Coupling stochastic behavior to metabolism: How a switch protein generates binary signaling programs in Escherichia coli

The process of cellular differentiation is vital for the development of multicellular organisms and includes the use of intra- and extracellular signaling molecules that govern precise tissue patterns. In contrast, unicellular organisms like bacteria use intracellular signaling molecules to adapt their behavior and morphology to environmental changes. While cell differentiation often results from hardwired deterministic processes, bacteria can stochastically develop phenotypic variability. Cellular individuality contributes to the fitness of bacterial cultures either because it serves to delegate different functional tasks or because it serves to minimize risks in a rapidly changing environment. Although cell differentiation and stochastic behavior are well-explored on the transcriptional level, it has remained unclear how bacteria convert gradual changes of diffusible small signaling molecules into specific and robust cellular responses, and which mechanisms bacteria exploit to establish stochastic behavior of such regulatory networks. To address these questions, this study investigates c-di-GMP, a signaling molecule that is conserved across all major bacterial phyla and controls important physiological and behavioral processes like surface colonization, virulence or cell cycle progression.
C-di-GMP signaling networks can adopt highly complex architectures with multiple enzymes involved in its synthesis or degradation. In the first results chapter of my thesis, I use E. coli to address the question how converging input from multiple enzymes is transformed into robust and unambiguous cellular responses. Together with my collaborators, I demonstrate that E. coli makes use of a simple switch to convert dynamic changes of c-di-GMP into discrete binary outputs. This is mediated by an ultrasensitive switch protein, PdeL, which senses the prevailing concentration of the signaling molecule in the cell and couples this information to c-di-GMP degradation and to a transcriptional feedback loop boosting its own expression. We demonstrate that PdeL acts as a digital filter that establishes bimodal populations where individual cells exhibit either high or low c-di-GMP. The observation that PdeL effectively protects E. coli against specific bacteriophage predators argues that this molecular switch also serves as a bet-hedging device to minimize life-style specific risks.
The second chapter investigates a regulatory link between metabolism and c-di-GMP turnover. Our studies identified the global metabolic regulator Cra as an essential activator of pdeL transcription. This raised the questions if, how and why metabolic processes or growth rate modulate c-di-GMP heterogeneity. Based on my observation that PdeL-mediated c-di-GMP heterogeneity strongly depends on the available carbon source, I set out to probe the role of Cra in this process and in regulating c-di-GMP distributions in individual bacteria. I first developed reporter tools to quantify Cra activity in individual cells. With this in hand, I could demonstrate that when grown on a glycolytic source, low Cra activity results in limited pdeL expression and in the loss of c-di-GMP bimodality. However, c-di-GMP bimodality could be restored at least partially by boosting Cra levels or by expressing a constitutively active form of Cra. The results advocate a model in which c-di-GMP heterogeneity is tightly interlinked with metabolic processes to fine tune developmental decisions with the cells’ nutrient status and growth rate.
Together, this work provides a molecular frame for how bacteria make use of simple switches to generate stochastic outcomes in signaling processes and how they tune such regulatory elements with their nutritional status. Future studies should investigate how and under which conditions the observed coupling between signaling and metabolism provides optimal fitness benefits for E. coli.