Whole-Genome Resequencing and Transcriptomic Analysis to Identify Genes Involved in Leaf-Color Diversity in Ornamental Rice Plants

作者 Kim, Chang-Kug; Seol, Young-Joo; Shin, Younhee; Lim, Hye-Min; Lee, Gang-Seob; Kim, A-Ram; Lee, Tae-Ho; Lee, Jae-Hee; Park, Dong-Suk; Yoo, Seungil; Kim, Yong-Hwan; Kim, Yong-Kab

期刊 PLOS ONE , APR 21 2015,4:10

DOI 10.1371/journal.pone.0124071 PubMed 25897514

摘要 Rice field art is a large-scale art form in which people design rice fields using various kinds of ornamental rice plants with different leaf colors. Leaf color-related genes play an important role in the study of chlorophyll biosynthesis, chloroplast structure and function, and anthocyanin biosynthesis. Despite the role of different metabolites in the traditional relationship between leaf and color, comprehensive color-specific metabolite studies of ornamental rice have been limited. We performed whole-genome resequencing and transcriptomic analysis of regulatory patterns and genetic diversity among different rice cultivars to discover new genetic mechanisms that promote enhanced levels of various leaf colors. We resequenced the genomes of 10 rice leaf-color accessions to an average of 40x reads depth and >95% coverage and performed 30 RNA-seq experiments using the 10 rice accessions sampled at three developmental stages. The sequencing results yielded a total of 1,814 x 106 reads and identified an average of 713,114 SNPs per rice accession. Based on our analysis of the DNA variation and gene expression, we selected 47 candidate genes. We used an integrated analysis of the whole-genome resequencing data and the RNA-seq data to divide the candidate genes into two groups: genes related to macronutrient (i.e., magnesium and sulfur) transport and genes related to flavonoid pathways, including anthocyanidin biosynthesis. We verified the candidate genes with quantitative real-time PCR using transgenic T-DNA insertion mutants. Our study demonstrates the potential of integrated screening methods combined with genetic-variation and transcriptomic data to isolate genes involved in complex biosynthetic networks and pathways.