Missense variants in human ACE2 strongly affect binding to SARS-CoV-2 Spike providing a mechanism for ACE2 mediated genetic risk in Covid-19: A case study in affinity predictions of interface variants

  1. Stuart A. MacGowan
  2. Michael I. Barton
  3. Mikhail Kutuzov
  4. Omer Dushek
  5. P. Anton van der Merwe
  6. Geoffrey J. Barton

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

SARS-CoV-2 Spike (Spike) binds to human angiotensin-converting enzyme 2 (ACE2) and the strength of this interaction could influence parameters relating to virulence. To explore whether population variants in ACE2 influence Spike binding and hence infection, we selected 10 ACE2 variants based on affinity predictions and prevalence in gnomAD and measured their affinities and kinetics for Spike receptor binding domain through surface plasmon resonance (SPR) at 37°C. We discovered variants that reduce and enhance binding, including three ACE2 variants that strongly inhibited (p.Glu37Lys, ΔΔG = –1.33 ± 0.15 kcal mol -1 and p.Gly352Val, predicted ΔΔG = –1.17 kcal mol -1 ) or abolished (p.Asp355Asn) binding. We also identified two variants with distinct population distributions that enhanced affinity for Spike. ACE2 p.Ser19Pro (ΔΔG = 0.59 ± 0.08 kcal mol -1 ) is predominant in the gnomAD African cohort (AF = 0.003) whilst p.Lys26Arg (ΔΔG = 0.26 ± 0.09 kcal mol -1 ) is predominant in the Ashkenazi Jewish (AF = 0.01) and European non-Finnish (AF = 0.006) cohorts. We compared ACE2 variant affinities to published SARS-CoV-2 pseudotype infectivity data and confirmed that ACE2 variants with reduced affinity for Spike can protect cells from infection. The effect of variants with enhanced Spike affinity remains unclear, but we propose a mechanism whereby these alleles could cause greater viral spreading across tissues and cell types, as is consistent with emerging understanding regarding the interplay between receptor affinity and cell-surface abundance. Finally, we compared mCSM-PPI2 ΔΔG predictions against our SPR data to assess the utility of predictions in this system. We found that predictions of decreased binding were well-correlated with experiment and could be improved by calibration, but disappointingly, predictions of highly enhanced binding were unreliable. Recalibrated predictions for all possible ACE2 missense variants at the Spike interface were calculated and used to estimate the overall burden of ACE2 variants on Covid-19.

Article activity feed

  1. Version published to 10.1371/journal.pcbi.1009922
  2. ScreenIT

    SciScore for 10.1101/2021.05.21.445118: (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
    SPR data fitting: Double referenced binding data was plotted and fit with GraphPad Prism (Supplementary Figure 5).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    These operations were run with our ProIntVar45 Python package, which processes all these data into conveniently accessible Pandas DataFrames.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Recalibrated scores were calculated with the predict.lm function and applied to ACE2 variants within 10 angstroms of Spike. Enumerating possible ACE2 missense SNPs: The ACE2 gene (ENSG00000130234) was retrieved from Ensembl in Jalview25.
    Ensembl
    suggested: (Ensembl, RRID:SCR_002344)
    Estimated Prevalence of Novel Rare Variants in ACE2 with Spike Affinity Phenotypes: Software: Jalview 2.1125 was used for interactive sequence data retrieval, sequence analysis, structure data analysis and figure generation.
    Jalview
    suggested: (Jalview, RRID:SCR_006459)
    Biopython was used to process sequence data.
    Biopython
    suggested: (Biopython, RRID:SCR_007173)
    Data analyses were coded in R and Python Jupyter Notebooks. Numpy, Pandas and Scipy were used for data analysis.
    Numpy
    suggested: (NumPy, RRID:SCR_008633)
    Scipy
    suggested: (SciPy, RRID:SCR_008058)
    Matplotlib, Seaborn and ggplot2 were used to plot data.
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    Results from OddPub: Thank you for sharing your code.

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

    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.

  3. Version published to 10.1101/2021.05.21.445118v1 on bioRxiv