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A Riboswitch-Based Inducible Gene Expression System for Mycobacteria

Overview of attention for article published in PLOS ONE, January 2012
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209 Mendeley
Title
A Riboswitch-Based Inducible Gene Expression System for Mycobacteria
Published in
PLOS ONE, January 2012
DOI 10.1371/journal.pone.0029266
Pubmed ID
Authors

Jessica C. Seeliger, Shana Topp, Kimberly M. Sogi, Mary L. Previti, Justin P. Gallivan, Carolyn R. Bertozzi

Abstract

Research on the human pathogen Mycobacterium tuberculosis (Mtb) would benefit from novel tools for regulated gene expression. Here we describe the characterization and application of a synthetic riboswitch-based system, which comprises a mycobacterial promoter for transcriptional control and a riboswitch for translational control. The system was used to induce and repress heterologous protein overexpression reversibly, to create a conditional gene knockdown, and to control gene expression in a macrophage infection model. Unlike existing systems for controlling gene expression in Mtb, the riboswitch does not require the co-expression of any accessory proteins: all of the regulatory machinery is encoded by a short DNA segment directly upstream of the target gene. The inducible riboswitch platform has the potential to be a powerful general strategy for creating customized gene regulation systems in Mtb.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 209 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 <1%
United States 2 <1%
United Kingdom 1 <1%
Hungary 1 <1%
Denmark 1 <1%
Iran, Islamic Republic of 1 <1%
Unknown 201 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 53 25%
Researcher 45 22%
Student > Master 28 13%
Student > Bachelor 22 11%
Student > Postgraduate 10 5%
Other 24 11%
Unknown 27 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 77 37%
Biochemistry, Genetics and Molecular Biology 59 28%
Chemistry 14 7%
Immunology and Microbiology 10 5%
Engineering 7 3%
Other 14 7%
Unknown 28 13%