GC-MS Metabolomics Of Protective Activities Of Phytoestrogens On PC 12 Cell Induced By Aβ_25-35

Alzheimer’s disease(AD) is associated with severe cognitive impairment and degeneration of specific vulnerable neurons. Multiple neurotransmitter systems are believed to be involved in the cognitive decline of AD patients, hallmarked by cholinergic neuronal dysfunction and loss. The majority of AD cases are sporadic in origin, and the exact causes of sporadic AD are currently unknown. From the epidemiology studies, Women are 2 times more likely to develop AD than men. This highlights the prospect that estrogens may participate in normal brain maintenance and that estrogen deprivation plays a role in age-related neurodegenerative diseases. Phytoestrogen derived from natural plants. Retrospective studies have shown an inverse, dose and duration dependent correlation between estrogen replacement therapy(ERT) and incidence of AD, suggesting a role for estrogens as therapy for preventing the onset of AD symptoms. Researcher has recently demonstrated that ERT delays the onset of AD and that this effect is independent of ethnic origin. Moreover, small, open clinical trials of ERT support the usefulness of ERT in improving memory/cognitive function in AD patients. Kaempferol and naringin have the same effect of phytoestrogen. The experimental results of present study support the neuroprotection of kaempferol and naringin and provide a new strategy of preventing and curing AD.ObjectiveUsing Aβ25-35 incubated PC12 cells to establish AD in vitro cell model. The metabonomic studies of PC12 cells after administration are conducted based on the GC-MS metabonomic research platform.Analysis PC12 cell endogenous metabolitesand identify potential biomarkers, to clarify the protection mechanism of kaempferol and naringin.Methods1. PC12 cell model of AD was established, after comparison of Aβ25-35 concentration and the time of hatching.2. Used the group of donepezil hydrochloride and 17β-estradiol as contrast group to explored the protection of flavonoids of rhizome drynariae to PC12 which were damaged by Aβ25-35.3. PC12 GC-MS cell lines analysis method was established after silylation column derivatization, quenching, crushing, extracting agent, oximation conditions, d e r i v a t i z a t i o n. T h e s t a b l e m e t a b o l i t e p r o f i l e o f P C 1 2 w a s o b t a i n e d.4. Established a stable metabolic profile by GC-MS analysis method, used NIST05 a standard MS library search, embedding peaks analysis of PC12 samples accurate qualitative analysis of metabolites in a sample. The mass spectra obtained by MZmine were transformed into the available data, and the endogenous metabolites were identified by PCA and PLS-DA analysis, and the potential biomarkers were identified by SIMCA-P12.0. T-test analysis of the relative peak area of potential biomarkers by SPSS11.5, observed of potential control group, model group and drug intervention group biomarkers comparison of change in trend. By biochemical and bioinformatics database of metabolites difference bioinformatic analysis, researched for differences in the metabolic pathway metabolites, and explained the relationship between AD and kaempferol and naringin.Results1. Compared with the control group, PC12 cells injuried by 20 μM Aβ25-35(P< 0.01) for 24 h were used as AD cell model.2. Compared with the model group( Aβ injuried group), 10-1μM kaempferol(P < 0.01) and 10-1μM naringin(P < 0.01) had obvious protective effection.3. By comparing the peak area and the peak number, the best method of sample treatment is, the cell were quenched in liquid nitrogen.The extraction agent was 1.5m L acetonitrile water(4:1, V/V), and the broken condition was broken by ultrasonic cell. 524 metabolites were detected.4. GC-MS analysis method was established to stabilize the metabolite profile of PC12 cell, using SIMCA-P 12.0 PCA pattern recognition and PLS-DA analysis of endogenous metabolites. 27 potential biomarkers were identified: Glycine, Acetamide, Lactic acid, L-alanine, L-valine, Phosphate, Amino malonic acid, Proline, D-malic acid, L-serine, Phenylalanine, Keto glutarate, 1,4-butyl-diamine, Citric acid, 3-carboxylic acid, Leucine, L-threonine, Inositol and Inositol, Acetic acid, Stearic acid, Cholesterol, Palmitic acid, Uridine, D-glucopyranose,Oleic acid amide, Ribose phosphate. The metabolic pathways of these 27 potential biomarkers are as follows:(1) glycine, serine, threonine metabolism;(2) alanine, aspartate and glutamate metabolism;(3) fatty acid metabolism;(4) phenylalanine metabolism;(5) valine, leucine and isoleucine biosynthesis;(6) galactose metabolism;(7) inositol phosphate metabolism;(8) arginine and proline metabolism.Conclusions Methodology this study was based on the basis of GC-MS metabolomics, through to control group, model group and drug intervention group, combined with multivariate statistical analysis, AD screening potential biomarkers by in-depth analysis of the metabolic pathways.To investigate the role of protection mechanism of kaempferol and naringin, which provides a theoretical basis for the treatment of AD.