Skip to main content
Download PDF

Reductive acetogenesis is a dominant process in the ruminant hindgut

Microbiome volume 13, Article number: 28 (2025) Cite this article

Abstract

Background

The microbes residing in ruminant gastrointestinal tracts play a crucial role in converting plant biomass to volatile fatty acids, which serve as the primary energy source for ruminants. This gastrointestinal tract comprises a foregut (rumen) and hindgut (cecum and colon), which differ in structures and functions, particularly with respect to feed digestion and fermentation. While the rumen microbiome has been extensively studied, the cecal microbiome remains much less investigated and understood, especially concerning the assembling microbial communities and overriding pathways of hydrogen metabolism.

Results

To address this gap, we comparatively investigated the composition, capabilities, and activities of the rumen and the cecum microbiome using goats as an experimental ruminant model. In situ measurements showed significantly higher levels of dissolved hydrogen and acetate in the cecum than in the rumen. Increased dissolved hydrogen indicated distinct processes and reduced coupling between fermentative H2 production and utilization, whereas higher levels of acetate could be caused by slower VFA absorption through cecal papillae than through the rumen papillae. Microbial profiling indicated that the cecum harbors a greater abundance of mucin-degrading microbes and fermentative hydrogen producers, whereas the rumen contains a higher abundance of fibrolytic fermentative bacteria, hydrogenotrophic respiratory bacteria, and methanogenic archaea. Most strikingly, reductive acetogenic bacteria were 12-fold more abundant in the cecum. Genome-resolved metagenomic analysis unveiled that the cecum acetogens are both phylogenetically and functionally distinct from those found in the rumen. Further supporting these findings, two in vitro experiments demonstrated a marked difference in hydrogen metabolism pathways between the cecum and the rumen, with increased acetate production and reduced methanogenesis in the cecum. Moreover, comparative analysis across multiple ruminant species confirmed a strong enrichment of reductive acetogens in the hindguts, suggesting a conserved functional role.

Conclusions

These findings highlight an enrichment of acetogenesis in a key region of the gastrointestinal tract and reshape our understanding of ruminant hydrogen metabolism and how the H2 can be managed in accord to livestock methane mitigation efforts.

Video Abstract

Introduction

Ruminants, a mainstay of the livestock industry and the first organism domesticated for husbandry, have a unique digestive system that relies on mutualistic microbes and release greenhouse gas methane [1]. These microbes enable ruminants to derive most of their energy and nutrients from otherwise indigestible fibrous plant cell wall materials [2, 3]. The key digestive organ characteristic of ruminants is their rumen, a permissive environment for a diverse community of bacteria, protists, fungi, methanogens, and viruses [2, 4]. Within this largest gastrointestinal chamber with a thick wall of keratinized epithelium and muscle pillars [5, 6], this rumen harbors a diverse population of cellulose- and hemicellulose-degrading microorganisms [3, 7, 8]. The cecum, situated at the junction of the small and large intestines within the diverticulum, serves as an additional fermentation chamber, facilitates the absorption of fermentation products and water, and forms feces from undigested matter [9, 10]. Differences in the morphological and nutrient composition of rumen and cecum likely select for compositionally and functionally distinct microbiota.

Molecular hydrogen (H2) and volatile fatty acids (VFAs) are central intermediates in the digestive processes of ruminants. In the ruminant gastrointestinal tract, bacteria, protists, and fungi hydrolyse and ferment carbohydrates to H2 and VFAs [11, 12]. Hydrogen production is mediated by hydrogenases, primarily fermentative and electron-bifurcating [FeFe]-hydrogenases, which reoxidize reduced cofactors from carbohydrate fermentation while converting the derived electrons into H2 [13]. A range of VFAs are also produced during these processes, including acetate, propionate, and butyrate, through a range of pathways, and the VFAs are absorbed as nutrients by the ruminants [8, 14]. H2 formation only remains thermodynamically favorable if H2 partial pressures are kept low, with various ruminant bacteria and archaea using H2 as an electron donor for energy and carbon acquisition, often through syntrophic relationships with fermenters [15,16,17]. Archaeal methanogenesis is the major H2 disposal pathway, keeping H2 partial pressures low but producing methane [18]. Diverse hydrogenotrophic bacteria also inhabit the ruminant gastrointestinal tract, such as reductive acetogens, fumarate- and nitrate-, and sulfate-reducing bacteria, which compete with methanogens for H2 [3, 19, 20].

It is well-konwn that most of the enteric methane produced in ruminants originates in the rumen rather than the hindgut, but little is known about the variation in H2 cycling and VFA production across different gastrointestinal regions in ruminants. Notably, acetate levels and acetate-to-propionate ratios are higher in the cecum than in the rumen [10, 21, 22], which suggests distinct electron flows and H2 metabolism between these two microbiomes [23]. This is further supported by higher H2 disposal through methanogenesis of goat rumen (85%) compared to rabbit cecum (25%) [24]. However, we currently lack a detailed species-resolved understanding of the processes and mediators controlling nutrient cycling between the two regions. To address this knowledge gap, we systematically compared the microbiome and its functions, VFA and gas levels, and host gene expression between the cecum and rumen, with goats as the experimental model and verification using in situ measurements and in vitro experiments. We provide multiple layers of evidence that unique acetogens are enriched and active in the cecum of goats, with potential relevance to other ruminants. 

Results and discussion

Hydrogen and acetate levels are elevated in the cecum compared to the rumen

We collected epithelium and digesta samples from the rumen and cecum of 16 growing healthy goats before morning feeding (Fig. 1A). The cecum exhibited a distinct epithelial structure and papillary morphology, including rougher and rounder papillae, in comparison with the rumen (Fig. 1B). Although the cecum had a slightly higher pH (p < 0.001, Fig. 1C), it contained a higher concentration of total VFAs (p < 0.01), as well as a higher concentration and molar proportion of acetate (p < 0.001) but a lower molar proportion of propionate (p < 0.01) and butyrate (p < 0.001) (Fig. 1E, F; Additional Fig. S1). These differences may reflect increased VFA production within the cecal microbiome and/or reduced absorption through the cecal epithelium. Measurement of dissolved H2 (dH2), a central electron carrier produced during fermentation and mainly consumed by methanogens in the rumen [3], revealed a four-fold higher dH2 concentration in the cecum compared to the rumen (p < 0.001, Fig. 1D). Such significantly increased dH2 concentrations indicate distinct processes and reduced coupling between fermentative H2 production and utilization in the cecum.

Fig. 1
figure 1

Distinct microbiomes, metabolomes, and epithelial functions in the rumen and cecum. A Graphical representation of samples collected from the two distinct digestive tract segments of goats, B Epithelial tissue morphology, C Lumen pH, D lumen dH2 concentrations, E Total lumen VFA concentrations, F Molar proportions of VFAs, G Protein expression of G-protein coupled receptors 43 (GPR43) as visualized after immunofluorescent staining, and H Top 10 enriched KEGG pathways in the ruminal and the cecal epithelium (other pathways are shown in Additional File S2). The undefined Metabolic pathways category was the result of too many genes being enriched under this category. I Differentially expressed genes (DEGs) involved in VFA absorption and intracellular pH regulation. J 16S rRNA gene copy numbers of total bacteria. K 16S rRNA gene copy numbers of total methanogens. L Relative abundance of major bacterial phyla. M Most differentially abundance bacterial genera identified using LEfSe (only the genera with LDA > 4.2 are shown, with others shown in Additional File S3). Abbreviations: VFA, volatile fatty acid; dH2, dissolved hydrogen; HMGCS, 3-hydroxy-3-methylglutaryl-CoA synthase; HMGCL, 3-hydroxy-3-methylglutaryl-CoA lyase; ATPeV1B, vacuolar H+ ATPase subunit B; BDH, β-hydroxybutyrate dehydrogenase. ** p < 0.01, *** p < 0.001, n = 16 per group

Full size image

To understand the varying VFA concentrations between the rumen and the cecum, we compared the expression of genes involved in VFA absorption. Immunofluorescent staining revealed reduced levels of GPR43 in cecum papillae (Fig. 1G), a VFA receptor widely distributed throughout the gastrointestinal tract involved in controlling host energy accumulation [25]. Transcriptomic analysis identified 2276 differentially expressed genes (fold-change > 2, p < 0.05) in the epithelia of the rumen and cecum (Additional File S1). Principal component analysis (PCA) further showed that these genes were distinct between the rumen and cecum (Additional Fig. S2) and were primarily associated with undefined metabolic pathways (Fig. 1H, Additional File S2). For example, proteins involved in VFA absorption and intracellular pH regulation, including 3-hydroxy-3-methylglutaryl-CoA synthase (HMGCS), 3-hydroxy-3-methylglutaryl-CoA lyase (HMGCL), vacuolar H+ ATPase subunit B (ATPeV1B), and β-hydroxybutyrate dehydrogenase (BDH) [26,27,28], were expressed at a higher level in the rumen than in the cecum (p < 0.001, Fig. 1I). These results indicate that VFA absorption through cecal papillae is probably slower than that through the rumen papillae, explaining the relatively higher VFA concentration in the cecum, at least in part.

We analyzed the rumen and cecum microbiomes through 16S rRNA gene amplicon sequencing and qPCR to understand their contribution to the observed VFA disparity. Compared to the rumen, the cecum contained a significantly lower population of total bacteria (6.7-fold lower, p < 0.01, Fig. 1J) and archaeal methanogens (2.8-fold lower, p < 0.01, Fig. 1K), but a higher ratio of methanogens to bacteria (3.1% vs 1.3%). Principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarity revealed distinct bacterial communities (p < 0.001) between the rumen and cecum (Additional Fig. S3). At the phylum level, Bacteroidota and Spirochaetota were enriched in the rumen microbiota, whereas Firmicutes, Verrucomicrobiota, Proteobacteria, and Actinobacteriota were relatively more abundant in the cecum microbiota (p < 0.001, Fig. 1L, Additional File S3). Linear discriminant analysis (LDA) identified 15 key bacterial genera differentially abundance between the two gastrointestinal regions [29] (p < 0.001, Fig. 1M, Additional File S4). Among these genera, Prevotella and Lachnospiraceae XPB1014, two genera with known to participate in forage fiber degradation [30, 31]. In contrast, Ruminococcaceae UCG-005 and Christensenellaceae R7 were the most abundant and representative cecal genera (Additional Fig. S4), previously known for their roles in sugar degradation and gut health maintenance [32, 33]. Acetate levels positively correlated with these two genera and several other genera, with the strongest correlation with Akkermansia, Bacteroidales UCG_010_ge, Ruminococcaceae UCG_002, and unclassified Lachnospiraceae (Additional Fig. S5). Dissolved H2 concentrations were also exhibited a positive correlation with several genera of microbes, particularly Christensenellaceae R7, Monoglobus, and unclassified Ruminococaceae (Additional Fig. S5). Thus, these differentially enriched taxa may contribute to the differential electron flows and H2 metabolism observed between the rumen and the cecum.

Distinct processes of carbohydrate, VFA, and hydrogen metabolism in the cecum

We screened contigs assembled from the cecum and rumen metagenomes for carbohydrate-active enzymes (CAZymes), the microbial enzymes responsible for degrading plant biomass to hexoses and pentoses [34,35,36], which can be then fermented to VFA. These two types of metagenomes exhibited distinct CAZymes as demonstrated by PCoA, and some CAZymes genes were more abundant in the rumen microbiome (p < 0.01, Additional Fig. S6). The most abundant family of CAZymes in both regions were glycoside hydrolases (GH) (Additional File S5), which hydrolyze glycosidic linkages within a wide range of polysaccharides [37, 38]. The rumen had a higher abundance GHs degrading cellulose (p < 0.001, 3.80-fold higher) and hemicellulose (p < 0.001, 3.45-fold higher), such as β-xylosidase GH43 (p < 0.001, Fig. 2A; Additional Fig. S7, Additional File S6), along with three starch-degrading enzymes (p < 0.001, 3.60-fold higher; Additional Fig. S8, Additional File S6). Whereas the cecum exhibited a higher abundance of GHs degrading host-derived glycans (p < 0.05, 1.25-fold higher), including α-N-acetylgalactosaminidase GH123 (p < 0.05, Fig. 2A; Additional Fig. S7, Additional File S6). Mucin degradation may weaken the protective mucosal layer, and emerging evidence suggests that it also stimulates mucin secretion, mucosal renewal, and thickening, an important mechanism for repelling pathogens and selecting favorable bacteria [20, 39]. The two gastrointestinal regions also differed in the types of genes encoding hemicellulases, with the rumen enriched in 17 GHs (e.g., GH2, GH10) and the cecum enriched in 7 GHs (e.g., GH16, GH141). In cases where fold changes in GH enzymes were significant but numerically close to unity, their biological significance regarding the digestive process might be minor or negligible, a fact that would need to be verified by studying enzyme activity. Additionally, the cecum and the rumen microbiomes differed in the abundance of genes related to VFA production from carbohydrates (Additional Fig. S8). The cecum microbiome had a greater abundance of four key genes involved in fermentative acetate production (i.e., porA, porD, porG, and ackA, p < 0.05) (Fig. 2B, Additional Fig. S9, Additional File S7), whereas the rumen microbiome showed a greater abundance of six key genes involved in butyrate production (i.e., crt, fabV, atoA, atoD, ptb, and buk genes, p < 0.001) and three key genes involved in propionate production via the succinate pathway (i.e., fumA, sucC, and MCEE genes, p < 0.001) (Fig. 2B, Additional Fig. S9, Additional File S7). These results suggest that the cecum may select for microorganisms that favor fermentative acetate production by enriching GHs hydrolyzing host glycans.

Fig. 2
figure 2

Distinct functions in the rumen and cecum microbiomes. A Abundance of GH families and their enrichment in various prokaryotic phyla. B Relative abundance of genes involved in acetate, propionate, butyrate production, and the Wood-Ljungdahl pathway, and their enrichment in various bacterial phyla. C Relative abundance and phylum-level distribution of hydrogenases and terminal reductases expressed as transcripts per million (TPM). Classifications: Bifurcating hydrogenases (group A3 and A4 [FeFe]-hydrogenases), fermentative hydrogenases (groups B, A1 and A2 [FeFe]-hydrogenases), energy-converting hydrogenases (groups 4a, 4c, 4e, 4f, 4 g [NiFe]-hydrogenases), sensory hydrogenases (group C [FeFe]-hydrogenases), methanogenic hydrogenases (group 3a, 3c, 4h, 4i [NiFe]-hydrogenases, [Fe]-hydrogenases). HydB, hydrogenase-associated diaphorase. H2 uptake pathways can be coupled to methanogenesis (McrA, methyl-CoM reductase), reductive acetogenesis (AcsB, acetyl-CoA synthase), fumarate reduction (FrdA, fumarate reductase), nitrate ammonification (NrfA, ammonia-forming nitrite reductase; NarG, dissimilatory nitrate reductase; NapA, periplasmic nitrate reductase), sulfate and sulfite reduction (AprA, adenylylsulfate reductase; AsrA, alternative sulfite reductase; DsrA, dissimilatory sulfite reductase), dimethyl sulfoxide and trimethylamine N-oxide reduction (DmsA, DMSO and TMAO reductase). Only genes with an average abundance > 5 TPM were shown, while others are shown in Additional File S8. Abbreviations: AA, acetyl-CoA to acetate; AB, acetyl-CoA to butyrate; PLP, propionate formation via the lactate pathway; PSP, propionate formation via the succinate pathway; WLP, Wood-Ljungdahl pathway. Data with error bars are expressed as mean ± standard error. Significance was tested using independent two-group Wilcoxon rank-sum tests. * p < 0.05, *** p < 0.001, n = 16 per group

Full size image

We subsequently examined the pathways of H2 production and consumption by identifying the hydrogenase genes. A total of 16,883 gene sequences were annotated as hydrogenase, which were distributed among 306 microbial genera across 23 phyla (Additional File S8). The trimeric group A3 [FeFe]-hydrogenases (identified based on the presence of genes encoding the bifurcating hydrogenase subunit hydA and diaphorase subunit hydB) were the most abundant in both gastrointestinal regions and were highly enriched in the cecum (Fig. 2C). Primarily encoded by the classes Clostridia and Bacteroidia (Fig. 2C, Additional File S8), these hydrogenases were hypothesized to be mainly responsible for H2 production and utilization in the rumen [13]. The group B [FeFe]-hydrogenases were abundant and highly enriched (eight-fold) in the cecum. This group of hydrogenases, though functionally unresolved was the most predominant hydrogenases and was implicated in fermentative H2 production in the human colon [40]. Additionally, several other hydrogenases were also more enriched in the cecum (sensory group C1 and C3 [FeFe]-, energy-converting group 4g [NiFe]-, and hypothetical group A2 [FeFe]-hydrogenases (p < 0.001, Fig. 2C, Additional File S8). In contrast, the rumen displayed a greater abundance of sensory group C2 [FeFe]-hydrogenases (4.1-fold higher) and energy-converting 4e [NiFe]-hydrogenases (1.7-fold higher) (p < 0.001, Fig. 2C, Additional File S8). Altogether, these findings suggest elevated H2 production and distinct H2 cycling processes in the cecum, which aligns with observed higher dH2 concentrations detected in the cecal samples (Fig. 1D).

The gastrointestinal regions also contained distinct hydrogenotrophic microorganisms, based on profiling of their hydrogenases and terminal reductases. The capacity for methanogenesis appeared to be similar between these two regions, as indicated by the similar abundance of genes encoding methyl-CoM reductase and methanogen-specific hydrogenases ([Fe]-hydrogenases, groups 3a, 3c, and 4i [NiFe]-hydrogenases) (Fig. 2C, Additional File S8, Additional Fig. S10, Additional File S8). Several hydrogenotrophic bacteria compete with methanogens for H2 in the gastrointestinal region of ruminants, including those that mediate reductive acetogenesis, fumarate reduction, nitrate ammonification, and sulfate reduction [13, 41]. The signature gene for reductive acetogenesis (acetyl-CoA synthase, acsB, p < 0.001) was 12-fold more abundant in the cecum than in the rumen (Fig. 2C, Additional File S8). This enrichment of reductive acetogenesis is also reflected by increased levels of nine genes (i.e., fhs, acsB, acsC, acsD, acsE, cooS, metF, fchA, folD) of the Wood-Ljungdahl pathway (Additional Fig. S9, Additional File S7). In contrast, the rumen microbiome encoded a greater proportion of genes involved in fumarate reduction (fumarate reductase, frdA, p < 0.001, 2-folder higher), nitrate ammonification (ammonia-forming nitrite reductase, nrfA and dissimilatory nitrate reductase, narG, p < 0.001, 51-folder and ten-folder higher), and sulfate reduction (adenylylsulfate reductase, aprA, p < 0.001, 7.4-folder higher) (Fig. 2C, Additional File S8). Altogether, these findings suggest that the cecum favors reductive acetogenesis over hydrogenotrophic respiration, consistent with the higher concentration of dH2 in the cecum (Fig. 1D), which can enhance the competitiveness of reductive acetogenesis [42, 43]. Compared to the rumen, such predominance of reductive acetogenesis was consistent with the higher molar proportion of acetate and acetate to propionate ratio in the cecum (Fig. 1F).

Genome-centric metagenomic confirms novel acetogens are enriched in the cecum

To gain insights into the aforementioned differences at the species level, we performed metagenomic binning, yielding 2358 medium- or higher-quality metagenome-assembled genomes (MAGs, completeness ≥ 50% and contamination ≤ 10%) after quality control and dereplication (Additional File S9). The 2324 bacterial MAGs were distributed across 19 phyla, with Firmicutes (represented by 1463 MAGs), Bacteroidota (637 MAGs), and Spirochaetota (70 MAGs) being the most predominant (Fig. 3A, Additional File S9). In addition to the bacterial diversity, we detected 34 methanogenic MAGs classified into two archaeal phyla, Methanobacteriota (primarily Methanobrevibacter spp.) and Halobacteriota (primarily Methanocorpusculum spp.) (Additional Fig. S11, Additional Fig. S12, Additional File S9). Interestingly, while Halobacteriota MAGs were distributed in both regions, they were enriched in the cecum, which aligns with their reported roles in low-methane production during hindgut fermentation in monogastric animals such as horses [44].

Fig. 3
figure 3

Phylogenetically and functionally distinct microbial MAGs are enriched in the rumen and the cecum microbiomes. A A phylogenetic tree of 2324 MAGs encoding CAZymes or hydrogenases. B A heatmap depicting the microbial phyla and their enzymes identified from the 2324 MAGs. Z-scores between alignments of rumen/cecum samples are used for correcting abundance, and major phyla are expressed as log10 (TPM + 1). Significance was tested using independent two-group Wilcoxon rank-sum tests. * p < 0.05, ** p < 0.01, *** p < 0.001, n = 16 per group

Full size image

The MAGs were functionally annotated to comparatively examine their potential capacity in carbohydrate degradation, fermentation, and H2 cycling. Most bacterial MAGs encoded GHs, with 80% encoding enzymes involved in the degradation of cellulose (80% MAGs), hemicellulose (81%), and host glycans (72%). Notably, the cecum was enriched with MAGs classified as Faecousia, Akkermansia, and Alistipes, and these MAGs encoded GH123, a GH enzyme involved in hydrolyzing host glycans (p < 0.05, Fig. 3B, Additional File S10). In contrast, the rumen-enriched MAGs were assigned to Prevotella, Cryptobacteroides, Ruminococcus, and Butyrivibrio (p < 0.01), and they mainly encode cellulases (e.g., GH3) and hemicellulases (e.g., GH43) (Fig. 3B, Additional File S10). Additionally, fermentative acetate-producing bacteria were enriched in the cecum (e.g., MAG587, MAG985).

Two-thirds of the MAGs carried hydrogenase genes, encompassing a wide range of subgroups. MAGs encoding H2-producing [FeFe]-hydrogenases were particularly prevalent, with 1488 MAGs (63% of the total) identified in both gastrointestinal regions (Fig. 3A, Additional File S10, and S11). The cecum was enriched with MAGs predicted to produce H2 using the electron-confurcating trimeric group A3 [FeFe]-hydrogenases (e.g., MAG264) and the fermentative group B [FeFe]-hydrogenases (e.g., MAG270). A total of 300 MAGs (13%) encoded both H2-consuming hydrogenases and terminal reductases, including those involved in reductive acetogenesis (52 MAGs), fumarate reduction (176 MAGs), nitrate ammonification (34 MAGs), and sulfate reduction (65 MAGs) (Additional File S11). In the rumen microbiome, the most enriched were Desulfovibrio and Succiniclasticum MAGs capable of hydrogenotrophic sulfate and fumarate reduction, respectively (p < 0.001, Fig. 3B, Additional File S11). Notably, putative reductive acetogens from the class Clostridia were enriched in the cecum microbiome, each encoding both [FeFe]-hydrogenases and the acetyl-CoA synthase (p < 0.001, Fig. 3B, Additional File S11). These results point to distinct microbes selected in the rumen and the cecum that utilize different pathways of H2 metabolism, with acetogenesis likely being a significant H2 sink within the cecal microbiome.

We comprehensively compared the nine acetogenic MAGs obtained from this study with 15 reference genomes of acetogens [45, 46]. Genome-based phylogenetic analysis showed that these MAGs lack cultured representatives, spanning five new genera, namely SIG483 and RGIG7114 from the family Oscillospiraceae and HGM12587, SIG280, and RGIG5612 from the family Lachnospiraceae (Fig. 4A, Additional File S12). Nevertheless, a phylogenetic tree based on the AcsB sequences revealed that these organisms encoded either one or two copies of the canonical acetyl-CoA synthase gene, suggesting their potential to mediate acetogenesis (Fig. 4B). This is further supported by detailed annotation, which shows that the MAGs from each of the five novel genera encoded the complete set of enzymes of the Wood-Ljungdahl pathway, together with the group A3 [FeFe]-hydrogenase, Nfn transhydrogenase, and energy-converting Rnf or Ech enzymes. This highlights a more comprehensive view of carbon fixation and energy conservation through reductive acetogenesis in these organisms (Fig. 4C). The H2-dependent carbon dioxide reductase (HDCR) [47,48,49] was absent in all but one MAG (MAG2125), suggesting formate, rather than CO2 is utilized in the Wood-Ljungdahl pathway by these organisms. This is consistent with the adaptation of gastrointestinal acetogens to their formate-rich habitats [46] or through metabolic cross-feeding of formate generated by other bacteria [50]. The HGM12587 MAGs also lacked the genes involved in the further conversion of acetyl-CoA to acetate. Finally, we compared the composition of the acetogenic communities between the rumen and cecum microbiomes based on both the novel and reference MAGs [45, 46]. The Bray–Curtis analysis confirmed that the two communities were distinct (p < 0.001, Additional Fig. S13), with a greater number of acetogens enriched in the cecum, where 15 MAGs (e.g., HGM12587 and SIG483) enriched in the cecum and 9 others (e.g., SIG280 and RGIG7114) enriched in the rumen (Fig. 4A).

Fig. 4
figure 4

Maximum-likelihood phylogenetic trees of acetogenic MAGs, the AcsB gene sequences, and the Wood-Ljungdahl pathway reconstructed from the identified genes. A A maximum-likelihood phylogenetic tree of nine acetogenic MAGs obtained from this study and 15 reference genomes of acetogens. The heatmap indicates the relative abundance of MAG based on short reads in the rumen and the cecum samples. B A maximum-likelihood phylogenetic tree based on 36 AcsB sequences encoded by the 24 MAGs. C The Wood-Ljungdahl pathway reconstructed from nine novel acetogenic MAGs at the genus level. For both trees, the triangles show the reference genomes of acetogens, and the pentagrams show acetogenic MAGs assembled in this study. MAGs are colored based on their phylogenetic affiliation at the family level. Bootstrap values > 70% are indicated as black circles at the nodes, and scale bars indicate the mean number of substitutions per site. Detailed data are presented in Additional File S12. Abbreviations: HDCR, hydrogen-dependent carbon dioxide reductase; Fhs, formate-tetrahydrofolate (THF) ligase; Fch, methenyl-THF cyclohydrolase; Fol, methylene-THF dehydrogenase; Met, methylene-THF reductase; MT, methyltransferase; CODH/ACS, carbon monoxide dehydrogenase/acetyl-CoA synthetase

Full size image

In vitro experiments reveal the distinct pathway of hydrogen metabolism in the cecum

To confirm the distinct pathway of hydrogen metabolism in cecal microbiomes, we conducted two in vitro experiments comparing the biochemical activities of the cecum and rumen microbiomes. In the first experiment, corn stover served as the substrate, the rumen inocula demonstrated increased dry matter degradability (DMD), VFA concentrations, and methane production, whereas the cecum inocula exhibited greater H2 accumulation (p < 0.05, Fig. 5A). These findings indicate that the rumen microbiome possesses a greater capacity for fiber degradation and hydrogenotrophic methanogenesis, leading to lower H2 accumulation. In the second in vitro experiment, we further compared the ability of the rumen and the cecum microbiome to utilize H2. Both rumen and cecal microbiomes significantly decreased H2 levels compared to the uninoculated control and increased headspace methane concentration, approaching an asymptote (p < 0.05, Fig. 5B). After 48 h of incubation, the rumen microbiota consumed more gaseous H2 and produced more methane than the cecum microbiome (p < 0.05, Fig. 5B). These results further confirm the greater capacity of hydrogenotrophic methanogenesis for rumen microbiome.

Fig. 5
figure 5

In vitro metabolic activities of the rumen and the cecum microbiomes. A Feed degradation, methane (CH4) production, gaseous hydrogen (gH2) accumulation, and VFA profiles after 72-h in vitro incubation with corn stover as a substrate. B Utilization of headspace H2 and CH4 production during 48-h incubation in experiment 2 without adding any substrate. * p < 0.05, ** p < 0.01, *** p < 0.001, n = 6 per group

Full size image

Further analysis of the VFA profiles indicated that rumen inocula had a higher molar proportion of propionate whereas the cecum inocula resulted in a higher molar proportion of acetate with a concurrently increased acetate-to-propionate ratio (p < 0.05, Fig. 5A). Inoculation with fiber-selected microbiome showed the paradoxically increased acetate production with decreased methane production than starch-selected microbiome, and could be attributed to the greater community of reductive acetogens [41]. Acetate production during fermentative carbohydrate fermentation is accompanied by H2 release, whereas acetate production through reductive acetogenesis is an H2 sink [51]. Generally, elevated fermentative acetate production is consistently associated with an increase in methane production [52, 53]. Heightened acetate production with reduced methane production by the cecal microbiome is further supported by the enrichment of reductive acetogenesis in the cecal microbiome, which might have incorporated electrons into reductive acetate production, leading to a reduced H2 production for methanogenesis. This aligns with the enrichment MAGs classified as acetogens in the cecal microbiome.

Acetogens are enriched in the hindguts of diverse ruminants

Building upon the metagenomic data reported in our previous study [19], we sought to determine whether our findings could be extended to other ruminants, including dairy cattle, sheep, roe deer, buffalo, water deer, and yak. Our comparative analysis revealed a significant enrichment of the acsB gene a marker signature gene of the reductive acetogenesis pathway in the cecal microbiomes of all the ruminants relative to their rumen microbiome (p < 0.001): by 22-fold in dairy cows, 58-fold in goats, 111-fold in roe deer, 14-fold in sheep, ten-fold in water buffalo, 133-fold in water deer, and 20-fold in yak (Fig. 6A). Notably, reductive acetogens were particularly abundant in the cecum of dairy cattle and buffalo, while water deer exhibited relatively lower abundance. The taxonomic classification assigned nearly all detected acetogens to the class Clostridia. The enrichment of reductive acetogenesis in the cecal microbiome was further supported by a high abundance of other genes encoding key enzymes involved in the Wood-Ljungdahl pathway (Additional File S13). Collectively, these results suggest a generalized phenomenon of more significant enrichment of reductive acetogenesis in ruminants’ cecum.

Fig. 6
figure 6

Distinct hydrogen metabolism in the rumen and cecum of different ruminants. A Relative abundance of bacterial phyla whose genomes carry acsB, the marker gene of the reductive acetogenesis pathway. The data were obtained from Xie et al. [19]. B Summary of key pathways, enzymes, microorganisms, and metabolites involved in hydrogen metabolism in the rumen (left-hand part) and the cecum (right-hand part) microbiomes. The data are presented in detail in Additional File S14

Full size image

Conclusion

In this study, we provide a consolidated view of the distinct microbes and processes in the rumen and cecum, the two primary fermentation sites of ruminants (Fig. 6B). The rumen shows a greater capacity for fiber degradation, propionate formation, and hydrogenotrophic methanogenesis; this is reflected in lower dH2 concentrations in the foregut, likely attributed to enhanced H2 consumption by methanogenesis and reduction of fumarate, nitrate, and sulfate. In contrast, the cecum has a higher capacity for utilizing host-derived glycans, evidenced by the enrichment of mucin-degrading bacteria. Additionally, the cecum is enriched with H2-producing fermentative bacteria and novel lineages of reductive acetogens that convert formate to acetate. Figure 6B presents a comparative overview of microbial communities and metabolic processes in the rumen and the cecum. Collectively, our findings provide novel insights about distinct microbial community and dominant reductive acetogenesis in the cecum microbiome, and help to understand gastrointestinal energy harvesting strategies, hydrogenotrophic growth potential, and enteric methanogenesis in ruminants. Given the finding that acetogenesis is a dominant process in the cecum, further work is needed to isolate the acetogens from the hindgut, investigate its metabolic features, and demonstrate the potential for redirecting electron and H2 flows from methane to acetate amid ongoing ruminant methane mitigation.

Materials and methods

Animals, and sampling and measurements

Sixteen healthy goats (Capra hircus), with an average body weight of 19.1 ± 2.75 kg, were employed as experimental ruminants and fed a solid diet with a concentrate and forage ratio of 60:40 for 120 days. The goats were slaughtered before morning feeding to collect luminal contents and epithelial tissues from the rumen and the cecum for subsequent measurement of pH, dH2, and VFA concentrations, microbial DNA extraction, and tissue RNA extraction. Full details of these methods are included in the supplementary materials and methods.

Immunofluorescence staining

The rumen and cecum tissue samples were fixed with paraformaldehyde, embedded in paraffin, and then sectioned at 4 µm on a rotary microtome. The paraffin sections were stained using a SABC kit (Boster, Wuhan, China) and then incubated with the GPR43 (1:400 diluted) antibody (Santa Cruz Biotechnology, Texas) at 4°C overnight. Images were acquired from each slide under a laser confocal microscope (Beckman, German) with the same immunofluorescence microscopy parameters within a single section to minimize variations in background levels.

Tissue RNA-Seq and bioinformatic analysis

Full details of RNA extraction are available in the supplementary materials. Sequencing libraries were prepared utilizing the NEBNext UltraTM RNA Library Prep Kit for the Illumina system (New England Biolabs, Ipswich, MA, USA), with index barcodes added for demultiplexing after sequencing. The libraries were pooled and paired-end sequenced on the Hiseq XTEN platform (Illumina, San Diego, CA). The raw sequencing data were quality-filtered using Trimmomatic [54] (version 0.36). Then, the remaining reads were aligned to the reference genome of the domesticated goat (Capra hircus) using HISAT2 [55] (version 2.2.1) to remove host sequences. mRNA expression profiles were quantified for each sample using featureCounts [56] (version 2.0.1), with read counts normalized to transcripts per million (TPM). Differentially expressed genes (DEGs) were identified using edgeR [57] and were filtered based on fold change and false discovery rate. KEGG enrichment analyses of DEGs were conducted using KOBAS [58] (version 3.0).

Quantification of select bacteria and methanogens using qPCR

Quantitative real-time PCR (qPCR) was performed using the procedures detailed by Ma et al. [59]. In brief, the qPCR assays were performed on a LightCycler 480 (Roche Molecular Systems Inc., Pleasanton, CA) using SYBR and primers validated in our laboratory (Supplemental Table S1). Absolute abundance was expressed as copies of the 16S rRNA gene per milliliter of sample (log10 transformed).

16S ribosomal RNA sequencing and analysis

Individual amplicon libraries were prepared by PCR amplification of the V3-V4 region of the prokaryotic 16S rRNA gene using primers 341F (5′-barcode-CCTAYGGGRBGCASCAG-3′) and 806R (5′- GGACTACNNGGGTATCTAAT −3′), each with a unique 6-bp barcode [60]. All amplicon libraries were pooled at an equal molar ratio and then paired-end sequenced using a MiSeq platform (Illumina, San Diego, CA). The detailed sequencing information for each sample is listed in Supplemental Table S2. Amplicon sequence variants (ASVs) were then used to analyze the prokaryotic community with fully detailed methodologies included in the supplementary materials and methods.

Shotgun metagenome sequencing

For each sample, 1 μg of metagenomic DNA was fragmented using the Covaris S220 Focused-ultrasonicator (Woburn, MA, USA) to generate DNA fragments with an average length of approximately 350 bp. These DNA fragments were then used to prepare sequencing libraries. Subsequently, the libraries from all samples were pooled and paired-end sequenced (2 × 150 bp) on the HiSeq X platform (Illumina, San Diego, CA). The detailed sequencing information is listed in Supplemental Table S2. Prediction of open reading frames (ORFs) within the assembled contigs was conducted and quantified after eliminating adaptor contaminants and low-quality reads. The detailed methodologies were included in the supplementary materials and methods.

Metagenomic assembly and binning

The quality control and assembly details for metagenomes were included in the supplementary materials and methods. The assembled contigs from each sample were binned using MetaBAT [61] (version 0.32.5), MetaBAT2 [62] (version 2.12.1), MaxBin2 [63] (version 2.2.4–1), and CONCOCT [64] (version 1.0.0). The bins resulting from all the binning methods were then integrated using the DAS tool [65] (version 1.1.6). The completeness and contamination rates of MAGs were assessed using CheckM2 [66] (version 1.0.1). The MAGs with a completeness of ≥ 50% and a contamination rate of ≤ 10% (3439 MAGs) were then dereplicated using dRep [67] (version 3.3.0) at a 99% average nucleotide identity (ANI) cutoff. This resulted in a total of 2358 nonredundant MAGs for further analysis. The relative abundance of individual MAGs in each sample was estimated using CoverM (version 0.6.1, https://github.com/wwood/CoverM).

Functional annotation

The gene catalog and genomes were aligned with the carbohydrate-active enzymes (CAZymes) database [36] using HMMER [68] and DIAMOND [64] to obtain CAZymes annotations through three approaches. A query was acceptable only if all three approaches (DIAMOND, dbCAN_sub, and dbCAN), reported the same match. Functional annotation of the catalog was performed using KofamKOALA [69] (https://www.genome.jp/tools/kofamkoala/), with a focus on metabolic pathways for the production of VFA (i.e., acetate, propionate, and butyrate) through carbohydrate fermentation. Hydrogenases were identified and classified using HydDB [70] with DIAMOND [71], with genes encoding terminal reductases screened as done by Chris et al. [13] and Li et al. [41]. Specific parameters are detailed in the supplementary materials.

Taxonomic, phylogenetic, and functional analyses of genomes

The MAGs were taxonomically classified using GTDB-Tk [72] (version 2.1.1). IQ-TREE [73] (version 2.2.2.7) was used to construct a maximum-likelihood phylogenetic tree. KOs of MAGs were annotated using METABOLIC [74] (version 4.0) based on KEGG. Protein sequences encoded by the 2358 MAGs were also screened against the HydDB database [70] to identify the catalytic subunits of the three classes of hydrogenases and terminal reductases using DIAMOND [71]. Novel acetogens were identified based on the marker gene acsB. The phylogenetic tree of novel acetogens and reference acetogen genomes was constructed using PhyloPhlAn [75]. Function protein sequences (i.e., AcsB) were trimmed by trimAI [76], and then multiple sequence alignment was performed with MAFFT [77]. IQ-TREE [73] was used to construct a phylogenetic tree of AcsB. All the above phylogenetic trees were visualized using iTOL [78] (https://itol.embl.de/).

Measurements of microbiome activity through in vitro experiments

The first in vitro experiment (“In vitro Experiment 1”) was conducted to compare the fermentative activities of the rumen and cecal inocula with corn stover as the sole substrate, as described by Wang et al. [79]. The second in vitro experiment (“In vitro Experiment 2”) was conducted to compare the hydrogenotrophic activities of the rumen and cecum microbiomes by adding H2 gas to the headspace of the incubation bottles at the beginning of the incubations. Liquid samples of the in vitro experiments were collected after incubation for 48 h. VFA profiles were analyzed in the same way as the rumen and cecal samples [12]. Refer to the supplementary materials for detailed information regarding the in vitro ruminal fermentation, sample collection, and measurements.

Statistical analysis

The metabolite concentration and production in both the in vivo and in vitro experiment were subjected to a paired samples t-test using SPSS 21.0 software (SPSS Inc., Chicago, IL). Microbial community composition, qPCR results, and the relative abundance of functional genes, derived from amplicon and metagenomic data [80], were analyzed using the Wilcoxon rank-sum test in JMP Pro software (version 16.1.0, SAS Institute Inc., Cary, NC, USA). All p values were adjusted for FDR using the Benjamini–Hochberg method. Statistical significance was considered at p ≤ 0.05, and tendencies were declared at 0.05 < p ≤ 0.10.

Data availability

Amplicon and metagenomic sequences are available at the National Center for Biotechnology Information (project number PRJNA1068802 and PRJNA1068803, respectively). All the bioinformatic scripts are available at https://github.com/liqiushuang596/Rumen-Cecum. All other data supporting the results of this study are available in the article or Supplementary information.

References

  1. Wang M, Wan D, Xie X, Bai Z, Wang R, Zhang X, et al. Crop-livestock integration: Implications for food security resource efficiency and greenhouse gas mitigation The Innovation Life. 2024;2:100103.

  2. Pérez-Barbería FJ. The Ruminant: Life History and Digestive Physiology of a Symbiotic Animal. In: García-Yuste S, editor. Sustainable and Environmentally Friendly Dairy Farms. Cham: Springer International Publishing; 2020:19–45.

  3. Mizrahi I, Wallace RJ, Moraïs S. The rumen microbiome: balancing food security and environmental impacts. Nat Rev Microbiol. 2021;19:553–66.

    Article  PubMed  CAS  Google Scholar 

  4. H. Karasov W, Douglas AE. Comparative Digestive Physiology. Compr Physiol. Compr Physiol. 2013; 3: 741–783.

  5. Janis C. Evolution of Horns in Ungulates: Ecology and Paleoecology. Biol Rev. 1982;57:261–318.

    Article  Google Scholar 

  6. Miller WF, Titgemeyer EC, Nagaraja TG, Watanabe DHM, Felizari LD, Millen DD, et al. Influence of cane molasses inclusion to dairy cow diets during the transition period on rumen epithelial development. Animals. 2021;11:1230.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Moraïs S, Mizrahi I. Islands in the stream: from individual to communal fiber degradation in the rumen ecosystem. Fems Microbiol Rev. 2019;43:362–79.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lean IJ, Golder HM, Hall MB. Feeding, evaluating, and controlling rumen function. Vet Clin North Am Food Anim Pract. 2014;30:539–75.

    Article  PubMed  Google Scholar 

  9. Grond K, Kurtz CC, Hatton J, Sonsalla MM, Duddleston KN. Gut microbiome is affected by gut region but robust to host physiological changes in captive active-season ground squirrels. Anim Microbiome. 2021;3:56.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Dixon RM, Nolan JV. Studies of the large intestine of sheep: Fermentation and absorption in sections of the large intestine. Br J Nutr. 1982;47:289–300.

    Article  PubMed  CAS  Google Scholar 

  11. Ellis JL, Dijkstra J, Kebreab E, Bannink A, Odongo NE, McBride BW, et al. Aspects of rumen microbiology central to mechanistic modelling of methane production in cattle. J Agric Sci. 2008;146:213–33.

    Article  CAS  Google Scholar 

  12. Wang M, Sun XZ, Janssen PH, Tang SX, Tan ZL. Responses of methane production and fermentation pathways to the increased dissolved hydrogen concentration generated by eight substrates in in vitro ruminal cultures. Anim Feed Sci Tech. 2014;194:1–11.

    Article  Google Scholar 

  13. 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;13:2617–32.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Gressley TF, Hall MB, Armentano LE. RUMINANT NUTRITION SYMPOSIUM: Productivity, digestion, and health responses to hindgut acidosis in ruminants. J Anim Sci. 2011;89:1120–30.

    Article  PubMed  CAS  Google Scholar 

  15. Janssen PH. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Anim Feed Sci Tech. 2010;160:1–22.

    Article  CAS  Google Scholar 

  16. Chin K-J, Janssen PH. Propionate Formation by Opitutus terrae in Pure Culture and in Mixed Culture with a Hydrogenotrophic Methanogen and Implications for Carbon Fluxes in Anoxic Rice Paddy Soil. Appl Environ Microbiol. 2002;68:2089–92.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Kappler O, Janssen PH, Kreft J-U, Schink B. Effects of alternative methyl group acceptors on the growth energetics of the O-demethylating anaerobe Holophaga foetida. Microbiology+ 1997; 143: 1105–1114.

  18. Wang R, Bai Z, Chang J, Li Q, Hristov AN, Smith P, et al. China’s low-emission pathways toward climate-neutral livestock production for animal-derived foods. The Innovation. 2022;3: 100220.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Xie F, Jin W, Si H, Yuan Y, Tao Y, Liu J, et al. An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants. Microbiome. 2021;9:137.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Lin L, Lai Z, Zhang J, Zhu W, Mao S. The gastrointestinal microbiome in dairy cattle is constrained by the deterministic driver of the region and the modified effect of diet. Microbiome. 2023;11:10.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Popova M, Morgavi DP, Martin C. Methanogens and Methanogenesis in the Rumens and Ceca of Lambs Fed Two Different High-Grain-Content Diets. Appl Environ Microb. 2013;79:1777–86.

    Article  CAS  Google Scholar 

  22. Popova M, McGovern E, McCabe MS, Martin C, Doreau M, Arbre M, et al. The Structural and Functional Capacity of Ruminal and Cecal Microbiota in Growing Cattle Was Unaffected by Dietary Supplementation of Linseed Oil and Nitrate. Front Microbiol. 2017;8:937.

  23. Leahy SC, Janssen PH, Attwood GT, Mackie RI, McAllister TA, Kelly WJ. Electron flow: key to mitigating ruminant methanogenesis. Trends Microbiol. 2022;30:209–12.

    Article  PubMed  CAS  Google Scholar 

  24. Karekar S, Stefanini R, Ahring B. Homo-Acetogens: Their Metabolism and Competitive Relationship with Hydrogenotrophic Methanogens. Microorganisms Microorganisms. 2022;10:397.

    Article  PubMed  CAS  Google Scholar 

  25. Kimura I, Inoue D, Hirano K, Tsujimoto G. The SCFA Receptor GPR43 and Energy Metabolism. Front Endocrinol. 2014;5:85.

  26. Yan L, Zhang B, Shen Z. Dietary modulation of the expression of genes involved in short-chain fatty acid absorption in the rumen epithelium is related to short-chain fatty acid concentration and pH in the rumen of goats. J Dairy Sci. 2014;97:5668–75.

    Article  PubMed  CAS  Google Scholar 

  27. Laarman AH, Ruiz-Sanchez AL, Sugino T, Guan LL, Oba M. Effects of feeding a calf starter on molecular adaptations in the ruminal epithelium and liver of Holstein dairy calves. J Dairy Sci. 2012;95:2585–94.

    Article  PubMed  CAS  Google Scholar 

  28. Xie F, Xu L, Wang Y, Mao S. Metagenomic Sequencing Reveals that High-Grain Feeding Alters the Composition and Metabolism of Cecal Microbiota and Induces Cecal Mucosal Injury in Sheep. MSystems. 2021;6:e00915–e921.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Betancur-Murillo CL, Aguilar-Marín SB, Jovel J. Prevotella: A Key Player in Ruminal Metabolism. Microorganisms. 2023;11:1.

    Article  CAS  Google Scholar 

  31. Li D, Leahy S, Henderson G, Kelly W, Cookson A, Attwood G, et al. Atypical bacterial rRNA operon structure is prevalent within the Lachnospiraceae, and use of the 16S–23S rRNA internal transcribed spacer region for the rapid identification of ruminal Butyrivibrio and Pseudobutyrivibrio strains. Ann Microbiol. 2014;64:1623–31.

    Article  CAS  Google Scholar 

  32. Ma Y, Deng X, Yang X, Wang J, Li T, Hua G, et al. Characteristics of Bacterial Microbiota in Different Intestinal Segments of Aohan Fine-Wool Sheep. Front Microbiol. 2022;13:874536.

  33. Wang X, Hu L, Liu H, Xu T, Zhao N, Zhang X, et al. Characterization of the bacterial microbiota across the different intestinal segments of the Qinghai semi-fine wool sheep on the Qinghai-Tibetan Plateau. Anim Biosci. 2021;34:1921–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Kameshwar AKS, Qin W. Genome Wide Analysis Reveals the Extrinsic Cellulolytic and Biohydrogen Generating Abilities of Neocallimastigomycota Fungi. J Genomics. 2018;6:74–87.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Baldwin RL, Connor EE. Rumen Function and Development. Vet Clin North Am Food Anim Pract. 2017;33:427–39.

    Article  PubMed  Google Scholar 

  36. Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–495.

    Article  PubMed  CAS  Google Scholar 

  37. Wardman JF, Bains RK, Rahfeld P, Withers SG. Carbohydrate-active enzymes (CAZymes) in the gut microbiome. Nat Rev Microbiol. 2022;20:542–56.

    Article  PubMed  CAS  Google Scholar 

  38. Lin L, Ma H, Zhang J, Yang H, Zhang J, et al. Lignocellulolytic microbiomes orchestrating degradation cascades in the rumen of dairy cattle and their diet-influenced key degradation phases. Animal Adv. 2024;1:e002.

  39. La Rosa SL, Ostrowski MP, Vera-Ponce de León A, McKee LS, Larsbrink J, Eijsink VG, et al. Glycan processing in gut microbiomes. Curr Opin Microbiol. 2022; 67:102143.

  40. Wolf PG, Biswas A, Morales SE, Greening C, Gaskins HR. H2 metabolism is widespread and diverse among human colonic microbes. Gut Microbes. 2016;7:235–45.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. 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;16:1–12.

  42. Ungerfeld EM. A theoretical comparison between two ruminal electron sinks. Front Microbiol. 2013;4:319.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ma ZY, Zhang XM, Wang M, Wang R, Jiang ZY, Tan ZL, et al. Molecular hydrogen produced by elemental magnesium inhibits rumen fermentation and enhances methanogenesis in dairy cows. J Dairy Sci. 2019;102:5566–76.

    Article  PubMed  CAS  Google Scholar 

  44. Li C, Li X, Guo R, Ni W, Liu K, Liu Z, et al. Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse. Microbiome. 2023;11:7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Drake HL, Gössner AS, Daniel SL. Old acetogens, new light. Ann N Y Acad Sci. 2008;1125:100–28.

    Article  PubMed  CAS  Google Scholar 

  46. Yao Y, Fu B, Han D, Zhang Y, Wei Z, Liu H. Reduction, evolutionary pattern and positive selection of genes encoding formate dehydrogenase in Wood-Ljungdahl pathway of gastrointestinal acetogens suggests their adaptation to formate-rich habitats. Env Microbiol Rep. 2023;15:129–41.

    Article  CAS  Google Scholar 

  47. Schuchmann K, Müller V. Direct and Reversible Hydrogenation of CO2 to Formate by a Bacterial Carbon Dioxide Reductase. Science. 2013;342:1382–5.

    Article  PubMed  CAS  Google Scholar 

  48. Dietrich HM, Righetto RD, Kumar A, Wietrzynski W, Trischler R, Schuller SK, et al. Membrane-anchored HDCR nanowires drive hydrogen-powered CO2 fixation. Nature. 2022;607:823–30.

    Article  PubMed  CAS  Google Scholar 

  49. Müller V. New Horizons in Acetogenic Conversion of One-Carbon Substrates and Biological Hydrogen Storage. Trends Biotechnol. 2019;37:1344–54.

    Article  PubMed  Google Scholar 

  50. Ni G, Walker N, Fischer A, Stemmler RT, Schmidt O, Jain S, et al. Methanogenesis inhibition remodels microbial fermentation and stimulates acetogenesis in ruminants. 2024. Preprint at https://www.biorxiv.org/content/10.1101/2024.08.15.608071v1.

  51. Ungerfeld EM. Metabolic Hydrogen Flows in Rumen Fermentation: Principles and Possibilities of Interventions. Front Microbiol. 2020;11:589.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Wang M, Wang R, Xie TY, Janssen PH, Sun XZ, Beauchemin KA, et al. Shifts in Rumen fermentation and microbiota are associated with dissolved ruminal hydrogen concentrations in lactating dairy cows fed different types of carbohydrates. J Nutr. 2016;146:1714–21.

    Article  PubMed  CAS  Google Scholar 

  53. Beauchemin KA, Ungerfeld EM, Eckard RJ, Wang M. Review: Fifty years of research on rumen methanogenesis: lessons learned and future challenges for mitigation. Animal. 2020;14:s2–16.

    Article  PubMed  CAS  Google Scholar 

  54. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-Based Genome Alignment and Genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907–15.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

    Article  PubMed  CAS  Google Scholar 

  57. Dimont E, Shi J, Kirchner R, Hide W. edgeRun: an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test. Bioinformatics. 2015;31:2589–90.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011; 39: W316–W322.

  59. Ma ZY, Zhang XM, Wang R, Wang M, Liu T, Tan ZL. Effects of chemical and mechanical Lysis on microbial DNA yield, Integrity, and downstream amplicon sequencing of rumen bacteria and protozoa. Front Microbiol. 2020;11:581227.

  60. Zakrzewski M, Goesmann A, Jaenicke S, Jünemann S, Eikmeyer F, Szczepanowski R, et al. Profiling of the metabolically active community from a production-scale biogas plant by means of high-throughput metatranscriptome sequencing. J Biotechnol. 2012;158:248–58.

    Article  PubMed  CAS  Google Scholar 

  61. Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.

  62. Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7: e7359.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinform. 2016; 32: 605–607.

  64. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.

    Article  PubMed  CAS  Google Scholar 

  65. Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Chklovski A, Parks DH, Woodcroft BJ, Tyson GW. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods. 2023;20:1203–12.

    Article  PubMed  CAS  Google Scholar 

  67. Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Wheeler TJ, Eddy SR. nhmmer: DNA homology search with profile HMMs. Bioinformatics. 2013;29:2487–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2020;36:2251–2.

    Article  PubMed  CAS  Google Scholar 

  70. Søndergaard D, Pedersen CNS, Greening C. HydDB: A web tool for hydrogenase classification and analysis. Sci Rep. 2016;6:34212.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.

    Article  PubMed  CAS  Google Scholar 

  72. Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformat. 2019;36:1925–7.

  73. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol Biol Evol. 2015;32:268–74.

    Article  PubMed  CAS  Google Scholar 

  74. Zhou Z, Tran PQ, Breister AM, Liu Y, Kieft K, Cowley ES, et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome. 2022;10:33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Asnicar F, Thomas AM, Beghini F, Mengoni C, Manara S, Manghi P, et al. Precise phylogenetic analysis of microbial isolates and genomes from metagenomes using PhyloPhlAn 3.0. Nat Commun. 2020; 11:2500.

  76. Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Rozewicki J, Li S, Amada KM, Standley DM, Katoh K. MAFFT-DASH: integrated protein sequence and structural alignment. Nucleic Acids Res. 2019;47:W5–10.

    PubMed  PubMed Central  CAS  Google Scholar 

  78. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Wang M, Wang R, Tang SX, Tan ZL, Zhou CS, Han XF, et al. Comparisons of manual and automated incubation systems: Effects of venting procedures on in vitro ruminal fermentation. Livest Sci. 2016;184:41–5.

    Article  Google Scholar 

  80. Xu S, Zhan L, Tang W, Wang Q, Dai Z, Zhou L, et al. MicrobiotaProcess: A comprehensive R package for deep mining microbiome. The Innovation. 2023;4: 100388.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Funding

This work was supported by the National Key Research and Development Program of China (Grant No. 2023YFD1300902), National Natural Science Foundation of China (Grant No. U22A20512), Hunan Province Science and Technology Plan (2022RC3058), Ningxia Province Science and Technology Plan (2021BEF02020), China Agriculture Research System of MOF and MARA. An NHMRC EL2 Fellowship (APP1178715; awarded to C.G) and a Monash Faculty of Medicine, Nursing, and Health Sciences Early Career Postdoctoral Fellowship 2023 (ECPF23–8566329039; awarded to G.N.).

Author information

Author notes

  1. Qiushuang Li and Jiabin Huo contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, Hunan, China

    Qiushuang Li, Jiabin Huo, Fan Zhang, Shizhe Zhang, Xiumin Zhang, Rong Wang, Jinzhen Jiao, Xuanxuan Pu, Yipeng Yue, Xiaoli Zhang, Jian Wu, Zhiliang Tan & Min Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Qiushuang Li, Jiabin Huo, Shizhe Zhang, Zhiliang Tan & Min Wang

  3. Biomedicine Discovery Institute, Department of Microbiology, Monash University, Clayton, Australia

    Gaofeng Ni & Chris Greening

  4. Department of Animal Sciences, The Ohio State University, Columbus, OH, USA

    Zhongtang Yu

  5. Centro Regional de Investigación Carillanca, Instituto de Investigaciones Agropecuarias (INIA), Temuco, Chile

    Emilio M. Ungerfeld

Authors
  1. Qiushuang Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jiabin Huo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Gaofeng Ni
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Fan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Shizhe Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Xiumin Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Rong Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Jinzhen Jiao
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Zhongtang Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Xuanxuan Pu
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Yipeng Yue
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Emilio M. Ungerfeld
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Xiaoli Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  14. Jian Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  15. Zhiliang Tan
    View author publications

    You can also search for this author inPubMed Google Scholar

  16. Chris Greening
    View author publications

    You can also search for this author inPubMed Google Scholar

  17. Min Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Conceptualization and research design: MW, JH, QL, ZT; Research conduction and data acquisition: JH, QL, FZ; Data analysis: QL, JH, SZ, GN, XP, YY, XZ, MW, JW; Investigation: XZ, RW, JJ, JW, ZT, MW; Writing—original draft: QL, JH, MW, CG, EU, ZY; Writing—reviewing & editing: all authors. We also thank Xiyang Dong, Qiang Qiu, and Zhipeng Li for their discussions. 

Corresponding author

Correspondence to Min Wang.

Ethics declarations

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.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1. Differentially expressed genes in the epithelia of the rumen and cecum.

Additional file 2. Enriched KEGG pathways in the ruminal and cecal epithelia.

Additional file 3. The relative abundance of rumen and cecum microbes.

Additional file 4. The linear discriminant analysis (LDA) score of the biomarkers.

Additional file 5. The relative abundance of CAZymes families enriched in the rumen and cecum microbiomes.

Additional file 6. The relative abundance of GH families enriched in the rumen and cecum microbiome.

40168_2024_2018_MOESM7_ESM.xlsx

Additional file 7. Phylogenetic distribution of sequences of KEGG pathways associated with VFA production enriched in the rumen and cecum microbiome.

40168_2024_2018_MOESM8_ESM.xlsx

Additional file 8. Phylogenetic distribution of sequences of hydrogenase and terminal reductase genes enriched in the rumen and cecum microbiome.

Additional file 9. Profiles of MAGs.

Additional file 10. Function annotations of MAGs.

Additional file 11. Sequences of hydrogenase and terminal reductase genes identified among MAGs.

Additional file 12. Profiles of acetogenic MAGs.

Additional file 13. The relative abundance of the Wood-Ljungdahl pathway enriched in the rumen and cecum microbiome.

Additional file 14. Consolidated results from recruitment analysis of enzymes and genomes.

Additional file 15. Supporting information.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Q., Huo, J., Ni, G. et al. Reductive acetogenesis is a dominant process in the ruminant hindgut. Microbiome 13, 28 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-024-02018-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40168-024-02018-1

Keywords

Microbiome

ISSN: 2049-2618

Contact us