Potential impact on coagulopathy of gene variants of coagulation related proteins that interact with SARS-CoV-2

  1. David Holcomb
  2. Aikaterini Alexaki
  3. Nancy Hernandez
  4. Kyle Laurie
  5. Jacob Kames
  6. Nobuko Hamasaki-Katagiri
  7. Anton A. Komar
  8. Michael DiCuccio
  9. Chava Kimchi-Sarfaty

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Abstract

Thrombosis has been one of the complications of the Coronavirus disease of 2019 (COVID-19), often associated with poor prognosis. There is a well-recognized link between coagulation and inflammation, however, the extent of thrombotic events associated with COVID-19 warrants further investigation. Poly(A) Binding Protein Cytoplasmic 4 (PABPC4), Serine/Cysteine Proteinase Inhibitor Clade G Member 1 (SERPING1) and Vitamin K epOxide Reductase Complex subunit 1 (VKORC1), which are all proteins linked to coagulation, have been shown to interact with SARS proteins. We computationally examined the interaction of these with SARS-CoV-2 proteins and, in the case of VKORC1, we describe its binding to ORF7a in detail. We examined the occurrence of variants of each of these proteins across populations and interrogated their potential contribution to COVID-19 severity. Potential mechanisms by which some of these variants may contribute to disease are proposed. Some of these variants are prevalent in minority groups that are disproportionally affected by severe COVID-19. Therefore, we are proposing that further investigation around these variants may lead to better understanding of disease pathogenesis in minority groups and more informed therapeutic approaches.

Author summary

Increased blood clotting, especially in the lungs, is a common complication of COVID-19. Infectious diseases cause inflammation which in turn can contribute to increased blood clotting. However, the extent of clot formation that is seen in the lungs of COVID-19 patients suggests that there may be a more direct link. We identified three human proteins that are involved indirectly in the blood clotting cascade and have been shown to interact with proteins of SARS virus, which is closely related to the novel coronavirus. We examined computationally the interaction of these human proteins with the viral proteins. We looked for genetic variants of these proteins and examined how these variants are distributed across populations. We investigated whether variants of these genes could impact severity of COVID-19. Further investigation around these variants may provide clues for the pathogenesis of COVID-19 particularly in minority groups.

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    SciScore for 10.1101/2020.09.08.272328: (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
    SentencesResources
    In addition, to verify the binding of PABPC4 and SERPING1 with SARS-CoV-2 proteins, we created homology models for each using I-TASSER and Robetta [26] [27].
    I-TASSER
    suggested: (I-TASSER, RRID:SCR_014627)
    For this reason, we used Blast and Clustal Omega to create multiple sequence alignments (MSAs) of proteins similar to interacting SARS proteins, and computed the percent of columns of the homologous SARS-CoV-2 protein matching the SARS protein, as well as a loglikelihood score to measure the probability that the SARS-CoV-2 homolog would be included in the MSA (Table 1).
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    All human proteins interacting with VKORC1 were taken from BIOGRID, the Biological General Repository for Interaction Datasets [29] [30].
    BIOGRID
    suggested: (BioGrid Australia, RRID:SCR_006334)
    We characterized these variants in terms of splicing, using hexamer scoring tools [33] [34], ESEfinder [35] [36], ExonScan [37] [38] [39], and FAS-ESS [37].
    ESEfinder
    suggested: (ESEfinder 3.0, RRID:SCR_007088)
    We also analyzed miRNA binding changes using miRDB [45] [46].
    miRDB
    suggested: (miRDB, RRID:SCR_010848)
    (Supplementary Table S4) variants of VKORC1, SERPING1 and PABPC4 genes [47] from NCBI’s Single Nucleotide Polymorphism Database (dbSNP) [48] and characterized them in terms of (i) population prevalence in the Genome Aggregation Database (gnomAD) [49] [50], (ii) the percent of sequences matching the WT at that position in a multiple sequence alignment (MSA) [51], (iii) likelihood of the variant in the column of an MSA, (iv) mRNA MFE computed by both Kinefold and mFold, (v) relative synonymous codon usage (RSCU) and (vi) relative synonymous codon pair usage (RSCPU) [52] [53], (vii) rare codon enrichment [54], (viii) and %MinMax codon usage [55].
    dbSNP
    suggested: (dbSNP, RRID:SCR_002338)
    For nonsynonymous variants, we additionally used amino acid fraction matching in an MSA, likelihood of the variant amino acid in an amino acid MSA, SIFT [56] [50], and Polyphen [57] [50].
    SIFT
    suggested: (SIFT, RRID:SCR_012813)
    Polyphen
    suggested: None

    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.

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  2. Version published to 10.1101/2020.09.08.272328v2 on bioRxiv
  3. Version published to 10.1101/2020.09.08.272328v1 on bioRxiv