A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments

A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments Yingjian Wu, Na Gao, Chuqing Sun, Tong Feng, Qingyou Liu, Wei-Hua Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2712043/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Apr, 2024 Read the published version in Microbiome → Version 1 posted 4 You are reading this latest preprint version Abstract Background : Ruminants are important livestock animals that have a unique digestive system comprising multiple stomach compartments. Despite significant progress in the study of microbiome in the gastrointestinal tract (GIT) sites of ruminants, we still lack an understanding of the viral community of ruminants. Here, we surveyed its viral ecology using 2333 samples from ten sites along the GIT of eight ruminant species. Results : We present the Unified Ruminant Phage Catalogue (URPC), a comprehensive survey of phages in the GITs of ruminants including 64,922 non-redundant phage genomes. We characterized the distributions of the phage genomes in different ruminants and GIT sites and found that most phages were organism-specific. We revealed that ~60% of the ruminant phages were lytic, which was the highest as compared with those in all other environments and certainly will facilitate their applications in microbial interventions. To further facilitate the future applications of the phages, we also constructed a comprehensive virus-bacteria/archaea interaction network and identified dozens of phages that may have lytic effects on methanogenic archaea. Conclusions : The URPC dataset represents a useful resource for future microbial interventions to improve ruminant production and ecological environmental qualities. Phages have great potential for controlling pathogenic bacterial/archaeal species and reducing methane emissions. Our findings provide insights into the virome ecology research of the ruminant GIT and offer a starting point for future research on phage therapy in ruminants. Ruminants gastrointestinal tract microbiome virome bacteriophages methane production Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Ruminants have been an important part of human society for centuries [1, 2], providing us with a variety of economic products including meat, milk, and fur [3]. The unique digestive system of the multi-chambered stomach including the rumen, reticulum, omasum, and abomasum allows them to return the semi-digested food fibers to the mouth for further digestion [4]. This makes them well-suited to grazing on pastures and other types of land that are not suitable for other forms of agriculture [5]. Recent research has shown that the gastrointestinal tract (GIT) of ruminants contains a great diversity of prokaryotic and eukaryotic microorganisms [6-9]. Due to the various compositions of microorganisms among different locations, the microbiomes distributed in different sites play different roles but perform equally important physiological functions in ruminants’ survival [10, 11]. The GIT microbes enable the ruminants to digest lignocellulose and other plant feedstuffs [12, 13] and protect the animal host from harmful bacteria and other pathogens [14]. However, there can also be negative effects of the GIT microbiome on ruminant health and productivity. For example: a disruption in the microbial balance in the rumen can lead to the overproduction of lactic acid, which can lower the pH and cause ruminal acidosis, a common metabolic disorder in ruminants [15]. Precision regulation of the gastrointestinal tract microbiome in ruminants is crucial for improving animal health and productivity, and reducing the environmental impact of animal agriculture. Methane-producing archaea exist in the GIT of ruminants and are one of the main sources of greenhouse gases, which have been targeted for eradication or reduction [16, 17]. However, achieving precision regulation of methane-producing archaea or pathogenic bacteria in ruminants is not a simple task, as it requires a deep understanding of the complex interplay between the microbiome, diet, and host physiology. Currently, there is a lack of system tools available to enable precise manipulation of the microbiome in ruminants [18]. Bacteriophages (phages) are a critical component of the ruminant GIT microbiome and play crucial roles in shaping microbial composition [19]. In addition, phages hold great promise for the precision manipulation of the bacteriome (i.e., the bacterial and archaeal microbes) because of their narrow microbial-host range (i.e., often at species and even strain levels [20, 21]), providing alternative ways to suppress pathogenic bacterial/archaeal species [22] and control methane emissions [16, 23]. The lifestyles of viruses can be broadly classified into two categories: lytic and lysogenic. The separation of lytic phages is important in practical applications, as lytic phages are typically more convenient to work with and have more immediate applications, such as using phages as antimicrobials against bacterial infections in animals. For example, phages have been used to control bacterial infections in dairy cattle with mastitis, which is a common and costly disease in the dairy industry [24]. Despite tremendous success in identifying viruses from various environmental sources such as the ruminants rumen [25-31], human gut [21, 32-38], aquatic, terrestrial, plants, as well as other mammals (i.e., IMG/VR v3 [39]), the virome structure remains the “dark matter” in different ruminant GIT sites, especially those other than the rumen, compared with other environments. A comprehensive resource reference phage genome is required to further characterize the viral community of the ruminant GIT and enable genome-resolution research across ruminants. Here, we present the Unified Ruminant Phage Catalogue (URPC), a comprehensive survey of phages in the gastrointestinal tracts of ruminants. Currently, the URPC contains 64,922 non-redundant phage genomes identified using 2,333 bulk metagenomics sequencing samples from 18 published works (Table S1), covering ten gastrointestinal sites from eight different ruminant species. We found that 60.53% (n = 39,300) of phage genomes were novel compared with those in the public viral datasets, supporting the novelty of our dataset. We characterized the distributions of the phage genomes in different ruminants and GIT sections, as well as the lifestyles of the phages. Strikingly, we revealed that ~60% of the ruminant phages were lytic, which was the highest as compared with those in all other environments and certainly will facilitate their applications in microbial interventions. To further facilitate the future applications of the phage, we also constructed a comprehensive virus-bacteria/archaea interaction network and identified dozens of phages that may have lytic effects on methanogenic bacteria. Together, our URPC dataset represents a useful resource for future microbial interventions to improve ruminant production and ecological environmental qualities. Methods Data collection, quality control, and removal of host- and food-associated genomes To perform a comprehensive search for phages of the ruminant gastrointestinal tract (GIT), publicly available sequencing reads of 2,333 ruminant metagenomic samples were downloaded from National Center for Biotechnology Information/NLM/NIH (NCBI) (Fig. S1; Table S1), covering eight ruminants (buffalo, camel, cattle, cow, deer, goat, sheep, yak) and ten GIT sites (rumen, reticulum, omasum, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum/feces) (Table S2). Raw reads were trimmed by Trimmomatic (v 0.39) [40] with the options ‘ILLUMINACLIP: TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:15 MINLEN:50 LEADING:3 TRAILING:3’. To decrease potential DNA contamination from the animal hosts, reads that could be aligned to their closest genomes from NCBI ( Capra hircus , GCF_001704415.1; Bubalus bubalis , GCA_004794615.1; Camelus bactrianus , GCF_000767855.1; Camelus dromedarius, GCF_000803125.2; Bos taurus ,GCF_002263795.1; Capra hircus , GCF_001704415.1; Alces alces , GCA_007570765.1; Cervus elaphus , GCF_910594005.1; Rangifer tarandus caribou , GCA_019903745.1; Capreolus capreolus ,GCA_000751575.1; Ovis aries , GCF_016772045.1; Hydropotes inermis , GCA_020226075.1; Bos grunniens , GCA_005887515.2), and some food-associated genomes such as Glycine max , Zea Mays , and Medicago truncatula were filtered out using Bowtie2 (v 2.3.5.1) [41] with options ‘--very-sensitive’. The remaining paired reads were then used for further analyses. Metagenomic assembly and viral contigs prediction Unless otherwise stated, default parameters were used. Each sample was assembled using MEGAHIT (v 1.2.8) [42] with options ‘--min-contig-len 1000’. Assembled contigs of ≥1.5kb in size were used to identify viral sequences using VirSorter2 (v 2.1) [43] with options ‘--include-groups "dsDNAphage, ssDNA" --min-score 0.7’ and VirFinder (v 1.1) [44] with default parameters. Contigs were identified as phages by both VirSorter2 and VirFinder (score≥0.6 and p <0.05). Quality evaluation of phage genomes and dereplication of URPC datasets The completeness of the viral contigs was estimated using CheckV (v 0.8.1) [45]. A total of 74,519 identified viral contigs with >50% completeness were then selected and renamed according to their animal hosts. The sequences of these contigs were merged into a single file and dereplicated using CD-HIT [46](v4.8.1, parameters: -c 0.95 -n 8) using a global identity threshold of 95%. The resulting non-redundant representative viral genomes consisted of a total of 64,922 viral populations (VPs) and were referred to as the Unified Ruminant Phage Catalogue (URPC). Comparing the URPC genomes with public viral datasets To estimate the proportion of novel phage genomes in the URPC genomes, the BLASTn tool (v 2.5.0) [47] was used to search all its sequences against a list of public viral databases including four public rumen virome datasets from the rumen virome database (RVD) [31], Hitch et al. [25] Solden et al. [26] and Friedersdorff et al. [27], NCBI viral Reference genomes, Release 201 (Jul 06, 2020), IMG/VR v3 [39], and four public human virome datasets such as GVD [33], GPD [32], MGV [21], and CHGV [34] (Table S3). Average nucleotide identity (ANI) was calculated by merging the BLASTn hit regions with identity ≥90% and hit length ≥ 500bp, then calculating the coverage of these regions. Based on the overall ANI, a viral sequence was considered to be novel if it has <95% ANI as compared with other viral sequences. Clustering viral contigs into viral clusters (VCs) The clustering of viral contigs into viral clusters (VCs) was performed using a strategy adopted from the GPD [32]. Briefly, the BLASTn algorithm with default parameters was used to search the nucleotide sequences of the URPC genomes and the environmental viral sequences of various habitats (e.g., Terrestrial, Freshwater, Plants and etc.) in the IMG/VR v3 database against themselves for homologous sequences. An E -value threshold of 1E-10 was first used to filter the BLASTn results; the BLASTn query-hit pairs were further filtered to retain those with a coverage > 70% on the larger genomes and a coverage >90% on the smaller genomes. Here, the coverage was calculated by merging the aligned fraction length of BLASTn high-scoring pair (HSP) sequences that shared at least 90% nucleotide similarity. Finally, a Markov clustering algorithm (MCL v14-137) [48] was used with an inflation value of 6.0, which took the filtered BLASTn results as input, carried out graph-based clustering, and clustered the viral contigs into 55,635 VCs. Prediction of viral lifestyles The lifestyle classifications of all the URPC genomes were analyzed using DeePhage v1.0 [49] with the default parameters . DeePhage uses a scoring system to classify phage genomes into four categories, including temperate (with scores ≤0.3), uncertain temperate (0.3~0.5), uncertain virulent (0.5~0.7), and virulent (>0.7). Higher scores indicate higher virulence. According to a benchmark study [50], DeePhage can classify short contigs from metagenomic data and has the best reported performance on lifestyle prediction, while BACPHLIP [51] is only designed for complete phage genomes. And DeePhage has better generalization ability on novel phages as using a deep neural network to learn features from both DNA and protein sequences of phages, while BACPHLIP relies on a set of conserved protein domains that are associated with lysogeny. Therefore, we chose DeePhage to predict phage lifestyles of URPC. Taxonomic annotation of the URPC phages To taxonomically classify the phage contigs, VirusTaxo (https://github.com/omics-lab/VirusTaxo, downloaded on 19th April 2022) [52] was used to compare the nucleotide sequences against those in the prebuilt database of VirusTaxo and assign them to a known viral genus at an entropy index threshold of <0.5. A Demovir script (https://github.com/feargalr/Demovir; downloaded on 6th January 2022) was then used to predict family and order ranks for the remaining genomes by searching for viral marker genes at the amino acid level. Co-diversification analysis of phages with their animal hosts For all VCs that contained phages from three and more animal hosts, a phylogenetic analysis was performed. In total, 80 VCs were selected. First, Prokka v1.13 [53] (--kingdom Phages) was used to annotate the phage genomes for protein-coding genes. Pan-genome analysis was carried out for each of the VCs with Roary [54] to identify core genes and create a multiFASTA alignment of core genes using MAFFT [55] by using the multiFASTA alignment as input. A phylogenetic tree was then built using FastTree [56] v2.1.10 with default parameters. All the phylogenetic trees were then visualized and annotated using iTol [57]. For each VC, the branch length between any two phage genomes was calculated. Two single-tailed Wilcoxon rank sum tests were performed on the branch lengths from the same animal hosts and those from different animal hosts. The p-value of the hypothesis that phages from different animal hosts had higher branch lengths were used to determine whether the phages significantly co-evolved or not co-evolved with their animal hosts. Microbial host analysis of the URPC phages To find putative microbial hosts for the URPC phages, ruminant metagenome-assembled genomes (MAGs) from four publications were downloaded, including the buffalo GIT [6], ruminants GIT [7], cattle rumen [58], and goat GIT (NCBI SRA database PRJNA723432). In addition, MAGs from the Global Microbial Gene Catalog (GMGC) [59] that covered 14 different habitats were also downloaded. To establish phage-microbial host relationships between these MAGs and the URPC phages, two bioinformatic methods were used that included the CRISPR-spacer matches and nucleotide sequence similarity searches. The CRISPR spacers of the MAGs were identified using CRT (v 1.2) [60] and MinCED (v 0.4.2, https://github.com/ctSkennerton/minced). The union of the CRISPR spacers was then aligned to the viral populations using BLASTn (v 2.5.0) [47] with options of ‘-word_size 10 -dust no -max_target_seqs 10000’. Matches with mismatch≤1 and alignment length > 95% spacer length were retained. In addition, BLASTn was used to compare the viral populations with the MAGs. A putative viral-host relationship could be established if their nucleotide sequences shared >90% identity over >500 bps. Phylogenetic analysis of animal hosts The phylogenetic analysis of the eight ruminant species was carried out using a method based on a previous study [61]. Briefly, the genomic sequences of the eight ruminant species were downloaded from the NCBI Genome database. Then the universally conserved single-copy marker genes from each genome were identified using fetchMG [62]. The protein sequences of the markers were then aligned using MUSCLE [63] (-maxiters 100). To eliminate divergent regions from the resulting multiFASTA alignment, Gblocks [64] were used (parameters: -t=p -b3=8 -b4=2 -b5=h). The maximum likelihood trees were built with RAxML [65] with default parameters. Statistical analysis All statistical analyses were conducted using R (v4.0.4) with a two-sided Wilcoxon rank sum test unless otherwise stated. Results A unified catalog of 64,922 phage genomes from the ruminant gastrointestinal tract To provide a comprehensive overview of the phages associated with the gastrointestinal tract (GIT) of ruminants, we collected a total of 2,333 metagenomic samples from 18 previously published research [6, 7, 13, 23, 26, 58, 66-78] (Table S1) that covered ten GIT sites from eight ruminant species, including (Fig 1A and Tables S1, S2). including buffalo (n = 745), cattle (n = 930), goat (n = 563), sheep (n = 133), deer (n = 115), yak (n = 50) and cow (n = 46). After quality filtering and removing host DNA sequences (Methods), a total of 14.17 terabytes (Tb) of clean data with more than 33 million reads and 9 billion bases per sample were retained (Table S1). We assembled them into a total of 302,721,852 contigs using MEGAHIT [42], averaging 132,251 contigs per sample with an N50 length of 3,836 (Table S4). To identify putative phage genomes, we screened the assembled contigs using a bioinformatics pipeline adopted from Luis et al. [32] (Methods), followed by quality assessment for viral genome completeness using CheckV [45] and dereliction using CD-HIT [46]. We obtained a total of 74,519 viral contigs (mostly bacteriophages) with >50% completeness and length >1.5 kb, corresponding to 64,922 non-redundant viral populations (VPs), i.e., species-level clusters at an Average Nucleotide Identity (ANI) of 95%. We defined the latter (i.e., the 64,922 non-redundant VPs) as the Unified Ruminant Phage Catalogue (URPC). Among these, 6,035 (9.29%), 3,085 (4.75%), and 55,802 (85.95%) were classified as complete, high- and medium-quality, respectively, according to the CheckV tool (Fig 1B; Table S5). Previous studies have commonly employed a 5kb threshold for identifying metagenome-based viral genomes [31, 39]. In our study, we opted for a 1.5kb threshold. To substantiate this choice, we categorized all URPC phages into four length groups: 60kb (n = 14,484). We first compared the qualities of phages in each group and were intrigued to discover that the <5kb group exhibited the highest proportion of complete (29.81%) phage genomes, as determined by CheckV, in comparison to the other three groups (Fig. S2A). Moreover, taxonomic annotation, as per our methods, was successful for 91.48% of phages in the <5kb group, surpassing the rates observed in all other groups (Fig. S2B). Therefore, our findings indicate that the utilization of short contigs (i.e., 1.5~5k) not only aids in more accurately estimating the number of phages but also surprisingly enhances the annotation rates. We used the rarefaction analysis to show that the saturation curve is far from plateaued, and more samples are required for the discovery of ruminant GIT phages (Fig. 1A). Similar trends were observed in human gut virome catalogs such as the Metagenomic Gut Virus (MGV) [21] and a phage genome catalogue of the Japanese [20]. Among all the animal hosts, we obtained the highest number of VPs (n = 33,156) in the buffalo, followed by the cattle (n = 10,589), goat (n = 8,756), and sheep (n = 5,876). And the number of viruses identified varied in different GIT sites (Fig. S3), which correlated with the number of samples we collected. The genome size and viral taxa vary among different animal hosts, indicating species-specific viral composition. Given the recent interest in human gut phageome, we then compared the genome length of URPC and other published metagenome-assembled human gut viral genomes, and found that URPC genomes were significantly longer than those in the human gut ( p < 2.22e−16, Wilcoxon Rank Sum test; Fig. S4). We annotated the VPs using VirusTaxo [52] and Demovir (https://github.com/feargalr/Demovir) (Fig. S5; Table S5) and assigned 74.69% of the VPs at the family level (Fig 1C). Among the annotated VPs, 16,507 (25.42% of the total) belong to the Siphoviridae , followed by Poxviridae (n = 6,327), Mimiviridae (n = 5,409), Baculoviridae (n = 3,962), Myoviridae (n = 3,846), Podoviridae (n = 2,360) and Microviridae (n = 2,291). The overall taxonomic distribution, dominated by viral families such as Siphoviridae , Microviridae , Myoviridae , Podoviridae , was consistent with other metagenome-derived viral catalogs in ruminant rumen (RVD) [31] and human gut [21, 33]. Particularly, we reannotated the viral genomes from RVD using our pipeline for taxonomic classification (see Methods), and we found that the families Siphoviridae , Podoviridae , Myoviridae , Baculoviridae , and Myoviridae accounted for majority of the viral genomes in both URPC and RVD datasets. However, we also identified more phages from family Podoviridae than RVD (3.6% in URPC, and 0.5% in RVD) (Fig. S6; Table S6) indicating that URPC expands the diversity of the ruminant gastrointestinal phage genomes. We then examined the novelty of the URPC phage genomes by comparing them with several public viral databases including the NCBI viral Reference genomes (Release 201, Jul 06, 2020), IMG/VR v3 [39], four public rumen virome datasets [25-27, 31] and four human gut virome genome catalogs [21, 32-34] (Table S3). Applying an Average Nucleotide Identity (ANI) threshold of 95%, we observed that URPC exhibited the highest number of shared viral populations (VPs) with the RVD (Fig. S7A). Notably, 28.11% of URPC genomes were found in the RVD, while 33.84% of RVD genomes were identified in URPC (Fig. S7A). The substantial overlap between the two datasets can be attributed to the similar number of rumen samples used in URPC (826) compared to the RVD (975), despite variations in tools and criteria for viral contig identification in the latter [31] (refer to Table S7 for a detailed comparison). For a fair comparison, only 41,738 VPs from the RVD dataset meeting the same criteria as our dataset (i.e., completeness > 50%) were considered. At these criteria, this study identified a significantly higher number of VPs (64,145) compared to the RVD (Table S7). Furthermore, with the inclusion of three additional public rumen phage datasets, a total of 46,668 (71.89%) URPC phages were determined to be novel at a 95% ANI threshold (Fig. S7A), signifying URPC’s substantial contribution to expanding the ruminant gastrointestinal tract phage dataset despite prior outstanding works. When considering all the aforementioned public viral datasets, we found that 60.53% (n = 39,300) of VPs were considered novel at the 95% ANI threshold, indicating that the majority of URPC phages are novel (Fig. S7B). Organism-specific distribution of URPC genomes in animal hosts To investigate the correlation between the composition of VPs and their animal hosts, we first calculated the distribution of the VPs in each animal host. We discovered that 99.91% (n = 64,863) of VPs had only one animal host (referred to as organism-specific from now on), while only a few (n = 59) appeared in two or three animal hosts (Fig. 2A). To evaluate the distribution of phages with their animal hosts under higher level, we clustered the VPs into viral clusters (VCs) using methods adopted from the GPD [32] (Methods) and generated a total of 55,635 VCs. Among these, 99.06% of (n = 55,122) the VCs are organism-specific. Similarly, most (91.43%, n=50,874) of the VCs were distributed only in one GIT sit (Fig. 2B). Among the 4,761 VCs that were distributed in two or more GIT sites, 92.69% (n = 4,413) came from the same animal host, indicating an organism-specific distribution of the phages in the animal hosts. Among the 80 “broad-range” VCs (presented in three or more animal hosts), most of their animal hosts were goats (n = 60), buffaloes (n = 55), cattle (n = 52), sheep (n = 51), and deer (n = 39), while there were fewer in yaks (n= 4), camels (n = 2), and cows (n = 1) (Fig. 2C), which might be due to fewer available GIT samples of the latter three animals. To find out whether these “broad-range” VCs were food-related, we also include the VPs from the IMG/VR v3 database and re-did the viral clustering. We found that two of the “broad-range” VCs could be clustered with phages found in the terrestrial, freshwater, and plants (Fig. 2C), which confirmed previous research that phages could be readily introduced into the rumen from water sources, as well as housing and farm infrastructure [19]. However, the origins of the other 78 broad-range VCs remained to be identified. We next characterized the largest VCs (i.e., the VCs were ranked according to the number of containing VPs). 27 out of the top 50 had two or more animal hosts, suggesting that the diverse VCs were also the well-adapted ones; this trend was more apparent among the top ten VCs (i.e., of these, eight were found in two and more animal hosts). In addition, we observed that most of the top ten VCs consisted of lytic bacteriophages (Fig. 2E; the lifestyles were determined using a DeePhage tool; Methods). Most of the phages in the top ten VCs were 30~60kb in size, which were well within the size range of typical phage genomes; in contrast, crAssphages and Gubaphages were the most diverse in the human gut virome, which were significantly longer (~100kb in size) [32, 79]. Interestingly, the majority of broad-range phages in the top ten VCs were identified in the rumen (63%; Table S5), much higher than those identified in the rectum (including fecal samples; 17%), despite that we had comparable samples from the two GIT sites (826 vs. 753, Fig. 1A). These results strongly suggested that at least some of the rumen phages were likely originated from the environment. In summary, we found that most of the ruminant phages are organism- and GIT site-specific at both the VP and VC levels, with a few broad-range ones, likely originating from the environment. Co-diversification of phages with their animal hosts We next investigated whether the phage genomes in the broad-range VCs could show co-diversification patterns with their animal hosts. We focused on the 80 broad-range VCs that were presented in three or more animal hosts; for each of the VCs, we used a phylogenetic tree-based method to test whether phages from the same animal host were significantly closer than those of different animal hosts (Methods). We observed that in 83.75% (n=67) of the VCs, the phages tend to significantly co-evolute with their animal hosts (i.e., VPs from the same animal host in a VC were clustered together on the evolutionary tree and had significantly closer evolutionary distance), while only 2.5% were classified as significantly not co-evolution (Fig. 3A). We included in Fig. 3B-F a few typical examples to showcase our analysis. As shown in Fig. 3B, we identified two deer phages in VC_1167, which showed closer phylogenetic relationships to phages of other ruminants than to each other, indicating significant non-co-evolution. Conversely, the Fig. 3C-F showed a few cases of significant co-evolution in VC_1341, VC_1220, VC_35, and VC_95, in which multiple phages from the same animal hosts often cluster together in their respective phylogenetic trees. Further efforts would be required to illustrate whether the co-evolution was due to the adaption of the microbial hosts of the phages to the ruminant species. The URPC contains the highest proportion of lytic phages as compared with other environments The observation that the lytic phages account for seven of the top ten VCs encouraged us to further characterize the lifestyle of the ruminant phages. Because the metagenome samples we collected were not separated viral particles, we expected that many phages were derived from bacterial cells and were more likely to be integrated into the bacterial chromosomes as temperate phages. However, we found that the majority (59.60%, n = 38,696) of the phages were classified as lytic phages (virulent or uncertain virulent) using a DeePhage tool [49], which outperformed several existing tools in terms of accuracy. We found similar proportions of lytic phages in the eight ruminants (55.15% to 88.59%, with an interquartile range (IQR) of 55.77% to 61.86%; Fig. 4A) as well as across GIT sites (53.76% to 61.51%, with an IQR of 57.35 to 59.08%; Fig. S8). Similarly, we also identified a comparable percentage of lytic phages in two rumen viral genome datasets (48.55% in RVD, and 52.43% in moose rumen [26]) that contained at least 200 phage genomes (Table S3), further supporting our findings. We then compared with public datasets and found surprisingly that phages from all other environments had lower proportions of lytic phages. Since the length and completeness of the virus affect the number of genes detected in the viral genome, we performed the same quality control consistent with our URPC (filtering length >1.5k and completeness >50% estimated using CheckV [45]) on the viral genomes from public databases (Table S3). For example, 44.40% of phages in the IMG/VR v3, the most comprehensive phage database so far, are lytic, which is significantly lower than the URPC. We found the same results when stratifying the IMG/VR phages according to the habits (Fig. 4A). In addition, we found an overall of 32% lytic phages in several human gut virome datasets (24.02% to 40.81%, interquartile range (IQR): 28.00% to 38.03%; Table S3), consistent with previous observations that the human gut phages were mostly temperate [20, 32]. Interestingly, out of all the human gut virome/metagenome datasets we have analyzed, we found only one that contained a similar proportion of lytic phages to the URPC: the Tanzania hunter gut metagenome. As shown in Fig. 4B, 57.5% of the phages identified in the Tanzania hunter dataset were lytic, similar to that of the URPC ( p = 0.61, Chi-square test), while both were significantly different from the other human gut virome datasets (p < 0.01, Chi-square test). We speculate that the different lifestyles between the Tanzania hunter and the other human samples might underlie the different phage lifestyles, such as the consumption of raw or less processed foods and high exposure to microbe-enriched environments of the hunters [80]. However, due to the limited numbers of samples (i.e., 40 metagenomic samples from the NCBI SRA database; PRJNA392180) and identified viral contigs (i.e., 40 non-redundant viral contigs with completeness above 50%), our hypothesis should be tested using larger datasets. Bacterial and archaeal host prediction of the URPC phages identifies dozens of lytic phages targeting methane-producers Predicting viral hosts is crucial for understanding their roles and impacts [82], and phages can serve as ideal tools to regulate ruminant GIT ecosystems by limiting the number of their microbial hosts through lytic infections [83]. We thus predicted hosts for all the VPs using metagenome-assembled genomes (MAGs) from public datasets [6, 7, 58, 59] using two different methods, namely the CRISPR-spacer- and sequence similarity-based methods (Methods). We were able to assign a total of 9,271 phages (14.28% out of the total) to their putative bacterial/archaeal hosts, including 4,690 (50.59%) to a public ruminant GIT genome collection and 5,562 (59.99%) to the MAGs in the Global Microbial Gene Catalog (GMGC [59]). Among these, 754 phages could be assigned consistently to the same hosts by both methods (e.g., the highly confident prediction results; Fig. 5A, Table S8). We observed little overlaps between the predicted virus-host connection pairs produced by the two methods, consistent with previous results [84, 85]. In general, a total of 7,227 (77.95%) phages were classified as a specialist (Fig. 5B), meaning that they infect only one genus (i.e., specialist phages), while the others were predicted to infect two or more genera (i.e. generalist phages), which confirmed previous research that phages have a limited host range [20, 86, 87]. Among all the predicted virus-microbial host relationships, Firmicutes (the combination of Firmicutes_A , Firmicutes, and Firmicutes_C ) was the most common phylum targeted by the URPC phages (n = 5,214, i.e., the number of interacting phages), followed by Bacteroidetes (n = 3,554) (Fig. 5C), which were the two groups of beneficial bacteria that were dominant in the ruminant GIT [88]. Many of the functionally important genera were targeted by the phages. At the genus level, the most predicted hosts were Prevotella (n = 1,035; one of the most abundant and versatile genera that contributes to hemicellulose degradation, lignocellulose pretreatment, and feruloyl esterase activity [89]), followed by cellulose digestive Bacteroides (n = 625) which secrete cellulases and hemicellulases to degrade cellulose and hemicellulose into glucose and other sugars for ruminants [12, 90], Lachnospira (n = 524) and Roseburia (n = 381) major short-chain fatty acids (SCFAs) producers in the rumen providing energy and anti-inflammatory effects [12, 91, 92]. Our results suggested important regulatory roles of phages in the ruminant GIT microbial structures and functions. Phage could be an ideal tool to inhibit the growth of methane-producing archaea in the GIT of ruminants [93]. However, no lytic phages targeting methane producers have been identified [16]. Here, we retrieved 109 phages that infected methanogenic archaea from the phage-microbial host analysis (Fig. 5D). Of these, 74 were lytic (virulent or uncertain-virulent; Method) and could target the six genera of methanogens (i.e ISO4 , Methanobrevibacter , Methanobrevibacter_A, Methanobrevibacter_B, Methanocorpusculum , Methanosphaera ) annotated by GTDB-Tk [94]. These results should facilitate targeted isolation of phages and experimental validation of their lysis efficiency against methanogenic archaea. Discussions Many ruminant animals are important livestock and have more complicated gastrointestinal tracts (GITs) than other mammals. It has been well established that the GIT microbiome plays important roles in not only feedstuff digestion and absorption [12, 14], but also the development, health, and diseases as well as the quality of animal products such as meat, milk, and fur [3]. So far, there has been significant progress in the study of ruminant microbiomes, particularly bacteria/archaea [6-8]. However, we still lack systematic tools to precisely manipulate the microbiomes to improve the wellness of the animals and the qualities of their products. Phages (bacteriophages and archaeal viruses), especially lytic ones, are ideal tools for such purposes because of their abundance in nature and high microbial host specificity [93]. However, there is still a lack of comprehensive research on ruminant phages, especially at GIT sites other than the rumen [25-31]. In this study, we filled this gap by mining 2,333 metagenome samples from eight ruminant species, covering all major sites along the GIT (ten sites, including the rumen). Based on the data, we constructed a Unified Ruminant Phage Catalogue (URPC) comprising 64,922 phage genomes. Of which, 60.53% were novel as compared with public virome databases, indicating that the URPC represents a significant expansion to ruminant GIT phages and is the most comprehensive dataset so far. We first examined the distributions of the URPC in the eight ruminants and across different GIT sites. Broad-range phages, i.e., those found in multiple hosts are of higher values because they could be applied to multiple animals, e.g., to kill pathogens. However, we found that most phages were organism-specific, which was expected given the results that the rarefaction curve was far from saturation (Fig. 1A) and consistent with the previous observations in humans that the gut virome was often individual-specific [20, 32]. Nevertheless, these results also indicate that we have a much larger pool of arsenals from which we can find phages targeting specific bacterial/archaeal species of interest. Lytic phages often have higher application potentials because they are easier to isolate and more efficient in killing their microbial hosts. Surprisingly, we found that ~60% of the URPC phages are lytic, higher than any other environments we have surveyed, including the terrestrial, marine, aquatic, freshwater, plants, and human gut (Fig. 4A). Moreover, we also observed a similar elevated proportion of lytic phages within two rumen viral genome dataset (RVD and moose rumen [26]). Lytic phages are often isolated from the sewage [22]; our results thus provided better alternatives for lytic phage isolation. To further facilitate future application of the URPC phages, we predicted their microbial hosts using public MAG datasets, including several ruminant GIT MAGs and those of the other environments. Of particular interest, we obtained 109 phages targeting methane-producing species in all six archaeal genera by mining the phage-host relationships; of which 74 were lytic ones. Previous studies have shown that phages targeting methanogens may help reduce methane emissions [13], but we lack a large-scale method for identifying such phages [95]. Therefore, our results will facilitate the targeted isolation of lytic phages against methanogens and other bacterial/archaeal species in general. Overall, our assembly and analysis of the URPC phages massively expanded the ruminant GIT phages and pave the way for microbiome intervention to improve the ruminant and environmental quality. Conclusions We filled the gap in ruminant viral ecology research by providing a catalog of phage genomes, and identifying many lytic viruses that could target methane producers. Our findings provide insights into the phage community of the ruminant GIT and can be used as a starting point for future research on microbiome manipulation in ruminants. Declarations Ethical Approval and Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The Unified Ruminant Phage Catalogue (URPC) used in this study are available in the Figshare database under accession code 22219093 (https://doi.org/10.6084/m9.figshare.22219093.v2). Competing interests The authors declare that they have no competing interests. Funding The present study was granted and supported by NNSF-VR Sino-Swedish Joint Research Programme (82161138017 to WHC), National Natural Science Foundation of China (32070660 to WHC) and Key Support Project of the National Natural Science Foundation of China Joint Fund (U20A201297 to QYL). Author contributions WHC and QYL designed and directed the research. Conceptualization, data curation, formal analysis, investigation, visualization: YJW, CQS and NG; Methodology: WYJ, CQS, NG and TF; Writing-original draft: YJW and CQS; Writing-review and editing: all authors; Funding acquisition: WHC and QYL; Supervision: YJW, WHC and QYL. All authors read and approved the final manuscript. Acknowledgments Not applicable. Author information Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China Yingjian Wu, Chuqing Sun, Tong Feng and Wei-Hua Chen Institution of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China. Wei-Hua Chen Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, 528225, China Qingyou Liu State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530005, China Qingyou Liu Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China References Oltjen JW, Beckett JL: Role of ruminant livestock in sustainable agricultural systems . J Anim Sci 1996, 74 (6):1406-1409. Chen L, Qiu Q, Jiang Y, Wang K, Lin Z, Li Z, Bibi F, Yang Y, Wang J, Nie W et al : Large-scale ruminant genome sequencing provides insights into their evolution and distinct traits . Science 2019, 364 (6446). Matthews C, Crispie F, Lewis E, Reid M, O'Toole PW, Cotter PD: The rumen microbiome: a crucial consideration when optimising milk and meat production and nitrogen utilisation efficiency . 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Table S1 Detailed sampling information for 2333 samples, Table S2 Number of ruminant gastrointestinal tract samples, Table S3 Detailed information for public viral reference databases used in this study, Table S4Statistics of assembly results of 2333 samples, Table S5 Statistical information and classification level annotation of 64,922 phages, Table S6 Comparison of methods between URPC and RVD. Table S7 Classification level annotation of 41,738 viral genomes of RVD.Table S8 An overview of URPC phages and their bacterial/archaeal host prediction. Supplementfigure.pdf Additional file 2 Supplement figures S1-S8. Fig. S1 Overview of the custom pipeline employed for the reconstruction of URPC, Fig. S2 Phage quality assessment and annotation at different lengths, Fig. S3 The number of phages identified along the ruminant gastrointestinal tract, Fig. S4 The URPC shows longer length than viral genomes from human gut, Fig. S5 Taxonomic annotation of the URPC genomes, Fig. S6 Family annotation of RVD viral genomes by VirusTaxo and Demovir, Fig. S7 Percentages of novel phages in URPC as compared with public datasets at 95% average nucleotide identity (ANI) thresholds, Fig. S8Lifestyle analysis of the phages identified in the ruminants of different GIT sites. Cite Share Download PDF Status: Published Journal Publication published 04 Apr, 2024 Read the published version in Microbiome → Version 1 posted Reviewers agreed at journal 05 Dec, 2023 Reviewers invited by journal 04 Dec, 2023 Submission checks completed at journal 27 Nov, 2023 First submitted to journal 26 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2712043","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":253804713,"identity":"e1b21b37-0190-459b-84a6-6b9592597c55","order_by":0,"name":"Yingjian Wu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yingjian","middleName":"","lastName":"Wu","suffix":""},{"id":253804714,"identity":"41d1fc39-0080-484d-b743-d452a9e0b929","order_by":1,"name":"Na Gao","email":"","orcid":"","institution":"Zhongnan Hospital of Wuhan University, Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Gao","suffix":""},{"id":253804715,"identity":"ea2f837d-ce76-4505-8aca-c9ce33d4a33e","order_by":2,"name":"Chuqing Sun","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chuqing","middleName":"","lastName":"Sun","suffix":""},{"id":253804716,"identity":"6cfe661f-5bd2-42fe-a306-a31cad2d02c6","order_by":3,"name":"Tong Feng","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Feng","suffix":""},{"id":253804717,"identity":"00071c8c-e515-4836-b4b5-ac7deaad951f","order_by":4,"name":"Qingyou Liu","email":"","orcid":"","institution":"Foshan University","correspondingAuthor":false,"prefix":"","firstName":"Qingyou","middleName":"","lastName":"Liu","suffix":""},{"id":253804718,"identity":"87c07a6b-bfc8-48a4-b91e-f58fa6b05f52","order_by":5,"name":"Wei-Hua Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFACxjYQmcDAwHwMKpJAtBa2NCBtQIwWBjaoFh4z4rQYHG9ue8xTY5fHL93z7THPnz8M/Ow5Bgw/d+DRcuZguzHPseRiyTlntxvzthkwSPa8MWDsPYNbi9mNxDZpHrYDiRtu5G6T5m0wYDC4kWPADPEgDi33HwK1/DuQuP9GzjNpnj8GDPYEtdxgbJPmbQPaIpHDBrQOaIsEAS32ZxLbJOf2JSfOuJFmJjm3zZhH4syzgoO9eLRIth9/JvHmm11i/4xkIOOPnBx/e/LGBz/xaMEAPCDiAAkaRsEoGAWjYBRgAQAxPU8wzD7szAAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Wei-Hua","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2023-03-20 02:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2712043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2712043/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40168-024-01784-2","type":"published","date":"2024-04-04T15:01:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51831388,"identity":"c1530a92-6af3-4055-a6c9-5402fe5069e4","added_by":"auto","created_at":"2024-02-29 18:36:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124719,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReconstruction of the phage genomes from the ruminant gastrointestinal tract (GIT).\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e Generation of the Unified Ruminant Phage Catalogue (URPC) using 2,333 GIT microbiome samples from ten GIT sites and eight ruminant species. The upper-left panel shows a graphical representation of the ruminant gastrointestinal tract (GIT), with arrows indicating the direction of food flow through the stomach. The GIT sites in this study are divided into ten sections. The bottom-left panel shows the number of samples taken from the GIT sites or sections of the ruminants. The top-right panel shows the rarefaction analysis of the unique number of VPs (Y-axis) as a function of collected samples (X-axis), while the bottom-right panel shows the statistics on the identified phages from each of the eight ruminant species, including the number, genome size and taxonomy. \u003cstrong\u003eB\u003c/strong\u003e Pie chart showing the distribution of estimated quality of the VPs in the URPC into quality tiers estimated by CheckV (complete, n = 6,035; high-quality, n = 3,085; medium-quality, n = 55,802). Column chart showing the quality distribution of VPs in each animal host. \u003cstrong\u003eC\u003c/strong\u003e Pie chart showing the proportion of annotated VPs in the URPC at the family level by using VirusTaxo and Demovir (see Methods).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712043/v1/a6e164ef2e5d1a3d111f7692.jpg"},{"id":51831495,"identity":"b202f327-a1c9-449a-afac-d2b24af9f431","added_by":"auto","created_at":"2024-02-29 18:44:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistributions of the URPC phages in animal hosts and GIT sites. A \u003c/strong\u003eThe distribution of the URPC phages at the viral population (VP) level across the animal hosts. The UpSet pot shows the numbers of unique and shared VPs for the eight ruminant animals, while the bar chart shows the number of animal hosts for the VPs. \u003cstrong\u003eB\u003c/strong\u003e The distributions of the URPC phages at the viral cluster (VC) level across the animal hosts (left) and GIT sites (right). \u003cstrong\u003eC\u003c/strong\u003e An UpSet plot shows the overlaps between the broad-range phages (i.e., those that were found in two or more ruminant species) and the phage genomes collected in the IMG/VR v3 database (Methods). \u003cstrong\u003eD\u003c/strong\u003eTop 50 most diverse VCs, ordered by their cluster size. The color and size of the VCs correspond to the number of animal hosts in which they were found. \u003cstrong\u003eE\u003c/strong\u003e Characterizations of the top 10 VCs, including the size, distributions in the GIT sites and animal hosts, genome size and lifestyle. Lifestyles of phage in each VCs were predicted by DeePhage, which classified phages into four groups, including virulent (red), uncertain virulent (pink), uncertain temperate (light blue) and temperate (dark blue). Dark green and light green respectively indicate whether VCs are found in the public datasets.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712043/v1/9c6feaab8c1a7feea0ddc2bb.jpg"},{"id":51831390,"identity":"fb3436ab-ab50-4d20-81d6-e70c34e5d344","added_by":"auto","created_at":"2024-02-29 18:36:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-evolution analysis of the broad-range VCs with the animal hosts. \u0026nbsp;A \u003c/strong\u003eOverall statistics of the co-evolution analysis.\u003cstrong\u003e \u003c/strong\u003eHere the density and bar plots show the likelihood (\u003cem\u003ep\u003c/em\u003e-values) distributions of the phages in the 80 broad-range VCs were co-evolved (red line and bars) or not co-evolved (orange line and bars). One-tailed Wilcoxon rank sum tests were performed on the branch lengths from the same animal hosts and different animal hosts (Methods). The pie chart shows the proportion of coevolved and non-coevolved viral clusters (VCs) with three or more animal hosts. \u003cstrong\u003eB-F\u003c/strong\u003e Example phylogenetic trees of VCs with their animal hosts in which the phages showed significant not co-evolution (\u003cstrong\u003eB\u003c/strong\u003e) or co-evolution (\u003cstrong\u003eC-F\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712043/v1/0c4b3a80befa9734916a59d0.jpg"},{"id":51831392,"identity":"9e79a2af-b4af-47a5-b21b-767a7a162a41","added_by":"auto","created_at":"2024-02-29 18:36:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLifestyle analysis of the phages identified in the ruminant GIT and other environments. A \u003c/strong\u003ePhage lifestyle analysis of the ruminant GIT, rumen, human gut, and other habitats in the IMG/VR v3 database. Due to the previous utilization of Virsorter1 [81] for viral identification, we reannotated the viral genomes from the moose rumen [26] using our viral identification pipeline (i.e., VirSorter2, VirFinder and CheckV; see Methods), which has been marked with *. A DeePhage tool was used to analyze the phage lifestyles, which classified phages into four groups, including virulent (red), uncertain virulent (pink), uncertain temperate (light blue) and temperate (dark blue). The proportions next to the bar plots indicate the overall proportion of lytic phages (i.e., the virulent and uncertain virulent combined) in each dataset, while the numbers in the parentheses indicate the overall phage numbers in the corresponding datasets that passed our filtering criteria (i.e., CheckV completeness score \u0026gt; 50% and length \u0026gt;1.5k). \u003cstrong\u003eB\u003c/strong\u003e Comparisons in the phage lifestyles among the ruminants, Tanzania hunters, and the combination of public human gut virome datasets including the GPD, GVD, MGV and CHGV. \u003cem\u003eP\u003c/em\u003e-values were calculated using the Chi-square test.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712043/v1/010f594f8e127fc3b88d4242.jpg"},{"id":51831391,"identity":"1ea0c16d-022c-407e-a56a-73f773ae8ee2","added_by":"auto","created_at":"2024-02-29 18:36:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94908,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHost prediction of the ruminant GIT phages and identification of lytic phages targeting methane producers. A \u003c/strong\u003eStatistics on the viral-microbial host relationships using two different methods, namely CRISPR-spacer and sequence homology-(blastn) based methods. The UpSet plot shows the number of unique and shared viral-host interactions according to the two methods. The pie chart shows the proportion of phages whose host(s) could be predicted by these methods. \u003cstrong\u003eB \u003c/strong\u003eHistogram showing the number of phages (Y-axis) as a function of the number of predicted hosts at the genus level (X-axis). The phages could be divided into a specialist (number of host genus = 1) and a generalist (number of host genera \u0026gt;1). \u003cstrong\u003eC \u003c/strong\u003eCharacteristics of the phages stratified by their predicted microbial hosts at the phylum level, including the genome size, annotation rate, host specificity, and lifestyles. The lifestyles were predicted using DeePhage and classified into two groups (virulent: DeePhage score \u0026gt;= 0.5, temperate: score \u0026lt; 0.5).\u003cstrong\u003eD \u003c/strong\u003eThe interaction network between phages and methane-producers (i.e, archaea) predicted by phage-host relationships.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2712043/v1/d9f509ccefd225fb11f3e47d.jpg"},{"id":54304561,"identity":"caeee16b-8831-417e-92a0-f884781909b1","added_by":"auto","created_at":"2024-04-08 15:16:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2950031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2712043/v1/d32dea19-2aaf-4617-8122-c103aa2b8603.pdf"},{"id":51831394,"identity":"07ec7b50-38c8-4fbd-9b5d-9581b59e4cf1","added_by":"auto","created_at":"2024-02-29 18:36:40","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8816417,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional files\u003c/p\u003e\n\u003cp\u003eAdditional file 1\u003c/p\u003e\n\u003cp\u003eSupplement Table S1-S8. Table S1 Detailed sampling information for 2333 samples, Table S2 Number of ruminant gastrointestinal tract samples, Table S3 Detailed information for public viral reference databases used in this study, Table S4Statistics of assembly results of 2333 samples, Table S5 Statistical information and classification level annotation of 64,922 phages, Table S6 Comparison of methods between URPC and RVD. Table S7 Classification level annotation of 41,738 viral genomes of RVD.Table S8 An overview of URPC phages and their bacterial/archaeal host prediction.\u003c/p\u003e","description":"","filename":"SupplementTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-2712043/v1/17921861843f051a216400a1.xlsx"},{"id":51831393,"identity":"092a2d53-b5c4-4f40-bfa4-0974fa2e6ac4","added_by":"auto","created_at":"2024-02-29 18:36:40","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8175074,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2\u003c/p\u003e\n\u003cp\u003eSupplement figures S1-S8. Fig. S1 Overview of the custom pipeline employed for the reconstruction of URPC, Fig. S2 Phage quality assessment and annotation at different lengths, Fig. S3 The number of phages identified along the ruminant gastrointestinal tract, Fig. S4 The URPC shows longer length than viral genomes from human gut, Fig. S5 Taxonomic annotation of the URPC genomes, Fig. S6 Family annotation of RVD viral genomes by VirusTaxo and Demovir, Fig. S7 Percentages of novel phages in URPC as compared with public datasets at 95% average nucleotide identity (ANI) thresholds, Fig. S8Lifestyle analysis of the phages identified in the ruminants of different GIT sites.\u003c/p\u003e","description":"","filename":"Supplementfigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2712043/v1/7f462e7b077a8b23cd91ab85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments","fulltext":[{"header":"Background","content":"\u003cp\u003eRuminants have been an important part of human society for centuries [1, 2], providing us with a variety of economic products including meat, milk, and fur [3]. \u0026nbsp;The unique digestive system of the multi-chambered stomach including the rumen, reticulum, omasum, and abomasum allows them to return the semi-digested food fibers to the mouth for further digestion [4]. This makes them well-suited to grazing on pastures and other types of land that are not suitable for other forms of agriculture [5].\u003c/p\u003e\n\u003cp\u003eRecent research has shown that the gastrointestinal tract (GIT) of ruminants contains a great diversity of prokaryotic and eukaryotic microorganisms [6-9]. Due to the various compositions of microorganisms among different locations, the microbiomes distributed in different sites play different roles but perform equally important physiological functions in ruminants\u0026rsquo; survival [10, 11]. The GIT microbes enable the ruminants to digest lignocellulose and other plant feedstuffs [12, 13] and protect the animal host from harmful bacteria and other pathogens [14]. However, there can also be negative effects of the GIT microbiome on ruminant health and productivity. For example: a disruption in the microbial balance in the rumen can lead to the overproduction of lactic acid, which can lower the pH and cause ruminal acidosis, a common metabolic disorder in ruminants [15]. Precision regulation of the gastrointestinal tract microbiome in ruminants is crucial for improving animal health and productivity, and reducing the environmental impact of animal agriculture. Methane-producing archaea exist in the GIT of ruminants and are one of the main sources of greenhouse gases, which have been targeted for eradication or reduction [16, 17]. However, achieving precision regulation of methane-producing archaea or pathogenic bacteria in ruminants is not a simple task, as it requires a deep understanding of the complex interplay between the microbiome, diet, and host physiology. Currently, there is a lack of system tools available to enable precise manipulation of the microbiome in ruminants [18].\u003c/p\u003e\n\u003cp\u003eBacteriophages (phages) are a critical component of the ruminant GIT microbiome and play crucial roles in shaping microbial composition [19]. In addition, phages hold great promise for the precision manipulation of the bacteriome (i.e., the bacterial and archaeal microbes) because of their narrow microbial-host range (i.e., often at species and even strain levels [20, 21]), providing alternative ways to suppress pathogenic bacterial/archaeal species [22] and control methane emissions [16, 23]. The lifestyles of viruses can be broadly classified into two categories: lytic and lysogenic. The separation of lytic phages is important in practical applications, as lytic phages are typically more convenient to work with and have more immediate applications, such as using phages as antimicrobials against bacterial infections in animals. For example, phages have been used to control bacterial infections in dairy cattle with mastitis, which is a common and costly disease in the dairy industry [24]. Despite tremendous success in identifying viruses from various environmental sources such as the ruminants rumen [25-31], human gut [21, 32-38], aquatic, terrestrial, plants, as well as other mammals (i.e., IMG/VR v3 \u0026nbsp;[39]), the virome structure remains the \u0026ldquo;dark matter\u0026rdquo; \u0026nbsp;in different ruminant GIT sites, especially those other than the rumen, compared with other environments. A comprehensive resource reference phage genome is required to further characterize the viral community of the ruminant GIT and enable genome-resolution research across ruminants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we present the Unified Ruminant Phage Catalogue (URPC), a comprehensive survey of phages in the gastrointestinal tracts of ruminants. Currently, the URPC contains 64,922 non-redundant phage genomes identified using 2,333 bulk metagenomics sequencing samples from 18 published works (Table S1), covering ten gastrointestinal sites from eight different ruminant species. We found that 60.53% (n = 39,300) of phage genomes were novel compared with those in the public viral datasets, supporting the novelty of our dataset. We characterized the distributions of the phage genomes in different ruminants and GIT sections, as well as the lifestyles of the phages. Strikingly, we revealed that ~60% of the ruminant phages were lytic, which was the highest as compared with those in all other environments and certainly will facilitate their applications in microbial interventions. To further facilitate the future applications of the phage, we also constructed a comprehensive virus-bacteria/archaea interaction network and identified dozens of phages that may have lytic effects on methanogenic bacteria. Together, our URPC dataset represents a useful resource for future microbial interventions to improve ruminant production and ecological environmental qualities.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eData collection,\u0026nbsp;quality control, and removal of host- and food-associated genomes\u003c/h3\u003e\n\u003cp\u003eTo perform a comprehensive search for phages of the ruminant gastrointestinal tract (GIT), publicly available sequencing reads of 2,333 ruminant metagenomic samples were downloaded from National Center for Biotechnology Information/NLM/NIH (NCBI) (Fig. S1; Table S1), covering eight ruminants (buffalo, camel, cattle, cow, deer, goat, sheep, yak) and ten GIT sites (rumen, reticulum, omasum, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum/feces) (Table S2). Raw reads were trimmed by Trimmomatic (v 0.39)\u0026nbsp;[40]\u0026nbsp;with the options \u0026lsquo;ILLUMINACLIP: TruSeq3-PE.fa:2:30:10 SLIDINGWINDOW:4:15 MINLEN:50 LEADING:3 TRAILING:3\u0026rsquo;. To decrease potential DNA contamination from the animal hosts, reads that could be aligned to their closest genomes from NCBI (\u003cem\u003eCapra hircus\u003c/em\u003e, GCF_001704415.1; \u003cem\u003eBubalus bubalis\u003c/em\u003e, GCA_004794615.1; \u003cem\u003eCamelus bactrianus\u003c/em\u003e, GCF_000767855.1; Camelus dromedarius, GCF_000803125.2; \u003cem\u003eBos taurus\u003c/em\u003e,GCF_002263795.1; \u003cem\u003eCapra hircus\u003c/em\u003e, GCF_001704415.1; \u003cem\u003eAlces alces\u003c/em\u003e, GCA_007570765.1; \u003cem\u003eCervus elaphus\u003c/em\u003e, GCF_910594005.1; \u003cem\u003eRangifer tarandus caribou\u003c/em\u003e, GCA_019903745.1; \u003cem\u003eCapreolus capreolus\u003c/em\u003e,GCA_000751575.1; \u003cem\u003eOvis aries\u003c/em\u003e, GCF_016772045.1; \u003cem\u003eHydropotes inermis\u003c/em\u003e, GCA_020226075.1; \u003cem\u003eBos grunniens\u003c/em\u003e, GCA_005887515.2), and some food-associated genomes such as \u003cem\u003eGlycine max\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Zea Mays\u003c/em\u003e, and \u003cem\u003eMedicago truncatula\u003c/em\u003e were filtered out using Bowtie2 (v 2.3.5.1)\u0026nbsp;[41]\u0026nbsp;with options \u0026lsquo;--very-sensitive\u0026rsquo;. The remaining paired reads were then used for further analyses.\u003c/p\u003e\n\u003ch3\u003eMetagenomic assembly and viral contigs prediction\u003c/h3\u003e\n\u003cp\u003eUnless otherwise stated, default parameters were used. Each sample was assembled using MEGAHIT (v 1.2.8)\u0026nbsp;[42]\u0026nbsp;with options \u0026lsquo;--min-contig-len 1000\u0026rsquo;. Assembled contigs of \u0026ge;1.5kb in size were used to identify viral sequences using VirSorter2 (v 2.1)\u0026nbsp;[43]\u0026nbsp;with options \u0026lsquo;--include-groups \u0026quot;dsDNAphage, ssDNA\u0026quot; --min-score 0.7\u0026rsquo; and VirFinder (v 1.1)\u0026nbsp;[44]\u0026nbsp;with default parameters. Contigs were identified as phages by both VirSorter2 and VirFinder (score\u0026ge;0.6 and \u003cem\u003ep\u003c/em\u003e \u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eQuality evaluation of phage genomes and dereplication of URPC datasets\u003c/h3\u003e\n\u003cp\u003eThe completeness of the viral contigs was estimated using CheckV (v 0.8.1)\u0026nbsp;[45]. A total of 74,519 identified viral contigs with \u0026gt;50% completeness were then selected and renamed according to their animal hosts. The sequences of these contigs were merged into a single file and dereplicated using CD-HIT\u0026nbsp;[46](v4.8.1, parameters: -c 0.95 -n 8) using a global identity threshold of 95%. The resulting non-redundant representative viral genomes consisted of a total of 64,922 viral populations (VPs) and were referred to as the Unified Ruminant Phage Catalogue (URPC).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eComparing the URPC genomes with public viral datasets\u003c/h3\u003e\n\u003cp\u003eTo estimate the proportion of novel phage genomes in the URPC genomes, the BLASTn tool (v 2.5.0)\u0026nbsp;[47]\u0026nbsp;was used to search all its sequences against a list of public viral databases including four public rumen virome datasets from the rumen virome database (RVD)\u0026nbsp;[31], Hitch \u003cem\u003eet al.\u003c/em\u003e [25]\u0026nbsp;Solden \u003cem\u003eet al.\u003c/em\u003e [26]\u0026nbsp;and Friedersdorff \u003cem\u003eet al.\u003c/em\u003e [27],\u0026nbsp;NCBI viral Reference genomes, Release 201\u0026nbsp;(Jul 06, 2020), IMG/VR v3\u0026nbsp;[39], and four public human virome datasets such as GVD\u0026nbsp;[33], GPD\u0026nbsp;[32], MGV\u0026nbsp;[21], and CHGV\u0026nbsp;[34]\u0026nbsp;(Table S3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAverage nucleotide identity (ANI) was calculated by merging the BLASTn hit regions with identity \u0026ge;90% and hit length \u0026ge; 500bp, then calculating the coverage of these regions. Based on the overall ANI, a viral sequence was considered to be novel if it has \u0026lt;95% ANI as compared with other viral sequences.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eClustering viral contigs into viral clusters (VCs)\u003c/h3\u003e\n\u003cp\u003eThe clustering of viral contigs into viral clusters (VCs) was performed using a strategy adopted from the GPD\u0026nbsp;[32]. Briefly, the BLASTn algorithm with default parameters was used to search the nucleotide sequences of the URPC genomes and the environmental viral sequences of various habitats (e.g., Terrestrial, Freshwater, Plants and etc.) in the IMG/VR v3 database against themselves for homologous sequences. An \u003cem\u003eE\u003c/em\u003e-value threshold of 1E-10 was first used to filter the BLASTn results; the BLASTn query-hit pairs were further filtered to retain those with a coverage \u0026gt; 70% on the\u0026nbsp;larger\u0026nbsp;genomes and a coverage \u0026gt;90% on the smaller genomes. Here,\u0026nbsp;the coverage was calculated by merging the aligned fraction length of BLASTn high-scoring pair (HSP)\u0026nbsp;sequences\u0026nbsp;that shared at least 90% nucleotide similarity. Finally, a Markov\u0026nbsp;clustering algorithm\u0026nbsp;(MCL v14-137)\u0026nbsp;[48]\u0026nbsp;was used with an inflation value of 6.0, which took the filtered BLASTn results as input, carried out graph-based clustering, and clustered the viral contigs into 55,635 VCs.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003ePrediction of viral lifestyles\u003c/h3\u003e\n\u003cp\u003eThe lifestyle classifications of all the URPC genomes were analyzed using DeePhage v1.0\u0026nbsp;[49]\u0026nbsp;with the default parameters\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eDeePhage uses a scoring system to classify phage genomes into four categories, including temperate (with scores \u0026le;0.3), uncertain temperate (0.3~0.5), uncertain virulent (0.5~0.7), and virulent (\u0026gt;0.7). Higher scores indicate higher virulence. According to a benchmark study\u0026nbsp;[50], DeePhage can classify short contigs from metagenomic data and has the best reported performance on lifestyle prediction, while BACPHLIP\u0026nbsp;[51]\u0026nbsp;is only designed for complete phage genomes. And DeePhage has better generalization ability on novel phages\u0026nbsp;as\u0026nbsp;using a deep neural network to learn features from both DNA and protein sequences of phages, while BACPHLIP relies on a set of conserved protein domains that are associated with lysogeny. Therefore, we chose DeePhage to predict phage lifestyles of URPC.\u003c/p\u003e\n\u003ch3\u003eTaxonomic annotation of the URPC phages\u003c/h3\u003e\n\u003cp\u003eTo taxonomically classify the phage contigs, VirusTaxo (https://github.com/omics-lab/VirusTaxo, downloaded on 19th April 2022) [52] was used to compare the nucleotide sequences against those in the prebuilt database of VirusTaxo and assign them to a known viral genus at an entropy index threshold of \u0026lt;0.5. A Demovir script (https://github.com/feargalr/Demovir; downloaded on 6th January 2022) was then used to predict family and order ranks for the remaining genomes by searching for viral marker genes at the amino acid level.\u003c/p\u003e\n\u003ch3\u003eCo-diversification analysis of phages with their animal hosts\u003c/h3\u003e\n\u003cp\u003eFor all VCs that contained phages from three and more animal hosts, a phylogenetic analysis was performed. In total, 80 VCs were selected. First, Prokka v1.13\u0026nbsp;[53]\u0026nbsp;(--kingdom Phages) was used to annotate the phage genomes for protein-coding genes. Pan-genome analysis was carried out for each of the VCs with Roary\u0026nbsp;[54]\u0026nbsp;to identify core genes and create a multiFASTA alignment of core genes using MAFFT\u0026nbsp;[55]\u0026nbsp;by using the multiFASTA alignment as input. A phylogenetic tree was then built using FastTree\u0026nbsp;[56]\u0026nbsp;v2.1.10 with default parameters. All the phylogenetic trees were then visualized and annotated using iTol\u0026nbsp;[57]. For each VC, the branch length between any two phage genomes was calculated. Two single-tailed Wilcoxon rank sum tests were performed on the branch lengths from the same animal hosts and those from different animal hosts. The p-value of the hypothesis that phages from different animal hosts had higher branch lengths were used to determine whether the phages significantly co-evolved or not co-evolved with their animal hosts.\u003c/p\u003e\n\u003ch3\u003eMicrobial host analysis of the URPC phages\u003c/h3\u003e\n\u003cp\u003eTo find putative microbial hosts for the URPC phages, ruminant metagenome-assembled genomes (MAGs) from four publications were downloaded, including the buffalo GIT\u0026nbsp;[6], ruminants GIT\u0026nbsp;[7], cattle rumen\u0026nbsp;[58], and goat GIT (NCBI SRA database PRJNA723432). In addition, MAGs from the Global Microbial Gene Catalog (GMGC)\u0026nbsp;[59]\u0026nbsp;that covered 14 different habitats were also downloaded. To establish phage-microbial host relationships between these MAGs and the URPC phages, two bioinformatic methods were used that included the CRISPR-spacer matches and nucleotide sequence similarity searches. The CRISPR spacers of the MAGs were identified using CRT (v 1.2)\u0026nbsp;[60]\u0026nbsp;and MinCED (v 0.4.2, https://github.com/ctSkennerton/minced). The union of the CRISPR spacers was then aligned to the viral populations using BLASTn (v 2.5.0)\u0026nbsp;[47]\u0026nbsp;with options of \u0026lsquo;-word_size 10 -dust no -max_target_seqs 10000\u0026rsquo;. Matches with mismatch\u0026le;1 and alignment length \u0026gt; 95% spacer length were retained. In addition, BLASTn was used to compare the viral populations with the MAGs. A putative viral-host relationship could be established if their nucleotide sequences shared \u0026gt;90% identity over \u0026gt;500 bps.\u003c/p\u003e\n\u003ch3\u003ePhylogenetic analysis of animal hosts\u003c/h3\u003e\n\u003cp\u003eThe phylogenetic analysis of the eight ruminant species was carried out using a method based on a previous study\u0026nbsp;[61]. Briefly, the genomic sequences of the eight ruminant species were downloaded from the NCBI Genome database. Then the universally conserved single-copy marker genes from each genome were identified using fetchMG\u0026nbsp;[62]. The protein sequences of the markers were then aligned using MUSCLE\u0026nbsp;[63]\u0026nbsp;(-maxiters 100). To eliminate divergent regions from the resulting multiFASTA alignment, Gblocks\u0026nbsp;[64]\u0026nbsp;were used (parameters: -t=p -b3=8 -b4=2 -b5=h). The maximum likelihood trees were built with RAxML\u0026nbsp;[65]\u0026nbsp;with default parameters.\u003c/p\u003e\n\u003ch3\u003eStatistical analysis\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were conducted using R (v4.0.4) with a two-sided Wilcoxon rank sum test unless otherwise stated.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eA unified catalog of 64,922 phage genomes from the ruminant gastrointestinal\u0026nbsp;tract\u003c/h3\u003e\n\u003cp\u003eTo provide a comprehensive overview of the phages associated with the gastrointestinal tract (GIT) of ruminants, we collected a total of 2,333 metagenomic samples from 18 previously published research [6, 7, 13, 23, 26, 58, 66-78] (Table S1) that covered ten GIT sites from eight ruminant species, including \u0026nbsp;(Fig 1A and Tables S1, S2). including buffalo (n = 745), cattle (n = 930), goat (n = 563), sheep (n = 133), deer (n = 115), yak (n = 50) and cow (n = 46). After quality filtering and removing host DNA sequences (Methods), a total of 14.17 terabytes (Tb) of clean data with more than 33 million reads and 9 billion bases per sample were retained (Table S1). We assembled them into a total of 302,721,852 contigs using MEGAHIT [42], averaging 132,251 contigs per sample with an N50 length of 3,836 (Table S4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify putative phage genomes, we screened the assembled contigs using a bioinformatics pipeline adopted from Luis \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003e[32]\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Methods), followed by\u0026nbsp;quality assessment for viral genome completeness using\u0026nbsp;CheckV\u0026nbsp;[45]\u0026nbsp;and dereliction using\u0026nbsp;CD-HIT\u0026nbsp;[46].\u0026nbsp;We obtained a total of 74,519 viral contigs\u0026nbsp;(mostly bacteriophages)\u0026nbsp;with \u0026gt;50% completeness and\u0026nbsp;length \u0026gt;1.5 kb, corresponding to 64,922 non-redundant viral populations (VPs), i.e., species-level clusters at\u0026nbsp;an\u0026nbsp;Average Nucleotide Identity (ANI) of\u0026nbsp;95%. We defined the latter (i.e., the 64,922 non-redundant VPs) as the\u0026nbsp;Unified Ruminant Phage Catalogue\u0026nbsp;(URPC). Among these, 6,035 (9.29%), 3,085 (4.75%), and 55,802 (85.95%) were classified as complete, high- and medium-quality, respectively, according to the CheckV tool (Fig 1B;\u0026nbsp;Table S5).\u003c/p\u003e\n\u003cp\u003ePrevious studies have commonly employed a 5kb threshold for identifying metagenome-based viral genomes [31, 39]. In our study, we opted for a 1.5kb threshold. To substantiate this choice, we categorized all URPC phages into four length groups: \u0026lt;5kb (n = 777), 5~30kb (n = 14,462), 30~60kb (n = 35,199) and \u0026gt;60kb (n = 14,484). We first compared the qualities of phages in each group and were intrigued to discover that the \u0026lt;5kb group exhibited the highest proportion of complete (29.81%) phage genomes, as determined by CheckV, in comparison to the other three groups (Fig. S2A). Moreover, taxonomic annotation, as per our methods, was successful for 91.48% of phages in the \u0026lt;5kb group, surpassing the rates observed in all other groups (Fig. S2B). Therefore, our findings indicate that the utilization of short contigs (i.e., 1.5~5k) not only aids in more accurately estimating the number of phages but also surprisingly enhances the annotation rates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used the rarefaction analysis to show that the saturation curve is far from plateaued, and more samples are required for the discovery of ruminant GIT phages (Fig. 1A). Similar trends were observed in human gut virome catalogs such as the Metagenomic Gut Virus (MGV) [21] and a phage genome catalogue of the Japanese [20]. Among all the animal hosts, we obtained the highest number of VPs (n = 33,156) in the buffalo, followed by the cattle (n = 10,589), goat (n = 8,756), and sheep (n = 5,876). And the number of viruses identified varied in different GIT sites (Fig. S3), which correlated with the number of samples we collected. The genome size and viral taxa vary among different animal hosts, indicating species-specific viral composition. Given the recent interest in human gut phageome, we then compared the genome length of URPC and other published metagenome-assembled human gut viral genomes, and found that URPC genomes were significantly longer than those in the human gut (\u003cem\u003ep\u003c/em\u003e \u0026lt; 2.22e\u0026minus;16, Wilcoxon Rank Sum test; Fig. S4).\u003c/p\u003e\n\u003cp\u003eWe annotated the VPs using VirusTaxo [52] and Demovir (https://github.com/feargalr/Demovir) (Fig. S5; Table S5) and assigned 74.69% of the VPs at the family level (Fig 1C). Among the annotated VPs, 16,507 (25.42% of the total) belong to the \u003cem\u003eSiphoviridae\u003c/em\u003e, followed by \u003cem\u003ePoxviridae\u003c/em\u003e (n = 6,327), \u003cem\u003eMimiviridae\u003c/em\u003e (n = 5,409), \u003cem\u003eBaculoviridae\u003c/em\u003e (n = 3,962), \u003cem\u003eMyoviridae\u003c/em\u003e (n = 3,846), \u003cem\u003ePodoviridae\u003c/em\u003e (n = 2,360) and \u003cem\u003eMicroviridae\u003c/em\u003e (n = 2,291). The overall taxonomic distribution, dominated by viral families such as \u003cem\u003eSiphoviridae\u003c/em\u003e, \u003cem\u003eMicroviridae\u003c/em\u003e, \u003cem\u003eMyoviridae\u003c/em\u003e, \u003cem\u003ePodoviridae\u003c/em\u003e, was consistent with other metagenome-derived viral catalogs in ruminant rumen (RVD) [31] and human gut [21, 33]. Particularly, we reannotated the viral genomes from RVD using our pipeline for taxonomic classification (see Methods), and we found that the families \u003cem\u003eSiphoviridae\u003c/em\u003e, \u003cem\u003ePodoviridae\u003c/em\u003e, \u003cem\u003eMyoviridae\u003c/em\u003e, \u003cem\u003eBaculoviridae\u003c/em\u003e, and \u003cem\u003eMyoviridae\u003c/em\u003e accounted for majority of the viral genomes in both URPC and RVD datasets. However, we also identified more phages from family \u003cem\u003ePodoviridae\u003c/em\u003e than RVD (3.6% in URPC, and 0.5% in RVD) (Fig. S6; Table S6) indicating that URPC expands the diversity of the ruminant gastrointestinal phage genomes.\u003c/p\u003e\n\u003cp\u003eWe then examined the novelty of the URPC phage genomes by comparing them with several public viral databases including the NCBI viral Reference genomes (Release 201, Jul 06, 2020), IMG/VR v3 [39], four public rumen virome datasets \u0026nbsp;[25-27, 31] and four human gut virome genome catalogs [21, 32-34] (Table S3). Applying an Average Nucleotide Identity (ANI) threshold of 95%, we observed that URPC exhibited the highest number of shared viral populations (VPs) with the RVD (Fig. S7A). Notably, 28.11% of URPC genomes were found in the RVD, while 33.84% of RVD genomes were identified in URPC (Fig. S7A). The substantial overlap between the two datasets can be attributed to the similar number of rumen samples used in URPC (826) compared to the RVD (975), despite variations in tools and criteria for viral contig identification in the latter [31] (refer to Table S7 for a detailed comparison). For a fair comparison, only 41,738 VPs from the RVD dataset meeting the same criteria as our dataset (i.e., completeness \u0026gt; 50%) were considered. At these criteria, this study identified a significantly higher number of VPs (64,145) compared to the RVD (Table S7).\u003c/p\u003e\n\u003cp\u003eFurthermore, with the inclusion of three additional public rumen phage datasets, a total of 46,668 (71.89%) URPC phages were determined to be novel at a 95% ANI threshold (Fig. S7A), signifying URPC\u0026rsquo;s substantial contribution to expanding the ruminant gastrointestinal tract phage dataset despite prior outstanding works. When considering all the aforementioned public viral datasets, we found that 60.53% (n = 39,300) of VPs were considered novel at the 95% ANI threshold, indicating that the majority of URPC phages are novel (Fig. S7B).\u003c/p\u003e\n\u003ch3\u003eOrganism-specific distribution of URPC genomes in animal hosts\u003c/h3\u003e\n\u003cp\u003eTo investigate the correlation between the composition of VPs and their animal hosts, we first calculated the distribution of the VPs in each animal host. We discovered that 99.91% (n = 64,863) of VPs had only one animal host (referred to as organism-specific from now on), while only a few (n = 59) appeared in two or three animal hosts (Fig. 2A). \u0026nbsp;To evaluate the distribution of phages with their animal hosts under higher level, we clustered the VPs into viral clusters (VCs) using methods adopted from the GPD [32] (Methods) and generated a total of 55,635 VCs. Among these, 99.06% of (n = 55,122) the VCs are organism-specific. Similarly, most (91.43%, n=50,874) of the VCs were distributed only in one GIT sit (Fig. 2B). Among the 4,761 VCs that were distributed in two or more GIT sites, 92.69% (n = 4,413) came from the same animal host, indicating an organism-specific distribution of the phages in the animal hosts. Among the 80 \u0026ldquo;broad-range\u0026rdquo; VCs (presented in three or more animal hosts), most of their animal hosts were goats (n = 60), buffaloes (n = 55), cattle (n = 52), sheep (n = 51), and deer (n = 39), while there were fewer in yaks (n= 4), camels (n = 2), and cows (n = 1) (Fig. 2C), which might be due to fewer available GIT samples of the latter three animals. To find out whether these \u0026ldquo;broad-range\u0026rdquo; VCs were food-related, we also include the VPs from the IMG/VR v3 database and re-did the viral clustering. We found that two of the \u0026ldquo;broad-range\u0026rdquo; VCs could be clustered with phages found in the terrestrial, freshwater, and plants (Fig. 2C), which confirmed previous research that phages could be readily introduced into the rumen from water sources, as well as housing and farm infrastructure [19]. However, the origins of the other 78 broad-range VCs remained to be identified.\u003c/p\u003e\n\u003cp\u003eWe next characterized the largest VCs (i.e., the VCs were ranked according to the number of containing VPs). 27 out of the top 50 had two or more animal hosts, suggesting that the diverse VCs were also the well-adapted ones; this trend was more apparent among the top ten VCs (i.e., of these, eight were found in two and more animal hosts). In addition, we observed that most of the top ten VCs consisted of lytic bacteriophages (Fig. 2E; the lifestyles were determined using a DeePhage tool; Methods). Most of the phages in the top ten VCs were 30~60kb in size, which were well within the size range of typical phage genomes; in contrast, crAssphages and Gubaphages were the most diverse in the human gut virome, which were significantly longer (~100kb in size) [32, 79]. Interestingly, the majority of broad-range phages in the top ten VCs were identified in the rumen (63%; Table S5), much higher than those identified in the rectum (including fecal samples; 17%), despite that we had comparable samples from the two GIT sites (826 vs. 753, Fig. 1A). These results strongly suggested that at least some of the rumen phages were likely originated from the environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, we found that most of the ruminant phages are organism- and GIT site-specific at both the VP and VC levels, with a few broad-range ones, likely originating from the environment.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eCo-diversification of phages with their animal hosts\u003c/h3\u003e\n\u003cp\u003eWe next investigated whether the phage genomes in the broad-range VCs could show co-diversification patterns with their animal hosts. We focused on the 80 broad-range VCs that were presented in three or more animal hosts; for each of the VCs, we used a phylogenetic tree-based method to test whether phages from the same animal host were significantly closer than those of different animal hosts (Methods). We observed that in 83.75% (n=67) of the VCs, the phages tend to significantly co-evolute with their animal hosts (i.e., VPs from the same animal host in a VC were clustered together on the evolutionary tree and had significantly closer evolutionary distance), while only 2.5% were classified as significantly not co-evolution (Fig. 3A). We included in Fig. 3B-F a few typical examples to showcase our analysis. As shown in Fig. 3B, we identified two deer phages in VC_1167, which showed closer phylogenetic relationships to phages of other ruminants than to each other, indicating significant non-co-evolution. Conversely, the Fig. 3C-F showed a few cases of significant co-evolution in VC_1341, VC_1220, VC_35, and VC_95, in which multiple phages from the same animal hosts often cluster together in their respective phylogenetic trees. Further efforts would be required to illustrate whether the co-evolution was due to the adaption of the microbial hosts of the phages to the ruminant species.\u003c/p\u003e\n\u003ch3\u003eThe URPC contains the highest proportion of lytic phages as compared with other environments\u003c/h3\u003e\n\u003cp\u003eThe observation that the lytic phages account for seven of the top ten VCs encouraged us to further characterize the lifestyle of the ruminant phages. Because the metagenome samples we collected were not separated viral particles, we expected that many phages were\u0026nbsp;derived\u0026nbsp;from bacterial cells and were more likely to be integrated into the bacterial chromosomes as temperate phages. However, we found that the majority (59.60%, n = 38,696) of the phages were classified as lytic phages (virulent or uncertain virulent) using a DeePhage tool\u0026nbsp;[49], which outperformed several existing tools in terms of accuracy. We found similar proportions of lytic phages in the eight ruminants (55.15% to 88.59%, with an interquartile range (IQR) of 55.77% to 61.86%; Fig. 4A) as well as across GIT sites (53.76% to 61.51%, with an IQR of 57.35 to 59.08%; Fig. S8). Similarly, we also identified a comparable percentage of lytic phages in two rumen viral genome datasets (48.55% in RVD, and 52.43% in moose rumen\u0026nbsp;[26]) that contained at least 200 phage genomes (Table S3), further supporting our findings.\u003c/p\u003e\n\u003cp\u003eWe then compared with public datasets and found surprisingly that phages from all other environments had lower proportions of lytic phages. Since the length and completeness of the virus affect the number of genes detected in the viral genome, we performed the same quality control consistent with our URPC (filtering length \u0026gt;1.5k and completeness \u0026gt;50% estimated using CheckV [45]) on the viral genomes from public databases (Table S3). \u0026nbsp;For example, 44.40% of phages in the IMG/VR v3, the most comprehensive phage database so far, are lytic, which is significantly lower than the URPC. We found the same results when stratifying the IMG/VR phages according to the habits (Fig. 4A). In addition, we found an overall of 32% lytic phages in several human gut virome datasets (24.02% to 40.81%, interquartile range (IQR): 28.00% to 38.03%; Table S3), consistent with previous observations that the human gut phages were mostly temperate [20, 32].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, out of all the human gut virome/metagenome datasets we have analyzed, we found only one that contained a similar proportion of lytic phages to the URPC: the Tanzania hunter gut metagenome. As shown in Fig. 4B, 57.5% of the phages identified in the Tanzania hunter dataset were lytic, similar to that of the URPC (\u003cem\u003ep\u003c/em\u003e = 0.61, Chi-square test), while both were significantly different from the other human gut virome datasets (p \u0026lt; 0.01, Chi-square test). We speculate that the different lifestyles between the Tanzania hunter and the other human samples might underlie the different phage lifestyles, such as the consumption of raw or less processed foods and high exposure to microbe-enriched environments of the hunters [80]. However, due to the limited numbers of samples (i.e., 40 metagenomic samples from the NCBI SRA database; PRJNA392180) and identified viral contigs (i.e., 40 non-redundant viral contigs with completeness above 50%), our hypothesis should be tested using larger datasets.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eBacterial and archaeal host prediction of the URPC phages identifies dozens of lytic phages targeting methane-producers\u003c/h3\u003e\n\u003cp\u003ePredicting viral hosts is crucial for understanding their roles and impacts [82], and phages can serve as ideal tools to regulate ruminant GIT ecosystems by limiting the number of their microbial hosts through lytic infections [83]. We thus predicted hosts for all the VPs using metagenome-assembled genomes (MAGs) from public datasets [6, 7, 58, 59] using two different methods, namely the CRISPR-spacer- and sequence similarity-based methods (Methods). We were able to assign a total of 9,271 phages (14.28% out of the total) to their putative bacterial/archaeal hosts, including 4,690 (50.59%) to a public ruminant GIT genome collection and 5,562 (59.99%) to the MAGs in the Global Microbial Gene Catalog (GMGC [59]). Among these, 754 phages could be assigned consistently to the same hosts by both methods (e.g., the highly confident prediction results; Fig. 5A, Table S8). We observed little overlaps between the predicted virus-host connection pairs produced by the two methods, consistent with previous results [84, 85]. \u0026nbsp;In general, a total of 7,227 (77.95%) phages were classified as a specialist (Fig. 5B), meaning that they infect only one genus (i.e., specialist phages), while the others were predicted to infect two or more genera (i.e. generalist phages), which confirmed previous research that phages have a limited host range [20, 86, 87].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong all the predicted virus-microbial host relationships, \u003cem\u003eFirmicutes\u003c/em\u003e (the combination of \u003cem\u003eFirmicutes_A\u003c/em\u003e, \u003cem\u003eFirmicutes,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Firmicutes_C\u003c/em\u003e) was the most common phylum targeted by the URPC phages (n = 5,214, i.e., the number of interacting phages), followed by \u003cem\u003eBacteroidetes\u003c/em\u003e (n = 3,554) (Fig. 5C), which were the two groups of beneficial bacteria that were dominant in the ruminant GIT [88]. Many of the functionally important genera were targeted by the phages. At the genus level, the most predicted hosts were \u003cem\u003ePrevotella\u003c/em\u003e (n = 1,035; one of the most abundant and versatile genera that contributes to hemicellulose degradation, lignocellulose pretreatment, and feruloyl esterase activity [89]), followed by cellulose digestive\u003cem\u003e\u0026nbsp;Bacteroides\u003c/em\u003e (n = 625) which secrete cellulases and hemicellulases to degrade cellulose and hemicellulose into glucose and other sugars for ruminants [12, 90], \u003cem\u003eLachnospira\u0026nbsp;\u003c/em\u003e(n = 524) and \u003cem\u003eRoseburia\u003c/em\u003e (n = 381) major short-chain fatty acids (SCFAs) producers in the rumen providing energy and anti-inflammatory effects [12, 91, 92]. Our results suggested important regulatory roles of phages in the ruminant GIT microbial structures and functions.\u003c/p\u003e\n\u003cp\u003ePhage could be an ideal tool to inhibit the growth of methane-producing archaea in the GIT of ruminants [93]. However, no lytic phages targeting methane producers have been identified [16]. Here, we retrieved 109 phages that infected methanogenic archaea from the phage-microbial host analysis (Fig. 5D). Of these, 74 were lytic (virulent or uncertain-virulent; Method) and could target the six genera of methanogens (i.e \u003cem\u003eISO4\u003c/em\u003e, \u003cem\u003eMethanobrevibacter\u003c/em\u003e, \u003cem\u003eMethanobrevibacter_A, Methanobrevibacter_B, \u0026nbsp;Methanocorpusculum\u003c/em\u003e, \u003cem\u003eMethanosphaera\u003c/em\u003e) annotated by GTDB-Tk [94]. These results should facilitate targeted isolation of phages and experimental validation of their lysis efficiency against methanogenic archaea.\u003c/p\u003e"},{"header":"Discussions","content":"\u003cp\u003eMany ruminant animals are important livestock and have more complicated gastrointestinal tracts (GITs) than other mammals. It has been well established that the GIT microbiome plays important roles in not only feedstuff digestion and absorption [12, 14], but also the development, health, and diseases as well as the quality of animal products such as meat, milk, and fur [3]. So far, there has been significant progress in the study of ruminant microbiomes, particularly bacteria/archaea [6-8]. However, we still lack systematic tools to precisely manipulate the microbiomes to improve the wellness of the animals and the qualities of their products. Phages (bacteriophages and archaeal viruses), especially lytic ones, are ideal tools for such purposes because of their abundance in nature and high microbial host specificity [93]. However, there is still a lack of comprehensive research on ruminant phages, especially at GIT sites other than the rumen [25-31]. In this study, we filled this gap by mining 2,333 metagenome samples from eight ruminant species, covering all major sites along the GIT (ten sites, including the rumen). Based on the data, we constructed a Unified Ruminant Phage Catalogue (URPC) comprising 64,922 phage genomes. Of which, 60.53% were novel as compared with public virome databases, indicating that the URPC represents a significant expansion to ruminant GIT phages and is the most comprehensive dataset so far.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe first examined the distributions of the URPC in the eight ruminants and across different GIT sites. Broad-range phages, i.e., those found in multiple hosts are of higher values because they could be applied to multiple animals, e.g., to kill pathogens. However, we found that most phages were organism-specific, which was expected given the results that the rarefaction curve was far from saturation (Fig. 1A) and consistent with the previous observations in humans that the gut virome was often individual-specific [20, 32]. Nevertheless, these results also indicate that we have a much larger pool of arsenals from which we can find phages targeting specific bacterial/archaeal species of interest.\u003c/p\u003e\n\u003cp\u003eLytic phages often have higher application potentials because they are easier to isolate and more efficient in killing their microbial hosts. Surprisingly, we found that ~60% of the URPC phages are lytic, higher than any other environments we have surveyed, including the terrestrial, marine, aquatic, freshwater, plants, and human gut (Fig. 4A). Moreover, we also observed a similar elevated proportion of lytic phages within two rumen viral genome dataset (RVD and moose rumen [26]). Lytic phages are often isolated from the sewage [22]; our results thus provided better alternatives for lytic phage isolation.\u003c/p\u003e\n\u003cp\u003eTo further facilitate future application of the URPC phages, we predicted their microbial hosts using public MAG datasets, including several ruminant GIT MAGs and those of the other environments. Of particular interest, we obtained 109 phages targeting methane-producing species in all six archaeal genera by mining the phage-host relationships; of which 74 were lytic ones. Previous studies have shown that phages targeting methanogens may help reduce methane emissions [13], but we lack a large-scale method for identifying such phages [95]. Therefore, our results will facilitate the targeted isolation of lytic phages against methanogens and other bacterial/archaeal species in general.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, our assembly and analysis of the URPC phages massively expanded the ruminant GIT phages and pave the way for microbiome intervention to improve the ruminant and environmental quality.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe filled the gap in ruminant viral ecology research by providing a catalog of phage genomes, and identifying many lytic viruses that could target methane producers. Our findings provide insights into the phage community of the ruminant GIT and can be used as a starting point for future research on microbiome manipulation in ruminants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval and Consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe Unified Ruminant Phage Catalogue (URPC) used in this study are available in the Figshare database under accession code 22219093 (https://doi.org/10.6084/m9.figshare.22219093.v2).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe present study was granted and supported by NNSF-VR Sino-Swedish Joint Research Programme (82161138017 to WHC), National Natural Science Foundation of China (32070660 to WHC) and Key Support Project of the National Natural Science Foundation of China Joint Fund (U20A201297 to QYL).\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u003c/h2\u003e\n\u003cp\u003eWHC\u0026nbsp;and QYL\u0026nbsp;designed and directed the research. Conceptualization, data curation, formal analysis, investigation, visualization: YJW, CQS and NG; Methodology: WYJ, CQS, NG and TF; Writing-original draft: YJW and CQS; Writing-review and editing: all authors; Funding acquisition: WHC\u0026nbsp;and QYL; Supervision: YJW, WHC and\u0026nbsp;QYL. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAuthor information\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eKey Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYingjian Wu, Chuqing Sun, Tong Feng and Wei-Hua Chen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitution of Medical Artificial Intelligence, Binzhou Medical University, Yantai 264003, China.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWei-Hua Chen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan, 528225, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQingyou Liu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eState Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530005, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQingyou Liu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOltjen JW, Beckett JL: \u003cstrong\u003eRole of ruminant livestock in sustainable agricultural systems\u003c/strong\u003e. \u003cem\u003eJ Anim Sci \u003c/em\u003e1996, \u003cstrong\u003e74\u003c/strong\u003e(6):1406-1409.\u003c/li\u003e\n\u003cli\u003eChen L, Qiu Q, Jiang Y, Wang K, Lin Z, Li Z, Bibi F, Yang Y, Wang J, Nie W\u003cem\u003e et al\u003c/em\u003e: 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Despite significant progress in the study of microbiome in the gastrointestinal tract (GIT) sites of ruminants, we still lack an understanding of the viral community of ruminants. Here, we surveyed its viral ecology using 2333 samples from ten sites along the GIT of eight ruminant species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: We present the Unified Ruminant Phage Catalogue (URPC), a comprehensive survey of phages in the GITs of ruminants including 64,922 non-redundant phage genomes. We characterized the distributions of the phage genomes in different ruminants and GIT sites and found that most phages were organism-specific. We revealed that ~60% of the ruminant phages were lytic, which was the highest as compared with those in all other environments and certainly will facilitate their applications in microbial interventions. To further facilitate the future applications of the phages, we also constructed a comprehensive virus-bacteria/archaea interaction network and identified dozens of phages that may have lytic effects on methanogenic archaea.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The URPC dataset represents a useful resource for future microbial interventions to improve ruminant production and ecological environmental qualities. Phages have great potential for controlling pathogenic bacterial/archaeal species and reducing methane emissions. Our findings provide insights into the virome ecology research of the ruminant GIT and offer a starting point for future research on phage therapy in ruminants.\u003c/p\u003e","manuscriptTitle":"A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 18:36:35","doi":"10.21203/rs.3.rs-2712043/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"47a459b6-78d7-4d71-bd05-4c21ff9a5a4c","date":"2023-12-05T16:21:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-04T18:58:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-11-28T02:22:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbiome","date":"2023-11-27T03:16:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbio","sideBox":"Learn more about [Microbiome](http://microbiomejournal.biomedcentral.com/)","snPcode":"40168","submissionUrl":"https://submission.nature.com/new-submission/40168/3","title":"Microbiome","twitterHandle":"@MicrobiomeJ","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e4db3b9-12e2-450d-bd9c-9b32e867f6a8","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-08T15:12:38+00:00","versionOfRecord":{"articleIdentity":"rs-2712043","link":"https://doi.org/10.1186/s40168-024-01784-2","journal":{"identity":"microbiome","isVorOnly":false,"title":"Microbiome"},"publishedOn":"2024-04-04 15:01:33","publishedOnDateReadable":"April 4th, 2024"},"versionCreatedAt":"2024-02-29 18:36:35","video":"","vorDoi":"10.1186/s40168-024-01784-2","vorDoiUrl":"https://doi.org/10.1186/s40168-024-01784-2","workflowStages":[]},"version":"v1","identity":"rs-2712043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2712043","identity":"rs-2712043","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}