Salvianolic Acid C Inhibits Methane Emissions in Dairy Cows by Targeting MCR and Reshaping the Rumen Microbial Community

Salvianolic Acid C Inhibits Methane Emissions in Dairy Cows by Targeting MCR and Reshaping the Rumen Microbial Community | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Salvianolic Acid C Inhibits Methane Emissions in Dairy Cows by Targeting MCR and Reshaping the Rumen Microbial Community Zihao Liu, Li Xiao, Xiangfang Tang, Yue He, Xuemei Nan, Hui Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6850632/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Journal of Animal Science and Biotechnology → Version 1 posted 5 You are reading this latest preprint version Abstract Background Methane (CH₄) emissions from ruminants significantly contribute to greenhouse gas effects and energy loss in livestock production. Methyl-coenzyme M reductase (MCR) is the key enzyme in methanogenesis, making it a promising target for CH₄ mitigation. This study aimed to identify and validate plant-derived inhibitors by employing molecular docking to screen compounds with strong binding affinity to the F430 active site of MCR and assessing their efficacy in reducing CH₄ emissions. Results Molecular docking analysis identified Salvianolic acid C (SAC) as a potent inhibitor of MCR, exhibiting a strong binding affinity to the F430 active site (binding energy: −8.2 kcal/mol). Enzymatic inhibition assays confirmed its inhibitory effect, with a half-maximal inhibitory concentration (IC₅₀) of 692.3 µM. In vitro rumen fermentation experiments demonstrated that SAC supplementation (1.5 mg/g DM) significantly reduced CH₄ production ( P < 0.01) without negatively affecting key fermentation parameters. Microbial community analysis using 16S rRNA sequencing and metagenomics revealed that SAC selectively altered the rumen microbiota, increasing the relative abundance of Bacteroidota while significantly reducing Methanobrevibacter ( P = 0.04). Additionally, metagenomic analysis indicated the downregulation of key methanogenesis-related genes ( mcrA , rnfC ), suggesting a dual mechanism involving direct enzymatic inhibition and microbial community modulation. Conclusions These findings indicate that SAC effectively reduces CH₄ production by inhibiting MCR activity and reshaping the rumen microbial community. As a plant-derived compound with strong inhibitory effects on methanogenesis, SAC presents a promising and sustainable alternative to synthetic methane inhibitors, offering potential applications in mitigating CH₄ emissions in livestock production. Salvianolic acid C Methane mitigation Methyl-coenzyme M reductase Rumen microbiota Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Since the beginning of the 21st century, the problem of global warming caused by greenhouse gas (GHG) emissions has become increasingly serious and has received widespread attention [1, 2]. Human induced GHG emissions come from a wide range of sources, including industry, transportation, agriculture, forestry and other land use, construction, and other energy related activities [3]. The agricultural sector is considered the second largest anthropogenic GHG. It is estimated that in 2010, the agricultural sector accounted for approximately 11% of global anthropogenic GHG emissions [1, 4]. In the agricultural sector, the main GHG emissions are from livestock production [1, 5]. Methane (CH 4 ) produced by ruminant gut fermentation is one of the main GHG in livestock production, and it is increasing year by year [3]. Ruminants, due to their unique and complex rumen system, can convert plant fibers that are difficult to digest and utilize by other livestock into volatile fatty acids (VFA) and microbial proteins that can be absorbed and utilized [6, 7]. However, CH 4 is a byproduct produced during the digestion process. It is the second largest GHG after carbon dioxide (CO 2 ), and its potential contribution to the greenhouse effect is more than 25 times that of CO 2 [8]. In addition, the excretion of CH 4 as a byproduct of the digestion process also results in energy loss [9]. According to statistics, about 6% of gross energy intake is lost in the form of CH 4 [10]. Therefore, reducing CH 4 emissions from ruminants is of great significance in mitigating the greenhouse effect and improving feed energy utilization efficiency. The CH 4 emissions of ruminants are mainly produced by the rumen, accounting for up to 87% [11, 12]. In the rumen, feed carbohydrates are fermented and decomposed by rumen microorganisms into VFA, CO 2 and H 2 [13, 14]. Methanogenic archaea generate CH 4 using acetate, formate, methanol, or CO 2 and H 2 as substrates [15]. There are currently three main pathways known for CH 4 production, namely the H 2 nutrition pathway, the acetate cleavage pathway, and the methyl nutrition pathway [16]. Methyl-coenzyme M reductase (MCR) plays a unique role as a key enzyme in the CH 4 generation pathway, the final step of all CH 4 generation pathways requires the catalysis of MCR [17, 18]. MCR catalyzes the reduction of methyl-coenzyme M and coenzyme B to CH 4 and heterodisulfide under anaerobic conditions [19]. However, this process requires the presence of coenzyme F430 [20]. Coenzyme F430 is the active site of MCR, and its nickel center must be in the Ni (I) oxidation state for the enzyme to be active [21]. Therefore, inhibiting the activity of MCR is an effective strategy to reduce CH 4 generation. There have been studies reporting on some MCR inhibitors, such as 3-nitrooxypropanol (3-NOP), 2-bromoethanesulfonate (BES), and bromopropanesulfonate (BPS). Both BES and BPS can inhibit the activity of MCR [22]. However, due to the susceptibility of BES to drug resistance and the inability of BPS to be transported to methanogenic cells [23], as well as toxicological reasons, they cannot be widely promoted in application [24]. 3-NOP is an artificially synthesized compound that can inhibit CH 4 emissions, but it can lead to a significant increase in H 2 emissions, and the direction of the increased H 2 is not yet clear [25]. Plant extracts have a wide range of sources and rich functions[26]. Compared to chemical additives, plant extracts are economical and safe, and have received widespread attention in recent years [27]. It has been found that some plant extracts can inhibit CH 4 generation, such as saponins, tannins, and flavonoids [27]. However, these plant extracts have issues such as affecting digestion and metabolism, toxicity, and limited effectiveness [27, 28]. Therefore, exploring specific plant extract inhibitors targeting MCR is crucial. At the same time, there are currently few studies that use high-throughput virtual screening methods and explore the mechanism of inhibitors inhibiting CH 4 generation. The aim of this study is to target MCR and use molecular docking high-throughput virtual screening technology to screen for a plant derived CH 4 inhibitor with significant effects, and to elucidate its mechanism of action. Materials and methods Molecular docking and virtual screening MCR is a multi-subunit enzyme complex, with mcrA , mcrB , and mcrG genes encoding α、β And γ subunit, respectively [29]. Download the 3D structure of MCR (PDB ID: 5G0R) from the RCSB PDB database. The Protein Preparation Wizard module was used to hydrogenate and delete the D/E/F chain of the protein, remove water molecules, and delete small molecules such as TP7, AGM, DY A, GL3, MGN, MHS, SMC, etc. The energy was then optimized (OPLS2005 field, RMSD 0.30 Å). The processed proteins were made into grid files using the Receptor Grid Generation module, and the grid files were generated with small molecule F430 binding pocket as the center, and the box size was set to 20 Å × 20 Å × 20 Å. The 2D format of Natural Product Library (containing 3.9K compounds) was adopted by LigPrep Module of Schrödinger performs hydrogenation, energy optimization, and outputs 3D structures for virtual screening. Utilize the Virtual Screening Workflow module for the virtual screening process, import the prepared compounds, and employ the Glide module for molecular docking. Firstly, the high-throughput virtual screening (HTVS) mode in the Glide module was used to dock the compounds in the Natural Product Library with the target protein MCR for the standard precision (SP) mode for the first round of screening. Subsequently, the top 15% of docking scores were selected for the second round of screening using extra precision (XP) mode. Finally, check the target and compound binding force, as well as the structure of the compound. Measurement of MCR inhibitory activity MCR activity inhibited by plant extracts was measured using a microbial MCR ELISA kit (Meimian Industrial Co., Ltd, Jiangsu, China). All plant extracts were purchased from MedChemExpress (Shanghai, China) and dissolved in dimethyl sulfoxide (DMSO), then diluted to the appropriate concentrations using 50 mM HEPES buffer (pH 7.5). In each well, 40 µL of MCR and 10 µL of plant extract (final concentration 100 µM) were added. The plate was gently mixed and incubated at 37°C for 30 minutes. After incubation, the liquid was discarded, excess liquid was shaken off, and the plate was washed. Then, 50 µL of enzyme substrate was added to each well, and incubation continued for another 30 minutes at 37°C. Afterward, 50 µL of chromogenic agent A and 50 µL of chromogenic agent B were added to each well, followed by incubation for 10 minutes at 37°C, shielded from light. To terminate the reaction, 50 µL of terminating solution was added, and the absorbance (OD) at 450 nm was measured using a microplate reader (Thermo Labserv K3 TOUCH, Thermo Scientific, Waltham, MA, USA). MCR activity inhibited by plant extracts was measured using a microbial MCR ELISA kit (Meimian Industrial Co., Ltd, Jiangsu, China). Plant extracts were purchased from MedChemExpress (Shanghai, China) and dissolved in dimethyl sulfoxide (DMSO), then diluted to the appropriate concentrations using 50 mM HEPES buffer (pH 7.5). In each well, 40 µL of MCR and 10 µL of plant extract (final concentration 100 µM) were added. The plate was gently mixed and incubated at 37°C for 30 minutes. After incubation, the liquid was discarded, excess liquid was shaken off, and the plate was washed. Then, 50 µL of enzyme substrate was added to each well, and incubation continued for another 30 minutes at 37°C. Afterward, 50 µL of chromogenic agent A and 50 µL of chromogenic agent B were added to each well, followed by incubation for 10 minutes at 37°C, shielded from light. To terminate the reaction, 50 µL of terminating solution was added, and the absorbance (OD) at 450 nm was measured using a microplate reader (Thermo Labserv K3 TOUCH, Thermo Scientific, Waltham, MA, USA). Salvianolic acid C (SAC), a potent MCR inhibitor, was selected for further investigation based on its ability to effectively inhibit MCR activity. To determine its IC50 value, SAC was added to the MCR enzyme solution at concentrations of 0, 25, 50, 100, 250, 500, 800, 1000, and 1500 µM. The same procedure was followed as described above, and the IC50 was calculated using nonlinear regression curve fitting in GraphPad Prism (version 9, GraphPad, La Jolla, CA, USA). SAC was chosen for subsequent studies due to its promising inhibitory effects on MCR activity. In vitro fermentation experiment The experimental procedures involving animals have been approved by the Animal Care and Use Committee of the Chinese Academy of Agricultural Sciences (IAS2023-131; Beijing, China). Rumen fluid was obtained from three rumen-fistulated Holstein dairy cows (average body weight: 618 ± 100 kg; milk yield: 23 ± 2.8 kg/d; 3 ± 1 of parity) fed a consistent total mixed ration (TMR). The TMR composition (on a dry matter basis) was as follows: corn silage (25.65%), alfalfa hay (18.59%), steam-flaked corn (26.02%), soybean meal (7.43%), cottonseed meal (7.43%), beet meal (5.58%), distillers dried grains with solubles (7.43%), and minerals and vitamins (1.86%). Approximately 2 h after morning feeding, rumen content was collected, placed in prewarmed insulated bottles, and transported to the laboratory within 30 minutes. The rumen content was filtered through four layers of sterile cheesecloth and mixed with buffer solution at a 1:2 ratio (v/v) under continuous CO₂ flushing at 39°C to prepare the inoculum. The buffer solution was prepared according to Liu et al. (2023) [30]. An in vitro batch culture system was employed to evaluate the effects of SAC on rumen fermentation. Fermentation substrate was prepared by drying the TMR to 55°C for 48 h, grinding to pass through a 1 mm sieve, and weighing 0.5 g into each 120 mL fermentation vessel. Each vessel received 75 mL of inoculum and a corresponding dose of SAC (0, 0.25, 0.5, 1.0, 1.5, or 2.0 mg/g of substrate DM), with SAC added in dissolved form (1 mg/mL, dissolved in water). The system was flushed with CO₂, sealed with a rubber stopper and aluminum foil, and incubated in a shaking water bath at 39°C, 60 rpm, for 24 h. Each treatment included four replicates and four blank vessels (inoculum only) for gas calibration. The experiment was repeated three times on separate days. After 24 hours of fermentation, all fermentation tanks were immediately removed and placed in ice water to terminate the fermentation process. The gas produced during fermentation is collected in airtight aluminum foil bags, and the total gas production is obtained by drawing out the gas using a graduated syringe. Take 5ml of gas from each gas bag for measuring CH 4 concentration. The pH value of the fluid sample was analyzed using a portable pH meter (Seven Go, Mettler Toledo). Take 4ml x 3 of fermentation substance (solid-liquid mixture) from each fermentation tank for VFA (-20℃) and microbial (-80℃) analysis. The remaining fermentation content in each tank was filtered using nylon bags and rinsed thoroughly with water until the effluent became clear. The nylon bags were then dried at 55°C for 48 hours to determine dry matter digestibility (DMD). The concentrations of CH₄ and VFAs were analyzed using a gas chromatograph (8860, Agilent Technologies, CA, USA), following the parameters and procedures described by Liu et al. (2023)[30]. Genomic DNA extraction and sequencing The extraction of microbial DNA uses the HiPure Stool DNA extraction kit (Magen, Guangzhou, China), the extraction process begins with mixing the sample with the buffer SOL, swirling it for 5–10 mins for initial cracking. Then, the buffer SDS was added and swirled for 15s, and the mixture was heated at 70 ° C for 10 mins for thermal cracking. After centrifugation, buffer PS and absorption solution were added, mixed and allowed to stand for 5 mins. The supernatant was collected by centrifugation at 13000g, mixed with buffer GDP, and passed through a DNA column for two rounds for centrifugation separation. Afterward, the DNA column is washed twice with buffer GDP and GW2 each to ensure the purity of the DNA. The DNA column was centrifuged and dried for 2 mins, and DNA was eluted with preheated buffer AE. The extracted DNA should be stored at 2–8 ℃ or -20 ℃ for long-term storage. The extracted DNA samples were tested for integrity by AGAR gel electrophoresis, and the concentration and purity of the DNA samples were tested by NanoDrop microspectrophotometer (NanoDrop 2000, Thermo Fisher Technologies, USA). The V4-V5 region of 16S rRNA was amplified by primers Arch519F (5'-CAGCMGCCGCGGTAA-3') and Arch915R (5 '-GTGCTCCCCCGCCAATTCCT-3'). Perform PCR amplification procedure as described by Z Liu, K Wang, Y Zhao, X Nan, L Yang, M Zhou, X Tang and B Xiong [31]. The obtained amplicons underwent evaluation through 2% agarose gels and were subsequently purified using AMPure XP Beads (Beckman, CA, USA). The purified amplicons were then pooled and sequenced on the Novaseq 6000 platform using PE250 mode. The raw reads were then deposited in the NCBI Sequence Read Archive (SRA) database, with the accession number SRP516729. Raw reads were filtered using FASTP v0.18.0 to remove reads with ≥ 10% ambiguous bases (N), those with > 50% of bases having quality scores ≤ 20, and adapter-contaminated sequences. Paired reads were merged using FLASH v1.2.11 (≥ 10 bp overlap, ≤ 2% mismatch), and low-quality tags were trimmed (quality ≤ 3 for ≥ 3 bases), retaining tags with high-quality length ≥ 75% of the total. Operational taxonomic units (OTUs) were clustered at 97% similarity using USEARCH (UPARSE algorithm, v11.0.667), and chimeric sequences were removed using UCHIME. Taxonomic annotation of representative OTU sequences was performed by alignment against the SILVA v138.2, Greengenes2 v2022.10, and UNITE v10.0 databases using the RDP Classifier v2.2. For metagenomic sequencing, qualified genomic DNA is fragmented into about 350 bp segments by sonication, then undergoes end-repair and A-tailing. Illumina sequencing adapters are added using the NEBNext® Ultra™ Kit (NEB, USA), forming the sequencing library. Next, 300–400 bp fragments are selected, amplified by PCR, and purified to remove impurities. The library's quality, concentration, and size distribution are checked with an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA), and quantified using real-time PCR. Finally, the library is sequenced on an Illumina Novaseq 6000, where both ends of each fragment are read for 150 bp, generating high-quality sequencing data. The raw sequencing reads were subsequently deposited into the SRA database, assigned with the unique accession number SRP527714. Raw reads were quality-filtered using fastp (v0.20.0) to remove reads containing ≥ 10% ambiguous bases, ≥ 50% low-quality bases (Phred ≤ 20), adapter contamination, or shorter than 50 bp. Clean reads were assembled with MEGAHIT (v1.2.9) using a multi-k-mer strategy (k = 27–127), retaining contigs ≥ 500 bp. Gene prediction was conducted with MetaGeneMark (v3.38), and predicted genes were clustered using CD-HIT (v4.6) at ≥ 95% identity and ≥ 90% coverage to construct a non-redundant gene catalog. Clean reads were aligned to the gene set using Bowtie2 (v2.3.5.1), and gene abundance was calculated with read reassignment via Pathoscope (v2.0.7), removing genes supported by < 2 reads. Functional annotation was performed using DIAMOND (v0.9.25, e-value < 1e-5) against multiple databases, including NR (2024.07.25), KEGG (Release 111), eggNOG (v5.0), CAZy (2024.07.14), PHI-base (v4.17), CARD (v3.3.0), QS-related genes, NCycDB, SCycDB, PCycDB, and MCycDB. Mobile genetic elements were annotated using BLASTn (v2.6.0) against the MGE database (2017 version; identity and coverage > 40%), and resistance genes were identified using BacMet-Scan (v1.1) with the BacMet database (v2.0). Functional abundance data were visualized using R packages. Statistical analysis The experimental results were analyzed using the mixed model procedure of SAS (Version 9.4, SAS Institute Inc., Cary, NC) and the UNIV variable program was used to test the normality of the data. The model was as follows: Yijk = µ + Gi + Sj + eij, where Yijk is the dependent variable, µ is the grand mean, Gi is the treatment group, Sj is the treatment fixed effect (CON vs SAC), and eij is the error term. Tukey's method was used for multiple mean comparisons, with P ≤ 0.05 declaring a significant difference, and 0.05 < P ≤ 0.10 declaring a trend. Results Screening of plant extracts The three-dimensional structure of the methane-generating enzyme MCR (methyl-coenzyme M reductase) was modeled, with its active sites (F430 coenzyme-binding pockets) clearly identified (Fig. 1 ). Molecular docking was performed to screen plant extracts for potential inhibitors targeting the F430 site. The docking scores, reflecting binding affinity strength, identified four extracts with scores exceeding − 8 kcal/mol (Table 1 ). Subsequent in vitro inhibition assays at 100 µM revealed divergent outcomes: Hispidin and SAC significantly suppressed MCR activity by 25.80% and 34.50%, respectively, while the other two extracts showed no inhibitory effects (Fig. 2 ). Table 1 Molecular docking parameters and details of plant extracts with inhibition of MCR activity Name docking score CAS No. Structure Mw Secoisolariciresinol diglucoside -10.839 257930-74-8 686.70 Kinsenoside -8.749 151870-74-5 264.23 Salvianolic acid C -8.124 115841-09-3 492.43 Hispidin -8.097 555-55-5 246.22 Molecular binding mode of SAC to MCR The chemical structure of SAC is shown in Fig. 3 a. The interaction between SAC and MCR was explored through molecular docking and high-throughput screening, with a binding free energy of -8.124 kcal/mol (Table 1 ), suggesting a robust affinity between SAC and the MCR active site. The binding modes of SAC and MCR are illustrated in Fig. 3 b-c. In the 3D diagram of McrG , McrB , and McrA proteins, the C-skeleton is displayed in green, the O atoms are shown in bright red, the H atoms are presented in off-white, and SAC is indicated as light blue sticks. The hydrogen bond lengths are represented by red dashed lines. The docking results show that SAC forms five hydrogen bonds with MCR, with two phenolic hydroxyl groups donating hydrogen bonds to GLY142 and MET229 (1.8 Å and 2.0 Å), a carboxyl group forming a bond with GLN230 (2.0 Å), and an ester group forming two bonds with VAL146 and GLN147 (2.3 Å and 2.1 Å). Kinetics of SAC inhibition To further quantify the inhibitory effect of SAC on MCR activity, the activity of MCR was assessed under different concentrations of SAC treatment, and a dose-response curve was plotted (Fig. 4 ). As shown in the figure, the inhibitory effect of SAC on MCR activity increases significantly with rising concentrations, exhibiting a typical concentration-effect relationship. At low concentrations (around Log10[µM] = 2), the residual activity of MCR remains close to 100%. However, as the concentration of SAC gradually increases, the residual activity of MCR decreases progressively. At high concentrations (around Log10[µM] = 4), the residual activity of MCR approaches zero. By fitting the concentration-effect curve, the half-maximal inhibitory concentration (IC50) of SAC on MCR activity was calculated to be 692.3 µM. Effect of SAC on CH 4 production and fermentation in vitro In this study, the effects of SAC on in vitro CH 4 production, total VFA concentration, and other fermentation parameters in dairy cow rumen are shown in Table 2 . The data indicates that SAC significantly reduced in vitro CH 4 production ( P < 0.01). In the treatment group, the CH 4 production was about 6.01%-14.8% lower than that of the control group. In line with the changes in CH 4 production, total gas production also significantly decreased with the increasing concentration of SAC ( P = 0.025). In the 2.0 mg/mL group, the total gas production was 147.5 mL, which was approximately 8% lower than that of the control group (160.3 mL). The addition of SAC did not show a significant effect on the pH value and DMD of rumen fermentation fluid ( P > 0.05). In terms of VFA concentration, no significant differences were observed between the treatment and control groups ( P = 0.945). However, in terms of individual VFA components, the proportion of butyrate significantly decreased with the increasing concentration of SAC ( P = 0.045), while other major VFA components (such as acetate and propionate) were not significantly affected. Additionally, the acetate/propionate ratio showed a decreasing trend across the groups ( P = 0.096), decreasing from 2.85 in the control group to 2.77. Table 2 Effects of Salvianolic Acid C on CH₄ production and other fermentation parameters in vitro Items Treatment SEM P-value CON 0.25 0.5 1 1.5 2 pH 6.97 6.97 6.96 6.95 6.97 6.96 0.007 0.952 DMD 0.79 0.80 0.79 0.79 0.80 0.81 0.212 0.114 Gas production (mg/d) 160.3 a 152.0 b 149.5 b 148.8 b 148.0 b 147.5 b 1.250 0.025 CH 4 (mg/d) 25.78 a 24.23 b 23.55 b 22.59 b 21.95 c 21.97 c 0.199 0.001 Total VFA Concentrations (mM) 105.91 106.85 109.12 108.51 107.25 108.68 0.847 0.945 Individual, mol/100 mol Acetate 61.78 61.81 61.72 61.81 61.89 61.90 0.515 0.145 Propionate 21.76 21.89 21.47 22.15 22.27 22.35 0.143 0.106 Isobutyrate 1.06 1.05 1.06 1.02 1.02 1.04 0.016 0.174 Butyrate 11.69 ab 11.55 ab 11.99 a 11.41 ab 11.23 ab 11.09 b 0.176 0.045 Isovalerate 2.03 2.01 2.06 1.95 1.92 1.97 0.041 0.379 Valerate 1.69 1.69 1.71 1.67 1.66 1.67 0.023 0.489 Acetate / Propionate 2.85 2.83 2.89 2.79 2.78 2.77 0.013 0.096 1 Treatment = were control (no Salvianolic Acid C) and Salvianolic Acid C included at 0,0.25,0.5,1.0,1.5 and 2.0mg/g of DM (0.25,0.5,1.0, 1.5 and 2.0 respectively). 2 DMD = apparent disappearance of dry matter. Impact on rumen microbial communities Figure 5 a-b show the α-diversity indices of bacterial and archaeal communities under SAC treatments, assessing microbial richness and evenness. No significant differences were observed between SAC treatments and control group, though slight variations appeared at certain concentrations. Figure 5 c-d show the principal coordinate analysis (PCoA) of bacterial and archaeal communities, which visualizes the distance between communities to assess the differences among treatment groups. Higher SAC concentrations exhibited distinct shifts from the control. Fig e-f (community composition at the phylum and genus levels for bacteria) and Fig g-h (community composition at the phylum and genus levels for archaea) show the changes in microbial community composition under different concentrations of SAC treatment. For bacteria, Bacteroidota and Firmicutes are the predominant phyla in the rumen, dominating all treatment groups. At the genus level, Prevotella , Rikenellaceae_RC9_gut_group , and Succiniclasticum are the major bacterial genera. For archaea, Euryarchaeota dominate all treatment groups, and at the genus level, Methanobrevibacter is the primary genus of archaea. Fig i-l and Fig m-n collectively provide an in-depth analysis of the bacterial and archaeal community shifts in response to the SAC treatment (1.5 mg) compared to the control group (CK). Fig i-l highlight the results of the linear discriminant analysis effect size (LEfSe) analysis. In Fig i , the linear discriminant analysis (LDA)bar plot reveals that bacterial taxa such as Bacteroidota and Bacteroidales are significantly enriched in the SAC treatment group, as indicated by the positive LDA scores. In contrast, taxa such as Firmicutes , Actinomycetales , and Ruminococcaceae are enriched in the control group, as indicated by the negative LDA scores. Fig j complements this by providing a phylogenetic cladogram, where green-highlighted branches represent taxa enriched in the treatment group (e.g., Bacteroidota and Bacteroidales ), and red-highlighted branches denote taxa enriched in the control group (e.g., Firmicutes and Ruminococcaceae ). Similarly, for archaeal taxa, Fig k demonstrates that members of the Methanomassiliicoccales and Thermaplasmata are enriched in the treatment group, whereas Euryarchaeota and Methanobrevibacter are more abundant in the control group. This is further visualized in Fig l , where green branches highlight archaeal taxa enriched in the treatment group, such as Methanomassiliicoccales , and red branches show taxa like Methanobrevibacter , enriched in the control group. Fig m-n further support these findings through statistical analysis of the relative abundances of bacterial and archaeal genera. In Fig m , the mean relative abundance of Prevotella is higher in the control group, although this change is not statistically significant. In Fig n , the analysis of archaeal genera reveals that Methanobrevibacter , the dominant methanogen genus, has a significantly higher relative abundance in the control group compared to the treatment group ( P < 0.01). Effects of SAC on functional gene expression and methanogenesis The SAC treatment group exhibited significant enrichment of functional genes associated with carbohydrate metabolism and starch and sucrose metabolism (Fig. 6 a, 6 c), alongside notable upregulation of the pentose phosphate pathway (Fig. 6 c). The genes involved in steroid degradation were markedly enriched (Fig. 6 c-d). The key genes critical to CH 4 production— mcrA (encoding the α-subunit of MCR) and rnfC (implicated in electron transport during methanogenesis)—were significantly downregulated in the SAC group (Fig. 6 b). The DNA replication related genes were more abundant in the control group compared to the SAC treatment (Fig. 6 d). Discussion The molecular docking results confirmed that SAC is an effective non-covalent inhibitor of MCR, with a GlideScore of -8.124 kcal/mol. This strong predicted binding affinity is primarily attributed to the formation of five key hydrogen bonds, as depicted in the 3D interaction model (Fig. 3 ). The shortest hydrogen bond (1.8 Å) is formed between the phenolic hydroxyl group of SAC and GLY142, a residue situated near the active site of MCR. Although GLY142 has not been explicitly identified as a catalytic residue, its close proximity to the Ni-containing F430 cofactor suggests a possible structural role in ligand stabilization. In addition, hydrogen bonds with lengths shorter than 2.0 Å are generally considered particularly significant, as they tend to exhibit stronger binding interactions than those exceeding 2.5 Å[32]. SAC also forms additional interactions with MET229 (2.0 Å), GLN230 (2.0 Å), and GLN147 (2.1 Å), further stabilizing the compound within the MCR active site. These interactions enhance the binding affinity of SAC, supporting its potential as an effective MCR-targeting CH₄ inhibitor. Among the plant extracts tested in this study, SAC demonstrated the strongest inhibitory effect on MCR activity, achieving a 34.50% reduction (Fig. 2 ). Interestingly, although other extracts exhibited comparable predicted binding affinities in molecular docking, they did not produce similar levels of enzyme inhibition. This discrepancy indicates that binding affinity alone may not reliably predict enzyme inhibition[33]. To further quantify the inhibitory efficacy of SAC, the concentration dependent inhibition of MCR by SAC was evaluated, and its IC₅₀ was determined to be 692.3 µM. This value indicates that SAC is a moderately potent inhibitor of MCR. Although direct IC₅₀ comparisons are limited, similar to other plant derived polyphenols, relatively high concentrations are generally required to suppress CH 4 emissions or the expression of MCR genes [27, 34]. Synthetic compounds targeting MCR typically exhibit extremely low IC₅₀ values, such as 3-NOP (IC₅₀ = 0.1 µM) [21] and BES (IC₅₀ = 0.4 µM) [35], which are markedly lower than that of SAC. However, issues such as the need for high effective doses and concerns over cytotoxicity have limited the practical application of these synthetic inhibitors[25, 35]. In contrast, plant derived compounds generally offer higher biocompatibility[36]. SAC is a water-soluble polyphenolic compound derived from the root of Salvia miltiorrhiza , a traditional Chinese medicinal herb[37]. SAC has shown a broad spectrum of biological activities, including antioxidant, anti-inflammatory, and antimicrobial properties[38]. These properties are not only beneficial in health applications but also suggest the potential of SAC as an environmentally sustainable CH₄ inhibitor in agricultural settings. SAC significantly reduced total gas production in a concentration-dependent manner, primarily due to its inhibitory effect on CH₄ production. As a polyphenolic compound, its impact on CH₄ production is closely linked to its ability to interfere with methanogenic pathways while maintaining the overall stability of rumen fermentation. Previous studies have demonstrated that plant polyphenolic compounds can selectively reduce CH₄ production by inhibiting methanogen activity, reducing H 2 availability, or altering rumen microbial community composition [39]. For example, tannins and flavonoids reduce CH 4 production by binding to key enzymes in methanogens or disrupting their membrane stability [40]. Similarly, SAC likely interferes with MCR, a critical enzyme in CH₄ production, through mechanisms supported by molecular docking results. The strong binding affinity of SAC to the F430 active site of MCR and its hydrogen bonding interactions with key residues (e.g., GLY142 and MET229) provide molecular evidence for its ability to reduce CH₄ production. Additionally, the antioxidant properties of SAC may further enhance its inhibitory effects on CH₄ production. Antioxidants have been shown to mitigate free radical reactions that facilitate methanogenesis, thereby disrupting the metabolic processes of methanogens [41]. Despite the reduction in total gas production, the stability of VFA concentrations across treatments suggests that SAC selectively inhibits methanogenesis without impairing overall rumen fermentation. Although H₂ concentration was not directly measured in this study, previous studies have demonstrated that suppression of methanogenesis can lead to redirection of metabolic hydrogen ([H]) toward alternative sinks, such as propionate formation or microbial biomass synthesis, rather than CH 4 production[27, 42]. In this context, the significant decrease in butyrate proportion ( P = 0.045) and the trend toward a lower acetate/propionate (A/P) ratio ( P = 0.096) may suggest shifts in [H] utilization. Since butyrate production releases H₂ and propionate formation consumes it, these observations could reflect a redistribution of [H] away from methanogenesis[43, 44]. Future research should incorporate H₂ monitoring to validate the proposed mechanism by which SAC alters the destination of [H] flux. The pH values and DMD were not significantly affected across treatments, their stability indicates that SAC does not impair the fundamental fermentation processes of the rumen. Maintaining pH within a narrow range (6.95–6.97) is essential for optimal microbial activity, particularly for fiber-degrading bacteria responsible for fiber digestion [45]. In vitro fermentation systems often maintain slightly higher pH values due to the buffering capacity of the medium and limited acid accumulation during short incubation periods, as similarly reported by Ertl et al. (2015) in study with in vitro rumen fermentation[46]. The lack of significant changes in DMD suggests that SAC does not inhibit fiber degradation, aligning with the observation that VFA concentrations remained consistent across treatments. These findings further demonstrate that SAC selectively disrupts methanogenesis without negatively impacting other fermentation processes. SAC altered the abundance of certain bacterial and archaeal taxa, but overall microbial diversity and community evenness remained unaffected, suggesting that SAC selectively targets specific methanogenic archaea rather than broadly disrupting the rumen microbiota. In the bacterial community, Bacteroidota increased while Firmicutes and Ruminococcaceae decreased under SAC (1.5 mg/g DM). The increase of Bacteroidota is usually associated with enhanced fiber degradation and lower H 2 availability for methanogenesis, while Firmicutes , especially Ruminococcae , are H 2 producing bacteria that supply methanogens[47, 48]. These shifts suggest that the reroute of fermentation end products may indirectly reduce the substrates available to methanogens. On the archaeal side, Methanobrevibacter abundance declined markedly with SAC, while Methanomassiliicoccales increased. Methanobrevibacter is the primary hydrogenotrophic methanogen in the rumen, and its suppression directly reduces CH₄ output[49]. Methanomassiliicoccales rely on methylated substrates (e.g., methanol) and generate less CH₄ per unit of substrate, representing a lower efficiency methanogenic pathway[50]. Thus, SAC appears to shift the archaea community toward lower efficiency methanogens. Metagenomic analysis revealed that SAC enriched genes involved in carbohydrate metabolism, starch and sucrose metabolism, and the pentose phosphate pathway. These pathways can generate additional NADPH and fermentable intermediates, improving energy yields without increasing H 2 available for methanogenesis. SAC treatment also enriched steroid degradation genes, indicating a shift in lipid turnover and possibly enhanced microbial capacity to process complex carbon sources[51]. SAC treatment also enriched steroid degradation genes, indicating a shift in lipid turnover and possibly enhanced microbial capacity to process complex carbon sources[52]. Importantly, SAC downregulated two key methanogenesis genes: mcrA (methyl-coenzyme M reductase α-subunit) and rnfC (energy-conserving electron transport). Downregulation of mcrA directly impairs the enzyme that catalyzes CH₄ formation[53], while downregulation of rnfC may limit H 2 utilization for methanogenesis[54]. In contrast, DNA_replication genes were more enriched in the control group, which may reflect a higher proliferative activity of certain microorganisms, such as Methanobrevibacter . Overall, the transformation of these communities and functions further enhances the selective inhibition potential of SAC for methanogenesis. Conclusion This study suggests that SAC may have potential as a CH₄ inhibitor in the rumen. SAC demonstrated significant inhibitory effects on CH₄ production by selectively targeting Methanobrevibacter , the dominant methanogen genus involved in hydrogenotrophic methanogenesis. The modulation of the rumen microbiota, particularly the enrichment of beneficial bacterial genera such as Bacteroidota , suggests that SAC may indirectly inhibit CH₄ production by altering the H 2 balance in the rumen. Furthermore, SAC has the ability to reduce the expression of key genes involved in CH₄ metabolism, including mcrA and rnfC , highlights its potential to interfere with the enzymatic processes responsible for CH₄ generation. Importantly, SAC did not adversely affect overall fermentation efficiency, as evidenced by the stable pH, DMD, and VFA concentrations. These findings support the feasibility of incorporating SAC as a feed additive to reduce CH₄ emissions in dairy cow, with further studies needed to optimize its application and explore its long-term effects on animal health and productivity. Abbreviations 3-NOP (3-nitrooxypropanol), A/P ratio (acetate-to-propionate ratio), BES (2-bromoethanesulfonate), BPS (bromopropanesulfonate), CH₄ (methane), CO₂ (carbon dioxide), DMD (dry matter digestibility), DM (dry matter), ELISA (enzyme-linked immunosorbent assay), GHG (greenhouse gas), IC₅₀ (half-maximal inhibitory concentration), LDA (linear discriminant analysis), LEfSe (linear discriminant analysis effect size), MCR (methyl-coenzyme M reductase), mcrA (methyl-coenzyme M reductase subunit A gene), NADPH (nicotinamide adenine dinucleotide phosphate, reduced form), PCoA (principal coordinate analysis), rnfC (electron transport complex Rnf subunit C gene), SAC (salvianolic acid C), SI (Supporting Information) and VFA (volatile fatty acid). Declarations Ethics approval and consent to participate The experimental procedures involving animals have been approved by the Animal Care and Use Committee of the Chinese Academy of Agricultural Sciences (IAS2023-131; Beijing, China). Availability of data The 16S rRNA raw sequencing reads have been deposited in the NCBI Sequence Read Archive (SRA) under the accession number SRP516729. The metagenomic raw sequencing reads have also been deposited in the NCBI SRA database under the accession number SRP527714. Additional raw data are available from the corresponding author upon reasonable request. Consent for publication Not applicable. Funding This research was funded by the Integrated Demonstration of Scalable and Efficient Healthy Breeding for Cattle and Sheep (Grant No. 2022YFD1301100) and Instant Intelligent Diagnosis and Risk Warning Methods for Nutritional and Metabolic-Type Periparturient Cow Paralysis (Grant No. 2024-YWF-ZYSQ-10). Author Contributions Zihao Liu: Conceptualization, methodology, data analysis, writing – original draft. Li Xiao: Investigation, data collection, analysis. Xiangfang Tang: Investigation, funding acquisition, analysis. Yue He: Data collection, analysis, writing – review and editing. Xuemei Nan: Methodology, writing – review and editing. Hui Wang: Data analysis, funding acquisition. Yuming Guo: Supervision, project administration, writing – review and editing. Benhai Xiong: Supervision, project administration, analysis, writing – review and editing. Acknowledgments Graphical abstract created in BioRender. Liu, Z. (2025) https://BioRender.com/k72v884. References Rotz CA. Modeling greenhouse gas emissions from dairy farms. Journal of Dairy Science. 2018;101(7):6675-90; doi: 10.3168/jds.2017-13272. Sakatani M. The role of reproductive biology in SDGs Global warming and cattle reproduction: Will increase in cattle numbers progress to global warming? Journal of Reproduction and Development. 2022;68(2):90 − 5; doi: 10.1262/jrd.2021-149. Shukla PR, Skea J, Slade R, Khourdajie, A.A., Rv, et al.: IPCC Sixth Assessment Report (AR6): Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. In. Cambridge University Press,Cambridge, UK and New York, NY, USA.; 2022. Smith P, Bustamante M, Ahammad H, Clark H, Dong H, Elsiddig E, et al.: Agriculture, Forestry and Other Land Use (AFOLU). Pages 811–922 in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In. Cambridge University Press, Cambridge,UK.; 2014. Gerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J, et al. Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. 2013. Flint HJ. Polysaccharide breakdown by anaerobic microorganisms inhabiting the Mammalian gut. Adv Appl Microbiol. 2004;56:89–120; doi: 10.1016/S0065-2164(04)56003-3. Abbas W, Howard JT, Paz HA, Hales KE, Wells JE, Kuehn LA, et al. Influence of host genetics in shaping the rumen bacterial community in beef cattle. Sci Rep. 2020;10(1):15101; doi: 10.1038/s41598-020-72011-9. Wang K, Xiong B, Zhao X. Could propionate formation be used to reduce enteric methane emission in ruminants? Sci Total Environ. 2023;855:158867; doi: 10.1016/j.scitotenv.2022.158867. Alvarez-Hess PS, Williams SRO, Jacobs JL, Hannah MC, Beauchemin KA, Eckard RJ, et al. Effect of dietary fat supplementation on methane emissions from dairy cows fed wheat or corn. J Dairy Sci. 2019;102(3):2714-23; doi: 10.3168/jds.2018-14721. Appuhamy JA, France J, Kebreab E. Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand. Glob Chang Biol. 2016;22(9):3039-56; doi: 10.1111/gcb.13339. Lascano C, Cárdenas E. Alternatives for methane emission mitigation in livestock systems. Revista Brasileira de Zootecnia. 2010;39:175 − 82; doi: 10.1590/S1516-35982010001300020. Lan W, Yang C. Ruminal methane production: Associated microorganisms and the potential of applying hydrogen-utilizing bacteria for mitigation. Sci Total Environ. 2019;654:1270-83; doi: 10.1016/j.scitotenv.2018.11.180. Kumar S, Choudhury PK, Carro MD, Griffith GW, Dagar SS, Puniya M, et al. New aspects and strategies for methane mitigation from ruminants. Appl Microbiol Biotechnol. 2014;98(1):31–44; doi: 10.1007/s00253-013-5365-0. Kamra DN. Rumen microbial ecosystem. Current Science. 2005;89(1):124 − 35. Qiao J, Tan Z, Wang M. Potential and existing mechanisms of enteric methane production in ruminants. Scientia Agricola. 2014;71(5):430 − 40; doi: 10.1590/0103-9016-2013-0423. Bueno de Mesquita CP, Wu D, Tringe SG. Methyl-Based Methanogenesis: an Ecological and Genomic Review. Microbiol Mol Biol Rev. 2023;87(1):e0002422; doi: 10.1128/mmbr.00024-22. Thauer RK. Methyl (Alkyl)-Coenzyme M Reductases: Nickel F-430-Containing Enzymes Involved in Anaerobic Methane Formation and in Anaerobic Oxidation of Methane or of Short Chain Alkanes. Biochemistry. 2019;58(52):5198 − 220; doi: 10.1021/acs.biochem.9b00164. Scheller S, Goenrich M, Mayr S, Thauer RK, Jaun B. Intermediates in the catalytic cycle of methyl coenzyme M reductase: isotope exchange is consistent with formation of a sigma-alkane-nickel complex. Angew Chem Int Ed Engl. 2010;49(44):8112-5; doi: 10.1002/anie.201003214. Ermler U. On the mechanism of methyl-coenzyme M reductase. Dalton Trans. 2005;(21):3451-8; doi: 10.1039/b506697b. Prakash D, Wu Y, Suh SJ, Duin EC. Elucidating the process of activation of methyl-coenzyme M reductase. J Bacteriol. 2014;196(13):2491-8; doi: 10.1128/JB.01658-14. Duin EC, Wagner T, Shima S, Prakash D, Cronin B, Yanez-Ruiz DR, et al. Mode of action uncovered for the specific reduction of methane emissions from ruminants by the small molecule 3-nitrooxypropanol. Proc Natl Acad Sci U S A. 2016;113(22):6172-7; doi: 10.1073/pnas.1600298113. Goenrich M, Mahlert F, Duin EC, Bauer C, Jaun B, Thauer RK. Probing the reactivity of Ni in the active site of methyl-coenzyme M reductase with substrate analogues. J Biol Inorg Chem. 2004;9(6):691–705; doi: 10.1007/s00775-004-0552-1. Ungerfeld EM, Rust SR, Boone DR, Liu Y. Effects of several inhibitors on pure cultures of ruminal methanogens. J Appl Microbiol. 2004;97(3):520-6; doi: 10.1111/j.1365-2672.2004.02330.x. Grawert T, Hohmann HP, Kindermann M, Duval S, Bacher A, Fischer M. Inhibition of methyl-CoM Reductase from Methanobrevibacter ruminantium by 2-bromoethanesulfonate. J Agric Food Chem. 2014;62(52):12487-90; doi: 10.1021/jf505056g. Hristov AN, Oh J, Giallongo F, Frederick TW, Harper MT, Weeks HL, et al. An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production. Proc Natl Acad Sci U S A. 2015;112(34):10663-8; doi: 10.1073/pnas.1504124112. Wu T, Zhu W, Chen L, Jiang T, Dong Y, Wang L, et al. A review of natural plant extracts in beverages: Extraction process, nutritional function, and safety evaluation. Food Res Int. 2023;172:113185; doi: 10.1016/j.foodres.2023.113185. Patra AK, Saxena J. A new perspective on the use of plant secondary metabolites to inhibit methanogenesis in the rumen. Phytochemistry. 2010;71(11–12):1198 − 222; doi: 10.1016/j.phytochem.2010.05.010. Holtshausen L, Chaves AV, Beauchemin KA, McGinn SM, McAllister TA, Odongo NE, et al. Feeding saponin-containing Yucca schidigera and Quillaja saponaria to decrease enteric methane production in dairy cows. J Dairy Sci. 2009;92(6):2809-21; doi: 10.3168/jds.2008-1843. Cedervall PE, Dey M, Pearson AR, Ragsdale SW, Wilmot CM. Structural insight into methyl-coenzyme M reductase chemistry using coenzyme B analogues. Biochemistry. 2010;49(35):7683-93; doi: 10.1021/bi100458d. Liu Z, Wang K, Nan X, Yang L, Wang Y, Zhang F, et al. Effects of combined addition of 3-nitrooxypropanol and vitamin B(12) on methane and propionate production in dairy cows by in vitro-simulated fermentation. J Dairy Sci. 2023;106(1):219 − 32; doi: 10.3168/jds.2022-22207. Liu Z, Wang K, Zhao Y, Nan X, Yang L, Zhou M, et al. Combined effects of vitamin B12 and fumarate on rumen propionate production. Animal Research and One Health. 2023;1(2):204 − 18; doi: 10.1002/aro2.27. Mildvan AS, Massiah MA, Harris TK, Marks GT, Harrison DHT, Viragh C, et al. Short, strong hydrogen bonds on enzymes: NMR and mechanistic studies. Journal of Molecular Structure. 2002;615(1):163 − 75; doi: https://doi.org/10.1016/S0022-2860(02)00212-0. Caldwell GW, Yan Z, Lang W, Masucci JA. The IC(50) concept revisited. Curr Top Med Chem. 2012;12(11):1282-90; doi: 10.2174/156802612800672844. Guo YQ, Liu JX, Lu Y, Zhu WY, Denman SE, McSweeney CS. Effect of tea saponin on methanogenesis, microbial community structure and expression of mcrA gene, in cultures of rumen micro-organisms. Lett Appl Microbiol. 2008;47(5):421-6; doi: 10.1111/j.1472-765X.2008.02459.x. Gräwert T, Hohmann HP, Kindermann M, Duval S, Bacher A, Fischer M. Inhibition of methyl-CoM Reductase from Methanobrevibacter ruminantium by 2-bromoethanesulfonate. J Agric Food Chem. 2014;62(52):12487-90; doi: 10.1021/jf505056g. Wang J, Deng L, Chen M, Che Y, Li L, Zhu L, et al. Phytogenic feed additives as natural antibiotic alternatives in animal health and production: A review of the literature of the last decade. Anim Nutr. 2024;17:244 − 64; doi: 10.1016/j.aninu.2024.01.012. Shen H, Pei H, Zhai L, Guan Q, Wang G. Salvianolic acid C improves cerebral ischemia reperfusion injury through suppressing microglial cell M1 polarization and promoting cerebral angiogenesis. Int Immunopharmacol. 2022;110:109021; doi: 10.1016/j.intimp.2022.109021. Xu MJ, Jiang LF, Wu T, Chu JH, Wei YD, Aa JY, et al. Inhibitory Effects of Danshen components on CYP2C8 and CYP2J2. Chem Biol Interact. 2018;289:15–22; doi: 10.1016/j.cbi.2018.04.011. Patra AK, Saxena J. Exploitation of dietary tannins to improve rumen metabolism and ruminant nutrition. J Sci Food Agric. 2011;91(1):24–37; doi: 10.1002/jsfa.4152. Jayanegara A, Leiber F, Kreuzer M. Meta-analysis of the relationship between dietary tannin level and methane formation in ruminants from in vivo and in vitro experiments. J Anim Physiol Anim Nutr (Berl). 2012;96(3):365 − 75; doi: 10.1111/j.1439-0396.2011.01172.x. Lambo MT, Ma H, Liu R, Dai B, Zhang Y, Li Y. Review: Mechanism, effectiveness, and the prospects of medicinal plants and their bioactive compounds in lowering ruminants' enteric methane emission. Animal. 2024;18(4):101134; doi: 10.1016/j.animal.2024.101134. Ungerfeld EM. Shifts in metabolic hydrogen sinks in the methanogenesis-inhibited ruminal fermentation: a meta-analysis. Front Microbiol. 2015;6:37; doi: 10.3389/fmicb.2015.00037. Morgavi DP, Forano E, Martin C, Newbold CJ. Microbial ecosystem and methanogenesis in ruminants. Animal. 2010;4(7):1024-36; doi: 10.1017/s1751731110000546. Hook SE, Wright AD, McBride BW. Methanogens: methane producers of the rumen and mitigation strategies. Archaea. 2010;2010:945785; doi: 10.1155/2010/945785. Russell JB, Wilson DB. Why are ruminal cellulolytic bacteria unable to digest cellulose at low pH? J Dairy Sci. 1996;79(8):1503-9; doi: 10.3168/jds.S0022-0302(96)76510-4. Ertl P, Knaus W, Metzler-Zebeli BU, Klevenhusen F, Khiaosa-Ard R, Zebeli Q. Substitution of common concentrates with by-products modulated ruminal fermentation, nutrient degradation, and microbial community composition in vitro. J Dairy Sci. 2015;98(7):4762-71; doi: 10.3168/jds.2014-9063. Snelling TJ, Auffret MD, Duthie CA, Stewart RD, Watson M, Dewhurst RJ, et al. Temporal stability of the rumen microbiota in beef cattle, and response to diet and supplements. Anim Microbiome. 2019;1(1):16; doi: 10.1186/s42523-019-0018-y. Janssen PH, Kirs M. Structure of the archaeal community of the rumen. Appl Environ Microbiol. 2008;74(12):3619-25; doi: 10.1128/aem.02812-07. Weimar MR, Cheung J, Dey D, McSweeney C, Morrison M, Kobayashi Y, et al. Development of Multiwell-Plate Methods Using Pure Cultures of Methanogens To Identify New Inhibitors for Suppressing Ruminant Methane Emissions. Appl Environ Microbiol. 2017;83(15); doi: 10.1128/aem.00396-17. Weil M, Wang H, Zak D, Urich T. Spatial and temporal niche separation of Methanomassiliicoccales phylotypes in temperate fens. FEMS Microbiol Ecol. 2023;99(6); doi: 10.1093/femsec/fiad049. Patra KC, Hay N. The pentose phosphate pathway and cancer. Trends Biochem Sci. 2014;39(8):347 − 54; doi: 10.1016/j.tibs.2014.06.005. Brzostek A, Rumijowska-Galewicz A, Dziadek B, Wojcik EA, Dziadek J. ChoD and HsdD can be dispensable for cholesterol degradation in mycobacteria. J Steroid Biochem Mol Biol. 2013;134:1–7; doi: 10.1016/j.jsbmb.2012.09.028. Alvarado A, Montañez-Hernández LE, Palacio-Molina SL, Oropeza-Navarro R, Luévanos-Escareño MP, Balagurusamy N. Microbial trophic interactions and mcrA gene expression in monitoring of anaerobic digesters. Front Microbiol. 2014;5:597; doi: 10.3389/fmicb.2014.00597. Schmehl M, Jahn A, Meyer zu Vilsendorf A, Hennecke S, Masepohl B, Schuppler M, et al. Identification of a new class of nitrogen fixation genes in Rhodobacter capsulatus: a putative membrane complex involved in electron transport to nitrogenase. Mol Gen Genet. 1993;241(5–6):602 − 15; doi: 10.1007/bf00279903. Supplementary Files CoverLetterforResubmission.docx Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Journal of Animal Science and Biotechnology → Version 1 posted Editorial decision: Major revision 22 Aug, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 10 Jun, 2025 First submitted to journal 09 Jun, 2025 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. 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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-6850632","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469529823,"identity":"ec43fe2a-3d3b-493f-9866-5291a2ca9fd9","order_by":0,"name":"Zihao Liu","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Animal Science","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Liu","suffix":""},{"id":469529824,"identity":"decaffdb-a5fe-4375-8958-6da2559d00b1","order_by":1,"name":"Li Xiao","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Animal Science","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xiao","suffix":""},{"id":469529825,"identity":"4759010a-dcbd-44ee-8016-a079895f5154","order_by":2,"name":"Xiangfang Tang","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Animal Science","correspondingAuthor":false,"prefix":"","firstName":"Xiangfang","middleName":"","lastName":"Tang","suffix":""},{"id":469529826,"identity":"ab052b5f-c2fe-4de9-afcd-a5d94de98ddf","order_by":3,"name":"Yue He","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Animal Science","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"He","suffix":""},{"id":469529827,"identity":"481f66e3-8d5a-4f8c-808e-7be4faac0d0f","order_by":4,"name":"Xuemei Nan","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Animal Science","correspondingAuthor":false,"prefix":"","firstName":"Xuemei","middleName":"","lastName":"Nan","suffix":""},{"id":469529828,"identity":"19839ff6-9887-4e14-95d1-9fbd3109baeb","order_by":5,"name":"Hui Wang","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Animal Science","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":469529829,"identity":"5c2a6147-d2ac-4b9a-a8ae-4a40c5a27e4e","order_by":6,"name":"Yuming Guo","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuming","middleName":"","lastName":"Guo","suffix":""},{"id":469529830,"identity":"4e6547a4-ae69-4038-a9f8-9d43b37ac7f1","order_by":7,"name":"Benhai Xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACZiBmbGBgMGBgbHxAspZmA+JtgmhhYJMgSrXBceZjD7/usMkzZz/cVvGjxs6egf3sAbxaJJvZ0o1lz6QVW/Yktt3sOZac2MCTl4BXCz8zj5m0ZNvhxA0HEttuM7AxJzBI8OD3ExtEy//EDecfthUz/Ku3J6gFZIvkx7YDiRtuJLYxM7YdZmwgpAXolzRpxjPJQC0PmyV7+44ntvHk4NdicP7wMcmfO+yADkt/+OHHt2p7fvYzhOOHmQfFdwTVAwHjD2JUjYJRMApGwcgFAOpfQ9yYhak3AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2773-5739","institution":"State Key Laboratory of Animal Nutrition","correspondingAuthor":true,"prefix":"","firstName":"Benhai","middleName":"","lastName":"Xiong","suffix":""}],"badges":[],"createdAt":"2025-06-09 04:52:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6850632/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6850632/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40104-025-01285-8","type":"published","date":"2025-11-17T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84580695,"identity":"33039096-8e37-44a2-b863-3025c0e75b24","added_by":"auto","created_at":"2025-06-13 18:29:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":577802,"visible":true,"origin":"","legend":"\u003cp\u003eThe 3D structure and active sites of MCR. α subunit in green; β subunit in cyan; γ subunit in purple-pink; active site F430 in yellow.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6850632/v1/7e44e23c6c42e46ad2d1e5d2.png"},{"id":84581193,"identity":"14b3bc5f-2b90-464b-8a1f-fdf3d0eadd6b","added_by":"auto","created_at":"2025-06-13 18:45:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":495096,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different plant extracts on inhibition rate of MCR activity. Columns with the same lowercase letters indicate no significant difference (P \u0026gt; 0.05), while columns with different lowercase letters denote a significant difference (P \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6850632/v1/6e719b43feac20fc538be910.png"},{"id":84580909,"identity":"53712b66-2f03-4547-9934-915ce5811fcb","added_by":"auto","created_at":"2025-06-13 18:37:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":869118,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of SAC and its Binding Mode with MCR. (a) Chemical structure of SAC. (b) Interaction of SAC with key amino acid residues in the active site of MCR. SAC, blue chemical framework; MCR residues, represented as leaf-shaped markers in different colors; purple arrows indicate hydrogen bond formation. (c) The binding sites and binding modes of SAC and MCR active pockets. The enlarged view shows the binding interaction between SAC and active site residues. The MCR protein C backbone in green; O atoms in bright red, H atoms in white; SAC is represented as light blue sticks; Hydrogen bonds are shown as red dashed lines, with numbers indicating distances (Å)\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6850632/v1/491dbef8445494fd71839dfb.png"},{"id":84580703,"identity":"ba2e52a9-e94a-4771-8819-293075f37e88","added_by":"auto","created_at":"2025-06-13 18:29:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355094,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response curve for the inhibition of MCR activity by SAC. The y-axis represents the residual activity of MCR (%), and the x-axis represents the log-transformed concentration of SAC (Log10 μM). The IC50 value, representing the concentration at which 50% of the MCR activity is inhibited. Error bars represent standard deviation (SD) for triplicate experiments.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6850632/v1/25568d5257212dbee5028a9f.png"},{"id":84580911,"identity":"bd68bac2-035e-4c42-a8fa-ece7d7e09d48","added_by":"auto","created_at":"2025-06-13 18:37:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":620061,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of different concentrations of SAC on rumen microbial community. (a-b) Alpha diversity of bacteria and archaea. (c-d) Principal coordinate analysis (PCoA) of bacteria and archaea. (e-f) Community composition at the phylum and genus levels of bacteria. (g-h) Community composition at the phylum and genus levels of archaea. (i-j) Linear discriminant analysis effect size (LEfSe) of bacteria. (k-i) Linear discriminant analysis effect size (LEfSe) of archaea. (m) Differences in relative abundance of bacteria at genus level (1.5mg/g DM of SAC). (n) Differences in relative abundance of archaea at genus level (1.5mg/g DM of SAC).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6850632/v1/470dcb8165d14d76419c7911.png"},{"id":84580737,"identity":"7d70c662-ce1f-4add-a471-1dc0e771257c","added_by":"auto","created_at":"2025-06-13 18:29:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":337749,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of SAC (1.5mg/g DM) on metagenomic functional genes of rumen microbiota. (a) Random Forest analysis of functional genes based on KEGG pathway. The horizontal axis represents the Gini index of average impurity reduction, and the larger the Gini index of a species, the more pronounced its ability to distinguish between two groups. (b) Heatmap of methane metabolism related genes. (c-d) Linear discriminant analysis effect size (LEfSe) of functional genes based on KEGG pathway\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6850632/v1/bf01db9b466ca108001cdd02.png"},{"id":96650035,"identity":"320b62da-a6dc-44c6-bbe5-c6ff52d9ca92","added_by":"auto","created_at":"2025-11-24 16:06:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4336431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6850632/v1/76d8fe79-5da4-4806-8cfe-1734211762c8.pdf"},{"id":84580704,"identity":"6e2b0727-ed76-44d1-8272-3b6fa9c36fa4","added_by":"auto","created_at":"2025-06-13 18:29:10","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":27361,"visible":true,"origin":"","legend":"","description":"","filename":"CoverLetterforResubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-6850632/v1/46c430cb1e9b5f16cd7caa3d.docx"}],"financialInterests":"","formattedTitle":"Salvianolic Acid C Inhibits Methane Emissions in Dairy Cows by Targeting MCR and Reshaping the Rumen Microbial Community","fulltext":[{"header":"Background","content":"\u003cp\u003eSince the beginning of the 21st century, the problem of global warming caused by greenhouse gas (GHG) emissions has become increasingly serious and has received widespread attention [1, 2]. Human induced GHG emissions come from a wide range of sources, including industry, transportation, agriculture, forestry and other land use, construction, and other energy related activities [3]. The agricultural sector is considered the second largest anthropogenic GHG. It is estimated that in 2010, the agricultural sector accounted for approximately 11% of global anthropogenic GHG emissions [1, 4]. In the agricultural sector, the main GHG emissions are from livestock production [1, 5]. Methane (CH\u003csub\u003e4\u003c/sub\u003e) produced by ruminant gut fermentation is one of the main GHG in livestock production, and it is increasing year by year [3]. Ruminants, due to their unique and complex rumen system, can convert plant fibers that are difficult to digest and utilize by other livestock into volatile fatty acids (VFA) and microbial proteins that can be absorbed and utilized [6, 7]. However, CH\u003csub\u003e4\u003c/sub\u003e is a byproduct produced during the digestion process. It is the second largest GHG after carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), and its potential contribution to the greenhouse effect is more than 25 times that of CO\u003csub\u003e2\u003c/sub\u003e [8]. In addition, the excretion of CH\u003csub\u003e4\u003c/sub\u003e as a byproduct of the digestion process also results in energy loss [9]. According to statistics, about 6% of gross energy intake is lost in the form of CH\u003csub\u003e4\u003c/sub\u003e [10]. Therefore, reducing CH\u003csub\u003e4\u003c/sub\u003e emissions from ruminants is of great significance in mitigating the greenhouse effect and improving feed energy utilization efficiency.\u003c/p\u003e \u003cp\u003eThe CH\u003csub\u003e4\u003c/sub\u003e emissions of ruminants are mainly produced by the rumen, accounting for up to 87% [11, 12]. In the rumen, feed carbohydrates are fermented and decomposed by rumen microorganisms into VFA, CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e [13, 14]. Methanogenic archaea generate CH\u003csub\u003e4\u003c/sub\u003e using acetate, formate, methanol, or CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003e as substrates [15]. There are currently three main pathways known for CH\u003csub\u003e4\u003c/sub\u003e production, namely the H\u003csub\u003e2\u003c/sub\u003e nutrition pathway, the acetate cleavage pathway, and the methyl nutrition pathway [16]. Methyl-coenzyme M reductase (MCR) plays a unique role as a key enzyme in the CH\u003csub\u003e4\u003c/sub\u003e generation pathway, the final step of all CH\u003csub\u003e4\u003c/sub\u003e generation pathways requires the catalysis of MCR [17, 18]. MCR catalyzes the reduction of methyl-coenzyme M and coenzyme B to CH\u003csub\u003e4\u003c/sub\u003e and heterodisulfide under anaerobic conditions [19]. However, this process requires the presence of coenzyme F430 [20]. Coenzyme F430 is the active site of MCR, and its nickel center must be in the Ni (I) oxidation state for the enzyme to be active [21]. Therefore, inhibiting the activity of MCR is an effective strategy to reduce CH\u003csub\u003e4\u003c/sub\u003e generation.\u003c/p\u003e \u003cp\u003eThere have been studies reporting on some MCR inhibitors, such as 3-nitrooxypropanol (3-NOP), 2-bromoethanesulfonate (BES), and bromopropanesulfonate (BPS). Both BES and BPS can inhibit the activity of MCR [22]. However, due to the susceptibility of BES to drug resistance and the inability of BPS to be transported to methanogenic cells [23], as well as toxicological reasons, they cannot be widely promoted in application [24]. 3-NOP is an artificially synthesized compound that can inhibit CH\u003csub\u003e4\u003c/sub\u003e emissions, but it can lead to a significant increase in H\u003csub\u003e2\u003c/sub\u003e emissions, and the direction of the increased H\u003csub\u003e2\u003c/sub\u003e is not yet clear [25]. Plant extracts have a wide range of sources and rich functions[26]. Compared to chemical additives, plant extracts are economical and safe, and have received widespread attention in recent years [27]. It has been found that some plant extracts can inhibit CH\u003csub\u003e4\u003c/sub\u003e generation, such as saponins, tannins, and flavonoids [27]. However, these plant extracts have issues such as affecting digestion and metabolism, toxicity, and limited effectiveness [27, 28]. Therefore, exploring specific plant extract inhibitors targeting MCR is crucial. At the same time, there are currently few studies that use high-throughput virtual screening methods and explore the mechanism of inhibitors inhibiting CH\u003csub\u003e4\u003c/sub\u003e generation.\u003c/p\u003e \u003cp\u003eThe aim of this study is to target MCR and use molecular docking high-throughput virtual screening technology to screen for a plant derived CH\u003csub\u003e4\u003c/sub\u003e inhibitor with significant effects, and to elucidate its mechanism of action.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking and virtual screening\u003c/h2\u003e \u003cp\u003eMCR is a multi-subunit enzyme complex, with \u003cem\u003emcrA\u003c/em\u003e, \u003cem\u003emcrB\u003c/em\u003e, and \u003cem\u003emcrG\u003c/em\u003e genes encoding α、β And γ subunit, respectively [29]. Download the 3D structure of MCR (PDB ID: 5G0R) from the RCSB PDB database. The Protein Preparation Wizard module was used to hydrogenate and delete the D/E/F chain of the protein, remove water molecules, and delete small molecules such as TP7, AGM, DY A, GL3, MGN, MHS, SMC, etc. The energy was then optimized (OPLS2005 field, RMSD 0.30 \u0026Aring;). The processed proteins were made into grid files using the Receptor Grid Generation module, and the grid files were generated with small molecule F430 binding pocket as the center, and the box size was set to 20 \u0026Aring; \u0026times; 20 \u0026Aring; \u0026times; 20 \u0026Aring;. The 2D format of Natural Product Library (containing 3.9K compounds) was adopted by LigPrep Module of Schr\u0026ouml;dinger performs hydrogenation, energy optimization, and outputs 3D structures for virtual screening. Utilize the Virtual Screening Workflow module for the virtual screening process, import the prepared compounds, and employ the Glide module for molecular docking. Firstly, the high-throughput virtual screening (HTVS) mode in the Glide module was used to dock the compounds in the Natural Product Library with the target protein MCR for the standard precision (SP) mode for the first round of screening. Subsequently, the top 15% of docking scores were selected for the second round of screening using extra precision (XP) mode. Finally, check the target and compound binding force, as well as the structure of the compound.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of MCR inhibitory activity\u003c/h3\u003e\n\u003cp\u003eMCR activity inhibited by plant extracts was measured using a microbial MCR ELISA kit (Meimian Industrial Co., Ltd, Jiangsu, China). All plant extracts were purchased from MedChemExpress (Shanghai, China) and dissolved in dimethyl sulfoxide (DMSO), then diluted to the appropriate concentrations using 50 mM HEPES buffer (pH 7.5). In each well, 40 \u0026micro;L of MCR and 10 \u0026micro;L of plant extract (final concentration 100 \u0026micro;M) were added. The plate was gently mixed and incubated at 37\u0026deg;C for 30 minutes. After incubation, the liquid was discarded, excess liquid was shaken off, and the plate was washed. Then, 50 \u0026micro;L of enzyme substrate was added to each well, and incubation continued for another 30 minutes at 37\u0026deg;C. Afterward, 50 \u0026micro;L of chromogenic agent A and 50 \u0026micro;L of chromogenic agent B were added to each well, followed by incubation for 10 minutes at 37\u0026deg;C, shielded from light. To terminate the reaction, 50 \u0026micro;L of terminating solution was added, and the absorbance (OD) at 450 nm was measured using a microplate reader (Thermo Labserv K3 TOUCH, Thermo Scientific, Waltham, MA, USA). MCR activity inhibited by plant extracts was measured using a microbial MCR ELISA kit (Meimian Industrial Co., Ltd, Jiangsu, China). Plant extracts were purchased from MedChemExpress (Shanghai, China) and dissolved in dimethyl sulfoxide (DMSO), then diluted to the appropriate concentrations using 50 mM HEPES buffer (pH 7.5). In each well, 40 \u0026micro;L of MCR and 10 \u0026micro;L of plant extract (final concentration 100 \u0026micro;M) were added. The plate was gently mixed and incubated at 37\u0026deg;C for 30 minutes. After incubation, the liquid was discarded, excess liquid was shaken off, and the plate was washed. Then, 50 \u0026micro;L of enzyme substrate was added to each well, and incubation continued for another 30 minutes at 37\u0026deg;C. Afterward, 50 \u0026micro;L of chromogenic agent A and 50 \u0026micro;L of chromogenic agent B were added to each well, followed by incubation for 10 minutes at 37\u0026deg;C, shielded from light. To terminate the reaction, 50 \u0026micro;L of terminating solution was added, and the absorbance (OD) at 450 nm was measured using a microplate reader (Thermo Labserv K3 TOUCH, Thermo Scientific, Waltham, MA, USA).\u003c/p\u003e \u003cp\u003eSalvianolic acid C (SAC), a potent MCR inhibitor, was selected for further investigation based on its ability to effectively inhibit MCR activity. To determine its IC50 value, SAC was added to the MCR enzyme solution at concentrations of 0, 25, 50, 100, 250, 500, 800, 1000, and 1500 \u0026micro;M. The same procedure was followed as described above, and the IC50 was calculated using nonlinear regression curve fitting in GraphPad Prism (version 9, GraphPad, La Jolla, CA, USA). SAC was chosen for subsequent studies due to its promising inhibitory effects on MCR activity.\u003c/p\u003e\n\u003ch3\u003eIn vitro fermentation experiment\u003c/h3\u003e\n\u003cp\u003eThe experimental procedures involving animals have been approved by the Animal Care and Use Committee of the Chinese Academy of Agricultural Sciences (IAS2023-131; Beijing, China). Rumen fluid was obtained from three rumen-fistulated Holstein dairy cows (average body weight: 618\u0026thinsp;\u0026plusmn;\u0026thinsp;100 kg; milk yield: 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 kg/d; 3\u0026thinsp;\u0026plusmn;\u0026thinsp;1 of parity) fed a consistent total mixed ration (TMR). The TMR composition (on a dry matter basis) was as follows: corn silage (25.65%), alfalfa hay (18.59%), steam-flaked corn (26.02%), soybean meal (7.43%), cottonseed meal (7.43%), beet meal (5.58%), distillers dried grains with solubles (7.43%), and minerals and vitamins (1.86%). Approximately 2 h after morning feeding, rumen content was collected, placed in prewarmed insulated bottles, and transported to the laboratory within 30 minutes. The rumen content was filtered through four layers of sterile cheesecloth and mixed with buffer solution at a 1:2 ratio (v/v) under continuous CO₂ flushing at 39\u0026deg;C to prepare the inoculum. The buffer solution was prepared according to Liu et al. (2023) [30]. An in vitro batch culture system was employed to evaluate the effects of SAC on rumen fermentation. Fermentation substrate was prepared by drying the TMR to 55\u0026deg;C for 48 h, grinding to pass through a 1 mm sieve, and weighing 0.5 g into each 120 mL fermentation vessel. Each vessel received 75 mL of inoculum and a corresponding dose of SAC (0, 0.25, 0.5, 1.0, 1.5, or 2.0 mg/g of substrate DM), with SAC added in dissolved form (1 mg/mL, dissolved in water). The system was flushed with CO₂, sealed with a rubber stopper and aluminum foil, and incubated in a shaking water bath at 39\u0026deg;C, 60 rpm, for 24 h. Each treatment included four replicates and four blank vessels (inoculum only) for gas calibration. The experiment was repeated three times on separate days.\u003c/p\u003e \u003cp\u003eAfter 24 hours of fermentation, all fermentation tanks were immediately removed and placed in ice water to terminate the fermentation process. The gas produced during fermentation is collected in airtight aluminum foil bags, and the total gas production is obtained by drawing out the gas using a graduated syringe. Take 5ml of gas from each gas bag for measuring CH\u003csub\u003e4\u003c/sub\u003e concentration. The pH value of the fluid sample was analyzed using a portable pH meter (Seven Go, Mettler Toledo). Take 4ml x 3 of fermentation substance (solid-liquid mixture) from each fermentation tank for VFA (-20℃) and microbial (-80℃) analysis. The remaining fermentation content in each tank was filtered using nylon bags and rinsed thoroughly with water until the effluent became clear. The nylon bags were then dried at 55\u0026deg;C for 48 hours to determine dry matter digestibility (DMD). The concentrations of CH₄ and VFAs were analyzed using a gas chromatograph (8860, Agilent Technologies, CA, USA), following the parameters and procedures described by Liu et al. (2023)[30].\u003c/p\u003e\n\u003ch3\u003eGenomic DNA extraction and sequencing\u003c/h3\u003e\n\u003cp\u003eThe extraction of microbial DNA uses the HiPure Stool DNA extraction kit (Magen, Guangzhou, China), the extraction process begins with mixing the sample with the buffer SOL, swirling it for 5\u0026ndash;10 mins for initial cracking. Then, the buffer SDS was added and swirled for 15s, and the mixture was heated at 70 \u0026deg; C for 10 mins for thermal cracking. After centrifugation, buffer PS and absorption solution were added, mixed and allowed to stand for 5 mins. The supernatant was collected by centrifugation at 13000g, mixed with buffer GDP, and passed through a DNA column for two rounds for centrifugation separation. Afterward, the DNA column is washed twice with buffer GDP and GW2 each to ensure the purity of the DNA. The DNA column was centrifuged and dried for 2 mins, and DNA was eluted with preheated buffer AE. The extracted DNA should be stored at 2\u0026ndash;8 ℃ or -20 ℃ for long-term storage. The extracted DNA samples were tested for integrity by AGAR gel electrophoresis, and the concentration and purity of the DNA samples were tested by NanoDrop microspectrophotometer (NanoDrop 2000, Thermo Fisher Technologies, USA).\u003c/p\u003e \u003cp\u003eThe V4-V5 region of 16S rRNA was amplified by primers Arch519F (5'-CAGCMGCCGCGGTAA-3') and Arch915R (5 '-GTGCTCCCCCGCCAATTCCT-3'). Perform PCR amplification procedure as described by Z Liu, K Wang, Y Zhao, X Nan, L Yang, M Zhou, X Tang and B Xiong [31]. The obtained amplicons underwent evaluation through 2% agarose gels and were subsequently purified using AMPure XP Beads (Beckman, CA, USA). The purified amplicons were then pooled and sequenced on the Novaseq 6000 platform using PE250 mode. The raw reads were then deposited in the NCBI Sequence Read Archive (SRA) database, with the accession number SRP516729. Raw reads were filtered using FASTP v0.18.0 to remove reads with \u0026ge;\u0026thinsp;10% ambiguous bases (N), those with \u0026gt;\u0026thinsp;50% of bases having quality scores\u0026thinsp;\u0026le;\u0026thinsp;20, and adapter-contaminated sequences. Paired reads were merged using FLASH v1.2.11 (\u0026ge;\u0026thinsp;10 bp overlap, \u0026le;\u0026thinsp;2% mismatch), and low-quality tags were trimmed (quality\u0026thinsp;\u0026le;\u0026thinsp;3 for \u0026ge;\u0026thinsp;3 bases), retaining tags with high-quality length\u0026thinsp;\u0026ge;\u0026thinsp;75% of the total. Operational taxonomic units (OTUs) were clustered at 97% similarity using USEARCH (UPARSE algorithm, v11.0.667), and chimeric sequences were removed using UCHIME. Taxonomic annotation of representative OTU sequences was performed by alignment against the SILVA v138.2, Greengenes2 v2022.10, and UNITE v10.0 databases using the RDP Classifier v2.2.\u003c/p\u003e \u003cp\u003eFor metagenomic sequencing, qualified genomic DNA is fragmented into about 350 bp segments by sonication, then undergoes end-repair and A-tailing. Illumina sequencing adapters are added using the NEBNext\u0026reg; Ultra\u0026trade; Kit (NEB, USA), forming the sequencing library. Next, 300\u0026ndash;400 bp fragments are selected, amplified by PCR, and purified to remove impurities. The library's quality, concentration, and size distribution are checked with an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA), and quantified using real-time PCR. Finally, the library is sequenced on an Illumina Novaseq 6000, where both ends of each fragment are read for 150 bp, generating high-quality sequencing data. The raw sequencing reads were subsequently deposited into the SRA database, assigned with the unique accession number SRP527714. Raw reads were quality-filtered using fastp (v0.20.0) to remove reads containing\u0026thinsp;\u0026ge;\u0026thinsp;10% ambiguous bases, \u0026ge;\u0026thinsp;50% low-quality bases (Phred\u0026thinsp;\u0026le;\u0026thinsp;20), adapter contamination, or shorter than 50 bp. Clean reads were assembled with MEGAHIT (v1.2.9) using a multi-k-mer strategy (k\u0026thinsp;=\u0026thinsp;27\u0026ndash;127), retaining contigs\u0026thinsp;\u0026ge;\u0026thinsp;500 bp. Gene prediction was conducted with MetaGeneMark (v3.38), and predicted genes were clustered using CD-HIT (v4.6) at \u0026ge;\u0026thinsp;95% identity and \u0026ge;\u0026thinsp;90% coverage to construct a non-redundant gene catalog. Clean reads were aligned to the gene set using Bowtie2 (v2.3.5.1), and gene abundance was calculated with read reassignment via Pathoscope (v2.0.7), removing genes supported by \u0026lt;\u0026thinsp;2 reads.\u003c/p\u003e \u003cp\u003eFunctional annotation was performed using DIAMOND (v0.9.25, e-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-5) against multiple databases, including NR (2024.07.25), KEGG (Release 111), eggNOG (v5.0), CAZy (2024.07.14), PHI-base (v4.17), CARD (v3.3.0), QS-related genes, NCycDB, SCycDB, PCycDB, and MCycDB. Mobile genetic elements were annotated using BLASTn (v2.6.0) against the MGE database (2017 version; identity and coverage\u0026thinsp;\u0026gt;\u0026thinsp;40%), and resistance genes were identified using BacMet-Scan (v1.1) with the BacMet database (v2.0). Functional abundance data were visualized using R packages.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe experimental results were analyzed using the mixed model procedure of SAS (Version 9.4, SAS Institute Inc., Cary, NC) and the UNIV variable program was used to test the normality of the data. The model was as follows:\u003c/p\u003e \u003cp\u003eYijk\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;Gi\u0026thinsp;+\u0026thinsp;Sj\u0026thinsp;+\u0026thinsp;eij,\u003c/p\u003e \u003cp\u003ewhere Yijk is the dependent variable, \u0026micro; is the grand mean, Gi is the treatment group, Sj is the treatment fixed effect (CON vs SAC), and eij is the error term. Tukey's method was used for multiple mean comparisons, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 declaring a significant difference, and 0.05\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.10 declaring a trend.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eScreening of plant extracts\u003c/h2\u003e \u003cp\u003eThe three-dimensional structure of the methane-generating enzyme MCR (methyl-coenzyme M reductase) was modeled, with its active sites (F430 coenzyme-binding pockets) clearly identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Molecular docking was performed to screen plant extracts for potential inhibitors targeting the F430 site. The docking scores, reflecting binding affinity strength, identified four extracts with scores exceeding \u0026minus;\u0026thinsp;8 kcal/mol (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequent in vitro inhibition assays at 100 \u0026micro;M revealed divergent outcomes: Hispidin and SAC significantly suppressed MCR activity by 25.80% and 34.50%, respectively, while the other two extracts showed no inhibitory effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking parameters and details of plant extracts with inhibition of MCR activity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edocking score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAS No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMw\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecoisolariciresinol diglucoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e257930-74-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e686.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKinsenoside\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e151870-74-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e264.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalvianolic acid C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e115841-09-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e492.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispidin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e555-55-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e246.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular binding mode of SAC to MCR\u003c/h3\u003e\n\u003cp\u003eThe chemical structure of SAC is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. The interaction between SAC and MCR was explored through molecular docking and high-throughput screening, with a binding free energy of -8.124 kcal/mol (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting a robust affinity between SAC and the MCR active site. The binding modes of SAC and MCR are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c. In the 3D diagram of \u003cem\u003eMcrG\u003c/em\u003e, \u003cem\u003eMcrB\u003c/em\u003e, and \u003cem\u003eMcrA\u003c/em\u003e proteins, the C-skeleton is displayed in green, the O atoms are shown in bright red, the H atoms are presented in off-white, and SAC is indicated as light blue sticks. The hydrogen bond lengths are represented by red dashed lines. The docking results show that SAC forms five hydrogen bonds with MCR, with two phenolic hydroxyl groups donating hydrogen bonds to GLY142 and MET229 (1.8 \u0026Aring; and 2.0 \u0026Aring;), a carboxyl group forming a bond with GLN230 (2.0 \u0026Aring;), and an ester group forming two bonds with VAL146 and GLN147 (2.3 \u0026Aring; and 2.1 \u0026Aring;).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eKinetics of SAC inhibition\u003c/h2\u003e \u003cp\u003eTo further quantify the inhibitory effect of SAC on MCR activity, the activity of MCR was assessed under different concentrations of SAC treatment, and a dose-response curve was plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As shown in the figure, the inhibitory effect of SAC on MCR activity increases significantly with rising concentrations, exhibiting a typical concentration-effect relationship. At low concentrations (around Log10[\u0026micro;M]\u0026thinsp;=\u0026thinsp;2), the residual activity of MCR remains close to 100%. However, as the concentration of SAC gradually increases, the residual activity of MCR decreases progressively. At high concentrations (around Log10[\u0026micro;M]\u0026thinsp;=\u0026thinsp;4), the residual activity of MCR approaches zero. By fitting the concentration-effect curve, the half-maximal inhibitory concentration (IC50) of SAC on MCR activity was calculated to be 692.3 \u0026micro;M.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEffect of SAC on CH\u003csub\u003e4\u003c/sub\u003e production and fermentation in vitro\u003c/h2\u003e \u003cp\u003eIn this study, the effects of SAC on in vitro CH\u003csub\u003e4\u003c/sub\u003e production, total VFA concentration, and other fermentation parameters in dairy cow rumen are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The data indicates that SAC significantly reduced in vitro CH\u003csub\u003e4\u003c/sub\u003e production (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In the treatment group, the CH\u003csub\u003e4\u003c/sub\u003e production was about 6.01%-14.8% lower than that of the control group. In line with the changes in CH\u003csub\u003e4\u003c/sub\u003e production, total gas production also significantly decreased with the increasing concentration of SAC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025). In the 2.0 mg/mL group, the total gas production was 147.5 mL, which was approximately 8% lower than that of the control group (160.3 mL). The addition of SAC did not show a significant effect on the pH value and DMD of rumen fermentation fluid (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In terms of VFA concentration, no significant differences were observed between the treatment and control groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.945). However, in terms of individual VFA components, the proportion of butyrate significantly decreased with the increasing concentration of SAC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), while other major VFA components (such as acetate and propionate) were not significantly affected. Additionally, the acetate/propionate ratio showed a decreasing trend across the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096), decreasing from 2.85 in the control group to 2.77.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of Salvianolic Acid C on CH₄ production and other fermentation parameters in vitro\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGas production (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148.8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e147.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.59\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.95\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.97\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal VFA Concentrations (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e108.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual, mol/100 mol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropionate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsobutyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButyrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.69\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.55\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.99\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.41\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.23\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.09\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsovalerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetate / Propionate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e1\u003c/sup\u003eTreatment = were control (no Salvianolic Acid C) and Salvianolic Acid C included at 0,0.25,0.5,1.0,1.5 and 2.0mg/g of DM (0.25,0.5,1.0,\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e1.5 and 2.0 respectively).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e2\u003c/sup\u003eDMD = apparent disappearance of dry matter.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImpact on rumen microbial communities\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b show the α-diversity indices of bacterial and archaeal communities under SAC treatments, assessing microbial richness and evenness. No significant differences were observed between SAC treatments and control group, though slight variations appeared at certain concentrations. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d show the principal coordinate analysis (PCoA) of bacterial and archaeal communities, which visualizes the distance between communities to assess the differences among treatment groups. Higher SAC concentrations exhibited distinct shifts from the control. \u003cb\u003eFig e-f\u003c/b\u003e (community composition at the phylum and genus levels for bacteria) and \u003cb\u003eFig g-h\u003c/b\u003e (community composition at the phylum and genus levels for archaea) show the changes in microbial community composition under different concentrations of SAC treatment. For bacteria, \u003cem\u003eBacteroidota\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e are the predominant phyla in the rumen, dominating all treatment groups. At the genus level, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e, and \u003cem\u003eSucciniclasticum\u003c/em\u003e are the major bacterial genera. For archaea, \u003cem\u003eEuryarchaeota\u003c/em\u003e dominate all treatment groups, and at the genus level, \u003cem\u003eMethanobrevibacter\u003c/em\u003e is the primary genus of archaea.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFig i-l\u003c/b\u003e and \u003cb\u003eFig m-n\u003c/b\u003e collectively provide an in-depth analysis of the bacterial and archaeal community shifts in response to the SAC treatment (1.5 mg) compared to the control group (CK). \u003cb\u003eFig i-l\u003c/b\u003e highlight the results of the linear discriminant analysis effect size (LEfSe) analysis. In \u003cb\u003eFig i\u003c/b\u003e, the linear discriminant analysis (LDA)bar plot reveals that bacterial taxa such as \u003cem\u003eBacteroidota\u003c/em\u003e and \u003cem\u003eBacteroidales\u003c/em\u003e are significantly enriched in the SAC treatment group, as indicated by the positive LDA scores. In contrast, taxa such as \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eActinomycetales\u003c/em\u003e, and \u003cem\u003eRuminococcaceae\u003c/em\u003e are enriched in the control group, as indicated by the negative LDA scores. \u003cb\u003eFig j\u003c/b\u003e complements this by providing a phylogenetic cladogram, where green-highlighted branches represent taxa enriched in the treatment group (e.g., \u003cem\u003eBacteroidota\u003c/em\u003e and \u003cem\u003eBacteroidales\u003c/em\u003e), and red-highlighted branches denote taxa enriched in the control group (e.g., \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e). Similarly, for archaeal taxa, \u003cb\u003eFig k\u003c/b\u003e demonstrates that members of the \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e and \u003cem\u003eThermaplasmata\u003c/em\u003e are enriched in the treatment group, whereas \u003cem\u003eEuryarchaeota\u003c/em\u003e and \u003cem\u003eMethanobrevibacter\u003c/em\u003e are more abundant in the control group. This is further visualized in \u003cb\u003eFig l\u003c/b\u003e, where green branches highlight archaeal taxa enriched in the treatment group, such as \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e, and red branches show taxa like \u003cem\u003eMethanobrevibacter\u003c/em\u003e, enriched in the control group. \u003cb\u003eFig m-n\u003c/b\u003e further support these findings through statistical analysis of the relative abundances of bacterial and archaeal genera. In \u003cb\u003eFig m\u003c/b\u003e, the mean relative abundance of \u003cem\u003ePrevotella\u003c/em\u003e is higher in the control group, although this change is not statistically significant. In \u003cb\u003eFig n\u003c/b\u003e, the analysis of archaeal genera reveals that \u003cem\u003eMethanobrevibacter\u003c/em\u003e, the dominant methanogen genus, has a significantly higher relative abundance in the control group compared to the treatment group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEffects of SAC on functional gene expression and methanogenesis\u003c/h2\u003e \u003cp\u003eThe SAC treatment group exhibited significant enrichment of functional genes associated with carbohydrate metabolism and starch and sucrose metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), alongside notable upregulation of the pentose phosphate pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). The genes involved in steroid degradation were markedly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec-d). The key genes critical to CH\u003csub\u003e4\u003c/sub\u003e production\u0026mdash;\u003cem\u003emcrA\u003c/em\u003e (encoding the α-subunit of MCR) and \u003cem\u003ernfC\u003c/em\u003e (implicated in electron transport during methanogenesis)\u0026mdash;were significantly downregulated in the SAC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The DNA replication related genes were more abundant in the control group compared to the SAC treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe molecular docking results confirmed that SAC is an effective non-covalent inhibitor of MCR, with a GlideScore of -8.124 kcal/mol. This strong predicted binding affinity is primarily attributed to the formation of five key hydrogen bonds, as depicted in the 3D interaction model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The shortest hydrogen bond (1.8 \u0026Aring;) is formed between the phenolic hydroxyl group of SAC and GLY142, a residue situated near the active site of MCR. Although GLY142 has not been explicitly identified as a catalytic residue, its close proximity to the Ni-containing F430 cofactor suggests a possible structural role in ligand stabilization. In addition, hydrogen bonds with lengths shorter than 2.0 \u0026Aring; are generally considered particularly significant, as they tend to exhibit stronger binding interactions than those exceeding 2.5 \u0026Aring;[32]. SAC also forms additional interactions with MET229 (2.0 \u0026Aring;), GLN230 (2.0 \u0026Aring;), and GLN147 (2.1 \u0026Aring;), further stabilizing the compound within the MCR active site. These interactions enhance the binding affinity of SAC, supporting its potential as an effective MCR-targeting CH₄ inhibitor. Among the plant extracts tested in this study, SAC demonstrated the strongest inhibitory effect on MCR activity, achieving a 34.50% reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, although other extracts exhibited comparable predicted binding affinities in molecular docking, they did not produce similar levels of enzyme inhibition. This discrepancy indicates that binding affinity alone may not reliably predict enzyme inhibition[33]. To further quantify the inhibitory efficacy of SAC, the concentration dependent inhibition of MCR by SAC was evaluated, and its IC₅₀ was determined to be 692.3 \u0026micro;M. This value indicates that SAC is a moderately potent inhibitor of MCR. Although direct IC₅₀ comparisons are limited, similar to other plant derived polyphenols, relatively high concentrations are generally required to suppress CH\u003csub\u003e4\u003c/sub\u003e emissions or the expression of MCR genes [27, 34]. Synthetic compounds targeting MCR typically exhibit extremely low IC₅₀ values, such as 3-NOP (IC₅₀ = 0.1 \u0026micro;M) [21] and BES (IC₅₀ = 0.4 \u0026micro;M) [35], which are markedly lower than that of SAC. However, issues such as the need for high effective doses and concerns over cytotoxicity have limited the practical application of these synthetic inhibitors[25, 35]. In contrast, plant derived compounds generally offer higher biocompatibility[36]. SAC is a water-soluble polyphenolic compound derived from the root of \u003cem\u003eSalvia miltiorrhiza\u003c/em\u003e, a traditional Chinese medicinal herb[37]. SAC has shown a broad spectrum of biological activities, including antioxidant, anti-inflammatory, and antimicrobial properties[38]. These properties are not only beneficial in health applications but also suggest the potential of SAC as an environmentally sustainable CH₄ inhibitor in agricultural settings.\u003c/p\u003e \u003cp\u003eSAC significantly reduced total gas production in a concentration-dependent manner, primarily due to its inhibitory effect on CH₄ production. As a polyphenolic compound, its impact on CH₄ production is closely linked to its ability to interfere with methanogenic pathways while maintaining the overall stability of rumen fermentation. Previous studies have demonstrated that plant polyphenolic compounds can selectively reduce CH₄ production by inhibiting methanogen activity, reducing H\u003csub\u003e2\u003c/sub\u003e availability, or altering rumen microbial community composition [39]. For example, tannins and flavonoids reduce CH\u003csub\u003e4\u003c/sub\u003e production by binding to key enzymes in methanogens or disrupting their membrane stability [40]. Similarly, SAC likely interferes with MCR, a critical enzyme in CH₄ production, through mechanisms supported by molecular docking results. The strong binding affinity of SAC to the F430 active site of MCR and its hydrogen bonding interactions with key residues (e.g., GLY142 and MET229) provide molecular evidence for its ability to reduce CH₄ production. Additionally, the antioxidant properties of SAC may further enhance its inhibitory effects on CH₄ production. Antioxidants have been shown to mitigate free radical reactions that facilitate methanogenesis, thereby disrupting the metabolic processes of methanogens [41].\u003c/p\u003e \u003cp\u003eDespite the reduction in total gas production, the stability of VFA concentrations across treatments suggests that SAC selectively inhibits methanogenesis without impairing overall rumen fermentation. Although H₂ concentration was not directly measured in this study, previous studies have demonstrated that suppression of methanogenesis can lead to redirection of metabolic hydrogen ([H]) toward alternative sinks, such as propionate formation or microbial biomass synthesis, rather than CH\u003csub\u003e4\u003c/sub\u003e production[27, 42]. In this context, the significant decrease in butyrate proportion (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) and the trend toward a lower acetate/propionate (A/P) ratio (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096) may suggest shifts in [H] utilization. Since butyrate production releases H₂ and propionate formation consumes it, these observations could reflect a redistribution of [H] away from methanogenesis[43, 44]. Future research should incorporate H₂ monitoring to validate the proposed mechanism by which SAC alters the destination of [H] flux.\u003c/p\u003e \u003cp\u003eThe pH values and DMD were not significantly affected across treatments, their stability indicates that SAC does not impair the fundamental fermentation processes of the rumen. Maintaining pH within a narrow range (6.95\u0026ndash;6.97) is essential for optimal microbial activity, particularly for fiber-degrading bacteria responsible for fiber digestion [45]. In vitro fermentation systems often maintain slightly higher pH values due to the buffering capacity of the medium and limited acid accumulation during short incubation periods, as similarly reported by Ertl et al. (2015) in study with in vitro rumen fermentation[46]. The lack of significant changes in DMD suggests that SAC does not inhibit fiber degradation, aligning with the observation that VFA concentrations remained consistent across treatments. These findings further demonstrate that SAC selectively disrupts methanogenesis without negatively impacting other fermentation processes.\u003c/p\u003e \u003cp\u003eSAC altered the abundance of certain bacterial and archaeal taxa, but overall microbial diversity and community evenness remained unaffected, suggesting that SAC selectively targets specific methanogenic archaea rather than broadly disrupting the rumen microbiota. In the bacterial community, \u003cem\u003eBacteroidota\u003c/em\u003e increased while \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eRuminococcaceae\u003c/em\u003e decreased under SAC (1.5 mg/g DM). The increase of \u003cem\u003eBacteroidota\u003c/em\u003e is usually associated with enhanced fiber degradation and lower H\u003csub\u003e2\u003c/sub\u003e availability for methanogenesis, while \u003cem\u003eFirmicutes\u003c/em\u003e, especially \u003cem\u003eRuminococcae\u003c/em\u003e, are H\u003csub\u003e2\u003c/sub\u003e producing bacteria that supply methanogens[47, 48]. These shifts suggest that the reroute of fermentation end products may indirectly reduce the substrates available to methanogens.\u003c/p\u003e \u003cp\u003eOn the archaeal side, \u003cem\u003eMethanobrevibacter\u003c/em\u003e abundance declined markedly with SAC, while \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e increased. \u003cem\u003eMethanobrevibacter\u003c/em\u003e is the primary hydrogenotrophic methanogen in the rumen, and its suppression directly reduces CH₄ output[49]. \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e rely on methylated substrates (e.g., methanol) and generate less CH₄ per unit of substrate, representing a lower efficiency methanogenic pathway[50]. Thus, SAC appears to shift the archaea community toward lower efficiency methanogens.\u003c/p\u003e \u003cp\u003eMetagenomic analysis revealed that SAC enriched genes involved in carbohydrate metabolism, starch and sucrose metabolism, and the pentose phosphate pathway. These pathways can generate additional NADPH and fermentable intermediates, improving energy yields without increasing H\u003csub\u003e2\u003c/sub\u003e available for methanogenesis. SAC treatment also enriched steroid degradation genes, indicating a shift in lipid turnover and possibly enhanced microbial capacity to process complex carbon sources[51]. SAC treatment also enriched steroid degradation genes, indicating a shift in lipid turnover and possibly enhanced microbial capacity to process complex carbon sources[52].\u003c/p\u003e \u003cp\u003eImportantly, SAC downregulated two key methanogenesis genes: \u003cem\u003emcrA\u003c/em\u003e (methyl-coenzyme M reductase α-subunit) and \u003cem\u003ernfC\u003c/em\u003e (energy-conserving electron transport). Downregulation of \u003cem\u003emcrA\u003c/em\u003e directly impairs the enzyme that catalyzes CH₄ formation[53], while downregulation of \u003cem\u003ernfC\u003c/em\u003e may limit H\u003csub\u003e2\u003c/sub\u003e utilization for methanogenesis[54]. In contrast, \u003cem\u003eDNA_replication\u003c/em\u003e genes were more enriched in the control group, which may reflect a higher proliferative activity of certain microorganisms, such as \u003cem\u003eMethanobrevibacter\u003c/em\u003e. Overall, the transformation of these communities and functions further enhances the selective inhibition potential of SAC for methanogenesis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study suggests that SAC may have potential as a CH₄ inhibitor in the rumen. SAC demonstrated significant inhibitory effects on CH₄ production by selectively targeting \u003cem\u003eMethanobrevibacter\u003c/em\u003e, the dominant methanogen genus involved in hydrogenotrophic methanogenesis. The modulation of the rumen microbiota, particularly the enrichment of beneficial bacterial genera such as \u003cem\u003eBacteroidota\u003c/em\u003e, suggests that SAC may indirectly inhibit CH₄ production by altering the H\u003csub\u003e2\u003c/sub\u003e balance in the rumen. Furthermore, SAC has the ability to reduce the expression of key genes involved in CH₄ metabolism, including \u003cem\u003emcrA\u003c/em\u003e and \u003cem\u003ernfC\u003c/em\u003e, highlights its potential to interfere with the enzymatic processes responsible for CH₄ generation. Importantly, SAC did not adversely affect overall fermentation efficiency, as evidenced by the stable pH, DMD, and VFA concentrations. These findings support the feasibility of incorporating SAC as a feed additive to reduce CH₄ emissions in dairy cow, with further studies needed to optimize its application and explore its long-term effects on animal health and productivity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e3-NOP (3-nitrooxypropanol), A/P ratio (acetate-to-propionate ratio), BES (2-bromoethanesulfonate), BPS (bromopropanesulfonate), CH₄ (methane), CO₂ (carbon dioxide), DMD (dry matter digestibility), DM (dry matter), ELISA (enzyme-linked immunosorbent assay), GHG (greenhouse gas), IC₅₀ (half-maximal inhibitory concentration), LDA (linear discriminant analysis), LEfSe (linear discriminant analysis effect size), MCR (methyl-coenzyme M reductase), mcrA (methyl-coenzyme M reductase subunit A gene), NADPH (nicotinamide adenine dinucleotide phosphate, reduced form), PCoA (principal coordinate analysis), rnfC (electron transport complex Rnf subunit C gene), SAC (salvianolic acid C), SI (Supporting Information) and VFA (volatile fatty acid).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental procedures involving animals have been approved by the Animal Care and Use Committee of the Chinese Academy of Agricultural Sciences (IAS2023-131; Beijing, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 16S rRNA raw sequencing reads have been deposited in the NCBI Sequence Read Archive (SRA) under the accession number SRP516729. The metagenomic raw sequencing reads have also been deposited in the NCBI SRA database under the accession number SRP527714. Additional raw data are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Integrated Demonstration of Scalable and Efficient Healthy Breeding for Cattle and Sheep (Grant No. 2022YFD1301100) and Instant Intelligent Diagnosis and Risk Warning Methods for Nutritional and Metabolic-Type Periparturient Cow Paralysis (Grant No. 2024-YWF-ZYSQ-10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZihao Liu: Conceptualization, methodology, data analysis, writing – original draft. Li Xiao: Investigation, data collection, analysis. Xiangfang Tang: Investigation, funding acquisition, analysis. Yue He: Data collection, analysis, writing – review and editing. Xuemei Nan: Methodology, writing – review and editing. Hui Wang: Data analysis, funding acquisition. Yuming Guo: Supervision, project administration, writing – review and editing. Benhai Xiong: Supervision, project administration, analysis, writing – review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGraphical abstract created in BioRender. Liu, Z. (2025) https://BioRender.com/k72v884.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRotz CA. Modeling greenhouse gas emissions from dairy farms. Journal of Dairy Science. 2018;101(7):6675-90; doi: 10.3168/jds.2017-13272.\u003c/li\u003e\n\u003cli\u003eSakatani M. The role of reproductive biology in SDGs Global warming and cattle reproduction: Will increase in cattle numbers progress to global warming? Journal of Reproduction and Development. 2022;68(2):90\u0026thinsp;\u0026minus;\u0026thinsp;5; doi: 10.1262/jrd.2021-149.\u003c/li\u003e\n\u003cli\u003eShukla PR, Skea J, Slade R, Khourdajie, A.A., Rv, et al.: IPCC Sixth Assessment Report (AR6): Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. In. Cambridge University Press,Cambridge, UK and New York, NY, USA.; 2022.\u003c/li\u003e\n\u003cli\u003eSmith P, Bustamante M, Ahammad H, Clark H, Dong H, Elsiddig E, et al.: Agriculture, Forestry and Other Land Use (AFOLU). Pages 811\u0026ndash;922 in Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In. Cambridge University Press, Cambridge,UK.; 2014.\u003c/li\u003e\n\u003cli\u003eGerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J, et al. Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. 2013.\u003c/li\u003e\n\u003cli\u003eFlint HJ. Polysaccharide breakdown by anaerobic microorganisms inhabiting the Mammalian gut. Adv Appl Microbiol. 2004;56:89\u0026ndash;120; doi: 10.1016/S0065-2164(04)56003-3.\u003c/li\u003e\n\u003cli\u003eAbbas W, Howard JT, Paz HA, Hales KE, Wells JE, Kuehn LA, et al. Influence of host genetics in shaping the rumen bacterial community in beef cattle. Sci Rep. 2020;10(1):15101; doi: 10.1038/s41598-020-72011-9.\u003c/li\u003e\n\u003cli\u003eWang K, Xiong B, Zhao X. Could propionate formation be used to reduce enteric methane emission in ruminants? Sci Total Environ. 2023;855:158867; doi: 10.1016/j.scitotenv.2022.158867.\u003c/li\u003e\n\u003cli\u003eAlvarez-Hess PS, Williams SRO, Jacobs JL, Hannah MC, Beauchemin KA, Eckard RJ, et al. Effect of dietary fat supplementation on methane emissions from dairy cows fed wheat or corn. J Dairy Sci. 2019;102(3):2714-23; doi: 10.3168/jds.2018-14721.\u003c/li\u003e\n\u003cli\u003eAppuhamy JA, France J, Kebreab E. Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand. Glob Chang Biol. 2016;22(9):3039-56; doi: 10.1111/gcb.13339.\u003c/li\u003e\n\u003cli\u003eLascano C, C\u0026aacute;rdenas E. Alternatives for methane emission mitigation in livestock systems. Revista Brasileira de Zootecnia. 2010;39:175\u0026thinsp;\u0026minus;\u0026thinsp;82; doi: 10.1590/S1516-35982010001300020.\u003c/li\u003e\n\u003cli\u003eLan W, Yang C. Ruminal methane production: Associated microorganisms and the potential of applying hydrogen-utilizing bacteria for mitigation. Sci Total Environ. 2019;654:1270-83; doi: 10.1016/j.scitotenv.2018.11.180.\u003c/li\u003e\n\u003cli\u003eKumar S, Choudhury PK, Carro MD, Griffith GW, Dagar SS, Puniya M, et al. New aspects and strategies for methane mitigation from ruminants. Appl Microbiol Biotechnol. 2014;98(1):31\u0026ndash;44; doi: 10.1007/s00253-013-5365-0.\u003c/li\u003e\n\u003cli\u003eKamra DN. Rumen microbial ecosystem. Current Science. 2005;89(1):124\u0026thinsp;\u0026minus;\u0026thinsp;35.\u003c/li\u003e\n\u003cli\u003eQiao J, Tan Z, Wang M. Potential and existing mechanisms of enteric methane production in ruminants. Scientia Agricola. 2014;71(5):430\u0026thinsp;\u0026minus;\u0026thinsp;40; doi: 10.1590/0103-9016-2013-0423.\u003c/li\u003e\n\u003cli\u003eBueno de Mesquita CP, Wu D, Tringe SG. Methyl-Based Methanogenesis: an Ecological and Genomic Review. Microbiol Mol Biol Rev. 2023;87(1):e0002422; doi: 10.1128/mmbr.00024-22.\u003c/li\u003e\n\u003cli\u003eThauer RK. Methyl (Alkyl)-Coenzyme M Reductases: Nickel F-430-Containing Enzymes Involved in Anaerobic Methane Formation and in Anaerobic Oxidation of Methane or of Short Chain Alkanes. Biochemistry. 2019;58(52):5198\u0026thinsp;\u0026minus;\u0026thinsp;220; doi: 10.1021/acs.biochem.9b00164.\u003c/li\u003e\n\u003cli\u003eScheller S, Goenrich M, Mayr S, Thauer RK, Jaun B. Intermediates in the catalytic cycle of methyl coenzyme M reductase: isotope exchange is consistent with formation of a sigma-alkane-nickel complex. Angew Chem Int Ed Engl. 2010;49(44):8112-5; doi: 10.1002/anie.201003214.\u003c/li\u003e\n\u003cli\u003eErmler U. On the mechanism of methyl-coenzyme M reductase. Dalton Trans. 2005;(21):3451-8; doi: 10.1039/b506697b.\u003c/li\u003e\n\u003cli\u003ePrakash D, Wu Y, Suh SJ, Duin EC. Elucidating the process of activation of methyl-coenzyme M reductase. J Bacteriol. 2014;196(13):2491-8; doi: 10.1128/JB.01658-14.\u003c/li\u003e\n\u003cli\u003eDuin EC, Wagner T, Shima S, Prakash D, Cronin B, Yanez-Ruiz DR, et al. Mode of action uncovered for the specific reduction of methane emissions from ruminants by the small molecule 3-nitrooxypropanol. Proc Natl Acad Sci U S A. 2016;113(22):6172-7; doi: 10.1073/pnas.1600298113.\u003c/li\u003e\n\u003cli\u003eGoenrich M, Mahlert F, Duin EC, Bauer C, Jaun B, Thauer RK. Probing the reactivity of Ni in the active site of methyl-coenzyme M reductase with substrate analogues. J Biol Inorg Chem. 2004;9(6):691\u0026ndash;705; doi: 10.1007/s00775-004-0552-1.\u003c/li\u003e\n\u003cli\u003eUngerfeld EM, Rust SR, Boone DR, Liu Y. Effects of several inhibitors on pure cultures of ruminal methanogens. J Appl Microbiol. 2004;97(3):520-6; doi: 10.1111/j.1365-2672.2004.02330.x.\u003c/li\u003e\n\u003cli\u003eGrawert T, Hohmann HP, Kindermann M, Duval S, Bacher A, Fischer M. Inhibition of methyl-CoM Reductase from Methanobrevibacter ruminantium by 2-bromoethanesulfonate. J Agric Food Chem. 2014;62(52):12487-90; doi: 10.1021/jf505056g.\u003c/li\u003e\n\u003cli\u003eHristov AN, Oh J, Giallongo F, Frederick TW, Harper MT, Weeks HL, et al. An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production. Proc Natl Acad Sci U S A. 2015;112(34):10663-8; doi: 10.1073/pnas.1504124112.\u003c/li\u003e\n\u003cli\u003eWu T, Zhu W, Chen L, Jiang T, Dong Y, Wang L, et al. A review of natural plant extracts in beverages: Extraction process, nutritional function, and safety evaluation. Food Res Int. 2023;172:113185; doi: 10.1016/j.foodres.2023.113185.\u003c/li\u003e\n\u003cli\u003ePatra AK, Saxena J. A new perspective on the use of plant secondary metabolites to inhibit methanogenesis in the rumen. Phytochemistry. 2010;71(11\u0026ndash;12):1198\u0026thinsp;\u0026minus;\u0026thinsp;222; doi: 10.1016/j.phytochem.2010.05.010.\u003c/li\u003e\n\u003cli\u003eHoltshausen L, Chaves AV, Beauchemin KA, McGinn SM, McAllister TA, Odongo NE, et al. Feeding saponin-containing Yucca schidigera and Quillaja saponaria to decrease enteric methane production in dairy cows. J Dairy Sci. 2009;92(6):2809-21; doi: 10.3168/jds.2008-1843.\u003c/li\u003e\n\u003cli\u003eCedervall PE, Dey M, Pearson AR, Ragsdale SW, Wilmot CM. Structural insight into methyl-coenzyme M reductase chemistry using coenzyme B analogues. Biochemistry. 2010;49(35):7683-93; doi: 10.1021/bi100458d.\u003c/li\u003e\n\u003cli\u003eLiu Z, Wang K, Nan X, Yang L, Wang Y, Zhang F, et al. Effects of combined addition of 3-nitrooxypropanol and vitamin B(12) on methane and propionate production in dairy cows by in vitro-simulated fermentation. J Dairy Sci. 2023;106(1):219\u0026thinsp;\u0026minus;\u0026thinsp;32; doi: 10.3168/jds.2022-22207.\u003c/li\u003e\n\u003cli\u003eLiu Z, Wang K, Zhao Y, Nan X, Yang L, Zhou M, et al. Combined effects of vitamin B12 and fumarate on rumen propionate production. Animal Research and One Health. 2023;1(2):204\u0026thinsp;\u0026minus;\u0026thinsp;18; doi: 10.1002/aro2.27.\u003c/li\u003e\n\u003cli\u003eMildvan AS, Massiah MA, Harris TK, Marks GT, Harrison DHT, Viragh C, et al. Short, strong hydrogen bonds on enzymes: NMR and mechanistic studies. Journal of Molecular Structure. 2002;615(1):163\u0026thinsp;\u0026minus;\u0026thinsp;75; doi: https://doi.org/10.1016/S0022-2860(02)00212-0.\u003c/li\u003e\n\u003cli\u003eCaldwell GW, Yan Z, Lang W, Masucci JA. The IC(50) concept revisited. Curr Top Med Chem. 2012;12(11):1282-90; doi: 10.2174/156802612800672844.\u003c/li\u003e\n\u003cli\u003eGuo YQ, Liu JX, Lu Y, Zhu WY, Denman SE, McSweeney CS. Effect of tea saponin on methanogenesis, microbial community structure and expression of mcrA gene, in cultures of rumen micro-organisms. Lett Appl Microbiol. 2008;47(5):421-6; doi: 10.1111/j.1472-765X.2008.02459.x.\u003c/li\u003e\n\u003cli\u003eGr\u0026auml;wert T, Hohmann HP, Kindermann M, Duval S, Bacher A, Fischer M. Inhibition of methyl-CoM Reductase from Methanobrevibacter ruminantium by 2-bromoethanesulfonate. J Agric Food Chem. 2014;62(52):12487-90; doi: 10.1021/jf505056g.\u003c/li\u003e\n\u003cli\u003eWang J, Deng L, Chen M, Che Y, Li L, Zhu L, et al. Phytogenic feed additives as natural antibiotic alternatives in animal health and production: A review of the literature of the last decade. Anim Nutr. 2024;17:244\u0026thinsp;\u0026minus;\u0026thinsp;64; doi: 10.1016/j.aninu.2024.01.012.\u003c/li\u003e\n\u003cli\u003eShen H, Pei H, Zhai L, Guan Q, Wang G. Salvianolic acid C improves cerebral ischemia reperfusion injury through suppressing microglial cell M1 polarization and promoting cerebral angiogenesis. Int Immunopharmacol. 2022;110:109021; doi: 10.1016/j.intimp.2022.109021.\u003c/li\u003e\n\u003cli\u003eXu MJ, Jiang LF, Wu T, Chu JH, Wei YD, Aa JY, et al. Inhibitory Effects of Danshen components on CYP2C8 and CYP2J2. Chem Biol Interact. 2018;289:15\u0026ndash;22; doi: 10.1016/j.cbi.2018.04.011.\u003c/li\u003e\n\u003cli\u003ePatra AK, Saxena J. Exploitation of dietary tannins to improve rumen metabolism and ruminant nutrition. J Sci Food Agric. 2011;91(1):24\u0026ndash;37; doi: 10.1002/jsfa.4152.\u003c/li\u003e\n\u003cli\u003eJayanegara A, Leiber F, Kreuzer M. Meta-analysis of the relationship between dietary tannin level and methane formation in ruminants from in vivo and in vitro experiments. J Anim Physiol Anim Nutr (Berl). 2012;96(3):365\u0026thinsp;\u0026minus;\u0026thinsp;75; doi: 10.1111/j.1439-0396.2011.01172.x.\u003c/li\u003e\n\u003cli\u003eLambo MT, Ma H, Liu R, Dai B, Zhang Y, Li Y. Review: Mechanism, effectiveness, and the prospects of medicinal plants and their bioactive compounds in lowering ruminants' enteric methane emission. Animal. 2024;18(4):101134; doi: 10.1016/j.animal.2024.101134.\u003c/li\u003e\n\u003cli\u003eUngerfeld EM. Shifts in metabolic hydrogen sinks in the methanogenesis-inhibited ruminal fermentation: a meta-analysis. Front Microbiol. 2015;6:37; doi: 10.3389/fmicb.2015.00037.\u003c/li\u003e\n\u003cli\u003eMorgavi DP, Forano E, Martin C, Newbold CJ. Microbial ecosystem and methanogenesis in ruminants. Animal. 2010;4(7):1024-36; doi: 10.1017/s1751731110000546.\u003c/li\u003e\n\u003cli\u003eHook SE, Wright AD, McBride BW. Methanogens: methane producers of the rumen and mitigation strategies. Archaea. 2010;2010:945785; doi: 10.1155/2010/945785.\u003c/li\u003e\n\u003cli\u003eRussell JB, Wilson DB. Why are ruminal cellulolytic bacteria unable to digest cellulose at low pH? J Dairy Sci. 1996;79(8):1503-9; doi: 10.3168/jds.S0022-0302(96)76510-4.\u003c/li\u003e\n\u003cli\u003eErtl P, Knaus W, Metzler-Zebeli BU, Klevenhusen F, Khiaosa-Ard R, Zebeli Q. Substitution of common concentrates with by-products modulated ruminal fermentation, nutrient degradation, and microbial community composition in vitro. J Dairy Sci. 2015;98(7):4762-71; doi: 10.3168/jds.2014-9063.\u003c/li\u003e\n\u003cli\u003eSnelling TJ, Auffret MD, Duthie CA, Stewart RD, Watson M, Dewhurst RJ, et al. Temporal stability of the rumen microbiota in beef cattle, and response to diet and supplements. Anim Microbiome. 2019;1(1):16; doi: 10.1186/s42523-019-0018-y.\u003c/li\u003e\n\u003cli\u003eJanssen PH, Kirs M. Structure of the archaeal community of the rumen. Appl Environ Microbiol. 2008;74(12):3619-25; doi: 10.1128/aem.02812-07.\u003c/li\u003e\n\u003cli\u003eWeimar MR, Cheung J, Dey D, McSweeney C, Morrison M, Kobayashi Y, et al. Development of Multiwell-Plate Methods Using Pure Cultures of Methanogens To Identify New Inhibitors for Suppressing Ruminant Methane Emissions. Appl Environ Microbiol. 2017;83(15); doi: 10.1128/aem.00396-17.\u003c/li\u003e\n\u003cli\u003eWeil M, Wang H, Zak D, Urich T. Spatial and temporal niche separation of Methanomassiliicoccales phylotypes in temperate fens. FEMS Microbiol Ecol. 2023;99(6); doi: 10.1093/femsec/fiad049.\u003c/li\u003e\n\u003cli\u003ePatra KC, Hay N. The pentose phosphate pathway and cancer. Trends Biochem Sci. 2014;39(8):347\u0026thinsp;\u0026minus;\u0026thinsp;54; doi: 10.1016/j.tibs.2014.06.005.\u003c/li\u003e\n\u003cli\u003eBrzostek A, Rumijowska-Galewicz A, Dziadek B, Wojcik EA, Dziadek J. ChoD and HsdD can be dispensable for cholesterol degradation in mycobacteria. J Steroid Biochem Mol Biol. 2013;134:1\u0026ndash;7; doi: 10.1016/j.jsbmb.2012.09.028.\u003c/li\u003e\n\u003cli\u003eAlvarado A, Monta\u0026ntilde;ez-Hern\u0026aacute;ndez LE, Palacio-Molina SL, Oropeza-Navarro R, Lu\u0026eacute;vanos-Escare\u0026ntilde;o MP, Balagurusamy N. Microbial trophic interactions and mcrA gene expression in monitoring of anaerobic digesters. Front Microbiol. 2014;5:597; doi: 10.3389/fmicb.2014.00597.\u003c/li\u003e\n\u003cli\u003eSchmehl M, Jahn A, Meyer zu Vilsendorf A, Hennecke S, Masepohl B, Schuppler M, et al. Identification of a new class of nitrogen fixation genes in Rhodobacter capsulatus: a putative membrane complex involved in electron transport to nitrogenase. Mol Gen Genet. 1993;241(5\u0026ndash;6):602\u0026thinsp;\u0026minus;\u0026thinsp;15; doi: 10.1007/bf00279903.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-animal-science-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jasb","sideBox":"Learn more about [Journal of Animal Science and Biotechnology](http://jasbsci.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jasb/default.aspx","title":"Journal of Animal Science and Biotechnology","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Salvianolic acid C, Methane mitigation, Methyl-coenzyme M reductase, Rumen microbiota","lastPublishedDoi":"10.21203/rs.3.rs-6850632/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6850632/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMethane (CH₄) emissions from ruminants significantly contribute to greenhouse gas effects and energy loss in livestock production. Methyl-coenzyme M reductase (MCR) is the key enzyme in methanogenesis, making it a promising target for CH₄ mitigation. This study aimed to identify and validate plant-derived inhibitors by employing molecular docking to screen compounds with strong binding affinity to the F430 active site of MCR and assessing their efficacy in reducing CH₄ emissions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMolecular docking analysis identified Salvianolic acid C (SAC) as a potent inhibitor of MCR, exhibiting a strong binding affinity to the F430 active site (binding energy: \u0026minus;8.2 kcal/mol). Enzymatic inhibition assays confirmed its inhibitory effect, with a half-maximal inhibitory concentration (IC₅₀) of 692.3 \u0026micro;M. In vitro rumen fermentation experiments demonstrated that SAC supplementation (1.5 mg/g DM) significantly reduced CH₄ production (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) without negatively affecting key fermentation parameters. Microbial community analysis using 16S rRNA sequencing and metagenomics revealed that SAC selectively altered the rumen microbiota, increasing the relative abundance of \u003cem\u003eBacteroidota\u003c/em\u003e while significantly reducing \u003cem\u003eMethanobrevibacter\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). Additionally, metagenomic analysis indicated the downregulation of key methanogenesis-related genes (\u003cem\u003emcrA\u003c/em\u003e, \u003cem\u003ernfC\u003c/em\u003e), suggesting a dual mechanism involving direct enzymatic inhibition and microbial community modulation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings indicate that SAC effectively reduces CH₄ production by inhibiting MCR activity and reshaping the rumen microbial community. As a plant-derived compound with strong inhibitory effects on methanogenesis, SAC presents a promising and sustainable alternative to synthetic methane inhibitors, offering potential applications in mitigating CH₄ emissions in livestock production.\u003c/p\u003e","manuscriptTitle":"Salvianolic Acid C Inhibits Methane Emissions in Dairy Cows by Targeting MCR and Reshaping the Rumen Microbial Community","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 18:29:05","doi":"10.21203/rs.3.rs-6850632/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-08-22T04:59:44+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-19T17:04:17+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-11T03:27:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T10:09:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Animal Science and Biotechnology","date":"2025-06-09T11:16:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-animal-science-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jasb","sideBox":"Learn more about [Journal of Animal Science and Biotechnology](http://jasbsci.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jasb/default.aspx","title":"Journal of Animal Science and Biotechnology","twitterHandle":"@animalplantsci","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"196fa305-6dda-4e4d-a3f0-91cd2ee37748","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:00:22+00:00","versionOfRecord":{"articleIdentity":"rs-6850632","link":"https://doi.org/10.1186/s40104-025-01285-8","journal":{"identity":"journal-of-animal-science-and-biotechnology","isVorOnly":false,"title":"Journal of Animal Science and Biotechnology"},"publishedOn":"2025-11-17 15:57:20","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2025-06-13 18:29:05","video":"","vorDoi":"10.1186/s40104-025-01285-8","vorDoiUrl":"https://doi.org/10.1186/s40104-025-01285-8","workflowStages":[]},"version":"v1","identity":"rs-6850632","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6850632","identity":"rs-6850632","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}