| Literature DB >> 31768118 |
Shuhei Yasumoto1, Naoyuki Umemoto2, Hyoung Jae Lee3, Masaru Nakayasu3, Satoru Sawai1, Tetsushi Sakuma4, Takashi Yamamoto4, Masaharu Mizutani3, Kazuki Saito2, Toshiya Muranaka1.
Abstract
Potato (Solanum tuberosum) is one of the most important crops in the world. However, it is generally difficult to breed a new variety of potato crops because they are highly heterozygous tetraploid. Steroidal glycoalkaloids (SGAs) such as α-solanine and α-chaconine found in potato are antinutritional specialized metabolites. Because of their toxicity following intake, controlling the SGA levels in potato varieties is critical in breeding programs. Recently, genome-editing technologies using artificial site-specific nucleases such as TALEN and CRISPR-Cas9 have been developed and used in plant sciences. In the present study, we developed a highly active Platinum TALEN expression vector construction system, and applied to reduce the SGA contents in potato. Using Agrobacterium-mediated transformation, we obtained three independent transgenic potatoes harboring the TALEN expression cassette targeting SSR2 gene, which encodes a key enzyme for SGA biosynthesis. Sequencing analysis of the target sequence indicated that all the transformants could be SSR2-knockout mutants. Reduced SGA phenotype in the mutants was confirmed by metabolic analysis using LC-MS. In vitro grown SSR2-knockout mutants exhibited no differences in morphological phenotype or yields when compared with control plants, indicating that the genome editing of SGA biosynthetic genes such as SSR2 could be a suitable strategy for controlling the levels of toxic metabolites in potato. Our simple and powerful plant genome-editing system, developed in the present study, provides an important step for future study in plant science.Entities:
Keywords: TALEN; genome editing; potato; steroidal glycoalkaloids
Year: 2019 PMID: 31768118 PMCID: PMC6854339 DOI: 10.5511/plantbiotechnology.19.0805a
Source DB: PubMed Journal: Plant Biotechnol (Tokyo) ISSN: 1342-4580 Impact factor: 1.133