A stochastic model of Escherichia coli AI‐2 quorum signal circuit reveals alternative synthesis pathways

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Abstract
Quorum sensing (QS) is an important determinant of bacterial phenotype. Many cell functions are regulated by intricate and multimodal QS signal transduction processes. The LuxS/AI‐2 QS system is highly conserved among Eubacteria and AI‐2 is reported as a ‘universal’ signal molecule. To understand the hierarchical organization of AI‐2 circuitry, a comprehensive approach incorporating stochastic simulations was developed. We investigated the synthesis, uptake, and regulation of AI‐2, developed testable hypotheses, and made several discoveries: (1) the mRNA transcript and protein levels of AI‐2 synthases, Pfs and LuxS, do not contribute to the dramatically increased level of AI‐2 found when cells are grown in the presence of glucose; (2) a concomitant increase in metabolic flux through this synthesis pathway in the presence of glucose only partially accounts for this difference. We predict that ‘high‐flux’ alternative pathways or additional biological steps are involved in AI‐2 synthesis; and (3) experimental results validate this hypothesis. This work demonstrates the utility of linking cell physiology with systems‐based stochastic models that can be assembled de novo with partial knowledge of biochemical pathways. ### Synopsis Bacteria can communicate by releasing and detecting small chemical signal molecules in a process termed ‘quorum sensing’. This confers the ability of single‐celled bacteria to coordinate their behavior as a multicellular unit ([Miller and Bassler, 2001][1]; [Withers et al , 2001][2]). There are several quorum‐sensing systems differentiated by the mode of signal transduction (e.g., freely diffusible signals, cell‐surface receptor‐mediated phosphorelay systems, and post‐translationally modified peptide signals). The luxS‐ derived autoinducer, AI‐2, is of great interest owing to its cross‐species and pro/eukaryotic connectivity ([Xavier and Bassler, 2003][3]; [Vendeville et al , 2005][4]). Elucidating the intricacies of AI‐2‐mediated regulation and the mechanisms associated with AI‐2 synthesis and transport will play an important role in deciphering the behavior of luxS ‐containing bacteria in various environments. In Escherichia coli cultures, the presence of glucose enhances AI‐2 production substantially ([Wang et al , 2005a][5]). In order to elucidate the possible mechanisms underpinning this phenomenon, we simulated the appearance and disappearance of AI‐2 in extracellular fluids using a stochastic Petri net (SPN) model, based on the algorithms of [Gillespie (1977)][6]. This approach has been proven successful in modeling several biological systems ([Arkin et al , 1998][7]; [Matsuno et al , 2000][8]). An SPN model ([Figure 2][9]) was built from the known AI‐2 biosynthesis pathways and the partially elucidated AI‐2 uptake processes of E. coli ([Wang et al , 2005a][5], [2005b][10]) A base model was constructed for growth in LB medium, wherein an AI‐2 synthesis rate was optimized to match our experimental data. Specifically, a piecewise constant optimization was carried out using an identically constructed ODE model with median output quantities for state variables. Our decision to optimize the AI‐2 synthesis rate was based on our experimental observations that AI‐2 synthase LuxS and Pfs transcript levels are high during exponential growth and that the transcription of the AI‐2 uptake transporter is not initiated until late exponential phase ([Wang et al , 2005a][5], [2005b][10]). Hence, synthesis is regulated first, followed by uptake. The simulation results were in good agreement with experimental data. The stimulation of AI‐2 owing to the presence of glucose was then investigated. Experimental results revealed an increase in luxS mRNA in the presence of glucose and an overall decrease in time as the cells entered the stationary phase. A similar time course was found for pfs mRNA, although there was no apparent difference due to glucose. Corresponding simulation results showed that an increase in luxS transcript levels does not appreciably enhance AI‐2 formation. The network was then perturbed by overexpression of luxS and pfs , wherein expression vectors for His‐LuxS and His‐Pfs were introduced into W3110 cells, respectively. The cells were induced by IPTG in growth media both with and without glucose. LuxS and Pfs protein levels increased by more than 200‐fold compared to controls. Surprisingly, AI‐2 production increased only 16%. Hence, the dramatic increase in enzyme only marginally enhanced AI‐2 production. Simulations were remarkably consistent with experimental results. In the presence of glucose, luxS and pfs overexpression resulted in a two‐fold increase in AI‐2. As a more than 200‐fold increase in LuxS and Pfs alone does not appreciably increase AI‐2, we surmised that the carbon fluxes through these metabolic pathways acted in tandem with the increased enzyme level. Based on previous research ([Holms, 1996][11]; [Oh and Liao, 2000][12]), a 50% flux increase in carbon flux could be expected when cells are grown in the presence of glucose. To interrogate a flux‐based hypothesis, the overexpression of luxS and pfs was simulated with increased carbon flux. The results showed enhanced AI‐2, but not at levels that would corroborate experimental observations. We based further computational experiments on the work of [Almaas et al (2004)][13], who suggested that high‐flux reactions experience noticeable flux changes whereas low‐flux reactions are minimally affected. Specifically, we suspected that the reaction from 4,5‐dihydroxy‐2,3‐pentanedione (DPD) (the immediate product of LuxS) to AI‐2 is a high‐flux reaction for AI‐2...