Asymmetric strand segregation: epigenetic costs of genetic fidelity?

Is adult stem cell aging driven by conflicting modes of chromatin remodeling?

Epigenetic control of gene expression by chromatin remodeling is critical for adult stem cell function. A decline in stem cell function is observed during aging, which is accompanied by changes in the chromatin structure that are currently unexplained. Here, we hypothesize that these epigenetic changes originate from the limited cellular capability to inherit epigenetic information. We suggest that spontaneous loss of histone modification, due to fluctuations over short time scales, gives rise to long-term changes in DNA methylation and, accordingly, in gene expression. These changes are assumed to impair stem cell function and, thus, to contribute to aging. We discuss cell replication as a major source of fluctuations in histone modification patterns. Gene silencing by our proposed mechanism can be interpreted as a manifestation of the conflict between the stem cell plasticity required for tissue regeneration and the permanent silencing of potentially deleterious genomic sequences.

STATISTICAL INFERENCE OF TRANSMISSION FIDELITY OF DNA METHYLATION PATTERNS OVER SOMATIC CELL DIVISIONS IN MAMMALS

We develop Bayesian inference methods for a recently-emerging type of epigenetic data to study the transmission fidelity of DNA methylation patterns over cell divisions. The data consist of parent-daughter double-stranded DNA methylation patterns with each pattern coming from a single cell and represented as an unordered pair of binary strings. The data are technically difficult and time-consuming to collect, putting a premium on an efficient inference method. Our aim is to estimate rates for the maintenance and de novo methylation events that gave rise to the observed patterns, while accounting for measurement error. We model data at multiple sites jointly, thus using whole-strand information, and considerably reduce confounding between parameters. We also adopt a hierarchical structure that allows for variation in rates across sites without an explosion in the effective number of parameters. Our context-specific priors capture the expected stationarity, or near-stationarity, of the stochastic process that generated the data analyzed here. This expected stationarity is shown to greatly increase the precision of the estimation. Applying our model to a data set collected at the human FMR1 locus, we find that measurement errors, generally ignored in similar studies, occur at a non-trivial rate (inappropriate bisulfite conversion error: 1.6% with 80% CI: 0.9-2.3%). Accounting for these errors has a substantial impact on estimates of key biological parameters. The estimated average failure of maintenance rate and daughter de novo rate decline from 0.04 to 0.024 and from 0.14 to 0.07, respectively, when errors are accounted for. Our results also provide evidence that de novo events may occur on both parent and daughter strands: the median parent and daughter de novo rates are 0.08 (80% CI: 0.04-0.13) and 0.07 (80% CI: 0.04-0.11), respectively.