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

细胞在细胞膜上大量运输分子的能力是用于生产可再生生物化学物质的微生物系统的一个重要特征。该项目研究了通过操纵细胞用于从细胞中输出生化物质的系统来改造细菌细胞以增加微生物过程的生产特性的方法。该项目的核心概念被整合到一系列有影响力的学习体验中，旨在吸引和培训学生在STEM教育管道的多个阶段。这项活动的目的是提高学生在STEM领域继续深造的兴趣。在另一项单独的活动中，本研究的科学内容被纳入在线和面对面的合成生物学教育模块，以提高系统和合成生物学的教学课程。该项目还包括一个桥梁项目，通过研讨会和职业咨询活动促进生物学研究生的职业发展。来自科学领域代表性不足群体的学生被积极招募，并参与到该项目的各个方面。该项目的总体目标是对大肠杆菌中可再生化学品的出口和流出系统提供系统的了解，包括短链单羧酸和二羧酸以及小芳烃。在本项目获得的大量实验数据集的帮助下，将开发和优化机器学习预测算法，以准确预测选定的单羧酸和二羧酸的本地出口商。预测的转运体将通过实验验证其潜在的输出功能。使用类似的方法，还将确定具有选定芳烃活性的异源外排系统。结合实验和计算数据的迭代优化将改进预测算法。最后，将建立一个完整的质粒集合，编码所有假定的大肠杆菌膜蛋白，以促进大肠杆菌膜蛋白质组的系统级研究，支持高效的微生物生产生化物质。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.