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. 2022 Nov 15;13:1027112. doi: 10.3389/fphar.2022.1027112

Exploring the mechanism of Alisma orientale for the treatment of pregnancy induced hypertension and potential hepato-nephrotoxicity by using network pharmacology, network toxicology, molecular docking and molecular dynamics simulation

Yilin Liao 1, Yiling Ding 1, Ling Yu 1, Cheng Xiang 2, Mengyuan Yang 1,*
PMCID: PMC9705790  PMID: 36457705

Abstract

Background: Pregnancy-induced Hypertension (PIH) is a disease that causes serious maternal and fetal morbidity and mortality. Alisma Orientale (AO) has a long history of use as traditional Chinese medicine therapy for PIH. This study explores its potential mechanism and biosafety based on network pharmacology, network toxicology, molecular docking and molecular dynamics simulation.

Methods: Compounds of AO were screened in TCMSP, TCM-ID, TCM@Taiwan, BATMAN, TOXNET and CTD database; PharmMapper and SwissTargetPrediction, GeneCards, DisGeNET and OMIM databases were used to predict the targets of AO anti-PIH. The protein-protein interaction analysis and the KEGG/GO enrichment analysis were applied by STRING and Metascape databases, respectively. Then, we constructed the “herb-compound-target-pathway-disease” map in Cytoscape software to show the core regulatory network. Finally, molecular docking and molecular dynamics simulation were applied to analyze binding affinity and reliability. The same procedure was conducted for network toxicology to illustrate the mechanisms of AO hepatotoxicity and nephrotoxicity.

Results: 29 compounds with 78 potential targets associated with the therapeutic effect of AO on PIH, 10 compounds with 117 and 111 targets associated with AO induced hepatotoxicity and nephrotoxicity were obtained, respectively. The PPI network analysis showed that core therapeutic targets were IGF, MAPK1, AKT1 and EGFR, while PPARG and TNF were toxicity-related targets. Besides, GO/KEGG enrichment analysis showed that AO might modulate the PI3K-AKT and MAPK pathways in treating PIH and mainly interfere with the lipid and atherosclerosis pathways to induce liver and kidney injury. The “herb-compound-target-pathway-disease” network showed that triterpenoids were the main therapeutic compounds, such as Alisol B 23-Acetate and Alisol C, while emodin was the main toxic compounds. The results of molecular docking and molecular dynamics simulation also showed good binding affinity between core compounds and targets.

Conclusion: This research illustrated the mechanism underlying the therapeutic effects of AO against PIH and AO induced hepato-nephrotoxicity. However, further experimental verification is warranted for optimal use of AO during clinical practice.

Keywords: pregnancy induced hypertension, Alisma orientale, network pharmacology, network toxicology, molecular docking

Introduction

Pregnancy-induced Hypertension (PIH) encompasses a series of serious pregnancy complications in the second trimester (after 20 weeks of gestation), including pre-eclampsia, eclampsia, chronic hypertension with pre-eclampsia and chronic hypertension with pregnancy (Chen et al., 2019). Little is currently known about the exact etiology, although it has been established that genetic, immunological and oxidative stress are risk factors (Magalhães et al., 2020). The pathological features include endothelial cell dysfunction, multiple cytokine stimulation and activation of the coagulation system with vasospasm and increased vascular reactivity, which lead to inadequate spiral arterioles and decreased invasion of trophoblasts into the maternal decidua of the uterus, leading to a series of clinical symptoms, such as generalized edema, vomiting, blurred vision, proteinuria and even disseminated intravascular coagulation (DIC) (Chaiworapongsa et al., 2014). Furthermore, it causes fetal growth retardation, premature birth and perinatal stillborn fetus (Al Khalaf et al., 2022). Current evidence suggests that PIH is a significant threat to more than 70,000 gravidas and half a million fetuses worldwide yearly, with a high maternal mortality rate ranging from 10 to 16% (Bruno et al., 2022).

The complexity of the interactions also limits the efficacy of treatment. Currently available management consists of symptomatic treatment, including spasmolytics (magnesium sulfate), anticoagulants (aspirin), antihypertensive drugs, and termination of pregnancy as a last resort (Zhang et al., 2022a). Nevertheless, the clinical curative efficacy is highly heterogeneous and has been reported to cause maternal and fetal adverse effects (Ni et al., 2022).

Traditional Chinese medicine (TCM) therapy is an ancient practice used for more than 2000 years in China (Tian et al., 2021). The application of TCM therapy in modern society provides a novel approach for complementary and alternative medicine (CAM) treatment and is recognized by more and more people worldwide (Chu et al., 2022). Zhong-jing Zhang originally described PIH in “Synopsis in the Golden Chamber” as “nausea in pregnancy” “eclampsia” and “edema during pregnancy” (Xue et al., 2016). According to the syndrome differentiation theory, PIH is caused by Yin deficiency and Yang excess, deficiency of both spleen and kidney and blood stasis (Yeh et al., 2009). Over the years, many therapeutic methods and prescriptions of TCM have been used to treat PIH, and countless patients have benefited from it (Perry et al., 2018).

Alisma Orientale (AO), also called Ze Xie in Chinese, is a high-efficacy and low-toxicity herb medicine primarily used in Southeast Asian countries. Its efficacy is mainly mediated by removing dampness and promoting water metabolism (Wang et al., 2022), accounting for its efficacy in treating oliguria, edema and hypertension. “Women’s prescription” also recorded that AO could relieve systemic edema of gravida. In recent years, pharmacological research has demonstrated that AO compounds exhibit diuretic, anti-atherosclerotic and immunomodulatory activities, which all suitable for the treatment of PIH (Loh et al., 2017). Chen et al. reported that the natural compound of AO, alisol B 23-acetate, could inhibit Ang II-induced RAS/Wnt/β-catenin axis and attenuate podocyte injury (Chen et al., 2017). Moreover, it could inhibit collagen I, vimentin and α-smooth muscle actin at the mRNA and protein levels in rats (Chen et al., 2020). Notwithstanding that hundreds of active compounds have been isolated from AO, the specific compounds and correlative mechanisms have been largely understudied. Therefore, a novel method is urgently needed to reveal the complex treatment network and identify compounds underlying the therapeutic effect of AO against PIH. Besides, toxicology studies on AO have revealed that chronic administration may induce mild nephrotoxicity and hepatotoxicity (Yuen et al., 2006), emphasizing the need to verify the toxicity and safety profile of AO compounds and their molecular mechanisms.

The concept of network pharmacology was first officially reported by the British Pharmacologist Hopkins in 2007 (Hopkins, 2008), while network toxicology was originally proposed by Academician Liu of Chinese in 2011 (Li et al., 2019a). Both methods were based on the theories of “multi-target” and “multi-pathway” between the drug and disease, which are consistent with the characteristics of TCM therapy (Li et al., 2022).

In this study, we applied network pharmacology to screen the main therapeutic compounds of AO and predicted the potential mechanism. In addition, we analyzed the potential hepato-nephrotoxicity mechanism of AO compounds by network toxicology during the treatment of PIH. Finally, molecular docking and molecular dynamics simulation analyses were used to estimate the binding stability. Overall, this study improves our current understanding of the potential pharmacodynamic and toxicity mechanism of AO in PIH patients and explores how to enhance efficacy and reduce toxicity. The detailed research content of this study is presented in the flow chart (Figure 1).

FIGURE 1.

FIGURE 1

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The flow chart of this study to explore the potential molecular mechanism of AO in the treatment of PIH and toxic compounds induced hepato-nephrotoxicity.

Methods

Obtaining therapeutic and toxicity-related compounds

The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP http://lsp.nwu.edu.cn/tcmsp.php) database (Ru et al., 2014), Traditional Chinese Medicine Information Database (Kang et al., 2013) (TCM-ID http://bidd.group), BATMAN-TCM (Liu et al., 2016) (a bioinformatics analysis Tool for molecular mechanism of traditional Chinese medicine http://bionet.ncpsb.org.cn/) and TCM@Taiwa (Chen, 2011) (http://tcm.cmu.edu.tw/zh-tw/) database were used to acquire detailed information on identified AO compounds. Oral bioavailability (OB) refers to the dosage of drugs that enter the blood circulation through oral absorption and act on local tissues and organs to produce corresponding pharmacological effects. Drug-likeness (DL) is defined as the similarity of the chemical structure of a drug compared with known drugs. Both characteristics are important to assess the potential medicinal value of compounds (Dai et al., 2022). We selected the biologically active compounds of AO in the TCMSP database using the screening criteria: OB ≥ 30 and DL ≥ 0.18 (Gao et al., 2022). Similarly, compounds from other database sources were screened in SwissADME (Daina et al., 2017) (http://www.swissadme.ch) database according to their pharmacokinetic parameters. The toxicity compound of AO was further searched in Toxicology data network (TOXNET, http://toxnet.nlm.nih.gov/index.html) database (Fowler and Schnall, 2014) and Comparative Toxicogenomics Database (CTD, https://toxnet.nlm.nih.gov/newtoxnet/ctd.htm) to analyze the biosafety profile of AO in the treatment of PIH (Stanic et al., 2021).

Potential targets related to AO compounds, PIH, liver and kidney injury

The structural formulas of therapeutic and toxic compounds obtained in the previous step were input into the PharmMapper (Liu et al., 2010) (http://www.lilab-ecust.cn/pharmmapper/) and SwissTargetPrediction database (Daina et al., 2019) (http://www.swisstargetprediction.ch/) to predict the possible targets based on the spatial conformation. The uniport ID of the target was converted to the standardized gene name by Uniport (Holzhüter and Geertsma, 2022) (https://www.uniprot.org/) database. MeSH database (https://www.ncbi.nlm.nih.gov/mesh) was utilized to verify the standard name of the disease as “Pregnancy-induced Hypertension” “drug-induced liver injury” (DILI) and “drug-induced kidney injury” (DIKI) (Zouhal et al., 2021). Then, GeneCards database (Barshir et al., 2021) (https://www.genecards.org/), DisGeNET database (Piñero et al., 2020) (https://www.disgenet.org) and Online Mendelian Inheritance in Man (Li et al., 2012) (OMIM, https://omim.org) database were retrieved to obtain potential targets using the keywords “Pregnancy-induced Hypertension” “drug-induced liver injury” and “drug-induced kidney injury”. After merging and removing duplicate targets, the intersected targets in the Venn plot were defined as potential targets of OA compounds during PIH treatment and toxic compounds associated with hepato-nephrotoxicity.

Constructing PPI network to screen core target

A protein-protein interaction (PPI) network was generated to identify interacting proteins. STRING (Szklarczyk et al., 2021) (http://string-db.org, Version 11.5) database was used to construct a PPI network of the anti-PIH effect of AO, with species limited to “Homo sapiens” and interaction score >0.4. The Cytoscape plug-ins Cytohubba and MCODE (molecular complex detection) were used to analyze the topological parameters and select the core targets of AO anti-HIP with high accuracy and exhibit the complex relationship between disease and herb (Ye et al., 2022). The same steps were used to construct the PPI network of network toxicology.

GO and KEGG pathway enrichment analysis

Functional enrichment analysis was conducted to better understand the functions of the screened genes. Gene Ontology (GO) enables the analysis of gene function based on the cellular compound (CC), molecular function (MF) and biological process (BP). Kyoto Encyclopedia of Genes and Genomes (KEGG) enables an understanding of the biological pathways associated with genes. Both were used to illustrate the core pathway and mechanism of AO anti-HIP. Targets were inputted in the Metascape database (Zhou et al., 2019) (http://www.metascape.org/), and the cut-off p-value, min overlap and enrichment value were set to 0.01, 3 and 1.5, respectively. And q-value < 0.05 (Benjamini–Hochberg method) was used to remove the false positive enrichment results (Zou et al., 2016). The R package available on the bioinformatics website (http://www.bioinformatics.com.cn/) was used to visualize the enriched results as bar and bubble plots. Then, the KEGG (Kanehisa and Sato, 2020) (http://www.kegg.jp.org/) database was used to map and color the detailed target messages in the most significantly enriched pathway. Finally, the complex regulatory network of AO in the treatment of PIH was shown by the “herb-compound-target-pathway-disease” network, incorporating the elements of AO, therapeutic compound, potential target, and top 10 enriched pathway, and visualized by Cytoscape software (v.3.9.1, https://cytoscape.org/). The same steps were used to analyze the core pathways of toxic AO compounds associated with hepato-nephrotoxicity.

Molecular dock verified the binding affinity

The Swiss dock platform (Grosdidier et al., 2011) (http://www.swissdock.ch/) is an online molecular docking (MD) tool that enables the calculation of the binding affinity of each binding site between small molecule ligand and receptor protein. The X-ray diffraction of the protein crystal structure of core targets was downloaded from the Protein Data Bank (PDB) database (www.rcsb.org) (Nakamura et al., 2022). The results were ranked by the binding affinity score, and the lowest binding affinity score value corresponded to the best binding site. The binding affinity < -7 kcal/mol indicates a strong combination possibility, which was visualized in Discovery Studio 2019 software (https://www.3ds.com/) (Sultana et al., 2022).

Molecular dynamics simulation verified the binding stability

Molecular Dynamics simulations (MDS) were further conducted to investigate the stability of small molecule ligands in proteins by the “Standard Dynamics Cascade” unit of Discovery Studio2019 software for ligand-protein complexes with the lowest binding affinity after molecular docking (Hu et al., 2022). The ligand-protein complex was placed in a solvent chamber, filled with water molecules and stabilized the electrically neutral system with Cl− and Na+. After balancing the system by the NPT ensemble (fixed the pressure, temperature and number of particles), the simulation time value was set to 500 ps and heating, equilibrium and production phases were conducted. Finally, the result was derived by trajectories analyzed with Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) and hydrogen bond properties.

Result

Target prediction of AO compounds, PIH, liver and kidney injury

There were 77 compounds of AO obtained from TCMSP, TCM-ID, TCM@Taiwan and BATMAN database (Figure 3A) (Details were listed in Supplementary Table S1). Based on the screening criteria OB ≥ 30 and DL ≥ 0.18, 10 eligible active compounds were obtained from the TCMSP database, including sitosterol (MOL000359), Alisol B (MOL000830), Alisol B monoacetate (MOL000831), alisol b 23 acetate (MOL000832), 16β-methoxyalisol B monoacetate (MOL000849), alisol B (MOL000853), alisol C (MOL000854), alisol C monoacetate (MOL000856), 1-Monolinolein (MOL002464) and Alisol B acetate (MOL000862). There were 19 bioactive compounds from other database sources, including 13Β,17Β-Epoxyalisol A, 24-Deacetylalisol O, 25-Anhydroalisol F, Alismol, Alisol B 23-Acetate, Alisol E 23-Acetate, Alisol E 24-Acetate, Alizexol A, Neoalisol, Oriediterpenol, Oriediterpenoside and other eight compounds without formally name. The 10 toxic compounds retrieved from TOXNET and CTD databases were choline (MOL000394), emodin (MOL000472), 5-hydroxymethylfurfural (HMF, MOL000748), 1 h-indole-3-carboxylic acid (MOL000823), sucrose (MOL000842), nicotinamide (NCA, MOL000857), stearic acid (MOL000860), Healip (MOL000861), 2-Furaldehyde and 2-Furancarboxylic acid (details shown in Table1, chemistry structure shown in Figure 2).

FIGURE 3.

FIGURE 3

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The PPI network of AO active compounds in the treatment of PIH (A), Venn diagram of AO identified compounds from TCMSP, TCM-ID, TCM@Taiwan and BATMAN-TCM database (B), Venn diagram of potential targets to the AO treat PIH (C), The degree value of compound potential targets (D), The 78 targets PPI network gain from STRING database (E), Plug-in of Cytoscape to screen core targets.

TABLE 1.

The chemical characteristics of 39 AO bioactive compounds obtained in TCMSP, TCM-ID, TCM@Taiwan, and BATMAN-TCM database.

No. Molecule name Molecular weight InCHIKey ID Compound OB (%) DL Source
1 1-Monolinolein 354.59 WECGLUPZRHILCT-GSNKCQISSA-N Treat 37.18 0.3 TCMSP
2 Sitosterol 414.79 KZJWDPNRJALLNS-ZFVHJZABSA-N Treat 36.91 0.75 TCMSP
3 Alisol B 444.72 XOWUWSRAQZUPJP-ASMOWKBLSA-N Treat 36.76 0.82 TCMSP
4 Alisol B monoacetate 514.82 NLOAQXKIIGTTRE-JOGPTBIUSA-N Treat 35.58 0.81 TCMSP
5 Alisol B Acetate 514.82 NLOAQXKIIGTTRE-JSWHPQHOSA-N Treat 35.58 0.81 TCMSP
6 Alisol B 472.78 GBJKHDVRXAVITG-HKXAQQBESA-N Treat 34.47 0.82 TCMSP
7 Alisol C monoacetate 514.77 KOOCQNIPRJEMDH-QSKXMHMESA-N Treat 33.06 0.83 TCMSP
8 Alisol C 486.76 DORJGGFFCMZTHW-KXVAGGRESA-N Treat 32.7 0.82 TCMSP
9 Alisol,b,23-acetate 446.74 QIMPSOXWCKBEBD-PQZUBQKGSA-N Treat 32.52 0.82 TCMSP
10 16β-methoxyalisol B monoacetate 544.85 UJRPGLKPBYUOLM-XBKPOWCMSA-N Treat 32.43 0.77 TCMSP
11 NCA 122.14 DFPAKSUCGFBDDF-UHFFFAOYSA-N Toxic 71.13 0.02 TCMSP
12 HMF 126.12 NOEGNKMFWQHSLB-UHFFFAOYSA-N Toxic 45.07 0.02 TCMSP
13 Choline 104.2 GDPPXFUBIJJIKR-UHFFFAOYSA-N Toxic 0.47 0.01 TCMSP
14 Healip 326.68 NOPFSRXAKWQILS-UHFFFAOYSA-N Toxic 11.65 0.22 TCMSP
15 Emodin 270.25 RHMXXJGYXNZAPX-UHFFFAOYSA-N Toxic 24.4 0.24 TCMSP
16 1h-indole-3-carboxylic,acid 161.17 KUQQAVBKLJHQJI-UHFFFAOYSA-O Toxic 25.83 0.05 TCMSP
17 Stearic acid 284.54 QIQXTHQIDYTFRH-UHFFFAOYSA-N Toxic 17.83 0.14 TCMSP
18 Sucrose 342.34 CZMRCDWAGMRECN-UGDNZRGBSA-N Toxic 7.17 0.23 TCMSP
NO. Molecule name Molecular weight InCHIKey ID Compound GI absorption Lipinski RO5 Source
19 13Β,17Β-Epoxyalisol A 506.7 BLYMKZZHAHHGBK-KOMKTNACSA-N Treat High Yes TCM-ID
20 24-Deacetylalisol O 470.34 ZQFQEWOYCZTQFY-OZBSICFQSA-N Treat High Yes TCM-ID
21 25-Anhydroalisol F 470.34 MRBFVJVQQIVGMY-CDJAXXMBSA-N Treat High Yes TCM-ID
22 Alismol 220.35 BUPJOLXWQXEJSQ-GIJJTGMTSA-N Treat High Yes BATMAN
23 Alisol B 23-Acetate 516.35 CPYFCYMXHGAZTF-WXYIYAPGSA-N Treat High Yes TCM-ID
24 Alisol E 23-Acetate 532.8 KRZLECBBHPYBFK-GLHMJAHESA-N Treat High Yes TCM-ID
25 Alisol E 24-Acetate 532.8 WXHUQVMHWUQNTG-GLHMJAHESA-N Treat High Yes TCM-ID
26 Alizexol A 530.7 KFWYQAKZMXFEFB-XKFNBYHKSA-N Treat High Yes TCM-ID
27 Neoalisol 488.7 QOEBTWRYOBEBPF-FZUOWIMQSA-N Treat High Yes TCM-ID
28 Oriediterpenol 304.5 ZALNTAHRBOFRCM-SVGWTELYSA-N Treat High Yes BATMAN
29 Oriediterpenoside 436.6 ITBIOEXYDKFWDZ-SUMMCVSUSA-N Treat High Yes BATMAN
30 TCMC1688 484.32 GHJSMGTZWLBPHU-DSAUVPRUSA-N Treat High Yes TCM-ID
31 TCMC1689 468.32 MPDDCFALNXXKHF-AJFHEMKZSA-N Treat High Yes TCM-ID
32 TCMC1691 518.4 QVVRXENOONWNGX-IPAXGOGQSA-N Treat High Yes TCM-ID
33 TCMC1692 488.35 OVJNKAWWCRMIMP-UNPOXIGHSA-N Treat High Yes TCM-ID
34 TCMC1694 474.33 DCYOPZMJKBYLCO-QJDZRUNDSA-N Treat High Yes TCM-ID
35 TCMC1695 486.33 PGQUPZCFLHXEHV-KPYCMCGFSA-N Treat High Yes TCM-ID
36 TCMC1696 530.36 QJQGTCCCAJRIPR-QRPPABIJSA-N Treat High Yes TCM-ID
37 TCMC4823 254.36 JNTOHIOAISZSEJ-SAAWNECCSA-N Treat High Yes TCM-ID
38 2-Furaldehyde 96.08 HYBBIBNJHNGZAN-UHFFFAOYSA-N Toxic High Yes TCM-ID
39 2-Furancarboxylic acid 112.08 SMNDYUVBFMFKNZ-UHFFFAOYSA-N Toxic High Yes TCM-ID/TCM@Taiwan
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OB, oral bioavailability; DL, drug-likeness; GI absorption, gastrointestinal absorption; HMF, 5-Hydroxymethyl-2-furaldehyde; NCA, nicotinamide; TCMC1688, (24R)-24,25-Dihydroxyprotosta-11,13(17)-Diene-3,16,23-Trione; TCMC1689, (23S,24R)-23,24-Dihydroxyprotosta-11,13(17),25-Triene-3,16-Dione; TCMC1691, (23S,24R)-11beta,23,24-Trihydroxy-25-Ethoxyprotosta-13(17)-Ene-3-One; TCMC1692, (23S,24R)-11beta,23,24,25-Tetrahydroxyprotosta-13(17),15-Diene-3-One; TCMC1694, (23S,24R)-11beta,23,24,25-Tetrahydroxy-29-Nor-3,4-Didehydroprotosta-13(17)-Ene-2-One; TCMC1695, (5R,8S,9S,10S,14R)-4,4,8,10,14-pentamethyl-17-[(2R,4S,5S)-4,5,6-trihydroxy-6-methylheptan-2-yl]-2,5,6,7,9,15-hexahydro-1H-cyclopenta[a]phenanthrene-3,16-dione; TCMC1696, [(1S,2R,4S,6S,7S,9R,13S,14S,15S,20R)-13-hydroxy-6-(2-hydroxypropan-2-yl)-1,2,9,15,19,19-hexamethyl-18-oxo-5-oxapentacyclo[12.8.0.02,11.04,10.015,20]docos-10-en-7-yl]; TCMC4823, (1S,2S,5R,6S,7R,8S)-1,5-dimethyl-8-propan-2-yl-11-oxatricyclo[6.2.1.02,6]undecane-5,7-diol.

FIGURE 2.

FIGURE 2

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The chemical structure of 39 AO bioactive compounds obtained from TCMSP, TCM-ID, TCM@Taiwan and BATMAN-TCM database (A), The chemical structure of top 10 therapeutic compounds (B) and (C), The chemical structure of remaining 19 therapeutic compounds (D), The chemical structure of 10 toxic compounds.

After screening and removing duplicates, we identified 138 potential targets associated with 29 therapeutic compounds and 160 targets associated with 10 AO toxic compounds. Besides, 1136 putative targets of PIH, 2188 targets of drug-induced liver injury and 2094 targets of drug-induced kidney injury were retrieved from the GeneCards, DisGeNET and OMIM databases. 78 targets overlapped between therapeutic compounds of AO and PIH, while 117 and 111 targets overlapped between AO toxic compounds and liver and kidney injury were obtained by Venn (detailed targets provided in Table 2).

TABLE 2.

The detail gene name of potential targets of AO in the treatment of PIH and AO toxic compounds induced hepato-nephrotoxicity.

No. Target Symbol Entrez ID Compound NO. Target Symbol Entrez ID Compound
1 Angiotensin-converting enzyme ACE P12821 Treat/Toxic 79 Acetylcholinesterase ACHE P22303 Toxic
2 Androgen receptor AR P10275 Treat/Toxic 80 Long-chain-fatty-acid--CoA ligase 1 ACSL1 P33121 Toxic
3 Cholinesterase BCHE P06276 Treat/Toxic 81 Actin, aortic smooth muscle ACTA2 P62736 Toxic
4 Carbonic anhydrase 2 CA2 P00918 Treat/Toxic 82 All-trans-retinol dehydrogenase ADH1B P00325 Toxic
5 Caspase-3 CASP3 P42574 Treat/Toxic 83 Alcohol dehydrogenase 1C ADH1C P00326 Toxic
6 Cyclin-dependent kinase 2 CDK2 P24941 Treat/Toxic 84 Adenosylhomocysteinase AHCY P23526 Toxic
7 Aromatase CYP19A1 P11511 Treat/Toxic 85 Aldo-keto reductase family 1 member B1 AKR1B1 P15121 Toxic
8 Dipeptidyl peptidase 4 DPP4 P27487 Treat/Toxic 86 Aldehyde dehydrogenase, mitochondrial ALDH2 P05091 Toxic
9 Epidermal growth factor receptor EGFR P00533 Treat/Toxic 87 Arginase-1 ARG1 P05089 Toxic
10 Neutrophil elastase ELANE P08246 Treat/Toxic 88 Beta-secretase 1 BACE1 P56817 Toxic
11 Bifunctional epoxide hydrolase 2 EPHX2 P34913 Treat/Toxic 89 Tyrosine-protein kinase BTK BTK Q06187 Toxic
12 Estrogen receptor ESR1 P03372 Treat/Toxic 90 Cyclin-A2 CCNA2 P20248 Toxic
13 Estrogen receptor beta ESR2 Q92731 Treat/Toxic 91 Cyclin-dependent kinase 6 CDK6 Q00534 Toxic
14 Coagulation factor X F10 P00742 Treat/Toxic 92 Cyclin-dependent kinase inhibitor 1 CDKN1A P38936 Toxic
15 Prothrombin F2 P00734 Treat/Toxic 93 Liver carboxylesterase 1 CES1 P23141 Toxic
16 Fatty acid-binding protein, adipocyte FABP4 P15090 Treat/Toxic 94 Serine/threonine-protein kinase Chk1 CHEK1 O14757 Toxic
17 Glycogen synthase kinase-3 beta GSK3B P49841 Treat/Toxic 95 Chitinase-3-like protein 1 CHI3L1 P36222 Toxic
18 3-hydroxy-3-methylglutaryl-coenzyme A reductase HMGCR P04035 Treat/Toxic 96 Muscarinic acetylcholine receptor M1 CHRM1 P11229 Toxic
19 11-beta-hydroxysteroid dehydrogenase 1 HSD11B1 P28845 Treat/Toxic 97 Muscarinic acetylcholine receptor M2 CHRM2 P08172 Toxic
20 Heat shock protein HSP 90-alpha HSP90AA1 P07900 Treat/Toxic 98 Muscarinic acetylcholine receptor M3 CHRM3 P20309 Toxic
21 Tyrosine-protein kinase JAK2 JAK2 O60674 Treat/Toxic 99 Collagen alpha-1(I) chain COL1A1 P02452 Toxic
22 Vascular endothelial growth factor receptor 2 KDR P35968 Treat/Toxic 100 Collagen alpha-1(VII) chain COL7A1 Q02388 Toxic
23 Mast/stem cell growth factor receptor Kit KIT P10721 Treat/Toxic 101 Granulocyte-macrophage colony-stimulating factor CSF2 P04141 Toxic
24 Galectin-3 LGALS3 P17931 Treat/Toxic 102 Casein kinase II subunit alpha CSNK2A1 P68400 Toxic
25 Mitogen-activated protein kinase 14 MAPK14 Q16539 Treat/Toxic 103 Cytochrome P450 1A1 CYP1A1 P04798 Toxic
26 Mitogen-activated protein kinase 8 MAPK8 P45983 Treat/Toxic 104 Cytochrome P450 1A2 CYP1A2 O77810 Toxic
27 Hepatocyte growth factor receptor MET P08581 Treat/Toxic 105 Dihydroorotate dehydrogenase (quinone) DHODH Q02127 Toxic
28 Interstitial collagenase MMP1 P03956 Treat/Toxic 106 Pro-epidermal growth factor EGF P01133 Toxic
29 72 kDa type IV collagenase MMP2 P08253 Treat/Toxic 107 Coagulation factor VII F7 P08709 Toxic
30 Stromelysin-1 MMP3 P08254 Treat/Toxic 108 Vascular endothelial growth factor receptor 1 FLT1 P17948 Toxic
31 Matrix metalloproteinase-9 MMP9 P14780 Treat/Toxic 109 Vascular endothelial growth factor receptor 3 FLT4 P35916 Toxic
32 Oxysterols receptor LXR-beta NR1H2 P55055 Treat/Toxic 110 Glutamate carboxypeptidase 2 FOLH1 Q04609 Toxic
33 Bile acid receptor NR1H4 Q96RI1 Treat/Toxic 111 Protein c-Fos FOS P01100 Toxic
34 Glucocorticoid receptor NR3C1 P59667 Treat/Toxic 112 Glucose-6-phosphatase catalytic subunit 1 G6PC1 P35575 Toxic
35 Mineralocorticoid receptor NR3C2 P08235 Treat/Toxic 113 Gamma-aminobutyric acid receptor subunit alpha-1 GABRA1 P14867 Toxic
36 Poly [ADP-ribose] polymerase 1 PARP1 P09874 Treat/Toxic 114 Gamma-aminobutyric acid receptor subunit alpha-2 GABRA2 P47869 Toxic
37 Progesterone receptor PGR P06401 Treat/Toxic 115 Glutamine synthetase GLUL P15104 Toxic
38 Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform PIK3CG P48736 Treat/Toxic 116 Glutamate receptor 2 GRIA2 P42262 Toxic
39 Peroxisome proliferator-activated receptor alpha PPARA Q07869 Treat/Toxic 117 Glutathione reductase, mitochondrial GSR P00390 Toxic
40 Peroxisome proliferator-activated receptor gamma PPARG P37231 Treat/Toxic 118 Histone deacetylase 8 HDAC8 Q9BY41 Toxic
41 Prostaglandin G/H synthase 1 PTGS1 P23219 Treat/Toxic 119 Hexokinase-1 HK1 P19367 Toxic
42 Tyrosine-protein phosphatase non-receptor type 1 PTPN1 P18031 Treat/Toxic 120 Immunoglobulin heavy constant gamma 1 IGHG1 P01857 Toxic
43 Retinoic acid receptor alpha RARA P10276 Treat/Toxic 121 Interleukin-1 beta IL1B P01584 Toxic
44 Retinol-binding protein 4 RBP4 P02753 Treat/Toxic 122 Interleukin-2 IL2 P60568 Toxic
45 Sex hormone-binding globulin SHBG P04278 Treat/Toxic 123 Integrin alpha-L ITGAL P20701 Toxic
46 Vitamin D3 receptor VDR F8VRJ4 Treat/Toxic 124 Kinesin-like protein KIF11 KIF11 P52732 Toxic
47 Tyrosine-protein kinase ABL1 ABL1 P00519 Treat 125 Tyrosine-protein kinase Lck LCK P06239 Toxic
48 Disintegrin and metalloproteinase domain-containing protein 17 ADAM17 P78536 Treat 126 Lysozyme C LYZ P61626 Toxic
49 RAC-alpha serine/threonine-protein kinase AKT1 P31749 Treat 127 Amine oxidase [flavin-containing] B MAOB P27338 Toxic
50 Bcl-2-like protein 1 BCL2L1 Q07817 Treat 128 Macrophage migration inhibitory factor MIF P14174 Toxic
51 Cathepsin D CTSD P07339 Treat 129 Macrophage metalloelastase MMP12 P39900 Toxic
52 Cytochrome P450 2C9 CYP2C9 P11712 Treat 130 Myc proto-oncogene protein MYC P01106 Toxic
53 Fibroblast growth factor receptor 1 FGFR1 P11362 Treat 131 Nuclear receptor subfamily 1 group I member 3 NR1I3 Q14994 Toxic
54 Hexokinase-4 GCK P35557 Treat 132 Serine/threonine-protein kinase pim-1 PIM1 P11309 Toxic
55 Glutathione S-transferase Mu 1 GSTM1 P09488 Treat 133 Serine/threonine-protein kinase PLK1 PLK1 P53350 Toxic
56 Glutathione S-transferase P GSTP1 P09211 Treat 134 Peroxisome proliferator-activated receptor delta PPARD Q03181 Toxic
57 Heme oxygenase 1 HMOX1 P09601 Treat 135 cAMP-dependent protein kinase catalytic subunit alpha PRKACA P17612 Toxic
58 Insulin-like growth factor I IGF1 P05019 Treat 136 Protein kinase C delta type PRKCD Q05655 Toxic
59 Insulin-like growth factor 1 receptor IGF1R P08069 Treat 137 Protein kinase C epsilon type PRKCE Q02156 Toxic
60 Insulin receptor INSR P06213 Treat 138 Prostaglandin G/H synthase 2 PTGS2 P27607 Toxic
61 Dual specificity mitogen-activated protein kinase kinase 1 MAP2K1 Q02750 Treat 139 Glycogen phosphorylase, liver form PYGL P06737 Toxic
62 Mitogen-activated protein kinase 1 MAPK1 P28482 Treat 140 RAF proto-oncogene serine/threonine-protein kinase RAF1 P04049 Toxic
63 Mitogen-activated protein kinase 10 MAPK10 P53779 Treat 141 Retinoic acid receptor beta RARB P10826 Toxic
64 E3 ubiquitin-protein ligase Mdm2 MDM2 Q00987 Treat 142 Retinoic acid receptor RXR-alpha RXRA P19793 Toxic
65 Neprilysin MME P08473 Treat 143 Solute carrier family 2, facilitated glucose transporter member 1 SLC2A1 P11166 Toxic
66 Matrilysin MMP7 P09237 Treat 144 Solute carrier family 2, facilitated glucose transporter member 4 SLC2A4 P14672 Toxic
67 Neutrophil collagenase MMP8 P22894 Treat 145 Transcription factor Sp1 SP1 P08047 Toxic
68 Nitric oxide synthase, inducible NOS2 P35228 Treat 146 Sterol regulatory element-binding protein 1 SREBF1 P36956 Toxic
69 Nuclear receptor subfamily 1 group I member 2 NR1I2 O75469 Treat 147 Tyrosine-protein kinase SYK SYK P43405 Toxic
70 cAMP-specific 3′,5′-cyclic phosphodiesterase 4D PDE4D Q08499 Treat 148 Transforming growth factor beta-1 proprotein TGFB1 P01137 Toxic
71 cGMP-specific 3′,5′-cyclic phosphodiesterase PDE5A O76074 Treat 149 Tumor necrosis factor TNF P01375 Toxic
72 Phosphatidylinositol 3-kinase regulatory subunit alpha PIK3R1 P27986 Treat 150 DNA topoisomerase 2-alpha TOP2A P11388 Toxic
73 Phospholipase A2, membrane associated PLA2G2A P14555 Treat 151 Cellular tumor antigen p53 TP53 P04637 Toxic
74 Tyrosine-protein phosphatase non-receptor type 11 PTPN11 Q90687 Treat 152 Transthyretin TTR P02766 Toxic
75 Renin REN P00797 Treat — — — — —
76 Proto-oncogene tyrosine-protein kinase Src SRC P12931 Treat — — — — —
77 TGF-beta receptor type-1 TGFBR1 P36897 Treat — — — — —
78 Thyroid hormone receptor beta THRB P10828 Treat — — — — —
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PPI network analysis

The 78 targets of AO in the treatment of PIH were incorporated into the STRING database to construct the PPI network. 78 nodes and 771 edges of protein-protein interaction were visually displayed in Cytoscape software, topological analysis including degree centrality (DC) and combined score, and the maximal clique centrality (MCC) algorithm was further used to screen the core targets. The top 10 targets were AKT1, BCL2L1, CASP3, EGFR, ESR1, HSP90AA1, IGF1, MAPK1, MAPK8 and SRC (Figure 3). Besides, after removing the disconnect node, the PPI network of toxic compounds-induced liver injury included 116 nodes and 1176 edges, the top 10 targets were CASP3, EGF, EGFR, ESR1, HSP90AA1, IL1B, MYC, PPARG, TNF and TP53 (Figures 5A,B). And the PPI network of toxic compounds-induced kidney injury included 109 nodes and 1130 edges, the top 10 targets were CASP3, EGF, EGFR, FOS, HSP90AA1, MMP9, MYC, PTGS2, TNF and TP53 (Supplementary Figure S1A,B).

FIGURE 5.

FIGURE 5

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The PPI network and GO/KEGG enrichment analysis of AO-induced liver injury (A), Venn diagram of potential targets to the AO-induced hepatotoxicity (B), The 116 targets PPI network and plug-in of Cytoscape to screen core targets (C), The bar graph of top 10 BP, CC and MF items (D), The bubble diagram of the top 10 KEGG pathway (E), The lipid and atherosclerosis pathway mapped by KEGG Mapper database (F), The H-C-T-P-D network showed the regulation mechanism of AO induced liver injury.

GO and KEGG pathway enrichment analysis

A total of 1022 biological process, 50 cellular compound and 93 molecular function items were obtained from the Metascape database, and 151 KEGG pathways were significantly enriched (p < 0.01, q < 0.05), demonstrating the characteristic of multi-pathway of AO in the treatment of PIH. The top 10 biological processes included the response to hormone, regulation of kinase activity, enzyme-linked receptor protein signaling pathway, cellular response to hormone stimulus, regulation of MAPK cascade cellular response to lipid, cellular response to nitrogen compound, positive regulation of protein phosphorylation, transmembrane receptor protein tyrosine kinase and positive regulation of cell migration (Figures 4A–C).

FIGURE 4.

FIGURE 4

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GO/KEGG enrichment analysis of AO treat PIH (A), The bar graph of top 10 GO items, including biological process (BP), cellular compound (CC) and molecular function (MF) (B), The bubble diagram of the top 10 KEGG pathway (C), GO chord chart (D), The core pathway, PI3K-AKT cascade mapped by KEGG Mapper database, whereby therapeutic targets were colored in red, targets of AO but not in the treatment of PIH were in blue, the other targets of PIH were yellow (E), The H-C-T network map showed the importance of each AO compounds, whereby 29 compounds ranked by degree value (F), The H-C-T-P-D network map showed the complex regulation of AO treat PIH. The deep green diamond represents herb (AO), the light green hexagon represents the therapeutic compound of AO, the red and orange circles represent targets based on the degree value, the blue V icon represents the pathway involved, and the yellow rectangle is the disease (PIH).

The top 10 significantly enriched KEGG pathways included Pathways in cancer, MAPK signaling pathway, PI3K-Akt signaling pathway, Proteoglycans in cancer, Prostate cancer, Lipid and atherosclerosis, Ras signaling pathway, Endocrine resistance, Chemical carcinogenesis -receptor activation and reactive oxygen species. Based on the enrichment ratio of pathways and their correlation with diseases, the PI3K-Akt signaling pathway was the most important. We further colored the detailed targets of the PI3K-Akt pathway in the KEGG mapper database, whereby therapeutic targets were colored in red, targets of AO but not in the treatment of PIH were in blue, the other targets of PIH were yellow (Figure 4D). Besides, a total of 1262 biological process, 55 cellular compound and 117 molecular function and 168 KEGG pathway items were significantly enriched (p < 0.01, q < 0.05), and the lipid and atherosclerosis pathway was significantly associated with AO induced liver injury (Figures 5C–E). And a total of 1260BP, 52 CC, 115 MF items, and 161 KEGG pathway were enriched (p < 0.01, q < 0.05), the lipid and atherosclerosis pathway was also significantly associated with AO induced kidney injury (Supplementary Figures S1C,E).

Herb-compound-target-pathway-disease network analysis

After identifying the core targets and the top 10 pathways from previous analysis, a “herb-compound-target-pathway-disease” (H-C-T-P-D) network was constructed by Cytoscape 3.9.1 to reveal the complex molecular mechanism and multi-compound, multi-target and multi-pathway characteristic of AO in the treatment of PIH (Figures 4E,F). The major active compounds from the network were Alisol B 23-Acetate, Alisol E 24-Acetate and alisol C. The deep green diamond represents herb (AO), the light green hexagon represents the therapeutic compound of AO, the red and orange circles represent targets based on the degree value, the blue V icon represents the pathway involved, and the yellow rectangle is the disease (PIH). Similarly, the “H-C-T-P-D” network was used to illustrate the mechanism of AO toxic compounds induced liver injury (Figure 5F) and kidney injury (Supplementary Figure S1F), whereby the green diamond, the light green hexagon, the red circle, the blue Vicon and the yellow rectangle represent the herb, the toxic compounds of AO, targets, pathways, and disease (AO induced hepato-nephrotoxicity). The main toxic compound was emodin (MOL000472).

Molecular docking

Supplementary Table S2 exhibits the binding affinity of the top 10 therapeutic compounds and emodin with their core targets, respectively. The results showed that the active compounds had good binding activities with all targets. A binding affinity < -5.0 kcal/mol indicated that the small molecule ligand has a good binding activity with the receptor protein, and binding affinity < -7.0 kcal/mol indicated stronger binding activity. Among them, we found a binding affinity < -7.0 kcal/mol between all compounds and IGF1 as well as emodin with TNF and PPARG, indicating that IGF1 may be an important target mediating the therapeutic effect against PIH, and PPARG and TNF may be the main targets of AO-induced liver injury. Figure 6A was the binding affinity heatmap of ligand-protein complexes, and Figures 6B–D shows the details of molecular docking between Alisol B monoacetate and IGF1 (lowest binding affinity); the intermolecular forces include alkyl bonding, π-alkyl bonding, conventional hydrogen bonding and carbon-hydrogen bonding. Figure 7 shows the results of emodin binding with PPARG and TNF.

FIGURE 6.

FIGURE 6

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The molecular docking results of AO therapeutic compounds with core targets (A), The binding affinity heatmap of ligand-receptor complex (B), 3D structures of the Alisol B monoacetate-IGF1 complex (C), spatial structure of Alisol B monoacetate-IGF1 show binding details (D), 2D structure of Alisol B monoacetate-IGF1 complex show intermolecular force types.

FIGURE 7.

FIGURE 7

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The molecular docking results of AO toxic compounds with corresponding core targets (A), emodin docking with TNF (B), emodin docking with PPARG (From left to right: 3D, spatial and 2D structure).

Molecular dynamics simulation

Molecular dynamics simulation represents an important technology for analyzing the ligand-protein complex’s conformational change and stability after docking. To explore the binding stability of Alisol B monoacetate with IGF1, the RMSD, RMSF curves, energies alternate tendency and hydrogen bonding heat map were calculated. Figure 8 shows that after 50 ps, the trajectories of all molecules and energy levels tend to stabilized. The RMSD, RMSF curve and hydrogen bond heat map exhibited good stability.

FIGURE 8.

FIGURE 8

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The molecular dynamic simulations results of Alisol B monoacetate binding with IGF1 (A), RMSD and (B), RMSF curves of MDS (C), Hydrogen bond heat map of Alisol B monoacetate binding with IGF1.

Discussion

Pregnancy-induced hypertension is a common disease during pregnancy, with a 10–12% incidence rate (Lan et al., 2022). Elevated arterial pressure and proteinuria are the most prominent clinical manifestations, and disease progression can cause reversible pathological damage to multiple systems and organs. Given the importance of the safety profile of drugs indicated for pregnant women and the unknown pathogenesis of PIH, significant emphasis has been placed on better understanding the underlying therapeutic and toxic mechanisms of these drugs (Reddy et al., 2022).

As mentioned above, oxidative stress, immune dysregulation, endothelial cell malfunction and other perplex factors have been associated with the pathogenesis of PIH. Unlike Western medicine which adopts a targeted approach, TCM fosters a more holistic approach based on the multi-compound, multi-target and multi-pathway of TCM drugs and has huge prospects for use as complementary and alternative medicine.

Alisma Orientale, also known as “Ze Xie” in Chinese, is the most important herbal compound in Ze Xie decoction indicated for treating hypertension, according to ancient records. Modern studies have isolated more than 100 active compounds, mainly terpenoids and small amounts of flavonoids, alkaloids, phytosterols and fatty acids. It is worth mentioning that protostane triterpenoids are the characteristic compounds, including alisol A-I and their derivatives (Shu et al., 2016). It has been established that Alisol B 23-acetate is an important compound with excellent bioactivities used to characterize AO in the “Pharmacopoeia of the People’s Republic of China” (Li et al., 2019b). Our findings showed that compounds with therapeutic effects included Alisol B 23-acetate, Alisol E 24-acetate and other derivatives, consistent with the “H-C-T-P-D” network.

Previous studies have demonstrated that AO plants, especially triterpene compounds, have diuretic activity. Zhang et al. reported that Alisol B, alisol B 23-acetate and other derivatives could interfere with the Na+, K+, and Cl− co-transport carrier on the luminal membrane of the Henle and sodium–chloride co-transporter in the renal distal convoluting tubule to exert diuretic action (Zhang et al., 2017). It could also significantly affect K+ excretion by competitively binding the receptor site in the collecting tube to influence sodium, potassium exchange and acid absorption. Besides, the immunomodulatory activity of the methanol extract of triterpenoids from the AO plant (alisol A, B and their acetate derivatives) significantly inhibited paw swelling in rats showing type I-IV hypersensitivity (Shao et al., 2018). In addition, alisol B and alisol B monoacetate could inhibit the complement system through the antigen–antibody-mediated process, exhibiting a good therapeutic effect against immune-related diseases (Lee et al., 2003). Further studies illustrated that alisol B 23-acetate could prevent lipid peroxidation and regulate inducible nitric oxide synthase (iNOS) expression, with significant reticuloendothelial system-potentiating activities (Matsuda et al., 1999). In conclusion, the active compounds of AO possess diuretic, immunomodulatory and vascular endothelial modulatory activities, exhibiting multi-compound, multi-target and cooperation characteristics as well as multi-compound amplification effects in the treatment of PIH.

Few studies have hitherto assessed the toxicity and side effects of AO and its compounds to determine its biosafety profile (Tian et al., 2014). Accordingly, network toxicology was applied to explore the potential mechanism in this research. Our findings showed that emodin belonging to flavonoids was the main compound that induced hepato-nephrotoxicity, and other toxic compounds with smaller proportions, including fatty acids and carbohydrates, such as stearic acid and sucrose. PPI network and GO/KEGG pathway enrichment analysis showed that AO toxic compounds mainly targeted PPARG and TNF and regulated the lipid and atherosclerosis signaling pathway. PPARG, also known as peroxisome proliferator-activated receptor gamma, is a nuclear receptor binding peroxisome proliferator that regulates adipocyte differentiation and controls the peroxisomal beta-oxidation pathway of fatty acids (Małodobra-Mazur et al., 2022). Tumor necrosis factor (TNF) is mainly secreted by macrophages and can induce insulin resistance through inhibition of Insulin receptor substrate 1 (IRS1), tyrosine phosphorylation and G kinase-anchoring protein 1 (GKAP42) degradation in adipocytes (Ando et al., 2015). Taken together, we provide preliminary evidence that the potential mechanism in AO-induced liver injury may mediate lipid metabolism via network toxicology.

PPI network analysis showed that IGF, AKT1, EGFR and MAPK1 were the main targets of AO in the treatment of PIH; the molecular docking and molecular dynamics simulation results also exhibited good binding affinity and stability. Besides, KEGG pathway enrichment analysis showed that MAPK and PI3K-AKT signal pathways were significantly enriched. Insulin-like growth factor I (IGF1) can bind to the alpha subunit of IGF1 receptor and modulate the tyrosine kinase activity to initiate the down-stream signaling events activation of PI3K-AKT and Ras-MAPK pathways (Mohindra et al., 2022). It has been reported that IGF1 is differentially expressed between PIH and normal pregnancy newborns, and IGF1 could stimulate extra-villous trophoblast (EVT) cell migration and invasion, given that insufficient invasion into the uterine endometrium may be the crucial reason for PIH (Seedorf et al., 2020). Epidermal growth factor receptor (EGFR) and RAC-alpha serine/threonine-protein kinase (AKT1) are important hub genes that regulate a series of biological functions, including angiogenesis (Zhang et al., 2022b). Mitogen-activated protein kinase 1 (MAPK1) has been associated with the transduction of endothelial inflammatory response and inflammatory factor expression action (Galganska et al., 2021). Wang Z et al. indicated that overexpressing miR-106 and downregulating the MAPK signaling cascade could attenuate oxidative stress injury and inflammatory response in the liver of mice with PIH (Wang et al., 2019). Li X et al. found significant Cx43 protein overexpression on human umbilical vein endothelial cells (HUVECs) in PIH patients, which may lead to monocyte-endothelial adhesion increase by PI3K-AKT signaling pathway activation and upregulate the downstream genes of VCAM-1 and ICAM-1 (Li et al., 2019c).

In conclusion, we found that the main compounds of AO in treating PIH are triterpenoids, especially Alisol B, Alisol C and their derivatives, which target IGF, AKT1, EGFR and MAPK1 and mediate the MAPK and PI3K-AKT signal pathways to produce diuretic and immunomodulatory activities and regulate the function of endothelial cells, anti-vascular inflammation and oxidative stress. In addition, our study preliminarily indicated that the toxic compounds in AO were mainly emodin and small amounts of fatty acids, which may produce hepato-nephrotoxicity by interfering with lipid metabolism. Furthermore, our study suggests that an optimal therapeutic effect against PIH may be achieved by increasing the triterpenoid content and reducing toxic compounds through appropriate methods. However, some shortcomings in the present study affect the robustness of our findings to some extent. First, our study was based on the identified and main bioactive compounds of AO, which may have some selection bias. Second, and crucially, further in vivo and in vitro basic research is needed to confirm and extend our conclusions. And this was exactly what we need to do next, to verify the mechanisms of AO core compounds with therapeutic efficacy in PIH and hepato-nephrotoxicity in future research.

Conclusion

This study was based on the bioinformatics analysis of network pharmacology, network toxicology, molecular docking and molecular dynamics simulation. The finding shows that the triterpenoids were the main therapeutic compounds and emodin was the main toxic compound of Alisma Orientale. And also illustrated the potential mechanism underlying the therapeutic effects of AO against PIH and AO induced hepato-nephrotoxicity. In short, our research could guide directions for subsequent basic experimental verification and provide points of view for enhance efficacy and reduce toxicity in clinical.

Acknowledgments

We sincerely appreciate the databases of TCMSP, TCM-ID, TCM@Taiwan, BATMAN-TCM, TOXNET, CTD, PharmMapper, Swiss Institute of Bioinformatics (SIB, including SwissADME, SwissTargetPrediction and Swiss Dock), Uniport, GeneCards, DisGeNET, OMIM, STRING, Metascape, bioinformatics and Cytoscape, Discovery Studio 2019 software for provided the data and making the charts.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Author contributions

YL designed the study and wrote the manuscript, MY derived the datasets and performed statistical analyses; YD revised the manuscript; LY and CX draw all figures and prepared the supplementary materials.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2022.1027112/full#supplementary-material

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Associated Data

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Supplementary Materials

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Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

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