Animal experiments, both in vitro and in vivo, were conducted in accordance with the guidelines approved by the Jilin Agricultural University Animal Research Ethics Committee (approval number: JLAU-ACUC2022-004).

Rumen fermentation fluid samples were collected after 3, 6, 12, and 24 h of cultivation and divided into five parts: 1.5 mL for VFA concentration, 1 mL for ammonia nitrogen (NH₃-N) concentration, 2 mL for microbial flora analysis, and 2 mL for metabolomics. The remaining fluid was used for pH determination. Samples for VFA and NH₃-N determination were frozen at -40°C, while those for microbial flora and metabolomics were frozen at -80°C. Rumen fluid for VFA determination was centrifuged at 10,000 g for 10 minutes at 4°C. Subsequently, 1 mL of the supernatant and 0.2 mL of a 25% metaphosphate solution were added to a 2 mL centrifuge tube and allowed to precipitate overnight. Afterward, the mixture was centrifuged again at 10,000 g for 10 minutes at 4°C, and the supernatant was collected in a new 2 mL centrifuge tube for further analysis.

VFA concentrations were measured using a gas chromatograph (7890 B; Agilent Technologies, Santa Clara, CA, USA) (19). The pH was measured using a pH meter (MP523-04; Shanghai Sanxin Instrument Co. Ltd., Shanghai, China). NH₃-N levels were quantified using a spectrophotometer (UV-1201, Shimadzu Corporation, Kyoto, Japan) (20).

Blood indicators including inflammatory ( IL-6, IL-10, IL-1β, TNF-α, and IFN-γ), immune (IgA and IgM), and oxidation/antioxidant (CAT, MDA, GSH-Px, SOD, TAS, and TOS). Indicators were measured using enzyme-linked immunosorbent assay kits (Lengton, Shanghai, China), and the tests were performed in accordance with the manufacturer’s instructions.

The bacterial 16S rRNA gene V3–V4 region was amplified by PCR using the forward primer (F): ACTCCTACGGGAGGCAGCA and reverse primer (R): GGACTACHVGGGTWTCTAAT. Sample-specific 7-bp barcodes were included in the primers for multiplex sequencing. The PCR mixture included 5 μL of buffer (5×), 0.25 μL of fast pfu DNA polymerase (5U/μL), 2 μL (2.5 mM) of dNTPs, 1 μL (10 µM) of each forward and reverse primer, 1 μL of DNA template, and 14.75 μL of ddH₂O. The thermal cycling protocol consisted of an initial denaturation at 98°C for 5 min, followed by 25 cycles of denaturation at 98°C for 30 s, annealing at 53°C for 30 s, and extension at 72°C for 45 s, with a final extension at 72°C for 5 min. PCR amplicons were purified using Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China), quantified with a Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA), and then pooled in equal amounts for pair-end 250-bp sequencing on the Illumina NovaSeq platform with the NovaSeq 6000 SP Reagent Kit (500 cycles) at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China). Tax4Fun was employed to predict functional characteristics consolidated according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway classification system. The data presented in the study are deposited in the NCBl Sequence Read Archive repository, accession number PRJNA1101182.

The sample (100 μL) was mixed with an extraction solution (MeOH: ACN, 1:1[v/v], 400 μL) containing deuterated internal standards. The mixture was vortexed for 30 s, sonicated for 10 min in a 4°C water bath, and incubated for 1 h at 40°C for 1 h to precipitate proteins. After centrifugation at 12000 rpm (RCF=13800 [× g], R=8.6 cm) for 15 min at 4°C, the supernatant was transferred to a fresh glass vial for further analysis. Quality control samples were prepared by mixing equal aliquots of the supernatants.

LC-MS analyses were performed using an Ultra-High Performance Liquid Chromatography system (Vanquish, Thermo Fisher Scientific) with a Waters ACQUITY UPLC BEH Amide (2.1 mm × 50 mm, 1.7 μm) coupled to Orbitrap Exploris 120 mass spectrometer (Orbitrap MS, Thermo). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 mmol/L ammonia hydroxide in water (pH 9.75) (A) and acetonitrile (B). The auto-sampler temperature was 4°C, and the injection volume was 2 μL. An Orbitrap Exploris 120 mass spectrometer was used to acquire MS/MS spectra in information-dependent acquisition mode under the control of an acquisition software (Xcalibur, Thermo). In this mode, the acquisition software continuously evaluated the full-scan MS spectrum. The Electrospray ionization source conditions were set as follows: sheath gas flow rate at 50 Arb, Aux gas flow rate at 15 Arb, capillary temperature at 320°C, full MS resolution at 60000, MS/MS resolution at 15000, collision energy: SNCE 20/30/40, and spray voltage at 3.8 kV (positive) or 3.4 kV (negative), respectively.

The raw data were converted to the mzXML format using ProteoWizard and subsequently processed with an in-house program developed in R, which is based on XCMS for peak detection, extraction, alignment, and integration.

Data were analyzed using SPSS Statistics software (version 22.0, IBM). The experiment used a one-way analysis of variance for data analysis. Spearman’s correlation coefficient was utilized to analyze the correlation between microbial communities at the genus level and fermentation indicators, and Mantel’s test was used to explore the relationship between differential metabolites, microbial communities, and fermentation indicators.

The effects of myo-inositol on in vitro ruminal fermentation are summarized in Table 2 . After 3 h of in vitro fermentation, the pH of the YC, IN65, IN85, and IN205 groups were significantly lower than the CON group (P < 0.01). After 6 h of in vitro fermentation, the pH of the IN85 and IN205 groups were significantly lower compared to the CON group (P < 0.05). After 12 h of in vitro fermentation, the pH of the IN125 group was significantly lower than that of the YC group (P < 0.01). Following 24 h of in vitro fermentation, the pH of the YC, IN65, and IN85 groups were significantly higher than that of the CON group (P < 0.01).

After 3 h of in vitro fermentation, NH₃-N concentrations in YC, IN65, IN85, IN125, and IN205 were significantly higher than those in the CON group (P < 0.01), with NH₃-N concentrations in IN125 and IN205 being significantly higher than those in the YC group (P < 0.01). After 6 h of in vitro fermentation, NH₃-N concentrations in IN85 and IN125 were significantly higher than those in the CON and YC groups (P < 0.01). After 12 h of in vitro fermentation, the NH₃-N concentration in the YC group was significantly higher than that in the CON group (P < 0.01). Notably, after 6 h of in vitro fermentation, the acetic acid/propionic acid ratio (A/P ratio), six VFA, 3type TVFA and 6type TVFA in the IN65 group were significantly higher than those in the CON group ( Table 2 ; Figures 1A, B ).

Alpha diversity analysis was conducted to assess the effect of YC and myo-inositol on rumen microbial abundance. The Chao1, Faith_pd, Shannon, and Simpson indices were used to evaluate the richness, diversity, and evenness of the microbiota. After 6 h of in vitro fermentation, the Shannon index was significantly higher in the IN65 group compared to the YC group (P < 0.05). After 24 h of in vitro fermentation, the Chao1 and Faith_pd indices were significantly lower in the IN85 and IN125 groups compared to the YC group (P < 0.01). The Simpson indices of the YC, IN65, IN85, IN125, and IN205 groups were all significantly higher than the CON group, with the IN85 group showing a significantly lower Simpson index than the YC group (P < 0.01; Table 3 ).

The 16S rRNA gene was sequenced using amplicon sequencing, resulting in 3,473,478 high-quality sequences. On average, each sample had 289,457 sequences. The main microbial phyla consisted of 10 taxa with relative abundances > 0.10%. The most abundant phyla were Bacteroidetes (61.10%), Firmicutes (33.29%), Verrucomicrobia (1.84%), and Proteobacteria (1.11%) ( Figure 1C ). At the genus level, the main microbiota included 20 genera with relative abundances exceeding 0.10%. The most abundant genera were Prevotella (28.51%), Butyrivibrio (2.10%), and Succinivibrio (1.51%) ( figure 1D ).

As shown in Table 4 , the microbiota at the phylum level were significantly affected by YC and myo-inositol. After 6 h of in vitro fermentation, the Firmicutes abundance in the IN65 group was significantly higher compared to the YC group (P < 0.01), whereas the relative abundances of Bacteroidetes and Lentisphaerae were significantly lower than those in the YC group (P < 0.01). After 24 h in vitro fermentation, the IN65 group showed significantly higher relative abundances of Bacteroidetes, Verrucomicrobia, Proteobacteria, and Lentisphaerae compared to the CON group (P < 0.01), whereas the relative abundances of Firmicutes, and SR1 were significantly lower than that in the CON group (P < 0.01). As shown in Table 5 , at 6 h of in vitro, the IN65 group showed a significantly higher relative abundance of Moryella at the genus level compared to the YC group and CON group (P < 0.01); at 24 h of in vitro, the relative abundances of Prevotella, CF231, BF311, YRC22, Selenomonas, and Desulfovibrio in the IN65 group were significantly higher than that in the CON group (P < 0.01).

Jaccard dissimilarity analysis showed significant differences in microbiota composition among six groups with the same fermentation time. The addition of YC and myo-inositol affected the richness, diversity, and uniformity of the rumen microbial communities. At 6 h of in vitro fermentation, the YC group closely resembled was close to the IN65 group, while the other four groups clustered together, suggesting that adding a small amount of myo-inositol and a specific amount of YC had a significant effect on rumen microorganisms. The IN85 group, IN125 group, IN205 group, and YC group were similar to the CON group, indicating a minimal effect of these four groups on rumen microorganisms. At 24 h of in vitro, the treatment groups with myo-inositol addition showed similarities but differed significantly from the CON group, suggesting little variation among the IN65 group, the IN85 group, the IN125 group, and the IN205 group, and a large difference between CON and the treatment groups with myo-inositol addition ( Figures 1E, F ).

At 21 days postpartum, compared with the control group and YC group, a numerically higher milk yield (+4.00% and +4.05%) was observed in the IN65 group, though statistical significance was not achieved due to limited sample size and high variability ( Figure 2A ). Table 6 shows no significant differences in oxidation, immune, or inflammatory indicators at calving. On the 21 days postpartum, GSH-Px level in the IN65 group was significantly lower compared to the YC and CON groups (P < 0.01), and SOD level was significantly lower than that in the CON group (P < 0.01). These findings suggested that myo-inositol affects oxidative stress status in peripartum dairy cows. Following a period of YC or myo-inositol feeding, TNF-α levels in the IN65 group were significantly lower than those in the YC group (P < 0.01), and IgM levels in the YC group were significantly higher than those in the CON group (P < 0.01). From day 1 to day 21 postpartum, SOD, TAS, TOS and IL-10 levels in the CON group showed a significant increase (P < 0.01). Notably, this trend was significantly mitigated after the addition of myo-inositol ( Table 7 ). The findings indicate that myo-inositol affected oxidative stress in peripartum dairy cows ( Figure 2B ).

Principal component analysis (PCA) revealed a clear separation of metabolites at 6 h and 24 h ( Figure 2C ). A total of 170 differential metabolites were identified between the 6 h and 24 h groups compared to the CON group, with variable importance in projection (VIP) > 1 and P < 0.05. These metabolites were lipids and lipid-like molecules and organic acids and derivatives ( Figure 2D ). At 6 h of in vitro fermentation, 65 metabolites were upregulated in the YC group compared to the CON group ( Figure 2E ). At 24 h of in vitro fermentation, 36 differential metabolites were found to be upregulated ( Figure 2F ).

The study highlights the need for a comprehensive evaluation of the impact of YC and myo-inositol on ruminal fermentation in dairy cows. Further analysis of rumen metabolites and their pathways revealed key metabolic pathways with significant impact. Enrichment analysis identified pathways with P < 0.05 and impact > 0.2 as crucial in understanding the effects of these supplements.

The effects of supplementing myo-inositol on in vitro rumen fermentation in dairy cows were shown by examining the differential metabolites and enriched metabolic pathways between the YC and CON groups. After 6 h of in vitro fermentation, analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that YC mainly affects the biosynthesis of plant secondary metabolites and unsaturated fatty acids. During 24 h of in vitro fermentation, KEGG pathway enrichment analysis demonstrated that YC primarily affected the biosynthesis of plant secondary metabolites, terpenoids, and steroids.

Analysis of the differential metabolites and enriched differential metabolic pathways between the YC group and the myo-inositol-supplemented (IN65, IN85, IN125, and IN205) groups indicated that the inclusion of myo-inositol in the diet affected rumen fermentation in dairy cows. KEGG pathway enrichment analysis after 6 h of in vitro fermentation revealed that myo-inositol mainly affected AA metabolism, particularly D-amino acid metabolism, and taurine and hypotaurine metabolism ( Figure 2G ). After 24 h of in vitro fermentation, KEGG pathway enrichment analysis indicated that myo-inositol predominantly affected styrene degradation and fatty acid biosynthesis.

PCA analysis showed a clear separation between metabolites from cows at day 1 and day 21 postpartum ( Figure 3A ). In the serum samples of the three groups of dairy cows 21 days after calving, 307 differential metabolites with VIP > 1 and P < 0.05 were identified and then classified. The most abundant differential metabolites included lipids and lipid-like molecules, and organic acids and their derivatives ( Figure 3B ). In comparison to the CON group, the YC group had 62 upregulated and 141 downregulated differential metabolites ( Figure 3C ). Metabolic pathways with P < 0.05 and impact > 0.2, such as galactose metabolism and other differential metabolic pathways, were identified through enrichment analysis of the pathways where the differential metabolites were located ( Figure 3E ).

In the IN65 group, 166 differential metabolites were upregulated and 27 were downregulated compared to the YC group ( Figure 3D ). Enrichment analysis of the differential metabolites between IN65 and CON showed significant metabolic pathways such as galactose metabolism, pentose and glucuronate interconversions, taste transduction, and carbohydrate digestion and absorption ( Figure 3F ).

PCA analysis showed a clear separation between metabolites from cows at day 1 and day 21 postpartum ( Figure 3G ). In the milk samples collected from the three groups of cows 21 days after calving, a total of 382 differential metabolites with VIP > 1 and P < 0.05 were screened and then classified. The most differentially metabolites included organic acids and derivatives, lipids and lipid-like molecules ( Figure 3H ). In comparison to the CON group, the YC group had 72 upregulated and 238 downregulated differential metabolites ( Figure 3I ). In comparison to the YC group, the IN65 group had 54 upregulated and 31 downregulated differential metabolites ( Figure 3J ). Enrichment analysis of differential metabolites between the CON and YC groups revealed significant metabolic pathways with P < 0.05 and impact > 0.2, such as glucosinolate biosynthesis and tropane, piperidine, and pyridine alkaloid biosynthesis ( Figure 3K ). Additionally, metabolic pathways with P < 0.05 and impact > 0.2 were selected through enrichment analysis, including the sphingolipid signaling pathway.

Correlation analyses were performed between the differential metabolites in rumen fluid, fermentation indicators, and rumen bacteria ( Figure 4 ). Acetic acid was positively correlated with D-glyceric acid and xanthosine ( Figures 4A, C ). Similarly, D-glyceric acid, inosine 5-monophosphate, and xanthosine were positively correlated with genus, such as Bulleidia and Moryella ( Figures 4D, F ). This suggested that myo-inositol affects the production of acetic acid and butyric acid, by regulating the composition of rumen bacteria and their metabolism.