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Wardeh, M.

Publications and source records attributed to Wardeh, M..

2 recordsLinked to original sources

Predicting mammalian hosts in which novel coronaviruses can be generated.

Novel pathogenic coronaviruses - including SARS-CoV and SARS-CoV-2 - arise by homologous recombination in a host cell1,2. This process requires a single host to be infected with more than one type of coronavirus, which recombine to form novel strains of virus with unique combinations of genetic material. Identifying possible sources of novel coronaviruses requires identifying hosts (termed recombination hosts) of more than one coronavirus type, in which recombination might occur. However, the majority of coronavirus-host interactions remain unknown, and therefore the vast majority of recombination hosts for coronaviruses cannot be identified. Here we show that there are 11.5-fold more coronavirus-host associations, and over 30-fold more potential SARS-CoV-2 recombination hosts, than have been observed to date. We show there are over 40-fold more host species with four or more different subgenera of coronaviruses. This underestimation of both number and novel coronavirus generation in wild and domesticated animals. Our results list specific high-risk hosts in which our model predicts homologous recombination could occur, our model identifies both wild and domesticated mammals including known important and understudied species. We recommend these species for coronavirus surveillance, as well as enforced separation in livestock markets and agriculture.

bioinformatics

Divide and conquer: machine-learning integrates mammalian, viral, and network traits to predict unknown virus-mammal associations

Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify zoonotic and animal-disease risks. Furthermore, such understanding can be used to mitigate against viral spill-over from animal reservoirs into human population. To address this knowledge-gap we apply a divide-and-conquer approach which separates viral, mammalian and network features into three unique perspectives, each predicting associations independently to enhance predictive power. Our approach predicts over 20,000 unknown associations between known viruses and mammalian hosts, suggesting that current knowledge underestimates the number of associations in wild and semi-domesticated mammals by a factor of 4.3, and the average mammalian host-range of viruses by a factor of 3.2. In particular, our results highlight a significant knowledge gap in the wild reservoirs of important zoonotic and domesticated mammals viruses: specifically, lyssaviruses, bornaviruses and rotaviruses.

ecology