Limits...
DNA methylation and gene expression in Mimulus guttatus.

Colicchio JM, Miura F, Kelly JK, Ito T, Hileman LC - BMC Genomics (2015)

Bottom Line: Additionally, we find that DNA methylation is significantly depleted near gene transcriptional start sites, which may explain the recently discovered elevated rate of recombination in these same regions.Using a model-based approach, we demonstrate that methylation patterns are an important predictor of variation in gene expression.This model provides a novel approach for differential methylation analysis that generates distinct and testable hypotheses regarding gene expression.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, 66045, USA. Colicchio@ku.edu.

ABSTRACT

Background: The presence of methyl groups on cytosine nucleotides across an organism's genome (methylation) is a major regulator of genome stability, crossing over, and gene regulation. The capacity for DNA methylation to be altered by environmental conditions, and potentially passed between generations, makes it a prime candidate for transgenerational epigenetic inheritance. Here we conduct the first analysis of the Mimulus guttatus methylome, with a focus on the relationship between DNA methylation and gene expression.

Results: We present a whole genome methylome for the inbred line Iron Mountain 62 (IM62). DNA methylation varies across chromosomes, genomic regions, and genes. We develop a model that predicts gene expression based on DNA methylation (R(2) = 0.2). Post hoc analysis of this model confirms prior relationships, and identifies novel relationships between methylation and gene expression. Additionally, we find that DNA methylation is significantly depleted near gene transcriptional start sites, which may explain the recently discovered elevated rate of recombination in these same regions.

Conclusions: The establishment here of a reference methylome will be a useful resource for the continued advancement of M. guttatus as a model system. Using a model-based approach, we demonstrate that methylation patterns are an important predictor of variation in gene expression. This model provides a novel approach for differential methylation analysis that generates distinct and testable hypotheses regarding gene expression.

No MeSH data available.


Gene Ontology classes over and underrepresented in highly gene body methylated genes. Genes Ontology terms significantly enriched and depleted in genes in the top 10 % for gene body CG methylation. X-axis shows the percent of genes in both the high CG methylated portion (blue) as well as the remainder of the transcriptome (red) that contained the given GO terms. Text color represents the class of GO term: blue-molecular functions, grey-biological processes, and green-cellular component
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4492170&req=5

Fig7: Gene Ontology classes over and underrepresented in highly gene body methylated genes. Genes Ontology terms significantly enriched and depleted in genes in the top 10 % for gene body CG methylation. X-axis shows the percent of genes in both the high CG methylated portion (blue) as well as the remainder of the transcriptome (red) that contained the given GO terms. Text color represents the class of GO term: blue-molecular functions, grey-biological processes, and green-cellular component

Mentions: Comparing genes in the top 10 % genome wide for gene body CG methylation with the remainder of the genome, we found numerous gene categories that are either enriched or depleted in our set of highly CG methylated genes. Genes coding for proteins with kinase activity, involved in signal transduction, and nucleotide binding were among those which tended to be highly methylated, while proteins functioning in the thylakoid, plastid, and ribosome, as well as proteins involved in primary metabolism, photosynthesis, and RNA binding tended to be lowly or moderately methylated (Fig. 7). Similar results have been found in Brachypodium, rice [29], and Arabidopsis [3].Fig. 7


DNA methylation and gene expression in Mimulus guttatus.

Colicchio JM, Miura F, Kelly JK, Ito T, Hileman LC - BMC Genomics (2015)

Gene Ontology classes over and underrepresented in highly gene body methylated genes. Genes Ontology terms significantly enriched and depleted in genes in the top 10 % for gene body CG methylation. X-axis shows the percent of genes in both the high CG methylated portion (blue) as well as the remainder of the transcriptome (red) that contained the given GO terms. Text color represents the class of GO term: blue-molecular functions, grey-biological processes, and green-cellular component
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4492170&req=5

Fig7: Gene Ontology classes over and underrepresented in highly gene body methylated genes. Genes Ontology terms significantly enriched and depleted in genes in the top 10 % for gene body CG methylation. X-axis shows the percent of genes in both the high CG methylated portion (blue) as well as the remainder of the transcriptome (red) that contained the given GO terms. Text color represents the class of GO term: blue-molecular functions, grey-biological processes, and green-cellular component
Mentions: Comparing genes in the top 10 % genome wide for gene body CG methylation with the remainder of the genome, we found numerous gene categories that are either enriched or depleted in our set of highly CG methylated genes. Genes coding for proteins with kinase activity, involved in signal transduction, and nucleotide binding were among those which tended to be highly methylated, while proteins functioning in the thylakoid, plastid, and ribosome, as well as proteins involved in primary metabolism, photosynthesis, and RNA binding tended to be lowly or moderately methylated (Fig. 7). Similar results have been found in Brachypodium, rice [29], and Arabidopsis [3].Fig. 7

Bottom Line: Additionally, we find that DNA methylation is significantly depleted near gene transcriptional start sites, which may explain the recently discovered elevated rate of recombination in these same regions.Using a model-based approach, we demonstrate that methylation patterns are an important predictor of variation in gene expression.This model provides a novel approach for differential methylation analysis that generates distinct and testable hypotheses regarding gene expression.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, 66045, USA. Colicchio@ku.edu.

ABSTRACT

Background: The presence of methyl groups on cytosine nucleotides across an organism's genome (methylation) is a major regulator of genome stability, crossing over, and gene regulation. The capacity for DNA methylation to be altered by environmental conditions, and potentially passed between generations, makes it a prime candidate for transgenerational epigenetic inheritance. Here we conduct the first analysis of the Mimulus guttatus methylome, with a focus on the relationship between DNA methylation and gene expression.

Results: We present a whole genome methylome for the inbred line Iron Mountain 62 (IM62). DNA methylation varies across chromosomes, genomic regions, and genes. We develop a model that predicts gene expression based on DNA methylation (R(2) = 0.2). Post hoc analysis of this model confirms prior relationships, and identifies novel relationships between methylation and gene expression. Additionally, we find that DNA methylation is significantly depleted near gene transcriptional start sites, which may explain the recently discovered elevated rate of recombination in these same regions.

Conclusions: The establishment here of a reference methylome will be a useful resource for the continued advancement of M. guttatus as a model system. Using a model-based approach, we demonstrate that methylation patterns are an important predictor of variation in gene expression. This model provides a novel approach for differential methylation analysis that generates distinct and testable hypotheses regarding gene expression.

No MeSH data available.