Skip to Content
MDPIMDPI

Advanced

MicroorganismsMicroorganisms
PDF
  • Article
  • Open Access

24 January 2026

21 Pages

Dietary Supplementation of Concentrate Improves Lactation Performance and Immune Function in Grazing Lactating Yaks Through Changes in Rumen Microbial Population and Metabolites

,
,
,
,
,
and
1
College of Life Science, Northwest A&F University, Yangling, Xianyang 712100, China
2
College of Animal Husbandry and Veterinary Sciences, Qinghai University, Xining 810016, China
3
Key Laboratory of Qinghai-Tibet Plateau Grazing Yak and Tibetan Sheep Animal Nutrition and Feed-Forage, Ministry of Agriculture and Rural Affairs, Qinghai University, Xining 810016, China
4
Key Laboratory of Plateau Grazing Animal Nutrition and Feed Science of Qinghai Province, Qinghai University, Xining 810016, China
Microorganisms2026, 14(2), 278;https://doi.org/10.3390/microorganisms14020278
This article belongs to the Section Veterinary Microbiology
Version Notes
Order Reprints

Abstract

Grazing yaks often face protein deficiency due to low-quality pasture, which limits milk production. This study aimed to investigate the effects of varying protein levels in concentrate supplementation on lactational performance, immune function, and rumen microbial and metabolites in grazing lactating yaks. Thirty-six lactating Qinghai Plateau yaks (172.78 ± 11.70 kg) were assigned to four treatments for 70 d (10 d adaptation + 60 d trial): grazing only (CON) or grazing plus 1.50 kg/d concentrate containing 15.09% (CP15), 17.00% (CP17), or 18.98% CP (CP19). Concentrate supplementation significantly increased average daily gain (ADG; 0.22 vs. 0.72–0.90 kg/d; p < 0.001) and milk yield (622.18 vs. 1094.25–1385.73 g/d; p < 0.001), and milk yield showed a linear increase with higher dietary protein levels (p < 0.001). Milk protein yield (29.99 vs. 56.00–68.60 g/d; p < 0.001) and milk lactose yield (40.71 vs. 79.85–93.53 g/d; p < 0.001) were also increased. Milk composition, including fat, protein, and lactose, also improved across supplementation groups, with the greatest enhancement observed at the CP17 group (p < 0.05). Rumen pH and volatile fatty acids did not differ between treatments, while microbial protein increased with supplementation (10.88 vs. 12.72–15.00 mg/dL; p = 0.041) and showed a linear response to dietary CP level (p = 0.033). Concentrate supplementation significantly altered the rumen microbial structure (ANOSIM, p = 0.036), enriching Succinivibrionaceae_UCG-002, Fibrobacter, Ruminobacter, and Succinimonas and reducing Saccharofermentans in CP17 yaks (p < 0.05). Untargeted metabolomics further indicated a marked shift in the ruminal metabolite profile. Compared with CON, CP17 yaks exhibited higher levels of calcium propionate, 2-nitrofuran, curvulalide, and 2,5-dihydroxybenzoic acid, but lower levels of 4-pyridoxic acid, L-carnitine, bitocholic acid, and taurodehydrocholic acid. Pathway enrichment analysis identified vitamin B6 metabolism as significantly enriched. Collectively, these findings suggest that moderate protein concentrate supplementation (CP17) may enhance lactation performance by modulating rumen microbiota and metabolism in lactating yaks.

1. Introduction

Yaks (Bos grunniens), known as the “ship of the plateau”, are a unique livestock species, mainly inhabiting the Qinghai–Tibetan Plateau (QTP) and adjacent high-altitude regions, ranging from 3000 to 5000 m above sea level; the yak population is estimated to be 14–15 million, with the majority raised in China [1]. They play a pivotal role in sustaining the livelihoods of local herders by providing milk, meat, fiber, and transport under extreme environments characterized by hypoxia, low temperature, and sparse vegetation [2]. Traditionally, the Tibetan diet has relied heavily on yak milk—often referred to as “liquid gold”—and its derived products, which serve as major sources of energy, essential vitamins, and nutrients [3]. Compared to ordinary cow’s milk, yak milk has high total solids (13.97–18.57%), fat (5.6–8.8%), and protein (4.6–6.4%), as well as functional and bioactive components, including unique amino acids and fatty acids, high levels of vitamins, specific enzymes, and beneficial microbes [3,4]. Tibetan herders reportedly consume little to no vegetables or fruits throughout the year, yet show no obvious symptoms of vitamin or mineral deficiency [5], suggesting that yak milk is crucial for sustaining the health of the Tibetan population. However, the productivity of yaks, particularly during lactation, is constrained by a pronounced nutritional imbalance between seasonal pasture supply and the nutritional requirements of lactating yaks [6], resulting in an average annual milk yield of only 0.82–3.18 kg/d [7].
During lactation, yaks experience a marked increase in energy and protein requirements to sustain milk synthesis. Inadequate nutrition during this period can lead to reduced milk yield, deterioration in milk composition, body weight loss, and weakened health status [8]. To address nutrition deficiencies, concentrate supplementation has been widely recognized as an effective nutritional strategy. Studies demonstrate that providing concentrate to grazing yaks improves growth performance through regulating rumen microbes, fermentation parameters, and host metabolism [9,10]. Although concentrate supplementation has been widely applied to improve the nutritional status of grazing yaks, existing studies have primarily focused on the energy supply or the amount of concentrate provided, while research on dietary protein level and utilization remains limited. In ruminants, protein is not only a critical substrate for microbial protein secretion and milk protein secretion but also a regulator of immune and metabolic pathways [11]. Recent studies in ruminants suggest that dietary protein level and degradability (Rumen-degradable protein/bypass protein ratio) substantially influence rumen fermentation, microbial nitrogen flow, and host immunity [12,13]. In grazing yaks, insufficient protein intake limits rumen microbial protein synthesis and metabolizable protein supply, leading to negative nitrogen balance and reduced milk yield [7]. Conversely, excessive protein supply may increase ruminal deamination and ammonia accumulation, promote urea synthesis, and elevate urinary and fecal nitrogen excretion, and can exacerbate environmental burdens through ammonia volatilization, nitrate leaching, and nitrous oxide emissions. Moreover, the rumen, as the metabolic center of ruminants, hosts diverse microbial communities that ferment fibrous forages into VFAs, microbial proteins, and ruminal metabolites. The composition and activity of rumen microbiota are highly sensitive to dietary inputs. In a recent study, Liu et al. [7] reported that supplementing concentrate alters the microbial community structure, often increasing the relative abundance of Firmicutes and starch-degrading microbes, which are positively associated with milk production. Importantly, rumen microbes also produce metabolites that influence host immune function, including butyrate, which modulates inflammatory responses [10]. Hence, a systematic exploration of the rumen microbial and metabolic responses to varying protein concentrate supplementation can be used to elucidate the mechanism behind improved lactational performance and immune function of grazing yaks.
We hypothesize that optimal protein-level supplementation enhances yak lactational performance and immune function, modulating rumen microbiota, rumen microbes, and metabolites. To investigate this hypothesis, 16S rRNA sequencing was used alongside untargeted metabolomics technology to identify key metabolic pathways and differential microbes associated with lactational performance and immune function in grazing yak-supplemented optimal protein. These findings provide evidence for protein–microbe–host interactions and may inform sustainable nutritional strategies for grazing lactating yaks.

2. Materials and Methods

2.1. Description of Experiment Area and Animal Ethics Statement

The experiment was performed from August to October in 2023 in natural grassland (99.121° E, 37.55° N) at an altitude of 3615 m above sea level in Tianjun County, Qinghai Province. The annual average temperature and precipitation are −1.0 °C and 300 mm in this grassland, respectively. The dominant forage species were Kobresia humilis, Carex moorcroftii, Elymus nutans, and Oxytropis coerulea f. albiflora. All animal procedures were approved by the College of Animal Husbandry and Veterinary Sciences, Qinghai University (approval number: 2023-QHMKY-005).

2.2. Experiment Design and Diets

The experiment was conducted with 36 lactating Qinghai Plateau yaks with similar body weight (172.78 ± 11.70 kg). All yaks were clinically healthy at the start of the experiment, and their age and parity were 6.0 ± 0.82 and 2.0 ± 0.82, respectively. Thirty-six lactating yaks were studied using a randomized complete block design, with a 10-day adaptation and a 60-day experimental period. All yaks were randomly assigned to 4 dietary treatments (n = 9 per group): a natural grazing group (CON), grazing supplemented with a low-protein concentrate (CP15 group; 15.09%), grazing supplemented with a medium-protein concentrate (CP17 group; 17.00%), and grazing supplemented with a high-protein concentrate (CP19 group; 18.98%). The groups were separated by fencing to prevent intermixing. Grazing occurred daily from 08:00 to 18:00. Upon returning from pasture, yaks in the supplementation groups received 1.5 kg of concentrate per day. The concentrates were formulated according to the Beef Cattle Feeding Standard (NY/T 815-2004) [14]; the nutrient compositions of the forage and concentrates are presented in Table 1.
Table 1. Ingredients and chemical composition of the forage and concentrate.

2.3. Body Weight, Milk Yield, and Milk Composition

During the morning feed, yak body weight was measured at the beginning and at the end of the trial using floor scales. The ADG was calculated as =(Final BW − Initial BW)/60. To simulate the forage selection behavior of grazing yaks, mixed forage samples were collected monthly from the yak-grazed pasture using randomly placed 0.5 × 0.5 m quadrats. Forage and concentrate samples were dried in a forced-air oven (DGG-9240B; Shanghai Shen Xin Inc., Shanghai, China) at 55 °C for 48 h, then ground through a 1 mm screen (Wiley mill, Arthur H. Thomas Co., Philadelphia, PA, USA) before analysis.
Standard procedures of the Association of Official Analytical Chemists were used to determine dry matter (DM; method 935.29; AOAC, 2006) and crude ash (method 942.05; AOAC, 2006). The organic matter (OM) content was calculated using DM, subtracting crude ash content. The crude protein (CP) concentration was determined using the Macro-Kjeldahl procedure (method 990.03; AOAC, 2006). The ether extract (EE) content was determined using the AOAC method 920.39 (AOAC, 2006). Neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents were analyzed according to the method described by Van Soest et al. [15]. At each sampling time point, milk samples were collected from the yaks before grazing for three consecutive days (the sampling day and the two preceding days), and the daily milk yield of each yak was recorded. The collected milk samples were analyzed using a calibrated milk composition analyzer (Julie Z10; Scope Electric, Razgrad, Bulgaria) to determine the contents of milk fat, protein, and lactose.

2.4. Blood Cell Parameters and Rumen Fermentation Parameters

Blood samples (5 mL) were collected from the jugular vein of each yak into Vacutainer tubes containing EDTA (Vacutainer, Becton Dickinson, Franklin Lakes, NJ, USA) after morning milking on the final day (day 60) of the experiment. Immediately after collection, the tubes were gently inverted 6–8 times, chilled on ice, and transported to the laboratory at the College of Veterinary Medicine, Qinghai University, for analysis. Hematological parameters (immune-related indicators), including white blood cell count (WBC), neutrophils (NE, %), lymphocytes (LY, %), red blood cell count (RBC), hemoglobin (HGB), and and platelet count (PLT), were determined using a hematology analyzer (XN-350, Sysmex Asia Pacific) following the procedures described by Fujimaki et al. [16].
After morning milking on the final day (day 60), approximately 100 mL of rumen fluid was collected from each yak using a stomach tube, according to the method described by Liu et al. [17], and ruminal pH was measured immediately using a portable pH meter (Testo 206; Testo SE & Co. KGaA, Lenzkirch, Germany). Then, each sample was divided into two 50 mL centrifuge tubes and immediately frozen in liquid nitrogen. One of the aliquots was used for microbiota and metabolome analyses, whereas the other was used to determine volatile fatty acids (VFAs), ammonia nitrogen (NH3-N), and microbial protein (MCP). The VFA concentrations in rumen fluid samples were measured according to the method described by Dai et al. [18] with minor modifications. Briefly, the rumen fluid samples were thawed at room temperature and then centrifuged at 16,000× g for 15 min at 4 °C (5840R; Eppendorf SE, Hamburg, Germany). Subsequently, 1.0 mL of the supernatant was transferred into a 1.5 mL centrifuge tube, and 200 μL of 25% (v/v) metaphosphoric acid (Beijing Yili Fine Chemicals Co., Ltd., Beijing, China) was added. The mixture was incubated in an ice bath for 30 min and then centrifuged again at 16,000× g for 10 min at 4 °C. The resulting supernatant was filtered through a 0.22 μm syringe filter and transferred into 1.0 mL GC vials for analysis. Subsequently, the VFA concentrations were determined using a gas chromatograph (GC-2014, Shimadzu, Shanghai, China) equipped with a DB-FFAP capillary column (30 m × 0.32 mm × 0.5 μm). The acquisition conditions were as follows: the temperatures of the injector and detector were maintained at 220 °C and 250 °C, respectively, with a splitting ratio of 30:1 and a high-purity nitrogen flow of 40 mL/min. The concentration of NH3-N in rumen fluid was determined using the phenol-hypochlorite colorimetric method [19]. Briefly, ammonia in the rumen fluid reacts with phenol and hypochlorite under alkaline conditions to form indophenol blue, and the absorbance was measured spectrophotometrically at 630 nm. NH3-N concentration was calculated from an ammonium standard curve and expressed as mg/dL. The MCP concentration was measured according to the procedure described by Makkar et al. [20]. Briefly, microbial cells were isolated from rumen liquor by differential centrifugation, followed by alkaline lysis, and the released protein was quantified using a colorimetric protein assay.

2.5. Rumen Fluid DNA Extraction, 16S rRNA Sequencing, and Bioinformatics Analysis

Total microbial DNA was extracted from rumen fluid samples using the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s instructions. DNA concentration and purity were measured using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA), and DNA integrity was confirmed by 1% agarose gel electrophoresis. The V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified using the forward primer 341F (5′-ACTCCTRCGGGAGGCAGCAG-3′) and the reverse primer 806R (5′-GGACTACCVGGGTATCTAAT-3′) was utilized as described by Guo et al. [21]. PCR reactions were performed in triplicate using TransStart® FastPfu DNA Polymerase (TransGen Biotech, Beijing, China) under the following cycling conditions: 95 °C for 3 min, followed by 27 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s, with a final extension at 72 °C for 10 min. Amplicons were pooled, purified with the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and quantified by QuantiFluor™-ST (Promega, Madison, WI, USA). Equimolar amplicons were pooled and sequenced on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) with paired-end (2 × 300 bp).
The original reads were demultiplexed, quality-filtered, trimmed, denoised, and merged using the DADA2 (q2-dada2 version 2023.2.0) pipeline implemented in QIIME2 (version 2023.2) to generate high-resolution amplicon sequence variants (ASVs) [22]. Chimeric sequences were removed, and taxonomic assignment of ASVs was conducted against the SILVA rRNA database (release 138) using a Naive Bayes classifier, with the resulting microbial community composition characterized at various taxonomic levels, including phylum, class, order, family, genus, and species. Alpha diversity indices, including Shannon and Chao1, were calculated using QIIME 2 to assess the richness and evenness of the microbial communities. The microbial β-diversity was assessed based on Bray–Curtis distance matrices, which were subsequently used for principal coordinate analysis (PCoA) to visualize community dissimilarities; the significance of group separation was further evaluated using ANOSIM. To identify differentially abundant taxa between treatment groups, LEfSe (linear discriminant analysis effect size) analysis was conducted as described by Segata et al. [23]. Additionally, co-occurrence network analysis was conducted using the top 20 genera, and significant correlations among genera (p < 0.05 and |r| > 0.6) were visualized.

2.6. Rumen Fluid Metabolite Extraction, LC-MS/MS Analysis, and Data Processing

Frozen rumen fluid samples were thawed on ice and vortexed to ensure homogeneity. For extraction, 100 µL of rumen fluid was mixed with 400 µL of ice-cold methanol/acetonitrile (1:1, v/v) to precipitate proteins and extract metabolites. The mixture was vortexed for 60 s, sonicated in an ice-water bath for 10 min, and then incubated at −20 °C for 1 h. After centrifugation (14,000× g, 15 min, 4 °C), the supernatant was collected, dried in a vacuum concentrator, and reconstituted in 100 µL of 50% methanol prior to LC–MS/MS analysis [24]. Metabolomic profiling was performed using an ultra-high-performance liquid chromatography (UHPLC) system (Vanquish; Thermo Fisher Scientific, Waltham, MA, USA) equipped with an ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm) (Waters, Milford, MA, USA). The mobile phases consisted of water with 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B) using a linear gradient from 2% B to 98% B over 30 min at a flow rate of 0.3 mL/min. Mass spectrum was operated in either the positive or negative ion mode as described by Wang et al. [25].
Raw LC–MS/MS files were converted to mzXML format using ProteoWizard (v3.0). Data preprocessing, including peak detection, retention time alignment, and integration, was conducted in XCMS (V3.16.1, R package) following established workflows [26]. Metabolite features were annotated by matching accurate mass and MS/MS spectra against reference databases, including the Human Metabolome Database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Multivariate analyses, including principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA), were performed using SIMCA-P (version 11.0; Umetrics AB, Umeå, Sweden). The differentially expressed metabolites (DEMs) were evaluated by combining the variable importance in the projection (VIP > 1.5) and the corrected p-values (<0.05) via Student’s t-test. The online platform, MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/; accessed 12 November 2025) was used to conduct the metabolic pathway analysis (MetPA) based on DEM via the library of Bos Taurus (cow) of KEGG [25].

2.7. Statistical Analysis

Data statistics were performed using SPSS Statistics (version 22, IBM, New York, NY, USA). For ADG, milk yield and composition, blood cell parameters, and rumen fermentation parameters, orthogonal polynomial contrasts were performed to evaluate the effects of concentrate supplementation (supplemented groups combined vs. the control group). In addition, regression models were constructed to evaluate the effects of dietary levels across CP15–CP19 (with CON excluded from the trend). The ADG, milk yield and composition, blood cell parameters, and rumen fermentation parameters were set as dependent variables; dietary protein level was set as the independent variable; and yak diet was considered a random effect. Linear and quadratic effects of increasing dietary crude protein were evaluated using orthogonal polynomial regression. The linear regression model is as follows:
Y = β0 + β1X + ε,
where Y is the dependent variable; X is the independent variable; β0 is the intercept; β1 is the slope; and ε is the error term. The quadratic regression model is as follows:
Y = β0 + β1X + β2X2 + ε,
where Y is the dependent variable; X is the independent variable; β0 is the intercept; β1 is the linear coefficient for X; β2 is the quadratic coefficient for X2; and ϵ is the error term. Results are expressed as means ± standard error of the mean (SEM). Statistical significance was declared at p < 0.05, and 0.05 ≤ p < 0.10 was considered a tendency. Spearman’s rank correlation coefficients were calculated to assess the relationships between differentially abundant bacteria (DAB) and DEM, DAB and performance data. Correlation analyses were performed in R software (version 4.2.3), and the results were visualized using the R package ggplot2 (version 4.0.0); correlations with |r| > 0.5 and p < 0.05 were considered biologically relevant [27].

3. Results

3.1. Effect of Different Protein Level Concentrate Supplementations on Body Weight, Milk Production, and Composition in Grazing Lactating Yaks

As shown in Table 2, supplementing concentrate significantly increased final BW and ADG compared with the CON group (p < 0.001). Within the supplemented groups, final BW showed no linear or quadratic trends (p ≥ 0.10), while ADG tended to show a quadratic effect (p = 0.091). For lactation performance, milk yield was significantly increased by concentrate supplementation and showed a significant linear increase as protein levels increased from CP15 to CP19 (p < 0.001). Milk fat concentration was significantly lower in supplemented groups and tended to decrease linearly (p = 0.051). Conversely, milk fat yield was significantly higher in supplemented groups and showed tendencies for both linear (p = 0.090) and quadratic (p = 0.100) responses. Milk protein concentration was significantly higher in supplemented groups and tended to decrease linearly with increasing protein levels (p = 0.067). In contrast, milk protein yield was significantly increased by supplementing (p < 0.001), but exhibited neither linear nor quadratic trends (p ≥ 0.10). Milk lactose concentration and yield were significantly higher in supplemented groups (p = 0.022; p <0.001). ECM did not vary with protein level among supplemented groups, whereas 3.5% FCM exhibited linear and quadratic tendencies (p = 0.073; p = 0.086).
Table 2. Different protein level concentrate supplementations on body weight, milk production, and composition in grazing lactating yaks.

3.2. Effect of Different Protein Level Concentrate Supplementations on Rumen Fermentation Parameters in Grazing Lactating Yaks

As summarized in Table 3, ruminal pH was unaffected by concentrate supplementation or protein level (p ≥ 0.10). NH3-N concentration did not differ between CON and supplemented groups but showed both linear and quadratic responses across protein levels (p = 0.047; p = 0.046). MCP concentration was higher with supplementation and increased linearly with protein level within supplemented treatments (p = 0.041; p = 0.033). Concentrations of total volatile fatty acids (TVFAs) and individual volatile fatty acids (VFAs), including acetate, propionate, isobutyrate, butyrate, isovalerate, and valerate, were not influenced by concentrate supplementation or by protein level among supplemented groups (p ≥ 0.10).
Table 3. Effect of different protein level concentrate supplementations on rumen fermentation parameters in grazing lactating yaks.

3.3. Effect of Different Protein Level Concentrate Supplementations on Blood Cell Parameters in Grazing Lactating Yaks

As shown in Table 4, WBC, NE, LY, RBC, and HGB did not differ between supplementation or protein level (p ≥ 0.10). PLT did not differ between CON and supplemented groups but exhibited significant linear and quadratic responses to increasing dietary protein among supplemented treatments (p = 0.014; p = 0.015).
Table 4. Effect of different protein level concentrate supplementations on blood cell parameters in grazing lactating yaks.

3.4. Effect of Concentrate Supplementation on Rumen Bacterial Community in Grazing Lactating Yaks

Based on the production performance and rumen fermentation parameters, CP17 showed the most favorable overall response. Therefore, rumen fluid samples from these two groups were selected for subsequent 16S rRNA sequencing and metabolomics analyses to characterize the microbial community and rumen metabolic alterations underlying the observed phenotypic differences. A total of 1,614,748 high-quality sequences were obtained from rumen samples using 16S rRNA sequencing, with an average of 89,708 ± 20,890 (mean ± SD) reads per sample. After quality filtering, denoising, and chimera removal, a total of 7507 bacterial amplicon sequence variants (ASVs) were identified across all rumen samples, with an average of 1518 ± 284 ASVs per sample. The higher values of bacterial richness (Chao1; 1654.68 ± 296.86 vs. 1413.77 ± 239.54; p = 0.016) and diversity (Shannon; 8.68 ± 0.42 vs. 8.02 ± 0.65; p = 0.009) indexes were observed on the CON group compared to the CP17 group (Figure 1A,B), indicating that the supplementation of concentrate significantly reduces the diversity and richness of the rumen microbial community. Furthermore, beta diversity was evaluated by principal coordinate analysis (PCoA) based on Bray–Curtis distances, and a distinct separation was observed between the CON and CP17 groups (ANOSIM: p = 0.036; Figure 1C), suggesting that supplementing concentrate altered ruminal microbial structure. Bacteroidota (62.37%), Firmicutes (17.42%), Verrucomicrobiota (6.05%), Fibrobacterota (3.41%), and Patescibacteria (1.76%) were the dominant bacterial phyla in both groups (Figure 1D). Compared to the CON group, supplementing the concentrate in grazing lactating yaks significantly increased the relative abundances of Proteobacteria and Fibrobacterota, whereas a lower abundance of Firmicutes was found in the CP17 group (p < 0.05; Figure S1). The ruminal microbial community was dominated by Prevotella (27.51%), Prevotellaceae UCG-001 (3.96%), Rikenellaceae_RC9 gut group (5.85%), Prevotellaceae UCG-003 (2.37%), and Fibrobacter (3.40%) at the genus level (Figure 1E). Supplementing the concentrate in grazing lactating yaks significantly increased the relative abundances of Succinivibrionaceae_UCG-002, Fibrobacter, Ruminobacter, Succinimonas, and Acinetobacter when compared to those in the CON group, while a lower abundance of Saccharofermentans was observed in the CP17 group than in the CON group (p < 0.05; Figure 1F). We used LEfSe analysis (LDA ≥ 4, p < 0.05) to identify marker microbial taxa between CON and CP17. Ruminobacter_amylophilus, Ruminobacter, Succinivibrionaceae_UCG_002, Succinivibrionaceae, Enterobacterales, Proteobacteria, and Gammaproteobacteria were enriched in the CP17 group, and Firmicutes, Clostridia, Bacteroidales_BS11_gut_group, and Oscillospirales were enriched in the CON group (Figure 1G). To further explore the interaction patterns of the rumen microbiota, we constructed genus-level co-occurrence networks using the top 20 bacterial genera. The network of the CON group consisted of 11 nodes and 10 edges, with five positive and five negative correlations (Figure 1H). The network of the CP17 group consisted of 14 nodes and 13 edges, with eight positive and five negative correlations (Figure 1I), suggesting that concentrate supplementation promoted stronger cooperative interactions among dominant genera and enhanced the overall connectivity of the rumen microbial community.
Figure 1. Effect of concentrate supplementation (CP17; 1.50 kg/d, 17.00% CP) on diversity and composition of the rumen bacterial community in grazing lactating yaks (CON vs. CP17; n = 9 per group). Rumen bacteria were analyzed using 16S rRNA gene sequencing (V3–V4 region). (A) Chao1 richness index. (B) Shannon diversity index. (C) PCoA based on Bray–Curtis distances with ANOSIM testing. (D) Relative abundance of dominant bacterial phyla. (E) Relative abundance of dominant genera. (F) Differences in the top 15 genera between CON and CP17 assessed using the Mann–Whitney U test. (G) Linear discriminant analysis effect size approach identifying biomarker genera between CON and CP17 groups (LDA score ≥ 4, p < 0.05). Based on the top 20 genera, co-occurrence networks in the CON (H) and CP17 groups (I) were generated using Spearman correlations (|r| > 0.60 and p < 0.05); positive correlations are shown as red edges and negative correlations are shown as green edges. CON = grazing only; CP17 = grazing supplemented with concentrate containing 17.00% CP. Only the significant differences were marked (* p < 0.05, ** 0.01 < p < 0.05, *** p < 0.001).

3.5. Effect of Concentrate Supplementation on Ruminal Metabolite Profiles in Grazing Lactating Yaks

To investigate the effects of supplementing concentrate intervention on rumen metabolome in grazing lactating yaks, the UHPLC was employed to analyze rumen fluid. In total, 2961 metabolic features were obtained, and the annotated metabolites were predominantly classified as lipids, organic acids, and benzenoids (Table S1). The OPLS-DA plot of the metabolite profile clearly revealed a significant separation between the CON and CP17 groups (R2Y = 0.894, Q2Y = 0.596; Figure 2A,B); the modes were all above 0.40, suggesting good model reliability and predictive capability. The Q2 intercept value was less than 0.05, signifying that no over-fitting was present. Based on the criteria of VIP > 1.5 and p < 0.05, a total of 64 differential metabolites were identified between the two groups, including 25 differential up-regulated metabolites and 39 down-regulated differential metabolites in the CP17 group compared to the CON group (Figure 2C,D). As shown in Figure 2E, the top significantly up-regulated metabolites were calcium propionate, 2-nitrofuran, curvulalide, acetophenone, and 2,5-dihydroxybenzoic acid, whereas down-regulated metabolites primarily included 4-pyridoxic acid, L-carnitine, bitocholic acid, isoleucyl-glutamate, and taurodehydrocholic acid. To further explore the biological significance of these differential metabolites, KEGG pathway enrichment analysis was performed, revealing that the differential metabolites were mainly involved in taste transduction, receptor activation, antifolate resistance, and vitamin B6 metabolism (p < 0.05; Figure 2F).
Figure 2. Effects of moderate-protein concentrate supplementation (CP17; 1.50 kg/d, 17.00% CP) on rumen metabolite profiles in grazing lactating yaks (CON vs. CP17; n = 9 per group). Rumen fluid samples were analyzed using UHPLC–MS/MS-based untargeted metabolomics. (A) Partial least squares discriminant analysis (OPLS-DA) score plot showing the separation of metabolite profiles between CON and CP17 groups. (B) OPLS-DA permutation test (n = 200 permutations) for model validation of the ruminal metabolites. (C) Volcano plot of DEMs between CP17 and CON groups. (D) The heatmaps of differential metabolites in rumen fluid (VIP > 1.5, and p < 0.05); dark green represents the up-regulated metabolites, and dark brown represents the down-regulated metabolite. (E) Lollipop plot of the top 20 differential metabolites ranked by absolute fold change (|FC|) (F) KEGG pathway enrichment analysis of the differential metabolites. CON = grazing only; CP17 = grazing supplemented with concentrate containing 17.00% CP. Only the significant differences were marked (* p < 0.05, ** 0.01 < p < 0.05, *** p < 0.001).

3.6. Correlation Analysis of Microbial Genera of Rumen Fluid with Production Data, and Differential Metabolites

To further explore the potential associations between ruminal microbial and metabolites, and host phenotypes, Spearman’s correlation analysis was performed between the dominant bacterial genera, key performance, and differential metabolites (|r| > 0.5, p < 0.05), as shown in Figure 3A,B. Succiniclasticum and Prevotella were positively associated with milk yield, propionate, and MCP, while milk fat was negatively associated with Succiniclasticum and Prevotella. Saccharofermentans was negatively associated with NH3-N, milk lactose, milk yield, and propionate. Fibrobacter showed a positive correlation with NH3-N and milk lactose, and propionate was negatively associated with Fibrobacter (Figure 3A). The correlation analysis was performed between the dominant bacterial genera and differential metabolites (top 20), as shown in Figure 3B, and P-Cresol was positively associated with Saccharofermentans, Ruminococcus, and NK4A214_group, while Fibrobacter was negatively associated with P-Cresol. Furthermore, bitocholic acid was negatively associated with Saccharofermentans and NK4A214_group, and Prevotellaceae_UCG-003 was negatively associated with L-carnitine and bitocholic acid. These results indicate a concentrated supplementation microbe–metabolite network structure, thereby improving lactation performance.
Figure 3. Correlation analysis of dominant rumen bacterial genera, performance, rumen fermentation, and differential metabolites in grazing lactating yaks (CON vs. CP17; n = 9 per group). (A) Spearman’s correlation heatmap between dominant bacterial genera and production parameters, including milk yield, milk composition, and rumen fermentation parameters (pH, NH3-N, MCP, and VFAs). (B) Spearman’s correlation heatmap between dominant bacterial genera and the top 20 differential metabolites. Red and blue colors indicate positive and negative correlations, respectively. Only correlations with |r| > 0.5 and p < 0.05 are shown. CON = grazing only; CP17 = grazing supplemented with concentrate containing 17.00% CP. (* p < 0.05, ** 0.01 < p < 0.05).

4. Discussion

To address nutrition deficiencies of grazing yaks, concentrate supplementation has been widely recognized as an effective nutritional strategy; multiple studies have demonstrated that concentrate supplementation can improve yak growth and rumen fermentation [7,28]. However, the optimal protein level of supplemental concentrate required to support lactation and weight remains poorly defined. In this context, the present study investigated the effects of protein-level concentrate supplementation (15–19% CP) on milk production, rumen fermentation, microbial composition, and immune parameters in grazing lactating yaks. Our results clearly demonstrate that protein supplementation enhanced ADG and milk yield, indicating that balanced protein supply effectively alleviates nutritional stress during lactation. Specifically, milk yield showed a linear increase with rising dietary protein level, which is consistent with the notion that additional nitrogen may improve the synchrony between ruminally degradable protein and fermentable energy, potentially supporting microbial protein synthesis and the supply of precursors for milk production [11,29]. Although milk fat percentage decreased slightly, milk fat yield increased significantly, which may be attributed to concentrate supplementation generally decreasing ruminal acetate concentration and the acetate-to-propionate ratio due to enhanced fermentation of readily fermentable carbohydrates, thereby reducing de novo fatty acid synthesis in the mammary gland [30,31]. Nevertheless, the milk fat content of yaks remains higher than that of dairy cows, likely due to species-specific metabolic adaptations and lower milk yield [32]. In the present study, concentrate supplementation increased both milk lactose and protein concentrations, which is consistent with previous findings in yaks and dairy cows [7,13]. The increased availability of fermentable nutrients under concentrate supplementation may enhance overall energy supply to the host, thereby supporting glucose availability and lactose synthesis. In parallel, the observed increases in milk protein concentration and yield may reflect improved amino acid supply and enhanced nitrogen utilization efficiency as a result of optimized ruminal fermentation [33].
A stable ruminal environment is fundamental for maintaining microbial activity, optimizing nutrient fermentation, and supporting efficient growth and production performance in ruminants [34]. In our study, supplementation with concentrates did not significantly affect TVFA concentrations or ruminal pH, which remained above 7.0, suggesting that a concentrate supplementation level of 1.5 kg/d is within the optimal range and does not affect the ruminal environment or induce subacute ruminal acidosis. In contrast to the minimal effects on VFA concentrations and ruminal pH, microbial crude protein (MCP) synthesis was significantly enhanced with concentrate supplementation, which supports the importance of nitrogen–energy synchrony in promoting microbial growth and nitrogen utilization efficiency [19]. The observed increase in MCP yield with higher dietary crude protein intake is consistent with the findings from Hristov et al. [12], who reported that moderate protein supplementation stimulates microbial growth and improves nitrogen utilization efficiency. However, a quadratic response in NH3–N was observed at the highest CP level (CP19), indicating that excessive protein intake may exceed microbial nitrogen requirements, leading to unnecessary nitrogen turnover without additional improvements in performance; these findings corroborate similar observations in dairy cows and sheep [35,36], where a surplus of protein failed to further enhance microbial protein synthesis or overall animal performance. It should be noted that the protein level in the concentrates was increased mainly by adjusting the inclusion rates of rapeseed meal and soybean meal (Table 1), which are not only sources of crude protein but may also contain minor bioactive constituents. Rapeseed meal is rich in phenolic compounds dominated by sinapic acid derivatives (e.g., sinapine and sinapic acid), and its phenolic fraction has been associated with antioxidant potential [37,38]. In addition, rapeseed co-products may contain glucosinolates, the metabolites of which can influence rumen fermentation and the rumen microbial community depending on dose and source [39,40]. Soybean meal may contain isoflavones and soyasaponins, which have been reported to exert antioxidant and anti-inflammatory/immunomodulatory activities in animal models and production animals [41,42]. Cereal by-products such as wheat bran can also contribute phenolic acids (notably ferulic acid) that are linked to antioxidant capacity [43,44]. However, in the present study, bioactive compounds in the ingredients were not quantified. Therefore, the observed improvements in lactation performance, rumen fermentation (e.g., MCP), and immune-related indices are more likely attributable to the increased supply of fermentable nutrients and improved nitrogen–energy synchrony, while potential contributions of ingredient-derived bioactives cannot be excluded and warrant targeted quantification in future studies.
In this study, blood cell parameters, including white WBC, NE, and PLT, were analyzed to evaluate the impact of dietary protein supplementation on immune function, showing that supplementation with protein significantly influenced immune function. While no significant changes were observed in the WBC or NE, platelet counts significantly increased in response to protein supplementation, particularly at higher protein levels (CP17 and CP19). Platelets are essential components of the innate immune system, playing a crucial role in inflammation and the regulation of immune responses [45]. The increase in platelet count observed in this study suggests that protein supplementation, especially at higher levels, may enhance an animal’s immune status. This finding is consistent with previous research indicating that protein supplementation can improve immune function by supporting the production of immune cells and inflammatory mediators [46,47]. Nevertheless, platelet count alone is an incomplete proxy for systemic immune competence; future studies should combine platelet measurements with additional immunological indicators for a comprehensive assessment. The absence of significant changes in WBC and neutrophil counts suggests that the overall immune cell population may not have been directly affected by dietary protein, potentially because the animals were not under stress or infection conditions that would normally induce a marked immune response.
Rumen microbiomes play crucial roles in regulating nutrient degradation and host productivity by facilitating the fermentation of carbohydrates, proteins, and lipids into VFAs, ammonia, and microbial protein [48]. In the present study, concentrate supplementation significantly altered the rumen microbial structure, characterized by reduced Chao1 and Shannon indices and a distinct separation in β-diversity, which are consistent with previous research, showing that increased inclusion of fermentable carbohydrates promoted a less diverse but more specialized microbial community [9,49]. Prevotella are proteolytic and saccharolytic bacteria that play a key role in ruminal protein and carbohydrate degradation, breaking down dietary proteins into amino acids and peptides for microbial protein synthesis [50]. In our study, we observed a high Prevotella abundance in the CP17 compared to the CON group, although this difference was not statistically significant. This is consistent with previous research in cattle or yaks, where an increase in starch or fermentable carbohydrate availability led to a rise in Prevotella population [51,52]. Meanwhile, higher MCP concentration and milk yields were observed in the concentrate supplementation group, which also indirectly supports this mechanism. Succiniclasticum, playing crucial roles in conversion of succinate to propionate, is a gluconeogenic precursor that supports glucose and lactose synthesis in lactating ruminants [53]. Our findings were consistent with previous studies in dairy cows and yak, where starch supplementation led to improvements in both energy efficiency and milk yield [54,55]. The increase in Succiniclasticum suggested a shift in fermentation pathways toward propionate production, enhancing both energy supply and microbial protein yield. This shift was also reflected in the improved milk yield observed in our study. Fibrobacter, known for their role in cellulolysis, are essential for the breakdown of plant cell wall polysaccharides into sugars and peptides, which supports other microbial populations [56]. Interestingly, the abundance of Fibrobacter was increased in the CP17 group despite the increased concentrate supplementation, which contrasts most previous reports indicating that higher concentrate levels tend to reduce or have no significant effect on cellulolytic bacteria [57,58]. This may be attributed to the moderate level of concentrate used in our study, which possibly provided additional readily fermentable substrates without substantially lowering ruminal pH. Moreover, moderate concentrate inclusion may have enhanced cross-feeding interactions between amylolytic and cellulolytic microbes, supporting Fibrobacter proliferation [59].
To investigate the impact of concentrate supplementation on rumen metabolism, we employed LC–MS to study the rumen metabolic profile in grazing lactating yaks. A well-defined separation between the CP17 and CON groups was found, indicating that concentrate feeding significantly altered the overall rumen metabolic profile. Moreover, differential metabolites were mainly concentrated in carbohydrates, amino acids, and fatty acids, which are linked to microbial fermentation processes and host energy metabolism. An important observation was a numerically higher propionate level in the CP17 group, which was supported by higher concentrations of propionate-related metabolites, such as calcium propionate; propionate serves as a critical precursor for gluconeogenesis and influences energy balance in ruminants [60]. One of the key findings was the enrichment of vitamin B6 metabolism and folate resistance pathways in the CP17 group, suggesting that the concentrate diet enhanced the synthesis of microbial cofactors and one-carbon metabolism [61]. Vitamin B6 (predominantly as pyridoxal-5′-phosphate, PLP) is a key cofactor for aminotransferases and other enzymes involved in amino acid interconversion and nitrogen assimilation, thereby supporting microbial growth and microbial protein synthesis [62]. In parallel, folate-mediated one-carbon metabolism provides one-carbon units required for purine/pyrimidine biosynthesis and methionine regeneration, which are fundamental for microbial DNA synthesis and biomass formation [63]. These findings align with those of Seck et al. [64] and Zhang et al. [65], who reported similar changes in dairy cows and sheep when supplemented with concentrates. The increase in microbial vitamin synthesis and one-carbon metabolism likely improves microbial protein synthesis, thereby benefiting the host’s growth and immune function [66]. Furthermore, there was a reduction in L-carnitine, hippuric acid, and taurocholic acid in the CP17 group; the reduction in lipid-related metabolites supports the hypothesis that the energy metabolism in these animals shifts from fat breakdown to carbohydrate fermentation [52].
To elucidate the potential functional interplay between rumen microbiota, metabolite profiles, and host phenotypes, a comprehensive correlation analysis was conducted based on Spearman’s rank coefficients. These findings provide an insight into how microbial communities and metabolites interact to influence lactation performance and energy utilization in grazing lactating yaks. The positive correlations between Succiniclasticum and Prevotella with milk yield, propionate, and microbial MCP underscore the critical role of these genera in carbohydrate fermentation and energy production. However, the negative correlation between these microbes and milk fat suggests that concentrate supplementation influenced ruminal lipid biohydrogenation/oxidation pathways [67]. In contrast, Saccharofermentans showed negative correlations with NH3-N, milk lactose, milk yield, and propionate, indicating that this genus may be less efficient under concentrate supplementation conditions. Previous studies have reported that Saccharofermentans, a fiber-degrading bacterium, presents a higher abundance of Saccharofermentans in carbohydrates such as cellulose and hemicellulose [68,69]. In this study, the correlation analysis between metabolites and microbes further reinforced these findings. For instance, p-Cresol was positively correlated with Saccharofermentans, Ruminococcus, and NK4A214_group, while Fibrobacter exhibited a negative correlation with p-Cresol, suggesting that certain microbial groups may specifically influence the production of aromatic metabolites, potentially linked to rumen health and fermentation efficiency [70]. Similarly, bitocholic acid was negatively correlated with Saccharofermentans and NK4A214_group, and Prevotellaceae_UCG-003 was negatively correlated with both L-carnitine and bitocholic acid. These findings align with Zhang et al. [71], who reported that rumen microbes mediate bile acid transformations and affect host lipid metabolism and energy regulation. However, because the analysis is correlation-based, the directionality and causality of these relationships cannot be inferred; future studies combining longitudinal sampling, absolute abundance quantification, and functional assays will be required to validate these putative microbe–metabolite interactions.

5. Conclusions

In this study, the effects of different protein levels in concentrate supplementation on lactational performance, immune function, and rumen microbial and metabolite profiles in grazing lactating yaks were explored. Our findings demonstrate that concentrate supplementation, particularly with the 17% crude protein (CP17), significantly enhances milk yield, microbial protein synthesis, and energy metabolism. These improvements are attributed to the modulation of the rumen microbiota, with key genera such as Succiniclasticum and Prevotella supporting the MCP synthesis. Furthermore, the metabolomic analysis revealed important shifts in the rumen metabolite profile, including increased enrichment of propionate-related metabolites (e.g., calcium propionate) and microbial vitamin synthesis, suggesting enhanced energy efficiency and MCP synthesis, which are crucial for lactation performance. These findings provide practical evidence that moderate protein concentrate supplementation can improve productive performance and the physiological status of grazing lactating yaks under forage-limited plateau conditions. Future studies should assess the long-term implications of this strategy for animal health, reproductive performance, economic profitability, and ecological sustainability in grazing yak production systems.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/microorganisms14020278/s1. Figure S1: Differences in the top 10 bacterial phylum were analyzed using the Mann–Whitney U test; Table S1: Metabolites in the CON and CP17 groups.

Author Contributions

Conceptualization, S.L. and S.T.; methodology, S.L.; software, S.L. and S.T.; validation, X.W., H.W. and Z.C. and S.C.; formal analysis, L.S.; investigation, S.L. and S.T.; resources, L.S.; data curation, L.S.; writing—original draft preparation, L.S., S.L. and S.T.; writing—review and editing, S.L.; visualization, S.L.; supervision, S.L. and S.T.; project administration, L.S.; funding acquisition, S.L. and S.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Top Talent Project of “Kunlun Talents-High-Level Innovation and Entrepreneurship Talents” for Outstanding Talents Training in Qinghai Province (Grant No.2022) and supported by the Top Talent Project of “Kunlun Talents-High-Level Innovation and Entrepreneurship Talents” for Leading Talents Training in Qinghai Province (Grant No.2021).

Institutional Review Board Statement

The animal experiment was reviewed and approved by the Ethical Review Committee of the College of Animal Husbandry and Veterinary Sciences, Qinghai University. Approval Code: 2023-QHMKY-005; Approval Date: (13 June 2023).

Data Availability Statement

The 16S rRNA gene amplicon sequencing data were deposited in the National Center for Biotechnology Information database under accession number (PRJNA1381585).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Qiu, Q.; Zhang, G.; Ma, T.; Qian, W.; Wang, J.; Ye, Z.; Cao, C.; Hu, Q.; Kim, J.; Larkin, D.M.; et al. The yak genome and adaptation to life at high altitude. Nat. Genet. 2012, 44, 946–949. [Google Scholar] [CrossRef] [Scilit]
  2. Lan, D.; Xiong, X.; Wei, Y.; Xu, T.; Zhong, J.; Zhi, X.; Wang, Y.; Li, J. RNA-Seq analysis of yak ovary: Improving yak gene structure information and mining reproduction-related genes. Sci. China Life Sci. 2014, 57, 925–935. [Google Scholar] [CrossRef] [Scilit]
  3. Guo, X.; Long, R.; Kreuzer, M.; Ding, L.; Shang, Z.; Zhang, Y.; Yang, Y.; Cui, G. Importance of functional ingredients in yak milk-derived food on health of Tibetan nomads living under high-altitude stress: A review. Crit. Rev. Food Sci. Nutr. 2014, 54, 292–302. [Google Scholar] [CrossRef] [Scilit]
  4. Liu, H.N.; Ren, F.Z.; Jiang, L.; Ma, Z.L.; Qiao, H.J.; Zeng, S.S.; Gan, B.Z.; Guo, H.Y. Fatty acid profile of yak milk from the Qinghai-Tibetan Plateau in different seasons and for different parities. J. Dairy Sci. 2011, 94, 1724–1731. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  5. Ding, W.; Shi, C.; Chen, M.; Zhou, J.; Long, R.; Guo, X. Screening for lactic acid bacteria in traditional fermented Tibetan yak milk and evaluating their probiotic and cholesterol-lowering potentials in rats fed a high-cholesterol diet. J. Funct. Foods 2017, 32, 324–332. [Google Scholar] [CrossRef] [Scilit]
  6. Xue, B.; Zhao, X.Q.; Zhang, Y.S. Seasonal changes in weight and body composition of yak grazing on alpine-meadow grassland in the Qinghai–Tibetan Plateau of China. J. Anim. Sci. 2005, 83, 1908–1913. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  7. Liu, H.; Hao, L.; Cao, X.; Yang, G.; Degen, A.A.; Xiao, L.; Liu, S.; Zhou, J. Effects of supplementary concentrate and/or rumen-protected lysine plus methionine on productive performance, milk composition, rumen fermentation, and bacterial population in grazing, lactating yaks. Anim. Feed Sci. Technol. 2023, 297, 115591. [Google Scholar] [CrossRef] [Scilit]
  8. Shang, K.; Guan, J.; An, T.; Zhao, H.; Bai, Q.; Li, H.; Sha, Q.; Jiang, M.; Zhang, X.; Luo, X. Effects of perinatal nutrition supplementation and early weaning on serum biochemistry, metabolomics, and reproduction in yaks. Front. Vet. Sci. 2024, 11, 1443856. [Google Scholar] [CrossRef] [Scilit]
  9. Dai, D.; Pang, K.; Liu, S.; Wang, X.; Yang, Y.; Chai, S.; Wang, S. Effects of concentrate supplementation on growth performance, rumen fermentation, and bacterial community composition in grazing yaks during the warm season. Animals 2022, 12, 1398. [Google Scholar] [CrossRef] [Scilit]
  10. Liu, K.; Zhang, Y.; Yu, Z.; Xu, Q.; Zheng, N.; Zhao, S.; Huang, G.; Wang, J. Ruminal microbiota–host interaction and its effect on nutrient metabolism. Anim. Nutr. 2021, 7, 49–55. [Google Scholar] [CrossRef] [Scilit]
  11. Allen, M.S.; Bradford, B.J.; Oba, M. Board Invited Review: The hepatic oxidation theory of the control of feed intake and its application to ruminants. J. Anim. Sci. 2009, 87, 3317–3334. [Google Scholar] [CrossRef] [Scilit]
  12. Hristov, A.N.; Etter, R.P.; Ropp, J.K.; Grandeen, K.L. Effect of dietary crude protein level and degradability on ruminal fermentation and nitrogen utilization in lactating dairy cows. J. Anim. Sci. 2004, 82, 3219–3229. [Google Scholar] [CrossRef] [Scilit]
  13. Zhang, F.; Zhao, Y.; Wang, H.; Nan, X.; Wang, Y.; Guo, Y.; Xiong, B. Alterations in the milk metabolome of dairy cows supplemented with different levels of calcium propionate in early lactation. Metabolites 2022, 12, 699. [Google Scholar] [CrossRef] [Scilit]
  14. NY/T 815-2004; Feeding Standard of Beef Cattle. Ministry of Agriculture of the People’s Republic of China: Beijing, China, 2004.
  15. Van Soest, P.J.; Robertson, J.B.; Lewis, B.A. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 1991, 74, 3583–3597. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  16. Fujimaki, K.; Hummel, K.; Magonde, I.; Dammert, K.; Hamaguchi, Y.; Mintzas, K.; Saker, J.; Valina, O.; Otte, K. Performance evaluation of the new Sysmex XR-Series haematology analyser. Pract. Lab. Med. 2024, 39, e00370. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  17. Liu, E.; Vandehaar, M.J. Relationship of residual feed intake and protein efficiency in lactating cows fed high-or low-protein diets. J. Dairy Sci. 2020, 103, 3177–3190. [Google Scholar] [CrossRef] [Scilit]
  18. Dai, D.; Dong, C.; Kong, F.; Wang, S.; Wang, S.; Wang, W.; Li, S. Dietary supplementation of S cutellariae radix flavonoid extract improves lactation performance in dairy cows by regulating gastrointestinal microbes, antioxidant capacity and immune function. Anim. Nutr. 2025, 20, 499–508. [Google Scholar] [CrossRef] [Scilit]
  19. Broderick, G.A.; Kang, J.H. Automated simultaneous determination of ammonia and total amino acids in ruminal fluid and in vitro media. J. Dairy Sci. 1980, 63, 64–75. [Google Scholar] [CrossRef] [Scilit]
  20. Makkar, H.P.; Sharma, O.P.; Dawra, R.K.; Negi, S.S. Simple determination of microbial protein in rumen liquor. J. Dairy Sci. 1982, 65, 2170–2173. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  21. Guo, M.; Wu, F.; Hao, G.; Qi, Q.; Li, R.; Li, N.; Wei, L.; Chai, T. Bacillus subtilis improves immunity and disease resistance in rabbits. Front. Immunol. 2017, 8, 354. [Google Scholar] [CrossRef] [Scilit]
  22. Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 2019, 37, 852–857, Correction in Nat. Biotechnol. 2019, 37, 1091. https://doi.org/10.1038/s41587-019-0252-6. [Google Scholar] [CrossRef] [Scilit]
  23. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef] [Scilit]
  24. Alseekh, S.; Aharoni, A.; Brotman, Y.; Contrepois, K.; D’Auria, J.; Ewald, J.; Ewald, J.C.; Fraser, P.D.; Giavalisco, P.; Hall, R.D.; et al. Mass spectrometry-based metabolomics: A guide for annotation, quantification and best reporting practices. Nat. Methods 2021, 18, 747–756. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  25. Wang, B.; Ma, M.P.; Diao, Q.Y.; Tu, Y. Saponin-induced shifts in the rumen microbiome and metabolome of young cattle. Front. Microbiol. 2019, 10, 356. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  26. Schrimpe-Rutledge, A.C.; Codreanu, S.G.; Sherrod, S.D.; McLean, J.A. Untargeted metabolomics strategies—Challenges and emerging directions. J. Am. Soc. Mass Spectrom. 2016, 27, 1897–1905. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  27. Mukaka, M.M. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med. J. 2012, 24, 69–71. [Google Scholar]
  28. Yi, S.; Wu, H.; Liu, Y.; Dai, D.; Meng, Q.; Chai, S.; Liu, S.; Zhou, Z. Concentrate supplementation improves cold-season environmental fitness of grazing yaks: Responsive changes in the rumen microbiota and metabolome. Front. Microbiol. 2023, 14, 1247251. [Google Scholar] [CrossRef] [Scilit]
  29. Tang, Q.; Shao, P.; Wen, C.; Bu, Z.; Qin, G.; Huang, Y.; Pan, Y.; Li, Z.; Wei, K.; Li, S.; et al. Effects of dietary rumen-degradable protein on the growth performance, energy, and nitrogen metabolism of dairy buffalo heifers. Trop. Anim. Health Prod. 2025, 57, 245. [Google Scholar] [CrossRef] [Scilit]
  30. Benoit, A.C.; Dos Santos Neto, J.M.; Lock, A.L. Mammary gland responses to altering the supply of de novo fatty acid substrates and preformed fatty acids on the yields of milk components and milk fatty acids. J. Dairy Sci. 2024, 107, 10653–10666. [Google Scholar] [CrossRef] [Scilit]
  31. Della Rosa, M.M.; Bosher, T.J.; Khan, M.A.; Sandoval, E.; Dobson-Hill, B.; Duranovich, F.N.; Jonker, A. Effect of supplementing high-fiber or high-starch concentrates or a 50:50 mix of both to late-lactation dairy cows fed cut herbage on methane production, milk yield, and ruminal fermentation. J. Dairy Sci. 2025, 108, 7036–7050. [Google Scholar] [CrossRef] [Scilit]
  32. Zhang, X.; Kreuzer, M.; Niu, M.; Li, S. Differences in milk production efficiency and physiology between lactating Holstein cows and yaks born and kept at about 4000 m of altitude when fed the same diet. J. Anim. Physiol. Anim. Nutr. 2025, 109, 1091–1100. [Google Scholar] [CrossRef] [Scilit]
  33. Laroche, J.P.; Gervais, R.; Lapierre, H.; Ouellet, D.R.; Tremblay, G.F.; Halde, C.; Boucher, M.S.; Charbonneau, E. Milk production and efficiency of utilization of nitrogen, metabolizable protein, and amino acids are affected by protein and energy supplies in dairy cows fed alfalfa-based diets. J. Dairy Sci. 2022, 105, 329–346. [Google Scholar] [CrossRef] [Scilit]
  34. Keum, G.B.; Pandey, S.; Kim, E.S.; Doo, H.; Kwak, J.; Ryu, S.; Choi, Y.; Kang, J.; Kim, S.; Kim, H.B. Understanding the diversity and roles of the ruminal microbiome. J. Microbiol. 2024, 62, 217–230. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  35. Jetana, T.; Abdullah, N.; Halim, R.A.; Jalaludin, S.; Ho, Y.W. Effects of energy and protein supplementation on microbial-N synthesis and allantoin excretion in sheep fed guinea grass. Anim. Feed Sci. Technol. 2000, 84, 167–181. [Google Scholar] [CrossRef] [Scilit]
  36. Schwab, C.G.; Broderick, G.A. A 100-Year Review: Protein and amino acid nutrition in dairy cows. J. Dairy Sci. 2017, 100, 10094–10112. [Google Scholar] [CrossRef] [Scilit]
  37. Vuorela, S.; Meyer, A.S.; Heinonen, M. Quantitative analysis of the main phenolics in rapeseed meal and oils processed differently using enzymatic hydrolysis and HPLC. Eur. Food Res. Technol. 2003, 217, 517–523. [Google Scholar] [CrossRef] [Scilit]
  38. Le, T.T.; Framboisier, X.; Aymes, A.; Ropars, A.; Frippiat, J.-P.; Kapel, R. Identification and capture of phenolic compounds from a rapeseed meal protein isolate production process by-product by macroporous resin and valorization their antioxidant properties. Molecules 2021, 26, 5853. [Google Scholar] [CrossRef] [Scilit]
  39. Gao, J.; Sun, Y.; Bao, Y.; Zhou, K.; Kong, D.; Zhao, G. Effects of different levels of rapeseed cake containing high glucosinolates in steer ration on rumen fermentation, nutrient digestibility and the rumen microbial community. Br. J. Nutr. 2021, 125, 266–274. [Google Scholar] [CrossRef] [Scilit]
  40. Gao, M.; Irawan, A.; El-Sherbiny, M.; Szumacher-Strabel, M.; Cieślak, A.; Setiawan, M.A.; Jallal, H.; Fusaro, I.; Jayanegara, A.; Yanza, Y.R. Meta-Analysis of Incorporating Glucosinolates into Diets and Their Effects on Ruminant Performance, Ruminal Fermentation, Methane Emissions, Milk Composition, and Metabolic Biochemical Attributes. Animals 2025, 15, 1480. [Google Scholar] [CrossRef] [Scilit]
  41. Deng, Z.; Kim, S.W. Opportunities and Challenges of Soy Proteins with Different Processing Applications. Antioxidants 2024, 13, 569. [Google Scholar] [CrossRef] [Scilit]
  42. White, C.S.; Dilger, R.N. Immunomodulatory potential of dietary soybean-derived saponins. J. Anim. Sci. 2024, 102, skae349. [Google Scholar] [CrossRef] [Scilit]
  43. Adom, K.K.; Liu, R.H. Antioxidant activity of grains. J. Agric. Food Chem. 2002, 50, 6182–6187. [Google Scholar] [CrossRef] [Scilit]
  44. Liyana-Pathirana, C.M.; Shahidi, F. Importance of insoluble-bound phenolics to antioxidant properties of wheat. J. Agric. Food Chem. 2006, 54, 1256–1264. [Google Scholar] [CrossRef] [Scilit]
  45. Sonmez, O.; Sonmez, M. Role of platelets in immune system and inflammation. Porto Biomed. J. 2017, 2, 311–314. [Google Scholar] [CrossRef] [Scilit]
  46. Coleman, D.N.; Lopreiato, V.; Alharthi, A.; Loor, J.J. Amino acids and the regulation of oxidative stress and immune function in dairy cattle. J. Anim. Sci. 2020, 98, S175–S193. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  47. Khan, M.Z.; Liu, S.; Ma, Y.; Ma, M.; Ullah, Q.; Khan, I.M.; Wang, J.; Xiao, J.; Chen, T.; Khan, A.; et al. Overview of the effect of rumen-protected limiting amino acids (methionine and lysine) and choline on the immunity, antioxidative, and inflammatory status of periparturient ruminants. Front. Immunol. 2022, 13, 1042895. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  48. Yang, J.; Li, Y.; Sun, M.; Guo, S.; Lin, P.; Wang, A.; Jin, Y. Understanding the differences in rumen bacteria and their impact on dairy cows’ production performance: A review. Anim. Nutr. 2025, 22, 259–279. [Google Scholar] [CrossRef] [Scilit]
  49. Belanche, A.; Palma-Hidalgo, J.M.; Jiménez, E.; Yáñez-Ruiz, D.R. Enhancing rumen microbial diversity and its impact on energy and protein metabolism in forage-fed goats. Front. Vet. Sci. 2023, 10, 1272835. [Google Scholar] [CrossRef] [Scilit]
  50. Betancur-Murillo, C.L.; Aguilar-Marín, S.B.; Jovel, J. Prevotella: A key player in ruminal metabolism. Microorganisms 2023, 11, 1. [Google Scholar] [CrossRef] [Scilit]
  51. Ge, T.; Yang, C.; Li, B.; Huang, X.; Zhao, L.; Zhang, X.; Tian, L.; Zhang, E. High-energy diet modify rumen microbial composition and microbial energy metabolism pattern in fattening sheep. BMC Vet. Res. 2023, 19, 32. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  52. Pang, K.; Dai, D.; Yang, Y.; Wang, X.; Liu, S.; Huang, W.; Xue, B.; Chai, S.; Wang, S. Effects of high concentrate rations on ruminal fermentation and microbiota of yaks. Front. Microbiol. 2022, 13, 957152. [Google Scholar] [CrossRef] [Scilit]
  53. Firkins, J.L.; Mitchell, K.E. Invited review: Rumen modifiers in today’s dairy rations. J. Dairy Sci. 2023, 106, 3053–3071. [Google Scholar] [CrossRef] [Scilit]
  54. Goetsch, A.L. Recent research of feeding practices and the nutrition of lactating dairy goats. J. Appl. Anim. Res. 2019, 47, 103–114. [Google Scholar] [CrossRef] [Scilit]
  55. Luo, G.; Xu, W.; Yang, J.; Li, Y.; Zhang, L.; Wang, Y.; Lin, C.; Zhang, Y. Effects of ruminally degradable starch levels on performance, nitrogen balance, and nutrient digestibility in dairy cows fed low corn-based starch diets. Asian-Australas. J. Anim. Sci. 2017, 30, 653–659. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  56. Fakih, I.; Got, J.; Robles-Rodriguez, C.E.; Siegel, A.; Forano, E.; Munoz-Tamayo, R. Dynamic genome-based metabolic modeling of the predominant cellulolytic rumen bacterium Fibrobacter succinogenes S85. mSystems 2023, 8, e0102722. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  57. Fernando, S.C.; Purvis, H.N.; Najar, F.Z.; Sukharnikov, L.O.; Krehbiel, C.R.; Nagaraja, T.G.; Roe, B.A.; Desilva, U. Rumen microbial population dynamics during adaptation to a high-grain diet. Appl. Environ. Microbiol. 2010, 76, 7482–7490. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  58. Mavrommatis, A.; Skliros, D.; Sotirakoglou, K.; Flemetakis, E.; Tsiplakou, E. The effect of forage-to-concentrate ratio on Schizochytrium spp.-supplemented goats: Modifying rumen microbiota. Animals 2021, 11, 2746. [Google Scholar] [CrossRef] [Scilit]
  59. Wei, Z.; Xie, X.; Xue, M.; Valencak, T.G.; Liu, J.; Sun, H. The effects of non-fiber carbohydrate content and forage type on rumen microbiome of dairy cows. Animals 2021, 11, 3519. [Google Scholar] [CrossRef] [Scilit]
  60. Agle, M.; Hristov, A.N.; Zaman, S.; Schneider, C.; Ndegwa, P.M.; Vaddella, V.K. Effect of dietary concentrate on rumen fermentation, digestibility, and nitrogen losses in dairy cows. J. Dairy Sci. 2010, 93, 4211–4222. [Google Scholar] [CrossRef] [Scilit]
  61. Das, P.; Babaei, P.; Nielsen, J. Metagenomic analysis of microbe-mediated vitamin metabolism in the human gut microbiome. BMC Genom. 2019, 20, 208. [Google Scholar] [CrossRef] [Scilit]
  62. Liang, J.; Han, Q.; Tan, Y.; Ding, H.; Li, J. Current Advances on Structure–Function Relationships of Pyridoxal 5′-Phosphate-Dependent Enzymes. Front. Mol. Biosci. 2019, 6, 4. [Google Scholar] [CrossRef] [Scilit]
  63. Feng, Y.; Chang, S.K.; Portnoy, D.A. The major role of Listeria monocytogenes folic acid metabolism during infection is the generation of N-formylmethionine. mBio 2023, 14, e01074-23. [Google Scholar] [CrossRef] [Scilit]
  64. Seck, M.; Linton, J.; Allen, M.S.; Castagnino, D.S.; Chouinard, P.Y.; Girard, C.L. Apparent ruminal synthesis of B vitamins in lactating dairy cows fed diets with different forage-to-concentrate ratios. J. Dairy Sci. 2017, 100, 1914–1922. [Google Scholar] [CrossRef] [Scilit]
  65. Zhang, R.; Cheng, Z.; Zang, C.; Cui, C.; Zhang, C.; Jiao, Y.; Li, F.; Li, X.; Yang, K.; Luo, Q. Supplementation of 5,6-dimethylbenzimidazole and cobalt in high-concentrate diet improves the ruminal vitamin B12 synthesis and fermentation of sheep. Fermentation 2023, 9, 956. [Google Scholar] [CrossRef] [Scilit]
  66. McFadden, J.W.; Girard, C.L.; Tao, S.; Zhou, Z.; Bernard, J.K.; Duplessis, M.; White, H.M. Symposium review: One-carbon metabolism and methyl donor nutrition in the dairy cow. J. Dairy Sci. 2020, 103, 5668–5683. [Google Scholar] [CrossRef] [Scilit]
  67. Dewanckele, L.; Toral, P.G.; Vlaeminck, B.; Fievez, V. Invited review: Role of rumen biohydrogenation intermediates and rumen microbes in diet-induced milk fat depression: An update. J. Dairy Sci. 2020, 103, 7655–7681. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  68. Li, J.; Guan, F.; Liu, P.; Ma, H.; Zhang, J.; Ma, Y.; Mao, S.; Xiang, X.; Jin, W. Effects of dietary NDF/NFC ratios on in vitro rumen fermentation, methane emission, and microbial community composition. Front. Vet. Sci. 2025, 12, 1588357. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  69. Kim, G.; Lee, W.; Park, T. Exploring the impact of forage-to-concentrate ratios on the ruminal bacteriome in vitro focusing on ciliate-associated bacteria. Sci. Rep. 2025, 15, 26467. [Google Scholar] [CrossRef] [Scilit]
  70. Wang, D.; Chen, L.; Tang, G.; Yu, J.; Chen, J.; Li, Z.; Cao, Y.; Lei, X.; Deng, L.; Wu, S.; et al. Multi-omics revealed the long-term effect of ruminal keystone bacteria and the microbial metabolome on lactation performance in adult dairy goats. Microbiome 2023, 11, 215. [Google Scholar] [CrossRef] [Scilit]
  71. Zhang, B.; Jiang, X.; Yu, Y.; Cui, Y.; Wang, W.; Luo, H.; Stergiadis, S.; Wang, B. Rumen microbiome-driven insight into bile acid metabolism and host metabolic regulation. ISME J. 2024, 18, wrae098. [Google Scholar] [CrossRef] [Scilit]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Implementation of SARS-CoV-2 Wastewater Surveillance Systems in Germany—Pilot Study in the Federal State of Thuringia
Previous
Prevotella-Based Bacterial Mixture Influences Gut Microbiota Composition in Weaned Piglets
Next