Rumen DNA virome and its relationship with feed efficiency in dairy cows

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However, the knowledge of rumen viruses is limited, and their relationship with production traits (e.g., feed efficiency) has not been reported. In this study, we combined next-generation sequencing (NGS) and HiFi sequencing to investigate the rumen DNA virome and reveal the potential mechanisms of how viruses influence feed efficiency in dairy cows. Results Compared with NGS, HiFi sequencing improved the length, completeness, and resolution of viral operational taxonomic units (vOTUs) obviously. A total of 6,922 vOTUs were recruited, including 4,716 lytic and 1,961 temperate vOTUs. At family level, lytic viruses were mainly composed of Siphoviridae (30.35%) and Schitoviridae (23.93%), while temperate viruses were predominantly Siphoviridae (67.21%). A total of 2,382 auxiliary metabolic genes (AMGs) were annotated, which involved in the pathways of carbon metabolism, nitrogen metabolism, energy metabolism, etc. A total of 2,232 vOTU-hMAG (host metagenome-assembled genome) linkages were predicted, with Firmicutes_A (33.60%) and Bacteroidota (33.24%) being the most common host at phylum level. Differential viruses were detected between high and low feed efficiency groups at the family, genus and species levels ( P < 0.05). By integrating differential viruses, vOTU-hMAG linkages and AMGs, two pathways have been proposed for how rumen viruses affect feed efficiency in dairy cows: 1) lytic viruses lyse host related to cattle phenotypes, such as vOTU1836 can lyse Ruminococcaceae that have a positive effect on organic acids, and 2) AMG-mediated modulation of host metabolism, for example, GT2 carried by vOTU0897 may enhance the fermentation capacity of Lachnosopraceae to produce more organic acids. Conclusions Overall, we constructed a rumen DNA virome profile of Holstein dairy cows, showing the structural and functional composition of rumen viruses, the roles of AMGs carried by vOTUs and the linkages between vOTUs and their hosts. By integrating the above information, we proposed potential mechanisms through which rumen viruses influence feed efficiency in dairy cows, providing new insights into the regulation of feed digestion and nutrient utilization in dairy cows. HiFi sequencing Lytic Temperate Auxiliary metabolic genes Feed efficiency Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Increasing feed efficiency (FE) of dairy cows is an effective way to achieve the sustainable development of dairy industry [ 1 ]. Massive and different kingdom of rumen microbes can utilize plant biomass to provide a large proportion of nutrient precursors to dairy cows, thus determine the FE to some extent. Most of the related papers have focused on bacteria and archaea [ 2 – 5 ], for example, the positive interaction between Selenomonas and members of the Succinivibrionaceae family may be linked to the high efficiency of dairy cows [ 2 ]. Also, a few of studies are involved in rumen fungi and protozoa [ 6 , 7 ]. However, there is a lack of research on rumen viruses. Viruses are the most abundant non-cellular entities in the world [ 8 ]. In the rumen, the number of free viruses ranges from 5 × 10^ 7 to 1.4 × 10^ 10 /mL [ 9 ], representing large under-investigated members and functions. According to their life cycle, viruses can be classified into lytic and temperate viruses. Lytic viruses will infect host (referring to the parasitic host of the virus in this context) cells, replicate rapidly, and cause cell lysis, leading to the release of new viral particles that can infect other cells [ 10 ]. On the contrary, temperate viruses usually enter a dormant state by integrating their genetic material into the host genome, establishing a long-term relationship with the host [ 11 ]. These two different types of infection exert distinct modes of function. Viruses can influence the host metabolism through different mechanisms, including host cells lysis [ 12 ], horizontal gene transfer mediation [ 10 , 13 ], and the presence of auxiliary metabolic genes (AMGs) that can selectively alter host metabolism [ 14 , 15 ]. AMGs include a range of genes that regulate host metabolism and life processes [ 16 ]. These genes are involved in photosynthesis and synthesis of photosynthetic pigments, phosphate metabolism, central carbon metabolism, nutrient cycling, nucleotide biosynthesis and so on [ 14 , 17 , 18 ]. Research on rumen viruses in ruminant animals has undergone three stages, from morphological studies to molecular biology research, and finally to the latest omics-based studies. As early as 1890s, researchers have discovered the presence of viruses in the rumen [ 19 , 20 ], and the morphological structures of certain viruses, including the families of Siphoviridae, Podoviridae, Myoviridae, and some non-tailed phages were observed [ 21 ]. Subsequent molecular biology research focused primarily on the length of bacteriophage genomes and restriction endonuclease patterns. Specific bacteriophages targeting certain rumen-specific bacterial species were identified and isolated, including phages specific to Streptococcus bovis , Selenomonas ruminantium [ 22 , 23 ]. Current omics-based research in this field advances the identification and characterization of virus [ 24 , 25 ]. However, rare studies explored the roles of rumen virus in animal phenotypes. It has evidenced that viruses can interact with all other microbial kingdoms present in the rumen [ 9 ]. Based on the aforementioned background, we propose potential mechanisms of how rumen viruses affect FE in dairy cows: 1) Direct lysis of host cells related to production traits, thus affecting FE. 2) Viral AMGs alter host metabolism to improve or decline the organic matters positive with FE. To validate our hypothesis, we measured the FE of 53 mid-lactation Holstein dairy cows and subsequently selected 15 high feed efficiency (HE) and 15 low feed efficiency (LE) cows. Then, we performed next-generation sequencing (NGS) and HiFi sequencing to construct rumen DNA virome, including the taxonomy, composition, diversity, virus-host linkages, and information on carried AMGs of the viruses. By integrating these data and the differential viruses between the groups as “biomarkers”, the potential roles of the viruses in FE are initially explored. Results Animal phenotypes and metagenomic data The energy correct milk (ECM, P = 0.0003), milk yield ( P = 0.0002) and ECM/DMI (dry matter intake, 1.593 ± 0.017 vs. 1.374 ± 0.019, P < 0.0001) were significantly higher in the HE group. However, there were no differences in DMI ( P = 0.5216), milk protein content ( P = 0.1280), and milk fat content ( P = 0.9918) between HE and LE groups (Table 1 , more details in Table S1 ). Table 1 Milk production and feed efficiency in cows selected for HE and LE Items Groups SEM P -value LE HE DMI (kg/d) 25.030 26.701 0.951 0.5216 Milk Yield (kg) 30.560 36.416 1.393 0.0002 Milk fat content (%) 4.143 4.145 0.193 0.9918 Milk protein content (%) 3.377 3.279 0.062 0.1280 ECM 33.348 39.600 1.534 0.0003 ECM/DMI 1.374 1.593 0.025 < 0.0001 LE: low efficiency; HE: high efficiency; DMI: dry matter intake; ECM: energy corrected milk. ECM = (0.3246 × kg of milk)+(13.86 × kg of milk fat)+(7.04 × kg of milk protein). NGS sequenced an average of 40.51 ± 2.04 Gb (mean ± SEM) data per sample. A total of 8,102,834,056 raw reads were generated from the NGS of 30 rumen fluid samples, with 270,094,468 ± 13,573,491 per sample (Table S2). After quality control and host gene removal, 7,974,091,852 clean reads were retained, with 265,803,062 ± 13,460,468 per sample (Table S3). A total of 15.60 Gb data (average length of reads: 6.03 Kb) were generated from the HiFi sequencing for one rumen fluid samples (more details in Table S4). Overview of rumen DNA virome With the increasing length of filtered contigs, the number of vOTUs decreased for both NGS and NGS + HiFi samples (Fig. 1 a, Table S5). The average length of vOTUs obtained from NGS + HiFi was significantly longer than those only from NGS (Fig. 1 a, Table S5). Under the filtering condition of the length ≥ 10k, the quality of vOTUs obtained from NGS + HiFi was higher than those from NGS. The number of complete vOTUs has a 91.6% increase (from 132 to 253) (Fig. 1 b, Table S5). Compared to NGS, the overall proportion of complete, high-quality, and low-quality vOTUs increased from 7.8–20.0% in NGS + HiFi (Fig. 1 b). Totally 6,922 vOTUs were recruited, including 4,716 lytic, 1,961 temperate, and 245 unknown vOTUs (Fig. 1 c, Table S6). At the family level, the majority of vOTUs (57.87%) remained unannotated. The total relative abundance of Siphoviridae and Myoviridae families accounted for 23.68% and 7.96% of total abundance, respectively (Fig. 1 c, Tables S6 and 7). The longest length of observed vOTU was 318,474 bp, while the shortest was 10,031 bp, with 4,421.4 ± 343.3 bp per sample (Fig. 1 c, Table S6). In total, 5,665 vOTUs were detected or optimized by the HiFi sequencing platform, while 1,257 vOTUs were obtained from the NGS platform (Fig. 1 c, Table S6). A total of 1,963 vOTUs were annotated with host information. The most common hosts were Firmicutes_A (33.60%) and Bacteroidota (33.24%) at phylum level (Fig. 1 c, Tables S8 and 9). Structural composition of lifestyle-dependent viruses in the rumen In all 30 samples, the vOTU counts ( P < 0.0001), relative abundance ( P < 0.0001), and Shannon index ( P = 0.0277) were significantly higher for lytic viruses compared to temperate viruses (Fig. 2 a). At the family, genus, and species levels, the quantity and diversity of lytic viruses were greater than those of temperate viruses (Fig. 2 b). However, at these three taxonomic levels, there were overlaps of lytic and temperate viruses (Fig. 2 b). The majority of vOTUs at the family level remain unannotated, representing 40.78% and 47.59% in lytic and temperate viruses, respectively (Fig. 2 c). In lytic viruses, the most abundant families were Siphoviridae and Schitoviridae, accounting for 30.35% and 23.93% of the annotated vOTUs, respectively. For temperate viruses, Siphoviridae was the most abundant family, representing 67.21% of the total annotated vOTUs (Fig. 2 c and Tables S6 and 7). AMGs of lifestyle-dependent viruses in the rumen A total of 6,868 non-provirus vOTUs were selected to predict AMGs, and finally 2,382 vOTU sequences were predicted as 56 categories of AMGs, of which 1,752 (51 categories) and 589 (29 categories) were lytic and temperate viruses, respectively (Figs. S1a and 1b). The majority (79.5%) of detected vOTUs carried only 1 AMG, whereas the remaining vOTUs carried 2 or more AMGs (Table S10). Specifically, there were two lytic vOTUs, vOTU3931 and vOTU3877, carried 6 AMGs (Fig. S1 e and Table S10). The categories of AMGs involved in carbon metabolism was the most numerous which up to 29, followed by miscellaneous (MISC) including 14 categories (Fig. 3 a). However, the count of AMGs involved in MISC metabolism was the most (Table S10). The most prevalent AMG in both lytic and temperate viruses was dut , which involved in nucleotides metabolism, encoding dUTP pyrophosphatase that can hydrolyze dUTP to dUMP and pyrophosphate (Figs. S1c and 1d, Table S10). Besides, some other genes, such as nrdD , metK and DNMT1 , were in high abundance in both lytic and temperate viruses. Notably, cysH was more abundant in lytic viruses, a gene encoding a phosphoadenosine phosphate sulfate reductase protein involved in the synthesis of sulfite from sulfate. To show which kind of metabolism of the host are affected by AMG clearly, we divided the metabolism pathways into 10 sub-categories (Fig. 3 b and Table S10). In this study, there were 25 categories of AMGs specific to lytic viruses and only 3 specific to temperate viruses (Fig. 3 b). For example, lytic-specific fiC and fliD involved in the metabolism of flagella structure, temperate-specific GH28 and GT25 belonging to CAZymes (Fig. 3 b). Host prediction and correlation of lifestyle-dependent viruses A total of 2,232 virus-host correspondences were annotated, including 1,377 lytic vOTUs-hosts and 780 temperate vOTUs-hosts linkages (Table S8). The hosts of lytic vOTUs belong to 458 metagenome assembled genomes (MAGs), while the hosts of temperate vOTUs belong to 366 MAGs (Table S8). Almost all of vOTUs (88.95%) were annotated to single host, and those hosts shared the same vOTUs were generally from the same family (Figs. S2a and 2b, Table S8). However, vOTU4641, vOTU6113, and vOTU6884 possessed the highest number of hosts (n = 5; Table S8). Additionally, 32 vOTUs were found to have multiple hosts at family level, for example, vOTU6884 had 5 hosts belonging to 3 different families: UBA66, Bacteroidaceae, and Paludibacteraceae (Table S8). CowSGB-5648, an archaea MAG belonging to Methanobrevibacter (the most abundant methanogens genus), was predicted to be the host of 140 vOTUs (87.14% of which are lytic) (Table S8). We observed 4 pairs exhibiting negative correlation and 578 pairs exhibiting positive correlation in the linkage between lytic vOTUs and their host (MAG level), and 294 positive correlation in the linkage between temperate vOTUs and their hosts (Spearman, P < 0.05, | r | ≥ 0.5)( Table S11). The hosts of lytic viruses belong to 15 different phyla, while the hosts of temperate viruses belong to 11 different phyla. The hosts of both types of viruses were mainly belonged to Firmicutes_A (32.2% and 38.9% of the hosts of lytic and temperate viruses, respectively) and Bacteriodota (24.4% and 45.0% of the hosts of lytic and temperate viruses, respectively) (Fig. 4 ), which consistent with the two bacteria with the highest relative abundance (Figs. S3a and 3b, Table S9). Compositional differences of the rumen viruses between the HE and LE groups There was no difference between HE and LE groups in Richness ( P = 0.2854), Shannon ( P = 0.9349) and Simpson ( P = 0.6827) index of total vOTUs (Fig. 5 a), the same results were observed in both lytic (Fig. S4a) and temperate vOTUs (Fig. S4c). The PCoA analysis showed no difference in vOTUs numbers and distribution of vOTUs’ relative abundance between the two groups (Fig. 5 b and Figs. S4b and 4d). Moreover, the total relative abundance of lytic viruses and temperate viruses did not differ between two groups (Fig. 5 c). For the correlation analysis between relative abundance of vOTUs and FE or some FE-related phenotypes (DMI, Milk yield, Milk protein content, and Milk fat content), 23 lytic (positive:19; negative:4) and 11 temperate (positive:10; negative:1) viruses (at vOTU level) were significantly correlated with ECM/DMI (Spearman, P < 0.05, | r | ≥ 0.5) (Fig. 5 d). For both lytic and temperate vOTUs, DMI and milk yield were consistently associated with ECM/DMI (Fig. 5 d). At the family level, Drexlerviridae ( P = 0.0255) exhibited significantly lower abundances in the HE group among the lytic viruses, while Leisingerviridae ( P = 0.0396) was more abundant in the HE group among the temperate viruses. For lytic viruses, there were 2 genus ( Klausavirus , Chatterjeevirus ) and 6 species ( Phifelvirus FL3 , Biseptimavirus P630 , Gofduovirus edno5 , Chatterjeevirus N4 , Laroyevirus salgado , Klausavirus kburrousTX ) of viruses that had higher abundance in the HE group. Furthermore, the number of high-abundance differentially viruses in the HE group was less than the number observed in the LE group (Fig. 5 e). However, in temperate viruses, the opposite results were found. Except for Clostridium phage phiCP13O (species level), the remaining differentially viruses showed higher abundance in the HE group (Fig. 5 e). Mechanism of how rumen viruses be linked to feed efficiency Since no differences were observed in AMGs abundance and the enriched pathway abundances between the HE and LE groups (Fig. S5, Tables S12-14), we can only interpret the impact of AMGs on feed efficiency based on their functional properties. Another approach to understand the behind mechanism of how viruses affect FE is to explore how differential viruses affect their hosts and subsequently influence FE. Therefore, we obtained the vOTUs of 18 differential viruses, along with their hosts’ MAGs that predicted earlier. Based on our results and literature review, we summarized the host-influenced cow traits, which can contribute to the FE directly or indirectly. Obviously, the viruses can affect their host through the following two pathways: 1) The lytic viruses can lyse their host directly; 2) The AMGs carried by both types of viruses, especially temperate viruses can alter their host metabolism. Both pathways had the potential to change the structural and functional composition of the rumen microbiome, thus further influencing the traits such as FE and methane emissions in dairy cows (Fig. 6 ). For example, there are 6 lytic vOTUs capable of lysing bacteria associated with methane emissions, resistance to parasitic infections, formate, acetate, succinate, and NH 3 -H; and the vOTU0897 carrying GT2 may enhance the fermentation capacity of Lachnosopraceae to produce more acid, which in turn contribute to the improvement of FE (Fig. 6 ). Discussion As the “dark matter” in the gut microbiome, virome and its roles attracted more and more attentions. However, due to the limitations in sequencing depth and computation tools, whether and how gut viruses be linked to human/animal phenotypes remain unknown. In this study, integrated deep NGS sequencing (5 time over general sequencing depth) and HiFi-based third-generation long reads sequencing, a fundamental understanding of the structural and functional composition of rumen virus in dairy cattle had been obtained. By conducting differential and correlation analysis, FE associated rumen viruses have been identified. Furthermore, based on the predicted hosts and annotated AMG of targeted viruses, the current study provided novel insights into the potential mechanisms of how different viruses affect FE in dairy cows. To date, only a few of studies explored rumen viruses/phages. Similar to other reports that using high-throughput technologies, a significant portion of viruses are un-annotated [ 24 , 26 , 27 ], suggesting that there are still many “unresolved mysteries” surrounding rumen viruses. The rumen virome database generated by Yan et al. showed that at the family level, Siphoviridae (79.6% of known viruses) was the most abundant, followed by Myoviridae (14.3%) and Podoviridae (4.5%) [ 27 ]. In our results, Siphoviridae (34.9%) was also the most abundant, however, the second and third abundant viral family were Schitoviridae (19.0%) and Myoviridae (14.5%), respectively. Since Yan’s data included ruminant animals from Holstein cows as well as other 12 different species, the above difference may be attributed to the distinct patterns or specific viruses existing in the rumen of different ruminant species. Therefore, our data provides a more accurate description of the composition of DNA viruses in the rumen of Holstein cows. According to the different life cycles of lytic and temperate viruses, they have distinct impacts on the rumen environment [ 10 ]. Therefore, the structural and functional composition of lytic and temperate viruses should be investigated separately, which are largely ignored in other studies. There are some unique viral families to each of the two types, such as the lytic virus-specific family Salasmaviridae and the temperate virus-specific family Iridoviridae, confirmed that studying lytic and temperate viruses separately may provide a more accurate assessment of the role of certain virus. Viruses can be linked to the animals’ phenotypes by interacting with their host firstly and directly, AMGs is an important way [ 27 , 28 ]. Our results revealed a total of 54 types of annotated AMGs, with 28 types being carbohydrate-active enzyme (CAZyme) genes. Previous studies have reported the presence of a large number of CAZyme modules in 186 rumen bacterial genomes [ 29 ]. This observation may suggest that during the long-term co-evolution between rumen viruses and their host microbes, the viruses acquired common and vital genes from their hosts, which play an auxiliary role in fiber degradation. The most abundant AMG identified in this study was dut , which encods dUTP pyrophosphatase and can catalyze the hydrolysis of dUTP into dUMP and PPi, playing a crucial role in preventing erroneous insertions during DNA synthesis and maintaining nucleotide balance [ 30 ]. Other high abundant AMGs, such as DNMT1 , metK , and nrdD , also contributed to DNA synthesis and repair [ 31 – 33 ], reflecting the importance of nucleic acid metabolism-related genes in rumen viruses in dairy cows. There are 25 types of AMGs specific to lytic viruses, while only 3 types specific to temperate viruses, indicating that lytic viral AMGs likely to exhibit higher diversity and involve more metabolic pathways, which is consistent with previous environmental microbiome studies [ 34 ]. Lytic viruses require specific AMGs to hijack the host’s metabolism for their own replication, while temperate viruses do not rely on these kinds of genes [ 34 , 35 ]. Our findings indicated that lytic viruses possess 3 distinct classes of AMGs in the “information system”, which involved in DNA synthesis, modification, glycosylation and so on. For example, the tmk generated dUDP and dTDP, which are potentially involved in DNA synthesis and nucleic acid modification [ 36 ]. The summarized schematic diagram of AMGs involved in host metabolism in the rumen provided a comprehensive understanding of how AMGs influence rumen holistic metabolism. There were no significant differences in α and β diversity of vOTUs, as well as AMGs, indicating that healthy cows within the same dairy farm may share a common viral source and exhibit a relatively stable composition of rumen viruses. However, as crucial “biomarkers”, the viruses with different relative abundances between the 2 groups provide a breakthrough for exploring the relationship between rumen viruses and FE in dairy cows. Interestingly, most differential lytic viruses showed lower relative abundance in HE groups, whereas differential temperate viruses were more numerous in HE animals. To explain this phenomenon, we speculate that lytic viruses may replacate whthin and lyse bacteria/archaea that are positively associated with production traits, leading to an increase in their own abundance and a decrease in FE [ 37 ]. On the other hand, temperate viruses may play some beneficial roles, such as carrying AMGs that can enhance the competitiveness of the host, thereby increasing in abundance with the increase in host abundance. However, these hypotheses still require detailed analysis of virus-host relationships and dynamic experimental validation [ 38 ]. To investigate how viruses affect FE, we focused on the differential viruses between the HE and LE groups, combined the information on AMGs and cows’ phenotypes that viral hosts (other microorganisms in the rumen) can influence [ 39 – 44 ]. This because most phenotypes are related to metabolism and can have direct or indirect effects on FE, and some studies have verified the correlations between ruminal metabolites (e.g., VFAs) and FE in cattle [ 45 – 47 ]. We established the associations between viruses (and AMGs), hosts, and phenotypes of dairy cows. For example, as an important source of energy for cows [ 48 ], the concentration of acetate is higher in the rumen of high-FE cows [ 46 ], and Lachnospiraceae and Ruminococcaceae are the two acetate producers. On the one hand, as a temperate viruse, vOTU0897 carried the GT2 may enhance the function of acetate production of Lachnospiraceae , thereby improving FE. On the other hand, as lytic viruses, vOTU1836 and vOTU5102 may decrease acetate production by lysing Ruminococcaceae , consequently reducing FE. Therefore, it was plausible to explain how viruses impact their hosts by establishing a “virus-host-FE” pathway, which can further elucidate how viruses affect FE. In addition to the viruses shown in Fig. 6 , other virus-host linkages also provide valuable information, for example, the 142 lytic archaeal viruses (Table S8) have the potential to directly lyse methanogens, thereby reducing methane production. Due to space limitations, a more in-depth exploration was not conducted in this study, further investigation should be carried out to dig out the related mechanism. Although we had conducted high-depth NGS and HiFi sequencing to gain a more detailed and accurate understanding of the structural and functional composition of rumen virome in dairy cows, however, like many other studies focusing on rumen viruses [ 23 , 27 – 29 ], we haven’t investigated RNA viruses which may have important functions [ 50 ]. In the future, it is important to explore this area to enhance our active functional knowledge of the intraruminal viruses. Currently, many published virome papers have conducted virus filtration and enrichment [ 24 , 34 , 51 , 52 ]. However, considering the potential loss of information on temperate viruses, we did metagenomic sequencing without enrichment. Additionally, to minimize bias caused by a low proportion of viral reads in the whole DNA sequencing, we selected a deep sequencing depth. There is no data comparing the advantages and disadvantages of these two methods, so further research should be conducted to explore the difference and specificity. In this study, we selected the differential viruses between the HE and LE groups as “biomarkers”, and then proceeded to link them with their hosts and FE of dairy cows. Taking a different perspective, the hosts also can also serve as “biomarkers”. Many differential bacteria/archaea between the HE and LE groups had already reported [ 2 , 5 , 53 ], for example, Selenomonas , which positively related to FE, has 7 vOTUs in our results. By integrating the virus-host linkages, AMGs and the host-traits relationship, both types of “biomarkers” can provide insights for elucidating the mechanism of “virus-host-FE” in our study. Moreover, the validation of the mechanism needs to be complemented by more explicit details as well as wet experiments. Conclusion The research on the viruses of the bovine rumen is still limited. Utilizing omics technologies, we conducted a comprehensive analysis of viruses in 30 rumen fluid samples. Our study has preliminarily constructed a DNA virome profile in the rumen of dairy cows, including the composition of viral structures, functional elements, and virus-host interactions. We have demonstrated the role of AMGs in influencing pathways such as rumen carbon metabolism, nitrogen cycling, and nucleic acid metabolism. Through the identification of differentially viruses between HE and LE groups, along with their associated AMGs and hosts, we have revealed potential pathways through which viruses may impact FE in cows. More specifically, viruses can exert lytic effects or selectively modify the metabolism of bacteria/archaea associated with FE through AMGs, thereby influencing the feed efficiency of dairy cows. Method Animals and samples In this article, all procedures involving animals were approved by the Animal Use and Health Committee of Zhejiang University (Hangzhou, China, No. 12410). A total of 53 multiparous mid­lactating Holstein cows were selected for the 57 days (with an adaptation of 7 days) experiment. During this period, all cows were housed in a free­stall barn with access to a total mixed ration, and were fed three times per day (06:30, 14:30, and 21:30) with a corn-based high-grain diet. Cows are weighed every morning of the week, immediately after milking. The DMI of each cow was determined daily by an automatic feed system (Zhenghong Co., Shanghai, China). Samples of rumen fluid were collected by oral stomachs tube before morning feeding and stored at -80°C until subsequent treatment. The milk yield of each cow was recorded at 3 milking time points (06: 00, 14:00, and 21:00) for 2 consecutive days. One subsample was stored at 4°C for analysis of milk composition using infrared spectroscopy. Feed efficiency was calculated using ECM/DMI, and 15 high efficiency (HE, ECM/DMI = 1.593 ± 0.063) and 15 low efficiency (LE, ECM/DMI = 1.374 ± 0.071)) cows were selected for further analysis. DNA extraction, library preparation, and next generation sequencing All 30 rumen fluid samples were not filtered prior to DNA extraction, so all free viruses and prophages were retained. The DNA was extracted from 1.5 g rumen fluid samples using the E.Z.N.A.® stool DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to manufacturer’s protocols. NGS libraries were prepared following TruSeqTM Nano DNA sample preparation Kit from Illumina (San Diego, CA), using 1ug of total DNA. DNA end repair, A-base addition and ligation of the Illumina-indexed adaptors were performed according to Illumina’s protocol. Libraries were then size selected for DNA target fragments of ~ 400 bp on 2% Low Range Ultra Agarose followed by PCR amplified using Phusion DNA polymerase (New England Biolabs, USA) for 15 PCR cycles. All samples were sequenced by the Illumina NovaSeq 6000 platform (150bp×2, Shanghai Biozeron Biotechnology Co., Ltd, Shanghai, China). HiFi sequencing A total of 5 µg of DNA (from HE_6) was used to prepare a SMRTbell library with the PacBio SMRTbell prep kit 3.0 (Pacific Biosciences, CA, USA, Part Number: 102-182-700) according to the manufacturer’s recommendations. Damaged double-stranded DNA in the initial DNA sample was repaired using the New England BioLabs PreCR® Repair Mix Kit according to the manufacturer’s instructions before library preparation. Then, the repaired DNA was size-selected by using the BluePippin system (Sage Science, MA, USA) to obtain molecules larger than 3 kb. The SMRTbell library was sequenced with v3 chemistry on a PacBio Sequel IIe instrument (Pacific Biosciences, CA, USA) using SMRT 8M cells (Part Number: 101-389-001). HiFi reads were then generated with the ‘ccs’ module (parameters: --min-length 500 --min-passes 3 --min-rq 0.99) within the SMRT Link v10.0 package (Pacific Biosciences, CA, USA). Metagenomic assembly and prokaryotic genome binning NGS raw sequence reads underwent quality trimming using Trimmomatic (v0.36, adapters.fa:2:30:10 SLIDINGWINDOW:4:15 MINLEN:75) [ 54 ] to remove adaptor contaminants and low quality reads. Reads passed quality control were then mapped against Bovine genome by BWA mem algorithm, with the parameters “-M -k 32 -t 16”. The reads removing host-genome contaminations and low-quality data were called as clean reads and used for the further analysis. Clean reads were generated a set of contigs of each sample using MegaHit [ 55 ] (v1.1.1-2-g02102e1) with “--min-contig-len 500” parameters. Metagenomic binning was performed in each sample contigs. First, binning software metaBAT2 v2.11.1 [ 56 ] was used to do binning separately. The completeness, and contamination of all bins was obtained using CheckM v1.1.1 [ 57 ]. All bins with completeness > 50%, contamination < 10% were considered as MAGs. Finally, 13,572 MAGs were annotated. All MAGs were taxonomically annotated using GTDB-Tk (r207) [ 58 ] based on the Genome Taxonomy Database, which produced the standardized taxonomic labels of bacteria and archaea. vOTU identification and taxonomic classification For greater accuracy and confidence, VirSorter2 (v2.2.3, max_score ≥ 0.7) [ 59 ] and DeepVirFinder (v1.0, Score ≥ 0.7, pvalue ≤ 0.05) [ 60 ] were used to identify viral contigs from metagenomic assemblies. Viral contigs longer than 10k were manually screened for subsequent analysis. To remove some non-viral sequences during the VirSorter2 analysis, CheckV v0.7.0 [ 61 ] was conducted to quality assessment. Finally, all potential viral contigs were further checked using VIBRANT v1.2.1 [ 62 ], with default settings. The identified viral contigs were clustered by using Mummer software (95% ANI, ≥ 85% coverage), and the longest representative one within each cluster was considered as vOTUs. To prevent information loss due to excessively large filter lengths, 10k was selected as the filter parameter, finally a total of 6,922 vOTUs were recruited for further analysis. Virus lysis was detected by VIBRANT v1.2.1 and provirus is detected by CheckV v0.7.0. Taxonomic annotation of viruses was performed using Blastx [ 63 ] according to the recently published NCBI virus classification list (e-value ≤ 1e-5, by November 2022). Since the differences between vOTUs at the genomic (DNA) level are so large that it is hardly to use conventional global comparison for phylogenetic tree construction, the similarity matrix at the protein level was generated by ViPTree v1.1.2 [ 64 ] based on the genome-wide sequence similarity computed by tBLASTX, which in turn led to the construction of the phylogenetic tree. Calculation of vOTU and MAG relative abundance In order to more accurately reflect the true state of the viral and procaryotic “genome” in the rumen environment, sequences of vOTUs and MAGs are packaged together to calculate the relative abundance. Then metaWRAP quant_bins module with default parameters was applied to calculate the relative abundance of each vOTU and MAGs, which was prescented as “TPM” (transcripts per kilobase of exon model per million mapped reads). Virus-host linkage predication In order to extend the virus-host linkage on an accurate basis, we added the downloaded and reconstructed prokaryotic genome (unpublished data) as a reference database for hosts. Finally, the 6,922 vOTUs were putatively linked to 13,572 (13,412 bacterial and 160 archaeal genomes) MAGs using two in silico methods: (1) similarity of CRISPR spacers between bacterial and viral sequence, with “blastn similarity ≥ 97, Comparison length ≥ 30”; (2) VirHostMatcher with “distance ≤ 0.2”. As a result, the hosts predicted by either of two methods were combined into a final potential host of the viruses. All virus-host linkages and the respective methods used were listed in Table S7. AMG identification, classification and abundance In order to more accurately predict and assign the levels of viral carbohydrate metabolism substrates and their contribution to geochemical cycling, DRAM-v (v 1.2.0 [ 65 ] was chosen to annotate AMGs, with the “default parameters, 1 ≤ score ≤ 3, AMG flags of -M and -F”. Statistical analysis Data statistics and visualization in this study were performed using R (v 4.2.1) ( https://www.r-project.org/ ) and GraphPad Prism 8.0 ( https://www.graphpad.com/ ). The α diversity between the HE and LE groups were determined using Mann–Whitney U test. PCoA analysis was conducted to cluster the vOTUs of different samples based on the Bray–Curtis distance. Spearman correlations were calculated to reveal the relationships between the relative abundance of vOTUs and the production traits of dairy cows. Differential viruses between HE and LE groups were verified using “STAMP” analysis. Spearman correlations were calculated to reveal the relationships between the relative abundance of vOTUs and the relative abundance of their hosts. Declarations Acknowledgments We acknowledge the members in the Institute of Dairy Science of Zhejiang University (Hangzhou, China) for their assistance in the field sampling and data analysis. Authors’ contributions Xiaohan Liu and Hui-Zeng Sun designed the study. Xiaohan Liu and Yifan Tang conducted bioinformatic analysis. Xiaohan Liu wrote the first draft manuscript. Hongyi Chen collected data. Hui-Zeng Sun and Jian-Xin Liu edited and revised the manuscript. All authors read and approved the final manuscript. Funding This research was supported by grants from the National Natural Science Foundation of China (32322077, Beijing) and the National Key R&D Program of China (2023YFE0123100). Availability of data and materials The data that support the findings of this study are available in the National Genomics Data Center (NGDC) database with the accession number of PRJCA021193, PRJCA020785 and PRJCA023564. Ethics approval and consent to participate Animal care and experimental procedures were approved by the Animal Care Committee of Zhejiang University (Hangzhou, China), and were under the university’s guidelines for animal research . Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Liu X, Tang Y, Wu J, Liu J-X, Sun H-Z. Feedomics provides bidirectional omics strategies between genetics and nutrition for improved production in cattle. Anim Nutr. 2022;9:314–9. Xue M-Y, Xie Y-Y, Zhong Y, Ma X-J, Sun H-Z, Liu J-X. 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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-4199008","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291422452,"identity":"4f888cff-f816-43b1-8430-d0792a6a9895","order_by":0,"name":"Xiaohan Liu","email":"","orcid":"","institution":"Institute of Dairy Science, MoE Key Laboratory of Molecular Animal Nutrition, College of Animal Sciences, Zhejiang University, Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Xiaohan","middleName":"","lastName":"Liu","suffix":""},{"id":291422453,"identity":"78fc98a5-a8b9-4fa8-b1c1-b8a2e24715e0","order_by":1,"name":"Yifan Tang","email":"","orcid":"","institution":"Institute of Dairy Science, MoE Key Laboratory of Molecular Animal Nutrition, College of Animal Sciences, Zhejiang University, Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Yifan","middleName":"","lastName":"Tang","suffix":""},{"id":291422454,"identity":"e0dea7af-00b6-4370-b09e-a9673d159732","order_by":2,"name":"Hongyi Chen","email":"","orcid":"","institution":"Institute of Dairy Science, MoE Key Laboratory of Molecular Animal Nutrition, College of Animal Sciences, Zhejiang University, Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Hongyi","middleName":"","lastName":"Chen","suffix":""},{"id":291422455,"identity":"85088d00-1ef0-479e-9596-5f5915fcd3c9","order_by":3,"name":"Jian-Xin Liu","email":"","orcid":"","institution":"Institute of Dairy Science, MoE Key Laboratory of Molecular Animal Nutrition, College of Animal Sciences, Zhejiang University, Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Jian-Xin","middleName":"","lastName":"Liu","suffix":""},{"id":291422456,"identity":"c301f6b3-e76c-4bf9-b0da-8581ca5f2beb","order_by":4,"name":"Hui-Zeng Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAwbGBhAtx8BwAEixkaDFmBQtEJAI1kiUFnOJ5OYPH3fUps9vPGPA8KHsMAP/7Ab8WixnJDYYzjxzPLex4YwB44xzhxkk7hwg4LAbiQ3JvG3HcpsZzhgw87YdZjCQSCCs5fDftmPpbCAtf4nU0tjM2FaTwAPSwkiUljMPmxl72w4YzmA4VnCw51w6j8QNQlqOpz/+8LOtTl5+xuGND36UWcvxzyCgBQoOMzBIHABHJg9R6oGgjoGBv4FYxaNgFIyCUTDSAADQm0koEpFnrQAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Dairy Science, MoE Key Laboratory of Molecular Animal Nutrition, College of Animal Sciences, Zhejiang University, Hangzhou, China","correspondingAuthor":true,"prefix":"","firstName":"Hui-Zeng","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-04-01 07:50:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4199008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4199008/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40168-024-02019-0","type":"published","date":"2025-01-16T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54903021,"identity":"87213829-cda2-418d-8b5d-4aa8bbd145d7","added_by":"auto","created_at":"2024-04-18 10:48:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3030902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of NGS and HiFi sequencing technologies and overview of DNA virome\u003c/strong\u003e. (a) The number and average length of vOTUs under the threshold of contigs greater than 2k, 5k, 8k, 10k or 15k, respectively. (b) The quality of vOTUs obtained by NGS and NGS+HiFi when the length of contigs greater than 10k. (c) Overview of DNA virome in dairy cows including family taxonomy of vOTUs, life-cycle types of vOTUs, sequencing platforms, phylum taxonomy of vOTUs’ host, relative abundance of vOTUs, and length of vOTUs.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4199008/v1/a7d87e36171a753bfff42ba1.png"},{"id":54903022,"identity":"df4f9f8c-5f52-49f7-ac68-f82407bf571d","added_by":"auto","created_at":"2024-04-18 10:48:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural composition of lytic and temperate viruses in all samples\u003c/strong\u003e. (a) The number of vOTU (left) and the α diversity of vOTU between lytic and temperate viruses (middle: richness indexes, right: shannon indexes). The significance of the differences was determined by Mann–Whitney U test. (b) The number of vOTUs and viruses at family, genus, and species levels. The bar chart represents the number of vOTUs. The red color in the pie chart represents lytic viruses, and the blue color represents temperate viruses. (c) Community compositions (family level) of lytic and temperate viruses, only shows the most 10 abundant viruses of the two types of viruses.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4199008/v1/c7a15abfe30b6023872c5aa3.png"},{"id":54902421,"identity":"c08cf3f5-8ce4-4eaf-99f2-f9b0273e1958","added_by":"auto","created_at":"2024-04-18 10:40:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":624891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional composition of rumen viruses and AMGs involved in virus-host interactions.\u003c/strong\u003e (a) The relative abundance of AMGs in lytic and temperate viruses. (b) AMGs involved in virus-host interactions of 10 metabolism pathways. Red markers: AMGs specific to lytic viruses; Blue markers: AMGs specific to temperate viruses; Grey markers: AMGs both in lytic and temperate viruses. AMG: Auxiliary Metabolic Gene.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4199008/v1/1528ad32e7be3cb4e0e36948.png"},{"id":54902418,"identity":"e3879478-446d-47a4-ba63-d2cf65fcb790","added_by":"auto","created_at":"2024-04-18 10:40:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":743336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrespondences of viruses and their host\u003c/strong\u003e. A chord diagram linking viruses to their hosts, with viruses as the starting point and hosts as the endpoints. The upper half of the figure displays viruses at the family level, while the lower half shows hosts at the phylum level. In either outer ring of the two parts, each color represents a virus/host, and the segment proportion represents the percentage of quantity within that half. The color of inner ring in the viruse part represents the composition of their hosts.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4199008/v1/8acaed8cab92ba8ffd2b8964.png"},{"id":54902422,"identity":"a15e9e23-4a00-41d7-820e-bc6bbc02496b","added_by":"auto","created_at":"2024-04-18 10:40:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":476132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompositional differences of the rumen viruses between the HE and LE groups.\u003c/strong\u003e (a) The α diversity of vOTUs between HE and LE groups. The significance of the differences was determined by Mann–Whitney U test. (b) The β diversity of vOTUs between HE and LE groups based on Bray–Curtis dissimilarity. (c) The relative abundance of lytic and temperate vOTUs bewteen HE and LE groups. (d) The Spearman’s correlations between the relative abundance of lytic/temperate vOTUs and the phenotypes of dairy cows. (e) Significantly different vOTUs at family, genus and species level between HE and LE groups based on STAMP analysis (wilcox.test, 95% confidence intervals). HE: High Efficiency; LE: Low Efficiency; STMAP: Spatial Transcriptomics Analysis Pipeline.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4199008/v1/b95aa813451c7010fd4f8a44.png"},{"id":54902420,"identity":"7b366bc9-769c-49b3-a7bf-826338864e87","added_by":"auto","created_at":"2024-04-18 10:40:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":215831,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanism of which rumen viruses impact feed efficiency through affecting their host\u003c/strong\u003e. The figure shows the type, vOTU ID and host of different viruses between HE and LE groups, the phenotype of cattle associated with the host, and AMGs carried by the vOTU (red star). The lytic viruses can lyse their host directly, and viruses carrying AMGs can alter the metabolism of their host, and the structural and functional composition of the rumen microbiome may be changed, thus influencing the productive traits of dairy cows. HE: High Efficiency; LE: Low Efficiency.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4199008/v1/43f38d968ec07c84a4b882db.png"},{"id":74285684,"identity":"7990ca02-8188-4f31-964b-0d3f2e5d50ab","added_by":"auto","created_at":"2025-01-20 16:14:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5560063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4199008/v1/ba8c176d-7f74-4a09-ba6a-ebe4364227fb.pdf"},{"id":54902425,"identity":"dd2b4b38-173c-4938-97c8-70042d7e8bb4","added_by":"auto","created_at":"2024-04-18 10:40:53","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8137162,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-4199008/v1/821580ce42344f7a43b7bdd0.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rumen DNA virome and its relationship with feed efficiency in dairy cows","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIncreasing feed efficiency (FE) of dairy cows is an effective way to achieve the sustainable development of dairy industry [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Massive and different kingdom of rumen microbes can utilize plant biomass to provide a large proportion of nutrient precursors to dairy cows, thus determine the FE to some extent. Most of the related papers have focused on bacteria and archaea [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], for example, the positive interaction between \u003cem\u003eSelenomonas\u003c/em\u003e and members of the Succinivibrionaceae family may be linked to the high efficiency of dairy cows [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Also, a few of studies are involved in rumen fungi and protozoa [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there is a lack of research on rumen viruses. Viruses are the most abundant non-cellular entities in the world [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the rumen, the number of free viruses ranges from 5 \u0026times; 10^\u003csup\u003e7\u003c/sup\u003e to 1.4 \u0026times; 10^\u003csup\u003e10\u003c/sup\u003e/mL [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], representing large under-investigated members and functions.\u003c/p\u003e \u003cp\u003eAccording to their life cycle, viruses can be classified into lytic and temperate viruses. Lytic viruses will infect host (referring to the parasitic host of the virus in this context) cells, replicate rapidly, and cause cell lysis, leading to the release of new viral particles that can infect other cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On the contrary, temperate viruses usually enter a dormant state by integrating their genetic material into the host genome, establishing a long-term relationship with the host [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These two different types of infection exert distinct modes of function. Viruses can influence the host metabolism through different mechanisms, including host cells lysis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], horizontal gene transfer mediation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the presence of auxiliary metabolic genes (AMGs) that can selectively alter host metabolism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. AMGs include a range of genes that regulate host metabolism and life processes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These genes are involved in photosynthesis and synthesis of photosynthetic pigments, phosphate metabolism, central carbon metabolism, nutrient cycling, nucleotide biosynthesis and so on [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch on rumen viruses in ruminant animals has undergone three stages, from morphological studies to molecular biology research, and finally to the latest omics-based studies. As early as 1890s, researchers have discovered the presence of viruses in the rumen [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and the morphological structures of certain viruses, including the families of Siphoviridae, Podoviridae, Myoviridae, and some non-tailed phages were observed [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Subsequent molecular biology research focused primarily on the length of bacteriophage genomes and restriction endonuclease patterns. Specific bacteriophages targeting certain rumen-specific bacterial species were identified and isolated, including phages specific to \u003cem\u003eStreptococcus bovis\u003c/em\u003e, \u003cem\u003eSelenomonas ruminantium\u003c/em\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Current omics-based research in this field advances the identification and characterization of virus [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, rare studies explored the roles of rumen virus in animal phenotypes.\u003c/p\u003e \u003cp\u003eIt has evidenced that viruses can interact with all other microbial kingdoms present in the rumen [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Based on the aforementioned background, we propose potential mechanisms of how rumen viruses affect FE in dairy cows: 1) Direct lysis of host cells related to production traits, thus affecting FE. 2) Viral AMGs alter host metabolism to improve or decline the organic matters positive with FE. To validate our hypothesis, we measured the FE of 53 mid-lactation Holstein dairy cows and subsequently selected 15 high feed efficiency (HE) and 15 low feed efficiency (LE) cows. Then, we performed next-generation sequencing (NGS) and HiFi sequencing to construct rumen DNA virome, including the taxonomy, composition, diversity, virus-host linkages, and information on carried AMGs of the viruses. By integrating these data and the differential viruses between the groups as \u0026ldquo;biomarkers\u0026rdquo;, the potential roles of the viruses in FE are initially explored.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimal phenotypes and metagenomic data\u003c/h2\u003e \u003cp\u003eThe energy correct milk (ECM, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003), milk yield (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002) and ECM/DMI (dry matter intake, 1.593\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017 vs. 1.374\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were significantly higher in the HE group. However, there were no differences in DMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5216), milk protein content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1280), and milk fat content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9918) between HE and LE groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, more details in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMilk production and feed efficiency in cows selected for HE and LE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMI (kg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk Yield (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk fat content (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk protein content (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECM/DMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eLE: low efficiency; HE: high efficiency; DMI: dry matter intake; ECM: energy corrected milk. ECM = (0.3246 \u0026times; kg of milk)+(13.86 \u0026times; kg of milk fat)+(7.04 \u0026times; kg of milk protein).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNGS sequenced an average of 40.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04 Gb (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) data per sample. A total of 8,102,834,056 raw reads were generated from the NGS of 30 rumen fluid samples, with 270,094,468\u0026thinsp;\u0026plusmn;\u0026thinsp;13,573,491 per sample (Table S2). After quality control and host gene removal, 7,974,091,852 clean reads were retained, with 265,803,062\u0026thinsp;\u0026plusmn;\u0026thinsp;13,460,468 per sample (Table S3). A total of 15.60 Gb data (average length of reads: 6.03 Kb) were generated from the HiFi sequencing for one rumen fluid samples (more details in Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOverview of rumen DNA virome\u003c/h2\u003e \u003cp\u003eWith the increasing length of filtered contigs, the number of vOTUs decreased for both NGS and NGS\u0026thinsp;+\u0026thinsp;HiFi samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Table S5). The average length of vOTUs obtained from NGS\u0026thinsp;+\u0026thinsp;HiFi was significantly longer than those only from NGS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Table S5). Under the filtering condition of the length\u0026thinsp;\u0026ge;\u0026thinsp;10k, the quality of vOTUs obtained from NGS\u0026thinsp;+\u0026thinsp;HiFi was higher than those from NGS. The number of complete vOTUs has a 91.6% increase (from 132 to 253) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, Table S5). Compared to NGS, the overall proportion of complete, high-quality, and low-quality vOTUs increased from 7.8\u0026ndash;20.0% in NGS\u0026thinsp;+\u0026thinsp;HiFi (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eTotally 6,922 vOTUs were recruited, including 4,716 lytic, 1,961 temperate, and 245 unknown vOTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Table S6). At the family level, the majority of vOTUs (57.87%) remained unannotated. The total relative abundance of Siphoviridae and Myoviridae families accounted for 23.68% and 7.96% of total abundance, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Tables S6 and 7). The longest length of observed vOTU was 318,474 bp, while the shortest was 10,031 bp, with 4,421.4\u0026thinsp;\u0026plusmn;\u0026thinsp;343.3 bp per sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Table S6). In total, 5,665 vOTUs were detected or optimized by the HiFi sequencing platform, while 1,257 vOTUs were obtained from the NGS platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Table S6). A total of 1,963 vOTUs were annotated with host information. The most common hosts were Firmicutes_A (33.60%) and Bacteroidota (33.24%) at phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, Tables S8 and 9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStructural composition of lifestyle-dependent viruses in the rumen\u003c/h2\u003e \u003cp\u003eIn all 30 samples, the vOTU counts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), relative abundance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and Shannon index (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0277) were significantly higher for lytic viruses compared to temperate viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). At the family, genus, and species levels, the quantity and diversity of lytic viruses were greater than those of temperate viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). However, at these three taxonomic levels, there were overlaps of lytic and temperate viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The majority of vOTUs at the family level remain unannotated, representing 40.78% and 47.59% in lytic and temperate viruses, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). In lytic viruses, the most abundant families were Siphoviridae and Schitoviridae, accounting for 30.35% and 23.93% of the annotated vOTUs, respectively. For temperate viruses, Siphoviridae was the most abundant family, representing 67.21% of the total annotated vOTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and Tables S6 and 7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAMGs of lifestyle-dependent viruses in the rumen\u003c/h2\u003e \u003cp\u003eA total of 6,868 non-provirus vOTUs were selected to predict AMGs, and finally 2,382 vOTU sequences were predicted as 56 categories of AMGs, of which 1,752 (51 categories) and 589 (29 categories) were lytic and temperate viruses, respectively (Figs. S1a and 1b). The majority (79.5%) of detected vOTUs carried only 1 AMG, whereas the remaining vOTUs carried 2 or more AMGs (Table S10). Specifically, there were two lytic vOTUs, vOTU3931 and vOTU3877, carried 6 AMGs (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ee and Table S10). The categories of AMGs involved in carbon metabolism was the most numerous which up to 29, followed by miscellaneous (MISC) including 14 categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). However, the count of AMGs involved in MISC metabolism was the most (Table S10). The most prevalent AMG in both lytic and temperate viruses was \u003cem\u003edut\u003c/em\u003e, which involved in nucleotides metabolism, encoding dUTP pyrophosphatase that can hydrolyze dUTP to dUMP and pyrophosphate (Figs. S1c and 1d, Table S10). Besides, some other genes, such as \u003cem\u003enrdD\u003c/em\u003e, \u003cem\u003emetK\u003c/em\u003e and \u003cem\u003eDNMT1\u003c/em\u003e, were in high abundance in both lytic and temperate viruses. Notably, \u003cem\u003ecysH\u003c/em\u003e was more abundant in lytic viruses, a gene encoding a phosphoadenosine phosphate sulfate reductase protein involved in the synthesis of sulfite from sulfate. To show which kind of metabolism of the host are affected by AMG clearly, we divided the metabolism pathways into 10 sub-categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and Table S10). In this study, there were 25 categories of AMGs specific to lytic viruses and only 3 specific to temperate viruses (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). For example, lytic-specific \u003cem\u003efiC\u003c/em\u003e and \u003cem\u003efliD\u003c/em\u003e involved in the metabolism of flagella structure, temperate-specific GH28 and GT25 belonging to CAZymes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHost prediction and correlation of lifestyle-dependent viruses\u003c/h2\u003e \u003cp\u003eA total of 2,232 virus-host correspondences were annotated, including 1,377 lytic vOTUs-hosts and 780 temperate vOTUs-hosts linkages (Table S8). The hosts of lytic vOTUs belong to 458 metagenome assembled genomes (MAGs), while the hosts of temperate vOTUs belong to 366 MAGs (Table S8). Almost all of vOTUs (88.95%) were annotated to single host, and those hosts shared the same vOTUs were generally from the same family (Figs. S2a and 2b, Table S8). However, vOTU4641, vOTU6113, and vOTU6884 possessed the highest number of hosts (n\u0026thinsp;=\u0026thinsp;5; Table S8). Additionally, 32 vOTUs were found to have multiple hosts at family level, for example, vOTU6884 had 5 hosts belonging to 3 different families: UBA66, Bacteroidaceae, and Paludibacteraceae (Table S8). CowSGB-5648, an archaea MAG belonging to \u003cem\u003eMethanobrevibacter\u003c/em\u003e (the most abundant methanogens genus), was predicted to be the host of 140 vOTUs (87.14% of which are lytic) (Table S8).\u003c/p\u003e \u003cp\u003eWe observed 4 pairs exhibiting negative correlation and 578 pairs exhibiting positive correlation in the linkage between lytic vOTUs and their host (MAG level), and 294 positive correlation in the linkage between temperate vOTUs and their hosts (Spearman, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |\u003cem\u003er\u003c/em\u003e| \u0026ge; 0.5)( Table S11). The hosts of lytic viruses belong to 15 different phyla, while the hosts of temperate viruses belong to 11 different phyla. The hosts of both types of viruses were mainly belonged to Firmicutes_A (32.2% and 38.9% of the hosts of lytic and temperate viruses, respectively) and Bacteriodota (24.4% and 45.0% of the hosts of lytic and temperate viruses, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which consistent with the two bacteria with the highest relative abundance (Figs. S3a and 3b, Table S9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCompositional differences of the rumen viruses between the HE and LE groups\u003c/h2\u003e \u003cp\u003eThere was no difference between HE and LE groups in Richness (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2854), Shannon (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9349) and Simpson (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6827) index of total vOTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), the same results were observed in both lytic (Fig. S4a) and temperate vOTUs (Fig. S4c). The PCoA analysis showed no difference in vOTUs numbers and distribution of vOTUs\u0026rsquo; relative abundance between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and Figs. S4b and 4d). Moreover, the total relative abundance of lytic viruses and temperate viruses did not differ between two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). For the correlation analysis between relative abundance of vOTUs and FE or some FE-related phenotypes (DMI, Milk yield, Milk protein content, and Milk fat content), 23 lytic (positive:19; negative:4) and 11 temperate (positive:10; negative:1) viruses (at vOTU level) were significantly correlated with ECM/DMI (Spearman, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |\u003cem\u003er\u003c/em\u003e| \u0026ge; 0.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). For both lytic and temperate vOTUs, DMI and milk yield were consistently associated with ECM/DMI (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). At the family level, Drexlerviridae (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0255) exhibited significantly lower abundances in the HE group among the lytic viruses, while Leisingerviridae (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0396) was more abundant in the HE group among the temperate viruses. For lytic viruses, there were 2 genus (\u003cem\u003eKlausavirus\u003c/em\u003e, \u003cem\u003eChatterjeevirus\u003c/em\u003e) and 6 species (\u003cem\u003ePhifelvirus FL3\u003c/em\u003e, \u003cem\u003eBiseptimavirus P630\u003c/em\u003e, \u003cem\u003eGofduovirus edno5\u003c/em\u003e, \u003cem\u003eChatterjeevirus N4\u003c/em\u003e, \u003cem\u003eLaroyevirus salgado\u003c/em\u003e, \u003cem\u003eKlausavirus kburrousTX\u003c/em\u003e) of viruses that had higher abundance in the HE group. Furthermore, the number of high-abundance differentially viruses in the HE group was less than the number observed in the LE group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). However, in temperate viruses, the opposite results were found. Except for \u003cem\u003eClostridium phage phiCP13O\u003c/em\u003e (species level), the remaining differentially viruses showed higher abundance in the HE group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003ee).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMechanism of how rumen viruses be linked to feed efficiency\u003c/h2\u003e \u003cp\u003eSince no differences were observed in AMGs abundance and the enriched pathway abundances between the HE and LE groups (Fig. S5, Tables S12-14), we can only interpret the impact of AMGs on feed efficiency based on their functional properties. Another approach to understand the behind mechanism of how viruses affect FE is to explore how differential viruses affect their hosts and subsequently influence FE. Therefore, we obtained the vOTUs of 18 differential viruses, along with their hosts\u0026rsquo; MAGs that predicted earlier. Based on our results and literature review, we summarized the host-influenced cow traits, which can contribute to the FE directly or indirectly. Obviously, the viruses can affect their host through the following two pathways: 1) The lytic viruses can lyse their host directly; 2) The AMGs carried by both types of viruses, especially temperate viruses can alter their host metabolism. Both pathways had the potential to change the structural and functional composition of the rumen microbiome, thus further influencing the traits such as FE and methane emissions in dairy cows (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For example, there are 6 lytic vOTUs capable of lysing bacteria associated with methane emissions, resistance to parasitic infections, formate, acetate, succinate, and NH\u003csub\u003e3\u003c/sub\u003e-H; and the vOTU0897 carrying GT2 may enhance the fermentation capacity of Lachnosopraceae to produce more acid, which in turn contribute to the improvement of FE (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs the \u0026ldquo;dark matter\u0026rdquo; in the gut microbiome, virome and its roles attracted more and more attentions. However, due to the limitations in sequencing depth and computation tools, whether and how gut viruses be linked to human/animal phenotypes remain unknown. In this study, integrated deep NGS sequencing (5 time over general sequencing depth) and HiFi-based third-generation long reads sequencing, a fundamental understanding of the structural and functional composition of rumen virus in dairy cattle had been obtained. By conducting differential and correlation analysis, FE associated rumen viruses have been identified. Furthermore, based on the predicted hosts and annotated AMG of targeted viruses, the current study provided novel insights into the potential mechanisms of how different viruses affect FE in dairy cows.\u003c/p\u003e \u003cp\u003eTo date, only a few of studies explored rumen viruses/phages. Similar to other reports that using high-throughput technologies, a significant portion of viruses are un-annotated [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], suggesting that there are still many \u0026ldquo;unresolved mysteries\u0026rdquo; surrounding rumen viruses. The rumen virome database generated by Yan et al. showed that at the family level, Siphoviridae (79.6% of known viruses) was the most abundant, followed by Myoviridae (14.3%) and Podoviridae (4.5%) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In our results, Siphoviridae (34.9%) was also the most abundant, however, the second and third abundant viral family were Schitoviridae (19.0%) and Myoviridae (14.5%), respectively. Since Yan\u0026rsquo;s data included ruminant animals from Holstein cows as well as other 12 different species, the above difference may be attributed to the distinct patterns or specific viruses existing in the rumen of different ruminant species. Therefore, our data provides a more accurate description of the composition of DNA viruses in the rumen of Holstein cows. According to the different life cycles of lytic and temperate viruses, they have distinct impacts on the rumen environment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, the structural and functional composition of lytic and temperate viruses should be investigated separately, which are largely ignored in other studies. There are some unique viral families to each of the two types, such as the lytic virus-specific family Salasmaviridae and the temperate virus-specific family Iridoviridae, confirmed that studying lytic and temperate viruses separately may provide a more accurate assessment of the role of certain virus.\u003c/p\u003e \u003cp\u003eViruses can be linked to the animals\u0026rsquo; phenotypes by interacting with their host firstly and directly, AMGs is an important way [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our results revealed a total of 54 types of annotated AMGs, with 28 types being carbohydrate-active enzyme (CAZyme) genes. Previous studies have reported the presence of a large number of CAZyme modules in 186 rumen bacterial genomes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This observation may suggest that during the long-term co-evolution between rumen viruses and their host microbes, the viruses acquired common and vital genes from their hosts, which play an auxiliary role in fiber degradation. The most abundant AMG identified in this study was \u003cem\u003edut\u003c/em\u003e, which encods dUTP pyrophosphatase and can catalyze the hydrolysis of dUTP into dUMP and PPi, playing a crucial role in preventing erroneous insertions during DNA synthesis and maintaining nucleotide balance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Other high abundant AMGs, such as \u003cem\u003eDNMT1\u003c/em\u003e, \u003cem\u003emetK\u003c/em\u003e, and \u003cem\u003enrdD\u003c/em\u003e, also contributed to DNA synthesis and repair [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], reflecting the importance of nucleic acid metabolism-related genes in rumen viruses in dairy cows. There are 25 types of AMGs specific to lytic viruses, while only 3 types specific to temperate viruses, indicating that lytic viral AMGs likely to exhibit higher diversity and involve more metabolic pathways, which is consistent with previous environmental microbiome studies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Lytic viruses require specific AMGs to hijack the host\u0026rsquo;s metabolism for their own replication, while temperate viruses do not rely on these kinds of genes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our findings indicated that lytic viruses possess 3 distinct classes of AMGs in the \u0026ldquo;information system\u0026rdquo;, which involved in DNA synthesis, modification, glycosylation and so on. For example, the \u003cem\u003etmk\u003c/em\u003e generated dUDP and dTDP, which are potentially involved in DNA synthesis and nucleic acid modification [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The summarized schematic diagram of AMGs involved in host metabolism in the rumen provided a comprehensive understanding of how AMGs influence rumen holistic metabolism.\u003c/p\u003e \u003cp\u003eThere were no significant differences in α and β diversity of vOTUs, as well as AMGs, indicating that healthy cows within the same dairy farm may share a common viral source and exhibit a relatively stable composition of rumen viruses. However, as crucial \u0026ldquo;biomarkers\u0026rdquo;, the viruses with different relative abundances between the 2 groups provide a breakthrough for exploring the relationship between rumen viruses and FE in dairy cows. Interestingly, most differential lytic viruses showed lower relative abundance in HE groups, whereas differential temperate viruses were more numerous in HE animals. To explain this phenomenon, we speculate that lytic viruses may replacate whthin and lyse bacteria/archaea that are positively associated with production traits, leading to an increase in their own abundance and a decrease in FE [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. On the other hand, temperate viruses may play some beneficial roles, such as carrying AMGs that can enhance the competitiveness of the host, thereby increasing in abundance with the increase in host abundance. However, these hypotheses still require detailed analysis of virus-host relationships and dynamic experimental validation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo investigate how viruses affect FE, we focused on the differential viruses between the HE and LE groups, combined the information on AMGs and cows\u0026rsquo; phenotypes that viral hosts (other microorganisms in the rumen) can influence [\u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This because most phenotypes are related to metabolism and can have direct or indirect effects on FE, and some studies have verified the correlations between ruminal metabolites (e.g., VFAs) and FE in cattle [\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. We established the associations between viruses (and AMGs), hosts, and phenotypes of dairy cows. For example, as an important source of energy for cows [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], the concentration of acetate is higher in the rumen of high-FE cows [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e are the two acetate producers. On the one hand, as a temperate viruse, vOTU0897 carried the \u003cem\u003eGT2\u003c/em\u003e may enhance the function of acetate production of \u003cem\u003eLachnospiraceae\u003c/em\u003e, thereby improving FE. On the other hand, as lytic viruses, vOTU1836 and vOTU5102 may decrease acetate production by lysing \u003cem\u003eRuminococcaceae\u003c/em\u003e, consequently reducing FE. Therefore, it was plausible to explain how viruses impact their hosts by establishing a \u0026ldquo;virus-host-FE\u0026rdquo; pathway, which can further elucidate how viruses affect FE. In addition to the viruses shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e, other virus-host linkages also provide valuable information, for example, the 142 lytic archaeal viruses (Table S8) have the potential to directly lyse methanogens, thereby reducing methane production. Due to space limitations, a more in-depth exploration was not conducted in this study, further investigation should be carried out to dig out the related mechanism.\u003c/p\u003e \u003cp\u003eAlthough we had conducted high-depth NGS and HiFi sequencing to gain a more detailed and accurate understanding of the structural and functional composition of rumen virome in dairy cows, however, like many other studies focusing on rumen viruses [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], we haven\u0026rsquo;t investigated RNA viruses which may have important functions [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In the future, it is important to explore this area to enhance our active functional knowledge of the intraruminal viruses. Currently, many published virome papers have conducted virus filtration and enrichment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, considering the potential loss of information on temperate viruses, we did metagenomic sequencing without enrichment. Additionally, to minimize bias caused by a low proportion of viral reads in the whole DNA sequencing, we selected a deep sequencing depth. There is no data comparing the advantages and disadvantages of these two methods, so further research should be conducted to explore the difference and specificity. In this study, we selected the differential viruses between the HE and LE groups as \u0026ldquo;biomarkers\u0026rdquo;, and then proceeded to link them with their hosts and FE of dairy cows. Taking a different perspective, the hosts also can also serve as \u0026ldquo;biomarkers\u0026rdquo;. Many differential bacteria/archaea between the HE and LE groups had already reported [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], for example, \u003cem\u003eSelenomonas\u003c/em\u003e, which positively related to FE, has 7 vOTUs in our results. By integrating the virus-host linkages, AMGs and the host-traits relationship, both types of \u0026ldquo;biomarkers\u0026rdquo; can provide insights for elucidating the mechanism of \u0026ldquo;virus-host-FE\u0026rdquo; in our study. Moreover, the validation of the mechanism needs to be complemented by more explicit details as well as wet experiments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe research on the viruses of the bovine rumen is still limited. Utilizing omics technologies, we conducted a comprehensive analysis of viruses in 30 rumen fluid samples. Our study has preliminarily constructed a DNA virome profile in the rumen of dairy cows, including the composition of viral structures, functional elements, and virus-host interactions. We have demonstrated the role of AMGs in influencing pathways such as rumen carbon metabolism, nitrogen cycling, and nucleic acid metabolism. Through the identification of differentially viruses between HE and LE groups, along with their associated AMGs and hosts, we have revealed potential pathways through which viruses may impact FE in cows. More specifically, viruses can exert lytic effects or selectively modify the metabolism of bacteria/archaea associated with FE through AMGs, thereby influencing the feed efficiency of dairy cows.\u003c/p\u003e "},{"header":"Method","content":"\u003ch2\u003eAnimals and samples\u003c/h2\u003e\u003cp\u003e In this article, all procedures involving animals were approved by the Animal Use and Health Committee of Zhejiang University (Hangzhou, China, No. 12410). A total of 53 multiparous mid­lactating Holstein cows were selected for the 57 days (with an adaptation of 7 days) experiment. During this period, all cows were housed in a free­stall barn with access to a total mixed ration, and were fed three times per day (06:30, 14:30, and 21:30) with a corn-based high-grain diet. Cows are weighed every morning of the week, immediately after milking. The DMI of each cow was determined daily by an automatic feed system (Zhenghong Co., Shanghai, China). Samples of rumen fluid were collected by oral stomachs tube before morning feeding and stored at -80°C until subsequent treatment. The milk yield of each cow was recorded at 3 milking time points (06: 00, 14:00, and 21:00) for 2 consecutive days. One subsample was stored at 4°C for analysis of milk composition using infrared spectroscopy. Feed efficiency was calculated using ECM/DMI, and 15 high efficiency (HE, ECM/DMI = 1.593 ± 0.063) and 15 low efficiency (LE, ECM/DMI = 1.374 ± 0.071)) cows were selected for further analysis.\u003c/p\u003e\u003ch2\u003eDNA extraction, library preparation, and next generation sequencing\u003c/h2\u003e\u003cp\u003eAll 30 rumen fluid samples were not filtered prior to DNA extraction, so all free viruses and prophages were retained. The DNA was extracted from 1.5 g rumen fluid samples using the E.Z.N.A.® stool DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to manufacturer’s protocols. NGS libraries were prepared following TruSeqTM Nano DNA sample preparation Kit from Illumina (San Diego, CA), using 1ug of total DNA. DNA end repair, A-base addition and ligation of the Illumina-indexed adaptors were performed according to Illumina’s protocol. Libraries were then size selected for DNA target fragments of ~ 400 bp on 2% Low Range Ultra Agarose followed by PCR amplified using Phusion DNA polymerase (New England Biolabs, USA) for 15 PCR cycles. All samples were sequenced by the Illumina NovaSeq 6000 platform (150bp×2, Shanghai Biozeron Biotechnology Co., Ltd, Shanghai, China).\u003c/p\u003e\u003ch2\u003eHiFi sequencing\u003c/h2\u003e\u003cp\u003eA total of 5 µg of DNA (from HE_6) was used to prepare a SMRTbell library with the PacBio SMRTbell prep kit 3.0 (Pacific Biosciences, CA, USA, Part Number: 102-182-700) according to the manufacturer’s recommendations. Damaged double-stranded DNA in the initial DNA sample was repaired using the New England BioLabs PreCR® Repair Mix Kit according to the manufacturer’s instructions before library preparation. Then, the repaired DNA was size-selected by using the BluePippin system (Sage Science, MA, USA) to obtain molecules larger than 3 kb. The SMRTbell library was sequenced with v3 chemistry on a PacBio Sequel IIe instrument (Pacific Biosciences, CA, USA) using SMRT 8M cells (Part Number: 101-389-001). HiFi reads were then generated with the ‘ccs’ module (parameters: --min-length 500 --min-passes 3 --min-rq 0.99) within the SMRT Link v10.0 package (Pacific Biosciences, CA, USA).\u003c/p\u003e\u003ch2\u003eMetagenomic assembly and prokaryotic genome binning\u003c/h2\u003e\u003cp\u003eNGS raw sequence reads underwent quality trimming using Trimmomatic (v0.36, adapters.fa:2:30:10 SLIDINGWINDOW:4:15 MINLEN:75) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] to remove adaptor contaminants and low quality reads. Reads passed quality control were then mapped against Bovine genome by BWA mem algorithm, with the parameters “-M -k 32 -t 16”. The reads removing host-genome contaminations and low-quality data were called as clean reads and used for the further analysis.\u003c/p\u003e\u003cp\u003eClean reads were generated a set of contigs of each sample using MegaHit [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] (v1.1.1-2-g02102e1) with “--min-contig-len 500” parameters. Metagenomic binning was performed in each sample contigs. First, binning software metaBAT2 v2.11.1 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] was used to do binning separately. The completeness, and contamination of all bins was obtained using CheckM v1.1.1 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. All bins with completeness \u0026gt; 50%, contamination \u0026lt; 10% were considered as MAGs. Finally, 13,572 MAGs were annotated. All MAGs were taxonomically annotated using GTDB-Tk (r207) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] based on the Genome Taxonomy Database, which produced the standardized taxonomic labels of bacteria and archaea.\u003c/p\u003e\u003ch2\u003evOTU identification and taxonomic classification\u003c/h2\u003e\u003cp\u003eFor greater accuracy and confidence, VirSorter2 (v2.2.3, max_score ≥ 0.7) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and DeepVirFinder (v1.0, Score ≥ 0.7, pvalue ≤ 0.05) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] were used to identify viral contigs from metagenomic assemblies. Viral contigs longer than 10k were manually screened for subsequent analysis. To remove some non-viral sequences during the VirSorter2 analysis, CheckV v0.7.0 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] was conducted to quality assessment. Finally, all potential viral contigs were further checked using VIBRANT v1.2.1 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], with default settings. The identified viral contigs were clustered by using Mummer software (95% ANI, ≥ 85% coverage), and the longest representative one within each cluster was considered as vOTUs. To prevent information loss due to excessively large filter lengths, 10k was selected as the filter parameter, finally a total of 6,922 vOTUs were recruited for further analysis. Virus lysis was detected by VIBRANT v1.2.1 and provirus is detected by CheckV v0.7.0. Taxonomic annotation of viruses was performed using Blastx [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] according to the recently published NCBI virus classification list (e-value ≤ 1e-5, by November 2022). Since the differences between vOTUs at the genomic (DNA) level are so large that it is hardly to use conventional global comparison for phylogenetic tree construction, the similarity matrix at the protein level was generated by ViPTree v1.1.2 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] based on the genome-wide sequence similarity computed by tBLASTX, which in turn led to the construction of the phylogenetic tree.\u003c/p\u003e\u003ch2\u003eCalculation of vOTU and MAG relative abundance\u003c/h2\u003e\u003cp\u003eIn order to more accurately reflect the true state of the viral and procaryotic “genome” in the rumen environment, sequences of vOTUs and MAGs are packaged together to calculate the relative abundance. Then metaWRAP quant_bins module with default parameters was applied to calculate the relative abundance of each vOTU and MAGs, which was prescented as “TPM” (transcripts per kilobase of exon model per million mapped reads).\u003c/p\u003e\u003ch2\u003eVirus-host linkage predication\u003c/h2\u003e\u003cp\u003eIn order to extend the virus-host linkage on an accurate basis, we added the downloaded and reconstructed prokaryotic genome (unpublished data) as a reference database for hosts. Finally, the 6,922 vOTUs were putatively linked to 13,572 (13,412 bacterial and 160 archaeal genomes) MAGs using two \u003cem\u003ein silico\u003c/em\u003e methods: (1) similarity of CRISPR spacers between bacterial and viral sequence, with “blastn similarity ≥ 97, Comparison length ≥ 30”; (2) VirHostMatcher with “distance ≤ 0.2”. As a result, the hosts predicted by either of two methods were combined into a final potential host of the viruses. All virus-host linkages and the respective methods used were listed in Table S7.\u003c/p\u003e\u003ch2\u003eAMG identification, classification and abundance\u003c/h2\u003e\u003cp\u003eIn order to more accurately predict and assign the levels of viral carbohydrate metabolism substrates and their contribution to geochemical cycling, DRAM-v (v 1.2.0 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] was chosen to annotate AMGs, with the “default parameters, 1 ≤ score ≤ 3, AMG flags of -M and -F”.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData statistics and visualization in this study were performed using R (v 4.2.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GraphPad Prism 8.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The α diversity between the HE and LE groups were determined using Mann–Whitney U test. PCoA analysis was conducted to cluster the vOTUs of different samples based on the Bray–Curtis distance. Spearman correlations were calculated to reveal the relationships between the relative abundance of vOTUs and the production traits of dairy cows. Differential viruses between HE and LE groups were verified using “STAMP” analysis. Spearman correlations were calculated to reveal the relationships between the relative abundance of vOTUs and the relative abundance of their hosts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the members in the Institute of Dairy Science of Zhejiang University (Hangzhou, China) for their assistance in the field sampling and data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaohan Liu and Hui-Zeng Sun designed the study. Xiaohan Liu and Yifan Tang conducted bioinformatic analysis. Xiaohan Liu wrote the first draft manuscript. Hongyi Chen collected data. Hui-Zeng Sun and Jian-Xin Liu edited and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by grants from the National Natural Science Foundation of China (32322077, Beijing) and the\u0026nbsp;National Key R\u0026amp;D Program\u0026nbsp;of China\u0026nbsp;(2023YFE0123100).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available in the National Genomics Data Center (NGDC) database with the accession number of PRJCA021193, PRJCA020785 and PRJCA023564.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimal care and experimental procedures were approved by the Animal Care Committee of Zhejiang University (Hangzhou, China), and were under the university\u0026rsquo;s guidelines for animal research\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu X, Tang Y, Wu J, Liu J-X, Sun H-Z. 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Nucleic Acids Research. 2020;48:8883\u0026ndash;900. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"HiFi sequencing, Lytic, Temperate, Auxiliary metabolic genes, Feed efficiency","lastPublishedDoi":"10.21203/rs.3.rs-4199008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4199008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere are numerous viruses in the rumen that interact with other microorganisms, which play crucial roles in regulating rumen environmental metabolism. However, the knowledge of rumen viruses is limited, and their relationship with production traits (e.g., feed efficiency) has not been reported. In this study, we combined next-generation sequencing (NGS) and HiFi sequencing to investigate the rumen DNA virome and reveal the potential mechanisms of how viruses influence feed efficiency in dairy cows.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with NGS, HiFi sequencing improved the length, completeness, and resolution of viral operational taxonomic units (vOTUs) obviously. A total of 6,922 vOTUs were recruited, including 4,716 lytic and 1,961 temperate vOTUs. At family level, lytic viruses were mainly composed of Siphoviridae (30.35%) and Schitoviridae (23.93%), while temperate viruses were predominantly Siphoviridae (67.21%). A total of 2,382 auxiliary metabolic genes (AMGs) were annotated, which involved in the pathways of carbon metabolism, nitrogen metabolism, energy metabolism, etc. A total of 2,232 vOTU-hMAG (host metagenome-assembled genome) linkages were predicted, with Firmicutes_A (33.60%) and Bacteroidota (33.24%) being the most common host at phylum level. Differential viruses were detected between high and low feed efficiency groups at the family, genus and species levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). By integrating differential viruses, vOTU-hMAG linkages and AMGs, two pathways have been proposed for how rumen viruses affect feed efficiency in dairy cows: 1) lytic viruses lyse host related to cattle phenotypes, such as vOTU1836 can lyse Ruminococcaceae that have a positive effect on organic acids, and 2) AMG-mediated modulation of host metabolism, for example, \u003cem\u003eGT2\u003c/em\u003e carried by vOTU0897 may enhance the fermentation capacity of Lachnosopraceae to produce more organic acids.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOverall, we constructed a rumen DNA virome profile of Holstein dairy cows, showing the structural and functional composition of rumen viruses, the roles of AMGs carried by vOTUs and the linkages between vOTUs and their hosts. By integrating the above information, we proposed potential mechanisms through which rumen viruses influence feed efficiency in dairy cows, providing new insights into the regulation of feed digestion and nutrient utilization in dairy cows.\u003c/p\u003e","manuscriptTitle":"Rumen DNA virome and its relationship with feed efficiency in dairy cows","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 10:40:48","doi":"10.21203/rs.3.rs-4199008/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-22T16:47:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-15T13:37:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-03T08:09:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Microbiome","date":"2024-04-01T07:48:17+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":"b0c2edce-3801-4a76-8bec-264be2cc3de5","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:10:27+00:00","versionOfRecord":{"articleIdentity":"rs-4199008","link":"https://doi.org/10.1186/s40168-024-02019-0","journal":{"identity":"microbiome","isVorOnly":false,"title":"Microbiome"},"publishedOn":"2025-01-16 15:57:51","publishedOnDateReadable":"January 16th, 2025"},"versionCreatedAt":"2024-04-18 10:40:48","video":{"identity":"8d0041d8c4a077396c0eb9bf7f74c974"},"vorDoi":"10.1186/s40168-024-02019-0","vorDoiUrl":"https://doi.org/10.1186/s40168-024-02019-0","workflowStages":[]},"version":"v1","identity":"rs-4199008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4199008","identity":"rs-4199008","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}