Network Pharmacology Analysis Uncovers the Potential Anti-Hypertensive Mechanisms of Xia Sang Ju Granule

doi:10.14218/JERP.2020.00008

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Network Pharmacology Analysis Uncovers the Potential Anti-Hypertensive Mechanisms of Xia Sang Ju Granule

Minhua Peng^1,2,*

Journal of Exploratory Research in Pharmacology 2020;5(4):51-60

doi: 10.14218/JERP.2020.00008

Received: April 22, 2020

Revised: May 16, 2020

Accepted: May 23, 2020

Published online: June 3, 2020

Author information

Citation: Peng M. Network Pharmacology Analysis Uncovers the Potential Anti-Hypertensive Mechanisms of Xia Sang Ju Granule. J Explor Res Pharmacol. 2020;5(4):51-60. doi: 10.14218/JERP.2020.00008.

Abstract

Background and objectives

Xia Sang Ju (XSJ) granule, a Chinese drug and herbal tea made up of Prunellae spica (Xia Ku Cao), Mori folium (Sang Ye), and Flos Chrysanthemi Indici (Ye Ju Hua), is commonly used for fever, headache, and sore throat. The underlying pharmacological mechanism of XSJ on hypertension treatment is described here, based on network pharmacology.

Methods

The compounds in XSJ were searched using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (commonly known as TCMSP), and the active components, according to oral bioavailability and drug likeness, were screened. Compounds targets were predicted by the SwissTargetPrediction web server, while hypertension targets were collected from the Online Mendelian Inheritance in Man (commonly known as OMIM) and GeneCards databases. The interaction of targets was analyzed by STRING. The compound-compound target network was constructed by Cytoscape. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (commonly known as KEGG) pathways were analyzed by the Database for Annotation, Visualization and Integrated Discovery (commonly known as DAVID).

Results

Forty-five active compounds were obtained from 359 ingredients present in the XSJ decoction, corresponding to 237 targets. In addition, 189 genes were found to be related to hypertension, of which 11 overlapped with XSJ targeted by 28 compounds and were thus considered therapeutically-relevant. ESR2 was the most frequent gene targeted by the compounds, while NR3C1 showed the most interaction with other genes. These results revealed that the anti-hypertensive activity of XSJ may directly relate to the regulation of several hypertension-associated biological processes and pathways, such as cellular nitrogen compound biosynthetic process, positive regulation of the nitrogen compound metabolic process, steroid hormone biosynthesis, and aldosterone-regulated sodium reabsorption.

Conclusions

These findings provide a reference for further interpretation of the potential mode of action of XSJ against hypertension and serve as an example for elucidation of the Traditional Chinese Medicine concept of “multiple compounds-multiple targets-multiple effects”.

Keywords

Xia Sang Ju granule, Hypertension, Network pharmacology, Multiple compounds-multiple targets-multiple effects

Introduction

Xia Sang Ju (XSJ) granule is a traditional Chinese drug as well as a kind of Chinese herbal tea which is made up of Prunellae spica (Xia Ku Cao), Mori folium (Sang Ye), and Flos Chrysanthemi Indici (Ye Ju Hua). It originates from the classic prescription called Sang Ju Yin that was recorded in the Treatise on Differentiation and Treatment of Epidemic Febrile Diseases (Wen Bing Tiao Bian) by Wu Jutong in the Qing Dynasty. Though XSJ is well-known for treating fever, headache, and sore throat, hypertension is also one of the main functions of XSJ.¹ However, how XSJ plays a part in anti-hypertensive activity remains unclear, due to the complexity of Traditional Chinese medicine (TCM).

Hypertension is characterized by elevated blood pressure in arteries, and is the most common of the chronic diseases and one of the most important risk factors for cerebrovascular diseases; causing an estimated 7.5 million deaths, it accounts for 12.8% of the total deaths.² So far, commonly-used anti-hypertensive drugs include diuretics, beta-blockers, angiotensin converting enzyme inhibitors, calcium channel blockers, angiotensin receptor blockers, etc.³ Unfortunately, no specific medicine can yet cure high blood pressure.

TCMs are extensively used in eastern countries, as treatments for such chronic diseases as hypertension, diabetes and stroke, and their advantages have been gradually recognized through the increasing number of people who seek natural herbal remedies in western countries.⁴ Most existing research is limited to a certain gene target while interpreting the mechanism of a drug, an approach which may ignore the multi-component, multi-target, multi-pathway characteristics of Chinese herbal formulae.⁴ Network pharmacology, based on an integrated multidisciplinary concept, is a powerful tool that analyzes the multi-level network of molecular-target-pathway-disease through the interaction between TCM and disease from a holistic perspective.^5–9

In this study, firstly the active compounds of XSJ were screened computationally, according to oral bioavailability (OB) and drug likeness (DL)¹⁰ and then the potential compound targets and hypertension-related targets were predicted. Finally the XSJ-compound-hypertension networks were constructed, so as to deeply understand the potential underlying mechanism of the anti-hypertensive effect of XSJ.

Materials and methods

Compounds of XSJ

The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (commonly known as TCMSP; http://www.tcmspw.com/tcmsp.php , version 2.3)¹¹ was used to collect the compound information of XSJ. A total of 60 compounds in Prunellae spica, 269 compounds in Mori folium, and 30 compounds in Flos Chrysanthemi Indici were found. To select the potential active compounds, OB and DL,¹⁰ the most important criteria for drug screening, was set to be ≥30% and ≥0.18, respectively.¹²

Compound targets

To predict the most relevant targets of compounds, the simplified molecular-input line-entry system (referred to as SMILES) format of each compound was input into the SwissTargetPrediction website (http://www.swisstargetprediction.ch/ ) with the organism limited to Home sapiens.^13,14

Hypertension targets

Genes associated with hypertension were searched from the Online Mendelian Inheritance in Man (commonly known as OMIM) database (http://www.omim.org/ )^15,16 and GeneCards database (https://www.genecards.org/ )¹⁷ using the keywords “hypertension” or “high blood pressure”.

Protein-protein interaction

The STRING database (https://string-db.org/ , version 10.5) was used to analysis the protein-protein interaction.¹⁸ Protein names were input and organism was limited to Homo sapiens. Data of protein-protein interactions were obtained and saved as TSV files.

GeneMANIA analysis

A weighted composite functional interaction network for hypertension-related genes were constructed by GeneMANIA (https://genemania.org/ ).¹⁹ Genes of interest were input and organism was limited to Homo sapiens.

Network construction

All the networks were constructed by Cytoscape software (https://cytoscape.org/ , version 3.6.1).²⁰

Gene ontology enrichment analysis

Gene ontology enrichment analysis for biological processes and Kyoto Encyclopedia of Genes and Genomes (commonly known as KEGG) pathways were performed by Database for Annotation, Visualization and Integrated Discovery, commonly known as DAVID, 6.8 server (https://david.ncifcrf.gov/ ).^21,22

Results

Screen of active compounds

In total, 359 compounds in XSJ were obtained from the TCMSP database. After filtering by OB and drug likeness parameters, 11 compounds from Prunellae spica, 29 compounds from Mori folium, and 12 compounds from Flos Chrysanthemi Indici with favorable pharmacokinetic profiles were included for further investigation (Table 1). Specifically, beta-sitosterol and quercetin were found in all three of the herbs, and kaempferol as well as stigmasterol were originated from both Prunellae spica and Mori folium, while luteolin was found in Prunellae spica and Flos Chrysanthemi Indici.

Table 1

Active compounds in the herbs and their properties

Mol ID	Compound	OB, %	DL	Herbs
MOL000358	Beta-sitosterol	36.91	0.75	Prunellae spica, Mori folium, Flos Chrysanthemi Indici
MOL000422	Kaempferol	41.88	0.24	Prunellae spica, Mori folium
MOL004355	Spinasterol	42.98	0.76	Prunellae spica
MOL000449	Stigmasterol	43.83	0.76	Prunellae spica, Mori folium
MOL004798	Delphinidin	40.63	0.28	Prunellae spica
MOL000006	Luteolin	36.16	0.25	Prunellae spica, Flos Chrysanthemi Indici
MOL006767	Vulgaxanthin-I	56.14	0.26	Prunellae spica
MOL006772	Poriferasterol monoglucoside_qt	43.83	0.76	Prunellae spica
MOL006774	Stigmast-7-enol	37.42	0.75	Prunellae spica
MOL000737	Morin	46.23	0.27	Prunellae spica
MOL000098	Quercetin	46.43	0.28	Prunellae spica, Mori folium, Flos Chrysanthemi Indici
MOL001771	Poriferast-5-en-3beta-ol	36.91	0.75	Mori folium
MOL002218	Scopolin	56.45	0.39	Mori folium
MOL002773	Beta-carotene	37.18	0.58	Mori folium
MOL003842	Albanol	83.16	0.24	Mori folium
MOL003847	Inophyllum E	38.81	0.85	Mori folium
MOL003850	26-Hydroxy-dammara-20,24-dien-3-one	44.41	0.79	Mori folium
MOL003851	Isoramanone	39.97	0.51	Mori folium
MOL003856	Moracin B	55.85	0.23	Mori folium
MOL003857	Moracin C	82.13	0.29	Mori folium
MOL003858	Moracin D	60.93	0.38	Mori folium
MOL003859	Moracin E	56.08	0.38	Mori folium
MOL003860	Moracin F	53.81	0.23	Mori folium
MOL003861	Moracin G	75.78	0.42	Mori folium
MOL003862	Moracin H	74.35	0.51	Mori folium
MOL003879	4-Prenylresveratrol	40.54	0.21	Mori folium
MOL000433		68.96	0.71	Mori folium
MOL000729	Oxysanguinarine	46.97	0.87	Mori folium
MOL001439	Arachidonic acid	45.57	0.2	Mori folium
MOL001506	Supraene	33.55	0.42	Mori folium
MOL003759	Iristectorigenin A	63.36	0.34	Mori folium
MOL003975	Icosa-11,14,17-trienoic acid methyl ester	44.81	0.23	Mori folium
MOL006630	Norartocarpetin	54.93	0.24	Mori folium
MOL007179	Linolenic acid ethyl ester	46.1	0.2	Mori folium
MOL007879	Tetramethoxyluteolin	43.68	0.37	Mori folium
MOL013083	Skimmin (8CI)	38.35	0.32	Mori folium
MOL001689	Acacetin	34.97	0.24	Flos Chrysanthemi Indici
MOL001790	Linarin	39.84	0.71	Flos Chrysanthemi Indici
MOL000359	Sitosterol	36.91	0.75	Flos Chrysanthemi Indici
MOL008173	Daucosterol_qt	36.91	0.75	Flos Chrysanthemi Indici
MOL008915	Acacetin-7-O-β-D-galactopyranoside	50.19	0.77	Flos Chrysanthemi Indici
MOL008918	Arteglasin A	52.45	0.33	Flos Chrysanthemi Indici
MOL008919	(2S,6S,7aR)-2-[(1E,3E,5E,7E,9E,11E,13E,15E)-16-[(4S)-4-Hydroxy-2,6,6-trimethyl-1-cyclohexenyl]-1,5,10,14-tetramethylhexadeca-1,3,5,7,9,11,13,15-octaenyl]-4,4,7a-trimethyl-2,5,6,7-tetrahydrobenzofuran-6-ol	59.52	0.55	Flos Chrysanthemi Indici
MOL008924	Azuleno(4,5-b)furan-2(3H)-one, 4-(acetyloxy)-3a,4,5,6,6a,7,9a,9b-octahydro-6-hydroxy-6,9-dimethyl-3-methylene-, (3aR-(3aalpha,4alpha,6alpha,6aalpha,9aalpha,9bbeta))-	68.44	0.27	Flos Chrysanthemi Indici
MOL008925	(3aR,4S,6R,6aR,9aR,9bR)-4,6-Dihydroxy-6,9-dimethyl-3-methylene-4,5,6a,7,9a,9b-hexahydro-3aH-azuleno[5,4-d]furan-2-one	40.08	0.19	Flos Chrysanthemi Indici

DL, drug likeness.

Hypertension network analysis

In total, 189 genes associated with hypertension were obtained from the OMIM and GeneCards databases after elimination of false positives and repetitive genes (Table s1). The interaction of hypertension target genes was analyzed by GeneMANIA (Fig. 1, Table s2) and a network containing 274 nodes and 10,742 edges was constructed. This result showed that 55.08% of genes were co-expressed, and 20.87% were expressed in the same tissue or their products in the same cellular location. Among the genes, 11.02% were found to be involved in physical interaction, while 4.86% were engaged in predicted functional relationships. Up to 3.61% were identified as possibly participating in the same pathway, 3.01% had shared protein domains, and 1.55% had genetic interactions that were functionally associated.

Fig. 1 Protein-protein interaction network of hypertension targets.

Analysis of compound-compound target network

The SMILES format of each compound was input into SwissTargetPrediction, and predicted compound targets were obtained (Table s3). A compound-compound target network was constructed, consisting of 282 nodes and 703 edges (Fig. 2). These results showed that some target genes may be modulated by many compounds, such as the ESR1, AR, MAPT, CYP19A1, and HMGCR genes. While the AOX1, CTSK, OCD1, SRC, RARA, NOX4, and CDC25B genes are hit by only one compound. Interestingly, both SLC6A4 and P05093 can be regulated by poriferast-5-en-3beta-ol, beta-sitosterol, poriferasterol mo noglucoside_qt, stigmast-7-enol, spinasterol, daucosterol_qt, and sitosterol. Both the NR1H2 and NR1H3 genes can be modulated by 26-hydroxy-dammara-20,24-dien-3, poriferast-5-en-3beta-ol, beta-sitosterol, poriferasterol monoglucoside_qt, stigmast-7-enol, spinasterol, stigmasterol, daucosterol_qt, and sitosterol. This predicted compound-compound target network strengthens the concepts of multi-compound-multi-target of TCM, in which different active components in XSJ may regulate the same targets and one active ingredient may also modulate various targets.

Fig. 2 Compound-compound target network of Xia Sang Ju (XSJ).

Compound targets are denoted by green hexagons; Prunellae spica by blue circles; Mori folium by red circles; Flos Chrysanthemi Indici by yellow circles. “Azuleno(4,5-b)furan-2(3H)-one, 4-(acetyloxy)-3a,4,5,6,6a,7,9a,9b-octahydro-6-hydroxy-6,9-dimethyl-3-methylene-, (3aR-(3aalpha,4alpha,6alpha,6aalpha,9aalpha,9bbeta))” is abbreviated to “Azuleno-furan”; “(2S,6S,7aR)-2-[(1E,3E,5E,7E,9E,11E,13E,15E)-16-[(4S)-4-hydroxy-2,6,6-trimethyl-1-cyclohexenyl]-1,5,10,14-tetramethylhexadeca-1,3,5,7,9,11,13,15-octaenyl]-4,4,7a-trimethyl-2,5,6,7-tetrahydrobenzofuran-6-ol)” is abbreviated to “tetrahydrobenzofuran”; “(3aR,4S,6R,6aR,9aR,9bR)-4,6-dihydroxy-6,9-dimethyl-3-methylene-4,5,6a,7,9a,9b-hexahydro-3aH-azuleno[5,4-d]furan-2-one” is abbreviated to “azuleno-furan”.)

Hypertension-related compound target network analysis

Eleven genes with commonalities between hypertension genes and compound targets were found and a hypertension-related compound target network was constructed (Fig. 3, Table 2), which contained 39 nodes and 39 edges. Among the 28 compounds directly interacting with these genes, 8 of them came from Prunellae spica, 18 were from Mori folium, and 5 were from Flos Chrysanthemi Indici. The protein classes for the 11 common genes were obtained from the DisGeNET database. The XSJ and hypertension-related targets’ protein-protein interaction network is shown in Figure 4. ESR2 and SLC6A2, both of which play a role in nucleic acid binding, as receptor and transcription factor, or transporter, were the most frequent genes targeted by the compounds. ESR2 and SLC6A2 are known to be important to cardiovascular physiology and blood pressure regulation.^23–27 These results suggested that the anti-hypertension effect of XSJ may be regulated mainly by ESR2 and SLC6A2 (Table 3).

Fig. 3 XSJ-hypertension network.

Compound targets and hypertension targets are denoted by green hexagons; Prunellae spica by blue circles; Mori folium by red circles; Flos Chrysanthemi Indici by yellow circles; Prunellae spica and Mori folium by pink circle; Prunellae spica and Mori folium and Flos Chrysanthemi Indici by purple circle.

Table 2

Candidate compounds from Xia Sang Ju and their potential targets associated with hypertension

No.	Compound	Target gene code	Herbs
1	Stigmast-7-enol	SLC6A2	Prunellae spica
2	Spinasterol	SLC6A2, ESR2	Prunellae spica
3	Poriferasterol monoglucoside_qt	SLC6A2	Prunellae spica
4	Delphinidin	ADORA2A	Prunellae spica
5	Morin	ESR2	Prunellae spica
6	Stigmasterol	ESR2	Prunellae spica, Mori folium
7	Vulgaxanthin-I	MGAM	Prunellae spica
8	Beta-sitosterol	SLC6A2	Prunellae spica, Mori folium, Flos Chrysanthemi Indici
9	Poriferast-5-en-3beta-ol	SLC6A2, ESR2	Mori folium
10	Isoramanone	ESR2, NR3C2, NR3C1	Mori folium
11	Beta-carotene	ADRA2B, ESR2	Mori folium
12	Moracin F	ESR2	Mori folium
13	Moracin G	ESR2	Mori folium
14	Moracin H	ESR2	Mori folium
15	Moracin E	ESR2	Mori folium
16	Moracin D	ESR2	Mori folium
17	Moracin B	ESR2	Mori folium
18	Iristectorigenin A	ESR2, HSD11B2	Mori folium
19	Albanol	HIF1A, ESR2	Mori folium
20	26-Hydroxy-dammara-20,24-dien-3	HSD11B1	Mori folium
21	Icosa-11,14,17-trienoic acid methyl ester	HSD11B1, PPARG	Mori folium
22	4-Prenylresveratrol	PPARG	Mori folium
23	Arachidonic acid	PPARG	Mori folium
24	Tetramethoxyluteolin	ADORA2A	Mori folium
25	Arteglasin A	SLC6A2, ESR2	Flos Chrysanthemi Indici
26	Daucosterol_qt	SLC6A2, ESR2	Flos Chrysanthemi Indici
27	Sitosterol	SLC6A2, ESR2	Flos Chrysanthemi Indici
28	Acacetin-7-O-β-D-galactopyranos	ADORA2A	Flos Chrysanthemi Indici

Fig. 4 XSJ and hypertension-related targets’ protein-protein interaction network.

Table 3

Hypertension-related targets of Xia Sang Ju

No.	Target	Uniprot ID	Gene code	Protein class	Frequency
1	Estrogen receptor-beta	Q92731	ESR2	Nucleic acid binding; receptor; transcription factor	17
2	Solute carrier family 6 member 2	P23975	SLC6A2	Transporter	8
3	Nuclear receptor subfamily 3, group C, member 1	P04150	NR3C1	Nucleic acid binding; receptor; transcription factor	1
4	Nuclear receptor subfamily 3, group C, member 2	P08235	NR3C2	Nucleic acid binding; receptor; transcription factor	1
5	Adenosine receptor A2a	P29274	ADORA2A	Receptor	3
6	Adrenoceptor alpha 2B	P18089	ADRA2B	Receptor	1
7	Maltase-glucoamylase	O43451	MGAM	Hydrolase	1
8	Hypoxia-inducible factor 1, alpha subunit	Q16665	HIF1A	Nucleic acid binding; transcription factor	1
9	11-Beta-hydroxysteroid dehydrogenase, type I	P28845	HSD11B1	None	1
10	Peroxisome proliferator-activated receptor-gamma	P37231	PPARG	Nucleic acid binding; receptor; transcription factor	3
11	Corticosteroid 11-beta-dehydrogenase isozyme 2	P80365	HSD11B2	Oxidoreductase	1

Biological functional analysis

Biological functions of the hypertension-related compound targets were annotated to explain the possible mode of action of XSJ in hypertension. Gene ontology enrichment analysis was performed on the 11targets by DAVID. The top five biological processes were cellular nitrogen compound biosynthetic process, organic cyclic compound biosynthetic process, aromatic compound biosynthetic process, heterocycle biosynthetic process, and nucleobase-containing compound biosynthetic process (Fig. 5a). The significant KEGG pathways included neuroactive ligand-receptor interaction, steroid hormone biosynthesis, aldosterone-regulated sodium reabsorption, PPAR signaling pathway, and thyroid cancer (Fig. 5b). These results elucidated that XSJ may exert anti-hypertension activity through multi-biological processes as well as multi-pathways.

Fig. 5 Gene ontology functional analysis.

(a) Biological processes terms. (b) Significant KEGG pathways.

Discussion

The escalation of hypertension cases global effects. Coupled with lack of any promising hypotensor, this then requires multiple approaches for treatment, including lifestyle modifications and new drugs. Though XSJ is generally used for treatment of fever, headache, sore throat, and as a beverage for clearing heat, hypertension is also one of the major functions.¹ Nevertheless, the mechanism of action for XSJ working on hypertension remains to be fully understood.

During the development of hypertension, endothelin, nitric oxide, and angiotensin II are key factors. Vascular endothelial cells can produce both systolic and vasoactive substances for maintaining vasomotor balance and normal tension. Endothelin is the strongest vasoconstrictor and promotes smooth muscle proliferation,²⁸ while nitric oxide is the main vasodilator substance released by vascular endothelial cells. Endothelin harbors angiotensin-converting enzyme activity that catalyzes the synthesis of angiotensin II; however, angiotensin II can induce expression of the endothelin gene in endothelial cells. Nitric oxide inhibits the production and release of endothelin, and also inhibits the release of renin, which in turn inhibits the production of angiotensin II.^29–31

Despite few publications in the publicly available literature describing the anti-hypertension activity of XSJ so far, recent studies have proven that the extracts and some compounds of all three herbs in XSJ have direct or indirect anti-hypertensive effect, consistent with some of the biological processes found in our study. Ethanol extract of Prunella vulgaris L has been shown to increase the content of nitric oxide, to decrease the content of endothelin and angiotensin II, and finally to reduce blood pressure significantly in a spontaneously hypertensive rat model (e.g., positive regulation of the nitric oxide biosynthetic process, regulation of the systemic arterial blood pressure by endothelin).³² Flavonoid compounds in Mori folium have also been found to expand the coronary vessels, improve myocardial circulation, and reduce blood pressure (e.g., regulation of blood pressure).³³ Ethanol extract of Flos Chrysanthemi Indici has shown hypotensive effect in clinical studies.³⁴ Intriguingly, luteolin from Prunella vulgaris L and Flos Chrysanthemi Indici might inhibit vascular smooth muscle cell proliferation and migration, which is pivotal in the development of arterial remodeling during hypertension (e.g., blood vessel remodeling), by suppressing transforming growth factor-β receptor 1 signaling.³⁵

Luteolin can ameliorate hypertensive vascular remodeling through inhibition of proliferation and migration of angiotensin II-induced vascular smooth muscle cells, a process that is mediated by the regulation of MAPK signaling pathway and the production of reactive oxygen species (e.g., blood vessel remodeling, positive regulation of the reactive oxygen species metabolic process).³⁶ Quercetin from Prunella vulgaris L and Flos Chrysanthemi Indici can attenuate hypertension via reduction in oxidative stress and improving endothelial function, as shown in an acute fluoride-induced hypertension and cardiovascular complications model.³⁷ Furthermore, quercetin was shown to reduce hypertension-induced vascular remodeling, oxidative stress and MMP-2 activity in aortas in the two-kidney one-clip hypertensive Wistar rat model (e.g., blood vessel remodeling).³⁸ Quercetin can also attenuate vascular contraction through the LKB1-AMPK signaling pathway (e.g., regulation of vasoconstriction).³⁹ Delphinidin and quercetin were shown to block the renin-angiotensin system signaling pathway through inhibition of angiotensin-converting enzyme activity and decreasing the production of its mRNA.⁴⁰ Finally, linarin from Flos Chrysanthemi Indici was shown to directly or indirectly activate macrophages and affect the inhibition of nitric oxide that is responsible for vasodilation and hypotension (e.g., vasodilation).⁴¹

The current study provided a prediction of the potential mechanism of XSJ as treatment of hypertension, based on a computational approach. There are some limitations of this work. First, the components in TCM herbs have not yet been completely identified; thus, the databases of compounds are not complete, precluding their ability to represent the integral spectrum of compounds responsible for the anti-hypertension effect. Second, all of the data were based on silico analysis, and as such there may be many false positive and false negative interactions between the found compound-protein and protein-protein interactions. What’s more, the relationship between XSJ and anti-hypertension activity was identified by enrichment analysis. Therefore, the associations presented herein should be further investigated for experimental verification to achieve more accurate and reliable inferences in the future.

Future directions

The associated biological processes and pathways need further investigation for confirmation of the exact mechanism of XSJ in hypertension treatment.

Conclusions

Collectively, the findings presented herein suggest that the compounds in XSJ exert their anti-hypertensive effect via multiple biological processes, such as regulation of blood pressure, blood vessel remodeling, regulation of the nitric oxide biosynthetic process and so on, which is in accord with the TCM therapy concept of “multiple compounds-multiple targets-multiple effects”. Though further experiments are needed to verify this finding, this study revealed the potential anti-hypertensive mechanism of XSJ from holistic and systematic perspectives by using network pharmacology.

Supporting information

Supplementary material for this article is available at https://doi.org/10.14218/JERP.2020.00008 .

Table s1

Hypertension targets.

(XLS)

Click here for additional data file.

Table s2

Interaction of genes related to hypertension.

(XLSX)

Click here for additional data file.

Table s3

Relationships between compounds and targets.

(XLSX)

Click here for additional data file.

Abbreviations

DAVID:: Database for Annotation, Visualization and Integrated Discovery

KEGG:: Kyoto Encyclopedia of Genes and Genomes

OB:: oral bioavailability

DL:: drug likeness

OMIM:: Online Mendelian Inheritance in Man

SMILES:: simplified molecular-input line-entry system

TCMSP:: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform

TCM:: traditional Chinese medicine

XSJ:: Xia Sang Ju

Declarations

Acknowledgement

The author wishes to thank Zhong Xiaotian and Gao Jiansheng for valuable comments.

Funding

This study was supported by Grants from Shenzhen Overseas High-Caliber Peacock Foundation (KQTD2015071414385495).

Conflict of interest

None.

References

1	Lin L, Xu ZD, Yao JX, Liu JY, Li C, Wang ZM. Quality standards for Xiasangju Granules (in Chinese). Chin Tradit Pat Med 2012;34(08):1500-1505

2	Lackland DT, Weber M A. Global burden of cardiovascular disease and stroke: hypertension at the core. Can J Cardiol 2015;31(5):569-571 View Article

3	Dustan HP, Roccella EJ, Garrison HH. Controlling hypertension. A research success story. Arch Intern Med 1996;156(17):1926-1935 View Article

4	Peng M, Watanabe S, Chan K, He Q, Zhao Y, Zhang Z, et al. Luteolin restricts dengue virus replication through inhibition of the proprotein convertase furin. Antiviral Research 2017;143:176-185 View Article

5	Hopkins AL. Network pharmacology. Nat Biotechnol 2007;25(10):1110-1111 View Article

6	Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 2008;4(11):682-690 View Article

7	Zhang R, Zhu X, Bai H, Ning K. Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment. Front Pharmacol 2019;10:123 View Article

8	Casas AI, Hassan AA, Larsen SJ, Gomez-Rangel V, Elbatreek M, Kleikers PWM, et al. From single drug targets to synergistic network pharmacology in ischemic stroke. PNAS 2019;116(14):7129-7136 View Article

9	Ma C, Xu T, Sun X, Zhang S, Liu S, Fan S, et al. Network Pharmacology and Bioinformatics Approach Reveals the Therapeutic Mechanism of Action of Baicalein in Hepatocellular Carcinoma. Evid Based Complement Alternat Med 2019;2019:7518374 View Article

10	Doak BC, Over B, Giordanetto F, Kihlberg J. Oral Druggable Space beyond the Rule of 5: Insights from Drugs and Clinical Candidates. Chem Biol 2014;21(9):1115-1142 View Article

11	Ru J, Li P, Wang J, Zhou W, Li B, Huang C, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform 2014;6:13 View Article

12	Walters WP, Murcko MA. Prediction of ‘drug-likeness’. Adv Drug Deliv Rev 2002;54(3):255-271 View Article

13	Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res 2014;42(Web Server issue):W32-W38 View Article

14	Gfeller D, Michielin O, Zoete V. Shaping the interaction landscape of bioactive molecules. Bioinformatics 2013;29(23):3073-3079 View Article

15	Amberger J, Bocchini C, Hamosh A. A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®). Hum Mutat 2011;32(5):564-567 View Article

16	Hu YA, Cui YX. Application of Online Mendelian Inheritance in Man to medical genetics (in Chinese). Zhonghua Nan Ke Xue 2011;17(7):639-643

17	Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics 2016;54:1.30.1-1.30.33 View Article

18	von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, et al. STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res 2005;33(Database issue):D433-D437 View Article

19	Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010;38(Web Server issue):W214-W220 View Article

20	Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003;13(11):2498-2504 View Article

21	Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4(1):44-57 View Article

22	Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009;37(1):1-13 View Article

23	Hutson DD, Gurrala R, Ogola BO, Zimmerman MA, Mostany R, Satou R, et al. Estrogen receptor profiles across tissues from male and female Rattus norvegicus. Biol Sex Differ 2019;10(1):4 View Article

24	Zhao L, Fan X, Zuo L, Guo Q, Su X, Xi G, et al. Estrogen receptor 1 gene polymorphisms are associated with metabolic syndrome in postmenopausal women in China. BMC Endocr Disord 2018;18(1):65 View Article

25	Ogawa S, Emi M, Shiraki M, Hosoi T, Ouchi Y, Inoue S. Association of estrogen receptor beta (ESR2) gene polymorphism with blood pressure. J Hum Genet 2000;45(6):327-330 View Article

26	Eikelis N, Marques FZ, Hering D, Marusic P, Head GA, Walton AS, et al. A polymorphism in the noradrenaline transporter gene is associated with increased blood pressure in patients with resistant hypertension. J Hypertens 2018;36(7):1571-1577 View Article

27	Zolk O, Ott C, Fromm MF, Schmieder RE. Effect of the rs168924 single-nucleotide polymorphism in the SLC6A2 catecholamine transporter gene on blood pressure in Caucasians. J Clin Hypertens (Greenwich) 2012;14(5):293-298 View Article

28	Yanagisawa M, Kurihara H, Kimura S, Tomobe Y, Kobayashi M, Mitsui Y, et al. A novel potent vasoconstrictor peptide produced by vascular endothelial cells. Nature 1988;332(6163):411-415 View Article

29	Halushka MK, Fan JB, Bentley K, Hsie L, Shen N, Weder A, et al. Patterns of single-nucleotide polymorphisms in candidate genes for blood-pressure homeostasis. Nat Genet 1999;22(3):239-247 View Article

30	Gohlke P, Bünning P, Unger T. Distribution and metabolism of angiotensin I and II in the blood vessel wall. Hypertension 1992;20(2):151-157 View Article

31	Joannides R, Haefeli WE, Linder L, Richard V, Bakkali EH, Thuillez C, et al. Nitric oxide is responsible for flow-dependent dilatation of human peripheral conduit arteries in vivo. Circulation 1995;91(5):1314-1319 View Article

32	Liang JQ, Xiong WN, Luo Y, Wang SS. Extract of Prunella vulgaris L. reduces blood pressure in essential hypertension rats (in Chinese). Journal of Chinese Medicine Materials 2011;34(1):99-100

33	Lu JP. An overview of the pharmacological effects of Mori Folium (in Chinese). Chinese Journal of Rural Medicine and Pharmacy 2013;20(12):83-84

34	Liu JF, Chu CC, Chien MK, Ting KS. Studies on antihypertensive drugs. XIII. Experimental therapy and toxicity of HC-1, an extract from Chrysanthemum Indicum (in Chinese). Acta Pharmaceutica Sinica 1962;9(3):151-154

35	Wu YT, Chen L, Tan ZB, Fan HJ, Xie LP, Zhang WT, et al. Luteolin Inhibits Vascular Smooth Muscle Cell Proliferation and Migration by Inhibiting TGFBR1 Signaling. Front Pharmacol 2018;9:1059 View Article

36	Su J, Xu HT, Yu JJ, Gao JL, Lei J, Yin QS, et al. Luteolin Ameliorates Hypertensive Vascular Remodeling through Inhibiting the Proliferation and Migration of Vascular Smooth Muscle Cells. Evid Based Complement Alternat Med 2015;2015:364876 View Article

37	Oyagbemi AA, Omobowale TO, Ola-Davies OE, Asenuga ER, Ajibade TO, Adejumobi OA, et al. Quercetin attenuates hypertension induced by sodium fluoride via reduction in oxidative stress and modulation of HSP 70/ERK/PPARγ signaling pathways. Biofactors 2018;44(5):465-479 View Article

38	Pereira SC, Parente JM, Belo VA, Mendes AS, Gonzaga NA, do Vale GT, et al. Quercetin decreases the activity of matrix metalloproteinase-2 and ameliorates vascular remodeling in renovascular hypertension. Atherosclerosis 2018;270:146-153 View Article

39	Kim SG, Kim JR, Choi HC. Quercetin-Induced AMP-Activated Protein Kinase Activation Attenuates Vasoconstriction Through LKB1-AMPK Signaling Pathway. J Med Food 2018;21(2):146-153 View Article

40	Parichatikanond W, Pinthong D, Mangmool S. Blockade of the renin-angiotensin system with delphinidin, cyanin, and quercetin. Planta Med 2012;78(15):1626-1632 View Article

41	Han S, Sung KH, Yim D, Lee S, Lee CK, Ha NJ, et al. The effect of linarin on LPS-induced cytokine production and nitric oxide inhibition in murine macrophages cell line RAW264.7. Arch Pharm Res 2002;25(2):170-177 View Article

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