Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2

  1. Harlan Barker
  2. Seppo Parkkila

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Abstract

The World Health Organization declared the COVID-19 epidemic a public health emergency of international concern on March 11th, 2020, and the pandemic is rapidly spreading worldwide. COVID-19 is caused by a novel coronavirus SARS-CoV-2, which enters human target cells via angiotensin converting enzyme 2 (ACE2). We used a number of bioinformatics tools to computationally characterize ACE2 by determining its cell-specific expression in trachea, lung, and small intestine, derive its putative functions, and predict transcriptional regulation. The small intestine expressed higher levels of ACE2 than any other organ. The large intestine, kidney and testis showed moderate signals, whereas the signal was weak in the lung. Single cell RNA-Seq data from trachea indicated positive signals along the respiratory tract in key protective cell types including club, goblet, proliferating, and ciliary epithelial cells; while in lung the ratio of ACE2-expressing cells was low in all cell types (<2.6%), but was highest in vascular endothelial and goblet cells. Gene ontology analysis suggested that, besides its classical role in renin-angiotensin system, ACE2 may be functionally associated with angiogenesis/blood vessel morphogenesis. Using a novel tool for the prediction of transcription factor binding sites we identified several putative binding sites within two tissue-specific promoters of the ACE2 gene. Our results also confirmed that age and gender play no significant role in the regulation of ACE2 mRNA expression in the lung.

IMPORTANCE

Vaccines and new medicines are urgently needed to prevent spread of COVID-19 pandemic, reduce the symptoms, shorten the duration of disease, prevent virus spread in the body, and most importantly to save lives. One of the key drug targets could be angiotensin-converting enzyme 2 (ACE2), which is a crucial receptor for the corona virus (SARS-CoV-2). It is known that SARS coronavirus infections lead to worse outcome in the elderly and in males. Therefore, one aim of the present study was to investigate whether age or sex could contribute to the regulation of ACE2 expression. We also decided to explore the transcriptional regulation of ACE2 gene expression. Since data on ACE2 distribution is still conflicting, we aimed to get a more comprehensive view of the cell types expressing the receptor of SARS-CoV-2. Finally, we studied the coexpression of ACE2 with other genes and explored its putative functions using gene ontology enrichment analysis.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.13.038752: (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

    Antibodies
    SentencesResources
    According to Protein Expression Atlas the immunostainings were performed with the rabbit anti-human polyclonal antibody (HPA000288; Sigma Aldrich, St. Louis, MO) raised against 111 N-terminal amino acids of ACE2 and diluted 1:250 for the staining.
    anti-human polyclonal antibody
    suggested: None
    Software and Algorithms
    SentencesResources
    ] Python libraries.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Boxplots for tissues of relevance were generated using Matplotlib and Seaborn libraries.
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    ] Python library to identify possible enriched terms in biological process (BP), molecular function (MF), cellular component (CC), human phenotype (HP), KEGG pathway, and WikiPathways (WP) ontologies.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    WikiPathways
    suggested: (WikiPathways, RRID:SCR_002134)
    ACE2 protein expression: Immunohistochemical localization of human ACE2 was evaluated from immunostained specimens provided by Protein Expression Atlas (https://www.proteinatlas.org/).
    https://www.proteinatlas.org/
    suggested: (HPA, RRID:SCR_006710)
    According to Protein Expression Atlas the immunostainings were performed with the rabbit anti-human polyclonal antibody (HPA000288; Sigma Aldrich, St. Louis, MO) raised against 111 N-terminal amino acids of ACE2 and diluted 1:250 for the staining.
    HPA000288; Sigma Aldrich
    suggested: None
    Promoter Analysis: Analysis of ACE2 promoter regions was performed using the TFBSfootprinter tool (https://github.com/thirtysix/TFBS_footprinting) which uses transcription-relevant data from several major databases to enhance prediction of putative TFBSs, including: ATAC-Seq data from ENCODE [64]
    ENCODE
    suggested: (Encode, RRID:SCR_015482)
    , transcription start sites and expression data from FANTOM5 [65], expression quantitative trail loci from GTEx [66], TFBS metacluster data from GTRD [67], TFBS binding profile data from JASPAR [68], and sequence and conservation data from Ensembl [69].
    JASPAR
    suggested: (JASPAR, RRID:SCR_003030)
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    Batch correction by individual and sample region was performed with SCANPY using the ComBat function.
    ComBat
    suggested: (ComBat, RRID:SCR_010974)
    Comparisons of ACE2 expression values in different tissues and between groups delineated by age or sex, were carried out by one-way ANOVA using the stats package in the SciPy [62
    SciPy
    suggested: (SciPy, RRID:SCR_008058)

    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 10.1101/2020.04.13.038752 on bioRxiv