An in-silico study on SARS-CoV-2: Its compatibility with human tRNA pool, and the polymorphism arising in a single lineage over a month

  1. Manish Prakash Victor
  2. Rohit Das
  3. Tapash Chandra Ghosh

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Abstract

SARS-CoV-2 has caused a global pandemic that has costed enormous human lives in the recent past. The present study is an investigation of the viral codon adaptation, ORFs’ stability and tRNA co-adaptation with humans. We observed that for the codon usage bias in viral ssRNA, ORFs have near values of folding free energies and codon adaptation index with mRNAs of the human housekeeping CDS. However, the correlation between the stability of the ORFs in ssRNA and CAI is stronger than the mRNA stability and CAI of HKG, suggesting a greater expression capacity of SARS-CoV-2. Mutational analysis reflects polymorphism in the virus for ORF1ab, surface glycoprotein and nucleocapsid phosphoprotein ORFs. Non-synonymous mutations have shown non-polar substitutions. Out of the twelve mutations nine are for a higher t-RNA copy number. Viruses in general have high mutation rates. To understand the chances of survival for the mutated SARS-CoV-2 we did simulation for synonymous mutations. It resulted in 50% ORFs with higher stability than their native equivalents. Thus, considering only the synonymous mutations the virus can exhibit a lot of polymorphism. Collectively our data provides new insights for SARS-CoV-2 mutations and the human t-RNA compatibility.

Significance

Survivability of SARS-CoV-2 in humans is essential for its spread. It has overlapping genes exhibiting a high codon optimization with humans even after a higher codon usage bias. They seem to possess cognizance for high copy number t-RNA (cognate or near-cognate) in humans, while mutating. Even though, it has been well established that native transcripts posses the highest stability, our in-silico studies show that SARS-CoV-2 under mutations give rise to ORFs with higher stability. These results significantly present the virus’s ability and the credibility of survival for the mutants. Despite its focus on a geographical location it explains the ongoing behavior of SARS-CoV-2 for a steady existence in humans as all the different lineages have a common origin. Wuhan, China.

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    SciScore for 10.1101/2020.07.23.217083: (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
    Copy number for the Homo sapiens tRNA molecules containing anticodons for each 61 codons were obtained from GtRNAdb 2.0 release 18.1(20).
    GtRNAdb
    suggested: None
    R language and environment (https://www.r-project.org/) was used to perform statistical analyses.
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)
    Python package dnds_cal-0.0.2 with attribute for polymorphism (pn, ps) calculations was used.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 10.1101/2020.07.23.217083v1 on bioRxiv