Computational protocol: Genome-Wide DNA Methylation Profiling Reveals Epigenetic Changes in the Rat Nucleus Accumbens Associated With Cross-Generational Effects of Adolescent THC Exposure

Similar protocols

Protocol publication

[…] Samples were processed following procedures outlined in . Bilateral NAc tissue was dissected (15-gauge punch), from frozen brains of 32 F1 rats (PND~62) from adult offspring of parents with repeated exposure to either THC or VEH during adolescence (). Eight females and eight males from both the THC- and VEH-exposed lines were derived from five to six different mothers and fathers in each group. Genomic DNA was extracted (DNeasy Blood & Tissue Kit; Qiagen, Valencia, CA, USA) and ~250 ng DNA/sample was processed for ERRBS following established protocols (). ERRBS sequencing libraries were constructed and sequenced on a HiSeq2000 (Weill Cornell genomics core facility, New York, NY). As per Alkalin et al (), reads were aligned to an in silico bisulfite-converted version of the rn4 reference genome assembly using Bismark (), allowing 2 bp mismatches per read, and discarding reads mapping to multiple locations. [...] Following read mapping, methylation levels were estimated on a per CpG basis by taking the fraction of reads containing methylated non-bisulfite-converted C nucleotides over the sum of all reads mapping to a given CpG, and multiplying this fraction by 100 (ie, CpG percent methylation=(count C)/(count C+count T) × 100). All processing of CpG read count files, sample quality control, and initial statistical analyses were carried out using the package methylKit (), implemented in R v3.0 ( with either fewer than 30 mapped reads or read counts >99th percentile (to exclude sites with potential PCR clonal amplification bias) were removed. CpG read depths across the 32 samples were normalized using the ‘normalizeCoverage' function in methylKit (Alkalin et al, 2012b). CpGs not represented in all samples and those mapping to sex chromosomes were not considered, leaving a total of 776 220 CpGs genome wide. Using this filtered and normalized set of CpGs, principal component analysis (PCA) was applied to identify potential sample outliers. Following outlier removal, we discarded CpGs at which the range in observed CpG methylation values (ie, maximum observed methylation value − minimum observed methylation value) within either group was ≥30% (n=208 914). At the remaining autosomal CpGs (n=567 306), logistic regression was used to test for differential methylation between THC and VEH groups. Observed P-values were adjusted using the Benjamini–Hochberg false discovery rate (FDR) method ().A novel sliding-window method, written in PERL, was used to identify clusters of neighboring CpGs exhibiting concordant changes in methylation associated with cross-generational THC exposure (termed differentially methylated regions, DMRs; ). DMRs were defined as regions of the genome containing at least three neighboring CpGs within a 500-bp interval; we required the presence of a minimum of three statistically significant CpGs (q<0.01) with a concordant (either hypo- or hypermethylated) mean methylation difference >2% between THC and VEH groups, representing at least 50% of CpGs within a given window/DMR. […]

Pipeline specifications

Software tools Bismark, methylKit
Application BS-seq analysis
Organisms Rattus norvegicus, Cannabis sativa
Diseases Substance-Related Disorders