牛磺鹅去氧胆酸抗炎作用机制的网络药理学研究

引用本文

LI Lei, TUMEN Ba-ya-er, LI Pei-feng, LIU Chang, LI Sheng-he, NING Kang-jian, JIANG Jin-peng. Mechanisms of anti-inflammation of taurochenodeoxycholic acid based on network pharmacology[J]. Acta Pharmaceutica Sinica, 2018, 53(12): 2064-2075.

李磊, 图门巴雅尔, 李培锋, 刘畅, 李升和, 宁康健, 姜锦鹏. 牛磺鹅去氧胆酸抗炎作用机制的网络药理学研究[J]. 药学学报, 2018, 53(12): 2064-2075.

牛磺鹅去氧胆酸抗炎作用机制的网络药理学研究

李磊¹, 图门巴雅尔², 李培锋³, 刘畅¹, 李升和¹, 宁康健¹, 姜锦鹏¹

1. 安徽科技学院动物科学学院, 农业农村部猪肉质量安全控制重点实验室, 安徽凤阳 233100;
2. 山西省动物疫病预防控制中心, 山西太原 030027;
3. 内蒙古农业大学兽医学院, 农业农村部动物疾病临床诊疗技术重点实验室, 内蒙古呼和浩特 010018

收稿日期: 2018-07-01; 修回日期: 2018-07-31

基金项目: 国家自然科学基金资助项目（31802242）；安徽省高校自然科学研究重点项目（KJ2017A503，KJ2017A504）；安徽科技学院人才引进项目（ZRC2014447）；安徽科技学院重点学科建设项目（AKZDXK2015A04）

^*通讯作者: 刘畅, Tel:86-550-6732040, E-mail:liuchang052@126.com
李升和, E-mail:lish@ahstu.edu.cn

摘要: 为了探讨牛磺鹅去氧胆酸（taurochenodeoxycholic acid，TCDCA）的抗炎作用机制，通过PubChem查找TCDCA的分子结构并下载SDF格式文件，经PharmMapper服务器、GeneCards数据库预测并筛选TCDCA抗炎靶点。将靶点导入STRING数据库得到蛋白互作关系并通过Cytoscape进行可视化处理，然后将靶点导入STRING数据库进行GO及KEGG通路分析，通过分子对接对TCDCA与主要靶点结合活性进行验证，并通过DisGeNET数据库获取靶点类型信息。筛选得到TCDCA涉及抗炎作用的靶点89个，网络分析结果表明，TCDCA主要涉及刺激反应（response to stimulus）、多细胞生物过程（multicellular organismal process）、单细胞生物过程（single-multicellular organism process）、对化学刺激反应（response to chemical）及有机物反应（response to organic substance）等68个生物过程，通过调节癌症相关通路（pathways in cancer）、孕酮介导的卵母细胞成熟（progesterone-mediated oocyte maturation）、MAPK信号通路（MAPK signaling pathway）、蛋白多糖在癌症中的作用（proteoglycans in cancer）等51条信号通路来发挥抗炎作用。本研究反映出TCDCA经多靶点、多途径发挥抗炎作用的特点，为后续开展其抗炎作用机制的研究指明了方向。

关键词: 牛磺鹅去氧胆酸炎症网络药理学药理机制分子对接

Mechanisms of anti-inflammation of taurochenodeoxycholic acid based on network pharmacology

LI Lei¹, TUMEN Ba-ya-er², LI Pei-feng³, LIU Chang¹, LI Sheng-he¹, NING Kang-jian¹, JIANG Jin-peng¹

1. Key Laboratory of Quality and Safety Control for Pork, Ministry of Agriculture and Rural, College of Animal Science, Anhui Science and Technology University, Fengyang 233100, China;
2. Shanxi Animal Disease Control Center, Taiyuan 030027, China;
3. Key Laboratory of Clinical Diagnosis and Treatment Techniques for Animal Disease, Ministry of Agriculture and Rual, College of Veterinary Medicine, Inner Mongolia Agricultural University, Hohhot 010018, China

Abstract: To investigate the anti-inflammatory mechanisms of taurochenodeoxycholic acid (TCDCA), the molecule structure file of TCDCA was downloaded from PubChem database, PharmMapper and GeneCards were used to predict and screen the targets of TCDCA. STRING database and Cytoscape software were used to construct protein interactions network. GO and KEGG analysis was preformed through STRING database. The key targets were validated by molecular docking and the targets type was attributed by DisGeNET database. The network showed that 89 targets were involved in 68 biological processes including response to stimulus, multicellular organismal process, single-multicellular organism process, response to chemical, response to organic substance, by adjusting 51 signaling pathways, such as pathways in cancer, progesterone-mediated oocyte maturation, MAPK signaling pathway, proteoglycans in cancer. These findings provide an overview of anti-inflammation of TCDCA, which reflects the characteristic of multi-targets and multi-pathways of TCDCA. It pointed out the direction for further research on anti-inflammatory mechanism of TCDCA.

Key words: taurochenodeoxycholic acid inflammation network pharmacology pharmacological mechanism molecular docking

胆汁, 性味苦寒, 具有清热解毒、清肝明目、清肺止咳、滋阴润肺之功效, 为我国传统动物来源中药, 应用历史悠久, 因其来源广泛, 疗效确切, 备受关注^[1]。早在《神农本草经》中就有关于牛胆的记载, 后来的《唐本草》中还记载了熊胆汁的应用, 《本草纲目》与《中华本草》中更是分别收录了31和44种动物胆汁^[2], 上千年的用药历史证明了胆汁的确切疗效。

现代药理学研究发现, 动物胆汁在治疗呼吸系统疾病(如急慢性气管炎、咳嗽等)、肝胆疾病(如胆固醇性结石)、消化系统疾病(如小儿单纯性消化不良)等方面具有显著作用^{[3, 4]}, 此外, 动物胆汁还具有解热镇痛^[5]、抑菌、抗炎^{[6, 7]}、免疫调节^[8]等多方面的药理作用。不同动物胆汁组成成分略有差异, 但均主要包含胆汁酸、胆色素、脂类、蛋白、微量元素等成分, 其中胆汁酸被认为是胆汁发挥功效的物质基础。按照来源可将胆汁酸分为初级和次级胆汁酸, 前者由胆固醇经多次酶促反应在肝脏合成, 后者主要由前者在肠道细菌水解后经7-位脱羟基作用而产生; 按照化学结构可将胆汁酸分为游离型和结合型胆汁酸。

在各类型胆汁酸中, 牛磺鹅去氧胆酸(taurochenodeoxycholic acid, TCDCA, 图 1)作为结合型胆汁酸主要存在于多种动物(如鸡、鸭、鹅、蛇等)胆汁中, 不同动物胆汁中TCDCA含量有一定差异, 其中以鸡、鸭、鹅等家禽胆汁含量较高, 均超过38%^[9]。课题组前期研究中发现, TCDCA对多种原因引起的急慢性炎症反应均具有良好的抑制作用^[10], 但其确切作用机制尚未阐明, 本研究拟通过网络药理学分析TCDCA抗炎作用机制, 为深入揭示TCDCA的药理作用提供支持。

Figure 1 The chemical structure of taurochenodeoxycholic acid (TCDCA)

材料与方法

TCDCA作用靶点的获取 登录PubChem服务器, 查询并下载TCDCA的3D分子结构式, 储存为SDFile (*.sdf)格式。然后将TCDCA.sdf文件输入PharmMapper服务器^[11], 获得TCDCA作用靶点。利用UniProt数据库的Retrieve/ID Mapping功能^[12], 将靶点的UniProt ID转换为Gene Symbol。

炎症相关靶点的筛选 在GeneCards服务器输入inflammation和anti-inflammation搜索已报道的炎症相关基因^[13], 去除重复基因, 与PharmMapper返回的TCDCA靶点进行匹配, 得到TCDCA抗炎作用靶点。

蛋白相互作用网络构建分析 将TCDCA抗炎作用靶点信息导入STRING数据库(https://string-db.org/, Version 10.5)^[14], 设定物种为人, 可获得靶点蛋白相互作用关系, 从结果中提取node1、node2及combine score等相关信息导入Cytoscape程序构建靶点蛋白网络^[15], 并对网络进行分析, 保存分析结果, 设置结点颜色和大小以反映degree的大小, 设置边的粗细反映combine score的大小, 建立靶点蛋白相互作用网络。

生物功能与通路分析 将TCDCA作用靶点信息输入STRING数据库, 进行GO富集分析和KEGG通路注释分析, 保存结果, 设定阈值为P < 0.05, 筛选生物过程或通路。

分子对接验证 选择KEGG通路中富集的靶点, 查找其PDB ID, 输入Systems Dock Web Site (http://systemsdock.unit.oist.jp, Version 2.0)与TCDCA进行分子对接^[16], 保存对接结果, 对其对接打分进行分析, 用以评价TCDCA与各靶点间的结合活性。

靶点类型归属 将能够与TCDCA发生对接的靶点信息输入DisGeNET数据库^[17], 获取靶点类型相关信息。

结果 1 靶点预测

将TCDCA在PharmMapper服务器返回的前100个潜在作用靶点依据fit score进行排序, 通过UniProt数据库将UniProt ID转换为Gene Symbol, 然后与GeneCards中炎症相关基因进行比对, 筛选出89个TCDCA抗炎的潜在作用靶点, 详见表 1。

Table 1 Information of potential targets from TCDCA

No.	UniProt ID	Fit score	Gene symbol	Protein name
1	P22830	6.231	FECH	Ferrochelatase
2	P06702	5.847	S100A9	S100 calcium binding protein A9
3	P28845	5.427	HSD11B1	Hydroxysteroid 11-beta dehydrogenase 1
4	P51161	5.344	FABP6	Fatty acid binding protein 6
5	Q04828	3.81	AKR1C1	Aldo-keto reductase family 1 member C1
6	Q96AZ6	3.786	ISG20	Interferon stimulated exonuclease gene 20
7	P24941	3.775	CDK2	Cyclin dependent kinase 2
8	P50579	3.623	METAP2	Methionine aminopeptidase 2
9	P08235	3.61	NR3C2	Nuclear receptor subfamily 3 group C member 2
10	P02768	3.595	ALB	Albumin
11	Q13370	3.584	PDE3B	Phosphodiesterase 3B
12	Q06520	3.571	SULT2A1	Sulfotransferase family 2A member 1
13	P22894	3.553	MMP8	Matrix metallopeptidase 8
14	Q01469	3.513	FABP5	Fatty acid binding protein 5
15	P49638	3.453	TTPA	Alpha tocopherol transfer protein
16	Q16539	3.433	MAPK14	Mitogen-activated protein kinase 14
17	P11142	3.369	HSPA8	Heat shock protein family A (Hsp70) member 8
18	P02774	3.341	GC	GC, vitamin D binding protein
19	Q07343	3.119	PDE4B	Phosphodiesterase 4B
20	Q13231	3.11	CHIT1	Chitinase 1
21	P27487	2.997	DPP4	Dipeptidyl peptidase 4
22	P52895	2.996	AKR1C2	Aldo-keto reductase family 1 member C2
23	P00325	2.981	ADH1B	Alcohol dehydrogenase 1B (class I), beta polypeptide
24	P04818	2.978	TYMS	Thymidylate synthetase
25	P35398	2.975	RORA	RAR related orphan receptor A
26	P10275	2.974	AR	Androgen receptor
27	P18031	2.961	PTPN1	Protein tyrosine phosphatase, non-receptor type 1
28	P00533	2.958	EGFR	Epidermal growth factor receptor
29	P45452	2.956	MMP13	Matrix metallopeptidase 13
30	P11309	2.954	PIM1	Pim-1 proto-oncogene, serine/threonine kinase
31	P47929	2.947	LGALS7	Galectin 7
32	P12931	2.945	SRC	SRC proto-oncogene, non-receptor tyrosine kinase
33	P49354	2.942	FNTA	Farnesyltransferase, CAAX box, alpha
34	P43155	2.939	CRAT	Carnitine O-acetyltransferase
35	P09211	2.936	GSTP1	Glutathione S-transferase pi 1
36	P80188	2.936	LCN2	Lipocalin 2
37	P00374	2.925	DHFR	Dihydrofolate reductase
38	P00734	2.925	F2	Coagulation factor Ⅱ, thrombin
39	P00491	2.903	PNP	Purine nucleoside phosphorylase
40	P02766	2.899	TTR	Transthyretin
41	P78536	2.896	ADAM17	ADAM metallopeptidase domain 17
42	P11586	2.893	MTHFD1	Methylenetetrahydrofolate dehydrogenase, cyclohydrolase and formyltetrahydrofolate synthetase 1
43	P25311	2.893	AZGP1	Alpha-2-glycoprotein 1, zinc-binding
44	P03372	2.888	ESR1	Estrogen receptor 1
45	P15056	2.885	BRAF	Serine/threonine-protein kinase B-raf
46	P53779	2.885	MAPK10	Mitogen-activated protein kinase 10
47	P00742	2.881	F10	Coagulation factor X
48	P00918	2.877	CA2	Carbonic anhydrase 2
49	P26196	2.873	DDX6	DEAD-box helicase 6
50	P00747	2.872	PLG	Plasminogen
51	P06401	2.87	PGR	Progesterone receptor
52	P43235	2.867	CTSK	Cathepsin K
53	P08758	2.864	ANXA5	Annexin A5
54	Q13126	2.853	MTAP	Methylthioadenosine phosphorylase
55	P29474	2.851	NOS3	Nitric oxide synthase 3
56	O14965	2.848	AURKA	Aurora kinase A
57	P04062	2.846	GBA	Glucosylceramidase beta
58	P07900	2.836	HSP90AA1	Heat shock protein 90 alpha family class A member 1
59	P68400	2.836	CSNK2A1	Casein kinase 2 alpha 1
60	P04278	2.829	SHBG	Sex hormone binding globulin
61	P03950	2.809	ANG	Angiogenin
62	P15090	2.806	FABP4	Fatty acid binding protein 4
63	O15530	2.806	PDPK1	3-Phosphoinositide dependent protein kinase 1
64	P02743	2.792	APCS	Amyloid P component, serum
65	P18075	2.785	BMP7	Bone morphogenetic protein 7
66	Q15075	2.78	EEA1	Early endosome antigen 1
67	P20248	2.759	CCNA2	Cyclin A2
68	P23141	2.755	CES1	Carboxylesterase 1
69	P42330	2.732	AKR1C3	Aldo-keto reductase family 1 member C3
70	P14174	2.721	MIF	Macrophage migration inhibitory factor (glycosylation-inhibiting factor)
71	P00915	2.706	CA1	Carbonic anhydrase 1
72	P12643	2.652	BMP2	Bone morphogenetic protein 2
73	P28482	2.646	MAPK1	Mitogen-activated protein kinase 1
74	P62937	2.643	PPIA	Peptidylprolyl isomerase A
75	O14757	2.628	CHEK1	Checkpoint kinase 1
76	P15121	2.622	AKR1B1	Aldo-keto reductase family 1 member B
77	P08842	2.616	STS	Steroid sulfatase (microsomal), isozyme S
78	P02652	2.586	APOA2	Apolipoprotein A2
79	P00751	2.549	CFB	Complement factor B
80	P30044	2.546	PRDX5	Peroxiredoxin 5
81	P07339	2.521	CTSD	Cathepsin D
82	P45983	2.47	MAPK8	Mitogen-activated protein kinase 8
83	P37231	2.377	PPARG	Peroxisome proliferator activated receptor gamma
84	P56817	2.373	BACE1	Beta-secretase 1
85	P42574	2.342	CASP3	Caspase 3
86	P20701	2.256	ITGAL	Integrin subunit alpha L
87	P52758	2.195	RIDA	Reactive intermediate imine deaminase A homolog
88	P49137	2.192	MAPKAPK2	Mitogen-activated protein kinase-activated protein kinase 2
89	P23946	2.055	CMA1	Chymase 1

Table 1 Information of potential targets from TCDCA

2 蛋白相互作用网络构建与分析

将上述靶点蛋白信息导入STRING数据库, 获得靶点蛋白相互作用关系, 利用Cytoscape程序对靶点蛋白相互作用网络进行可视化处理(图 2)。图中的节点表示靶点蛋白分子, 边表示各靶点间的相互关系, 共包含85个节点, 356条边(其中HRSP12、RORA、CRAT和ISG20与其他靶点蛋白不存在相互作用, 故不在网络中体现)。图中节点颜色和大小反映了靶点蛋白degree的值, 颜色由绿变红、节点越大表示该靶点蛋白的degree值越大, 反之表示degree值越小。边的粗细程度表示靶点蛋白间的combine score值, 线条越粗, 表示该值越大。

Figure 2 Target protein interaction network of TCDCA

3 GO分析结果

通过STRING数据库分析了TCDCA抗炎靶点的生物学过程、分子功能和细胞成分, 结果表明, 这些靶点被富集到68个生物学过程中(表 2), 其中排名前5的生物学过程主要包括:对刺激的反应(response to stimulus)、多细胞生物过程(multicellular organismal process)、单细胞生物过程(single-multicellular organism process)、对化学刺激的反应(response to chemical)及对有机物的反应(response to organic substance)等。分子功能GO分析表明(表 3), TCDCA的靶点主要富集于蛋白结合(protein binding)、脂质结合(lipid binding)、受体结合(receptor binding)、脂肪酸结合(fatty acid binding)、受体信号蛋白丝氨酸/苏氨酸激酶活性(receptor signaling protein serine/threonine kinase activity)及MAPK蛋白激酶活性(MAP kinase activity)等6方面。在细胞组分方面(表 4), 这些靶点主要为细胞质(cytoplasmic part、cytosol)、细胞外成分(extracellular region)、膜结合囊泡(membrane-bounded vesicle)、囊泡(vesicle)、外泌体(extracellular exosome)与内涵体(endosome)等。

Table 2 Gene ontology terms for biological process of TCDCA

GO-ID	GO term	Degree	P-value
GO.0050896	Response to stimulus	58	2.06E-02
GO.0032501	Multicellular organismal process	51	1.61E-02
GO.0044707	Single-multicellular organism process	50	1.02E-02
GO.0042221	Response to chemical	47	7.79E-03
GO.0010033	Response to organic substance	39	3.56E-03
GO.0065008	Regulation of biological quality	37	1.54E-02
GO.0009628	Response to abiotic stimulus	31	3.89E-07
GO.0050790	Regulation of catalytic activity	31	2.58E-02
GO.0051246	Regulation of protein metabolic process	31	2.58E-02
GO.0009719	Response to endogenous stimulus	30	3.47E-04
GO.0042127	Regulation of cell proliferation	30	5.75E-03
GO.0002376	Immune system process	29	1.61E-02
GO.0033993	Response to lipid	28	3.51E-06
GO.0009605	Response to external stimulus	28	3.99E-03
GO.0080134	Regulation of response to stress	27	2.02E-03
GO.1901700	Response to oxygen-containing compound	27	3.89E-03
GO.0009725	Response to hormone	26	5.28E-05
GO.0014070	Response to organic cyclic compound	25	2.95E-05
GO.0031399	Regulation of protein modification process	24	2.35E-02
GO.0006952	Defense response	23	1.61E-02
GO.0042592	Homeostatic process	23	1.61E-02
GO.0006955	Immune response	23	2.85E-02
GO.0048545	Response to steroid hormone	21	2.42E-06
GO.0051240	Positive regulation of multicellular organismal process	21	4.73E-02
GO.0051338	Regulation of transferase activity	19	1.61E-02
GO.0010243	Response to organonitrogen compound	18	9.65E-03
GO.0031347	Regulation of defense response	18	1.24E-02
GO.0032101	Regulation of response to external stimulus	18	2.58E-02
GO.0045859	Regulation of protein kinase activity	17	1.92E-02
GO.0009611	Response to wounding	17	2.58E-02
GO.1901698	Response to nitrogen compound	17	2.58E-02
GO.0050878	Regulation of body fluid levels	17	3.69E-02
GO.0042060	Wound healing	16	3.01E-02
GO.0043627	Response to estrogen	15	6.63E-06
GO.0071396	Cellular response to lipid	15	5.47E-03
GO.0007596	Blood coagulation	15	2.48E-02
GO.0032870	Cellular response to hormone stimulus	15	4.84E-02
GO.0097305	Response to alcohol	14	3.04E-03
GO.0071900	Regulation of protein serine/threonine kinase activity	14	1.24E-02
GO.1903034	Regulation of response to wounding	14	1.61E-02
GO.0001775	Cell activation	14	2.06E-02
GO.1901652	Response to peptide	13	1.61E-02
GO.0071407	Cellular response to organic cyclic compound	13	1.70E-02
GO.1901615	Organic hydroxy compound metabolic process	13	4.77E-02
GO.0030198	Extracellular matrix organization	12	1.70E-04
GO.0009314	Response to radiation	11	2.84E-02
GO.0022411	Cellular component disassembly	10	7.79E-03
GO.0071383	Cellular response to steroid hormone stimulus	10	7.79E-03
GO.0044772	Mitotic cell cycle phase transition	10	9.65E-03
GO.0009416	Response to light stimulus	10	1.70E-02
GO.0008202	Steroid metabolic process	10	1.91E-02
GO.0050727	Regulation of inflammatory response	10	2.58E-02
GO.0010038	Response to metal ion	10	3.16E-02
GO.0030168	Platelet activation	9	3.31E-02
GO.0070482	Response to oxygen levels	9	3.78E-02
GO.0022617	Extracellular matrix disassembly	8	1.70E-04
GO.0032355	Response to estradiol	8	1.61E-02
GO.0043401	Steroid hormone mediated signaling pathway	7	3.78E-02
GO.0000302	Response to reactive oxygen species	7	4.38E-02
GO.0009636	Response to toxic substance	7	4.66E-02
GO.2000379	Positive regulation of reactive oxygen species metabolic process	6	4.64E-02
GO.2001057	Reactive nitrogen species metabolic process	5	1.08E-02
GO.0030574	Collagen catabolic process	5	1.26E-02
GO.0031100	Organ regeneration	5	3.78E-02
GO.0046464	Acylglycerol catabolic process	4	1.92E-02
GO.0006730	One-carbon metabolic process	4	3.86E-02
GO.0031659	Positive regulation of cyclin-dependent protein serine/threonine kinase activity involved in G₁/S transition of mitotic cell cycle	3	7.79E-03
GO.0060694	Regulation of cholesterol transporter activity	2	2.58E-02

Table 2 Gene ontology terms for biological process of TCDCA

Table 3 Gene ontology terms for molecular functions of TCDCA

Table 4 Gene ontology terms for cellular components of TCDCA

4 KEGG分析结果

TCDCA所有潜在抗炎靶点中共有44个靶点富集到51条信号通路(表 5), 排名靠前的通路包括癌症相关通路(pathways in cancer)、孕酮介导的卵母细胞成熟(progesterone-mediated oocyte maturation)、MAPK信号通路(MAPK signaling pathway)、蛋白多糖在癌症中的作用(proteoglycans in cancer)等。此外, 如胰岛素信号通路(insulin signaling pathway)、FoxO信号通路(FoxO signaling pathway)、雌激素信号通路(estrogen signaling pathway)、PPAR信号通路(PPAR signaling pathway)、NOD样信号通路(NOD- like receptor signaling pathway)、Toll样受体信号通路(Toll-like receptor signaling pathway)、肿瘤坏死因子信号通路(TNF signaling pathway)及血管内皮生长因子受体信号通路(VEGF signaling pathway)等均与炎症反应密切相关, 体现了TCDCA多途径的作用特点。

Table 5 Enriched KEGG pathways of potential targets of TCDCA

No.	Pathway	Count	P-value
1	Pathways in cancer	11	3.33E-03
2	Progesterone-mediated oocyte maturation	9	3.18E-06
3	MAPK signaling pathway	9	4.48E-03
4	Proteoglycans in cancer	8	2.56E-02
5	Epithelial cell signaling in Helicobacter pylori infection	7	7.95E-05
6	Toxoplasmosis	7	3.23E-03
7	Tuberculosis	7	3.23E-03
8	Hepatitis C	7	3.23E-03
9	Neurotrophin signaling pathway	7	4.77E-03
10	Insulin signaling pathway	7	5.83E-03
11	FoxO signaling pathway	7	7.07E-03
12	Estrogen signaling pathway	7	7.32E-03
13	Prostate cancer	7	7.32E-03
14	Focal adhesion	7	1.11E-02
15	Epstein-Barr virus infection	7	2.08E-02
16	ErbB signaling pathway	6	2.28E-03
17	GnRH signaling pathway	6	3.23E-03
18	Prolactin signaling pathway	6	4.10E-03
19	PPAR signaling pathway	6	4.20E-03
20	Osteoclast differentiation	6	7.32E-03
21	Hepatitis B	6	7.32E-03
22	Influenza A	6	8.94E-03
23	Viral carcinogenesis	6	1.20E-02
24	Regulation of actin cytoskeleton	6	1.88E-02
25	Rap1 signaling pathway	6	2.08E-02
26	NOD-like receptor signaling pathway	5	1.69E-03
27	Shigellosis	5	1.69E-03
28	Pertussis	5	2.28E-03
29	Metabolism of xenobiotics by cytochrome P450	5	3.23E-03
30	Colorectal cancer	5	3.23E-03
31	Pancreatic cancer	5	4.31E-03
32	Toll-like receptor signaling pathway	5	6.87E-03
33	Adherens junction	5	1.02E-02
34	TNF signaling pathway	5	1.11E-02
35	VEGF signaling pathway	5	1.30E-02
36	Fc epsilon RI signaling pathway	5	1.78E-02
37	Steroid hormone biosynthesis	4	7.32E-03
38	Salmonella infection	4	9.12E-03
39	Complement and coagulation cascades	4	1.20E-02
40	Chemical carcinogenesis	4	1.23E-02
41	Natural killer cell mediated cytotoxicity	4	2.83E-02
42	Endometrial cancer	4	3.36E-02
43	Chagas disease (American trypanosomiasis)	4	3.49E-02
44	Retrograde endocannabinoid signaling	4	3.79E-02
45	One carbon pool by folate	3	5.83E-03
46	Thyroid cancer	3	9.76E-03
47	Staphylococcus aureus infection	3	1.75E-02
48	Bladder cancer	3	2.15E-02
49	Type Ⅱ diabetes mellitus	3	2.75E-02
50	RIG-I-like receptor signaling pathway	3	2.89E-02
51	Dorso-ventral axis formation	2	4.63E-02

Table 5 Enriched KEGG pathways of potential targets of TCDCA

5 分子对接分析

将上述富集到51条通路中的44个靶点的PDB ID输入到System Dock Web Site服务器中, 采用分子对接的方式进一步确认这些靶点与TCDCA的结合活性, 研究发现只有24个分子可以与TCDCA发生结合, 其中docking score大于7.0的有18个靶点, TCDCA与其中5个靶点的打分甚至超过8.0 (表 6, 图 3), 介于5.0~7.0之间的有6个靶点。该对接程序认为docking score大于4.25表示药物分子与靶蛋白间存在结合能力, 大于5.0时表示结合活性较强, 大于7.0时表示结合能力极强^[18]。分子对接结果表明, TCDCA与预测靶点的结合能力均较强, 体现了TCDCA多靶点的作用特点。

Table 6 Molecular docking of TCDCA

Figure 3 Results of molecular docking. A, C, E, G, I: Molecular docking results for TCDCA with HSPA8, CASP3, ADH1B, PPARG and HSP90AA1 respectively; B, D, F, H, J: Positive control

6 作用靶点归属

将上述可与TCDCA结合的24个靶点信息输入DisGeNET数据库查找各靶点作用类型(protein class, 表 7), 发现这些靶点多数为蛋白激酶、氧化还原酶、转录因子、受体等, 表明TCDCA可作为信号分子与这些靶点发生结合, 调控相关靶点的生物学功能, 这与近年来对于胆汁酸生理功能的研究结果相吻合^{[19, 20]}。

Table 7 The protein class of potential anti-inflammation targets of TCDCA

讨论

胆汁酸具有乳化脂肪、增强脂肪酶活性, 促进脂肪及脂溶性维生素的消化与吸收, 促进胆汁分泌防止胆石生成等作用^{[21, 22]}。近年来发现, 胆汁酸可作为信号分子, 激活细胞内信号转导通路发挥抗炎、免疫调节等药理作用^{[19, 20]}。研究发现, TCDCA具有显著的抗炎、免疫调节作用, 对物理、化学、细菌等原因引起的炎症反应均具有显著的抑制作用^[10], 随着研究深入, 本课题组以佐剂性关节炎(adjuvant arthritis, AA)模型为研究对象探讨了TCDCA对糖皮质激素受体(glucocorticoid receptor, GR)介导的信号通路的影响, 结果表明TCDCA可激活GR, 显著抑制AA大鼠足跖肿胀, 改善AA大鼠骨骼关节变形等情况, 表现出明显的抗炎作用^[23], TCDCA对AA的治疗作用与其抑制AA大鼠滑膜组织与细胞中核因子-κB (nuclear factor-κB, NF-κB)转录活性, 进而抑制肿瘤坏死因子-α (tumor necrosis factor-α, TNF-α)、白细胞介素(interleukin, IL)-1β、IL-6等细胞因子及细胞间黏附分子-1的表达^{[23, 24]}, 以及诱导AA大鼠成纤维样滑膜细胞凋亡有关^[25], 但同时研究也发现, 阻断GR信号通路并不能完全拮抗TCDCA的抗炎作用, 这表明还存在其他通路参与了TCDCA抗炎作用。因此, 通过网络药理学的方式深入挖掘TCDCA的作用靶点对于揭示其作用机制具有重要意义。

本研究通过PharmMapper服务器与GeneCards数据库预测到TCDCA的潜在抗炎作用靶点共89个。接着通过STRING数据库分析发现, 在这些靶点中, 共有85个蛋白存在相互作用关系, 这表明TCDCA抗炎作用并非依靠作用于单一靶点起效, 而是多靶点彼此协同作用。随后利用STRING数据库对这些靶点进行了GO分析, 结果表明, 这些靶点主要分布于细胞外、细胞质、膜结合囊泡、外泌体、内涵体等部位, 具有与蛋白、脂质、受体、脂肪酸结合的功能, 还具有蛋白激酶的活性, 可参与机体对多种物质刺激的反应过程, 参与蛋白、脂质代谢, 调控细胞增殖、免疫应答, 调控多种蛋白激酶活性等作用, 这表明TCDCA的抗炎作用可通过多层次、多途径实现。

对上述靶点进行KEGG通路分析发现, 共有44个靶点可富集到51条信号通路中, 其中24个靶点可与TCDCA发生结合, 它们主要为酶类(包括蛋白酶、蛋白激酶、氧化还原酶、转移酶、水解酶等)、受体、转录因子、转运蛋白、钙结合蛋白、伴侣分子、细胞骨架蛋白等。进一步分析发现, 这些靶点中MAPK1 (41)、MAPK8 (31)、MAPK10 (31)、MAPK14 (26)、BRAF (19)、EGFR (18)、SRC (15)、CASP3 (12)、PDPK1 (11)等均参与了10条以上信号通路(表 6), 以上靶点可能对TCDCA的抗炎作用具有重要的影响。

上述靶点中, MAPK1、MAPK8、MAPK10、MAPK14均为丝裂原活化蛋白激酶(mitogen-activated protein kinase, MAPK)家族成员, 它们是一组在真核生物中高度保守的蛋白激酶, 其介导的信号转导通路在调节细胞增殖、分化、凋亡过程中发挥重要作用, 此外, MAPK通路还参与了机体对外界环境应激的适应、炎症反应等多种生理、病理过程, 是多种抗炎药物作用的共同通路^[26]。Dong等^[27]发现低浓度胆汁酸能够促进大鼠肝细胞JNK蛋白表达, Alpini等^[28]研究发现, TCDCA的差向异构体牛磺熊去氧胆酸可通过MAPK通路抑制胆管上皮癌细胞系Mz-ChA-1细胞的增殖, Ko等^[29]报道证实, 熊去氧胆酸(ursodeoxycholic acid, UDCA)能够调控MAPK和NF-κB通路抑制脂多糖(lipopolysaccharide, LPS)诱导RAW 264.7巨噬细胞中IL-1β、TNF-α、IL-6等细胞因子的表达, 同时还能促进抗炎细胞因子IL-10的表达, 以上研究表明胆汁酸可以调控不同细胞中的MAPK通路。Wang等^[30]报道了TCDCA可以通过PKC/JNK通路诱导NR8383细胞凋亡, 这与本研究网络药理学的预测基本一致, 表明MAPK通路可能是TCDCA发挥抗炎作用的重要途径。

BRAF作为一种蛋白激酶参与了促有丝分裂信号从细胞膜到细胞核的传递, 能够磷酸化MAP2K1激活MAPK/ERK信号通路。在类风湿性关节炎等炎症疾病中, BRAF-MAPK/ERK信号通路被激活, 促进成纤维样滑膜细胞增殖, 可加重关节炎症反应^[31], Zhang^[32]发现白芍药苷可抑制该通路显著缓解类风湿性关节炎。课题组前期研究发现, TCDCA可促进细胞凋亡, 抑制成纤维样滑膜细胞过度增殖^[25], 在这一过程中TCDCA对BRAF-MAPK/ERK通路的影响尚不清楚。

表皮生长因子受体(epidermal growth factor receptor, EGFR)是一种糖蛋白, 位于细胞膜表面, 属于酪氨酸激酶受体, 与其配体结合后形成二聚体, 并发生磷酸化激活, 活化的EGFR可进一步激活下游的RAS-RAF-MEK-ERK、PI3K-AKT、PLC-PKC、STATs与NF-κB等信号通路, 而这些信号通路都与炎症反应密切相关。Im等^[33]发现, UDCA可以通过调控EGFR/Raf-1/ERK通路抑制脱氧胆酸诱导的结肠癌细胞凋亡, 表明胆汁酸可通过调控EGFR相关通路发挥生物学效应^[34]。Rust等^[35]报道, TCDCA可以激活EGFR下游PI3K/PKC通路, 但对于PI3K下游另一重要蛋白AKT无影响; TCDCA还可以抑制NF-κB的转录活性^[36], 但由于NF-κB与多条信号通路均存在密切联系, TCDCA是否能够抑制EGFR进而抑制NF-κB还需进行实验验证。

蛋白激酶SRC广泛分布于组织细胞内, 可与多条信号通路中的关键分子发生相互作用而参与调控细胞代谢、生长、发育和分化等过程。SRC可直接激活MAPK通路上游活化蛋白RAS, 活化的RAS可依次激活RAF、MEK、MAPK/ERK引发一系列生物学效应^[37]; SRC还与EGFR通路密切相关, EGFR的活性常随着SRC活性升高而升高^[38];在LPS诱导的巨噬细胞炎症反应中, 抑制SRC活性可阻断诱导型一氧化氮合酶(inducible nitric oxide synthase, iNOS)、环氧合酶(cyclooxygenase, COX)-2及TNF-α的表达产生抗炎作用^{[39, 40]}。TCDCA对LPS诱导的大鼠腹腔巨噬细胞中iNOS、COX-2及TNF-α的表达均有抑制作用^{[23, 36]}, 提示TCDCA也可能通过抑制SRC活性而产生抗炎作用。

PDPK1在磷脂酰肌醇-3, 4, 5-三磷酸存在的条件下可快速磷酸化AKT激活PI3K/AKT信号通路及下游NF-κB通路^{[41, 42]}, PDPK1的高表达间接促进了NF-κB通路的活化^[43], 然而也有研究发现, 在巨噬细胞中PDPK1可以负反馈抑制Toll样受体介导的NF-κB通路^[44], 因此, PDPK1对于炎症的影响可能是双重的。尽管PDPK1在PI3K/AKT及NF-κB信号通路中发挥重要作用, 胆汁酸也可以通过调控这两条通路产生相关生物学效应, 但目前胆汁酸对PDPK1有何影响尚不明确。

除了上述参与多条通路的靶点外, HSPA8、CASP3、ADH1B、PPARG、HSP90AA1等靶点在与TCDCA的分子对接中得分较高, 可能对TCDCA的抗炎作用也具有重要作用。这些靶点中, HSPA8、HSP90AA均与LPS诱导细胞分泌TNF-α有关, TCDCA已被证实在多种情况下均可抑制TNF-α表达^{[45, 46]};前期研究证实TCDCA可通过活化CASP3诱导佐剂性关节炎模型大鼠成纤维样滑膜细胞凋亡^[25]; ADH1B与PPARG可调控体内物质代谢, 前者参与体内乙醇的代谢, 而PPARG主要与脂质代谢有关, 同时, PPARG在调控机体炎症反应方面也显示出重要作用, 但目前对于TCDCA与这两个靶点的研究尚未见相关报道。

综合以上信息不难发现, TCDCA的上述靶点主要集中于调控RAS-RAF-MAPK、PI3K-AKT、NF-κB等信号通路, 这些通路均与炎症反应密切相关, 其中部分靶点在TCDCA抗炎效果中的作用已有相关文献报道, 在一定程度上确证了本研究有关网络药理学预测的准确性, 其余靶点的作用尚需进一步确证。本研究证明了TCDCA的抗炎作用是通过多靶点、多途径、多通路实现的, 为今后更加深入地阐明TCDCA的抗炎机制提供了新方向, 也为以胆汁酸为基础的创新抗炎药物研制奠定了基础。

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药学学报 2018, Vol. 53 Issue (12): 2064-2075	PDF