花青素
生物
MYB公司
栽培
转录组
基因
基因表达
小RNA
RNA序列
遗传学
植物
作者
Jie Yang,Juan Meng,Xiaolin Liu,Junshu Hu,Yuntao Zhu,Yiran Zhao,Gui-Xia Jia,Hengbin He,Tao Yuan
标识
DOI:10.1016/j.plaphy.2021.05.035
摘要
Anthocyanins are one of the main components of pigments, that are responsible for a wide range of colors in plants. To clarify the regulatory mechanism of anthocyanin biosynthesis in oriental hybrid lily, UPLC/MS analysis was performed to identify the pigments in two cultivars (white and pink). Four major anthocyanins were identified in pink cultivar, and no anthocyanins were detected in white cultivar. Transcriptome and small RNA sequencing (sRNAseq) analyses were performed using tepal tissues at two floral developmental stages from the two cultivars. In total, 55,698 transcripts were assembled, among which 233 were annotated as putative anthocyanin-related transcripts. Differential expression analysis and qRT-PCR results confirmed that most of the anthocyanin-related structural genes had higher expression levels in pink cultivar than in white cultivar. Conversely, LhANR showed a significantly high expression level in white cultivar. Annotated transcription factors (TFs), including MYB activators, MYB repressors and bHLHs, that putatively inhibit or enhance the expression of anthocyanin-related genes were identified. LhMYBA1, an anthocyanin activator, was isolated, and its heterologous expression resulted in a remarkably high level of anthocyanin accumulation. Additionally, 73 differentially expressed microRNAs (miRNAs), including 23 known miRNAs, were detected through sRNAseq. The miRNA target prediction showed that several anthocyanin-related genes might be targeted by miRNAs. Expression profile analysis revealed that these miRNAs showed higher expression levels at later floral developmental stages in white cultivar than in pink cultivar. The results indicated that anthocyanin deficiency in white cultivar might be influenced by multiple levels of suppressive mechanisms, including mRNAs and sRNAs.
科研通智能强力驱动
Strongly Powered by AbleSci AI