The milk flavor can be attributed to the presence of numerous flavor molecules and precursors. In this study, we employed widely targeted metabolomic analysis techniques to analyze the metabolic profiles of various milk samples obtained from goats, sheep, dairy cows, and buffaloes. A total of 631 metabolites were identified in the milk samples, which were further categorized into 16 distinct classes. Principal component analysis (PCA) suggested that the metabolite profiles of samples from the same species exhibit clustering, while separated patterns of metabolite profiles are observed across goat, sheep, cow, and buffalo species. The differential metabolites between the groups of each species were screened based on fold change and variable importance in projection (VIP) values. Five core differential metabolites were subsequently identified, including 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid, inosine 5′-triphosphate, methylcysteine, N-cinnamylglycine, and small peptide (L-tyrosine–L-aspartate). Through multiple comparisons, we also screened biomarkers of each type of milk. Our metabolomic data showed significant inter-species differences in the composition and concentration of some compounds, such as organic acids, amino acids, sugars, nucleotides, and their derivatives, which may affect the overall flavor properties of the milk sample. These findings provided insights into the molecular basis underlying inter-species variations in milk flavor.

Follicle development in dairy goats is lower after induced estrus during the non-breeding season, reducing conception rates and challenging year-round milk supply. This study investigated follicle development during the breeding and non-breeding seasons and explored molecular mechanisms for variations in the proportions of follicles of different sizes using ovarian RNA-seq and in vitro experiments. Induced estrus during the non-breeding season used a simulated breeding season short photoperiod and male effect methods, while the male effect method was used during the breeding season. This study identified an increase in follicle size during the breeding season and performed RNA-seq on ovaries to explore the underlying causes. The RNA-seq analysis elucidated pathways associated with cellular and hormonal metabolism and identified adenylyl cyclase 5 (ADCY5) as a key differentially expressed gene. In vitro experiments demonstrated that interfering with ADCY5 in ovarian granulosa cells (GCs) reduced steroid synthesis. Conversely, the overexpression of ADCY5 increased steroid synthesis. ADCY5 affects the biological function of GCs and consequently influences follicle development through the cAMP-response element binding protein (CREB) and p38 mitogen-activated protein kinase phosphorylation (MAPK) pathways. Overall, our findings demonstrate that follicle development in dairy goats differs between the breeding and non-breeding seasons and that the differential expression levels of the ADCY5 gene contribute to this discrepancy.

Embryos contain a large number of lipid droplets, and lipid metabolism is gradually activated during embryonic development to provide energy. However, the regulatory mechanisms remain to be investigated. Stearoyl-CoA desaturase 1 (Scd1) is a fatty acid desaturase gene that is mainly involved in intracellular monounsaturated fatty acid production, which takes part in many physiological processes. Analysis of transcripts at key stages of embryo development revealed that Scd1 was important and expressed at an increased level during the cleavage and blastocyst stages. Knockout Scd1 gene by CRISPR/Cas9 from zygotes revealed a decrease in lipid droplets (LDs) and damage in the inner cell mass (ICM) formation of blastocyst. Comparative analysis of normal and knockout embryo transcripts showed a suppression of ribosome protein (RPs) genes, leading to the arrest of ribosome biogenesis at the 2-cell stage. Notably, the P53-related pathway was further activated at the blastocyst stage, which eventually caused embryonic development arrest and apoptosis. In summary, Scd1 helps in providing energy for embryonic development by regulating intra-embryonic lipid droplet formation. Moreover, deficiency activates the RPs-Mdm2-P53 pathway due to ribosomal stress and ultimately leads to embryonic development arrest. The present results suggested that Scd1 gene is essential to maintain healthy development of embryos by regulating energy support.

The cashmere fibers produced by the secondary hair follicles of cashmere goats are precious textile raw materials. The secondary hair follicles exhibit a distinct annual cyclical pattern, comprising anagen, catagen, and telogen phases. Although a large amount of skin transcriptome data is currently available for cashmere goats, this study aims to systematically investigate the regulatory factors controlling the cyclical dynamics of secondary hair follicles through re-analysis of these data. Skin transcriptome data of Inner Mongolian cashmere goats were downloaded from the Sequence Read Archive (SRA) database. Bioinformatics analyses, including quality control, read mapping, quantification, differential expression analysis, Gene Ontology (GO) enrichment analysis, and KEGG pathway analysis, were performed to identify key regulatory genes governing the secondary hair follicle cycle. The results revealed a total of 1232 differentially expressed genes (DEGs) from comparisons across different phases. KEGG pathway analysis identified three signaling pathways associated with cashmere development: the cAMP signaling pathway (chx04024), the relaxin signaling pathway (chx04926), and the estrogen signaling pathway (chx04915). GO analysis yielded 335 terms, of which 236 were statistically significant (P-adj < 0.05). The DEGs were predominantly involved in biological processes such as regulation of transcription by RNA polymerase II, proteolysis, and positive regulation of transcription by RNA polymerase II. Significantly enriched cellular components included the plasma membrane, membrane, and extracellular space, and molecular functions were mainly related to protein binding, identical protein binding, and DNA-binding transcription factor activity, RNA polymerase II-specific. Based on their expression patterns, five key genes were selected: KRT25, KRT39, MAPK12, SPP1, and TCHHL1. These genes displayed significantly distinct expression profiles across the hair follicle cycle. TCHHL1 exhibited the highest expression in anagen, intermediate expression in catagen, and the lowest expression in telogen, with significant differences among all three phases. MAPK12 was specifically upregulated in telogen and was significantly more highly expressed than in both anagen and catagen. SPP1 showed high expression in anagen and catagen but extremely low expression in telogen. KRT39 displayed significantly higher expression in both anagen and catagen compared to telogen. KRT25 reached its highest expression in catagen, which was significantly different from that in telogen, while its expression in anagen showed no significant difference from either telogen or catagen. This study characterized the regulatory genes of the secondary hair follicle cycle in cashmere goats at the transcriptional level and analyzed the association between their expression patterns and cycle regulation.

Genomic selection (GS) provides an effective approach to accelerating genetic gain in dairy goats, but the prediction performance is strongly influenced by the statistical model, marker density, phenotype adjustment strategy, and biological architecture of the target trait. In this study, dairy goat populations comprising Xinong Saanen and Saanen dairy goats from major production regions in China were used to evaluate genomic prediction for milk yield (MY), milk fat percentage (MFP), and milk protein percentage (MPP). Genotypes from 1034 dairy goats were generated using low-coverage whole-genome sequencing (lcWGS), imputed to improve genotype completeness and accuracy; a high-quality chip-based dataset was also constructed from previously developed 25K single-nucleotide polymorphism (SNP) chip loci. Conventional genomic best linear unbiased prediction (GBLUP) models, Bayesian regression models, and machine learning algorithms were compared using 10-fold cross-validation. Bayesian models showed clear trait-specific advantages, with BayesB improving MFP prediction by approximately 12.9% relative to GBLUP under the 25K chip-based strategy. Among machine learning methods, gradient boosting models performed strongly; extreme gradient boosting (XGBoost) improved the prediction accuracy for MY, MFP, and MPP by 14.3%, 17.9%, and 18.5%, respectively, relative to GBLUP under the chip-based strategy. Incorporating genome-wide association study (GWAS)-derived prior information and selection signature priors further improved the prediction accuracy, particularly for milk composition traits. Overall, the results indicate that genomic prediction in dairy goats can be optimized by matching models, genotyping platforms, and prior biological information to the genetic characteristics of the target trait.

Low-coverage whole-genome resequencing (lcWGS) is emerging as a powerful population-scale genomic strategy for livestock and poultry research. By integrating sparse sequencing reads with genotype likelihoods, haplotype information and imputation models, lcWGS enables genome-wide variant discovery and genetic inference across large animal cohorts. This feature is particularly valuable for breeding populations, indigenous breeds and conservation resources, where broad sampling is essential for capturing population-specific variation and linking genomic diversity with economically and adaptively important traits. In this review, we synthesize the statistical foundations, analytical workflows and major applications of lcWGS in livestock and poultry genomics. We discuss how lcWGS supports genetic diversity assessment, population structure analysis, genome-wide association studies, genomic selection, selection-signature detection, environmental adaptation research and genetic resource conservation. We further highlight the importance of coordinated study design, including sequencing depth, sample size, reference-panel construction, imputation strategy, phenotype quality and downstream analytical models. Beyond its role as a cost-efficient genotyping approach, lcWGS provides a flexible framework for integrating population genomics with functional annotation, multi-omics resources, long-read assemblies, graph pan-genomes and interpretable prediction models. These developments are expanding the potential of lcWGS from variant discovery toward biological interpretation, precision breeding, climate-resilient animal production and the sustainable management of livestock and poultry genetic resources.