The Mechanism of Ermiao Powder In Treating Prostatitis: A Study Based on Network Pharmacology and Experimental Verification

Summary

Employing a network pharmacology approach, this study revealed that Ermiao Powder exerts its therapeutic effects against prostatitis by inhibiting inflammatory factors and associated signaling pathways. Subsequent in vitro experiments further demonstrated that its aqueous extract significantly suppressed the expression of key inflammatory cytokines in an LPS-induced model of prostate epithelial cells.

Abstract

This protocol presents a combined network pharmacology and experimental validation approach to investigate the mechanism of action of Ermiao Powder (EMP) in the treatment of prostatitis. The workflow follows a stepwise design: (i) database-driven screening of active compounds and targets, (ii) construction and analysis of interaction networks, and (iii) in vitro experimental validation. First, active ingredients of EMP and their corresponding targets were retrieved from the TCMSP database, while prostatitis-related targets were obtained from the GeneCards database. The intersection of EMP targets and disease targets was used to identify key therapeutic targets. Second, a “Drug–Active Ingredient–Target–Disease” network was constructed using Cytoscape. Protein–protein interaction (PPI) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment were performed on the key targets, revealing that inflammatory factors such as IL6 and TNF served as core targets, and pathways including PI3K/AKT and MAPK were significantly enriched. Finally, in vitro validation was conducted using an LPS-induced inflammatory model of prostate epithelial cells. Treatment with EMP significantly reduced the release of pro-inflammatory factors IL-6 and TNF-α compared to the LPS-treated group (p < 0.05), confirming the anti-inflammatory efficacy of EMP. This integrative approach provides a reproducible framework that can be adapted to investigate other multi-component therapeutic systems. In conclusion, this study preliminarily elucidates that EMP exerts its anti-prostatitis effects through a multi-component, multi-target, and multi-pathway mechanism, closely associated with the suppression of inflammatory responses, thereby offering a scientific basis for its clinical application.

Introduction

Prostatitis, particularly chronic non-bacterial prostatitis, represents a prevalent urological disorder characterized by complex etiology and a lack of highly effective therapies, imposing a significant burden on patients' quality of life^1,2,3. Current clinical management of prostatitis employs an integrated approach including antibiotics, α-receptor blockers, and non-steroidal anti-inflammatory drugs^4,5,6. The principal challenges include the absence of etiology-specific therapies and the complex interplay between psychological factors and physical symptoms, which collectively contribute to unsatisfactory treatment outcomes and frequent recurrence⁷. In recent years, integrative medicine approaches, such as acupuncture and herbal medicine, have been widely applied in the management of prostatitis and have demonstrated promising efficacy. The safety and efficacy of these therapeutic approaches have been substantiated by a series of high-quality research evidence^8,9,10,11,12.

In this context, traditional Chinese medicine (TCM) formulations like Ermiao Powder (EMP), known for their anti-inflammatory and detoxifying properties, offer a promising alternative therapeutic approach^13,14,15. However, the precise pharmacological mechanisms underlying the efficacy of EMP against prostatitis remain incompletely elucidated, primarily due to its multi-component nature. The advent of network pharmacology provides a powerful tool for deciphering the complex "multi-component, multi-target, multi-pathway" mode of action characteristic of TCM formulas^16,17. Our strategy involved identifying the active components of EMP and its potential targets, followed by network analysis to pinpoint key targets and pathways. Subsequent experiments were conducted to biologically validate the anti-inflammatory effects and core mechanisms, aiming to establish a scientific basis for the application of EMP. This study introduces a combined network pharmacology-experimental validation framework that enhances the mechanistic interpretation of multi-component therapies

Protocol

Ethical approval was not required for this study, as it did not involve human participants or clinical samples. All procedures involving cell lines were conducted in accordance with institutional biosafety guidelines and regulations. The prostate epithelial cell line used in this study was obtained from a certified commercial supplier.

NOTE: All reagents, instruments, and software used in this study are listed in the Table of Materials. The flowchart of this study is presented in Figure 1.

1. Screening of active ingredients

1. Access the TCMSP database.
2. Select "Herb name" in the search box, enter "Huangbo", and click "Search" .
3. Click the “OB” filter, set “≥30”, and apply.
4. Click the “DL” filter, set “≥0.18”, and apply.
5. Copy the filtered results to an Excel spreadsheet.
6. Apply the same screening method to obtain the active ingredients of "Cangzhu".
7. Remove the duplicate active ingredients by importing all datasets into Microsoft Excel (version 2019) and merging the compound lists in Excel format.
8. Remove duplicate active ingredients by standardizing compound names and identifying identical entries. When multiple records corresponded to the same compound, only one representative entry was retained.
9. Establish associations between "Huangbo", "Cangzhu", and active ingredients.

2. Obtain the targets corresponding to the active ingredients

1. Access the TCMSP database.
2. Select "Chemical name" in the search box, enter the active ingredients, and click "Search".
3. Copy the Target name corresponding to each active ingredient.
4. Remove duplicate Target names and establish associations between active ingredients and Target names.
5. Access the UniProt database (https://www.uniprot.org/)¹⁸, retrieve the official gene symbols for each Target name, and systematically replace them.

3. Acquisitions of disease targets

1. Access the GeneCards database (https://www.genecards.org)¹⁹.
2. Enter “Prostatitis” in the search field and press the search icon to obtain the relevant gene list.
3. Sort genes in descending order by Relevance score.
4. Export the relevant genes in Excel format.
5. Targets with a Score ≥1 were selected as prostatitis-related targets.

4. Preparation for the construction of a drug-active ingredient-target-disease regulatory network

1. Obtain the intersection between the targets related to the active ingredients and the prostatitis-related targets, and define it as the targets of EMP for treating prostatitis.
2. Establish the association between the active ingredients and therapeutic targets of EMP for prostatitis.
3. Create an Excel file named "Network" with the first three columns named "Node1", "Node2", and "Net".
4. Paste the associations between EMP and the drug into the "Node1" and "Node2" columns, while entering "drug" in the "Net" column for all rows.
5. Paste the drug-active ingredient associations into the "Node1" and "Node2" columns, while entering "ingredient" in the "Net" column for all rows.
6. Populate the "Node1" and "Node2" columns with the active ingredient-target associations for targets of EMP for treating prostatitis, with "target" entered in the "Net" column.
7. Paste the therapeutic targets of EMP for prostatitis into the "Node1" column, while entering "prostatitis" in the "Node2" column and "disease" in the "Net" column for all rows.
8. Save the Excel file "Network" as a txt format file.
9. Create an Excel file named "Type" with the first two columns named "Node" and "Type".
10. Populate the "Node" column with "EMP", with "formula" entered in the "Type" column.
11. Enter "Huangbo" and "Cangzhu" into the "Node" column; enter "drug" in the "Type" column for all rows.
12. Enter active ingredients into the "Node" column, while entering "ingredient" in the "Type" column for all corresponding rows.
13. Populate the "Node" column with the therapeutic targets of EMP for prostatitis, with "target" entered in the "Type" column.
14. Enter the prostatitis-related targets into the "Node" column, while entering "disease" in the "Type" column for all corresponding rows.
15. Save the Excel file "Type" as a .txt format file.

5. Visualization of the "Drug-Active Ingredient-Target-Disease" regulatory network by Cytoscape

1. Launch Cytoscape.
2. Go to “File > Import > Network from File System” and open the “network.txt” file.
3. Open the “Style” panel. Select “Shape”, choose “Type” from the “Column” dropdown, and set “Mapping Type” to “Discrete Mapping”.
4. Assign distinct node shapes to the following categories: formula, drug, ingredient, target, disease.
5. Select “Fill Color”, choose “Type” from the “Column” dropdown, and set “Mapping Type” to “Discrete Mapping”.
6. Assign distinct node colors to the following categories: formula, drug, ingredient, target, disease.
7. Select “Height” and “Width”, choose “Type” from the “Column” dropdown, and set “Mapping Type” to “Discrete Mapping”.
8. Assign distinct node sizes to the following categories: formula, drug, ingredient, target, disease.
9. Select “Label Font Size”, choose “Type” from the “Column” dropdown, and set “Mapping Type” to “Discrete Mapping”.
10. Assign distinct label font sizes to the following categories: formula, drug, ingredient, target, disease.
11. Manually reposition nodes to optimize the network layout.
12. Go to “File > Export > Network Image to File” and save the network as an image.

6. PPI network analysis and visualization

1. Access https://cn.string-db.org/ and go to the “Multiple Proteins” module.
2. Enter the target gene list in the search box, select “Homo sapiens” as the organism, and click “Search”.
3. After the results load, click “Continue”.
4. Open the “Settings” panel, set “minimum required interaction score” to “high confidence (0.700)”, and check “hide disconnected nodes in the network”.
5. Click “Update”, then export the PPI association data in TSV format via the “Export” menu.
6. Launch Cytoscape, navigate to the File menu, and select "Import Network from File System" to open your TSV file.
7. Navigate to the “Tools” menu, then select “NetworkAnalyzer > Network Analysis > Network Interpretation > Treat the network as undirected”.
8. Navigate to the “File” menu, select “Export Table to the file” system, and export the network data.
9. Navigate to the “Style” panel and configure the “Shape”, “Fill Color”, “Height”, and “Width” attributes.
10. Adjust the PPI network layout by repositioning the nodes.
11. Navigate to the “File” menu, select “Export Network Image to File”, and export the visualized network as an image.
12. Open the exported PPI network data with WPS and sort it in descending order by the "Degree" column.
13. Select the top 20 sorted targets, generate a bar chart, and save the chart as an image.

7. KEGG pathway enrichment analysis

1. Open a terminal or command prompt, type R, and press Enter.
2. In the R console, run the following commands one by one:
   install.packages(c("RSQLite", "colorspace", "stringi", "BiocManager"))
   BiocManager::install(c("org.Hs.eg.db", "DOSE", "clusterProfiler", "pathview"))
3. Convert the Gene Symbols of the targets of EMP for treating prostatitis to Entrez IDs using org.Hs.eg.db.
4. Read the Entrez IDs and perform KEGG enrichment analysis using the clusterProfiler package with parameters: organism = "hsa", pvalueCutoff = 0.05, qvalueCutoff = 0.05.
5. After removing human disease and metabolism-related pathways from the obtained KEGG enrichment analysis results, sort the remaining pathways according to the count of enriched genes.
6. Establish the association between the top 15-ranked signaling pathways and the targets.
7. Prepare a two-column file with "pathway" and "target" associations, then import it into Cytoscape via “File > Import > Network from File”.
8. Set node shapes and colors to distinguish pathways from targets using the “Style” panel.
9. Adjust the layout manually and export the network as an image.

8. Pharmaceutical preparation

NOTE: The formulation EMP is composed of 15 g of Phellodendri Chinensis Cortex (Huangbo) and 15 g of Atractylodis Rhizoma (Cangzhu). The water extraction and alcohol precipitation method was employed to obtain the extract for subsequent cell-based experiments²⁰.

1. Immerse the drug in a 10-fold mass of water for 1 h.
2. Decoct for 1 h, then filter through gauze to obtain the decoction.
3. Add a 10-fold mass of water to the drug again, decoct for 30 min, and filter through gauze to obtain the decoction.
4. Add a 10-fold mass of water to the drug once more, decoct for 30 min, and filter through gauze to obtain the decoction.
5. Combine the three filtered decoctions, then filter the mixture through filter paper.
6. Concentrate the decoction under reduced pressure to 10% of its original volume.
7. Cool the concentrate, slowly add ethanol to 75% with stirring, followed by sealing and storage at 4 °C for 12 h.
8. Centrifuge the mixture and collect the supernatant.
9. Continue with distillation under reduced pressure.
10. Finally, lyophilize the product using a vacuum freeze-dryer.
    NOTE: The lyophilized powder can be stored at –20ºC for up to 3 months, which serves as a safe pause point. To resume, reconstitute the powder in an appropriate solvent as needed.

9. Cell thawing and culture

NOTE: The purchased cells were delivered to the laboratory on dry ice. Prior to the commencement of the experiment, Keratinocyte Serum Free Medium (K-SFM) was prepared in advance by supplementing it with human recombinant epidermal growth factor (rEGF) to a final concentration of 5 ng/mL, bovine pituitary extract to a final concentration of 50 µg/mL, and 1% Penicillin-Streptomycin.

1. Activate the water bath and adjust the temperature to 37 °C.
2. Remove the cells from the dry ice and immediately agitate them in the water bath until fully thawed.
3. Add 5 mL of K-SFM to the cells. Mix gently, and then centrifuge the mixture (4 °C, 500 × g, 5 min).
4. Remove the supernatant and re-suspend the pellet in complete K‑SFM.
5. Transfer the cells to a T-25 flask and place them in a cell culture incubator.
   NOTE: The environmental parameters of the cell culture incubator were maintained at 37 °C, 5% CO₂, and ≥ 95% relative humidity.

10. Cell subculturing

NOTE: Subculturing was performed when cells reached 80–90% confluence, with a subculture ratio of 1:2 to 1:3.

1. Aspirate the culture medium supernatant, followed by two washes of the cells with PBS without calcium and magnesium.
2. Introduce 2 mL of trypsin-EDTA and let the cells sit for 3 min for enzymatic dissociation.
   CAUTION: This protocol involves the use of hazardous reagents, including lipopolysaccharide (LPS), chloroform, ethanol, and TRIzol reagent. All procedures involving hazardous chemicals should be performed in a certified chemical fume hood while wearing appropriate personal protective equipment (lab coat, gloves, and eye protection). Biological materials should be handled in accordance with institutional biosafety guidelines.
3. Add K-SFM medium supplemented with 10% fetal bovine serum to terminate the digestion.
4. Transfer the cell mixture to a 15 mL centrifuge tube, centrifuge (4 °C, 500 × g, 5 min), and remove the supernatant.
5. The cell pellet was resuspended in complete K-SFM and subsequently transferred into new culture flasks.

11. Determination of maximum drug treatment concentration via MTS assay

NOTE: Each drug concentration was tested with 5 technical replicates.

1. Digest RWPE-1 cells in the logarithmic growth phase.
2. Resuspend the cells in complete K-SFM medium, then determine the cell concentration by counting.
3. Adjust the cell suspension to a density of 1 × 10⁵ cells/mL with complete K-SFM medium.
4. Dispense the cell suspension into a 96-well plate with 100 µL per well.
5. The 96-well plate was placed in a cell culture incubator for cultivation.
6. After 24 h, once the cells had fully adhered, the culture medium was aspirated and discarded.
7. Replace the medium with complete K-SFM medium containing different concentrations of EMP solution.
8. Place the plate back into the cell culture incubator for another 24 h of incubation.
9. Aspirate the medium, then rinse the cells twice using PBS.
10. Add 100 µL of complete K-SFM medium to each well, then incubate the plate in the cell culture incubator for another 4 h.
11. Dispense 10 µL of MTS solution into each well, and determine the OD at 490 nm following a 1 h incubation.
12. Using the blank control group (cells cultured in complete medium without LPS stimulation or drug treatment) as the baseline, cell proliferation under each treatment concentration was calculated.
    NOTE: The highest concentration exhibiting no significant effect on cell proliferation was defined as the high-dose treatment, followed by its half concentration as the medium-dose treatment, and quarter concentration as the low-dose treatment.

12. Cellular intervention

1. Digest RWPE-1 cells in the logarithmic growth phase.
2. The cells were resuspended in complete K-SFM medium, and the cell concentration was determined by cell counting.
3. The cell suspension was adjusted to a density of 5 × 10⁵ cells/mL using complete K-SFM medium.
4. The cell suspension was seeded into a 6-well plate at 2 mL per well.
5. The plate was placed in a culture incubator and incubated for 12 h to allow for cell adhesion.
6. Cells were treated with low (150 µg/mL), medium (300 µg/mL), and high-dose (600 µg/mL) EMP concentrations, respectively.
7. After 1 h, 10 µg/mL LPS solution was added to the culture medium.
8. Following 24 h of treatment, the supernatant was collected for ELISA, and the cells were harvested for RT-PCR analysis.
   NOTE: LPS was selected to induce an inflammatory response due to its well-established ability to activate pro-inflammatory signaling pathways in prostate epithelial cells; the concentration (10 µg/mL) was chosen based on previous studies demonstrating robust cytokine induction.

13. Isolation of total RNA

NOTE: To prevent RNA degradation, gloves and masks were worn throughout the experimental procedure, and RNase-free pipette tips and centrifuge tubes were used.

1. Following the intervention, rinse the cells twice with PBS.
2. Add 1 mL of Trizol reagent to the cells and mix it with a pipette repeatedly to ensure homogenization.
   CAUTION: Performing all procedures in a chemical fume hood while wearing appropriate personal protective equipment.
3. Transfer the lysate to a 1.5 mL microcentrifuge tube and allow it to incubate at room temperature for 15 min.
4. Subsequently, add 0.2 mL of chloroform and vortex the sample vigorously.
5. After letting it stand at room temperature for 5 min, centrifuge the sample at 10,000 × g for 15 min at 4 °C.
6. After centrifugation, carefully transfer the upper colorless aqueous phase to a fresh microcentrifuge tube.
7. Introduce an equal volume of pre-cooled isopropanol, mix thoroughly, then incubate at room temperature for 10 min.
8. Centrifuge the sample once more (4 °C, 10,000 × g, 10 min), afterward discard the liquid phase and retain the precipitate.
9. Rinse the pellet with 1 mL of pre-cooled 75% ethanol and gently resuspend by pipetting.
10. Perform a final centrifugation at 4 °C and 7,500 × g for 10 min, then remove the supernatant and allow the RNA pellet to air-dry in a biosafety cabinet.
11. Dissolve the purified RNA in 50 µL of DEPC-treated water.
12. Determine the RNA concentration, A260/A280 ratio, and A260/A230 ratio using a NanoDrop spectrophotometer to evaluate RNA purity and ensure minimal protein and solvent contamination prior to downstream analyses.

14. RT-PCR

1. Use 1 µg of total RNA to prepare a 20 µL reverse transcription reaction system.
2. Incubate the reaction system at 42 °C for 15 min, followed by 95 °C for 5 min, then cool to 4 °C.
3. Dilute the resulting cDNA 10-fold using distilled water.
4. Prepare a 20 µL PCR reaction system using the diluted cDNA.
5. Program the thermal cycler and set an initial hold at 95 °C for 60 s, followed by 40 cycles of 95 °C for 15 s and 60 °C for 60 s.
6. Perform a melting curve analysis and record the CT values for all target genes and reference genes after completion.
7. Use the 2^(-ΔΔCT) method to determine the normalized expression levels of the target genes.

15. ELISA assay

NOTE: Retrieve the ELISA kit from storage and leave it to equilibrate at room temperature for 30 min prior to use.

1. Prepare the standard solution according to the manufacturer's instructions, and perform appropriate dilutions for standards, biotinylated antibodies, enzyme conjugates, and wash buffer.
2. Centrifuge the collected cell culture supernatant (4 °C, 10,000 × g, 10 min) and collect the supernatant.
3. Transfer 100 µL of the standard dilutions and test samples to the pre-coated wells.
4. Seal the wells with an adhesive strip and incubate the plate at 37 °C for 90 min.
5. Discard the liquid from the wells and wash the plate using an automated plate washer.
   NOTE: Set the automated washer parameters to: 350 µL wash buffer per well, 15-second soak time between dispensing and aspiration, 5 complete cycles.
6. Dispense 100 µL of biotinylated antibody working solution into each well. Allow to incubate at 37 °C for 60 min.
7. Discard the solution and wash the plate as described in 15.5.
8. Pipette 100 µL of enzyme conjugate working solution per well, then incubate at 37 °C for 60 min.
9. Discard the solution and wash the plate as described in 15.5.
10. To each well, add 100 µL of chromogenic substrate. Maintain at 37 °C for 15 min while protecting from light.
11. Pipette 100 µL of stop solution per well, then gently mix.
12. Immediately measure the optical density (OD) at 450 nm.
13. Establish a standard curve by plotting the mean OD values against the standard concentrations using Curve Expert software.
14. Determine the concentration of each sample by interpolation from the standard curve using its OD value.

16. Statistical analysis

1. Operate the SPSS software (Version 20.0).
2. Import the dataset into SPSS Statistics and define variables appropriately. Use Analyze > Descriptive Statistics> Explore to calculate the mean and standard deviation (SD).
3. Use Analyze > Compare Means > One-Way ANOVA for multiple groups comparisons.
4. Obtain p-values directly from the test output tables and report them accordingly.
5. Present all results as mean ± SD and consider differences statistically significant when p < 0.05.

Results

Identification of active compounds and target prediction

These results demonstrate the effectiveness of our integrated network pharmacology and in vitro approach for identifying and validating EMP’s therapeutic targets in prostatitis. A search of the TCMSP database identified 46 bioactive components from the constituent herbs of EMP, corresponding to 178 potential targets. From the Genecards database, 13,960 prostatitis-related targets were acquired. The 162 targets c...

Discussion

This protocol integrates network pharmacology with experimental validation to provide a systematic framework for investigating multi-component therapeutic mechanisms. Critical steps include accurate screening of active compounds using defined thresholds (OB ≥ 30%, DL ≥ 0.18), reliable network construction, and appropriate selection of experimental validation models. Careful parameter selection in bioinformatics analyses and strict control of experimental conditions are essential to ensure reproducibility.

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Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Human IL-6 ELISA KIT	4Abio	CHE0009
Human TNF-α ELISA KIT	4Abio	CHE0019
K-SFM supplements	Procell	CM-0200
Lipopolysaccharides	Solarbio	L8880
MTS	Promega	G1111
Reverse Transcription System	Promega	A3500
RWPE-1	Procell	CL-0200	RRID:CVCL_EQ24
RWPE-1 Cell Complete Medium	Procell	CM-0200
SYBR Green Realtime PCR Master Mix	Toyobo	QPK-201
Trizol reagent	Thermo Fisher Scientific (Invitrogen)	15596026
trypsin-EDTA	Gibco (Thermo Fisher)	25200072