The growth and development of muscle tissue play a pivotal role in the economic value and quality of meat in agricultural animals, garnering close attention from breeders and researchers. The quality and palatability of muscle tissue directly determine the market competitiveness of meat products and the satisfaction of consumers. Therefore, a profound understanding and management of muscle growth is essential for enhancing the overall economic efficiency and product quality of the meat industry. Despite this, systematic research on muscle development-related genes across different species still needs to be improved. This study addresses this gap through extensive cross-species muscle transcriptome analysis, combined with interpretable machine learning models. Utilizing a comprehensive dataset of 275 publicly available transcriptomes derived from porcine, bovine, and ovine muscle tissues, encompassing samples from ten distinct muscle types such as the semimembranosus and longissimus dorsi, this study analyzes 113 porcine (n = 113), 94 bovine (n = 94), and 68 ovine (n = 68) specimens. We employed nine machine learning models, such as Support Vector Classifier (SVC) and Support Vector Machine (SVM). Applying the SHapley Additive exPlanations (SHAP) method, we analyzed the muscle transcriptome data of cattle, pigs, and sheep. The optimal model, adaptive boosting (AdaBoost), identified key genes potentially influencing muscle growth and development across the three species, termed SHAP genes. Among these, 41 genes (including NANOG, ADAMTS8, LHX3, and TLR9) were consistently expressed in all three species, designated as homologous genes. Specific candidate genes for cattle included SLC47A1, IGSF1, IRF4, EIF3F, CGAS, ZSWIM9, RROB1, and ABHD18; for pigs, DRP2 and COL12A1; and for sheep, only COL10A1. Through the analysis of SHAP genes utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, relevant pathways such as ether lipid metabolism, cortisol synthesis and secretion, and calcium signaling pathways have been identified, revealing their pivotal roles in muscle growth and development.

The average daily gain (ADG) is a critical index for evaluating growth rates in cattle and is closely linked to the economic benefits of the cattle industry. Heredity is one of the factors affecting the daily gain of cattle. However, the molecular mechanisms regulating ADG remain incompletely understood. This study aimed to systematically unravel the molecular mechanisms underlying the divergence in ADG between high average daily gain (HADG) and low average daily gain (LADG) Angus cattle through integrated multi-omics analyses (microbiome, metabolome, and transcriptome), hypothesizing that the gut microbiota–host gene–metabolism axis is a key regulatory network driving ADG divergence. Thirty Angus cattle were classified according to their HADG and LADG. Fecal and serum samples were collected for 16S, fecal metabolome, and blood transcriptome analysis. The results showed that compared with the LADG group, the abundance of Firmicutes increased in the HADG group, while the abundance of Bacteroidetes and Proteobacteria decreased. Metabolomics and transcriptomic analysis revealed that KEGG pathways associated with differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) were enriched in bile acid metabolism. Spearman correlation analysis showed that Oscillospira was positively correlated with ZBTB20 and negatively correlated with RADIL. ZBTB20 was negatively correlated with dgA-11_gut_group. This study analyzed the regulatory mechanism of average daily gain of beef cattle from genetic, metabolic, and microbial levels, providing a theoretical basis for analyzing the mechanism of differential daily gain of beef cattle, and has important significance for improving the production performance of beef cattle. The multi-omics network provides biomarker foundations for machine learning-based ADG prediction models, offering potential applications in precision breeding. While these biomarkers show promise for precision breeding, their causal roles require further validation. The conclusions are derived from a single breed (Angus) and gender (castrated males). Future studies should include females and diverse breeds to assess generalizability.

Muscle is a crucial component of cattle, playing a vital role in determining the final quality of beef. This study aimed to identify candidate genes associated with muscle growth and lipid metabolism in beef and dairy cattle by utilizing the public database of the National Center for Biotechnology Information (NCBI) to download bovine muscle transcriptome data. Through differential expression analysis, weighted gene co-expression network analysis (WGCNA), and SHapley Additive exPlanation (SHAP) explains machine learning models, we integrated and screened for relevant genes. The results showed a total of 2588 differentially expressed genes (DEGs), with 933 upregulated and 1655 downregulated in beef cattle compared to dairy cattle. In the WGCNA, the purple, black, green, red, brown, and blue modules were identified as significant modules. Based on the results of five different machine learning models, the Adaptive Boosting (AdaBoost) model demonstrated superior classification performance (accuracy = 0.84) compared to the other four models and was therefore selected as the optimal model. SHAP analysis was then employed to interpret the results, yielding the top 500 SHAP genes. In combination with DEGs and WGCNA, a total of 117 genes were identified. Subsequent functional enrichment analysis of these 117 genes revealed significant enrichment in pathways such as lipoprotein metabolic process, muscle contraction, and cytoskeleton in muscle cells, followed by interaction network analysis of genes and pathways. Ultimately, the APOA1, ACTB, S1PR1, PKLR, and SLC27A6 genes were identified as potential key regulators of lipid metabolism and muscle growth in beef and dairy cattle. In summary, this study provides a feasible method for handling large-scale transcriptome data and lays a foundation for future research on meat quality and improving the economic benefits of Holstein cattle.

Elucidating the molecular mechanisms underlying beef quality differences is crucial for precision breeding of high-quality cattle. In this study, we first characterized the myofibrillar morphology of high-quality (H group) and low-quality (L group) beef samples using hematoxylin–eosin (HE) staining. Transcriptomic and metabolomic analyses were then conducted to reveal the molecular regulatory basis of quality variation. HE staining revealed highly significant differences in muscle fiber area and diameter between H and L groups (p < 0.01), along with significant differences in muscle fiber density (p < 0.05), but no significant differences in muscle fiber perimeter. Furthermore, by focusing on five core metabolic pathways shared across the transcriptome and metabolome datasets, 30 differentially expressed genes (DEGs) and 14 differentially accumulated metabolites (DAMs) were identified. Pearson correlation analysis revealed synergistic regulation between DEGs and DAMs: AMPD2 modulates umami flavor by regulating inosine accumulation via the purine metabolism pathway; ACOX3 promotes unsaturated fatty acid synthesis and intramuscular fat deposition through carbohydrate metabolism; genes in the glycolysis/gluconeogenesis pathway maintain post-slaughter muscle pH homeostasis, thereby influencing beef tenderness. Collectively, this study integrates morphological and molecular evidence to elucidate the multi-level basis of beef quality formation, providing key candidate genes, metabolites, and pathways for molecular breeding. These findings offer comprehensive theoretical and technical support for the sustainable development of the premium beef industry.

Objective: Intramuscular fat (IMF) is key for meat quality. Although fibro/adipogenic progenitor cells (FAPs) participate in adipogenesis, roles of other stromal cells remain unclear. This study characterized cellular heterogeneity in cattle IMF deposition and identified essential cell types and mechanisms regulating intramuscular adipogenesis.Methods: We performed single-nucleus RNA sequencing on longissimus dorsi muscle samples from three cattle breeds (Angus, Hereford, and Simmental) with divergent IMF content. Cell populations were annotated using multiple reference-based methods. We applied Scissor to identify phenotype-associated cells, CellChat to infer intercellular communication, high-dimensional weighted gene co-expression network analysis to detect co-expression modules, and single-cell flux estimation analysis to evaluate metabolic activity. Virtual gene perturbation was evaluated using scTenifoldKnk, and drug-responsive cell subpopulations were assessed with scRank. Additional in vitro validation experiments were conducted to confirm the regulatory effect of BMPR1A.Results: Ten cell types were identified. Angus (high marbling) showed elevated proportions of adipocytes, FAPs, and vascular endothelial cells (VEndoCs), while Hereford were enriched in type II fibers. VEndoCs were consistently linked to high IMF, acting as central signaling hubs that interact extensively with adipocytes and FAPs, and exhibited high glucose metabolic activity. Co-expression analysis within VEndoCs revealed 40 candidate regulators including nine BMP signaling pathway genes. Virtual knockdown identified BMPR1A as top regulator, altering expression of 13 lipid metabolism-related genes. In vitro validation confirmed that BMPR1A inhibition downregulated PPARγ mRNA, validating its positive regulatory role in intramuscular adipogenesis. Neurocytes showed highest sensitivity to resveratrol (via ALDH2), whereas FAPs were least responsive.Conclusion: Our findings indicate VEndoCs actively regulate intramuscular adipogenesis via a BMPR1A-mediated signaling pathway, supported by in vitro validation. These results provide insights into the cellular and molecular basis of marbling in cattle, highlighting BMPR1A as a potential target for genetic or nutritional strategies to improve meat quality.