Long-Term Alcohol-Induced Activation of Mammalian Target of Rapamycin is a Key Risk Factor of Epilepsy.

BACKGROUND The aim of this study was to determine whether activation of mammalian target of rapamycin (mTOR) is a key epileptogenic mechanism in the development of alcohol-related seizure. MATERIAL AND METHODS C57BL/6 mice were administered 10% ethanol in drinking water for 9 weeks. Video-electroencephalography (EEG) monitoring was then used to assess seizure frequency after alcohol and rapamycin treatment. In addition, mouse neuroblastoma NG108-15 cells were treated ethanol for 3 days and subsequently treated with AKT inhibitor LY294002 for 2-12 h. The in vitro kinase assay was performed for determining mTOR activity. Western blot analysis was used to determine the expression of P-AKT, P-S6K, and P-S6. RESULTS Long-term ethanol treatment markedly increased the seizure frequency of C57/BL6 mice over time. Moreover, ethanol treatment increased the expression level of P-S6 over time. Ethanol-induced seizure can be reversed by rapamycin. In addition, the in vitro kinase assay showed mTOR activity was activated by ethanol. Compared with NG108-15 cells treated without both ethanol and LY294002, ethanol increased the expression level of P-AKT, P-S6K, and P-S6, whereas LY294002 had opposite effects on expression levels of these proteins. CONCLUSIONS Our findings indicate that long-term alcohol intake increases the risk of epilepsy via activation of mTOR signaling. Moreover, ethanol-induced mTOR activation may be dependent on the AKT-mTOR signaling pathway. The key molecules involved in AKT-mTOR signaling pathway may serve as potential targets in the treatment of epilepsy.

Relationship Between the ApoE Gene Polymorphism and Type 2 Diabetes Mellitus Complications.

Background: Apolipoprotein E (ApoE) polymorphisms are associated with type 2 diabetes mellitus (T2DM) and its complications, but studies have shown conflicting results. Objective: To examine the relationship of ApoE gene polymorphisms with T2DM and its complications. Materials and Methods: This case-control study of patients with T2DM was conducted between June 2016 and July 2019. Healthy individuals were recruited as controls. The patients were grouped according to coronary heart disease (CHD), cerebral infarction (CI), diabetic nephropathy (DN), and neurological complications. The ApoE genotype was determined using a commercial gene chip. Results: Compared with controls, the frequencies of genotype ɛ3/4 (20.8% vs. 11.7%, p = 0.04) and allele ɛ4 (14.3% vs. 8.3%, p = 0.03) of patients with T2DM were higher. The frequency of genotype ɛ3/4 was higher in the T2DM with CHD group (30.4% vs. 17.4%, p = 0.01 vs. non-CHD) and in T2DM with CI (29.2% vs. 18.1%, p = 0.045 vs. non-CI). The frequency of genotype ɛ2/3 was higher in the T2DM with DN group (19.3% vs. 9.1%, p = 0.01 vs. non-DN). There were no significant differences between T2DM with and without neuropathy (p > 0.05). Conclusion: The ApoE allele ɛ4 may be a risk factor for T2DM, CHD in T2DM, and CI in T2DM, while the ApoE allele ɛ2 may be a risk factor for DN.

Automics: an integrated platform for NMR-based metabonomics spectral processing and data analysis.

BACKGROUND: Spectral processing and post-experimental data analysis are the major tasks in NMR-based metabonomics studies. While there are commercial and free licensed software tools available to assist these tasks, researchers usually have to use multiple software packages for their studies because software packages generally focus on specific tasks. It would be beneficial to have a highly integrated platform, in which these tasks can be completed within one package. Moreover, with open source architecture, newly proposed algorithms or methods for spectral processing and data analysis can be implemented much more easily and accessed freely by the public. RESULTS: In this paper, we report an open source software tool, Automics, which is specifically designed for NMR-based metabonomics studies. Automics is a highly integrated platform that provides functions covering almost all the stages of NMR-based metabonomics studies. Automics provides high throughput automatic modules with most recently proposed algorithms and powerful manual modules for 1D NMR spectral processing. In addition to spectral processing functions, powerful features for data organization, data pre-processing, and data analysis have been implemented. Nine statistical methods can be applied to analyses including: feature selection (Fisher's criterion), data reduction (PCA, LDA, ULDA), unsupervised clustering (K-Mean) and supervised regression and classification (PLS/PLS-DA, KNN, SIMCA, SVM). Moreover, Automics has a user-friendly graphical interface for visualizing NMR spectra and data analysis results. The functional ability of Automics is demonstrated with an analysis of a type 2 diabetes metabolic profile. CONCLUSION: Automics facilitates high throughput 1D NMR spectral processing and high dimensional data analysis for NMR-based metabonomics applications. Using Automics, users can complete spectral processing and data analysis within one software package in most cases. Moreover, with its open source architecture, interested researchers can further develop and extend this software based on the existing infrastructure.