Wildlife reservoirs of broad-host-range viruses have the potential to enable evolution of viral variants that can emerge to infect humans. In North America, there is phylogenomic evidence of continual transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from humans to white-tailed deer (Odocoileus virginianus) through unknown means, but no evidence of transmission from deer to humans. We carried out an observational surveillance study in Ontario, Canada during November and December 2021 (n = 300 deer) and identified a highly divergent lineage of SARS-CoV-2 in white-tailed deer (B.1.641). This lineage is one of the most divergent SARS-CoV-2 lineages identified so far, with 76 mutations (including 37 previously associated with non-human mammalian hosts). From a set of five complete and two partial deer-derived viral genomes we applied phylogenomic, recombination, selection and mutation spectrum analyses, which provided evidence for evolution and transmission in deer and a shared ancestry with mink-derived virus. Our analysis also revealed an epidemiologically linked human infection. Taken together, our findings provide evidence for sustained evolution of SARS-CoV-2 in white-tailed deer and of deer-to-human transmission.
Wildlife reservoirs of SARS-CoV-2 can lead to viral adaptation and spillback from wildlife to humans (Oude Munnink et al., 2021). In North America, there is evidence of spillover of SARS-CoV-2 from humans to white-tailed deer (Odocoileus virginianus), but no evidence of transmission from deer to humans (Hale et al., 2021; Kotwa et al., 2022; Kuchipudi et al., 2021). Through a multidisciplinary research collaboration for SARS-CoV-2 surveillance in Canadian wildlife, we identified a new and highly divergent lineage of SARS-CoV-2. This lineage has 76 consensus mutations including 37 previously associated with non-human animal hosts, 23 of which were not previously reported in deer. There were also mutational signatures of host adaptation under neutral selection. Phylogenetic analysis revealed an epidemiologically linked human case from the same geographic region and sampling period. Together, our findings represent the first evidence of a highly divergent lineage of SARS-CoV-2 in white-tailed deer and of deer-to-human transmission.
Mixed community or environmental DNA marker gene sequencing has become a commonly used technique for biodiversity analyses in freshwater systems. Many cytochrome c oxidase subunit I (COI) primer sets are now available for such work. The purpose of this study is to test whether COI primer choice affects the recovery of arthropod richness, beta diversity, and recovery of target assemblages in the benthos kick-net samples typically used in freshwater biomonitoring. We examine six commonly used COI primer sets on samples collected from six freshwater sites. Biodiversity analyses show that richness is sensitive to primer choice and the combined use of multiple COI amplicons recovers higher richness. Thus, to recover maximum richness, multiple primer sets should be used with COI metabarcoding. In ordination analyses based on community dissimilarity, samples consistently cluster by site regardless of amplicon choice or PCR replicate. Thus, for broadscale community analyses, overall beta diversity patterns are robust to COI marker choice. Recovery of traditional freshwater bioindicator assemblages such as Ephemeroptera, Trichoptera, Plectoptera, and Chironomidae as well as Arthropoda site indicators were differentially detected by each amplicon tested. This work will help future biodiversity and biomonitoring studies develop not just standardized, but optimized workflows that either maximize taxon-detection or the selection of amplicons for water quality or Arthropoda site indicators.
Background Identification of biomarkers, which are measurable characteristics of biological datasets, can be challenging. Although amplicon sequence variants (ASVs) can be considered potential biomarkers, identifying important ASVs in high-throughput sequencing datasets is challenging. Noise, algorithmic failures to account for specific distributional properties, and feature interactions can complicate the discovery of ASV biomarkers. In addition, these issues can impact the replicability of various models and elevate false-discovery rates. Contemporary machine learning approaches can be leveraged to address these issues. Ensembles of decision trees are particularly effective at classifying the types of data commonly generated in high-throughput sequencing (HTS) studies due to their robustness when the number of features in the training data is orders of magnitude larger than the number of samples. In addition, when combined with appropriate model introspection algorithms, machine learning algorithms can also be used to discover and select potential biomarkers. However, the construction of these models could introduce various biases which potentially obfuscate feature discovery. Results We developed a decision tree ensemble, LANDMark, which uses oblique and non-linear cuts at each node. In synthetic and toy tests LANDMark consistently ranked as the best classifier and often outperformed the Random Forest classifier. When trained on the full metabarcoding dataset obtained from Canada’s Wood Buffalo National Park, LANDMark was able to create highly predictive models and achieved an overall balanced accuracy score of 0.96 ± 0.06. The use of recursive feature elimination did not impact LANDMark’s generalization performance and, when trained on data from the BE amplicon, it was able to outperform the Linear Support Vector Machine, Logistic Regression models, and Stochastic Gradient Descent models (p ≤ 0.05). Finally, LANDMark distinguishes itself due to its ability to learn smoother non-linear decision boundaries. Conclusions Our work introduces LANDMark, a meta-classifier which blends the characteristics of several machine learning models into a decision tree and ensemble learning framework. To our knowledge, this is the first study to apply this type of ensemble approach to amplicon sequencing data and we have shown that analyzing these datasets using LANDMark can produce highly predictive and consistent models.
DNA-based biodiversity analysis has gained major attention due to the use of high throughput sequencing technology in approaches such as mixed community or environmental DNA metabarcoding. Many cytochrome c oxidase subunit I (COI) primer sets are now available for such work. The purpose of this study is to look at how COI primer choice affects the recovery of arthropod richness, beta diversity, and recovery of site indicator taxa in benthos kick-net samples typically used in freshwater biomonitoring. We examine 6 commonly used COI primer sets, on samples collected from 6 freshwater sites. Richness is sensitive to primer choice and the combined use of additional multiple COI amplicons recovers higher richness. Thus, to recover maximum richness, multiple primer sets should be used with COI metabarcoding. Samples consistently cluster by site regardless of amplicon choice or PCR replicate. Thus, for broadscale community analyses, overall beta diversity patterns are robust to COI marker choice. Additionally, the recovery of traditional freshwater bioindicator assemblages such as Ephemeroptera, Trichoptera, Plectoptera, and Diptera may not fully capture the diversity of broadscale arthropod site indicators that can be recovered from COI metabarcoding. Based on these results, studies that use different COI amplicons may not be directly comparable. This work will help future biodiversity and biomonitoring studies develop not just standardized, but optimized workflows that maximize taxon-detection or order taxa along gradients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.