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Corrected on Oct 2, 2018.

Source: the publisher, via Crossref · checked Oct 17, 2026 · Read the notice

Bacterial Quorum Sensing and Microbial Community Interactions

Rhea G. Abisado, Saida Benomar, Jennifer R. Klaus and 2 more

mBio · 2018 · Review · CC BY

doi:10.1128/mbio.02331-17

Abstract

Many bacteria use a cell-cell communication system called quorum sensing to coordinate population density-dependent changes in behavior. Quorum sensing involves production of and response to diffusible or secreted signals, which can vary substantially across different types of bacteria. In many species, quorum sensing modulates virulence functions and is important for pathogenesis. Over the past half-century, there has been a significant accumulation of knowledge of the molecular mechanisms, signal structures, gene regulons, and behavioral responses associated with quorum-sensing systems in diverse bacteria. More recent studies have focused on understanding quorum sensing in the context of bacterial sociality. Studies of the role of quorum sensing in cooperative and competitive microbial interactions have revealed how quorum sensing coordinates interactions both within a species and between species. Such studies of quorum sensing as a social behavior have relied on the development of “synthetic ecological” models that use nonclonal bacterial populations. In this review, we discuss some of these models and recent advances in understanding how microbes might interact with one another using quorum sensing. The knowledge gained from these lines of investigation has the potential to guide studies of microbial sociality in natural settings and the design of new medicines and therapies to treat bacterial infections.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 17, 2026. Paragraph numbers are ours, so an answer can point at one.

INTRODUCTION

Studies over the past half-century have revealed that bacteria can communicate among themselves to carry out a wide range of complex social behaviors, including cooperation. Such social behaviors are widespread in bacteria. It is now clear that social behaviors have important consequences in shaping the behavior and structure of polymicrobial communities. The developing interest in understanding bacterial social behaviors has led to innovative approaches for studying dynamic, mixed microbial communities. In particular, experiments using multiple-strain and multiple-species laboratory and infection models have provided critical new insights into bacterial sociality. In this minireview, we will focus on a type of cell-cell signaling in bacteria called quorum sensing (QS), which has emerged as one model for understanding bacterial sociality. We will review the basic molecular mechanisms of quorum sensing, primarily focusing on Proteobacteria. We highlight recent studies of quorum sensing that use laboratory, in situ, and in vivo models of multiple-strain and multiple-species communities and describe how these studies have contributed to our current practical and fundamental understanding of quorum sensing, communication, and competition in bacteria.

OVERVIEW OF QUORUM SENSING

Quorum sensing (QS) is a type of population density-dependent cell-cell signaling that triggers changes in behavior when the population reaches a critical density. QS systems rely on the production and sensing of extracellular signals. Typically, bacteria continually generate the signal starting at a low concentration in a fresh culture, and the signal accumulates in the local environment as the population density increases. Once a threshold concentration is reached, the signal interacts with a receptor protein, causing a coordinated change in gene expression in the population. Several types of QS signals exist: many Proteobacteria utilize acyl-homoserine lactone (AHL)-type signals, and Gram-positive species utilize short oligopeptide signals that are often chemically modified (Fig. 1). The QS systems in many bacterial species are well understood at the molecular level, and extensively reviewed elsewhere. In this review, we focus on models that have been developed to study how QS systems increase the success of individuals in multiple-strain and multiple-species communities. In many cases, AHL signaling systems are the focus of these studies; however, other types of QS systems have also been studied.

AHL QS and diversity of QS systems.

AHL QS was first described in the 1960s and 1970s in the bioluminescent marine bacterium Vibrio fischeri through the identification of “autoinducing” activity in conditioned medium of high-density cultures, which controls bioluminescence as part of symbiotic associations between V. fischeri and the pinecone fish, Monocentris japonica, and the Hawaiian bobtail squid, Euprymna scolopes (for reviews, see references and ). Bioluminescence is thought to help the pinecone fish and the squid mask their shadows during predator avoidance. At high cell densities, V. fischeri activates bioluminescence through the QS proteins LuxR and LuxI. LuxI is a signal synthase that produces N-3-oxo-hexanoyl-homoserine lactone (3OC6-HSL). 3OC6-HSL specifically binds to LuxR, a transcription factor that activates expression of the luxCDABEG operon, which contains the genes that enable bioluminescence and about 20 others. Similar AHL QS systems have since been shown to be widely distributed in Proteobacteria, where they control diverse behaviors, such as production of secreted toxins and virulence factors, biofilm matrix components, and DNA conjugation (for reviews, see references to and ). Many QS architectures involve more than one signal-receptor combination: for example, Pseudomonas aeruginosa has two complete LuxR-I-type circuits, LasR-I and RhlR-I, which function in a hierarchy, and Burkholderia thailandensis has three complete LuxR-I circuits. The selective pressures that result in multiple AHL signaling circuits are not clear, although it has been proposed that the different properties of AHL signals might provide specific benefits in different environments.

LuxR- and LuxI-type proteins have now been studied across many different bacterial species. Most LuxI-type AHL synthases produce AHLs from S-adenosylmethionine (SAM) and an acylated acyl carrier protein (ACP) from the fatty acid biosynthesis pathway. The fatty acyl side chains can vary in length and oxidation at the third carbon position, and this variation accounts for the specificity of the different systems. LuxR proteins contain a conserved N-terminal AHL-binding domain and a C-terminal helix-turn-helix DNA-binding domain. The N terminus of LuxR interacts with AHLs, and this interaction induces homodimerization of the LuxR homologue and subsequent binding to a specific DNA sequence called the lux box in the target promoter. The AHL-bound LuxR often induces transcription of its cognate luxI signal synthase gene, resulting in a positive-feedback loop that further increases the concentration of AHLs. Although most LuxR family proteins act as transcriptional activators, a few are repressors (for a review, see reference ). Cognate LuxI and LuxR family proteins are often encoded adjacent to one another. However, some species, including P. aeruginosa and B. thailandensis, encode LuxR-type proteins with no cognate LuxI family AHL synthase; these are termed orphan LuxR receptors.

Other types of signal-receptor QS systems exist in both proteobacterial and Gram-positive organisms. There is an alternative form of QS signaling in Vibrio harveyi and Vibrio cholerae (for a review, see reference ). QS in these species involves the signals CAI-1 and AI-2 and a third signal, HAI-1, in V. harveyi, which each specifically bind to different cell surface receptors. The receptors act in parallel to control expression of QS genes by modulating the phosphorylation state of two phosphorelay response regulators (LuxU and LuxO), regulatory small RNAs, and finally, a master QS gene transcription regulator. Gram-positive bacteria have peptide-based QS systems, of which the most well studied is the agr system of Staphylococcus aureus (for a review, see reference ). In this system, the QS signal is produced by AgrB and AgrD. agrD encodes a prosignal, which is exported and modified by the protein AgrB. At sufficient concentrations, the signal binds to the cell surface receptor AgrC, a histidine kinase that in turn activates the response regulator AgrA and affects changes in gene expression through a small RNA called RNAIII.

In addition to bacterial QS systems, QS-like systems have also been reported in eukaryotic microbes, viruses, and even higher-order species such as ants. These examples provide evidence that population density-dependent responses have an important role across many different domains of life. In bacteria, many of the factors controlled by QS systems are secreted or excreted goods that can be shared by the entire group and are important for cooperation. Some of the cooperative activities, such as secretion of toxins, might also be important for competition with other strains or species of bacteria. Studies of QS in natural mixed microbial communities are difficult because of the variability and complexity of these communities. A significant advance in QS research has been the use of models where nonclonal bacterial populations are grown in well-controlled environments. These models have provided significant new insights into the mechanisms driving microbial community interactions and how these interactions influence QS evolution. In the remainder of this minireview, we discuss the ways by which these models have shaped our current understanding of how QS control of certain factors increases reproductive success in environments where there is fierce competition for resources. Together, these studies have established a framework to think about the selective advantages and disadvantages of using QS to regulate the production of a variety of extracellular products.

QS control of cooperative behaviors.

Many QS-regulated products are shared “public goods” that can be used by any member of the community. Typically these are secreted or excreted products, such as secreted proteases. Synthesis of public goods imparts a metabolic cost for an individual cell, but is beneficial for all the other cells within the population. The cost involved in the production of QS-dependent public goods makes it prone to exploitation, or social cheating. Social cheating can offer growth or survival advantages to individual bacteria. Since cheaters thrive at the expense of cooperators, their presence may destabilize cooperation: if the proportion of social cheaters becomes too high, the population will no longer produce sufficient public goods. If public goods are needed for growth, the entire population stops growing and, ultimately, collapses. Although cheating has typically been described as a within-species interaction, rivalrous species can also exploit the cooperative behaviors of microbes. Therefore, understanding cooperation and cheating within species has served as a foundation for expanding our knowledge of community interactions.

Many laboratory studies have demonstrated that QS systems can be cooperative (Table 1). In situations where QS controls the production of public goods, the social cheaters that arise are QS receptor mutants. These studies rely on laboratory models of nonclonal populations grown under conditions requiring production of QS-controlled goods, such as proteases that are needed for bacteria to obtain nutrients from protein. The studies have shown that (i) cooperators have a growth advantage over cheaters when they are grown separately under these conditions, and (ii) cheaters exploit the cooperators and proliferate when they are grown as a mixed culture. The laboratory models used for these experiments have provided a critical step forward for studies to understand bacterial cooperation and polymicrobial community interactions in more natural environments.

Laboratory models of cooperation.

QS regulation of cooperation and cheating was first demonstrated using P. aeruginosa. P. aeruginosa uses QS to control production of a protease called elastase. Elastase production is required for growth when populations are grown on casein as the sole source of carbon and energy. When P. aeruginosa populations are passaged in casein broth, mutations in the gene encoding the QS receptor LasR emerge within <100 generations. The mutations are typically single-nucleotide changes that abolish or significantly reduce LasR function. These mutants are cheaters, as they are unable to grow by themselves in casein broth; however, they proliferate when grown in a mixed culture with the wild type. In the passaged populations, LasR mutants rapidly increase until they reach 25 to 50% of the population. It is thought that the mutants are maintained at this frequency by a policing mechanism that will be discussed below. Similar models of cooperation based on protease production have been developed using Chromobacterium violaceum, V. harveyi, and Vibrio cholerae. A protease-dependent laboratory model has also been used to demonstrate QS-dependent cooperation of P. aeruginosa cells grown under biofilm conditions.

Other systems for studying cooperation and cheating have since been developed. For example, swarming is a social trait due to cooperative production of secreted surfactants. QS controls surfactant production, and thus swarming, in several bacterial species, including P. aeruginosa and Bacillus subtilis, and has been used as the basis for a laboratory model to study QS and sociality. QS exploitation by cheaters has also been demonstrated using in vivo models. Rumbaugh and colleagues demonstrated that P. aeruginosa LasR mutants act as cheaters during wound infections in mice. In the study, LasR mutants were attenuated compared to the wild type in single-strain infections. However, when a mixture of both strains was used as an infection inoculum, the LasR mutants outgrew the wild type and ultimately dominated the population. Further, mice infected with the mixed population had reduced virulence relative to wild-type-infected mice. Similar results were observed with S. aureus and AgrC-null QS mutants in a wax moth larva infection model. Together, these experiments show that QS in many species of bacteria can be exploited under certain laboratory and infection conditions.

It is possible that QS-null bacteria have nonsocial advantages in some settings. Surveys of some communities from infections and other environments indicate QS mutants (signal receptor deficient) can readily be isolated from diverse bacterial species, including P. aeruginosa, V. cholerae, S. aureus, and Enterococcus faecalis. However, it remains unclear whether these mutants function as cheaters in these natural communities. In one study of P. aeruginosa infections of ventilator-intubated patients, LasR mutants were shown to proliferate only when QS-intact cells were present, supporting the idea that the LasR mutants are social cheaters. However, in other studies, LasR mutants appear to have an intrinsic growth advantage and to be better adapted to certain growth environments than the wild type, suggesting there is a selective advantage of mutating LasR. These types of experiments highlight the complexity of QS systems and the need for robust experimental systems in which social and nonsocial behaviors can be disentangled. Information from studies of laboratory models can reveal conditions and circumstances where social or nonsocial behavior is favored in natural communities. These systems are also useful to understand other aspects of QS and sociality, such as how QS contributes to the control of cheating.

Mechanisms to stabilize cooperative behaviors that rely on QS.

Because the rise of cheaters can threaten cooperation in a population, a recurring question in evolutionary biology is how do cooperative systems persist, despite the ongoing threat of cheating? Microbial systems are emerging as an excellent tool for studying cheater control because microbes have the advantage of rapid growth, high population yields, and reproducible growth in the laboratory. Studies in these systems suggest cheater control is widespread in bacteria. The laboratory models of QS and cooperation such as those highlighted in the previous section are particularly straightforward. These systems have provided novel insights into cheater control and proven a valuable tool for biologists to study cooperation and cheating.

QS can stabilize cooperation by decreasing the incentives to cheat or by sanctioning cheaters. One such mechanism of cheater control is through pleiotropy, where QS coregulates public goods with goods that provide an individual benefit (private goods). A similar phenomenon occurs in the slime mold Dictyostelium discoideum in a process that does not involve QS. Linking public and private goods through pleiotropy causes a disincentive to cheat due to loss of the private good. In P. aeruginosa, in addition to the public good elastase, the LasR-I QS system controls a periplasmic enzyme important for adenosine catabolism, a “private good”. When P. aeruginosa populations are passaged on adenosine-supplemented casein medium, LasR mutant cheaters do not emerge as they do when casein is the sole carbon and energy source. The LasR mutants are constrained by the availability of adenosine, which provides a direct benefit to QS-proficient cooperators in the population. Cheater control through pleiotropy is also a feature of C. violaceum, in which QS coregulates production of a secreted protease with a membrane-localized antibiotic efflux pump. QS mutants are more sensitive to certain antibiotics and do not emerge when cooperating populations are passaged in the presence of these antibiotics. In the case of C. violaceum, QS stabilization of cooperation relies on antibiotics produced by other species. Although pleiotropic mechanisms can stabilize QS, it is thought that properties other than cheater control drive selection of QS regulation of private goods. In the case of adenosine catabolism and antibiotic resistance, these advantages remain unclear.

QS can also stabilize cooperation through a mechanism involving selective harm of cheaters, a type of policing or enforcement mechanism similar to that described in animals. Cheaters are typically punished through intoxication by factors produced by cooperators. In P. aeruginosa, QS controls production of hydrogen cyanide and also the induction of cyanide resistance. In cooperating populations grown on casein, cyanide produced by cooperators limits growth of LasR mutants. Interestingly, growth under certain conditions can enhance policing effects, leading to greater stability of cooperation. Another form of policing is observed in Burkholderia thailandensis, where QS controls a type VI secretion (T6S) toxin immunity system. In T6S systems, a toxin is transferred from a donor to a recipient cell during direct contact. Cells that make an immunity protein, typically close relatives (kin) of the donor, can defend against the toxin. Cells with no immunity protein are killed, allowing kin discrimination. In B. thailandensis, QS controls both toxin delivery and toxin immunity; thus, QS-defective cheaters are sensitive to killing by cooperator-produced T6S toxins.

Cheating can also be deterred through “metabolic prudence”—that is, delaying production of costly products until nutrients required for growth are exhausted. QS provides one means of delaying production of costly public goods. For example, in swarming P. aeruginosa colonies, cheaters exploit cooperating cells that secrete rhamnolipid biosurfactants, which are needed to swarm. However, the cheaters swarm as well as the wild type when mixed in the same colony. This is because production of rhamnolipid is delayed until the cells are in the stationary phase, when the costs of production are relatively low. This delay was due to regulation by QS and also by nitrogen and carbon availability. The delay in producing rhamnolipids minimized the benefit of cheating, and swarming becomes cheatable when cooperators are genetically modified to produce rhamnolipids constitutively. Thus, QS can provide protection against cheating by delaying costly goods production.

Evolution theory predicts that limited dispersal through spatial structuring or high viscosity is also protective against cheating. Conditions of limited dispersal increase the probability that interacting individuals are close relatives, such that cooperative public goods are shared only among related cooperator cells. Indeed, QS is protected from cheaters in P. aeruginosa populations grown on casein under conditions of high relatedness. Many microbes grow in structured communities called biofilms, in which cell aggregates are encased in a self-produced extracellular matrix (ECM). Several recent experimental studies support the idea that biofilm formation promotes cooperation. QS controls biofilm formation in many bacterial species (for a review, see reference ). Thus, QS-dependent biofilm formation and spatial structuring might increase cooperation, which could be important for stabilizing QS in natural environments.

QS control of behaviors associated with competition.

Many bacterial species use QS to control production of secreted or cell-targeted toxins: for example, bacteriocins in Streptococcus species and type VI secretion effectors in B. thailandensis. (For a review of secreted QS-controlled toxins, see reference.) Many such toxins are thought to promote competition with other strains or species of bacteria. Thus, QS activation would be predicted to influence species dynamics in polymicrobial communities. Early support for this idea came from studies of the wheat rhizosphere. In these soil communities, the saprophytes and biocontrol agents Pseudomonas fluorescens 2-79 and Pseudomonas aureofaciens 30-84 use QS-regulated antibiotic phenazines to fight the fungus Gaeumannomyces graminis var. tritici and colonize the plant. Since these early in situ studies, the importance of QS in competition has been demonstrated in other bacteria, primarily using laboratory models of dual-species competition (Table 1).

Why are many competition-associated factors under QS control? The QS-dependent delay in antibiotic production is thought to mitigate the metabolic costs of production until the population can produce a sufficient concentration to kill a competitor. This delay could also deprive competitors of the ability to mount a defensive response to subinhibitory antibiotic concentrations. Population density might also be one of several types of information used by bacteria to infer the ecologic potential for competition. High cell density might be a good indicator that nutrient concentrations will soon become limited and could allow regulatory changes that broadly prepare the cell for such a situation. In support of this idea, although unrelated to competition, QS regulates changes in metabolism that prepare the population for stationary-phase-induced alkaline stress. Although the design of studies to understand the advantages of QS regulation of competition-associated factors can be technically challenging, several models have been developed that serve as a starting point to begin to understand the role of QS in competition.

Laboratory models of QS and interspecies competition.

Dual-species competition models present unique challenges, including differences in growth conditions and growth rates. Most model systems use species or strains with compatible growth rates and growth requirements and that are also likely to interact in natural communities. One such model uses the saprophytes B. thailandensis and C. violaceum. These two species grow at similar rates under the same conditions and are also both isolated from soil and water environments. In both species, QS controls the production of secreted antibiotics. In the case of B. thailandensis, the antibiotic is a ribosome-targeting polyketide, bactobolin. The C. violaceum antibiotic active in this model is unknown. For each species, mutations disrupting QS reduce competitiveness. This dual-species competition was also used to develop an in silico model. In the in silico model, increasing the cost of antibiotic production or producing it too early slows population growth and decreases killing efficiency. The in silico results support the idea that QS regulation of antibiotic production provides a significant cost savings to populations.

The C. violaceum-B. thailandensis model also demonstrates “eavesdropping,” or detection of other species’ AHLs. Although many AHL receptors specifically recognize their cognate AHLs and a narrow range of structurally related analogues, the C. violaceum AHL receptor CviR has a broad spectrum of AHL response. In the competition model, C. violaceum CviR could detect and respond to B. thailandensis AHLs, and this ability to eavesdrop increased C. violaceum competitiveness in some situations. These results suggest a potential benefit of broad-range AHL detection by some LuxR homologues. This also may account for some orphan QS receptors in bacteria that lack cognate signal synthases. AHL-dependent eavesdropping has also been demonstrated for other species, such as between endophytes of plants and between P. aeruginosa and Burkholderia cenocepacia, two pathogens known to coinfect the lungs of patients with the genetic disease cystic fibrosis. Although AHL signaling has traditionally been thought of as an intraspecies communication system, the results of these studies suggest AHLs might also be used to sense and respond to potential competitors in the environment. Another QS molecule, AI-2, is also thought to be important for interspecies signaling. Recent studies support that AI-2 may play a role in bacterial community dynamics in the mammalian gut, although it is not clear if this is through a signal receptor-dependent process.

A substantial effort has been made to understand the interactions between P. aeruginosa and S. aureus, as these bacteria are commonly cocultured from chronic wound infections. P. aeruginosa and S. aureus interaction has been studied in coculture in vitro, in a model mimicking the wound environment, in an in vivo rat infection model, and using a range of clinical strains in vitro and in vivo (for a review, see reference ). When both bacteria are together in vitro or in vivo, P. aeruginosa usually surpasses or decreases the S. aureus population. This effect is largely due to compounds controlled by QS. The first such compound characterized was 4-hydroxy-2-heptylquinoline N-oxide (HQNO), which was originally described as an antistaphylococcal compound. HQNO decelerates growth by inhibiting oxidative respiration via the cytochrome system, although it is not bactericidal. Exposure of S. aureus to HQNO does not result in eradication of S. aureus but rather in the emergence of small colony S. aureus variants. Another such QS-regulated antistaphylococcal compound is pyocyanin, a phenazine produced by P. aeruginosa. Pyocyanin, like HQNO, blocks oxidative respiration and induces the formation of small colonies. Finally, P. aeruginosa uses the QS-regulated protease LasA to degrade pentaglycine from the S. aureus cell wall, inducing cell lysis, which may be beneficial to P. aeruginosa.

The P. aeruginosa QS products hydrogen cyanide, rhamnolipid, pyocyanin, and pyoverdine are also important for interactions with Proteobacteria. For example, in P. aeruginosa and Burkholderia multivorans cocultures, QS-dependent production of hydrogen cyanide is important for P. aeruginosa to outcompete B. multivorans. In the experiments, QS-dependent antibiotic production could prevent B. multivorans from exploiting QS-dependent public goods. These results demonstrate another facet of QS-dependent pleiotropy: coregulation of antibiotics with public goods can stabilize cooperative behavior in mixed microbial communities.

QS and models that mimic polymicrobial in vivo infections.

Laboratory models can be used to mimic host conditions to make inferences about the role of QS in polymicrobial infections. A recent study suggests host factors might modulate P. aeruginosa QS during coinfections with S. aureus. These studies were conducted using a laboratory chronic wound model that more closely mimics the chronic wound environment, which includes plasma and red blood cells. In the chronic wound model, plasma albumin allowed S. aureus to survive coculture with P. aeruginosa by sequestering P. aeruginosa AHLs and reducing QS activation of anti-S. aureus toxins. Because many P. aeruginosa QS-controlled toxins are also virulence factors, these results also suggest that P. aeruginosa virulence might be reduced by albumin-dependent QS inhibition during infections.

Serum can also change cell-cell interactions by modulating signaling in the Gram-positive species Enterococcus faecalis. E. faecalis uses a peptide signaling system to control plasmid conjugation (reviewed in reference ). This signaling system triggers conjugation when a sufficient quorum of plasmid-free recipient cells is detected. In this case, albumin sequesters a peptide inhibitor that normally prevents conjugation in the absence of recipient cells. Growth in serum increased conjugation, presumably because albumin-dependent sequestration of the inhibitor caused conjugation to go unchecked. Results of these two studies suggest the outcome of QS-mediated species interactions might be very different in a host environment from that observed under standard laboratory growth conditions, an idea that requires further study in infections in vivo. A key challenge moving into polymicrobial infection models is to develop systems such as these that mimic the host environment in a context where variables, such as key nutrients and host-supplied factors, can be controlled or removed. In this way, the conditions and types of infections that drive cell-cell interactions can be delineated.

CONCLUSIONS

There is now a wealth of knowledge of how bacterial populations use QS systems to communicate and coordinate diverse behaviors. In the past decade, this knowledge has served as a foundation to build approaches for studying QS in polymicrobial communities. This emerging field of investigation is relevant to our understanding of how QS contributes to the success of bacteria in diverse environments—from polymicrobial infections to natural communities—and how these systems might be manipulated to encourage specific outcomes, such as altering community dynamics of microbiomes or ecologically important soil communities. Advances in this field have relied on laboratory and in vivo models of nonclonal bacterial populations to model natural communities that can be prohibitively complex to study directly. By studying polymicrobial model systems, we have learned that QS is important for cooperation and for competition among and between species. These models have also been useful to test predictions about the evolution of QS and social behavior. We anticipate existing models will continue to provide new insights into QS and sociality, either as they are or when adapted for new purposes or increased complexity. We also look forward to results of studies with newly developed models (e.g., the three-dimensional protein-based picoliter-scale microcavities [termed bacterial “lobster traps” in reference ], in vitro wound models, and alginate bead aggregates) and their application to designing studies of more natural ecosystems and infections. Key questions include understanding how QS drives polymicrobial interactions across different host and nonhost environments and how these interactions drive the evolution of QS and ultimately shape the structure and behavior of these communities.

References 143

  1. 1.

    Fuqua WC, Winans SC, Greenberg EP 1994 Quorum sensing in bacteria: the LuxR-LuxI family of cell density-responsive transcriptional regulators. J Bacteriol 176:269–275. doi:.

    Not in our library10.1128/jb.176.2.269-275.1994

  2. 2.

    Waters CM, Bassler BL 2005 Quorum sensing: cell-to-cell communication in bacteria. Annu Rev Cell Dev Biol 21:319–346. doi:.

    Not in our library10.1146/annurev.cellbio.21.012704.131001

  3. 3.

    Papenfort K, Bassler BL 2016 Quorum sensing signal-response systems in Gram-negative bacteria. Nat Rev Microbiol 14:576–588. doi:.

    Not in our library10.1038/nrmicro.2016.89

  4. 4.

    Schuster M, Sexton DJ, Diggle SP, Greenberg EP 2013 Acyl-homoserine lactone quorum sensing: from evolution to application. Annu Rev Microbiol 67:43–63. doi:.

    Not in our library10.1146/annurev-micro-092412-155635

  5. 5.

    Cook LC, Federle MJ 2014 Peptide pheromone signaling in Streptococcus and Enterococcus. FEMS Microbiol Rev 38:473–492. doi:.

    Not in our library10.1111/1574-6976.12046

  6. 6.

    Nealson KH, Hastings JW 1979 Bacterial bioluminescence: its control and ecological significance. Microbiol Rev 43:496–518.

    Not in our library10.1128/mr.43.4.496-518.1979

  7. 7.

    Fuqua C, Parsek MR, Greenberg EP 2001 Regulation of gene expression by cell-to-cell communication: acyl-homoserine lactone quorum sensing. Annu Rev Genet 35:439–468. doi:.

    Not in our library10.1146/annurev.genet.35.102401.090913

  8. 8.

    Visick KL, McFall-Ngai MJ 2000 An exclusive contract: specificity in the Vibrio fischeri-Euprymna scolopes partnership. J Bacteriol 182:1779–1787. doi:.

    Not in our library10.1128/jb.182.7.1779-1787.2000

  9. 9.

    Eberhard A, Burlingame AL, Eberhard C, Kenyon GL, Nealson KH, Oppenheimer NJ 1981 Structural identification of autoinducer of Photobacterium fischeri luciferase. Biochemistry 20:2444–2449. doi:.

    Not in our library10.1021/bi00512a013

  10. 10.

    Adar YY, Ulitzur S 1993 GroESL proteins facilitate binding of externally added inducer by LuxR protein-containing E. coli cells. J Biolumin Chemilumin 8:261–266. doi:.

    Not in our library10.1002/bio.1170080506

  11. 11.

    Hanzelka BL, Greenberg EP 1995 Evidence that the N-terminal region of the Vibrio fischeri LuxR protein constitutes an autoinducer-binding domain. J Bacteriol 177:815–817. doi:.

    Not in our library10.1128/jb.177.3.815-817.1995

  12. 12.

    Engebrecht J, Silverman M 1984 Identification of genes and gene products necessary for bacterial bioluminescence. Proc Natl Acad Sci U S A 81:4154–4158. doi:.

    Not in our library10.1073/pnas.81.13.4154

  13. 13.

    Engebrecht J, Nealson K, Silverman M 1983 Bacterial bioluminescence: isolation and genetic analysis of functions from Vibrio fischeri. Cell 32:773–781. doi:.

    Not in our library10.1016/0092-8674(83)90063-6

  14. 14.

    Antunes LC, Schaefer AL, Ferreira RB, Qin N, Stevens AM, Ruby EG, Greenberg EP 2007 Transcriptome analysis of the Vibrio fischeri LuxR-LuxI regulon. J Bacteriol 189:8387–8391. doi:.

    Not in our library10.1128/jb.00736-07

  15. 15.

    Whitehead NA, Barnard AM, Slater H, Simpson NJ, Salmond GP 2001 Quorum-sensing in Gram-negative bacteria. FEMS Microbiol Rev 25:365–404. doi:.

    Not in our library10.1111/j.1574-6976.2001.tb00583.x

  16. 16.

    Cornforth DM, Popat R, McNally L, Gurney J, Scott-Phillips TC, Ivens A, Diggle SP, Brown SP 2014 Combinatorial quorum sensing allows bacteria to resolve their social and physical environment. Proc Natl Acad Sci U S A 111:4280–4284. doi:.

    Not in our library10.1073/pnas.1319175111

  17. 17.

    Schaefer AL, Val DL, Hanzelka BL, Cronan JE Jr, Greenberg EP 1996 Generation of cell-to-cell signals in quorum sensing: acyl homoserine lactone synthase activity of a purified Vibrio fischeri LuxI protein. Proc Natl Acad Sci U S A 93:9505–9509. doi:.

    Not in our library10.1073/pnas.93.18.9505

  18. 18.
    Acyl homoserine-lactone quorum-sensing signal generation · 1999
    Not checked
  19. 19.

    Moré MI, Finger LD, Stryker JL, Fuqua C, Eberhard A, Winans SC 1996 Enzymatic synthesis of a quorum-sensing autoinducer through use of defined substrates. Science 272:1655–1658. doi:.

    Not in our library10.1126/science.272.5268.1655

  20. 20.

    Stevens AM, Dolan KM, Greenberg EP 1994 Synergistic binding of the Vibrio fischeri LuxR transcriptional activator domain and RNA polymerase to the lux promoter region. Proc Natl Acad Sci U S A 91:12619–12623. doi:.

    Not in our library10.1073/pnas.91.26.12619

  21. 21.

    Zhang RG, Pappas KM, Brace JL, Miller PC, Oulmassov T, Molyneaux JM, Anderson JC, Bashkin JK, Winans SC, Joachimiak A 2002 Structure of a bacterial quorum-sensing transcription factor complexed with pheromone and DNA. Nature 417:971–974. doi:.

    Not in our library10.1038/nature00833

  22. 22.

    Devine JH, Shadel GS, Baldwin TO 1989 Identification of the operator of the lux regulon from the Vibrio fischeri strain ATCC 7744. Proc Natl Acad Sci U S A 86:5688–5692. doi:.

    Not in our library10.1073/pnas.86.15.5688

  23. 23.

    Tsai CS, Winans SC 2010 LuxR-type quorum-sensing regulators that are detached from common scents. Mol Microbiol 77:1072–1082. doi:.

    Not in our library10.1111/j.1365-2958.2010.07279.x

  24. 24.

    Fuqua C 2006 The QscR quorum-sensing regulon of Pseudomonas aeruginosa: an orphan claims its identity. J Bacteriol 188:3169–3171. doi:.

    Not in our library10.1128/jb.188.9.3169-3171.2006

  25. 25.

    Freeman JA, Bassler BL 1999 A genetic analysis of the function of LuxO, a two-component response regulator involved in quorum sensing in Vibrio harveyi. Mol Microbiol 31:665–677. doi:.

    Not in our library10.1046/j.1365-2958.1999.01208.x

  26. 26.

    Lilley BN, Bassler BL 2000 Regulation of quorum sensing in Vibrio harveyi by LuxO and sigma-54. Mol Microbiol 36:940–954. doi:.

    Not in our library10.1046/j.1365-2958.2000.01913.x

  27. 27.

    Wei Y, Ng WL, Cong J, Bassler BL 2012 Ligand and antagonist driven regulation of the Vibrio cholerae quorum-sensing receptor CqsS. Mol Microbiol 83:1095–1108. doi:.

    Not in our library10.1111/j.1365-2958.2012.07992.x

  28. 28.

    Lenz DH, Mok KC, Lilley BN, Kulkarni RV, Wingreen NS, Bassler BL 2004 The small RNA chaperone Hfq and multiple small RNAs control quorum sensing in Vibrio harveyi and Vibrio cholerae. Cell 118:69–82. doi:.

    Not in our library10.1016/j.cell.2004.06.009

  29. 29.

    Lyon GJ, Novick RP 2004 Peptide signaling in Staphylococcus aureus and other Gram-positive bacteria. Peptides 25:1389–1403. doi:.

    Not in our library10.1016/j.peptides.2003.11.026

  30. 30.

    Ji G, Beavis RC, Novick RP 1995 Cell density control of staphylococcal virulence mediated by an octapeptide pheromone. Proc Natl Acad Sci U S A 92:12055–12059. doi:.

    Not in our library10.1073/pnas.92.26.12055

  31. 31.

    Saenz HL, Augsburger V, Vuong C, Jack RW, Götz F, Otto M 2000 Inducible expression and cellular location of AgrB, a protein involved in the maturation of the staphylococcal quorum-sensing pheromone. Arch Microbiol 174:452–455. doi:.

    Not in our library10.1007/s002030000223

  32. 32.

    Novick RP, Ross HF, Projan SJ, Kornblum J, Kreiswirth B, Moghazeh S 1993 Synthesis of staphylococcal virulence factors is controlled by a regulatory RNA molecule. EMBO J 12:3967–3975.

    Not in our library10.1002/j.1460-2075.1993.tb06074.x

  33. 33.

    Hornby JM, Jensen EC, Lisec AD, Tasto JJ, Jahnke B, Shoemaker R, Dussault P, Nickerson KW 2001 Quorum sensing in the dimorphic fungus Candida albicans is mediated by farnesol. Appl Environ Microbiol 67:2982–2992. doi:.

    Not in our library10.1128/aem.67.7.2982-2992.2001

  34. 34.

    Kügler S, Schurtz Sebghati T, Groppe Eissenberg L, Goldman WE 2000 Phenotypic variation and intracellular parasitism by Histoplasma capsulatum. Proc Natl Acad Sci U S A 97:8794–8798. doi:.

    Not in our library10.1073/pnas.97.16.8794

  35. 35.

    Erez Z, Steinberger-Levy I, Shamir M, Doron S, Stokar-Avihail A, Peleg Y, Melamed S, Leavitt A, Savidor A, Albeck S, Amitai G, Sorek R 2017 Communication between viruses guides lysis-lysogeny decisions. Nature 541:488–493. doi:.

    Not in our library10.1038/nature21049

  36. 36.

    Mallon EB, Pratt SC, Franks NR 2001 Individual and collective decision-making during nest site selection by the ant Leptothorax albipennis. Behav Ecol Sociobiol 50:352–359. doi:.

    Not in our library10.1007/s002650100377

  37. 37.

    Pratt SC, Mallon EB, Sumpter DJT, Franks NR 2002 Quorum sensing, recruitment, and collective decision-making during colony emigration by the ant Leptothorax albipennis. Behav Ecol Sociobiol 52:117–127. doi:.

    Not in our library10.1007/s00265-002-0487-x

  38. 38.

    West SA, Griffin AS, Gardner A, Diggle SP 2006 Social evolution theory for microorganisms. Nat Rev Microbiol 4:597–607. doi:.

    Not in our library10.1038/nrmicro1461

  39. 39.

    Schuster M, Greenberg EP 2006 A network of networks: quorum-sensing gene regulation in Pseudomonas aeruginosa. Int J Med Microbiol 296:73–81. doi:.

    Not in our library10.1016/j.ijmm.2006.01.036

  40. 40.

    Majerczyk C, Brittnacher M, Jacobs M, Armour CD, Radey M, Schneider E, Phattarasokul S, Bunt R, Greenberg EP 2014 Global analysis of the Burkholderia thailandensis quorum sensing-controlled regulon. J Bacteriol 196:1412–1424. doi:.

    Not in our library10.1128/jb.01405-13

  41. 41.

    Popat R, Pollitt EJ, Harrison F, Naghra H, Hong KW, Chan KG, Griffin AS, Williams P, Brown SP, West SA, Diggle SP 2015 Conflict of interest and signal interference lead to the breakdown of honest signaling. Evolution 69:2371–2383. doi:.

    Not in our library10.1111/evo.12751

  42. 42.

    Greig D, Travisano M 2004 The Prisoner’s Dilemma and polymorphism in yeast SUC genes. Proc Biol Sci 271(Suppl 3):S25–S26. doi:.

    Not in our library10.1098/rsbl.2003.0083

  43. 43.

    Velicer GJ, Kroos L, Lenski RE 2000 Developmental cheating in the social bacterium Myxococcus xanthus. Nature 404:598–601. doi:.

    Not in our library10.1038/35007066

  44. 44.

    Rainey PB, Rainey K 2003 Evolution of cooperation and conflict in experimental bacterial populations. Nature 425:72–74. doi:.

    Not in our library10.1038/nature01906

  45. 45.

    Evans KC, Benomar S, Camuy-Vélez LA, Nasseri EB, Wang X, Neuenswander B, Chandler JR 2018 Quorum-sensing control of antibiotic resistance stabilizes cooperation in Chromobacterium violaceum. ISME J 12:1263–1272. doi:.

    Not in our library10.1038/s41396-018-0047-7

  46. 46.

    Dandekar AA, Chugani S, Greenberg EP 2012 Bacterial quorum sensing and metabolic incentives to cooperate. Science 338:264–266. doi:.

    Not in our library10.1126/science.1227289

  47. 47.

    Smalley NE, An D, Parsek MR, Chandler JR, Dandekar AA 2015 Quorum sensing protects Pseudomonas aeruginosa against cheating by other species in a laboratory coculture model. J Bacteriol 197:3154–3159. doi:.

    Not in our library10.1128/jb.00482-15

  48. 48.

    Diggle SP, Griffin AS, Campbell GS, West SA 2007 Cooperation and conflict in quorum-sensing bacterial populations. Nature 450:411–414. doi:.

    Not in our library10.1038/nature06279

  49. 49.

    Sandoz KM, Mitzimberg SM, Schuster M 2007 Social cheating in Pseudomonas aeruginosa quorum sensing. Proc Natl Acad Sci U S A 104:15876–15881. doi:.

    Not in our library10.1073/pnas.0705653104

  50. 50.

    Pollitt EJ, West SA, Crusz SA, Burton-Chellew MN, Diggle SP 2014 Cooperation, quorum sensing, and evolution of virulence in Staphylococcus aureus. Infect Immun 82:1045–1051. doi:.

    Not in our library10.1128/iai.01216-13

  51. 51.

    Bruger EL, Waters CM 2016 Bacterial quorum sensing stabilizes cooperation by optimizing growth strategies. Appl Environ Microbiol 82:6498–6506. doi:.

    Not in our library10.1128/aem.01945-16

  52. 52.

    Katzianer DS, Wang H, Carey RM, Zhu J 2015 “Quorum non-sensing”: social cheating and deception in Vibrio cholerae. Appl Environ Microbiol 81:3856–3862. doi:.

    Not in our library10.1128/aem.00586-15

  53. 53.

    Pollak S, Omer-Bendori S, Even-Tov E, Lipsman V, Bareia T, Ben-Zion I, Eldar A 2016 Facultative cheating supports the coexistence of diverse quorum-sensing alleles. Proc Natl Acad Sci U S A 113:2152–2157. doi:.

    Not in our library10.1073/pnas.1520615113

  54. 54.

    Whiteley M, Lee KM, Greenberg EP 1999 Identification of genes controlled by quorum sensing in Pseudomonas aeruginosa. Proc Natl Acad Sci U S A 96:13904–13909. doi:.

    Not in our library10.1073/pnas.96.24.13904

  55. 55.

    Wang M, Schaefer AL, Dandekar AA, Greenberg EP 2015 Quorum sensing and policing of Pseudomonas aeruginosa social cheaters. Proc Natl Acad Sci U S A 112:2187–2191. doi:.

    Not in our library10.1073/pnas.1500704112

  56. 56.
    Quorum-sensing and cheating in bacterial biofilms · 2012
    No notice
  57. 57.

    Xavier JB, Kim W, Foster KR 2011 A molecular mechanism that stabilizes cooperative secretions in Pseudomonas aeruginosa. Mol Microbiol 79:166–179. doi:.

    Not in our library10.1111/j.1365-2958.2010.07436.x

  58. 58.

    Oslizlo A, Stefanic P, Dogsa I, Mandic-Mulec I 2014 Private link between signal and response in Bacillus subtilis quorum sensing. Proc Natl Acad Sci U S A 111:1586–1591. doi:.

    Not in our library10.1073/pnas.1316283111

  59. 59.

    Venturi V, Bertani I, Kerényi A, Netotea S, Pongor S 2010 Co-swarming and local collapse: quorum sensing conveys resilience to bacterial communities by localizing cheater mutants in Pseudomonas aeruginosa. PLoS One 5:e9998. doi:.

    Not in our library10.1371/journal.pone.0009998

  60. 60.

    Even-Tov E, Bendori SO, Valastyan J, Ke X, Pollak S, Bareia T, Ben-Zion I, Bassler BL, Eldar A 2016 Social evolution selects for redundancy in bacterial quorum sensing. PLoS Biol 14:e1002386. doi:.

    Not in our library10.1371/journal.pbio.1002386

  61. 61.

    Zhou L, Slamti L, Nielsen-LeRoux C, Lereclus D, Raymond B 2014 The social biology of quorum sensing in a naturalistic host pathogen system. Curr Biol 24:2417–2422. doi:.

    Not in our library10.1016/j.cub.2014.08.049

  62. 62.

    Rumbaugh KP, Trivedi U, Watters C, Burton-Chellew MN, Diggle SP, West SA 2012 Kin selection, quorum sensing and virulence in pathogenic bacteria. Proc Biol Sci 279:3584–3588. doi:.

    Not in our library10.1098/rspb.2012.0843

  63. 63.

    D’Argenio DA, Wu M, Hoffman LR, Kulasekara HD, Déziel E, Smith EE, Nguyen H, Ernst RK, Larson Freeman TJ, Spencer DH, Brittnacher M, Hayden HS, Selgrade S, Klausen M, Goodlett DR, Burns JL, Ramsey BW, Miller SI 2007 Growth phenotypes of Pseudomonas aeruginosa lasR mutants adapted to the airways of cystic fibrosis patients. Mol Microbiol 64:512–533. doi:.

    Not in our library10.1111/j.1365-2958.2007.05678.x

  64. 64.

    Harrison F, Muruli A, Higgins S, Diggle SP 2014 Development of an ex vivo porcine lung model for studying growth, virulence, and signaling of Pseudomonas aeruginosa. Infect Immun 82:3312–3323. doi:.

    Not in our library10.1128/iai.01554-14

  65. 65.

    Cabrol S, Olliver A, Pier GB, Andremont A, Ruimy R 2003 Transcription of quorum-sensing system genes in clinical and environmental isolates of Pseudomonas aeruginosa. J Bacteriol 185:7222–7230. doi:.

    Not in our library10.1128/jb.185.24.7222-7230.2003

  66. 66.

    Dénervaud V, TuQuoc P, Blanc D, Favre-Bonté S, Krishnapillai V, Reimmann C, Haas D, van Delden C 2004 Characterization of cell-to-cell signaling-deficient Pseudomonas aeruginosa strains colonizing intubated patients. J Clin Microbiol 42:554–562. doi:.

    Not in our library10.1128/jcm.42.2.554-562.2004

  67. 67.

    Hamood AN, Griswold J, Colmer J 1996 Characterization of elastase-deficient clinical isolates of Pseudomonas aeruginosa. Infect Immun 64:3154–3160.

    Not in our library10.1128/iai.64.8.3154-3160.1996

  68. 68.
    Parallel Evolution in <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> over 39,000 Generations <italic toggle="yes">In Vivo</italic> · 2010
    Not checked
  69. 69.

    Salunkhe P, Smart CH, Morgan JA, Panagea S, Walshaw MJ, Hart CA, Geffers R, Tümmler B, Winstanley C 2005 A cystic fibrosis epidemic strain of Pseudomonas aeruginosa displays enhanced virulence and antimicrobial resistance. J Bacteriol 187:4908–4920. doi:.

    Not in our library10.1128/jb.187.14.4908-4920.2005

  70. 70.

    Schaber JA, Carty NL, McDonald NA, Graham ED, Cheluvappa R, Griswold JA, Hamood AN 2004 Analysis of quorum sensing-deficient clinical isolates of Pseudomonas aeruginosa. J Med Microbiol 53:841–853. doi:.

    Not in our library10.1099/jmm.0.45617-0

  71. 71.

    Smith EE, Buckley DG, Wu Z, Saenphimmachak C, Hoffman LR, D’Argenio DA, Miller SI, Ramsey BW, Speert DP, Moskowitz SM, Burns JL, Kaul R, Olson MV 2006 Genetic adaptation by Pseudomonas aeruginosa to the airways of cystic fibrosis patients. Proc Natl Acad Sci U S A 103:8487–8492. doi:.

    Not in our library10.1073/pnas.0602138103

  72. 72.

    Sokurenko EV, Tchesnokova V, Yeung AT, Oleykowski CA, Trintchina E, Hughes KT, Rashid RA, Brint JM, Moseley SL, Lory S 2001 Detection of simple mutations and polymorphisms in large genomic regions. Nucleic Acids Res 29:E111. doi:.

    Not in our library10.1093/nar/29.22.e111

  73. 73.

    Wilder CN, Allada G, Schuster M 2009 Instantaneous within-patient diversity of Pseudomonas aeruginosa quorum-sensing populations from cystic fibrosis lung infections. Infect Immun 77:5631–5639. doi:.

    Not in our library10.1128/iai.00755-09

  74. 74.

    Zhu H, Bandara R, Conibear TC, Thuruthyil SJ, Rice SA, Kjelleberg S, Givskov M, Willcox MD 2004 Pseudomonas aeruginosa with lasI quorum-sensing deficiency during corneal infection. Invest Ophthalmol Vis Sci 45:1897–1903. doi:.

    Not in our library10.1167/iovs.03-0980

  75. 75.

    Joelsson A, Liu Z, Zhu J 2006 Genetic and phenotypic diversity of quorum-sensing systems in clinical and environmental isolates of Vibrio cholerae. Infect Immun 74:1141–1147. doi:.

    Not in our library10.1128/iai.74.2.1141-1147.2006

  76. 76.

    Shopsin B, Drlica-Wagner A, Mathema B, Adhikari RP, Kreiswirth BN, Novick RP 2008 Prevalence of agr dysfunction among colonizing Staphylococcus aureus strains. J Infect Dis 198:1171–1174. doi:.

    Not in our library10.1086/592051

  77. 77.

    Shopsin B, Eaton C, Wasserman GA, Mathema B, Adhikari RP, Agolory S, Altman DR, Holzman RS, Kreiswirth BN, Novick RP 2010 Mutations in agr do not persist in natural populations of methicillin-resistant Staphylococcus aureus. J Infect Dis 202:1593–1599. doi:.

    Not in our library10.1086/656915

  78. 78.

    Traber KE, Lee E, Benson S, Corrigan R, Cantera M, Shopsin B, Novick RP 2008 agr function in clinical Staphylococcus aureus isolates. Microbiology 154:2265–2274. doi:.

    Not in our library10.1099/mic.0.2007/011874-0

  79. 79.

    Nakayama J, Kariyama R, Kumon H 2002 Description of a 23.9-kilobase chromosomal deletion containing a region encoding fsr genes which mainly determines the gelatinase-negative phenotype of clinical isolates of Enterococcus faecalis in urine. Appl Environ Microbiol 68:3152–3155. doi:.

    Not in our library10.1128/aem.68.6.3152-3155.2002

  80. 80.

    Köhler T, Buckling A, van Delden C 2009 Cooperation and virulence of clinical Pseudomonas aeruginosa populations. Proc Natl Acad Sci U S A 106:6339–6344. doi:.

    Not in our library10.1073/pnas.0811741106

  81. 81.

    West SA, Griffin AS, Gardner A 2007 Evolutionary explanations for cooperation. Curr Biol 17:R661–R672. doi:.

    Not in our library10.1016/j.cub.2007.06.004

  82. 82.

    García-Contreras R, Nuñez-López L, Jasso-Chávez R, Kwan BW, Belmont JA, Rangel-Vega A, Maeda T, Wood TK 2015 Quorum sensing enhancement of the stress response promotes resistance to quorum quenching and prevents social cheating. ISME J 9:115–125. doi:.

    Not in our library10.1038/ismej.2014.98

  83. 83.

    Majerczyk C, Schneider E, Greenberg EP 2016 Quorum sensing control of type VI secretion factors restricts the proliferation of quorum-sensing mutants. eLife 5:e14712. doi:.

    Not in our library10.7554/elife.14712

  84. 84.

    Foster KR, Shaulsky G, Strassmann JE, Queller DC, Thompson CR 2004 Pleiotropy as a mechanism to stabilize cooperation. Nature 431:693–696. doi:.

    Not in our library10.1038/nature02894

  85. 85.

    Schuster M, Sexton DJ, Hense BA 2017 Why quorum sensing controls private goods. Front Microbiol 8:885. doi:.

    Not in our library10.3389/fmicb.2017.00885

  86. 86.

    Clutton-Brock TH, Parker GA 1995 Punishment in animal societies. Nature 373:209–216. doi:.

    Not in our library10.1038/373209a0

  87. 87.

    Yan H, Wang M, Sun F, Dandekar AA, Shen D, Li N 2018 A metabolic trade-off modulates policing of social cheaters in populations of Pseudomonas aeruginosa. Front Microbiol 9:337. doi:.

    Not in our library10.3389/fmicb.2018.00337

  88. 88.

    Mougous JD, Cuff ME, Raunser S, Shen A, Zhou M, Gifford CA, Goodman AL, Joachimiak G, Ordoñez CL, Lory S, Walz T, Joachimiak A, Mekalanos JJ 2006 A virulence locus of Pseudomonas aeruginosa encodes a protein secretion apparatus. Science 312:1526–1530. doi:.

    Not in our library10.1126/science.1128393

  89. 89.

    Russell AB, Hood RD, Bui NK, LeRoux M, Vollmer W, Mougous JD 2011 Type VI secretion delivers bacteriolytic effectors to target cells. Nature 475:343–347. doi:.

    Not in our library10.1038/nature10244

  90. 90.

    Hamilton WD 1964 The genetical evolution of social behaviour. I and II. J Theor Biol 7:1–16. doi:.

    Not in our library10.1016/0022-5193(64)90038-4

  91. 91.

    Chao L, Levin BR 1981 Structured habitats and the evolution of anticompetitor toxins in bacteria. Proc Natl Acad Sci U S A 78:6324–6328. doi:.

    Not in our library10.1073/pnas.78.10.6324

  92. 92.
    Extracellular-matrix-mediated osmotic pressure drives Vibrio cholerae biofilm expansion and cheater exclusion · 2017
    No notice
  93. 93.

    van Gestel J, Weissing FJ, Kuipers OP, Kovács AT 2014 Density of founder cells affects spatial pattern formation and cooperation in Bacillus subtilis biofilms. ISME J 8:2069–2079. doi:.

    Not in our library10.1038/ismej.2014.52

  94. 94.

    Irie Y, Roberts AEL, Kragh KN, Gordon VD, Hutchison J, Allen RJ, Melaugh G, Bjarnsholt T, West SA, Diggle SP 2017 The Pseudomonas aeruginosa PSL polysaccharide is a social but noncheatable trait in biofilms. mBio 8:e00374-17. doi:.

    Not in our library10.1128/mbio.00374-17

  95. 95.

    Nadell CD, Drescher K, Wingreen NS, Bassler BL 2015 Extracellular matrix structure governs invasion resistance in bacterial biofilms. ISME J 9:1700–1709. doi:.

    Not in our library10.1038/ismej.2014.246

  96. 96.

    Parsek MR, Greenberg EP 2005 Sociomicrobiology: the connections between quorum sensing and biofilms. Trends Microbiol 13:27–33. doi:.

    Not in our library10.1016/j.tim.2004.11.007

  97. 97.
    The Evolution of Quorum Sensing as a Mechanism to Infer Kinship. · 2016
    Not checked
  98. 98.

    van der Ploeg JR 2005 Regulation of bacteriocin production in Streptococcus mutans by the quorum-sensing system required for development of genetic competence. J Bacteriol 187:3980–3989. doi:.

    Not in our library10.1128/jb.187.12.3980-3989.2005

  99. 99.

    Fontaine L, Boutry C, Guédon E, Guillot A, Ibrahim M, Grossiord B, Hols P 2007 Quorum-sensing regulation of the production of Blp bacteriocins in Streptococcus thermophilus. J Bacteriol 189:7195–7205. doi:.

    Not in our library10.1128/jb.00966-07

  100. 100.

    Hibbing ME, Fuqua C, Parsek MR, Peterson SB 2010 Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol 8:15–25. doi:.

    Not in our library10.1038/nrmicro2259

  101. 101.

    Cornforth DM, Foster KR 2013 Competition sensing: the social side of bacterial stress responses. Nat Rev Microbiol 11:285–293. doi:.

    Not in our library10.1038/nrmicro2977

  102. 102.

    Mazzola M, Cook RJ, Thomashow LS, Weller DM, Pierson LS III 1992 Contribution of phenazine antibiotic biosynthesis to the ecological competence of fluorescent pseudomonads in soil habitats. Appl Environ Microbiol 58:2616–2624.

    Not in our library10.1128/aem.58.8.2616-2624.1992

  103. 103.

    Goo E, Majerczyk CD, An JH, Chandler JR, Seo YS, Ham H, Lim JY, Kim H, Lee B, Jang MS, Greenberg EP, Hwang I 2012 Bacterial quorum sensing, cooperativity, and anticipation of stationary-phase stress. Proc Natl Acad Sci U S A 109:19775–19780. doi:.

    Not in our library10.1073/pnas.1218092109

  104. 104.

    Chandler JR, Heilmann S, Mittler JE, Greenberg EP 2012 Acyl-homoserine lactone-dependent eavesdropping promotes competition in a laboratory co-culture model. ISME J 6:2219–2228. doi:.

    Not in our library10.1038/ismej.2012.69

  105. 105.

    Truong TT, Seyedsayamdost M, Greenberg EP, Chandler JR 2015 A Burkholderia thailandensis acyl-homoserine lactone-independent orphan LuxR homolog that activates production of the cytotoxin malleilactone. J Bacteriol 197:3456–3462. doi:.

    Not in our library10.1128/jb.00425-15

  106. 106.

    Seyedsayamdost MR, Chandler JR, Blodgett JA, Lima PS, Duerkop BA, Oinuma K, Greenberg EP, Clardy J 2010 Quorum-sensing-regulated bactobolin production by Burkholderia thailandensis E264. Org Lett 12:716–719. doi:.

    Not in our library10.1021/ol902751x

  107. 107.

    McClean KH, Winson MK, Fish L, Taylor A, Chhabra SR, Camara M, Daykin M, Lamb JH, Swift S, Bycroft BW, Stewart GS, Williams P 1997 Quorum sensing and Chromobacterium violaceum: exploitation of violacein production and inhibition for the detection of N-acylhomoserine lactones. Microbiology 143:3703–3711. doi:.

    Not in our library10.1099/00221287-143-12-3703

  108. 108.

    Lee JH, Lequette Y, Greenberg EP 2006 Activity of purified QscR, a Pseudomonas aeruginosa orphan quorum-sensing transcription factor. Mol Microbiol 59:602–609. doi:.

    Not in our library10.1111/j.1365-2958.2005.04960.x

  109. 109.

    Sjöblom S, Brader G, Koch G, Palva ET 2006 Cooperation of two distinct ExpR regulators controls quorum sensing specificity and virulence in the plant pathogen Erwinia carotovora. Mol Microbiol 60:1474–1489. doi:.

    Not in our library10.1111/j.1365-2958.2006.05210.x

  110. 110.

    Lindemann A, Pessi G, Schaefer AL, Mattmann ME, Christensen QH, Kessler A, Hennecke H, Blackwell HE, Greenberg EP, Harwood CS 2011 Isovaleryl-homoserine lactone, an unusual branched-chain quorum-sensing signal from the soybean symbiont Bradyrhizobium japonicum. Proc Natl Acad Sci U S A 108:16765–16770. doi:.

    Not in our library10.1073/pnas.1114125108

  111. 111.

    Ahlgren NA, Harwood CS, Schaefer AL, Giraud E, Greenberg EP 2011 Aryl-homoserine lactone quorum sensing in stem-nodulating photosynthetic bradyrhizobia. Proc Natl Acad Sci U S A 108:7183–7188. doi:.

    Not in our library10.1073/pnas.1103821108

  112. 112.

    Dulla GF, Lindow SE 2009 Acyl-homoserine lactone-mediated cross talk among epiphytic bacteria modulates behavior of Pseudomonas syringae on leaves. ISME J 3:825–834. doi:.

    Not in our library10.1038/ismej.2009.30

  113. 113.

    Lewenza S, Visser MB, Sokol PA 2002 Interspecies communication between Burkholderia cepacia and Pseudomonas aeruginosa. Can J Microbiol 48:707–716. doi:.

    Not in our library10.1139/w02-068

  114. 114.

    LeRoux M, Peterson SB, Mougous JD 2015 Bacterial danger sensing. J Mol Biol 427:3744–3753. doi:.

    Not in our library10.1016/j.jmb.2015.09.018

  115. 115.

    Ryan RP, Fouhy Y, Garcia BF, Watt SA, Niehaus K, Yang L, Tolker-Nielsen T, Dow JM 2008 Interspecies signalling via the Stenotrophomonas maltophilia diffusible signal factor influences biofilm formation and polymyxin tolerance in Pseudomonas aeruginosa. Mol Microbiol 68:75–86. doi:.

    Not in our library10.1111/j.1365-2958.2008.06132.x

  116. 116.

    Lloyd DG, Whitworth DE 2017 The myxobacterium Myxococcus xanthus can sense and respond to the quorum signals secreted by potential prey organisms. Front Microbiol 8:439. doi:.

    Not in our library10.3389/fmicb.2017.00439

  117. 117.

    Winans SC 2002 Bacterial Esperanto. Nat Struct Biol 9:83–84. doi:.

    Not in our library10.1038/nsb0202-83

  118. 118.

    Bäumler AJ, Sperandio V 2016 Interactions between the microbiota and pathogenic bacteria in the gut. Nature 535:85–93. doi:.

    Not in our library10.1038/nature18849

  119. 119.

    Thompson JA, Oliveira RA, Djukovic A, Ubeda C, Xavier KB 2015 Manipulation of the quorum sensing signal AI-2 affects the antibiotic-treated gut microbiota. Cell Rep 10:1861–1871. doi:.

    Not in our library10.1016/j.celrep.2015.02.049

  120. 120.
    Lack of genomic evidence of AI-2 receptors suggests a non-quorum sensing role for luxS in most bacteria · 2008
    No notice
  121. 121.

    Fazli M, Bjarnsholt T, Kirketerp-Møller K, Jørgensen B, Andersen AS, Krogfelt KA, Givskov M, Tolker-Nielsen T 2009 Nonrandom distribution of Pseudomonas aeruginosa and Staphylococcus aureus in chronic wounds. J Clin Microbiol 47:4084–4089. doi:.

    Not in our library10.1128/jcm.01395-09

  122. 122.

    Hoffman LR, Déziel E, D’Argenio DA, Lépine F, Emerson J, McNamara S, Gibson RL, Ramsey BW, Miller SI 2006 Selection for Staphylococcus aureus small-colony variants due to growth in the presence of Pseudomonas aeruginosa. Proc Natl Acad Sci U S A 103:19890–19895. doi:.

    Not in our library10.1073/pnas.0606756104

  123. 123.

    Palmer KL, Mashburn LM, Singh PK, Whiteley M 2005 Cystic fibrosis sputum supports growth and cues key aspects of Pseudomonas aeruginosa physiology. J Bacteriol 187:5267–5277. doi:.

    Not in our library10.1128/jb.187.15.5267-5277.2005

  124. 124.

    Palmer KL, Aye LM, Whiteley M 2007 Nutritional cues control Pseudomonas aeruginosa multicellular behavior in cystic fibrosis sputum. J Bacteriol 189:8079–8087. doi:.

    Not in our library10.1128/jb.01138-07

  125. 125.

    Biswas L, Biswas R, Schlag M, Bertram R, Götz F 2009 Small-colony variant selection as a survival strategy for Staphylococcus aureus in the presence of Pseudomonas aeruginosa. Appl Environ Microbiol 75:6910–6912. doi:.

    Not in our library10.1128/aem.01211-09

  126. 126.

    DeLeon S, Clinton A, Fowler H, Everett J, Horswill AR, Rumbaugh KP 2014 Synergistic interactions of Pseudomonas aeruginosa and Staphylococcus aureus in an in vitro wound model. Infect Immun 82:4718–4728. doi:.

    Not in our library10.1128/iai.02198-14

  127. 127.

    Mashburn LM, Jett AM, Akins DR, Whiteley M 2005 Staphylococcus aureus serves as an iron source for Pseudomonas aeruginosa during in vivo coculture. J Bacteriol 187:554–566. doi:.

    Not in our library10.1128/jb.187.2.554-566.2005

  128. 128.
    Adaptation of Pseudomonas aeruginosa in Cystic Fibrosis Airways Influences Virulence of Staphylococcus aureus In Vitro and Murine Models of Co-Infection · 2014
    No notice
  129. 129.
    In vivo and In vitro Interactions between Pseudomonas aeruginosa and Staphylococcus spp. · 2017
    No notice
  130. 130.

    Lightbown JW, Jackson FL 1956 Inhibition of cytochrome systems of heart muscle and certain bacteria by the antagonists of dihydrostreptomycin: 2-alkyl-4-hydroxyquinoline N-oxides. Biochem J 63:130–137. doi:.

    Not in our library10.1042/bj0630130

  131. 131.

    Machan ZA, Taylor GW, Pitt TL, Cole PJ, Wilson R 1992 2-Heptyl-4-hydroxyquinoline N-oxide, an antistaphylococcal agent produced by Pseudomonas aeruginosa. J Antimicrob Chemother 30:615–623. doi:.

    Not in our library10.1093/jac/30.5.615

  132. 132.

    Schuster M, Lostroh CP, Ogi T, Greenberg EP 2003 Identification, timing, and signal specificity of Pseudomonas aeruginosa quorum-controlled genes: a transcriptome analysis. J Bacteriol 185:2066–2079. doi:.

    Not in our library10.1128/jb.185.7.2066-2079.2003

  133. 133.

    An D, Danhorn T, Fuqua C, Parsek MR 2006 Quorum sensing and motility mediate interactions between Pseudomonas aeruginosa and Agrobacterium tumefaciens in biofilm cocultures. Proc Natl Acad Sci U S A 103:3828–3833. doi:.

    Not in our library10.1073/pnas.0511323103

  134. 134.

    Costello A, Reen FJ, O’Gara F, Callaghan M, McClean S 2014 Inhibition of co-colonizing cystic fibrosis-associated pathogens by Pseudomonas aeruginosa and Burkholderia multivorans. Microbiology 160:1474–1487. doi:.

    Not in our library10.1099/mic.0.074203-0

  135. 135.

    Smith AC, Rice A, Sutton B, Gabrilska R, Wessel AK, Whiteley M, Rumbaugh KP 2017 Albumin inhibits Pseudomonas aeruginosa quorum sensing and alters polymicrobial interactions. Infect Immun 85:e00116-17. doi:.

    Not in our library10.1128/iai.00116-17

  136. 136.

    Chandler JR, Hirt H, Dunny GM 2005 A paracrine peptide sex pheromone also acts as an autocrine signal to induce plasmid transfer and virulence factor expression in vivo. Proc Natl Acad Sci U S A 102:15617–15622. doi:.

    Not in our library10.1073/pnas.0505545102

  137. 137.

    Dunny GM 2013 Enterococcal sex pheromones: signaling, social behavior, and evolution. Annu Rev Genet 47:457–482. doi:.

    Not in our library10.1146/annurev-genet-111212-133449

  138. 138.

    Connell JL, Wessel AK, Parsek MR, Ellington AD, Whiteley M, Shear JB 2010 Probing prokaryotic social behaviors with bacterial “lobster traps.” mBio 1:e00202-10. doi:.

    Not in our library10.1128/mbio.00202-10

  139. 139.

    Sønderholm M, Kragh KN, Koren K, Jakobsen TH, Darch SE, Alhede M, Jensen PØ, Whiteley M, Kühl M, Bjarnsholt T 2017 Pseudomonas aeruginosa aggregate formation in an alginate bead model system exhibits in vivo-like characteristics. Appl Environ Microbiol 83:e00113-17. doi:.

    Not in our library10.1128/aem.00113-17

  140. 140.

    Moons P, Van Houdt R, Aertsen A, Vanoirbeek K, Engelborghs Y, Michiels CW 2006 Role of quorum sensing and antimicrobial component production by Serratia plymuthica in formation of biofilms, including mixed biofilms with Escherichia coli. Appl Environ Microbiol 72:7294–7300. doi:.

    Not in our library10.1128/aem.01708-06

  141. 141.

    Moons P, Van Houdt R, Aertsen A, Vanoirbeek K, Michiels CW 2005 Quorum sensing dependent production of antimicrobial component influences establishment of E. coli in dual species biofilms with Serratia plymuthica. Commun Agric Appl Biol Sci 70:195–198.

    Not in our library

  142. 142.

    McNab R, Ford SK, El-Sabaeny A, Barbieri B, Cook GS, Lamont RJ 2003 LuxS-based signaling in Streptococcus gordonii: autoinducer 2 controls carbohydrate metabolism and biofilm formation with Porphyromonas gingivalis. J Bacteriol 185:274–284. doi:.

    Not in our library10.1128/jb.185.1.274-284.2003

  143. 143.

    Rickard AH, Palmer RJ Jr, Blehert DS, Campagna SR, Semmelhack MF, Egland PG, Bassler BL, Kolenbrander PE 2006 Autoinducer 2: a concentration-dependent signal for mutualistic bacterial biofilm growth. Mol Microbiol 60:1446–1456. doi:.

    Not in our library10.1111/j.1365-2958.2006.05202.x

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