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Review 2: "Crossing Host Boundaries: The Evolutionary Drivers and Correlates of Viral Host Jumps"

This preprint finds more human-to-animal than zoonotic viral transmission, but reviewers recommend validating results with Bayesian phylogenetics, accounting for database biases, and clarifying analysis limitations that may impact reconstructing transmission histories.

Published onOct 30, 2023
Review 2: "Crossing Host Boundaries: The Evolutionary Drivers and Correlates of Viral Host Jumps"
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Crossing host boundaries: the evolutionary drivers and correlates of viral host jumps
Crossing host boundaries: the evolutionary drivers and correlates of viral host jumps

Abstract Most emerging and re-emerging infectious diseases stem from viruses that naturally circulate in non-human vertebrates. When these viruses cross over into humans, they can cause disease outbreaks and epidemics. While zoonotic host jumps have been extensively studied from an ecological perspective, little attention has gone into characterising the evolutionary drivers and correlates underlying these events. To address this gap, we harnessed the entirety of publicly available viral genomic data, employing a comprehensive suite of network and phylogenetic analyses. We address a series of questions concerning the evolutionary mechanisms underpinning viral host jumps. Notably, we challenge conventional assumptions about the directionality of host jumps, demonstrating that humans are as much a source as a sink for viral spillover events, insofar we infer more viruses to have jumped from humans to other animals, than from animals to humans. Moreover, we demonstrate heightened evolution in viral lineages that involve putative host jumps. We further observe that the mutational threshold associated with a host jump is lower for viruses with broad host ranges. Finally, we show that the genomic targets of natural selection upon a successful host jump vary across different viral families with either structural or auxiliary genes being the prime targets of selection. Collectively, our results illuminate some of the evolutionary drivers underlying viral host jumps that may contribute to mitigating viral threats across species boundaries.

 RR:C19 Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.



Humans and animals interact in many ways, and although cases of agricultural animal to human transfer, and human to agricultural animal transfer, get studied and published due to the economic impacts, cases of transfer between pets and humans or between wild anmals and humans, are very rarely studied and published. Compare the number of published cases of zoo animals infected with SARS-CoV-2 to those of hoshold cats and dogs infected, for example. 

This study indicates that there are more human to non-human transfers of viruses, than non-human to human, based on complete or near-complete virus genome sequences in GenBank. The authors note that the data set is highly biased, with 68% of the set coming from SARS-CoV-2 and close relatives, a majority of the data from the USA and China, and a majority of the data from human samples. I was not able to review the supplementary tables and figures, so I am not certain what strengths or weaknesses a large scale analysis such as this has. I am very familiar with GenBank and viruses, in general. I know, for example, that there are over 4,500 Filovirus sequences in GenBank, the vast majority of which are from humans. Even though, the epidemiology strongly suggests or proves that there have been many animal to human transfers of a given lineage such as "Ebola-Zaire" each sparking a new "outbreak", the animal host and especially complete genome sequences taken from the animal, have not been identified. So in that case, a phylogenetic approach would undercount the number of animal to human transfers. A similar problem exists with influenza A viruses, where it is often documented that a human has been infected by a porcine strain of virus, or a pig has been infected by a human strain, but the GenBank records do not allow accurate reconstruction of the transfers. The spread of SARS-CoV-2 to farmed mink in Europe, and to white tailed deer in the USA, both indicate many human to animal transfers and at least a few animal to human transfers, but again these cannot be reconstructed from sequence data alone and require additional knowledge of the epidemiology of the cases involved. The paper does note the bias in the data, and the list of citations includes many papers that discuss these issues. But the conclusions in the paper do not note how the bias in the data may have mislead the conclusions. It would require too much additional effort to provide case studies (such as predicted number of animal to human transfers of Ebola Zaire, vs what is known from epidemiology) for a few lineages of viruses, but the authors could at least note that their predictions are likely to be biased by the bias of the data set. 

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