Project description: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.
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.
Project description:Early life adversity has been linked to altered reproductive development in humans, including changes in the timing of pubertal onset and sexual activity. One common form of early life adversity is having limited access to resources. This form of early life adversity can be modeled in rodents using the limited bedding and nesting model (LBN), in which rat dams and pups are placed in a low resource environment from postnatal day (PND) 2 through 9. Our laboratory has previously shown that male rats raised in LBN conditions have elevated levels of plasma estradiol compared to control males. Female rats, on the other hand, show no effect of LBN on plasma hormone levels, pubertal timing, or estrous cycle duration in adulthood. Here, we find that LBN males also show changes in adult reproductive behaviors. LBN males acquired the suite of reproductive behaviors more quickly than their control counterparts over the course of 3 weeks of testing, showing shorter latencies to mount, intromit, and ejaculate compared to controls prior to the final week of testing. We also characterized LBN-induced gene transcription changes across sex in the medial preoptic area (mPOA) which underlies reproductive behaviors. Interestingly, there was no effect of LBN on puberty onset (as measured by preputial separation) or masculinization of the sexually dimorphic nucleus of the preoptic area (SDN/POA; as measured by calbindin immunoreactivity) in males, suggesting LBN may not exert effects on hormone-dependent measures until after puberty.
Project description:Early-life adversity (ELA) is associated with lifelong memory deficits, yet the responsible mechanisms remain unclear. We imposed ELA by rearing rat pups in simulated poverty, assessed hippocampal memory, and probed changes in gene expression, their transcriptional regulation and the consequent changes in hippocampal neuronal structure. ELA rats had poor hippocampal memory and stunted hippocampal pyramidal neurons, associated with ~140 differentially expressed genes. Upstream regulators of the altered genes included glucocorticoid receptor and, unexpectedly, the transcription factor neuron-restrictive silencer factor (NRSF/REST). NRSF contributed critically to the memory deficits because blocking its function transiently following ELA rescued spatial memory and restored the dendritic arborization of hippocampal pyramidal neurons in ELA rats. Blocking NRSF function in vitro augmented dendritic complexity of developing hippocampal neurons, suggesting that NRSF represses genes involved in neuronal maturation. These findings establish important, surprising contributions of NRSF to ELA-induced transcriptional programming that disrupts hippocampal maturation and memory function.
Project description:Methylomics and transcriptomics array-based analysis of 36 placentas: 28 with IUGR (Intra-Uterine Growth Restriction) vs. 8 control pregnancies. Methylomics data were obtained using Illumina HumanMethylation-450k microarrays. Transcriptomics data were obtained using Illumina HumanHT-12 v4 Expression BeadChips.
Project description:We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: (a) implication of three different normalization techniques, and (b) implication of differential analysis using the generalized linear model (GLM). We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.