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.

Dysregulated expression of proteoglycans influences the outcome and progression of numerous cancers. Several studies have investigated the role of individual glypicans in cancer, however, the impact of the whole glypican family of heparan sulfate proteoglycans on prognosis of a large patient cohort of breast cancer patients has not yet been investigated. In the present study, our aim was to investigate the prognostic power of the glypicans in breast cancer patients.

Aortic valve disease is a complex process characterized by valve interstitial cell activation, disruption of the extracellular matrix culminating in valve mineralization occurring over many years. We explored the function of the retinoblastoma protein (pRb) in aortic valve disease, given its critical role in mesenchymal cell differentiation including bone development and mineralization.

Currently, the multifaceted role of the extracellular matrix (ECM) in tumourigenesis has been realized. One ECM macromolecule exhibiting potent oncosuppressive actions in tumourigenesis is decorin, the prototype of the small leucine-rich proteoglycan gene family. The actions of decorin include its ability to function as an endogenous pan-receptor tyrosine kinase inhibitor, a regulator of both autophagy and mitophagy, as well as a modulator of the immune system. In this review, we will discuss these topics in more detail. We also provide a summary of preclinical studies exploring the value of decorin-mediated oncosuppression, as a potential future adjuvant therapy for epithelial cancers. LINKED ARTICLES: This article is part of a themed section on Translating the Matrix. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.1/issuetoc.

The extracellular matrix (ECM) is a three-dimensional network of proteins of diverse nature, whose interactions are essential to provide tissues with the correct mechanical and biochemical cues they need for proper development and homeostasis. Changes in the quantity of extracellular matrix (ECM) components and their balance within the tumor microenvironment (TME) accompany and fuel all steps of tumor development, growth and metastasis, and a deeper and more systematic understanding of these processes is fundamental for the development of future therapeutic approaches. The wealth of "big data" from numerous sources has enabled gigantic steps forward in the comprehension of the oncogenic process, also impacting on our understanding of ECM changes in the TME. Most of the available studies, however, have not considered the network nature of ECM and the possibility that changes in the quantity of components might be regulated (co-occur) in cancer and significantly "rebound" on the whole network through its connections, fundamentally altering the matrix interactome. To facilitate the exploration of these network-scale effects we have implemented MatriNet (www.matrinet.org), a database enabling the study of structural changes in ECM network architectures as a function of their protein-protein interaction strengths across 20 different tumor types. The use of MatriNet is intuitive and offers new insights into tumor-specific as well as pan-cancer features of ECM networks, facilitating the identification of similarities and differences between cancers as well as the visualization of single-tumor events and the prioritization of ECM targets for further experimental investigations.

The extracellular matrix (ECM) is a complex meshwork of proteins that forms the scaffold of all tissues in multicellular organisms. It plays crucial roles in all aspects of life - from orchestrating cell migration during development, to supporting tissue repair. It also plays critical roles in the etiology or progression of diseases. To study this compartment, we have previously defined the compendium of all genes encoding ECM and ECM-associated proteins for multiple organisms. We termed this compendium the 'matrisome' and further classified matrisome components into different structural or functional categories. This nomenclature is now largely adopted by the research community to annotate '-omics' datasets and has contributed to advance both fundamental and translational ECM research. Here, we report the development of Matrisome AnalyzeR, a suite of tools including a web-based application and an R package. The web application can be used by anyone interested in annotating, classifying and tabulating matrisome molecules in large datasets without requiring programming knowledge. The companion R package is available to more experienced users, interested in processing larger datasets or in additional data visualization options.

Integrin adhesion complexes (IACs) bridge the extracellular matrix to the actin cytoskeleton and transduce signals in response to both chemical and mechanical cues. The composition, interactions, stoichiometry, and topological organization of proteins within IACs are not fully understood. To address this gap, we used multiplexed proximity biotinylation (BioID) to generate an in situ, proximity-dependent adhesome in mouse pancreatic fibroblasts. Integration of the interactomes of 16 IAC-associated baits revealed a network of 147 proteins with 361 proximity interactions. Candidates with underappreciated roles in adhesion were identified, in addition to established IAC components. Bioinformatic analysis revealed five clusters of IAC baits that link to common groups of prey, and which therefore may represent functional modules. The five clusters, and their spatial associations, are consistent with current models of IAC interaction networks and stratification. This study provides a resource to examine proximal relationships within IACs at a global level.

Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson's Disease (PD) and Alzheimer's Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences.

Self-interacting proteins (SIPs), whose more than two identities can interact with each other, play significant roles in the understanding of cellular process and cell functions. Although a number of experimental methods have been designed to detect the SIPs, they remain to be extremely time-consuming, expensive, and challenging even nowadays. Therefore, there is an urgent need to develop the computational methods for predicting SIPs. In this study, we propose a deep forest based predictor for accurate prediction of SIPs using protein sequence information. More specifically, a novel feature representation method, which integrate position-specific scoring matrix (PSSM) with wavelet transform, is introduced. To evaluate the performance of the proposed method, cross-validation tests are performed on two widely used benchmark datasets. The experimental results show that the proposed model achieved high accuracies of 95.43 and 93.65% on human and yeast datasets, respectively. The AUC value for evaluating the performance of the proposed method was also reported. The AUC value for yeast and human datasets are 0.9203 and 0.9586, respectively. To further show the advantage of the proposed method, it is compared with several existing methods. The results demonstrate that the proposed model is better than other SIPs prediction methods. This work can offer an effective architecture to biologists in detecting new SIPs.

Studying protein interaction networks of all proteins in an organism ("interactomes") remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow-up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.

Although dysfunctional protein homeostasis (proteostasis) is a key factor in many age-related diseases, the untargeted identification of structurally modified proteins remains challenging. Peptide location fingerprinting is a proteomic analysis technique capable of identifying structural modification-associated differences in mass spectrometry (MS) data sets of complex biological samples. A new webtool (Manchester Peptide Location Fingerprinter), applied to photoaged and intrinsically aged skin proteomes, can relatively quantify peptides and map statistically significant differences to regions within protein structures. New photoageing biomarker candidates were identified in multiple pathways including extracellular matrix organisation (collagens and proteoglycans), protein synthesis and folding (ribosomal proteins and TRiC complex subunits), cornification (keratins) and hemidesmosome assembly (plectin and integrin α6β4). Crucially, peptide location fingerprinting uniquely identified 120 protein biomarker candidates in the dermis and 71 in the epidermis which were modified as a consequence of photoageing but did not differ significantly in relative abundance (measured by MS1 ion intensity). By applying peptide location fingerprinting to published MS data sets, (identifying biomarker candidates including collagen V and versican in ageing tendon) we demonstrate the potential of the MPLF webtool for biomarker discovery.

Balance between pro-tumor and anti-tumor effects may be affected by molecular interactions within tumor microenvironment. On this basis we searched for molecular partners of ADAMTS-12, a secreted metalloprotease that shows both oncogenic and tumor-suppressive effects. Using its spacer region as a bait in a yeast two-hybrid screen, we identified fibulin-2 as a potential ADAMTS-12-interacting protein. Fibulins are components of basement membranes and elastic matrix fibers in connective tissue. Besides this structural function, fibulins also play crucial roles in different biological events, including tumorigenesis. To examine the functional consequences of the ADAMTS-12/fibulin-2 interaction, we performed different in vitro assays using two breast cancer cell lines: the poorly invasive MCF-7 and the highly invasive MDA-MB-231. Overall our data indicate that this interaction promotes anti-tumor effects in breast cancer cells. To assess the in vivo relevance of this interaction, we induced tumors in nude mice using MCF-7 cells expressing both ADAMTS-12 and fibulin-2 that showed a remarkable growth deficiency. Additionally, we also found that ADAMTS-12 may elicit pro-tumor effects in the absence of fibulin-2. Immunohistochemical staining of breast cancer samples allowed the detection of both ADAMTS-12 and fibulin-2 in the connective tissue surrounding tumor area in less aggressive carcinomas. However, both proteins are hardly detected in more aggressive tumors. These data and survival analysis plots of breast cancer patients suggest that concomitant detection of ADAMTS-12 and fibulin-2 could be a good prognosis marker in breast cancer diagnosis.

Every year more than 8 million people suffer from cancer-related deaths worldwide [1]. Metastasis, the spread of cancer to distant sites, accounts for 90% of these deaths. A promising target for blocking tumor progression, without causing severe side effects [2], is Tumor Endothelial Marker 8 (TEM8), an integrin-like cell surface protein expressed predominantly in the tumor endothelium and in cancer cells [3, 4]. Here, we have investigated the role of TEM8 in cancer progression, angiogenesis and metastasis in invasive breast cancer, and validated the main findings and important results in colorectal cancer. We show that the loss of TEM8 in cancer cells results in inhibition of cancer progression, reduction in tumor angiogenesis and reduced metastatic burden in breast cancer mouse models. Furthermore, we show that TEM8 regulates cancer progression by affecting the expression levels of cell cycle-related genes. Taken together, our findings may have broad clinical and therapeutic potential for breast and colorectal primary tumor and metastasis treatment by targeting TEM8.

Salmonella is the only bacterium able to enter a host cell by the two known mechanisms: trigger and zipper. The trigger mechanism relies on the injection of bacterial effectors into the host cell through the Salmonella type III secretion system 1. In the zipper mechanism, mediated by the invasins Rck and PagN, the bacterium takes advantage of a cellular receptor for invasion. This study describes the transcriptomic reprogramming of the IEC-6 intestinal epithelial cell line to Salmonella Typhimurium strains that invaded cells by a trigger, a zipper, or both mechanisms. Using S. Typhimurium strains invalidated for one or other entry mechanism, we have shown that IEC-6 cells could support both entries. Comparison of the gene expression profiles of exposed cells showed that irrespective of the mechanism used for entry, the transcriptomic reprogramming of the cell was nearly the same. On the other hand, when gene expression was compared between cells unexposed or exposed to the bacterium, the transcriptomic reprogramming of exposed cells was significantly different. It is particularly interesting to note the modulation of expression of numerous target genes of the aryl hydrocarbon receptor showing that this transcription factor was activated by S. Typhimurium infection. Numerous genes associated with the extracellular matrix were also modified. This was confirmed at the protein level by western-blotting showing a dramatic modification in some extracellular matrix proteins. Analysis of a selected set of modulated genes showed that the expression of the majority of these genes was modulated during the intracellular life of S. Typhimurium.

Activation of hepatic stellate cells (HSCs) and subsequent uncontrolled accumulation of altered extracellular matrix (ECM) underpin liver fibrosis, a wound healing response to chronic injury, which can lead to organ failure and death. We sought to catalogue the components of fibrotic liver ECM to obtain insights into disease etiology and aid identification of new biomarkers. Cell-derived ECM was isolated from the HSC line LX-2, an in vitro model of liver fibrosis, and compared to ECM from human foreskin fibroblasts (HFFs) as a control. Mass spectrometry analyses of cell-derived ECMs identified, with ≥99% confidence, 61 structural ECM or secreted proteins (48 and 31 proteins for LX-2 and HFF, respectively). Gene ontology enrichment analysis confirmed the enrichment of ECM proteins, and hierarchical clustering coupled with protein-protein interaction network analysis revealed a subset of proteins enriched to fibrotic ECM, highlighting the existence of cell type-specific ECM niches. Thirty-six proteins were enriched to LX-2 ECM as compared to HFF ECM, of which Wnt-5a and CYR61 were validated by immunohistochemistry in human and murine fibrotic liver tissue. Future studies will determine if these and other components may play a role in the etiology of hepatic fibrosis, serve as novel disease biomarkers, or open up new avenues for drug discovery.

Microbiome-host interactions play significant roles in health and in various diseases including autoimmune disorders. Uncovering these inter-kingdom cross-talks propels our understanding of disease pathogenesis and provides useful leads on potential therapeutic targets. Despite the biological significance of microbe-host interactions, there is a big gap in understanding the downstream effects of these interactions on host processes. Computational methods are expected to fill this gap by generating, integrating, and prioritizing predictions-as experimental detection remains challenging due to feasibility issues. Here, we present MicrobioLink, a computational pipeline to integrate predicted interactions between microbial and host proteins together with host molecular networks. Using the concept of network diffusion, MicrobioLink can analyse how microbial proteins in a certain context are influencing cellular processes by modulating gene or protein expression. We demonstrated the applicability of the pipeline using a case study. We used gut metaproteomic data from Crohn's disease patients and healthy controls to uncover the mechanisms by which the microbial proteins can modulate host genes which belong to biological processes implicated in disease pathogenesis. MicrobioLink, which is agnostic of the microbial protein sources (bacterial, viral, etc.), is freely available on GitHub.

Amyotrophic Lateral Sclerosis is a fatal, progressive neurodegenerative disease characterized by loss of motor neuron function for which there is no effective treatment. One of the main difficulties in developing new therapies lies on the multiple events that contribute to motor neuron death in amyotrophic lateral sclerosis. Several pathological mechanisms have been identified as underlying events of the disease process, including excitotoxicity, mitochondrial dysfunction, oxidative stress, altered axonal transport, proteasome dysfunction, synaptic deficits, glial cell contribution, and disrupted clearance of misfolded proteins. Our approach in this study was based on a holistic vision of these mechanisms and the use of computational tools to identify polypharmacology for targeting multiple etiopathogenic pathways. By using a repositioning analysis based on systems biology approach (TPMS technology), we identified and validated the neuroprotective potential of two new drug combinations: Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine. In addition, we estimated their molecular mechanisms of action in silico and validated some of these results in a well-established in vitro model of amyotrophic lateral sclerosis based on cultured spinal cord slices. The results verified that Aliretinoin and Pranlukast, and Aliretinoin and Mefloquine promote neuroprotection of motor neurons and reduce microgliosis.

Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by k-mers. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on yeast and human datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.

The multifaceted action of thrombospondin-1 (TSP-1) depends on its ability to physically interact with different ligands, including structural components of the extracellular matrix, other matricellular proteins, cell receptors, growth factors, cytokines and proteases. Through this network, TSP-1 regulates the ligand activity, availability and structure, ultimately tuning the cell response to environmental stimuli in a context-dependent manner, contributing to physiological and pathological processes. Complete mapping of the TSP-1 interactome is needed to understand its diverse functions and to lay the basis for the rational design of TSP-1-based therapeutic approaches. So far, large-scale approaches to identify TSP-1 ligands have been rarely used, but many interactions have been identified in small-scale studies in defined biological systems. This review, based on information from protein interaction databases and the literature, illustrates current knowledge of the TSP-1 interactome map.

Self-interacting Proteins (SIPs) play an essential role in a wide range of biological processes, such as gene expression regulation, signal transduction, enzyme activation and immune response. Because of the limitations for experimental self-interaction proteins identification, developing an effective computational method based on protein sequence to detect SIPs is much important. In the study, we proposed a novel computational approach called RVMBIGP that combines the Relevance Vector Machine (RVM) model and Bi-gram probability (BIGP) to predict SIPs based on protein sequence. The proposed prediction model includes as following steps: (1) an effective feature extraction method named BIGP is used to represent protein sequences on Position Specific Scoring Matrix (PSSM); (2) Principal Component Analysis (PCA) method is employed for integrating the useful information and reducing the influence of noise; (3) the robust classifier Relevance Vector Machine (RVM) is used to carry out classification. When performed on yeast and human datasets, the proposed RVMBIGP model can achieve very high accuracies of 95.48% and 98.80%, respectively. The experimental results show that our proposed method is very promising and may provide a cost-effective alternative for SIPs identification. In addition, to facilitate extensive studies for future proteomics research, the RVMBIGP server is freely available for academic use at http://219.219.62.123:8888/RVMBIGP.