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A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
Published on: November 13, 2016
Identifying disulfidptosis-related biomarkers in epilepsy based on integrated bioinformatics and experimental
Sijun Li1, Lanfeng Sun1, Hongmi Huang1
1Department of Neurology, the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China.
Abstract:
One of the underlying mechanisms of epilepsy (EP), a brain disease characterized by recurrent seizures, is considered to be cell death. Disulfidptosis, a proposed novel cell death mechanism, is thought to play a part in the pathogenesis of epilepsy, but the exact role is unclear. The gene expression omnibus series (GSE) 33000 and GSE63808 datasets were used to search for differentially expressed disulfidptosis-related molecules (DE-DRMs). A correlation between the DE-DRMs was discovered. Individuals with epilepsy were then used to investigate molecular clusters based on the expression of DE-DRMs. Following that, the best machine learning model which is validated by GSE143272 dataset and predictor molecules were identified. The correlation between predictive molecules and clinical traits was determined. Based on the in vitro and in vivo seizures models, experimental analyses were applied to verify the DE-DRMs expressions and the correlation between them. Nine molecules were identified as DE-DRMs: glycogen synthase 1 (GYS1), solute carrier family 3 member 2 (SLC3A2), solute carrier family 7 member 11 (SLC7A11), NADH:ubiquinone oxidoreductase core subunit S1 (NDUFS1), 3-oxoacyl-ACP synthase, mitochondrial (OXSM), leucine rich pentatricopeptide repeat containing (LRPPRC), NADH:ubiquinone oxidoreductase subunit A11 (NDUFA11), NUBP iron‑sulfur cluster assembly factor, mitochondrial (NUBPL), and NCK associated protein 1 (NCKAP1). NDUFS1 interacted with NDUFA11, NUBPL, and LRPPRC, while SLC3A2 interacted with SLC7A11. The optimal machine learning model was revealed to be the random forest (RF) model. G protein guanine nucleotide-binding protein alpha subunit q (GNAQ) was linked to sodium valproate resistance. The experimental analyses suggested an upregulated SLC7A11 expression, an increased number of formed SLC3A2 and SLC7A11 complexes, and a decreased number of formed NDUFS1 and NDUFA11 complexes. This study provides previously undocumented evidence of the relationship between disulfidptosis and EP. In addition to suggesting that SLC7A11 may be a specific DRM for EP, this research demonstrates the alterations in two disulfidptosis-related protein complexes: SLC7A11-SLC3A2 and NDUFS1-NDUFA11.
Insights
This study identifies nine key molecules involved in disulfidptosis, a cell death process linked to epilepsy. Findings reveal specific molecular changes and a machine learning model for predicting epilepsy, highlighting SLC7A11 as a potential biomarker.
Area of Science:
- Neuroscience
- Cell Biology
- Genetics
Background:
- Epilepsy (EP) is a brain disorder characterized by recurrent seizures, with cell death implicated in its pathogenesis.
- Disulfidptosis is a novel cell death mechanism potentially involved in epilepsy, but its precise role remains unclear.
- Identifying molecular mechanisms underlying epilepsy is crucial for developing targeted therapies.
Purpose of the Study:
- To investigate the role of disulfidptosis-related molecules (DRMs) in the pathogenesis of epilepsy.
- To identify differentially expressed DRMs (DE-DRMs) in epilepsy using gene expression datasets.
- To develop a predictive model for epilepsy based on DE-DRM expression.
Main Methods:
- Analysis of Gene Expression Omnibus (GEO) datasets (GSE33000, GSE63808, GSE143272) to identify DE-DRMs.
- Correlation analysis and molecular clustering based on DE-DRM expression in epilepsy patients.
- Machine learning model development (Random Forest) and validation using independent datasets.
- Experimental verification of DE-DRM expression and interactions in in vitro and in vivo seizure models.
Main Results:
- Nine DE-DRMs were identified: GYS1, SLC3A2, SLC7A11, NDUFS1, OXSM, LRPPRC, NDUFA11, NUBPL, and NCKAP1.
- Specific interactions were found between NDUFS1-NDUFA11-NUBPL-LRPPRC and SLC3A2-SLC7A11.
- The Random Forest model demonstrated optimal predictive performance for epilepsy.
- GNAQ was associated with sodium valproate resistance.
- Experimental validation confirmed upregulated SLC7A11, increased SLC3A2-SLC7A11 complexes, and decreased NDUFS1-NDUFA11 complexes.
Conclusions:
- This study provides novel evidence linking disulfidptosis to epilepsy pathogenesis.
- SLC7A11 is proposed as a specific DRM for epilepsy.
- Alterations in the SLC7A11-SLC3A2 and NDUFS1-NDUFA11 protein complexes are associated with epilepsy.
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