NMR-based metabolomics in pediatric drug resistant epilepsy - preliminary results

Metabolomics-Based Study on the Anticonvulsant Mechanism of Acorus tatarinowii: GABA Transaminase Inhibition Alleviates PTZ-Induced Epilepsy in Rats

BACKGROUND/OBJECTIVES

Epilepsy is a common chronic and recurrent neurological disorder that poses a threat to human health, and Acorus tatarinowii Schott (ATS), a traditional Chinese medicine, is used to treat it. This study aimed to determine its effects on plasma metabolites. Moreover, the possible mechanism of its intervention in epilepsy was preliminarily explored, combined with network pharmacology.

METHODS

An epileptic model of rats was established using pentylenetetrazol. The potential targets and pathways of ATS were predicted by network pharmacology. Ultra Performance Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometrynce Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometryance Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometry and statistical analyses were used to profile plasma metabolites and identify ATS's effects on epilepsy.

RESULTS

Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that ATS was involved in regulating multiple signaling pathways, mainly including the neuroactive ligand-receptor interaction and GABAerGamma-aminobutyrate transaminaseAminobutyrate Transaminaseapse signaling pathway. ATS treatment restored 19 metabolites in epiGamma-aminobutyrate transaminaseminobutyrate Transaminase rats, affecting lysine, histidine, and purine metabolism. GABA-T was found as a new key target for treating epilepsy with ATS. The IC50 of ATS for inhibiting GABA-T activity was 57.9 μg/mL. Through metabolomic analysis, we detected changes in the levels of certain metabolites related to the GABAergic system. These metabolite changes can be correlated with the targets and pathways predicted by network pharmacology. One of the limitations of this study is that the correlation analysis between altered metabolites and seizure severity remains unfinished, which restricts a more in-depth exploration of the underlying biological mechanisms. In the future, our research will focus on conducting a more in-depth exploration of the correlation analysis between altered metabolites and seizure severity.

CONCLUSIONS

These results improved our understanding of epilepsy and ATS treatment, potentially leading to better therapies. The identification of key metabolites and their associated pathways in this study offers potential novel therapeutic targets for epilepsy. By modulating these metabolites, future therapies could be designed to better manage the disorder. Moreover, the insights from network pharmacology can guide the development of more effective antiepileptic drugs, paving the way for improved clinical outcomes for patients.

Comprehensive predictors of drug-resistant epilepsy in MELAS: clinical, EEG, imaging, and biochemical factors

BACKGROUND

Seizures are a common but often overlooked manifestation of MELAS. This study aimed to describe the characteristics of seizures in MELAS and to analyze the clinical, electroencephalographic, imaging, and biochemical factors associated with drug-resistant epilepsy.

METHODS

A single-center retrospective study was conducted to investigate the clinical characteristics of seizures in MELAS patients. The study collected data on clinical features, muscle biopsy results, genetic testing, seizure symptoms, electroencephalography (EEG), neuroimaging findings, cerebrospinal fluid and blood biochemistry, and the modified Rankin Scale (mRS). We also investigated the correlation between seizure frequency and mRS scores. In addition, we analyzed the risk factors for drug-resistant epilepsy in MELAS.

RESULTS

A total of 37 patients with confirmed MELAS (24 males and 13 females) were included in the study. All patients experienced seizures, with an onset age ranging from 14 to 53 years and a mean of 32 years. These MELAS patients experienced a variety of seizure types, with generalized seizures being the most common. EEG findings revealed background rhythm abnormalities in all patients, and epileptiform discharges were observed in 37.8% of patients during the interictal phase. Status epilepticus (OR 16.499; 95% CI, 1.615-168.557; P = 0.018) and elevated resting serum lactate levels (OR 8.594; 95% CI, 1.342-59.733; P = 0.024) were identified as independent risk factors for drug-resistant epilepsy. In addition, changes in the seizure frequency at the last follow-up compared to baseline were positively correlated with the mRS score. (r = 0.533, p < 0.001).

CONCLUSION

Status epilepticus and elevated resting serum lactate levels were predictive of the development of drug-resistant epilepsy in MELAS. Poor seizure control was significantly associated with increased clinical disability. Early identification of high-risk patients for drug-resistant epilepsy could facilitate the development of more effective treatment plans.

The Metabolic Profile of Plasma During Epileptogenesis in a Rat Model of Lithium-Pilocarpine-Induced Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) arises mostly because of an initial injury. Certain stimuli can make a normal brain prone to repeated, spontaneous seizures via a process called epileptogenesis. This study examined the plasma metabolomics profile in rats with the induced TLE to identify feasible biomarkers that can distinguish progression of epileptogenesis in three different time points and reveal the underlying mechanisms of epileptogenesis. Status epilepticus (SE) was induced by repetitive intraperitoneal injections of low-dose lithium chloride-pilocarpine hydrocholoride. Blood samples were collected 48 h, 1 week, and 6 weeks after SE, respectively. Plasma metabolites were analyzed by nuclear magnetic resonance (NMR) spectrometry. Statistical analysis was performed using MetaboAnalyst 6.0. An orthogonal partial least squares discriminant analysis (OPLS-DA) model was employed to represent variations between the TLE model groups and respective controls. Volcano plot analysis was used to identify key features, applying a fold-change criterion of 1.5 and a t-test threshold of 0.05. 48 h after SE, dimethyl sulfone (DMSO2) and creatinine levels were decreased, whereas glycine and creatine levels were increased. The only metabolite that changed 1 week after SE was pyruvic acid, which was increased compared to its control level. Lactic acid, pyruvic acid, and succinic acid levels were increased 6 weeks after SE. The identified metabolites were especially related to the tricarboxylic acid cycle and glycine, serine, and threonine metabolism. The results illustrate that distinct plasma metabolites can function as phase-specific biomarkers in TLE and reveal new insights into the mechanisms underlying SE.

Molecular Insights of Drug Resistance in Epilepsy: Multi-omics Unveil

Epilepsy is a devastating neurological disorder mainly associated with impaired synchronic discharge that leads to sensory, motor, and psychomotor impairments. Till now, about 30 anti-seizure medications (ASMs) have been approved for the management of epilepsy, yet one-third of individuals still have uncontrollable epilepsy and develop resistance. Drug resistance epilepsy (DRE) is defined as the condition where two ASMs fail to control the seizure in epileptic patients. The leading cause of the resistance was the extended use of ASMs. According to various studies, alterations in some genes and their expressions, along with specific metabolic impairments, are suggested to be associated with ASMs resistance and DRE pathophysiology. Several factors aid in the pathophysiology of DRE, such as alterations in protein-encoding genes such as neurotransmitter receptors, drug transporters, ion channels, and drug targets. Furthermore, the altered metabolite levels of metabolites implicated in neurotransmitter signaling, energetic pathways, oxidative stress, and neuroinflammatory signaling differentiate the epileptic patient from the DRE patient. Various DRE biomarkers can be identified using the "integrated omics approach," which includes the study of genomics, transcriptomics, and metabolomics. The current review has been compiled to understand the pathophysiological mechanisms of DRE by focusing on genomics, transcriptomics, and metabolomics. An effort has also been made to identify the therapeutic targets based on identifying significant markers by a multi-omics approach. This has the potential to develop novel therapeutic interventions in the future.

GC-MS uncovers unique metabolic markers of drug-resistant epilepsy in capillary but not venous dried blood spots

This study compared the effectiveness of capillary dried blood spots (DBS) versus venous DBS in detecting metabolic changes related to drug-resistant epilepsy (DRE). DBS samples were collected from 142 epilepsy patients (58 drug-resistant, 84 drug-responsive) via venipuncture or fingerstick capillary sampling. Metabolomic analysis using gas chromatography-mass spectrometry compared DBS metabolite profiles between the two groups. While venous DBS profiles showed no distinct patterns, capillary DBS profiles revealed clustering patterns in principal components analysis, with the first two principal components explaining 14.5 %, and 13.5 % of the total variance, respectively. Orthogonal PLS-DA confirmed group discrimination (R2Y=0.989, Q2=0.742). Drug-resistant patients exhibited elevated capillary DBS levels of glutamine, pyruvic acid, and serine, and decreased palmitic acid compared to drug-responsive patients. Pathway analysis revealed disruptions in amino acid metabolism, neurotransmission, and cellular energy regulation. Elevated glutamine levels may contribute to an imbalance between excitatory glutamate and inhibitory GABA neurotransmission, key factors in epileptogenesis and drug resistance. Capillary DBS, likely enriched with arterial blood supply to the brain, appears to better capture central nervous system metabolic disturbances compared to venous DBS containing systemic contributions. This minimally invasive capillary DBS approach offers effective metabolic profiling of brain conditions like DRE, for monitoring disease progression and treatment response, enhancing personalized patient management in epilepsy.

Diagnosis of epilepsy by machine learning of high-performance plasma metabolic fingerprinting

Epilepsy is a chronic neurological disorder that causes a major threat to public health and the burden of disease worldwide. High-performance diagnostic tools for epilepsy need to be developed to improve diagnostic accuracy and efficiency while still missing. Herein, we utilized nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI MS) to acquire plasma metabolic fingerprints (PMFs) from epileptic and healthy individuals for timely and accurate screening of epilepsy. The NELDI MS enabled high detection speed (∼30 s per sample), high throughput (up to 384 samples per run), and favorable reproducibility (coefficients of variation <15 %), acquiring high-performed PMFs. We next constructed an epilepsy diagnostic model by machine learning of PMFs, achieving desirable diagnostic capability with the area under the curve (AUC) value of 0.941 for the validation set. Furthermore, four metabolites were identified as a diagnostic biomarker panel for epilepsy, with an AUC value of 0.812-0.860. Our approach provides a high-performed and high-throughput platform for epileptic diagnostics, promoting the development of metabolic diagnostic tools in precision medicine.

Hippocampal metabolic profile during epileptogenesis in the pilocarpine model of epilepsy

Temporal lobe epilepsy (TLE) is a common form of refractory epilepsy in adulthood. The metabolic profile of epileptogenesis is still poorly investigated. Elucidation of such a metabolic profile using animal models of epilepsy could help identify new metabolites and pathways involved in the mechanisms of epileptogenesis process. In this study, we evaluated the metabolic profile during the epileptogenesis periods. Using a pilocarpine model of epilepsy, we analyzed the global metabolic profile of hippocampal extracts by untargeted metabolomics based on ultra-performance liquid chromatography-high-resolution mass spectrometry, at three time points (3 h, 1 week, and 2 weeks) after status epilepticus (SE) induction. We demonstrated that epileptogenesis periods presented different hippocampal metabolic profiles, including alterations of metabolic pathways of amino acids and lipid metabolism. Six putative metabolites (tryptophan, N-acetylornithine, N-acetyl-L-aspartate, glutamine, adenosine, and cholesterol) showed significant different levels during epileptogenesis compared to their respective controls. These putative metabolites could be associated with the imbalance of neurotransmitters, mitochondrial dysfunction, and cell loss observed during both epileptogenesis and epilepsy. With these findings, we provided an overview of hippocampal metabolic profiles during different stages of epileptogenesis that could help investigate pathways and respective metabolites as predictive tools in epilepsy.

Exploration of urine metabolic biomarkers for new-onset, untreated pediatric epilepsy: A gas and liquid chromatography mass spectrometry-based metabolomics study

OBJECTIVE

The discovery of objective indicators for recent epileptic seizures will help confirm the diagnosis of epilepsy and evaluate therapeutic effects. Past studies had shortcomings such as the inclusion of patients under treatment and those with various etiologies that could confound the analysis results significantly. We aimed to minimize such confounding effects and to explore the small molecule biomarkers associated with the recent occurrence of epileptic seizures using urine metabolomics.

METHODS

This is a multicenter prospective study. Subjects included pediatric patients aged 2 to 12 years old with new-onset, untreated epilepsy, who had had the last seizure within 1 month before urine collection. Controls included healthy children aged 2 to 12 years old. Those with underlying or chronic diseases, acute illnesses, or recent administration of medications or supplements were excluded. Targeted metabolome analysis of spot urine samples was conducted using gas chromatography (GC)- and liquid chromatography (LC)-tandem mass spectrometry (MS/MS).

RESULTS

We enrolled 17 patients and 21 controls. Among 172 metabolites measured by GC/MS/MS and 41 metabolites measured by LC/MS/MS, only taurine was consistently reduced in the epilepsy group. This finding was subsequently confirmed by the absolute quantification of amino acids. No other metabolites were consistently altered between the two groups.

CONCLUSIONS

Urine metabolome analysis, which covers a larger number of metabolites than conventional biochemistry analyses, found no consistently altered small molecule metabolites except for reduced taurine in epilepsy patients compared to healthy controls. Further studies with larger samples, subjects with different ages, expanded target metabolites, and the investigation of plasma samples are required.

Untargeted urine metabolomics and machine learning provide potential metabolic signatures in children with autism spectrum disorder

Background

Complementary to traditional biostatistics, the integration of untargeted urine metabolomic profiling with Machine Learning (ML) has the potential to unveil metabolic profiles crucial for understanding diseases. However, the application of this approach in autism remains underexplored. Our objective was to delve into the metabolic profiles of autism utilizing a comprehensive untargeted metabolomics platform coupled with ML.

Methods

Untargeted metabolomics quantification (UHPLC/Q-TOF-MS) was performed for urine analysis. Feature selection was conducted using Lasso regression, and logistic regression, support vector machine, random forest, and extreme gradient boosting were utilized for significance stratification. Pathway enrichment analysis was performed to identify metabolic pathways associated with autism.

Results

A total of 52 autistic children and 40 typically developing children were enrolled. Lasso regression identified ninety-two urinary metabolites that significantly differed between the two groups. Distinct metabolites, such as prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, were revealed to be associated with autism through the application of four different ML methods (p<0.05). The alterations observed in the phosphatidylinositol and inositol phosphate metabolism pathways were linked to the pathophysiology of autism (p<0.05).

Conclusion

Significant urinary metabolites, including prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, exhibit associations with autism. Additionally, the involvement of the phosphatidylinositol and inositol phosphate pathways suggests their potential role in the pathophysiology of autism.

Metabolomics of cerebrospinal fluid reveals prognostic biomarkers in pediatric status epilepticus

AIMS

Status epilepticus (SE) is the most common neurological emergency in pediatric patients. This study aimed to screen for prognostic biomarkers of SE in the cerebrospinal fluid (CSF) using metabolomics.

METHODS

Ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS) was conducted to identify prognostic biomarkers in CSF metabolomics by comparing the poor outcome group (N = 13) with the good outcome group (N = 15) of children with SE. Differentially expressed metabolites were identified using Mann-Whitney U test corrected by Benjamini-Hochberg and partial least squares discriminant analysis (PLS-DA).

RESULTS

The PLS-DA model identified and validated significant metabolic differences between the poor and good outcome groups of children with SE (PLS-DA with R2 Y = 0.992 and Q2  = 0.798). A total of 49 prognosis-related metabolites were identified. Of these metabolites, 20 including glutamyl-glutamine, 3-iodothyronamine, and L-fucose had an area under the curve (AUC) ≥ 80% in prognostic prediction of SE. The logistic regression model combining glutamyl-glutamine and 3-iodothyronamine produced an AUC value of 0.976, with a sensitivity of 0.863 and specificity of 0.956. Pathway analysis revealed that dysregulation of the citrate cycle (TCA) and arginine biosynthesis may contribute to poor SE prognosis.

CONCLUSIONS

This study highlighted the prognosis-related metabolomic disturbances in the CSF of children with SE and identified potential prognostic biomarkers. A prognostic prediction model combining glutamyl-glutamine and 3-iodothyronamine with high predictive value was established.

The role of the gut-microbiome-brain axis in metabolic remodeling amongst children with cerebral palsy and epilepsy

BACKGROUND

Epilepsy-associated dysbiosis in gut microbiota has been previously described, but the mechanistic roles of the gut microbiome in epileptogenesis among children with cerebral palsy (CP) have yet to be illustrated.

METHODS

Using shotgun metagenomic sequencing coupled with untargeted metabolomics analysis, this observational study compared the gut microbiome and metabolome of eight children with non-epileptic cerebral palsy (NECP) to those of 13 children with cerebral palsy with epilepsy (CPE). Among children with CPE, 8 had drug-sensitive epilepsy (DSE) and five had drug-resistant epilepsy (DRE). Characteristics at enrollment, medication history, and 7-day dietary intake were compared between groups.

RESULTS

At the species level, CPE subjects had significantly lower abundances of Bacteroides fragilis and Dialister invisus but higher abundances of Phascolarctobacterium faecium and Eubacterium limosum. By contrast, DRE subjects had a significantly higher colonization of Veillonella parvula. Regarding microbial functional pathways, CPE subjects had decreased abundances of pathways for serine degradation, quinolinic acid degradation, glutamate degradation I, glycerol degradation, sulfate reduction, and nitrate reduction but increased abundances of pathways related to ethanol production. As for metabolites, CPE subjects had higher concentrations of kynurenic acid, 2-oxindole, dopamine, 2-hydroxyphenyalanine, 3,4-dihydroxyphenylglycol, L-tartaric acid, and D-saccharic acid; DRE subjects had increased concentrations of indole and homovanilic acid.

CONCLUSIONS

In this study, we found evidence of gut dysbiosis amongst children with cerebral palsy and epilepsy in terms of gut microbiota species, functional pathways, and metabolites. The combined metagenomic and metabolomic analyses have shed insights on the potential roles of B. fragilis and D. invisus in neuroprotection. The combined analyses have also provided evidence for the involvement of GMBA in the epilepsy-related dysbiosis of kynurenine, serotonin, and dopamine pathways and their complex interplay with neuroimmune and neuroendocrinological pathways.

Integrating metabolomics and lipidomics revealed a decrease in plasma fatty acids but an increase in triglycerides in children with drug-refractory epilepsy

OBJECTIVE

The drug-refractory epilepsy (DRE) in children is commonly observed but the underlying mechanisms remain elusive. We examined whether fatty acids (FAs) and lipids are potentially associated with the pharmacoresistance to valproic acid (VPA) therapy.

METHODS

This single-center, retrospective cohort study was conducted using data from pediatric patients collected between May 2019 and December 2019 at the Children's Hospital of Nanjing Medical University. Ninety plasma samples from 53 responders with VPA monotherapy (RE group) and 37 non-responders with VPA polytherapy (NR group) were collected. Non-targeted metabolomics and lipidomics analysis for those plasma samples were performed to compare the potential differences of small metabolites and lipids between the two groups. Plasma metabolites and lipids passing the threshold of variable importance in projection value >1, fold change >1.2 or <0.8, and p-value <0.05 were regarded as statistically different substances.

RESULTS

A total of 204 small metabolites and 433 lipids comprising 16 different lipid subclasses were identified. The well-established partial least squares-discriminant analysis (PLS-DA) revealed a good separation of the RE from the NR group. The FAs and glycerophospholipids status were significantly decreased in the NR group, but their triglycerides (TG) levels were significantly increased. The trend of TG levels in routine laboratory tests was in line with the lipidomics analysis. Meanwhile, cases from the NR group were characterized by a decreased level of citric acid and L-thyroxine, but with an increased level of glucose and 2-oxoglutarate. The top two enriched metabolic pathways involved in the DRE condition were biosynthesis of unsaturated FAs and linoleic acid metabolism.

SIGNIFICANCE

The results of this study suggested an association between metabolism of FAs and the medically intractable epilepsy. Such novel findings might propose a potential mechanism linked to the energy metabolism. Ketogenic acid and FAs supplementation might therefore be high-priority strategies for DRE management.

Insights into the development of pentylenetetrazole-induced epileptic seizures from dynamic metabolomic changes

Epilepsy is often considered to be a progressive neurological disease, and the nature of this progression remains unclear. Understanding the overall and common metabolic changes of epileptic seizures can provide novel clues for their control and prevention. Herein, a chronic kindling animal model was established to obtain generalized tonic-clonic seizures via the repeated injections of pentylenetetrazole (PTZ) at subconvulsive dose. Dynamic metabolomic changes in plasma and urine from PTZ-kindled rats at the different kindling phases were explored using NMR-based metabolomics, in combination with behavioral assessment, brain neurotransmitter measurement, electroencephalography and histopathology. The increased levels of glucose, lactate, glutamate, creatine and creatinine, together with the decreased levels of pyruvate, citrate and succinate, ketone bodies, asparagine, alanine, leucine, valine and isoleucine in plasma and/or urine were involved in the development and progression of seizures. These altered metabolites reflected the pathophysiological processes including the compromised energy metabolism, the disturbed amino acid metabolism, the peripheral inflammation and changes in gut microbiota functions. NMR-based metabolomics could provide brain disease information by the dynamic plasma and urinary metabolic changes during chronic epileptic seizures, yielding classification of seizure stages and profound insights into controlling epilepsy via targeting deficient energy metabolism.

Multi-omic Analysis of the Gut Microbiome in Rats with Lithium-Pilocarpine-Induced Temporal Lobe Epilepsy

Evidence supports that the gut microbiota and bacteria-dependent metabolites influence the maintenance of epileptic brain activity. However, the alterations in the gut microbiota between epileptic versus healthy individuals are poorly understood. We used a multi-omic approach to evaluate the changes in the composition of gut metagenome as well in the fecal metabolomic profile in rats before and after being submitted to status epilepticus (SE)-induced temporal lobe epilepsy (TLE). The 16S ribosomal RNA (rRNA) sequencing of fecal samples coupled to bioinformatic analysis revealed taxonomic, compositional, and functional shifts in epileptic rats. The species richness (Chao1 index) was significantly lower in the post-TLE group, and the β-diversity analysis revealed clustering separated from the pre-TLE group. The taxonomic abundance analysis showed a significant increase of phylum Desulfobacterota and a decrease of Patescibacteria in the post-TLE group. The DESEq2 and LEfSe analysis resulted in 18 genera significantly enriched between post-TLE and pre-TLE groups at the genus level. We observed that epileptic rats present a peculiar metabolic phenotype, including a lower concentration of D-glucose and L-lactic acid and a higher concentration of L-glutamic acid and glycine. The microbiota-host metabolic correlation analysis showed that the genera differentially abundant in post-TLE rats are associated with the altered metabolites, especially the proinflammatory Desulfovibrio and Marvinbryantia, which were enriched in epileptic animals and positively correlated with these excitatory neurotransmitters and carbohydrate metabolites. Therefore, our data revealed a correlation between dysbacteriosis in epileptic animals and fecal metabolites that are known to be relevant for maintaining epileptic brain activity by enhancing chronic inflammation, an excitatory-inhibitory imbalance, and/or a metabolic disturbance. These data are promising and suggest that targeting the gut microbiota could provide a novel avenue for preventing and treating acquired epilepsy. However, the causal relationship between these microbial/metabolite components and the SRS occurrence still needs further exploration.

Circulating Metabolites as Biomarkers of Disease in Patients with Mesial Temporal Lobe Epilepsy

A major challenge in the clinical management of patients with mesial temporal lobe epilepsy (MTLE) is identifying those who do not respond to antiseizure medication (ASM), allowing for the timely pursuit of alternative treatments such as epilepsy surgery. Here, we investigated changes in plasma metabolites as biomarkers of disease in patients with MTLE. Furthermore, we used the metabolomics data to gain insights into the mechanisms underlying MTLE and response to ASM. We performed an untargeted metabolomic method using magnetic resonance spectroscopy and multi- and univariate statistical analyses to compare data obtained from plasma samples of 28 patients with MTLE compared to 28 controls. The patients were further divided according to response to ASM for a supplementary and preliminary comparison: 20 patients were refractory to treatment, and eight were responsive to ASM. We only included patients using carbamazepine in combination with clobazam. We analyzed the group of patients and controls and found that the profiles of glucose (p = 0.01), saturated lipids (p = 0.0002), isoleucine (p = 0.0001), β-hydroxybutyrate (p = 0.0003), and proline (p = 0.02) were different in patients compared to controls (p < 0.05). In addition, we found some suggestive metabolites (without enough predictability) by multivariate analysis (VIP scores > 2), such as lipoproteins, lactate, glucose, unsaturated lipids, isoleucine, and proline, that might be relevant to the process of pharmacoresistance in the comparison between patients with refractory and responsive MTLE. The identified metabolites for the comparison between MTLE patients and controls were linked to different biological pathways related to cell-energy metabolism and pathways related to inflammatory processes and the modulation of neurotransmitter release and activity in MTLE. In conclusion, in addition to insights into the mechanisms underlying MTLE, our results suggest that plasma metabolites may be used as disease biomarkers. These findings warrant further studies exploring the clinical use of metabolites to assist in decision-making when treating patients with MTLE.

Revealing the Antiepileptic Effect of α-Asaronol on Pentylenetetrazole-Induced Seizure Rats Using NMR-Based Metabolomics

α-Asaronol from Acorus tatarinowii (known as "Shichangpu" in Traditional Chinese medicine) has been proved to possess more efficient antiepileptic activity and lower toxicity than α-asarone (namely "Xixinnaojiaonang" as an antiepileptic drug in China) in our previous study. However, the molecular mechanism of α-asaronol against epilepsy needs to be known if to become a novel antiepileptic medicine. Nuclear magnetic resonance (NMR)-based metabolomics was applied to investigate the metabolic patterns of plasma and the brain tissue extract from pentylenetetrazole (PTZ)-induced seizure rats when treated with α-asaronol or α-asarone. The results showed that α-asaronol can regulate the metabolomic level of epileptic rats to normal to some extent, and four metabolic pathways were associated with the antiepileptic effect of α-asaronol, including alanine, aspartate, and glutamate metabolism; synthesis and degradation of ketone bodies; glutamine and glutamate metabolism; and glycine, serine, and threonine metabolism. It was concluded that α-asaronol plays a vital role in enhancing energy metabolism, regulating the balance of excitatory and inhibitory neurotransmitters, and inhibiting cell membrane damage to prevent the occurrence of epilepsy. These findings are of great significance in developing α-asaronol into a promising antiepileptic drug derived from Traditional Chinese medicine.

Harnessing Metabolomics to Advance Epilepsy Research

Metabolomics is the laboratory analysis and scientific study of the metabolome—that is, the entire collection of small molecule chemicals in an organism. The metabolome represents the functional state of an organism and provides a multifaceted readout of the aggregate activity of endogenous (cellular) and exogenous (environmental) processes. In this review, we discuss how the integrative and dynamic properties of the metabolome create unique opportunities to study complex pathologies that evolve and oscillate over time, like epilepsy. We explain how the scientific progress and clinical applications of metabolomics remain hampered by biological and technical challenges, and we propose best practices to overcome these challenges so that metabolomics can be used in a rigorous and effective manner to further epilepsy research.

Metabolomics Provides Novel Insights into Epilepsy Diagnosis and Treatment: A Review

Epilepsy is one of the most common diseases of the central nervous system. The diagnosis of epilepsy mainly depends on electroencephalograms and symptomatology, while diagnostic biofluid markers are still lacking. In addition, approximately 30% of patients with epilepsy (PWE) show a poor response to the currently available anti-seizure medicines. An increasing number of studies have reported alterations in the blood, brain tissue, cerebrospinal fluid and urine metabolome in PWE and animal models of epilepsy. The aim of this review was to identify potential metabolic biomarkers and pathways that might facilitate diagnostic, therapeutic and prognostic determination in PWE and the understanding of the pathogenesis of the disease. The PubMed and Embase databases were searched for metabolomic studies of PWE and epileptic models published before December 2020. The study objectives, types of models and reported differentially altered metabolites were examined and compared. Pathway analyses were performed using MetaboAnalyst 5.0 online software. Thirty-five studies were included in this review. Metabolites such as glutamate, lactate and citrate were disturbed in both PWE and epileptic models, which might be potential biomarkers of epilepsy. Metabolic pathways including alanine, aspartate and glutamate metabolism; glycine, serine and threonine metabolism; glycerophospholipid metabolism; glyoxylate and dicarboxylate metabolism; and arginine and proline metabolism were involved in epilepsy. These pathways might play important roles in the pathogenesis of the disease. This review summarizes metabolites and metabolic pathways related to epilepsy and provides a novel perspective for the identification of potential biomarkers and therapeutic targets for epilepsy.

Generation and Characterization of the Drosophila melanogaster paralytic Gene Knock-Out as a Model for Dravet Syndrome

Dravet syndrome is a severe rare epileptic disease caused by mutations in the SCN1A gene coding for the Nav1.1 protein, a voltage-gated sodium channel alpha subunit. We have made a knock-out of the paralytic gene, the single Drosophila melanogaster gene encoding this type of protein, by homologous recombination. These flies showed a heat-induced seizing phenotype, and sudden death in long term seizures. In addition to seizures, neuromuscular alterations were observed in climbing, flight, and walking tests. Moreover, they also manifested some cognitive alterations, such as anxiety and problems in learning. Electrophysiological analyses from larval motor neurons showed a decrease in cell capacitance and membrane excitability, while persistent sodium current increased. To detect alterations in metabolism, we performed an NMR metabolomic profiling of heads, which revealed higher levels in some amino acids, succinate, and lactate; and also an increase in the abundance of GABA, which is the main neurotransmitter implicated in Dravet syndrome. All these changes in the paralytic knock-out flies indicate that this is a good model for epilepsy and specifically for Dravet syndrome. This model could be a new tool to understand the pathophysiology of the disease and to find biomarkers, genetic modifiers and new treatments.

Metabolic Changes in Rat Plasma After Epilepsy by UPLC-MS/MS

Introduction:

Epilepsy is one of the most common neurological diseases in clinical practice. The combined application of metabolomics technology plays a great advantage in the screening of biomarkers.

Methods:

In this study, Wistar rats were used as experimental subjects to model intractable epilepsy and to detect the metabolic changes of small molecules in plasma. UPLC-MS/MS was used to determine the small molecules in rat plasma. UPLC HSS C18 (2.1mm×100mm, 1.7 μm) column was used for separation, column temperature of 40°C. The initial mobile phase was acetonitrile -0.3% formic acid with gradient elution, the flow rate was 0.3 mL/min, total running time 4.0 min. Quantitative analysis was performed with multi-response monitoring (MRM).

Results:

Compared to the control group, the L-Alanine and L-Arginine decreased in the Epilepsy group (p<0.05); while Cytosine, Adenosine, L-Tyrosine, Citric acid, Fructose increased (p<0.05).

Conclusion:

In the screening of epilepsy biomarkers using metabolomics, various amino acids that lead to increased energy production and neurotransmitter imbalance play an important role in epileptic seizures.

The First Metabolome Analysis in Children with Epilepsy and ALG13-CDG Resulting from c.320A>G Variant

BACKGROUND

ALG13-CDG belongs to the congenital disorders of glycosylation (CDG), which is an expanding group of multisystemic metabolic disorders caused by the N-linked, O-linked oligosaccharides, shared substrates, glycophosphatidylinositol (GPI) anchors, and dolichols pathways with high genetic heterogeneity. Thus, as far as clinical presentation, laboratory findings, and treatment are concerned, many questions are to be answered. Three individuals presented here may serve as a good example of clinical heterogeneity. This manuscript describes the first metabolomic analysis using NMR in three patients with epileptic encephalopathy due to the recurrent c.320A>G variant in ALG13, characterized to date only in about 60 individuals (mostly female). This is an important preliminary step in the understanding of the pathogenesis of the disease associated with this variant in the rare genetic condition. The disease is assumed to be a disorder of N-glycosylation given that this is the only known function of the ALG13 protein. Despite this, protein electrophoresis, which is abnormal in most conditions due to abnormalities in N-glycosylation, has been normal or only mildly abnormal in the ALG13 patients.

METHODS

Nuclear magnetic resonance (NMR) spectroscopy in conjunction with multivariate and univariate modelling were used to analyze the metabolic profile of the blood serum samples acquired from the studied patients.

RESULTS

Three metabolites were identified as potential biomarkers: betaine, N-acetyl-glycoprotein, and carnitine.

CONCLUSIONS

Since presented data are the first to be collected so far, they need be verified in further studies. Our intention was to turn attention toward possible CDG-ALG13 laboratory markers that would have clinical significance.

CE-MS metabolic profiling of volume-restricted plasma samples from an acute mouse model for epileptic seizures to discover potentially involved metabolomic features

Currently, a high variety of analytical techniques to perform metabolomics is available. One of these techniques is capillary electrophoresis coupled to mass spectrometry (CE-MS), which has emerged as a rather strong analytical technique for profiling polar and charged compounds. This work aims to discover with CE-MS potential metabolic consequences of evoked seizures in plasma by using a 6Hz acute corneal seizure mouse model. CE-MS is an appealing technique because of its capability to handle very small sample volumes, such as the 10 μL plasma samples obtained using capillary microsampling in this study. After liquid-liquid extraction, the samples were analyzed with CE-MS using low-pH separation conditions, followed by data analysis and biomarker identification. Both electrically induced seizures showed decreased values of methionine, lysine, glycine, phenylalanine, citrulline, 3-methyladenine and histidine in mice plasma. However, a second provoked seizure, 13 days later, showed a less pronounced decrease of the mean concentrations of these plasma metabolites, demonstrated by higher fold change ratios. Other obtained markers that can be related to seizure activities based on literature data, are isoleucine, serine, proline, tryptophan, alanine, arginine, valine and asparagine. Most amino acids showed relatively stable plasma concentrations between the basal levels (Time point 1) and after the 13-day wash-out period (Time point 3), which suggests its effectiveness. Overall, this work clearly demonstrated the possibility of profiling metabolite consequences related to seizure activities of an intrinsically low amount of body fluid using CE-MS. It would be useful to investigate and validate, in the future, the known and unknown metabolites in different animal models as well as in humans.

Oxidative Stress, a Crossroad Between Rare Diseases and Neurodegeneration

Oxidative stress is an imbalance between production and accumulation of oxygen reactive species and/or reactive nitrogen species in cells and tissues, and the capacity of detoxifying these products, using enzymatic and non-enzymatic components, such as glutathione. Oxidative stress plays roles in several pathological processes in the nervous system, such as neurotoxicity, neuroinflammation, ischemic stroke, and neurodegeneration. The concepts of oxidative stress and rare diseases were formulated in the eighties, and since then, the link between them has not stopped growing. The present review aims to expand knowledge in the pathological processes associated with oxidative stress underlying some groups of rare diseases: Friedreich’s ataxia, diseases with neurodegeneration with brain iron accumulation, Charcot-Marie-Tooth as an example of rare neuromuscular disorders, inherited retinal dystrophies, progressive myoclonus epilepsies, and pediatric drug-resistant epilepsies. Despite the discrimination between cause and effect may not be easy on many occasions, all these conditions are Mendelian rare diseases that share oxidative stress as a common factor, and this may represent a potential target for therapies.