The global effect of heat on gene expression in cultured bovine mammary epithelial cells

Abstract

Heat stress (HS) in hot climates is a major cause that strongly negatively affects milk yield in dairy cattle, leading to immeasurable economic loss. The heat stress response of bovine mammary epithelial cells (BMECs) is one component of the acute systemic response to HS. Gene networks of BMECs respond to environmental heat loads with both intra- and extracellular signals that coordinate cellular and whole-animal metabolism. Our experimental objective was to characterize the direct effects of heat stress on the cultured bovine mammary epithelial cells by microarray analyses. The data identified 2716 differentially expressed genes in 43,000 transcripts which were changed significantly between heat-stressed and normal bovine mammary epithelial cells (fold change ≥2, P ≤ 0.001). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that these differentially expressed genes are involved in different pathways that regulate cytoskeleton, cell cycle, and stress response processes. Our study provides an overview of gene expression profile and the interaction between gene expression and heat stress, which will lead to further understanding of the potential effects of heat stress on bovine mammary glands.

Fig. 1

Effect of heat treatment on cell viability. Cell viability was estimated by MTT, expressed as the rate of the value obtained with the control culture (37 °C). Asterisks and double asterisks separately represent values significantly different from the controls (P < 0.05 and P < 0.01)

Fig. 2

Cell cycle progression by flow cytometry. Double asterisks represent values significantly different from the controls (P < 0.01)

Fig. 3

Highly enriched GO terms analyzed of upregulated (a) and downregulated (b) genes with fold change ≥3 by Gene Ontology analysis (P values <0.05). Only the most specific subcategories of GO terms under biological process, cellular component, and molecular function which were considered significant by the Benjamini-Yuketeli test (P < 0.05) were ranked

Fig. 4

Validation of differentially expressed genes by real-time qPCR. Relative quantification of ten representative genes was performed. qRT-PCR values were determined from the ^ΔΔCt for the target genes relative to 18S. Note: double asterisks indicate a statistical difference (P < 0.01)

Fig. 5

Comparison of RT-PCR findings to microarray analysis results by fold change of ten selected genes