Quorum sensing protects bacterial co-operation from exploitation by cheats

Abstract

Quorum sensing (QS) is a cell-cell communication system found in many bacterial species, commonly controlling secreted co-operative traits, including extracellular digestive enzymes. We show that the canonical QS regulatory architecture allows bacteria to sense the genotypic composition of high-density populations, and limit co-operative investments to social environments enriched for co-operators. Using high-density populations of the opportunistic pathogen Pseudomonas aeruginosa we map per-capita signal and co-operative enzyme investment in the wild type as a function of the frequency of non-responder cheats. We demonstrate mathematically and experimentally that the observed response rule of 'co-operate when surrounded by co-operators' allows bacteria to match their investment in co-operation to the composition of the group, therefore allowing the maintenance of co-operation at lower levels of population structuring (that is, lower relatedness). Similar behavioural responses have been described in vertebrates under the banner of 'generalised reciprocity'. Our results suggest that mechanisms of reciprocity are not confined to taxa with advanced cognition, and can be implemented at the cellular level via positive feedback circuits.

Figure 1

QS network architecture produces the generalised reciprocity rule of ‘co-operate when surrounded by co-operators'. (Top) When co-operators (blue) are common positive signal autoregulation in co-operators leads to high environmental signal concentrations. In response to high signal concentration co-operators express more secreted enzyme, increasing population yield. Cheats (red) lack a signal receptor, they do not respond to signal and so do not express signal or secreted enzyme above basal levels. They are able to exploit co-operators, increasing in frequency. (Bottom) When co-operators are rare, there are few cells undergoing positive feedback in signal production (autoregulation) so signal levels remain low. Co-operators invest little in protease production (acting as phenotypic cheats), keeping costs of co-operation low and reducing exploitation by cheats.

Figure 2

Per-capita signal and secreted enzyme investment increases with wild-type co-operator frequency. Mixed populations of cheats and co-operators were grown for 6 h in quorum-sensing media. After growth, AHL signal and secreted protease concentrations were measured for the population. Plate counts were used to determine co-operator proportion at 6 h. Equations for fitted models are presented on the graphs. Insets show a transformation of the fitted models to show the change in per-capita WT phenotype. (a) Total 3-oxo-C12 N- acyl HSL concentration (inset, per WT individual). (b) Total C4 N-acyl HSL concentration (inset, per WT individual). (c) Total protease (elastase) concentration (inset, per WT individual), the equivalent constitutive response (constant maximal WT investment) is shown as dashed lines.

Figure 3

Generalised reciprocity limits social exploitation when WT co-operators are rare. Competition between WT co-operators and cheats (ΔlasR) over 40 h in media requiring secreted protease enzyme for growth. (a) Log growth rate of the two strains averaged over the length of competition. (b) The predicted linear effect of group co-operative effort on log growth rate (effect of group co-operation: β=4.43, s.e.=0.26, F_1,53=134.33, P<0.0001). (c) The predicted quadratic effect of individual co-operative effort on log growth rate (effect of individual co-operation: β=1.81, s.e.=0.54, F_1,49=210.79, P<0.0001; effect of individual co-operation squared: β=–4.91, s.e.=0.69, F_1,49=50.16, P<0.0001). (d) How population yield (CFU/100 μl) varies with WT frequency. (e) How WT relative fitness varies with WT frequency. All solid lines are fits of our model to the data shown in (a) assuming co-operative effort varies with WT frequency according to the solid fits in Figure 2c. Dashed lines are predictions for constitutive co-operative effort (assuming co-operative effort varies with WT frequency according to dashed lines in (2c), individual points are direct measures of growth rate, yield and relative fitness measured from competition experiments.

Figure 4

Generalised reciprocity stabilises co-operation over a larger range of conditions in a metapopulation. Selection determined using statistical model fits shown in Figure 3 and the metapopulation framework described in the methods. Dots represent equilibria for constitutive (black), QS-regulated (white), and either form of co-operation (grey). Constitutive co-operation is only favoured when the population has a clonal structure (1 founder per subpopulation). QS-regulated (facultative) co-operation is favoured for lower levels of relatedness, including a well-mixed population (infinite number of founders).