High-Throughput Transcriptome Analysis of the Leafy Flower Transition of Catharanthus roseus Induced by Peanut Witches’-Broom Phytoplasma Infection

多叶的 长春花 生物 植物菌原体 转录组 基因 RNA序列 遗传学 植物 使负有责任或义务 从头转录组组装 基因表达 聚合酶链反应 限制性片段长度多态性
作者
Liyu Liu,Hsin-I Tseng,Chan‐Pin Lin,Yen‐Yu Lin,Yuan-Hung Huang,Chien‐Kang Huang,Tean-Hsu Chang,Shih‐Shun Lin
出处
期刊:Plant and Cell Physiology [Oxford University Press]
卷期号:55 (5): 942-957 被引量:63
标识
DOI:10.1093/pcp/pcu029
摘要

Peanut witches'-broom (PnWB) phytoplasma are obligate bacteria that cause leafy flower symptoms in Catharanthus roseus. The PnWB-mediated leafy flower transitions were studied to understand the mechanisms underlying the pathogen-host interaction; however, our understanding is limited because of the lack of information on the C. roseus genome. In this study, the whole-transcriptome profiles from healthy flowers (HFs) and stage 4 (S4) PnWB-infected leafy flowers of C. roseus were investigated using next-generation sequencing (NGS). More than 60,000 contigs were generated using a de novo assembly approach, and 34.2% of the contigs (20,711 genes) were annotated as putative genes through name-calling, open reading frame determination and gene ontology analyses. Furthermore, a customized microarray based on this sequence information was designed and used to analyze samples further at various stages of PnWB infection. In the NGS profile, 87.8% of the genes showed expression levels that were consistent with those in the microarray profiles, suggesting that accurate gene expression levels can be detected using NGS. The data revealed that defense-related and flowering gene expression levels were altered in S4 PnWB-infected leafy flowers, indicating that the immunity and reproductive stages of C. roseus were compromised. The network analysis suggested that the expression levels of >1,000 candidate genes were highly associated with CrSVP1/2 and CrFT expression, which might be crucial in the leafy flower transition. In conclusion, this study provides a new perspective for understanding plant pathology and the mechanisms underlying the leafy flowering transition caused by host-pathogen interactions through analyzing bioinformatics data obtained using a powerful, rapid high-throughput technique.
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