Combinatorial quorum sensing allows bacteria to resolve their social and physical environment

Abstract

Quorum sensing (QS) is a cell-cell communication system that controls gene expression in many bacterial species, mediated by diffusible signal molecules. Although the intracellular regulatory mechanisms of QS are often well-understood, the functional roles of QS remain controversial. In particular, the use of multiple signals by many bacterial species poses a serious challenge to current functional theories. Here, we address this challenge by showing that bacteria can use multiple QS signals to infer both their social (density) and physical (mass-transfer) environment. Analytical and evolutionary simulation models show that the detection of, and response to, complex social/physical contrasts requires multiple signals with distinct half-lives and combinatorial (nonadditive) responses to signal concentrations. We test these predictions using the opportunistic pathogen Pseudomonas aeruginosa and demonstrate significant differences in signal decay between its two primary signal molecules, as well as diverse combinatorial responses to dual-signal inputs. QS is associated with the control of secreted factors, and we show that secretome genes are preferentially controlled by synergistic "AND-gate" responses to multiple signal inputs, ensuring the effective expression of secreted factors in high-density and low mass-transfer environments. Our results support a new functional hypothesis for the use of multiple signals and, more generally, show that bacteria are capable of combinatorial communication.

Keywords: bacterial cooperation; bacterial signaling; collective behavior; diffusion sensing; efficiency sensing.

Fig. 1.

Multiple signals allow greater environmental resolution. (A) With one auto-inducing signal molecule, populations can discriminate two environmental states in density/mass-transfer space (0,1). (B) With two signals, populations can, in principle, discriminate four states [(0,0),(0,1),(1,0), and (1,1)], if one signal (red) is more fragile than the other (blue). Signal-molecule concentration dynamics are given by by dS_k/dt = (p + a_kS_k)N − (m + u_k)S_k, where N is cell density, is baseline production, is the increased production due to autoinduction, is mass-transfer rate, and is decay rate. We define a signal to be “ON” (autoinduced) when is above an unstable equilibrium = Np/(m − a_kN + u_k), which occurs when is negative: that is, when Na_k > m + u_k. See SI Text for details.

Fig. 2.

Multiple signals enhance fitness in evolving in silico simulations. (A) Schematic model of agent-based simulation structure. In silico populations evolve in environments characterized by four distinct regimes (a–d) of density and mass transfer, each requiring a distinct pattern of gene expression. (B) The behavior (gene-expression pattern) of the best performing clone in our simulations, with colors corresponding to the target environments in A. The use of multiple signals allows individuals to approximately map their gene expression to the different environments. Logic gates for each regulon are also indicated (X_f indicates “exclusive fragile” and X_d indicates “exclusive durable” gates). (C) The performance of a two-signal system, relative to the theoretical maximal performance of a single-molecule system (dashed line) when environments are fluctuating. Values shown are means ± 1 SD. (D) Signal-decay properties quickly diverge in the two-signal case. Values are log₁₀ of the signal-decay rate ± 1 SD. See SI Text, Simulation Model for full details of the simulation procedure.

Fig. 3.

P. aeruginosa responds combinatorially to multiple signal inputs. (A) The set of QS-regulated genes in P. aeruginosa is partitioned into 14 distinct regulons, differentiated by distinct expression patterns across the four signal-addition treatments. The line plots represent the mean-centered and scaled expression profiles for each cluster of genes. Coarse-graining the expression data to discrete on/off states allows assignment of discrete logic-gate families, highlighting the prevalence and diversity of combinatorial processing rules. (B) Combining the distinct combinatorial responses to dual signal inputs with knowledge of relative signal stability (Table 1), we use our model to infer under which population-density and mass-transfer regimes each gene cluster would be expressed. The region plots (B) represent the inferred density/mass transfer target. Gene expression was measured using microarray. Cultures of PAO-JG1 were initiated with either (i) no signals, (ii) 15 µM C4-HSL, (iii) 15 µM 3-oxo-C12-HSL, or (iv) both 15 µM C4-HSL and 15 µM 3-oxo-C12-HSL in shaken cultures of LB broth for 8 h before RNA extraction. Regulon partitioning was achieved via k-means clustering, with selection of cluster number by BIC minimization (Fig. S6). Regulon gene content is detailed in Fig. S7.

Fig. 4.

Secretome genes are under adaptive synergistic (AND-gate) control. (A) Secreted factors are predicted to be beneficial in high-density, low mass-transfer environments. The (gray) region where there is enough product to be advantageous is upward-curved and can be better approximated with two signals (versus one), with a synergistic response. The concentration of secreted molecules is described by dX/dt = PN − (m + f)X, where P and f are the production and decay rates; the formation of the beneficial product is described by dY/dt = qX − (cN + m + e)Y, where q is the rate of conversion, c is the rate of per-cell consumption, and e is its decay rate (SI Text and Fig. S8). (B) Gene expression among QS-regulated genes for secreted proteins (red) and nonsecreted proteins (blue). Expression of nonsecretome genes shows interference between signals; expression is lower when both signals are present than the sum of the individual effects of the signals. However, as predicted, secretome genes show synergistic expression in response to the combination of signals: i.e., expression is higher when both signals are present than the sum of the individual signal effects. Data shown are means ± SEM.