Network embedding unveils the hidden interactions in the mammalian virome

Timothée Poisot; Marie-Andrée Ouellet; Nardus Mollentze; Maxwell J Farrell; Daniel J Becker; Liam Brierley; Gregory F Albery; Rory J Gibb; Stephanie N Seifert; Colin J Carlson

doi:10.1016/j.patter.2023.100738

Network embedding unveils the hidden interactions in the mammalian virome

Patterns (N Y). 2023 Apr 24;4(6):100738. doi: 10.1016/j.patter.2023.100738. eCollection 2023 Jun 9.

Authors

Affiliations

¹ Département de Sciences Biologiques, Université de Montréal, Montréal, QC, Canada.
² School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
³ MRC - University of Glasgow Centre for Virus Research, Glasgow, UK.
⁴ Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada.
⁵ Department of Biology, University of Oklahoma, Norman, OK, USA.
⁶ Department of Health Data Science, University of Liverpool, Liverpool, UK.
⁷ Department of Biology, Georgetown University, Washington, DC, USA.
⁸ Center for Biodiversity & Environment Research, University College, London, UK.
⁹ Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA.
¹⁰ Center for Global Health Science and Security, Georgetown University, Washington, DC, USA.

Abstract

Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases. We find that the mammalian virome is under-characterized anywhere in the world. We suggest that future virus discovery efforts could prioritize the Amazon Basin (for its unique coevolutionary assemblages) and sub-Saharan Africa (for its poorly characterized zoonotic reservoirs). Graph embedding of the imputed network improves predictions of human infection from viral genome features, providing a shortlist of priorities for laboratory studies and surveillance. Overall, our study indicates that the global structure of the mammal-virus network contains a large amount of information that is recoverable, and this provides new insights into fundamental biology and disease emergence.

Keywords: imputation; singular value decomposition; virome; zoonotic viruses.