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Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing., Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors., Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19., Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies., Competing Interests: AN collaborates with SANOFI-AVENTIS R&D via a public–private partnership grant CIFRE contract, n° 2020/0766. DM and AB are employed at Labvantage-Biomax GmbH and will be affected by any effect of this publication on the commercial version of the AILANI software. JB and BG received consulting fees from Two Six Labs, LLC. TH has served as a shareholder and has consulted for Discovery Collective, Inc. RB and RS are founders and shareholders of MEGENO SA and ITTM SA. JS-R reports funding from GSK, Pfizer and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer and Grunenthal. JP and LF are employees and shareholders of MedBioinformatics Solutions SL. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Niarakis, Ostaszewski, Mazein, Kuperstein, Kutmon, Gillespie, Funahashi, Acencio, Hemedan, Aichem, Klein, Czauderna, Burtscher, Yamada, Hiki, Hiroi, Hu, Pham, Ehrhart, Willighagen, Valdeolivas, Dugourd, Messina, Esteban-Medina, Peña-Chilet, Rian, Soliman, Aghamiri, Puniya, Naldi, Helikar, Singh, Fernández, Bermudez, Tsirvouli, Montagud, Noël, Ponce-de-Leon, Maier, Bauch, Gyori, Bachman, Luna, Piñero, Furlong, Balaur, Rougny, Jarosz, Overall, Phair, Perfetto, Matthews, Rex, Orlic-Milacic, Gomez, De Meulder, Ravel, Jassal, Satagopam, Wu, Golebiewski, Gawron, Calzone, Beckmann, Evelo, D’Eustachio, Schreiber, Saez-Rodriguez, Dopazo, Kuiper, Valencia, Wolkenhauer, Kitano, Barillot, Auffray, Balling, Schneider and the COVID-19 Disease Map Community.)

Why multisystem inflammatory syndrome in children (MIS-C) develops after SARS-CoV-2 infection in a subset of children is unknown. We hypothesized that aberrant virus-specific T cell responses contribute to MIS-C pathogenesis. We quantified SARS-CoV-2-reactive T cells, serologic responses against major viral proteins, and cytokine responses from plasma and peripheral blood mononuclear cells in children with convalescent COVID-19, in children with acute MIS-C, and in healthy controls. Children with MIS-C had significantly lower virus-specific CD4+ and CD8+ T cell responses to major SARS-CoV-2 antigens compared with children convalescing from COVID-19. Furthermore, T cell responses in participants with MIS-C were similar to or lower than those in healthy controls. Serologic responses against spike receptor binding domain (RBD), full-length spike, and nucleocapsid were similar among convalescent COVID-19 and MIS-C, suggesting functional B cell responses. Cytokine profiling demonstrated predominant Th1 polarization of CD4+ T cells from children with convalescent COVID-19 and MIS-C, although cytokine production was reduced in MIS-C. Our findings support a role for constrained induction of anti-SARS-CoV-2-specific T cells in the pathogenesis of MIS-C.

There is limited understanding of the viral antibody fingerprint following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children. Herein, SARS-CoV-2 proteome-wide immunoprofiling of children with mild/moderate or severe coronavirus disease 2019 (COVID-19) versus multisystem inflammatory syndrome in children versus hospitalized control patients revealed differential cytokine responses, IgM/IgG/IgA epitope diversity, antibody binding and avidity. Apart from spike and nucleocapsid, IgG/IgA recognized epitopes in nonstructural protein (NSP) 2, NSP3, NSP12-NSP14 and open reading frame (ORF) 3a-ORF9. Peptides representing epitopes in NSP12, ORF3a and ORF8 demonstrated SARS-CoV-2 serodiagnosis. Antibody-binding kinetics with 24 SARS-CoV-2 proteins revealed antibody parameters that distinguish children with mild/moderate versus severe COVID-19 or multisystem inflammatory syndrome in children. Antibody avidity to prefusion spike correlated with decreased illness severity and served as a clinical disease indicator. The fusion peptide and heptad repeat 2 region induced SARS-CoV-2-neutralizing antibodies in rabbits. Thus, we identified SARS-CoV-2 antibody signatures in children associated with disease severity and delineate promising serodiagnostic and virus neutralization targets. These findings might guide the design of serodiagnostic assays, prognostic algorithms, therapeutics and vaccines in this important but understudied population., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

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., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

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]