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

Abstract

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.

Keywords: Acorus tatarinowii Schott; Gamma-aminobutyrate transaminase; epilepsy; metabolomics; plasma.

Figure 1

Network pharmacology analysis. (A) PPI network and topological analysis diagram of target points for treating epilepsy with ATS; (B) bubble plot of KEGG enrichment analysis.

Figure 2

Pearson correlation analysis of QC samples. (A) Correlation between positive QC samples; (B) correlation between negative QC samples.

Figure 3

PCA score plots in different ion modes. (A) Positive PCA score plots and (B) negative PCA score plots.

Figure 4

OPLS-DA score plots in different ion modes. (A) Positive OPLS-DA score plots and (B) negative OPLS-DA score plots.

Figure 5

Permutation test results of OPLS-DA model. (A) Positive scatter plots for control group vs. model group; (B) positive scatter plots for model group vs. ATS group; (C) negative scatter plots for control group vs. model group; and (D) negative scatter plots for model group vs. ATS group.

Figure 6

Volcano plots of differential metabolites in the OPLS-DA model. (A) Positive volcano plot and (B) negative volcano plot.

Figure 7

Metabolic pathway enrichment analysis of differential metabolites.

Figure 8

Inhibition curve of ATS on GABA-T (n = 3).