Xavier JB.. Social interaction in synthetic and natural microbial communities. Mol Syst Biol 7: 483

Program in Computational Biology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA.
Molecular Systems Biology (Impact Factor: 10.87). 04/2011; 7(1):483. DOI: 10.1038/msb.2011.16
Source: PubMed


Social interaction among cells is essential for multicellular complexity. But how do molecular networks within individual cells confer the ability to interact? And how do those same networks evolve from the evolutionary conflict between individual- and population-level interests? Recent studies have dissected social interaction at the molecular level by analyzing both synthetic and natural microbial populations. These studies shed new light on the role of population structure for the evolution of cooperative interactions and revealed novel molecular mechanisms that stabilize cooperation among cells. New understanding of populations is changing our view of microbial processes, such as pathogenesis and antibiotic resistance, and suggests new ways to fight infection by exploiting social interaction. The study of social interaction is also challenging established paradigms in cancer evolution and immune system dynamics. Finding similar patterns in such diverse systems suggests that the same 'social interaction motifs' may be general to many cell populations.

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    • "Bacterial signaling and especially quorum sensing (QS) is a possible target for such treatments that will control and reduce bacterial virulence [4], [5]. QS is a signaling mechanism that bacteria use to communicate during the infection process [6], [7]. "
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    ABSTRACT: Quorum sensing (QS) is a signaling mechanism that pathogenic bacteria use to communicate and synchronize the production of exofactors to attack their hosts. Understanding and controlling QS is an important step towards a possible solution to the growing problem of antibiotic resistance. QS is a cooperative effort of a bacterial population in which some of the bacteria do not participate. This phenomenon is usually studied using game theory and the non-participating bacteria are modeled as cheaters that exploit the production of common goods (exofactors) by other bacteria. Here, we take a different approach to study the QS dynamics of a growing bacterial population. We model the bacterial population as a growing graph and use spectral graph theory to compute the evolution of its synchronizability. We also treat each bacterium as a source of signaling molecules and use the diffusion equation to compute the signaling molecule distribution. We formulate a cost function based on Lagrangian dynamics that combines the time-like synchronization with the space-like diffusion of signaling molecules. Our results show that the presence of non-participating bacteria improves the homogeneity of the signaling molecule distribution preventing thus an early onset of exofactor production and has a positive effect on the optimization of QS signaling and on attack synchronization.
    IEEE Transactions on NanoBioscience 06/2015; 14(4):440. DOI:10.1109/TNB.2014.2385109 · 2.31 Impact Factor
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    • "In several cases it is favorable for bacteria to cooperate and act as a multicellular organism [2], [3]. One of these cases is the process of infection, where bacteria produce virulence factors or exofactors, such as host cell wall degrading enzymes, which are secreted into the surrounding environment to cause degradation of host cells so that the bacteria can uptake nutrients [3], [4]. "
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    ABSTRACT: To coordinate their behavior and virulence and to synchronize attacks against their hosts, bacteria communicate by continuously producing signaling molecules (called autoinducers) and continuously monitoring the concentration of these molecules. This communication is controlled by biological circuits called quorum sensing (QS) circuits. Recently QS circuits and have been recognized as an alternative target for controlling bacterial virulence and infections without the use of antibiotics. Pseudomonas aeruginosa is a Gram-negative bacterium that infects insects, plants, animals and humans and can cause acute infections. This bacterium has three interconnected QS circuits that form a very complex and versatile QS system, the operation of which is still under investigation. Here we use Boolean networks to model the complete QS system of Pseudomonas aeruginosa and we simulate and analyze its operation in both synchronous and asynchronous modes. The state space of the QS system is constructed and it turned out to be very large, hierarchical, modular and scale-free. Furthermore, we developed a simulation tool that can simulate gene knock-outs and study their effect on the regulons controlled by the three QS circuits. The model and tools we developed will give to life scientists a deeper insight to this complex QS system.
    IEEE Transactions on NanoBioscience 08/2014; 13(3):1-7. · 2.31 Impact Factor
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    • "Bacteria secrete small-molecular-weight molecules, i.e., autoinducers , to communicate with each other, termed as quorum sensing (QS) [1]. QS has been found to regulate biofilm formation, virulence , production of antibiotics, formation of fruiting body, and gene transfer [2] [3]. In the past two decades, QS has been extensively studied because of its important roles in the fields of health and environment. "
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    ABSTRACT: Autoinducer-2 (AI-2), as a small-molecular-weight organic molecule secreted and perceived by various bacteria, enables intra- and inter-species communications. Quantitative determination of AI-2 is essential for exploring the bacterial AI-2-related physiological and biochemical processes. However, current strategies for sensitive detection of AI-2 require sophisticated instruments and complicated procedures. In this work, on the basis of the derivatization of AI-2 with 2,3-diaminonaphthalene, a simple, sensitive and cost-effective high-performance liquid chromatography with fluorescence detector (HPLC-FLD) method is developed for the quantitative detection of AI-2. Under the optimized conditions, this method had a broad linear range of 10-14,000ng/ml (R(2)=0.9999), and a low detection limit of 1.0ng/ml. Furthermore, the effectiveness of this approach was further validated through measuring the AI-2 concentrations in the cell-free culture supernatants of both Escherichia coli and Vibrio harveyi.
    Journal of Chromatography A 08/2014; 1361. DOI:10.1016/j.chroma.2014.07.103 · 4.17 Impact Factor
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