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Article

Verification of the Potential Targets of the Herbal Prescription Sochehwan for Drug Repurposing Processes as Deduced by Network Pharmacology

by
Dong-Woo Lim
1,2,3,
Da-Hoon Kim
2,
Ga-Ram Yu
2,
Won-Hwan Park
2 and
Jai-Eun Kim
1,*
1
Department of Pathology, College of Oriental Medicine, Dongguk University, Dongguk-Ro 32, Goyang 10326, Korea
2
Department of Diagnostic, College of Oriental Medicine, Dongguk University, Dongguk-Ro 32, Goyang 10326, Korea
3
Institute of Korean Medicine, Dongguk University, Dongguk-Ro 32, Goyang 10326, Korea
*
Author to whom correspondence should be addressed.
Processes 2021, 9(11), 2034; https://doi.org/10.3390/pr9112034
Submission received: 18 October 2021 / Revised: 29 October 2021 / Accepted: 10 November 2021 / Published: 14 November 2021
(This article belongs to the Special Issue Network Pharmacology Modelling for Drug Discovery)
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Abstract

:
Network pharmacology (NP) is a useful, emerging means of understanding the complex pharmacological mechanisms of traditional herbal medicines. Sochehwan (SCH) is a candidate herbal prescription for drug repurposing as it has been suggested to have beneficial effects on metabolic syndrome. In this study, NP was adopted to complement the shortcomings of literature-based drug repurposing strategies in traditional herbal medicine. We conducted in vitro studies to confirm the effects of SCH on potential pharmacological targets identified by NP analysis. Herbal compounds and molecular targets of SCH were explored and screened from a traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and an oriental medicine advanced searching integrated system (OASIS). Forty-seven key targets selected from a protein-protein interaction (PPI) network were analyzed with gene ontology (GO) term enrichment and KEGG pathway enrichment analysis to identify relevant categories. The tumor necrosis factor (TNF) and mitogen-activated protein kinase (MAPK) signaling pathways were presented as significant signaling pathways with lowest p-values by NP analysis, which were downregulated by SCH treatment. The signal transducer and activator of transcription 3 (STAT3) was identified as a core key target by NP analysis, and its phosphorylation ratio was confirmed to be significantly suppressed by SCH. In conclusion, the NP-based approach used for target prediction and experimental data obtained from Raw 264.7 cells strongly suggested that SCH can attenuate inflammatory status by modulating the phosphorylation status of STAT3.

1. Introduction

Network pharmacology (NP) is a powerful tool that is based on the concepts of system biology and bioinformatics, as supported by extensive pharmacological databases [1]. NP has the potential to contribute to novel drug discovery, the repurposing of existing drugs [2], and the identification of synergistic ingredient pairs [3]. The NP analysis also addresses the safety and efficacy issues of existing medications with an understanding of possible toxicity and side-effects [2]. In traditional herbal medicine, prescriptions are typically composed of several medicinal herbs, and multi-compound, multi-target theory provides a practical means of replacing the one drug-one target paradigm [4]. Given access to the many bioactive compounds of herbs screened using adsorption, distribution, metabolism, excretion (ADME), and pharmacokinetic profiles and their associated targets drawn from data archives [5], comprehensive networks can be established that demonstrate how these compounds work in integrated ways [6].
The network pharmacologic approach has become an emerging topic of study in traditional medicine during the last decade, and great progress has been made in terms of the quantity and quality of studies performed [7]. In particular, it has been demonstrated that network pharmacology-based target prediction is a feasible strategy with various methods. Protein-protein interaction (PPI) networks provide better understanding of the functions and interactions of key targets predicted by network analysis parameters in a broader view [8]. Annotation of specified Gene Ontology (GO) terms into the three categories, namely, biological process, cellular compartment, and molecular function, provides an understanding of the biological roles of target proteins [9]. Furthermore, GO term enrichment analysis suggests associated terms of hierarchically classified categories [10] and provides functional interpretations of sets of genes using various tools. Therefore, when linked with pharmacological databases of components, network pharmacology can be used to identify potential targets and predict the therapeutic effects of herbal prescriptions [11,12].
Sochehwan (SCH) is a traditional Korean medicine and an effective, safe herbal digestive agent that is available over the counter to treat dyspepsia, abdominal pain, and distention [13]. The prescription has been used for many centuries and is listed in medical classics among gastrointestinal indications [14]. However, we expect medicinal usages of SCH in other diseases regarding the ingredients. In a previous study conducted to screen for new indications for herbal drugs, we demonstrated that SCH can alleviate metabolic syndrome through multifaceted mechanisms in vitro and in vivo [14]. However, literature-based methods of drug repurposing in traditional medicine lack objective evaluation and statistical support. Recently, data mining and systematic pharmacology methods were used together to address these problems by other researchers [15,16]. Advanced methodology used in the area adopted a network-based algorithm to screen efficacious repurposing drugs against COVID-19 and was successfully validated by gene set enrichment analysis (GSEA) [17].
To approach this topic objectively, we adopted NP methods with the intention of establishing standardized procedures for repurposing traditional herbal medicines for the treatment of different diseases and to target disease-associated molecules. Thus, we investigated significant key targets of SCH using NP-based approaches. Significant KEGG pathways, which are enriched with key targets of SCH, were identified in web databases. GO enrichment analysis results were visualized to understand the biological effects of SCH, and a series of in vitro experiments were conducted to verify the functions and targets predicted by NP analysis. Finally, we provide an integrated explanation of the main mechanism of SCH using NP and in vitro data.

2. Materials and Methods

2.1. Acquisition of Potential Active Ingredients and Targets of SCH as Determined Using Web Databases

The SCH used in this study consisted of three different medicinal herbs, namely, Pharbitis Semen (PS), Trogopterorum Faeces (TF), and Cyperi Rhizoma (CR) (PS:TF:CR = 2:1:1, w/w). The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP, https://old.tcmsp-e.com/tcmsp.php (accessed on 2 July 2021)) [18] was used as a repository to collect information about the ingredients and targets of SCH. Potential bioactive ingredients in each herb were screened using ADME properties, especially for drug-likeness (DL, ≥0.18) and oral bioavailability (OB, ≥30%). However, due to incompleteness of the ingredient database of TF, we screened for more ingredients of TF in the traditional Chinese medicine information database (TCM-ID, http://bidd.group/TCMID/ (accessed on 2 July 2021)) [19] and the oriental medicine advanced searching integrated system (OASIS, https://oasis.kiom.re.kr/ (accessed on 2 July 2021)) [20], verified in Pubchem databases (https://pubchem.ncbi.nlm.nih.gov/ (accessed on 27 October 2021)) [21]. Pubchem_CID was used to identify ingredients.
Relevant proteins targeted by each ingredient acquired from TCMSP were validated using the Uniprot database (https://www.uniprot.org/ (accessed on 5 July 2021)) [22,23]. All target proteins were converted to official gene names in Homo sapiens using the Genecards web database (https://www.genecards.org/ (accessed on 5 July 2021)) [24]. Lists of ingredients and targets were sorted and uploaded as groups for each herb to the bioinformatics and evolutionary genomics website (http://bioinformatics.psb.ugent.be/webtools/Venn/ (accessed on 20 July 2021)) to obtain Venn diagrams.

2.2. Key SCH Target Screening for Unspecified Diseases Using the STRING Database and Cytoscape

All targets of SCH ingredients were arranged as lists and uploaded to the STRING database (https://string-db.org/ (accessed on 6 July 2021)) [25] to construct a protein-protein interaction (PPI) network. The minimum required interaction score was set at 0.4, and separated target nodes without known interactions were hidden. PPI network interactions were exported as a file to Cytoscape [26]. The network file was imported into Cytoscape version 3.8.2 and topological analysis of the networks was performed using a built-in network analyzer. Two topological parameters, “degree” and “betweenness centrality”, were adopted as criteria for selecting key targets of SCH. Targets with greater figures above average for both degree and betweenness centrality were regarded as key targets.

2.3. Construction of a Compound-Target-Pathway Network

A network of herbal ingredient-key target-pathways was constructed and visualized to better understand connectivity between components. Nodes represent herb ingredients, targets, and pathways, while edges represent interactions. Colors of nodes represent source herbs (PS-blue, CR-green, TF-red), and visibility of interactions were improved by importing attribute data of nodes. When a compound-target network was constructed, target-pathway networks were additionally combined using the ‘Merge tool’ in Cytoscape.

2.4. The KEGG Pathway and Gene Ontology Enrichment Analyses

The Kyoto Encyclopedia of Genes and Genomes (KEGG) [27] pathway and gene ontology (GO) enrichment analyses [28] were performed by uploading key target genes to the DAVID database platform (https://david.ncifcrf.gov/ (accessed on 7 July 2021)) [29]. A list of all key genes was uploaded, and the identifier was set as “official gene symbol”. Annotations of key genes were analyzed in different categories, including the KEGG pathway and the three GO terms, that is, BP (biological process), CC (cellular component), and MF (molecular function). The top 20 results with lowest p-values in each category were visualized as a bubble chart with p-values, gene counts, and gene ratios. Bubble plots were created using R Studio and the ‘ggplot2’ package with public R script properly modified to our study [30]. During the analysis of KEGG pathway enrichment, we separated signaling and disease pathway results.

2.5. Herbal Formula Extraction

The three constituents of SCH (Pharbitis Semen: Cyperi Rhizoma: Trogopterorum Faeces = 2: 1: 1, w/w) were obtained from Humanherb (Gyeongsangbukdo, South Korea). SCH powder was prepared by extracting a mix of the three herbs in 30% ethanol for 1 h with heat. The crude extract was filtered, condensed using a rotary evaporator (Buchi, Switzerland) at 95 °C, and freeze-dried to obtain a powder (SCH extract), which was eluted with DPBS and filtered through a 0.22 μm syringe filter before use.

2.6. Cell Culture and Cell Viability Assessment

Raw 264.7 cells (ATCC TIB-71) were grown in Dulbecco’s Modified Eagle Medium (DMEM, Gibco, Carlsbad, CA, USA), supplemented with 10% fetal bovine serum (FBS) and 100 U/mL penicillin and streptomycin (Gibco, USA). Cells were incubated at 37 °C in a humidified 5% CO2 atmosphere and maintained at ~70% confluence before being used in experiments.
For viability determinations, cells were seeded in 96 well plates in FBS-free DMEM at a density of 2 × 103 cells per well and then incubated with various concentrations of SCH for 24 h when the medium was replaced with DMEM, containing 2 μg/mL of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide; Invitrogen, Carlsbad, CA, USA), for 2 h. Formazan crystals were dissolved at DMSO and optical densities were measured at 540 nm using a microplate spectrophotometer (VersaMax, Molecular Devices, San Jose, CA, USA).

2.7. Cell Treatment and Determination of Nitric Oxide Levels

Raw 264.7 cells were seeded in 12 well plates at a density of 2 × 104 cells/mL for nitric oxide (NO) measurements or in 6 well plates at a density of 2 × 105 cells/mL for inflammatory protein isolation. At 24 h after seeding, culture media were changed to FBS-free DMEM, and cells were pretreated with SCH at 0–20 μg/mL for 1 h. SCH-treated cells were then treated with or without 1 μg/mL of LPS (lipopolysaccharide) for 24 h and centrifuged, then supernatants and cell pellets were collected.
NO amounts released to media were determined using a Griess reaction assay. Cell supernatants (100 µL) were mixed with 100 µL of Griess reagent (a mixture of 0.1% N-(1-naphthyl) ethylenediamine (NED) and 1% sulfanilamide dissolved in 5% phosphoric acid) and incubated for 15 min. Absorbances were measured using a microplate spectrophotometer at 540 nm (VersaMax, Molecular Devices, USA).
Intracellular NO levels were determined by measuring DAF-2 fluorescence intensities. Cells were seeded in a 96 clear-bottom black well plate at a density of 2 × 104 cells/mL and incubated using the conditions mentioned above and then with 10 μM of DAF-FM DA for 2 h. Relative fluorescence intensities were measured using a fluorescence spectrophotometer (SpectraMax Gemini EM, Molecular Devices, USA) at excitation/emission wavelengths of 485 and 520 nm, respectively.

2.8. Western Blotting of Inflammatory Mediators and Pathway Proteins

Whole proteins were isolated from Raw 264.7 cells using ice-cold radioimmunoprecipitation assay buffer (RIPA, Thermo Fisher Scientific, Waltham, MA, USA) containing 1x protease and phosphatase inhibitor cocktail (Gendepot, Katy, TX, USA). Protein concentrations in cell lysates were determined using a commercial BCA kit (Thermo Fisher Scientific, Waltham, MA, USA). Protein samples (20 µg) were mixed with 5× loading buffer (BioRad, Hercules, CA, USA) and denatured at 95 °C for 10 min before being loaded into 10% SDS-PAGE gel. Proteins were separated by electrophoresis and transferred to PVDF membranes using the Mini Transblot Electrophoretic Transfer Cell device (BioRad, Hercules, CA, USA). Membranes were then blocked in TBST containing 5% BSA for 1 h, incubated with the primary antibody (1:1000 diluted in 3% BSA in TBST) overnight with gentle shaking and washed. Incubation was followed with the secondary antibody (1:2000 diluted in 1% BSA in TBST) for 1 h, anprd otein signals were detected using ECL chemiluminescence reagent (Thermo Fisher Scientific, Carlsbad, CA, USA) using a blot imaging and analysis system (Fusion solo, Vilber Lourmat, Collégien, France).

2.9. Real-Time Quantitative PCR of Inflammatory Markers

Raw cells were seeded in 60 mm culture plates for 24 h and treated with SCH and LPS using the same conditions as those described above, and total mRNA was isolated using Trizol reagent (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer’s instructions. Isolated mRNA was subjected to cDNA synthesis using the AccuPower RT Premix kit (Bioneer, Daejeon, South Korea) and oligo (dt) primers (Invitrogen, Carlsbad, CA, USA). The PCR mix contained 2× SYBR green master mix, ultrapure water, 10 pmol/μL of gene primers, and 10 pmol/μL of template cDNA. Amplification was conducted using 45 cycles of 10 s of 95 °C (denaturation), 20 s of 50–60 °C (annealing), and 20 s of 72 °C (extension). Amplicons were subjected to melting curve analysis to check PCR results.

2.10. Immunofluorescence Staining and Imaging of NF-κB Complex

Raw 264.7 cells were seeded in chamber slides (Lab-Tek II, Nunc, Naperville, IL, USA) and treated with SCH and LPS, as described above. After 24 h of co-incubation, cells were fixed with 4% formaldehyde for 10 min and permeabilized in 0.1% Triton X-100 in BSA for 10 min. Cells were then blocked with 1% BSA in PBS at room temperature and incubated with anti-NF-κB antibody for 3 h. Slides were cover slipped using mounting media containing 4′,6-diamidino-2-phenylindole (DAPI) (Vector Lab Inc., Burlingame, CA, USA), and fluorescence images were acquired under a fluorescence microscope at 400×.

2.11. Statistical Analysis

The analysis was conducted using one-way ANOVA in Graphpad Prism 5.0 (Graphpad Software, USA). The significances of differences between non-treated controls and LPS treated cells and between LPS-treated and SCH-treated cells were determined. Results are presented as the means ± SDs of at least three independent experiments, and statistical significance was accepted for p values < 0.05. Figures and tables were created and exported from Graphpad Prism 5.0.

3. Results

3.1. Active Compound Screening and Key Targets of PS, CR, or TF

According to the TCMSP database, 14 and 16 ingredients in PS and CR, respectively, meet OB and DL criteria (Table 1). Although TCMSP did not provide any results for TF, TCM-ID and OASIS provided seven ingredients and references to an article with in vivo pharmacokinetic data as evidence of oral bioavailability and target information [31,32,33,34,35]. No common compound was found in TF and CR or in TF and PS. Three common ingredients were found in PS and CR (8-isopentenyl-kaempferol, beta-sitosterol, sitosterol) (Figure 1a, Table 1).
Lists of potential targets of PS, TF, and CR active ingredients were obtained and analyzed, and as a result, 283 targets were identified. Numbers of targets for PS, TF, and CR were 88, 98, and 232, respectively. PS and CR shared 80 targets, TF and CR 55 and PS and TF 24. Twenty-four targets were commonly targeted by the three herbs (Figure 1b).

3.2. Construction of a PPI Network and Screening of Key Targets

In total, 283 target genes were uploaded into the STRING database to produce a PPI network between target proteins. Disconnected target proteins without known interaction, duplicated targets (typographical error of target names in the database), and targets from different species (host) were removed during gene validation (16 genes were removed). The interaction network between the 267 target genes was analyzed in Cytoscape, and network parameters of betweenness centrality and degree were evaluated. Finally, 47 target proteins were found to be highly interconnected and thus were selected as key targets of SCH. A list of key targets arranged by degree and betweenness centrality is shown in Table 2. Figure 2 shows the PPI network consisting of 47 key targets as nodes and 284 interactions between targets as edges. STAT3 was found to be a core member of the PPI network with the highest degree of 48 (Table 2).

3.3. KEGG Pathway and GO Enrichment Analysis and the Main Targets of SCH

We uploaded 47 key genes into the DAVID database and obtained 20 results for the KEGG pathway and GO enrichment analysis with the least p-values (Figure 3). Of the 20 pathways, 11 were involved in human disease pathways, 8 in signaling pathways, and 1 in a cellular community-related pathway (Gap junction). Among BP terms, response to drug and positive regulation of transcription from RNA polymerase II promoter were the most prominent results. For CC terms, nucleoplasm was the most significant term, followed by protein-containing complex, and for MF, enzyme binding and protein-containing complex binding were the most significant. Details of the top 20 enriched KEGG pathways are presented in Table 3.

3.4. Compound-Target-Pathway Network of SCH

Using the key targets of SCH elicited by PPI network analysis, we constructed a compound-target-pathway network in Cytoscape (Figure 4). The complete compound-target-pathway network consisted of 93 nodes and 449 edges. PS, CR, and TF possessed 10, 13, and 5 active compounds, respectively, related to key targets. Twenty-four targets were linked with CR, 8 of which were also linked with PS. However, no key target was solely linked with PS. In addition, TF had 23 key targets and shared 18 targets with CR and 7 targets with PS and CR. The seven common key targets shared by the three herbs were Androgen receptor (AR), Muscarinic acetylcholine receptor M1 (CHRM1), Beta-2 adrenergic receptor (ADRB2), Prostaglandin G/H synthase 2 (PTGS2), Caspase-3 (CASP3), Caspase-8 (CASP8), and Estrogen Receptor 1 (ESR1). For SCH, numbers of pathways related to disease were 28 for cancer, 20 for hepatitis B, and 18 for proteoglycans in cancer, which were ranked first to third. Meanwhile, for pathways related to signaling, 16 involved the MAPK signaling pathway, 16 the PI3K-AKT signaling pathway, and 13 the TNF signaling pathway.

3.5. SCH Reduced Nitric Oxide Production in Raw 264.7 Cells

SCH at ≤20 µg/mL had no significant effect on Raw 264.7 cell viability, and thus, 20 µg/mL was used as the maximum concentration (Figure 5a). Nitric oxide levels in medium were significantly increased by LPS at 1 μg/mL (Figure 5b) but reduced by SCH at 5 to 20 μg/mL and maximally reduced at 20 μg/mL. DAF-FM DA assays were used to access intracellular nitric oxide levels (Figure 5c). As we expected, LPS treatment significantly increased fluorescence intensity, but pretreatment with SCH at all concentrations markedly suppressed this LPS-induced increase.

3.6. SCH Inhibited LPS-Induced MAPK Signaling Pathways and Inflammatory Mediator Productions

Immunoblot images of whole lysates from LPS-treated Raw 264.7 cells showed significant augmentation of inflammatory mediators, including iNOS and COX-2 (Figure 6A–C). LPS-induced increases in the protein levels of iNOS and COX-2 were markedly suppressed by SCH treatment at 10 and 20 μg/mL. Furthermore, phosphorylated protein levels of mitogen-activated protein kinase (MAPK) signaling, including those of JNK, ERK, and p38, were notably elevated in Raw 264.7 cells by LPS (Figure 6d–g). However, these changes were significantly inhibited by SCH pretreatment at all concentrations.

3.7. SCH Regulated the Phosphorylation and Translocation of NF-κB Transcription Factor Complex

Immunoblot images of NF-κB complex and its phosphorylated form suggested that LPS treatment induced the phosphorylations of IκB and NF-κB RelA/p65 (Figure 7a–c). However, SCH suppressed these LPS-induced phosphorylations, which implies that SCH blocked the nuclear translocation of NF-κB and thus reduced proinflammatory macrophage responses. The nuclear localization of NF-κB p65 in LPS-activated Raw 264.7 cells was investigated with immunofluorescence staining and microscopy. As shown in Figure 7d, LPS treatment induced the nuclear translocation of NF-κB in macrophages, but SCH pretreatment suppressed this LPS-induced translocation.

3.8. SCH Modulated the JAK2-STAT3 Pathway as Was Predicted by Network Pharmacology Analysis

PPI network analysis of key target genes confirmed STAT3 as a key target with high rank of degree and betweenness centrality (Table 2). Western blot images of whole protein lysates from LPS-treated Raw 264.7 cells showed that SCH treatment (at all concentrations) notably reduced STAT3 phosphorylation ratios (Figure 8a,b). Furthermore, levels of phosphorylated JAK2 (an upstream signaling molecule of STAT3) were also inhibited by SCH in a dose-dependent manner (Figure 8c,d).

4. Discussion

In the present study, we investigated in vitro a means of verifying the key protein targets of a herbal medicine identified by NP analysis. This type of analysis broadens understanding of the pharmacological mechanisms of traditional herbal medicines, which typically include several herbs that each contain compounds of potential interest and allows researchers to identify main targets [5]. Furthermore, NP analysis has attracted much interest in the traditional herbal medicine research field. Although NP has some limitations, such as a lack of information about lesser-known compounds or annotation biases of some predominant genes [36,37], the NP method and its outputs are widely accepted as meaningful by researchers working on traditional herbal medicines [38].
KEGG pathway enrichment analysis conducted on 47 key targets of SCH elucidated 11 human disease-related pathways and 8 signaling pathways, and of these signaling pathways, the TNF and MAPK signaling pathways were most significantly enriched with key targets of SCH. As has been well established, the TNF signaling pathway leads to the activations of NF-κB [39] and MAPKs [40], and we found that SCH pretreatment suppressed LPS-induced NF-κB and MAPK inflammatory responses.
GO enrichment analysis of BP terms revealed ‘response to drug’ as the term most significantly relevant SCH term. This term explains cellular processes associated with exposure to xenobiotics, such as bacterial lipopolysaccharides [41,42]. Key proteins with this BP term targeted by SCH included RELA, CASP3, IL4, IL6, PTGS2, STAT1, and STAT3 (18 genes in total), which could explain the overall attenuating effect of SCH on LPS-induced responses.
Meanwhile, the BP term with the second lowest significance, ‘positive regulation of transcription form RNA polymerase II promoter’, was closely related to pro-inflammatory cytokines (IL4 and IL6), signal transducers and the activator of transcription (STAT1 and STAT3), the transcription factor RELA, and MAPK (JUN, MAPK14) proteins. In addition to the high relevance of ‘transcription factor binding’ among MF terms, nucleoplasm was the most significant CC term related to SCH. Overall, GO enrichment results implied SCH attenuated inflammatory status by regulating the transcriptions of key target genes.
Ingredients of SCH have been studied by several researchers to determine their pharmacological efficacies on various targets related to inflammation. CR extract had a suppressive effect on nitric oxide and superoxide anion production in vitro [43], and neolignan and monoterpene glycoside, components of PS, reduced nitric oxide production in BV2 microglial cells [44]. Additionally, a water extract of TF reduced inflammatory cytokine levels and inhibited the phosphorylations of MAPKs [45], and in a previous study, we found that SCH reduced systematic low-grade inflammation in an obese mouse model and serum IL-6 and IL-1β levels [14]. Ursolic acid, a triterpenoid from TF, was reported to suppress inflammation by targeting NF-κB and STAT3 [46]. Additionally, amentoflavone, a biflavonoid from TF, was reported to inhibit nitric oxide synthase and NF-κB activation in LPS-activated Raw 264.7 cells [47]. These results suggest that SCH might be therapeutically useful for treating LPS-induced acute inflammation, whereas in the present study, we identified its targets by evidence-based NP analysis.
Furthermore, we found that SCH reduced production of NO, which coincides with decreased iNOS expression in protein levels. The observations are necessarily related with decreased activity of transcription factors, which produce pro-inflammatory mediators, including COX-2 and interleukins. As we expected, reductions in the activities of NF-κB and STAT3 were observed through downregulated phosphorylation levels of these proteins.
A mechanism whereby SCH reduced inflammation via JAK2/STAT3 signaling was suggested by network pharmacological analysis, especially PPI network analysis. STAT3 is essentially required for activating the transcriptions of inflammatory mediators, such as iNOS, COX-2, and interleukins, which drive the early cellular inflammatory phase [48]. In addition, IL-6/JAK2/STAT3 signaling is activated in an autocrine or paracrine manner [49] and amplifies signals intracellularly or disperse signals to adjacent cells [50].
SCH-induced reductions in JAK2/STAT3 phosphorylation observed in the current study indicate pro-inflammatory feedback were weakened by SCH treatment. We tentatively suggest that the above-mentioned anti-inflammatory effects of SCH were at least partially attributable to STAT3 inactivation.
The present study demonstrates that the NP-based approach is a useful, preliminary tool for target prediction of herbal drugs with verification data in vitro. Our data suggest that SCH might be used as a drug for various indications related with acute inflammatory response. Differentially expressed gene (DEG) analysis, using gene expression microarray or PCR array data, would provide deeper insight into SCH-specific pathways regulation for target disease. Additional in vivo studies should be performed to reconfirm the efficacy and mechanism of SCH in certain diseases. Additionally, we suggest further study to be conducted to identify other valid pathways and their potential therapeutic indications.

5. Conclusions

NP analysis showed that SCH has several targets that can modulate inflammatory response, and this was confirmed experimentally in Raw 264.7 cells. Anti-inflammatory property of SCH against LPS was attributable to inactivation of the IL6/JAK2/STAT3 pathway. Further in vivo study must be performed to assess pharmacological efficacy of SCH for target diseases.

Author Contributions

D.-W.L. performed the NP analysis and wrote the manuscript. D.-H.K. and G.-R.Y. performed the in vitro study. W.-H.P. supervised the project and contributed to the final draft of the paper. J.-E.K. reviewed the manuscript and supervised the project. All authors read and approved the final version of the manuscript.

Funding

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea by the Ministry of Science and ICT (Grant no. NRF-2019R1F1A1051652). This work was supported by Basic Science Research Program through the National Research Foundation of Korea by the Ministry of Education (Grant no. NRF-2018R1D1A1B07048467).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The authors thank Hyuck Kim for help with discussion of general study design and management.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Li, R.; Li, Y.; Liang, X.; Yang, L.; Su, M.; Lai, K.P. Network Pharmacology and bioinformatics analyses identify intersection genes of niacin and COVID-19 as potential therapeutic targets. Brief. Bioinform. 2021, 22, 1279–1290. [Google Scholar] [CrossRef] [PubMed]
  2. Chandran, U.; Mehendale, N.; Patil, S.; Chaguturu, R.; Patwardhan, B. Network pharmacology. Innov. Approaches Drug Discov. 2017, 127. [Google Scholar]
  3. Li, S.; Zhang, B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin. J. Nat. Med. 2013, 11, 110–120. [Google Scholar] [CrossRef]
  4. Zhang, G.B.; Li, Q.Y.; Chen, Q.L.; Su, S.B. Network pharmacology: A new approach for chinese herbal medicine research. Evid. Based Complement Alternat. Med. 2013, 2013, 621423. [Google Scholar] [CrossRef] [Green Version]
  5. Zhang, R.; Zhu, X.; Bai, H.; Ning, K. Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment. Front. Pharmacol. 2019, 10, 123. [Google Scholar] [CrossRef] [Green Version]
  6. Xu, H.; Zhang, Y.; Wang, P.; Zhang, J.; Chen, H.; Zhang, L.; Du, X.; Zhao, C.; Wu, D.; Liu, F.; et al. A comprehensive review of integrative pharmacology-based investigation: A paradigm shift in traditional Chinese medicine. Acta Pharm. Sin. B 2021, 11, 1379–1399. [Google Scholar] [CrossRef]
  7. Lee, W.Y.; Lee, C.Y.; Kim, Y.S.; Kim, C.E. The Methodological Trends of Traditional Herbal Medicine Employing Network Pharmacology. Biomolecules 2019, 9, 362. [Google Scholar] [CrossRef] [Green Version]
  8. Feng, Y.; Wang, Q.; Wang, T. Drug Target Protein-Protein Interaction Networks: A Systematic Perspective. Biomed. Res. Int. 2017, 2017, 1289259. [Google Scholar] [CrossRef] [Green Version]
  9. Zhang, J.; Liu, X.; Wu, J.; Zhou, W.; Tian, J.; Guo, S.; Jia, S.S.; Meng, Z.; Ni, M. A bioinformatics investigation into the pharmacological mechanisms of the effect of the Yinchenhao decoction on hepatitis C based on network pharmacology. BMC Complementary Med. Ther. 2020, 20, 1–17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Bauer, S.; Grossmann, S.; Vingron, M.; Robinson, P.N. Ontologizer 2.0—a multifunctional tool for GO term enrichment analysis and data exploration. Bioinformatics 2008, 24, 1650–1651. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Wu, W.; Yang, S.; Liu, P.; Yin, L.; Gong, Q.; Zhu, W. Systems Pharmacology-Based Strategy to Investigate Pharmacological Mechanisms of Radix Puerariae for Treatment of Hypertension. Front. Pharmacol. 2020, 11, 345. [Google Scholar] [CrossRef] [PubMed]
  12. Zhou, Z.; Chen, B.; Chen, S.; Lin, M.; Chen, Y.; Jin, S.; Chen, W.; Zhang, Y. Applications of Network Pharmacology in Traditional Chinese Medicine Research. Evid. Based Complement Alternat. Med. 2020, 2020, 1646905. [Google Scholar] [CrossRef] [PubMed]
  13. Song, C.; Baek, T. Effect of Sochehwan extract on indomethacin-induced gastric mucosal lesions in mice. J. Korean Orient. Intern. Med. 2010, 31, 401–414. [Google Scholar]
  14. Lim, D.W.; Kim, H.; Kim, Y.M.; Chin, Y.W.; Park, W.H.; Kim, J.E. Drug repurposing in alternative medicine: Herbal digestive Sochehwan exerts multifaceted effects against metabolic syndrome. Sci. Rep. 2019, 9, 9055. [Google Scholar] [CrossRef]
  15. Sun, J.H.; Sun, F.; Yan, B.; Li, J.Y.; Xin, L. Data mining and systematic pharmacology to reveal the mechanisms of traditional Chinese medicine in Mycoplasma pneumoniae pneumonia treatment. Biomed. Pharmacother. 2020, 125, 109900. [Google Scholar] [CrossRef]
  16. Zheng, W.; Wu, J.; Gu, J.; Weng, H.; Wang, J.; Wang, T.; Liang, X.; Cao, L. Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology-Based Identification. Front. Pharmacol. 2020, 11, 147. [Google Scholar] [CrossRef]
  17. Fiscon, G.; Conte, F.; Farina, L.; Paci, P. SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19. PLoS Comput. Biol. 2021, 17, e1008686. [Google Scholar] [CrossRef]
  18. Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 2014, 6, 13. [Google Scholar] [CrossRef] [Green Version]
  19. Wang, J.; Zhou, H.; Han, L.; Chen, X.; Chen, Y.; Cao, Z. Traditional Chinese medicine information database. Clin. Pharmacol. Ther. 2005, 78, 92–93. [Google Scholar] [CrossRef]
  20. Yea, S.-J.; Kim, H.-Y.; Kim, C.; Kim, J.-H.; Kim, S.-K.; Jang, H.-C.; Song, M.-Y. A Study on the User Needs Analysis to Improve OASIS. Korean J. Orient. Med. 2010, 16, 109–118. [Google Scholar]
  21. Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B. PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Res. 2021, 49, D1388–D1395. [Google Scholar] [CrossRef] [PubMed]
  22. Chou, K.-C.; Wu, Z.-C.; Xiao, X. iLoc-Euk: A multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. PLoS ONE 2011, 6, e18258. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res. 2021, 49, D480–D489. [CrossRef] [PubMed]
  24. Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform. 2016, 54, 1.30. 31–31.30. 33. [Google Scholar] [CrossRef]
  25. Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021, 49, D605–D612. [Google Scholar] [CrossRef] [PubMed]
  26. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
  27. Kanehisa, M.; Furumichi, M.; Sato, Y.; Ishiguro-Watanabe, M.; Tanabe, M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res. 2021, 49, D545–D551. [Google Scholar] [CrossRef]
  28. The Gene Ontology resource: Enriching a GOld mine. Nucleic Acids Res. 2021, 49, D325–D334. [CrossRef]
  29. Jiao, X.; Sherman, B.T.; Huang, D.W.; Stephens, R.; Baseler, M.W.; Lane, H.C.; Lempicki, R.A. DAVID-WS: A stateful web service to facilitate gene/protein list analysis. Bioinformatics 2012, 28, 1805–1806. [Google Scholar] [CrossRef] [Green Version]
  30. Bonnot, T.; Gillard, M.B.; Nagel, D.H. A simple protocol for informative visualization of enriched gene ontology terms. Bio-101 2019, 9, e3429. [Google Scholar] [CrossRef]
  31. Wei, Y.; He, J.; Qin, H.; Wu, X.; Yao, X. Determination of ferruginol in rat plasma via high-performance liquid chromatography and its application in pharmacokinetics study. Biomed. Chromatogr. 2009, 23, 1116–1120. [Google Scholar] [CrossRef] [PubMed]
  32. Yu, S.; Yan, H.; Zhang, L.; Shan, M.; Chen, P.; Ding, A.; Li, S.F. A Review on the Phytochemistry, Pharmacology, and Pharmacokinetics of Amentoflavone, a Naturally-Occurring Biflavonoid. Molecules 2017, 22, 299. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Li, J.J.; Li, Y.; Bai, M.; Tan, J.F.; Wang, Q.; Yang, J. Simultaneous determination of corosolic acid and euscaphic acid in the plasma of normal and diabetic rat after oral administration of extract of Potentilla discolor Bunge by high-performance liquid chromatography/electrospray ionization mass spectrometry. Biomed. Chromatogr. 2014, 28, 717–724. [Google Scholar] [CrossRef]
  34. Shan, C.X.; Guo, S.C.; Yu, S.; Shan, M.Q.; Li, S.F.Y.; Chai, C.; Cui, X.B.; Zhang, L.; Ding, A.W.; Wu, Q.N. Simultaneous Determination of Quercitrin, Afzelin, Amentoflavone, Hinokiflavone in Rat Plasma by UFLC-MS-MS and Its Application to the Pharmacokinetics of Platycladus orientalis Leaves Extract. J. Chromatogr. Sci. 2018, 56, 895–902. [Google Scholar] [CrossRef]
  35. Zhang, C.; Wang, C.; Li, W.; Wu, R.; Guo, Y.; Cheng, D.; Yang, Y.; Androulakis, I.P.; Kong, A.N. Pharmacokinetics and Pharmacodynamics of the Triterpenoid Ursolic Acid in Regulating the Antioxidant, Anti-inflammatory, and Epigenetic Gene Responses in Rat Leukocytes. Mol. Pharm. 2017, 14, 3709–3717. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Xu, J.; Bai, C.; Huang, L.; Liu, T.; Wan, Y.; Zheng, Z.; Ma, X.; Gao, F.; Yu, H.; Gu, X. Network pharmacology to dissect the mechanisms of Yinlai Decoction for pneumonia. BMC Complement Med. Ther. 2020, 20, 168. [Google Scholar] [CrossRef]
  37. Tomczak, A.; Mortensen, J.M.; Winnenburg, R.; Liu, C.; Alessi, D.T.; Swamy, V.; Vallania, F.; Lofgren, S.; Haynes, W.; Shah, N.H.; et al. Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations. Sci. Rep. 2018, 8, 5115. [Google Scholar] [CrossRef]
  38. Zhang, L.; Li, X.-p.; Lai, J.-q.; Zhang, L. Bioinformatics databases for network pharmacology research of traditional chinese medicine: A systematic review. In Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 13–16 November 2017, Kansas City, MO, USA; pp. 1400–1404.
  39. Sabio, G.; Davis, R.J. TNF and MAP kinase signalling pathways. Semin. Immunol. 2014, 26, 237–245. [Google Scholar] [CrossRef] [Green Version]
  40. Winston, B.W.; Lange-Carter, C.A.; Gardner, A.M.; Johnson, G.L.; Riches, D.W. Tumor necrosis factor alpha rapidly activates the mitogen-activated protein kinase (MAPK) cascade in a MAPK kinase kinase-dependent, c-Raf-1-independent fashion in mouse macrophages. Proc. Natl. Acad. Sci. USA 1995, 92, 1614–1618. [Google Scholar] [CrossRef] [Green Version]
  41. Ye, N.; Wang, H.; Li, Q.; Lin, C.; Feng, H.; Lin, S.; Hong, J.; Meng, C. Activation of PXR inhibits LPS-induced NF-κB activation by increasing IκBα expression in HepG2 cells. Mol. Cell. Toxicol. 2018, 14, 93–104. [Google Scholar] [CrossRef]
  42. Gunnett, C.A.; Berg, D.J.; Faraci, F.M.; Feuerstein, G. Vascular effects of lipopolysaccharide are enhanced in interleukin-10-deficient mice. Stroke 1999, 30, 2191–2195, discussion 2195–2196. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Seo, W.G.; Pae, H.O.; Oh, G.S.; Chai, K.Y.; Kwon, T.O.; Yun, Y.G.; Kim, N.Y.; Chung, H.T. Inhibitory effects of methanol extract of Cyperus rotundus rhizomes on nitric oxide and superoxide productions by murine macrophage cell line, RAW 264.7 cells. J. Ethnopharmacol. 2001, 76, 59–64. [Google Scholar] [CrossRef]
  44. Lee, S.R.; Moon, E.; Kim, K.H. Neolignan and monoterpene glycoside from the seeds of Pharbitis nil. Phytochem. Lett. 2017, 20, 98–101. [Google Scholar] [CrossRef]
  45. Kim, B.-J.; Ham, K.-W.; Park, K.-B.; Kim, D.-H.; Jo, B.-Y.; Cho, C.-R.; Cho, G.-H.; Bae, G.-S.; Park, K.-C.; Koo, B.-S. Inhibitory effect of extract of trogopterorum faeces on the production of inflammatory mediaters. Korea J. Herbol. 2009, 24, 153–160. [Google Scholar]
  46. Yadav, V.R.; Prasad, S.; Sung, B.; Kannappan, R.; Aggarwal, B.B. Targeting inflammatory pathways by triterpenoids for prevention and treatment of cancer. Toxins 2010, 2, 2428–2466. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Woo, E.R.; Lee, J.Y.; Cho, I.J.; Kim, S.G.; Kang, K.W. Amentoflavone inhibits the induction of nitric oxide synthase by inhibiting NF-kappaB activation in macrophages. Pharmacol. Res. 2005, 51, 539–546. [Google Scholar] [CrossRef]
  48. Kim, S.H.; Park, S.Y.; Park, Y.L.; Myung, D.S.; Rew, J.S.; Joo, Y.E. Chlorogenic acid suppresses lipopolysaccharideinduced nitric oxide and interleukin1beta expression by inhibiting JAK2/STAT3 activation in RAW264.7 cells. Mol. Med. Rep. 2017, 16, 9224–9232. [Google Scholar] [CrossRef] [Green Version]
  49. Sreenivasan, L.; Wang, H.; Yap, S.Q.; Leclair, P.; Tam, A.; Lim, C.J. Autocrine IL-6/STAT3 signaling aids development of acquired drug resistance in Group 3 medulloblastoma. Cell Death Dis. 2020, 11, 1035. [Google Scholar] [CrossRef]
  50. Tse, L.H.; Wong, Y.H. GPCRs in Autocrine and Paracrine Regulations. Front. Endocrinol. Lausanne 2019, 10, 428. [Google Scholar] [CrossRef]
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Figure 1. (a) Venn diagram showing the distribution of potent active compounds in SCH derived from the three constituent herbs; (b) Venn diagram showing all targets of each constituent contained in SCH. Data were obtained from the TCMSP database. PS—Pharbitis Semen, TF—Trogopterorum Faeces, and CR—Cyperi Rhizoma.
Figure 1. (a) Venn diagram showing the distribution of potent active compounds in SCH derived from the three constituent herbs; (b) Venn diagram showing all targets of each constituent contained in SCH. Data were obtained from the TCMSP database. PS—Pharbitis Semen, TF—Trogopterorum Faeces, and CR—Cyperi Rhizoma.
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Figure 2. PPI (protein-protein interaction) network of 47 key targets of SCH compounds. In total, 47 nodes of key targets and 284 edges of interaction are represented.
Figure 2. PPI (protein-protein interaction) network of 47 key targets of SCH compounds. In total, 47 nodes of key targets and 284 edges of interaction are represented.
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Figure 3. (a) Gene enrichment analysis in the KEGG Pathway. GO enrichment analysis of biological processes (b), cellular components (c), molecular functions, and (d) key targets of SCH. The X-axis shows numbers of genes annotated in each pathway/GO. Sizes of circles represent gene ratios (defined as the number of annotated genes divided by the total number of genes in a given pathway/GO).
Figure 3. (a) Gene enrichment analysis in the KEGG Pathway. GO enrichment analysis of biological processes (b), cellular components (c), molecular functions, and (d) key targets of SCH. The X-axis shows numbers of genes annotated in each pathway/GO. Sizes of circles represent gene ratios (defined as the number of annotated genes divided by the total number of genes in a given pathway/GO).
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Figure 4. Visualization of the compound-target-pathway network of SCH. In total, 47 nodes of key targets (middle circle) and 26 nodes of compounds (outer circle) related to key targets are presented. A total of 20 significant pathways related to 47 key targets are positioned inside the circle as a grid.
Figure 4. Visualization of the compound-target-pathway network of SCH. In total, 47 nodes of key targets (middle circle) and 26 nodes of compounds (outer circle) related to key targets are presented. A total of 20 significant pathways related to 47 key targets are positioned inside the circle as a grid.
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Figure 5. Effect of SCH on Raw 264.7 cell viability and NO production. (a) Effect of SCH on Raw 264.7 cell viability. (b,c) Effects of SCH on LPS-induced nitric oxide production as determined by (b) Griess reaction and (c) DAF-FM DA fluorescence assay. Results are presented as means ± SDs. # p < 0.05 vs. non-treated controls. * p < 0.05 vs. LPS treated cells.
Figure 5. Effect of SCH on Raw 264.7 cell viability and NO production. (a) Effect of SCH on Raw 264.7 cell viability. (b,c) Effects of SCH on LPS-induced nitric oxide production as determined by (b) Griess reaction and (c) DAF-FM DA fluorescence assay. Results are presented as means ± SDs. # p < 0.05 vs. non-treated controls. * p < 0.05 vs. LPS treated cells.
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Figure 6. Effects of SCH on iNOS, COX-2, and mitogen-activated protein kinase (MAPK) activities as determined by western blot in Raw 264.7 cells. (a) Representative immunoblot images of iNOS and COX-2. (b,c) Band intensities of immunoblots of iNOS and COX2 were normalized versus intensities in non-treated cells. (d) Representative immunoblot images of ERK, JNK, and p38 and their phosphorylated forms. (e–g) Band intensities of immunoblots of phosphorylated ERK, JNK, and p38 normalized versus their intensities in non-treated cells. Results are presented as means ± SDs. # p < 0.05, ## p < 0.01 vs. non-treated control cells. * p < 0.05, ** p < 0.01 vs. LPS-treated cells.
Figure 6. Effects of SCH on iNOS, COX-2, and mitogen-activated protein kinase (MAPK) activities as determined by western blot in Raw 264.7 cells. (a) Representative immunoblot images of iNOS and COX-2. (b,c) Band intensities of immunoblots of iNOS and COX2 were normalized versus intensities in non-treated cells. (d) Representative immunoblot images of ERK, JNK, and p38 and their phosphorylated forms. (e–g) Band intensities of immunoblots of phosphorylated ERK, JNK, and p38 normalized versus their intensities in non-treated cells. Results are presented as means ± SDs. # p < 0.05, ## p < 0.01 vs. non-treated control cells. * p < 0.05, ** p < 0.01 vs. LPS-treated cells.
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Figure 7. Effect of SCH on NF-κB activity as demonstrated by western blot and immunofluorescence analysis in LPS-induced Raw 264.7 cells. (a) Representative immunoblot images of NF-κB and IκB-α and their phosphorylated forms in cytosol. (b,c) The band intensities of immunoblots of phosphorylated NF-κB and IκB-α were measured and normalized versus intensities in non-treated cells. (d) Immunofluorescence images showing the effect of SCH treatment on the nuclear localization of NF-κB. Results are presented as means ± SDs. # p < 0.05, ## p < 0.01 vs. non-treated controls and * p < 0.05, ** p < 0.01 vs. LPS-induced cells.
Figure 7. Effect of SCH on NF-κB activity as demonstrated by western blot and immunofluorescence analysis in LPS-induced Raw 264.7 cells. (a) Representative immunoblot images of NF-κB and IκB-α and their phosphorylated forms in cytosol. (b,c) The band intensities of immunoblots of phosphorylated NF-κB and IκB-α were measured and normalized versus intensities in non-treated cells. (d) Immunofluorescence images showing the effect of SCH treatment on the nuclear localization of NF-κB. Results are presented as means ± SDs. # p < 0.05, ## p < 0.01 vs. non-treated controls and * p < 0.05, ** p < 0.01 vs. LPS-induced cells.
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Figure 8. Effect of SCH on the JAK2-STAT3 pathway, as demonstrated by western blot, in LPS-induced Raw 264.7 cells. (a) Representative immunoblot images of STAT3 and its phosphorylated forms in cytosol. (b) STAT3 band intensities in immunoblots were normalized versus the intensities of those of non-treated cells. (c) Representative immunoblot images of JAK2 and its phosphorylated forms in cytosol. (d) Band intensities of JAK2 immunoblots were normalized versus those of non-treated cells. Results are presented as means ± SDs. # p < 0.05, ## p < 0.01 vs. non-treated control cells and * p < 0.05, ** p < 0.01 vs. LPS-induced cells.
Figure 8. Effect of SCH on the JAK2-STAT3 pathway, as demonstrated by western blot, in LPS-induced Raw 264.7 cells. (a) Representative immunoblot images of STAT3 and its phosphorylated forms in cytosol. (b) STAT3 band intensities in immunoblots were normalized versus the intensities of those of non-treated cells. (c) Representative immunoblot images of JAK2 and its phosphorylated forms in cytosol. (d) Band intensities of JAK2 immunoblots were normalized versus those of non-treated cells. Results are presented as means ± SDs. # p < 0.05, ## p < 0.01 vs. non-treated control cells and * p < 0.05, ** p < 0.01 vs. LPS-induced cells.
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Table
Table 1. List of potent active compounds from three herbs. PS—Pharbitis Semen, TF—Trogopterorum Faeces, and CR—Cyperi Rhizoma.
Table 1. List of potent active compounds from three herbs. PS—Pharbitis Semen, TF—Trogopterorum Faeces, and CR—Cyperi Rhizoma.
SourceMolecule NamePubchem_CIDMWOBDL
PS, CR8-isopentenyl-kaempferol129716399354.3838.040.39
beta-sitosterol222284414.7936.910.75
sitosterol12303645414.7936.910.75
PSgibberellin6466346.4181.590.53
gibberellin A2914605548348.4392.380.53
gibberellin A44440915346.46101.610.54
anhydroicaritin44259058368.4145.410.44
phaseollidin119268324.452.040.53
penniclavin115247270.3648.150.31
chanoclavine5281381256.3862.640.18
lysergol72576078254.3648.110.27
agroclavin73484238.3647.710.24
elymoclavine16758153254.3672.870.27
rhein10168284.2347.070.28
CRisorhamnetin5281654316.2849.60.31
chryseriol5280666300.2835.850.27
isodalbergin5318543268.2835.450.2
khellin3828260.2633.190.19
resivit440833306.2930.840.27
rosenonolactone11723309316.4879.840.37
hyndarin72301355.4773.940.64
stigmasterol glucoside6602508412.7743.830.76
sugeonyl acetate46173924276.4145.080.2
kaempferol5280863286.2541.880.24
stigmasterol5280794412.7743.830.76
luteolin5280445286.2536.160.25
quercetin5280343302.2546.430.28
TFferruginol442027286.536.070.25
tormentic acid73193488.7811.40.71
euscaphic acid73193528.8511.50.46
ursolic acid64945456.7816.770.75
amentoflavone5281600538.482.950.65
afzelin5316673432.413.830.7
quercitrin5280459448.414.040.74
Table
Table 2. Degree and betweenness centrality of the 47 key target genes targeted by SCH compounds as predicted by the PPI network structure.
Table 2. Degree and betweenness centrality of the 47 key target genes targeted by SCH compounds as predicted by the PPI network structure.
GeneTarget NameDegreeBetweenness Centrality
STAT3Signal transducer and activator of transcription 3480.082929
MAPK1Mitogen-activated protein kinase 1440.082544
TP53Cellular tumor antigen p53440.055357
AKT1RAC-alpha serine/threonine-protein kinase430.062232
APPAmyloid beta A4 protein420.111229
JUNTranscription factor AP-1420.057458
TNFTumor necrosis factor370.038453
HSP90AA1Heat shock protein HSP 90360.074078
RELATranscription factor p65350.032286
CXCL8Interleukin-8340.040594
ANXA1Annexin A1310.027147
IL6Interleukin-6280.036648
MAPK14Mitogen-activated protein kinase 14280.022753
MAPK8Mitogen-activated protein kinase 8280.031284
VEGFAVascular endothelial growth factor A280.036532
CCND1G1/S-specific cyclin-D1270.02643
FOSProto-oncogene c-Fos270.018797
RB1Retinoblastoma-associated protein270.022973
ADCY2Adenylate cyclase type 2260.029752
CDKN1ACyclin-dependent kinase inhibitor 1250.012842
EGFREpidermal growth factor receptor250.06262
ESR1Estrogen Receptor 1240.021029
PRKCAProtein kinase C alpha type240.037977
MYCMyc proto-oncogene protein230.008995
NR3C1Nuclear Receptor Subfamily 3 Group C Member 1230.021282
CDK1Cell division control protein 2 homolog220.020226
RXRARetinoic acid receptor RXR-alpha220.053081
IL4Interleukin-4210.035382
CASP8Caspase-8200.039469
EGFPro-epidermal growth factor200.019467
PRKCBProtein kinase C beta type200.014805
DRD2D(2) dopamine receptor190.008889
NCOA1Nuclear receptor coactivator 1190.039744
PRKACAmRNA of PKA Catalytic Subunit C-alpha180.029475
STAT1Signal transducer and activator of transcription 1-alpha/beta180.013037
ARAndrogen receptor170.010352
CASP3Caspase-3160.016571
MMP9Matrix metalloproteinase-9160.025378
PPARAPeroxisome proliferator-activated receptor alpha150.024536
ADRB2Beta-2 adrenergic receptor140.019124
CHRM1Muscarinic acetylcholine receptor M1140.011633
PSMD326S proteasome non-ATPase regulatory subunit 3140.011488
HTR2A5-hydroxytryptamine 2A receptor130.008889
PTPN6Tyrosine-protein phosphatase non-receptor type 6120.01082
CYP1A1Cytochrome P450 1A1110.060846
MMP3Stromelysin-1100.010101
PTGS2Prostaglandin G/H synthase 2100.013843
Table
Table 3. Top 20 KEGG pathways enriched with 47 key targets of SCH, as determined using the DAVID database.
Table 3. Top 20 KEGG pathways enriched with 47 key targets of SCH, as determined using the DAVID database.
Pathway IDPathway NameGene Countp-ValueClass
Hsa05200Pathways in cancer287.30 × 10−23Human Diseases; Cancer: overview
Hsa05161Hepatitis B206.40 × 10−21Human Diseases; Infectious disease: viral
Hsa05205Proteoglycans in cancer182.10 × 10−15Human Diseases; Cancer: overview
Hsa05219Bladder cancer114.80 × 10−14Human Diseases; Cancer: specific types
Hsa05160Hepatitis C155.10 × 10−14Human Diseases; Infectious disease: viral
Hsa05212Pancreatic cancer121.80 × 10−13Human Diseases; Cancer: specific types
Hsa04468TNF signaling pathway132.00 × 10−12Environmental Information Processing; Signal transduction
Hsa04010MAPK signaling pathway162.60 × 10−11Environmental Information Processing; Signal transduction
Hsa05223Non-small cell lung cancer105.70 × 10−11Human Diseases; Cancer: specific types
hsa04919Thyroid hormone signaling pathway121.10 × 10−10Organismal Systems; Endocrine system
Hsa05215Prostate cancer111.50 × 10−10Human Diseases; Cancer: specific types
Hsa05214Glioma102.30 × 10−10Human Diseases; Cancer: specific types
hsa04066HIF-1 signaling pathway113.70 × 10−10Environmental Information Processing; Signal transduction
Hsa04917Prolactin signaling pathway105.20 × 10−10Organismal Systems; Endocrine system
Hsa04620Toll-like receptor signaling pathway111.00 × 10−9Organismal Systems; Immune system
Hsa04621NOD-like receptor signaling pathway92.00 × 10−9Organismal Systems; Immune system
Hsa04151PI3K-Akt signaling pathway162.10 × 10−9Environmental Information Processing; Signal transduction
hsa04540Gap junction103.70 × 10−9Cellular Processes; Cellular community-eukaryotes
hsa05140Leishmaniasis91.40 × 10−8Human Diseases; Infectious disease: parasitic
hsa05142Chagas disease101.60 × 10−8Human Diseases; Infectious disease: parasitic
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Lim, D.-W.; Kim, D.-H.; Yu, G.-R.; Park, W.-H.; Kim, J.-E. Verification of the Potential Targets of the Herbal Prescription Sochehwan for Drug Repurposing Processes as Deduced by Network Pharmacology. Processes 2021, 9, 2034. https://doi.org/10.3390/pr9112034

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Lim D-W, Kim D-H, Yu G-R, Park W-H, Kim J-E. Verification of the Potential Targets of the Herbal Prescription Sochehwan for Drug Repurposing Processes as Deduced by Network Pharmacology. Processes. 2021; 9(11):2034. https://doi.org/10.3390/pr9112034

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Lim, Dong-Woo, Da-Hoon Kim, Ga-Ram Yu, Won-Hwan Park, and Jai-Eun Kim. 2021. "Verification of the Potential Targets of the Herbal Prescription Sochehwan for Drug Repurposing Processes as Deduced by Network Pharmacology" Processes 9, no. 11: 2034. https://doi.org/10.3390/pr9112034

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