Agrobacterium tumefaciens-mediated transformation system for the important medicinal plant Dendrobium catenatum Lindl View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-06

AUTHORS

Ji Chen, Li Wang, Jianbin Chen, Jin Huang, Fan Liu, Rong Guo, Li Yang, Aby Grabon, Ke Zhao, Fanlei Kong, Chen Liu, Mengliang Tian

ABSTRACT

The orchid Dendrobium catenatum Lindl. has both ornamental and medicinal value. Studies on genetic transformation of other Dendrobium species have been reported; however, due to the heterogeneity among the Dendrobium species, an independent transformation system is required for optimum D. catenatum transformation. Agrobacterium tumefaciens-mediated transformation system for D. catenatum is reported herein. Three A. tumefaciens strains, GV3101, LBA4404, and EHA105, were tested for their ability to transform D. catenatum protocorms. It was found that infection with strain GV3101 at an OD600 of 0.6 yielded the highest transformation frequency of 56.5%. Addition of 0.001% (v/v) Triton™ X-100 or pretreatment with ultrasound (300 W, 40 kHz, 3 min) increased the transformation frequency by up to 2-fold. After 1 year, both green fluorescent protein (GFP) imaging and glucuronidase (GUS) histochemical analysis of transgenic plants demonstrated that DcObg-GFP and pDcObg-GUS construct DNA was transferred into the D. catenatum and expressed successfully. Functional and gene expression level analyses of transgenic plants containing a D. catenatum chloroplast-targeted homolog of Spo0B-associated GTP-binding protein (DcObgC) RNA interference construct (DcObgC-RNAi) further demonstrated that this transformation system was reliable and efficient for D. catenatum. The A. tumefaciens-based transformation protocol established and optimized in this current report could provide a powerful and efficient tool for academic research and genetic engineering-based breeding of D. catenatum. More... »

PAGES

228-239

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    http://scigraph.springernature.com/pub.10.1007/s11627-018-9903-4

    DOI

    http://dx.doi.org/10.1007/s11627-018-9903-4

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    https://app.dimensions.ai/details/publication/pub.1103660396


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