Association of CXCR6 with COVID-19 severity: Delineating the host genetic factors in transcriptomic regulation
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Abstract
Background
The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ∼80% asymptomatic or mild cases and ∼5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms.
Methods
We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients.
Results
We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on the lung and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis -expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung resident memory CD8 + T (T RM ) cells, we found a 3.32-fold decrease of cell proportion and lower expression of CXCR6 in the severe than moderate patients using the BALF transcriptomic dataset. Pro-inflammatory transcriptional programs were highlighted in T RM cells trajectory from moderate to severe patients.
Conclusions
CXCR6 from the 3p21 . 31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung T RM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis. We illustrate one potential mechanism of host genetic factor impacting the severity of COVID-19 through regulating the expression of CXCR6 and T RM cell proportion and stability. Our results shed light on potential therapeutic targets for severe COVID-19.
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SciScore for 10.1101/2021.02.17.431554: (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
Software and Algorithms Sentences Resources The eQTL associations and chromatin-state information and Hi-C interactions were processed and plotted using the R Bioconductor package gviz in R version 4.0.3 [29]. Bioconductorsuggested: (Bioconductor, RRID:SCR_006442)Cell trajectory and transcriptional program analysis in TRM cells: We used the R package Slingshot [34] to infer cell transition and pseudotime from the scRNA-seq data. Slingshotsuggested: (Slingshot, RRID:SCR_017012)DNA motif recognition analysis of genome-wide significant SNPs: We used the function “variation-scan” of the online tool RSAT … SciScore for 10.1101/2021.02.17.431554: (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
Software and Algorithms Sentences Resources The eQTL associations and chromatin-state information and Hi-C interactions were processed and plotted using the R Bioconductor package gviz in R version 4.0.3 [29]. Bioconductorsuggested: (Bioconductor, RRID:SCR_006442)Cell trajectory and transcriptional program analysis in TRM cells: We used the R package Slingshot [34] to infer cell transition and pseudotime from the scRNA-seq data. Slingshotsuggested: (Slingshot, RRID:SCR_017012)DNA motif recognition analysis of genome-wide significant SNPs: We used the function “variation-scan” of the online tool RSAT (http://rsat.sb-roscoff.fr/index.php, accessed on 01/15/2020) [40] to predict the binding effect of all the significant SNPs at the 3p21.31 locus. RSATsuggested: NoneThe position weight matrices (PWMs) for all the TFs were downloaded from cis-BP Database (http://cisbp.ccbr.utoronto.ca/) version 2019-06_v2.00) [41] and sequence logos representing motif binding sites were generated using R package seqLogo version 1.54.3 in R version 3.5.2. http://cisbp.ccbr.utoronto.ca/suggested: (CIS-BP, RRID:SCR_017236)seqLogosuggested: NoneResults from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Another limitation is that the scRNA-seq data only had nine COVID-19 patient samples (six severe and three moderate samples), which might not provide enough statistical power at the sample level as it is commonly considered each scRNA-seq data acts like a population. Finally, the TF binding site affinity alterations were assessed based on computational prediction, therefore, the in vivo effects require experimental validation. We anticipate more and larger datasets will be released in the near future. We will apply our integrative analysis approach to such new data.
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.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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