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.

Negative energy balance (NEB) during the perinatal period leads to metabolic and immunological disorders in dairy cows, resulting in systemic responses and inflammation. The innate immune system is crucial for the host’s protection and inflammatory response. However, systematic research is still lacking on how NEB affects the innate immune system to alter the ’host defense capability and inflammatory response. In this investigation, raw transcriptome data of adipose, blood, endometrial, hypothalamus, and liver tissues were downloaded from a public database, cleaned, aligned, quantified, and batch-corrected. The innate immune gene list was retrieved from innateDB, followed by the expression matrix of innate immune genes in various tissues for differential expression analysis, principle component analysis (PCA), and gene set enrichment analysis (GSEA). Under the effect of NEB, adipose tissue had the most differentially expressed genes, which were predominantly up-regulated, whereas blood GSEA had the most enriched biological processes, which were predominantly down-regulated. The gene sets shared by different tissues, which are predominantly involved in biological processes associated with defense responses and inflammation, were dramatically down-regulated in endometrial tissues and highly up-regulated in other tissues. Under the impact of NEB, LBP, PTX3, S100A12, and LCN2 play essential roles in metabolism and immunological control. In conclusion, NEB can downregulate the defensive response of innate immune genes in endometrial, upregulate the immune and inflammatory response of other tissues, activate the host defense response, and increase the systemic inflammatory response. The analysis of the effects of NEB on innate immune genes from the multiple tissues analysis provides new insights into the crosstalk between metabolism and immunity and also provides potential molecular targets for disease diagnosis and disease resistance breeding in dairy cows.

Xinggao sheep are a breed of Chinese domestic sheep that are adapted to the extremely cold climatic features of the Hinggan League in China. The economically vital reproductive trait of ewes (litter size, LS) and productive traits of lambs (birth weight, BWT; weaning weight, WWT; and average daily gain, ADG) are expressed in females and later in life after most of the selection decisions have been made. This study estimated the genetic parameters for four traits to explore the genetic mechanisms underlying the variation, and we performed genome-wide association study (GWAS) tests on a small sample size to identify novel marker trait associations (MTAs) associated with prolificacy and growth. We detected two suggestive significant single-nucleotide polymorphisms (SNPs) associated with LS and eight significant SNPs for BWT, WWT, and ADG. These candidate loci and genes also provide valuable information for further fine-mapping of QTLs and improvement of reproductive and productive traits in sheep.

The skeletal muscle of cattle is the main component of their muscular system, responsible for supporting and movement functions. However, there are still many unknown areas regarding the ranking of the importance of different types of cell populations within it. This study conducted in-depth research and made a series of significant findings. First, we trained 15 bovine skeletal muscle models and selected the best-performing model as the initial model. Based on the SHAP (Shapley Additive exPlanations) analysis of this initial model, we obtained the SHAP values of 476 important genes. Using the contributions of these 476 genes, we reconstructed a 476-gene SHAP value matrix, and relying solely on the interactions among these 476 genes, successfully mapped the single-cell atlas of bovine skeletal muscle. After retraining the model and further interpretation, we found that Myofiber cells are the most representative cell type in bovine skeletal muscle, followed by neutrophils. By determining the key genes of each cell type through SHAP values, we conducted analyses on the correlations among key genes and between cells for Myofiber cells, revealing the critical role these genes play in muscle growth and development. Further, by using protein language models, we performed cross-species comparisons between cattle and pigs, deepening our understanding of Myofiber cells as key cells in skeletal muscle, and exploring the common regulatory mechanisms of muscle development across species.