药学学报

No.	Province	No.	Province	No.	Province
1	Baishan, Jilin	31	Chengdu, Sichuan	61	Liuan, Anhui
2	Baoji, Shaanxi	32	Chengdu, Sichuan	62	Nantong, Jiangsu
3	Bayannaoer, Neimenggu	33	Chengdu, Sichuan	63	Qingdao, Shandong
4	Beijing	34	Chengdu, Sichuan	64	Qingyang, Gansu
5	Beijing	35	Chengdu, Sichuan	65	Sanming, Fujian
6	Beijing	36	Chengdu, Sichuan	66	Sanming, Fujian
7	Beijing	37	Chifeng, Neimenggu	67	Shenyang, Liaoning
8	Beijing	38	Chongqing	68	Shenyang, Liaoning
9	Beijing	39	Chuxiong, Yunnan	69	Shenyang, Liaoning
10	Beijing	40	Dalian, Liaoning	70	Shijiazhuang, Hebei
11	Beijing	41	Ganzhou, Jiangxi	71	Shijiazhuang, Hebei
12	Beijing	42	Guangzhou, Guangdong	72	Tangshan, Hebei
13	Beijing	43	Guiyang, Guizhou	73	Tianjin
14	Beijing	44	Guiyang, Guizhou	74	Tianjin
15	Beijing	45	Guiyang, Guizhou	75	Xining, Qinghai
16	Bozhou, Anhui	46	Guiyang, Guizhou	76	Xinxiang, Henan
17	Bozhou, Anhui	47	Guiyang, Guizhou	77	Xinxiang, Henan
18	Bozhou, Anhui	48	Guiyang, Guizhou	78	Xinxiang, Henan
19	Bozhou, Anhui	49	Guiyang, Guizhou	79	Yichun, Jiangxi
20	Bozhou, Anhui	50	Haerbin, Heilongjiang	80	Yichun, Jiangxi
21	Bozhou, Anhui	51	Haerbin, Heilongjiang	81	Yinchuan, Ningxia
22	Bozhou, Anhui	52	Haerbin, Heilongjiang	82	Yulin, Guangxi
23	Bozhou, Anhui	53	Haerbin, Heilongjiang	83	Yulin, Shaanxi
24	Bozhou, Anhui	54	Haikou, Hainan	84	Yuncheng, Shanxi
25	Changchun, Jilin	55	Huichun, Jilin	85	Yuncheng, Shanxi
26	Changchun, Jilin	56	Jieyang, Guangdong	86	Zhengzhou, Henan
27	Changchun, Jilin	57	Kashi, Xinjiang	87	Zhengzhou, Henan
28	Changge, Henan	58	Kashi, Xinjiang	88	Zibo, Shandong
29	Changsha, Hunan	59	Kunming, Yunnan	89	Zibo, Shandong
30	Chengdu, Sichuan	60	Kunming, Yunnan

No.	Province	No.	Province	No.	Province
1	Baishan, Jilin	31	Chengdu, Sichuan	61	Liuan, Anhui
2	Baoji, Shaanxi	32	Chengdu, Sichuan	62	Nantong, Jiangsu
3	Bayannaoer, Neimenggu	33	Chengdu, Sichuan	63	Qingdao, Shandong
4	Beijing	34	Chengdu, Sichuan	64	Qingyang, Gansu
5	Beijing	35	Chengdu, Sichuan	65	Sanming, Fujian
6	Beijing	36	Chengdu, Sichuan	66	Sanming, Fujian
7	Beijing	37	Chifeng, Neimenggu	67	Shenyang, Liaoning
8	Beijing	38	Chongqing	68	Shenyang, Liaoning
9	Beijing	39	Chuxiong, Yunnan	69	Shenyang, Liaoning
10	Beijing	40	Dalian, Liaoning	70	Shijiazhuang, Hebei
11	Beijing	41	Ganzhou, Jiangxi	71	Shijiazhuang, Hebei
12	Beijing	42	Guangzhou, Guangdong	72	Tangshan, Hebei
13	Beijing	43	Guiyang, Guizhou	73	Tianjin
14	Beijing	44	Guiyang, Guizhou	74	Tianjin
15	Beijing	45	Guiyang, Guizhou	75	Xining, Qinghai
16	Bozhou, Anhui	46	Guiyang, Guizhou	76	Xinxiang, Henan
17	Bozhou, Anhui	47	Guiyang, Guizhou	77	Xinxiang, Henan
18	Bozhou, Anhui	48	Guiyang, Guizhou	78	Xinxiang, Henan
19	Bozhou, Anhui	49	Guiyang, Guizhou	79	Yichun, Jiangxi
20	Bozhou, Anhui	50	Haerbin, Heilongjiang	80	Yichun, Jiangxi
21	Bozhou, Anhui	51	Haerbin, Heilongjiang	81	Yinchuan, Ningxia
22	Bozhou, Anhui	52	Haerbin, Heilongjiang	82	Yulin, Guangxi
23	Bozhou, Anhui	53	Haerbin, Heilongjiang	83	Yulin, Shaanxi
24	Bozhou, Anhui	54	Haikou, Hainan	84	Yuncheng, Shanxi
25	Changchun, Jilin	55	Huichun, Jilin	85	Yuncheng, Shanxi
26	Changchun, Jilin	56	Jieyang, Guangdong	86	Zhengzhou, Henan
27	Changchun, Jilin	57	Kashi, Xinjiang	87	Zhengzhou, Henan
28	Changge, Henan	58	Kashi, Xinjiang	88	Zibo, Shandong
29	Changsha, Hunan	59	Kunming, Yunnan	89	Zibo, Shandong
30	Chengdu, Sichuan	60	Kunming, Yunnan

Species	Sample No.	Variable site/bp	Genotype type
Rheum palmatum	59	G		C		T		G		A		C		C		C		A		T		C		A		C		G		C		T		C		C		G	Rp1
20, 36	*		*		*		*		*		*		A		*		*		*		*		C		*		*		*		*		*		*		*	Rp2
15, 34, 43, 48, 81	*		*		A		*		*		*		A		*		*		*		*		C		*		*		*		*		*		*		*	Rp3
1, 3, 4, 5, 6, 7, 9, 10, 13, 16, 17, 25, 26, 29, 37, 39, 40, 42, 47, 49, 52, 57, 61, 62, 65, 66, 68, 76, 77, 78, 80, 82, 84, 85, 87, 89	*		*		A		A		C		*		A		*		*		G		*		C		*		*		*		*		*		T		A	Rp4
8, 22, 30, 56, 64, 75, 83, 86	*		*		A		*		C		*		A		*		*		G		*		C		*		*		*		*		*		*		*	Rp5
2, 11, 12, 14, 19, 21, 23, 24, 27, 28, 31, 33, 35, 41, 44, 45, 46, 50, 51, 53, 54, 55, 58, 60, 67, 69, 70, 71, 72, 73, 74, 79, 88	A		*		A		*		C		*		A		*		*		G		*		C		*		*		*		C		*		*		*	Rp6
18, 32, 63	*		*		A		*		C		*		A		*		*		G		*		C		A		*		T		*		*		*		*	Rp10
38	*		*		A		*		*		*		A		*		*		G		*		C		A		*		T		*		*		*		*	Rp12

Species	Sample No.	Variable site/bp	Genotype type
Rheum palmatum	59	G		C		T		G		A		C		C		C		A		T		C		A		C		G		C		T		C		C		G	Rp1
20, 36	*		*		*		*		*		*		A		*		*		*		*		C		*		*		*		*		*		*		*	Rp2
15, 34, 43, 48, 81	*		*		A		*		*		*		A		*		*		*		*		C		*		*		*		*		*		*		*	Rp3
1, 3, 4, 5, 6, 7, 9, 10, 13, 16, 17, 25, 26, 29, 37, 39, 40, 42, 47, 49, 52, 57, 61, 62, 65, 66, 68, 76, 77, 78, 80, 82, 84, 85, 87, 89	*		*		A		A		C		*		A		*		*		G		*		C		*		*		*		*		*		T		A	Rp4
8, 22, 30, 56, 64, 75, 83, 86	*		*		A		*		C		*		A		*		*		G		*		C		*		*		*		*		*		*		*	Rp5
2, 11, 12, 14, 19, 21, 23, 24, 27, 28, 31, 33, 35, 41, 44, 45, 46, 50, 51, 53, 54, 55, 58, 60, 67, 69, 70, 71, 72, 73, 74, 79, 88	A		*		A		*		C		*		A		*		*		G		*		C		*		*		*		C		*		*		*	Rp6
18, 32, 63	*		*		A		*		C		*		A		*		*		G		*		C		A		*		T		*		*		*		*	Rp10
38	*		*		A		*		*		*		A		*		*		G		*		C		A		*		T		*		*		*		*	Rp12

R. palmatum	Intraspecific genetic distance	Intraspecific genetic distance (mean)
Rp1								0.004
Rp2	0.002
Rp3	0.002	0.001
Rp4	0.006	0.004	0.003
Rp5	0.004	0.002	0.002	0.002
Rp6	0.006	0.004	0.003	0.003	0.002
Rp10	0.010	0.008	0.007	0.007	0.006	0.007
Rp12	0.005	0.003	0.002	0.004	0.002	0.004	0.005

R. palmatum	Intraspecific genetic distance	Intraspecific genetic distance (mean)
Rp1								0.004
Rp2	0.002
Rp3	0.002	0.001
Rp4	0.006	0.004	0.003
Rp5	0.004	0.002	0.002	0.002
Rp6	0.006	0.004	0.003	0.003	0.002
Rp10	0.010	0.008	0.007	0.007	0.006	0.007
Rp12	0.005	0.003	0.002	0.004	0.002	0.004	0.005

Compd.	Regression equation	R²
Aloe-emodin	y = 15 659 262.663 1 x + 101 684.884 2	0.999 7
Rhein	y = 7 020 849.301 7 x - 2 872.391 2	0.999 9
Emodin	y = 12 387 879.266 9 x + 32 007.372 8	0.999 9
Chrysophanol	y = 13 257 038.455 2 x + 52 023.627 8	1.000 0
Physcion	y = 3 652 156.894 8 x + 97 584.840 1	0.999 1

Compd.	Regression equation	R²
Aloe-emodin	y = 15 659 262.663 1 x + 101 684.884 2	0.999 7
Rhein	y = 7 020 849.301 7 x - 2 872.391 2	0.999 9
Emodin	y = 12 387 879.266 9 x + 32 007.372 8	0.999 9
Chrysophanol	y = 13 257 038.455 2 x + 52 023.627 8	1.000 0
Physcion	y = 3 652 156.894 8 x + 97 584.840 1	0.999 1

Compd.	Regression equation	R²
Aloe-emodin	y = 15 503 230.514 6 x - 15 467.663 4	1.000 0
Rhein	y = 6 986 119.268 3 x + 6 708.475 8	0.999 3
Emodin	y = 12 659 622.823 2 x - 19 162.199 9	1.000 0
Chrysophanol	y = 13 912 884.710 9 x - 68 986.647 7	0.999 9
Physcion	y = 3 499 359.079 7 x + 69 196.113 3	0.998 9

Compd.	Regression equation	R²
Aloe-emodin	y = 15 503 230.514 6 x - 15 467.663 4	1.000 0
Rhein	y = 6 986 119.268 3 x + 6 708.475 8	0.999 3
Emodin	y = 12 659 622.823 2 x - 19 162.199 9	1.000 0
Chrysophanol	y = 13 912 884.710 9 x - 68 986.647 7	0.999 9
Physcion	y = 3 499 359.079 7 x + 69 196.113 3	0.998 9

Sample No.	Province	Code No.	Total anthraquinone content/%	Free anthraquinone content/%
1	Baishan, Jilin	RP4	2.25%	0.59%
2	Baoji, Shaanxi	RP6	3.22%	0.45%
3	Bayannaoer, Neimenggu	RP4	3.49%	0.55%
4	Beijing	RP4	4.54%	1.10%
5	Beijing	RP4	3.26%	0.75%
6	Beijing	RP4	3.83%	0.72%
7	Beijing	RP4	3.61%	0.67%
8	Beijing	RP5	2.93%	0.55%
9	Beijing	RP4	2.15%	0.48%
10	Beijing	RP4	3.86%	0.47%
11	Beijing	RP6	3.58%	0.47%
12	Beijing	RP6	3.56%	0.38%
13	Beijing	RP4	2.96%	0.36%
14	Beijing	RP6	3.21%	0.26%
15	Beijing	RP3	3.89%	0.09%
16	Bozhou, Anhui	RP4	2.81%	0.63%
17	Bozhou, Anhui	RP4	2.53%	0.47%
18	Bozhou, Anhui	RP10	2.43%	0.35%
19	Bozhou, Anhui	RP6	5.03%	0.31%
20	Bozhou, Anhui	RP2	3.64%	0.27%
21	Bozhou, Anhui	RP6	2.82%	0.26%
22	Bozhou, Anhui	RP5	2.43%	0.21%
23	Bozhou, Anhui	RP6	3.22%	0.18%
24	Bozhou, Anhui	RP6	2.85%	0.13%
25	Changchun, Jilin	RP4	3.90%	0.65%
26	Changchun, Jilin	RP4	3.02%	0.64%
27	Changchun, Jilin	RP6	2.85%	0.29%
28	Changge, Henan	RP6	2.70%	0.38%
29	Changsha, Hunan	RP4	2.49%	0.41%
30	Chengdu, Sichuan	RP5	2.22%	0.73%
31	Chengdu, Sichuan	RP6	3.42%	0.57%
32	Chengdu, Sichuan	RP10	2.59%	0.54%
33	Chengdu, Sichuan	RP6	3.39%	0.40%
34	Chengdu, Sichuan	RP3	3.18%	0.30%
35	Chengdu, Sichuan	RP6	2.55%	0.21%
36	Chengdu, Sichuan	RP2	2.86%	0.20%
37	Chifeng, Neimenggu	RP4	1.45%	0.28%
38	Chongqing	RP12	3.31%	0.21%
39	Chuxiong, Yunnan	RP4	3.43%	0.10%
40	Dalian, Liaoning	RP4	2.61%	0.59%
41	Ganzhou, Jiangxi	RP6	3.60%	0.48%
42	Guangzhou, Guangdong	RP4	2.24%	0.75%
43	Guiyang, Guizhou	RP3	3.52%	0.65%
44	Guiyang, Guizhou	RP6	2.46%	0.60%
45	Guiyang, Guizhou	RP6	3.10%	0.41%
46	Guiyang, Guizhou	RP6	3.75%	0.38%
47	Guiyang, Guizhou	RP4	3.05%	0.36%
48	Guiyang, Guizhou	RP3	3.90%	0.32%
49	Guiyang, Guizhou	RP4	3.59%	0.32%
50	Haerbin, Heilongjiang	RP6	3.28%	0.50%
51	Haerbin, Heilongjiang	RP6	3.91%	0.47%
52	Haerbin, Heilongjiang	RP4	1.57%	0.45%
53	Haerbin, Heilongjiang	RP6	3.18%	0.31%
54	Haikou, Hainan	RP6	4.59%	0.43%
55	Huichun, Jilin	RP6	5.06%	0.33%
56	Jieyang, Guangdong	RP5	3.34%	0.31%
57	Kashi, Xinjiang	RP4	3.46%	0.61%
58	Kashi, Xinjiang	RP6	3.97%	0.35%
59	Kunming, Yunnan	RP1	3.99%	0.51%
60	Kunming, Yunnan	RP6	2.17%	0.27%
61	Liuan, Anhui	RP4	2.24%	0.67%
62	Nantong, Jiangsu	RP4	2.63%	0.46%
63	Qingdao, Shandong	RP10	3.29%	0.50%
64	Qingyang, Gansu	RP5	3.48%	0.81%
65	Sanming, Fujian	RP4	2.98%	0.72%
66	Sanming, Fujian	RP4	3.57%	0.65%
67	Shenyang, Liaoning	RP6	1.94%	0.49%
68	Shenyang, Liaoning	RP4	2.29%	0.43%
69	Shenyang, Liaoning	RP6	1.27%	0.19%
70	Shijiazhuang, Hebei	RP6	2.73%	0.39%
71	Shijiazhuang, Hebei	RP6	2.81%	0.35%
72	Tangshan, Hebei	RP6	2.47%	0.71%
73	Tianjin	RP6	3.49%	0.38%
74	Tianjin	RP6	4.50%	0.32%
75	Xining, Qinghai	RP5	2.31%	0.23%
76	Xinxiang, Henan	RP4	4.60%	0.62%
77	Xinxiang, Henan	RP4	3.83%	0.55%
78	Xinxiang, Henan	RP4	4.07%	0.29%
79	Yichun, Jiangxi	RP6	3.32%	0.95%
80	Yichun, Jiangxi	RP4	2.97%	0.73%
81	Yinchuan, Ningxia	RP3	2.20%	0.19%
82	Yulin, Guangxi	RP4	1.98%	0.48%
83	Yulin, Shaanxi	RP5	1.89%	0.35%
84	Yuncheng, Shanxi	RP4	2.92%	0.61%
85	Yuncheng, Shanxi	RP4	3.75%	0.32%
86	Zhengzhou, Henan	RP5	2.95%	0.37%
87	Zhengzhou, Henan	RP4	4.58%	0.34%
88	Zibo, Shandong	RP6	4.31%	0.45%
89	Zibo, Shandong	RP4	3.00%	0.31%

Sample No.	Province	Code No.	Total anthraquinone content/%	Free anthraquinone content/%
1	Baishan, Jilin	RP4	2.25%	0.59%
2	Baoji, Shaanxi	RP6	3.22%	0.45%
3	Bayannaoer, Neimenggu	RP4	3.49%	0.55%
4	Beijing	RP4	4.54%	1.10%
5	Beijing	RP4	3.26%	0.75%
6	Beijing	RP4	3.83%	0.72%
7	Beijing	RP4	3.61%	0.67%
8	Beijing	RP5	2.93%	0.55%
9	Beijing	RP4	2.15%	0.48%
10	Beijing	RP4	3.86%	0.47%
11	Beijing	RP6	3.58%	0.47%
12	Beijing	RP6	3.56%	0.38%
13	Beijing	RP4	2.96%	0.36%
14	Beijing	RP6	3.21%	0.26%
15	Beijing	RP3	3.89%	0.09%
16	Bozhou, Anhui	RP4	2.81%	0.63%
17	Bozhou, Anhui	RP4	2.53%	0.47%
18	Bozhou, Anhui	RP10	2.43%	0.35%
19	Bozhou, Anhui	RP6	5.03%	0.31%
20	Bozhou, Anhui	RP2	3.64%	0.27%
21	Bozhou, Anhui	RP6	2.82%	0.26%
22	Bozhou, Anhui	RP5	2.43%	0.21%
23	Bozhou, Anhui	RP6	3.22%	0.18%
24	Bozhou, Anhui	RP6	2.85%	0.13%
25	Changchun, Jilin	RP4	3.90%	0.65%
26	Changchun, Jilin	RP4	3.02%	0.64%
27	Changchun, Jilin	RP6	2.85%	0.29%
28	Changge, Henan	RP6	2.70%	0.38%
29	Changsha, Hunan	RP4	2.49%	0.41%
30	Chengdu, Sichuan	RP5	2.22%	0.73%
31	Chengdu, Sichuan	RP6	3.42%	0.57%
32	Chengdu, Sichuan	RP10	2.59%	0.54%
33	Chengdu, Sichuan	RP6	3.39%	0.40%
34	Chengdu, Sichuan	RP3	3.18%	0.30%
35	Chengdu, Sichuan	RP6	2.55%	0.21%
36	Chengdu, Sichuan	RP2	2.86%	0.20%
37	Chifeng, Neimenggu	RP4	1.45%	0.28%
38	Chongqing	RP12	3.31%	0.21%
39	Chuxiong, Yunnan	RP4	3.43%	0.10%
40	Dalian, Liaoning	RP4	2.61%	0.59%
41	Ganzhou, Jiangxi	RP6	3.60%	0.48%
42	Guangzhou, Guangdong	RP4	2.24%	0.75%
43	Guiyang, Guizhou	RP3	3.52%	0.65%
44	Guiyang, Guizhou	RP6	2.46%	0.60%
45	Guiyang, Guizhou	RP6	3.10%	0.41%
46	Guiyang, Guizhou	RP6	3.75%	0.38%
47	Guiyang, Guizhou	RP4	3.05%	0.36%
48	Guiyang, Guizhou	RP3	3.90%	0.32%
49	Guiyang, Guizhou	RP4	3.59%	0.32%
50	Haerbin, Heilongjiang	RP6	3.28%	0.50%
51	Haerbin, Heilongjiang	RP6	3.91%	0.47%
52	Haerbin, Heilongjiang	RP4	1.57%	0.45%
53	Haerbin, Heilongjiang	RP6	3.18%	0.31%
54	Haikou, Hainan	RP6	4.59%	0.43%
55	Huichun, Jilin	RP6	5.06%	0.33%
56	Jieyang, Guangdong	RP5	3.34%	0.31%
57	Kashi, Xinjiang	RP4	3.46%	0.61%
58	Kashi, Xinjiang	RP6	3.97%	0.35%
59	Kunming, Yunnan	RP1	3.99%	0.51%
60	Kunming, Yunnan	RP6	2.17%	0.27%
61	Liuan, Anhui	RP4	2.24%	0.67%
62	Nantong, Jiangsu	RP4	2.63%	0.46%
63	Qingdao, Shandong	RP10	3.29%	0.50%
64	Qingyang, Gansu	RP5	3.48%	0.81%
65	Sanming, Fujian	RP4	2.98%	0.72%
66	Sanming, Fujian	RP4	3.57%	0.65%
67	Shenyang, Liaoning	RP6	1.94%	0.49%
68	Shenyang, Liaoning	RP4	2.29%	0.43%
69	Shenyang, Liaoning	RP6	1.27%	0.19%
70	Shijiazhuang, Hebei	RP6	2.73%	0.39%
71	Shijiazhuang, Hebei	RP6	2.81%	0.35%
72	Tangshan, Hebei	RP6	2.47%	0.71%
73	Tianjin	RP6	3.49%	0.38%
74	Tianjin	RP6	4.50%	0.32%
75	Xining, Qinghai	RP5	2.31%	0.23%
76	Xinxiang, Henan	RP4	4.60%	0.62%
77	Xinxiang, Henan	RP4	3.83%	0.55%
78	Xinxiang, Henan	RP4	4.07%	0.29%
79	Yichun, Jiangxi	RP6	3.32%	0.95%
80	Yichun, Jiangxi	RP4	2.97%	0.73%
81	Yinchuan, Ningxia	RP3	2.20%	0.19%
82	Yulin, Guangxi	RP4	1.98%	0.48%
83	Yulin, Shaanxi	RP5	1.89%	0.35%
84	Yuncheng, Shanxi	RP4	2.92%	0.61%
85	Yuncheng, Shanxi	RP4	3.75%	0.32%
86	Zhengzhou, Henan	RP5	2.95%	0.37%
87	Zhengzhou, Henan	RP4	4.58%	0.34%
88	Zibo, Shandong	RP6	4.31%	0.45%
89	Zibo, Shandong	RP4	3.00%	0.31%

基于叶绿体基因matK及UPLC对市售大黄的种质资源鉴定和质量分析

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满金辉 ^#^,¹ , 石玥 ^#^,¹ , 张靖晗 ¹ , 张志飞 ¹ , 尹光耀 ¹ , 王馨 ¹ , 刘凤波 ¹^,³ , 张媛 ¹^,³ , 王晓晖 ¹^,²^,^* , 魏胜利 ¹^,³^,^*

药学学报 | 研究论文 2022,57(2): 514-524

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药学学报 | 研究论文 2022, 57(2): 514-524

基于叶绿体基因matK及UPLC对市售大黄的种质资源鉴定和质量分析

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满金辉^#^,¹, 石玥^#^,¹, 张靖晗¹, 张志飞¹, 尹光耀¹, 王馨¹, 刘凤波¹^,³, 张媛¹^,³, 王晓晖¹^,²^,^*, 魏胜利¹^,³^,^*

作者信息

1.北京中医药大学中药学院, 北京 102488

2.北京中医药大学中药学院中药现代研究中心, 北京 100029

3.中药材规范化生产教育部工程研究中心, 北京 100102

通讯作者:

*魏胜利, Tel: 13683336930, E-mail: wsl7491@126.com;

王晓晖, Tel: 15201166873, E-mail: wangxhui2014@163.com

Molecular identification and quality analysis of Rhei Rhizoma based on chloroplast matK gene and UPLC

Jin-hui MAN¹, Yue SHI¹, Jing-han ZHANG¹, Zhi-fei ZHANG¹, Guang-yao YIN¹, Xin WANG¹, Feng-bo LIU¹^,³, Yuan ZHANG¹^,³, Xiao-hui WANG¹^,²^,^*, Sheng-li WEI¹^,³^,^*

Affiliations

1. School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 102488, China

2. Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China

3. Engineering Research Center of Good Agricultural Practice for Chinese Crude Drugs of Ministry of Education, Beijing 100102, China

出版时间: 2022-02-12 doi: 10.16438/j.0513-4870.2021-1225

文章导航

摘要

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大黄是多基原大宗常用药材，不同基原大黄其药理活性存在一定差异。本研究基于叶绿体基因matK序列对市售大黄饮片的种质资源进行鉴定，通过UPLC对其总蒽醌和游离蒽醌含量进行测定，以期确定市售大黄的主要种质及质量。本研究从27省40市收集89份市售大黄样品并提取DNA，通过PCR扩增matK基因，扩增的matK基因序列的分析结果表明，收集的市售大黄样品都是同一基原，即掌叶大黄，共鉴定了其8个基因型：Rp1、Rp2、Rp3、Rp4、Rp5、Rp6、Rp10和Rp12，其中来自甘肃、四川和云南省的Rp4和Rp6是主要流通基因型，分别占总样品量40.45%和37.08%。系统发育树结果表明，8个基因型主要分成2大支，其中主要基因型Rp4和Rp6聚在同一大支，遗传距离分析结果表明8个基因型遗传距离在0.001~0.010之间。利用UPLC检测市场样品中的总蒽醌和游离蒽醌含量结果表明，93.26%样品符合药典标准。样品之间总蒽醌含量和游离蒽醌的含量差异显著，其中游离蒽醌相差1.01%；总蒽醌含量相差3.79%，说明市售大黄样品质量存在参差不齐的现象，但是不同采集省份之间市售大黄总蒽醌和游离蒽醌含量没有显著性差异，各基因型之间的大黄样品中总蒽醌和游离蒽醌含量没有显著性差异。本文对大黄市场样品的种质资源、产地来源和药材质量进行研究，有利于指导大黄市场样品流通及临床科学合理用药，为其他中药材市场样品种质及质量鉴定提供参考。

关键词

大黄 / matK基因 / 分子鉴定 / 质量评价

Abstract

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Rhei Rhizoma is commonly used as a traditional Chinese medicine with multiple botanical origins. Different botanical sources may have different pharmacological activities. The germplasm resources of commercial Rhei Rhizoma were determined based on the chloroplast gene matK, and the anthraquinone and free anthraquinone content was determined by UPLC to analyze quality of commercial Rhei Rhizoma. Eighty-nine commercial Rhei Rhizoma samples were collected from 40 cities in 27 provinces. DNA was extracted and the matK gene was amplified by PCR. Results indicated that the collected samples were from the same botanical origin, Rheum palmatum, and 8 genotypes were identified, including Rp1, Rp2, Rp3, Rp4, Rp5, Rp6, Rp10 and Rp12. Rp4 and Rp6, cultivated in Gansu, Sichuan and Yunnan provinces were the main circulating genotypes, representing 40.45% and 37.08% of the total samples, respectively. Phylogenetic tree analysis showed that the eight genotypes were mainly divided into two branches, of which the main genotypes Rp4 and Rp6 were in one branch. Genetic distance analysis indicated that the genetic separation of the eight genotypes was between 0.001 and 0.010. UPLC analysis indicated that 93.26% of the samples met the Pharmacopoeia standards. There were significant differences in the content of total anthraquinone and free anthraquinone among the samples, in which the difference in free anthraquinone was 1.01% and the difference in total anthraquinone content was 3.79%, indicating that the quality of commercial Rhei Rhizoma samples varies considerably. There was no significant difference in the content of total anthraquinone and free anthraquinone in commercial Rhei Rhizoma among different collection provinces and genotypes. This study will help guide the circulation of Rhei Rhizoma in the market and provides valuable insights for molecular identification and quality analysis of other traditional Chinese medicines.

Key words

Rhei Rhizoma / matK gene / molecular identification / quality evaluation

引用本文

满金辉, 石玥, 张靖晗, 张志飞, 尹光耀, 王馨, 刘凤波, 张媛, 王晓晖, 魏胜利. 基于叶绿体基因matK及UPLC对市售大黄的种质资源鉴定和质量分析. 药学学报, 2022 , 57 (2) : 514 -524 . DOI: 10.16438/j.0513-4870.2021-1225

Jin-hui MAN, Yue SHI, Jing-han ZHANG, Zhi-fei ZHANG, Guang-yao YIN, Xin WANG, Feng-bo LIU, Yuan ZHANG, Xiao-hui WANG, Sheng-li WEI. Molecular identification and quality analysis of Rhei Rhizoma based on chloroplast matK gene and UPLC[J]. Acta Pharmaceutica Sinica, 2022 , 57 (2) : 514 -524 . DOI: 10.16438/j.0513-4870.2021-1225

正文

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大黄(Rhei Rhizoma) 是常用大宗药材之一^[1], 具有泻下攻积、清热泻火、凉血解毒、逐瘀痛经、利湿退黄的功效^[2], 临床上常用于治疗慢性肝炎^[3]、肝纤维化^[4]、便秘^[5]、慢性肾衰^[6]、黄疸^[7]、上消化道出血等多种疾病^[8]。大黄是一种典型的多基原、多炮制、多功效药材^[9], 其正品来源为蓼科植物掌叶大黄(Rheum palmatum L.)、唐古特大黄(Rheum tanguticum Maxim.ex Balf.) 或药用大黄(Rheum officinale Baill) 的根及根茎^[2]。已有研究表明, 不同基原大黄化学成分含量存在一定差别, 其中蒽醌类及其衍生物差别较大^{[10, 11]}, 并且3种基原大黄其药理活性也存在不同差异^[12], 其中大黄蒽醌类化合物是发挥泻下攻积作用的主要成分^{[13, 14]}, 鞣质类成分为清热泻火的药效物质基础^{[15, 16]}, 为确保大黄更为精确的对症用药, 有必要针对不同功效用途, 选取不同基原大黄入药, 因此, 鉴别大黄市场样品基原及种质具有重要的意义。

近年来, 基于DNA序列标记的中药材分子鉴定技术在中药鉴定领域被广泛应用, 与中药材性状鉴定、药材理化鉴定等方法相比, DNA序列标记技术重复性好并且准确度高^{[17, 18]}。叶绿体基因通过母系遗传给后代, 结构简单、分子量小、序列保守, 因此广泛用于中药材种质资源鉴定^{[19, 20]}及系统进化分析^[21-23]。叶绿体基因matK具有分子质量小、序列和结构保守性强、进化速率快等特点, 常被应用于中药材分子鉴定^[24]及亲缘性研究^[25]。如matK基因作为石斛属系统进化关系及亲缘关系分析具有更高的遗传多样^[24]。课题组前期研究结果表明matK基因能够鉴别大黄的真伪^[9], 及不同产地野生型和栽培型大黄的种质, 因此可以利用matK基因实现对市场销售的大黄进行种质资源鉴别, 并推断其产地来源。

大黄临床应用范围广, 组方中包含有大黄的中成药有868种, 如防风通圣丸、牛黄上清丸等, 均配有大黄药材, 大黄的质量直接关系到中药处方和制剂的临床疗效。大黄的质量与大黄的有效成分息息相关, 《中华人民共和国药典》 (2020版) 中明确规定大黄药材中总蒽醌含量应不低于2.0%, 游离蒽醌含量应不低于0.2%。因此, 检测市场销售大黄的总蒽醌和游离蒽醌的含量有利于确定大黄药材的质量, 确保大黄临床疗效。本研究从27省40市收集89份大黄市场样品, 通过检测matK基因型确定市售样品的基原、种质, 判断其产地, 通过UPLC检测市售大黄样品的总蒽醌和游离蒽醌含量, 确定大黄质量, 为后续指导大黄市场样品流通及大黄临床用药奠定基础, 同时也对其他多基原、多种质的中药材的市场样品检测及科学合理临床用药具有重要指导意义。

材料与方法

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材料本研究样本于2020年11月至2021年5月从全国27省40市收集89份市售大黄饮片, 将其粉碎, 过6号筛, 样品信息见表 1。植物基因组提取试剂盒(Vazyme, 中国), 产物纯化试剂盒(Vazyme, 中国), Taq DNA聚合酶(TaKaRa), 琼脂糖(Biowest Agrose, 西班牙), 核酸Marker和核酸染料购自北京拜尔迪生物科技有限公司, 芦荟大黄素对照品(T28D6F8264)、大黄酸对照品(F27J7S18516)、大黄素对照品(T17A10F95418)、大黄酚对照品(T23A8F42188)、大黄素甲醚对照品(T18O10F100404) (上海源叶生物科技有限公司), matK引物由北京六合华大基因科技有限公司合成。

仪器冷冻高速离心机(TGL-16, 湘仪离心机仪器有限公司), 粉碎机(BJ-150, 拜杰多功能粉碎机), 电热恒温水浴锅(HH-S4A, 北京科伟永兴仪器有限公司), 涡旋器(GL-88B, 海门市其林贝尔仪器制造有限公司), 超高效液相(岛津SPD-M40), 色谱柱(Waters, 2.1 mm × 100 mm, 1.7 μm), 超声波清洗机(GW1030, 深圳市冠博科技实业有限公司), 电子天平(PL602E, METTLER TOLEDO), 凝胶成像仪(WD-9413B, 北京六一生物科技有限公司), 电泳仪(JY300E, 北京君意东方电泳设备有限公司), 超微量分光光度计(NanoDrop, Thermo Fisher), PCR仪(T100, Bio-Rad)。

DNA提取与检测 按照植物DNA提取试剂盒(Vazyme, 中国) 实验操作步骤进行DNA提取, 利用NanoDrop检测DNA浓度, 同时利用1.0%琼脂糖凝胶电泳检测DNA的完整性和质量。

PCR扩增与测序 本研究根据大黄matK片段设计特异性引物matK-F: ATGGAGGAATTCCAAGGATATT TAG, matK-R: ATAATTATGACTGGCCAAATCATAA, 并对89份样品进行PCR扩增。扩增体系为DNA 1 μL、10×Taq buffer 5 μL、dNTP Mix (2.5 mmol·L^-1) 4 μL、rTaq (2.5 U·μL^-1) 1 μL、0.1% BSA 1 μL、10 μmol primer各2 μL, ddH₂O补足至终体积50 μL。扩增程序: 95 ℃预变性5 min, 94 ℃ 30 s, 50 ℃ 50 s, 72 ℃ 1 min, 35个循环, 72 ℃延伸反应10 min, 4 ℃保存。1.0%琼脂糖凝胶电泳检测PCR产物, 目的片段回收和纯化按照胶回收试剂盒说明书进行。回收产物送北京六合华大基因科技有限公司测序。

对照品溶液制备 分别精密称取芦荟大黄素、大黄酸、大黄素、大黄酚和大黄素甲醚对照品, 依次为1.88、1.66、1.25、3.55和0.95 mg, 加纯甲醇溶液超声使之充分溶解, 分别定容至10 mL量瓶中, 备用; 将上述溶液作为对照品贮备溶液, 置于4 ℃冰箱中备用。

供试品溶液制备 市售大黄饮片游离蒽醌和总蒽醌提取参考^{[26, 27]}等方法。取市售大黄粉末1.0 g, 精密称定, 置具50 mL塞锥形瓶中, 加入45 mL甲醇, 称定质量。超声处理30 min, 放至室温, 加甲醇补足质量, 摇匀, 过滤, 取续滤液过0.45 μm微孔滤膜, 作为游离蒽醌供试品溶液。取市售大黄粉末0.1 g, 精密称定, 置100 mL量瓶中, 加入约99 mL甲醇, 超声提取0.5 h, 放至室温, 加甲醇定容至刻度, 摇匀, 滤过。取续滤液15 mL置圆底烧瓶中, 加盐酸0.8 mL, 80 ℃水浴加热回流30 min, 放至室温, 加10% NaOH溶液3 mL调节pH值为3~4, 摇匀, 转移至25 mL容量瓶中, 加甲醇至刻度线, 摇匀, 过滤, 取续滤液过0.45 μm微孔滤膜, 作为总蒽醌供试品溶液。

色谱条件 采用UPLC进行含量测定, 总蒽醌UPLC条件: 岛津SPD-M40 (日本), 色谱柱为Waters Acquity UPLC^® BEH C18色谱柱(2.1 mm × 100 mm, 1.7 μm); 流动相为乙腈(A)-0.1%磷酸水(B) 梯度洗脱。洗脱条件为0 min 97% B, 1.5 min 97% B, 3 min 96% B, 5 min 95% B, 8 min 90% B, 14 min 85% B, 18 min 75% B, 20 min 64% B, 25 min 40% B, 40 min 40% B; 流速0.3 mL·min^-1; 检测波长254 nm; 柱温40 ℃。游离蒽醌UPLC条件: 岛津SPD-M40 (日本), 色谱柱为Waters Acquity UPLC^® BEH C18色谱柱(2.1 mm × 100 mm, 1.7 μm); 流动相为乙腈(A)-0.1%磷酸水(B) 梯度洗脱。洗脱条件为0 min 64% B, 3 min 45% B, 5 min 41% B, 6 min 40% B, 10 min 40% B; 流速0.3 mL·min^-1; 检测波长254 nm; 柱温40 ℃。

序列分析及数据处理 采用ContingExpress校对拼接, 去除低质量序列及引物区。用DNAman软件将测序得到的matK序列与文献中大黄序列进行碱基分析, 获得市场大黄饮片基因型信息, 并根据基因型判断市场大黄饮片基原。以本研究收集的大黄饮片为材料, 采用MEGA X软件中的K2P (kimura2-parameter) 模型计算不同种间和种内的遗传距离, 生成距离矩阵以及构建基因型NJ树。采取SPSS 19.0统计软件中的单因素分析比较采集地与总蒽醌和游离蒽醌的相关性以及单倍型与总蒽醌和游离蒽醌的相关性。

结果与分析

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1 市售大黄样品的收集

本实验收集27个省40个市89份市场销售大黄, 样品信息见表 1, 其中北京市12份样品, 安徽省10份样品, 四川省和贵州省各7份样品, 河南省6份样品, 吉林省5份样品, 辽宁省和黑龙江省各4份样品, 云南省、江西省、河北省和山东省各3份样品, 内蒙古自治区、新疆维吾尔自治区、陕西省、广东省、天津市、福建省和山西省各2份样品, 重庆市、湖南省、海南省、江苏省、甘肃省、青海省、宁夏回族自治区和广西壮族自治区各1份样品。

2 市售大黄DNA条形码matK基因的扩增

成功提取89份市售大黄样品DNA, DNA浓度是33.2~107.0 ng·μL^-1, 具体结果如图 1所示; 以提取的DNA为模板, 通过PCR扩增matK基因, 89份样品中都扩增到了1 500 bp的片段, 具体结果如图 2所示。PCR产物经纯化后进行双向测序, 测序结果经拼接后, 通过与NCBI中大黄matK序列(AB115669) 进行比对, 确定其为大黄matK基因序列。

3 市售大黄DNA条形码比对结果

本课题组前期研究结果表明, 目前检测的中国地区的大黄正品及伪品共41个单倍型, 3个基原的大黄共29个单倍型(基因型), 其中药用大黄5个、掌叶大黄16个、唐古特大黄8个。本研究89份市场样品均为正品大黄, 而且均是掌叶大黄的单倍型, 共8个基因型, 分别为Rp1、Rp2、Rp3、Rp4、Rp5、Rp6、Rp10和Rp12, 基因型序列变异位点见表 2, 其中基因型Rp4数量最多, 占总样本量40.45%, 共36份, 其次是Rp6, 共有33份, 占总样本量37.08%; 基因型数量由高至低排列如下: Rp4 > Rp6 > Rp5 > Rp3 > Rp10 > Rp2 > Rp1 = Rp12 (图 3)。市售大黄中Rp4基因型分布在18个省, 占全国省级行政区的52.94%, 包括北京市、广东省、云南省、河南省、吉林省、福建省、内蒙古自治区、贵州省、湖南省、山西省、广西壮族自治区、黑龙江省、辽宁省、新疆维吾尔自治区、安徽省、江西省、江苏省和山东省; 以前的研究结果表明Rp4基因型来自甘肃省、云南省和宁夏回族自治区, 其中云南省和甘肃省为栽培大黄, 宁夏回族自治区为野生大黄; 说明来自甘肃、云南的栽培大黄和来自宁夏的野生大黄可能销售到全国18个省份。Rp6基因型分布16个省份, 占全国省级行政区的47.06%, 包括北京市、辽宁省、四川省、安徽省、云南省、吉林省、黑龙江省、天津市、新疆维吾尔自治区、河北省、河南省、贵州省、海南省、陕西省、山东省和江西省, 课题组前期研究结果表明Rp6基因型来自四川省的栽培大黄; 说明四川的栽培大黄销售到全国16个省份。以上结果表明, 市售大黄饮片的基因型比较集中, 来源地也相对集中。

4 市售大黄饮片基因型系统进化及遗传距离分析

采用MEGA X将市售大黄8个matK基因型序列构建进化树。从进化树可以看出8个基因型共分成两个大支, 其中Rp10和Rp12聚为一大支, 支持率为76%; Rp1、Rp2、Rp3、Rp4、Rp5和Rp6聚为另一大支(图 4)。对获得的8个基因型进行种内遗传距离分析, 8个基因型平均遗传距离为0.004, 遗传距离在0.001~0.010之间, 最大遗传距离为0.010, 发生在Rp10和Rp1之间, 最小遗传距离为0.001, 发生Rp2和Rp3之间(表 3)。此外, 研究发现市售大黄饮片主要流通的Rp4和Rp6基因型聚在一小支上, 支持率为56%, 其遗传距离为0.003, 表明2个基因型亲缘关系较近。

5 市售大黄饮片采集地含量及基因型含量分析

采用UPLC对大黄中游离蒽醌和总蒽醌成分(芦荟大黄素、大黄酸、大黄素、大黄酚和大黄素甲醚) 进行线性关系考察(图 5、6, 表 4、5), 结果表明游离蒽醌和总蒽醌成分相关系数R²符合分析要求。采用该方法对89份市售大黄饮片进行游离蒽醌和总蒽醌含量测定, 结果表明89份样品中83份市售大黄饮片总蒽醌和游离蒽醌含量符合药典标准, 合格率为93.26%, 其中辽宁省、内蒙古自治区、黑龙江省、广西壮族自治区和陕西省大黄饮片存在总蒽醌含量不合格情况, 北京市、云南省、安徽省、宁夏回族自治区和辽宁省大黄饮片存在游离蒽醌含量不合格情况(表 6)。市售大黄中总蒽醌的含量为1.27%~5.06%, 总蒽醌含量最高的省份是海南省, 平均值为4.59%, 含量最低的省份是广西壮族自治区, 平均值为1.98%; 市售大黄样品的游离蒽醌含量为1.10%~0.09%。游离蒽醌含量最高的省份为甘肃省, 平均值为0.81%, 含量最低的省份为宁夏回族自治区, 平均值为0.19% (图 7), 这些结果表明市售大黄样品总蒽醌和游离蒽醌含量参差不齐。实验采用SPSS 19.0对采集省份的市售大黄游离蒽醌和总蒽醌含量进行相关性分析, 发现各采集省份之间的市售大黄游离蒽醌和总蒽醌含量无显著性差别。对市售大黄8个基因型的总蒽醌含量和游离蒽醌含量进行相关性分析, 总蒽醌含量最高的基因型为Rp1, 含量最低的基因型为Rp10; 游离蒽醌含量最高的基因型为Rp4, 含量最低的基因型为Rp12 (图 8); 采用SPSS 19.0对各基因型大黄中总蒽醌和游离蒽醌含量进行相关性分析, 结果表明各基因型间大黄总蒽醌和游离蒽醌含量无显著性差别。

讨论

收起

2020版《中华人民共和国药典》规定正品来源是掌叶大黄、唐古特大黄和药用大黄, 因此大黄是多基原药材^[2], 有文献表明不同基原药材的药理活性不同^{[12, 16]}, 对市场上大黄的种质资源鉴定对指导临床上合理用药尤为重要。传统的药材鉴定方法无法实现对药材基原及种质的鉴别^[9]。近年来, 基于DNA序列标记的中药材分子鉴定技术在中药鉴定领域得到了广泛的应用^[9]。其中叶绿体基因matK、trnH/trnL等基因和核基因ITS^[18]序列常用于中药材分子鉴定, 目前的研究表明matK在大黄正品及伪品鉴别、基原鉴定、种质资源鉴别过程中起到重要的作用^{[28, 29]}。但是由于大黄是多炮制的药材, 炮制后的大黄DNA样品不易提取, PCR片段不易扩增等原因, 对大黄市场样品的种质资源的鉴定目前还未见报道。本研究首次从全国27省40市收集89份药材提取的DNA样品中扩增到matK基因的全长并进行序列分析, 令人惊奇的是, 89份来自不同省市的大黄市售样品都是同一基原, 为掌叶大黄, 共发现掌叶大黄的8个单倍型, 其中Rp4和Rp6基因型数量为36份和33份, 占样品总体40.45%和37.08%; 以前的研究结果表明Rp4基因型主要来源于云南省、甘肃省和宁夏回族自治区, 其中云南和甘肃省的大黄是栽培大黄, 宁夏回族自治区为野生大黄, RP6基因型主要是四川省的栽培大黄^[9], 进化树和遗传距离分析结果表明RP4和RP6的基因型亲缘关系比较接近, 遗传距离较小; 这些实验结果表明市售大黄饮片的基原较单一, 基因型集中, 大部分可能是人工栽培的掌叶大黄。

中药质量与中药处方和制剂的临床疗效和安全息息相关, 中药的质量主要是由中药的活性成分决定的^[30]。如现代药理学研究表明大黄结合蒽醌具有致泻作用^[13], 游离蒽醌具有抑菌抗炎等功效^[15]。《中华人民共和国药典》 (2020版) 明确规定大黄中总蒽醌的含量应不低于2.0%, 游离蒽醌的含量应不低于0.2%; 王红娟等^[31]利用紫外-可见分光光度测定不同产地大黄中游离蒽醌结合蒽醌含量, 发现不同产地大黄中结合蒽醌成分含量范围为7.38%~6.15%; 游离蒽醌成分含量为0.563%~0.668%; 已有文献报道掌叶大黄和唐古特大黄质量较好, 药用大黄质量较差, 产地不同, 大黄的质量差异也比较大^[32], 但是目前对市售大黄的质量研究比较少。本研究利用UPLC对市售样品的总蒽醌和游离蒽醌进行测定, 结果表明有83份样品中总蒽醌和游离蒽醌含量符合药典规定; 但是样品间总蒽醌含量和游离蒽醌的含量差异显著, 样品中游离蒽醌最高含量是1.10%, 最低含量是0.09%, 相差1.01%; 总蒽醌含量最高为5.06%, 最低为1.27%, 相差3.79%; 同时对不同采集省份的市售大黄样品的总蒽醌和游离蒽醌含量进行分析, 结果表明相同采集省份的大黄的总蒽醌和游离蒽醌的含量差异比较大, 导致地区之间没有显著性差异。对不同基因型的大黄市售样品的总蒽醌和游离蒽醌含量进行分析, 结果表明相同基因型的样品之间差异比较大, 如RP4和RP6收集的样品中, 总蒽醌最高含量是5.06%, 最低含量是1.27%; 游离蒽醌最高含量是1.10%, 最低含量是0.10%; 总蒽醌和游离蒽醌含量差异最大的基因型均是Rp4, 但是基因型之间的市售大黄的总蒽醌和游离蒽醌含量没有显著性差异。这些结果表明大部分市场销售的大黄质量符合《中华人民共和国药典》的要求, 但是样品质量参差不齐, 采集地区和基因型与大黄的质量关联性不强。本研究为后续指导大黄的市场流通和科学合理的临床用药具有重要意义。

作者贡献: 满金辉负责matK基因的扩增、序列分析和数分析; 石玥负责市场大黄样品中总蒽醌和游离蒽醌含量检测、数据分析和论文初步撰写; 张靖晗、张志飞、尹光耀负责市场大黄样品打粉、DNA提取; 王馨、刘风波和张媛负责全国各地区样品的收集; 王晓晖和魏胜利负责设计论文的实验思路, 指导学生实验, 论文撰写及修改。

利益冲突: 无相关利益冲突。

基金

收起

国家自然科学基金资助项目(3207020660)
北京市科学技术委员会基金资助项目(Z201100005420005)

参考文献

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2022年第57卷第2期

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文章信息

doi: 10.16438/j.0513-4870.2021-1225

接收时间：2021-08-24
首发时间：2025-12-22
出版时间：2022-02-12

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出版历史

收稿日期：2021-08-24
修回日期：2021-09-13

基金

国家自然科学基金资助项目(3207020660)

北京市科学技术委员会基金资助项目(Z201100005420005)

作者信息

1.北京中医药大学中药学院, 北京 102488

2.北京中医药大学中药学院中药现代研究中心, 北京 100029

3.中药材规范化生产教育部工程研究中心, 北京 100102

通讯作者:

*魏胜利, Tel: 13683336930, E-mail: wsl7491@126.com;

王晓晖, Tel: 15201166873, E-mail: wangxhui2014@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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No.	Province	No.	Province	No.	Province
1	Baishan, Jilin	31	Chengdu, Sichuan	61	Liuan, Anhui
2	Baoji, Shaanxi	32	Chengdu, Sichuan	62	Nantong, Jiangsu
3	Bayannaoer, Neimenggu	33	Chengdu, Sichuan	63	Qingdao, Shandong
4	Beijing	34	Chengdu, Sichuan	64	Qingyang, Gansu
5	Beijing	35	Chengdu, Sichuan	65	Sanming, Fujian
6	Beijing	36	Chengdu, Sichuan	66	Sanming, Fujian
7	Beijing	37	Chifeng, Neimenggu	67	Shenyang, Liaoning
8	Beijing	38	Chongqing	68	Shenyang, Liaoning
9	Beijing	39	Chuxiong, Yunnan	69	Shenyang, Liaoning
10	Beijing	40	Dalian, Liaoning	70	Shijiazhuang, Hebei
11	Beijing	41	Ganzhou, Jiangxi	71	Shijiazhuang, Hebei
12	Beijing	42	Guangzhou, Guangdong	72	Tangshan, Hebei
13	Beijing	43	Guiyang, Guizhou	73	Tianjin
14	Beijing	44	Guiyang, Guizhou	74	Tianjin
15	Beijing	45	Guiyang, Guizhou	75	Xining, Qinghai
16	Bozhou, Anhui	46	Guiyang, Guizhou	76	Xinxiang, Henan
17	Bozhou, Anhui	47	Guiyang, Guizhou	77	Xinxiang, Henan
18	Bozhou, Anhui	48	Guiyang, Guizhou	78	Xinxiang, Henan
19	Bozhou, Anhui	49	Guiyang, Guizhou	79	Yichun, Jiangxi
20	Bozhou, Anhui	50	Haerbin, Heilongjiang	80	Yichun, Jiangxi
21	Bozhou, Anhui	51	Haerbin, Heilongjiang	81	Yinchuan, Ningxia
22	Bozhou, Anhui	52	Haerbin, Heilongjiang	82	Yulin, Guangxi
23	Bozhou, Anhui	53	Haerbin, Heilongjiang	83	Yulin, Shaanxi
24	Bozhou, Anhui	54	Haikou, Hainan	84	Yuncheng, Shanxi
25	Changchun, Jilin	55	Huichun, Jilin	85	Yuncheng, Shanxi
26	Changchun, Jilin	56	Jieyang, Guangdong	86	Zhengzhou, Henan
27	Changchun, Jilin	57	Kashi, Xinjiang	87	Zhengzhou, Henan
28	Changge, Henan	58	Kashi, Xinjiang	88	Zibo, Shandong
29	Changsha, Hunan	59	Kunming, Yunnan	89	Zibo, Shandong
30	Chengdu, Sichuan	60	Kunming, Yunnan

No.

Province

No.

Province

No.

Province

Baishan, Jilin

Chengdu, Sichuan

Liuan, Anhui

Baoji, Shaanxi

Chengdu, Sichuan

Nantong, Jiangsu

Bayannaoer, Neimenggu

Chengdu, Sichuan

Qingdao, Shandong

Beijing

Chengdu, Sichuan

Qingyang, Gansu

Beijing

Chengdu, Sichuan

Sanming, Fujian

Beijing

Chengdu, Sichuan

Sanming, Fujian

Beijing

Chifeng, Neimenggu

Shenyang, Liaoning

Beijing

Chongqing

Shenyang, Liaoning

Beijing

Chuxiong, Yunnan

Shenyang, Liaoning

Beijing

Dalian, Liaoning

Shijiazhuang, Hebei

Beijing

Ganzhou, Jiangxi

Shijiazhuang, Hebei

Beijing

Guangzhou, Guangdong

Tangshan, Hebei

Beijing

Guiyang, Guizhou

Tianjin

Beijing

Guiyang, Guizhou

Tianjin

Beijing

Guiyang, Guizhou

Xining, Qinghai

Bozhou, Anhui

Guiyang, Guizhou

Xinxiang, Henan

Bozhou, Anhui

Guiyang, Guizhou

Xinxiang, Henan

Bozhou, Anhui

Guiyang, Guizhou

Xinxiang, Henan

Bozhou, Anhui

Guiyang, Guizhou

Yichun, Jiangxi

Bozhou, Anhui

Haerbin, Heilongjiang

Yichun, Jiangxi

Bozhou, Anhui

Haerbin, Heilongjiang

Yinchuan, Ningxia

Bozhou, Anhui

Haerbin, Heilongjiang

Yulin, Guangxi

Bozhou, Anhui

Haerbin, Heilongjiang

Yulin, Shaanxi

Bozhou, Anhui

Haikou, Hainan

Yuncheng, Shanxi

Changchun, Jilin

Huichun, Jilin

Yuncheng, Shanxi

Changchun, Jilin

Jieyang, Guangdong

Zhengzhou, Henan

Changchun, Jilin

Kashi, Xinjiang

Zhengzhou, Henan

Changge, Henan

Kashi, Xinjiang

Zibo, Shandong

Changsha, Hunan

Kunming, Yunnan

Zibo, Shandong

Chengdu, Sichuan

Kunming, Yunnan

Species	Sample No.	Variable site/bp	Genotype type
Rheum palmatum	59	G		C		T		G		A		C		C		C		A		T		C		A		C		G		C		T		C		C		G	Rp1
20, 36	*		*		*		*		*		*		A		*		*		*		*		C		*		*		*		*		*		*		*	Rp2
15, 34, 43, 48, 81	*		*		A		*		*		*		A		*		*		*		*		C		*		*		*		*		*		*		*	Rp3
1, 3, 4, 5, 6, 7, 9, 10, 13, 16, 17, 25, 26, 29, 37, 39, 40, 42, 47, 49, 52, 57, 61, 62, 65, 66, 68, 76, 77, 78, 80, 82, 84, 85, 87, 89	*		*		A		A		C		*		A		*		*		G		*		C		*		*		*		*		*		T		A	Rp4
8, 22, 30, 56, 64, 75, 83, 86	*		*		A		*		C		*		A		*		*		G		*		C		*		*		*		*		*		*		*	Rp5
2, 11, 12, 14, 19, 21, 23, 24, 27, 28, 31, 33, 35, 41, 44, 45, 46, 50, 51, 53, 54, 55, 58, 60, 67, 69, 70, 71, 72, 73, 74, 79, 88	A		*		A		*		C		*		A		*		*		G		*		C		*		*		*		C		*		*		*	Rp6
18, 32, 63	*		*		A		*		C		*		A		*		*		G		*		C		A		*		T		*		*		*		*	Rp10
38	*		*		A		*		*		*		A		*		*		G		*		C		A		*		T		*		*		*		*	Rp12

Species

Sample No.

Variable site/bp

Genotype type

Rheum palmatum

Rp1

20, 36

Rp2

15, 34, 43, 48, 81

Rp3

1, 3, 4, 5, 6, 7, 9, 10, 13, 16, 17, 25, 26, 29, 37, 39, 40, 42, 47, 49, 52, 57, 61, 62, 65, 66, 68, 76, 77, 78, 80, 82, 84, 85, 87, 89

Rp4

8, 22, 30, 56, 64, 75, 83, 86

Rp5

2, 11, 12, 14, 19, 21, 23, 24, 27, 28, 31, 33, 35, 41, 44, 45, 46, 50, 51, 53, 54, 55, 58, 60, 67, 69, 70, 71, 72, 73, 74, 79, 88

Rp6

18, 32, 63

Rp10

Rp12

R. palmatum	Intraspecific genetic distance	Intraspecific genetic distance (mean)
Rp1								0.004
Rp2	0.002
Rp3	0.002	0.001
Rp4	0.006	0.004	0.003
Rp5	0.004	0.002	0.002	0.002
Rp6	0.006	0.004	0.003	0.003	0.002
Rp10	0.010	0.008	0.007	0.007	0.006	0.007
Rp12	0.005	0.003	0.002	0.004	0.002	0.004	0.005

R. palmatum

Intraspecific genetic distance

Intraspecific genetic distance (mean)

Rp1

0.004

Rp2

0.002

Rp3

0.002

0.001

Rp4

0.006

0.004

0.003

Rp5

0.004

0.002

Rp6

0.006

0.004

0.003

0.002

Rp10

0.010

0.008

0.007

0.006

0.007

Rp12

0.005

0.003

0.002

0.004

0.002

0.004

0.005

Compd.	Regression equation	R²
Aloe-emodin	y = 15 659 262.663 1 x + 101 684.884 2	0.999 7
Rhein	y = 7 020 849.301 7 x - 2 872.391 2	0.999 9
Emodin	y = 12 387 879.266 9 x + 32 007.372 8	0.999 9
Chrysophanol	y = 13 257 038.455 2 x + 52 023.627 8	1.000 0
Physcion	y = 3 652 156.894 8 x + 97 584.840 1	0.999 1

Compd.

Regression equation

R²

Aloe-emodin

y = 15 659 262.663 1 x + 101 684.884 2

0.999 7

Rhein

y = 7 020 849.301 7 x - 2 872.391 2

0.999 9

Emodin

y = 12 387 879.266 9 x + 32 007.372 8

0.999 9

Chrysophanol

y = 13 257 038.455 2 x + 52 023.627 8

1.000 0

Physcion

y = 3 652 156.894 8 x + 97 584.840 1

0.999 1

Compd.	Regression equation	R²
Aloe-emodin	y = 15 503 230.514 6 x - 15 467.663 4	1.000 0
Rhein	y = 6 986 119.268 3 x + 6 708.475 8	0.999 3
Emodin	y = 12 659 622.823 2 x - 19 162.199 9	1.000 0
Chrysophanol	y = 13 912 884.710 9 x - 68 986.647 7	0.999 9
Physcion	y = 3 499 359.079 7 x + 69 196.113 3	0.998 9

Compd.

Regression equation

R²

Aloe-emodin

y = 15 503 230.514 6 x - 15 467.663 4

1.000 0

Rhein

y = 6 986 119.268 3 x + 6 708.475 8

0.999 3

Emodin

y = 12 659 622.823 2 x - 19 162.199 9

1.000 0

Chrysophanol

y = 13 912 884.710 9 x - 68 986.647 7

0.999 9

Physcion

y = 3 499 359.079 7 x + 69 196.113 3

0.998 9

Sample No.	Province	Code No.	Total anthraquinone content/%	Free anthraquinone content/%
1	Baishan, Jilin	RP4	2.25%	0.59%
2	Baoji, Shaanxi	RP6	3.22%	0.45%
3	Bayannaoer, Neimenggu	RP4	3.49%	0.55%
4	Beijing	RP4	4.54%	1.10%
5	Beijing	RP4	3.26%	0.75%
6	Beijing	RP4	3.83%	0.72%
7	Beijing	RP4	3.61%	0.67%
8	Beijing	RP5	2.93%	0.55%
9	Beijing	RP4	2.15%	0.48%
10	Beijing	RP4	3.86%	0.47%
11	Beijing	RP6	3.58%	0.47%
12	Beijing	RP6	3.56%	0.38%
13	Beijing	RP4	2.96%	0.36%
14	Beijing	RP6	3.21%	0.26%
15	Beijing	RP3	3.89%	0.09%
16	Bozhou, Anhui	RP4	2.81%	0.63%
17	Bozhou, Anhui	RP4	2.53%	0.47%
18	Bozhou, Anhui	RP10	2.43%	0.35%
19	Bozhou, Anhui	RP6	5.03%	0.31%
20	Bozhou, Anhui	RP2	3.64%	0.27%
21	Bozhou, Anhui	RP6	2.82%	0.26%
22	Bozhou, Anhui	RP5	2.43%	0.21%
23	Bozhou, Anhui	RP6	3.22%	0.18%
24	Bozhou, Anhui	RP6	2.85%	0.13%
25	Changchun, Jilin	RP4	3.90%	0.65%
26	Changchun, Jilin	RP4	3.02%	0.64%
27	Changchun, Jilin	RP6	2.85%	0.29%
28	Changge, Henan	RP6	2.70%	0.38%
29	Changsha, Hunan	RP4	2.49%	0.41%
30	Chengdu, Sichuan	RP5	2.22%	0.73%
31	Chengdu, Sichuan	RP6	3.42%	0.57%
32	Chengdu, Sichuan	RP10	2.59%	0.54%
33	Chengdu, Sichuan	RP6	3.39%	0.40%
34	Chengdu, Sichuan	RP3	3.18%	0.30%
35	Chengdu, Sichuan	RP6	2.55%	0.21%
36	Chengdu, Sichuan	RP2	2.86%	0.20%
37	Chifeng, Neimenggu	RP4	1.45%	0.28%
38	Chongqing	RP12	3.31%	0.21%
39	Chuxiong, Yunnan	RP4	3.43%	0.10%
40	Dalian, Liaoning	RP4	2.61%	0.59%
41	Ganzhou, Jiangxi	RP6	3.60%	0.48%
42	Guangzhou, Guangdong	RP4	2.24%	0.75%
43	Guiyang, Guizhou	RP3	3.52%	0.65%
44	Guiyang, Guizhou	RP6	2.46%	0.60%
45	Guiyang, Guizhou	RP6	3.10%	0.41%
46	Guiyang, Guizhou	RP6	3.75%	0.38%
47	Guiyang, Guizhou	RP4	3.05%	0.36%
48	Guiyang, Guizhou	RP3	3.90%	0.32%
49	Guiyang, Guizhou	RP4	3.59%	0.32%
50	Haerbin, Heilongjiang	RP6	3.28%	0.50%
51	Haerbin, Heilongjiang	RP6	3.91%	0.47%
52	Haerbin, Heilongjiang	RP4	1.57%	0.45%
53	Haerbin, Heilongjiang	RP6	3.18%	0.31%
54	Haikou, Hainan	RP6	4.59%	0.43%
55	Huichun, Jilin	RP6	5.06%	0.33%
56	Jieyang, Guangdong	RP5	3.34%	0.31%
57	Kashi, Xinjiang	RP4	3.46%	0.61%
58	Kashi, Xinjiang	RP6	3.97%	0.35%
59	Kunming, Yunnan	RP1	3.99%	0.51%
60	Kunming, Yunnan	RP6	2.17%	0.27%
61	Liuan, Anhui	RP4	2.24%	0.67%
62	Nantong, Jiangsu	RP4	2.63%	0.46%
63	Qingdao, Shandong	RP10	3.29%	0.50%
64	Qingyang, Gansu	RP5	3.48%	0.81%
65	Sanming, Fujian	RP4	2.98%	0.72%
66	Sanming, Fujian	RP4	3.57%	0.65%
67	Shenyang, Liaoning	RP6	1.94%	0.49%
68	Shenyang, Liaoning	RP4	2.29%	0.43%
69	Shenyang, Liaoning	RP6	1.27%	0.19%
70	Shijiazhuang, Hebei	RP6	2.73%	0.39%
71	Shijiazhuang, Hebei	RP6	2.81%	0.35%
72	Tangshan, Hebei	RP6	2.47%	0.71%
73	Tianjin	RP6	3.49%	0.38%
74	Tianjin	RP6	4.50%	0.32%
75	Xining, Qinghai	RP5	2.31%	0.23%
76	Xinxiang, Henan	RP4	4.60%	0.62%
77	Xinxiang, Henan	RP4	3.83%	0.55%
78	Xinxiang, Henan	RP4	4.07%	0.29%
79	Yichun, Jiangxi	RP6	3.32%	0.95%
80	Yichun, Jiangxi	RP4	2.97%	0.73%
81	Yinchuan, Ningxia	RP3	2.20%	0.19%
82	Yulin, Guangxi	RP4	1.98%	0.48%
83	Yulin, Shaanxi	RP5	1.89%	0.35%
84	Yuncheng, Shanxi	RP4	2.92%	0.61%
85	Yuncheng, Shanxi	RP4	3.75%	0.32%
86	Zhengzhou, Henan	RP5	2.95%	0.37%
87	Zhengzhou, Henan	RP4	4.58%	0.34%
88	Zibo, Shandong	RP6	4.31%	0.45%
89	Zibo, Shandong	RP4	3.00%	0.31%

Sample No.

Province

Code No.

Total anthraquinone content/%

Free anthraquinone content/%

Baishan, Jilin

RP4

2.25%

0.59%

Baoji, Shaanxi

RP6

3.22%

0.45%

Bayannaoer, Neimenggu

RP4

3.49%

0.55%

Beijing

RP4

4.54%

1.10%

Beijing

RP4

3.26%

0.75%

Beijing

RP4

3.83%

0.72%

Beijing

RP4

3.61%

0.67%

Beijing

RP5

2.93%

0.55%

Beijing

RP4

2.15%

0.48%

Beijing

RP4

3.86%

0.47%

Beijing

RP6

3.58%

0.47%

Beijing

RP6

3.56%

0.38%

Beijing

RP4

2.96%

0.36%

Beijing

RP6

3.21%

0.26%

Beijing

RP3

3.89%

0.09%

Bozhou, Anhui

RP4

2.81%

0.63%

Bozhou, Anhui

RP4

2.53%

0.47%

Bozhou, Anhui

RP10

2.43%

0.35%

Bozhou, Anhui

RP6

5.03%

0.31%

Bozhou, Anhui

RP2

3.64%

0.27%

Bozhou, Anhui

RP6

2.82%

0.26%

Bozhou, Anhui

RP5

2.43%

0.21%

Bozhou, Anhui

RP6

3.22%

0.18%

Bozhou, Anhui

RP6

2.85%

0.13%

Changchun, Jilin

RP4

3.90%

0.65%

Changchun, Jilin

RP4

3.02%

0.64%

Changchun, Jilin

RP6

2.85%

0.29%

Changge, Henan

RP6

2.70%

0.38%

Changsha, Hunan

RP4

2.49%

0.41%

Chengdu, Sichuan

RP5

2.22%

0.73%

Chengdu, Sichuan

RP6

3.42%

0.57%

Chengdu, Sichuan

RP10

2.59%

0.54%

Chengdu, Sichuan

RP6

3.39%

0.40%

Chengdu, Sichuan

RP3

3.18%

0.30%

Chengdu, Sichuan

RP6

2.55%

0.21%

Chengdu, Sichuan

RP2

2.86%

0.20%

Chifeng, Neimenggu

RP4

1.45%

0.28%

Chongqing

RP12

3.31%

0.21%

Chuxiong, Yunnan

RP4

3.43%

0.10%

Dalian, Liaoning

RP4

2.61%

0.59%

Ganzhou, Jiangxi

RP6

3.60%

0.48%

Guangzhou, Guangdong

RP4

2.24%

0.75%

Guiyang, Guizhou

RP3

3.52%

0.65%

Guiyang, Guizhou

RP6

2.46%

0.60%

Guiyang, Guizhou

RP6

3.10%

0.41%

Guiyang, Guizhou

RP6

3.75%

0.38%

Guiyang, Guizhou

RP4

3.05%

0.36%

Guiyang, Guizhou

RP3

3.90%

0.32%

Guiyang, Guizhou

RP4

3.59%

0.32%

Haerbin, Heilongjiang

RP6

3.28%

0.50%

Haerbin, Heilongjiang

RP6

3.91%

0.47%

Haerbin, Heilongjiang

RP4

1.57%

0.45%

Haerbin, Heilongjiang

RP6

3.18%

0.31%

Haikou, Hainan

RP6

4.59%

0.43%

Huichun, Jilin

RP6

5.06%

0.33%

Jieyang, Guangdong

RP5

3.34%

0.31%

Kashi, Xinjiang

RP4

3.46%

0.61%

Kashi, Xinjiang

RP6

3.97%

0.35%

Kunming, Yunnan

RP1

3.99%

0.51%

Kunming, Yunnan

RP6

2.17%

0.27%

Liuan, Anhui

RP4

2.24%

0.67%

Nantong, Jiangsu

RP4

2.63%

0.46%

Qingdao, Shandong

RP10

3.29%

0.50%

Qingyang, Gansu

RP5

3.48%

0.81%

Sanming, Fujian

RP4

2.98%

0.72%

Sanming, Fujian

RP4

3.57%

0.65%

Shenyang, Liaoning

RP6

1.94%

0.49%

Shenyang, Liaoning

RP4

2.29%

0.43%

Shenyang, Liaoning

RP6

1.27%

0.19%

Shijiazhuang, Hebei

RP6

2.73%

0.39%

Shijiazhuang, Hebei

RP6

2.81%

0.35%

Tangshan, Hebei

RP6

2.47%

0.71%

Tianjin

RP6

3.49%

0.38%

Tianjin

RP6

4.50%

0.32%

Xining, Qinghai

RP5

2.31%

0.23%

Xinxiang, Henan

RP4

4.60%

0.62%

Xinxiang, Henan

RP4

3.83%

0.55%

Xinxiang, Henan

RP4

4.07%

0.29%

Yichun, Jiangxi

RP6

3.32%

0.95%

Yichun, Jiangxi

RP4

2.97%

0.73%

Yinchuan, Ningxia

RP3

2.20%

0.19%

Yulin, Guangxi

RP4

1.98%

0.48%

Yulin, Shaanxi

RP5

1.89%

0.35%

Yuncheng, Shanxi

RP4

2.92%

0.61%

Yuncheng, Shanxi

RP4

3.75%

0.32%

Zhengzhou, Henan

RP5

2.95%

0.37%

Zhengzhou, Henan

RP4

4.58%

0.34%

Zibo, Shandong

RP6

4.31%

0.45%

Zibo, Shandong

RP4

3.00%

0.31%