药学学报

Symbol	Gene ID	Gene name
DRD3	1 814	Dopamine receptor D3
DRD2	1 813	Dopamine receptor D2
DRD4	1 815	Dopamine receptor D4
BCL2L1	598	BCL2 like 1
ADORA1	134	Adenosine A1 receptor
DCT	1 638	Dopachrome tautomerase
TYR	7 299	Tyrosinase
STAT4	6 775	Signal transducer and activator of transcription 4
BCL2	596	BCL2, apoptosis regulator
CNR1	1 268	Cannabinoid receptor 1
MAPT	4 137	Microtubule associated protein tau
AR	367	Androgen receptor
PTPN2	5 771	Protein tyrosine phosphatase, non-receptor type 2
MAPK11	5 600	Mitogen-activated protein kinase 11
MAPK10	5 602	Mitogen-activated protein kinase 10
FUCA2	2 519	Fucosidase, alpha-L-2, plasma
STAT1	6 772	Signal transducer and activator of transcription 1
FUCA1	2 517	Fucosidase, alpha-L-1, tissue
STAT3	6 774	Signal transducer and activator of transcription 3
STAT2	6 773	Signal transducer and activator of transcription 2
ALOX15	246	Arachidonate 15-lipoxygenase
HIF1A	3 091	Hypoxia inducible factor 1 alpha subunit
CA9	768	Carbonic anhydrase 9
ALOX15B	247	Arachidonate 15-lipoxygenase, type B
MAPK14	1 432	Mitogen-activated protein kinase 14
AKR1B10	57 016	Aldo-keto reductase family 1 member B10
CHRM1	1 128	Cholinergic receptor muscarinic 1
AKR1B1	231	Aldo-keto reductase family 1 member B
MAPK9	5 601	Mitogen-activated protein kinase 9
MAPK8	5 599	Mitogen-activated protein kinase 8
ALOX5	240	Arachidonate 5-lipoxygenase
PTPN1	5 770	Protein tyrosine phosphatase, non-receptor type 1
CA1	759	Carbonic anhydrase 1
PTAFR	5 724	Platelet activating factor receptor
HTR2A	3 356	5-Hydroxytryptamine receptor 2A

Symbol	Gene ID	Gene name
DRD3	1 814	Dopamine receptor D3
DRD2	1 813	Dopamine receptor D2
DRD4	1 815	Dopamine receptor D4
BCL2L1	598	BCL2 like 1
ADORA1	134	Adenosine A1 receptor
DCT	1 638	Dopachrome tautomerase
TYR	7 299	Tyrosinase
STAT4	6 775	Signal transducer and activator of transcription 4
BCL2	596	BCL2, apoptosis regulator
CNR1	1 268	Cannabinoid receptor 1
MAPT	4 137	Microtubule associated protein tau
AR	367	Androgen receptor
PTPN2	5 771	Protein tyrosine phosphatase, non-receptor type 2
MAPK11	5 600	Mitogen-activated protein kinase 11
MAPK10	5 602	Mitogen-activated protein kinase 10
FUCA2	2 519	Fucosidase, alpha-L-2, plasma
STAT1	6 772	Signal transducer and activator of transcription 1
FUCA1	2 517	Fucosidase, alpha-L-1, tissue
STAT3	6 774	Signal transducer and activator of transcription 3
STAT2	6 773	Signal transducer and activator of transcription 2
ALOX15	246	Arachidonate 15-lipoxygenase
HIF1A	3 091	Hypoxia inducible factor 1 alpha subunit
CA9	768	Carbonic anhydrase 9
ALOX15B	247	Arachidonate 15-lipoxygenase, type B
MAPK14	1 432	Mitogen-activated protein kinase 14
AKR1B10	57 016	Aldo-keto reductase family 1 member B10
CHRM1	1 128	Cholinergic receptor muscarinic 1
AKR1B1	231	Aldo-keto reductase family 1 member B
MAPK9	5 601	Mitogen-activated protein kinase 9
MAPK8	5 599	Mitogen-activated protein kinase 8
ALOX5	240	Arachidonate 5-lipoxygenase
PTPN1	5 770	Protein tyrosine phosphatase, non-receptor type 1
CA1	759	Carbonic anhydrase 1
PTAFR	5 724	Platelet activating factor receptor
HTR2A	3 356	5-Hydroxytryptamine receptor 2A

No.	Compound	CAS	Closeness	Betweenness	Degree
E1	Eleutheroside A	474-58-8	0.186 471 04	0.450 549 45	13
E2	Eleutheroside B	118-34-3	0.025 209 9	0.294 964 03	4
E3	Eleutheroside B1	16845-16-2	0.099 564 75	0.344 537 82	5
E4	Eleutheroside C	15486-24-5	0.139 129 06	0.383 177 57	6
E5	Eleutheroside D	79484-75-6	0.089 527 33	0.427 083 33	7
E6	Eleutheroside E	39432-56-9	0.089 527 33	0.427 083 33	7
E7	Sesamin	607-80-7	0.175 687 82	0.422 680 41	10

No.	Compound	CAS	Closeness	Betweenness	Degree
E1	Eleutheroside A	474-58-8	0.186 471 04	0.450 549 45	13
E2	Eleutheroside B	118-34-3	0.025 209 9	0.294 964 03	4
E3	Eleutheroside B1	16845-16-2	0.099 564 75	0.344 537 82	5
E4	Eleutheroside C	15486-24-5	0.139 129 06	0.383 177 57	6
E5	Eleutheroside D	79484-75-6	0.089 527 33	0.427 083 33	7
E6	Eleutheroside E	39432-56-9	0.089 527 33	0.427 083 33	7
E7	Sesamin	607-80-7	0.175 687 82	0.422 680 41	10

Target	PDBID	Comp	PC	Score (Comp)	Score (PC)
STAT3	1BG1	Eleutheroside A	Niclosamide	5.047	1.541
PTPN1	2H4K	Eleutheroside D	PTP1B-IN-1	7.106	5.952
JNK1	3PZE	Sesamin	Tanzisertib	6.689	7.249
JNK2	3NPC	Sesamin	Tanzisertib	6.964	7.64
JNK3	2B1P	Sesamin	Tanzisertib	6.154	7.065
p38α	5LAR	Sesamin	SB202190	6.603	2.473
p38β	3GP0	Sesamin	SB202190	6.884	1.789

Target	PDBID	Comp	PC	Score (Comp)	Score (PC)
STAT3	1BG1	Eleutheroside A	Niclosamide	5.047	1.541
PTPN1	2H4K	Eleutheroside D	PTP1B-IN-1	7.106	5.952
JNK1	3PZE	Sesamin	Tanzisertib	6.689	7.249
JNK2	3NPC	Sesamin	Tanzisertib	6.964	7.64
JNK3	2B1P	Sesamin	Tanzisertib	6.154	7.065
p38α	5LAR	Sesamin	SB202190	6.603	2.473
p38β	3GP0	Sesamin	SB202190	6.884	1.789

基于网络药理学对刺五加总苷治疗2型糖尿病作用机制研究

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袁文彬 , 韦艳美 , 陈勇 ^* , 梁继超 ^*

药学学报 | 研究论文 2019,54(11): 1982-1989

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药学学报 |研究论文 2019 , 54 (11) : 1982 -1989

基于网络药理学对刺五加总苷治疗2型糖尿病作用机制研究

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袁文彬, 韦艳美, 陈勇^*, 梁继超^*

作者信息

湖北大学, 中药生物技术湖北省重点实验室, 药物高通量筛选技术国家地方联合工程研究中心, 湖北武汉 430062

通讯作者:

*陈勇, Tel: 86-27-88663590, E-mail: cy101610@qq.com;

梁继超, Tel: 86-27-88663882, E-mail: liang529114@163.com

Mechanisms of eleutheroside for treatment of diabetes mellitus type 2 based on network pharmacology

Wen-bin YUAN, Yan-mei WEI, Yong CHEN^*, Ji-chao LIANG^*

Affiliations

Hubei Province Key Laboratory of Biotechnology of Chinese Traditional Medicine, National and Local Joint Engineering Research Center of High-throughput Drug Screening Technology, Hubei University, Wuhan 430062, China

出版时间: 2019-11-12 doi: 10.16438/j.0513-4870.2019-0350

文章导航

摘要

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本文以刺五加总苷中的7种主要成分为研究对象，利用网络药理学方法对刺五加总苷治疗2型糖尿病（diabetes mellitus type 2）的作用机制进行探讨。首先运用SwissTargetPrediction、GeneCard、String平台在线预测这7种成分与2型糖尿病相关的潜在靶点共35个。然后使用Cytoscape3.6.1构建“成分-靶点”网络图，并利用内置的Networkanalyzer工具进行拓扑学分析。在DAVID6.8平台上进行基因功能（gene ontology，GO）富集分析和KEGG通路富集分析，根据富集结果构建“成分-靶点-通路”网络图。并筛选出可能主要作用于2型糖尿病的成分作为核心成分，然后将核心成分与关键疾病靶蛋白进行分子对接验证，并深入分析刺五加总苷作用的潜在机制。结果经筛选，共有8条与2型糖尿病相关的重要通路。本研究表明，刺五加苷A、刺五加苷D、刺五加苷E和芝麻素在胰岛素抵抗、细胞凋亡和炎症相关通路起关键作用。刺五加总苷可能主要通过作用信号转导子和转录激活子（signal transducer and activator of transcription，STATs）、非受体型蛋白酪氨酸磷酸酶（polyclonalantibody to protein tyrosine phosphatase，non receptor type，PTPN）1、PTPN2、c-Jun氨基末端激酶（c-Jun N-terminal kinase，JNK）、p38蛋白等靶点，通过多通路的方式共同作用，起到对2型糖尿病的综合治疗效果，并为今后的刺五加总苷作为抗2型糖尿病药物的开发奠定了基础。

关键词

网络药理学 / 刺五加总苷 / 2型糖尿病 / 作用机制 / 靶点

Abstract

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Seven main components in eleutheroside were used as research objects, and the mechanism of action of total eleutheroside for treatment of diabetes mellitus type 2 was investigated by network pharmacology. The SwissTargetPrediction, GeneCard, and String platforms were used to predict the 35 potential targets of these 7 components that are related to diabetes mellitus type 2. Then we used cytoscape 3.6.1 to build a " component-target" network map and used the Networkanalyzer tool for topology analysis. Gene ontology (GO) enrichment analysis and KEGG pathway enrichment analysis were performed on the DAVID6.8 platform, and the " component-target-path" network map was constructed based on the enrichment results. Those components mainly used in diabetes mellitus type 2 were screened as core components, and the core components were docked with key disease target proteins to verify the potential mechanism of the total eleutheroside. After screening, 8 important pathways associated with diabetes mellitus type 2 were identified. This study showed that eleutheroside A, eleutheroside D, eleutheroside E and sesamin played key roles in insulin resistance, apoptosis and inflammation pathways. The total eleutheroside may ameliorate type 2 diabetes mainly through regulating signal transducer and activator of transcription factors (STATs), non-receptor protein tyrosine phosphatase (PTPN) 1, PTPN2, c-Jun N-terminal kinase (JNK), and p38 mitogen activate protein kinase. These components worked together through multiple signaling pathway. Based on our data, eleutheroside is proposed as a novel therapeutic strategy for treatment of type 2 diabetes.

Key words

network pharmacology / eleutheroside / diabetes mellitus type 2 / mechanism / target

引用本文

袁文彬, 韦艳美, 陈勇, 梁继超. 基于网络药理学对刺五加总苷治疗2型糖尿病作用机制研究. 药学学报, 2019 , 54 (11) : 1982 -1989 . DOI: 10.16438/j.0513-4870.2019-0350

Wen-bin YUAN, Yan-mei WEI, Yong CHEN, Ji-chao LIANG. Mechanisms of eleutheroside for treatment of diabetes mellitus type 2 based on network pharmacology[J]. Acta Pharmaceutica Sinica, 2019 , 54 (11) : 1982 -1989 . DOI: 10.16438/j.0513-4870.2019-0350

正文

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糖尿病是一种由于葡萄糖代谢紊乱而导致血糖水平过高的疾病。糖尿病可以导致一系列的并发症, 降低患者的生活质量, 增加患者死亡率^[1]。近年来, 2型糖尿病发病率逐年攀升, 其主要特点是胰岛素抵抗和胰岛β细胞功能障碍导致, 而胰岛素抵抗参与了2型糖尿病的全过程, 主要体现在葡萄糖不耐受和血管功能障碍^[2]。其发病原因主要由于遗传基因和不良的生活方式造成的。目前无法治愈, 只有通过药物、运动、饮食来调理。由此可见, 寻找2型糖尿病的新靶点以及开发新药受到了越来越多研究人员的关注, 并渴望从传统中医药以及天然植物中寻找有价值的潜在化合物。

刺五加为五加科植物刺五加Acanthopanax senticosus (Rupr.et Maxim.) Harms的干燥根和根茎。广泛分布于中国、韩国、日本的一种植物。刺五加的各个部分, 包括根、茎、叶, 在很长的一段时间里被用作保肝、补肾、补骨、刺激食欲、提高记忆力的中药^[3]。对刺五加的研究发现, 刺五加根茎部位的主要化学成分是苷类化合物^[4], 目前已有研究表明刺五加总苷具有明显的预防糖尿病并发症以及降糖活性^[5]。但其作用机制却鲜有研究, 然而鉴于中药的多成分、多靶点、多通路的特点, 刺五加总苷抗2型糖尿病的作用机制利用传统的分离单体和药理学研究方法将耗资巨大, 周期长, 难以被阐明。其混合物活性成分靶点的不明确性, 严重影响进一步的药物筛选与开发。

网络药理学是近年来新兴的一种研究药物、疾病、靶点之间相互作用关系的网络分析方法, 利用现代计算机信息技术结合生物信息学、药理学、分子生物学、统计学等多学科交叉。通过反向分子对接技术预测成分的潜在靶点, 并对这些靶点进行生物信息学分析, 建立“成分-靶点”和“成分-靶点-通路”网络来分析预测多种药物成分在体内的相互作用关系。网络药理学强调信号通路的多靶点多途径调节, 在新药研发特别是在中药的创新药物研发中可发挥重要作用^[6]。因此常被用来预测与揭示中药多成分、多靶点的作用机制, 能够大幅降低不确定性和盲目性, 大大节约初期药物筛选的成本, 越来越得到了研究人员的重视。为传统中医药的现代化提供了新的思路。本研究将使用网络药理学方法, 对刺五加总苷主要成分进行分析, 揭示其潜在的抗2型糖尿病作用机制, 为今后的实验研究以及药物筛选和开发奠定了基础。

材料与方法

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刺五加总苷化合物的获取和筛选 刺五加根茎的主要成分为酚苷类化合物, 通过文献检索选取含量较高的苷类成分, 使用PubChem数据库(https://pubchem.ncbi.nlm.nih.gov/)检索得到刺五加总苷化合物的结构文件。

刺五加总苷化合物的靶点预测 将准备好的刺五加总苷化合物的结构文件, 导入SwissTargetPrediction (http://www.swisstargetprediction.ch/)平台^[7]进行反向分子对接靶点预测, 设置种属为“homo sapiens”来预测活性靶点, 平台自动将化合物与后台靶点数据库进行计算机模拟对接, 选择亲和力最高的前15位潜在活性靶点。

2型糖尿病疾病靶点的获取 利用GeneCards在线数据库(http://www.genecards.org/)检索2型糖尿病英文名“type 2 diabetes”, 得到与2型糖尿病相关的基因, 下载后存为Excel文件并备用。

刺五加总苷“成分-靶点”网络的建立 通过对刺五加总苷的相关靶点预测, 在String平台(https://string-db.org/)构建疾病-靶点蛋白相互作用关系, 置信度设置为0.9, 与刺五加总苷潜在靶点取交集后将结果导入到Cytoscape3.6.1软件中并建立成分-靶点相互作用网络并做网络分析。

GO富集分析 将靶点的基因名利用DAVID6.8 (https://david.ncifcrf.gov/)^{[8, 9]}转换成基因ID, 基因ID是基因的唯一标识码, 并对刺五加总苷的核心靶点可能存在的生物过程、分子功能、细胞组分进行GO富集分析(P < 0.05), 并根据P值绘制柱状图。

KEGG通路富集分析 通过DAVID6.8, 设置P < 0.05, 导入刺五加总苷靶点的Gene ID后进行KEGG通路富集分析, 并根据富集程度和P值并使用R 3.6.1软件对其可视化作图。筛选出2型糖尿病主要信号通路导入到Cytoscape3.6.1进行“成分-靶点-通路”相互作用网络的构建并可视化。

分子对接验证 采用在线平台系统对接平台Systems Dock (http://systemsdock.unit.oist.jp/iddp/home/index)对筛选出来的核心成分靶点进行进一步的分子对接验证。Systems Dock以对接分数(score)评价活性成分与靶点交互作用^[10]。对接分数是实验解离/抑制常数值(pK_d/pK_i)的负对数, 数值通常为0~10之间(由弱至强), 当对接分数 > 4.25时, 认为具有一定的结合活性; 当对接分数 > 5时, 认为结合活性较好; 当对接分数 > 7时, 认为有强烈的结合活性^[11]。再次证明靶点和活性成分结合的性能。

结果

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1 刺五加总苷成分筛选结果

通过文献报道^[12], 刺五加总苷主要有7种苷类成分, 并且根和根茎部分含量丰富, 分别占干药材重量的0.6%~0.9%和0.6%~1.5%。这7种主要成分分别是刺五加苷A (eleutheroside A)、刺五加苷B (eleutheroside B)、刺五加苷B1 (eleutheroside B1)、刺五加苷C (eleutheroside C)、还有刺五加苷D和E (eleutheroside D, eleutheroside E)、芝麻素(sesamin)。并且刺五加苷B和刺五加苷E在茎中的含量高于根部^[13]。这7种苷类化合物的成分比大约为8:30:10:12:4:2:1, 构成了刺五加根茎苷类化合物的主要成分, 以此作为本文研究对象。

2 活性靶点预测

利用SwissTargetPrediction平台预测活性化合物的潜在靶点, 共获得105个基因靶点。在GeneCards检索到的2型糖尿病靶点共有511个, 整理合并后得到刺五加总苷抗2型糖尿病的潜在相互作用靶点共35个靶点, 如表 1所示。

3 “成分-靶点”的构建

将筛选出的与2型糖尿病相关的靶点构建疾病-靶点相互作用网络, 并与成分建立关联, 导入Cytoscap3.6.1中, 合并成“成分-靶点”相互作用关系。利用Cytoscape3.6.1中内置的Networkanalyzer工具对网络关系的特性进行拓扑学分析, 得到“度值(degree)”、“节点紧密度(closeness)”和“节点介度(betweenness)”等拓扑学参数信息如表 2所示。

4 GO富集分析结果

使用DAVID6.8对刺五加总苷7个化合物的靶基因进行了GO富集分析(P < 0.05)。富集分析结果如图 1所示, 包括生化过程, 细胞组分, 分子功能。结果可以看出, 刺五加总苷可能参与众多过程, 包括葡萄糖稳态、药物反应、MAPK活性、JNK活性等。

5 KEGG通路富集分析结果

对刺五加总苷7个化合物预测的靶点使用DAVID6.8进行KEGG通路富集分析, 筛选出与2型糖尿病相关的8条主要通路, 包括胰岛素抵抗信号通路、FoxO信号通路和MAPK信号通路等, 并将其结果可视化, 如图 2所示。

6 “成分-靶点-通路”网络构建

根据KEGG通路富集分析的结果, 本研究筛选出8条主要与2型糖尿病相关的信号通路, 同时与参与相关通路的靶点建立联系。并与“成分-靶点”将相互作用关系整合并导入Cytoscape3.6.1中, 并可视化得到“成分-靶点-通路”网络图, 如图 3所示。

7 分子对接验证

结合上述KEGG富集分析结果, 从胰岛素抵抗、细胞凋亡和炎症3个方面选取了胰岛素抵抗信号通路、Jak-STAT3信号通路和TNF信号通路, 并结合网络药理学预测的靶点作为关键靶蛋白并与其对应的成分进行分子对接, 并选择商品化小分子抑制剂作为阳性对照, 详细信息和对接情况如表 3所示。由前面的对接标准可以得知, 当对接分数 > 4.25时, 说明具有一定的结合活性; 当对接分数 > 5时, 说明结合活性较好; 当对接分数 > 7时, 说明有强烈的结合活性。结果显示, 相对于商品化的小分子抑制剂而言, 筛选出来的核心成分与靶点均具有较好的结合活性, 其中刺五加苷D和PTPN1还具有强烈的结合活性。对接空间图和部分相互作用的氨基酸残基如图 4所示, 由图可以看出, 除E1_刺五加苷A与STAT3对接之外, 其余成分与靶点对接均有丰富的氨基酸残基与其相互作用。

讨论

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通过分析结果可以看出, 刺五加总苷主要是通过调控2型糖尿病、胰岛素抵抗、TNF信号通路、Jak-SATA信号通路、FoxO信号通路、胰岛素信号通路、cAMP信号通路、MAPK信号通路来起到治疗糖尿病的作用。根据KEGG富集分析的结果绘制了刺五加总苷治疗糖尿病的主要通路, 并且筛选出刺五加总苷中的核心成分, 分别包括E1_刺五加苷A、E5_刺五加苷D、E6_刺五加苷E和E7_芝麻素4个化合物。如图 5所示黄色代表刺五加总苷的潜在干预靶点。

不难看出, E1_刺五加苷A的潜在靶点是STATs, 并且在总苷中含量较高, 通过对STAT3进行进一步的分子对接验证可以看出, E1_刺五加苷A和STAT3具有良好的亲和力, 在与阳性对照化合物氯硝柳胺(niclosamide)的分子对接对比中可以发现, E1_刺五加苷A与STAT3的亲和力明显高于氯硝柳胺。目前对E1_刺五加苷A在抗糖尿病方面的研究仍然比较浅显。Sheng等^[14]研究发现, E1_刺五加苷A是一种α-葡萄糖苷酶抑制剂, 并且效果明显强于已经上市的阿卡波糖。Gao等^[15]的研究发现, E1_刺五加苷A是一种良好的过氧化物酶体增殖物激活受体β (peroxisome proliferators-activated receptor β, PPARβ)激动剂。但E1_刺五加苷A在体内的降糖作用机制尚未有研究涉及。本研究利用网络药学预测出了其对STAT3具有良好的亲和力, 而STAT3可以通过作用于SOCS3来影响胰岛素受体底物1 (insulin receptor substrate-1, IRS-1)的活性^[16], SOCS3已被认为可介导肥胖患者的中枢瘦素抵抗^[17]。下丘脑瘦素信号转导能调控肝脏葡萄糖和脂代谢^[18], 并且调控肝脏糖异生^[19]。由此可以看出STAT3在调节糖代谢紊乱方面起着重要的作用。因此, E1_刺五加苷A可能通过该途径来抑制STATs, 从而提高IRS的活性降低FoxO1活性, 提高胰岛素敏感性, 抑制肝脏糖异生。暗示E1_刺五加苷A通过STAT3对调节葡萄糖稳态具有重大开发价值。不仅如此, 在2型糖尿病中, 胰岛素抵抗合并内脏肥胖会导致葡萄糖毒性效应, 使胰岛β细胞异常凋亡^[20]。STATs基因和凋亡密切相关^{[21, 22]}, 根据KEGG分析发现E1_刺五加苷A能够通过STATs影响BCL2和BCLXL表达, 而BLC2和BCLXL同样也是E1_刺五加苷A的靶点。Jiang等^[23]研究发现在神经细胞中, E1_刺五加苷A能通过上调BCL2起到抗凋亡作用, 这与本研究预测抗凋亡作用相类似。并推测E1_刺五加苷A对在高糖环境下导致的神经细胞损伤具有保护作用, 甚至在保护胰岛β细胞方面也可能存在着类似作用, 结论有待进一步研究去论证。因此由图 6可以得出结论, E1_刺五加苷A可能通过共同作用STATs、BCL2和BCLXL来治疗由于高血糖导致的细胞异常凋亡。

E5_刺五加苷D和E6_刺五加苷E都可以作用于PTPN1和PTPN2, 可能与它们的化学结构类似有关, 为紫丁香树脂酚葡萄糖苷的两种不同构型, 通过以E5_刺五加苷D为例与PTPN1蛋白进行分子对接验证, 发现E5_刺五加苷D的结合性能略优于商品化小分子抑制剂PTP1B-IN-1。目前已有文献^[24]报道, E6_刺五加苷E能够改善2型糖尿病db/db小鼠胰岛素抵抗, 能在Ser473位点磷酸化AKT, 但深入机制尚未研究。而PTPN1的缺陷可以改善PI3K的活性, 从而提高胰岛素敏感性^[25], PTPN2同样也是负调控胰岛素信号通路^[26], 本研究解释了E6_刺五加苷E可能是通过PTPN1途径从而间接激活AKT。不仅如此, E5_刺五加苷D和E6_刺五加苷E还可以作用于PTPN2, 而PTPN2可以减弱肝脏中的SATA3活性和胰岛素信号通路, 从而调节糖异生^[27], 这说明E5_刺五加苷D和E6_刺五加苷E还可能通过抑制肝脏中PTPN2可以改善2型糖尿病和肥胖。

糖尿病患者在整个病程中都会伴随着炎症反应, 肿瘤坏死因子α (tumor necrosis factor-α, TNF-α)、白介素6 (interleukin-6, IL-6)和白介素1B (interleukin-1B, IL1B)等炎症因子含量会上升^[28], E7_芝麻素的潜在靶点是MAPK8、MAPK9、MAPK10、MAPK11和MAPK14, 也分别被称作JNK1、JNK2、JNK3、p38α和p38β。炎症反应常常与MAPKs有关, 包括JNK信号通路^[29], 抑制JNK可以改善由于炎症导致的细胞损伤。由分子对接结果可以看出, E7_芝麻素与JNK商品化小分子抑制剂tanzisertib相比具有相似的亲和能力, 以及比SB202190 (FHPI)更优良的p38结合力。Fan等^[30]和刘彦群等^[31]的研究证实这一结论, E7_芝麻素可以通过JNK和p38来抑制炎症反应和细胞凋亡。而p38可以使FoxO1核移位^[32], 因此本研究推断E7_芝麻素可能通过阻断p38来抑制肝脏的糖异生。不仅如此, 抑制JNK同样可以激活IRS^[33]和抑制FoxO1^[34]。说明E7_芝麻素在抗炎、改善胰岛素抵抗和抑制肝脏糖异生方面具有良好的治疗潜力。Mohammad等^[35]的研究结果表明, E7_芝麻素具有明显的抗炎活性, 能显著降低人体TNF-α和IL-6的水平, 与本研究预测的抗炎结果相一致。说明E7_芝麻素可能通过直接作用JNK来控制糖尿病发生的炎症反应, 还可能通过共同作用JNK和p38下调FoxO1来抑制肝脏糖异生的发生来调节血糖。

由此可见, 刺五加总苷调节其中多条通路, 在2型糖尿病导致的胰岛素抵抗、炎症、细胞凋亡等方面都可能发挥重要的作用, 这是传统“一药一靶点”模式难以做到的, 而这也符合中药多成分、多靶点、多通路对疾病起到综合治疗作用的特点。

网络药理学是一门基于高通量统计分析的新兴学科, 为传统中医药的研究提供了一条独特的创新之路, 包括单组分和多组分的作用机制。本研究利用网络药理学的方法, 对刺五加苷类化合物的主要成分进行了研究, 构建了“成分-靶点”网络和“成分-靶点-通路”网络, 挖掘了其可能存在的机制, 并利用分子对接与文献验证了其预测的结果, 证明了网络药理学具有一定的准确性, 并对刺五加总苷抗糖尿病的机制进行了预测和猜想, 大大节省了前期探索的人力物力, 减少了后续对刺五加总苷研究过程中的不确定性和盲目性, 使探索目的更加明确。但预测结果不能完全证明其正确性, 所以本课题组也将根据本研究的结果开展系统严谨的生物学验证。

基金

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国家自然科学基金资助项目(81400791)

参考文献

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文献

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2019年第54卷第11期

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doi: 10.16438/j.0513-4870.2019-0350

接收时间：2019-05-05
首发时间：2026-01-26
出版时间：2019-11-12

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收稿日期：2019-05-05
修回日期：2019-07-10

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国家自然科学基金资助项目(81400791)

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湖北大学, 中药生物技术湖北省重点实验室, 药物高通量筛选技术国家地方联合工程研究中心, 湖北武汉 430062

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*陈勇, Tel: 86-27-88663590, E-mail: cy101610@qq.com;

梁继超, Tel: 86-27-88663882, E-mail: liang529114@163.com

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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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RefWorks
TxT

Symbol	Gene ID	Gene name
DRD3	1 814	Dopamine receptor D3
DRD2	1 813	Dopamine receptor D2
DRD4	1 815	Dopamine receptor D4
BCL2L1	598	BCL2 like 1
ADORA1	134	Adenosine A1 receptor
DCT	1 638	Dopachrome tautomerase
TYR	7 299	Tyrosinase
STAT4	6 775	Signal transducer and activator of transcription 4
BCL2	596	BCL2, apoptosis regulator
CNR1	1 268	Cannabinoid receptor 1
MAPT	4 137	Microtubule associated protein tau
AR	367	Androgen receptor
PTPN2	5 771	Protein tyrosine phosphatase, non-receptor type 2
MAPK11	5 600	Mitogen-activated protein kinase 11
MAPK10	5 602	Mitogen-activated protein kinase 10
FUCA2	2 519	Fucosidase, alpha-L-2, plasma
STAT1	6 772	Signal transducer and activator of transcription 1
FUCA1	2 517	Fucosidase, alpha-L-1, tissue
STAT3	6 774	Signal transducer and activator of transcription 3
STAT2	6 773	Signal transducer and activator of transcription 2
ALOX15	246	Arachidonate 15-lipoxygenase
HIF1A	3 091	Hypoxia inducible factor 1 alpha subunit
CA9	768	Carbonic anhydrase 9
ALOX15B	247	Arachidonate 15-lipoxygenase, type B
MAPK14	1 432	Mitogen-activated protein kinase 14
AKR1B10	57 016	Aldo-keto reductase family 1 member B10
CHRM1	1 128	Cholinergic receptor muscarinic 1
AKR1B1	231	Aldo-keto reductase family 1 member B
MAPK9	5 601	Mitogen-activated protein kinase 9
MAPK8	5 599	Mitogen-activated protein kinase 8
ALOX5	240	Arachidonate 5-lipoxygenase
PTPN1	5 770	Protein tyrosine phosphatase, non-receptor type 1
CA1	759	Carbonic anhydrase 1
PTAFR	5 724	Platelet activating factor receptor
HTR2A	3 356	5-Hydroxytryptamine receptor 2A

Symbol

Gene ID

Gene name

DRD3

1 814

Dopamine receptor D3

DRD2

1 813

Dopamine receptor D2

DRD4

1 815

Dopamine receptor D4

BCL2L1

598

BCL2 like 1

ADORA1

134

Adenosine A1 receptor

DCT

1 638

Dopachrome tautomerase

TYR

7 299

Tyrosinase

STAT4

6 775

Signal transducer and activator of transcription 4

BCL2

596

BCL2, apoptosis regulator

CNR1

1 268

Cannabinoid receptor 1

MAPT

4 137

Microtubule associated protein tau

367

Androgen receptor

PTPN2

5 771

Protein tyrosine phosphatase, non-receptor type 2

MAPK11

5 600

Mitogen-activated protein kinase 11

MAPK10

5 602

Mitogen-activated protein kinase 10

FUCA2

2 519

Fucosidase, alpha-L-2, plasma

STAT1

6 772

Signal transducer and activator of transcription 1

FUCA1

2 517

Fucosidase, alpha-L-1, tissue

STAT3

6 774

Signal transducer and activator of transcription 3

STAT2

6 773

Signal transducer and activator of transcription 2

ALOX15

246

Arachidonate 15-lipoxygenase

HIF1A

3 091

Hypoxia inducible factor 1 alpha subunit

CA9

768

Carbonic anhydrase 9

ALOX15B

247

Arachidonate 15-lipoxygenase, type B

MAPK14

1 432

Mitogen-activated protein kinase 14

AKR1B10

57 016

Aldo-keto reductase family 1 member B10

CHRM1

1 128

Cholinergic receptor muscarinic 1

AKR1B1

231

Aldo-keto reductase family 1 member B

MAPK9

5 601

Mitogen-activated protein kinase 9

MAPK8

5 599

Mitogen-activated protein kinase 8

ALOX5

240

Arachidonate 5-lipoxygenase

PTPN1

5 770

Protein tyrosine phosphatase, non-receptor type 1

CA1

759

Carbonic anhydrase 1

PTAFR

5 724

Platelet activating factor receptor

HTR2A

3 356

5-Hydroxytryptamine receptor 2A

No.	Compound	CAS	Closeness	Betweenness	Degree
E1	Eleutheroside A	474-58-8	0.186 471 04	0.450 549 45	13
E2	Eleutheroside B	118-34-3	0.025 209 9	0.294 964 03	4
E3	Eleutheroside B1	16845-16-2	0.099 564 75	0.344 537 82	5
E4	Eleutheroside C	15486-24-5	0.139 129 06	0.383 177 57	6
E5	Eleutheroside D	79484-75-6	0.089 527 33	0.427 083 33	7
E6	Eleutheroside E	39432-56-9	0.089 527 33	0.427 083 33	7
E7	Sesamin	607-80-7	0.175 687 82	0.422 680 41	10

No.

Compound

CAS

Closeness

Betweenness

Degree

Eleutheroside A

474-58-8

0.186 471 04

0.450 549 45

Eleutheroside B

118-34-3

0.025 209 9

0.294 964 03

Eleutheroside B1

16845-16-2

0.099 564 75

0.344 537 82

Eleutheroside C

15486-24-5

0.139 129 06

0.383 177 57

Eleutheroside D

79484-75-6

0.089 527 33

0.427 083 33

Eleutheroside E

39432-56-9

0.089 527 33

0.427 083 33

Sesamin

607-80-7

0.175 687 82

0.422 680 41

Target	PDBID	Comp	PC	Score (Comp)	Score (PC)
STAT3	1BG1	Eleutheroside A	Niclosamide	5.047	1.541
PTPN1	2H4K	Eleutheroside D	PTP1B-IN-1	7.106	5.952
JNK1	3PZE	Sesamin	Tanzisertib	6.689	7.249
JNK2	3NPC	Sesamin	Tanzisertib	6.964	7.64
JNK3	2B1P	Sesamin	Tanzisertib	6.154	7.065
p38α	5LAR	Sesamin	SB202190	6.603	2.473
p38β	3GP0	Sesamin	SB202190	6.884	1.789

Target

PDBID

Comp

Score (Comp)

Score (PC)

STAT3

1BG1

Eleutheroside A

Niclosamide

5.047

1.541

PTPN1

2H4K

Eleutheroside D

PTP1B-IN-1

7.106

5.952

JNK1

3PZE

Sesamin

Tanzisertib

6.689

7.249

JNK2

3NPC

Sesamin

Tanzisertib

6.964

7.64

JNK3

2B1P

Sesamin

Tanzisertib

6.154

7.065

p38α

5LAR

Sesamin

SB202190

6.603

2.473

p38β

3GP0

Sesamin

SB202190

6.884

1.789