Integrative targeted blood gene-expression profiling, coding-region genetic variants, and machine learning identify candidate biomarkers of milk fever in Holstein dairy cattle

Abstract

Background Milk fever is a serious metabolic disease affecting high-yielding dairy cows during the transition period and can result in substantial economic losses due to reduced milk production, impaired health, increased treatment costs, and culling. Methods The present study aimed to identify molecular signatures associated with milk fever in Holstein dairy cows using targeted blood gene-expression profiling, genetic variation, and artificial neural network (ANN) modeling. One hundred multiparous Holstein cows were enrolled in the study, fifty of them clinically healthy and fifty diagnosed with milk fever based on clinical signs and serum calcium levels below 2.0 mmol/L. Quantitative real-time PCR was used to measure relative mRNA expression of 13 potential calcium signaling, endocrine control and cellular metabolism-related genes. Results Single nucleotide polymorphisms (SNPs) were then detected by PCR product sequencing. ANN and hierarchical cluster analysis were used to assess the diagnostic performance of the biomarkers tested. Milk fever cows showed significant upregulation of PKIB, ASIC2, PER1, NUAK1, STX1A, SRI and ITPR1, and significant downregulation of CAMK2A, ANXA6, CACNA1S, VDR, CAMK4 and NESP55 (p < 0.05). Sequencing identified 23 SNPs in the coding region, of which 17 were synonymous and 6 non-synonymous. These variants showed significant differences in their distribution between healthy and affected cows (p < 0.001). The most informative biomarkers for the classification of the disease by ANN analysis were NUAK1 and NESP55. Hierarchical clustering based on normalized gene-expression profiles also showed distinct clustering patterns between healthy and affected cows. Discussion The identified gene-expression signatures and coding-region variants may provide candidate biomarkers and genetic markers for future studies aimed at reducing susceptibility to milk fever in dairy cattle. These results show that the combination of targeted blood gene-expression profiling, functional genetic variation, and machine-learning methods offers a powerful framework for genomic prediction, precision livestock management, and future marker-assisted breeding strategies.