药学学报

No.	Medicinal origin
S5	Tuoli County, Tacheng, Xinjiang
S6	Yumin County, Tacheng, Xinjiang
S7	Tacheng, Xinjiang
S8	Tacheng, Xinjiang
S9	Tacheng, Xinjiang
S10	Tacheng, Xinjiang
S11	Tacheng, Xinjiang
S12	Tacheng, Xinjiang
S13	Tacheng, Xinjiang
S17	Kuohongqi Village, Chabuchaer County, Yili, Xinjiang
S21	Kan Village, Chabuchaer County, Yili, Xinjiang
S22	Jiagesitai Village, Chabuchaer County, Yili, Xinjiang
S23	Hainuke Village, Chabuchaer County, Yili, Xinjiang
S24	Nuke Village, Chabuchaer County, Yili, Xinjiang
S25	Chabuchaer County, Yili, Xinjiang
S26	Huocheng County, Yili, Xinjiang
S28	Nileke County, Yili, Xinjiang
S29	Jimusaer County, Changji, Xinjiang
S30	Yining County, Yili, Xinjiang
S31	Jimusaer County, Changji, Xinjiang
S32	Jimusaer County, Changji, Xinjiang
S33	Gongliu County, Yili, Xinjiang

No.	Medicinal origin
S5	Tuoli County, Tacheng, Xinjiang
S6	Yumin County, Tacheng, Xinjiang
S7	Tacheng, Xinjiang
S8	Tacheng, Xinjiang
S9	Tacheng, Xinjiang
S10	Tacheng, Xinjiang
S11	Tacheng, Xinjiang
S12	Tacheng, Xinjiang
S13	Tacheng, Xinjiang
S17	Kuohongqi Village, Chabuchaer County, Yili, Xinjiang
S21	Kan Village, Chabuchaer County, Yili, Xinjiang
S22	Jiagesitai Village, Chabuchaer County, Yili, Xinjiang
S23	Hainuke Village, Chabuchaer County, Yili, Xinjiang
S24	Nuke Village, Chabuchaer County, Yili, Xinjiang
S25	Chabuchaer County, Yili, Xinjiang
S26	Huocheng County, Yili, Xinjiang
S28	Nileke County, Yili, Xinjiang
S29	Jimusaer County, Changji, Xinjiang
S30	Yining County, Yili, Xinjiang
S31	Jimusaer County, Changji, Xinjiang
S32	Jimusaer County, Changji, Xinjiang
S33	Gongliu County, Yili, Xinjiang

Argument	Ingredient-target
Clustering coefficient	0.0
Connected components	1
Network diameter	5
Network radius	3
Network centralization	0.78
Shortest paths	509 082 (100%)
Characteristic path length	2.359
Avg. number of neighbors	9.09
Number of nodes	714
Number of edges	3 245
Network density	0.013
Network heterogeneity	2.88
Isolated nodes	0
Number of self-loops	0
Multi-edge node pairs	0
-	-

Argument	Ingredient-target
Clustering coefficient	0.0
Connected components	1
Network diameter	5
Network radius	3
Network centralization	0.78
Shortest paths	509 082 (100%)
Characteristic path length	2.359
Avg. number of neighbors	9.09
Number of nodes	714
Number of edges	3 245
Network density	0.013
Network heterogeneity	2.88
Isolated nodes	0
Number of self-loops	0
Multi-edge node pairs	0
-	-

Class	Compound	Code	Average shortest path length	Betweenness centrality	Closeness centrality	Degree
Flavonoid	Safflower yellow A	HH094	2.499	2.15×10^-2	0.4	126
Saffloryellow	HH093	2.533	1.69×10^-2	0.395	118
Hydroxysafflor yellow A	HH054	2.547	1.58×10^-2	0.393	117
Quercetin	HH090	2.698	6.90×10^-3	0.371	67
Kaempferol	HH058	2.743	3.30×10^-3	0.365	45
Apigenin	HH028	2.805	1.61×10^-3	0.357	32
Luteolin	HH064	2.816	1.47×10^-3	0.355	30
Onjixanthone I	HH077	2.842	9.67×10^-4	0.352	27
Myricetin	HH068	2.822	8.80×10^-4	0.354	21
Organic acids	Lauric acid	HH059	2.67	1.25×10^-2	0.374	68
Stearic acid	HH099	2.69	9.44×10^-3	0.372	62
Linolenic acid	HH062	2.72	7.66×10^-3	0.368	59
Isovaleric acid	HH057	2.87	1.32×10^-3	0.348	28
4-Coumaric acid	HH040	2.84	8.81×10^-4	0.352	24
Oleic acid	HH076	2.84	1.06×10^-3	0.353	21
Cinnamic acid	HH038	2.86	8.16×10^-4	0.35	21
Dehydroshikimic acid	HH044	2.93	9.12×10^-4	0.341	21
Coumarins	Bargapten	HH031	2.71	3.38×10^-3	0.369	50
Psoralene	HH088	2.71	3.53×10^-3	0.369	48
8-Hydroxy-5-methoxypsoralen	HH022	2.74	2.37×10^-3	0.366	45
Xanthotoxol	HH111	2.74	2.41×10^-3	0.365	46
Demethylfuropinarine	HH045	2.76	1.80×10^-3	0.362	38
Umbelliferone	HH109	2.77	1.77×10^-3	0.361	37
Isopimpinellin	HH056	2.81	8.61×10^-4	0.356	25
Alloimperatorin	HH025	2.86	5.64×10^-4	0.349	24
Sterols	Stigmasterol	HH101	2.81	1.13×10^-3	0.357	25
Alkaloids	Rutecarpine	HH091	2.78	2.69×10^-3	0.36	33
3-Formylindole	HH016	2.79	1.68×10^-3	0.359	32
Others	α-Tocopherol	HH113	2.30	7.29×10^-2	0.435	216
Adeninenucleoside	HH024	2.47	3.86×10^-2	0.405	142
Myristicin	HH070	2.79	3.62×10^-3	0.359	44
Benzothiazole	HH032	2.77	2.05×10^-3	0.361	37
Carvacrol	HH037	2.83	1.99×10^-3	0.354	31
1, 2, 3-Trimethoxy-5-methyl benzene	HH010	2.84	7.30×10^-4	0.352	26
4-Hydroxyacetophenone	HH019	2.84	8.96×10^-4	0.352	25

Class	Compound	Code	Average shortest path length	Betweenness centrality	Closeness centrality	Degree
Flavonoid	Safflower yellow A	HH094	2.499	2.15×10^-2	0.4	126
Saffloryellow	HH093	2.533	1.69×10^-2	0.395	118
Hydroxysafflor yellow A	HH054	2.547	1.58×10^-2	0.393	117
Quercetin	HH090	2.698	6.90×10^-3	0.371	67
Kaempferol	HH058	2.743	3.30×10^-3	0.365	45
Apigenin	HH028	2.805	1.61×10^-3	0.357	32
Luteolin	HH064	2.816	1.47×10^-3	0.355	30
Onjixanthone I	HH077	2.842	9.67×10^-4	0.352	27
Myricetin	HH068	2.822	8.80×10^-4	0.354	21
Organic acids	Lauric acid	HH059	2.67	1.25×10^-2	0.374	68
Stearic acid	HH099	2.69	9.44×10^-3	0.372	62
Linolenic acid	HH062	2.72	7.66×10^-3	0.368	59
Isovaleric acid	HH057	2.87	1.32×10^-3	0.348	28
4-Coumaric acid	HH040	2.84	8.81×10^-4	0.352	24
Oleic acid	HH076	2.84	1.06×10^-3	0.353	21
Cinnamic acid	HH038	2.86	8.16×10^-4	0.35	21
Dehydroshikimic acid	HH044	2.93	9.12×10^-4	0.341	21
Coumarins	Bargapten	HH031	2.71	3.38×10^-3	0.369	50
Psoralene	HH088	2.71	3.53×10^-3	0.369	48
8-Hydroxy-5-methoxypsoralen	HH022	2.74	2.37×10^-3	0.366	45
Xanthotoxol	HH111	2.74	2.41×10^-3	0.365	46
Demethylfuropinarine	HH045	2.76	1.80×10^-3	0.362	38
Umbelliferone	HH109	2.77	1.77×10^-3	0.361	37
Isopimpinellin	HH056	2.81	8.61×10^-4	0.356	25
Alloimperatorin	HH025	2.86	5.64×10^-4	0.349	24
Sterols	Stigmasterol	HH101	2.81	1.13×10^-3	0.357	25
Alkaloids	Rutecarpine	HH091	2.78	2.69×10^-3	0.36	33
3-Formylindole	HH016	2.79	1.68×10^-3	0.359	32
Others	α-Tocopherol	HH113	2.30	7.29×10^-2	0.435	216
Adeninenucleoside	HH024	2.47	3.86×10^-2	0.405	142
Myristicin	HH070	2.79	3.62×10^-3	0.359	44
Benzothiazole	HH032	2.77	2.05×10^-3	0.361	37
Carvacrol	HH037	2.83	1.99×10^-3	0.354	31
1, 2, 3-Trimethoxy-5-methyl benzene	HH010	2.84	7.30×10^-4	0.352	26
4-Hydroxyacetophenone	HH019	2.84	8.96×10^-4	0.352	25

Shared	Uniprot	Target	Average shortest path length	Betweenness centrality	Closeness centrality	Degree
PTGS2	P35354	Prostaglandin G/H synthase 2	2.00	1.76×10^-2	0.50	64
NOS2	P35228	Nitric oxide synthase, inducible	2.04	1.23×10^-2	0.49	55
ESR1	P03372	Estrogen receptor	2.07	1.60×10^-2	0.48	54
NOS3	P29474	Nitric oxide synthase, endothelial	2.03	1.84×10^-2	0.49	53
PTGS1	P23219	Prostaglandin G/H synthase 1	2.05	1.04×10^-2	0.49	51
ACHE	P22303	Acetylcholinesterase	2.06	1.49×10^-2	0.49	49
DPP4	P27487	Dipeptidyl peptidase Ⅳ	2.07	1.60×10^-2	0.48	48
CDK2	P24941	Cell division protein kinase 2	2.11	7.58×10^-2	0.48	43
AR	P10275	Androgen receptor	2.09	5.45×10^-3	0.48	33
PPARG	P37231	Peroxisome proliferator-activated receptor gamma	2.12	7.90×10^-3	0.47	32
SCN5A	Q14524	Sodium channel protein type 5 subunit alpha	2.12	5.27×10^-3	0.47	32

Shared	Uniprot	Target	Average shortest path length	Betweenness centrality	Closeness centrality	Degree
PTGS2	P35354	Prostaglandin G/H synthase 2	2.00	1.76×10^-2	0.50	64
NOS2	P35228	Nitric oxide synthase, inducible	2.04	1.23×10^-2	0.49	55
ESR1	P03372	Estrogen receptor	2.07	1.60×10^-2	0.48	54
NOS3	P29474	Nitric oxide synthase, endothelial	2.03	1.84×10^-2	0.49	53
PTGS1	P23219	Prostaglandin G/H synthase 1	2.05	1.04×10^-2	0.49	51
ACHE	P22303	Acetylcholinesterase	2.06	1.49×10^-2	0.49	49
DPP4	P27487	Dipeptidyl peptidase Ⅳ	2.07	1.60×10^-2	0.48	48
CDK2	P24941	Cell division protein kinase 2	2.11	7.58×10^-2	0.48	43
AR	P10275	Androgen receptor	2.09	5.45×10^-3	0.48	33
PPARG	P37231	Peroxisome proliferator-activated receptor gamma	2.12	7.90×10^-3	0.47	32
SCN5A	Q14524	Sodium channel protein type 5 subunit alpha	2.12	5.27×10^-3	0.47	32

Peak	t_R	Ultraviolet (UV)	Molecular ion	Fragment ion	Compound	Fragmentor	Collision voltage	Reference
1	5.38	205, 262	267.9[M+H]⁺	135.8	Adenosine*	100	10	[10]
2	7.04	255, 280	283.9[M+H]⁺	152.1	Vernine*	100	10	[11]
3	17.07	220, 275	205.0[M+H]⁺	188.3, 146.1, 117.7	Tryptophan	100	5
4	18.7	220, 265	395[M+H]⁺	232.3, 364.5	Syringin*	150	20	[12]
5	20.2	228, 270, 340	789.5[M+H]⁺	627.5, 465.4, 303.3	Quercetin-3-O-β-D-glucose-7-O-β-D-gentiobioside*	150	12
7	23.9	230, 400	612.9[M+H]⁺	451.1, 433.0, 396.9, 355.1, 331.1	Hydroxysafflor yellow A*	100	10	[13]
8	28.7	230, 240, 340	772.7[M+H]⁺	626.7, 465.0, 302.5	Quercetin-triglucose or 6-hydroxykaempferol-triglucose	100	15	[13]
9	29.52	230, 270, 340	626.9[M+H]⁺	465.0, 302.5	Quercetin-3, 6-diglucoside or 6-hydroxykaempferol-3, 6-diglucoside	50	12	[13]
10	33.18	270, 333	610.9[M+H]⁺	464.7, 303.0, 146.9	6-Hydroxykaempferol-3-O-rutinoside	100	8	[13]
11	34.44	277, 337	464.8[M+H]⁺	402.9, 303.1, 96.9, 85.0	6-Hydroxykaempferol-glucoside	100	10	[13]
12	36.21	200, 265, 245	610.7[M+H]⁺	286.9, 126.7	Kaempferol-3-O-β-D-sophoroside*	100	3	[12]
12	36.21	265, 345	165[M+H]⁺	146.8	4-Coumaric acid (coefflux)*	100	10	[12]
13	39.42	270, 340	625.0[M+H]⁺	479.0, 316.8	6-Methoxykaempferol-O-β-D-glucoside	100	5	[13]
14	41.11	265, 350	595.0[M+H]⁺	448.8, 286.8, 128.9	Kaempferol-3-O-β-D-rutinoside*	100	5	[13]
15	42.61	244, 406	1 045.0[M+H]⁺	883.7, 721.0, 312.8, 433.2	Anhydrosafflor yellow B*	150	15	[14]
16	48.67	210, 240, 252, 267, 282	311.0[M+H]⁺	149	8(Z)-decene-4, 6-diyne-1-O-β-D-glucoside*	100	5	[12] [15]
17	53.80	230, 270	583.8[M+H]⁺	438.2, 420.3, 275.0, 203.9, 147.1	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
18	55.70	230, 278	583.8[M+H]⁺	437.9, 420.2, 275.0, 292.5	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
19	57.31	230, 278	584.4[M+H]⁺	438.2, 420.3, 146.6, 203.7	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
20	58.09	230, 290	583.5[M+H]⁺	438.2, 419.9, 275.1	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
21	59.63	225, 296	584.3[M+H]⁺	438.2, 420.2, 275.2, 203.6, 147.0	N1, N5, N10-(E)-tri-p-coumarinyl spermidine*	150	15	[14]
22	66.10	243, 375, 520	910[M+H]⁺	250.1, 320.4, 375.3	Carthamine*	150	15	[13]

Peak	t_R	Ultraviolet (UV)	Molecular ion	Fragment ion	Compound	Fragmentor	Collision voltage	Reference
1	5.38	205, 262	267.9[M+H]⁺	135.8	Adenosine*	100	10	[10]
2	7.04	255, 280	283.9[M+H]⁺	152.1	Vernine*	100	10	[11]
3	17.07	220, 275	205.0[M+H]⁺	188.3, 146.1, 117.7	Tryptophan	100	5
4	18.7	220, 265	395[M+H]⁺	232.3, 364.5	Syringin*	150	20	[12]
5	20.2	228, 270, 340	789.5[M+H]⁺	627.5, 465.4, 303.3	Quercetin-3-O-β-D-glucose-7-O-β-D-gentiobioside*	150	12
7	23.9	230, 400	612.9[M+H]⁺	451.1, 433.0, 396.9, 355.1, 331.1	Hydroxysafflor yellow A*	100	10	[13]
8	28.7	230, 240, 340	772.7[M+H]⁺	626.7, 465.0, 302.5	Quercetin-triglucose or 6-hydroxykaempferol-triglucose	100	15	[13]
9	29.52	230, 270, 340	626.9[M+H]⁺	465.0, 302.5	Quercetin-3, 6-diglucoside or 6-hydroxykaempferol-3, 6-diglucoside	50	12	[13]
10	33.18	270, 333	610.9[M+H]⁺	464.7, 303.0, 146.9	6-Hydroxykaempferol-3-O-rutinoside	100	8	[13]
11	34.44	277, 337	464.8[M+H]⁺	402.9, 303.1, 96.9, 85.0	6-Hydroxykaempferol-glucoside	100	10	[13]
12	36.21	200, 265, 245	610.7[M+H]⁺	286.9, 126.7	Kaempferol-3-O-β-D-sophoroside*	100	3	[12]
12	36.21	265, 345	165[M+H]⁺	146.8	4-Coumaric acid (coefflux)*	100	10	[12]
13	39.42	270, 340	625.0[M+H]⁺	479.0, 316.8	6-Methoxykaempferol-O-β-D-glucoside	100	5	[13]
14	41.11	265, 350	595.0[M+H]⁺	448.8, 286.8, 128.9	Kaempferol-3-O-β-D-rutinoside*	100	5	[13]
15	42.61	244, 406	1 045.0[M+H]⁺	883.7, 721.0, 312.8, 433.2	Anhydrosafflor yellow B*	150	15	[14]
16	48.67	210, 240, 252, 267, 282	311.0[M+H]⁺	149	8(Z)-decene-4, 6-diyne-1-O-β-D-glucoside*	100	5	[12] [15]
17	53.80	230, 270	583.8[M+H]⁺	438.2, 420.3, 275.0, 203.9, 147.1	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
18	55.70	230, 278	583.8[M+H]⁺	437.9, 420.2, 275.0, 292.5	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
19	57.31	230, 278	584.4[M+H]⁺	438.2, 420.3, 146.6, 203.7	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
20	58.09	230, 290	583.5[M+H]⁺	438.2, 419.9, 275.1	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
21	59.63	225, 296	584.3[M+H]⁺	438.2, 420.2, 275.2, 203.6, 147.0	N1, N5, N10-(E)-tri-p-coumarinyl spermidine*	150	15	[14]
22	66.10	243, 375, 520	910[M+H]⁺	250.1, 320.4, 375.3	Carthamine*	150	15	[13]

No.	Similarity
S5	0.98	0.93
S6	1	0.96
S7	1	1
S8	1	0.98
S9	1	1
S10	0.99	0.99
S11	0.99	1
S12	1	0.99
S13	0.99	0.99
S17	0.99	0.99
S21	0.98	0.97
S22	0.97	0.98
S23	1	0.98
S24	1	0.98
S25	1	1
S26	1	0.99
S28	0.99	0.99
S29	0.98	0.98
S30	1	0.97
S31	0.98	0.97
S32	1	0.97
S33	1	0.92

No.	Similarity
S5	0.98	0.93
S6	1	0.96
S7	1	1
S8	1	0.98
S9	1	1
S10	0.99	0.99
S11	0.99	1
S12	1	0.99
S13	0.99	0.99
S17	0.99	0.99
S21	0.98	0.97
S22	0.97	0.98
S23	1	0.98
S24	1	0.98
S25	1	1
S26	1	0.99
S28	0.99	0.99
S29	0.98	0.98
S30	1	0.97
S31	0.98	0.97
S32	1	0.97
S33	1	0.92

基于网络药理学的红花注射液用红花药材质量控制方法研究

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胡晓茹 ¹ , 郑海荣 ² , 党海霞 ³ , 胡秦 ² , 康帅 ¹ , 戴忠 ¹^,^* , 马双成 ¹^,^*

药学学报 | 研究论文 2019,54(11): 2074-2082

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药学学报 |研究论文 2019 , 54 (11) : 2074 -2082

基于网络药理学的红花注射液用红花药材质量控制方法研究

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胡晓茹¹, 郑海荣², 党海霞³, 胡秦², 康帅¹, 戴忠¹^,^*, 马双成¹^,^*

作者信息

1.中国食品药品检定研究院, 北京 100050

2.北京工业大学, 北京 100124

3.中国中医科学院中医药发展研究中心, 北京 100700

通讯作者:

*戴忠, Tel: 86-10-67095150, E-mail: daizhong@nifdc.org.cn;

马双成, E-mail:mashuangcheng@nifdc.org.cn

Pharmacological network-based study on quality control of Carthamus tinctorius L. for safflower injection

Xiao-ru HU¹, Hai-rong ZHENG², Hai-xia DANG³, Qin HU², Shuai KANG¹, Zhong DAI¹^,^*, Shuang-cheng MA¹^,^*

Affiliations

1. National Institutes for Food and Drug Control, Beijing 100050, China

2. Beijing University of Technology, Beijing 100124, China

3. Research Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China

出版时间: 2019-11-12 doi: 10.16438/j.0513-4870.2019-0432

文章导航

摘要

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建立的红花“化学成分-靶点-疾病”网络，进行拓扑学分析得到网络中与脉管炎、冠心病和脑血管疾病相关的核心成分，基于此建立红花注射液用红花药材的质量控制方法。首先经TCMID和TCMSP数据库检索得到红花中132个化合物，利用PharmMapp和ChemMapper数据库对上述成分的靶点进行预测，利用CardioGenBase、Therapeutic Target Database、DisGeNET数据库收集脉管炎、冠心病和脑血管疾病冠心病相关靶点，经过映射对接，建立“化学成分-靶点”对应关系；然后，利用Cytoscape软件构建“药效成分-靶点-疾病”网络模型，借助Cytohubba插件进行核心网络筛选和关键节点分析，自由度排名靠前的24个关键化学成分为α-生育酚、腺苷，以及羟基红花黄色素A、saffloryellow等醌式查耳酮色素类，槲皮素、山柰酚等黄酮类，硬脂酸、亚麻酸、4-香豆酸和肉桂酸等有机酸，佛手柑内酯等香豆精类成分等。在上述核心成分的基础上，结合指纹图谱可同时对多成分进行控制的特点，建立注射用红花药材的指纹图谱，并对样品进行测定。所建立的指纹图谱可以反映出腺苷、羟基红花黄色素A、Saffloryellow、4-香豆酸等成分，通过对所收集到的红花药材进行分析，结果上述成分在药材中的含量均一性良好。本方法基于网络药理学的研究方法，从药材化学成分和疾病出发构建药材“化学成分-靶点-疾病”网络，通过拓扑学分析方法获取核心化学成分并作为质控指标，并基于此开展中药质量控制方法的研究，为中药药材的质控指标研究提供了新的研究思路。

关键词

网络药理学 / 红花 / 指纹图谱 / 质量控制

Abstract

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We determined a component-target-disease network for Carthamus tinctorius L. and the key compounds, identified by topological analysis, were related to vasculitis, coronary heart and cerebrovascular disease. Based on these compounds, the chromatographic fingerprint of Carthamus tinctorius L. was established. Firstly, 132 compounds were obtained from TCMID and TCMSP databases. Their targets were predicted in the PharmMapp and HemMapper databases. CardioGenBase, Therapeutic Target Database and DisGeNET databases were used to collect targets of vasculitis, coronary heart disease and cerebrovascular disease. The corresponding relationships between component and target protein were established by mapping. Finally, the " component-target-disease" network was built with Cytoscape software. The core network and key nodes were analyzed with the Cytohubba plug-in. The results showed that the 24 key compounds were alpha-tocopherol, adenosine, quinone chalcone pigments such as hydroxysafflor yellow A, safflower yellow, quercetin, kaempferol and other flavonoids, organic acids such as stearic acid, linolenic acid, coumaric acid and cinnamic acid. This resulting chromatographic fingerprint of Carthamus tinctorius L. showed good consistency, and the core chemical compounds obtained by topological analysis of the network of " component-target-disease" , could be used as quality control markers. Our research provides a new approach for the identification of quality control indicators in Chinese medicinal materials.

Key words

network pharmacological / Carthamus tinctorius L. / fingerprint / quality control

引用本文

胡晓茹, 郑海荣, 党海霞, 胡秦, 康帅, 戴忠, 马双成. 基于网络药理学的红花注射液用红花药材质量控制方法研究. 药学学报, 2019 , 54 (11) : 2074 -2082 . DOI: 10.16438/j.0513-4870.2019-0432

Xiao-ru HU, Hai-rong ZHENG, Hai-xia DANG, Qin HU, Shuai KANG, Zhong DAI, Shuang-cheng MA. Pharmacological network-based study on quality control of Carthamus tinctorius L. for safflower injection[J]. Acta Pharmaceutica Sinica, 2019 , 54 (11) : 2074 -2082 . DOI: 10.16438/j.0513-4870.2019-0432

正文

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随着现代分析仪器的发展与进步, 中药质量控制指标的选择成为中药质量控制的核心问题。目前较为普遍的研究方法是采用体内外药理实验对中药中化学成分的生物活性进行测定, 依据测定的结果选择合适的质控指标。但是目前传统的药理实验主要基于某一疾病模型进行药理药效的评价, 而中药多成分、多靶点, 在临床上“一药多用”的现象较为普遍, 那么选择单一药理模型进行中药材质控指标的评价就有待商榷。

网络药理学是2008年Hopkins^[1]在Nat Chem Biol杂志上首次明确提出的新概念, 是从系统生物学和生物网络平衡的角度阐发疾病的发生发展过程, 从改善、恢复生物网络平衡的整体观认识药物和集体的相互作用^[2]。通过“疾病-靶点-药物”网络分析, 系统分析药物与疾病的联系, 阐释多分子药物协同作用于人体的机制, 这与中药多成分、多途径、多靶点协同作用的原理不谋而合, 亦成为新药研发理论方法的一个研究热点^{[3, 4]}。目前中国学者也开展了中药网络药理学的相关研究, 主要通过网络药理学对化学成分、靶点和疾病之间进行数据分析、挖掘和关联, 对中药复方化学成分与临床疾病的机制进行探讨, 通过研究一方面揭示了疾病与疾病之间的共有靶点和通路, 另一方面对药物治疗相关疾病的机制进行了预测制^{[5, 6]}。基于上述研究作者提出采用网络药理学的方法, 建立中药化学成分、作用靶点和临床多种疾病网络, 依据数学分析结果寻找与多疾病作用靶点相关的核心化学成分, 并结合质控指标选择的原则, 进而选择基于“一药多效”的中药质控指标。此方法基于多成分、多靶点和多疾病的网络分析, 寻找能够作用于多种疾病靶点的核心化学成分, 对中药质控指标的选择, 尤其是具有“一药多效”的中药材质控指标选择提供新的方法和思路。

红花是菊科植物红花(Carthamus tinctorius L.)的管状干燥花, 性温、味辛, 归心、肝经, 具有活血化瘀、通经止痛的功效。红花注射液是以红花药材水提醇沉后制得中药制剂, 在临床上主要用于闭塞性心脑血管疾病、冠心病、脉管炎3种疾病的治疗。目前红花药材的质控指标为羟基红花黄色素A和山柰酚, 未能全面体现红花注射液的内在质量, 所以开展了基于分子对接与网络药理学的红花“一药多效”核心质量控制指标的研究, 目的是为了寻找红花中与红花注射液临床治疗疾病靶点相关的药效成分, 进而建立注射液用红花药材的质控方法。

材料和方法

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红花“成分-靶点-疾病”网络的构建

红花化学成分的获取从中国中药整合数据库TCMID数据库、传统中药系统药理学数据库TCMSP数据库及相关文献^[7-9]中共收集到132个红花的化学成分。

验证靶点的收集 TCMSP数据库是检索中药成分最常用的数据库, 共收录了500味中药及30 069个中药所包含的化合物, 提供每个化合物的药代动力学性质相关数据, 主要包括口服生物利用度、类药性、小肠上皮通透性、药物半衰期、血脑屏障通透性、药物成药五原则、溶解度等, 并提供活性分子的靶点、疾病信息。以单味药名称“Honghua”为关键词搜索红花化学成分及其验证靶点, 共得到183个验证靶点。TCMID数据库包含更多中药和靶点信息, 并且整合多个数据库, 包括TCM@Taiwan、TCM-ID、HIT、BOOK数据库, 共计收录8 159味中药及43 413个化合物, 数据信息更全面。以单味药拼音“Honghua”为关键词搜索红花化学成分和验证靶点, 共得到2 347个验证靶点。TCMSP数据库与TCMID数据库所得到的靶点进行合并后, 共得到2 442个验证靶点。

预测靶点的收集将检索到的132个红花化学成分分别输入到PubChem Project数据库, 以SDF格式导出化合物结构, 并将SDF格式文件导入PharmMapper数据库和ChemMapper数据库得到化合物的预测靶点。PharmMapper数据库和ChemMapper数据库都是基于配体结构特征预测化合物靶点, 得分越高说明该蛋白是化合物靶点的可能性越大, 选择匹配得分(Fit Score) > 3的靶点, 共得到480个预测靶点。ChemMapper数据库是采用随机游走算法以配体2D和3D结构相似性预测化合物靶点, 本实验中选择3D相似性, 相似度阈值> 3.0共得到738个预测靶点。合并后共得到596个靶点。

疾病靶点的收集根据红花注射液在临床上主要应用于冠心病、脑血管疾病和脉管炎的治疗。从CardioGenBase数据库、Therapeutic Target Database 、DisGeNET 数据库以关键词“coronary artery disease”收集冠心病相关靶点, 去掉重复靶点后共得到1 679个相关基因。从CardioGenBase数据库以关键词“cerebrovascular disease”收集脑血管疾病相关基因共243个。从DisGeNET数据库以关键词“angeitides”收集脉管炎相关基因共136个。

红花“成分-靶点-疾病”网络分析对所收集到的疾病-靶点数据合并, 去除冗余数据后得到2 061个疾病-靶点数据与8 097个化合物-靶点数据进行映射得到化合物-靶点-疾病数据, 导入Cytoscape3.6.1软件, 构建红花“化学成分-靶点-疾病”网络。借助Network Analyzer插件对已构建的网络进行拓扑学分析, 计算网络拓扑参数。采用Cytohubba插件对核心网络进行分析, 并对节点的属性进行排名。

红花指纹图谱的构建

药品与试剂羟基红花黄色素A对照品(批号111637-201810)、腺苷(批号110879-201703)、鸟苷(批号111977-201501)、紫丁香苷(批号111574-201605)、槲皮素-3-O-β-D-葡萄糖-7-O-β-D-龙胆双糖(批号111854-201704)、6-羟基山柰酚-3-O-芸香糖苷(批号112007-201602)、4-香豆酸(批号112037-201801)和山柰酚-3-O-β-D-芸香糖苷(批号112007-201602), 均购自中国食品药品检定研究院。山柰酚-3-O-β-D-槐糖苷(B21127)、脱水红花黄色素B (B24457)和红花红色素(B24709), 均购自上海源叶生物科技有限公司; 8(Z)-癸烯-4, 6-二炔-1-O-β-葡萄糖苷和N1, N5, N10-(E)-三-对香豆酰基亚精胺由山西大学提供。实验用水均为超纯水, 乙腈为色谱纯(186341), 购自Fisher Chemical公司; 三氟乙酸(S4741160711)购自Merck公司; 甲醇(10014118)购自国药集团化学试剂有限公司。22批红花药材来自新疆基地, 见表 1。

仪器 Waters 2695高效液相色谱仪(Waters公司, 美国), Empower数据处理系统(Waters公司, 美国), Agilent 1200-6410B QQQ三重四极杆质谱联用仪(Agilent公司, 美国), Masshunter数据处理系统(Agilent公司, 美国), MiLLi-Q超纯水处理系统美国(美国MiLLipore公司), KQ-300DA型数控超声波清洗仪(中国昆山市超声仪器有限公司), Mettler XS105型电子分析天平(瑞士Mettler公司)。

色谱条件色谱柱为Waters SymmetryshieLd^TM C₁₈柱(250 mm×4.6 mm, 5 μm); 柱温为30 ℃。流动相A: 0.05%三氟乙酸溶液, 流动相B:乙腈。梯度洗脱: 0~15 min 2%~12% B; 15~35 min 12%~23% B; 35~40 min 23% B; 40~48 min 23%~32% B, 48~75 min 32%~42% B; 75~76 min 42%~54% B; 76~80 min 54%~65% B; 80~90 min 65% B; 流速为1.0 mL·min^-1; 进样量: 10 μL, 检测波长270 nm和475 nm。

质谱条件采用ESI电喷雾离子化源, MS² Product正离子检测模式进行定性检测, 正离子模式, 脱溶剂气流速9.0 L·min^-1, 脱溶剂气温350 ℃ (氮气), 分流比为1:2。

对照品溶液的配制取羟基红花黄色素A对照品适量, 精密称定, 加甲醇制成每1 mL含0.1 mg的溶液, 即得。

供试品溶液的配制取红花药材粉末约1.0 g, 精密称定, 置50 mL具塞三角瓶中, 精密加入75%甲醇25 mL, 超声处理(功率300 W, 频率40 kHz) 30 min, 放冷, 再次称重, 用75%甲醇补足失重, 摇匀, 过0.45 μm微孔滤膜, 取续滤液即得。

方法学验证

精密度与重复性取红花药材(S15)按供试品溶液的配制进行前处理, 连续进样5次, 对所得的色谱图进行数据分析处理。取同一批次红花药材, 制备5份供试品溶液, 进样分析, 对所得色谱图进行数据分析处理, 通过相似度结果进行评价。

稳定性取红花药材(S15)按供试品溶液的配制进行前处理, 分别于0、1、2、4、8和16 h精密吸取同一供试品溶液10 μL, 对所得的色谱图进行数据分析处理, 通过相似度结果进行评价。

耐用性取红花药材(S15)供试品溶液的配制进行前处理, 分别采用3根Waters Symmetryshield^TM C₁₈型号的色谱柱(序列号02073713833866、02143834413856和02143834413814)对同一批次红花药材进行分析, 并计算相似度, 通过相似度结果进行评价。

色谱峰指认采用液质联用的方法, 按照LC-MS条件进样, 对所建立的指纹图谱色谱峰进行归属。结合色谱峰的质谱和紫外信息, 通过与对照品比对, 并结合化学成分的结构特点, 对网络药理学中所得到的醌式查耳酮、核苷类和黄酮类等核心化学成分的色谱峰进行归属。

相似度评价采用国家药典委员会相似度评价软件对所收集到的红花药材进行相似度评价。首先建立通过上述方法对22批红花药材进行分析, 并对色谱图进行积分处理, 导出AIA格式数据。选择红花基地10批药材的数据导入到相似度软件中, 生成对照图谱。然后用所得到的对照图谱计算其他批次红花药材的相似度, 进而对红花药材的质量均一性进行评价。

结果

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1 红花“成分-靶点-疾病”网络构建与分析

采用上述方法, 构建红花“成分-靶点-疾病”网络, 从左到右依次为化合物、靶点蛋白和疾病, 网络包括红花化学成分节点121个、作用靶点蛋白(节点) 590个和疾病节点3个, 共计714个节点, 潜在化学成分-靶点的结合关系(边)共计3 245种。Network Analyzer插件对所构建的网络进行拓扑学分析, 网络的平均自由度为9.09, 网络中心性为0.78, 具体参数拓扑参数见表 2。根据拓扑学参数中的自由度计算结果, 选择自由度大于10的节点, 进行红花“成分-靶点-疾病”核心网络的筛选, 得到红花药材与冠心病、脑血管疾病和脉管炎相关的核心网络, 网络共包括143个节点, 其中化学成分节点65个、作用靶点蛋白(节点) 75个, 疾病节点3个, 潜在化学成分-靶点的结合关系(边)共计1 281种。见图 1。

采用Cytohubba进行所建网络的关键节点分析, 化合物中的关键节点除了α-生育酚和腺苷之外, 包括红花黄色素A、saffloryellow、羟基红花黄色素A、槲皮素等黄酮类, 月桂酸、亚麻酸、4-香豆酸等有机酸类和佛手柑内酯补骨脂素等香豆素类成分, 这些节点在自由度、接近中心性、介数和平均最短路径中均获得很高排名, 见表 3。表明红花注射液所含的上述化学成分能和较多的闭塞性心脑血管疾病、冠心病、脉管炎3种疾病相关的蛋白靶点结合, 具有潜在的药理作用, 其中羟基红花黄花素A能与117个蛋白靶点结合。根据自由度排名前6的靶点为环加氧酶2 (prostaglandin G/H synthase 2, PTGS2)、一氧化氮合酶2 (nitric oxide synthase 2, NOS2)、雌激素受体1 (estrogen receptor, ESR1)、一氧化氮合酶3 (nitric oxide synthase 3, NOS3)、环加氧酶1 (prostaglandin G/H synthase 1, PTGS1)、乙酰胆碱酯酶(acetylcholinesterase, ACHE), 其中环加氧酶2、雌激素受体1、一氧化氮合酶3和乙酰胆碱酯酶在自由度、接近中心性、介数和平均最短路径中均获得很高排名, 被认为是红花治疗脉管炎、冠心病和心脑血管疾病的关键作用靶点, 见表 4。一氧化氮合酶2和环加氧酶1的介数排名靠后外, 可能是在局部网络中具有重要作用的靶点。

2 红花指纹图谱

2.1 样品分析

根据上述拓扑分析的结果, 核心化学成分包括羟基红花黄色素A、saffloryellow等醌式查耳酮色素类, 槲皮素、山柰酚等黄酮类, 硬脂酸、亚麻酸、4-香豆酸和肉桂酸等有机酸, 佛手柑内酯等香豆精类和腺苷等成分, 所以红花药材的质量控制指标不能仅局限于一种或者一类成分。依据红花注射液的制法工艺, 并结合拟定的中药质控指标选择原则:药理活性明确、化学成分的可测性和化学成分具有一定的代表性, 开展以腺苷、羟基红花黄色素A、槲皮素、山柰酚、4-香豆酸等成分为质控指标的质控方法研究。结合中药指纹图谱可同时对多类型多成分进行表征的特点, 采用指纹图谱的方法开展红花药材质控方法的研究, 以期通过指纹图谱对红花“化学成分-靶点-疾病”网络药理学发现的红花核心化学成分进行质量控制。结合核苷类、醌式查耳酮色素类、黄酮类和有机酸类成分的极性大小分布和紫外吸收特点, 开展色谱条件研究。采用上述指纹图谱构建方法对红花样品进行处理, 以乙腈-三氟乙酸体系为流动相梯度洗脱, 以270 nm和475 nm为检测波长, 得到红花指纹图谱色谱图, 见图 2。

2.2 方法学考察

2.2.1 精密度

结果显示, 在270 nm处相似度均值为0.99, RSD为0.20%;在475 nm处相似度均值为0.99, RSD为0.36%, 表明仪器精密度良好。

2.2.2 重复性

结果显示, 在270 nm处相似度均值为0.99, 计算结果的RSD为0.20%;在475 nm处相似度均值为0.99计算结果的RSD为0.44%, 表明该方法重复性良好。

2.2.3 稳定性

结果表明, 在270 nm处相似度均值为0.99, RSD为0.18%;在475 nm处相似度均值为0.94, RSD为0.50%, 表明供试品溶液在16 h内稳定。

2.2.4 耐用性

结果3根柱子相似度分别为0.99、0.98和0.99, 表明不同批次的色谱柱结果基本一致。分别采用Waters 2695和Agilent进行指纹图谱检测, 相似度评价0.98和0.99, 表明所建立的方法具有较好的耐用性。

2.3 核心化学成分归属

按照LC-MS条件进样, 对所建立的指纹图谱色谱峰进行归属。结合色谱峰的质谱和紫外信息, 通过与对照品比对, 并结合化学成分的结构特点, 对色谱图中醌式查耳酮、核苷类和黄酮类成分的色谱峰进行归属, 包括核苷类成分腺苷(峰1)、鸟苷(峰2);醌式查耳酮类成分羟基红花红色素A (峰7)、脱水红花黄色素B (峰15)和红花红色素(峰22);黄酮类成分山柰酚-3-O-β-D-槐糖苷(峰12)、槲皮素-3-O-β-D-葡萄糖-7-O-β-D-龙胆双糖苷(峰5)和山柰酚-3-O-β-D-芸香糖苷(峰14)等; 以及有机酸类成分4-香豆酸(峰12), 除此之外还有紫丁香苷、8(Z)-癸烯-4, 6-二炔-1-O-β-D-葡萄糖苷和N1, N5, N10-(E)-三-对香豆酰基亚精胺及其4个同分异构体, 见表 5^[10-15]。通过指纹图谱中上述色谱峰峰面积的大小和比例反映羟基红花黄色素A、腺苷等核心指标性成分的含量情况, 进而对药材的整体质量情况进行控制。

2.4 相似度评价

采用国家药典委员会相似度评价软件对所收集到的红花药材进行相似度评价。首先建立通过上述方法对红花注射液基地10批红花药材进行分析, 生成红花药材对照谱图, 其中270 nm下有21个共有色谱峰, 包含有核苷类成分、黄酮类、亚精胺类成分; 475 nm下有4个共有峰, 主要体现是红花药材中色素类成分, 对照图谱见图 3。对所收集的22批样品色谱图进行色谱分析, 以上述对照图谱进行相似度计算, 结果大部分红花药材色谱图中共有峰的峰面积和比例基本一致, 270 nm和475 nm下结果均大于0.9, 表明新疆基地红花药材质量较为稳定, 见表 6。

讨论

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在采用网络药理学方法选择质量控制指标的研究中, 本文利用TCMSP、TCMID、ChemMapper、PharmMapper数据库收集红花药材中的化合物及其靶点, 利用CardioGenBase、Therapeutic Target Database和DisGeNET数据库收集冠心病、脑血管疾病和脉管炎的靶点, 采用Cytoscape软件构建红花“化合物-靶点-疾病”网络。在所建的红花“化合物-靶点-疾病”网络中关键化学成分节点是醌式查耳酮色素、黄酮和有机酸类等成分, 与PTGS2、NOS2、ESR1、NOS3、PTGS1和ACHE等靶点均具有潜在的结合作用。这些靶点与活性氧簇生物合成, 细胞脂质应答反应, 调控血管直径, 氧化压力应答, 正向调节脂质的代谢和负性调节急性炎症反应等生物过程相关, 这与红花注射液具有抑制内皮素和血栓素, 提高NO和前列环素的作用^[16], 其主要的活性成分羟基红花黄色素A具有显著的抗血小板活化因子诱导的血小板聚集作用, 有效改善大鼠心肌线粒体损伤的研究相一致^{[17, 18]}。

在上述研究的基础上, 选择黄酮类成分、醌式查耳酮类和有机酸类成分作为红花注射液制剂生产用红花药材的质控指标。结合上述成分类型的特点, 采用270 nm和475 nm作为检测波长, 针对中低等极性的化学成分开展色谱分离, 建立尽可能反映上述关键化学成分节点的红花药材指纹图谱方法, 并对所收集到的22批次红花药材进行检测。通过上述研究, 本文基于网络药理学的研究方法, 从药材化学成分和疾病出发构建药材“化合物-靶点-疾病”网络, 通过拓扑学分析方法获取核心化学成分并作为质控指标, 在此基础上开展中药质量控制方法的研究, 较好的彰显了中药一药多治和多靶点的特点, 为中药材的质控指标研究提供了新的研究思路。

本文基于红花“化合物-靶点-疾病”网络的研究结果筛选出红花药材的核心化学成分, 将其作为质控指标应用于注射液用红花药材的质量控制研究。研究工作一方面对从疾病靶点出发选择中药质控指标的研究思路进行了有益的探索, 通过网络药理学建立了中药临床应用和化学成分的联系, 进而建立基于临床疾病相关的中药质控方法; 另一方面, 网络药理学是可基于多种疾病靶点构建网络, 这与中药“一药多治”的特点十分契合, 从网络分析数据中可挖掘出作用于多种疾病靶点的化学成分, 可从一定程度上弥补实验药理学药效评价单一的问题。

目前关于采用网络药理学开展中药质量控制指标的研究可能主要存在两个弊端。其一, 网络药理学本身是基于文献和数据库, 拓扑学分析的核心成分是在整个网络中连通性和紧密性方面都占据主要地位的成分, 所以作用靶点研究较为充分的化学成分, 且与相关疾病靶点存在直接或者间接的联系, 在网络中更易成为重要节点, 但某些结构类型新颖由于相关研究较少, 几乎不可能成为重要节点。其二, 由于网络药理学中关键节点的判断是依据节点在整个网络中的重要性, 强调的是化学成分作用的靶点多与少, 所以网络药理学不能体现化学成分生物活性作用的强弱, 只能体现成分在疾病相关的作用靶点的重要性。当然评价化学成分生物活性首先是基于单一药效模型的研究, 而人体本身就是一个复杂的综合体, 中药的特点本身就是多靶点作用, 仅仅依赖单一药效评价其作用强弱可能存在局限性。

任何一个技术和方法的发展和应用都会存在一定的问题。在本研究中, 基于网络药理学分析得到化学成分建立相应的质控方法, 但是在实际色谱分离中存在某些化学成分含量低、色谱分离困难等问题, 而前处理的方法不够专属, 导致所建立的指纹图谱只能反映部分目标化学成分, 所以还需要进一步研究。目前网络药理学在中药质量控制方面的研究还仅仅是局限于化学成分指标选择的领域, 但网络药理学作为一种基于文献的药理学数据分析和计算, 其还可对与疾病相关的关键靶点进行挖掘, 结合这些研究结果可以开展相关的活性评价研究, 为中药质量控制的研究提供新的研究内容和方法, 还有大量的工作去研究和探讨。

致谢山西大学张立伟教授和中国医学科学院药用植物研究所刘海涛研究员对本项工作给予大力支持。

基金

收起

国家中药标准化项目(ZYBZH-C-JIN-44)

参考文献

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文献

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参考文献引证文献

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2019年第54卷第11期

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文章信息

doi: 10.16438/j.0513-4870.2019-0432

接收时间：2019-06-01
首发时间：2026-01-26
出版时间：2019-11-12

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文章信息

作者

出版历史

收稿日期：2019-06-01
修回日期：2019-08-09

基金

国家中药标准化项目(ZYBZH-C-JIN-44)

作者信息

1.中国食品药品检定研究院, 北京 100050

2.北京工业大学, 北京 100124

3.中国中医科学院中医药发展研究中心, 北京 100700

通讯作者:

*戴忠, Tel: 86-10-67095150, E-mail: daizhong@nifdc.org.cn;

马双成, E-mail:mashuangcheng@nifdc.org.cn

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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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TxT

No.	Medicinal origin
S5	Tuoli County, Tacheng, Xinjiang
S6	Yumin County, Tacheng, Xinjiang
S7	Tacheng, Xinjiang
S8	Tacheng, Xinjiang
S9	Tacheng, Xinjiang
S10	Tacheng, Xinjiang
S11	Tacheng, Xinjiang
S12	Tacheng, Xinjiang
S13	Tacheng, Xinjiang
S17	Kuohongqi Village, Chabuchaer County, Yili, Xinjiang
S21	Kan Village, Chabuchaer County, Yili, Xinjiang
S22	Jiagesitai Village, Chabuchaer County, Yili, Xinjiang
S23	Hainuke Village, Chabuchaer County, Yili, Xinjiang
S24	Nuke Village, Chabuchaer County, Yili, Xinjiang
S25	Chabuchaer County, Yili, Xinjiang
S26	Huocheng County, Yili, Xinjiang
S28	Nileke County, Yili, Xinjiang
S29	Jimusaer County, Changji, Xinjiang
S30	Yining County, Yili, Xinjiang
S31	Jimusaer County, Changji, Xinjiang
S32	Jimusaer County, Changji, Xinjiang
S33	Gongliu County, Yili, Xinjiang

No.

Medicinal origin

Tuoli County, Tacheng, Xinjiang

Yumin County, Tacheng, Xinjiang

Tacheng, Xinjiang

S10

Tacheng, Xinjiang

S11

Tacheng, Xinjiang

S12

Tacheng, Xinjiang

S13

Tacheng, Xinjiang

S17

Kuohongqi Village, Chabuchaer County, Yili, Xinjiang

S21

Kan Village, Chabuchaer County, Yili, Xinjiang

S22

Jiagesitai Village, Chabuchaer County, Yili, Xinjiang

S23

Hainuke Village, Chabuchaer County, Yili, Xinjiang

S24

Nuke Village, Chabuchaer County, Yili, Xinjiang

S25

Chabuchaer County, Yili, Xinjiang

S26

Huocheng County, Yili, Xinjiang

S28

Nileke County, Yili, Xinjiang

S29

Jimusaer County, Changji, Xinjiang

S30

Yining County, Yili, Xinjiang

S31

Jimusaer County, Changji, Xinjiang

S32

Jimusaer County, Changji, Xinjiang

S33

Gongliu County, Yili, Xinjiang

Argument	Ingredient-target
Clustering coefficient	0.0
Connected components	1
Network diameter	5
Network radius	3
Network centralization	0.78
Shortest paths	509 082 (100%)
Characteristic path length	2.359
Avg. number of neighbors	9.09
Number of nodes	714
Number of edges	3 245
Network density	0.013
Network heterogeneity	2.88
Isolated nodes	0
Number of self-loops	0
Multi-edge node pairs	0
-	-

Argument

Ingredient-target

Clustering coefficient

0.0

Connected components

Network diameter

Network radius

Network centralization

0.78

Shortest paths

509 082 (100%)

Characteristic path length

2.359

Avg. number of neighbors

9.09

Number of nodes

714

Number of edges

3 245

Network density

0.013

Network heterogeneity

2.88

Isolated nodes

Number of self-loops

Multi-edge node pairs

Class	Compound	Code	Average shortest path length	Betweenness centrality	Closeness centrality	Degree
Flavonoid	Safflower yellow A	HH094	2.499	2.15×10^-2	0.4	126
Saffloryellow	HH093	2.533	1.69×10^-2	0.395	118
Hydroxysafflor yellow A	HH054	2.547	1.58×10^-2	0.393	117
Quercetin	HH090	2.698	6.90×10^-3	0.371	67
Kaempferol	HH058	2.743	3.30×10^-3	0.365	45
Apigenin	HH028	2.805	1.61×10^-3	0.357	32
Luteolin	HH064	2.816	1.47×10^-3	0.355	30
Onjixanthone I	HH077	2.842	9.67×10^-4	0.352	27
Myricetin	HH068	2.822	8.80×10^-4	0.354	21
Organic acids	Lauric acid	HH059	2.67	1.25×10^-2	0.374	68
Stearic acid	HH099	2.69	9.44×10^-3	0.372	62
Linolenic acid	HH062	2.72	7.66×10^-3	0.368	59
Isovaleric acid	HH057	2.87	1.32×10^-3	0.348	28
4-Coumaric acid	HH040	2.84	8.81×10^-4	0.352	24
Oleic acid	HH076	2.84	1.06×10^-3	0.353	21
Cinnamic acid	HH038	2.86	8.16×10^-4	0.35	21
Dehydroshikimic acid	HH044	2.93	9.12×10^-4	0.341	21
Coumarins	Bargapten	HH031	2.71	3.38×10^-3	0.369	50
Psoralene	HH088	2.71	3.53×10^-3	0.369	48
8-Hydroxy-5-methoxypsoralen	HH022	2.74	2.37×10^-3	0.366	45
Xanthotoxol	HH111	2.74	2.41×10^-3	0.365	46
Demethylfuropinarine	HH045	2.76	1.80×10^-3	0.362	38
Umbelliferone	HH109	2.77	1.77×10^-3	0.361	37
Isopimpinellin	HH056	2.81	8.61×10^-4	0.356	25
Alloimperatorin	HH025	2.86	5.64×10^-4	0.349	24
Sterols	Stigmasterol	HH101	2.81	1.13×10^-3	0.357	25
Alkaloids	Rutecarpine	HH091	2.78	2.69×10^-3	0.36	33
3-Formylindole	HH016	2.79	1.68×10^-3	0.359	32
Others	α-Tocopherol	HH113	2.30	7.29×10^-2	0.435	216
Adeninenucleoside	HH024	2.47	3.86×10^-2	0.405	142
Myristicin	HH070	2.79	3.62×10^-3	0.359	44
Benzothiazole	HH032	2.77	2.05×10^-3	0.361	37
Carvacrol	HH037	2.83	1.99×10^-3	0.354	31
1, 2, 3-Trimethoxy-5-methyl benzene	HH010	2.84	7.30×10^-4	0.352	26
4-Hydroxyacetophenone	HH019	2.84	8.96×10^-4	0.352	25

Class

Compound

Code

Average shortest path length

Betweenness centrality

Closeness centrality

Degree

Flavonoid

Safflower yellow A

HH094

2.499

2.15×10^-2

0.4

126

Saffloryellow

HH093

2.533

1.69×10^-2

0.395

118

Hydroxysafflor yellow A

HH054

2.547

1.58×10^-2

0.393

117

Quercetin

HH090

2.698

6.90×10^-3

0.371

Kaempferol

HH058

2.743

3.30×10^-3

0.365

Apigenin

HH028

2.805

1.61×10^-3

0.357

Luteolin

HH064

2.816

1.47×10^-3

0.355

Onjixanthone I

HH077

2.842

9.67×10^-4

0.352

Myricetin

HH068

2.822

8.80×10^-4

0.354

Organic acids

Lauric acid

HH059

2.67

1.25×10^-2

0.374

Stearic acid

HH099

2.69

9.44×10^-3

0.372

Linolenic acid

HH062

2.72

7.66×10^-3

0.368

Isovaleric acid

HH057

2.87

1.32×10^-3

0.348

4-Coumaric acid

HH040

2.84

8.81×10^-4

0.352

Oleic acid

HH076

2.84

1.06×10^-3

0.353

Cinnamic acid

HH038

2.86

8.16×10^-4

0.35

Dehydroshikimic acid

HH044

2.93

9.12×10^-4

0.341

Coumarins

Bargapten

HH031

2.71

3.38×10^-3

0.369

Psoralene

HH088

2.71

3.53×10^-3

0.369

8-Hydroxy-5-methoxypsoralen

HH022

2.74

2.37×10^-3

0.366

Xanthotoxol

HH111

2.74

2.41×10^-3

0.365

Demethylfuropinarine

HH045

2.76

1.80×10^-3

0.362

Umbelliferone

HH109

2.77

1.77×10^-3

0.361

Isopimpinellin

HH056

2.81

8.61×10^-4

0.356

Alloimperatorin

HH025

2.86

5.64×10^-4

0.349

Sterols

Stigmasterol

HH101

2.81

1.13×10^-3

0.357

Alkaloids

Rutecarpine

HH091

2.78

2.69×10^-3

0.36

3-Formylindole

HH016

2.79

1.68×10^-3

0.359

Others

α-Tocopherol

HH113

2.30

7.29×10^-2

0.435

216

Adeninenucleoside

HH024

2.47

3.86×10^-2

0.405

142

Myristicin

HH070

2.79

3.62×10^-3

0.359

Benzothiazole

HH032

2.77

2.05×10^-3

0.361

Carvacrol

HH037

2.83

1.99×10^-3

0.354

1, 2, 3-Trimethoxy-5-methyl benzene

HH010

2.84

7.30×10^-4

0.352

4-Hydroxyacetophenone

HH019

2.84

8.96×10^-4

0.352

Shared	Uniprot	Target	Average shortest path length	Betweenness centrality	Closeness centrality	Degree
PTGS2	P35354	Prostaglandin G/H synthase 2	2.00	1.76×10^-2	0.50	64
NOS2	P35228	Nitric oxide synthase, inducible	2.04	1.23×10^-2	0.49	55
ESR1	P03372	Estrogen receptor	2.07	1.60×10^-2	0.48	54
NOS3	P29474	Nitric oxide synthase, endothelial	2.03	1.84×10^-2	0.49	53
PTGS1	P23219	Prostaglandin G/H synthase 1	2.05	1.04×10^-2	0.49	51
ACHE	P22303	Acetylcholinesterase	2.06	1.49×10^-2	0.49	49
DPP4	P27487	Dipeptidyl peptidase Ⅳ	2.07	1.60×10^-2	0.48	48
CDK2	P24941	Cell division protein kinase 2	2.11	7.58×10^-2	0.48	43
AR	P10275	Androgen receptor	2.09	5.45×10^-3	0.48	33
PPARG	P37231	Peroxisome proliferator-activated receptor gamma	2.12	7.90×10^-3	0.47	32
SCN5A	Q14524	Sodium channel protein type 5 subunit alpha	2.12	5.27×10^-3	0.47	32

Shared

Uniprot

Target

Average shortest path length

Betweenness centrality

Closeness centrality

Degree

PTGS2

P35354

Prostaglandin G/H synthase 2

2.00

1.76×10^-2

0.50

NOS2

P35228

Nitric oxide synthase, inducible

2.04

1.23×10^-2

0.49

ESR1

P03372

Estrogen receptor

2.07

1.60×10^-2

0.48

NOS3

P29474

Nitric oxide synthase, endothelial

2.03

1.84×10^-2

0.49

PTGS1

P23219

Prostaglandin G/H synthase 1

2.05

1.04×10^-2

0.49

ACHE

P22303

Acetylcholinesterase

2.06

1.49×10^-2

0.49

DPP4

P27487

Dipeptidyl peptidase Ⅳ

2.07

1.60×10^-2

0.48

CDK2

P24941

Cell division protein kinase 2

2.11

7.58×10^-2

0.48

P10275

Androgen receptor

2.09

5.45×10^-3

0.48

PPARG

P37231

Peroxisome proliferator-activated receptor gamma

2.12

7.90×10^-3

0.47

SCN5A

Q14524

Sodium channel protein type 5 subunit alpha

2.12

5.27×10^-3

0.47

Peak	t_R	Ultraviolet (UV)	Molecular ion	Fragment ion	Compound	Fragmentor	Collision voltage	Reference
1	5.38	205, 262	267.9[M+H]⁺	135.8	Adenosine*	100	10	[10]
2	7.04	255, 280	283.9[M+H]⁺	152.1	Vernine*	100	10	[11]
3	17.07	220, 275	205.0[M+H]⁺	188.3, 146.1, 117.7	Tryptophan	100	5
4	18.7	220, 265	395[M+H]⁺	232.3, 364.5	Syringin*	150	20	[12]
5	20.2	228, 270, 340	789.5[M+H]⁺	627.5, 465.4, 303.3	Quercetin-3-O-β-D-glucose-7-O-β-D-gentiobioside*	150	12
7	23.9	230, 400	612.9[M+H]⁺	451.1, 433.0, 396.9, 355.1, 331.1	Hydroxysafflor yellow A*	100	10	[13]
8	28.7	230, 240, 340	772.7[M+H]⁺	626.7, 465.0, 302.5	Quercetin-triglucose or 6-hydroxykaempferol-triglucose	100	15	[13]
9	29.52	230, 270, 340	626.9[M+H]⁺	465.0, 302.5	Quercetin-3, 6-diglucoside or 6-hydroxykaempferol-3, 6-diglucoside	50	12	[13]
10	33.18	270, 333	610.9[M+H]⁺	464.7, 303.0, 146.9	6-Hydroxykaempferol-3-O-rutinoside	100	8	[13]
11	34.44	277, 337	464.8[M+H]⁺	402.9, 303.1, 96.9, 85.0	6-Hydroxykaempferol-glucoside	100	10	[13]
12	36.21	200, 265, 245	610.7[M+H]⁺	286.9, 126.7	Kaempferol-3-O-β-D-sophoroside*	100	3	[12]
12	36.21	265, 345	165[M+H]⁺	146.8	4-Coumaric acid (coefflux)*	100	10	[12]
13	39.42	270, 340	625.0[M+H]⁺	479.0, 316.8	6-Methoxykaempferol-O-β-D-glucoside	100	5	[13]
14	41.11	265, 350	595.0[M+H]⁺	448.8, 286.8, 128.9	Kaempferol-3-O-β-D-rutinoside*	100	5	[13]
15	42.61	244, 406	1 045.0[M+H]⁺	883.7, 721.0, 312.8, 433.2	Anhydrosafflor yellow B*	150	15	[14]
16	48.67	210, 240, 252, 267, 282	311.0[M+H]⁺	149	8(Z)-decene-4, 6-diyne-1-O-β-D-glucoside*	100	5	[12] [15]
17	53.80	230, 270	583.8[M+H]⁺	438.2, 420.3, 275.0, 203.9, 147.1	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
18	55.70	230, 278	583.8[M+H]⁺	437.9, 420.2, 275.0, 292.5	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
19	57.31	230, 278	584.4[M+H]⁺	438.2, 420.3, 146.6, 203.7	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
20	58.09	230, 290	583.5[M+H]⁺	438.2, 419.9, 275.1	Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine	150	15	[15]
21	59.63	225, 296	584.3[M+H]⁺	438.2, 420.2, 275.2, 203.6, 147.0	N1, N5, N10-(E)-tri-p-coumarinyl spermidine*	150	15	[14]
22	66.10	243, 375, 520	910[M+H]⁺	250.1, 320.4, 375.3	Carthamine*	150	15	[13]

Peak

t_R

Ultraviolet (UV)

Molecular ion

Fragment ion

Compound

Fragmentor

Collision voltage

Reference

5.38

205, 262

267.9[M+H]⁺

135.8

Adenosine*

100

[10]

7.04

255, 280

283.9[M+H]⁺

152.1

Vernine*

100

[11]

17.07

220, 275

205.0[M+H]⁺

188.3, 146.1, 117.7

Tryptophan

100

18.7

220, 265

395[M+H]⁺

232.3, 364.5

Syringin*

150

[12]

20.2

228, 270, 340

789.5[M+H]⁺

627.5, 465.4, 303.3

Quercetin-3-O-β-D-glucose-7-O-β-D-gentiobioside*

150

23.9

230, 400

612.9[M+H]⁺

451.1, 433.0, 396.9, 355.1, 331.1

Hydroxysafflor yellow A*

100

[13]

28.7

230, 240, 340

772.7[M+H]⁺

626.7, 465.0, 302.5

Quercetin-triglucose or 6-hydroxykaempferol-triglucose

100

[13]

29.52

230, 270, 340

626.9[M+H]⁺

465.0, 302.5

Quercetin-3, 6-diglucoside or 6-hydroxykaempferol-3, 6-diglucoside

[13]

33.18

270, 333

610.9[M+H]⁺

464.7, 303.0, 146.9

6-Hydroxykaempferol-3-O-rutinoside

100

[13]

34.44

277, 337

464.8[M+H]⁺

402.9, 303.1, 96.9, 85.0

6-Hydroxykaempferol-glucoside

100

[13]

36.21

200, 265, 245

610.7[M+H]⁺

286.9, 126.7

Kaempferol-3-O-β-D-sophoroside*

100

[12]

36.21

265, 345

165[M+H]⁺

146.8

4-Coumaric acid (coefflux)*

100

[12]

39.42

270, 340

625.0[M+H]⁺

479.0, 316.8

6-Methoxykaempferol-O-β-D-glucoside

100

[13]

41.11

265, 350

595.0[M+H]⁺

448.8, 286.8, 128.9

Kaempferol-3-O-β-D-rutinoside*

100

[13]

42.61

244, 406

1 045.0[M+H]⁺

883.7, 721.0, 312.8, 433.2

Anhydrosafflor yellow B*

150

[14]

48.67

210, 240, 252, 267, 282

311.0[M+H]⁺

149

8(Z)-decene-4, 6-diyne-1-O-β-D-glucoside*

100

[12] [15]

53.80

230, 270

583.8[M+H]⁺

438.2, 420.3, 275.0, 203.9, 147.1

Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine

150

[15]

55.70

230, 278

583.8[M+H]⁺

437.9, 420.2, 275.0, 292.5

Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine

150

[15]

57.31

230, 278

584.4[M+H]⁺

438.2, 420.3, 146.6, 203.7

Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine

150

[15]

58.09

230, 290

583.5[M+H]⁺

438.2, 419.9, 275.1

Isomers of N1, N5, N10-(E)-tri-p-coumarinyl spermidine

150

[15]

59.63

225, 296

584.3[M+H]⁺

438.2, 420.2, 275.2, 203.6, 147.0

N1, N5, N10-(E)-tri-p-coumarinyl spermidine*

150

[14]

66.10

243, 375, 520

910[M+H]⁺

250.1, 320.4, 375.3

Carthamine*

150

[13]

No.	Similarity
S5	0.98	0.93
S6	1	0.96
S7	1	1
S8	1	0.98
S9	1	1
S10	0.99	0.99
S11	0.99	1
S12	1	0.99
S13	0.99	0.99
S17	0.99	0.99
S21	0.98	0.97
S22	0.97	0.98
S23	1	0.98
S24	1	0.98
S25	1	1
S26	1	0.99
S28	0.99	0.99
S29	0.98	0.98
S30	1	0.97
S31	0.98	0.97
S32	1	0.97
S33	1	0.92

No.

Similarity

475 nm

270 nm

0.98

0.93

0.96

0.98

S10

0.99

S11

0.99

S12

0.99

S13

0.99

S17

0.99

S21

0.98

0.97

S22

0.97

0.98

S23

0.98

S24

0.98

S25

S26

0.99

S28

0.99

S29

0.98

S30

0.97

S31

0.98

0.97

S32

0.97

S33

0.92