COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

Abstract

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Multi-omics approach to COVID-19: a domain-based literature review

“…In addition, the studies published by Ostaszewski et al [ 7 , 8 ] were selected for introducing the concept of Disease Map, since they represent theoretical and hierarchical models of complex biological mechanisms of interaction, describing the involvement of specific pathways, consistent with specific disease phenotypes.…”

“…The visual exploration of diagrams on COVID-19 Disease Map repository allows to parse such clusters at mechanistic level, pointing out potential molecular interactions to be added to the existing diagrams on the repository (e.g., SARS-CoV-2 E protein interactions) [ 7 , 8 ].…”

The reactome pathway knowledgebase 2022

“…Finally, as experimental studies of SARS-CoV-2 have emerged, we have used these results to confirm and, where necessary, revise and extend the inferred SARS-CoV-2 pathways. Working with the COVID-19 Disease Map Community ( 12 , 13 ), we continue to revise and extend our annotations and to integrate them with annotations generated by other members of the community to maintain a comprehensive and up-to-date description of the SARS-CoV-2 infection process (Table 2 ).…”

Human phospho-signaling networks of SARS-CoV-2 infection are rewired by population genetic variants

“…Another related candidate gene BCLAF1 encodes a pro-apoptotic transcription and splicing factor and includes 18 pSNVs ( FDR = 4.2 × 10 −6 ). Apoptosis is triggered in infected host cells as a final form of cellular defense, while activation of anti-apoptotic pathways is a strategy to maximize viral replication 30 . BCLAF1 is also involved in type I interferon signaling and regulates antiviral gene expression upon virus infection 31 .…”

“…The Reactome pathway COPI-dependent Golgi-to-ER retrograde traffic ( FDR = 0.031; R-HSA-6811434) with 19 pSNVs in eight genes included the kinesins KIF1A and KIF3B involved in intracellular transport, and the GTPase signaling protein RACGAP1 . SARS-CoV-2 uses the host translational machinery in the endoplasmic reticulum (ER) for replication, resulting in increased ER stress and activation of unfolded protein response 30,38 . Gene sets related to chromatin organization, DNA repair, cell adhesion, and mitotic cell cycle processes were also enriched.…”