ACE2 Netlas: In silico Functional Characterization and Drug-Gene Interactions of ACE2 Gene Network to Understand Its Potential Involvement in COVID-19 Susceptibility
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Abstract
Angiotensin-converting enzyme-2 ( ACE2 ) receptor has been identified as the key adhesion molecule for the transmission of the SARS-CoV-2. However, there is no evidence that human genetic variation in ACE2 is singularly responsible for COVID-19 susceptibility. Therefore, we performed an integrative multi-level characterization of genes that interact with ACE2 (ACE2-gene network) for their statistically enriched biological properties in the context of COVID-19. The phenome-wide association of 51 genes including ACE2 with 4,756 traits categorized into 26 phenotype categories, showed enrichment of immunological, respiratory, environmental, skeletal, dermatological, and metabolic domains ( p < 4e-4). Transcriptomic regulation of ACE2-gene network was enriched for tissue-specificity in kidney, small intestine, and colon ( p < 4.7e-4). Leveraging the drug-gene interaction database we identified 47 drugs, including dexamethasone and spironolactone, among others. Considering genetic variants within ± 10 kb of ACE2-network genes we identified miRNAs whose binding sites may be altered as a consequence of genetic variation. The identified miRNAs revealed statistical over-representation of inflammation, aging, diabetes, and heart conditions. The genetic variant associations in RORA , SLC12A6 , and SLC6A19 genes were observed in genome-wide association study (GWAS) of COVID-19 susceptibility. We also report the GWAS-identified variant in 3p21.31 locus, serves as trans-QTL for RORA and RORC genes. Overall, functional characterization of ACE2-gene network highlights several potential mechanisms in COVID-19 susceptibility. The data can also be accessed at https://gpwhiz.github.io/ACE2Netlas/ .
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SciScore for 10.1101/2020.10.27.20220665: (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 5.1 Gene network collection: Information regarding ACE2 gene network was mined from GeneMANIA [57], Stringdb [58], APID [59], GeneNetwork [60], Biogrid[61] and FunctionalNet [62]. GeneMANIAsuggested: (GeneMANIA, RRID:SCR_005709)GeneNetworksuggested: (GeneNetwork, RRID:SCR_002388)5.5 Characterization of SNPs: Single nucleotide polymorphism (SNPs) were extracted based on the genomic coordinates of the genes (± 10kb) for GrCh37; dbSNP153 from the UCSC browser [66] using bigbed utilities [67], and referred to as ‘ACE2-network SNPs.’ ACE2-network SNPs were annotated for global allele … SciScore for 10.1101/2020.10.27.20220665: (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 5.1 Gene network collection: Information regarding ACE2 gene network was mined from GeneMANIA [57], Stringdb [58], APID [59], GeneNetwork [60], Biogrid[61] and FunctionalNet [62]. GeneMANIAsuggested: (GeneMANIA, RRID:SCR_005709)GeneNetworksuggested: (GeneNetwork, RRID:SCR_002388)5.5 Characterization of SNPs: Single nucleotide polymorphism (SNPs) were extracted based on the genomic coordinates of the genes (± 10kb) for GrCh37; dbSNP153 from the UCSC browser [66] using bigbed utilities [67], and referred to as ‘ACE2-network SNPs.’ ACE2-network SNPs were annotated for global allele frequency, Combined Annotation-Dependent Depletion (CADD) score [12], deep learning based algorithm framework (DeepSEA) [13], and target miRNAs using SNPnexus [68]. UCSC browsersuggested: NoneSNPnexussuggested: (SNPnexus, RRID:SCR_005192)The identified miRNAs were tested for over-represented miRNA clusters, functions, and diseases using TAM 2.0 [69] TAMsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This limitation is particularly relevant with respect to the ACE2 network genetic associations. Due to the limited statistical power of the genome-wide data available to date, none of the risk alleles identified as functionally relevant survive genome-wide testing correction. Further analyses will be needed to validate our current findings.
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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