Foodomics E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Wiley-Interscience Series on Mass Spectrometry
• Sprache: Englisch

Seitenzahl: 1098

Veröffentlichungsjahr: 2013

Beschreibung

Provides the latest "-omics" tools to advance the study of food and nutrition The rapidly emerging field of foodomics examines food and nutrition by applying advanced "-omics" technologies in order to improve people's health, well-being, and knowledge. Using tools from genomics, transcriptomics, epigenomics, proteomics, and metabolomics, foodomics offers researchers new analytical approaches to solve a myriad of current challenges in food and nutrition science. This book presents the fundamentals of foodomics, exploring the use of advanced mass spectrometry techniques in food science and nutrition in the post-genomic era. The first chapter of the book offers an overview of foodomics principles and applications. Next, the book covers: * Modern instruments and methods of proteomics, including the study and characterization of food quality, antioxidant food supplements, and food allergens * Advanced mass spectrometry-based methods to study transgenic foods and the microbial metabolome * Mass spectrometry-based metabolomics in nutrition and health research * Foodomics' impact on our current understanding of micronutrients (phenolic compounds and folates), optimal nutrition, and personalized nutrition and diet related diseases * Principles and practices of lipidomics and green foodomics * Use of chemometrics in mass spectrometry and foodomics The final chapter of Foodomics explores the potential of systems biology approaches in food and nutrition research. All the chapters conclude with references to the primary literature, enabling readers to explore individual topics in greater depth. With contributions from a team of leading pioneers in foodomics, this book enables students and professionals in food science and nutrition to take advantage of the latest tools to advance their research and open up new areas of food and nutrition investigation.

Leseprobe

Contents

Cover

Wiley Series on Mass Spectrometry

Title Page

Copyright

Dedication

Preface

Contributors

Chapter 1: Foodomics: Principles and Applications

1.1 INTRODUCTION TO FOODOMICS

1.2 FOODOMICS APPLICATIONS: CHALLENGES, ADVANTAGES, AND DRAWBACKS

1.3 FOODOMICS, SYSTEMS BIOLOGY, AND FUTURE TRENDS

ACKNOWLEDGMENTS

REFERENCES

Chapter 2: Next Generation Instruments and Methods for Proteomics

2.1 INTRODUCTION

2.2 EMERGING METHODS IN PROTEOMICS

2.3 THE MOVE FROM SHOTGUN TO TARGETED PROTEOMICS APPROACHES

2.4 NEW INSTRUMENTAL METHODS FOR PROTEOMICS

2.5 BIOINFORMATICS TOOLS

REFERENCES

Chapter 3: Proteomic-based Techniques for the Characterization of Food Allergens

3.1 INTRODUCTION: WHAT IS FOOD ALLERGY?

3.2 FOOD ALLERGY: FEATURES AND BOUNDARIES OF THE DISEASE

3.3 IMMUNOPATHOLOGY OF FOOD ALLERGY AND ROLE OF PROTEOMICS

3.4 IDENTIFICATION OF FOOD ALLERGY EPITOPES

3.5 EXPRESSION PROTEOMICS AND FUNCTIONAL PROTEOMICS IN FOOD ALLERGY

3.6 IDENTIFICATION OF ALLERGENS IN TRANSFORMED PRODUCTS

3.7 CONCLUDING REMARKS

REFERENCES

Chapter 4: Examination of the Efficacy of Antioxidant Food Supplements using Advanced Proteomics Methods

4.1 INTRODUCTION

4.2 METHODS FOR STUDYING THE EFFICACY OF ANTIOXIDANTS

4.3 STRATEGIES USED FOR PROTEOMIC ANALYSIS OF CARBONYLATED PROTEINS AND THE IMPACT OF ANTIOXIDANTS

4.4 STUDYING OXIDATION MECHANISMS

4.5 QUANTIFICATION OF CARBONYLATION SITES

4.6 BIOMEDICAL CONSEQUENCE OF PROTEIN OXIDATION AND THE IMPACT OF ANTIOXIDANTS

4.7 REDOX PROTEOMICS AND TESTING THE EFFICACY OF ANTIOXIDANTS

REFERENCES

Chapter 5: Proteomics in Food Science

5.1 PROTEOMICS

5.2 APPLICATIONS IN FOOD SCIENCE

5.3 SPECIES IDENTIFICATION AND GEOGRAPHIC ORIGIN

5.4 DETECTION AND IDENTIFICATION OF SPOILAGE AND PATHOGENIC MICROORGANISMS

5.5 CHANGES DURING FOOD STORAGE AND PROCESSING AND THEIR RELATIONSHIP TO QUALITY

5.6 PROTEOMICS DATA INTEGRATION TO EXPLORE FOOD METABOLIC PATHWAYS AND PHYSIOLOGICAL ACTIVITY OF FOOD COMPONENTS

5.7 NUTRIPROTEOMICS

5.8 FINAL CONSIDERATIONS AND FUTURE TRENDS

REFERENCES

Chapter 6: Proteomics in Nutritional Systems Biology: Defining Health

6.1 INTRODUCTION

6.2 FROM FOOD PROTEINS TO NUTRIPROTEOMICS

6.3 NUTRITIONAL PEPTIDE AND PROTEIN BIOACTIVES

6.4 NUTRITIONAL PEPTIDE AND PROTEIN BIOMARKERS

6.5 ECOSYSTEM-LEVEL UNDERSTANDING OF NUTRITIONAL HOST HEALTH

6.6 CONCLUSIONS AND PERSPECTIVES

REFERENCES

Chapter 7: MS-Based Methodologies for Transgenic Foods Development and Characterization

7.1 INTRODUCTION

7.2 CONTROVERSIAL SAFETY ASPECTS AND LEGISLATION ON GMOs

7.3 ANALYSIS OF GMOs: TARGETED PROCEDURES AND PROFILING METHODOLOGIES

7.4 CONCLUSIONS AND FUTURE OUTLOOK

7.5 ACKNOWLEDGMENTS

REFERENCES

Chapter 8: MS-Based Methodologies to Study the Microbial Metabolome

8.1 INTRODUCTION

8.2 THE GUT MICROBIOTA AND THEIR ROLE IN METABOLISM

8.3 METAGENOMICS

8.4 METABOLOMICS

8.5 MICROBIAL METABOLITES IN THE HUMAN GUT

8.6 ANALYSIS OF THE MICROBIAL METABOLOME

8.7 IMPLICATIONS FOR HUMAN HEALTH AND DISEASE

8.8 SUMMARY

ACKNOWLEDGMENTS

REFERENCES

Chapter 9: MS-Based Metabolomics in Nutrition and Health Research

9.1 INTRODUCTION

9.2 MS-BASED METABOLOMICS WORKFLOW

9.3 METABOLOMICS IN NUTRITION-RELATED STUDIES

9.4 DIET/NUTRITION AND DISEASE: METABOLOMICS APPLICATIONS

9.5 OTHER APPLICATIONS IN NUTRITIONAL METABOLOMICS

9.6 INTEGRATION WITH OTHER “OMICS”

9.7 CONCLUDING REMARKS

ACKNOWLEDGMENTS

REFERENCES

Chapter 10: Shaping the Future of Personalized Nutrition with Metabolomics

10.1 INTRODUCTION

10.2 METABOLOMICS TECHNOLOGIES

10.3 PERSONALIZED NUTRITION

10.4 CONCLUSION

REFERENCES

Chapter 11: How Does Foodomics Impact Optimal Nutrition?

11.1 INTRODUCTION

11.2 NUTRIGENOMICS

11.3 NUTRIGENETICS AND PERSONALIZED NUTRITION

11.4 THE ADDED VALUE OF FOODOMICS FOR THE FOOD INDUSTRY

11.5 CONCLUDING REMARKS

REFERENCES

Chapter 12: Lipidomics

12.1 DEFINITION AND ANALYTICAL CHALLENGES IN LIPIDOMICS

12.2 LIPIDOMICS IN NUTRITION AND HEALTH RESEARCH

12.3 LIPIDOMICS AND FOOD SCIENCE

12.4 FUTURE PERSPECTIVES

REFERENCES

Chapter 13: Foodomics Study of Micronutrients: The Case of Folates

13.1 FOLATES IN THE DIET

13.2 FOLATE AND HUMAN HEALTH

13.3 MEASURING FOLATES IN HUMAN BIOMONITORING

13.4 FOLATE AND COLON CANCER: ESTABLISHING MECHANISMS OF GENOMIC INSTABILITY USING A COMBINED PROTEOMIC AND FUNCTIONAL APPROACH

13.5 FOLATE DEFICIENCY AND ABNORMAL DNA METHYLATION: A COMMON MECHANISM LINKING CANCER AND ATHEROSCLEROSIS

13.6 SUMMARY

ACKNOWLEDGMENTS

REFERENCES

Chapter 14: Metabolomics Markers in Acute and Endurance/ Resistance Physical Activity: Effect of the Diet

14.1 INTRODUCTION

14.2 METABOLOMICS CONSEQUENCES OF PHYSICAL ACTIVITY: METABOLITES AND PHYSIOLOGICAL PATHWAYS AFFECTED

14.3 METABOLOMICS AND PHYSICAL ACTIVITY: EFFECT OF THE DIET

14.4 CONCLUDING REMARKS AND FUTURE PERSPECTIVES

ACKNOWLEDGMENTS

REFERENCES

Chapter 15: MS-Based Omics Evaluation of Phenolic Compounds as Functional Ingredients

15.1 INTRODUCTION

15.2 USE OF METABOLOMICS IN NUTRITIONAL TRIALS

15.3 STATISTIC TOOLS IN NUTRITIONAL METABOLOMICS

15.4 METABOLOMICS FROM CLINICAL TRIALS AFTER INTAKE OF POLYPHENOL-RICH FOODS

15.5 HUMAN METABOLOME IN LOW AND NORMAL POLYPHENOL DIETARY INTAKE

15.6 CONCLUDING REMARKS AND FUTURE PERSPECTIVES

15.7 ACKNOWLEDGMENTS

REFERENCES

Chapter 16: Metabolomics of Diet-related Diseases

16.1 INTRODUCTION

16.2 ANALYSIS OF THE METABOLOME: METABOLOMICS

16.3 DIET-RELATED DISEASES

REFERENCES

Chapter 17: MS-based Metabolomics Approaches for Food Safety, Quality, and Traceability

17.1 INTRODUCTION

17.2 MS-BASED METABOLOMICS FOR FOOD SAFETY

17.3 MS-BASED METABOLOMICS TO ASSESS FOOD QUALITY

17.4 MS-BASED METABOLOMICS STRATEGIES FOR FOOD TRACEABILITY

17.5 CONCLUSIONS AND FUTURE OUTLOOK

ACKNOWLEDGMENTS

REFERENCES

Chapter 18: Green Foodomics

18.1 BASIC CONCEPTS OF FOODOMICS (AND HOW TO MAKE IT GREENER)

18.2 BASIC CONCEPTS OF GREEN CHEMISTRY

18.3 GREEN PROCESSES TO PRODUCE FUNCTIONAL FOOD INGREDIENTS

18.4 DEVELOPMENT OF GREEN ANALYTICAL PROCESSES FOR FOODOMICS

18.5 COMPARATIVE LCA STUDY OF GREEN ANALYTICAL TECHNIQUES: CASE STUDY

18.6 CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

Chapter 19: Chemometrics, Mass Spectrometry, and Foodomics

19.1 FOODOMICS STUDIES

19.2 XC-MS DATA

19.3 DATA STRUCTURES AND MODELS

19.4 CONCLUSION

REFERENCES

Chapter 20: Systems Biology in Food and Nutrition Research

20.1 SYSTEMS BIOLOGY—NEW OPPORTUNITY FOR FOOD AND NUTRITION RESEARCH

20.2 SYSTEMS APPROACH TO IDENTIFY MOLECULAR NETWORKS BEHIND HEALTH AND DISEASE

20.3 FOOD METABOLOME AND ITS EFFECT ON HOST PHYSIOLOGY

20.4 BUILDING A SYSTEMS BIOLOGY PLATFORM FOR FOOD AND NUTRITION RESEARCH

20.5 FUTURE PERSPECTIVES

REFERENCES

Index

WILEY SERIES ON MASS SPECTROMETRY

Series Editors

Dominic M. DesiderioDepartments of Neurology and BiochemistryUniversity of Tennessee Health Science Center

Nico M. M. NibberingVrije Universiteit Amsterdam, The Netherlands

A complete list of the titles in this series appears at the end of this volume.

Copyright © 2013 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data

Foodomics : advanced mass spectrometry in modern food science and nutrition / edited by Alejandro Cifuentes. p. cm Includes bibliographical references and index. ISBN 978-1-118-16945-2 (cloth) 1. Food–Analysis. 2. Mass spectrometry. I. Cifuentes, Alejandro, editor of compilation.

TX547.F66 2013 664′.07–dc23 2012035736

ISBN: 9781118169452

To the three women in my life, Susana, Claudia and Fernanda, every day they make of this world a better place to be.

A las tres mujeres de mi vida, Susana, Claudia y Fernanda, porque cada día ellas hacen de este mundo un lugar mejor donde vivir.

PREFACE

The impressive analytical developments achieved at the end of the twentieth century have made possible the sequencing of nearly the whole human genome at the beginning of the twenty-first century, opening the so-called postgenomic era. These advances have made feasible analytical instruments and methodological developments that were unthinkable a few decades ago. These impressive developments have traditionally found their first application in the biotechnological or biochemical field many times linked to pharmaceutical, medical, or clinical needs. The huge amount of money allocated to these fields of research is logically an additional push to be considered when selecting the area in which a new analytical method can be probed, a good way to compensate the efforts behind any innovative analytical development. As a result, biotech, pharmaceutical, and clinical related industries have usually been the first targets for analytical chemists and instrumentation companies. This has left food analysis overshadowed and connected to the use of more traditional analytical approaches. Nowadays, boundaries among the different research fields are becoming more and more diffuse giving rise to impressive possibilities in the emerging interdisciplinary areas, for example, health and food. As a result, researchers in food science and nutrition are being pushed to move from classical methodologies to more advanced strategies usually borrowing methods well established in medical, pharmacological, and/or biotechnology research. This trend has generated the emergence of new areas of research for which a new terminology is required. In this context, our group defined a few years ago Foodomics, asa discipline that studies the food and nutrition domains through the application of advanced omics technologies to improve consumer's well-being, health, and confidence. The main idea behind the use of this new term has been not only to use it as a flag of the new times for food analysis but also to highlight that the investigation into traditional and new problems in food analysis in the postgenomic era can find exciting opportunities and new answers through the use of genomics, transcriptomics, epigenetics, proteomics, and metabolomics tools. Indeed, Foodomics is opening a new and unexpected land still wild, still unexplored, to a new generation of researchers who, using the everyday more powerful omics technologies, can find original search possibilities and innovative answers to crucial questions not only related to food science but also related to its complex links with our health.

The interest of the scientific community in modern food analysis and Foodomics, and the different trends in this hot area of research are well documented in the 20 chapters that compose this volume on “Foodomics: Advanced Mass Spectrometry in Modern Food Science and Nutrition”, the first book devoted to this new discipline in which the authors present their advanced perspective of the topic. Namely, in the first chapter the principles of Foodomics are presented, the next five chapters (chapter 2 to 6) are devoted to proteomics applications in Foodomics, including a description of modern instruments and methods for proteomics, proteomic-based techniques for food science and food allergens characterization, examination of antioxidant food supplements using advanced proteomics methods and proteomics in nutritional systems biology. The next two chapters (chapters 7 and 8) are devoted to the description of advanced MS-based methodologies to study transgenic foods development and characterization and the microbial metabolome. The following nine chapters (chapters 9 to 17) are devoted to metabolomics developments in Foodomics with special emphasis on the possibilities of MS-based metabolomics in nutrition and health research, for food safety, quality, and traceability, the investigations on future personalized nutrition, the study of the effect of the diet on acute and endurance exercise, the investigation on diet-related diseases, and the study on how Foodomics impact optimal nutrition or can provide crucial information on micronutrients (the case of folates), phenolic compounds as functional ingredients, and lipids (lipidomics). The following two chapters (chapters 18 and 19) present the main principles of Green Foodomics and the use of chemometrics in mass spectrometry and Foodomics. The last chapter of the book is devoted to the description of the possibilities of systems biology in food and nutrition research.

As editor of this book devoted to “Foodomics: Advanced Mass Spectrometry in Modern Food Science and Nutrition”, I would like to thank all the authors for their suitable contributions, Dom Desiderio for inviting me to prepare this piece of work, Michael Leventhal for his help and support, and to those in the John Wiley & Sons team who contributed their effort to the preparation of this volume.

Alejandro Cifuentes

CONTRIBUTORS

Francesco Addeo, Dipartimento di Scienza degli Alimenti, University of Naples Federico II, Naples, Italy

Juan Pablo Albar, Functional Proteomics Group, Centro Nacional de Biotecnología–CSIC, Madrid, Spain

Lluís Arola, Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, Reus, Spain; Departament de Bioquímica i Biotecnologia, Nutrigenomics Research Group, Universitat Rovira i Virgili, Tarragona, Spain

Anna Arola-Arnal, Departament de Bioquímica i Biotecnologia, Nutrigenomics Research Group, Universitat Rovira i Virgili, Tarragona, Spain

Coral Barbas, Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Boadilla del Monte, Madrid, Spain

Isabel Bondia-Pons, Quantitative Biology and Bioinformatics, VTT Technical Research Centre of Finland, Espoo, Finland

Antoni Caimari, Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, Reus, Spain

Mónica Carrera, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland

María Castro-Puyana, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Alejandro Cifuentes, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Sebastiano Collino, BioAnalytical Science, Nestle Research Center, Lausanne, Switzerland

Anna Crescenti, Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, Reus, Spain

Josep M. del Bas, Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, Reus, Spain

Sylvia H. Duncan, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, UK

Susan J. Duthie, Natural Products Group, Division of Lifelong Health, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, UK

Søren B. Engelsen, Faculty of Science, University of Copenhagen, Copenhagen, Denmark

Marcela A. Erazo, Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Boadilla del Monte, Madrid, Spain

Laurent Fay, R&D Infant Formulae, Nestlé Nutrition, Vevey, Switzerland

Pasquale Ferranti, Istituto di Scienze dell'Alimentazione, CNR, Avellino, Italy; Dipartimento di Scienza degli Alimenti, University of Naples Federico II, Naples, Italy

Federico Ferreres, Department of Food Science and Technology, CEBAS-CSIC, Murcia, Spain

Jose M. Gallardo, Marine Research Institute, CSIC, Vigo, Pontevedra, Spain

Antonia García, Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Boadilla del Monte, Madrid, Spain

Virginia García-Cañas, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Cristina García-Viguera, Department of Food Science and Technology, CEBAS-CSIC, Murcia, Spain

José Ignacio Gil, Service of Radiodiagnostic, Mammary Pathology Department, Hospital José María Morales Meseguer, Murcia, Spain

Angel Gil-Izquierdo, Department of Food Science and Technology, CEBAS-CSIC, Murcia, Spain

Jean-Philippe Godin, BioAnalytical Science, Nestle Research Center, Lausanne, Switzerland

Miguel Herrero, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Tuulia Hyötyläinen, Quantitative Biology and Bioinformatics, VTT Technical Research Centre of Finland, Espoo, Finland

Clara Ibáñez, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Elena Ibáñez, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Elsa M. Janle, Department of Foods and Nutrition, Purdue University, West Lafayette, Indiana, USA

Peter Kastenmayer, BioAnalytical Science, Nestle Research Center, Lausanne, Switzerland

Martin Kussmann, Proteomics/Metabonomics Core, Nestlé Institute of Health Sciences, Lausanne, Switzerland; Faculty of Science, Aarhus University, Aarhus, Denmark

Ashraf G. Madian, Department of Chemistry, Purdue University, West Lafayette, Indiana, USA

Gianfranco Mamone, Istituto di Scienze dell'Alimentazione, CNR, Avellino, Italy

François-Pierre Martin, BioAnalytical Science, Nestle Research Center, Lausanne, Switzerland

Sonia Medina, Department of Food Science and Technology, CEBAS-CSIC, Murcia, Spain

María del Carmen Mena, Functional Proteomics Group, Centro Nacional de Biotecnología–CSIC, Madrid, Spain

José A. Mendiola, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Sofia Moco, BioAnalytical Science, Nestle Research Center, Lausanne, Switzerland

Chiara Nitride, Dipartimento di Scienza degli Alimenti, University of Naples Federico II, Naples, Italy

Matej Oreši, Systems Biology and Bioinformatics, VTT Technical Research Centre of Finland, Espoo, Finland

Ignacio Ortea, Health Research Institute of Santiago de Compostela, A Coruña, Spain

Gianluca Picariello, Istituto di Scienze dell'Alimentazione, CNR, Avellino, Italy

Francesc Puiggròs, Centre Tecnològic de Nutrició i Salut (CTNS), TECNIO, Reus, Spain

Fred E. Regnier, Department of Chemistry, Purdue University, West Lafayette, Indiana, USA

Serge Rezzi, BioAnalytical Science, Nestle Research Center, Lausanne, Switzerland

Alastair Ross, BioAnalytical Science, Nestle Research Center, Lausanne, Switzerland

Francisco J. Rupérez, Center for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Boadilla del Monte, Madrid, Spain

Wendy R. Russell, Rowett Institute of Nutrition and Health, University of Aberdeen, Aberdeen, UK

Max Scherer, BioAnalytical Science, Nestle Research Center, Lausanne, Switzerland

Thomas Skov, Faculty of Science, University of Copenhagen, Copenhagen, Denmark

Carolina Simó, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Manuel Suárez, Departament de Bioquímica i Biotecnologia, Nutrigenomics Research Group, Universitat Rovira i Virgili, Tarragona, Spain

Francisco A. Tomás-Barberán, Department of Food Science and Technology, CEBAS-CSIC, Murcia, Spain

Alberto Valdés, Laboratory of Foodomics, Institute of Food Science Research (CIAL), National Research Council (CSIC), Madrid, Spain

Débora Villaño, Department of Food Science and Technology, CEBAS-CSIC, Murcia, Spain

1

FOODOMICS: PRINCIPLES AND APPLICATIONS

Alejandro Cifuentes

1.1 INTRODUCTION TO FOODOMICS

Research in food science and nutrition has grown parallel to the consumers' concern about what is in their food and the safety of the food they eat. To give an adequate answer to the rising consumer demands, food and nutrition researchers around the world are facing increasingly complex challenges that require the use of the best available science and technology. A good portion of this complexity is due to the so-called Globalization and the movement of food and related raw materials worldwide, which are generating contamination episodes that are also becoming global. An additional difficulty is that many products contain multiple and processed ingredients, which are often shipped from different parts of the world, and share common storage spaces and production lines. As a result, ensuring the safety, quality, and traceability of food has never been more complicated and necessary than today.

The first goal of food science has traditionally been, and still is, to ensure food safety. To meet this goal, food laboratories are being pushed to exchange their classical procedures for modern analytical techniques that allow them to give an adequate answer to this global demand. Besides, the new European regulations in the European Union countries (e.g., Regulation EC 258/97 or EN 29000 and subsequent issues), the Nutrition Labeling and Education Act in the USA, and the Montreal Protocol have had a major impact on food laboratories. Consequently, more powerful, cleaner, and cheaper analytical procedures are now required by food chemists, regulatory agencies, and quality control laboratories. These demands have increased the need for more sophisticated instrumentation and more appropriate methods able to offer better qualitative and quantitative results while increasing the sensitivity, precision, specificity, and/or speed of analysis.

Currently, there is also a general trend in food science toward the connection between food and health. Thus, food is considered today not only a source of energy but also an affordable way to prevent future diseases. The number of opportunities (e.g., new methodologies, new generated knowledge, new products) derived from this trend is impressive and it includes, for example, the possibility to account for food products tailored to promote the health and well-being of groups of population identified on the basis of their individual genomes. Interaction of modern food science and nutrition with disciplines such as pharmacology, medicine, or biotechnology provides impressive new challenges and opportunities. As a result, researchers in food science and nutrition are moving from classical methodologies to more advanced strategies, and usually borrow methods well established in medical, pharmacological, and/or biotechnology research. As a result, advanced analytical methodologies, “omics” approaches, and bioinformatics—frequently together with in vitro, in vivo, and/or clinical assays—are applied to investigate topics in food science and nutrition that were considered unapproachable few years ago.

In modern food science and nutrition, terms such as nutrigenomics, nutrigenetics, nutritional genomics, transgenics, functional foods, nutraceuticals, genetically modified (GM) foods, microbiomics, toxicogenomics, nutritranscriptomics, nutriproteomics, nutrimetabolomics, and systems biology are expanding. This novelty has also brought about some problems related to the poor definition of part of this terminology or their low acceptance, probably due to the difficulty to work in a developing field in which several emerging strategies are frequently put together.

1.1.1 Definition of Foodomics

Although the term Foodomics is being used in different web pages and scientific meetings since 2007 (see e.g., Slater and Wilson, 2007 or Capozzi and Placucci, 2009), Foodomics was for the first time defined in an SCI journal in 2009 as a new discipline that studies the food and nutrition domains through the application of advanced omics technologies to improve consumer's well-being, health, and confidence (Cifuentes, 2009; Herrero et al., 2010, 2012). Thus, Foodomics is not only an useful concept that comprises in a simple and straightforward way all of the emerging terms aforementioned (e.g., nutrigenomics, nutrigenetics, microbiomics, toxicogenomics, nutritranscriptomics, nutriproteomics, nutrimetabolomics), but more importantly, Foodomics is a global discipline that includes all the working areas in which food (including nutrition) and advanced omics tools are put together.

A representation of the areas covered by Foodomics and the tools employed can be seen in Figure 1.1. Just to name a few topics that could be addressed by this new discipline, Foodomics would help: (a) to understand the gene-based differences among individuals in response to a specific dietary pattern following nutrigenetic approaches; (b) to understand the biochemical, molecular, and cellular mechanisms that underlie the beneficial or adverse effects of certain bioactive food components following nutrigenomic approaches; (c) to determine the effect of bioactive food constituents on crucial molecular pathways; (d) to know the identity of genes that are involved in the previous stage to the onset of the disease, and, therefore, possible molecular biomarkers; (e) to establish the global role and functions of gut microbiome, a topic that is expected to open an impressive field of research in the near future; (f) to carry out the investigation on unintended effects in GM crops; (g) to understand the stress adaptation responses of food-borne pathogens to ensure food hygiene, processing, and preservation; (h) to investigate the use of food microorganisms as delivery systems including the impact of gene inactivation and deletion systems; (i) in the comprehensive assessment of food safety, quality, and traceability ideally as a whole; (j) to understand the molecular basis of biological processes with agronomic interest and economic relevance, such as the interaction between crops and its pathogens, as well as physicochemical changes that take place during fruit ripening; and (k) to fully understand postharvest phenomena through a global approach that links genetic and environmental responses and identifies the underlying biological networks. In this regard, it is expected that the new omics technologies combined with systems biology, as proposed by Foodomics, can lead postharvest research into a new era. The interest in Foodomics also coincides with a clear shift in medicine and biosciences toward prevention of future diseases through adequate food intakes, and the development of the so-called functional foods that are discussed below.

FIGURE 1.1 Foodomics: covered areas and tools.

1.1.2 Foodomics Tools

As can be seen in Figure 1.1, Foodomics involves the use of multiple tools to deal with its different subdisciplines and applications. Thus, the use of omics tools such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics is a must in this new discipline. Although a detailed description on these tools is out of the scope of this chapter, some fundamentals about these techniques are provided below.

Epigenomics studies the mechanisms of gene expression that can be maintained across cell divisions, and thus the life of the organism, without changing the DNA sequence. The epigenetic mechanisms are related to the changes induced (e.g., by toxins or bioactive food ingredients) in gene expression via altered DNA methylation patterns, altered histone modifications, or noncoding RNAs, including small RNAs. In mammals, many dietary components, including folate, vitamin B6, vitamin B12, betaine, methionine, and choline, have been linked to changes in DNA methylation. These nutrients can all affect the pathways of one-carbon metabolism that determine the amount of available S-adenosylmethionine, which is the methyl donor for DNA methylation and histone methylation. Although it is too early to apply epigenetic alterations that are induced by dietary ingredients as biomarkers in public health and medicine, research in this area is expected to be boosted by the expanding use of next-generation DNA sequencing technologies. Applications include chromatin immunoprecipitation followed by DNA sequencing (ChIP–seq) to assess the genomic distribution of histone modifications, histone variants and nuclear proteins, and global DNA methylation analysis through the sequencing of bisulphite-converted genomic DNA. Combined with appropriate statistical and bioinformatic tools, these methods will permit the identification of all the loci that are epigenetically altered.

Regarding transcriptomics, the global analysis of gene expression offers impressive opportunities in Foodomics (e.g., for the identification of the effect of bioactive food constituents on homeostatic regulation and how this regulation is potentially altered in the development of certain chronic diseases). Two conceptually different analytical approaches have emerged to allow quantitative and comprehensive analysis of changes in mRNA expression levels of hundreds or thousands of genes. One approach is based on microarray technology, and the other group of techniques is based on DNA sequencing. Next, typically real-time PCR is applied to confirm the up- or down-regulation of a selected number of genes.

In proteomics, the huge dynamic concentration range of proteins in biological samples causes many detection difficulties because many proteins are below the sensitivity threshold of the most advanced instruments. For this reason, fractionation and subsequent concentration of the proteome is often needed. Besides, the use and development of high-resolving separation techniques as well as highly accurate mass spectrometers is nowadays essential to solve the proteome complexity. Currently, more than a single electrophoretic or chromatographic step is used to separate the thousands of proteins found in a biological sample. This separation step is followed by analysis of the isolated proteins (or peptides) by mass spectrometry (MS) via the so-called “soft ionization” techniques, such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI), combined with the everyday more powerful mass spectrometers. Two fundamental analytical strategies can be employed: the bottom-up and the top-down approach. Both methodologies differ on the separation requirements and the type of MS instrumentation. New proteomic approaches based on array technology are also being employed. Protein microarrays can be composed by recombinant protein molecules or antibodies immobilized in a high-density format on the surface of a substrate material. There are two major classes of protein micro- (nano-) arrays: analytical and functional protein microarrays, being the antibody-based microarray the most common platform in proteomic studies.

Metabolome can be defined as the full set of endogenous or exogenous low molecular weight metabolic entities of approximately <1000 Da (metabolites), and the small pathway motifs that are present in a biological system (cell, tissue, organ, organism, or species). Unlike nucleic acid or protein-based omics techniques, which intend to determine a single chemical class of compounds, metabolomics has to deal with very different compounds of very diverse chemical and physical properties. Moreover, the relative concentration of metabolites in the biological fluids can vary from millimolar (or higher) to picomolar level, making it easy to exceed the linear range of the analytical techniques employed. Metabolites are, in general, the final downstream products of the genome, and reflect most closely the operation of the biological system, its phenotype. The analysis of metabolic patterns and the changes in the metabolism in the nutrition field can be, therefore, very interesting to locate; for example, variations in different metabolic pathways due to the consumption of different compounds in the diet. One of the main challenges in metabolomics is to face the complexity of any metabolome, usually composed by a huge number of compounds of very diverse chemical and physical properties (sugars, amines, amino acids, organic acids, steroids, etc.). Sample preparation is especially important in metabolomics, because the procedure used for metabolite extraction has to be robust and highly reproducible. Sample preparation will depend on the sample type and the targeted metabolites of interest (fingerprinting or profiling approach). Moreover, no single analytical methodology or platform is applicable to detect, quantify, and identify all metabolites in a certain sample. Two analytical platforms are currently used for metabolomic analyses: MS- and NMR-based systems. These techniques, either stand-alone or combined with separation techniques (typically, LC-NMR, GC-MS, LC-MS, and CE-MS) can produce complementary analytical information to attain more extensive metabolome coverage. There are three basic approaches that can be used in metabolomics: target analysis, metabolic profiling, and metabolic fingerprinting. Target analysis aims the quantitative measurement of selected analytes, such as specific biomarkers or reaction products. Metabolic profiling is a nontargeted strategy that focuses on the study of a group of related metabolites or a specific metabolic pathway. It is one of the basic approaches to phenotyping, because the study of metabolic profiles of a cell gives a more accurate description of a phenotype. Meanwhile, metabolic fingerprinting does not aim to identify all metabolites, but to compare the patterns of metabolites that change in response to the cellular environment.

Due to the huge amount of data usually obtained from omics studies, it has been necessary to develop strategies to convert the complex raw data obtained into useful information. Thus, bioinformatics has also become a crucial tool in Foodomics. Over the last years, the use of biological knowledge accumulated in public databases by means of bioinformatics allows to systematically analyze large data lists in an attempt to assemble a summary of the most significant biological aspects. Also, statistical tools are usually applied for exploratory data analysis to determine correlations among samples (which can be caused by either a biological difference or a methodological bias), for discriminating the complete data list and reducing it with the most relevant ones for biomarkers discovery, etc.

1.2 FOODOMICS APPLICATIONS: CHALLENGES, ADVANTAGES, AND DRAWBACKS

Although there is still a large number of gaps to be filled in our current knowledge on food science and nutrition, the great analytical potential of Foodomics can help resolve many issues and questions related to food safety, traceability, quality, new foods, transgenic foods, functional foods, nutraceuticals, etc.

1.2.1 Food Safety, Quality, and Traceability

Foodomics can help solve some of the new challenges that modern food safety, quality, and traceability have to face. These challenges encompass the multiple analyses of contaminants and allergens; the establishment of more powerful analytical methodologies to guarantee food origin, traceability, and quality; the discovery of biomarkers to detect unsafe products; the capability to detect food safety problems before they grow and affect more consumers; etc.

1.2.2 Transgenic Foods

Although this book includes a chapter devoted to transgenic foods, a brief outline on this topic is given below. Recombinant DNA technology, or genetic engineering, allows selected individual gene sequences to be transferred from an organism into another and also between nonrelated species. Genetic engineering has been used in agriculture and food industries in the past years in order to improve the performance of plant varieties (resistance to plagues, herbicides, and hydric or saline stresses), improve technological properties during storage and processing (firmness of fruits), or improve the sensorial and nutritional properties of food products (starch quality, content of vitamins or essential amino acids). The organisms derived from recombinant DNA technology are termed genetically modified organisms (GMOs). Transgenic food is a food that is derived from or contains GMOs.

The use of genetic engineering in the production of foods is constantly growing since the past years as well as the concern in part of the public opinion. This is due to the increasing impact of this technology in foodstuff production, by one side, and to the continued campaign against GMO crops lead by ecologist organizations, by the other. Claims about the advantages derived from GMO crops include those from the biotechnology companies and most of the scientific community, stressing the benefits for the agriculture and the food industry and the lack of scientific evidence on any detrimental effects on human health. On the other side, ecologists groups are concerned about the impact of GM plants on human health and on the environment. In this context, most governments have dictated regulations on the use, spreading, and marketing of GMOs, in order to regain the confidence of the consumers. Owing to the complexity that entails the compositional study of a biological system such as GMO, the study of substantial equivalence as well as the detection of any unintended effect should be approached with advanced profiling techniques, with the potential to extend the breadth of comparative analyses. However, there is no single technique currently available to acquire significant amounts of data in a single experimental analysis to detect all compounds found in GMOs or any other organism. In consequence, multiple analytical techniques have to be combined to improve analytical coverage of proteins and metabolites. Namely, the European Food Safety Authority (EFSA) (EFSA, 2006) has recommended the monitoring of the composition, traceability, and quality of these GM foods using advanced analytical techniques including omics techniques to provide a broad profile of these GM foods (Levandi et al., 2008; Garcia-Villaba et al., 2008, 2010; Simó et al., 2010; Garcia-Cañas et al., 2011). The development of new analytical strategies based on Foodomics will provide extraordinary opportunities to increase our understanding about GMOs, including the investigation on unintended effects in GM crops, or the development of the so-called second-generation GM foods. Besides, Foodomics has to deal with the particular difficulties commonly found in food analysis, such as the huge dynamic concentration range of food components as well as the heterogeneity of food matrices and the analytical interferences typically found in these complex matrices.

1.2.3 Foodomics in Nutrition and Health Research

Nowadays, food is investigated not only as a source of energy but also as a potential health promoter. As a result, food scientists and nutritionists have to face a large number of challenges to adequately answer the new questions emerging from this new field of research. One of the main challenges is to improve our limited understanding of the roles of nutritional compounds at molecular level (i.e., their interaction with genes and their subsequent effect on proteins and metabolites) for the rational design of strategies to manipulate cell functions through diet, which is expected to have an extraordinary impact on our health (Garcia-Cañas et al., 2010). The problem to be resolved is huge and it includes the study of the individual variations in gene sequences, particularly in single nucleotide polymorphisms (SNPs), and their expected different answer to nutrients. Moreover, nutrients can be considered as signaling molecules that are recognized by specific cellular-sensing mechanisms. However, unlike pharmaceuticals, the simultaneous presence of a variety of nutrients with diverse chemical structures and concentrations and having numerous targets with different affinities and specificities increases enormously the complexity of the problem. Therefore, it is necessary to look at hundreds of test compounds simultaneously and observe the diverse temporal and spatial responses.

Foodomics can be an adequate strategy to investigate the complex issues related to prevention of future diseases and health promotion through food intake. It is now well known that health is heavily influenced by genetics. However, diet, lifestyle, and environment can have a crucial influence on the epigenome, gut microbiome, and, by association, the transcriptome, proteome, and, ultimately, the metabolome. When the combination of genetics and nutrition/lifestyle/environment is not properly balanced, poor health is a result. Foodomics can be a major tool for detecting small changes induced by food ingredient(s) at different expression levels. A representation of an ideal Foodomics strategy to investigate the effect of food ingredient(s) on a given system (cell, tissue, organ, or organism) is shown in Figure 1.2. Following this Foodomics strategy, results on the effect of food ingredient(s) at genomic/transcriptomic/proteomic and/or metabolomic levels are obtained, making possible new investigations on food bioactivity and its effect on human health at molecular level. The interest in Foodomics also coincides with a clear shift in medicine and biosciences toward prevention of future diseases through adequate food intakes, and the development of the so-called functional foods. It has been mentioned that it is probably too early to conclude on the value of many substances for health, and the same can apply to other health relationships that are still under study. In this regard, it is interesting to remark that several of the health benefits assigned to many dietary constituents are still under controversy as can be deduced from the large number of applications rejected by the EFSA about health claims of new foods and ingredients (EFSA, 2010; Gilsenan, 2011). More sound scientific evidences are needed to demonstrate the claimed beneficial effects of these new foods and constituents. In this sense, the advent of new postgenomic strategies as Foodomics seems to be essential to understand how the bioactive compounds from diet interact at molecular and cellular levels, as well as to provide better scientific evidences on their health benefits. The combination of the information from the three expression levels (gen, protein, and metabolite) can be crucial to adequately understand and scientifically sustain the health benefits from food ingredients. To achieve this goal, it will be necessary to carry out more studies to discover more polymorphisms of one nucleotide, to identify genes related to complex disorders, to extend the research on new food products, and to demonstrate a higher degree of evidence through epidemiological studies based in Foodomics that can lead to public recommendations. Moreover, in spite of the significant outcomes expected from a global Foodomics strategy, practically there are no papers published in literature in which results from the three expression levels (transcriptomics, proteomics, and metabolomics) are simultaneously presented and merged. Figure 1.3 shows the results from a global Foodomics study on the chemopreventive effect of dietary polyphenols against HT29 colon cancer cells (Ibáñez et al., 2012). Figure 1.3 shows the genes, proteins, and metabolites that were identified (after transcriptomic, proteomic, and metabolomic analysis) to be involved in the principal biological processes altered in HT29 colon cancer cells after the treatment with rosemary polyphenols. In order to demonstrate all its value, Foodomics still needs to be translated to methods or approaches with medicinal impact, for example, through the so-called personalized nutrition. In this regard, data interpretation and integration when dealing with such complex systems is not straightforward and has been detected as one of the main bottlenecks.

FIGURE 1.2 Scheme of an ideal Foodomics strategy to investigate the health benefits from dietary constituents, including methodologies and expected outcomes. Modified from Ibáñez et al. (2012) with permission from Elsevier.

FIGURE 1.3 Foodomics identification of the proteins, genes, and metabolites involved in three of the principal biological processes altered in HT29 colon cancer cells after the treatment with rosemary polyphenols. Underlined, down-regulated; Not underlined, up-regulated. Modified from Ibáñez et al., 2012, with permission from Elsevier.

In Foodomics, to carry out a comprehensive elucidation of the mechanisms of action of natural compounds, specific nutrients, or diets, in vitro assays or animal models are mainly used because (a) they are genetically homogeneous within a particular assay or animal model and (b) environmental factors can be controlled. Moreover, these assays allow the study of certain tissues that would not be possible to obtain from humans. On the other hand, the main difficulty in the study of diets is the simultaneous presence of a variety of nutrients, with diverse chemical structures, that can have numerous targets with different affinities and specificities. Ideally, the final demonstration on the bioactivity of a given food constituent should be probed by Foodomics based on a global omics study of the biological samples generated during a clinical trial.

It is interesting to mention that there are still rather limited studies on the effect of specific natural compounds, nutrients, or diet on the transcriptome/proteome/metabolome of organisms, tissues, or cells, being the number of review papers on this topic higher than the number of research papers.

1.3 FOODOMICS, SYSTEMS BIOLOGY, AND FUTURE TRENDS

Analytical strategies used in Foodomics have to face important difficulties derived, among others, from food complexity, the huge natural variability, the large number of different nutrients and bioactive food compounds, their very different concentrations, and the numerous targets with different affinities and specificities that they might have. In this context, proteomics and metabolomics (plus transcriptomics) represent powerful analytical platforms developed for the analysis of proteins and metabolites (plus gene expression). However, “omics” platforms must be integrated in order to understand the biological meaning of the results on the investigated system (e.g., cell, tissue, organ) ideally through a holistic strategy as proposed by Systems Biology. Thus, Systems Biology can be defined as an integrated approach to study biological systems at the levels of cells, organs, or organisms, by measuring and integrating genomic, proteomic, and metabolic data (Panagiotou and Nielsen, 2009). Systems Biology approaches might encompass molecules, cells, organs, individuals, or even ecosystems, and it is regarded as an integrative approach of all information at the different levels of genomic expression (mRNA, protein, metabolite).

Although Systems Biology has been scarcely applied to Foodomics studies, its potential is underlined by its adoption by other related disciplines. For instance, Systems Biology has been applied to understand the complexity of the processes in the intestinal tract (dos Santos et al., 2010). This study is based on human adult microbiota characterization by deep metagenomic sequencing, identification of several hundreds of intestinal genomes at the sequence level, identification of the transcriptional response of the host and selected microbes in animal model systems and in humans, determination of the transcriptional response of the host to different diets in humans, germ-free and gene knockout animals, together with different metabolomics and proteomics studies. The long-term goal is to understand how specific nutrients, diets, and environmental conditions influence cell and organ function, and how they thereby impact on health and disease. This systems knowledge will be pivotal for the development of rational intervention strategies for the prevention of diseases such as diabetes, metabolic syndrome, obesity, and inflammatory bowel diseases.

The challenge in the combination of Foodomics and Systems Biology is not only at the technological level where great improvements are being made and expected in the “omics” technologies but also in improving our limited knowledge on many biological processes that can have place at molecular level. Last but not the least, bioinformatics (including data processing, clustering, dynamics, or integration of the various “omics” levels) will have to progress for Systems Biology to demonstrate all its potential in the new Foodomics discipline. In this regard, it is also interesting to mention that the traditional medical world has often noted that although many of the omics tools and Foodomics approaches provide academically interesting research, they have not been translated to methods or approaches with medicinal impact and value because the data integration when dealing with such complex systems is not straightforward.

In the future, the combination of Foodomics and Systems Biology can provide crucial information on, for example, host–microbiome interactions, nutritional immunology, food microorganisms including pathogens resistance, postharvest, plant biotechnology, or farm animal production. Besides, it is also foreseen the emerging of other innovative approaches as, for example, green Foodomics (see the chapter on this topic in this book), green systems biology (Weckwerth, 2011) or the human gutome.

ACKNOWLEDGMENTS

This work was supported by AGL2011-29857-C03-01 (Ministerio de Ciencia e Innovación, Spain) and CSD2007-00063 FUN-C-FOOD (Programa CONSOLIDER, Ministerio de Educacion y Ciencia, Spain) projects.

REFERENCES

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dos Santos VM, Müller M, de Vos WM (2010). Systems biology of the gut: the interplay of food, microbiota and host at the mucosal interface. Current Opinion in Biotechnology 21:539–550.

EFSA (2006). Guidance document of the scientific panel on genetically modified organisms for the risk assessment of genetically modified plants and derived food and feed. EFSA Communications Departmente, Parma, Italy.

EFSA (2010). Opinions of the NDA panel published on 2009 and 2010. http://www.efsa.europa.eu/cs/Satellite (accessed on April 7, 2010).

Garcia-Cañas V, Simó C, Leon C, Cifuentes A (2010). Advances in nutrigenomics research: novel and future analytical approaches to investigate the biological activity of natural compounds and food functions. Journal of Pharmaceutical and Biomedical Analysis 51:290–304.

Garcia-Cañas V, Simó C, León C, Ibáñez E, Cifuentes A (2011). MS-based analytical methodologies to characterize genetically modified crops. Mass Spectrometry Reviews 30:396–416.

Garcia-Villalba R, León C, Dinelli G, Segura-Carretero A, Fernandez-Gutierrez A, Garcia-Cañas V, Cifuentes A (2008). Comparative metabolomic study of transgenic versus conventional soybean using capillary electrophoresis–time-of-flight mass spectrometry. Journal of Chromatography A 1195:164–173.

Gilsenan MB (2011). Nutrition & health claims in the European Union: a regulatory overview. Trends in Food Science and Technology 22:536–542.

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2

NEXT GENERATION INSTRUMENTS AND METHODS FOR PROTEOMICS

María del Carmen Mena and Juan Pablo Albar

2.1 INTRODUCTION

The proteomic methods have a prominent role in the analysis of complex proteomes contributing to the characterization of the great diversity of proteins present in living organisms. Mass spectrometry (MS) has increasingly become a powerful analytical tool for both qualitative and quantitative analysis of protein samples over a wide dynamic range. MS-based proteomics is a discipline made possible by the availability of genome sequence databases and advances in many areas. Although much ground has been covered, continued advances in methods, instrumentation, and computational analysis is needed to get a complete analysis of biological systems. Recently, Foodomics has been defined as a new discipline that studies food and nutrition domains through the application of omics technologies in which MS techniques and proteomics are considered cornerstone players (Herrero et al., 2012).

2.1.1 History of Mass Spectrometry-Based Proteomics

With the rapid advances in protein analytical technologies in the early 1990s, it became possible to perform large-scale protein studies identifying the expression of many of the proteins resolvable by two-dimensional electrophoresis (2-DE). Gel electrophoresis was successfully developed for oligonucleotide sequencing in the late 1970 (Maxam and Gilbert, 1977) and it was also developed to separate proteins about the same time (O'Farrell, 1975). In 1994 the term “proteome” was coined (Wilkins et al., 1996) and defined as the set of proteins expressed by the genome, and the study of proteomes was named as “proteomics.”

In its short history, proteomics has lastly evolved. Indeed, just a decade ago, all proteomic data were generated on instruments with low mass accuracy and resolution, and limited scan speed and sensitivity, compared to high-performance hybrid mass spectrometers presently in common use.

2.1.2 Overview of Classical Proteomics Techniques

2.1.2.1 Separation Techniques by Chromatographic Methods

Chromatography has been used for decades as a separation technique and, over time, has developed into a sophisticated analytical technique. The most used methods for protein separation are liquid chromatographic techniques (e.g., ion exchange, size exclusion, affinity, and reversed-phase (RP)), as well as electrophoretic separation in liquid-phase techniques (capillary isoelectric focusing, capillary zone electrophoresis, capillary gel electrophoresis, and free-flow electrophoresis). Modern RP-HPLC utilizes a wide selection of chromatographic packing materials to separate proteins and peptides. The separation efficiency is determined by particle size and pore, surface area, stationary phase, as well as the chemistry of the substrate surface. The most popular column packing is based on spherical silica particles where the surface is modified by alkyl chains varying in length from C4 to C18 (Neverova and Van Eyk, 2005). The C18 bound phase is the most used, offering retention and selectivity for a wide range of compounds containing different polar and non-polar groups on their surface. C4 and C8 phases are used preferentially for separation of proteins and C18 for peptides (Wagner et al., 2002).

2.1.2.2 Two-Dimensional Electrophoresis

The introduction of sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) allowed the use of the electrophoretic mobility to study the occurrence of multiple protein forms. Nevertheless, single-dimension separations are inadequate for effectively resolving complex protein mixtures. Separation of proteins by 2-DE dates back to the 1950s (Smithies and Poulik, 1956) and is still one of the most frequently used techniques to separate complex protein mixtures prior to characterization by MS. The development of the modern 2-DE began with the combination of separation by isoelectric focusing (IEF) in the first dimension and SDS-PAGE in the second dimension, a technique published in 1969 by different authors (Dale and Latner, 1969; Macko and Stegemann, 1969).

The most used methods to visualize proteins after 2-DE are Coomassie and silver staining, both compatible with downstream MS analyses (Shevchenko et al., 1996) and whose limits of detection are at picomole and femtomole order, respectively (Miller et al., 2006). Colloidal Coomassie staining is more sensitive than the classical Coomassie. There are several fluorescent dyes to quantify the relative abundances of protein amounts in 2-DE gels, such as Nile red, SYPRO Orange, SYPRO Red, and SYPRO Tangerine, but the ruthenium-based dye SYPRO Ruby, with sensitivity similar to silver staining and extended dynamic range, nowadays is one of the most appropriate staining dyes in proteomics.

After electrophoresis separation and gel staining, statistical analysis is performed via one of the powerful software packages specifically designed to match protein spots of gel replicates for the different conditions, to compare protein patterns, and to detect protein changes, both qualitative (presence/absence) and quantitative (spot intensities) (Rotilio et al., 2012).

The main limitation of this technique is that some proteins are not suitable for separation by 2-DE. Proteins with a molecular weight lower than 10 kDa or higher than 150 kDa or with very basic isoelectric point (pI) are seldom detected using conventional gels. Moreover, hydrophobic proteins with low solubility cannot enter the gels. In addition, the detection of low-abundance proteins can be hindered by proteins with similar size and charge or by protein expression levels below the detection limits of the technique (Monteoliva and Albar, 2004).

2.1.2.3 Difference in Gel Electrophoresis

Differential proteomics, that is the comparison of different proteomes or different samples such as healthy versus diseased, allows to perfom sensitive, accurate, and reproducible quantitative proteomics studies (Monteoliva and Albar, 2004). 2-DE technique does not accomplish this goal, because two different samples cannot be distinguished into the same gel. Instead, difference in-gel electrophoresis (DIGE), a modified form of 2-DE, allows different proteins to be quantified and even different isoforms of proteins that have different migration patterns on the 2-DE gel.

DIGE technology will be explained in more detail below.

2.1.2.4 Protein Identification by Mass Spectrometry

Identification by Two-Dimensional Electrophoresis Combined with Mass Spectro-metry

The classical workflow in MS-based proteomics includes the protein separation by 2-DE and staining. Afterward, gel images are analyzed and spots of interest are cut and de-stained to prevent staining interference with MS analysis. Some samples may also need to be desalted and concentrated by using pipette tips containing C18 or C4 resin. Then the proteins are digested being trypsin the most common enzyme used, as it very specifically cleaves proteins at the C-terminal side of lysine and arginine, and generates peptides in the preferred mass range for subsequent MS analysis. On the other hand, protein mixtures may be directly digested without previous separation and then peptide mixtures are analyzed by LC–MS.

Ionization Techniques for Peptides and Proteins

To measure the mass or, more specifically, the mass-to-charge ratio (m/z) in a mass spectrometer, peptides and proteins must first be ionized and transferred into the high vacuum system of the instrument. In the late 1980s, two methods were developed for the ionization at high sensitivity: matrix-assisted laser desorption ionization (MALDI) (Karas and Hillenkamp, 1988) and electrospray ionization (ESI) (Fenn et al., 1989).

Matrix-Assisted Laser Desorption Ionization

MALDI is one of the ionization techniques more widely used in MS. MALDI time-of-flight (MALDI-TOF) is one of the most commonly used mass spectrometers and consists of an ion source, a mass analyzer, and a detector. The ion source has the purpose to convert sample molecules from solution or solid phase into ionized analytes. Firstly, analytes are co-crystallized with an organic matrix, such as α-cyano-4-hydroxycinnamic acid and sinapinic acid, on a metal target. This MALDI matrix absorbs laser energy and transfers it to the acidified analyte, whereas the rapid pulsed laser is used to excite the matrix, which causes rapid thermal heating of the molecules and eventually desorption of ions into the gas phase. After ionization, the samples reach the TOF mass analyzer where ions are separated on the basis of their m/z. Ion motion in the mass analyzer can be manipulated by electric or magnetic fields to direct ions to a detector, which registers the number of ions at each individual m/z value (Rotilio et al., 2012). MALDI ionization requires several hundred laser shots to achieve an acceptable signal-to-noise ratio for ion detection, and the generated ions are predominantly singly charged (Sze et al., 2002).

The drawbacks of this type of ionization are low shot-to-shot reproducibility and strong dependence on sample preparation methods. In general, the mass resolution and accuracy of a MALDI-TOF mass spectrometer is not high enough to give a non-ambiguous identification of a peptide.

The concept of MALDI has led to techniques such as surface-enhanced laser desorption ionization (SELDI) that introduce surface affinity toward various protein and peptide molecules.

Electrospray Ionization

Unlike MALDI, the ESI source produces ions from solution. The use of ESI coupled to MS was introduced in 1989 and led to the Nobel Prize for Chemistry in 2002 (Fenn et al., 1989). During ESI ionization, a high voltage is applied between the emitter at the end of the separation pipeline and the inlet of the mass spectrometer. Physicochemical processes of ESI involve creation of electrically charged spray, followed by formation and desolvation of analyte-solvent droplets is aided by a heated capillary and, in some cases, by heated gas flow at the mass spectrometer inlet (Steen and Mann, 2004). An important development in ESI technique includes micro- and nano-ESI, in which peptide mixtures are sprayed into the mass spectrometer at a very low flow rates improving the method's sensitivity.

Peptide Mass Fingerprinting

The development of new MALDI instruments allows to know sequence of peptides, where a MALDI source is coupled to a double time-of-flight section (MALDI-TOF-TOF), a hybrid quadruple TOF or an ion trap. MALDI-TOF/TOF MS is widely used in proteomics to identify proteins by a process called peptide mass fingerprinting (PMF). The main limitations of the MALDI-based PMF approach are that proteins must be completely sequenced and annotated in databases; it cannot identify proteins containing post-translational modifications (PTMs); it requires a complete protein separation and it is not appropriate for proteins with extensive cross-similarity.

Tandem Mass Spectrometry

Tandem mass spectrometry (MS/MS) is a process in which an ion formed in an ion source is mass-selected in the first phase, reacted and fragmented, and then the charged products from the reaction are analyzed in the second phase. The high precision of MS spectrometric measurements can analyze small molecules and distinguish closely related species, and MS/MS can provide structural information on molecular ions that can be specifically isolated on the basis of their m/z and fragmented in the gas phase within the instrument.

In LC–MS/MS, peptides generated from the digestion of complex mixtures of proteins are separated on the basis of their hydrophobicity and introduced into the mass spectrometer, in most of the cases directly via online ESI. The ESI source can be coupled to several mass analyzers, as quadrupole, ion trap, orbitrap, or Fourier transform ion cyclotron resonance system, whose accuracy and sensitivity is extremely different (Yates et al., 2009). After ESI and detection, the final step in this process is the identification of the proteins by the MS/MS fragmentation spectra using specific databases.

2.1.3 Sample Preparation Methods

Sample preparation is critically important in proteomics experiments. Less soluble proteins are difficult to study and the detection of low-abundance proteins is a great challenge for proteomics.

Adjuvants and contaminants, such as salts, detergents, or stabilizers, can interfere with the results of mass spectrometric analysis. In case of LC coupled to ESI-MS, salts and detergents can be removed online within the HPLC setup (e.g., guard column or trapping column). For higher concentrations and for MALDI-MS applications, spinning columns, dialysis, or precipitation are the methods which are mostly applied. Nevertheless, to avoid losses or modifications of the proteins, sample preparation steps have to be limited to the minimum steps needed.

2.2 EMERGING METHODS IN PROTEOMICS

2.2.1 Bottom-up and Top-down Proteomics

The field of MS-based proteomics can be broadly categorized into two fundamental approaches: the increasingly popular top-down proteomic approach that focuses on the direct analysis by MS of entire intact proteins after being subjected to gas-phase fragmentation; and the more widely used bottom-up proteomic approach that focuses on the analysis of peptides obtained after proteolytic digestion of proteins (Fig. 2.1). With top-down analysis, all PTMs will be subjected to analysis, while bottom-up analysis may skip the fragments with these types of modifications.

FIGURE 2.1 Representation of top-down and bottom-up proteomics approaches.

2.2.1.1 Bottom-up Proteomics

Bottom-up analyses are performed by initial proteolytic digestion of the protein of interest, followed by LC–MS analysis of the resultant peptides whose sequences are used to identify the corresponding proteins. The enzyme most used for protein digestion is trypsin, which is very well suited to downstream analysis by the most common MS and tandem MS/MS techniques. However, information regarding PTMs or protein isoforms could be missed, and it is often worth considering other proteolytic enzymes or applying a panel of enzymes (Swaney et al., 2010). The digestion of proteins greatly increases the complexity of samples and it is essential to separate them into manageable, reproducible fractions. In addition, pre-analytical sample processing must be considered especially for high-complexity samples for large-scale analyses.

Experimentally determined peptide masses that differ from those predicted from the primary protein sequence allow for identification of modified regions within the protein (Henzel et al., 1993). Analysis of these peptide ions by MS/MS may be used for further characterization of the modification, including its localization to a specific site within the peptide sequence. It is common, however, that some of the peptides resulting from bottom-up digestion strategies are not observed upon mass spectrometric analysis due to their poor chromatographic retention behavior, or inefficient ionization (Kapp et al., 2003). The main pre-fractionation methods used are SDS-PAGE, size-exclusion, anion-exchange, cation-exchange, lectin-affinity chromatography, RP-LC in basic media, free solution IEF, and high-abundance protein depletion.

Some of the advantages of the bottom-up approach include better front-end separation of peptides compared with proteins and higher sensitivity than the top-down method. Drawbacks of the bottom-up approach include limited protein sequence coverage by identified peptides, loss of labile PTMs, and ambiguity of the origin for redundant peptide sequences (Yates et al., 2009).

2.2.1.2 Top-down Proteomics