sloan Research Fellowship. H.L.B. acknowledges the National Institutes of Health Biotechnology Training Grant 5T32 GM08349. References 1. Robinson, T.E. and Justice, J.B. (eds.) (1991) Microdialysis in the Neurosciences. Elsevier Science Publishing Company, London. 2. Skiebe, P. (2001) Neuropeptides are ubiquitous chemical mediators: using the stomatogastric nervous system as a model system. J. Exp. Biol. 204, 2035–2048. 3. Nusbaum, M.P. and Beenhakker, M.P. (2002) A small-systems approach to motor pattern generation. Nature 417, 343–350. 4. Turrigiano, G.G. and Selverston, A.I. (1990) A cholecystokinin-like hormone activates a feeding-related neural circuit in lobster. Nature 344, 866–868. 5. Li, L., Kelley, W.P., Billimoria, C.P., Christie, A.E., Pulver, S.R., Sweedler, J.V., and Marder, E. (2003) Mass spectrometric investigation of the neuropeptide complement and release in the pericardial organs of the crab, Cancer borealis. J. Neurochem. 87, 642–656. 6. Messinger, D.I., Kutz, K.K., Le, T., Verley, D.R., Hsu, Y.W., Ngo, C.T., Cain, S.D., Birmingham, J.T., Li, L., and Christie, A.E. (2005) Identification and characterization of a tachykinin-containing neuroendocrine organ in the commissural ganglion of the crab Cancer productus. J. Exp. Biol. 208, 3303–3319. 7. DeKeyser, S.S., Kutz-Naber, K.K., Schmidt,
8.
9.
10.
11.
12.
J.J., Barrett-Wilt, G.A., and Li, L. (2007) Imaging mass spectrometry of neuropeptides in decapod crustacean neuronal tissues. J. Proteome Res. 6, 1782–1791. Stemmler, E.A., Gardner, N.P., Guiney, M.E., Bruns, E.A., and Dickinson, P.S. (2006) The detection of red pigmentconcentrating hormone (RPCH) in crustacean eyestalk tissues using matrix-assisted laser desorption/ionization-Fourier transform mass spectrometry: [M + Na]+ ion formation in dried droplet tissue preparations. J. Mass Spectrom. 41, 295–311. Saideman, S.R., Ma, M., Kutz-Naber, K.K., Cook, A., Torfs, P., Schoofs, L., Li, L., and Nusbaum, M.P. (2007) Modulation of rhythmic motor activity by pyrokinin peptides. J. Neurophysiol. 97, 579–595. Duo, J., Fletcher, H., and Stenken, J.A. (2006) Natural and synthetic affinity agents as microdialysis sampling mass transport enhancers: current progress and future perspectives. Biosens. Bioelectron. 22, 449–457. Rojas, C., Nagaraja, N.V., and Derendorf, H. (2000) In vitro recovery of triamcinolone acetonide in microdialysis. Pharmazie 55, 659–662. Lanckmans, K., Sarre, S., Smolders, I., and Michotte, Y. (2008) Quantitative liquid chromatography/mass spectrometry for the analysis of microdialysates. Talanta 74, 458–469.
In Vivo Monitoring of Neuropeptides by Microdialysis and MS 13. Trickler, W.J. and Miller, D.W. (2003) Use of osmotic agents in microdialysis studies to improve the recovery of macromolecules. J. Pharm. Sci. 92, 1419–1427. 14. Guiard, B.P., David, D.J., Deltheil, T., Chenu, F., Le Maitre, E., Renoir, T., LerouxNicollet, I., Sokoloff, P., Lanfumey, L., Hamon, M., Andrews, A.M., Hen, R., and Gardier, A.M. (2008) Brain-derived neurotrophic factor-deficient mice exhibit a hippocampal hyperserotonergic phenotype. Int. J. Neuropsychopharmacol. 11, 79–92. 15. Pettersson, A., Amirkhani, A., Arvidsson, B., Markides, K., and Bergquist, J. (2004) A feasibility study of solid supported enhanced microdialysis. Anal. Chem. 76, 1678–1682. 16. Andren, P.E. and Caprioli, R.M. (1999) Determination of extracellular release of neurotensin in discrete rat brain regions utilizing in vivo microdialysis/electrospray mass spectrometry. Brain Res. 845, 123–129. 17. Bengtsson, J., Jansson, B., and Hammarlund-Udenaes, M. (2005) On-line desalting and determination of morphine, morphine-3-glucuronide and morphine-6glucuronide in microdialysis and plasma samples using column switching and liquid chromatography/tandem mass spectrometry. Rapid Commun. Mass Spectrom. 19, 2116–2122. 18. Jakubowski, J.A., Hatcher, N.G., and Sweedler, J.V. (2005) Online microdialysisdynamic nanoelectrospray ionization-mass spectrometry for monitoring neuropeptide secretion. J. Mass Spectrom. 40, 924–931. 19. Lanckmans, K., Stragier, B., Sarre, S., Smolders, I., and Michotte, Y. (2007) Nano-LC– MS/MS for the monitoring of angiotensin IV in rat brain microdialysates: limitations and possibilities. J. Sep. Sci. 30, 2217–2224. 20. Baseski, H.M., Watson, C.J., Cellar, N.A., Shackman, J.G., and Kennedy, R.T. (2005) Capillary liquid chromatography with MS3 for the determination of enkephalins in microdialysis samples from the striatum of anesthetized and freely-moving rats. J. Mass Spectrom. 40, 146–153. 21. Reed, B., Zhang, Y., Chait, B.T., and Kreek, M.J. (2003) Dynorphin A(1–17) biotransformation in striatum of freely moving rats using microdialysis and matrix-assisted laser
22.
23.
24.
25.
26.
27.
28.
29.
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desorption/ionization mass spectrometry. J. Neurochem. 86, 815–823. Zhang, H., Stoeckli, M., Andren, P.E., and Caprioli, R.M. (1999) Combining solid-phase preconcentration, capillary electrophoresis and off-line matrix-assisted laser desorption/ionization mass spectrometry: intracerebral metabolic processing of peptide E in vivo. J. Mass Spectrom. 34, 377–383. Wilson, S.R., Boix, F., Holm, A., Molander, P., Lundanes, E., and Greibrokk, T. (2005) Determination of bradykinin and argbradykinin in rat muscle tissue by microdialysis and capillary column-switching liquid chromatography with mass spectrometric detection. J. Sep. Sci. 28, 1751–1758. Haskins, W.E., Wang, Z., Watson, C.J., Rostand, R.R., Witowski, S.R., Powell, D.H., and Kennedy, R.T. (2001) Capillary LC– MS2 at the attomole level for monitoring and discovering endogenous peptides in microdialysis samples collected in vivo. Anal. Chem. 73, 5005–5014. Davies, M.I., Cooper, J.D., Desmond, S.S., Lunte, C.E., and Lunte, S.M. (2000) Analytical considerations for microdialysis sampling. Adv. Drug Deliv. Rev. 45, 169–188. Shackman, H.M., Shou, M., Cellar, N.A., Watson, C.J., and Kennedy, R.T. (2007) Microdialysis coupled on-line to capillary liquid chromatography with tandem mass spectrometry for monitoring acetylcholine in vivo. J. Neurosci. Methods 159, 86–92. Myasein, K.T., Pulido, J.S., Hatfield, R.M., McCannel, C.A., Dundervill, R.F., 3rd, and Shippy, S.A. (2007) Sub-microlitre dialysis system to enable trace level peptide detection from volume-limited biological samples using MALDI-TOF-MS. Analyst 132, 1046–1052. Cruz-Bermudez, N.D., Fu, Q., Kutz-Naber, K.K., Christie, A.E., Li, L., and Marder, E. (2006) Mass spectrometric characterization and physiological actions of GAHKNYLRFamide, a novel FMRFamide-like peptide from crabs of the genus Cancer. J. Neurochem. 97, 784–799. Ma, M., Kutz-Naber, K.K., and Li, L. (2007) Methyl esterification assisted MALDI FTMS characterization of the orcokinin neuropeptide family. Anal. Chem. 79, 673–681.
Chapter 6 Protocols for Peptidomic Analysis of Spider Venoms Liang Songping Abstract Spider venom contains a complex mixture of components with a large range of molecular masses (0.1–60 kDa) exhibiting a diverse array of actions. Most of these components are proteinaceous molecules – biologically active proteins and peptides. Proteomics profiling of spider venoms (the components with MW >10 kDa) could be achieved through conventional 2-DE-based proteomics methods combined with MS or MS/MS detection. Peptidomic profiling (of the components with MW below ∼10 kDa) is usually achieved through off-line separation by a combination of ion-exchange and reverse-phase chromatography, and it relies more heavily on de novo sequencing by Edman degradation or MS/MS for peptide identification. Key words: Spider venom, peptidomics, multidimensional separations, mass spectrometer.
1. Introduction Spider venom is a complex mixture of components with a large range of molecular masses (0.1–60 kDa), exhibiting a diverse array of functional activities. Spider venoms are chemically diverse and include proteins, peptides and small organic molecules such as acylpolyamines. It has been estimated that spider venoms may contain of the order of ∼500 different proteinaceous components of varying weight, pI, hydrophobicity and of highly variable abundance (1–3). Extracting proteins from spider venoms presents essentially the same challenges as does the protein and peptide extraction from cells, tissues and body fluids (the latter typically available in abundance, compared to venom samples). In addition to the variability of their physical and chemical properties, the abundance of individual components of spider toxins varies M. Soloviev (ed.), Peptidomics, Methods in Molecular Biology 615, DOI 10.1007/978-1-60761-535-4 6, © Humana Press, a part of Springer Science+Business Media, LLC 2010
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significantly. Estimates indicate up to 6 orders of magnitude differences in protein and peptide expression level within individual venoms. In classical proteomics most of the proteins have molecular weights above 10 kDa, whilst in peptidomics these are typically below 10 kDa. Spider venoms contain a complex mixture of peptides and proteins and therefore a special strategy is required for their efficient isolation. The rapid progress of proteomics and mass spectrometry technologies makes it possible to access the full peptide and protein complement of spider venoms. Proteomic and Peptidomic analyses have been successfully applied to the studies of venoms from several spider species including Ornithoctonus huwena, Chilobrachys jingzhao, Atrax robustus and Hadronyche versuta (1, 3–5). Figure 6.1 shows a typical strategy for the combined proteomics and Peptidomics analyses of spider venom by using the
Fig. 6.1. Schematic overview of the strategy for the proteomic and peptidomic analyses of spider venoms.
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combination 2-DE and mass spectrometry or a multidimensional liquid chromatography (MDLC) in combination with MALDITOF or Q-TOF analyses and peptide sequencing. The protein components of the spider venoms having molecular weight over ∼10,000 Da could be analysed using conventional 2-DE-based proteomic approach. Peptidomic profiling is however more difficult because of the huge diversity of venom peptides and the lack of genomic data to support peptide mass matching analysis and peptide identification by mass spectrometry. Therefore, de novo sequencing becomes the main approach for peptide identification from venoms. After an off-line separation using a combination of ion-exchange and reverse-phase HPLC, de novo peptide sequencing can be attempted using either automatic Edman degradation or tandem mass spectrometry.
2. Materials 1. Sephadex G-75, IPG Buffer pH 3–10, DryStrips (180×30×0.5 mm), Cover fluid, Agarose and colloidal Coomassie blue (GE Healthcare, formerly Amersham Biosciences). 2. Deionized water was prepared with a tandem Milli-Q system and used for the preparation of all buffers. 3. Rehydration solution: 8 M Urea, 2 M Thiourea, 4% CHAPS, 20 mM Tris-base, 0.5% (v/v) IPG buffer, 18 mM DTT, bromophenol blue (trace amount to facilitate visualization of the samples), pH3. 4. Reduction solution: 50 mM Tris-HCl, 6 M urea, 30% glycerol, 2% SDS, 125 mM DTT, pH 6.8. 5. Alkylation solution: 50 mM Tris-HCl, 6 M urea, 30% glycerol, 2% SDS, 125 mM iodoacetamide, pH 6.8. 6. LC solvents. Buffer A: 0.1% formic acid, 4.9% ACN, 95% H2 O (v/v/v); Buffer B: 0.1% formic acid, 4.9% H2 O, 95% ACN (v/v/v); Buffer C: 0.1% v/v TFA in water; Buffer D: 0.1% v/v TFA in acetonitrile. 7. Gel staining with Coomassie Brilliant Blue G250: Dissolve 100 mg of Brilliant Blue G250 in 50 mL of 95% ethanol. Mix with 100 mL of 85% phosphoric acid and made up to 1 L with distilled water. 8. Matrix solution: CHCA, saturated in 97% Acetone/0.1% TFA solution Recrystallization solution: 100 mg CHCA dissolved in 10 mL of the solution ethanol/acetone/0.1% TFA (6:3:1).
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9. IPGphor IEF system (Amersham Pharmacia Biotech). 10. Protean II Electrophoresis system (Bio-Rad). 11. ProXPESS 2D Proteomic Imaging System (Perkin Elmer). 12. PDQuest spot detection software software Version 6.1 (Bio-Rad). 13. Q-TOF mass spectrometer with a nanoelectrospray ionization source (Micromass). 14. MassLynx software for MS/MS data analysis (Micromass). 15. MALDI-TOF-TOF mass spectrometer (UltraFlex I), AnchorChipTM (Bruker Daltonics). 16. BioTools v2.2 software for the analysis of LIFT-MS/MS spectra (Bruker). 17. Accell Plus Sep-Pak CM cation exchange cartridges (10 mm ×100 mm, Waters). 18. Vydac C18 reversed-phase HPLC column (218TP54, ˚ 4.6 mm × 250 mm, Grace Davison Discovery Sci300 A, ences). 19. HPLC capillary column CapLC-MS/MS (75 ␮m×150 mm, Waters) for protein identification with CapLC-MS/MS (Waters). 20. HiprepTM 16/10 CM FF pre-packed column (Pharmacia). 21. Precise 491A sequencer (Applied Biosystems). 22. The venoms were obtained by stimulating the cheliceral claw of spiders using electro-pulse stimulator. The expressed venom was collected from the fang tips into a glass vial and freeze-dried. 23. Protein Assay Kit for protein concentration determination (Bio-Rad).
3. Methods 3.1. Gel Filtration and SDS-PAGE
1. Pool a few venom samples from several spiders of the same species and of the same sex (see Note 1). 2. Load pooled venom samples (10 mg) onto a 10 × 450 mm Sephadex G-75 column pre-equilibrated with 20 mM NH4 HCO3 at pH 6.8. 3. Elute the venom using the equilibration buffer with a flow rate of 1.0 mL/min at room temperature (25◦ C). Monitor the elution at 215 and 280 nm. Collect 500 ␮L fractions (see Note 2).
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4. Check molecular weight of the collected peptides by SDSPAGE, using 15% separation gel and 4.8% stacking gel. 5. Pool fractions containing venomous proteins with MW above 10 kDa for 2-DE analysis. Pool the remaining fractions (MW below 10 kDa) for HPLC separation. 6. Determine protein concentration using Bio-Rad Protein Assay Kit or a similar method. 3.2. Separation of Venom Proteins by 2D Electrophoresis
Run 2D electrophoresis using IPGphor IEF system or a similar system. 1. Combine 300 ␮g of the pooled venom protein samples after the gel filtration separation with 50 ␮L of the rehydration solution; apply to IPG dry strips. 2. Rehydrate for 14 h, run IEF using step-n-hold protocol: 500 V for 1 h; 1000 V for 1 h; and 8000 V for 6 h at 50 ␮A/strip. 3. After focusing, soak the strips for 20 min in the reduction solution followed by 20 min incubation in alkylation solution. 4. Carry out the second-dimensional run on incontinuity SDSpolyacrylamide vertical slab gels 1 mm thick, with 4.8% stacking gels and 12.5% separation, in a Bio-Rad Protein II electrophoresis apparatus. Run the gel at a 12.5 mA/gel constant current, use water cooling to maintain the temperature at 10◦ C. 5. Stain the gel with Coomassie Brilliant Blue. Scan the gel with ProXPESS 2D Imaging System. For spot detection use PDQuest software.
3.3. Protein In-Gel Digestion
1. Manually excise the Coomassie blue-stained protein spots from the 2-DE gel using a puncher and place them into 500␮L microcentrifuge tubes. Store excised samples at –20◦ C prior to the digestion. 2. To perform in-gel digestion, first destain each spot with 50 ␮L of 50% ACN in 25 mM NH4 HCO3 . Incubate at 37◦ C for 30 min, change the destaining buffer once and repeat the incubation. 3. Reduce proteins with 10 mM DTT solution in 25 mM NH4 HCO3 at 56◦ C for 1 h and then alkylate proteins with 55 mM iodoacetamide solution in 25 mM NH4 HCO3 ) in the dark at room temperature for 45 min in situ. 4. Wash the gel slices or spots with 25 mM NH4 HCO3 in water/acetonitrile (1:1, v/v) solution and dry completely in a SpeedVac. Then digest the protein in-gel using 25 ␮L of trypsin solution (10 ng/␮L in 25 mM NH4 HCO3 ) by
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incubation overnight at 37◦ C. Extract the peptides in 50 ␮L of 50% acetonitrile containing 2.5% TFA. Concentrate the sample to about 4 ␮L for MALDI-TOF/TOF analysis. 3.4. MALDI-TOF/TOF and Q-TOF Analysis of Tryptic Peptides (see Note 3)
Protein digests obtained in Section 3.3 above are analysed with MALDI-TOF/TOF MS, followed by protein identification with peptide mass fingerprinting (PMF) and LIFT-MS/MS (see Note 4) data searching. 1. Touch the surface of the AnchorChipTM MALDI plate with a pipette tip containing 1 ␮L of cyano-4-hydroxycinnamic acid (CHCA) matrix; aspirate the excess of the matrix with the same tip. The CHCA thin layer is formed within seconds. 2. Apply 2 ␮L of the extracted peptides directly onto the AnchorChipTM plate preloaded with CHCA matrix and left to dry for 3 min. Add 2 ␮L 0.1%TFA to the sample to wash out contaminants, and 4 s later remove the remaining solution with a pipette. Subsequently, add 1 ␮L of the recrystallization solution to the sample; ensure that sample is concentrated in the centre of the anchors. 3. For the calibration use a mixture of peptide standards, such as for example Bruker Daltonics Pepmix containing Angiotensin II, [M+H]+ = 1046.5420; Angiotensin I, [M+H]+ = 1296.6853; Substance P, [M+H]+ = 1347.7361; Bombesin, [M+H]+ = 1619.8230; ACTH clip 1–17, [M+H]+ = 2093.0868; ACTH clip 18–39, [M+H]+ = 2465.1990; Somatostatin 28, [M+H]+ = 3147.4714. 4. Set up the UltraflexTM TOF/TOF mass spectrometer using the FlexControl (TM) software; choose the reflectron mode and the accelerating voltage of 25 kV. 5. For the MS/MS analysis, choose a maximum of four precursor ions per sample. In the TOF1 stage, accelerate all ions to 8 kV to promote metastable fragmentation conditions. Select the jointly migrating parent and fragment ions in a timed ion gate; lift them to high potential energy in the LIFT cell (19 kV). Their masses could be analysed simultaneously and with high accuracy in the reflectron mode. 6. For the Q-TOF MS analysis of the peptide mixtures from the in-gel digestions use nanoelectrospray ionization source coupled with the HPLC capillary column (CapLCMS/MS). 7. Reconstitute peptides in an aqueous solution of 5% ACN before injection. 8. For the on-line LC separation use a gradient elution (Buffer A/Buffer B) as follows: (i) (95/5–50/50%) for 65 min,
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followed by (35/65–5/95%) for 10 min, followed by (5%/95%) elution for 10 min. Set flow rate to 3 ␮L/min. Use nanoelectrospray to inject the eluted peptides into the coupled Q-TOF MS. 9. Use the
Eluted sample, e.g. 1A
Amp ..
Titer =
Amplified sample, e.g. 1A'
P ..
Titer =
Eluted sample, e.g.1A'A
Amp ..
Titer =
EB ..
P ..
Eluted sample, e.g.1A'B
EB ..
Eluted sample, e.g. 1B
Amp ..
Amplified sample, e.g. 1B' Titer =
Titer =
Panning round 1
P ..
Eluted sample, e.g.1B'A
Amp ..
Eluted sample, e.g.1B'B
Amplified sample, e.g. 1A'B' Titer =
Amp ..
Amplified sample, e.g.1B'A' Titer =
Titer =
EB ..
Amplified sample, e.g.1A'A' Titer =
Titer =
EA ..
323
Amp ..
Titer =
Amplified sample, e.g.1B'B' Titer =
Panning round 2
Fig. 23.2. Phage panning flowchart. Label each eluted phage such that the identity of the target and of the panning stage is clear. “P” denotes panning, “EA ” and “EB ” denote Elutions, whilst the superscript denotes elution conditions, e.g. specific and non-specific elution, or soft and stringent elution. “Amp” denotes amplification. Panning round 2 is shown to contain two elutions for each of the two samples, this is optional. For the simplification of the next panning rounds, 0006 0006 0006 0006 0006 0006 Phage sample 1B A could be joined with 1A A and 1A B with 1B0006 B0006 Draft flowchart covering four panning rounds should be sufficient for most of the applications. Sample identifies, dates and the titer values should be entered into the table as the work progresses.
2. Pre-warm LB/IPTG/Xgal plates by incubating them at +37◦ C for at least 1 h (see Note 22). 3. Whilst the cells are growing, melt the agarose in a microwave and dispense 3-ml aliquots into sterile 15-ml Falcon tubes. Make one Falcon tube per phage dilution. Keep tubes in a +45◦ C water bath until use (see Note 23). 4. Prepare dilutions of phage in LB (total volume 10 ␮l) in sterile 1.5-ml microcentrifuge tubes. 5. Once the culture has reached the mid-log phase, pour 400 ␮l of the culture into 1.5-ml tubes containing phage dilutions. 6. Vortex and incubate at room temperature for 5 min to allow infection. 7. Transfer the infected cells to tubes containing warm agarose, mix with the pipette tip (do not vortex) and immediately pour onto a pre-warmed LB/IPTG/Xgal plate. Spread the agarose evenly by tilting the plate. 8. Allow plates to cool for ∼5 min, or until the top agarose solidifies, invert and incubate overnight at +37◦ C. 9. The following day inspect the plates and count the plaques. Multiply the number of plaques by the dilution factor for that plate to get the phage titer (in pfu per 10 ␮l).
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3.2.3. DNA Extraction
1. Inoculate 20 ml LB medium with E. coli, incubate in a shaking incubator at +37◦ C (250 rpm) for 2 h. 2. Following that initial incubation, dilute the bacterial culture with fresh LB so that the final OD600 = 0.01; transfer 2-ml aliquots into sterile 50-ml Falcon tubes. Pick a single blue plaque from the plates used for titering the phage and add it to the 2 ml E. coli culture; incubate in a shaking incubator at +37◦ C (250 rpm) for 4.5 h. 3. Transfer the culture to 2.0-ml microcentrifuge tubes and centrifuge for 10 min at 10,000×g at +4◦ C. Transfer the supernatant to a fresh tube and centrifuge again for 10 min at 10,000×g at +4◦ C. 4. Carefully collect the upper 80% (1.6 ml) of the supernatant and transfer to a fresh tube, add 1/5 volume (400 ␮l) of PEG/NaCl. Allow phage to precipitate at +4◦ C overnight. 5. Precipitate the phage by spinning for 15 min at 10,000×g at +4◦ C. Carefully remove the supernatant (do not discard it until the phage pellet is recovered), re-spin the tubes for 5 s at room temperature and carefully remove the residual supernatant with a fine pipette. 6. Resuspend the pellet in 100 ␮l TE Buffer Mix. Add 100 ␮l phenol. Vortex. 7. Centrifuge at 10,000×g for 5 min at room temperature. Collect 80% of the upper (water) phase to fresh tubes. 8. Add 100 ␮l of phenol:chloroform (1:1). Vortex 3 min at room temperature. Centrifuge at 10,000 rpm for 5 min at room temperature. 9. Transfer the top 80% of the upper water phase to fresh tubes. Add 70 ␮l of chloroform. Vortex for 3 min at room temperature. Centrifuge at 10,000×g for 5 min at room temperature. 10. Transfer the top 80% of the upper water phase to fresh tubes. Add 1/10 volume of 3 M Sodium Acetate pH 5.5 and 2.5 volumes of ethanol. Vortex briefly. Incubate at –20◦ C for at least 30 min or overnight. 11. Centrifuge the tubes at 14,000×g at +4◦ C for 20 min. 12. Remove supernatant, wash once with 1 ml of cold 70% ethanol. Dry at room temperature (for approximately 1 h). 13. Add 30 ␮l of deionised H2 O to suspend the DNA pellets. Store at –20◦ C. These single-stranded DNAs are ready for sequencing.
3.3. Affinity Peptidomics: Antibody Microarrays
Microarrays allow miniaturisation and multiplexing of affinitybased assays, and a large number of array formats and assays have been reported to date, including for the analysis of serum
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samples. The availability and affordability of anti-protein antibodies is often an issue, whilst another typical issue in any proteinbased assay is sample stability and preservation (34). Affinity peptidomics relies on the successfully proven approach used widely in mass-spectrometry-based protein analysis, where protein samples are proteolytically digested prior to the analysis. Such treatment removes the need to preserve protein samples. To further streamline the affinity assay, we have chosen to use single-label competitive assays rather than traditional direct binding two-colour assays. The justification of the choice can be found here (11, 35); briefly, this approach allows to avoid repetitive labelling of the experimental samples and compensates for the heterogeneity of the antibody affinities. Our protocols were originally devised for use with recombinant scFv anti-peptide antibodies developed using Phage display (8), but were later adapted for use with traditional anti-peptide polyclonal antibodies. Such peptide affinity assays are widely applicable to the detection and quantification of the proteolytic or naturally occurring peptides. 3.3.1. Proteolysis and Labelling of Serum Protein Samples
1. Aliquot the required amount of sera (e.g. we used 100 ␮l of each of the serum samples to be tested), add a few microlitres of 1 M K2 HPO4 or 1 M Tris pH 9 to bring the pH of the sample to pH 8, check pH by spotting a fraction of a microlitre of the buffered serum onto pH paper (see Note 24). 2. Make one additional pooled serum sample by mixing equal volumes from all serum samples being tested (see Note 25). 3. Add Trypsin to each sample, including the pooled serum sample, use 1 ␮g per ∼20–50 ␮g of the total serum protein and incubate at 37◦ C overnight (see Note 26). 4. Stop the digestions by adding 20 ␮l of 10 mM PMSF (see Note 27). 5. To fluorescently label the pooled serum sample, take a 20␮l aliquot, add 80 ␮l PBS and add 100 ␮l of 1% RITC. Incubate at room temperature for 30–60 min. 6. Stop the labelling reaction by adding 20 ␮l of 1 M Tris pH 8. Proceed with purification (Section 3.3.2).
3.3.2. Purification of the Peptides (see Note 28)
1. Calibrate the SEC column by injecting Trypsin diluted in PBS (see Note 29). 2. Monitor absorbance at 280 nm. Identify the elution peak for trypsin, the end of which will indicate when to commence peptide collection during SEC purification (see Notes 30 and 31). 3. Load the labelled pooled serum (from Step 6, Section 3.3.1) onto the SEC column. Commence the collection
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at a time determined in the previous step. Stop collection when the unincorporated Rhodamine peak (slowly moving band) reaches the end of the column. Store the collected peptide on ice (short-term) or freeze –20◦ C for the longer term storage (see Note 32). 3.3.3. Microarrays for Fluorescent Detection and Quantification of Peptides (see Note 33)
1. Set up the microarray spotting instrument. The Flexys microarray gridding robot allows for three washing buffers to be used for cleaning the pins and the washing program should be set as follows: 1% Tween 20 wash for 30 s, followed by PBS wash for 10 s, followed by another wash in 1% Tween 20 for 30 s and PBS wash for 10 s. The final wash is in 0.1% BSA in PBS with 0.1% Tween 20 for 30 s (see Note 34). 2. Fix membranes on glass slides, e.g. using small paper stickers or small pieces of tape and place the slides in the robot holder (see Note 35). 3. To check pins quality and to match the pins, perform a trial run by spotting the same fluorescently labelled protein and scan the slides to determine the efficiency of protein transfer for each individual pin (see Notes 36, 37, and 38). 4. To measure sample volumes required for spotting, add an even number of identical ∼20-␮l aliquots of any sample to the microwell plate, and insert it in the robotic spotter. Samples should have the same protein concentration and buffer as that in the antibody samples to be spotted. Choose the wells (or pins) such that half of the samples are transferred to the membrane, and half are not used. Run a number of transfers (e.g. ∼100). Remove the plate from the robot and measure the remaining sample volumes, compare volume in the used and unused wells, average the difference and divide by the number of transfers (see Note 39). 5. Add the required amount of antibodies to microwell plates, insert them into the robot holder and run the spotting program using the parameters specified and tested in previous steps (see Note 40). 6. Remove membranes from the robot and transfer them into a sealed chamber containing a few millilitres of 37% formaldehyde. Incubate overnight in a fume hood at room temperature (see Note 41). 7. Block the membranes using large volume of Microarray blocking and assay buffer (∼10 ml per membrane for at least 2 h) (see Note 42). 8. Assemble the assay mixtures as follows (exemplified for 200 ␮l final volume sample): Use ∼10 ␮l of the unlabelled
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serum digest (or the equivalent amount of the purified proteolytic peptides), add 1 ␮l of the 25× Protease Inhibitor Cocktail, incubate for 15 min at room temperature. Add 50 ␮l of the labelled and purified pooled sera digest and 140 ␮l of the fresh Microarray blocking and assay buffer. Assemble an individual assay mixture for each of the tested sera samples (see Notes 43 and 44). 9. Trim the array membrane to minimal size. Add 100 ␮l of the assay mix to a small Petri dish, place the array membrane face down in incubation mix, and add the remaining ∼100 ␮l on top of the membrane. Close the Petri dish; incubate at room temperature in the dark for 2 h. 10. To wash the membranes transfer them to a flask containing ∼50 ml of the Microarray washing buffer for 10 s, change buffer and incubate for 5 min, change buffer again and incubate for 10 min. (see Note 45). 11. Dry membranes on blotting paper (arrayed side up) in darkness (see Note 46). 12. Mount the dried membranes on glass slides using doublesided adhesive tape and scan using a suitable instrument. We use a BioChip microarray Scanner. The scanner settings (focus, laser intensity and photomultiplier attenuation) should not be changed between the different slides. Figure 23.3 illustrates a fragment of the scanned microarray, and shows all the normalisation and control spots. 13. Data analysis depends on whether competitive or noncompetitive assay was used and also on the set of normalisation spots used. In most cases, however, readouts should be normalised pin-to-pin and array-to-array (see Note 47). 3.4. Peptide Assays on Hydrogels
Porous membranes provide a convenient support material, which is strong and for which a variety of materials and protocols are available. Hydrogels cannot compete with membranes in terms of strength and durability, but they provide the best 3D support for the immobilisation of test molecules (whether proteins or peptides) in their native functional state in highly porous hydrogel substrate suitable for both functional assays (36) and immunoassays (26, 37, 38). When hydrated, the hydrogels swell, allowing easy access for the molecules and short diffusion times, but when dried, the gel thickness is reduced significantly, resulting in focussing of the trapped fluorescence in a thinner layer. This increases fluorescent readouts (especially on confocal scanners), whilst the background fluorescence remains extremely low (no autofluorescence, no non-specific protein sorption). Furthermore, hydrogels are also suitable for use as MALDI-MS substrates (8, 39). Having an anti-peptide antibody immobilised on
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Fig. 23.3. Affinity peptidomics microarray assay. Fluorescent readout at 550 nm of a fragment of a microarray containing two grids with 33 anti-peptide antibodies, spotted onto positively charged nylon membrane and incubated with the proteolytic serum peptides in a competitive binding assay. Shapes denote Coomassie spots (rectangles, dashed line), total IgG negative control (rectangles, solid line), fluorescent references for pin calibration and grid normalisation – circle, solid line (active channel 550 nm), circle, dashed line (different channel 650 nm).
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a hydrogel slide therefore allows to bypass multidimensional separation stages and to capture peptides for MALDI-MS analysis directly from crude Tryptic digests. We report here a set of distinct hydrogel-based assays, suitable for a variety of applications and also for further method development. 3.4.1. Making Hydrogels
1. Rinse microscope glass slides in 100% ethanol and soak in 10% sodium hydroxide overnight. 2. Rinse the slides four times in deionised water and twice in 100% ethanol. 3. Treat the slides with binding silan solution for 5 min, wash with 100% ethanol and dry at room temperature (see Note 48). 4. Assemble the adhesive gaskets onto the defined area of the slides. Attach two gaskets to each glass slide (see Note 49). 5. Make fresh polymerisation mix: 1 M acrylamide, 20 mM 0.1% TEMED. Add N,N0006 -Methylenebisacrylamide, 1 mg/ml ammonium persulfate (see Note 50). 6. Add 80 ␮l of the assembled polymerisation mix into the frame and carefully seal the frame with the plastic cover slips (provided with frames). 7. When gel is formed and polymerisation is finished, remove the plastic coverslip (leave the gasket on the slide) and wash the hydrogel in water overnight (see Note 51). 8. Dry the hydrogel slides at room temperature. Store in a dry clean slide box until use.
3.4.2. Target Immobilisation on Hydrogels
1. Activate hydrogels by immersing the slides in 25% glutaraldehyde overnight. 2. Wash the slides with deionised water twice for 5 min and dry the slides at room temperature (see Note 52). 3. Immobilise the antibodies by spotting 0.5 ␮l of 1 mg/ml antibody solution onto the hydrogel pads (see Notes 53–55). 4. Allow the spots to dry fully at room temperature, transfer the slides to a humidified chamber and incubate at +4◦ C overnight. 5. Rehydrate the hydrogels fully in the Hydrogel assay buffer prior to running affinity assays (see Note 56).
3.4.3. Affinity Binding Assays on Hydrogels (see Note 57)
Hydrogels can be used to assay a variety of biological targets, including endogenous proteins or peptides (as in traditional Peptidomics applications) (40, 41), proteolytic peptides (as in Affinity Peptidomics) (8, 10), synthetic peptides (e.g. for validation
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experiments), glycans or small-molecule ligands (42, 43). We will exemplify hydrogel affinity assays using a simple example of fluorescently labelled synthetic peptide, but the protocol would remain essentially the same for crude peptide digests (see Sections 3.3.1, 3.3.2, and 3.3.3). The same hydrogels may be probed with MALDI-MS. 1. To fluorescently label peptides obtained after proteolytic digestion of serum, or synthetic peptides having free amino groups (unprotected N-termini, Lysines), follow Section 3.3.1 (Steps 4 and 5). 2. To fluorescently label synthetic peptides having free sulfhydryl groups (Cysteines) mix 10 ␮l of 1% peptide solution with 70 ␮l labelling buffer, add 2 ␮l of 200 mM TBP (final concentration 5 mM) and incubate the mixture at room temperature for 30 min (see Notes 58 and 59). Add 30 ␮l of 0.1% NIR-664-iodoacetamide fluorescent dye to the mixture and incubate at room temperature for 1 h in dark. 3. Whilst incubating the labelling reactions, prepare spin columns for SEC purification of the labelled peptides (one column per labelling reaction). Remove a plunger from 1 ml disposable syringe; cut filter paper to just over twice cross-sectional area of syringe, fold and push to the bottom of the syringe using the plunger; remove the plunger. Load R G-25 gel into the syringe column; 1 ml of 75% Sephadex0002 insert syringe column into a 15 ml Falcon tube and spin at 1000×g for 5 min (see Note 60). 4. Replace the Falcon tube, load the labelled sample (∼112 ␮l) to the centre of the spin column and centrifuge at 1000×g for 5 min. 5. Dispose the spin column, transfer the purified peptide sample to a fresh microcentrifuge tube. Store at –20◦ C. 6. To fluorescently assay proteolytic peptides in competitive assays, e.g. as in Affinity peptidomics assays (as described in Section 3.3.3, Steps 8–13), mix the equimolar amounts of the unlabelled peptide test samples and the labelled reference peptide samples, use Hydrogel assay buffer to make up the volume to at least 65 ␮l per single hydrogel pad (see Note 61). 7. To fluorescently assay individual peptides, e.g. in validation experiments, prepare two assays for each peptide tested. Use Hydrogel assay buffer to make up the volume to at least 65 ␮l per single hydrogel pad (see Note 62). 8. Add the assay mixture to fully hydrated Hydogel pads (see Note 56), incubate at room temperature overnight in the dark.
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5.00E+07 4.50E+07 4.00E+07 3.50E+07 3.00E+07 2.50E+07 2.00E+07 1.50E+07 1.00E+07 5.00E+06 0.00E+00
IGF1
Insulin
Fig. 23.4. Peptide binding on hydrogels. (a) Peptide SALNTPN binds to IGF1 but not to Insulin. (b) Same as above, but in the presence of 100-fold excess of the unlabelled peptide SALNTPN. (c) Mean values of the fluorescence intensities for the above are shown (±stdev).
9. Wash slides in Hydrogel washing buffer three times ×5 min, and in deionised water twice ×5 min. 10. Air-dry the slides and scan with fluorescent scanners at the appropriate wavelength. Affinity capture of a peptide on hydrogel with the immobilised IGF1 and insulin protein is shown on Fig. 23.4. Insulin is used as the negative control (an irrelevant protein). 11. To prepare hydrogels for MALDI-MS, add matrix on top of the hydrogel as follows: apply MALDI matrix #1, air dry; apply MALDI matrix #2, air dry. The hydrogels can be examined on MALDI-TOF MS (see Note 63).
4. Notes 1. We used a recombinant analogue of human insulin like growth factor 1 (Long R3 IGF-1, Sigma-Aldrich) to exemplify this protocol, which otherwise is easily adaptable for
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use with other proteins. Following the recommendation of the provider, the protein was dissolved in 1 ml of 10 mM HCl. Other protein targets may require different buffers and preparation procedures. 2. Membranes provide 3D porous substrate with very high protein binding capacity and are therefore preferred over flat 2D substrates. Ready-made and commercially available membrane substrates such as immobilised Nitrocellulose (available from multiple suppliers) or FASTTM and CASTTM slides from Schleicher and Schuel could be used, but these would provide a more expensive alternative to ordinary membranes. 3. Liquid chromatography setups vary and any suitable equipment and properly sized columns could be used. Gravity flow may also be used for peptide purification, but care should be taken to properly calibrate the elution times of the protein (Trypsin) fraction, the peptides and the unincorporated RITC. The flow rate will vary if gravity flow is used, so calibration should be done by the volume eluted (weigh each tube containing each sample and subtract the weight of the tube) rather than the elution time. 4. We are currently using 70 sera samples raised against 35 peptide markers (two rabbits per peptide). Antibody sample purity and the protein binding capacity of the microarray substrate material will affect the amount of retained antibodies and therefore the maximum signal obtainable. Surfaces with lower binding capacities may be used with purified antibodies. Total IgGs will require supports with higher protein binding capacities to ensure that sufficient amount of the specific antibody is attached to the membrane. 5. Use a fresh pack of glass slides. Glass slides may not work with MALDI-MS detection, in which case Silicon wafers should be used (8). 6. Entering UniProtKB, Swiss-Prot or TrEMBL accession numbers is the preferred option, since this would allow to also include in the analysis the post-translational modification, database sequence conflicts, alternative splicing variants and polymorphisms. 7. This tool is convenient for the analysis of individual or small sets of proteins. We created a simple proteolytic digestion tool using EXCEL, which we use for in silico digestion and comparison of individual or groups of proteins. Other methods for predicting proteolytic peptides can be used; the choice of the method should not affect the outcome of the predictions.
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8. Although mass calculations are not critical at this point, it is worth selecting this and other options, as these would become useful later. 9. Having this information handy will help to avoid errors in the subsequent anti-peptide antibody generation programme, which may be costly and which may cause very long delays, e.g. if an antibody has to be re-made. 10. The first step in anti-protein antibody generation is to make synthetic peptides. There is a list of criteria to bear in mind when selecting suitable peptide sequences, briefly: i. Peptide lengths should be between 5 and 30 amino acids. Short peptides are difficult to purify following the synthesis, whilst ∼30-mers and above will have lower yields because of the increased error rate. Price will play a significant role too. Often 12–15 amino-acid-long peptides work best for anti-peptide antibody generation. ii. Avoid multiple Prolines, Serines, Aspatic Acid and Glycines. iii. Avoid the following duplets of amino acids: Ser-Ser, Asp-Gly, Asp-Pro. iv. Avoid the following triplets of amino acids: Gly-AsnGly, Gly-Pro-Gly. v. Avoid charge clustering and fewer than 1 in 5 charged amino acid side chains. The selection of the subset of suitable peptide can be achieved simply by selecting the range of lengths 10–15 amino acids in the EXCEL file, containing the output of the PeptideMass program (from the previous step), followed by a quick check for any of the unwanted amino acids (as outlined above). We have entered the above rules into a Visual Basic Macro which is run in Excel, making the selection easy even if multiple proteins are analysed. Having sorted the PeptideMass results by mass (Step 2) allows to very easily select a range of peptides of suitable size. We also used truncated tryptic sequences (i.e. just partial peptide sequence, if the predicted peptides were too long). 11. Much has been published on the prediction of antigenic epitopes from protein sequences (44–50). Most of the simpler tools however are based on the amino acid propensity scales, which take into account the hydrophilicity, surface accessibility and segmental mobility of amino acids (51) and are not therefore suitable for selecting tryptic peptides for anti-peptide antibody generation. Surprisingly, the “old ideas” of relying on hydrophobicity scales (52–54) appear to work better than any of the more modern tools
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Table 23.1 Five peptides from human vascular cell adhesion molecule (VCAM) chosen for antipeptide scFv(s) antibody development (8) Peptide sequence detection
Amino acid length
Hydrophilicitya
SQEFLEDADR
10
1.44
GRAVYb
EMBOSS antigenicc
Peptide detection on MALDI-MS
–1.44
0.998
Very strong
TQIDSPLNGK
10
0.96
–0.96
1.038
Strong
LHIDDMEFEPK
11
0.88
–0.882
No epitope detected, no score
Weak
VTNEGTTSTLT MNPVS FGNEHSY
23
0.58
–0.583
1.031
Very weak
SSEGLPAPE IFWSK
14
0.35
–0.35
1.077
Not detected
a Hydrophilicity
was calculated using Kyte Doolittle Hydrophilicity scale from (53). indexes were calculated using ProtParam tool (www.expasy.org/tools/protparam) c EMBOSS Antigenic scores calculated using (liv.bmc.uu.se/cgi-bin/emboss/antigenic) tool. b GRAVY
which we tested. Although the original idea published by Hopp and Woods was to look for hydrophilic regions, because they were the most likely ones to represent surfaceexposed fragments, the same principle seems to work for selecting tryptic peptides, although it is not clear why. We use our own ranking tool (a Macro run within EXCEL), which uses Kyte Doolittle hydrophilicities (53). Any similar tools, including on-line tools, might be used, for example the ProtParam tool. Use the grand average of hydropathicity (GRAVY) index. Table 23.1 shows VCAM peptides used for the generation of single-chain Fv(s) antipeptide antibodies from a Phage display library (CAT, Melbourne, UK) (8), their calculated GRAVY indexes and their ability to enrich peptides from crude tryptic digests for direct MALDI-MS detection from hydrogel arrays (8). For comparison, the same table shows antigenic scores generated using EMBOSS Antigenic prediction tool (liv.bmc.uu.se/cgi-bin/emboss/antigenic). There is a clear correlation between the MALDI-MS detection (directly from the immobilised antibodies) and the peptides’ hydrophilicity; but no correlation is observed between MALDI detection and the peptide antigenic scores. 12. When a Universal Covalent surface is used to covalently immobilise protein via an abstractable hydrogen using UV illumination, a calibration step is required to calibrate the UV exposure. Typically, a set of UV-sensitive calibration
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labels will be supplied with the pack of Universal Covalent Plates or Strips for that purpose. 13. The required Library dilution will depend on the Library complexity and the phage titer. One library may be sufficient for panning against more than one target. We routinely diluted one Ph.D.-C7CTM Phage Display library in 300 ␮l of 0.1 mg/ml BSA (in 0.1% TBST) and used 50 ␮l of the obtained dilution per target, to screen six targets in one experiment. 14. Incubating the LB media with E. coli cultures gives best results for phage amplification when it is incubated for 2 h. If OD600 <0.1 after 2 h, the culture has to be incubated further until the absorbance exceeds 0.1. E. coli grown on an LB-tet plate and kept at +4◦ C in the dark will be alive for 1 week, so new E. coli needs to be grown every week. Fresh E. coli will reach the mid-log phase much faster (∼2 h) compared to the older (end of the week) cultures, which may take up to ∼4 h to reach mid-log phase. 15. Use the amount of washing buffer sufficient to completely fill the wells. It is important to make sure here that all unbound phages are washed off. We found that ten changes of the washing buffer are required to completely remove the unbound phages. The total washing time must be kept short. 16. Eluting the bound phages by adding large excess of the target protein appears to provide better results. To be most certain that all the bound phages have eluted, both steps can be done one after another. For example, start by eluting the phage with the free target protein and then elute with low pH. In our experience, specific elution was successful with the most of the protein targets tested so far. The nonspecific eluates may be kept as backups, in case the panning has to be repeated. 17. It may be difficult to predict even approximately the concentration of the eluted phages. Therefore a set of different dilutions ranging 102 –108 should be made. For dilution purposes, one may assume that the concentration of the eluted phage will be 1/100,000th of that in the starting material (i.e. of the phage display library in the first panning round, or of the amplified phage solutions in further rounds). 18. Using small volumes of E. coli (2 ml) yields the best amplification (∼106 fold). Using larger volumes of E. coli cultures will yield poorer results. 19. In order for the phage particles to precipitate, they need to be incubated with PEG/NaCl for at least overnight or
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longer. One PEG/NaCl precipitation is usually sufficient to precipitate all or most of the phages and does not have to be repeated. However, if the phage titer is low the precipitation may need to be repeated (repeat the overnight incubation at +4◦ C and centrifugation steps). 20. The phage pellet is not necessarily visible after the centrifugation and removal of the PEG/NaCl supernatant. However, if precipitate is visible, this would indicate the total phage content of above ∼105 pfu. 21. Although the kit manual recommends to use 109 –1011 pfu of the amplified phage for the subsequent screening rounds, the phage titers as low as 105 pfu have proven to work well. 22. The LB/IPTG/X-gal plates need to be pre-warmed at least 1 h before the agarose is being added to prevent the agarose from cooling too quickly and forming lumps on the agar surface. The latter would make the titering results inaccurate. 23. Keeping 3 ml of agarose top in 15-ml Falcon tubes makes sure that the agarose top does not solidify quickly after being taken out of the water bath. Other tubes, such as 50-ml Falcon tubes cool down quicker and are therefore not suitable. 24. This amount (∼100 ␮l) should be sufficient for more than one assay, but much would depend on the volume of the assay chosen by the user. 25. The pooled serum will be used for fluorescent labelling and as a reference sample in a competitive binding assay. We first make a pooled sample and then proteolytically digest it. Alternatively, individually digested samples can be pooled after the proteolysis. 26. It may be assumed that total serum protein concentration is below 10%, hence 100 ␮l of serum should not contain more than 10 mg protein. Hence 0.2–0.5 mg Trypsin should be added. 27. PMSF will inactivate Trypsin irreversibly. PMSF will hydrolyse in water, especially at high pH, and may not work at high salt concentrations, so if in doubt, samples should be diluted and the pH shall be adjusted to pH7 prior to adding PMSF. Alternatively, trypsin may be inactivated by boiling. However, the high total protein concentration in the sample could result in the formation of protein precipitate which will complicate the extraction of peptides. 28. Crude Tryptic digests may be used for affinity assays with or without additional purification (as long as Trypsin is
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inactivated). Fluorescently labelled peptides must be purified from the unincorporated fluorescent molecules. We R G-25 to separate the labelled pepuse SEC on Sephadex0002 tides from both Trypsin and the unincorporated RITC. The same procedure can be applied to unlabelled tryptic digests. 29. Dilute Trypsin similarly to the dilution used in Section 3.3.1 (Step 3). Inject the same volume as the volume of the peptide sample to be purified, i.e. ∼220 ␮l, obtained in Section 3.3.1 (Step 5). 30. The Trypsin calibration sample may be spiked with RITC. Elution can then be monitored by measuring fluorescence on-line or off-line. Inevitably some Trypsin will be labelled but some Rhodamine will remain unincorporated, resulting in that both Trypsin peak and the small-molecule fraction (Rhodamine) will be identified. The gap between the two peaks will determine the elution window for the peptides. 31. Ensure that the column is thoroughly washed and equilibrated with the running buffer after each Trypsin run. 32. The collected eluates may be hand-spotted and scanned for fluorescence to more accurately determine the start and the end of the peptide fraction. 33. Irrespective of the type of spotting instrument used (even if using a hand-held “MicroCaster” spotter, Whatman/Schleicher and Schuell), similar key principles have to be followed: i. Spotting should be done at least in triplicate for each individual antibody. The number of replicates is usually not a limiting factor (hundreds or thousands of spots can be made on each array), we found that having six replicates is sufficient in most cases. ii. Careful consideration must be given to the array layout: replicates should be spread over the whole array area to minimise staining and scanning artefacts. Our instrument (Flexys robotic spotter) produces blocks of densely arranged spots (grids, having from 5 × 5 to 12 × 12 spots each) whilst each grid is well separated from each other. In such a case each grid may contain only a single copy of any antibody, but the patterns should be replicated at least three (better six) times and be spread over the whole array area. iii. Relevant negative controls must be included. For example, if polyclonal rabbit anti-peptide antibodies are used, pre-immunisation sera or just total rabbit IgGs would make a suitable negative control. IgG concentration should be ideally the same as in other (specific)
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antibody samples and at least the same number of replicates should be made. These will provide an important reference point for the data analysis; any errors in determining the non-specific background may affect quantification. iv. Reference spots (fluorescently labelled protein) should be added to each array, we have at least one reference spot per grid of spots. These are necessary for signal normalisation during scanning and for pin calibration (see Step 2 of the Section 3.3.3). v. Coloured spots should be added to ease array handling. These can be e.g. Coomassie Brilliant Blue or Coomassie-stained protein. These will help to identify the correct membrane surface, distinguish front from the back of the membrane and identify array borders. vi. If using contact spotting, pins should be either matched or calibrated. These issues are addressed in Step 2 of the Section 3.3.3. 34. Pin washing and reconditioning is very important for the avoidance of carry-over contaminations and for achieving high reproducibility of spotting. Pin washing procedures and buffers differ significantly from DNA gridding protocols. 35. We use positively charged nylon. Other membranes such as supported nitrocellulose membranes or immobilised membranes may also be used. Using the unsupported nitrocellulose membranes should be avoided (very fragile nature of nitrocellulose makes it nearly impossible to handle). Any tape can be used. Having some overhanging tape facilitates handling of the membrane strips. 36. If a large number of pins is available to the user, the simplest way would be to select those which result in the identical efficiency of protein transfer from the microwell plates to the membrane (array). If this is not possible, pins should be calibrated (by measuring the fluorescence in each spot) from multiple replicates and the values should be taken into the account when interpreting the main assay results. Alternatively, calibration controls (fluorescence reference spots) should be included for each individual pin when spotting the antibodies. 37. Multiple transfers should be made for each spot (i.e. the material spotted repeatedly onto the same spot on the membrane). This will dramatically increase the reproducibility of antibody transfer and increase the amount of the spotted antibodies (leading to the stronger and more reproducible signals and lesser variability between spots).
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We routinely use between 6 and 10 transfers per spot. Further increases are counterproductive as the procedure becomes very long and sample evaporation becomes an issue. 38. High humidity should be maintained inside the robot whilst spotting, especially for longer runs. 39. When using contact spotting, the volume transferred by the pins will depend on many parameters, such as sample viscosity, surface tension, cleanliness of the pins, contact time and the material and porosity of the membrane. These are difficult to predict but easy to measure. We typically have values of ∼20 nl per single transfer per pin. 40. Making small batches of arrays (up to 10 arrays per batch) works best in our hands. Increasing the number of arrays further increases variations in the efficiency of transfer. This is likely due to the built up of dry residue on the pins, which causes the changes. As a rule keep the total number of transfers between pin washes below ∼50. 41. Because protein cross-linking with formaldehyde occurs slowly, long incubation time is necessary. This will also ensure better reproducibility of the cross-linking. Blocking the unreacted groups with glycine or Tris buffer is optional; we found no clear evidence for including this step, perhaps because blocking might be accomplished during the subsequent steps during incubation of the membranes in the blocking and assay buffers containing high concentration of BSA. 42. Ensure that membranes do not adhere to each other, otherwise blocking may be incomplete. Ideally, block individual membranes in separate vessels: 15-ml Falcon tubes or flatbottom scintillation tubes or similar work well. 43. Because of the competitive nature of the assay, higher concentration of the unlabelled peptide (test sample) will yield weaker fluorescent staining (higher degree of displacement of the labelled reference). 44. The protocol described here is most suitable for running a number of different affinity assays and for relative quantification of the peptide levels. The pooled serum sample will serve as a good reference sample. Alternatively any one of the samples can be used, e.g. any normal serum sample. The concentration (or the dilution) of the unlabelled proteolytic peptides should be approximately equivalent to the concentration of pooled labelled peptides. This will provide the most accurate measurements. Before running large series, it is worth running a pilot experiment to check that addition of the unlabelled test sample does not reduce the
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fluorescent signal more than twice. Use two identical slides, make the assay mixture for two arrays, but only add unlabelled serum to one of the arrays (use equivalent volume of 9% BSA in PBS for the other array). 45. We use 50-ml Falcon tubes for washes. For convenience and to avoid handling mistakes, we use sets of three tubes for each array, filled with 50 ml of the washing buffer. The membranes are transferred from one flask to another at preset intervals. Optionally membranes can be rinsed in water prior to the next step. 46. It may take up to an hour to dry the membranes completely. The filters may be left to dry overnight. 47. In competitive assays a higher readout would indicate lower competition for the immobilised binding site from the unlabelled sample and therefore lower concentration of the competing unlabelled peptide. Lower fluorescence would indicate increased competition for binding sites (higher concentration of the matching peptide in the test sample). 48. The slides have to be dried completely to achieve better attachment of the hydrogel. 49. If the specified gaskets (1.5 × 1.6 cm) are used, two can be fitted on a single microscope slide. This adds the advantage of running binding and displacement assays for the same target on the same slide. 50. The volume of ammonium persulfate has to be adjusted experimentally, to allow sufficient handling time yet to ensure fast polymerisation (within ∼30 min). Making acrylamide hydrogels is very similar to making SDS-PAGE gels, except that no SDS should be present. Pre-made 0006 Acrylamide:N,N -Methylenebisacrylamide mixtures can be used. Glycerol may be added to the gel up to 40% final concentration. It improves mechanical properties of the gel, aids handling but requires longer washing times and does not significantly improve the binding assays to justify its use. However, if photochemical polymerisation is used instead of the chemical (TEMED/persulphate), the addition of glycerol is beneficial (26). 51. It is important to wash the hydrogel pads thoroughly in order to remove any unpolymerised acrylamide. 52. The slides can be dried in an incubator (∼20–25◦ C), but the drying time should not exceed 10 min. 53. Any protein or peptide containing free amino groups could be immobilised. Importantly, the target should not be in Tris buffer (otherwise, the buffer should be changed e.g. to
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phosphate buffer using SEC0. Micro Bio-Spin 30 Columns from BioRad are suitable for desalting ∼30–70 ␮l samples). 54. If robotic spotting is sought, follow Section 3.3.3. 55. Normally three spots for each of the target protein/antibody, negative and/or positive controls are sufficient. The gaskets used (1.5 × 1.6 cm) allow up to 4 × 4 hand spots but significantly higher number of spots if robotic arrayer is used (up to 30 × 30 of 250 ␮m spots). 56. The blocking step is not necessary for hydrogels (unlike membrane-based blots and arrays), but adding ∼0.01% BSA to the Hydrogel assay buffer (PBST) may help to further reduce any background, especially if home-made hydrogels are used. No BSA shall be used if MALDI-MS detection is sought. 57. In our experience peptide assays with fluorescent detection on hydrogels often outperform ELISA-based assays despite the lack of signal amplification. We attribute this to the advantages of the hydrogel 3D matrix and target protein immobilisation. 58. The final concentration of sulfhydryl groups in the peptide labelling reaction should be below 5 mM. 59. The fluorescence dye, NIR-664-iodoacetamide, labels peptides through cysteine side chains. Final concentration of TBP in the sample should be below 5 mM, but TBP should be in molar excess to the sulfhydryl groups. 60. If air becomes trapped in the syringe during loading, dilute the gel medium slightly. The volume of the settled gel after spinning the columns should be no less than 0.7 ml. 61. At least two samples should be assayed, so relative concentrations of the assayed peptides can be compared between the two samples, or between one unknown sample and one known or pooled reference sample. Labelled peptides’ concentrations may be high, ideally above their binding KD values. 62. One assay mixture should contain only labelled peptides, but no unlabelled peptide should be added. Another assay mixture (displacement assay) should also contain a 100fold excess of the unlabelled peptide. The unlabelled and labelled peptides should be mixed prior to the incubation with the target protein spotted on hydrogels. 63. The sample holder may need to be modified to accommodate the hydrogel slides. The latter should be cast on silicon wafers and be mounted on standard MALDI plates.
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References 1. Guschin, D., Yershov, G., Zaslavsky, A., Gemmell, A., Shick, V., Proudnikov, D., Arenkov, P. and Mirzabekov, A. (1997) Manual manufacturing of oligonucleotide, DNA, and protein microchips. Anal. Biochem. 250, 203–211. 2. Vasiliskov, A.V., Timofeev, E.N., Surzhikov, S.A., Drobyshev, A.L., Shick, V.V. and Mirzabekov, A.D. (1999) Fabrication of microarray of gel-immobilized compounds on a chip by copolymerization. Biotechniques 27, 592–594. 3. Lueking, A., Horn, M., Eickhoff, H., Bussow, K., Lehrach, H. and Walter, G. (1999) Protein microarrays for gene expression and antibody screening. Anal. Biochem. 270, 103–111. 4. MacBeath, G. and Schreiber, S.L. (2000) Printing proteins as microarrays for highthroughput function determination. Science 289, 1760–1763. 5. de Wildt, R.M., Mundy, C.R., Gorick, B.D. and Tomlinson, I.M. (2000) Antibody arrays for high-throughput screening of antibody– antigen interactions. Nat. Biotechnol. 18, 989–994. 6. Arenkov, P., Kukhtin, A., Gemmell, A., Voloshchuk, S., Chupeeva, V. and Mirzabekov, A. (2000) Protein microchips: use for immunoassay and enzymatic reactions. Anal. Biochem. 278, 123–131. 7. Soloviev, M., Barry, R. and Terrett, J. (2004) Chip based proteomics technology, in Molecular Analysis and Genome Discovery (Rapley R., Harbron S., ed.), John Wiley and Sons Ltd, New York, pp. 217–249. 8. Scrivener, E., Barry, R., Platt, A., Calvert, R., Masih, G., Hextall, P., Soloviev, M. and Terrett, J. (2003) Peptidomics: a new approach to affinity protein microarrays. Proteomics 3, 122–128. 9. Barry, R. and Soloviev, M. (2004) Peptidomics approaches to proteomics research. BIOforum Eur. 1, 34–36. 10. Soloviev, M. and Finch, P. (2005) Peptidomics, current status. J. Chromatogr. B. 815, 11–24. 11. Soloviev, M. and Terrett, J. (2005) Practical guide to protein microarrays: assay systems, methods and algorithms, in Protein Microarrays (Schena M., ed.), Jones and Bartlett Publishers, Boston, MA, pp. 43–56. 12. Clarckson, T., Hoogenboom, H.R., Griffiths, A.D. and Winter, G. (1991) Making antibody fragments using phage display libraries. Nature 352, 624–628.
13. Knappik, A., Ge, L., Honegger, A., Pack, P., Fischer, M., Wellnhofer, G., Hoess, A., Wolle, J., Pluckthun, A. and Virnekas, B. (2000) Fully synthetic human combinatorial antibody libraries (HuCAL). J. Mol. Biol. 296, 57–86. 14. Binz, K.H. and Pl¨uckthun, A. (2005) Engineered proteins as specific binding reagents. Curr. Opin. Biotechnol. 16, 459–469. 15. Messmer, B.T., Benham, C.J. and Thaler, D.S. (2000) Sequential determination of ligands binding to discrete components in heterogeneous mixtures by iterative panning and blocking (IPAB). J. Mol. Biol. 296, 821–832. 16. Kay, B.K., Kasanov, J. and Yamabhai, M. (2001) Screening phage-displayed combinatorial peptide libraries. Methods 24, 240–246. 17. Noren, K.A. and Noren, C.J. (2001) Construction of high-complexity combinatorial phage display peptide libraries. Methods 23, 169–178. 18. Smith, G.P. (1985) Filamentous fusion phage: novel expression vectors that display clones antigens on the virion surface. Science 228, 1315–1317. 19. Ditzel, H.J., Barbas, S.M., Barbas, C.F., 3rd and Burton, D.R. (1994) The nature of the autoimmune antibody repertoire in human immunodeficiency virus type 1 infection. Proc. Natl Acad. Sci. USA. 91, 3710–3714. 20. Scott, J.K. and Smith, G.P. (1990) Searching for peptide ligands with an epitope library. Science 249, 386–390. 21. Cwirla, S.E., Peters, E.A., Barrett, R.W. and Dower, W.J. (1990) Peptides on phage: a vast library of peptides for identifying ligands. Proc. Natl Acad. Sci. USA. 87, 6378–6382. 22. Devlin, J.J., Panganiban, L.C. and Devlin, P.E. (1990) Random peptide libraries: a source of specific protein binding molecules. Science. 249, 404–406. 23. Barry, R., Scrivener, E., Soloviev, M. and Terrett, J. (2002) Chip-based proteomics technologies. Int. Genomic Proteomic Technol. Feb, 14–22. 24. Quanjun, L., Wei, Z., Hong, W., Jujun, Z., Yujie, Z. and Zuhong, L. (2002) Detection YMDD mutation of HBV with a polyacrylamide film immobilized molecular beacon array. 8th International conference on Electronic Materials, IUMRS-ICEM 2002, Xi’an, China, pp. 387–39.2 25. Brueggemeier, S.B., Kron, S.J. and Palecek, S.P. (2004) Use of protein-acrylamide copolymer hydrogels for measuring protein
Affinity Peptidomics
26.
27. 28.
29.
30.
31.
32.
33.
34.
concentration and activity. Anal. Biochem. 329, 180–189. Burnham, M.R., Turner, J.N., Szarowski, D. and Martin, D.L. (2006) Biological functionalization and surface micropatterning of polyacrylamide hydrogels. Biomaterials 27, 5883–5891. Salata, O. (2007) Nanotechnology in therapeutics: hydrogels and beyond. J. Nanobiotechnol. 5, 5. Jacchetti, E., Emilitri, E., Rodighiero, S., Indrieri, M., Gianfelice, A., Lenardi, C., Podest`a, A., Ranucci, E., Ferruti, P. and Milani, P. (2008) Biomimetic poly(amidoamine) hydrogels as synthetic materials for cell culture. J. Nanobiotechnol. 6, 14. Tang, J. and Xiao, P. (2009) Polymerizing immobilization of acrylamide-modified nucleic acids and its application. Biosens. Bioelectron. 24, 1817–1824. Barbas, C.F., 3rd, Kang, A.S., Lerner, R.A. and Benkovic, S.J. (1991) Assembly of combinatorial antibody libraries on phage surfaces: the gene III site. Proc. Natl Acad. Sci. USA. 88, 7978–7982. Hoogenboom, H.R., Griffiths, A.D., Johnson, K.S., Chiswell, D.J., Hudson, P. and Winter, G. (1991) Multi-subunit proteins on the surface of filamentous phage: methodologies for displaying antibody (Fab) heavy and light chains. Nucleic Acids Res. 19, 4133–4137. Griffiths, A.D., Williams, S.C., Hartley, O., Tomlinson, I.M., Waterhouse, P., Crosby, W.L., Kontermann, R.E., Jones, P.T., Low, N.M., Allison, T.J., Prospero, T.D., Hoogenboom, H.R., Nissim, A., Cox, J.P.L., Harrison, J.L., Zaccolo, M., Gherardy, E. and Winter, G. (1994) Isolation of high affinity human antibodies directly from large synthetic repertoires. EMBO J. 13, 3245–3260. Vaughan, T.J., Williams, A.J., Pritchard, K., Osbourn, J.K., Pope, A.R., Earnshaw, J.C., McCafferty, J., Hodits, R.A., Wilton, J. and Johnson, K.S. (1996) Human antibodies with sub-nanomolar affinities isolated from a large non-immunized phage display library. Nat. Biotechnol. 14, 309–314. Rai, A.J., Gelfand, C.A., Haywood, B.C., Warunek, D.J., Yi, J., Schuchard, M.D., Mehigh, R.J., Cockrill, S.L., Scott, G.B., Tammen, H., Schulz-Knappe, P., Speicher, D.W., Vitzthum, F., Haab, B.B., Siest, G. and Chan, D.W. (2005) HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics 5, 3262–3277.
343
35. Barry, R., Diggle, T., Terrett, J. and Soloviev, M. (2003) Competitive assay formats for high-throughput affinity arrays. J. Biomol. Screen. 8, 257–263. 36. Heo, J. and Crooks, R.M. (2005) Microfluidic biosensor based on an array of hydrogelentrapped enzymes. Anal. Chem. 77, 6843–6851. 37. Zubtsov, D.A., Savvateeva, E.N., Rubina, A.Y., Pan’kov, S.V., Konovalova, E.V., Moiseeva, O.V., Chechetkin, V.R. and Zasedatelev, A.S. (2007) Comparison of surface and hydrogel-based protein microchips. Anal. Biochem. 368, 205–213. 38. Thomas, G., El-Giar, E.M., Locascio, L.E. and Tarlov, M.J. (2006) Hydrogelimmobilized antibodies for microfluidic immunoassays: hydrogel immunoassay. Methods Mol. Biol. 321, 83–95. 39. Parker, L.L., Brueggemeier, S.B., Rhee, W.J., Wu, D., Kent, S.B., Kron, S.J. and Palecek, S.P. (2006) Photocleavable peptide hydrogel arrays for MALDI-TOF analysis of kinase activity. Analyst 131, 1097–1104. 40. Rubina, A.Y., Dyukova, V.I., Dementieva, E.I., Stomakhin, A.A., Nesmeyanov, V.A., Grishin, E.V. and Zasedatelev, A.S. (2005) Quantitative immunoassay of biotoxins on hydrogel-based protein microchips. Anal. Biochem. 340, 317–329. 41. Tanaka, H., Hanasaki, M., Isojima, T., Takeuchi, H., Shiroya, T. and Kawaguchi, H. (2009) Enhancement of sensitivity of SPR protein microarray using a novel 3D protein immobilization. Colloids Surf. B Bioint. 70, 259–265. 42. Dyukova, V.I., Dementieva, E.I., Zubtsov, D.A., Galanina, O.E., Bovin, N.V. and Rubina, A.Y. (2005) Hydrogel glycan microarrays. Anal. Biochem. 347, 94–105. 43. Charles, P.T., Taitt, C.R., Goldman, E.R., Rangasammy, J.G. and Stenger, D.A. (2004) Immobilization strategy and characterization of hydrogel-based thin films for interrogation of ligand binding with staphylococcal enterotoxin B (SEB) in a protein microarray format. Langmuir 20, 270–272. 44. Hofmann, H.J. and H¨adge, D. (1987) On the theoretical prediction of protein antigenic determinants from amino acid sequences. Biomed. Biochim. Acta 46, 855–866. 45. Pellequer, J.L. and Westhof, E. (1993) PREDITOP: a program for antigenicity prediction. J. Mol. Graph. 11, 204–210. 46. Ponomarenko, J.V. and Bourne, P.E. (2007) Antibody–protein interactions: benchmark datasets and prediction tools evaluation. BMC Struct. Biol. 7, 64.
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Zhang et al.
47. Flower, D.R. (2007) Immunoinformatics and the in silico prediction of immunogenicity. An introduction. Methods Mol. Biol. 409, 1–15. 48. El-Manzalawy, Y., Dobbs, D. and Honavar, V. (2008) Predicting linear B-cell epitopes using string kernels. J. Mol. Recognit. 21, 243–255. 49. Chen, J., Liu, H., Yang, J. and Chou, K.C. (2007) Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33, 423–428. 50. Ponomarenko, J., Bui, H.H., Li, W., Fusseder, N., Bourne, P.E., Sette, A. and Peters, B. (2008) ElliPro: a new structurebased tool for the prediction of antibody epitopes. BMC Bioinformatics 9, 514.
51. Van Regenmortel, M.H. and Pellequer, J.L. (1994) Predicting antigenic determinants in proteins: looking for unidimensional solutions to a three-dimensional problem?. Pept. Res. 7, 224–228. 52. Hopp, T.P. and Woods, K.R. (1981) Prediction of protein antigenic determinants from amino acid sequences. Proc. Natl Acad. Sci. USA. 78, 3824–3828. 53. Kyte, J. and Doolittle, R.F. (1982) A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157, 105–312. 54. Hopp, T.P. (1993) Retrospective: 12 years of antigenic determinant predictions, and more. Pept. Res. 6, 183–190.
Chapter 24 In Situ Biosynthesis of Peptide Arrays Mingyue He and Oda Stoevesandt Abstract Polypeptide and protein arrays enable high-throughput screening capabilities for studying molecular interactions and profiling of biomarkers, and provide a powerful functional screening tool for peptidomics. To overcome the limitations of conventional arraying methods, we have exploited cell-free systems for generating arrays of polypeptides by direct on-chip biosynthesis from DNA templates. Here we describe two protocols: (i) Protein In Situ Array (PISA), which allows the generation of polypeptide arrays in a single reaction by spotting cell-free lysate together with PCR DNA on a glass surface pre-coated with a capturing reagent, and (ii) DNA Array to Protein Array (DAPA), which is capable of producing multiple copies of a polypeptide array from a single DNA array template. The main advantage of these methods is in using an in vitro coupled transcription and translation system which circumvents the need to synthesise and purify individual polypeptides. Our methods allow making polypeptide arrays using amplified linear DNA fragments. Key words: Peptide array, cell-free protein synthesis.
1. Introduction Peptidomics requires technologies for high-throughput, multiplexed interaction assays. Peptide arrays can be used for simultaneous analysis of a large number of protein–peptide interactions and protein signalling pathways in a time- and cost-effective manner (1). One of the major bottlenecks in making peptide arrays is ensuring the supply of a large number of peptides for immobilisation. Chemical synthesis of peptides remains an expensive option, while expression and purification of large numbers of polypeptides or proteins in heterologous hosts is a time-consuming process. Cell-free synthesis can be used to overcome these problems M. Soloviev (ed.), Peptidomics, Methods in Molecular Biology 615, DOI 10.1007/978-1-60761-535-4 24, © Humana Press, a part of Springer Science+Business Media, LLC 2010
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(2–8). It directs the synthesis of polypeptides and proteins from added PCR DNA templates without the need for bacterial cloning, providing a rapid and economic means for conversion of genetic information into polypeptides. DNA fragments encoding peptides can be synthesised routinely, rapidly and inexpensively. By coupling cell-free synthesis and in situ protein immobilisation on the array surface, we have developed two cell-free methods, termed PISA and DAPA, for making protein arrays on demand directly from DNA molecules (2, 4, 8). These approaches eliminate the need for separate expression, purification and printing of individual proteins, and help to avoid the risk of deterioration in protein function during storage, as protein arrays can be produced in a matter of hours immediately prior to their application, as shown in Fig. 24.1. Our methods can also be used for arraying functional full-length proteins (5).
Fig. 24.1. Principle of PISA and DAPA. (a) Scheme of Protein In Situ Array procedure (PISA). (b) Example of a PISA protein array. (c) Scheme of DNA Array to Protein Array procedure (DAPA). (d) Example of a template DNA array (left) and the synthesised DAPA protein array (right).
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2. Materials 2.1. Cell-Free Expression
1. Primers for the amplification of DNA template for use with Rabbit Reticulocyte Lysate System: – “T7/back(R)”: 50006 -GCAGCTAATACGACTCACTATA GGAA CAGACCACCATG-30006 , an upstream primer containing T7 promoter (italics) and Kozak sequence (underlined) and the start codon ATG (doubly underlined). – “G/back(R)”: 50006 -TAGGAACAGACCACCATG 0006 (N)15–25 -3 , an upstream primer for PCR amplification of target genes. It contains a sequence overlapping with “T7/back (R)” primer and 15–25 nucleotides (N) matching the 50006 sequence of the gene of interest (see Note 1). a – “G/for”:50006 -CACCGCCTCTAGAGCG(N)15–25 -30006 , downstream primer for PCR amplification of target genes. It contains a sequence overlapping with a PCR fragment encoding a C-terminal region of the expression construct and 15–25 nucleotides complementary to the 30006 region of a target gene (see Note 1). 2. Primers for the amplification of DNA template for use with E. coli S30 Extracts: – “RTST7/back”: 50006 -GATCTCGATCCCGCG-30006 , an upstream primer for the amplification of T7 fragment (in combination with the “RTST7/for” primer or a full-length construct in combination with “T-term/for” primer). – “RTST7/for”: 50006 -CATGGTATATCTCCTTCTTAAAG30006 , a downstream primer for the amplification of T7 fragment in combination with the “RTST7/back” primer. – “G/back(E)”: 50006 -CTTTAAGAAGGAGATATACCATG (N)15–25 -30006 , an upstream primer for the amplification of target genes. It contains a sequence overlapping with the T7 fragment and 15–25 nucleotides from the 50006 sequence of the gene of interest (see Note 1). – “G/for”: 50006 -CACCGCCTCTAGAGCG(N)15–25 -30006 , a downstream primer for the amplification of a target gene. It contains a sequence overlapping with a PCR fragment encoding a C-terminal region of the expression construct and 15–25 nucleotides complementary to the 30006 region of the target gene (see Note 1).
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3. Primers for the amplification of the C-terminal region of the expression construct: – “Linker-tag/back”: 50006 -GCTCTAGAGGCGGTGGC-30006 , an upstream primer for the amplification of a termination region in combination with the “T-term/for” primer. – “T-term/for”: 50006 -TCCGGATATAGTTCCTCC-30006 , a downstream primer for the amplification of the termination region in combination with the “Linker-tag/back” primer or the amplification of the full-length construct in combination with one of the “RTST7/back” or “T7/back(R)” primers. 4. Cy5 and NH2 -modified primers – “Cy5-RTST7/back”: 50006 -Cy5-GATCTCGATCCCGCG30006 , an upstream primer for the amplification of the full-length construct in combination with the “NH2 Tterm/F” primer (see Note 2). – “NH2 -T-term/for”: 50006 -NH2 -TCCGGATATAGTTCCTCC-30006 , a downstream primer for PCR generation of the full-length construct in combination with “Cy5-RTST7/B” primer (see Note 3). 5. T7 regulatory fragment for E. coli cell-free expression: 50006 GATCTCGATCCCGCGAAATTAATACGACTCACTATA GGGAGACCACAACGGTTTCCCTCTAGAAATAATTT TGTTTAACTTTAAGAAGGAGATATACCATG-30006 . T7 promoter is underlined; the ribosome binding site (underlined italics) and the start codon ATG (doubly underlined) are indicated (see Note 4). 6. C-terminal region regulatory fragment for E. coli cell-free expression: 50006 -GCTCTAGAggcggtggctctggtggcggttctggcggtggcaccggtggcggttctggcggtggcAAACGGGCTGATGCTGC ACATCACCATCACCATCACTCTAGAGCTTGGC GTCACCCGC CAGTTCGGTGGTCACCACCACCACC ACCACTAATAA(A)28 CCGCTGAGCAATAACTAGCA T-AACCCCTTGGGGCCTCTAAACGGGTCTTGAGGG GTTTTTTGCTGAAAGGAGGAACTATATCCGGA-30006 . This fragment encodes a C-terminal region, composed of a flexible 19 amino acid linker (lower case), a double (His)6 tag (underlined), two consecutive stop codons (doubly underlined), a poly(A) tail and a transcription termination region (shown in italics) (see Note 5). 7. Cell-free systems, molecular biology reagents and kits: Rabbit Reticulocyte T&T T7 Quick for PCR DNA (Promega, UK); RTS100 E. coli HY (Roche Molecular Biochemicals, UK); GenEluteTM Gel Extraction kit (Sigma, UK); GenEluteTM PCR Clean-Up kit (Sigma, UK);
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8. Arrays and slides: NexterionTM slide E (epoxysilane coated, Schott Nexterion, UK); Durapore 0.22-␮m membrane filters (Millipore, UK); Ni-NTA-coated microscope slides (Xenopore, USA) 9. PBS: Phosphate-buffered saline, pH 7.4 10. Wash buffer 1: PBS, 300 mM NaCl, 20 mM imidazole, pH 8.0 11. Wash buffer 2: PBS, 0.05% Tween20 12. 6× spotting buffer: 300 mM sodium phosphate, pH 8.5 13. Saturated NaCl solution: 30% NaCl, boil and cool down to make saturated solution 14. Quenching buffer: 0.1 M Tris-HCl, pH 9.0. Add ethanolamine to a final concentration of 50 mM immediately before use. 15. Other buffers: 100 mM magnesium acetate; 0.1% Tween20 in H2 O; 1 mM HCl; 100 mM KCl.
3. Methods 3.1. Amplification of cDNA Constructs for Cell-Free Expression
PCR-amplified DNA fragments make suitable templates especially for short polypeptide synthesis using cell-free systems. The PCR construct should contain the essential regulatory elements for transcription and translation. These include a promoter (usually T7), translation initiation site and sequences for transcription and translation termination. The translation initiation site for eukaryotic systems is different to that for prokaryotic E. coli S30 extracts. A poly(A) tail should also be included after the stop codon. For in situ immobilisation of polypeptides on a surface, an affinity tag sequence should be placed at either N- or C-terminus of the polypeptide (see Note 6). To simplify the generation of templates for cell-free expression, these common sequence elements should be made and cloned. Plasmids make convenient templates for PCR amplification. Figure 24.2 summarises the process of generating the DNA fragments. The T7 promoter and translation initiation site should be present upstream of the target cDNA. These can be introduced either by using a long primer containing the required sequences (an approach most suitable for the rabbit reticulocyte system) or by using a PCR-amplified DNA derived from the cloned T7 fragment (the approach more suitable for the E. coli expression system). A DNA fragment encoding C-terminal immobilisation tag and containing transcription and translation
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Fig. 24.2. PCR strategy for the generation of constructs for cell-free expression. The primers used are (1) “RTST7/back”, (2) “RTST7/for”, (3) “G/back”, (4) “G/for”, (5) “Linker-tag/back”, (6) “T-term/for”, (7) “T7/back (R)”. (i) PCR amplification strategy for E. coli cell-free system. (ii) PCR amplification strategy for rabbit reticulocyte lysate.
termination sequences should be placed downstream of the target DNA. 3.1.1. Amplification of Target Genes, the C-Terminal Region and the T7 Domain (see Note 7)
1. Set up a standard 50 ␮l PCR reaction using e.g. Qiagen Taq system. Use “G/for” primer together with either “G/back(R)” primer (for rabbit reticulocyte lysate system) or with “G/back(E)” primer (for E. coli cell-free system). Carry out thermal cycling for 30 cycles (94◦ C for 30 s, 54◦ C for 30 s and 72◦ C for 1 min). 2. Set up standard 50 ␮l PCR reaction using e.g. Qiagen Taq system. Use primers “Linker-tag/back” and “T-term/for” to amplify the C-terminal region (see Section 2, Step 7). Carry out thermal cycling for 30 cycles (94◦ C for 30 s, 54◦ C for 30 s and 72◦ C for 1 min). 3. For the E. coli expression system only, set up a standard 50 ␮l PCR reaction using e.g. Qiagen Taq system. Use
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primers “RTST7/back” and “RTST7/for” to amplify the T7 domain from the control plasmid. Carry out thermal cycling for 30 cycles (94◦ C for 30 s, 54◦ C for 30 s and 72◦ C for 1 min). 4. Analyse the amplified fragments on 1% agarose gel. Isolate the expected fragments using GenEluteTM or similar gel extraction kit. 3.1.2. Assembly of the DNA Constructs by PCR
1. For the rabbit reticulocyte lysate system, set up a PCR reaction using e.g. Qiagen Taq system; mix the target gene and the C-terminal region in equimolar ratios (total DNA 50– 100 ng), no oligonucleotide primers needed at this stage, total volume 25 ␮l. Carry out thermal cycling for eight cycles (94◦ C for 30 s, 54◦ C for 1 min and 72◦ C for 1 min) to assemble the fragments (see Note 8). 2. To further amplify the assembled product, transfer 2 ␮l of the assembled construct (from the Step 1 above) to another standard PCR reaction mix, add primers “T7/back(R)” and “T-term/for” and amplify for 30 cycles (94◦ C for 30 s, 54◦ C for 1 min and 72◦ C for 1.2 min). 3. For the E. coli system, set up a PCR reaction using e.g. Qiagen Taq system; mix T7 domain, target gene and the Cterminal region in equimolar ratios (total DNA 50–100 ng), no oligonucleotide primers needed at this stage, total volume 25 ␮l. Carry out thermal cycling for 8 cycles (94◦ C for 30 s, 54◦ C for 1 min and 72◦ C for 1 min) to assemble the fragments (see Note 8). 4. To further amplify the assembled product, transfer 2 ␮l of the assembled construct (from the Step 3 above) to another standard PCR reaction mix, add primers “RTST7/back” and “T-term/for” and amplify for 30 cycles (94◦ C for 30 s, 54◦ C for 1 min and 72◦ C for 1.2 min). 5. Analyse the amplified fragments on 1% agarose gel. Isolate the expected fragments using GenEluteTM or similar gel extraction kit (see Note 9). 6. Confirm the construct identity by PCR mapping (see Note 10). The resulting PCR construct, either purified or unpurified, is ready for use for peptide arrays. The construct may be stored at –20◦ C for at least 6 months.
3.1.3. Assembly of the Fluorescently Labelled DNA Construct for Use with E. coli Cell-Free Expression System
1. Assemble a PCR reaction using e.g. Qiagen Taq system; mix T7 domain, target gene and the C-terminal region in equimolar ratios (total DNA 50–100 ng), no oligonucleotide primers needed at this stage, total volume 25 ␮l. Carry out thermal cycling for eight cycles (94◦ C for 30 s,
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54◦ C for 1 min and 72◦ C for 1 min) to assemble the fragments (see Note 8). 2. To further amplify the assembled product, transfer 2 ␮l of the assembled construct (from the Step 3 above) to another standard PCR reaction mix, add primers “Cy5RTST7/back” and “T-term/for” and amplify for 30 cycles (94◦ C for 30 s, 54◦ C for 1 min and 72◦ C for 1.2 min). 3. Analyse the amplified fragments on 1% agarose gel. Isolate the expected fragments using GenEluteTM or similar gel extraction kit (see Note 9). 4. Measure the concentration and purity of the PCR product by absorption at 260 nm and 280 nm or by gel electrophoresis. DNA concentration of 100 ng/␮L is recommended for spotting (see Note 11). 3.2. In Situ Peptide Arrays on Nickel-Coated Glass Slides
1. To set up T&T reaction using Rabbit Reticulocyte Lysate system, mix the following kit components: Rabbit Reticulocyte Lysate T&T system for PCR DNA (40 ␮l), 1 mM Methionine (1 ␮l), 100 mM magnesium acetate (1 ␮l), assembled cDNA expression construct (50–100 ng), H2 O (to 50 ␮l final volume) (see Note 12). 2. To set up T&T reaction using RTS100 E. coli HY, mix the following kit components: E. coli lysate (12 ␮l), Reaction mix from the kit (10 ␮l), Amino acids (12 ␮l), Methionine (1 ␮l), Reconstitution buffer (5 ␮l), assembled cDNA expression construct (50–100 ng), H2 O (to 50 ␮l final volume) (see Note 13). 2. Spot the T&T mixture onto a Ni-NTA-coated glass slide (40 nl per spot) (see Note 14). 3. Incubate the slide in a humidified chamber (see Note 15) at 30◦ C for 2 h (see Note 16). 4. Wash three times with the wash buffer 1 (see Note 17) or with the wash buffer 2, followed by a final wash with 100 ␮l PBS, pH 7.4.
3.3. DNA Array to Protein Array
DNA Array to Protein Array (DAPA) is achieved using cellfree synthesis of polypeptides within a membrane held between the surfaces of two glass slides. One of the slides carries an array of immobilised PCR molecules, the other slide is coated with a reagent to capture the newly synthesised polypeptides. After synthesis within the membrane, individual polypeptides bind to the capturing surface, creating a polypeptide array with the layout mirroring that of the DNA array. We use epoxysilaneactivated slides for DNA immobilisation, E. coli cell-free system for polypeptide synthesis, and Ni-NTA-coated slides for capturing His-tagged polypeptides.
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1. Add 1 volume of 6× spotting buffer to 5 volumes of the assembled DNA PCR product (3.1.3. Step 4). 2. Spot DNA samples on the epoxysilane slide (see Note 18) with spot-to-spot distances of 1 mm and volumes per spot of 2–3 nl. Incubate spotted slides in a humidified chamber at room temperature for 1 h. (see Note 15). 3. Incubate slides at 60◦ C for 30 min. 4. Wash the slides once with 0.1% Tween-20 for 5 min, twice with 1 mM HCl for 2 min, once with 100 mM KCl for 10 min and once with ddH2 O for 1 min (all washes should be performed at room temperature). 5. Quench the remaining epoxy groups by incubating slides in 0.1 M Tris-HCl pH 9.0, 50 mM ethanolamine at 50◦ C for 15 min. Rinse slides with ddH2 O for 1 min and dry either by pressurised air or by centrifugation at 2000 rpm for 1 min. 6. Scan the slides in a suitable microarray scanner to confirm immobilisation of Cy5-labelled DNA. The slides should stored in the dark at 4◦ C until use.
3.3.2. Printing Polypeptide Array Using the DNA Array Template
1. Use a slide holder similar to the prototype shown in Fig. 24.3.
Fig. 24.3. Schematic cross-section of DAPA assembly. The numbering of components is the same as in Section 3.3.2 (Step 3).
2. Cut a Durapore membrane filter large enough to cover the area of the DNA template array. Prepare E. coli cell-free lysate, make 10 ␮l of the lysate per 1 cm2 of the membrane area. 3. Assemble the slide holder in the following order (as shown in Fig. 24.3):
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(i) Put a rubber spacer (Fig. 24.3, #2) in the bottom plate (Fig. 24.3, #1), followed by a layer of parafilm (Fig. 24.3, #3); (ii) Place a Ni-NTA-coated slide (Fig. 24.3, #4) with the capturing surface facing up onto the parafilm (see Note 18); (iii) Spread the required volume of E. coli cell-free lysate on the surface of the Ni-NTA slide (Fig. 24.3, #4), cover with the membrane filter (Fig. 24.3, #5) allowing it to soak up the lysate (see Note 19); (iv) Position the DNA template slide (Fig. 24.3, #6) with DNA surface facing down on the membrane filter. Cover with another layer of parafilm (Fig. 24.3, #7) and another rubber spacer (Fig. 24.3, #8) (see Note 20); (v) Close the slide holder with the top plate (Fig. 24.3, #9); ensure even pressure by carefully tightening screws. 4. Incubate the assembled slide holder at 30◦ C for 2–4 h. 5. Disassemble the slide holder and wash the Ni-NTA slide (peptide arrays) three times with washing buffer 2. At this stage the peptide array is ready for use in downstream applications. 6. Rinse the DNA template slide with ddH2 O, dry and store at 4◦ C; the DNA array can be used for making more than one peptide array. 7. A standard direct binding immunoassay can be used to detect immobilised polypeptides on the array or for quality control purposes (see Note 21).
4. Notes 1. Sequence of the fragment marked as “(N)15–25 ” will depend on the particular target gene used and on the position along that sequence and has to be devised by the user. 2. Cy5 fluorescent label allows detection and quantification of the immobilised PCR product. 3. The coupled NH2 group allows immobilisation of the PCR product on epoxy-activated slides. 4. This fragment can be obtained from the control plasmid included with the RTS100 E. coli HY kit (Roche). 5. The encoded double-(His)6 tag has shown an order of magnitude or greater affinity for Ni-NTA modified surfaces compared to a conventional single-(His)6 tag (2, 6).
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6. The location of a tag can be at both the N- and C-termini of the polypeptide, although C-terminal immobilisation tags are preferable, as their presence ensures that the entire polypeptide is expressed. 7. The C-terminal region and the T7 domain can be produced in large quantities by PCR and stored at –20◦ C until use. 8. Alternatively, long oligonucleotides (about 100 bases) encoding peptides can be synthesised and then assembled with the 50006 T7 domain and the C-terminal domain by PCR. 9. If multiple PCR bands are generated, the expected PCR fragment with the correct size should be isolated by gel extraction and used as template for PCR re-amplification. In general, unpurified PCR fragments can be directly used for protein synthesis in cell-free systems. However, if purification is needed, a Sigma GenEluteTM PCR Clean-Up kit can be used. 10. A construct can be confirmed by PCR mapping, which is performed by using a combination of primers annealing at different positions along the construct. If all PCR products give the expected size, it suggests the correct construction. 11. If the eluted PCR product is below this range, it can be concentrated in a vacuum centrifuge. 12. Magnesium acetate added to rabbit reticulocyte lysate TNT mixture during translation was found to improve protein expression. We produced a better yield for single-chain antibodies and other protein when additional Mg2+ concentrations ranging from 0.5 to 2 mM were included in this system. 13. RTS100 E. coli HY can yield 3–25 ␮g of protein or polypeptide in a 50 ␮l reaction. 14. The Ni-NTA-coated glass slides are capable of capturing His-tagged polypeptides. 15. A humidified chamber can be prepared using a box containing saturated NaCl solution. 16. Depending on the polypeptide and the planned downstream application, the time can vary. For rabbit reticulocyte lysate T&T system 1–2 h is most suitable, or 1–4 h for RTS E. coli HY System. 17. Rabbit reticulocyte lysate contains large amounts of haemoglobin which sometimes binds to Ni-coated slides. More washes may be required to remove haemoglobin from the slides. 18. Mark glass slides and their orientation with a diamondtipped pen. Any possible glass splinters or dust from the slide surfaces can be removed by using pressurised air.
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19. The soaking should take just a few seconds. It is important to avoid drying the cell-free lysate within the membrane filter. 20. The parafilm must form an airtight seal around the slide sandwich (as shown in Fig. 24.3) in order to prevent evaporation of cell-free lysate. 21. Fluorescently labelled antibodies or signal amplification, e.g. with horseradish peroxidase/tyramide-Cy3 system can be used with commonly available microarray scanners, most of which are capable of fluorescence detection in the Cy3 and Cy5 range (550 and 650 nm, respectively).
Acknowledgements We thank Hong Liu for technical assistance. Research at the Babraham Institute is supported by Biotechnology and Biological Sciences Research Council (BBSRC), UK. References 1. Uttamchandani, M. and Yao, S.Q. (2008) Review: Peptide microarrays: next generation biochips for detection, diagnostics and highthroughput screening. Curr. Pharm. Des. 14, 2428–2438. 2. He, M. and Taussig, M.J.(2001). Single step generation of protein arrays from DNA by cell-free expression and in situ immobilization (PISA method). Nucleic Acids Res. 29, e73. 3. Ramachandran, N., Hainsworth, E., Bhullar, B., Eisenstein, S., Rosen, B., Lau, A.Y., Walter, J.C., and LaBaer, J. (2004) Selfassembling protein microarrays. Science 305, 86–90. 4. Angenendt, P., Kreutzberger, J., Glokler, J., and Hoheisel, J.D. (2006) Generation of high density protein microarrays by cell-free in situ expression of unpurified
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PCR products. Mol. Cell. Proteomics 5, 1658–1666. He, M. and Taussig, M.J.(2003) DiscernArrayTM technology: a cell-free method for the generation of protein arrays from PCR DNA. J. Immunol. Methods 274, 265–270. Khan, F., He, M., and Taussig, M.J. (2006) A double-His tag with high affinity binding for protein immobilisation, purification, and detection on Ni-NTA surfaces. Anal. Chem. 78, 3072–3079 He, M., Stoevesandt, O., Palmer, E.A., Khan, F., Ericsson, O., and Taussig, M.J. (2008) Printing protein arrays from DNA arrays. Nat. Methods 5, 175–177. He, M., Stoevesandt, O., and Taussig, M.J. (2008) In situ synthesis of protein arrays. Curr. Opin. Biotechnol. 19, 4–9.
Chapter 25 Bioinformatic Approaches to the Identification of Novel Neuropeptide Precursors Elke Clynen, Feng Liu, Steven J. Husson, Bart Landuyt, Eisuke Hayakawa, Geert Baggerman, Geert Wets, and Liliane Schoofs Abstract With the entire genome sequence of several animals now available, it is becoming possible to identify in silico all putative peptides and their precursors in an organism. In this chapter we describe a searching algorithm that can be used to scan the genome for predicted proteins with the structural hallmarks of (neuro)peptide precursors. We also describe how to use search strings such as the presence of a glycine residue as a putative amidation site, dibasic cleavage sites, the presence of a signal peptide, and specific peptide motifs to improve a standard BLAST search and make it suitable for searching (neuro)peptides in EST data. We briefly explain how bioinformatic tools and in silico predicted peptide precursor sequences can aid experimental peptide identification with mass spectrometry. Key words: Bioinformatics, BLAST, expressed sequence tags, Mascot, mass spectrometry, neuropeptide prediction, Sequest.
1. Introduction 1.1. Prediction of Peptide Precursor Genes from the Genome
Since the advent of genome projects, computational methods have become especially important in predicting novel putative peptides and their precursor genes. The genome of an organism may be screened for peptide-coding genes based on sequence similarity to known peptide genes from other organisms using Basic Local Alignment Searching Tool (BLAST). For example, BLAST helped to identify 36 peptide genes found in Drosophila melanogaster (1, 2). However, for in silico prediction of peptide precursor genes, the performance of the BLAST tool is limited because putative peptide precursor sequences, for which no homologous biologically
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active peptides or their precursors have been identified as yet, will not be revealed. Peptide precursors are a special class of proteins because they undergo extensive posttranslational processing before producing final mature peptides. In most cases, only a short conserved motif might be responsible for the function of a particular peptide. The remainder of the peptide precursor sequence may be essentially irrelevant and show no significant sequence similarity (3). Due to the limited sequence conservation between peptides or their precursors, the BLAST tool is also not very effective at identifying new members of known peptide families. BLAST is suitable for scanning databases for protein sequences in which the sequence similarity is expected along the entire or most part of the sequences (global alignment) or when the similarity is limited to a specific domain (local alignment). But, it is far less efficient at finding similarity to short conserved regions spanning only few amino acids. For large peptide precursors which are between 50 and 500 amino acids in length and for which the biologically conserved regions are limited, the relevant motifs are often masked by random matches with long but unrelated sequence regions. This is because for any two random large protein sequences, BLAST usually can find a relatively long local alignment. That alignment is likely to be longer than any typical conserved peptide motif, and therefore BLAST would assign higher scores to long “random” alignments rather than to the short peptide conservative motifs. If a pair of homologous proteins share only a short mature peptide sequence, BLAST may not be able to detect the homology because the short alignment makes the pairwise sequence alignment less likely to obtain a significant BLAST score (e.g., e-value < 0.001) (4, 5). Many bioactive peptides have been sequenced by now, several of these are short and no precursors are yet known for these. There is a growing need to take advantage of these mature peptides in identifying homologous peptides and peptide precursors. Here we describe a searching algorithm for systematic search and identification (in silico) of all peptide precursor proteins in a specific species. Our method uses BLAST but also relies on the detection of additional structural hallmarks of peptides and their precursor sequences. The original study was performed in D. melanogaster, where 76 additional putative secretory peptide genes were discovered in addition to 43 known sequences (6). This bioinformatic study opens perspectives for the genome-wide analysis of (neuro)peptide genes in other eukaryotic model organisms. 1.2. Prediction of Peptide Precursor Proteins from EST Data
In many organisms (neuro)peptide research is hampered by the absence of genomic information. In its absence the Expressed Sequence Tag (EST) databases can be interrogated for (neuro)peptide precursors. ESTs are short, single-read cDNA
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sequences, usually between 200 and 500 nucleotides, derived from a particular tissue and/or a particular stage in development. One disadvantage of ESTs is the high sequencing error rate. Also, information at the transcriptome level varies in time and place. However, for many organisms EST libraries comprise the largest pool of sequence data available and often contain portions of transcripts from many uncharacterized genes. Here, we describe how EST databases can be searched for (neuro)peptide precursors using a simple BLAST search. For such a search, one has to take into account the peptide’s small size and its very limited sequence similarity, and to take advantage of the structural hallmarks of peptide precursor sequences. To validate our approach we searched an EST database of the locust Locusta migratoria, which contained 12,161 clustered Unigenes, and compared our predictions against known locust neuropeptide precursors (7). Using neuropeptide precursors from D. melanogaster as a query, we annotated six novel neuropeptide precursors.
1.3. Identification of Peptides by Mass Spectrometry (MS)
Expression of the predicted peptides in different tissues can be confirmed with mass spectrometric techniques. This not only shows which peptides are cleaved from the precursor proteins but could also reveal their posttranslational modifications. A number of databases and web tools exist that can be used to speed up the process of identifying endogenous peptides analyzed by mass spectrometry. One way to identify peptides in a biological extract is by matching experimental peptide masses against theoretically calculated masses in a database, both with and without annotated posttranslational modifications, using a selected mass tolerance based on the mass accuracy of the mass spectrometer used. The SwePep database consists of approximately 4200 annotated endogenous peptides originating from 394 different species, which are divided into three classes (i) biologically active peptides, (ii) potential biologically active peptides, and (iii) uncharacterized peptides (8). Another database PeptideDB represents the most complete collection of metazoan peptides, peptide motifs, and peptide precursor proteins identified to date (9). It contains 20,027 peptides that are processed from 19,438 precursor proteins. The peptides include neuropeptides, growth factors, peptide toxins, and antibacterial peptides and have currently been retrieved from 2820 different metazoan species. However, a peptide identity based solely on the observed molecular mass is only a suggestion and needs to be confirmed by sequence analysis of the corresponding tandem mass spectra (MS/MS). We here describe how sequence information can be retrieved by MS/MS ion searches and de novo sequencing.
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2. Materials 1. Personal computer installed with SAS (Statistical Analysis System – a statistical software package), a web browser, and Internet access 2. Swepep database (www.swepep.org) 3. PeptideDB (www.peptides.be) 4. Uniprot protein database 5. SignalP (www.cbs.dtu.dk/services/SignalP) 6. NCBI BLAST (www.ncbi.nlm.nih.gov/blast/Blast.cgi) 7. tBLASTn (www.ncbi.nlm.nih.gov/blast/Blast.cgi) 8. TMpred (www.ch.embnet.org/software/TMPRED form. html) 9. SOSUI (bp.nuap.nagoya-u.ac.jp/sosui/sosuiG/sosuigsubmit.html) 10. Translate tool (www.expasy.ch/tools/dna.html) 11. ClustalW (www.ebi.ac.uk/clustalw/) 12. MS/MS fragmentation data – peak list files 13. Mascot (www.matrixscience.com) 14. Sequest (fields.scripps.edu/sequest) 15. Peaks (www.bioinformaticssolutions.com) 16. PepNovo (proteomics.bioprojects.org/MassSpec/) 17. ProP software tool (www.cbs.dtu.dk/services/ProP/) 18. NeuroPred (neuroproteomics.scs.uiuc.edu/neuropred.html)
3. Methods 3.1. Prediction of Peptide Precursor Genes from the Genome
The existence of the common structural characteristics of known peptide precursors (see Note 1) allows to devise a sensitive searching procedure capable of identifying peptide genes. We have originally developed such program for D. melanogaster, but because the structural hallmarks of peptide precursor sequences are highly conserved across phyla, the established searching algorithm can be easily adapted for the genome-wide analysis of peptide precursor genes in other animal model systems that have their genome sequenced (see Note 1). The general principles of our algorithm are exemplified below for D. melanogaster. The same steps can be used in relation with other species.
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1. From D. melanogaster genome database select protein sequences that are shorter than 500 amino acids and that contain a signal peptide sequence. 2. Cleave these proteins in silico at typical cleavage sites (see Note 1) and use BLAST to compare these polypeptide fragments (subsequences) against full-length protein sequences to identify proteins which match at least two similar polypeptide fragments. 3. Compare the fragments obtained at the Step 2 (above) with all known bioactive peptide sequences from all metazoan organisms (see Note 2). 4. Based on the sequence comparison results, two types of screening procedures can be constructed (see Note 3): i. Finding the precursor proteins which encode multiple highly related putative peptides ii. Finding the precursors containing a single putative peptide or multiple unrelated putative peptides that share conserved motifs with known bioactive peptides. The program is implemented in SAS – a powerful integrated software for accessing, management, and analysis of large datasets (see Note 4). External tools such as SignalP, BLAST, TMpred, and SOSUI need to be run independently. Text files are used to exchange the data between the different programs. The SAS program includes a few subprograms listed below. 3.1.1. Protein.SAS
This subprogram is the first part of the SAS program, and it serves to select a subset of candidate protein sequences from any given species. For example, in D. melanogaster, the input of the subprogram consists of the Uniprot protein database file and additional D. melanogaster genes at GenBank identified by Hild et al. (10). The algorithm and the operational procedure are outlined below. 1. The relevant information for each of the proteins, such as accession number, protein name, gene name, protein sequence, signal peptide information, length, and mass, is entered into SAS. The first 70 amino acids of every protein sequence serve as output to a text file in FASTA format, which is used as the input for SignalP. 2. SignalP for eukaryotes is then run to predict the presence and location of a signal peptide in each protein sequence (11). 3. The subprogram reads the output file from SignalP, and another SAS dataset is created that includes the predicted signal peptide information for each protein. 4. The dataset containing the predicted signal peptide information is then checked against all the proteins, and the pro-
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teins are retained if they are either annotated to have signal peptides in Uniprot or predicted to have signal peptides by SignalP. The result is a dataset of proteins having aminoterminal signal peptides. 5. From this dataset, only proteins that are shorter than 500 amino acids are retained. This subprogram is used to cleave the protein sequences in the D. melanogaster protein dataset into polypeptide fragments following the removal of the signal peptide sequences. A number of conserved precursor proteins cleavage motifs have been reported (12). These are GKR, GRK, GRR, GKK, KR, RK, RR, KK, GR, GK (see Note 5). Table 25.1 compares the frequency of occurrence of these motifs in the proteome of D. melanogaster and compares that with the frequencies that these basic sites are actually used as cleavage sites in all of the annotated peptides from D. melanogaster. A similar analysis is shown for the vertebrate Mus musculus in Table 25.2. All the protein fragments, obtained by cleavage through these cleavage motifs form the D. melanogaster subsequence dataset (see Notes 6 and 7). Flow chart shown in Fig. 25.1 summarizes Protein.SAS and Cleavage.SAS procedures.
3.1.2. Cleavage.SAS
Table 25.1 Frequencies of known consensus cleavage sites in known peptides in D. melanogastera GKR
GRK
GRR
GKK
KR
RK
RR
KK
Cleaved sitesb
18(C)
1(C)
6(C)
0
35(N) 2(N) 13(C) 3(C)
16(N) 3(N) 10(C)
Uncleaved sitesc
1
3
1
4
12
30
25
Percentage (%)d
94.7
25.0
85.7
0
80.0
14.3
51.0
GR
GK
R
K
11(N) 1(N) 17(C) 5(C)
6(N) 2(C)
1(N)
29
21
13
260
305
9.4
57.1
31.6
3.0
0.3
a The
numbers are based on the analysis of 146 annotated peptides in D. melanogaster. The total number of amino acids in all these peptides is 7346 (the flanking basic cleavage sites not included). b Cleaved sites: Number of consensus sites at which cleavage process occurs. The (N) or (C) following the number indicates whether the cleavage site is located at the amino- or carboxy-terminus of the peptide sequence. c Uncleaved sites: Number of consensus sites at which no cleavage occurs. d Percentage (%): The number of sites at which cleavage occurs relative to the total number of consensus sites found sites (expressed in %): Cleaved Cleaved sites+Uncleaved sites × 100.
3.1.3. Peptide.SAS and the BLAST Analysis
This subprogram searches the UniProt database for all the annotated bioactive peptides from all metazoan organisms. The summary of Peptide.SAS is shown in Fig. 25.2. The algorithm and the operational procedure are outlined below. 1. All proteins from Metazoa, which function as mature peptides or peptide precursor proteins, are assembled into a dataset of peptides and precursors. A protein sequence has characteristics of a peptide or peptide precursor if its name
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Table 25.2 Frequencies of known consensus cleavage sites in known peptides in Mus musculusa GKR
GRK
GRR
GKK
KR
RK
RR
Cleaved sitesb
23(C)
0(C)
19(C)
8(C)
88(N) 6(N) 29(C) 1(C)
Uncleaved sitesc
11
7
53
14
165
Percentage (%)d
67.6
0
26.4
36.4
41.5
KK
GR
GK
R
44(N) 6(N) 17(C) 4(C)
1(N) 6(C)
1(N) 4(C)
48(N) 6(N) 17(C) 1(C)
225
230
160
170
169
2416
2251
3.0
21.0
5.9
4.0
2.9
2.6
0.3
a The
K
numbers are based on the analysis of 595 annotated peptides in Mus musculus. The total number of amino acids in all these peptides is 54,621 (the flanking basic cleavage sites not included). b Cleaved sites: Number of consensus sites at which cleavage process occurs. The (N) or (C) following the number indicates whether the cleavage site is located at the amino- or carboxy-terminus of the peptide sequence. c Uncleaved sites: Number of consensus sites at which no cleavage occurs. d Percentage (%): The number of sites at which cleavage occurs relative to the total number of consensus sites found Cleaved sites (expressed in %): Cleaved sites+Uncleaved sites × 100.
Begin
Uniprot protein database and additional D. melanogaster proteins
SignalP online tool
Protein database
Sequences in FASTA format
Select proteins Drosophila melanogaster
txt-file
Predicted signal peptide information
D. melanogaster proteins
SignalP output
Read in
Combine
D. melanogaster proteins that have a signal peptide and that are less than 500 amino acids in length
D. melanogaster protein dataset
Cleave sequences at basic cleavage sites
D. melanogaster subsequences
Fig. 25.1. Protein.SAS and Cleavage.SAS, Modified from 6.
D. melanogaster subsequences dataset
SAS datasets
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Uniprot protein database
Select proteins with keywords: (neuro)peptide, hormone or neurotransmitter; Exclude proteins with keywords: receptor, signal anchor, transmembrane, binding protein, DNA binding, nuclear protein, nuclear transport, enzyme or words ending in “ase”
Peptide and peptide precursor proteins
Peptide and precursor dataset
Peptide sequences are extracted if they are either annotated as “peptide” or “chain” in the “feature table” of their precursor protein files or if they are annotated as mature peptides
All bioactive peptide sequences
Peptide dataset
Fig. 25.2. Peptide.SAS, Modified from 6.
contains typical peptide keywords or if it is annotated with peptide keywords in the “Keywords” line in UniProt. The bioactive peptide keywords include (neuro)peptide, hormone, and neurotransmitter. 2. Proteins defined as membrane proteins (as indicated in UniProt) or proteins having the keywords such as receptor, signal anchor, transmembrane, binding protein, DNA binding, nuclear protein, nuclear transport, enzyme, or words ending in “ase” are excluded. 3. Bioactive peptide sequences are then extracted in silico from each precursor protein present in the peptide and precursor dataset. Peptides are extracted if they are annotated with the keyword peptide or chain in the “Feature table” of their corresponding precursor protein files in the UniProt. The endpoint specifications “from” and “to” indicate beginning and the end of the peptide fragments. The conserved basic cleavage sites flanking the peptides are also extracted (see Note 8). 4. Database entries from the peptide and precursor dataset that represent mature peptide sequences are also retained in the peptide dataset (see Note 9). 5. All the selected metazoan peptides (the above dataset) are exported as a single FASTA formatted file “peptide.txt”. 6. All the selected amino acid sequences in the D. melanogaster dataset (from the Sections 3.1.1 and 3.1.2) are exported as a single FASTA formatted file “subsequence.txt”. 7. Standalone BLAST is applied to compare the two sequence files. The score matrix “PAM30” is used, and the expectation value (e-value) and the parameter “word size” are set
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to 6 and 2, respectively, in order to find short but strong similarities (see Note 10). 3.1.4. Extract.SAS, Motif.SAS, and Shift.SAS
These subprograms are used to screen the alignment results output by BLAST and determine the biologically significant matches. The summary of the procedure is shown in Fig. 25.3. 1. Extract.SAS reads the alignments between D. melanogaster fragments (see Section 3.1.2) with themselves and extracts the proteins that have at least two similar subsequences within the protein. 2. Motif.SAS reads the alignments between D. melanogaster fragments and known peptide sequences as well as the alignments among peptide sequences themselves and identifies the fragments that contain conserved peptide motifs. 3. Shift.SAS reads the alignment results and computes the shift value. The shift value is the minimal distance between the amino- or carboxy-termini of the aligned sequence and the matching amino acids (sequence tags) in the sequence. The shift value is set to be no larger than 3 in the program (see Note 11).
3.1.5. The Implementation of Screening Procedures
The subprograms described in the previous section (Extract.SAS, Motif.SAS, and Shift.SAS) facilitate peptide screening procedures, the principles of which were described in Section 3.1. 1. The first procedure searches for proteins which contain the following sequence pattern:
· · · [cleavage1] − x1(3, 60) − [cleavage2] − · · · −[cleavage3] − x2(3, 60) − [cleavage4] · · · In this formula, “x1 (3, 60)” and “x2 (3, 60)” are two similar fragments which are between 3 and 60 amino acids long (see Note 12). “[cleavage1–4]” can be any conventional cleavage site. The fragments do not need to be adjacent within the precursor, and the matching amino acid sequence should be present close to the amino- or carboxy-termini of at least one of the fragments (shift value ≤ 3). To implement such screening procedure, the file “subsequence.txt” should be compared with itself using BLAST. Then the subprograms Extract.SAS and Shift.SAS should be used to select those proteins which match the first structural pattern of a putative peptide precursor (containing multiple highly related putative peptides). 2. The second procedure looks for proteins that meet other structural characteristics of peptide precursors (containing
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D. melanogaster subsequences
subsequence.txt
peptide.txt
Peptide sequences
Run BLAST: Query: D. melanogaster subsequences; target: D. melanogaster subsequences Query: Peptide sequences; target: D. melanogaster subsequences Query: Peptide sequences; target: Peptide sequences txt-file Alignment results among D. melanogaster subsequences
Alignment results between peptide sequences and D. melanogaster subsequences
Alignment results among peptide sequences
Shift.SAS
Extract.SAS Extract D. melanogaster proteins having at least 2 similar subsequences within the protein
Compute shift value
If the shift value of the compared subsequence ≤ 3
Shift.SAS Compute shift value
Motif.SAS
If the shift value of at least one subsequence ≤ 3
If the D. melanogaster subsequence is similar to a motif
D. melanogaster proteins that contain at least 2 similar subsequences
D. melanogaster proteins that contain at least one subsequence similar to a peptide motif
If the D. melanogaster protein has one single transmembrane region
TMpred or SOSUI online tools
The D. melanogaster putative peptide precursors
Fig. 25.3. BLAST analysis and screening of the alignment results, Modified from 6.
a single putative peptide or multiple unrelated putative peptides): i. The protein should contain at least one fragment that shares at least 60% amino acid sequence identity with a known peptide sequence, and the identical amino acids are situated close to the amino- or carboxy-termini of that fragment (shift value ≤ 3).
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ii. The identical amino acids should be similar to a conserved motif present in other known peptide sequences. This screening procedure involves the following sequence comparison by BLAST: i. The sequences in the “peptide.txt” are compared with each other and the obtained similar amino acid sequence tags are considered as possible conserved peptide motifs. ii. The file “peptide.txt” is compared with the “subsequence.txt” and those D. melanogaster fragments that display sequence similarities to any peptide motifs from the previous step are retained. 3.1.6. TMpred and SOSUI
3.2. Prediction of Peptide Precursor Proteins from EST Data
These tools are available online and could be used to identify the presence of a single transmembrane region at the amino-terminus of a protein. The length of the hydrophobic part of the transmembrane region should be set to between 17 and 33 amino acids. For the TMpred program a score above 500 for both inside to outside as well as outside to inside helixes is considered to be significant for the presence of the amino-terminal transmembrane region. A score of 250 is considered to be significant for the presence of an inside to outside helix of any second or third transmembrane region. A putative peptide precursor is retained if any of the programs predicts a single transmembrane region at the protein amino-terminus. When both programs predict the absence of an amino-terminal transmembrane region, the protein sequence is removed from the list (see Note 13). 1. Go to NCBI BLAST website and select tBLASTn (see Note 14). Enter the query sequence (see Note 15). Select the expressed sequence tags (EST) database from the pull-down menu and limit the search by specifying the correct species or entering the EST accession numbers under “entrez query” (see Note 16). Other BLAST parameters are left at their default values. 2. When a significant match is found (e.g., e-value < 0.001) the corresponding EST sequences should be further analyzed for the presence of start and stop codons and for the typical peptide precursor features (see Note 1). For this the EST sequence must be translated. In the BLAST result page, click on the accession number to display the full EST sequence. Go to www.expasy.ch/tools/dna.html, paste the EST sequence in FASTA format, and select to translate the sequence. Specify the correct frame. The frame number is displayed on the BLAST result page. The “+1” corresponds to 50006 30006 frame 1, “–1” corresponds to 30006 50006 frame 1, and so on. Select the amino acid sequence between the first start (Met) and stop codon. Paste this sequence into ClustalW. Replace
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all Met by M and delete the word STOP. Align this sequence with the homologous peptide precursor(s) (the ones that were used to perform the BLAST search) and perform a multiple sequence alignment. Analyze the results. Look for the presence of cleavage sites and the conservation of motifs (see Note 17). 3.3. Identification of Peptides in an MS/MS Ion Search
The types of peptide fragment ions observed in an MS/MS spectrum depend on many factors including the primary sequence, the mode of energy introduction, and the charge state. Fragments can only be detected if they carry at least one charge. If this charge is retained on the amino-terminal fragment, the ions are classified as either a, b, or c. If the charge is retained on the carboxyterminal fragment, the ions are classified as either x, y, or z. The subscripts indicate the number of residues in a certain fragment (summarized in Fig. 25.4). In a typical MS/MS ion search, all MS/MS data of every peptide selected for fragmentation during a liquid chromatography (LC)-MS/MS run are combined in a single peak list file. This simple type of file contains the monoisotopic masses and associated intensity values of all the parent ions and their corresponding fragmentation ions, and can be used for further bioinformatics analyses such as MS/MS ion searches and de novo sequencing. The peptides can be identified by comparing the experimentally obtained fragmentation spectra with the theoretical fragmentation spectra in databases (13).
Fig. 25.4. Possible peptide fragmentation patterns.
1. The peak list files can be used to query MS/MS data using Mascot and Sequest tools. Settings for use of endogenous peptides should be as follows: variable modifications; carboxy-terminal amidation; oxidation of methionine; and pyro-glu (N-term Q). Set enzyme to “none”. 2. A FASTA protein database containing all the (in silico) identified putative peptide precursors should be constructed and loaded onto the Mascot or Sequest server and be used for the identification of peptides using an MS/MS ion search (see Note 18).
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3. Posttranslational peptide modifications may hinder confident identification with MS/MS data. To overcome this problem one should use tools like Peaks and PepNovo (14) which assist in determining the amino acid sequence of peptides from the raw MS/MS data. De novo sequencing will further increase the number of reliable protein identifications.
4. Notes 1. Structural hallmarks of peptide precursor sequences can be arranged in three major categories: i. Almost all known peptide precursors are less than 500 amino acids in length and contain one single transmembrane region at the amino-terminus corresponding to the signal peptide that directs them into the secretory pathway of the cell. ii. The precursor is processed into bioactive peptides by a series of enzymatic steps. After cleavage of the signal peptide, prohormone convertases break the precursor protein into smaller peptides by cleaving mainly at paired dibasic residues. Carboxy-terminal basic residues are subsequently removed by carboxypeptidases, and peptides with a carboxy-terminal glycine are converted into the amide by peptidylglycine ␣-amidating monooxygenase thereby stabilizing the C-terminus. Also other posttranslational modifications occur, e.g., N-terminal glutamine residues often cyclize resulting in pyroglutamate. iii. Many peptide precursors encode multiple bioactive peptides that are often highly related. Peptide genes encoding multiple, unrelated bioactive peptides or genes encoding just a single bioactive peptide also occur. 2. The direct alignment of short D. melanogaster polypeptide fragments and the metazoan peptide sequences increases the sensitivity of finding and matching short (conserved) peptide motifs and thus overcomes the shortcomings of BLAST when searching long sequences for short matching fragments. 3. Because each D. melanogaster protein sequence was cleaved into a number of subsequences and because all of these subsequences were subsequently compared with each other or with all known metazoan peptide sequences, a very large
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number of alignments were obtained, with a high score. Because similarity does not imply homology, only the alignments which were filtered by the screening procedure were considered as candidate putative peptide precursors. 4. The software tool is not limited to SAS. Any software, which is capable of dealing with large datasets, can be applied to implement the program. 5. The cleavage of peptide precursors does not occur at every basic site (as evident from Tables 25.1 and 25.2). In the described program, we cleave a protein sequence into short fragments at every position where motifs GKR, GRK, GRR, GKK, KR, RK, RR, KK, GR, and GK occur. This results in the maximum possible number of candidate fragments. A statistical analysis on all known peptides in Metazoa shows that the minimal distance between the amino- or carboxy-termini of a peptide sequence and the conserved region (motif) in the peptide sequence is usually small (the distance is defined as “shift” in Section 3.1.4). This means that the conserved peptide motif should be close to a cleavage site in the peptide precursor. Based on this observation, our program identifies a sequence as a potential peptide if the sequence possesses a conserved peptide motif near its amino- or carboxy-terminus. We do not consider monobasic sites R and K as cleavage sites because of the low probability of their occurrence –3.5% (260/7346) and 4.1% (305/7346), respectively, as seen from Table 25.1. Furthermore, many conserved peptide motifs contain the amino acids R and K, such as, for example, the motif “[LVMI]-[MLIV]-R-F” from the peptide families “FMR Famide and related neuropeptides” and “K-[KN]-[YF]G-G-F-M” motif from adrenocorticotropic hormone domain and opioids neuropeptides (3). 6. It has been suggested that the cleavage process also depends on the amino acids that are at the proximity of the cleavage site. For example, aliphatic amino acids (leucine, isoleucine, valine, methionine) are rarely present immediately after the consensus cleavage site of the subtilisin/kexin-like proprotein convertases [R/K]-(X) n-[R/K] ↓ (where X is any amino acid except cysteine and n is equal to 0, 2, 4 or 6) (15). Several prediction tools have been developed to identify putative cleavage sites in peptide precursors. For example, the ProP software and online tool NeuroPred predict basic cleavage sites of peptide precursors based on biochemical sequence data (16, 17). NeuroPred also has the capability to calculate the mass of the neuropeptides resulting from the predicted cleavages. The resulting mass list aids the discovery and confirmation of
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new neuropeptides using MS techniques. The ProP and NeuroPred prediction tools can be used as the alternative to the Cleavage.SAS program (6). 7. In addition to the basic cleavage sites, peptide precursors may cleave at other non-basic sites (18). It will be a challenge to consider the existence of these unconventional cleavage sites in the further refinement of this method. 8. If the residues flanking the peptides are a combination of a few consecutive K or R, the combination is extracted as the cleavage site. 9. Many bioactive mature peptides are identified by direct protein sequencing techniques and their precursor proteins are unknown. 10. The expected value (e-value) is set to 6 because of the short length of the sequence fragments being compared. 11. Based on the statistical analysis of the peptide precursors in the peptide and precursor dataset, the shift value should be low. This means that the motifs should be in the close vicinity of a cleavage site. 12. For the majority (∼98%) of the known peptide precursors that encode such multiple related peptides, the length of the fragments does not exceed 60 amino acids. 13. We predicted 76 additional putative secretory peptide genes in D. melanogaster (6). Some of these predicted novel precursors contain two or more fragments that share significant sequence similarities and others share conserved peptide motifs with known vertebrate or invertebrate peptides. These similarities could not be discovered by BLAST scanning of the whole D. melanogaster genome. Only one of the characterized peptide precursors in D. melanogaster was not identified by our method, i.e., the diuretic hormone precursor CG8348, because it has four transmembrane regions. Our procedure yielded four false positives (6). 14. Since EST sequences are not annotated, no protein translations are available for the BLAST search of EST databases. Hence the tBLASTn search is the only way to search for these potential coding regions at the protein level. TBLASTn compares protein query sequences against a nucleotide sequence database dynamically translated in all reading frames. 15. The BLAST search program is not suitable for the detection of small peptides. To circumvent this problem, one could combine several peptide isoforms and (posttranslational) processing sites in a single sequence query. For example,
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(neuro)peptide sequences that are expected to originate from a single precursor should be flanked by typical processing sites [(G)KR, (G)RK, (G)(R)R or (G)(K)K] and combined into a single sequence; all possible combinations should be entered. The BLAST query sequence box accepts a number of different types of inputs and it will automatically determine the format used. 16. EST sequences reside in a specific division within GenBank, the dbEST database. For example, the ESTs of L. migratoria are deposited in the GenBank database under the accession numbers C0819675–C0832059 and C0832067– C0865130. These ESTs can be searched by selecting the EST database and entering “CO819675:CO832059[accn] OR CO832067:CO865130[accn]” in the “entrez query” box. 17. In the L. migratoria study, some of the known neuropeptide precursors were not found when searching EST databases (7). It is possible that these sequences were not present in the EST database. Alternatively, because ∼3% of ESTs are estimated to contain sequencing errors, these could easily mask or disrupt short peptide alignments. 18. Mascot or Sequest should be set and run locally. Online versions of Mascot and Sequest are available, but are limited to using large databases like Swiss-Prot or NCBInr. These are less suitable for peptide searches. Also, most proteomic identification tools, including Mascot, are designed to identify a protein from several individual peptides or fragments originating from the same protein. The protein score in a peptide summary is derived from the ion scores of the individual peptides. Many peptide precursors give rise to only one or a very limited number of bioactive peptides, and because peptidomic experiments focus on the peptides themselves rather than on the peptide precursor proteins, only peptide scores can be taken into the account for peptide identification. In addition, because the exact processing mechanisms involved in the production of any particular peptide are unknown, no cleavage enzyme can be selected for identification. All these features of naturally occurring peptides should be considered to allow peptide identification. Our research indicates that improved success rate of identification of secretory peptides could be achieved using restricted databases of predicted peptide precursor proteins.
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Acknowledgments This work was supported by grants of the Fund for Scientific Research (FWO)-Flanders (1.5.137.06) and the Institute for the Promotion of Innovation through Science and Technology (IWT)-Flanders (SBO 335605). The authors also acknowledge Prometa, the Interfacultary Centre for Proteomics and Metabolomics at K.U. Leuven. E. Clynen and S.J. Husson are postdoctoral fellows of the FWO-Flanders and B. Landuyt is a postdoctoral fellow of the IWT-Flanders. References 1. Hewes, R.S. and Taghert, P.H. (2001) Neuropeptides and neuropeptide receptors in the Drosophila melanogaster genome. Genome Res. 11, 1126–1142. 2. Vanden Broeck, J. (2001) Neuropeptides and their precursors in the fruitfly, Drosophila melanogaster. Peptides 22, 241–254. 3. Liu, F., Baggerman, G., Schoofs, L., and Wets, G. (2006) Uncovering conserved patterns in bioactive peptides in Metazoa. Peptides 27, 3137–3153. 4. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. (1997) Gapped BLAST and PSIBLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402. 5. Altschul, S.F., Bundschuh, R., Olsen, R., and Hwa, T. (2001) The estimation of statistical parameters for local alignment score distributions. Nucleic Acids Res. 29, 351–361. 6. Liu, F., Baggerman, G., D‘Hertog, W., Verleyen, P., Schoofs, L., and Wets, G. (2006) In silico identification of new secretory peptide genes in Drosophila melanogaster. Mol. Cell. Proteomics 5, 510–522. 7. Clynen, E., Huybrechts, J., Verleyen, P., De Loof, A., and Schoofs, L. (2006) Annotation of novel neuropeptide precursors in the migratory locust based on transcript screening of a public EST database and mass spectrometry. BMC Genom. 7, 201. 8. F¨alth, M., Skold, K., Norrman, M., Svensson, M., Fenyo, D., and Andren, P.E. (2006) SwePep, a database designed for endogenous peptides and mass spectrometry. Mol. Cell. Proteomics 5, 998–1005. 9. Liu, F., Baggerman, G., Schoofs, L., and Wets, G. (2008) The construction of a bioac-
10.
11.
12.
13.
14.
15.
16.
tive peptide database in Metazoa. J. Proteome Res. 7, 4119–4131. Hild, M., Beckmann, B., Haas, S.A., Koch, B., Solovyev, V., Busold, C., Fellenberg, K., Boutros, M., Vingron, M., Sauer, F., Hoheisel, J.D., and Paro, R. (2003) An integrated gene annotation and transcriptional profiling approach towards the full gene content of the Drosophila genome. Genome Biol. 5, R3. Bendtsen, J.D., Nielsen, H., von Heijne, G., and Brunak, S. (2004) Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–795. Veenstra, J.A. (2000) Mono- and dibasic proteolytic cleavage sites in insect neuroendocrine peptide precursors. Arch. Insect Biochem. Physiol. 43, 49–63. Perkins, D.N., Pappin, D.J., Creasy, D.M., and Cottrell, J.S. (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567. Ma, B., Zhang, K., Hendrie, C., Liang, C., Li, M., Doherty-Kirby, A., and Lajoie, G. (2003) PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun. Mass Spectrom. 17, 2337–2342. Rholam, M., Brakch, N., Germain, D., Thomas, D.Y., Fahy, C., Boussetta, H., Boileau, G., and Cohen, P. (1995) Role of amino acid sequences flanking dibasic cleavage sites in precursor proteolytic processing. The importance of the first residue Cterminal of the cleavage site. Eur. J. Biochem. 227, 707–714. Duckert, P., Brunak, S., and Blom, N. (2004) Prediction of proprotein convertase cleavage sites. Protein Eng. Des. Sel. 17, 107–112.
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17. Southey, B.R., Amare, A., Zimmerman, T.A., Rodriguez-Zas, S.L., and Sweedler, J.V. (2006) NeuroPred: a tool to predict cleavage sites in neuropeptide precursors and provide the masses of the resulting peptides. Nucleic Acids Res. 34, W267–W272. 18. Seidah, N.G., Benjannet, S., Wickham, L., Marcinkiewicz, J., Jasmin, S.B., Stifani, S.,
Basak, A., Prat, A., and Chretien, M. (2003) The secretory proprotein convertase neural apoptosis-regulated convertase 1 (NARC-1): liver regeneration and neuronal differentiation. Proc. Natl. Acad. Sci. USA 100, 928–933.
Chapter 26 Bioinformatic Identification of Plant Peptides Kevin A. Lease and John C. Walker Abstract Plant peptides play a number of important roles in defence, development and many other aspects of plant physiology. Identifying additional peptide sequences provides the starting point to investigate their function using molecular, genetic or biochemical techniques. Due to their small size, identifying peptide sequences may not succeed using the default bioinformatic approaches that work well for average-sized proteins. There are two general scenarios related to bioinformatic identification of peptides to be discussed in this paper. In the first scenario, one already has the sequence of a plant peptide and is trying to find more plant peptides with some sequence similarity to the starting peptide. To do this, the Basic Local Alignment Search Tool (BLAST) is employed, with the parameters adjusted to be more favourable for identifying potential peptide matches. A second scenario involves trying to identify plant peptides without using sequence similarity searches to known plant peptides. In this approach, features such as protein size and the presence of a cleavable amino-terminal signal peptide are used to screen annotated proteins. A variation of this method can be used to screen for unannotated peptides from genomic sequences. Bioinformatic resources related to Arabidopsis thaliana will be used to illustrate these approaches. Key words: Peptide, peptidomics, bioinformatics, Arabidopsis thaliana.
1. Introduction Plant peptides can be defined as small proteins, below an arbitrary molecular weight or length cut-off (1). Peptides can be generated either from a gene encoding a small open reading frame, or they can be produced from a larger protein that undergoes posttranslational proteolytic cleavages that give rise to one or more smaller peptides. Proteolytically produced peptides may be bioactive functional peptides or they may represent non-functional turnover of formerly active proteins. The cleavage sites of plant M. Soloviev (ed.), Peptidomics, Methods in Molecular Biology 615, DOI 10.1007/978-1-60761-535-4 26, © Humana Press, a part of Springer Science+Business Media, LLC 2010
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proteolytic enzymes are not well established, so it is not possible to look at primary amino acid sequences and identify which proteins will be processed or which mature peptides will result. Nine plant signalling peptides have been intensively characterized by biochemical and molecular genetic experiments (2–13). These founding peptides coupled with the availability of genome sequences have led to the identification of additional peptides through bioinformatics analyses (9, 10, 14–18). In addition, based on the collective properties of identified plant peptides, some general properties have emerged. This information has been exploited to find additional peptides (19). The purpose of this review is to suggest how to use available tools and resources with the goal of identifying plant peptides of interest for further investigation. BLAST (20, 21) is a useful and well-known bioinformatic tool that can be used to find additional members of a gene family, if a founding member is available to use as a query. For example, many plant peptides that were originally identified through genetic or biochemical studies were found to belong to families of genes encoding similar peptides (9, 15). Using BLAST with default settings is not ideal for plant peptide studies. Various parameters involved in BLAST searches will be discussed as well as the rationale for changing them. The overall goal of the specific parameters suggested is to increase sensitivity. Following these changes, one will greatly increase the odds of finding meaningful similar sequences in the database when searching with short queries. At the same time spurious matches will increase, so healthy scepticism, sound judgement and further investigation will be required.
2. Materials 1. A personal computer with a web browser installed and Internet access are required. 2. TAIR BLAST at The Arabidopsis Information Resource (http://www.arabidopsis.org/Blast). 3. SignalP3.0 (http://www.cbs.dtu.dk/services/SignalP). 4. TMHMM2.0 (http://www.cbs.dtu.dk/services/TMHMM). 5a. TAIR bulk data retrieval and analysis tools (http://www. arabidopsis.org/tools/bulk/index.jsp). 5b. TAIR bulk protein search page (http://www. arabidopsis.org/tools/bulk/protein/index.jsp).
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5c. TAIR gene description search and download page (http://www.arabidopsis.org/tools/bulk/genes/index. jsp). 5d. TAIR sequence bulk download and analysis page (http://www.arabidopsis.org/tools/bulk/sequences/ index.jsp). 6a. The Arabidopsis Unannotated Secreted Peptide Database AUSP (http://peptidome.missouri.edu). 6b. AUSP search page (http://peptidome.missouri.edu/ cgi-bin/getprotein.cgi). 7. The Arabidopsis Transcriptome Genome Expression Database ATGED (http://signal.salk.edu/cgi-bin/atta). 8. NCBI BLAST (http://blast.ncbi.nlm.nih.gov/Blast.cgi). 9. BLASTCLUST(http://toolkit.tuebingen.mpg.de/ blastclust#;). 10. REPRO (http://zeus.cs.vu.nl/programs/reprowww/). 11a. Multiple Em for Motif Search MEME (http://meme. sdsc.edu/meme/). 11b. Motif Alignment & Search Tool MAST (http://meme. sdsc.edu/meme/cgi-bin/mast.cgi).
3. Methods 3.1. Identifying Peptides with Sequence Similarity to Another Peptide Sequence
When conducting BLAST searches over the internet, The Arabidopsis Information Resource TAIR BLAST tool is a good choice. It contains plant-specific datasets to search and usually returns the results faster than NCBI BLAST. To access the additional parameters discussed below that may not be displayed initially, click on the “+” sign on the Advanced BLAST Parameter Options line on the webpage. 1. Select the appropriate BLAST Program. To start with, select BLASTP – it is often the best choice (see Notes 1–4). 2. Select suitable BLAST Dataset. To search Arabidopsis alone, select the A. thaliana GB all database; to search other higher plants, the Green plant GB all database should be chosen (see Notes 5–7). 3. Enter the sequence query. Full or partial sequence may be used. To start with, use the full precursor sequence (see Notes 8 and 9). 4. Turn off all filters on the BLAST input page by un-checking the “Filter query” box (see Note 10).
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5. Select appropriate weight matrix. PAM70, PAM30 or BLOSUM45 are suitable matrices for peptide searches because they are less stringent for scoring amino acid similarities (see Note 11). 6. Choose appropriate word size. For peptide searches, setting the word size to 2 makes the search more sensitive, albeit slower (see Note 12). 7. Increase the “expect score” value to 1000 (see Note 13). 8. Select the scoring penalties (gap opening and gap extension penalties). The default gap opening penalty is 11 and the gap extension penalty is 1. To achieve better sensitivity with short sequence queries, lower gap opening and extension penalties should be selected. Reasonable choice would be 7–10 for the gap opening penalty and 1–2 for the gap extension penalty (see Note 14). 9. Increase the number of reported scores (“Max scores”) to 500 (see Note 15). 10. Analysis of the matched sequence . 3.2. Analysis of Structural Features in the Matched Putative Peptide Sequences
Analysis of structural features in the found sequences may be very useful for discriminating potentially meaningful matches, identified, e.g. using Section 3.1 (see above). The choice as to which structural characteristics to analyse would depend on the target peptide and is likely to be different in each individual case. Two common examples are described here. 1. If the query sequence represents a secreted peptide, one might expect the matched putative peptide also to be secreted. Signal peptide and transmembrane domain prediction tools, such as SignalP3.0 and TMHMM2.0, respectively, should be used to evaluate this possibility (see Note 16). 2. The length of the database sequence might be expected to conform to size limits seen in known plant peptide precursors. For example, the largest of known plant peptide precursors is 200 amino acid long Systemin from tomato (2). This is a sensible upper limit to have in mind when evaluating the peptides and protein precursors identified (see Note 17).
3.3. Peptide Identification Based on PeptideAssociated Characteristics
In addition to the use of sequence similarity searches, peptides may be identified through the analysis of features common to known plant peptides (19). Most such peptides are produced from larger precursor proteins. The precursor proteins typically have ∼200 or fewer amino acids. Additionally, most peptide precursors have cleavable amino-terminal signal peptides that direct the protein to the secretory pathway. TAIR Bulk Data Retrieval and Analysis Tools offer a simple solution to the identification
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of all of the annotated genes in Arabidopsis that are predicted to encode proteins with the aforementioned qualities (i.e. small size and having a signal peptide). 3.3.1. Bulk Protein Search
1. Open TAIR bulk protein search page (http://www. arabidopsis.org/tools/bulk/protein/index.jsp). 2. The format should be set to “html”, output boxes should be unchecked, the predicted molecular weight range should be set to “0 to 22,000” (based on 110 Da per amino acid and an estimated 200 amino acids maximum precursor length). “Secreted Proteins” should be checked under the choice of “Predicted sub-cellular location.” 3. After clicking “Get Protein Data”, approximately 2000 gene IDs will be returned. Select all and copy the gene IDs onto clipboard, just as one would copy text in a word processor program. 4. Open the TAIR gene description search and download page, and paste the gene IDs into the search box. Run the search. One can peruse the gene descriptions or follow the hypertext links to obtained detailed information about the gene. 5. Open TAIR sequence bulk download and analysis page; paste the gene IDs to download all of the amino acid sequences in FASTA format for further evaluation.
3.3.2. Arabidopsis Unannotated Secreted Peptide Database Searches
Given that small genes are often poorly annotated, many potential peptides are not included in the TAIR annotated list (19). A complementary resource that can be used to address this issue is the Arabidopsis Unannotated Secreted Peptide Database AUSP. This is a searchable database of more than 30,000 unannotated open reading frames that may encode small secreted proteins. 1. Open the AUSP search page.(http://peptidome.missouri. edu/cgi-bin/getprotein.cgi). 2. Set the Chromosome to “All”, Select “Both Strands” and click “Search and View.” 3. Evaluation of the peptide expression levels. Some expression data are available in AUSP, but more extensive expression data are available from the Arabidopsis Transcriptome Genome Expression Database ATGED.
3.3.3. Other Structural Considerations – Internal Repeats
There exists one example of a plant peptide precursor that contains two bioactive peptides which share sequence similarity to one another (8). In invertebrates, this is a common finding (22). For example, multiple FMRFamide peptides may be encoded by a single precursor protein. If this pattern can be extrapolated to additional plant peptides, they may be putatively identified by
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looking for internally repeated sequences. A bioinformatics tool called REPRO is available on the web to identify internal repeats. 3.3.4. Other Structural Considerations – Sequence Patterns
Multiple Em for Motif Elicitation MEME is an algorithm that can be used to find patterns among a group of peptides (18, 23). The patterns found by MEME can be used to search with Motif Alignment & Search Tool MAST for other proteins sharing this pattern.
4. Notes 1. BLASTP is generally the best choice for BLAST program. BLASTP is used with an amino acid query to search a database of proteins. BLASTP is a better choice for this application than BLASTN because conservation of sequence similarity at the amino acid level is higher than nucleic acid sequence similarity. It is worth noting that genome annotations are not static and results of searches may vary over time as the database is updated. Therefore, it is important to pay attention to which database and which version of that database is available 2. In some circumstances it may be advantageous to choose TBLASTN over BLASTP. Many small genes are not well annotated; in such cases, the protein encoded by that gene may not be included in the protein database. TBLASTN will deal with this by searching genomic DNA databases translated in all six reading frames, using a protein query. 3. Position-specific iterated BLAST (PSI-BLAST) is another variant of BLAST that can be used to find additional members of a plant peptide family (14, 18). PSI-BLAST searches are not available at the TAIR website, but are available through NCBI BLAST. 4. Many plant peptides belong to gene families, rather than being “singletons.” BLASTCLUST is a useful way to group a large list of peptide precursors in FASTA format into groups that have sequence similarity. BLASTCLUST uses single-linkage clustering. This may be a useful filter for screening TAIR proteins. If the “cluster” box is checked at AUSP, one can see whether the peptides in the search results have sequence similarity to other peptides in AUSP. 5. Database selection (referred to as “Datasets” on TAIR website) could greatly influence the results. There are eight protein databases available at TAIR. Larger databases are normally most suitable if searching for peptides. Increasing
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the size of the dataset by selecting more than one library makes it more likely to find a match based on sequence similarity alone. However, if the sequence is not in the database, the match can never be found. 6. If the same sequence fragment is found in two databases, the expect value for the same peptide sequence match in this larger dataset will be higher. This means that the probability of the match being significant will be lower. 7. The protein databases at TAIR are organized to be mutually exclusive for searching Arabidopsis or non-Arabidopsis higher plants. Currently there is no protein database at TAIR that would combine Arabidopsis and other higher plant sequences in a single search. 8. Search using the full-length precursor sequence typically produces fewer false positives and the results obtained are usually easier to interpret. 9. One might suspect that the mature peptide sequence would make a better query because this sequence might be better conserved. This is not always the case. For example, the signal peptides within the same gene family could be more similar to each other than the rest of the protein sequences (14). 10. These filters mask out part of your query which might eliminate potential matches. 11. The matrix chosen for the BLAST search can affect results. Many matrices have been optimized for searching proteins not peptides and are only suitable for detecting high sequence similarity between the query and database subjects. 12. The BLAST algorithm requires an initial exact match with a “word” which is your query sequence broken into small chunks. Increasing the word size parameter speeds up BLAST search, but makes it less sensitive and will yield fewer hits. 13. The BLAST “expected score” indicates the statistical significance of the sequence matches found. These depend on the degree of sequence identity (better match results in lower “expect” values) and on the size of the database (in larger databases, the chance of having a random match is higher). When searching with very short queries, it makes sense to sacrifice selectivity for sensitivity and to consider “expect score” values. Some such matches may be biologically significant and meaningful, but one must regard these with a sceptical eye.
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14. When a query sequence and a database sequence are aligned, inserting gaps in one of these sequences may increase the apparent quality and the degree of alignment. However, inserting a gap will reduce the overall alignment score. Similarly, extending the gap length may improve the alignment but will also decrease the alignment score. If the overall improvement in the alignment outweighs the gap penalties incurred, the alignment is accepted by the system as the preferred fit. 15. The default setting is 50. Changing this value will not affect the search, but will simply increase the number of alignments reported. One line identifiers can be checked quickly. 16. As with many prediction tools, care should be taken in using SignalP3.0 and TMHMM2.0 prediction tools and interpreting the results obtained (24, 25). 17. When analysing the proteins, one should not rely exclusively on the short descriptors of the protein sequences. These may appear to be useful, but they can also be misleading, in that annotation of gene function can be speculative in some cases. 18. In ATGED gene expression data are displayed graphically. These can be used to evaluate expression of the peptides from AUSP, as well as any other genes from TAIR. The annotation track AUSP links to the Arabidopsis Unannotated Secreted Peptide sequences. References 1. Farrokhi, N., Whitelegge, J.P., and Brusslan, J.A. (2007) Plant peptides and peptidomics. Plant Biotechnol. J. 6, 105–134. 2. McGurl, B., Pearce, G., Orozco-Cardenas, M., and Ryan, C.A. (1992) Structure, expression, and antisense inhibition of the systemin precursor gene. Science 255, 1570–1573. 3. Matsubayashi, Y., and Sakagami, Y. (1996) Phytosulfokine, sulfated peptides that induce the proliferation of single mesophyll cells of Asparagus officinalis L. Proc. Natl. Acad. Sci. USA 93, 7623–7627. 4. Fletcher, J.C., Brand, U., Running, M.P., Simon, R., and Meyerowitz, E.M. (1999) Signaling of cell fate decisions by CLAVATA3 in Arabidopsis shoot meristems. Science 283, 1911–1914. 5. Kondo, T., Sawa, S., Kinoshita, A., Mizuno, S., Kakimoto, T., Fukuda, H., and Sakagami, Y. (2006) A plant peptide encoded by CLV3 identified by in situ MALDI-TOF MS analysis. Science 313, 845–848.
6. Schopfer, C.R., Nasrallah, M.E., and Nasrallah, J.B. (1999) The male determinant of self-incompatibility in Brassica. Science 5445, 1697–1700. 7. Takayama, S., Shiba, H., Iwano, M., Shimosato, H., Che, F.S., Kai, N., Watanabe, M., Suzuki, G., Hinata, K., and Isogai, A. (2000) The pollen determinant of selfincompatibility in Brassica campestris. Proc. Natl. Acad. Sci. USA 97, 1920–1925. 8. Pearce, G., Moura, D.S., Stratmann, J., and Ryan, C.A. (2001) Production of multiple plant hormones from a single polyprotein precursor. Nature 411, 817–820 9. Pearce, G., Moura, D.S., Stratmann, J., and Ryan, C.A. (2001) RALF, a 5-kDa ubiquitous polypeptide in plants, arrests root growth and development. Proc. Natl. Acad. Sci. USA 98, 12843–12847. 10. Butenko, M.A., Patterson, S.E., Grini, P.E., Stenvik, G.E., Amundsen, S.S., Mandal, A., and Aalen, R.B. (2003) Inflorescence deficient in abscission controls floral organ
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11.
12.
13.
14.
15.
16.
abscission in Arabidopsis and identifies a novel family of putative ligands in plants. Plant Cell 15, 2296–2307. Huffaker, A., Pearce, G., and Ryan, CA (2006) An endogenous peptide signal in Arabidopsis activates components of the innate immune response. Proc. Natl. Acad. Sci. USA 103, 10098–10103. Stenvik, G., Tandstad, N.M, Guo, Y., Shi, C., Kristiansen, W., Holmgren, A., Clark, S.E., Aalen, R., and Butenko, M.A. (2008) The EPIP Peptide of INFLORESCENCE DEFICIENT IN ABSCISSION is sufficient to induce abscission in Arabidopsis through the receptor-like kinases HAESA and HAESALIKE2. Plant Cell 20, 1805–1817. Cho, S.K., Larue, C.T., Chevalier, D., Wang, H., Jinn, T., Zhang, S., and Walker, J.C. (2008). Regulation of floral organ abscission in Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA 105, 15629–15634. Vanoosthuyse, V., Miege, C., Dumas, C., and Cock, J.M. (2001) Two large Arabidopsis thaliana gene families are homologous to the Brassica gene superfamily that encodes pollen coat proteins and the male component of the self-incompatibility response. Plant Mol. Biol. 46, 17–34. Cock, J.M., and McCormick, S. (2001) A large family of genes that share homology with CLAVATA3. Plant Physiol. 126, 939–942. Yang, H., Matsubayashi, Y., Nakamura, K., Sakagami, Y. (2001) Diversity of Arabidopsis genes encoding precursors for phytosulfakine, a peptide growth factor. Plant Physiol. 127, 842–851.
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17. Olsen, A.N., Mundy, J., and Skriver, K. (2002) Peptomics, identification of novel cationic Arabidopsis peptides with conserved sequence motifs. In Silico Biol. 2, 441–451 18. Oelkers, K., Goffard, N., Weiller, G.F., Gresshoff, P.M., Mathesius, U., and Frickey, T. (2008) Bioinformatic analysis of the CLE signaling peptide family. BMC Plant Biol. 8, 1. 19. Lease, K.A., and Walker, J.C. (2006) The Arabidopsis unannotated secreted peptide database, a resource for plant peptidomics. Plant Physiol. 142, 831–838. 20. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. (1990) Basic local alignment search tool. J. Mol. Biol. 215, 403–410. 21. Poole, R.L. (2007). The TAIR Database. Methods Mol. Biol. 406, 179–212. 22. Baggerman, G., Cerstiaens, A., De Loof, A., and Schoofs, L. (2002). Peptidomics of the larval Drosophila melanogaster central nervous system. J. Biol. Chem. 277, 40368– 40374. 23. Baggerman, G., Liu, F., Wets, G., and Schoofs, L. (2006) Bioinformatic analysis of peptide precursor proteins. Ann. NY Acad. Sci. 1040, 59–65. 24. Bendtsen, J.D., Nielsen, H., von Heijne, G., and Brunak, S. (2004) Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–787. 25. Krogh, A., Larsson, B., von Heijne, G., and Sonnhammer, E.L. (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580.
INDEX
A Abdomen . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118, 120–121, 125 Absorbance. . .41, 55, 90, 92–93, 95, 97–98, 105, 113, 154, 167, 171, 236, 250, 256, 304–306, 325, 352 Abundance . . . . . . . 19–25, 83, 96, 124, 178, 207–208, 218, 227–228, 260 Accuracy . . . . . . . . . . . . . . . 41, 80, 83, 86, 96, 156, 198–199, 237–238, 359 Acetic acid . . . . . . . 15, 17, 37, 172, 209, 220, 224, 249, 252, 254–256, 280, 318 Acetone . . . . . . 38, 41, 51, 53, 229, 234, 251, 268–269, 277, 279–280, 285 Acetonitrile . . . . . . 15, 18, 37, 40, 43, 68, 77, 79–80, 88–89, 111–113, 133–134, 147–150, 197, 220, 251, 253–256, 280–281, 285–287, 318 Acetylation . . . . . . . . . . . . . . . . . . . . . . 123–124, 200, 218, 222 Acidified methanol . . . . . . . . . . . . . . . 193, 203, 220–221, 224 ACN, see Acetonitrile Acrylamide . . . . . . . . . . . . . . . . . 229, 236, 313, 318–319, 340 ACTH . . . . . . . . . . . . . . . . . . . . 38, 42, 80, 148, 153, 195, 199 Activity antifungal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 broad-spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 cardiovascular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 peptidase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90, 312 physiological . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171, 217 Adduct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Adjuvant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294, 298–299 Adrenaline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 Affinity antibody . . . . . . . . . . . . . . . . 302–305, 307, 312, 314–315 assay. . . . . . . . . . .296, 304, 312, 314, 325, 328–330, 336 peptidomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5, 313–341 purification . . . . . . . . . . . . . . . . . . . . . . . . . . . 296, 301–304 reagent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296, 304, 315 selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .313–341 tag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349, 354 Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Agelenidae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Alcohol precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Alignment . . . . . 82, 124, 133, 171, 357–358, 365, 368–370 Alkylate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79, 92, 239 Alpha-cyano-4-hydroxycinnamic acid, see Cyano-4-hydroxycinnamic acid Alzheimer’s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Amidation . . . . . . . . 110, 126, 171, 200, 218, 222, 279, 293, 368–369 Amino acid antigenicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 modification . . . . . . . . . . . . . . . . . . 20, 126, 222, 368–369 propensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160, 333
sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165, 192, 225 Ammonium acetate . . . . . . . . . . . . . . . 43, 147, 187, 195, 268 Ammonium bicarbonate . . . . . . . . . . . . . . . . . . . . 15, 209, 213 Ammonium persulfate . . . . . . . . . . . 229, 236, 318, 329, 340 AMP . . . . . . . . . . . . . . . . . . 160–163, 165–167, 169–175, 323 See also Peptide, antimicrobial Amphibian. . . . . . . . 145–156, 159–175, 178–180, 182–184, 186–188 See also Frog Amphibian antimicrobial peptides . . . . . . . . . . . . . . . 177–188 See also Peptides, amphibian Amphibian skin . . . . . . . . 145–156, 160, 173, 178, 182–184 Amplification . . . . . . . . . . 159–175, 185, 248, 252, 323, 335, 347–350, 356 Anatomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30, 125 AnchorChip . . . . . . . . . . . . . . . . . . . . . . . 38, 45, 261–263, 285 Angiotensin . . . . . . . . . . 38, 42, 80, 103, 108, 146, 148, 195, 199, 202 Anhydride . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124, 230, 243 Anhydride, acetic/methanol . . . . . . . . . . . . . . . . . . . . . . . . . 124 Animal immunization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228, 360 venom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75–84, 87–99 Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380, 382 ANOVA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21, 23, 202 Antennal lobe . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130, 132, 134 Anterior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121, 122, 125 Anthopleura elegantissima . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Anti-bacterial, see Antimicrobial Antibiotic. . . . . . . . . . . . . . . . . . . . .14, 88, 159, 164–165, 177 Antibodies affinity-purified . . . . . . . . . . . . . . . . . . . . . . . 302–304, 312 Anti-BrdU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277, 284 anti-peptide . . . . . . . . 279, 296, 302, 304–306, 314–315, 317, 319–320, 325, 327–328, 333–334, 337 anti-protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325, 333 anti-tachykinin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 biotinylation of . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307, 310 immobilised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 labelled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 monoclonal . . . . . . . . . . . . . . . . . . . . . . . . . . . 284, 313–314 polyclonal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317, 325 primary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230, 238, 244 purified . . . . . . . . . . . . . . . . . . . . . . 299, 302–304, 312, 332 recombinant. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .314 single-chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 spotted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 Antibody affinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 Antibody array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Antibody characterisation . . . . . . . . . . . . . . . . . . . . . . 314–315
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PEPTIDOMICS
386 Index
Antibody development . . . . . . . . . . . . . . . . 315, 319, 333–334 Antibody epitope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Antibody fragment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314 Antibody microarray . . . . . . . . . . . . . . . . . . . . . . 317, 324–325 See also Arrays, protein Antibody-protein interaction . . . . . . . . . . . . . . . . . . . 314–315 Antibody screening . . . . . . . . . . . . . . . . . . . 208, 365, 367, 380 Anticoagulant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Antigen . . . . . . 297, 301, 307, 311, 314–316, 319, 333–334 Anti-infection, see Antimicrobial Antimicrobial activity . . . . . . . . . . . . . . . . . . . . 90, 95, 160, 178, 184–188 assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 peptide . . . . . . . . . . . . . 99, 145–146, 156, 159–175, 180, 183–185, 188 precursor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179–180 properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 substances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Antiserum . . . . . . . . . . . . . . . . . . . . . . 295, 299, 302–303, 311 Aplysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30, 118 Arabidopsis thaliana . . . . . . . . . . . . . . 376–377, 379, 381–382 Arginine vasopressin peptide, see AVP Array high throughput . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21, 345 hydrogel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 membrane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 peptide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352–353 protein . . . . . . . . . . . . . . . . . . . . . . . . 5, 314, 346, 352–354 Artemia salina . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Arthropod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Ascaris suum. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36 Aspergillus flavus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Assay affinity . . . . . . . . . . . . . . . . . . . . . . 325, 329–330, 336, 339 cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16, 283, 338 competitive . . . . . . . . . . . . . . . . . . . . . . . 325, 327, 330, 340 displacement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340–341 multiplexed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Atrax robustus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Autoclave . . . . . . . . . . . . . . . . . . . . . . . . 37, 161, 164, 181, 316 Autofluorescence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315, 327 Automation . . . . . . . 6, 40, 42–43, 45, 54–55, 71, 77, 81–84, 92, 94, 105, 112, 198, 204, 223, 242, 249, 255–256, 263, 265–267, 277, 372 Avian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163, 294, 297 AVP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59–62, 65–66, 68 Azide, sodium . . . . . . . . . . . . . . . . . . . . . . . 295–296, 303–304
B Bacillus dysenteriae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Bacillus megatherium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Bacillus pyocyaneus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Bacterial growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67, 95, 214 lawn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 pathogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 suspension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Basic Local Alignment Search Tool see BLAST BCA . . . . . . . . . . . . . . . . . . . . . . . . . . 16–17, 21, 148, 229, 235 Beads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15, 17, 66, 262, 269 Beetle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Bicinchoninic acid . . . . . . . . . . . . . . . . . . . . . . . . . 16, 148, 235 BigDye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165, 175 Binding . . . . . . . . . . 154–155, 164, 168, 260–262, 311, 318, 320–321, 325, 331–332, 340
Bioactive peptides See also Peptides, active BioChip microarray scanner . . . . . . . . . . . . . . . . 317–318, 327 Bioinformatics . . . . . . . . 42, 82–83, 192, 199, 225, 260–261, 263–266, 357–373, 375–382 Biological activity . . . . . . . . . . . . . . . . . . . . . . 4, 7, 109, 145, 286, 313 assay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276, 282–286 fluid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5, 7, 71, 269 samples . . . . . . . . . . . . . . . . . . . . 3, 17, 142, 207, 224, 250 system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13–14, 227 Biomarker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4, 7, 207–208 Biotin . . . . . . . . . . . . . . . . . . . . . . . . . . 296–297, 304–308, 310 BioTools . . . . . . . . . . . . . . . . . . . . . . 78, 81, 195, 199–200, 231 BLAST . . . . . . . . . . 81–82, 84, 110, 119, 124, 131, 133, 171, 357–362, 365, 367–369, 371–372, 376–377, 380–381 Block . . . . . . . 17, 19, 24–25, 70, 87, 94, 230, 237–238, 277, 321, 326–327, 339 See also Solution, blocking Blood . . . . . . . . . . . . 3, 57–58, 208, 210, 248–249, 251, 255, 260–261, 299 Blot . . . . 60, 62–64, 104, 230, 234, 236–237, 244, 327, 341 Body fluid . . . . . . . . . . . . . . . . . . . . . 7, 75, 192, 208, 259–260 Boiling . . . . . . . . . . . . . 54, 193, 218, 236, 279–281, 336, 349 Bombesin . . . . . . . . . . . . . . . . . 38, 42, 80, 146, 178, 195, 199 Bombina maxima . . . . . . . . . . . . . . . . . 180–181, 183–184, 187 Bombina microdeladigitora . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Bombina orientalis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Bombina variegata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Bovine serum albumin, see BSA Bradford . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249, 255 Bradykinin . . . . . . . . . . . . . 103, 146, 148, 153, 156, 178, 200 Brain . . . . . . 3, 49–50, 52, 57, 107, 118, 120, 129–132, 135, 192–197, 220–221, 223–224, 228, 232, 320 BrdU . . . . . . . . . . . . . . . . . . . . . . . . . . . 277, 283–285, 288–289 Breast cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Brevinins . . . . . . . . . . . . . . . . . . . . . . . 153, 162, 179–180, 188 Bromodeoxyuridine, see BrdU Broth . . . . . . . . . . . . . . . . . 16, 38, 99, 164–165, 170–171, 186 BSA . . . . . . . . . . 68, 171, 229, 235–236, 295, 297, 316–318, 321–322, 335, 340–341
C C15 . . . . . . . 18–19, 37–39, 45, 82, 103, 148, 181, 194, 197, 203, 220, 241, 260, 269, 285 CaCl2 . . . 37, 51, 58–59, 103, 118, 130, 138, 229, 232–233, 277, 297, 316–318 Caenorhabditis elegans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29–46 Calibration . . . . . . 38, 42, 80, 103, 105, 108, 113, 142, 148, 151–156, 198, 254, 263, 268, 328, 332, 334–338 California sea slug, see Aplysia Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58, 260 See also Tumour Cancer borealis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Capillary . . . . . . . . . 15, 18, 71, 80, 120, 122, 140, 194, 209, 278, 303 Carboxypeptidase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192, 369 Cardiovascular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 CDNA . . . . 31, 84, 160–164, 168–169, 175, 185, 281, 320, 349–352 Cell culture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171, 252, 270 density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252, 255
PEPTIDOMICS 387 Index differentiation . . . . . . . . . . . . . . . . . . . . . . . . . 275, 283, 286 free expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345–351 growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14, 16, 315 line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248, 252 lysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15, 170, 249, 255 pellet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17, 252 proliferation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Cells dried . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141–142 muscle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 nerve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49, 283, 288–289 red blood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251, 255 vascular endothelial. . . . . . . . . . . . . . . . . . . . . . . . .260, 270 Central nervous system, see CNS Centrifuge . . . . . . . 35, 37, 39, 61, 65, 68, 70, 105, 111, 125, 166, 170, 174, 183, 196–197, 210, 232–234, 239–241, 250–253, 277, 280, 299, 308–309, 318, 322, 324 Cerebrospinal fluid, see Fluid, cerebrospinal Chamber, humidified . . . . . . . . . . . . . . . . . 329, 352–353, 355 Characterisation . . . . . . . 7, 87–99, 101–114, 192, 275–291, 294, 313–314, 320, 360, 364, 378–380 Charge state . . . . . . . . 19–20, 61–62, 68, 198, 201, 214, 368 CHCA matrix, see Cyano-4-hydroxycinnamic acid CHCA see Cyano-4-hydroxycinnamic acid Chemical depletion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Chemical derivatization . . . . . . . . . . . . . . . . . . . . . . 71–72, 225 Chemical modification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Childhood ataxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248, 254 Chilobrachys jingzhao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Chloroform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161, 288, 324 Chromatography Cartridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 Column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19, 183, 212 DEAE-Sephadex . . . . . . . . . . . . . . . . . 181, 183–184, 187 gel filtration . . . . . . . . . . . . . 78–79, 81, 83, 175, 183–184 Hydrophobic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 ion exchange . . . . . . . . . . . 18, 38, 42–43, 77–78, 91, 181, 183–184, 187–188, 194, 204, 287 liquid . . . . . 3, 5–6, 15–16, 18–19, 21, 36–38, 40, 42–43, 50–51, 54, 58, 61, 65, 67–68, 71, 92–93, 103, 181, 183, 193–198, 208, 220, 223, 241–242, 248, 250, 368 multidimensional . . . 3–6, 15, 18–19, 36, 40, 50–51, 58, 92–93, 105, 172, 181, 183, 208, 210, 220, 223, 231, 270, 368 nanoLC . . . . . . 38, 42–44, 192, 195, 197–198, 201–204, 208, 213, 215 reverse phase . . 71, 77, 82, 194, 241, 277–278, 285, 287 See also HPLC, reversed-phase size exclusion . . . . 88, 92, 183, 187, 207–215, 285, 297, 309–311, 317 CID, see Collision-induced dissociation Circadian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Classification . . . . . . . . . . . . . . . . . . . . 146–147, 265–266, 269 Cleavage . . . 20, 24–25, 81, 95, 99, 102, 110, 114, 118, 124, 160, 224, 319, 359, 361–365, 368–372 Clipper scissors . . . . . . . . . . . . . . . . . . . . . . . . . . . 118, 130, 138 Clogging . . . . . . . . . . . . . . . . . . . 18, 67, 69–70, 214, 308, 312 Cloning . . . . . . 160, 163–165, 167, 169, 174, 181, 185, 346 ClustalW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82, 360, 368 Cluster analysis . . . . 15–16, 21–24, 260–261, 263–266, 333, 359, 380 CMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59, 66 CM-Sephadex . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181, 183–184 CNBr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89, 94, 96
CNS . . . . . . . . . . . . . . 121, 125, 129–130, 132, 141, 227–228 Cockroach . . . . . . . . . . . . . . . . . . . . . . . 97, 107, 109, 117, 138 Collagenase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194, 196 Collision . . . . . . . . . . . . . 42–43, 54, 102, 123, 126, 133, 143, 198, 213 Collision-induced dissociation . . . 6, 54, 102, 106, 110, 242 Column analytical . . . . . . . . . . . . . . . . 195, 197, 220, 222, 280–281 assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 capillary . . . . . . . . . . . . . . . . . . . . . . . . . . 18, 71, 78, 80, 241 equilibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 guard . . . . . . . . . . . . . . . . . . . . . . . . . 38, 103, 113, 195, 250 nano . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42–43, 53–54, 278 pre-packed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 preparative . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148, 150–151 reversed phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42, 253 semi-preparative . . . . . . . . . . . . . . . . . . . . . . . 148, 150–151 size exclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 solid phase extraction . . . . . . . . . . . . . . . . . . . . . . . . . 51, 53 strong cation exchange . . . . . . . . 18, 38, 42–43, 194, 204 trap . . . . . . . . . . . . . . . . . . . . . . . . . . 43, 220, 222, 231, 241 volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97, 183, 298 Combinatorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 CompassXport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195, 200–201 Competitive assay . . . . . . . . . . . . . . . . . . . . 325, 327, 330, 340 Complexity . . . . . . . . . . . . 18, 50, 83, 91, 138, 207, 228, 248, 294, 335 Composition . . . . . . . . . . . . . . . . . 96, 111, 118, 174, 199, 314 Conjugate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296, 301 Connective tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . 52, 64, 139 Consensus sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . 362–363 Conservation . . . 7, 109, 160–161, 165–166, 178, 185, 293, 358, 362, 364–365, 367–371 Contaminant . . . . . . . . . . . . . . . . . . . . . . . 55, 68, 80, 168, 223 Control, negative . . . . . 95–96, 167–169, 318, 328, 331, 337 Cooling . . . . . . . . . . . . . . . . . . . . 16–17, 24, 79, 237, 244, 336 Coomassie Brilliant Blue . . 77, 79, 296, 304–306, 312, 317, 338 Corpora Allata . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107, 112, 119 Corpora Cardiaca . . . . . . . . . . . . . . . . 107, 112, 118, 120, 123 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214, 319, 334 Crab live . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58–59, 62–65 nervous system . . . 50, 58, 119, 121–122, 125, 191, 208, 219, 227 Crustacean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Crystals . . . . . . . . . . . . 55, 102, 122, 125, 142, 156, 254, 296 C-terminal . . . . 98, 102, 110, 171–172, 192, 200, 218, 222, 276, 279, 293, 347–351, 355, 368–369 C-terminal, amidation . . . . . . . . . . . 126, 171–172, 218, 222 C-terminal, fragment ion . . . . . . . . . 20, 55, 80, 83–84, 102, 108–110, 243, 266, 368 Culture bacterial . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90, 95, 186, 324 conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 medium . . . . . . . 181, 248, 252, 255, 277, 282–283, 288 Cutoff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Cy3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138, 317, 356 Cy5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245, 348, 352–354, 356 Cyano-4-hydroxycinnamic acid . . . . . 38, 41, 45, 51, 54–56, 71, 80, 103, 119, 130, 139, 142, 148, 151, 195, 198, 200, 251, 253–256, 268 See also Matrix Cyanogen bromide . . . . . . . . . . . . 89, 94, 296, 301, 303–304 Cysteine . . . . . . . . . . 81, 89, 94–95, 110, 188, 243, 297–298, 311, 330, 370
PEPTIDOMICS
388 Index
Cytochrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209–211 Cyto-insectotoxin, see Insectotoxin Cytokine . . . . . . . . . . . . . . . . . . . . . . . . . . 4, 208, 260, 270, 313
D DAPA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346, 352–353 Data analysis . . . 14, 16, 19, 21, 23, 78, 124, 195, 199, 210, 213, 222–223, 228, 231, 242, 327, 338 Database conflicts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319, 332 protein . . . . . . . . . . . . . . 42, 46, 81–82, 84, 268, 360–361, 363–364, 369, 380–381 searching . . . . . . . . . . . . . . . . . . . . . . 81, 84, 210, 213, 242 Data mass spectrometric . . . 49–56, 84, 117–126, 217–225, 242, 359 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263, 265, 270 DD-PCR . . . . . . . . . . . . . . . . . . 277–279, 281–282, 286, 288 DeCyder MS . . . . . . . . . . . . . . . . . . . . . . . . . 193, 195, 200–202 Degas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60, 62, 148, 154 Degradation . . . . . . . . . 13–14, 21, 33, 35–36, 45, 70, 77, 89, 93–94, 98, 101, 114, 147, 156, 175, 184–185, 188, 193, 208, 214, 223, 243, 260, 270, 279, 282, 288, 314 See also Protein degradation Delayed extraction . . . . . . . . . . . . . . . . . . . . . . . . 102, 156, 254 Denaturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168, 281, 314 See also Solution, denaturing De novo sequencing . . . . . . . . . . 46, 77, 81–82, 84, 102, 107, 110, 118, 223, 225, 359, 368–369 DEPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172, 288 Dermal . . . . . . . . . . . . . . . . 146–147, 182, 284, 288, 290–291 Desalt . . . . . . . . . . . 39–40, 43, 58, 61, 65, 67, 70–71, 94–95, 230, 341 Detection . . . . 21, 50, 57–58, 68, 71, 78–79, 130, 134, 138, 192–193, 198, 244, 256, 260, 284, 311–312, 320, 326–327, 334, 341, 356, 372 Detection, fluorescent . . . . . . . . . . . . . . . . . 320, 326–327, 341 Detergent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148, 151, 236 Development . . . . . . . . . . 5, 14, 25, 138, 165, 177, 228, 276, 278, 283, 307–308, 314–315, 319–320, 334, 359 Dextran . . . . . . . . . . . . . . . . . . . . 138–140, 143, 194, 196, 203 DHBA, see Dihydroxybenzoic acid DHB, see Dihydroxybenzoic acid Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . 5, 208, 248, 259, 266 DIC, see Differential interference contrast Diethyl pyrocarbonate, see DEPC Differential display PCR, see DD-PCR Differential expression, see Expression, differential Differential interference contrast . . . . . . . . . . . . . . . . . . 30–31 Differential isotopic labelling . . . . . . . . . . 230–231, 239–243 Digest . . 5–6, 15, 19, 21, 23–24, 79–80, 173, 175, 196, 213, 215, 228, 230, 239–240, 314–315, 325, 327, 329–330, 332, 334 See also Protein digestion Dihydroxybenzoic acid . . . . . . . . . 51, 55, 71, 126, 139, 142, 204, 210, 212 Dimethyl sulfoxide . . . . . . . . . . 230, 287, 294, 296, 298, 304 Direct mass spectrometric peptide profiling . . . . . . 117–126, 129–135 Disease . . . 25, 192–193, 247–248, 260–261, 263, 265–266, 270, 320 Disease markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268, 270 Display technology . . . . . . . . . . . . . . . . . . . . . . . . 314, 320–324
Dissection cryostatm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218–219 enhanced . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Disulfide bonds . . . . . . . . . . . . . 59–60, 82, 93, 126, 239, 320 Dithiothreitol, see DTT Diuretic hormone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49–50, 77, 248 DMSO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .230, 287, 294, 318 DNA Amplification . . . . . . . . . . . . . . . . . . . . . . . . . 159–175, 181 Array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346, 352–354 Polymerase . . . . . . . . . . . . . . . . . . . . . . . 163, 167, 169, 173 Template . . . . . . . . . . . . . . . . . . . . . . . . . 346–347, 353–354 DNTP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163, 167, 169, 175 Domain . . . . . . . . . . . . 50, 160, 171, 350–351, 370, 355, 358 Dorsal . . . . . . . . . . . . . . . . . . 63, 120–122, 125, 141, 149, 182 Droplets . . . . . . . . . . . . . . . . . . . . . . . . . 41, 105, 112, 187, 198 Droplets pre-mixing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Drosophila melanogaster . . . . . 30, 40, 97, 117–127, 133–134, 140–141, 192, 357–363, 365, 367, 369, 371 Drosophila neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138, 141 Drosophila, see Drosophila melanogaster Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7, 227 Dry ice . . . . . . . . 95, 166, 174, 193, 220–221, 223–224, 228, 231–232, 299, 304, 306 Drying . . . . . . . . . . . . 18, 61, 67, 69, 98, 112, 155, 204, 253, 309–310, 340 DTT . . . . . . . . . . 77, 79, 82, 89, 93, 96, 165, 209, 213, 230, 236, 239 Dye . . . . . 59, 64–65, 70, 139–140, 143, 237, 318, 330, 341
E EBV, see Epstein-Barr virus E.coli, see Escherichia coli Econo-Column . . . . . . . . . . . . . . . . . . 294, 296, 298, 302–306 Ectoderm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284, 288, 290–291 Edman degradation . . . . . . . . . . . 33, 35–36, 77, 89, 94, 147, 184–185, 188, 218, 282, 288 EDTA . . . . . . 82, 89–90, 161, 164, 183, 185, 254, 296, 303, 304, 316, 317 Electrodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182, 315 Electrophoresis . . . 71, 78–79, 163–164, 168–170, 175, 192, 230, 236, 248, 281, 288, 352 Electro-pulse stimulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 Electrospray, see ESI Elution buffer . . . . . . . . . . . . . . . 88–89, 92–94, 96, 296, 304, 317 isocraticm . . . . . . . . . . . . . . . . . . . 198, 210–211, 285, 287 profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150–151, 287 stepwise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 time . . . . . . . . . . . . . . . . . . . . . . 18–19, 201–202, 204, 332 EMBOSS Antigenic prediction . . . . . . . . . . . . . . . . . 315, 334 Endogenous biomolecules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 peptidases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 peptides . . . . . . . . . . . . . . . 17, 29–46, 208, 225, 359, 368 Endokinins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 Endopeptidase . . . . . . . . . . . . . . . . . . . . . . . . . . 85, 89–90, 260 Endoproteinase, see Endopeptidase Enzymes . . . 31, 36, 89–90, 96, 99, 146, 172–173, 191, 208, 376 Epithelial . . . . . . . . . . . . . . . . . . . 275, 283–284, 286, 288–289 Epstein-Barr virus . . . . . . . . . . . . . . . . . . . . . . . . 248–249, 252 Escherichia coli . . . . 37–39, 90, 167, 171, 181, 316, 321–322, 324, 335, 347–355
PEPTIDOMICS 389 Index Esculentin . . . . . . . . . . . . . . . . . . 153, 156, 162, 179–180, 188 ESI . . . . . . . . . . . . . . . . . 37–38, 40, 42–44, 83, 102, 193, 250 ESI Q-TOF . . . . . . . . . . . . . . 37–38, 40, 43–44, 83, 102, 193 EST databases . . . . . . . . . . . . . . 110, 113, 358–359, 371–372 EST sequences . . . . . . . . . . . . . . . . . . 358–359, 367, 371–372 Ethanol . . . . . 37, 51, 54, 59–60, 62, 66, 77, 92–93, 96, 125, 133–134, 151, 164, 167–169, 174–175, 200, 223–224, 228–229, 268–269, 316–318, 329 Ethanolamine . . . . . . . . . . . . . . . . . . . . . . . . 296, 303, 349, 353 Ether . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183, 187 Ethidium bromide . . . . . . . . . . . . . . . . . . . . 164, 168, 173, 175 Eukaryotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14, 349, 358, 361 Evaporation . . . . . . . 39, 94–95, 97, 104, 112, 134, 168, 198, 280–281, 321, 339, 356 Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7, 50, 88, 146, 269 Exiqon plate . . . . . . . . . . . . . . . . . . . . . . . . . 300–301, 306, 308 Exopeptidases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 Exoprotease, see Exopeptidase ExPASy . . . . . . . . . . . . . . . . . . . . . 82, 269, 315, 334, 360, 367 Expression cell-free . . . . . . . . . . . . . . . . . . . . . . . . . . 347–350, 351–352 differential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50, 247, 259 Extraction buffer . . . . . . . . . . . . . . . . . . . 209–210, 218, 297, 308, 312 cold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 solid phase . . . . . . . . 15, 18, 37, 39, 45, 51, 53, 197, 203, 249–250, 253 Extracts bacterial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15–16 lymphoblastoid . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248, 254 yeast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164, 181, 186, 316 See also Gene expression
F FA, see Formic acid Fat . . . . . . . . . . . . . . . . . . . . . . . . . 121, 131, 139–140, 192, 196 FEP . . . . . . . . . . . . . . . . . . . . . . . . . . . 59–60, 62, 64, 66, 69–70 Fiber pads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Fibrinopeptide . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103, 148, 153 FlexAnalysis . . . . . . . . . 38, 119, 131, 139, 199–200, 261, 266 FlexControl . . . . . . . . . . . . . . . . . . . 38, 80, 261–262, 263, 266 Flow rate . . . . . 43, 45, 53–54, 58, 66–68, 81–82, 92–93, 99, 148–151, 184–185, 210–211, 213–215, 222, 253, 280–281, 308–309, 332 Fluid, cerebrospinal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260, 269 Fluid, synovial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Fluorescence . . . . . . 138–141, 143, 314–315, 327, 337–338, 340–341, 356 Fluorinated ethylene propylene, see FEP Fly . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30, 90, 99, 118, 120–121 FMRFamide. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29, 31, 125 Fold . . . . . . . . . . . . . 71, 93, 95, 105, 167, 330–331, 335, 341 Formaldehyde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326, 339 Formic acid . . . . 37, 40, 43, 59, 61, 65, 67, 77, 96, 194–198, 209, 220, 222, 230–231, 243, 296–297 Fourier transform ion cyclotron resonance, see FTICR-MS Fractionation . . . . . . . . . 18, 50–51, 53, 88–89, 91, 184, 224, 228–229, 232–234, 244, 255, 276, 280–281, 309, 313 Fractionation subcellular . . . . . . . . . . . . . . . . . . . . . . . 228, 232 Fraction collector . . . . . . . . . . . . . . . . . . . . . 113, 155, 250, 256 Fragmentation . . . 42, 45, 83, 102, 108, 110, 143, 198–200, 225, 243, 360, 368 Fragment ions . . . . . . 55–56, 80, 83–84, 102, 108, 110, 243, 266, 368
Freeze . . . . 78, 82, 89, 93–96, 155, 166, 183–185, 211, 232, 253, 277, 298–299, 326 Frog . . . . . . . . . . 146, 150–151, 153, 155, 160, 162, 173, 187 See also Amphiobian Frog skin . . . . . . . . . . 146, 153, 155, 162, 166–167, 173–174 Fruit fly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30, 118 FTICR-MS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15, 18–19, 83 Function . . 4, 13, 31, 49, 130, 137, 171–172, 208, 270, 275, 358, 382 Functional activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Functional assays . . . . . . . . . . . . . . . . . . . . . . . . . . 278, 287, 327 Functional genomics . . . . . . . . . . . . . . . . . . . . . . . . . . . 177–178 Fungi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145, 159, 181, 186 FXPRLamides, see Pyrokinins
G G–25 . . . . . . . . . 294, 296, 298, 304–305, 317–318, 330, 337 Ganglia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139, 141, 143 Ganglionic sheath . . . . . . . . . . . 121–122, 125, 131, 140–141 Gas-phase sequencing . . . . . . . . . . . . . . . . . . . . 36, 82, 84, 282 Gel . . . . . . . . 63–64, 69, 77–81, 83, 163–164, 168–169, 175, 183–184, 230, 236–237, 311 Gel electrophoresis, see Electrophoresis Gel electrophoresis, two-dimensional . . . . . . . . . . . . . . . . 248 Gel, stacking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79, 236–237 Gene expression . . . . . . . . . . . . . . . . . . . . . . . . . . 278, 287, 382 See also Expression Gene families . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 GenElute . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349, 351–352, 355 Genes . . . . . . 14, 31, 36, 110, 130, 137, 146, 160, 275, 278, 347, 350–351, 357–361, 371, 376–377 Genome . . . . . . . . . 30–31, 84, 137, 147, 276, 279, 360–361, 371, 380 GFP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140, 143 Gland . . . . . . . . . . . . . . 89, 119–120, 122, 178, 286, 288–289 Gland cells . . . . . . . . . . . . . . . . . . . . . . . . . . 284, 286, 288–289 Gland secretions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 Glass capillary . . . . . . . . . . . . . . 120, 122, 131–132, 140–141 Glass slides . . . . . . . . . . . . . . . . . . . . . . 326–327, 332, 352, 355 Gluteraldehyde . . . . . . . . . . . . . . . . . . . . . . . . . . . 294, 297–299 Glycerol . . . . . . . . . . . . . . . . . . . . . . . . . 77, 164, 182, 224, 230 Glycine . . . . . . 229–230, 236, 239, 279, 293, 317, 321, 333, 339, 369 Glycosylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50, 218 G-protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Gradient elution . . . . . . . . . . . . . . . . . . . . . 18, 80, 94, 155, 204, 213 linear . . . . . . . . . . 40, 43, 81–82, 91, 105, 150, 197, 253, 280–281, 285, 309–310 Granular glands . . . . . . . . . . . . . . . . . . . . . . . . . . . 146, 178, 183 Grid voltage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106, 142 Growth conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22–23 factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331–332 Guanidinium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82, 89, 161 Guanidinium isothiocyanate, see Guanidinium Guide wire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106, 142, 152
H Hadronyche versuta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Haemoglobin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5, 259, 355 Hank’s medium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194, 196 HB buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 HCCA, see Cyano-4-hydroxycinnamic acid 4-HCCA, see Cyano-4-hydroxycinnamic acid
PEPTIDOMICS
390 Index
Head regeneration assay . . . . . . . . . . . . . . . . . . . . . . . . 276, 282 Heart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63, 65, 83, 121, 251 Heliothis virescens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Hemokinin . . . . . . . . . . . . . . . . . 294–295, 297–298, 307, 310 Hemolymph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58, 63, 69, 71 HEPES . . . . . . . . . . . . . . . . . . . . . 51, 103, 118, 194, 203, 254 Hermaphrodites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . 6, 83, 314, 325 High performance liquid chromatography, see HPLC Homogenate. . .98, 155, 166, 221, 224, 232, 278, 280–281, 308 Homogenize . . . . . . . . 39, 50, 112, 161, 166, 173, 182, 196, 220–221, 224, 229, 232–234, 252, 254–255, 277, 279–280, 308 Homology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31–32, 358, 370 Honey bee. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .107 Hormones. . . . . . . .4, 49, 110, 113, 117–118, 260, 275, 313 HPLC Column . . . . . . . . . . . 78, 82, 97, 105, 148, 184, 250, 277 Fractionation . . . . . . 40–41, 45, 54, 79, 94–95, 104–107, 112, 151, 188, 198, 248, 280–281 reversed-phase . . . . . . 71, 78, 82, 89, 91–95, 97–98, 107, 147, 150–151, 156, 183–184, 197–198, 241, 248, 250, 277–278, 285, 287 Human placenta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 Hydra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275–291 Hydra attenuate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Hydractinia echinata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Hydra EST database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 Hydra magnipapillata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Hydrogel assay . . . . . . . . . . . . . . . . . . . . . . . 318, 329–330, 341 Hydrogels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313–341 Hydrophilicity . . . . . . . . . . . . . . . . . . . . . . . 333–334, 315, 319 Hydrophobic . . . . 68, 75, 160, 178, 256, 260, 293, 333, 367 Hylidae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146, 178 Hym neuropeptides . . . . . . . . . . . . . . 286–287, 289, 291, 310 Hyperoliidae. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .178 Hypersil BDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181, 184–185 Hypothalamus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192–196
I Identification . . . . . . . . . . 7, 18–21, 25, 77, 81, 84, 102, 113, 140–141, 143, 145–156, 191–205, 218, 225, 245, 259–270, 275–291, 357–373 See also Protein identification IGF1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331 IgG . . . . . . . . . . . . . . . 296, 301, 304–305, 312, 317, 328, 337 Immobilisation . . . . 315, 327, 329, 341, 345–346, 349–350, 352–355 See also Protein immobilisation Immortalization . . . . . . . . . . . . . . . . . . . . . . 248–249, 251–252 Immunoassays . . . . . . . . . . . . . . . . . . . 130, 276, 307, 310, 327 Immunogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294, 298–299 Injector . . . . . . . 68, 81, 92–93, 111, 113, 119, 131, 142, 241 Innate immunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Insect neuropeptides . . . . . . . . . . . . . . . . . . . . . . . . . 101–114, 126 peptides . . . . . . . . . . . . . . . . . . . . . . . . . . 106, 108, 109, 112 saline . . . . . . . . . . . . . . . . . . . . . . . . 103, 122, 131, 139–141 Insecticidal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88, 90–91, 96 Insectotoxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 In situ peptide arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Insulin . . . . 5, 29, 36, 45, 148, 153, 203, 209–211, 254, 331 Integration . . . . . . . . . . . . . . . . . . . . . . . . . 58–59, 68, 130, 265 Interactions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31, 315, 345
Invertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7, 101, 379 Iodoacetamide . . . . . . . . 77, 79, 82, 209, 230, 318, 330, 341 Ion intensities . . . . . . 126, 129, 132–134, 142–143, 193, 202 source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198–199 trap . . . . . . . . . . . . . . . . . . . 15, 18, 71, 194, 210, 213, 245 Ionization . . . . . . . . 18, 37–38, 40–42, 78, 80, 94, 209, 224, 243, 245, 250 Ions immonium . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102, 108–110 positive . . . . . . . . . . . . . . . . . . . . . . 105–107, 113, 182, 213 total . . . . . . . . . . . . . . . . . . . . . . . . . 193, 201, 204–205, 242 Islets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193–197, 200–203 Islets of Langerhans . . . . . . . . . . . . . . . . . . 193, 196, 200–203 Isopentanem . . . . . . . . . . . . . . . . . . . . 294, 298–299, 304, 306 Isopropanol . . . . 15, 17–18, 89, 96, 229, 231–232, 236, 317 Isothiocyanate, guanidinium, see Guanidinium isothiocyanate
K KCl . . . . . . . . . . . . 51, 58, 118, 130, 163, 265, 269, 349, 353 Kit . . . . . . . . . 16, 78–79, 103, 148, 165, 181, 185, 229, 260, 277, 281, 320, 336, 349, 354–355
L Label . . . . . 6, 18, 50, 52, 134, 139–141, 143, 164, 193, 228, 239–243, 245, 283–284, 289, 318, 323, 325–327, 330–331, 336–341, 351–354, 356 Label isotopic . . . . . . . . . . . . . . . 228, 230–231, 239–243, 245 Lachesana tarabaevi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Larvae . . . . . . . . . . . . . . . . . 30–31, 96, 99, 118–125, 173, 276 Laser . . . . . . . . . 37, 40–42, 54, 55, 102, 104–106, 108, 124, 129, 133, 142–144, 152, 182, 198, 224, 250, 261–263, 317 Laser intensity . . . . . . 42, 106, 108, 142, 152, 154, 198, 327 Laser power. . . . . . . . . . . . . . . . . . . . . . . . . .262–263, 267, 270 Laser shots . . . . . 54, 104, 106, 122, 133, 143–144, 263, 285 LC-MS . . 14–15, 18–21, 58–59, 61–62, 65, 67–68, 83, 98, 117, 192–200, 208, 213, 215, 220, 222, 240 LC Packings . . . . . . . . . . . . . . . . . . . . . . . 38, 42, 195, 231, 241 LC, see Chromatography, liquid Leech . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117, 129 Leucosep tube . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249, 251, 255 Library . . . . . . . . . . . . . . . . . . . . . 316, 320–323, 334–335, 381 LIFT-MS/MS . . . . . . . . . . . . . . . . . . . . . . . . . . . 78, 80–81, 83 Ligation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164, 169, 173 Lipids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45, 218, 256 Liquid chromatograph and mass spectrometry, see LC-MS Liquid chromatography, see Chromatography, liquid Liver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207–215, 320 Local alignment . . . . . . . . . . . . . . . . . 124, 133, 171, 357–358 Locusta migratoria . . . . . . . . . . . . . . . . . . . . . . . . . 131, 192, 359 Lymphoblastoid cell lines . . . . . . . . . . . . . . . . . . . . . . 247–256 Lymphoblasts . . . . . . . . . . . . . . . . . . . 247–248, 250–253, 255 Lymphocytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248, 251–252 Lysates cell-free . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353–354, 356 rabbit reticulocyte . . . . . . . . . . . . . . . . . 347, 350–352, 355 Lysobacter enzymogenes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89–90
M Magnesium acetate . . . . . . . . . . . . . . . . . . . 254, 349, 352, 355 Malassez cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249, 252, 255 MALDI matrix, see Matrix
PEPTIDOMICS 391 Index MALDI MS/MS, see MALDI-TOF/TOF MALDI MS, see MALDI-TOF MALDI sample plate . . . . . . . 40, 51–55, 80, 119–120, 131, 133, 139, 141–143, 151–152, 198, 200, 212, 254, 261, 341 MALDI, see MALDI-TOF MALDI-TOF . . . . . . . . 33, 35–37, 40–41, 80–81, 101–114, 117–127, 129–135, 139, 141, 147–148, 153, 181–182, 184, 188, 198, 204, 210, 248, 250–251, 253–254 See also Mass spectrometry, time-of-flight MALDI-TOF/TOF . . . 40, 80–81, 83, 123, 193, 195, 198, 200, 204, 261 See also Mass spectrometry, tandem Mammals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159, 293 Manduca sexta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Mascot . . . 38, 43, 46, 81, 84, 126, 195, 199–200, 222–223, 231, 242, 291, 360, 368–369, 372 Mass accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41, 156, 359 fingerprint . . . . . . . . . . . . 80, 84, 123, 223, 248, 250, 254 range . . . . . . . . . 7, 20, 24, 68, 70–71, 81, 105–106, 114, 152–153, 174, 192, 199, 214, 228, 251, 300, 302, 307, 314, 333 spectra . . . . . . . . . . 55, 84, 123, 125–126, 133–135, 138, 142–143, 209, 212, 218, 222–223, 254–256, 359 spectrometry data . . . . . . . . . . . . . . . . 15, 41, 81, 124, 200, 217, 263 electrospray . . . . . . . . . . . 18–19, 38, 42, 78, 220, 245 hybrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83, 314 linear ion trap . . . . . . . . . . . . . . . . . . . . . . . . . . 210, 213 nano-LC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 quadrupole time-of-flight . . . . . . . . . 38, 71, 102, 220 reflectron . . . . . . . . . . . 41–42, 80, 101–102, 105, 198 tandem . . . . . . . . . . . . . . . . . . . . . . . . 84, 147, 208, 282 See also MALDI-TOF/TOF time-of-flight . . . . . . . . . . 38, 42, 102, 182, 209, 220 See also MALDI-TOF Masses, matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77, 215 Mass tolerance . . . . . . . . . . . . . . . . 20, 81, 200, 222, 242, 359 Matrix crystals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55, 122, 125, 142 solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Matrix-assisted laser desorption ionization mass spectrometer, see MALDI-TOF Mature peptides . . . . . . . . . . . . . . . . . . . . . . 358, 364, 371, 376 Membrane . . . 58–62, 67, 69, 168, 214, 230, 234, 237–238, 320–321, 326–328, 338, 349, 353–354, 364 Membrane filter . . . . . . . . . . . . . . . . . . . . . . . . . . 256, 353–354 Metazoa . . . . . . . . . . . . 31, 275, 359, 361–362, 364, 369–370 Methanol . . . . . . . . . . . 15, 17, 37, 39, 45, 68, 103–105, 109, 111–112, 124, 130, 133–134, 142, 194, 197, 203, 219–221, 224, 229, 249, 253, 280–281, 296–297, 308–309, 318 Methanol extracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104–105 Methionine . . . . 81, 155, 200, 222, 243, 319, 352, 368, 370 MgCl2 . . . . . . . . . . . . . . 51, 58, 103, 118, 164–165, 167, 316 MHC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7, 208 Mice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192, 194–196, 209 Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21, 313–341 Microdialysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57–72 Microscope . . . 30, 44, 52–53, 103, 119, 138, 141, 186, 317, 329, 340, 349 Microscope dissecting . . . . . . . . . . . . . . . . . . 44, 103, 119, 130 Microwave irradiation . . . . . . . . . . . . . . . . . . . . . 192, 218, 223
Mining sequence databases . . . . . . . . . . . . . . . . 119, 131, 139 See also BLAST Mmonium hydroxide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Model organisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58, 358 Molecular mass profiling . . . . . . . . . . . . . . . . . . . . . . . 250–251 Molecular weight markers . . . . . . . . . . . . . . . . . 164, 229, 237 Molecules signalling . . . . . . . . . . . . . 276, 278–279, 285, 287 Molluscan brainm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Molluscs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Monoisotopic . . . . . . . . . . 105–106, 108, 110, 153–154, 200, 213, 319, 368 Morphine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .228, 245 Morphology . . . . . . . . . . . . . . . . 125, 223, 278, 282, 286, 289 Motifs . . . . . . . . . . . . . . . . . 358–359, 361–362, 365, 367–371 Mouse . . . . . . . . . . . . . . 30, 89, 195–196, 204, 207–215, 223 Mouse brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Mouse liver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207–215 MS mode . . . . . . . . . . . . . . . . . . 54–55, 81, 94, 198–199, 222 MS/MS, see Mass spectrometry, tandem and MALDI-TOF/TOF MS, see Mass spectrometry and MALDI-TOF Multicellular organisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Multivariate analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .270 Musca domestica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Muscle fibres . . . . . . . . . . . 121, 131, 139–140, 284, 290–291 Mus musculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30, 362–363 Myoactivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284–286, 289 Myoglobin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251, 254
N Na2 HPO4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277, 295 NaCl . . . . . . . . . 37, 39, 44, 51, 58, 103, 118, 138, 164, 277, 294–299, 304, 306, 308, 316, 335, 349, 355 NaHCO3 . . . . . . . . . . 51, 138, 206, 239, 294–296, 298–299, 301, 304, 311 NaN3 . . . . . . . . . . . . . . . . . 214, 230, 244–245, 295–296, 322 NanoLC, see Chromatography, nanoLC NaOH . . . . . . . . . . . . . . . . . . . . . 230, 239, 245, 296, 301, 311 NCBI BLAST, see BLAST Nematodes . . . . . . . . . . . . . . . . . . . . . . . . . . . 36–37, 39, 44–45 Nerves . . . . . 50, 52, 101, 120–121, 139–140, 143, 195, 289 Nervous system . . . . . . . . . . . . . . . 50, 58, 119, 121–122, 125, 191, 217, 219, 226 Neuroendocrine tissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Neurohemal organs . . . . . . . . . . . . . . 118, 129–130, 133, 135 Neurohormones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191, 217 Neurokinin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Neuromodulators . . . . . . . . . . . . . . . . . . . . . 7, 49, 58, 191, 217 Neurons . . . . . . . . . . . 29–31, 50, 52, 125, 130, 137–144, 276 Neurons, peptidergic . . . . . . . . . . . . . . . . . . . . . . . 49, 132, 138 Neuropeptides expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 extraction . . . . . . . . . . . . . . . . . . . . . 53, 193–197, 217–225 identification of . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218, 225 precursor . . . . . 31, 36, 42, 110, 124, 133, 290, 357–373 Neuropeptides, invertebrate . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Neuropil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130–134 Neuroproteomics . . . . . . . . . . . . . . . . 119, 131, 227–245, 360 Neurosecretions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121, 125 Neurotransmitters . . . . . . . . . . . . . . . 191, 217, 228, 275, 293 NH4 HCO3 . . . . . . . . . . . . . . . . . . . . . . . . . 17–18, 79, 90, 230 N-hexane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37, 39 Ni-NTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349, 352, 354–355 NIR-664-iodoacetamide . . . . . . . . . . . . . . . . . . . 318, 330, 341 3-nitrobenzyl alcohol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
PEPTIDOMICS
392 Index
Nitrocellulose membrane . . . . . . . . . . . . . . . . . . 230, 237, 338 Nitrogen . . . 82, 93, 102, 166, 171–172, 183, 193–195, 204, 209, 277, 304 Norepinephrine . . . . . . . . . . . . . . . . . . . . . . 146–147, 149, 154 Normalization . . . . . . . . . . 193, 202, 204–205, 327–328, 338 N-terminal . . . . . 72, 94, 102, 110, 165, 178, 222, 242–243, 294–295, 297, 299, 302, 310, 369 N-terminal, acetylation . . . . . . . . . . . . . . . . . . . . . . . . 222, 288 Nucleic acids . . . . . . . . . . . . 84, 164, 173, 279, 314–315, 380 Nucleotides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347–348, 358 Nucleotide sequences . . . 146, 160–161, 162, 165–166, 171
O Oligonucleotides . . . . . . . . . . . . . . . . . 161–163, 314, 351, 355 Optimisation. . . . .50–51, 83, 106, 118, 152–153, 174, 193, 204, 225, 231, 238, 250, 312, 381 Organic solvents . . . . . . . . . . . 39, 45, 71, 112, 155, 193, 204, 241, 253, 309 Organisms . . . 4, 6–7, 16, 29–31, 57–58, 71, 118, 137, 146, 159, 171, 177–178, 181, 207, 224–225, 227, 278, 357–359, 361–362 Organs . . . . . . . . . . . . . . . . 30, 50–51, 53, 117–118, 121–122, 129–130, 133, 135, 192, 207–208
P PAM30 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124, 365, 378 Pancreas . . . . . . . . . . . . . . . . 89, 191–194, 196–197, 251, 254 Panning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320–323, 335 Paraffin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234, 236, 244 Parafilm . . . . . . . . . . . . . . . . . . . . . . . . . . 61, 105, 112, 354, 356 Parent ions . . . . . . . . . . 20, 43, 102, 124, 133, 143, 198, 368 Patients . . . . . . . . . . . . . . . . 248, 254, 260–261, 265, 269–270 Pattern recognition . . . . . . . . . . . . . . . . . . . 260–261, 263–266 PBS . . . . . . . . . . 16, 103, 184, 229, 231, 249, 251–252, 254, 277, 284, 289, 316–318, 320–321, 325–326, 340–341, 349, 352 PBS-Tween . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 PCR . . . . . . . . . 170, 173, 175, 185, 281, 350–352, 354, 355 See also RT-PCR PCR primers . . . . . . . . . . . . . . . . . . . . . . . . . 162, 164–166, 185 Peaks. . .19, 82, 97, 102, 105, 113–114, 124–126, 150–151, 154, 156, 184, 187–188, 199, 201, 242, 245, 256, 265–267, 269–270, 337 intensities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55, 243, 265 ion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45, 114, 266–267 See also Mass spectra PEG/NaCl. . . . . . . . . . . . . . . . . . . . . .316, 322, 324, 335–336 Pellet . . . . . . . . . . 16–17, 21, 39, 45, 65, 164, 166–168, 170, 174, 221, 233–234, 239, 244, 251–252, 308–309, 311, 322, 324, 336 Peptide antibody . . . . . . . 279, 295–296, 300–301, 304–306, 315, 319–320, 325, 327–328, 332–334, 337 arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345–356 calibration . . . . . . . . . . 148, 151–152, 154, 156, 268, 276 complement . . . . . . . . . . . . . . . . . . . . . . 129, 138, 146, 260 content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3–4, 7, 40 degradation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70, 279 detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71, 200, 334 diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49–50 elution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82, 256 expression . . . . . . . . . . . . . . . . . . . . . . . . . 76, 218, 247, 379 extraction . . . . . 14–18, 39–40, 45, 50, 75, 80, 103–105, 112, 137, 156, 194–197, 217–225, 252–253 families . . . 7, 57–58, 118, 188, 293–294, 358, 370, 380
fractionation . . . . . . . . . . 14, 96, 155, 181, 184, 188, 212, 250, 253, 278, 337 genes . . . . . . . . . . . . . . . . . . . 160, 357–358, 360, 369, 371 growth factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 hormones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117–118, 364 identification . . . . . . 19–21, 25, 71–72, 77, 84, 113, 126, 191–192, 195, 199–202, 259–270, 372–373, 378–380 labelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 loss . . . . . . . . . . . . . . . . . . . . . . . . . 112, 124–125, 133, 143 markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259–270, 332 mass . . 18, 20, 77, 80, 84, 103, 113, 125–126, 152, 182, 200, 222–223, 243, 266, 319, 359 modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126, 369 motifs . . . . . . . . . . . . . . . . . . 358–359, 365, 367, 369–371 peaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82, 265, 270 precursors amphibian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178, 180 insect . . . . . . . . . . . . . . . . . . . . . . . . . 106, 108, 109, 112 plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375–382 profiling . . . . . . . . . . . . . . . . . . . . . 117–126, 129–135, 248 purification . . . . . . . . . . . . . . . . . . . . . . . . . 88–89, 269, 332 quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 searches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372, 378 selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .313–341 separation . . . . . . . . . . . . . . 6, 50, 150–151, 198, 203, 245 sequencing . . . . . . 77, 82, 85, 89, 96, 118, 192, 248, 282 signalling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7, 279, 285 toxins See also Peptides, venom Peptidergic . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49, 132, 137–138 Peptides active . . . . . . . . . . . . 5, 31, 36, 87–99, 145–156, 188, 218, 224–225, 358–359, 361–362, 369, 372, 379 amphibian . . . . . . . . . . . . . . . . . . . . . . . . 145–156, 177–188 annotated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362–363 antibacterial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 antigenic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 binding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 bradykinin-related . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 characterization of . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 circularised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 concentration . . . . . . . . 17, 21, 70, 97–98, 106, 135, 144, 203, 208, 282 cytolytic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 functional . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275–291, 375 insecticidal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 invertebrate . . . . . . . . . . . . . . . . . . . . . . . . . . . 110, 192, 371 lepidopteran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113–114 molluscan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 morphogenetic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 mouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 myoactivity of . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285, 289 myotropic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145–146 neuroendocrine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 neuromodulatory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375–382 predicted. . . . . . . . . . . . . . . . . . . . . . .36, 42, 333, 359, 373 reduced . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 regulatory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 serum-derived . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 skin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146, 153 synthetic . . . . . . 110, 113, 124, 143–144, 260, 282, 286, 294–295, 300, 330, 333 tachykinin-related . . . . . . . . . . . . 118, 130, 132, 134, 146
PEPTIDOMICS 393 Index venom . . . . . . . . . . . . . . . . . . . . . 77, 81–82, 88–91, 93–96 See also Peptide toxins Perfusate . . . . . . . . . . . . . . . . . . . . . . . . 60–62, 67–68, 104–106 Pericardial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58, 63–65, 69 Perisympathetic organs . . . . . . . . . . . . . . . . . . . . 118, 121–122 Phage . . . . . . . . . . . . . . . . . . . . . . 314–316, 320–324, 335–336 Phage display . . . . . . . . . . . . . . . 314–317, 320–324, 334–335 Phenol . . . . . . . . . . . . . . . . . . . . . 161, 166, 172–173, 288, 324 Phosphate buffer. . . . .81, 89, 184–185, 214, 230, 295, 297, 317–318, 341 See also PBS Phylogenetic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82, 146, 178 Pipidae, family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 PISA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 Pituitary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192–196 Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177, 377, 381 Plasma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255, 259–260 Polyacrylamide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281, 315 Polyps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277, 281–285, 288 Post-source decay, see PSD Post-translational modifications . . . . . . 36, 50, 81, 110, 114, 126, 137, 218, 224, 231, 245, 358–359, 369, 372, 375 Precipitation . . . . . . . . . . . 17, 21, 45, 54, 164, 166–167, 169, 174–175, 234, 239, 280, 309, 311, 322, 324, 335–336 Precursor ions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80, 242 proteins . . . . . . . . 6–7, 31, 160, 279, 358–359, 361–362, 367–368, 371–373 sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358–360, 369 Prediction tools . . . . . . . . . . . . . . . . . . . . . . 319, 371, 378, 382 Presynaptic . . . . . . . . . . . . . . . . . . . . . . 228, 232, 234, 237–238 Primers . . . . . . . . . . . 160–166, 174, 178, 185, 277, 347–348, 350–352, 355 Processing enzymes . . . . . . . . . . . . . . . . 31, 36, 160, 172, 191 Promoter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347–349 Proprotein convertases . . . . . . . . . . . . . . . . . . . . . . . . . 192, 370 Protease inhibitor . . . . . . . . 15–16, 181, 183, 187, 217–225, 229, 232, 317, 327 Proteases . . . 15–16, 23–24, 89–90, 183, 187, 192, 217–225, 312 Protein analysis . . . . . . . . . . . . . . . . . . . . . . . . . 6, 24, 255, 268, 325 degradation . . . . . . . . . . . . . . . . . . . . 13–14, 214, 218, 243 See also Degradation Digestion . . . . . . . . . . . . . . . . . . . . . . . . . 15, 230, 239–240 See also Digest expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270, 355 identification . . . . . . . . . 24–25, 78, 80–81, 195, 245, 369 See also Identification Immobilisation . . . . . . . . . . 315, 327, 329, 345, 352, 355 See also Immobilisation Microarrays, see Arrays, protein In Situ Array, see PISA ProteinProspector . . . . . . . . . . . . . . . . . 110, 119, 131, 139, 204 Proteins parent. . . . . . . . . . . . . . . . . . . .20, 260, 266, 268, 270, 319 predicted . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14, 31 standard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209–210 Proteolysis . . . . . . . 4–6, 24, 93–94, 112, 192, 214, 325, 336 Proteolytic enzymes . . . . . . . . . . . . . . . . 89–90, 146, 208, 376 Proteolytic peptides . . . . . . . . . 192, 205, 319, 327, 329–330, 332, 339 Proteome . . . . . . . . . . . . . . . . . . . 14, 21, 24, 83, 207, 248, 362 Proteomics, spectrometry-based . . . . . . . . . . . . . . . . . . 50, 325
PSD . . . . . . . . . . . . . . . . . . . . 83, 102, 104, 106–110, 114, 234 Pseudomonas fragi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 PSI-BLAST, see BLAST PTM, see Post-translational modifications Pyroglutamate aminopeptidase . . . . . . . . . . . . . . . . . . . . . . . 89 Pyrokinins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118, 123
Q Q-TOF, see Mass spectrometry, quadrupole time-of-flight Quantification . . . 6, 16, 18, 24–25, 58, 146, 167, 191–205, 235, 249, 314, 325–326, 338–339, 354 Quantitative proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
R RACE . . . . . . . . . . . . . . . . . . . . . . . . . . 160–163, 165, 167–168 See also PCR Radioimmunoassay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57, 218 Rana cancrivora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Rana grahami . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181, 185 Rana Japonica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Rana luteiventris . . . . . . . . . . . . . . . . . . . . . . . . . . 150–151, 153 Rana nigrovittata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Rana ornativentris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Rana palustris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Rana pleuraden . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Rana tagoiare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 Random peptide libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 Ranidae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146, 178–179, 185 Rat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217–225, 294, 310 Rat brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217–225 Recovery. . . . . .17–18, 58–59, 61–62, 66–68, 148, 253, 255 Rehydration solution . . . . . . . . . . . . . . . . . . . . . . . . 77, 79, 329 Reproducibility . . . . . . 18, 61, 126, 142, 193, 208, 214, 223, 249, 255, 338–339 Resolution . . . . . . . . . . 15, 18, 41–42, 96–97, 102, 106, 150, 152–154, 156, 203–204, 243 Retention time . . . . . . . . . . . 61, 98, 147, 156, 200, 242–243 RFamides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Rhodamine . . . . . . . . . . . . . . . . . . . . . . . . . . 317–318, 326, 337 RITC, see Rhodamine RNA . . . . . . . . . 30, 160–161, 163, 166–168, 173–175, 181, 185, 281, 288 RNase inhibitor . . . . . . . . . . . . . . . . . . . . . . . . . . . 163, 167, 288 RNA total . . . . . . . . . 160, 166–167, 173–174, 185, 281, 288 RP-HPLC, see HPLC, reversed-phase and chromatography, reverse phase RT-PCR . . . . . . . . . . . . . . . . . . . . . . . . 160–163, 167–168, 174 RTS . . . . . . . . . . . . . . . . . . . . . . . 347–348, 350–352, 354–355
S Salmonella enterica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13–25 Salt plugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43, 204 Salt washes . . . . . . . . . . . . . . . . . . . . . . . . . . 296, 302–303, 312 Sample complexity . . . . . . . . . . . . . . . . . . . . . . . 18, 50, 83, 138 Sample plate see MALDI sample plate Screening . . . . . . . . . . . 40, 99, 169–170, 175, 178, 185, 336, 365, 367, 370, 380 SDS-PAGE . . . . . . . 15, 17, 78–79, 192, 229, 236–238, 340 Sea anemone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Secondary antibodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Secretions . . . . . . . . . . . . . . 121, 125, 145–156, 178, 182–188 Sensitivity . . . . 4, 18, 42, 45, 50, 55, 57, 67, 71, 83–84, 101, 111, 198, 204, 256, 266, 270, 381 Separation, multidimensional . . . . . . . . . . . . . . . . . . . 248, 329
PEPTIDOMICS
394 Index
Sephadex . . . . . . . 77–78, 181, 183–185, 187–188, 294, 296, 298, 304, 318 Sepharose 4B . . . . . . . . . . . . . . . . . . . . . . . . 296, 301, 303–304 SepPak C18 . . . 37, 39–40, 45, 148–149, 150, 155, 194, 197 Sequence similarity . . . . . . . . . . . . . . . . . . . 357–358, 377–381 Serum . . . . . . . . . . . . . 4–5, 68, 203, 229, 235, 248–249, 254, 260–264, 267, 269–270, 297, 299–303, 317, 325, 327–328, 336, 339–340 Serum peptides, see Peptides, serum-derived Signal intensity . . . . . . . . . 55, 102, 106–107, 123, 132, 134, 138, 142 Signalling peptides, see Peptide signalling Signal-to-noise ratio . . . . 54–55, 71, 84, 106, 135, 152, 154 SignalP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360–363 Signal peptides . . . . . . . . . . . . . . . . . . . . . . . . . . . 362, 378, 381 Single cell analysis . . . . . . . . . . . . . . . . . . . . . . 51–52, 137–138 Single neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Sites, translation initiation . . . . . . . . . . . . . . . . . . . . . . . . . . 349 Size exclusion chromatography see Chromatography, size exclusion Skin . . . . . . . . . . . . . . . 52, 145–156, 159–175, 178, 182–188 Skin secretions . . . . . . . . . . . . . . . . . . . . . . . 145–155, 182–188 SOB medium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 SOC medium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164, 169 Sodium acetate . . . . 161, 166, 168–169, 174, 296, 303–304, 317, 324 Solid-phase extraction. . . . . . . 15, 18–19, 37, 39, 45, 51, 53, 194, 197, 203, 248–250, 253, 256 Solution, blocking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277, 284 Solution, denaturing . . . . . . . . . . . . . . . . . . 161, 166, 172–173 Sonication . . . . . . . . . . 37, 39, 112, 172–173, 194, 196, 203, 210, 234, 252, 254–255 Spectrum . . . . . . . . . . . . . . 18, 20, 24, 42, 102, 106–110, 122, 132–133, 143, 152–154, 198, 250, 263, 270, 368 Spectrum acquisition . . . . . . . . . . . . . 122–124, 133, 143, 152 Spiders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7, 78, 83 Spider toxins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75–76, 88 Spider venoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75–84, 87–99 See also Venom Spin columns. . . . . . . . . . .37, 40, 60, 65, 165, 170, 230, 240 Splicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50, 332 Spotting . . . . . . 112, 317, 323, 325–326, 337–339, 341, 349 SSC buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 Staphylococcus aureus . . . . . . . . . . . . . . . . . . . . . 89, 90, 171, 181 Stationary phase . . . . . . . . . . . . . . . . . . . . . . . . . . 15–16, 96, 98 Statistical analysis . . . . . . . . . . . . 21, 193, 202, 360, 370–371 Stem cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286, 288–289 Stimulated extraction . . . . . . . . . . . . . . . . . . . . . . . . . . 182–183 Stimulation electrical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Stimulation, mechanical . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Stomach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64, 320 Streptavidin . . . . . . . . . . . . . . . . . . . . . . . . . . 297, 305–306, 308 Streptomycin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203, 249 Striatum . . . . . . . . . . . . . . . . . . . . . . . . 193, 221, 223–224, 231 Sucrose . . . . . . . . . . . . . . 37, 39, 118, 229, 232–233, 244, 307 Sucrose gradient . . . . . . . . . . . . . . . . . . . . . . . . . . 232–233, 244 Sulfhydryl groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330, 341 Superdex . . . . . . . . . . . . . . . . . . . . . . . . 183, 277, 285, 309–310 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Surfactants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 SwePep database . . . . . . . . . . . . . . . . . 201–202, 225, 359–360 Swiss-Prot database . . . . . . . . . . . . . . 109–110, 222, 332, 372 Sylgard dish . . . . . . . . . . . . . . . . . . . . . . . 51–52, 119, 130, 138 Synapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227–245 Synaptic junctions . . . . . . . . . . . . . . . . . . . . . . . . 233–234, 244
Synaptosomes . . . . . . . . . . . . . . . . . . . . . . . . 228, 232–233, 244 Syntaxin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237–238
T T&T reaction . . . . . . . . . . . . . . . . . . . . . . . . 347–348, 352, 355 TAC4 . . . . . . . . . . . . . . . . . . . . . . 294–295, 297, 299, 302, 310 Tachykinin peptides . . . . . . . . . . . . . . . . . . . . . . . . . 7, 293–312 Tachykinins, see Tachykinin peptides TA-cloning . . . . . . . . . . . . . . . . . . . . . . . . . . 164–165, 169, 174 TAIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376–382 Target plate, see MALDI sample plate Taxonomic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146–147, 178 TBE buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164, 168 TBLASTN, see BLAST TBS buffer . . . . . . . . . . . . . . . . . 230, 238, 316, 321–322, 335 TBST buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 TE buffer . . . . . . . . . . . . . . . . . . 138, 161, 165, 167, 169, 316 TEMED . . . . . . . . . . . . . . . . . . . . . . . 229, 236, 318, 329, 340 Template . . . . . 165, 167–168, 281, 346–347, 349, 353–355 Tentacles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282, 289 Tetramethyl benzidine, see TMB TFA . . 15, 18, 21, 37–38, 40, 45, 51, 53–54, 67, 77, 80, 88, 103–105, 111, 125, 130, 139, 142, 148–149, 197, 209–211, 214, 243, 249–250, 253, 256, 280, 318 Thermal cycler . . . . . . . . . . . . . . . . . . 167–169, 171, 174, 181 Threshold . . . . . . . . . . . . . . . . . . 21, 46, 81, 84, 199, 201, 242 Throughput . . . . . . 4, 18, 21, 36, 40, 43, 208, 225, 285, 345 TIGR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Time-of-flights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 See also Mass spectrometry, time-of-flight Tissue analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103–104, 139 Tissue collection . . . . . . . . . . . . . . . . . . . . . 217–225, 228, 231 Tissue extracts . . . . . . . . . . . . . . . . . . . . 49, 105–106, 137, 225 Tissue regeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Tissue samples . . . . . . . . . 104, 129, 133, 135, 138, 223–225 Tissues, dried . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104, 122, 133 TMB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297, 305–306, 308 Toads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145, 182, 183, 186 TOF, see Mass spectrometry, time-of-flight TOF/TOF, see Mass spectrometry, tandem Toxins . . . . . . . . . . . . . . . . . . 4, 7, 75, 84, 88, 97, 99, 313, 359 Tracer antibody . . . . . . . . . . . . . . . . . . . . . . . . . . . 307–308, 310 Trachea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121, 125, 131, 140 Transcription . . 160, 163, 167–168, 291, 347–350, 359, 379 Transfer buffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229, 237 Translation . . 36, 50, 81, 110, 114, 118, 124, 126, 137, 199, 218, 224, 319, 332, 347, 349, 359, 369, 371–372, 375 Transmembrane regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Trifluoroacetic acid, see TFA Tris . . . . 15, 17, 77, 82, 89–90, 96, 161, 163–165, 183, 187, 229–230, 233–234, 236, 244, 277, 284, 294–296, 304, 316, 322, 325, 353 Triton X–100 . . . . . . . . . . 229, 233–234, 295–297, 306, 308 TRIzol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181, 185 Trypsin . . . . . . . . . . 15, 19–21, 24, 79, 89–90, 209, 213, 215, 230, 317, 325, 332, 336–337 Tryptic peptides . . . . . . . . . 20, 23, 25, 80–81, 243, 245, 319, 333–334 Tryptone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164, 316 TSK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88, 91, 209–210, 214 Tumour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320 See also Cancer Tween . . . . 20, 230, 238, 244–245, 316–318, 326, 349, 353
PEPTIDOMICS 395 Index Tyndall effect . . . . . . . . . . . . . . . . . . . . . . . . 125, 140–141, 143 Typhimurium, see Salmonella enterica
U Ultrafiltration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 UltraFlex . . . 45, 78, 98, 111, 119, 131, 139, 195, 199–200, 204, 251, 261–263, 266, 278 Ultrasonic . . . . . . . . . . . . . . . . . . . 37, 148, 151, 154, 194, 196 UniProt database. . . . . . . . . . . . . . . . . . . . . . . . . .315, 360–364 Urea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77, 230 UTR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160, 165–166 UV-absorbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41, 150–151 UV detection . . . . . . . . . . . . . . . . . . . . 113, 209–211, 334–335 UV source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316, 321
V Vacuum concentrator . . . . . . . . . . . 15, 17–18, 37, 39–40, 53, 79–80, 89, 92, 94, 98, 194, 309–310 Values, shift . . . . . . . . . . . . . . . . . . . . . . . . . . 365, 367, 370–371 Vasopressin peptide, see AVP VCAM peptides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Vector AccepTor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164, 169, 174 pSTBlue–1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164, 173 Venom components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92–93, 96 crude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91–92, 97 fractionation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88–89, 91
peptides, see Peptides, venom See also Toxins Venomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Vertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7, 101, 147, 379 Vesicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140–141, 218 4-vinylpyridine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 Visualisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200–201 Voltage, accelerating . . . . . . . . . . . . . . . . . . . . . . . . 80, 106, 152 Voyager . . . . . . . . . . . 106–108, 111, 119, 131, 139, 156, 182 4-VP, see 4-vinylpyridine Vydac . . . . . . . . . . . . . . . . . 78, 82, 89, 91, 148, 150–151, 155
W Western Blotting . . . . . . . . . . . . . . . . . . . . . . . . . 230, 237–238 Whatman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54, 220, 337 Worms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39, 44
X Xenopus laevis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 X-gal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316, 336
Y Yeasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7, 164, 181, 186, 316
Z ZipTip . . . . . . . . 59, 61, 65, 67–68, 71, 89, 94–95, 194, 203