Poisot Timothée, Ouellet Marie-Andrée, Mollentze Nardus, Farrell Maxwell J, Becker Daniel J, Brierley Liam, Albery Gregory F, Gibb Rory J, Seifert Stephanie N, Carlson Colin J
Département de Sciences Biologiques, Université de Montréal, Montréal, QC, Canada.
School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
Patterns (N Y). 2023 Apr 24;4(6):100738. doi: 10.1016/j.patter.2023.100738. eCollection 2023 Jun 9.
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.
预测宿主与病毒的相互作用本质上是一个网络科学问题。我们开发了一种用于二分网络预测的方法，该方法将推荐系统（线性过滤）与基于低秩图嵌入的插补算法相结合。我们通过将该方法应用于哺乳动物 - 病毒相互作用的全球数据库来对其进行测试，结果表明它能做出具有生物学合理性且对数据偏差具有鲁棒性的预测。我们发现，全球任何地方的哺乳动物病毒组都未得到充分表征。我们建议，未来的病毒发现工作可以将亚马逊盆地（因其独特的共同进化组合）和撒哈拉以南非洲（因其特征不明的人畜共患病宿主）作为优先区域。插补网络的图嵌入改进了基于病毒基因组特征对人类感染的预测，为实验室研究和监测提供了一份优先事项清单。总体而言，我们的研究表明，哺乳动物 - 病毒网络的全球结构包含大量可恢复的信息，这为基础生物学和疾病出现提供了新的见解。
