Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort
Raphael Carapito
(1, 2, 3, 4, 5)
,
Richard Li
(6)
,
Julie Helms
(1, 2, 3, 5, 7)
,
Christine Carapito
(7, 5)
,
Sharvari Gujja
(6)
,
Véronique Rolli
(1, 2, 3, 4, 5)
,
Raony Guimaraes
(6)
,
Jose Malagon-Lopez
(6)
,
Perrine Spinnhirny
(1, 2, 3, 5)
,
Alexandre Lederle
(1, 2, 3, 5)
,
Razieh Mohseninia
(8)
,
Aurélie Hirschler
(5, 7)
,
Leslie Muller
(5, 7)
,
Paul Bastard
(9, 10, 11)
,
Adrian Gervais
(10, 11)
,
Qian Zhang
(9, 10, 11)
,
François Danion
(1, 2, 3, 5)
,
Yvon Ruch
(5, 12)
,
Maleka Schenck
(5, 12, 13)
,
Olivier Collange
(5, 12, 4)
,
Thiên-Nga Chamaraux-Tran
(5, 14)
,
Anne Molitor
(5, 1, 2, 3)
,
Angélique Pichot
(5, 1, 2, 3)
,
Alice Bernard
(5, 1, 2, 3)
,
Ouria Tahar
(4, 5)
,
Sabrina Bibi-Triki
(5, 1, 2, 3)
,
Haiguo Wu
(6)
,
Nicodème Paul
(5, 1, 2, 3)
,
Sylvain Mayeur
(5, 1, 2, 3)
,
Annabel Larnicol
(5, 1, 2)
,
Géraldine Laumond
(5, 1, 2, 3)
,
Julia Frappier
(5, 1, 2, 3)
,
Sylvie Schmidt
(5, 1, 2, 3)
,
Antoine Hanauer
(5, 1, 2, 3)
,
Cécile Macquin
(5, 1, 2, 3)
,
Tristan Stemmelen
(5, 1, 2, 3, 4)
,
Michael Simons
(14)
,
Xavier Mariette
(15, 16)
,
Olivier Hermine
(11, 17)
,
Samira Fafi-Kremer
(5, 1, 2, 3, 12)
,
Bernard Goichot
(5, 12, 13)
,
Bernard Drenou
(18)
,
Khaldoun Kuteifan
(18)
,
Julien Pottecher
(5, 13)
,
Paul-Michel Mertes
(5, 4)
,
Shweta Kailasan
(19)
,
M. Javad Aman
(19)
,
Elisa Pin
(20)
,
Peter Nilsson
(20)
,
Anne Thomas
(21)
,
Alain Viari
(22, 23)
,
Damien Sanlaville
(21)
,
Francis Schneider
(5, 13)
,
Jean Sibilia
(5, 1, 2, 3, 12)
,
Pierre-Louis Tharaux
(24)
,
Jean-Laurent Casanova
(9, 10, 11)
,
Yves Hansmann
(5, 12)
,
Daniel Lidar
(8)
,
Mirjana Radosavljevic
(5, 1, 2, 3)
,
Jeffrey Gulcher
(6)
,
Ferhat Meziani
(5, 4)
,
Christiane Moog
(5, 1, 2, 3)
,
Thomas Chittenden
(25)
,
Seiamak Bahram
(5, 1, 2, 3, 4)
1
IRM -
Immuno-Rhumatologie Moléculaire
2 GENOMAX [plateforme]
3 UNISTRA - Université de Strasbourg
4 Nouvel Hôpital Civil de Strasbourg
5 FMTS - Fédération de Médecine Translationnelle de Strasbourg
6 Genuity Science [Boston]
7 DSA-IPHC - Département Sciences Analytiques et Interactions Ioniques et Biomoléculaires
8 USC - University of Southern California
9 Rockefeller University [New York]
10 Hôpital Necker - Enfants Malades [AP-HP]
11 Imagine - U1163 - Imagine - Institut des maladies génétiques (IHU)
12 HUS - Les Hôpitaux Universitaires de Strasbourg
13 Hôpital de Hautepierre [Strasbourg]
14 YSM - Yale School of Medicine [New Haven, Connecticut]
15 Hôpital Bicêtre [AP-HP, Le Kremlin-Bicêtre]
16 IMVA-HB - Immunologie des maladies virales, auto-immunes, hématologiques et bactériennes
17 CRESS (U1153 / UMR_A 1125) - Centre for Research in Epidemiology and Statistics | Centre de Recherche Épidémiologie et Statistiques
18 Groupe hospitalier de la région de Mulhouse et Sud-Alsace
19 Integrated biotherapeutics [Rockville]
20 KTH - KTH Royal Institute of Technology [Stockholm]
21 Auragen [Lyon]
22 ERABLE - Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale
23 Synergie Lyon Cancer-Platform of Bioinformatics-Gilles Thomas
24 PARCC (UMR_S 970/ U970) - Paris-Centre de Recherche Cardiovasculaire
25 HMS - Harvard Medical School [Boston]
2 GENOMAX [plateforme]
3 UNISTRA - Université de Strasbourg
4 Nouvel Hôpital Civil de Strasbourg
5 FMTS - Fédération de Médecine Translationnelle de Strasbourg
6 Genuity Science [Boston]
7 DSA-IPHC - Département Sciences Analytiques et Interactions Ioniques et Biomoléculaires
8 USC - University of Southern California
9 Rockefeller University [New York]
10 Hôpital Necker - Enfants Malades [AP-HP]
11 Imagine - U1163 - Imagine - Institut des maladies génétiques (IHU)
12 HUS - Les Hôpitaux Universitaires de Strasbourg
13 Hôpital de Hautepierre [Strasbourg]
14 YSM - Yale School of Medicine [New Haven, Connecticut]
15 Hôpital Bicêtre [AP-HP, Le Kremlin-Bicêtre]
16 IMVA-HB - Immunologie des maladies virales, auto-immunes, hématologiques et bactériennes
17 CRESS (U1153 / UMR_A 1125) - Centre for Research in Epidemiology and Statistics | Centre de Recherche Épidémiologie et Statistiques
18 Groupe hospitalier de la région de Mulhouse et Sud-Alsace
19 Integrated biotherapeutics [Rockville]
20 KTH - KTH Royal Institute of Technology [Stockholm]
21 Auragen [Lyon]
22 ERABLE - Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale
23 Synergie Lyon Cancer-Platform of Bioinformatics-Gilles Thomas
24 PARCC (UMR_S 970/ U970) - Paris-Centre de Recherche Cardiovasculaire
25 HMS - Harvard Medical School [Boston]
Raphael Carapito
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- ORCID : 0000-0002-7036-442X
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Christine Carapito
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Aurélie Hirschler
- Function : Author
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Paul Bastard
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Adrian Gervais
- Function : Author
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- ORCID : 0000-0002-1083-5787
Qian Zhang
- Function : Author
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- ORCID : 0000-0002-9040-3289
Maleka Schenck
- Function : Author
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- ORCID : 0000-0002-1043-6452
Nicodème Paul
- Function : Author
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Xavier Mariette
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Olivier Hermine
- Function : Author
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Samira Fafi-Kremer
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Julien Pottecher
- Function : Author
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Paul-Michel Mertes
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Peter Nilsson
- Function : Author
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Alain Viari
- Function : Author
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- IdHAL : alain-viari
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Damien Sanlaville
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Francis Schneider
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Pierre-Louis Tharaux
- Function : Author
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Jean-Laurent Casanova
- Function : Author
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Yves Hansmann
- Function : Author
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Christiane Moog
- Function : Author
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- IdRef : 074287788
Abstract
The etiopathogenesis of critical COVID-19 remains unknown. Indeed given major confounding factors (age and comorbidities), 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 comorbidities 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 non-critical 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 non-critical patients, several driver genes promoted critical COVID-19 among which the upregulated metalloprotease ADAM9 was key. This gene signature was supported in a second 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.
Domains
Genomics [q-bio.GN]| Origin | Files produced by the author(s) |
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