Kundariya, H. | bioRxiv Science

bioRxiv2026

Intragenic methylation repatterning is associated with alternative splicing and unique epigenetic phenotypes

The role of intragenic cytosine methylation in shaping phenotypes has been contentious. Recent studies show association between stress and alternative splicing of transcripts, but without functional genome-wide or single-position analysis. We utilized the msh1 experimental system in Arabidopsis as a model of reproducible epigenetic states with stress-responsive phenotypes, including commitment to heritable memory for at least seven generations. We mapped the methylome to single-cytosine resolution with signal-detection, verified by machine learning. Differentially methylated genes were overlapped with msh1-derived transcript isoforms to show that different patterns of exonic methylation led to different levels of isoform expression. Alternatively spliced and differentially methylated genes were enriched in key regulators of growth and development and spliceosome components. Genes targeted for differential methylation also contained a known CTT motif. These results demonstrate a direct relationship in plants between environmentally responsive differential methylation and alternative splicing behavior leading to phenotype changes.

plant biology↗

bioRxiv2018

Enhancing resolution of natural methylome reprogramming behavior in plants

BackgroundNatural methylome reprogramming within chromatin involves changes in local energy landscapes that are subject to thermodynamic principles. Signal detection permits the discrimination of methylation signal from dynamic background noise that is induced by thermal fluctuation. Current genome-wide methylation analysis methods do not incorporate biophysical properties of DNA, and focus largely on DNA methylation density changes, which limits resolution of natural, more subtle methylome behavior in relation to gene activity.\n\nResultsWe present here a novel methylome analysis procedure, Methyl-IT, based on information thermodynamics and signal detection. Methylation analysis involves a signal detection step, and the method was designed to discriminate methylation regulatory signal from background variation. Comparisons with commonly used programs and two publicly available methylome datasets, involving stages of seed development and drought stress effects, were implemented. Information divergence between methylation levels from different groups, measured in terms of Hellinger divergence, provides discrimination power between control and treatment samples. Differentially informative methylation positions (DIMPs) achieved higher sensitivity and accuracy than standard differentially methylated positions (DMPs) identified by other methods. Differentially methylated genes (DMG) that are based on DIMPs were significantly enriched in biologically meaningful networks.\n\nConclusionsMethyl-IT analysis enhanced resolution of natural methylome reprogramming behavior to reveal network-associated responses, offering resolution of gene pathway influences not attainable with previous methods.

genomics↗