Rheologically tuned diffusion modulates quorum sensing in Vibrio fischeri

TL;DR

The paper investigates how fluid rheology modulates Vibrio fischeri motility and QS-controlled luminescence. Using 3D tracking of single cells in Newtonian and non-Newtonian fluids, it quantifies motility modes, run durations, turning angles, and derives an effective diffusion coefficient $D$. They show that in Newtonian fluids $D$ is nonmonotonic with viscosity, aligning with a nonmonotonic luminescence response, while in viscoelastic Methocel $D$ decreases monotonically and luminescence declines, with a diffusion-based model reproducing these trends. The results link environmental rheology to collective signaling, revealing search efficiency as a physical bridge between individual motility and QS activation in host-like habitats.

Abstract

Understanding how the physical properties of a fluid influence bacterial behavior is essential for explaining how microorganisms interact with their environment and with animal hosts. Here, we examine how changes in fluid viscosity and rheological properties affect the locomotion of the marine bacterium Vibrio fischeri and its ability to produce luminescence through cell--cell communication. We track the three-dimensional motion of single cells in well-defined fluids with different physical properties and measure the luminescence emitted by cell populations. We find that fluids with higher viscosity cause V. fischeri to spend more time in a slower, turning-focused swimming mode, which reduces how effectively cells spread out and encounter the chemical signals required to activate luminescence. As a result, luminescence first increases and then decreases in Newtonian fluids, but decreases monotonically in fluids that exhibit non-Newtonian rheological behavior. Computer simulations based on our measurements confirm that the ability of cells to explore their surroundings plays a central role in determining when and how strongly they communicate. These findings reveal a direct link between the physical environment, bacterial movement, and collective behavior, and offer new insight into how microorganisms adapt to complex fluid habitats, including those found inside animal hosts.

Figures (4)

• Figure 1: Schematic of quorum-sensing (QS) regulation in V. fischeri. At low cell and signal density, LuxR remains inactive; accumulation of autoinducer molecules at high density activates LuxR binding to the lux-type box, triggering bioluminescence. Conceptual model of V. fischeri colonization in the light organ of the Hawaiian bobtail squid (Euprymna scolopes). Beating cilia transport bacteria toward the light-organ ducts while secreting non-Newtonian mucus. Bacteria with swimming speeds that are too fast or too slow are inefficiently captured, whereas an appropriate swimming speed promotes entry into the shelter zone and successful colonization. Increased viscosity and viscoelasticity reduce propulsion efficiency, leading to slower swimming in non-Newtonian fluids, consistent with the experimentally observed motility reduction and its impact on QS-mediated bioluminescence. Illustration of bacterial space exploration characterized by different effective diffusion coefficients. Larger effective diffusion coefficients correspond to more efficient exploration and higher encounter rates between cells and signal molecules.
• Figure 2: Viscosity-dependent modulation of quorum-sensing–mediated bioluminescence in V. fischeri. (A) Representative false-color images of bioluminescence intensity for bacterial suspensions at fixed cell concentration in motility buffers containing PVP360k (Newtonian fluid, top row) and Methocel (shear-thinning viscoelastic fluid, bottom row) at different viscosities. Color scale indicates luminescence intensity. Temporal evolution of the spatially averaged luminescence intensity in PVP360k (B) and Methocel (C) containing motility buffer for viscosities ranging from 1 to 2 cP. The solid points represent the average intensity, while the shaded areas represent the standard deviation. The mean luminescence intensity exhibits a nonmonotonic dependence on viscosity, with a maximum at intermediate viscosity.
• Figure 3: Motility modes and viscosity-dependent swimming behavior of V. fischeri in Newtonian and non-Newtonian fluids. (A) Schematic illustration of the three characteristic motility modes of V. fischeri: push, pull, and wrap. Push and pull modes correspond to straight swimming driven by flagellar rotation, whereas the wrap mode arises from flagellar bundling around the cell body and is associated with reduced propulsion efficiency. (B) Fraction of trajectories exhibiting push/pull and wrap modes at selected viscosities. The upper panel shows data obtained in PVP360k-containing Newtonian fluids, whereas the lower panel corresponds to Methocel-containing viscoelastic fluids. (C) Swimming speed as a function of viscosity for different motility modes in motility buffers containing PVP360k (Newtonian fluid) and Methocel (shear-thinning viscoelastic fluid).
• Figure 4: Motility statistics, effective diffusion, and their connection to quorum-sensing activity in V. fischeri. (A) Schematic illustration of the motility statistics used to characterize cell reorientation and environmental exploration. Straight swimming in push and pull modes is intermittently interrupted by wrap events of duration $\tau_{\mathrm{wrap}}$, leading to a reorientation characterized by the turning angle $\theta$ and an effective run duration $\tau_{\mathrm{run}}$. Representative trajectories illustrate how different motility statistics give rise to smaller or larger effective diffusion coefficients $D$. (B) Mean turning angle as a function of the wrap duration $\tau_{\mathrm{wrap}}$ for cells swimming in PVP360k- and Methocel-containing motility buffers. Dash line represents exponential fits to the data. (C) Mean run duration $\tau_{\mathrm{run}}$ as a function of viscosity. Inset: probability density function (PDF) of run durations with an exponential fit, supporting a Poisson description of reorientation events. (D) Effective diffusion coefficient $D$ calculated from the measured swimming speed, run duration, and turning-angle statistics. (E) Comparison between the normalized mean luminescence intensity measured experimentally and simulation results based on a diffusion-controlled interaction model between cells and signal molecules.