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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Measuring and Modeling of Phenylpropanoid Metabolic Flux in Arabidopsis

Peng Wang (5930384) 12 October 2021 (has links)
<p>Plants naturally deposit a significant amount of carbon towards lignin, a polymer that imparts mechanical strength to cell walls but impedes our utilization of the polysaccharides in lignocellulosic biomass. Genetic engineering of lignin has demonstrated profound success in improving the processing of the biomass. Lignin is derived from the phenylpropanoid pathway, the architecture of which is well understood based upon the biochemical and genetic studies conducted to date. In contrast, we lack a systematic and quantitative view of the factors that determine carbon flux into and within this branched metabolic pathway in plants. To explore the control of carbon allocation for phenylalanine and lignin biosynthesis, we have developed a kinetic model of the pathway in Arabidopsis to test the regulatory role of several key enzymatic steps. We first established a <sup>13</sup>C isotope feeding system for the measurement of flux using excised wild-type Arabidopsis stems. The excised stems continued to grow and lignify in our feeding system. When ring <sup>13</sup>C<sub>6</sub>-labeled phenylalanine ([<sup>13</sup>C<sub>6</sub>]-Phe) was supplied to excised stems, isotope label was rapidly incorporated into soluble intermediates and lignin. Using this approach, we then analyzed metabolite pool sizes and isotope abundances of the pathway intermediates in a time course from stems fed with [<sup>13</sup>C<sub>6</sub>]-Phe of different concentrations, and used these data to parameterize a kinetic model constructed with Michaelis-Menten kinetics. Our model of the general phenylpropanoid pathway captured the dynamic trends of metabolite pools <i>in vivo</i>and predicted the metabolic profiles of an independent feeding experiment. Based on the model simulation, we found that subcellular sequestration of pathway intermediates is necessary to maintain lignification homeostasis when metabolites are over-accumulated. Both the measurements and simulation suggested that theavailability of substrate Phe is one limiting factor for lignin flux in developing stems. This finding indicates new gene targets for lignin manipulation in plants. To extend our kinetic model to simulate flux distribution in response to genetic perturbations, we conducted an RNA-sequencing experiment in wild type and 13 plants with modified lignification, and integrated the transcriptional data with the metabolic profiles. We found that the biosynthesis of Phe and lignification are tightly coordinated at transcriptional level. The coregulation of the shikimate and phenylpropanoid pathways involves transcriptional and post-translational regulatory mechanisms to maintain pathway homeostasis. Our results also indicate that induction of Phe supply and enhancement of PAL activity are both effective strategies to increase carbon flux into the phenylpropanoid network.</p><p>In this interdisciplinary project, we have taken various system biology approaches to understand metabolic flux towards lignin, the second most abundant carbon sink in nature. We have combined isotope labeling aided flux measurements and mathematical simulation, and have integrated metabolome data with transcriptome profiles. The experiments and analysis have been conducted in both wild-type Arabidopsis and those with perturbed lignification. The novel work not only provides insight into our knowledge of phenylpropanoid metabolism, but also creates a framework to systematically assemble gene expression, enzyme activity, and metabolite accumulation to study metabolic fluxes, the ultimate functional phenotypes of biochemical networks.</p>

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