bioRxivMicrobiology
Local rather than global H3K27me3 dynamics associates with differential gene expression in Verticillium dahliae
Abstract
Differential growth conditions typically trigger global transcriptional responses in filamentous fungi. Such fungal responses to environmental cues involve epigenetic regulation, including chemical histone modifications. It has been proposed that conditionally expressed genes, such as those that encode secondary metabolites but also effectors in pathogenic species, are often associated with a specific histone modification, lysine27 methylation of H3 (H3K27me3). However, thus far no analyses on the global H3K27me3 profiles have been reported under differential growth conditions in order to assess if H3K27me3 dynamics governs differential transcriptional. Using ChIP- and RNA-sequencing data from the plant pathogenic fungus Verticillium dahliae grown in three in vitro cultivation media, we now show that a substantial number of the identified H3K27me3 domains globally display stable profiles among these growth conditions. However, we do observe local quantitative differences in H3K27me3 ChIP-seq signal that associate with a subset of differentially transcribed genes between media. Comparing the in vitro results to expression during plant infection suggests that in planta-induced genes may require chromatin remodelling to achieve expression. Overall, our results demonstrate that some loci display H3K27me3 dynamics associated with concomitant transcriptional variation, but many differentially expressed genes are associated with stable H3K27me3 domains. Thus, we conclude that while H3K27me3 is required for transcriptional repression, it does not appear that transcriptional activation requires global erasure of H3K27me3. We propose that the H3K27me3 domains that do not undergo dynamic methylation may contribute to transcription through other mechanisms or may serve additional genomic regulatory functions.
- organism: Drosophila
- assay: ChIP-seq
- research focus: chromatin & epigenetic regulation
- approach: statistical model
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