Integration of Omics Approaches and Systems Biology for Clinical Applications

ANTONIA VLAHOU is Co-director of the Proteomics Research Unit at the Biomedical Research Foundation, Academy of Athens. FULVIO MAGNI is a Full Professor at the Faculty of Medicine and Surgery, University Milano-Bicocca. HARALD MISCHAK is Professor for Proteomics and Systems Medicine at the University of Glasgow and is the Director of Mosaiques diagnostics. JEROME ZOIDAKIS is a Research Scientist at the Proteomics Research Unit at the Biomedical Research Foundation, Academy of Athens.

• List of Contributors xvPreface xixAcknowledgement xxPart I Platforms for Molecular Data Acquisition and Analysis 11 Clinical Data Collection and Patient Phenotyping 3Katerina Markoska and Goce Spasovski1.1 Clinical Data Collection 31.1.1 Data Collection for Clinical Research 31.1.2 Clinical Data Management 31.1.3 Creating Data Forms 41.1.3.1 Different Data Forms According to the Type of Study 41.1.4 Case Report Form (CRF) 51.1.4.1 CRF Standards Characterization 51.1.4.2 Electronic and Paper CRFs 61.1.5 Methods and Forms for Clinical Data Collection and/or Extraction from Patient’s Records 61.1.5.1 Electronic Health Records (EHRs) 61.1.6 Data Collection Workflow 71.1.6.1 Defining Baseline and Follow]Up Data 71.1.6.2 Medical Coding 71.1.6.3 Errors in Data Collection and Missing Data 81.1.6.4 Data Linkage, Storage, and Validation 81.2 Patient Phenotyping 81.2.1 Approaches in Defining Patient Phenotype 91.2.2 Phenotyping CKD Patients 91.3 Concluding Remarks 10References 102 Biobanking, Ethics, and Relevant Legal Issues 13Brigitte Lohff, Thomas Illig, and Dieter Tröger2.1 Introduction 132.2 Brief Historical Derivation to the Ethical Guidelines in Medical Research 132.2.1 1900: Directive to the Head of the Hospitals, Polyclinics, and Other Hospitals 142.2.2 1931: Guidelines for Novel Medical Treatments and Scientific Experimentation 142.2.3 1947: The Nuremberg Code 142.2.4 1964: The Declaration of Helsinki 142.2.5 The Declaration of Helsinki and Research on Human Materials and Data 152.2.6 2013: Current Valid Declaration of Helsinki in the 7th Revision 15References 152.3 Biobanking: Definition, Role, and Guidelines of National and International Biobanks 162.3.1 Introduction 162.3.2 Definition of Biobanks 172.3.3 Human Biobank Types 172.3.4 Clinical Biobanks 172.3.5 Governance in HUB 182.3.6 Epidemiological Biobanks 182.3.7 Quality of Samples 192.3.8 Harmonization and Cooperation of Biobanks 192.3.9 Situation in Germany 202.3.10 Situation in Europe and Worldwide 202.3.11 Definition of Ownership, Access Rights, and Governance of Biobanks 202.3.12 IT in Biobanks 212.3.13 Financial Aspects and Sustainability 212.3.14 Conclusion 21References 222.4 Tasks of Ethics Committees in Research with Biobank Materials 232.4.1 General Basic Concept 232.4.1.1 The Application Procedure 232.4.2 About the Respective Ethics Commissions 232.4.3 The Establishment of Biobanks 24Further Reading 243 Nephrogenetics and Nephrodiagnostics: Contemporary Molecular Approaches in the Genomics Era 26Constantinos Deltas3.1 Introduction 263.2 Applications of Molecular Diagnostics 273.3 Aims of Present]Day Molecular Genetic Investigations 283.4 Material Used for Genetic Testing 283.5 Clinical, Genetic, and Allelic Heterogeneity 293.6 Oligogenic Inheritance 313.7 ADPKD, Phenotypic Heterogeneity, and Genetic Modifiers 323.8 Collagen IV Nephropathies, Genetic and Phenotypic Heterogeneity, and Genetic Modifiers 333.9 CFHR5 Nephropathy, Phenotypic Heterogeneity, and Genetic Modifiers 363.10 Unilocus Mutational and Phenotypic Diversity (UMPD) 383.11 Next]Generation Sequencing (NGS) 393.12 Conclusions 40Acknowledgments 41References 414 The Use of Transcriptomics in Clinical Applications 49Daniel M. Borràs and Bart Janssen4.1 Introduction 494.2 Clinical Applications of Transcriptomics: Cases and Potential Examples 534.2.1 PCR Applications 534.2.2 Microarrays 554.2.3 Sequencing 574.2.4 Discussion 60References 63Further Reading 665 miRNA Analysis 67Theofilos Papadopoulos, Julie Klein, Jean]Loup Bascands, and Joost P. Schanstra5.1 miRNA Biogenesis, Function, and Annotation 675.2 Annotation of miRNAs 695.3 miRNAs: Location, Stability, and Research Methods 695.3.1 miRNA Analysis and Tissue Distribution 695.3.2 miRNAs in Body Fluids 695.3.3 Stability of miRNAs 715.3.4 Methods to Study miRNAs 715.3.4.1 Sampling 715.3.4.2 Extraction Protocols 715.3.4.3 miRNA Detection Techniques 725.3.4.4 Data Processing and Molecular Integration 735.3.4.5 In Vitro Target Validation 775.4 Use of miRNA In Vivo 795.4.1 Chemically Modified miRNAs 825.4.2 miRNA Sponges or Decoys 825.4.3 Modified Viruses 825.4.4 Microvesicles 825.4.5 The Polymers 835.4.6 Inorganic Nanoparticles 835.5 miRNAs as Potential Therapeutic Agents and Biomarkers: Lessons Learned So Far 835.5.1 miRNAs as Potential Therapeutic Agents 835.5.2 miRNAs as Potential Biomarkers 845.5.2.1 Cancer 845.5.2.2 Metabolic and Cardiovascular Diseases 845.5.2.3 Miscellaneous Diseases 845.6 Conclusion 84References 856 Proteomics of Body Fluids 93Szymon Filip and Jerome Zoidakis6.1 Introduction 936.2 General Workflow for Obtaining High]Quality Proteomics Results 936.3 Body Fluids 956.3.1 Blood 956.3.1.1 Plasma 956.3.1.2 Serum 966.3.2 Urine 966.3.3 Cerebrospinal Fluid (CSF) 966.3.4 Saliva 966.4 Sample Collection and Storage 976.5 Sample Preparation for MS/MS Analysis 976.5.1 Protein Separation 976.5.1.1 Electrophoresis]Based Methods 986.5.1.2 Liquid Chromatography Methods 986.5.2 Sample Preparation for MS/MS (Tryptic Digestion) 1026.5.3 Separation of Peptides 1026.6 Analytical Instruments 1036.7 Data Processing and Bioinformatics Analysis 1036.7.1 Peptide and Protein Identification 1036.7.2 Protein Quantitation 1036.7.3 Data Normalization (Example of Label]Free Proteomics Using Ion Intensities) 1046.7.4 Statistics in Proteomics Analysis 1056.8 Validation of Findings 1056.9 Clinical Applications of Body Fluid Proteomics 1066.10 Conclusions 109References 1097 Peptidomics of Body Fluids 113Prathibha Reddy, Claudia Pontillo, Joachim Jankowski, and Harald Mischak7.1 Introduction 1137.2 Clinical Application of Peptidomics 1137.3 Different Types of Body Fluids Used in Biomarker Research 1137.3.1 Blood 1137.3.2 Urine 1147.4 Sample Preparation and Separation Methods for Mass Spectrometric Analysis 1157.4.1 Depletion Strategies 1157.4.1.1 Ultrafiltration 1157.4.1.2 Precipitation 1167.4.1.3 Liquid Chromatography 1167.4.1.4 Capillary Electrophoresis 1167.4.1.5 Instrumentation 1177.5 Identification of Peptides and Their Posttranslational Modifications 1177.6 Urinary Peptidomics for Clinical Application 1187.6.1 Kidney Disease 1187.6.2 Urogenital Cancers 1197.6.3 Blood Peptides as Source of Biomarkers 1207.6.4 Proteases and Their Role in Renal Diseases and Cancer 1207.7 Concluding Remarks 122References 1228 Tissue Proteomics 129Agnieszka Latosinska, Antonia Vlahou, and Manousos Makridakis8.1 Introduction 1298.2 Tissue Proteomics Workflow 1308.3 Tissue Sample Collection and Storage 1328.4 Sample Preparation 1338.4.1 Homogenization of Fresh]Frozen Tissue 1338.4.1.1 Mechanical Methods of Tissue Homogenization 1358.4.1.2 Chemical Methods of Tissue Homogenization 1368.4.2 LCM 1368.4.3 Protein Digestion 1378.5 Overcoming Tissue Complexity and Protein Dynamic Range: Separation Techniques 1388.5.1 Subcellular Fractionation 1398.5.2 Gel]Based Approaches 1398.5.3 Gel]Free Approaches 1408.6 Instrumentation 1418.6.1 LTQ Orbitrap 1418.6.2 LTQ Orbitrap Velos 1428.6.3 Q Exactive 1428.7 Quantitative Proteomics 1438.8 Functional Annotation of Proteomics Data 1448.9 Application of MS]Based Tissue Proteomics in Bladder Cancer Research 1458.10 Conclusions 148References 1489 Tissue MALDI Imaging 156Andrew Smith, Niccolò Mosele, Vincenzo L’Imperio, Fabio Pagni, and Fulvio Magni9.1 Introduction 1569.1.1 MALDI]MSI: General Principles 1579.2 Experimental Procedures 1599.2.1 Sample Handling: Storage, Embedding, and Sectioning 1599.2.2 Matrix Application 1609.2.3 Spectral Processing 1629.2.3.1 Baseline Removal 1629.2.3.2 Smoothing 1649.2.3.3 Spectral Normalization 1649.2.3.4 Spectral Realignment 1669.2.3.5 Generating an Overview Spectrum 1669.2.3.6 Peak Picking 1669.2.4 Data Elaboration 1689.2.4.1 Unsupervised Data Mining 1689.2.4.2 Supervised Data Mining 1689.2.5 Correlating MALDI]MS Images with Pathology 1699.3 Applications in Clinical Research 169References 17110 Metabolomics of Body Fluids 173Ryan B. Gill and Silke Heinzmann10.1 Introduction to Metabolomics 17310.2 Analytical Techniques 17410.2.1 NMR 17410.2.1.1 Sample Preparation for Urine 17510.2.1.2 Sample Preparation for Blood 17710.2.1.3 Sample Preparation for Tissue 17710.2.1.4 Instrumental Setup 17710.2.2 MS 17810.2.2.1 Ionization 17810.2.2.2 Mass Analyzers 17910.2.2.3 Coupled Separation Methods 17910.2.2.4 MS Sample Pretreatment Techniques 18010.2.3 Protein Removal (PPT) 18110.2.4 LLE 18210.2.5 Solid]Phase Extraction (SPE) 18210.3 Statistical Tools and Systems Integration 18210.3.1 Post]Measurement Spectral Processing 18310.3.2 Spectral Alignment 18310.3.3 Normalization and Scaling 18410.3.4 Peak Versus Feature Detection 18410.3.5 Data Analysis 18410.3.6 Unsupervised 18410.3.7 Supervised 18510.3.8 Spectral Databases and Metabolite Identification 18510.3.9 Pathway Analysis 18610.3.10 Validation and Performance Assessment 18610.3.11 Application into Systems Biology 18710.4 Metabolomics in CKD 18710.4.1 Uremic Toxins and New Biomarkers of eGFR and CKD Stage 18710.4.2 Dimethylarginine 18810.4.3 p]Cresol Sulfate (PCS) 18810.4.4 Indoxyl Sulfate (IS) 18810.4.5 Gut Microbiota 18910.4.6 Osmolytes 19010.5 Conclusions 190References 19111 Statistical Inference in High]Dimensional Omics Data 196Eleni]Ioanna Delatola and Mohammed Dakna11.1 Introduction 19611.2 From Raw Data to Expression Matrices 19611.3 Brief Introduction R and Bioconductor 19711.4 Feature Selection 19711.5 Sample Classification 19911.6 Real Data Example 20011.7 Multi]Platform Data Integration 20011.7.1 Early]Stage Integration 20111.7.2 Late]Stage Integration 20111.7.3 Intermediate]Stage Integration 20211.7.4 Intermediate]Stage Integration: Matrix Factorization 20211.7.5 Intermediate]Stage Integration: Unsupervised Methods 20211.8 Discussion and Further Challenges 202References 20312 Epidemiological Applications in ]Omics Approaches 207Elena Critselis and Hiddo Lambers Heerspink12.1 Overview: Importance of Study Design and Methodology 20712.2 Principles of Hypothesis Testing 20712.2.1 Definition of Research Hypotheses and Clinical Questions 20712.2.2 Hypothesis Testing in Relation to Types of Biomarkers Under Assessment 20812.3 Selection of Appropriate Epidemiological Study Design for Hypothesis Testing 20812.4 Types of Epidemiological Study Designs 20912.4.1 Observational Studies 20912.4.1.1 Cross]Sectional Studies 20912.4.1.2 Case]Control Studies 21012.4.1.3 Cohort Studies 21112.4.1.4 Health Economics Assessment 21112.5 Selection of Appropriate Statistical Analyses for Hypothesis Testing 21112.6 Summary 212References 213Part II Progressing Towards Systems Medicine 21513 Introduction into the Concept of Systems Medicine 217Stella Logotheti and Walter Kolch13.1 Medicine of the Twenty]First Century: From Empirical Medicine and Personalized Medicine to Systems Medicine 21713.2 The Emerging Concept of Systems Medicine 21813.2.1 The Need for Establishment of Systems Medicine and the Field of Application 21813.2.2 Bridging the Gap: From Systems Biology to Systems Medicine 21913.2.3 Attempting a Definition 22013.2.4 The Network]Within]a]Network Approach in Systems Medicine 22013.2.4.1 Great Expectations for Systems Medicine: The P4 Vision 22113.2.4.2 How Systems Medicine Will Transform Healthcare 22213.2.4.3 The Five Pillars of Systems Medicine 22313.2.4.4 The Stakeholders of Systems Medicine 22313.2.4.5 The Key Areas for Successful Implementation 22313.2.4.6 Improvement of the Design of Clinical Trials 22313.2.4.7 Development of Methodology and Technology, with Emphasis on Modeling 22413.2.4.8 Generation of Data 22413.2.4.9 Investment on Technological Infrastructure 22413.2.4.10 Improvement of Patient Stratification 22413.2.4.11 Cooperation with the Industry 22413.2.4.12 Defining Ethical and Regulatory Frameworks 22413.2.4.13 Multidisciplinary Training 22513.3 Networking Among All Key Stakeholders 22513.4 Coordinated European Efforts for Dissemination and Implementation 22513.5 The Contributions of Academia in Systems Medicine 22613.6 Data Generation: Omics Technologies 22613.7 Data Integration: Identifying Disease Modules and Multilayer Disease Modules 22713.8 Modeling: Computational and Animal Disease Models for Understanding the Systemic Context of a Disease 22813.9 Examples and Success Stories of Systems Medicine]Based Approaches 22813.10 Limitations, Considerations, and Future Challenges 229References 23014 Knowledge Discovery and Data Mining 233Magdalena Krochmal and Holger Husi14.1 Introduction 23314.2 Knowledge Discovery Process 23314.2.1 Defining the Concept and Goals 23414.2.2 Data Preparation/Preprocessing 23514.2.3 Database Systems 23614.2.4 Data Mining Tasks and Methods 23614.2.4.1 Statistics 23814.2.4.2 Machine Learning 23914.2.4.3 Text Mining 24114.2.5 Pattern Evaluation 24214.3 Data Mining in Scientific Applications 24214.3.1 Genomics Data Mining 24314.3.2 Proteomics Data Mining 24314.4 Bioinformatics Data Mining Tools 24414.5 Conclusions 244References 24515 -Omics and Clinical Data Integration 248Gaia De Sanctis, Riccardo Colombo, Chiara Damiani, Elena Sacco, and Marco Vanoni15.1 Introduction 24815.2 Data Sources 24915.3 Integration of Different Data Sources 25215.4 Integration of Different ]Omics Data 25215.4.1 Integrating Transcriptomics and Proteomics 25215.4.2 Integrating Transcriptomics and Interactomics 25315.4.3 Integrating Transcriptomics and Metabolic Pathways 25415.5 Visualization of Integrated ]Omics Data 25515.6 Integration of ]Omics Data into Models 26015.6.1 Multi]Omics Data Integration into Genome]Scale Constraint]Based Models 26215.7 Data Integration and Human Health 26315.7.1 Applications to Metabolic Diseases 26315.7.2 Applications to Cancer Research 26415.8 Conclusions 265References 26516 Generation of Molecular Models and Pathways 274Amel Bekkar, Julien Dorier, Isaac Crespo, Anne Niknejad, Alan Bridge, and Ioannis Xenarios16.1 Introduction 27416.2 PKN Construction Through Expert Biocuration 27416.3 Modeling and Simulating the Dynamical Behavior of Networks 27616.3.1 Logic Models 27616.3.1.1 Boolean Networks 27616.3.1.2 Probabilistic Boolean Networks (PBN) 27816.3.1.3 Multiple Value Modeling 27816.3.1.4 Fuzzy Logic]Based Modeling 27816.3.1.5 Contextualization of PKNs Using Experimental Data 27916.3.1.6 Ordinary Differential Equations 28016.3.1.7 Piecewise Linear Differential Equations 28016.3.1.8 Constraint]Based Modeling 28116.3.1.9 Hybrid Models 28216.4 Conclusions 283References 28317 Database Creation and Utility 286Magdalena Krochmal, Katryna Cisek, and Holger Husi17.1 Introduction 28617.2 Database Systems 28617.2.1 Introduction to Databases 28617.2.2 Data Life Cycle and Objectives of Database Systems 28617.2.3 Advantages and Limitations 28817.2.4 Database Design Models 28817.2.5 Development Life Cycle 29117.2.6 Database Transactions, Structured Query Language (SQL) 29217.2.7 Data Analysis and Visualization 29217.3 Biological Databases 29317.3.1 Development Life Cycle 29417.3.1.1 Data Extraction 29417.3.1.2 Semantic Tools for ]Omics 29417.3.2 Existing Biological Repositories 29517.3.2.1 Information Sources for ]Omics 29517.3.2.2 Renal Information Sources for ]Omics 29617.3.3 Application in Research 29717.3.3.1 Data Mining on Large Multi]Omics Datasets 29717.3.3.2 Multi]Omics Tools for Researchers 29717.3.3.3 Limitations of Multi]Omics Tools 29717.3.3.4 Future Outlook for Multi]Omics 29817.4 Conclusions 298References 298Part III Test Cases CKD and Bladder Carcinoma 30118 Kidney Function, CKD Causes, and Histological Classification 303Franco Ferrario, Fabio Pagni, Maddalena Bolognesi, Elena Ajello, Vincenzo L’Imperio, Cristina Masella, and Giovambattista Capasso18.1 Introduction 30318.2 The Evaluation of Glomerular Filtration Rate 30318.3 Causes of CKD 30518.3.1 Histological Classification of CKD 30718.4 Assessment of Disease Progression and Response to Therapy for the Individual: Interval Renal Biopsy 31018.5 Recent Advances: Pathology at the Molecular Level 31018.6 Digital Pathology 31318.7 Conclusions 315References 31519 CKD: Diagnostic and Other Clinical Needs 319Alberto Ortiz19.1 The Evolving Concept of Chronic Kidney Disease 31919.2 A Growing Epidemic 32019.3 Increasing Mortality from Chronic Kidney Disease 32119.4 The Issue of Cause and Etiologic Therapy 32219.5 Unmet Medical Needs: Biomarkers and Therapy 32319.6 Conclusions 324Acknowledgments 324References 32420 Molecular Model for CKD 327Marco Fernandes, Katryna Cisek, and Holger Husi20.1 Introduction 32720.2 Data]Driven Approaches and Multiomics Data Integration 32720.2.1 Database Resources 32820.2.2 Software Tools and Solutions 33020.2.2.1 Gene Ontology (GO) and Pathway]Term Enrichment 33120.2.2.2 Disease–Gene Associations 33120.2.2.3 Resolving Molecular Interactions (Protein–Protein Interaction, Metabolite–Reaction–Protein–Gene) 33220.2.2.4 Transcription Factor(TF)]Driven Modules and microRNA–Target Regulation 33220.2.2.5 Pathway Visualization and Mapping 33320.2.2.6 Data Harmonization: Merging and Mapping 33320.2.3 Computational Drug Discovery 33420.2.3.1 High]Throughput Virtual Screening (HTVS) 33420.2.3.2 Advantages and Limitations of HTVS 33420.3 Chronic Kidney Disease (CKD) Case Study 33520.3.1 Dataspace Description: Demographics and Omics Platforms Information 33720.3.2 Dataspace Description: No. of Associated Molecules Per Omics Platform 33720.3.3 Data Reduction by Principal Component Analysis (PCA) 33820.3.4 Gene Ontology (GO) and Pathway]Term Clustering 33920.3.5 Interactome Analysis: PPIs and Regulatory Interactions 34220.3.5.1 Protein–Protein Interactions (PPIs) 34220.3.5.2 Regulatory Interactions 34320.3.6 Interactome Analysis: Metabolic Reactions 34320.4 Final Remarks 343Acknowledgments 343Conflict of Interest Statement 343References 34521 Application of Omics and Systems Medicine in Bladder Cancer 347Maria Frantzi, Agnieszka Latosinska, Murat Akand, and Axel S. Merseburger21.1 Introduction 34721.2 Bladder Cancer Pathology and Clinical Needs 34821.2.1 Epidemiological Facts and Histological Classification 34821.2.2 Current Diagnostic Means 34821.2.3 Treatment Options 34921.2.4 Recurrence and Progression 34921.2.5 Molecular Classification 35021.2.6 Biomarkers for Bladder Cancer 35021.2.7 Considerations on Patient Management 35121.2.8 Defining the Disease]Associated Clinical Needs 35121.3 Systems Medicine in Bladder Cancer 35121.3.1 Omics Datasets for Biomarker Research 35321.3.1.1 Diagnostic Biomarkers for Disease Detection/Monitoring 35321.3.1.2 Prognostic Signatures 35421.3.1.3 Predictive Molecular Profiles 35521.3.1.4 Molecular Sub]Classification 35621.4 Outlook 357Acknowledgments 357References 358Index 361