LsrR Quorum Sensing “Switch” Is Revealed by a Bottom-Up Approach

Sara Hooshangi and William E Bentley

PLOS Computational Biology, 2011, vol. 7, issue 9, 1-11

Abstract: Quorum sensing (QS) enables bacterial multicellularity and selective advantage for communicating populations. While genetic “switching” phenomena are a common feature, their mechanistic underpinnings have remained elusive. The interplay between circuit components and their regulation are intertwined and embedded. Observable phenotypes are complex and context dependent. We employed a combination of experimental work and mathematical models to decipher network connectivity and signal transduction in the autoinducer-2 (AI-2) quorum sensing system of E. coli. Negative and positive feedback mechanisms were examined by separating the network architecture into sub-networks. A new unreported negative feedback interaction was hypothesized and tested via a simple mathematical model. Also, the importance of the LsrR regulator and its determinant role in the E. coli QS “switch”, normally masked by interfering regulatory loops, were revealed. Our simple model allowed mechanistic understanding of the interplay among regulatory sub-structures and their contributions to the overall native functioning network. This “bottom up” approach in understanding gene regulation will serve to unravel complex QS network architectures and lead to the directed coordination of emergent behaviors. Author Summary: Quorum sensing is a mechanism by which bacterial cells communicate within a population. One particular form of communication in E. coli is through a universal signaling molecule known as autoinducer 2. Although the importance of this form of cell-cell interaction has been recognized in the formation of biofilms and virulent infections, the mechanisms by which this form of communication is regulated is still not well understood. In this paper, we presented a method of unraveling these mechanisms by using a combination of experimental work and mathematical models. We took apart the network architecture and isolated the different components. The examination of these isolated sub-networks provided us with a better understanding of the underlying mechanisms that control and regulate bacterial quorum sensing. We were also able to predict new network interactions with the help of our mathematical models. This bottom up approach, combined with our modeling efforts, proved effective in unraveling the mechanisms of quorum sensing in E. coli.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1002172

DOI: 10.1371/journal.pcbi.1002172

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