Introduction Ketosis is a common metabolic disease in periparturient dairy cows. Monitoring and early warning based on blood indicators have become a key research focus in this field. Current diagnosis mainly relies on detecting plasma β-hydroxybutyric acid (BHBA). This study aims to explore the pathogenesis of ketosis at the blood genetic level, with the goal of achieving the early warning and prevention of the disease. Methods According to China’s “gold standard” diagnostic method for dairy cow ketosis, blood samples were collected from different groups. Biochemical and oxidative stress indicators were then measured to evaluate organ function. Transcriptome sequencing analysis was performed on healthy cows and sick cows (both pre- and post-onset) to identify differentially expressed genes (DEGs). The functions of DEGs and their enriched pathways were analyzed through GO and KEGG pathway analyses. Weighted Gene Co-expression Network Analysis (WGCNA) was used to analyze the transcriptomic data, identify co-expression modules, and explore the relevance between these modules and the traits associated with ketosis occurrence. A dual standard curve method was established with the AHR gene as the core for early warning and detection. Then the specificity, sensitivity, and repeatability of the primers were validated. Finally, the method was applied to detect both positive samples (diseased cows) and healthy dairy cow samples to analyze the relative expression level of the AHR gene and the prediction accuracy. Results Three biochemical indicators BHBA, non-esterified fatty acid (NEFA), and glucose (GLU) were identified as key diagnostic markers for ketosis in postpartum dairy cows. Changes in NEFA and GLU levels prior to parturition may indicate the risk of ketosis. A large number of DEGs were screened out, among which the AHR gene was identified as a candidate molecular marker gene for ketosis prediction. The early warning and detection method based on the AHR gene demonstrated high primer specificity, and the detection method itself exhibited excellent specificity, sensitivity, and repeatability. The prediction accuracy for healthy dairy cows reached 91.11%. Discussion This study demonstrates that the detection method established based on the AHR gene is efficient and reliable, effectively distinguishing between early ketosis individuals and healthy ones based on gene expression differences. This study provides an efficient and reliable technical tool for the early warning of ketosis in dairy cows.
Wang et al. (Wed,) studied this question.