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.

sn2-15-HpETE-PE generated by mammalian 15-lipoxygenase/phosphatidylethanolamine binding protein-1 (PEBP1) complex is a death signal in a recently identified type of programmed cell demise, ferroptosis. How the enzymatic complex selects sn2-ETE-PE as the substrate among 1 of ~100 total oxidizable membrane PUFA phospholipids is a central yet unresolved question. To unearth the highly selective and specific mechanisms of catalytic competence, we used a combination of redox lipidomics, mutational and computational structural analysis to show they stem from: i) reactivity towards readily accessible hexagonally organized membrane sn2-ETE-PEs, ii) relative preponderance of sn2-ETE-PE species vs. other sn2-ETE-PLs, iii) allosteric modification of the enzyme in the complex with PEBP1. This emphasizes the role of enzymatic vs. random stochastic free radical reactions in ferroptotic death signaling.

In our previous work, we built the model of PPARγ dependent pathways involved in the development of the psoriatic lesions. Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor and transcription factor which regulates the expression of many proinflammatory genes. We tested the hypothesis that low levels of PPARγ expression promote the development of psoriatic lesions triggering the IL17-related signaling cascade. Skin samples of normally looking and lesional skin donated by psoriasis patients and psoriatic CD3+ Tcells samples (n = 23) and samples of healthy CD3+ T cells donated by volunteers (n = 10) were analyzed by real-time PCR, ELISA and immunohistochemistry analysis. We found that the expression of PPARγ is downregulated in human psoriatic skin and laser treatment restores the expression. The expression of IL17, STAT3, FOXP3, and RORC in psoriatic skin before and after laser treatment were correlated with PPARγ expression according to the reconstructed model of PPARγ pathway in psoriasis.In conclusion, we report that PPARγ weakens the expression of genes that contribute in the development of psoriatic lesion. Our data show that transcriptional regulation of PPARγ expression by FOSL1 and by STAT3/FOSL1 feedback loop may be central in the psoriatic skin and T-cells.

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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.

We hereby 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. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists. We highlight the role of relevant databases and text mining approaches in enrichment and validation of the curated mechanisms. We describe the contents of the map and their relevance to the molecular pathophysiology of COVID-19 and the analytical and computational modelling approaches that can be applied to the contents of the COVID-19 Disease Map for mechanistic data interpretation and predictions. We conclude by demonstrating concrete applications of our work through several use cases.

Interactions of genes in intersecting signaling pathways, as well as environmental influences, are required for the development of psoriasis. Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor and transcription factor which inhibits the expression of many proinflammatory genes. We tested the hypothesis that low levels of PPARγ expression promote the development of psoriatic lesions. We combined experimental results and network functional analysis to reconstruct the model of PPARγ-downregulated signaling in psoriasis. We hypothesize that the expression of IL17, STAT3, FOXP3, and RORC and FOSL1 genes in psoriatic skin is correlated with the level of PPARγ expression, and they belong to the same signaling pathway that regulates the development of psoriasis lesion.

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