Rumen-derived Prevotella and Megasphaera elsdenii mitigate methane production through functional modulation of rumen microbial metabolism

Rumen-derived Prevotella and Megasphaera elsdenii mitigate methane production through functional modulation of rumen microbial metabolism | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rumen-derived Prevotella and Megasphaera elsdenii mitigate methane production through functional modulation of rumen microbial metabolism Banglin He, Min Xia, Xin Wang, Chenlong Ding, Mudasir Nazar, Dingfu Xiao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8723424/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background: Enteric methane production represents a major energy loss in ruminant systems and contributes substantially to agricultural greenhouse gas emissions. Increasing ruminal propionate production has been proposed as an effective strategy to redirect metabolic hydrogen away from methanogenesis. However, the functional mechanisms by which specific rumen bacteria regulate methane production remain incompletely understood Results: In this study, four rumen-derived Prevotella strains and one Megasphaera elsdenii strain were isolated, genomically characterized, and evaluated using an in vitro rumen fermentation model. Supplementation with these strains significantly reduced methane yield while increasing total gas production and volatile fatty acid concentrations, particularly propionate. Shotgun metagenomic analysis revealed that methane mitigation was not associated with major alterations in archaeal abundance, but rather with a pronounced functional suppression of methanogenic pathways. Specifically, the dominant hydrogenotrophic (CO₂-reduction) methanogenesis module was significantly downregulated in all supplemented treatments. Concurrently, pathways and enzymes involved in carbohydrate fermentation and propionate synthesis were enriched, indicating a redirection of metabolic hydrogen toward alternative microbial sinks. Conclusions: These findings demonstrate that rumen-derived Prevotella and Megasphaera elsdenii can reduce methane production primarily through functional regulation of microbial metabolism rather than displacement of methanogens. This study provides mechanistic insights into microbially mediated hydrogen redistribution in the rumen and offers a functional basis for developing future probiotic strategies aimed at improving fermentation efficiency and mitigating enteric methane emissions. Methane Prevotella M. elsdenii Metagenomic sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction As concerns over global warming escalate, how to find measures to reduce greenhouse gas emissions has been highlighted by researchers worldwide. Greenhouse gases mainly include carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O), which significant contribute to rising surface temperatures [1] . Anthropogenic methane accounts for 85% of the global methane gas and agriculture accounts for 40% of global methane emissions [2] . Notably, ruminant farming is the primary source of methane emissions from agricultural production, intestinal fermentation accounts for 60% of methane emissions from agricultural production [3] . Due to the specificity of ruminant rumen fermentation this allows ruminants to utilize cellulose, because of the ruminant microbes can ferment cellulose and so on to produce volatile fatty acids (VFA) [4] . However, rumen microbes produce VFA while fermenting cellulose with a large amount of gas, in which the resulting CO 2 and H 2 are reduced to methane gas by methanogenic archaea, which eventually is expelled by eructation [5] . This way of emitting methane is important for maintaining the stability of the microbiota within the rumen, but the production of methane from the rumen can lead to waste of nutrients and its impact on the greenhouse effect is significantly [6, 7] . Therefore, the use of direct-fed microbials (DFM) becomes a suitable option, as it has no negative effects on the host and the environment, and is acceptable to both consumers and producers [8] . Prevotella , as a core microbe involved in carbohydrate and hydrogen metabolism within the rumen, has an abundance that correlates with rumen health status [9-11] . Furthermore, Prevotella could break down various polysaccharides and possesses the ability to synthesize propionate. Propionate not only serves as the most important substrate for hepatic gluconeogenesis in ruminants, but its formation within the rumen also utilizes hydrogen, thereby influencing rumen methane synthesis [12, 13] . A series of studies have demonstrated a close correlation between the abundance of Prevotella and animals exhibiting low methane emissions [9, 14, 15] . In the rumen, 60%–80% of propionate is primarily produced via the acrylate pathway, and Megasphaera elsdenii , as a major lactate-utilizing bacterium in the rumen, is also considered a key contributor to ruminal propionate production. [16] . Furthermore, Megasphaera elsdenii can ferment both the L- and D-isomers of lactate via racemase and produce propionate through the acrylate pathway [17, 18] . Since Prevotella primarily utilizes the succinate pathway to produce propionate, this pathway directly consumes hydrogen from substrates, thereby reducing methane production in the rumen [19] . Whereas Megasphaera elsdenii mainly employs the acrylate pathway to increase ruminal propionate concentration, effectively blocking the conversion of lactate to hydrogen [18] . Therefore, in our study, we aim to evaluate—using an in vitro fermentation model supplemented with rumen-isolated strains of Prevotella and Megasphaera elsdenii —whether these two bacteria, both capable of elevating ruminal propionate concentration, can reduce methane emissions in ruminants. Furthermore, we will employ metagenomic analysis to assess the impact of supplementing Prevotella and Megasphaera elsdenii on the functionality and composition of the rumen microbiome. Methods Animal feeding management For single bacterial isolation and in vitro fermentation models, rumen fluid was obtained from three lactating Holstein dairy cows serving as donors at Nanjing Weigang Dairy Co., Ltd. (Table S1) shows the composition of the dairy cows diets and also lists the detailed composition and specific nutritional profile of the substrates used in the in vitro experiments. All trials were approved by the Experimental Animal Welfare and Ethics Committee of Jiangsu Academy of Agricultural Sciences (Jiangsu Nanjing). Rumen liquid collection and isolation of rumen bacteria To avoid interference from feed, rumen contents were collected from three lactating dairy cows (611 ± 18.7 kg) using an oral tube after milking and before morning feeding. The contents were placed into a thermos flask, mixed, and transported back to the laboratory. After filtration through four layers of gauze, 1 mL of rumen fluid was aspirated and transferred into an anaerobic workstation (AW400SG, Electrotek, England). Gradient dilutions were performed using sterile physiological saline. Eight dilution gradients were prepared (10⁻¹ to 10⁻⁸). From each dilution, 100 μL was aspirated and spread evenly onto kanamycin-vancomycin laked blood agar (KVLB, Haibo China) solid medium. Each dilution gradient was plated in duplicate. Plates were incubated anaerobically at 37 °C for growth. After 24 hours of incubation, distinct colonies were selected, re-diluted, and repeatedly streaked onto KVLB solid medium for purification. Following three rounds of purification, single bacterial strains were obtained. The bacterial strains ultimately isolated from the lactating cow rumen fluid were designated RH3, RH14, RH19, RH27, and RH35. The physiological and biochemical characteristics of the isolated strains were preliminarily determined using Gram staining and a Gram-negative bacteria identification system (Haibo China). To assess the growth kinetics of the isolated strains, 1 mL of bacterial culture was inoculated into 150 mL of Schaedler broth (Solarbio China). The flask was sealed and transferred from the anaerobic workstation to a shaking incubator. Bacterial culture samples were collected aseptically using a syringe at 2-hour intervals. The optical density at 600 nm (OD₆₀₀) of each sample was measured to plot the growth curve. Bacterial whole-genome analysis Bacterial DNA extraction and 16S rRNA sequencing Bacterial cells (1 mL) at the exponential growth phase were harvested, and genomic DNA was isolated using a commercial bacterial DNA extraction kit (Tiangen, Beijing). The extracted DNA served as the template for amplifying the 16S rRNA gene using PCR with the universal primers 27F (5'-GAGTTTGATCTGGCTCAG-3') and 1492R (5'-ACGGCTACCTTGTTACGACTT-3'). Each 20 μL PCR reaction contained 10 μL of PrimeSTAR Max Premix (2×, Takara Bio), 0.5 μL of each primer, 0.5 μL of genomic DNA, and 8.5 μL of nuclease-free water. PCR products were resolved on a 1% agarose gel, and amplicons showing a single band of the expected size were purified and submitted to Qingke Biotechnology (Nanjing, China) for Sanger sequencing. The obtained sequences were queried against the NCBI nucleotide database using BLAST. Multiple-sequence alignments and phylogenetic tree construction were performed in MEGA v12.0.0. Library construction and NGS sequencing Next-generation sequencing was carried out by Shanghai Biozeron Biotechnology Co., Ltd. (Shanghai, China). For paired-end NGS sequencing of each bacterial strain, at least 1 μg of high-quality genomic DNA was used for library preparation. Paired-end libraries with an approximate insert size of 400 bp were constructed following standard protocols for genomic DNA library generation. Briefly, purified genomic DNA was fragmented to the desired size using a Covaris system, and the resulting fragments were end-repaired with T4 DNA polymerase. An adenine residue was added to the 3′ termini of phosphorylated blunt-end fragments, after which sequencing adapters were ligated. Size-selected fragments were subsequently isolated by gel electrophoresis, followed by selective enrichment and amplification through PCR. Index sequences were introduced into the adapters during the PCR step when required, and library quality was assessed before sequencing. Libraries meeting quality criteria were subjected to paired-end sequencing (150 bp × 2) on the NGS platform at Shanghai Biozeron. Genome assembly Raw paired-end reads were subjected to quality trimming and filtering using Trimmomatic (version 0.36) [20] with parameters (SLIDINGWINDOW:4:15 MINLEN:75). The resulting high-quality reads were used for downstream analysis. De novo genome assembly was performed with ABySS 2.2.0 [21] , employing multiple k-mer values to identify the optimal assembly. Finally, GapCloser [22] was utilized to close residual gaps and correct single-base polymorphisms within the draft assembly. Genome Annotation For prokaryotic strains, gene models for Prevotella and Megasphaera elsdenii were predicted using an ab initio approach with GeneMark 4.17. [23] (http://topaz.gatech.edu/GeneMark/). Predicted genes were then functionally annotated by performing BLASTp searches against multiple databases, including NCBI non-redundant (NR), SwissProt (http://uniprot.org), KEGG (http://www.genome.jp/kegg/), COG (http://www.ncbi.nlm.nih.gov/COG), CAZy (http://www.cazy.org/), CARD (https://card.mcmaster.ca/), PHI, TCDB (http://www.tcdb.org/), VFDB, BacMet, as well as SignalP (http://www.cbs.dtu.dk/services/SignalP/) and TMHMM (http://www.cbs.dtu.dk/services/TMHMM/) for signal peptide and transmembrane domain prediction. In addition, tRNA genes were identified using tRNAscan-SE v2.0.4 [24] , and rRNA genes were detected with RNAmmer v1.2 [25] . Batch culture of rumen in vitro Rumen fluid was collected from the same three lactating dairy cows mentioned previously. The collected rumen fluid was filtered through four layers of cheesecloth. 20 mL of the filtered rumen fluid was mixed with 40 mL of artificial saliva (prepared according to the method described by Menke. [26] ) in a 150 mL anaerobic fermentation bottle. The fermentation substrate consisted of 0.5 g of the dairy farm's Total Mixed Ration (TMR). Prior to fermentation, air within the bottle was displaced by flushing with CO₂. Once air evacuation was complete, the bottle was immediately sealed with a butyl rubber stopper and secured with an aluminum cap. At the start of fermentation, 1 mL of bacteria suspension was added. This suspension contained bacteria pre-revived to the exponential growth phase and diluted to a concentration of 2 × 10¹⁰ CFU/g. 1 mL of sterilized Schaedler broth was added instead to the control group. A pre-evacuated gas collection bag (E-Switch, Shanghai) was attached to the top of the stopper before fermentation began. The mixture was incubated at 39 °C in a constant-temperature shaker under anaerobic conditions. Gas and fermentation broth samples were collected at the 12, 24, 48 and 72 h. Upon completion of each fermentation period, the bottles were immediately removed and placed on ice to halt microbial activity. The pH of the fermentation broth was measured immediately. Each treatment was performed in triplicate. Determination of gas production volume, hydrogen and methane in samples At the end of each fermentation period, the gas collection bag attached to the fermentation bottle was removed, and total gas production was quantified by measuring the displacement of the syringe plunger. A 0.5 mL aliquot of the collected gas was analyzed for hydrogen and methane using a gas chromatograph (GC, model CP-3800, Varian Inc., Palo Alto, CA). Separation was performed on a 13× molecular sieve column (45–60 mesh; 2.0 mm × 3.2 mm × 2.0 mm, stainless steel) with a thermal conductivity detector. Instrument settings were as follows: oven temperature, 60 °C; injector and TCD temperature, 120 °C; flame ionization detector, 200°C. Nitrogen was used as the carrier gas at a flow rate of 50 mL/min. Determination of pH, NH 3 -N, MCP, and VFA Immediately after the completion of fermentation, the pH of the fermentation broth was measured using a pH meter (FE28, Mettler-Toledo, Switzerland). Ammonia nitrogen (NH₃-N) concentrations were determined via the phenol–hypochlorite colorimetric assay [27] . MCP was determined using the purine method. Microbial protein nitrogen (mg/mL) was calculated according to the formula: Microbial Protein Nitrogen (mg/mL) = (Measured RNA (mg/mL) × Nitrogen Content in RNA) / (Nitrogen Content in Bacterial RNA) × Dilution Factor. Subsequently, microbial protein concentration was calculated using the formula: Microbial Protein Concentration (mg/mL) = Microbial Protein Nitrogen (mg/mL) × 6.25. The VFA concentration in the fermentation broth was determined according to the method described by Erwin [28] . Briefly, 1 mL of 25% metaphosphoric acid was added to 5 mL of fermentation broth. After centrifugation (12,000 × g, 20 min, 4 °C), the supernatant was filtered through a 0.22-μm membrane. The filtrate was analyzed using a gas chromatograph (GC-14B, Shimadzu, Japan) equipped with a flame ionization detector (FID). Instrument settings were: column temperature 100 °C, detector temperature 200 °C, and injector temperature 200 °C. DNA extraction, library construction, and metagenomic sequencing Total genomic DNA was extracted from rumen fluid samples using the Mag-Bind® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) following the manufacturer’s instructions. DNA concentration and purity were assessed with TBS-380 and NanoDrop 2000, respectively, and integrity was verified by 1% agarose gel electrophoresis. For library construction, genomic DNA was fragmented to an average size of ~400 bp using a Covaris M220 system (Gene Company Limited, China). Paired-end libraries were prepared using the NEXTFLEX Rapid DNA-Seq kit (Bioo Scientific, Austin, TX, USA), with adapters containing full sequencing primer sites ligated to blunt-ended fragments. Sequencing was performed on an Illumina NovaSeq platform (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using the NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles). Raw paired-end reads were processed on the Majorbio Cloud Platform (www.majorbio.com). Adapter sequences and low-quality reads (length < 50 bp, quality score < 20, or containing N bases) were removed using fastp v0.20.0 [29] . Host-derived reads were filtered by aligning to the bovine reference genome with BWA v0.7.9a [30] . Remaining high-quality reads were assembled de novo using MEGAHIT v1.1.2 [31] , and contigs ≥300 bp were retained for downstream analyses. Open reading frames (ORFs) were predicted from assembled contigs using Prodigal [32] or MetaGene [33] . With ORFs ≥100 bp translated into amino acid sequences based on the NCBI translation table. Redundant sequences were clustered with CD-HIT v4.6.1 [34] at 90% sequence identity and 90% coverage to generate a non-redundant gene catalog. Gene abundance was estimated by mapping reads to the catalog using SOAPaligner v2.21 [35] with 95% identity. Representative sequences from the non-redundant catalog were taxonomically annotated using Diamond v0.8.35 [36] (http://www.diamondsearch.org/index.php) against the NCBI NR database (e-value ≤1e-5). Concurrently, functional annotation was conducted by assigning Cluster of Orthologous Groups (COG) categories through alignment against the eggNOG database. The Kyoto Encyclopedia of Genes and Genome (KEGG) annotation was conducted using Diamond v0.8.35 [36] against the KEGG database (http://www.genome.jp/keeg/) with an e-value cutoff of 1e -5 . The CAZy annotation was performed using hmmscan (http://hmmer.janelia.org/search/hmmscan). The abundances of annotated KEGG Orthologs (KOs), pathways, enzymes, modules, and carbohydrate-active enzyme (CAZymes) were first quantified and normalized as counts per million reads (CPM). For subsequent analysis, we retained only those KEGG modules, pathways, enzymes, and CAZymes that exhibited a CPM value greater than 5 in at least 50% of the animals within any given experimental group. Statistical analysis Gas composition and rumen fermentation parameters were analyzed using one-way ANOVA in SPSS 22.0 (IBM, New York, USA). Differences in rumen microbial communities at the domain, phylum, genus, and species levels were assessed using the Kruskal–Wallis test for multiple comparisons, with P < 0.05 considered statistically significant. Similarly, the abundances of microbial metabolic pathways, modules, KEGG pathways and CAZymes across the six groups were compared using the Kruskal–Wallis test. Spearman rank correlation coefficients between rumen microbial taxa were calculated in SPSS 22.0, with P < 0.05 indicating significance. Results Isolation, Identification, and Genomic Characterization of Rumen Bacteria A total of 5 bacterial strains were isolated from rumen liquid and designated as RH3, RH14, RH19, RH27, and RH35. Phylogenetic analysis based on 16S rRNA sequencing revealed that strains RH3, RH14, RH27, and RH35 were most closely related to Prevotella _ sp ., while RH19 was most closely related to Megasphaera elsdenii strain PC404 (Fig. 1A, B). Colony morphology observations showed that strains RH3, RH14, RH27, and RH35 exhibited circular colonies with smooth surfaces, slightly convex profiles, translucent and pale yellow pigmentation, a viscous consistency, and entire margins. In contrast, strain RH19 formed circular, pale yellow colonies with convex, smooth, opaque surfaces, a viscous consistency, and entire margins (Fig. 1C). Gram staining confirmed all five strains as Gram-negative bacteria, with Prevotella strains predominantly appearing as short rods and M. elsdenii RH19 exhibiting cocci (Fig. 1D). Growth curve determination indicated that strains RH14 and RH35 reached peak OD at 12 h, RH3 and RH27 peaked at 14 h, and RH19 achieved maximum OD at 16 h (Fig 3E). Based on biochemical characterization results (Table S2), Prevotella strains were positive for utilization of ONPG, VP, and various carbohydrates, whereas M. elsdenii RH19 utilized arginine, lysine, ornithine, glucose, maltose, and mannitol. Whole-genome sequencing revealed chromosome lengths of 5,241,519 bp for RH3, 4,637,358 bp for RH14, 2,429,558 bp for RH19, 4,638,824 bp for RH27, and 4,639,038 bp for RH35 (Fig 1E), with corresponding GC contents of 37.49%, 50.48%, 53.26%, 50.76%, and 51.12% (Table S3). A total of 5519, 4514, 2258, 4515, and 4516 coding genes were predicted, respectively (Table S3). Comparative CAZymes profile analysis showed RH3 possessed a higher proportion of Carbohydrate Esterases (CE, 22.60%) compared to RH14/RH27/RH35 (avg. 15.7% CE). M. elsdenii RH19 showed minimal Polysaccharide Lyases (PLs, 0%) but dominant Glycosyl Transferases (GTs, 50.8%) (Fig. S1). KEGG annotation indicated Prevotella strains had a higher number of genes assigned to carbohydrate metabolism (e.g., 452 genes in RH14) compared to RH19 (195 genes), while RH19 had more genes in amino acid metabolism (163 genes) (Fig. 2A). eggNOG annotation showed RH3 possessed a high number of genes for amino acid transport and metabolism (446 genes) (Fig. 2B). In Vitro Rumen Fermentation Characteristics Fig. 3C shows the effect of supplementing Prevotella and Megasphaera elsdenii on gas production over time. At 12 h, all supplemented treatments produced significantly more gas than the NC group ( P < 0.05). Beyond 12 h, cumulative gas production remained significantly higher in the RH3, RH19, and RH27 groups compared to NC, RH14, and RH35 ( P < 0.05). As shown in Fig. 3A, methane production increased linearly from 24 to 48 h, but all bacterial treatments yielded significantly less methane than the control ( P < 0.05). At 48 h, RH14 produced the lowest methane among all groups ( P < 0.05). In contrast, CO₂ levels followed an opposite trend (Fig. 3B), with the control showing significantly lower CO₂ than all bacterial treatments at 48 h ( P < 0.05). Regarding pH (Fig. 3D), all supplemented treatments had significantly lower pH than the control at 12 h ( P < 0.05). At 24 h, only RH35 did not differ from NC, while the rest remained lower. Throughout the incubation, RH3 maintained the lowest pH among all treatments ( P < 0.05). As presented in Table 1, NH₃-N concentration increased from 12 to 48 h in all groups. RH3 consistently had higher NH₃-N than the control at all times ( P < 0.05), while RH14 was higher at 24 h and 72 h ( P < 0.05). RH27 and RH35 initially had lower NH₃-N than the control at 12 h ( P < 0.05), but surpassed it after 48 h. For microbial crude protein (MCP), RH3 showed higher MCP than NC at 12 h and 48 h ( P < 0.05). RH14 had lower MCP than other treatments at 12 h but exceeded the control at 24 h and 48 h ( P < 0.05). Table 2 shows that RH3 had higher total volatile fatty acid (TVFA) than NC throughout fermentation ( P < 0.05). RH19 and RH35 also had higher TVFA than NC at 24 h and 72 h ( P < 0.05). RH14 and RH27 only exceeded the control at 72 h. Acetate content did not differ significantly from NC at 12, 24, or 72 h in any treatment, though RH3, RH14, RH19, and RH35 remained lower throughout. At 48 h, acetate in RH27 was significantly lower than NC ( P < 0.05). Propionate content did not differ from NC except in RH3 at 12 h, where it was higher ( P < 0.05). The acetate-to-propionate ratio was significantly lower in RH3 at 12 h and in RH14 at 48 h compared to other groups ( P < 0.05). Metagenomic analysis of in vitro supplementation with Prevotella and Megasphaera elsdenii Metagenomic sequencing generated a total of 1,302,559,334 reads, with 72,364,407 ± 930,848 reads per sample (mean ± SEM). Furthermore, a total of 1,294,690,664 reads were retained, with 71,927,259 ± 929,658 reads per sample (Table S4). Comparative analysis of the rumen microbiota across six treatments revealed no significant differences at the microbial domain level. As shown in Table S5, Bacteria, Archaea, Eukaryota, and Viruses demonstrated no statistically significant variations among the treatments ( P > 0.05). For bacterial α-diversity, the Shannon index was significantly higher in the RH27 treatment compared to RH19 ( P < 0.05), while the Simpson index was significantly higher in RH19 than in RH27 ( P < 0.05). Additionally, no significant differences were observed in other α-diversity indices among the remaining treatments (Fig 4A). Regarding archaea α-diversity (Fig 4B), no significant differences were observed across any treatments ( P > 0.05). Furthermore, PCoA analysis of bacterial β-diversity revealed significant separation between in vitro supplementation groups of Prevotella and Megasphaera elsdenii based on Bray-Curtis distances. The first principal coordinate (PC1) accounted for 50.01% of variation, while PC2 explained 15.28% (Fig 4C). For archaeal communities, PCoA analysis showed that PC1 and PC2 explained 76.19% and 15.39% of variation, respectively (Fig 4D). Abundance characteristics and taxonomic differences of rumen microbiota For bacterial composition (Fig 4E), predominant phyla included Bacteroidetes (66.58 ± 1.14%), Bacillota (21.64 ± 0.90%), and Pseudomonadota (4.66 ± 0.66%). Dominant genera comprised Prevotella (43.95 ± 0.92%), Candidatus _ Cryptobacteroides (6.62 ± 0.46%), Xylanibacter (2.93 ± 0.14%), Candidatus _ Limimorpha (2.81 ± 0.12%), and Succiniclasticum (2.36 ± 0.31%). Prevalent species featured Prevotella _ sp . (39.97 ± 0.83%), Candidatus _ Cryptobacteroides_sp . (5.36 ± 0.41%), Xylanibacter _ ruminicola (2.77 ± 0.13%), Candidatus _Limimorpha_equi (2.06 ± 0.10%), and Eubacterium _sp. (2.01 ± 0.20%). In the differential abundance analysis of rumen microbiota (Fig 5A) at the phylum level, RH3 and RH19 significantly increased Bacteroidetes abundance compared to the NC treatment ( P < 0.05), while significantly decreasing Bacillota abundance ( P < 0.05). Additionally, RH14 treatment also exhibited significantly lower Bacillota abundance than NC ( P < 0.05). Furthermore, RH14 showed significantly reduced Spirochaetota abundance relative to all other treatments ( P < 0.05). At the genus level, Prevotella abundance was significantly higher in RH3, RH14, and RH19 treatments compared to NC group ( P < 0.05). Additionally, Eubacterium abundance in RH14 and RH19 was significantly lower than in all other treatments ( P < 0.05). For Treponema abundance, all treatments except RH14 showed significantly higher levels than NC ( P < 0.05), while RH14 exhibited significantly reduced abundance ( P < 0.05). Regarding Ruminococcus abundance, only RH19 showed a significant decrease ( P 0.05). Notably, Candidatus _ Cryptobacteroides abundance in RH27 was significantly higher than in all other treatment groups ( P < 0.05). At the species level, RH3, RH14, and RH19 had higher Prevotella _sp. abundance than NC, while RH27 and RH35 had lower ( P < 0.05). The RH19 treatment group demonstrated a significantly higher abundance of M. elsdenii in the fermentation broth compared to other treatments ( P < 0.05, Fig. S3A). The archaeal community was dominated by the phylum Euryarchaeota (91.60 ± 1.06%) and the genus Methanobrevibacter (80.92 ± 2.38%) (Fig 4F). No significant differences were found at the phylum or genus level ( P > 0.05), but a downward trend in the abundance of Euryarchaeota and Methanobrevibacter was observed in the supplemented groups (Fig 5B). Figure S2 presents the abundance profiles and differential analysis of eukaryotic species after supplementation with Prevotella and Megasphaera elsdenii . At the phylum level, Ciliophora was the most abundant group, though no significant differences were observed among treatments ( P > 0.05). The fungal phylum Mucoromycota was the only one significantly reduced following supplementation ( P < 0.05). At the genus level, no significant differences in abundance were detected. Species-level analysis revealed that Blepharisma stoltei was significantly lower in groups RH3 and RH27 compared to NC ( P < 0.05), while other eukaryotic species showed no significant changes. Since enteric methane in dairy cows is primarily produced by archaea, and bacteria—which dominate the rumen microbiota—generate the key substrates for methanogenesis (e.g., H₂, CO₂, volatile fatty acids, and methyl compounds), only Bacteria and Archaea were included in subsequent comparative analyses [37] . Functional Profiling of the Rumen Microbiota Functional profiling of the rumen microbiota was conducted through annotation of metagenomic sequences against KEGG pathways and CAZyme genes. Sequence mapping identified 321 KEGG level‑3 pathways representing core rumen metabolic functions. Among the top 50 differentially abundant pathways (Fig. 5C), methane metabolism was significantly downregulated in all treatments compared to the NC group ( P 0.05), while all other treatments were significantly downregulated ( P < 0.05). Key pathways related to amino acid, carbohydrate, lipid, and energy metabolism were further examined due to their link with enteric methane emissions and energy use (Fig S3C-F). In amino acid metabolism, six pathways showed significant differences ( P < 0.05). For lipid metabolism, only three pathways differed significantly ( P < 0.05). In energy metabolism, solely Carbon fixation by Calvin cycle exhibited significant differential abundance ( P < 0.05). KEGG module analysis (Fig. 5D) revealed that among the top 50 modules. The cobalamin (vitamin B12) biosynthesis module remained unchanged in RH19 but was downregulated in all other treatments (P < 0.05). Additionally, the acetoclastic methanogenesis pathway was significantly downregulated in RH14, RH19, and RH35 ( P < 0.05). However, given the minor role of acetoclastic methanogenesis in the rumen ecosystem, its downregulation, while statistically significant, likely contributes less to the overall methane mitigation [38, 39] . More importantly, differential analysis of other core methanogenic modules showed that the Carbon dioxide reduction to methane module—the dominant hydrogenotrophic pathway in the rumen—along with the methylamine and methanol methanogenesis modules, were significantly enriched in the NC group compared to the supplemented treatments ( P < 0.05) [40, 41] . A total of 613 CAZyme‑encoding genes were detected, classified into: 17 AAs, 77 CBMs, 17 CEs, 338 GHs, 92 GTs, and 71 PLs. Total CAZyme abundance differed between groups (Fig. 6A): RH35 was higher than the NC and RH14 ( P < 0.05), and RH27 was higher than RH14 ( P < 0.05). However, no significant differences were found in the abundance of any individual CAZyme class (AA, CBM, CE, GH, GT, PL) across treatments ( P < 0.05; Fig. 6B). Screening within the six classes identified specific changes after supplementation with Prevotella and Megasphaera elsdenii , significant differences ( P < 0.05) were detected in 2 AAs, 9 CBMs, 40 GHs, 5 CEs, 8 GTs, and 1 PL (Fig. 6C‑F). To pinpoint genes involved in fiber degradation, CAZyme genes encoding cellulases, hemicellulases, and ligninases were examined (Fig. 6G, Table S6). Among 64 GH families analyzed, only seven (GH92, GH67, GH39, GH1, GH4, GH38, and GH113) showed significant differential abundance ( P < 0.05). Analysis of fiber‑degradation pathways revealed multiple enzymes that drive the fermentation of dietary substrates to volatile fatty acids (VFAs). Since propionate formation consumes H₂—a substrate for methanogenesis—increasing propionate production can reduce methane emissions. Focusing on glucose fermentation pathways to acetate, propionate, and butyrate, we identified 14 key enzymes (Fig. 6H). Among these, the abundances of EC:5.3.1.9 and EC:2.7.1.11 (Fig. 6I) were significantly higher in RH27 compared to other treatments ( P < 0.05). Microbial Interactions and Correlations with Fermentation Parameters Co-occurrence network analysis uncovered potential interactions among microbes. Bacterial network analysis identified a total of 5342 significant associations, with distinct structures for each treatment (Fig S4). The most frequent negative correlations were found between the Bacteroidetes and Bacillota phyla. Archaeal co-occurrence network analysis identified 5558 significant relationships, with the most prevalent positive correlations occurring within the Euryarchaeota phylum (Fig S5). Supplementation with rumen-derived bacteria reduced interconnectivity within the archaeal network. Correlation analysis revealed significant associations between microbial taxa and fermentation parameters (Fig 6J, K). Prevotella _sp. showed a significant positive correlation with TVFA ( P < 0.05). Eubacterium _sp. exhibited a positive correlation with methane content ( P < 0.05). Among archaea, Methanobrevibacter _sp. exhibited significant negative correlations with NH₃-N and acetate ( P < 0.05). Discussion This study successfully isolated and characterized four Prevotella strains (RH3, RH14, RH27, RH35) and one Megasphaera elsdenii strain (RH19) from the rumen. Their functional roles in rumen fermentation and methane mitigation were systematically evaluated through genomic annotation, in vitro fermentation, and metagenomic analyses. The functional roles of these isolates in rumen fermentation are rooted in their distinct genomic architectures [42] . RH3 had larger genome and higher proportion of CEs compared to other Prevotella strains suggest an expanded carbohydrate esterification capacity [43] . In contrast, M. elsdenii RH19 minimal PLs but dominant GTs align with its role in lactate-to-propionate conversion requiring glycosyl modifications [44] . The high GH activity in Prevotella strains is crucial for plant polysaccharide degradation [45] . While M. elsdenii's genomic specialization in amino acid metabolism corroborates its known proteolytic and lactate-utilizing functions [46] . The observed in vitro rumen fermentation dynamics provide a direct link between the supplemented strains and methane mitigation. The significantly lower methane yields across all bacterial treatments, with RH14 being the most effective, align with RH14’s high GH-mediated carbohydrate fermentation, promoting propionate formation (a hydrogen sink) over acetoclastic methanogenesis [47] . The reduced acetate/propionate ratio in RH3 and RH14 further supports their role in redirecting metabolic hydrogen toward propionigenesis, thereby competitively inhibiting methanogenesis [48] . The elevated NH₃-N and MCP in the RH3 treatment are likely due to its exceptional peptidase activity enhancing proteolysis. Metagenomic insights elucidate the ecological and functional mechanisms behind the methane reduction. The significant increase in Bacteroidetes (particularly Prevotella ) and decrease in Bacillota (e.g., Ruminococcus ) in treatments like RH3 and RH19 indicate niche competition for fibrous substrates. This nutritional competition may decrease the availability of H₂ for hydrogenotrophic methanogenesis [49, 50] . The enrichment of Candidatus_Cryptobacteroides in RH27, as a hydrogen-consuming propionate producer, may further suppress methanogenesis through competitive H₂ consumption [49-51] . Additionally, following in vitro supplementation, the RH19 treatment group demonstrated a significantly higher abundance of M. elsdenii n the fermentation broth compared to other treatments. which also confirms the successful colonization of M. elsdenii via in vitro supplementation. As the relative abundance of M. elsdenii in the rumen is notably low (typically below 1%) [52] , it was consequently not represented in the previous analysis of dominant bacterial populations. The downward trend in the hydrogenotrophic genus Methanobrevibacter , coupled with the significant functional downregulation of the dominant "Carbon dioxide reduction to methane" pathway, strongly suggests that methane mitigation was achieved primarily by limiting the substrate (H₂) and suppressing the activity of the major metabolic route methanogens depend on, rather than eliminating them [37, 41, 53, 54] . The significant downregulation of the acetoclastic pathway, while statistically significant, likely contributes less to overall mitigation given its minor role in the rumen [38, 39] . This pattern strongly suggests that the primary inhibitory effect of the supplemented bacteria on methanogenesis is directed against the hydrogenotrophic (CO₂-reduction) pathway [37] . This finding aligns perfectly with the earlier observed ecological shifts, where bacterial competitors like Candidatus_Cryptobacteroides likely reduced H₂ availability [50] , and the downward trend in the hydrogenotrophic genus Methanobrevibacter. Thus, the functional genomics data confirm that methane reduction was achieved not by eliminating methanogens, but by strategically limiting the substrate (H₂) and suppressing the activity of the major metabolic route (CO₂ reduction) they depend on. Furthermore, the downregulation of the cobalamin (B12) biosynthesis module in most treatments likely compounded the functional suppression, Vitamin B12 is a crucial coenzyme for several key steps in methanogenesis [55] . Its reduced biosynthesis likely compounded the functional suppression of methanogenic pathways, further constraining methane production capacity in these treatments [56] . The shift in specific fiber-degrading CAZyme families may redirect H₂ flux by modulating fermentation end-products [57] . The synergistic upregulation of key glycolytic enzymes (EC:5.3.1.9 and EC:2.7.1.11) in the RH27 treatment enhances flux towards pyruvate and propionate synthesis. Notably, the EC:5.3.1.9 (glucose isomerase) and EC:2.7.1.11 (phosphofructokinase) holds synergistic significance. These two enzymes collectively enhance the flux of the glycolytic (EMP) pathway, directing more carbon substrates toward pyruvate, thereby providing ample precursors for subsequent propionate synthesis. Since propionate generation is a process that consumes reducing power (NADH) and/or H₂, the intensification of this metabolic flow directly competes with methanogenic archaea for available H₂, serving as the core biochemical driver for methane mitigation in the treatment [47] . The microbial interaction networks and correlation analyses integrate these findings. The elevated Bacteroidetes-to-Bacillota ratio in supplemented groups is significant, as previous research correlates an increased ratio with enhanced energy utilization efficiency in dairy cows [58, 59] . This suggests that supplementation of rumen microorganisms may improve feed utilization. The reduced interconnectivity within the archaeal network upon bacterial supplementation reflects diminished archaeal activity and suppressed dominant populations [60] . The significant positive correlation between Prevotella _sp. and TVFA is consistent with reports linking its enrichment to enhanced VFA production [61] , while the positive correlation of Eubacterium _sp. with methane highlights its potential role in methanogenic niches. In conclusion, supplementation with rumen-derived Prevotella and M. elsdenii strains consistently reduced methane production in vitro through a multi-faceted mechanism. This involved redirecting hydrogen towards propionate synthesis, competitively altering the rumen bacterial community to reduce H₂ availability for archaea, and functionally suppressing the expression of key methanogenic pathways, particularly the dominant hydrogenotrophic CO₂-reduction route. Conclusion In this study, we successfully isolated and identified four Prevotella strains and one Megasphaera elsdenii strain from the rumen. By combining genomic, in vitro fermentation, and metagenomic analyses, we demonstrated that these strains modulate the ruminal environment by enhancing carbohydrate fermentation, promoting propionate production, and globally suppressing the "methane metabolism" pathway, particularly the dominant hydrogenotrophic (CO₂-reduction) methanogenesis pathway in the rumen. The observed methane reduction was driven more by functional suppression of methanogenic pathways rather than by displacement of the archaeal community. These findings highlight the potential of using rumen-derived bacteria as probiotics to improve rumen fermentation efficiency and thereby reduce methane emissions in ruminant production. Declarations Acknowledgements The authors would like to acknowledge the Institute of Animal Science of Jiangsu Academy of Agricultural Sciences and Nanjing Weigang Dairy Co., Ltd. for their assistance in providing rumen fluid donors, sample processing, and data collection. Special thanks are extended to Professor Yanfen Cheng from Nanjing Agricultural University for her financial support of this project. Conflict of Interest Author disclosures: HBL, CYF, LBY, and XDF, no conflicts of interest. Author Contribution HBL and LBY designed the study. WX and XM collected the samples. CYF and DCL provide the funding declaration. Nazar and XDF revised the manuscript. The authors read and approved the final manuscript. Funding Declaration This work was supported by the National Key Research and Development Program of China (2023YFD1300903). Data Availability Statement The nucleotide sequences of the 16S rRNA gene from five strains of rumen bacteria isolated from dairy cows have been submitted to GenBank and assigned the following accession numbers: strain RH3 (accession number: PX426733), strain RH14 (PX426734), strain RH19 (PX426735), strain RH27 (PX426736), and strain RH35 (PX426737). 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Tables Table 1 Effect of adding rumen-derived bacteria on artificial rumen fermentation parameters Items Sampling time /h Treatments 1 SEM 2 P -values NC RH3 RH14 RH19 RH27 RH35 pH 12 6.64 a 6.57 b 6.56 b 6.56 b 6.57 b 6.55 b 0.01 <0.01 24 6.63 a 6.51 c 6.57 b 6.57 b 6.57 b 6.62 ab 0.01 0.01 48 6.62 a 6.50 c 6.55 b 6.66 a 6.66 a 6.64 a 0.02 <0.01 72 6.62 a 6.51 b 6.63 a 6.63 a 6.62 a 6.65 a 0.01 <0.01 NH 3 -N (mg/dL) 12 12.76 ab 13.79 a 12.25 b 12.89 ab 9.03 c 11.95 b 0.42 <0.01 24 18.99 c 23.19 a 21.90 ab 20.57 bc 18.86 c 18.12 c 0.38 0.01 48 28.80 30.12 32.33 30.80 29.92 31.25 0.58 0.67 72 28.76 d 33.59 a 33.37 ab 30.75 bcd 29.77 cd 32.25 abc 0.52 <0.01 MCP（mg/mL） 12 3.44 bc 4.27 a 2.75 d 3.79 b 3.29 c 3.91 ab 0.13 <0.01 24 3.82 ab 3.52 b 3.58 b 4.32 a 3.80 ab 4.02 ab 0.07 0.02 48 1.87 ab 1.93 a 1.93 a 1.75 bc 1.72 c 1.44 d 0.04 <0.01 72 1.64 c 1.80 bc 2.17 a 1.87 abc 1.92 abc 2.05 ab 0.05 0.04 Note: 1 NC= Negative control, RH3 = Prevotella RH3 ; RH14 = Prevotella RH14 ; RH19 = Megasphaera elsdenii RH19 ; RH27 = Prevotella RH27 ; RH35 = Prevotella RH35 . Data were analyzed using the one-way ANOVA procedure (n = 3 per group). a,b Means bearing different superscripts in the same row differ significantly ( P < 0.05). 2 SEM: standard error of the mean. Table 2 Effect of supplemented rumen-derived bacteria on VFAs in rumen fermentation broth in vitro Items Sampling time /h Treatments 1 SEM 2 P -values NC RH3 RH14 RH19 RH27 RH35 TVFA (mmol) 12 76.05 b 93.24 a 79.47 b 77.78 b 64.37 b 71.39 b 2.87 0.01 24 79.65 b 96.41 a 79.69 b 97.33 a 78.73 b 92.49 a 2.15 <0.01 48 98.69 101.39 104.80 102.61 98.54 103.50 1.19 0.63 72 82.35 d 104.69 a 101.67 a 95.43 b 89.05 c 94.68 bc 1.92 <0.01 Acetate (%) 12 56.97 53.21 58.20 58.45 58.59 58.84 0.75 0.24 24 56.65 53.85 57.96 57.37 56.52 58.06 0.63 0.45 48 62.29 a 61.22 ab 59.62 ab 59.90 ab 58.06 b 60.53 ab 0.47 0.04 72 60.85 60.20 59.40 59.21 61.36 59.75 0.38 0.44 Propionate (%) 12 20.71 b 24.98 a 20.55 b 20.30 b 20.42 b 20.21 b 0.55 0.04 24 19.62 21.08 21.18 20.50 21.05 21.54 0.29 0.51 48 17.94 20.86 20.65 18.54 18.51 18.62 0.44 0.24 72 19.30 19.61 20.02 18.92 19.14 19.50 0.21 0.80 Butyrate (%) 12 12.45 11.45 11.49 11.79 11.49 11.22 0.26 0.86 24 12.67 ab 13.75 a 12.08 ab 12.58 ab 11.54 b 11.39 b 0.27 0.05 48 10.75 10.74 10.75 12.07 12.43 11.67 0.28 0.29 72 11.01 b 12.08 ab 11.89 ab 12.78 a 11.96 ab 12.78 a 0.19 0.04 Valerate (%) 12 2.43 2.39 1.81 2.66 2.32 2.14 0.13 0.55 24 2.13 2.50 1.97 2.74 2.53 2.02 0.16 0.70 48 1.88 c 1.94 c 2.29 b 1.84 c 2.53 b 2.88 a 0.10 <0.01 72 2.00 bc 2.18 ab 2.14 ab 1.79 cd 1.50 d 2.47 a 0.08 <0.01 Acetate/ Propionate 12 2.63 a 2.15 b 2.61 a 2.88 a 2.87 a 2.91 a 0.08 <0.01 24 2.90 2.55 2.75 2.82 2.69 2.88 0.07 0.74 48 3.49 a 3.21 ab 2.90 b 3.09 ab 3.15 ab 3.25 ab 0.07 0.05 72 3.09 3.28 2.98 3.14 3.21 3.06 0.05 0.70 Note: 1 NC= Negative control, RH3 = Prevotella RH3 ; RH14 = Prevotella RH14 ; RH19 = Megasphaera elsdenii RH19 ; RH27 = Prevotella RH27 ; RH35 = Prevotella RH35 .Data were analyzed using the one-way ANOVA procedure (n = 3 per group). a,b Means bearing different superscripts in the same row differ significantly ( P < 0.05). 2 SEM: standard error of the mean. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx GraphicalAbstract.png Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 28 Jan, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8723424","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588069108,"identity":"8640738e-1f25-4b43-a111-4ceef5e86fd9","order_by":0,"name":"Banglin He","email":"","orcid":"","institution":"Hunan Agriculture University","correspondingAuthor":false,"prefix":"","firstName":"Banglin","middleName":"","lastName":"He","suffix":""},{"id":588069112,"identity":"f58e2433-50c2-46bf-bf3f-1da9ac2a3f8d","order_by":1,"name":"Min Xia","email":"","orcid":"","institution":"Hunan Agriculture University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Xia","suffix":""},{"id":588069114,"identity":"bece2517-6675-46a6-8980-23d730ea61ae","order_by":2,"name":"Xin Wang","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":588069115,"identity":"5019cd0c-afe2-41fe-9ccf-b8d5b049f90e","order_by":3,"name":"Chenlong Ding","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chenlong","middleName":"","lastName":"Ding","suffix":""},{"id":588069117,"identity":"b4ec9682-6645-4078-92ac-c0711042f98d","order_by":4,"name":"Mudasir Nazar","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mudasir","middleName":"","lastName":"Nazar","suffix":""},{"id":588069123,"identity":"ce481790-902e-45ec-91ab-b69e4b4570a4","order_by":5,"name":"Dingfu Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACfvbmww8SKv7JARkHiNMi2XMszeDDmQPGQEYCcVoMbvgYSM5sO5C44UaOAZEuu8GWYMzbdiex4cyZjzfeMNjJ6TYQ0ME4u/nAY55zz4wb23s3W85hSDY2O0BAC7PMsQRjnjJm2Waes9ukeRgOJG4jpIVNIsdAmoeNmbFNIucZcVp4gFokZ7QdVuyRyGEjTosEDziQ04yBDGPLOQZE+MX+ODgqbeSAjIc33lTYyRHUgmYlsVGDpIVUHaNgFIyCUTAiAABV9En+6yxInwAAAABJRU5ErkJggg==","orcid":"","institution":"Hunan Agriculture University","correspondingAuthor":true,"prefix":"","firstName":"Dingfu","middleName":"","lastName":"Xiao","suffix":""},{"id":588069124,"identity":"6ac275bc-9e07-46dc-98f6-96dab6424339","order_by":6,"name":"Yanfen Cheng","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yanfen","middleName":"","lastName":"Cheng","suffix":""},{"id":588069126,"identity":"79c0c870-b486-4aa7-995e-4d5034d23d2c","order_by":7,"name":"Beiyi Liu","email":"","orcid":"","institution":"Jiangsu Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Beiyi","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-28 16:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8723424/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8723424/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102321716,"identity":"452ad1e0-176e-410f-9f17-964d0b6a510f","added_by":"auto","created_at":"2026-02-10 13:50:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2176838,"visible":true,"origin":"","legend":"\u003cp\u003e(A-B) Phylogenetic tree of the four \u003cem\u003ePrevotella\u003c/em\u003estrains and one \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e. (C) Colonial morphology of the isolated \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e strains. (D) Gram staining identification of the isolated \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e strains. (E) Circular genome maps of \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e. The outermost circle indicates genome size scale. The second and third circles represent coding sequences (CDS) on the forward and reverse strands, respectively, with colors designating functional classifications according to the Clusters of Orthologous Groups (COG) database. The fourth circle shows rRNA and tRNA locations. The fifth circle displays GC content: outward red peaks indicate regions with GC content higher than the genomic average (peak height corresponds to the degree of deviation), while inward blue peaks indicate regions with GC content lower than the genomic average. The innermost circle represents the GC skew value.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/c36710c1f2f39b15b571c9dd.png"},{"id":102397338,"identity":"887d6176-c41c-4d2e-8e00-31dd32f229be","added_by":"auto","created_at":"2026-02-11 10:15:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":787941,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Classification statistics of KEGG pathway annotations for \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e. The Y-axis indicates the names of KEGG metabolic pathways, while the X-axis represents both the number of genes annotated to each pathway and their proportion relative to the total number of annotated genes. (B) COG functional classification statistics for \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/ae2f3f7af45dc60e0c44d295.png"},{"id":102321717,"identity":"da5834fc-422b-4198-9834-e0b6b8a88a38","added_by":"auto","created_at":"2026-02-10 13:50:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1413749,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Methane production following in vitro supplementation with \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e at different incubation times. (B) Carbon dioxide (CO₂) content following in vitro supplementation with \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e at different incubation times. (C) Total gas production following in vitro supplementation with \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e at different incubation times. (D) pH of the fermentation broth following in vitro supplementation with \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e at different incubation times. (E) Growth curves of the \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e strains. Data were expressed as mean ± SEM (n = 3). Different lowercase letters above bars within the same time point indicate significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/f6e1c2353ccbc5292b90de76.png"},{"id":102321718,"identity":"9c54fc91-3833-40b6-8095-e210408d5426","added_by":"auto","created_at":"2026-02-10 13:50:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1204366,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of \u003cem\u003ePrevotella\u003c/em\u003eand \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e on rumen microbial community composition and diversity in the in vitro fermentation model. (A) Bacteria alpha diversity. (B) Archaea alpha diversity. (C) Bacteria beta diversity (PCoA based on Bray-Curtis distance). (D) Archaea beta diversity (PCoA based on Bray-Curtis distance). (E) Relative abundance of bacteria communities at phylum, genus, and species levels. (F) Relative abundance of archaea communities at phylum, genus, and species levels. The difference among six groups was identified by Kruskal–Wallis multiple comparisons, and asterisk indicated the significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/239b568b680bb55f488ed369.png"},{"id":102321721,"identity":"081f9659-959a-4482-a866-b642100644cc","added_by":"auto","created_at":"2026-02-10 13:50:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1331322,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of rumen microbial communities and KEGG functional profiles following in vitro supplementation with \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e. (A) Differential abundance analysis of bacteria at phylum, genus, and species levels. (B) Differential abundance analysis of archaea at phylum, genus, and species levels. Asterisks indicate significant differences (P \u0026lt; 0.05). (C) Comparison of rumen microbial KEGG metabolic pathways across different treatment groups. (D) Comparison of rumen microbial KEGG modules across different treatment groups. Purple indicates enrichment in the control group (NC); red indicates enrichment in treatment groups. Asterisks denote pathways with reporter scores \u0026gt;1.65 or \u0026lt;−1.65. The Kruskal–Wallis multiple comparisons were used for mean comparison.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/827f7866a3fe24be23df2a3b.png"},{"id":102321720,"identity":"efe611ce-5a40-4b5d-a3a9-9acbcdc6526a","added_by":"auto","created_at":"2026-02-10 13:50:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1691576,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Comparison of total CAZyme gene abundance in the rumen microbiome. (B) Comparison of CAZyme gene family abundance in the rumen microbiome. (C-F) Comparisons of the gene abundance of the CAZymes families (CBM, AA, GH, and CE, PL, GT) of the rumen microbiomes of cows; only families with significant differences among groups were shown. (G) Differential abundance of CAZyme genes involved in the degradation of structural carbohydrates (cellulose, hemicellulose, and lignin). (H) Comparisons of the abundance of KO enzymes related to the acetate, propionate, and butyrate production pathway of cows. The Kruskal–Wallis multiple comparisons was used for mean comparison, and asterisk indicated the significant difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). (I) Differential abundance analysis of KO enzymes across different treatments. (J-K) Heatmaps display the Spearman’s correlation coefficients among rumen microbiomes and rumen fermentation characteristics. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/309773e4ed81029538175e73.png"},{"id":102745377,"identity":"77a6b5a9-6359-4aa4-b15a-e342c34d0196","added_by":"auto","created_at":"2026-02-16 08:46:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6762302,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/bda477ad-1bb5-404e-8d25-7bb8be7d9dad.pdf"},{"id":102321723,"identity":"ea75d6c0-e149-47e4-b85d-04dd37f8a9a4","added_by":"auto","created_at":"2026-02-10 13:50:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14384027,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/b7345a618345c2d41571c7ac.docx"},{"id":102321724,"identity":"d1433547-27cc-4e4d-83fd-1795b51a9f11","added_by":"auto","created_at":"2026-02-10 13:50:24","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1112212,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8723424/v1/d61442cbbeb2034f168f8f0d.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rumen-derived Prevotella and Megasphaera elsdenii mitigate methane production through functional modulation of rumen microbial metabolism","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs concerns over global warming escalate, how to find measures to reduce greenhouse gas emissions has been highlighted by researchers worldwide. Greenhouse gases mainly include carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), methane (CH\u003csub\u003e4\u003c/sub\u003e) and nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO), which significant contribute to rising surface temperatures \u003csup\u003e[1]\u003c/sup\u003e. Anthropogenic methane accounts for 85% of the global methane gas and agriculture accounts for 40% of global methane emissions \u003csup\u003e[2]\u003c/sup\u003e.\u0026nbsp;Notably, ruminant farming is the primary source of methane emissions from agricultural production, intestinal fermentation accounts for 60% of methane emissions from agricultural production\u0026nbsp;\u003csup\u003e[3]\u003c/sup\u003e.\u0026nbsp;Due to the specificity of ruminant rumen fermentation this allows ruminants to utilize cellulose, because of the ruminant microbes can ferment cellulose and so on to produce volatile fatty acids (VFA)\u0026nbsp;\u003csup\u003e[4]\u003c/sup\u003e. However, rumen microbes produce VFA while fermenting cellulose with a large amount of gas, in which the resulting CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e are reduced to methane gas by methanogenic archaea, which eventually is expelled by eructation\u0026nbsp;\u003csup\u003e[5]\u003c/sup\u003e.\u0026nbsp;This way of emitting methane is important for maintaining the stability of the microbiota within the rumen, but the production of methane from the rumen can lead to waste of nutrients and its impact on the greenhouse effect is significantly\u0026nbsp;\u003csup\u003e[6, 7]\u003c/sup\u003e.\u0026nbsp;Therefore, the use of direct-fed microbials (DFM) becomes a suitable option, as it has no negative effects on the host and the environment, and is acceptable to both consumers and producers\u0026nbsp;\u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrevotella\u003c/em\u003e, as a core microbe involved in carbohydrate and hydrogen metabolism within the rumen, has an abundance that correlates with rumen health status \u003csup\u003e[9-11]\u003c/sup\u003e. Furthermore, \u003cem\u003ePrevotella\u003c/em\u003e could break down various polysaccharides and possesses the ability to synthesize propionate. Propionate not only serves as the most important substrate for hepatic gluconeogenesis in ruminants, but its formation within the rumen also utilizes hydrogen, thereby influencing rumen methane synthesis \u003csup\u003e[12, 13]\u003c/sup\u003e. A series of studies have demonstrated a close correlation between the abundance of \u003cem\u003ePrevotella\u003c/em\u003e and animals exhibiting low methane emissions \u003csup\u003e[9, 14, 15]\u003c/sup\u003e.\u0026nbsp;In the rumen, 60%\u0026ndash;80% of propionate is primarily produced via the acrylate pathway, and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e, as a major lactate-utilizing bacterium in the rumen, is also considered a key contributor to ruminal propionate production.\u0026nbsp;\u003csup\u003e[16]\u003c/sup\u003e. Furthermore, \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e can ferment both the L- and D-isomers of lactate via racemase and produce propionate through the acrylate pathway\u0026nbsp;\u003csup\u003e[17, 18]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSince \u003cem\u003ePrevotella\u003c/em\u003e primarily utilizes the succinate pathway to produce propionate, this pathway directly consumes hydrogen from substrates, thereby reducing methane production in the rumen \u003csup\u003e[19]\u003c/sup\u003e. Whereas \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e mainly employs the acrylate pathway to increase ruminal propionate concentration, effectively blocking the conversion of lactate to hydrogen \u003csup\u003e[18]\u003c/sup\u003e.\u0026nbsp;Therefore, in our study, we aim to evaluate\u0026mdash;using an in vitro fermentation model supplemented with rumen-isolated strains of \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e\u0026mdash;whether these two bacteria, both capable of elevating ruminal propionate concentration, can reduce methane emissions in ruminants.\u0026nbsp;Furthermore, we will employ metagenomic analysis to assess the impact of supplementing \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e on the functionality and composition of the rumen microbiome.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eAnimal feeding management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor single bacterial isolation and in vitro fermentation models, rumen fluid was obtained from three lactating Holstein dairy cows serving as donors at Nanjing Weigang Dairy Co., Ltd. (Table S1) shows the composition of the dairy cows diets and also lists the detailed composition and specific nutritional profile of the substrates used in the in vitro experiments. All trials were approved by the Experimental Animal Welfare and Ethics Committee of Jiangsu Academy of Agricultural Sciences (Jiangsu Nanjing).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRumen liquid collection and isolation of rumen bacteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo avoid interference from feed, rumen contents were collected from three lactating dairy cows (611 \u0026plusmn; 18.7 kg) using an oral tube after milking and before morning feeding. The contents were placed into a thermos flask, mixed, and transported back to the laboratory. After filtration through four layers of gauze, 1 mL of rumen fluid was aspirated and transferred into an anaerobic workstation (AW400SG, Electrotek, England). Gradient dilutions were performed using sterile physiological saline. Eight dilution gradients were prepared (10⁻\u0026sup1; to 10⁻⁸). From each dilution, 100 \u0026mu;L was aspirated and spread evenly onto kanamycin-vancomycin laked blood agar (KVLB, Haibo China) solid medium. Each dilution gradient was plated in duplicate. Plates were incubated anaerobically at 37 \u0026deg;C for growth. After 24 hours of incubation, distinct colonies were selected, re-diluted, and repeatedly streaked onto KVLB solid medium for purification. Following three rounds of purification, single bacterial strains were obtained. The bacterial strains ultimately isolated from the lactating cow rumen fluid were designated RH3, RH14, RH19, RH27, and RH35. The physiological and biochemical characteristics of the isolated strains were preliminarily determined using Gram staining and a Gram-negative bacteria identification system (Haibo China). To assess the growth kinetics of the isolated strains, 1 mL of bacterial culture was inoculated into 150 mL of Schaedler broth (Solarbio China). The flask was sealed and transferred from the anaerobic workstation to a shaking incubator. Bacterial culture samples were collected aseptically using a syringe at 2-hour intervals. The optical density at 600 nm (OD₆₀₀) of each sample was measured to plot the growth curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBacterial whole-genome analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBacterial DNA extraction and 16S rRNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBacterial cells (1 mL) at the exponential growth phase were harvested, and genomic DNA was isolated using a commercial bacterial DNA extraction kit (Tiangen, Beijing). The extracted DNA served as the template for amplifying the 16S rRNA gene using PCR with the universal primers 27F (5\u0026apos;-GAGTTTGATCTGGCTCAG-3\u0026apos;) and 1492R (5\u0026apos;-ACGGCTACCTTGTTACGACTT-3\u0026apos;). Each 20 \u0026mu;L PCR reaction contained 10 \u0026mu;L of PrimeSTAR Max Premix (2\u0026times;, Takara Bio), 0.5 \u0026mu;L of each primer, 0.5 \u0026mu;L of genomic DNA, and 8.5 \u0026mu;L of nuclease-free water. PCR products were resolved on a 1% agarose gel, and amplicons showing a single band of the expected size were purified and submitted to Qingke Biotechnology (Nanjing, China) for Sanger sequencing. The obtained sequences were queried against the NCBI nucleotide database using BLAST. Multiple-sequence alignments and phylogenetic tree construction were performed in MEGA v12.0.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLibrary construction and NGS sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext-generation sequencing was carried out by Shanghai Biozeron Biotechnology Co., Ltd. (Shanghai, China). For paired-end NGS sequencing of each bacterial strain, at least 1 \u0026mu;g of high-quality genomic DNA was used for library preparation. Paired-end libraries with an approximate insert size of 400 bp were constructed following standard protocols for genomic DNA library generation. Briefly, purified genomic DNA was fragmented to the desired size using a Covaris system, and the resulting fragments were end-repaired with T4 DNA polymerase. An adenine residue was added to the 3\u0026prime; termini of phosphorylated blunt-end fragments, after which sequencing adapters were ligated. Size-selected fragments were subsequently isolated by gel electrophoresis, followed by selective enrichment and amplification through PCR. Index sequences were introduced into the adapters during the PCR step when required, and library quality was assessed before sequencing. Libraries meeting quality criteria were subjected to paired-end sequencing (150 bp \u0026times; 2) on the NGS platform at Shanghai Biozeron.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome assembly\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw paired-end reads were subjected to quality trimming and filtering using Trimmomatic (version 0.36) \u003csup\u003e[20]\u003c/sup\u003e with parameters (SLIDINGWINDOW:4:15 MINLEN:75). The resulting high-quality reads were used for downstream analysis. De novo genome assembly was performed with ABySS 2.2.0 \u003csup\u003e[21]\u003c/sup\u003e, employing multiple k-mer values to identify the optimal assembly. Finally, GapCloser \u003csup\u003e[22]\u003c/sup\u003e was utilized to close residual gaps and correct single-base polymorphisms within the draft assembly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome Annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor prokaryotic strains, gene models for \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e were predicted using an ab initio approach with GeneMark 4.17. \u003csup\u003e[23]\u003c/sup\u003e (http://topaz.gatech.edu/GeneMark/). \u003c/p\u003e\n\u003cp\u003ePredicted genes were then functionally annotated by performing BLASTp searches against multiple databases, including NCBI non-redundant (NR), SwissProt (http://uniprot.org), KEGG (http://www.genome.jp/kegg/), COG (http://www.ncbi.nlm.nih.gov/COG), CAZy (http://www.cazy.org/), CARD (https://card.mcmaster.ca/), PHI, TCDB (http://www.tcdb.org/), VFDB, BacMet, as well as SignalP (http://www.cbs.dtu.dk/services/SignalP/) and TMHMM (http://www.cbs.dtu.dk/services/TMHMM/) for signal peptide and transmembrane domain prediction. In addition, tRNA genes were identified using tRNAscan-SE v2.0.4 \u003csup\u003e[24]\u003c/sup\u003e, and rRNA genes were detected with RNAmmer v1.2 \u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBatch culture of rumen in vitro\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRumen fluid was collected from the same three lactating dairy cows mentioned previously. The collected rumen fluid was filtered through four layers of cheesecloth. 20 mL of the filtered rumen fluid was mixed with 40 mL of artificial saliva (prepared according to the method described by Menke. \u003csup\u003e[26]\u003c/sup\u003e) in a 150 mL anaerobic fermentation bottle. The fermentation substrate consisted of 0.5 g of the dairy farm\u0026apos;s Total Mixed Ration (TMR). Prior to fermentation, air within the bottle was displaced by flushing with CO₂. Once air evacuation was complete, the bottle was immediately sealed with a butyl rubber stopper and secured with an aluminum cap. At the start of fermentation, 1 mL of bacteria suspension was added. This suspension contained bacteria pre-revived to the exponential growth phase and diluted to a concentration of 2 \u0026times; 10\u0026sup1;⁰ CFU/g. 1 mL of sterilized Schaedler broth was added instead to the control group. A pre-evacuated gas collection bag (E-Switch, Shanghai) was attached to the top of the stopper before fermentation began. The mixture was incubated at 39 \u0026deg;C in a constant-temperature shaker under anaerobic conditions. Gas and fermentation broth samples were collected at the 12, 24, 48 and 72 h. Upon completion of each fermentation period, the bottles were immediately removed and placed on ice to halt microbial activity. The pH of the fermentation broth was measured immediately. Each treatment was performed in triplicate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of gas production volume, hydrogen and methane in samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the end of each fermentation period, the gas collection bag attached to the fermentation bottle was removed, and total gas production was quantified by measuring the displacement of the syringe plunger. A 0.5 mL aliquot of the collected gas was analyzed for hydrogen and methane using a gas chromatograph (GC, model CP-3800, Varian Inc., Palo Alto, CA). Separation was performed on a 13\u0026times; molecular sieve column (45\u0026ndash;60 mesh; 2.0 mm \u0026times; 3.2 mm \u0026times; 2.0 mm, stainless steel) with a thermal conductivity detector. Instrument settings were as follows: oven temperature, 60 \u0026deg;C; injector and TCD temperature, 120 \u0026deg;C; flame ionization detector, 200\u0026deg;C. Nitrogen was used as the carrier gas at a flow rate of 50 mL/min.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of pH, NH\u003csub\u003e3\u003c/sub\u003e-N, MCP, and VFA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmediately after the completion of fermentation, the pH of the fermentation broth was measured using a pH meter (FE28, Mettler-Toledo, Switzerland). Ammonia nitrogen (NH₃-N) concentrations were determined via the phenol\u0026ndash;hypochlorite colorimetric assay \u003csup\u003e[27]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMCP was determined using the purine method. Microbial protein nitrogen (mg/mL) was calculated according to the formula: Microbial Protein Nitrogen (mg/mL) = (Measured RNA (mg/mL) \u0026times; Nitrogen Content in RNA) / (Nitrogen Content in Bacterial RNA) \u0026times; Dilution Factor. Subsequently, microbial protein concentration was calculated using the formula: Microbial Protein Concentration (mg/mL) = Microbial Protein Nitrogen (mg/mL) \u0026times; 6.25.\u003c/p\u003e\n\u003cp\u003eThe VFA concentration in the fermentation broth was determined according to the method described by Erwin \u003csup\u003e[28]\u003c/sup\u003e. Briefly, 1 mL of 25% metaphosphoric acid was added to 5 mL of fermentation broth. After centrifugation (12,000 \u0026times; g, 20 min, 4 \u0026deg;C), the supernatant was filtered through a 0.22-\u0026mu;m membrane. The filtrate was analyzed using a gas chromatograph (GC-14B, Shimadzu, Japan) equipped with a flame ionization detector (FID). Instrument settings were: column temperature 100 \u0026deg;C, detector temperature 200 \u0026deg;C, and injector temperature 200 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction, library construction, and metagenomic sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal genomic DNA was extracted from rumen fluid samples using the Mag-Bind\u0026reg; Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) following the manufacturer\u0026rsquo;s instructions. DNA concentration and purity were assessed with TBS-380 and NanoDrop 2000, respectively, and integrity was verified by 1% agarose gel electrophoresis.\u003c/p\u003e\n\u003cp\u003eFor library construction, genomic DNA was fragmented to an average size of ~400 bp using a Covaris M220 system (Gene Company Limited, China). Paired-end libraries were prepared using the NEXTFLEX Rapid DNA-Seq kit (Bioo Scientific, Austin, TX, USA), with adapters containing full sequencing primer sites ligated to blunt-ended fragments. Sequencing was performed on an Illumina NovaSeq platform (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using the NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles).\u003c/p\u003e\n\u003cp\u003eRaw paired-end reads were processed on the Majorbio Cloud Platform (www.majorbio.com). Adapter sequences and low-quality reads (length \u0026lt; 50 bp, quality score \u0026lt; 20, or containing N bases) were removed using fastp v0.20.0 \u003csup\u003e[29]\u003c/sup\u003e. Host-derived reads were filtered by aligning to the bovine reference genome with BWA v0.7.9a \u003csup\u003e[30]\u003c/sup\u003e. Remaining high-quality reads were assembled de novo using MEGAHIT v1.1.2 \u003csup\u003e[31]\u003c/sup\u003e, and contigs \u0026ge;300 bp were retained for downstream analyses. Open reading frames (ORFs) were predicted from assembled contigs using Prodigal \u003csup\u003e[32]\u003c/sup\u003e or MetaGene \u003csup\u003e[33]\u003c/sup\u003e. With ORFs \u0026ge;100 bp translated into amino acid sequences based on the NCBI translation table. Redundant sequences were clustered with CD-HIT v4.6.1 \u003csup\u003e[34]\u003c/sup\u003e at 90% sequence identity and 90% coverage to generate a non-redundant gene catalog. Gene abundance was estimated by mapping reads to the catalog using SOAPaligner v2.21 \u003csup\u003e[35]\u003c/sup\u003e with 95% identity.\u003c/p\u003e\n\u003cp\u003eRepresentative sequences from the non-redundant catalog were taxonomically annotated using Diamond v0.8.35\u003csup\u003e[36]\u003c/sup\u003e (http://www.diamondsearch.org/index.php) against the NCBI NR database (e-value \u0026le;1e-5). Concurrently, functional annotation was conducted by assigning Cluster of Orthologous Groups (COG) categories through alignment against the eggNOG database. The Kyoto Encyclopedia of Genes and Genome (KEGG) annotation was conducted using Diamond v0.8.35 \u003csup\u003e[36]\u003c/sup\u003e against the KEGG database (http://www.genome.jp/keeg/) with an e-value cutoff of 1e\u003csup\u003e-5\u003c/sup\u003e. The CAZy annotation was performed using hmmscan (http://hmmer.janelia.org/search/hmmscan). The abundances of annotated KEGG Orthologs (KOs), pathways, enzymes, modules, and carbohydrate-active enzyme (CAZymes) were first quantified and normalized as counts per million reads (CPM). For subsequent analysis, we retained only those KEGG modules, pathways, enzymes, and CAZymes that exhibited a CPM value greater than 5 in at least 50% of the animals within any given experimental group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGas composition and rumen fermentation parameters were analyzed using one-way ANOVA in SPSS 22.0 (IBM, New York, USA). Differences in rumen microbial communities at the domain, phylum, genus, and species levels were assessed using the Kruskal\u0026ndash;Wallis test for multiple comparisons, with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 considered statistically significant. Similarly, the abundances of microbial metabolic pathways, modules, KEGG pathways and CAZymes across the six groups were compared using the Kruskal\u0026ndash;Wallis test. Spearman rank correlation coefficients between rumen microbial taxa were calculated in SPSS 22.0, with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 indicating significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIsolation, Identification, and Genomic Characterization of Rumen Bacteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 5 bacterial strains were isolated from rumen liquid and designated as RH3, RH14, RH19, RH27, and RH35. Phylogenetic analysis based on 16S rRNA sequencing revealed that strains RH3, RH14, RH27, and RH35 were most closely related to \u003cem\u003ePrevotella\u003c/em\u003e_\u003cem\u003esp\u003c/em\u003e., while RH19 was most closely related to \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e strain PC404 (Fig. 1A, B).\u003c/p\u003e\n\u003cp\u003eColony morphology observations showed that strains RH3, RH14, RH27, and RH35 exhibited circular colonies with smooth surfaces, slightly convex profiles, translucent and pale yellow pigmentation, a viscous consistency, and entire margins. In contrast, strain RH19 formed circular, pale yellow colonies with convex, smooth, opaque surfaces, a viscous consistency, and entire margins (Fig. 1C). Gram staining confirmed all five strains as Gram-negative bacteria, with \u003cem\u003ePrevotella\u003c/em\u003e strains predominantly appearing as short rods and \u003cem\u003eM. elsdenii\u003c/em\u003e RH19 exhibiting cocci (Fig. 1D). Growth curve determination indicated that strains RH14 and RH35 reached peak OD at 12 h, RH3 and RH27 peaked at 14 h, and RH19 achieved maximum OD at 16 h (Fig 3E). Based on biochemical characterization results (Table S2), \u003cem\u003ePrevotella\u003c/em\u003e strains were positive for utilization of ONPG, VP, and various carbohydrates, whereas \u003cem\u003eM. elsdenii\u003c/em\u003e RH19 utilized arginine, lysine, ornithine, glucose, maltose, and mannitol.\u003c/p\u003e\n\u003cp\u003eWhole-genome sequencing revealed chromosome lengths of 5,241,519 bp for RH3, 4,637,358 bp for RH14, 2,429,558 bp for RH19, 4,638,824 bp for RH27, and 4,639,038 bp for RH35 (Fig 1E), with corresponding GC contents of 37.49%, 50.48%, 53.26%, 50.76%, and 51.12% (Table S3). A total of 5519, 4514, 2258, 4515, and 4516 coding genes were predicted, respectively (Table S3). Comparative CAZymes profile analysis showed RH3 possessed a higher proportion of Carbohydrate Esterases (CE, 22.60%) compared to RH14/RH27/RH35 (avg. 15.7% CE). \u003cem\u003eM. elsdenii\u003c/em\u003e RH19 showed minimal Polysaccharide Lyases (PLs, 0%) but dominant Glycosyl Transferases (GTs, 50.8%) (Fig. S1). KEGG annotation indicated \u003cem\u003ePrevotella\u003c/em\u003e strains had a higher number of genes assigned to carbohydrate metabolism (e.g., 452 genes in RH14) compared to RH19 (195 genes), while RH19 had more genes in amino acid metabolism (163 genes) (Fig. 2A). eggNOG annotation showed RH3 possessed a high number of genes for amino acid transport and metabolism (446 genes) (Fig. 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn Vitro Rumen Fermentation Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFig. 3C shows the effect of supplementing \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e on gas production over time. At 12 h, all supplemented treatments produced significantly more gas than the NC group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Beyond 12 h, cumulative gas production remained significantly higher in the RH3, RH19, and RH27 groups compared to NC, RH14, and RH35 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;As shown in Fig. 3A, methane production increased linearly from 24 to 48 h, but all bacterial treatments yielded significantly less methane than the control (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). At 48 h, RH14 produced the lowest methane among all groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). In contrast, CO₂ levels followed an opposite trend (Fig. 3B), with the control showing significantly lower CO₂ than all bacterial treatments at 48 h (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;Regarding pH (Fig. 3D), all supplemented treatments had significantly lower pH than the control at 12 h (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). At 24 h, only RH35 did not differ from NC, while the rest remained lower. Throughout the incubation, RH3 maintained the lowest pH among all treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eAs presented in Table 1, NH₃-N concentration increased from 12 to 48 h in all groups. RH3 consistently had higher NH₃-N than the control at all times (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), while RH14 was higher at 24 h and 72 h (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). RH27 and RH35 initially had lower NH₃-N than the control at 12 h (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), but surpassed it after 48 h. For microbial crude protein (MCP), RH3 showed higher MCP than NC at 12 h and 48 h (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). RH14 had lower MCP than other treatments at 12 h but exceeded the control at 24 h and 48 h (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eTable 2 shows that RH3 had higher total volatile fatty acid (TVFA) than NC throughout fermentation (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). RH19 and RH35 also had higher TVFA than NC at 24 h and 72 h (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). RH14 and RH27 only exceeded the control at 72 h. Acetate content did not differ significantly from NC at 12, 24, or 72 h in any treatment, though RH3, RH14, RH19, and RH35 remained lower throughout. At 48 h, acetate in RH27 was significantly lower than NC (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Propionate content did not differ from NC except in RH3 at 12 h, where it was higher (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The acetate-to-propionate ratio was significantly lower in RH3 at 12 h and in RH14 at 48 h compared to other groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetagenomic analysis of in vitro supplementation with \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetagenomic sequencing generated a total of 1,302,559,334 reads, with 72,364,407 \u0026plusmn; 930,848 reads per sample (mean \u0026plusmn; SEM). Furthermore, a total of 1,294,690,664 reads were retained, with 71,927,259 \u0026plusmn; 929,658 reads per sample (Table S4).\u003c/p\u003e\n\u003cp\u003eComparative analysis of the rumen microbiota across six treatments revealed no significant differences at the microbial domain level. As shown in Table S5, Bacteria, Archaea, Eukaryota, and Viruses demonstrated no statistically significant variations among the treatments (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u0026nbsp;For bacterial \u0026alpha;-diversity, the Shannon index was significantly higher in the RH27 treatment compared to RH19 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), while the Simpson index was significantly higher in RH19 than in RH27 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Additionally, no significant differences were observed in other \u0026alpha;-diversity indices among the remaining treatments (Fig 4A).\u0026nbsp;Regarding archaea\u0026nbsp;\u0026alpha;-diversity (Fig 4B), no significant differences were observed across any treatments (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u0026nbsp;Furthermore, PCoA analysis of bacterial\u0026nbsp;\u0026beta;-diversity revealed significant separation between in vitro supplementation groups of\u003cem\u003e\u0026nbsp;Prevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e based on Bray-Curtis distances. The first principal coordinate (PC1) accounted for 50.01% of variation, while PC2 explained 15.28% (Fig 4C). For archaeal communities, PCoA analysis showed that PC1 and PC2 explained 76.19% and 15.39% of variation, respectively (Fig 4D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbundance characteristics and taxonomic differences of rumen microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor bacterial composition (Fig 4E), predominant phyla included \u003cem\u003eBacteroidetes\u003c/em\u003e (66.58 \u0026plusmn; 1.14%), \u003cem\u003eBacillota\u003c/em\u003e (21.64 \u0026plusmn; 0.90%), and \u003cem\u003ePseudomonadota\u003c/em\u003e (4.66 \u0026plusmn; 0.66%). Dominant genera comprised \u003cem\u003ePrevotella\u0026nbsp;\u003c/em\u003e(43.95 \u0026plusmn; 0.92%), \u003cem\u003eCandidatus\u003c/em\u003e_\u003cem\u003eCryptobacteroides\u003c/em\u003e (6.62 \u0026plusmn; 0.46%), \u003cem\u003eXylanibacter\u003c/em\u003e (2.93 \u0026plusmn; 0.14%), \u003cem\u003eCandidatus\u003c/em\u003e_\u003cem\u003eLimimorpha\u003c/em\u003e (2.81 \u0026plusmn; 0.12%), and \u003cem\u003eSucciniclasticum\u003c/em\u003e (2.36 \u0026plusmn; 0.31%). Prevalent species featured \u003cem\u003ePrevotella\u003c/em\u003e_\u003cem\u003esp\u003c/em\u003e. (39.97 \u0026plusmn; 0.83%), \u003cem\u003eCandidatus\u003c/em\u003e_\u003cem\u003eCryptobacteroides_sp\u003c/em\u003e. (5.36 \u0026plusmn; 0.41%), \u003cem\u003eXylanibacter\u003c/em\u003e_\u003cem\u003eruminicola\u003c/em\u003e (2.77 \u0026plusmn; 0.13%), \u003cem\u003eCandidatus\u003c/em\u003e_Limimorpha_equi (2.06 \u0026plusmn; 0.10%), and \u003cem\u003eEubacterium\u003c/em\u003e_sp. (2.01 \u0026plusmn; 0.20%).\u0026nbsp;In the differential abundance analysis of rumen microbiota (Fig 5A) at the phylum level, RH3 and RH19 significantly increased \u003cem\u003eBacteroidetes\u003c/em\u003e abundance compared to the NC treatment (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), while significantly decreasing \u003cem\u003eBacillota\u003c/em\u003e abundance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Additionally, RH14 treatment also exhibited significantly lower \u003cem\u003eBacillota\u003c/em\u003e abundance than NC (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Furthermore, RH14 showed significantly reduced \u003cem\u003eSpirochaetota\u003c/em\u003e abundance relative to all other treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;At the genus level, \u003cem\u003ePrevotella\u003c/em\u003e abundance was significantly higher in RH3, RH14, and RH19 treatments compared to NC group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Additionally, \u003cem\u003eEubacterium\u003c/em\u003e abundance in RH14 and RH19 was significantly lower than in all other treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). For \u003cem\u003eTreponema\u003c/em\u003e abundance, all treatments except RH14 showed significantly higher levels than NC (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), while RH14 exhibited significantly reduced abundance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Regarding \u003cem\u003eRuminococcus\u003c/em\u003e abundance, only RH19 showed a significant decrease (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), with no significant differences observed among other treatments (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Notably, \u003cem\u003eCandidatus\u003c/em\u003e_\u003cem\u003eCryptobacteroides\u003c/em\u003e abundance in RH27 was significantly higher than in all other treatment groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;At the species level, RH3, RH14, and RH19 had higher \u003cem\u003ePrevotella\u003c/em\u003e_sp. abundance than NC, while RH27 and RH35 had lower (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The RH19 treatment group demonstrated a significantly higher abundance of \u003cem\u003eM. elsdenii\u003c/em\u003e in the fermentation broth compared to other treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Fig. S3A).\u0026nbsp;The archaeal community was dominated by the phylum Euryarchaeota (91.60 \u0026plusmn; 1.06%) and the genus \u003cem\u003eMethanobrevibacter\u003c/em\u003e (80.92 \u0026plusmn; 2.38%) (Fig 4F). No significant differences were found at the phylum or genus level (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05), but a downward trend in the abundance of Euryarchaeota and \u003cem\u003eMethanobrevibacter\u003c/em\u003e was observed in the supplemented groups (Fig 5B).\u003c/p\u003e\n\u003cp\u003eFigure S2 presents the abundance profiles and differential analysis of eukaryotic species after supplementation with \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e. At the phylum level, \u003cem\u003eCiliophora\u003c/em\u003e was the most abundant group, though no significant differences were observed among treatments (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). The fungal phylum \u003cem\u003eMucoromycota\u003c/em\u003e was the only one significantly reduced following supplementation (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). At the genus level, no significant differences in abundance were detected. Species-level analysis revealed that \u003cem\u003eBlepharisma stoltei\u003c/em\u003e was significantly lower in groups RH3 and RH27 compared to NC (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), while other eukaryotic species showed no significant changes.\u003c/p\u003e\n\u003cp\u003eSince enteric methane in dairy cows is primarily produced by archaea, and bacteria\u0026mdash;which dominate the rumen microbiota\u0026mdash;generate the key substrates for methanogenesis (e.g., H₂, CO₂, volatile fatty acids, and methyl compounds), only Bacteria and Archaea were included in subsequent comparative analyses \u003csup\u003e[37]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Profiling of the Rumen Microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional profiling of the rumen microbiota was conducted through annotation of metagenomic sequences against KEGG pathways and CAZyme genes.\u003c/p\u003e\n\u003cp\u003eSequence mapping identified 321 KEGG level‑3 pathways representing core rumen metabolic functions. Among the top 50 differentially abundant pathways (Fig. 5C), methane metabolism was significantly downregulated in all treatments compared to the NC group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Within Metabolic pathways, only RH27 group showed no difference from the control (\u003cem\u003eP \u003c/em\u003e \u0026gt; 0.05), while all other treatments were significantly downregulated (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eKey pathways related to amino acid, carbohydrate, lipid, and energy metabolism were further examined due to their link with enteric methane emissions and energy use (Fig S3C-F). In amino acid metabolism, six pathways showed significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). For lipid metabolism, only three pathways differed significantly (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). In energy metabolism, solely Carbon fixation by Calvin cycle exhibited significant differential abundance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). KEGG module analysis (Fig. 5D) revealed that among the top 50 modules. The cobalamin (vitamin B12) biosynthesis module remained unchanged in RH19 but was downregulated in all other treatments (P \u0026lt; 0.05). Additionally, the acetoclastic methanogenesis pathway was significantly downregulated in RH14, RH19, and RH35 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). However, given the minor role of acetoclastic methanogenesis in the rumen ecosystem, its downregulation, while statistically significant, likely contributes less to the overall methane mitigation \u003csup\u003e[38, 39]\u003c/sup\u003e. More importantly, differential analysis of other core methanogenic modules showed that the Carbon dioxide reduction to methane module\u0026mdash;the dominant hydrogenotrophic pathway in the rumen\u0026mdash;along with the methylamine and methanol methanogenesis modules, were significantly enriched in the NC group compared to the supplemented treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) \u003csup\u003e[40, 41]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eA total of 613 CAZyme‑encoding genes were detected, classified into: 17 AAs, 77 CBMs, 17 CEs, 338 GHs, 92 GTs, and 71 PLs. Total CAZyme abundance differed between groups (Fig. 6A): RH35 was higher than the NC and RH14 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), and RH27 was higher than RH14 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). However, no significant differences were found in the abundance of any individual CAZyme class (AA, CBM, CE, GH, GT, PL) across treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig. 6B). Screening within the six classes identified specific changes after supplementation with \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e, significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) were detected in 2 AAs, 9 CBMs, 40 GHs, 5 CEs, 8 GTs, and 1 PL (Fig. 6C‑F). To pinpoint genes involved in fiber degradation, CAZyme genes encoding cellulases, hemicellulases, and ligninases were examined (Fig. 6G, Table S6). Among 64 GH families analyzed, only seven (GH92, GH67, GH39, GH1, GH4, GH38, and GH113) showed significant differential abundance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Analysis of fiber‑degradation pathways revealed multiple enzymes that drive the fermentation of dietary substrates to volatile fatty acids (VFAs). Since propionate formation consumes H₂\u0026mdash;a substrate for methanogenesis\u0026mdash;increasing propionate production can reduce methane emissions. Focusing on glucose fermentation pathways to acetate, propionate, and butyrate, we identified 14 key enzymes (Fig. 6H). Among these, the abundances of EC:5.3.1.9 and EC:2.7.1.11 (Fig. 6I) were significantly higher in RH27 compared to other treatments (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobial Interactions and Correlations with Fermentation Parameters\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Co-occurrence network analysis uncovered potential interactions among microbes. Bacterial network analysis identified a total of 5342 significant associations, with distinct structures for each treatment (Fig S4). The most frequent negative correlations were found between the Bacteroidetes and Bacillota phyla. Archaeal co-occurrence network analysis identified 5558 significant relationships, with the most prevalent positive correlations occurring within the Euryarchaeota phylum (Fig S5). Supplementation with rumen-derived bacteria reduced interconnectivity within the archaeal network.\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed significant associations between microbial taxa and fermentation parameters (Fig 6J, K). \u003cem\u003ePrevotella\u003c/em\u003e_sp. showed a significant positive correlation with TVFA (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cem\u003eEubacterium\u003c/em\u003e_sp. exhibited a positive correlation with methane content (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Among archaea, \u003cem\u003eMethanobrevibacter\u003c/em\u003e_sp. exhibited significant negative correlations with NH₃-N and acetate (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study successfully isolated and characterized four \u003cem\u003ePrevotella\u003c/em\u003e strains (RH3, RH14, RH27, RH35) and one \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e strain (RH19) from the rumen. Their functional roles in rumen fermentation and methane mitigation were systematically evaluated through genomic annotation, in vitro fermentation, and metagenomic analyses.\u0026nbsp;The functional roles of these isolates in rumen fermentation are rooted in their distinct genomic architectures \u003csup\u003e[42]\u003c/sup\u003e.\u0026nbsp;RH3 had larger genome and higher proportion of CEs compared to other \u003cem\u003ePrevotella\u003c/em\u003e strains suggest an expanded carbohydrate esterification capacity\u0026nbsp;\u003csup\u003e[43]\u003c/sup\u003e.\u0026nbsp;In contrast, \u003cem\u003eM. elsdenii\u003c/em\u003e RH19 minimal PLs but dominant GTs align with its role in lactate-to-propionate conversion requiring glycosyl modifications\u0026nbsp;\u003csup\u003e[44]\u003c/sup\u003e.\u0026nbsp;The high GH activity in \u003cem\u003ePrevotella\u003c/em\u003e strains is crucial for plant polysaccharide degradation\u0026nbsp;\u003csup\u003e[45]\u003c/sup\u003e.\u0026nbsp;While \u003cem\u003eM. elsdenii\u0026apos;s\u003c/em\u003e genomic specialization in amino acid metabolism corroborates its known proteolytic and lactate-utilizing functions\u0026nbsp;\u003csup\u003e[46]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe observed in vitro rumen fermentation dynamics provide a direct link between the supplemented strains and methane mitigation. The significantly lower methane yields across all bacterial treatments, with RH14 being the most effective, align with RH14\u0026rsquo;s high GH-mediated carbohydrate fermentation, promoting propionate formation (a hydrogen sink) over acetoclastic methanogenesis \u003csup\u003e[47]\u003c/sup\u003e.\u0026nbsp;The reduced acetate/propionate ratio in RH3 and RH14 further supports their role in redirecting metabolic hydrogen toward propionigenesis, thereby competitively inhibiting methanogenesis\u0026nbsp;\u003csup\u003e[48]\u003c/sup\u003e.\u0026nbsp;The elevated NH₃-N and MCP in the RH3 treatment are likely due to its exceptional peptidase activity enhancing proteolysis.\u003c/p\u003e\n\u003cp\u003eMetagenomic insights elucidate the ecological and functional mechanisms behind the methane reduction. The significant increase in Bacteroidetes (particularly \u003cem\u003ePrevotella\u003c/em\u003e) and decrease in Bacillota (e.g., \u003cem\u003eRuminococcus\u003c/em\u003e) in treatments like RH3 and RH19 indicate niche competition for fibrous substrates. This nutritional competition may decrease the availability of H₂ for hydrogenotrophic methanogenesis \u003csup\u003e[49, 50]\u003c/sup\u003e.\u0026nbsp;The enrichment of \u003cem\u003eCandidatus_Cryptobacteroides\u003c/em\u003e in RH27, as a hydrogen-consuming propionate producer, may further suppress methanogenesis through competitive H₂ consumption\u003csup\u003e[49-51]\u003c/sup\u003e. Additionally, following in vitro supplementation, the RH19 treatment group demonstrated a significantly higher abundance of \u003cem\u003eM. elsdenii\u003c/em\u003e n the fermentation broth compared to other treatments. which also confirms the successful colonization of \u003cem\u003eM. elsdenii\u003c/em\u003e via in vitro supplementation. As the relative abundance of \u003cem\u003eM. elsdenii\u003c/em\u003e in the rumen is notably low (typically below 1%)\u0026nbsp;\u003csup\u003e[52]\u003c/sup\u003e, it was consequently not represented in the previous analysis of dominant bacterial populations.\u003c/p\u003e\n\u003cp\u003eThe downward trend in the hydrogenotrophic genus \u003cem\u003eMethanobrevibacter\u003c/em\u003e, coupled with the significant functional downregulation of the dominant \u0026quot;Carbon dioxide reduction to methane\u0026quot; pathway, strongly suggests that methane mitigation was achieved primarily by limiting the substrate (H₂) and suppressing the activity of the major metabolic route methanogens depend on, rather than eliminating them \u003csup\u003e[37, 41, 53, 54]\u003c/sup\u003e. The significant downregulation of the acetoclastic pathway, while statistically significant, likely contributes less to overall mitigation given its minor role in the rumen \u003csup\u003e[38, 39]\u003c/sup\u003e. This pattern strongly suggests that the primary inhibitory effect of the supplemented bacteria on methanogenesis is directed against the hydrogenotrophic (CO₂-reduction) pathway \u003csup\u003e[37]\u003c/sup\u003e. This finding aligns perfectly with the earlier observed ecological shifts, where bacterial competitors like \u003cem\u003eCandidatus_Cryptobacteroides\u003c/em\u003e likely reduced H₂ availability \u003csup\u003e[50]\u003c/sup\u003e, and the downward trend in the hydrogenotrophic genus Methanobrevibacter. Thus, the functional genomics data confirm that methane reduction was achieved not by eliminating methanogens, but by strategically limiting the substrate (H₂) and suppressing the activity of the major metabolic route (CO₂ reduction) they depend on.\u003c/p\u003e\n\u003cp\u003eFurthermore, the downregulation of the cobalamin (B12) biosynthesis module in most treatments likely compounded the functional suppression, Vitamin B12 is a crucial coenzyme for several key steps in methanogenesis\u003csup\u003e[55]\u003c/sup\u003e. Its reduced biosynthesis likely compounded the functional suppression of methanogenic pathways, further constraining methane production capacity in these treatments\u003csup\u003e[56]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe shift in specific fiber-degrading CAZyme families may redirect H₂ flux by modulating fermentation end-products \u003csup\u003e[57]\u003c/sup\u003e. The synergistic upregulation of key glycolytic enzymes (EC:5.3.1.9 and EC:2.7.1.11) in the RH27 treatment enhances flux towards pyruvate and propionate synthesis. Notably, the EC:5.3.1.9 (glucose isomerase) and EC:2.7.1.11 (phosphofructokinase) holds synergistic significance. These two enzymes collectively enhance the flux of the glycolytic (EMP) pathway, directing more carbon substrates toward pyruvate, thereby providing ample precursors for subsequent propionate synthesis. Since propionate generation is a process that consumes reducing power (NADH) and/or H₂, the intensification of this metabolic flow directly competes with methanogenic archaea for available H₂, serving as the core biochemical driver for methane mitigation in the treatment \u003csup\u003e[47]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe microbial interaction networks and correlation analyses integrate these findings. The elevated Bacteroidetes-to-Bacillota ratio in supplemented groups is significant, as previous research correlates an increased ratio with enhanced energy utilization efficiency in dairy cows \u003csup\u003e[58, 59]\u003c/sup\u003e. This suggests that supplementation of rumen microorganisms may improve feed utilization. The reduced interconnectivity within the archaeal network upon bacterial supplementation reflects diminished archaeal activity and suppressed dominant populations \u003csup\u003e[60]\u003c/sup\u003e.\u0026nbsp;The significant positive correlation between \u003cem\u003ePrevotella\u003c/em\u003e_sp. and TVFA is consistent with reports linking its enrichment to enhanced VFA production\u0026nbsp;\u003csup\u003e[61]\u003c/sup\u003e,\u0026nbsp;while the positive correlation of \u003cem\u003eEubacterium\u003c/em\u003e_sp. with methane highlights its potential role in methanogenic niches.\u003c/p\u003e\n\u003cp\u003eIn conclusion, supplementation with rumen-derived \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eM. elsdenii\u003c/em\u003e strains consistently reduced methane production in vitro through a multi-faceted mechanism. This involved redirecting hydrogen towards propionate synthesis, competitively altering the rumen bacterial community to reduce H₂ availability for archaea, and functionally suppressing the expression of key methanogenic pathways, particularly the dominant hydrogenotrophic CO₂-reduction route.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we successfully isolated and identified four \u003cem\u003ePrevotella\u003c/em\u003e strains and one \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e strain from the rumen. By combining genomic, in vitro fermentation, and metagenomic analyses, we demonstrated that these strains modulate the ruminal environment by enhancing carbohydrate fermentation, promoting propionate production, and globally suppressing the \u0026quot;methane metabolism\u0026quot; pathway, particularly the dominant hydrogenotrophic (CO₂-reduction) methanogenesis pathway in the rumen. The observed methane reduction was driven more by functional suppression of methanogenic pathways rather than by displacement of the archaeal community. These findings highlight the potential of using rumen-derived bacteria as probiotics to improve rumen fermentation efficiency and thereby reduce methane emissions in ruminant production.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the Institute of Animal Science of Jiangsu Academy of Agricultural Sciences and Nanjing Weigang Dairy Co., Ltd. for their assistance in providing rumen fluid donors, sample processing, and data collection. Special thanks are extended to Professor Yanfen Cheng from Nanjing Agricultural University for her financial support of this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor disclosures: HBL, CYF, LBY, and XDF, no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHBL and LBY designed the study. WX and XM collected the samples. CYF and DCL provide the funding declaration. Nazar and XDF\u0026nbsp;revised the manuscript.\u0026nbsp;The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2023YFD1300903).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nucleotide sequences of the 16S rRNA gene from five strains of rumen bacteria isolated from dairy cows have been submitted to GenBank and assigned the following accession numbers: strain RH3 (accession number: PX426733), strain RH14 (PX426734), strain RH19 (PX426735), strain RH27 (PX426736), and strain RH35 (PX426737). The rumen metagenome sequences and bacterial whole-genome sequences were deposited into NCBI Sequence Read Archive (SRA) under the accession number of\u0026nbsp;PRJNA1312568 and PRJNA1314459.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFILONCHYK M, PETERSON M P, ZHANG L, et al. Greenhouse gases emissions and global climate change: Examining the influence of CO2, CH4, and N2O [J]. Science of The Total Environment, 2024, 935: 173359.\u003c/li\u003e\n\u003cli\u003eANDERSON B, BARTLETT K B, FROLKING S, et al. Methane and nitrous oxide emissions from natural sources [J]. 2010.\u003c/li\u003e\n\u003cli\u003eSTEINFELD H. Livestock\u0026apos;s long shadow: environmental issues and options [M]. Food \u0026amp; Agriculture Org., 2006.\u003c/li\u003e\n\u003cli\u003eBERGMAN E. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species [J]. 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Animals, 2020, 10(10): 1855.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Effect of adding rumen-derived bacteria on artificial rumen fermentation parameters\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSampling time\u0026nbsp;/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003eTreatments\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSEM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eRH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eRH14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eRH19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eRH27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eRH35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.56\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.56\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.51\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.62\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.50\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.64\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.51\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.65\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 643px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.76\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.89\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.03\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.95\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.99\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.19\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.90\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.57\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.86\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.12\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.76\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.37\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.75\u003csup\u003ebcd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.77\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.25\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 643px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eMCP（mg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.44\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.27\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.75\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.79\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.29\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.91\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.82\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.52\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.58\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.80\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.02\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.87\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.93\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.75\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.72\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.44\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.64\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.80\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.87\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.92\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.05\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.04\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\u003eNote: \u003csup\u003e1\u003c/sup\u003e NC= Negative control, RH3 =\u0026nbsp;\u003cem\u003ePrevotella RH3\u003c/em\u003e; RH14 =\u0026nbsp;\u003cem\u003ePrevotella RH14\u003c/em\u003e; RH19 =\u0026nbsp;\u003cem\u003eMegasphaera elsdenii\u003c/em\u003e \u003cem\u003eRH19\u003c/em\u003e; RH27 =\u0026nbsp;\u003cem\u003ePrevotella RH27\u003c/em\u003e; RH35 =\u003cem\u003e\u0026nbsp;Prevotella RH35\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eData were analyzed using the one-way ANOVA procedure (n\u0026thinsp;=\u0026thinsp;3 per group). \u003csup\u003ea,b\u003c/sup\u003eMeans bearing different superscripts in the same row differ significantly (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e SEM: standard error of the mean.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Effect of supplemented rumen-derived bacteria on VFAs in rumen fermentation broth in vitro\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11px;\"\u003e\n \u003cp\u003eSampling time\u0026nbsp;/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 57px;\"\u003e\n \u003cp\u003eTreatments\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 8px;\"\u003e\n \u003cp\u003eSEM\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eRH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eRH14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eRH19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eRH27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eRH35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eTVFA (mmol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e76.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e93.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e79.47\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e77.78\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e64.37\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e71.39\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e79.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e96.41\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e79.69\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e97.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e78.73\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e92.49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e98.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e101.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e104.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e102.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e98.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e103.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e82.35\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e104.69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e101.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e95.43\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e89.05\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e94.68\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e56.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e53.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e58.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e58.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e58.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e58.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e56.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e57.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e57.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e56.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e58.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e62.29\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e61.22\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e59.62\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e59.90\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e58.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e60.53\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e60.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e60.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e59.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e59.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e61.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e59.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003ePropionate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e24.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.30\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.42\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.21\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e21.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e21.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e21.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e21.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e17.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e18.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e18.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e18.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e20.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e18.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e19.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eButyrate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.67\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e13.75\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.08\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.58\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.54\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.39\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e10.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.01\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.08\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.89\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e11.96\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e12.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eValerate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.88\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.94\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.29\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.84\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.53\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.88\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.00\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.18\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.14\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.79\u003csup\u003ecd\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.50\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 12px;\"\u003e\n \u003cp\u003eAcetate/ Propionate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.15\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.61\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.88\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.21\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.90\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.09\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.15\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.25\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.70\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\u003eNote: \u003csup\u003e1\u003c/sup\u003e NC= Negative control, RH3 =\u0026nbsp;\u003cem\u003ePrevotella RH3\u003c/em\u003e; RH14 =\u0026nbsp;\u003cem\u003ePrevotella RH14\u003c/em\u003e; RH19 =\u0026nbsp;\u003cem\u003eMegasphaera elsdenii\u003c/em\u003e \u003cem\u003eRH19\u003c/em\u003e; RH27 =\u0026nbsp;\u003cem\u003ePrevotella RH27\u003c/em\u003e; RH35 =\u003cem\u003e\u0026nbsp;Prevotella RH35\u003c/em\u003e.Data were analyzed using the one-way ANOVA procedure (n\u0026thinsp;=\u0026thinsp;3 per group). \u003csup\u003ea,b\u003c/sup\u003eMeans bearing different superscripts in the same row differ significantly (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e SEM: standard error of the mean.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-animal-science-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jasb","sideBox":"Learn more about [Journal of Animal Science and Biotechnology](http://jasbsci.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jasb/default.aspx","title":"Journal of Animal Science and Biotechnology","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Methane, Prevotella, M. elsdenii, Metagenomic sequencing","lastPublishedDoi":"10.21203/rs.3.rs-8723424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8723424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Enteric methane production represents a major energy loss in ruminant systems and contributes substantially to agricultural greenhouse gas emissions. Increasing ruminal propionate production has been proposed as an effective strategy to redirect metabolic hydrogen away from methanogenesis. However, the functional mechanisms by which specific rumen bacteria regulate methane production remain incompletely understood\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In this study, four rumen-derived \u003cem\u003ePrevotella\u003c/em\u003estrains and one \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e strain were isolated, genomically characterized, and evaluated using an in vitro rumen fermentation model. Supplementation with these strains significantly reduced methane yield while increasing total gas production and volatile fatty acid concentrations, particularly propionate. Shotgun metagenomic analysis revealed that methane mitigation was not associated with major alterations in archaeal abundance, but rather with a pronounced functional suppression of methanogenic pathways. Specifically, the dominant hydrogenotrophic (CO₂-reduction) methanogenesis module was significantly downregulated in all supplemented treatments. Concurrently, pathways and enzymes involved in carbohydrate fermentation and propionate synthesis were enriched, indicating a redirection of metabolic hydrogen toward alternative microbial sinks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e These findings demonstrate that rumen-derived \u003cem\u003ePrevotella\u003c/em\u003eand \u003cem\u003eMegasphaera elsdenii\u003c/em\u003e can reduce methane production primarily through functional regulation of microbial metabolism rather than displacement of methanogens. This study provides mechanistic insights into microbially mediated hydrogen redistribution in the rumen and offers a functional basis for developing future probiotic strategies aimed at improving fermentation efficiency and mitigating enteric methane emissions.\u003c/p\u003e","manuscriptTitle":"Rumen-derived Prevotella and Megasphaera elsdenii mitigate methane production through functional modulation of rumen microbial metabolism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 13:50:19","doi":"10.21203/rs.3.rs-8723424/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-26T05:55:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T15:21:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T13:33:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213722637990435628554918813835341097868","date":"2026-03-12T06:41:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259055809752667383234648423291555240308","date":"2026-03-10T10:10:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T21:15:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254738657063693389643048059471883012676","date":"2026-02-12T06:31:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70014399255629547203197237452044650812","date":"2026-02-06T13:43:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164436757161737504359491514689340466495","date":"2026-02-06T11:06:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T07:59:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T07:33:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-31T02:35:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Animal Science and Biotechnology","date":"2026-01-28T15:43:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-animal-science-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jasb","sideBox":"Learn more about [Journal of Animal Science and Biotechnology](http://jasbsci.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jasb/default.aspx","title":"Journal of Animal Science and Biotechnology","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7dff3a40-39a9-45a8-a7da-d37df5850bc9","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T12:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 13:50:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8723424","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8723424","identity":"rs-8723424","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}