Chromatin modifications and genomic contexts linked to dynamic DNA methylation patterns across human cell types

Differences in CpG island distribution between exogenous and endogenous jaagsiekte sheep retrovirus strains

The jaagsiekte sheep retrovirus (JSRV), belonging to the betaretrovirus genus of the retroviridae family, includes both exogenous and endogenous jaagsiekte sheep retroviruses (exJSRV and enJSRV, respectively). At the proviral genome level, exJSRV and enJSRV strains have a high degree of similarity with their main variation regions being the LTR, gag, and env genes. In this study, for the first time, we investigated and compared the distribution of CpG islands between these enJSRV and exJSRV strains. Specifically, we analyzed a total of 42 full-length JSRV genomic sequences obtained from the GenBank® database to identify CpG islands in the exJSRV and enJSRV genomes using the MethPrimer software. Our results showed that the CpG islands in the two JSRV strains were mainly distributed in the LTR, gag, and env genes. In exJSRVs, 66.66% (6/9), 33.33% (3/9), and 100% (9/9) of the sequences presented at least one CpG island in LTR, gag, env genes, respectively, and for enJSRVs, 84.84% (28/33), 57.57% (19/33), and 96.96% (32/33) of the sequences presented at least one CpG island in the LTR, gag, and env genes. These findings suggested that the distribution, length, and genetic traits of CpG islands were different for the exJSRV and enJSRV strains. In future, it would be necessary to demonstrate the biological significance of CpG islands within these genes in exJSRV and enJSRV genomes. This will enhance understanding regarding the potential role of CpG islands in epigenetic regulation.

Regulatory functions of FKBP5 intronic regions associated with psychiatric disorders

The FKBP5 gene codifies a co-chaperone protein associated with the modulation of glucocorticoid receptor interaction involved in the adaptive stress response. The FKBP5 intracellular concentration affects the binding affinity of the glucocorticoid receptor (GR) to glucocorticoids (GCs). This gene has glucocorticoid response elements (GREs) located in introns 2, 5 and 7, which affect its expression. Recent studies have examined GRE activity and the effects of genetic variants on transcript efficiency and their contribution to susceptibility to behavioral disorders. Epigenetic changes and environmental factors can influence the effects of these allele-specific variants, impacting the response to GCs of the FKBP5 gene. The main epigenetic mark investigated in FKBP5 intronic regions is DNA methylation, however, few studies have been performed for all GREs located in these regions. One of the major findings was the association of low DNA methylation levels in the intron 7 of FKBP5 in patients with psychiatric disorders. To date, there are no reports of DNA methylation in introns 2 and 5 of the gene associated with diagnoses of psychiatric disorders. This review highlights what has been discovered so far about the relationship between polymorphisms and epigenetic targets in intragenic regions, and reveals the gaps that need to be explored, mainly concerning the role of DNA methylation in these regions and how it acts in psychiatric disease susceptibility.

Revealing Hi-C subcompartments by imputing inter-chromosomal chromatin interactions

Higher-order genome organization and its variation in different cellular conditions remain poorly understood. Recent high-coverage genome-wide chromatin interaction mapping using Hi-C has revealed spatial segregation of chromosomes in the human genome into distinct subcompartments. However, subcompartment annotation, which requires Hi-C data with high sequencing coverage, is currently only available in the GM12878 cell line, making it impractical to compare subcompartment patterns across cell types. Here we develop a computational approach, SNIPER (Subcompartment iNference using Imputed Probabilistic ExpRessions), based on denoising autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. SNIPER accurately reveals subcompartments using moderate coverage Hi-C datasets and outperforms an existing method that uses epigenomic features in GM12878. We apply SNIPER to eight additional cell lines and find that chromosomal regions with conserved and cell-type specific subcompartment annotations have different patterns of functional genomic features. SNIPER enables the identification of subcompartments without high-coverage Hi-C data and provides insights into the function and mechanisms of spatial genome organization variation across cell types.

Perspectives and applications of machine learning for evolutionary developmental biology

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, "omic" studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growing amount of information. Among computational approaches, machine learning stands out as a promising field to generate knowledge and trace new research perspectives for bioinformatics. In this review, we aim to expose the current advances of machine learning applied to evolution and development. We draw clear perspectives and argue how evolution impacted machine learning techniques.

Passing experiences on to future generations: endocrine disruptors and transgenerational inheritance of epimutations in brain and sperm

All animals have body burdens of polychlorinated biphenyls (PCBs) despite their ban decades ago. These and modern endocrine-disrupting chemicals (EDCs) such as the fungicide vinclozolin (VIN) perturb hormone signaling and lead to dysfunctions following prenatal exposures. Beyond direct exposures, transgenerational disease phenotypes can persist for multiple generations without subsequent exposure. The mechanisms of action of these EDCs differ: VIN is anti-androgenic while the PCB mixture Aroclor 1221 (A1221) is weakly estrogenic. Based on limited evidence for the inheritance of epimutations in germline, we measured DNA methylation in brain and sperm of rats. Pregnant dams were exposed from day 8-18 of gestation to low dosages of VIN, A1221, or the vehicle. To produce paternal lineages, exposed F1 males were bred with untreated females, creating the F2 and subsequently F3 generations. In adult F1 and F3 males, mature sperm was collected, and brain nuclei involved in anxiety and social behaviors (CA3 of the hippocampus; central amygdala) were selected for assays of epimutations in CpG islands using reduced representation bisulfite sequencing. In F1 sperm, VIN and PCBs induced differential methylation in 215 and 284 CpG islands, respectively, compared to vehicle. The majority of effects were associated with hypermethylation. Fewer epimutations were detected in the brain. A subset of differentially methylated regions were retained from the F1 to the F3 generation, suggesting a common mechanism of EDC and germline epigenome interaction. Thus, EDCs can cause heritable epimutations in the sperm that may embody the future phenotype of brain-behavior disorders caused by direct or transgenerational exposures.

DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes

Motivation

5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible in some contexts and leave a portion of the genome unqueried. Hence, we devised a novel, supervised, integrative learning framework to perform whole-genome methylation and hydroxymethylation predictions in CpG dinucleotides. Our framework can also perform imputation of missing or low quality data in existing sequencing datasets. Additionally, we developed infrastructure to perform in silico, high-throughput hypotheses testing on such predicted methylation or hydroxymethylation maps.

Results

We test our approach on H1 human embryonic stem cells and H1-derived neural progenitor cells. Our predictive model is comparable in accuracy to other state-of-the-art DNA methylation prediction algorithms. We are the first to predict hydroxymethylation in silico with high whole-genome accuracy, paving the way for large-scale reconstruction of hydroxymethylation maps in mammalian model systems. We designed a novel, beam-search driven feature selection algorithm to identify the most discriminative predictor variables, and developed a platform for performing integrative analysis and reconstruction of the epigenome. Our toolkit DIRECTION provides predictions at single nucleotide resolution and identifies relevant features based on resource availability. This offers enhanced biological interpretability of results potentially leading to a better understanding of epigenetic gene regulation.

Availability and implementation

http://www.pradiptaray.com/direction, under CC-by-SA license.

Contacts

pradiptaray@gmail.com or mchen@utdallas.edu or michael.zhang@utdallas.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

DNA hypomethylation of Synapsin II CpG islands associates with increased gene expression in bipolar disorder and major depression

BACKGROUND

The Synapsins (SYN1, SYN2, and SYN3) are important players in the adult brain, given their involvement in synaptic transmission and plasticity, as well as in the developing brain through roles in axon outgrowth and synaptogenesis. We and others previously reported gene expression dysregulation, both as increases and decreases, of Synapsins in mood disorders, but little is known about the regulatory mechanisms leading to these differences. Thus, we proposed to study DNA methylation at theses genes' promoter regions, under the assumption that altered epigenetic marks at key regulatory sites would be the cause of gene expression changes and thus part of the mood disorder etiology.

METHODS

We performed CpG methylation mapping focusing on the three genes' predicted CpG islands using the Sequenom EpiTYPER platform. DNA extracted from post-mortem brain tissue (BA10) from individuals who had lived with bipolar disorder (BD), major depressive disorder (MDD), as well as psychiatrically healthy individuals was used. Differences in methylation across all CpGs within a CpG island and between the three diagnostic groups were assessed by 2-way mixed model analyses of variance.

RESULTS

We found no significant results for SYN1 or SYN3, but there was a significant group difference in SYN2 methylation, as well as an overall pattern of hypomethylation across the CpG island. Furthermore, we found a significant inverse correlation of DNA methylation with SYN2a mRNA expression.

CONCLUSIONS

These findings contribute to previous work showing dysregulation of Synapsins, particularly SYN2, in mood disorders and improve our understanding of the regulatory mechanisms that precipitate these changes likely leading to the BD or MDD phenotype.