Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2
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Abstract
Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein–protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals—an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown 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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