dc.contributor.author |
Niarakis, Anna |
dc.contributor.author |
Piñero González, Janet, 1977- |
dc.contributor.author |
Furlong, Laura I., 1971- |
dc.contributor.author |
COVID-19 Disease Map Community |
dc.date.accessioned |
2024-10-15T06:20:12Z |
dc.date.available |
2024-10-15T06:20:12Z |
dc.date.issued |
2024 |
dc.identifier.citation |
Niarakis A, Ostaszewski M, Mazein A, Kuperstein I, Kutmon M, Gillespie ME, et al. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. Front Immunol. 2024 Feb 13;14:1282859. DOI: 10.3389/fimmu.2023.1282859 |
dc.identifier.issn |
1664-3224 |
dc.identifier.uri |
http://hdl.handle.net/10230/61400 |
dc.description.abstract |
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. |
dc.description.sponsorship |
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. AN acknowledges support from SANOFI-AVENTIS R&D via the CIFRE contract, n° 2020/0766. MK, FH, NP, FE, and CE acknowledge the support of the ZonMw COVID-19 programme (Grant No. 10430012010015). JD Spanish Ministry of Science and Innovation (Grant no. PID2020-117979RB-I00) and Instituto de Salud Carlos III (Grant no. IMP/00019). MAi, KK, FS: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-ID 251654672 - TRR 161 and under Germany’s Excellence Strategy - EXC 2117 - 422037984. FM: “5 per 1000–2021” grant of the Italian Ministry of Health (Grant No. 5M-2021-23683787) and European Commission with HORIZON programme, BY-COVID project (Grant No. 101046203—BY-COVID). National Institute for Infectious Diseases Lazzaro Spallanzani–IRCCS received financial support from the Italian Ministry of Health grant “Ricerca Corrente”. JP, LF: IMI2-JU grants, resources which are composed of financial contributions from the European Union’s Horizon 2020 Research and Innovation Programme and EFPIA [GA: 777365 eTRANSAFE], and the EU H2020 Programme [GA:964537 RISKHUNT3R]; Project 001-P-001647—Valorisation of EGA for Industry and Society funded by the European Regional Development Fund (ERDF) and Generalitat de Catalunya; Institute of Health Carlos III (project IMPaCT-Data, exp. IMP/00019), co-funded by the European Union, European Regional Development Fund (ERDF, “A way to make Europe”). AMo, MP and AV acknowledge the support of the European Commission under the INFORE project (H2020-ICT-825070) and the PerMedCoE (H2020-ICT-951773). Contributions by TH and BLP were supported by NIH grant #R35GM119770 to TH. MaGo acknowledges funding from Deutsche Forschungsgemeinschaft (DFG) through grants no. 442326535 (NFDI4Health) and 451265285 (NFDI4Health Task Force COVID-19), from the European Commission through the Horizon 2020 framework program under grant no. 825843 (EU-STANDS4PM) and through the Digital Europe program under grant no. 101083771 (EDITH), as well as from the Klaus Tschira Foundation. AL acknowledges support from the Intramural Research Program of the National Library of Medicine (NLM), National Institutes of Health (NIH). |
dc.format.mimetype |
application/pdf |
dc.language.iso |
eng |
dc.publisher |
Frontiers |
dc.relation.ispartof |
Front Immunol. 2024 Feb 13;14:1282859 |
dc.rights |
© 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. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
dc.rights.uri |
http://creativecommons.org/licenses/by/4.0/ |
dc.title |
Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches |
dc.type |
info:eu-repo/semantics/article |
dc.identifier.doi |
http://dx.doi.org/10.3389/fimmu.2023.1282859 |
dc.subject.keyword |
SARS-CoV-2 |
dc.subject.keyword |
Disease maps |
dc.subject.keyword |
Dynamic models |
dc.subject.keyword |
Large-scale community effort |
dc.subject.keyword |
Mechanistic models |
dc.subject.keyword |
Systems biology |
dc.subject.keyword |
Systems medicine |
dc.relation.projectID |
info:eu-repo/grantAgreement/ES/2PE/PID2020-117979RB-I00 |
dc.relation.projectID |
info:eu-repo/grantAgreement/EC/HE/101046203 |
dc.relation.projectID |
info:eu-repo/grantAgreement/EC/H2020/777365 |
dc.relation.projectID |
info:eu-repo/grantAgreement/EC/H2020/825070 |
dc.relation.projectID |
info:eu-repo/grantAgreement/EC/H2020/951773 |
dc.relation.projectID |
info:eu-repo/grantAgreement/EC/H2020/825843 |
dc.rights.accessRights |
info:eu-repo/semantics/openAccess |
dc.type.version |
info:eu-repo/semantics/publishedVersion |