Escherichia coli chemotaxis to competing stimuli in a microfluidic device with a constant gradient

Abstract

In natural systems bacteria are exposed to many chemical stimulants; some attract chemotactic bacteria as they promote survival, while others repel bacteria because they inhibit survival. When faced with a mixture of chemoeffectors, it is not obvious which direction the population will migrate. Predicting this direction requires an understanding of how bacteria process information about their surroundings. We used a multiscale mathematical model to relate molecular level details of their two-component signaling system to the probability that an individual cell changes its swimming direction to the chemotactic velocity of a bacterial population. We used a microfluidic device designed to maintain a constant chemical gradient to compare model predictions to experimental observations. We obtained parameter values for the multiscale model of Escherichia coli chemotaxis to individual stimuli, α-methylaspartate and nickel ion, separately. Then without any additional fitting parameters, we predicted the response to chemoeffector mixtures. Migration of E. coli toward α-methylaspartate was modulated by adding increasing concentrations of nickel ion. Thus, the migration direction was controlled by the relative concentrations of competing chemoeffectors in a predictable way. This study demonstrated the utility of a multiscale model to predict the migration direction of bacteria in the presence of competing chemoeffectors.

Marine Bacteria Chemotaxis to Crude Oil Components with Opposing Effects

“…Values of κ = 2.7 and stimuli sensitivity coefficient for repellent σ 2 = 3 s were obtained by fitting the model to data from the repellent experiments. As an initial estimate for regressing the parameter κ, we used a value of 9 that was previously determined for E. coli and nickel ion (Zhao et al, 2022). For the dissociation constant 𝐾 𝑑𝐶 , a value on the order of 10 −3 M was used because this value corresponded to the concentration where bacteria exhibited the greatest repulsion from Cu 2+ .…”

“…Signaling efficiency 4 4 [1] σ 1 Stimuli sensitivity coefficient for attractant (s) 6 11 [2] σ 2 Stimuli sensitivity coefficient for repellent (s) 3 11 [2] 𝜅 Repellent sensitivity coefficient 2.7 9 [1] 𝑣 Swimming speed (µm/s) 50 22 [3] 𝜇 0 Random Motility Coefficient (𝑚 2 /𝑠) 2.5× 10 −10 1.5× 10 −9 [4] [1] Zhao et al, 2022;[2] Middlebrooks et al, 2021;[3] Wang, 2013;[4] de Sá nchez and Schiffrin, 1996.…”