Diversity of bet-hedging strategies in microbial communities-Recent cases and insights

Abstract

Microbial communities are continuously exposed to unpredictable changes in their environment. To thrive in such dynamic habitats, microorganisms have developed the ability to readily switch phenotypes, resulting in a number of differently adapted subpopulations expressing various traits. In evolutionary biology, a particular case of phenotypic heterogeneity that evolved in an unpredictably changing environment has been defined as bet-hedging. Bet-hedging is a risk-spreading strategy where isogenic populations stochastically (randomly) diversify their phenotypes, often resulting in maladapted individuals that suffer lower reproductive success. This fitness trade-off in a specific environment may have a selective advantage upon the sudden environmental shift. Thus, a bet-hedging strategy allows populations to persist in very dynamic habitats, but with a particular fitness cost. In recent years, numerous examples of phenotypic heterogeneity in different microorganisms have been observed, some suggesting bet-hedging. Here, we highlight the latest reports concerning bet-hedging phenomena in various microorganisms to show how versatile this strategy is within the microbial realms. This article is categorized under: Infectious Diseases > Molecular and Cellular Physiology.

Keywords: adaptation; bet-hedging; evolutionary strategy; persisters; phenotypic heterogeneity.

FIGURE 1

Responsive versus stochastic switching (adapted from Kussell et al., 2005). Isogenic cell populations adapt to changing conditions by switching their phenotype, either responsively (upper panel) or stochastically (lower panel). A schematic representation of the switching strategies is shown, in which the color of the fittest individuals matches the color of the environment. In responsive switching, cells change their phenotype when sensing an environmental change to maximize temporal fitness. The population survives if the majority of the individuals successfully commit to the switch. However, when the environment changes in a stochastic manner, the stochastic switching strategy becomes critical for adaptation. Populations that randomly employ stochastic switching, express a number of maladapted phenotypes of reduced fitness that may suit another environment in the future

FIGURE 2

Schematic representation of persister cell formation in Caulobacter crescentus. It has been proposed that antibiotic persistence in C. crescentus is promoted by HipA1 and HipA2 toxins, which are serine/threonine kinases that phosphorylate the aminoacyl‐tRNA synthetases GltX and TrpS, preventing synthesis of charged tRNAs (Huang et al., 2020). Phosphorylation of GltX/TrpS leads to translation arrest and activation of the amino acid starvation‐signaling pathway (SpoT). Activation of SpoT is also stochastically triggered by carbon or nitrogen starvation (indicated by the dashed arrow). Elevated levels of (p)ppGpp, a stringent response alarmone, contribute to translational arrest. Activation of SpoT and further transcriptional changes in the isogenic population of C. crescentus allow most cells to adapt to the starvation conditions, whereas only a fraction of the population becomes dormant (phenotypic heterogeneity). Dormant cells (blue cells) are not metabolically active and can survive high doses of antibiotics. The persister state is reversible; therefore, when the optimal conditions arrive, dormant cells can repopulate the environment (orange cells). The gradient red‐colored bar indicates starvation stress

FIGURE 3

Overview of recent studies on phenotypic heterogeneity and possible employment of bet‐hedging strategies in various microorganisms. In this work, we highlight some recent studies regarding bet‐hedging traits that fall into several categories of microbial lifestyle, including signaling (purple), dormancy (yellow), and resource use (blue). In some cases, the same population manifests different bet‐hedging strategies because of direct or indirect relationships between traits. With an asterisk, we marked examples of studies where a population was shown to employ several bet‐hedging strategies, for example, nutrient utilization is directly involved in spore or persisters formation

FIGURE 4

Nutrient fluctuations and cell consequences during a bet‐hedging strategy. Bacteria develop phenotypic heterogeneity during changes in environmental conditions (Environment A to B), and a bet‐hedging strategy results in subpopulations of cells with different fitness. While cells with low fitness are subjected to different outcomes (e.g., sporulation), the fitter cells thrive. Eventually, (change to Environment C) the cells display the diversity in phenotypes by a random switch