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.
Subject(s)
COVID-19/immunology , Computational Biology/methods , Databases, Factual , SARS-CoV-2/immunology , Software , Antiviral Agents/therapeutic use , COVID-19/genetics , COVID-19/virology , Computer Graphics , Cytokines/genetics , Cytokines/immunology , Data Mining/statistics & numerical data , Gene Expression Regulation , Host Microbial Interactions/genetics , Host Microbial Interactions/immunology , Humans , Immunity, Cellular/drug effects , Immunity, Humoral/drug effects , Immunity, Innate/drug effects , Lymphocytes/drug effects , Lymphocytes/immunology , Lymphocytes/virology , Metabolic Networks and Pathways/genetics , Metabolic Networks and Pathways/immunology , Myeloid Cells/drug effects , Myeloid Cells/immunology , Myeloid Cells/virology , Protein Interaction Mapping , SARS-CoV-2/drug effects , SARS-CoV-2/genetics , SARS-CoV-2/pathogenicity , Signal Transduction , Transcription Factors/genetics , Transcription Factors/immunology , Viral Proteins/genetics , Viral Proteins/immunology , COVID-19 Drug TreatmentABSTRACT
PRL-3 belongs to the PRL phosphatase family. Its physiological role remains unclear, but many studies have identified that PRL-3 is a marker of cancer progression and shown it to be associated with metastasis. Evidence implicating PRL-3 in various elements of the metastatic process, such as the cell cycle, survival, angiogenesis, adhesion, cytoskeleton remodeling, EMT, motility and invasion, has been reported. Furthermore, several molecules acting as direct or indirect substrates have been identified. However, this information was obtained in many different studies, and it remains difficult to see the larger picture. We therefore systematically collected the published information together and used it to develop a comprehensive signaling network map. By analyzing this network map, we were able to retrieve the signaling pathways via which PRL-3 governs the key steps of the metastatic process in cancer. In this review, we summarize current knowledge of the role of PRL-3 in cancer and the molecular mechanisms involved. We also provide the web-based open-source PRL-3 signaling network map, for use in further studies.
