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

点击翻译按钮获取中文摘要

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.