Global Characterization of the Avian Gut Virome Reveals Extensive Viral Diversity and Functional Implications

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However, its taxonomic and functional diversity remains poorly characterized due to the absence of a dedicated reference database. This limitation hampers our understanding of the complex interactions among viruses, their bacterial hosts, and the overarching avian host, as well as viral contributions to gut microbial ecology. Results To address this gap, we developed the Avian Virome Database (AvianViromeDB) by integrating 2,692 gut metagenomic samples from poultry and wild birds. This effort yielded 252,752 viral contigs, which are clustered into 61,608 high-quality, species-level viral operational taxonomic units (vOTUs). Taxonomic analysis revealed that 99.05% of these vOTUs belonged to the class Caudoviricetes , yet only 4.69% could be assigned to known viral families—suggesting over 95% likely represent novel viral lineages. Prediction of prokaryotic hosts indicated that these viruses primarily target core gut microbiota, particularly Bacillota and Bacteroidota , both central to carbohydrate metabolism. Functional annotation uncovered tens of thousands of auxiliary metabolic genes (AMGs), with enrichments in glycolysis, amino acid metabolism, and nucleotide biosynthesis pathways. Conclusion These findings demonstrate that avian gut viruses may modulate microbial communities both through direct lysis of their bacterial hosts (“top-down” control) and by altering host metabolism via AMGs (“bottom-up” modulation). The resulting high-quality genome catalog reveals the remarkable diversity and functional potential of the avian gut virome, offering a valuable resource for future research into avian microbial ecology and the intricate interplay between viruses, bacteria, and their avian hosts. The AvianViromeDB is publicly accessible at: https://phagebyte.github.io/avianviromedb . Avian Metagenomics Gut virome Microbiome Bacteriophage Antibiotic resistance genes Auxiliary metabolic genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The gut microbiome is a complex microbial ecosystem, encompassing bacteria, viruses, fungi, and other prokaryotic organisms, playing a crucial role in digestion, nutrient utilization, immune regulation, and disease prevention in humans and animals [ 1 – 3 ]. Despite often being overlooked, the gut harbors a highly diverse and stable viral community, predominantly composed of bacteriophages (or phages) [ 4 , 5 ] that specifically infect bacteria. Phages exert profound effects on the composition and function of gut bacterial communities through multiple mechanisms, including horizontal gene transfer (e.g., antibiotic resistance genes (ARGs)) to disseminate genetic material [ 6 ], the delivery of auxiliary metabolic genes (AMGs) that enhance bacterial stress tolerance or metabolic potential [ 7 , 8 ], and integrating into bacterial genomes as prophages (temperate) or directly lysing bacteria (virulent) [ 9 , 10 ]. By influencing both community structure and microbial metabolism, these phage-driven processes play a vital role in maintaining gut homeostasis and ensuring host physiological performance, which is particularly important for animal health and production outcomes [ 11 ]. Although several databases have been established to identify viromes from various sources, such as the human gut [ 12 – 14 ], marine environments [ 15 ], plants [ 16 ], and other animals [ 17 – 19 ], the virome structure in birds remains largely unexplored and is often considered ‘viral dark matter’ in comparison to these environments. A comprehensive reference database of viral genomes is urgently needed to better characterize viral communities within the avian gut microbiota and to enable genome-resolved metagenomic studies focused on the interactions between viruses and their bacterial hosts within the context of the avian host. As monophyletic vertebrates, avian occupy a wide range of ecological niches [ 20 , 21 ] across the globe due to their diverse physiology and broad geographic distribution [ 22 ], playing vital roles in natural ecosystems. Similar to other animals, the avian gut harbors a large number of phages, many of which remain poorly characterized, and their roles in shaping the gut microbial communities and influencing host health are not yet fully understood. To date, research on the avian gut microbiome has primarily focused on poultry, generating extensive metagenomic sequencing data, particularly from chickens [ 23 – 28 ], ducks [ 29 – 31 ], and geese [ 32 – 34 ]. These datasets enable scientists to capture both intracellular and extracellular viral sequences through computational tools [ 35 , 36 ], including integrated prophages, without being affected by whole-genome amplification biases [ 37 ]. However, due to thousands of years of artificial selection, poultry has diverged from wild birds and may not fully represent all avian species. Nevertheless, the insights and methodologies developed through poultry studies provide a valuable foundation for investigating the gut virome diversity of wild bird species. In this study, we utilized ViroProfiler [ 38 ], a newly developed containerized bioinformatic pipeline, to ensure computational reproducibility. Using this pipeline, we analyzed 2,692 avian gut and fecal metagenomic samples, which culminated in the creation of the Avian Virome Database (AvianViromeDB) – a global catalog of viral genomes derived from these diverse avian sources. Addressing the notable research bias wherein approximately 97.65% of available avian gut metagenomes originate from poultry, leaving wild bird microbiomes comparatively under-characterized, AvianViromeDB was specifically designed to encompass data from both agriculturally significant avian species (e.g., poultry) and a curated selection of wild bird populations. This comprehensive database currently comprises 61,608 high-quality viral operational taxonomic unit (vOTU) genomes, assembled from these manually curated whole metagenomic samples sourced from 28 distinct studies (Fig. 1 ; Table S1 ). Analysis of these viral genomes highlights the remarkable diversity and complexity of the avian gut virome. Furthermore, comparison with existing public viral datasets reveals that 95.31% of these vOTUs represent novel viral entities. Collectively, these genomes substantially expand the known viral diversity within the avian gut microbiome and provide critical insights into interactions between viruses and their bacterial hosts. We anticipate that AvianViromeDB will serve as an invaluable foundational resource for future investigations into the avian gut virome, its ecological roles, and its implications for avian health and ecosystem dynamics. Materials and Methods Metagenomic dataset collection and curation To construct a comprehensive catalog of the avian gut virome, we sourced 2,692 publicly available whole-metagenome sequencing (WMS) datasets from avian gut content and fecal samples, originating from 28 published studies (detailed in Table S1 ). These datasets represented a diverse range of avian hosts, including chicken ( Gallus gallus domesticus ), duck (e.g., Anas platyrhynchos domesticus ), goose (e.g., Anser anser domesticus ), quail ( Coturnix coturnix ), great egret ( Ardea alba ), pigeon ( Columba livia ), and a collective group of migratory bird species (refer to Table S1 for species details where available). We specifically selected WMS data to enable the recovery of viral genomes directly from bulk DNA, thereby facilitating the detection of both lytic viruses and integrated proviruses without the biases associated with virus-like particle (VLP) enrichment protocols. This approach also provides the necessary genomic context for subsequent virus-host association predictions [ 39 ]. The curated raw sequencing data totaled approximately 2.5 TB. Bioinformatic pipeline for genome assembly and virus identification The raw WMS reads were processed using our previously described ViroProfiler pipeline (version 0.2.5) [ 38 ]. Key steps relevant to this study are outlined below. First, raw reads underwent quality control, including adapter trimming and low-quality read filtering, using fastp (version 0.23.2) [ 40 ] with default parameters. High-quality reads from each sample were then independently assembled de novo into contigs using MEGAHIT (version 1.2.9) [ 41 ] with default settings. Subsequently, contigs longer than 5 kb, or those identified as circular by VirSorter2 (version 2.2.3) [ 42 ], were selected from all individual sample assemblies and pooled to create a comprehensive contig set. To identify and remove potential host DNA contamination from proviral regions within this set, CheckV (version 1.0.1) [ 43 ] was employed for provirus detection and trimming. The resulting cleaned contigs were then dereplicated to generate a non-redundant catalog of viral sequences. Specifically, pairwise average nucleotide identity (ANI) was calculated among these contigs using the clustering algorithm within CheckV, applying thresholds of ≥ 95% ANI and ≥ 85% alignment fraction (AF) to define species-level viral operational taxonomic units (vOTUs). The longest contig within each vOTU cluster was selected as its representative sequence. These representative vOTU sequences were subjected to viral identification using a multi-tool consensus approach. Three distinct viral identification tools were employed: VirSorter2 [ 42 ] and VIBRANT (version 1.2.1) [ 44 ], which were run as part of the ViroProfiler pipeline, and geNomad (version 1.8.0) [ 45 ], which was executed separately due to its exclusion from ViroProfiler. All tools were run with their respective default parameters. A contig was confirmed as viral if identified as such by at least two of these three tools. Finally, the quality of all confirmed vOTUs, including genome completeness and contamination ratios, was assessed using CheckV (version 1.0.1) [ 43 ]. Only vOTUs classified by CheckV as “Complete”, “High-quality”, or “Medium-quality” were retained for downstream analyses and inclusion in AvianViromeDB. Taxonomic annotation, clustering, and phylogenetic analysis of vOTUs Taxonomic classification of the identified vOTUs was performed using a combination of geNomad (version 1.8.0) [ 45 ] and VITAP (version 1.7.1) [ 46 ], referencing the viral taxonomy from the International Committee on Taxonomy of Viruses (ICTV) [ 47 ]. GeNomad employs a hybrid strategy, integrating alignment-free and gene-based methods with a comprehensive dataset of taxonomically informed hidden Markov model (HMM) protein profiles. It assigns a taxonomic classification to each gene based on these markers and subsequently aggregates these assignments using a weighted majority vote to generate a consensus lineage for each sequence. VITAP utilizes alignment-based techniques coupled with graph-based analysis for classifying viral sequences. Given VITAP’s potential for more specific annotations (often to genus or species level with high precision), its taxonomic assignments were prioritized. For vOTUs not annotated by VITAP, classifications provided by geNomad were used. While geNomad frequently assigned taxonomy at higher ranks (e.g., class level), it provided valuable classifications for a larger number of vOTUs. This combined approach enabled taxonomic assignment for approximately 28,000 vOTUs. To further investigate relationships among the identified vOTUs, compare them with known viral genomes, and identify potential novel viral clusters, gene-sharing network analysis was conducted using vConTACT3 ( https://bitbucket.org/MAVERICLab/vcontact3/src/master/ ). This analysis focused on vOTUs meeting specific criteria: a minimum genome length of 10 kb and a quality designation of “Complete” or “High-quality” as determined by CheckV [ 43 ]. These selected vOTUs were analyzed alongside reference viral genomes from the NCBI Virus RefSeq database (version 223) [ 48 ]. vConTACT3 groups viral genomes into viral clusters (VCs) based on shared protein clusters (PCs). These VCs represent groups of related viral genomes, offer insights into potential genus-level relationships, and highlight vOTUs that may constitute novel taxa. The vConTACT3 analysis outputs separate UPGMA-based phylogenetic trees for major viral groups. Given our focus on prokaryotic phages, which constituted the vast majority of viruses identified in this study, we only used the phylogenetic tree for the Duplodnaviria realm for subsequent visualization with iTOL [ 49 ]. Functional annotation, prokaryotic host prediction, and lifestyle classification of vOTUs Protein-coding genes (CDS) within the vOTU sequences were predicted using Prodigal (version 2.6.3) [ 50 ] in metagenomic mode. Functional annotation of these predicted CDS was performed using eggNOG-mapper (version 2.1.12) [ 51 ] against the eggNOG database (version 5.0) [ 52 ]. Putative auxiliary metabolic genes (AMGs) were identified using a combined approach to maximize detection sensitivity. This involved predictions from both VIBRANT (version 1.2.1) [ 44 ] and DRAM-v (version 1.3) [ 53 ] with their default settings. Antimicrobial resistance genes (ARGs) potentially encoded by the vOTUs were identified using Abricate (version 1.0.1, https://github.com/tseemann/abricate ). Predicted genes were queried against multiple databases: the Comprehensive Antibiotic Resistance Database (CARD, version 3.2.9) [ 54 ], Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT, version V6) [ 55 ], NCBI AMRFinderPlus (version v3.10.1) [ 56 ], ResFinder (version 4.1.3) [ 57 , 58 ], and the Virulence Factor Database (VFDB, version 2019) [ 59 ]. Hits were considered significant if they met stringent criteria of ≥ 95% nucleotide identity and ≥ 75% query coverage (Tables S5-S8). Putative bacterial hosts for the vOTUs were predicted using iPHoP (version 1.3.3) [ 60 ] with default parameters. Phage lifestyle (i.e., temperate or virulent) was predicted using BACPHLIP (version 0.9.6) [ 61 ] with default settings. Results Characterization of the avian gut virome genome catalog Analysis of 2,692 whole-metagenome sequencing datasets from gut and fecal samples of diverse avian species across multiple geographic regions (Fig. 1 ; Table S1 ) was performed to establish a comprehensive avian virome resource (Avian Virome Database, AvianViromeDB). The dataset was predominantly composed of samples from chicken (n = 2,043), duck (n = 459), and goose (n = 123), with smaller contributions from quail, great egret, migratory birds, and pigeon (Fig. 2 D). Following the bioinformatic pipeline described in the Methods section, our analysis yielded 61,608 non-redundant viral operational taxonomic units (vOTUs), which constitute the core of AvianViromeDB (Table S2 ). To assess the quality and integrity of this catalog, each vOTU was evaluated for genome completeness using CheckV. This assessment revealed that 5,829 vOTUs (9.5% of the total) were classified as “complete” genomes (Fig. 2 B, C). Further characterization of the vOTU genomes showed a wide distribution of lengths, with a significant portion ranging from 10 to 20 kb (Fig. 2 A). We categorized the vOTUs into five size bins and observed a strong positive correlation between genome length and quality. Notably, vOTUs in the > 40 kb size bin exhibited the highest proportion of complete genomes (29.79%) compared to the other size groups (Fig. S1 ). This large-scale assembly and quality assessment provides a robust foundation for detailed downstream analyses of avian gut virome diversity and function. Avian host specificity and lifestyle predominance of vOTUs Among the avian hosts, samples from chickens yielded the highest number of vOTUs (n = 46,494), followed by ducks (n = 13,795) and geese (n = 3,260) (Fig. 2 D). The number of viruses identified in different avian species correlated with the respective sample sizes. Furthermore, viral genome sizes and taxonomic compositions varied among avian hosts, suggesting species-specific characteristics of the avian virome. Analysis of vOTU distribution across different hosts (chicken, duck, goose, and a combined group of other bird species) indicated that a high proportion, 90.10% (n = 55,511), of vOTUs exhibited host specificity, infecting only a single host type, with a smaller fraction shared between two or three host groups (Fig. 3 A). Using protein sequence similarity-based clustering (as described in Methods), we identified 33,076 distinct viral clusters (VCs) (Fig. 3 B; Table S3 ). Chickens, ducks, geese, and other birds harbored 17,773, 4,590, 3,673, and 1,314 VCs, respectively (Fig. S2 ). Similarly, 82.69% (n = 27,305) of VCs were associated with a single host group. Examination of the top 10 VCs, ranked by the number of vOTUs they contained (Fig. 3 C), revealed that 8 of these VCs were present in more than two host groups, indicating that these diverse VCs possess considerable adaptability across different avian hosts. The majority of phages within these top VCs had genome sizes ranging from 1 to 50 kb, consistent with typical bacteriophage genome sizes [ 62 , 63 ]. Prediction of phage lifestyles indicated that virulent phages were significantly more numerous than temperate phages (Fig. 3 D). This is consistent with the fact that virulent phages predominantly drive bacterial lysis in the gut environment [ 64 , 65 ], which could be due to virulent phages having broader ecological adaptability or higher enrichment potential. Taxonomic profiling reveals vast undiscovered viral lineages Taxonomic annotation of the 61,608 high-quality vOTUs in AvianViromeDB revealed that the vast majority (99.05%) were classified within the class Caudoviricetes , which primarily encompasses tailed bacteriophages (Fig. 4 A, C; Table S4 ). Despite this high-level classification, a striking observation was the limited resolution at lower taxonomic ranks. Only 4.69% of all vOTUs could be assigned to a known viral family, with Microviridae , Peduoviridae , and Drexlerviridae being among the more frequently identified families (Fig. 4 B, C). This indicates that over 95% of the vOTUs likely represent novel viral families or genera not yet characterized in existing databases. Even among the vOTUs with complete genomes, a substantial portion remained unclassified at the family level, underscoring the significant divergence of these avian gut viruses from currently known phages and highlighting the vast uncharted diversity within this ecosystem. These findings are consistent with observations from other environmental virome studies [ 66 ], pointing to substantial gaps in our current understanding of global viral taxonomy. Avian phages predominantly target core gut microbes To gain insights into the ecological roles of the identified viruses, we predicted their prokaryotic hosts using iPHoP [ 60 ], focusing on 8,853 high-quality vOTUs (defined in Methods as having CheckV completeness > 90% and contamination < 5%). The analysis revealed that avian phages identified in this study are predicted to infect bacteria across 38 phyla, predominantly targeting members of the core gut microbiota (Fig. 5 A, B). The most frequently predicted host phyla included Bacillota_A (targeted by n = 3,517 vOTUs), Bacillota (targeted by n = 2,104 vOTUs), Bacteroidota (targeted by n = 1,466 vOTUs), Pseudomonadota (targeted by n = 800 vOTUs), and Actinomycetota (targeted by n = 522 vOTUs), which play key roles in carbohydrate metabolism, gut homeostasis, and immune regulation [ 67 ]. At the genus level, prominent predicted hosts included Phocaeicola and Bacteroides (phylum Bacteroidota ), Lactobacillus and Limosilactobacillus (phylum Bacillota ), Mediterraneibacter (phylum Bacillota_A ), and Escherichia (phylum Pseudomonadota ) (Fig. 5 D). Among the vOTUs with predicted prokaryotic hosts, 69.41% were classified as specialist bacteriophages, predicted to infect only a single bacterial genus, while the remaining 30.59% were generalists, capable of infecting multiple prokaryotic genera (Fig. 5 C). Significant variations in viral genome sizes and GC content were observed across different predicted host phyla (Fig. 5 A). For instance, phages predicted to infect Bacteroidota , Bacillota_C , and Pseudomonadota generally had larger genome sizes (median: 45.85–56.97 kb) compared to those targeting Actinomycetota and Thermoplasmatota (median: 34.62–38.04 kb). Prokaryotic host specificity also varied at the genus level; phages targeting Lactobacillus , Limosilactobacillus , and Ligilactobacillus were predominantly specialists (93.96–98.47%), whereas those infecting Phocaeicola , Bacteroides , and Mediterraneibacter were mostly generalists (84.74–92.64%) (Fig. 5 D). Furthermore, predicted phage lifestyles differed across host taxa. Phages infecting Bifidobacterium , Lachnospira , Streptococcus , Acutalibacter , and Parabacteroides were primarily predicted to be virulent (73.53–80.92%), while those targeting Fusicatenibacter , Mediterraneibacter , Ligilactobacillus , and CAG-273 were predominantly temperate (75.00–81.08%) (Fig. 5 D). Phylogenetic analysis uncovers potential novel viral lineages A phylogenetic tree constructed from 7,569 representative high-quality viral genomes from AvianViromeDB illustrated the broad diversity within the avian gut virome (Fig. 6 A). The majority of these phages clustered within two major taxonomic groups: Caudoviricetes (tail phages) and Tectiliviricetes (twin-portal phages). At the family level, while some vOTUs were classified into known families such as Autographiviridae , Drexlerviridae , Demerecviridae , Herelleviridae , Rountreeviridae , Tectiviridae , and Straboviridae , a significant portion remained unclassified, consistent with the taxonomy classification results. To further explore evolutionary relationships and identify novel lineages, a gene-sharing network was constructed using these 7,569 vOTUs along with reference viral genomes from NCBI Virus RefSeq database (Fig. 6 B). Most metagenome-assembled vOTUs from AvianViromeDB showed interconnections with reference phages, supporting their viral nature. Following an edge-weight filtering step to reduce network complexity and enhance interpretability (as described in Methods), 220 distinct viral clusters (VCs) were identified. Notably, one prominent VC (designated as VCX) was composed exclusively of vOTUs from AvianViromeDB and shared no discernible gene content with any NCBI reference phages (Fig. 6 C). This suggests the presence of a potentially novel viral lineage previously unrepresented in public databases. Further analysis of this unique cluster revealed that multiple vOTUs within it were predicted to infect bacteria belonging to the order Oscillospirales (phylum Bacillota , class Clostridia ). The size of the nodes in Fig. 6 C, representing these vOTUs, indicates their detection across multiple distinct avian hosts. Avian gut virome harbors a repertoire of clinically relevant ARGs Our investigation into AvianViromeDB revealed that the avian gut virome serves as a notable reservoir for diverse antibiotic resistance genes (ARGs). Employing a stringent identification pipeline (as detailed in Methods, Tables S5-S8), we identified seven distinct ARGs within the viral contigs (Table 1 ). These genes encode resistance mechanisms primarily involving enzymatic modification of antibiotics and target site alteration. Among the identified ARGs, those conferring resistance to macrolides, lincosamides, and streptogramin B (MLSB antibiotics) were particularly prevalent. The gene erm(X) was the most frequently detected ARG (n = 25 occurrences), predominantly identified in vOTUs derived from chicken metagenomes. Other members of the erm family, namely erm(Y) , erm(G) , and erm(B) , which also mediate MLSB resistance through methylation of the 23S rRNA [ 68 ], were also present. Further contributing to lincosamide resistance, the lnu(C) gene, responsible for enzymatic drug modification [ 69 ], was detected three times. Resistance determinants against other critical antibiotic classes were also found. Two instances of aac(6’)-Im , an aminoglycoside-modifying enzyme that confers resistance to important aminoglycosides such as amikacin, gentamicin, and tobramycin via acetylation [ 70 ], were identified. Additionally, the β-lactamase gene cfxA4 , which can hydrolyze cephalosporin antibiotics [ 71 ], was detected twice in vOTUs originating from both chicken and duck samples. The detection of these ARGs, some of which target antibiotics commonly used in poultry production and human medicine, across different avian hosts (chickens and ducks) points to a potentially widespread distribution of these resistance determinants within avian gut viral communities. Critically, the presence of these ARGs within viral genomes strongly suggests the potential for phage-mediated horizontal gene transfer (HGT), highlighting a significant pathway for the propagation and dissemination of antibiotic resistance from the avian gut virome to the broader microbial ecosystem, and posing potential risks to animal and human health under the One Health paradigm. Table 1 The ARGs are derived from the avian gut virome. GENE Number ACCESSION Resistance Mechanism Antibiotic Classes Animal-Host Erm(X) 25 NG_047853.1 Methylates 23S rRNA Macrolides, Lincosamides, Streptogramin B (Erythromycin, Clindamycin) Chicken Lnu(C) 3 NG_047924.1 Modifies lincosamides Lincosamides (Clindamycin) Chicken, Duck Erm(Y) 3 NG_047855.1 Methylates 23S rRNA Macrolides, Lincosamides, Streptogramin B Chicken, Duck Aac(6’)-Im 2 NG_052222.1 Acetylates antibiotics Aminoglycosides (Amikacin, Gentamicin, Tobramycin) Chicken Cfxa4 2 NG_047642.1 Hydrolyzes cephalosporins Cephalosporins (Cefotaxime, Ceftazidime) Chicken, Duck Erm(B) 1 NG_047793.1 Methylates 23S rRNA Macrolides, Lincosamides, Streptogramin B (Erythromycin, Clindamycin) Chicken Erm(G) 1 NG_047827.1 Methylates 23S rRNA Macrolides, Lincosamides, Streptogramin B Duck Viral AMGs implicate phages in modulating prokaryotic host metabolism Functional annotation of predicted viral genes based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways identified 10,095 putative auxiliary metabolic genes (AMGs) corresponding to 112 third-level KEGG pathways (Table S9 ). These AMGs were broadly categorized into five major first-level KEGG modules: “Metabolism”, “Genetic Information Processing”, “Environmental Information Processing”, “Cellular Processes”, and “Organismal Systems”. For clarity in our analysis, these were further grouped into more general functional categories relevant to viral impact on prokaryotic host metabolism, as depicted in Fig. 7 . The most abundant AMGs fell into categories related to “Information systems” (n = 6,486 AMGs, primarily DNA replication and repair), “Amino Acid Metabolism” (n = 2,737), and “Carbohydrate Metabolism” (including CAZymes, n = 235) (Fig. 7 A, B). Further analysis at the KEGG pathway level (third-level) revealed significant enrichment of AMGs in pathways crucial for viral replication and host modulation, including glycolysis, amino acid metabolism (e.g., cysteine and methionine metabolism), and nucleotide biosynthesis (e.g., pyrimidine and purine metabolism) (Fig. 7 C). Notably, AMGs associated with nucleotide metabolism, such as those involved in the synthesis of CDP, dCTP, and dTMP (Fig. 8 A), were frequently identified, suggesting a viral strategy to enhance the host’s nucleotide pool for efficient phage replication. AMGs involved in carbohydrate metabolism were also prominent, including genes encoding glycoside hydrolases (GHs) from families such as GH5 , GH13 , and GH16 (Fig. 8 B; Table S9 ). These enzymes can facilitate the breakdown of complex polysaccharides (e.g., cellulose, starch) into simpler sugars. Other AMGs were linked to downstream pathways such as glycolysis and one-carbon metabolism, potentially influencing the production of pyruvate, formate, and acetyl-CoA, which are precursors for short-chain fatty acids (SCFAs) (Fig. 8 B). Discussion This study presents Avian Virome Database (AvianViromeDB), the most comprehensive catalog of the avian gut virome to date, comprising 61,608 viral operational taxonomic units (vOTUs) derived from 2,692 metagenomic samples. Our analyses reveal remarkable viral diversity, high avian host specificity, and significant functional potential encoded by these viruses, including a repertoire of auxiliary metabolic genes (AMGs) and antibiotic resistance genes (ARGs). These findings substantially expand our understanding of the viral landscape within avian gut ecosystems and provide a critical resource for future research into virus-host interactions and their ecological implications. A key finding of this study is the vast extent of uncharacterized viral diversity. Despite the large scale of our dataset, over 95% of the identified vOTUs could not be classified to known viral families, highlighting a significant ‘viral dark matter’ within the avian gut. This observation is consistent with virome studies in other environments, such as the human gut and various aquatic and terrestrial ecosystems [ 66 ], underscoring that current viral databases represent only a small fraction of global viral diversity. The predominance of Caudoviricetes among the classifiable vOTUs aligns with their known ubiquity in gut environments [ 10 , 72 , 73 ]. However, the inability to assign most vOTUs to lower taxonomic levels, even for complete genomes, suggests that the avian gut harbors numerous novel viral lineages that have diverged significantly from currently characterized phages. Addressing this knowledge gap will require concerted efforts in isolating and characterizing these novel viruses, alongside the development of improved bioinformatic tools for viral classification and phylogenomic analysis. The unique viral cluster identified (VCX) in our gene-sharing network analysis, composed entirely of AvianViromeDB vOTUs with no homology to known reference phages and predicted to infect Oscillospirales bacteria, exemplifies this hidden diversity and warrants further investigation. Members of Oscillospirales are known to play roles in avian host metabolism, particularly in the degradation of complex carbohydrates and production of short-chain fatty acids (SCFAs); thus, phages targeting these bacteria could indirectly influence avian host nutrient acquisition and gut health, representing an important facet of the virus-bacteria-avian host tripartite interaction. The prediction of phage lifestyles indicated a predominance of virulent phages over temperate ones in the avian gut virome. This contrasts with some mammalian gut virome studies where temperate phages are often reported as highly abundant, potentially contributing to prokaryotic host genome evolution and stability through lysogeny [ 74 , 75 ]. However, other studies, particularly those focusing on active viral replication, have also reported higher proportions of virulent phages [ 64 , 65 ]. The higher prevalence of virulent phages in our dataset could reflect several factors, including a more dynamic “kill-the-winner” ecological interaction in the avian gut, differences in avian host immune pressures, or biases in viral recovery from metagenomic data that might favor the detection of actively replicating lytic phages. Further research, perhaps incorporating metatranscriptomics, would be needed to differentiate between these possibilities and understand the true balance of lytic versus lysogenic cycles in this ecosystem. The identification of seven distinct ARGs within the avian gut virome, including those conferring resistance to clinically important antibiotics like macrolides, lincosamides, aminoglycosides, and cephalosporins, is a significant concern from a One Health perspective. These antibiotics are widely used in both poultry production and human medicine [ 76 – 78 ]. The presence of ARGs in viral genomes, particularly in phages capable of infecting diverse bacterial hosts, highlights the potential for phage-mediated horizontal gene transfer (HGT) to disseminate these resistance determinants. This finding aligns with previous reports of ARGs in viromes from various environments, including the human and pig gut, and chicken meat [ 79 – 89 ]. The erm family genes, frequently detected in our study, are known to confer MLSB resistance via 23S rRNA methylation, while lnu(C) modifies lincosamides, aac(6’)-Im acetylates aminoglycosides, and cfxA4 hydrolyzes β-lactams. The detection of these ARGs in phages from different avian hosts (chickens and ducks) suggests a widespread distribution and underscores the avian gut virome as a potential reservoir and vehicle for ARG propagation, necessitating closer surveillance and strategies to mitigate this risk. Our study also uncovered a rich repertoire of viral AMGs, implicating avian gut phages in the modulation of prokaryotic host metabolism. The abundance of AMGs related to “Information systems” (DNA replication and repair) is expected, as phages often carry genes to supplement or co-opt host machinery for their own replication. More interestingly, we found numerous AMGs involved in amino acid and nucleotide metabolism. For instance, the frequent identification of viral genes encoding enzymes like ribonucleoside-diphosphate reductase (K00525) and thymidylate synthase (K00560) suggests a viral strategy to boost the host’s pyrimidine deoxyribonucleotide pool, thereby facilitating efficient phage DNA synthesis. This aligns with findings from marine viromes where phages were shown to enhance host nucleotide biosynthesis [ 90 – 92 ]. Furthermore, the prominence of AMGs related to carbohydrate metabolism, including various glycoside hydrolases (GHs) such as GH5 , GH13 , and GH16 , suggests that avian gut phages actively participate in the breakdown of complex polysaccharides. For example, GH5 enzymes are known for cellulose degradation, GH13 for starch and glycogen breakdown, and GH16 for hemicellulose hydrolysis [ 93 – 97 ]. The degradation of these complex plant-based fibers into simpler sugars by phage-encoded AMGs could provide readily available energy sources for the bacterial host, and subsequently for the phage itself. Similar roles for phage AMGs in lignocellulose degradation have been reported in ruminant animals [ 98 , 99 ]. These AMGs, coupled with others involved in downstream pathways like glycolysis and one-carbon metabolism (e.g., Wood-Ljungdahl pathway), could significantly influence the production of key metabolites such as pyruvate, formate, and acetyl-CoA, which are precursors for SCFAs. SCFAs are crucial for gut homeostasis and host energy supply. Thus, avian gut phages, through their AMGs, may not only reprogram host metabolism to optimize their replication but also contribute significantly to the overall metabolic output of the gut microbiota, impacting nutrient availability and avian host physiology. This represents another layer of the virus-bacteria-avian host interaction. Despite the comprehensive nature of AvianViromeDB, this study has limitations. Firstly, our analysis relied on publicly available metagenomic data, which may have inherent biases related to sample collection, DNA extraction methods, and sequencing depth across different original studies. Secondly, while we employed multiple tools for viral identification and prokaryotic host prediction, these in silico predictions require experimental validation. The functions of many identified AMGs and the impact of ARGs also warrant further experimental investigation. Thirdly, the database, while extensive, still represents a snapshot and does not capture the temporal dynamics or the full diversity across all avian species and geographical locations. In conclusion, AvianViromeDB provides an unprecedented view into the diversity and functional potential of the avian gut virome. Our findings reveal a vast uncharted viral space, highlight the ecological roles of phages in targeting core microbiota, and underscore their potential to modulate host metabolism and disseminate ARGs. This resource will be invaluable for future research aimed at understanding the complex interplay among viruses, their prokaryoitc hosts, and the avian host, and could inform the development of novel strategies, such as phage therapy or microbial community intervention, to improve avian host health and productivity, particularly in poultry farming. Continued exploration of this “viral dark matter” and functional characterization of novel viral genes will undoubtedly yield further insights into the critical roles viruses play in shaping gut ecosystems and influencing avian biology. Conclusion This study established Avian Virome Database (AvianViromeDB), the most comprehensive catalog of the avian gut virome to date, by analyzing 2,692 diverse avian metagenomes. We unveiled a vast and largely uncharacterized viral diversity, with over 95% of the 61,608 identified vOTUs representing novel viral lineages, predominantly within the Caudoviricetes . Our findings demonstrate high avian host specificity of these viruses and reveal their significant functional potential, evidenced by a rich repertoire of auxiliary metabolic genes (AMGs) implicated in modulating prokaryotic host metabolism, and the presence of clinically relevant antibiotic resistance genes (ARGs). The AvianViromeDB significantly expands the known viral sequence space and provides a critical resource for future investigations into viral ecology, virus-host interactions, and the evolutionary dynamics within avian gut ecosystems. The characterization of novel viral lineages, AMGs, and ARGs opens new avenues for understanding the mechanistic roles of viruses in avian health, disease, and nutrient processing, as well as their influence on the broader gut microbial community. Ultimately, this work lays a foundation for developing novel strategies, such as phage-based interventions, to improve avian health and productivity in agricultural settings and to better understand the ecological impact of viruses in wild bird populations. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article (and its supplementary information files).The AvianViromeDB are freely available at https://phagebyte.github.io/avianviromedb. All raw sequencing data used in this study are publicly available and can be accessed through the NCBI Sequence Read Archive under the project accession numbers listed in Table S1. The code used for data analysis is available at the GitHub repository: https://github.com/rujinlong/avianviromedb. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation - Emmy Noether program, Project No. 273124240; and SPP2330, Project No. 464797012), and European Research Council Starting grant (ERC StG 803077) awarded to LD; National Natural Science Foundation of China (#32341055) awarded to XW; China Agriculture Research System of MOF and MARA (CARS-42-2) awarded to ZGH. Authors' contributions All authors contributed intellectually to and agreed to this submission. XQY, JLR and XW designed the experiments. PYL, ZZW, DH, LY and YSZ conducted the experiments and collected the data. XQY, PYL, ZZW and JLR analyzed the data. XQY, PYL, YSZ prepared figures. XQY wrote the initial draft of the manuscript, while JLR, XW and LD provided substantial feedback. JLR, XW, and LD contributed to the conceptual design of the study. XW, ZGH and LD provided funding support. All authors read and approved the final manuscript. Acknowledgements The authors sincerely thank the members of the Deng laboratory for their constructive discussions and valuable suggestions. We are also deeply grateful to Prof. Yingping Xiao from the Zhejiang Academy of Agricultural Sciences for generously providing the duck metagenomic datasets used in this study. Additionally, we acknowledge the Technical University of Munich (TUM), the Helmholtz Zentrum München (HMGU), and Northwest A&F University (NWAFU) for providing computing resources. References Cui L, Morris A, Ghedin E. The human mycobiome in health and disease. Genome Med. 2013;5(7):63. Chin VK, Yong VC, Chong PP, Amin NS, Basir R, Abdullah M. Mycobiome in the Gut: A Multiperspective Review. Mediators Inflamm. 2020;2020:9560684. Pottenger S, Watts A, Wedley A, Jopson S, Darby AC, Wigley P. Timing and delivery route effects of cecal microbiome transplants on Salmonella Typhimurium infections in chickens: potential for in-hatchery delivery of microbial interventions. 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The world map indicates sampling locations across 63 countries and regions. The size of each pie chart is proportional to the number of metagenomic samples from that location, and the colored segments within each pie chart represent the proportion of samples from different avian host species (see legend inset for species color key). The enlarged inset shows a detailed view of sampling sites in China.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/135cf92047f680c17d66b7eb.png"},{"id":93694989,"identity":"ce435178-e876-4acc-a454-52e3a4e2bfa3","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90747,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of viral operational taxonomic units (vOTUs) in Avian Virome Database (AvianViromeDB). \u003cstrong\u003ea\u003c/strong\u003e Distribution of genome lengths for all identified vOTUs. \u003cstrong\u003eb\u003c/strong\u003e Overall quality assessment of vOTUs. The pie chart shows the proportion of vOTUs in different quality tiers (Complete, High-quality, Medium-quality, Low-quality, and Not Determined) as assessed by CheckV. The stacked bar chart on the right shows the relative proportions of these quality tiers for vOTUs derived from different avian host species. \u003cstrong\u003ec\u003c/strong\u003eDensity plot illustrating the distribution of genome lengths (log10 scale) for vOTUs within each CheckV quality tier. The numbers in the legend indicate the count of vOTUs in each quality category. \u003cstrong\u003ed\u003c/strong\u003e Summary statistics for different avian hosts. Bar charts show the number of input metagenomic samples (left panel) and the total number of vOTUs identified (middle panel) for each avian species. Violin plots combined with boxplots (right panel) illustrate the distribution of vOTU genome sizes for each avian species.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/7d214e5f6bc3f99a34aaf78c.png"},{"id":93695864,"identity":"4b09a664-d03b-47d1-bbab-f5a721b9cbb4","added_by":"auto","created_at":"2025-10-16 14:45:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59520,"visible":true,"origin":"","legend":"\u003cp\u003eHost distribution and lifestyle prediction of avian gut vOTUs. For panels A and B, avian species with smaller sample sizes (quail, great egret, migratory birds, pigeon) were grouped as “Other birds”. \u003cstrong\u003ea\u003c/strong\u003e UpSet plot illustrating the number of unique and shared vOTUs among different avian host groups (Chicken, Duck, Goose, Other birds). \u003cstrong\u003eb\u003c/strong\u003e UpSet plot showing the number of unique and shared viral clusters (VCs) among the avian host groups. \u003cstrong\u003ec\u003c/strong\u003e Characteristics of the top 10 largest VCs (ranked by the number of constituent vOTUs). For each VC, panels show: total number of vOTUs (VC size, left), distribution of vOTU genome sizes (middle), and proportion of vOTUs derived from each animal host group (right). \u003cstrong\u003ed\u003c/strong\u003e Proportion of vOTUs predicted to have virulent versus temperate lifestyles.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/2808e7e185e47973426646e7.png"},{"id":93694992,"identity":"f9acc0de-4de6-4fd5-9f68-10a531810fba","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134430,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic composition of AvianViromeDB vOTUs. \u003cstrong\u003ea\u003c/strong\u003e Pie chart showing the proportional distribution of vOTUs at the viral class level. The enlarged section highlights less abundant classes. \u003cstrong\u003eb\u003c/strong\u003e Pie chart illustrating the proportional distribution of vOTUs at the viral family level. The enlarged section highlights less abundant families. \u003cstrong\u003ec\u003c/strong\u003e Sankey diagram illustrating the flow of taxonomic assignments for vOTUs from the realm to the family level, showing the number of vOTUs assigned at each rank.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/082d3bbf11380189e28884a9.png"},{"id":93694995,"identity":"17bb6ef3-c400-4d10-9b2a-3505fce5297e","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":136995,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic features and predicted host specificity of avian gut vOTUs. \u003cstrong\u003ea\u003c/strong\u003e Scatter plot showing the relationship between vOTU genome sizes (log10 scale) and GC content. Points are colored according to the predicted bacterial host phylum of the vOTU. Marginal bar plots display the overall distributions of genome sizes (top) and GC content (right). \u003cstrong\u003eb\u003c/strong\u003e Bar chart showing the number of vOTUs predicted to target bacteria from different phyla. \u003cstrong\u003ec\u003c/strong\u003e Bar chart illustrating the distribution of host range breadths at the genus level, categorizing vOTUs as specialists (predicted to infect a single bacterial genus) or generalists (predicted to infect multiple bacterial genera). \u003cstrong\u003ed\u003c/strong\u003e Comprehensive characterization of vOTUs based on their predicted host genera (showing the top 41 most frequently predicted host genera, each targeted by \u0026gt;3 vOTUs). For each host genus, the panels display: the total number of vOTUs predicted to infect it, the distribution of their genome sizes, the taxonomic phylum of the host genus, the proportion of temperate versus virulent vOTUs, and the proportion of specialist versus generalist vOTUs.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/6ed1c0fff3922541592f58ec.png"},{"id":93695871,"identity":"41340053-22b2-46b0-8cc5-dadb2dce082e","added_by":"auto","created_at":"2025-10-16 14:45:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":440406,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogeny, gene-sharing network, and predicted host associations of avian gut virome. \u003cstrong\u003ea\u003c/strong\u003e Phylogenetic tree of 7,569 representative high-quality vOTUs from AvianViromeDB. Concentric outer rings display annotations for each vOTU: viral class, viral family, novelty status (TRUE for newly identified in this study, FALSE for matching known viruses ), predicted host bacterial phylum, and genome sizes category. \u003cstrong\u003eb\u003c/strong\u003e Gene-sharing network of the 7,569 AvianViromeDB vOTUs and reference viral genomes from NCBI Virus RefSeq. Nodes represent individual viral genomes, and edges connect genomes that share significant numbers of protein clusters. Node colors indicate major viral taxonomic groups. \u003cstrong\u003ec\u003c/strong\u003e Network illustrating predicted interactions between a novel viral cluster (VCX , composed exclusively of AvianViromeDB vOTUs) and their predicted prokaryotic hosts at the order level. Blue circular nodes represent vOTUs, and red elliptical nodes represent prokaryotic host orders. Edges denote predicted infection events. The size of each blue vOTU node is proportional to the number of distinct avian host species from which it was identified.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/22025b60bc24024f5e91ab2a.png"},{"id":93696771,"identity":"a5741962-8808-4792-8891-d4e018689bb3","added_by":"auto","created_at":"2025-10-16 14:53:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":187813,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional profile of auxiliary metabolic genes (AMGs) identified in the avian gut virome. \u003cstrong\u003ea\u003c/strong\u003ePie chart illustrating the proportion of identified AMGs assigned to broad functional categories based on KEGG. \u003cstrong\u003eb\u003c/strong\u003eBar chart showing the absolute number of AMGs within various detailed functional subcategories. The number to the right of each bar indicates the count of AMGs in that subcategory. \u003cstrong\u003ec\u003c/strong\u003eTop enriched KEGG pathways (third-level) among the identified AMGs. The length of the bars corresponds to the number of vOTU genomes (log10 scale) encoding AMGs in each pathway. The stacked bar chart on the right shows the proportion of these vOTU genomes derived from different avian host groups.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/2f339c68b9186e1aecfa905a.png"},{"id":93695010,"identity":"d69ca42f-55e5-4e9d-a3ea-496764c81c8d","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":163332,"visible":true,"origin":"","legend":"\u003cp\u003eProposed metabolic pathways in avian gut bacteria potentially modulated by phage-encoded AMGs. \u003cstrong\u003ea\u003c/strong\u003eSchematic of pyrimidine deoxyribonucleotide biosynthesis pathways. Viral AMGs encoding key enzymes (green ellipses, labeled with EC numbers; corresponding KEGG Orthology (KO) identifiers in orange) are highlighted, potentially augmenting the host’s nucleotide pool for viral replication. \u003cstrong\u003eb\u003c/strong\u003e Overview of carbohydrate metabolism and related pathways influenced by viral AMGs. This includes the breakdown of complex polysaccharides (e.g., cellulose) by phage-encoded glycoside hydrolases (GHs, blue boxes with EC numbers; associated KOs in red italics), feeding into central carbon metabolism (glycolysis, pentose phosphate pathway), one-carbon metabolism (Wood-Ljungdahl pathway), and the subsequent production of short-chain fatty acids (SCFAs). Key substrates, intermediates, and products are shown. Pathways are color-coded by general metabolic function.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/3f254c6703ac0290a76ef99a.png"},{"id":96247096,"identity":"990d3f48-91b5-4b55-bb88-3e021a2e52ce","added_by":"auto","created_at":"2025-11-19 07:27:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2138800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/27ee57b4-eea6-4da2-a270-79b5a9538718.pdf"},{"id":93694988,"identity":"288e9737-0b89-48d4-b391-060686fd4f25","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20898,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/a09527b6eb1ed3eb5d5ac87b.xlsx"},{"id":93694993,"identity":"7b5e045a-2e3d-4f55-850a-c4599ce9b206","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18766,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/0c7fcac47d5ed298d82ecb5b.xlsx"},{"id":93697721,"identity":"532ad97f-4642-4c9c-8c18-2b5e39a4984a","added_by":"auto","created_at":"2025-10-16 15:01:31","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20033,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/410e38f263ecb1eb6095bd5e.xlsx"},{"id":93696773,"identity":"1bc30276-51eb-495f-823c-a9fe4635d27f","added_by":"auto","created_at":"2025-10-16 14:53:31","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":25912,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/ba5d91a43a4bcb9e4429710d.xlsx"},{"id":93695004,"identity":"9ef140e4-55ab-4f1d-a8fd-0b5a45c84cbf","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":111197,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.S2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/5f4ee9e9d2e019f3cf79933d.pdf"},{"id":93695880,"identity":"e0249bcd-32cb-4b31-a3b5-a26888cabe92","added_by":"auto","created_at":"2025-10-16 14:45:31","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":95361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementarytableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/ecc8e2a05482ec7d39d6d961.xlsx"},{"id":93695012,"identity":"100f07e6-2b83-4c7f-a3ba-0fc6ad9903e1","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":828584,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/1816e8647e44a70f5a178be6.xlsx"},{"id":93697724,"identity":"2a564566-d39e-44cf-a898-9f3b1a974bc3","added_by":"auto","created_at":"2025-10-16 15:01:31","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":704019,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.S1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/e705e586591bc19a3612d5be.pdf"},{"id":93695033,"identity":"27b7f886-65e1-4b00-b959-7da014ce6c03","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":3600598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementarytableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/0e15c21dc2cc893ae4118906.xlsx"},{"id":93695040,"identity":"2a813856-9fd8-4da5-992f-190533cb65f2","added_by":"auto","created_at":"2025-10-16 14:37:32","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":5837754,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table S3: Statistical information and classification level annotation of phages.\u003c/p\u003e","description":"","filename":"SupplementarytableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/94ff746e53cc97a0a0f7cf73.xlsx"},{"id":93695042,"identity":"c061317e-41cb-4991-9d9a-79d299f8ab05","added_by":"auto","created_at":"2025-10-16 14:37:32","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":9419872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementarytableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/3cc81f49b0b068cc7de0b580.xlsx"},{"id":93695015,"identity":"418c8042-fc8d-4fd5-b64b-a7a39d208b94","added_by":"auto","created_at":"2025-10-16 14:37:31","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":13937,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6913311/v1/7dc4b6a7de787ae978ecbf4b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Characterization of the Avian Gut Virome Reveals Extensive Viral Diversity and Functional Implications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe gut microbiome is a complex microbial ecosystem, encompassing bacteria, viruses, fungi, and other prokaryotic organisms, playing a crucial role in digestion, nutrient utilization, immune regulation, and disease prevention in humans and animals [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite often being overlooked, the gut harbors a highly diverse and stable viral community, predominantly composed of bacteriophages (or phages) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] that specifically infect bacteria. Phages exert profound effects on the composition and function of gut bacterial communities through multiple mechanisms, including horizontal gene transfer (e.g., antibiotic resistance genes (ARGs)) to disseminate genetic material [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], the delivery of auxiliary metabolic genes (AMGs) that enhance bacterial stress tolerance or metabolic potential [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and integrating into bacterial genomes as prophages (temperate) or directly lysing bacteria (virulent) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. By influencing both community structure and microbial metabolism, these phage-driven processes play a vital role in maintaining gut homeostasis and ensuring host physiological performance, which is particularly important for animal health and production outcomes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough several databases have been established to identify viromes from various sources, such as the human gut [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], marine environments [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], plants [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and other animals [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the virome structure in birds remains largely unexplored and is often considered \u0026lsquo;viral dark matter\u0026rsquo; in comparison to these environments. A comprehensive reference database of viral genomes is urgently needed to better characterize viral communities within the avian gut microbiota and to enable genome-resolved metagenomic studies focused on the interactions between viruses and their bacterial hosts within the context of the avian host.\u003c/p\u003e\u003cp\u003eAs monophyletic vertebrates, avian occupy a wide range of ecological niches [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] across the globe due to their diverse physiology and broad geographic distribution [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], playing vital roles in natural ecosystems. Similar to other animals, the avian gut harbors a large number of phages, many of which remain poorly characterized, and their roles in shaping the gut microbial communities and influencing host health are not yet fully understood. To date, research on the avian gut microbiome has primarily focused on poultry, generating extensive metagenomic sequencing data, particularly from chickens [\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], ducks [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and geese [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These datasets enable scientists to capture both intracellular and extracellular viral sequences through computational tools [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], including integrated prophages, without being affected by whole-genome amplification biases [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, due to thousands of years of artificial selection, poultry has diverged from wild birds and may not fully represent all avian species. Nevertheless, the insights and methodologies developed through poultry studies provide a valuable foundation for investigating the gut virome diversity of wild bird species.\u003c/p\u003e\u003cp\u003eIn this study, we utilized ViroProfiler [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], a newly developed containerized bioinformatic pipeline, to ensure computational reproducibility. Using this pipeline, we analyzed 2,692 avian gut and fecal metagenomic samples, which culminated in the creation of the Avian Virome Database (AvianViromeDB) \u0026ndash; a global catalog of viral genomes derived from these diverse avian sources.\u003c/p\u003e\u003cp\u003eAddressing the notable research bias wherein approximately 97.65% of available avian gut metagenomes originate from poultry, leaving wild bird microbiomes comparatively under-characterized, AvianViromeDB was specifically designed to encompass data from both agriculturally significant avian species (e.g., poultry) and a curated selection of wild bird populations. This comprehensive database currently comprises 61,608 high-quality viral operational taxonomic unit (vOTU) genomes, assembled from these manually curated whole metagenomic samples sourced from 28 distinct studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Analysis of these viral genomes highlights the remarkable diversity and complexity of the avian gut virome. Furthermore, comparison with existing public viral datasets reveals that 95.31% of these vOTUs represent novel viral entities. Collectively, these genomes substantially expand the known viral diversity within the avian gut microbiome and provide critical insights into interactions between viruses and their bacterial hosts. We anticipate that AvianViromeDB will serve as an invaluable foundational resource for future investigations into the avian gut virome, its ecological roles, and its implications for avian health and ecosystem dynamics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMetagenomic dataset collection and curation\u003c/h2\u003e\u003cp\u003eTo construct a comprehensive catalog of the avian gut virome, we sourced 2,692 publicly available whole-metagenome sequencing (WMS) datasets from avian gut content and fecal samples, originating from 28 published studies (detailed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These datasets represented a diverse range of avian hosts, including chicken (\u003cem\u003eGallus gallus domesticus\u003c/em\u003e), duck (e.g., \u003cem\u003eAnas platyrhynchos domesticus\u003c/em\u003e), goose (e.g., \u003cem\u003eAnser anser domesticus\u003c/em\u003e), quail (\u003cem\u003eCoturnix coturnix\u003c/em\u003e), great egret (\u003cem\u003eArdea alba\u003c/em\u003e), pigeon (\u003cem\u003eColumba livia\u003c/em\u003e), and a collective group of migratory bird species (refer to Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e for species details where available). We specifically selected WMS data to enable the recovery of viral genomes directly from bulk DNA, thereby facilitating the detection of both lytic viruses and integrated proviruses without the biases associated with virus-like particle (VLP) enrichment protocols. This approach also provides the necessary genomic context for subsequent virus-host association predictions [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The curated raw sequencing data totaled approximately 2.5 TB.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBioinformatic pipeline for genome assembly and virus identification\u003c/h3\u003e\n\u003cp\u003eThe raw WMS reads were processed using our previously described ViroProfiler pipeline (version 0.2.5) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Key steps relevant to this study are outlined below. First, raw reads underwent quality control, including adapter trimming and low-quality read filtering, using fastp (version 0.23.2) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] with default parameters. High-quality reads from each sample were then independently assembled \u003cem\u003ede novo\u003c/em\u003e into contigs using MEGAHIT (version 1.2.9) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] with default settings. Subsequently, contigs longer than 5 kb, or those identified as circular by VirSorter2 (version 2.2.3) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], were selected from all individual sample assemblies and pooled to create a comprehensive contig set. To identify and remove potential host DNA contamination from proviral regions within this set, CheckV (version 1.0.1) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] was employed for provirus detection and trimming. The resulting cleaned contigs were then dereplicated to generate a non-redundant catalog of viral sequences. Specifically, pairwise average nucleotide identity (ANI) was calculated among these contigs using the clustering algorithm within CheckV, applying thresholds of \u0026ge;\u0026thinsp;95% ANI and \u0026ge;\u0026thinsp;85% alignment fraction (AF) to define species-level viral operational taxonomic units (vOTUs). The longest contig within each vOTU cluster was selected as its representative sequence. These representative vOTU sequences were subjected to viral identification using a multi-tool consensus approach. Three distinct viral identification tools were employed: VirSorter2 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and VIBRANT (version 1.2.1) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which were run as part of the ViroProfiler pipeline, and geNomad (version 1.8.0) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], which was executed separately due to its exclusion from ViroProfiler. All tools were run with their respective default parameters. A contig was confirmed as viral if identified as such by at least two of these three tools. Finally, the quality of all confirmed vOTUs, including genome completeness and contamination ratios, was assessed using CheckV (version 1.0.1) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Only vOTUs classified by CheckV as \u0026ldquo;Complete\u0026rdquo;, \u0026ldquo;High-quality\u0026rdquo;, or \u0026ldquo;Medium-quality\u0026rdquo; were retained for downstream analyses and inclusion in AvianViromeDB.\u003c/p\u003e\n\u003ch3\u003eTaxonomic annotation, clustering, and phylogenetic analysis of vOTUs\u003c/h3\u003e\n\u003cp\u003eTaxonomic classification of the identified vOTUs was performed using a combination of geNomad (version 1.8.0) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and VITAP (version 1.7.1) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], referencing the viral taxonomy from the International Committee on Taxonomy of Viruses (ICTV) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. GeNomad employs a hybrid strategy, integrating alignment-free and gene-based methods with a comprehensive dataset of taxonomically informed hidden Markov model (HMM) protein profiles. It assigns a taxonomic classification to each gene based on these markers and subsequently aggregates these assignments using a weighted majority vote to generate a consensus lineage for each sequence. VITAP utilizes alignment-based techniques coupled with graph-based analysis for classifying viral sequences. Given VITAP\u0026rsquo;s potential for more specific annotations (often to genus or species level with high precision), its taxonomic assignments were prioritized. For vOTUs not annotated by VITAP, classifications provided by geNomad were used. While geNomad frequently assigned taxonomy at higher ranks (e.g., class level), it provided valuable classifications for a larger number of vOTUs. This combined approach enabled taxonomic assignment for approximately 28,000 vOTUs.\u003c/p\u003e\u003cp\u003eTo further investigate relationships among the identified vOTUs, compare them with known viral genomes, and identify potential novel viral clusters, gene-sharing network analysis was conducted using vConTACT3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bitbucket.org/MAVERICLab/vcontact3/src/master/\u003c/span\u003e\u003cspan address=\"https://bitbucket.org/MAVERICLab/vcontact3/src/master/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This analysis focused on vOTUs meeting specific criteria: a minimum genome length of 10 kb and a quality designation of \u0026ldquo;Complete\u0026rdquo; or \u0026ldquo;High-quality\u0026rdquo; as determined by CheckV [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These selected vOTUs were analyzed alongside reference viral genomes from the NCBI Virus RefSeq database (version 223) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. vConTACT3 groups viral genomes into viral clusters (VCs) based on shared protein clusters (PCs). These VCs represent groups of related viral genomes, offer insights into potential genus-level relationships, and highlight vOTUs that may constitute novel taxa. The vConTACT3 analysis outputs separate UPGMA-based phylogenetic trees for major viral groups. Given our focus on prokaryotic phages, which constituted the vast majority of viruses identified in this study, we only used the phylogenetic tree for the \u003cem\u003eDuplodnaviria\u003c/em\u003e realm for subsequent visualization with iTOL [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eFunctional annotation, prokaryotic host prediction, and lifestyle classification of vOTUs\u003c/h3\u003e\n\u003cp\u003eProtein-coding genes (CDS) within the vOTU sequences were predicted using Prodigal (version 2.6.3) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] in metagenomic mode. Functional annotation of these predicted CDS was performed using eggNOG-mapper (version 2.1.12) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] against the eggNOG database (version 5.0) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Putative auxiliary metabolic genes (AMGs) were identified using a combined approach to maximize detection sensitivity. This involved predictions from both VIBRANT (version 1.2.1) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and DRAM-v (version 1.3) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] with their default settings. Antimicrobial resistance genes (ARGs) potentially encoded by the vOTUs were identified using Abricate (version 1.0.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/abricate\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/abricate\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Predicted genes were queried against multiple databases: the Comprehensive Antibiotic Resistance Database (CARD, version 3.2.9) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT, version V6) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], NCBI AMRFinderPlus (version v3.10.1) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], ResFinder (version 4.1.3) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and the Virulence Factor Database (VFDB, version 2019) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Hits were considered significant if they met stringent criteria of \u0026ge;\u0026thinsp;95% nucleotide identity and \u0026ge;\u0026thinsp;75% query coverage (Tables S5-S8). Putative bacterial hosts for the vOTUs were predicted using iPHoP (version 1.3.3) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] with default parameters. Phage lifestyle (i.e., temperate or virulent) was predicted using BACPHLIP (version 0.9.6) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] with default settings.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCharacterization of the avian gut virome genome catalog\u003c/h2\u003e\u003cp\u003eAnalysis of 2,692 whole-metagenome sequencing datasets from gut and fecal samples of diverse avian species across multiple geographic regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) was performed to establish a comprehensive avian virome resource (Avian Virome Database, AvianViromeDB). The dataset was predominantly composed of samples from chicken (n\u0026thinsp;=\u0026thinsp;2,043), duck (n\u0026thinsp;=\u0026thinsp;459), and goose (n\u0026thinsp;=\u0026thinsp;123), with smaller contributions from quail, great egret, migratory birds, and pigeon (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Following the bioinformatic pipeline described in the Methods section, our analysis yielded 61,608 non-redundant viral operational taxonomic units (vOTUs), which constitute the core of AvianViromeDB (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To assess the quality and integrity of this catalog, each vOTU was evaluated for genome completeness using CheckV. This assessment revealed that 5,829 vOTUs (9.5% of the total) were classified as \u0026ldquo;complete\u0026rdquo; genomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). Further characterization of the vOTU genomes showed a wide distribution of lengths, with a significant portion ranging from 10 to 20 kb (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We categorized the vOTUs into five size bins and observed a strong positive correlation between genome length and quality. Notably, vOTUs in the \u0026gt;\u0026thinsp;40 kb size bin exhibited the highest proportion of complete genomes (29.79%) compared to the other size groups (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This large-scale assembly and quality assessment provides a robust foundation for detailed downstream analyses of avian gut virome diversity and function.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAvian host specificity and lifestyle predominance of vOTUs\u003c/h3\u003e\n\u003cp\u003eAmong the avian hosts, samples from chickens yielded the highest number of vOTUs (n\u0026thinsp;=\u0026thinsp;46,494), followed by ducks (n\u0026thinsp;=\u0026thinsp;13,795) and geese (n\u0026thinsp;=\u0026thinsp;3,260) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The number of viruses identified in different avian species correlated with the respective sample sizes. Furthermore, viral genome sizes and taxonomic compositions varied among avian hosts, suggesting species-specific characteristics of the avian virome. Analysis of vOTU distribution across different hosts (chicken, duck, goose, and a combined group of other bird species) indicated that a high proportion, 90.10% (n\u0026thinsp;=\u0026thinsp;55,511), of vOTUs exhibited host specificity, infecting only a single host type, with a smaller fraction shared between two or three host groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Using protein sequence similarity-based clustering (as described in Methods), we identified 33,076 distinct viral clusters (VCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Chickens, ducks, geese, and other birds harbored 17,773, 4,590, 3,673, and 1,314 VCs, respectively (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Similarly, 82.69% (n\u0026thinsp;=\u0026thinsp;27,305) of VCs were associated with a single host group. Examination of the top 10 VCs, ranked by the number of vOTUs they contained (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), revealed that 8 of these VCs were present in more than two host groups, indicating that these diverse VCs possess considerable adaptability across different avian hosts. The majority of phages within these top VCs had genome sizes ranging from 1 to 50 kb, consistent with typical bacteriophage genome sizes [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Prediction of phage lifestyles indicated that virulent phages were significantly more numerous than temperate phages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). This is consistent with the fact that virulent phages predominantly drive bacterial lysis in the gut environment [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], which could be due to virulent phages having broader ecological adaptability or higher enrichment potential.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eTaxonomic profiling reveals vast undiscovered viral lineages\u003c/h3\u003e\n\u003cp\u003eTaxonomic annotation of the 61,608 high-quality vOTUs in AvianViromeDB revealed that the vast majority (99.05%) were classified within the class \u003cem\u003eCaudoviricetes\u003c/em\u003e, which primarily encompasses tailed bacteriophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, C; Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Despite this high-level classification, a striking observation was the limited resolution at lower taxonomic ranks. Only 4.69% of all vOTUs could be assigned to a known viral family, with \u003cem\u003eMicroviridae\u003c/em\u003e, \u003cem\u003ePeduoviridae\u003c/em\u003e, and \u003cem\u003eDrexlerviridae\u003c/em\u003e being among the more frequently identified families (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). This indicates that over 95% of the vOTUs likely represent novel viral families or genera not yet characterized in existing databases. Even among the vOTUs with complete genomes, a substantial portion remained unclassified at the family level, underscoring the significant divergence of these avian gut viruses from currently known phages and highlighting the vast uncharted diversity within this ecosystem. These findings are consistent with observations from other environmental virome studies [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], pointing to substantial gaps in our current understanding of global viral taxonomy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAvian phages predominantly target core gut microbes\u003c/h2\u003e\u003cp\u003eTo gain insights into the ecological roles of the identified viruses, we predicted their prokaryotic hosts using iPHoP [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], focusing on 8,853 high-quality vOTUs (defined in Methods as having CheckV completeness\u0026thinsp;\u0026gt;\u0026thinsp;90% and contamination\u0026thinsp;\u0026lt;\u0026thinsp;5%). The analysis revealed that avian phages identified in this study are predicted to infect bacteria across 38 phyla, predominantly targeting members of the core gut microbiota (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). The most frequently predicted host phyla included \u003cem\u003eBacillota_A\u003c/em\u003e (targeted by n\u0026thinsp;=\u0026thinsp;3,517 vOTUs), \u003cem\u003eBacillota\u003c/em\u003e (targeted by n\u0026thinsp;=\u0026thinsp;2,104 vOTUs), \u003cem\u003eBacteroidota\u003c/em\u003e (targeted by n\u0026thinsp;=\u0026thinsp;1,466 vOTUs), \u003cem\u003ePseudomonadota\u003c/em\u003e (targeted by n\u0026thinsp;=\u0026thinsp;800 vOTUs), and \u003cem\u003eActinomycetota\u003c/em\u003e (targeted by n\u0026thinsp;=\u0026thinsp;522 vOTUs), which play key roles in carbohydrate metabolism, gut homeostasis, and immune regulation [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. At the genus level, prominent predicted hosts included \u003cem\u003ePhocaeicola\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e (phylum \u003cem\u003eBacteroidota\u003c/em\u003e), \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u003c/em\u003e (phylum \u003cem\u003eBacillota\u003c/em\u003e), \u003cem\u003eMediterraneibacter\u003c/em\u003e (phylum \u003cem\u003eBacillota_A\u003c/em\u003e), and \u003cem\u003eEscherichia\u003c/em\u003e (phylum \u003cem\u003ePseudomonadota\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Among the vOTUs with predicted prokaryotic hosts, 69.41% were classified as specialist bacteriophages, predicted to infect only a single bacterial genus, while the remaining 30.59% were generalists, capable of infecting multiple prokaryotic genera (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Significant variations in viral genome sizes and GC content were observed across different predicted host phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). For instance, phages predicted to infect \u003cem\u003eBacteroidota\u003c/em\u003e, \u003cem\u003eBacillota_C\u003c/em\u003e, and \u003cem\u003ePseudomonadota\u003c/em\u003e generally had larger genome sizes (median: 45.85\u0026ndash;56.97 kb) compared to those targeting \u003cem\u003eActinomycetota\u003c/em\u003e and \u003cem\u003eThermoplasmatota\u003c/em\u003e (median: 34.62\u0026ndash;38.04 kb). Prokaryotic host specificity also varied at the genus level; phages targeting \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eLimosilactobacillus\u003c/em\u003e, and \u003cem\u003eLigilactobacillus\u003c/em\u003e were predominantly specialists (93.96\u0026ndash;98.47%), whereas those infecting \u003cem\u003ePhocaeicola\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eMediterraneibacter\u003c/em\u003e were mostly generalists (84.74\u0026ndash;92.64%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Furthermore, predicted phage lifestyles differed across host taxa. Phages infecting \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eLachnospira\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eAcutalibacter\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e were primarily predicted to be virulent (73.53\u0026ndash;80.92%), while those targeting \u003cem\u003eFusicatenibacter\u003c/em\u003e, \u003cem\u003eMediterraneibacter\u003c/em\u003e, \u003cem\u003eLigilactobacillus\u003c/em\u003e, and \u003cem\u003eCAG-273\u003c/em\u003e were predominantly temperate (75.00\u0026ndash;81.08%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePhylogenetic analysis uncovers potential novel viral lineages\u003c/h2\u003e\u003cp\u003eA phylogenetic tree constructed from 7,569 representative high-quality viral genomes from AvianViromeDB illustrated the broad diversity within the avian gut virome (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The majority of these phages clustered within two major taxonomic groups: \u003cem\u003eCaudoviricetes\u003c/em\u003e (tail phages) and \u003cem\u003eTectiliviricetes\u003c/em\u003e (twin-portal phages). At the family level, while some vOTUs were classified into known families such as \u003cem\u003eAutographiviridae\u003c/em\u003e, \u003cem\u003eDrexlerviridae\u003c/em\u003e, \u003cem\u003eDemerecviridae\u003c/em\u003e, \u003cem\u003eHerelleviridae\u003c/em\u003e, \u003cem\u003eRountreeviridae\u003c/em\u003e, \u003cem\u003eTectiviridae\u003c/em\u003e, and \u003cem\u003eStraboviridae\u003c/em\u003e, a significant portion remained unclassified, consistent with the taxonomy classification results. To further explore evolutionary relationships and identify novel lineages, a gene-sharing network was constructed using these 7,569 vOTUs along with reference viral genomes from NCBI Virus RefSeq database (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Most metagenome-assembled vOTUs from AvianViromeDB showed interconnections with reference phages, supporting their viral nature. Following an edge-weight filtering step to reduce network complexity and enhance interpretability (as described in Methods), 220 distinct viral clusters (VCs) were identified. Notably, one prominent VC (designated as VCX) was composed exclusively of vOTUs from AvianViromeDB and shared no discernible gene content with any NCBI reference phages (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). This suggests the presence of a potentially novel viral lineage previously unrepresented in public databases. Further analysis of this unique cluster revealed that multiple vOTUs within it were predicted to infect bacteria belonging to the order \u003cem\u003eOscillospirales\u003c/em\u003e (phylum \u003cem\u003eBacillota\u003c/em\u003e, class \u003cem\u003eClostridia\u003c/em\u003e). The size of the nodes in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC, representing these vOTUs, indicates their detection across multiple distinct avian hosts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eAvian gut virome harbors a repertoire of clinically relevant ARGs\u003c/h2\u003e\u003cp\u003eOur investigation into AvianViromeDB revealed that the avian gut virome serves as a notable reservoir for diverse antibiotic resistance genes (ARGs). Employing a stringent identification pipeline (as detailed in Methods, Tables S5-S8), we identified seven distinct ARGs within the viral contigs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These genes encode resistance mechanisms primarily involving enzymatic modification of antibiotics and target site alteration. Among the identified ARGs, those conferring resistance to macrolides, lincosamides, and streptogramin B (MLSB antibiotics) were particularly prevalent. The gene \u003cem\u003eerm(X)\u003c/em\u003e was the most frequently detected ARG (n\u0026thinsp;=\u0026thinsp;25 occurrences), predominantly identified in vOTUs derived from chicken metagenomes. Other members of the erm family, namely \u003cem\u003eerm(Y)\u003c/em\u003e, \u003cem\u003eerm(G)\u003c/em\u003e, and \u003cem\u003eerm(B)\u003c/em\u003e, which also mediate MLSB resistance through methylation of the 23S rRNA [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], were also present. Further contributing to lincosamide resistance, the \u003cem\u003elnu(C)\u003c/em\u003e gene, responsible for enzymatic drug modification [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], was detected three times. Resistance determinants against other critical antibiotic classes were also found. Two instances of \u003cem\u003eaac(6\u0026rsquo;)-Im\u003c/em\u003e, an aminoglycoside-modifying enzyme that confers resistance to important aminoglycosides such as amikacin, gentamicin, and tobramycin via acetylation [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], were identified. Additionally, the β-lactamase gene \u003cem\u003ecfxA4\u003c/em\u003e, which can hydrolyze cephalosporin antibiotics [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], was detected twice in vOTUs originating from both chicken and duck samples. The detection of these ARGs, some of which target antibiotics commonly used in poultry production and human medicine, across different avian hosts (chickens and ducks) points to a potentially widespread distribution of these resistance determinants within avian gut viral communities. Critically, the presence of these ARGs within viral genomes strongly suggests the potential for phage-mediated horizontal gene transfer (HGT), highlighting a significant pathway for the propagation and dissemination of antibiotic resistance from the avian gut virome to the broader microbial ecosystem, and posing potential risks to animal and human health under the One Health paradigm.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe ARGs are derived from the avian gut virome.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGENE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eACCESSION\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eResistance Mechanism\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAntibiotic Classes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAnimal-Host\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eErm(X)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNG_047853.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethylates 23S rRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMacrolides, Lincosamides, Streptogramin B (Erythromycin, Clindamycin)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChicken\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLnu(C)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNG_047924.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModifies lincosamides\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLincosamides (Clindamycin)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChicken, Duck\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eErm(Y)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNG_047855.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethylates 23S rRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMacrolides, Lincosamides, Streptogramin B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChicken, Duck\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAac(6\u0026rsquo;)-Im\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNG_052222.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcetylates antibiotics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAminoglycosides (Amikacin, Gentamicin, Tobramycin)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChicken\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCfxa4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNG_047642.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHydrolyzes cephalosporins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCephalosporins (Cefotaxime, Ceftazidime)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChicken, Duck\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eErm(B)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNG_047793.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethylates 23S rRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMacrolides, Lincosamides, Streptogramin B (Erythromycin, Clindamycin)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eChicken\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eErm(G)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNG_047827.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethylates 23S rRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMacrolides, Lincosamides, Streptogramin B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDuck\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eViral AMGs implicate phages in modulating prokaryotic host metabolism\u003c/h2\u003e\u003cp\u003eFunctional annotation of predicted viral genes based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways identified 10,095 putative auxiliary metabolic genes (AMGs) corresponding to 112 third-level KEGG pathways (Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e). These AMGs were broadly categorized into five major first-level KEGG modules: \u0026ldquo;Metabolism\u0026rdquo;, \u0026ldquo;Genetic Information Processing\u0026rdquo;, \u0026ldquo;Environmental Information Processing\u0026rdquo;, \u0026ldquo;Cellular Processes\u0026rdquo;, and \u0026ldquo;Organismal Systems\u0026rdquo;. For clarity in our analysis, these were further grouped into more general functional categories relevant to viral impact on prokaryotic host metabolism, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The most abundant AMGs fell into categories related to \u0026ldquo;Information systems\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;6,486 AMGs, primarily DNA replication and repair), \u0026ldquo;Amino Acid Metabolism\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;2,737), and \u0026ldquo;Carbohydrate Metabolism\u0026rdquo; (including CAZymes, n\u0026thinsp;=\u0026thinsp;235) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Further analysis at the KEGG pathway level (third-level) revealed significant enrichment of AMGs in pathways crucial for viral replication and host modulation, including glycolysis, amino acid metabolism (e.g., cysteine and methionine metabolism), and nucleotide biosynthesis (e.g., pyrimidine and purine metabolism) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Notably, AMGs associated with nucleotide metabolism, such as those involved in the synthesis of CDP, dCTP, and dTMP (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA), were frequently identified, suggesting a viral strategy to enhance the host\u0026rsquo;s nucleotide pool for efficient phage replication. AMGs involved in carbohydrate metabolism were also prominent, including genes encoding glycoside hydrolases (GHs) from families such as \u003cem\u003eGH5\u003c/em\u003e, \u003cem\u003eGH13\u003c/em\u003e, and \u003cem\u003eGH16\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB; Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e). These enzymes can facilitate the breakdown of complex polysaccharides (e.g., cellulose, starch) into simpler sugars. Other AMGs were linked to downstream pathways such as glycolysis and one-carbon metabolism, potentially influencing the production of pyruvate, formate, and acetyl-CoA, which are precursors for short-chain fatty acids (SCFAs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents Avian Virome Database (AvianViromeDB), the most comprehensive catalog of the avian gut virome to date, comprising 61,608 viral operational taxonomic units (vOTUs) derived from 2,692 metagenomic samples. Our analyses reveal remarkable viral diversity, high avian host specificity, and significant functional potential encoded by these viruses, including a repertoire of auxiliary metabolic genes (AMGs) and antibiotic resistance genes (ARGs). These findings substantially expand our understanding of the viral landscape within avian gut ecosystems and provide a critical resource for future research into virus-host interactions and their ecological implications.\u003c/p\u003e\u003cp\u003eA key finding of this study is the vast extent of uncharacterized viral diversity. Despite the large scale of our dataset, over 95% of the identified vOTUs could not be classified to known viral families, highlighting a significant \u0026lsquo;viral dark matter\u0026rsquo; within the avian gut. This observation is consistent with virome studies in other environments, such as the human gut and various aquatic and terrestrial ecosystems [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], underscoring that current viral databases represent only a small fraction of global viral diversity. The predominance of \u003cem\u003eCaudoviricetes\u003c/em\u003e among the classifiable vOTUs aligns with their known ubiquity in gut environments [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. However, the inability to assign most vOTUs to lower taxonomic levels, even for complete genomes, suggests that the avian gut harbors numerous novel viral lineages that have diverged significantly from currently characterized phages. Addressing this knowledge gap will require concerted efforts in isolating and characterizing these novel viruses, alongside the development of improved bioinformatic tools for viral classification and phylogenomic analysis. The unique viral cluster identified (VCX) in our gene-sharing network analysis, composed entirely of AvianViromeDB vOTUs with no homology to known reference phages and predicted to infect \u003cem\u003eOscillospirales\u003c/em\u003e bacteria, exemplifies this hidden diversity and warrants further investigation. Members of \u003cem\u003eOscillospirales\u003c/em\u003e are known to play roles in avian host metabolism, particularly in the degradation of complex carbohydrates and production of short-chain fatty acids (SCFAs); thus, phages targeting these bacteria could indirectly influence avian host nutrient acquisition and gut health, representing an important facet of the virus-bacteria-avian host tripartite interaction.\u003c/p\u003e\u003cp\u003eThe prediction of phage lifestyles indicated a predominance of virulent phages over temperate ones in the avian gut virome. This contrasts with some mammalian gut virome studies where temperate phages are often reported as highly abundant, potentially contributing to prokaryotic host genome evolution and stability through lysogeny [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. However, other studies, particularly those focusing on active viral replication, have also reported higher proportions of virulent phages [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The higher prevalence of virulent phages in our dataset could reflect several factors, including a more dynamic \u0026ldquo;kill-the-winner\u0026rdquo; ecological interaction in the avian gut, differences in avian host immune pressures, or biases in viral recovery from metagenomic data that might favor the detection of actively replicating lytic phages. Further research, perhaps incorporating metatranscriptomics, would be needed to differentiate between these possibilities and understand the true balance of lytic versus lysogenic cycles in this ecosystem.\u003c/p\u003e\u003cp\u003eThe identification of seven distinct ARGs within the avian gut virome, including those conferring resistance to clinically important antibiotics like macrolides, lincosamides, aminoglycosides, and cephalosporins, is a significant concern from a One Health perspective. These antibiotics are widely used in both poultry production and human medicine [\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The presence of ARGs in viral genomes, particularly in phages capable of infecting diverse bacterial hosts, highlights the potential for phage-mediated horizontal gene transfer (HGT) to disseminate these resistance determinants. This finding aligns with previous reports of ARGs in viromes from various environments, including the human and pig gut, and chicken meat [\u003cspan additionalcitationids=\"CR80 CR81 CR82 CR83 CR84 CR85 CR86 CR87 CR88\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. The erm family genes, frequently detected in our study, are known to confer MLSB resistance via 23S rRNA methylation, while \u003cem\u003elnu(C)\u003c/em\u003e modifies lincosamides, \u003cem\u003eaac(6\u0026rsquo;)-Im\u003c/em\u003e acetylates aminoglycosides, and \u003cem\u003ecfxA4\u003c/em\u003e hydrolyzes β-lactams. The detection of these ARGs in phages from different avian hosts (chickens and ducks) suggests a widespread distribution and underscores the avian gut virome as a potential reservoir and vehicle for ARG propagation, necessitating closer surveillance and strategies to mitigate this risk.\u003c/p\u003e\u003cp\u003eOur study also uncovered a rich repertoire of viral AMGs, implicating avian gut phages in the modulation of prokaryotic host metabolism. The abundance of AMGs related to \u0026ldquo;Information systems\u0026rdquo; (DNA replication and repair) is expected, as phages often carry genes to supplement or co-opt host machinery for their own replication. More interestingly, we found numerous AMGs involved in amino acid and nucleotide metabolism. For instance, the frequent identification of viral genes encoding enzymes like ribonucleoside-diphosphate reductase (K00525) and thymidylate synthase (K00560) suggests a viral strategy to boost the host\u0026rsquo;s pyrimidine deoxyribonucleotide pool, thereby facilitating efficient phage DNA synthesis. This aligns with findings from marine viromes where phages were shown to enhance host nucleotide biosynthesis [\u003cspan additionalcitationids=\"CR91\" citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Furthermore, the prominence of AMGs related to carbohydrate metabolism, including various glycoside hydrolases (GHs) such as \u003cem\u003eGH5\u003c/em\u003e, \u003cem\u003eGH13\u003c/em\u003e, and \u003cem\u003eGH16\u003c/em\u003e, suggests that avian gut phages actively participate in the breakdown of complex polysaccharides. For example, GH5 enzymes are known for cellulose degradation, GH13 for starch and glycogen breakdown, and GH16 for hemicellulose hydrolysis [\u003cspan additionalcitationids=\"CR94 CR95 CR96\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. The degradation of these complex plant-based fibers into simpler sugars by phage-encoded AMGs could provide readily available energy sources for the bacterial host, and subsequently for the phage itself. Similar roles for phage AMGs in lignocellulose degradation have been reported in ruminant animals [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. These AMGs, coupled with others involved in downstream pathways like glycolysis and one-carbon metabolism (e.g., Wood-Ljungdahl pathway), could significantly influence the production of key metabolites such as pyruvate, formate, and acetyl-CoA, which are precursors for SCFAs. SCFAs are crucial for gut homeostasis and host energy supply. Thus, avian gut phages, through their AMGs, may not only reprogram host metabolism to optimize their replication but also contribute significantly to the overall metabolic output of the gut microbiota, impacting nutrient availability and avian host physiology. This represents another layer of the virus-bacteria-avian host interaction.\u003c/p\u003e\u003cp\u003eDespite the comprehensive nature of AvianViromeDB, this study has limitations. Firstly, our analysis relied on publicly available metagenomic data, which may have inherent biases related to sample collection, DNA extraction methods, and sequencing depth across different original studies. Secondly, while we employed multiple tools for viral identification and prokaryotic host prediction, these in silico predictions require experimental validation. The functions of many identified AMGs and the impact of ARGs also warrant further experimental investigation. Thirdly, the database, while extensive, still represents a snapshot and does not capture the temporal dynamics or the full diversity across all avian species and geographical locations.\u003c/p\u003e\u003cp\u003eIn conclusion, AvianViromeDB provides an unprecedented view into the diversity and functional potential of the avian gut virome. Our findings reveal a vast uncharted viral space, highlight the ecological roles of phages in targeting core microbiota, and underscore their potential to modulate host metabolism and disseminate ARGs. This resource will be invaluable for future research aimed at understanding the complex interplay among viruses, their prokaryoitc hosts, and the avian host, and could inform the development of novel strategies, such as phage therapy or microbial community intervention, to improve avian host health and productivity, particularly in poultry farming. Continued exploration of this \u0026ldquo;viral dark matter\u0026rdquo; and functional characterization of novel viral genes will undoubtedly yield further insights into the critical roles viruses play in shaping gut ecosystems and influencing avian biology.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study established Avian Virome Database (AvianViromeDB), the most comprehensive catalog of the avian gut virome to date, by analyzing 2,692 diverse avian metagenomes. We unveiled a vast and largely uncharacterized viral diversity, with over 95% of the 61,608 identified vOTUs representing novel viral lineages, predominantly within the \u003cem\u003eCaudoviricetes\u003c/em\u003e. Our findings demonstrate high avian host specificity of these viruses and reveal their significant functional potential, evidenced by a rich repertoire of auxiliary metabolic genes (AMGs) implicated in modulating prokaryotic host metabolism, and the presence of clinically relevant antibiotic resistance genes (ARGs). The AvianViromeDB significantly expands the known viral sequence space and provides a critical resource for future investigations into viral ecology, virus-host interactions, and the evolutionary dynamics within avian gut ecosystems. The characterization of novel viral lineages, AMGs, and ARGs opens new avenues for understanding the mechanistic roles of viruses in avian health, disease, and nutrient processing, as well as their influence on the broader gut microbial community. Ultimately, this work lays a foundation for developing novel strategies, such as phage-based interventions, to improve avian health and productivity in agricultural settings and to better understand the ecological impact of viruses in wild bird populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article (and its supplementary information files).The AvianViromeDB are freely available at https://phagebyte.github.io/avianviromedb. All raw sequencing data used in this study are publicly available and can be accessed through the NCBI Sequence Read Archive under the project accession numbers listed in Table S1. The code used for data analysis is available at the GitHub repository: https://github.com/rujinlong/avianviromedb.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was supported by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation - Emmy Noether program, Project No. 273124240; and SPP2330, Project No. 464797012), and European Research Council Starting grant (ERC StG 803077) awarded to LD; National Natural Science Foundation of China (#32341055) awarded to XW; China Agriculture Research System of MOF and MARA (CARS-42-2) awarded to ZGH.\u003c/p\u003e\n\u003ch3\u003eAuthors\u0026apos; contributions\u003c/h3\u003e\n\u003cp\u003eAll authors contributed intellectually to and agreed to this submission. XQY, JLR and XW designed the experiments. PYL, ZZW, DH, LY and YSZ conducted the experiments and collected the data. XQY, PYL, ZZW and JLR analyzed the data. XQY, PYL, YSZ prepared figures. XQY wrote the initial draft of the manuscript, while JLR, XW and LD provided substantial feedback. JLR, XW, and LD contributed to the conceptual design of the study. XW, ZGH and LD provided funding support. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eThe authors sincerely thank the members of the Deng laboratory for their constructive discussions and valuable suggestions. We are also deeply grateful to Prof. Yingping Xiao from the Zhejiang Academy of Agricultural Sciences for generously providing the duck metagenomic datasets used in this study. 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ISME Commun. 2025;5(1):ycaf021.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Avian, Metagenomics, Gut virome, Microbiome, Bacteriophage, Antibiotic resistance genes, Auxiliary metabolic genes","lastPublishedDoi":"10.21203/rs.3.rs-6913311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6913311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe avian gut virome plays a crucial role in shaping the gastrointestinal microbial ecosystem of birds. However, its taxonomic and functional diversity remains poorly characterized due to the absence of a dedicated reference database. This limitation hampers our understanding of the complex interactions among viruses, their bacterial hosts, and the overarching avian host, as well as viral contributions to gut microbial ecology.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo address this gap, we developed the Avian Virome Database (AvianViromeDB) by integrating 2,692 gut metagenomic samples from poultry and wild birds. This effort yielded 252,752 viral contigs, which are clustered into 61,608 high-quality, species-level viral operational taxonomic units (vOTUs). Taxonomic analysis revealed that 99.05% of these vOTUs belonged to the class \u003cem\u003eCaudoviricetes\u003c/em\u003e, yet only 4.69% could be assigned to known viral families\u0026mdash;suggesting over 95% likely represent novel viral lineages. Prediction of prokaryotic hosts indicated that these viruses primarily target core gut microbiota, particularly \u003cem\u003eBacillota\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e, both central to carbohydrate metabolism. Functional annotation uncovered tens of thousands of auxiliary metabolic genes (AMGs), with enrichments in glycolysis, amino acid metabolism, and nucleotide biosynthesis pathways.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThese findings demonstrate that avian gut viruses may modulate microbial communities both through direct lysis of their bacterial hosts (\u0026ldquo;top-down\u0026rdquo; control) and by altering host metabolism via AMGs (\u0026ldquo;bottom-up\u0026rdquo; modulation). The resulting high-quality genome catalog reveals the remarkable diversity and functional potential of the avian gut virome, offering a valuable resource for future research into avian microbial ecology and the intricate interplay between viruses, bacteria, and their avian hosts. The AvianViromeDB is publicly accessible at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phagebyte.github.io/avianviromedb\u003c/span\u003e\u003cspan address=\"https://phagebyte.github.io/avianviromedb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e","manuscriptTitle":"Global Characterization of the Avian Gut Virome Reveals Extensive Viral Diversity and Functional Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 14:37:26","doi":"10.21203/rs.3.rs-6913311/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0185b603-1f33-4820-9153-3c16d974ddcf","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T12:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 14:37:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6913311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6913311","identity":"rs-6913311","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}