Predicting the mutational drivers of future SARS-CoV-2 variants of concern

Maher, M. C. et al. (2022) Predicting the mutational drivers of future SARS-CoV-2 variants of concern. Science Translational Medicine, 14(633), eabk3445. (doi: 10.1126/scitranslmed.abk3445) (PMID:35014856) (PMCID:PMC8939770)

[img] Text
263897.pdf - Published Version
Available under License Creative Commons Attribution.

2MB

Abstract

SARS-CoV-2 evolution threatens vaccine- and natural infection–derived immunity and the efficacy of therapeutic antibodies. To improve public health preparedness, we sought to predict which existing amino acid mutations in SARS-CoV-2 might contribute to future variants of concern. We tested the predictive value of features comprising epidemiology, evolution, immunology, and neural network–based protein sequence modeling and identified primary biological drivers of SARS-CoV-2 intrapandemic evolution. We found evidence that ACE2-mediated transmissibility and resistance to population-level host immunity has waxed and waned as a primary driver of SARS-CoV-2 evolution over time. We retroactively identified with high accuracy (area under the receiver operator characteristic curve = 0.92 to 0.97) mutations that will spread, at up to 4 months in advance, across different phases of the pandemic. The behavior of the model was consistent with a plausible causal structure where epidemiological covariates combine the effects of diverse and shifting drivers of viral fitness. We applied our model to forecast mutations that will spread in the future and characterize how these mutations affect the binding of therapeutic antibodies. These findings demonstrate that it is possible to forecast the driver mutations that could appear in emerging SARS-CoV-2 variants of concern. We validated this result against Omicron, showing elevated predictive scores for its component mutations before emergence and rapid score increase across daily forecasts during emergence. This modeling approach may be applied to any rapidly evolving pathogens with sufficiently dense genomic surveillance data, such as influenza, and unknown future pandemic viruses.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Robertson, Professor David
Authors: Maher, M. C., Bartha, I., Weaver, S., di Iulio, J., Ferri, E., Soriaga, L., Lempp, F. A., Hie, B. L., Bryson, B., Berger, B., Robertson, D. L., Snell, G., Corti, D., Virgin, H. W., Kosakovsky Pond, S. L., and Telenti, A.
College/School:College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research
Journal Name:Science Translational Medicine
Publisher:American Association for the Advancement of Science
ISSN:1946-6234
ISSN (Online):1946-6242
Published Online:11 January 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Science Translational Medicine 14(633): eabk3445
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.5281/zenodo.5799744

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172630014Cross-Cutting Programme – Viral Genomics and Bioinformatics (Programme 9)David RobertsonMedical Research Council (MRC)MC_UU_12014/12III - Centre for Virus Research