Study of cell to cell transmission of SARS CoV 2 virus particle using gene network from microarray data

  1. Anamika Basu
  2. Anasua Sarkar
  3. Ujjwal Maulik

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Microarray data from SARS CoV2 patients helps to construct a gene network relating to this disease. Analysis of these genes, present in network, highlights their biological functions and related cellular pathways. With the assistance of these information, a drug(s) can be identified to treat COVID-19. A detailed analysis of the human host response to SARS CoV 2 with expression profiling by high throughput sequencing has executed with primary human lung epithelium cell lines. Clustered genes annotation and gene network construction are performed with the help of String database. Among four cluster of genes from microarray data, cluster 1 is identified as basal cells with p= 1.37e −05 with 44 genes. Functional enrichment analysis of genes present in gene network has completed using String database, ToppFun online tool and NetworkAnalyst tool. For SARS CoV2 virus particles, keratin proteins, which are part of cytoskeleton structure of host cell, play a major role in cell to cell virus particle transmission. Among three types of cell- cell communication, only anchoring junction between basal cell membrane and basal lamina, is involved in this virus transmission. In this junction point, hemidesmosome structure play a vital role in virus spread from one cell to basal lamina in respiratory tract. In this protein complex structure, keratin, integrin and laminin proteins of host cell is used to promote the spread of virus infection into extracellular matrix. So, small molecular blockers of different anchoring junction proteins i.e. ITGA3, ITGA2 can provide efficient protection against this deadly virus disease. Understanding the human host response against this virus is very important to develop novel therapeutics for the treatment of SARS-CoV 2.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.26.116780: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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.
    • No funding statement was detected.
    • 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.

  2. Version published to 10.1101/2020.05.26.116780 on bioRxiv