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. 2026 Feb 27;8:36. doi: 10.1186/s42523-026-00530-3

Age-dependent gut microbiota associated with intramuscular fat deposition in Longdong cashmere goats

Heyu Chai 1, Haowen Cheng 1, Jiayang Sun 1, Yuzhu Luo 1, Bingang Shi 1, Jiqing Wang 1, Shaobin Li 1,✉, Jing Luo 2,✉, Fangfang Zhao 1,✉
PMCID: PMC13049821  PMID: 41761371

Abstract

Intramuscular fat (IMF) is a key determinant of meat quality, influencing tenderness, juiciness, and flavor. Previous studies have reported that the deposition of IMF is controlled by various factors. However, there is a shortage of research exploring the variations in IMF deposition across age groups from a microbial perspective. This study evaluated the differences in IMF deposition between yearling (1-year-old) and mature (4-year-old) Longdong Cashmere goats and analyzed its association with gut microbiota. The results revealed that the IMF content in shoulder meat and blood lipid levels increased with age (p < 0.05). Conversely, the contents of lipoprotein lipase (LPL) in the liver and duodenum significantly decreased with age. Microbial diversity differed between the two age groups, with specific microbiota identified from the gut of goats involved in the lipid metabolism pathway. The concentrations of valeric and isovaleric acids in the rumen, as well as acetic, propionic and isovaleric acids in the colon, were higher in yearling goats than in mature goats (p < 0.05). Spearman correlation analysis of IMF deposition indicators with gut microbiota revealed that, within the rumen, the abundances of CAG-791 and Sodaliphilus were positively correlated with IMF content in shoulder meat and TG levels, while exhibiting a negative correlation with the contents of valeric acids. Furthermore, the abundance of Clostridium_R showed a positive association with IMF content in shoulder meat and with the abundances of CAG-791and Sodaliphilus. In contrast, the abundance of Bact_11 was negatively correlated with IMF content in shoulder meat, TG levels, and the abundances of CAG-791, Sodaliphilus and Clostridium_R. Within the abomasum, the abundances of UMGS and Hylemonella_582308 were correlated with IMF content in the shoulder meat, as well as serum LDL and VLDL levels. This study provides significant insights into the age-dependent gut microbiota associated with intramuscular fat deposition in goats and identifies several potential gut microbiota for further research on their impacts on IMF deposition.

Graphical abstract

graphic file with name 42523_2026_530_Figa_HTML.jpg

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-026-00530-3.

Keywords: Longdong cashmere goats, Age groups, IMF deposition, Blood lipid levels, Lipid metabolizing enzymes, Gut microbiota, Short chain fatty acids, Correlation analysis

Highlights

  • The intramuscular fat deposition in Longdong Cashmere goats increases with age.

  • Mature goats have higher levels of blood lipids, lower levels of lipoprotein lipase, and reduced concentrations of short-chain fatty acids.

  • The gut microbial diversity differs between yearling and mature goats, with alpha diversity being higher in the older goats.

  • Several bacteria in the rumen and colon are significantly associated with intramuscular fat deposition, enzyme content, and short-chain fatty acid levels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-026-00530-3.

Introduction

As the global economy advances and living standards improve, consumers are prioritizing the taste, flavor, and nutritional value of meat. Juiciness and flavour increased linearly with Intramuscular fat (IMF) [1]. This is due to a moderate level of IMF can disrupt the structural integrity of muscle fiber bundles, thereby enhancing meat tenderness [2]. In addition, IMF largely contains phospholipids and unsaturated fatty acids such as palmitic, stearic, oleic, and linoleic acids, which can produce aroma though Maillard reactions during cooking [3].

IMF content varies among species, among breeds and among muscle types within the same breed. Other factors are involved in the variation of IMF content in animals, including gender, age and feeding [4]. For example, Polidori et al. [5] and Zhang et al. [6] revealed a steady increase in IMF content with ageing in elder Fabrianese lambs, sika deer, respectively. Similarly, another research demonstrated IMF deposition occurred later in an animal’s development [7].

The mechanism of IMF deposition has been demonstrated at both molecular and epigenetic levels in previous studies, a number of genes that potentially regulate IMF development and growth in cattle have been identified [8]. New omics developments and application advanced our understanding of the roles of gut microbes in the lipid metabolism of human and animals. For example, Megamonas rupellensis as a myoinositol degrader that enhances lipid absorption and contributes to obesity [9]. Lactobacillus dietary supplements impacted the variation of IMF deposition and FAS composition by altering the lipid metabolism pathways of Sunit sheep and then changed the edible quality and nutritional value [10]. Recent research has revealed that age-related alterations in gut microbiota associated with aging can influence lipid metabolism in the brain and liver [11]. Additionally, Yin et al. [12] reported that certain age-associated changes in the microbial genera correlate with variations in the concentrations of volatile fatty acids and the levels of microbial crude protein in the rumen. Those reports indicated that age-dependent gut microbiota may be involved in the deposition of IMF in elder goats.

The Longdong Cashmere goat is a dual-purpose breed valued for producing both cashmere and meat in the Longdong region of China. It is renowned for its strong tolerance to coarse feed, cold-resistance, and high adaptability. These goats are primarily grazed on tidal flats, river valleys, and slopes, which are rich in natural vegetation, including Stipa capillata L., Agropyron cristatum L., Peganum harmala L., Artemisia L., and other clumping shrubs such as Hippophae rhamnoides subsp. sinensis Rousi, and Caragana korshinskii Kom. Its meat is highly favored by local consumers for its flavorful, tender, and low cholesterol content, especially in mature goats, despite its price being slightly higher than that of regular mutton. Therefore, we selected 20 co-grazing Longdong Cashmere goats − 10 yearlings (1-year-old) and 10 mature (4-year-old) goats, to evaluate variations in IMF-associated traits and gut microbial diversity. By conducting further correlation analyses among those factors, we aim to identify potential bacterial targets for future regulation to improve livestock meat quality. Our findings will advance the understanding of the role of gut microbiota in IMF deposition in meat-producing animals and provide novel insights into the regulation of meat quality.

Materials and methods

Animals

Twenty Longdong cashmere goats (10 yearlings and 10 mature individuals), managed and fed using the same practices by a local farmer in Maojing Town, Huanxian County, Gansu Province, China (35°12′N, 106°40′E), were selected as trial animals. They were co-grazed on the same natural grasslands throughout the year, following a full-day grazing pattern: leaving in the morning and returning in the evening, without any supplementary feeding. The primary forage species they consumed included Medicago sativa, Agropyron cristatum, Stipa capillata L., Artemisia frigida, Sonchus arvensis, and Stipa bungeana.

Ethics approval

This study protocol was approved by the Ethics Committee of Gansu Agricultural University (protocol code GSAU-Eth-AST-2023–037).

Sample collection

All twenty goats were slaughtered on September 23, 2023. Prior to slaughter, they were herded into a quiet environment free from distracting noises or devices and fasted for 12 hours with access to water. After fasting, blood was collected from jugular vein using vacuum blood collection tubes, and live weight was measured. Centrifuge at 1509.3 × g for 10 minutes to obtain serum. Then, all goats were slaughtered and their carcass weight were recorded. Muscle tissues (shoulder and rump), organs (liver, pancreas, duodenum), and gut chyme (rumen, abomasum, colon) were collected within half an hour after slaughter. All samples were immediately snap-frozen in liquid nitrogen and then stored at −80 °C until use.

Determination of phenotypic indicators

The IMF content of muscle tissues was measured using Soxhlet extraction method (XT15 Extractor, ANKOM Technology, Macedon, NY, USA). The free fatty acids (FFA), triglycerides (TG), total cholesterol (TC), and lipoprotein cholesterol fractions (high-density, low-density, and very-low-density) in serum were determined by using commercial ELISA kits (Shanghai Enzyme-linked Biotechnology Co., Ltd., China).The levels of fatty acid synthase (FAS), hormone-sensitive lipase (HSL) and lipoprotein lipase (LPL) in organs and gut chyme were measured using goat-specific FAS ELISA kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China), goat-specific HSL and LPL ELISA kits (Fankew, Shanghai Kexing Trading Co., Ltd, Shanghai, China), respectively. The concentrations of short-chain fatty acids (SCFAs) in gut chyme were determined by using the method described by Tangerman [13] with a gas chromatograph (Agilent 6890 N Network Gas Chromatograph, Agilent Technologies, Santa Clara, CA, USA). The structure and composition of gut bacteria were analyzed using 16S ribosomal RNA (16S rRNA) sequencing technology and bioinformatics techniques.

16S rRNA analysis

DNA extractions, library preparation and sequencing

Microbial DNA was extracted from 200 mg of each gut chyme sample (ruminal, abomasum and colonic contents) using the E.Z.N.A. ® Stool DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s instructions. Then, the 27F (5’-AGRGTTYGATYMTGGC TCAG-3’) and 1492 R (5’-RGYTACCTTGTTACGACTT-3’) primer sets were used to amplify the full-length 16S rRNA genes [14].

Amplified DNA was used to construct SMRTbell libraries following the manufacturer’s protocol (Pacific Biosciences, Menlo Park, CA, USA). Then, the libraries were sequenced on a PacBio Sequel II platform using the Sequencing Kit 2.0 chemistry. Raw reads were processed using the SMRT Link Analysis software (v0.9.0) to generate de-multiplexed circular consensus sequences [15]. Sequence length (<800bp or >2500bp) and quality were assessed using SMRT Portal, including filtering for sequences outside the 800 to 2500 base pair range and those containing more than 10 consecutive identical bases. Barcodes, primer sequences, and chimeric sequences were also removed.

High quality sequences were clustered into operational taxonomic units (OTUs) at 98.65% similarity threshold using UPARSE (v0.7.1). Residual chimeric sequences were further detected and removed using UCHIME (v0.7.1) [16]. The phylogenetic affiliation of each 16S rRNA gene sequence was analyzed against the Greengenes2 database using UCLUST algorithm (v0.1.2.22q) [17] with a confidence threshold of 80% [18]. These sequence data have been submitted to the NCBI database under accession number PRJNA1225419.

Alpha and beta diversity analyses

Alpha diversity was evaluated through the Chao1, ACE, and Shannon indices. Beta diversity was assessed using principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) analysis.

Functional prediction

The OUT mapping table was converted into Greengenes2 OTU IDs by using Mothur [19] for phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt2) analysis. Functional profiles of bacteria in each group were predicted using the PICRUSt2 program with reference to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Statistical analysis

A t-test was employed to compare IMF content, blood lipid profiles, enzyme activities, and SCFA concentrations between yearling and mature goats. The Chao1, ACE, and Shannon indices were calculated based on the OTU abundance using the vegan package in R (v. 4.1.0). Statistical differences between groups were assessed using the Kruskal-Wallis test. PCoA plotting analysis was conducted based on the Bray-Curtis distance matrix among samples utilizing the ape package [20]. NMDS plotting analysis was performed based on the unweighted UniFrac distance matrix using the vegan package (v0.4.1.0) [21]. Differences in beta diversity indices between groups were evaluated by using one-way permutational analysis of variance (PERMANOVA). Variations of microbial abundance between groups were analyzed by using the Kruskal–Wallis test. Differences in functional pathways were evaluated with the Wilcoxon rank-sum test. Spearman correlation analysis was conducted to assess relationships among age-dependent IMF content, lipid profiles, microbiota, and SCFA concentrations. Correlation coefficients (|r|) greater than 0.45 were interpreted as indicating moderate correlation, while those exceeding 0.6 were considered indicative of strong correlation. A false discovery rate (FDR)-adjusted p value of 0.05 or less was regarded as statistically significant.

Results

Age-related differences in IMF content of Longdong cashmere goats

The dressing rate and IMF content in the shoulder and rump meat of yearling and mature goats were analyzed to evaluate the age-related differences in IMF deposition (Fig. 1a). The results demonstrated that IMF content in the shoulder meat of mature goats was significantly higher than that of yearling goats (T-test, p < 0.05, Fig. 1b and Supplementary Table 1). Furthermore, a strong positive correlation was observed between age and IMF content in the shoulder meat of Longdong cashmere goats (p = 0.006, r = 0.625, Fig. 1c).

Fig. 1.

Fig. 1

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The differences in dressing rate and IMF content of yearling and mature goats. (a) Schematic diagram of the collected muscle tissue site; (b) the differences in dressing rate and IMF content of yearling and mature goats (n = 10 per group); (c) Correlations between age and IMF deposition indices

Age-related variations in lipid levels and organ enzyme contents of Longdong cashmere goats

The serum concentrations of TC, TG, FFA, HDL, LDL and VLDL, as well as the levels of FAS, HSL and LPL in the liver, pancreas, and duodenum were measured in this study. The results revealed a significant differences in serum lipid levels and enzyme contents between yearling and mature goats. Specifically, mature goats exhibited higher serum lipid levels (T-test, p < 0.01) and lower LPL content in both the liver and duodenum (T-test, p < 0.05) compared to yearling goats (Fig. 2 and Supplementary Table 2). No significant differences were observed in FAS and HSL concentrations between the two age groups.

Fig. 2.

Fig. 2

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Variations in serum lipids levels and enzymes between yearling and mature goats. (a) Differences in levels serum lipids levels between yearling and mature goats; (b) Differences in levels of FAS, HSL and LPL in the liver, pancreas and duodenum between yearling and mature goats

Age-related variations in the concentrations of SCFAs and enzymes in the gut chyme between yearling and mature Longdong cashmere goats

The concentrations of SCFAs and enzymes in the rumen, abomasum, and colon chyme differed between yearling and mature goats (Fig. 3). In yearling goats, the levels of valeric and isovaleric acids in the rumen chyme, along with acetic acid, propionic acid, and isovaleric acid in the colonic chyme, were significantly higher(T-test, p < 0.05; Supplementary Table 3(a)). Mature goats exhibited a higher FAS content in the colon chyme and a lower HSL content in the abomasal chyme compared to yearling goats (T-test, p < 0.05; Supplementary Table 3(b)).

Fig. 3.

Fig. 3

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Variations in SCFAs and lipid-metabolizing enzymes concentrations in gut chyme between yearling and mature goats. (a) Heat map and bubble plots of SCFA profiles varied between yearling and mature goats. (b) Differences in FAS, HSL and LPL contents in the gut chyme between yearling and mature goats

Differences in gut microbial diversity and functional profiles between yearling and mature Longdong cashmere goats

Alpha and beta diversities of gut microbiota in the rumen, abomasum and colon of mature and yearling goats were analyzed based on the OTUs abundance (see “Materials and methods”, Supplementary Table 4). The Chao1, ACE and Shannon indices in the abomasum were significantly higher in mature goats compared to yearling goats (Kruskal-Wallis test, p < 0.05, Fig. 4A). PCoA and NMDS plotting analyzes of ruminal and colonic microbiota showed a distinct separation between age groups (PERMANOVA, p < 0.05; Fig. 4B).

Fig. 4.

Fig. 4

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The differences in microbial diversity of yearling and mature goats (n = 10 per group). A. Variations in alpha diversity indices. (a) Chao1 index of ruminal microbiota, abomasum microbiota and colonic microbioata in yearling and mature goats; (b) ACE index of ruminal microbiota, abomasum microbiota and colonic microbioata in yearling and mature goats; (c) Shannon index of ruminal microbiota, abomasum microbiota and colonic microbiota in yearling and mature goats. B. Variations in beta diversity. (a) PCoA plotting of gut microbiota between yearling and mature goats and differences across age groups; (b) NMDS plotting of gut microbiota between yearling and mature goats and differences across age groups

Microbial structure differed significantly between yearling and mature goats. 29 specific bacteria taxa in the rumen, 21 in the abomasum, and 28 in the colon were identified from age groups (Kruskal-Wallis test, p < 0.05). In mature goats, the abundances of Prevotella, Sodaliphilus and CAG-791 in the rumen, as well as Bact-11, Peptococcus, Agathobacter_164117 and Copromonas in the colon, were significantly higher (p < 0.05). Conversely, the abundance of Bifidobacterium_387352 in the abomasum (p = 0.04), and Faecousia in the colon (p = 0.02) were significantly lower (Fig. 5A).

Fig. 5.

Fig. 5

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Specific bacteria of age groups and their functional pathways. A. Specific bacteria of two age groups in the rumen, abomasum and colon. (a) Specific bacteria identified from the rumen of yearling and mature goats. (b) Specific bacteria identified from the abomasum of yearling and mature goats. (c) Specific bacteria identified from the colon of yearling and mature goats. B. Functional variations in specific bacteria identified from the rumen, abomasum and colon of two distinct age goats. (a) Functional variations in specific bacteria identified from the rumen of yearling and mature goats. (b) Functional variations in specific bacteria identified from the abomasum of yearling and mature goats. (c) Functional variations in specific bacteria identified from the colon of yearling and mature goats

Functional predictions of the microbiota indicated that bacteria in mature sheep possess a greater potential for lipid metabolism. Notably, bacteria in the rumen and abomasum exhibited a significant increase in lipid metabolism activity (Wilcoxon rank-sum test, p < 0.05; Fig. 5B).

Correlations of specific gut bacteria with IMF deposition indicators

Spearman correlation analysis revealed significant association between gut bacteria and IMF deposition indicators (Fig. 6, Supplementary Table 6). Within the rumen, the abundances of CAG-791 (FDR = 0.03, r = 0.628) and Sodaliphilus (FDR = 0.00, r = 0.767) exhibited positive associations with IMF content in shoulder meat and serum TG levels (FDR ≤ 0.04, 0.56 < r < 0.65), while demonstrating negative correlations with valeric acid content (FDR = 0.04, r < −0.56). Additionally, the abundance of Clostridium_R was positively associated with IMF content in shoulder meat (FDR = 0.00, r = 0.765), as well as with the abundance of CAG-791 (FDR = 0.01, r = 0.675) and Sodaliphilus (FDR = 0.01, r = 0.723). In contrast, the abundance of Bact_11 was negatively correlated with IMF content in shoulder meat (FDR = 0.00, r = −0.825), TG levels (FDR = 0.03, r = −0.611), and the abundance of CAG-791 (FDR = 0.01, r = −0.681), Sodaliphilus (FDR = 0.03, r = −0.597) and Clostridium_R (FDR = 0.01, r = −0.704).

Fig. 6.

Fig. 6

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Correlations of specific gut bacteria with IMF deposition indicators. (a) Correlation network of specific ruminal bacteria with age-related IMF deposition indicators; (b) Correlation network of specific bacteria in the colon with age-related IMF deposition indicators; (c) Correlation network colonic bacteria with age-related IMF deposition indicator. Only the results with significantly moderate or strong correlations are displayed (｜r｜> 0.45, p < 0.05)

Within the abomasum, the abundance of UMGS exhibited a positive correlation with IMF content in the shoulder meat (FDR = 0.01, r = 0.589) and serum LDL level (FDR = 0.02, r = 0.659, Fig. 6b). Conversely, the abundance of Hylemonella_582308 was negatively correlated with IMF content in the shoulder meat (FDR = 0.01, r = −0.719), while showing a positive association with serum VLDL level (FDR = 0.03, r = −0.584) and the abundance of UMGS (FDR = 0.05, r = −0.554).

Discussion

IMF deposition are positively or negatively regulated by genetic and nongenetic factors (such as food nutrition, animal age and breed). Previous studies have reported that IMF contents in muscles increases with age [22–24]. Kirkland et al. [24] demonstrated that carcass fat class, marbling score, and internal fat depots were significantly higher in 610-day-old beef compared with 485-day-old beef. Consistent with these findings, our research showed that IMF content in shoulder muscle of mature goats was significantly greater than in yearlings.

Recent research has reported that IMF content of chicken meat, as well as serum FFA, PL, TC, TG, and VLDL contents, increase with age Li et al. [25]. Rauw et al. [26] found that elevated levels of TG, TC, HDL, LDL, and VLDL were closely associated with increased IMF content in pork muscle. Consistent with these findings, this study revealed a significant increase in TG, TC, HDL, LDL, VLDL and FFA levels in mature goats, corresponding with enhanced IMF deposition. Higher concentrations of TG and TC in the blood may facilitate their uptake by adipocytes, leading to greater fat accumulation [27, 28].

IMF primarily contains phospholipids, TGs, and TCs. Its deposition is regulated by the balance between lipogenesis and lipolysis [29], progress mediated by enzyme-catalyzed reactions [30]. FAS is the final enzyme in the de novo lipogenesis progress, responsible for converting acetyl-CoA and malonyl-CoA into long-chain fatty acids. These fatty acids are subsequently used to synthesize phospholipids, TGs and cholesteryl ester [31]. HSL and LPL are pivotal enzymes that catalyzing the hydrolysis of TGs and cholesteryl ester to produce free fatty acids and glycerol. The suppression of their activity is essential to normal regulation of fat deposition [32]. In this study, mature goats exhibited a significant decrease in HSL and LPL levels in the liver, duodenum and abomasal chyme, but a notable increase in FAS content in the colonic chyme compared to yearling goats. These findings highlight the coordinated contribution of these enzymes in promoting fat deposition and suggest a potential role of the gut microbiota in the progression of lipid metabolism. However, it should be noted that the enzymes levels measured in gut chyme in this study may not accurately reflect the true role of the gut microbiota in fat deposition. These measurements could include enzymes produced by the host that enter the gastrointestinal tract. In the future, multi-omic analysis needed to be performed to verify the role of gut microbiota in fat metabolism and to elucidate the mechanisms of host-microbiota interaction.

SCFAs produced by the gut microbiota serve as pivotal regulators of host lipid metabolism, including enhancing fatty acid oxidation, suppressing fatty acid synthesis, and reducing fat deposition [33]. Yang et al. [34] reported that elevated concentrations of propionic acid in the colonic chyme were associated with decreased IMF content in the shoulder and rump meat of ruminant animals. Consistent with these findings, this study demonstrated a significant decrease in the concentrations of certain SCFAs in mature goats, which corresponded with a significant increase in IMF content in the shoulder muscle. This is likely because SCFAs attenuate triglyceride synthesis by suppressing sterol regulatory element-binding protein-1c (SREBP-1c)-mediated lipogenic signaling, thereby inhibiting lipogenesis [35].

Microbial diversity differed between yearling and mature goats, particularly in the rumen and abomasum, indicating that microbial composition shifts with age. Mature goats exhibited higher alpha diversity compared to yearlings, suggesting that specific microbial communities may be involved in the progression of later fat deposition. Several candidate bacteria were identified from two different age groups. Functional analyses demonstrated that they were involved in lipid metabolism. However, PICRUSt2, used to infer microbial functional potential from 16SrRNA gene data, carries inherent limitations that may affect the accuracy of its predictions. Future studies should integrate metagenomics and metabolomics analyses to validate these predictions.

Correlation analysis further revealed that the abundance of specific bacterial members may be associated with IMF deposition in Longdong cashmere goats. For example, the abundances of Closridium_R in the rumen were strongly positively correlated with IMF content in the shoulder meat. Kübeck et al. [36] revealed that the abundance of Clostridium was significantly increased in the occurrence of fat accumulation. Similarly, Just et al. [37] reported that the mice fed lard-based diet were characterized by increased relative abundances of Clostridium. These results are generally consistent with our findings, suggesting a potential role of Closridium_R in IMF deposition. In addition, decreased concentrations of valeric acids were associated with elevated IMF content in Longdong cashmere goats. Jiao et al. [38] reported that propionic acid supplementation in pigs significantly reduced serum lipid levels and stimulated SlRT1 mRNA expression in the longissimus dorsi, suggesting a critical role in reducing lipogenesis and promoting lipolysis. Notably, previous studies have primarily focused on the roles of acetate, propionate, and butyrate in IMF deposition [39–41], with limited research on valeric acids. This study identifies additional potential fatty acid for further validation.

Overall, our investigation identified several candidate bacteria, including Sodaliphilus, Clostridium_R, and Copromonas, however, their roles in IMF deposition remain poorly understood. The functions of these potential bacteria merit further validation. Future research should focus on isolating these bacteria and employing germ-free animal models colonized with targeted gut microbiota to elucidate the causal relationships between gut microbiota and IMF deposition.

Conclusion

This research revealed that Longdong cashmere goats exhibited age-dependent characteristics in IMF deposition. The IMF content in shoulder meat increased with age, corresponding with elevated serum lipid levels. However, the concentrations of valeric and isovaleric acids in the rumen chyme, as well as acetic, propionic, and isovaleric acids in the colonic chyme, decreased significantly in mature goats. Microbial diversity differed between yearling and mature goats. Specific gut microbiota, particularly CAG-791, Sodaliphilus, Clostridium_R and Bact_11 in the rumen, were positively correlated with IMF deposition, probably by influencing the production of propionic and valeric acids. These findings suggest that age-related shifts in gut bacteria may contribute to differences in IMF content among goats.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (470.8KB, pdf)
Supplementary material 2 (195KB, xlsx)

Acknowledgements

All authors thank the Gansu Provincial Key Laboratory of Herbivore Biotechnology for providing the platform.

Abbreviations

IMF

Intramuscular fat

FFA

Free Fatty Acid

TG

Triglyceride

TC

Total Cholesterol

HDL

High density Lipoprotein

LDL

Low density lipoprotein

VLDL

Very low density lipoprotein

FAS

Fatty acid synthetase

HSL

Hormone sensitive lipase

LPL

Lipoprotein lipase

SCFAs

Short chain fatty acids

Author contributions

Heyu Chai: Formal analysis, Writing – original draft. Haowen Cheng: Methodology. Jiayang Sun: Data curation. Yuzhu Luo: Resources. Bingang Shi: Investigation. Jiqing Wang: Funding acquisition. Shaobin Li: Funding acquisition, Project administration. Jing Luo: Resources, Investigation. Fangfang Zhao: Conceptualization, Project administration, Writing – review & editing.

Funding

This research was funded by Lanzhou Youth Science and Technology Talent Innovation Project (2023-QN-186), Fuxi Young Talents Fund of Gansu Agricultural University (Gaufx-03Y04), Discipline Team Project of Gansu Agricultural University (GAUXKTD-2022-21) and Research Fund Project of Gansu Agricultural University (0722019).

Data availability

Our research data are available within the article and its supplementary materials. The raw sequence data of 16S rRNA are openly accessible in the NCBI database under the accession number PRJNA1225419 at http://www.ncbi.nlm.nih.gov/bioproject/1225419.

Declarations

Competing interest

The authors declare no competing interests.

Ethics approval

This study protocol was approved by the Ethics Committee of Gansu Agricultural University in June 20, 2023 (protocol code GSAU-Eth-AST-2023–037).

Footnotes

Publisher’s Note

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

Contributor Information

Shaobin Li, Email: lisb@gsau.edu.cn.

Jing Luo, Email: luojing@njau.edu.cn.

Fangfang Zhao, Email: zhaofangfang@gsau.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1 (470.8KB, pdf)
Supplementary material 2 (195KB, xlsx)

Data Availability Statement

Our research data are available within the article and its supplementary materials. The raw sequence data of 16S rRNA are openly accessible in the NCBI database under the accession number PRJNA1225419 at http://www.ncbi.nlm.nih.gov/bioproject/1225419.

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