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.

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.

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.

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.

Few pieces of evidence have been published on the use of Apatinib Mesylate (AM) against EGFR-TKI resistance in lung adenocarcinoma (LA) patients. Here, we investigate the clinical efficacy and safety of AM in the treatment of advanced progressed epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) resistant LA patients. We conducted a double-blind, randomized controlled trial in 68 patients admitted to 18 hospitals of Anhui province in China. The efficacy and safety of AM treatment were evaluated in terms of progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR), as well as related adverse events (AE). A literature knowledge database analysis and a pathway model reconstruction were performed to decipher the relevant mechanism may be involved. Our results showed that, compared to the control group, AM presented improved efficacy in PFS (P = 0.033), ORR (P < 0.001), and DCR (P < 0.001). No significant difference was observed between case and control group in terms of AE, and no drug-related death occurred. Pathway analysis supports that Apatinib can be repurposed for the treatment of LA. Our results suggested that AM could be a potential option for advanced progressed LA patients to combat EGFR-TKI resistance.