bioRxivPlant BiologyPreprint
The rapid evolution of alternative splicing in plants
Zhihao LingiD, Thomas BrockmölleriD, Ian T. Baldwin, Shuqing XuiD
Abstract
Alternative pre-mRNA splicing (AS) is prevalent among all plants and is involved in many interactions with environmental stresses. However, the evolutionary patterns and underlying mechanisms of AS in plants remain unclear. By analyzing the transcriptomes of six plant species, we revealed that AS diverged rapidly among closely related species, largely due to the gains and losses of AS events among orthologous genes. Furthermore, AS that generates transcripts containing premature termination codons (PTC), although only representing a small fraction of the total AS, are more conserved than those that generate non-PTC containing transcripts, suggesting that AS coupled with nonsense-mediated decay (NMD) might play an important role in regulating mRNA levels post-transcriptionally. With a machine learning approach we analyzed the key determinants of AS to understand the mechanisms underlying its rapid divergence. Among the studied species, the presence/absence of alternative splicing site (SS) within the junction, the distance between the authentic SS and the nearest alternative SS, the size of exon-exon junctions were the major determinants for both alternative 5 donor site and 3acceptor site, suggesting a relatively conserved AS mechanism. Comparative analysis further demonstrated that variations of the identified AS determinants, mostly are located in introns, significantly contributed to the AS turnover among closely related species in both Solanaceae and Brassicaceae taxa. These new mechanistic insights into the evolution of AS in plants highlight the importance of post-transcriptional regulation in mediating plant-environment interactions.\n\nOne sentence summaryChanges of intron located splicing regulators contributed to the rapid evolution of alternative splicing in plants.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: chromatin & epigenetic regulation
- research focus: development & morphogenesis
- approach: machine learning (classical)
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