Information processing and signal integration in bacterial quorum sensing

Abstract

Bacteria communicate using secreted chemical signaling molecules called autoinducers in a process known as quorum sensing. The quorum‐sensing network of the marine bacterium Vibrio harveyi uses three autoinducers, each known to encode distinct ecological information. Yet how cells integrate and interpret the information contained within these three autoinducer signals remains a mystery. Here, we develop a new framework for analyzing signal integration on the basis of information theory and use it to analyze quorum sensing in V. harveyi . We quantify how much the cells can learn about individual autoinducers and explain the experimentally observed input–output relation of the V. harveyi quorum‐sensing circuit. Our results suggest that the need to limit interference between input signals places strong constraints on the architecture of bacterial signal‐integration networks, and that bacteria probably have evolved active strategies for minimizing this interference. Here, we analyze two such strategies: manipulation of autoinducer production and feedback on receptor number ratios. ### Synopsis Unicellular organisms live in complex and dynamic environments. They sense and respond to both external environmental cues and to each other through quorum sensing, that is, cell‐to‐cell communication. Adapting to changing environments often requires cells to simultaneously integrate information from multiple environmental inputs, and cells have developed elaborate signaling networks to accomplish this feat. However, the design principles underlying the architectures of these networks remain largely mysterious. For example, in the model quorum‐sensing bacterium Vibrio harveyi , three chemical communication signals are integrated to regulate gene expression, but the logic and mechanism underlying this integration are poorly understood. Such open questions highlight the need for new conceptual and theoretical tools to supplement ongoing experimental work. Here, we present a new theoretical framework for understanding signal integration on the basis of information theory ([Shannon, 1948][1]), and we use it to study information processing in the V. harveyi quorum‐sensing circuit. The V. harveyi quorum‐sensing circuit is among the best characterized of all quorum‐sensing networks ([Figure 1A][2]). V. harveyi produces and detects three chemical signaling molecules called autoinducers (AIs), AI‐1, CAI‐1, and AI‐2. Although AI‐1 is produced only by V. harveyi , CAI‐1 is produced by other Vibrio species, and AI‐2 is produced by a large variety of both Gram‐negative and Gram‐positive bacteria, and probably functions as a universal signaling molecule. Thus, the use of multiple AIs potentially provides bacteria with information about the local density of V. harveyi , all Vibrio species, and total bacteria ([Waters and Bassler, 2005][3]). Sensory information from the three AIs is channeled through a common phosphorelay (see [Figure 1][2]). Consequently, it is unclear how much bacteria can learn about each individual input. Even less clear is how the architecture and kinetic parameters (e.g. kinase and phosphatase rates) of the quorum‐sensing network affect its signal transduction properties. We address these questions using information theory. Our studies reveal that there are two distinct mechanisms that limit information transmission when bacteria integrate multiple signals, biochemical noise and interference between different signals. Although the former limits the total information that bacteria can learn about all the inputs, signal interference is the primary impediment to learning about individual input signals. Furthermore, we showed that because of signal interference, V. harveyi cells must precisely tune the kinase activity of each input branch of the quorum‐sensing pathway to simultaneously learn about individual autoinducer inputs (see [Figure 4][4]). These theoretically motivated conclusions are consistent with recent quantitative experiments on V. harveyi showing that the maximal kinase activities of the AI‐1 (LuxN) and AI‐2 (LuxPQ) pathways are nearly equal (see [Figure 1][2]). Our information theory analysis also indicates that bacteria can increase how much they learn about individual inputs by manipulating the different autoinducer production rates. In addition, we show that bacteria can learn preferentially about a particular signal in a particular environment, even with a single‐output pathway, by using simple feedback loops to control receptor numbers. Our detailed analysis of the V. harveyi quorum‐sensing network has implications for other prokaryotic signal integration networks. Signal integration is a common feature of many organisms, and bacteria have developed sophisticated molecular mechanisms for integrating signals from a broad range of inputs using two‐component systems and phosphorelays ([Perego, 1998][5]; [Bassler and Losick, 2006][6]; [Kato et al , 2007][7]; [Mitrophanov and Groisman, 2008][8]). Our information theory analysis suggests that the need to minimize interference between signals probably places strong constraints on the design of such signal integration networks. In particular, our study indicates that information transmission properties are likely to be extremely sensitive to changes in kinase and phosphatase rates, and that bacteria may have evolved strategies for minimizing interference. Our results suggest that information theory may prove to be a powerful general tool for analyzing biological signaling networks. Information theory provides a natural language for formulating questions about information processing and signaling integration in . For these reasons, we expect the application of information theory to yield new biological insights into cellular signaling in the future. Bacteria communicate using secreted...