INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
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2025
·Vol. 26
·期 10
·4578
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IF 4.9
Yaping Chen,Qiuqi Wen,Bing Yang,Liang Feng,Xiaobin Jia
Traditional Chinese Medicine (TCM) is recognized for its complex composition and multiple therapeutic targets. Current pharmacological research often concentrates on extracts or individual components. The former approach faces numerous challenges, whereas the latter oversimplifies and disregards the synergistic effects of TCM components. This study aimed to address this limitation by evaluating the therapeutic efficacy and mechanisms of Huang-Qi San (HQS) active constituent (AC) against type 2 diabetes (T2DM). Active components of HQS were identified using network pharmacology and spectrum–effect correlation analysis. The reconstituted AC group was assessed both in vitro (for glucose consumption and glycogen synthesis) and in vivo (in T2DM mice), with metabolomics and molecular docking techniques used to elucidate the underlying mechanisms. Eight components exhibiting a correlation degree greater than 0.85 were identified as the representative components of HQS intervention for T2DM. These eight components were then mixed in equal proportions to produce AC. The AC group demonstrated increased glucose uptake and glycogen synthesis in vitro, surpassing both the HQS extract and individual components. In diabetic mice, AC significantly increased the insulin sensitivity, outperforming the HQS extract and matching the efficacy of metformin. Metabolomics analysis identified pentose and glucuronic acid interconversion as a critical metabolic pathway, with strong binding affinity (less than −15 kJ/mol) between AC and key enzymes. This research further substantiates the scientific validity and feasibility of emphasizing active constituents in the evaluation of TCM efficacy. Additionally, it provides a scientific foundation for the clinical application of HQS. Most importantly, this study serves as a demonstration of the development of new TCM drugs characterized by clear ingredients, safety, and effectiveness.
研究领域:
中药学
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药理学
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代谢生物学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
CHEMICO-BIOLOGICAL INTERACTIONS
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2016
·
IF 2.618
Xia Wu,Jian-Cheng Zhu,Yu Zhang,Wei-Min Li,Xiang-Lu Rong,Yi-Fan Feng
Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes.
研究领域:
代谢组学
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中医药学
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生物信息学
✓ 已验证
DOI
高影响因子
BIOMEDICINE & PHARMACOTHERAPY
·
2020
·
IF 4.545
Yan Li,Chunyi Wang,Yu Jin,Haibo Chen,Meiqun Cao,Weimin Li,Huanmin Luo,Zhengzhi Wu
Numerous researches supported that non-alcoholic fatty liver disease (NAFLD) was an emerging problem associated with increased visceral adiposity (obesity), diabetes and related metabolic disorders. Huang-Qi San (HQS) is composed of three traditional Chinese medicines (Astragali Radix, Pueraria Radix and Cortex Mori Radicis) with a weight ratio of 1:2:1. HQS has been reported to be effective in improving glucose-lipid metabolism, but its underlying mechanism on NAFLD has not been fully understood. The purpose of the present study was to assess the protective effects of HQS on obesity-induced hepatic steatosis in rats fed with high fat diet (HFD). Our data revealed that administration of HQS (1.2 and 2.4 g/kg body weight) resulted in significant reduction in body weight (BW) and organs coefficients of visceral fat. The full-Body CT scan demonstrated that HQS reduced liver fat ratio, visceral and subcutaneous fat mass in a dose-dependent manner. Additionally, HQS decreased plasma TC, TG, FFA and FABP4 levels, normalized glucose and insulin levels, and improved the glucose tolerance. Pathological examination showed that HQS alleviated hepatic steatosis and reduced the cell size of epididymal visceral adipose tissue. Hepatic lipid accumulation was also reduced by HQS treatment compared with HFD fed rats. RNA-Seq analysis combining with qPCR demonstrated that the mRNA expression of some important glucose and lipid metabolism-related genes including Acat2, Apoc4, Bhmt, Cyp3a62, Cyp51, Egln3 (Phd3), Fads1, Fads2, Gnmt, Hmgcs1 and Pemt, were significantly changed by HQS treatment. Taken together, these results suggested that HQS had beneficial effects on glucose-lipid metabolism and hepatic steatosis, and its mechanism might be related to the functions of the genes in regulating glucose and lipid metabolism.
研究领域:
药理学
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营养学
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代谢学
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分子生物学
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基因组学
✓ 已验证
DOI
PubMed
高影响因子
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