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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.


DNA methylation modeling to predict gene expression. A visual depiction of our simplified model showing the effect of gene body CG methylation and an increasing complexity of interaction terms on gene expression. a A scatterplot comparing Z-transformed gene body CG methylation values with log(gene expression) values. The black line shows the linear term, green line includes both the linear and quiadratic term, and the blue line includes linear, quadratic, and cubic terms. b Interaction plot depicting the interaction between gene CG methylation and exonlength, up-stream CHH methylation, gene body CHH methylation, and gene body CG methylation on gene expression. Summed terms across these four terms are considered ranging from −1.6 (dark purple) to 1.6 (yellow). Points represent actual genes CG gene body methylation, gene expression, and their color represents their interaction sum on the same scale as the model colors. c The second order interaction term of squared gene body CG methylation by exon length is added to the model depicted in b. As exon length increases (goes from red to blue) gene body CG methylation is found to have a more positive effect on gene expression. Points represent genes, and colors represent the exon length of these genes on the same scale as the model colors. d The independent effect of exon length on gene expression is added to the model depicted in c. The shape of the lines does not change, however predicted gene expression is altered (the lines move up or down on the y-axis) depending on the predicted effects of exon length on gene expression
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Fig6: DNA methylation modeling to predict gene expression. A visual depiction of our simplified model showing the effect of gene body CG methylation and an increasing complexity of interaction terms on gene expression. a A scatterplot comparing Z-transformed gene body CG methylation values with log(gene expression) values. The black line shows the linear term, green line includes both the linear and quiadratic term, and the blue line includes linear, quadratic, and cubic terms. b Interaction plot depicting the interaction between gene CG methylation and exonlength, up-stream CHH methylation, gene body CHH methylation, and gene body CG methylation on gene expression. Summed terms across these four terms are considered ranging from −1.6 (dark purple) to 1.6 (yellow). Points represent actual genes CG gene body methylation, gene expression, and their color represents their interaction sum on the same scale as the model colors. c The second order interaction term of squared gene body CG methylation by exon length is added to the model depicted in b. As exon length increases (goes from red to blue) gene body CG methylation is found to have a more positive effect on gene expression. Points represent genes, and colors represent the exon length of these genes on the same scale as the model colors. d The independent effect of exon length on gene expression is added to the model depicted in c. The shape of the lines does not change, however predicted gene expression is altered (the lines move up or down on the y-axis) depending on the predicted effects of exon length on gene expression

Mentions: Linear Effects: log(GE) = 2.61 − 0.07mcg = f  1, where mcg is gene body CG methylation and GE is gene expression. Controlling for gene architecture and other forms of methylation, we observe a negative linear effect of gene body CG methylation on gene expression (Figs. 5 and 6a. black line). The effect size of gene body CG methylation (mcg) is −0.07 (Table 3); a gene with mcg = − 1 (32 %) is predicted to have 35 % higher expression than one with mcg = 1 (80 %) (Fig. 6a, black line). Previous studies report that gene body CG methylation is positively correlated with gene expression [3, 10, 19, 20]. While the linear component of the model seems to contradict these previous reports, it cannot be interpreted in isolation. The polynomial and interaction terms indicate that gene body methylation has neither universally positive nor negative effects on gene expression. Traditional methods that looked for associations between gene expression and gene body CG methylation (which find a positive correlation between the two), and modeling methods as applied here followed by only analysis of the simple linear terms (which finds a negative correlation) come up quite short in portraying the role of gene body CG methylation in transcriptional regulation. By considering non-linear effects of methylation on gene expression we can begin to increase our understanding of the role of gene body CG methylation in gene regulation.Fig. 6


DNA methylation and gene expression in Mimulus guttatus.

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

DNA methylation modeling to predict gene expression. A visual depiction of our simplified model showing the effect of gene body CG methylation and an increasing complexity of interaction terms on gene expression. a A scatterplot comparing Z-transformed gene body CG methylation values with log(gene expression) values. The black line shows the linear term, green line includes both the linear and quiadratic term, and the blue line includes linear, quadratic, and cubic terms. b Interaction plot depicting the interaction between gene CG methylation and exonlength, up-stream CHH methylation, gene body CHH methylation, and gene body CG methylation on gene expression. Summed terms across these four terms are considered ranging from −1.6 (dark purple) to 1.6 (yellow). Points represent actual genes CG gene body methylation, gene expression, and their color represents their interaction sum on the same scale as the model colors. c The second order interaction term of squared gene body CG methylation by exon length is added to the model depicted in b. As exon length increases (goes from red to blue) gene body CG methylation is found to have a more positive effect on gene expression. Points represent genes, and colors represent the exon length of these genes on the same scale as the model colors. d The independent effect of exon length on gene expression is added to the model depicted in c. The shape of the lines does not change, however predicted gene expression is altered (the lines move up or down on the y-axis) depending on the predicted effects of exon length on gene expression
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig6: DNA methylation modeling to predict gene expression. A visual depiction of our simplified model showing the effect of gene body CG methylation and an increasing complexity of interaction terms on gene expression. a A scatterplot comparing Z-transformed gene body CG methylation values with log(gene expression) values. The black line shows the linear term, green line includes both the linear and quiadratic term, and the blue line includes linear, quadratic, and cubic terms. b Interaction plot depicting the interaction between gene CG methylation and exonlength, up-stream CHH methylation, gene body CHH methylation, and gene body CG methylation on gene expression. Summed terms across these four terms are considered ranging from −1.6 (dark purple) to 1.6 (yellow). Points represent actual genes CG gene body methylation, gene expression, and their color represents their interaction sum on the same scale as the model colors. c The second order interaction term of squared gene body CG methylation by exon length is added to the model depicted in b. As exon length increases (goes from red to blue) gene body CG methylation is found to have a more positive effect on gene expression. Points represent genes, and colors represent the exon length of these genes on the same scale as the model colors. d The independent effect of exon length on gene expression is added to the model depicted in c. The shape of the lines does not change, however predicted gene expression is altered (the lines move up or down on the y-axis) depending on the predicted effects of exon length on gene expression
Mentions: Linear Effects: log(GE) = 2.61 − 0.07mcg = f  1, where mcg is gene body CG methylation and GE is gene expression. Controlling for gene architecture and other forms of methylation, we observe a negative linear effect of gene body CG methylation on gene expression (Figs. 5 and 6a. black line). The effect size of gene body CG methylation (mcg) is −0.07 (Table 3); a gene with mcg = − 1 (32 %) is predicted to have 35 % higher expression than one with mcg = 1 (80 %) (Fig. 6a, black line). Previous studies report that gene body CG methylation is positively correlated with gene expression [3, 10, 19, 20]. While the linear component of the model seems to contradict these previous reports, it cannot be interpreted in isolation. The polynomial and interaction terms indicate that gene body methylation has neither universally positive nor negative effects on gene expression. Traditional methods that looked for associations between gene expression and gene body CG methylation (which find a positive correlation between the two), and modeling methods as applied here followed by only analysis of the simple linear terms (which finds a negative correlation) come up quite short in portraying the role of gene body CG methylation in transcriptional regulation. By considering non-linear effects of methylation on gene expression we can begin to increase our understanding of the role of gene body CG methylation in gene regulation.Fig. 6

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.