Ruijun Wang

Background/objectives: Melatonin (MT) promotes secondary hair follicle development and cashmere growth in cashmere goats; however, the underlying molecular mechanisms remain incompletely understood. This study investigated whether MT-associated changes in chi-miR-1388-3p are related to the regulation of cashmere goats dermal papilla cell (DPC) phenotypes through PDGFA , and examined the association of this regulatory axis with MAPK-related factors. Methods: Candidate targets of chi-miR-1388-3p were predicted using the TargetScan database and previously generated transcriptomic data. The direct interaction between chi-miR-1388-3p and the PDGFA 3′ untranslated region (UTR) was evaluated using a dual-luciferase reporter assay. Lentiviral vectors were used to knock down or overexpress chi-miR-1388-3p in DPCs isolated from three independent cashmere goats donors. Each donor was treated as one biological replicate, and technical replicates were averaged before statistical analysis. Cell proliferation, cell-cycle distribution, and apoptosis were assessed using CCK-8 assays and flow cytometry. The expression of PDGFA , MAP3K1 , and FGF21 was examined by quantitative reverse-transcription PCR and Western blotting. MT treatment was performed for 48 h. Results: Chi-miR-1388-3p directly targets the 3′ UTR of PDGFA . Its overexpression can inhibit the proliferation of DPC, increase G0/G1 phase arrest, and increase the apoptosis rate from 9.28% ± 0.17% to 17.61% ± 0.29% ( p < 0.01). Conversely, the inhibition of chi-miR-1388-3p promotes proliferation and reduces the apoptosis rate to 3.07% ± 0.11% ( p < 0.01). The overexpression of chi-miR-1388-3p can significantly reduce the expression levels of PDGFA, MAP3K1 and FGF21 ( p < 0.01), while the inhibitory effect has the opposite effect ( p < 0.01), while inhibition has the opposite effect. MT treatment can decrease the expression of chi-miR-1388-3p, accompanied by changes in DPC proliferation, apoptosis, and PDGFA expression. These results indicate that there is a correlation between the MT/chi-miR-1388-3p/ PDGFA axis and the expression of MAPK-related factors. Conclusion: This study uncovers a novel mechanism by which MT regulates hair follicle growth and provides a foundation for future in vivo studies exploring miRNA-based strategies to enhance cashmere production.

Background: Melatonin is an important regulator of seasonal hair follicle activity in Inner Mongolia cashmere goats, but the miRNA-mediated mechanisms linking melatonin signaling to dermal papilla cell (DPC) function remain incompletely understood. In the present study, we examined the role of chi-miR-27a-3p in melatonin-treated DPCs. Methods: DPCs were isolated from Inner Mongolia cashmere goats and treated with melatonin or transduced with lentiviral constructs for chi-miR-27a-3p knockdown or overexpression. Cell proliferation was assessed by CCK8 and Ki67 immunofluorescence, cell-cycle distribution and apoptosis by flow cytometry, and the chi-miR-27a-3p–Wnt7B interaction by dual-luciferase reporter assay; the expression of Wnt7B, Wnt10b, β-catenin, SFRP1 and DKK1 was measured by qRT-PCR and Western blotting. Results: Melatonin treatment was accompanied by downregulation of chi-miR-27a-3p. Chi-miR-27a-3p overexpression reduced DPC proliferation and increased apoptosis, whereas its inhibition increased CCK-8 signal and promoted cell-cycle progression while reducing apoptosis; Ki67 fluorescence was not significantly changed in the knockdown group. Dual-luciferase assays showed that chi-miR-27a-3p directly targets the Wnt7B 3′-UTR in a sequence-dependent manner, and chi-miR-27a-3p manipulation was accompanied by reciprocal changes in Wnt7B, Wnt10b, β-catenin, SFRP1 and DKK1 expression. Conclusions: These findings support a potential regulatory relationship between melatonin, chi-miR-27a-3p and Wnt7B in DPCs; however, whether chi-miR-27a-3p is required for melatonin-induced proliferation and whether Wnt7B mediates its cellular effects require further rescue experiments and in vivo validation.

Non-coding negative regulation is an important but incompletely resolved component of gene-expression control in livestock. We conducted a narrative review using PubMed, Web of Science Core Collection, and Scopus, prioritizing primary studies in pigs, cattle, sheep, goats, and poultry and using model-organism studies only for directly relevant mechanistic or methodological evidence. We distinguish sequence-defined silencers, context-dependent enhancer-silencer elements, functional microRNA-binding sites, long non-coding RNA-associated regulation, and candidate repressive chromatin regions, and evaluate representative livestock studies using a four-tier framework comprising candidate annotation, molecular functional evidence, endogenous causal evidence, and animal-level validation. Current evidence is strongest for a small number of loci, whereas many of the livestock candidates reviewed here are supported primarily by association, chromatin state, or in vitro assays. We further assess how multi-omics data can prioritize candidates and assign target genes, while emphasizing that reporter activity, repressive chromatin marks, and population association do not establish endogenous causality. Progress will depend on locus-specific perturbation, rescue experiments, relevant animal phenotyping, and inheritance testing. This evidence-centered framework provides a focused basis for interpreting negative regulatory variation in livestock and identifying robust targets for future genetic improvement.

Genomic selection (GS) has revolutionized animal breeding by accelerating genetic gain through genome-wide marker data. As genotyping technologies advance and data dimensionality grows, the statistical foundations of GS are shifting from classical linear frameworks, which assume additive genetic effects, toward advanced computational models that capture complex nonlinear relationships in genomic data. The commercialization of genotyping arrays for livestock and poultry, coupled with steadily declining sequencing costs, has led to an exponential increase in the availability of high-density genomic data. However, challenges persist, including scenarios where the number of genetic markers far exceeds the number of samples with phenotypic data, and the growing complexity of relationships within genomic data. These issues significantly limit the applicability of traditional evaluation models. In parallel, computational power has increased significantly over the last few decades, providing the capacity necessary for highly complex analyses. While traditional linear mixed models provide a robust framework for incorporating biological priors and modeling additive genetic effects, they often rely on simplified assumptions. In contrast, machine learning (ML) and deep learning (DL) algorithms, which do not rely on predefined parametric models, are well-suited to capturing complex nonlinear relationships and offer effective solutions to the aforementioned challenges. This review provides a comprehensive overview of GS methodologies. We first cover the statistical foundations of linear mixed and Bayesian models, and then survey modern ML and DL approaches. We discuss the assumptions, advantages, and limitations of each method and, by comparing the computational efficiency and predictive accuracy of these diverse approaches, aim to provide practical guidance for optimizing genomic evaluation strategies in the era of big data breeding.

Objective: Genome-wide association study (GWAS) and haplotype analysis were employed to identify molecular markers and candidate genes associated with fleece traits in Inner Mongolia cashmere goats (IMCGs).Methods: GWASs using whole-genome resequencing data together with phenotypic data from 2,299 IMCGs, applying four models: mixed linear model, multiple locus mixed linear model, fixed and random model circulating probability unification, and Bayesian-information and linkage-disequilibrium iteratively nested keyway. We focused on the GWAS signals to conduct gene annotation and performed functional enrichment analyses to explore the biological processes underlying these signals. Additionally, haplotypes were constructed for the significant loci, and haplotype-phenotype association analyses were performed to identify molecular markers and candidate genes associated with these fleece traits in IMCGs.Results: We identified 542 SNPs and 179 candidate genes linked to fleece traits through GWAS and gene annotation. Genes such as LAMA3, KCTD1, PTK7, FGFR3, LEF1, TAPT1, PTCH1, ELOVL6, and EVC have emerged as important candidates that may influence fleece traits. Furthermore, 11 haplotype blocks related to fleece traits were constructed, among which A1A1, C1C1, E2E2, F1F1, G1G1, H1H1 and K1K1 were identified as the superior haplotype combinations for fleece traits. These could serve as important molecular markers to improve the accuracy of early selection and the economic efficiency of breeding programs for fleece traits in IMCGs.Conclusion: This study successfully employed GWAS to identify key genetic loci significantly associated with the fleece traits of IMCGs. The genetic basis of these traits was revealed through additional gene annotation and haplotype analysis. The findings provide important theoretical and practical foundations for molecular breeding in IMCGs.

Introduction: To facilitate the conservation and sustainable utilization of the Inner Mongolia Cashmere Goat (IMCG)-an important genetic resource and economically valuable breed-this study performed a genome homozygosity assessment of its Erlangshan subtype population, analyzed the distribution characteristics of runs of homozygosity (ROH), and compared inbreeding coefficients estimated using different methods. Methods: A total of 384 individuals were genotyped using the Illumina Goat SNP70K BeadChip. PLINK software was employed to conduct ROH analysis, estimate genomic inbreeding coefficients, and identify ROH islands based on the obtained SNP data. Results: The results revealed 3,622 ROH segments in the population, with an average total ROH length of 71.48 Mb per individual. The inbreeding coefficients based on ROH (F _ROH ) and observed homozygosity (F _HOM ) were 0.0290 and −0.0126, respectively, showing a strong positive correlation ( r = 0.97). In addition, 14 ROH islands were identified on chromosomes 2, 5, 7, 8, 9, 13, 17, 19, 24, 25, and 26. Within these regions, 238 genes were annotated, including key genes such as HDAC1 , MIR21 , DNASE1L2 , and ACACA , which may be implicated in reproductive traits, hair follicle development, and milk fat metabolism. Conclusion: This study systematically characterized ROH patterns in IMCG, indicating a generally low level of genomic inbreeding in the population, though certain individuals still require attention. The identification of genes related to economically important traits further reveals the genetic distinctiveness and breeding potential of IMCG, providing valuable insights for informing future conservation and breeding strategies.

Sheep and goats, among the earliest domesticated animals, hold significant historical importance in Chinese animal husbandry. The rapid advancement of modern biotechnology has rendered genome wide association studies (GWAS) and selection signature analyses have become indispensable tools in livestock genetic research. This review summarizes recent progress in the application of these methods to sheep and goat breeding in China, analyzes existing challenges in the research, and suggests potential research strategies. GWAS and selection signature analyses have identified key genomic regions associated with agronomically important traits. Genes such as LHX2, FGF12 and Notch3 are associated with hair follicle growth and development, while RBM11, SMARCA5 and GAB1 are linked to body size in sheep. Additionally, BMPRIB has been identified as a determinant of reproductive performance and growth rate in both sheep and goats. Despite progress, several challenges remain, including incomplete reference genomes, insufficient phenotypic data, inadequate algorithms, and a lack of functional validation and practical application of findings. Future work must prioritize genomic refinement, the integration of multi-omics, and the development of algorithm. Enhanced international collaboration is crucial to deciphering the genetic basis of key traits, thereby advancing global industry. These initiatives will enable highly precise breeding strategies. Through precise identification and selection of individuals with desirable genetic traits, breeders can significantly enhance the efficiency of genetic improvement. Using Hua sheep 50k chip as an example, it effectively identifies individuals with superior reproductive genes (such as BMPRIB) at an early stage, increases the lambing rate by 27.7%, accelerates the propagation of high-quality breeding groups, provides a large number of breeding sheep with excellent genetic traits for the global sheep industry, and improve the overall quality of germplasm. This will ultimately enhance the sustainable development of global sheep and goat production.

Chinese indigenous goat breeds exhibit rich genetic diversity and a long history of domestication, endowing these native populations with superior traits i