Enrichment of SARS-CoV-2 Entry Factors and Interacting Intracellular Genes in Tissue and Circulating Immune Cells

  1. Abhinandan Devaprasad
  2. Aridaman Pandit

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Abstract

SARS-CoV-2 uses ACE2 and TMPRSS2 to gain entry into the cell. However, recent studies have shown that SARS-CoV-2 may use additional host factors that are required for the viral lifecycle. Here we used publicly available datasets, CoV-associated genes, and machine learning algorithms to explore the SARS-CoV-2 interaction landscape in different tissues. We found that in general a small fraction of cells express ACE2 in the different tissues, including nasal, bronchi, and lungs. We show that a small fraction of immune cells (including T cells, macrophages, dendritic cells) found in tissues also express ACE2. We show that healthy circulating immune cells do not express ACE2 and TMPRSS2. However, a small fraction of circulating immune cells (including dendritic cells, monocytes, T cells) in the PBMC of COVID-19 patients express ACE2 and TMPRSS2. Additionally, we found that a large spectrum of cells (in tissues and circulation) in both healthy and COVID-19-positive patients were significantly enriched for SARS-CoV-2 factors, such as those associated with RHOA and RAB GTPases, mRNA translation proteins, COPI- and COPII-mediated transport, and integrins. Thus, we propose that further research is needed to explore if SARS-CoV-2 can directly infect tissue and circulating immune cells to better understand the virus’ mechanism of action.

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  1. Version published to 10.3390/v13091757
  2. ScreenIT

    SciScore for 10.1101/2021.03.29.437515: (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
    This score reproduces the gene score in Seurat pipeline (Satija et al. 2015).
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Reactome pathway enrichment analysis was performed on the top 25 percentile genes in each ranking cluster for the DIME results of the different gene lists (Supplementary Figure 5).
    DIME
    suggested: (DIME, RRID:SCR_010874)
    The single cell RNA-Seq analysis was performed in python (version = 3.6) using scanpy package and Wilcoxon statistics using stat package from python.
    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2021.03.29.437515v1 on bioRxiv