Male C57/BL6 mice (6–8 weeks old, weighing 18–20g) were purchased from the Animal Center of Southern Medical University (Guangzhou, China). The mice were housed in a Specific Pathogen-Free (SPF) facility with ad libitum access to food and water. They were randomly assigned to three groups (5 mice in each group): the sham surgery group, the CFA group, and the CFA + ponicidin group. An inflammatory pain model was established in mice by subcutaneous injection of CFA (20 μL, Sigma, St. Louis, MO), while the sham surgery group received an equivalent volume of saline. The CFA + ponicidin group received intraperitoneal injections of ponicidin (10 mg/kg, MedChemExpress, USA) at 8, 24, and 48 h post-CFA injection. On the third day following CFA injection, mice spinal cords or hind paw subcutaneous tissues were collected for experimentation. All animal studies were approved by the Experimental Animal Ethics Committee of the Experimental Animal Center at Southern Medical University (IACUC-LAC-20240618-008).

Behavioral tests in mice were conducted 1 day before modeling and on days 1, 3, 5, and 7 post-modeling. Mice were acclimated to the testing environment for at least 2 days prior to baseline testing. The mechanic threshold of mice was assessed using the Von Frey monofilament test. Mice were placed in a box with an elevated metal mesh floor and allowed to acclimate for 30 min before testing. The Von Frey filament was applied perpendicularly to the plantar surface of the mouse's paw. Each mouse was tested three times, and the average threshold was taken, with a minimum 10-min rest period between each test. Thermal sensitivity was evaluated using the Hargreaves apparatus (Ugo Basile), which applies infrared heat to the plantar surface of the hind paw, measuring the paw withdrawal latency. A cutoff time of 20 s was set. The thermal test was repeated three times at 20-min intervals, and the average value was taken. Investigators were blinded to the behavioral group assignments.

The left hind paw subcutaneous tissues from mice of different treatment groups were lysed and cytokine levels were determined. The concentrations of IL-1β (Proteintech, KE10003), TNF-α (Proteintech, KE10002), and IL-6 (Proteintech, KE10007) levels were measured by ELISA according to the manufacturer's instructions. Optical density (OD) measurements were taken at 450 nm using a microplate reader (Thermo Scientific).

On the third day following CFA injection, the left hind paw subcutaneous tissues of mice were harvested for experimentation. Mice were anesthetized with isoflurane (2%) and sequentially perfused with 37°C saline and a 4% paraformaldehyde solution (pH 7.4; 4°C). The tissue was then immediately excised and fixed in 10% buffered formalin for 48 h to prepare for routine paraffin histological examination. Paraffin-embedded sections of 5 μm thickness from different groups were stained with H&E.

Samples of hind paw subcutaneous tissue and corresponding L4-5 spinal cord were sectioned into 20-μm-thick cryosections using a cryostat microtome. The tissue sections were fixed in 4% paraformaldehyde (Solarbio, China) at room temperature for 10 min, then permeabilized and blocked with 0.5% Triton X-100 (Sigma-Aldrich) and 3% bovine serum albumin (BSA, Solarbio, China) at room temperature for 1 h. Subsequently, the sections were incubated with diluted primary antibodies against CD206 (1:500 dilution, Proteintech 18,704-1-AP), Iba1 (1:1000 dilution, Wako 559-24761), calcitonin gene-related peptide (CGRP) (1:100 dilution, Santa Cruz SC57053), GFAP (1:100 dilution, Proteintech 16,825-1-AP), and inducible nitric oxide synthase (iNOS; 1:200 dilution, Proteintech 18,985-1-AP). The sections were then incubated with appropriate secondary antibodies (1:500 dilution, Alexa Fluor 488-labeled goat anti-rabbit, mouse IgG, Jackson Immuno Research, West Grove, PA) at room temperature for 1 h. Finally, the slides were mounted with DAPI (4′,6-diamidino-2-phenylindole) containing anti-fade fluorescence mounting medium. Images were acquired using an upright manual fluorescence microscope (Zeiss, Imager D2, Germany) and a confocal laser scanning microscope system (Zeiss, LSM 980, Germany), and then processed using Adobe Photoshop 8.0 software (Adobe Systems, Mountain View, CA). Fluorescence images of iNOS, CD206, CGRP, and IBA1 were semi-quantitatively analyzed using ImageJ software version 1.8.0 (National Institutes of Health, Bethesda, MA, USA).

Based on the findings, we selected the highest degree node in the PPI network as the receptor for docking with the ligand ponicidin. The structure of the target protein was retrieved from the Protein Data Bank (PDB; http://www.rcsb.org/) and subsequently processed and docked using the AutoDockVina software. The level of binding free energy was used as the evaluation standard of the binding degree of compounds. As a result, a binding energy ≤ −7.0 kJ/mol was used as the screening criterion. The results were then imported into the PyMOL software for visualization analysis.

The BV2 cell line was sourced from Servicebio Biological Technology Co. Ltd (Wuhan, China). The immortalized mouse BV2 microglial cell line was cultured in Dulbecco's Modified Eagle's Medium (DMEM, Gibco, USA) supplemented with 10% fetal bovine serum (FBS, Gibco, USA) at 37°C in a humidified atmosphere containing 5% carbon dioxide. Upon reaching 50% confluence, the cells were treated with phosphate-buffered saline (PBS, Gibco, USA) or ponicidin and incubated for 24 h. Subsequently, the BV2 cells were rinsed with PBS and treated with lipopolysaccharide (LPS, Sigma, USA) at a concentration of 1 μg/mL for an additional 24 h.

Initially, cytotoxicity assays were conducted to ascertain the non-toxic concentrations of the extract. A total of 1.0 × 10⁵ cells per well were seeded onto a 96-well plate and incubated at 37°C for 12 h. Subsequently, the cells were exposed to various concentrations of ponicidin (5, 10, 25, and 50 μmol/L) and incubated at 37°C for durations of 12, 24, and 48 h, respectively. The cell viability was assessed using the Cell Counting Kit-8 (Beyotime, China).

Total RNA was extracted from spinal cord tissue using Trizol reagent (Thermo Fisher Scientific, USA). The total RNA was reverse-transcribed into cDNA using a Vazyme reverse transcription kit (Vazyme, China). Expression of mRNA was assessed using the Hieff^® qPCR SYBR Green Master Mix (YEASEN, Shanghai, China) for real-time quantitative PCR (qRT-PCR). The qRT-PCR was performed on an ABI QuantStudio 6 Flex system (Applied Biosystems, USA). Primers for mouse genes were synthesized by RiboBio (Guangzhou, China), and their sequences are provided in Table 1.

Microglia in the spinal cord played a crucial role in pain modulation (21). We established a neuroinflammatory model by stimulating BV-2 microglia with LPS and employed qRT-PCR to verify whether Toll-like Receptor 4 (TLR4) and Hypoxia-Inducible Factor 1-alpha (HIF-1A), identified from the molecular docking results, are targets of ponicidin within this model.

Statistical analyses were conducted using Prism software (GraphPad, San Diego, CA). Data are presented as mean ± standard error of the mean (SEM). For data obtained from behavioral tests, a two-way repeated measures analysis of variance (ANOVA) was employed, followed by Tukey's post-hoc test to analyze differences among groups. Data acquired from ELISA and immunofluorescence staining were analyzed using one-way ANOVA and Tukey's post-hoc test for multiple comparisons. The Shapiro-Wilk normality test confirmed a normal distribution of the data; hence, parametric tests were utilized for comparisons. A p-value of < 0.05 was considered to indicate statistically significant differences.

First, we established a mouse model of inflammatory pain by administering a subcutaneous injection of 20 μL of CFA into the hind paw of mice. Ponicidin (10 mg/kg) was administered intraperitoneally at 8, 24, and 48 h post-CFA injection (Figure 1A). The results demonstrated that the CFA + Ponicidin group exhibited significant anti-nociceptive effects compared to the CFA group on days 3, 5, and 7 post-modeling (Figures 1B, C). Compared to the CFA group, the CFA + Ponicidin group maintained a significant increase in mechanical threshold and thermal latency until day 7, although the analgesic effect began to decline from day 5 (Figures 1B, C). The results of the rotarod experiments indicate no significant differences in the mice's motor function (Supplementary Figure S1). These findings suggest that ponicidin can produce a potent analgesic effect in CFA mice in the short term.

On day 3 post-CFA injection, we collected hind paw tissue from mice to assess changes in inflammation-related cytokines. We found that, compared to the CFA group, the CFA + Ponicidin group showed a significant downregulation of IL-1β, IL-6, and TNF-α in the hind paw tissue (Figures 1D–F). Collectively, these results indicate that ponicidin can alleviate CFA-induced inflammatory pain and suppress inflammation in the paw tissue of mice.

H&E staining of mouse hind paw tissues revealed significant inflammatory infiltration and tissue structural damage in the subcutaneous tissues of the CFA group (Figure 2A). Compared to the CFA group, the CFA + Ponicidin group exhibited a marked reduction in inflammatory infiltration of the subcutaneous tissues. Subsequently, we analyzed the expression of iNOS and CD206 in the mouse hind paw tissues. The expression of iNOS was significantly increased (Figures 2B, C), while the expression of CD206 was significantly decreased in the hind paw tissues of the CFA group (Figures 2D, E). In contrast, the CFA + Ponicidin group showed a significant decrease in iNOS expression and a significant increase in CD206 expression in the hind paw tissues (Figures 2B–E). These results indicate that ponicidin can significantly alleviate local subcutaneous inflammation in mice following CFA modeling.

We investigated the impact of ponicidin on the production of CGRP in the spinal cord. Our findings revealed that compared to the CFA group, the CFA + Ponicidin group exhibited a significant decrease in CGRP levels in the dorsal horn of the spinal cord, suggesting that the antinociceptive mechanism of ponicidin may involve the inhibition of spinal CGRP production (Figures 3A, B). We also examined the impact of ponicidin on the activation of spinal neurons. Our findings revealed that compared to the CFA group, the CFA + Ponicidin group exhibited a significant reduction in spinal cFos levels (Supplementary Figure S2), suggesting that ponicidin may exert analgesic effects by suppressing the activation of spinal neurons. Subsequently, we analyzed the changes in Iba1-positive microglia in the L4-5 dorsal horn of the spinal cord. We observed that microglia in the L4-5 dorsal horn were significantly activated following the induction of inflammatory pain by CFA (Figures 3C, D). Compared to the CFA group, the number of activated microglia was markedly reduced in the CFA + Ponicidin group (Figures 3C, D). These results indicate that ponicidin may suppress spinal neuroinflammation by inhibiting the production of CGRP and the activation of microglia in the spinal cord of CFA mice.

We identified 2,277 pain-related targets from four disease databases. From two traditional Chinese medicine databases, we obtained 195 drug targets associated with ponicidin. By intersecting these datasets using a Venn diagram, 84 common targets between ponicidin and pain were discovered (Figure 4A). These targets are considered candidate targets for the analgesic effects of ponicidin.

We performed functional annotation clustering on the common targets of ponicidin and pain using the DAVID database. Ponicidin was mainly enriched in response to positive regulation of transcription from RNA polymerase II promoter and protein phosphorylation in the biological process (BP) category. For cellular component (CC), ponicidin was mainly enriched in the plasma membrane, an integral component of the membrane, and nucleoplasm. For molecular function (MF), it was mainly enriched in ATP binding, G-protein coupled receptor activity, and protein serine/threonine/tyrosine kinase activity (Figure 5A).

Additionally, 10 KEGG-enriched pathways were obtained after the intersection with Metascape (Figure 5B, pathways arranged by p-value) and KEGG database. In addition, KEGG enrichment pathways were obtained in the KEGG database (Figure 5C, taxonomic arrangement of pathways and number of enriched genes). Enriched pathways included Pathways in cancer, Alzheimer's disease, Neuroactive ligand-receptor interaction, and Pathways of neurodegeneration- multiple diseases. Subsequently, we employed Cytoscape 3.9.1 to visualize the relationships between the top 10 targets by node degree and the top 20 pathways (Figure 6A). Our analysis revealed that these genes are predominantly involved in pathways associated with inflammation and oxidative stress.

We imported the 84 targets into Metascape for MCODE clustering analysis, yielding five functional modules (Figure 6B). Notably, the MCODE2 module (score = 2.0, depicted in purple in Figure 6C) encompasses six of the top 10 nodes by degree in the PPI network, while the MCODE3 module (score = 1.25, also depicted in purple in Figure 6D) contains one such node. This suggests a close association between these two functional modules and the analgesic effects of ponicidin. The pathways enriched in MCODE2 include prostate cancer, RANKL-RANK signaling pathway, and chemical carcinogenesis—receptor activation. The key genes involved are HSP90AA1 (degree = 16), NFKB1 (degree = 9), mTOR (degree = 8), PIK3R1 (degree = 8), MAPK1 (degree = 7), and AKT2 (degree = 6). MCODE3 is primarily enriched in pathways related to the response to reactive oxygen species, oxygen levels, and oxidative stress, with HIF1A (degree = 10) identified as a key gene. Furthermore, we identified the top 10 transcription factors associated with ponicidin's analgesic effects, complete with P-values (Figure 7A). Utilizing Metascape, we also obtained ponicidin-associated disease phenotypes with P-values (Figure 7B). The top 10 enriched phenotypes related to ponicidin-induced diseases are predominantly linked to analgesics, with the target diseases ranked as follows: second for neuralgia, fourth for hyperalgesia, and ninth for inflammation (Figure 7B).

We imported the 3D structures of the top 10 targets from the PPI network into Autodock for docking with ponicidin. The binding energies of each target from the docking results are presented in Table 2. Ponicidin exhibited favorable docking outcomes with the corresponding proteins, with key amino acids primarily interacting through hydrogen bonds. The relatively low docking energy values suggest that the compound can stably bind to the receptor proteins and exert its effects. We selected TLR4 and HIF1A, which had the lowest binding energies, for visualization (Figures 7C, D) and subsequent experimental validation.

To evaluate the outcomes of the systematic network pharmacology analysis, we employed qPCR to examine the mRNA expression levels of TLR4 and HIF1A, which were identified in the molecular docking results, within an acute inflammatory model induced by LPS stimulation in BV2 cells. The mRNA expression levels of TLR4 and HIF1A were significantly reduced in the ponicidin group compared to the LPS group (Figures 7E–G). The validation results were consistent with the predicted outcomes.