SARS-CoV-2 within-host population expansion, diversification and adaptation in zoo tigers, lions and hyenas

Domain: biology
Published: 2025-12-13
Editorial outcome: major_revision

Abstract
SARS-CoV-2 rapidly adapts to new hosts following cross-species transmission; this is highly relevant as unique within-host variants have emerged following infection of susceptible wild and domestic animal species. Furthermore, SARS-CoV-2 transmission from animals (e.g., white-tailed deer, mink, domestic cats, and others) back to humans has been observed, documenting the potential of animal-derived variants to infect humans. Here, we investigate SARS-CoV-2 evolution and host-specific adaptation during an outbreak in Amur tigers (Panthera tigris altaica), African lions (Panthera leo), and spotted hyenas (Crocuta crocuta) at Denver Zoo in 2021. SARS-CoV-2 genomes from longitudinal samples from 16 individuals are evaluated for within-host variation and genomic signatures of selection, and we determine that the outbreak was likely initiated by a single spillover of a rare Delta sublineage. Within-host virus populations rapidly expand and diversify, and we detect signatures of purifying and positive selection, including strong positive selection in hyenas and in the nucleocapsid (N) gene in all animals. Four candidate species-specific adaptive mutations are identified: N A254V in lions and hyenas, and ORF1a E1724D, spike T274I, and N P326L in hyenas. These results reveal accelerated SARS-CoV-2 adaptation following host shifts in three non-domestic species in daily contact with humans.

Introduction
The evolution of SARS-CoV-2, and the emergence of successive new variant lineages, moved to the forefront of global public health awareness during the COVID-19 pandemic. In addition to humans, the virus infects a wide range of other animal species1–7, and it is widely accepted that SARS-CoV-2 spilled over from an animal host into human populations in late 20198. Although thousands of studies have been conducted since the start of this pandemic, key questions remain unanswered—including identifying the drivers of SARS-CoV-2 evolution and transmission dynamics in non-human animals, and the origins of variant lineages infecting human and animal populations.

SARS-CoV-2 within-host evolution is shaped by both deterministic effects of selection, and stochastic effects of genetic drift. Selection acts to maximize the fitness of the population of viruses, often referred to as quasispecies, within an individual host. Limited within-host viral variation is observed in most human SARS-CoV-2 infections, with the exception of prolonged infections in immunocompromised individuals9–12. In these cases, a persistent infection in a single patient can result in the accumulation of a large number of selectively beneficial mutations. Similarly, virus populations can experience unique and strong selective pressures following host shifts (reviewed in ref. 13).

These spillover events may accelerate viral variant emergence through selection for genetic variation that improves virus fitness in a novel host environment. Capturing the initial viral dynamics following a host shift in the wild poses logistical challenges and these attributes are therefore more commonly evaluated in experimental infection systems14,15. In this study, we investigated SARS-CoV-2 host adaptation following a uniquely well-documented spillover event: an outbreak in large felids and hyenas living at Denver Zoo in Colorado, USA16.

In March 2020, SARS-CoV-2 sequence analysis revealed human-to-animal transmission in five tigers and three lions at the Bronx Zoo, making non-domestic felids among the earliest documented non-human animals infected with SARS-CoV-2 through viral spillover17,18. Large felids have since constituted the majority of animal infections reported in zoological settings, with cases most commonly associated with respiratory signs and virus detection in oronasal samples and/or feces16,19–32. Furthermore, putative lion-to-human spillback has also been reported33. As opposed to experimental infections using model organisms, zoos offer a unique setting to understand natural infection, transmission dynamics, and viral evolution of zoonotic viruses in understudied natural hosts. Published studies most likely represent a small fraction of the true number of infections in these animals. SARS-CoV-2 infections in zoo settings offer an opportunity to evaluate risk factors and study the dynamics of variant emergence following cross-species transmission.

A detailed review of the Denver Zoo outbreak in the context of other SARS-CoV-2 outbreaks in conservatory animals has previously been reported16. Nasal swab samples from two Amur tigers (Panthera tigris altaica), eleven African lions (Panthera leo), and four spotted hyenas (Crocuta crocuta) were evaluated by SARS-CoV-2 qRT-PCR between October and December 2021, revealing variation in viral shedding among species. The findings from this comprehensive surveillance elicited further questions surrounding the exposure timeline, transmission dynamics and potential for host adaptation. Here, we evaluate SARS-CoV-2 genomes recovered from infected animals for variant lineage, phylogenomic epidemiology, within-host variation and genomic signatures of selection. Our findings document a significant impact of host shifts on virus evolution.

Results
Longitudinal high-quality full genome SARS-CoV-2 sequences from zoo tigers, lions and hyenas

Nasal swab samples were collected from two tigers, eleven lions, and four hyenas over an approximately three-month period at Denver Zoo for diagnostic purposes following observation of mild respiratory signs (Fig. 1, Table S1). Detailed descriptions of all infections and complete qRT-PCR datasets have been previously described16. Clinical signs were observed first in tigers, followed by lions, and then hyenas. The tigers were exhibited separately ∼200 m away from lions and hyenas, and supervised by different animal care specialists. Lions were housed communally in two prides and hyenas in two clans, all of which rotated through the same spaces without direct contact. Positive tests in the older hyena clan (Hyenas A and B) represent the only documented cases of SARS-CoV-2 infection in this species. Four lions had positive vRNA tests after weeks of no virus detection, including Lion B with a positive vRNA test 60 days after the first positive test in this individual.

SARS-CoV-2 vRNA was sequenced in technical duplicate to identify SARS-CoV-2 lineage, within-host variation and genomic signatures of selection. High-quality SARS-CoV-2 genome sequencing datasets were obtained from 63 biological samples from two tigers, eleven lions, and three hyenas collected at multiple timepoints between October 7th and December 13th, 2021 (Fig. 1). This corresponded to one or more timepoints for each tiger and hyena, and three to seven timepoints for each lion. Of the 68 total qRT-PCR-positive swab samples shown in Fig. 1, 57 (84%) yielded high-quality sequencing datasets. Of the 47 qRT-PCR-inconclusive samples, we acquired high-quality sequencing datasets from 6 (13%) samples.

SARS-CoV-2 Delta sublineage AY.20 was transmitted from tigers to lions to hyenas

To obtain a focused phylogenomic epidemiology of this outbreak, a time-based tree was generated using SARS-CoV-2 sequence data from zoo animals and humans in Colorado collected during this time period (Fig. 2A, B). All sequences from animals were classified as Delta variant sublineage, Pangolin lineage AY.20, and generally clustered by species. Viruses from lions and hyenas were more closely related to each other than to those from tigers, and the two prides of lions were intermixed throughout the phylogeny. Due to limited genetic variability across all sequences, we are unable to conclusively determine if positive samples detected in lions in November and December were recrudescent infections or reinfections; however, this homogeneity is suggestive of recrudescent infections.

The AY.20 sublineage was circulating at low rates (less than 1%) in humans globally between early 2021 and the spring of 2022, and only 0.6% of sequences in the GISAID database from Colorado between October and December 2021 were classified as AY.2034. Excepting twelve sequences from Denver Zoo animals generated by the United States Department of Agriculture National Veterinary Services Laboratories (USDA NVSL) following the outbreak, only three other AY.20 sequences recovered from non-human animals have been submitted to the GISAID database: two from white-tailed deer (from New York and North Carolina in November 2021), and one from a domestic cat (California in December 2021). Interestingly, these sequences group together, but were distinct from the lion, tiger and hyena genomes identified in this study (Fig. S1). This suggests that the outbreak was derived from a single introduction of AY.20 that spread among the animals at the zoo.

To further explore potential directions of transmission among animals, we generated a haplotype network (Fig. 2C). The species-specific clusters evident in this network are consistent with the observed onset of clinical signs, supporting the conclusion that SARS-CoV-2 infection began in tigers, then moved to lions, then to hyenas. Although viral genomic similarity is consistent with a single source of infection, these data do not definitively demonstrate directionality, and it is possible multiple spillover events occurred. To determine if species-specific patterns observed in this study have been replicated on a global scale, we inferred a third phylogeny from all publicly available felid- and hyena-derived SARS-CoV-2 sequences (Fig. S2). We did not observe shared mutations in this tree that would indicate felid-specific viral adaptation.

Sequences clustered due to mutations shared by more than one individual of the same species provided further evidence for the direction of transmission (Fig. 2D, Supplementary Data 1). The A254V mutation in the nucleocapsid (N) gene differentiated all hyena and lion sequences from three tiger-derived sequences, and was supported by robust sequencing coverage at that genomic position across all samples. SARS-CoV-2 sequences derived from Hyenas A, B, and C contained mutations P326L in N, T274I in the spike (S) gene, and E1724D in open reading frame (ORF) 1a (nonstructural protein (nsp) 3 E906D). These mutations were not detected in lion or tiger samples (see methods for additional details on variant calling parameters).

SARS-CoV-2 within-host sequence variation points to host adaptation

We identified 63 unique mutations relative to the ancestral Wuhan-1 reference sequence (Supplementary Data 1). Thirty of these were defining mutations of the Pangolin AY.20 lineage, of which the majority (N=27) were nonsynonymous single nucleotide variants (SNVs) (Sup S 2). The remaining 33 non-AY.20 mutations were SNVs distributed throughout the genome (11 synonymous, 12 nonsynonymous, and 10 noncoding mutations).

Based on the conclusion that the two tigers were the first to be infected, we interrogated our sequencing datasets for SARS-CoV-2 within-host variation relative to the consensus sequence from the first tiger samples (tiger reference sequence). Fourteen unique mutations remained following this approach, with limited sub-consensus variation (Fig. 3A). The 14 unique mutations were observed 94 times in our dataset, and were almost always fixed; 14 of these observations (14.9%) were detected at <99% allele frequency, and just five of these (5.3%) were detected at <50% allele frequency. There was more within-host variation in samples from hyenas compared to lions (Fig. 3B; lion mean=1.33, median=1 within-host variant per sample; hyena mean=4.17, median=5; Wilcoxon two-sample two-sided test, W=273, p=0.000107). The N A254V mutation was shared across every sample from lions and hyenas, and was the lone within-host variant detected in multiple animals (Lions B, C, D, J and Hyena D) (Fig. 3A).

The first SARS-CoV-2 sequences from Hyenas A and B (October 28th) were identical at the consensus level. All Hyena A samples share an additional mutation, ORF1b G2068E, which was detected sub-consensus on October 28th, and not observed at any level in the other two hyenas or any lion samples (Fig. 3A). The sequence obtained from a qRT-PCR-inconclusive sample from Hyena D was more closely related to sequences recovered from lions than to those from the older Hyenas A and B (housed separately from the younger clan) (Fig. 1).

The majority of substitutions observed in our dataset were transitions (Fig. 3C). Within-host variation was dominated by nonsynonymous SNVs, which constituted nine (64%) of the fourteen unique variants, and 87 (93%) of 94 observations of these variants (Fig. 3D). The nine nonsynonymous SNVs were located in ORF1a (N=2 in nsp3, papain-like protease), ORF1b (N=2 in nsp15, endoribonuclease), S (N=2), ORF3a (N=1) and N (N=2). The two synonymous SNVs were both in ORF1a (nsp1, leader protein and nsp3), and the two noncoding variants were located at the same nucleotide position in the 5’ UTR.

Within-host virus populations were characterized by expansion, diversification, and selection

To characterize selective pressures on within-host SARS-CoV-2 populations, we calculated nucleotide diversity (π) across the virus genome at nonsynonymous and synonymous sites (Supplementary Data 5 and 6, Fig. 4A, B). In lions, which were sampled repeatedly over time, we observed a significant increase in within-host nucleotide diversity over time, accounting for repeated measures of the same individuals (Fig. 4A, Table 1). Within individual lions, synonymous nucleotide diversity also increased over time (Fig. 4C, Table 1).

Genomic signatures of positive selection (πN > πS) were detected in 49% (31 of 63) of virus samples, whereas 51% (32 of 63) had genomic signatures of purifying selection (πS > πN). Although these signatures do not conclusively determine what type of selection dominated over the course of the entire infection, they reveal the type of selection occurring within an individual at one point in time. There was no overall pattern in the detection of purifying or positive selection relative to infection timeline. Positive selection at the genome level was observed in all six samples from hyenas (Hyenas A, B and D). Differences in the strength of selection acting on SARS-CoV-2 genomes among host species was particularly evident in the final vRNA samples obtained from each individual, with positive selection in hyenas and purifying selection in tigers (Fig. 5A). Although we considered signatures of selection from all three species together to examine overall patterns during the sampling period, it is important to note that the sampling period varied among species. When nucleotide diversity was calculated separately for each SARS-CoV-2 gene, interesting patterns of selection were observed. Strong positive selection was detected in the N gene from all samples across all timepoints, and most S genes across all species, whereas both positive and purifying selection were detected in ORF1ab (Fig. 5B, C). The remaining gene segments showed varied or no signatures of selection (Fig. 5C, D).

Methods
Next-generation sequencing of qRT-PCR positive and inconclusive nasal swab samples

This study was approved by the Association of Zoos & Aquariums standardized research proposal process (Denver Zoo Conservation Alliance approval number 2023-11). Animals, sample collection, and diagnostic testing are described in Table S1 and previous published16. The sample size was predetermined by the number of individuals within each species housed at the zoo. Sex was not considered in the study design and analysis because the entire population of each species was included in sampling. During the time period of the outbreak (October 2021-January 2022), Denver Zoo was open to visitors while high-level biosecurity measures were in place. Virological controls included use of personal protective equipment by animal care staff, and assignment of different staff to care for each species. Animals were fed a variety of commercially sourced meat products from human grocery suppliers. Most commonly large felids were fed ground beef and venison; less frequently, they fed meat and bone products from pork, beef, bison, goat, rabbit, and chicken. Other diet components included whole prey (rabbits, guinea pigs, chicks, and quail), fish, eggs, canned cat foods, and produce that were commercially sourced.

Aliquots of all nasal swab samples that were positive or inconclusive by qRT-PCR16 analysis were thawed and inactivated with Trizol reagent (Invitrogen) for five minutes at room temperature prior to RNA isolation using a modified protocol for the Zymo RNA Clean & Concentrator-5 kit with New England Biolabs DNase I. RNA was eluted in nuclease-free water, and kept on ice or stored at -80°C before continuing with reverse transcription. Blank RNA extraction negative controls were also generated using nuclease-free water as input. Complementary DNA (cDNA) was generated using SuperScript IV Reverse Transcriptase (Invitrogen) and stored at -20°C prior to tiled amplicon PCR. Complete protocols for RNA and cDNA are available in the project Github repository49. To control for false-positives resulting from PCR amplification, all biological samples were processed in two technical replicates for cDNA generation onwards. Replicate samples were enriched for SARS-CoV-2 genomic material using a tiled amplicon PCR protocol developed by the ARTIC Network. The ARTIC version 4 (V4) primer scheme was used to generate a pool of overlapping amplicons that cover the entire SARS-CoV-2 genome (Integrated DNA Technologies 10011442; Supplementary Data 7). Pooled PCR products were visualized on 1% agarose gels, and quantified using a Qubit dsDNA 1x High Sensitivity Assay kit (Thermo Fisher Scientific). All samples that amplified successfully and contained sufficient genomic material continued to library preparation. Libraries were prepared using 100 ng DNA as input using NEBNext Ultra II DNA Library Prep kits (New England Biolabs), and individual replicates were uniquely dual indexed with NEBNext Multiplex Oligos for Illumina (New England Biolabs). All bead cleanups were performed using Ampure XP beads (Beckman Coulter). Of the 68 total positive samples, we acquired high-quality sequencing libraries from 57 (84%) samples. Of the 47 total inconclusive samples, we acquired high-quality sequencing libraries from 6 (13%) samples.

All qRT-PCR positive samples were sequenced on the same Illumina MiSeq instrument at the Colorado State University Next Generation Sequencing Facility using v2 500 cycle 2 × 250bp kits (Illumina). The technical replicate libraries for Lion F collected on October 21st were pooled and sequenced as a test run with a separate project. All other individual libraries from positive samples were pooled evenly into three sequencing libraries corresponding to three separate sequencing runs, each containing a blank RNA extraction negative control library that was subjected to all of the processing steps. All qRT-PCR inconclusive samples were sequenced on one run of an Illumina MiSeq instrument at the University of Colorado Boulder Center for Microbial Exploration Sequencing Center using a v2 500 cycle 2×250bp kit (Illumina). A spreadsheet detailing the samples and index sequences in each library is available in the project Github repository49.

Variant calling and bioinformatics

Sequencing data in FASTQ file format were input into the nf-core/viralrecon pipeline version 2.6.0 for detailed quality control metrics, lineage assignment, within-host viral variant calling, variant annotation, and the generation of consensus sequences. The pipeline is designed to perform low-frequency variant calling for viral samples. For initial analyses, we used the nf-core/viralrecon default as a SARS-CoV-2 reference sequence, GenBank MN908947.3. However, we used a modified GFF for MN908947.3 that separates ORF1a and ORF1b to account for the -1 ribosomal frameshift. This is consistent with the approach used by Nextstrain.

Briefly, nf-core/viralrecon merges paired FASTQ files and read quality is assessed (FastQC) prior to trimming for quality and adapters (fastp) and removal of host reads (Kraken2). Reads are aligned to a reference sequence, and variant calling is performed (Bowtie2, SAMtools, iVar, picard) followed by variant annotation (SnpEff, SnpSift). The pipeline uses the default iVar parameters including a minimum base quality of 20 and a minimum allele frequency of 0.03 for variant calling. Finally, consensus sequences are generated and assessed (BCFtools, QUAST, Pangolin, Nextclade). Coverage and quality metrics and a complete variant table, modified minimally for clarity, are available in Supplementary Data 3 and 4.

At this stage sequence data were subjected to a manual round of quality control prior to downstream analyses, using the metrics reported in the nf-core/viralrecon pipeline multiqc report (Supplementary Data 3). Replicate NGS datasets that had greater than or equal to 75% 1X and 10X coverage and less than 25,000 Ns per 100kb were retained for within-host variant analysis. This removed three datasets generated from blank RNA extraction negative control samples, 16 datasets corresponding to both technical replicates for eight biological samples, and two single replicate datasets corresponding to two other biological samples (Lion I on November 22nd and Lion C on November 4th). After quality control, 124 NGS datasets corresponding to 63 biological samples from 16 individuals (three hyenas, 11 lions and two tigers) were retained. The mean sequencing depth of a dataset ranged from 2012X to 6997X. The overall mean and median sequencing depth of coverage across the full SARS-CoV-2 genome were 3754X and 2826X respectively.

For analysis requiring all consensus sequences in our dataset (the phylogenetic tree) one of the two technical replicates was selected using the quality control metrics generated by the nf-core/viralrecon pipeline. Of the two technical replicates, the sequence with a higher value for % coverage > 10x was selected. If this metric was the same, the sequence with fewer Ns per 100kb was selected. If the number of Ns was the same, the sequence with a higher overall median coverage value was selected.

For analyses involving just one consensus sequence per individual (the haplotype network), we aimed to select the highest quality sequence collected at the latest date. To do this, the sequence generated at the latest timepoint with high coverage metrics (≥99% coverage > 1X and ≥99 % coverage > 10X and <1225 Ns per 100kb) was selected. The dataset generated from the sample collected on Nov. 22nd from Hyena D passed the initial manual quality control step after NGS, but did not meet this more stringent cutoff. However, it was still included because it was the only sequence generated from this individual.

For a subset of additional analyses, a second bioinformatics pipeline for viral variant analysis was employed [https://github.com/stenglein-lab/viral_variant_caller]. This pipeline outputs raw sequencing depth across all datasets at all genomic positions which we used to calculate summary statistics. It was also used to generate .vcf files in the appropriate format for input in the SNPGenie pipeline (see below).

Based on our conclusion that the two tigers were infected first, we also assessed within-host variation relative to what we refer to as the tiger reference sequence: the consensus SARS-CoV-2 sequence of the virus samples recovered from the two tigers tested on October 7th. The two tiger sequences were aligned with MUSCLE, and a consensus sequence was generated with emboss. This consensus was then used as the reference sequence for running the nf-core/viralrecon pipeline with a custom configuration, using the remaining NGS datasets from lions and hyenas (only those with two high quality technical replicates) as input.

Mutation naming conventions and validation

For all mutations described and visualized in this report, the naming convention used for mutations in the region that codes for the ORF1ab polyprotein is described as its position within either ORF1a or ORF1b. To help provide additional context, for mutations in this region we also list the specific nonstructural protein affected. We also interrogated sequencing coverage at the genomic position corresponding to each mutation discussed in this report to and confirmed >100X coverage in both sequencing replicates.

To identify mutations in our dataset that are characteristic of SARS-CoV-2 lineage AY.20, a list of 32 characteristic mutations was obtained from https://outbreak.info/situation-reports?pango=AY.20. This list is available as Supplementary Data 2. We modified this table with information about the nonstructural protein impacted by mutations in ORF1ab, and with alternate names for mutations based on the annotation and naming convention output by the nf-core/viralrecon pipeline.

Briefly, the outbreak.info mutation list includes E156G and del157/158 in S, whereas our dataset has the mutation E156_R158delinsG. This is the same change with a different name. Similarly, outbreak.info lists del119/120 in ORF8, whereas our dataset has D119_F120del. Finally, our dataset describes a C>T change at genomic position 27874, which would cause a T to an I amino acid change. In our annotation schema, this effect is described as upstream of ORF8, and in outbreak.info it is listed as T40I occurring in ORF7b. These are also the same change with a different name. Finally, S84L in ORF8 is listed as a characteristic mutation of AY.20 in the outbreak.info list. However, this mutation was already present in the reference sequence used in our analysis (GenBank MN908947.3), and all sequences we generated also had the L residue at this position. Therefore, after accounting for these annotation disparities, all mutations characteristic of AY.20 were identified in our sequences.

Some mutations that appear in the Nextstrain phylogeny are not discussed in our results due to insufficient sequencing depth or failure to be detected in both technical replicates; these may represent sequencing errors.

Public SARS2-CoV-2 data, phylogenetic trees and haplotype network

A time-based phylogenetic tree was generated with Nextstrain software following the ncov SARS-CoV-2 workflow50. All sequence data not generated in this study were obtained from GISAID34. The tree (Fig. 2A, B) included three data inputs: (1) sixty-three SARS-CoV-2 consensus sequences generated from zoo animals, (2) all human-derived sequences classified as AY.20 in the state of Colorado between September 23rd and November 4th, 2021 (N=198 sequences), and (3) a random subsample of human-derived sequences in Colorado from the days leading up to the zoo outbreak (100 sequences/day from September 23rd to October 7th, 2021; N=1500 sequences). The tree was visualized in Taxonium and screenshots were used for Fig. 2A and B51. The interactive version is publicly available at https://nextstrain.org/community/laurabashor/DZSARS2/COAY20. A second tree (Figure S1) was generated with Nextstrain using the above method, including two data inputs: SARS-CoV-2 consensus sequences generated from the zoo outbreak (N=63), and all publicly available sequences from animals classified as AY.20 (N=3, two white-tailed deer and one cat). A third phylogenetic tree (Figure S2) was generated with Nextstrain using the above method, and including four data inputs: (1) sixteen SARS-CoV-2 consensus sequences from the Denver Zoo outbreak corresponding to the highest quality sequence collected at the latest date from each individual animal, (2) a random subsample of human-derived sequences in Colorado from the days leading up to the zoo outbreak (100 sequences/day from September 23rd to October 7th, 2021; N=1500 sequences), (3) all felid- and hyena- derived SARS-CoV-2 sequences available in the GISAID database prior to December 2021, and (4) contextual sequences collected in the same time and place as each animal sequence (100 human-derived sequence per animal-derived sequence from the United States; 10 sequences for animal-derived sequences from other locations). Felids include domestic cats, lions, tigers, snow leopards, a fishing cat, and a leopard cat. The second tree is publicly available at https://nextstrain.org/community/laurabashor/DZSARS2/felidshumans. Detailed acknowledgements for all phylogenetic trees including the authors, originating and submitting laboratories of all sequence and metadata obtained from GISAID are available in GISAID EPI_SET_240407tz and EPI_SET_240407wv (see Supplementary Information). A haplotype network was generated with the ‘pegas’ package in R using an alignment (MUSCLE) of the latest high-quality sequence obtained from each individual.

SARS-CoV-2 genomes from nasal swab samples from all eleven lions collected on October 14th and one hyena collected on October 28th were also sequenced by USDA NVSL at the time of the outbreak (accessions included in GISAID EPI_SET_240407tz). A comparison of the consensus sequences obtained in our study from lions and hyenas on these dates reveals high overall identity, with two SNPs observed in the APHIS sequences that were not detected in our datasets. Based on shared SNPs, the hyena sequence (GISAID accession EPI_ISL_6810900) was likely generated from a sample from Hyena A. All other GISAID sequences discussed in this report are included in GISAID EPI_SET_240407tz. Unless otherwise specified in the text, all GISAID searches were carried out with the follow options selected: complete and low coverage excluded.

Within-host virus population demographics and signatures of selection

Within-host virus population demographics and signatures of selection were calculated with SNPGenie52. The SNPGenie pipeline calculates nucleotide diversity as the mean number of pairwise differences per nonsynonymous or synonymous site in the genome from next-generation virus sequencing data, and estimates are weighted by allele frequencies at these sites. This analysis was done at both the population, or virus sample level, and by SARS-CoV-2 gene product.

Linear mixed models were used to evaluate statistical differences in π and πS over time (measured as days since first positive test), using the lmer() function in the ‘lme4’ package in R. Time was a fixed effect, with animal ID as a random effect to account for repeated measures of the same individual over time. Due to the limitation of the population size of the zoologic collection, which included only two tigers and four hyenas, we restricted our statistical analysis to lion samples in these models. The residuals of both models were normally distributed, and the statistical summary table was generated with the tab_model() function in the ‘sjPlot’ package in R. In this table, the conditional R2 for the πS model on the right hand side is NA because zero variance was explained by the animal ID grouping factor. No formal analysis was done to distinguish endpoint πN/πS ratios for Fig. 5A due to insufficient sample size.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript entitled “SARS-CoV-2 within-host population expansion, diversification, and adaptation in zoo tigers, lions, and hyenas” by Bashor et al., provides a comprehensive analysis of SARS-CoV-2 genomic evolution and adaptation following a documented transmission event among non-domestic animal species, Amur tigers (Panthera tigris altaica), African lions (Panthera leo), and spotted hyenas (Crocuta crocuta) in a zoological setting. The investigation is methodologically robust, leveraging high-quality genomic sequencing and detailed bioinformatic and statistical analysis. The findings significantly advance our understanding of within-host SARS-CoV-2 evolution and species-specific adaptations. I recommend this manuscript for publication after addressing a few minor issues detailed below.\nIn the “Introduction” section, Authors can discuss in detail why zoological environments offer valuable contexts to study viral evolution, to further highlight the uniqueness and relevance of this research context. In the “Discussion” Authors should clearly outline the limitations of the study, including the relatively small sample size and limited geographic scope, to contextualize the generalizability of the findings. Additionally, authors could more explicitly suggest potential public health strategies for surveillance or biosecurity measures informed by the findings of this study.\nFinally, I would like to ask the Authors, whether during the period under study, virological controls were also carried out on personnel responsible for managing these animals. This would be a very important aspect to evaluate to add evidence to the spillback and reverse zoonotic mechanisms that have characterized SARS-CoV-2.", "reviewer_2": "Dear Authors,\nThe manuscript entitled \"SARS-CoV-2 within-host population expansion, diversification and adaptation in zoo tigers, lions and hyenas\" represents an exceptional study of genomic surveillance of SARS-CoV-2 in 2 Amur tigers, 11 African lions and 4 spotted hyenas at the Denver Zoo, Colorado, USA. This study was conducted following an apparent spillover event from humans to tigers reported in previous studies, and was specifically designed to characterize the genomic evolution of the virus within the three host populations over a four-month period between October 2021 and January 2022. One of the main limitations of working with \"non-experimental laboratory species\" is often the uncertainty associated with uncontrolled environmental conditions and, in this case, the possibility of multiple viral transmissions and (re)infections from unknown sources. In my modest opinion, the phylogenetic trees provided with all SARS-CoV-2 delta AY.20 sublineages, including human and animal isolates (Figure 2a and 2b), and the network (Figure 2C) are not robust enough to support viral transmission from tigers to lions and hyenas. Despite the onset of symptoms in tigers, the lack of direct contact between the three zoo species, and the close phylogenetic position of the viruses isolated from lions and hyenas to those isolated from humans (gray lines), raises the question of the possibility of multiple infections from humans or other sources to lions and hyenas. The author's argument that “The species-specific clusters evident in this network are consistent with the observed onset of clinical signs, supporting the conclusion that SARS-CoV-2 infection began in tigers, then moved to lions, then to hyenas.” is not robust, as networks do not provide directionality of transmission and only represent within-strain variability. There are other important questions: Was the zoo closed to humans during the study period? What is the food source of these animals? Are the human AY.20 cases found among zoo staff? What is the possible role of re-infection between individuals? What is the phylogenetic relationship of the studied variants with the three other animal variants of AY.20 (2 white-tailed deer from New York and North Carolina in November 202; and 1 from a domestic cat in California in December 2021)? Are the mutations found in the study similar to those found in other animals? I suggest that the authors clarify some of these issues, if possible, and examine the data in the light of this evidence.\nBest regards", "reviewer_3": "I have read the work by Bashor and colleagues. Its significance is encapsulated by this line:\nL331: “The SARS-CoV-2 variation in lions and hyenas described in this study highlights the plasticity of within-host virus populations experiencing selection and genetic drift over time.”\nGiven the work on white-tailed deer this finding is not novel per se. Is it interesting because it is on charismatic megafauna (albeit in zoos)? I won’t judge that. But I wasn’t convinced throughout that their research goes deep enough or is of general interest. In addition, the authors overstate some of their results concerning hyenas and tigers, which rest on very few samples, and an uneven sampling design that likely missed early phases of the infections (at the very least in hyenas).\nBelow I list a few of my concerns:\nL93: How do you explain the positive tests weeks after the infection? Could you determine whether they are in the normal range of false positives?\nFigure 2A and B I find difficult to read and visually not very convincing – is there maybe a nicer way to plot this than via nextstrain?\nL210: why would an increase in synonymous nucleotide diversity over time matter, since it doesn’t change AAs and functions?\nFig 4A: I am not convinced by a correlation consisting of 3 and 5 data point in tigers and hyenas respectively. I think the authors should focus on the lion results.\nFig 5. Are the authors sure that given the wide spread of the data in lions, the results from tigers and hyenas with such few samples are conclusively arguing for one or the other type of selection? Besides not all points are fully visible; on top I find it bizarre to compare all samples pairwise when some are taken early and some later in the infection. That surely has an impact on within host selection on the virus. Was purifying or positive selection more common among earlier or later samples?\nL289: wouldn’t that argue for tigers not to be the first link in the transmission chain? Could it not be independent transmissions? AY.20 with the mutation could be transmitted to the lions and hyenas while a different AY.20 without the mutation was transmitted to tigers. Additionally, hyenas were tested later. How can you even be sure they got it last?\nL348: However, samples from hyenas were taken much latter than those from tigers and lions. You are likely missing the early infection period. I think this a strong assertion standing on shaky ground with few samples to back this up.\nL355: I don’t think this species effect on nucleotide diversity would hold up if more samples from all species existed and from early on in the infection. The tigers also have just 3 positive samples. Have you tested whether you have enough power to support your statistics?"}, "author_response": "Response to Reviews\nWe thank the reviewers for their thoughtful reading of, and comments on our manuscript. We have\nprovided a point-by-point response to their comments below.\nReviewer #1\nThe manuscript entitled “SARS-CoV-2 within-host population expansion, diversification, and adaptation\nin zoo tigers, lions, and hyenas” by Bashor et al., provides a comprehensive analysis of SARS-CoV-2\ngenomic evolution and adaptation following a documented transmission event among non-domestic\nanimal species, Amur tigers (Panthera tigris altaica), African lions (Panthera leo), and spotted hyenas\n(Crocuta crocuta) in a zoological setting. The investigation is methodologically robust, leveraging high-\nquality genomic sequencing and detailed bioinformatic and statistical analysis. The findings significantly\nadvance our understanding of within-host SARS-CoV-2 evolution and species-specific adaptations. I\nrecommend this manuscript for publication after addressing a few minor issues detailed below.\nWe thank the reviewer for their thoughtful and positive assessment of our work.\nIn the “Introduction” section, Authors can discuss in detail why zoological environments offer valuable\ncontexts to study viral evolution, to further highlight the uniqueness and relevance of this research\ncontext.\nWe thank the reviewer for pointing out the uniqueness and valuable context offered by our\nresearch collaboration with a zoo. We have added text to the introduction (lines 66-68) and\ndiscussion (lines 315-321) highlighting how this setting offered a unique opportunity for studying\nviral evolution.\nIn the “Discussion” Authors should clearly outline the limitations of the study, including the relatively\nsmall sample size and limited geographic scope, to contextualize the generalizability of the findings.\nWe have modified the text of the discussion to clearly outline these limitations as suggested\n(lines 381-382 and 388-391).\nAdditionally, authors could more explicitly suggest potential public health strategies for surveillance or\nbiosecurity measures informed by the findings of this study.\nWe have modified the text of the discussion (lines 315-321) to discuss public health strategies\nand reference the companion article Gallichotte et al. 2025\n(https://doi.org/10.1128/msphere.00989-24) in which these topics are discussed more\ncomprehensively.\nFinally, I would like to ask the Authors, whether during the period under study, virological controls were\nalso carried out on personnel responsible for managing these animals. This would be a very important\naspect to evaluate to add evidence to the spillback and reverse zoonotic mechanisms that have\ncharacterized SARS-CoV-2.\nThe virological controls carried out by zoo personnel are described fully in a companion article\ndescribing this outbreak (Gallichotte et al. 2025 https://doi.org/10.1128/msphere.00989-24). We\nupdated this citation (was previously cited as a preprint), in the references section (lines 639-\n640), and added additional information about zoo virological controls to the methods section\n(lines 412-414).\nReviewer #2\nDear Authors,\nThe manuscript entitled \"SARS-CoV-2 within-host population expansion, diversification and adaptation\nin zoo tigers, lions and hyenas\" represents an exceptional study of genomic surveillance of SARS-CoV-\n2 in 2 Amur tigers, 11 African lions and 4 spotted hyenas at the Denver Zoo, Colorado, USA. This study\nwas conducted following an apparent spillover event from humans to tigers reported in previous\nstudies, and was specifically designed to characterize the genomic evolution of the virus within the\nthree host populations over a four-month period between October 2021 and January 2022.\nWe thank the reviewer for their thoughtful comments, and appreciate that our manuscript\n“represents an exceptional study of genomic surveillance”.\nOne of the main limitations of working with \"non-experimental laboratory species\" is often the\nuncertainty associated with uncontrolled environmental conditions and, in this case, the possibility of\nmultiple viral transmissions and (re)infections from unknown sources. In my modest opinion, the\nphylogenetic trees provided with all SARS-CoV-2 delta AY.20 sublineages, including human and animal\nisolates (Figure 2a and 2b), and the network (Figure 2C) are not robust enough to support viral\ntransmission from tigers to lions and hyenas. Despite the onset of symptoms in tigers, the lack of direct\ncontact between the three zoo species, and the close phylogenetic position of the viruses isolated from\nlions and hyenas to those isolated from humans (gray lines), raises the question of the possibility of\nmultiple infections from humans or other sources to lions and hyenas.\nWe’ve updated the language of the results (lines 137, 140-142) and discussion (lines 305-306)\nto address these limitations and the possibility of multiple infections. We also note that this\nreviewer’s suggestion to evaluate additional other AY.20 genotypes (addressed below)\nillustrates the high identity among the Denver Zoo isolates relative to other animal-derived\nAY.20 isolates. This further supports a single infection event that subsequently spread within the\nzoo.\nThe author's argument that “The species-specific clusters evident in this network are consistent with the\nobserved onset of clinical signs, supporting the conclusion that SARS-CoV-2 infection began in tigers,\nthen moved to lions, then to hyenas.” is not robust, as networks do not provide directionality of\ntransmission and only represent within-strain variability.\nWe’ve revised this statement in the results and clarified that analysis cannot determine\ndirectionality (lines 140-142).\nThere are other important questions: Was the zoo closed to humans during the study period? What is\nthe food source of these animals? Are the human AY.20 cases found among zoo staff?\nWe have added additional information in the methods noting that the zoo was open to humans\nduring the time period and the source of food for the animals (lines 412-420). Due to protections\nprovided in the 1996 Health Insurance Portability and Accountability Act (HIPAA), we are\nunable to identify specific individuals in Colorado who were infected with AY.20.\nWhat is the possible role of re-infection between individuals?\nBased on our viral genomic analysis, we are unable to distinguish between recrudescence and\nreinfection and have noted this on lines 122-125.\nWhat is the phylogenetic relationship of the studied variants with the three other animal variants of\nAY.20 (2 white-tailed deer from New York and North Carolina in November 202; and 1 from a domestic\ncat in California in December 2021)? Are the mutations found in the study similar to those found in\nother animals? I suggest that the authors clarify some of these issues, if possible, and examine the\ndata in the light of this evidence.\nWe have generated a new supplemental figure that incorporates the three other animal variants\nof AY.20 and our zoo sequences (Figure S1 in the supplementary information, also publicly\navailable at https://nextstrain.org/community/laurabashor/DZSARS2/AY20animals). These three\nanimal variants of AY.20 were distinct from those seen at the zoo, providing further support for\nour hypothesis that this outbreak was caused by a single introduction. We discuss this on lines\n133-136 of the results. We have updated all text that re-designates Figure S1 as Figure S2 in\nthis revision and revised: (1) the methods (lines 554-558, 570; (2) the data availability statement\n(line 765), and (3), the supplementary information (SI lines 35, 76-82).\nReviewer #3\nI have read the work by Bashor and colleagues. Its significance is encapsulated by this line:\nL331: “The SARS-CoV-2 variation in lions and hyenas described in this study highlights the plasticity of\nwithin-host virus populations experiencing selection and genetic drift over time.”\nGiven the work on white-tailed deer this finding is not novel per se. Is it interesting because it is on\ncharismatic megafauna (albeit in zoos)? I won’t judge that. But I wasn’t convinced throughout that their\nresearch goes deep enough or is of general interest. In addition, the authors overstate some of their\nresults concerning hyenas and tigers, which rest on very few samples, and an uneven sampling design\nthat likely missed early phases of the infections (at the very least in hyenas).\nWe thank the reviewer for the comprehensive reading and assessment of our manuscript. We\nhave revised the text throughout to highlight the limitations of our study and to address concerns\nrelating to novelty of these findings.\nBelow I list a few of my concerns:\nL93: How do you explain the positive tests weeks after the infection? Could you determine whether they\nare in the normal range of false positives?\nOur companion article describing this outbreak (Gallichotte et al. 2025\nhttps://doi.org/10.1128/msphere.00989-24) describes the comprehensive scheme for\ndetermining positive, negative and inconclusive results, demonstrates recovery of infectious\nvirus in a subset of those positive samples, and discusses this in the broader context of SARS-\nCoV-2 rebound infections. We think that this publication addresses this comment, and\naccordingly have included the reference to the testing result classification to the methods\nsection (lines 419-420).\nFigure 2A and B I find difficult to read and visually not very convincing – is there maybe a nicer way to\nplot this than via nextstrain?\nThank you for this helpful comment. We’ve updated figures 2A and B to enhance the readability\nand impact of this figure. Specifically, we have made the following modifications: (1) we have\nvisualized the Figure 2 phylogenetic tree in Taxonium to facilitate interpretation; (2) we have\nincreased text size, removed gridlines, and highlighted external nodes; (3) date labels in\nsubfigure 2D have been updated in BioRender to be consistent with subfigures 2A and B: (4)\nthe figure legend (lines 161 and 168-169) and (5) the methods (lines 552-553) have been\nupdated to reflect these changes.\nL210: why would an increase in synonymous nucleotide diversity over time matter, since it doesn’t\nchange AAs and functions?\nWe’ve added to the discussion highlighting the importance of genetic diversity for viral evolution,\nand the potential significance of this diversity outside of functional changes (e.g, alternating\ncodon usage, RNA secondary structure, innate immune evasion, etc.) to address this point\n(lines 366-369).\nFig 4A: I am not convinced by a correlation consisting of 3 and 5 data point in tigers and hyenas\nrespectively. I think the authors should focus on the lion results.\nWe agree with the reviewer that a larger sample size for tigers and hyenas would have been\npreferred but also acknowledge that this study represents a unique number of sequentially\nsampled naturally infected individuals compared to similar studies in wildlife (see recent Nature\nCommunications article: https://doi.org/10.1038/s41467-024-53766-5 for one example). We\nhave modified the text to more clearly take note of how sample size limits our conclusions\nbased on species in the results (line 220), discussion (lines 381-382 and 388-391) and methods\n(lines 596-598).\nFig 5. Are the authors sure that given the widespread of the data in lions, the results from tigers and\nhyenas with such few samples are conclusively arguing for one or the other type of selection? Besides\nnot all points are fully visible; on top I find it bizarre to compare all samples pairwise when some are\ntaken early and some later in the infection.\nIn order to address this concern, we have revised the text of the figure legend and results\nassociated with Figure 5 regarding conclusions about type of selection (lines 227-229),\ninterspecific sample comparisons (lines 234-236), and results that address whether purifying or\npositive selection was most common among earlier or later samples (lines 229-230). We have\nalso described how to interpret points for which the πN/πS ratio is infinite in the figure legend\n(lines 271-273).\nL289: wouldn’t that argue for tigers not to be the first link in the transmission chain? Could it not be\nindependent transmissions? AY.20 with the mutation could be transmitted to the lions and hyenas while\na different AY.20 without the mutation was transmitted to tigers. Additionally, hyenas were tested later.\nHow can you even be sure they got it last?\nClinical signs reported support tigers as the initially infected species, and the genomic data\npresented support this conclusion. We have, however, revised text in the results and legends to\nnote that it is possible that multiple independent transmission events occurred (lines 140-142\nand 305-306).\nL348: However, samples from hyenas were taken much later than those from tigers and lions. You are\nlikely missing the early infection period. I think this is a strong assertion standing on shaky ground with\nfew samples to back this up.\nWe appreciate the reviewer’s caution to avoid overstating our findings, and have revised the text\nto note this limitation (lines 376-379).\nL355: I don’t think this species effect on nucleotide diversity would hold up if more samples from all\nspecies existed and from early on in the infection. The tigers also have just 3 positive samples. Have\nyou tested whether you have enough power to support your statistics?\nAlthough a larger sample size would have increased our power and been ideal, the sample size\nwas predetermined by the number of individuals within each species housed at the zoo (note\nthat the entire population of each species was included in sampling). The longitudinal sampling\nprotocol providing repeated screening and defined the period that viral nucleic acid sequences\ncould be detected as reported in Gallichotte et al. 2025 (https://doi.org/10.1128/msphere.00989-\n24). While overall population size is small, these sampling data are unprecedented in natural\ndisease outbreaks and provide a comparatively comprehensive analysis of a natural viral\noutbreak. Further, our statistical models took sample size into account when comparing among\nspecies. We have added additional text to the results (line 220), the discussion (lines 381-382\nand 388-391) and the methods (lines 596-598) to clearly note sample size limitations. We did\nnot carry out a power calculation before collecting samples or to plan an experimental design\nbecause samples were collected for surveillance purposes, and all animals within an exhibit\nwere sampled."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Dear authors,\nThank you for your thorough and thoughtful revisions. You have addressed all my concerns as well as the other reviewer in a clear and satisfactory manner. The changes made have significantly improved the clarity and overall quality of the manuscript.", "reviewer_3": "This is the second time I am reviewing this manuscript and I am surprised at how few of the reasonable requests made by the reviewers have been addressed. In particular, reviewer 2’s point regarding the uncertainty of transmission time line and the fact that the network analysis does not count as enough proof. I have raised a similar concern, and voiced that the uneven sampling design, particularly the fact that hyenas miss samples early on, leaves me unconvinced of the authors claim. Many responses were defensive and changes mainly concerned addendums to paragraphs without rethinking their approach.\nResponse to L93: Papers such as this manuscript have to stand on their own. Methods or results necessary to understand or strengthen the arguments should but included (even if only as shortened version) rather than citing another paper reporting similar findings and relying on the same data as the present one. If anything, their work (Gallichotte et al. 2025) brings into question how new the paper is they are trying to publish in Nature Communications now given that the take-home (i.e., supposed course of transmission from tigers to lions to hyenas) is already spelled out there (and even some graphics are stikingly similar e.g., Fig 4 in the msystems pub is Fig 1 in this MS). Generally, the practice of parsing out data into separate studies with only incremental knowledge gain is bad practice.\nResponse to Fig 4A: Justification based on the fact that someone else has done something does not make the statistics sound. Given the variability clearly visible in the data points in Figure 4A I don’t understand why the authors seem to think that the hyena’s correlation is meaningful. To spell it out: three of the 5 data points are at day 0 (somewhat clustered), the fourth is lower, whereas the fifth is super high, pulling the correlation into the shape it has, and let’s not talk about the three data points for tigers.\nIn terms of sample sizes, I appreciate that the authors are limited with that respect and I do acknowledge the dense virological surveillance data they were able to collect. Still, given Figure 1, I believe that had to be a conversation on what samples were included for the subsequent analysis. Apart from the samples collected close to the outbreak, the selection seems random. Here I would have appreciated a more thought through approach or is their approach and final sample sizes down to what the authors describe in L450 and following?"}, "author_response": "Response to Reviews\nThank you very much for the opportunity to revise our manuscript, “SARS-CoV-2 within-host population expansion, diversification and adaptation in zoo tigers, lions and hyenas,” for Nature\nCommunications. We have provided responses to each of the Reviewers’ queries.\nReviewer comments:\nReviewer #2 (Remarks to the Author):\nDear authors,\nThank you for your thorough and thoughtful revisions. You have addressed all my concerns as\nwell as the other reviewer in a clear and satisfactory manner. The changes made have\nsignificantly improved the clarity and overall quality of the manuscript.\nWe thank the reviewer for their reviewing efforts, positive comments and support of the revised\nmanuscript.\nReviewer #3 (Remarks to the Author):\nThis is the second time I am reviewing this manuscript and I am surprised at how few of the\nreasonable requests made by the reviewers have been addressed. In particular, reviewer 2’s\npoint regarding the uncertainty of transmission time line and the fact that the network analysis\ndoes not count as enough proof. I have raised a similar concern, and voiced that the uneven\nsampling design, particularly the fact that hyenas miss samples early on, leaves me\nunconvinced of the authors claim. Many responses were defensive and changes mainly\nconcerned addendums to paragraphs without rethinking their approach.\nIn our previous revisions we addressed the uneven sampling design (which was conducted by\nzoologic staff as part of a rigorous diagnostic and biosecurity protocol, not as an experimental\ndesign), and added additional text highlighting these limitations (lines 380-381, 385-388). We\nalso addressed Reviewer #2’s point regarding the uncertainty of the transmission timeline by\nupdating the language of the results (lines 141-143) and discussion (lines 304-305) to clarify\nthat our analysis cannot determine directionality and address the possibility of multiple\ninfections. As noted above, Reviewer #2 was satisfied with these revisions.\nResponse to L93: Papers such as this manuscript have to stand on their own. Methods or\nresults necessary to understand or strengthen the arguments should but included (even if only\nas shortened version) rather than citing another paper reporting similar findings and relying on\nthe same data as the present one.\nby-point response to Reviewer #2 concerns. We failed to point to this revised text in our\nprevious point-by-point response to Reviewer #3’, although we feel that it directly responds to\ntheir query in their initial review:\nLines 123-126:\nDue to limited genetic variability across all sequences, we are unable to conclusively determine if\npositive samples detected in lions in November and December were recrudescent infections or\nreinfections; however, this homogeneity is suggestive of recrudescent infections.\nIf anything, their work (Gallichotte et al. 2025) brings into question how new the paper is they\nare trying to publish in Nature Communications now given that the take-home (i.e., supposed\ncourse of transmission from tigers to lions to hyenas) is already spelled out there (and even\nsome graphics are strikingly similar e.g., Fig 4 in the msystems pub is Fig 1 in this MS).\nGenerally, the practice of parsing out data into separate studies with only incremental\nknowledge gain is bad practice.\nGallichotte et al. 2025 provides an epidemiological description of the outbreak in the\ncontext of other SARS2 outbreaks in conservatory animals, but does not describe the\norder of transmission between the animals, viral evolution or viral genomic analysis\nwithin and among the different species. Multiple publications describing different aspects\nof complex disease outbreaks is a common practice. (Relevant example: SARS2 at the\nBronx Zoo, https://pmc.ncbi.nlm.nih.gov/articles/PMC7554670/ and\nhttps://pubmed.ncbi.nlm.nih.gov/33480553/).\nWhile Figure 4 in Gallichotte et al and Figure 1 in our manuscript are similar, they are\nused to illustrate entirely different points. Figure 4 in the mSphere paper demonstrates\nthe result of the vRNA testing and PCR diagnosis of animals over time. Figure 1 in this\nmanuscript demonstrates the outcome/success rate of next-generation sequencing\nattempts of PCR positive and inconclusive samples, highlighting which samples were\ndeep-sequenced for the subsequent analyses reported. Providing Figure 1 in this paper\nallows readers to obtain critical information needed to understand sampling strategy and\norigin and temporal relationship of viral samples that were interrogated in this study.\nAs noted below in our comments in response to sample sizes, we have updated the\nresults text for (lines 103-105) Figure 1 and its figure legend (lines 106-114) to\nemphasize the findings presented in this figure, specifically, that all of these samples\nwere subjected to an NGS workflow, and which samples produced high-quality\nsequence data (all of which were included in genomic analysis). We have modified\nFigure 1 by adding a more distinctive, wider diamond-shaped border to each sample\nwas successfully sequenced with high quality resolvable data for further analysis. We\nalso updated the text of the methods (lines 407-450) to further clarify.\nalone and presents a body of novel genomic sequencing data generated that has\nprovided novel and important findings about natural SARS-CoV2 disease cross-species\ntransmission, and within and between host and species evolution.\nResponse to Fig 4A: Justification based on the fact that someone else has done something\ndoes not make the statistics sound. Given the variability clearly visible in the data points in\nFigure 4A I don’t understand why the authors seem to think that the hyena’s correlation is\nmeaningful. To spell it out: three of the 5 data points are at day 0 (somewhat clustered), the\nfourth is lower, whereas the fifth is super high, pulling the correlation into the shape it has, and\nlet’s not talk about the three data points for tigers.\nWe’re grateful for the reviewer’s persistence in helping us improve this part of the\nanalysis. We’ve considered the dataset and we can see how although our model was\nstatistically significant, the biological significance is unclear given how little data was\navailable for hyenas and tigers. We therefore modified our statistical models to evaluate\nonly the lion sample (N=54 samples from 11 different individuals) dataset. We have\nsubsequently generated a new Figure 4 (lines 240-246). that visualizes the data from\nlions separately, with the lions now shown in panel A, and tigers and hyenas in panel B.\nWe revised the results and discussion text (lines 219-222, 385-388) and updated Table\n1 accordingly (lines 248-254). Finally, we updated the methods section (lines 585-588)\nto provide complete details of this analysis.\nExcerpted revised figure and table legends and results text:\nLines 240-246:\nFigure 4. SARS-CoV-2 within-host populations underwent expansion and diversification over time.\nEach point indicates the nucleotide diversity of a SARS-CoV-2 virus population observed in one sample,\ncalculated as the mean of two sequencing replicates. (A, B) Within-host nucleotide diversity (π) by the\nnumber of days after an individual’s first positive SARS-CoV-2 test, in (A) lions (N=54 samples) and (B)\ntigers (N=3 samples) and hyenas (N=6 samples). (C) Within-host synonymous nucleotide diversity (πS) over\ntime by individual animal.\nLines 248-254:\nTable 1. Summary of linear mixed models of nucleotide diversity (π) of SARS-CoV-2 in lions as a\nfunction of time. Time was measured as days since first positive qRT-PCR test. Time was a fixed effect,\nwith animal ID as a random effect to account for repeated measures of the same individual (N=54 samples\nfrom 11 lions) over time. The model was fit with the lmer() function in the ‘lme4’ package and this table was\ngenerated with the tab_model() function in the ‘sjPlot’ package in R.\nLines 219-222:\nTo characterize selective pressures on within-host SARS-CoV-2 populations, we calculated nucleotide\ndiversity (p) across the virus genome at nonsynonymous and synonymous sites (Tables S6 and S7, Figure\n4A, B). In lions, which were sampled repeatedly over time, we observed a significant increase in within-host\n3\nnucleotide diversity over time, accounting for repeated measures of the same individuals (Figure 4A, Table\n1). Within individual lions, synonymous nucleotide diversity also increased over time (Figure 4C, Table 1).\nIn terms of sample sizes, I appreciate that the authors are limited with that respect and I do\nacknowledge the dense virological surveillance data they were able to collect. Still, given Figure\n1, I believe that had to be a conversation on what samples were included for the subsequent\nanalysis. Apart from the samples collected close to the outbreak, the selection seems random.\nHere I would have appreciated a more thought through approach or is their approach and final\nsample sizes down to what the authors describe in L450 and following?\nWe are thankful to the reviewer for drawing our attention to the need to clarify the\ndescription of the sample selection process. In this study we attempted to sequence all\nthe positive (red, N=68) and inconclusive (yellow, N=47) samples shown in Figure 1,\nwhich represent all the samples collected and tested as positive or inconclusive during\nthe outbreak. We were able to successfully generate high-quality NGS data from 63\nsamples outlined in blue in Figure 1 (57 positives, 6 inconclusives).\nAs noted above, we have revised Figure 1 and its figure legend (lines 106-114) to\nemphasize that this is the rationale for providing this figure. We added a more distinctive,\nwider diamond-shaped border to each sample that yielded high-quality genomic\nsequencing libraries.\nWe have also updated the results text (lines 103-105) accordingly. We also modified\nand reorganized the methods section (lines 407-450) to describe this approach.\nSpecifically, we added clarifying text, and combined the two sections describing positive\nand inconclusive sample library preparation into one section to clarify that all the\nsamples with positive or inconclusive PCR results were subjected to the same workflow.\nWe thank the reviewers for their helpful suggestions and hope that these clarifications\nand modifications address these outstanding concerns.\nExcerpted revised text from manuscript:\nLines 103-105:\nOf the 68 total qRT-PCR-positive swab samples shown in Figure 1, 57 (84%) yielded high-quality\nsequencing datasets. Of the 47 qRT-PCR-inconclusive samples, we acquired high-quality\nsequencing datasets from 6 (13%) samples.\nLines 106-114:\nFigure 1. SARS-CoV-2 RNA was persistently detected in tigers, lions and hyenas by qRT-PCR\nfollowed by next-generation sequencing. qRT-PCR was performed on nasal swabs from tigers\n(N=2), lions (N=11) and hyenas (N=4) sampled repeatedly between October 2021 and January\n2022. Each point represents a nasal swab sample tested for three SARS-CoV-2 gene targets,\n4\ncolored by the result determined by diagnostic software. All the positive (red, N=68) and\ninconclusive (yellow, N=47) samples shown were subjected to the NGS library preparation\nworkflow. Points with a blue diamond border indicate samples from which high-quality NGS data\nwas successfully generated (N=57 positive and N=6 inconclusive samples, N=63 total).\nLine 407:\nNext-generation sequencing of qRT-PCR positive and inconclusive nasal swab samples\nLine 417:\nAliquots of all nasal swab samples that tested positive or inconclusive by qRT-PCR…\nLines 432-434:\nAll samples that amplified successfully and contained sufficient genomic material continued to\nlibrary preparation.\n5"}, {"round": 3, "reviewer_comments": {"reviewer_3": "I thank the authors for their patience with the reviewing process. I do think the manuscript has improved from its original version thanks to the contributions of all reviewers. I am satisfied with the revisions layed out before me in this last round. The authors recognise the shortcomings of their data sufficiently. I wish you much success with the final editorial process and future work in the field."}, "author_response": ""}]

Decision letter