Hydrogen-Rich Water as a potential strategy for improving ruminant nutrition and mitigating methane emissions

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The experimental design included four treatment groups: control group (CON), 200ppb HRW group (HRW 200ppb ), 400ppb HRW group (HRW 400ppb ), and 800ppb HRW group (HRW 800ppb ). Each group was analyzed at 12-hour (h) and 48-hour (h) time points with five replicates, totaling 40 samples. The results showed that the highest gas production and methane content were observed in the 800ppb HRW group among the four groups. However, the 200ppb HRW group had significantly lower methane content during both 12 h and 48 h fermentations compared to the other treatment groups ( P < 0.05). In terms of rumen fermentation indicators, the 400ppb HRW group significantly increased the levels of ammonia nitrogen (NH 3 -N) and microbial crude protein (MCP), but reduced the dry matter degradation rate at 12 h fermentation ( P < 0.05). After the 48 h fermentation, the HRW 400ppb group had the highest MCP content ( P 0.05). Although HRW did not significantly benefit the synthesis of total volatile fatty acids (TVFA) and individual VFA, the HRW 800ppb group significantly increased the ratio of acetate to propionate ( P < 0.05). Based on these results, we selected the HRW 400ppb group for subsequent bacterial community analysis. Bacterial community analysis showed that compared with the CON group, the HRW 400ppb group had significant increases in the Simpson index, Firmicutes, Streptococcus , Schwartzia , Prevotellaceae_YAB2003_group , and Oribacterium , and significant decreases in the Prevotella , Ruminobacter , Succinivibrio , unclassified Succinivibrionaceae , and Prevotellaceae_UCG-003 at 12 h fermentation ( P < 0.05). As fermentation time extended to 48 h, the differential bacterial communities changed. The abundance of Prevotellaceae_YAB2003_group and Oribacterium significantly increased, while the abundance of Rikenellaceae_RC9_gut_group and Succiniclasticum significantly decreased in the HRW group ( P < 0.05). Correlation analysis revealed the negative associations between CH 4 and Streptococcus . Moreover, the abundance of Rikenellaceae_RC9_gut_group positively correlated with the CH 4 . Collectively, these results indicate that HRW can modulate rumen fermentation and microbial community structure to reduce methane emissions without significantly affecting VFA synthesis, highlighting its potential as drinking water for enhancing ruminant nutrition and mitigating the environmental impact of livestock farming. drinking water hydrogen-rich water microbial diversity in vitro ruminal fermentation methanogenesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Ruminants can convert fibrous plants into edible meat and milk products for human consumption. This process requires the participation of rumen microorganisms, including bacteria, archaea, fungi, and ciliated protozoa,which can produce volatile fatty acids (VFA), microbial proteins (MCP), and vitamins for the host animals[ 1 , 2 ]. VFA supplies 70–80% of the energy to ruminants[ 3 ] and MCP provides a high level of protein resources for host animals[ 4 ]. While this fermentation is vital for the nutritional enhancement of dietary intake, it also inevitably leads to methane production—a potent greenhouse gas. Research has found that efficient beef cattle produce 20% less methane than inefficient ones[ 5 ]. Therefore, exploring strategies to regulate the activity of rumen microbiota to reduce methane production while maintaining animal production efficiency is of great scientific and practical significance. Hydrogen-rich water (HRW) is a form of potable water that has been super-saturated with molecular hydrogen gas (H 2 ) through pressurized dissolution[ 6 ]. The hydrogen molecules are extremely small, so they can easily penetrate water and stay dissolved for a while. In recent years, it has been widely used and applied in many fields such as medicine, agriculture, sports, and beauty[ 7 ]. The widespread adoption of HRW can be largely attributed to its beneficial properties, such as its antioxidant, anti-inflammatory, and anti-apoptotic effects, coupled with a proven high safety profile[ 8 ]. However, there are few studies on hydrogen-rich water in ruminants. Kuru[ 8 ] found that administering HRW to goats during the peripartum period may improve the health and survival of kids and reduce their mortality. At present, the specific mechanism of HRW is still unclear in ruminants. Some studies speculated that intestinal microorganisms might be the main target organ of hydrogen molecules[ 10 ]. Hydrogen metabolism is related to many microorganisms in the intestinal microbiota[ 11 ]. HRW intake could increase the abundance of Lactobacillus, Ruminococcus, and Clostridium[ 12 ], fortifying intestinal structural integrity and upregulation of butyrate-producing bacteria, in turn, ameliorated clinical features associated with gut microbiota disturbance[ 10 ]. On the other hand, in ruminants, improving the metabolic efficiency of hydrogen can affect the proliferation of hydrogenotrophic bacteria, thereby reducing the production of ruminal methane[ 13 ]. However, there is a lack of in-depth research on the impact of HRW on the structure and function of the rumen microbiota in ruminant animals, as well as its mechanism of action on the rumen fermentation and methane production process. This study aims to fill this research gap by using a comprehensive set of technical methods, including in vitro fermentation tests, microbial community analysis, and metabolite detection, to explore the potential impact of HRW on rumen microbiota. We hypothesize that HRW may alter the hydrogen metabolism pathways within the rumen by regulating the composition and metabolic activities of rumen microbiota, thereby exerting a regulatory effect on methane production. The expected results of this study will provide new insights into the understanding and regulation of rumen microbiota metabolic activities, and offer potential solutions for reducing methane emissions from ruminants. Results Total gas production and methane production The production of total gas and methane at 12 h and 48 h fermentation are shown in Fig. 1 . After 12 h of fermentation, the HRW 800ppb group demonstrated the highest production of total gas and methane gas, reaching 53.35 mL and 13.58 mL, respectively, with the total gas production significantly exceeding the other three groups ( P 0.05). At 48 h of fermentation, HRW800 ppb maintained the highest production of total gas and methane gas, with 78.56 mL and 12.80 mL, respectively, both of which were significantly higher than those in the HRW 200ppb and HRW 400ppb groups ( P < 0.05). Nevertheless, there were no significant differences compared to the CON group. The methane gas production of the HRW 200ppb group was significantly lower than the other three groups at both 12 and 48 h of fermentation. Rumen Characteristics The results of rumen fermentation characteristics are shown in Table 2 . Different concentration of HRW affected rumen fermentation parameters. After 12 h of fermentation, significant differences in pH values were observed among the four groups, with HRW 800ppb (pH = 6.59) and HRW 400ppb (pH = 7.03) showing significantly lower pH values compared to HRW 200ppb (pH = 7.25, P < 0.001) and the CON group (pH = 6.43, P < 0.001). In terms of MCP, the HRW 400ppb group exhibited the highest MCP content at 31.67 mg/dL, followed by the HRW 200ppb group at 27.45 mg/dL, while the HRW 800ppb group had a significantly lower MCP content than the CON group (20.85 mg/dL vs. 28.38 mg/dL, P < 0.001). At 48 h of fermentation, the trend in pH values was similar to that at 12 h, with HRW 400ppb and HRW 800ppb groups showing significantly higher pH values compared to the HRW 200ppb and CON groups ( P < 0.001). Regarding MCP, the HRW 400ppb group maintained the highest MCP content, followed by the HRW 200ppb group, and both were significantly higher compared to the CON group ( P = 0.001). After 12 h of fermentation, the HRW group of NH 3 -N levels were significantly higher than that in the CON group ( P < 0.001). While, at 48 h of fermentation, the CON group exhibited the highest NH 3 -N levels at 12.21 mg/dL, which were significantly different from those in the HRW groups ( P = 0.018). The dry matter degradation rate at 12 h was significantly higher in the CON and HRW 800ppb groups compared to the HRW 200ppb and HRW 400ppb groups ( P < 0.001), while there was no significant effects between the HRW groups and the CON group ( P = 0.187) at 48 h. Table 1 Composition and nutrient levels of experimental diet (air-dry basis, %) Ingredients Content Nutritional composition g/kg of DM Wheat Straw 50.71 Metabolic energy (ME), MJ/kg 9.59 Corn 20.00 Crude protein (CP) 141.2 wheat bran 3.49 MP to CP ratio, MJ/g 0.068 Soybean meal 19.80 Neutral detergent fiber 428.0 Calcium bicarbonate 0.50 Acid detergent fiber 266.4 Calcium hydrophosphate 0.50 Premix a 4.00 Limestone 0.50 Salt 0.50 Tatol 100 1 The premix (per kg of diet) is: 1400 mg of Fe, 1200 mg of Zn, 250 mg of Cu, 900 mg of Mn, 100,000 IU of vitamin A, 27,000 IU of vitamin D3, and 800 IU of vitamin E. Table 2 Rumen fermentation characteristics in vitro rumen fermentation. Item CON HRW 200ppb HRW 400ppb HRW 800ppb SEM P -value pH value 12 h 6.43 d 7.20 a 7.03 b 6.59 c 0.025 < 0.001 48 h 6.60 b 6.98 a 7.08 a 6.50 b 0.027 < 0.001 Microbial crude protein, mg/dL 12 h 28.38 ab 27.45 b 31.67 a 20.85 c 0.588 < 0.001 48 h 36.08 b 45.23 a 45.97 a 34.34 b 0.986 0.001 Ammonia nitrogen, mg/dL 12 h 4.64 c 7.66 a 6.62 ab 5.99 b 0.194 < 0.001 48 h 12.21 a 9.76 bc 11.95 ab 8.86 c 0.387 0.018 Dry matter degradability, % 12 h 45.07 a 30.16 c 40.66 b 48.53 a 0.725 < 0.001 48 h 65.35 ab 62.71 b 66.13 ab 68.62 a 0.907 0.187 a,b Means within a row with no common superscript differ significantly ( P < 0.05). CON = control; HRW = hydrogen-rich water. SEM = stand error of mean. The results of VFAs are shown in Table 3 . After 12 h of fermentation, the CON group demonstrated the highest levels of acetate, propionate, branched-chain amino acids, and TVFA, with concentrations of 32.39 mM, 16.16 mM, 0.56 mM, and 52.20 mM, respectively, which were significantly higher than those in the HRW 200ppb and HRW 400ppb groups ( P < 0.05). Notably, the HRW 800ppb group exhibited the lowest propionate content ( P < 0.05). The acetate-to-propionate ratio and the non-glucogenic-to-glucogenic acids ratio at 12 h of fermentation were significantly higher in the HRW 800ppb group compared to the other groups ( P < 0.05). After 48 h of fermentation, the HRW 200ppb group exhibited markedly reduced isobutyrate levels in comparison to the other groups ( P < 0.05). In contrast, the HRW 800ppb group displayed significantly elevated butyrate concentration and an increased acetate-to-propionate ratio among the four groups ( P < 0.05). The CON group had significantly higher valerate and isovalerate contents compared to the HRW 200ppb and HRW 800ppb groups ( P < 0.05). Additionally, the TVFA content in CON group’s also significantly higher than the other 3 groups ( P < 0.05), with a notable decrease observed in the HRW 200ppb group ( P < 0.05). The HRW 800ppb group had a highest non-glucogenic to glucogenic acids ratio, but a lowest fermentation efficiency compared to the CON and HRW 200ppb groups ( P < 0.05). At 48 h, the HRW 200ppb group had a significantly lower non-glucogenic to glucogenic acids ratio and a higher fermentation efficiency than the other groups ( P < 0.05) Table 3 Rumen fermentation total volatile fatty acids (VFA) and individual VFAs in vitro rumen fermentation. Item DW HRW 200ppb HRW 400ppb HRW 800ppb SEM P -value Acetate, mM 12 h 32.39 a 21.58 c 26.54 b 32.59 a 0.455 < 0.001 48 h 35.44 a 23.10 c 30.14 b 30.88 b 0.503 < 0.001 Propionate, mM 12 h 16.16 a 11.17 b 11.47 b 7.63 c 0.270 < 0.001 48 h 19.76 a 14.91 b 15.88 b 14.73 b 0.235 < 0.001 Isobutyrate, mM 12 h 0.04 0.04 0.05 0.05 0.002 0.282 48 h 0.13 a 0.08 b 0.14 a 0.17 a 0.006 0.002 Butyrate, mM 12 h 3.19 3.77 3.09 4.33 0.301 0.457 48 h 4.55 b 4.15 bc 3.64 c 7.40 a 0.142 < 0.001 Isovalerate, mM 12 h 0.14 ab 0.12 b 0.14 ab 0.15 a 0.003 0.011 48 h 0.36 a 0.21 c 0.32 ab 0.27 bc 0.015 0.017 Valerate, mM 12 h 0.38 a 0.28 b 0.29 b 0.31 b 0.005 < 0.001 48 h 0.39 a 0.27 b 0.43 a 0.29 b 0.013 0.001 Branched-chain volatile fatty acids, mM 12 h 0.56 a 0.45 c 0.48 bc 0.51 ab 0.009 0.003 48 h 0.87 a 0.57 b 0.90 a 0.73 a 0.031 0.006 Total volatile fatty acids, mM 12 h 52.20 a 36.97 b 41.59 b 43.49 b 0.921 < 0.001 48 h 60.62 a 42.73 c 50.56 b 53.74 b 0.711 < 0.001 Acetate to propionate ratio 12 h 2.00 b 1.95 b 2.32 b 4.21 a 0.100 < 0.001 48 h 1.79 b 1.55 c 1.90 b 2.10 a 0.032 < 0.001 Non-glucogenic to glucogenic acids ratio 12 h 2.36 c 2.59 bc 2.80 b 5.10 a 0.069 < 0.001 48 h 2.23 a 2.09 b 2.32 a 3.07 a 0.043 < 0.001 Fermentation efficiency 12 h 0.78 a 0.78 a 0.77 a 0.72 b 0.003 < 0.001 48 h 0.79 b 0.80 a 0.78 bc 0.77 c 0.002 < 0.001 a,b Means within a row with no common superscript differ significantly ( P < 0.05). CON = control; HRW = hydrogen-rich water; Branched-chain volatile fatty acids are the sum of isobutyrate, valerate, and isovalerate. SEM = stand error of mean. Rumen Bacteria From 20 samples, a total of 909,772 clean reads were detected with an average of 45488.6 for each sample (Table. S1). The composition of bacteria across the 20 samples was dominated by 951 OTU, 16 phyla, and 224 genera (Table. S2). The alpha diversity at 12 h and 48 h of fermentation was estimated by the Simpson and Ace (Fig. 2 ). Compared with the CON group, the HRW group significantly increased the Simpson index (Fig. 2 A, C), whereas no significant differences were observed in Ace at 12 h and 48 h (Fig. 2 B, D). To measure the extent of similarity between the microbial communities, beta diversity was calculated using a weighted normalized UniFrac, and the PCoA was performed. As shown in Fig. 2 E, F. The microbial community profiles of the HRW were grouped to the right of the PCoA, and CON was grouped to the left of the PCoA. PERMANOVA analysis found that the two groups were significantly different at 12 h and 48 h (R = 0.988, P = 0.004; R = 0.660, P = 0.004). The taxonomic analysis of the reads revealed that the dominant phyla were Firmicutes, Bacteroidota, and Proteobacteria at 12 h and 48 h of fermentation, accounting for > 99% of total reads (Fig. 3 A, B). Among the three phyla, supplementing with HRW could significantly increase the relative abundance of Firmicutes, and decrease the relative abundance of Bacteroidota and Proteobacteria at 12 h of fermentation (Fig. 4 A, B, C, P < 0.05), while no significant differences were observed at 48 h of fermentation (Fig. 5 A, B, C, P < 0.05). At the genus level, the predominance of the genus is depicted in Figs. 3 C and 3 D for the 12 h and 48 h fermentation stages, respectively. Difference analysis of TOP 12 genera (Fig. 4 D-O) indicated that the abundance of Streptococcus , Schwartzia , Prevotellaceae_YAB2003_group , and Oribacterium were significantly higher, and Prevotella , Succinivibrio , unclassified_f__Succinivibrionaceae , and Prevotellaceae_ UCG-003 were significantly lower in the HRW group compared with the CON group at 12 h of fermentation ( P < 0.05). While, among the 5 differential genera at 48 h (Fig. 5 D, G, H, M, N), the abundance of Prevotellaceae_YAB200 3_group , Oribacterium , Streptococcus , and Ruminobacter were significantly increased, and Rikenellaceae_RC9_gut_group and Succiniclasticum were significantly decreased in the HRW group compared with the CON group ( P < 0.05). Correlation Analysis Correlations analysis was conducted between rumen fermentation characteristics and main bacteria in genus level at 12 h and 48 h (Fig. 6 ). There were 9 significant correlations at 12 h of fermentation (|R| > 0.5, P < 0.05). The acetate and propionate were significantly positively related to Prevotella , Ruminobacter , unclassified_f__Succinivibrionaceae , and Prevotellaceae_UCG-003 ( P < 0.05), while significantly negatively related to Streptococcus , Schwartzia , Prevotellaceae_YAB2003_group , and Oribacterium ( P < 0.05). The NH 3 -N was significantly positively related to Streptococcus , Schwartzia , Prevotellaceae_YAB2003_group , and Oribacterium ( P < 0.05), and significantly negatively related to Prevotella , Ruminobacter , Succinivibrio , unclassified_f__Succinivibrionaceae , and Prevotellaceae_UCG-003 ( P 0.5, P < 0.05). The acetate and propionate were significantly positively related to Rikenellaceae_RC9_gut_group and Succiniclasticum ( P < 0.05), and significantly negatively related to Prevotellaceae_YAB2003_grou , Oribacterium , and Streptococcus ( P < 0.05). Discussion HRW, which is derived through a unique technological process that integrates hydrogen gas into water, boasts numerous beneficial effects on human health[ 7 ]. Nevertheless, the realm of research pertaining to its utilization in ruminants remains relatively unexplored, with the majority of studies predominantly focused on monogastric animals. Under normal growth conditions, HRW treatment did not affect the feed intake and growth performance in the piglets and broiler chickens[ 14 , 15 ], which might be related to nutrient digestibility. In general, the improvement in nutrient digestibility is accompanied by an elevation in growth performance[ 16 ]. Therefore, we speculate that HRW has no significant effect on nutrient digestibility. In our study, there was no significant difference in dry matter degradability between the HRW group and CON group at the 48 h of fermentation, which was consistent with our hypothesis. Fermentation gas is derived from the digestion of carbohydrates during the fermentation process and is associated with rumen degradability of the organic matter[ 17 ]. It has been reported that the greater the dry matter degradability, the greater the gas production[ 18 ]. what is inconsistent with our result that HRW 800ppb has the highest dry matter degradability and gas production at 12 h and 48 h, but no significant difference compared with the control group. CH 4 , a potent greenhouse gas, is predominantly generated through microbial fermentation in the rumen ecosystem. In this process, methanogenic archaea play a pivotal role by engaging in methanogenesis, a metabolic pathway that assimilates hydrogen and carbon dioxide, thereby converting them into methane[ 19 ]. HRW 200ppb significantly decreased the content of CH 4 at 12 h and 48 h fermentation (Fig. 1 C, D). But interestingly, as the content of HRW increases, CH 4 production also increases. It might be that low doses of hydrogen can alter the fermentation pathways of rumen microorganisms, while high content of hydrogen provides a substrate for methane production[ 20 ]. The rumen pH, NH 3 -N, MCP, and VFA are important indicators for evaluating rumen function. The rumen pH fluctuates from 6.0 to 7.2, which is conducive to rumen microorganisms and the normal function of ruminants[ 21 ]. In this experiment, the pH of the rumen in each group fluctuated within the normal range of 6.43 to 7.2, indicating that HRW supplementation did not disrupt the balance of the acid-base environment. The fluctuation of NH 3 -N concentration in the rumen reflects the degradation of dietary N and the utilization of NH 3 -N by rumen microorganisms[ 22 ]. The content of NH 3 -N in the HRW 400ppb group has no significant difference compared with the CON group at 48 h of fermentation, which might be that HRW 400ppb did not promote the dry matter degradability (Table 2 ). The result that lack of significant effect on nitrogenous was consistent with other studies. For instance, Choi et al.[ 23 ] found that the use of HRW had no significant effect on the quality of duck manure in Beijing ducks, including pH, total nitrogen, and ammonia nitrogen. Although the HRW 400ppb does not affect the rumen ammonia nitrogen content, it increases the content of MCP, which can be explained by the higher bacterial diversity (Fig. 2 C). The increasing diversity of rumen microorganisms may be improving the utilization efficiency of available nitrogen. VFA is known as the main end product of carbohydrates, which can provide 70–80% of ruminant energy needs[ 24 ]. Structural carbohydrates and nonstructural carbohydrates continue to degrade as the fermentation process advances, producing acetate and propionate, respectively[ 25 ]. In this study, at 48 h of fermentation, although the levels of TVFA, individual VFAs, and BCVFA were higher, the contents of TVFA, acetate, and propionate were lower in the HRW group compared to the CON group. To date, despite the absence of direct studies exploring the effect of HRW on ruminal microorganisms, research findings have pointed towards HRW’s capacity to modulate the gut microbiota in humans[ 10 ]. In light of this, we employed 16S rRNA sequencing technology to delve into the potential effects of HRW on the structure of the ruminal microbiota, and to subsequently dissect the intricate relationship between these alterations and the production of VFA. Utilizing this approach, we aim to gain a clearer understanding of the mechanisms by which HRW modulates ruminal microbial activity and VFA production, thereby providing a scientific basis for optimizing the feeding management of ruminants. Based on rumen fermentation indicators and methane gas production, HRW 400ppb (abbreviated as HRW hereafter) was selected as the subsequent treatment group for analysis. Bacterial alpha diversity includes species richness and diversity, which are primarily described by Ace and Simpson indexes, respectively. In this study, differences were found in diversity between the HRW and CON groups at 12 h and 48 h of fermentation. Currently, the effects of HRW on gut microbial diversity are inconsistent. Under normal physiological conditions, HRW has been observed to exert no significant influence on the α-diversity of gut bacteria in mice[ 12 ]. However, conversely, research has demonstrated a marked enhancement in the α-diversity of gut bacteria among female athletes[ 26 ]. This discrepancy suggests that the impact of HRW on gut microbiota may vary among different populations and distinct physiological states, necessitating in-depth research to unravel the underlying mechanisms and explore potential applications. Bacteroidetes, Firmicutes, and Proteobacteria were regarded as the three phyla with the most abundance in ruminal bacteria[ 27 ], which was consistent with our results (Fig. 3 A, B). Firmicutes are capable of breaking down cellulose into VFA, thereby supplying energy to the host, and Bacteroidetes contribute to the enhancement of the host’s nutrient utilization by degrading carbohydrates and proteins[ 28 ]. Firmicutes degrade dietary fiber to produce acetate and butyrate, while Bacteroidota mainly produces propionate through nonfibrous substance degradation. In this study, at 12 h of fermentation, we observed a correlation between Bacteroidota and propionate, with their trends moving in tandem. In contrast, an increase in Firmicutes abundance was associated with a decrease in both acetate and butyrate contents. This discrepancy may be attributed to the enriched HRW, which potentially enhanced the capacity of Firmicutes to synthesize MCP. Consistent with our findings, previous studies have reported that the Firmicutes phylum accelerates the utilization of ruminal ammonia-N and the synthesis of MCP[ 29 ], thereby underscoring its pivotal role in the metabolic transformations within the rumen ecosystem. At the same time, the abundance of Proteobacteria decreased in the HRW group at 12 h. The phylum Proteobacteria plays an essential role in the rumen microbiome, particularly in the degradation of carbohydrates, where they are primarily responsible for the breakdown of cellulose and hemicellulose. Consequently, a decrease in the abundance of Proteobacteria may directly lead to a reduced efficiency of fibrous material degradation in the rumen[ 30 ]. This change not only affects the metabolic activities of the rumen microbiota but also subsequently impacts the production of VFA. In the present study, it is evident that the variation in the abundance of Proteobacteria significantly influences the efficiency of rumen fermentation, and its decline could be a key factor contributing to the decrease in dry matter degradation rate and VFA production. But at 48 h of fermentation, the abundance of Bacteroidota, Firmicutes, and Proteobacteria had no significant difference between the HRW and CON groups. It might be that, as fermentation time passes, the hydrogen in the HRW gradually gets consumed, thereby leading to the normalization of its fermentation pattern. Subsequently, a deeper analysis was conducted on the differential bacteria genera at 12 h of fermentation, the results showed that the relative abundance of Prevotella and Prevotellaceae_UCG-003 were significantly decreased in the HRW group (Fig. 4 D, N). Prevotella and Prevotellaceae_UCG-003 , belonging to the Bacteroidetes phylum, both possess a potent capacity to degrade nonstructural carbohydrates and proteins. Additionally, they are capable of fermenting sugars via the acrylic and succinic acid pathways, leading to the production of propionate[ 31 ]. Meanwhile, both of these also had a positive correlation with propionate (Fig. 6 ). Thus, the decrease in propionate levels in the HRW group is directly associated with the reduced abundance of Prevotella . Additionally, Prevotella , Ruminobacter , and Succinivibrio are all hydrogen-producing bacteria[ 32 ], and the supplementation of HRW may have suppressed their activity. On the other hand, in the HRW group, there was a significant increase in the abundance of the Streptococcus genus. Although no studies have directly investigated the correlation between the increased abundance of Streptococcus and the utilization of ruminal nitrogen, the findings of Jin et al.[ 33 ] suggest that the Streptococcus genus possesses unique advantages in the utilization of nitrogen within the rumen. Based on this, we hypothesize that the increased MCP synthesis may be associated with the rise of Streptococcus abundance. However, in this study, after 48 h of fermentation, the types of bacteria changed varied. Previous research has reported that Rikenellaceae_RC9_gut_group plays a crucial role in the degradation of carbohydrates within the gut microbiota[ 34 ], while Succinivibrionaceae exhibits a significant positive correlation with the production of total VFA as well as the contens of acetate and propionate[ 35 ]. The reduction in the abundance of these two bacterial families in our experiment is consistent with the observed trends in VFAs. However, the precise biological mechanisms underlying their influence necessitate further research for clarification. Additionally, correlation analysis revealed a significant positive relationship between Rikenellaceae_RC9_gut_group and CH 4 content, which may suggest a role for this family in the decline of methane levels during this period. This finding provides a novel perspective for further exploration of the potential role of Rikenellaceae_RC9_gut_group in regulating methane production. In addition, a significant negative correlation was observed between the presence of Streptococcus and CH4 production (Fig. 6 ). This phenomenon suggests that bacteriocins produced by Streptococcus may play a role in inhibiting the activity of methanogenic archaea or facilitate the redirection of H 2 towards other reductive microorganisms that do not generate CH4[ 36 ]. Consequently, the reduction in methane levels we observed is likely associated with an increase in Streptococcus abundance, which may be attributed to the antimicrobial effects of bacteriocins or the metabolic redirection they induce. Particular attention was given to the genus Oribacterium , which was significantly increased in the HRW group at 12 and 48 h of fermentation. Oribacterium has been identified as one of the primary bacteria in the rumen of cows fed with forage[ 37 , 38 ]. However, current research on this bacterium is still limited, with existing studies merely speculating a relationship between ruminal Oribacterium and the production of alanine[ 39 ]. Therefore, the mechanism by which HRW affects Oribacterium requires further investigation. Conclusion In conclusion, our findings indicate that HRW at 400ppb significantly enhances rumen fermentation, thereby improving the overall efficiency of the rumen ecosystem. Contrary to initial hypotheses, HRW does not directly contribute to the synthesis of ruminal VFA. Nonetheless, it exhibits a notable capacity to mitigate methane emissions, which correlates with Streptococcus and Rikenellaceae_RC9_gut_group , offering a critical environmental benefit. Additionally, HRW’s influence on the rumen microbiota’s composition indirectly facilitates the synthesis of MCP, which is essential for ruminant nutrition. These results underscore the potential of HRW as a sustainable feed additive, offering dual advantages in enhancing ruminant nutrition and reducing the environmental footprint of livestock farming. This research thus contributes pivotal insights into the strategic integration of HRW in ruminant diets for improved animal health and environmental stewardship. Abbreviations HRW hydrogen-rich water CON Control MCP Microbial crude protein NH 3 -N Ammonia nitrogen VFA Volatile fatty acids OUT Operational taxonomic units CH 4 Methane Methods Preparation of hydrogen-rich water Preparation of 800 ppb HRW: Distilled water (2 L) was added to a negative ion water generator (Model V8, Mrs. Li’s Electrical Appliance Co., Ltd., Zhongshan City) using a measuring cylinder. The apparatus was powered for 0.5 h to produce alkaline hydrogen-rich electrolyzed water. The resulting alkaline electrolyzed water had a pH of 8.69, an ORP of -554 mV, and a hydrogen gas concentration of 0.81 mg/L. Preparation of 400 ppb: 1 L of HRW at a concentration of 800 ppb was mixed with 1 L of distilled water to obtain 2 L of HRW at a concentration of 400 ppb. The resulting alkaline electrolyzed water had a pH of 7.64, an ORP of -72 mV, and a hydrogen gas concentration of 0.44 mg/L Preparation of 200 ppb HRW: 1 L of HRW at a concentration of 400 ppb was mixed with 1 L of distilled water to obtain 2 L of HRW at a concentration of 200 ppb. The resulting alkaline electrolyzed water had a pH of 7.52, an ORP of -14 mV, and a hydrogen gas concentration of 0.22 mg/L. Rumen Fluid Collection Three Jinjiang cattle with permanent ruminal fistula installed (weight = 365.2 ± 27.4 kg) were taken as the rumen fluid donors for rumen content collection. The rumen content was obtained 1 h before morning feeding and then was filtered by four layers of gauze. All three collections from bulls were evenly mixed into a sterile bottle, which was finally used as the rumen fluid (culture medium) for the in vitro test. The rumen fluid pH of three cattle, measured immediately with a Rex PHBJ-260 meter upon arrival at the laboratory using a Rex PHBJ-260 pH meter (Shanghai INESA Scientific Instrument Co., Ltd., Shanghai, China), averaged 6.82. The fermentation substrate was the total mixed ration for Jinjiang cattle, the ingredients and nutrient composition of the diet are listed in Table 1. In vitro cultivation medium and experimental design Mixing the following reagents in volume as cultivation medium: 520.2 mL of distilled water (treatment group using 200 ppb, 400ppb, and 800ppb HRW), 208.1 mL of buffer solution (4.0 g NH 4 HCO 3 + 35 g NaHCO 3 dissolved in distilled water and made up to 1000 mL), 208.1 mL of constant element solution (9.45 g Na 2 HPO 4 ·12H 2 O + 6.2 g anhydrous KH 2 PO 4 + 0.6 g MgSO 4 ·7H 2 O dissolved in distilled water and made up to 1000 mL), 0.1 mL of trace element solution (13.2 g CaCl 2 ·2H 2 O + 10.0 g MnCl 2 ·4H 2 O + 1.0 g CoCl 2 ·6H 2 O + 8.0 g FeCl 3 ·6H 2 O dissolved in distilled water and made up to 1000 mL), and 62.4 mL of reducing solution (4.0 mL of 1 mol/L NaOH + 625 mg Na 2 S·9H 2 O + 625 mg cysteine hydrochloride + 95 mL distilled water), which was bubbled with CO 2 until the solution turned colorless from light blue. The prepared cultivation medium was warmed at 39 ℃. Proportionally prepared fermentation substrate (0.50 g) was placed in a glass bottle with a total volume of 100 mL, and then 40 mL of pre-warmed cultivation medium and 20 mL of rumen fluid were added to the above bottle and CO 2 was injected to get rid of oxygen. The bottle was incubated in SHA-B oscillators (Guohua Enterprise, Changzhou, Jiangsu, China) for in vitro gastric fermentation experiments. The experiment comprised 4 groups, including CON (control), 200 ppb, 400 ppb, and 800ppb HRW, with 10 replicates for each group (5 replicates were stopped at 12 h (hours) and the other 5 at 48 h), and the indicators were strictly measured according to the experimental steps and requirements. Rumen fermentation characteristics were determined at the incubation time of 12 h and 48 h. Rumen fermentation parameter determination After 12 h and 48 h incubation, fermented contents were filtered with four layers of gauze to obtain supernatant samples. The pH value was measured by a pH meter (Testo 206-pH1, Desto Instrument Co., LTD, Shenzhen, China). These supernatant samples were stored at -80℃ to a determination of VFA, ammonia nitrogen (NH 3 –N), MCP, and rumen microorganisms. The NH 3 -N concentration was determined using the method of phenol-hypochlorite reaction as described in Broderick and Kang[40]. The Folin phenol method based on Lowry’s assay was taken to determine the concentration of microbial crude protein (MCP), as described by Makkar et al[41]. The VFA measurements were determined according to the method of Qiu et al[42]: using a gas chromatograph (GC-2014Shimadzu Corporation, Kyoto, Japan) equipped with a 30 m capillary column (Rtx-Wax, 0.25 mm ID × 0.25 µm film, Restek, Evry, France) to determine the contents of acetic acid, propionic acid, iso-butyric acid, butyric acid, iso-valeric acid and valeric acid. The sum of the six VFAs was defined as total VFA (TVFA), and the sum of iso-butyric acid and iso-valeric acid was defined as branched-chain VFA. The peak area method was used for identification and content conversion of each VFA based on relative retention time. The standard curve was prepared under the same conditions using the same method. The non-glucogenic to glucogenic acids ratio (NGR) and fermentation efficiency (FE) were calculated as follows: NGR = (C 2 + 2 × C 4 + C 5 ) / (C 3 + C 5 ) FE = (0.622 × C 2 + 1.092 × C 3 + 1.56 × C 4 ) / (C 2 + C 3 + 2 × C 4 ) The culture medium was filtered through gauze, and the filter cake was transferred without damage into a nylon bag, which was then placed in a 65°C drying oven to determine the solids content and calculate the degradation rate. The 12 h and 48 h solids were dried by reference to the method in GB/T6435-2006. The in vitro solids degradation rate (V) = (W2-W3) / W1 W1 = The weight of fermentation substrate (g) W2 = The total weight of fermentation substrate and nylon bag (g) W3 = The total weight of fermentation substrate and nylon bag after in vitro fermentation (g) Net Gas Production Rate and Gas Production Parameters The gas production was measured after incubating the culture for 3, 6, 9, 12, 18, 24, 27, 30, 36, and 48 h. The culture tubes were quickly removed from incubation and the piston displacement (mL) was immediately recorded. The net gas production for each period was calculated as: Net gas production (mL) = Gas production at a time point (mL) - Gas production of blank at the same time point (mL) Methane production (The CH 4 production was estimated using the equation described by Moss et al[43]. CH 4 (mmol/L) = 0.45 × C 2 - 0.275 × C 3 + 0.40 × C 4 Note: C 2 = Concentration of acetate (mmol/L), C 3 = Concentration of propionate (mmol/L), C 4 = Concentration of butyrate (mmol/L). Bacterial Community Analysis A total of twenty microbial community genomic DNA using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) were transported to the Shanghai Majorbio Bio‐pharm Technology Co., Ltd. (Shanghai, China) or PCR amplification and MiSeq sequencing. The DNA extract was checked on 1% agarose gel, and DNA concentration and purity were determined with a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA). The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with primer pairs 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R(5’-GGACTACHVGGGTWTCTAAT-3’) by an ABI GeneAmp® 9700 PCR thermocycler (ABI, CA, USA). The amplification reaction system and program were the same as Mao et al.[27] report. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions and quantified using Quantus™ Fluorometer (Promega, USA). Purified amplicons were pooled in equimolar and paired-end sequenced on an Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA1103729). The raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by Trimmomatic and merged by FLASH with the following criteria: (i) the 300 bp reads were truncated at any site receiving an average quality score of <20 over a 50 bp sliding window, and the truncated reads shorter than 50 bp were discarded, reads containing ambiguous characters were also discarded; (ii) only overlapping sequences longer than 10 bp were assembled according to their overlapped sequence. The maximum mismatch ratio of the overlap region is 0.2. Reads that could not be assembled were discarded; (iii) Samples were distinguished according to the barcode and primers, and the sequence direction was adjusted, exact barcode matching, 2 nucleotide mismatch in primer matching. Operational taxonomic units (OTUs) with 97% similarity cut-off were clustered using UPARSE (version 7.1, http://drive5.com/uparse/), and chimeric sequences were identified and removed. The taxonomy of each OTU representative sequence was analyzed by the RDP Classifier (http://rdp.cme.msu.edu/) against the 16S rRNA database (eg. Silva v138) using a confidence threshold of 0.7. Correlations between rumen fermentation characteristics and rumen bacterial community were presented with a heat map, which was performed using SPSS (version 17.0, IBM, Armonk, NY, USA) and Origin (version 2018, Origin Software, Inc., Northampton, Massachusetts, USA) Statistical analysis Data processing was performed using SPSS (version 17.0, IBM, Armonk, NY, USA). The results are shown as the mean and standard error mean (SEM). Differences among means were determined using Tukey’s multiple range test was done when the interaction was significant. The level of statistical significance was set at P < 0.05. Declarations Acknowledgments The authors appreciate all the help from our colleagues and collaborators. Authors’ contributions KM and GL were responsible for the conception and design of the study. KM and GL were responsible for data extraction and interpretation of the results, and GL and YZ carried out the statistical analysis. YZ, GL, QQ, and KO supervised the research activity. KM and GL were mainly responsible for drafting the manuscript. YL, KM, and MQ were involved in revising the draft. All authors read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China (No. 3230810), the Young Talents Training Program for Academic and Technical Leaders of Major Disciplines in Jiangxi Province (20232BCJ23016), and the China Agriculture Research System of MOF and MARA (CARS-37). Availability of data and materials The datasets analyzed during the current study are available from the corresponding author upon reasonable request. Ethics approval Animal care and experimental procedures were approved by the Animal Care Committee of Jiangxi Agricultural University (Nanchang, China), and were under the university’s guidelines for animal research. Ethics approval and consent to participate This experiment was approved by the Committee for the Care and Use of Experimental Animals at Jiangxi Agricultural University (JXAULL-2021-10). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Clinical Trial Number Animal care and experimental procedures were approved by the Committee for the Care and Use of Experimental Animals at Jiangxi Agricultural University (JXAULL-2021-10). References Xue MY, Sun HZ, Wu XH, Liu JX, Guan LL. Multi-omics reveals that the rumen microbiome and its metabolome together with the host metabolome contribute to individualized dairy cow performance. Microbiome. 2020;8(1):64. 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Supplementary Files Supplementarytable.xlsx Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2024 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 26 Sep, 2024 Reviews received at journal 25 Sep, 2024 Reviews received at journal 20 Sep, 2024 Reviewers agreed at journal 19 Sep, 2024 Reviewers agreed at journal 18 Sep, 2024 Reviewers invited by journal 17 Sep, 2024 Editor invited by journal 16 Sep, 2024 Editor assigned by journal 13 Sep, 2024 Submission checks completed at journal 13 Sep, 2024 First submitted to journal 05 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5037482","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":359380385,"identity":"b62ed09a-a82c-45c1-826c-c9e2bd62b299","order_by":0,"name":"Kang Mao","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kang","middleName":"","lastName":"Mao","suffix":""},{"id":359380386,"identity":"ed26e07d-17d2-4440-bc90-7ee4a287da1d","order_by":1,"name":"Guwei Lu","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Guwei","middleName":"","lastName":"Lu","suffix":""},{"id":359380387,"identity":"950ce56e-ca65-4096-8045-f1318d1cd018","order_by":2,"name":"Yitian Zang","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yitian","middleName":"","lastName":"Zang","suffix":""},{"id":359380388,"identity":"ceeb6d83-f226-4e67-8c62-315dee49aaf6","order_by":3,"name":"Qinghua Qiu","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Qinghua","middleName":"","lastName":"Qiu","suffix":""},{"id":359380389,"identity":"82e5e402-185c-4980-b5f0-5e20df0b7050","order_by":4,"name":"Xianghui Zhao","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xianghui","middleName":"","lastName":"Zhao","suffix":""},{"id":359380390,"identity":"d2541577-455f-471c-b31e-f9fbd89bedea","order_by":5,"name":"Kehui Ouyang","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kehui","middleName":"","lastName":"Ouyang","suffix":""},{"id":359380391,"identity":"5b897c9f-3b3d-4a8c-bd5b-b7cdfe3a617a","order_by":6,"name":"Mingren Qu","email":"","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mingren","middleName":"","lastName":"Qu","suffix":""},{"id":359380392,"identity":"b3efe839-d20e-4d83-a3b8-08a0df827051","order_by":7,"name":"Yanjiao Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3PsUoDQRDG8TkW5prhbCOI8RFGhGCx5Fn2CGx1SB7AYkFIJdZ2voKPsHHwbJS0V6RIlUrIQUBShOCmtNm7Usj+y2V/fAxAKvVPYwNwgfmD9y3rcW9CSHU5f57aSe8lgkF1I9S+Za5z4ePLT1f3mgowLJq9glzeX6Pk886wqS0heCMVLwsga5so8RWzQSHMnA9krWBAozhZfAdyCERlTm5ZMtdJmrBSzgJBBQJ9yHkTVsqncEu4Zv7IdoJdtxSLanS9+9GXw5fNtt3t9fgslzpKrjwg/3nB2PdjQwdq1fUplUqlTrxfxEFMr1LRLzkAAAAASUVORK5CYII=","orcid":"","institution":"Jiangxi Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Yanjiao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-09-05 10:36:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5037482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5037482/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-024-03638-1","type":"published","date":"2024-11-12T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68417687,"identity":"feb7b36d-dad2-4169-b504-383e88113621","added_by":"auto","created_at":"2024-11-07 05:35:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":718790,"visible":true,"origin":"","legend":"\u003cp\u003eTotal gas production and methane production at 12 h and 48 h. \u003cstrong\u003eA \u003c/strong\u003e12 h gas production;\u003cstrong\u003e B \u003c/strong\u003e48 h gas production;\u003cstrong\u003e C \u003c/strong\u003e12 h methane production;\u003cstrong\u003e D \u003c/strong\u003e48 h methane production. CON = control, HRW = hydrogen-rich water; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5037482/v1/1821b0e949fb63e506cc65b4.png"},{"id":68417688,"identity":"a7551e74-8db6-46b5-a246-fdb7b95818b4","added_by":"auto","created_at":"2024-11-07 05:35:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":479578,"visible":true,"origin":"","legend":"\u003cp\u003eBacteria alpha diversity and principal-coordinate analysis (PCoA) based on OUT level. \u003cstrong\u003eA-D\u003c/strong\u003e: Bacteria alpha diversity analysis between HRW and CON group at 12 h and 48 h fermentation; \u003cstrong\u003eE-F\u003c/strong\u003e: Bacteria PCoA analysis between HRW and CON group at 12 h and 48 h fermentation. CON =control, HRW = hydrogen-rich water ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5037482/v1/ac25f1ac3276c36f8dcb7ab1.png"},{"id":68417386,"identity":"00bd4bc8-cb0d-4f8f-b9b7-64b879725fce","added_by":"auto","created_at":"2024-11-07 05:27:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1925823,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial compositional profiles of phylum and genus. \u003cstrong\u003eA-B: \u003c/strong\u003eMicrobial compositional profiles of phylum between CON and HRW group at 12 h and 48 h fermentation. \u003cstrong\u003eC-D\u003c/strong\u003e: Microbial compositional profiles of genus between CON and HRW group at 12 h and 48 h fermentation. CON = control, HRW = hydrogen-rich water\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5037482/v1/e43547dbadafdd80db8a896a.png"},{"id":68417383,"identity":"06d81e6d-273e-4f6a-8a73-fd606c3a10cf","added_by":"auto","created_at":"2024-11-07 05:27:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":400299,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential rumen bacteria phylum and genus at 12 h fermentation. \u003cstrong\u003eA-C\u003c/strong\u003e: Analysis of differences in abundance among the top 3 phylum level bacteria between CON and HRW group; D-O: Analysis of differences in abundance among the top 12 phylum level bacteria between CON and HRW group. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5037482/v1/c3e20a9f2d4a10f6c0f4c222.png"},{"id":68417389,"identity":"12808bf3-0131-41ee-acce-53698d67dd0a","added_by":"auto","created_at":"2024-11-07 05:27:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":933978,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential rumen bacteria phylum and genus at 48 h fermentation. \u003cstrong\u003eA-C\u003c/strong\u003e: Analysis of differences in abundance among the top 3 phylum level bacteria between CON and HRW group; D-O: Analysis of differences in abundance among the top 12 phylum level bacteria between CON and HRW group. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5037482/v1/8f14045f02ac23527aa17999.png"},{"id":68417385,"identity":"96c01c9c-9ba2-4e1f-a65c-b1d68a901fa3","added_by":"auto","created_at":"2024-11-07 05:27:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":663653,"visible":true,"origin":"","legend":"\u003cp\u003eSperman\u003cstrong\u003e \u003c/strong\u003ecorrelations between rumen fermentation characteristics and TOP 12 rumen bacterial community in genus level at 12 h and 48 h fermentation. |R| \u0026gt; 0.5 and \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05 indicate significant correlation\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5037482/v1/314c29a47df67ce6f023d503.png"},{"id":69285522,"identity":"f3680ce4-fe83-4fa1-9ca6-31b469d43d83","added_by":"auto","created_at":"2024-11-18 19:26:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5852913,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5037482/v1/d8d3bf79-4ca9-4bc6-8cde-39efd447e0d6.pdf"},{"id":68420096,"identity":"510f0b9b-49ed-476f-9f45-b483e5c4fa3d","added_by":"auto","created_at":"2024-11-07 05:59:03","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":172974,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5037482/v1/6a7aff1aa808904d36377544.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hydrogen-Rich Water as a potential strategy for improving ruminant nutrition and mitigating methane emissions","fulltext":[{"header":"Background","content":"\u003cp\u003eRuminants can convert fibrous plants into edible meat and milk products for human consumption. This process requires the participation of rumen microorganisms, including bacteria, archaea, fungi, and ciliated protozoa,which can produce volatile fatty acids (VFA), microbial proteins (MCP), and vitamins for the host animals[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. VFA supplies 70\u0026ndash;80% of the energy to ruminants[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and MCP provides a high level of protein resources for host animals[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While this fermentation is vital for the nutritional enhancement of dietary intake, it also inevitably leads to methane production\u0026mdash;a potent greenhouse gas. Research has found that efficient beef cattle produce 20% less methane than inefficient ones[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, exploring strategies to regulate the activity of rumen microbiota to reduce methane production while maintaining animal production efficiency is of great scientific and practical significance.\u003c/p\u003e \u003cp\u003eHydrogen-rich water (HRW) is a form of potable water that has been super-saturated with molecular hydrogen gas (H\u003csub\u003e2\u003c/sub\u003e) through pressurized dissolution[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The hydrogen molecules are extremely small, so they can easily penetrate water and stay dissolved for a while. In recent years, it has been widely used and applied in many fields such as medicine, agriculture, sports, and beauty[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The widespread adoption of HRW can be largely attributed to its beneficial properties, such as its antioxidant, anti-inflammatory, and anti-apoptotic effects, coupled with a proven high safety profile[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there are few studies on hydrogen-rich water in ruminants. Kuru[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] found that administering HRW to goats during the peripartum period may improve the health and survival of kids and reduce their mortality.\u003c/p\u003e \u003cp\u003eAt present, the specific mechanism of HRW is still unclear in ruminants. Some studies speculated that intestinal microorganisms might be the main target organ of hydrogen molecules[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Hydrogen metabolism is related to many microorganisms in the intestinal microbiota[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. HRW intake could increase the abundance of Lactobacillus, Ruminococcus, and Clostridium[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], fortifying intestinal structural integrity and upregulation of butyrate-producing bacteria, in turn, ameliorated clinical features associated with gut microbiota disturbance[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On the other hand, in ruminants, improving the metabolic efficiency of hydrogen can affect the proliferation of hydrogenotrophic bacteria, thereby reducing the production of ruminal methane[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, there is a lack of in-depth research on the impact of HRW on the structure and function of the rumen microbiota in ruminant animals, as well as its mechanism of action on the rumen fermentation and methane production process.\u003c/p\u003e \u003cp\u003eThis study aims to fill this research gap by using a comprehensive set of technical methods, including \u003cem\u003ein vitro\u003c/em\u003e fermentation tests, microbial community analysis, and metabolite detection, to explore the potential impact of HRW on rumen microbiota. We hypothesize that HRW may alter the hydrogen metabolism pathways within the rumen by regulating the composition and metabolic activities of rumen microbiota, thereby exerting a regulatory effect on methane production. The expected results of this study will provide new insights into the understanding and regulation of rumen microbiota metabolic activities, and offer potential solutions for reducing methane emissions from ruminants.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTotal gas production and methane production\u003c/h2\u003e \u003cp\u003eThe production of total gas and methane at 12 h and 48 h fermentation are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After 12 h of fermentation, the HRW\u003csub\u003e800ppb\u003c/sub\u003e group demonstrated the highest production of total gas and methane gas, reaching 53.35 mL and 13.58 mL, respectively, with the total gas production significantly exceeding the other three groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the production of methane gas showed no significant difference compared to the CON group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). At 48 h of fermentation, HRW800\u003csub\u003eppb\u003c/sub\u003e maintained the highest production of total gas and methane gas, with 78.56 mL and 12.80 mL, respectively, both of which were significantly higher than those in the HRW\u003csub\u003e200ppb\u003c/sub\u003e and HRW\u003csub\u003e400ppb\u003c/sub\u003e groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Nevertheless, there were no significant differences compared to the CON group. The methane gas production of the HRW\u003csub\u003e200ppb\u003c/sub\u003e group was significantly lower than the other three groups at both 12 and 48 h of fermentation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRumen Characteristics\u003c/h2\u003e \u003cp\u003eThe results of rumen fermentation characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Different concentration of HRW affected rumen fermentation parameters. After 12 h of fermentation, significant differences in pH values were observed among the four groups, with HRW\u003csub\u003e800ppb\u003c/sub\u003e (pH\u0026thinsp;=\u0026thinsp;6.59) and HRW\u003csub\u003e400ppb\u003c/sub\u003e (pH\u0026thinsp;=\u0026thinsp;7.03) showing significantly lower pH values compared to HRW\u003csub\u003e200ppb\u003c/sub\u003e (pH\u0026thinsp;=\u0026thinsp;7.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the CON group (pH\u0026thinsp;=\u0026thinsp;6.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of MCP, the HRW\u003csub\u003e400ppb\u003c/sub\u003e group exhibited the highest MCP content at 31.67 mg/dL, followed by the HRW\u003csub\u003e200ppb\u003c/sub\u003e group at 27.45 mg/dL, while the HRW\u003csub\u003e800ppb\u003c/sub\u003e group had a significantly lower MCP content than the CON group (20.85 mg/dL vs. 28.38 mg/dL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At 48 h of fermentation, the trend in pH values was similar to that at 12 h, with HRW\u003csub\u003e400ppb\u003c/sub\u003e and HRW\u003csub\u003e800ppb\u003c/sub\u003e groups showing significantly higher pH values compared to the HRW\u003csub\u003e200ppb\u003c/sub\u003e and CON groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding MCP, the HRW\u003csub\u003e400ppb\u003c/sub\u003e group maintained the highest MCP content, followed by the HRW\u003csub\u003e200ppb\u003c/sub\u003e group, and both were significantly higher compared to the CON group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). After 12 h of fermentation, the HRW group of NH\u003csub\u003e3\u003c/sub\u003e-N levels were significantly higher than that in the CON group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While, at 48 h of fermentation, the CON group exhibited the highest NH\u003csub\u003e3\u003c/sub\u003e-N levels at 12.21 mg/dL, which were significantly different from those in the HRW groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). The dry matter degradation rate at 12 h was significantly higher in the CON and HRW\u003csub\u003e800ppb\u003c/sub\u003e groups compared to the HRW\u003csub\u003e200ppb\u003c/sub\u003e and HRW\u003csub\u003e400ppb\u003c/sub\u003e groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while there was no significant effects between the HRW groups and the CON group (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.187) at 48 h.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComposition and nutrient levels of experimental diet (air-dry basis, %)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIngredients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNutritional composition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eg/kg of DM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheat Straw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolic energy (ME), MJ/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude protein (CP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewheat bran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMP to CP ratio, MJ/g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeutral detergent fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e428.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium bicarbonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcid detergent fiber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e266.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium hydrophosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremix\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimestone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTatol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003eThe premix (per kg of diet) is: 1400 mg of Fe, 1200 mg of Zn, 250 mg of Cu, 900 mg of Mn, 100,000 IU of vitamin A, 27,000 IU of vitamin D3, and 800 IU of vitamin E.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRumen fermentation characteristics in vitro rumen fermentation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCON\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHRW\u003csub\u003e200ppb\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRW\u003csub\u003e400ppb\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHRW\u003csub\u003e800ppb\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003epH value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.43\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.03\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.59\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.60\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.08\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.50\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMicrobial crude protein, mg/dL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.38\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.45\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.85\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.08\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.97\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.34\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAmmonia nitrogen, mg/dL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.64\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.66\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.62\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.99\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.76\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.95\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.86\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eDry matter degradability, %\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.16\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.66\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.53\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.35\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.13\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea,b\u003c/sup\u003e Means within a row with no common superscript differ significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CON\u0026thinsp;=\u0026thinsp;control; HRW\u0026thinsp;=\u0026thinsp;hydrogen-rich water. SEM\u0026thinsp;=\u0026thinsp;stand error of mean.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of VFAs are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. After 12 h of fermentation, the CON group demonstrated the highest levels of acetate, propionate, branched-chain amino acids, and TVFA, with concentrations of 32.39 mM, 16.16 mM, 0.56 mM, and 52.20 mM, respectively, which were significantly higher than those in the HRW\u003csub\u003e200ppb\u003c/sub\u003e and HRW\u003csub\u003e400ppb\u003c/sub\u003e groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, the HRW\u003csub\u003e800ppb\u003c/sub\u003e group exhibited the lowest propionate content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The acetate-to-propionate ratio and the non-glucogenic-to-glucogenic acids ratio at 12 h of fermentation were significantly higher in the HRW\u003csub\u003e800ppb\u003c/sub\u003e group compared to the other groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After 48 h of fermentation, the HRW\u003csub\u003e200ppb\u003c/sub\u003e group exhibited markedly reduced isobutyrate levels in comparison to the other groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, the HRW\u003csub\u003e800ppb\u003c/sub\u003e group displayed significantly elevated butyrate concentration and an increased acetate-to-propionate ratio among the four groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The CON group had significantly higher valerate and isovalerate contents compared to the HRW\u003csub\u003e200ppb\u003c/sub\u003e and HRW\u003csub\u003e800ppb\u003c/sub\u003e groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, the TVFA content in CON group\u0026rsquo;s also significantly higher than the other 3 groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a notable decrease observed in the HRW\u003csub\u003e200ppb\u003c/sub\u003e group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The HRW\u003csub\u003e800ppb\u003c/sub\u003e group had a highest non-glucogenic to glucogenic acids ratio, but a lowest fermentation efficiency compared to the CON and HRW\u003csub\u003e200ppb\u003c/sub\u003e groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). At 48 h, the HRW\u003csub\u003e200ppb\u003c/sub\u003e group had a significantly lower non-glucogenic to glucogenic acids ratio and a higher fermentation efficiency than the other groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRumen fermentation total volatile fatty acids (VFA) and individual VFAs in vitro rumen fermentation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHRW\u003csub\u003e200ppb\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRW\u003csub\u003e400ppb\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHRW\u003csub\u003e800ppb\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAcetate, mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.39\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.58\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.54\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.59\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.10\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.14\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.88\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePropionate, mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.17\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.47\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.63\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.91\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.88\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.73\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eIsobutyrate, mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eButyrate, mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.55\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.15\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.64\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.40\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eIsovalerate, mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eValerate, mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBranched-chain volatile fatty acids, mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTotal volatile fatty acids, mM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.97\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.59\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.49\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.73\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.56\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.74\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAcetate to propionate ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.21\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNon-glucogenic to glucogenic acids ratio\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.59\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.80\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.10\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eFermentation efficiency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48 h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea,b\u003c/sup\u003e Means within a row with no common superscript differ significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CON\u0026thinsp;=\u0026thinsp;control; HRW\u0026thinsp;=\u0026thinsp;hydrogen-rich water; Branched-chain volatile fatty acids are the sum of isobutyrate, valerate, and isovalerate. SEM\u0026thinsp;=\u0026thinsp;stand error of mean.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRumen Bacteria\u003c/h2\u003e \u003cp\u003eFrom 20 samples, a total of 909,772 clean reads were detected with an average of 45488.6 for each sample (Table. S1). The composition of bacteria across the 20 samples was dominated by 951 OTU, 16 phyla, and 224 genera (Table. S2). The alpha diversity at 12 h and 48 h of fermentation was estimated by the Simpson and Ace (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with the CON group, the HRW group significantly increased the Simpson index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, C), whereas no significant differences were observed in Ace at 12 h and 48 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, D). To measure the extent of similarity between the microbial communities, beta diversity was calculated using a weighted normalized UniFrac, and the PCoA was performed. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F. The microbial community profiles of the HRW were grouped to the right of the PCoA, and CON was grouped to the left of the PCoA. PERMANOVA analysis found that the two groups were significantly different at 12 h and 48 h (R\u0026thinsp;=\u0026thinsp;0.988, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; R\u0026thinsp;=\u0026thinsp;0.660, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe taxonomic analysis of the reads revealed that the dominant phyla were Firmicutes, Bacteroidota, and Proteobacteria at 12 h and 48 h of fermentation, accounting for \u0026gt;\u0026thinsp;99% of total reads (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). Among the three phyla, supplementing with HRW could significantly increase the relative abundance of Firmicutes, and decrease the relative abundance of Bacteroidota and Proteobacteria at 12 h of fermentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, C, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no significant differences were observed at 48 h of fermentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, C, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). At the genus level, the predominance of the genus is depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD for the 12 h and 48 h fermentation stages, respectively. Difference analysis of TOP 12 genera (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-O) indicated that the abundance of \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSchwartzia\u003c/em\u003e, \u003cem\u003ePrevotellaceae_YAB2003_group\u003c/em\u003e, and \u003cem\u003eOribacterium\u003c/em\u003e were significantly higher, and \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eSuccinivibrio\u003c/em\u003e, \u003cem\u003eunclassified_f__Succinivibrionaceae\u003c/em\u003e, and \u003cem\u003ePrevotellaceae_ UCG-003\u003c/em\u003e were significantly lower in the HRW group compared with the CON group at 12 h of fermentation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). While, among the 5 differential genera at 48 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, G, H, M, N), the abundance of \u003cem\u003ePrevotellaceae_YAB200 3_group\u003c/em\u003e, \u003cem\u003eOribacterium\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eand Ruminobacter\u003c/em\u003e were significantly increased, and \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e and \u003cem\u003eSucciniclasticum\u003c/em\u003e were significantly decreased in the HRW group compared with the CON group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003eCorrelations analysis was conducted between rumen fermentation characteristics and main bacteria in genus level at 12 h and 48 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). There were 9 significant correlations at 12 h of fermentation (|R| \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The acetate and propionate were significantly positively related to \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRuminobacter\u003c/em\u003e, \u003cem\u003eunclassified_f__Succinivibrionaceae\u003c/em\u003e, and \u003cem\u003ePrevotellaceae_UCG-003\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while significantly negatively related to \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSchwartzia\u003c/em\u003e, \u003cem\u003ePrevotellaceae_YAB2003_group\u003c/em\u003e, and \u003cem\u003eOribacterium\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The NH\u003csub\u003e3\u003c/sub\u003e-N was significantly positively related to \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSchwartzia\u003c/em\u003e, \u003cem\u003ePrevotellaceae_YAB2003_group\u003c/em\u003e, and \u003cem\u003eOribacterium\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and significantly negatively related to \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRuminobacter\u003c/em\u003e, \u003cem\u003eSuccinivibrio\u003c/em\u003e, \u003cem\u003eunclassified_f__Succinivibrionaceae\u003c/em\u003e, and \u003cem\u003ePrevotellaceae_UCG-003\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). On the other hand, there were 7 significant correlations at 48 h of fermentation (|R| \u0026gt; 0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The acetate and propionate were significantly positively related to \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e and \u003cem\u003eSucciniclasticum\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and significantly negatively related to \u003cem\u003ePrevotellaceae_YAB2003_grou\u003c/em\u003e, \u003cem\u003eOribacterium\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHRW, which is derived through a unique technological process that integrates hydrogen gas into water, boasts numerous beneficial effects on human health[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nevertheless, the realm of research pertaining to its utilization in ruminants remains relatively unexplored, with the majority of studies predominantly focused on monogastric animals. Under normal growth conditions, HRW treatment did not affect the feed intake and growth performance in the piglets and broiler chickens[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which might be related to nutrient digestibility. In general, the improvement in nutrient digestibility is accompanied by an elevation in growth performance[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, we speculate that HRW has no significant effect on nutrient digestibility. In our study, there was no significant difference in dry matter degradability between the HRW group and CON group at the 48 h of fermentation, which was consistent with our hypothesis. Fermentation gas is derived from the digestion of carbohydrates during the fermentation process and is associated with rumen degradability of the organic matter[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It has been reported that the greater the dry matter degradability, the greater the gas production[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. what is inconsistent with our result that HRW\u003csub\u003e800ppb\u003c/sub\u003e has the highest dry matter degradability and gas production at 12 h and 48 h, but no significant difference compared with the control group. CH\u003csub\u003e4\u003c/sub\u003e, a potent greenhouse gas, is predominantly generated through microbial fermentation in the rumen ecosystem. In this process, methanogenic archaea play a pivotal role by engaging in methanogenesis, a metabolic pathway that assimilates hydrogen and carbon dioxide, thereby converting them into methane[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. HRW\u003csub\u003e200ppb\u003c/sub\u003e significantly decreased the content of CH\u003csub\u003e4\u003c/sub\u003e at 12 h and 48 h fermentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). But interestingly, as the content of HRW increases, CH\u003csub\u003e4\u003c/sub\u003e production also increases. It might be that low doses of hydrogen can alter the fermentation pathways of rumen microorganisms, while high content of hydrogen provides a substrate for methane production[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The rumen pH, NH\u003csub\u003e3\u003c/sub\u003e-N, MCP, and VFA are important indicators for evaluating rumen function. The rumen pH fluctuates from 6.0 to 7.2, which is conducive to rumen microorganisms and the normal function of ruminants[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this experiment, the pH of the rumen in each group fluctuated within the normal range of 6.43 to 7.2, indicating that HRW supplementation did not disrupt the balance of the acid-base environment. The fluctuation of NH\u003csub\u003e3\u003c/sub\u003e-N concentration in the rumen reflects the degradation of dietary N and the utilization of NH\u003csub\u003e3\u003c/sub\u003e-N by rumen microorganisms[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The content of NH\u003csub\u003e3\u003c/sub\u003e-N in the HRW\u003csub\u003e400ppb\u003c/sub\u003e group has no significant difference compared with the CON group at 48 h of fermentation, which might be that HRW\u003csub\u003e400ppb\u003c/sub\u003e did not promote the dry matter degradability (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The result that lack of significant effect on nitrogenous was consistent with other studies. For instance, Choi et al.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] found that the use of HRW had no significant effect on the quality of duck manure in Beijing ducks, including pH, total nitrogen, and ammonia nitrogen. Although the HRW\u003csub\u003e400ppb\u003c/sub\u003e does not affect the rumen ammonia nitrogen content, it increases the content of MCP, which can be explained by the higher bacterial diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The increasing diversity of rumen microorganisms may be improving the utilization efficiency of available nitrogen. VFA is known as the main end product of carbohydrates, which can provide 70\u0026ndash;80% of ruminant energy needs[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Structural carbohydrates and nonstructural carbohydrates continue to degrade as the fermentation process advances, producing acetate and propionate, respectively[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, at 48 h of fermentation, although the levels of TVFA, individual VFAs, and BCVFA were higher, the contents of TVFA, acetate, and propionate were lower in the HRW group compared to the CON group. To date, despite the absence of direct studies exploring the effect of HRW on ruminal microorganisms, research findings have pointed towards HRW\u0026rsquo;s capacity to modulate the gut microbiota in humans[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In light of this, we employed 16S rRNA sequencing technology to delve into the potential effects of HRW on the structure of the ruminal microbiota, and to subsequently dissect the intricate relationship between these alterations and the production of VFA. Utilizing this approach, we aim to gain a clearer understanding of the mechanisms by which HRW modulates ruminal microbial activity and VFA production, thereby providing a scientific basis for optimizing the feeding management of ruminants. Based on rumen fermentation indicators and methane gas production, HRW\u003csub\u003e400ppb\u003c/sub\u003e (abbreviated as HRW hereafter) was selected as the subsequent treatment group for analysis. Bacterial alpha diversity includes species richness and diversity, which are primarily described by Ace and Simpson indexes, respectively. In this study, differences were found in diversity between the HRW and CON groups at 12 h and 48 h of fermentation. Currently, the effects of HRW on gut microbial diversity are inconsistent. Under normal physiological conditions, HRW has been observed to exert no significant influence on the α-diversity of gut bacteria in mice[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, conversely, research has demonstrated a marked enhancement in the α-diversity of gut bacteria among female athletes[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This discrepancy suggests that the impact of HRW on gut microbiota may vary among different populations and distinct physiological states, necessitating in-depth research to unravel the underlying mechanisms and explore potential applications. Bacteroidetes, Firmicutes, and Proteobacteria were regarded as the three phyla with the most abundance in ruminal bacteria[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], which was consistent with our results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). Firmicutes are capable of breaking down cellulose into VFA, thereby supplying energy to the host, and Bacteroidetes contribute to the enhancement of the host\u0026rsquo;s nutrient utilization by degrading carbohydrates and proteins[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Firmicutes degrade dietary fiber to produce acetate and butyrate, while Bacteroidota mainly produces propionate through nonfibrous substance degradation. In this study, at 12 h of fermentation, we observed a correlation between Bacteroidota and propionate, with their trends moving in tandem. In contrast, an increase in Firmicutes abundance was associated with a decrease in both acetate and butyrate contents. This discrepancy may be attributed to the enriched HRW, which potentially enhanced the capacity of Firmicutes to synthesize MCP. Consistent with our findings, previous studies have reported that the Firmicutes phylum accelerates the utilization of ruminal ammonia-N and the synthesis of MCP[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], thereby underscoring its pivotal role in the metabolic transformations within the rumen ecosystem. At the same time, the abundance of Proteobacteria decreased in the HRW group at 12 h. The phylum Proteobacteria plays an essential role in the rumen microbiome, particularly in the degradation of carbohydrates, where they are primarily responsible for the breakdown of cellulose and hemicellulose. Consequently, a decrease in the abundance of Proteobacteria may directly lead to a reduced efficiency of fibrous material degradation in the rumen[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This change not only affects the metabolic activities of the rumen microbiota but also subsequently impacts the production of VFA. In the present study, it is evident that the variation in the abundance of Proteobacteria significantly influences the efficiency of rumen fermentation, and its decline could be a key factor contributing to the decrease in dry matter degradation rate and VFA production. But at 48 h of fermentation, the abundance of Bacteroidota, Firmicutes, and Proteobacteria had no significant difference between the HRW and CON groups. It might be that, as fermentation time passes, the hydrogen in the HRW gradually gets consumed, thereby leading to the normalization of its fermentation pattern. Subsequently, a deeper analysis was conducted on the differential bacteria genera at 12 h of fermentation, the results showed that the relative abundance of \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003ePrevotellaceae_UCG-003\u003c/em\u003e were significantly decreased in the HRW group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, N). \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003ePrevotellaceae_UCG-003\u003c/em\u003e, belonging to the Bacteroidetes phylum, both possess a potent capacity to degrade nonstructural carbohydrates and proteins. Additionally, they are capable of fermenting sugars via the acrylic and succinic acid pathways, leading to the production of propionate[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Meanwhile, both of these also had a positive correlation with propionate (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Thus, the decrease in propionate levels in the HRW group is directly associated with the reduced abundance of \u003cem\u003ePrevotella\u003c/em\u003e. Additionally, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRuminobacter\u003c/em\u003e, and \u003cem\u003eSuccinivibrio\u003c/em\u003e are all hydrogen-producing bacteria[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and the supplementation of HRW may have suppressed their activity. On the other hand, in the HRW group, there was a significant increase in the abundance of the \u003cem\u003eStreptococcus\u003c/em\u003e genus. Although no studies have directly investigated the correlation between the increased abundance of \u003cem\u003eStreptococcus\u003c/em\u003e and the utilization of ruminal nitrogen, the findings of Jin et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] suggest that the \u003cem\u003eStreptococcus\u003c/em\u003e genus possesses unique advantages in the utilization of nitrogen within the rumen. Based on this, we hypothesize that the increased MCP synthesis may be associated with the rise of \u003cem\u003eStreptococcus\u003c/em\u003e abundance. However, in this study, after 48 h of fermentation, the types of bacteria changed varied. Previous research has reported that \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e plays a crucial role in the degradation of carbohydrates within the gut microbiota[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], while \u003cem\u003eSuccinivibrionaceae\u003c/em\u003e exhibits a significant positive correlation with the production of total VFA as well as the contens of acetate and propionate[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The reduction in the abundance of these two bacterial families in our experiment is consistent with the observed trends in VFAs. However, the precise biological mechanisms underlying their influence necessitate further research for clarification. Additionally, correlation analysis revealed a significant positive relationship between \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e and CH\u003csub\u003e4\u003c/sub\u003e content, which may suggest a role for this family in the decline of methane levels during this period. This finding provides a novel perspective for further exploration of the potential role of \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e in regulating methane production. In addition, a significant negative correlation was observed between the presence of Streptococcus and CH4 production (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This phenomenon suggests that bacteriocins produced by Streptococcus may play a role in inhibiting the activity of methanogenic archaea or facilitate the redirection of H\u003csub\u003e2\u003c/sub\u003e towards other reductive microorganisms that do not generate CH4[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Consequently, the reduction in methane levels we observed is likely associated with an increase in Streptococcus abundance, which may be attributed to the antimicrobial effects of bacteriocins or the metabolic redirection they induce. Particular attention was given to the genus \u003cem\u003eOribacterium\u003c/em\u003e, which was significantly increased in the HRW group at 12 and 48 h of fermentation. \u003cem\u003eOribacterium\u003c/em\u003e has been identified as one of the primary bacteria in the rumen of cows fed with forage[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, current research on this bacterium is still limited, with existing studies merely speculating a relationship between ruminal \u003cem\u003eOribacterium\u003c/em\u003e and the production of alanine[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, the mechanism by which HRW affects \u003cem\u003eOribacterium\u003c/em\u003e requires further investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings indicate that HRW at 400ppb significantly enhances rumen fermentation, thereby improving the overall efficiency of the rumen ecosystem. Contrary to initial hypotheses, HRW does not directly contribute to the synthesis of ruminal VFA. Nonetheless, it exhibits a notable capacity to mitigate methane emissions, which correlates with \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e, offering a critical environmental benefit. Additionally, HRW\u0026rsquo;s influence on the rumen microbiota\u0026rsquo;s composition indirectly facilitates the synthesis of MCP, which is essential for ruminant nutrition. These results underscore the potential of HRW as a sustainable feed additive, offering dual advantages in enhancing ruminant nutrition and reducing the environmental footprint of livestock farming. This research thus contributes pivotal insights into the strategic integration of HRW in ruminant diets for improved animal health and environmental stewardship.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehydrogen-rich water\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCON\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrobial crude protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNH\u003csub\u003e3\u003c/sub\u003e-N\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmmonia nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVolatile fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOUT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOperational taxonomic units\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCH\u003csub\u003e4\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMethane\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003ePreparation of hydrogen-rich water\u003c/h2\u003e\n\u003cp\u003ePreparation of 800 ppb HRW: Distilled water (2 L) was added to a negative ion water generator (Model V8, Mrs. Li\u0026rsquo;s Electrical Appliance Co., Ltd., Zhongshan City) using a measuring cylinder. The apparatus was powered for 0.5 h to produce alkaline hydrogen-rich electrolyzed water. The resulting alkaline electrolyzed water had a pH of 8.69, an ORP of -554 mV, and a hydrogen gas concentration of 0.81 mg/L.\u003c/p\u003e\n\u003cp\u003ePreparation of 400 ppb: 1 L of HRW at a concentration of 800 ppb was mixed with 1 L of distilled water to obtain 2 L of HRW at a concentration of 400 ppb. The resulting alkaline electrolyzed water had a pH of 7.64, an ORP of -72 mV, and a hydrogen gas concentration of 0.44 mg/L\u003c/p\u003e\n\u003cp\u003ePreparation of 200 ppb HRW: 1 L of HRW at a concentration of 400 ppb was mixed with 1 L of distilled water to obtain 2 L of HRW at a concentration of 200 ppb. The resulting alkaline electrolyzed water had a pH of 7.52, an ORP of -14 mV, and a hydrogen gas concentration of 0.22 mg/L.\u003c/p\u003e\n\u003ch2\u003eRumen Fluid Collection\u003c/h2\u003e\n\u003cp\u003eThree Jinjiang cattle with permanent ruminal fistula installed (weight = 365.2 \u0026plusmn; 27.4 kg) were taken as the rumen fluid donors for rumen content collection. The rumen content was obtained 1 h before morning feeding and then was filtered by four layers of gauze. All three collections from bulls were evenly mixed into a sterile bottle, which was finally used as the rumen fluid (culture medium) for the in \u003cem\u003evitro\u003c/em\u003e test. The rumen fluid pH of three cattle, measured immediately with a Rex PHBJ-260 meter upon arrival at the laboratory using a Rex PHBJ-260 pH meter (Shanghai INESA Scientific Instrument Co., Ltd., Shanghai, China), averaged 6.82. The fermentation substrate was the total mixed ration for Jinjiang cattle, the ingredients and nutrient composition of the diet are listed in Table 1.\u003c/p\u003e\n\u003ch2\u003eIn vitro cultivation medium and experimental design\u003c/h2\u003e\n\u003cp\u003eMixing the following reagents in volume as cultivation medium: 520.2 mL of distilled water (treatment group using 200 ppb, 400ppb, and 800ppb HRW), 208.1 mL of buffer solution (4.0 g NH\u003csub\u003e4\u003c/sub\u003eHCO\u003csub\u003e3\u003c/sub\u003e + 35 g NaHCO\u003csub\u003e3\u003c/sub\u003e dissolved in distilled water and made up to 1000 mL), 208.1 mL of constant element solution (9.45 g Na\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e\u0026middot;12H\u003csub\u003e2\u003c/sub\u003eO + 6.2 g anhydrous KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e + 0.6 g MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO dissolved in distilled water and made up to 1000 mL), 0.1 mL of trace element solution (13.2 g CaCl\u003csub\u003e2\u003c/sub\u003e\u0026middot;2H\u003csub\u003e2\u003c/sub\u003eO + 10.0 g MnCl\u003csub\u003e2\u003c/sub\u003e\u0026middot;4H\u003csub\u003e2\u003c/sub\u003eO + 1.0 g CoCl\u003csub\u003e2\u003c/sub\u003e\u0026middot;6H\u003csub\u003e2\u003c/sub\u003eO + 8.0 g FeCl\u003csub\u003e3\u003c/sub\u003e\u0026middot;6H\u003csub\u003e2\u003c/sub\u003eO dissolved in distilled water and made up to 1000 mL), and 62.4 mL of reducing solution (4.0 mL of 1 mol/L NaOH + 625 mg Na\u003csub\u003e2\u003c/sub\u003eS\u0026middot;9H\u003csub\u003e2\u003c/sub\u003eO + 625 mg cysteine hydrochloride + 95 mL distilled water), which was bubbled with CO\u003csub\u003e2\u003c/sub\u003e until the solution turned colorless from light blue.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prepared cultivation medium was warmed at 39 ℃. Proportionally prepared fermentation substrate (0.50 g) was placed in a glass bottle with a total volume of 100 mL, and then 40 mL of pre-warmed cultivation medium and 20 mL of rumen fluid were added to the above bottle and CO\u003csub\u003e2\u003c/sub\u003e was injected to get rid of oxygen. The bottle was incubated in SHA-B oscillators (Guohua Enterprise, Changzhou, Jiangsu, China) for in vitro gastric fermentation experiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experiment comprised 4 groups, including CON (control), 200 ppb, 400 ppb, and 800ppb HRW, with 10 replicates for each group (5 replicates were stopped at 12 h (hours) and the other 5 at 48 h), and the indicators were strictly measured according to the experimental steps and requirements. Rumen fermentation characteristics were determined at the incubation time of 12 h and 48 h.\u003c/p\u003e\n\u003ch2\u003eRumen fermentation parameter determination\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAfter 12 h and 48 h incubation, fermented contents were filtered with four layers of gauze to obtain supernatant samples. The pH value was measured by a pH meter (Testo 206-pH1, Desto Instrument Co., LTD,\u0026nbsp;Shenzhen, China). These supernatant samples were stored at -80℃ to a determination of VFA, ammonia nitrogen (NH\u003csub\u003e3\u003c/sub\u003e\u0026ndash;N), MCP, and rumen microorganisms. The NH\u003csub\u003e3\u003c/sub\u003e-N concentration was determined using the method of phenol-hypochlorite reaction as described in Broderick and Kang[40]. The Folin phenol method based on Lowry\u0026rsquo;s assay was taken to determine the concentration of microbial crude protein (MCP), as described by Makkar et al[41]. The VFA measurements were determined according to the method of Qiu et al[42]: using a gas chromatograph (GC-2014Shimadzu Corporation, Kyoto, Japan) equipped with a 30 m capillary column (Rtx-Wax, 0.25 mm ID \u0026times; 0.25 \u0026micro;m film, Restek, Evry, France) to determine the contents of acetic acid, propionic acid, iso-butyric acid, butyric acid, iso-valeric acid and valeric acid. The sum of the six VFAs was defined as total VFA (TVFA), and the sum of iso-butyric acid and iso-valeric acid was defined as branched-chain VFA. The peak area method was used for identification and content conversion of each VFA based on relative retention time. The standard curve was prepared under the same conditions using the same method.\u0026nbsp;The non-glucogenic to glucogenic acids ratio (NGR) and fermentation efficiency (FE) were calculated as follows:\u003c/p\u003e\n\u003cp\u003eNGR = (C\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e+ 2 \u0026times; C\u003csub\u003e4\u0026nbsp;\u003c/sub\u003e+ C\u003csub\u003e5\u003c/sub\u003e) / (C\u003csub\u003e3\u0026nbsp;\u003c/sub\u003e+ C\u003csub\u003e5\u003c/sub\u003e)\u003c/p\u003e\n\u003cp\u003eFE = (0.622 \u0026times; C\u003csub\u003e2\u003c/sub\u003e + 1.092 \u0026times; C\u003csub\u003e3\u003c/sub\u003e + 1.56 \u0026times; C\u003csub\u003e4\u003c/sub\u003e) / (C\u003csub\u003e2\u003c/sub\u003e + C\u003csub\u003e3\u003c/sub\u003e + 2 \u0026times; C\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n\u003cp\u003eThe culture medium was filtered through gauze, and the filter cake was transferred without damage into a nylon bag, which was then placed in a 65\u0026deg;C drying oven to determine the solids content and calculate the degradation rate. The 12 h and 48 h solids were dried by reference to the method in GB/T6435-2006.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe in \u003cem\u003evitro\u003c/em\u003e solids degradation rate (V) = (W2-W3) / W1\u003c/p\u003e\n\u003cp\u003eW1 = The weight of fermentation substrate (g)\u003c/p\u003e\n\u003cp\u003eW2 = The total weight of fermentation substrate and nylon bag (g)\u003c/p\u003e\n\u003cp\u003eW3 = The total weight of fermentation substrate and nylon bag after in \u003cem\u003evitro\u003c/em\u003e fermentation (g)\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eNet Gas Production Rate and Gas Production Parameters\u003c/h2\u003e\n\u003cp\u003eThe gas production was measured after incubating the culture for 3, 6, 9, 12, 18, 24, 27, 30, 36, and 48 h. The culture tubes were quickly removed from incubation and the piston displacement (mL) was immediately recorded. The net gas production for each period was calculated as:\u003c/p\u003e\n\u003cp\u003eNet gas production (mL) = Gas production at a time point (mL) - Gas production of blank at the same time point (mL)\u003c/p\u003e\n\u003cp\u003eMethane production (The CH\u003csub\u003e4\u003c/sub\u003e production was estimated using the equation described by Moss et al[43].\u003c/p\u003e\n\u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e (mmol/L) = 0.45 \u0026times; C\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e- 0.275 \u0026times; C\u003csub\u003e3\u003c/sub\u003e + 0.40 \u0026times; C\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eNote: C\u003csub\u003e2\u003c/sub\u003e = Concentration of acetate (mmol/L), C\u003csub\u003e3\u003c/sub\u003e = Concentration of propionate (mmol/L), C\u003csub\u003e4\u003c/sub\u003e = Concentration of butyrate (mmol/L).\u003c/p\u003e\n\u003ch2\u003eBacterial Community Analysis\u003c/h2\u003e\n\u003cp\u003eA total of twenty microbial community genomic DNA using the E.Z.N.A.\u0026reg; soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) were transported to the Shanghai Majorbio Bio‐pharm Technology Co., Ltd. (Shanghai, China) or PCR amplification and MiSeq sequencing. The DNA extract was checked on 1% agarose gel, and DNA concentration and purity were determined with a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA). The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with primer pairs 338F (5\u0026apos;-ACTCCTACGGGAGGCAGCAG-3\u0026apos;) and 806R(5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-3\u0026rsquo;) by an ABI GeneAmp\u0026reg; 9700 PCR thermocycler (ABI, CA, USA). The amplification reaction system and program were the same as Mao et al.[27]\u0026nbsp;report. The PCR product was extracted from 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer\u0026rsquo;s instructions and quantified using Quantus\u0026trade; Fluorometer (Promega, USA). Purified amplicons were pooled in equimolar and paired-end sequenced on an Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA1103729).\u003c/p\u003e\n\u003cp\u003eThe raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by Trimmomatic and merged by FLASH with the following criteria: (i) the 300 bp reads were truncated at any site receiving an average quality score of \u0026lt;20 over a 50 bp sliding window, and the truncated reads shorter than 50 bp were discarded, reads containing ambiguous characters were also discarded; (ii) only overlapping sequences longer than 10 bp were assembled according to their overlapped sequence. The maximum mismatch ratio of the overlap region is 0.2. Reads that could not be assembled were discarded; (iii) Samples were distinguished according to the barcode and primers, and the sequence direction was adjusted, exact barcode matching, 2 nucleotide mismatch in primer matching.\u003c/p\u003e\n\u003cp\u003eOperational taxonomic units (OTUs) with 97% similarity cut-off were clustered using UPARSE (version 7.1, http://drive5.com/uparse/), and chimeric sequences were identified and removed. The taxonomy of each OTU representative sequence was analyzed by the RDP Classifier (http://rdp.cme.msu.edu/) against the 16S rRNA database (eg. Silva v138) using a confidence threshold of 0.7. Correlations between rumen fermentation characteristics and rumen bacterial community were presented with a heat map, which was performed using SPSS (version 17.0, IBM, Armonk, NY, USA) and Origin (version 2018, Origin Software, Inc., Northampton, Massachusetts, USA)\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eData processing was performed using SPSS (version 17.0, IBM, Armonk, NY, USA). The results are shown as the mean and standard error mean (SEM). Differences among means were determined using Tukey\u0026rsquo;s multiple range test was done when the interaction was significant. The level of statistical significance was set at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors appreciate all the help from our colleagues and collaborators.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eKM and GL were responsible for the conception and design of the study. KM and GL were responsible for data extraction and interpretation of the results, and GL and YZ carried out the statistical analysis. YZ, GL, QQ, and KO supervised the research activity. KM and GL were mainly responsible for drafting the manuscript. YL, KM, and MQ were involved in revising the draft. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 3230810), the Young Talents Training Program for Academic and Technical Leaders of Major Disciplines in Jiangxi Province (20232BCJ23016), and the China Agriculture Research System of MOF and MARA (CARS-37).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003eAnimal care and experimental procedures were approved by the Animal Care Committee of Jiangxi Agricultural University (Nanchang, China), and were under the university\u0026rsquo;s guidelines for animal research.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis experiment was approved by the Committee for the Care and Use of Experimental Animals at Jiangxi Agricultural University (JXAULL-2021-10).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eClinical Trial Number\u003c/p\u003e\n\u003cp\u003eAnimal care and experimental procedures were approved by the Committee for the Care and Use of Experimental Animals at Jiangxi Agricultural University (JXAULL-2021-10).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXue MY, Sun HZ, Wu XH, Liu JX, Guan LL. 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Effect of dietary inclusion of tea residue and tea leaves on ruminal fermentation characteristics and methane production. Anim Biotechnol. 2023;34(4):825\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiebig MA, Gross JR, Kronberg SL, Phillips RL, Hanson JD. Grazing management contributions to net global warming potential: a long-term evaluation in the northern great plains. J Environ Qual. 2010;39(3):799\u0026ndash;809.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"drinking water, hydrogen-rich water, microbial diversity, in vitro ruminal fermentation, methanogenesis","lastPublishedDoi":"10.21203/rs.3.rs-5037482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5037482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of this study was to evaluate the effects of different concentrations of hydrogen-rich water (HRW) on \u003cem\u003ein vitro\u003c/em\u003e rumen fermentation characteristics and the dynamics of bacterial communities. The experimental design included four treatment groups: control group (CON), 200ppb HRW group (HRW\u003csub\u003e200ppb\u003c/sub\u003e), 400ppb HRW group (HRW\u003csub\u003e400ppb\u003c/sub\u003e), and 800ppb HRW group (HRW\u003csub\u003e800ppb\u003c/sub\u003e). Each group was analyzed at 12-hour (h) and 48-hour (h) time points with five replicates, totaling 40 samples. The results showed that the highest gas production and methane content were observed in the 800ppb HRW group among the four groups. However, the 200ppb HRW group had significantly lower methane content during both 12 h and 48 h fermentations compared to the other treatment groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of rumen fermentation indicators, the 400ppb HRW group significantly increased the levels of ammonia nitrogen (NH\u003csub\u003e3\u003c/sub\u003e-N) and microbial crude protein (MCP), but reduced the dry matter degradation rate at 12 h fermentation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After the 48 h fermentation, the HRW\u003csub\u003e400ppb\u003c/sub\u003e group had the highest MCP content (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but there were no significant differences in NH\u003csub\u003e3\u003c/sub\u003e-N and dry matter degradation rate compared to the CON group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Although HRW did not significantly benefit the synthesis of total volatile fatty acids (TVFA) and individual VFA, the HRW\u003csub\u003e800ppb\u003c/sub\u003e group significantly increased the ratio of acetate to propionate (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Based on these results, we selected the HRW\u003csub\u003e400ppb\u003c/sub\u003e group for subsequent bacterial community analysis. Bacterial community analysis showed that compared with the CON group, the HRW\u003csub\u003e400ppb\u003c/sub\u003e group had significant increases in the Simpson index, Firmicutes, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eSchwartzia\u003c/em\u003e, \u003cem\u003ePrevotellaceae_YAB2003_group\u003c/em\u003e, and \u003cem\u003eOribacterium\u003c/em\u003e, and significant decreases in the \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRuminobacter\u003c/em\u003e, \u003cem\u003eSuccinivibrio\u003c/em\u003e, \u003cem\u003eunclassified Succinivibrionaceae\u003c/em\u003e, and \u003cem\u003ePrevotellaceae_UCG-003\u003c/em\u003e at 12 h fermentation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As fermentation time extended to 48 h, the differential bacterial communities changed. The abundance of \u003cem\u003ePrevotellaceae_YAB2003_group\u003c/em\u003e and \u003cem\u003eOribacterium\u003c/em\u003e significantly increased, while the abundance of \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e and \u003cem\u003eSucciniclasticum\u003c/em\u003e significantly decreased in the HRW group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Correlation analysis revealed the negative associations between CH\u003csub\u003e4\u003c/sub\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e. Moreover, the abundance of \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e positively correlated with the CH\u003csub\u003e4\u003c/sub\u003e. Collectively, these results indicate that HRW can modulate rumen fermentation and microbial community structure to reduce methane emissions without significantly affecting VFA synthesis, highlighting its potential as drinking water for enhancing ruminant nutrition and mitigating the environmental impact of livestock farming.\u003c/p\u003e","manuscriptTitle":"Hydrogen-Rich Water as a potential strategy for improving ruminant nutrition and mitigating methane emissions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-07 05:26:58","doi":"10.21203/rs.3.rs-5037482/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-26T13:06:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-25T14:06:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-20T05:14:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229178675485322634242312126290695729775","date":"2024-09-20T01:29:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335307730893504466193255437761223236619","date":"2024-09-18T04:07:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-18T02:24:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-16T11:22:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-13T05:16:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-13T05:15:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2024-09-05T10:33:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5186f09f-ec1a-44c5-94f9-32b15fa88684","owner":[],"postedDate":"November 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T19:20:53+00:00","versionOfRecord":{"articleIdentity":"rs-5037482","link":"https://doi.org/10.1186/s12866-024-03638-1","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2024-11-12 15:57:05","publishedOnDateReadable":"November 12th, 2024"},"versionCreatedAt":"2024-11-07 05:26:58","video":"","vorDoi":"10.1186/s12866-024-03638-1","vorDoiUrl":"https://doi.org/10.1186/s12866-024-03638-1","workflowStages":[]},"version":"v1","identity":"rs-5037482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5037482","identity":"rs-5037482","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}