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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.


Related in: MedlinePlus

Correlation matrix between forms of methylation at individual genes. Clouds represent density, and lines show the slope of the correlation. Green lines indicate forms of methylation with a positive correlation, while red represents negative correlation. Numbers represent the Pearson correlation (r) value, bolded numbers highlight correlations with an r > 0.35. All correlations were found to be statistically significant (n = 17,038, p < 0.05)
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Fig4: Correlation matrix between forms of methylation at individual genes. Clouds represent density, and lines show the slope of the correlation. Green lines indicate forms of methylation with a positive correlation, while red represents negative correlation. Numbers represent the Pearson correlation (r) value, bolded numbers highlight correlations with an r > 0.35. All correlations were found to be statistically significant (n = 17,038, p < 0.05)

Mentions: The methylation levels in all contexts (CG, CHG, CHH) and genic positions (up-stream, down-stream, and gene body) at a given gene were significantly correlated with one another (Fig. 4). These were positive correlations for all cases but two. The two exceptions were negative correlations between up- and down-stream CHH methylation with gene body CG methylation. The most significant positive correlations were found between CHG and CHH or CG methylation levels at both up-stream and down-stream regions, as well as between CHG and CHH gene body methylation. Interestingly, the methylation levels for all three contexts vary greatly across the three gene regions in a fairly unpredictable manner. For instance, correlation between up-stream CG methylation and gene body CG methylation is only r = 0.14. This highlights the disparate functions of regulatory region methylation with that of gene body methylation [54]. The extremely high correlations between CHG and CHH methylation (Fig. 4, r > 0.67) in all three regions is likely due to the involvement of similar enzymatic machinery in the propagation of both types of non-CG methylation [55].Fig. 4


DNA methylation and gene expression in Mimulus guttatus.

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

Correlation matrix between forms of methylation at individual genes. Clouds represent density, and lines show the slope of the correlation. Green lines indicate forms of methylation with a positive correlation, while red represents negative correlation. Numbers represent the Pearson correlation (r) value, bolded numbers highlight correlations with an r > 0.35. All correlations were found to be statistically significant (n = 17,038, p < 0.05)
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig4: Correlation matrix between forms of methylation at individual genes. Clouds represent density, and lines show the slope of the correlation. Green lines indicate forms of methylation with a positive correlation, while red represents negative correlation. Numbers represent the Pearson correlation (r) value, bolded numbers highlight correlations with an r > 0.35. All correlations were found to be statistically significant (n = 17,038, p < 0.05)
Mentions: The methylation levels in all contexts (CG, CHG, CHH) and genic positions (up-stream, down-stream, and gene body) at a given gene were significantly correlated with one another (Fig. 4). These were positive correlations for all cases but two. The two exceptions were negative correlations between up- and down-stream CHH methylation with gene body CG methylation. The most significant positive correlations were found between CHG and CHH or CG methylation levels at both up-stream and down-stream regions, as well as between CHG and CHH gene body methylation. Interestingly, the methylation levels for all three contexts vary greatly across the three gene regions in a fairly unpredictable manner. For instance, correlation between up-stream CG methylation and gene body CG methylation is only r = 0.14. This highlights the disparate functions of regulatory region methylation with that of gene body methylation [54]. The extremely high correlations between CHG and CHH methylation (Fig. 4, r > 0.67) in all three regions is likely due to the involvement of similar enzymatic machinery in the propagation of both types of non-CG methylation [55].Fig. 4

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.


Related in: MedlinePlus