Transcriptome Analysis Reveals Potential Regulatory Genes Related to Heat Tolerance in Holstein Dairy Cattle

Abstract

Heat stress affects the physiology and production performance of Chinese Holstein dairy cows. As such, the selection of heat tolerance in cows and elucidating its underlying mechanisms are vital to the dairy industry. This study aimed to investigate the heat tolerance associated genes and molecular mechanisms in Chinese Holstein dairy cows using a high-throughput sequencing approach and bioinformatics analysis. Heat-induced physiological indicators and milk yield changes were assessed to determine heat tolerance levels in Chinese Holstein dairy cows by Principal Component Analysis method following Membership Function Value Analysis. Results indicated that rectal temperature (RT), respiratory rate (RR), and decline in milk production were significantly lower (p < 0.05) in heat tolerant (HT) cows while plasma levels of heat shock protein (HSP: HSP70, HSP90), and cortisol were significantly higher (p < 0.05) when compared to non-heat tolerant (NHT) Chinese Holstein dairy cows. By applying RNA-Seq analysis, we identified 200 (81 down-regulated and 119 up-regulated) significantly (|log2fold change| ≥ 1.4 and p ≤ 0.05) differentially expressed genes (DEGs) in HT versus NHT Chinese Holstein dairy cows. In addition, 14 of which were involved in protein-protein interaction (PPI) network. Importantly, several hub genes (OAS2, MX2, IFIT5 and TGFB2) were significantly enriched in immune effector process. These findings might be helpful to expedite the understanding for the mechanism of heat tolerance in Chinese Holstein dairy cows.

Keywords: RNA-Seq; dairy cows; heat tolerance; hub genes; milk yield.

Figure 1

The change in respiratory rate (RR), rectal temperature (RT) and milk yield with increase of temperature-humidity index (THI) between HT and NHT Chinese Holstein dairy cows. (A) The change in RT with increase of THI; (B) The change in RR with increase of THI; (C) The change in milk yield with increase of THI. The left and right represent the changes in June and August, respectively.

Figure 2

MA plot showing differentially expressed genes between HT and NHT Chinese Holstein dairy cows. M is the intensity ratio representing the Y-axis; A is the average intensity for a dot representing the X-axis. Red and blue dots represent up- and down-regulated genes, respectively while black dots represent the genes without significant differential expression.

Figure 3

Functional annotation for the differentially expressed genes (DEGs). (A) Summary of Gene Ontology (GO) analysis present under three categories: molecular function, cellular component, and biological process; (B) The top 20 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for DEGs. Rich factor: the ratio of the number of genes divided by the number of all the genes in each pathway. Gene number: number of genes in each pathway.

Figure 4

A protein–protein interaction (PPI) network of hub genes regarding heat stress. Yellow and red marker genes represent up- and down-regulated genes, respectively.

Figure 5

Validation of RNA-Seq results by qPCR. Bars of RNA-Seq and qPCR data were colored by black and white, respectively.