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

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.