Intraindividual dynamics of transcriptome and genome-wide stability of DNA methylation

Abstract

Cytosine methylation at CpG dinucleotides is an epigenetic mechanism that affects the gene expression profiles responsible for the functional differences in various cells and tissues. Although gene expression patterns are dynamically altered in response to various stimuli, the intraindividual dynamics of DNA methylation in human cells are yet to be fully understood. Here, we investigated the extent to which DNA methylation contributes to the dynamics of gene expression by collecting 24 blood samples from two individuals over a period of 3 months. Transcriptome and methylome association analyses revealed that only ~2% of dynamic changes in gene expression could be explained by the intraindividual variation of DNA methylation levels in peripheral blood mononuclear cells and purified monocytes. These results showed that DNA methylation levels remain stable for at least several months, suggesting that disease-associated DNA methylation markers are useful for estimating the risk of disease manifestation.

Figure 1. Study design.

(a) Study workflow. (b) Schedule of blood collection. Magenta bars indicate the day of blood collection. (c,d) Participant’s health monitoring. Body temperatures (c) and high-sensitivity C-reactive protein (hs-CRP) serum levels (d) of the participants over the blood collection period.

Figure 2. Sample summaries.

(a) Monocyte gating strategy. Typical light-scatter density-plot of PBMCs (left) and CD14^highCD16^low monocytes were isolated from the monocyte-containing gate based on CD14 and CD16 expression (right). (b) Isolated CD14^highCD16^low monocyte population (96.7% ± 1.0% purity). (c) Cell numbers (left) and DNA (middle) and RNA (right) yields obtained for each cell population. (d) Estimated cellular composition for each sample as determined by reference information on cell-specific DNA methylation signatures using the “estimateCellCounts” function implemented in the minfi package. Bars in (c,d) represent mean ± standard deviation (grey, participant #1; black, participant #2).

Figure 3. Extraction of dynamically expressed genes.

(a) Dynamically expressed genes were defined as coefficient of variation (CV) outliers for each gene with 0.05-interval bins of the average of log₁₀ [FPKM + 1]. (b) Association analysis between cell-type composition and variation in the expression of dynamically expressed genes in each cell population. The vertical axis shows the distribution of the negative log₁₀ (p values) obtained from linear regression and ANOVA testing.

Figure 4. Contribution of DNA methylation to gene expression.

(a) The distribution of the R² values—representing the proportion of gene expression variation due to DNA methylation—was computed by linear regression analysis. (b) The distribution of negative log₁₀ (p values) obtained from Spearman’s rank correlation and permutation test. (c) The standard deviation distribution for DNA methylation at each CpG site near the dynamic non-CEA genes. (a,b) All FPKM and beta values were converted to relative values using the values of Day 1 as a reference.