Metatranscriptomic changes in the propionate pathway and carbohydrate enzyme revealed the rebalancing mechanism of rumen microbiota after CH4 inhibition by 3-NOP

Metatranscriptomic changes in the propionate pathway and carbohydrate enzyme revealed the rebalancing mechanism of rumen microbiota after CH4 inhibition by 3-NOP | 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 Metatranscriptomic changes in the propionate pathway and carbohydrate enzyme revealed the rebalancing mechanism of rumen microbiota after CH4 inhibition by 3-NOP Yurong Cao, Shizhe Zhang, Qiushuang Li, Xiang Zhou, Wenxing Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7121971/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Inhibition of methanogen by 3-NOP will produce excess hydrogen gas, which affects the nutrient digestion and fermentation functions of rumen microbiota. Certain hydrogenotrophic microorganisms contribute to a fraction of the hydrogen sink via processes such as propionate production, acetogenesis, and anion reduction. However, these mechanisms alone are insufficient to fully account for the sustained methane generation and the decline in hydrogen gas levels. A more comprehensive understanding is needed on the rebalancing process of rumen microbiota, the temporal dynamics and microbial drivers of 3-NOP resistance. Results We opted for a fully-automated fermentation system in vitro to closely monitor the process by which 3-NOP inhibits methane production. At the onset of net hydrogen consumption (24h), we collected fermentation fluid samples (n = 12) for metatranscriptomic sequencing, along with conducting a quantitative analysis of key microorganisms. The inhibition experiments of pure culture were also conducted on three different nutritional types of methane bacteria. 3-NOP significantly reduced the transcripts of the methane metabolism pathway, but significantly increased the transcripts of two propionate pathways(lactate and succinate). Based on annotating gene sets and mapping the transcriptomic reads to the assembled genome database, the transcripts of Methanomassiliicoccales and a portion of Methanobrevibacter were significantly elevated. Analysis of carbohydrate enzymes showed that 3-NOP significantly increased the transcription of cellulase in fungi ( Neocallimastigaceae ) and Fibrobacter succinogenes , but significantly reduced the transcription of Ruminococcus flavefaciens and Ruminococcus albus . Methanogens that form symbiotic relationships with fungi ( Neocallimastigaceae ) remain unaffected by 3-NOP through a coupled mechanism involving cellulose degradation and hydrogenosome activity in the rumen. These adaptive strategies of methanogenesis—non-hydrogenotrophic and the symbiotic partnership with rumen fungi—enable methanogens to attenuate the inhibitory effects of 3-NOP, potentially leading to resistance following prolonged exposure. Conclusion These findings underscore the need for caution in the sustained use of 3-NOP as a standalone methane mitigation strategy and highlight critical targets for developing next-generation inhibitors. 3-nitrooxypropanol ruminant methane mitigation molecular hydrogen propionate pathway carbohydrate-active enzymes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The 3-nitrooxypropanol (3-NOP), pioneered by DSM (Dutch State Mines) nutritional products in the Netherlands in 2014, was served as a feed additive for ruminant and is presently employed to regulate ruminant CH 4 production [ 1 ]. As a methyl-coenzyme M reductase (Mcr) analog, 3-NOP can displace methyl-coenzyme M in binding to coenzyme B, thereby impeding the conversion process of methyl-coenzyme M into CH 4 [ 2 ]. Multiple studies have demonstrated that 3-NOP diets lead to a reduction in ruminal CH 4 production (7%-60%) without compromising the animals’ production performance (beef cattle [ 3 – 6 ], dairy cows [ 7 – 9 ], and sheep [ 2 ]). Following the supplement of 3-NOP, methanogens were predominantly inhibited, resulting in an environment with elevated hydrogen partial pressure [ 10 ]. Consequently, rumen microorganisms were subjected to dual stressors: the influence of 3-NOP and the resultant high hydrogen partial pressure. How does rumen microbiota adapt to the high-H 2 environment after methane inhibition? More affordable and highly precise omics technologies now enable the accurate capture of these changes as they occur during the process. An animal experiment has demonstrated that the inhibitor 3-NOP can exert distinct effects on various methanogens through metagenomic and metatranscriptomic analyses [ 7 ]. These differential effects are likely attributable to the varying mechanisms of methyl group formation and the diverse sources of hydrogen involved [ 7 , 11 ]. The diversity of rumen microbiota was also affected by 3-NOP, come in for multiple factors including diet composition, dry matter intake, host genetics, and rumen conditions such as pH, volatile fatty acid (VFA) profile, and H 2 partial pressure in the rumen [ 11 – 13 ]. The theory of hydrogen sink was a widely recognized perspective, which suggests that under the inhibition of methane production, an increase in dissolved H 2 may lead to a shift in the production and the utilization of H 2 in microorganisms, resulting in the formation of a new steady state in the rumen microbiota [ 14 , 15 ]. However, it is worth noting that in addition to hydrogenotrophic bacteria, some fungi, and protozoa are also involved in hydrogen metabolism. Research indicates that coculturing methanogens with anaerobic fungi upregulates multiple carbohydrate-active enzymes (CAZyme) families within the fungi, thereby enhancing their capacity to sense and degrade carbohydrates [ 16 ]. Some studies have also indicated that the methane-inhibiting effect of 3-NOP diminishes as the proportion of fiber in the diet increases [ 8 ]. This phenomenon may arise from the symbiotic interaction between methanogens and anaerobic fungi. Additionally, in vitro pure culture experiments demonstrate that 3-NOP exerts an inhibitory effect exceeding 90% on methane production [ 2 ], but the methane-inhibiting efficacy of 3-NOP varies between 15% and 65% in animal trials [ 8 ]. These divergent phenomenon(three different experiments) necessitate additional data to illuminate the response mechanism of rumen microbiota to 3-NOP. In this study, we observed that 3-NOP altered the rumen environment through inhibited methane and elevated H 2 , promoting rumen microbiota to shift towards the propionate production pathway. This result was also observed in experiments with low methane ruminants and other methane inhibitors [ 3 , 17 , 18 ]. As for other non-methane hydrogen sinks, the Wood-Ljungdahl processes, as well as other hydrogen sinks (sulfates and nitrate, metal ions, etc.) are also significantly affected by the elevated rumen hydrogen, but these pathways provide a small contribution to the redirection of H 2 in ruminants [ 18 , 19 ]. On the other hand, the two types of methanogens (non-hydrogenotrophic and symbiotic with fungi) were less affected by 3-NOP. Furthermore, we found an increase in the activity of the CAZy enzyme in fungi by matching the analysis of transcripts and species in the cascade of degradation of cellulose. We predicted that the hydrogen production process of fiber degradation via fungi may assist the methanogens that are symbiotic with fungi to be independent of 3-NOP influence. Overall, these results help us understand the main destination and source of hydrogen after 3-NOP inhibits methane, and reveal the methanogens that are not affected by 3-NOP and their escape mechanisms. Materials and methods The present investigation was sanctioned by the Animal Care Committee under approval number W202502 at the Institute of Subtropical Agriculture, Chinese Academy of Sciences. In vitro fermentation experiment The rumen fluid in this experiment was obtained from three healthy fistula sheep. The obtained rumen fluid preserved in a CO 2 gas-filled thermos flask and promptly transport it to the laboratory. Filter the fluid using 4 layers of gauze and reserve for further experiment. The artificial rumen buffer was meticulously prepared following the methodology outlined by Menke et al [ 20 ]. Detailed operating methods and procedures are shown in supplementary file S1. Using 1g of dried whole-plant maize silage as a substrate, a automated fermentation system in vitro was employ to record the complete processes of methane and hydrogen production. The fermentation bottles were divided into CON and 3-NOP (n = 12). Prior to inoculating the rumen fluid, the artificial rumen buffer solution was pre-heated to 39.5℃ using a constant-temperature magnetic stirrer. An anaerobic environment was meticulously maintained by continuously flushing the system with CO 2 (note: the resazurin color change to colorless). A set of six fermentation bottles was allowed to ferment for a duration of 48 hours. Meanwhile, another six bottles were designated for sampling at the 24th hour mark. This sampling time was chosen because the hydrogen concentration started to decline at the 24th hour, a decision based on the findings of numerous prior experiments. Following fermentation, the fermentation liquid was promptly extracted, rapidly frozen using liquid nitrogen, and subsequently stored at -80℃. This storage was intended for metatranscriptome sequencing and subsequent analyses. Pure culture of inhibition experiment To clarify the effect of 3-NOP on methane from different methyl sources, this experiment selected three different types of methanogens-namely hydrogen reducing CO 2 - Methanobrevibacter , hydrogen-reducing methyltrophicocta- Methanosarcina sp. , methyltrophic- Methanomassiliicoccales sp. Methanobrevibacter and Methanosarcina are taken in the same medium-rumen fluid medium [ 21 ]. Methanobrevibacter -The strains were obtained from the laboratory ( Methanobrevibacter olleyae ), Methanosarcina and Methanomassiliicoccales were obtained from the Institute of Microbiology, Chinese Academy of Sciences. The strain frozen in-80℃ refrigerator was revived in 37℃ water bath, and directly inoculated into the anaerobic bottle. When the optical density at 600 nm (OD 600 ) of the culture increased to 0.8, inoculation was carried out. The inoculation volume was 2 ml mother liquor, and the standard gas (H 2 : CO 2 = 4:1) was added at 0.1 Mpa. Methanomassiliicoccales was suited to grow in a methanol medium [ 22 ], and its recovery process was slow, and the OD 600 reached 0.8 after 20 days. Before inoculation, 0.1 mL of 0.25 mmol/L 3-NOP solution was added to the anaerobic bottle. After inoculation, methane concentration was measured every two days. All mediums were handled under strictly anaerobic conditions and sterilized using high-temperature and high-pressure autoclaving. The detection of methane was carried out by using a 50uL micro-injector to draw 40ul from the anaerobic bottle and injected it into the gas chromatograph (SHIMADZU GC-14B) Metatranscriptome sequencing and mapping Total RNA was extracted from fermentation fluid samples using the TRIzol Reagent. The residual DNase was conducted using DNase I (Takara). Ribosomal RNA was depleted by using the Ribo Zero TM rRNA Removal Kit [ 23 ]. Metatranscriptome sequencing was conducted utilizing the HiSeq 2500 System by Illumina, generating paired-end reads with a length of 150 bp in both the forward and reverse directions. Metatranscriptome sequencing and analysis of rumen prokaryotic were undertaken by Shanghai Biozeron Biological Technology Co. Ltd. Detailed analysis steps of the biological information are shown in the supplementary file (Supplementary file S1). For the quality control of sequencing results, FASTP software is used to complete this study. The FASTP software streamlines the data processing workflow by scanning the data file, effectively integrating the functionalities of Fastqc, Cutadapt, and Trimmomatic into a single operation [ 24 ]. During this process, it calculates the average quality scores for both the head and tail regions of each sequence. Subsequently, it excises the subsequence with the lower average quality value, ensuring high-quality data for downstream analyses. Bowtie2 was used to align the reads after quality control with the sheep genome [ 25 ], and then the sequence of the host was removed (the generated SAM file was transferred to samtools through the pipeline, and the SAM file was converted into BAM file). Averaging 13.5 gigabases of paired-end reads per sample, suitable for subsequent analysis. MEGAHIT (v1.2.9, minimum length threshold is 500) was used to predict the contigs from each sample [ 26 ], and Prodigal was used to predict the genes corresponding to each contig [ 27 ] and filter out coding sequences with a length less than 100. Non-redundant UniGene catalogs were constructed with 95% identity and 90% coverage by CD-HIT (v4.8.1) [ 28 ]. Relative gene expression levels were determined following established methodologies [ 29 ]. In general, the metatranscriptomic reads of each sample were aligned to predicted Open Reading Frames (ORFs) identified in metagenomes, with Transcript Per Million (TPM) values utilized for data analysis in this study. Functional annotation and phylogenetic analysis To clarify the composition of active microorganisms, species annotation of transcriptome sequences was performed using kranken2 [ 30 ]. The reference database ( https://github.com/DerrickWood/kraken2 ) was used to classify and annotate each sequence based on the K-mer present in each sequence. The transcriptome sequences of fungi and protozoa were extracted using the deep learning tools-GutEuk tool [ 31 ], and abundance calculations and differential analysis were performed on the two groups [ 32 ]. All UniGene catalogs were searched against NCBI non-redundant proteins, String, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases using BLASTp. These searches were performed utilizing hidden Markov models (HMMs) and homology-based search strategies. Furthermore, gene annotations were cross-referenced with the KEGG (Kyoto Encyclopedia of Genes and Genomes) database to investigate carbohydrate metabolism [ 17 ]. This analysis involved HMM searches with default parameters, culminating in the aggregation of data based on the abundance of pathways at Level 3 and Level 2, together with the identification of KEGG Orthologs (KOs) implicated in volatile fatty acid production [ 17 ].To gain insights into the genes involved in the pathway of these organic acids, we conducted an analysis implicated in the organic acid and methane metabolism pathway based on the classification table formulated by Li et al [ 15 ]. These pathways include methanogenesis, the Pyr->Ace and Pyr->But, Ace->But, Pyr->Lacate->Pro and Pyr->Suc->Pro (Ace, acetate; But, butyrate; Pro, propionate; Pyr, pyruvate; ) and two organic acids (Lactate; Suc, Succinate). To investigate the machinery associated with reductant disposal through electron transfer reactions pertinent to carbohydrate degradation, crucial enzymes such as acetate production enzymes ( Pta , Ld , Por ), propionate production enzymes ( Mdh , Fum , Frd/Sdh , Aarc , LcdA , LcdB , PC , meaA , Suc ), and butyrate production enzymes ( FadJ , FabV , Ptb , buk , atoA ). These genes are represented at the VFA metabolism profile respectively. These enzymes underwent annotation via hidden-Markov model (HMM) searches against a localized protein database, except for SdhA and FrdA , which were annotated using BLAST against a customized database [ 18 ]. To investigate the expression of carbohydrate enzymes, this study predicted carbohydrate enzyme genes based on protein sequences using Run-dbcan V4 using dbCAN (carbohydrate enzyme gene database) [ 33 ]. And classify and organize cellulases, ligninases, hemicellulases, amylases, and fructosases. After aligning protein sequence annotation with expression abundance, we obtained the relative expression abundance of carbohydrate enzymes. Based on the classification of carbohydrate enzymes by Lin et al. [ 34 ], we constructed a similar metabolic pathway diagram according to the degradation process of cellulose, hemicellulose, and lignin. NCBI-NR (October 2018; ~550M sequence) analysis of marker genes for species classification and functional allocation was performed by the DIAMOND searches (v.2.0.4) based on the BLASTP search [ 35 ]. We created a map based on the classification of carbohydrate-active enzymes conducted by Liang et al. in the rumen microbiota of ruminant animals [ 36 ]. To figure out the response of the rumen microbiota to 3-NOP, the mRNA sequences were aligned to a database of rumen microbiota genomes ( https://figshare.com/projects/RGMC/228963 ). TPM values were used to represent the relative abundances of microorganisms. Sorted BAM files generated by SAMtools v1.9 were utilized for computing sample coverage using CoverM(v0.6.1) [ 37 ] with parameters ‘-m TPM’. The completeness and contamination of all bins were estimated using CheckM2 (v1.0.1), with the following criteria: >50% genome completeness, 50 estimated quality score (completeness-5×contamination). A total of 1717 MAGs were matched, including 59 MAGs for archaea and 1658 MAGs for bacteria. The distribution of the two key genes involved in lactate and succinate was aggregated and the MAGs corresponding to the LDH and sdhA/frdA genes in the assembled genome were curated and statistically analyzed at the class level to visualize the diagonal heatmap. Quantification of select bacteria and methanogens using qPCR To investigate the transcriptional differences of 3-NOP in fiber-degrading bacteria, fungi, protozoa, and methanogenic archaea, qPCR quantification was performed in this experiment on Fibrobacter succinogenes , Ruminococcus albus , Ruminococcus flavefaciens , fungi, protozoa, and methanogen (the primer sequences are displayed in supplementary file table S1 ) This quantification was performed on LightCycler 480 (Roche Molecular Systems, Pleasanton, California) using SYBR and reference primers for qPCR detection. Absolute abundance is expressed as the copy number of 16S rRNA genes per milliliter of sample (log 10 copy number). Statistical analyses The data of fermentation end products were initially organized and compiled using Excel 2021. Subsequently, a one-way analysis of variance was conducted using JPM Pro 13. The outcomes were presented as mean values accompanied by standard errors, with statistical significance denoted by P < 0.05, indicating a notable difference. Differential analyses between predominant genera, species, carbohydrate enzyme genes, and distinct KEGG pathways were conducted utilizing the Wilcoxon rank-sum test method by the Wilcoxon rank-sum test in the JMP Pro software (JMP Pro version 13.2.1, SAS Institute, Cary, NC). Statistical significance was defined as P < 0.05 indicating a significant difference, and 0.05 ≤ P < 0.10 indicating a trend of difference. Results Increasing the hydrogen by 3-NOP results in changes in the fermentation pattern but does not influence the degradability The experimental results show that, during the fermentation period (0-48h), the CH 4 yield in the 3-NOP group was lower than that in the CON group, while H 2 yield was higher in the 3-NOP group (Fig. 1 A, Fig. 1 B). Specifically, within the initial 0–10 hours, the CH 4 concentration in the 3-NOP group was negligible; At 15 hours, the concentration of methane in 3-NOP began to rise, but the concentration of hydrogen continued to increase;The hydrogen concentration began to decrease at 24h. A notable increase is observed after 20 hours in 3-NOP group and ultimately stabilized at a production of 19.1 ml/g, but the CH 4 production of the CON increased from 0.07 ml/g to 41.6 ml/g within 48h. The H 2 production in the 3-NOP group rose sharply and reached a peak of 8.9 ml/g at the 18th hour. In contrast, the H 2 concentration in the CON group remained nearly at the zero-level curve during the initial 10h period. Subsequently, it exhibited a slight increase, reaching 0.2 ml/g at the 48 - hour mark (as depicted in Fig. 1 B). For the VFA, the ratio of acetate and propionate in the 3-NOP group was significantly reduced, while total VFA had no significant effect. For the VFA profile, the results indicated that 3-NOP markedly decreased the proportion of acetate, while notably increasing the proportions of propionate and isobutyrate (Fig. 1 D). For the concentration, 3-NOP had no significant effect on the concentration of acetate, but significantly increased the concentration of propionate (Additional Figures S1 ). To gain insights into the genes involved in the pathway of these organic acids, we conducted an analysis implicated in the organic acid and methane metabolism pathway based on the classification table formulated by Li et al [ 15 ]. Detailed results are shown in supplement table 1. The results revealed that 3-NOP substantially decreased the transcripts of methane metabolism, the Pyr->Ace and Pyr->But pathways, whereas significantly increased the transcripts of the Pyr->Lacate->Pro and Pyr->Suc->Pro pathways (Fig. 1 D). For the two types of propionate pathways, we measured the concentration of lactate and succinate after 48h. The results indicated that 3-NOP significantly elevated the concentrations of lactate and succinate in the fermentation fluid (P < 0.05, Fig. 1 D). Concerning the degradability, no significant difference was observed in the degradability of dry matter (DMD) and neutral detergent fiber (NDFD) between with two groups (Fig. 1 E). The transcriptome analysis further showed that 3-NOP had no significant effect on the abundance of total carbohydrate enzymes, but increased the transcripts of carbohydrate-binding module (CBM), carbohydrate esterases (CE) and glycosyltransferase (GT, P < 0.05, Fig. 1 F). Detailed results are shown in supplement table 2. However, no significant difference was noted in the glycoside hydrolases (GH), polysaccharide lyases (PL), and total transcripts of carbohydrate enzymes (P > 0.05, Fig. 1 F). Transcript changes of rumen microbiota after addition of 3-NOP and validation of pure bacterial culture experiments The diversity analysis was based on the genus level, and the results showed that principal component analysis (PCoA) revealed significant differences in the diversity of active bacteria (Fig. 2 A, P = 0.003) and archaea (Fig. 2 A, P = 0.024) in the addition of 3-NOP. The Shannon index further indicated that 3-NOP significantly decreased the diversity of active bacteria, while significantly increasing the diversity of active archaea (Fig. 2 B). Detailed results are presented in supplement table 3. The major bacteria are consistent species composition in two groups, such as the Clostridia , Gammaproteobacteria , Negativicutes , Bacteroidia , Fibrobacteria , Bacilli , and Actinobacteria constituted the predominant bacterial populations in both experimental groups, and collectively represented approximately 81% and 87% in the two groups. Compared to the CON group, the relative abundance of Negativicutes and Fibrobacteria in the 3-NOP group exhibited a significant increase (Fig. 2 C, P < 0.05). In contrast to the CON group the relative abundance of Actinobacteria , Alphaproteobacteria , Deltaproteobacteria , Coriobacteriia , Betaproteobacteria , and Spirochaetia was significantly reduced in the 3-NOP group (Fig. 2 C, P < 0.05). Within the archaea domain, Methanobacteriales , Methanomicrobiales , Methanomassiliicoccales , and Methanosarcinales prevailed in both groups, collectively representing approximately 98% in the CON group and 3-NOP group. Notably, the relative abundance of Methanobacteriales and other classes in the 3-NOP group was markedly lower (Fig. 2 D, P < 0.05) compared to the CON group. Conversely, the relative abundance of Methanomassiliicoccales , primarily utilizing exogenous H 2 to reduce the C1 compound (methyl donor) for methane, was significantly higher in the 3-NOP group (Fig. 2 D, P < 0.05). Detailed results are shown in supplement table 4. The results showed that in the 3-NOP group, a total of 195 contigs showed a significant increase in fungal transcripts, while only 9 contigs showed a decrease; 370 contigs of protozoa transcripts significantly increased, while only 65 contigs showed a significant decrease. To further demonstrate the differences, the transcriptomes of fungi and protozoa were quantified. The results showed that the copy number of fungi in the 3-NOP group was significantly higher than that in the CON group (Fig. 2 E, P < 0.05, CON vs. 3-NOP = 10 6.11 vs. 10 6.70 ). The copy number of protozoa was slightly increased, but there was no significant difference (Fig. 2 E, P = 0.127). To further understand the effect of 3-NOP on methanogens, four different types of methanogens were selected for pure culture experiments by adding 3-NOP. The CH 4 concentration showed no significant difference in Methanobrevibacter between the two groups on the second day and then The CH 4 concentration of 3-NOP was lower than that of the CON group after 3-NOP adding. The curve of Methanosarcina also showed a significant reduction, but on the ninth day, methane concentrations were trended to be stable. Methanomassiliicoccales are methylotrophic methanogens and have a much longer growth cycle than other methanogens [ 38 ]. Therefore, the 3-NOP was immediately added to the Methanomassiliicoccales medium after inoculation. Methanomassiliicoccales medium was supplemented with 0.2 mol/L of methanol as the only source of methyl groups. The results showed that the addition of 3-NOP to Methanomassiliicoccales medium did not completely stop the growth of Methanomassiliicoccales during the first methane test in seven days. Within 7-16d, the concentration of methane was significantly inhibited by 3-NOP.Considering the above results, 3-NOP reduced the methane production of the four methanogens (Fig. 1 C), but the methane decrease of Methanobrevibacter in initial testing was the largest ( Methanobrevibacter vs Methanosarcina vs Methanomassiliicoccales = -3.07 vs -2.64 vs -2.11) after the addition of 3-NOP.These results showed that compared with the other three methanogens, Methanobrevibacter is the most sensitive to 3-NOP. 3-NOP downregulated the transcript of hydrogenotrophic methanogenic and butyrate pathways but upregulated the propionate production pathway The macrotranscriptome analysis revealed that the addition of 3-NOP led to a significant decrease in the transcript of fwd/fmd , Ftr , Mch , Hmd / Mtd , Mer , Mtr , Mcr , and Hdr - Mvh (Fig. 3 A, P < 0.05). Detailed results are shown in supplement table 5. These genes are eight important genes for hydrogenotrophic methanogens to produce methane by reducing carbon dioxide with hydrogen gas. Moreover, there was a notable reduction in the gene transcription of methanol-utilizing methanogens ( MtaA , MtaB , MtaC , Fig. 3 A, P < 0.05), while no distinct impact was observed on acetate-utilizing ( Ack , Acs , Cdh ), methylamine-utilizing ( MtmB , MtmC ), and trimethylamine-utilizing ( MttB , MttC ) methanogens. Interestingly, the presence of 3-NOP resulted in an increasing transcription of dimethylamine-utilizing methanogens ( MtbA , MtbB , MtbC , Fig. 3 B, P < 0.05). Furthermore, the addition of 3-NOP stimulated the transcript of Fdh (Fig. 3 A, P < 0.05), while significantly reducing the transcript of sdh / frd (a key gene involved in the conversion of F 420 H 2 energy from NADPH, Fig. 3 A, P < 0.05). 3-NOP significantly downregulated the expression of the McrA and McrB genes (Fig. 3 A, P < 0.05), while having no notable impact on the expression of McrC , McrD , and McrG genes. This downregulation is the direct cause of the reduction in methane production. Regarding VFA metabolism, transcription results indicated that 3-NOP notably upregulated the expression of LDH , lcdA , and lcdB in the lactate pathway (Fig. 3 B, P < 0.05), as well as increased the transcript levels of mdh , fum , sdh / frd , Aarc , CS , and Eda in the succinate pathway (Fig. 3 B, P < 0.05). Detailed results are shown in supplement table 5. Conversely, in the acetate and butyrate pathways, 3-NOP significantly decreased the transcription level of Por (a key gene for pyruvate-producing acetyl-CoA, Fig. 3 B, P < 0.05). The downregulation of the Por gene directly impacted the overall reduction in gene transcript for both acetate and butyrate, consequently reducing their production, aligning with the VFA experimental results. Notably, there was a significant increase in the transcript of the pta gene (Fig. 3 B, P < 0.05)-a gene associated with high-energy phosphate bond formation. Moreover, 3-NOP significantly decreased the transcript of key enzymes involved in VFA metabolism, including FadJ , Ptb , buk , and atoA in the butyrate pathway (Fig. 3 B, P < 0.05). Interestingly, 3-NOP led to a significant increase in the transcript levels of fabV (Fig. 3 B, P < 0.05), which plays a pivotal role in converting But-c2-enoyl-CoA to Butanoyl-CoA by utilizing hydrogen from NADH during butyrate synthesis[ 39 ]. This gene is essential for mitigating the redundancy of reductants in the butyrate metabolism pathway, suggesting its involvement in the modulation of reductants during butyrate production. 3-NOP promoted transcripts in encoding fiber degrading enzymes fungal ( Neocallimastigaceae ) and alter the dominant fibro-degradable bacteria Hydrolytic digestion of carbohydrates in the rumen was conducted by the microflora inhabiting it and their end-products of ruminal fermentation include the VFA, CH 4 , CO 2 , NH 3 , and small molecule ingredients [ 40 ]. We created a classification map based on Lin et al., classification of carbohydrate-active enzymes in the rumen microbiota of ruminant animals [ 36 ]. Specifically, for cellulose degradation, the relevant carbohydrate-active enzyme family includes endoglucanases, exoglucanases, and β-glucosidases. Detailed results are shown in supplement table 6. We found that 3-NOP notably increased the transcript of GH9, GH8, GH74, GH44, GH45, and GH5 for endoglucanases, but it significantly reduced the expression of GH148 (Fig. 4 A, P < 0.05). Among the microorganisms expressing endoglucanases, the Neocallimastigaceae (1.94-fold), Clostridiaceae (1.21-fold), Fibrobacteraceae (1.80-fold), Prevotellaceae (1.52-fold), Bacteroidales_norank (1.30-fold), Bacteroidaceae (1.41-fold) in the 3-NOP group was higher than that in the CON group, Ruminococcus of 3-NOP group showed a 80.88% of the CON group. Regarding exoglucanases, the expression levels of GH9 and GH5 were significantly elevated by 3-NOP (Fig. 4 A, P < 0.05). In terms of exoglucanases, the Neocallimastigaceae (1.97-fold), Clostridiaceae (1.22-fold), Fibrobacteraceae (2.04-fold), Prevotellaceae (1.55-fold). Bacteroidales_norank (1.37-fold), Bacteroidaceae (1.37-fold) in the 3-NOP group was higher than that in the CON group, and Ruminococcus of the 3-NOP group showed 78.11% of the CON group. For β-glucosidases, the addition of 3-NOP significantly increased GH116 and GH5 expression but significantly reduced the expression of GH1 (Fig. 4 A, P < 0.05). In microorganisms expressing β-glucosidases, the Neocallimastigaceae (1.67-fold), Fibrobacteraceae (1.97-fold), and Spirochaetaceae (1.45-fold) in the 3-NOP group was higher than that in the CON group, Prevotellaceae and Ruminococcus of the 3-NOP group showed 79.69% and 82.58% of the CON group. For the degradation of hemicellulose, its carbohydrate-active enzyme family is composed of debranching enzymes, endoxyianases, and exoxylanases. The results showed that 3-NOP significantly increased the expression of CE4, GH57, GH5, CE6, CE7, CE3, CE15, CE67, CE4 in debranching enzymes, but significantly reduced expression of CE1, GH97, GH36, GH1, GH110 (Fig. 4 A, P < 0.05). In microorganisms expressing debranching enzymes, the Neocallimastigaceae (1.54-fold), Fibrobacteraceae (2.05-fold), Bacteroidaceae (1.22-fold) in the 3-NOP group was higher than that in the CON group, Prevotellaceae and Ruminococcus of the 3-NOP group showed 85.19% and 84.23% of the CON group. As for endoxyianases, 3-NOP significantly increased the expression of GH5, GH8, GH141, and GH11 in endoxyianases, but no significant difference in GH43, GH51, GH10, GH30, GH98, and GH48 (Fig. 4 A, P < 0.05). For microorganisms expressing endoxyianases, the Neocallimastigaceae (1.76-fold), Clostridiaceae (1.21-fold), Fibrobacteraceae (1.72-fold), Prevotellaceae (1.42-fold), Bacteroidaceae (1.31-fold) in the 3-NOP group was higher than that in the CON group, Ruminococcus of 3-NOP group showed a 79.25% of the CON group. For exoxylanases, 3-NOP significantly increased the expression level of GH5, but significantly reduced expression of GH1, GH120, GH54, and GH116 (Fig. 4 A, P < 0.05). For exoxylanases, the Neocallimastigaceae (1.65-fold), Fibrobacteraceae (1.92-fold), and Bacteroidaceae (1.23-fold) in the 3-NOP group was higher than that in the CON group, Prevotellaceae and Ruminococcus of the 3-NOP group showed 89.56% and 82.46% of the CON group. The results of this study revealed that the incorporation of 3-NOP had a significant impact on the gene transcript levels of various carbohydrate-binding modules (CBMs). Specifically, it notably elevated the expression of major CBM groups such as CBM4, CBM30, CBM32, CBM35, CBM51, and CBM91. Detailed results are shown in supplement table 2. However, it also significantly decreased the transcript levels of CBM3 and CBM11 (Fig. 4 B, P < 0.05). After species annotation of all CBMs (NCBI-NR (October 2018; ~550M sequence)), it is interesting that the three CBMs with the most significant increase in transcripts (CBM4, 30, 35) are all from Fibrobacter succinogene s, which indicates that 3-NOP does not have a negative effect on Fibrobacter succinogenes . In addition, through the PCR quantification of transcripts, the results showed that 3-NOP significantly increased the amount of Fibrobacter succinogenes in the transcripts but markedly reduced the numbers of two rumen bacteria ( Ruminococcus flavefaciens and Ruminococcus albus , Fig. 4 C, P < 0.05). These findings indicate that 3-NOP significantly depresses the activity of both types of rumen bacteria but has no effect on or can create an environment favorable for Fibrobacter succinogenes (Fig. 4 C, P < 0.05). Similarly, as for glycosyl transferases (GTs), the addition of 3-NOP significantly increased the expression of GT2, GT4, GT5, GT9, GT26, GT30, GT35, GT51, GT66, GT111, and GT113. Conversely, it significantly reduced the expression of GT3, GT11, GT81, GT105, and GT112 genes (Fig. 4 D, P < 0.05). Mapping genome confirms non-hydrogenotrophic ( Methanomassiliicoccales ) and propionate pathways are enriched in the 3-NOP group To further investigate the impact of 3-NOP on rumen microbiota, we aligned the transcriptome sequences with the database of rumen microbiota genomes ( https://figshare.com/projects/RGMC/228963 ). From these metagenome-assembled genomes (MAGs), we constructed a phylogenetic tree and correlated key genes with the respective MAGs. Detailed results are shown in supplement table 7. Our findings revealed that 45 archaeal MAGs were successfully matched and Methanobrevibacter was the main species (Fig. 5 A). Analysis of the five subunits of methyl-CoM reductase indicated that not all methanogens expressed a complete set of five subunits, potentially attributed to the incomplete nature of some MAGs in the database. Furthermore, both Methanobrevibacter and Methanobrevibacter-A (Each one has a single MAG) were found to possess two sets of methyl-CoM reductase subunits (MCR-ABDG), but McrC has only a single copy. Furthermore, a rank sum test was conducted on the abundance of each MAG in this study, revealing that the relative abundance of certain Methanobrevibacter species was higher in the 3-NOP group compared to the CON group (Fig. 5 A, P < 0.05). The previous studies indicated that methanogens in the rumen could be categorized into different states: free-living, associated with fungi, and symbiotic with protozoa [ 40 ], but free-living methanogens were the most abundant [ 42 ]. Notably, protozoa and anaerobic fungi in the rumen contain hydrogenosomes, and these Methanobrevibacter species, associated with fungi and protozoa may not be susceptible to the effects of 3-NOP. It is worth noting that JAKSHX01 belongs to the methylnutritive methane bacteria ( Methanomassiliicoccales ), which was significantly increased in the 3-NOP group, which is consistent with previous results. The study showed that the addition of 3-NOP had a more profound effect on non-hydrogenated methanogens than the results detected [ 11 ]. For bacteria, a total of 1658 bacterial MAGs were matched (Fig. 5 A) and were mainly distributed in Clostridia and Bacteria . Interestingly, LDH genes were mainly enriched in Clostridia , while sdhA / frdA was mainly enriched in Bacteria . In this experiment, two key genes were annotated in more detail, and the abundance difference between the two groups was analyzed by rank sum test. The results showed that 3-NOP significantly increased the expression of the LDH gene in c_Spirochaetia , c_Bacilli_A , c_Clostridia (Fig. 5 B, P < 0.05), and significantly decreased the expression of LDH in c_Anaerolineae , c_Endomicrobiia (Fig. 5 B, P < 0.05). Detailed results are shown in supplement table 8. Similarly, 3-NOP significantly increased the transcriptome of sdhA / frdA in Fibrobacteria and Bacteroides and significantly decreased the transcriptome of sdhA / frdA expression in c_Dehalobacteria and c_Desulfuromonadia . These results suggest that the propionate pathway plays an important role in the health fermentation and diversity rebalancing of rumen microbiota after 3-NOP addition. Discussion The 3-NOP is internationally recognized as a highly efficient and specialized methane inhibitor in the current field of methane inhibitors [ 43 ]. After the reduction of methane, the generation rate of hydrogen in rumen fluid increased significantly from 0.01 to 0.16g/h [ 1 ]. In this study, the hydrogen concentration increased by 60 fold within 48 h. The methane pathway, as the largest hydrogen sink, was inhibited, and the increase in hydrogen pressure put the rumen microbiota in a state of hydrogen stress [ 44 , 45 ]. The process of bacterial fiber degradation is also accompanied by the production of hydrogen, some bacterial fiber degradation may even stop [ 46 ]. In this state, how do the rumen microbiota adapt to this shift and re-establish a new balance? This study integrates metatranscriptomic analysis with pure bacterial inhibition assays to quantitatively identify and characterize microorganisms exhibiting specific functional roles, thereby validating the enhanced capacity of two propionate (lactate and succinate) pathways to act as hydrogen sinks under elevated hydrogen partial pressure conditions. It also revealed some methanogens that form symbiotic partnerships with anaerobic fungi via cellulose degradation in the rumen and rely on non-hydrogenotrophic methanogenesis pathways may remain independent of 3-NOP affect. The first important finding of this study is that, compared with the control group, 3-NOP reduced the activity of most hydrogenotrophic methanogen, while non-hydrogenotrophic methanogen and methanogen symbiotic with fungi or protozoa may be less affected by 3-NOP. Firstly, 3-NOP was found to markedly decrease the transcriptional activity of hydrogenotrophic methanogens in the rumen, while exerting either negligible or minor effects on non-hydrogenotrophic methanogens (Fig. 2 D, Fig. 3 A, Fig. 5 A). Another piece of evidence, the transcripts of the genes encoding hydrotrophic methanogenesis were significantly decreased within 24 hours in 3-NOP group ( fwd / fmd , ftr , Mch , Hmd / Mtd , Mer , Mtr , Mcr , and Hdr - Mvh ), and the key genes ( MtaA , MtaB , MtaC ) were also significantly downregulated, but the key acetate, methanine, dimethylamine and trimethylamine methanogenesis were not negatively affected (Fig. 3 A). A methanogen that converts methylamine to methane ( Methanomassiliicoccales ) was increased this abundance in the transcriptome alignment results (Fig. 6A). Previous research reported that CO 2 -reducing Methanobrevibacter species have higher H 2 thresholds (> 5.0 MPa) compared with methanol-utilizing Methanosphaera (< 1.0 Pa) and methylamine-utilizing Methanomassiliicoccales (< 0.1 Pa), suggesting that methylamine- and methanol-utilizing methanogens have an advantage over CO 2 -reducing methanogens [ 11 , 47 , 48 ]. This is consistent with the experimental transcripts and pure culture results (Fig. 2 D, Fig. 3 A). Another worth considering result is that the transcriptional and quantitative results of fungi in the 3-NOP group showed a consistent increase (Fig. 2 E), and the abundance of Neocallimastigaceae was significantly increased in the microorganisms expressing cellulose-degrading enzymes. In addition, the results of the mapping transcript sequencing with the complete assembled genome database showed that some transcripts of hydrogenotrophic methanogen (Fig. 5 A, Methanobrevibacter , Methanomassiliicoccales ) in the 3-NOP group were even larger than those in the control group. In the rumen, a variety of fungi [ 49 ] and protozoa [ 50 ] contain hydrogenosomes. Hydrogenosome plays an important role in inter-specific hydrogen transfer and metabolic balance. The mutual association of methanogens with both fungi and parasites provides a natural barrier for methanogens to avoid the effects of 3-NOP [ 49 ], which may explain the elevation of some methanogen transcripts in this study (Fig. 5 A, Methanobrevibacter , Methanomassiliicoccales ). Some non-hydrogenotrophic and no-free methanogens may make a greater contribution to total methanogenesis than was originally thought [ 11 , 49 ]. These two adaptive mechanisms of the methanogen, non-hydrogenotrophic and hydrogenosome-relied methanogens, were able to weaken the inhibitory effect of 3-NOP and even develop resistance because of long-term use, which needs to warrant vigilance. The second important finding of this study is that the 3-NOP enhances the hydrogen sink via the propionate pathway after CH 4 inhibition in the rumen. The results of transcriptome encoding in reductant disposal indicated that the VFA profiles exhibited a tight correlation with active rumen microbial species, distribution, and quantification. Studies have shown that when methane decreases or the methane production pathway is inhibited, the high concentration of hydrogen may even hinder the development of biochemical reactions and alter microbial metabolism and VFA metabolite profiles[ 46 ]. This led to a decrease in the redox potential within the fermentation system. Thermodynamically, this is unfavorable for microorganisms that derive energy and carbon sources from fermenting feed substrates. However, it is beneficial for microorganisms that can utilize hydrogen to metabolize fumarate and lactate, or these microorganisms may be less affected by hydrogen [ 51 ]. For example, a significant increase in transcripts encoding lactate ( LDH ) and succinate ( mdh , sdhA / frdA , et al.) pathways was also observed in this experiment (Fig. 3 B), which was confirmed in the mapping of the assembly genome (Fig. 5 B) and consistent with majority previous results with 3-NOP addition [ 11 , 14 ]. The results of active microbial composition based on the Krakan2 database showed that 3-NOP significantly increased the transcript abundance of Negativicutes and Fibrobacteria . Studies showed that Negativicutes and Fibrobacteria were closely related to propionate metabolism (lactate and succinate pathways) and contained multiple genera ( Acidaminococcus , Succiniclasticum , Megasphaera , Anaerovibrio , Schwartzia , Selenomonas , Mitsuokella ) [ 48 , 51 ]. As for other non-methane hydrogen sinks, the Wood-Ljungdahl processes, as well as other hydrogen sinks (sulfates and nitrate, metal ions, etc.) are also significantly affected by the elevated rumen hydrogen, but these pathways provide a small contribution to the redirection of H 2 in ruminants [ 18 , 19 ]. Detailed results are shown in supplement table 9. The propionate synthesis is the preferred hydrogen sink pathway after methanogens inhibition by 3-NOP, which ensures that microorganisms maintain normal metabolism. The third important finding is that 3-NOP promoted the transcripts of fibro-degrading enzymes of fungi ( Neocallimastigaceae ) and altered the composition of fibro-degradable bacteria. There was no significant difference in the overall transcripts of fiber degrading enzymes by 3-NOP and no difference in dry matter and fiber degradation. However, we found that 3-NOP significantly increased the transcripts of fungal ( Neocallimastigaceae ), as well as some fiber-degrading enzyme family of bacterial ( Lachnospiraceae_norank , Prevotella , Bacteroidales_norank , Fibrobacter ), but significantly decreased the Ruminococcus . The quantitative results are consistent with this result (Fig. 2 E, Fig. 4 C). The unaltered fiber degradation is consistent with the previous in vivo [ 4 , 6 , 13 ] and in vitro [ 14 , 48 ] studies. However, some studies have shown that the inhibitory effect of 3-NOP is significantly negatively correlated with the proportion of NDF in the diet [ 43 , 52 ]. This suggests that methane production is directly related to fiber degradation in the rumen [ 49 , 50 ]. Studies have shown that different microbial communities adopt different enzymatic strategies in the degradation of specific lignocellulosic components (including cellulose, hemicellulose, and lignin) [ 53 ]. Studies have shown that Fibrobacter and Ruminococcus are predicted to be powerful degraders, and capable of using endocytic and exoglucosanases on amorphous and crystalline cellulose, respectively [ 54 , 55 ]. Compared to Ruminococcus , the outer membrane vesicles in Fibrobacter contain a series of carbohydrate-active enzymes that can degrade multiple polysaccharides and efficiently decompose cellulose into simple sugars [ 56 ]. However, the cellulases and hemicellulases secreted by Ruminococcus directly work on the degradation of complex carbohydrates that exist in the plant cell wall [ 57 ]. Moreover, Fibrobacter succinogene has a succinate-producing function by utilizing hydrogen and is more adaptable to the environment with low redox potential with high hydrogen pressure. This carbohydrate-active enzymes system of Ruminococcus was more negative affected by 3-NOP or high hydrogen pressure. Neocallimastigaceae is a fungi with existing differences in this experiment and is a very critical and highly proportional fiber-degrading anaerobic fungi in the rumen [ 57 ]. To maintain the redox balance in cells, the hydrogenosome of anaerobic fungi couples H + reduction with NAD(P)H oxidation to produce H 2 , and provide sufficient growth substrate and methanogenic substrates for parasitic-methanogens through interspecific H 2 transfer [ 49 , 58 ]. These conclusions may explain the significant increase in the Neocallimastigaceae transcripts encoding the cellulase after the addition of 3-NOP. In this experiment, the changes in transcripts of some cellulase illustrated the adjustments and adaptations made by rumen organisms in the mode of fiber degradation. The 3-NOP significantly increased the transcripts of GH5 and GH9 family enzymes, whereas significantly decreased the transcripts of the GH1 family. The study shows that GH5 and GH9 family enzymes, with broad substrate specificity able to hydrolyze a variety of different types of carbohydrates, including cellulose, hemicellulose as well as other complex polysaccharide structures, participate in multiple cascades of cellulose, hemicellulose degradation [ 59 ]. However, the GH1 family (β-glucosidase and β-galactosidase), which are key enzymes for microbial degradation of disaccharides into monosaccharides, are also involved in branched-chain removal in hemicellulose, which is the richest fiber-degrading enzyme in this study. In this study, a large number of transcription of CBM and GT enzyme families was found, with the large carbohydrate modules CBM30 and CBM35 and glycosyltransferases (GT2, GT4, GT5, GT35, GT51), which significantly increased after methane inhibition by 3-NOP, while CBM3 and CT3, CT81 transcripts were significantly decreased. These increased carbohydrate enzymes are mainly from anaerobic fungi and partly from fiber-degrading bacteria [ 36 ]. This result explains the the third important finding. Rumininal anaerobic fungi producing cellulosomes contain many cellulases including CBM and GT, which are six times more efficient than free enzymes, and can degrade lignocellulose and resistant starch [ 60 ]. However, Ruminococcus does not form cellulosomes but rather performs the degradation activity through a mechanism involving CBM adhesion to the substrate [ 61 ]. The sensitivity of Ruminococcus exhibits to 3-NOP may be responsible for its reduced transcript abundance, and the same result was observed in some in vivo studies [ 4 , 11 ]. These results suggest that the degradation mode of fibers in the rumen may have some alteration and some of the fiber degradation is transferred to fungal after the application of 3-NOP. Conclusion In this experiment, we monitored the dynamics of hydrogen (H 2 ) production and methane (CH 4 ) reduction following 3-NOP-mediated methane inhibition using a fully automated in vitro fermentation system. We collected RNA samples in real time for macrotranscriptomic sequencing. The rise in hydrogen partial pressure induces the propionate pathway to compensate for the disrupted methanogenic hydrogen (H 2 ) sink. Additionally, some methanogens was independent of 3-NOP affect through symbiotic associations with fungi or protozoa, utilizing H 2 for methanogenesis. Transcriptional upregulation of lactate and succinate pathways further supports the hypothesis of H 2 sink compensation. The increase in fungal fiber-degrading enzyme transcripts and reduction in bacteria explain the paradoxical rise in some methanogen transcripts, likely reflecting indirect metabolic interactions. Methanogens that form symbiotic partnerships with anaerobic fungi via cellulose degradation in the rumen and rely on non-hydrogenotrophic methanogenesis pathways may remain unaffected by 3-NOP. This study provides new insights into the sink of hydrogen gas after methane inhibition in ruminants and the rebalancing of microorganisms. These results provide a theoretical foundation for optimizing strategies to mitigate methane emissions in ruminants. Declarations DATA AVAILABILITY All data are available in the main text or the supplementary materials. Raw reads of macrotranscriptome sequencing of ruminal microbiota are available at the National Center for Biotechnology Information (NCBI, project number PRJNA1257059). Authors ’ contributions Conceptualization and research design: MW, YC, XZ; Research conduction and data acquisition: QL, SZ, XZ, WW; Data analysis: QL, SZ, XZ, XZ, WW, MW; Investigation: XZ, ZT, MW; Writing—original draft: YC, SZ, MW; Writing—reviewing & editing: all authors Funding This work was supported by the National Key R&D Program of China (Grant No. 2023YFD1300900, 2023YFD1300903), the Science and Technology Innovation Program of Hunan Province (Grant No. 2023RC3206, 2022RC3058), and Natural Science Foundation of Hunan Province (2025JJ70628). Ethics approval and consent to participate Animal experiments followed the Animal Care and Use Guidelines of the Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China. Acknowledgements I would like to express my sincere gratitude to xiuzhu Dong and Jie Li , two researchers of the Institute of Microbiology, Chinese Academy of Sciences, who provided technical support and experimental guidance for the pure bacteria experiment in this manuscript. Consent for publication Not applicable References References Gruninger RJ, Zhang XM, Smith ML, Kung L Jr., Vyas D, McGinn SM, et al. Application of 3-nitrooxypropanol and canola oil to mitigate enteric methane emissions of beef cattle results in distinctly different effects on the rumen microbial community. Anim Microbiome. 2022;4(1):35. Duin EC, Wagner T, Shima S, Prakash D, Cronin B, Yá?ez-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. 2016:201600298. Kirwan SF, Tamassia LFM, Walker ND, Karagiannis A, Kindermann M, Waters SM. Effects of dietary supplementation with 3-nitrooxypropanol on enteric methane production, rumen fermentation, and performance in young growing beef cattle offered a 50:50 forage:concentrate diet. J Anim Sci. 2024;102. Alemu AW, Gruninger RJ, Zhang XM, O'Hara E, Kindermann M, Beauchemin KA. 3-Nitrooxypropanol supplementation of a forage diet decreased enteric methane emissions from beef cattle without affecting feed intake and apparent total-tract digestibility. J Anim Sci. 2023;101. Alemu AW, Shreck AL, Booker CW, McGinn SM, Pekrul LKD, Kindermann M et al. Use of 3-nitrooxypropanol in a commercial feedlot to decrease enteric methane emissions from cattle fed a corn-based finishing diet. J Anim Sci. 2021;99(1). Zhang XM, Smith ML, Gruninger RJ, Kung L, Vyas D, McGinn SM et al. Combined effects of 3-nitrooxypropanol and canola oil supplementation on methane emissions, rumen fermentation and biohydrogenation, and total tract digestibility in beef cattle. J Anim Sci. 2021;99(4). Pitta DW, Melgar A, Hristov AN, Indugu N, Narayan KS, Pappalardo C, Hennessy ML, Vecchiarelli B, Kaplan-Shabtai V, Kindermann M, Walker N. Temporal changes in total and metabolically active ruminal methanogens in dairy cows supplemented with 3-nitrooxypropanol. J Dairy Sci. 2021;104(8):8721–35. Maigaard M, Weisbjerg MR, Ohlsson C, Walker N, Lund P. Effects of different doses of 3-nitrooxypropanol combined with varying forage composition on feed intake, methane emission, and milk production in dairy cows. J Dairy Sci. 2024. Costigan H, Shalloo L, Egan M, Kennedy M, Dwan C, Walsh S, et al. The effect of twice daily 3-nitroxypropanol supplementation on enteric methane emissions in grazing dairy cows. J Dairy Sci. 2024;107(11):9197–208. Zhang XM, Gruninger RJ, Alemu AW, Wang M, Tan ZL, Kindermann M, et al. 3-Nitrooxypropanol supplementation had little effect on fiber degradation and microbial colonization of forage particles when evaluated using the in situ ruminal incubation technique. J Dairy Sci. 2020;103(10):8986–97. Pitta DW, Indugu N, Melgar A, Hristov AE, Challa K, Vecchiarelli B et al. The effect of 3-nitrooxypropanol, a potent methane inhibitor, on ruminal microbial gene expression profiles in dairy cows. Microbiome. 2022;10. Martínez-Fernández G, Abecia L, Arco A, Cantalapiedra-Hijar G, Martín-García AI, Molina-Alcaide E, et al. Effects of ethyl-3-nitrooxy propionate and 3-nitrooxypropanol on ruminal fermentation, microbial abundance, and methane emissions in sheep. J Dairy Sci. 2014;97(6):3790–9. Martinez-Fernandez G, Duval S, Kindermann M, Schirra HJ, Denman SE, McSweeney CS. 3-NOP vs. Halogenated Compound: Methane Production, Ruminal Fermentation and Microbial Community Response in Forage Fed Cattle. Front Microbiol. 2018;9:1582. Liu Z, Wang K, Nan X, Cai M, Yang L, Xiong B, et al. Synergistic Effects of 3-Nitrooxypropanol with Fumarate in the Regulation of Propionate Formation and Methanogenesis in Dairy Cows In Vitro. Appl Environ Microbiol. 2022;88(6):e0190821. Li Q, Ma Z, Huo J, Zhang X, Wang R, Zhang S et al. Distinct microbial hydrogen and reductant disposal pathways explain interbreed variations in ruminant methane yield. ISME J. 2024;18(1). Swift CL, Brown JL, Seppälä S, O’Malley MA. Co-cultivation of the anaerobic fungus Anaeromyces robustus with Methanobacterium bryantii enhances transcription of carbohydrate active enzymes. J Ind Microbiol Biotechnol. 2019;46(9–10):1427–33. Li QS, Wang R, Ma ZY, Zhang XM, Jiao JZ, Zhang ZG, et al. Dietary selection of metabolically distinct microorganisms drives hydrogen metabolism in ruminants. ISME J. 2022;16(11):2535–46. Greening C, Geier R, Wang C, Woods LC, Morales SE, McDonald MJ, et al. Diverse hydrogen production and consumption pathways influence methane production in ruminants. ISME J. 2019;13(10):2617–32. Denman SE, Martinez Fernandez G, Shinkai T, Mitsumori M, McSweeney CS. Metagenomic analysis of the rumen microbial community following inhibition of methane formation by a halogenated methane analog. Front Microbiol. 2015;6:1087. Menke KH, Raab L, Salewski A, Steingass H, Fritz D, Schneider W. The estimation of the digestibility and metabolizable energy content of ruminant feedingstuffs from the gas production when they are incubated with rumen liquor in vitro. J Agric Sci. 979;93(1):217–22. Miller TL, Wolin MJ, Conway de Macario E, Macario AJ. Isolation of Methanobrevibacter smithii from human feces. Appl Environ Microbiol. 1982;43(1):227–32. Xie F, Zhao S, Zhan X, Zhou Y, Li Y, Zhu W, Pope PB, Attwood GT, Jin W, Mao S. Unraveling the phylogenomic diversity of Methanomassiliicoccales and implications for mitigating ruminant methane emissions. Genome Biol. 2024;25(1):32. Gil JC, Hird SM. Multiomics Characterization of the Canada Goose Fecal Microbiome Reveals Selective Efficacy of Simulated Metagenomes. Microbiol Spectr. 2022;10(6):e0238422. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357–9. Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta. 2023;2(2):e107. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674–6. Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11(1):119. Zhou Y, Yang Z, Liu J, Li X, Wang X, Dai C, et al. Crop rotation and native microbiome inoculation restore soil capacity to suppress a root disease. Nat Commun. 2023;14(1):8126. Wang D, Wang Y, Liu L, Chen Y, Wang C, Li Y-Y, et al. Response and resilience of anammox consortia to nutrient starvation. Microbiome. 2022;10(1):23. Ye SH, Siddle KJ, Park DJ, Sabeti PC. Benchmarking Metagenomics Tools for Taxonomic Classification. Cell. 2019;178(4):779–94. Yan M, Andersen TO, Pope PB, Yu Z. Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases. Genome Res. 2025;35(2):368–78. Aroney STN, Newell RJP, Nissen JN, Camargo AP, Tyson GW, Woodcroft BJ. CoverM: read alignment statistics for metagenomics. Bioinformatics. 2025;41(4). Huang L, Zhang H, Wu P, Entwistle S, Li X, Yohe T, et al. dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation. Nucleic Acids Res. 2018;46(D1):D516–21. Lin L, Ma H, Zhang J, Yang H, Zhang J, Lai Z et al. Lignocellulolytic microbiomes orchestrating degradation cascades in the rumen of dairy cattle and their diet-influenced key degradation phases. Anim Adv. 2024;1(1). Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59–60. Liang J, Zhang R, Chang J, Chen L, Nabi M, Zhang H, et al. Rumen microbes, enzymes, metabolisms, and application in lignocellulosic waste conversion - A comprehensive review. Biotechnol Adv. 2024;71:108308. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. Cozannet M, Borrel G, Roussel E, Moalic Y, Allioux M, Sanvoisin A et al. New Insights into the Ecology and Physiology of Methanomassiliicoccales from Terrestrial and Aquatic Environments. Microorganisms. 2020;9(1). Massengo-Tiassé RP, Cronan JE. Vibrio cholerae FabV Defines a New Class of Enoyl-Acyl Carrier Protein Reductase. J Biol Chem. 2008;283(3):1308–16. Janssen PH. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim Feed Sci Tech. 2010;160(1):1–22. Patra A, Park T, Kim M, Yu Z. Rumen methanogens and mitigation of methane emission by anti-methanogenic compounds and substances. J Anim Sci Biotechno. 2017;8(1):13. Morgavi DP, Forano E, Martin C, Newbold CJ. Microbial ecosystem and methanogenesis in ruminants. Animal. 2010;4(7):1024–36. Gastelen S, Burgers EEA, Dijkstra J, de Mol R, Muizelaar W, Walker N, et al. Long-term effects of 3-nitrooxypropanol on methane emission and milk production characteristics in Holstein-Friesian dairy cows. J Dairy Sci. 2024;107(8):5556–73. Benoit SL, Maier RJ, Sawers RG, Greening C. Molecular Hydrogen Metabolism: a Widespread Trait of Pathogenic Bacteria and Protists. Microbiol Mol Biol Rev. 2020;84(1). Ungerfeld EM. Metabolic Hydrogen Flows in Rumen Fermentation: Principles and Possibilities of Interventions. Front Microbiol. 2020;11:589. Leahy SC, Janssen PH, Attwood GT, Mackie RI, McAllister TA, Kelly WJ. Electron flow: key to mitigating ruminant methanogenesis. Trends Microbiol. 2022;30(3):209–12. Feldewert C, Lang K, Brune A. The hydrogen threshold of obligately methyl-reducing methanogens. FEMS Microbiol Lett. 2020;367(17). Tan J, Wang Y, Niu H, Li L, Zhao H, Fang L, et al. Metagenomic insights into the mechanistic differences of plant polyphenols and nitrocompounds in reducing methane emissions using the rumen simulation technique. Sci Total Environ. 2024;953:176135. Ma J, Zhong P, Li Y, Sun Z, Sun X, Aung M, et al. Hydrogenosome, Pairing Anaerobic Fungi and H2-Utilizing Microorganisms Based on Metabolic Ties to Facilitate Biomass Utilization. J Fungi. 2022;8(4):338. Akhmanova A, Voncken F, van Alen T, van Hoek A, Boxma B, Vogels G, et al. A hydrogenosome with a genome. Nature. 1998;396(6711):527–8. Wang K, Xiong B, Zhao X. Could propionate formation be used to reduce enteric methane emission in ruminants? Sci Total Environ. 2023;855. Yu G, Beauchemin KA, Dong R. A Review of 3-Nitrooxypropanol for Enteric Methane Mitigation from Ruminant Livestock. Animals. 2021;11(12). Wang K, Gao P, Geng L, Liu C, Zhang J, Shu C. Lignocellulose degradation in Protaetia brevitarsis larvae digestive tract: refining on a tightly designed microbial fermentation production line. Microbiome. 2022. Froidurot A, Julliand V. Cellulolytic bacteria in the large intestine of mammals. Gut Microbes. 2022;14(1):2031694. Hagen LH, Brooke CG, Shaw CA, Norbeck AD, Piao H, Arntzen MØ, et al. Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber. ISME J. 2021;15(2):421–34. Yeoman CJ, Fields CJ, Lepercq P, Ruiz P, Forano E, White BA et al. In Vivo Competitions between Fibrobacter succinogenes, Ruminococcus flavefaciens, and Ruminoccus albus in a Gnotobiotic Sheep Model Revealed by Multi-Omic Analyses. mBio. 2021;12(2). Gruninger RJ, Puniya AK, Callaghan TM, Edwards JE, Youssef N, Dagar SS, et al. Anaerobic fungi (phylum Neocallimastigomycota): advances in understanding their taxonomy, life cycle, ecology, role and biotechnological potential. FEMS Microbiol Ecol. 2014;90(1):1–17. López-García A, Saborío-Montero A, Gutiérrez-Rivas M, Atxaerandio R, Goiri I, García-Rodríguez A, et al. Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle. GigaScience. 2022;11:giab088. Lankiewicz TS, Lillington SP, O'Malley MA. Enzyme Discovery in Anaerobic Fungi (Neocallimastigomycetes) Enables Lignocellulosic Biorefinery Innovation. Microbiol Mol Biol Rev. 2022;86(4):e0004122. Tsai SL, Oh J, Singh S, Chen R, Chen W. Functional assembly of minicellulosomes on the Saccharomyces cerevisiae cell surface for cellulose hydrolysis and ethanol production. Appl Environ Microbiol. 2009;75(19):6087–93. Dassa B, Borovok I, Ruimy-Israeli V, Lamed R, Flint HJ, Duncan SH, et al. Rumen cellulosomics: divergent fiber-degrading strategies revealed by comparative genome-wide analysis of six ruminococcal strains. PLoS ONE. 2014;9(7):e99221. Additional Declarations No competing interests reported. Supplementary Files supplementtable1.xlsx supplementtable2.xlsx supplementtable3.xlsx supplementtable4.xlsx supplementtable5.xlsx supplementtable6.xlsx supplementtable7.xlsx supplementtable8.xlsx supplementtable9.xlsx Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7121971","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498414557,"identity":"809bfab2-14bf-453b-b222-241fc00c8b9a","order_by":0,"name":"Yurong Cao","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yurong","middleName":"","lastName":"Cao","suffix":""},{"id":498414558,"identity":"c0bf8f78-1ca0-4a41-8a8f-2db24b0e01f3","order_by":1,"name":"Shizhe Zhang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shizhe","middleName":"","lastName":"Zhang","suffix":""},{"id":498414559,"identity":"0d945ccd-c252-4abe-a8fc-ffd63daac128","order_by":2,"name":"Qiushuang Li","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qiushuang","middleName":"","lastName":"Li","suffix":""},{"id":498414560,"identity":"67202caf-653c-433d-bd52-40ab3c359fb1","order_by":3,"name":"Xiang Zhou","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Zhou","suffix":""},{"id":498414561,"identity":"17e66a14-7568-4368-87b5-933e5a8d31d1","order_by":4,"name":"Wenxing Wang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenxing","middleName":"","lastName":"Wang","suffix":""},{"id":498414564,"identity":"da216d61-22c6-4af1-8093-54d1c74af559","order_by":5,"name":"Bingzi Ouyang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bingzi","middleName":"","lastName":"Ouyang","suffix":""},{"id":498414566,"identity":"e6750c79-ea74-45c0-98c5-4a2f354a55bb","order_by":6,"name":"Xiumin Zhang","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiumin","middleName":"","lastName":"Zhang","suffix":""},{"id":498414569,"identity":"c73a021d-a149-4e4b-9c25-5b7232942f47","order_by":7,"name":"Zhiliang Tan","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhiliang","middleName":"","lastName":"Tan","suffix":""},{"id":498414571,"identity":"9684dd41-fe16-4bf0-aeed-ba4e74ee5b39","order_by":8,"name":"Min Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYNCCCgmStZwhWQtjGymqdWckP3v4dZ6FPX8D88MPDDV3CGsxu5Fmbiy7TSJxxgE2YwmGY8+I0HI7wUxacptEAsMBBjMGxobDxGhJ/yYtOUfCXv4A+zditeSYSX5skGDccICHWFvuvymTZjgmkbjxME+xRMIxYrScOb5N8kdNnb3c8faNHz7UEKEFBJh5wCQQJxCnARiTP4hVOQpGwSgYBSMTAACqrTaotGxkAgAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-14 14:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7121971/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7121971/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88821634,"identity":"3a932691-3448-49a3-b75f-27ee6b95097e","added_by":"auto","created_at":"2025-08-11 17:49:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3753410,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of 0.5 mmol/kg 3-NOP on greenhouse gas emissions and methanogens of different trophic types \u003cem\u003ein vitro\u003c/em\u003e, as well as the effects of VFA and VFA metabolic pathways in transcriptomics. (A) Experimental design; (B) The real-time changes in methane and hydrogen from maize silage fermentation, and the final degradation rate; (C) The VFA of the \u003cem\u003ein vitro\u003c/em\u003e experiments; (D) Effect of 3-NOP on the microbial transcription involved in VFA metabolic pathways(Ace, acetate; But, butyrate; Pro, propionate; Pyr, pyruvate; ) and two organic acids (Lactate; Suc, Succinate); (E) Effect of 3-NOP on the digestibility (DMD, dry material digestibility; NDFD, neutral detergent fiber digestibility); (F) Effect of 3-NOP on the carbohydrate enzyme (CBM, Carbohydrate-Binding Module; CE, Carbohydrate Esterases; PL, Polysaccharide Lyases; GT, Glycosyltransferases; GH, \u003ca href=\"https://www.cazy.org/Glycoside-Hydrolases.html\"\u003eGlycoside Hydrolases\u003c/a\u003e). * 0.05\u0026lt; P \u0026lt; 0.10, ** P \u0026lt; 0.05, n = 6.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/7bf5039ecd876e4a6ef52d79.png"},{"id":88821638,"identity":"79de6f38-4fdf-4f7a-a048-45483ff361c8","added_by":"auto","created_at":"2025-08-11 17:49:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4822623,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of 3-NOP on the diversity and composition of active microorganisms. (A) The beta diversity of bacteria and archaea in the transcriptome \u003cem\u003ein vitro\u003c/em\u003e; (B) The alpha diversity of bacteria and archaea in the transcriptome \u003cem\u003ein vitro\u003c/em\u003e; (C) and (D) Effect of 3-NOP on the composition of active microorganisms (bacteria and archaea); (E) Effects of 3-NOP on three different types of methanogens. * 0.05\u0026lt; P \u0026lt; 0.10, ** P \u0026lt; 0.05, n = 6.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/af7ab0cd0f47dc601da19055.png"},{"id":88822251,"identity":"43d85a51-29a5-41bd-ac0f-fc01d48295d8","added_by":"auto","created_at":"2025-08-11 17:57:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5041080,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of 3-NOP on transcription of genes involved in methane and VFA pathways in fermentation broth. (A) Methane production pathway based on methane metabolism (KEGG databases--M00563 Methanogenesis); (B) VFA production pathway. Red represents a significant increase in the 3-NOP group, green represents a significant decrease in the 3-NOP group, and black represents no significant difference; * 0.05\u0026lt; P \u0026lt; 0.10, ** P \u0026lt; 0.05, n = 6.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/b123cacf375139fff9b37a11.png"},{"id":88821639,"identity":"a719a35c-fea5-42a0-993c-e270dcc49c3f","added_by":"auto","created_at":"2025-08-11 17:49:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5905008,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of 3-NOP on microbial carbohydrate-active enzymes in the fermentation broth. (A) Effect of 3-NOP on degrading enzymes of different cellulose, hemicellulose, and lignin based on Lin et al. research of carbohydrate-active enzymes. Orange indicates elevated transcription in 3-NOP group, light blue indicates elevated transcription in CON; (B) effect of 3-NOP on transcript abundance of Carbohydrate-Binding Modules (CBMs); (C) \u0026nbsp;QPCR quantitative results of \u003cem\u003eFibrobacter succinogenes\u003c/em\u003e,\u003cem\u003e Ruminococcus flavefaciens\u003c/em\u003e and \u003cem\u003eRuminococcus albus\u003c/em\u003e (D)effect of 3-NOP on transcript abundance of GlycosylTransferases (GTs).* 0.05\u0026lt; P \u0026lt; 0.10, ** P \u0026lt; 0.05, n = 6.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/376eaf2aa3c775f29114e621.png"},{"id":88821642,"identity":"bcbd695d-6a39-46ae-aa09-b7b8d40902c3","added_by":"auto","created_at":"2025-08-11 17:49:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11111565,"visible":true,"origin":"","legend":"\u003cp\u003eAssembled genome evolutionary trees through transcriptome mapping with a database of ruminant microbial genomes (https://figshare.com/projects/RGMC/228963) and the changes of abundance in key genes. (A) The phylogenetic trees of archaeal assembled genomes and distribution mcr of methanogens; (B) The phylogenetic tree of bacterial assembled genomes; distribution of sdhA / frdA and LDH genes; * 0.05\u0026lt; P \u0026lt; 0.10, ** P \u0026lt; 0.05, n = 6.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/93ea8dd2b3988f4743aa2865.png"},{"id":89885777,"identity":"1347496a-c1fe-4c4f-85f2-fef134b647e8","added_by":"auto","created_at":"2025-08-26 06:32:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29184419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/93831639-9b45-40fc-8e91-4c0aa264f577.pdf"},{"id":88821636,"identity":"b2d6c633-2031-4fc9-9f5b-ed595dd6a0ba","added_by":"auto","created_at":"2025-08-11 17:49:54","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18811,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/45a2137497778e3cb1c4ff3b.xlsx"},{"id":88822250,"identity":"d06f70fd-b471-4abb-b728-7c538b772af0","added_by":"auto","created_at":"2025-08-11 17:57:54","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":62357,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/6190a269021bda939a2f0066.xlsx"},{"id":88823046,"identity":"8b738f53-8c97-4e83-a788-8642a2155afd","added_by":"auto","created_at":"2025-08-11 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18:05:54","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":15688,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/832fd56c4a5fba6e5ed0a534.xlsx"},{"id":88821649,"identity":"4828d072-a240-4767-bae6-a0164554cf1c","added_by":"auto","created_at":"2025-08-11 17:49:54","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":74907,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7121971/v1/878221491ae2c9d2590ae33c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metatranscriptomic changes in the propionate pathway and carbohydrate enzyme revealed the rebalancing mechanism of rumen microbiota after CH4 inhibition by 3-NOP","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe 3-nitrooxypropanol (3-NOP), pioneered by DSM (Dutch State Mines) nutritional products in the Netherlands in 2014, was served as a feed additive for ruminant and is presently employed to regulate ruminant CH\u003csub\u003e4\u003c/sub\u003e production [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a methyl-coenzyme M reductase (Mcr) analog, 3-NOP can displace methyl-coenzyme M in binding to coenzyme B, thereby impeding the conversion process of methyl-coenzyme M into CH\u003csub\u003e4\u003c/sub\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Multiple studies have demonstrated that 3-NOP diets lead to a reduction in ruminal CH\u003csub\u003e4\u003c/sub\u003e production (7%-60%) without compromising the animals\u0026rsquo; production performance (beef cattle [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], dairy cows [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and sheep [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]). Following the supplement of 3-NOP, methanogens were predominantly inhibited, resulting in an environment with elevated hydrogen partial pressure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, rumen microorganisms were subjected to dual stressors: the influence of 3-NOP and the resultant high hydrogen partial pressure. How does rumen microbiota adapt to the high-H\u003csub\u003e2\u003c/sub\u003e environment after methane inhibition?\u003c/p\u003e\u003cp\u003eMore affordable and highly precise omics technologies now enable the accurate capture of these changes as they occur during the process. An animal experiment has demonstrated that the inhibitor 3-NOP can exert distinct effects on various methanogens through metagenomic and metatranscriptomic analyses [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These differential effects are likely attributable to the varying mechanisms of methyl group formation and the diverse sources of hydrogen involved [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The diversity of rumen microbiota was also affected by 3-NOP, come in for multiple factors including diet composition, dry matter intake, host genetics, and rumen conditions such as pH, volatile fatty acid (VFA) profile, and H\u003csub\u003e2\u003c/sub\u003e partial pressure in the rumen [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The theory of hydrogen sink was a widely recognized perspective, which suggests that under the inhibition of methane production, an increase in dissolved H\u003csub\u003e2\u003c/sub\u003e may lead to a shift in the production and the utilization of H\u003csub\u003e2\u003c/sub\u003e in microorganisms, resulting in the formation of a new steady state in the rumen microbiota [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, it is worth noting that in addition to hydrogenotrophic bacteria, some fungi, and protozoa are also involved in hydrogen metabolism. Research indicates that coculturing methanogens with anaerobic fungi upregulates multiple carbohydrate-active enzymes (CAZyme) families within the fungi, thereby enhancing their capacity to sense and degrade carbohydrates [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Some studies have also indicated that the methane-inhibiting effect of 3-NOP diminishes as the proportion of fiber in the diet increases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This phenomenon may arise from the symbiotic interaction between methanogens and anaerobic fungi. Additionally, \u003cem\u003ein vitro\u003c/em\u003e pure culture experiments demonstrate that 3-NOP exerts an inhibitory effect exceeding 90% on methane production [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], but the methane-inhibiting efficacy of 3-NOP varies between 15% and 65% in animal trials [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These divergent phenomenon(three different experiments) necessitate additional data to illuminate the response mechanism of rumen microbiota to 3-NOP.\u003c/p\u003e\u003cp\u003eIn this study, we observed that 3-NOP altered the rumen environment through inhibited methane and elevated H\u003csub\u003e2\u003c/sub\u003e, promoting rumen microbiota to shift towards the propionate production pathway. This result was also observed in experiments with low methane ruminants and other methane inhibitors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As for other non-methane hydrogen sinks, the Wood-Ljungdahl processes, as well as other hydrogen sinks (sulfates and nitrate, metal ions, etc.) are also significantly affected by the elevated rumen hydrogen, but these pathways provide a small contribution to the redirection of H\u003csub\u003e2\u003c/sub\u003e in ruminants [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. On the other hand, the two types of methanogens (non-hydrogenotrophic and symbiotic with fungi) were less affected by 3-NOP. Furthermore, we found an increase in the activity of the CAZy enzyme in fungi by matching the analysis of transcripts and species in the cascade of degradation of cellulose. We predicted that the hydrogen production process of fiber degradation via fungi may assist the methanogens that are symbiotic with fungi to be independent of 3-NOP influence. Overall, these results help us understand the main destination and source of hydrogen after 3-NOP inhibits methane, and reveal the methanogens that are not affected by 3-NOP and their escape mechanisms.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe present investigation was sanctioned by the Animal Care Committee under approval number W202502 at the Institute of Subtropical Agriculture, Chinese Academy of Sciences.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003efermentation experiment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe rumen fluid in this experiment was obtained from three healthy fistula sheep. The obtained rumen fluid preserved in a CO\u003csub\u003e2\u003c/sub\u003e gas-filled thermos flask and promptly transport it to the laboratory. Filter the fluid using 4 layers of gauze and reserve for further experiment.\u003c/p\u003e\u003cp\u003eThe artificial rumen buffer was meticulously prepared following the methodology outlined by Menke et al [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Detailed operating methods and procedures are shown in supplementary file S1. Using 1g of dried whole-plant maize silage as a substrate, a automated fermentation system \u003cem\u003ein vitro\u003c/em\u003e was employ to record the complete processes of methane and hydrogen production. The fermentation bottles were divided into CON and 3-NOP (n\u0026thinsp;=\u0026thinsp;12). Prior to inoculating the rumen fluid, the artificial rumen buffer solution was pre-heated to 39.5℃ using a constant-temperature magnetic stirrer. An anaerobic environment was meticulously maintained by continuously flushing the system with CO\u003csub\u003e2\u003c/sub\u003e (note: the resazurin color change to colorless). A set of six fermentation bottles was allowed to ferment for a duration of 48 hours. Meanwhile, another six bottles were designated for sampling at the 24th hour mark. This sampling time was chosen because the hydrogen concentration started to decline at the 24th hour, a decision based on the findings of numerous prior experiments. Following fermentation, the fermentation liquid was promptly extracted, rapidly frozen using liquid nitrogen, and subsequently stored at -80℃. This storage was intended for metatranscriptome sequencing and subsequent analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePure culture of inhibition experiment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo clarify the effect of 3-NOP on methane from different methyl sources, this experiment selected three different types of methanogens-namely hydrogen reducing CO\u003csub\u003e2\u003c/sub\u003e-\u003cem\u003eMethanobrevibacter\u003c/em\u003e, hydrogen-reducing methyltrophicocta-\u003cem\u003eMethanosarcina sp.\u003c/em\u003e, methyltrophic-\u003cem\u003eMethanomassiliicoccales sp. Methanobrevibacter\u003c/em\u003e and \u003cem\u003eMethanosarcina\u003c/em\u003e are taken in the same medium-rumen fluid medium [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. \u003cem\u003eMethanobrevibacter\u003c/em\u003e-The strains were obtained from the laboratory (\u003cem\u003eMethanobrevibacter olleyae\u003c/em\u003e), \u003cem\u003eMethanosarcina\u003c/em\u003e and \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e were obtained from the Institute of Microbiology, Chinese Academy of Sciences. The strain frozen in-80℃ refrigerator was revived in 37℃ water bath, and directly inoculated into the anaerobic bottle. When the optical density at 600 nm (OD\u003csub\u003e600\u003c/sub\u003e) of the culture increased to 0.8, inoculation was carried out. The inoculation volume was 2 ml mother liquor, and the standard gas (H\u003csub\u003e2\u003c/sub\u003e : CO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4:1) was added at 0.1 Mpa. \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e was suited to grow in a methanol medium [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and its recovery process was slow, and the OD\u003csub\u003e600\u003c/sub\u003e reached 0.8 after 20 days. Before inoculation, 0.1 mL of 0.25 mmol/L 3-NOP solution was added to the anaerobic bottle. After inoculation, methane concentration was measured every two days. All mediums were handled under strictly anaerobic conditions and sterilized using high-temperature and high-pressure autoclaving. The detection of methane was carried out by using a 50uL micro-injector to draw 40ul from the anaerobic bottle and injected it into the gas chromatograph (SHIMADZU GC-14B)\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetatranscriptome sequencing and mapping\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal RNA was extracted from fermentation fluid samples using the TRIzol Reagent. The residual DNase was conducted using DNase I (Takara). Ribosomal RNA was depleted by using the Ribo Zero TM rRNA Removal Kit [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Metatranscriptome sequencing was conducted utilizing the HiSeq 2500 System by Illumina, generating paired-end reads with a length of 150 bp in both the forward and reverse directions. Metatranscriptome sequencing and analysis of rumen prokaryotic were undertaken by Shanghai Biozeron Biological Technology Co. Ltd.\u003c/p\u003e\u003cp\u003eDetailed analysis steps of the biological information are shown in the supplementary file (Supplementary file S1). For the quality control of sequencing results, FASTP software is used to complete this study. The FASTP software streamlines the data processing workflow by scanning the data file, effectively integrating the functionalities of Fastqc, Cutadapt, and Trimmomatic into a single operation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. During this process, it calculates the average quality scores for both the head and tail regions of each sequence. Subsequently, it excises the subsequence with the lower average quality value, ensuring high-quality data for downstream analyses. Bowtie2 was used to align the reads after quality control with the sheep genome [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and then the sequence of the host was removed (the generated SAM file was transferred to samtools through the pipeline, and the SAM file was converted into BAM file). Averaging 13.5 gigabases of paired-end reads per sample, suitable for subsequent analysis. MEGAHIT (v1.2.9, minimum length threshold is 500) was used to predict the contigs from each sample [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and Prodigal was used to predict the genes corresponding to each contig [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and filter out coding sequences with a length less than 100. Non-redundant UniGene catalogs were constructed with 95% identity and 90% coverage by CD-HIT (v4.8.1) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Relative gene expression levels were determined following established methodologies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In general, the metatranscriptomic reads of each sample were aligned to predicted Open Reading Frames (ORFs) identified in metagenomes, with Transcript Per Million (TPM) values utilized for data analysis in this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional annotation and phylogenetic analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo clarify the composition of active microorganisms, species annotation of transcriptome sequences was performed using kranken2 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The reference database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/DerrickWood/kraken2\u003c/span\u003e\u003cspan address=\"https://github.com/DerrickWood/kraken2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to classify and annotate each sequence based on the K-mer present in each sequence. The transcriptome sequences of fungi and protozoa were extracted using the deep learning tools-GutEuk tool [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and abundance calculations and differential analysis were performed on the two groups [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAll UniGene catalogs were searched against NCBI non-redundant proteins, String, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases using BLASTp. These searches were performed utilizing hidden Markov models (HMMs) and homology-based search strategies. Furthermore, gene annotations were cross-referenced with the KEGG (Kyoto Encyclopedia of Genes and Genomes) database to investigate carbohydrate metabolism [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This analysis involved HMM searches with default parameters, culminating in the aggregation of data based on the abundance of pathways at Level 3 and Level 2, together with the identification of KEGG Orthologs (KOs) implicated in volatile fatty acid production [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].To gain insights into the genes involved in the pathway of these organic acids, we conducted an analysis implicated in the organic acid and methane metabolism pathway based on the classification table formulated by Li et al [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These pathways include methanogenesis, the Pyr-\u0026gt;Ace and Pyr-\u0026gt;But, Ace-\u0026gt;But, Pyr-\u0026gt;Lacate-\u0026gt;Pro and Pyr-\u0026gt;Suc-\u0026gt;Pro (Ace, acetate; But, butyrate; Pro, propionate; Pyr, pyruvate; ) and two organic acids (Lactate; Suc, Succinate).\u003c/p\u003e\u003cp\u003eTo investigate the machinery associated with reductant disposal through electron transfer reactions pertinent to carbohydrate degradation, crucial enzymes such as acetate production enzymes (\u003cem\u003ePta\u003c/em\u003e, \u003cem\u003eLd\u003c/em\u003e, \u003cem\u003ePor\u003c/em\u003e), propionate production enzymes (\u003cem\u003eMdh\u003c/em\u003e, \u003cem\u003eFum\u003c/em\u003e, \u003cem\u003eFrd/Sdh\u003c/em\u003e, \u003cem\u003eAarc\u003c/em\u003e, \u003cem\u003eLcdA\u003c/em\u003e, \u003cem\u003eLcdB\u003c/em\u003e, \u003cem\u003ePC\u003c/em\u003e, \u003cem\u003emeaA\u003c/em\u003e, \u003cem\u003eSuc\u003c/em\u003e), and butyrate production enzymes (\u003cem\u003eFadJ\u003c/em\u003e, \u003cem\u003eFabV\u003c/em\u003e, \u003cem\u003ePtb\u003c/em\u003e, \u003cem\u003ebuk\u003c/em\u003e, \u003cem\u003eatoA\u003c/em\u003e). These genes are represented at the VFA metabolism profile respectively. These enzymes underwent annotation via hidden-Markov model (HMM) searches against a localized protein database, except for \u003cem\u003eSdhA\u003c/em\u003e and \u003cem\u003eFrdA\u003c/em\u003e, which were annotated using BLAST against a customized database [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To investigate the expression of carbohydrate enzymes, this study predicted carbohydrate enzyme genes based on protein sequences using Run-dbcan V4 using dbCAN (carbohydrate enzyme gene database) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. And classify and organize cellulases, ligninases, hemicellulases, amylases, and fructosases. After aligning protein sequence annotation with expression abundance, we obtained the relative expression abundance of carbohydrate enzymes. Based on the classification of carbohydrate enzymes by Lin et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], we constructed a similar metabolic pathway diagram according to the degradation process of cellulose, hemicellulose, and lignin. NCBI-NR (October 2018; ~550M sequence) analysis of marker genes for species classification and functional allocation was performed by the DIAMOND searches (v.2.0.4) based on the BLASTP search [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. We created a map based on the classification of carbohydrate-active enzymes conducted by Liang et al. in the rumen microbiota of ruminant animals [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo figure out the response of the rumen microbiota to 3-NOP, the mRNA sequences were aligned to a database of rumen microbiota genomes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/projects/RGMC/228963\u003c/span\u003e\u003cspan address=\"https://figshare.com/projects/RGMC/228963\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). TPM values were used to represent the relative abundances of microorganisms. Sorted BAM files generated by SAMtools v1.9 were utilized for computing sample coverage using CoverM(v0.6.1) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] with parameters \u0026lsquo;-m TPM\u0026rsquo;. The completeness and contamination of all bins were estimated using CheckM2 (v1.0.1), with the following criteria: \u0026gt;50% genome completeness, \u0026lt; 5% contamination, and \u0026gt;\u0026thinsp;50 estimated quality score (completeness-5\u0026times;contamination). A total of 1717 MAGs were matched, including 59 MAGs for archaea and 1658 MAGs for bacteria. The distribution of the two key genes involved in lactate and succinate was aggregated and the MAGs corresponding to the \u003cem\u003eLDH\u003c/em\u003e and \u003cem\u003esdhA/frdA\u003c/em\u003e genes in the assembled genome were curated and statistically analyzed at the class level to visualize the diagonal heatmap.\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantification of select bacteria and methanogens using qPCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the transcriptional differences of 3-NOP in fiber-degrading bacteria, fungi, protozoa, and methanogenic archaea, qPCR quantification was performed in this experiment on \u003cem\u003eFibrobacter succinogenes\u003c/em\u003e, \u003cem\u003eRuminococcus albus\u003c/em\u003e, \u003cem\u003eRuminococcus flavefaciens\u003c/em\u003e, fungi, protozoa, and methanogen (the primer sequences are displayed in supplementary file table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) This quantification was performed on LightCycler 480 (Roche Molecular Systems, Pleasanton, California) using SYBR and reference primers for qPCR detection. Absolute abundance is expressed as the copy number of 16S rRNA genes per milliliter of sample (log\u003csub\u003e10\u003c/sub\u003e copy number).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data of fermentation end products were initially organized and compiled using Excel 2021. Subsequently, a one-way analysis of variance was conducted using JPM Pro 13. The outcomes were presented as mean values accompanied by standard errors, with statistical significance denoted by P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, indicating a notable difference.\u003c/p\u003e\u003cp\u003eDifferential analyses between predominant genera, species, carbohydrate enzyme genes, and distinct KEGG pathways were conducted utilizing the Wilcoxon rank-sum test method by the Wilcoxon rank-sum test in the JMP Pro software (JMP Pro version 13.2.1, SAS Institute, Cary, NC). Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating a significant difference, and 0.05\u0026thinsp;\u0026le;\u0026thinsp;P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 indicating a trend of difference.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIncreasing the hydrogen by 3-NOP results in changes in the fermentation pattern but does not influence the degradability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe experimental results show that, during the fermentation period (0-48h), the CH\u003csub\u003e4\u003c/sub\u003e yield in the 3-NOP group was lower than that in the CON group, while H\u003csub\u003e2\u003c/sub\u003e yield was higher in the 3-NOP group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Specifically, within the initial 0\u0026ndash;10 hours, the CH\u003csub\u003e4\u003c/sub\u003e concentration in the 3-NOP group was negligible; At 15 hours, the concentration of methane in 3-NOP began to rise, but the concentration of hydrogen continued to increase;The hydrogen concentration began to decrease at 24h. A notable increase is observed after 20 hours in 3-NOP group and ultimately stabilized at a production of 19.1 ml/g, but the CH\u003csub\u003e4\u003c/sub\u003e production of the CON increased from 0.07 ml/g to 41.6 ml/g within 48h. The H\u003csub\u003e2\u003c/sub\u003e production in the 3-NOP group rose sharply and reached a peak of 8.9 ml/g at the 18th hour. In contrast, the H\u003csub\u003e2\u003c/sub\u003e concentration in the CON group remained nearly at the zero-level curve during the initial 10h period. Subsequently, it exhibited a slight increase, reaching 0.2 ml/g at the 48 - hour mark (as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the VFA, the ratio of acetate and propionate in the 3-NOP group was significantly reduced, while total VFA had no significant effect. For the VFA profile, the results indicated that 3-NOP markedly decreased the proportion of acetate, while notably increasing the proportions of propionate and isobutyrate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). For the concentration, 3-NOP had no significant effect on the concentration of acetate, but significantly increased the concentration of propionate (Additional Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To gain insights into the genes involved in the pathway of these organic acids, we conducted an analysis implicated in the organic acid and methane metabolism pathway based on the classification table formulated by Li et al [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. \u003cb\u003eDetailed results are shown in supplement table 1.\u003c/b\u003e The results revealed that 3-NOP substantially decreased the transcripts of methane metabolism, the Pyr-\u0026gt;Ace and Pyr-\u0026gt;But pathways, whereas significantly increased the transcripts of the Pyr-\u0026gt;Lacate-\u0026gt;Pro and Pyr-\u0026gt;Suc-\u0026gt;Pro pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). For the two types of propionate pathways, we measured the concentration of lactate and succinate after 48h. The results indicated that 3-NOP significantly elevated the concentrations of lactate and succinate in the fermentation fluid (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eConcerning the degradability, no significant difference was observed in the degradability of dry matter (DMD) and neutral detergent fiber (NDFD) between with two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The transcriptome analysis further showed that 3-NOP had no significant effect on the abundance of total carbohydrate enzymes, but increased the transcripts of carbohydrate-binding module (CBM), carbohydrate esterases (CE) and glycosyltransferase (GT, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). \u003cb\u003eDetailed results are shown in supplement table 2.\u003c/b\u003e However, no significant difference was noted in the glycoside hydrolases (GH), polysaccharide lyases (PL), and total transcripts of carbohydrate enzymes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTranscript changes of rumen microbiota after addition of 3-NOP and validation of pure bacterial culture experiments\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe diversity analysis was based on the genus level, and the results showed that principal component analysis (PCoA) revealed significant differences in the diversity of active bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, P\u0026thinsp;=\u0026thinsp;0.003) and archaea (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, P\u0026thinsp;=\u0026thinsp;0.024) in the addition of 3-NOP. The Shannon index further indicated that 3-NOP significantly decreased the diversity of active bacteria, while significantly increasing the diversity of active archaea (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). \u003cb\u003eDetailed results are presented in supplement table 3.\u003c/b\u003e The major bacteria are consistent species composition in two groups, such as the \u003cem\u003eClostridia\u003c/em\u003e, \u003cem\u003eGammaproteobacteria\u003c/em\u003e, \u003cem\u003eNegativicutes\u003c/em\u003e, \u003cem\u003eBacteroidia\u003c/em\u003e, \u003cem\u003eFibrobacteria\u003c/em\u003e, \u003cem\u003eBacilli\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e constituted the predominant bacterial populations in both experimental groups, and collectively represented approximately 81% and 87% in the two groups. Compared to the CON group, the relative abundance of \u003cem\u003eNegativicutes\u003c/em\u003e and \u003cem\u003eFibrobacteria\u003c/em\u003e in the 3-NOP group exhibited a significant increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast to the CON group the relative abundance of \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eDeltaproteobacteria\u003c/em\u003e, \u003cem\u003eCoriobacteriia\u003c/em\u003e, \u003cem\u003eBetaproteobacteria\u003c/em\u003e, and \u003cem\u003eSpirochaetia\u003c/em\u003e was significantly reduced in the 3-NOP group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Within the archaea domain, \u003cem\u003eMethanobacteriales\u003c/em\u003e, \u003cem\u003eMethanomicrobiales\u003c/em\u003e, \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e, and \u003cem\u003eMethanosarcinales\u003c/em\u003e prevailed in both groups, collectively representing approximately 98% in the CON group and 3-NOP group. Notably, the relative abundance of \u003cem\u003eMethanobacteriales\u003c/em\u003e and other classes in the 3-NOP group was markedly lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the CON group. Conversely, the relative abundance of \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e, primarily utilizing exogenous H\u003csub\u003e2\u003c/sub\u003e to reduce the C1 compound (methyl donor) for methane, was significantly higher in the 3-NOP group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cb\u003eDetailed results are shown in supplement table 4.\u003c/b\u003e The results showed that in the 3-NOP group, a total of 195 contigs showed a significant increase in fungal transcripts, while only 9 contigs showed a decrease; 370 contigs of protozoa transcripts significantly increased, while only 65 contigs showed a significant decrease. To further demonstrate the differences, the transcriptomes of fungi and protozoa were quantified. The results showed that the copy number of fungi in the 3-NOP group was significantly higher than that in the CON group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, CON vs. 3-NOP\u0026thinsp;=\u0026thinsp;10\u003csup\u003e6.11\u003c/sup\u003e vs. 10\u003csup\u003e6.70\u003c/sup\u003e). The copy number of protozoa was slightly increased, but there was no significant difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, P\u0026thinsp;=\u0026thinsp;0.127).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further understand the effect of 3-NOP on methanogens, four different types of methanogens were selected for pure culture experiments by adding 3-NOP. The CH\u003csub\u003e4\u003c/sub\u003e concentration showed no significant difference in \u003cem\u003eMethanobrevibacter\u003c/em\u003e between the two groups on the second day and then The CH\u003csub\u003e4\u003c/sub\u003e concentration of 3-NOP was lower than that of the CON group after 3-NOP adding. The curve of \u003cem\u003eMethanosarcina\u003c/em\u003e also showed a significant reduction, but on the ninth day, methane concentrations were trended to be stable. \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e are methylotrophic methanogens and have a much longer growth cycle than other methanogens [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, the 3-NOP was immediately added to the \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e medium after inoculation. \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e medium was supplemented with 0.2 mol/L of methanol as the only source of methyl groups. The results showed that the addition of 3-NOP to \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e medium did not completely stop the growth of \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e during the first methane test in seven days. Within 7-16d, the concentration of methane was significantly inhibited by 3-NOP.Considering the above results, 3-NOP reduced the methane production of the four methanogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), but the methane decrease of \u003cem\u003eMethanobrevibacter\u003c/em\u003e in initial testing was the largest (\u003cem\u003eMethanobrevibacter\u003c/em\u003e vs \u003cem\u003eMethanosarcina\u003c/em\u003e vs \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e = -3.07 vs -2.64 vs -2.11) after the addition of 3-NOP.These results showed that compared with the other three methanogens, \u003cem\u003eMethanobrevibacter\u003c/em\u003e is the most sensitive to 3-NOP.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3-NOP downregulated the transcript of hydrogenotrophic methanogenic and butyrate pathways but upregulated the propionate production pathway\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe macrotranscriptome analysis revealed that the addition of 3-NOP led to a significant decrease in the transcript of \u003cem\u003efwd/fmd\u003c/em\u003e, \u003cem\u003eFtr\u003c/em\u003e, \u003cem\u003eMch\u003c/em\u003e, \u003cem\u003eHmd\u003c/em\u003e/\u003cem\u003eMtd\u003c/em\u003e, \u003cem\u003eMer\u003c/em\u003e, \u003cem\u003eMtr\u003c/em\u003e, \u003cem\u003eMcr\u003c/em\u003e, and \u003cem\u003eHdr\u003c/em\u003e-\u003cem\u003eMvh\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cb\u003eDetailed results are shown in supplement table 5.\u003c/b\u003e These genes are eight important genes for hydrogenotrophic methanogens to produce methane by reducing carbon dioxide with hydrogen gas. Moreover, there was a notable reduction in the gene transcription of methanol-utilizing methanogens (\u003cem\u003eMtaA\u003c/em\u003e, \u003cem\u003eMtaB\u003c/em\u003e, \u003cem\u003eMtaC\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no distinct impact was observed on acetate-utilizing (\u003cem\u003eAck\u003c/em\u003e, \u003cem\u003eAcs\u003c/em\u003e, \u003cem\u003eCdh\u003c/em\u003e), methylamine-utilizing (\u003cem\u003eMtmB\u003c/em\u003e, \u003cem\u003eMtmC\u003c/em\u003e), and trimethylamine-utilizing (\u003cem\u003eMttB\u003c/em\u003e, \u003cem\u003eMttC\u003c/em\u003e) methanogens. Interestingly, the presence of 3-NOP resulted in an increasing transcription of dimethylamine-utilizing methanogens (\u003cem\u003eMtbA\u003c/em\u003e, \u003cem\u003eMtbB\u003c/em\u003e, \u003cem\u003eMtbC\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, the addition of 3-NOP stimulated the transcript of \u003cem\u003eFdh\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while significantly reducing the transcript of \u003cem\u003esdh\u003c/em\u003e/\u003cem\u003efrd\u003c/em\u003e (a key gene involved in the conversion of F\u003csub\u003e420\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003e energy from NADPH, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). 3-NOP significantly downregulated the expression of the \u003cem\u003eMcrA\u003c/em\u003e and \u003cem\u003eMcrB\u003c/em\u003e genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while having no notable impact on the expression of \u003cem\u003eMcrC\u003c/em\u003e, \u003cem\u003eMcrD\u003c/em\u003e, and \u003cem\u003eMcrG\u003c/em\u003e genes. This downregulation is the direct cause of the reduction in methane production.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegarding VFA metabolism, transcription results indicated that 3-NOP notably upregulated the expression of \u003cem\u003eLDH\u003c/em\u003e, \u003cem\u003elcdA\u003c/em\u003e, and \u003cem\u003elcdB\u003c/em\u003e in the lactate pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as increased the transcript levels of \u003cem\u003emdh\u003c/em\u003e, \u003cem\u003efum\u003c/em\u003e, \u003cem\u003esdh\u003c/em\u003e/\u003cem\u003efrd\u003c/em\u003e, \u003cem\u003eAarc\u003c/em\u003e, \u003cem\u003eCS\u003c/em\u003e, and \u003cem\u003eEda\u003c/em\u003e in the succinate pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cb\u003eDetailed results are shown in supplement table 5.\u003c/b\u003e Conversely, in the acetate and butyrate pathways, 3-NOP significantly decreased the transcription level of \u003cem\u003ePor\u003c/em\u003e (a key gene for pyruvate-producing acetyl-CoA, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The downregulation of the \u003cem\u003ePor\u003c/em\u003e gene directly impacted the overall reduction in gene transcript for both acetate and butyrate, consequently reducing their production, aligning with the VFA experimental results. Notably, there was a significant increase in the transcript of the \u003cem\u003epta\u003c/em\u003e gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)-a gene associated with high-energy phosphate bond formation. Moreover, 3-NOP significantly decreased the transcript of key enzymes involved in VFA metabolism, including \u003cem\u003eFadJ\u003c/em\u003e, \u003cem\u003ePtb\u003c/em\u003e, \u003cem\u003ebuk\u003c/em\u003e, and \u003cem\u003eatoA\u003c/em\u003e in the butyrate pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interestingly, 3-NOP led to a significant increase in the transcript levels of \u003cem\u003efabV\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which plays a pivotal role in converting But-c2-enoyl-CoA to Butanoyl-CoA by utilizing hydrogen from NADH during butyrate synthesis[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This gene is essential for mitigating the redundancy of reductants in the butyrate metabolism pathway, suggesting its involvement in the modulation of reductants during butyrate production.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3-NOP promoted transcripts in encoding fiber degrading enzymes fungal (\u003c/b\u003e\u003cb\u003eNeocallimastigaceae\u003c/b\u003e\u003cb\u003e) and alter the dominant fibro-degradable bacteria\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHydrolytic digestion of carbohydrates in the rumen was conducted by the microflora inhabiting it and their end-products of ruminal fermentation include the VFA, CH\u003csub\u003e4\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e, NH\u003csub\u003e3\u003c/sub\u003e, and small molecule ingredients [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. We created a classification map based on Lin et al., classification of carbohydrate-active enzymes in the rumen microbiota of ruminant animals [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Specifically, for cellulose degradation, the relevant carbohydrate-active enzyme family includes endoglucanases, exoglucanases, and β-glucosidases. \u003cb\u003eDetailed results are shown in supplement table 6.\u003c/b\u003e We found that 3-NOP notably increased the transcript of GH9, GH8, GH74, GH44, GH45, and GH5 for endoglucanases, but it significantly reduced the expression of GH148 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among the microorganisms expressing endoglucanases, the \u003cem\u003eNeocallimastigaceae\u003c/em\u003e (1.94-fold), \u003cem\u003eClostridiaceae\u003c/em\u003e (1.21-fold), \u003cem\u003eFibrobacteraceae\u003c/em\u003e (1.80-fold), \u003cem\u003ePrevotellaceae\u003c/em\u003e (1.52-fold), \u003cem\u003eBacteroidales_norank\u003c/em\u003e (1.30-fold), \u003cem\u003eBacteroidaceae\u003c/em\u003e (1.41-fold) in the 3-NOP group was higher than that in the CON group, \u003cem\u003eRuminococcus\u003c/em\u003e of 3-NOP group showed a 80.88% of the CON group. Regarding exoglucanases, the expression levels of GH9 and GH5 were significantly elevated by 3-NOP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of exoglucanases, the \u003cem\u003eNeocallimastigaceae\u003c/em\u003e (1.97-fold), \u003cem\u003eClostridiaceae\u003c/em\u003e (1.22-fold), \u003cem\u003eFibrobacteraceae\u003c/em\u003e (2.04-fold), \u003cem\u003ePrevotellaceae\u003c/em\u003e (1.55-fold). \u003cem\u003eBacteroidales_norank\u003c/em\u003e (1.37-fold), \u003cem\u003eBacteroidaceae\u003c/em\u003e (1.37-fold) in the 3-NOP group was higher than that in the CON group, and \u003cem\u003eRuminococcus\u003c/em\u003e of the 3-NOP group showed 78.11% of the CON group. For β-glucosidases, the addition of 3-NOP significantly increased GH116 and GH5 expression but significantly reduced the expression of GH1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In microorganisms expressing β-glucosidases, the \u003cem\u003eNeocallimastigaceae\u003c/em\u003e (1.67-fold), \u003cem\u003eFibrobacteraceae\u003c/em\u003e (1.97-fold), and \u003cem\u003eSpirochaetaceae\u003c/em\u003e (1.45-fold) in the 3-NOP group was higher than that in the CON group, \u003cem\u003ePrevotellaceae\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e of the 3-NOP group showed 79.69% and 82.58% of the CON group. For the degradation of hemicellulose, its carbohydrate-active enzyme family is composed of debranching enzymes, endoxyianases, and exoxylanases. The results showed that 3-NOP significantly increased the expression of CE4, GH57, GH5, CE6, CE7, CE3, CE15, CE67, CE4 in debranching enzymes, but significantly reduced expression of CE1, GH97, GH36, GH1, GH110 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn microorganisms expressing debranching enzymes, the \u003cem\u003eNeocallimastigaceae\u003c/em\u003e (1.54-fold), \u003cem\u003eFibrobacteraceae\u003c/em\u003e (2.05-fold), \u003cem\u003eBacteroidaceae\u003c/em\u003e (1.22-fold) in the 3-NOP group was higher than that in the CON group, \u003cem\u003ePrevotellaceae and Ruminococcus\u003c/em\u003e of the 3-NOP group showed 85.19% and 84.23% of the CON group. As for endoxyianases, 3-NOP significantly increased the expression of GH5, GH8, GH141, and GH11 in endoxyianases, but no significant difference in GH43, GH51, GH10, GH30, GH98, and GH48 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For microorganisms expressing endoxyianases, the \u003cem\u003eNeocallimastigaceae\u003c/em\u003e (1.76-fold), \u003cem\u003eClostridiaceae\u003c/em\u003e (1.21-fold), \u003cem\u003eFibrobacteraceae\u003c/em\u003e (1.72-fold), \u003cem\u003ePrevotellaceae\u003c/em\u003e (1.42-fold), \u003cem\u003eBacteroidaceae\u003c/em\u003e (1.31-fold) in the 3-NOP group was higher than that in the CON group, \u003cem\u003eRuminococcus\u003c/em\u003e of 3-NOP group showed a 79.25% of the CON group. For exoxylanases, 3-NOP significantly increased the expression level of GH5, but significantly reduced expression of GH1, GH120, GH54, and GH116 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For exoxylanases, the \u003cem\u003eNeocallimastigaceae\u003c/em\u003e (1.65-fold), \u003cem\u003eFibrobacteraceae\u003c/em\u003e (1.92-fold), and \u003cem\u003eBacteroidaceae\u003c/em\u003e (1.23-fold) in the 3-NOP group was higher than that in the CON group, \u003cem\u003ePrevotellaceae and Ruminococcus\u003c/em\u003e of the 3-NOP group showed 89.56% and 82.46% of the CON group.\u003c/p\u003e\u003cp\u003eThe results of this study revealed that the incorporation of 3-NOP had a significant impact on the gene transcript levels of various carbohydrate-binding modules (CBMs). Specifically, it notably elevated the expression of major CBM groups such as CBM4, CBM30, CBM32, CBM35, CBM51, and CBM91. \u003cb\u003eDetailed results are shown in supplement table 2.\u003c/b\u003e However, it also significantly decreased the transcript levels of CBM3 and CBM11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After species annotation of all CBMs (NCBI-NR (October 2018; ~550M sequence)), it is interesting that the three CBMs with the most significant increase in transcripts (CBM4, 30, 35) are all from \u003cem\u003eFibrobacter succinogene\u003c/em\u003es, which indicates that 3-NOP does not have a negative effect on \u003cem\u003eFibrobacter succinogenes\u003c/em\u003e. In addition, through the PCR quantification of transcripts, the results showed that 3-NOP significantly increased the amount of \u003cem\u003eFibrobacter succinogenes\u003c/em\u003e in the transcripts but markedly reduced the numbers of two rumen bacteria (\u003cem\u003eRuminococcus flavefaciens\u003c/em\u003e and \u003cem\u003eRuminococcus albus\u003c/em\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings indicate that 3-NOP significantly depresses the activity of both types of rumen bacteria but has no effect on or can create an environment favorable for \u003cem\u003eFibrobacter succinogenes\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, as for glycosyl transferases (GTs), the addition of 3-NOP significantly increased the expression of GT2, GT4, GT5, GT9, GT26, GT30, GT35, GT51, GT66, GT111, and GT113. Conversely, it significantly reduced the expression of GT3, GT11, GT81, GT105, and GT112 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMapping genome confirms non-hydrogenotrophic (\u003c/b\u003e\u003cb\u003eMethanomassiliicoccales\u003c/b\u003e\u003cb\u003e) and propionate pathways are enriched in the 3-NOP group\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the impact of 3-NOP on rumen microbiota, we aligned the transcriptome sequences with the database of rumen microbiota genomes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/projects/RGMC/228963\u003c/span\u003e\u003cspan address=\"https://figshare.com/projects/RGMC/228963\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). From these metagenome-assembled genomes (MAGs), we constructed a phylogenetic tree and correlated key genes with the respective MAGs. \u003cb\u003eDetailed results are shown in supplement table 7.\u003c/b\u003e Our findings revealed that 45 archaeal MAGs were successfully matched and \u003cem\u003eMethanobrevibacter\u003c/em\u003e was the main species (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Analysis of the five subunits of methyl-CoM reductase indicated that not all methanogens expressed a complete set of five subunits, potentially attributed to the incomplete nature of some MAGs in the database. Furthermore, both \u003cem\u003eMethanobrevibacter\u003c/em\u003e and \u003cem\u003eMethanobrevibacter-A\u003c/em\u003e (Each one has a single MAG) were found to possess two sets of methyl-CoM reductase subunits (MCR-ABDG), but \u003cem\u003eMcrC\u003c/em\u003e has only a single copy. Furthermore, a rank sum test was conducted on the abundance of each MAG in this study, revealing that the relative abundance of certain \u003cem\u003eMethanobrevibacter\u003c/em\u003e species was higher in the 3-NOP group compared to the CON group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The previous studies indicated that methanogens in the rumen could be categorized into different states: free-living, associated with fungi, and symbiotic with protozoa [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], but free-living methanogens were the most abundant [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Notably, protozoa and anaerobic fungi in the rumen contain hydrogenosomes, and these \u003cem\u003eMethanobrevibacter\u003c/em\u003e species, associated with fungi and protozoa may not be susceptible to the effects of 3-NOP. It is worth noting that JAKSHX01 belongs to the methylnutritive methane bacteria (\u003cem\u003eMethanomassiliicoccales\u003c/em\u003e), which was significantly increased in the 3-NOP group, which is consistent with previous results. The study showed that the addition of 3-NOP had a more profound effect on non-hydrogenated methanogens than the results detected [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor bacteria, a total of 1658 bacterial MAGs were matched (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and were mainly distributed in \u003cem\u003eClostridia\u003c/em\u003e and \u003cem\u003eBacteria\u003c/em\u003e. Interestingly, \u003cem\u003eLDH\u003c/em\u003e genes were mainly enriched in \u003cem\u003eClostridia\u003c/em\u003e, while \u003cem\u003esdhA\u003c/em\u003e/\u003cem\u003efrdA\u003c/em\u003e was mainly enriched in \u003cem\u003eBacteria\u003c/em\u003e. In this experiment, two key genes were annotated in more detail, and the abundance difference between the two groups was analyzed by rank sum test. The results showed that 3-NOP significantly increased the expression of the \u003cem\u003eLDH\u003c/em\u003e gene in \u003cem\u003ec_Spirochaetia\u003c/em\u003e, \u003cem\u003ec_Bacilli_A\u003c/em\u003e, \u003cem\u003ec_Clostridia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and significantly decreased the expression of \u003cem\u003eLDH\u003c/em\u003e in \u003cem\u003ec_Anaerolineae\u003c/em\u003e, \u003cem\u003ec_Endomicrobiia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cb\u003eDetailed results are shown in supplement table 8.\u003c/b\u003e Similarly, 3-NOP significantly increased the transcriptome of \u003cem\u003esdhA\u003c/em\u003e/\u003cem\u003efrdA\u003c/em\u003e in \u003cem\u003eFibrobacteria\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e and significantly decreased the transcriptome of \u003cem\u003esdhA\u003c/em\u003e/\u003cem\u003efrdA\u003c/em\u003e expression in \u003cem\u003ec_Dehalobacteria\u003c/em\u003e and \u003cem\u003ec_Desulfuromonadia\u003c/em\u003e. These results suggest that the propionate pathway plays an important role in the health fermentation and diversity rebalancing of rumen microbiota after 3-NOP addition.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe 3-NOP is internationally recognized as a highly efficient and specialized methane inhibitor in the current field of methane inhibitors [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. After the reduction of methane, the generation rate of hydrogen in rumen fluid increased significantly from 0.01 to 0.16g/h [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In this study, the hydrogen concentration increased by 60 fold within 48 h. The methane pathway, as the largest hydrogen sink, was inhibited, and the increase in hydrogen pressure put the rumen microbiota in a state of hydrogen stress [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The process of bacterial fiber degradation is also accompanied by the production of hydrogen, some bacterial fiber degradation may even stop [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In this state, how do the rumen microbiota adapt to this shift and re-establish a new balance? This study integrates metatranscriptomic analysis with pure bacterial inhibition assays to quantitatively identify and characterize microorganisms exhibiting specific functional roles, thereby validating the enhanced capacity of two propionate (lactate and succinate) pathways to act as hydrogen sinks under elevated hydrogen partial pressure conditions. It also revealed some methanogens that form symbiotic partnerships with anaerobic fungi via cellulose degradation in the rumen and rely on non-hydrogenotrophic methanogenesis pathways may remain independent of 3-NOP affect.\u003c/p\u003e\u003cp\u003eThe first important finding of this study is that, compared with the control group, 3-NOP reduced the activity of most hydrogenotrophic methanogen, while non-hydrogenotrophic methanogen and methanogen symbiotic with fungi or protozoa may be less affected by 3-NOP. Firstly, 3-NOP was found to markedly decrease the transcriptional activity of hydrogenotrophic methanogens in the rumen, while exerting either negligible or minor effects on non-hydrogenotrophic methanogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Another piece of evidence, the transcripts of the genes encoding hydrotrophic methanogenesis were significantly decreased within 24 hours in 3-NOP group (\u003cem\u003efwd\u003c/em\u003e/\u003cem\u003efmd\u003c/em\u003e, \u003cem\u003eftr\u003c/em\u003e, \u003cem\u003eMch\u003c/em\u003e, \u003cem\u003eHmd\u003c/em\u003e/\u003cem\u003eMtd\u003c/em\u003e, \u003cem\u003eMer\u003c/em\u003e, \u003cem\u003eMtr\u003c/em\u003e, \u003cem\u003eMcr\u003c/em\u003e, and \u003cem\u003eHdr\u003c/em\u003e-\u003cem\u003eMvh\u003c/em\u003e), and the key genes (\u003cem\u003eMtaA\u003c/em\u003e, \u003cem\u003eMtaB\u003c/em\u003e, \u003cem\u003eMtaC\u003c/em\u003e) were also significantly downregulated, but the key acetate, methanine, dimethylamine and trimethylamine methanogenesis were not negatively affected (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A methanogen that converts methylamine to methane (\u003cem\u003eMethanomassiliicoccales\u003c/em\u003e) was increased this abundance in the transcriptome alignment results (Fig.\u0026nbsp;6A). Previous research reported that CO\u003csub\u003e2\u003c/sub\u003e-reducing \u003cem\u003eMethanobrevibacter species\u003c/em\u003e have higher H\u003csub\u003e2\u003c/sub\u003e thresholds (\u0026gt;\u0026thinsp;5.0 MPa) compared with methanol-utilizing \u003cem\u003eMethanosphaera\u003c/em\u003e (\u0026lt;\u0026thinsp;1.0 Pa) and methylamine-utilizing \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e (\u0026lt;\u0026thinsp;0.1 Pa), suggesting that methylamine- and methanol-utilizing methanogens have an advantage over CO\u003csub\u003e2\u003c/sub\u003e-reducing methanogens [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This is consistent with the experimental transcripts and pure culture results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Another worth considering result is that the transcriptional and quantitative results of fungi in the 3-NOP group showed a consistent increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), and the abundance of \u003cem\u003eNeocallimastigaceae\u003c/em\u003e was significantly increased in the microorganisms expressing cellulose-degrading enzymes. In addition, the results of the mapping transcript sequencing with the complete assembled genome database showed that some transcripts of hydrogenotrophic methanogen (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cem\u003eMethanobrevibacter\u003c/em\u003e, \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e) in the 3-NOP group were even larger than those in the control group. In the rumen, a variety of fungi [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and protozoa [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] contain hydrogenosomes. Hydrogenosome plays an important role in inter-specific hydrogen transfer and metabolic balance. The mutual association of methanogens with both fungi and parasites provides a natural barrier for methanogens to avoid the effects of 3-NOP [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which may explain the elevation of some methanogen transcripts in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cem\u003eMethanobrevibacter\u003c/em\u003e, \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e). Some non-hydrogenotrophic and no-free methanogens may make a greater contribution to total methanogenesis than was originally thought [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These two adaptive mechanisms of the methanogen, non-hydrogenotrophic and hydrogenosome-relied methanogens, were able to weaken the inhibitory effect of 3-NOP and even develop resistance because of long-term use, which needs to warrant vigilance.\u003c/p\u003e\u003cp\u003eThe second important finding of this study is that the 3-NOP enhances the hydrogen sink via the propionate pathway after CH\u003csub\u003e4\u003c/sub\u003e inhibition in the rumen. The results of transcriptome encoding in reductant disposal indicated that the VFA profiles exhibited a tight correlation with active rumen microbial species, distribution, and quantification. Studies have shown that when methane decreases or the methane production pathway is inhibited, the high concentration of hydrogen may even hinder the development of biochemical reactions and alter microbial metabolism and VFA metabolite profiles[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This led to a decrease in the redox potential within the fermentation system. Thermodynamically, this is unfavorable for microorganisms that derive energy and carbon sources from fermenting feed substrates. However, it is beneficial for microorganisms that can utilize hydrogen to metabolize fumarate and lactate, or these microorganisms may be less affected by hydrogen [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. For example, a significant increase in transcripts encoding lactate (\u003cem\u003eLDH\u003c/em\u003e) and succinate (\u003cem\u003emdh\u003c/em\u003e, \u003cem\u003esdhA\u003c/em\u003e/\u003cem\u003efrdA\u003c/em\u003e, et al.) pathways was also observed in this experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), which was confirmed in the mapping of the assembly genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and consistent with majority previous results with 3-NOP addition [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The results of active microbial composition based on the Krakan2 database showed that 3-NOP significantly increased the transcript abundance of \u003cem\u003eNegativicutes\u003c/em\u003e and \u003cem\u003eFibrobacteria\u003c/em\u003e. Studies showed that \u003cem\u003eNegativicutes\u003c/em\u003e and \u003cem\u003eFibrobacteria\u003c/em\u003e were closely related to propionate metabolism (lactate and succinate pathways) and contained multiple genera (\u003cem\u003eAcidaminococcus\u003c/em\u003e, \u003cem\u003eSucciniclasticum\u003c/em\u003e, \u003cem\u003eMegasphaera\u003c/em\u003e, \u003cem\u003eAnaerovibrio\u003c/em\u003e, \u003cem\u003eSchwartzia\u003c/em\u003e, \u003cem\u003eSelenomonas\u003c/em\u003e, \u003cem\u003eMitsuokella\u003c/em\u003e) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. As for other non-methane hydrogen sinks, the Wood-Ljungdahl processes, as well as other hydrogen sinks (sulfates and nitrate, metal ions, etc.) are also significantly affected by the elevated rumen hydrogen, but these pathways provide a small contribution to the redirection of H\u003csub\u003e2\u003c/sub\u003e in ruminants [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. \u003cb\u003eDetailed results are shown in supplement table 9.\u003c/b\u003e The propionate synthesis is the preferred hydrogen sink pathway after methanogens inhibition by 3-NOP, which ensures that microorganisms maintain normal metabolism.\u003c/p\u003e\u003cp\u003eThe third important finding is that 3-NOP promoted the transcripts of fibro-degrading enzymes of fungi (\u003cem\u003eNeocallimastigaceae\u003c/em\u003e) and altered the composition of fibro-degradable bacteria. There was no significant difference in the overall transcripts of fiber degrading enzymes by 3-NOP and no difference in dry matter and fiber degradation. However, we found that 3-NOP significantly increased the transcripts of fungal (\u003cem\u003eNeocallimastigaceae\u003c/em\u003e), as well as some fiber-degrading enzyme family of bacterial (\u003cem\u003eLachnospiraceae_norank\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eBacteroidales_norank\u003c/em\u003e, \u003cem\u003eFibrobacter\u003c/em\u003e), but significantly decreased the \u003cem\u003eRuminococcus\u003c/em\u003e. The quantitative results are consistent with this result (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The unaltered fiber degradation is consistent with the previous \u003cem\u003ein vivo\u003c/em\u003e [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] studies. However, some studies have shown that the inhibitory effect of 3-NOP is significantly negatively correlated with the proportion of NDF in the diet [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This suggests that methane production is directly related to fiber degradation in the rumen [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Studies have shown that different microbial communities adopt different enzymatic strategies in the degradation of specific lignocellulosic components (including cellulose, hemicellulose, and lignin) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Studies have shown that \u003cem\u003eFibrobacter\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e are predicted to be powerful degraders, and capable of using endocytic and exoglucosanases on amorphous and crystalline cellulose, respectively [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Compared to \u003cem\u003eRuminococcus\u003c/em\u003e, the outer membrane vesicles in \u003cem\u003eFibrobacter\u003c/em\u003e contain a series of carbohydrate-active enzymes that can degrade multiple polysaccharides and efficiently decompose cellulose into simple sugars [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However, the cellulases and hemicellulases secreted by \u003cem\u003eRuminococcus\u003c/em\u003e directly work on the degradation of complex carbohydrates that exist in the plant cell wall [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Moreover, \u003cem\u003eFibrobacter succinogene\u003c/em\u003e has a succinate-producing function by utilizing hydrogen and is more adaptable to the environment with low redox potential with high hydrogen pressure. This carbohydrate-active enzymes \u003cem\u003esystem\u003c/em\u003e of \u003cem\u003eRuminococcus\u003c/em\u003e was more negative affected by 3-NOP or high hydrogen pressure. \u003cem\u003eNeocallimastigaceae\u003c/em\u003e is a fungi with existing differences in this experiment and is a very critical and highly proportional fiber-degrading anaerobic fungi in the rumen [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. To maintain the redox balance in cells, the hydrogenosome of anaerobic fungi couples H\u003csup\u003e+\u003c/sup\u003e reduction with NAD(P)H oxidation to produce H\u003csub\u003e2\u003c/sub\u003e, and provide sufficient growth substrate and methanogenic substrates for parasitic-methanogens through interspecific H\u003csub\u003e2\u003c/sub\u003e transfer [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. These conclusions may explain the significant increase in the \u003cem\u003eNeocallimastigaceae\u003c/em\u003e transcripts encoding the cellulase after the addition of 3-NOP.\u003c/p\u003e\u003cp\u003eIn this experiment, the changes in transcripts of some cellulase illustrated the adjustments and adaptations made by rumen organisms in the mode of fiber degradation. The 3-NOP significantly increased the transcripts of GH5 and GH9 family enzymes, whereas significantly decreased the transcripts of the GH1 family. The study shows that GH5 and GH9 family enzymes, with broad substrate specificity able to hydrolyze a variety of different types of carbohydrates, including cellulose, hemicellulose as well as other complex polysaccharide structures, participate in multiple cascades of cellulose, hemicellulose degradation [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. However, the GH1 family (β-glucosidase and β-galactosidase), which are key enzymes for microbial degradation of disaccharides into monosaccharides, are also involved in branched-chain removal in hemicellulose, which is the richest fiber-degrading enzyme in this study. In this study, a large number of transcription of CBM and GT enzyme families was found, with the large carbohydrate modules CBM30 and CBM35 and glycosyltransferases (GT2, GT4, GT5, GT35, GT51), which significantly increased after methane inhibition by 3-NOP, while CBM3 and CT3, CT81 transcripts were significantly decreased. These increased carbohydrate enzymes are mainly from anaerobic fungi and partly from fiber-degrading bacteria [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This result explains the the third important finding. Rumininal anaerobic fungi producing cellulosomes contain many cellulases including CBM and GT, which are six times more efficient than free enzymes, and can degrade lignocellulose and resistant starch [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. However, \u003cem\u003eRuminococcus\u003c/em\u003e does not form cellulosomes but rather performs the degradation activity through a mechanism involving CBM adhesion to the substrate [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The sensitivity of \u003cem\u003eRuminococcus\u003c/em\u003e exhibits to 3-NOP may be responsible for its reduced transcript abundance, and the same result was observed in some \u003cem\u003ein vivo\u003c/em\u003e studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These results suggest that the degradation mode of fibers in the rumen may have some alteration and some of the fiber degradation is transferred to fungal after the application of 3-NOP.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this experiment, we monitored the dynamics of hydrogen (H\u003csub\u003e2\u003c/sub\u003e) production and methane (CH\u003csub\u003e4\u003c/sub\u003e) reduction following 3-NOP-mediated methane inhibition using a fully automated \u003cem\u003ein vitro\u003c/em\u003e fermentation system. We collected RNA samples in real time for macrotranscriptomic sequencing. The rise in hydrogen partial pressure induces the propionate pathway to compensate for the disrupted methanogenic hydrogen (H\u003csub\u003e2\u003c/sub\u003e) sink. Additionally, some methanogens was independent of 3-NOP affect through symbiotic associations with fungi or protozoa, utilizing H\u003csub\u003e2\u003c/sub\u003e for methanogenesis. Transcriptional upregulation of lactate and succinate pathways further supports the hypothesis of H\u003csub\u003e2\u003c/sub\u003e sink compensation. The increase in fungal fiber-degrading enzyme transcripts and reduction in bacteria explain the paradoxical rise in some methanogen transcripts, likely reflecting indirect metabolic interactions. Methanogens that form symbiotic partnerships with anaerobic fungi via cellulose degradation in the rumen and rely on non-hydrogenotrophic methanogenesis pathways may remain unaffected by 3-NOP. This study provides new insights into the sink of hydrogen gas after methane inhibition in ruminants and the rebalancing of microorganisms. These results provide a theoretical foundation for optimizing strategies to mitigate methane emissions in ruminants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available in the main text or the supplementary materials. Raw reads of macrotranscriptome sequencing of ruminal microbiota are available at the National Center for Biotechnology Information (NCBI, project number PRJNA1257059).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e’\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization and research design: MW, YC, XZ; Research conduction and data acquisition: QL, SZ, XZ, WW; Data analysis: QL, SZ, XZ, XZ, WW, MW; Investigation: XZ, ZT, MW; Writing—original draft: YC, SZ, MW; Writing—reviewing \u0026amp; editing: all authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (Grant No. 2023YFD1300900, 2023YFD1300903), the Science and Technology Innovation Program of Hunan Province (Grant No. 2023RC3206, 2022RC3058), and Natural Science Foundation of Hunan Province (2025JJ70628).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimal experiments followed the Animal Care and Use Guidelines of the Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to express my sincere gratitude to xiuzhu Dong and Jie Li , two researchers of the Institute of Microbiology, Chinese Academy of Sciences, who provided technical support and experimental guidance for the pure bacteria experiment in this manuscript.\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\u003eReferences\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGruninger RJ, Zhang XM, Smith ML, Kung L Jr., Vyas D, McGinn SM, et al. Application of 3-nitrooxypropanol and canola oil to mitigate enteric methane emissions of beef cattle results in distinctly different effects on the rumen microbial community. Anim Microbiome. 2022;4(1):35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuin EC, Wagner T, Shima S, Prakash D, Cronin B, Y\u0026aacute;?ez-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. 2016:201600298.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirwan SF, Tamassia LFM, Walker ND, Karagiannis A, Kindermann M, Waters SM. Effects of dietary supplementation with 3-nitrooxypropanol on enteric methane production, rumen fermentation, and performance in young growing beef cattle offered a 50:50 forage:concentrate diet. J Anim Sci. 2024;102.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlemu AW, Gruninger RJ, Zhang XM, O'Hara E, Kindermann M, Beauchemin KA. 3-Nitrooxypropanol supplementation of a forage diet decreased enteric methane emissions from beef cattle without affecting feed intake and apparent total-tract digestibility. J Anim Sci. 2023;101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlemu AW, Shreck AL, Booker CW, McGinn SM, Pekrul LKD, Kindermann M et al. Use of 3-nitrooxypropanol in a commercial feedlot to decrease enteric methane emissions from cattle fed a corn-based finishing diet. J Anim Sci. 2021;99(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang XM, Smith ML, Gruninger RJ, Kung L, Vyas D, McGinn SM et al. Combined effects of 3-nitrooxypropanol and canola oil supplementation on methane emissions, rumen fermentation and biohydrogenation, and total tract digestibility in beef cattle. J Anim Sci. 2021;99(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePitta DW, Melgar A, Hristov AN, Indugu N, Narayan KS, Pappalardo C, Hennessy ML, Vecchiarelli B, Kaplan-Shabtai V, Kindermann M, Walker N. Temporal changes in total and metabolically active ruminal methanogens in dairy cows supplemented with 3-nitrooxypropanol. J Dairy Sci. 2021;104(8):8721\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaigaard M, Weisbjerg MR, Ohlsson C, Walker N, Lund P. Effects of different doses of 3-nitrooxypropanol combined with varying forage composition on feed intake, methane emission, and milk production in dairy cows. J Dairy Sci. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCostigan H, Shalloo L, Egan M, Kennedy M, Dwan C, Walsh S, et al. The effect of twice daily 3-nitroxypropanol supplementation on enteric methane emissions in grazing dairy cows. J Dairy Sci. 2024;107(11):9197\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang XM, Gruninger RJ, Alemu AW, Wang M, Tan ZL, Kindermann M, et al. 3-Nitrooxypropanol supplementation had little effect on fiber degradation and microbial colonization of forage particles when evaluated using the in situ ruminal incubation technique. J Dairy Sci. 2020;103(10):8986\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePitta DW, Indugu N, Melgar A, Hristov AE, Challa K, Vecchiarelli B et al. The effect of 3-nitrooxypropanol, a potent methane inhibitor, on ruminal microbial gene expression profiles in dairy cows. Microbiome. 2022;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez-Fern\u0026aacute;ndez G, Abecia L, Arco A, Cantalapiedra-Hijar G, Mart\u0026iacute;n-Garc\u0026iacute;a AI, Molina-Alcaide E, et al. Effects of ethyl-3-nitrooxy propionate and 3-nitrooxypropanol on ruminal fermentation, microbial abundance, and methane emissions in sheep. J Dairy Sci. 2014;97(6):3790\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartinez-Fernandez G, Duval S, Kindermann M, Schirra HJ, Denman SE, McSweeney CS. 3-NOP vs. Halogenated Compound: Methane Production, Ruminal Fermentation and Microbial Community Response in Forage Fed Cattle. Front Microbiol. 2018;9:1582.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Wang K, Nan X, Cai M, Yang L, Xiong B, et al. Synergistic Effects of 3-Nitrooxypropanol with Fumarate in the Regulation of Propionate Formation and Methanogenesis in Dairy Cows In Vitro. Appl Environ Microbiol. 2022;88(6):e0190821.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Q, Ma Z, Huo J, Zhang X, Wang R, Zhang S et al. Distinct microbial hydrogen and reductant disposal pathways explain interbreed variations in ruminant methane yield. ISME J. 2024;18(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwift CL, Brown JL, Sepp\u0026auml;l\u0026auml; S, O\u0026rsquo;Malley MA. Co-cultivation of the anaerobic fungus Anaeromyces robustus with Methanobacterium bryantii enhances transcription of carbohydrate active enzymes. J Ind Microbiol Biotechnol. 2019;46(9\u0026ndash;10):1427\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi QS, Wang R, Ma ZY, Zhang XM, Jiao JZ, Zhang ZG, et al. Dietary selection of metabolically distinct microorganisms drives hydrogen metabolism in ruminants. ISME J. 2022;16(11):2535\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreening C, Geier R, Wang C, Woods LC, Morales SE, McDonald MJ, et al. Diverse hydrogen production and consumption pathways influence methane production in ruminants. ISME J. 2019;13(10):2617\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDenman SE, Martinez Fernandez G, Shinkai T, Mitsumori M, McSweeney CS. Metagenomic analysis of the rumen microbial community following inhibition of methane formation by a halogenated methane analog. Front Microbiol. 2015;6:1087.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMenke KH, Raab L, Salewski A, Steingass H, Fritz D, Schneider W. The estimation of the digestibility and metabolizable energy content of ruminant feedingstuffs from the gas production when they are incubated with rumen liquor in vitro. J Agric Sci. 979;93(1):217\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller TL, Wolin MJ, Conway de Macario E, Macario AJ. Isolation of Methanobrevibacter smithii from human feces. Appl Environ Microbiol. 1982;43(1):227\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie F, Zhao S, Zhan X, Zhou Y, Li Y, Zhu W, Pope PB, Attwood GT, Jin W, Mao S. Unraveling the phylogenomic diversity of Methanomassiliicoccales and implications for mitigating ruminant methane emissions. Genome Biol. 2024;25(1):32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGil JC, Hird SM. Multiomics Characterization of the Canada Goose Fecal Microbiome Reveals Selective Efficacy of Simulated Metagenomes. Microbiol Spectr. 2022;10(6):e0238422.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLangmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta. 2023;2(2):e107.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11(1):119.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou Y, Yang Z, Liu J, Li X, Wang X, Dai C, et al. Crop rotation and native microbiome inoculation restore soil capacity to suppress a root disease. Nat Commun. 2023;14(1):8126.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang D, Wang Y, Liu L, Chen Y, Wang C, Li Y-Y, et al. Response and resilience of anammox consortia to nutrient starvation. Microbiome. 2022;10(1):23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe SH, Siddle KJ, Park DJ, Sabeti PC. Benchmarking Metagenomics Tools for Taxonomic Classification. Cell. 2019;178(4):779\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan M, Andersen TO, Pope PB, Yu Z. Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases. Genome Res. 2025;35(2):368\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAroney STN, Newell RJP, Nissen JN, Camargo AP, Tyson GW, Woodcroft BJ. CoverM: read alignment statistics for metagenomics. Bioinformatics. 2025;41(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang L, Zhang H, Wu P, Entwistle S, Li X, Yohe T, et al. dbCAN-seq: a database of carbohydrate-active enzyme (CAZyme) sequence and annotation. Nucleic Acids Res. 2018;46(D1):D516\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin L, Ma H, Zhang J, Yang H, Zhang J, Lai Z et al. Lignocellulolytic microbiomes orchestrating degradation cascades in the rumen of dairy cattle and their diet-influenced key degradation phases. Anim Adv. 2024;1(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang J, Zhang R, Chang J, Chen L, Nabi M, Zhang H, et al. Rumen microbes, enzymes, metabolisms, and application in lignocellulosic waste conversion - A comprehensive review. Biotechnol Adv. 2024;71:108308.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCozannet M, Borrel G, Roussel E, Moalic Y, Allioux M, Sanvoisin A et al. New Insights into the Ecology and Physiology of Methanomassiliicoccales from Terrestrial and Aquatic Environments. Microorganisms. 2020;9(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMassengo-Tiass\u0026eacute; RP, Cronan JE. Vibrio cholerae FabV Defines a New Class of Enoyl-Acyl Carrier Protein Reductase. J Biol Chem. 2008;283(3):1308\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanssen PH. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim Feed Sci Tech. 2010;160(1):1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatra A, Park T, Kim M, Yu Z. Rumen methanogens and mitigation of methane emission by anti-methanogenic compounds and substances. J Anim Sci Biotechno. 2017;8(1):13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorgavi DP, Forano E, Martin C, Newbold CJ. Microbial ecosystem and methanogenesis in ruminants. Animal. 2010;4(7):1024\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGastelen S, Burgers EEA, Dijkstra J, de Mol R, Muizelaar W, Walker N, et al. Long-term effects of 3-nitrooxypropanol on methane emission and milk production characteristics in Holstein-Friesian dairy cows. J Dairy Sci. 2024;107(8):5556\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenoit SL, Maier RJ, Sawers RG, Greening C. Molecular Hydrogen Metabolism: a Widespread Trait of Pathogenic Bacteria and Protists. Microbiol Mol Biol Rev. 2020;84(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUngerfeld EM. Metabolic Hydrogen Flows in Rumen Fermentation: Principles and Possibilities of Interventions. Front Microbiol. 2020;11:589.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeahy SC, Janssen PH, Attwood GT, Mackie RI, McAllister TA, Kelly WJ. Electron flow: key to mitigating ruminant methanogenesis. Trends Microbiol. 2022;30(3):209\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFeldewert C, Lang K, Brune A. The hydrogen threshold of obligately methyl-reducing methanogens. FEMS Microbiol Lett. 2020;367(17).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan J, Wang Y, Niu H, Li L, Zhao H, Fang L, et al. Metagenomic insights into the mechanistic differences of plant polyphenols and nitrocompounds in reducing methane emissions using the rumen simulation technique. Sci Total Environ. 2024;953:176135.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa J, Zhong P, Li Y, Sun Z, Sun X, Aung M, et al. Hydrogenosome, Pairing Anaerobic Fungi and H2-Utilizing Microorganisms Based on Metabolic Ties to Facilitate Biomass Utilization. J Fungi. 2022;8(4):338.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAkhmanova A, Voncken F, van Alen T, van Hoek A, Boxma B, Vogels G, et al. A hydrogenosome with a genome. Nature. 1998;396(6711):527\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang K, Xiong B, Zhao X. Could propionate formation be used to reduce enteric methane emission in ruminants? Sci Total Environ. 2023;855.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu G, Beauchemin KA, Dong R. A Review of 3-Nitrooxypropanol for Enteric Methane Mitigation from Ruminant Livestock. Animals. 2021;11(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang K, Gao P, Geng L, Liu C, Zhang J, Shu C. Lignocellulose degradation in Protaetia brevitarsis larvae digestive tract: refining on a tightly designed microbial fermentation production line. Microbiome. 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFroidurot A, Julliand V. Cellulolytic bacteria in the large intestine of mammals. Gut Microbes. 2022;14(1):2031694.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHagen LH, Brooke CG, Shaw CA, Norbeck AD, Piao H, Arntzen M\u0026Oslash;, et al. Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber. ISME J. 2021;15(2):421\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYeoman CJ, Fields CJ, Lepercq P, Ruiz P, Forano E, White BA et al. In Vivo Competitions between Fibrobacter succinogenes, Ruminococcus flavefaciens, and Ruminoccus albus in a Gnotobiotic Sheep Model Revealed by Multi-Omic Analyses. mBio. 2021;12(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGruninger RJ, Puniya AK, Callaghan TM, Edwards JE, Youssef N, Dagar SS, et al. Anaerobic fungi (phylum Neocallimastigomycota): advances in understanding their taxonomy, life cycle, ecology, role and biotechnological potential. FEMS Microbiol Ecol. 2014;90(1):1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Garc\u0026iacute;a A, Sabor\u0026iacute;o-Montero A, Guti\u0026eacute;rrez-Rivas M, Atxaerandio R, Goiri I, Garc\u0026iacute;a-Rodr\u0026iacute;guez A, et al. Fungal and ciliate protozoa are the main rumen microbes associated with methane emissions in dairy cattle. GigaScience. 2022;11:giab088.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLankiewicz TS, Lillington SP, O'Malley MA. Enzyme Discovery in Anaerobic Fungi (Neocallimastigomycetes) Enables Lignocellulosic Biorefinery Innovation. Microbiol Mol Biol Rev. 2022;86(4):e0004122.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsai SL, Oh J, Singh S, Chen R, Chen W. Functional assembly of minicellulosomes on the Saccharomyces cerevisiae cell surface for cellulose hydrolysis and ethanol production. Appl Environ Microbiol. 2009;75(19):6087\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDassa B, Borovok I, Ruimy-Israeli V, Lamed R, Flint HJ, Duncan SH, et al. Rumen cellulosomics: divergent fiber-degrading strategies revealed by comparative genome-wide analysis of six ruminococcal strains. PLoS ONE. 2014;9(7):e99221.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"3-nitrooxypropanol, ruminant methane mitigation, molecular hydrogen, propionate pathway, carbohydrate-active enzymes","lastPublishedDoi":"10.21203/rs.3.rs-7121971/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7121971/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eInhibition of methanogen by 3-NOP will produce excess hydrogen gas, which affects the nutrient digestion and fermentation functions of rumen microbiota. Certain hydrogenotrophic microorganisms contribute to a fraction of the hydrogen sink via processes such as propionate production, acetogenesis, and anion reduction. However, these mechanisms alone are insufficient to fully account for the sustained methane generation and the decline in hydrogen gas levels. A more comprehensive understanding is needed on the rebalancing process of rumen microbiota, the temporal dynamics and microbial drivers of 3-NOP resistance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe opted for a fully-automated fermentation system \u003cem\u003ein vitro\u003c/em\u003e to closely monitor the process by which 3-NOP inhibits methane production. At the onset of net hydrogen consumption (24h), we collected fermentation fluid samples (n\u0026thinsp;=\u0026thinsp;12) for metatranscriptomic sequencing, along with conducting a quantitative analysis of key microorganisms. The inhibition experiments of pure culture were also conducted on three different nutritional types of methane bacteria. 3-NOP significantly reduced the transcripts of the methane metabolism pathway, but significantly increased the transcripts of two propionate pathways(lactate and succinate). Based on annotating gene sets and mapping the transcriptomic reads to the assembled genome database, the transcripts of \u003cem\u003eMethanomassiliicoccales\u003c/em\u003e and a portion of \u003cem\u003eMethanobrevibacter\u003c/em\u003e were significantly elevated. Analysis of carbohydrate enzymes showed that 3-NOP significantly increased the transcription of cellulase in fungi (\u003cem\u003eNeocallimastigaceae\u003c/em\u003e) and \u003cem\u003eFibrobacter succinogenes\u003c/em\u003e, but significantly reduced the transcription of \u003cem\u003eRuminococcus flavefaciens\u003c/em\u003e and \u003cem\u003eRuminococcus albus\u003c/em\u003e. Methanogens that form symbiotic relationships with fungi (\u003cem\u003eNeocallimastigaceae\u003c/em\u003e) remain unaffected by 3-NOP through a coupled mechanism involving cellulose degradation and hydrogenosome activity in the rumen. These adaptive strategies of methanogenesis\u0026mdash;non-hydrogenotrophic and the symbiotic partnership with rumen fungi\u0026mdash;enable methanogens to attenuate the inhibitory effects of 3-NOP, potentially leading to resistance following prolonged exposure.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese findings underscore the need for caution in the sustained use of 3-NOP as a standalone methane mitigation strategy and highlight critical targets for developing next-generation inhibitors.\u003c/p\u003e","manuscriptTitle":"Metatranscriptomic changes in the propionate pathway and carbohydrate enzyme revealed the rebalancing mechanism of rumen microbiota after CH4 inhibition by 3-NOP","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 17:49:49","doi":"10.21203/rs.3.rs-7121971/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"706bd7dc-ad13-4cea-998e-8d4f479eb2da","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T06:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-11 17:49:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7121971","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7121971","identity":"rs-7121971","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}