Autonomous Reshaping of Expression Landscapes by DNA Methylation

TL;DR

This paper demonstrates that DNA methylation actively influences gene regulation dynamics, reshaping expression landscapes and cell fate decisions, rather than merely serving as a passive memory mark.

Contribution

It introduces a model where DNA methylation acts as an internal control variable, dynamically shaping gene expression landscapes and cell fate outcomes.

Findings

Methylation shifts transcription-factor binding thresholds, influencing gene regulation.

Feedback loops between methylation and expression enable autonomous landscape reshaping.

Evolving methylation reduces fate reversals, improving fate prediction accuracy.

Abstract

DNA methylation is usually treated as an epigenetic memory mark: transcriptional history is written into regulatory DNA and later stabilizes a chosen cell identity. This picture explains persistence, but it makes memory passive. Here we show that the same promoter-level coupling required for methylation memory can instead turn methylation into an internal control variable for regulatory dynamics. Transcription-factor occupancy protects regulatory DNA from methylation, while methylation shifts later transcription-factor binding thresholds. Under time-scale separation, this reciprocal loop separates into fast expression dynamics conditioned on methylation and a slow methylation flow written by expression. Minimal promoter, self-activation, and fate-toggle models show that this feedback does more than preserve a past state: it autonomously reshapes the expression landscape. In a methylation-coupled toggle, the preferred expression state can move continuously through single-well drift, allowing commitment without first entering a multiwell regime. Stochastic simulations further show that evolving methylation reduces fate reversals relative to a frozen landscape, making weak early expression bias more predictive of later fate. These results recast DNA methylation from a downstream stabilizer of cell identity into a slow dynamical coordinate that can help determine how regulatory states are chosen.