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Uncovering Metabolic Dynamics in Epileptic Phenotypes Using a Systems Biology Approach
Epilepsy is a progressive, neurological disorder characterized by unprovoked seizures. Despite continued efforts to develop anti-epileptic drugs (AEDs), ~30% of all epileptic patients are resistant to traditional AED therapy. Additionally, AEDs alleviate symptoms of epilepsy rather than slow its progression, necessitating the identification and development of other therapeutic targets. Impairments in central carbon metabolism are widely observed in epilepsy, suggesting a non-preference for glucose as a fuel source. Metabolism-based therapies, such as the KD, have shown success as a treatment for children’s epilepsy, but mixed efficacy in adults. Despite previous studies’ characterization of alterations in redox homeostasis, central carbon, and purine metabolism through isolated, mechanistic approaches, the metabolic underpinnings of epilepsy remain ill-defined. “Omics” techniques can be leveraged to characterize these alterations while identifying alterations that were not previously considered, especially at a systems level. This work utilizes metabolomics and proteomics approaches to characterize metabolic dynamics in epilepsy across genetic and pharmacological mouse models of epilepsy in addition to metabolic profiles obtained from dried plasma spots (DPS) from patients in an epilepsy monitoring unit. In the genetic mouse model, we identify differential lipid profiles in the hippocampus and cortex, suggesting that each region has a unique metabolic contribution to seizure generation. In the brain of a pharmacological (kainic acid) mouse model, alterations to glucose metabolism, TCA cycle, purine, and hexosamine pathways are identified. Further, we find that miRNAs within bovine-milk exosomes demonstrate their therapeutic potential in epilepsy by their reversal of seizure-induced changes in mice fed a diet in which miRNAs are depleted from exosomes. The liver of the kainic acid mouse model showed a distinct, seizure-induced metabolic phenotype suggestive of a sympathetic response. Finally, DPS samples of patients in an epilepsy monitoring unit were collected for the duration of their stay using a novel DPS collection device. We find alterations in amino acid metabolism, TCA cycle, and fatty acid metabolism over time in patients and identify potential biomarker candidates for future validation.
Johnson, Alicia L, "Uncovering Metabolic Dynamics in Epileptic Phenotypes Using a Systems Biology Approach" (2022). ETD collection for University of Nebraska - Lincoln. AAI29999536.