Data for: Mouse sperm DNA-methylation changes induced by age and mechanistic target of rapamycin (mTOR) manipulation in Sertoli cells

This study was designed to study changes in sperm DNA methylation induced by age and by changes in activity of two complexes of the mechanistic target of rapamycin (mTOR) in Sertoli cells. Two transgenic mouse models were used. To inactivate mTOR complex one (mTORC1), we used Raptor (component of mTORC1) Sertoli-specific knockout model, and to inactivate mTOR complex two (mTORC2), we used Rictor (component of mTORC2) Sertoli-specific knockout model. Data was collected at two time-pints: postnatal week 8 and postnatal week 22. Sperm DNA methylation of all age/genotype groups was analyzed. Genes associated with differentially methylated regions were identified and biological categories enriched with these genes were identified.

Methods
To achieve Sertoli-specific knockouts, mice with floxed Rictor or Raptor (Rictortm1.1Klg/SjmJ and B6.Cg-Rptortm1.1Dmsa/J respectively) were crossed with mice with Cre recombinase controlled by the Amh Sertoli-specific promoter (129S.FVB-Tg(Amh-cre)8815Reb/J). All mice were purchased from Jackson Laboratories (Stock ##: 020649, 013188 and 007915 respectively). F3 wildtype and knockout male mice were euthanized at 8 or 22 weeks of age (n = 4 per genotype/timepoint). All procedures followed the guidelines of the National Institutes of Health Guide for the Care and Use of Laboratory Animals and the approval for this study was received from the Institutional Animal Care and Use Committee at University of Massachusetts, Amherst.

At each euthanasia cauda epididymides were incised, cut three times, and incubated at 37C for 30 minutes in 1 ml of sperm wash buffer (Cat. # ART1006, Origio, Denmark). After incubation DNA extraction was done following the rapid method (1). Bisulfite conversion was performed on 100 ng of genomic DNA using the EpiTect Fast DNA Bisulfite kit from Qiagen (Cat.# 59824) and DNA libraries were constructed using the NUGEN Ovation RRBS Methyl-seq System (Cat.# 0353) according to the manufacturer’s instructions. Libraries were sequenced on Illumina HiSeq 4000 at the Deep Sequencing Core Facility of the University of Massachusetts Medical School (Schrewsburry, MA) with an average of 22.7 million single end reads (50-bp) per sample.

Raw reads were trimmed using TrimGalore and a NuGEN-specific adaptor trimming scripts available from GitHub (nugentechnologies/ NuMetRRBS). Trimmed reads were mapped using Bismark-Bowtie2 with no mismatch allowed. Methylation counts were called using Bismark extract. Differentially methylated regions were identified using the Methyl kit (v1.24.0) pipeline (2). In brief, the genome was tiled into sliding windows and a weighted methylation level was calculated for each window. We used a logistic regression model for p-value calculation subsequently adjusted for multiple comparison (FDR) using the SLIM method for final DMR identification. Individual DMRs were identified for a 100 bp sliding window with a minimum of 1 CpG. DMRs with methylation difference at FDR < 0.05 were used to compare different age/genotype groups. The tiles identified in at least 3 samples out of 4 in an age/genotype group were included in comparisons. Each DMR was assigned to the closest gene (<5 kb upstream transcription start site, promoter, 5’UTR, exon, or intron) or intergenic region using annotatr package (v1.24.0) (3) and mouse genome (mm10) assembly data from ENSEMBL. We used Metascape (4) to analyze biological categories associated with differentially methylated regions.

References

(1)       Wu, H, et al. Rapid Method for the Isolation of Mammalian Sperm DNA. BioTechniques 2015, 58 (6).

(2)       Akalin, A, et al. MethylKit: A Comprehensive R Package for the Analysis of Genome-Wide DNA Methylation Profiles. Genome Biol. 2012, 13 (10), R87.

(3)       R. G. Cavalcante, M. A. Sartor, annotatr: genomic regions in context. Bioinforma. Oxf. Engl. 33, 2381–2383 (2017) (3).

(4).      Zhou, Y, et al. Metascape Provides a Biologist-Oriented Resource for the Analysis of Systems-Level Datasets. Nat. Commun. 2019, 10 (1).