Exploratory Metabolomics Profiling in the Kainic Acid Rat Model Reveals Depletion of 25-Hydroxyvitamin D3 during Epileptogenesis

Abstract

Currently, no reliable markers are available to evaluate the epileptogenic potential of a brain injury. The electroencephalogram is the standard method of diagnosis of epilepsy; however, it is not used to predict the risk of developing epilepsy. Biomarkers that indicate an individual’s risk to develop epilepsy, especially those measurable in the periphery are urgently needed. Temporal lobe epilepsy (TLE), the most common form of acquired epilepsy, is characterized by spontaneous recurrent seizures following brain injury and a seizure-free “latent” period. Elucidation of mechanisms at play during epilepsy development (epileptogenesis) in animal models of TLE could enable the identification of predictive biomarkers. Our pilot study using liquid chromatography-mass spectrometry metabolomics analysis revealed changes (p-value ≤ 0.05, ≥1.5-fold change) in lipid, purine, and sterol metabolism in rat plasma and hippocampus during epileptogenesis and chronic epilepsy in the kainic acid model of TLE. Notably, disease development was associated with dysregulation of vitamin D3 metabolism at all stages and plasma 25-hydroxyvitamin D3 depletion in the acute and latent phase of injury-induced epileptogenesis. These data suggest that plasma VD3 metabolites reflect the severity of an epileptogenic insult and that a panel of plasma VD3 metabolites may be able to serve as a marker of epileptogenesis.

Regulation of kynurenine metabolism by a ketogenic diet

“…Other potential, oftendiscussed mechanisms are increases of the anticonvulsant adenosine, changes in amino acids and neurotransmitters, such as disruption of glutamatergic synaptic transmission, and activation of adenosine triphosphate-sensitive potassium channels (2). Overall, despite many hypotheses and a few validated targets such as amino acid metabolism (3), redox systems and mitochondria (13,14), and the Nrf2 pathway (15), the mechanisms of action of KDs remain largely unknown. Hence it is reasonable to expect that yet unknown mechanisms including pathway changes associated with KDs are involved.…”

“…HPLC-MS metabolomics analysis. One hundred microliters of plasma or tissue homogenate (1:10 tissue:water with 0.1% ammonium acetate and 0.03% butylated hydroxytoluene) were used per sample for analysis as described previously (15). Briefly, internal standards were added as quality controls for metabolite extraction, i.e., creatinine-d3, D-glucose-13 C 6 , valine-d8, testosterone-d2, C17 ceramide, FA unsaturated C19:1, heptadecenoic acid, and cis-10-nonadecenoic acid.…”

AMON: Annotation of metabolite origins via networks to better integrate microbiome and metabolome data

“…The hydrophobic and hydrophilic fractions were run on Agilent Technologies (Santa Clara, CA) 6520 and 6550 Quadrupole Time of Flight (QTOF) mass spectrometers, respectively. Both fractions were run in positive and negative electrospray ionization (ESI) modes, as previously described (Heischmann et al , 2016).…”

“…Compound data was extracted using Agilent Technologies (Santa Clara, CA) Mass Hunter Profinder Version B.08 (Profinder) software in combination with Agilent Technologies Mass Profiler Professional Version 14 (MPP) as described previously (Heischmann et al , 2016). Briefly, a naive feature finding algorithm, Find By Molecular Feature, was used in Profinder to extract compound data from all samples and sample preparation blanks.…”

Knockout of the non-essential gene SUGCT creates diet-linked, age-related microbiome disbalance with a diabetes-like metabolic syndrome phenotype

“…All experimental mice were co-housed and matched gender-(male), age-(15 weeks), and backgroundwise (C57BL/6J), which means that usually, 2 wild type and 2 SugctKO mice were housed in a single cage. We detected a total of 669 compounds and tentatively annotated metabolites with an associated unique molecular mass, which in numerous cases was followed up by identity confirmation either via chemical standards or using metabolite fragmentation against libraries for possible matches [14] (Tables S2, S3, see "Materials and methods"). The data were visualized in a Volcano plot representing the differences in detected metabolites between WT and SugctKO mouse kidney ( Fig.…”