RNA-Seq Analysis of Peripheral Whole Blood from Dairy Bulls with High and Low Antibody-Mediated Immune Responses-A Preliminary Study

Abstract

Enhancing the immune response through breeding is regarded as an effective strategy for improving animal health, as dairy cattle identified as high immune responders are reported to have a decreased prevalence of economically significant diseases. The identification of differentially expressed genes (DEGs) associated with immune responses might be an effective tool for breeding healthy dairy cattle. In this study, antibody-mediated immune responses (AMIRs) were induced by the immunization of hen egg white lysozyme (HEWL) in six Chinese Holstein dairy bulls divided into high- and low-AMIR groups based on their HEWL antibody level. Then, RNA-seq was applied to explore the transcriptome of peripheral whole blood between the two comparison groups. As a result, several major upregulated and downregulated genes were identified and attributed to the regulation of locomotion, tissue development, immune response, and detoxification. In addition, the result of the KEGG pathway analysis revealed that most DEGs were enriched in pathways related to disease, inflammation, and immune response, including antigen processing and presentation, Staphylococcus aureus infection, intestinal immune network for IgA production, cytokine-cytokine receptor interaction, and complement and coagulation cascades. Moreover, six genes (BOLA-DQA5, C5, CXCL2, HBA, LTF, and COL1A1) were validated using RT-qPCR, which may provide information for genomic selection in breeding programs. These results broaden the knowledge of the immune response mechanism in dairy bulls, which has strong implications for breeding cattle with an enhanced AMIR.

Keywords: RNA-seq; antibody-mediated immune response; dairy bulls; transcriptome analysis.

Figure 1

Workflow of this study.

Figure 2

HEWL antibody titers measured by ELISA on days 0, 7, 14, 21, and 28. The mean and standard error are shown. Means without a common letter differ significantly (p < 0.05). (A) Trend of changes in the titer of antibody against HEWL in all experimental animals; (B) trend of changes in the titer of antibody against HEWL in the high-AMIR group; (C) trend of changes in the titer of antibody against HEWL in the low-AMIR group.

Figure 3

The level of immune response in experimental animals based on the phenotypic deviation of the serum antibody response. (A) The AMIR14d value is the ratio of optical density values at d14 of the immunization protocol to those at d0; (B) sample-to-positive (S/P) ratio of HEWL antibody level from serum IgG1 at d14 of the immunization protocol. Red pillars represent high-AMIR animals, and blue pillars represent low-AMIR animals.

Figure 4

Cluster of high-AMIR and low-AMIR based on read counts. PCA, cluster dendrogram, and correlation matrix were performed for blood samples collected at d14 of the immunization protocol. Red points in the dot plot represent high-AMIR samples. Blue points in the dot plot represent low-AMIR samples.

Figure 5

Volcano plot displaying significantly expressed genes in the comparison group, low-AMIR vs. high-AMIR. The red and green dots represent genes that have been significantly upregulated or downregulated, respectively.

Figure 6

Analysis of DEGs using gene ontology (GO) functional enrichment. Only the top 20 biological processes are mentioned.

Figure 7

The main enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for DEGs. Count: number of genes in each pathway.

Figure 8

A protein–protein interaction (PPI) network construction based on the STRING database. The interaction was carried out with a confidence score of 0.7. Nodes represent genes, and lines between nodes refer to edges indicating various types of interactions, which are denoted by different colors and defined by the legends in the figure; stand-alone nodes without edges were removed.

Figure 9

Validation of RNA-Seq results using qPCR. The qPCR and RNA-Seq data bars are colored red and green, respectively.

Figure 10

Gene clusters with similar expression patterns. The ‘Mfuzz’ R package was used to analyze DEG expression patterns. Based on the similarity of expression patterns, five clusters were identified.