<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 ¹³C 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 ¹³C₆-labeled phenylalanine ([¹³C₆]-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 [¹³C₆]-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>

A mild traumatic brain injury (mTBI), commonly referred to as a concussion, is when the brain experiences an abrupt acceleration and/or deceleration that sends shock waves through the brain tissue, upsetting its structure and function. A mTBI is a heterogeneous condition with acute and chronic outcomes for patients. The chronic form of mTBI can lead to a wide range of neurological, behavioral, and cognitive symptoms. Critically, this injury is not defined by a simple process or pathophysiological event but rather biomechanical and neurological brain damage that can trigger highly complex physiological cascades. These further lead to a wide range of cellular, molecular, and functional changes that alter genes and associated metabolites. These changes, if specifically characterized, could be used to predict a patient’s outcome and recovery timeline. Recently, genetic studies showed that specific genotypes could increase an individual’s risk of more severe injury and impaired recovery following mTBI. Consequently, an improved understanding of gene alteration and genetic changes is necessary to develop personalized diagnostic approaches which can guide the design of novel treatments. The current study proposes utilizing bioinformatic tools, biological networks, and databases to identify potential genes and microRNAs associated with the mTBI in order to aid the early diagnosis of mTBI and track recovery for individual patients. With bioinformatic techniques, we were able to identify and compare genetic and epigenetic data associated with mTBI, as well as understand the various aspects of molecular changes after brain injury. Ultimately, we analyzed and cataloged the biological pathways and networks associated with this injury. A critical search of online bioinformatics databases was performed to determine interactions between mTBI-related genes, and relevant molecular processes. The major finding was that APOE, S100B, GFAP, BDNF, AQP4, COMT, MBP, UCHL1, DRD2, ASIC1, and CACNA1A genes were significantly associated with mTBI outcome. Those genes are primarily involved in different neurological tasks and neurological pathways such as neuron projection regeneration, regulation of neuronal synaptic plasticity, cognition, memory function, neuronal cell death and the dopaminergic pathway. This study predicted specific miRNAs linked to mTBI outcomes and candidate genes (hsa-miR-204-5p, hsa-miR-16-5p, hsa-miR-10a-5p, has-miR-218-5p, has-miR-34a-5p), and RNA-seq analysis on the GSE123336 data revealed that one miRNA found (hsa-miR-10a-5p) matched our predictions related to mTBI outcomes. Pathway analysis revealed that the predicted miRNA targets were mainly engaged in nervous system signaling, neuron projection and cell differentiation. These findings may contribute to developing diagnostic procedures and treatments for mTBI patients who are still experiencing symptoms, but validation of these genetic markers for mTBI assessment requires patient participation and correlation with advanced personalized MRI methods that show concussion related changes. / Thesis / Master of Applied Science (MASc) / Traumatic brain injury (TBI) is a highly prevalent neurological injury affecting millions of individuals globally. Mild TBI (mTBI), sometimes called concussion, makes up over 85% of TBI cases. A mTBI is a heterogeneous condition with acute and chronic outcomes for patients and involves complex cascades of cellular and molecular events that can lead to functional changes in genes and associated metabolites. In recent genetic studies, it has been shown that certain genotypes are associated with a higher risk of experiencing a more serious injury and a slower recovery after mTBI. These genes can be utilized as crucial biomarkers to predict how long it will take for a person to recover from a concussion. The purpose of this study was to find potential biomarkers that could help in the early detection of mTBI and the monitoring of individual patients’ recovery. It was hypothesized that genes and miRNAs (and their associated proteins) involved in neuronal body, axonal and myelin integrity and regeneration would be identified as important markers of severity.