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Golden Gate Shuffling: A One-Pot DNA Shuffling Method Based on Type IIs Restriction Enzymes

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Title
Golden Gate Shuffling: A One-Pot DNA Shuffling Method Based on Type IIs Restriction Enzymes
Published in
PLOS ONE, May 2009
DOI 10.1371/journal.pone.0005553
Pubmed ID
Authors

Carola Engler, Ramona Gruetzner, Romy Kandzia, Sylvestre Marillonnet

Abstract

We have developed a protocol to assemble in one step and one tube at least nine separate DNA fragments together into an acceptor vector, with 90% of recombinant clones obtained containing the desired construct. This protocol is based on the use of type IIs restriction enzymes and is performed by simply subjecting a mix of 10 undigested input plasmids (nine insert plasmids and the acceptor vector) to a restriction-ligation and transforming the resulting mix in competent cells. The efficiency of this protocol allows generating libraries of recombinant genes by combining in one reaction several fragment sets prepared from different parental templates. As an example, we have applied this strategy for shuffling of trypsinogen from three parental templates (bovine cationic trypsinogen, bovine anionic trypsinogen and human cationic trypsinogen) each divided in 9 separate modules. We show that one round of shuffling using the 27 trypsinogen entry plasmids can easily produce the 19,683 different possible combinations in one single restriction-ligation and that expression screening of a subset of the library allows identification of variants that can lead to higher expression levels of trypsin activity. This protocol, that we call 'Golden Gate shuffling', is robust, simple and efficient, can be performed with templates that have no homology, and can be combined with other shuffling protocols in order to introduce any variation in any part of a given gene.

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Geographical breakdown

Country Count As %
United States 35 2%
United Kingdom 14 <1%
Germany 12 <1%
Canada 5 <1%
Spain 5 <1%
France 3 <1%
Netherlands 3 <1%
China 3 <1%
Mexico 3 <1%
Other 24 1%
Unknown 1837 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 446 23%
Researcher 364 19%
Student > Master 285 15%
Student > Bachelor 276 14%
Student > Doctoral Student 76 4%
Other 200 10%
Unknown 297 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 819 42%
Biochemistry, Genetics and Molecular Biology 530 27%
Engineering 59 3%
Chemistry 58 3%
Medicine and Dentistry 31 2%
Other 119 6%
Unknown 328 17%