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OBJECTIVE To investigate the autophagic effect of the compounds from the Chinese medicinal herbs, Radix polygalae as a potential neuroprotective agent that enhances the clearance of mutant Huntingtin and α- synuclein in PC- 12 cells. METHODS Radix polygalae was extracted with 75% ethanol using refluxing method, and its quality was assayed by UHPLC-TOF-MS. The autophagic effect of Radix polygalae extract, and its major components including polygalacic acid, senegenin and onjisaponin B were investigated using the green fluorescent protein-light chain 3 (GFP-LC3) autophagy detection and Western blot platforms for detecting the autophagic markers, GFP-LC3 puncta formation and LC3 II expression. The degradation of A53T α- synuclein and the inhibition ofα-synuclein oligo merization related to the Parkinson disease were assayed using Western blot and flow cytometer analysis, respectively. While the cytotoxicity and the degradation of the mutant Huntingtin associated with the Huntingtin disease were investigated using MTT method and flow cytometer analysis. RESULTS Radix polygalae ethanol extract and onjisaponin B improved the GFP-LC3 puncta formation and expression of LC3Ⅱ with time and dose manner, and this induction was activated via AMPK-mTOR and Atg 7 gene pathway. Furthermore, the clearance of α-synuclein and mutant Huntingtin was enhanced via autophagy induction with the treatment of Radix polygalae ethanol extract and onjisaponin B. CONCLUSION Findings in the current study provide detailed insights into the protective mechanism of a novel autophagy inducer, onjisaponin B, which is valuable for further investigation as a new candidate agent for modulating neurodegenerative disorders through the reduction of toxicity and clearance of mutant proteins in the cellular level.
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@#The purpose of this research was to explore the differences of the components of Radix Polygalae in herbal pair of Radix Polygalae and Rhizoma Acori Tatarinowii. An HPLC-Q-TOF-MS/MS method and an HPLC-UV method were established for the identification and determination of the components of Radix Polygalae, respectively. HPLC separation was carried out on a C18 column(250 mm×4. 6 mm, 5 μm)with linear gradient elution using a mobile phase consisting of acetonitrile and 0. 1% formic acid aqueous solution. Mass spectrometry with ESI source was performed in the positive ion mode to scan MS data including total ion chromatograms and ion peaks of Radix Polygalae. Eight components including 3, 4, 5-trimethoxycinnamic acid, p-methoxycinnamic acid, tenuifolin, sibiricose A5, polygalaxanthone III, tenuifoliside B, 3, 6′-disinapoly sucrose, and tenuifoliside A were identified according to the reference substance retention time, MS data and literatures. There was no significant variation found in the contents of eight chemical constituents of Radix Polygalae. The qualification and quantitation study of the components in herbal pair of Radix Polygalae and Rhizoma Acori Tatarinowii provide the methodological basis for compatibility mechanism exploration in vivo.
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OBJECTIVE:To analyze the effects of environmental factors (soil,climate,etc.) on the quality of Radix Polygalae.METHODS:The contents of polygalic acid were determined by HPLC.Climate factors including altitude,average annual temperature,average temperature of July,annual precipitation,annual sunshine hours and non-frost days were collected through on-site inspection or from the local meteorological data. Water content,pH and nutritional elements of the soils were determined by routine method,and the reletionship between the content of polygalic acid and the factors were analyzed.RESULTS:There was linear relationship between the content of polygalic acid and climate factors such as average temperature of July,annual precipitation,annual sunshine hours and non-frost days.There was also linear relationship between polygalic acid and soil factors such as total nitrogen,total phosphor,available phosphor,available kalium,organic matters and water content. CONCLUSION:The main climate factors that affect polygalic acid content were annual sunshine hours and non-frost days,while the main soil factors affecting the content of polygalic acid were the content of available phosphor and soil water content.
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Objective To establish HPLC-UV fingerprints of genuine Radix Polygalae from Hebei Province and get the control fingerprint.To compare the fingerprints of genuine Radix Polygalae collected from different habitats with the control fingerprint so as to establish a specific method for the quality con- trol of genuine Radix Polygalae.Methods The fingerprints of 19 batches of genuine Radix Polygalae were obtained from Waters 1525 pump.The chromatographic procedure was carried out with Diamonsil~(TM) C_(18)(250 mm)?4.6 mm,5?m)as an analytic column and a mixture consisting of acetonitrile and 0.2% formic acid in gradient as mobile phase.The detection wavelength was 316 nm.The flow rate was 1.0 mL/min.The temperature of column was 35 C.Results The control fingerprint of HPLC-UV was set up.The fingerprints of genuine Radix Polygalae from different habitats were compared.Conclusion The operation of this method is simple,quick,accurate,and could be used for the identification and quality control of genuine Radix Polygalae.
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AIM: To optimize the process of extracting effective constituents from Radix Polygalae by central composite design/response surface methodology. METHODS: Independent variables were ethanol concentration,reflux time and solvent fold,dependent variable was extraction rate of senegenin in Radix Polygalae.Linear or nolinear mathematic models were used to estimate the relationship between independent and dependent variables.Response surface methodology was used to optimize the process of extraction.Prediction was carried out through comparing the observed and predicted values. RESULTS: Regression coefficients of binomial fitting complex model was as high as 0.979 0, the optimum conditions of extraction process were 60% ethanol,2.5 hours for reflux,10-fold solvent and 2 times for extraction.Bias between observed and predicted values was-5.93%. CONCLUSION: It shows that the optimum model is highly predictive.