Differentially regulated genes in Esr2-mutant rat granulosa cells

RNA seq analyses were performed in granulosa cells (GCs) collected from gonadotropin treated ESR2 mutant rats. Data obtained from a null mutant with Esr2 exon 3 deletion (∆3) and another DNA binding domain (DBD) mutant with exon 4 deletion (∆4) were compared to that of wildtype (WT) rats. The raw data were analyzed using CLC genomics workbench. High quality RNA-sequencing reads were aligned to the Rattus norvegicus genome. Differentially expressed genes in ∆3 or ∆4 Esr2-mutant GCs were identified based on the following criteria: FDR p-Value ≤0.05 and an absolute fold change of 2. Fewer differentially expressed genes were identified in ∆3 compared to the ∆4 mutant group. As both mutant groups demonstrated a common phenotype of ovulation failure, differentially expressed genes common to both in ∆3 and ∆4 mutant rats were emphasized and further analyzed in the companion article “ESR2 regulates granulosa cell genes essential for follicle maturation and ovulation” [1].

a b s t r a c t RNA seq analyses were performed in granulosa cells (GCs) collected from gonadotropin treated ESR2 mutant rats. Data obtained from a null mutant with Esr2 exon 3 deletion (Δ3) and another DNA binding domain (DBD) mutant with exon 4 deletion (Δ4) were compared to that of wildtype (WT) rats. The raw data were analyzed using CLC genomics workbench. High quality RNAsequencing reads were aligned to the Rattus norvegicus genome. Differentially expressed genes in Δ3 or Δ4 Esr2-mutant GCs were identified based on the following criteria: FDR p-Value r 0.05 and an absolute fold change of 2. Fewer differentially expressed genes were identified in Δ3 compared to the Δ4 mutant group. As both mutant groups demonstrated a common phenotype of ovulation failure, differentially expressed genes common to both in Δ3 and Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/dib Δ4 mutant rats were emphasized and further analyzed in the companion article "ESR2 regulates granulosa cell genes essential for follicle maturation and ovulation" [1]. &

ESR2 mutant rats
All procedures were performed in accordance with the protocols approved by the University of Kansas Medical Center Animal Care and Use Committee. Holtzman Sprague-Dawley (HSD) Esr2mutant rat models were generated by targeting exon 3 (Δ3) or exon 4 (Δ4) in the Esr2 gene as described previously [2]. Δ3 caused a frameshift and null mutation in the ESR2 coding sequence while Δ4 resulted in an ESR2 protein lacking part of the DBD [2]. All animals were screened for mutation by PCR based genotyping using tail-tip DNA samples (RED extract-N-Amp Tissue PCR Kit, Sigma-Aldrich) and primers targeting the flanking intron sequences [2].

Treatment with exogenous gonadotropins
Four-week-old Esr2-mutant (Δ3 and Δ4) and age-matched wildtype (WT) female rats were used for the gonadotropin induced follicular development. Synchronized follicular growth was initiated by intraperitoneal injection of 30 IU Pregnant Mare's Serum Gonadotropin (PMSG, National Hormone and Peptide Program). 48 h after the PMSG injection, 30 IU of Human Chorionic Gonadotropin (hCG, National Hormone and Peptide Program) was injected intraperitoneally.

Sample collection and processing
Animals were sacrificed 10 h after exogenous gonadotropin administration. GCs were collected from the gonadotropin treated WT, Δ3 and Δ4 ovaries, and total RNA was extracted by using TRI Reagent (Millipore-Sigma) following the manufactures instruction. RNA quantification was performed by using nanodrop (Thermo Scientific) and approximately 500 ng of total RNA was used for the RNAseq library preparation. Libraries were prepared by using TruSeq standard mRNA kit (Illumina) following the manufacturer's instruction. The cDNA libraries were sequenced at the Molecular Biology Core Laboratory of Mayo Clinic (Rochester, MN).

RNA-seq data analyses
RNA-Seq data were analyzed by using the CLC Genomics Workbench (Qiagen Bioinformatics) to identify the differentially expressed genes. All clean reads were obtained by removing low quality reads by trimming, and the high-quality reads were aligned to the Rattus norvegicus genome (downloaded from NCBI database) using default parameters: (a) maximum number of allowable mismatches was 2 (b) minimum length and similarity fraction was set at 0.8; and (c) minimum number of hits per read was 10. A total of 32,623 genes were detected in each group of GCs. Expression values were measured in RPKM (Reads per kilobase of exon model per million mapped reads) [3]. The threshold p-value was determined according to the false discovery rate (FDR). In this study, genes that were considered differentially regulated met the following criteria: FDR p-value r0.05 and absolute fold change was 2.

Statistical analysis
For RNA Seq, each study group contained three library samples. Each library sample was made by pooling two RNA samples from two individual rats from the same genotype. In CLC Genomics Workbench, the 'Differential Expression for RNA-Seq tool' performs some multi-factorial statistics on a set of Expression Tracks based on a negative binomial Generalized Linear Model (GLM). The final GLM fit and dispersion estimate calculate the total likelihood of the model given the data, and the uncertainty on each fitted coefficient. Two statistical tests-Wald test and Likelihood Ratio test, each make use of one of these values. The Likelihood Ratio test is used in the Across groups (ANOVA-like) comparison.

Transparency document. Supporting information
Transparency data associated with this article can be found in the online version at https://doi.org/ 10.1016/j.dib.2018.05.098.