Water buffalo, Sunda pangolins and mink are among the 540 mammals predicted to be likely to spread the coronavirus based on their biology and where they live
17 November 2021
The SARS-CoV-2 coronavirus, which causes covid-19, invades human and animal tissues by engaging the ACE2 protein on host cells with its spike protein. This step is required to infect an animal, a prerequisite for potential onward transmission to other hosts.
Distinct species have different versions of the ACE2 protein, so understanding how well their version binds to the coronavirus spike protein can help us predict which animals are most likely to get, and hence possibly spread, covid-19. But the amino acid sequences that make up ACE2 are available for only around 300 species.
To get around this, Barbara Han at the Cary Institute of Ecosystem Studies in New York and her colleagues built a machine-learning tool to predict whether the ACE2 protein from 5400 mammalian species can bind strongly enough to the spike protein from the original coronavirus variant to harbour the virus, even without knowing their ACE2 amino acid sequences.
The species predicted to be able to do this include white-tailed deer, which were recently found to have very high rates of infection in North America.
Striped skunk and 76 rodent species including some species of rat and deer mice were also deemed likely to spread the coronavirus, along with some farmed species such as water buffalo.
To create the model, the team first estimated how strongly the spike protein binds to the ACE2 protein from 142 mammalian species for which ACE2 sequences are known, and whether or not these species are likely to spread the coronavirus.
They fed the AI information on transmissibility and around 60 ecological and biological traits for the 142 species so it could discern links between transmissibility and the various traits. The traits included where the animals live, how much their habitats overlap with human populations, their lifespan, how varied their diet is and their body mass.
The resulting model, when given biological and ecological trait data for the other species, could then guess the likelihood of the 5400 mammalian species studied being able to spread the coronavirus.
These results must be followed up with systematic surveillance and lab studies to test and validate the predictions, says Han.
“This is an incredibly useful approach to prioritise animal species for surveillance,” says Arinjay Banerjee at the University of Saskatchewan in Canada. Surveillance will help track viral infections and the possible emergence of animal-adapted coronavirus variants, he says.
Journal reference: Proceedings of the Royal Society B, DOI: 10.1098/rspb.2021.1651
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