Transcriptomic and bioinformatics analysis of the early time-course of the response to prostaglandin F2 alpha in the bovine corpus luteum

Abstract

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools’ predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article “Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling” [1].

Early transcriptome responses of the bovine midcycle corpus luteum to prostaglandin F2α includes cytokine signaling

“…There were no significant differences among groups in CL weight, ovary dimensions, and antral follicle counts [ Fig. 2 in (Talbott et al, 2017)]. Despite the decrease in progesterone secretion by 2 h after PGF2a, there were no changes within our study in transcripts of proteins that directly control progesterone synthesis (Fig.…”

“…Validation of target transcripts was performed after reverse transcription of 1 mg RNA using SuperScript II Reverse Transcriptase (Invitrogen, Grand Island, NY) followed by quantitative real-time PCR (qPCR) using gene-specific primers [Supplemental Table 1] on a CFX96 Touch™ Real-Time PCR Cycler (Bio-Rad, Hercules, CA) with SsoFast™ EvaGreen ® Supermix (Bio-Rad, Hercules, CA). Comprehensive microarray methods and data are available in the Gene Expression Omnibus (GEO) database under accession GSE94069 and are described in the accompanying Data in Brief article (Talbott et al, 2017).…”

“…Transcription factors were particularly prominent early in the time-course response to PGF2a and although the number of transcription factors continued to increase, they made up a lower proportion of differentially expressed genes as the time-course proceeded [ Fig. 1 in (Talbott et al, 2017)]. One-half hour after PGF2a treatment, 34.5% of the mapped differentially expressed genes had a transcription factor classification using DAVID molecular function analysis and at 4 h after PGF2a, only 1.9% of the differentially expressed genes were classified as transcription factors [ Fig.…”

“…One-half hour after PGF2a treatment, 34.5% of the mapped differentially expressed genes had a transcription factor classification using DAVID molecular function analysis and at 4 h after PGF2a, only 1.9% of the differentially expressed genes were classified as transcription factors [ Fig. 1 in (Talbott et al, 2017)]. Transcription factors that were upregulated at all times investigated included ATF3, BTG2, FOS, FOSB, EGR3, JUNB, NR4A1, NR4A2, NR4A3, and ZFP36 (Supplemental Table 2).…”

“…3A. Most of these upstream regulators were predicted to have the greatest effect at 2 h. A curated list of upstream regulators predicted by IPA to be involved during the PGF2a time-course is available in Supplemental Table 1 in (Talbott et al, 2017).…”