Biology subjects

Kakoulidou, I.

Publications and source records attributed to Kakoulidou, I..

3 recordsLinked to original sources

bioRxiv2024

Accelerated growth increases the somatic epimutation rate in trees

Trees are critical components of ecosystems and of major economic importance. Due to their extraordinary longevity and well-defined modular architecture they have also emerged as model systems to study the long-term accumulation of somatic mutations in plants. Coupled with retrospective life-history and environmental data, trees can offer unique insights into mutational processes that would be difficult to obtain with prospective studies. In addition to genetic mutations, somatic epimutations in the form of stochastic gains and losses of DNA cytosine methylation have been shown to accumulate rapidly during ontogeny. One hypothesis is that somatic epimutations originate from DNA methylation maintenance errors during mitotic cell divisions, which would predict that their rate of accumulation scales with growth rate, rather than with age. Here we test this hypothesis in European beech. We analyzed one of the oldest continuously measured experimental plots in the world. The plot contains an even-aged beech stand that was established in 1822 and monitored for growth at regular intervals until the present. Starting [~]150 years ago, alternative thinning strategies were applied to subplots of this experiment, resulting in differential stem growth rates among trees. We show that accelerated growth significantly increased the per-year somatic epimutation rate at CG dinucleotides, and that this effect is accompanied by differences in cell division rates. Hence, faster growth elevates the chances for methylation maintenance errors during DNA replication per unit time. As somatic CG epimutations can be stably inherited to subsequent generations in plants, our insights have direct ecological and evolutionary implications.

genomics↗

bioRxiv2022

Parental pericentromeric methylation status drives methylome remodelling and heterosis in epigenetic hybrids

Heterosis is the superior phenotypic performance of F1 hybrids relative to their parents. Although this phenomenon is extensively exploited commercially, its molecular causes remain elusive. A central challenge is to understand how specific features of parental (epi)genomes contribute to the widespread functional remodelling that occurs in hybrids. Using Arabidopsis, we show that differentially methylated regions (DMRs) in parental pericentromeres act as major re-organizers of hybrid methylomes and transcriptomes, even in the absence of genetic variation. We demonstrate that these parental DMRs facilitate methylation changes in the hybrids not only in cis, but also in trans at thousands of target regions throughout the genome. Many of these trans-induced changes facilitate the expression of nearby genes, and are significantly associated with phenotypic heterosis. Our study establishes the epigenetic status of parental pericentromeres as an important predictor of heterosis and elucidates its pleiotropic potential in the functional remodelling of hybrid genomes.

genomics↗

bioRxiv2022

MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant WGBS data

Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Many WGBS-based studies aim to identify biologically relevant loci that display differential methylation between genotypes, treatment groups, tissues, or developmental stages. Over the years, different tools have been developed to extract differentially methylated regions (DMRs) from whole-genome data. Often, such tools are built upon assumptions from mammalian data and do not consider the substantially more complex and variable nature of plant DNA methylation. Here, we present MethylScore, a pipeline to analyze WGBS data and to account for plant-specific DNA methylation properties. MethylScore processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. It uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation, substantially reducing the number of necessary statistical tests while increasing the signal-to-noise ratio and the statistical power. We show how MethylScore can identify DMRs from hundreds of samples and how its data-driven approach can stratify associated samples without prior information. We identify DMRs in the A. thaliana 1001 Genomes dataset to unveil known and unknown genotype-epigenotype associations. MethylScore is an accessible pipeline for plant WGBS data, with unprecedented features for DMR calling in small- and large-scale datasets; it is built as a Nextflow pipeline and its source code is available at https://github.com/Computomics/MethylScore.

plant biology↗