A synthetic biology approach to analyze evolution of programmed bacterial death

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DESCRIPTION (provided by applicant): A synthetic biology approach to analyze evolution of programmed bacterial death Programmed death is commonly associated with a bacterial response to stressful conditions, such as starvation, presence of competitors, and antibiotic treatment. As death offers no benefit to its actor, evolution of programmed bacterial death is a fundamental, unresolved problem in biology. A popular explanation is that the death is "altruistic": it can provide direct or indirect benefitsto the survivors. In other words, death may represent the ultimate form of cooperation. By making this assumption, evolution of programmed death can be analyzed under the general framework of public-good cooperation. Using this framework, studies have suggested possible public goods resulting from death in various bacterial pathogens. However, there remains a fundamental gap in the definitive understanding of microbial social behavior in general and programmed bacterial death in particular. Indeed, advantage of altruistic death has never been unequivocally demonstrated in an experimental system. A major challenge in tackling this problem is the complexity of natural biological processes, where numerous confounding factors obscure interpretation and quantitative analysis of the benefits associated with death. For example, previous work has been criticized because gene manipulations involved led to multiple effects and so it is hard to tease apart different fitness consequences. These issues make the results open to alternative explanations, such as PCD representing a maladaptive response to stress. We propose to use a combination of synthetic-biology techniques and microfluidics to overcome these limitations. In particular, using a set of synthetic gene circuits in bacterium Escherichia coli to implement tunable altruistic death, we will quantitatively define the condition under which altruistic death can become advantageous at the population level and examine their evolutionary dynamics in the presence of cheating. To enable such analysis, we will develop a novel droplet-based platform to examine the evolutionary dynamics under different conditions. Building on such understanding, we will develop and evaluate new treatment strategies that will exploit the evolutionary dynamics. It is our vision that the proposed research will have several broad impacts. First, it will fill the critical conceptual gap in our understandig of the evolution of programmed death. Second, it will generate novel insights into how bacteria respond to antibiotic-mediated stress, which has implications for designing novel therapeutic strategies against bacterial pathogens.