Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort.

Abstract

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 “critical” (in the intensive care unit under mechanical ventilation) and 25 “non-critical” (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

• Authors: Raphael Carapito, Richard Li, Julie Helms, Christine Carapito, Sharvari Gujja, Véronique Rolli, Raony Guimaraes, Jose Malagon-Lopez, Perrine Spinnhirny, Alexandre Lederle, Razieh Mohseninia, Aurélie Hirschler, Leslie Muller, Paul Bastard, Adrian Gervais, Qian Zhang, François Danion, Yvon Ruch, Maleka Schenck, Olivier Collange, Thiên-Nga Chamaraux-Tran, Anne Molitor, Angélique Pichot, Alice Bernard, Ouria Tahar, Sabrina Bibi-Triki, Haiguo Wu, Nicodème Paul, Sylvain Mayeur, Annabel Larnicol, Géraldine Laumond, Julia Frappier, Sylvie Schmidt, Antoine Hanauer, Cécile Macquin, Tristan Stemmelen, Michael Simons, Xavier Mariette, Olivier Hermine, Samira Fafi-Kremer, Bernard Goichot, Bernard Drenou, Khaldoun Kuteifan, Julien Pottecher, Paul-Michel Mertes, Shweta Kailasan, M Javad Aman, Elisa Pin, Peter Nilsson, Anne Thomas, Alain Viari, Damien Sanlaville, Francis Schneider, Jean Sibilia, Pierre-Louis Tharaux, Jean-Laurent Casanova, Yves Hansmann, Daniel Lidar, Mirjana Radosavljevic, Jeffrey R Gulcher, Ferhat Meziani, Christiane Moog, Thomas W Chittenden, Seiamak Bahram
• Journal: Science translational medicine (Sci Transl Med) Vol. 14 Issue 628 Pg. eabj7521 (Jan 19 2022) ISSN: 1946-6242 [Electronic] United States
• PMID: 34698500 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.)

Chemical References

• Membrane Proteins
• ADAM Proteins
• ADAM9 protein, human

Topics

• ADAM Proteins
• Artificial Intelligence
• COVID-19
• Humans
• Intensive Care Units
• Membrane Proteins
• Respiration, Artificial
• SARS-CoV-2