Deep sequencing of the transcriptome in the anterior pituitary of heifers before and after ovulation

J Vet Med Sci. 2017 Jun 10;79(6):1003-1012. doi: 10.1292/jvms.16-0531. Epub 2017 Apr 23.

Abstract

We aimed to determine gene expression patterns in the anterior pituitary (AP) of heifers before and after ovulation via deep sequencing of the transcriptome (RNA-seq) to identify new genes and clarify important pathways. Heifers were slaughtered on the estrus day (pre-ovulation; n=5) or 3 days after ovulation (post-ovulation; n=5) for AP collection. We randomly selected 4 pre-ovulation and 4 post-ovulation APs, and the ribosomal RNA-depleted poly (A)+RNA were prepared to assemble next-generation sequencing libraries. The bovine APs expressed 12,769 annotated genes at pre- or post-ovulation. The sum of the reads per kilobase of exon model per million mapped reads (RPKM) values of all transcriptomes were 599,676 ± 38,913 and 668,209 ± 23,690, and 32.2 ± 2.6% and 44.0 ± 4.4% of these corresponded to the AP hormones in the APs of pre- and post-ovulation heifers, respectively. The bovine AP showed differential expression of 396 genes (P<0.05) in the pre- and post-ovulation APs. The 396 genes included two G-protein-coupled receptor (GPCR) genes (GPR61 and GPR153) and those encoding 13 binding proteins. The AP also expressed 259 receptor and other 364 binding proteins. Moreover, ingenuity pathway analysis for the 396 genes revealed (P=2.4 × 10-3) a canonical pathway linking GPCR to cytoskeleton reorganization, actin polymerization, microtubule growth, and gene expression. Thus, the present study clarified the novel genes found to be differentially expressed before and after ovulation and clarified an important pathway in the AP.

Keywords: G-protein-coupled receptor; RNA-seq; Rho family GTPase; ruminant.

MeSH terms

  • Animals
  • Cattle / genetics*
  • Female
  • Gene Regulatory Networks
  • High-Throughput Nucleotide Sequencing
  • Molecular Sequence Annotation
  • Ovulation / genetics*
  • Pituitary Gland, Anterior / metabolism*
  • Real-Time Polymerase Chain Reaction
  • Receptors, Cell Surface / genetics
  • Sequence Analysis, RNA
  • Software
  • Transcriptome*

Substances

  • Receptors, Cell Surface