A new study used a novel modelling approach to predict the zoonotic capacity of 5,400 mammal species, extending predictive capacity by an order of magnitude. Of the high risk species flagged, many live near people and in COVID-19 hotspots.
A major bottleneck to predicting high-risk mammal species is limited data on ACE2, the cell receptor that SARS-CoV-2 binds to in animals. ACE2 allows SARS-CoV-2 to enter host cells, and is found in all major vertebrate groups. It is likely that all vertebrates have ACE2 receptors, but sequences were only available for 326 species.
To overcome this obstacle, the team developed a machine learning model that combined data on the biological traits of 5,400 mammal species with available data on ACE2. The goal: to identify mammal species with high ‘zoonotic capacity’ — the ability to become infected with SARS-CoV-2 and transmit it to other animals and people. The method they developed could help extend predictive capacity for disease systems beyond COVID-19.
Co-lead author Ilya Fischhoff, a postdoctoral associate at Cary Institute of Ecosystem Studies, comments, «SARS-CoV-2, the virus that causes COVID-19, originated in an animal before making the jump to people. Now, people have caused spillback infections in a variety of mammals, including those kept in farms, zoos, and even our homes. Knowing which mammals are capable of re-infecting us is vital to preventing spillback infections and dangerous new variants.»
When a virus passes from people to animals and back to people it is called secondary spillover. This phenomenon can accelerate new variants establishing in humans that are more virulent and less responsive to vaccines. Secondary spillover of SARS-CoV-2 has already been reported among farmed mink in Denmark and the Netherlands, where it has led to at least one new SARS-CoV-2 variant.
Senior author and Cary Institute disease ecologist, Barbara Han, says, «Secondary spillover allows SARS-CoV-2 established in new hosts to transmit potentially more infectious strains to people. Identifying mammal species that are efficient at transmitting SARS-CoV-2 is an important step in guiding surveillance and preventing the virus from continually circulating between people and other animals, making disease control even more costly and difficult.»
Binding to ACE2 receptors is not always enough to facilitate SARS-CoV-2 viral replication, shedding, and onward transmission. The team trained their models on a conservative binding strength threshold informed by published ACE2 amino acid sequences of vertebrates, analyzed using a software tool called HADDOCK (High Ambiguity Driven protein-protein DOCKing). This software scored each species on predicted binding strength; stronger binding likely promotes successful infection and viral shedding.
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Materials provided by Cary Institute of Ecosystem Studies. Note: Content may be edited for style and length.