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

Carapito, Raphael; Li, Richard; Helms, Julie; Carapito, Christine; Gujja, Sharvari; Rolli, Véronique; Guimaraes, Raony; Malagon-Lopez, Jose; Spinnhirny, Perrine; Lederle, Alexandre; Mohseninia, Razieh; Hirschler, Aurélie; Muller, Leslie; Bastard, Paul; Gervais, Adrian; Zhang, Qian; Danion, François; Ruch, Yvon; Schenck, Maleka; Collange, Olivier; Chamaraux-Tran, Thiên-Nga; Molitor, Anne; Pichot, Angélique; Bernard, Alice; Tahar, Ouria; Bibi-Triki, Sabrina; Wu, Haiguo; Paul, Nicodème; Mayeur, Sylvain; Larnicol, Annabel; Laumond, Géraldine; Frappier, Julia; Schmidt, Sylvie; Hanauer, Antoine; Macquin, Cécile; Stemmelen, Tristan; Simons, Michael; Mariette, Xavier; Hermine, Olivier; Fafi-Kremer, Samira; Goichot, Bernard; Drenou, Bernard; Kuteifan, Khaldoun; Pottecher, Julien; Mertes, Paul-Michel; Kailasan, Shweta; Aman, M. Javad; Pin, Elisa; Nilsson, Peter; Thomas, Anne; Viari, Alain; Sanlaville, Damien; Schneider, Francis; Sibilia, Jean; Tharaux, Pierre-Louis; Casanova, Jean-Laurent; Hansmann, Yves; Lidar, Daniel; Radosavljevic, Mirjana; Gulcher, Jeffrey R.; Meziani, Ferhat; Moog, Christiane; Chittenden, Thomas W.; Bahram, Seiamak

doi:10.1126/scitranslmed.abj7521

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

Journal Article · 18 January 2022 · Science Translational Medicine

DOI: https://doi.org/10.1126/scitranslmed.abj7521 · OSTI ID:1980750

^[1];

^[2]; Helms, Julie ^[3];

^[4]; Gujja, Sharvari ^[2];

^[5]; Guimaraes, Raony ^[2]; Malagon-Lopez, Jose ^[2]; Spinnhirny, Perrine ^[6]; Lederle, Alexandre ^[6]; Mohseninia, Razieh ^[7];

^[4]; Muller, Leslie ^[4];

^[8];

^[9];

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^[11]; Collange, Olivier ^[11] more »

University of Strasbourg (France); Nouvel Hôpital Civil, Strasbourg (France); Fédération Hospitalo-Universitaire (FHU), Starsbourg (France); OSTI
Genuity AI Research Institute, Boston, MA (United States)
University of Strasbourg (France); Fédération Hospitalo-Universitaire (FHU), Starsbourg (France); Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg (France)
Fédération Hospitalo-Universitaire (FHU), Starsbourg (France); University of Strasbourg (France)
University of Strasbourg (France); Nouvel Hôpital Civil, Strasbourg (France); Fédération Hospitalo-Universitaire (FHU), Starsbourg (France)
University of Strasbourg (France); Fédération Hospitalo-Universitaire (FHU), Starsbourg (France)
University of Southern California, Los Angeles, CA (United States)
Rockefeller University, New York, NY (United States); Necker Hospital for Sick Children, Paris (France); University of Paris (France)
Necker Hospital for Sick Children, Paris (France); University of Paris (France)
University of Strasbourg (France); Fédération Hospitalo-Universitaire (FHU), Starsbourg (France); Hôpitaux Universitaires de Strasbourg, Strasbourg (France)
Fédération Hospitalo-Universitaire (FHU), Starsbourg (France); Hôpitaux Universitaires de Strasbourg, Strasbourg (France)
Nouvel Hôpital Civil, Strasbourg (France); Fédération Hospitalo-Universitaire (FHU), Starsbourg (France)
Yale University, New Haven, CT (United States). School of Medicine
Hôpital Bicêtre, Assistance Publique-Hôpitaux de Paris (France); Univ. Paris-Saclay, Le Kremlin Bicetre (France)
University of Paris (France); University of Paris, Assistance Publique-Hôpitaux de Paris (France)
Fédération Hospitalo-Universitaire (FHU), Starsbourg (France); Hôpitaux Universitaires de Strasbourg (France)
Groupe Hospitalier de la région Mulhouse Sud Alsace, Mulhouse (France)
Integrated BioTherapeutics Inc., Rockville, MD (United States)
KTH Royal Institute of Technology, Stockholm (Sweden)
Plateforme Auragen, Lyon (France)
University of Paris (France)
Rockefeller University, New York, NY (United States); Necker Hospital for Sick Children, Paris (France); University of Paris (France); Howard Hughes Medical Institute, New York, NY (United States)
Fédération Hospitalo-Universitaire (FHU), Starsbourg (France); Nouvel Hôpital Civil, Hôpitaux Universitaires de Strasbourg (France)
Genuity AI Research Institute, Boston, MA (United States); Harvard Medical School, Boston, MA (United States)

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.

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Research Organization:: California Institute of Technology (CalTech), Pasadena, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP); University of Strasbourg; IdEx Unistra; SFRI-STRAT’US; Institut Universitaire de France; Institut National de la Santé et de la Recherche Médicale; University of Strasbourg; European Union; French Proteomic Infrastructure; QCCFP/Quantum Machine Learning and Quantum Computation Frameworks (QCCFP-QMLQCF)

Grant/Contract Number:: SC0019219

OSTI ID:: 1980750

Journal Information:: Science Translational Medicine, Journal Name: Science Translational Medicine Journal Issue: 628 Vol. 14; ISSN 1946-6234

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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Building Predictive Models in R Using the caret Package Kuhn, Max Journal of Statistical Software, Vol. 28, Issue 5 https://doi.org/10.18637/jss.v028.i05	journal	January 2008
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Sentieon DNASeq Variant Calling Workflow Demonstrates Strong Computational Performance and Accuracy Kendig, Katherine I.; Baheti, Saurabh; Bockol, Matthew A. Frontiers in Genetics, Vol. 10 https://doi.org/10.3389/fgene.2019.00736	journal	August 2019
An Overview of ADAM9: Structure, Activation, and Regulation in Human Diseases Chou, Cheng-Wei; Huang, Yu-Kai; Kuo, Ting-Ting International Journal of Molecular Sciences, Vol. 21, Issue 20 https://doi.org/10.3390/ijms21207790	journal	October 2020
ADAM9 Is a Novel Product of Polymorphonuclear Neutrophils: Regulation of Expression and Contributions to Extracellular Matrix Protein Degradation during Acute Lung Injury Roychaudhuri, Robin; Hergrueter, Anja H.; Polverino, Francesca The Journal of Immunology, Vol. 193, Issue 5 https://doi.org/10.4049/jimmunol.1303370	journal	September 2014
COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis Chua, Robert Lorenz; Lukassen, Soeren; Trump, Saskia figshare https://doi.org/10.6084/m9.figshare.12436517.v2	dataset	January 2020
Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study Kokla, Marietta; Virtanen, Jyrki; Kolehmainen, Marjukka figshare https://doi.org/10.6084/m9.figshare.c.4694753.v1	collection	January 2019
Mortality rates of patients with COVID-19 in the intensive care unit: a systematic review of the emerging literature Quah, Pipetius; Li, Andrew; Phua, Jason figshare https://doi.org/10.6084/m9.figshare.c.5104175	collection	January 2020
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