ArticlePlant, cell & environment2025
Short-Term High Light Stress Analysis Through Differential Methylation Identifies Root Architecture and Cell Size Responses.
Article in Plant, cell & environment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Intragenic methylation repatterning is associated with alternative splicing and unique epigenetic phenotypes.bioRxiv : the preprint server for biology · 2026Article
- Intragenic methylation repatterning is associated with alternative splicing and unique epigenetic phenotypes.Environmental epigenetics · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
DNA methylation repatterning is an epigenomic component of plant stress response, but the extent that methylome data can elucidate changes in plant growth following stress onset is not known. We applied high-resolution DNA methylation analysis to decode plant responses to short- and long-term high light stress and, integrating with gene expression data, attempted to predict components of plant growth response. We identified 105 differentially methylated genes (DMGs) following 1 h of high light treatment and 193 DMGs following 1 week of intermittent high light treatment. Two distinct methylome-predicted plant growth responses to high light treatment could be confirmed by linking methylome changes in auxin response pathways to observed changes in root architecture and methylome changes in cell cycle pathway components to endoreduplication and palisade cell enlargement. We observed methylome changes in a cyclic GMP-dependent protein kinase in association with high light stress signalling. The ability to associate intragenic methylation repatterning with predictable plant phenotypic outcomes after a limited period of high light treatment allows for data-based early prediction of plant growth responses. The approach also permits the dissection of gene networks underpinning plant growth adjustments during environmental change to uncover dynamic phenotype determinants.
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Registered trials
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