EAGER: Systematic Approach to Improve Probability of Metabolic Engineering Success

代谢工程领域继续在从简单的可再生原料中生产特种化学品和商品化学品方面表现出色，但专注于该领域在文学和媒体上的成功，忽视了更大的图景：许多代谢工程的努力在研究的早期阶段产生与工业无关的滴度，有时在整个项目期间。这个EAGER奖项将解决在代谢工程项目早期是否可以采取基本步骤以最大限度地提高其成功率的问题。PI将阐述他的假设，即辅助因子（使某些酶发挥作用的小分子）数量的调节是控制细胞代谢途径的核心，从而能够为生物制造目的生产有用的生化物质。此外，PI将培养研究生和本科生进行跨学科研究，并让高中生接触该领域的前沿研究。技术描述：研究者将采用广泛适用的系统方法，通过对大肠杆菌中复杂生化产物的生产进行严格的案例研究，来提高代谢工程努力的成功率。PI假设辅因子生物合成的动态调控是代谢工程成功的关键，并将使用CRISPR干扰（CRISPRi）系统实现动态辅因子表达控制，以验证其假设。他将通过脉冲追踪代谢分析和LC-MS/MS定量来研究积累辅助因子和细胞反应的命运。将使用比较RNA-seq（转录组）和iTRAQ（蛋白质组）分析来监测野生型和工程菌株对辅助因子积累的细胞反应。然后，PI将使用该案例研究的结果开始概括有关辅助因子调节和对代谢工程成功的影响的规则。

The field of metabolic engineering continues to excel at producing specialty and commodity chemicals from simple, renewable feedstocks, but focus on the field's successes in literature and media neglect the larger picture: many metabolic engineering efforts produce industrially irrelevant titers in the early stages of research and sometimes throughout the duration of the project. This EAGER award will address the question of whether there are fundamental steps that can be taken early in metabolic engineering projects to maximize their success rates. The PI will address his hypothesis that the regulation of the amount of cofactors (small molecules that enable certain enzymes to function) is central to the ability to control metabolic pathways in cells in such a way as to enable the production of useful biochemicals for biomanufacturing purposes. In addition, the PI will train graduate and undergraduate students in interdisciplinary research and expose high school students to cutting edge research in this field.Technical description: The investigator will apply a broadly applicable, systematic approach to improve the success rate of metabolic engineering endeavors by performing a rigorous case study of the production of a complex biochemical in E. coli. The PI hypothesizes that the dynamic regulation of cofactor biosynthesis is key to metabolic engineering success, and will use the CRISPR interference (CRISPRi) system to achieve dynamic cofactor expression control in order to test his hypothesis. He will examine the fate of accumulating cofactor and cellular response via pulse-chase metabolic analysis and quantification with LC-MS/MS. Cellular response to cofactor accumulation will be monitored using comparative RNA-seq (transcriptomic) and iTRAQ (proteomic) analysis between wild-type and engineered strains. The PI will then use results from this case study to begin to generalize rules concerning co-factor regulation and impacts on metabolic engineering success.