Search

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.

Graphical 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. SYNOPSIS: COVID‐19 Disease Map is a large‐scale collection of curated computational models and diagrams of molecular mechanisms involved in SARS‐CoV‐2 infection. The map supports the computational exploration of pathways affected by the virus. COVID‐19 Disease Map was built by over 20 independent biocuration teams and harmonised using systems biology standards.Biocuration efforts were assisted by the systematic use of text‐ and AI‐assisted mining of relevant bioinformatic databases and platforms.Case studies illustrate the applications of the map for visual exploration and computational analysis of SARS‐CoV‐2 pathways in combination with omic data.The map is an open‐access effort, with all content and code shared in public repositories. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Landscape Architecture Frontiers is the property of Higher Education Press Limited Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

COVID-19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms GLO:1CEJ/01dec21:msb202110851-toc-0001.jpg PHOTO (COLOR): . gl The authors requested to correct the spelling of Egon Willighagen and Andrea Senff-Ribeiro's names, as well as the following affiliations: Charles Auffray to: "European Institute for Systems Biology and Medicine (EISBM), Vourles, France"; Noriko Hiori to: "Graduate School of Media and Governance, Keio Research Institute at SFC, Keio University, Kanagawa, Japan"; and Leonard Schmeister to: "Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Garching, Germany and Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany". [Extracted from the article]

Motivation: Immune cell dynamics is a critical factor of disease-associated pathology (immunopathology) that also impacts the levels of mRNAs in diseased tissue. Deconvolution algorithms attempt to infer cell quantities in a tissue/organ sample based on gene expression profiles and are often evaluated using artificial, non-complex samples. Their accuracy on estimating cell counts given temporal tissue gene expression data remains not well characterized and has never been characterized when using diseased lung. Further, how to remove the effects of cell migration on transcript counts to improve discovery of disease factors is an open question. Results: Four cell count inference (i.e., deconvolution) tools are evaluated using microarray data from influenza-infected lung sampled at several time points post-infection. The analysis finds that inferred cell quantities are accurate only for select cell types and there is a tendency for algorithms to have a good relative fit (R 2 ) but a poor absolute fit (normalized mean squared error; NMSE), which suggests systemic biases exist. Nonetheless, using cell fraction estimates to adjust gene expression data, we show that genes associated with influenza virus replication and increased infection pathology are more likely to be identified as significant than when applying traditional statistical tests. [ABSTRACT FROM AUTHOR]