Role of DNA methylation in altered gene expression patterns in adult zebrafish (Danio rerio) exposed to 3, 3', 4, 4', 5-pentachlorobiphenyl (PCB 126)

Abstract

There is growing evidence that environmental toxicants can affect various physiological processes by altering DNA methylation patterns. However, very little is known about the impact of toxicant-induced DNA methylation changes on gene expression patterns. The objective of this study was to determine the genome-wide changes in DNA methylation concomitant with altered gene expression patterns in response to 3, 3', 4, 4', 5-pentachlorobiphenyl (PCB126) exposure. We used PCB126 as a model environmental chemical because the mechanism of action is well-characterized, involving activation of aryl hydrocarbon receptor, a ligand-activated transcription factor. Adult zebrafish were exposed to 10 nM PCB126 for 24 h (water-borne exposure) and brain and liver tissues were sampled at 7 days post-exposure in order to capture both primary and secondary changes in DNA methylation and gene expression. We used enhanced Reduced Representation Bisulfite Sequencing and RNAseq to quantify DNA methylation and gene expression, respectively. Enhanced reduced representation bisulfite sequencing analysis revealed 573 and 481 differentially methylated regions in the liver and brain, respectively. Most of the differentially methylated regions are located more than 10 kilobases upstream of transcriptional start sites of the nearest neighboring genes. Gene Ontology analysis of these genes showed that they belong to diverse physiological pathways including development, metabolic processes and regeneration. RNAseq results revealed differential expression of genes related to xenobiotic metabolism, oxidative stress and energy metabolism in response to polychlorinated biphenyl exposure. There was very little correlation between differentially methylated regions and differentially expressed genes suggesting that the relationship between methylation and gene expression is dynamic and complex, involving multiple layers of regulation.

Keywords: PCBs; RNAseq; brain; enhanced reduced representation bisulfite sequencing (eRRBS); liver.

Figure 1:

PCB126-induced cyp1a gene expression in the liver (A) and brain (B). Cyp1a expression relative to the reference gene was calculated using the delta Ct method. β-Actin was used as a reference gene. * Represents significant difference from DMSO control (One-way ANOVA; P < 0.01)

Figure 2:

Tissue-specific DNA methylation profiles and their genomic location. Percentage of methylation levels in the proximal promoter regions (A) and CpG islands (B) in the liver and brain are plotted. (C) Percentage of 300 bp tiles overlapping with different genomic regions. These plots are based on the tiles represented in all samples. Proximal (up to 5 kb upstream) and distal promoter (>5 kb) regions are classified based on the distance from the transcriptional start site

Figure 3:

PCB126-induced tissue-specific changes in DNA methylation. Volcano plots showing DMRs in response to PCB126 exposure in the liver (A) and brain (B). Percent methylation difference (x-axis) between PCB126 and Control are plotted against q-value (y-axis). Red vertical lines represent the 25% methylation difference and the blue horizontal line represents a q-value of 0.05, which are used as a statistical cutoff in differential methylation analysis. Each green and red spot represents a statistically significant hypo and hypermethylated region, respectively

Figure 4:

GO term analysis of DMRs in the liver (A, C) and brain (B, D). Top panel represents GO biological process terms and the bottom panel contains the GO molecular function terms. GO term analysis was performed on DMRs using GREAT. Only statistically significant GO terms are shown. The fold enrichment of GO terms and the genes associated with each term are provided in the Supplementary Material

Figure 5:

Tissue-specific gene expression changes. MA plots showing the DEGs in the liver (A) and brain (B). Horizontal blue lines represent fold change of ± 2. Each red spot represent a statistically differentially expressed gene