ACE2 Interaction Networks in COVID-19: A Physiological Framework for Prediction of Outcome in Patients with Cardiovascular Risk Factors

  1. Zofia Wicik
  2. Ceren Eyileten
  3. Daniel Jakubik
  4. Sérgio N. Simões
  5. David C. Martins
  6. Rodrigo Pavão
  7. Jolanta M. Siller-Matula
  8. Marek Postula

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Abstract

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (coronavirus disease 2019; COVID-19) is associated with adverse outcomes in patients with cardiovascular disease (CVD). The aim of the study was to characterize the interaction between SARS-CoV-2 and Angiotensin-Converting Enzyme 2 (ACE2) functional networks with a focus on CVD. Methods: Using the network medicine approach and publicly available datasets, we investigated ACE2 tissue expression and described ACE2 interaction networks that could be affected by SARS-CoV-2 infection in the heart, lungs and nervous system. We compared them with changes in ACE-2 networks following SARS-CoV-2 infection by analyzing public data of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). This analysis was performed using the Network by Relative Importance (NERI) algorithm, which integrates protein-protein interaction with co-expression networks. We also performed miRNA-target predictions to identify which miRNAs regulate ACE2-related networks and could play a role in the COVID19 outcome. Finally, we performed enrichment analysis for identifying the main COVID-19 risk groups. Results: We found similar ACE2 expression confidence levels in respiratory and cardiovascular systems, supporting that heart tissue is a potential target of SARS-CoV-2. Analysis of ACE2 interaction networks in infected hiPSC-CMs identified multiple hub genes with corrupted signaling which can be responsible for cardiovascular symptoms. The most affected genes were EGFR (Epidermal Growth Factor Receptor), FN1 (Fibronectin 1), TP53, HSP90AA1, and APP (Amyloid Beta Precursor Protein), while the most affected interactions were associated with MAST2 and CALM1 (Calmodulin 1). Enrichment analysis revealed multiple diseases associated with the interaction networks of ACE2, especially cancerous diseases, obesity, hypertensive disease, Alzheimer’s disease, non-insulin-dependent diabetes mellitus, and congestive heart failure. Among affected ACE2-network components connected with the SARS-Cov-2 interactome, we identified AGT (Angiotensinogen), CAT (Catalase), DPP4 (Dipeptidyl Peptidase 4), CCL2 (C-C Motif Chemokine Ligand 2), TFRC (Transferrin Receptor) and CAV1 (Caveolin-1), associated with cardiovascular risk factors. We described for the first time miRNAs which were common regulators of ACE2 networks and virus-related proteins in all analyzed datasets. The top miRNAs regulating ACE2 networks were miR-27a-3p, miR-26b-5p, miR-10b-5p, miR-302c-5p, hsa-miR-587, hsa-miR-1305, hsa-miR-200b-3p, hsa-miR-124-3p, and hsa-miR-16-5p. Conclusion: Our study provides a complete mechanistic framework for investigating the ACE2 network which was validated by expression data. This framework predicted risk groups, including the established ones, thus providing reliable novel information regarding the complexity of signaling pathways affected by SARS-CoV-2. It also identified miRNAs that could be used in personalized diagnosis in COVID-19.

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  1. Version published to 10.3390/jcm9113743
  2. ScreenIT

    SciScore for 10.1101/2020.05.13.094714: (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
    Data collection: ACE2-associated genes used for constructing interaction networks were extracted from KEGG database (23 genes from renin-angiotensin system-RAS pathway) [20]; stringApp (top 40 ACE2 interactors) [21], Archs4 database https://amp.pharm.mssm.edu/archs4 (top 20 genes with correlated expression), Genecards database https://www.genecards.org (5 interactors and 4 sister terms), literature search [22–24].
    Archs4
    suggested: (ARCHS4, RRID:SCR_015683)
    Genecards
    suggested: (GeneCards, RRID:SCR_002773)
    Tissues 2.0 database integrates:
    Tissues
    suggested: None
    Enrichment analysis of the diseases and networks was done with the EnrichR database [32], using Fisher’s exact with Benjamini and Hochberg correction, while the reference was precomputed background for each term in each gene set library.
    EnrichR
    suggested: (Enrichr, RRID:SCR_001575)
    Signalling pathways were analyzed using BioPlanet2019 and Human KEGG 2019 datasets.
    BioPlanet2019
    suggested: None
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Interaction networks between ACE2-related genes and miRNAs were constructed in R and exported to Cytoscape 3.7.2.
    Cytoscape
    suggested: (Cytoscape, RRID:SCR_003032)

    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.

    About SciScore

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  3. Version published to 10.1101/2020.05.13.094714 on bioRxiv