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Kakoulidou, I.

Publications and source records attributed to Kakoulidou, I..

2 recordsLinked to original sources

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↗

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↗