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Article(id=1220364244265123930, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1220364233427043161, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2019-0455, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1559750400000, receivedDateStr=2019-06-06, revisedDate=1569772800000, revisedDateStr=2019-09-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1768887109104, onlineDateStr=2026-01-20, pubDate=1581436800000, pubDateStr=2020-02-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768887109104, onlineIssueDateStr=2026-01-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768887109104, creator=13701087609, updateTime=1768887109104, updator=13701087609, issue=Issue{id=1220364233427043161, tenantId=1146029695717560320, journalId=1189982191388893191, year='2020', volume='55', issue='2', pageStart='181', pageEnd='348', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1768887106520, creator=13701087609, updateTime=1768887715499, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1220366787728822983, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1220364233427043161, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1220366787728822984, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1220364233427043161, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=247, endPage=255, ext={EN=ArticleExt(id=1220364244873298047, articleId=1220364244265123930, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Potential mechanism of Huangqin decoction for the treatment of ulcerative colitis based on network pharmacology, columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

To study the mechanism of Huangqin decoction (HQT) in the treatment of ulcerative colitis (UC) by using network pharmacology, chemical components and targets related to the four herbs of Chinese meteria medical in HQT were searched through the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) to construct the interaction network diagram of the target point of the compounds. The UC-related targets were screened through OMIM, TTD, and GeneCard databases. The compound-target network was constructed using Cytoseape_v3.7.1 software; based on the STRING database, a target interaction network for HQT for UC was constructed, and the core target of HQT for UC was selected based on topological parameters. GO (gene ontology) biological process enrichment analysis and KEGG (KEGG pathway analysis) pathway annotation analysis were performed on the disease and drug intersection targets using the R package clusterprofile version 3.12.0 in Bioconductor. The HQT compound-UC target network contains 128 compounds and corresponding targets 141. The core targets are AKTI, IL6, PTGS2, IL10, IL1β and so on. GO functional enrichment analysis yielded 151 GO terms, and KEGG pathway enrichment screening resulted in 33 associations with UC, mainly involving PI3K-AKT signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway and so on. The synergetic effect of HQT with multi-components and multi-pathway was confirmed by network pharmacology, and the main possible mechanism of HQT in treating UC was predicted, which lay a foundation for the identification of effective components, the mechanism of action, and clinical application.

, correspAuthors=Wei-peng YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2020 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=Hong-xin SONG, Xu-ran MA, Dun-fang WANG, Yan-li WANG, Di-xin ZOU, Jin-xue MIAO, Han WANG, Wei-peng YANG), CN=ArticleExt(id=1220364246752346392, articleId=1220364244265123930, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=基于网络药理学的黄芩汤治疗溃疡性结肠炎的潜在机制研究, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

利用网络药理学和系统生物学方法探究黄芩汤治疗溃疡性结肠炎（ulcerative colitis，UC）分子机制。从中药系统药理学分析平台TCMSP挖掘黄芩汤中4味中药相关的化学成分和作用靶点；通过OMIM数据库、TTD数据库以及GeneCard数据库筛选UC的预测靶点。利用Cytoseape_v3.7.1软件构建化合物-靶点网络；基于STRING数据库，构建黄芩汤治疗UC靶点互作网络，根据拓扑学参数筛选黄芩汤治疗UC的核心靶点。利用Bioconductor中的R包clusterprofileversion 3.12.0对疾病与药物交集靶点进行GO（gene ontology）生物学过程富集分析和KEGG（KEGG pathway analysis）通路注释分析。结果发现，黄芩汤化合物-UC靶点网络包含128个化合物和相应靶点141个，核心靶点涉及AKTI、IL6、PTGS2、IL10、IL1β等。GO功能富集分析得到151个GO条目，KEGG富集筛选得到33条和UC有关通路，主要涉及PI3K-AKT信号通路、NF-κB信号通路、TNF信号通路、Toll样受体信号通路等。本文预测了黄芩汤治疗UC疾病的可能作用机制，为进一步寻找其有效成分和作用机制奠定基础。

, correspAuthors=杨伟鹏, authorNote=null, correspAuthorsNote=
*杨伟鹏, Tel:86-10-64093014, Fax:86-10-64013996, E-mail:
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Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
2. Heilongjiang University of Traditional Chinese Medicine, Harbin 150040, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1220364250200064446, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, authorId=1220364249965183410, language=CN, stringName=苗金雪, firstName=金雪, middleName=null, lastName=苗, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=1.中国中医科学院中药研究所, 北京100700
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Int J Colorectal Dis, 2013, 28: 1049-1055., articleTitle=Endoscopic finding of spontaneous hemorrhage correlates with tumornecrosis factor alpha expression in colonic mucosa of patients with ulcerative colitis, refAbstract=null), Reference(id=1220364258534146702, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[23], rfOrder=22, authorNames=null, journalName=null, refType=null, unstructuredReference=Jiang Y. Effect of Baicalin on Ulcerative Colitis PI3K/Akt/NF-κB Signaling Pathways and Its Underlying Mechanism (黄芩苷对溃疡性结肠炎PI3K/Akt/NF-kB信号通路的影响及其机制研究)[D]. Nanjing: Nanjing University of Chinese Medicine, 2016., articleTitle=null, refAbstract=null), Reference(id=1220364258618032784, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, doi=10.1016/j.jchromb.2015.01.018, pmid=null, pmcid=null, year=2015, volume=985, issue=null, pageStart=91, pageEnd=102, url=null, language=null, rfNumber=[24], rfOrder=23, authorNames=Zhang J, Cai W, Zhou Y, journalName=J Chromatogr B Analyt Technol Biomed Life Sci, refType=null, unstructuredReference= Zhang J , Cai W , Zhou Y et al . Profiling and identification of the metabolites of baicalin and study on their tissue distribution in rats by ultra-high-performance liquid chromatography with linear ion trap-orbitrap mass spectrometer[J]. J Chromatogr B Analyt Technol Biomed Life Sci, 2015, 985: 91-102., articleTitle=Profiling and identification of the metabolites of baicalin and study on their tissue distribution in rats by ultra-high-performance liquid chromatography with linear ion trap-orbitrap mass spectrometer, refAbstract=null)], funds=[Fund(id=1220364255279366722, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, awardId=81273662, language=CN, fundingSource=国家自然科学基金资助项目(81273662), fundOrder=null, country=null), Fund(id=1220364255375835719, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, awardId=81473592, language=CN, fundingSource=国家自然科学基金资助项目(81473592), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1220364246970450212, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, xref=null, ext=[AuthorCompanyExt(id=1220364246978838821, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, companyId=1220364246970450212, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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The sky blue nodes represent Gancao, the dark blue nodes represent Huangqin, the yellow nodes represent Dazhao, the purple nodes represent Baishao, the green nodes represent the target point, the red nodes represent key pharmacodynamic molecules, compound number is short for TCMSP-ID , figureFileSmall=mDbzKQKl/f1MtGbZuvLL9Q==, figureFileBig=WSjfOeW2zCglsO5fEcF2mQ==, tableContent=null), ArticleFig(id=1220364253698114056, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=EN, label=null, caption=null, figureFileSmall=ePT9gnMJXWgPXPfsHtuPrg==, figureFileBig=MA2fXtPX6cHC2kqhW4bfCg==, tableContent=null), ArticleFig(id=1220364253786194443, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=CN, label=Figure 2, caption= Huangqin decoction (HQT) core target network diagram. The nodes of network 1 represent the direct targets of HQT for ulcerative colitis, and the nodes of network 2 represent the indirect targets of HQT for ulcerative colitis, the color shade and node size represent the position of the network relationship , figureFileSmall=ePT9gnMJXWgPXPfsHtuPrg==, figureFileBig=MA2fXtPX6cHC2kqhW4bfCg==, tableContent=null), ArticleFig(id=1220364253891052048, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=EN, label=null, caption=null, figureFileSmall=hiHX8vfEH1ja5UOXBkxn3Q==, figureFileBig=PIHEq91X3nH7kNS1dd8XFQ==, tableContent=null), ArticleFig(id=1220364253995909650, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=CN, label=Figure 3, caption= Heat map for binding energy of key targets with active ingredients of HQT. 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Red nodes represent pathways, yellow nodes represent core targets, and green nodes represent active compounds , figureFileSmall=mCwXE7ctgSFr+o25eanC6w==, figureFileBig=+Pj82yM3vOJ2IH+G7+uKjA==, tableContent=null), ArticleFig(id=1220364254708941355, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
TCMSP-ID Compound Betweenness centrality Degree Source
MOL000422 Kaempferol 0.062 084 15 51 BS
MOL000358 Beta-sitosterol 0.008 639 78 36 BS
MOL000492 (+)-Catechin 0.008 152 7 8 BS
MOL000098 Quercetin 0.453 396 26 155 DZ
MOL007213 Nuciferin 0.011 412 15 8 DZ
MOL000354 Isorhamnetin 0.005 020 65 11 GC
MOL000497 Licochalcone A 0.006 692 93 11 GC
MOL004328 Naringenin 0.027 635 27 11 GC
MOL000392 Formononetin 0.001 708 54 10 GC
MOL004978 2-[(3R)-8, 8-Dimethyl-3, 4-dihydro-2H-pyrano[6, 5-f]chromen-3-yl]-5-methoxyphenol 0.003 773 62 10 GC
MOL003896 7-Methoxy-2-methylIsoflavone 0.001 708 54 9 GC
MOL004891 Shinpterocarpin 0.005 805 47 9 GC
MOL004908 Glabridin 0.002 395 44 9 GC
MOL004912 Glabrone 0.001 708 54 9 GC
MOL004974 3'-Methoxyglabridin 0.001 708 54 9 GC
MOL005003 Licoagrocarpin 0.003 463 38 9 GC
MOL000417 Calycosin 0.001 229 24 8 GC
MOL000500 Vestitol 0.001 229 24 8 GC
MOL004811 Glyasperin C 0.000 855 51 8 GC
MOL004820 Kanzonols W 0.001 229 24 8 GC
MOL004824 (2S)-6-(2, 4-Dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2, 3-dihydrofuro[3, 2-g]chromen-7-one 0.000 855 51 8 GC
MOL004833 Phaseolinisoflavan 0.000 855 51 8 GC
MOL004856 Gancaonin A 0.000 855 51 8 GC
MOL004884 Licoisoflavone B 0.000 855 51 8 GC
MOL004885 Licoisoflavanone 0.001 502 66 8 GC
MOL004907 Glyzaglabrin 0.001 229 24 8 GC
MOL004957 3-(4-Hydroxyphenyl)-7-methoxychromen-4-one 0.001 229 24 8 GC
MOL004966 3'-Hydroxy-4'-O-methylglabridin 0.001 229 24 8 GC
MOL004991 7-Acetoxy-2-methylisoflavone 0.001 431 26 8 GC
MOL005007 Glyasperins M 0.001 502 66 8 GC
MOL005016 Odoratin 0.001 229 24 8 GC
MOL000173 Wogonin 0.084 177 82 31 HQ
MOL002714 Baicalein 0.054 621 48 22 HQ
MOL001689 Acacetin 0.017 291 6 14 HQ
MOL002928 Oroxylin A 0.012 981 98 10 HQ
MOL002933 5, 7, 4'-Trihydroxy-8-methoxyflavone 0.004 526 95 10 HQ
MOL008206 Moslosooflavone 0.003 929 63 10 HQ
MOL002934 Neobaicalein 0.002 296 41 9 HQ
MOL000552 5, 2'-Dihydroxy-6, 7, 8-trimethoxyflavone 0.004 380 39 8 HQ
MOL002897 Epiberberine 0.000 829 04 8 HQ
), ArticleFig(id=1220364254801216046, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=CN, label=Table 1, caption=

Network characteristics of key component-target network nodes. BS: Baishao; DZ: Dazhao; GC: Gancao; HQ: Huangqin

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TCMSP-ID Compound Betweenness centrality Degree Source
MOL000422 Kaempferol 0.062 084 15 51 BS
MOL000358 Beta-sitosterol 0.008 639 78 36 BS
MOL000492 (+)-Catechin 0.008 152 7 8 BS
MOL000098 Quercetin 0.453 396 26 155 DZ
MOL007213 Nuciferin 0.011 412 15 8 DZ
MOL000354 Isorhamnetin 0.005 020 65 11 GC
MOL000497 Licochalcone A 0.006 692 93 11 GC
MOL004328 Naringenin 0.027 635 27 11 GC
MOL000392 Formononetin 0.001 708 54 10 GC
MOL004978 2-[(3R)-8, 8-Dimethyl-3, 4-dihydro-2H-pyrano[6, 5-f]chromen-3-yl]-5-methoxyphenol 0.003 773 62 10 GC
MOL003896 7-Methoxy-2-methylIsoflavone 0.001 708 54 9 GC
MOL004891 Shinpterocarpin 0.005 805 47 9 GC
MOL004908 Glabridin 0.002 395 44 9 GC
MOL004912 Glabrone 0.001 708 54 9 GC
MOL004974 3'-Methoxyglabridin 0.001 708 54 9 GC
MOL005003 Licoagrocarpin 0.003 463 38 9 GC
MOL000417 Calycosin 0.001 229 24 8 GC
MOL000500 Vestitol 0.001 229 24 8 GC
MOL004811 Glyasperin C 0.000 855 51 8 GC
MOL004820 Kanzonols W 0.001 229 24 8 GC
MOL004824 (2S)-6-(2, 4-Dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2, 3-dihydrofuro[3, 2-g]chromen-7-one 0.000 855 51 8 GC
MOL004833 Phaseolinisoflavan 0.000 855 51 8 GC
MOL004856 Gancaonin A 0.000 855 51 8 GC
MOL004884 Licoisoflavone B 0.000 855 51 8 GC
MOL004885 Licoisoflavanone 0.001 502 66 8 GC
MOL004907 Glyzaglabrin 0.001 229 24 8 GC
MOL004957 3-(4-Hydroxyphenyl)-7-methoxychromen-4-one 0.001 229 24 8 GC
MOL004966 3'-Hydroxy-4'-O-methylglabridin 0.001 229 24 8 GC
MOL004991 7-Acetoxy-2-methylisoflavone 0.001 431 26 8 GC
MOL005007 Glyasperins M 0.001 502 66 8 GC
MOL005016 Odoratin 0.001 229 24 8 GC
MOL000173 Wogonin 0.084 177 82 31 HQ
MOL002714 Baicalein 0.054 621 48 22 HQ
MOL001689 Acacetin 0.017 291 6 14 HQ
MOL002928 Oroxylin A 0.012 981 98 10 HQ
MOL002933 5, 7, 4'-Trihydroxy-8-methoxyflavone 0.004 526 95 10 HQ
MOL008206 Moslosooflavone 0.003 929 63 10 HQ
MOL002934 Neobaicalein 0.002 296 41 9 HQ
MOL000552 5, 2'-Dihydroxy-6, 7, 8-trimethoxyflavone 0.004 380 39 8 HQ
MOL002897 Epiberberine 0.000 829 04 8 HQ
), ArticleFig(id=1220364254893490738, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Target Target-name Degree
AKT1 RAC-alpha serine/threonine-protein kinase 110
IL6 Interleukin-6 102
CASP3 Caspase-3 92
PTGS2 Prostaglandin G/H synthase 2 89
MAPK8 Mitogen-activated protein kinase 8 86
CXCL8 Interleukin-8 84
MAPK1 Mitogen-activated protein kinase 1 82
IL1β Interleukin-1 beta 76
IL10 Interleukin-10 73
CCL2 C-C motif chemokine 2 71
CAT Catalase 67
MAPK14 Mitogen-activated protein kinase 14 67
ALB Serum albumin 100
VEGFA Vascular endothelial growth factor A 99
JUN Transcription factor AP-1 92
EGFR Epidermal growth factor receptor 88
MYC Myc proto-oncogene protein 85
MMP9 Matrix metalloproteinase-9 84
EGF Pro-epidermal growth factor 83
FOS Proto-oncogene c-Fos 76
CCND1 G1/S-specific cyclin-D1 75
ESR1 Estrogen receptor 75
FN1 Fibronectin 75
), ArticleFig(id=1220364254968988214, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=CN, label=Table 2, caption=

Information for 23 core targets of topological analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
Target Target-name Degree
AKT1 RAC-alpha serine/threonine-protein kinase 110
IL6 Interleukin-6 102
CASP3 Caspase-3 92
PTGS2 Prostaglandin G/H synthase 2 89
MAPK8 Mitogen-activated protein kinase 8 86
CXCL8 Interleukin-8 84
MAPK1 Mitogen-activated protein kinase 1 82
IL1β Interleukin-1 beta 76
IL10 Interleukin-10 73
CCL2 C-C motif chemokine 2 71
CAT Catalase 67
MAPK14 Mitogen-activated protein kinase 14 67
ALB Serum albumin 100
VEGFA Vascular endothelial growth factor A 99
JUN Transcription factor AP-1 92
EGFR Epidermal growth factor receptor 88
MYC Myc proto-oncogene protein 85
MMP9 Matrix metalloproteinase-9 84
EGF Pro-epidermal growth factor 83
FOS Proto-oncogene c-Fos 76
CCND1 G1/S-specific cyclin-D1 75
ESR1 Estrogen receptor 75
FN1 Fibronectin 75
), ArticleFig(id=1220364255057068601, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Target Uniprot-ID PDB-ID Ligand-ID
AKTI P31749 6HHJ G4H
IL6 P05231 4NI9 OMG
PTGS2 P35354 5FLA MAN
JUN P05412 1JUN ACE
MAPK8 P45983 4UX9 ANP
CXCL8 P10145 1QE6 SO4
CASP3 P42574 6BF1 AZI
MAPK1 P28482 6Q7S IPH
IL10 P22301 2ILK SO4
IL1β P01584 5BVP PCA
), ArticleFig(id=1220364255174509117, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1220364244265123930, language=CN, label=Table 3, caption=

Information on 10 proteins involved in molecular docking

, figureFileSmall=null, figureFileBig=null, tableContent=
Target Uniprot-ID PDB-ID Ligand-ID
AKTI P31749 6HHJ G4H
IL6 P05231 4NI9 OMG
PTGS2 P35354 5FLA MAN
JUN P05412 1JUN ACE
MAPK8 P45983 4UX9 ANP
CXCL8 P10145 1QE6 SO4
CASP3 P42574 6BF1 AZI
MAPK1 P28482 6Q7S IPH
IL10 P22301 2ILK SO4
IL1β P01584 5BVP PCA
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基于网络药理学的黄芩汤治疗溃疡性结肠炎的潜在机制研究
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宋红新 1 , 马旭冉 1 , 王敦方 1 , 王彦礼 1 , 邹迪新 1 , 苗金雪 1, 2 , 王汉 3 , 杨伟鹏 1, *
药学学报 | 研究论文 2020,55(2): 247-255
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药学学报 | 研究论文 2020, 55(2): 247-255
基于网络药理学的黄芩汤治疗溃疡性结肠炎的潜在机制研究
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宋红新1, 马旭冉1, 王敦方1, 王彦礼1, 邹迪新1, 苗金雪1, 2, 王汉3, 杨伟鹏1, *
作者信息
  • 1.中国中医科学院中药研究所, 北京100700
  • 2.黑龙江中医药大学, 黑龙江 哈尔滨 150040
  • 3.长春中医药大学, 吉林 长春 130117

通讯作者:

*杨伟鹏, Tel:86-10-64093014, Fax:86-10-64013996, E-mail:
Potential mechanism of Huangqin decoction for the treatment of ulcerative colitis based on network pharmacology
Hong-xin SONG1, Xu-ran MA1, Dun-fang WANG1, Yan-li WANG1, Di-xin ZOU1, Jin-xue MIAO1, 2, Han WANG3, Wei-peng YANG1, *
Affiliations
  • 1. Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
  • 2. Heilongjiang University of Traditional Chinese Medicine, Harbin 150040, China
  • 3. Changchun University of Traditional Chinese Medicine, Changchun 130117, China
出版时间: 2020-02-12 doi: 10.16438/j.0513-4870.2019-0455
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摘要
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利用网络药理学和系统生物学方法探究黄芩汤治疗溃疡性结肠炎（ulcerative colitis，UC）分子机制。从中药系统药理学分析平台TCMSP挖掘黄芩汤中4味中药相关的化学成分和作用靶点；通过OMIM数据库、TTD数据库以及GeneCard数据库筛选UC的预测靶点。利用Cytoseape_v3.7.1软件构建化合物-靶点网络；基于STRING数据库，构建黄芩汤治疗UC靶点互作网络，根据拓扑学参数筛选黄芩汤治疗UC的核心靶点。利用Bioconductor中的R包clusterprofileversion 3.12.0对疾病与药物交集靶点进行GO（gene ontology）生物学过程富集分析和KEGG（KEGG pathway analysis）通路注释分析。结果发现，黄芩汤化合物-UC靶点网络包含128个化合物和相应靶点141个，核心靶点涉及AKTI、IL6、PTGS2、IL10、IL1β等。GO功能富集分析得到151个GO条目，KEGG富集筛选得到33条和UC有关通路，主要涉及PI3K-AKT信号通路、NF-κB信号通路、TNF信号通路、Toll样受体信号通路等。本文预测了黄芩汤治疗UC疾病的可能作用机制，为进一步寻找其有效成分和作用机制奠定基础。

网络药理学  /  黄芩汤  /  溃疡性结肠炎  /  作用机制  /  靶点

To study the mechanism of Huangqin decoction (HQT) in the treatment of ulcerative colitis (UC) by using network pharmacology, chemical components and targets related to the four herbs of Chinese meteria medical in HQT were searched through the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) to construct the interaction network diagram of the target point of the compounds. The UC-related targets were screened through OMIM, TTD, and GeneCard databases. The compound-target network was constructed using Cytoseape_v3.7.1 software; based on the STRING database, a target interaction network for HQT for UC was constructed, and the core target of HQT for UC was selected based on topological parameters. GO (gene ontology) biological process enrichment analysis and KEGG (KEGG pathway analysis) pathway annotation analysis were performed on the disease and drug intersection targets using the R package clusterprofile version 3.12.0 in Bioconductor. The HQT compound-UC target network contains 128 compounds and corresponding targets 141. The core targets are AKTI, IL6, PTGS2, IL10, IL1β and so on. GO functional enrichment analysis yielded 151 GO terms, and KEGG pathway enrichment screening resulted in 33 associations with UC, mainly involving PI3K-AKT signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway and so on. The synergetic effect of HQT with multi-components and multi-pathway was confirmed by network pharmacology, and the main possible mechanism of HQT in treating UC was predicted, which lay a foundation for the identification of effective components, the mechanism of action, and clinical application.

network pharmacology  /  Huangqin decoction  /  ulcerative colitis  /  mechanism  /  target
宋红新, 马旭冉, 王敦方, 王彦礼, 邹迪新, 苗金雪, 王汉, 杨伟鹏. 基于网络药理学的黄芩汤治疗溃疡性结肠炎的潜在机制研究. 药学学报, 2020 , 55 (2) : 247 -255 . DOI: 10.16438/j.0513-4870.2019-0455
Hong-xin SONG, Xu-ran MA, Dun-fang WANG, Yan-li WANG, Di-xin ZOU, Jin-xue MIAO, Han WANG, Wei-peng YANG. Potential mechanism of Huangqin decoction for the treatment of ulcerative colitis based on network pharmacology[J]. Acta Pharmaceutica Sinica, 2020 , 55 (2) : 247 -255 . DOI: 10.16438/j.0513-4870.2019-0455
正文
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溃疡性结肠炎(ulcerative colitis, UC)是一种病因和发病机制尚不明确的慢性肠道炎症疾病。本病主要以大肠黏膜及黏膜下层发生慢性、非特异性炎症性病变为中心, 黏膜上皮及腺体破坏、增生或萎缩, 淋巴细胞、中性粒细胞和单核细胞浸润, 隐窝脓肿形成, 因而呈现黏膜充血、糜烂、溃疡及增殖性改变[1]。中医将UC归为“肠僻”、“大瘾泄”、“下利”、“痢疾”和“滞下”等范畴, 并认为UC多因感受外邪、内伤饮食、情志失调和脾胃虚弱所致, 病位在脾胃与大小肠。其发病机制为浊邪壅塞肠中, 传导失司, 气机不通, 气血壅滞, 肠络受损, 下痢赤白黏液。《济生方·痢疾论治》“胃者脾之腑, 为水谷之海, 营卫充焉。夫人饮食起居失其宜, 运动劳役失其度, 则脾胃不充, 大肠虚弱, 而风冷暑湿之邪, 得以乘间而入, 故为痢疾。”《类证治裁·痢症》谓“由胃腑湿蒸热壅, 致气血凝结, 夹糟粕积滞, 进入大小腑, 倾刮脂液, 化脓血下注。”
黄芩汤见于《伤寒论辨·太阳病脉证并治》, 该方由黄芩、白芍、大枣、炙甘草4味药组成。方中以黄芩为主, 苦寒坚阴而清里热; 芍药味酸微苦, 敛阴和营, 缓急止痛; 芩芍配伍, 酸苦相济, 调中存阴以止利, 是治热之要药, 炙甘草、大枣益气和中, 调补正气。诸药合用, 共奏清热止痢, 缓急止痛之功。此方以其严谨的理法方药和确切的临床疗效, 对后世产生了深刻的影响。现代药理研究表明, 黄芩汤有非常明显的抑菌、抗炎、解痉、镇痛、解热、增强免疫和镇静等作用[2]。黄芩汤治疗UC在调节免疫功能, 抗氧化应激, 以及通过调节肠道菌群恢复微生态的相对平衡、抑制炎症反应、保护肠黏膜屏障等作用显著。
因中药及其复方具有多成分、多途径、多靶点协同作用的特点, 使得中药药效物质基础不明确、作用机制不清楚, 从整体到组织器官、细胞和分子水平都很难进行全面系统地研究。近年来网络药理学在国际上兴起, 它融合系统生物学、多向药理学、计算生物学和网络分析等多学科的技术和内容, 进行多层次网络的构建, 从整体角度探索药物与疾病间的关联性, 提供了一种从系统水平寻找中药复方潜在活性成分和作用靶点的新策略, 特别适用于反映及阐释中药多成分-多靶点的作用关系, 与中药从整体水平调控机体、发挥治疗作用的观点相吻合[3]。为此, 本研究采用网络药理学方法, 分析黄芩汤治疗UC的药效物质基础和可能的分子作用机制, 建立“有效成分-靶点-通路”之间的关系, 探究该方治疗UC的多成分、多靶点和多途径作用机制, 为后续的研究工作提供思路。
材料与方法
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黄芩汤活性成分和相关靶点筛选通过TCMSP (http://lsp.nwu.edu.cn/tcmsp.php)寻找黄芩汤中4味中药化学成分, 筛选口服利用度(OB)≥30%且类药性(DL)≥0.18[4]的活性化合物, 获得黄芩汤活性化合物及其所作用的蛋白质靶点。
UC相关靶点的筛选  本研究基于GeneCards (http://www.genecards.org/)、TTD (http://bidd.nus.edu.sg/BIDD-Databases/TTD/TTD.asp)以及OMIM[5] (http://www.omim.org)数据库综合遴选UC相关靶点。将上述疾病靶点与黄芩汤潜在靶点取交集, 共收集到141个基因, 作为疾病和药物的共同潜在靶点。
黄芩汤化合物-靶点网络关系的构建  将筛选后的黄芩汤中活性化合物与上述141个靶点对应关系上传至Cytoseape_v3.7.1软件(http://www.cytoscape.org/)生成体现化合物-靶点相互作用的网络图, 进行拓扑参数[6]分析, 根据介度(betweenness centrality, BC)、紧密度(closeness centrality, CC)以及连接度(Degree)的中位数来分析化合物-靶点网络核心节点。
黄芩汤治疗UC核心靶点分析  将上述141个靶点上传至STRING数据库(https://string-db.org/), 构建黄芩汤治疗UC靶点互作网络, 将网络中节点交互作用关系参数导入Cytoscape软件绘制相互作用网络, 根据BC、CC和Degree值的中位数来分析黄芩汤治疗UC的靶点互作网络核心节点。
分子对接验证  利用分子对接模拟软件AutoDock_Vina, 对Degree≥10的关键药效分子与筛选的10个核心靶点进行受体-配体对接模拟计算, 以阳性药柳氮磺胺吡啶(SASP)与核心靶点对接结果进行对照。完成全部的对接模拟后, 根据关键药效分子和核心靶点最低结合能(binding energy)值做热图。经大量文献查阅[7], 小分子与靶点蛋白最低结合能≤ -5.0 kJ·mol-1表明黄芩汤的关键药效分子与靶点蛋白结合较好。
靶点通路注释分析  利用Bioconductor中的R包clusterprofile version 3.12.0对疾病与药物交集靶点进行GO (gene ontology)生物学过程富集分析和KEGG (KEGG pathway analysis)通路注释分析, 设定阈值P < 0.05[8], 并根据生物学信息筛选与UC有关的通路, 构建化合物-靶点-通路网络图。
结果
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1 黄芩汤化合物-靶点网络的构建及核心靶点的筛选
根据OB与DL的阈值, 剔除重复化合物和缺少靶点预测的化合物, 并结合多方数据库和文献检索确认, 最终得出128个活性成分, 其中黄芩26个化学成分, 白芍6个化学成分, 大枣15个化学成分, 甘草81个化学成分。利用128个化合物与靶点的关系构建化合物-靶点网络(图 1)。其中天蓝色节点代表甘草, 深蓝色结点代表黄芩, 黄色节点代表大枣, 紫色节点代表白芍, 绿色节点为靶点, 红色节点代表核心节点。在化合物-靶点相互作用网络中, 共包括269个节点(128个化合物节点和141个靶点节点)和72 092条边。每条边则表示化合物分子与靶点之间的相互作用关系。经Network Analyzer计算该化合物-靶点网络中, Degree值中位数2倍为8, BC和CC中位数分别为0.000 890 1和0.709 163 34, 核心节点需满足以上参数的中位数卡值。表 1列出了化合物-靶点网络中核心化合物的Degree值, 从表 1可见, 槲皮素(MOL000098)的潜在靶点155个, 最多; 其次是山柰酚(MOL000422), 51个; β-谷甾醇(MOL000358)潜在靶点36个; 汉黄芩素(MOL000173) 31个; 黄芩素(MOL002714) 22个; 金合欢素(MOL001689) 14个; 异鼠李素(MOL000354)和甘草查尔酮A (MOL000497)各11个。这些Degree值较高的关键有效分子有可能在黄芩汤的药理功能中发挥着相对重要的作用。
2 黄芩汤治疗UC核心靶点分析
本研究基于STRING数据库分析黄芩汤治疗UC的靶点之间的交互作用, 将黄芩汤治疗UC的141个靶点相互作用关系导入Cytoscape软件进行分析, 根据BC ≥ 0.001 676 35、CC ≥ 0.555 555 56以及Degree ≥ 66 (中位数2倍)条件筛选出23个核心靶点, 如图 2所示。节点的大小和颜色与Degree值正相关, Degree值越大, 节点越大, 颜色越深, 表明靶点在此网络关系越重要。其中Network 1为黄芩汤治疗UC的直接作用靶点, Network 2为黄芩汤治疗UC的间接靶点。表 2列出了23个核心靶点的Degree值, 在黄芩汤治疗UC靶点交互作用网络中, RAC-α丝氨酸/苏氨酸蛋白激酶(AKT1, 110)、白介素6 (IL6, 102)、半胱天冬酶3 (CASP3, 92)、前列腺素G/H合酶2 (PTGS2, 89)、转录因子AP-1 (JUN, 92)、丝裂原激活的蛋白激酶8 (MAPK8, 86)、白介素8 (CXCL8, 84)、丝裂原激活的蛋白激酶1 (MAPK1, 82)、白介素1β (IL1β, 76)和白介素10 (IL10, 73)在黄芩汤治疗UC的直接作用靶点中处于核心位置, 可能在黄芩汤治疗UC中发挥重要作用。
3 分子对接验证结果
对14个关键药效分子与10个靶点蛋白对接的最低结合能作图, 如图 3所示。其中横坐标为靶蛋白, 纵坐标为黄芩汤的活性成分, 颜色的深浅代表最低结合能的大小。由图可见, 所有分子与标靶蛋白的最低结合能都小于0, 说明配体与受体可以自发结合, 最低结合能越小说明分子与靶点蛋白结合越好。对接结果显示, 14个活性化合物与蛋白靶点AKTI、IL6、PTGS2、MAPK8、CXCL8、CASP3最低结合能小于-5.0 kJ·mol-1, 说明这些活性化合物与以上靶点蛋白结合较好。表 3列出靶点蛋白基本信息, 包括配体和蛋白的PDB-ID编号及配体ID编号。阳性药物与靶点蛋白对接的最低结合能和黄芩汤中关键药效分子与靶点蛋白的最低结合能相差不大, 验证了靶点蛋白晶体及配体选择的合理性。
4 靶点生物功能及靶点-通路分析
利用R包clusterprofile共富集出151个GO条目, 选取P值最小的前20个GO条目作图, 如图 4所示。Y轴代表GO条目, X轴和气泡面积大小代表目标基因集中属于GO的基因数量, 气泡颜色代表富集显著性, 即P值的大小。其中生物学过程辅因子结合、细胞因子受体结合、蛋白质丝氨酸/苏氨酸激酶活性、受体配体活性、细胞因子活性、DNA结合转录激活因子活性、RNA聚合酶Ⅱ特异性和RNA聚合酶Ⅱ转录因子结合富集的基因数排名靠前。
KEGG通路富集共获得151条富集通路, 如图 5所示。Y轴代表pathway名称, X轴和条形面积大小代表目标基因集中属于pathway的基因数量, 条形图颜色代表富集显著性, 即P值的大小。其中与UC有关的33条通路主要包括PI3K-AKT信号通路(PI3K-AKT signaling pathway)、NF-κB信号通路(NF-kappa B signaling pathway)、TNF信号通路(TNF signaling pathway)、Toll样受体信号通路(Toll-like receptor signaling pathway), 表明黄芩汤的有效成分可能通过作用于这些信号通路达到治疗疾病的目的。
采用Cytoscape软件构建“成分-靶点-通路”(图 6), 其中红色节点代表根据生物信息学筛选的通路, 黄色和绿色节点分别代表核心靶点和核心活性化合物。从图中可见, 黄芩汤41个活性成分的作用靶点分布于不同的代谢通路, 共同调节机体水平, 体现了药物多成分、多靶点治疗UC的作用机制。
讨论
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黄芩汤具有清热止痢, 缓急止痛之功效, 临床用于治疗湿热型UC, 具有较好的临床效果。本研究基于多成分、多靶点作用的研究思路, 应用网络药理学技术, 分析黄芩汤治疗UC作用靶点, 阐述黄芩汤治疗UC潜在作用机制, 为黄芩汤临床应用提供科学依据。
基于黄芩汤活性成分-作用靶点网络分析, Degree值排名前10位的关键药效分子有9个属于黄酮类化合物, 其中包括黄酮类化合物黄芩素(MOL002714)、汉黄芩素(MOL000173)、槲皮素(MOL000098)、山柰酚(MOL000422)、金合欢素(MOL001689)、异鼠李素(MOL000354)、芒柄花黄素(MOL000392)等、二氢黄酮类化合物柚皮素(MOL004328)及查尔酮类化合物甘草查尔酮A (MOL000497)。究其原因, 主要是黄酮类在中草药及天然产物中分布广泛, 并具有抗炎、抗菌和抗氧化活性[9]。
基于STRING交互作用网络数据库构建黄芩汤治疗UC靶点交互作用网络, 筛选出23个核心靶点, 结合分子对接结果, 发现AKT1、IL6、PTGS2、IL10、IL1β、MAPK8、CASP3是黄芩汤治疗UC关键作用靶点, 这7个靶点可能在黄芩汤治疗UC作用的发挥重要作用。分子对接结果显示14个关键药效成分与AKT1、IL6、IL10、PTGS2对接结合能都小于-5.0 kJ·mol-1, 14个活性化合物与以上靶点蛋白结合较好。免疫调节异常是UC的主要发病因素, 其中IL6、IL1β、IL10在免疫细胞的发育、分化、免疫应答及某些细胞的激活过程中有重要调节作用, 并与UC的发生、发展和转归等具有密切相关性[10]。IL6主要由活化的巨噬细胞、淋巴细胞及上皮细胞分泌产生, IL6过度表达使体内环境紊乱, 导致肠上皮细胞的电解质分泌失调, 使其通透性增强, 进而使黏附其上的中性粒细胞游出并浸润到炎症部位[11]。IL1β是IL1的一种, 在免疫与炎症启动中扮演着重要调节作用, 可增加由巨噬细胞所产生的细胞因子如: IL6、TNFα、IL18等, 使得中性粒细胞浸润于肠道病变部位, 引起一系列的肠道炎症病变[12]。KEGG通路富集分析显示, IL6、IL1β与UC有关炎症通路TNF、TLRS和NF-κB信号通路密切相关。TNF信号通路: TNF→TNFR1→NF-κB→IL1β、IL6; TLRs信号通路: TLR1/TLR2→NF-κB→IL1β、IL6, 得出黄芩汤可能作用于信号通路中潜在中心NF-κB, 从而介导下游IL1β及IL6的合成。IL6、IL1β在炎症反应过程中产生协同作用, 与炎症分级密切相关。IL10主要由Th2型细胞分泌, 具有多种生物学作用, 能够通过抑制激活单核细胞、巨噬细胞、粒细胞和T细胞等, 调节免疫功能的一种细胞因子。IL10在黏膜免疫中发挥重要调节作用, 又能很好的稳定肠道黏膜的内环境[13]。文献报道黄芩汤有效成分黄芩素、汉黄芩素[14]、槲皮素[15]和山柰酚[16]能够抑制炎症模型细胞或实验动物IL6、IL1β的表达, Chen等[17]发现黄芩汤辅助柳氮磺胺吡啶可降低结肠炎患者IL6和IL1β, 升高IL10, 进一步说明筛选黄芩汤治疗UC核心靶点结果可靠。
PTGS又称为环氧酶(COX), 是催化花生四烯酸合成各种前列腺素(PGs)和其他二十烷类的关键酶, COX有两种同工酶, 即组成型酶COX1和诱导性酶COX2, 其中COX2可被多种炎性介质及细胞因子诱导, 参与组织炎症过程及细胞的分化增殖过程[18]。研究表明, 正常结肠黏膜不表达COX2, 而炎症黏膜上皮细胞及结肠固有层单核炎性细胞大量表达COX2, 并且在炎症活动期结肠固有层和肌间神经丛中COX2 mRNA表达水平明显高于非活动期[19]。本文分子对接结果显示, 黄芩汤中14个关键药效分子可与PTGS2结合最低能量为-6.0 kJ·mol-1, 14个关键药效分子与10个蛋白靶点的对接最低结合能除β-谷甾醇(MOL000358)外都小于-5 kJ·mol-1, 提示黄芩汤与COX2活性位点结合能力较强, 可推测黄芩汤作用于PTGS2, 调节炎症活动期结肠固有层和肌间神经丛中COX2 mRNA表达, 抑制前列腺素生成, 减少炎性介质的释放。
AKT在黄芩汤治疗UC的关键靶点网络关系中处于核心地位。AKT是一种与蛋白激酶C相关的丝氨酸或苏氨酸激酶, 也被称为蛋白激酶B, 是PI3K下游的直接作用靶点。有研究显示, PI3K通路的异常激活在UC的发病机制中起到了重要作用, PI3K是一系列胞内传导酶家族, 当所有位点被磷酸化, 细胞膜上的AKT被完全激活, AKT从细胞膜释放进入细胞浆或细胞核中进行信号的传输, 执行其生物学功能[20]。因此, AKT的磷酸化可以被作为判断PI3K活性的指标。KEGG通路富集结果显示PI3K/AKT是基因富集最多的一条通路。近年来, PI3K/AKT信号通路在肠道炎症和肿瘤中的作用日益受到重视, PI3K/AKT信号传导通路与细胞因子的调控密切相关, 在肠道炎症反应中发挥着重要作用[21]。PI3K/AKT信号通路可激活NF-κB, 起到多向性的调节作用, 可调节免疫炎症相关的多种细胞的基因表达。在炎症反应的过程中, NF-κB能起到关键的调控作用, 能提高诸如IL1β、IL6等炎症因子的基因转录, 增加IL1β、IL6的表达[22], 加速炎症反应, 增加UC的发生。Jiang等[23]通过体内动物实验和体外细胞实验发现黄芩苷能减轻大鼠UC的炎症反应, 抑制PI3K磷酸化, 下调AKT的活化, 从而抑制TNFα、IL6等炎症因子的分泌, 发挥其抗炎效应, 提示黄芩苷可能通过以抑制AKT的活化、抑制NF-κB的核转位发挥其抗炎的作用。黄芩苷作为黄芩汤有效成分之一, 在体内可被代谢为黄芩素、7-甲氧基黄芩素和5-甲氧基黄芩素等[24], 由此提示, 黄芩汤可能通过抑制PI3K/AKT/NF-κB信号通路的激活而发挥抗炎作用, 减轻UC的炎症反应。
综上所述, 本文采用网络药理学的方法对黄芩汤治疗溃疡性结肠的分子机制进行探讨, 黄芩汤由4种药材配伍组成, 不同的活性成分通过调控不同的靶蛋白, 作用于不同的通路, 对UC起到治疗作用, 体现中药方剂整体性、系统性的特点。本研究发现, 黄芩汤可能从以下3个方面发挥治疗UC的作用: ①作用于PTGS2靶点, 调节炎症活动期结肠固有层和肌间神经丛中COX2 mRNA表达。控制花生四烯酸代谢途径, 抑制前列腺素生成, 减少炎性介质的释放; ②调节IL6、IL1β和IL10等多种细胞因子的表达, 调节UC发生发展, 转归过程中的免疫异常, 稳定肠道黏膜的内环境; ③抑制PI3K/AKT/NF-κB、TNF和TLRs等信号通路的活化, 进而下调IL6、IL1β和COX2等炎性因子的表达而发挥抗炎作用。此次运用网络药理学的方法探究了黄芩汤治疗UC的作用机制, 同时预测的PI3K/AKT/NF-κB信号通路, 为后续实验验证提供依据。预测所得黄芩汤成分疾病靶点与已有文献较为接近, 表明基于网络药理学与分子对接相结合的技术手段探讨其治疗UC的作用机制具有一定的准确性, 同时也揭示了黄芩汤多成分、多靶点的特点, 为后续的相关研究提供参考。
值得注意的是, CASP3和MAPK8的Degree值很高, 处于黄芩汤治疗UC关键靶点的核心位置, 且与黄芩汤的关键药效分子结合较好, 推测黄芩汤可能还影响细胞凋亡、自噬过程, 有待进一步研究。
基金
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  • 国家自然科学基金资助项目(81273662)
  • 国家自然科学基金资助项目(81473592)
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2020年第55卷第2期
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doi: 10.16438/j.0513-4870.2019-0455
  • 接收时间：2019-06-06
  • 首发时间：2026-01-20
  • 出版时间：2020-02-12
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  • 收稿日期：2019-06-06
  • 修回日期：2019-09-30
基金
国家自然科学基金资助项目(81273662)
国家自然科学基金资助项目(81473592)
作者信息
    1.中国中医科学院中药研究所, 北京100700
    2.黑龙江中医药大学, 黑龙江 哈尔滨 150040
    3.长春中医药大学, 吉林 长春 130117

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*杨伟鹏, Tel:86-10-64093014, Fax:86-10-64013996, E-mail:
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https://castjournals.cast.org.cn/joweb/yxxb/CN/10.16438/j.0513-4870.2019-0455
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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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