Bacterial quorum sensing allows graded and bimodal cellular responses to variations in population density

Abstract

Quorum sensing (QS) is a mechanism of cell-to-cell communication via diffusible signal molecules that controls multiple secreted factors including virulence factors in bacterial pathogens [1,2]. While the standard view is that QS functions as a density-sensing mechanism, the functional and evolutionary context of QS continues to be disputed [3][4][5][6][7][8][9][10][11]. A critical step in assessing the various adaptive hypotheses is establishing the functional capacities and limits of QS. Current functional studies largely focus on a dichotomy of QS on/off (or, quorate / sub-quorate) states, despite the increasing amount of heterogeneity on a cellular scale [4,[12][13][14][15][16], overlooking the potential for intermediate, graded responses. In this paper we explore the functional capacity of QS to resolve finely graded environmental densities and introduce the use of reaction norms as a way to holistically characterize QS response. Here we show that Pseudomonas aeruginosa can deliver a graded response to variation in environmental population density on both the population and individual scales. We further resolve the linear population response to be the product of two component cellular reaction norms: the likelihood of being responsive and the intensity of response. Overall, this work reveals that there is no critical cell mass or 'quorum', at which QS is activated on either the individual cell or population scale.

Pseudomonas aeruginosa reaches collective decisions via transient segregation of quorum sensing activities across cells

“…Both effects together resulted in a sigmoid, yet slowly progressing QS responses at the population level. Thus, our findings support a graded 33,55 and not an all-or-none gene expression transition 40,56,57 . While our findings support the function of QS as a means to coordinate collective actions at the group level, the path to reach collective decisions is much more intricate than population-level observations would suggest.…”

ThelasandrhlQuorum Sensing Systems inPseudomonas aeruginosaForm a Multi-Signal Reciprocal Network Which Can Tune Reactivity to Variations in Physical and Social Environments

“…Top panels (A and B) show estimated fold-change in expression as a function of population density approximately equivalent to OD600. The range of OD600 values is consistent withRattray et al (2022), data from which was used to calibrate parameters. Bottom panels (C and D) show percentage decline from maximum expression as a function of mass transfer rate normalized to C4-HSL decay rate.…”

“…Right panels (B and D) emphasize qualitative rather than quantitative differences by scaling the parameters of Table 3 to effect the same maximum lasI and rhlI expression in all architectures. The native reciprocal architecture (blue line) in panels A and B broadly captures the wildtype NPAO1 behavior reported in Rattray et al (2022). Compared to this baseline, the Yigure predicts that removing the inYluence of rhl on las (hierarchical architecture, dark green and red lines) results in a delayed response to increasing density.…”

“…Figure10plots lasB expression as reaction norms(Stearns 1989;Rattray et al 2022) against population density (A, B) and mass transfer rate (C, D) for the three different architectures. As expected, there is a quantitative difference in the behavior of the architectures: in the reciprocal architecture C4-HSL increases the expression level of lasI and without its inYluence the lasB fold-change is reduced.…”

“…To Yind solutions for the steady state signal concentrations Si* in this model, we estimate expression levels Ei (S) from our experimental data (Equation 2, Table2), approximate the proportionality constant c based on data collected forRattray et al 2022 (details in supplementary information), and note that the measurements ofCornforth at el. (2014) …”