Novel ACE2 protein interactions relevant to COVID-19 predicted by evolutionary rate correlations

  1. Austin A. Varela
  2. Sammy Cheng
  3. John H. Werren

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Angiotensin-converting enzyme 2 (ACE2) is the cell receptor that the coronavirus SARS-CoV-2 binds to and uses to enter and infect human cells. COVID-19, the pandemic disease caused by the coronavirus, involves diverse pathologies beyond those of a respiratory disease, including micro-thrombosis (micro-clotting), cytokine storms, and inflammatory responses affecting many organ systems. Longer-term chronic illness can persist for many months, often well after the pathogen is no longer detected. A better understanding of the proteins that ACE2 interacts with can reveal information relevant to these disease manifestations and possible avenues for treatment. We have undertaken an approach to predict candidate ACE2 interacting proteins which uses evolutionary inference to identify a set of mammalian proteins that “coevolve” with ACE2. The approach, called evolutionary rate correlation (ERC), detects proteins that show highly correlated evolutionary rates during mammalian evolution. Such proteins are candidates for biological interactions with the ACE2 receptor. The approach has uncovered a number of key ACE2 protein interactions of potential relevance to COVID-19 pathologies. Some proteins have previously been reported to be associated with severe COVID-19, but are not currently known to interact with ACE2, while additional predicted novel ACE2 interactors are of potential relevance to the disease. Using reciprocal rankings of protein ERCs, we have identified strongly interconnected ACE2 associated protein networks relevant to COVID-19 pathologies. ACE2 has clear connections to coagulation pathway proteins, such as Coagulation Factor V and fibrinogen components FGA, FGB, and FGG, the latter possibly mediated through ACE2 connections to Clusterin (which clears misfolded extracellular proteins) and GPR141 (whose functions are relatively unknown). ACE2 also connects to proteins involved in cytokine signaling and immune response ( e.g . XCR1, IFNAR2 and TLR8), and to Androgen Receptor (AR). The ERC prescreening approach has elucidated possible functions for relatively uncharacterized proteins and possible new functions for well-characterized ones. Suggestions are made for the validation of ERC-predicted ACE2 protein interactions. We propose that ACE2 has novel protein interactions that are disrupted during SARS-CoV-2 infection, contributing to the spectrum of COVID-19 pathologies.

Article activity feed

  5. Version published to 10.7717/peerj.12159
  6. Version published to 10.1101/2021.05.24.445517v3 on bioRxiv
  7. Version published to 10.1101/2021.05.24.445517v2 on bioRxiv
  8. ScreenIT

    SciScore for 10.1101/2021.05.24.445517: (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
    Well-defined orthologous sequence data is sourced from OrthoDB v10 (Kriventseva et al., 2019) at the “mammalia” (taxonomic id: 40674) taxonomic level.
    OrthoDB
    suggested: (OrthoDB, RRID:SCR_011980)
    Phylogenetic Calculations and protein alignments: To prepare orthologous sequence data for ERC calculation, each set of protein sequences are first aligned using the MAFFT software package (Katoh & Standley, 2013) using the following arguments: “--maxiterate 1000 --localpair --anysymbol”.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    The alignments are trimmed using the trimAl software package (Capella-Gutiérrez, Silla-Martínez, & Gabaldón, 2009) using the “-automated1” argument to remove poorly aligned regions.
    trimAl
    suggested: (trimAl, RRID:SCR_017334)
    The IQ-TREE software package (Minh et al., 2020) is used to estimate protein branch lengths (equivalent to average substitution counts per site).
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    The resultant branch lengths are paired with corresponding branches in the TimeTree to quantify branch-specific rates to be used for ERC calculations (described below).
    TimeTree
    suggested: (TimeTree, RRID:SCR_021162)
    Protein set enrichment analyses are performed using the Enrichr service (Xie et al., 2021) via the Python bindings provided by the “GSEApy” Python package (Fang et al., 2021) given the background of the full set of 1,953 proteins.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and 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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    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.

  9. Version published to 10.1101/2021.05.24.445517v1 on bioRxiv