bioRxivPlant BiologyPreprint
Gaining comprehensive biological insight into chloroplast RNA editing by performing a broad-spectrum RNA-seq analysis
Abstract
BackgroundRNA editing is a post-transcriptional modification that complement variation at the DNA level. Until now, different RNA editing systems were found in the major eukaryotic lineages. However, the evolution trajectory in plant chloroplast remains unclear. To gain a better understanding of RNA editing in plant chloroplast, in this study, based on publicly available RNA-seq data across three plant lineages (fern, gymnosperm, and angiosperm), we provided a detailed analysis of RNA editing events in plant chloroplasts and discussed the evolution of RNA editing in land plants. ResultsThere were a total of 5,389 editing sites located in leaf chloroplast identified across 21 plants after rigorous screening. We found that the cluster of RNA editing sites across 21 plants complied with the phylogenetic tree based on linked protein sequences approximately, and majority ([~] 95%) of the editing events resulted in non-synonymous codon changes, RNA editing occurred in second codon position was mainly the largest. Additionally, RNA editing caused an overall increase in hydrophobicity of the resulting proteins. The analyses also revealed that there is an uneven distribution of editing sites among species, genes, and codon positions, the average RNA editing extent varied among different plant species as well as genes. Finally, we found that the loss of editing sites along angiosperm evolution is mainly occurring by reduce of cytosines content, fern plants has the highest cytosine content, with the evolution of plants, cytosine were lost in RNA edited genes. ConclusionsMany of the identified sites in our study have not been previously reported and represent a valuable data set for future research community. Our findings provide valuable information for evolution of RNA editing in plants.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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