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. 2026 Mar 18;47(2):606–620. doi: 10.24272/j.issn.2095-8137.2025.134

Comparison of gut viromes across captive mammals reveals extensive genetic diversity in bacteriophage dark matter and mammalian viruses

Juan Lu 1,2,#, Hao-Ning Wang 3,6,#, Chun-Mei Wang 4,#, Juan Xu 5,#, Chukwudozie Kingsley Ikechukwu 1,#, Wang Li 5, Song-Yi Ning 1, Ping Wu 1, Yu-Wei Liu 1, Quan Shen 1, Li-Kai Ji 1, Xiao-Chun Wang 1, Shi-Xing Yang 1, Cheng-Ling Zhou 5,*, Xiao-Long Wang 7,*, Wen Zhang 1,2,*, Tong-Ling Shan 4,*
PMCID: PMC13425396  PMID: 42011762

Abstract

Comprehensive characterization of mammalian gut viromes is essential for early detection of commensal and potentially zoonotic viruses and for reducing the risk of cross-species transmission. Viral metagenomics was applied to profile gut viral communities from zoo mammals maintained across multiple zoological institutions in China. Viral communities differed markedly among host dietary guilds, with herbivores exhibiting the highest viral species diversity. In total, 1 027 viral sequences representing five major viral groups were recovered, including multiple mammal-associated astroviruses, picornaviruses, and parvoviruses with potential infectivity. Phylogenetic reconstruction based on viral hallmark genes demonstrated extensive genomic diversification across recovered lineages. Hosts for most microviruses were predicted to belong to the bacterial family Bacteroidaceae. In addition, 10 previously unreported crAss-like phages were identified in mammalian samples and showed close evolutionary relationships with proposed crAssphages from the human gut virome. Antibiotic resistance genes identified in the mammalian gut viromes primarily belonged to tetracyclines. These findings substantially expand current understanding of viral community structure in captive animals in China and provide a foundation for proactive surveillance frameworks targeting emerging mammalian viruses with zoonotic potential.

Keywords: Viral metagenomics, Zoo animals, Gut viromes, Bacteriophages, Mammalian viruses

INTRODUCTION

The accelerating emergence of zoonotic diseases has been ascribed to ecological disruption, intensified human mobility, altered land use, and socioeconomic factors, with projections indicating continued growth in outbreak frequency (Jones et al., 2008). Increasingly dense and complex interactions among human, livestock, wildlife, and companion animals have created epidemiological conditions that facilitate cross-species transmission and zoonotic spillover events (Sreenivasan et al., 2021). Mammals constitute major reservoirs of viral diversity and account for the majority of emerging viral infections in human populations (Woolhouse et al., 2012). Spillover of mammalian viruses into new animal hosts or humans therefore represents a substantial and persistent global health burden (Woolhouse et al., 2012). Zoological collections and captive facilities house taxonomically diverse mammals within spatially restricted settings, resulting in frequent interspecific and human-animal contact. Such environments increase opportunities for cross-species viral transmission and pathogen exchange among resident animals and between animals and humans. During the COVID-19 pandemic, bidirectional transmission of SARS-CoV-2 was documented between humans and multiple zoo mammals, including tigers, lions, and gorillas (Sharun et al., 2021), underscoring the vulnerability of captive systems to viral spillover and reverse zoonosis. Given the intensity of human-animal contact in zoological settings, a substantial reservoir of undiscovered viruses with zoonotic potential likely remains uncharacterized. Systematic investigation of gut viral communities in captive mammals is therefore critical for identifying circulating viral pathogens and assessing potential sources of future spillover events.

Current strategies addressing zoonotic emergence remain largely reactive, emphasizing containment of human-to-human transmission after pathogens have already established in human populations (Clifford Astbury et al., 2023). Because most pandemics originate from animal reservoirs, effective prevention requires proactive detection of viruses with zoonotic potential prior to large-scale transmission. Metagenomic surveillance of domestic and wild animal viromes provides a powerful framework for identifying known zoonotic agents, discovering previously unknown viruses, and characterizing reservoir hosts (Orf et al., 2023). Continuous monitoring of viral diversity within animal populations enables early recognition of lineages with spillover capacity and informs targeted interventions designed to reduce transmission risk (Sanyal et al., 2022). Metagenomic approaches have increasingly been applied to characterize gut viral diversity across animal taxa, including dogs (Wang et al., 2023), rodents (Wu et al., 2018), and bats (Bergner et al., 2020). In parallel, the World Health Organization (WHO) has advanced the One Health framework, advocating integrated, cross-sectoral strategies to address potential or current health threats arising from interactions among humans, animals, and ecosystems (Sanyal et al., 2022).

Bacteriophages, although incapable of infecting eukaryotic cells, exert substantial influence on gut microbiomes by restructuring bacterial communities and mediating dissemination of antibiotic resistance genes (ARGs) (von Wintersdorff et al., 2016). These processes indirectly shape host health and susceptibility to opportunistic pathogens (Boyd, 2012). Phage-driven transfer of ARGs in captive animals may expand resistance reservoirs, thereby complicating strategies aimed at mitigating zoonotic spillover (Colomer-Lluch et al., 2011). Dynamic phage-bacteria interactions in captive animals may destabilize gut microbial networks, creating ecological conditions conducive to pathogen emergence (Boyd, 2012). Moreover, phage-encoded virulence factors and ARGs may accelerate bacterial adaptation under anthropogenic antibiotic pressure, indirectly elevating zoonotic risk (Gillings & Stokes, 2012; Modi et al., 2013). Viral metagenomics provides an unbiased framework for the characterization of viral communities and serves as an early-warning system for pathogen discovery (Santiago-Rodriguez & Hollister, 2022). Even in the absence of overt pathogens, viral “dark matter” and resistance determinants detected through metagenomic approaches may modulate zoonotic risk via microbial dysbiosis and horizontal gene transfer (Santiago-Rodriguez & Hollister, 2022).

China features diverse landscapes and harbors exceptional animal diversity across multiple ecosystems, providing a unique context for investigating mammalian viromes. In this study, viral metagenomics combined with high-throughput sequencing was applied to characterize gut viromes from zoo mammals in densely populated regions in China, followed by phylogenetic reconstruction of representative viral families using hallmark genes. Detailed characterization was conducted for crAssphages, the most abundant viral group in the human intestine, and widespread ARGs were detected across mammalian gut viromes. Findings from this study deepen current understanding of gut virome composition in captive mammals, reveal substantial hidden diversity of mammalian viruses in China, and establish a practical framework for proactive surveillance and early prevention of zoonotic disease outbreaks driven by mammalian viruses.

MATERIALS AND METHODS

Sample collection and preparation

From May to September 2022, 1 145 fecal samples were collected from mammals representing eight orders housed in zoological institutions across Southwest, Northeast, and Southeast China (Figure 1; Supplementary Table S1). Ethical approval was granted by the Ethics Committee of the Chinese Academy of Agricultural Sciences (Approval No.: SVRI2017091) and the Ethics Committee of Jiangsu University (Approval No.: 2018ujs18023), covering all participating facilities. Sampled taxa comprised Diprotodontia (marsupials), Primates (non-human primates), Carnivora (carnivorous mammals), Artiodactyla (even-toed ungulates), Perissodactyla (odd-toed ungulates), Pilosa (anteaters and sloths), Proboscidea (elephants), and Rodentia (rodents). At least three individual fecal specimens per species were analyzed, with sample sizes ranging from three to 66 depending on species availability and ethical authorization. No animals exhibited diarrhea or acute clinical symptoms during sampling. Fecal material was collected using sterile disposable gloves and sterile swabs, immediately transferred into pre-sterilized cryogenic tubes, and flash-frozen within 30 min on dry ice for transport. Samples were combined into 130 species-specific pools using equal fecal mass per individual (1 g per specimen). For endangered species with limited availability, all collected specimens were included. Sample pools were assigned to three dietary categories: herbivore, omnivore, and carnivore. Pooled samples were homogenized in 10 volumes of phosphate-buffered saline (PBS), vortexed vigorously for 5 min, and subjected to three freeze-thaw cycles on dry ice. Supernatants were recovered after centrifugation (10 min, 15 000 ×g, 4°C) and stored at −80°C until use.

Figure 1.

Figure 1

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Geographic distribution of sampling sites across Southwest, Northeast, and Southeast China

Pie charts illustrate sample size and composition at each sampling site, with categories distinguished by color according to the legend. Base map data were obtained from Geospatial Data Cloud.

Viral metagenomic library construction

A volume of 500 µL from each supernatant was passed through a 0.45 µm filter (Millipore, USA) to remove eukaryotic and bacterial cell-sized particles. Filtrates enriched in viral particles were treated with DNase and RNase at 37°C for 60 min to digest unprotected nucleic acids (Zhang et al., 2014, 2016, 2017). Remaining total nucleic acids were isolated using a QIAamp Viral RNA Mini Kit (QIAGEN, Germany) according to the manufacturer’s protocols. For library construction, double-stranded DNA (dsDNA) was generated from both RNA and DNA viruses. For RNA viruses, reverse transcription was performed using SuperScript III Reverse Transcriptase (Thermo Fisher, USA), followed by second-strand synthesis with DNA polymerase I large fragment (Klenow). For single-stranded DNA (ssDNA) viruses, conversion to dsDNA was achieved simultaneously using the Klenow reaction. Specifically, 12 µL of nucleic acid extract was added to each 20 µL reaction mixture for dsDNA synthesis from RNA and ssDNA templates. All resulting dsDNA products were used for library preparation. A total of 130 sequencing libraries were constructed using a Nextera XT DNA Sample Preparation Kit (Illumina, USA), with quality assessment performed by agarose gel electrophoresis and by analysis on an Agilent Bioanalyzer 2100 (China). Amplification during library preparation was limited to 16 PCR cycles to reduce duplication bias and preserve library complexity. Libraries were sequenced on the Illumina NovaSeq 6000 platform (Illumina, USA) using 250 bp paired-end reads with dual barcoding for each sample pool (Liu et al., 2016). Read counts for all libraries are provided in Supplementary Table S1.

Bioinformatics analysis

Paired-end 250 bp reads generated on the NovaSeq platform were demultiplexed using sample-specific dual-index barcodes with Illumina bcl2fastq Conversion Software (v.2.20) under default parameters, assigning sequences to their original 130 sample pools. Data processing was performed using an in-house pipeline deployed on a 32-node Linux computing cluster. Reads were classified as duplicates when bases 5–55 were identical, and a single randomly selected representative read was retained from each duplicate group. Low-quality terminal regions were trimmed using a Phred quality score of 30 as the threshold. Adaptor sequences were removed using VecScreen implemented through NCBI BLASTn (v.2.13.0+) with parameters optimized for adapter detection. Clean reads were de novo assembled within each barcode group, followed by chimera detection and length filtering using ENSEMBLE assembler v.1.0 with default parameters (Deng et al., 2015). Host-associated sequences were removed using Bowtie2 (v.2.4.5; --very-sensitive-local mode) (Langmead & Salzberg, 2012), discarding reads exhibiting at least 90% nucleotide identity and covering at least 90% of read length relative to host reference genomes. Resulting contigs were independently evaluated with VirSorter2 (v.2.2.3; --include-groups “dsDNAphage, ssDNA, RNA”) (Guo et al., 2021) and CheckV (v.1.0.1; default parameters) (Nayfach et al., 2021) to classify viral sequences and estimate genome completeness. Both contigs and singleton reads were queried against a customized viral proteome database using Diamond BLASTx (v.2.1.8; --evalue 1e-5 --max-target-seqs 10). The viral reference database was constructed from NCBI virus reference proteomes (ftp://ftp.ncbi.nih.gov/refseq/release/viral/) and viral protein sequences extracted from NCBI nr fasta files based on Virus Kingdom taxonomic annotation. Candidate viral hits were subsequently screened against an in-house non-virus non-redundant (NVNR) protein database using an E-value cutoff of <10−5 to remove false-positive viral hits. This non-virus database comprised protein sequences extracted from NCBI nr fasta files after exclusion of Virus Kingdom annotations. Contigs lacking significant BLASTx similarity to viral proteomes were further searched against viral protein families in the vFam database (Skewes-Cox et al., 2014) using HMMER v.3.3.2 (Eddy, 2009; Finn et al., 2011; Johnson et al., 2010) to identify remote viral homologs.

Viral community analysis

BLASTx outputs were imported into MEGAN (v.6.25.10) (Huson et al., 2007) for taxonomic binning using the Lowest Common Ancestor (LCA) algorithm with default parameters (min-score: 50, top-percent: 10, min-support: 5). Viral composition across 130 viromes was compared at species and family levels using the compare function in MEGAN (Huson et al., 2007). Community similarity patterns were visualized using principal coordinate analysis (PCoA) based on Bray-Curtis distance matrices with Unweighted Pair Group Method clustering at species resolution. Analysis of Similarities (ANOSIM) was applied to assess intergroup differences in viral communities using the vegan package (v.2.6-8) in R (v.4.3.1) (https://CRAN.R-project.org/package=vegan). Viral community structure and richness at the family level were visualized using heatmaps, UpSet plots, and bar charts generated using pheatmap (v.1.0.12, https://CRAN.R-project.org/package=pheatmap), UpSetR (v.1.4.0, https://CRAN.R-project.org/package=UpSetR), and ggplot2 (v.3.5.1, https://ggplot2.tidyverse.org), respectively.

Viral sequence extension and annotation

Viral contigs inferred to originate from the same genome but lacking detectable overlaps were merged using Low Sensitivity/Fastest mode in Geneious Prime v.2024.0.5 (Kearse et al., 2012). Individual contigs were subsequently used as references for read mapping against corresponding raw reads from original barcode pools under identical alignment settings. Putative viral open reading frames (ORFs) were predicted in Geneious Prime v.2024.0.5 using built-in parameters (minimum size: 300; genetic code: standard; start codons: ATG) (Kearse et al., 2012) and further validated by BLASTp comparison against related viral proteins in NCBI. Functional annotation of predicted ORFs was performed through searches against the Conserved Domain Database using RPS-BLAST with an E-value cutoff of <10−5. Contigs encoding hallmark genes from major viral groups were retained, and sequences containing complete ORFs were selected for downstream phylogenetic analyses. Hallmark markers included phage terminase large subunit (TerL) for crAss-like phages, major capsid protein (MCP) for Microviridae, nonstructural protein 1 (NS1) for Parvoviridae, replication-associated protein (Rep) for CRESS-DNA viruses, and RNA-dependent RNA polymerase (RdRp) for Riboviria (Supplementary Table S2).

Phylogenetic analysis

Phylogenetic reconstruction was conducted using predicted hallmark protein sequences identified in this study together with representative reference sequences from corresponding viral groups downloaded from GenBank. Protein alignments were generated using MUSCLE v.5.1 implemented in MEGA v.10.1.8 (Kumar et al., 2018) under default settings (BLOSUM62 matrix, gap penalties: -11/-1). Alignment columns containing more than 50% gaps were removed using trimAl v.1.4.1 (-gt 0.5 parameter) (Capella-Gutiérrez et al., 2009), followed by manual trimming of hypervariable regions to preserve conserved domains critical for phylogenetic inference. Bayesian trees were constructed using MrBayes v.3.2.7 (Ronquist et al., 2012) with Markov chains run for a maximum of 1 000 000 generations, sampling every 50 generations, and discarding the first 25% of Markov chain Monte Carlo (MCMC) samples as burn-in. Convergence of MCMC runs was assessed using Tracer v.1.7.2. Maximum-likelihood trees were independently generated in MEGA v.10.1.8 (Kumar et al., 2018) to validate Bayesian topologies. Only nodes supported by posterior probabilities greater than 0.95 and bootstrap scores greater than 70% were displayed.

Bacteriophage analysis

Genomic relatedness among five high-quality near-complete crAss-like genomes and reference sequences was evaluated using EasyFig v.2.2.5 (Sullivan et al., 2011) with default tBLASTx parameters. Intergenomic similarity across crAss-like genomes was quantified using VIRIDIC (2022 release) (Moraru et al., 2020) with default settings. Host prediction for bacteriophages, including crAss-like phages and microviruses, was conducted using iPHoP (v.1.3.0), integrating phage-based classifiers (RaFAH, vHULK, VPF-Class, and HostPhinder) and host-based approaches (BLAST, CRISPR, SpacePharer, VirHostMatcher, WIsH, and PHP), with confidence scores exceeding 90% (Roux et al., 2023). Host assignment outputs are provided in Supplementary Table S3. ARGs within gut viromes were annotated, classified, and quantified using deepARG (v.1.0.2) (Arango-Argoty et al., 2018) through a short-read deep learning workflow with default parameters. ARGs with prediction probability greater than 0.8 were retained for downstream analysis, with annotation results summarized in Supplementary Table S4. ARG-carrying contigs were screened against the NCBI Plasmid Reference Database using BLASTn (E-value≤1e−5, ≥90% identity) to remove plasmid-associated ARGs. Contigs encoding ARGs were further examined manually to confirm co-localization with viral genes and absence of plasmid markers. Comparative ARG profiles across different dietary groups were visualized using heatmaps, PCoA, Venn diagrams, and Circos plots generated with pheatmap (v.1.0.12, https://CRAN.R-project.org/package=pheatmap), vegan (v.2.6-8, https://CRAN.R-project.org/package=vegan), ggvenn (v.0.1.10, https://CRAN.R-project.org/package=ggvenn), and circlize (Gu et al., 2014).

Quality control

All experimental procedures followed standardized contamination-control protocols to minimize cross-sample contamination and nucleic acid degradation. Aerosol-resistant filter tips were used to reduce sample cross contamination, and all consumables contacting nucleic acids, including microcentrifuge tubes and pipette tips, were certified RNase- and DNase-free. Nucleic acid extraction and downstream processing were performed under sterile conditions. Extracted nucleic acids were dissolved in DEPC-treated water and supplemented with RNase inhibitors. All laboratory operations were performed within a biological safety cabinet.

RESULTS

Overview of sequencing outcomes

Gut viral communities in zoo mammals were investigated through integrated viral metagenomic profiling of DNA and RNA viruses across 1 145 fecal samples collected from mammals representing distinct dietary guilds housed in zoological institutions across five provinces of China (Figure 1). Samples were consolidated into 130 species-level pools prior to sequencing. Following library preparation and high-throughput sequencing, the 130 libraries produced a total of 356 030 150 raw paired-end reads (mean 2 738 693 per library), with an average read length of 245.6 bp and average GC content of 47.5%. Reads were binned by barcode, quality filtered, and assembled into larger contigs. De novo assembly combined with BLASTx-based alignment against viral protein databases yielded 396 902 viral contigs, with an average length of 1 645.4 bp (Supplementary Table S1).

Characterization of viral communities

To examine gut virome composition and distribution across dietary categories, 130 libraries were classified into herbivore (65), omnivore (44), and carnivore (21) groups, followed by comparative community analyses. At the family level, viral contigs and singleton reads were assigned to 18 viral families, including eight dsDNA viral families, seven ssDNA viral families, and three RNA viral families (Figure 2A). Gut viromes were overwhelmingly dominated by bacteriophage lineages, particularly Siphoviridae, Myoviridae, and Microviridae, whereas ssDNA viral families, such as Parvoviridae, Genomoviridae, and Smacoviridae, exhibited patchy distributions. RNA families, including Astroviridae and Picornaviridae, were detected at comparatively low abundance.

Figure 2.

Figure 2

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Comparative analysis of gut viral communities among mammals with distinct feeding habits

A: Circular heatmap displaying normalized viral family abundance expressed as transcripts per million (TPM) across zoo mammal gut viromes. Library IDs are labeled along the outer perimeter (black text). Host species, animal orders, and feeding categories are indicated with corresponding colors (see legend). Viral genome composition is represented by different colored rectangles with taxon names indicated on the right. B, C: PCoA (B) and ANOSIM (C) analysis based on the Bray-Curtis dissimilarity at the species level across herbivore, omnivore, and carnivore groups. Post-hoc pairwise comparisons revealed significant differences between all groups (*: P<0.05). Dietary categories are distinguished by color (see legend). The “between group” category in the boxplot represents pairwise dissimilarities calculated between samples from different groups. D: UpSet plot depicting shared and unique viral species (species-level OTUs) among dietary groups. Connected filled circles indicate intersections, whereas unfilled light gray dots denote non-intersecting sets. Upper bars represent the number of species within the intersection and left bars represent the total number of viral species in each virome set. E: Bar plots showing normalized relative abundance of viral families across the three dietary groups.

Bray-Curtis ANOSIM and PCoA demonstrated statistically significant divergence in viral community composition among dietary groups (P<0.01; Figure 2B, C). Group-specific patterns were further visualized using UpSet and bar plots at species and family levels, respectively (Figure 2D, E). In total, 2 838 viral species-level OTUs were identified across the 130 gut viromes. Herbivores exhibited the highest species-level richness, followed by omnivores and carnivores, and contained the largest number of unique species-level OTUs (709). Enrichment of herbivore-exclusive viral taxa suggested a distinct gut virome signature associated with plant-based diets. Across all dietary categories, 899 viral species-level OTUs were shared, representing 40.9% to 65.2% of detected taxa within each group. At the family level, Parvoviridae displayed pronounced unevenness in distribution, with predominance in the carnivore and omnivore groups. Smacoviridae and Genomoviridae were primarily detected in herbivores, whereas Anelloviridae occurred mainly in carnivore-associated viromes. Collectively, these results support a strong association between feeding habit and gut virome structure in zoo mammals.

Identification of viral sequences

Following contig extension and functional annotation, 1 027 viral sequences encoding conserved hallmark genes were identified (Supplementary Table S2). These sequences represented five major viral groups that exhibited high read abundance and were defined by canonical hallmark domains (Koonin et al., 2006, 2020), including MCP for Microviridae, NS1 for Parvoviridae, Rep for the CRESS-DNA viruses, RdRp domain for Riboviria, and TerL for CrAss-like phages. BLASTx analysis revealed broad sequence divergence, with amino acid identities relative to closest database matches ranging from 20.7% to 99.7%. Genome lengths spanned 1 618 bp to 103 056 bp, with a mean length of 5 354 bp. Complete ORFs corresponding to hallmark genes were extracted from all sequences and used for downstream phylogenetic reconstruction to resolve evolutionary relationships among identified viral lineages.

Phylogenetic analyses of major viral groups

Phylogenetic relationships among dominant viral groups were reconstructed using amino acid sequences of conserved hallmark regions together with representative reference sequences retrieved from GenBank. Within ssDNA bacteriophages, Microviridae constituted the largest lineage, and MCP served as a highly conserved evolutionary marker within this family. A total of 619 MCP sequences identified in the study were analyzed, including 345 from herbivore-associated libraries, 213 from omnivore-associated libraries, and 12 from carnivore-associated libraries (Figure 3A). Phylogenetic topology revealed extensive divergence, with most MCP sequences failing to affiliate with established Microviridae subfamilies. Instead, three deeply branching, unclassified clades emerged with strong nodal support (posterior probability >0.95; bootstrap >70%), sharing only 20.7%–52.8% amino acid identity with closest reference genomes, highlighting substantial uncharacterized genetic diversity among ssDNA bacteriophages. A subset of MCP sequences clustered with recognized subfamilies, including Gokushovirinae, Bullavirinae, Sukshmavirinae, and Alpavirinae. Among these, Gokushovirinae represented the largest assemblage and was dominated by sequences derived from herbivores and omnivores. Host prediction indicated that most microviruses were associated with bacterial family Bacteroidaceae, including genera Bacteroides, Mediterranea, Phocaeicola, and Prevotella (Figure 3B).

Figure 3.

Figure 3

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Phylogenetic relationships and predicted bacterial hosts of Microviridae detected in zoo mammal fecal samples

A: Phylogenetic tree reconstructed from amino acid sequences of MCP from Microviridae. Sequences identified in this study are color-coded according to host animal source (see legend). Major viral clades are annotated around the tree using distinct color schemes. Node support is indicated by purple dots, with dot size proportional to bootstrap support. Only nodes with posterior probability>0.95 and bootstrap support>70% are marked. Scale bar indicates amino acid substitutions. Putative subfamilies denote candidate lineages that have not been formally recognized by the ICTV (Wang et al., 2019). B: Predicted bacterial hosts of microviruses inferred using iPHoP. Only best matches with confidence scores>90 are shown. Bacterial host families are marked with corresponding colors.

In contrast, phylogenetic patterns among other ssDNA viruses displayed closer relationships to previously characterized taxa. Analysis of NS1 sequences from Parvoviridae showed that the 60 identified sequences clustered with known viral species, including 12 from herbivores, 34 from omnivores, and 14 from carnivores (Figure 4). Sequences assigned to Hamaparvovirinae were primarily derived from carnivorous hosts, including lions, tigers, and leopards. Parvovirinae sequences clustered with bocaparvoviruses and protoparvoviruses that infect vertebrates, whereas the remaining sequences clustered with ambidensoviruses associated with diverse invertebrate hosts. Similarly, phylogenetic reconstruction based on Rep sequences from CRESS-DNA viruses indicated that most of the 319 sequences clustered with established viral lineages, including 74 within Genomoviridae, 141 within Smacoviridae, one within Circoviridae, and 103 within known CRESS-DNA clades; only 22 sequences formed two distinct divergent clusters outside known groups (Figure 5). Herbivore-derived CRESS-DNA viruses accounted for the majority of sequences (257/319) and were widely distributed across nearly all clades, whereas carnivore-associated sequences were largely confined to Genomoviridae, a lineage infecting multiple vertebrate hosts.

Figure 4.

Figure 4

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Phylogenetic relationships of Parvoviridae detected in zoo mammal fecal samples

Phylogenetic tree reconstructed from amino acid sequences of NS1 from Parvoviridae. Viral sequences identified in this study are highlighted by colored lines and red-filled sectors. Major viral clades are indicated by corresponding color annotations (see legend). Node support is represented by black dots, with dot size proportional to bootstrap values. Only nodes with posterior probability>0.95 and bootstrap support>70% are shown. Scale bar indicates amino acid substitutions.

Figure 5.

Figure 5

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Phylogenetic relationships of CRESS-DNA viruses identified in zoo mammal fecal samples

Phylogenetic tree reconstructed from amino acid sequences of Rep from CRESS-DNA viruses. Viral sequences identified in this study are highlighted by colored lines. Different viral groups are marked with corresponding colors (see legend). Scale bar indicates amino acid substitutions. Dro-, Drosmacovirus; Drag-, Dragsmacovirus; Bovi-, Bovismacovirus; Un, Unclassified.

RNA viruses detected in zoo mammal gut viromes were segregated primarily into two families based on RdRp phylogeny. Three sequences grouped with Astroviridae (Figure 6A). One sequence recovered from Amur tiger (C_AA081436.1) clustered with avastroviruses associated with poultry, suggesting dietary origin. A sequence from rhinoceros (C_AA081437.1) clustered with mamastroviruses from rodents, sharing >90% RdRp identity, whereas another sequence from otter (C_AA081435.1) remained highly divergent and clustered with unclassified astroviruses from aquatic organisms. The remaining 16 RNA viruses clustered with viral strains belonging to Picornaviridae (Figure 6B), predominantly derived from primates (13/16), and grouped with Enterovirus and Salivirus lineages known to infect humans and other mammals. One sequence from sambar deer (C_AA081444.1) affiliated with Bopivirus, a lineage primarily detected in the mammalian order Artiodactyla, sharing >70% RdRp identity. Two additional sequences recovered from otters clustered with Paavivirinae and Kobuvirus, viral groups associated with fish and humans, respectively.

Figure 6.

Figure 6

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Phylogenetic relationships of RNA viruses identified in zoo mammal fecal samples

A: Phylogenetic tree reconstructed from amino acid sequences of ORF1b from Astroviridae. B: Phylogenetic tree reconstructed from amino acid sequences of RdRp from Picornaviridae. Only nodes with posterior probability>0.95 and bootstrap support>70% are shown. Viral sequences identified in this study are highlighted with colored letters. Different taxonomic clusters were represented by different color-filled rectangles and taxon names are indicated on the right. Each scale bar indicates amino acid substitutions per site. Caphtho-, Caphthovirinae; Paavi-, Paavivirinae; Kodimesa-, Kodimesavirinae.

CrAss-like phages identified in zoo mammals

CrAssphages represent a dominant lineage of dsDNA bacteriophages in human gut viromes. In the present dataset, 10 crAss-like phages were detected in mammalian gut viromes from zoo animals. Phylogenetic reconstruction based on TerL protein sequences demonstrated that all recovered crAss-like sequences clustered within previously proposed Alpha, Beta, and Delta subfamilies (Figure 7A). Two sequences derived from carnivores were grouped with crAssphages within genus I. Four sequences obtained from white kangaroos and monkeys were assigned to genus VI. The remaining four sequences from herbivores, including golden monkeys, sloths, and takins, fell within genera VII and X of the Delta clade. Relative abundance profiles across host viromes indicated host-associated distribution patterns (Figure 7B). For instance, sequence C_AA081131.1 was broadly detected among members of the Cercopithecidae family, whereas sequences C_AA081132.1, C_AA081135.1, and C_AA081137.1 were largely restricted to the host categories from which they were identified. Sequence C_AA081129.1, initially detected in white kangaroo, was also observed in golden monkey samples, and sequence C_AA081133.1 detected in golden monkeys was additionally identified in takin samples.

Figure 7.

Figure 7

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Phylogenetic relationships and relative abundance of crAss-like phages detected in zoo mammal fecal samples

A: Phylogenetic tree reconstructed from amino acid sequences of TerL from crAss-like phages. Representative strains from 10 proposed candidate genera are included and labeled with Roman numerals. Only nodes with posterior probability>0.95 and bootstrap support>70% are shown. Viral sequences identified in this study are color-coded by host animal groups (see legend). Animal silhouettes indicate host categories in which each crAss-like phage was detected. Scale bar indicates amino acid substitutions. Different taxonomic clusters are represented by different color-filled rectangles and taxon names are indicated on the right. B: Bubble chart showing relative abundance of 10 identified crAss-like phages across all host gut viromes analyzed. Bubble size and shape correspond to relative abundance, calculated as transcripts per million (TPM). Bubbles outlined in black denote host viromes in which each phage was first identified. Host animal families are indicated by corresponding colors (see legend).

Five crAss-like phages with near-complete genomes belonging to Beta and Delta subfamilies were further compared with three reference human-associated crAssphages at the whole-genome scale (Figure 8A). Genomes C_AA081129.1 and C_AA081132.1 shared the highest intergenomic similarity (62.9%), whereas similarity among the remaining genome pairs within the Beta subfamily ranged from 15.0% to 29.1%. Within the Delta subfamily, similarity between C_AA081130.1 and reference genome NC_055873 reached 35.5%, while genome C_AA081134.1 exhibited the lowest similarity relative to other sequences. The genome structures of the crAss-like phages are presented in Figure 8B. Genome lengths of the five identified crAss-like phages ranged from 91 220 bp to 103 056 bp. All genomes displayed conserved modular architecture comprising transcription, assembly, and replication modules, with stabilization protein, portal protein, and terminase large subunit genes retained across all genomes. Despite conservation of core functional modules, overall nucleotide similarity across genomes was limited, with the exception of the closely related pair C_AA081129.1 and C_AA081132.1. Host prediction analyses suggested associations with common intestinal bacterial genera, including Prevotella, Lachnospira, and Eubacterium (Figure 8B). Collectively, these findings reveal substantial previously uncharacterized diversity of crAss-like phages in mammalian gut viromes.

Figure 8.

Figure 8

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Genome organization and intergenomic similarity of complete crAss-like phage genomes identified in the study

A: VIRIDIC heatmap displaying intergenomic similarity values (right half) and alignment metrics (left half). Right panel shows pairwise nucleotide similarity percentages derived from BLASTn comparisons. Left panel presents three alignment indicators per genome pair, listed from top to bottom: aligned fraction for genome 1 (row), length ratio between genome 1 and genome 2, and aligned fraction for genome 2 (column). B: Whole-genome comparisons of Beta and Delta crAss-like phage genomes. Genomes identified in this study (highlighted in red) were compared with three reference sequences. Regions of tBLASTx homology are indicated with corresponding colors (see legend). Predicted bacterial host genera inferred by iPHoP are shown in brackets beneath accession numbers.

Characterization of ARGs

Profiles of ARGs associated with bacteriophages were compared across herbivore, omnivore, and carnivore groups. Tetracycline resistance genes constituted the predominant class across all three dietary categories. Multidrug resistance genes were primarily detected in herbivore and omnivore viromes, whereas aminoglycoside resistance genes were strongly represented in carnivore-associated samples (Figure 9A). PCoA revealed significant differences in ARG abundance patterns among dietary groups (P<0.001; Figure 9B). A total of 219 distinct ARGs were identified across zoo mammal gut viromes, with 75 genes shared among all groups. Herbivore and omnivore viromes exhibited the greatest overlap in ARG composition (17.8%), whereas omnivores possessed the highest proportion of unique resistance genes (15.1%) (Figure 9C). Among detected ARGs, rpoB2 was the most abundant gene in the herbivore and omnivore groups. Tetracycline resistance genes, including tetQ, tetW, and tetP, were broadly distributed across all dietary categories (Figure 9D). Overall, herbivore and omnivore viromes demonstrated more similar ARG composition patterns relative to carnivores.

Figure 9.

Figure 9

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Distribution of antibiotic resistance genes (ARGs) across gut viromes of zoo mammals with distinct feeding habits

A: Circos plots showing relative abundance of the top 10 ARG drug classes across herbivore, omnivore, and carnivore groups. B: PCoA of ARG composition across dietary categories, with groups distinguished by color. C: Venn diagram depicting shared and unique ARGs among the three dietary groups. D: Heatmap showing exponential-scale abundance of the top 30 ARGs across dietary categories. Drug classes are indicated by corresponding colors (see legend).

DISCUSSION

Frequent and intensifying interactions between humans and animals have created ecological conditions that facilitate zoonotic spillover (Yadana et al., 2022). Although the global burden of zoonotic diseases is widely recognized, comparatively limited attention has been directed toward elucidating the ecological and virological determinants that govern emergence and transmission (Kelly et al., 2017). In the present study, comprehensive profiling of 1 145 fecal samples spanning eight mammalian orders across zoological institutions in Southwest, Northeast, and Southeast China provided a broad view of gut virome composition in captive mammals and enabled assessment of viral lineages with potential zoonotic relevance. Zoological settings represent high-risk environments for cross-species viral exchange, given the close spatial proximity among diverse animal taxa and routine human contact. Results revealed marked differences in gut virome structure among herbivores, omnivores, and carnivores, indicating that dietary ecology strongly shapes viral community composition. More than half of emerging infectious diseases are zoonotic in origin, and many are linked to animal-derived viruses transmitted through fecal-oral pathways (Ryu et al., 2017). In densely populated regions with frequent human-animal interactions, viruses harbored by captive wildlife may represent an underrecognized source of human exposure.

Bacteriophages constitute a dominant component of gut viral communities and are recognized as key regulators of microbial ecosystem dynamics (Cadwell, 2015). The current study indicated that dsDNA bacteriophages, particularly Siphoviridae, predominated across zoo mammal viromes, while Microviridae accounted for a substantial proportion of ssDNA bacteriophages. A total of 619 complete Microviridae sequences were recovered. Phylogenetic reconstruction revealed that the majority clustered within deeply divergent, unclassified Microviridae lineages, sharing less than 60% MCP amino acid identity with reference taxa, underscoring extensive uncharacterized genetic diversity. Host prediction analyses indicated that most microviruses were associated with the bacterial family Bacteroidaceae, a prevalent component of human gut microbiota (Zafar & Saier, 2021). Consistent with zoo mammal gut viromes, Microviridae in the human gut exhibit high genetic diversity, particularly among lineages infecting Bacteroidaceae (Shkoporov et al., 2019), and are predominantly associated with bacterial genera Bacteroides and Prevotella, highlighting overlap between human- and animal-associated Microviridae (Shkoporov & Hill, 2019). Such phage-bacterium interactions may influence Bacteroidaceae population structure and modulate microbial function, including polysaccharide metabolism and immune regulation (Cadwell, 2015; Fujimoto et al., 2022). In captive mammals, particularly species maintained on specialized diets, these dynamics may have implications for nutritional physiology and health status (Avellaneda-Franco et al., 2023).

CrAssphages represent one of the most abundant viral lineages in the human gut and are considered a core component of the human gut phageome (Dutilh et al., 2014). Despite their prevalence, little is known yet about the biological characteristics and evolutionary origins of crAssphages, leading to their historic categorization as viral dark matter (Dutilh, 2014; Ogilvie & Jones, 2015). Complete crAssphage genomes recovered from human fecal viromes are typically reconstructed as circular or terminally redundant ssDNA molecules of approximately 97 kb (Yutin et al., 2018). Previous phylogenetic analyses have organized human-associated crAssphages into four distinct subfamilies comprising 10 genera (Guerin et al., 2018). In the present study, 10 previously unreported crAss-like phages were identified in zoo mammal gut viromes, all clustering within established crAssphage subfamilies defined in human-associated datasets. Despite the ubiquitous nature of crAssphages in the human gut, several crAss-like phages detected in zoo mammals exhibited apparent host-category specificity and were predominantly confined to the animal groups from which they were recovered. The evolutionary relationship between these animal-associated crAss-like phages and human-associated lineages remains unresolved and warrants further investigation.

Widespread and indiscriminate antibiotic use has accelerated the emergence of resistant bacterial populations, particularly in densely populated environments (Calero-Cáceres & Balcázar, 2019). ARGs can be packaged within bacteriophages and disseminated through horizontal gene transfer across viral communities (von Wintersdorff et al., 2016). Viral particles exhibit substantial environmental stability in soil, water, and fecal matrices, enabling the transport of ARGs from zoological enclosures into surrounding ecosystems. Zookeepers, visitors, and nearby livestock may subsequently acquire ARG-bearing phages, facilitating the transmission of resistance determinants into human-associated microbiomes (Calero-Cáceres et al., 2019). Zoological institutions in China typically maintain high animal densities alongside frequent public contact, creating conditions conducive to ARG dissemination among animals and humans. In this study, 219 ARGs were identified in zoo mammal gut viromes, with tetracycline-associated resistance genes representing the dominant class, consistent with extensive tetracycline usage and broad environmental dissemination of corresponding ARGs. Dietary ecology appeared to influence ARG composition, with carnivores exhibiting reduced representation of multidrug resistance genes and enrichment of aminoglycoside resistance relative to herbivores and omnivores. These patterns underscore the need for evidence-based antibiotic stewardship and strengthened regulatory oversight in zoological settings. Integration of veterinary treatment records with zoo management practices in future investigations will be essential for resolving causal links between antibiotic exposure and ARG prevalence.

CRESS-DNA viruses were detected at high abundance across zoo mammal fecal samples, particularly within herbivorous hosts. Eukaryotic-associated CRESS-DNA viruses have been classified by the International Committee on Taxonomy of Viruses (ICTV) into six families: Bacilladnaviridae, Nanoviridae, Genomoviridae, Geminiviridae, Circoviridae, and Smacoviridae (Varsani & Krupovic, 2018). The CRESS-DNA lineages detected in the present study were dominated by Smacoviridae and Genomoviridae, viral families broadly distributed among mammals and implicated in intestinal colonization in humans (Díez-Villaseñor & Rodriguez-Valera, 2019; Krupovic et al., 2016; Varsani & Krupovic, 2018). Genomoviruses have been reported from diverse sources, including fungal hosts (Yu et al., 2010), fecal material from multiple animal species (Sikorski et al., 2013; Steel et al., 2016) and plant-derived samples (Fontenele et al., 2020). Elevated abundance of CRESS-DNA viruses in herbivores, particularly within Artiodactyla, may reflect high intake of plant material and associated microbial communities. Conversely, another ssDNA virus family, Parvoviridae, was primarily identified in omnivore and carnivore groups, with primates harboring the greatest parvovirus diversity. Members of Parvoviridae infect both vertebrate and invertebrate hosts, and parvoviruses recovered from goats and sheep feces have previously shown close evolutionary relationships with human-associated lineages (Reuter et al., 2022). Carnivores, including lions, tigers, and leopards, represented the sole source of Hamaparvovirinae in this dataset, while Parvovirinae sequences clustered with bocaparvoviruses and protoparvoviruses known to infect humans. Given that zoological environments bring domestic animals, wildlife, and humans into close proximity, viral lineages with demonstrated affinity for mammalian hosts warrant heightened surveillance due to potential zoonotic risk.

High mutation rates and rapid evolutionary turnover are defining features of RNA viruses, facilitating adaptive flexibility and increasing the likelihood of successful cross-species transmission (Woolhouse et al., 2012). Within this context, the Picornaviridae family contains a diverse assemblage of pathogens capable of infecting both humans and animals, with documented zoonotic potential (Palombieri et al., 2022). In the present study, Picornaviridae genomes were detected across multiple dietary guilds in zoo animals, indicating a broad host range. Most sequences originated from primates and were clustered within the Enterovirus and Salivirus lineages known to infect humans and other mammals, underscoring potential bidirectional transmission risk. In addition, three sequences closely clustered with viruses belonging to Astroviridae, a family associated with mammalian gastroenteritis characterized by diarrhea, vomiting, and fever (Johnson et al., 2017). Although sampled animals exhibited no overt clinical symptoms, subclinical infection cannot be excluded. In addition, the absence of TEM imaging precluded direct visualization of viral particles; however, bioinformatic analyses supported the presence of viral sequences. Integration of ultrastructural validation and host receptor-based functional assays, including pseudovirus entry assays and receptor-blocking approaches in primate cell models, will be essential for refining mechanistic understanding of spillover susceptibility. Despite these limitations, our results reinforce the necessity of continuous virome surveillance in zoological environments and adjacent ecosystems through viral metagenomics to enable early detection of zoonotic threats and timely mitigation.

In conclusion, given the close contact among animal caretakers, visitors, and captive wildlife, routine gut virome monitoring represents a critical component of zoonotic risk management in zoological settings. Comprehensive characterization of viral communities in zoo mammals across China expands current understanding of mammalian viromes and highlights substantial hidden viral diversity with potential public health relevance. These findings support implementation of longitudinal surveillance frameworks and advocate coordinated, interdisciplinary research strategies to strengthen preparedness against emerging and re-emerging zoonotic diseases.

SUPPLEMENTARY DATA

Supplementary data to this article can be found online.

zr-47-2-606-S1.zip (227.8KB, zip)

Acknowledgments

COMPETING INTERESTS

The authors declare that they have no competing interests.

AUTHORS’ CONTRIBUTIONS

W.Z., T.L.S., X.L.W., and C.L.Z. designed the study and methods. J.L., H.N.W., and C.M.W. constructed the libraries. J.L., J.X., C.K.I., W.L., S.Y.N., P.W., Q.S., S.X.Y., L.K.J., Y.W.L., X.C.W., and W.Z. completed the data analysis. The first draft of this manuscript was prepared by J.L. All authors read and approved the final version of the manuscript.

ACKNOWLEDGMENTS

The authors thank Chengdu Zoo, Chongqing Zoo, Dragon Dream Animal World, Hangzhou Zoo, Suzhou Shangfangshan Forest Zoo, Ningbo Youngor Zoo, Dalian Forest Zoo, and Harbin North Forest Zoo for sample support.

Funding Statement

This work was supported by the National Key Research and Development Programs of China (2023YFD1801301, 2022YFC2603801) and National Natural Science Foundation of China (82341106)

Contributor Information

Cheng-Ling Zhou, Email: 18762340015@njmu.edu.cn.

Xiao-Long Wang, Email: yttuhh@yeah.net.

Wen Zhang, Email: z0216wen@yahoo.com.

Tong-Ling Shan, Email: shantongling@shvri.ac.cn.

DATA AVAILABILITY

The raw sequence reads analyzed in this study are available from the China National Center for Bioinformation (CNCB) (CNCB-NGDC Members and Partners, 2024) Genome Sequence Archive (GSA) (BioProject PRJCA027173; https://ngdc.cncb.ac.cn/gsa/browse/CRA019794), with BioSample accession numbers listed in Supplementary Table S1. All viral sequences with hallmark genes identified in this study were deposited in GenBase (Bu et al., 2024) (accession number in Supplementary Table S2). Sequencing data were also uploaded to the NCBI database (accession number PRJNA1335397) and Science Data Bank (https://doi.org/10.57760/sciencedb.j00139.00267).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data to this article can be found online.

zr-47-2-606-S1.zip (227.8KB, zip)

Data Availability Statement

The raw sequence reads analyzed in this study are available from the China National Center for Bioinformation (CNCB) (CNCB-NGDC Members and Partners, 2024) Genome Sequence Archive (GSA) (BioProject PRJCA027173; https://ngdc.cncb.ac.cn/gsa/browse/CRA019794), with BioSample accession numbers listed in Supplementary Table S1. All viral sequences with hallmark genes identified in this study were deposited in GenBase (Bu et al., 2024) (accession number in Supplementary Table S2). Sequencing data were also uploaded to the NCBI database (accession number PRJNA1335397) and Science Data Bank (https://doi.org/10.57760/sciencedb.j00139.00267).

Articles from Zoological Research are provided here courtesy of Editorial Office of Zoological Research, Kunming Institute of Zoology, The Chinese Academy of Sciences

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