This book covers different omics aspects related to the extracellular matrix (ECM), namely specific omics resources focused on the extracellular matrix (e.g., databases, repositories and atlases), quantitative proteomics applied to specific extracellular matrices (e.g. basement membranes), biological processes such as ECM degradation (degradomics), cell-matrix interactions (adhesomes), signaling pathways, biomarker discovery and diseases, and interactomics (extracellular matrix interaction networks including not only protein-protein but also protein-glycosaminoglycan interactions). The volume also includes recent advances in glycomics and glycobioinformatics applied to proteoglycans and glycosaminoglycans, which are key biological players. The use of omics data to build dynamic models of ECM-regulated biological pathways is addressed, together with the requirement to standardize omic data, which is a prerequisite for the FAIR (Findability, Accessibility, Interoperability, and Reusability) guiding principles for scientific data management.
This book will be of great interest to a broad readership from beginners to advanced researchers, who are interested in extracellular matrix omics and will inspire future research topics.
Author(s): Sylvie Ricard-Blum
Series: Biology of Extracellular Matrix, 7
Publisher: Springer
Year: 2021
Language: English
Pages: 225
City: Cham
Preface
Contents
Chapter 1: The Extracellular Matrix Goes -Omics: Resources and Tools
1.1 Introduction
1.2 ECM Knowledge Databases
1.2.1 The Matrisome Project and MatrisomeDB
1.2.2 The Laminin Database
1.2.3 MatrixDB, the ECM Interaction Database
1.2.4 The Consensus Adhesome
1.3 Resources for the Study of ECM-Related Diseases
1.4 Conclusions and Perspectives
References
Chapter 2: The Matrisome of Model Organisms: From In-Silico Prediction to Big-Data Annotation
2.1 Introduction
2.2 Gene Ontology Annotations of ECM Proteins
2.3 Prototypical Organization of an ECM Protein
2.3.1 Protein Export and Signal-Peptide Prediction
2.3.2 What Are Protein Domains and How Can We Predict Them?
2.3.2.1 The Example of the von Willebrand Factor A Domain
2.3.2.2 Profile Hidden Markov Models to Predict Domain Homology
2.3.2.3 The Special Case of Repeats or Motifs
2.3.3 Predicting ECM Proteins by Machine-Learning-Based Approaches
2.4 Combining Structural Features and Prior Knowledge to Define the Matrisome of Organisms
2.4.1 Defining the Human and Murine Matrisomes
2.4.2 The Zebrafish Matrisome
2.4.3 The Quail Matrisome
2.4.4 The Drosophila Matrisome
2.4.5 The C. elegans Matrisome
2.4.6 The Planarian Matrisome
2.5 Conclusion and Future Directions
References
Chapter 3: Detecting Changes to the Extracellular Matrix in Liver Diseases
3.1 The Extracellular Matrix (ECM) of the Liver
3.2 Balance and Imbalance of ECM Turnover in the Liver
3.2.1 De Novo Synthesis
3.2.2 Maturation of ECM Through Post-Translational Modifications
3.2.3 ECM Degradation
3.3 Critical Role of Inflammation in Chronic Liver Disease
3.4 The Role of the Extracellular Matrix in Liver Diseases-More than Fibrosis and Collagen
3.5 The Hepatic Matrisome and the Control of Inflammation
3.5.1 Maintenance of Structure
3.5.2 Facilitation of Infiltration
3.5.3 Management of Storage, Presentation and Sensing
3.6 ECM Remodeling and the ``Degradome´´
3.7 Proteomic Analysis of the Hepatic Matrisome
3.7.1 Overview
3.7.2 3-Step ECM Extraction (Fig. 3.3)
3.7.3 Sample Cleanup and Preparation for Liquid Chromatography and Mass Spectrometry
3.7.4 Liquid Chromatography and Tandem Mass Spectrometry
3.7.5 Informatics
3.8 Summary and Conclusions
References
Chapter 4: Characterization of Proteoglycanomes by Mass Spectrometry
4.1 Introduction
4.2 Extraction of Proteoglycans from Tissue
4.3 Proteoglycan Isolation by Anion Exchange Chromatography
4.4 Analysis of Isolated Proteoglycans by Mass Spectrometry
4.5 Conclusions and Future Perspectives
References
Chapter 5: Historical Overview of Integrated GAG-omics and Proteomics
5.1 Introduction
5.2 Glycosaminoglycan Analysis/GAG-omics
5.2.1 Overview of GAGs
5.2.2 Analytical Challenges of GAG Analysis
5.2.3 GAG LC-MS/MS Analysis Using SEC and Amide-HILIC
5.2.4 GAG CE-LIF Analysis
5.2.5 GAG HILIC-CHIP-MS Based Analysis
5.2.6 Tetraplex Stable Isotope-Coded Based Quantitative GAG Glycomics
5.2.7 GAG Disaccharide Analysis Using HILIC LC-MS
5.3 On-Slide Tissue Digestion Coupled with LC-MS/MS for Integrated Glycomics and Proteomics
5.4 In Solution Tissue Digestion for Integrated Proteomics and Glycomics
5.5 Deep Sequencing of Proteoglycans
5.6 Conclusions
References
Chapter 6: Extracellular Matrix Networks: From Connections to Functions
6.1 Introduction
6.2 Experimental Identification of ECM Protein and Proteoglycan Interactions
6.2.1 Yeast Two-Hybrid Assays
6.2.2 ECM Protein, Peptide and Glycosaminoglycan Arrays
6.2.3 Affinity Purification: Mass Spectrometry (AP-MS)
6.2.4 Interaction Databases and Large Interaction Datasets
6.3 Extracellular Matrix Interaction Networks
6.3.1 Interaction Networks of Individual ECM Components
6.3.2 Interactomes of ECM Protein, Glycosaminoglycan or Proteoglycan Families
6.3.3 Networks of ECM Supramolecular Assemblies and Basement Membranes
6.3.4 Networks Associated with ECM Degradation and ECM-Cell Interactions
6.3.5 ECM Interaction Networks Associated with Physiological and Pathological Processes
6.3.5.1 Development and Aging
6.3.5.2 Cancer and Angiogenesis
6.3.5.3 Alzheimer´s Disease
6.3.5.4 Host ECM-Pathogen Interactions
6.3.5.5 Heritable Diseases
6.4 Concluding Remarks and Perspectives
References
Chapter 7: Integration of Matrisome Omics: Towards System Biology of the Tumor Matrisome
7.1 Introduction
7.2 Transcriptomics of the Tumor Matrisome
7.2.1 Integrative Expression Profiling and ``Classical´´ Gene Signature Studies
7.2.2 Walkthrough: A Guided Example of Pan-Cancer Integrative Analysis of the Tumor Matrisome Using CBioPortal
7.2.3 From Transcriptomics to Regulomics
7.3 Analysis of Cancer Matrisome at the Protein Level: Towards the Matrisome ``Integrome´´
7.3.1 Mass Spectrometry and Proteomics are Powerful Tools to Unveil Protein Composition in Tissue Samples
7.3.2 Proteomics Data Repositories and Portals
7.3.3 Proteomics Reveal the Basic Components of the Tumor Matrisome
7.3.4 Connecting Proteomics to Other Research Methods in Tumor Matrisome
7.4 Future Perspectives
References
Chapter 8: Proteomic and Degradomic Analysis of Body Fluids: Applications, Challenges and Considerations
8.1 Body Fluids
8.1.1 Blood
8.1.2 Urine
8.1.3 Cerebrospinal Fluid
8.1.4 Synovial Fluid
8.1.5 Salivary and Tear Fluid
8.1.6 Seminal Fluid and Sweat
8.1.7 Wound Exudate
8.1.8 Other Body Fluids and Exosomes
8.2 Mass Spectrometry-Based Proteomics
8.2.1 Discovery Proteomics
8.2.2 Targeted Proteomics
8.3 Sample Preparation
8.3.1 Sample Handling
8.3.2 Dynamic Range Reduction
8.3.2.1 Depletion of High-Abundance Proteins
8.3.2.2 Dynamic Range Compression
8.4 N-Terminal Enrichment and Degradomics
8.4.1 Positive Enrichment
8.4.2 Negative Enrichment
8.4.2.1 Combined Fractional Diagonal Chromatography
8.4.2.2 Terminal Amine Isotopic Labeling of Substrates
8.5 Conclusions
References
Chapter 9: Regulation of Cell-Matrix Adhesion Networks: Insights from Proteomics
9.1 Introduction
9.2 Regulation of Cell-Matrix Adhesions
9.2.1 Bidirectional Integrin Signalling
9.2.2 Adhesion Complex Assembly
9.2.3 Focal Adhesion Architecture
9.2.4 Role of ECM in Cell-Matrix Signalling
9.3 Proteomic Analysis of Cell-Matrix Adhesion
9.3.1 Isolation of Adhesion Complexes
9.3.2 Proteomic Characterisation of Adhesion Complexes
9.3.3 Proximal Adhesion Protein Associations
9.3.4 Phosphoproteomic Analysis of Adhesion Signalling
9.4 Perspectives and Future Directions
References
Chapter 10: Integrative Models for TGF-β Signaling and Extracellular Matrix
10.1 TGF-β and Extracellular Matrix, a Win-Win Relationship
10.2 Modeling Approaches for TGF-β Signaling
10.3 Kappa, a Formalism Adapted to Model the Biological Component Networks of the Extracellular Matrix
10.4 Mesoscale and Multi-Scale Tissue Models Integrating TGF-β Signaling and its Interaction with the ECM
10.5 Conclusion
References
