Ovarian transcriptomic profiling of fitness-related genes in F4 inbred mice and to validate differentially expressed genes using qRT-PCR

Purpose: To investigate the differentially expressed genes in high litter size samples v/s low litter size samples of F4 inbred Swiss albino mice using RNA-Seq and to validate some identified differentially expressed genes using qRT-PCR. Methods: Intact ovarian tissue samples were obtained from six F4 female [from two groups i.e. three from high litter size group (HLS; n=3) and three from low litter size group (LLS; n=3)] inbred mice, after assessment of estrous phase of ovarian cycle by vaginal cytology smear technique (Byers et al., 2012). After sacrifice of mice, their intact ovaries were rapidly harvested and immediately preserve in RNAlater (Invitrogen). The QC passed libraries were subjected to RNA sequencing by use of HiSeq X10 of Illumina® platform. The paired ends (2 X 150 bp) reads were sequenced and about 3GB raw data were generated for both high and low groups, separately. The provided raw data were processed with trimgalore for quality purpose and kallisto pipeline to get transcript abundances. Tximport was used to generate gene counts and DESeq2 was performed on gene count data of RNA-Seq experiment to get DEGs. DEGs in HLS v/s LLS groups in Swiss albino mice were subjected to functional enrichment using bioinformatics tools, eg: Profiler and GSEA. The interaction of important DEGs having fold change ≥ ± 1.5 and Padj ˂ 0.05 was studied using Network Analyst 3.0. A total 5 DEGs identified through RNA-Seq in high and low litter size groups were validated by quantitative real time PCR (qRT-PCR). Results: All QC passed transcriptomic libraries were sequenced in paired end read sequencing platform and on an average 49.7 million reads of 150bp per sample were generated. All six samples produced a total of 44.7 GB of data, with an average Phred score of more than 35 per sample. All six samples yielded a total of 42.23 GB of trimmed data. The average mapping percentage was 78.5. The average GC percent and unique reads percent for clean data samples were 50 and 55.66, respectively. DESeq2 analysis yielded the 1018 genes under R environment with 5 % FDR. 347 genes were found to be up regulated, and about 671 genes were found to be down regulated out of 1018 genes. Based on functional enrichment analysis of 659 DEGs with fold change ≥ ± 1.5 and Padj ˂ 0.05, 166 significantly enriched gene ontology (GO) classes were identified. Out of 166 categories, 17, 109, 28, and 12 were significantly enriched in molecular function (MF), biological process (BP), cellular component (CC), KEGG, and REAC, respectively. Conclusion: In the present analysis of ovarian transcriptome, cell–cell signaling and metabolic related pathways were significantly enriched in HLS as compare to LLS group of F4 inbred Swiss albino mice. In these pathways, TP63, SLC16A10, APOD, S100A8, LRRK2, GPD1, CNR1, HDAC9, WNT11, LPIN1, SLC5A7, KLB, GNAI1, IRS2, TIMP4, PRKAR2B, SLC1A3, GSTZ1, FAH, CLYBL, SYNGR1, FAT4, CIDEA, ECHDC1, FMO5, GPT and ALDH1A1genes were significantly up-regulated in HLS group of inbred mice.