Features that matter: Evolutionary signatures can predict viral transmission routes.

Autor: Wardeh M; Department of Computer Science, University of Liverpool, Liverpool, United Kingdom.; Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom., Pilgrim J; Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom., Hui M; Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom., Kotsiri A; Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom., Baylis M; Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom., Blagrove MSC; Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.
Jazyk: angličtina
Zdroj: PLoS pathogens [PLoS Pathog] 2024 Oct 21; Vol. 20 (10), pp. e1012629. Date of Electronic Publication: 2024 Oct 21 (Print Publication: 2024).
DOI: 10.1371/journal.ppat.1012629
Abstrakt: Routes of virus transmission between hosts are key to understanding viral epidemiology. Different routes have large effects on viral ecology, and likelihood and rate of transmission; for example, respiratory and vector-borne viruses together encompass the majority of rapid outbreaks and high-consequence animal and plant epidemics. However, determining the specific transmission route(s) can take months to years, delaying mitigation efforts. Here, we identify the viral features and evolutionary signatures which are predictive of viral transmission routes and use them to predict potential routes for fully-sequenced viruses in silico and rapidly, for both viruses with no observed routes, as well as viruses with missing routes. This was achieved by compiling a dataset of 24,953 virus-host associations with 81 defined transmission routes, constructing a hierarchy of virus transmission encompassing those routes and 42 higher-order modes, and engineering 446 predictive features from three complementary perspectives. We integrated those data and features to train 98 independent ensembles of LightGBM classifiers. We found that all features contributed to the prediction for at least one of the routes and/or modes of transmission, demonstrating the utility of our broad multi-perspective approach. Our framework achieved ROC-AUC = 0.991, and F1-score = 0.855 across all included transmission routes and modes, and was able to achieve high levels of predictive performance for high-consequence respiratory (ROC-AUC = 0.990, and F1-score = 0.864) and vector-borne transmission (ROC-AUC = 0.997, and F1-score = 0.921). Our framework ranks the viral features in order of their contribution to prediction, per transmission route, and hence identifies the genomic evolutionary signatures associated with each route. Together with the more matured field of viral host-range prediction, our predictive framework could: provide early insights into the potential for, and pattern of viral spread; facilitate rapid response with appropriate measures; and significantly triage the time-consuming investigations to confirm the likely routes of transmission.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Wardeh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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