隔山消治疗溃疡性结肠炎的机制：基于UPLC-QE-MS、网络药理学及代谢组学技术

doi:10.12122/j.issn.1673-4254.2024.08.07

摘要/Abstract

摘要：

目的基于UPLC-QE-MS与网络药理学挖掘隔山消防治溃疡性结肠炎（UC）的靶点及通路，并结合代谢组学技术探讨作用机制。方法采用UPLC-QE-MS技术鉴定隔山消醇提物化学成分，基于Swiss Target Prediction、GeneCards、Pubchem等数据库筛选相应靶点，获取核心PPI，进行GO、KEGG富集分析。将40只雄性C57小鼠随机分为正常组、模型组、美沙拉嗪组（0.2 g/kg）、隔山消组（2.28 g/kg），每组10只，除正常组外，其余各组自由饮用2.5% DSS诱导UC模型，造模期间给药组给予药物灌胃干预。通过体质量变化率、DAI得分评价治疗效果；HE及AB-PAS染色观察结肠组织病理变化；Western blotting技术检测JAK2、STAT3蛋白水平；代谢组学技术鉴别差异代谢物并挖掘代谢通路。结果鉴定出隔山消醇提物化学成分240个，其中甾体类（高含量）19个，得到隔山消（甾体类）靶点177个，UC基因5406个，隔山消与UC交集基因117个，JAK2、STAT3等为核心PPI，在脂质与动脉粥样硬化等通路富集显著。动物实验结果显示，经隔山消治疗后，小鼠体质量变化率上升、DAI评分显著下降（P<0.05），肠组织病理改变明显缓解，JAK2、STAT3蛋白水平显著降低（P<0.05）。鉴定出正常组、模型组及隔山消组之间交集差异代谢物83个，以甘油磷脂、类花生酸、氨基酸成分为主，与甘油磷脂代谢等通路相关。整合分析显示隔山消治疗UC的核心靶点参与了代谢物的调节。结论隔山消可通过调节JAK2、STAT3等核心靶点表达及内源性代谢物水平来缓解脂质及氨基酸代谢紊乱，发挥治疗UC的作用。

关键词: 隔山消, 溃疡性结肠炎, 代谢组学, UPLC-QE-MS, 甾体类

Abstract:

Objective To explore the targets and pathways of Cynanchum wilfordii for treatment of ulcerative colitis (UC). Methods UPLC-QE-MS was used to identify the components of Cynanchum wilfordii ethanol extract, and their targets were screened using public databases for construction of the core protein-protein interaction (PPI) network and GO and KEGG enrichment analyses. Forty male C57 mice were randomized into normal control group, model group, mesalazine group and Cynanchum wilfordii group (n=10), and in the latter 3 groups, mouse UC models were established by treatment with 2.5% DSS and the latter 2 groups drug interventions by gavage. The therapeutic effect was evaluated by recording body weight changes and DAI score. Pathological changes of the colon tissue were observed with HE and AB-PAS staining, and JAK2 and STAT3 protein expressions were detected with Western blotting. The metabolites and metabolic pathways were identified by metabonomics analysis. Results We identified 240 chemical components in Cynanchum wilfordii alcoholic extracts, including 19 steroids. A total of 177 Cynanchum wilfordii targets, 5406 UC genes, and 117 intersection genes were obtained. JAK2 and STAT3 were the core targets and significantly enriched in lipid and atherosclerosis pathways. Cynanchum wilfordii treatment significantly increased the body weight and decreased DAI score of UC mice (P<0.05), alleviated intestinal pathologies, and decreased JAK2 and STAT3 protein expressions in the colon tissues. Most of the 83 intersecting differential metabolites between the control, model and Cynanchum wilfordii groups were identified as glycerophospholipids, arachidonic acid, and amino acids involving glycerophospholipid metabolism and other pathways. Correlation analysis suggested that the core targets of Cynanchum wilfordii for UC participated in regulation of the metabolites. Conclusion Cynanchum wilfordii alleviates lipid and amino acid metabolism disorders to lessen UC in mice by regulating the core targets including JAK2 and STAT3 and the levels of endogenous metabolites.

Key words: Cynanchum wilfordii (Maxim.) Hook. F, ulcerative colitis, metabolomics, UPLC-QE-MS, steroids

于官正, 程炜强, 涂星, 张满, 李鸿, 聂娟. 隔山消治疗溃疡性结肠炎的机制：基于UPLC-QE-MS、网络药理学及代谢组学技术[J]. 南方医科大学学报, 2024, 44(8): 1485-1496.

Guanzheng YU, Weiqiang CHENG, Xing TU, Man ZHANG, Hong LI, Juan NIE. Therapeutic mechanism of Cynanchum wilfordii for ulcerative colitis: an analysis using UPLC-QE-MS, network pharmacology and metabolomics[J]. Journal of Southern Medical University, 2024, 44(8): 1485-1496.

图/表 13

表1 隔山消甾体类鉴定结果

Tab.1 Results of identification of the steroids in Cynanchum wilfordii

NO.	t_R/min	Chemical compound	Chemical formula	Ionization mode	m/z	Theoretical m/z
1	11.54	Qingyangshengenin B	C₄₉H₇₈O₁₆	M-H, M+FA-H	967.5273	967.5272
2	9.72	Qingyangshengenin A	C₄₉H₇₂O₁₇	M+NH₄, M+Na	955.4642	955.4662
3	5.53	Convallatoxin	C₂₉H₄₂O₁₀	M+FA-H	595.2765	595.2760
4	9.7	Caudatin	C₂₈H₄₂O₇	M-H, M+FA-H	535.2915	535.2912
5	10.22	Cynatratoside A	C₂₈H₄₀O₈	M+H-H₂O	487.2679	487.2690
6	6.45	Qingyangshengenin	C₂₈H₃₆O₈	M+Na	523.2292	523.2303
7	7.33	5beta-Dihydrocortisol	C₂₁H₃₂O₅	M+H	365.2315	365.2323
8	9.13	Acetylarenobufagin	C₂₆H₃₄O₇	M+Na	481.2214	481.2196
9	9.36	Polyphyllin VI	C₃₉H₆₂O₁₃	M+K	777.3818	777.3822
10	9.2	Oleandrin	C₃₂H₄₈O₉	M+Na	599.3215	599.3190
11	10.47	Gracillin	C₄₅H₇₂O₁₇	M-H₂O-H	865.4589	865.4591
12	7.57	19-Oxocinobufagin	C₂₆H₃₂O₇	2M-H	911.4252	911.4223
13	10.28	Dioscin	C₄₅H₇₂O₁₆	M+FA-H	913.4814	913.4802
14	11.52	(25R)-Spirost-4-en-3,12-dion	C₂₇H₃₈O₄	M+NH₄	444.3099	444.3109
15	10.14	3-O-Acetylbufotalin	C₂₈H₃₈O₇	M+Na	509.2527	509.2509
16	11.57	Timosaponin A1	C₃₃H₅₄O₈	M+Na	601.3699	601.3711
17	5.83	Bufarenogin	C₂₄H₃₂O₆	2M+Na	855.4328	855.4289
18	5.38	Digoxigenin	C₂₃H₃₄O₅	M+H-H₂O	373.236	373.2373
19	4.82	Rubrosterone	C₁₉H₂₆O₅	M+NH₄	352.2113	352.2119

图1 Venn图、网络调控图及核心PPI

Fig.1 Venn diagrams, network regulation charts and core PPI. A: Venn diagram. B: Network regulation charts. C: PPI network. D: Core PPI. CW: Cynanchum wilfordii.

图2 GO富集分析

Fig.2 GO enrichment analysis. A: Biological process. B: Cellular components. C: Molecular function.

图3 KEGG富集分析

Fig.3 KEGG enrichment analysis.

图4 体质量变化率及DAI评分

Fig.4 Weight change rate and DAI score of the mouse models of UC. A: Weight change rate. B: DAI score. N: Normal group. M: Model group. Y: Mesalazine group. G: Cynanchum wilfordii Group. #P<0.05 vs normal group; *P<0.05 vs model group.

图5 HE染色及AB-PAS染色

Fig.5 HE staining and AB-PAS staining of the colon tissue of the mice. A: Normal group. B: Model group. C: mesalazine group. D: Cynanchum wilfordii Group.

图6 各组小鼠结肠组织中JAK2和STAT3蛋白表达

Fig.6 Expression of JAK2 and STAT3 proteins in the colon tissue of the mice detected by Western blotting. CW: Cynanchumwilfordii Group. #P<0.05 vs normal group; *P<0.05 vs model group;△P<0.05 vs mesalazine group.

图7 基峰图

Fig.7 Base peak chromatogram. A: Normal group. B: Model group. C: mesalazine group. D: Cynanchum wilfordii Group.

图8 代谢组学多元统计分析

Fig.8 Metabolomics multivariate statistical analysis. A: PCA. B, C: OPLS-DA.

图9 差异代谢物火山图

Fig.9 Volcano map based on different metabolites. A:M vs N; B: M vs G.

图10 差异代谢物热图

Fig.10 Heatmaps based on different metabolites.

图11 差异代谢物富集代谢通路

Fig.11 Differential metabolite-enriched metabolic pathways.

图12 代谢物-靶点基因网络

Fig.12 Metabolite-target gene network. Squares represent differential genes, circles represent the core target. The darker the color, the stronger the correlation.

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