MatrixDB: integration of new data with a focus on glycosaminoglycan interactions

Abstract

MatrixDB (http://matrixdb.univ-lyon1.fr/) is an interaction database focused on biomolecular interactions established by extracellular matrix (ECM) proteins and glycosaminoglycans (GAGs). It is an active member of the International Molecular Exchange (IMEx) consortium (https://www.imexconsortium.org/). It has adopted the HUPO Proteomics Standards Initiative standards for annotating and exchanging interaction data, either at the MIMIx (The Minimum Information about a Molecular Interaction eXperiment) or IMEx level. The following items related to GAGs have been added in the updated version of MatrixDB: (i) cross-references of GAG sequences to the GlyTouCan database, (ii) representation of GAG sequences in different formats (IUPAC and GlycoCT) and as SNFG (Symbol Nomenclature For Glycans) images and (iii) the GAG Builder online tool to build 3D models of GAG sequences from GlycoCT codes. The database schema has been improved to represent n-ary experiments. Gene expression data, imported from Expression Atlas (https://www.ebi.ac.uk/gxa/home), quantitative ECM proteomic datasets (http://matrisomeproject.mit.edu/ecm-atlas), and a new visualization tool of the 3D structures of biomolecules, based on the PDB Component Library and LiteMol, have also been added. A new advanced query interface now allows users to mine MatrixDB data using combinations of criteria, in order to build specific interaction networks related to diseases, biological processes, molecular functions or publications.

Figure 1.

Biomolecule Report page of a glycosaminoglycan entry (GAG_13, a heparin nonasaccharide) where the sequence of the GAG is displayed as an SNFG image and GlycoCT format together with a 3D model of the GAG.

Figure 2.

Interaction networks integrating quantitative proteomic data built with the iNavigator of MatrixDB. An advanced search was performed with ‘Basement membrane’ (BM) query in ‘Biomolecule information’. The query was restricted to human biomolecules only and to those involved in at least one interaction. All the primary hits and the secondary hits were included in the interaction network (A). The global network was then filtered with quantitative proteomic data from human glomerular basement membrane (51 nodes, B), human retinal vascular basement membrane (45 nodes, C), human lens capsule basement membrane (40 nodes, D), and human inner limiting membrane (M) (27 nodes, E). The nodes corresponding to proteins, which were not detected in the proteomic datasets of these membranes (peptide abundance: 0) were deleted from the networks.

Figure 3.

Interaction network of the human glomerular basement membrane integrating quantitative proteomic data (i.e. different threshold values of peptide abundance). The human glomerular basement membrane network was built with different threshold values of peptide abundance in arbitrary units (AU): 0–10⁸ (A), >10⁹ (B), >10¹⁰ (C), >10¹¹ (D) and >10¹² (E).

Figure 4.

Use of the new advanced queries interface to study Ehlers-Danlos syndromes. ‘Ehlers-Danlos’ was used as a search string in several subsections of the advanced queries interface, as shown in the green ‘query history’ window. All queries were restricted to human biomolecules involved in at least one interaction. The ‘diseases’ subsection yielded 13 different syndromes/subtypes, which are associated with a total of 12 proteins in MatrixDB. The ‘publications’ subsection found 61 articles whose titles contain ‘Ehlers-Danlos’ (Primary Hits), and which are associated with 13 biomolecules altogether; and also 25 additional articles as Secondary Hits shown here in blue, whose abstracts contain ‘Ehlers-Danlos’. Moving the mouse over each secondary hit pops up the abstract with the search string highlighted, allowing to easily decide whether the biomolecules associated with a publication should be added to the list of biomolecules of interest, shown in the pink ‘cart’ section. Clicking on ‘build interaction network’ in this cart section launches iNavigator to build a network comprising all selected biomolecules and their partners as a starting point. The interaction networks can then be filtered using gene expression data, proteomic data and interaction detection methods.