Variation in the ACE2 receptor has limited utility for SARS-CoV-2 host prediction (preprint)

Transmission of SARS-CoV-2 from humans to other species threatens wildlife conservation and may create novel sources of viral diversity for future zoonotic transmission. A variety of computational heuristics have been developed to pre-emptively identify susceptible host species based on variation in the ACE2 receptor used for viral entry. However, the predictive performance of these heuristics remains unknown. Using a newly-compiled database of 96 species we show that, while variation in ACE2 can be used by machine learning models to accurately predict animal susceptibility to sarbecoviruses (accuracy = 80.2%, binomial confidence interval [CI]: 70.8 - 87.6%), the sites informing predictions have no known involvement in virus binding and instead recapitulate host phylogeny. Models trained on host phylogeny alone performed equally well (accuracy = 84.4%, CI: 75.5 - 91.0%) and at a level equivalent to retrospective assessments of accuracy for previously published models. These results suggest that the predictive power of ACE2-based models derives from strong correlations with host phylogeny rather than processes which can be mechanistically linked to infection biology. Further, biased availability of ACE2 sequences misleads projections of the number and geographic distribution of at-risk species. Models based on host phylogeny reduce this bias, but identify a very large number of susceptible species, implying that model predictions must be combined with local knowledge of exposure risk to practically guide surveillance. Identifying barriers to viral infection or onward transmission beyond receptor binding and incorporating data which are independent of host phylogeny will be necessary to manage the ongoing risk of establishment of novel animal reservoirs of SARS-CoV-2.

Zoonotic Risk Technology Enters the Viral Emergence Toolkit (preprint)

In light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programs will identify hundreds of novel viruses that might someday pose a threat to humans. Our capacity to identify which viruses are capable of zoonotic emergence depends on the existence of a technology—a machine learning model or other informatic system—that leverages available data on known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions: What are the prerequisites, in terms of open data, equity, and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it, and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges?

Identifying and prioritizing potential human-infecting viruses from their genome sequences (preprint)

Rapid assessment of which animal viruses may be capable of infecting humans is currently intractable, but would allow their prioritization for further investigation and pandemic preparedness. We developed machine learning algorithms that identify candidate zoonoses using evolutionary signals of host range encoded in viral genomes. This reduces lists of hundreds of viruses with uncertain human infectivity to tractable numbers for prioritized research, generalizes to virus families excluded from model training, can distinguish high risk viruses within families that contain a minority of zoonotic species, and could have identified the exceptional risk of SARS-CoV-2 prior to its emergence. Genome-based risk assessment allows identification of high-risk viruses immediately upon discovery, increasing both the feasibility and likelihood of downstream virological and ecological characterization and allowing for evidence-driven virus surveillance.