A number of plant genomes have been fully sequenced, but we still do not know exactly how many genes are encoded in each genome. One of the factors contributing to this challenge is alternative splicing, in which particular exons of a gene may be included or excluded from the final processed mRNA. Because of alternative splicing, a single gene can code for multiple proteins. This functions as a key regulatory mechanism increasing transcriptome and proteome diversity. Recent genome-wide studies have substantially expanded our estimation of the frequency of alternative splicing in plants. However, the proportion of alternative splicing events that lead to increased proteome diversity in plants---as opposed to imperfect pre-mRNA processing---remains unknown.
A team of scientists led by Yuling Jiao from the IGDB has provided the first genome-wide estimation of the contribution of alternative splicing to proteome diversity in Arabidopsis thaliana. The research, which is published in Molecular Plant (DO I: 10.1016/j.molp.2015.12.018), analyzes alternative splicing events in presumably translated mRNAs, i.e., mRNAs associated with polysomes. The researchers found that 35% of alternative splicing events identified in total mRNAs occur in polysome-associated mRNAs. Furthermore, over 80% of these presumably translated alternative isoforms lead to diversified protein-coding capacity. In contrast, weakly translated---but not non-translated---alternative isoforms likely undergo nonsense-mediated mRNA decay. Sequence analyses identified structural features of transcripts and cis-elements that were associated with alternative splicing. The results suggest that alternative splicing in plants increases proteome complexity, but that it show clear difference from alternative splicing in animals.
This work was supported by a National Basic Research Program of China (973 Program), National Natural Science Foundation of China grants, National Program for Support of Top-Notch Young Professionals, and by the State Key Laboratory of Plant Genomics. The research team also included Haopeng Yu and Caihuan Tian (co-first authors) and Yang Yu, from IGDB.
By Jiao Yuling
