Staphylococcus aureus- induced mastitis is one of the most intractable problems for the dairy industry, which causes loss of milk yield and early slaughter of cows worldwide. Few studies have used a comprehensive approach based on the integrative analysis of miRNA and mRNA expression profiles to explore molecular mechanism in bovine mastitis caused by S. aureus. In this study, S. aureus (A1, B1 and C1) and sterile phosphate buffered saline (PBS) (A2, B2 and C2) were introduced to different udder quarters of three individual cows, and transcriptome sequencing and microarrays were utilized to detected miRNA and gene expression in mammary glands from the challenged and control groups. A total of 77 differentially expressed microRNAs (DE miRNAs) and 1625 differentially expressed genes (DEGs) were identified. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that multiple DEGs were enriched in significant terms and pathways associated with immunity and inflammation. Integrative analysis between DE miRNAs and DEGs proved that miR-664b, miR-23b-3p, miR-331-5p, miR-19b and miR-2431-3p were potential factors regulating the expression levels of CD14 Molecule (CD14), G protein subunit gamma 2 (GNG2), interleukin 17A (IL17A), collagen type IV alpha 1 chain (COL4A1), microtubule associated protein RP/EB family member 2 (MAPRE2), member of RAS oncogene family (RAP1B), LDOC1 regulator of NFKB signaling (LDOC1), low-density lipoprotein receptor (LDLR) and S100 calcium binding protein A9 (S100A9) in bovine mastitis caused by S. aureus. These findings could enhance the understanding of the underlying immune response in bovine mammary glands against S. aureus infection and provide a useful foundation for future application of the miRNA–mRNA-based genetic regulatory network in the breeding cows resistant to S. aureus.

The aim of the study was to understand the internal relationship between milk quality and lipid metabolism in cow mammary glands. A serial of studies was conducted to assess the molecular mechanism of PRL/microRNA-183/IRS1 (Insulin receptor substrate) pathway, which regulates milk fat metabolism in dairy cows. microRNA-183 (miR-183) was overexpressed and inhibited in cow mammary epithelial cells (CMECs), and its function was detected. The function of miR-183 in inhibiting milk fat metabolism was clarified by triglycerides (TAG), cholesterol and marker genes. There is a CpG island in the 5′-flanking promoter area of miR-183, which may inhibit the expression of miR-183 after methylation. Our results showed that prolactin (PRL) inhibited the expression of miR-183 by methylating the 5′ terminal CpG island of miR-183. The upstream regulation of PRL on miR-183 was demonstrated, and construction of the lipid metabolism regulation network of microRNA-183 and target gene IRS1 was performed. These results reveal the molecular mechanism of PRL/miR-183/IRS1 pathway regulating milk fat metabolism in dairy cows, thus providing an experimental basis for the improvement of milk quality.

Background: Time-restricted feeding (TRF) is a dieting strategy based on nutrients availability and diurnal rhythm, shown to improve lipid metabolism efficiency. We have demonstrated previously that retinoic acid-related (RAR) orphan receptor (ROR) γ is the primary transcription factor controlling cholesterol (CHO) biosynthesis program of animals. However, the functional role of RORγ in liver physiology of pigs in response to TRF has not been determined, largely due to the lack of functional models and molecular tools. In the present study, we established porcine liver organoids and subjected them to restricted nutrients supply for 10-h during the light portion of the day. Results: Our results showed that TRF regimen did not alter hepatocyte physiology, including unchanged cell viability, caspase 3/7 enzyme activity and the gene signature of cell proliferation in porcine liver organoids, compared to the control group (P > 0.05). Furthermore, we found that TRF downregulated the hepatic CHO biosynthesis program at both mRNA and protein levels, along with the reduced cellular CHO content in porcine liver organoids (P < 0.05). Using unbiased bioinformatic analysis of a previous ChIP-seq data and ChIP-qPCR validation, we revealed RORγ as the predominant transcription factor that responded to TRF, amongst the 12 targeted nuclear receptors (NRs) (P < 0.05). This was likely through RORγ direct binding to the MVK gene (encoding mevalonate kinase). Finally, we showed that RORγ agonists and overexpression enhanced the enrichment of co-factor p300, histone marks H3K27ac and H3K4me1/2, as well as RNA polymerase II (Pol-II) at the locus of MVK, in TRF-porcine liver organoids, compared to TRF-vector control (P < 0.05). Conclusions: Our findings demonstrate that TRF triggers the RORγ-mediated chromatin remodeling at the locus of CHO biosynthesis genes in porcine liver organoids and further improves lipid metabolism.

As AI becomes increasingly integrated into higher education, its contribution to sustainable agricultural education requires assessment beyond technology use alone. This is particularly important in higher agricultural education, where future professionals require disciplinary, digital, and sustainability-related competencies. However, multidimensional evidence linking students AI Literacy, perceived educational value, institutional support, and perceived educational effects remains limited. This study assessed students’ perceptions of AI-driven educational development among 417 undergraduate students from five universities in Jiangsu Province, China. Measurement quality was evaluated using reliability analysis and WLSMV confirmatory factor analysis, followed by multidimensional statistical analyses. Educational Value of AI and Intention to Use It received the highest mean score (3.52), whereas Institutional Support for AI Education was lowest (2.74). Perceived effects differed across domains, with Development of Talent for Sustainable Agriculture rated highest (3.54) and Educational Resources and Equity lowest (3.26; p < 0.001). Educational Value of AI and Intention to Use It (ρ = 0.578) and AI Literacy (ρ = 0.538) showed the strongest associations with perceived effects. Students with prior AI-related educational exposure scored higher across all major constructs. These findings suggest that sustainable AI integration in higher agricultural education is associated with students’ AI Literacy, institutional support, equitable access to digital educational resources, and the integration of AI into authentic agricultural learning. The study provides evidence for aligning AI-enabled education with educational quality, equity, and the development of competencies required for sustainable agriculture.