CAREER: Systems-Level Identification and Characterization of Cellular Export and Efflux Systems for Renewable Chemicals

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The capacity of cells to mass transport molecules across the cell membrane is an important feature of microbial systems that are used for the production of renewable biochemicals. This project investigates the means by which bacterial cells can be engineered to increase the production characteristics of microbial processes by manipulating the systems that cells use to export biochemicals from the cells. Core concepts from this project are integrated into a series of impactful learning experiences designed to engage and train students at multiple stages along the STEM education pipeline. The aim of this activity is to promote the students' interest in pursuing advanced studies in STEM areas. In a separate activity, the scientific content of this research is incorporated into online and in-person synthetic biology education module in an effort to enhance the teaching curriculum for systems and synthetic biology. The project also incorporates a bridge program to promote career development, for graduate students in biology, through workshop and career consultation activities. Students from underrepresented groups in science are proactively recruited and engaged in all aspects of this project. The overarching goal of this project is to provide a systematic understanding of export and efflux systems in Escherichia coli for renewable chemicals, including short chain mono- and dicarboxylic acids as well as small aromatics. Aided by the large amount of experimental dataset obtained in this project, machine-learning prediction algorithms will be developed and optimized to accurately predict native exporters for select mono- and dicarboxylic acids. The predicted transporters will be experimentally validated for their potential export functions. Using similar approaches, heterologous efflux systems with activities for select aromatics will also be identified. The iterative optimization aided by integrated experimental and computational data will improve the prediction algorithm. Finally, a complete plasmid collection encoding all putative E. coli membrane proteins will be created to facilitate systems-level study of the E. coli membrane proteome in support of efficient microbial production of biochemicals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.