3D spatial exploration by E. coli echoes motor temporal variability

TL;DR

This study reveals significant variability in E. coli's 3D movement, linking motor stochasticity to behavioral diversity, which impacts bacterial transport models and our understanding of microbial spatial exploration.

Contribution

It provides the first direct experimental evidence that chemotactic noise influences macroscopic bacterial motility, challenging the traditional run-and-tumble model.

Findings

Large distribution of run times observed in E. coli trajectories

Significant changes in motility over long timescales

Implications for active matter transport modeling

Abstract

Unraveling bacterial strategies for spatial exploration is crucial for understanding the complexity in the organization of life. Bacterial motility determines the spatio-temporal structure of microbial communities, controls infection spreading and the microbiota organization in guts or in soils. Most theoretical approaches for modeling bacterial transport rely on their run-and-tumble motion. For Escherichia coli, the run time distribution was reported to follow a Poisson process with a single characteristic time related to the rotational switching of the flagellar motors. However, direct measurements on flagellar motors show heavy-tailed distributions of rotation times stemming from the intrinsic noise in the chemotactic mechanism. Currently, there is no direct experimental evidence that the stochasticity in the chemotactic machinery affect the macroscopic motility of bacteria. In stark contrast with the accepted vision of run-and-tumble, here we report a large behavioral variability of wild-type \emph{E. coli}, revealed in their three-dimensional trajectories. At short observation times, a large distribution of run times is measured on a population and attributed to the slow fluctuations of a signaling protein triggering the flagellar motor reversal. Over long times, individual bacteria undergo significant changes in motility. We demonstrate that such a large distribution of run times introduces measurement biases in most practical situations. Our results reconcile the notorious conundrum between run time observations and motor switching statistics. We finally propose that statistical modeling of transport properties currently undertaken in the emerging framework of active matter studies, should be reconsidered under the scope of this large variability of motility features.