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Preprint Dans Anglais | medRxiv | ID: ppmedrxiv-21257822


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. One sentence summaryEtiopathogenesis of severe COVID19 in a young patient population devoid of comorbidities.

Preprint Dans Anglais | bioRxiv | ID: ppbiorxiv-156166


Rapid and accurate diagnosis is crucial for successful outbreak containment. During the current coronavirus disease 2019 (COVID-19) public health emergency, the gold standard for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection diagnosis is the detection of viral RNA by reverse transcription (RT)-PCR. Additional diagnostic methods enabling the detection of current or past SARS-CoV-2 infection would be highly beneficial to ensure the timely diagnosis of all infected and recovered patients. Here, we investigated several serological tools, i.e., two immunochromatographic lateral flow assays (LFA-1 (Biosynex COVID-19 BSS) and LFA-2 (COVID-19 Sign IgM/IgG)) and two enzyme-linked immunosorbent assays (ELISAs) detecting IgA (ELISA-1 Euroimmun), IgM (ELISA-2 EDI) and/or IgG (ELISA-1 and ELISA-2) based on well-characterized panels of serum samples from patients and healthcare workers with PCR-confirmed COVID-19 and from SARS-CoV-2-negative patients. A total of 272 serum samples were used, including 62 serum samples from hospitalized patients (panel 1 and panel 3), 143 serum samples from healthcare workers (panel 2) diagnosed with COVID-19 and 67 serum samples from negative controls. Diagnostic performances of each assay were assessed according to days after symptom onset (dso) and the antigenic format used by manufacturers. We found overall sensitivities ranging from 69% to 93% on panels 1 and 2 and specificities ranging from 83% to 98%. The clinical sensitivity varied greatly according to the panel tested and the dso. The assays we tested showed poor mutual agreement. A thorough selection of serological assays for the detection of ongoing or past infections is advisable.

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