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

 Raphael Carapito, Richard Li, Julie Helms, Christine Carapito, Sharvari Gujja, Veronique Rolli, Raony Guimaraes, Jose Malagon-Lopez, Perrine Spinnhirny, Razieh Mohseninia, Aurelie Hirschler, Leslie Muller, Paul Bastard, Adrian Gervais, Qian Zhang, Francois Danion, Yvon Ruch, Maleka Schenck-Dhif, Olivier Collange, Thien-Nga Chamaraux-Tran, Anne Molitor, Angelique Pichot, Alice Bernard, Ouria Tahar, Sabrina Bibi-Triki, Haiguo Wu, Nicodeme Paul, Sylvain Mayeur, Annabel Larnicol, Geraldine Laumond, Julia Frappier, Sylvie Schmidt, Antoine Hanauer, Cecile 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, 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

doi: https://doi.org/10.1101/2021.06.21.21257822

This article is a preprint and has not been certified by peer review [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.

PDF: https://www.medrxiv.org/content/10.1101/2021.06.21.21257822v1.full.pdf

Abstract

The etiopathogenesis of severe COVID-19 remains unknown. Indeed given major confounding factors (age and co-morbidities), true drivers of this condition have remained elusive. Here, we employ an unprecedented multi-omics analysis, combined with artificial intelligence, in a young patient cohort where major co-morbidities have been excluded at the onset. Here, we established a three-tier cohort of individuals younger than 50 years without major comorbidities. These included 47 critical (in the ICU under mechanical ventilation) and 25 non-critical (in a noncritical care ward) COVID-19 patients as well as 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cells proteomics, cytokine profiling and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing and structural causal modeling led to key findings. Critical patients were characterized by exacerbated inflammation, perturbed lymphoid/myeloid compartments, coagulation and viral cell biology. Within a unique gene signature that differentiated critical from noncritical patients, several driver genes promoted severe COVID-19 among which the upregulated metalloprotease ADAM9 was key. This gene signature was replicated in an independent cohort of 81 critical and 73 recovered COVID-19 patients, as were ADAM9 transcripts, soluble form and proteolytic activity. Ex vivo ADAM9 inhibition affected SARS-CoV-2 uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, COVID-19 cohort, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. The key driver, ADAM9, interfered with SARS-CoV-2 biology. A repositioning strategy for anti-ADAM9 therapeutic is feasible.

Competing Interest Statement

RL, RG, SG, JL, HW, JRG and TWC are employees of Genuity Science. TWC, RC and SB, are, thru their employers, named as inventors on two patent applications covering findings reported in this work. The remaining authors have no conflicts of interest to declare.

Funding Statement

This work was supported by the Strasbourg Interdisciplinary Thematic Institute (ITI) for Precision Medicine, TRANSPLANTEX NG, as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and INSERM, funded by IdEx Unistra (ANR-10-IDEX-0002 to S. Bahram) and SFRI-STRATUS (ANR-20-SFRI-0012 to S. Bahram); Institut National de la Sante et de la Recherche Medicale UMR_S 1109, the Institut Universitaire de France, and MSD-Avenir grant AUTOGEN (all to S. Bahram); the University of Strasbourg (including Initiative d'Excellence IDEX UNISTRA; to S. Bahram and R. Carapito); the European Regional Development Fund (European Union) INTERREG V program PERSONALIS (to R. Carapito and S. Bahram); the French Proteomic Infrastructure (ProFI; ANR-10-INBS-08-03; to C. Carapito); France Medecine Genomique 2025 (to D. Sanlaville); and Genuity Science. This work was partially supported by a DOE/HEP QuantISED program grant, QCCFP/Quantum Machine Learning and Quantum Computation Frameworks (QCCFP-QMLQCF) for HEP (Grant No. DE-SC0019219 to DAL).

https://www.medrxiv.org/content/10.1101/2021.06.21.21257822v1