bioRxivGenomicsPreprint
Heritable changes of epialleles in maize can be triggered in the absence of DNA methylation
Beibei LiuiD, Diya YangiD, Dafang WangiD, Chun LiangiD, Jianping WangiD, Damon R. LischiD, Meixia ZhaoiD
Abstract
Trans-chromosomal interactions resulting in changes in DNA methylation during hybridization have been observed in several plant species. However, very little is known about the causes or consequences of these interactions. Here, we compared DNA methylomes of F1 hybrids that are mutant for a small RNA biogenesis gene, Mop1 (mediator of paramutation1) with that of their parents, wild type siblings, and backcrossed progeny in maize. Our data show that hybridization triggers global changes in both trans-chromosomal methylation (TCM) and trans-chromosomal demethylation (TCdM), most of which involved changes in CHH methylation. In more than 60% of these TCM differentially methylated regions (DMRs) in which small RNAs are available, no significant changes in the quantity of small RNAs were observed. Methylation at the CHH TCM DMRs was largely lost in the mop1 mutant, although the effects of this mutant varied depending on the location of the CHH DMRs. Interestingly, an increase in CHH at TCM DMRs was associated with enhanced expression of a subset of highly expressed genes and suppressed expression of a small number of lowly expressed genes. Examination of the methylation levels in backcrossed plants demonstrates that TCM and TCdM can be maintained in the subsequent generation, but that TCdM is more stable than TCM. Surprisingly, although increased CHH methylation in F1 plants did require Mop1, initiation of the changes in the epigenetic state of TCM DMRs did not require a functional copy of this gene, suggesting that initiation of these changes is not dependent on RNA-directed DNA methylation.
- organism: zebrafish
- assay: DNA methylation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 2
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