ERI: Development of Non-native Sigma Factors for Metabolic Engineering

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。可再生资源是可持续经济的关键要素。如果设计得当，微生物将发挥重要作用。需要提高产品产量。通过不同的代谢途径有效地输送物质是提高产量的关键。该项目的目标是通过细胞代谢和产物合成途径平衡物质和能量流动。将创建并测试控制路径流量的sigma因子库。这项研究将与外展和劳动力发展活动联系在一起。为了学习合成生物学技术，一年级本科生和高中教师将描述他们选择的sigma因子。酶表达的转录平衡和对生物生产的资源重新分配是导致生物生产增强的两种一般策略。替代sigma因子自然地通过在压力时将转录资源重定向到新的遗传程序来执行这些功能。通过这项工作，我们将评估是否替代sigma因子将赋予大肠杆菌的生物生产效益。启动子间隔区域的突变以及间隔长度的变化将为一组非原生sigma因子提供转录多样性。深度测序后的筛选方法将确定产生特定转录量的启动子变异的身份，从而提高对启动子变化如何影响转录的预测。启动子库将应用于具有多个副反应的异源途径，以在整个途径中提供平衡的通量。此外，使用基于多西格玛因子的方法，我们将协调需要中间构建的复杂途径的酶表达，以提高特异性。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Renewable resources are a key element of a sustainable economy. Microbes will play an important role if properly engineered. Product yield increases are needed. Routing material efficiently through different metabolic pathways is critical for increasing yield. The objective of this project is to balance material and energy flows through cell metabolism and product synthesis pathways. A library of sigma factors, which control pathway flows, will be created, and tested. The research will be tied to outreach and workforce development activities. To learn synthetic biology techniques, first year undergraduates and high school teachers will characterize a sigma factor of their choice. Transcriptional balancing of enzyme expression and resource reallocation towards bioproduction are two general strategies that have resulted in enhanced bioproduction. Alternative sigma factors naturally carry out these functions by redirecting transcriptional resources towards new genetic programs in times of stress. Through this work, we will evaluate whether alternative sigma factors will confer bioproduction benefits in E. coli. Mutations to promoter spacer region as well as changes in spacer length will provide transcriptional diversity for a set of non-native sigma factors. A screening approach followed by deep sequencing will determine the identity of promoter variants that give rise to specific transcriptional amounts, improving the prediction of how changes in promoters influence transcription. Libraries of promoters will be applied to heterologous pathways that have multiple side reactions to provide a balanced flux throughout the pathway. Additionally, using a multi-sigma factor-based approach, we will coordinate enzyme expression for complicated pathways that require intermediate build-up to improve specificity.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.