Association analysis between reproduction genes INHA, PGR, RARG with lamb and other traits of Liaoning cashmere goats.

Reproductive traits have a high economic value in goat breeding, and increasing the number of lambs produced by ewes is of great importance to improve the production efficiency of goat farming. Lambing traits in goats are low heritability traits, but their genetic basis is ultimately determined by genes. This study aimed to investigate the relationship between INHA, RARG, and PGR gene polymorphisms and production performance, such as lambing, cashmere production, milk production, and body size in Liaoning cashmere goats. A total of six single nucleotide polymorphisms (SNPs) loci were identified in these three genes, G144A and T504C on the INHA gene, A56G, G144A, G490C on the RARG gene, and G109519T on the PGR gene. For lambing and cashmere production traits, the AA genotype of G144A on the INHA gene, TT on the T504C genotype, GG genotype of G144A on the INHA gene, A56G, G144A, and T504C on RARG and G109519T on PGR gene are dominant genotypes. AATT is a dominant haplotype combination. Allele G can be used as a molecular marker for lambing, cashmere, and milk production traits in Liaoning cashmere goats. Marker-assisted selection can be used for early selection to achieve improvement of genetic traits in Liaoning cashmere goats.

Microproteomic-Based Analysis of the Goat Milk Protein Synthesis Network and Casein Production Evaluation.

Goat milk has been consumed by humans since ancient times and is highly nutritious. Its quality is mainly determined by its casein content. Milk protein synthesis is controlled by a complex network with many signal pathways. Therefore, the aim of our study is to clearly depict the signal pathways involved in milk protein synthesis in goat mammary epithelial cells (GMECs) using state-of-the-art microproteomic techniques and to identify the key genes involved in the signal pathway. The microproteomic analysis identified more than 2253 proteins, with 323 pathways annotated from the identified proteins. Knockdown of IRS1 expression significantly influenced goat casein composition (α, ß, and κ); therefore, this study also examined the insulin receptor substrate 1 (IRS1) gene more closely. A total of 12 differential expression proteins (DEPs) were characterized as upregulated or downregulated in the IRS1-silenced sample compared to the negative control. The enrichment and signal pathways of these DEPs in GMECs were identified using GO annotation and KEGG, as well as KOG analysis. Our findings expand our understanding of the functional genes involved in milk protein synthesis in goats, paving the way for new approaches for modifying casein content for the dairy goat industry and milk product development.

Methodology for the identification of relevant loci for milk traits in dairy cattle, using machine learning algorithms.

Machine learning methods were considered efficient in identifying single nucleotide polymorphisms (SNP) underlying a trait of interest. This study aimed to construct predictive models using machine learning algorithms, to identify loci that best explain the variance in milk traits of dairy cattle. Further objectives involved validating the results by comparison with reported relevant regions and retrieving the pathways overrepresented by the genes flanking relevant SNPs. Regression models using XGBoost (XGB), LightGBM (LGB), and Random Forest (RF) algorithms were trained using estimated breeding values for milk production (EBVM), milk fat content (EBVF) and milk protein content (EBVP) as phenotypes and genotypes on 40417 SNPs as predictor variables. To evaluate their efficiency, metrics for actual vs. predicted values were determined in validation folds (XGB and LGB) and out-of-bag data (RF). Less than 4500 relevant SNPs were retrieved for each trait. Among the genes flanking them, signaling and transmembrane transporter activities were overrepresented. The models trained:•Predicted breeding values for animals not included in the dataset.•Were efficient in identifying a subset of SNPs explaining phenotypic variation. The results obtained using XGB and LGB algorithms agreed with previous results. Therefore, the method proposed could be applied for future association studies on milk traits.

Whole Genome DNA Methylation Variations in Mammary Gland Tissues from Holstein Cattle Producing Milk with Various Fat and Protein Contents.

Milk fat and protein contents are among key elements of milk quality, and they are attracting more attention in response to consumers' demand for high-quality dairy products. To investigate the potential regulatory roles of DNA methylation underlying milk component yield, whole genome bisulfite sequencing was employed to profile the global DNA methylation patterns of mammary gland tissues from 17 Canada Holstein cows with various milk fat and protein contents. A total of 706, 2420 and 1645 differentially methylated CpG sites (DMCs) were found between high vs. low milk fat (HMF vs. LMF), high vs. low milk protein (HMP vs. LMP), and high vs. low milk fat and protein (HMFP vs. LMFP) groups, respectively (q value < 0.1). Twenty-seven, 56 and 67 genes harboring DMCs in gene regions (denoted DMC genes) were identified for HMF vs. LMF, HMP vs. LMP and HMFP vs. LMFP, respectively. DMC genes from HMP vs. LMP and HMFP vs. LMFP comparisons were significantly overrepresented in GO terms related to aerobic electron transport chain and/or mitochondrial ATP (adenosine triphosphate) synthesis coupled electron transport. A total of 83 (HMF vs. LMF), 708 (HMP vs. LMP) and 408 (HMFP vs. LMFP) DMCs were co-located with 87, 147 and 158 quantitative trait loci (QTL) for milk component and yield traits, respectively. In conclusion, the identified methylation changes are potentially involved in the regulation of milk fat and protein yields, as well as the variation in reported co-located QTLs.

l-Lactate Dehydrogenase B Chain Associated with Milk Protein Content in Dairy Cows.

This study aimed to explore genes associated with milk protein content in dairy cows and their relationships with l-leucine. Ten primiparous Holstein cows (93.8 ± 11.56 milking days) fed the same diet were divided into two groups depending on their milk protein contents (group High, 3.34 ± 0.10%; and group Low, 2.86 ± 0.05%). Milk epithelial cells (MECs) were isolated from the collected morning milk and differentially expressed proteins in MECs were explored by two-dimensional gel electrophoresis (2-DE). Then, the mRNA expression of these proteins was detected by real time PCR in MAC-T cells incubated with three different media named positive control (PC), negative control (NC), and l-leucine depletion (NO-leu). Results showed that ten proteins were differentially expressed in MECs from cows in group High. They included seven down-regulated ones (heat shock protein beta-1 (HSPB1), 78 kDa glucose-regulated protein (GRP-78), l-lactate dehydrogenase B chain (LDH-B), malate dehydrogenase, cytoplasmic (MDH1), annexin I (ANXA1), cytokeratin-7 (CK-7), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH)), and three up-regulated ones (prohibitin (PHB), beta casein (CSN2), and alpha S1 casein (CSN1S1)). When l-leucine was depleted from the medium, not only proteins content was lowered (p < 0.05), but also the LDH-B mRNA expression was decreased in MAC-T cells (p < 0.05). In conclusion, LDH-B is negatively associated with the milk protein content of dairy cows and has a positive association with l-leucine.