MatrixDB, the extracellular matrix interaction database

Network-based proteomic approaches reveal the neurodegenerative, neuroprotective and pain-related mechanisms involved after retrograde axonal damage

Neurodegenerative processes are preceded by neuronal dysfunction and synaptic disconnection. Disconnection between spinal motoneuron (MN) soma and synaptic target leads either to a retrograde degenerative process or to a regenerative reaction, depending injury proximity among other factors. Distinguished key events associated with one or other processes may give some clues towards new therapeutical approaches based on boosting endogenous neuroprotective mechanisms. Root mechanical traction leads to retrograde MN degeneration, but share common initial molecular mechanisms with a regenerative process triggered by distal axotomy and suture. By 7 days post-injury, key molecular events starts to diverge and sign apart each destiny. We used comparative unbiased proteomics to define these signatures, coupled to a novel network-based analysis to get biological meaning. The procedure implicated the previous generation of combined topological information from manual curated 19 associated biological processes to be contrasted with the proteomic list using gene enrichment analysis tools. The novel and unexpected results suggested that motoneurodegeneration is better explained mainly by the concomitant triggering of anoikis, anti-apoptotic and neuropathic-pain related programs. In contrast, the endogenous neuroprotective mechanisms engaged after distal axotomy included specifically rather anti-anoikis and selective autophagy. Validated protein-nodes and processes are highlighted across discussion.

Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study

The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative–molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

Protein-protein interaction databases

Years of meticulous curation of scientific literature and increasingly reliable computational predictions have resulted in creation of vast databases of protein interaction data. Over the years, these repositories have become a basic framework in which experiments are analyzed and new directions of research are explored. Here we present an overview of the most widely used protein-protein interaction databases and the methods they employ to gather, combine, and predict interactions. We also point out the trade-off between comprehensiveness and accuracy and the main pitfall scientists have to be aware before adopting protein interaction databases in any single-gene or genome-wide analysis.

MatrixDB, the extracellular matrix interaction database: updated content, a new navigator and expanded functionalities

MatrixDB (http://matrixdb.ibcp.fr) is a freely available database focused on interactions established by extracellular proteins and polysaccharides. It is an active member of the International Molecular Exchange (IMEx) consortium and has adopted the PSI-MI standards for annotating and exchanging interaction data, either at the MIMIx or IMEx level. MatrixDB content has been updated by curation and by importing extracellular interaction data from other IMEx databases. Other major changes include the creation of a new website and the development of a novel graphical navigator, iNavigator, to build and expand interaction networks. Filters may be applied to build sub-networks based on a list of biomolecules, a specified interaction detection method and/or an expression level by tissue, developmental stage, and health state (UniGene data). Any molecule of the network may be selected and its partners added to the network at any time. Networks may be exported under Cytoscape and tabular formats and as images, and may be saved for subsequent re-use.

New tricks for "old" domains: how novel architectures and promiscuous hubs contributed to the organization and evolution of the ECM

The extracellular matrix (ECM) is a defining characteristic of metazoans and consists of a meshwork of self-assembling, fibrous proteins, and their functionally related neighbours. Previous studies, focusing on a limited number of gene families, suggest that vertebrate complexity predominantly arose through the duplication and subsequent modification of retained, preexisting ECM genes. These genes provided the structural underpinnings to support a variety of specialized tissues, as well as a platform for the organization of spatio-temporal signaling and cell migration. However, the relative contributions of ancient versus novel domains to ECM evolution have not been quantified across the full range of ECM proteins. Here, utilizing a high quality list comprising 324 ECM genes, we reveal general and clade-specific domain combinations, identifying domains of eukaryotic and metazoan origin recruited into new roles in approximately two-third of the ECM proteins in humans representing novel vertebrate proteins. We show that, rather than acquiring new domains, sampling of new domain combinations has been key to the innovation of paralogous ECM genes during vertebrate evolution. Applying a novel framework for identifying potentially important, noncontiguous, conserved arrangements of domains, we find that the distinct biological characteristics of the ECM have arisen through unique evolutionary processes. These include the preferential recruitment of novel domains to existing architectures and the utilization of high promiscuity domains in organizing the ECM network around a connected array of structural hubs. Our focus on ECM proteins reveals that distinct types of proteins and/or the biological systems in which they operate have influenced the types of evolutionary forces that drive protein innovation. This emphasizes the need for rigorously defined systems to address questions of evolution that focus on specific systems of interacting proteins.

The complex portal--an encyclopaedia of macromolecular complexes

The IntAct molecular interaction database has created a new, free, open-source, manually curated resource, the Complex Portal (www.ebi.ac.uk/intact/complex), through which protein complexes from major model organisms are being collated and made available for search, viewing and download. It has been built in close collaboration with other bioinformatics services and populated with data from ChEMBL, MatrixDB, PDBe, Reactome and UniProtKB. Each entry contains information about the participating molecules (including small molecules and nucleic acids), their stoichiometry, topology and structural assembly. Complexes are annotated with details about their function, properties and complex-specific Gene Ontology (GO) terms. Consistent nomenclature is used throughout the resource with systematic names, recommended names and a list of synonyms all provided. The use of the Evidence Code Ontology allows us to indicate for which entries direct experimental evidence is available or if the complex has been inferred based on homology or orthology. The data are searchable using standard identifiers, such as UniProt, ChEBI and GO IDs, protein, gene and complex names or synonyms. This reference resource will be maintained and grow to encompass an increasing number of organisms. Input from groups and individuals with specific areas of expertise is welcome.

VirusMentha: a new resource for virus-host protein interactions

Viral infections often cause diseases by perturbing several cellular processes in the infected host. Viral proteins target host proteins and either form new complexes or modulate the formation of functional host complexes. Describing and understanding the perturbation of the host interactome following viral infection is essential for basic virology and for the development of antiviral therapies. In order to provide a general overview of such interactions, a few years ago we developed VirusMINT. We have now extended the scope and coverage of VirusMINT and established VirusMentha, a new virus-virus and virus-host interaction resource build on the detailed curation protocols of the IMEx consortium and on the integration strategies developed for mentha. VirusMentha is regularly and automatically updated every week by capturing, via the PSICQUIC protocol, interactions curated by five different databases that are part of the IMEx consortium. VirusMentha can be freely browsed at http://virusmentha.uniroma2.it/ and its complete data set is available for download.

Building and exploring an integrated human kinase network: global organization and medical entry points

Biological matter is organized in functional networks of different natures among which kinase-substrate and protein-protein interactions play an important role. Large public data collections allowed us to compile an important corpus of interaction data around human protein kinases. One of the most interesting observations analyzing this network is that coherence in kinase functional activity relies on kinase substrate interactions primarily and not on which protein complexes are formed around them. Further dissecting the two types of interactions at the level of kinase groups (CMGCs, Tyrosine kinases, etc.) we show a prevalence of intra-group interconnectivity, which we can naturally relate to current scenarios of evolution of biological networks. Tracking publication dates we observe high correlation of kinase interaction research focus with general kinase research. We find a similar bias in the targets of kinase inhibitors that feature high redundancy. Finally, intersecting kinase inhibitor specificity with sets of kinases located at specific positions in the kinase network, we propose alternative options for future therapeutic strategies using these compounds.

BIOLOGICAL SIGNIFICANCE

Despite its importance for cellular regulation and the fact that protein kinases feature prominent targets of modern therapeutic approaches, the structure and logic of the global, integrated protein phosphorylation network have not been investigated intensively. To focus on the regulatory skeleton of the phosphorylation network, we contemplated a network consisting of kinases, their substrates, and publicly available physical protein interactions. Analysis of this network at multiple levels allowed establishing a series of interesting properties such as prevalence of kinase substrate interactions as opposed to general protein-protein interactions for establishing a holistic control over kinases activities. Kinases controlling many or a few only other kinases, in addition to non-kinases, were distributed in cellular compartments differently. They were also targeted by kinase inhibitors with distinct success rates. Non-kinases tightly regulated by a large number of kinases were involved in biological processes both specific and shared with their regulators while being preferably localized in the nucleus. Collectively, these observations may provide for a new perspective in the elaboration of pharmacological intervention strategies. We complemented our study of kinase interactions with a perspective of how this type of data is generated in comparison with general research about those enzymes. Namely, what was the temporal evolution of the research community attention for interaction versus non-interaction-based kinase experiments. This article is part of a Special Issue entitled: "20years of Proteomics" in memory of Viatliano Pallini" Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

Engineering of synthetic cellular microenvironments: implications for immunity

In this article, we discuss novel synthetic approaches for studying the interactions of cells with their microenvironment. Notably, critical cellular processes such as growth, differentiation, migration, and fate determination, are tightly regulated by interactions with neighboring cells, and the surrounding extracellular matrix. Given the huge complexity of natural cellular environments, and their rich molecular and physical diversity, the mission of understanding "environmental signaling" at a molecular-mechanistic level appears to be extremely challenging. To meet these challenges, attempts have been made in recent years to design synthetic matrices with defined chemical and physical properties, which, artificial though they may be, could reveal basic "design principles" underlying the physiological processes. Here, we summarize recent developments in the characterization of the chemical and physical properties of cell sensing and adhesion, as well as the design and use of engineered, micro- to nanoscale patterned and confined environments, for systematic, comprehensive modulation of the cells' environment. The power of these biomimetic surfaces to highlight environmental signaling events in cells, and in immune cells in particular, will be discussed.

Protein-protein interactions and genetic diseases: The interactome

Protein-protein interactions mediate essentially all biological processes. Despite the quality of these data being widely questioned a decade ago, the reproducibility of large-scale protein interaction data is now much improved and there is little question that the latest screens are of high quality. Moreover, common data standards and coordinated curation practices between the databases that collect the interactions have made these valuable data available to a wide group of researchers. Here, I will review how protein-protein interactions are measured, collected and quality controlled. I discuss how the architecture of molecular protein networks has informed disease biology, and how these data are now being computationally integrated with the newest genomic technologies, in particular genome-wide association studies and exome-sequencing projects, to improve our understanding of molecular processes perturbed by genetics in human diseases. This article is part of a Special Issue entitled: From Genome to Function.

Extended interaction network of procollagen C-proteinase enhancer-1 in the extracellular matrix

PCPE-1 (procollagen C-proteinase enhancer-1) is an extracellular matrix glycoprotein that can stimulate procollagen processing by procollagen C-proteinases such as BMP-1 (bone morphogenetic protein 1). PCPE-1 interacts with several proteins in addition to procollagens and BMP-1, suggesting that it could be involved in biological processes other than collagen maturation. We thus searched for additional partners of PCPE-1 in the extracellular matrix, which could provide new insights into its biological roles. We identified 17 new partners of PCPE-1 by SPR (surface plasmon resonance) imaging. PCPE-1 forms a transient complex with the β-amyloid peptide, whereas it forms high or very high affinity complexes with laminin-111 (KD=58.8 pM), collagen VI (KD=9.5 nM), TSP-1 (thrombospondin-1) (KD1=19.9 pM, KD2=14.5 nM), collagen IV (KD=49.4 nM) and endostatin, a fragment of collagen XVIII (KD1=0.30 nM, KD2=1.1 nM). Endostatin binds to the NTR (netrin-like) domain of PCPE-1 and decreases the degree of superstimulation of PCPE-1 enhancing activity by heparin. The analysis of the PCPE-1 interaction network based on Gene Ontology terms suggests that, besides its role in collagen deposition, PCPE-1 might be involved in tumour growth, neurodegenerative diseases and angiogenesis. In vitro assays have indeed shown that the CUB1CUB2 (where CUB is complement protein subcomponents C1r/C1s, urchin embryonic growth factor and BMP-1) fragment of PCPE-1 inhibits angiogenesis.

Presence and utility of intrinsically disordered regions in kinases

We reveal presence of intrinsically disordered regions in human kinome and build a kinase–kinase interaction network identifying a novel SRC–SMAD relationship.

The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases

IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).

Mass spectrometry for proteomics-based investigation

Within the past years, we have witnessed a great improvement in mass spectrometry (MS) and proteomics approaches in terms of instrumentation, protein fractionation, and bioinformatics. With the current technology, protein identification alone is no longer sufficient. Both scientists and clinicians want not only to identify proteins but also to identify the protein's posttranslational modifications (PTMs), protein isoforms, protein truncation, protein-protein interaction (PPI), and protein quantitation. Here, we describe the principle of MS and proteomics and strategies to identify proteins, protein's PTMs, protein isoforms, protein truncation, PPIs, and protein quantitation. We also discuss the strengths and weaknesses within this field. Finally, in our concluding remarks we assess the role of mass spectrometry and proteomics in scientific and clinical settings in the near future. This chapter provides an introduction and overview for subsequent chapters that will discuss specific MS proteomic methodologies and their application to specific medical conditions. Other chapters will also touch upon areas that expand beyond proteomics, such as lipidomics and metabolomics.

Large-scale investigation of Leishmania interaction networks with host extracellular matrix by surface plasmon resonance imaging

We have set up an assay to study the interactions of live pathogens with their hosts by using protein and glycosaminoglycan arrays probed by surface plasmon resonance imaging. We have used this assay to characterize the interactions of Leishmania promastigotes with ~70 mammalian host biomolecules (extracellular proteins, glycosaminoglycans, growth factors, cell surface receptors). We have identified, in total, 27 new partners (23 proteins, 4 glycosaminoglycans) of procyclic promastigotes of six Leishmania species and 18 partners (15 proteins, 3 glycosaminoglycans) of three species of stationary-phase promastigotes for all the strains tested. The diversity of the interaction repertoires of Leishmania parasites reflects their dynamic and complex interplay with their mammalian hosts, which depends mostly on the species and strains of Leishmania. Stationary-phase Leishmania parasites target extracellular matrix proteins and glycosaminoglycans, which are highly connected in the extracellular interaction network. Heparin and heparan sulfate bind to most Leishmania strains tested, and 6-O-sulfate groups play a crucial role in these interactions. Numerous Leishmania strains bind to tropoelastin, and some strains are even able to degrade it. Several strains interact with collagen VI, which is expressed by macrophages. Most Leishmania promastigotes interact with several regulators of angiogenesis, including antiangiogenic factors (endostatin, anastellin) and proangiogenic factors (ECM-1, VEGF, and TEM8 [also known as anthrax toxin receptor 1]), which are regulated by hypoxia. Since hypoxia modulates the infection of macrophages by the parasites, these interactions might influence the infection of host cells by Leishmania.

Heparin-protein interactions: from affinity and kinetics to biological roles. Application to an interaction network regulating angiogenesis

Numerous extracellular proteins, growth factors, chemokines, cytokines, enzymes, lipoproteins, involved in a variety of biological processes, interact with heparin and/or heparan sulfate at the cell surface and in the extracellular matrix (ECM). The goal of this study is to investigate the relationship(s) between affinity and kinetics of heparin-protein interactions and the localization of the proteins, their intrinsic disorder and their biological roles. Most proteins bind to heparin with a higher affinity than their fragments and form more stable complexes with heparin than with heparan sulfate. Lipoproteins and matrisome-associated proteins (e.g. growth factors and cytokines) bind to heparin with very high affinity. Matrisome-associated proteins form transient complexes with heparin. However they bind to this glycosaminoglycan with a higher affinity than the proteins of the core matrisome, which contribute to ECM assembly and organization, and than the secreted proteins which are not associated with the ECM. The association rate of proteins with heparin is related to the intrinsic disorder of heparin-binding sites. Enzyme inhibitor activity, protein dimerization, skeletal system development and pathways in cancer are functionally associated with proteins displaying a high or very high affinity for heparin (KD<100 nM). Besides their use in investigating molecular recognition and functions, kinetics and affinity are essential to prioritize interactions in networks and to build network models as discussed for the interaction network established at the surface of endothelial cells by endostatin, a heparin-binding protein regulating angiogenesis.

DBATE: database of alternative transcripts expression

The use of high-throughput RNA sequencing technology (RNA-seq) allows whole transcriptome analysis, providing an unbiased and unabridged view of alternative transcript expression. Coupling splicing variant-specific expression with its functional inference is still an open and difficult issue for which we created the DataBase of Alternative Transcripts Expression (DBATE), a web-based repository storing expression values and functional annotation of alternative splicing variants. We processed 13 large RNA-seq panels from human healthy tissues and in disease conditions, reporting expression levels and functional annotations gathered and integrated from different sources for each splicing variant, using a variant-specific annotation transfer pipeline. The possibility to perform complex queries by cross-referencing different functional annotations permits the retrieval of desired subsets of splicing variant expression values that can be visualized in several ways, from simple to more informative. DBATE is intended as a novel tool to help appreciate how, and possibly why, the transcriptome expression is shaped.

Database URL:http://bioinformatica.uniroma2.it/DBATE/.

Complementing the Eukaryotic Protein Interactome

Protein interaction networks are important for the understanding of regulatory mechanisms, for the explanation of experimental data and for the prediction of protein functions. Unfortunately, most interaction data is available only for model organisms. As a possible remedy, the transfer of interactions to organisms of interest is common practice, but it is not clear when interactions can be transferred from one organism to another and, thus, the confidence in the derived interactions is low. Here, we propose to use a rich set of features to train Random Forests in order to score transferred interactions. We evaluated the transfer from a range of eukaryotic organisms to S. cerevisiae using orthologs. Directly transferred interactions to S. cerevisiae are on average only 24% consistent with the current S. cerevisiae interaction network. By using commonly applied filter approaches the transfer precision can be improved, but at the cost of a large decrease in the number of transferred interactions. Our Random Forest approach uses various features derived from both the target and the source network as well as the ortholog annotations to assign confidence values to transferred interactions. Thereby, we could increase the average transfer consistency to 85%, while still transferring almost 70% of all correctly transferable interactions. We tested our approach for the transfer of interactions to other species and showed that our approach outperforms competing methods for the transfer of interactions to species where no experimental knowledge is available. Finally, we applied our predictor to score transferred interactions to 83 targets species and we were able to extend the available interactome of B. taurus, M. musculus and G. gallus with over 40,000 interactions each. Our transferred interaction networks are publicly available via our web interface, which allows to inspect and download transferred interaction sets of different sizes, for various species, and at specified expected precision levels.

AVAILABILITY

http://services.bio.ifi.lmu.de/coin-db/.

Optimization criteria and biological process enrichment in homologous multiprotein modules

Biological process enrichment is a widely used metric for evaluating the quality of multiprotein modules. In this study, we examine possible optimization criteria for detecting homologous multiprotein modules and quantify their effects on biological process enrichment. We find that modularity, linear density, and module size are the most important criteria considered, complementary to each other, and that graph theoretical attributes account for 36% of the variance in biological process enrichment. Variations in protein interaction similarity within module pairs have only minor effects on biological process enrichment. As random modules increase in size, both biological process enrichment and modularity tend to improve, although modularity does not show this upward trend in modules with size at most 50 proteins. To adjust for these trends, we recommend a size correction based on random sampling of modules when using biological process enrichment or other attributes to evaluate module boundaries. Characteristics of homologous multiprotein modules optimized for each of the optimization criteria are examined.

Data standardization and sharing-the work of the HUPO-PSI

Significant advances have been made over the past ten years to standardize the data emerging from the proteomic workflows adopted by laboratories all over the world. Differences in workflows, instrumentation, analysis software and reporting methods initially resulted in very disparate data being generated by many of these research groups, making data storage and comparison challenging. As the data standards proposed by the HUPO-PSI have increasingly been adopted, and tools and databases implementing these data formats have become more readily available, data generated by these complex experimental procedures is now becoming easier to manipulate, to visualize and to analyse. Public domain databases now exist to collate the information generated by experimentalists and to make the generation of specific protein expression maps, and monitoring of changes in protein expression levels in response to external stimuli a real possibility. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Defining the extracellular matrix using proteomics

The cell microenvironment has a profound influence on the behaviour, growth and survival of cells. The extracellular matrix (ECM) provides not only mechanical and structural support to cells and tissues but also binds soluble ligands and transmembrane receptors to provide spatial coordination of signalling processes. The ability of cells to sense the chemical, mechanical and topographical features of the ECM enables them to integrate complex, multiparametric information into a coherent response to the surrounding microenvironment. Consequently, dysregulation or mutation of ECM components results in a broad range of pathological conditions. Characterization of the composition of ECM derived from various cells has begun to reveal insights into ECM structure and function, and mechanisms of disease. Proteomic methodologies permit the global analysis of subcellular systems, but extracellular and transmembrane proteins present analytical difficulties to proteomic strategies owing to the particular biochemical properties of these molecules. Here, we review advances in proteomic approaches that have been applied to furthering our understanding of the ECM microenvironment. We survey recent studies that have addressed challenges in the analysis of ECM and discuss major outcomes in the context of health and disease. In addition, we summarize efforts to progress towards a systems-level understanding of ECM biology.

A survey of protein interaction data and multigenic inherited disorders

Background

Multigenic diseases are often associated with protein complexes or interactions involved in the same pathway. We wanted to estimate to what extent this is true given a consolidated protein interaction data set. The study stresses data integration and data representation issues.

Results

We constructed 497 multigenic disease groups from OMIM and tested for overlaps with interaction and pathway data. A total of 159 disease groups had significant overlaps with protein interaction data consolidated by iRefIndex. A further 68 disease overlaps were found only in the KEGG pathway database. No single database contained all significant overlaps thus stressing the importance of data integration. We also found that disease groups overlapped with all three interaction data types: n-ary, spoke-represented complexes and binary data – thus stressing the importance of considering each of these data types separately.

Conclusions

Almost half of our multigenic disease groups could potentially be explained by protein complexes and pathways. However, the fact that no database or data type was able to cover all disease groups suggests that no single database has systematically covered all disease groups for potential related complex and pathway data. This survey provides a basis for further curation efforts to confirm and search for overlaps between diseases and interaction data. The accompanying R script can be used to reproduce the work and track progress in this area as databases change. Disease group overlaps can be further explored using the iRefscape plugin for Cytoscape.

The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies

BACKGROUND

BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research.

RESULTS

The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization.

CONCLUSION

We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

Applications of Mass Spectrometry in Proteomics

Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

Visualization and analysis of biological networks

The study of the interactome-the totality of the protein-protein interactions taking place in a cell-has experienced an enormous growth in the last few years. Biological networks representation and analysis has become an everyday tool for many biologists and bioinformatics, as these interaction graphs allow us to map and characterize signaling pathways and predict the function of unknown proteins. However, given the size and complexity of interactome datasets, extracting meaningful information from interaction networks can be a daunting task. Many different tools and approaches can be used to build, represent, and analyze biological networks. In this chapter, we will use a practical example to guide novice users through this process. We will be making use of the popular open source tool Cytoscape and of other resources such as : the PSICQUIC client to access several protein interaction repositories and the BiNGO plugin to perform GO enrichment analysis of the resulting network.

Overview of extracellular matrix

The extracellular matrix provides an environment for cells. It is produced, assembled, and modified by cells and in turn, it modifies the functions and behavior of the cells it encounters. The molecules that make up the matrix are diverse in both structure and function. This well-illustrated unit provides an introduction to the structure and function of the major components of the extracellular matrix and serves as a background for the other units in the chapter, which include protocols for isolation and analysis of individual components.

Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction

Background

Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction.

Results

Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins – this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein’s membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn.

Conclusions

These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.

The ConsensusPathDB interaction database: 2013 update

Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

Molecular interaction databases

Molecular interaction databases are playing an ever more important role in our understanding of the biology of the cell. An increasing number of resources exist to provide these data and many of these have adopted the controlled vocabularies and agreed-upon standardised data formats produced by the Molecular Interaction workgroup of the Human Proteome Organization Proteomics Standards Initiative (HUPO PSI-MI). Use of these standards allows each resource to establish PSI Common QUery InterfaCe (PSICQUIC) service, making data from multiple resources available to the user in response to a single query. This cooperation between databases has been taken a stage further, with the establishment of the International Molecular Exchange (IMEx) consortium which aims to maximise the curation power of numerous data resources, and provide the user with a non-redundant, consistently annotated set of interaction data.

Knowledge building insights on biomarkers of arsenic toxicity to keratinocytes and melanocytes

Exposure to inorganic arsenic induces skin cancer and abnormal pigmentation in susceptible humans. High-throughput gene transcription assays such as DNA microarrays allow for the identification of biological pathways affected by arsenic that lead to initiation and progression of skin cancer and abnormal pigmentation. The overall purpose of the reported research was to determine knowledge building insights on biomarker genes for arsenic toxicity to human epidermal cells by integrating a collection of gene lists annotated with biological information. The information sets included toxicogenomics gene-chemical interaction; enzymes encoded in the human genome; enriched biological information associated with genes; environmentally relevant gene sequence variation; and effects of non-synonymous single nucleotide polymorphisms (SNPs) on protein function. Molecular network construction for arsenic upregulated genes TNFSF18 (tumor necrosis factor [ligand] superfamily member 18) and IL1R2 (interleukin 1 Receptor, type 2) revealed subnetwork interconnections to E2F4, an oncogenic transcription factor, predominantly expressed at the onset of keratinocyte differentiation. Visual analytics integration of gene information sources helped identify RAC1, a GTP binding protein, and TFRC, an iron uptake protein as prioritized arsenic-perturbed protein targets for biological processes leading to skin hyperpigmentation. RAC1 regulates the formation of dendrites that transfer melanin from melanocytes to neighboring keratinocytes. Increased melanocyte dendricity is correlated with hyperpigmentation. TFRC is a key determinant of the amount and location of iron in the epidermis. Aberrant TFRC expression could impair cutaneous iron metabolism leading to abnormal pigmentation seen in some humans exposed to arsenicals. The reported findings contribute to insights on how arsenic could impair the function of genes and biological pathways in epidermal cells. Finally, we developed visual analytics resources to facilitate further exploration of the information and knowledge building insights on arsenic toxicity to human epidermal keratinocytes and melanocytes.

Negative protein-protein interaction datasets derived from large-scale two-hybrid experiments

Negative protein-protein interaction datasets are needed for training and evaluation of interaction prediction methods, as well as validation of high-throughput interaction discovery experiments. In large-scale two-hybrid assays, the direct interaction of a large number of protein pairs is systematically probed. We present a simple method to harness two-hybrid data to obtain negative protein-protein interaction datasets, which we validated using other available experimental data. The method identifies interactions that were likely tested but not observed in a two-hybrid screen. For each negative interaction, a confidence score is defined as the shortest-path length between the two proteins in the interaction network derived from the two-hybrid experiment. We show that these high-quality negative datasets are particularly important when a specific biological context is considered, such as in the study of protein interaction specificity. We also illustrate the use of a negative dataset in the evaluation of the InterPreTS interaction prediction method.

Protein interaction data curation: the International Molecular Exchange (IMEx) consortium

The International Molecular Exchange (IMEx) consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website (http://www.imexconsortium.org/). Common curation rules have been developed, and a central registry is used to manage the selection of articles to enter into the dataset. We discuss the advantages of such a service to the user, our quality-control measures and our data-distribution practices.

Relationships between the amyloid precursor protein and its various proteolytic fragments and neuronal systems

Alzheimer's disease (AD) is a progressive neurodegenerative disease and in its familial form is associated with mutations in the amyloid precursor protein (APP) and the presenilins (PSs). Much data regarding the interactions of APP, its proteolytic fragments and PS have been generated, expanding our understanding of the roles of these proteins in mechanisms underlying cognitive function and revealing many complex relationships with wide ranging cellular systems. In this review, we examine the multiple interactions of APP and its proteolytic fragments with other neuronal systems in terms of feedback loops and use these relationships to build a map. We highlight the complexity involved in the APP proteolytic system and discuss alternative perspectives on the roles of APP and its proteolytic fragments in dynamic processes associated with disease progression in AD. We highlight areas where data are missing and suggest potential confounding factors. We suggest that a systems biology approach enhances representations of the data and may be more useful in modelling both normal cognition and disease processes.

How to link ontologies and protein-protein interactions to literature: text-mining approaches and the BioCreative experience

There is an increasing interest in developing ontologies and controlled vocabularies to improve the efficiency and consistency of manual literature curation, to enable more formal biocuration workflow results and ultimately to improve analysis of biological data. Two ontologies that have been successfully used for this purpose are the Gene Ontology (GO) for annotating aspects of gene products and the Molecular Interaction ontology (PSI-MI) used by databases that archive protein–protein interactions. The examination of protein interactions has proven to be extremely promising for the understanding of cellular processes. Manual mapping of information from the biomedical literature to bio-ontology terms is one of the most challenging components in the curation pipeline. It requires that expert curators interpret the natural language descriptions contained in articles and infer their semantic equivalents in the ontology (controlled vocabulary). Since manual curation is a time-consuming process, there is strong motivation to implement text-mining techniques to automatically extract annotations from free text. A range of text mining strategies has been devised to assist in the automated extraction of biological data. These strategies either recognize technical terms used recurrently in the literature and propose them as candidates for inclusion in ontologies, or retrieve passages that serve as evidential support for annotating an ontology term, e.g. from the PSI-MI or GO controlled vocabularies. Here, we provide a general overview of current text-mining methods to automatically extract annotations of GO and PSI-MI ontology terms in the context of the BioCreative (Critical Assessment of Information Extraction Systems in Biology) challenge. Special emphasis is given to protein–protein interaction data and PSI-MI terms referring to interaction detection methods.

Analyzing protein-protein interaction networks

The advent of the "omics" era in biology research has brought new challenges and requires the development of novel strategies to answer previously intractable questions. Molecular interaction networks provide a framework to visualize cellular processes, but their complexity often makes their interpretation an overwhelming task. The inherently artificial nature of interaction detection methods and the incompleteness of currently available interaction maps call for a careful and well-informed utilization of this valuable data. In this tutorial, we aim to give an overview of the key aspects that any researcher needs to consider when working with molecular interaction data sets and we outline an example for interactome analysis. Using the molecular interaction database IntAct, the software platform Cytoscape, and its plugins BiNGO and clusterMaker, and taking as a starting point a list of proteins identified in a mass spectrometry-based proteomics experiment, we show how to build, visualize, and analyze a protein-protein interaction network.

Toward a systems level view of the ECM and related proteins: a framework for the systematic definition and analysis of biological systems

Advances in high throughput 'omic technologies are starting to provide unprecedented insights into how components of biological systems are organized and interact. Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems-level view of the human extracellular matrix (ECM)--a three-dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene-disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue-specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM.

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks

Background

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph.

Results

We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation.

Conclusions

The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.

Reactome pathway analysis to enrich biological discovery in proteomics data sets

Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).

Identifying gene interaction networks

In this chapter, we introduce interaction networks by describing how they are generated, where they are stored, and how they are shared. We focus on publicly available interaction networks and describe a simple way of utilizing these resources. As a case study, we used Cytoscape, an open source and easy-to-use network visualization and analysis tool to first gather and visualize a small network. We have analyzed this network's topological features and have looked at functional enrichment of the network nodes by integrating the gene ontology database. The methods described are applicable to larger networks that can be collected from various resources.

Integrated inference and analysis of regulatory networks from multi-level measurements

Regulatory and signaling networks coordinate the enormously complex interactions and processes that control cellular processes (such as metabolism and cell division), coordinate response to the environment, and carry out multiple cell decisions (such as development and quorum sensing). Regulatory network inference is the process of inferring these networks, traditionally from microarray data but increasingly incorporating other measurement types such as proteomics, ChIP-seq, metabolomics, and mass cytometry. We discuss existing techniques for network inference. We review in detail our pipeline, which consists of an initial biclustering step, designed to estimate co-regulated groups; a network inference step, designed to select and parameterize likely regulatory models for the control of the co-regulated groups from the biclustering step; and a visualization and analysis step, designed to find and communicate key features of the network. Learning biological networks from even the most complete data sets is challenging; we argue that integrating new data types into the inference pipeline produces networks of increased accuracy, validity, and biological relevance.

Overview of the matrisome--an inventory of extracellular matrix constituents and functions

Completion of genome sequences for many organisms allows a reasonably complete definition of the complement of extracellular matrix (ECM) proteins. In mammals this "core matrisome" comprises ∼300 proteins. In addition there are large numbers of ECM-modifying enzymes, ECM-binding growth factors, and other ECM-associated proteins. These different categories of ECM and ECM-associated proteins cooperate to assemble and remodel extracellular matrices and bind to cells through ECM receptors. Together with receptors for ECM-bound growth factors, they provide multiple inputs into cells to control survival, proliferation, differentiation, shape, polarity, and motility of cells. The evolution of ECM proteins was key in the transition to multicellularity, the arrangement of cells into tissue layers, and the elaboration of novel structures during vertebrate evolution. This key role of ECM is reflected in the diversity of ECM proteins and the modular domain structures of ECM proteins both allow their multiple interactions and, during evolution, development of novel protein architectures.

MINT, the molecular interaction database: 2012 update

The Molecular INTeraction Database (MINT, http://mint.bio.uniroma2.it/mint/) is a public repository for protein-protein interactions (PPI) reported in peer-reviewed journals. The database grows steadily over the years and at September 2011 contains approximately 235,000 binary interactions captured from over 4750 publications. The web interface allows the users to search, visualize and download interactions data. MINT is one of the members of the International Molecular Exchange consortium (IMEx) and adopts the Molecular Interaction Ontology of the Proteomics Standard Initiative (PSI-MI) standards for curation and data exchange. MINT data are freely accessible and downloadable at http://mint.bio.uniroma2.it/mint/download.do. We report here the growth of the database, the major changes in curation policy and a new algorithm to assign a confidence to each interaction.

Intrinsic disorder of the extracellular matrix

The extracellular matrix is very well organized at the supramolecular and tissue levels and little is known on the potential role of intrinsic disorder in promoting its organization. We predicted the amount of disorder and identified disordered regions in the human extracellular proteome with established computational tools. The extracellular proteome is significantly enriched in proteins comprising more than 50% of disorder compared to the complete human proteome. The enrichment is mostly due to long disordered regions containing at least 100 consecutive disordered residues. The amount of intrinsic disorder is heterogeneous in the extracellular protein families, with the most disordered being collagens and the small integrin-binding ligand N-linked glycoproteins. Although most domains found in extracellular proteins are structured, the fibronectin III domains contain a variable amount of disordered residues (up to 92%). Binding sites for heparin and integrins are found in disordered sequences of extracellular proteins. Intrinsic disorder is evenly distributed in hubs and ends in the interaction network of extracellular proteins with their extracellular partners. In contrast, extracellular hubs are significantly enriched in disorder in the network of extracellular proteins with their extracellular, membrane and intracellular partners. Disorder could thus provide the structural plasticity required for the hubs to interact with membrane and intracellular proteins. Organization and assembly of the extracellular matrix, development of mineralized tissues and cell-matrix adhesion are the biological processes overrepresented in the most disordered extracellular proteins. Extracellular disorder is associated with binding to growth factors, glycosaminoglycans and integrins at the molecular level.