Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2
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Abstract
Back and forth transmission of SARS-CoV-2 between humans and animals may lead to wild reservoirs of virus that can endanger efforts toward long-term control of COVID-19 in people, and protecting vulnerable animal populations that are particularly susceptible to lethal disease. Predicting high risk host species is key to targeting field surveillance and lab experiments that validate host zoonotic potential. A major bottleneck to predicting animal hosts is the small number of species with available molecular information about the structure of ACE2, a key cellular receptor required for viral cell entry. We overcome this bottleneck by combining species’ ecological and biological traits with 3D modeling of virus and host cell protein interactions using machine learning methods. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for over 5,000 mammals — an order of magnitude more species than previously possible. The high accuracy predictions achieved by this approach are strongly corroborated by in vivo empirical studies. We identify numerous common mammal species whose predicted zoonotic capacity and close proximity to humans may further enhance the risk of spillover and spillback transmission of SARS-CoV-2. Our results reveal high priority areas of geographic overlap between global COVID-19 hotspots and potential new mammal hosts of SARS-CoV-2. With molecular sequence data available for only a small fraction of potential host species, predictive modeling integrating data across multiple biological scales offers a conceptual advance that may expand our predictive capacity for zoonotic viruses with similarly unknown and potentially broad host ranges.
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SciScore for 10.1101/2021.02.18.431844: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Protein sequence and alignment: We assembled a dataset of ACE2 NCBI GenBank accessions that are known human ACE2 orthologs or have high similarity to known orthologs as determined using BLASTx (Altschul et al., 1990). BLASTxsuggested: (BLASTX, RRID:SCR_001653)We supplemented these sequences by manually downloading four additional sequences from the MEROPS database (Rawlings et al., 2018). MEROPSsuggested: (MEROPS, RRID:SCR_007777)In short, sequences of ACE2 orthologs were aligned using MAFFT (Katoh et al., 2002) and trimmed to the region resolved in the template crystal structure of hACE2 … SciScore for 10.1101/2021.02.18.431844: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Protein sequence and alignment: We assembled a dataset of ACE2 NCBI GenBank accessions that are known human ACE2 orthologs or have high similarity to known orthologs as determined using BLASTx (Altschul et al., 1990). BLASTxsuggested: (BLASTX, RRID:SCR_001653)We supplemented these sequences by manually downloading four additional sequences from the MEROPS database (Rawlings et al., 2018). MEROPSsuggested: (MEROPS, RRID:SCR_007777)In short, sequences of ACE2 orthologs were aligned using MAFFT (Katoh et al., 2002) and trimmed to the region resolved in the template crystal structure of hACE2 bound to the SARS-CoV-2 spike (PDB ID: 6m0j, (Lan et al., 2020). MAFFTsuggested: (MAFFT, RRID:SCR_011811)For each ortholog, we generated 10 homology models using MODELLER 9.24 (Sali and Blundell, 1993; Webb and Sali, 2016), with restricted optimization (fastest schedule) and refinement (very_fast schedule) settings, and selected a representative model based on the normalized DOPE score. MODELLERsuggested: (MODELLER, RRID:SCR_008395)Trait data collection and cleaning: We gathered ecological and life history trait data from AnAge (de Magalhães and Costa, 2009) AnAgesuggested: (anage, RRID:SCR_001470)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Insofar as data limitations (e.g., limited ACE2 sequences or species trait data) preclude perfect computational predictions of zoonotic capacity, laboratory experiments are also limited in assessing true zoonotic capacity. For SARS-CoV-2 and other host-pathogen systems, animals that are readily infected in the lab appear to be less susceptible in non-lab settings (ferrets in the lab vs. mixed results in ferrets as pets (OIE, 2021; Sawatzki et al., 2020; Schlottau et al., 2020); rabbits in the lab vs. rabbits as pets (Mykytyn et al., 2021; Ruiz-Arrondo et al., 2020)). Moreover, wildlife hosts that are confirmed to shed multiple zoonotic viruses in natural settings (e.g., bats, (Peel et al., 2019)) can be much less tractable for laboratory investigations (for instance, requiring high biosecurity containment and very limited sample sizes). While laboratory experiments are critical for understanding mechanisms of pathogenesis and disease, without field surveillance and population-level studies they are only partial reflections of zoonotic capacity in the natural world. These examples illustrate that there is no single methodology sufficient to understand and predict zoonotic transmission, for SARS-CoV-2 or any zoonotic pathogen, and further demonstrate the need for coordination among theoretical and statistical models, lab work, and field work to improve zoonotic predictive capacity (Restif et al., 2012). As new SARS-CoV-2 variants continue to emerge, our work demonstrates the ut...
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