GSE119640
Intra-individual methylomics detects the impact of early-life adversity
Genetic and environmental factors interact during sensitive periods early in life to influence mental health and disease via epigenetic processes such as DNA methylation. However, it is not known if DNA methylation changes outside the brain provide an 'epigenetic signature' of early-life experiences. Here, we employed a novel intra-individual approach by testing DNA methylation from buccal cells of individual rats before and immediately after exposure to one week of typical or adverse life experience. We find that whereas inter-individual changes in DNA methylation reflect the effect of age, DNA methylation changes within paired DNA samples from the same individual reflect the impact of diverse neonatal experiences. Genes coding for critical cellular–metabolic enzymes, ion channels and receptors were more methylated in pups exposed to the adverse environment, predictive of their repression. In contrast, the adverse experience was associated with less methylation on genes involved in pathways of death and inflammation as well as cell-fate related transcription factors, indicating their potential upregulation. Thus, intra-individual methylome signatures indicate large-scale transcription-driven alterations of cellular fate, growth and function. Overall design: The experimental paradigm involved rearing pups and dams in “impoverished” cages for a week (P2-P9). Briefly, routine rat cages were fitted with a plastic-coated aluminum mesh platform sitting ~2.5 cm above the cage floor (allowing collection of droppings). Bedding was reduced to only cover cage floor sparsely, and one-half of a single paper towel was provided for nesting material, creating a limited bedding and nesting (LBN) cage. Control dams and their litters resided in standard bedded cages. For each experiment, pups form several litters were mixed, and then assigned randomly to a control or LBN dam. Control and experimental cages were undisturbed during P2–P9, housed in a quiet room with constant temperature and a strong laminar airflow. For technical reasons the study was conducted in two 'batches' (cohorts). These differed solely in the dates at which they were conducted. The first buccal swab was collected from both cheeks of each pup prior to randomization on P2, using Hydraflock swab (Puritan diagnostics, LLC). Pups were then randomized to controls or LBN cages. On P10, buccal swabs were collected as described for P2. The Buccal swab was placed into the DNA shields™ (Zymo Research) immediately after swabbing. DNA was prepared from the DNA shields solution using the Quick-gDNA™ MiniPrep kit (Zymo Research) following the manufacturer's protocol. The quantity of double stranded DNA was analyzed using Qubit, and RRBS Libraries were prepared from 40 ng of genomic DNA digested with Msp I and then extracted with DNA Clean & Concentrator™-5 kit (Zymo Research). Fragments were ligated to pre-annealed adapters containing 5'-methylcytosine instead of cytosine according to Illumina's specified guidelines (www.illumina.com). Adaptor-ligated fragments were then bisulfite-treated using the EZ DNA MethylationLightning™ Kit (Zymo Research). Preparative-scale PCR was performed and the resulting products were purified with DNA Clean & Concentrator for sequencing. Amplified RRBS libraries were quantified and qualified by Qubit, Bioanalyzer (Agilent), and Kapa library quant (Kapa systems), and then sequenced on the Illumina NextSeq 500 platform. Adaptor and low quality reads were trimmed and filtered using Trim Galore! 0.4.3. Reads were aligned to the rat genome (RGSC 6.0/rn6) by using Bismark 0.16.3. CpG sites were called by “bismark_methylation_extractor” function from Bismark. Single CpG sites with more than 10 reads coverage were kept for DMR calling. Differential methylation sites (DMSs) were first called using MethyKit (R 3.3.2) between P2 and P10 from the same individual with a false discovery rate (FDR) lower than 0.05. DMSs were shared in at least two individuals in either control or LBN groups were kept and DMSs falling within 100 base pairs were then merged into DMRs. The methylation percentage/level was calculated as the ratio of the methylated read counts over the sum of both methylated and unmethylated read counts for a single CpG site or across CpGs for a region. The delta methylation was calculated using the log2 transformation of the ratio of methylation level in the P10 sample and the methylation level in the P2 sample, defined as log2(P10/P2). Increased methylation in P10 is shown as a positive value while decreased methylation in P10 is shown as a negative value.