High-throughput transcriptome analysis of the leafy flower transition of catharanthus roseus induced by peanut witches'-broom phytoplasma infection
Journal
Plant and Cell Physiology
Journal Volume
55
Journal Issue
5
Pages
942-957
Date Issued
2014
Author(s)
Liu L.-Y.D.
Tseng H.-I.
Lin C.-P.
Lin Y.-Y.
Huang Y.-H.
Huang C.-K.
Chang T.-H.
CHAN-PIN LIN
Abstract
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.
Type
journal article