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Codon optimization significantly enhanced the expression of human 37-kDa iLRP in Escherichia coli

Overview of attention for article published in 3 Biotech, April 2018
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Title
Codon optimization significantly enhanced the expression of human 37-kDa iLRP in Escherichia coli
Published in
3 Biotech, April 2018
DOI 10.1007/s13205-018-1234-y
Pubmed ID
Authors

Bainan Liu, Qianqian Kong, Dong Zhang, Lingli Yan

Abstract

37-kDa immature laminin receptor protein (iLRP), the precursor of 67-kDa laminin receptor protein (LRP), is overexpressed on the surface of most cancer cells and recognized as a universal tumor antigen. The role makes it a potential target for cancer immunotherapy, which has been well-studied. Our study aimed to produce high quality of human iLRP in bacteria so that the needs in research of its clinical application could be met. The powerful system for heterologous protein expression, pET system was used. Two types of DNA sequences encoding the same amino acid sequences were separately cloned into the vector pET30a(+). One of the resulting vectors includes the wild-type iLRP, and other one includes the codon-optimized iLRP. The expression by both genes was then compared in Escherichia coli BL21(DE3). Our results revealed that the performance of codon optimization was crucial for the expression of human iLRP in Escherichia coli. The yield was significantly enhanced up to 300 mg/L of bacterial culture by this approach.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 26%
Student > Master 3 13%
Student > Doctoral Student 2 9%
Student > Ph. D. Student 2 9%
Unknown 10 43%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 30%
Agricultural and Biological Sciences 2 9%
Immunology and Microbiology 2 9%
Chemistry 1 4%
Unknown 11 48%