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Using Synthetic Biology to Distinguish and Overcome Regulatory and Functional Barriers Related to Nitrogen Fixation

Overview of attention for article published in PLOS ONE, July 2013
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Title
Using Synthetic Biology to Distinguish and Overcome Regulatory and Functional Barriers Related to Nitrogen Fixation
Published in
PLOS ONE, July 2013
DOI 10.1371/journal.pone.0068677
Pubmed ID
Authors

Xia Wang, Jian-Guo Yang, Li Chen, Ji-Long Wang, Qi Cheng, Ray Dixon, Yi-Ping Wang

Abstract

Biological nitrogen fixation is a complex process requiring multiple genes working in concert. To date, the Klebsiella pneumoniae nif gene cluster, divided into seven operons, is one of the most studied systems. Its nitrogen fixation capacity is subject to complex cascade regulation and physiological limitations. In this report, the entire K. pneumoniae nif gene cluster was reassembled as operon-based BioBrick parts in Escherichia coli. It provided ~100% activity of native K. pneumoniae system. Based on the expression levels of these BioBrick parts, a T7 RNA polymerase-LacI expression system was used to replace the σ(54)-dependent promoters located upstream of nif operons. Expression patterns of nif operons were critical for the maximum activity of the recombinant system. By mimicking these expression levels with variable-strength T7-dependent promoters, ~42% of the nitrogenase activity of the σ(54)-dependent nif system was achieved in E. coli. When the newly constructed T7-dependent nif system was challenged with different genetic and physiological conditions, it bypassed the original complex regulatory circuits, with minor physiological limitations. Therefore, we have successfully replaced the nif regulatory elements with a simple expression system that may provide the first step for further research of introducing nif genes into eukaryotic organelles, which has considerable potentials in agro-biotechnology.

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Geographical breakdown

Country Count As %
Brazil 2 2%
United Kingdom 2 2%
United States 2 2%
China 1 <1%
Canada 1 <1%
Unknown 113 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 26%
Researcher 21 17%
Student > Bachelor 13 11%
Student > Master 12 10%
Professor 5 4%
Other 19 16%
Unknown 20 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 39%
Biochemistry, Genetics and Molecular Biology 33 27%
Chemistry 3 2%
Social Sciences 3 2%
Immunology and Microbiology 3 2%
Other 10 8%
Unknown 22 18%