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Multi-omics reveals the mechanism of quality discrepancy between Gayal (Bos frontalis) and yellow cattle beef

BMC Genomics volume 26, Article number: 351 (2025) Cite this article

Abstract

Background

Producing high-quality beef with enhanced muscle composition and reduced fat content is critical for meeting consumer preferences and supporting a balanced diet. Given the substantial variability in beef quality across cattle breeds, this study aimed to identify key determinants of meat quality by examining Gayal (Bos frontalis) and yellow cattle (Bos taurus) through a multi-disciplinary approach.

Results

The results demonstrated that Gayal cattle exhibited superior meat quality, characterized by higher levels of protein, flavor-enhancing and essential amino acids, total amino acids, and polyunsaturated fatty acids (PUFAs), alongside reduced fat content, with similar trends observed in serum hormone and amino acid profiles. Distinct differences in gut microbial composition, enzymatic activities, and metabolites were observed between the breeds. Gayal displayed increased abundances of key bacterial taxa such as Akkermansia, Paeniclostridium, Escherichia-Shigella, and Clostridium sensu stricto 1, which were associated with enhanced volatile fatty acids (VFAs), ammoniacal nitrogen, and enzymatic activity in the colon. Transcriptomic analysis of the psoas major (PM) muscle revealed significant changes in genes linked to muscle development, amino acid metabolism, and lipid metabolism. Genes related to intestinal amino acid absorption were upregulated in Gayal, while those connected to short-chain fatty acid absorption were downregulated. Correlation analyses underscored the role of gut microbiota and metabolic profiles in modulating gene expression associated with lipid and amino acid metabolism, ultimately influencing meat flavor and quality.

Conclusions

These findings provide actionable insights into the genetic and microbial factors underlying beef quality, offering a foundation for enhancing local cattle resources, optimizing breeding programs, and advancing the production of premium beef to meet both market and dietary needs.

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Introduction

Livestock meat serves as a primary source of protein and is an essential component of the human diet, directly impacting human health. Beef, in particular, is of significant nutritional value, providing high-quality proteins, including essential amino acids, along with other vital nutrients. As the beef cattle industry has developed, consumer demand has shifted from prioritizing quantity to emphasizing meat quality. Numerous factors influence beef quality, including breed, nutrition, production systems, and post-slaughter handling. Among these, breed plays a pivotal role in determining beef quality [1]. Recently, the direction of breeding to improve beef quality has been emphasized by researchers, resulting in attention being given to some beef cattle breeds in semi-wild environments. Gayal (Bos frontalis), primarily found in the high-altitude regions of southwestern China, are a unique grazing breed that provides affordable and highly nutritious meat to the local tribal population [2, 3]. Additionally, Gayal meat commands a premium price in the market, with traditional Gayal meat products experiencing high demand [4]. Moreover, there exists another breed of beef cattle that meets the local human food needs in the same living environment, namely the yellow cattle (Bos taurus). yellow cattle, native to the plateau region, are typically reared at altitudes between 800 and 1250 m. However, there are differences in meat quality between the different breeds of beef cattle. Comparative studies had demonstrated that Gayal exhibited superior growth rates and meat quality compared to yellow cattle, characterized by finer muscle fibers, enhanced tenderness, and greater juiciness [5, 6]. Beef quality is determined by an intricate combination of factors, including color, marbling, intramuscular fat (IMF), tenderness, pH, water-holding capacity, and the content of proteins, fatty acids (FAs), and amino acids (AAs). While marbling is a desirable trait for many consumers, there is increasing interest in leaner beef alternatives. Thus, identifying the factors influencing these critical meat quality traits is essential for developing sustainable utilization strategies and selecting high-quality beef cattle breeds.

Ruminants possess the ability to convert indigestible biomolecules from feed into high-quality meat for human consumption via their gastrointestinal system [7]. A critical factor influencing meat quality is the interaction between gastrointestinal microorganisms and the host. The gastrointestinal microbiota produces various metabolites, such as short-chain fatty acids (SCFAs), which are absorbed by intestinal cells and circulate through the bloodstream, influencing peripheral organs that, in turn, regulate intestinal function [7, 8]. Notably, SCFAs produced in the hindgut can bypass the liver and enter systemic circulation directly via the internal iliac vein [9], thereby modulating muscle growth and metabolism [10, 11]. Numerous studies have highlighted the colon's critical role in determining ruminant meat quality [12]. While the intestinal-muscle axis has been extensively studied in humans [13], avians [14], and monogastrics [15], research on the gut-muscle axis in large ruminants remains limited. Microbial communities in the gut, which include bacteria, archaea, fungi, and protozoa, have garnered significant attention [16], with bacteria accounting for 95% of gut microbes [1, 17]. Given the importance of gut bacteria in ruminant digestion, it has been hypothesized that muscle growth and metabolism in ruminants are primarily mediated by the gut-muscle axis, with colonic bacteria playing a pivotal role in this process.

The objective of this study was to elucidate the mechanisms underlying quality variations between Gayal and yellow cattle via the gut-muscle axis. We posited that divergences in the growth and development of these bovine muscles are mediated through this axis. To corroborate this hypothesis, we employed phenotyping, microbiomics, and transcriptomics to ascertain species-specific traits, and constructed a meat-microbe-gene interplay network to delineate the regulatory interactions within the host-gut-muscle axis. The findings of this research are poised to enhance understanding of the factors influencing meat quality across different bovine genetic backgrounds, contribute theoretical insights into the application of axis theory in beef production.

Materials and methods

Ethics statement

This experiment was reviewed and approved by the Animal Conservation Committee of the College of Animal Science and Technology, Yunnan Agricultural University (Kunming, China) in June 2022 under the approval number YNAU20220638 - 1.

Experimental design, animals and sample collection

This study was conducted at the experimental pasture of the Phoenix Mountain Gayal Breeding and Expansion Base in Lushui City, Nujiang Prefecture, Yunnan Province, China. The pasture is located at an altitude of 2,700 m (Fig. 1A, B). A total of 16 bulls were selected for this study, including 8 male healthy Gayal (Bos frontalis) and 8 male healthy local yellow cattle (Bos taurus) from the Bilu Xueshan herd. The cattle were selected based on age (2 years) and body weight (240 ± 7.0 kg; mean ± SEM) and were divided into two groups: the Gayal group and the local yellow cattle group. The selection process lasted for over a month due to the semi-wild nature of these animals, making the procedure more challenging. During the experimental period, all cattle were allowed to graze in the trail pasture throughout the day and had unrestricted access to water and salt blocks. The cattle completed the entire 45-day experimental period in good health, without exhibiting any major symptoms that could have affected the results. At the end of the experiment, the cattle were fasted overnight while retaining access to water. Blood samples (5 mL) were drawn using EDTA-K2 vacuum tubes, stored at 4℃ for 30 min, and centrifuged at 3,000 rpm for 15 min. The serum was separated and frozen at − 20 °C for further analysis. Then, to collect intestinal and muscle-related samples, all cattle were anesthetized and euthanized with 0.3 mg/kg∙BW of xylazine hydrochloride injection according to the manufacturer's instructions. A cotton thread was used to separate the end of the cecum from the middle of the ascending colon, which was divided with scissors, retaining the ascending colon portion, and then its contents and colonic tissue samples were collected through 5 mL sterile test tubes and fast-frozen in liquid nitrogen for assessment of fermentation metabolites, enzyme activity, microbial composition, and transcriptomic data. A sample of approximately 500 g of psoas major (PM) muscle was collected between the 12 th and 13 th ribs of the experimental cattle. This sample was processed in three parts: the first part was immediately stored in liquid nitrogen for subsequent mRNA sequencing; the second part was stored in dry ice for subsequent chemical analyses; and the remaining part of the muscle sample was stored at 4 °C for meat quality assessment.

Fig. 1
figure 1

Distribution, biochemical indicators, and muscle quality of Gayal and yellow cattle. A The map delineating the worldwide distribution of Gayal, utilizing bubble coloration to identify distinct geographical locales: Bhutan is designated by light brown, Nujiang Prefecture, Yunnan Province, China by red, India by green, and Myanmar by orange. Bubble size on this map serves as a proxy for population density, where the largest bubbles represent regions with the densest populations. The elevation is depicted through a color gradient, with red intensifying at higher altitudes. B A map depicting the regional distribution of Gayal in Yunnan Province, China, employs varying bubble colors to signify different geographic areas. Here, bubble dimensions are directly related to the concentration of the cattle population, with the largest bubbles highlighting areas of maximum density. Echoing the method used in part (A), the map uses a red hue's deepening to signal higher elevations. Additionally, the map pinpoints the sites of the study's trials on Gayal and yellow Cattle, with a particular emphasis on the Gayal Breeding and Conservation Site at Fenghuang Mountain, Lushui County, Yunnan Province, China, marked by red dots. C The heatmap was constructed using the R package Complex Heatmap version 2.18.0 for serum indicators and meat physicochemical property data, values were z-score normalised, and samples were divided into two groups (yellow cattle and Gayal) without clustering. Among them, biochemical indicators assessed include triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), non-esterified fatty acids (NEFA), and serum amino acid levels (TAAs- 1). Muscle quality parameters include lightness (L*), redness (R*), yellowness (Y*), and intramuscular fat (IMF). Statistical significance is represented as ns (P > 0.05), * (P < 0.05), ** (P < 0.01), and *** (P < 0.001)

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Determination of biochemical indices and amino acid levels in serum

Serum concentrations of total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and non-esterified fatty acids (NEFA) were determined in all cattle using a colorimetric method with Thermo Fisher automated enzyme-labeling instruments (Thermo Fisher Scientific, USA). Additionally, the absolute amino acids (AAs) content in serum was quantified using a Sykam S- 433D automatic amino acid analyzer (Sykam Scientific Instruments, Beijing, China) based on the GB 5009.124–2016 standard (China), with minor modifications.

Measurement of PM muscle meat quality

The pH of the PM muscle was measured 45 min and 24 h postmortem using a portable pH meter (PHBJ- 260, INESA Scientific Instrument, Shanghai, China). Meat color parameters—lightness (L*), redness (a*), and yellowness (b*)—were evaluated after 1 h of blooming on freshly cut surfaces of samples aged for 24 h, using a chromameter (CR- 410, Konica Minolta, Tokyo, Japan) with an illuminant D65, 10° observer angle, and 5.0 mm aperture. Calibration was performed using a white standard, and measurements were taken at three different sample locations. Shear force and cooking loss were assessed following the method of Wang, An et al. [1]. Samples (approximately 60 g) were vacuum-sealed in polyethylene bags and cooked in a water bath at 80℃. Once the internal temperature reached 70℃, samples were cooled to room temperature. Cooking loss was calculated as: ((W1 − W2)/W1) × 100%, where W1 and W2 represent sample weights before and after cooking. For shearing force measurement, rectangular pieces (1 cm2 cross-sectional area) were tested using a tenderness meter equipped with a strain gauge load cell (50 kg capacity), with each sample tested six or more times. Moisture, crude ash, intramuscular fat (IMF), and protein content were determined using AOAC (2005) methods [18]. AAs content in freeze-dried PM muscle (1.5 g) was analyzed following GB 5009. 124–2016, with modifications. Samples were hydrolyzed in 6 mol/L HCl at 110℃ for 22 h under nitrogen, centrifuged, and dried with an evaporator. Residues were dissolved in sodium citrate buffer and filtered through 0.22-μm membranes before analysis using an automatic amino acid analyzer (Sykam S- 433D, Sykam Scientific Instruments, Beijing, China). FAs content in PM muscle was analyzed using LC–MS/MS, following a modified protocol [7]. Approximately 80 mg of muscle tissue was homogenized in liquid nitrogen, mixed with water, and vortexed. A 100 μL aliquot was combined with isopropanol/acetonitrile, centrifuged at 13,400 × g for 10 min, and the supernatant analyzed. Separation was performed on a Waters ACQUITY UPLC BEH C18 column using a gradient mobile phase of 0.05% formic acid in water and isopropanol/acetonitrile. Mass spectrometry parameters were optimized for FA detection, and calibration was performed with known FA standards in a fatty acid-free matrix.

Determination of volatile fatty acids and enzyme activities in colonic digesta

After all cattle were slaughtered, samples of mixed colonic digesta were collected and the pH was determined immediately using a digital pH meter (Hanna Instruments, Woonsocket, RI, USA). Subsequently, 1 g of fresh colonic digest was taken and mixed with 5 mL of double-distilled water to ensure homogeneous dissolution and stored at − 20 °C. Three replicates were performed for each sample. After the samples were brought back to the laboratory, the thawed mixture was placed in a 24-well No H2402 centrifugal rotor and centrifuged at 5400 rpm for 15 min at 4 °C using a HC- 3018R high-speed cryo-centrifuge. Next, the filtrate obtained was kept in a solution containing 25% crotonic acid metaphosphate (5 mL colon filtrate: 1 mL crotonic acid metaphosphate) and incubated in an ice bath for 30 min. After incubation, the solution was centrifuged at 10,000 rpm for 10 min and then filtered through a 0.22 μm needle filter. The filtrate was ultimately stored in an autosampling vial for gas chromatographic analysis. For analysis, 1 μL of the sample was used for injection and the concentration of volatile fatty acids (VFAs) was determined using a gas chromatograph (GC- 18B, Shimadzu, Japan). To ensure accurate quantitative analysis, mixed standard solutions including acetate, propionate, isobutyrate, butyrate, isovalerate, and valerate were prepared at concentrations of 3.46 g/L, 3.97 g/L, 0.29 g/L, 1.53 g/L, 0.38 g/L, and 0.47 g/L, respectively. The standard samples were spiked with 0.2 mL of croscarmellose metaphosphate in a 1.5 mL centrifuge tube and 1 mL of mixed standard solution was added. The relative correction factors of organic acids such as acetate, propionate and butyrate were obtained by calculating the ratio of concentration to peak area of the standard samples and the internal standard crotonate, so as to further extrapolate the concentrations of these organic acids in each sample based on the positive relationship between the concentrations of acetate, propionate and butyrate and their peak areas. The operating parameters of the gas chromatograph were set as follows: a SH-Polar D column (30 m × 0.25 mm × 0.25 μm, P/N: R221 - 75,981–30) was used, and the initial temperature was set at 50 ℃ and held for 2 min, after which the temperature was increased to 150 ℃ at a rate of 8 ℃/min, and then increased to 220 ℃ at 20 ℃/min and held for 5 min. The carrier gas was nitrogen at a pressure set to 60 kPa, and hydrogen and oxygen at 50 kPa, respectively. Total VFAs (TVFAs) content was derived by summing the concentrations of the individual acids. The ELISA kit was used in this study for both lipase and protease activities in colonic fluids by double-antibody sandwich assay (ranges of 12–500 ng/L and 25–850 ng/L, respectively), and the experimental procedure consisted of two incubation steps at 37 °C: a first antigen–antibody binding incubation for 30 min, incubation of the enzyme-labelled antibody for 30 min, and incubation of the colour development reaction for 10 min. Quality control measures included: (1) the simultaneous establishment of standard curves (5 gradient concentrations, prepared by serial dilution) for each batch of experiments, and the use of compound wells is recommended to reduce the chance error; (2) the use of a pipette gun to calibrate the volume of the sample (50 μl and 10 μl for the standard and samples, respectively), and the control of the addition time of 5 min in order to avoid reagent volatilisation; (3) a rigorous washing procedure (each well filled with washing solution, let stand for 30 s and then discarded, repeat 5 times to ensure that the non-specific binding compounds are completely removed); (4) the kit is stored at 4 ℃, equilibrated at room temperature for 1 h before use, and the components of different batches are prohibited from mixing to ensure the consistency of the reaction system; (5) if the absorbance of the samples is out of the range of the standard curve, it is necessary to pre-diluted according to the requirements of the instruction manual (such as fivefold dilution) and corrected for the final concentration.

Transcriptome analysis of PM and Colon

The transcriptomic analysis was performed using bulk RNA sequencing. After the experiment, Gayal and yellow cattle were slaughtered, and total RNA was extracted from the colon and PM muscle tissues. Total RNA purification, reverse transcription, library construction, and sequencing were conducted at Shanghai Majorbio Bio-pharm Biotechnology Co., Ltd. (Shanghai, China) according to the manufacturer’s instructions. Total RNA of colon and PM muscle tissues was extracted using QIAzolLysis Reagent (Qiagen, German). RNA quality was determined by 5300 Bioanalyser (Agilent Technologies, Inc., USA) and NanoDrop ND- 1000 (Thermo Fisher Scientific, Inc., USA). Only high-quality RNA sample (OD260/280 = 1.8 ~ 2.2, OD260/230 ≥ 2.0; RQN ≥ 6.5) was used to construct sequencing library. The RNA-seq group libraries for colon and PM muscle were prepared with 1 μg of total RNA following the Illumina® Stranded mRNA Prep, Ligation (San Diego, CA) method. The sequencing library was performed on NovaSeq X Plus platform (Illumina, Inc., USA) using NovaSeq Reagent Kit, and using 2 × 150 bp paired-ended sequencing. After quality control (fastx_toolkit, Version 0.0.14), the clean reads were separately aligned to Bos_taurus (reference genome version: GCF_002263795.3, reference genome source: https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_002263795.3/) with orientation mode using HISAT2 v2.0.1 [19]. The number of mapped reads per sample was assembled by StringTie in a reference-based method [20]. Expression levels were calculated for each transcript based on the number of fragments per kilobase per exon (TPM) per million mapped reads using the Ballgown package in StringTie v1.2.2 and R. The expression level of each transcript was calculated using the Ballgown package in StringTie v1.2.2 and R. Only genes expressed in at least 50% of the cattle in each group were used for subsequent analyses. KEGG functional enrichment analyses were performed by Python scipy software [21]. GO term annotation and enrichment analyses were performed using Blast2GO and goatools software [22]. In brief, the data were analyzed online using the Majorbio Cloud Platform, and visualizations were generated with R packages. The raw sequencing data have been deposited in the National Genomics Data Center Genome Sequence Archive (CRA) under the accession number CRA021301.

DNA extraction, 16S rRNA sequencing and analysis

DNA was extracted from 16 colonic samples and sequenced on the Illumina MiSeq platform (Illumina, San Diego, USA) by Majorbio Biomedical Technology Ltd. (Shanghai, China) according to standard protocols. The V3-V4 hypervariable regions of the bacterial 16S rRNA gene were amplified using primers 338 F (5′-ACTCCTACGGGAGGCAGCAG- 3′) and 806R (5′-GGACTACHVGGGTWTCTAAT- 3′) via PCR (GeneAmp 9700, ABI, USA) under the following conditions: initial denaturation at 95℃ for 3 min, followed by 27 cycles of 30 s at 95℃, 30 s of annealing at 55℃, and 45 s of extension at 72℃, with a final extension at 72℃ for 10 min. PCR was performed in triplicate with each 20 μL reaction mixture containing 4 μL of 5 × FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu polymerase, and 10 ng of template DNA [23]. After sequencing, paired-end reads were quality-controlled and filtered for sequencing accuracy. The processed data were then analyzed using the DADA2 method to obtain Operational Taxonomic Units (OTUs) based on both sequence and abundance information. Various analyses, including OTU profiling, community diversity, species comparisons, correlation, and Tax4 Fun2-based functional predictions, were performed on the Majorbio Cloud Platform (www.majorbio.com). Sequences were clustered into OTUs at a 97% similarity threshold using UPARSE software and classified using the RDP classifier against the SILVA database. Alpha diversity metrics (e.g., Chao1, Simpson, Shannon) and beta diversity (via PCoA based on Bray–Curtis distances) were calculated in QIIME. LEfSe analysis was used to identify microbial biomarkers in each group, and STAMP analysis was employed to confirm differences in species abundance across the groups. Lastly, the metabolic functions of the colonic microbiota were predicted using the KEGG database [23]. The raw sequencing data have been deposited in the National Genomics Data Center Genome Sequence Archive (CRA) under the accession number CRA021295.

Determination of crucial genes by real-time Quantitative PCR (qPCR)

The quantification of gene expression of some differentially expressed genes (DEGs) in muscle and colon was performed using real-time quantitative PCR (qPCR) analysis. β-actin served as the reference gene. Primer sequences for the target genes are provided in Table S1. Reverse transcription was performed using the GeneAmp® PCR System 9700 (Applied Biosystems, USA) under the following conditions: 0.5 μg total RNA, 2 μL Buffer GE, and nuclease-free water were incubated at 42 °C for 5 min, followed by a 1-min incubation on ice. The reaction mixture was then supplemented with 4 μL of 5 × Buffer BC3, 1 μL Control P2, 2 μL RE3 Reverse Transcriptase Mix, and 3 μL RNase-free water, and incubated at 42 °C for 15 min and at 95 °C for 5 min. Afterward, 91 μL of RNase-free water was added, and the reaction was stored at − 20 °C. cDNA synthesis was mixed with 1350 μL of 2 × RT2 SYBR Green Mastermix (Qiagen), and the final volume was adjusted to 2700 μL with RNase-free water. A 25 μL aliquot of this mixture was transferred to each well of the RT2 Profiler PCR Array (96-well format). The PCR amplification was carried out as follows: an initial denaturation step at 95 °C for 10 min, followed by 45 cycles of 95 °C for 15 s and 60 °C for 1 min, using the LightCycler® 480 II Real-time PCR System (Roche, Switzerland). A melting curve analysis was conducted at the end of the PCR to confirm the specificity of the amplified products. Gene expression levels were calculated using the 2−ΔΔCt method.

Statistical analysis

There were at least three independent experiments for each phenotypic indicator, and the phenotypic data were checked for normal distribution by the Kolmogorov–Smirnov test in SPSS 22.0. Phenotypic data, including serum indices, meat quality, and gut fermentation parameters, were analyzed using unpaired two-tailed t-tests in R (v4.3.1), with results visualized as heatmaps. In constructing the heatmaps, we used the R package ComplexHeatmap version 2.18.0 for clustering analysis (hierarchical clustering) and normalised the values by z-score. Significant differences in microbial community structure between groups were determined using the wilcoxon rank sum test in R, and standardised differences between the two data sets were measured based on Cohen's d effect values. For the muscle and colon transcriptome data, P values were adjusted for false discovery rate (FDR) using the method described by Benjamini and Hochberg [24]. Genes that were differentially expressed with a |log2 fold change (FC)|≥ 1 and an FDR < 0.05 were deemed significant, as determined by DESeq2 [25]. PCA and GO and KEGG pathway enrichment analyses were performed and visualized using the Majorbio Cloud platform (https://cloud.majorbio.com) and R-based tools. Statistical significance is indicated as ns (P > 0.05), * (P < 0.05), ** (P < 0.01), and *** (P < 0.001). The correlation analyses of phenotypic data, microbial abundance and gene expression were performed using the person correlation coefficient test and visualised using ggraph.

Result

Serum amino acids and biochemical indicators

The serum biochemical parameters and AAs profiles of Gayal and yellow cattle are illustrated in Fig. 1. Compared to yellow cattle, Gayal displayed significantly lower concentrations of triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), and non-esterified fatty acids (NEFAs, Fig. 1C). Moreover, the majority of serum amino acids were more abundant in Gayal (Table S2), leading to substantially elevated total amino acids (TAAs) levels relative to their yellow cattle counterparts.

Meat quality assessment

A total of 16 cattle (8 Gayal and 8 yellow cattle) were slaughtered to evaluate meat quality (Fig. 1, 2). Gayal showed a significantly higher a* value than yellow cattle. No significant differences were observed between the breeds in terms of drip loss, cooking loss, or shear force (Fig. 1C). Notably, the PM muscle of Gayal had a significantly higher protein content but lower IMF. Further analysis revealed that the PM muscle of Gayal contained elevated levels of aspartic acid (Asp), glutamic acid (Glu), essential AAs (EAAs), and TAAs (Fig. 2A). These characteristics contribute positively to flavor, suggesting Gayal beef as a valuable source of EAAs. FAs profiles (Fig. 2B and Table S3) demonstrated that Gayal muscle had lower relative levels of C12:0, C16:0, and C18:1 cis but higher levels of C18:3n6 cis, C20:4n- 6, C15:0, C15:1 cis, C16:1, and C24:1. Additionally, Gayal had significantly higher levels of total polyunsaturated FAs (PUFAs), aligning with consumer preferences for nutritionally beneficial beef.

Fig. 2
figure 2

Amino acid and fatty acid composition of PM muscle in Gayal and yellow cattle. The heatmap was constructed using the R package Complex Heatmap version 2.18.0 for muscle amino acid and fatty acid content, values were z-score normalised, and samples were divided into two groups (Yellow cattle and Gayal) without clustering. A Analysis of amino acids in PM muscle showed that total amino acids (TAAs- 2) consist of essential amino acids (EAAs) such as histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, and valine, and non-essential amino acids (NEAAs) including alanine, aspartic acid, arginine, glutamic acid, glycine, serine, tyrosine, proline, and cysteine. B Fatty acid composition analysis revealed that saturated fatty acids (SFAs) were calculated as the sum of individual fatty acids (e.g., C6:0–C24:0). Monounsaturated fatty acids (MUFAs) included C14:1, c-C15:1, C16:1, c-C17:1, c-C18:1, t-C18:1, c-C20:1, and C24:1, while unsaturated fatty acids (UFAs) were derived by subtracting SFAs from total fatty acids. Polyunsaturated fatty acids (PUFAs) included c-C18:2n6, t-C18:2n6, c-C18:3n6, C18:3n3, C20:4n6, C20:3n3, c-C20:5n3, and c-C22:6n3. c: cis; t: trans. Statistical significance is indicated as ns (P > 0.05), * (P < 0.05), ** (P < 0.01), and *** (P < 0.001), and Z-scores reflect deviations from the mean

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Colonic fermentation and enzyme activity

To explore how breed influences hindgut fermentation, we assessed colonic fermentation products and enzymatic activities (Fig. 3). Gayal exhibited obviously higher molar ratios of acetate compared to yellow cattle. However, there were no significant differences in pH, molar ratios of TVFA and other VFA in the colon, resulting in no significant changes in the acetate-to-propionate ratios. Levels of ammonium nitrogen were elevated in Gayal relative to yellow cattle (Fig. 3A), mirroring the trend observed for protea yellow cattle (Fig. 3B).

Fig. 3
figure 3

Colonic fermentation parameters in Gayal and yellow cattle. A Total volatile fatty acids (TVFAs) were quantified in mmol/100 mmol, with individual VFAs (acetate, propionate, butyrate, valerate, isobutyrate, isovalerate) reported in the same units. TVFAs include acetate (A), propionate (P), butyrate (B), isobutyrate (Isob) and isovalerate (Isov). The acetate-to-propionate (A/P) ratio was calculated as a percentage. A_N for ammonia nitrogen. B Comparison of differences in colonic enzyme activities. Significance is indicated as * (P < 0.05) or ns (P ≥ 0.05)

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Microbial diversity and composition analysis

Using 16 s rRNA sequencing, we assessed the microbial community structure in the colonic digesta of Gayal and yellow cattle (Fig. 4). Sequencing depth of 16 s rRNA sequencing data averaged 65,000 sequences, while read lengths ranged from the shortest 239 bp to the longest 541 bp. Shannon and rank abundance curves confirmed that the sequencing depth was adequate and reliable for comprehensive microbiome analysis (Fig S1A and B). Across the 16 colonic digesta samples, a total of 975,711 high-quality reads and 2938 OTUs were identified, with 477 and 473 unique OTUs found in Gayal and yellow cattle, respectively (Fig. 4A). PCoA revealed distinct microbial community structures between the two breeds (Fig. 4B), although diversity indices did not differ significantly (Fig. 4C). At the phylum level, both groups were dominated by Firmicutes, Bacteroidota, Verrucomicrobiota, and Proteobacteria, but Gayal exhibited higher relative abundances of Proteobacteria, Actinobacteriota, and Chloroflexi (Fig. 4D, E). Genus-level analysis showed that Gayal had significantly higher levels of Romboutsia, Akkermansia, Paeniclostridium, Escherichia-Shigella, Clostridium_sensu_stricto_1, and Turicibacter, while the abundances of Roseburia and Candidatus_Soleaferrea were lower (Fig. 4F, G). Functional predictions using Tax4 Fun2 indicated enrichment in pathways associated with fatty acid degradation, amino acid metabolism, and the tricarboxylic acid (TCA) cycle (Fig. 4H). Pearson’s correlation analysis demonstrated positive associations of p_Actinobacteriota, p_Chloroflexi, g_Akkermansia, and g_Paeniclostridium with pathways related to fat degradation and protein synthesis, while g_Roseburia showed negative correlations with protein synthesis and lipid metabolism pathways (Fig. 4I). These findings highlight significant differences in colonic flora structure and microbial content associated with protein and lipid metabolism between Gayal and yellow cattle.

Fig. 4
figure 4

Microbial diversity and composition. A Venn diagrams illustrate shared and unique OTUs between species. B PCoA plots depict microbial community clustering. C Alpha diversity metrics assess microbial richness and evenness. D–F Microbial composition and differences at the phylum and genus levels. H KEGG functional pathway enrichment of microbial taxa. I Correlation analysis links microbial composition to functional pathways. Statistical significance is represented as * (P < 0.05), ** (P < 0.01), and *** (P < 0.001)

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Transcriptomic analysis of the colon and PM muscle

We analyzed RNA-seq data from 32 PM muscle and colon samples (Fig. 5), generating a total of 214.86 GB of data, with over 94.98% of bases meeting q30 quality standards (Table S4). The average GC content was 50.69%, ranging from 47.69% to 55.03%, reflecting high-quality sequencing and reliable base composition. Transcript expression levels, measured in transcripts per million (TPM), were comprehensively documented for each sample (Table S4). PCA revealed distinct separation between the two cattle breeds, with PC1 and PC2 explaining 76.18% and 4.10% of the variance, respectively (Fig. 5A). Heatmap analysis supported these findings, showing similar clustering patterns (Fig. 5B). A total of 21,696 genes were identified across all RNA-seq data, of which 10,986 (67.01%) were shared between the PM muscle and colon (Fig. 5C). In the colon, 1,831 DEGs were identified, including 913 upregulated and 918 downregulated genes (Fig. 5D). In the PM muscle, 1,669 DEGs were detected, with 864 genes upregulated and 805 downregulated (Fig. 5D). KEGG pathway analysis indicated that DEGs in the colon were predominantly enriched in ABC transporters, glutathione metabolism, Ras signaling, retinol metabolism, and cell adhesion pathways (Fig. 5E). Similarly, GO analysis highlighted significant enrichment in processes such as glutathione metabolism, protein refolding, nitrogen metabolism, G protein-coupled receptor signaling, ATPase activation, lipid droplet formation, and protein-lipid complexes. In Gayal colon, SLC5 A8 and SLC2 A5 expression were significantly downregulated, whereas GRM2 and SLC38 A3 were markedly upregulated. In the PM muscle, KEGG analysis identified significant enrichment in the PPAR signaling pathway, glutathione metabolism, fatty acid degradation, butanoate metabolism, cardiac muscle contraction, and alanine, aspartate, and glutamate metabolism. DEGs associated with these pathways include CYP4 A22, ACOX3, ACSL6, EHHADH, FABP3, and ANGPTL4, among others (Table S5). GO analysis further revealed that DEGs were significantly enriched in processes such as glutathione metabolism, monocarboxylic acid transport, long-chain fatty acid metabolism, eicosanoid metabolism, and protein-lipid complex formation. Pathway enrichment analysis revealed that upregulated genes in Gayal were primarily involved in AAs synthesis and lipid degradation, whereas downregulated genes in yellow cattle were linked to lipid biosynthesis. These findings align with the observed lower fat content and higher protein and AAs levels in Gayal.

Fig. 5
figure 5

Transcriptomic profiling of colon and PM muscle. A PCA visualizes transcriptomic differences. B Heatmaps of DEGs in colon and PM muscle. C Distribution of DEGs in the tissues. D The number of upward and downward DEGs (FDR < 0.05). E KEGG and GO analyses of DEGs indicate pathway enrichment (FDR < 0.05, rich factor > 0.1)

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qRT-PCR Verification of DEGs

We randomly selected MYH10, AC5L6, GSTA2, GSTM3, FOXO6, WNT5B, EHHADH and other DEGs for qRT-PCR validation. The results in PM muscle showed that the relative mRNA expression levels of key genes related to muscle development and lipid metabolism were higher in Gayal than in yellow cattle, including METTL21 C, MYH10, GLULP, GSTA2, GSTM3, CYP4 A22, ACSL6, and ACOX3, whereas the opposite results were observed for FABP3 and ANGPTL4. In Gayal colon, the relative mRNA expression levels of GRM2 and SLC38 A3 were considerably higher than those of yellow cattle, while SLC5 A8 and SLC2 A5 were remarkably lower than those of yellow cattle (Fig. 6). The tendency of the relative mRNA levels of the chosen genes was in agreement with the results of the transcriptomic analyses.

Fig. 6
figure 6

The qRT-PCR validation of PM muscle and colonic DEGs in Gayal and yellow cattle. The results were analyzed by using the independent samples t-test in SPSS 20.0 (SPSS INC, USA). Abbreviations: MYH10,Myosin Heavy Chain 10; ACSL6, Acyl-CoA Synthetase Long-Chain Family Member 6; ASPA, Asparagine Synthetase; ASNS, Asparagine Synthetase; GSTA2, Glutathione S-transferase A2; GSTM3, Glutathione S-transferase M3; FOXO6,Forkhead Box O6; WNT5B, Wingless-Type MMTV Integration Site Family, Member 5B; EHHADH, Enoyl-CoA Hydratase/3-Hydroxyacyl Coenzyme A Dehydrogenase; GPX1, Glutathione Peroxidase 1; FABP3, Fatty Acid Binding Protein 3; ANGPTL4, Angiopoietin-Like Protein 4;METTL21 C, Methyltransferase Like 21 C;GLULP, Glutamine Synthetase;ACOX3, Acyl-Coenzyme A Oxidase 3;CYP4 A22, Cytochrome P450, Family 4, Subfamily A, Polypeptide 22;GRM2, Glutamate Receptor, Metabotropic 2;SLC2 A5, Solute Carrier Family 2, Member 5;SLC5 A8, Solute Carrier Family 5, Member 8; SLC38 A3, Solute Carrier Family 38, Member 3

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Correlation analysis between the multi-omics data

Our study identified significant correlations between dietary components and various physiological and microbial metrics in experimental cattle (Fig. 7). The abundance of specific gut microbiota, including p_Proteobacteria, p_Actinobacteriota, p_Chloroflexi, g_Romboutsia, g_Akkermansia, g_Paeniclostridium, g_Escherichia-Shigella, g_Clostridium_sensu_stricto_1, and g_Turicibacter, was positively associated with C18:3n6, C20:4n6, EAAs, NEAAs, and muscle protein levels. Conversely, these microbial taxa were negatively correlated with IMF content. Additionally, colonic acetate, ammonium nitrogen levels, and protease activity exhibited strong positive correlations with muscle levels of Asp, C18:3n6, and C20:4n6, but negative associations with IMF and SFAs. Serum concentrations of NEFA, TC, and LDL were strongly correlated with IMF but inversely related to muscle protein, AAs, and PUFAs. Furthermore, key genes involved in SFAs degradation, PUFAs synthesis, and muscle development were significantly upregulated. In contrast, genes associated with fat deposition showed the opposite expression pattern. Finally, we summarized the pattern of gut-muscle axis regulation based on Gayal and yellow cattle in Fig. 8.

Fig. 7
figure 7

Correlation and network analysis. A Pearson correlation analysis reveals relationships among serum indicators, meat quality traits, differentially expressed genes (DEGs), colonic fermentation parameters, enzyme activities, and microbiota. Positive correlations are displayed in red and negative correlations in blue, with darker shades indicating stronger associations. Colored rectangles represent distinct indicators for each cattle group. B Pearson correlation and network analyses were employed to construct an interaction network diagram integrating DEGs, microbes, metabolites, serum parameters, and meat quality traits. The diagram uses pink solid or dashed lines to represent positive correlations and light blue solid or dashed lines for negative correlations, with the thickness of the lines indicating the strength of each correlation. Bubble colors denote various parameters: orange for psoas major (PM) muscle DEGs, light brown for colon DEGs, green for differential microbes, light red for colon metabolites, light purple for serum indices, and light blue for meat nutritional components. The size of each bubble corresponds to the correlation coefficient, providing a comprehensive visualization of the complex interactions among multidimensional biological features. Statistical significance is indicated as * (P < 0.05), ** (P < 0.01), and *** (P < 0.001)

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Fig. 8
figure 8

Gut microbiota’s influence on gene expression and meat quality. This figure showed the mechanisms of gut-muscle axis regulation mediated by the gut microbiota through its metabolites, which in turn influenced host muscle development and meat quality characteristics. In particular genera (e.g. Romboutsia, Akkermansia, Paeniclostridium, Escherichia-Shigella) and their metabolites (e.g. volatile fatty acids, amino acids) are allowed to enter the host circulatory system via intestinal epithelial transporters (e.g. SLC5 A8, SLC38 A3) to regulate expression of key genes (e.g. METTL21 C, ACOX3, GSTM3, and CYP4 A22) in muscle tissue. This regulatory effect further affected the fatty acid composition (e.g. C16:1, C18:3n6c) and levels of essential amino acids (EAAs) to optimise meat quality. This study revealed a multi-level regulatory network between microorganisms and the host, which provided an important molecular basis for the improvement of livestock production through targeted regulation of intestinal flora. Key mechanisms include: ① Colonic microbial metabolites; ② Differential microbiota expression in the colon; ③ Differentially expressed genes (DEGs) regulating metabolite absorption and transport in colon tissue; ④ Serum biochemical markers; ⑤ DEGs in PM muscle involved in growth and development; ⑥ Differences in meat nutrient composition (Color coding details are available in the Web version)

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Discussion

Gayal (Bos frontalis), a unique semi-wild cattle breed mainly found in the alpine and subtropical rainforest areas of the Nujiang River Basin in Yunnan Province, southwestern China, consumes mainly bamboo, and is regarded as a natural green food due to its pollution-free living environment [26]. Studies have been done to show that breed is one of the key factors affecting meat quality [27]. In this study, we evaluated the physical properties (shear, drip loss and cooking loss) of Gayal and yellow cattle beef, and showed that no significant differences were found between the two in terms of pH, drip loss and cooking loss, suggesting that they have similar muscle physical properties. However, the higher a* value of Gayal beef was probably due to its increased myoglobin content in the hypoxic environment of the plateau. To adapt to the low oxygen conditions, the enzyme activity in the muscle of Gayal was enhanced to keep the iron ions (Fe2+) in the reduced state, resulting in a darker red color [28]. With this finding, it was consistent with the results of protein and AAs content of muscles in the present experiment [29, 30], which was similar to the findings in yak [28]. Compared to yellow cattle, muscles of Gayal had lower IMF but higher protein content, consistent with their serum levels of TG, TC, LDL, NEFA and AAs. Studies have shown that meat from heavier animals contain a greater MUFAs proportion and a lower PUFAs proportion, and that PUFAs is negatively correlated with IMF [1, 23]. FAs and AAs composition have notable effects on meat flavor, juiciness, and nutrition, with PUFAs being particularly healthier [1, 7]. Higher n- 6 PUFAs content in Gayal muscle indicated that it has higher nutritional and health value than yellow cattle beef [1, 23]. Hu et al. [31] noted that both lower SFAs intake and higher PUFAs/SFAs ratios contributed to a risk reduction for coronary heart disease. Further, the TAAs content of Gayal PM muscle was considerably larger than that of yellow cattle, which was consistent with their colonic ammonium nitrogen and serum AAs levels [32]. In particular, Asp and Glu, as “flavor AAs”, are essential for meat preservation and myofibroblast formation, with Glu being particularly critical in flavor formation [1, 32].

Studies over the past decade have revealed the critical role of the gut microbiota in host energy harvesting and fat metabolism [7, 10,11,12,13] and have abundantly demonstrated the close association of skeletal muscle properties (e.g., lipid metabolic profiles and fibrous structure) with obesity [7, 8, 10, 13, 15]. The concept of “gut microbiota-muscle axis” has been proposed in some literatures [11, 12], but the relationship between gut microbiota and meat quality in large animals is still less studied. We found in present study that the dominant bacteria in the cattle were bacteroidetes, firmicutes and proteobacteria, which resembled the intestinal bacterial flora of sheep [12, 13], beef cattle [7], and camels [33]. Significant differences in gut microbial composition at the phylum and genus levels were found between Gayal and yellow cattle, a marked increment in muscle protein content and a considerable reduction in IMF content in Gayal, as well as remarkable differences in AAs and FAs compositions between the two. The preliminary implication suggested by these results is that the differences in meat quality between Gayal and yellow cattle were probably closely related to the bacterial composition. Established studies have shown that the composition of gut microorganisms varies among different breeds of animals [11]. Within ruminants, food is fermented by microorganisms in the rumen to produce ammoniacal nitrogen, VFAs, and the remaining undigested material passes into the colon [7], where it provides energy for physiological activities and participates in the regulation of lipid storage in the body [15, 16]. The present study revealed, for the first time, differences in the composition of the colonic microbiota of Gayal and yellow cattle. The Firmicutes/Bacteroidetes ratio was slightly higher in yellow cattle than in Gayal, and the higher ratio was correlated with energy absorption efficiency and fat storage capacity [33, 34], which explained the higher IMF levels in yellow cattle. In comparison, Gayal's muscles were more protein- and AAs-rich, possibly due to greater levels of proteobacteria and ammoniacal nitrogen, where ammonia-oxidizing bacteria efficiently utilize nitrogen-containing compounds to provide the precursors needed for muscle protein and AAs synthesis [35]. Also, enhancement of the gut microbiota affected the VFAs levels and enzyme activities, which in turn regulated lipid metabolism and meat quality [16]. TG, TC, LDL and NEFA were found in this study to be negatively correlated with acetate, ammonia–nitrogen and protease in the colon of Gayal, whereas positively correlated with lipase in yellow cattle. Protease activity was affected by ammonia–nitrogen concentration, which increased CP and AAs utilization but decreased fat deposition [36]. In contrast, lipase activity was positively correlated with fat deposition [37]. Similar results have been presented in other literatures [38]. Interestingly, SCFAs provide energy to colon cells and the organism, and typically obese individuals have higher SCFAs concentrations [7]. However, in this study, intestinal luminal SCFAs concentrations were higher in Gayal than in yellow cattle, which may be attributed to the greater uptake of SCFAs by the colonic cells of yellow cattle [7]. Microbial composition also directly regulated lipid metabolism, Actinobacteriota, Chloroflexi and Proteobacteria were positively correlated with protease, and protease increased protein and AAs digestibility and ammoniacal nitrogen concentration, consistent with increased AAs in the blood of Gayal. Romboutsia, Akkermansia, Paeniclostridium, Escherichia-Shigella, Clostridium_sensu_stricto_1 and Turicibacter, which are negatively correlated with lipid metabolism, were detected in Gayal, while Candidatus_Soleaferrea and Roseburia, which were positively correlated with lipid metabolism, were increased in cattle, indicating that beef quality was improved by regulating the intestinal flora. Paeniclostridium and Turicibacter (all belonging to the phylum Thick-walled Bacteria) have been shown to promote the TCA cycle and improve protein utilization efficiency, thereby increasing muscle AAs content [23]. Clostridium_sensu_stricto_1 affects host metabolism by enhancing nutrient uptake, promoting muscle protein synthesis, enhancing fatty acid β-oxidation and reducing fat deposition [39], and synthesizing muscle AAs and PUFAs, especially n- 6 FAs, while inhibiting C18:1 and MUFAs production for health benefits [40]. Increased abundance of Romboutsia [41] and Escherichia- Shigella [42] was associated with decreased fat deposition and increased PUFAs, which may explain the increase in AAs and PUFAs in Gayal PM muscle. Studies have shown that the amount of Akkermansia was diminished in mice on a high-fat diet and that Akkermansia was negatively correlated with fat deposition [43]. Similar results were obtained in sheep [12]. In the present study, the observed increased abundance of Akkermansia in the Gayal may explained the low-fat deposition characteristic. To summarize, the increased abundance of Romboutsia, Akkermansia, Paeniclostridium, Escherichia-Shigella, Clostridium_sensu_stricto_1, and Turicibacter in Gayal colon, along with the concomitant increase in TG, TC, and LDL levels were reduced, revealing the unique lipid metabolic profile of Gayal and its potential in meat improvement.

The intestinal transcriptome analysis in this study indicated that the expression of SLC38 family (especially SLC38 A3) was up-regulated, while SLC5 A8 and SLC2 A5 were down-regulated in Gayal colon compared to that of yellow cattle. It was noted that SMCT1 (SLC5 A8) is a sodium-dependent monocarboxylate transporter protein mainly localised at the brush border of enterocytes and is responsible for the uptake of short-chain fatty acids (SCFAs) produced by fermentation of intestinal flora [44]. In addition, the fructose transporter carrier encoded by SLC2 A5 is predominantly distributed in the parietal membrane of intestinal epithelial cells, which may be associated with the absorption mechanism of SCFAs [45]. Notably, the SLC38 family is widely distributed in cells in vivo and is mainly responsible for sodium-dependent uptake and efflux of small neutral amino acids (AAs) and plays a key role in amino acid signalling [46]. In summary, the colon cells of Gayal are more efficient in the absorption of amino acids and metabolites, whereas the yellow cattle may have a greater advantage in the absorption of SCFAs. These gene expression patterns reveal the low-fat, high-protein and amino acid-rich nature of Gayal meat, further highlighting the uniqueness of nutrient uptake and metabolic preferences between Gayal and yellow cattle.

The histological analyses in the current study revealed a variety of DEGs that are closely related to muscle growth, lipid and amino acid metabolism, mainly enriched in the GO and KEGG pathways of muscle development, lipid metabolism and amino acid metabolism. Regulation of these genes was complicated and highly specific, which provided insights into the biological mechanisms of meat quality. Muscle development, which not only influenced yield but also had economic value, was particularly regulated by genes such as METTL21 C [47], MYH10 [48], and GLULP [49]. METTL21 C was specifically expressed in skeletal muscle as a lysine methyltransferase by interaction with proteins such as HSPA8 and p97 (VCP) [47, 50], regulating protein degradation to promote muscle hypertrophy [47, 51]. The MYH10 gene is responsible for muscle fibre diversity, which is closely related to muscle morphology and biochemical properties, and its high expression has been shown to be positively correlated with muscle development in pigs [52,53,54]. GLULP agonists, such as Exendin- 4, promote the development of muscle mass and function by repressing the expression of muscle atrophy-associated genes (e.g., MSTN, atrogin- 1, and MuRF- 1), and activating the PKA and AKT pathways, further contributing to the increase in muscle mass and function [49, 55]. Besides, antioxidant and cell metabolism-related genes such as GSTA2 [56]and GSTM3 [57] support the normal physiological functions of muscle cells by regulating antioxidant responses, maintaining cellular metabolic homeostasis, and protecting cells from oxidative damage. Thus, the up-regulation of these genes in Gayal indicated that they may provide a potential molecular basis for the optimisation of Gayal meat quality by promoting muscle growth, enhanced muscle mass and improved meat quality characteristics. Correlation analysis of lipid metabolism revealed remarkable associations between the expression of genes related to fatty acid degradation and PPAR signaling pathway and meat quality, and these genes may play key roles in the differences in lipid metabolism, meat quality, and flavor between Gayal and yellow cattle. The importance of the PPAR pathway and its related genes in regulating differences in muscle lipid metabolism is further supported by our findings. CYP4 A22, a member of the cytochrome P450 (CYP) family, primarily catalyzes the ω-hydroxylation of medium-chain FAs (e.g., lauric acid LA and myristic acid MA), and participates in fatty acid metabolism and the synthesis of biologically active substances through the introduction of a hydroxyl group (-OH) into the terminal carbon atoms (ω-carbons) of FAs [58]. Recent studies showed that hydroxylases of the CYP4 A family were found to play important roles in rat skeletal and arterial myocytes, participating in the production of 20-hydroxyeicosatetraenoic acid and eicosatrienoic acid, further validating the potential function of CYP4 A22 in fatty acid metabolism [59]. ACSL6, on the other hand, catalysed the conversion of long-chain FAs to lipoyl-coenzyme A for participation in Lipid oxidation [60], which augmented PUFAs content [61]. Acyl-coenzyme A oxidase 3 encoded by ACOX3 is involved in dehydrogenation of 2-methyl-branched-chain FAs in peroxisomes [62]. Studies have shown that ACOX3 was found to play an essential function in IMF regulation in broiler chickens [63] and was able to oxidize bovine straight-chain FAs [64]. However, the specific regulatory mechanism of ACOX3 has not been fully elucidated. We speculated that up-regulation of the remarkable up-regulation of these genes in Gayal possibly promoted muscle growth and development by reducing intramuscular lipid deposition, optimizing muscle fiber properties, and thus enhancing their adaptability to extreme environments [60]. In particular, gene upregulation may further support survival and adaptation in extreme environments by enhancing mitochondrial antioxidant function and attenuating muscle damage from oxidative stress [65]. In addition to this, the PPAR signaling pathway can be activated by SCFAs [66], which promote FAs uptake by selectively inducing the upregulation of genes such as FABP3, thereby increasing fatty acid synthesis in mammals [67]. Meanwhile, ANGPTL4 is involved in lipolytic regulation by inhibiting LPL activity, which was previously shown to be positively correlated with TG and NEFA [68, 69], consistent with the results of the present experiment. Similar results were validated in periparturient cows [69]. The down-regulation of ANGPTL4 and FABP3 in Gayal is correlated closely with their lower fat deposition characteristics, showing that the breed has a unique metabolic adaptation mechanism to cope with the specific demands of the environment in which it is found. The regulatory mechanisms governing beef quality and flavor formation are highly intricate and remain only partially understood. Thus, further studies are essential to deepen our knowledge and validate these processes.

Conclusion

This study examined the role of the gut-muscle axis in mediating breed-specific differences in meat quality between Gayal and yellow cattle. Gayal demonstrated superior meat quality, characterized by higher protein and PUFAs content, which is likely linked to their genetic adaptations and a diverse microbiome, including Akkermansia, Paeniclostridium, Escherichia-Shigella, Clostridium_sensu_stricto_1, and Turicibacter. At the genetic level, key genes involved in muscle development (e.g., METTL21 C, MYH10), lipid metabolism (e.g., CYP4 A22, ACSL6), and amino acid transport appear to be crucial regulators of the enhanced meat quality observed in Gayal. Additionally, Akkermansia, Paeniclostridium, Escherichia-Shigella, Clostridium_sensu_stricto_1, and Turicibacter were positively correlated with genes associated with muscle development, lipid metabolism, and intestinal absorption. These findings emphasize the critical role of the gut-muscle axis in mediating breed-specific meat quality differences, shedding light on the molecular and microbial factors contributing to the superior quality of Gayal beef. This study highlights the importance of the gut-muscle axis and provides a foundation for optimizing breeding strategies and dietary interventions to improve beef quality. While these insights are significant, the genetic and molecular mechanisms underlying beef quality and flavor remain complex. Further investigation is needed to explore the regulatory networks of key metabolites and genes.

The raw sequence data reported in this paper is available in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA021295 and CRA021301) at https://bigd.big.ac.cn/gsa/browse/CRA021295 and https://bigd.big.ac.cn/gsa/browse/CRA021301.

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Acknowledgements

The authors sincerely thank the members of the Key Laboratory of Animal Nutrition and Feed Science, Yunnan Agricultural University, and the Gastrointestinal Microbiology Laboratory of the National International Research Center for Animal Intestinal Nutrition, Nanjing Agricultural University for their support. We also thank Biorender for providing the platform and RPython public number.

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) did not use any AI and AI-assisted technologies.

Funding

This work was supported by the projects of the “Yunnan Revitalization Talent Support Program”, China; the National Natural Science Foundation of China (32260841, U2202203).

Yunnan Revitalization Talent Support Program,National Natural Science Foundation of China,32260841

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  1. Lin Han and Runqi Fu contributed equally to this work.

Authors and Affiliations

  1. Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming, 650201, China

    Lin Han, Runqi Fu, Chunjia Jin, Huan Gao, Binlong Fu, Qian Li, Ye Yu, Min Qi & Jing Leng

  2. Key Laboratory of Animal Nutrition and Feed Science of Yunnan Province, Yunnan Agricultural University, Kunming, 650201, China

    Lin Han, Runqi Fu, Chunjia Jin, Huan Gao, Binlong Fu, Qian Li, Ye Yu, Min Qi & Jing Leng

  3. Laboratory of Gastrointestinal Microbiology, National Center for International Research on Animal Gut Nutrition, Nanjing Agricultural University, Nanjing, 210095, China

    Jiawei Zhang & Shengyong Mao

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Contributions

J. L, R. F and S. M conceived and designed the study. J. Z, Y. Y, B. F, and Q. L performed the research. C. J, M. Q and H. G completed data collection. L. H, and R. F analyzed, interpreted the data and wrote the manuscript. L. H visualized the data. R. F and L. H gave advice during the experiments and revised the manuscript. All the authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Jing Leng.

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Ethics approval and consent to participate

The study protocol and its details were approved by the Animal Care Committee at the Faculty of Animal Science and Technology, Yunnan Agricultural University (Kunming, P. R. China). All animal experiments were conducted in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, China, 2004). Sample collection adhered to the guidelines set by the Institutional Management Committee and the Laboratory Animal Ethics Committee at Yunnan Agricultural University (Kunming, P. R. China).

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Human participants were not involved in this research.

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The authors declare no competing interests.

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Han, L., Fu, R., Jin, C. et al. Multi-omics reveals the mechanism of quality discrepancy between Gayal (Bos frontalis) and yellow cattle beef. BMC Genomics 26, 351 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12864-025-11519-8

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BMC Genomics

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