Predicting mammalian hosts in which novel coronaviruses can be generated

Novel pathogenic coronaviruses – such as SARS-CoV and probably SARS-CoV-2 – arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-h...

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Bibliographic Details
Published in:Nature communications 2021-02, Vol.12 (1), p.780-780, Article 780
Main Authors: Wardeh, Maya, Baylis, Matthew, Blagrove, Marcus S. C.
Format: Article
Language:English
Subjects:
631/114/1305
631/158/1469
631/158/2463
631/326/596/2564
Animals
Coronaviridae
Coronavirus - physiology
Coronavirus Infections - virology
Coronaviruses
Domestic animals
Health surveillance
Homologous recombination
Homology
Host-Pathogen Interactions
Humanities and Social Sciences
Humans
Learning algorithms
Machine learning
Mammals
Mammals - virology
Models, Biological
multidisciplinary
Phylogeny
Recombination, Genetic - genetics
Reproducibility of Results
Science
Science (multidisciplinary)
Severe acute respiratory syndrome
Severe acute respiratory syndrome coronavirus 2
Viral diseases
Viruses
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Summary:Novel pathogenic coronaviruses – such as SARS-CoV and probably SARS-CoV-2 – arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-host interactions remain unknown. Here, by deploying a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), we predict which mammals are hosts of multiple coronaviruses. We predict that there are 11.5-fold more coronavirus-host associations, over 30-fold more potential SARS-CoV-2 recombination hosts, and over 40-fold more host species with four or more different subgenera of coronaviruses than have been observed to date at >0.5 mean probability cut-off (2.4-, 4.25- and 9-fold, respectively, at >0.9821). Our results demonstrate the large underappreciation of the potential scale of novel coronavirus generation in wild and domesticated animals. We identify high-risk species for coronavirus surveillance. Homologous recombination between co-infecting coronaviruses can produce novel pathogens. Here, Wardeh et al. develop a machine learning approach to predict associations between mammals and multiple coronaviruses and hence estimate the potential for generation of novel coronaviruses by recombination.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-021-21034-5