Noise in Bacterial Chemotaxis: Sources, Analysis, and Control

Annotation of chemotaxis gene clusters and proteins involved in chemotaxis of Bacillus subtilis strain MB378 capable of biodecolorizing different dyes

This study explores the chemotactic potential of Bacillus subtilis MB378 against industrial dyes. Initial screening with swim plate assay showed significant movement of Bacillus subtilis MB378 towards test compounds. According to quantitative capillary assay, B. subtilis MB378 exhibited high chemotaxis potential towards Acid Orange 52 (CI: 9.52), followed by Direct Red 28 (CI: 8.39) and Basic Green 4 (CI: 5.21) in glucose-supplemented medium. Sequencing and gene annotation results evidently showed presence of chemotaxis genes and flagellar motor proteins in Bacillus subtilis draft genome. Methyl-accepting proteins (involved in chemotaxis regulation) belonged to pfam00672, pfam00072, and pfam00015 protein families. Annotated chemotaxis machinery of MB378 comprised 8 Che genes, 5 chemoreceptor genes, associated flagellar proteins, and rotary motors. Chemotaxis genes of B. subtilis MB378 were compared with genes of closely related Bacillus strains (168, WK1, and HTA426), depicting highly conserved regions showing evolutionary relation between them. Considering results of present study, it can be speculated that test compounds triggered chemotactic genes, which made these compounds bioavailable to the bacterium. Hence, the bacterium recognized and approached these compounds and facilitated biodegradation and detoxification of these compounds.

Statistical signatures of a targeted search by bacteria

Chemoattractant gradients are rarely well-controlled in nature and recent attention has turned to bacterial chemotaxis toward typical bacterial food sources such as food patches or even bacterial prey. In environments with localized food sources reminiscent of a bacterium's natural habitat, striking phenomena-such as the volcano effect or banding-have been predicted or expected to emerge from chemotactic models. However, in practice, from limited bacterial trajectory data it is difficult to distinguish targeted searches from an untargeted search strategy for food sources. Here we use a theoretical model to identify statistical signatures of a targeted search toward point food sources, such as prey. Our model is constructed on the basis that bacteria use temporal comparisons to bias their random walk, exhibit finite memory and are subject to random (Brownian) motion as well as signaling noise. The advantage with using a stochastic model-based approach is that a stochastic model may be parametrized from individual stochastic bacterial trajectories but may then be used to generate a very large number of simulated trajectories to explore average behaviors obtained from stochastic search strategies. For example, our model predicts that a bacterium's diffusion coefficient increases as it approaches the point source and that, in the presence of multiple sources, bacteria may take substantially longer to locate their first source giving the impression of an untargeted search strategy.

Inferring Models of Bacterial Dynamics toward Point Sources

Experiments have shown that bacteria can be sensitive to small variations in chemoattractant (CA) concentrations. Motivated by these findings, our focus here is on a regime rarely studied in experiments: bacteria tracking point CA sources (such as food patches or even prey). In tracking point sources, the CA detected by bacteria may show very large spatiotemporal fluctuations which vary with distance from the source. We present a general statistical model to describe how bacteria locate point sources of food on the basis of stochastic event detection, rather than CA gradient information. We show how all model parameters can be directly inferred from single cell tracking data even in the limit of high detection noise. Once parameterized, our model recapitulates bacterial behavior around point sources such as the “volcano effect”. In addition, while the search by bacteria for point sources such as prey may appear random, our model identifies key statistical signatures of a targeted search for a point source given any arbitrary source configuration.