DOI: 10.5061/dryad.h18931zj8
title: PCB126 exposure revealed alterations in m6A RNA modifications in transcripts associated with AHR activation

description: Chemical modifications of proteins, DNA and RNA moieties play critical
 roles in regulating gene expression. Emerging evidence suggests
 the RNA modifications (epitranscriptomics) have substantive roles in basic
 biological processes. One of the most common modifications in mRNA and
 noncoding RNAs is N6-methyladenosine (m6A). In a subset of mRNAs, m6A
 sites are preferentially enriched near stop codons, in 3′ UTRs, and within
 exons, suggesting an important role in the regulation of mRNA processing
 and function including alternative splicing and gene expression. Very
 little is known about the effect of environmental chemical exposure on m6A
 modifications. As many of the commonly occurring environmental
 contaminants alter gene expression profiles and have detrimental effects
 on physiological processes, it is important to understand the effects of
 exposure on this important layer of gene regulation. Hence, the objective
 of this study was to characterize the acute effects of developmental
 exposure to PCB126, an environmentally relevant dioxin-like PCB, on m6A
 methylation patterns. We exposed zebrafish embryos to PCB126 for 6 hours
 starting from 72 hours post-fertilization and profiled m6A RNA using
 methylated RNA immunoprecipitation followed by sequencing (MeRIP-seq). Our
 analysis revealed 117 and 217 m6A peaks in the DMSO and PCB126 samples
 (FDR 5%), respectively. The majority of the peaks were preferentially
 located around the 3’UTR and stop codons. Statistical analysis revealed 15
 m6A marked transcripts to be differentially methylated by PCB126 exposure.
 These include transcripts that are known to be activated by AHR agonists
 (e.g., ahrra, tiparp, nfe2l2b) as well as others that are important for
 normal development (vgf, cebpd, sned1). These results suggest that
 environmental chemicals such as dioxin-like PCBs could affect
 developmental gene expression patterns by altering m6A levels. Further
 studies are necessary to understand the functional consequences of
 exposure-associated alterations in m6A levels.
description: Zebrafish embryos were exposed to PCB126 for 6 hours starting
 from 72 hours post-fertilization and profiled m6A RNA using Methylated RNA
 immunoprecipitation followed by sequencing (MeRIP-seq). m6A and input RNA
 libraries were prepared using the SMARTer stranded total RNA-seq kit
 (Clontech) and sequenced on the Illumina NextSeq500 platform (75bp, paired
 end reads). <i>MeRIP sequencing
 (MeRIP-seq) data analysis involved the following 4
 steps.</i> <i>1. Mapping</i>: Raw
 reads from individual samples were pre-processed using Trimmomatic to
 remove adapters and low quality reads. Ribosomal reads were removed from
 downstream processing by mapping the pre-processed reads to the ribosomal
 sequences. The zebrafish ribosomal sequences were downloaded from the
 Silva database (https://www.arb-silva.de/).  Ribosomal-free reads were
 mapped to the zebrafish genome (GRCz11 version 96) using the STAR aligner
 version 2.6.0d (Dobin et al. 2013). The resulting unique reads were used
 for peak calling. <i>2. Peak calling and
 visualization</i>: We used Model-based Analysis of ChIP-Seq (MACS)
 algorithm (version 1.4.2) for identification of the m6A peaks. The
 immunoprecipitated (m6A) and input (fragmented RNA) samples were used as
 treatment and control input files, respectively. The effective genome size
 (--gsize) was set to 117,608,789. The coverage data (wiggle files) was
 normalized to obtain comparable read density between input and IP samples
 and visualized using the UCSC genome browser. <i>3. Motif search and peak
 annotation</i>: <i>De novo</i> motif finding was done
 using MEME (Bailey et al. 2009). The FASTA file of the 50 bp region
 flanking the peak summits was used as input and the top 3 consensus motifs
 were retrieved. CentriMO was used for visualization of the positional
 distribution of the best match of the m6A motif in the 300 bp centered
 around the peak summits. <i>4. Statistical
 analysis</i>: Differential methylation analysis was done using
 DiffBind, a bioconductor package, for differential m6A peak calling (Stark
 and Brown 2011).