Microbes can form communities called biofilms that affect many aspects of human activities. For example, microbes can be used to treat wastewater and improve agricultural soil productivity. Understanding how microbes organize and persist in different environments is important for promoting beneficial outcomes, as well as mitigating harmful effects. This project will focus on an amazing ability microbes have for surviving in changing environments--through a process called phenotypic diversity--which enables them to produce progeny with a wide range of behaviors or characteristics. Predicting phenotypic diversity behaviors in microbes is a major unsolved problem. This project will use the environmental bacterium and human pathogen, Vibrio cholerae, to investigate the mechanisms used by bacteria to generate phenotypic diversity. The fundamental principles learned from this project will be directly relevant to other microorganisms, thus, facilitating the management and engineering of microbes for beneficial outcomes. In addition, this project will provide research training for undergraduate and graduate students, including individuals from underrepresented groups. New knowledge will be shared with a broad audience through collaborations with secondary science educators and Impression 5, a science museum in Lansing, MI.
This project will investigate the role of the second messenger bis-(3-5)-cyclic dimeric guanosine monophosphate (c-di-GMP) in controlling phenotypic diversity in clonal populations of Vibrio cholerae. c-di-GMP integrates environmental signals through a sophisticated signaling network to control, among other factors, the transition between motile and biofilm lifestyles. The hypothesis is that c-di-GMP signaling changes the sensitivity of transcriptional regulation to stochastic fluctuations inherent to molecular processes to control the proportions of different phenotypes. This project will use a combination of mathematical modeling, quantitative microscopy, and behavioral tracking to quantify the regulation of phenotypic diversity under different experimental conditions. Cell tracking and epifluorescence microscopy will be used to correlate signaling activity with behavior directly in single cells. The transition rates between motile and biofilm-forming phenotypes in growing cells will be quantified using time-lapse experiments. Finally, a genetic approach will be used to manipulate c-di-GMP levels and transcription regulation to test specific predictions from theoretical models. Overall, this project will elucidate design principles that allow signaling networks to integrate stochastic fluctuations with environmental signals to generate and control advantageous phenotypic diversity in clonal populations.
