Theoretical studies on sRNA-mediated regulation in bacteria
Chang Xiao-Xue (常晓雪)b, Xu Liu-Fang (徐留芳)a b, Shi Hua-Lin (史华林)b
a Department of Physics and Theoretical Physics Center, Jilin University, Changchun 130012, China;
b State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
Small RNA(sRNA)-mediated post-transcriptional regulation differs from protein-mediated regulation. Through base-pairing, sRNA can regulate the target mRNA in a catalytic or stoichiometric manner. Some theoretical models were built for comparison of the protein-mediated and sRNA-mediated modes in the steady-state behaviors and noise properties. Many experiments demonstrated that a single sRNA can regulate several mRNAs, which causes crosstalk between the targets. Here, we focus on some models in which two target mRNAs are silenced by the same sRNA to discuss their crosstalk features. Additionally, the sequence-function relationship of sRNA and its role in the kinetic process of base-pairing have been highlighted in model building.
Project supported by the National Basic Research Program of China (Grant No. 2013CB834100), the National Natural Science Foundation of China (Grant Nos. 11121403 and 11274320), the Open Project Program of State Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, China (Grant No. Y4KF171CJ1), the National Natural Science Foundation for Young Scholar of China (Grant No. 11304115), and the China Postdoctoral Science Foundation (Grant No. 2013M541282).
Corresponding Authors:
Xu Liu-Fang, Shi Hua-Lin
E-mail: lfxuphy@jlu.edu.cn;shihl@itp.ac.cn
Cite this article:
Chang Xiao-Xue (常晓雪), Xu Liu-Fang (徐留芳), Shi Hua-Lin (史华林) Theoretical studies on sRNA-mediated regulation in bacteria 2015 Chin. Phys. B 24 128703
[1]
Masse E, Vanderpool C K and Gottesman S 2005 J. Bacteriol. 187 6962
[2]
Opdyke J A, Kang J G and Storz G 2004 J. Bacteriol. 186 6698
[3]
Bejerano-Sagie M and Xavier K B 2007 Curr. Opin. Microbiol. 10 189
[4]
Vogel J and Papenfort K 2006 Curr. Opin. Microbiol. 9 605
[5]
Brantl S 2009 Future Microbiol. 4 85
[6]
Storz G, Vogel J and Wassarman K M 2011 Mol. Cell 43 880
[7]
De Lay N, Schu D J and Gottesman S 2013 J. Biol. Chem. 288 7996
[8]
Geissmann T A and Touati D 2004 EMBO J. 23 396
[9]
Henderson C A, Vincent H A, Casamento A, Stone C M, Phillips J O, Cary P D, Sobott F, Gowers D M, Taylor J E and Callaghan A J 2013 RNA 19 1089
[10]
Otaka H, Ishikawa H, Morita T and Aiba H 2011 Proc. Natl. Acad. Sci. USA 108 13059
[11]
Sauer E, Schmidt S and Weichenrieder O 2012 Proc. Natl. Acad. Sci. USA 109 9396
[12]
Aiba H 2007 Curr. Opin. Microbiol. 10 134
[13]
Desnoyers G and Masse E 2012 Genes Dev. 26 726
[14]
Prevost K, Salvail H, Desnoyers G, Jacques J F, Phaneuf E and Masse E 2007 Mol. Microbiol. 64 1260
[15]
Soper T, Mandin P, Majdalani N, Gottesman S and Woodson S A 2010 Proc. Natl. Acad. Sci. USA 107 9602
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