ACE2 Expression Is Increased in the Lungs of Patients With Comorbidities Associated With Severe COVID-19

  1. Bruna G G Pinto
  2. Antonio E R Oliveira
  3. Youvika Singh
  4. Leandro Jimenez
  5. Andre N A Gonçalves
  6. Rodrigo L T Ogava
  7. Rachel Creighton
  8. Jean Pierre Schatzmann Peron
  9. Helder I Nakaya

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Abstract

Patients who died from COVID-19 often had comorbidities, such as hypertension, diabetes, and chronic obstructive lung disease. Although angiotensin-converting enzyme 2 (ACE2) is crucial for SARS-CoV-2 to bind and enter host cells, no study has systematically assessed the ACE2 expression in the lungs of patients with these diseases. Here, we analyzed over 700 lung transcriptome samples from patients with comorbidities associated with severe COVID-19 and found that ACE2 was highly expressed in these patients compared to control individuals. This finding suggests that patients with such comorbidities may have higher chances of developing severe COVID-19. Correlation and network analyses revealed many potential regulators of ACE2 in the human lung, including genes related to histone modifications, such as HAT1, HDAC2, and KDM5B. Our systems biology approach offers a possible explanation for increased COVID-19 severity in patients with certain comorbidities.

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  1. Version published to 10.1093/infdis/jiaa332
  2. ScreenIT

    SciScore for 10.1101/2020.03.21.20040261: (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
    Literature curation: Relevant scientific literature related to key COVID-19 morbidities was retrieved from PubMed.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Gene IDs were converted to gene symbols using the biomaRt R package [16, 17], and disease IDs were converted to disease MeSH terms using the Entrez Programming Utilities to query the Entrez database provided by the National Center for Biotechnology Information.
    Entrez
    suggested: (Entrez, RRID:SCR_016640)
    The data was then further filtered to retain disease MeSH terms relevant to reported clinical COVID-19 comorbidities [3].
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    The final gene-disease data set was used to generate a network utilizing Gephi software where the nodes were genes and diseases, and the edge weight was determined by the number of analyzed papers containing the gene-disease combination [18].
    Gephi
    suggested: (Gephi, RRID:SCR_004293)
    Meta-Analysis: We manually curated Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/) to find lung and blood transcriptome datasets related to “Pulmonary Arterial Hypertension” (PAH), “Chronic Obstructive Pulmonary Disease” (COPD), “Severe Acute Respiratory Syndrome” (SARS), “H1N1 Influenza”, “Tuberculosis” and “Smoking”.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Author-normalized expression values and metadata from these datasets were downloaded using the GEOquery package [19].
    GEOquery
    suggested: (GEOquery, RRID:SCR_000146)
    We performed differential expression analyses between patients with a disease and the control individuals (see Table S1) using the limma package [20].
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    Meta-analysis was performed with the MetaVolcanoR package [22] by combining the p-values using Fisher’s method.
    MetaVolcanoR
    suggested: (MetaVolcanoR, RRID:SCR_018549)
    For enrichment analyses, we utilized the EnrichR tool [23] with the “GO Biological Process 2018” and “BioPlanet 2019” databases.
    EnrichR
    suggested: (Enrichr, RRID:SCR_001575)
    “GO Biological
    suggested: None
    The network was created in Cytoscape [24].
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    Results from OddPub: Thank you for sharing your data.

    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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  3. Version published to 10.1101/2020.03.21.20040261v1 on medRxiv