Ecological memory of hydrodynamic cues shapes growth and migration of motile microorganisms

TL;DR

These results establish a mechanistic framework for flow-induced memory in motile microbes, revealing how past fluidic cues shape future growth and migration and advances predictive understanding of motile microbes in natural and engineered hydrodynamic systems experiencing increasing variability under global environmental changes.

Abstract

Microorganisms live in inherently dynamic environments where fluctuations in biotic and abiotic factors shape their behaviour, physiology, and fitness. The concept of ecological memory: the lasting imprint of prior environmental cues, suggests that past exposures can exert prolonged effects on microbial growth, resilience, and phenotypic expressions. For motile microbes in aquatic ecosystems, environmental variability is mediated by fluid motion, which may engender a form of hydrodynamic memory, whereby prior exposure to specific spatio-temporal cues influence future growth and migratory behaviour. Yet, the emergence of such flow-induced memory, or its long-term consequences for trait evolution and population dynamics, remain unexplored. We integrate millifluidic flow control, high-resolution cell tracking, and tunable hydrodynamic cues to quantify growth and migration of Heterosigma akashiwo, a model microbe, across growth stages. Using two complementary perturbation scenarios: standard (flow after static conditions) and reverse (flow before static growth), we test how the temporal structure of forcing shapes multigenerational responses. This combinatorial design disentangles exposure history from its duration, and reveals how prior flow modulates sensitivity, generating legacy effects. Compared with static controls, repeated hydrodynamic exposure alters doubling time, carrying capacity, gravitactic stability, and swimming speed distributions; shifting growth phase progression and tolerance to subsequent perturbations. These results establish a mechanistic framework for flow-induced memory in motile microbes, revealing how past fluidic cues shape future growth and migration. Our study advances predictive understanding of motile microbes in natural and engineered hydrodynamic systems experiencing increasing variability under global environmental changes.

Figures (26)

• Figure 1: Tracking ecological memory in motile microorganisms. (A)--(D): Standard scenario (static $\rightarrow$ shaker): microbial growth initially under static condition (red hue), followed by hydrodynamic perturbations (blue hue). (A) 0-hour: cells transferred to the shaker immediately post-inoculation, while control cultures are maintained under static conditions within the same incubator. (B) 120-hour delay: 120 h (early exponential phase) of static condition, followed by hydrodynamic perturbation. Similarly, (C) captures 168 h delay, corresponding to mid-exponential phase. (D) 348 h delay: cultures are exposed to perturbation after 348 h of growth (stationary phase) under static condition. (E)--(F): Reverse scenarios (shaker $\rightarrow$ static). (E) 135 h lead perturbation: cells are first exposed to hydrodynamic perturbation for 135 h (right after inoculation), thereafter transferred to static condition for rest of the experiment. Similarly, (F) 168 h lead refers to transfer from dynamic to static condition after 168 h.
• Figure 2: Quantifying swimming and reorientation dynamics of motile microbes. Raw (A) and binarized (B) images of swimming cells; insets present magnified view. (C) Swimming trajectories; inset presents zoom-in views of a selection of trajectories. (D) Trajectories visualized in the milifluidic chamber reveals diversity of trajectories and reorientation events (E), visualized as curved trajectories. (F) Instantaneous swimming angle ($\theta$), plotted against rotational velocity ($\omega$) provides reorientation time scales, following sinusoidal fitting. Representative reorientation plots of UP-swimming (negative gravitaxis) and DOWN-swimming cells (positive gravitaxis). Experimental data are fitted with a sinusoidal function ($\kappa$ is an imposed phase shift), which determines the reorientation timescale,($\tau_r$), from the best-fit sinusoid.
• Figure 3: Impact of standard hydrodynamic scenarios (static-to-perturbed) on the growth of HA452 population. (A)--(D): Growth curves of HA452 cells for both the experimental perturbed samples and the static control samples under the following perturbation scenarios: (A) 0-hour, (B) 120-hour delay, (C) 168-hour delay, and (D) 348-hour delay. In all panels, the inset displays the logistic models fitted to the corresponding growth curves. For each data point, two biological replicates and two technical replicates were measured. In the growth curves, the standard deviation is represented by the shaded region surrounding the solid curve, which shows the mean values.
• Figure 4: Impact of standard hydrodynamic scenarios (static-to-perturbed) on the growth of HA3107 population. (A)--(D): Growth curves of HA3107 cells for both experimental perturbed samples and static control samples under the following perturbation scenarios: (A) 0-hour, (B) 120-hour delay, (C) 168-hour delay, and (E) 348-hour delay. In all panels, the inset displays the logistic models fitted to the corresponding growth curves. Each data point represents two biological replicates and two technical replicates. For the growth curves, the shaded regions around the solid curves represent the standard deviations.
• Figure 5: Hydrodynamic perturbations impact doubling time and carrying capacity of motile microbes. (A) Doubling time ($T_d$, upper) and carrying capacity ($K$, lower) from logistic fits for HA452 under forward hydrodynamic-perturbation scenarios, comparing static (red) and perturbed (blue) cultures. Early perturbation (0-hour) accelerates growth without altering $K$, whereas perturbation imposed during exponential growth (120- and 168-h delay scenarios) reduces biomass yield and, at 168 h, prolongs $T_d$; late perturbation (348 h) has no detectable effect. (B) As in (A) for HA3107, which shows generally stronger reductions in $T_d$ and $K$ at 120- and 168-h delays. Error bars denote standard deviations. Statistical significance is indicated as follows: (*) for $P < 0.05$, (**) for $P < 0.01$, (***) for $P < 0.001$, and (****) for $P < 0.0001$.