Type 1 narcolepsy, a disorder caused by a lack of hypocretin (orexin), is so strongly associated with human leukocyte antigen (HLA) class II HLA-DQA1(∗)01:02-DQB1(∗)06:02 (DQ0602) that very few non-DQ0602 cases have been reported. A known triggering factor for narcolepsy is pandemic 2009 influenza H1N1, suggesting autoimmunity triggered by upper-airway infections. Additional effects of other HLA-DQ alleles have been reported consistently across multiple ethnic groups. Using over 3,000 case and 10,000 control individuals of European and Chinese background, we examined the effects of other HLA loci. After careful matching of HLA-DR and HLA-DQ in case and control individuals, we found strong protective effects of HLA-DPA1(∗)01:03-DPB1(∗)04:02 (DP0402; odds ratio [OR] = 0.51 [0.38-0.67], p = 1.01 × 10(-6)) and HLA-DPA1(∗)01:03-DPB1(∗)04:01 (DP0401; OR = 0.61 [0.47-0.80], p = 2.07 × 10(-4)) and predisposing effects of HLA-DPB1(∗)05:01 in Asians (OR = 1.76 [1.34-2.31], p = 4.71 × 10(-05)). Similar effects were found by conditional analysis controlling for HLA-DR and HLA-DQ with DP0402 (OR = 0.45 [0.38-0.55] p = 8.99 × 10(-17)) and DP0501 (OR = 1.38 [1.18-1.61], p = 7.11 × 10(-5)). HLA-class-II-independent associations with HLA-A(∗)11:01 (OR = 1.32 [1.13-1.54], p = 4.92 × 10(-4)), HLA-B(∗)35:03 (OR = 1.96 [1.41-2.70], p = 5.14 × 10(-5)), and HLA-B(∗)51:01 (OR = 1.49 [1.25-1.78], p = 1.09 × 10(-5)) were also seen across ethnic groups in the HLA class I region. These effects might reflect modulation of autoimmunity or indirect effects of HLA class I and HLA-DP alleles on response to viral infections such as that of influenza.

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.

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Cancer initiation and progression are associated with multiple molecular mechanisms. The knowledge of these mechanisms is expanding and should be converted into guidelines for tackling the disease. Here, we discuss the formalization of biological knowledge into a comprehensive resource: the Atlas of Cancer Signalling Network (ACSN) and the Google Maps-based tool NaviCell, which supports map navigation. The application of ACSN for omics data visualization, in the context of signalling maps, is possible via the NaviCell Web Service module and through the NaviCom tool. It allows generation of network-based molecular portraits of cancer using multilevel omics data. We review how these resources and tools are applied for cancer preclinical studies. Structural analysis of the maps together with omics data helps to rationalize the synergistic effects of drugs and allows design of complex disease stage-specific druggable interventions. The use of ACSN modules and maps as signatures of biological functions can help in cancer data analysis and interpretation. In addition, they empowered finding of associations between perturbations in particular molecular mechanisms and the risk to develop a specific type of cancer. These approaches are helpful, among others, to study the interplay between molecular mechanisms of cancer. It opens an opportunity to decipher how gene interactions govern the hallmarks of cancer in specific contexts. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other diseases.

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.