Bacterial Quorum Sensing Signals Self-Assemble in Aqueous Media to Form Micelles and Vesicles: An Integrated Experimental and Molecular Dynamics Study

Abstract

Many species of common bacteria communicate and coordinate group behaviors, including toxin production and surface fouling, through a process known as quorum sensing (QS). In Gram-negative bacteria, QS is regulated by N-acyl-L-homoserine lactones (AHLs) that possess a polar homoserine lactone headgroup and a nonpolar aliphatic tail. Past studies demonstrate that AHLs can aggregate in water or adsorb at interfaces, suggesting that molecular self-assembly could play a role in processes that govern bacterial communication. We used a combination of biophysical characterization and atomistic molecular dynamics (MD) simulations to characterize the selfassembly behaviors of 12 structurally related AHLs. We used static light scattering and measurements of surface tension to characterize the assembly of four naturally occurring AHLs (3-oxo-C8-AHL, 3-oxo-C12-AHL, C12-AHL, and C16-AHL) in aqueous media and determine their critical aggregation concentrations (CACs). MD simulations and alchemical free energy calculations were used to predict thermodynamically preferred aggregate structures for each AHL. Those calculations predicted that AHLs with 10 or 12 tail carbon atoms should form spherical micelles and that AHLs with 14 or 16 tail carbon atoms should form vesicles in solution. Characterization of solutions of AHLs using negative stain transmission electron microscopy (TEM) and dynamic light scattering (DLS) revealed aggregates with sizes consistent with spherical micelles or small unilamellar vesicles for 3-oxo-C12-AHL and C12-AHL and the formation of large vesicles (∼250 nm) in solutions of C16-AHL. These experimental findings are in general agreement with our simulation predictions. Overall, our results provide insight into processes of self-assembly that can occur in this class of bacterial amphiphiles and, more broadly, provide a potential basis for understanding how AHL structure could influence processes that bacteria use to drive important group behaviors.

Bacterial Quorum Sensing Signals Promote Large-Scale Remodeling of Lipid Membranes

“…Additionally, 3-oxo-C12-AHL exhibited a break in a plot of surface tension vs concentration (denoted by an arrow in Figure S1), suggesting that it aggregates at a concentration between 100 and 125 μM under the conditions used here. The location of this break occurs in a range that is generally consistent with the value reported for the critical aggregation concentration (CAC) for this AHL in our past study using a combination of light scattering and measurements of surface tension in water (88 ± 20 μM and 97 ± 11 μM, respectively; see that past study for a more detailed discussion on the solution-phase self-assembly of 3-oxo-C12-AHL in aqueous media) . In contrast, 3-oxo-C12-HS did not exhibit a break in plots of surface tension vs concentration in the range of concentrations examined in this study; as a result, we conclude that this hydrolyzed compound does not aggregate in this range of concentrations.…”

“…The location of this break occurs in a range that is generally consistent with the value reported for the critical aggregation concentration (CAC) for this AHL in our past study using a combination of light scattering and measurements of surface tension in water (88 ± 20 μM and 97 ± 11 μM, respectively; see that past study for a more detailed discussion on the solution-phase self-assembly of 3-oxo-C12-AHL in aqueous media). 26 In contrast, 3-oxo-C12-HS did not exhibit a break in plots of surface tension vs concentration in the range of concentrations examined in this study; as a result, we conclude that this hydrolyzed compound does not aggregate in this range of concentrations. These results, taken together, suggest that chemical hydrolysis of the homoserine lactone head group of 3-oxo-C12-AHL alters its physical behavior in solution and at interfaces, leading to 3oxo-C12-AHL being more surface-active than 3-oxo-C12-HS.…”

“…Of particular interest to the work described here are past reports demonstrating that AHLs, which possess a relatively polar L-homoserine lactone "head group" and an aliphatic "tail" of four to 20 carbons (Figure 1), can self-assemble in aqueous solution and at interfaces. 24−26 For example, past work by our group 26 and others 25 has demonstrated that long-chained AHLs (e.g., those possessing eight or more carbons in their tails) can self-assemble in solution to form spherical micelles or small (∼20 nm) vesicles in a tail length-dependent manner. Daniels assemble and undergo phase changes when compressed at air− water interfaces.…”

Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks

“…1, a-d), from multiple literature sources to form our dataset. 1,12,17,34 All CMCs were measured at room temperature (between 20-25 °C) in water and converted to log CMC values (base 10). The dataset was split into training (~90%) and testing (~10%) subsets, and we performed k-fold crossvalidation (CV) for hyperparameter tuning.…”

Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks

“…[12][13][14] As an alternative to experiments, computational methods such as molecular dynamics (MD) simulations [15][16][17] and descriptor-based quantitative structure-property relationship (QSPR) models 12,[18][19][20][21][22][23] have been used to predict CMCs. These approaches have been shown to predict CMCs with relatively high accuracy, but they have a number of limitations; for instance, MD simulations usually require large system sizes, long simulation times, and assumptions regarding the number of surfactants within a micelle, [15][16][17] whereas QSPR models are often applicable to a single class of surfactants and may need density functional theory calculations to obtain quantum-chemical molecular descriptors. 24 Recent advances in machine learning methods for molecular property prediction can help overcome some of these obstacles.…”