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484. Functional Proteomics: Methods and Protocols,
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483. Recombinant Proteins From Plants: Methods and
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482. Stem Cells in Regenerative Medicine: Methods
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454. SARS- and Other Coronaviruses: Laboratory
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450. Germline Stem Cells, edited by Steven X. Hou and
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449. Mesenchymal Stem Cells: Methods and Protocols,
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448. Pharmacogenomics in Drug Discovery and
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447. Alcohol: Methods and Protocols, edited by
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446. Post-translational Modifications of Proteins: Tools
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445. Autophagosome and Phagosome, edited by Vojo
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M E T H O D S I N M O L E C U L A R B I O L O G YTM
Functional Proteomics
Methods and Protocols
Edited by
Julie D. Thompson
Christine Schaeffer-Reiss
Marius Ueffing

Editors
Julie D. Thompson Christine Schaeffer-Reiss
Laboratoire de Bioinformatique et LSMBO, ECPM
Génomique Intégratives Institut Pluridisciplinaire Hubert Curien
Institut de Génétique et Strasbourg, France
de Biologie Moléculaire et Cellulaire
Illkirch, France
Marius Ueffing
Department of Protein Science
Helmholtz Zentrum München
German Research Center for
Environmental Health
Munich-Neuherberg, Germany
Series Editor
John M. Walker
School of Life Sciences
University of Hertfordshire
Hatfield, Hertfordshire Al10 9 AB
UK
ISBN: 978-1-58829-971-0 e-ISBN: 978-1-59745-398-1
DOI: 10.1007/978-1-59745-398-1
Library of Congress Control Number: 2008921788
© 2008 Humana Press, a part of Springer Science+Business Media, LLC
All rights reserved. This work may not be translated or copied in whole or in part without the written
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Preface
Recent progress in experimental techniques has led to a revolutionary change
in life science research. High-throughput genome sequencing and assembly
techniques, together with new information resources, such as structural and
functional proteomics, transcriptome data from microarray analyses, or light
microscopy images of living cells, have led to a rapid increase in the amount of
data available, ranging from complete genome sequences to cellular, structure,
phenotype, and other types of biologically relevant information. As a conse-
quence, novel system-level studies are now being performed with the goal of
understanding and predicting the behavior of complex systems, such as cells,
tissues, organs, and even whole organisms. The field of proteomics plays an
essential role in this new systems approach to molecular and cellular studies by
identifying the genes involved and determining their functional significance; this
makes it possible to understand the complex functional networks and control
mechanisms that govern the system’s response to perturbations, such as environ-
mental changes or genetic mutations.
Research in the emerging field of proteomics is growing at an extremely rapid
rate. The real challenge is the relative quantification of proteins, targeted by
their function. Mass spectrometry-based strategies were developed to identify
modifications in the proteome profile in correlation with functional changes. In
practice, the task involves the identification of peptides in a peptide mixture of
extremely high complexity. This identification and relative quantification will
allow researchers to study changes in the level of expression, in the processing,
or in the post translational modifications of a set of proteins. Recent technical
innovations in mass spectrometry-based techniques have resulted in a range of
highly sensitive and versatile instruments for high-throughput, high-sensitivity,
proteome-scale profiling and the door is now open for a wide range of appli-
cations exploiting these approaches. But mass spectrometry is only one among
many other techniques that are part of an analytical strategy. These alternative
or complementary technologies include two-dimensional gel electrophoresis,
protein microarrays, yeast two-hybrid systems, phage display, and immunopre-
cipitation. However, there is no one technology of choice and the most appro-
priate method will depend on the size and the nature of the system being studied
and the type of results desired. The principal aim of this volume is to describe
the latest protocols being developed to address the problems encountered in
high-throughput proteomics projects, with emphasis on the factors governing the
technical choices for a given application. The volume is aimed at researchers
v

vi Preface
working in the field of proteomics including chemical engineers, analytical
chemists, biochemists, cell and molecular biologists, clinical scientists, and
bioinformaticians, as well as those who are contemplating using proteomics for
functional studies.
In functional proteomics, successful characterization of proteins from mass
spectrometry experimental data will depend on the technological choices made
during the three main phases of the study:
1. The strategy used for the selection, purification, and preparation of the sample to
be analyzed by mass spectrometry.
2. The type of mass spectrometer used and the type of data to be obtained from it.
3. The method used for the interpretation of the mass spectrometry data and the search
engine used for the identification of the proteins in the different types of sequence
data banks available.
The mass spectrometry part itself is often seen as the most important one
because it corresponds to the largest budget. It is also time consuming, being
very complex and highly technical. Nevertheless, the sample preparation and
the data analysis steps are equally important, if not more important, for the
success of a proteomic experiment. Therefore, in this volume, the case studies
presented will always insist on the three aspects of the experimental design.
In the initial chapters, different mass spectrometry instrumentation will be
introduced in the context of various applications, from the study of large
multiple protein complexes to complete organism proteomics. The advantages
and the best use of the following types of instruments will be discussed:
MALDI-TOF for simple mass finger printing protein identifications as well
as MALDI-TOF-TOF, LC-MALDI-TOF-TOF, and LC-ESI-MS-MS (at low,
average, and high resolution), detailing the characteristics and capabilities of
the different types of mass spectrometers in term of sensitivity, resolution,
accuracy, and MS-MS. Metabolomic studies, which are also experimentally
based on mass spectrometry, will also be presented, since metabolomic changes
obviously reveal functional changes. The following chapters describe the use of
mass spectrometry for the detection of protein–protein specific interactions and
posttranslational modifications.
High-throughput proteomics studies generate huge volumes of data, including
gel images, mass spectrometry spectra, and protein identifications. These data
have to be collected, stored, organized, and interpreted if they are to be used
effectively. Bioinformatics plays an important role by providing common data
representation standards to enable the comparison and transfer of information
between different systems and laboratories. The last chapters of this volume are
therefore dedicated to the most widely used database resources, as well as the
new computational techniques being developed to search and analyze proteomic
data. Finally, emerging computational systems biology methods are described

Preface vii
for the integration of data from multiple sources, in order to model complex
structures such as protein networks or regulatory pathways and their response to
external perturbations.
Julie D. Thompson
Marius Ueffing

Contents
Preface .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Part I: Introduction
1. A Brief Summary of the Different Types of Mass Spectrometers
Used in Proteomics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Experimental Setups and Considerations to Study Microbial
Interactions .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Petter Melin
Part II: Proteomics
3. Plant Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Eric Sarnighausen and Ralf Reski
4. Methods for Human CD8+
T Lymphocyte Proteome Analysis . . . . 45
Lynne Thadikkaran, Nathalie Rufer, Corinne Benay,
David Crettaz, and Jean-Daniel Tissot
5. Label-Free Proteomics of Serum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Natalia Govorukhina, Peter Horvatovich,
and Rainer Bischoff
6. Flow Cytometric Analysis of Cell Membrane Microparticles . . . . . 79
Monique P. Gelderman and Jan Simak
Part III: Protein Expression Profiling
7. Exosomes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Joost P. J. J. Hegmans, Peter J. Gerber,
and Bart N. Lambrecht
8. Toward a Full Characterization of the Human 20S Proteasome
Subunits and Their Isoforms by a Combination of Proteomic
Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Sandrine Uttenweiler-Joseph, Stéphane Claverol,
Loïk Sylvius, Marie-Pierre Bousquet-Dubouch,
Odile Burlet-Schiltz, and Bernard Monsarrat
ix

x Contents
9. Free-Flow Electrophoresis of the Human Urinary Proteome . . . . . . 131
Mikkel Nissum and Robert Wildgruber
10. Versatile Screening for Binary Protein–Protein Interactions
by Yeast Two-Hybrid Mating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Stef J. F. Letteboer and Ronald Roepman
11. Native Fractionation: Isolation of Native Membrane-Bound
Protein Complexes from Porcine Rod Outer Segments Using
Isopycnic Density Gradient Centrifugation . . . . . . . . . . . . . . . . . . . 161
Magdalena Swiatek-de Lange, Bernd Müller,
and Marius Ueffing
12. Mapping of Signaling Pathways by Functional
Interaction Proteomics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Alex von Kriegsheim, Christian Preisinger,
and Walter Kolch
13. Selection of Recombinant Antibodies by Eukaryotic
Ribosome Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
Mingyue He and Michael J. Taussig
14. Production of Protein Arrays by Cell-Free Systems. . . . . . . . . . . . . . . 207
Mingyue He and Michael J. Taussig
15. Nondenaturing Mass Spectrometry to Study Noncovalent
Protein/Protein and Protein/Ligand Complexes: Technical
Aspects and Application to the Determination of Binding
Stoichiometries.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Sarah Sanglier, Cédric Atmanene,
Guillaume Chevreux, and Alain Van Dorsselaer
16. Protein Processing Characterized by a Gel-Free
Proteomics Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
Petra Van Damme, Francis Impens,
Joël Vandekerckhove, and Kris Gevaert
17. Identification and Characterization of N-Glycosylated Proteins
Using Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
David S. Selby, Martin R. Larsen,
Cosima Damiana Calvano, and Ole Nørregaard Jensen
Part IV: Protein Analysis
18. Data Standards and Controlled Vocabularies for Proteomics . . . . . 279
Lennart Martens, Luisa Montecchi Palazzi,
and Henning Hermjakob
19. The PRIDE Proteomics Identifications Database: Data
Submission, Query, and Dataset Comparison. . . . . . . . . . . . . . . . . 287
Philip Jones and Richard Côté

Contents xi
20. Searching the Protein Interaction Space Through
the MINT Database.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
Andrew Chatr-aryamontri, Andreas Zanzoni,
Arnaud Ceol, and Gianni Cesareni
21. PepSeeker: Mining Information from Proteomic Data .. . . . . . . . . . . 319
Jennifer A. Siepen, Julian N. Selley, and Simon J. Hubbard
22. Toward High-Throughput and Reliable Peptide Identification
via MS/MS Spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
Jian Liu
23. MassSorter: Peptide Mass Fingerprinting Data Analysis . . . . . . . . . . 345
Ingvar Eidhammer, Harald Barsnes, and Svein-Ole Mikalsen
24. Database Similarity Searches .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361
Frédéric Plewniak
25. Protein Multiple Sequence Alignment. . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Chuong B. Do and Kazutaka Katoh
26. Discovering Biomedical Knowledge from the Literature . . . . . . . . . 415
Jasmin Šarić, Henriette Engelken, and Uwe Reyle
27. Protein Subcellular Localization Prediction Using Artificial
Intelligence Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435
Rajesh Nair and Burkhard Rost
28. Protein Functional Annotation by Homology . . . . . . . . . . . . . . . . . . . . 465
Raja Mazumder, Sona Vasudevan,
and Anastasia N. Nikolskaya
29. Designability and Disease .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491
Philip Wong and Dmitrij Frishman
30. Prism: Protein–Protein Interaction Prediction by Structural
Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505
Ozlem Keskin, Ruth Nussinov, and Attila Gursoy
31. Prediction of Protein Interaction Based on Similarity
of Phylogenetic Trees.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523
Florencio Pazos, David Juan, Jose M. G. Izarzugaza,
Eduardo Leon, and Alfonso Valencia
32. Large Multiprotein Structures Modeling
and Simulation: The Need for Mesoscopic Models. . . . . . . . . . . . 537
Antoine Coulon, Guillaume Beslon, and Olivier Gandrillon
33. Dynamic Pathway Modeling of Signal Transduction
Networks: A Domain-Oriented Approach . . . . . . . . . . . . . . . . . . . . 559
Holger Conzelmann and Ernst-Dieter Gilles
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579

Contributors
CÉDRIC ATMANENE • Laboratoire de Spectrométrie de Masse
Bio-Organique, Institut Pluridisciplinaire Hubert Curien, UMR 7178 CNRS /
Université Louis Pasteur, Strasbourg, France
HARALD BARSNES • Department of informatics, University of Bergen,
Bergen, Norway
CORINNE BENAY • Service Régional Vaudois de Transfusion Sanguine,
Lausanne, Switzerland
GUILLAUME BESLON • Laboratoire d’InfoRmatique en Images et Systèmes
d’information (LIRIS, UMR CNRS 5205), INSA-Lyon, Villeurbanne, France
RAINER BISCHOFF • University of Groningen, Centre of Pharmacy,
Analytical Biochemistry, Antonius, Groningen, The Netherlands
MARIE-PIERRE BOUSQUET-DUBOUCH • Institut de Pharmacologie et de
Biologie Structurale, UMR 5089, CNRS/Université Paul Sabatier, Toulouse,
France
ODILE BURLET-SCHILTZ • Institut de Pharmacologie et de Biologie
Structurale, UMR 5089, CNRS/Université Paul Sabatier, Toulouse, France
COSIMA DAMIANA CALVANO • Protein Research Group, Department of
Biochemistry and Molecular Biology, University of Southern Denmark,
Odense M, Denmark
ANDREW CHATR-ARYAMONTRI • Department of Biology, University of
Rome “Tor Vergata,” Rome, Italy
ARNAUD CEOL • Department of Biology, University of Rome “Tor Vergata,”
Rome, Italy
GIANNI CESARENI • Department of Biology, University of Rome “Tor
Vergata,” Rome, Italy
GUILLAUME CHEVREUX • Laboratoire de Spectrométrie de Masse
STÉPHANE CLAVEROL • Pole protéomique, Plateforme Génomique
Fonctionelle, Université V. Ségalen Bordeaux, Bordeaux, France
HOLGER CONZELMANN • Max Planck Institute for Dynamics of Complex
Technical Systems, Magdeburg, Germany
RICHARD CÔTÉ • EMBL-European Bioinformatics Institute, Wellcome Trust
Genome Campus, Hinxton, Cambridge, UK
xiii

xiv Contributors
ANTOINE COULON • Université de Lyon, Lyon, France; Université Lyon,
Lyon, France; Centre de Génétique Moléculaire et Cellulaire – UMR CNRS
5534, Villeurbanne, France
DAVID CRETTAZ • Service Régional Vaudois de Transfusion Sanguine,
CHUONG B. DO • Computer Science Department, Stanford University,
Stanford, CA, USA
INGVAR EIDHAMMER • Department of informatics, University of Bergen,
Bergen, Norway
HENRIETTE ENGELKEN • EML Research gGmbH, Heidelberg, Germany
DMITRIJ FRISHMAN • Institute for Bioinformatics, GSF-National Research
Center for Environment and Health, Neuherberg, Germany; Department
of Genome Oriented Bioinformatics, Technische Universität Munchen,
Freising, Germany
OLIVIER GANDRILLON • Université de Lyon, Lyon, France; Université
Lyon, Lyon, France; Centre de Génétique Moléculaire et Cellulaire – UMR
CNRS 5534, Villeurbanne, France
MONIQUE P. GELDERMAN • Laboratory of Cellular Hematology, CBER,
FDA, Rockville, MD, USA
PETER J. GERBER • Department of Pulmonary Medicine, Erasmus Medical
Centre, Rotterdam, The Netherlands
KRIS GEVAERT • Ghent University, Ghent, Belgium
ERNST-DIETER GILLES • Max Planck Institute for Dynamics of Complex
Technical Systems, Magdeburg, Germany
NATALIA GOVORUKHINA • University of Groningen, Centre of Pharmacy,
ATTILA GURSOY • Koc University, Center for Computational Biology and
Bioinformatics and College of Engineering, Istanbul, Turkey
MINGYUE HE • Technology Research Group, The Babraham Institute,
Cambridge, UK
JOOST P.J.J. HEGMANS • Department of Pulmonary Medicine, Erasmus
Medical Centre, Rotterdam, The Netherlands
HENNING HERMJAKOB • European Bioinformatics Institute, Wellcome Trust
PETER HORVATOVICH • University of Groningen, Centre of Pharmacy,
SIMON J HUBBARD • Michael Smith Building, Faculty of Life Sciences, The
University of Manchester, Manchester, UK
FRANCIS IMPENS • Ghent University, Ghent, Belgium
JOSE M. G. IZARZUGAZA • Structural Computational Biology Programme,
Spanish National Cancer Research Centre (CNIO), Madrid, Spain

Contributors xv
OLE NØRREGAARD JENSEN • Protein Research Group, Department of
Biochemistry and Molecular Biology, University of Southern Denmark,
Odense M, Denmark
PHILIP JONES • EMBL-European Bioinformatics Institute, Wellcome Trust
DAVID JUAN • Structural Computational Biology Programme, Spanish
National Cancer Research Centre (CNIO), Madrid, Spain
KAZUTAKA KATOH • Digital Medicine Initiative, Kyushu University,
Fukuoka, Japan
OZLEM KESKIN • Koc University, Center for Computational Biology and
Bioinformatics and College of Engineering, Istanbul, Turkey
WALTER KOLCH • Cancer Research Beatson Laboratories, Glasgow, UK
BART N. LAMBRECHT • Department of Pulmonary Medicine, Erasmus
Medical Centre, Rotterdam, The Netherlands
MARTIN R. LARSEN • Protein Research Group, Department of Biochemistry
and Molecular Biology, University of Southern Denmark, Odense M,
Denmark
EDUARDO LEON • Structural Computational Biology Programme, Spanish
National Cancer Research Centre (CNIO), Madrid, Spain
STEF J. F. LETTEBOER • Department of Human Genetics, Nijmegen Centre
for Molecular Life Sciences, Radboud University Nijmegen Medical Centre,
Nijmegen, The Netherlands
JIAN LIU • Center for Cellular and Biomolecular Research, University of
Toronto, Toronto, Ontario, Canada
LENNART MARTENS • European Bioinformatics Institute, Wellcome Trust
RAJA MAZUMDER • Protein Information Resource, Georgetown University
Medical Center, Washington, DC, USA
PETTER MELIN • Department of Microbiology, Swedish University of
Agricultural Sciences, Uppsala, Sweden
SVEIN-OLE MIKALSEN • Institute for Cancer Research,
Rikshospitalet-Radiumhospitalet University Hospital, Montebello, Oslo,
Norway
BERNARD MONSARRAT • Institut de Pharmacologie et de Biologie
Structurale, UMR 5089, CNRS/Université Paul Sabatier, Toulouse, France
LUISA MONTECCHI PALAZZI • European Bioinformatics Institute,
Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
BERND MÜLLER • Department I Biologie, Ludwig Maximilian University
Munich, Munich, Germany

xvi Contributors
RAJESH NAIR • CUBIC, Department of Biochemistry and Molecular
Biophysics and Center for Computational Biology and Bioinformatics,
Columbia University, New York, NY, USA
ANASTASIA N. NIKOLSKAYA • Protein Information Resource, Georgetown
University Medical Center, Washington, DC, USA
MIKKEL NISSUM • BD Diagnostics, Martinsried, Germany
RUTH NUSSINOV • Basic Research Program, SAIC-Frederick, Inc. Center for
Cancer Research Nanobiology Program NCI-Frederick, Frederick, MD,
USA; Sackler Institute of Molecular Medicine, Department of Human
Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv
University, Tel Aviv, Israel
FLORENCIO PAZOS • Computational Systems Biology Group, National
Centre for Biotechnology (CNB-CSIC), Madrid, Spain
FRÉDÉRIC PLEWNIAK • Plate-forme Bio-informatique de Strasbourg,
Institut de Génétique et de Biologie Moléculaire et Cellulaire, UMR 7104 –
CNRS – Inserm – ULP, Illkirch, France
CHRISTIAN PREISINGER • Cancer Research Beatson Laboratories,
Glasgow, UK
RALF RESKI • Plant Biotechnology, Faculty of Biology, University of
Freiburg, Freiburg, Germany
UWE REYLE • Institute for Computational Linguistics, University of Stuttgart,
Stuttgart, Germany
RONALD ROEPMAN • Department of Human Genetics, Nijmegen Centre for
Molecular Life Sciences, Radboud University Nijmegen Medical Centre,
Nijmegen, The Netherlands
BURKHARD ROST • CUBIC, Department of Biochemistry and Molecular
Biophysics, Columbia University and Center for Computational Biology and
Bioinformatics, Columbia University, New York, NY, USA
NATHALIE RUFER • NCCR Molecular Oncology; Swiss Institute for
Experimental Cancer Research (ISREC), Epalinges,
Switzerland
SARAH SANGLIER • Laboratoire de Spectrométrie de Masse Bio-Organique,
Institut Pluridisciplinaire Hubert Curien, UMR 7178 CNRS / Université
Louis Pasteur, Strasbourg, France
ERIC SARNIGHAUSEN • Plant Biotechnology, Faculty of Biology, University
of Freiburg, Freiburg, Germany
JASMIN ŠARIĆ • Boehringer Ingelheim Pharma GmbH & Co., Biberach,
Germany
CHRISTINE SCHAEFFER-REISS • Laboratoire de Spectrométrie de Masse

Contributors xvii
DAVID S. SELBY • Protein Research Group, Department of Biochemistry and
Molecular Biology, University of Southern Denmark, Odense M, Denmark
JULIAN N SELLEY • Michael Smith Building, Faculty of Life Sciences, The
JENNIFER A SIEPEN • Michael Smith Building, Faculty of Life Sciences, The
JAN SIMAK • Laboratory of Cellular Hematology, CBER, FDA, Rockville,
MD, USA
MAGDALENA SWIATEK-DE LANGE • Boehringer Ingelheim Pharma GmbH
& Co., Biberach an der Riss, Germany
LOÏK SYLVIUS • Plate-forme protéomique IFR-100, Etablissement Français
du Sang, Dijon, France
MICHAEL J TAUSSIG • Technology Research Group, The Babraham Institute,
Cambridge, UK
LYNNE THADIKKARAN • Service Régional Vaudois de Transfusion Sanguine,
JEAN-DANIEL TISSOT • Service Régional Vaudois de Transfusion Sanguine,
JULIE D. THOMPSON • Institut de Génétique et de Biologie, Moléculaire et
Cellulaire, Illkirch, France
MARIUS UEFFING • Institute of Human Genetics, GSF National-Research
Center for Environment and Health, Neuherberg, Germany
SANDRINE UTTENWEILER-JOSEPH • Institut de Pharmacologie et de
Biologie Structurale, UMR 5089, Centre National de la Recherche
Scientifique/Université Paul Sabatier, Toulouse, France
SONA VASUDEVAN • Protein Information Resource, Georgetown University
Medical Center, Washington, DC, USA
ALFONSO VALENCIA • Structural Computational Biology Programme,
Spanish National Cancer Research Centre (CNIO), C/ Melchor Fernandez
Almagro, Madrid, Spain
PETRA VAN DAMME • Ghent University, Ghent, Belgium
JOËL VANDEKERCKHOVE • Ghent University, Ghent, Belgium
ALAIN VAN DORSSELAER • Laboratoire de Spectrométrie de Masse
ALEX VON KRIEGSHEIM • Cancer Research Beatson Laboratories,
Glasgow, UK
ROBERT WILDGRUBER • BD Diagnostics, Martinsried, Germany
PHILIP WONG • Institute for Bioinformatics, GSF-National Research Center
for Environment and Health, Neuherberg, Germany
ANDREAS ZANZONI • Department of Biology, University of Rome “Tor
Vergata,” Rome, Italy

1
A Brief Summary of the Different Types of Mass
Spectrometers Used in Proteomics
Summary
Recent technical innovations in mass spectrometry-based techniques have resulted in
a range of highly sensitive and versatile instruments for high-throughput, high-sensitive,
proteome-scale profiling. This wide diversity of instrumentation commercially available
for mass spectrometry-based proteomics makes the choice of instrumentation sometimes
difficult. The choice of instruments depends on the biological problem and the proteomic
strategy chosen for protein identification. This chapter will give a short overview of the
instruments routinely used in proteomic laboratories and the technical criteria that should
be considered before instrument selection.
Key Words: Mass spectrometry instrumentation.
1. Introduction: The Special Role of Mass Spectrometry in Proteomics
The goal of proteomics is to identify, characterize, and quantify the whole
content of proteins that are present in complex biological materials (tissues,
cells in culture, organelles, or fluids). For the past decade, the interest for
proteomic studies kept growing exponentially and today, proteomic has reached
high-throughput analysis capabilities. This is the result of two major advances:
(1) the progress in mass spectrometry (MS) makes possible routine analysis
of peptides and proteins with improved sensitivity, reliability, speed, and
automation, and (2) the large scale genome sequence programs of the past 10
years provided large protein sequence databases for many organisms which are
essential to identify quickly proteins from MS data. As a result, MS has become
From: Methods in Molecular Biology, vol. 484: Functional Proteomics: Methods and Protocols
Edited by: J. D. Thompson et al., DOI: 10.1007/978-1-59745-398-1, © Humana Press, Totowa, NJ
3

4 Schaeffer-Reiss
a pillar analytical method in proteomic studies for the identification and charac-
terization of the proteins present in complex biological systems. A wide panel
of instrumental solutions is now available from several manufacturers and the
choice of the appropriate instrumentation can really be puzzling. This chapter
will give an overview of the instruments routinely used in proteomic laboratories
and the technical criteria that should be considered before instrument selection.
2. General Features and Key Characteristics of Mass Spectrometers
2.1. A Wide Variety of Mass Spectrometers with Very Different
Technical Solutions
A broad range of mass spectrometers is used in MS-based proteomic research.
Each type of instrument has unique design, data system, and performance speci-
fications, resulting in strengths and weaknesses depending on the types of exper-
iments.
Mass spectrometry is a two-step method: first, the analyte is volatilized and
ionized, while keeping intact its integrity, and second, the measurement of
the mass-to-charge ratio (m/z) of the ionized analyte is obtained. The mass
spectrometer is usually made of two distinct parts: the source, where the
volatilization/ionization step is performed, and the analyzer/detector, where
the ions are separated and the m/z ratio is measured by a physical device
(Fig. 1).
The “heart” of the mass spectrometer is the analyzer. Several analyzers can
be combined to perform “two-dimensional” MS. The analyzer separates the
Fig. 1. Simplified configuration of a mass spectrometer. The kinetic energy driving
the ions from the source to the analyzer is very different depending on the type of source
and analyzer.

Different Types of Mass Spectrometers Used in Proteomics 5
gas phase ions. The analyzer uses electrical or magnetic fields, or a combination
of both, to move and select the ions from the source to the detector. Because the
motion and separation of ions is based on electrical and/or magnetic fields, the
m/z ratio, and not only the mass, is of importance. The analyzer must be operated
under high vacuum, such that ions can travel without colliding with neutral gas
atoms and reach the detector with a sufficient yield.
In proteomic analysis, it is important to choose the right source-analyzer
association, and also the most adapted combination of analyzers in the case
of “two-dimensional” MS. The best mass spectrometer configuration depends
on the analytical strategy that will be used for protein identification. The most
popular strategies are summarized in the following chapters.
2.2. Key Characteristics of Instruments
For proteomic studies, the key mass spectrometer characteristics that must
be considered are (1) mass resolution (or resolving power), (2) mass accuracy,
(3) sensitivity, and (4) ability to perform MS/MS. The resolving power (R)
measures the ability of the instrument to distinguish between two ions of close
masses: if M is the mass of one ion and ⌬M the difference between the two
ion masses, then R is defined by the ratio M/⌬M. Mass accuracy describes
how closely experimental (or measured) mass (Mexp) matches theoretical (or
expected) mass (Mth). The mass accuracy is usually given in parts-per-million
(ppm): 106
× (Mth – Mexp)/Mexp. Mass accuracy is directly linked to the resolving
power. A low-resolution mass spectrometer cannot provide high accuracy.
In addition, several other specifications are important such as the possibility
for automation allowing high-throughput analysis and the scan speed of the
analyzer. Obviously, it is necessary to keep in mind that resolution, accuracy,
scan speed and sensitivity are linked in some ways.
3. Three Main Protein Identification Strategies in Proteomics
The classical strategies for protein identification consist in digesting proteins
into peptides that are subsequently analyzed by MS. These strategies are
described in detail in a variety of papers (1–7). Three main methodologies are
routinely used for protein identification: peptide mass fingerprinting (PMF),
peptide fragment fingerprinting (PFF), and de novo sequencing. All these
methods use proteolytic enzymes (typically trypsin) to specifically cleave
proteins into peptides with a mass suitable for MS and/or MS/MS analysis.

6 Schaeffer-Reiss
3.1. The Peptide Mass Fingerprinting (PMF) Strategy
In the case of PMF (8), the m/z ratio of each peptide obtained after enzymatic
digestion of a protein is measured with the highest possible accuracy. The
measured masses are then compared with the theoretical masses of all the
peptides, which has been obtained after in silico proteolytic digestion of a
selected protein database (calculated fingerprints). The degree of confidence in
protein identification with this approach will strongly depend on the tight corre-
lation between measured and theoretical masses. Therefore, the most important
specification of the instrument best suited for that approach is the accuracy of
mass measurement.
3.2. The Peptide Fragment Fingerprinting (PFF) Strategy
In the PFF approach, peptides are fragmented using a “two-dimensional”
mass spectrometer (MS/MS). Intact peptide ions are selected by a first analyzer
(MS1) and then dissociated by collisions, usually by passing through a neutral
gas (collision-induced dissociation, CID). This results in the fragmentation of
the parent peptide, which occurs at specific bonds of the polypeptide backbone.
Figure 2 presents the six most usual fragmentations obtained in those condi-
tions and the specific nomenclature of each fragment (9). Charged fragments are
then separated in a second analyzer (MS2) yielding to a fragmentation finger-
print (Fig. 3). Fragment masses obtained experimentally are compared with the
theoretical masses of all the fragments, which has been obtained after in silico
proteolytic digestion and fragmentation of a selected protein database (calcu-
lated fingerprints) (10–12). The complexity of the digestion peptide mixture will
be important for the choice of the instrument and its tuning. Samples of reduced
complexity are obtained when slices cut from one- or two-dimensional polyacry-
lamide gels are digested. When the total protein extract from the biological
sample is digested and directly analyzed by MS (for example, in shotgun
proteomics) (13,14), the peptide mixture is extremely complex and scanning
parameters will have to be optimized. In this approach, the specifications of the
Fig. 2. Nomenclature of the various fragments expected from peptide dissociation (9).

Fig. 3. Most popular analyzer configurations for “two-dimensional” mass spectro-
metry. Q-TOF and TOF-TOF are real tandem instruments. Ion trap and FT-ICR are using
the same analyzer for MS1 and MS2. The Orbitrap is more complex since it is always
hyphenated with an ion trap as first analyzer (see text). For simplicity, however, Orbitrap
has been compared to IT and FT-ICR.
best suited mass spectrometer must include (1) a collision cell generating a large
number of ionized fragments and (2) high accuracy of mass measurements.
These two first strategies require that the exact sequences of the studied
proteins are present in the protein databases and require specialized search
engines (Mascot, Sequest).
3.3. De Novo Strategy
If the protein database for the studied organism does not contain enough
information for the comparison of fragmentation fingerprints, an alternative
consists in using the so-called de novo sequencing approach. In this case,
sequence information is deduced directly from the experimental MS/MS
spectra by manual or automatic interpretation of the data. When a sequence
of a few amino acids is obtained from an MS/MS spectrum, it can be
used in a classical BLAST search to identify the protein(s) (15). For
this strategy, the same instrument specifications as the ones for PFF are
required, but the highest possible accuracy in MS2 mass measurements is
needed.

8 Schaeffer-Reiss
3.4. Guidelines for Protein Identification by Mass Spectrometry
The three approaches described above allow the identification of proteins, but
do not lead to their full characterization, for example in terms of posttranslational
modifications. It was previously pointed out that a high number of false protein
identifications was observed when experiments used instruments with inade-
quate performances or when the search criteria in the protein databases were
not stringent enough. Unfortunately, this tendency will keep increasing with the
number of protein sequences present in databases, making protein identification
based on experimental versus calculated “fingerprints” less and less reliable.
A series of guidelines for the identification of proteins in proteomic studies
have been proposed (16,17). Accordingly the most reliable identification of a
protein is now obtained using MS/MS strategies. These guidelines helps to select
accuracy of mass measurement needed, which depends on the appropriate choice
of the MS instrument. Very high resolution instruments still make PMF useful
provided the high-resolution mass spectrometer is properly used (18).
4. Ionization Methods
Matrix-assisted laser desorption ionization (MALDI) and electrospray
ionization (ESI) are the two techniques most commonly used to volatize and
ionize peptides and proteins in MS analysis (19,20). Both display femto-
molar sensitivity when used in optimal conditions. MALDI is performed on a
condensed phase. ESI works on a liquid phase thus allowing an easy coupling
with high-performance liquid chromatography (HPLC), which is not the case for
MALDI. For peptides and proteins, the charge is generally due to the addition
of a variable number of protons. However, the ions observed with MALDI
are typically only single charged while ESI adds multiple protons to the basic
residues generating multiply charged molecules. In theory all types of analyzers
can be adapted to both ionization sources.
4.1. MALDI
The sample is mixed with a saturated solution of matrix (an organic
compound with a strong absorption at the laser wavelength) and a microliter
drop is laid on the MALDI target (19). After solvent evaporation and matrix
crystallization, the target is positioned in the mass spectrometer source under
vacuum and irradiated with pulses of laser light. Once in the vapor phase, proton
transfer between matrix and analytes occurs, resulting in ion formation. Ions are
subsequently accelerated by applying a high potential (∼20 kV) to a series of
extraction electrodes and lenses (Fig. 1).

4.2. ESI
The sample in solution is infused through a silica capillary (spray capillary)
with a typical flow rate between 1 and 100 ␮L per minute. An electrical field,
applied at the extremity of the pneumatically assisted spray capillary, imparts
charges to the spray droplets (20). ESI is made at atmospheric pressure. Ions
are subsequently transferred in the vacuum of the analyzer after transitioning
through the interface, where they are accelerated and desolvated. An ESI source
can be readily coupled to liquid-based separation tools (chromatographic or
electrophoretic devices). Miniaturization of liquid chromatography (nano-LC)
with columns of 50–100 ␮m internal diameter allows routine subpicomole
sensitivities because a high concentration of analytes in the eluted chromato-
graphic peaks is obtained. On line separation prior to MS analysis is an obvious
advantage for ESI which is used mainly in the LC-ESI-MS/MS mode (21).
In the case of very complex mixtures, initial separation of individual peptides
is a strong advantage since “ion suppression” will be mostly avoided. Ion
suppression corresponds to the effect of highly ionizable peptides that suppress
the signal from less ionizable peptides.
5. Five Types of Analyzers Classically Used
The combination of ESI or MALDI with several types of mass analyzers
provides a wide variety of specialized mass spectrometers. Five types of
analyzers are currently used in proteomics: quadrupole (Q), ion trap (IT), time-
of-flight (TOF), Fourier transform ion-cyclotron resonance (FT-ICR or FT-
MS), and Orbitrap (OT). Analyzers are selected as a function of the analytical
problems and, obviously, their prices. The choice of a mass spectrometer will
strongly depend on the strategy preferred for protein identification and on the
biological question. Once these are clearly defined, the key characteristics and
performances of the instrument should be considered.
Quadrupoles and TOF are only able to perform “one-dimension” MS analysis.
Ion trap and FT-ICR can be used in MS and MS/MS analysis, since the same
analyzer is used sequentially as MS1 and MS2. Q-TOF and TOF-TOF are hybrid
instruments which are composed of two individual instruments in tandem.
The case of the OT is distinct since the available instrument commercialized
by Thermo Fisher Scientific is always hyphenated with an ion trap as a first
analyzer. Figure 4 summarizes the most popular source-analyzer configurations
routinely used in proteomic laboratories.
The following chapters will briefly present these five types of analyzers. The
principle of these techniques is comprehensively described in various reviews
and books (22,23).

10 Schaeffer-Reiss
Fig. 4. Most popular source-analyzer configurations routinely used for proteomics.
In proteomic studies, ESI-TOF is not used very often. “Off line” experiments coupling
HPLC with MALDI are not mentioned, but they are feasible and can be as powerful
as LC-ESI-MS/MS experiments when performed properly. Early on, triple quadrupoles
(Q-Q-Q) were widely used despite poor resolution. Currently other instruments are
better suited for proteomics.
5.1. Principle of the TOF Analyzer
Ions are maintained in a space as small as possible before being pushed with
the same kinetic energy (20–30 kV) through the analyzer (a tube of about 1 m)
toward the detector. Since the ions enter the TOF at the same time and with
the same kinetic energy, they will reach the detector with speeds directly corre-
lated to their m/z ratio. An accurate measurement of the time ions need to travel
from the source to the detector allows the ion m/z ratio to be determined. The
resolution is usually increased when using a reflectron, which has an effect of
energy focalization (24,25). TOF analyzers typically reach a resolution of about
20,000 and allow routine accuracy of ± 10–50 ppm.
5.2. Principle of the Quadrupole and Ion Trap Analyzers
These instruments use electrostatic fields to force ions to oscillate in a very
complex way. For quadrupole and ion trap analyzers, the equation of Matthieu
describes the movements of the ions and the basis for selecting m/z values to
allow specific ions to reach the detector and to generate a spectrum (26–28).
Quadrupoles are typically used as a first analyzer (MS1) in MS/MS instruments
because their resolution is good enough for molecular ion selection, but too weak
to provide an accuracy compatible with PMF identifications. The ion trap-based
instruments provide MS/MS capabilities. They are used in PFF identification
strategies and sometimes in MSn
analysis of modified peptides (PTM).
5.3. Principle of the FT-ICR
The basic principle of the FT-ICR is to measure ion cyclotronic frequency
in a magnetic field, which allows ion mass to be calculated. For this, a pulsed

radiofrequency signal is used to excite the ions while they are orbiting. Excited
ions generate signals that are processed by a Fourier transform (FT) to obtain the
component frequency of the different ions, which correspond to their m/z ratio.
Because ion frequency can be measured with high accuracy, their corresponding
m/z ratio is also calculated with high accuracy (29). One major drawback of
these instruments is their high cost, which is partly due to the supramagnetic
field required to induce ion circular motion. However, FT-ICR instruments have
the highest resolution capabilities.
5.4. Principle of the Orbitrap
This analyzer has some similarities to the FT-ICR, except that it uses complex
electrostatic fields instead of a magnetic field (30). An OT analyzer provides
routine resolution of about 60,000 and an accuracy of less than 2 ppm (using
internal standard) (31). OT-based instruments are less expensive than FT-ICR
instruments, their running cost is lower, and they are operated more easily. So
far, an OT analyzer is used exclusively to measure with high resolution and
accuracy the parent ions and the fragment ions selected by an ion trap (MS1).
The commercially available OT is therefore always an MS/MS instrument; it
is characterized by an excellent versatility, high sensitivity, and high routine
resolving power (32).
5.5. Analyzers Used in PMF Identification
MALDI-TOF is the most widely used instrument for PMF identification
in proteomic laboratories because it is easy to operate and very robust. The
mass accuracy of the MALDI-TOF is usually between 10 and 50 ppm (with a
resolution of about 15,000), which is enough to allow routine identification of
most proteins.
PMF analysis using MALDI-TOF is still widespread in many laboratories,
although the guidelines published by several journals (16,17) pointed out the
lack of specificity of this technology for protein identification. Its use should be
restricted to relatively simple peptide mixtures.
FT-ICR is also used for PMF identification in a nano-LC-MS mode (33).
The resolution of the FT-ICR allows an accuracy of about 1 ppm in routine
proteomic analysis. The dynamic range of the FT-ICR is also much higher and
low abundant peptides can be detected. FT-ICR analyzers display overall the
best performances for proteomic analysis. However, the complexity in operating
this system, the price of the machine, and its running cost must be seriously
considered before opting for that instrument.

12 Schaeffer-Reiss
The OT with its high routine resolution also seems well adapted for PMF
identification. The OT-based instrument is always hyphenated with an ion trap
as MS1. This type of instrument can perform PFF identification at any time.
5.6. MS/MS Analyzers Used in PFF Identification
Classical peptide sequencing (PFF approach) by “two-dimensional” mass
spectrometry mainly uses automated instruments including Q-TOF, IT and OT,
TOF-TOF, and seldom FT-ICR (Fig. 3). MS/MS instruments offer additional
possibilities and give access to sophisticated experiments for the characterization
of peptide families (phosphopeptides, peptide glycosylation, etc.).
To improve peptide sequencing, fragmentation techniques alternative to
classical CID have been developed: electron capture dissociation (ECD) and
electron transfer dissociation (ETD).
The advantage of ECD and ETD is to generate fragments that are evenly
distributed along the peptide backbone. In contrast, CID-induced fragments are
usually restricted to a more limited number of cleavage points in the peptide
and, therefore, yield less sequence information. This is a major advantage
for the study of PTMs. Indeed, the combination of CID and ECD fragmen-
tation methods (34) can be used, for example, to localize PTM on the peptide
backbone. However, ECD is not compatible with ion traps or Q-TOF and is
limited to FT-ICR instruments.
Electron transfer dissociation (ETD) is compatible with instruments that
utilize RF fields to trap ions (35–37). Peptide fragmentation is achieved through
gas-phase electron transfer from singly charged anions to multiply protonated
peptides and yields fragments that are complementary to the classical CID
method.
ETD and ECD are complementary to CID in the determination of sequence
information by peptide fragmentation (38). There is no doubt that many MS/MS
instruments will soon complement CID with ETD or ECD.
6. The Importance of Chromatography for Sensitivity
In the past few years, the miniaturization of chromatography has been a
major innovation to improve the sensitivity of LC-ESI-MS/MS analysis. Nano-
LC chromatographic separations are performed on a nanoscale column (75 ␮m
inner diameter) using flow rates in the nanoliter per minute range. This results
in high analytical sensitivity due to substantial concentration efficiency of the
eluted sample.
The need for increased sensitivity, robustness, and high throughput has
led to the recent introduction of nano-HPLC-Chip systems from Agilent

Technologies. The nano-HPLC-Chip system (39,40) consists of a device that
integrates on a single chip: an enrichment column, an analytical column, and
the electrospray nozzle. By minimizing the number of connections and dead
volumes, the chip offers better chromatographic performances in terms of repro-
ducibility, peak resolution, sensitivity, and spray stability, compared to classical
nanocolumns of 75 ␮m inner diameter. Enhanced sensitivity provided by this
system will be particularly interesting for the identification of rare proteins and
biomarkers.
It should be mentioned also that “off line” LC-MALDI-TOF-TOF can be
readily performed using micro- or nanocollectors, which in some cases may be
an interesting alternative to nano-ESI-LC-MS/MS (41).
7. Conclusions
A wide diversity of instrumentation is commercially available for MS-based
proteomics. Instrumentation will probably become more sophisticated in the
next years; however, the criteria for selecting the appropriate instrumentation
will still depend on the experimental strategy that has been decided to answer
the question(s) of the biologist.
Before electing an instrument, the following parameters must be considered:
the resolving power, the mass accuracy, the sensitivity, the possibility for “two-
dimensional” MS, the dynamic range, the time required for one analysis, the
automation possibility, the reliability, the complexity in operating the system,
Fig. 5. Relative comparison of the resolution, accuracy, sensitivity, and dynamic
range of the most popular instrument used in proteomic studies.

14 Schaeffer-Reiss
and, obviously, the price (Fig. 5). The biological problem (material availability,
complexity, etc.) and the protein identification approach will decide which of
these characteristics are the most important, allowing the appropriate system to
be selected accordingly.
It would be misleading to think that only one type of instrument is always the
best choice for a specific question. Indeed, the price of the instrument, its running
cost, the ease of use, and the robustness have to be evaluated individually in
each laboratory that wants to perform proteomic studies. Specialized proteomic
platforms may offer interesting options for specific biological questions, which
include (1) a combination of MALDI-TOF and nano-LC-ESI-IT, or (2) a combi-
nation of nano-LC with Q-TOF or OT.
Finally, looking at the equipment in laboratories specialized in proteomic
studies, it is evident that several technical solutions are often needed.
Additionally, the training of the scientists performing the experiments is crucial
for the success of proteomic research programs. This training must include the
correct operation of the instrument(s) and interpretation of MS data as well as
and most importantly, the thorough preparation of the biological samples.
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2
Experimental Setups and Considerations to Study
Microbial Interactions
Petter Melin
Summary
Within ecosystems microorganisms coexist and interact. Knowledge of these
interactions is of great importance in the fields of ecology, food production, and medicine.
Such interactions often involve the synthesis of antibiotic secondary metabolites. Different
kinds of s molecules or direct contacts are other forms of microbial interactions. Recently,
modern molecular methods such as microarrays and proteomics have been employed to
investigate such interactions. In this chapter, the use of proteomics for studies of microbial
interactions is discussed. The choice of experimental setup is dependent on the aims of
the specific study. One aspect of competition between microbes can be simulated by
treatment of one microbe with antibiotics produced by a competing microbe. A more
complicated approach involves cocultivation of the competitors, but in order to reveal
species-specific protein patterns it is advisable to keep the organisms separated. Alternative
techniques are to monitor alterations in the proteomes between the wild-type and mutant
strains. The mutant can be either natural or created using random or targeted mutage-
nesis. Generally, a proteomic study will reveal proteins with both expected and surprising
changes in abundance upon competition, but also previously unknown proteins are likely
to be identified. A proteomic approach is usually insufficient to obtain a complete data
set describing microbial interactions. Therefore, it is essential to follow up identification
of proteins with changed abundance by, e.g., the creation of knockout strains for pheno-
typic analyses. Despite the limitations, proteomics is a useful method, and an important
complement to other approaches for studies of microbial interactions.
Key Words: Proteomics; proteome analysis; interactions; microorganisms; fungi;
yeasts; bacteria; antibiotics; secondary metabolites.
17

18 Melin
1. Introduction
In most ecosystems various microorganisms occupy the same habitat and
coexist. Microbial interactions differ and can, for example, be mutual, parasitic,
and competitive. These events can be studied at different levels, ranging from
the whole ecosystem to the gene expression in a single organism. At the
ecosystem level, the main concern is to describe variations in the surrounding
environment and the content of species present. During the past decade, a
very large number of ecological studies have, besides classical methods, been
performed using various aspects of the polymerase chain reaction (1). These
studies have been aimed at describing discrete microbial communities and
monitoring changes in gene expression at the population level. In contrast, only
a limited number of studies have been aimed at the responses on the level of
protein synthesis. Moreover, most of the protein studies in the area have had a
medical rather than an ecological point of view. However, interesting general
data concerning microbial interactions can be obtained from these medical
studies. Likewise, more general studies of microbial stress responses may be
of great interest in medicine, e.g., to elucidate responses to antibiotics. In this
chapter, I intend to describe the potential and problems of using proteomics
to study responses when different microorganisms interact. It is likely that the
protein synthesis in a single microbe will adapt to a competitive environment.
These changes in the complement of proteins present in an organism can be
assessed by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE).
The term proteomics is very wide and can be used in all sorts of protein
biology (2), but for simplicity I decided to restrict the term proteomics to the
comparison of different protein patterns from a specific organism exposed to
different environments. Identified proteins can have an altered abundance due
to the interaction. Alternatively, the protein is modified resulting in a different
migration on the gel.
2. Why Study Microbial Interactions?
2.1. Antibiotic Secondary Metabolites
Almost all antibiotics used today are of microbial origin. In medicine we
experience an increasing problem with pathogenic microbes that becomes
resistant to the most commonly used antibiotics (3,4). Thus there is an
urgent need to develop new antimicrobial drugs. To use them in a safe way,
we have to understand both their mode of action and the pathways and
probabilities for development of resistance. Most studies concerning the compe-
tition between different microbes have aimed at elucidating the synthesis to
antibiotic secondary metabolites, or to reveal the effect on target organisms

Experimental Setups to Study Microbial Interactions 19
when encountering these metabolites. The predominant hypothesis is that these
secondary metabolites are synthesized to give the producing organism a compet-
itive advantage by killing or inhibiting growth of other microbes (5). According
to that proposal, the biosynthetic genes for a specific antibiotic are usually
located in the same gene cluster as the corresponding resistance genes, thus
relating synthesis of the antibiotic to competitive advantage (6). Alternative
hypotheses regarding the origin of secondary metabolites have been proposed,
e.g., the reduction of abnormally high concentrations of intermediate metabolites
during growth arrest. One argument states that the concentrations of secondary
metabolites in the field are not high enough to stop growth of other microbes
(7). However, it has been shown that an organism can change the expression
of several genes after encountering only subinhibitory concentrations of several
different antibiotics (8).
2.2. Human Health
Bacteria can be both good and bad, and within our bodies we have a
large bacterial flora that protects us from infection from pathogenic fungi and
bacteria. Bacterial populations play a role in a large number of fungal diseases,
e.g., by Candida albicans or Cryptococcus neoformans. The bacterium can be
coinfecting our bodies or play an important role in the defense (9). Also, the
consumption of probiotics, in general strains from the genus Lactobacillus, can
be a way to protect us from hostile bacteria (10).
2.3. Microorganisms in Food and Feed
Fungal infection of crops intended for food and feed is a serious agricultural
problem. Much effort is going on to replace or decrease the use of fungicides
by fungal antagonistic microbes, e.g., Pseudomonas species (11), or by several
strains within the filamentous fungi genus Trichoderma (12). When food and
feed are stored, some microbes such as lactic acid bacteria (13), and the yeasts
Candida sake (14) and Pichia anomala (15) can be used to protect the food from
toxic fungi such as Aspergillus, Botrytis, and Penicillium. Here it is essential not
only to decrease fungal growth, but also to know if the production/accumulation
of toxic compounds produced is decreased. Some food products actually consist
of several microbes, e.g., tempeh, which is a cake of soy beans (or other legumes
or cereals), and the fungus Rhizopus oligosporus as well as nonpathogenic
bacteria (16).
2.4. Microbial Interactions in Fundamental Ecology
In times with rising threats and an increased concern about the environment
it is important to understand how organisms interact within the ecosystems.

20 Melin
Although microbes are small in size, they are present in abundance, are
ubiquitous, and play decisive roles in all aspects of ecology. Fungi together with
algae or cyanobacteria can live in mutual dependence and form a unique group
of symbiotic organisms, the lichens. Fungi and plants can form mycorrhiza; the
fungus increases the effective root surface of the plants and facilitates uptake
of nutrients. In return, the plant provides the fungus with carbohydrates. It is
known that bacteria also have a role in this symbiosis (17). Since formation of
mycorrhiza is crucial for normal growth of many plants, knowledge of the nature
of this symbiosis, including all the organisms involved, is not only interesting
but also of great economic importance.
3. Materials
3.1. Simple Systems
In my opinion, the most important concern when studying microbial inter-
actions at the laboratory scale is the choice of a system that faithfully mimics
the situation of interest. This is independent of the techniques and is relevant
regardless of whether the studies are aimed at the proteome, the transcriptome,
or the metabolome. The simplest microbial interaction is when only one species
is involved. This phenomenon has been observed among bacteria and it is
called quorum sensing (18), and to my knowledge one such proteomics study
has been published (19). To simplify a microbial interaction consisting of
two different species, one of the organisms can be replaced by one or more
important metabolites produced by that strain. For example, if a researcher
wants to elucidate effects on the protein complement when a microbe is
subjected to one specific hostile antibiotic, the target organism can be culti-
vated in the presence and absence of the antibiotic. This kind of proteomic setup
has been used to study antibiotic resistance in the pathogenic gram-positive
bacterium Staphylococcus aureus (20). Moreover, in medical mycology this
experimental approach has been widely used to investigate several antifungals
with the potential to replace amphotericin B, which is nephrotoxic for humans
(21). For example, the responses to the antibiotic mulundocandin have been
monitored in the human pathogenic yeast C. albicans (22). Grinyer and co-
workers performed an interesting alternative approach in the area of biocontrol.
They studied changes in the proteome of the biocontrol filamentous fungus
Trichoderma atroviride. Prior to protein extraction they grew the Trichoderma
strain with cell wall material from the plant pathogenic fungus Rhizoctonia
solani as carbon sources compared to glucose in the control. In the study,
several cell wall degrading enzymes likely to play a role in the biocontrol were
identified (23).

3.2. Coculturing the Microorganisms
Replacing one interacting microbe with one or several of its metabolites is
not always doable. If growth of all the involved microbes is essential, it is
practical to keep the organisms separated, e.g., have a membrane that physi-
cally separates the organisms but allows metabolites to pass. We successfully
used that technique when we cocultured the fungus Aspergillus nidulans with
an antifungal strain of Lactobacillus plantarum (24). Growing the organisms
together, coextracting the materials from both organisms, and running the
proteins from two or more proteomes on a single gel may be achievable, but it
will complicate subsequent experiments, e.g., when identifying the proteins of
interest. A potential problem when evaluating the results from a proteomic study
from cocultured microorganisms is that not only changes in protein abundances
due to metabolites but also responses to the nutritional competition will be
monitored.
3.3. Comparing Different Strains
Besides coculturing or replacing a microbe with metabolites, there are several
other approaches that can be suitable for proteomic studies of microbial inter-
actions. If the specific target for an antibiotic is known, it is possible to disrupt
the gene encoding the target for the antibiotic and then monitor changes in
the proteome compared to the wild-type strain. Also, proteomics can be used
to characterize mutants with a specific phenotype. For example, this approach
was performed to investigate the proteome in a hygromycin-resistant strain
of C. albicans (25). Moreover, the proteomes of different strains of the same
bacteria can be studied, e.g., to find proteins that are unique or absent in strains
that are resistant to a specific antibiotic. This approach has been widely used
in studies of bacterial proteomes, e.g., in Lactobacillus sanfranciscensis (26),
S. aureus (27), and Streptococcus pneumonia (28).
3.4. Experimental Design
All the analytical approaches listed above can and have been used in combi-
nation in order to understand the proteomic changes in a microorganism.
For example, Yun et al. investigated the proteome of tetracycline treated
Pseudomonas putida, and to understand the antibiobic-induced stress they used
a strain that could tolerate high levels of tetracycline but did not carry resis-
tance genes (29). With multiple experiments and combining several different
approaches on the same system it should be possible to discriminate responses to
a specific antibiotic from the more complicated scenario in cocultures, or more
so in complex small ecosystems. This approach was successful in our study

22 Melin
when we cocultured A. nidulans with L. plantarum, we also grown the fungus
with each of the known the bacterial metabolites (24).
4. Methods
4.1. Preparation and Separation of the Protein Extract
The main limitation of proteomics is that, on each gel, only a fraction of
the proteins will be displayed, i.e., the prominent and successfully extracted
proteins, within the experimental parameters. However, more proteins could
be made detectable if the parameters are slightly altered. Thus, it is always
possible to change the pI intervals in the first dimension and the polyacry-
lamide concentration in the second. In addition, the method for protein extraction
can be adjusted. Another way to improve resolution is to start by separating
a specific organelle and then separating its protein components by 2D-PAGE.
Accordingly, both cell wall (30), plasma membrane (31), and mitochondrial (32)
proteins from S. cerevisiae have been successfully analyzed on 2D-PAGE. If the
number of different proteins is reduced in a preparation, even proteins present in
minor quantities can be displayed on the gel by increasing the amount of loaded
proteins. Moreover, the field of proteomics is expanding rapidly, and technical
improvements will further facilitate extraction, separation, and visualization of
proteins (33). It is possible that in the future all proteins in the proteome could be
analyzed using 2D-PAGE, although a large number of gels need to be analyzed.
The sensitivity of protein detection can also be improved by testing different
staining methods. In my experience, working with parallel silver-stained gels
and radiolabeled proteins, the latter provided the best resolution and the highest
reproducibility. Another advantage of using radiolabeled amino acids is the
ability to distinguish between short-term and long-term effects on the proteome.
With this approach, only proteins that were synthesized after a specific time
point will be visualized using autoradiography. In our experiments we studied
proteomic responses in A. nidulans when it encountered concanamycin, an
inhibitor of V-ATPases produced by Streptomyces sp. (34). To achieve a suffi-
cient amount of tissue for protein extraction, we have to preinoculate the fungus
before adding the antibiotic. By simultaneously adding labeled amino acids
only proteins synthesized after addition of the antibiotic were monitored on
2D-PAGE (35).
4.2. Choices of Microorganisms
Naturally, the use of proteomics alone does not provide comprehensive infor-
mation about how microbes interact in ecosystems. It is convenient to work
with an organism with an available fully sequenced genome. In addition, it is an

advantage if the genome is annotated and all hypothetical proteins are deduced.
The identification of full-length protein sequences, by blasting the sequences
to known protein databases, using only mass spectrometric data is problematic
and time consuming. Without a sequenced genome, or a great number of known
expressed sequence tags (EST) from a specific microbe, I would not recommend
performing proteomics on that organism. Anyhow, if a close relative organism
is sequenced, a correct identification of the proteins may be successful. In
contrast, different strains of the same bacterial species may be very different and
proteins identified by 2D-PAGE may not be fully deduced by blasting identified
peptides toward the genome. The same problem can occur if the coverage of the
sequence genome is low because parts of the genome are not sequenced. When
we performed our first proteomic study using the model fungus A. nidulans (34),
the genome was sequenced only with a 3× coverage; thus the full sequence
of one identified protein could only be partially deduced and the sequence of
one other protein could not be deduced at all. Another obstacle was that several
peptides (identified with mass spectrometry) were located on different exons
making the full detection of the complete protein and DNA sequences very time
consuming.
4.3. How to Interpret the Results?
Most proteomic reports describe up- or downregulation of proteins due to
a specific environmental change, e.g., a microbial interaction. Usually, several
of these proteins are already identified in previous studies. However, there is
often no logical explanation as to why these proteins should be involved in
the actual response. It is obvious that the mechanisms behind protein synthesis
are complicated events, and it is often impossible to predict secondary effects
that alter the synthesis of a specific protein. Additional experiments are often
required to provide answers. To learn more about an unknown protein, the most
straightforward approach is to disrupt the encoding gene and investigate pheno-
typical consequences. Repeating the proteomic approach using the mutant strain
is one method to study the new phenotype. Since additional studies are required
to understand observed changes in the proteomic pattern, I would recommend,
in addition to a complete genomic sequence, using a model organism with
developed molecular techniques, including a functional transformation system.
4.4. Comparison with Transcriptomics
In principal, the system designed for studying responses in the proteome,
using proteomics, can also be used to study gene expression, i.e., transcrip-
tomics. The observed changes in the proteome are the result of the interaction,
but since only the most abundant proteins will be displayed it is likely that

24 Melin
minor proteins, being very important in the response to other microorganisms,
may not be monitored. In this respect monitoring the transcriptome, e.g.,
with microarrays, is a more suitable approach. The important difference in
favor of proteomics is due to stability. Proteins tend to be stable whereas
mRNAs are relatively short-lived molecules. Therefore, short-term changes in
the expression/synthesis are probably most conveniently studied at the mRNA
level. On the other hand, since regulation often also occurs at posttranscriptional
levels, mRNA levels may be misleading, and a determination of the final gene
product, the protein, may be more instructive for general metabolic potential.
5. Conclusions
In this chapter I have summarized the use of proteomics to study microbial
interactions. Although proteomics is a comparatively new approach in functional
biology, it has been proven useful when elucidating molecular responses
in microorganisms upon microbial interactions. There are, however, several
inherent limitations with the technique. One fundamental problem with
proteomics is the choice of a system that faithfully mimics the interaction of
choice. However, this problem is encountered in any microbial study at the
laboratory scale. Another aspect more specifically connected to proteomics is
that the microbe may not change its protein production during competition to
detectable levels. For example, the molecular response to an antibiotic may be
extreme during laboratory conditions, but, in the field, the concentrations of
antibiotic secondary metabolites may not be high enough to cause the same
changes in protein synthesis. Despite these limitations I think the proteomic
approach in ecological studies is a useful complement to other techniques,
although the potential of proteomics is probably greater in medicine. The
knowledge of responses at the protein level to antibiotics is important in under-
standing the full mode of action as well as secondary responses in both the target
microbe and in the host.
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31. Navarre, C., Degand, H., Bennett, K. L., Crawford, J. S., Mortz, E., and Boutry, M.
(2002) Subproteomics: identification of plasma membrane proteins from the yeast
Saccharomyces cerevisiae. Proteomics 2, 1706–1714.
32. Zischka, H., Weber, G., Weber, P. J. A., Posch, A., Braun, R. J., Buhringer, D.,
Schneider, U., Nissum, M., Meitinger, T., Ueffing, M., and Eckerskorn, C. (2003)
Improved proteome analysis of Saccharomyces cerevisiae mitochondria by free-
flow electrophoresis. Proteomics 3, 906–916.
33. Harry, J. L., Wilkins, M. R., Herbert, B. R., Packer, N. H., Gooley, A. A., and
Williams, K. L. (2000) Proteomics: Capacity versus utility. Electrophoresis 21,
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34. Bowman, E. J., Siebers, A., and Altendorf, K. (1988) Bafilomycins: a class of
inhibitors of membrane ATPases from microorganisms, animal cells, and plant cells.
Proc. Natl. Acad. Sci. USA 85, 7972–7976.
35. Melin, P., Schnürer, J., and Wagner, E. G. H. (2002) Proteome analysis of
Aspergillus nidulans reveals proteins associated with the response to the antibiotic
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695–702.

3
Plant Proteomics
Eric Sarnighausen and Ralf Reski
Summary
An understanding of gene function requires a complementation of gene and gene
expression analysis by the systematic analysis of proteins. Progress in plant proteomics has
been lagging behind animal and microbial proteomics due to the lack of plant genome data
and the problems involved in successful protein extraction from plant material. With the
sequencing of more and more plant genomes, this slow progress will soon be overcome. The
moss Physcomitrella patens is a model organism in the field of plant functional genomics.
P. patens is the first seedless plant for which the complete genome was sequenced. Genome
annotation is currently in progress. While identification of proteins requires knowledge of
all coding genes of the organism under study, gene annotation and functional characteri-
zation benefit greatly from the findings of proteome analysis. The proteome of P. patens is
accessible and approaches are under way to increase the spectrum of proteomic methods
applied to this plant. Here we provide a protocol for the extraction of proteins from P.
patens and describe the basic and still most important method of proteome analysis, two-
dimensional polyacrylamide electrophoresis of proteins. As this technique (not entirely
unjustifiably) has the reputation of being unpredictably complicated, we provide a detailed
protocol intended to reduce the reluctance that many scientists may have in using this
technique.
Key Words: Plant proteomics; Physcomitrella patens; protein extraction;
two-dimensional electrophoresis; isoelectric focusing; SDS–PAGE.
1. Introduction
Progress in the field of plant proteomics has always lagged behind research
in the animal or microbial field (1). There are numerous reasons for this.
Compared with multicellular organisms, proteomes of unicellular prokaryotes
29

30 Sarnighausen and Reski
and eukaryotes are of reduced complexity and therefore more easily acces-
sible; at the same time these were the first organisms for which the genome
sequences were available. Furthermore, there is hardly any material that is more
reluctant to proteome analysis than plant tissue. The presence of a rigid cell
wall, which is often enforced through deposition of strengthening substances,
like lignin (wood), suberin (cork), or inorganic salts (calcification), can render
tissue disruption problematic. Compared to animal tissue, protein content in
most parts of the plant is rather low. On the other hand, plants contain a multitude
of substances that interfere strongly with a successful protein extraction process;
foremost among these are phenolic compounds, organic acids, and proteases—
compounds that tend to modify, inactivate, precipitate, aggregate, or degrade
proteins in crude extracts. Consequently, special techniques are required to
disrupt the cell walls and to protect proteins from damaging components released
on breakage. A direct single-step extraction of proteins, which is a general
procedure when working with bacteria (2), yeast, or animal tissue (3), is
therefore hardly ever the best choice for workers in the plant field (4). The
ultimate goal is to separate the total proteome from substances that interfere with
proteome analysis while at the same time avoiding quantitative or qualitative
modification of the proteome during this process. As protein extraction proce-
dures can hardly be automated, plant proteomics requires extensive processing
at a step that is considered most critical for the generation of reproducible
results. Protein purification procedures, required for the analysis of the plant
proteome, will inevitably be selective for certain proteins and will at the same
time discriminate others (5). Among the most commonly used plant protein
extraction procedures are acetone/trichloroacetic acid (TCA) precipitation (6),
phenolic extraction (7), and extraction of soluble proteins in combination with
acetone or TCA precipitation (8). While all these procedures can render high
quality separations of proteins on two-dimensional gels, protein spot patterns
obtained from the same tissues display considerable variations if extraction
methods are varied (9,10). Another problem researchers in plant proteomics have
to face is the unequal distribution of the concentration of distinct protein species
among the plant proteome. Proteins related to the photosynthetic apparatus can
represent far more than 50% of the total protein mass in plants and will always
dominate in the separation patterns while low abundant proteins are likely to
escape detection (5).
The moss Physcomitrella patens (Fig. 1A) has emerged as a model organism
in the field of functional genome analysis. P. patens is unique among land
plants as its nuclear genes can be directly targeted due to highly efficient
homologous recombination (11). In reverse genetics approaches, a gene of
interest is disrupted and the resulting phenotypical aberrations subsequently
allow conclusions to be drawn on the function of the gene (12). Due to its

Plant Proteomics 31
Fig. 1. Proteome analysis of Physcomitrella patens. (A) The moss P. patens is
a model organism in plant functional genomics. (Courtesy of Dr. Julia Schulte.)
(B) Proteins of P. patens were extracted with acetone/TCA and were subsequently
separated via isoelectric focusing in the first dimension and via SDS–PAGE in the second
dimension. (Courtesy of Anika Erxleben.)
outstanding features as a model organism (13), P. patens has been chosen as
the first seedless plant to have its full genome sequenced (http://www.jgi.doe.
gov/sequencing/why/CSP2005/physcomitrella.html). Knowledge of all coding
genes now adds additional weight to proteome analysis as a tool of functional
genomics in P. patens. Complementation of phenotypical analysis by differ-
ential or functional proteomics studies allows for the elucidation of regulatory
networks and a precise classification of gene functions in the context of complex
living systems.
From the repertoire of proteomic techniques used in our laboratory, this
chapter will focus on those methods of classical proteome analysis that will
most likely describe the most accessible approach for researchers interested
in the field. Plant protein extraction by acetone/TCA precipitation is straight-
forward, fast, and simple and yields samples of high purity. However, it
should be mentioned that sometimes (depending on the source tissue) the price
that needs to be paid for this degree of purity is reduced extractability, not
only of impurities but also of proteins (14). We describe a two-dimensional
(IEF/SDS–PAGE) electrophoresis system routinely used in our laboratory. The
high separation power of this system lies in the combination of two independent
protein separation techniques. Isoelectric focusing (IEF) as the first dimension
separates the proteins according to their intrinsic charge (their isoelectric points).

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The Project Gutenberg eBook of Tatlings

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Title: Tatlings
Author: Sydney Tremayne
Author of introduction, etc.: Fowl
Illustrator: Anne Harriet Fish
Release date: August 3, 2019 [eBook #60046]
Most recently updated: October 17, 2024
Language: English
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*** START OF THE PROJECT GUTENBERG EBOOK TATLINGS ***

Transcriber Notes
Obvious typos corrected.
Sydney Tremayne was the pseudonym of Sybil
Taylor Cookson, journalist and writer, according
to Wikipedia.

TATLINGS
by Sydney Tremayne
The Drawings
by Fish

TATLINGS
Epigrams
by Sydney Tremayne
The Drawings
by Fish
NEW YORK
E. P. Dutton and Company
1922

INTRODUCTION
H E R E I N T H E F O R T U N AT E R E A D E R S W I L L F I N D
T H E H A P P Y C O N J U N C T I O N of two very brilliant young
people, whose literary and artistic talents fit like the proverbial glove,
or the musical and lyrical alliance of those immortals, Gilbert and
Sullivan.

Never were epigrams more worthily illustrated, or more worthy of
illustration. The joie de vivre, the humour and the human
observation which run through this little volume, will I am sure make
a great appeal to the public possessing or admiring those qualities.
I am proud to think that I was responsible for the journalistic débuts
of both authors, whose work enriched the pages of The Tatler for
some years, and that I have been honoured in being asked to write
an introduction to their first collective effort.
E . H U S K I N S O N
Editor of The Tatler

ILLUSTRATIONS
Frontispiece
Most women if they had
to choose would ask for
a clear complexion in
preference to a clear
conscience
page
29
Men do not try to
escape temptations;
their only fear is that
some temptation should
escape them
pages
46-7
You can never forget a
sin you have confessed
page
63
Most women live for the
present, and the
handsomer the present
the better they live
page
71
Men always say that
they loathe being
flattered, but don’t take
any notice—no man has
ever known that he was
flattered
page
74
Letters that should
never have been written
page
78

and ought immediately
to be destroyed are the
only ones worth keeping
The husband who
counts is the one who
has something to count
page
83
When you see an old
man alone you are
looking at something
very sad. When you see
an old man with a
young woman you are
looking at something
rich
page
92
What a woman wears
reveals more than she
says
page
99

T
I
N
T
N
I
TATLINGS
H E L O O K I N G - G L A S S
reveals us as we are to
ourselves; the Wine-glass reveals
us as we are to others.
F A M A N puts a woman on a
pedestal someone else will help
her down.
O M A N gets what he wants,
though some may get what
they have wanted.
H E R E A S O N that a love
affair so seldom ends happily
is that one of the lovers is
generally unwilling for it to end at
all.
O O N E agrees with other
people’s opinions, they merely
agree with their own opinions
expressed by somebody else.

I
A
S
I
Y
S
T
A
T I S a poor doctor who cannot
prescribe an expensive cure for
a rich patient.
W O M A N alone is not
necessarily a temptation, if
she were a temptation she would
probably not be alone.
O M E people succeed in
preserving a youthful
appearance, but they show their
age in their opinions.
F Y O U G I V E a woman an
opportunity, she will take
everything else that she wants.
O U A R E much nearer success
when you are deplored than
when you are ignored.
O M A N Y young women have
glibly promised their lovers
that they would ‘never change’
and have been unrecognisable ten
years later.
O A W O M A N women are a
sex and men an individual.

A
I
A
A
O
S
O
W O M A N likes to know what
the man she loves was like
when he was a little boy; but a
man would rather know what the
woman he loves will be like when
she is an old woman.
T I S P R O B A B L E that if a
woman cannot see the point of
her husband’s jokes she will see
very little indeed of him.
W O M A N may have a small
mouth and yet be able to open
it very wide.
G I R L W H O spends her youth
learning philosophy will almost
certainly need it when her youth
is spent.
N E M A N ’ S love is often only
the bait with which another
man is caught.
O M E P E O P L E contrive to
make their ‘silent suffering’
simply deafening.
N E C A N forgive a person
lying about one and possibly
disprove them, but it is

W
I
N
I
A
T
A
unforgiveable if they tell the truth;
that is taking a mean advantage.
O M E N have been the same
through all the ages: the
only difference between a girl and
her mother is their feeling for her
father.
T I S difficult for a man to
understand that a woman who
would go through hell for love of
him is capable of leaving him
because he clears his throat or
uses a toothpick.
O T H I N G unites people like a
common sorrow, except,
perhaps, a vulgar joke.
F A P R E T T Y back view won’t
let you catch it up it has
probably got a horrible face.
S S O O N as a woman has put
a man in her power she puts
him out of her heart.
H E O N LY blows Fate seems
to deal some people are slaps
on the back.

A
S
A
I
W
A
A
W O M A N ’ S clothes should be
like an epigram, an adequate
expression of an idea without a
superfluous—syllable.
O M E M E N borrow a fiver and
behave for ever after as if the
only thing they owed you was a
grudge.
W O M A N I S not really
adequately clothed because
she is draped in mystery.
T I S inexplicable, but
undeniable, that a man often
prefers the woman he has to
make excuses for to the woman
he has to make excuses to.
H AT a woman costs and
what she is worth are two
entirely different things.
M B I T I O N S vary: Men may
want to do well, women may
want to look well, but the old only
want to sleep well.
W O M A N cares most for a
man when their love affair is
over, a man cares most for a

E
A
A
S
I
O
woman before their love affair has
begun.
V E R Y O N E likes to be run
after, but the difference
between men and women is that
men do not want to be caught
and women do.
W O M A N who can bear to
hear her husband praise
another woman is either different
to other wives or indifferent to her
husband.
M A N ’ S ‘for ever’ is just about
as long as a woman’s ‘five
minutes.’
O M E P E O P L E drain the cup
of life, and others stick to a
medicine glass.
T TA K E S a clever man to write
a good love letter, but only a
fool would do it.
D D LY enough the impression
made by the possession of
several different names is not
nearly so favourable as the
impression made by the

T
H
A
T
M
M
A
T
possession of several different
addresses.
H E M E A N S to an end may
put an end to one’s means.
E W H O C A N does, he who
can’t is shocked.
R O M A N C E is wonderful while
it lasts, but if it lasts it ceases
to be a romance.
O B E successful in love one
must know how to begin and
when to stop.
A N Y A M A N has ended by
running away with a woman
because he had not the sense to
begin by running away from her.
A N Y A N impecunious stylist
has found that a girl is more
easily won by an ordinary bank-
note than an extraordinary love
note.
N I N F A L L I B L E way of
acquiring a host of friends is to
be a host yourself.

T
I
W
I
M
W
A
T
H E R E A R E three stages in a
man’s infatuation for a
woman: making his way, having
his way, and going his way.
T I S T H E M A N who has no
right who generally comforts
the woman who has wrongs.
O M E N who are the easiest
to win are always the most
difficult to lose.
T I S perfectly saintly to love
some women; and that
presumably is sacred love. It is
perfectly natural to adore others;
and that probably is profane love.
A N Y A W O M A N ’ S
undoing is due to her maid.
H E N A M A N is lost to one
woman it is generally
because he has been found by
another.
M A N M A Y B E legally
attached to one woman and
yet sincerely attached to another.

T
I
B
T
I
A
I
O
O I N D U L G E in independent
ways one really needs to have
independent means.
T I S no use collecting notable
acquaintances unless you can
be sure that they will recollect
you.
Y A L L M E A N S tell a woman
you love her, but don’t tell her
anything else.
H AT A M A N and woman are
always together proves
nothing—but it is probably true.
F A W O M A N goes too far
with a man, she comes back
alone.
P R E T T Y woman in a
becoming gown is a
temptation—men love
temptations.
F Y O U C A N N O T be funny
without being shocking, it is
better to be shocking.

O
N
T
I
W
G
A
T
F C O U R S E it is quite
dreadful to lead another into
mischief, but it is almost
impossible to enjoy oneself alone.
O T H I N G is more infuriating
than to be accused of doing
something which one has taken
every precaution to keep secret.
H E W O M E N who have
nothing to show are the ones
who have nothing to hide.
F O N E lives long enough one is
bound to become respectable
and virtuous—hallowed by time.
O M E N are always asking
questions and men are
always inventing answers—and
women are none the wiser.
O O D N E S S is only a relative
term, and one that is always
on the tongue of relatives.
W O M A N ’ S accounts of how
she spent ‘the house money’
are only equalled in inventive
genius by a man’s accounts of
how he spent his time.

T
O
L
E
A
E
A
S
H E R E A R E two sorts of
lovers—those who forget and
those who are forgotten.
N E S O O N gets tired of
saying a thing over and over
again if nobody contradicts, just
as one soon gets tired of doing a
thing over again if no one says
one mayn’t.
O V E I S N I C E when it is
new, but it wears badly and is
impossible to renovate.
V E N T H E M O S T upright
man may be tempted by a
recumbent woman.
W O M A N may have no
reticence about her ankle or
even her knee if it is pretty, but
she will never show her hand.
V E R Y O N E must take chances
and if they turn out right they
are renamed opportunities.
M A N will forgive a woman
doing everything at his
expense except making a joke.

S
M
F
P
I
I
B
O M E M E N consider marriage
an unnecessary expense, and
some men simply won’t consider
it at all.
A N Y a woman has waited
patiently for years until the
man could afford to marry her,
and then he won’t wait patiently
for five minutes while she puts
her hat on.
L I R TAT I O N and office work
are the oil and water which the
devil sometimes tempts a man to
attempt to mix.
E O P L E who allow their
character to be diluted by
other people’s opinions are
naturally weak.
T I S O N LY a very great man
who, in a higher position, does
not look small to the man down
below.
T ’ S A M I S TA K E to take a
man into your confidence. If
you do you will probably never
trust him again and he will
certainly never trust you again.

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