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Binary vector copy number engineering improves Agrobacterium-mediated transformation

delete2024-11-04
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OA
AI
M
Matthew J. Szarzanowicz
L
Lucas Waldburger
M
Michael Busche
G
Gina M. Geiselman
L
Liam D. Kirkpatrick
A
Alexander J. Kehl
C
Claudine Tahmin
R
Rita Kuo
J
Joshua McCauley
H
H. Pannu
R
Ruoming Cui
S
Shuying Liu
N
Nathan J. Hillson
J
Jacob O. Brunkard
J
Jay D. Keasling
J
John M. Gladden
M
Mitchell G. Thompson
P
Patrick M. Shih *
DOI:10.1038/s41587-024-02462-2delete
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摘要

摘要

En 中文
The copy number of a plasmid is linked to its functionality, yet there have been few attempts to optimize higher-copy-number mutants for use across diverse origins of replication in different hosts. We use a high-throughput growth-coupled selection assay and a directed evolution approach to rapidly identify origin of replication mutations that influence copy number and screen for mutants that improve Agrobacterium-mediated transformation (AMT) efficiency. By introducing these mutations into binary vectors within the plasmid backbone used for AMT, we observe improved transient transformation of Nicotiana benthamiana in four diverse tested origins (pVS1, RK2, pSa and BBR1). For the best-performing origin, pVS1, we isolate higher-copy-number variants that increase stable transformation efficiencies by 60-100% in Arabidopsis thaliana and 390% in the oleaginous yeast Rhodosporidium toruloides. Our work provides an easily deployable framework to generate plasmid copy number variants that will enable greater precision in prokaryotic genetic engineering, in addition to improving AMT efficiency. Agrobacterium-mediated transformation of plants and fungi is enhanced by plasmid copy number variants.
Keyword:
HOST-RANGE PLASMID
T-DNA
GENE
REPLICATION
MUTATIONS
CELLS

期刊

Nature Biotechnology 封面图
Nature Biotechnology
IF:
41.7
论文数:
1.2W
被引数:
10.1W

机构

U
University of California Berkeley
学者数:
3.5W
论文数: 2.8W
被引数: 11.3W
U
united states department of energy (doe)
学者数:
11.3W
论文数: 9.6W
被引数: 246
University of California System 封面图
University of California System
学者数:
37.7W
论文数: 33.8W
被引数: 6.6K
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