Genome Biology and EvolutionGenomicsPeer-reviewed journal article
Robustness of Transposable Element regulation but no genomic shock observed in interspecific Arabidopsis hybrids
Ulrike Göbel, Agustín Lucas ArceiD, Fei HeiD, Alain Rico, Gregor SchmitziD, Juliette de MeauxiD
- Citations
- 36
- FWCI
- 3.53
- Field percentile
- top 8%
Abstract
The merging of two divergent genomes in a hybrid is believed to trigger a \"genomic shock\", disrupting gene regulation and transposable element (TE) silencing. Here, we tested this expectation by comparing the pattern of expression of transposable elements in their native and hybrid genomic context. For this, we sequenced the transcriptome of the Arabidopsis thaliana genotype Col-0, the A. lyrata genotype MN47 and their F1 hybrid. Contrary to expectations, we observe that the level of TE expression in the hybrid is strongly correlated to levels in the parental species. We detect that at most 1.1% of expressed transposable elements belonging to two specific subfamilies change their expression level upon hybridization. Most of these changes, however, are of small magnitude. We observe that the few hybrid-specific modifications in TE expression are more likely to occur when TE insertions are close to genes. In addition, changes in epigenetic histone marks H3K9me2 and H3K27me3 following hybridization do not coincide with TEs with changed expression. Finally, we further examined TE expression in parents and hybrids exposed to severe dehydration stress. Despite the major reorganization of gene and TE expression by stress, we observe that hybridization does not lead to increased disorganization of TE expression in the hybrid. We conclude that TE expression is globally robust to hybridization and that the term \"genomic shock\" is no longerappropriate to describe the anticipated consequences of merging divergent genomes in a hybrid.
- organism: Arabidopsis
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- approach: experimental assay
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