转化(遗传学)
生物
可选择标记
绿色荧光蛋白
农杆菌
转基因
根癌农杆菌
表达式向量
质粒
拉伤
转化效率
分子生物学
遗传学
DNA
基因
重组DNA
解剖
作者
Lihua Li,Xudan Tian,Lanlan Wang,Jianhua Zhao,Jie Zhou,He Haiyan,Liangying Dai,Shaohong Qu
标识
DOI:10.1007/s11033-022-07864-6
摘要
Biotechnologists seeking to develop marker-free transgenic plants have established co-transformation methods. For co-transformation using mixed Agrobacterium strains, the mix ratio of Agrobacterium strains and selection scheme may influence co-transformation frequency. This study used fluorescent GFP and RFP markers to compose different selection schemes for observation of the selective dynamics of transformed rice cells and to investigate the factors affecting co-transformation efficiency. We utilized GFP and RFP markers in co-transformation and tested the combinations of an antibiotic-selectable vector (pGFP-HPT) and a single RFP vector (pRFP) and of two antibiotic-selectable vectors (pGFP-HPT and pRFP-HPT) in rice. The pGFP-HPT/pRFP combination resulted in 70.9% to 81.2% of co-transformation frequencies while lower frequencies (56.6% on average) were obtained with the pGFP-HPT/pRFP-HPT combination. Based on GFP/RFP segregation patterns, 55% of the pGFP-HPT/pRFP co-transformants contained unlinked T-DNAs and segregated single RFP progeny, which simulated the selection process of marker-free transgenic plants that carry an actual gene of interest. Transgene expression levels in the rice lines varied as revealed by RT-PCR, and tandem-linked T-DNAs were detected in co-transformants, suggesting that transgene expression might be affected by duplicated T-DNA structures. Co-transformation via mixed Agrobacterium strains is feasible, and approximately 55% of the pGFP-HPT/pRFP co-transformants contained unlinked T-DNAs and segregated single RFP progeny. The pGFP-HPT/pRFP and the pGFP-HPT/pRFP-HPT vector combinations showed distinctive selective dynamics of transformed rice cells, suggesting that co-transformation efficiency depends on both vector system and selection scheme.
科研通智能强力驱动
Strongly Powered by AbleSci AI