Risk assessment of SARS-CoV-2 replicating and evolving in animals.

The retransmissions of SARS-CoV-2 from several mammals - primarily mink and white-tailed deer - to humans have raised concerns for the emergence of a new animal-derived SARS-CoV-2 variant to worsen the pandemic. Here, we discuss animal species that are susceptible to natural or experimental infection with SARS-CoV-2 and can transmit the virus to mates or humans. We describe cutting-edge techniques to assess the impact of a mutation in the viral spike (S) protein on its receptor and on antibody binding. Our review of spike sequences of animal-derived viruses identified nine unique amino acid exchanges in the receptor-binding domain (RBD) that are not present in any variant of concern (VOC). These mutations are present in SARS-CoV-2 found in companion animals such as dogs and cats, and they exhibit a higher frequency in SARS-CoV-2 found in mink and white-tailed deer, suggesting that sustained transmissions may contribute to maintaining novel mutations. Four of these exchanges, such as Leu452Met, could undermine acquired immune protection in humans while maintaining high affinity for the human angiotensin-converting enzyme 2 (ACE2) receptor. Finally, we discuss important avenues of future research into animal-derived viruses with public health risks.

Scalable Bayesian Divergence Time Estimation With Ratio Transformations.

Divergence time estimation is crucial to provide temporal signals for dating biologically important events from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original $N-1$ internal node heights into a space of one height parameter and $N-2$ ratio parameters. To make the analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in 4 pathogenic viruses (West Nile virus, rabies virus, Lassa virus, and Ebola virus) and the coralline red algae. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well as for the algae example. Our method now also makes it computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study, and reveals clearer multimodal distributions of the divergence times of some clades of interest.

Zoonotic risk assessment among farmed mammals.

Farmed mammals may act as hosts for zoonotic viruses that can cause disease outbreaks in humans. This SnapShot shows which farmed mammals, and to what extent, are of particular risk of harboring and spreading viruses from viral families that are commonly associated with zoonotic disease. It also discusses genome surveillance methods and biosafety measures. To view this SnapShot, open or download the PDF.