Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction

Mollentze, N. , Keen, D., Munkhbayar, U., Biek, R. and Streicker, D. G. (2022) Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction. eLife, 11, e80329. (doi: 10.7554/eLife.80329) (PMID:36416537) (PMCID:PMC9683784)

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Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the angiotensin-converting enzyme 2 (ACE2) receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly compiled database of 96 species, we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8–87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5–91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Streicker, Professor Daniel and Biek, Professor Roman and Mollentze, Dr Nardus and Munkhbayar, Miss Uuriintuya
Creator Roles:
Mollentze, N.Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing
Munkhbayar, U.Data curation
Biek, R.Conceptualization, Supervision, Investigation, Writing – review and editing
Streicker, D. G.Conceptualization, Supervision, Funding acquisition, Investigation, Writing – review and editing
Authors: Mollentze, N., Keen, D., Munkhbayar, U., Biek, R., and Streicker, D. G.
College/School:College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
College of Medical Veterinary and Life Sciences > School of Infection & Immunity
Journal Name:eLife
Publisher:eLife Sciences Publications
ISSN (Online):2050-084X
Copyright Holders:Copyright © Mollentze et al.
First Published:First published in eLife 11:e80329
Publisher Policy:Reproduced under a Creative Commons license

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
307106Epidemiology meets biotechnology: preventing viral emergence from batsDaniel StreickerWellcome Trust (WELLCOTR)217221/Z/19/ZInstitute of Biodiversity, Animal Health and Comparative Medicine
172630015Cross-Cutting Programme - Viral Genomics and Bioinformatics (Programme 9)Andrew DavisonMedical Research Council (MRC)MC_UU_12014/12III - Centre for Virus Research