Genotype count for all coding variants in type I IFN genes investigated in 659 life-threatening COVID-19 patients and 534 asymptomatic/mild infected controls
Cobat, Aurélie (2020), Genotype count for all coding variants in type I IFN genes investigated in 659 life-threatening COVID-19 patients and 534 asymptomatic/mild infected controls, Dryad, Dataset, https://doi.org/10.5061/dryad.8pk0p2nkk
Clinical outcome upon infection with SARS-CoV-2 ranges from silent infection to lethal COVID-19. We have found an enrichment in rare variants predicted to be loss-of-function (LOF) at the 13 human loci known to govern TLR3- and IRF7-dependent type I interferon (IFN) immunity to influenza virus, in 659 patients with life-threatening COVID-19 pneumonia, relative to 534 subjects with asymptomatic or benign infection. By testing these and other rare variants at these 13 loci, we experimentally define LOF variants in 23 patients (3.5%), aged 17 to 77 years, underlying autosomal recessive or dominant deficiencies. We show that human fibroblasts with mutations affecting this pathway are vulnerable to SARS-CoV-2. Inborn errors of TLR3- and IRF7-dependent type I IFN immunity can underlie life-threatening COVID-19 pneumonia in patients with no prior severe infection.
Epidemiological studies have identified three risk factors for severe disease: being male, elderly, and having other medical conditions. However, interindividual clinical variability remains huge in each demographic category. Discovering the root cause and detailed molecular, cellular, tissue- and body-level mechanisms underlying life-threatening COVID-19 is of the utmost biological and medical importance. We included in this study 659 patients with life-threatening COVID-19 pneumonia defined as patients with pneumonia who developed critical disease, whether pulmonary with mechanical ventilation (CPAP, BIPAP, intubation, hi-flow oxygen), septic shock, or with any other organ damage requiring admission to the ICU. Patients who developed Kawasaki-like syndrome were excluded. As controls, we enrolled 534 individuals infected with SARS-CoV-2 (based on a positive PCR and/or serological test and/or the presence of typical symptoms such as anosmia/ageusia after exposure to a confirmed COVID-19 case) who remained asymptomatic or developed mild, self-healing, ambulatory disease.
Genomic DNA was extracted from whole blood. For the 1193 patients and controls included, the whole exome (N=687) or whole genome (N=506) was sequenced at several sequencing centers, including the Genomics Core Facility of the Imagine Institute (Paris, France), the Yale Center for Genome Analysis (USA), the New-York Genome Center (NY, USA), and the American Genome Center (TAGC, USUHS, Bethesda, USA). For WES, libraries were generated with the Twist Bioscience kit (Twist Human Core Exome Kit), the xGen Exome Research Panel from Integrated DNA Technologies (IDT xGen), the Agilent SureSelect V7 kit or the SeqCap EZ MedExome kit from Roche. Massively parallel sequencing was performed on a NovaSeq6000 system (Illumina).
We used the Genome Analysis Software Kit (GATK) (version 3.4-46 or 4) best-practice pipeline to analyze our WES data (30). We aligned the reads obtained with the human
reference genome (hg19), using the maximum exact matches algorithm in the Burrowsâ€“ Wheeler Aligner (BWA). PCR duplicates were removed with Picard tools (picard.sourceforge.net). The GATK base quality score recalibrator was applied to correct sequencing artifacts.
Annotation was performed using Ensembl Variant Effect Predictor on the canonical transcript. All the variants were manually curated using IGV and confirmed to affect the main functional protein isoform by checking the protein sequence before inclusion in further analyzes. The analysis of IKBKG was customized to differentiate the duplicated region in IKBKG using a special pipeline previously described.
This dataset includes detailed genotype counts for all coding variants in the 13 genes investigated in the manuscript "Inborn errors of type I IFN immunity in patients with life-threatening COVID-19" by Zhang Q et al., Science 2020, in press.