Structural Variability, Expression Profile, and Pharmacogenetic Properties of TMPRSS2 Gene as a Potential Target for COVID-19 Therapy

  1. Aleksei Zarubin
  2. Vadim Stepanov
  3. Anton Markov
  4. Nikita Kolesnikov
  5. Andrey Marusin
  6. Irina Khitrinskaya
  7. Maria Swarovskaya
  8. Sergey Litvinov
  9. Natalia Ekomasova
  10. Murat Dzhaubermezov
  11. Nadezhda Maksimova
  12. Aitalina Sukhomyasova
  13. Olga Shtygasheva
  14. Elza Khusnutdinova
  15. Magomed Radzhabov
  16. Vladimir Kharkov

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Abstract

The human serine protease serine 2 TMPRSS2 is involved in the priming of proteins of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and represents a possible target for COVID-19 therapy. The TMPRSS2 gene may be co-expressed with SARS-CoV-2 cell receptor genes angiotensin-converting enzyme 2 (ACE2) and Basigin (BSG), but only TMPRSS2 demonstrates tissue-specific expression in alveolar cells according to single-cell RNA sequencing data. Our analysis of the structural variability of the TMPRSS2 gene based on genome-wide data from 76 human populations demonstrates that a functionally significant missense mutation in exon 6/7 in the TMPRSS2 gene is found in many human populations at relatively high frequencies, with region-specific distribution patterns. The frequency of the missense mutation encoded by rs12329760, which has previously been found to be associated with prostate cancer, ranged between 10% and 63% and was significantly higher in populations of Asian origin compared with European populations. In addition to single-nucleotide polymorphisms, two copy number variants were detected in the TMPRSS2 gene. A number of microRNAs have been predicted to regulate TMPRSS2 and BSG expression levels, but none of them is enriched in lung or respiratory tract cells. Several well-studied drugs can downregulate the expression of TMPRSS2 in human cells, including acetaminophen (paracetamol) and curcumin. Thus, the interactions of TMPRSS2 with SARS-CoV-2, together with its structural variability, gene–gene interactions, expression regulation profiles, and pharmacogenomic properties, characterize this gene as a potential target for COVID-19 therapy.

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  1. Version published to 10.3390/genes12010019
  2. ScreenIT

    SciScore for 10.1101/2020.06.20.156224: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: DNA samples were collected under informed consent and deposited to DNA bank of the Research Institute for Medical Genetics, Tomsk National Medical Research Center, Tomsk, Russia and DNA bank of the Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences.
    IRB: The study was approved by the Ethical Committee of the Research Institute for Medical Genetics, Tomsk National Medical Research Center.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    variability data: Allele frequency for worldwide populations were downloaded from GnomAD database containing information on the frequencies of genomic variants from more than 120 thousand exomes and 15 thousand of whole genomes [18].
    GnomAD
    suggested: (Genome Aggregation Database, RRID:SCR_014964)
    CNV search was performed using Markov model algorithm for high-resolution copy number variation detection in whole-genome SNP implemented in PennCNV tool [34].
    PennCNV
    suggested: (PennCNV, RRID:SCR_002518)
    To determine possible functional impact of detected SNVs, the Polymorphism Phenotyping v2 (Poly-Phen-2) tool was used [2].
    Poly-Phen-2
    suggested: None
    Poly-Phen estimates the impact of the mutation on the stability and function of the protein using the structural and evolutionary analyses of the amino acid substitution.
    Poly-Phen
    suggested: None
    Bioinformatics analysis of gene expression, miRNA intercaction and pharmacogenomics: Analysis of protein – protein interactions of SARS-CoV-2 interacting proteins was carried out using the GeneMANIA and STRING web resources [32, 38].
    GeneMANIA
    suggested: (GeneMANIA, RRID:SCR_005709)
    STRING
    suggested: (STRING, RRID:SCR_005223)
    Lung cells single cell RNA-seq data were obtained from the Sequence Read Archive (SRA) [22] and processed in R software environment using the Seurat package [31].
    Sequence Read Archive
    suggested: (DDBJ Sequence Read Archive, RRID:SCR_001370)
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Analysis of the interaction of miRNAs with target proteins was performed using information from two databases, miRTarBase, which contains information from more than 8000 referenced sources about experimentally confirmed micro RNA - protein interactions [16], and miRPathDB database containing experimentally confirmed and predicted miRNA-protein interactions [19].
    miRPathDB
    suggested: (miRpathDB, RRID:SCR_017356)
    DRUGBANK database [35] was used to search for the drugs which may change the level of protein expression.
    DRUGBANK
    suggested: (DrugBank, RRID:SCR_002700)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.06.20.156224v1 on bioRxiv