Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Anna Niarakis(Université Paris-Saclay), Marek Ostaszewski(University of Luxembourg), Alexander Mazein(University of Luxembourg), Inna Kuperstein(Inserm), Martina Kutmon(Maastricht University), Marc Gillespie(Ontario Institute for Cancer Research), Akira Funahashi(Keio University), Márcio Luís Acencio(University of Luxembourg), Ahmed Abdelmonem Hemedan(University of Luxembourg), Michael Aichem(University of Konstanz), Karsten Klein(University of Konstanz), Tobias Czauderna(Hochschule Mittweida), Felicia Burtscher(University of Luxembourg), Takahiro Yamada(Keio University), Yusuke Hiki(Keio University), Noriko Hiroi(Kanagawa Institute of Technology), Finterly Hu(Maastricht University), Nhung Pham(Maastricht University), Friederike Ehrhart(Maastricht University), Egon Willighagen(Maastricht University), Alberto Valdeolivas(Heidelberg University), Aurélien Dugourd(Heidelberg University), Francesco Messina(Istituto Nazionale per le Malattie Infettive Lazzaro Spallanzani), Marina Esteban‐Medina(Fundación Progreso y Salud), María Peña-Chilet(Fundación Progreso y Salud), Kinza Rian(Fundación Progreso y Salud), Sylvain Soliman(Centre Inria de Saclay), Sara Sadat Aghamiri(University of Nebraska–Lincoln), Bhanwar Lal Puniya(University of Nebraska–Lincoln), Aurélien Naldi(Centre Inria de Saclay), Tomáš Helikar(University of Nebraska–Lincoln), Vidisha Singh(Université Paris-Saclay), Marco Fariñas Fernández(Norwegian University of Science and Technology), Viviam Bermudez(Norwegian University of Science and Technology), Eirini Tsirvouli(Norwegian University of Science and Technology), Arnau Montagud(Barcelona Supercomputing Center), Vincent Noël(Inserm), Miguel Ponce-de-León(Barcelona Supercomputing Center), Dieter Maier(Biomax Informatics (Germany)), Angela Bauch(Biomax Informatics (Germany)), Benjamin M. Gyori(Harvard University), John A. Bachman(Harvard University), Augustin Luna(Harvard University), Janet Piñero(Universitat Pompeu Fabra), Laura I. Furlong(Universitat Pompeu Fabra), Irina Balaur(University of Luxembourg), Adrien Rougny(National Institute of Advanced Industrial Science and Technology), Yohan Jarosz(University of Luxembourg), Rupert W. Overall(Humboldt-Universität zu Berlin), Robert D. Phair, Livia Perfetto(Sapienza University of Rome), Lisa Matthews(NYU Langone Health), Devasahayam Arokia Balaya Rex(Yenepoya University), Marija Orlic-Milacic(Ontario Institute for Cancer Research), Luis Cristóbal Monraz Gómez(Inserm), Bertrand De Meulder, Jean‐Marie Ravel(Inserm), Bijay Jassal(Ontario Institute for Cancer Research), Venkata Satagopam(Goethe University Frankfurt), Guanming Wu(Oregon Health & Science University), Martin Golebiewski(Heidelberg Institute for Theoretical Studies), Piotr Gawron(University of Luxembourg), Laurence Calzone(Inserm), J. Beckmann(University of Lausanne), Chris T. Evelo(Maastricht University), Peter D’Eustachio(NYU Langone Health), Falk Schreiber(University of Konstanz), Julio Sáez-Rodríguez(Heidelberg University), Joaquı́n Dopazo(Fundación Progreso y Salud), Martin Kuiper(Norwegian University of Science and Technology), Alfonso Valencia(Institució Catalana de Recerca i Estudis Avançats), Olaf Wolkenhauer(Leibniz-Institute for Food Systems Biology at the Technical University of Munich), Hiroaki Kitano(Systems Biology Institute), Emmanuel Barillot(Inserm), Charles Auffray, Rudi Balling(University of Bonn), Reinhard Schneider(University of Luxembourg), the COVID-19 Disease Map Community
Frontiers in Immunology
February 13, 2024
Cited by 21Open Access
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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.


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