The Biochemical Profile of Post-Mortem Brain from People Who Suffered from Epilepsy Reveals Novel Insights into the Etiopathogenesis of the Disease

Abstract

Epilepsy not-otherwise-specified (ENOS) is one of the most common causes of chronic disorders impacting human health, with complex multifactorial etiology and clinical presentation. Understanding the metabolic processes associated with the disorder may aid in the discovery of preventive and therapeutic measures. Post-mortem brain samples were harvested from the frontal cortex (BA8/46) of people diagnosed with ENOS cases (n = 15) and age- and sex-matched control subjects (n = 15). We employed a targeted metabolomics approach using a combination of proton nuclear magnetic resonance (1H-NMR) and direct injection/liquid chromatography tandem mass spectrometry (DI/LC-MS/MS). We accurately identified and quantified 72 metabolites using 1H-NMR and 159 using DI/LC-MS/MS. Among the 212 detected metabolites, 14 showed significant concentration changes between ENOS cases and controls (p < 0.05; q < 0.05). Of these, adenosine monophosphate and O-acetylcholine were the most commonly selected metabolites used to develop predictive models capable of discriminating between ENOS and unaffected controls. Metabolomic set enrichment analysis identified ethanol degradation, butyrate metabolism and the mitochondrial beta-oxidation of fatty acids as the top three significantly perturbed metabolic pathways. We report, for the first time, the metabolomic profiling of postmortem brain tissue form patients who died from epilepsy. These findings can potentially expand upon the complex etiopathogenesis and help identify key predictive biomarkers of ENOS.

Central Nervous System Metabolism in Autism, Epilepsy and Developmental Delays: A Cerebrospinal Fluid Analysis

“…Loss of this enzyme results in a mitochondrial disease phenotype emphasizing the importance of NADP in mitochondrial function [ 46 ]. In addition, EPI was the only disorder associated with changes in lipid metabolism, consistent with previous studies [ 10 , 11 , 12 , 24 ]. This can be directly linked back to NADP/NADPH metabolism [ 42 , 43 ].…”

“…EPI was associated with abnormalities in several vitamin pathways, including folate, pyridoxine, riboflavin and nicotinamide. Studies on children with EPI have identified abnormalities in pyridoxine metabolism in CSF [ 22 ] and pyridoxine and nicotinamide metabolism in post-mortem brain [ 24 ]. Interesting studies in post-mortem brain have identified abnormalities in other vitamin pathways including thiamine as well as pantothenate and its important product for energy metabolism CoA [ 24 ].…”

“…One study on CSF in EPI found changes in metabolites associated with altered pyridoxine metabolism in preschool children and a reduction in 1,5-anhydroglucitol in school-aged children [ 22 ]. One study on post-mortem frontal lobes in adults with idiopathic EPI found alterations in metabolites involved in fatty acid, pentose phosphate pathways, vitamin pathways including thiamine, one-carbon, nicotinamide, pantothenate and CoA pathways as well as amino acid metabolism including phenylalanine and tyrosine [ 24 ].…”

Fatty Acids: An Insight into the Pathogenesis of Neurodegenerative Diseases and Therapeutic Potential

“…MCFAs may serve as agonists of peroxisome proliferator-activated receptors and are involved in several biological functions within the brain as enhancers of insulin sensitivity and acute inflammation. Moreover, they are involved in homeostatic functions, as well as energy processing, under pathological conditions (PD, MS, and epilepsy, among others) [ 238 , 239 ]. Importantly, the brain can generate ketone bodies through the oxidation of MCFAs [ 44 ].…”

Lipid Profiling of Alzheimer’s Disease Brain Highlights Enrichment in Glycerol(phospho)lipid, and Sphingolipid Metabolism

“…To prevent overfitting when too many variables are used and there are not enough observations, a single attribute was chosen. For each model, a five-fold cross validation was performed for parameter optimization [25,26]. In an attempt to systematically investigate the predictive performance of the various machine learning approaches, several models were tested.…”