Predicting mammalian hosts in which novel coronaviruses can be generated
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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.
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SciScore for 10.1101/2020.06.15.151845: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This ‘no-preconceptions’ approach enables us to analyse without being restricted by our current incomplete knowledge of the specific biological and molecular mechanisms which govern host-virus permissibility Additionally, the incorporation of similarity-based learners in our three-perspective approach enabled us to capture new hosts (i.e. with no known association with coronaviruses), thus avoiding a main limitation of approaches which rely only …
SciScore for 10.1101/2020.06.15.151845: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This ‘no-preconceptions’ approach enables us to analyse without being restricted by our current incomplete knowledge of the specific biological and molecular mechanisms which govern host-virus permissibility Additionally, the incorporation of similarity-based learners in our three-perspective approach enabled us to capture new hosts (i.e. with no known association with coronaviruses), thus avoiding a main limitation of approaches which rely only on networks and their topology. We acknowledge certain limitations in our methodology, primarily pertaining to current incomplete datasets in the rapidly developing but still understudied field. 1) The inclusion only of coronaviruses for which complete genomes could be found limited the number of coronaviruses (species or strain) for which we could compute meaningful similarities, and therefore predict potential hosts. The same applies for our mammalian species – we only included mammalian hosts for which phylogenetic, ecological, and geospatial data were available. As more data on sequenced coronaviruses or mammals become available in future, our model can be re-run to further improve predictions, and to validate predictions from earlier iterations. 2) Research effort, centering mainly on coronaviruses found in humans and their domesticated animals, can lead to overestimation of the potential of coronaviruses to recombine in frequently studied mammals, such as lab rodents which were excluded from the results reported here (similar to...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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