Phenotypic diversity in bacterial behavior

Proteins to behavior

Individual cells within an isogenic population exhibit considerable phenotypic heterogeneity even when grown in homogeneous conditions. Phenotypic diversity caused by random fluctuations at the molecular level often interferes with the robust performance of biochemical networks. On the other hand, phenotypic diversity can provide a selective advantage through bet-hedging when a population is faced with uncertain conditions.

Mapping phenotypic individuality to fluctuations in protein numbers is technically challenging when cells are moving. We developed FAST (Fluorescence Analysis with Single-cell Tracking) to characterize swimming behavior and quantify protein numbers in individual cells. This technique enables the investigation of the molecular origins of phenotypic diversity and characterize the selective advantages of phenotypic diversity in controlled environments.

Using microfluidics to observe bacterial motility through mucus


The mucosal tissues covering the gastrointestinal, respiratory, reproductive, and urinary tracts are the places of residence of much of our microbiota. The mucus layer is also the primary line of defense against invasion by pathogens. Secreted mucin glycoproteins are the major component of mucus and create an effective gel-like barrier that limits diffusion and bacterial motility.

In addition to evading the host immune system, pathogens must be able to penetrate the mucus layer to establish a successful infection. We use microfluidics and videomicroscopy to characterize the motility of bacteria in environments that mimic the physical properties of mucus. Our goal is to understand the importance of motility and chemotaxis for virulence, characterize the physical and biological properties of mucus, and develop strategies to prevent infections through the mucus layer.

Understanding chemotactic strategies using directed evolution

Bacterial motility

Chemotaxis and flagellar motility are used by bacteria with diverse lifestyles. Even though the core design of the chemotaxis pathway is conserved, the system has been successfully adapted to perform in environments with different physical and chemical characteristics. However, the parameters that determine the optimal chemotactic strategy given a set of environmental factors are not completely understood.

We use directed evolution under controlled experimental conditions to follow the adaptation of chemotactic behavior to specific changes in the environment physical properties. Genetic and phenotypic changes resulting from the selection process inform us of the relationships between specific behavior and environmental factors. We are particularly interested in understanding how pathogenic bacteria have adapted to penetrate the mucus layer protecting the host tissues.

Modeling bacterial behavior in complex environments

The ability to guide bacteria toward favorable conditions is an emergent property of the chemotaxis signaling pathway. The system has to be considered as a whole to understand how a relatively small set of proteins is able to integrate temporal changes in signal concentration and control the flagellar motor accordingly. Furthermore, because of the behavioral feedback on the input signal, the system operates at a dynamical equilibrium that is dependent on both the biochemical and environmental parameters. Therefore, defining the biological parameters that maximize chemotactic performance in various environments is a not a trivial problem.

We use mathematical modeling and simulations to examine how dynamical parameters of the chemotaxis system determine performance in various environments. The hypotheses generated by our theoretical analyses guide the design of new experiments. In turn, new data are used to improve the models and generate more accurate predictions. We are particularly interested in understanding the role of phenotypic diversity in overcoming fundamental performance trade-offs to increase fitness in complex environments.





This is a bacterial chemotaxis simulator. Cells have different phenotypes represented by different colors. Green cells tend to tumble more than red cells. Find out which phenotype performs the best in different environments.
Do you think you can do better than these virtual cells? Click here to play the game!