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. 2023 Aug 28;14(1):5105.
doi: 10.1038/s41467-023-40706-y.

Accelerated evolution of SARS-CoV-2 in free-ranging white-tailed deer

Dillon S McBride #  1 , Sofya K Garushyants #  2 , John Franks #  3 , Andrew F Magee  4 , Steven H Overend  1 , Devra Huey  1 , Amanda M Williams  5 , Seth A Faith  5 , Ahmed Kandeil  3   6 , Sanja Trifkovic  3 , Lance Miller  3 , Trushar Jeevan  3 , Anami Patel  7 , Jacqueline M Nolting  1 , Michael J Tonkovich  8 , J Tyler Genders  9 , Andrew J Montoney  9 , Kevin Kasnyik  10 , Timothy J Linder  11 , Sarah N Bevins  11 , Julianna B Lenoch  11 , Jeffrey C Chandler  12 , Thomas J DeLiberto  13 , Eugene V Koonin  2 , Marc A Suchard  4   14   15 , Philippe Lemey  16 , Richard J Webby  3 , Martha I Nelson  17 , Andrew S Bowman  18
Affiliations

Accelerated evolution of SARS-CoV-2 in free-ranging white-tailed deer

Dillon S McBride et al. Nat Commun. .

Abstract

The zoonotic origin of the COVID-19 pandemic virus highlights the need to fill the vast gaps in our knowledge of SARS-CoV-2 ecology and evolution in non-human hosts. Here, we detected that SARS-CoV-2 was introduced from humans into white-tailed deer more than 30 times in Ohio, USA during November 2021-March 2022. Subsequently, deer-to-deer transmission persisted for 2-8 months, disseminating across hundreds of kilometers. Newly developed Bayesian phylogenetic methods quantified how SARS-CoV-2 evolution is not only three-times faster in white-tailed deer compared to the rate observed in humans but also driven by different mutational biases and selection pressures. The long-term effect of this accelerated evolutionary rate remains to be seen as no critical phenotypic changes were observed in our animal models using white-tailed deer origin viruses. Still, SARS-CoV-2 has transmitted in white-tailed deer populations for a relatively short duration, and the risk of future changes may have serious consequences for humans and livestock.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Geographic distribution of SARS-CoV-2 in Ohio by county.
Counties classified as urban are colored gray and rural counties are white. The size of circles plotted over the county centroids indicate the number of samples collected and the color scale indicates SARS-CoV-2 estimated prevalence in each county by rRT-PCR (a) and seroprevalence by surrogate virus neutralization (b). Counties that are outlined in bold borders indicate counties from which we obtained SARS-CoV-2 genomic sequences (Table S2). Counties marked with an asterisk indicate counties from which samples were collected from culled WTD as a part of population management programs (Table S1). Map created with ArcMap (ESRI) using base layers and data from Esri, Garmin, OpenStreetMap, GIS user community, Infogroup and the US Census Bureau.
Fig. 2
Fig. 2. Human-to-deer transmission of SARS-CoV-2 in Ohio.
a MCC tree inferred for 786 B.1.1.7 viruses collected from humans and WTD. Branches shaded by host species and location. The two Ohio WTD clusters are labeled. b AY.25 subtree (entire delta MCC tree shown in Fig. S4, n = 1094 delta viruses). Ohio WTD virus transmission clusters are shaded similarly to Fig. 3, with the addition of black branches indicating Ohio WTD singleton detections from this study and dark gray branches indicating non-Ohio WTD singletons. c The number of bi-weekly COVID-19 cases in humans in Ohio from January 2021 to February 2022, shaded by the proportion of human SARS-CoV-2 sequences from Ohio that belong to one of four Pango lineages (or ‘other’). Red box delineates the B.1.1.7 wave in humans. Below, green bars show the estimated number of human-to-deer transmission events of B.1.1.7 viruses, per 20-week increments, based on “Markov jump” counts inferred on the alpha MCC tree (n = 786 B.1.1.7 viruses). Green circles indicate the collection dates of 9 B.1.1.7 viruses in Ohio WTD. d Similar to c, but inferred on the delta MCC tree (n = 1094). e The detection lag (months) is the time difference between a human-to-deer transmission event (estimated) and the first observed sequence from a WTD transmission cluster, shown for 14 delta and 2 alpha WTD transmission clusters, inferred from the MCC trees. Red lines represent the two time points of the two nodes defining the branch on the phylogenetic tree on which the human-to-deer transition occurred; thick black bar represents the mean time of the branch. f Estimated number of human-to-deer transmission events in Ohio and North America and long-distance deer-to-deer transmission events that span Ohio counties, inferred from the MCC trees. Data for North America does not include Ohio.
Fig. 3
Fig. 3. Map of SARS-CoV-2 transmission clusters in Ohio white-tailed deer.
Each shape represents a county in Ohio where SARS-CoV-2 virus was identified in WTD for this study (triangle = alpha variant; circle = delta variant). Large circles indicate WTD transmission clusters, as identified on the phylogenetic tree (black = clusters restricted to one county; shaded = clusters identified in more than one county). Large circles shaded the same color belong to the same transmission cluster. Small black circles indicate singleton WTD viruses. PANGO lineage provided for all clusters. Human population density is shown in the background (red = high; green = low) and major cities are labeled. Ohio population map [created by JimIrwin, from Wikimedia Commons] was used with modification under CC BY-SA 3.0 license.
Fig. 4
Fig. 4. Evolutionary rate of SARS-CoV-2 in humans and white-tailed deer.
a The posterior distributions of evolutionary rates (substitutions per site per year) for five partitions of the SARS-CoV-2 genome (ORF1a, ORF1b, ORF3–ORF8 plus envelope (E) and membrane (M), spike (S), and nucleocapsid (N)) are presented for human (pink) and WTD (blue) for the delta variant (Fig. S4). Alpha results (similar) are provided in Fig. S11. b Mutations in spike protein that were found in delta WTD clusters (orange), L18F is shown in red, alpha WTD clusters (green), and T29I found in both alpha and delta WTD clusters (yellow). Characteristic mutations for delta lineages (mutations present in human and WTD viruses belonging to delta compared to Wuhan reference genome) are shown in dark orange, while characteristic mutations for alpha lineages are shown in dark green. All recurrent mutations from WTD clusters are documented in Supplementary Data 2. The log deviation (random-effect) from HKY model relative rates is presented for c alpha, humans, d alpha, WTD, e alpha, WTD-to-human ratio, f delta, humans, g delta, WTD, and h delta, WTD-to-human ratio, inferred independently for alpha (n = 786) and delta (n = 1094). Box midlines indicate the median, the box limits show the upper and lower quartiles, and the whiskers extend to 1.5 times the interquartile range. Asterisks indicate transversions. WTD-to-human ratios that significantly differ from zero are highlighted.
Fig. 5
Fig. 5. Pathogenicity and replication of multiple strains of SARS-CoV-2 viruses in Golden Syrian hamsters.
Golden Syrian hamsters were challenged with Hu-WA.1 (unvaccinated n = 12, vaccinated n = 12), Hu-B.1.1.7 (unvaccinated n = 4), B.1.1.7-like (unvaccinated n = 13, vaccinated n = 13), Hu-B.1.617.2 (unvaccinated n = 11, vaccinated n = 12), AY.103 (unvaccinated n = 13, vaccinated n = 13), and AY.25 (unvaccinated n = 10). All sample sizes reflect biologically independent animals. a Microneutralization titers of a-BNT162b2 or lineage specific serum against representative viruses from this study. For b–e, the mean for each group is plotted, and bars indicate standard deviation. Titers expressed as log10 IC50 were plotted and described as a fold change from the reference strains. b Body weight loss comparison between unvaccinated and BNT162b2 vaccinated animals at the peak of infection, day 7. Mean weights are displayed as a percentage of starting weight. Nasal wash was collected (unvaccinated groups only) (c) or lung and nasal turbinate were harvested (d, e) and used to quantify viral titers. Viral titers expressed as the log10 TCID50 were plotted. Statistical analysis was performed using one-way ANOVA followed by a Tukey post hoc, p values displayed from Tukey’s test statistic, q, which controls for family wise error rate for multiple comparison.
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