Expression profiling of a high-fertility mouse line by microarray analysis and qPCR

In a recent study it was demonstrated that a largely increased ovulation number is responsible for high prolificacy in two mouse lines selected for fertility performance. The objective of the present study was to identify genes that are involved in increasing the ovulation number in one of these lines, FL1. For differential expression profiling, ovaries of FL1 and of a non-selected control line, DUKsi, both lines derived from the same genetic pool, were analyzed with microarray analysis and quantitative polymerase chain reaction (qPCR). Ovaries from 30 animals of each line were collected at the metestrous stage, combined to 6 pools each, and processed for microarray analysis.

The actual number of ova shed in FL1 exceeded that of the DUKsi control line more than twofold (26.6 vs. 12.9). 148 differentially expressed ovarian transcripts could be identified, 74 of them up- and 74 down-regulated. Of these, 47 significantly mapped to specific Gene Ontology (GO) terms representing different biological processes as steroid metabolism, folliculogenesis, immune response, intracellular signal transduction (particularly of the G protein signaling cascade), regulation of transcription and translation, cell cycle and others. qPCR was used to re-evaluate selected transcripts and to estimate inter-individual variation of expression levels. These data significantly correlated with microarray data in 12 out of 15 selected transcripts but revealed partly large variations of expression levels between individuals.

(1) The abundance of numerous ovarian transcripts was significantly different in FL1 compared to the non-selected control line DUKsi thus suggesting that at least some of the respective genes and corresponding biological processes are involved in improving reproductive traits, particularly by increasing the number of ovulation. (2) Selective qPCR re-evaluation largely confirmed the microarray data and in addition demonstrated that sample pooling can be beneficial to find out group-specific expression profiles despite of large inter-individual variation. (3) The present data will substantially help ongoing genetic association studies to identify candidate genes and causative mutations responsible for increased fertility performance in mice.