Genetic circuit design platforms for engineering living therapeutics

Synthetic biology tools enable the reprogramming of cells for numerous applications in the life sciences, medicine, and biotechnology. Genetic circuits are the foundation of cellular programming, and synthetic biologists seek to develop techniques to control networks of gene expression rationally and predictably in diverse organisms. Numerous tools have been developed to achieve this goal, but there are still key challenges that must be addressed to advance cellular programming for modern applications. First, genetic circuit design strategies need to be established for non-model organisms that are appropriate for the environment and application of interest—particularly, organisms that can be engineered to function as live therapeutics in the human body. Second, genetic circuits need to be designed so that they are more resource-efficient and less burdensome to their host cells, as this will expand their utility and long-term deployment outside of the controlled laboratory environment. Here, I focus on engineering bacteria that can be used for therapeutic applications in the human gastrointestinal tract, which is the primary location of the microbiome. As the gut microbiome is an indispensable component of our metabolism, immune system, and overall health, it represents a unique target to engineer for therapeutic endeavors. Members of the Bacteroides genus are amongst the most abundant and prevalent species in the gut microbiome, making them attractive candidates to engineer with synthetic biology tools. In Chapter 2, I first develop a genetic circuit design strategy for Bacteroides with the goal of controlling the expression of both native and heterologous genes. To achieve this, a dual strategy of protein and genetic engineering is implemented to build high-performance genetic circuits with predictable and reproducible behavior. These genetic circuits are then leveraged to control the fitness of individual species in synthetic consortia, allowing dynamic control of community composition. Systematically modulating the composition of the microbiome is a major challenge in the field of microbiome engineering, and the results here should improve our ability to program fitness in these microbes. While there are key advantages to engineering Bacteroides, long-term colonizers of the human gut, an alternative approach is to engineer probiotic bacteria that do not colonize the gut for transient applications. To achieve this, I engineer the probiotic Escherichia coli Nissle 1917 to function as a next-generation biotherapeutic chassis in Chapter 3. To enable total control of cellular functions such as decision-making, communication, and memory storage, I engineer a recombinase-based circuit platform for E. coli. Combining various circuit design strategies, I demonstrate the unified programming of cellular behavior for single-strain applications, as well as in E. coli-Bacteroides co-culture. This work provides a highly modular tool kit for engineering gene expression and community behavior in these important candidate therapeutic chassis. Finally, Chapter 4 focuses on addressing the major challenge of engineering genetic circuits—predictive design. Central to this challenge is the inherent non-modularity of genetic circuit components at the DNA, RNA, and protein levels. By developing genetic context-specific metrics for characterizing gene expression, I successfully overcome these obstacles to develop an improved circuit design technique for E. coli. When compared to the previous state-of-the-art predictive circuit design strategy, our technique is superior in terms of accuracy and reduced complexity. The circuits developed require significantly fewer parts to operate compared to previous strategies, an important factor in considering their burden to the host cell. Collectively, the strategies for cellular programming enabled by these genetic circuit design platforms should advance our ability to engineer bacteria as therapeutic agents with improved precision and performance.

Date

2024-04-25

Resource Type

Text

Resource Subtype

Dissertation

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