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

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.

TREAP: A New Topological Approach to Drug Target Inference.

Over 50% of drugs fail in stage 3 clinical trials, many because of a poor understanding of the drug's mechanisms of action (MoA). A better comprehension of drug MoA will significantly improve research and development (R&D). Current proposed algorithms, such as ProTINA and DeMAND, can be overly complex. Additionally, they are unable to predict whether the drug-induced gene expression or the topology of the networks used to model gene regulation primarily impacts accurate drug target inference. In this work, we evaluate how network and gene expression data affect ProTINA's accuracy. We find that network topology predominantly determines the accuracy of ProTINA's predictions. We further show that the size of an interaction network and/or selecting cell-specific networks has a limited effect on accuracy. We then demonstrate that a specific network topology measure, betweenness, can be used to improve drug target prediction. Based on these results, we create a new algorithm, TREAP, that combines betweenness values and adjusted p-values for target inference. TREAP offers an alternative approach to drug target inference and is advantageous because it is not computationally demanding, provides easy-to-interpret results, and is often more accurate at predicting drug targets than current state-of-the-art approaches.

Repurposing the psoriasis drug Oxarol to an ointment adjuvant for the influenza vaccine.

Aluminum precipitates have long been used as adjuvants for human vaccines, but there is a clear need for safer and more effective adjuvants. Here we report in a mouse model that the psoriasis drug Oxarol ointment is a highly effective vaccine adjuvant. By applying Oxarol ointment onto skin, humoral responses and germinal center (GC) reactions were augmented, and the treated mice were protected from death caused by influenza virus infection. Keratinocyte-specific vitamin D3 receptor (Vdr) gene expression was required for these responses through induction of the thymic stromal lymphopoietin (Tslp) gene. Experiments involving administration of recombinant TSLP or, conversely, anti-TSLP antibody demonstrated that TSLP plays a key role in the GC reactions. Furthermore, cell-type-specific Tslpr gene deletion or diphtheria toxin-mediated deletion of specific cell types revealed that CD11c+ cells excluding Langerhans cells were responsible for the Oxarol-mediated GC reactions. These results indicate that active vitamin D3 is able to enhance the humoral response via Tslp induction in the skin and serves as a new vaccine adjuvant.