Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort
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Abstract
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 with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9 . This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.
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SciScore for 10.1101/2021.06.21.21257822: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Autoantibodies against type I IFNs: The blocking activity of anti-IFNα and anti-IFNω autoantibodies was determined by assessing a reporter luciferase activity as previously described (Bastard et al., 2021). anti-IFNαsuggested: (Elabscience Cat# E-EL-M3054, RRID:AB_2891274)anti-IFNωsuggested: NoneThe membranes were blocked for 1 h in 5% skimmed milk/TBS 0.05%/Tween 20 and then incubated with the anti-ADAM9 antibody (ab218242; Abcam, Cambridge, UK) for 2 h at 4°C in 5% BSA/TBS 0.1% Tween at 1/1000 dilution. anti-ADAM9suggested: NoneAn anti-GAPDH antibody (MAB374, Merck Millipore, Burlington, MA, USA) … SciScore for 10.1101/2021.06.21.21257822: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Autoantibodies against type I IFNs: The blocking activity of anti-IFNα and anti-IFNω autoantibodies was determined by assessing a reporter luciferase activity as previously described (Bastard et al., 2021). anti-IFNαsuggested: (Elabscience Cat# E-EL-M3054, RRID:AB_2891274)anti-IFNωsuggested: NoneThe membranes were blocked for 1 h in 5% skimmed milk/TBS 0.05%/Tween 20 and then incubated with the anti-ADAM9 antibody (ab218242; Abcam, Cambridge, UK) for 2 h at 4°C in 5% BSA/TBS 0.1% Tween at 1/1000 dilution. anti-ADAM9suggested: NoneAn anti-GAPDH antibody (MAB374, Merck Millipore, Burlington, MA, USA) was used for loading control. anti-GAPDHsuggested: NoneFlow cytometry staining: The cells were fixed for 20 min in 3.6% paraformaldehyde at 4°C, washed in 5% FCS in PBS and stained with anti-nucleocapsid antibody (GTX135357, Genetex, Irvine, CA, USA) at a 1/200 dilution in Perm/WashTM (Becton, Dickinson and Company, Franklin Lakes, NJ, USA) for 45 min at room temperature. anti-nucleocapsidsuggested: (GeneTex Cat# GTX135357, RRID:AB_2868464)GTX135357suggested: (GeneTex Cat# GTX135357, RRID:AB_2868464)The antibody was then revealed by incubation with an Alexa 647-labeled goat anti-rabbit monoclonal antibody (Ab150083, Abcam, Cambridge, UK) diluted 1/200 in 5% FCS in PBS for 45 min at room temperature. anti-rabbitsuggested: (Abcam Cat# ab150083, RRID:AB_2714032)Experimental Models: Cell Lines Sentences Resources The three viral antigens included in our multiplex bead array comprise two representations of the Spike protein (a soluble trimeric form of the spike glycoprotein stabilized in the pre-fusion conformation expressed in HEK, and the Spike S1 domain expressed in CHO cells), and the C-terminal domain of the Nucleocapsid protein (expressed in E.coli). CHOsuggested: CLS Cat# 603479/p746_CHO, RRID:CVCL_0213)A monolayer of Vero cells seeded at 60000 cells/well in black-well plates was infected with the pseudotype virus (virus only) or virus and serum mixture overnight at 37°C - 5% CO2. Verosuggested: NoneBriefly, HEK293T cells were transfected with the firefly luciferase plasmids under the control of human ISRE promoters in the pGL4.45 backbone, and a constitutively expressing Renilla luciferase plasmid for normalization (pRL-SV40). HEK293Tsuggested: NoneCell culture: Vero 76 cell lines were grown at 37 °C under 5% CO2 and maintained in DMEM (Thermo Fisher Scientific, Waltham, MA, USA) containing 100 units/ml penicillin and supplemented with 10% fetal bovine serum (Pan Biotech, Aidenbach, Germany) Vero 76suggested: NoneACE2-expressing A549 cells (A549-ACE2) were grown at 37 °C under 5% CO2 and maintained in DMEM (Thermo Fisher Scientific, Waltham, MA, USA) containing 10 µg/ml of blasticidine S (Invitrogen, Carlsbad, CA, USA). A549suggested: NoneIn vitro viral infections: Vero 76 and A549-ACE2 cell lines were infected with wild-type SARS-CoV-2 virus at Multiplicities Of Infections (MOIs) of 10 and 400, respectively. A549-ACE2suggested: NoneRecombinant DNA Sentences Resources Briefly, HEK293T cells were transfected with the firefly luciferase plasmids under the control of human ISRE promoters in the pGL4.45 backbone, and a constitutively expressing Renilla luciferase plasmid for normalization (pRL-SV40). pGL4.45suggested: NoneRenilla luciferasesuggested: RRID:Addgene_118016)pRL-SV40suggested: NoneSoftware and Algorithms Sentences Resources The mass cytometry standard files produced with the Helios instrument were analyzed using Maxpar® Pathsetter software v. Maxpar® Pathsettersuggested: NoneThe FCS files from each group (healthy, critical, non-critical) were then concatenated using CyTOF® software v. CyTOF®suggested: (Fluidigm CyTOF, RRID:SCR_021055)7.0.8493.0 for viSNE analysis (Cytobank Inc, Mountain View, CA, USA). Cytobanksuggested: (Cytobank, RRID:SCR_014043)Differential protein expression analysis was performed using the limma bioconductor package in R (Ritchie et al., 2015). limmasuggested: (LIMMA, RRID:SCR_010943)bioconductorsuggested: (Bioconductor, RRID:SCR_006442)Differential protein expression analysis: Summed peptides normalized label-free quantification (LFQ values from MaxQuant software) values were used for differential protein expression analysis. MaxQuantsuggested: (MaxQuant, RRID:SCR_014485)The following day, cells were lysed using passive lysis buffer (Promega, Madison, WI, USA) and luciferase activity was measured upon addition of substrate (Promega® CellTiterGlo®, Madison, WI, USA) using Biotek plate reader and Gen5 software. Gen5suggested: (Gen5, RRID:SCR_017317)To determine neutralizing titers, the concentration of the antibody resulting in 50% neutralization (NT50) was determined using XLFit and graphed using Prism (GraphPad, San Diego, CA, USA) Prismsuggested: (PRISM, RRID:SCR_005375)GraphPadsuggested: (GraphPad Prism, RRID:SCR_002798)Whole-genome sequencing (WGS): WGS data was generated from DNA isolated from whole blood. WGSsuggested: NoneFunctional annotation of the variants was performed using Variant Effect Predictor from Ensembl (version 101). Variant Effect Predictorsuggested: NoneEnsemblsuggested: (Ensembl, RRID:SCR_002344)GATK version 4 (Van der Auwera et al., 2013; DePristo et al., 2011) was used for the joint genotyping process and variant quality score recalibration (VQSR). GATKsuggested: (GATK, RRID:SCR_001876)The raw sequencing data were aligned to a reference human genome build 38 (GRCh38) using the short reads aligner STAR (Dobin et al., 2013). STARsuggested: (STAR, RRID:SCR_004463)Quantification of gene expression was performed using RSEM (Li and Dewey, 2011) with GENCODE annotation v25 (http://www.gencodegenes.org). GENCODEsuggested: (GENCODE, RRID:SCR_014966)http://www.gencodegenes.orgsuggested: (HapMap 3 and ENCODE 3, RRID:SCR_004563)We then performed DGE analysis on all 69 samples using the trimmed mean of M-values method (TMM) from the edgeR R package (Robinson and Oshlack, 2010; Robinson et al., 2010). edgeRsuggested: (edgeR, RRID:SCR_012802)We used the TensorFlow (Abadi et al., 2016) Python package to construct the DANNs. Pythonsuggested: (IPython, RRID:SCR_001658)The membrane was then incubated with the secondary antibody coupled to HRP (Bio-Rad Laboratories, Hercules, CA, USA) Bio-Rad Laboratoriessuggested: (Bio-Rad Laboratories, RRID:SCR_008426)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
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Results from scite Reference Check: We found no unreliable references.
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