2.2.1 Replication of the HUA model

Forty-eight specific-pathogen-free Sprague Dawley male rats, weighing 180 ± 20 g, (Provided by Chengdu Dashuo Laboratory Animal Co. Ltd (Animal License No.: SCXK (Chuan) 2022-080). This study was approved by the Animal Ethics Committee of Shaanxi University of Traditional Chinese Medicine (SUCMDL20220703002). The rats were randomly assigned to six groups of eight rats each, following five days of acclimatization: blank control, model, positive drug, SLEO low-dose (20 mg/kg), SLEO medium-dose (40 mg/kg), and SLEO high-dose (80 mg/kg) (Honda et al., 2014). Rats in all groups were gavaged with adenine (25 mg/kg) and potassium salt of oxonic acid (300 mg/kg) diluted in 0.5 % sodium carboxymethylcellulose solution for 21 days in a row, with the exception of the blank control group. This was done to create a model of HUA (Qin et al., 2021). All the groups were administered their corresponding treatments 3 h after the induction of HUA. The blank and model groups received an aqueous solution of 1 % Tween 80 daily, the positive drug group received an aqueous solution of 1 % Tween 80 containing allopurinol (−), and the SLEO groups received the respective dose of SLEO for 21 consecutive days. Each dose of SLEO administered was configured with 1 % Tween 80 as the solvent. (Fig. 1A).

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Fig. 1

The serum levels of A. Hyperuricemia Modeling Flowchart, B. UA, C. BUN, D. CRE, E. XOD, F. TNF-α, G. IL-1β, H.IL-18 and I. IL-6 in each group of rats J. Liver index for each group of rats K. Levels of XDH protein expression levels in liver tissues of rats in each group (Standard deviation ± mean (n = 6), *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 vs. Model group., ^#P<0.05, ^##P<0.01, ^###P<0.001, ^####P<0.0001 vs. Control group.).

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2.2.2 Serum and tissue sample collection

After the last drug administration for 3 h in each group, the rats were anesthetized with 1 % pentobarbital sodium solution before being sacrificed. Blood was collected from each group of rats, and the serum was centrifuged and stored frozen at −80 °C. Both the kidneys and liver tissues were extracted and weighed.

2.2.3 Measurement of serum indicators of HUA

Serum samples were thawed at room temperature, and the levels of UA, CRE, BUN, XOD, and inflammatory TNF-α, IL-1β, and IL-6 were measured using enzyme-linked immunosorbent assay (ELISA) kits. Additionally, XDH protein expression in rat livers of rats was assessed.

2.4.1 Identification of the targets of SLEO active ingredients and HUA target

The CAS numbers of the active ingredients in SLEO were entered into PubChem (https://pubchem.ncbi.nlm.nih.gov/) to obtain their simplified molecular-input line-entry system notations. The relevant targets of the active ingredients in SLEO were obtained through the SwissTargetPrediction (https://www.swisstargetprediction.ch/) and TargetNet (https://targetnet.scbdd.com/) databases (Li et al., 2023). HUA targets were retrieved from GeneCards (https://www.genecards.org/), Online Mendelian Inheritance in Man (https://www.omim.org/, OMIM), and Comparative Toxicogenomics Database (https://ctdbase.org/, CTD).

2.4.2 Constructing a “component–disease–target” interaction network

The overlap between the targets of SLEO active ingredients and HUA disease targets was obtained using Venny 2.1.0 and imported into the STRING database (https://cn.string-db.org/) to construct protein–protein interaction (PPI) networks and to hide relationship line segments with very low interaction scores. The degrees of PPI were evaluated and ranked, and the screened target proteins were imported into Cytoscape 3.9.1 to construct a component–disease–target visualization network (Zhou et al., 2023b).

2.4.3 Establishment of a weighting factor for introducing “ weight coefficient ”

Considering the route of administration of SLEO, weighting coefficients were assigned to network pharmacological predictions to accurately reflect the significance of each key pharmacodynamic component within the pharmacological mechanism. The weighting coefficient for each active ingredient was calculated by multiplying the probability of its oral bioavailability by its relative content in SLEO (Wang et al., 2023). The established relationships were as follows:

2.4.4 Reordering of enriched pathways based on the “quantitative efficiency” weighting coefficients

To accurately elucidate the mechanism of action of SLEO in treating HUA, the enriched signaling pathways identified through KEGG and Gene Ontology analysis were reordered based on their weighting coefficients, calculated using formula (2), (3), and (4).

2.5.1 Cell culture

Human renal tubular epithelial cells (HK-2), obtained from Procell Life Science & Technology Co., Ltd., were cultured in DMEM with 10 % fetal bovine serum at 37 °C and 5 % CO₂. In subsequent experiments, HK-2 cells were pretreated with SLEO or 10 μM NLRP3 inhibitor (MCC950, HY-12815, MedChem Express) for 24 h, followed by stimulation with appropriate amounts of UA (Zhou et al., 2023a).

2.5.2 Cell viability assay

The viability of HK-2 cells assessed at varying concentrations of UA (400, 300, 200, 100, 50, and 25 μg/mL) and SLEO (1, 0.5, 0.25, 0.125, 0.0625 and 0.0375 μL/mL) according to the MTT kit instructions, with absorbance was measured at 490 nm (Cui et al., 2020). Uric acid sodium salt (UA, 1198–77-2, MedChem Express) was prepared to a concentration of 200 μg/mL of soluble UA at the time of prophylaxis, and 10 μM MCC950 was dissolved in 0.9 % sodium chloride solution.

2.5.3 ELISA assay

Determination of important inflammatory indicators in the supernatant of each cell group was performed using standard ELISA kits.

2.6.1 Preparation and analysis of metabolomic samples

Thaw serum stored at −80 °C at room temperature. To 50 μL of serum, the internal standard seventeen carbonic acid and methanol were added. The mixture is homogenized and allowed to stand at −20 °C for 10 min, then centrifuged to remove the proteins. The supernatant was aspirated and freeze-dried. Its lyophilised powder was collected and dissolved in methoxyaminopyridine solution (15 mg/mL) and then reacted with methoxyamine hydrochloride for 2 h at 60 °C with stirring. The reaction was sealed again by adding N,O-bis(trimethylsilyl)trifluoroacetamide, cooled at 4 °C, and centrifuged at 12,000 rpm for 10 min. The supernatant was analyzed by GC–MS. Repeat all the above steps to prepare quality control samples (Shan et al., 2021).

HP-5MS capillary column (30 m × 250 μm × 0.25 μm) was used for chromatography, and it was filled with 1.5 μL of high-purity helium and flowed at a rate of 1 mL/min. The temperature was ramped up to 260 °C at 10 °C/min, then to 300 °C at 2 °C/min in the planned ramping mode, without shunt, after being held at 80 °C for two minutes. Mass spectrometer conditions were as follows: full scan mode in the range of 20–450 atomic mass units; ion source, electron ionization; electron energy, 70 V; MS quadrupole temperature, 150 °C; solvent delay, 5 min (Chen et al., 2016).

2.6.2 Metabolomics analysis

Serum metabolomics data were analyzed using specialized software, and standard mass spectra from the NIST 20 database employed to match the purified mass spectra of the identified metabolites. Compounds with less than 80 matches in the serum were removed, and the peak area of each sample was used as a variable and normalized for all samples. The collated data were imported into MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) for principal component analysis (PCA) to reveal the distribution of metabolites in the rat serum samples. An orthogonal partial least squares discriminant analysis (OPLS-DA) model was constructed using the SIMCA software to visualize the metabolic changes after SLEO treatment. Screening criteria were set with a variable importance in projection (VIP) score > 1 and P value < 0.05 were levied as the screening criteria, and the screened metabolites were considered as differential metabolites, which were imported into MetaboAnalyst 5.0 for metabolic pathway analysis (Zhu et al., 2023).

2.7.1 RNA-seq sample preparation

Fifteen specific-pathogen-free, adult, male Sprague Dawley rats, each weighing approximately 200 g, were acclimatized to standard conditions of temperature (25 ± 2 °C) and humidity (60 ± 5 %) for 1 week before being randomly assigned to either the blank, model, or SLEO groups (five rats per group). The HUA model was established as described earlier, and the SLEO group received 40 mg/kg of SLEO for 21 days. At the end of the treatment, the rats were anesthetized with 1 % pentobarbital sodium before being sacrificed. Collect their kidney tissue and freeze it for preservation (Shen et al., 2023).

2.7.2 RNA-seq analysis

RNA-seq libraries were constructed using the Illumina NovaSeq Reagent Kit. Total RNA was extracted from rat kidney tissues using Trizol reagent. Subsequently, mRNA was isolated, fragmented, and primed, followed by synthesis of the first and second strands of cDNA were synthesized in a thermal cycler to construct a transcriptome library. RNA quality was assessed by the RNA integrity number (RIN) using the Agilent 2100 detector, where higher RIN values indicate better and more complete RNA. Finally, RNA-seq analysis was conducted using the free Majorbio Cloud platform (https://www.majorbio.com) (Wang et al., 2022a).

2.7.3 Weighted gene co-expression network analysis (WGCNA)

WGCNA can identifies highly synergistic gene sets and candidate biomarkers or therapeutic targets by examining gene set end-conjugation and gene set-phenotype associations. This method yields precise gene function enrichment results from transcriptome sequencing, revealing key hub genes (Kakati et al., 2019).

Differential gene and HUA trait files from transcriptome sequencing were analyzed using the Majorbio Cloud Platform (https://www.majorbio.com) WGCNA panel, excluding genes with expression levels ≤ 2 were screened out. Initially, the weighting coefficient β\betaβ gene correlations within of each sample group of samples were determined according to the scale-free network principle to construct the gene clustering tree and divide the gene modules. Subsequently, the gene module most associated with the trait was identified, based on module-trait associations and gene enrichment within the module. This approach established the gene regulatory network within the module uncovering the regulatory core genes (hub genes) and the regulatory relationships among individual genes (Cheng et al., 2022).

2.9.1 Greedy algorithm to screen possible key components of SLEO for HUA treatment

For precise identification and screening of key active ingredients in SLEO for molecular docking studies, the greedy algorithm (Doi and Imai 1999, Gu et al., 2024), was introduced as an innovative tool for drug ingredient screening. Initially, intersection targets of SLEO and ingredient-disease relationships were merged and analyzed to map their correspondence between SLEO and intersection targets. Subsequently, the greedy algorithm, as detailed in the supplementary material, was used to identify the set of SLEO bioactive ingredients that cover the possible targets for HUA treatment. The final screened active ingredients represent the core components of SLEO for treating HUA, enhancing the accuracy of molecular docking predictions.

2.9.2 Molecular docking

The pdb structure files of possible target proteins for HUA treatment, identified through “weighted” network pharmacology and multi-omics analysis, were downloaded. Based on GC–MS analysis, “weighted” network pharmacology predictions, and greedy algorithm screening 2D structure files of the component compounds in sdf format were also downloaded. These the structure files were then imported into Discovery Studio 4.0 and PyMOL software for molecular docking and binding energy calculations.

2.9.3 Hematoxylin and eosin (HE) staining of renal tissue

Rat left kidney tissues were embedded in paraffin, dehydrated using an ethanol gradient, fixed in 4 % paraformaldehyde, sliced to a thickness of 5 μm, stained with HE following standard protocol, and examined under a light microscope (Nikon (Japan) ECLIPSE 80I) (Li et al., 2022).

2.9.4 Immunohistochemistry

Protein detection in rat kidney tissues was performed using immunohistochemistry. Paraffin-embedded tissues were sectioned into 5-μm slices, hydrated, incubated in 3 % H₂O₂ at room temperature for antigen retrieval, blocked 30 min, and probed overnight at 4 °C in a humid chamber with the corresponding primary antibodies targeting IL-1β, p-NF-κB-p65, and ASC, left at room temperature for 1 h on the following day, rinsed thrice in phosphate-buffered saline for 3 min each time, incubated for 60 min at 37 °C with horseradish peroxidase-labeled secondary antibodies developed for color and sealed for microscopic observation (Ren et al., 2020).

2.9.5 Western blotting

Proteins extracted from kidney and liver tissues were stored at −80 °C until analysis. Proteins were separated by electrophoresis and transferred to a membrane, which was blocked at room temperature for 70 min. The membrane was then probed overnight at 4 °C with primary antibodies NF-κB-p65, p-NF-κB-p65, NLRP3 (1:1000; Abcam, ab214185, Shanghai, China), caspase-1 (1:1000; HUAbio, ET1608-69, Hangzhou, Zhejiang, China), IKK-β, p-IKK-β, glyceraldehyde 3-phosphate dehydrogenase (1:1000; Proteintech, 60004–1-Ig, Wuhan, Hubei, China), and XDH(1:1000; HUAbio, 55156–1-AP, Wuhan, Hubei, China). After three washes at room temperature with Tris-buffered saline containing Tween 20 for 5 min each the membrane was incubated for 50 min at room temperature with horseradish peroxidase-conjugated secondary antibody (1:10,000; Beyotime, P0258, Beijing, China). Finally, the membrane was washed three times with Tris-buffered saline containing Tween 20 for 5 min each and developed using an enhanced chemiluminescence reagent.

3.1.1 UA, CRE, BUN, and XOD levels after SLEO treatment

ELISA results indicated that serum levels of UA, CRE, BUN, and XOD were significantly elevated in the model group compared with the blank group (P<0.05), confirming the successful replication of the HUA model. Compared with the model group, the levels of UA, CRE, BUN, and XOD were significantly reduced compared to the model group (Fig. 1B–E). SLEO alleviated HUA by lowering UA levels and inhibiting XOD activity.

3.1.2 XDH expression and inflammatory factor levels in liver and serum after SLEO treatment

ELISA results, also showed that the serum levels of TNF-α, IL-1β, IL-6 and IL-18 in HUA rats were significantly different in HUA rats compared to the blank group (P<0.05). These levels were significantly reduced by SLEO treatment (P<0.05, Fig. 1F–I). In addition, the liver coefficients of HUA rats were significantly increased, whereas those of the SLEO high-dose group and the blank group did not significantly different (Fig. 1J). Western Blot analysis demonstrated that XDH protein expression was significantly reduced in the SLEO treatment group (P<0.01) (Fig. 1K).

3.3.1 Key target prediction and interaction network construction

A total of 486 SLEO component-related targets and 890 disease targets were obtained through database queries and data deduplication, resulting in 74 were intersecting targets (Fig. 2B). The connection degrees between targets were obtained from the PPI network (Fig. 2C), and they were ranked by importing them into Cytoscape 3.9.1. The prediction results indicated that SLEO might treat HUA through four main targets, namely, TNF-α, NF-κB, peroxisome proliferator-activated receptor, and PTGS2 (Fig. 2D). Disease–component network interactions were visualized using Cytoscape 3.9.1 (Fig. 2E).

3.3.2 Functional enrichment of targets after introducing the “ weight coefficient ”

The 74 intersecting targets underwent enrichment analysis for cellular components and molecular functions using R. Weighting coefficients were applied to reorder the enriched biological processes and KEGG pathways. Among the 121 enriched KEGG pathways that were reordered, the nucleotide-binding oligomerization domain-like (NOD-like) receptor signaling pathway was promoted from the 27th to the 4th position after reordering, indicating that SLEO may alleviate HUA via the NOD-like receptor signaling pathway (Fig. 3A–B). Significant changes were also observed in the ranking of biological processes post-reordering (Fig. 3C–D). Notably, the NOD-like receptor signaling pathway is a classical inflammatory pathway, and HUA is closely linked to the activation of NLRP3 inflammasomes. The top 10 cellular components and molecular functions of the mechanism of action associated with SLEO treatment of HUA have been enriched using R, as shown in Fig. 3E–F.

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Fig. 3

Changes in the ordering of KEGG pathways and BPs after introducing weighting coefficients A. KEGG pathways predicted by network pharmacology B. Reordering of KEGG pathways after introducing weighting factors C. BPs predicted by network pharmacology D. Reordering of BPs after introducing weighting factors E. Cellular components predicted by network pharmacology F. Molecular functions predicted by network pharmacology.

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3.5.1 Serum metabolomics and the screening of differential metabolites

The differential serum metabolites were analyzed using MetaboAnalyst 5.0 and identified by subjecting the quality control samples, the control, model, positive drug, and SLEO treatment groups to PCA. The SLEO group was considerably separated from the model group in the PCA scatter plot, indicating that the metabolite content was significantly altered after treatment and metabolic processes might also be affected or reversed to some extent (Fig. 6A). We used SIMCA to analyze metabolic differences between the blank control, model and SLEO groups. The metabolomes of the blank control and the model groups were well-separated (R2X [cumulative] = 0.725, R2Y=0.999, Q2 [cumulative] = 0.956), corroborating the successful establishment of the HUA model (Fig. 5A). Analysis of metabolic data from model and SLEO groups (R2X [cumulative] = 0.337, R2Y=0.981, Q2 [cumulative] = 0.85), suggesting that SLEO significantly altered the metabolic content in the serum of HUA rats (Fig. 5D). In addition, these results were validated by S-plot and permutation tests (n = 200) (Fig. 5 B, E). A total of 200 permutation tests were performed to confirm the validity and applicability of the OPLS-DA model (Fig. 5C, F). Applying variable importance in projection score > 1 and P<0.05 in the OPLS-DA model as screening criteria, 12 differential metabolites were identified between the blank control and the model groups and 18 between the SLEO and the model groups (Tables 2 and 3).

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Fig. 5

SIMCA Analysis of Metabolic Differentials. A. OPLS-DA of the blank control and model groups. B. OPLS-DA of the model and SLEO groups. C. S-plot of the control and model groups. D. S-plot of the model and SLEO groups. E. Stability experiments on the blank control and model groups. F. Stability experiments on the model and SLEO groups.

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Fig. 6

A. Plot of PCA scores for the five groups based on their serum metabolic profiles. Blue represents the control group, green represents the model group, red represents the positive group, orange represents the SLEO group, and purple represents the quality control group B. Heatmap of metabolic differences between the model and SLEO groups C. Sankey diagram depicting the functional enrichment of differential metabolites of the control and model groups. D. Functional enrichment of differential metabolites of the control and model groups. E. Sankey diagram depicting the functional enrichment of differential metabolites of the model and SLEO groups. F. Functional enrichment of differential metabolites of the model and SLEO groups.

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3.5.2 Differential metabolite screening and metabolic pathway analysis

The results indicated significant alterations in serum metabolite content of the rats changed after SLEO treatment (Fig. 6B). The differential metabolites identified between the model and SLEO groups were imported into MetaboAnalyst 5.0 to determine the metabolic pathways possibly altered. We discovered that 31 metabolic pathways were potentially enriched in the SLEO group relative to the model group, among which arginine biosynthesis, glutathione metabolism, arginine and proline metabolism, D-glutamine and D-glutamate metabolism, and the metabolism of glycine, serine, and threonine may be the key pathways involved in mediating the therapeutic effects of SLEO against HUA (Fig. 6D). Sankey diagrams illustrated the linkage of individual metabolites to the enriched pathways and to denote the type of metabolism (Fig. 6C). In addition, we also studied and analysed the metabolic pathways of the blank and model groups as a secondary reference for the administration group (Fig. 6E-F).

3.8.1 Molecular docking results

Sixteen active ingredients of SLEO identified through GC–MS were analyzed. Using the greedy algorithm, these ingredients that could cover the 75 intersecting targets by the correspondence between the 16 ingredients and the 75 intersecting targets of the ingredients and diseases predicted by network pharmacology. The active ingredient set includes 11 key active ingredients from SLEO. Among them, linalool, caryophyllene and β-pinene play important roles in the treatment of HUA by SLEO (Fig. 9F and Table S1).

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Fig. 9

Molecular docking results. A. Docking results of β-pinene and NLRP3 (left); positive drug indomethacin and NLRP3(right) B. Docking results of β-caryophyllen and COX-2(left); positive drug celecoxib and COX-2 (right) C. Docking results of β-caryophyllen and ASC (left); positive drug melphalan(right) and ASC D. Docking results of linalool and TLR4(left); positive drug alprazolam and TLR4 (right) E. Docking results of β-caryophyllen and XOD (left); positive drug allopurinol and XOD (right) F. Plot of key components in 11 SLEO screened by the greedy algorithm in relation to the target network. G The binding scores of the key active ingredients to the targets.

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Key target proteins such as TLR4, NLRP3, XOD, ASC, and COX-2 were selected as the key target proteins based on their PPI degree ranking and the core components of SLEO screened by greedy algorithm. The active ingredients of SLEO constituted the ligands, while fostamatinib, celecoxib, alprazolam, allopurinol, and melphalan were selected as positive control drugs. The binding scores of linalool to TLR4, β-caryophyllen to XOD, and β-pinene to NLRP3 were comparable to those of the positive control drugs to their protein targets, suggesting that linalool, β-caryophyllene, and β-pinene maybe the key active components of SLEO for the treatment of HUA and kidney injury (Fig. 9A-E). Binding fractions of active ingredients to proteins and 2D binding structural formulae (Fig. S1). The binding scores of the key active ingredients to the targets are shown in Fig. 9G.

3.8.2 Preliminary validation of molecular docking results

The molecular docking results were preliminarily verified by in vitro determination of the contents of the key factors in the relevant mechanism of action (Khalaf et al., 2022). First, the maximum administered dose of the three active ingredients was determined by the MTT, which yielded a cell survival rate of > 90 % at linalool (80 μM), β-pinene (10 μM) and β-stigmasterol (80 μM), shown in Fig. 10A-C. Two active components of SLEO, linalool and β-caryophyllene, exhibit strong binding properties with TLR4 and COX-2 on the NF-κB signaling pathway. To preliminarily verify this finding, classical inflammatory factors TNF-α and IL-6, downstream of NF-κB (Fu et al., 2023), were measured in cell supernatant after in vitro UA-induced HK-2 modeling and administration of linalool and β-caryophyllene (Wei et al., 2023). In vitro experiments with linalool and β-caryophyllene significantly reduced TNF-α and IL-6 levels (p < 0.05) are shown in Fig. 10G-H, suggesting that these components in SLEO may block the NF-κB pathway by binding to TLR4 and COX-2 targets. Additionally, β-pinene and β-caryophyllene were closely linked to the activation of NLRP3 inflammasomes. The activation of NLRP3 inflammasomes leads to increased levels of inflammatory factors, such as IL-1β and IL-18, exacerbating kidney injury (Özenver and Efferth 2021). In vitro experiments demonstrated that the administration of β-pinene and β-caryophyllene significantly reduced IL-1β and IL-18 levels (p < 0.05), shown in Fig. 10E-F. The study also provides preliminary validation of the molecular docking results through XOD in vitro activity inhibition assays, shown in Fig. 10D.

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Fig. 10

A Determination of the maximum administered dose of linalool by the MTT. B Determination of the maximum administered dose of β-pinene by the MTT. C Determination of the maximum administered dose of β-caryophyllene by the MTT. D Experimental results of XOD inhibition by β-caryophyllene. E Effects of β-caryophyllene and β-pinene on IL-1β in NLRP3 inflammasome activation. F Effects of β-caryophyllene and β-pinene on IL-18 in NLRP3 inflammasome activation. G Effect of linalool and β-caryophyllene on TNF-α content on the NF-κB pathway. H Effect of linalool and β-caryophyllene on IL-6 content on the NF-κB pathway. (*P<0.05, **P<0.01, ***P<0.001vs. Model group., ^##P<0.01, ^###P<0.001, ^####P<0.0001 vs. Control group.).

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3.9.1 HE staining of renal tissues

HE-stained sections of renal tissues revealed prominent infiltration of inflammatory cells (green arrows) in the tissues of the model group, while a small number of renal tubular epithelial cells appeared to be sparsely edematous and detached (yellow arrows). In contrast, the renal tissues of rats in the SLEO group showed a significant reduction in inflammatory cell infiltration, including a significant improvement in pathological features in the renal tissues of the high-dose group (Fig. 11).

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Fig. 11

HE staining of rat kidney tissues after treatment of hyperuricemia with SLEO. Renal tissues in the model group had more severe infiltration of inflammatory factors compared with those in the control group, while the renal histopathology of rats in the high-dose group was significantly improved by SLEO treatment.

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3.9.2 Immunohistochemistry

The expression of the inflammation-related proteins ASC, IL-1β, and p-NF-κB-p65 was higher in the renal tissues of rats in the model group compared with those in the blank group (P<0.05), but it was lower in the renal tissues of rats in the SLEO group compared with those in the model group (P<0.01) (Fig. 12A-D).

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Fig. 12

A-D The immunohistochemical detection of protein expression in the renal tissues of rats in various group. E-J The detection of protein expression in the kidney tissues of rats in each group by Western blotting. (Standard deviation ± mean (n = 3), *P<0.05, ^**P<0.01, ^***P<0.001, ^****P<0.0001 vs. Model group., ^#P<0.05, ^##P<0.01, ^###P<0.001, ^####P<0.0001 vs. Control group.).

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3.9.3 Western blotting

Since we have established that SLEO alleviates HUA by modulating the NF-κB/NLRP3 pathway, we investigated the changes in protein expression in this pathway after SLEO administration. The protein expression of TLR4, p-IKKB/IKKB, p-P65/P65, NLRP3, and caspase-1 was significantly increased in the renal tissues of the model group compared with those of the control group. SLEO treatment decreased the expression of these proteins in a dose-dependent manner (Fig. 12E-J). The results of Western blotting indicated that SLEO could inhibit the release of inflammatory factors mediated by NF-κB/NLRP3, thereby mitigating renal inflammation.