Signal discrimination by differential regulation of protein stability in quorum sensing

Abstract

Quorum sensing (QS) is a communication mechanism exploited by a large variety of bacteria to coordinate gene expression at the population level. In Gram-negative bacteria, QS occurs via synthesis and detection of small chemical signals, most of which belong to the acyl-homoserine lactone class. In such a system, binding of an acyl-homoserine lactone signal to its cognate transcriptional regulator (R-protein) often induces stabilization and subsequent dimerization of the R-protein, which results in the regulation of downstream gene expression. Existence of diverse QS systems within and among species of bacteria indicates that each bacterium needs to distinguish among a myriad of structurally similar chemical signals. We show, using a mathematical model, that fast degradation of an R-protein monomer can facilitate discrimination of signals that differentially stabilize it. Furthermore, our results suggest an inverse correlation between the stability of an R-protein and the achievable limits of fidelity in signal discrimination. In particular, an unstable R-protein tends to be more specific to its cognate signal, whereas a stable R-protein tends to be more promiscuous. These predictions are consistent with experimental data on well-studied natural and engineered R-proteins and thus have implications for understanding the functional design of QS systems.

Figure 1. Signal discrimination by a quorum sensing regulator (R)

A₁ is the cognate signal; A₂ is a non-cognate signal. R₁ and R₂ are the signal-monomer complexes. D₁ and D₂ are dimerized complexes. See main text and Table S1 for details on parameters.

Figure 2. Quantifying fidelity in signal recognition using two metrics

(A) Dose responses of D₁ and D₂ to A₁ and A₂ respectively. f_a is evaluated at the signal concentration (K_1/2) that leads to half-maximum induction of D₁. f_s is evaluated at a saturating signal concentration. (B) Modulation of fidelity (f_a or f_s) by α (for β = γ =10), which characterizes the differential protein stabilization by A₁ and A₂. A similar trend is observed for other values of β and γ (Figure S1).

Figure 3. Increasing the R degradation rate constant enhances signaling fidelity

(A) Dependence of f_a on α for varying d_R (β = γ = 10). Note that d_R determines the range of α for a constant d_R1 (= 0.023 min⁻¹). (B) Modulation of the maximal (α = 1) and minimal (α = d_R1/d_R) limits of fidelity by d_R. For both metrics, the maximal fidelity increases with d_R, but the minimal fidelty does not change with d_R. (C) The half-activation threshold (K_1/2, nM) by the cognate signal increases with d_R.

Figure 4. QS signal recognition as an example of kinetic proofreading

(A) The canonical Hopfield-Ninio (HN) model of kinetic proofreading with one cycle. (B) QS signaling as two-cycle kinetic proofreading. In QS, the degradation of the R-protein provides a second cycle of proofreading. R is a receptor protein (HN model) or an R-protein (QS model). A is a signaling molecule. RA is the complex of R and A. In the HN model, RA* the activated form RA. The star (*) in the QS model represents unspecified substrates or degradation products.