Development of DNA methylation-based epigenetic age predictors in loblolly pine (Pinus taeda)

Abstract

Biological ageing is connected to life history variation across ecological scales and informs a basic understanding of age-related declines in organismal function. Altered DNA methylation dynamics are a conserved aspect of biological ageing and have recently been modelled to predict chronological age among vertebrate species. In addition to their utility in estimating individual age, differences between chronological and predicted ages arise due to acceleration or deceleration of epigenetic ageing, and these discrepancies are linked to disease risk and multiple life history traits. Although evidence suggests that patterns of DNA methylation can describe ageing in plants, predictions with epigenetic clocks have yet to be performed. Here, we resolve the DNA methylome across CpG, CHG, and CHH-methylation contexts in the loblolly pine tree (Pinus taeda) and construct epigenetic clocks capable of predicting ages in this species within 6% of its maximum lifespan. Although patterns of CHH-methylation showed little association with age, both CpG and CHG-methylation contexts were strongly associated with ageing, largely becoming hypomethylated with age. Among age-associated loci were those in close proximity to malate dehydrogenase, NADH dehydrogenase, and 18S and 26S ribosomal RNA genes. This study reports one of the first epigenetic clocks in plants and demonstrates the universality of age-associated DNA methylation dynamics which can inform conservation and management practices, as well as our ecological and evolutionary understanding of biological ageing in plants.

Keywords: Pinus taeda; DNA methylation; biological age; chronological age; epigenetic clock.

FIGURE 1

Histograms displaying the distribution of spearman correlation coefficients of cytosine methylation status with age following filtering of invariant sites across CpG, CHG, and CHH‐methylation contexts from 24 loblolly pine trees of differing ages. (a) of 21,567 CpGs analysed, 533 (2.5%) showed correlations between methylation status and age of R > |.5|. (b) of 25,501 CHGs analysed, 869 (3.4%) displayed correlations between methylation status and age of R > |.5|. (c) of 33,151 CHHs analysed, 308 (0.93%) showed correlations between age and methylation status of R > |.5|.

FIGURE 2

Enrichment and depletion of age‐related DNA methylation within genomic regions with varying CpG densities. Cytosines for which methylation status was associated with age (R > |0.5|) were compared against all RBSS‐captured cytosines (background). Darker green shading indicates proportion of cytosines for which methylation decreased with age and lighter green shading indicates cytosines for which methylation increased with age. (a) Age‐associated CpG‐methylation was enriched in CpG island and shore regions and were depleted in CpG shelf and open‐sea regions. (b) Age‐associated CHG‐methylation was enriched in CpG islands and depleted in shore regions. (c) There were no differences in the distributions of age‐associated CHH‐methylated cytosines compared to background CHH‐methylated cytosine levels (asterisks indicate p < .05).

FIGURE 3

Accuracy and precision of age predictions using elastic net models to predict chronological age for n = 21 Pinus taeda trees (ages 1–55 years) using a leave‐one‐out‐cross‐validation approach. (a) Predicted ages were highly correlated with chronological ages (R ² = .85, obtained from a regression of predicted age with chronological age). (b) Precision of predicted ages was assessed by comparing estimated age for each individual to their chronological age and is expressed as mean absolute error (MAE) in years.

FIGURE 4

Elastic net models used to predict chronological age in Pinus taeda for both training and test subsets. Clock accuracy is measured by the Pearson's correlation coefficient and precision by the mean absolute error (MAE) (± standard error). (a) a total of 34 CpGs out of 18,844 CpGs following invariant filtering were selected in our elastic net model. (b) a total of 35 CHGs out of 22,263 CHGs were selected and incorporated into the clock. (c) Combining CpG and CHG data sets, there were 41,107 cytosines following invariant filtering, with 38 incorporated into the clock. Values of individual data points represent the error between predicted ages from our models compared to chronological ages among test individuals, with positive values indicating overestimation and negative values indicating underestimation of chronological age.

FIGURE 5

Pearson models incorporating the five cytosines with the strongest age‐associated methylation patterns predict chronological ages in Pinus taeda for both training and test subsets for CpG, CHG, and combined (CpG + CHG) models. Clock accuracy is measured by the Pearson's correlation coefficient and precision by the mean absolute error (MAE) (± standard error). Values of individual datapoints represent the error between predicted ages from our models compared to chronological ages among our test individuals, with positive values indicating overestimation and negative values indicating underestimation of chronological age.