Mollentze, N. , Babayan, S. A. and Streicker, D. G. (2021) Identifying and prioritizing potential human-infecting viruses from their genome sequences. PLoS Biology, 19(9), e3001390. (doi: 10.1371/journal.pbio.3001390) (PMID:34582436) (PMCID:PMC8478193)
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Abstract
Determining which animal viruses may be capable of infecting humans is currently intractable at the time of their discovery, precluding prioritization of high-risk viruses for early investigation and outbreak preparedness. Given the increasing use of genomics in virus discovery and the otherwise sparse knowledge of the biology of newly discovered viruses, we developed machine learning models that identify candidate zoonoses solely using signatures of host range encoded in viral genomes. Within a dataset of 861 viral species with known zoonotic status, our approach outperformed models based on the phylogenetic relatedness of viruses to known human-infecting viruses (area under the receiver operating characteristic curve [AUC] = 0.773), distinguishing high-risk viruses within families that contain a minority of human-infecting species and identifying putatively undetected or so far unrealized zoonoses. Analyses of the underpinnings of model predictions suggested the existence of generalizable features of viral genomes that are independent of virus taxonomic relationships and that may preadapt viruses to infect humans. Our model reduced a second set of 645 animal-associated viruses that were excluded from training to 272 high and 41 very high-risk candidate zoonoses and showed significantly elevated predicted zoonotic risk in viruses from nonhuman primates, but not other mammalian or avian host groups. A second application showed that our models could have identified Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a relatively high-risk coronavirus strain and that this prediction required no prior knowledge of zoonotic Severe Acute Respiratory Syndrome (SARS)-related coronaviruses. Genome-based zoonotic risk assessment provides a rapid, low-cost approach to enable evidence-driven virus surveillance and increases the feasibility of downstream biological and ecological characterization of viruses.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Babayan, Dr Simon and Streicker, Professor Daniel and Mollentze, Dr Nardus |
| Creator Roles: | Streicker, D. G.Conceptualization, Data curation, Writing – review and editing Babayan, S. A.Conceptualization, Writing – review and editing Mollentze, N.Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review and editing |
| Authors: | Mollentze, N., Babayan, S. A., and Streicker, D. G. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Infection & Immunity College of Medical Veterinary and Life Sciences > School of Infection & Immunity > Centre for Virus Research College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine |
| Journal Name: | PLoS Biology |
| Publisher: | Public Library of Science |
| ISSN: | 1544-9173 |
| ISSN (Online): | 1545-7885 |
| Published Online: | 28 September 2021 |
| Copyright Holders: | Copyright © 2021 Mollentze et al. |
| First Published: | First published in PLoS Biology 19(9): e3001390 |
| Publisher Policy: | Reproduced under a Creative Commons License |
| Related URLs: | |
| Data DOI: | 10.5281/zenodo.4271479 |
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