Non-Genetic Diversity in Chemosensing and Chemotactic Behavior

Abstract

Non-genetic phenotypic diversity plays a significant role in the chemotactic behavior of bacteria, influencing how populations sense and respond to chemical stimuli. First, we review the molecular mechanisms that generate phenotypic diversity in bacterial chemotaxis. Next, we discuss the functional consequences of phenotypic diversity for the chemosensing and chemotactic performance of single cells and populations. Finally, we discuss mechanisms that modulate the amount of phenotypic diversity in chemosensory parameters in response to changes in the environment.

Keywords: bacterial chemotaxis; chemoreceptors; fluctuations; phenotypic diversity; receptor clusters; signal processing; single cell; stochastic gene expression.

Figure 1

Phenotypic diversity arises from processes operating at different timescales. (a) Unequal partitioning of proteins and organelles upon cell division, (b) stochastic gene expression, and (c) stochasticity in the chemical reactions of signaling pathways all contribute to phenotypic diversity. (d) The number and size of chemoreceptor clusters varies from cell to cell due to random partitioning. This variation may lead to diverse sensitivities to stimuli. (e) The expression of class II motility genes such as fliA occurs in pulses lasting multiple generations. These pulses are stochastic, likely generating cells with very different sensory and swimming capabilities. (f) Fluctuations in the kinase activity of the chemotaxis network. In red are cells containing the adaptation enzymes CheR and CheB, while in blue are cheRB deletants. The cycle of methylation and demethylation, as well as receptor cooperativity both contribute to fluctuations. Panels d, e, and f are adapted from Koler et al., 2018 [34]; Kim et al., 2020 [35]; and Keegstra et al., 2017 [36].

Figure 2

Phenotypic diversity and signaling fluctuations affect ecological performance. (a) The distribution of run lengths in wild-type cells (red) has a fatter tail than in cells with the same mean tumble bias but no fluctuations in CheY phosphorylation (blue). This distribution of run lengths allows cells to efficiently explore the environment by Levy walk instead of a pure random walk. (b) The conflict between individuality and collective behavior is resolved by spatial sorting of phenotypes in traveling waves of bacteria. Cells with lower tumble bias (red) climb attractant gradients more quickly than cells with higher tumble bias (blue), and therefore are located near the front of the wave. Panels a and b are adapted with permission from Huo et al., 2021 [85]; Fu et al., 2018 [87].

Figure 3

Bacteria tune the degree of sensory diversity in response to chemoattractant. (a,b) The cumulative distribution and probability density functions of the inverse sensitivity (K_1/2 defined as the stimulus level that elicits half-maximal response amplitude) for methyl-aspartate (MeAsp). When there is no chemoattractant in the environment, the sensitivity distribution is broad. However, when cells are exposed to non-zero chemoattractant concentrations, the sensitivity distribution sharply narrows. (c) The ability to modulate the degree of diversity in the sensitivity distribution according to the background stimulus could allow the population to switch between two navigational strategies in different environments. When no signals are present, cells have no information about future environmental signals, so they diversify their sensitivities for different ligands, improving the readiness for potential upcoming conditions (bet-hedging strategy). However, once a signal is detected, the population is benefited by having most cells being sensitive to the change in the signal level (tracking strategy). Panels a and b are adapted with permission from Kamino et al., 2020 [61].