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Predicting mammalian hosts in which novel coronaviruses can be generated.
Wardeh, Maya; Baylis, Matthew; Blagrove, Marcus S C.
  • Wardeh M; Department of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK. maya.wardeh@liverpool.ac.uk.
  • Baylis M; Department of Mathematical Sciences, University of Liverpool, Liverpool, UK. maya.wardeh@liverpool.ac.uk.
  • Blagrove MSC; Department of Livestock and One Health, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK.
Nat Commun ; 12(1): 780, 2021 02 16.
Article in English | MEDLINE | ID: covidwho-1087442
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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Semantic information from SemMedBD (by NLM)
1. Cells LOCATION_OF Virus
Subject
Cells
Predicate
LOCATION_OF
Object
Virus
2. Cells LOCATION_OF Virus
Subject
Cells
Predicate
LOCATION_OF
Object
Virus
ABSTRACT
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Coronavirus / Host-Pathogen Interactions / Mammals Type of study: Prognostic study / Randomized controlled trials / Reviews Limits: Animals / Humans Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2021 Document Type: Article Affiliation country: S41467-021-21034-5

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Coronavirus / Host-Pathogen Interactions / Mammals Type of study: Prognostic study / Randomized controlled trials / Reviews Limits: Animals / Humans Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2021 Document Type: Article Affiliation country: S41467-021-21034-5