Identifying Mobile Genetic Elements in the Ruminal Microbiome of Nellore Cattle: An Initial Investigation

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Faleiros, Alanne T. Nunes, Osiel S. Gonçalves, Pâmela A. Alexandre, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3749940/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Metagenomics has made it feasible to elucidate the intricacies of the ruminal microbiome and its role in the differentiation of animal production phenotypes of significance. The search for mobile genetic elements (MGEs) has taken on great importance, as they play a critical role in the transfer of genetic material between organisms. Furthermore, these elements serve a dual purpose by controlling populations through lytic bacteriophages, thereby maintaining ecological equilibrium and driving the evolutionary progress of host microorganisms. In this study, we aimed to identify the association between ruminal bacteria and their MGEs in Nellore cattle using physical chromosomal links through the Hi-C method. Shotgun metagenomic sequencing and the proximity ligation method ProxiMeta™ were used to analyze DNA, getting 1,713,111,307 bp, which gave rise to 107 metagenome-assembled genomes from rumen samples of four Nellore cows maintained on pasture. Taxonomic analysis revealed that most of the bacterial genomes belonged to the families Lachnospiraceae , Bacteroidaceae , Ruminococcaceae , Saccharofermentanaceae , and Treponemataceae and mostly encoded pathways for central carbon and other carbohydrate metabolisms. A total of 31 associations between host bacteria and MGE were identified, including 17 links to viruses and 14 links to plasmids. Additionally, we found 12 antibiotic resistance genes. To our knowledge, this is the first study in Brazilian cattle that connect MGEs with their microbial hosts. It identifies MGEs present in the rumen of pasture-raised Nellore cattle, offering insights that could advance biotechnology for food digestion and improve ruminant performance in production systems. Biological sciences/Microbiology/Bacteriophages Biological sciences/Genetics/Microbial genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Advancements in metagenomics in rumen environments have enabled us to identify and classify the taxonomy and functional capabilities of bacteria that specialize in digesting plant material, which is the primary food source for ruminants. Given the impact of microorganisms on host cattle performance, microbiota modulation is crucial for developing a sustainable and productive livestock system 1 . For instance, feed efficiency is a trait with implications for economic and environmental sustainability 2 and is strongly associated with host animal microbiota profiles 3 – 5 . Ruminal methane production, which results from microbial fermentation in the rumen, is another example of trait that can be explained by genetic variation and the composition of the host animal microbiota 6 – 8 . Approximately 65% of rumen prokaryotic species have been classified and described in taurine breeds 9 ; however, less information is available on Indian cattle, which are the majority in the world, especially in Brazil. Understanding the role of specific phylotypes in the rumen during fermentation and identifying new enzymes through metagenomic analysis can improve biotechnology for food digestion and ruminant performance in production systems 10 . Underutilized components of the rumen, known as Mobile Genetic Elements (MGEs), possess great potential for manipulation and biotechnological applications owing to their ability to confer benefits to their hosts and participate in crucial functional modifications of their hosts, carrying auxiliary metabolic genes such as glycosidic hydrolases (GH), which contribute to the breakdown of complex carbohydrates. In addition, viruses carry genes that contribute positively their hosts in the metabolism of nutrients and stages of the biogeochemical cycles carried out by the hosts 11 , 12 . They are classified as mobile DNA elements that move within and between bacterial cells through agents, such as plasmids, bacteriophages, and transposons, resulting in the introduction of accessory genes that contribute to the evolution of host cells 13 , 14 . Solden et al. 15 reported that viruses play a crucial role in controlling the ecosystem functions in the rumen. These viruses have metabolic interdependence with their microbial hosts, carry genes that can redirect carbon metabolism, and infect dominant microorganisms that degrade carbohydrates. Early studies on bacteriophages in the rumen revealed that they can eliminate specific bacteria 16 . Recent research has indicated the potential use of lytic phages to regulate microorganisms in the rumen responsible for an increase in acidosis and methane production 17 , 18 . This suggests that the components in the rumen are interconnected with viruses that infect ruminal bacteria, the microbiome, and the host animal interacting and affecting each other. This can change the ruminal function and the nutritional capacity of the host animal 19 . Recent studies have employed the Hi-C approach, which captures intimate interactions between bacterial chromosomes within living cells, to investigate MGEs in microbial communities. This method has been used in conjunction with metagenomic sequencing and has proven effective in assembling genomes and uncovering new relationships between hosts and MGEs 20 – 23 . The objective of this study was to determine the existence of MGEs in the rumen content of Nellore cattle in Brazil, using the Hi-C method to establish a link between these elements and their microbial hosts. Results Metagenome of bovine rumen content samples In this study, the metagenomic sequencing was performed with DNA extracted from a pool of rumen content samples of four 4 Nellore cows, applying ProxiMeta™, a proximity linkage method based on Hi-C. This method used contigs derived from shotgun sequencing and reads from a library Hi-C, and generated an assembly length of 1,713,111,307 bp, with 834,164 contigs total and 16,035 contigs in clusters allowing the assembly of 107 genomes (Fig. 1 A). The most completely assembled genome was bin_4 (99.30%), which corresponded to Treponema D. sp016288205 . Other genomes classified as complete, with completeness greater than 95%, were UBA2868 sp003535955 (bin_1), Treponema D. sp017421145 (bin_9), Treponema D. sp902783155 (bin_6) and UBA2868 (bin _12). The clusters classified as excellent (> 90%) were dominated by species from the Lachnospiraceae family, namely UBA3766 sp902803015 (bin_22), UBA1712 (bin_14), UBA2868 (bin_3), UBA2868 sp003535955 (bin_17), NK4A144 sp902783395 (bin_7), Acetatifactor sp900066565 (bin_21), UBA1711 sp900317125 (bin_11), and UBA1711 sp001543385 (bin_18). Clusters belonging to the families Saccharofermentanaceae with Saccharofermentans (bin_19), and Bacteroidaceae with Prevotella sp000702825 (bin_5) also had an excellent completeness. The clusters considered good are presented in the Supplementary Table 1, and the 107 clusters subjected to taxonomic classification are represented in Fig. 1 B. Functional and carbohydrate-active enzymes (CAZYmes) characterization of bins Before analyzing the MGEs, we conducted a functional analysis to understand the functional characteristics encoded within metagenome-assembled genomes (MAGs) from the ruminal microbiome of Nellore cattle. For this, MAGs with completeness > 50% and those associated with MGEs were used, totaling 43 genomes (Supplementary Tables 8 and 9). This step provided essential insights into the interplay between microbial communities and the functional potential associated with MGEs in the rumen ecosystem. Our study evaluated the involvement of rumen microorganisms in these biogeochemical pathways, and we found that the majority were associated with the stages of the carbon, sulfur, nitrogen and iron cycles (Fig. 2 ). Regarding the carbon cycle, most of the genomes identified demonstrated involvement in the acetogenesis process and carbon degradation complex. More specifically, we identified two genomes belonging to Lachnospiraceae and Bacteroidales encoding the enzyme methane monooxygenase (mmoBD), which act in methane oxidation, and members of these clades also participated in the oxidation step of the sulfur cycle. In the nitrogen cycle, phylotypes belonging to Saccharofermentans and Treponema acted in the nitrogen fixation stage and members of the genus Provotella encoded genes for the reduction of nitrite into ammonia. All taxonomic classes presented members active in the iron oxidation stage, which is fundamental for most microorganisms, due to its redox properties, being used as an electron transporter by bacteria 24 , 25 . The most frequent processes included steps in the central carbon metabolism pathway, the metabolism of other carbohydrates, and purine metabolism. Among the bins examined, 69% contained genes that encoded the phosphate acetyltransferase-acetate kinase pathway, a reversible process where acetyl-CoA can be converted into acetate or acetate into acetyl-CoA. Additionally, the aromatic amino acid metabolism pathway with tryptophan biosynthesis was present in 64% of the bins (Fig. 3 A). The metagenomic approach allows for the evaluation of the potential of rumen microbial populations to break down plant lignocellulolytic materials, by annotating the genes associated with this process, known as CAZymes 10 . CAZymes are classified into several categories based on their degradation capabilities, including glycoside hydrolases (GHs), carbohydrate esterases (CEs), polysaccharide lyases (PLs), and others 26 . In this study, the bins identified were assessed for their ability to encode CAZymes, and the presence of GHs and PLs was confirmed (Supplementary Table 3). We found that GH2, GH3, GH5, GH13, and GH43 were the most abundant enzymes mapped in the bins (Fig. 3 B). Assignment of mobile genetic elements to their hosts Clustering with Hi-C allows not only the construction of MAGs but also the sequence grouping of mobile genetic elements with their core genome 27 . The sequences were obtained with sequencing and the use of the proximity linkage method to assign viruses and plasmids to their microbial hosts, the data obtained is summarized in Fig. 4 . Plasmids and the Identification of microbial resistance genes The reads derived from shotgun sequencing were used to evaluate the presence of plasmid sequences. Thirty-three DNA sequences were identified as plasmids, which were not integrated into the host genome. Among these, 14 had at least one connection with the prokaryotic host genome through Hi-C reads. Furthermore, two putative plasmids contigs k141_1760976 and k141_5289202 were shared between multiple host clusters (bin_3, bin_5, bin_20, bin_80) and (bin_1; bin_3; bin_11; bin_30; bin_88) (Supplementary Table 4). The bins shared by contig k141_1760976 belong to different genus, including UBA2868 , Prevotella and Saccharofermentans , which characterizes horizontal gene transfer mediated by plasmids. The plasmid k141_5289202 demonstrated greater specificity, being shared by three individuals belonging to Lachnospiracea and one belonging to Bacteroidales. Additionally, we identified 19 plasmid sequences integrated into the host genome sequences (Fig. 4 A). Within this set, it was observed that 50% of the total antibiotic resistance genes (ARGs) validated in our database were present (Fig. 5 ). All the identified resistance genes had an identity of more than 90% with the reference sequence (Supplementary Table 5). The tet32 gene sequence was associated with bin_14, which belongs to the Lachnospiraceae family. Virus found in the rumen A total of 11 high-quality metagenome-assembled viral genomes (vMAGs) were identified and characterized as viral sequences (as shown in Fig. 4 B). They had completeness rates of 92–100% and a length of 35 to 126 kbp. Another 22 MAGs were obtained with medium quality, of which 13 had completeness rates of 71–89% and a length of 25 to 90 kbp, and nine had completeness rates of 68–50% and lengths of 20 and 130 kbp. Any genomes with less than 50% completeness were considered low quality. All of the assembled genomes were determined to be free of contamination (Supplementary Table 6). The majority of vMAGs had their taxonomy associated with genomes that have not yet been assigned to the ICTV phage database 28 . There were vMAGs associated with the same viral cluster (VC); however, these do not yet have taxonomic assignment in the database: vMAG_4 and vMAG_35 were associated with the viral cluster VC_146, as well as vMAG_5 and vMAGs_18, with VC_147 and vMAG_25, and vMAG_56, with VC_200 (Supplementary Table 7). Among the viruses that could be assigned to known genomes, vMAG_1 and vMAG_48 were assigned to the Myoviridae family VC_155, vMAG_40, vMAG_46 and vMAG_57 were assigned to the Siphoviridae family, VC_102, vMAG_7, vMAG_16, 29 and vMAG_52 were also assigned to the Siphoviridae family, but with genomes associated with Streptomyces phage , VC_87 (Fig. 6 ). Virus-host association The viral clusters associated with the bacterial genomes determined by Hi-C mostly exhibited a lytic cycle. The vMAG_54, associated with the host UBA2868 sp003535955 ( Lachnospiraceae ), presented high quality, achieved 100% completeness in its genome, and was characterized by a lytic lifestyle (Table 1 ). Two co-infection events were identified, in which bacterial cells presented the assignment of more than one bacteriophage simultaneously, that is, different bacteriophages infecting the same bacterial host. Bacteriophages corresponding to vMAGs 18 and 45 infected the same bacterial cell corresponding to bin_28, and the same occurred with vMAGs 12 and 54, which infected the same bacterial cell corresponding to bin_30 (Fig. 7 ). Table 1 Viruses associated with bacterial hosts by Hi-C Name cluster host Host Name Cluster (phage) Life Cycle CheckV quality Completeness (phages) bin_30 UBA2868 sp003535955 (Lachnospiraceae) vMAG_54 Lytic High-quality 100.0 bin_41 g_UBA2868 (Lachnospiraceae) vMAG_13 - Medium-quality 87.8 bin_12 UBA2868 sp002368575 (Lachnospiraceae) vMAG_4 Lytic Medium-quality 85.14 bin_28 Saccharofermentans sp003543635 vMAG_45 Lytic Medium-quality 72.67 bin_22 UBA3766 sp902803015 (Lachnospiraceae) vMAG_35 Lytic Medium-quality 60.16 bin_30 UBA2868 sp003535955 (Lachnospiraceae) vMAG_12 Lytic Medium-quality 54.18 bin_28 Saccharofermentans sp003543635 vMAG_18 Lytic Medium-quality 50.67 bin_16 RUG12461 sp016286115 (Lachnospiraceae) vMAG_22 Lytic Low-quality 46.05 bin_102 Non-classified vMAG_43 Lytic Low-quality 39.81 bin_27 Butyrivibrio sp017620235 (Lachnospiraceae) vMAG_17 Lytic Low-quality 39.28 bin_58 g_F23-D06 vMAG_51 - Low-quality 37.32 bin_74 g_UBA1711 (P3) vMAG_42 Lytic Low-quality 34.54 bin_73 Non-classified vMAG_27 - Low-quality 25.64 bin_49 Non-classified vMAG_6 - Low-quality 24.02 bin_59 RUG191 sp002373675 (Lachnospiraceae) vMAG_44 Lytic Low-quality 23.57 bin_7 NK4A144 sp902783395 (Lachnospiraceae) vMAG_24 Lytic Low-quality 8.71 bin_26 Saccharofermentans sp902782235 vMAG_49 Lytic Not-determined NA g_(genus). Of the 17 bins identified to be associated with viruses, 8 belonged to the Lachnospiraceae family. The bin_30 and bin_41 belong to the same genus UBA2868 , and the other bins assigned to this family were: (bin_7) NK4A144 sp902783395 , (bin_12) UBA2868 sp002368575 , (bin_16) RUG12461 sp016286115 , (bin_22) UBA3766 sp902803015 , (bin_27) Butyrivibrio sp017620235 , and (bin_59) RUG191 sp002373675 . The bin_26 and bin_28 were assigned to Saccharofermentans sp902782235 and sp003543635 , respectively, and bin_58 was assigned to genus F23-D06 and bin_74 to genus UBA1711 , both belonging to the order Bacteriodales . Discussion Metagenomics has enabled the efficient identification of microorganisms, enzymes, and metabolic pathways that play a role in plant breakdown in the bovine rumen 29 . This tool has also proven to be crucial for identifying the microbiota and characterizing important traits such as methane production and feed efficiency 3 , 8 , 30 . This study is the first to explore rumen mobile genetic elements of Nellore cattle fed a grass diet by associating viruses and plasmids with their hosts through physical links between DNA molecules from the same genome using the Hi-C method 21 . We identified 31 links between bacteria and mobile genetic elements, with 14 of these links being plasmids and 17 links being viruses. Furthermore, we identified 12 genes conferring antibiotic resistance to tetracycline (6 genes), nitroimidazole (1 gene), lincosamide (1 gene), beta-lactam (1 gene), aminoglycoside (1 gene) and macrolide (1 gene). Additionally, we assembled a collection of 107 bacterial genomes, which mostly encoded pathways for central carbon and other carbohydrate metabolisms. In this study, we identified 52 plasmid sequences, 14 of which were linked to their hosts using Hi-C. Similarly, in their evaluation of the canine fecal metagenome, Cuscó et al. 23 found that six Hi-C plasmids were ligated into their bacterial hosts, including five circular plasmids and a plasmid carrying the linA resistance gene. In our investigation, we uncovered two plasmids, k141_1760976 and k141_5289202, which are associated with multiple host clusters. As previously reported by Mo et al. 31 and Stewart et al. 27 , sequences may be shared among various bacterial species found in the rumen. Through the Hi-C approach, Stalder et al. 32 found that plasmids are efficient vectors for horizontal gene transfer and that this method is useful for tracing microorganisms that harbor antibiotic-resistant genes. We identified 12 genes conferring antibiotic resistance, and among these, six genes were found to confer resistance to tetracycline: tet32 , tet40 , tet44 , tetO , tetQ , and tetW . These findings are consistent with a previous study by Jing and Yan 33 , in which tetracycline resistance genes ( tet44 , tetQ , and tetW ) were the most prevalent in rumen content samples and were the most abundant in the genome of Prevotellaceae , but Lachnospiraceae were also present. Furthermore, tetQ , tetW , and tet40 were also detected in most samples derived from the rumen of 48 beef cattle from three different taurine breeds 34 . In addition to the genes that confer resistance to tetracycline, other classes of antibiotics, such as nitroimidazole nimJ , macrolide mefA , lincosamide lnuC , beta-lactam blaACI-1 , and aminoglycoside aadE , were also identified. Genes that confer resistance to macrolides, lincosamide, and aminoglycosides were the most prevalent among ARGs in the study conducted by Ma et al. 34 , with the mefA gene being one of the most common. Genes that confer resistance to macrolides, lincosamide, and aminoglycosides were the most prevalent among ARGs in the study conducted by Ma et al. 34 , with the mefA gene being one of the most common. Additionally, ARGs associated with plasmids were identified, indicating that plasmids should be the focus of future investigations on antimicrobial resistance in livestock. Auffret et al. 35 also found an increase in ARGs in rumen content samples from cattle fed diets with higher concentrate content, with an abundance of genes related to resistance to macrolides and beta-lactams, suggesting that this increase is associated with dysbiosis caused by more concentrated diets. The findings of this study are in agreement with those of an earlier in silico study conducted by Sabino et al. 36 , who analyzed the rumen resistome in 435 microbial genomes and found a high prevalence of antibiotic resistance genes (ARGs) that provide resistance to β-lactams, glycopeptides, tetracyclines, and aminoglycosides. Additionally, recent metagenomic studies have indicated that bovine rumen is a significant contributor to antibiotic resistance 33 . However, advancements in bioinformatics have contributed to a deeper understanding and better characterization of the rumen virome. Yan et al. 37 identified 397,180 viral operational taxonomic units (vOTUs) from 975 metagenomes of 13 ruminant species, with the majority of metagenomes derived from taurine animals. Only 23 metagenomes were from Bos indicus raised in Kenya, and none were from Brazil, supporting the significance of this research. The viral metagenomic assemblages discovered in this study were mostly assigned to unclassified genomes, which is probably because of the limited rumen virome of Bos indicus data in the ICTV phage database. This finding is consistent with those of Sato et al. 38 , who observed low numbers of viral operational taxonomic units shared with the RefSeq database for samples from the rumen of Japanese cattle. The assigned vMAGs belong to the families Myoviridae and Siphoviridae , which are commonly found in the rumen environment and constitute the most abundant group, along with Mimiviridae and Podoviridae 12 , 15 , 38 , 39 . A group of bacteria classified as UBA2868 sp003535955 (unclassified Lachnospiraceae bacterium , NCBI) was found to have the highest percentage of complete vMAGs (100%) and was characterized as high and medium quality. This genus was previously identified in a community of uncultured microorganisms from the intestines of pigs through sequencing of fecal samples 40 , 41 . Bacteria from the unclassified family Lachnospiraceae are prevalent in the ruminal environment and serve as the central microbiome in cattle 9 . This allowed us to infer that population-associated viruses play a crucial role in the bovine rumen, rather than populations present in low abundance, which remains a challenge 20 . Friedersdorff et al. 42 successfully isolated and sequenced active lytic phages belonging to the Siphoviridae family that infect Butyrivibrio fibrisolvens , which was found in both the rumen and feces of cattle and sheep. We discovered a lytic phage in Butyrivibrio sp017620235 , which is commonly found in the rumen and promotes the degradation of lignocellulose and fermented carbohydrates into butyrate, formate, lactate, and acetate 43 – 45 . Two bacterial species classified as Rumen Uncultured Genomes (RUGs), (bin_16) RUG12461 sp016286115 and (bin_59) RUG191 sp002373675 , were also associated with viruses having a lytic life cycle. The study's assembly of 107 bacterial genomes achieved a high completeness (> 95%) for members of the Treponema genus, which are known as spirochetes and have been positively correlated with feed conversion in cattle 3 . These species are considered to be one of the main fiber degraders in the rumen of Gir cattle, along with other bacteria, such as Clostridium , Ruminococcus , Eubacterium , Butyrivibrio , Roseburia , Caldicellulosiruptor , and Rhodospirillum 46 . The bins identified were primarily assigned to families, such as Lachnospiraceae , Bacteroidaceae , P3 , Ruminococcaceae , Saccharofermentanaceae , and Treponemataceae . These bins mainly encoded pathways related to central carbon metabolism and metabolism of various carbohydrates. One notable pathway found between these bins is the phosphate acetyltransferase-acetate kinase pathway, which is activated at high acetate concentrations, requires less ATP, and plays a role in energy metabolism 47 . Additionally, the aromatic amino acid metabolism pathway with tryptophan biosynthesis was abundant among the bins in the current study. This pathway occurs only in microorganisms and plants, and tryptophan is obtained by animals via symbiosis for protein synthesis 48 . The identification of CAZymes in the bacterial genomes revealed the presence of genes encoding glycoside hydrolases and polysaccharide lyase groups, with GH2, GH3, GH5, GH13, and GH43 being the most prevalent. These groups degrade xylan polysaccharides, which are heterogeneous and require different catalytic enzymes 10 . In a study by Wang et al. 49 , the same GH families, GH2, GH3, GH13, and GH43, were found to comprise the majority of CAZymes present in the rumen of dairy cows fed different proportions of roughage and concentrate. The GH3 group was particularly more abundant in the high-roughage diet, and these observations are consistent with the current study's findings for pasture-fed Nellore cows, where glycoside hydrolase groups were the most prevalent. In summary, this study provides a thorough examination of the MGEs in the rumen of Nellore cattle, shedding new light on the connection between these elements and their microbial inhabitants. The impact and function of these MGEs in beef cattle are currently being investigated by our research group. Material and methods Animal characterization and sample collection Samples of ruminal content were collected from four Nellore ( Bos taurus indicus ) nulliparous cows cannulated in the rumen, with an average weight of (600 ± 50kg) and age between 9 and 6 years of the Ruminant Nutrition Laboratory, Faculty of Veterinary Medicine and Animal Science, FMVZ, Pirassununga campus . The experimental protocols were approved by Ethics Committee on the Use of Animals of Faculty of Veterinary Medicine and Animal Science (CEUA/FMVZ) under number 7333211118. These protocols adhere to both their guidelines and the ARRIVE guidelines. The animals received a diet exclusively based on pasture and mineral salts. Samples were kept on ice during collection until processing and were subsequently stored at -80°C. DNA extraction and sample preparation for Hi-C DNA extraction was performed using a QIAmp® Fast DNA Stool Mini Kit (Qiagen, USA). The crosslinking procedure was performed, in which the samples were processed according to the protocol recommended by the company Phase Genomics (Seattle, WA, USA) adapted by Burton et al. 50 . Briefly, the sample containing liquid and fiber (5 mL) were suspended in 1% formaldehyde (45 ml) and incubated (20 min) at room temperature with periodic shaking. Subsequently, glycine (1 g/100 mL) was added, which was then incubated (15 min) at room temperature with periodic shaking. The sample was centrifuged (1000 × g for 1 min) and washed with PBS. It was then centrifuged again and the supernatant was removed. The pellets obtained formed a pool and were transferred to a cryotube (2 mL), stored at -80°C until sent along with the DNA extracted from the same sample, to be processed and sequenced by Phase Genomics. Hi-C library preparation and short-read sequencing To prepare Hi-C libraries, the Phase Genomics ProxiMeta Hi-C kit v4.0 was used as described by the manufacturer 51 . After going through the crosslinking process, the pellets underwent lysis and their DNA-protein complexes were digested by the restriction enzymes (Sau3AI and MlucI) 31 , creating free ends on the DNA strands that had labeled nucleotides with biotin to create chimeric molecules composed of fragments from different physically close genome regions in vivo . Proximity-linked DNA molecules were drawn down using streptavidin beads and processed into an Illumina-compatible sequencing library. Separately, DNA was extracted from an aliquot of the original sample, and a shotgun metagenomic library was prepared using ProxiMeta library preparation reagents. Sequencing was performed using Illumina NovaSeq, generating pairs of PE150 reads for the Hi-C and shotgun libraries. Hi-C and shotgun metagenomic sequencing files were uploaded to the Phase Genomics cloud-based bioinformatics portal for subsequent analyses. Clustering, quality assessment, taxonomic and functional assignment of assembled microorganisms Shotgun-derived reads were filtered and adjusted for quality, normalized using fastp 52 , and assembled with MEGAHIT 53 using default options. Pairs of Hi-C reads were aligned to each assembly using BWA-MEM 54 with the − 5SP options specified and all other default options to disable attempts to pair the reads according to the normal Illumina settings. The SAMBLASTER program 55 was used to flag PCR duplicates that were subsequently excluded from the analysis. The alignments were then filtered with SAMtools 56 using the -F 2304 filter flag to remove nonprimary and secondary alignments. Deconvolution, a method that uses the intracellular proximity signal captured by Hi-C as an indicator of the cellular origin of metagenome sequences, was performed using ProxiMeta 27 , 31 to generate putative genomes and clusters of genome fragments. Prokaryotic clusters were assessed for quality using CheckM 57 and received preliminary taxonomic classifications using Mash, a generalized and automated method that identifies reference genomes compatible with the NCBI RefSeq database 58 (Supplementary Table 1). Subsequently, the bins were submitted to the Genome Taxonomy Database (GTDB) 59 for taxonomic assignment (Supplementary Table 2). The functional potential of the bins was evaluated by METABOLIC (METabolic and BiogeOchemistry analyses in microCrobes) 60 , which detected the presence or absence of KEGG modules for the most abundant central pathways present in the genome of the microorganisms evaluated, as well as how they attributed the CAZymes and the main biogeochemical cycles present. Mobile genetic elements Viral contigs were annotated and used for lifestyle and viral protein annotation per contig using VIBRANT 61 . The contigs were grouped into assembled genomes using ProxiPhage viral binning. Sequence integrity was validated with CheckV, viral genomes were classified into quality levels based on AAI or HMM estimation, high confidence (≥ 90% complete), medium confidence (80–90% complete) or low confidence (< 80% complete) 22,62 . For taxonomic assignment, the protein sequences of the viral clusters (vMAGs) were predicted using Prodigal 63 , and these sequences were used to build protein cluster profiles that were compared with sequences present in the ICTV phage database, generating networks of similarities between viral clusters with the using vCONTACT 64 . To identify sequences with plasmid characteristics, a BLAST (Basic Local Alignment Search Tool) alignment was performed against the NCBI plasmid database 31 . Sequences annotated as plasmids were associated with the host using a host-finding algorithm 22 . Microbial resistance genes, in turn, were identified with AMRFinderPlus, which allows the assessment of Antimicrobial Resistance gene content in the NCBI database 65 . Assignment of mobile genetic elements to their hosts The step of assigning mobile genetic elements to their hosts was composed of several filtering criteria, with the first phase of filtering restricting connections with less than 2 Hi-C read links. To remove false positives, a connectivity rate (≥ 0.1) was used for the number of copies of mobile elements per cell (for repeated vMAGs the highest number of counts per cell was considered) and host intra-MAG connectivity (≥ 10links). Finally, a receiver operating characteristic (ROC) curve was performed, which determines the ideal copy count cutoff value (≥ 0.1). This value is expected to be close to 1, as values close to or above 1 are considered reliable for attributing a real host 22 . In cases where the optimal copy count threshold is excessively low (< 0.1), it is replaced with 0.1. This occurred in the present study, where the calculated value was 0.01. Declarations Data Availability The data generated from the present study will be available in the Sequence Read Archive (SRA) database https://dataview.ncbi.nlm.nih.gov/object/PRJNA1054691?reviewer=vd7njv2i8dqvoj4d6tmt82kr1t Acknowledgements The authors express their gratitude to the funding agencies that facilitated the realization of this research. The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) grant 2022/05541-9 and the Fundação de Estudos Agrários Luiz de Queiroz (FEALQ). Contributions C.A.F collected the samples, performed the laboratory procedures until sending them to Phase Genomics for sequencing, analyzed the data obtained, compiled the results and wrote the initial manuscript. A.T.N. collaborated with sampling and writing the manuscript. M.D.P. contributed to data analysis and preparation of figures. E.C.M.O., O.S.G. and P.A.A. contributed with bioinformatics analyses. F.P.J. and P.H.M.R collected the samples and provided the animal data. HF was the project supervisor and reviewed the initial manuscript. All authors reviewed the final version of the manuscript. Ethics declarations Competing Interests The authors declare no competing interests. References González-Recio, O. et al. Invited review: Novel methods and perspectives for modulating the rumen microbiome through selective breeding as a means to improve complex traits: Implications for methane emissions in cattle. Livest Sci 269 , 105171 (2023). Nkrumah, J. D. et al. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J Anim Sci 84 , 145–153 (2006). Auffret, M. D. et al. Identification of Microbial Genetic Capacities and Potential Mechanisms Within the Rumen Microbiome Explaining Differences in Beef Cattle Feed Efficiency. Front Microbiol 11 , 1229 (2020). McGovern, E. et al. Investigation into the effect of divergent feed efficiency phenotype on the bovine rumen microbiota across diet and breed. Scientific Reports 2020 10:1 10 , 1–11 (2020). Liu, Y. et al. Rumen Microbiome and Metabolome of High and Low Residual Feed Intake Angus Heifers. Front Vet Sci 9 , 812861 (2022). Roehe, R. et al. Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. PLoS Genet 12 , e1005846 (2016). Zhang, Q. et al. Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows. ISME J 14 , 2019–2033 (2020). Martínez-Álvaro, M. et al. Bovine host genome acts on rumen microbiome function linked to methane emissions. Communications Biology 2022 5:1 5 , 1–16 (2022). Stewart, R. D. et al. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nature Biotechnology 2019 37:8 37 , 953–961 (2019). Gharechahi, J. et al. Lignocellulose degradation by rumen bacterial communities: New insights from metagenome analyses. Environ Res 229 , 115925 (2023). Luo, X. Q. et al. Viral community-wide auxiliary metabolic genes differ by lifestyles, habitats, and hosts. Microbiome 10 , 1–18 (2022). Anderson, C. L., Sullivan, M. B. & Fernando, S. C. Dietary energy drives the dynamic response of bovine rumen viral communities. Microbiome 2017 5:1 5 , 1–19 (2017). Kav, A. B. et al. Insights into the bovine rumen plasmidome. Proc Natl Acad Sci U S A 109 , 5452–5457 (2012). Lobo, R. R. & Faciola, A. P. Ruminal Phages – A Review. Front Microbiol 12 , 763416 (2021). Solden, L. M. et al. Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nature Microbiology 2018 3:11 3 , 1274–1284 (2018). Orpin, C. G. & Munn, E. A. The occurrence of bacteriophages in the rumen and their influence on rumen bacterial populations. Experientia 30 , 1018–1020 (1974). Altermann, E., Schofield, L. R., Ronimus, R. S., Beatty, A. K. & Reilly, K. Inhibition of Rumen methanogens by a novel archaeal lytic enzyme displayed on tailored bionanoparticles. Front Microbiol 9 , 2378 (2018). Park, S. Y. et al. Characterization of two lytic bacteriophages, infecting Streptococcus bovis/equinus complex (SBSEC) from Korean ruminant. Scientific Reports 2023 13:1 13 , 1–16 (2023). Ross, E. M., Petrovski, S., Moate, P. J. & Hayes, B. J. Metagenomics of rumen bacteriophage from thirteen lactating dairy cattle. BMC Microbiol 13 , 1–11 (2013). Bickhart, D. M. et al. Assignment of virus and antimicrobial resistance genes to microbial hosts in a complex microbial community by combined long-read assembly and proximity ligation. Genome Biology 2019 20:1 20 , 1–18 (2019). Marbouty, M., Baudry, L., Cournac, A. & Koszul, R. Scaffolding bacterial genomes and probing host-virus interactions in gut microbiome by proximity ligation (chromosome capture) assay. Sci Adv 3 , (2017). Uritskiy, G. et al. Accurate viral genome reconstruction and host assignment with proximity-ligation sequencing. bioRxiv 2021.06.14.448389 (2021) doi:10.1101/2021.06.14.448389. Cuscó, A., Pérez, D., Viñes, J., Fàbregas, N. & Francino, O. Novel canine high-quality metagenome-assembled genomes, prophages and host-associated plasmids provided by long-read metagenomics together with Hi-C proximity ligation. Microb Genom 8 , 802 (2022). Braun, V. & Killmann, H. Bacterial solutions to the iron-supply problem. Trends Biochem Sci 24 , 104–109 (1999). Canfield, D. E., Kristensen, E. & Thamdrup, B. The Iron and Manganese Cycles. Adv Mar Biol 48 , 269–312 (2005). Bohra, V., Dafale, N. A. & Purohit, H. J. Understanding the alteration in rumen microbiome and CAZymes profile with diet and host through comparative metagenomic approach. Arch Microbiol 201 , 1385–1397 (2019). Stewart, R. D. et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat Commun 9 , 870–870 (2018). Krupovic, M. et al. Bacterial Viruses Subcommittee and Archaeal Viruses Subcommittee of the ICTV: update of taxonomy changes in 2021. Arch Virol 166 , 3239–3244 (2021). Hua, D., Hendriks, W. H., Xiong, B. & Pellikaan, W. F. Starch and Cellulose Degradation in the Rumen and Applications of Metagenomics on Ruminal Microorganisms. Animals 2022, Vol. 12, Page 3020 12 , 3020 (2022). Wallace, R. J. et al. The rumen microbial metagenome associated with high methane production in cattle. BMC Genomics 2015 16:1 16 , 1–14 (2015). MO, P. et al. Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions. (2017) doi:10.1101/198713. Stalder, T., Press, M. O., Sullivan, S., Liachko, I. & Top, E. M. Linking the resistome and plasmidome to the microbiome. The ISME Journal 2019 13:10 13 , 2437–2446 (2019). Jing, R. & Yan, Y. Metagenomic analysis reveals antibiotic resistance genes in the bovine rumen. Microb Pathog 149 , 104350 (2020). Ma, T. et al. Expressions of resistome is linked to the key functions and stability of active rumen microbiome. Anim Microbiome 4 , 1–17 (2022). Auffret, M. D. et al. The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle. Microbiome 5 , 1–11 (2017). Sabino, Y. N. V. et al. Characterization of antibiotic resistance genes in the species of the rumen microbiota. Nature Communications 2019 10:1 10 , 1–11 (2019). Yan, M. et al. Interrogating the viral dark matter of the rumen ecosystem with a global virome database. Nature Communications 2023 14:1 14 , 1–16 (2023). Sato, Y. et al. Characteristics of the rumen virome in Japanese cattle. bioRxiv 2023.03.20.532305 (2023) doi:10.1101/2023.03.20.532305. Berg Miller, M. E. et al. Phage-bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome. Environ Microbiol 14 , 207–227 (2012). Zhou, S. et al. Characterization of Metagenome-Assembled Genomes and Carbohydrate-Degrading Genes in the Gut Microbiota of Tibetan Pig. Front Microbiol 11 , 595066 (2020). Crossfield, M. et al. Archaeal and Bacterial Metagenome-Assembled Genome Sequences Derived from Pig Feces. Microbiol Resour Announc 11 , (2022). Friedersdorff, J. C. A. et al. The Isolation and Genome Sequencing of Five Novel Bacteriophages From the Rumen Active Against Butyrivibrio fibrisolvens. Front Microbiol 11 , 522243 (2020). Kopečný, J., Zorec, M., Mrázek, J., Kobayashi, Y. & Marinšek-Logar, R. Butyrivibrio hungatei sp. nov. and Pseudobutyrivibrio xylanivorans sp. nov., butyrate-producing bacteria from the rumen. Int J Syst Evol Microbiol 53 , 201–209 (2003). Moon, C. D. et al. Reclassification of Clostridium proteoclasticum as Butyrivibrio proteoclasticus comb. nov., a butyrate-producing ruminal bacterium. Int J Syst Evol Microbiol 58 , 2041–2045 (2008). Palevich, N. et al. Complete Genome Sequence of the Polysaccharide-Degrading Rumen Bacterium Pseudobutyrivibrio xylanivorans MA3014 Reveals an Incomplete Glycolytic Pathway. Genome Biol. Evol 12 , 1566–1572 (2020). Pandit, R. J. et al. Microbiota composition, gene pool and its expression in Gir cattle (Bos indicus) rumen under different forage diets using metagenomic and metatranscriptomic approaches. Syst Appl Microbiol 41 , 374–385 (2018). Zhang, S. et al. Metabolic engineering for efficient supply of acetyl-CoA from different carbon sources in Escherichia coli. Microb Cell Fact 18 , 1–11 (2019). Parthasarathy, A. et al. A Three-Ring circus: Metabolism of the three proteogenic aromatic amino acids and their role in the health of plants and animals. Front Mol Biosci 5 , 342220 (2018). Wang, L., Zhang, G., Xu, H., Xin, H. & Zhang, Y. Metagenomic analyses of microbial and carbohydrate-active enzymes in the rumen of holstein cows fed different forage-to-concentrate ratios. Front Microbiol 10 , 441658 (2019). Burton, J. N., Liachko, I., Dunham, M. J. & Shendure, J. Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps. G3: Genes, Genomes, Genetics 4 , 1339–1346 (2014). Lieberman-Aiden, E. et al. Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome. Science (1979) 326 , 289–293 (2009). Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34 , i884–i890 (2018). Li, D. et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102 , 3–11 (2016). Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26 , 589–595 (2010). Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30 , 2503–2505 (2014). Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25 , 2078 (2009). Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25 , 1043 (2015). Ondov, B. D. et al. Mash Screen: High-throughput sequence containment estimation for genome discovery. Genome Biol 20 , 1–13 (2019). Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nature Biotechnology 2018 36:10 36 , 996–1004 (2018). Zhou, Z. et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome 10 , 1–22 (2022). Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: Automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8 , 1–23 (2020). Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nature Biotechnology 2020 39:5 39 , 578–585 (2020). Hyatt, D. et al. Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11 , 1–11 (2010). Bolduc, B. et al. vConTACT: An iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. PeerJ 2017 , e3243 (2017). Feldgarden, M. et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Scientific Reports 2021 11:1 11 , 1–9 (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2024 Reviews received at journal 19 Apr, 2024 Reviewers agreed at journal 04 Apr, 2024 Reviews received at journal 26 Feb, 2024 Reviewers agreed at journal 08 Feb, 2024 Reviewers invited by journal 06 Feb, 2024 Editor assigned by journal 29 Jan, 2024 Editor invited by journal 28 Dec, 2023 Submission checks completed at journal 28 Dec, 2023 First submitted to journal 13 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Faleiros","email":"","orcid":"","institution":"Faculty of Animal Science and Food Engineering (FZEA), University of Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Camila","middleName":"A.","lastName":"Faleiros","suffix":""},{"id":264592048,"identity":"ab6d9d94-96e5-41c8-ab91-ba98faa7a72f","order_by":1,"name":"Alanne T. Nunes","email":"","orcid":"","institution":"Faculty of Animal Science and Food Engineering (FZEA), University of Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Alanne","middleName":"T.","lastName":"Nunes","suffix":""},{"id":264592049,"identity":"65890242-36b0-42ab-8ee6-0b4859e16d4a","order_by":2,"name":"Osiel S. Gonçalves","email":"","orcid":"","institution":"Federal University of Viçosa","correspondingAuthor":false,"prefix":"","firstName":"Osiel","middleName":"S.","lastName":"Gonçalves","suffix":""},{"id":264592050,"identity":"10e8dbb1-bb0b-4cd6-acd5-24f49ed1a943","order_by":3,"name":"Pâmela A. Alexandre","email":"","orcid":"","institution":"Commonwealth Scientific and Industrial Research Organization (CSIRO), Agriculture and Food","correspondingAuthor":false,"prefix":"","firstName":"Pâmela","middleName":"A.","lastName":"Alexandre","suffix":""},{"id":264592051,"identity":"d68fbbc8-dfd1-43e9-81aa-000d9b1187d2","order_by":4,"name":"Mirele D. Poleti","email":"","orcid":"","institution":"Faculty of Animal Science and Food Engineering (FZEA), University of Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Mirele","middleName":"D.","lastName":"Poleti","suffix":""},{"id":264592052,"identity":"718d84b1-eefe-4f13-b6dc-1c4de3cc4af9","order_by":5,"name":"Elisângela C. M. Oliveira","email":"","orcid":"","institution":"Faculty of Animal Science and Food Engineering (FZEA), University of Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Elisângela","middleName":"C. M.","lastName":"Oliveira","suffix":""},{"id":264592053,"identity":"662979e3-e306-4c57-a028-c3fc9d2cb04c","order_by":6,"name":"Flavio Perna Junior","email":"","orcid":"","institution":"University of São Paulo (FMVZ-USP)","correspondingAuthor":false,"prefix":"","firstName":"Flavio","middleName":"Perna","lastName":"Junior","suffix":""},{"id":264592054,"identity":"0a804176-d409-48bf-bbe6-65ddeafe9275","order_by":7,"name":"Paulo H. Mazza Rodrigues","email":"","orcid":"","institution":"University of São Paulo (FMVZ-USP)","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"H. Mazza","lastName":"Rodrigues","suffix":""},{"id":264592055,"identity":"fe7711be-6b67-49e6-9f3e-b6f08d63e663","order_by":8,"name":"Heidge Fukumasu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBAC9gY2CIMfRDA2EKGF5wBUi2QDyVoMDhCtRSItTepGTV3i5hvpzx4w7rhHlJZj0jnHDiduu5FjbsB4ppiwFnuJ9DbpHLYDIC1sEoxtCcTYAtLyD+iwGenPiNUCdFhuG3PiBokEMyK18DxLts7tO2w848wbc4PEM8RoYU8zvJ3zrU62vx0YYh93EKEFGbAxkKgBpGUUjIJRMApGATYAAHvlOGKmhxiAAAAAAElFTkSuQmCC","orcid":"","institution":"Faculty of Animal Science and Food Engineering (FZEA), University of Sao Paulo","correspondingAuthor":true,"prefix":"","firstName":"Heidge","middleName":"","lastName":"Fukumasu","suffix":""}],"badges":[],"createdAt":"2023-12-13 19:14:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3749940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3749940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49045894,"identity":"87723922-17ab-45ec-82be-e8d73b3e266a","added_by":"auto","created_at":"2024-01-02 07:17:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211047,"visible":true,"origin":"","legend":"\u003cp\u003eRumen metagenome of Nellore cattle. (\u003cstrong\u003eA)\u003c/strong\u003e Completeness of metagenome-assembled genomes (MAGs), (\u003cstrong\u003eB\u003c/strong\u003e) Taxonomic classification of bins: graph on the left genus and right graph family.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/c3b1381973deab3742f02384.png"},{"id":49046485,"identity":"836a4ee1-0b7a-4821-8f4c-794bda8287ac","added_by":"auto","created_at":"2024-01-02 07:33:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191421,"visible":true,"origin":"","legend":"\u003cp\u003ePotential biogeochemical cycling processes by rumen bacteria. Bins are color-coded in each step within the (\u003cstrong\u003eA\u003c/strong\u003e) Carbon, (\u003cstrong\u003eB\u003c/strong\u003e) sulfur, (\u003cstrong\u003eC\u003c/strong\u003e) nitrogen, and (\u003cstrong\u003eD\u003c/strong\u003e) iron cycles. Each arrow in the figure represents a single transformation step within a cycle.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/c82a1f8996525b04c2259de2.jpeg"},{"id":49045889,"identity":"cff4611c-48f7-4488-9ed7-78c6c4fee3bc","added_by":"auto","created_at":"2024-01-02 07:17:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":356623,"visible":true,"origin":"","legend":"\u003cp\u003eCentral processes of metabolism and CAZymes present in the rumen microbiome of Nellore cattle. \u003cstrong\u003e(A)\u003c/strong\u003e Characterization of the metabolic potential of bins found in the rumen, (\u003cstrong\u003eB\u003c/strong\u003e) Identification and abundance of enzymes encoded by rumen microorganisms. The size of each circle corresponds tothe number of hits for the respective gene.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/53e19be6cf3eea99aba3b76d.png"},{"id":49045893,"identity":"f0d1529d-b8ab-45d0-b051-501dccd2d51b","added_by":"auto","created_at":"2024-01-02 07:17:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":144909,"visible":true,"origin":"","legend":"\u003cp\u003eMobile genetic elements of bovine rumen. (\u003cstrong\u003eA\u003c/strong\u003e) Each circle represents number of sequences identified as plasmids, integrated and non-host-integrated plasmids and plasmids associated with their hosts by the Hi-C method. (\u003cstrong\u003eB\u003c/strong\u003e) Each circle represents the number of identified viral sequences, viral sequences integrated into the host genome (Prophages), binned viral contigs, metagenome-assembled viral genomes (MAGs) and vMAGs associated with hosts by proximity linkage (Hi-C).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/45227b02ecb7b2da71f55c15.png"},{"id":49046034,"identity":"9fec707f-16fa-4be7-88b3-acecd80fb3e6","added_by":"auto","created_at":"2024-01-02 07:25:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":118332,"visible":true,"origin":"","legend":"\u003cp\u003eAntimicrobial Resistance Genes – ARGs. Class, subclass, and symbols of resistance genes.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/8084655967260ccadf5ed5e7.png"},{"id":49045892,"identity":"dd47074c-6d4a-4ccb-8ae6-b0d79e05ec1d","added_by":"auto","created_at":"2024-01-02 07:17:12","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":349580,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic assignment of rumen viruses. Network of viral clusters assigned by vCONTACT. Nodes with the same color have the same viral cluster, diamonds are clusters with known taxonomy and the nodes linked to them have the same viral cluster and can be assigned the same taxonomy, lilac nodes represent vMAGs that have not been assigned to clusters known.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/c1999dbf5a00f914018f86a2.jpeg"},{"id":49045888,"identity":"558d7190-5cf1-4bde-a174-7d35fb06ed82","added_by":"auto","created_at":"2024-01-02 07:17:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":131704,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between bacterial hosts and their Hi-C-linked mobile genetic elements. Circles represent bacterial hosts, hexagons represent bacteriophages, and rectangles represent plasmids.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/8964c2d897aecf83b98d6a59.png"},{"id":49046602,"identity":"8046b457-291b-4f9e-8d99-5465912087b9","added_by":"auto","created_at":"2024-01-02 07:41:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1717668,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/aca88af6-5bc5-4a73-8a84-bb5929d6a6a6.pdf"},{"id":49045895,"identity":"22655029-0758-4516-875e-c7dc1a6f58f2","added_by":"auto","created_at":"2024-01-02 07:17:12","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":189631,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3749940/v1/69a22a0a3700c97d2d492769.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying Mobile Genetic Elements in the Ruminal Microbiome of Nellore Cattle: An Initial Investigation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdvancements in metagenomics in rumen environments have enabled us to identify and classify the taxonomy and functional capabilities of bacteria that specialize in digesting plant material, which is the primary food source for ruminants. Given the impact of microorganisms on host cattle performance, microbiota modulation is crucial for developing a sustainable and productive livestock system\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. For instance, feed efficiency is a trait with implications for economic and environmental sustainability\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and is strongly associated with host animal microbiota profiles\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Ruminal methane production, which results from microbial fermentation in the rumen, is another example of trait that can be explained by genetic variation and the composition of the host animal microbiota\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Approximately 65% of rumen prokaryotic species have been classified and described in taurine breeds\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e; however, less information is available on Indian cattle, which are the majority in the world, especially in Brazil.\u003c/p\u003e \u003cp\u003eUnderstanding the role of specific phylotypes in the rumen during fermentation and identifying new enzymes through metagenomic analysis can improve biotechnology for food digestion and ruminant performance in production systems\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Underutilized components of the rumen, known as Mobile Genetic Elements (MGEs), possess great potential for manipulation and biotechnological applications owing to their ability to confer benefits to their hosts and participate in crucial functional modifications of their hosts, carrying auxiliary metabolic genes such as glycosidic hydrolases (GH), which contribute to the breakdown of complex carbohydrates.\u003c/p\u003e \u003cp\u003eIn addition, viruses carry genes that contribute positively their hosts in the metabolism of nutrients and stages of the biogeochemical cycles carried out by the hosts \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. They are classified as mobile DNA elements that move within and between bacterial cells through agents, such as plasmids, bacteriophages, and transposons, resulting in the introduction of accessory genes that contribute to the evolution of host cells\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Solden et al.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e reported that viruses play a crucial role in controlling the ecosystem functions in the rumen. These viruses have metabolic interdependence with their microbial hosts, carry genes that can redirect carbon metabolism, and infect dominant microorganisms that degrade carbohydrates.\u003c/p\u003e \u003cp\u003eEarly studies on bacteriophages in the rumen revealed that they can eliminate specific bacteria\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Recent research has indicated the potential use of lytic phages to regulate microorganisms in the rumen responsible for an increase in acidosis and methane production\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This suggests that the components in the rumen are interconnected with viruses that infect ruminal bacteria, the microbiome, and the host animal interacting and affecting each other. This can change the ruminal function and the nutritional capacity of the host animal\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies have employed the Hi-C approach, which captures intimate interactions between bacterial chromosomes within living cells, to investigate MGEs in microbial communities. This method has been used in conjunction with metagenomic sequencing and has proven effective in assembling genomes and uncovering new relationships between hosts and MGEs\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The objective of this study was to determine the existence of MGEs in the rumen content of Nellore cattle in Brazil, using the Hi-C method to establish a link between these elements and their microbial hosts.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eMetagenome of bovine rumen content samples\u003c/h2\u003e\n\u003cp\u003eIn this study, the metagenomic sequencing was performed with DNA extracted from a pool of rumen content samples of four 4 Nellore cows, applying ProxiMeta\u0026trade;, a proximity linkage method based on Hi-C. This method used contigs derived from shotgun sequencing and reads from a library Hi-C, and generated an assembly length of 1,713,111,307 bp, with 834,164 contigs total and 16,035 contigs in clusters allowing the assembly of 107 genomes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). The most completely assembled genome was bin_4 (99.30%), which corresponded to \u003cem\u003eTreponema D. sp016288205\u003c/em\u003e. Other genomes classified as complete, with completeness greater than 95%, were \u003cem\u003eUBA2868 sp003535955\u003c/em\u003e (bin_1), \u003cem\u003eTreponema D. sp017421145\u003c/em\u003e (bin_9), \u003cem\u003eTreponema D. sp902783155\u003c/em\u003e (bin_6) and \u003cem\u003eUBA2868\u003c/em\u003e (bin _12). The clusters classified as excellent (\u0026gt;\u0026thinsp;90%) were dominated by species from the \u003cem\u003eLachnospiraceae\u003c/em\u003e family, namely \u003cem\u003eUBA3766 sp902803015\u003c/em\u003e (bin_22), \u003cem\u003eUBA1712\u003c/em\u003e (bin_14), \u003cem\u003eUBA2868\u003c/em\u003e (bin_3), \u003cem\u003eUBA2868 sp003535955\u003c/em\u003e (bin_17), \u003cem\u003eNK4A144 sp902783395\u003c/em\u003e (bin_7), \u003cem\u003eAcetatifactor sp900066565\u003c/em\u003e (bin_21), \u003cem\u003eUBA1711 sp900317125\u003c/em\u003e (bin_11), and \u003cem\u003eUBA1711 sp001543385\u003c/em\u003e (bin_18). Clusters belonging to the families \u003cem\u003eSaccharofermentanaceae\u003c/em\u003e with \u003cem\u003eSaccharofermentans\u003c/em\u003e (bin_19), and \u003cem\u003eBacteroidaceae\u003c/em\u003e with \u003cem\u003ePrevotella sp000702825\u003c/em\u003e (bin_5) also had an excellent completeness. The clusters considered good are presented in the Supplementary Table\u0026nbsp;1, and the 107 clusters subjected to taxonomic classification are represented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eFunctional and carbohydrate-active enzymes (CAZYmes) characterization of bins\u003c/h2\u003e\n\u003cp\u003eBefore analyzing the MGEs, we conducted a functional analysis to understand the functional characteristics encoded within metagenome-assembled genomes (MAGs) from the ruminal microbiome of Nellore cattle. For this, MAGs with completeness\u0026thinsp;\u0026gt;\u0026thinsp;50% and those associated with MGEs were used, totaling 43 genomes (Supplementary Tables\u0026nbsp;8 and 9). This step provided essential insights into the interplay between microbial communities and the functional potential associated with MGEs in the rumen ecosystem. Our study evaluated the involvement of rumen microorganisms in these biogeochemical pathways, and we found that the majority were associated with the stages of the carbon, sulfur, nitrogen and iron cycles (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Regarding the carbon cycle, most of the genomes identified demonstrated involvement in the acetogenesis process and carbon degradation complex. More specifically, we identified two genomes belonging to \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eBacteroidales\u003c/em\u003e encoding the enzyme methane monooxygenase (mmoBD), which act in methane oxidation, and members of these clades also participated in the oxidation step of the sulfur cycle. In the nitrogen cycle, phylotypes belonging to \u003cem\u003eSaccharofermentans\u003c/em\u003e and \u003cem\u003eTreponema\u003c/em\u003e acted in the nitrogen fixation stage and members of the genus \u003cem\u003eProvotella\u003c/em\u003e encoded genes for the reduction of nitrite into ammonia. All taxonomic classes presented members active in the iron oxidation stage, which is fundamental for most microorganisms, due to its redox properties, being used as an electron transporter by bacteria \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe most frequent processes included steps in the central carbon metabolism pathway, the metabolism of other carbohydrates, and purine metabolism. Among the bins examined, 69% contained genes that encoded the phosphate acetyltransferase-acetate kinase pathway, a reversible process where acetyl-CoA can be converted into acetate or acetate into acetyl-CoA. Additionally, the aromatic amino acid metabolism pathway with tryptophan biosynthesis was present in 64% of the bins (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eThe metagenomic approach allows for the evaluation of the potential of rumen microbial populations to break down plant lignocellulolytic materials, by annotating the genes associated with this process, known as CAZymes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. CAZymes are classified into several categories based on their degradation capabilities, including glycoside hydrolases (GHs), carbohydrate esterases (CEs), polysaccharide lyases (PLs), and others\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In this study, the bins identified were assessed for their ability to encode CAZymes, and the presence of GHs and PLs was confirmed (Supplementary Table\u0026nbsp;3). We found that GH2, GH3, GH5, GH13, and GH43 were the most abundant enzymes mapped in the bins (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eAssignment of mobile genetic elements to their hosts\u003c/h2\u003e\n\u003cp\u003eClustering with Hi-C allows not only the construction of MAGs but also the sequence grouping of mobile genetic elements with their core genome\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The sequences were obtained with sequencing and the use of the proximity linkage method to assign viruses and plasmids to their microbial hosts, the data obtained is summarized in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003ePlasmids and the Identification of microbial resistance genes\u003c/h2\u003e\n\u003cp\u003eThe reads derived from shotgun sequencing were used to evaluate the presence of plasmid sequences. Thirty-three DNA sequences were identified as plasmids, which were not integrated into the host genome. Among these, 14 had at least one connection with the prokaryotic host genome through Hi-C reads. Furthermore, two putative plasmids contigs k141_1760976 and k141_5289202 were shared between multiple host clusters (bin_3, bin_5, bin_20, bin_80) and (bin_1; bin_3; bin_11; bin_30; bin_88) (Supplementary Table\u0026nbsp;4). The bins shared by contig k141_1760976 belong to different genus, including \u003cem\u003eUBA2868\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eSaccharofermentans\u003c/em\u003e, which characterizes horizontal gene transfer mediated by plasmids. The plasmid k141_5289202 demonstrated greater specificity, being shared by three individuals belonging to \u003cem\u003eLachnospiracea\u003c/em\u003e and one belonging to \u003cem\u003eBacteroidales.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, we identified 19 plasmid sequences integrated into the host genome sequences (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Within this set, it was observed that 50% of the total antibiotic resistance genes (ARGs) validated in our database were present (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). All the identified resistance genes had an identity of more than 90% with the reference sequence (Supplementary Table\u0026nbsp;5). The \u003cem\u003etet32\u003c/em\u003e gene sequence was associated with bin_14, which belongs to the \u003cem\u003eLachnospiraceae\u003c/em\u003e family.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eVirus found in the rumen\u003c/h2\u003e\n\u003cp\u003eA total of 11 high-quality metagenome-assembled viral genomes (vMAGs) were identified and characterized as viral sequences (as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). They had completeness rates of 92\u0026ndash;100% and a length of 35 to 126 kbp. Another 22 MAGs were obtained with medium quality, of which 13 had completeness rates of 71\u0026ndash;89% and a length of 25 to 90 kbp, and nine had completeness rates of 68\u0026ndash;50% and lengths of 20 and 130 kbp. Any genomes with less than 50% completeness were considered low quality. All of the assembled genomes were determined to be free of contamination (Supplementary Table\u0026nbsp;6).\u003c/p\u003e\n\u003cp\u003eThe majority of vMAGs had their taxonomy associated with genomes that have not yet been assigned to the ICTV phage database\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. There were vMAGs associated with the same viral cluster (VC); however, these do not yet have taxonomic assignment in the database: vMAG_4 and vMAG_35 were associated with the viral cluster VC_146, as well as vMAG_5 and vMAGs_18, with VC_147 and vMAG_25, and vMAG_56, with VC_200 (Supplementary Table\u0026nbsp;7).\u003c/p\u003e\n\u003cp\u003eAmong the viruses that could be assigned to known genomes, vMAG_1 and vMAG_48 were assigned to the \u003cem\u003eMyoviridae\u003c/em\u003e family VC_155, vMAG_40, vMAG_46 and vMAG_57 were assigned to the \u003cem\u003eSiphoviridae\u003c/em\u003e family, VC_102, vMAG_7, vMAG_16, 29 and vMAG_52 were also assigned to the \u003cem\u003eSiphoviridae\u003c/em\u003e family, but with genomes associated with \u003cem\u003eStreptomyces phage\u003c/em\u003e, VC_87 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eVirus-host association\u003c/h2\u003e\n\u003cp\u003eThe viral clusters associated with the bacterial genomes determined by Hi-C mostly exhibited a lytic cycle. The vMAG_54, associated with the host \u003cem\u003eUBA2868 sp003535955\u003c/em\u003e (\u003cem\u003eLachnospiraceae\u003c/em\u003e), presented high quality, achieved 100% completeness in its genome, and was characterized by a lytic lifestyle (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTwo co-infection events were identified, in which bacterial cells presented the assignment of more than one bacteriophage simultaneously, that is, different bacteriophages infecting the same bacterial host. Bacteriophages corresponding to vMAGs 18 and 45 infected the same bacterial cell corresponding to bin_28, and the same occurred with vMAGs 12 and 54, which infected the same bacterial cell corresponding to bin_30 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u0026nbsp;Viruses associated with bacterial hosts by Hi-C\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eName\u003c/p\u003e\n\u003cp\u003ecluster\u003c/p\u003e\n\u003cp\u003ehost\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHost\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eName Cluster (phage)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLife\u003c/p\u003e\n\u003cp\u003eCycle\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCheckV quality\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCompleteness (phages)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eUBA2868 sp003535955 (Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eg_UBA2868\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eUBA2868 sp002368575\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSaccharofermentans sp003543635\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eUBA3766 sp902803015\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eUBA2868 sp003535955\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSaccharofermentans sp003543635\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eRUG12461 sp016286115\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNon-classified\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eButyrivibrio sp017620235\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eg_F23-D06\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eg_UBA1711\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(P3)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.54\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNon-classified\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNon-classified\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eRUG191 sp002373675\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eNK4A144 sp902783395\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Lachnospiraceae)\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow-quality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ebin_26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSaccharofermentans sp902782235\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003evMAG_49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLytic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot-determined\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eg_(genus).\u003c/p\u003e\n\u003cp\u003eOf the 17 bins identified to be associated with viruses, 8 belonged to the \u003cem\u003eLachnospiraceae\u003c/em\u003e family. The bin_30 and bin_41 belong to the same genus \u003cem\u003eUBA2868\u003c/em\u003e, and the other bins assigned to this family were: (bin_7) \u003cem\u003eNK4A144 sp902783395\u003c/em\u003e, (bin_12) \u003cem\u003eUBA2868 sp002368575\u003c/em\u003e, (bin_16) \u003cem\u003eRUG12461 sp016286115\u003c/em\u003e, (bin_22) \u003cem\u003eUBA3766 sp902803015\u003c/em\u003e, (bin_27) \u003cem\u003eButyrivibrio sp017620235\u003c/em\u003e, and (bin_59) \u003cem\u003eRUG191 sp002373675\u003c/em\u003e. The bin_26 and bin_28 were assigned to \u003cem\u003eSaccharofermentans sp902782235\u003c/em\u003e and \u003cem\u003esp003543635\u003c/em\u003e, respectively, and bin_58 was assigned to genus \u003cem\u003eF23-D06\u003c/em\u003e and bin_74 to genus \u003cem\u003eUBA1711\u003c/em\u003e, both belonging to the order \u003cem\u003eBacteriodales\u003c/em\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMetagenomics has enabled the efficient identification of microorganisms, enzymes, and metabolic pathways that play a role in plant breakdown in the bovine rumen\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. This tool has also proven to be crucial for identifying the microbiota and characterizing important traits such as methane production and feed efficiency\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This study is the first to explore rumen mobile genetic elements of Nellore cattle fed a grass diet by associating viruses and plasmids with their hosts through physical links between DNA molecules from the same genome using the Hi-C method\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We identified 31 links between bacteria and mobile genetic elements, with 14 of these links being plasmids and 17 links being viruses. Furthermore, we identified 12 genes conferring antibiotic resistance to tetracycline (6 genes), nitroimidazole (1 gene), lincosamide (1 gene), beta-lactam (1 gene), aminoglycoside (1 gene) and macrolide (1 gene). Additionally, we assembled a collection of 107 bacterial genomes, which mostly encoded pathways for central carbon and other carbohydrate metabolisms.\u003c/p\u003e \u003cp\u003eIn this study, we identified 52 plasmid sequences, 14 of which were linked to their hosts using Hi-C. Similarly, in their evaluation of the canine fecal metagenome, Cusc\u0026oacute; et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e found that six Hi-C plasmids were ligated into their bacterial hosts, including five circular plasmids and a plasmid carrying the linA resistance gene. In our investigation, we uncovered two plasmids, k141_1760976 and k141_5289202, which are associated with multiple host clusters. As previously reported by Mo et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and Stewart et al.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, sequences may be shared among various bacterial species found in the rumen. Through the Hi-C approach, Stalder et al.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e found that plasmids are efficient vectors for horizontal gene transfer and that this method is useful for tracing microorganisms that harbor antibiotic-resistant genes. We identified 12 genes conferring antibiotic resistance, and among these, six genes were found to confer resistance to tetracycline: \u003cem\u003etet32\u003c/em\u003e, \u003cem\u003etet40\u003c/em\u003e, \u003cem\u003etet44\u003c/em\u003e, \u003cem\u003etetO\u003c/em\u003e, \u003cem\u003etetQ\u003c/em\u003e, and \u003cem\u003etetW\u003c/em\u003e. These findings are consistent with a previous study by Jing and Yan\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, in which tetracycline resistance genes (\u003cem\u003etet44\u003c/em\u003e, \u003cem\u003etetQ\u003c/em\u003e, and \u003cem\u003etetW\u003c/em\u003e) were the most prevalent in rumen content samples and were the most abundant in the genome of \u003cem\u003ePrevotellaceae\u003c/em\u003e, but \u003cem\u003eLachnospiraceae\u003c/em\u003e were also present. Furthermore, \u003cem\u003etetQ\u003c/em\u003e, \u003cem\u003etetW\u003c/em\u003e, and \u003cem\u003etet40\u003c/em\u003e were also detected in most samples derived from the rumen of 48 beef cattle from three different taurine breeds\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the genes that confer resistance to tetracycline, other classes of antibiotics, such as nitroimidazole \u003cem\u003enimJ\u003c/em\u003e, macrolide \u003cem\u003emefA\u003c/em\u003e, lincosamide \u003cem\u003elnuC\u003c/em\u003e, beta-lactam \u003cem\u003eblaACI-1\u003c/em\u003e, and aminoglycoside \u003cem\u003eaadE\u003c/em\u003e, were also identified. Genes that confer resistance to macrolides, lincosamide, and aminoglycosides were the most prevalent among ARGs in the study conducted by Ma et al.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, with the \u003cem\u003emefA\u003c/em\u003e gene being one of the most common. Genes that confer resistance to macrolides, lincosamide, and aminoglycosides were the most prevalent among ARGs in the study conducted by Ma et al.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, with the \u003cem\u003emefA\u003c/em\u003e gene being one of the most common. Additionally, ARGs associated with plasmids were identified, indicating that plasmids should be the focus of future investigations on antimicrobial resistance in livestock. Auffret et al.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e also found an increase in ARGs in rumen content samples from cattle fed diets with higher concentrate content, with an abundance of genes related to resistance to macrolides and beta-lactams, suggesting that this increase is associated with dysbiosis caused by more concentrated diets. The findings of this study are in agreement with those of an earlier \u003cem\u003ein silico\u003c/em\u003e study conducted by Sabino et al.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, who analyzed the rumen resistome in 435 microbial genomes and found a high prevalence of antibiotic resistance genes (ARGs) that provide resistance to β-lactams, glycopeptides, tetracyclines, and aminoglycosides. Additionally, recent metagenomic studies have indicated that bovine rumen is a significant contributor to antibiotic resistance\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, advancements in bioinformatics have contributed to a deeper understanding and better characterization of the rumen virome. Yan et al.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e identified 397,180 viral operational taxonomic units (vOTUs) from 975 metagenomes of 13 ruminant species, with the majority of metagenomes derived from taurine animals. Only 23 metagenomes were from \u003cem\u003eBos indicus\u003c/em\u003e raised in Kenya, and none were from Brazil, supporting the significance of this research. The viral metagenomic assemblages discovered in this study were mostly assigned to unclassified genomes, which is probably because of the limited rumen virome of \u003cem\u003eBos indicus\u003c/em\u003e data in the ICTV phage database. This finding is consistent with those of Sato et al.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, who observed low numbers of viral operational taxonomic units shared with the RefSeq database for samples from the rumen of Japanese cattle. The assigned vMAGs belong to the families \u003cem\u003eMyoviridae\u003c/em\u003e and \u003cem\u003eSiphoviridae\u003c/em\u003e, which are commonly found in the rumen environment and constitute the most abundant group, along with \u003cem\u003eMimiviridae\u003c/em\u003e and \u003cem\u003ePodoviridae\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA group of bacteria classified as \u003cem\u003eUBA2868 sp003535955\u003c/em\u003e (unclassified \u003cem\u003eLachnospiraceae bacterium\u003c/em\u003e, NCBI) was found to have the highest percentage of complete vMAGs (100%) and was characterized as high and medium quality. This genus was previously identified in a community of uncultured microorganisms from the intestines of pigs through sequencing of fecal samples\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Bacteria from the unclassified family \u003cem\u003eLachnospiraceae\u003c/em\u003e are prevalent in the ruminal environment and serve as the central microbiome in cattle\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This allowed us to infer that population-associated viruses play a crucial role in the bovine rumen, rather than populations present in low abundance, which remains a challenge\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFriedersdorff et al.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e successfully isolated and sequenced active lytic phages belonging to the \u003cem\u003eSiphoviridae\u003c/em\u003e family that infect \u003cem\u003eButyrivibrio fibrisolvens\u003c/em\u003e, which was found in both the rumen and feces of cattle and sheep. We discovered a lytic phage in \u003cem\u003eButyrivibrio sp017620235\u003c/em\u003e, which is commonly found in the rumen and promotes the degradation of lignocellulose and fermented carbohydrates into butyrate, formate, lactate, and acetate\u003csup\u003e\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Two bacterial species classified as Rumen Uncultured Genomes (RUGs), (bin_16) \u003cem\u003eRUG12461 sp016286115\u003c/em\u003e and (bin_59) \u003cem\u003eRUG191 sp002373675\u003c/em\u003e, were also associated with viruses having a lytic life cycle.\u003c/p\u003e \u003cp\u003eThe study's assembly of 107 bacterial genomes achieved a high completeness (\u0026gt;\u0026thinsp;95%) for members of the \u003cem\u003eTreponema\u003c/em\u003e genus, which are known as spirochetes and have been positively correlated with feed conversion in cattle\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These species are considered to be one of the main fiber degraders in the rumen of Gir cattle, along with other bacteria, such as \u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eEubacterium\u003c/em\u003e, \u003cem\u003eButyrivibrio\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eCaldicellulosiruptor\u003c/em\u003e, and \u003cem\u003eRhodospirillum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The bins identified were primarily assigned to families, such as \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003eP3\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eSaccharofermentanaceae\u003c/em\u003e, and \u003cem\u003eTreponemataceae\u003c/em\u003e. These bins mainly encoded pathways related to central carbon metabolism and metabolism of various carbohydrates. One notable pathway found between these bins is the phosphate acetyltransferase-acetate kinase pathway, which is activated at high acetate concentrations, requires less ATP, and plays a role in energy metabolism \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Additionally, the aromatic amino acid metabolism pathway with tryptophan biosynthesis was abundant among the bins in the current study. This pathway occurs only in microorganisms and plants, and tryptophan is obtained by animals via symbiosis for protein synthesis\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe identification of CAZymes in the bacterial genomes revealed the presence of genes encoding glycoside hydrolases and polysaccharide lyase groups, with GH2, GH3, GH5, GH13, and GH43 being the most prevalent. These groups degrade xylan polysaccharides, which are heterogeneous and require different catalytic enzymes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In a study by Wang et al.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, the same GH families, GH2, GH3, GH13, and GH43, were found to comprise the majority of CAZymes present in the rumen of dairy cows fed different proportions of roughage and concentrate. The GH3 group was particularly more abundant in the high-roughage diet, and these observations are consistent with the current study's findings for pasture-fed Nellore cows, where glycoside hydrolase groups were the most prevalent.\u003c/p\u003e \u003cp\u003eIn summary, this study provides a thorough examination of the MGEs in the rumen of Nellore cattle, shedding new light on the connection between these elements and their microbial inhabitants. The impact and function of these MGEs in beef cattle are currently being investigated by our research group.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAnimal characterization and sample collection\u003c/h2\u003e\n \u003cp\u003eSamples of ruminal content were collected from four Nellore (\u003cem\u003eBos taurus indicus\u003c/em\u003e) nulliparous cows cannulated in the rumen, with an average weight of (600\u0026thinsp;\u0026plusmn;\u0026thinsp;50kg) and age between 9 and 6 years of the Ruminant Nutrition Laboratory, Faculty of Veterinary Medicine and Animal Science, FMVZ, Pirassununga \u003cem\u003ecampus\u003c/em\u003e. The experimental protocols were approved by Ethics Committee on the Use of Animals of Faculty of Veterinary Medicine and Animal Science (CEUA/FMVZ) under number 7333211118. These protocols adhere to both their guidelines and the ARRIVE guidelines. The animals received a diet exclusively based on pasture and mineral salts. Samples were kept on ice during collection until processing and were subsequently stored at -80\u0026deg;C.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDNA extraction and sample preparation for Hi-C\u003c/h2\u003e\n \u003cp\u003eDNA extraction was performed using a QIAmp\u0026reg; Fast DNA Stool Mini Kit (Qiagen, USA). The crosslinking procedure was performed, in which the samples were processed according to the protocol recommended by the company Phase Genomics (Seattle, WA, USA) adapted by Burton et al.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Briefly, the sample containing liquid and fiber (5 mL) were suspended in 1% formaldehyde (45 ml) and incubated (20 min) at room temperature with periodic shaking. Subsequently, glycine (1 g/100 mL) was added, which was then incubated (15 min) at room temperature with periodic shaking. The sample was centrifuged (1000 \u0026times; g for 1 min) and washed with PBS. It was then centrifuged again and the supernatant was removed. The pellets obtained formed a pool and were transferred to a cryotube (2 mL), stored at -80\u0026deg;C until sent along with the DNA extracted from the same sample, to be processed and sequenced by Phase Genomics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eHi-C library preparation and short-read sequencing\u003c/h2\u003e\n \u003cp\u003eTo prepare Hi-C libraries, the Phase Genomics ProxiMeta Hi-C kit v4.0 was used as described by the manufacturer\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. After going through the crosslinking process, the pellets underwent lysis and their DNA-protein complexes were digested by the restriction enzymes (Sau3AI and MlucI)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, creating free ends on the DNA strands that had labeled nucleotides with biotin to create chimeric molecules composed of fragments from different physically close genome regions \u003cem\u003ein vivo\u003c/em\u003e. Proximity-linked DNA molecules were drawn down using streptavidin beads and processed into an Illumina-compatible sequencing library.\u003c/p\u003e\n \u003cp\u003eSeparately, DNA was extracted from an aliquot of the original sample, and a shotgun metagenomic library was prepared using ProxiMeta library preparation reagents. Sequencing was performed using Illumina NovaSeq, generating pairs of PE150 reads for the Hi-C and shotgun libraries. Hi-C and shotgun metagenomic sequencing files were uploaded to the Phase Genomics cloud-based bioinformatics portal for subsequent analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eClustering, quality assessment, taxonomic and functional assignment of assembled microorganisms\u003c/h2\u003e\n \u003cp\u003eShotgun-derived reads were filtered and adjusted for quality, normalized using fastp\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, and assembled with MEGAHIT\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e using default options. Pairs of Hi-C reads were aligned to each assembly using BWA-MEM\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e with the \u0026minus;\u0026thinsp;5SP options specified and all other default options to disable attempts to pair the reads according to the normal Illumina settings. The SAMBLASTER program\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e was used to flag PCR duplicates that were subsequently excluded from the analysis. The alignments were then filtered with SAMtools\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e using the -F 2304 filter flag to remove nonprimary and secondary alignments.\u003c/p\u003e\n \u003cp\u003eDeconvolution, a method that uses the intracellular proximity signal captured by Hi-C as an indicator of the cellular origin of metagenome sequences, was performed using ProxiMeta\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e to generate putative genomes and clusters of genome fragments. Prokaryotic clusters were assessed for quality using CheckM\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and received preliminary taxonomic classifications using Mash, a generalized and automated method that identifies reference genomes compatible with the NCBI RefSeq database\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e (Supplementary Table 1). Subsequently, the bins were submitted to the Genome Taxonomy Database (GTDB)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e for taxonomic assignment (Supplementary Table 2).\u003c/p\u003e\n \u003cp\u003eThe functional potential of the bins was evaluated by METABOLIC (METabolic and BiogeOchemistry analyses in microCrobes)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, which detected the presence or absence of KEGG modules for the most abundant central pathways present in the genome of the microorganisms evaluated, as well as how they attributed the CAZymes and the main biogeochemical cycles present.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eMobile genetic elements\u003c/h2\u003e\n \u003cp\u003eViral contigs were annotated and used for lifestyle and viral protein annotation per contig using VIBRANT\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The contigs were grouped into assembled genomes using ProxiPhage viral binning. Sequence integrity was validated with CheckV, viral genomes were classified into quality levels based on AAI or HMM estimation, high confidence (\u0026ge;\u0026thinsp;90% complete), medium confidence (80\u0026ndash;90% complete) or low confidence (\u0026lt;\u0026thinsp;80% complete)\u003csup\u003e22,62\u003c/sup\u003e. For taxonomic assignment, the protein sequences of the viral clusters (vMAGs) were predicted using Prodigal\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e, and these sequences were used to build protein cluster profiles that were compared with sequences present in the ICTV phage database, generating networks of similarities between viral clusters with the using vCONTACT\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eTo identify sequences with plasmid characteristics, a BLAST (Basic Local Alignment Search Tool) alignment was performed against the NCBI plasmid database\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Sequences annotated as plasmids were associated with the host using a host-finding algorithm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Microbial resistance genes, in turn, were identified with AMRFinderPlus, which allows the assessment of Antimicrobial Resistance gene content in the NCBI database\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eAssignment of mobile genetic elements to their hosts\u003c/h2\u003e\n \u003cp\u003eThe step of assigning mobile genetic elements to their hosts was composed of several filtering criteria, with the first phase of filtering restricting connections with less than 2 Hi-C read links. To remove false positives, a connectivity rate (\u0026ge;\u0026thinsp;0.1) was used for the number of copies of mobile elements per cell (for repeated vMAGs the highest number of counts per cell was considered) and host intra-MAG connectivity (\u0026ge;\u0026thinsp;10links). Finally, a receiver operating characteristic (ROC) curve was performed, which determines the ideal copy count cutoff value (\u0026ge;\u0026thinsp;0.1). This value is expected to be close to 1, as values close to or above 1 are considered reliable for attributing a real host\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In cases where the optimal copy count threshold is excessively low (\u0026lt;\u0026thinsp;0.1), it is replaced with 0.1. This occurred in the present study, where the calculated value was 0.01.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated from the present study will be available in the Sequence Read Archive (SRA) database https://dataview.ncbi.nlm.nih.gov/object/PRJNA1054691?reviewer=vd7njv2i8dqvoj4d6tmt82kr1t\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the funding agencies that facilitated the realization of this research. The Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior - Brasil (CAPES) - Finance Code 001, Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de S\u0026atilde;o Paulo (FAPESP) grant 2022/05541-9 and the Funda\u0026ccedil;\u0026atilde;o de Estudos Agr\u0026aacute;rios Luiz de Queiroz (FEALQ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.A.F collected the samples, performed the laboratory procedures until sending them to Phase Genomics for sequencing, analyzed the data obtained, compiled the results and wrote the initial manuscript. A.T.N. collaborated with sampling and writing the manuscript. M.D.P. contributed to data analysis and preparation of figures. E.C.M.O., O.S.G. and P.A.A. contributed with bioinformatics analyses. F.P.J. and P.H.M.R collected the samples and provided the animal data. HF was the project supervisor and reviewed the initial manuscript. All authors reviewed the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGonz\u0026aacute;lez-Recio, O. \u003cem\u003eet al.\u003c/em\u003e Invited review: Novel methods and perspectives for modulating the rumen microbiome through selective breeding as a means to improve complex traits: Implications for methane emissions in cattle. \u003cem\u003eLivest Sci\u003c/em\u003e \u003cstrong\u003e269\u003c/strong\u003e, 105171 (2023).\u003c/li\u003e\n\u003cli\u003eNkrumah, J. D. \u003cem\u003eet al.\u003c/em\u003e Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. \u003cem\u003eJ Anim Sci\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 145\u0026ndash;153 (2006).\u003c/li\u003e\n\u003cli\u003eAuffret, M. D. \u003cem\u003eet al.\u003c/em\u003e Identification of Microbial Genetic Capacities and Potential Mechanisms Within the Rumen Microbiome Explaining Differences in Beef Cattle Feed Efficiency. \u003cem\u003eFront Microbiol\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1229 (2020).\u003c/li\u003e\n\u003cli\u003eMcGovern, E. \u003cem\u003eet al.\u003c/em\u003e Investigation into the effect of divergent feed efficiency phenotype on the bovine rumen microbiota across diet and breed. \u003cem\u003eScientific Reports 2020 10:1\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1\u0026ndash;11 (2020).\u003c/li\u003e\n\u003cli\u003eLiu, Y. \u003cem\u003eet al.\u003c/em\u003e Rumen Microbiome and Metabolome of High and Low Residual Feed Intake Angus Heifers. \u003cem\u003eFront Vet Sci\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 812861 (2022).\u003c/li\u003e\n\u003cli\u003eRoehe, R. \u003cem\u003eet al.\u003c/em\u003e Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance. \u003cem\u003ePLoS Genet\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, e1005846 (2016).\u003c/li\u003e\n\u003cli\u003eZhang, Q. \u003cem\u003eet al.\u003c/em\u003e Bayesian modeling reveals host genetics associated with rumen microbiota jointly influence methane emission in dairy cows. \u003cem\u003eISME J\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 2019\u0026ndash;2033 (2020).\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-\u0026Aacute;lvaro, M. \u003cem\u003eet al.\u003c/em\u003e Bovine host genome acts on rumen microbiome function linked to methane emissions. \u003cem\u003eCommunications Biology 2022 5:1\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1\u0026ndash;16 (2022).\u003c/li\u003e\n\u003cli\u003eStewart, R. D. \u003cem\u003eet al.\u003c/em\u003e Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. \u003cem\u003eNature Biotechnology 2019 37:8\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 953\u0026ndash;961 (2019).\u003c/li\u003e\n\u003cli\u003eGharechahi, J. \u003cem\u003eet al.\u003c/em\u003e Lignocellulose degradation by rumen bacterial communities: New insights from metagenome analyses. \u003cem\u003eEnviron Res\u003c/em\u003e \u003cstrong\u003e229\u003c/strong\u003e, 115925 (2023).\u003c/li\u003e\n\u003cli\u003eLuo, X. Q. \u003cem\u003eet al.\u003c/em\u003e Viral community-wide auxiliary metabolic genes differ by lifestyles, habitats, and hosts. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1\u0026ndash;18 (2022).\u003c/li\u003e\n\u003cli\u003eAnderson, C. L., Sullivan, M. B. \u0026amp; Fernando, S. C. Dietary energy drives the dynamic response of bovine rumen viral communities. \u003cem\u003eMicrobiome 2017 5:1\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1\u0026ndash;19 (2017).\u003c/li\u003e\n\u003cli\u003eKav, A. B. \u003cem\u003eet al.\u003c/em\u003e Insights into the bovine rumen plasmidome. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 5452\u0026ndash;5457 (2012).\u003c/li\u003e\n\u003cli\u003eLobo, R. R. \u0026amp; Faciola, A. P. Ruminal Phages \u0026ndash; A Review. \u003cem\u003eFront Microbiol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 763416 (2021).\u003c/li\u003e\n\u003cli\u003eSolden, L. M. \u003cem\u003eet al.\u003c/em\u003e Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. \u003cem\u003eNature Microbiology 2018 3:11\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, 1274\u0026ndash;1284 (2018).\u003c/li\u003e\n\u003cli\u003eOrpin, C. G. \u0026amp; Munn, E. A. The occurrence of bacteriophages in the rumen and their influence on rumen bacterial populations. \u003cem\u003eExperientia\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 1018\u0026ndash;1020 (1974).\u003c/li\u003e\n\u003cli\u003eAltermann, E., Schofield, L. R., Ronimus, R. S., Beatty, A. K. \u0026amp; Reilly, K. Inhibition of Rumen methanogens by a novel archaeal lytic enzyme displayed on tailored bionanoparticles. \u003cem\u003eFront Microbiol\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 2378 (2018).\u003c/li\u003e\n\u003cli\u003ePark, S. Y. \u003cem\u003eet al.\u003c/em\u003e Characterization of two lytic bacteriophages, infecting Streptococcus bovis/equinus complex (SBSEC) from Korean ruminant. \u003cem\u003eScientific Reports 2023 13:1\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1\u0026ndash;16 (2023).\u003c/li\u003e\n\u003cli\u003eRoss, E. M., Petrovski, S., Moate, P. J. \u0026amp; Hayes, B. J. Metagenomics of rumen bacteriophage from thirteen lactating dairy cattle. \u003cem\u003eBMC Microbiol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1\u0026ndash;11 (2013).\u003c/li\u003e\n\u003cli\u003eBickhart, D. M. \u003cem\u003eet al.\u003c/em\u003e Assignment of virus and antimicrobial resistance genes to microbial hosts in a complex microbial community by combined long-read assembly and proximity ligation. \u003cem\u003eGenome Biology 2019 20:1\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1\u0026ndash;18 (2019).\u003c/li\u003e\n\u003cli\u003eMarbouty, M., Baudry, L., Cournac, A. \u0026amp; Koszul, R. Scaffolding bacterial genomes and probing host-virus interactions in gut microbiome by proximity ligation (chromosome capture) assay. \u003cem\u003eSci Adv\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e, (2017).\u003c/li\u003e\n\u003cli\u003eUritskiy, G. \u003cem\u003eet al.\u003c/em\u003e Accurate viral genome reconstruction and host assignment with proximity-ligation sequencing. \u003cem\u003ebioRxiv\u003c/em\u003e 2021.06.14.448389 (2021) doi:10.1101/2021.06.14.448389.\u003c/li\u003e\n\u003cli\u003eCusc\u0026oacute;, A., P\u0026eacute;rez, D., Vi\u0026ntilde;es, J., F\u0026agrave;bregas, N. \u0026amp; Francino, O. Novel canine high-quality metagenome-assembled genomes, prophages and host-associated plasmids provided by long-read metagenomics together with Hi-C proximity ligation. \u003cem\u003eMicrob Genom\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 802 (2022).\u003c/li\u003e\n\u003cli\u003eBraun, V. \u0026amp; Killmann, H. Bacterial solutions to the iron-supply problem. \u003cem\u003eTrends Biochem Sci\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 104\u0026ndash;109 (1999).\u003c/li\u003e\n\u003cli\u003eCanfield, D. E., Kristensen, E. \u0026amp; Thamdrup, B. The Iron and Manganese Cycles. \u003cem\u003eAdv Mar Biol\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 269\u0026ndash;312 (2005).\u003c/li\u003e\n\u003cli\u003eBohra, V., Dafale, N. A. \u0026amp; Purohit, H. J. Understanding the alteration in rumen microbiome and CAZymes profile with diet and host through comparative metagenomic approach. \u003cem\u003eArch Microbiol\u003c/em\u003e \u003cstrong\u003e201\u003c/strong\u003e, 1385\u0026ndash;1397 (2019).\u003c/li\u003e\n\u003cli\u003eStewart, R. D. \u003cem\u003eet al.\u003c/em\u003e Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 870\u0026ndash;870 (2018).\u003c/li\u003e\n\u003cli\u003eKrupovic, M. \u003cem\u003eet al.\u003c/em\u003e Bacterial Viruses Subcommittee and Archaeal Viruses Subcommittee of the ICTV: update of taxonomy changes in 2021. \u003cem\u003eArch Virol\u003c/em\u003e \u003cstrong\u003e166\u003c/strong\u003e, 3239\u0026ndash;3244 (2021).\u003c/li\u003e\n\u003cli\u003eHua, D., Hendriks, W. H., Xiong, B. \u0026amp; Pellikaan, W. F. Starch and Cellulose Degradation in the Rumen and Applications of Metagenomics on Ruminal Microorganisms. \u003cem\u003eAnimals 2022, Vol. 12, Page 3020\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 3020 (2022).\u003c/li\u003e\n\u003cli\u003eWallace, R. J. \u003cem\u003eet al.\u003c/em\u003e The rumen microbial metagenome associated with high methane production in cattle. \u003cem\u003eBMC Genomics 2015 16:1\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1\u0026ndash;14 (2015).\u003c/li\u003e\n\u003cli\u003eMO, P. \u003cem\u003eet al.\u003c/em\u003e Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions. (2017) doi:10.1101/198713.\u003c/li\u003e\n\u003cli\u003eStalder, T., Press, M. O., Sullivan, S., Liachko, I. \u0026amp; Top, E. M. Linking the resistome and plasmidome to the microbiome. \u003cem\u003eThe ISME Journal 2019 13:10\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 2437\u0026ndash;2446 (2019).\u003c/li\u003e\n\u003cli\u003eJing, R. \u0026amp; Yan, Y. Metagenomic analysis reveals antibiotic resistance genes in the bovine rumen. \u003cem\u003eMicrob Pathog\u003c/em\u003e \u003cstrong\u003e149\u003c/strong\u003e, 104350 (2020).\u003c/li\u003e\n\u003cli\u003eMa, T. \u003cem\u003eet al.\u003c/em\u003e Expressions of resistome is linked to the key functions and stability of active rumen microbiome. \u003cem\u003eAnim Microbiome\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1\u0026ndash;17 (2022).\u003c/li\u003e\n\u003cli\u003eAuffret, M. D. \u003cem\u003eet al.\u003c/em\u003e The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1\u0026ndash;11 (2017).\u003c/li\u003e\n\u003cli\u003eSabino, Y. N. V. \u003cem\u003eet al.\u003c/em\u003e Characterization of antibiotic resistance genes in the species of the rumen microbiota. \u003cem\u003eNature Communications 2019 10:1\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1\u0026ndash;11 (2019).\u003c/li\u003e\n\u003cli\u003eYan, M. \u003cem\u003eet al.\u003c/em\u003e Interrogating the viral dark matter of the rumen ecosystem with a global virome database. \u003cem\u003eNature Communications 2023 14:1\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1\u0026ndash;16 (2023).\u003c/li\u003e\n\u003cli\u003eSato, Y. \u003cem\u003eet al.\u003c/em\u003e Characteristics of the rumen virome in Japanese cattle. \u003cem\u003ebioRxiv\u003c/em\u003e 2023.03.20.532305 (2023) doi:10.1101/2023.03.20.532305.\u003c/li\u003e\n\u003cli\u003eBerg Miller, M. E. \u003cem\u003eet al.\u003c/em\u003e Phage-bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome. \u003cem\u003eEnviron Microbiol\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 207\u0026ndash;227 (2012).\u003c/li\u003e\n\u003cli\u003eZhou, S. \u003cem\u003eet al.\u003c/em\u003e Characterization of Metagenome-Assembled Genomes and Carbohydrate-Degrading Genes in the Gut Microbiota of Tibetan Pig. \u003cem\u003eFront Microbiol\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 595066 (2020).\u003c/li\u003e\n\u003cli\u003eCrossfield, M. \u003cem\u003eet al.\u003c/em\u003e Archaeal and Bacterial Metagenome-Assembled Genome Sequences Derived from Pig Feces. \u003cem\u003eMicrobiol Resour Announc\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eFriedersdorff, J. C. A. \u003cem\u003eet al.\u003c/em\u003e The Isolation and Genome Sequencing of Five Novel Bacteriophages From the Rumen Active Against Butyrivibrio fibrisolvens. \u003cem\u003eFront Microbiol\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 522243 (2020).\u003c/li\u003e\n\u003cli\u003eKopečn\u0026yacute;, J., Zorec, M., Mr\u0026aacute;zek, J., Kobayashi, Y. \u0026amp; Marin\u0026scaron;ek-Logar, R. Butyrivibrio hungatei sp. nov. and Pseudobutyrivibrio xylanivorans sp. nov., butyrate-producing bacteria from the rumen. \u003cem\u003eInt J Syst Evol Microbiol\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 201\u0026ndash;209 (2003).\u003c/li\u003e\n\u003cli\u003eMoon, C. D. \u003cem\u003eet al.\u003c/em\u003e Reclassification of Clostridium proteoclasticum as Butyrivibrio proteoclasticus comb. nov., a butyrate-producing ruminal bacterium. \u003cem\u003eInt J Syst Evol Microbiol\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 2041\u0026ndash;2045 (2008).\u003c/li\u003e\n\u003cli\u003ePalevich, N. \u003cem\u003eet al.\u003c/em\u003e Complete Genome Sequence of the Polysaccharide-Degrading Rumen Bacterium Pseudobutyrivibrio xylanivorans MA3014 Reveals an Incomplete Glycolytic Pathway. \u003cem\u003eGenome Biol. Evol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1566\u0026ndash;1572 (2020).\u003c/li\u003e\n\u003cli\u003ePandit, R. J. \u003cem\u003eet al.\u003c/em\u003e Microbiota composition, gene pool and its expression in Gir cattle (Bos indicus) rumen under different forage diets using metagenomic and metatranscriptomic approaches. \u003cem\u003eSyst Appl Microbiol\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 374\u0026ndash;385 (2018).\u003c/li\u003e\n\u003cli\u003eZhang, S. \u003cem\u003eet al.\u003c/em\u003e Metabolic engineering for efficient supply of acetyl-CoA from different carbon sources in Escherichia coli. \u003cem\u003eMicrob Cell Fact\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1\u0026ndash;11 (2019).\u003c/li\u003e\n\u003cli\u003eParthasarathy, A. \u003cem\u003eet al.\u003c/em\u003e A Three-Ring circus: Metabolism of the three proteogenic aromatic amino acids and their role in the health of plants and animals. \u003cem\u003eFront Mol Biosci\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 342220 (2018).\u003c/li\u003e\n\u003cli\u003eWang, L., Zhang, G., Xu, H., Xin, H. \u0026amp; Zhang, Y. Metagenomic analyses of microbial and carbohydrate-active enzymes in the rumen of holstein cows fed different forage-to-concentrate ratios. \u003cem\u003eFront Microbiol\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 441658 (2019).\u003c/li\u003e\n\u003cli\u003eBurton, J. N., Liachko, I., Dunham, M. J. \u0026amp; Shendure, J. Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps. \u003cem\u003eG3: Genes, Genomes, Genetics\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 1339\u0026ndash;1346 (2014).\u003c/li\u003e\n\u003cli\u003eLieberman-Aiden, E. \u003cem\u003eet al.\u003c/em\u003e Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome. \u003cem\u003eScience (1979)\u003c/em\u003e \u003cstrong\u003e326\u003c/strong\u003e, 289\u0026ndash;293 (2009).\u003c/li\u003e\n\u003cli\u003eChen, S., Zhou, Y., Chen, Y. \u0026amp; Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, i884\u0026ndash;i890 (2018).\u003c/li\u003e\n\u003cli\u003eLi, D. \u003cem\u003eet al.\u003c/em\u003e MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. \u003cem\u003eMethods\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 3\u0026ndash;11 (2016).\u003c/li\u003e\n\u003cli\u003eLi, H. \u0026amp; Durbin, R. Fast and accurate long-read alignment with Burrows\u0026ndash;Wheeler transform. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 589\u0026ndash;595 (2010).\u003c/li\u003e\n\u003cli\u003eFaust, G. G. \u0026amp; Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 2503\u0026ndash;2505 (2014).\u003c/li\u003e\n\u003cli\u003eLi, H. \u003cem\u003eet al.\u003c/em\u003e The Sequence Alignment/Map format and SAMtools. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 2078 (2009).\u003c/li\u003e\n\u003cli\u003eParks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. \u0026amp; Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. \u003cem\u003eGenome Res\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 1043 (2015).\u003c/li\u003e\n\u003cli\u003eOndov, B. D. \u003cem\u003eet al.\u003c/em\u003e Mash Screen: High-throughput sequence containment estimation for genome discovery. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 1\u0026ndash;13 (2019).\u003c/li\u003e\n\u003cli\u003eParks, D. H. \u003cem\u003eet al.\u003c/em\u003e A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. \u003cem\u003eNature Biotechnology 2018 36:10\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 996\u0026ndash;1004 (2018).\u003c/li\u003e\n\u003cli\u003eZhou, Z. \u003cem\u003eet al.\u003c/em\u003e METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 1\u0026ndash;22 (2022).\u003c/li\u003e\n\u003cli\u003eKieft, K., Zhou, Z. \u0026amp; Anantharaman, K. VIBRANT: Automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. \u003cem\u003eMicrobiome\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1\u0026ndash;23 (2020).\u003c/li\u003e\n\u003cli\u003eNayfach, S. \u003cem\u003eet al.\u003c/em\u003e CheckV assesses the quality and completeness of metagenome-assembled viral genomes. \u003cem\u003eNature Biotechnology 2020 39:5\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 578\u0026ndash;585 (2020).\u003c/li\u003e\n\u003cli\u003eHyatt, D. \u003cem\u003eet al.\u003c/em\u003e Prodigal: Prokaryotic gene recognition and translation initiation site identification. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1\u0026ndash;11 (2010).\u003c/li\u003e\n\u003cli\u003eBolduc, B. \u003cem\u003eet al.\u003c/em\u003e vConTACT: An iVirus tool to classify double-stranded DNA viruses that infect Archaea and Bacteria. \u003cem\u003ePeerJ\u003c/em\u003e \u003cstrong\u003e2017\u003c/strong\u003e, e3243 (2017).\u003c/li\u003e\n\u003cli\u003eFeldgarden, M. \u003cem\u003eet al.\u003c/em\u003e AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. \u003cem\u003eScientific Reports 2021 11:1\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1\u0026ndash;9 (2021).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3749940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3749940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetagenomics has made it feasible to elucidate the intricacies of the ruminal microbiome and its role in the differentiation of animal production phenotypes of significance. The search for mobile genetic elements (MGEs) has taken on great importance, as they play a critical role in the transfer of genetic material between organisms. Furthermore, these elements serve a dual purpose by controlling populations through lytic bacteriophages, thereby maintaining ecological equilibrium and driving the evolutionary progress of host microorganisms. In this study, we aimed to identify the association between ruminal bacteria and their MGEs in Nellore cattle using physical chromosomal links through the Hi-C method. Shotgun metagenomic sequencing and the proximity ligation method ProxiMeta\u0026trade; were used to analyze DNA, getting 1,713,111,307 bp, which gave rise to 107 metagenome-assembled genomes from rumen samples of four Nellore cows maintained on pasture. Taxonomic analysis revealed that most of the bacterial genomes belonged to the families \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eBacteroidaceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eSaccharofermentanaceae\u003c/em\u003e, and \u003cem\u003eTreponemataceae\u003c/em\u003e and mostly encoded pathways for central carbon and other carbohydrate metabolisms. A total of 31 associations between host bacteria and MGE were identified, including 17 links to viruses and 14 links to plasmids. Additionally, we found 12 antibiotic resistance genes. To our knowledge, this is the first study in Brazilian cattle that connect MGEs with their microbial hosts. It identifies MGEs present in the rumen of pasture-raised Nellore cattle, offering insights that could advance biotechnology for food digestion and improve ruminant performance in production systems.\u003c/p\u003e","manuscriptTitle":"Identifying Mobile Genetic Elements in the Ruminal Microbiome of Nellore Cattle: An Initial Investigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-02 07:17:07","doi":"10.21203/rs.3.rs-3749940/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-23T13:42:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-19T06:42:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23cccc9e-eceb-4350-a160-523e0ae2285b","date":"2024-04-04T18:14:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-26T12:01:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c7ee7a5e-8ac3-4b80-b5fd-a9eee93ec1f7","date":"2024-02-08T17:05:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-06T11:37:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-29T09:17:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-12-28T19:58:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-28T19:51:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-12-13T18:59:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d912251a-4bcc-47eb-8714-ceae88b250ab","owner":[],"postedDate":"January 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":27882312,"name":"Biological sciences/Microbiology/Bacteriophages"},{"id":27882313,"name":"Biological sciences/Genetics/Microbial genetics"}],"tags":[],"updatedAt":"2024-06-04T01:44:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-02 07:17:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3749940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3749940","identity":"rs-3749940","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}