CAREER: Dissecting a Metabolically Versatile Non-Model Bacterium's Lignin-Derived Compound Catabolism

该项目调查微生物适应各种条件(如有无氧)和碳源(如木质素化合物)的能力背后的潜在原因。如果一种参与分解碳源的酶是有效的，但就能量而言，它的合成成本很高，那么它催化的反应步骤可能是限速的。这一速率限制步骤可能决定微生物是否能够或在多大程度上能够依靠特定的碳源生存，或者它是否需要有第二个碳源才能生存。该项目揭示了在有氧或无氧的情况下分解特定碳源所涉及的特定代谢途径(即基因和酶)。生成一个计算机模型来预测该过程中的速率限制步骤(S)。对这些预测进行了实验研究，以探索特定基因在最佳生长条件下的作用。该项目的成果将使生物技术人员能够预测和有效地将最丰富的废物之一(即木质素)转化为有用的生物制品(例如可生物降解的聚合物)。此外，该项目的一个主要目标是通过一个面向所有受众(从学龄前儿童到退休成年人)的成熟的教育平台，改善公众对科学素养的看法。教育活动包括为学龄前儿童设计一系列生物书籍以及互动活动，并指导初中生、高中生和本科生推进STEM教育。研究生接受了广泛的计算驱动实验综合性质的培训，以解决相关的生物学问题。为了提高对科学的支持和认识，还组织了公开讲座和互动活动/示范。该项目研究了一种代谢功能广泛的紫色非硫细菌沼泽红假单胞菌如何控制木质素衍生化合物的分解代谢途径，并将这些途径与其他生物过程联系起来。虽然现有的文献表明，它有五条有注释的途径(包括具有广泛底物特异性的酶)来分解这些化合物，但该细菌如何或是否能够分解一些主要的木质素衍生化合物甚至聚合木质素尚不清楚。还缺乏对酶在有/无氧条件下的敏感性以及是否需要二次碳源的补充的了解。该项目的目标是将计算建模与实验方法相结合，以解决这些关键差距，从而揭示允许细菌适应广泛条件和底物的基本规则。分解代谢反应和相关基因/蛋白质的相对贡献通过代谢和表达(ME)模型确定。这个模型是根据现有的基因组注释以及从转录组学和定量蛋白质组学数据中获得的信息开发出来的，这些数据将在该项目中获得。一个合成生物学工具箱，包括可诱导启动子，将用于探索关键/限制基因在感兴趣的途径中的作用。该项目的发现有助于通过设计-建造-测试-精炼循环对迭代ME模型进行改进，以更定量和更机械地了解微生物中木质素衍生化合物的分解代谢。此外，该项目将成为提高公众对科学素养的认知的载体，并开发针对从学龄前儿童到退休成年人的广泛受众的教育平台。该项目由分子和细胞生物科学部门的系统和合成生物学集群以及既定的激励竞争研究计划(EPSCoR)共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project investigates the potential causal factors behind a microbe’s ability to adapt to a wide range of conditions (e.g., presence/absence of oxygen) and carbon sources (e.g., compounds derived from lignin). If an enzyme involved in breaking down a carbon source is efficient but expensive to synthesize, in terms of energy, the reaction step it catalyzes may be rate limiting. This rate limiting step may dictate if or to what extent the microbe is able to live on the specific carbon source or whether it needs to have a secondary carbon source to survive. The project reveals the specific metabolic pathways (i.e., genes and enzymes) involved in breaking down a specific carbon source in the presence or absence of oxygen. A computer model is generated to predict the rate limiting step(s) in this process. These predictions are experimentally investigated to probe the role of specific genes under optimal growth conditions. The results of this project will enable biotechnologists to predictably and efficiently convert one of the most abundant waste products (i.e., lignin) to useful bioproducts (e.g., a biodegradable polymer). In addition, a major goal of this project is to improve the public perception of science literacy through a well-developed education platform targeting all audiences (from preschoolers to retired adults). The education activities include designing a series of biology books as well as interactive activities for preschoolers and mentoring middle/high school and undergraduate students to advance STEM education. Graduate students are trained broadly in the integrated nature of computation-driven experimentation to address relevant biological questions. In order to improve support and awareness for science, public lectures and interactive activities/demonstrations are also organized. This project addresses how a metabolically versatile purple non-sulfur bacterium, Rhodopseudomonas palustris, controls the catabolic pathways of lignin-derived compounds and connects these pathways with other biological processes. Although existing literature shows it has five annotated pathways (comprising of enzymes of broad substrate specificities) for catabolizing these compounds, how or if the bacterium is able to catabolize some of the major lignin-derived compounds or even polymeric lignin is unknown. There is also a lack of understanding of how sensitive the enzymes are in the presence/absence of oxygen and if there is a need for complementation by a secondary carbon source. The goal of this project is to integrate computational modeling with experimental approaches to address these critical gaps, and consequently unlocking the fundamental rules that allow the bacterium to adapt to a wide range of conditions and substrates. Relative contributions of catabolic reactions and associated genes/proteins are determined through a metabolism and expression (ME) model. This model is developed from available genome annotations and information derived from transcriptomics and quantitative proteomics data that will be obtained in this project. A synthetic biology toolbox, including inducible promoters, will be used to probe the role of key/limiting genes in pathways of interest. Findings from this project contribute to iterative ME model improvement through design-build-test-refine cycles for a more quantitative and mechanistic understanding of lignin-derived compound catabolism in microbes. In addition the project will be the vehicle for improving the public perception of science literacy, and developing education platform targeting to a wide range of audiences, from preschoolers to retired adults.This project is jointly funded by the Systems and Synthetic Biology cluster in the Division of Molecular and Cellular Biosciences and the Established Program to Stimulate Competitive Research (EPSCoR).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.