Exploring the link between ruminal methane production and physiological resilience in Japanese Black cattle during fattening

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Here, we examined the characteristics of rumen microbiome and physiological resilience associated with methane production, a breed characterized by enhanced intramuscular fat deposition. Results: Methane emissions were measured during three phases: early (13 months), middle (20 months), and late (28 months), and the liver transcriptome, blood metabolites, hormones, rumen fermentation characteristics, and microbiota were analysed. Hydrogen-sinking microbes such as Anaerovorax and Succinivibrio were present at low levels, whereas the prevalence of hydrogen-producing microbes including Christensenellaceae , Clostridium methylpentosum , and Mogibacterium was high in cattle with high methane emissions. Functional profiling of rumen microbiota revealed decreased coenzyme M biosynthesis and an increased hydrogen sink from L-glutamate biosynthesis in low-emission cattle. In the liver, glutamate-derived ornithine and elevated ornithine transcarbamylase gene expression facilitated ammonia detoxification in low-emission cattle, whereas the glutamate transporter-encoding gene SLC1A1 was upregulated in high-emission cattle, thereby enhancing glutathione synthesis and reducing the oxidative stress induced by beta-hydroxybuteric acid. Conclusions: These ruminal and physiological changes reflect the resilience of these cattle to different rumen fermentation conditions, have potential as biomarkers for monitoring the methanogenic potential of Japanese Black cattle, and highlight the upstream oxoglutarate-to-glutamate biosynthesis pathway as a promising target for decreasing methane production by reducing hydrogen in the rumen. Methane production Hydrogen sink Physiological changes Japanese Black Cattle Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Methane (CH 4 ), a greenhouse gas, is 28–34 times more potent than carbon dioxide (CO 2 )[1]. It constitutes 14% of the total [2] and approximately 5% of the anthropogenic greenhouse gas emissions worldwide, primarily owing to the ruminal digestion of livestock [3]. Moreover, without proper management, the negative effects of enteric methane emissions from the livestock industry will likely worsen owing to the increasing demand for milk and meat fuelled by population growth and urbanisation. Therefore, coordinated efforts are essential to ensure animal production system sustainability and health. Ruminants, such as cows and sheep, have a long digestive tract that harbours diverse microbiota in their rumen. These microbiotas consist of numerous microorganisms, including bacteria, archaea, and methanogens, which enable ruminants to convert otherwise indigestible plant components into high-quality protein products such as milk and meat. During this process, ruminal microorganisms break down plant polymers and produce volatile fatty acids (VFAs), hydrogen (H 2 ), and carbon dioxide as the primary fermentation by-products. VFAs are absorbed along the ruminal epithelium, providing energy for host animal growth and production. However, as continuous ruminal fermentation requires hydrogen removal, methanogens metabolize hydrogen to facilitate CO 2 reduction to CH 4 , which is then emitted from the body. This process consumes 2–15% of the total ruminant energy intake [4], prompting farmers to compensate by increasing feed, which may lead to inefficiencies. Thus, in-depth research is necessary to understand the mechanisms of methane generation and develop strategies to reduce these emissions. Various methane mitigation strategies influence ruminal methanogenesis, including dietary manipulation, rumen control, and selective breeding [5-7]. However, despite extensive feeding additive research, few techniques for reducing enteric CH 4 emissions are cost-effective, non-residual, non-toxic, and acceptable to farmers [8]. Alternatively, a promising supplement-free strategy for regulating ruminal methane generation is to divert hydrogen from methanogenesis to more nutritionally advantageous pathways; e.g. by. reducing acetate and butyrate while increasing propionate production. Better understanding the causes of methane production and identifying biomarkers such as rumen fermentation characteristics, blood metabolites, and associated ruminal microorganisms are key to developing effective methane reduction strategies. We previously demonstrated that Japanese Black cattle exhibit unique physiological traits at different stages of fattening [9]. Furthermore, because these animals are raised under unique feeding systems aimed at enhancing intramuscular fat deposition, their ruminal environment and physiological characteristics likely differ from those of other beef cattle breeds. However, the relationship between ruminal microbiota and methane emissions during the fattening period and an in-depth understanding of the physiological changes related to methane production in this breed remain to be elucidated. Thus, in this study we analysed the physiological traits of Japanese Black steers raised using conventional livestock practices in Japan, examining the influence of ruminal microbiota on the gut–liver axis and blood metabolites. By providing comprehensive data on physiological features including blood metabolites, hepatic gene expression, and ruminal fermentation characteristics along with ruminal microbiota compositional and functional profiling, this study offers new insights regarding effectively minimizing methane emissions in Japanese Black cattle. Results Effect of methane production on the ruminal microbial community Figure 1a and 1b shows the dominant members of the bacterial and archaeal communities within the HME and LME groups at the phylum and genus. At the phylum level, Firmicutes and Bacteroidetes dominated the ruminal microbial community with relative abundances of 56.62 and 36.0%, respectively. Patescibacteria was the next most abundant bacterial phylum (2.83%), followed by Actinobacteriota (1.32%), Euryarchaeota (1.01%), Planctomycetota (0.65%), and Spirochaetota (0.63%). Prevotella was the most abundant bacterial genus (20.21% mean relative abundance across all samples), followed by Acetitomaculum (3.88%), Muribaculum (3.97%), and Ruminococcus (3.10%). Figure 1c shows the differentially abundant microbiota in the two groups. HME exhibited enriched Prevotella , Prevotellaceae , Bacteroidota, Christensenellaceae , Christensenellaceae R-7 group, and Methanobrevibacter , whereas LME showed enriched Succinivibrio , Succinivibrionaceae , Clostridia UCG-014 , Eubacterium nodatum group, Methanosphaera , Anaerovorax , and Firmicutes(all P < 0.05). Figure 1d and 1e present Venn diagrams illustrating shared and exclusive amplicon sequence variants (ASVs) in the ruminal microbial community. The HME group contained higher numbers of exclusive ASVs during each of the three (T1, T2, and T3) fattening periods. Among the periods, exclusively detected ASVs were more abundant during T2 in the HME and during T3 in the LME groups. As revealed by PCoA and permutational multivariate analysis of variance (PERMANOVA), the overall microbial community structure differed between the two groups at T2 (PERMANOVA; P < 0.05). Regression tree for methane emission in Japanese Black cattle Figure 2 presents the regression tree analysis with methane emissions as the dependent variable. Figure 2a shows all genus-level microbiota as independent variables. All cattle were split into subgroups based on Christensenellaceae abundance (Node 1); Nodes 2 and 3 represent cattle with abundance of ≤ 6.209 and > 6.209, respectively. At the second tree depth, Node 3 was partitioned based on Methanosphaera abundance; Nodes 4 and 5 reflect ≤ 3.765 and > 3.765 abundance, respectively. At the third tree depth, Node 5 was further split based on Prevotellaceae abundance, with respective Node 6 and 7 abundance of ≤ 25.214 and > 25.214. Figure 2b illustrates selected microbiota that were differentially abundant between HME and LME. All cattle were divided into subgroups based on Clostridium methylpentosum group abundance (Node 1); Nodes 2 and 5 represent cattle with abundance ≤ 0.307 and > 0.307, respectively. At the second tree depth, Node 2 was partitioned based on Mogibacterium abundance; Nodes 3 and 4 represent abundances of ≤ 0.16 and > 0.16. Node 5 was partitioned based on the abundance of the Lachnospiraceae NK3A20 group; Nodes 6 and 7 are indicative of ≤ 2.211 and > 2.211 abundance. At the third tree depth, Node 7 was further split based on Desulfobacteroita abundance, with Node 8 and 9 abundance of ≤ 0.03 and > 0.03, respectively. Functional profiles of ruminal microbiota related to methane emission Functional genetic profiling revealed a total of 398 MetaCyc pathways among the 62 ruminal microbiome samples across the entire fattening period. Various analyses were conducted to identify the MetaCyc pathways related to methane emissions levels (Fig. 3). Spearman's correlation analysis (correlation coefficient, |r|≥ 0.5; P < 0.05). revealed P261-PWY and PWY-7431 as positively and negatively correlated with methane emission levels, respectively (Fig. 3a). An ANOVA with mixed effects model revealed that PWY-5505 and GLUTORN-PWY levels were significantly higher (P<0.01) in the LME group (Fig. 3b). In a regression tree with methane emissions as the dependent variable and MetaCyc pathways as independent variables, all cattle were significantly split into subgroups based on P261-PWY pathway abundance (Node 1), with Nodes 2 and 5 representing cattle with abundance ≤ 0.016 and > 0.016, respectively (Fig. 3c). At the second tree depth, Node 2 was significantly partitioned based on PWY-5100 pathway abundance, with Node 3 and 4 abundance of ≤ 0.894 and > 0.894. Node 5 was significantly partitioned based on PWY-5695 pathway abundance, creating Nodes 6 and 7 at ≤ 0.754 and 0.754 abundance, respectively. Link between methane emissions, blood metabolites, hepatic transcriptome, ruminal microbiota, and fermentation To identify the physiological factors related to methane emission levels, ANOVA with a mixed-effect model was used to compare the HME and LME groups (Additional file 2: Table S2). The regression tree analysis utilized rumen fermentation characteristics, blood metabolites, blood amino acids, and hepatic genes as independent variables and methane emissions as the dependent variable (Additional file 3: Figure S1, Additional file 4: Figure S2). Ruminal fermentation characteristics (e.g. propionate, butyrate, and ammonia concentrations), blood metabolites (e.g. BHBA and ornithine), and hepatic genes (e.g. solute carrier family 1 member 1 ( SLC1A1 ), Ras-related protein Rab-6A ( RAB6A ), and ornithine transcarbamylase ( OTC )) significantly influenced the methane emission levels. Based on the regression tree and ANOVA results, the potential correlation between methane emission level and physiological parameters was examined using a Bayesian network (Fig. 4). The tabu search algorithm produced eight and six directed connections from the eight and six latent variables related to LME and HME, respectively. HME-group methane emissions were potentially moderated by butyrate (arc strength = 0.63), BHBA (arc strength = 0.68), and PWY-5505 (arc strength = 0.64), whereas PWY-5505 (arc strength = 0.69) potentially moderated LME-group emissions. Discussion Methane production in cattle is a thermodynamic requirement for the microbial conversion of feed into nutrients [10]. The associated removal of hydrogen is critical for the ruminal ecosystem and host because low hydrogenconcentrations ensure high fermentation rates and efficient feed digestion [11]. Therefore, methane production is a natural metabolic process, with an increase in methane production potentially indicating healthier and better-growing cattle. Enteric methane production in Japanese Black cattle is influenced by changes in the rumen microbiome and affects overall metabolism (Figs. 1–3), rendering it crucial to understand the mechanisms of methane production and its metabolic side effects for improving cattle growth and well-being. In particular, we hypothesized that the metabolic changes induced by methane production in cattle are reflected in the ruminal environment, blood metabolites, and liver metabolism. Rumen microbial features related to methane production A more diverse microbial community enables the rumen ecosystem to better adapt to dietary changes, which has been associated with improved ruminant growth performance [12], suggesting that the higher ruminal microbial diversity in the HME group facilitated better adaptation to dietary changes than that in the LME group. Additionally, diverse microbial communities can effectively degrade a wide range of plant materials, potentially enhancing the nutrient intake from feed [13]. The increased fiber degradation can lead to higher ruminal hydrogen production, potentially increasing methane production. Notably, ruminants with larger rumens emit increased methane levels, likely because of longer ruminal feed retention [14]. Therefore, the higher ruminal diversity in the HME group may be related to a larger rumen size than that in the LME group. Regression tree and mixed effect model analyses during the fattening period revealed several key microbiotas associated with methane emissions in Japanese Black cattle. Christensenellaceae , Clostridium methylpentosum , and Mogibacterium , which are related to hydrogen production, were more prevalent in HME cattle. Christensenellaceae , a crucial ruminal hydrogen-producing group [15], effectively breakdown carbohydrates, amino acids, and carboxylic acids to produce acetate and butyrate [16]. This family is associated with methane emissions in Holstein cows [17], sheep [18], and beef cattle [19]. Clostridium methylpentosum specializes in decomposing specific plant materials in the rumen that other bacteria may not utilize efficiently, producing acetate, glycolaldehyde, carbon dioxide, and hydrogen during the fermentation of L-lyxose and B-arabinose [20]. Mogibacterium , a hydrogen-producing fibrolytic bacterium, contributes to methane production by generating phenylacetate, which may facilitate cellulose degradation by R. albus strains [18]. Consistent with our findings, elevated Mogibacterium levels are also observed in cattle with high ruminal methane emissions [21, 22]. In contrast, Succinivibrionaceae , Succinivibrio , Anaerovorax , and Lachnospiraceae NK3A20 were more abundant in LME cattle. Succinivibrio , a member of the family Succinivibrionaceae , produces propionate as its primary fermentation product in the rumen [23]. This genus helps mitigate methane levels through hydrogen consumption and negatively correlates with methane emissions in sheep [24] and dairy cattle [25]. Although propionate production is the primary hydrogen sink, biohydrogenation also plays this role during ruminal conversion of unsaturated to saturated fatty acids [26, 27]. Identification of Anaerovorax as a potential biohydrogenating bacterium [28] also suggests a role in hydrogen sinking. In glucose-supplemented culture experiments, Lachnospiraceae NK3A20 produces hydrogen under low but not high hydrogen concentrations, whereupon fermentation shifts to more reduced organic acid products [29]. Although this bacterium was more abundant in LME cattle, it potentially increases methanogenesis in ruminal environments with lower hydrogen levels. Eubacterium nodatum produces acetate from lysine [30]. Although higher ruminal acetate concentrations were expected in the HME group owing to increased hydrogen production, the acetate generated from amino acids by this bacterium likely contributed to the comparable acetate levels between the HME and LME groups. Additionally, the PWY-5100 pathway, which involves pyruvate fermentation to acetate, was more active in some LME cattle. The equivalent acetate concentrations among HME and LME cattle are consistent with our previous finding [31]. Considering that the connection between ruminal fermentation traits and methane production is likely affected by factors such as breed and feeding management practices [32], high concentrate feeding in Japanese Black cattle likely reduces their reliance on the pyruvate-to-acetate microbial pathway in the rumen, leading to comparable acetate levels regardless of methane emission status. Prevotella can reduce methane emissions by channelling hydrogen into propionic acid production, thereby lowering methanogenesis [33]. Conversely, Prevotella levels were higher in the HME group. Similar observations were made in Holstein cattle, where Prevotella phylotypes were more abundant in animals with lower propionate levels [23]. In sheep, Prevotella bryantii is an indicator of low-methane ruminotypes, whereas other Prevotella phylotypes are associated with high-methane ruminotypes [34]. This discrepancy could be due to differences in propionate production at the phylotype level or variations in metabolic pathways. Metabolic profiling of Prevotella has revealed numerous pathways involving amino acid, carbohydrate, lipid, cofactor and vitamin, nucleotide, and energy (ATP) metabolism [33]. Therefore, further research is necessary to clarify the influence of Prevotella on methane production in Japanese Black cattle. The LME and HME groups had higher relative abundances of Methanosphaera and Methanobrevibacter , respectively, consistent with previous research. In particular, sheep with lower methane yields contained more Methanosphaera and fewer Methanobrevibacter [35]. Similarly, dairy cows with lower methane yields produced 26% less methane than their high methane-yield counterparts, exhibiting higher Methanosphaera and lower Methanobrevibacter abundance [36]. Methanosphaera is a methylotrophic methanogen that depends entirely on hydrogen and utilizes alcohols but not carbon dioxide, formate, or methylamines [37, 38]. Alternatively, Methanobrevibacter is a hydrogenotrophic methanogen that uses hydrogen along with carbon dioxide or formate to produce methane. These methanogens exhibit negative correlations in ruminants [39, 40]. Furthermore, Methanosphaera , with its low hydrogen threshold, can outcompete Methanobrevibacter at low hydrogen partial pressures [41]. Methanosphaera may thus have outcompeted Methanobrevibacter for hydrogen in the LME group, which had low hydrogen pressure, whereas Methanobrevibacter may have outcompeted Methanosphaera in the HME group owing to high ruminal hydrogen concentrations. Further research is needed to understand the competitive dynamics of hydrogen and factors influencing the selection of methanogenic lineages. Examining the relationships among methane, hydrogen, and specific methanogenic lineages could provide valuable insights regarding methanogenesis and help develop strategies to reduce enteric methane emissions in the rumen. KEGG pathways related to methane emission Several MetaCyc pathways associated with methane production were identified using correlation, mixed-effect modelling, and regression tree analyses. In particular, methyl-coenzyme M reductase is targeted by numerous inhibitors of rumen methanogenesis. For example, nitrocompounds such as nitroethane, 2-nitroethanol, and 2-nitro-1-propanol exhibit ability to decrease methyl-coenzyme M reductase activity and inhibit methanogens [42]. Additionally, 3-nitrooxypropanol specifically targets this enzyme, making it an effective tool for studying methane metabolism and potentially reducing methane emissions [43]. Methyl-coenzyme M reductase acts on methyl-coenzyme M (CH 3 -S-CoM), which is an immediate precursor to methane. Coenzyme M is thus essential for methanogenesis in rumen microorganisms during the final step of methane production [44]. Similarly, we identified coenzyme M biosynthesis (P261-PWY) as a critical factor influencing methane emissions during the fattening period in Japanese Black cattle. The PWY-5505 pathway involves a transamination process in which oxoglutarate and ammonium are converted into glutamate by glutamate dehydrogenase. This process is a major source of ammonia fixation and is important for converting non-protein nitrogen into proteins in ruminants [45]. The PWY-5505 pathway was more active in the LME group during the fattening period, which may reflect a lower ruminal ammonia concentration in this group than in HME animals. Additionally, oxoglutarate fermentation to glutamate represents an important hydrogen disposal pathway, wherein oxoglutarate combines with ammonium, NADH, and hydrogen to form glutamate, nicotinamide adenine dinucleotide (NAD + ), and H 2 O, respectively. Conversely, hydrogen production during NAD + conversion to NADH, which occurs during glutamate deamination to oxoglutarate, may be associated with methane synthesis [46]. Furthermore, transamination reactions of ruminal bacteria involving glutamate dehydrogenase proceed at a significantly faster rate than those involving other amino acids, underscoring the elevated dehydrogenation capability of ruminal glutamate biosynthesis [46]. Additionally, formate, a substrate for methanogenesis, can be produced from oxoglutarate by ruminal microbes via a pathway other than the pyruvate formate-lyase reaction [47]. Therefore, the conversion of oxoglutarate to glutamate may also reduce formate production from oxoglutarate. Induced physiological, ruminal, and hepatic changes according to methane production The ruminal ornithine biosynthetic pathway begins with oxoglutarate and proceeds in two stages: glutamate synthesis and ornithine synthesis [48]. The increased PWY-5505 and GLUTRON-PWY pathway activity in the LME group promotes oxoglutarate conversion first to glutamate then to ornithine, respectively. Increased GLUTRON-PWY pathway activity was associated with elevated blood ornithine levels in the LME group. Regression tree analysis of blood amino acids and hepatic gene expression revealed that increased blood ornithine levels and OTC activity were characteristic of LME animals. OTC plays a crucial role in ammonia detoxification and nitrogen waste removal by catalysing the reaction between carbamoyl phosphate and ornithine to form citrulline in the second step of the urea cycle [49]. The ornithine–urea cycle is the primary pathway for ammonia detoxification and urea synthesis in the livers of dairy cattle [50]. The enhanced conversion of ammonia to urea via ornithine and OTC, which helps maintain body health by detoxifying ammonia, may have contributed to the lower ruminal ammonia concentrations in the LME group. Nevertheless, the ammonia levels of HME group (14.55 mg/dL) were higher than those previously reported in Japanese Black cattle fed conventional high concentrate-to-forage ratios during the fattening period (5.5~7.2 [51], 4.2~6.4 [52], 8.85~12.9 [53], 3.5~7.5 [54] 7.42~11.44 mg/dL[31]). Elevated ammonia concentrations can adversely affect the production of VFAs and other fermentation end products, potentially leading to less efficient host nutrient utilisation and energy production [55]. Relatively low ammonia detoxification by the ornithine–urea cycle and transamination (PWY-5505) may lead to increased rumen ammonia levels in the HME group, which could negatively affect rumen fermentation. Regression tree analysis of hepatic genes indicated that SLC1A1 expression was higher in HME cattle. Lower GLUTRON-PWY activity may lead to relatively high ruminal glutamate concentrations, resulting in the elevated SLC1A1 expression in HME animals. SLC1A1 is overexpressed in the liver compared to other organs. Its protein product contributes to the biosynthesis of glutathione, an abundant natural antioxidant in the liver, by facilitating transport of the glutathione precursors L-glutamate and L-cysteine [56], thereby protecting the liver cells from oxidative stress [57]. Butyrate produced during rumen fermentation is absorbed across the ruminal epithelium and converted into BHBA, which is then transported through the bloodstream and used as an energy source in various tissues. Owing to the high-energy diets fed to Japanese Black cattle during the fattening period, BHBA may not be fully utilized as an energy source and may remain in the bloodstream and tissues, potentially inducing inflammatory injury and oxidative stress in cattle hepatocytes through the NF-κB signalling pathway [58]. The elevated hepatic SLC1A1 expression may help mitigate the oxidative stress induced by residual BHBA in the HME group. Moreover, Bayesian network analysis revealed a strong association between BHBA and SLC1A1 (0.987), further supporting the role of SLC1A1 in mitigating oxidative stress. Notably, although the differences in blood urea cycle components, ornithine concentration, and OTC and SLC1A1 expression levels do not directly explain methane production, they may indicate downstream metabolic effects derived from the ruminal oxoglutarate-to-glutamate biosynthesis (PWY-5505) pathway and its role as a hydrogen sink. However, the significant differences in these downstream metabolic processes according to methane-production status provide evidence that PWY-5505 could be an important pathway for decreasing methane production by reducing ruminal hydrogen. This study provides crucial insights regarding the ruminal microbial community of Japanese Black cattle, highlighting its association with blood metabolites, hepatic gene expression, and methane emissions (Fig. 5). The more diverse ruminal microbial communities in the HME group may be associated with higher hydrogen production. Christensenellaceae , Clostridium methylpentosum , and Mogibacterium , which are related to hydrogen production, were more prevalent in HME animals. In contrast, Succinivibrionaceae , Succinivibrio , and Anaerovorax , which are associated with hydrogen sinks, were more abundant in LME cattle. Methanobrevibacter may have outcompeted Methanosphaera for hydrogen because of the higher hydrogen concentrations in the HME group. Downstream metabolic effects from the oxoglutarate-to-glutamate biosynthesis pathway differed between the HME and LME groups. In LME cattle, improved ammonia conversion to urea via ornithine and OTC detoxified ammonia, thereby promoting body health. In HME animals, higher hepatic SLC1A1 expression may help mitigate the oxidative stress caused by elevated BHBA levels. Differences in these metabolic processes suggest that the oxoglutarate-to-glutamate biosynthesis pathway may contribute to hydrogen sinking, thereby serving as a crucial differentiating factor for methane emissions. Methods Animals and sample collection The study involved animal experiments conducted at the Hyogo Prefectural Technology Center of Agriculture, Forestry, and Fisheries in Japan. The experiments followed the guidelines provided by the Institute of Livestock and Grassland Science [59] and the ethical guidance of the Hyogo Prefectural Institute of Agriculture and Forestry and Fisheries Animal Care and Use Committee. The protocol for the experiments was evaluated and approved by the same committee, and all experiments were conducted in compliance with the ARRIVE guidelines. The study used 21 Japanese Black steers that were raised from 12 months of age (initial body weight, 335.6 ± 19.8 kg) until they reached 30 months of age (final body weight, 742.1 ± 49.9 kg). The experimental period was divided into three phases: early fattening (12~14 months; T1), middle fattening (15~22 months; T2), and late fattening (until 23 ~30 months; T3). During the experiment, the animals were fed concentrate and roughage twice daily, and other feeding management was conducted by the practices of the Hyogo Prefectural Technology Center. The growth performance, feed intake and nutritional composition was shown in Table S1 (Additional file 1). Experimental samples, including blood, liver tissue, and rumen fluid, were collected from 21 Japanese Black cattle during the early (13 months of age), middle (20 months of age), and late fattening phases (28 months of age). Blood samples were drawn from the jugular vein at 13:00, three hours post-morning feeding, using heparin-sodium tubes (Venoject II VP-H100K; Terumo, Tokyo, Japan). Rumen fluid was obtained via a suitable catheter, and liver tissue biopsies were performed as previously described [60]. All samples were appropriately processed and stored for subsequent metabolic profiling. Methane emissions measurement Methane emissions were measured in 21 Japanese Black steers at different stages of fattening: early (T1, 13 months old), middle (T2, 20 months old), and late (T3, 28 months old), as described in our previous research [12]. Briefly, Methane concentration was monitored for 6 minutes after feeding concentrate following roughage feeding, and six repeated measurements were taken for three consecutive days during each fattening period. CH 4 and CO 2 concentrations were measured by a Micro-Portable Greenhouse Gas Analyzer (Model 909-0050, LGR Inc., CA, USA). Methane emissions were calculated using the formula mentioned previously [61], and predicted values were obtained through a linear regression model of dry matter intake to evaluate the levels of methane emission (L/day). Residual methane emission (RME) was calculated as the difference between the methane emission (L/day) and the predicted value. The top six and bottom six individuals classified as the high- (HME) and low- (LME) methane-emitting groups based on RME. Metabolomics analyses of blood and rumen fluid Blood metabolites were analyzed using an Automatic Biochemical Analyzer (HITACHI 7070, Hitachi, Ltd., Tokyo, Japan), measuring total protein, albumin, blood urea nitrogen (BUN), creatinine, total cholesterol, triglycerides, non-esterified fatty acid (NEFA), glucose, alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LD), gamma(γ)-glutamyl transferase (γ-GTP), creatine kinase, acetate, BHBA, and total ketone bodies. Plasma levels of insulin, insulin-like growth factor 1 (IGH-1), and cortisol were determined by enzyme immunoassay using specific kits: the multi-species insulin ELISA kit (Mercodia bovine insulin ELISA, Mercodia AB, Uppsala, Sweden), the human IGF-1 ELISA kit (Human IGF-I, R&D Systems, Minneapolis, USA), and the cortisol ELISA kit (Cortisol ELISA kit, Enzo Life Sciences Inc., Budapest, Hungary), following the manufacturers' protocols. For blood amino acid concentrations, trichloroacetic acid was added to the plasma, and proteins were filtered through a membrane filter before analysis with a high-speed amino acid spectrometer (L-8900, Hitachi High Tech, Tokyo, Japan). Total VFA in the rumen fluid and individual components, such as acetic acid, propionic acid, butyric acid, and valeric acid were quantified by gas chromatography (GC2014, Shimadzu, Kyoto, Japan) using a Thermon-3000 [3%] packed glass column on a Shimalite TPA 60–80 support (Shinwa Chemical Industries Ltd., Kyoto, Japan). The gas chromatography was operated under the following conditions: nitrogen as the carrier gas at a flow rate of 30 mL/min, with the column injection and FID detection temperatures set at 220°C, and the column oven at 140°C. Ammonium nitrogen concentration in the rumen fluid was measured using the steam distillation method with an automatic nitrogen analyzer (Kjeltec Auto 1035, Tecator, Sweden). The ammonium nitrogen concentration in rumen fluid was measured using the steam distillation method with an automatic nitrogen analyzer (Kjeltec Auto 1035, Tecator, Sweden). Transcriptomics analyses of Hepatocytes Liver tissue samples from 20 cattle at early fattening (T1), 20 at middle fattening (T2), and 19 at late fattening (T3) were used for RNA-Seq. The tissues were homogenized in 200 μL RNAiso Plus (TAKARA Bio Inc., Shiga, Japan) with a Multibeads shocker (YASUIKIKAI Inc., Osaka, Japan) following the manufacturer’s instructions. Homogenization was performed twice at 2000 rpm for 10–15 seconds, followed by the addition of 800 μL RNAiso at 25 °C. The homogenate was collected in 1.5 mL tubes, mixed with 200 μL chloroform, and centrifuged at 12,000×g for 15 minutes at 4 °C. The supernatant was mixed with 500 μL isopropanol and centrifuged again to precipitate the RNA. The RNA pellet was washed twice with 75% cold ethanol, dissolved in RNase-free water, and quantified using a Nano Drop ND-1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). RNA purity was confirmed with an A260/A280 ratio >1.8 and verified via 1.0% agarose gel electrophoresis. RNA integrity was confirmed using a Tape Station 4200 (Agilent Technologies, Santa Clara, CA, USA) with an average RNA integrity number (RIN) of 7.1. RNA-seq libraries were prepared using the TruSeq stranded mRNA Kit (Illumina, San Diego, CA, USA) and sequenced on a NovaSeq 6000 platform at Macrogen Japan Corporation. Sequencing read quality was evaluated with FastQC software (version 0.11.8), trimmed with Trim Galore (version 0.5.0), and mapped to the ARS-UCD1.2 bovine reference genome using HISAT2 [62]. Read counts were calculated using StringTie [62] with the GTF Bovine gene annotation file. Normalization of counts and PCA were performed using the DESeq2 package [63] in R statistical software and two T2 samples identified as outliers were removed. Metataxnomics analyses of rumen microbiota Genomic DNA was extracted from rumen fluid using the Fast DNA kit (MP Biomedicals in California, USA) and quantified with the Nanodrop ND-1000 Spectrophotometer (Thermo Fisher Scientific, France), with integrity verified by agarose gel electrophoresis. One sample in T2 was excluded due to poor gDNA quality. Approximately 10µg of rumen gDNA was used for PCR amplification to generate libraries of the bacterial 16S rRNA V3-V4 region using 341F-806R primers (forward: CCTACGGGNGGCWGCAG, reverse: GACTACHVGGGTATCTAATCC) [15] and the archaeal 16S rRNA V4 region using Arch 349F-806R primers (forward: GYGCASCAGKCGMGAAW, reverse: GGACTACVSGGGTATCTAAT) [65] following the 16S metagenomics sequencing library preparation protocol. Sequencing was performed on Illumina’s MiSeq platform (San Diego, CA, United States) using the Paired-End method (2 × 300bp). Metataxonomic analysis of the bacterial and archaeal rumen microbiome was conducted using QIIME2 (version 2023.2) [66], with adapter and primer sequences trimmed using Cutadapt [67] and further processing with DADA2 [68] for chimera removal, denoising, and quality filtering. Sequences were clustered at 99% similarity using the Naive Bayes classifier [69] with the SILVA v13.8 database [70]. Taxonomic assignment was refined by excluding unassigned ASVs, chloroplast, mitochondria, and non-bacteria taxa. Beta diversity of overall rumen microbiotas between HME and LME across fattening stages were analyzed using MicrobiomeAnalyst [71], with principal component analysis (PCoA) based on Bray-Curtis dissimilarity [72]. A Venn diagram illustrated ASVs with 100% prevalence present in both groups and unique to each group using InteractiVenn software [73]. Functional genetic profiles were predicted using PICRUSt2 ( Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2) [74], with differences in Kyoto Encyclopedia for Genes and Genomes (KEGG) profiles between HME and LME groups analyzed. Statistical analysis The comparison of blood metabolites, hormones, amino acids, hepatic genes, rumen fermentation, rumen microbiota, functional profiles, and methane emission levels between the HME and LME groups was conducted using R (v.4.1.3) software. Specifically, a mixed model analysis of variance (ANOVA) was employed, incorporating the fixed effects of methane emission levels (HME and LME), periods (T1, T2, and T3), as well as their interaction, with the animal as a random effect. Spearman's correlation coefficients were analyzed using the 'hmisc' R package [75], with statistical significance determined at |r|≥ 0.5 and P ≤ 0.05. To identify parameters associated with methane emission, regression tree analysis was conducted using the 'lmertree' R package [76]. Methane emissions were treated as the dependent variable, while phenotypic factors served as independent variables. Bayesian networks were constructed to infer significant interactions between methane emission and host physiological parameters. The structure of the Bayesian network was developed using a tabu search algorithm in the 'bnlearn' R package [77], and the uncertainty of the edge strength and direction within the network was estimated using 2,000 bootstrap replicates. Declarations Acknowledgements We would like to acknowledge the Hyogo Prefectural Technology Center of Agriculture employees for their assistance with the care of experimental animals and sample collection. Funding This work was supported by the MAFF Commissioned project study on “Development of Technologies to Reduce Greenhouse Gas Emissions in the Livestock Sector” (Grant Number JPJ011299). This work was partly supported by JSPS KAKENHI (grant number: 23K18075). Huseong Lee was granted by JST (grant number: JPMJSP2114). Authors’ contributions H.L., M.K. contributed to sample collection, sample analysis, data analysis, and writing; T.M., K.I., and E.I. contributed to experimental design, sample collection, and data analyses; O.K., U.Y. contributed to data analysis, data interpretation, and manuscript revision; S.H., F.T. and S.R. contributed to experimental design, data interpretation, writing, and revision; all authors read and approved the final manuscript. Availability of data and material Raw sequence reads data obtained from the present study have been deposited in the NCBI BioProject database, assigned the project number PRJNA1031765. The data will be available with the following link: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1031765. Competing interests The authors declare that they have no competing interest. Consent for publication Not applicable. Ethics approval and consent to participate The experimental protocol was evaluated and approved by the Hyogo Prefectural Institute of Agriculture of Forestry and Fisheries Animal Care and Use Committee (approval number: H2018-01). References Stocker, T., Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. 2014: Cambridge university press. Adopted, I., Climate change 2014 synthesis report. IPCC: Geneva, Szwitzerland, 2014. Pörtner, H.-O., et al., Climate change 2022: Impacts, adaptation and vulnerability. IPCC Sixth Assessment Report, 2022. Van Nevel, C. and D. Demeyer, Control of rumen methanogenesis. Environ. Monit. Assess., 1996. 42 (1): p. 73-97. 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CRAN2018, 2019. 2019 : p. 235-236. Fokkema, M., A. Zeileis, and M.M. Fokkema, Package ‘glmertree’. . 2019. Scutari, M., Learning Bayesian networks with the bnlearn R package. arXiv preprint arXiv:0908.3817, 2009. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additionalfile2.docx Additionalfile3.docx Additionalfile4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5235475","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":365125382,"identity":"5ed4ddfa-e29f-4510-b3f1-11d745a9f8ca","order_by":0,"name":"Huseong Lee","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Huseong","middleName":"","lastName":"Lee","suffix":""},{"id":365125384,"identity":"45188a69-675a-451c-86c4-1a895ff77d02","order_by":1,"name":"Minji Kim","email":"","orcid":"","institution":"Kangwon National University","correspondingAuthor":false,"prefix":"","firstName":"Minji","middleName":"","lastName":"Kim","suffix":""},{"id":365125385,"identity":"67c27f89-125e-4ad5-b391-de33148ab520","order_by":2,"name":"Tatsunori Masaki","email":"","orcid":"","institution":"Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Tatsunori","middleName":"","lastName":"Masaki","suffix":""},{"id":365125386,"identity":"e057a17d-ea7f-4398-a00e-9aef2ea0dd5b","order_by":3,"name":"Kentaro Ikuta","email":"","orcid":"","institution":"Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Kentaro","middleName":"","lastName":"Ikuta","suffix":""},{"id":365125387,"identity":"de68aa92-3892-4149-92a9-09ae2d719f61","order_by":4,"name":"Eiji Iwamoto","email":"","orcid":"","institution":"Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries","correspondingAuthor":false,"prefix":"","firstName":"Eiji","middleName":"","lastName":"Iwamoto","suffix":""},{"id":365125388,"identity":"ffcdd60d-740b-4145-8064-ed91153a3430","order_by":5,"name":"Kohei Oikawa","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Kohei","middleName":"","lastName":"Oikawa","suffix":""},{"id":365125391,"identity":"9b670266-a5b0-4558-8969-8a48aa48ff70","order_by":6,"name":"Yoshinobu Uemoto","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Yoshinobu","middleName":"","lastName":"Uemoto","suffix":""},{"id":365125393,"identity":"1a3dc0fb-be63-4cbe-8a5c-b55897d393c3","order_by":7,"name":"Satoshi Haga","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Satoshi","middleName":"","lastName":"Haga","suffix":""},{"id":365125396,"identity":"2cdcd0ae-76e4-470d-9d17-66c05e98ecaa","order_by":8,"name":"Fuminori Terada","email":"","orcid":"","institution":"Institute of Livestock and Grassland Science","correspondingAuthor":false,"prefix":"","firstName":"Fuminori","middleName":"","lastName":"Terada","suffix":""},{"id":365125397,"identity":"3905ff6d-3851-4ded-8101-196953556045","order_by":9,"name":"Sanggun Roh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYHADHsYHDAwHGCQgPDaitDAbHCBVC5sEkhbcQL797OGXX9vuMJi39x6r/lBxR05yRgLjhx8MfHm4tBicyUuzlm17xiBz5lzajQNnnhlLSyQwS/YwsBXj1MKQY2Ys2XaYQUIix+zGwbbDifMkEhikgX5JbMDlsP43CC0FUC3Mv/FpYbiRY/zwI1QLA0jLbIkENry2GNx4Y8bMcO4wjwTPGWOJM2cOG0v2PGyz7DHA7Rf5/hzjjz/KDstJsPcYfqioADKOJx++8aPiGM4QAwI2aR5gpCAJMAKdZHAsAY8W5o8/sIjW4NMyCkbBKBgFIwsAANAEWEE9bfNuAAAAAElFTkSuQmCC","orcid":"","institution":"Tohoku University","correspondingAuthor":true,"prefix":"","firstName":"Sanggun","middleName":"","lastName":"Roh","suffix":""}],"badges":[],"createdAt":"2024-10-10 01:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5235475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5235475/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66665523,"identity":"8b91aa9e-f504-453a-bf8f-214f99fc28db","added_by":"auto","created_at":"2024-10-15 09:26:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":802955,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic changes of ruminal microbiota according to methane emission. (a, b) Relative microbial abundance at the phylum (a) and genus (b) level. (c) Relative fold change of differentially abundant microbiota between the HME and LME groups. (d, e) Shared core ASVs with 100% prevalence between the HME and LME groups, and among T1, T2 and T3. (f) PCoA plots based on Bary–Curtis for comparing the HME and LME groups. HME, group of high methane-emission cattle; LME, group of low methane-emission cattle; PCoA, principal components analysis; T1, early fattening period; T2, middle fattening period; T3, late fattening period. ***P \u0026lt; 0.001, **P \u0026lt; 0.01, and *P \u0026lt; 0.05, respectively.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/c2a2d2ba1a9161966ecc1841.png"},{"id":66663677,"identity":"6d2847bd-94b5-449f-86ac-5ca8bf36860c","added_by":"auto","created_at":"2024-10-15 09:10:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":752508,"visible":true,"origin":"","legend":"\u003cp\u003eThe linear mixed-effect model regression tree for methane emission. A total of 62 measurements for the early (n=21), middle (n=20), and late fattening (n=21) periods were used in the analysis. Methane emission (L/day) was used as a dependent variable. Ruminal microbiota selected using the mixed effect model analysis (a) and those with 100% prevalence (b) were used as independent variables.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/dfad909d5676fd1d570f6cfc.png"},{"id":66663676,"identity":"92bdb047-c4af-4eda-aa35-073d1e9b44fc","added_by":"auto","created_at":"2024-10-15 09:10:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1112099,"visible":true,"origin":"","legend":"\u003cp\u003eMetaCyc pathways related to methane production. (a) Metacyc pathways significantly correlated (Cor \u0026gt; |0.5|, P-value \u0026lt; 0.05) with methane production based on Spearman's rank correlation analysis. (b) Differentially abundant MetaCyc pathways between HME and LME groups based on a linear mixed effect model. (c) Linear mixed-effect model regression tree for methane emission (L/day), using MetaCyc pathways as independent variables, incorporating 62 measurements across the early (n=21), middle (n=20), and late (n=21) fattening periods. GLUTORN-PWY, L-ornithine biosynthesis; HME, group of high methane-emission cattle; LME, group of low methane-emission cattle; P261-PWY, coenzyme M biosynthesis; PWY-5100, pyruvate fermentation to acetate and lactate; PWY-5505, L-glutamate biosynthesis; PWY-5695, urate biosynthesis/inosine 5'-phosphate degradation; PWY-7431, aromatic biogenic amine degradation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/80d07cff69a028b44db1b356.png"},{"id":66663678,"identity":"242d5352-46a3-4bf0-b10d-23ae3c7b7aa3","added_by":"auto","created_at":"2024-10-15 09:10:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":410567,"visible":true,"origin":"","legend":"\u003cp\u003eBayesian networks considering both methane emission and selected phenotypic factors. The estimated phenotypic network structure of methane emission, PWY-5505, and the downstream metabolism is shown for low (a) and high (b) methane emission conditions. BHBA, beta-hydroxybutyric acid; C4, butyrate; CH4, methane emission; GLUTORN-PWY, L-ornithine biosynthesis; NH3, ammonia; OTC, ornithine transcarbamylase; PWY-5505, L-glutamate biosynthesis; SLC1A1, solute carrier family 1 member 1.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/bf142845542335f8e8d8f2fc.png"},{"id":66664195,"identity":"6142dbc7-cb58-427d-907c-87066384fee5","added_by":"auto","created_at":"2024-10-15 09:18:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":369324,"visible":true,"origin":"","legend":"\u003cp\u003eProposed model for blood metabolites, hepatic transcriptomes, and ruminal fermentation associated with high and low methane emission in Japanese Black Cattle. BHBA, beta-hydroxybutyric acid; GLUTORN-PWY, L-ornithine biosynthesis; OTC, ornithine transcarbamylase; PWY-5505, L-glutamate biosynthesis; SLC1A1, solute carrier family 1 member 1.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/dc3695b41e9f55722b53c8ad.png"},{"id":66676383,"identity":"55741916-b96e-4baf-8773-1796497a2eac","added_by":"auto","created_at":"2024-10-15 11:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3753320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/8bf4562b-843f-4a76-8af6-02d1153c947a.pdf"},{"id":66663673,"identity":"70fe067c-b0bf-4ef3-b0a5-cfe046bc96f6","added_by":"auto","created_at":"2024-10-15 09:10:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16610,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/ae7e949ca1974bd084e60be0.docx"},{"id":66664197,"identity":"a4b90ebd-2631-42b4-a975-c0b33844ae2a","added_by":"auto","created_at":"2024-10-15 09:18:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17560,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/6c2a174c29b85d83d8b042e2.docx"},{"id":66665524,"identity":"5dbc6839-12d0-429f-bec5-3b79eff3ffc6","added_by":"auto","created_at":"2024-10-15 09:26:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":255335,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/ecc48347a5e9dbecd92a9e8e.docx"},{"id":66664198,"identity":"bcfa51f3-2866-4206-b809-b4bb7d23b57f","added_by":"auto","created_at":"2024-10-15 09:18:22","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":203004,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5235475/v1/e7b00119cdc6086b33444957.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the link between ruminal methane production and physiological resilience in Japanese Black cattle during fattening","fulltext":[{"header":"Background","content":"\u003cp\u003eMethane (CH\u003csub\u003e4\u003c/sub\u003e), a greenhouse gas, is 28–34 times more potent than carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e)[1]. It constitutes 14% of the total [2] and approximately 5% of the anthropogenic greenhouse gas emissions worldwide, primarily owing to the ruminal digestion of livestock [3]. Moreover, without proper management, the negative effects of enteric methane emissions from the livestock industry will likely worsen owing to the increasing demand for milk and meat fuelled by population growth and urbanisation. Therefore, coordinated efforts are essential to ensure animal production system sustainability and health.\u003c/p\u003e\n\u003cp\u003eRuminants, such as cows and sheep, have a long digestive tract that harbours diverse microbiota in their rumen. These microbiotas consist of numerous microorganisms, including bacteria, archaea, and methanogens, which enable ruminants to convert otherwise indigestible plant components into high-quality protein products such as milk and meat. During this process, ruminal microorganisms break down plant polymers and produce volatile fatty acids (VFAs), hydrogen (H\u003csub\u003e2\u003c/sub\u003e), and carbon dioxide as the primary fermentation by-products. VFAs are absorbed along the ruminal epithelium, providing energy for host animal growth and production. However, as continuous ruminal fermentation requires hydrogen removal, methanogens metabolize hydrogen to facilitate CO\u003csub\u003e2\u003c/sub\u003e reduction to CH\u003csub\u003e4\u003c/sub\u003e, which is then emitted from the body. This process consumes 2–15% of the total ruminant energy intake [4], prompting farmers to compensate by increasing feed, which may lead to inefficiencies. Thus, in-depth research is necessary to understand the mechanisms of methane generation and develop strategies to reduce these emissions.\u003c/p\u003e\n\u003cp\u003eVarious methane mitigation strategies influence ruminal methanogenesis, including dietary manipulation, rumen control, and selective breeding [5-7]. However, despite extensive feeding additive research, few techniques for reducing enteric CH\u003csub\u003e4\u003c/sub\u003e emissions are cost-effective, non-residual, non-toxic, and acceptable to farmers [8]. Alternatively, a promising supplement-free strategy for regulating ruminal methane generation is to divert hydrogen from methanogenesis to more nutritionally advantageous pathways; e.g. by. reducing acetate and butyrate while increasing propionate production. Better understanding the causes of methane production and identifying biomarkers such as rumen fermentation characteristics, blood metabolites, and associated ruminal microorganisms are key to developing effective methane reduction strategies.\u003c/p\u003e\n\u003cp\u003eWe previously demonstrated that Japanese Black cattle exhibit unique physiological traits at different stages of fattening [9]. Furthermore, because these animals are raised under unique feeding systems aimed at enhancing intramuscular fat deposition, their ruminal environment and physiological characteristics likely differ from those of other beef cattle breeds. However, the relationship between ruminal microbiota and methane emissions during the fattening period and an in-depth understanding of the physiological changes related to methane production in this breed remain to be elucidated. Thus, in this study we analysed the physiological traits of Japanese Black steers raised using conventional livestock practices in Japan, examining the influence of ruminal microbiota on the gut–liver axis and blood metabolites. By providing comprehensive data on physiological features including blood metabolites, hepatic gene expression, and ruminal fermentation characteristics along with ruminal microbiota compositional and functional profiling, this study offers new insights regarding effectively minimizing methane emissions in Japanese Black cattle.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eEffect of methane production on the ruminal microbial community\u003c/h3\u003e\n\u003cp\u003eFigure 1a and 1b shows the dominant members of the bacterial and archaeal communities within the HME and LME groups at the phylum and genus. At the phylum level, Firmicutes and Bacteroidetes dominated the ruminal microbial community with relative abundances of 56.62 and 36.0%, respectively. Patescibacteria was the next most abundant bacterial phylum (2.83%), followed by Actinobacteriota (1.32%), Euryarchaeota (1.01%), Planctomycetota (0.65%), and Spirochaetota (0.63%). \u003cem\u003ePrevotella\u003c/em\u003e was the most abundant bacterial genus (20.21% mean relative abundance across all samples), followed by \u003cem\u003eAcetitomaculum\u003c/em\u003e (3.88%), \u003cem\u003eMuribaculum\u0026nbsp;\u003c/em\u003e(3.97%), and \u003cem\u003eRuminococcus\u003c/em\u003e (3.10%). Figure 1c shows the differentially abundant microbiota in the two groups. HME exhibited enriched \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003ePrevotellaceae\u003c/em\u003e, Bacteroidota, \u003cem\u003eChristensenellaceae\u003c/em\u003e, \u003cem\u003eChristensenellaceae\u003c/em\u003e R-7 group, and \u003cem\u003eMethanobrevibacter\u003c/em\u003e, whereas LME showed enriched \u003cem\u003eSuccinivibrio\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Succinivibrionaceae\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Clostridia UCG-014\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Eubacterium nodatum\u0026nbsp;\u003c/em\u003egroup,\u003cem\u003e\u0026nbsp;Methanosphaera\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Anaerovorax\u003c/em\u003e, and Firmicutes(all P \u0026lt; 0.05). Figure 1d and 1e present Venn diagrams illustrating shared and exclusive amplicon sequence variants (ASVs) in the ruminal microbial community. The HME group contained higher numbers of exclusive ASVs during each of the three (T1, T2, and T3) fattening periods. Among the periods, exclusively detected ASVs were more abundant during T2 in the HME and during T3 in the LME groups. As revealed by PCoA and permutational multivariate analysis of variance (PERMANOVA), the overall microbial community structure differed between the two groups at T2 (PERMANOVA; P \u0026lt; 0.05).\u003c/p\u003e\n\u003ch3\u003eRegression tree for methane emission in Japanese Black cattle\u003c/h3\u003e\n\u003cp\u003eFigure 2 presents the regression tree analysis with methane emissions as the dependent variable. Figure 2a shows all genus-level microbiota as independent variables. All cattle were split into subgroups based on \u003cem\u003eChristensenellaceae\u003c/em\u003e abundance (Node 1); Nodes 2 and 3 represent cattle with abundance of ≤ 6.209 and \u0026gt; 6.209, respectively. At the second tree depth, Node 3 was partitioned based on \u003cem\u003eMethanosphaera\u003c/em\u003e abundance; Nodes 4 and 5 reflect ≤ 3.765 and \u0026gt; 3.765 abundance, respectively. At the third tree depth, Node 5 was further split based on \u003cem\u003ePrevotellaceae\u003c/em\u003e abundance, with respective Node 6 and 7 abundance of ≤ 25.214 and \u0026gt; 25.214.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2b illustrates selected microbiota that were differentially abundant between HME and LME. All cattle were divided into subgroups based on \u003cem\u003eClostridium methylpentosum\u003c/em\u003e group abundance (Node 1); Nodes 2 and 5 represent cattle with abundance ≤ 0.307 and \u0026gt; 0.307, respectively. At the second tree depth, Node 2 was partitioned based on \u003cem\u003eMogibacterium\u003c/em\u003e abundance; Nodes 3 and 4 represent abundances of ≤ 0.16 and \u0026gt; 0.16. Node 5 was partitioned based on the abundance of the \u003cem\u003eLachnospiraceae\u003c/em\u003e NK3A20 group; Nodes 6 and 7 are indicative of ≤ 2.211 and \u0026gt; 2.211 abundance. At the third tree depth, Node 7 was further split based on \u003cem\u003eDesulfobacteroita\u003c/em\u003e abundance, with Node 8 and 9 abundance of ≤ 0.03 and \u0026gt; 0.03, respectively.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eFunctional profiles of ruminal microbiota related to methane emission\u003c/h3\u003e\n\u003cp\u003eFunctional genetic profiling revealed a total of 398 MetaCyc pathways among the 62 ruminal microbiome samples across the entire fattening period. Various analyses were conducted to identify the MetaCyc pathways related to methane emissions levels (Fig. 3). Spearman's correlation analysis (correlation coefficient, |r|≥ 0.5; P \u0026lt; 0.05). revealed P261-PWY and PWY-7431 as positively and negatively correlated with methane emission levels, respectively (Fig. 3a). An ANOVA with mixed effects model revealed that PWY-5505 and GLUTORN-PWY levels were significantly higher (P\u0026lt;0.01) in the LME group (Fig. 3b). In a regression tree with methane emissions as the dependent variable and MetaCyc pathways as independent variables, all cattle were significantly split into subgroups based on P261-PWY pathway abundance (Node 1), with Nodes 2 and 5 representing cattle with abundance ≤ 0.016 and \u0026gt; 0.016, respectively (Fig. 3c). At the second tree depth, Node 2 was significantly partitioned based on PWY-5100 pathway abundance, with Node 3 and 4 abundance of ≤ 0.894 and \u0026gt; 0.894. Node 5 was significantly partitioned based on PWY-5695 pathway abundance, creating Nodes 6 and 7 at ≤ 0.754 and 0.754 abundance, respectively.\u003c/p\u003e\n\u003ch3\u003eLink between methane emissions, blood metabolites, hepatic transcriptome, ruminal microbiota, and fermentation\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTo identify the physiological factors related to methane emission levels, ANOVA with a mixed-effect model was used to compare the HME and LME groups (Additional file 2: Table S2). The regression tree analysis utilized rumen fermentation characteristics, blood metabolites, blood amino acids, and hepatic genes as independent variables and methane emissions as the dependent variable (Additional file 3: Figure S1, Additional file 4: Figure S2). Ruminal fermentation characteristics (e.g. propionate, butyrate, and ammonia concentrations), blood metabolites (e.g. BHBA and ornithine), and hepatic genes (e.g. solute carrier family 1 member 1 (\u003cem\u003eSLC1A1\u003c/em\u003e), Ras-related protein Rab-6A (\u003cem\u003eRAB6A\u003c/em\u003e), and ornithine transcarbamylase (\u003cem\u003eOTC\u003c/em\u003e)) significantly influenced the methane emission levels. Based on the regression tree and ANOVA results, the potential correlation between methane emission level and physiological parameters was examined using a Bayesian network (Fig. 4). The tabu search algorithm produced eight and six directed connections from the eight and six latent variables related to LME and HME, respectively. HME-group methane emissions were potentially moderated by butyrate (arc strength = 0.63), BHBA (arc strength = 0.68), and PWY-5505 (arc strength = 0.64), whereas PWY-5505 (arc strength = 0.69) potentially moderated LME-group emissions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMethane production in cattle is a thermodynamic requirement for the microbial conversion of feed into nutrients\u0026nbsp;[10].\u0026nbsp;The associated removal of hydrogen is critical for the ruminal ecosystem and host because low hydrogenconcentrations ensure high fermentation rates and efficient feed digestion\u0026nbsp;[11].\u0026nbsp;Therefore, methane production is a natural metabolic process, with an increase in methane production potentially indicating healthier and better-growing cattle. Enteric methane production in Japanese Black cattle is influenced by changes in the rumen microbiome and affects overall metabolism (Figs. 1–3), rendering it crucial to understand the mechanisms of methane production and its metabolic side effects for improving cattle growth and well-being. In particular, we hypothesized that the metabolic changes induced by methane production in cattle are reflected in the ruminal environment, blood metabolites, and liver metabolism.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eRumen microbial features related to methane production\u003c/h3\u003e\n\u003cp\u003eA more diverse microbial community enables the rumen ecosystem to better adapt to dietary changes, which has been associated with improved ruminant growth performance [12], suggesting that the higher ruminal microbial diversity in the HME group facilitated better adaptation to dietary changes than that in the LME group. Additionally, diverse microbial communities can effectively degrade a wide range of plant materials, potentially enhancing the nutrient intake from feed [13]. The increased fiber degradation can lead to higher ruminal hydrogen production, potentially increasing methane production. Notably, ruminants with larger rumens emit increased methane levels, likely because of longer ruminal feed retention [14]. Therefore, the higher ruminal diversity in the HME group may be related to a larger rumen size than that in the LME group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegression tree and mixed effect model analyses during the fattening period revealed several key microbiotas associated with methane emissions in Japanese Black cattle. \u003cem\u003eChristensenellaceae\u003c/em\u003e, \u003cem\u003eClostridium methylpentosum\u003c/em\u003e, and \u003cem\u003eMogibacterium\u003c/em\u003e, which are related to hydrogen production, were more prevalent in HME cattle. \u003cem\u003eChristensenellaceae\u003c/em\u003e, a crucial ruminal hydrogen-producing group\u0026nbsp;[15], effectively breakdown carbohydrates, amino acids, and carboxylic acids to produce acetate and butyrate\u0026nbsp;[16].\u0026nbsp;This family is associated with methane emissions in Holstein cows [17], sheep [18], and beef cattle [19]. \u003cem\u003eClostridium methylpentosum\u0026nbsp;\u003c/em\u003especializes in decomposing specific plant materials in the rumen that other bacteria may not utilize efficiently, producing acetate, glycolaldehyde, carbon dioxide, and hydrogen during the fermentation of L-lyxose and B-arabinose [20]. \u003cem\u003eMogibacterium\u003c/em\u003e, a hydrogen-producing fibrolytic bacterium, contributes to methane production by generating phenylacetate, which may facilitate cellulose degradation by \u003cem\u003eR. albus\u003c/em\u003e strains [18]. Consistent with our findings, elevated \u003cem\u003eMogibacterium\u003c/em\u003e levels are also observed in cattle with high ruminal methane emissions [21, 22].\u003c/p\u003e\n\u003cp\u003eIn contrast, \u003cem\u003eSuccinivibrionaceae\u003c/em\u003e, \u003cem\u003eSuccinivibrio\u003c/em\u003e, \u003cem\u003eAnaerovorax\u003c/em\u003e, and \u003cem\u003eLachnospiraceae\u003c/em\u003e NK3A20 were more abundant in LME cattle. \u003cem\u003eSuccinivibrio\u003c/em\u003e, a member of the family \u003cem\u003eSuccinivibrionaceae\u003c/em\u003e, produces propionate as its primary fermentation product in the rumen [23]. This genus helps mitigate methane levels through hydrogen consumption and negatively correlates with methane emissions in sheep [24] and dairy cattle [25]. Although propionate production is the primary hydrogen sink, biohydrogenation also plays this role during ruminal conversion of unsaturated to saturated fatty acids [26, 27]. Identification of \u003cem\u003eAnaerovorax\u003c/em\u003e as a potential biohydrogenating bacterium [28] also suggests a role in hydrogen sinking. In glucose-supplemented culture experiments, \u003cem\u003eLachnospiraceae\u003c/em\u003e NK3A20 produces hydrogen under low but not high hydrogen concentrations, whereupon fermentation shifts to more reduced organic acid products [29]. Although this bacterium was more abundant in LME cattle, it potentially increases methanogenesis in ruminal environments with lower hydrogen levels.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEubacterium nodatum\u003c/em\u003e produces acetate from lysine [30]. Although higher ruminal acetate concentrations were expected in the HME group owing to increased hydrogen production, the acetate generated from amino acids by this bacterium likely contributed to the comparable acetate levels between the HME and LME groups. Additionally, the PWY-5100 pathway, which involves pyruvate fermentation to acetate, was more active in some LME cattle. The equivalent acetate concentrations among HME and LME cattle are consistent with our previous finding [31]. Considering that the connection between ruminal fermentation traits and methane production is likely affected by factors such as breed and feeding management practices [32], high concentrate feeding in Japanese Black cattle likely reduces their reliance on the pyruvate-to-acetate microbial pathway in the rumen, leading to comparable acetate levels regardless of methane emission status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePrevotella\u003c/em\u003e can reduce methane emissions by channelling hydrogen into propionic acid production, thereby lowering methanogenesis [33]. Conversely, \u003cem\u003ePrevotella\u003c/em\u003e levels were higher in the HME group. Similar observations were made in Holstein cattle, where \u003cem\u003ePrevotella\u003c/em\u003e phylotypes were more abundant in animals with lower propionate levels [23]. In sheep, \u003cem\u003ePrevotella bryantii\u0026nbsp;\u003c/em\u003eis an indicator of low-methane ruminotypes, whereas other \u003cem\u003ePrevotella\u003c/em\u003e phylotypes are associated with high-methane ruminotypes [34]. This discrepancy could be due to differences in propionate production at the phylotype level or variations in metabolic pathways. Metabolic profiling of \u003cem\u003ePrevotella\u003c/em\u003e has revealed numerous pathways involving amino acid, carbohydrate, lipid, cofactor and vitamin, nucleotide, and energy (ATP) metabolism [33]. Therefore, further research is necessary to clarify the influence of \u003cem\u003ePrevotella\u003c/em\u003e on methane production in Japanese Black cattle.\u003c/p\u003e\n\u003cp\u003eThe LME and HME groups had higher relative abundances of \u003cem\u003eMethanosphaera\u003c/em\u003e and \u003cem\u003eMethanobrevibacter\u003c/em\u003e, respectively, consistent with previous research. In particular, sheep with lower methane yields contained more \u003cem\u003eMethanosphaera\u003c/em\u003e and fewer \u003cem\u003eMethanobrevibacter\u0026nbsp;\u003c/em\u003e[35]. Similarly, dairy cows with lower methane yields produced 26% less methane than their high methane-yield counterparts, exhibiting higher \u003cem\u003eMethanosphaera\u003c/em\u003e and lower \u003cem\u003eMethanobrevibacter\u0026nbsp;\u003c/em\u003eabundance [36]. \u003cem\u003eMethanosphaera\u003c/em\u003e is a methylotrophic methanogen that depends entirely on hydrogen and utilizes alcohols but not carbon dioxide, formate, or methylamines [37, 38]. Alternatively, \u003cem\u003eMethanobrevibacter\u003c/em\u003e is a hydrogenotrophic methanogen that uses hydrogen along with carbon dioxide or formate to produce methane. These methanogens exhibit negative correlations in ruminants [39, 40]. Furthermore, \u003cem\u003eMethanosphaera\u003c/em\u003e, with its low hydrogen threshold, can outcompete \u003cem\u003eMethanobrevibacter\u003c/em\u003e at low hydrogen partial pressures [41]. \u003cem\u003eMethanosphaera\u003c/em\u003e may thus have outcompeted \u003cem\u003eMethanobrevibacter\u003c/em\u003e for hydrogen in the LME group, which had low hydrogen pressure, whereas \u003cem\u003eMethanobrevibacter\u003c/em\u003e may have outcompeted \u003cem\u003eMethanosphaera\u003c/em\u003e in the HME group owing to high ruminal hydrogen concentrations. Further research is needed to understand the competitive dynamics of hydrogen and factors influencing the selection of methanogenic lineages. Examining the relationships among methane, hydrogen, and specific methanogenic lineages could provide valuable insights regarding methanogenesis and help develop strategies to reduce enteric methane emissions in the rumen.\u003c/p\u003e\n\u003ch3\u003eKEGG pathways related to methane emission\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eSeveral MetaCyc pathways associated with methane production were identified using correlation, mixed-effect modelling, and regression tree analyses. In particular, methyl-coenzyme M reductase is targeted by numerous inhibitors of rumen methanogenesis. For example, nitrocompounds such as nitroethane, 2-nitroethanol, and 2-nitro-1-propanol exhibit ability to decrease methyl-coenzyme M reductase activity and inhibit methanogens [42]. Additionally, 3-nitrooxypropanol specifically targets this enzyme, making it an effective tool for studying methane metabolism and potentially reducing methane emissions [43]. Methyl-coenzyme M reductase acts on methyl-coenzyme M (CH\u003csub\u003e3\u003c/sub\u003e-S-CoM), which is an immediate precursor to methane. Coenzyme M is thus essential for methanogenesis in rumen microorganisms during the final step of methane production [44]. Similarly, we identified coenzyme M biosynthesis (P261-PWY) as a critical factor influencing methane emissions during the fattening period in Japanese Black cattle.\u003c/p\u003e\n\u003cp\u003eThe PWY-5505 pathway involves a transamination process in which oxoglutarate and ammonium are converted into glutamate by glutamate dehydrogenase. This process is a major source of ammonia fixation and is important for converting non-protein nitrogen into proteins in ruminants [45]. The PWY-5505 pathway was more active in the LME group during the fattening period, which may reflect a lower ruminal ammonia concentration in this group than in HME animals. Additionally, oxoglutarate fermentation to glutamate represents an important hydrogen disposal pathway, wherein oxoglutarate combines with ammonium, NADH, and hydrogen to form glutamate, nicotinamide adenine dinucleotide (NAD\u003csup\u003e+\u003c/sup\u003e), and H\u003csub\u003e2\u003c/sub\u003eO, respectively. Conversely, hydrogen production during NAD\u003csup\u003e+\u003c/sup\u003e conversion to NADH, which occurs during glutamate deamination to oxoglutarate, may be associated with methane synthesis [46]. Furthermore, transamination reactions of ruminal bacteria involving glutamate dehydrogenase proceed at a significantly faster rate than those involving other amino acids, underscoring the elevated dehydrogenation capability of ruminal glutamate biosynthesis [46]. Additionally, formate, a substrate for methanogenesis, can be produced from oxoglutarate by ruminal microbes via a pathway other than the pyruvate formate-lyase reaction [47]. Therefore, the conversion of oxoglutarate to glutamate may also reduce formate production from oxoglutarate.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eInduced physiological, ruminal, and hepatic changes according to methane production\u003c/h3\u003e\n\u003cp\u003eThe ruminal ornithine biosynthetic pathway begins with oxoglutarate and proceeds in two stages: glutamate synthesis and ornithine synthesis [48]. The increased PWY-5505 and GLUTRON-PWY pathway activity in the LME group promotes oxoglutarate conversion first to glutamate then to ornithine, respectively. Increased GLUTRON-PWY pathway activity was associated with elevated blood ornithine levels in the LME group. Regression tree analysis of blood amino acids and hepatic gene expression revealed that increased blood ornithine levels and OTC activity were characteristic of LME animals. OTC plays a crucial role in ammonia detoxification and nitrogen waste removal by catalysing the reaction between carbamoyl phosphate and ornithine to form citrulline in the second step of the urea cycle [49]. The ornithine–urea cycle is the primary pathway for ammonia detoxification and urea synthesis in the livers of dairy cattle [50]. The enhanced conversion of ammonia to urea via ornithine and OTC, which helps maintain body health by detoxifying ammonia, may have contributed to the lower ruminal ammonia concentrations in the LME group. Nevertheless, the ammonia levels of HME group (14.55 mg/dL) were higher than those previously reported in Japanese Black cattle fed conventional high concentrate-to-forage ratios during the fattening period (5.5~7.2\u0026nbsp;[51], 4.2~6.4 [52], 8.85~12.9 [53], 3.5~7.5 [54] 7.42~11.44 mg/dL[31]). Elevated ammonia concentrations can adversely affect the production of VFAs and other fermentation end products, potentially leading to less efficient host nutrient utilisation and energy production [55]. Relatively low ammonia detoxification by the ornithine–urea cycle and transamination (PWY-5505) may lead to increased rumen ammonia levels in the HME group, which could negatively affect rumen fermentation.\u003c/p\u003e\n\u003cp\u003eRegression tree analysis of hepatic genes indicated that \u003cem\u003eSLC1A1\u003c/em\u003e expression was higher in HME cattle. Lower GLUTRON-PWY activity may lead to relatively high ruminal glutamate concentrations, resulting in the elevated \u003cem\u003eSLC1A1\u003c/em\u003e expression in HME animals. \u003cem\u003eSLC1A1\u003c/em\u003e is overexpressed in the liver compared to other organs. Its protein product contributes to the biosynthesis of glutathione, an abundant natural antioxidant in the liver, by facilitating transport of the glutathione precursors L-glutamate and L-cysteine [56], thereby protecting the liver cells from oxidative stress [57]. Butyrate produced during rumen fermentation is absorbed across the ruminal epithelium and converted into BHBA, which is then transported through the bloodstream and used as an energy source in various tissues. Owing to the high-energy diets fed to Japanese Black cattle during the fattening period, BHBA may not be fully utilized as an energy source and may remain in the bloodstream and tissues, potentially inducing inflammatory injury and oxidative stress in cattle hepatocytes through the NF-κB signalling pathway [58]. The elevated hepatic \u003cem\u003eSLC1A1\u003c/em\u003e expression may help mitigate the oxidative stress induced by residual BHBA in the HME group. Moreover, Bayesian network analysis revealed a strong association between BHBA and \u003cem\u003eSLC1A1\u003c/em\u003e (0.987), further supporting the role of \u003cem\u003eSLC1A1\u003c/em\u003e in mitigating oxidative stress.\u003c/p\u003e\n\u003cp\u003eNotably, although the differences in blood urea cycle components, ornithine concentration, and \u003cem\u003eOTC\u003c/em\u003e and \u003cem\u003eSLC1A1\u003c/em\u003e expression levels do not directly explain methane production, they may indicate downstream metabolic effects derived from the ruminal oxoglutarate-to-glutamate biosynthesis (PWY-5505) pathway and its role as a hydrogen sink. However, the significant differences in these downstream metabolic processes according to methane-production status provide evidence that PWY-5505 could be an important pathway for decreasing methane production by reducing ruminal hydrogen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study provides crucial insights regarding the ruminal microbial community of Japanese Black cattle, highlighting its association with blood metabolites, hepatic gene expression, and methane emissions (Fig. 5). The more diverse ruminal microbial communities in the HME group may be associated with higher hydrogen production. \u003cem\u003eChristensenellaceae\u003c/em\u003e, \u003cem\u003eClostridium methylpentosum\u003c/em\u003e, and \u003cem\u003eMogibacterium\u003c/em\u003e, which are related to hydrogen production, were more prevalent in HME animals. In contrast, \u003cem\u003eSuccinivibrionaceae\u003c/em\u003e, \u003cem\u003eSuccinivibrio\u003c/em\u003e, and \u003cem\u003eAnaerovorax\u003c/em\u003e, which are associated with hydrogen sinks, were more abundant in LME cattle. \u003cem\u003eMethanobrevibacter\u003c/em\u003e may have outcompeted \u003cem\u003eMethanosphaera\u003c/em\u003e for hydrogen because of the higher hydrogen concentrations in the HME group. Downstream metabolic effects from the oxoglutarate-to-glutamate biosynthesis pathway differed between the HME and LME groups. In LME cattle, improved ammonia conversion to urea via ornithine and OTC detoxified ammonia, thereby promoting body health. In HME animals, higher hepatic \u003cem\u003eSLC1A1\u003c/em\u003e expression may help mitigate the oxidative stress caused by elevated BHBA levels. Differences in these metabolic processes suggest that the oxoglutarate-to-glutamate biosynthesis pathway may contribute to hydrogen sinking, thereby serving as a crucial differentiating factor for methane emissions.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eAnimals and sample collection\u003c/h3\u003e\n\u003cp\u003eThe study involved animal experiments conducted at the Hyogo Prefectural Technology Center of Agriculture, Forestry, and Fisheries in Japan. The experiments followed the guidelines provided by the Institute of Livestock and Grassland Science [59] and the ethical guidance of the Hyogo Prefectural Institute of Agriculture and Forestry and Fisheries Animal Care and Use Committee. The protocol for the experiments was evaluated and approved by the same committee, and all experiments were conducted in compliance with the ARRIVE guidelines. The study used 21 Japanese Black steers that were raised from 12 months of age (initial body weight, 335.6 ± 19.8 kg) until they reached 30 months of age (final body weight, 742.1 ± 49.9 kg). The experimental period was divided into three phases: early fattening (12~14 months; T1), middle fattening (15~22 months; T2), and late fattening (until 23 ~30 months; T3). During the experiment, the animals were fed concentrate and roughage twice daily, and other feeding management was conducted by the practices of the Hyogo Prefectural Technology Center. The growth performance, feed intake and nutritional composition was shown in Table S1 (Additional file 1). Experimental samples, including blood, liver tissue, and rumen fluid, were collected from 21 Japanese Black cattle during the early (13 months of age), middle (20 months of age), and late fattening phases (28 months of age). Blood samples were drawn from the jugular vein at 13:00, three hours post-morning feeding, using heparin-sodium tubes (Venoject II VP-H100K; Terumo, Tokyo, Japan). Rumen fluid was obtained via a suitable catheter, and liver tissue biopsies were performed as previously described [60]. All samples were appropriately processed and stored for subsequent metabolic profiling.\u003c/p\u003e\n\u003ch3\u003eMethane emissions measurement\u003c/h3\u003e\n\u003cp\u003eMethane emissions were measured in 21 Japanese Black steers at different stages of fattening: early (T1, 13 months old), middle (T2, 20 months old), and late (T3, 28 months old), as described in our previous research [12]. Briefly, Methane concentration was monitored for 6 minutes after feeding concentrate following roughage feeding, and six repeated measurements were taken for three consecutive days during each fattening period. CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e concentrations were measured by a Micro-Portable Greenhouse Gas Analyzer (Model 909-0050, LGR Inc., CA, USA). Methane emissions were calculated using the formula mentioned previously [61], and predicted values were obtained through a linear regression model of dry matter intake to evaluate the levels of methane emission (L/day). Residual methane emission (RME) was calculated as the difference between the methane emission (L/day) and the predicted value. The top six and bottom six individuals classified as the\u0026nbsp;high- (HME) and low- (LME) methane-emitting groups\u0026nbsp;based on RME.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eMetabolomics analyses of blood and rumen fluid\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eBlood metabolites were analyzed using an Automatic Biochemical Analyzer (HITACHI 7070, Hitachi, Ltd., Tokyo, Japan), measuring total protein, albumin, blood urea nitrogen (BUN), creatinine, total cholesterol, triglycerides, non-esterified fatty acid (NEFA), glucose, alkaline phosphatase (ALP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LD), gamma(γ)-glutamyl transferase (γ-GTP), creatine kinase, acetate, BHBA, and total ketone bodies. Plasma levels of insulin, insulin-like growth factor 1 (IGH-1), and cortisol were determined by enzyme immunoassay using specific kits: the multi-species insulin ELISA kit (Mercodia bovine insulin ELISA, Mercodia AB, Uppsala, Sweden), the human IGF-1 ELISA kit (Human IGF-I, R\u0026amp;D Systems, Minneapolis, USA), and the cortisol ELISA kit (Cortisol ELISA kit, Enzo Life Sciences Inc., Budapest, Hungary), following the manufacturers' protocols. For blood amino acid concentrations, trichloroacetic acid was added to the plasma, and proteins were filtered through a membrane filter before analysis with a high-speed amino acid spectrometer (L-8900, Hitachi High Tech, Tokyo, Japan). Total VFA in the rumen fluid and individual components, such as acetic acid, propionic acid, butyric acid, and valeric acid were quantified by gas chromatography (GC2014, Shimadzu, Kyoto, Japan) using a Thermon-3000 [3%] packed glass column on a Shimalite TPA 60–80 support (Shinwa Chemical Industries Ltd., Kyoto, Japan). The gas chromatography was operated under the following conditions: nitrogen as the carrier gas at a flow rate of 30 mL/min, with the column injection and FID detection temperatures set at 220°C, and the column oven at 140°C. Ammonium nitrogen concentration in the rumen fluid was measured using the steam distillation method with an automatic nitrogen analyzer (Kjeltec Auto 1035, Tecator, Sweden). The ammonium nitrogen concentration in rumen fluid was measured using the steam distillation method with an automatic nitrogen analyzer (Kjeltec Auto 1035, Tecator, Sweden).\u003c/p\u003e\n\u003ch3\u003eTranscriptomics analyses of Hepatocytes\u003c/h3\u003e\n\u003cp\u003eLiver tissue samples from 20 cattle at early fattening (T1), 20 at middle fattening (T2), and 19 at late fattening (T3) were used for RNA-Seq. The tissues were homogenized in 200 μL RNAiso Plus (TAKARA Bio Inc., Shiga, Japan) with a Multibeads shocker (YASUIKIKAI Inc., Osaka, Japan) following the manufacturer’s instructions. Homogenization was performed twice at 2000 rpm for 10–15 seconds, followed by the addition of 800 μL RNAiso at 25 °C. The homogenate was collected in 1.5 mL tubes, mixed with 200 μL chloroform, and centrifuged at 12,000×g for 15 minutes at 4 °C. The supernatant was mixed with 500 μL isopropanol and centrifuged again to precipitate the RNA. The RNA pellet was washed twice with 75% cold ethanol, dissolved in RNase-free water, and quantified using a Nano Drop ND-1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). RNA purity was confirmed with an A260/A280 ratio \u0026gt;1.8 and verified via 1.0% agarose gel electrophoresis. RNA integrity was confirmed using a Tape Station 4200 (Agilent Technologies, Santa Clara, CA, USA) with an average RNA integrity number (RIN) of 7.1. RNA-seq libraries were prepared using the TruSeq stranded mRNA Kit (Illumina, San Diego, CA, USA) and sequenced on a NovaSeq 6000 platform at Macrogen Japan Corporation. Sequencing read quality was evaluated with FastQC software (version 0.11.8), trimmed with Trim Galore (version 0.5.0), and mapped to the ARS-UCD1.2 bovine reference genome using HISAT2 [62]. Read counts were calculated using StringTie [62] with the GTF Bovine gene annotation file. Normalization of counts and PCA were performed using the DESeq2 package [63] in R statistical software and two T2 samples identified as outliers were removed.\u003c/p\u003e\n\u003ch3\u003eMetataxnomics analyses of rumen microbiota\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from rumen fluid using the Fast DNA kit (MP Biomedicals in California, USA) and quantified with the Nanodrop ND-1000 Spectrophotometer (Thermo Fisher Scientific, France), with integrity verified by agarose gel electrophoresis. One sample in T2 was excluded due to poor gDNA quality. Approximately 10µg of rumen gDNA was used for PCR amplification to generate libraries of the bacterial 16S rRNA V3-V4 region using 341F-806R primers (forward: CCTACGGGNGGCWGCAG, reverse: GACTACHVGGGTATCTAATCC) [15] and the archaeal 16S rRNA V4 region using Arch 349F-806R primers (forward: GYGCASCAGKCGMGAAW, reverse: GGACTACVSGGGTATCTAAT) [65] following the 16S metagenomics sequencing library preparation protocol. Sequencing was performed on Illumina’s MiSeq platform \u0026nbsp; (San Diego, CA, United States) using the Paired-End method (2 × 300bp). Metataxonomic analysis of the bacterial and archaeal rumen microbiome was conducted using QIIME2 (version 2023.2) [66], with adapter and primer sequences trimmed using Cutadapt [67] and further processing with DADA2 [68] for chimera removal, denoising, and quality filtering. Sequences were clustered at 99% similarity using the Naive Bayes classifier [69] with the SILVA v13.8 database [70]. Taxonomic assignment was refined by excluding unassigned ASVs, chloroplast, mitochondria, and non-bacteria taxa. Beta diversity of overall rumen microbiotas between HME and LME across fattening stages were analyzed using MicrobiomeAnalyst [71], with\u0026nbsp;principal component analysis (PCoA)\u0026nbsp;based on Bray-Curtis dissimilarity [72]. A Venn diagram illustrated ASVs with 100% prevalence present in both groups and unique to each group using InteractiVenn software [73]. Functional genetic profiles were predicted using PICRUSt2 ( Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2) [74], with differences in Kyoto Encyclopedia \u0026nbsp;for Genes and Genomes (KEGG) \u0026nbsp;profiles between HME and LME groups analyzed.\u003c/p\u003e\n\u003ch3\u003eStatistical analysis\u003c/h3\u003e\n\u003cp\u003eThe comparison of blood metabolites, hormones, amino acids, hepatic genes, rumen fermentation, rumen microbiota, functional profiles, and methane emission levels between the HME and LME groups was conducted using R (v.4.1.3) software. Specifically, a mixed model analysis of variance (ANOVA) was employed, incorporating the fixed effects of methane emission levels (HME and LME), periods (T1, T2, and T3), as well as their interaction, with the animal as a random effect. Spearman's correlation coefficients were analyzed using the 'hmisc' R package [75], with statistical significance determined at |r|≥ 0.5 and P ≤ 0.05. To identify parameters associated with methane emission, regression tree analysis was conducted using the 'lmertree' R package [76]. Methane emissions were treated as the dependent variable, while phenotypic factors served as independent variables. Bayesian networks were constructed to infer significant interactions between methane emission and host physiological parameters. The structure of the Bayesian network was developed using a tabu search algorithm in the 'bnlearn' R package [77], and the uncertainty of the edge strength and direction within the network was estimated using 2,000 bootstrap replicates.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the Hyogo Prefectural Technology Center of Agriculture employees for their assistance with the care of experimental animals and sample collection.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the MAFF Commissioned project study on \u0026ldquo;Development of Technologies to Reduce Greenhouse Gas Emissions in the Livestock Sector\u0026rdquo; (Grant Number JPJ011299). This work was partly supported by JSPS KAKENHI (grant number: 23K18075). Huseong Lee was granted by JST (grant number: JPMJSP2114).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eH.L., M.K. contributed to sample collection, sample analysis, data analysis, and writing; T.M., K.I., and E.I. contributed to experimental design, sample collection, and data analyses; O.K., U.Y. contributed to data analysis, data interpretation, and manuscript revision; S.H., F.T. and S.R. contributed to experimental design, data interpretation, writing, and revision; all authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eRaw sequence reads data obtained from the present study have been deposited in the NCBI BioProject database, assigned the project number PRJNA1031765. The data will be available with the following link: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1031765.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe experimental protocol was evaluated and approved by the Hyogo Prefectural Institute of Agriculture of Forestry and Fisheries Animal Care and Use Committee (approval number: H2018-01).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStocker, T., Climate change 2013: the physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. 2014: Cambridge university press.\u003c/li\u003e\n\u003cli\u003eAdopted, I., Climate change 2014 synthesis report. 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Harrell Jr, Package \u0026lsquo;hmisc\u0026rsquo;. CRAN2018, 2019. \u003cstrong\u003e2019\u003c/strong\u003e: p. 235-236.\u003c/li\u003e\n\u003cli\u003eFokkema, M., A. Zeileis, and M.M. Fokkema, Package \u0026lsquo;glmertree\u0026rsquo;. . 2019.\u003c/li\u003e\n\u003cli\u003eScutari, M., Learning Bayesian networks with the bnlearn R package. arXiv preprint arXiv:0908.3817, 2009.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Methane production, Hydrogen sink, Physiological changes, Japanese Black Cattle","lastPublishedDoi":"10.21203/rs.3.rs-5235475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5235475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGastroenteric release of methane from livestock accounts for a substantial portion of anthropogenic greenhouse gas emissions worldwide. Here, we examined the characteristics of rumen microbiome and physiological resilience associated with methane production, a breed characterized by enhanced intramuscular fat deposition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMethane emissions were measured during three phases: early (13 months), middle (20 months), and late (28 months), and the liver transcriptome, blood metabolites, hormones, rumen fermentation characteristics, and microbiota were analysed. Hydrogen-sinking microbes such as \u003cem\u003eAnaerovorax\u003c/em\u003eand \u003cem\u003eSuccinivibrio \u003c/em\u003ewere present at low levels, whereas the prevalence of hydrogen-producing microbes including \u003cem\u003eChristensenellaceae\u003c/em\u003e, \u003cem\u003eClostridium methylpentosum\u003c/em\u003e, and \u003cem\u003eMogibacterium\u003c/em\u003e was high in cattle with high methane emissions. Functional profiling of rumen microbiota revealed decreased coenzyme M biosynthesis and an increased hydrogen sink from L-glutamate biosynthesis in low-emission cattle. In the liver, glutamate-derived ornithine and elevated ornithine transcarbamylase gene expression facilitated ammonia detoxification in low-emission cattle, whereas the glutamate transporter-encoding gene \u003cem\u003eSLC1A1\u003c/em\u003ewas upregulated in high-emission cattle, thereby enhancing glutathione synthesis and reducing the oxidative stress induced by beta-hydroxybuteric acid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThese ruminal and physiological changes reflect the resilience of these cattle to different rumen fermentation conditions, have potential as biomarkers for monitoring the methanogenic potential of Japanese Black cattle, and highlight the upstream oxoglutarate-to-glutamate biosynthesis pathway as a promising target for decreasing methane production by reducing hydrogen in the rumen.\u003c/p\u003e","manuscriptTitle":"Exploring the link between ruminal methane production and physiological resilience in Japanese Black cattle during fattening","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 09:10:17","doi":"10.21203/rs.3.rs-5235475/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37fc8347-8cb7-4fc9-af96-494c4b7cb9d6","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-15T11:09:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 09:10:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5235475","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5235475","identity":"rs-5235475","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}