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Article(id=1210147813531447472, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1210147807885923054, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2022-0092, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1642435200000, receivedDateStr=2022-01-18, revisedDate=1647878400000, revisedDateStr=2022-03-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1766451322203, onlineDateStr=2025-12-23, pubDate=1652284800000, pubDateStr=2022-05-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1766451322203, onlineIssueDateStr=2025-12-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766451322203, creator=13701087609, updateTime=1766451322203, updator=13701087609, issue=Issue{id=1210147807885923054, tenantId=1146029695717560320, journalId=1189982191388893191, year='2022', volume='57', issue='5', pageStart='1219', pageEnd='1540', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766451320859, creator=13701087609, updateTime=1766451433476, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1210148280286179842, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1210147807885923054, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1210148280286179843, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1210147807885923054, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1387, endPage=1395, ext={EN=ArticleExt(id=1210147814785544413, articleId=1210147813531447472, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=A proposed mechanism by which Qishen Yiqi dropping pill improves cardiac energy metabolism in rats with heart failure based on metabolomics and network pharmacology, columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

"Omics" and bioinformatics have brought new ideas to the study of traditional Chinese medicine. This study used metabonomics and network pharmacology to investigate the pharmacodynamic basis and regulation of Qishen Yiqi dropping pill (QDP) improving cardiac energy metabolism in rats with heart failure (HF). 1H NMR metabonomics analysis showed that eight metabolites, including carnitine, glutamine, creatine, proline, homocitrulline, lactic acid, taurine and alanine appeared significant callback after QDP treatment for HF. The results indicate that QDP regulates the metabolism of carbohydrate, lipid, ATP and protein. The animal experiment was conducted in accordance with the regulations of the Ethics Committee for Experimental Animal Management and Animal Welfare of Institute of Materia Medica, Chinese Academy of Medical Sciences. A "drug-component-target-disease" network was established using network pharmacology, and the "component-target" sub-network related to the above energy metabolism processes was extracted by combining metabonomics results. Results revealed 79 chemical compounds and 47 potential targets of QDP involved in the regulation of energy metabolism, and identified key chemical components including ursolic acid, notoginsenoside G, ginsenoside-Rh1, and core targets such as INS, PPARG, and AKT1. The results also demonstrated the complex multi-target and multi-component relationship between QDP and HF from the perspective of energy metabolism. The molecular docking technique verified a strong interaction between some targets and chemical compounds, with affinities less than -5 kcal·mol-1. The results of this study provide useful information for the clinical application, development, and utilization of QDP.

, correspAuthors=Ying-hong WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2022 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Xue-qi LÜ, Cong-cong GUO, Yue XU, Xiang-ju JIN, Ying-hong WANG), CN=ArticleExt(id=1210147819877429927, articleId=1210147813531447472, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=基于代谢组学和网络药理学方法探讨芪参益气滴丸改善心衰大鼠心脏能量代谢的作用机制, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

组学与生物信息学为中药机制研究带来了新的思路。本研究采用代谢组学和网络药理学联合策略探讨芪参益气滴丸(Qishen Yiqi dropping pill, QDP) 改善心力衰竭(heart failure, HF) 大鼠心脏能量代谢的药效物质基础及网络调控机制。1H NMR代谢组学分析从HF大鼠心脏组织中发现肉碱、谷氨酰胺、肌酸、脯氨酸、高瓜氨酸、乳酸、牛磺酸和丙氨酸等8个代谢物在QDP治疗后显著回调, 表明QDP调控糖、脂、ATP和蛋白质代谢等过程。动物实验操作均遵循中国医学科学院药物研究所实验动物管理与动物福利伦理委员会的规定。利用网络药理学建立“药物-成分-靶点-疾病”网络, 并结合代谢组学结果提取与上述代谢过程相关的“成分-靶点”子网络, 显示了QDP调节能量代谢潜在的47个作用靶点和79个化学成分, 提炼了熊果酸、三七皂苷、人参皂苷等关键化学成分与INS、PPARG、AKT1等核心靶点, 同时也从能量代谢的角度展示了QDP与HF之间多靶点、多成分的复杂作用关系。分子对接技术验证了部分靶点与化学成分良好的相互作用, 其结合能均小于-5 kcal·mol-1。本研究结果为QDP临床应用及方剂开发利用提供了有用信息。

, correspAuthors=王映红, authorNote=null, correspAuthorsNote=
*王映红, Tel: 86-10-63165216, E-mail:
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Circ Res, 2016, 118: 1151-1169., articleTitle=Insulin signaling and heart failure, refAbstract=null)], funds=[Fund(id=1210147828274425979, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, awardId=2018ZX09711001-002-004, language=CN, fundingSource=国家科技重大专项项目(2018ZX09711001-002-004), fundOrder=null, country=null), Fund(id=1210147828366700672, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, awardId=2016-I2M-1-009, language=CN, fundingSource=中国医学科学院医学与健康科技创新工程项目(2016-I2M-1-009), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1210147820120699586, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, xref=null, ext=[AuthorCompanyExt(id=1210147820129088199, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, companyId=1210147820120699586, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China), AuthorCompanyExt(id=1210147820137476805, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, companyId=1210147820120699586, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=中国医学科学院、北京协和医学院药物研究所, 天然药物活性物质与功能国家重点实验室, 北京 100050)])], figs=[ArticleFig(id=1210147824398889931, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=UAoC4sj0DE5HJI0IDFDicA==, figureFileBig=m8ExQldoGNCYG1WkarOH+A==, tableContent=null), ArticleFig(id=1210147824507941847, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Figure 1, caption= Echocardiographic determination of cardiac function, as assessed by left ventricular ejection fraction (LVEF, A), fractional shortening (FS, B) and cardiac output (CO, C). S: Sham group; M: Model group; QDP: QDP-treated group (270 mg·kg<sup>-1</sup>·day<sup>-1</sup>); V: Valsartan-treated group (30 mg·kg<sup>-1</sup>·day<sup>-1</sup>); BA: Before administration; AA: After administration. <i>n</i> = 8, <span class="mag-xml-inline-formula">$ \bar{x} $</span> ± <i>s</i>. <sup>#</sup><i>P</i> < 0.05, <sup>####</sup> <i>P</i> < 0.000 1 <i>vs</i> S group; <sup>*</sup><i>P</i> < 0.05, <sup>***</sup><i>P</i> < 0.001, <sup>****</sup><i>P</i> < 0.000 1 <i>vs</i> M group , figureFileSmall=UAoC4sj0DE5HJI0IDFDicA==, figureFileBig=m8ExQldoGNCYG1WkarOH+A==, tableContent=null), ArticleFig(id=1210147824755405796, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=Z97UE/66SGqcJPdhfDWZaw==, figureFileBig=qL5gYYqzcrEi76MAWipqGw==, tableContent=null), ArticleFig(id=1210147824868652011, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Figure 2, caption= Multivariate analysis of NMR metabonomics data (<i>n</i> = 8). A: PLS-DA score plot of S, M and QDP, <i>R</i><sup><i>2</i></sup><i>X</i> = 0.547, <i>R</i><sup><i>2</i></sup><i>X</i> = 0.922, <i>Q</i><sup><i>2</i></sup> = 0.835; B: PLS-DA score plot of M and QDP, <i>R</i><sup><i>2</i></sup><i>X</i> = 0.51, <i>R</i><sup><i>2</i></sup><i>X</i> = 0.817, <i>Q</i><sup><i>2</i></sup> = 0.635; C: PLS-DA model verification of M and QDP; D: VIP value of variable in OPLS-DA model of M and QDP , figureFileSmall=Z97UE/66SGqcJPdhfDWZaw==, figureFileBig=qL5gYYqzcrEi76MAWipqGw==, tableContent=null), ArticleFig(id=1210147824973509613, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=WYtEzGTX2X5UKP9whXRSWg==, figureFileBig=l9qdG14DQvBvNv3XwLatlQ==, tableContent=null), ArticleFig(id=1210147825099338742, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Figure 3, caption= Results of metabonomics analysis of rat heart tissue. A: Differential metabolites in heart samples between S, M, QDP and V groups. The red cells indicate increase in metabolite levels, whereas the blue cells indicate decrease in metabolite levels (<i>P</i> < 0.05 and VIP > 1, <i>n</i> = 8); B: Metabolic pathway changes of heart samples between S, M and QDP groups. <sup>#</sup><i>P</i> < 0.05 <i>vs</i> S group; <sup>*</sup><i>P</i> < 0.05 <i>vs</i> M group , figureFileSmall=WYtEzGTX2X5UKP9whXRSWg==, figureFileBig=l9qdG14DQvBvNv3XwLatlQ==, tableContent=null), ArticleFig(id=1210147825200002046, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=BNJ8dK4deYrfeUWSYTCMlw==, figureFileBig=HFYJWHicJkKKHojO/mzImg==, tableContent=null), ArticleFig(id=1210147825321635847, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Figure 4, caption= The regulatory network of "compound-target-metabolic process". Green presents compounds. Purple presents targets. 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Biological processes associated with material and energy metabolism are in bold with underline , figureFileSmall=lhAXjTxoQ3d4HrukXbeVdA==, figureFileBig=SQ3DoOOJpnT5IZmx8++WFw==, tableContent=null), ArticleFig(id=1210147825954975791, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=Sjf1ViLzDyKDEoRM+RjSIw==, figureFileBig=iUsG+D+MO+yq2+NrDbUq4A==, tableContent=null), ArticleFig(id=1210147826101776442, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Figure 7, caption= KEGG enrichment analysis results associated with material and energy metabolism. A: KEGG enrichment analysis results of cluster 1; B: KEGG enrichment analysis results of cluster 3 , figureFileSmall=Sjf1ViLzDyKDEoRM+RjSIw==, figureFileBig=iUsG+D+MO+yq2+NrDbUq4A==, tableContent=null), ArticleFig(id=1210147827330707515, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=M8RnwD5eEpuBOSz0kMsdRg==, figureFileBig=gOKN+igA8CyyqlZ3eOMvmg==, tableContent=null), ArticleFig(id=1210147827448148037, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Figure 8, caption= Molecular docking modes of targets associated with energy metabolism, including PPARG and 2'-<i>O</i>-methyl isoliquiritigenin (A), AKT1 and baicalin (B), LPL and astragaloside Ⅷ (C), LPL and ginsenoside-Rh1 (D) , figureFileSmall=M8RnwD5eEpuBOSz0kMsdRg==, figureFileBig=gOKN+igA8CyyqlZ3eOMvmg==, tableContent=null), ArticleFig(id=1210147827578171469, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
MetaboliteM/SQDP/MV/M
FCP valueFCP valueFCP value
Methanol0.630.247 32.79*0.010 61.300.508 8
Carnitine0.65*0.000 01.35*0.004 31.18*0.049 6
Inosine0.72*0.009 21.220.260 30.960.820 6
Creatine0.84*0.001 61.13*0.036 81.070.274 6
Lactate0.83*0.009 81.16*0.037 81.050.546 0
Glycerol0.890.216 01.100.385 91.19*0.031 3
Alanine0.82*0.033 41.21*0.042 51.180.083 2
Glutamine0.83*0.003 41.14*0.013 61.14*0.026 2
Glutamate1.27*0.000 40.890.077 40.940.412 8
Aspartate1.89*0.001 00.720.090 90.870.335 3
Taurine1.21*0.001 50.87*0.024 10.86*0.006 0
Proline1.21*0.000 40.90*0.047 00.90*0.019 3
Valine1.26*0.021 30.890.376 11.150.197 2
Homocitrulline1.35*0.000 10.76*0.001 20.970.638 1
Acetate1.62*0.014 60.780.212 60.980.924 5
Propylene glycol1.75*0.005 20.820.330 60.75*0.033 5
Choline1.78*0.000 40.730.058 01.030.828 7
Uridine2.37*0.000 90.660.087 60.990.971 2
), ArticleFig(id=1210147827678834773, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Table 1, caption=

Differential metabolites in heart samples between S, M, QDP and V groups (n = 8). FC: Fold change value. FC with P value < 0.05 is marked by *

, figureFileSmall=null, figureFileBig=null, tableContent=
MetaboliteM/SQDP/MV/M
FCP valueFCP valueFCP value
Methanol0.630.247 32.79*0.010 61.300.508 8
Carnitine0.65*0.000 01.35*0.004 31.18*0.049 6
Inosine0.72*0.009 21.220.260 30.960.820 6
Creatine0.84*0.001 61.13*0.036 81.070.274 6
Lactate0.83*0.009 81.16*0.037 81.050.546 0
Glycerol0.890.216 01.100.385 91.19*0.031 3
Alanine0.82*0.033 41.21*0.042 51.180.083 2
Glutamine0.83*0.003 41.14*0.013 61.14*0.026 2
Glutamate1.27*0.000 40.890.077 40.940.412 8
Aspartate1.89*0.001 00.720.090 90.870.335 3
Taurine1.21*0.001 50.87*0.024 10.86*0.006 0
Proline1.21*0.000 40.90*0.047 00.90*0.019 3
Valine1.26*0.021 30.890.376 11.150.197 2
Homocitrulline1.35*0.000 10.76*0.001 20.970.638 1
Acetate1.62*0.014 60.780.212 60.980.924 5
Propylene glycol1.75*0.005 20.820.330 60.75*0.033 5
Choline1.78*0.000 40.730.058 01.030.828 7
Uridine2.37*0.000 90.660.087 60.990.971 2
), ArticleFig(id=1210147827808858207, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
No.CompoundMolecular formulaSource
1KumatakeninC17H14O6HUANGQI
2Danshenol BC22H26O4DANSHEN
3Miltionone ⅠC19H20O4DANSHEN
4SugiolC20H28O2DANSHEN
5SalviolC20H30O2DANSHEN
6Neocryptotanshinone ⅡC17H18O3DANSHEN
7Paramiltioic acidC19H24O5DANSHEN
8MiltipoloneC19H24O3DANSHEN
9Miltionone ⅡC19H20O4DANSHEN
101-Keto-isocryptotanshinoneC19H18O4DANSHEN
112-Isopropyl-8-methylphenanthrene-3, 4-dione (R0-090680)C18H16O2DANSHEN
12Hesperetic acidC10H10O4DANSHEN
13MiltironeC19H22O2DANSHEN
14FerruginolC20H30ODANSHEN
152, 6-Ditertbutyl-4-methyl phenolC15H24OSANQI
16SativanoneC17H16O5JIANGXIANG
17Melilotocarpan CC18H18O6JIANGXIANG
18OdoricarpanC18H18O6JIANGXIANG
193'-O-MethylviolanoneC18H18O6JIANGXIANG
20(3R)-5'-MethoxyvestitolC17H18O5JIANGXIANG
212', 7-Dihydroxy-4', 5'-dimethoxyisoflavoneC17H14O6JIANGXIANG
22IsoduartinC18H20O6JIANGXIANG
23DalbergichromeneC16H14O3JIANGXIANG
24Xenognosin BC16H12O5JIANGXIANG
253'-MethoxydaidzeinC16H12O5JIANGXIANG
26LiquiritigeninC15H12O4JIANGXIANG
27(3R)-VestitolC16H16O4JIANGXIANG
28BowdichioneC16H10O6JIANGXIANG
29(3R)-4'-Methoxy-2', 3, 7-trihydroxyisoflavanoneC16H14O6JIANGXIANG
30Methyl 2-hydroxy-3, 4-dimethoxy benzoateC10H12O5JIANGXIANG
31KoparinC16H12O6JIANGXIANG
32DalberginC16H12O4JIANGXIANG
33IsodalberginC16H12O4JIANGXIANG
), ArticleFig(id=1210147827913715810, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Table 2, caption=

Information of chemical compounds with good druglikeness

, figureFileSmall=null, figureFileBig=null, tableContent=
No.CompoundMolecular formulaSource
1KumatakeninC17H14O6HUANGQI
2Danshenol BC22H26O4DANSHEN
3Miltionone ⅠC19H20O4DANSHEN
4SugiolC20H28O2DANSHEN
5SalviolC20H30O2DANSHEN
6Neocryptotanshinone ⅡC17H18O3DANSHEN
7Paramiltioic acidC19H24O5DANSHEN
8MiltipoloneC19H24O3DANSHEN
9Miltionone ⅡC19H20O4DANSHEN
101-Keto-isocryptotanshinoneC19H18O4DANSHEN
112-Isopropyl-8-methylphenanthrene-3, 4-dione (R0-090680)C18H16O2DANSHEN
12Hesperetic acidC10H10O4DANSHEN
13MiltironeC19H22O2DANSHEN
14FerruginolC20H30ODANSHEN
152, 6-Ditertbutyl-4-methyl phenolC15H24OSANQI
16SativanoneC17H16O5JIANGXIANG
17Melilotocarpan CC18H18O6JIANGXIANG
18OdoricarpanC18H18O6JIANGXIANG
193'-O-MethylviolanoneC18H18O6JIANGXIANG
20(3R)-5'-MethoxyvestitolC17H18O5JIANGXIANG
212', 7-Dihydroxy-4', 5'-dimethoxyisoflavoneC17H14O6JIANGXIANG
22IsoduartinC18H20O6JIANGXIANG
23DalbergichromeneC16H14O3JIANGXIANG
24Xenognosin BC16H12O5JIANGXIANG
253'-MethoxydaidzeinC16H12O5JIANGXIANG
26LiquiritigeninC15H12O4JIANGXIANG
27(3R)-VestitolC16H16O4JIANGXIANG
28BowdichioneC16H10O6JIANGXIANG
29(3R)-4'-Methoxy-2', 3, 7-trihydroxyisoflavanoneC16H14O6JIANGXIANG
30Methyl 2-hydroxy-3, 4-dimethoxy benzoateC10H12O5JIANGXIANG
31KoparinC16H12O6JIANGXIANG
32DalberginC16H12O4JIANGXIANG
33IsodalberginC16H12O4JIANGXIANG
), ArticleFig(id=1210147828018573415, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
TargetCompoundAffinity / kcal·mol-1
IL6Ginsenoside-Rh1-6.4
IL6Sanchinoside B1-7.1
IL6Astragaloside Ⅷ-7.0
IL6Notoginsenoside G-6.8
IL6Quinquenoside Ⅳ-7.4
IL6Quinquenoside Ⅲ-6.6
IL6Quinquenoside Ⅰ-6.7
IL6Quinquenoside Ⅱ-6.0
PTGS2Enanthic acid-5.0
TNF3-O-Acetyloleanolic acid-7.3
TNFGinsenoside-Rh1-7.1
TNFSanchinoside B1-7.8
TNFAstragaloside Ⅷ-8.2
TNFNotoginsenoside G-7.3
TNFQuinquenoside Ⅳ-7.1
TNFQuinquenoside Ⅲ-7.3
TNFQuinquenoside Ⅰ-7.1
TNFQuinquenoside Ⅱ-7.0
IL1BGinsenoside-Rh1-7.0
IL1BSanchinoside B1-6.7
IL1BAstragaloside Ⅷ-7.4
IL1BNotoginsenoside G-6.6
IL1BQuinquenoside Ⅳ-6.9
IL1BQuinquenoside Ⅲ-7.4
IL1BQuinquenoside Ⅰ-7.0
IL1BQuinquenoside Ⅱ-6.7
CASP33-O-Acetyloleanolic acid-6.6
CASP3Ginsenoside-Rh1-7.0
CASP3Sanchinoside B1-7.6
CASP3Astragaloside Ⅷ-8.8
CASP3Notoginsenoside G-7.3
CASP3Quinquenoside Ⅳ-7.3
CASP3Quinquenoside Ⅲ-7.5
CASP3Quinquenoside Ⅰ-6.7
CASP3Quinquenoside Ⅱ-7.5
AKT1Guercetol, quercetin-6.4
AKT1Nordalbergin-6.2
AKT1Baicalin-6.7
), ArticleFig(id=1210147828140208243, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1210147813531447472, language=CN, label=Table 3, caption=

Docking results of key target molecules and the key compounds of QDP (affinity ≤ -5 kcal·mol-1)

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TargetCompoundAffinity / kcal·mol-1
IL6Ginsenoside-Rh1-6.4
IL6Sanchinoside B1-7.1
IL6Astragaloside Ⅷ-7.0
IL6Notoginsenoside G-6.8
IL6Quinquenoside Ⅳ-7.4
IL6Quinquenoside Ⅲ-6.6
IL6Quinquenoside Ⅰ-6.7
IL6Quinquenoside Ⅱ-6.0
PTGS2Enanthic acid-5.0
TNF3-O-Acetyloleanolic acid-7.3
TNFGinsenoside-Rh1-7.1
TNFSanchinoside B1-7.8
TNFAstragaloside Ⅷ-8.2
TNFNotoginsenoside G-7.3
TNFQuinquenoside Ⅳ-7.1
TNFQuinquenoside Ⅲ-7.3
TNFQuinquenoside Ⅰ-7.1
TNFQuinquenoside Ⅱ-7.0
IL1BGinsenoside-Rh1-7.0
IL1BSanchinoside B1-6.7
IL1BAstragaloside Ⅷ-7.4
IL1BNotoginsenoside G-6.6
IL1BQuinquenoside Ⅳ-6.9
IL1BQuinquenoside Ⅲ-7.4
IL1BQuinquenoside Ⅰ-7.0
IL1BQuinquenoside Ⅱ-6.7
CASP33-O-Acetyloleanolic acid-6.6
CASP3Ginsenoside-Rh1-7.0
CASP3Sanchinoside B1-7.6
CASP3Astragaloside Ⅷ-8.8
CASP3Notoginsenoside G-7.3
CASP3Quinquenoside Ⅳ-7.3
CASP3Quinquenoside Ⅲ-7.5
CASP3Quinquenoside Ⅰ-6.7
CASP3Quinquenoside Ⅱ-7.5
AKT1Guercetol, quercetin-6.4
AKT1Nordalbergin-6.2
AKT1Baicalin-6.7
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基于代谢组学和网络药理学方法探讨芪参益气滴丸改善心衰大鼠心脏能量代谢的作用机制
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吕雪琪 , 郭聪聪 , 许悦 , 靳香菊 , 王映红 *
药学学报 | 研究论文 2022,57(5): 1387-1395
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药学学报 | 研究论文 2022, 57(5): 1387-1395
基于代谢组学和网络药理学方法探讨芪参益气滴丸改善心衰大鼠心脏能量代谢的作用机制
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吕雪琪, 郭聪聪, 许悦, 靳香菊, 王映红*
作者信息
  • 中国医学科学院、北京协和医学院药物研究所, 天然药物活性物质与功能国家重点实验室, 北京 100050

通讯作者:

*王映红, Tel: 86-10-63165216, E-mail:
A proposed mechanism by which Qishen Yiqi dropping pill improves cardiac energy metabolism in rats with heart failure based on metabolomics and network pharmacology
Xue-qi LÜ, Cong-cong GUO, Yue XU, Xiang-ju JIN, Ying-hong WANG*
Affiliations
  • State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
出版时间: 2022-05-12 doi: 10.16438/j.0513-4870.2022-0092
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摘要
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组学与生物信息学为中药机制研究带来了新的思路。本研究采用代谢组学和网络药理学联合策略探讨芪参益气滴丸(Qishen Yiqi dropping pill, QDP) 改善心力衰竭(heart failure, HF) 大鼠心脏能量代谢的药效物质基础及网络调控机制。1H NMR代谢组学分析从HF大鼠心脏组织中发现肉碱、谷氨酰胺、肌酸、脯氨酸、高瓜氨酸、乳酸、牛磺酸和丙氨酸等8个代谢物在QDP治疗后显著回调, 表明QDP调控糖、脂、ATP和蛋白质代谢等过程。动物实验操作均遵循中国医学科学院药物研究所实验动物管理与动物福利伦理委员会的规定。利用网络药理学建立“药物-成分-靶点-疾病”网络, 并结合代谢组学结果提取与上述代谢过程相关的“成分-靶点”子网络, 显示了QDP调节能量代谢潜在的47个作用靶点和79个化学成分, 提炼了熊果酸、三七皂苷、人参皂苷等关键化学成分与INS、PPARG、AKT1等核心靶点, 同时也从能量代谢的角度展示了QDP与HF之间多靶点、多成分的复杂作用关系。分子对接技术验证了部分靶点与化学成分良好的相互作用, 其结合能均小于-5 kcal·mol-1。本研究结果为QDP临床应用及方剂开发利用提供了有用信息。

芪参益气滴丸  /  心力衰竭  /  代谢组学  /  网络药理学  /  分子对接

"Omics" and bioinformatics have brought new ideas to the study of traditional Chinese medicine. This study used metabonomics and network pharmacology to investigate the pharmacodynamic basis and regulation of Qishen Yiqi dropping pill (QDP) improving cardiac energy metabolism in rats with heart failure (HF). 1H NMR metabonomics analysis showed that eight metabolites, including carnitine, glutamine, creatine, proline, homocitrulline, lactic acid, taurine and alanine appeared significant callback after QDP treatment for HF. The results indicate that QDP regulates the metabolism of carbohydrate, lipid, ATP and protein. The animal experiment was conducted in accordance with the regulations of the Ethics Committee for Experimental Animal Management and Animal Welfare of Institute of Materia Medica, Chinese Academy of Medical Sciences. A "drug-component-target-disease" network was established using network pharmacology, and the "component-target" sub-network related to the above energy metabolism processes was extracted by combining metabonomics results. Results revealed 79 chemical compounds and 47 potential targets of QDP involved in the regulation of energy metabolism, and identified key chemical components including ursolic acid, notoginsenoside G, ginsenoside-Rh1, and core targets such as INS, PPARG, and AKT1. The results also demonstrated the complex multi-target and multi-component relationship between QDP and HF from the perspective of energy metabolism. The molecular docking technique verified a strong interaction between some targets and chemical compounds, with affinities less than -5 kcal·mol-1. The results of this study provide useful information for the clinical application, development, and utilization of QDP.

Qishen Yiqi dropping pill  /  heart failure  /  metabonomics  /  network pharmacology  /  molecular docking
吕雪琪, 郭聪聪, 许悦, 靳香菊, 王映红. 基于代谢组学和网络药理学方法探讨芪参益气滴丸改善心衰大鼠心脏能量代谢的作用机制. 药学学报, 2022 , 57 (5) : 1387 -1395 . DOI: 10.16438/j.0513-4870.2022-0092
Xue-qi LÜ, Cong-cong GUO, Yue XU, Xiang-ju JIN, Ying-hong WANG. A proposed mechanism by which Qishen Yiqi dropping pill improves cardiac energy metabolism in rats with heart failure based on metabolomics and network pharmacology[J]. Acta Pharmaceutica Sinica, 2022 , 57 (5) : 1387 -1395 . DOI: 10.16438/j.0513-4870.2022-0092
正文
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心力衰竭(heart failure, HF) 是各种心血管事件的最终结果和各种心脏异常的累积效应[1]。HF发生时心肌收缩力减弱, 心功能及血流动力学等多项参数均发生改变, 如左心室射血分数(left ventricular ejection fractions, LVEF)、短轴缩短率(fractional shortening, FS)、心输出量(cardiac output, CO) 等[2], 可导致相当高的发病率和死亡率[3]。近年来, 临床广泛使用利尿剂、血管扩张剂、正性肌力药、醛固酮受体拮抗剂等西药治疗HF[1], 但这些治疗模式为单一靶点的西药, 未能针对HF复杂的病理机制发挥治疗效果, 不能缓解长期缺血缺氧导致的微循环变化。中药具有多成分、多靶点的特点, 对HF等复杂心血管疾病具有良好的治疗或辅助治疗效果。
临床研究显示[4, 5], 芪参益气滴丸(Qishen Yiqi dropping pill, QDP) 用于HF的防治具有显著疗效, 且不良反应少。QDP是一种复方中药, 2003年被国家食品药品监督管理局批准用于治疗心功能障碍。QDP主要包含黄芪、丹参、三七、降香4味中药, 具有复杂化学组成成分和多靶点, 因此, 其药效物质基础与药效及作用机制间难以建立良好的对应关系, 这阻碍了QDP的进一步应用与国际化进程。
有研究报道[6], 在HF病理状态下, 心脏的能量代谢失衡, 能量利用是判断心脏功能的重要依据。前期研究[7]表明QDP可调节能量代谢对心肌梗死大鼠起保护作用, 然而支持QDP调节能量代谢的作用机制及背后的物质基础不明确。
QDP和HF之间的相互作用是系统到系统的非线性、复杂作用模式。因此, 采用组学和生物信息学的研究策略有助于揭示QDP对HF的整体调控机制。代谢组学作为系统生物学的重要组成部分[8], 研究机体小分子代谢物, 已被广泛用于探究代谢标志物与热点代谢途径。网络药理学[9]将中药成分与疾病靶点联系起来, 能够从改善或恢复生物网络平衡的整体观角度认识药物与机体相互作用。近年来, 分子对接常用于对网络药理学研究建立的活性成分与靶点关系进行初步理论验证, 是计算机辅助药物研究领域的一项重要技术[10]。本研究采用代谢组学和网络药理学联合方式研究QDP干预对HF大鼠心脏组织能量代谢的影响, 并初步探讨QDP治疗HF对能量代谢调节所涉及的相关药效物质基础、靶点及作用关系。
材料与方法
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主要材料  羧甲基纤维素钠(carboxymethyl cellulose sodium, CMC-Na, 批号: F20170810) 购自国药集团化学试剂有限公司; 芪参益气滴丸(批号: 国药准字Z20030139) 购自天士力医药集团股份有限公司; 缬沙坦(批号: 国药准字H20040217) 购自北京诺华制药有限公司; 甲醇(分析纯, 批号: 20211028)、氯仿(分析纯, 批号: 20191028) 均购自北京市通广精细化工公司; 重水(deuterium oxide, D2O) 由Sigma-Aldrich生产, 购自北京金鸥翔科贸有限公司; 2, 2-二甲基-2-硅氧烷-5-磺酸钠(sodium 2, 2-dimethyl-2-silapentane-5-sulfonate, DSS, 批号: l-15369) 购于青岛腾龙微波有限公司。
实验动物  32只雄性SD大鼠购于北京维通利华实验动物技术有限公司[合格证号: SCXK (京) 2016-0006], 饲养于中国医学科学院药物研究所动物实验中心屏障环境设施中。
实验仪器  多用途低温高速离心机(型号: 3K15), 德国Sigma公司; Bead Ruptor多样品研磨珠均质仪, 美国Omni Intenational公司; Bruker AVANCE Ⅲ-500超导傅里叶变化核磁共振波谱仪, 瑞士布鲁克公司; 天平(型号: BT124S), 德国赛多利斯公司; 冷冻干燥机(型号: FDU-1110), 日本东京理化器械(株) 独资工厂。
动物实验  运用左冠前降支结扎法建立心梗后心衰大鼠模型。在造模2周后进行超声检查, 判断造模是否成功, 共获得8只假手术处理大鼠与24只心衰模型大鼠。32只大鼠分为假手术组(S)、模型组(M)、芪参益气滴丸给药组(QDP) 和缬沙坦给药组(V), 给药方案如下: S组灌胃给予等体积对照溶剂(2.5% CMC-Na); M组灌胃给予等体积对照溶剂(2.5% CMC-Na); QDP组灌胃给予QDP剂量为270 mg·kg-1·day-1[11]; V组灌胃给予缬沙坦剂量为30 mg·kg-1·day-1[12]。给药4周后超声检测各组大鼠的心脏功能, 观察给药治疗的效果。动物实验操作均遵循中国医学科学院药物研究所实验动物管理与动物福利伦理委员会的规定。
基于1H NMR技术的大鼠心脏代谢组学实验  取大鼠心脏结扎附近区域与结扎部位远端(靠心尖部位) 心脏组织各150 mg分别进行代谢组学实验, 制备的样品在Bruker AVANCE Ⅲ-500 MHz谱仪上进行检测, 具体实验步骤参照文献[13]。1H NMR谱图采集与参数及数据处理步骤参考文献[14]。
QDP化学成分与靶点的筛选  将黄芪、丹参、三七和降香4味中药输入TCMIP数据库(http://www.tcmip.cn/)[15]中, 通过TCMIP中药材数据库和中药成分数据库挖掘QDP中4味中药的化学成分及其对应靶点, 将靶点导入David数据库中, 筛选与糖、脂、ATP、蛋白质等代谢相关通路的靶点。
HF相关靶点筛选  以“heart failure”作为检索词, 分别在DisGeNet数据库(http://www.disgenet.org/)、GeneCards数据库(http://www.genecards.org/) 和OMIM数据库(http://www.omim.org/) 检索与HF相关的靶蛋白。对上述3个数据库中检索到的靶蛋白进行筛选、汇总、去重后, 得到HF相关靶蛋白。
“药物-成分-靶点-疾病”网络的构建与分析  将QDP中化学成分所对应的靶点与HF相关靶点进行对比, 筛选出交集靶点, 作为QDP治疗HF的潜在作用靶点。将中药、潜在作用靶点及其对应的化学成分和疾病之间的两两关系信息导入Cytoscape3.7.1软件中, 构建QDP治疗HF潜在的“中药-成分-靶点-疾病”网络。对该网络进行拓扑分析, 依据分析结果中的degree项筛选QDP治疗HF的关键化学成分。将网络中靶点导入David数据库中, 筛选与糖、脂、ATP、蛋白质等代谢相关通路的靶点, 提取这些靶点与其对应化学成分组成的“成分-靶点”子网络。
蛋白相互作用网络的构建与分析  为明确QDP治疗HF潜在作用靶点在蛋白水平上的相互作用关系, 挖掘关键作用靶点, 构建了蛋白相互作用网络。将潜在作用靶点导入String数据库(http://string-db.org/) 中, Organism设为Homo sapiens, Minimum required interaction score设为0.4, 构建靶蛋白相互作用网络。利用CytoHubba挖掘蛋白相互作用网络中的关键靶点, 选用MCC算法; 应用Cytoscape的另一插件MCODE在蛋白相互作用网络中进行聚类构建功能模块。为进一步探讨功能模块潜在的生物学意义, 使用R包对每个模块中包含的靶点集分别进行GO和KEGG的富集分析, 筛选与糖、脂、ATP、蛋白质等代谢相关的生物学过程和信号通路。
化学成分-靶点分子对接  对“中药-成分-靶点-疾病”网络中的关键化学成分与核心靶点, 与糖、脂、ATP、蛋白质等代谢相关的“成分-靶点”对应关系中的部分化学成分与靶点进行分子对接验证。于RCSB PDB蛋白质数据库(https://www.rcsb.org/) 查找并下载靶点蛋白的3D结构, 于PubChem数据库(https://pubchem.ncbi.nlm.nih.gov) 和SciFinder数据库(https://scifinder-n.cas.org/) 查找并下载化学成分的3D结构。利用AutoDockTools-1.5.6软件对靶点和化学成分进行去水、加氢等处理并将文件转换为PDBQT格式, 应用AutoDock Vina软件对筛选得到的化学成分与靶点进行分子对接, 以结合能(affinity) 指标评价二者之间的亲和力。
统计学分析  数据采用单因素方差分析。所有数值均以means ± SD或SEM表示, P < 0.05认为差异有统计学意义。
结果
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1 QDP对心衰大鼠的治疗作用
给药QDP (270 mg·kg-1·day-1)、缬沙坦(30 mg·kg-1·day-1) 治疗HF大鼠, 观察其对模型动物心脏功能的影响。结果如图 1所示, 给药前与S组相比, 模型动物出现明显心力衰竭症状, 模型动物心脏LVEF、FS显著低于S组, 并且给药前M组与QDP组、V组均未见明显差异, 表明成功建立心衰模型。连续灌胃给药4周后, M组LVEF、FS和CO显著低于S组; 与M组相比, QDP组与V组大鼠心脏LVEF、FS、CO显著回升, 显示QDP有效地改善HF大鼠心脏功能。
2 HF和QDP对大鼠心脏代谢物的影响
主成分分析(principal component analysis, PCA) 结果显示, 在结扎附近区域给药组与模型组心脏组织样本代谢轮廓未显著分离, 而结扎部位远端心脏组织样本的代谢轮廓在组间具有显著差异, 给药组由模型组向假手术组回归。偏最小二乘法判别分析(partial least squares discriminant analysis, PLS-DA) 结果显示(图 2), 结扎部位远端组织S组与M组、M组与QDP组代谢轮廓可显著分离(R2和Q2均 > 0.5且P < 0.05)。
结扎部位远端组织主要差异代谢物和代谢通路结果如图 3和表 1所示, VIP > 1且P < 0.05的相应变量被选择为主要差异代谢物。与S组相比, M组中与ATP代谢相关的肌苷和肌酸、糖酵解通路中的乳酸和丙氨酸及与脂肪酸代谢相关的肉碱水平均显著下降, 而缬氨酸、谷氨酸、脯氨酸等显著升高, 这些差异代谢物主要富集通路包括丙氨酸、天冬氨酸和谷氨酸代谢, 精氨酸和脯氨酸代谢、丙酮酸代谢及糖酵解/糖异生等, 显示M组大鼠心脏结扎部位远端组织发生糖、脂质、ATP和蛋白质等代谢紊乱; 与M组相比, QDP组中部分代谢物显著回调, 包括脯氨酸、牛磺酸、肌酸、谷氨酰胺、乳酸、肉碱、丙氨酸等, 这些差异代谢物富集通路主要包括丙氨酸、天冬氨酸和谷氨酸代谢, 精氨酸和脯氨酸代谢及糖酵解/糖异生等, 提示QDP可影响糖、脂质、ATP和蛋白质代谢, 缓解部分能量代谢紊乱。
3 QDP化学成分与其对应靶点
在TCMIP数据中共得到124种化学成分, 并获得与之对应的潜在靶点400个。其中, 黄芪的化学成分有24种, 包括黄芪甲苷、葡萄糖醛酸、叶酸、胆碱等, 潜在靶点有266个, 包括糖、脂、ATP和蛋白质代谢相关靶点75个; 丹参的化学成分有35种, 包括原儿茶醛、迷迭香酸、丹酚酸等, 潜在靶点有216个, 包括糖、脂、ATP和蛋白质代谢相关靶点52个; 三七的化学成分有44种, 包括人参皂苷、三七皂苷等, 潜在靶点有210个, 包括糖、脂、ATP和蛋白质代谢相关靶点66个; 降香的化学成分有26种, 包括异黄酮类、甘草素等, 潜在靶点有101个, 包括糖、脂、ATP、蛋白代谢相关靶点20个。成药性等级为Good的化合物信息见表 2。
4 HF相关靶点
从DisGeNet数据库获得HF相关靶点660个, 从GeneCards数据库获得HF相关靶点1 124个, 从OMIM数据库获得HF相关靶点1 947个, 3个疾病数据库获得HF相关的靶点去除重复后共有1 598个。
5 “药物-成分-靶点-疾病”网络的构建与分析
将得到的HF相关靶点与QDP化学成分的靶点进行比对, 得到107个交集靶点。利用Cytoscape构建“药物-成分-靶点-疾病”的调控网络, 共包含222个节点和962条边, 拓扑分析获得19个关键化学成分, 包括熊果酸、齐墩果酸、槲皮素等。根据与糖、脂、ATP、蛋白质等代谢相关靶点及其对应化学成分提取子网络, 由47个糖、脂、ATP和蛋白代谢相关靶点和79个化学成分组成, 包括熊果酸、三七皂苷、人参皂苷等关键化学成分, 见图 4。其中, 与ATP代谢相关的靶点共9个, 其对应的化学成分共4个; 与糖代谢相关的靶点共27个, 其对应的化学成分共68个; 与脂代谢相关的靶点共23个, 与其对应的化学成分共70个; 与蛋白质代谢相关的靶点共2个, 与其对应的化学成分共40个。
6 蛋白相互作用网络的构建与分析
将107个交集靶点导入STRING数据库进行蛋白相互作用网络构建和筛选, 结果导入Cytoscape中。蛋白相互作用网络中包含105个节点和729条边。应用cytoHubba插件, 提取位于Top10的Hub靶点, 见图 5A。结果显示, 与能量代谢相关的过氧化物酶体增殖物激活受体γ (peroxisome proliferator-activated receptor gamma, PPARG)、RACα丝氨酸/苏氨酸蛋白激酶(RAC-alpha serine/threonine-protein kinase, AKT1) 和胰岛素(insulin, INS) 等核心节点可能是QDP调节能量代谢的重要靶点。应用MCODE插件对蛋白相互作用网络进行模块划分与聚类, 共得到7个模块, 依据聚类Score值, 将排名前三的模块作为核心模块。其中模块一(图 5B) 和模块三(图 5D) 包含较多与能量代谢相关的靶点, 模块二(图 5C) 中的靶点主要与钠离子通道相关。
7 GO富集分析和KEGG通路富集分析
7.1 GO富集分析
对每个模块进行了GO富集分析。模块一共富集出450个生物过程、8个细胞组分和32个分子功能, 主要涉及氧化应激与炎症反应、糖脂代谢和细胞凋亡等; 模块二共富集出29个生物过程、8个细胞组分和9个分子功能, 主要涉及动作电位、钠通道复合体等; 模块三共富集出7个生物过程、6个细胞组分和8个分子功能, 生物过程主要涉及有氧呼吸、ATP代谢过程等。GO富集结果(图 6) 显示QDP通过参与包括葡萄糖代谢、脂肪酸代谢、ATP代谢在内的多条生物学途径发挥作用。
7.2 KEGG通路富集分析
对每个模块进行了KEGG富集分析。模块一共富集出149条通路, 与能量代谢相关的通路主要包括脂质与动脉粥样硬化(lipid and atherosclerosis)、AMPK信号通路(AMPK signaling pathway) 和PPAR信号通路(PPAR signaling pathway) 等(图 7A); 模块二共富集出1条通路, 为心肌细胞中的肾上腺素信号通路(adrenergic signaling in cardiomyocytes); 模块三共富集出14条通路, 与能量代谢相关的通路主要包括氧化磷酸化(oxidative phosphorylation)、三羧酸循环(TCA cycle) 等(图 7B)。
8 化学成分-靶点分子对接验证
应用AutoDock Vina软件对IL6、PTGS2、TNF、IL1B、CASP3、PPARG、AKT1等关键靶点与网络中degree值≥ 12的化学成分进行分子对接验证。根据对接结果(表 3) 中的affinity指标判断靶点与化学成分之间的亲和力, 其中astragaloside Ⅷ与CASP3的结合能最低, 为-8.8 kcal·mol-1, 平均结合能为-6.2 kcal·mol-1, 显示网络中关键靶点与关键化学成分对接良好。同时对与糖、脂、ATP和蛋白质代谢相关靶点AKT1、PPARG、LPL与其对应化学成分分别进行分子对接验证, 用Pymol软件对其对接结果进行可视化(图 8), 显示QDP化学成分与这些靶点对接良好。
讨论
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HF的发病机制与心脏代谢功能密切相关, 在衰竭心脏中, 心肌细胞线粒体功能和高能磷酸盐异常等病理因素导致心脏代谢重塑, 进一步加速HF进程, 因此, 调节能量代谢是治疗HF的重要策略[16]。基础研究显示QDP能够改善心脏功能和能量代谢[17]。
本研究对大鼠心脏结扎部位与结扎部位远端的组织进行了代谢组学研究, 结果显示, 结扎部位各组样本给药组与模型组组间差异不显著, 这可能是由于手术结扎导致该部位组织受损严重, QDP治疗作用难以带来显著的改善。结扎部位远端组织代谢轮廓在组间具有显著分离, 表明QDP可通过干预心脏冠脉下游的微循环代谢网络对HF大鼠产生有益作用。通过差异代谢物及其富集通路分析发现, 在HF和QDP干预下, 大鼠心脏组织中丙酮酸代谢、脂肪酸代谢、氨基酸代谢、糖酵解及能量代谢等生物过程发生了相应的变化。肉碱作为脂肪酰辅酶A线粒体输入能力的标志物, 在晚期HF患者和多种动物模型心肌中降低[18, 19]。在本研究中, QDP干预后HF大鼠肉碱水平显著回升, 显示QDP改善了脂肪酸与葡萄糖代谢平衡。肌酸参与ATP的存储, HF患者总肌酸池大小减少[20]。QDP干预后, HF大鼠心肌组织中肌酸显著回升, 表明QDP可能通过调控肌酸水平改善心肌功能。QDP治疗的HF大鼠中谷氨酰胺水平显著回升, 研究报道谷氨酰胺可通过多种机制对冠状动脉疾病患者心脏发挥保护作用[21-23], 如参与心肌能量代谢[23], 提示QDP可能通过提高谷氨酰胺水平发挥调节心肌能量代谢的作用。因此, 从代谢组学结果来看, QDP对HF大鼠心脏组织的糖、脂、ATP和蛋白质代谢具有有益调节作用。
在网络药理学研究中, 提取出与糖、脂、ATP和蛋白质代谢相关靶点及其对应化学成分构成的子网络, 由47个靶点和79个化学成分构成, 包括熊果酸、三七皂苷、人参皂苷等关键化学成分和INS、PPARG、AKT1等核心靶点, QDP可广泛地调控能量代谢, 包括糖、脂、ATP和蛋白质等代谢过程, 其中主要调控糖脂代谢。有研究报道, 熊果酸可有效改善糖脂代谢紊乱, 提高胰岛素敏感性, 调节代谢相关酶类的表达[24]; 槲皮素可调节脂质代谢紊乱[25]; 人参皂苷Rh1可调节大鼠多条代谢通路, 特别是三羧酸循环代谢通路[26]。PPARG是脂肪细胞分化和葡萄糖稳态的关键调节因子, 与动脉粥样硬化密切相关。AKT对糖代谢的调节起着重要的作用, Zhang等[27]发现QDP通过激活PI3K-Akt信号通路抑制高糖诱导的心肌细胞凋亡。INS与受体结合介导胰岛素信号传导, 衰竭心脏心肌细胞内胰岛素信号的变化可能导致不良的左心室重构和线粒体功能障碍[28]。分子对接结果也进一步验证了化学成分与靶点之间良好的相互作用关系。
本研究采用代谢组学和网络药理学联合方式研究构建了QDP调节能量代谢相关“成分-靶点”网络, 该网络不仅展示了QDP调节糖、脂、ATP和蛋白质代谢潜在的作用靶点和药效物质基础, 提炼了其中的关键化学成分与核心靶点, 同时也从能量代谢的角度展示了QDP与HF之间多靶点、多成分的复杂作用关系, 以上研究结果为QDP临床应用及方剂开发利用提供了有用信息。
本研究同时也发现这种研究方式的不足, 首先采用数据库对化学成分进行靶点预测有一定的局限性; 其次, 在本研究中筛选得到的部分化合物在中药中含量低, 如quinquenosides在三七中含量很低, 这些微量成分对于复方药效的贡献度有多大值得探讨。因此, 本研究所提供的信息首先应该进行相应的验证分析, 这包括动物体内体外的一系列生物学验证及相关的组学研究。同时, 通过对中药及复方的定性定量分析后获得的化学成分准确信息后再进行靶点预测, 进而提高预测的准确性。
作者贡献: 吕雪琪和王映红负责整体设计、数据分析和文章撰写; 许悦和靳香菊负责心脏代谢组学实验; 郭聪聪负责文献整理和论文修改。
利益冲突: 本文无利益冲突。
基金
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  • 国家科技重大专项项目(2018ZX09711001-002-004)
  • 中国医学科学院医学与健康科技创新工程项目(2016-I2M-1-009)
文献
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参考文献 引证文献
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2022年第57卷第5期
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doi: 10.16438/j.0513-4870.2022-0092
  • 接收时间：2022-01-18
  • 首发时间：2025-12-23
  • 出版时间：2022-05-12
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  • 收稿日期：2022-01-18
  • 修回日期：2022-03-22
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国家科技重大专项项目(2018ZX09711001-002-004)
中国医学科学院医学与健康科技创新工程项目(2016-I2M-1-009)
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    中国医学科学院、北京协和医学院药物研究所, 天然药物活性物质与功能国家重点实验室, 北京 100050

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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