Transcriptomics,Genomics

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


ABSTRACT: The etiology of severe forms of COVID19, especially in young patients, remains a salient unanswered question. Here we build built on a 3-tier cohort where all individuals/patients were strictly below 50 years of age and where a number of comorbidities were excluded at study onset. Besides healthy controls (N=22), these included patients in the intensive care unit with Acute Respiratory Distress Syndrome (ARDS) (“critical group”; N=47), and those in a non-critical care ward under supplemental oxygen (“non-critical group”, N=25). This highly curated cohort allowed us to perform a deep multi-omics approach, which included whole genome sequencing, whole blood RNA-sequencing, plasma and peripheral-blood mononuclear cells proteomics, multiplex cytokine profiling, mass-cytometry-based immune cell profiling in conjunction with viral parameters i.e. anti-SARS-Cov-2 neutralizing antibodies and multi-target antiviral serology. Critical patients were characterized by an exacerbated inflammatory state, perturbed lymphoid and myeloid cell compartments, signatures of dysregulated blood coagulation and active regulation of viral entry into the cells. A unique gene signature that differentiates critical from non-critical patients was identified by an ensemble of machine learning, deep learning and quantum annealing approachmethods. Within this gene networksignature, Sstructural Causal causal Modeling modeling identified several genes that may potentially drivepromote ARDS driver genes etiology, among which the up-regulated metalloprotease ADAM9 seems to be a key driver. Inhibition of ADAM9 ex vivo interfered with SARS-Cov-2 uptake and replication in human epithelial cells. In brief, Hence we applyied an advanced integrated machine learning approach and probabilistic programming strategy to identify causal molecular driver geness for of severe forms of COVID-19 in a small, uncluttered tightly controlled cohort of patients, the importance of which were then validated with experiments.  Overall design: RNA-seq was performed on 69 whole blood RNA samples corresponding to 46 critical and 23 non-critical patients at hospitalization.

INSTRUMENT(S): Illumina NovaSeq 6000 (Homo sapiens)

ORGANISM(S): Homo sapiens  

SUBMITTER: Richard Li  

PROVIDER: GSE172114 | GEO | 2021-10-26

REPOSITORIES: GEO

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

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

Science translational medicine 20220119 628


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 wit  ...[more]

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