Sugars and Sucrose Transporters in Pollinia of Phalaenopsis aphrodite (Orchidaceae).
Wan-Ting SunShyi-Chyi ChengYa-Ting ChaoShu-Yao LinTing-Ting YangYi-Ping HoMing-Che ShihSwee-Suak KoPublished in: Journal of experimental botany (2023)
The pollen grains of Phalaenopsis orchids are clumped tightly together, packed in pollen dispersal units called pollinia. In this study, the morphology, cytology, biochemistry, and sucrose transporters in pollinia of Phalaenopsis orchids were investigated. Periodic acid-Schiff (PAS) counterstained with DAPI or aniline blue was used to characterize the distribution of sugars and callose at the different development stages of pollinia. Ultra performance liquid chromatography-high resolution-tandem mass spectrometry data indicated that Phal. orchid accumulated abundant saccharides such as sucrose, galactinol, myo-inositol, and glucose, and trace amounts of raffinose and trehalose in mature pollinia. We found that galactinol synthase (PAXXG304680) and trehalose-6-phosphate phosphatase (PAXXG016120) were preferentially expressed in mature pollinia. The Phal. aphrodite genome was identified as having 11 sucrose transporters (SUTs). Our qRT-PCR confirmed that two SUTs (PAXXG030250 and PAXXG195390) were preferentially expressed in the pollinia. Pollinia germinated in Brewbaker and Kwack's medium (BK) pollen germination media supplemented with 10% sucrose showed increased callose production and enhanced pollinia germination, but there was no callose and no germination in BK media without sucrose. We postulate that Phal. orchid accumulates high levels of sugars in mature pollinia providing nutrients and enhanced sucrose transporter gene expression for pollinia germination and tube growth.
Keyphrases
- tandem mass spectrometry
- liquid chromatography
- high resolution
- gene expression
- ultra high performance liquid chromatography
- mass spectrometry
- high performance liquid chromatography
- simultaneous determination
- gas chromatography
- high resolution mass spectrometry
- dna methylation
- high grade
- heavy metals
- plant growth
- adipose tissue
- blood pressure
- big data
- blood glucose
- machine learning
- electronic health record
- deep learning
- insulin resistance
- metabolic syndrome
- fine needle aspiration
- ultrasound guided