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Article

Effects of Age on Slaughter Performance and Meat Quality of Shanbei White Cashmere Goat and Optimization of Slaughter Strategies

by
Yanyi He
1,†,
Sina Lu
1,†,
Pengpeng Fu
1,
Shenghui Chen
2,
Pengyu Zhang
1 and
Xiaoyue Song
1,*
1
Modern Agricultural College, Yulin University, Yulin 719000, China
2
Shenmu Juke Agricultural and Animal Husbandry Development Co., Ltd., Yulin 719000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biology 2026, 15(4), 318; https://doi.org/10.3390/biology15040318
Submission received: 23 January 2026 / Revised: 5 February 2026 / Accepted: 9 February 2026 / Published: 11 February 2026
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Simple Summary

This study aimed to clarify the impact of age on growth performance and meat quality in Shanbei white cashmere (SWC) goats and the mechanism of action. The study measured the growth and meat quality of 6- and 12-month-old male goats and analyzed the expression of genes and metabolites in their muscles. The results showed that 12-month-old goats grew faster and had higher meat yields, while 6-month-old goats had tenderer and juicier meat. The study also identified the key genes and metabolites affecting these differences and their interaction modes. These age-related differences can be used to select the appropriate slaughter age according to production needs. This study’s findings can serve as a reference for goat breeders to help them optimize breeding and better meet consumer demands for different quality meats.

Abstract

To clarify the age-related differences in growth performance, meat quality, and the underlying molecular mechanisms of Shanbei white cashmere (SWC) goats, the slaughter performance and meat quality of 6-month-old (S group) and 12-month-old (T group) male goats were analyzed. This was combined with muscle transcriptome and untargeted metabolome analyses. The results showed that the T group had better growth performance, while the S group had superior meat quality. A total of 138 differentially expressed genes (DEGs) and 158 differentially expressed metabolites (DEMs) were identified, which are enriched in multiple pathways, and a meat quality-related gene–metabolite regulatory network was constructed. This study reveals the molecular mechanisms of age-dependent differences, providing theoretical support for goat breeding and slaughter strategy optimization.

1. Introduction

Shanbei white cashmere (SWC) goats are a dual-purpose breed that provide cashmere and meat and can adapt to the natural environment of drought, cold, and sandstorms in China’s Loess Plateau region [1]. The breed was developed using Liaoning cashmere goats as the male parent and Ziwuling black goats as the female parent. After more than 30 years of breeding, its growth, reproductive, and cashmere production performances have been significantly improved, and it has become the main germplasm foundation for the local goat industry [2]. However, with the improvement in living standards, consumer demand for high-quality mutton is increasing, and improving meat quality has become another major breeding direction following the improvement in growth performance and lambing performance.
Age is the dominant developmental factor influencing the dynamic changes in muscle tissue structure and functional properties. Existing studies on age effects on meat quality in small ruminants primarily focused on phenotypic characterization or single-omics approaches, with limited exploration of the crosstalk between transcriptional regulation and metabolite accumulation at the molecular level, particularly for breeds adapted to the Loess Plateau environment. Studies have shown that age affects the meat quality of Tibetan sheep by regulating the expression of myosin heavy chain (MyHC) isoforms, which determine the myofiber types, and 1.5 y Tibetan sheep meat was found to be the most suitable for human consumption [3]. However, the molecular regulatory mechanisms underlying the age-related variations in the muscle tissue of SWC goats remain poorly understood. The crosstalk between transcriptional regulation and metabolite accumulation during this process is particularly understudied. With the development of modern biotechnology, the rise of multi-omics technologies such as transcriptomics and metabolomics has greatly facilitated in-depth analyses of the molecular mechanisms underlying meat quality formation, including gene expression regulation and changes in metabolic pathways [4].
Therefore, in this study, the effects of age on slaughter performance, meat quality, and the underlying molecular regulatory mechanisms were investigated using 6- and 12-month-old male SWC goats. The results could have important theoretical and practical implications: Theoretically, the integrated transcriptome and metabolome analysis identifies the key genes, metabolites, and regulatory pathways in the muscles of goats that change with age, revealing the molecular mechanisms underlying the age-related trade-off between growth performance and meat quality. It fills the gap in the research on the genetic regulation of age-related meat quality in SWC goats and enriches our knowledge on goat muscle development and meat quality formation. The integration of multi-omics technologies in this study represents a novel approach that differs from previous single-omics studies, providing a more comprehensive perspective on age-related meat quality variations. From a practical perspective, this study finds that 6-month-old goats have better meat quality, while those at 12 months of age have superior meat production performance. This finding can provide a scientific basis for the selection of slaughter age in breeding production. The identified molecular targets provide support for genetic improvement and could help in optimizing feeding and breeding strategies, improving breeding benefits, and promoting the large-scale and high-quality development of the SWC goat industry. At the same time, it can aid in meeting the diverse needs of consumers desiring different quality mutton and contributes to the high-quality improvement in the livestock and poultry breeding industry in the context of rural revitalization.

2. Materials and Methods

2.1. Animal Care

All animal tests performed in this study were conducted under the supervision and guidance of the Ethics Committee of Yulin University (Approval No. YULLPZ-2025-009), and all procedures strictly adhered to the national standard “Guidelines for the Ethical Review of Animal Welfare in Scientific Research” (GB/T 35892-2018) and the “Regulations on the Administration of Laboratory Animals” (Order No. 676 of the State Council of the People’s Republic of China).

2.2. Animals, Diets, and Experimental Design

The experimental animals were selected from the Shaanxi Province Engineering and Technology Research Center of Shanbei Cashmere Goats in China. Sixty male SWC goats were divided into two groups according to their age: group S (4 months old) and group T (10 months old). The birth date and body weight of the goats in the same group were similar. All individuals were fed the same diet, following a unified management protocol (Table 1). All goats were raised in the same environment and under the same feeding conditions for 2 months. Three healthy S group goats with similar weights (12.7 ± 0.1 kg, 6 months old) and three T group goats with similar weights (49.6 ± 2.5 kg, 12 months old) were selected for slaughter after fasting for 24 h and water deprivation for 12 h. All goats were humanely anesthetized prior to tissue collection to minimize suffering.
The longissimus dorsi of the 6 goats were collected within 30 min after slaughter. Some muscle samples were stored at −80 °C. These samples were used for transcriptome sequencing and metabolomics analysis.

2.3. Determination of Slaughter Performance

After exsanguination and skinning, slaughter indices were measured using calibrated equipment (accuracy: 0.01 kg for mass metrics, 0.01 cm for length metrics) following the method described by “The Technical Code for Sheep and Goat Slaughtering” (T/CAAA 035-2020) with minor modifications. Carcass weight is the mass of the carcass after removal of the head, feet, internal organs (excluding kidneys and perirenal fat), and tail fat, which was recorded within 30 min post slaughter. Bone weight is the total mass of separated bones from the complete carcass after manual dissection of muscle and fat tissues. Net meat weight is the mass of lean muscle tissue obtained by trimming all visible fat and connective tissue from the carcass. Meat-to-bone ratio is calculated as the ratio of net meat weight to bone weight. Slaughter rate is calculated as (carcass weight/pre-slaughter live weight) × 100%. Net meat rate is calculated as (net meat weight/pre-slaughter live weight) × 100%. Backfat thickness is measured at three sites (shoulder, rib, and hip) using a digital caliper, with the average value recorded. GR value (grading value) represents the thickness of soft tissue (fat + muscle) at the 12th–13th rib interface, 6–8 cm from the midline, and is measured using a dedicated GR ruler. Marbling score is evaluated by three trained technicians based on the national standard for goat/sheep meat quality grading (GB/T22286-2008), with scores ranging from 1 (minimal marbling) to 5 (abundant marbling), and the median score used in the analysis [5].

2.4. Analysis of Longissimus Dorsi Meat Quality

Meat quality indices were determined following established protocols and national/international standards. To determine the cooking yield, a 5 g meat sample was weighed, sealed in a polyethylene bag, and heated in a water bath at 80 °C for 30 min; after cooling to room temperature, the sample was blotted dry and reweighed. Cooking yield was calculated as (cooked tissue mass/raw tissue mass) × 100%. To determine the shear force, the cooked sample (from the cooking yield assay) was cut into 1 cm × 1 cm × 3 cm strips (parallel to muscle fibers), and shear force was measured using a texture analyzer (TA-XT Plus, Stable Micro Systems, London, UK) with a Warner–Bratzler shear blade (crosshead speed: 2 mm/s) (GB/T 40467-2021). Crude protein content was determined using the Kjeldahl method (Kjeltec 8400, FOSS, Hilleroed, Denmark) with a conversion factor of 6.25 (GB/T 9695.11-2008), while crude fat content was measured using the Soxhlet extraction method (Soxtec 2050, FOSS, Hilleroed, Denmark) (GB/T 9695.7-2008). Water content was determined using the direct drying method (Memmert UN110, Memmert GmbH, Schwabach, Germany): the sample was incubated at 105 °C until a constant weight was reached (ISO 1442:2023). The fresh meat’s surface was immediately analyzed to measure meat color metrics, specifically lightness (L*), redness (a*), and yellowness (b*), utilizing a Minolta Chromameter (CR-210 Minolta, Minolta Co., Ltd., Osaka, Japan). Water-holding capacity was determined using the centrifugation method: 1 g of minced meat was placed in a centrifuge tube with filter paper and centrifuged at 3000× g for 10 min at 4 °C; the water-holding capacity was calculated as (tissue mass after centrifugation/initial tissue mass) × 100%. pH was measured at 45 min (pH45min) and 24 h (pH24h) post slaughter using a portable pH meter with a meat-specific electrode (GB/T 9695.5-2008).

2.5. RNA-Seq

RNA-seq was performed on a total of six tissue samples (three biological replicates per age group, including 6-month-old and 12-month-old SWC goats). This sample size was based on previous multi-omics studies on small ruminants and pre-experimental verification; it was deemed sufficient to ensure statistical power and effectively address potential concerns about biological variability. Sequencing experiments were performed using the Total RNA Extractor (Trizol) kit for library construction (Takara, Dalian, China). Total RNA was extracted from the tissue samples, and the concentration and purity of the extracted RNA were examined using a Qubit2.0 RNA Test kit (Thermo Fisher Scientific, Waltham, MA, USA); RNA integrity was determined using agarose gel electrophoresis. Each library was required to have a total RNA content ≥ 1 ug, RNA concentration ≥ 35 ng/µL, OD260/280 ≥ 1.8, and OD260/230 ≥ 1.0. A–T base pairing of poly A tails of mRNA to Oligo (dT)-coated magnetic beads was used to isolate mRNA from the total RNA. RNA-seq was performed using the Illumina Hiseq™ sequencing platform (Illumina, Inc., San Diego, California, USA). Clean data (reads) were mapped to the goat reference genome ARS1 (gca_001704415.1) [6] using HISAT2 (v2.2.1) [7]. Transcripts were assembled and the expression levels of the transcripts were quantified using StringTie software (v2.2.3) [8]. The number of transcripts was expressed as transcripts per million (TPM).

2.6. Muscle Metabolite Extraction

A total of six muscle samples (three biological replicates per age group) that were previously preserved in a −80 °C freezer were thawed in an ice bath prior to processing. This sample size was based on previous multi-omics studies on small ruminants and validated by pre-experiments; it was deemed adequate to ensure sufficient statistical power and address potential biological variability. The thawed samples were ground into a fine powder under liquid nitrogen. A 400 μL mixture of methanol and water (7:3, V/V) supplemented with an internal standard was added to 20 mg of the ground sample, which was then vortexed at 1500 rpm for 5 min. The mixture was incubated on ice for 15 min and subsequently centrifuged at 12,000 rpm for 10 min at 4 °C. A 300 μL aliquot of the supernatant was collected and stored at −20 °C for 30 min. The supernatant was subjected to a second centrifugation step at 12,000 rpm for 3 min at 4 °C. A 200 μL aliquot of the final supernatant was pipetted into appropriate vials for subsequent LC-MS analysis.

2.7. HPLC Conditions

All samples were analyzed using two distinct LC/MS analytical approaches. One aliquot was analyzed under positive ion mode and separated on a Waters ACQUITY Premier HSS T3 column (1.8 µm, 2.1 mm × 100 mm; Waters Corporation, Milford, MA, USA). The mobile phase consisted of solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid in acetonitrile) using the following gradient program: 5 to 20% over 2 min, increased to 60% over the following 3 min, increased to 99% over 1 min and held for 1.5 min, and then decreased to 5% solvent B over 0.1 min and held for 2.4 min. The analytical conditions were as follows: column temperature of 40 °C; flow rate of 0.4 mL/min; and injection volume of 4 μL. The other aliquot was analyzed under negative ion mode using the same elution gradient.

2.8. MS Conditions

Data acquisition was performed in information-dependent acquisition (IDA) mode using Analyst TF software (v1.8.1) (Sciex, Concord, ON, Canada). The ion source parameters were as follows: ion source gas 1 (GAS1) pressure of 50 psi; ion source gas 2 (GAS2) pressure of 50 psi; curtain gas (CUR) pressure of 25 psi; temperature (TEM) of 550 °C; declustering potential (DP) of 60 V and −60 V in positive and negative mode, respectively; and ion spray voltage floating (ISVF) of 5000 V and −4000 V in positive and negative mode, respectively. The TOF MS scan parameters were set as follows: mass range of 50–1000 Da; accumulation time of 200 ms; and dynamic background subtract was on. The product ion scanning parameters were as follows: mass range of 25–1000 Da; accumulation time of 40 ms; collision energy of 30 and −30 V in positive and negative mode, respectively; collision energy spread of 15; UNIT resolution; charge state of 1 to 1; intensity of 100 cps; excluding isotopes within 4 Da; mass tolerance of 50 ppm; and maximum number of candidate ions to monitor per cycle of 18.

2.9. Data Analysis Method

The slaughter and meat quality indicator data were subjected to the non-parametric Wilcoxon rank-sum test using IBM SPSS Statistics 27.0 software (IBM Corp., Armonk, New York, USA). The results were expressed as the mean ± standard error of the mean (SEM), with statistical significance set at p < 0.05.
In the RNA-seq analysis, DEGs were identified based on the fold change (FC) expression of genes, which were defined as genes with a greater than 2-fold change in expression (FC ≥ 2 (up-regulated) or FC ≤ 0.5 (down-regulated)) and p < 0.05 between the groups [9]. The GO (Gene ontology) enrichment analysis of the DEGs was performed using the GOATOOLS of Python (v3.15) [10] and Fisher’s exact test, with p < 0.05 indicating statistical significance. Hierarchical clustering analysis was performed using the fastcluster in R software (v4.3.1) [11]. The heatmap clustering analysis was performed using the gplots in R software (v4.3.1).
In the untargeted metabolomics analysis, unsupervised principal component analysis (PCA) was performed using the statistics function pr-comp within R (www.r-project.org). The data comprised unit variance scaled before unsupervised PCA. The hierarchical cluster analysis (HCA) results for samples and metabolites are presented as heatmaps with dendrograms, while Pearson correlation coefficients (PCCs) between samples were calculated using the cor function in R and presented as heatmaps only. Both HCA and PCC were carried using the R package complex heatmap. For the HCA, the normalized signal intensities of the metabolites (unit variance scaling) are visualized as a color spectrum. For the two-group analysis, differential metabolites were determined based on VIP (VIP > 1) and p-value (p < 0.05, t test). For the multi-group analysis, differential metabolites were determined based on VIP (VIP > 1) and p-value (p < 0.05, ANOVA). VIP values were extracted from the OPLS-DA results, which also contain score plots and permutation plots, and were generated using MetaboAnalystR in R software (v4.3.1). Log transformation (log2) and mean centering of the data were performed before OPLS-DA. To avoid overfitting, a permutation test (200 permutations) was performed. The identified metabolites were annotated using the KEGG compound database (http://www.kegg.jp/kegg/compound/, accessed on 1 December 2025), and the annotated metabolites were then mapped to the KEGG pathway database (http://www.kegg.jp/kegg/pathway.html, accessed on 5 December 2025).

3. Results

3.1. Analysis of Slaughter Performance and Muscle Quality

The live body weight, carcass weight, bone weight, net meat weight, meat-to-bone ratio, backfat thickness, and GR of the T group were significantly higher than those of the S group (p < 0.05), while the marbling score of the S group was significantly higher than that of the T group (p < 0.05) (Table 2). Analysis of routine meat quality indicators showed that the water content and lightness of the S group were significantly higher than those of the T group (p < 0.05), while the shear force, redness, and yellowness were significantly lower than those of the T group (p < 0.01) (Table 3). This indicates that the tenderness and juiciness of S group meat are superior to those of T group meat.

3.2. Transcriptome Profiling of Muscle

The transcriptome sequencing generated a total of 47 Gb of clean data. The effective data volume for the two groups of samples ranged from 6.58 to 8.71 Gb, the distribution of Q30 bases ranged from 95.23% to 95.60%, and the average GC content was 55.13%. Clean reads were mapped to the goat reference genome with mapping ratios ranging from 94.42% to 94.98% (Table 4). There were 138 DEGs in the muscle of the S group vs. T group, including 69 up-regulated genes and 69 down-regulated genes, and the cluster analysis of the DEGs showed significant differences between the two groups (Figure 1). The DEGs in the two groups showed enrichment of 65 GO entries (p < 0.05). Among them, many DEGs were associated with terms such as “growth”, “reproduction”, “regulation of biological process”, “cellular component organization or biogenesis”, “signaling”, “developmental process”, “metabolic process”, “antioxidant activity”, and “enzyme regulator activity” (Figure 2).
The KEGG database was used to analyze the pathways that the DEGs were associated with, and the results showed significant enrichment for 36 pathways (p < 0.05). The DEGs were mainly concentrated in the “p53 signaling pathway”, “PI3K–Akt signaling pathway”, “cholinergic synapse”, “cellular senescence”, “signaling pathways regulating pluripotency of stem cells”, “growth hormone synthesis, secretion, and action”, “thyroid hormone synthesis”, “regulation of lipolysis in adipocytes”, and “IL-17 signaling pathway” (Figure 3).

3.3. DEM Analysis

In this study, the muscle samples were also used for untargeted metabolite analysis. The PLS-DA results showed significant differences between the metabolites of the S and T groups (Figure 4a). A total of 158 DEMs were identified, of which 44 were up-regulated and 114 were down-regulated (Figure 4b).
Comparative analysis of the differentially expressed metabolites was conducted, and the results (organized by fold change from largest to smallest) are shown in Figure 4c. Compared with the T group, the main up-regulated metabolites in the S group included cytidine-5′-triphosphate, pizotifen, Arg-Tyr-Leu-Lys, 3-hydroxyhexadecanoic acid, noladin ether, and Gln-Asn-Phe-Glu. The down-regulated metabolites in the S group included difloxacin, mannose 1-phosphate, Arg-Ser-Met, 1-stearoyl-2-arachidonoyl PC-d8, propan-2-yl7-[3,5-dihydroxy-2-(3-hydroxy-4-phenoxybut-1-en-1-yl)cyclopentyl] hept-5-enoate, janthitrem E, cis-cyclohexa-3,5-diene-1,2-diol, N6-methyladenosine, 4-phenanthrenecarboxylic acid, nbd-ceramide, L-allysine ethylene acetal, 4-hydroxyphenylpyruvic acid, securinine, and gallacetophenone.
These 158 DEMs showed significant enrichment for several metabolism-related pathways. The top 20 pathways based on p-values are plotted from smallest to largest in Figure 5. The pathways with significant enrichment of DEMs between the S and T groups included tyrosine metabolism; starch and sucrose metabolism; carbohydrate digestion and absorption; glycerolipid metabolism; aminoacyl−tRNA biosynthesis; fat digestion and absorption; vitamin digestion and absorption; mannose type O−glycan biosynthesis; glutathione metabolism; and phenylalanine, tyrosine, and tryptophan biosynthesis.

3.4. Integrated Transcriptome and Metabolome Analysis

A joint KEGG analysis on the differentially expressed metabolites and genes in the longissimus dorsi muscle between the two goat groups found that they collectively showed enrichment of some metabolic pathways. Among them, pathways such as fructose and mannose metabolism, sphingolipid metabolism, mineral absorption, thyroid hormone synthesis, protein digestion and absorption, linoleic acid metabolism, fat digestion and absorption, arachidonic acid metabolism, regulation of lipolysis in adipocytes, glycerolipid metabolism, alpha-linolenic acid metabolism, and glycerophospholipid metabolism were associated with meat quality (Figure 6), indicating their critical role in the meat quality differences between the two groups.
A nine-quadrant plot was drawn to show the correlation between the differentially expressed genes and metabolites detected between the S and T groups. Among them, the genes and metabolites with positive correlations accounted for 0.28%, indicating that the expression changes in these metabolites may be positively regulated by genes; the genes and metabolites with negative correlations accounted for 4.37%, indicating that the expression changes in these metabolites may be negatively regulated by genes (Figure 7a). The analysis results show that genes such as ABCG4, ADCY1, CREB5, DRD1, GNAO1, HMOX1, FZD5, CISH, COL14A1, and COL2A1 were closely related to L-tyrosine, L-tryptophan, and glutathione through the pathways of mineral absorption, protein digestion and absorption, thyroid hormone synthesis, etc., which affect the metabolism of amino acids and their metabolites. The ABCB4 and PFKFB4 genes are associated with D-fructofuranose through the fructose and mannose metabolic pathways, which influence the biological processing of carbohydrates and their metabolites. Genes such as DRD1, GRID1, DGAT2, ADCY1, CREB5, GNAO1, NAPEPLD, and UGT8 are closely related to cis-3-hexenyl acetate, noladin ether, 1-(1Z-octadecenyl)-2-(9Z-octadecenoyl)-sn-glycero-3-phosphocholine, 1-stearoyl-2- arachidonoyl PC-d8, and C24:1 sphingomyelin through the pathways of alpha-linolenic acid metabolism, fat digestion and absorption, glycerolipid metabolism, regulation of lipolysis in adipocytes, glycerophospholipid metabolism, linoleic acid metabolism, and sphingolipid metabolism, and they affected lipid metabolism (Figure 7b).

4. Discussion

Mutton is an important protein source for human health. According to the Statistical Communique of China on National Economic and Social Development released in 2023 by the National Bureau of Statistics of China, mutton output in China has maintained a steady growth, becoming a key component of the human diet [12].
The SWC goat is a dominant breed in regional animal husbandry development in China, with a breeding stock exceeding 10 million in Yulin city and an annual output value of CYN 6.5 billion, accounting for over 70% of local farmers’ economic income. As a core enabling industry for rural revitalization in northern Shaanxi, the SWC goat industry was included in China’s national industrial cluster system in 2021 [13]. Developed through 30 years of crossbreeding, this dual-purpose breed (providing cashmere and meat) exhibits strong environmental adaptability, high cashmere yields, and superior cashmere quality, ranking among the most advanced levels in the goat industry in China [14].
This study investigated age-related differences in meat quality of SWC goats, revealing prominent phenotypic variations in growth performance and meat quality traits between 6-month-old (S group) and 12-month-old (T group) male goats and providing direct evidence for growth stage-dependent differences within the same breed. Growth-related traits (live body weight, carcass weight, meat yield, and fat deposition indicators) were significantly higher in the T group (p < 0.05), a biological phenomenon attributed to the longer growth cycle and more sufficient nutrient accumulation in older goats. Conversely, the S group showed more favorable meat quality traits, including higher marbling scores, water contents, and lightness, as well as lower shear forces (p < 0.05). These traits are key determinants of meat tenderness, juiciness, and sensory acceptance [15,16,17,18], collectively indicating that 6-month-old goats produce mutton that is more aligned with consumer preferences.
This highlights an apparent trade-off between growth performance and meat quality with advancing age in SWC goats: while growth rate and carcass yield improve with age, meat tenderness and juiciness tend to decrease. This dilemma is common in animal breeding, requiring balanced consideration in practical production. Consistent with previous findings in sheep and goats [19,20] which reported age-related trade-offs between growth and meat quality, our results suggest a broad occurrence of this phenomenon in ruminant breeding and provide species-specific insights for SWC goat production.
To elucidate the molecular mechanisms underlying these phenotypic differences, transcriptome sequencing was performed on muscle tissues. The high-quality sequencing data (meeting stringent high-throughput analysis standards) allowed for the identification of 138 differentially expressed genes (DEGs) between the two age groups, with similar numbers of up- and down-regulated DEGs. Cluster analysis confirmed distinct transcriptional profiles associated with age. Functional annotation of the DEGs revealed enrichment of GO terms such as “growth”, “developmental process”, “metabolic process”, “antioxidant activity”, and “enzyme regulator activity”. Terms related to growth and development explain the superior growth performance of the T group, while metabolic and antioxidant-related terms explain the better meat quality of the S group, which reflect the transcriptional regulation of age-dependent phenotypic traits. Similar to previous studies on black goat breeds [21,22], which linked age-related DEGs to intramuscular fat deposition and meat quality, our findings emphasize the role of transcriptional regulation in shaping goat growth and meat quality.
KEGG pathway enrichment analysis identified 36 significantly enriched pathways, including the PI3K-Akt signaling, p53 signaling, cellular senescence, and growth hormone synthesis/secretion pathways. The PI3K-Akt pathway (a central regulator of cell proliferation and metabolism) and p53 pathway (which regulates the cell cycle and apoptosis) collectively modulate muscle development and tissue homeostasis [23,24], while lipid metabolism-related pathways (e.g., regulation of lipolysis in adipocytes) contribute to marbling score differences [25,26]. These results indicate that growth and meat quality differences are mediated by a complex signaling network rather than individual genes or pathways, providing a holistic understanding of the molecular regulation of these processes.
Untargeted metabolome analysis further revealed substantial metabolic profile differences between the two age groups, with 158 differentially expressed metabolites (DEMs) identified (44 up-regulated and 114 down-regulated in the S group compared with the T group). The up-regulated metabolites are involved in nucleotide metabolism, peptide synthesis, and fatty acid metabolism (e.g., 3-hydroxyhexadecanoic acid, which may promote intramuscular fat accumulation [27]), while the down-regulated metabolites are associated with carbohydrate and amino acid metabolism. KEGG enrichment analysis of the DEMs revealed significant enrichment of multiple metabolism-related pathways, such as tyrosine metabolism, starch and sucrose metabolism, carbohydrate digestion and absorption, glycerolipid metabolism, and fat digestion and absorption. These pathways are directly involved in the synthesis and degradation of key substances that affect meat quality, including amino acids, carbohydrates, and lipids, and their differential expression between the two groups is closely related to the age difference (6 months vs. 12 months). For example, in SWC male goats, tyrosine metabolism is related to the formation of flavor substances in meat, while glycerolipid metabolism and fat digestion and absorption pathways regulate the deposition of intramuscular fat, which is closely linked to marbling score and meat tenderness [28,29,30]. During the early growth stage (represented by the S group), intramuscular fat synthesis may be prioritized, while during the rapid growth stage (T group), nutrients are redirected to skeletal muscle and bone development; these metabolic changes reflect the animals’ adaptation to the different demands of different growth stages. Consistent with previous studies on lipid metabolism in meat goats [31], our results confirm the importance of glycerolipid metabolism in regulating age-related meat quality differences.
The joint transcriptome–metabolome analysis results were used to construct an age-dependent gene–metabolite–phenotype regulatory network that comprehensively explains the growth and meat quality differences between the two age groups. Key genes (e.g., ABCG4, ADCY1, CREB5, and HMOX1) were correlated with metabolites such as L-tyrosine, L-tryptophan, and glutathione via pathways such as mineral absorption and protein digestion, which regulate amino acid metabolism and flavor precursor accumulation. In the S group, the active regulation of this network enhanced meat tenderness and taste, while down-regulation of this network in the T group supported rapid growth. Genes such as ABCB4 and PFKFB4 participate in carbohydrate metabolism (via fructose and mannose pathways) and changes in their expression could enable metabolic adaptation to different growth stages: the moderate energy metabolism in the S group maintains meat quality, while the active carbohydrate metabolism in the T group is needed to meet the energy demands for growth. Lipid metabolism-related genes (e.g., DRD1, GRID1, DGAT2, and UGT8) are involved in alpha-linolenic acid metabolism, glycerolipid metabolism, and fat digestion. DGAT2 has been positively correlated with intramuscular fat content in meat goats [32] and contributes to marbling score differences. As lipid metabolism is central to intramuscular fat deposition and meat juiciness [33,34,35], these findings highlight the combined role of amino acid, carbohydrate, and lipid metabolism pathways in regulating age-dependent phenotypic traits.
Collectively, our multi-omics analysis reveals the molecular mechanisms underlying age-related trade-offs between growth performance and meat quality in SWC goats. These insights have practical implications for optimizing slaughter age selection (6-month-old goats for high-quality meat, 12-month-old goats for high yield) and guiding breeding strategies (targeting key genes/metabolites to balance growth and meat quality). By providing a scientific basis for SWC goat production and genetic improvement, this study supports the sustainable development of the SWC goat industry, contributing to rural revitalization and meeting consumer demands for high-quality mutton.

5. Conclusions

This study systematically revealed the age-related phenotypic differences in growth performance and meat quality between 6- and 12-month-old SWC goats and uncovered the underlying molecular mechanisms through integrated transcriptome and metabolome analyses. The identification of key DEGs, DEMs, and their associated pathways not only deepens our understanding of the age-dependent genetic regulation of meat quality and growth traits in SWC goats but also provides valuable molecular targets for the genetic improvement in meat quality and the optimization of breeding and feeding strategies for different life stages. The findings of this study emphasize the complexity of the molecular regulatory network underlying age-related meat quality traits and provide a theoretical basis for resolving the trade-off between growth performance and meat quality in goat breeding. Specifically, for SWC goats, if the goal is to produce high-quality meat with high tenderness and juiciness, slaughtering at 6 months of age (consistent with the S group) may be more appropriate; if maximizing carcass yield and growth performance is the priority, slaughtering at 12 months of age (consistent with the T group) is more advantageous. Future research should focus on the functional verification of these key genes and metabolites that show age-related changes and explore their regulatory roles in vivo through gene editing or overexpression experiments to provide more direct theoretical support for the breeding of SWC goats with both excellent growth performance and meat quality and for the formulation of age-specific feeding management schemes.

Author Contributions

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

Funding

This research was funded by the Shaanxi Fundamental Science Research Project for Chemistry and Biology (22JHQ045), the Natural Science Basic Research Program of Shaanxi Province (2025JC-YBQN-315), and the Science and Technology Program Project of Yulin City (2025-CXY-130).

Institutional Review Board Statement

The experimental procedures and protocol used in this study were approved by the Ethics Committee of Yulin University (Approval No. YULLPZ-2025-009; date: 17 August 2025).

Data Availability Statement

The raw data supporting the conclusions of this study will be made available by the authors on request.

Acknowledgments

We thank Lei Qu from the Shaanxi Province Engineering and Technology Research Center of Cashmere Goats (Shaanxi, China) for providing the animals. In addition, we would like to thank Jianguo Shi for their help with the preliminary experiment.

Conflicts of Interest

Shenghui Chen is employed by Shenmu Juke Agricultural and Animal Husbandry Development Co., Ltd. The other authors do not have any commercial or financial relationships that could be construed as potential conflicts of interest.

References

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Biology 15 00318 g001
Figure 1. A comparison of transcriptomes of the longissimus dorsi of the S and T groups. (a) A volcano map of DEGs. Gray indicates non-differentially expressed genes, and red and green indicate up- and down-regulated DEGs. (b) Cluster analysis of DEGs. The abscissa indicates the sample and the ordinate indicates the DEGs, which are hierarchically clustered. The color indicates the expression level of the gene in the sample, with red representing high expression and green representing low expression. The left panel shows a dendrogram of gene clustering, where closer branches indicate higher expression levels. The left color indicates row grouping information, showing gene grouping information, and different colors represent functional categories and other classification information of genes.
Figure 1. A comparison of transcriptomes of the longissimus dorsi of the S and T groups. (a) A volcano map of DEGs. Gray indicates non-differentially expressed genes, and red and green indicate up- and down-regulated DEGs. (b) Cluster analysis of DEGs. The abscissa indicates the sample and the ordinate indicates the DEGs, which are hierarchically clustered. The color indicates the expression level of the gene in the sample, with red representing high expression and green representing low expression. The left panel shows a dendrogram of gene clustering, where closer branches indicate higher expression levels. The left color indicates row grouping information, showing gene grouping information, and different colors represent functional categories and other classification information of genes.
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Figure 2. GO annotations of differentially expressed genes.
Figure 2. GO annotations of differentially expressed genes.
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Figure 3. Enriched KEGG pathways for DEGs between the longissimus dorsi of the S and T groups. The top 30 enriched KEGG pathways are shown. The ordinate is the pathway and the abscissa is the enrichment factor.
Figure 3. Enriched KEGG pathways for DEGs between the longissimus dorsi of the S and T groups. The top 30 enriched KEGG pathways are shown. The ordinate is the pathway and the abscissa is the enrichment factor.
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Figure 4. Differentially expressed metabolites between the S and T groups. (a) PLS-DA analysis of metabolomic data. (b) A volcano plot of DEMs. (c) A bar chart of the difference multiple. The abscissa is the log2FC value and the ordinate is the DEM. Red and green represent up-regulated and down-regulated DEMs, respectively.
Figure 4. Differentially expressed metabolites between the S and T groups. (a) PLS-DA analysis of metabolomic data. (b) A volcano plot of DEMs. (c) A bar chart of the difference multiple. The abscissa is the log2FC value and the ordinate is the DEM. Red and green represent up-regulated and down-regulated DEMs, respectively.
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Figure 5. Differentially expressed metabolite KEGG pathway enrichment map.
Figure 5. Differentially expressed metabolite KEGG pathway enrichment map.
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Figure 6. Correlation analysis of transcriptomic and metabolomic data.
Figure 6. Correlation analysis of transcriptomic and metabolomic data.
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Figure 7. Correlation analysis of differentially expressed genes and metabolites. (a) A nine-quadrant diagram of correlation analysis. Each point represents a pair of correlations, and the abscissa and ordinate represent the log2FC of the genes and metabolites, respectively. Yellow indicates genes with opposite differential expression patterns to metabolites, red indicates genes and metabolites both upregulated, blue indicates genes and metabolites both downregulated, purple indicates genes and metabolites with no correlation in expression, and gray indicates genes and metabolites with no differential expression. (b) A correlation clustering heatmap. Each row represents a gene and each column represents a metabolite. Red represents a positive correlation between the gene and the metabolite, and blue represents a negative correlation between the gene and the metabolite.
Figure 7. Correlation analysis of differentially expressed genes and metabolites. (a) A nine-quadrant diagram of correlation analysis. Each point represents a pair of correlations, and the abscissa and ordinate represent the log2FC of the genes and metabolites, respectively. Yellow indicates genes with opposite differential expression patterns to metabolites, red indicates genes and metabolites both upregulated, blue indicates genes and metabolites both downregulated, purple indicates genes and metabolites with no correlation in expression, and gray indicates genes and metabolites with no differential expression. (b) A correlation clustering heatmap. Each row represents a gene and each column represents a metabolite. Red represents a positive correlation between the gene and the metabolite, and blue represents a negative correlation between the gene and the metabolite.
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Table 1. Composition and nutrient level of diets (DM basis).
Table 1. Composition and nutrient level of diets (DM basis).
ItemContent
Feed composition:
Alfalfa hay (%)10.5
Corn stalk (%)10.0
Soybean stalk (%)10.0
Grass powder (%)12.0
Corn (%)27.0
Soybean meal (%)12.0
Bran (%)3.0
Salt (%)0.5
Premix (%) 115.0
Nutritional level: 2
Digestible energy (MJ/kg)10.9
Crude fat (%)3.8
Crude protein (%)12.6
Calcium (%)0.4
Phosphorus (%)0.5
Acidic detergent fiber (%)36.6
Neutral detergent fiber (%)23.1
1 The premix contains 120,000 kIU of vitamin A, 120,000 kIU of vitamin D, 0.384 g of vitamin E, 0.468 g of copper, 1.508 g of iron, 1.560 g of zinc, 1.220 g of manganese, 8.020 mg of iodine, 6.120 mg of selenium, 10.160 mg of cobalt, 138.600 g of calcium, 43.000 g of sulfur, and 13.000 g of phosphorus per kilogram. 2 The nutritional levels are calculated values.
Table 2. Comparison of slaughter performance indicators between two groups.
Table 2. Comparison of slaughter performance indicators between two groups.
S GroupT Groupp
Live body weight (kg)12.7 ± 0.1 b49.6 ± 2.5 a0.046
Carcass weight (kg)6.1 ± 0.4 b23.6 ± 0.5 a0.049
Bone weight (kg)1.8 ± 0.1 b5.3 ± 0.1 a0.049
Net meat weight (kg)3.7 ± 0.2 b17.2 ± 0.2 a0.049
Meat-to-bone ratio2.1 ± 0.3 b3.2 ± 0.0 a0.046
Slaughter rate (%)48.2 ± 3.547.6 ± 1.40.827
Net meat rate (%)29.0 ± 1.834.8 ± 1.70.127
Backfat thickness (cm)0.2 ± 0.0 b0.8 ± 0.1 a0.046
GR value (mm)0.6 ± 0.0 b2.4 ± 0.1 a0.037
Marbling score4.1 ± 0.1 a2.6 ± 0.0 b0.046
No letter means no significant difference (p > 0.05); different superscript letters in the same row indicate significant differences (p < 0.05).
Table 3. Analysis of routine meat quality indicators of two groups.
Table 3. Analysis of routine meat quality indicators of two groups.
S GroupT Groupp
Cooking yield (%)97.2 ± 0.598.4 ± 0.70.275
Shear force (N)37.6 ± 0.3 b53.2 ± 0.2 a0.049
Fat (%)3.7 ± 0.43.9 ± 0.40.513
Protein (%)19.5 ± 0.519.2 ± 0.00.507
Water content (%)72.3 ± 1.3 a61.2 ± 0.6 b0.049
Lightness (L*)52.4 ± 1.0 a41.9 ± 0.9 b0.049
Redness (a*)10.2 ± 0.1 b10.8 ± 0.2 a0.049
Yellowness (b*)2.1 ± 0.1 b2.7 ± 0.1 a0.049
Water-holding capacity0.2 ± 0.00.1 ± 0.00.114
pH6.3 ± 0.16.4 ± 0.10.827
No letter indicates no significant difference (p > 0.05); different superscript letters in the same row indicate significant differences (p < 0.05).
Table 4. Sequencing data statistics.
Table 4. Sequencing data statistics.
Raw ReadsClean ReadsRaw BasesClean BasesGC Content≥Q30Total MappedReads Mapped in Proper Pairs
S156,103,20456,020,1028,415,480,6008,266,981,79454.68%95.43%52,811,610 (94.48%)46,752,130 (83.64%)
S244,309,74644,244,2666,646,461,9006,584,978,26755.56%95.47%41,756,656 (94.62%)36,898,020 (83.61%)
S353,589,67853,510,9448,038,451,7007,966,039,86854.73%95.47%50,381,805 (94.42%)43,580,760 (81.68%)
T158,605,19658,519,0408,790,779,4008,712,518,10254.86%95.55%49,035,212 (94.98%)43,613,208 (84.48%)
T254,714,14654,634,3988,207,121,9008,132,713,08754.95%95.23%51,474,389 (94.54%)45,483,992 (83.53%)
T351,890,14251,813,2307,783,521,3007,684,796,55755.98%95.60%55,146,163 (94.50%)48,796,086 (83.61%)
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MDPI and ACS Style

He, Y.; Lu, S.; Fu, P.; Chen, S.; Zhang, P.; Song, X. Effects of Age on Slaughter Performance and Meat Quality of Shanbei White Cashmere Goat and Optimization of Slaughter Strategies. Biology 2026, 15, 318. https://doi.org/10.3390/biology15040318

AMA Style

He Y, Lu S, Fu P, Chen S, Zhang P, Song X. Effects of Age on Slaughter Performance and Meat Quality of Shanbei White Cashmere Goat and Optimization of Slaughter Strategies. Biology. 2026; 15(4):318. https://doi.org/10.3390/biology15040318

Chicago/Turabian Style

He, Yanyi, Sina Lu, Pengpeng Fu, Shenghui Chen, Pengyu Zhang, and Xiaoyue Song. 2026. "Effects of Age on Slaughter Performance and Meat Quality of Shanbei White Cashmere Goat and Optimization of Slaughter Strategies" Biology 15, no. 4: 318. https://doi.org/10.3390/biology15040318

APA Style

He, Y., Lu, S., Fu, P., Chen, S., Zhang, P., & Song, X. (2026). Effects of Age on Slaughter Performance and Meat Quality of Shanbei White Cashmere Goat and Optimization of Slaughter Strategies. Biology, 15(4), 318. https://doi.org/10.3390/biology15040318

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