Mechanism of Jianpi Qingchang Huashi Recipe in treating ulcerative colitis: A study based on network pharmacology and molecular docking

Abstract

BACKGROUND

Ulcerative colitis (UC) is a refractory intestinal disease with alternating onset and remission and a long disease course, which seriously affects the health and quality of life of patients. The goal of treatment is to control clinical symptoms, induce and maintain remission, promote mucosal healing, and reduce recurrence. Clinical trials have shown unsatisfactory clinical response rates. As a supplementary alternative medicine, traditional Chinese medicine has a rich history and has shown good results in the treatment of UC. Because of the quality of herbal medicine and other factors, the curative effect of traditional Chinese medicine is not stable enough. The mechanism underlying the effect of Jianpi Qingchang Huashi Recipe (JPQCHSR) on inducing UC mucosal healing is not clear.

AIM

To investigate the potential mechanism of JPQCHSR for the treatment of UC based on network pharmacology and molecular docking.

METHODS

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to extract the active components and action targets of JPQCHSR, and the target names were standardized and corrected through UniProt database. The related targets of UC were obtained through GeneCards database, and the intersection targets of drugs and diseases were screened by jvenn online analysis tool. The visual regulatory network of "Traditional Chinese medicine-active components-target-disease" was constructed using Cytoscape software, the protein interaction network was constructed using STRING database, and enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways was conducted through R software. At last, the active components were docked with the core target through SYBYL-X 2.1.1 software.

RESULTS

Through database analysis, a total of 181 active components, 302 targets and 205 therapeutic targets were obtained for JPQCHSR. The key compounds include quercetin, luteolin, kaempferol, etc. The core targets involved STAT3, AKT1, TP53, MAPK1, MAPK3, JUN, TNF, etc. A total of 2861 items were obtained by GO enrichment analysis, and 171 items were obtained by KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis. The results of molecular docking showed that the key active components in JPQCHSR had certain affinity with the core target.

CONCLUSION

The treatment of UC with JPQCHSR is a complex process of multi-component, multi-target and multi-pathway regulation. The mechanism of this Recipe in the treatment of UC can be predicted through network pharmacology and molecular docking, so as to provide theoretical reference for it to better play its therapeutic role.

Key Words: Jianpi Qingchang Huashi Recipe, Ulcerative colitis, Network pharmacology, Molecular docking, Inflammatory disease

Core Tip: Ulcerative colitis (UC) is a chronic non-specific inflammatory disease that can cause varying degrees of mucosal inflammation from the rectum to the proximal colon. The mechanism of this Recipe in the treatment of UC can be predicted through network pharmacology and molecular docking, so as to provide theoretical reference for it to better play its therapeutic role.

INTRODUCTION

Ulcerative colitis (UC) is a chronic non-specific inflammatory disease that can cause varying degrees of mucosal inflammation from the rectum to the proximal colon[1]. Its typical clinical manifestations include diarrhea, mucus and blood in stools, and abdominal pain[2]. The disease can affect the intestinal mucosa and muscularis mucosa and is featured by a recurrent and prolonged course. Its pathogenesis involves genetic susceptibility, epithelial barrier defects, dysregulated immune response, and environmental factors[3]. As one of the common refractory diseases in the digestive system, UC has unclear and complex pathogenic mechanisms. Western medicine-based treatments for UC rely mostly on drug therapies. Although these drugs can achieve quick remission in most patients, there is no radical cure. Furthermore, patients receiving Western medicine-based therapies often suffer from problems including hormone dependence, drug resistance, and treatment-related toxicities[4]. Therefore, many patients with UC as well as physicians and researchers are increasingly considering complementary and alternative medicine (CAM) option[5]. In North American and European studies, the rate of current or past use of CAM for the treatment of UC is 21%-60%[6,7], of which herbal medicine, especially CAM intervention, is the first choice[8]. As a supplementary alternative medicine, traditional Chinese medicine (TCM) has a long history and has shown good results in the treatment of UC. Through multi-component, multi-target holistic therapies, TCM can effectively improve intestinal inflammation.

Jianpi Qingchang Recipe (JPQCR, or spleen-invigorating and intestine-clearing recipe) is modified from Shenling Baizhu Powder, which was recorded in Taiping Huimin Heji Ju Fang (Prescriptions of the Bureau of Taiping People's Welfare Pharmacy), and Diyu Powder, which was described in Sheng Ji Zonglu (General Records of Holy Universal Relief). Previous studies from our group showed that oral Chinese medicine compounds were effective for the treatment of UC, inducing and maintaining remission, although the time until the effect was evident was relatively slow. Many studies have shown that JPQCR can markedly improve dextran sulphate sodium (DSS)-induced UC in mouse models[9], which may be explained that JPQCR can inhibit the activation of nuclear factor-kappa B (NF-κB), down-regulate inflammatory mediators [e.g., MPO, interleukin (IL)-1β, IL-8, and tumor necrosis factor alpha (TNF-α)], and improve the barrier function of the intestinal epithelium. In addition, JPQCR can regulate DSS-induced abnormal intestinal motility in UC mice by inhibiting the cascade of intestinal inflammation and reducing autophagy in interstitial cells of Cajal[10,11]. Based on JPQCR, the Jianpi Qingchang Huashi Recipe (JPQCHSR) has newly added TCM drugs including Poria, Tangerine Peel, Coix Seed, Alisma, and Radix Scutellariae. The prescription is composed of Codonopsis, Astragalus, Rhizoma Coptis, Poria cocos, Tangerine Peel, Coix Seed, Alisma, Purslane, Radix Sanguisorbae, Radix Aucklandiae, Scutellaria, and Radix Glycyrrhizae and is effective for clinical treatment of UC.

TCM drugs are characterized by multi-component, multi-target, and multi-pathway. The TCM formulas are prepared in accordance with the principles of "Sovereign, Minister, Assistant, and Courier". Under the guidance of a patient-centered holistic view, TCM formulas start from the integration between “whole” and “parts” and systematically regulate human body through the in vivo metabolism of the active components in the drugs. However, it is often difficult to identify the mechanisms of action of a specific formula. Compared with western medicine[12], traditional Chinese medicine formula may not quickly induce UC remission and control clinical symptoms. In addition, because of the quality of herbal medicine and other factors, the curative effect of TCM is not stable enough. The patients reported a poor taste of traditional Chinese medicine formula and had poor follow-up compliance. TCM drugs exert their effects in both independent and synergistic manners. The “independence” is reflected in the direct or indirect influence or action of a certain drug component on a target, whereas the “synergy” refers that there are synergistic or antagonistic effects among multiple components of a TCM drug and they treat diseases as a whole. In recent years, network pharmacology has been proposed for overall, multi-level, and multi-grade research on TCM formulas. Based on systems biology and computer networks, network pharmacology is the study of the interactions among related nodes, so as to explain the mechanisms of action of drugs in treating diseases. The core concept of "multi-component, multi-target, and multi-pathway" of network pharmacology is consistent with the "holistic view" of TCM theory, and thus network pharmacology may enable the accurate prediction and analysis of the mechanisms of action of TCM compound prescriptions[13]. Molecular docking is a receptor-based virtual screening technology that mainly studies the interaction among molecules and predicts the binding mode and affinity between ligands and receptors[14,15]. In recent years, molecular docking has become an important technology in computer-aided drug design[16,17]. This study aims to explore the mechanisms of action of JPQCHSR in the treatment of UC through network pharmacology and molecular docking, in an attempt to further promote the development and use of JPQCHSR and provide new insights into the treatment of UC.

MATERIALS AND METHODS

Databases and software

Data were searched from databases including Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (https://tcmspw.com/tcmsp.php)[18], UniProt (https://www.uniprot.org/), GeneCards (https://www.genecards.org/), Jvenn (http://jvenn.toulouse.inra.fr/app/index.html)[19], STRING 11.0 (https://string-db.org/), and Chemical Book (https://www.chemicalbook.com/ProductIndex.aspx)[20]. The software used included Cytoscape version 3.7.2, R version 3.6.3, cluster Profiler, and SYBYL-X version 2.1.1.

Active components and targets of action of JPQCHSR

Using the keywords "Radix Codonopsis", "Astragalus", "Coptis", "Poria", "Tangerine Peel", "Coix Seed", "Alismaceae", "Herba Portulacae", "Radix Sanguisorbae", "Radix Aucklandiae", "Radix Scutellariae" and "Radix Glycyrrhizae", we retrieved the active components of 12 TCM drugs in JPQCHSR in the TCMSP database. According to the pharmacokinetic principles, the active components were screened using the criteria including oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. The relevant targets of the active components were extracted from the TCMSP database, and the targets of action were standardized and corrected using the human genes that have been validated in the UniProt database. Components without action targets and targets without standard names were deleted and active components and validated targets were collected, thus yielding the active components of JPQCHSR and the related targets of action in human beings.

Predicting the UC-related targets

Using the keyword “ulcerative colitis”, we searched the GeneCards database for UC-related targets.

Establishing the "TCM drugs-Active Components-Targets-Diseases" network

Using the jvenn online analysis tool, we mapped the active component targets obtained in Section 1.2 with the disease targets in Section 1.3, and the intersection was the potential targets of JPQCHSR in the treatment of UC. Files containing TCM drugs, active components, targets, disease information and their attribute files were imported into the Cytoscape version 3.7.2 to construct a visual network of "TCM Drugs-Active Components-Targets-Diseases”. With the help of the "Network analyzer" plug-in in the software, the topological properties of network nodes were analyzed, and the key active components of JPQCHSR for treating UC were obtained according to the “degree” values.

Establishing a protein-protein interaction network

The "Active components-Diseases" intersection target genes obtained in Section 1.4 were imported into the STRING 11.0 database (http://string-db.org), and the species were limited to "homo sapiens". Data with a confidence level higher than 0.900 were selected, and isolated nodes were removed. Then, a protein-protein interaction (PPI) network was constructed. According to the “degree” values, the core targets of JPQCHSR in the treatment of UC were screened.

GO and KEGG enrichment analysis

Based on the cluster Profiler package in the R 3.6.3 software, we performed GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses on the potential targets of active components. The results were filtered based on P values (P < 0.05). R 3.6.3 software was used to visualize the results, during which bubble charts and bar charts were drawn.

Molecular docking

The Chemical Book database was searched to obtain the active components (mol. files), and the results were imported into the SYBYL-X 2.1.1 software for energy optimization. The PDB files of the crystal structure of the core target proteins were downloaded from the RSCB PDB database. After a series of optimization steps (including ligand extraction, hydrogenation, and water extraction) in the SYBYL-X 2.1.1 software, molecular docking was performed using the Surflex-Dock module in the software, and the binding activity was evaluated through function scoring.

RESULTS

Active components and targets of action of JPQCHSR

After the TCM drugs in JPQCHSR were searched in the TCMSP database, 251 active components were obtained. Using the OB ≥ 30% and a DL index ≥ 0.18 as potential active compounds and excluding both non-target compounds and duplicate compounds, 181 active components corresponding to 302 targets were derived. The active components and targets of action of JPQCHSR are summarized in Table 1.

Table 1 Jianpi Qingchang Huashi Recipe main active ingredients.

No.	MOL ID	Compound	Source	No.	MOL ID	Compound	Source
1	MOL001006	poriferasta-7,22E-dien-3beta-ol	Codonopsis	92	MOL004806	euchrenone	Radix Glycyrrhizae
2	MOL002140	Perlolyrine	Codonopsis	93	MOL004808	glyasperin B	Radix Glycyrrhizae
3	MOL003036	ZINC03978781	Codonopsis	94	MOL004810	glyasperin F	Radix Glycyrrhizae
4	MOL004355	Spinasterol	Codonopsis	95	MOL004811	Glyasperin C	Radix Glycyrrhizae
5	MOL005321	Frutinone A	Codonopsis	96	MOL004814	Isotrifoliol	Radix Glycyrrhizae
6	MOL006774	stigmast-7-enol	Codonopsis	97	MOL004815	(E)-1-(2,4-dihydroxyphenyl)-3-(2,2-dimethylchromen-6-yl)prop-2-en-1-one	Radix Glycyrrhizae
7	MOL007059	3-beta-Hydroxymethyllenetanshiquinone	Codonopsis	98	MOL004820	kanzonols W	Radix Glycyrrhizae
8	MOL007514	methyl icosa-11,14-dienoate	Codonopsis	99	MOL004824	(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one	Radix Glycyrrhizae
9	MOL008393	7-(beta-Xylosyl)cephalomannine_qt	Codonopsis	100	MOL004827	Semilicoisoflavone B	Radix Glycyrrhizae
10	MOL008397	Daturilin	Codonopsis	101	MOL004828	Glepidotin A	Radix Glycyrrhizae
11	MOL008400	glycitein	Codonopsis	102	MOL004829	Glepidotin B	Radix Glycyrrhizae
12	MOL008407	(8S,9S,10R,13R,14S,17R)-17-[(E,2R,5S)-5-ethyl-6-methylhept-3-en-2-yl]-10,13-dimethyl-1,2,4,7,8,9,11,12,14,15,16,17-dodecahydrocyclopenta[a]phenanthren-3-one	Codonopsis	103	MOL004833	Phaseolinisoflavan	Radix Glycyrrhizae
13	MOL008411	11-Hydroxyrankinidine	Codonopsis	104	MOL004835	Glypallichalcone	Radix Glycyrrhizae
14	MOL000033	(3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol	Astragalus	105	MOL004838	8-(6-hydroxy-2-benzofuranyl)-2,2-dimethyl-5-chromenol	Radix Glycyrrhizae
15	MOL000371	3,9-di-O-methylnissolin	Astragalus	106	MOL004841	Licochalcone B	Radix Glycyrrhizae
16	MOL000378	7-O-methylisomucronulatol	Astragalus	107	MOL004848	licochalcone G	Radix Glycyrrhizae
17	MOL000379	9,10-dimethoxypterocarpan-3-O-β-D-glucoside	Astragalus	108	MOL004849	3-(2,4-dihydroxyphenyl)-8-(1,1-dimethylprop-2-enyl)-7-hydroxy-5-methoxy-coumarin	Radix Glycyrrhizae
18	MOL000380	(6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol	Astragalus	109	MOL004855	Licoricone	Radix Glycyrrhizae
19	MOL000387	Bifendate	Astragalus	110	MOL004856	Gancaonin A	Radix Glycyrrhizae
20	MOL000433	FA	Astragalus	111	MOL004857	Gancaonin B	Radix Glycyrrhizae
21	MOL000439	isomucronulatol-7,2'-di-O-glucosiole	Astragalus	112	MOL004863	3-(3,4-dihydroxyphenyl)-5,7-dihydroxy-8-(3-methylbut-2-enyl)chromone	Radix Glycyrrhizae
22	MOL000442	1,7-Dihydroxy-3,9-dimethoxy pterocarpene	Astragalus	113	MOL004864	5,7-dihydroxy-3-(4-methoxyphenyl)-8-(3-methylbut-2-enyl)chromone	Radix Glycyrrhizae
23	MOL001454	berberine	Rhizoma Coptis	114	MOL004866	2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-6-(3-methylbut-2-enyl)chromone	Radix Glycyrrhizae
24	MOL002894	berberrubine	Rhizoma Coptis	115	MOL004879	Glycyrin	Radix Glycyrrhizae
25	MOL002903	(R)-Canadine	Rhizoma Coptis	116	MOL004882	Licocoumarone	Radix Glycyrrhizae
26	MOL002904	Berlambine	Rhizoma Coptis	117	MOL004883	Licoisoflavone	Radix Glycyrrhizae
27	MOL002907	Corchoroside A_qt	Rhizoma Coptis	118	MOL004884	Licoisoflavone B	Radix Glycyrrhizae
28	MOL000622	Magnograndiolide	Rhizoma Coptis	119	MOL004885	licoisoflavanone	Radix Glycyrrhizae
29	MOL000785	palmatine	Rhizoma Coptis	120	MOL004891	shinpterocarpin	Radix Glycyrrhizae
30	MOL002668	Worenine	Rhizoma Coptis	121	MOL004898	(E)-3-[3,4-dihydroxy-5-(3-methylbut-2-enyl)phenyl]-1-(2,4-dihydroxyphenyl)prop-2-en-1-one	Radix Glycyrrhizae
31	MOL000273	(2R)-2-[(3S,5R,10S,13R,14R,16R,17R)-3,16-dihydroxy-4,4,10,13,14-pentamethyl-2,3,5,6,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl]-6-methylhept-5-enoic acid	Poria cocos	122	MOL004903	liquiritin	Radix Glycyrrhizae
32	MOL000275	trametenolic acid	Poria cocos	123	MOL004904	licopyranocoumarin	Radix Glycyrrhizae
33	MOL000279	Cerevisterol	Poria cocos	124	MOL004907	Glyzaglabrin	Radix Glycyrrhizae
34	MOL000282	ergosta-7,22E-dien-3beta-ol	Poria cocos	125	MOL004908	Glabridin	Radix Glycyrrhizae
35	MOL000283	Ergosterol peroxide	Poria cocos	126	MOL004910	Glabranin	Radix Glycyrrhizae
36	MOL005815	Citromitin	Tangerine Peel	127	MOL004911	Glabrene	Radix Glycyrrhizae
37	MOL005828	nobiletin	Tangerine Peel	128	MOL004912	Glabrone	Radix Glycyrrhizae
38	MOL001323	Sitosterol alpha1	Coix Seed	129	MOL004913	1,3-dihydroxy-9-methoxy-6-benzofurano[3,2-c]chromenone	Radix Glycyrrhizae
39	MOL001494	Mandenol	Coix Seed	130	MOL004914	1,3-dihydroxy-8,9-dimethoxy-6-benzofurano[3,2-c]chromenone	Radix Glycyrrhizae
40	MOL008121	2-Monoolein	Coix Seed	131	MOL004915	Eurycarpin A	Radix Glycyrrhizae
41	MOL000953	CLR	Coix Seed	132	MOL004924	(-)-Medicocarpin	Radix Glycyrrhizae
42	MOL000831	Alisol B monoacetate	Alisma	133	MOL004935	Sigmoidin-B	Radix Glycyrrhizae
43	MOL000849	16β-methoxyalisol B monoacetate	Alisma	134	MOL004941	(2R)-7-hydroxy-2-(4-hydroxyphenyl)chroman-4-one	Radix Glycyrrhizae
44	MOL000853	alisol B	Alisma	135	MOL004945	(2S)-7-hydroxy-2-(4-hydroxyphenyl)-8-(3-methylbut-2-enyl)chroman-4-one	Radix Glycyrrhizae
45	MOL000856	alisol C monoacetate	Alisma	136	MOL004948	Isoglycyrol	Radix Glycyrrhizae
46	MOL002464	1-Monolinolein	Alisma	137	MOL004949	Isolicoflavonol	Radix Glycyrrhizae
47	MOL000862	[(1S,3R)-1-[(2R)-3,3-dimethyloxiran-2-yl]-3-[(5R,8S,9S,10S,11S,14R)-11-hydroxy-4,4,8,10,14-pentamethyl-3-oxo-1,2,5,6,7,9,11,12,15,16-decahydrocyclopenta[a]phenanthren-17-yl]butyl] acetate	Alisma	138	MOL004957	HMO	Radix Glycyrrhizae
48	MOL001439	arachidonic acid	Purslane	139	MOL004959	1-Methoxyphaseollidin	Radix Glycyrrhizae
49	MOL003578	Cycloartenol	Purslane	140	MOL004961	Quercetin der.	Radix Glycyrrhizae
50	MOL002773	beta-carotene	Purslane	141	MOL004966	3'-Hydroxy-4'-O-Methylglabridin	Radix Glycyrrhizae
51	MOL006657	isobetanidin	Purslane	142	MOL000497	licochalcone a	Radix Glycyrrhizae
52	MOL006662	isobetanin_qt	Purslane	143	MOL004974	3'-Methoxyglabridin	Radix Glycyrrhizae
53	MOL005399	alexandrin_qt	Radix Sanguisorbae	144	MOL004978	2-[(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano[6,5-f]chromen-3-yl]-5-methoxyphenol	Radix Glycyrrhizae
54	MOL005853	methyl-2,3,6-tri-O-galloyl-β-D-glucopyranoside	Radix Sanguisorbae	145	MOL004980	Inflacoumarin A	Radix Glycyrrhizae
55	MOL005858	3,7,8-Tri-O-methylellagic acid	Radix Sanguisorbae	146	MOL004985	icos-5-enoic acid	Radix Glycyrrhizae
56	MOL005864	methyl-6-O-galloyl-β-D-glucopyranoside	Radix Sanguisorbae	147	MOL004988	Kanzonol F	Radix Glycyrrhizae
57	MOL005869	daucostero_qt	Radix Sanguisorbae	148	MOL004989	6-prenylated eriodictyol	Radix Glycyrrhizae
58	MOL010813	Benzo[a]carbazole	Radix Aucklandiae	149	MOL004990	7,2',4'-trihydroxy－5-methoxy-3－arylcoumarin	Radix Glycyrrhizae
59	MOL010828	cynaropicrin	Radix Aucklandiae	150	MOL004991	7-Acetoxy-2-methylisoflavone	Radix Glycyrrhizae
60	MOL001689	acacetin	Scutellaria	151	MOL004993	8-prenylated eriodictyol	Radix Glycyrrhizae
61	MOL000173	wogonin	Scutellaria	152	MOL004996	gadelaidic acid	Radix Glycyrrhizae
62	MOL000228	(2R)-7-hydroxy-5-methoxy-2-phenylchroman-4-one	Scutellaria	153	MOL000500	Vestitol	Radix Glycyrrhizae
63	MOL002714	baicalein	Scutellaria	154	MOL005000	Gancaonin G	Radix Glycyrrhizae
64	MOL002909	5,7,2,5-tetrahydroxy-8,6-dimethoxyflavone	Scutellaria	155	MOL005001	Gancaonin H	Radix Glycyrrhizae
65	MOL002910	Carthamidin	Scutellaria	156	MOL005003	Licoagrocarpin	Radix Glycyrrhizae
66	MOL002913	Dihydrobaicalin_qt	Scutellaria	157	MOL005007	Glyasperins M	Radix Glycyrrhizae
67	MOL002914	Eriodyctiol (flavanone)	Scutellaria	158	MOL005008	Glycyrrhiza flavonol A	Radix Glycyrrhizae
68	MOL002915	Salvigenin	Scutellaria	159	MOL005012	Licoagroisoflavone	Radix Glycyrrhizae
69	MOL002917	5,2',6'-Trihydroxy-7,8-dimethoxyflavone	Scutellaria	160	MOL005016	Odoratin	Radix Glycyrrhizae
70	MOL002925	5,7,2',6'-Tetrahydroxyflavone	Scutellaria	161	MOL005017	Phaseol	Radix Glycyrrhizae
71	MOL002927	Skullcapflavone II	Scutellaria	162	MOL005018	Xambioona	Radix Glycyrrhizae
72	MOL002928	oroxylin a	Scutellaria	163	MOL005020	dehydroglyasperins C	Radix Glycyrrhizae
73	MOL002932	Panicolin	Scutellaria	164	MOL002879	Diop	Codonopsis Scutellaria
74	MOL002933	5,7,4'-Trihydroxy-8-methoxyflavone	Scutellaria	165	MOL000449	Stigmasterol	Codonopsis Scutellaria Coix Seed Radix Aucklandiae
75	MOL002934	NEOBAICALEIN	Scutellaria	166	MOL003896	7-Methoxy-2-methyl isoflavone	Codonopsis Radix Glycyrrhizae
76	MOL002937	DIHYDROOROXYLIN	Scutellaria	167	MOL000006	luteolin	Codonopsis Purslane
77	MOL000525	Norwogonin	Scutellaria	168	MOL000211	Mairin	Astragalus Radix SanguisorbaeRadix Aucklandiae Radix Glycyrrhizae
78	MOL000552	5,2'-Dihydroxy-6,7,8-trimethoxyflavone	Scutellaria	169	MOL000239	Jaranol	Astragalus Radix Glycyrrhizae
79	MOL000073	ent-Epicatechin	Scutellaria	170	MOL000296	hederagenin	Astragalus Poria cocos
80	MOL001490	bis[(2S)-2-ethylhexyl] benzene-1,2-dicarboxylate	Scutellaria	171	MOL000354	isorhamnetin	Astragalus Radix Glycyrrhizae
81	MOL008206	Moslosooflavone	Scutellaria	172	MOL000392	formononetin	Astragalus Radix Glycyrrhizae
82	MOL010415	11,13-Eicosadienoic acid, methyl ester	Scutellaria	173	MOL000417	Calycosin	Astragalus Radix Glycyrrhizae
83	MOL012245	5,7,4'-trihydroxy-6-methoxyflavanone	Scutellaria	174	MOL000422	kaempferol	Astragalus Purslane Radix SanguisorbaeRadix Glycyrrhizae
84	MOL012246	5,7,4'-trihydroxy-8-methoxyflavanone	Scutellaria	175	MOL000098	quercetin	Astragalus Rhizoma Coptis PurslaneRadix SanguisorbaeRadix Glycyrrhizae
85	MOL012266	rivularin	Scutellaria	176	MOL002897	epiberberine	Rhizoma CoptisScutellaria
86	MOL001484	Inermine	Radix Glycyrrhizae	177	MOL001458	coptisine	Rhizoma CoptisScutellaria
87	MOL001792	DFV	Radix Glycyrrhizae	178	MOL000359	sitosterol	Tangerine Peel Coix Seed Alisma Radix Aucklandiae Scutellaria Radix Glycyrrhizae
88	MOL002311	Glycyrol	Radix Glycyrrhizae	179	MOL004328	naringenin	Tangerine Peel Radix Glycyrrhizae
89	MOL002565	Medicarpin	Radix Glycyrrhizae	180	MOL005100	5,7-dihydroxy-2-(3-hydroxy-4-methoxyphenyl)chroman-4-one	Tangerine Peel Purslane
90	MOL003656	Lupiwighteone	Radix Glycyrrhizae	181	MOL000358	beta-sitosterol	Purslane Radix SanguisorbaeScutellaria
91	MOL004805	(2S)-2-[4-hydroxy-3-(3-methylbut-2-enyl)phenyl]-8,8-dimethyl-2,3-dihydropyrano[2,3-f]chromen-4-one	Radix Glycyrrhizae

Prediction of UC-related targets

Using the key word “ulcerative colitis”, a total of 4622 UC-related targets were obtained in the GeneCards database.

"TCM drugs-Active Components-Targets-Diseases" network

Using the jvenn online analysis tool, after the intersection of the active component targets and the disease targets, a total of 205 potential targets of JPQCHSR in the treatment of UC were obtained (Figure 1). Then, a visual network of "TCM Drugs - Active Components - Targets - Diseases” was established using the Cytoscape version 3.7.2 (Figure 2). The network diagram contains 383 nodes, which included 205 target gene nodes, 165 active component nodes, 12 TCM drug nodes, and 1 disease node. With the help of the "Network analyzer" plug-in in the software, we analyzed the topological properties of the network nodes. The average node “degree” value of the compounds in the network was calculated to be 12.72. Table 2 shows the topological information of the active components with a “degree” value of ≥ 13. The degree of a node is the number of edges connected to the node. A higher degree means a more important role that a node plays in the network. As shown in Table 2, 77 compounds including quercetin (degree = 121), luteolin (degree = 52), kaempferol (degree = 46), calycosin (degree = 38), naringenin (degree = 32), nobiletin (degree = 30), baicalein (degree = 29), arachidonic acid (degree = 29), and formononetin (degree = 27) had degree values higher than the mean degree value. Thus, these components were predicted preliminarily as the core compounds of JPQCHSR for the treatment of UC.

Figure 1 Potential targets of action of Jianpi Qingchang Huashi Recipe in the treatment of ulcerative colitis. C-T: Active component targets; D-T: Ulcerative colitis-related targets.

Figure 2 The visual network of "traditional Chinese medicine drugs-Active Components-Targets-Diseases". The circles represent the active component, the diamonds represent traditional Chinese medicine drugs, the arrows represent the disease targets, and the hexagons represent the diseases.

Table 2 Compound degree information (degree ≥ 13).

Name	Degree	Name	Degree
MOL000098	121	MOL004857	17
MOL000006	52	MOL004849	17
MOL000422	46	MOL004835	17
MOL000173	38	MOL004808	17
MOL004328	32	MOL003656	17
MOL005828	30	MOL012266	16
MOL002714	29	MOL004915	16
MOL001439	29	MOL004883	16
MOL000392	27	MOL004864	16
MOL003896	25	MOL004856	16
MOL000497	24	MOL004833	16
MOL000378	24	MOL004820	16
MOL000354	24	MOL004815	16
MOL002773	22	MOL002934	16
MOL004959	21	MOL005012	15
MOL000358	21	MOL004961	15
MOL008206	20	MOL004885	15
MOL005003	20	MOL004884	15
MOL004978	20	MOL004863	15
MOL004974	20	MOL004841	15
MOL004891	20	MOL004810	15
MOL004828	20	MOL002933	15
MOL001689	20	MOL000552	15
MOL000500	20	MOL005008	14
MOL004991	19	MOL004990	14
MOL004966	19	MOL004911	14
MOL004811	19	MOL004827	14
MOL002565	19	MOL002927	14
MOL008400	18	MOL002917	14
MOL005007	18	MOL000449	14
MOL004912	18	MOL005020	13
MOL004824	18	MOL005017	13
MOL002928	18	MOL004904	13
MOL000417	18	MOL004879	13
MOL005016	17	MOL004848	13
MOL005000	17	MOL004814	13
MOL004957	17	MOL000380	13
MOL004908	17	MOL000371	13
MOL004907	17

Table 3 The degree information of targets in the protein-protein interaction network (degree ≥ 12).

Name	Degree	Name	Degree	Name	Degree
STAT3	49	CDKN1A	20	AR	14
AKT1	46	PRKCA	19	HIF1A	14
TP53	46	CASP3	19	ALB	14
MAPK1	44	TIMP1	19	PTGS2	14
MAPK3	43	FN1	19	PPARA	14
JUN	42	CASP8	19	PPARG	13
TNF	39	STAT1	19	GSK3B	13
RELA	35	PRKCD	18	IGF2	13
HSP90AA1	32	IL2	18	CCNA2	13
MAPK14	32	BCL2	18	TNFRSF1A	13
IL6	31	TGFB1	18	MMP2	13
MAPK8	30	IL4	18	ERBB2	13
VEGFA	28	IL1B	18	F2	12
FOS	28	BCL2L1	17	CXCL2	12
EGFR	25	NFKBIA	17	RAF1	12
RB1	24	MMP9	17	NOS3	12
CTNNB1	24	PRKCB	16	E2F1	12
MYC	24	CDK1	16	CHUK	12
CCND1	22	IL10	16	XIAP	12
ESR1	22	CREB1	15	IGFBP3	12
RXRA	21	CCL2	15	CCNB1	12
CXCL8	21	CDK2	15	CDK4	12
NR3C1	20	IFNG	15	PCNA	12
EGF	20	MDM2	14	MMP3	12

The PPI network

In order to understand the interactions among the UC-related target proteins after JPQCHSR treatment, the effective targets were imported into the STRING database for further analysis. The species default was "Homo sapiens", and the minimum interaction score threshold was set to 0.900. After the isolated nodes were removed, a PPI network graph was constructed (Figure 3). The network graph contained a total of 182 nodes and 1021 edges, with the average node degree value being 11.22. Table 3 shows the information of targets with degree values of ≥ 12. A total of 72 targets including the signal transducer and activator of transcription 3 (STAT3, degree = 49), protein kinase B (AKT1, degree = 46), tumor protein p53 (TP53, degree = 46), mitogen-activated protein kinase 1 (MAPK1, degree = 44), mitogen-activated protein kinase 3 (MAPK3, degree = 43), Jun N-terminal kinase (JNK, degree = 42), and tumor necrosis factor (TNF, degree = 39) might be the core targets of JPQCHSR in the treatment of UC.

Figure 3 Diagram of protein–protein interactions.

GO and KEGG enrichment analysis

The R 3.6.3 software was used to perform GO and KEGG enrichment analyses on 205 intersection targets, during which a total of 2861 GO terms and 171 KEGG pathways were obtained. The GO enrichment analysis was based on three aspects: Biological process (BP), cellular component (CC), and molecular function (MF). The enriched GO terms included 2681 BPs, 93 CCs, and 171 MFs. With P value set at < 0.05 and according to the number of enriched genes, the top 20 terms were used to draw a bubble chart (Figure 4), in which the vertical axis represents the function annotation, the horizontal axis represents the gene ratio, the bubble size represents the number of enriched genes, and the bubble color represents P value. BPs involved response to oxidative stress, response to lipopolysaccharide, response to molecule of bacterial origin and response to antibiotics; CCs involved membrane raft, membrane microdomain, membrane region, vesicle lumen, and cytoplasmic vesicle lumen; and MFs involved transcription factor activity, preceptor ligand activity, cytokine receptor binding, protein serine/threonine kinase activity, and phosphatase binding.

Figure 4 Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the potential targets of action of Jianpi Qingchang Huashi Recipe in the treatment of ulcerative colitis. BP: Biological process; CC: Cellular component; KEGG: Kyoto Encyclopedia of Genes and Genomes; MF: Molecular function.

The results of the KEGG pathway enrichment are displayed as a bar graph, in which the length of the bars represents the number of enriched genes and the color depth refers to P values. As shown in Figure 4, the results of KEGG metabolic pathway enrichment involved infection, apoptosis, nutrition, immunity, inflammation, and other pathways, which included AGE-RAGE signaling pathway, Kaposi sarcoma-associated herpesvirus infection, IL-17 signaling pathway, TNF signaling pathway, and human cytomegalovirus infection.

Molecular docking

Molecular docking is a computer-aided virtual screening technology that predicts the binding mode and affinities between ligands and their receptors, in accordance with geometric matching, energy matching, and other principles of interaction. It is generally believed that the docking score > 4.0 indicates that the docking molecules have certain binding activity with the target, the docking score > 5.0 indicates good binding activity, and the docking score > 7.0 indicates strong binding activity[21].

We selected compounds with the top ten “degree” values in Section 2.3 and target proteins with top five “degree” values in Section 2.4 for molecular docking. Except for baicalein, the remaining nine compounds had certain binding activity with AKT1, TP53, MAPK1, and MAPK3; among them, quercetin, wogonin, and naringenin had a docking score higher than 5.0 with AKT1, TP53, MAPK1, and MAPK3, suggesting good binding activity; nobiletin had a docking score higher than 7.0 with AKT1, MAPK1, and MAPK3, showing strong binding activity; all the docking scores of arachidonic acid with AKT1, TP53, MAPK1, and MAPK3 were higher than 7.0, indicating strong binding activity. As shown in the molecular docking pattern diagram (Figure 5), wogonin stably bound to the active site of STAT3 through hydrogen bonding interactions with amino acids including ARG609, GLU612, SER613, THR620, and VAL637 on the STAT3 target protein; through hydrogen bond interactions with LYS14, GLU17, ASN54, ARG86 and GLN79 on the AKT1 target protein, quercetin bound to the active site of AKT1 stably; luteolin stably bound to the active site of TP53 through SER1503, ASP1520, and MET1584 on the TP53 target protein; naringenin stably bound to the active site of MAPK1 through TYR36, GLU71, and GLN105 on the MAPK1 target protein; and kaempferol stably bound to the active site of MAPK3 through ILE48, MET125, LYS131, ASN171, and ASP184 on MAPK3 target protein.

Figure 5 Molecular docking patterns between AKT1, TP53, MAPK1 and MAPK3 and quercetin, luteolin, naringenin and kaempferol.

DISCUSSION

The incidence of UC has been increasing over the past years. It is characterized by a long disease course and complex pathogenic mechanisms. Western medicine-based treatments rely mainly on hormones and biological agents[22-25]. Although these treatments may achieve short-term therapeutic effects, they may cause severe adverse reactions with long-term use. Based on the combination of the data mining method and network pharmacology approach[26-28], we performed comprehensive and systematic prediction for the effect of JPQCHSR in treating UC, which has provided some new insights into future basic research and clinical application of JPQCHSR.

Using network pharmacology approach, we obtained 181 active components and 302 corresponding targets, among which there were 205 UC-related targets. As shown in the visual network of "TCM Drugs-Active Components-Targets-Diseases”, the key components of JPQCHSR in treating UC included quercetin, luteolin, kaempferol, calycosin, naringenin, nobiletin, baicalein, arachidonic acid, and formononetin. Quercetin is a plant flavonoid that is found in many types of fruits and vegetables[29,30]. It has anti-inflammatory, anti-oxidant, and anti-cancer activities[31-34]. Studies have shown that quercetin can lower the expressions of MMP2, MMP9, TLR4, NF-κBp65, and cadherin E; by suppressing the migration and invasion of Caco-2 cells via inhibiting the Toll-like Receptor 4/NF-κB pathway, quercetin may be effective in treating colitis[35]. Luteolin is a natural antioxidant and has the functions of scavenging oxygen free radicals and protecting cells. Li et al[36] found that luteolin could significantly reduce the expressions of inflammatory factors such as iNOS, TNF-α and IL-6 and increase the activities of superoxide dismutase and catalase in colon tissues; it was expected that luteolin might suppress experimental colitis via the Nrf2 pathway. In addition, luteolin can also exert its anti-inflammatory, anti-apoptotic, and anti-autophagy effects by inhibiting JNK1/2, p38, PI3K/AKT, NF-κB, and STAT3 pathways and inducing ERK1/2, thereby playing a role in the treatment of colitis[37]. Kaempferol is a flavonoid compound with anti-apoptosis, anti-inflammatory, and other pharmacological activities. It can play an anti-osteoarthritis role by down-regulating the expression of miR-146a[38].

As shown in the PPI network, 72 targets including STAT3, AKT1, TP53, MAPK1, MAPK3, JUN, and TNF may be the core targets of JPQCHSR in treating UC. Signal Transducer and Activator of Transcription (STAT) pathway is essential for cell proliferation and differentiation[39] and is involved in the pathogenesis of colorectal cancer. Research has shown that STAT3 expression in T cells, macrophages, and epithelial cells in patients with colitis is directly related to the severity of inflammation[40]. TP53, a tumor suppressor gene, is closely related to the occurrence of colon cancer and may serve as a key target for UC prevention and treatment[41,42].

Our enrichment analyses showed that the effective targets were involved in many BPs including response to oxidative stress, response to lipopolysaccharide, molecular response to bacterial origin, and response to antibiotics; they also participated in membrane rafts, membrane microdomains, membrane region, vesicle lumen, cytoplasmic vesicle lumen, and other CCs; finally, they were involved in transcription factor activity, preceptor ligand activity, cytokine receptor binding, protein serine/ threonine kinase activity, phosphatase binding, and other MFs. The 205 effective targets exerted their effects in treating UC mainly through AGE-RAGE signaling pathway, Kaposi sarcoma-associated herpesvirus infection, IL-17 signaling pathway, TNF signaling pathway, and human cytomegalovirus infection, which also reflected the mechanisms of TCM drugs in treating diseases via multiple components, multiple targets, and multiple pathways. The IL-17 pathway is involved in autoimmune diseases, self-defense, and regulation of autoimmune balance[43-46]. As a strong proinflammatory cytokine, IL-17 can increase cell permeability and promote the production of other pro-inflammatory factors and chemokines, thus playing an important role in the pathogenesis of UC[47-52].

Molecular docking showed that the active compounds of JPQCHSR for treating UC had certain affinities with the core targets. The active compounds interacted with the amino acids of the target proteins through hydrogen bonding, thereby stably binding to the active sites of the target proteins.

CONCLUSION

In summary, using the network pharmacology and molecular docking technology, we predicted that the active components such as quercetin, luteolin, and kaempferol in JPQCHSR act on targets including STAT3, AKT1, TP53, MAPK1, MAPK3, JUN, and TNF via IL-17, TNF, and other signaling pathways to reduce the expressions of inflammatory factors and repair intestinal mucosal damage, thereby exerting their roles in the treatment UC.

ARTICLE HIGHLIGHTS

Research background

Traditional Chinese medicine has played an important role in the treatment of ulcerative colitis (UC), but the specific mechanism of action has not been clarified, which needs further research.

Research motivation

To provide objective basis for the treatment of UC with traditional Chinese medicine.

Research objectives

To investigate the potential mechanism of Jianpi Qingchang Huashi Recipe (JPQCHSR) for the treatment of UC based on network pharmacology and molecular docking.

Research methods

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to extract the active components and action targets of JPQCHSR.

Research results

Through database analysis, a total of 181 active components, 302 targets and 205 therapeutic targets were obtained for JPQCHSR. The key compounds include quercetin, luteolin, kaempferol, etc. The core targets involved STAT3, AKT1, TP53, MAPK1, MAPK3, JUN, TNF, etc. Total 2861 items were obtained by GO enrichment analysis, and 171 items were obtained by KEGG pathway enrichment analysis. The results of molecular docking showed that the key active components in JPQCHSR had certain affinity with the core target.

Research conclusions

The treatment of UC with JPQCHSR is a complex process of multi-component, multi-target and multi-pathway regulation.

Research perspectives

Traditional Chinese medicine has a huge potential mechanism in the treatment of UC.