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Cross-species analysis of genic GC3 content and DNA methylation patterns.

ABSTRACT: The GC content in the third codon position (GC(3)) exhibits a unimodal distribution in many plant and animal genomes. Interestingly, grasses and homeotherm vertebrates exhibit a unique bimodal distribution. High GC(3) was previously found to be associated with variable expression, higher frequency of upstream TATA boxes, and an increase of GC(3) from 5' to 3'. Moreover, GC(3)-rich genes are predominant in certain gene classes and are enriched in CpG dinucleotides that are potential targets for methylation. Based on the GC(3) bimodal distribution we hypothesize that GC(3) has a regulatory role involving methylation and gene expression. To test that hypothesis, we selected diverse taxa (rice, thale cress, bee, and human) that varied in the modality of their GC(3) distribution and tested the association between GC(3), DNA methylation, and gene expression. We examine the relationship between cytosine methylation levels and GC(3), gene expression, genome signature, gene length, and other gene compositional features. We find a strong negative correlation (Pearson's correlation coefficient r = -0.67, P value < 0.0001) between GC(3) and genic CpG methylation. The comparison between 5'-3' gradients of CG(3)-skew and genic methylation for the taxa in the study suggests interplay between gene-body methylation and transcription-coupled cytosine deamination effect. Compositional features are correlated with methylation levels of genes in rice, thale cress, human, bee, and fruit fly (which acts as an unmethylated control). These patterns allow us to generate evolutionary hypotheses about the relationships between GC(3) and methylation and how these affect expression patterns. Specifically, we propose that the opposite effects of methylation and compositional gradients along coding regions of GC(3)-poor and GC(3)-rich genes are the products of several competing processes.

SUBMITTER: Tatarinova T

PROVIDER: S-EPMC3762193 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Cross-species analysis of genic GC3 content and DNA methylation patterns.

Tatarinova Tatiana T Elhaik Eran E Pellegrini Matteo M

Genome biology and evolution 20130101 8

The GC content in the third codon position (GC(3)) exhibits a unimodal distribution in many plant and animal genomes. Interestingly, grasses and homeotherm vertebrates exhibit a unique bimodal distribution. High GC(3) was previously found to be associated with variable expression, higher frequency of upstream TATA boxes, and an increase of GC(3) from 5' to 3'. Moreover, GC(3)-rich genes are predominant in certain gene classes and are enriched in CpG dinucleotides that are potential targets for m ...[more]

PMID: 23833164

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Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Dataset Information

Cross-species analysis of genic GC3 content and DNA methylation patterns.

Publications

Cross-species analysis of genic GC3 content and DNA methylation patterns.

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