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.

Age is an important physiological factor that affects the metabolism and immune function of beef cattle. While there have been many studies using the blood transcriptome to study the effects of age on gene expression, few have been reported on beef cattle. To this end, we used the blood transcriptomes of Japanese black cattle at different ages as the study subjects and screened 1055, 345, and 1058 differential expressed genes (DEGs) in the calf vs. adult, adult vs. old, and calf vs. old comparison groups, respectively. The weighted co-expression network consisted of 1731 genes. Finally, blue, brown, and yellow age-specific modules were obtained, in which genes were enriched in signaling pathways related to growth and development and immune metabolic dysfunction, respectively. Protein-protein interaction (PPI) analysis showed gene interactions in each specific module, and 20 of the highest connectivity genes were chosen as potential hub genes. Finally, we identified 495, 244, and 1007 genes by exon-wide selection signature (EWSS) analysis of different comparison groups. Combining the results of hub genes, we found that VWF, PARVB, PRKCA, and TGFB1I1 could be used as candidate genes for growth and development stages of beef cattle. CORO2B and SDK1 could be used as candidate marker genes associated with aging. In conclusion, by comparing the blood transcriptome of calves, adult cattle, and old cattle, the candidate genes related to immunity and metabolism affected by age were identified, and the gene co-expression network of different age stages was constructed. It provides a data basis for exploring the growth, development, and aging of beef cattle.

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.

The heritability of litter size in sheep is low and controlled by multiple genes, but the research on its related genes is not sufficient. Here, to explore the expression pattern of multi-tissue genes in Chinese native sheep, we selected 10 tissues of the three adult ewes with the highest estimated breeding value in the early study of the prolific Xinggao sheep population. The global gene expression analysis showed that the ovary, uterus, and hypothalamus expressed the most genes. Using the Uniform Manifold Approximation and Projection (UMAP) cluster analysis, these samples were clustered into eight clusters. The functional enrichment analysis showed that the genes expressed in the spleen, uterus, and ovary were significantly enriched in the Ataxia Telangiectasia Mutated Protein (ATM) signaling pathway, and most genes in the liver, spleen, and ovary were enriched in the immune response pathway. Moreover, we focus on the expression genes of the hypothalamic–pituitary–ovarian axis (HPO) and found that 11,016 genes were co-expressed in the three tissues, and different tissues have different functions, but the oxytocin signaling pathway was widely enriched. To further explore the differences in the expression genes (DEGs) of HPO in different sheep breeds, we downloaded the transcriptome data in the public data, and the analysis of DEGs (Xinggao sheep vs. Sunite sheep in Hypothalamus, Xinggao sheep vs. Sunite sheep in Pituitary, and Xinggao sheep vs. Suffolk sheep in Ovary) revealed the neuroactive ligand–receptor interactions. In addition, the gene subsets of the transcription factors (TFs) of DEGs were identified. The results suggest that 51 TF genes and the homeobox TF may play an important role in transcriptional variation across the HPO. Altogether, our study provided the first fundamental resource to investigate the physiological functions and regulation mechanisms in sheep. This important data contributes to improving our understanding of the reproductive biology of sheep and isolating effecting molecular markers that can be used for genetic selection in sheep.

Maternal parity is an important physiological factor influencing beef cow reproductive performance. However, there are few studies on the influence of different calving periods on early growth and postpartum diseases. Here, we conducted blood transcriptomic analysis on cows of different parities for gene discovery. We used Short Time Series Expression Miner (STEM) analysis to determine gene expression levels in cows of various parities and divided multiple parities into three main periods (nulliparous, primiparous, and multiparous) for subsequent analysis. Furthermore, the top 15,000 genes with the lowest median absolute deviation (MAD) were used to build a co-expression network using weighted correlation network analysis (WGCNA), and six independent modules were identified. Combing with Exon Wide Selection Signature (EWSS) and protein-protein interaction (PPI) analysis revealed that TPCN2, KIF22, MICAL3, RUNX2, PDE4A, TESK2, GPM6A, POLR1A, and KLHL6 involved in early growth and postpartum diseases. The GO and KEGG enrichment showed that the Parathyroid hormone synthesis, secretion, and action pathway and stem cell differentiation function-related pathways were enriched. Collectively, our study revealed candidate genes and gene networks regulating the early growth and postpartum diseases and provided new insights into the potential mechanism of reproduction advantages of different parity selection.

Follicle-stimulating hormone (FSH) regulates ovarian follicle development through specific gene expression programs. Granulosa cells (GCs) are somatic cells surrounding the oocytes, secreting gonadotropins to regulate ovulation and promote follicular development. By analyzing the effects of different doses of FSH on the proliferation of GCs, we found that adding 10 ng/mL of FSH, as the optimal concentration, could promote the growth of GCs. Furthermore, we have successfully constructed the first CRISPR-Cas9 knockout library targeting the genes on chromosomes 2 and 3 and the X chromosomes of the sheep massively parallel coding gene, as well as an ovarian GCs knockout cell library. For the first time, we have exposed the knockout cell library to a concentration of 10 ng/mL FSH to explore the underlying mechanisms. Through this screening, we have identified 836 positive–negative screening genes that are responsive to FSH, thereby revealing the regulatory mechanisms and screening the functionality of candidate genes. Next, RNA-Seq of control (0 ng/mL), low (10 ng/mL), and high (100 ng/mL) doses of FSH revealed 1708 differentially expressed genes, and combined with 836 genes, we obtained 129 FSH dose-dependent genes with extremely significant differences. This enables us to delve deeper into investigating and identifying the mechanisms by which FSH regulates GCs. More generally, we have discovered new regulatory factors and identified reproductivity-associated major effectors. These findings provide novel research directions for further studies on sheep reproduction.