CP1Hao Decoction Alleviates Chronic Prostatitis by Modulating Arachidonic Acid Metabolism: A Multi-Omics Analysis

Abstract

CP1Hao Decoction (CP1HD) is a traditional Chinese herbal prescription used to treat chronic prostatitis, but the therapeutic effects and underlying mechanisms of CP1HD, particularly those involving arachidonic acid metabolism and gut microbiota, remain unclear. This study evaluated the pharmacological efficacy of CP1HD in a carrageenan-induced rat model of prostatitis and explored its potential targets and pathways through integrated transcriptomics, 16S rRNA sequencing, and metabolomics analyses. CP1HD treatment significantly alleviated prostate histopathological damage, reduced the prostate index, and suppressed inflammatory responses. ELISA results showed that CP1HD markedly decreased the levels of TNF-α, IL-6, PGE2, LTB4, and COX-2, while regulating EETs levels, and inhibited M1 macrophage polarization and neutrophil infiltration while promoting M2 polarization. Transcriptomic analysis revealed that CP1HD reversed differentially expressed genes associated with inflammation and arachidonic acid metabolism, which was further confirmed by Western blot analysis of cPLA2, LTA4H, and sEH. In addition, CP1HD modulated gut microbiota composition by restoring the relative abundance of Bacteroides, Alistipes, and the Eubacterium_siraeum group. Metabolomic profiling demonstrated that CP1HD regulated arachidonic acid metabolism and related lipid mediators, with notable effects on phosphatidylethanolamine metabolism and fatty acid β-oxidation. Overall, CP1HD ameliorates prostatitis by reducing inflammation and prostate injury while modulating arachidonic acid metabolism, gut microbiota, and associated metabolic pathways.

1. Introduction

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is a common urological disorder characterized by persistent symptoms and substantial clinical complexity. Epidemiological data suggest that approximately 50% of men will experience symptoms consistent with prostatitis at some point in their lifetime (He et al., 2023). Notably, CP/CPPS is the predominant subtype, representing over 90% of prostatitis cases (Doiron et al., 2019). Characterized by persistent pelvic discomfort, voiding dysfunction, and sexual disturbances, this condition imposes a substantial long-term burden on patients (Borgert et al., 2025). Unlike acute infections, the clinical course of CP is often refractory and marked by frequent recurrence, leading to physical debilitation and psychological distress, including anxiety and depression (Piontek et al., 2019; Sugimoto et al., 2022).

Existing treatment approaches, such as antibiotics, α-blockers, and nonsteroidal anti-inflammatory drugs (NSAIDs), primarily focus on symptomatic relief rather than targeting the underlying pathogenesis (Borgert et al., 2025). Clinical outcomes remain variable, often marred by incomplete symptom resolution, high recurrence rates, and risks such as gastrointestinal toxicity and drug resistance (Barkin et al., 2003; Nickel et al., 2003; Polackwich & Shoskes, 2016). There remains a pressing unmet clinical need for the development of novel therapeutic agents with multi-target mechanisms that can effectively modulate the inflammatory microenvironment and restore physiological homeostasis.

Evidence from prostaglandin-related studies indicates that the arachidonic acid (AA) metabolic pathway plays a critical role in both the onset and progression of prostatic inflammation (Jiang et al., 2024; J. H. Wang et al., 2025a). Disruption of arachidonic acid metabolism results in elevated levels of pro-inflammatory mediators, including prostaglandins and leukotrienes, thereby contributing to tissue injury and pain. (Yao et al., 2025). Furthermore, recent paradigm shifts in microbiota research have highlighted the “gut-prostate axis” as a critical regulator of systemic and local immunity (S. W. Song et al., 2025a). Dysbiosis of the gut microbiota can influence host metabolic profiles and distant inflammatory responses, potentially modulating the AA cascade. The potential link between gut microbial metabolites and AA signaling in the context of CP remains poorly understood.

Traditional Chinese Medicine (TCM), characterized by a holistic framework and multi-component interactions, presents unique therapeutic advantages for complex inflammatory conditions, showing particular promise in alleviating the symptoms and pathology of chronic prostatitis (Ning et al., 2025). CP1HD is a Chinese herbal formulation developed for the management of CP, comprising eight traditional Chinese medicinal components as detailed in Section 2.1. This formula was designed based on the traditional therapeutic principles of invigorating Qi, promoting blood circulation, removing stasis, and clearing heat and detoxification, which are commonly applied in the management of CP(D. B. Li et al., 2026a). Accumulating evidence suggests that Huangqi exerts immunomodulatory effects by enhancing host defense and regulating inflammatory responses (Son et al., 2026). Danshen and Shuizhi have been reported to improve microcirculation and alleviate inflammation through anti-thrombotic and anti-inflammatory activities (Y. Li et al., 2026b; Pei et al., 2026). Yanhusuo and Wangbuliuxing are known to promote blood circulation and relieve pain, which may contribute to the alleviation of pelvic discomfort (Bing et al., 2024; Luo et al., 2025). Baijiangcao exhibits anti-inflammatory and antibacterial properties, making it relevant for inflammation-associated disorders (Gong et al., 2021). Shichangpu has been reported to modulate neuroinflammatory responses and improve the tissue microenvironment (Hu et al., 2025), while Gancao plays a harmonizing role and exerts anti-inflammatory effects (Zhu et al., 2026). Collectively, these herbs may act synergistically to target key pathological features of CP, including chronic inflammation, microcirculatory dysfunction, and immune imbalance, suggesting that CP1HD has the potential to exert therapeutic effects through multi-target regulation.

To elucidate the therapeutic mechanisms of CP1HD in CP, an integrated multi-omics strategy was applied combining fecal 16S rDNA sequencing, gut metabolomics, and prostate transcriptomics. Key pathways and potential interactions were identified through integrative analysis and subsequently validated by histopathology, ELISA, and Western blotting. This study provides a molecular basis for understanding the “gut-metabolite-prostate” axis in CP1HD treatment.

2. Materials and Methods

2.1. Preparation and Analysis of CP1HD

CP1HD is a Traditional Chinese Medicine formulation composed of eight medicinal components, including 15 g of Salvia miltiorrhiza Bunge (Danshen), 15 g of Patrinia villosa (Thunb.) Juss (Baijiangcao), 15 g of Astragalus membranaceus (Fisch.) Bunge (Huangqi), 10 g of Vaccaria segetalis (Neck.) Garcke (Wangbuliuxing), 10 g of Corydalis yanhusuo W.T. Wang (Yuanhu), 10 g of Acorus tatarinowii Schott (Shichangpu), 6 g of Glycyrrhiza uralensis Fisch. (Gancao), and 3 g of Whitmania pigra Whitman (Shuizhi). Detailed information regarding these components is listed in Table 1. All components were obtained as standardized formula granules from Tasly Pharmaceutical Group Co., Ltd. (Tianjin, China). The dosages indicated above refer to the equivalent weights of the raw medicinal materials. The granulated formulation was reconstituted in distilled water to achieve a final concentration of 0.18 g/mL (granule weight/volume), which corresponds to a daily dosage of 1.5 g raw material equivalent per kg body weight (1.5 g/kg). The solution was stored at 4°C for daily administration. The nomenclature of all plant components was validated using the Medicinal Plant Names Services (MPNS, https://mpns.kew.org). For quality control, aliquots of the CP1HD solution were preserved at −80°C and subsequently subjected to analysis using ultra-high-performance liquid chromatography coupled to quadrupole Orbitrap mass spectrometry (UHPLC–Q-Orbitrap-MS).

Table 1.

The Composition of CP1HD

Scientific name	Chinese name	Abbreviation	Latin name	Family	Used part	Grams,g
Salvia miltiorrhiza Bunge	Danshen	DS	Salviae Miltiorrhizae Radix et Rhizoma	Lamiaceae	the dried root and rhizome	15
Patrinia villosa (Thunb.) Juss.	Baijiangcao	BJC	Patriniae Herba	Caprifoliaceae	the dried whole plant	15
Astragalus membranaceus (Fisch.) Bunge	Huangqi	HQ	Astragali Radix	Leguminosae	the dried root	15
Vaccaria segetalis (Neck.) Garcke	Chinese name	WBLX	Vaccariae Semen	Caryophyllaceae	the dried seed	10
Corydalis yanhusuo W.T. Wang	Yuanhu	YH	Corydalis Rhizoma	Papaveraceae	the dried rhizome	10
Acorus tatarinowii Schott	Shichangpu	SCP	Acori Tatarinowii Rhizoma	Acoraceae	the dried rhizome	10
Glycyrrhiza uralensis Fisch.	Gancao	GC	Glycyrrhizae Radix et Rhizoma	Leguminosae	the dried root and rhizome	6
Whitmania pigra Whitman	Shuizhi	SZ	Hirudo	Hirudinidae	Whole animal body	3

CP1HD samples were thawed at 4 °C and extracted using methanol/acetonitrile (1:1, v/v), followed by vortexing, ultrasonication, and centrifugation. Supernatants were collected, lyophilized, and re-dissolved in 30% acetonitrile before analysis. Chromatographic separation was achieved on a UHPLC system coupled to a Q Exactive HF-X Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific, USA) with a Waters HSS T3 column (100 × 2.1 mm, 1.8 μm) at 40 °C. The mobile phase comprised water and acetonitrile (0.1% formic acid) at 0.3 mL/min. A 0–95% B gradient over 12 min was applied, followed by re-equilibration. MS data acquisition was performed in full MS/dd-MS² mode with a HESI source. Parameters included spray voltage (−2.8/3.0 kV), capillary temperature (320 °C), and auxiliary gas heater temperature (350 °C). Raw data were exported as mzML via ProteoWizard and processed in XCMS for peak detection and alignment. Metabolites were annotated using metDNA with in-house and public databases.

2.2. Animal Studies and Experimental Design

Twenty-one male specific pathogen-free (SPF) Sprague–Dawley rats (6-7 weeks, 200 ± 50 g) were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. (x; License No. x). Animals were maintained under controlled conditions (24-25 °C, 45%-65% humidity) and acclimatized for 7 days before experiments at the SPF Animal Facility, xx. All procedures were approved by the Animal Ethics Committee of xx (xx) and complied with institutional and national guidelines.

Rats were randomly assigned to four groups: Sham (n=5), CP model (n=5), CP1HD-Low (0.45 g/kg, n=5), and CP1HD-High (1.8 g/kg, n=6). CP1HD was dissolved in distilled water and administered daily by gavage. The CP model was induced via intraprostatic injection of λ-carrageenan (C1013-25G; Sigma, USA). Model and treated rats were anesthetized with 3% pentobarbital sodium. After iodine disinfection, a 1-cm midline incision exposed the prostate, followed by injection of 0.1 mL 1% λ-carrageenan into each lobe. Sham rats received equal saline. Incisions were sutured and disinfected. Treatment started on day 2 post-surgery and lasted 4 weeks. At the end, rats were anesthetized and weighed, and blood was collected from the abdominal aorta. Prostates were excised and weighed; most tissue was stored at −80 °C for biochemical assays, and the remainder fixed in 4% paraformaldehyde for histology.

2.3. Blood and Tissue Sample Collection

Two hours after the last administration, rats were weighed and anesthetized. Blood was drawn from the abdominal aorta and centrifuged at 3000 rpm for 10 min to obtain serum, then stored at −80 °C. Prostates were quickly excised on ice and weighed, then divided into two parts: one fixed in 4% paraformaldehyde for histology, and the other snap-frozen in liquid nitrogen and kept at −80 °C. For multi-omics analysis, fecal pellets from the distal colon were collected for 16S rDNA sequencing, while cecal contents were obtained for metabolomics. All samples were immediately frozen in liquid nitrogen and stored at −80 °C until analysis.

2.4. Pathological Assessment

2.4.1. Calculation of the Prostate Index

The prostate index (PI) was calculated as: PI=(prostate wet weight/rat weight)×1000.

2.4.2. Histological Examination

Prostate tissues fixed in 4% paraformaldehyde were processed in an automated dehydrator, paraffin-embedded, and sectioned at 4 μm. Sections were deparaffinized in xylene, rehydrated through graded alcohol, stained with hematoxylin and eosin, dehydrated, and cleared with xylene. Tissue morphology was examined under a light microscope.

2.4.3. Immunohistochemistry (IHC)

For IHC, paraffin-embedded prostate sections (4 μm) underwent antigen retrieval and were blocked with 5% BSA to reduce nonspecific staining. Sections were incubated with primary antibodies against myeloperoxidase (MPO), inducible nitric oxide synthase (iNOS), and CD206 overnight at 4 °C, followed by secondary antibodies for 1 h. Signals were detected using DAB staining. Images were captured and analyzed with ImageJ. Antibody details are provided in the Western blot section.

2.5. Integrated Multi-omics Analysis

2.5.1. Prostate Transcriptome Analysis

The CP1HD-L group was chosen as the representative treatment group for sequencing. Total RNA was isolated from prostate tissue using TRIzol (Invitrogen). RNA quality and concentration were evaluated with an Agilent 2100 Bioanalyzer and NanoDrop 2000 (Thermo Fisher Scientific). Libraries were prepared using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina (NEB) following standard protocols and sequenced on an Illumina NovaSeq 6000 platform to generate 150 bp paired-end reads. Clean reads were mapped to the reference genome with HISAT2. Differential expression analysis was performed using DESeq2 with |log2FoldChange| ≥ 1 and adjusted P < 0.05. GO and KEGG enrichment analyses were conducted via the Majorbio Cloud platform.

2.5.2. 16s rDNA Sequencing Analysis

Total genomic DNA was isolated using the Mag-Bind Soil DNA Kit (Omega Biotek, USA) following the manufacturer’s protocol. DNA quality and concentration were evaluated before amplification. The V3–V4 regions of bacterial 16S rRNA were amplified with barcoded primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 806R (5′-GACTACHVGGGTATCTAATCC-3′) using a high-fidelity polymerase. PCR products were verified by 2% agarose gel electrophoresis, excised, and purified. Amplicons were quantified with a PicoGreen dsDNA assay on a fluorescence microplate reader (BioTek FLx800, USA) and pooled as required. Libraries were constructed using the TruSeq Nano DNA LT kit (Illumina, USA) and assessed on an Agilent 2100 Bioanalyzer with Quantifluor (Promega, USA). Qualified libraries were sequenced on an Illumina NovaSeq 6000 platform using paired-end 2×250 bp reads.

2.5.3. Non-targeted Metabolomic Analysis

The CP1HD-L group was selected as the representative treatment group for metabolomic analysis. Approximately 50 mg of cecal contents were mixed with 400μL of a methanol/water solution (4:1, v/v) containing 4 μg/mL L-2-chlorophenylalanine as an internal standard. Samples were homogenized at 60 Hz for 2 min, sonicated in an ice-water bath for 10 min, and kept at −20 °C for 30 min for protein precipitation. After centrifugation (13,000 × g, 10 min, 4 °C), supernatants were dried and re-dissolved in acetonitrile/water (1:1, v/v) for LC–MS. Metabolomic analysis was conducted on an ACQUITY UPLC I-Class Plus system (Waters, USA) coupled with a Q-Exactive mass spectrometer. Data were processed using Progenesis QI. OPLS-DA was applied to discriminate groups, and differential metabolites were screened with VIP > 1.0 and P < 0.05. KEGG pathway analysis was subsequently performed.

2.6. Protein Expression Analysis

2.6.1. Enzyme-Linked Immunosorbent Assay

Blood samples were prepared as described above. The concentrations of inflammatory mediators and metabolites were quantified using commercial ELISA kits according to the manufacturers’ instructions. TNF-α (Cat. No. 88-7340) and IL-6 (Cat. No. 88-50625) were determined using kits from Invitrogen (Thermo Fisher Scientific, Waltham, MA, USA). LTB4 (Cat. No. CEA562Ge) and PGE2 (Cat. No. CEA538Ge) were measured using kits from Cloud-Clone Corp. (Katy, TX, USA). Additionally, levels of EETs were assessed using a kit from Ruiqing Biological (Cat. No. RQ-sw13500; Shenzhen, China). The level of COX-2 was further determined using a commercial ELISA kit (Cat. No. SED284Ra; Cloud-Clone Corp., Katy, TX, USA).

2.6.2. Western Blot Analysis

Prostate tissues stored in liquid nitrogen were thawed on ice, rinsed with cold PBS, and homogenized in RIPA buffer (G2002; Servicebio, China). After lysis and centrifugation (4 °C), supernatants were collected for protein extraction. Protein levels were measured using a BCA assay kit (G2026; Servicebio), and samples were mixed with loading buffer and denatured. Proteins were resolved by SDS-PAGE, transferred to PVDF membranes (GPWDF45; Servicebio), and blocked with 5% non-fat milk in TBST, followed by overnight incubation at 4 °C with primary antibodies against cPLA2 (1:1000), sEH (1:1000), and β-actin (1:5000). After washing, membranes were incubated with HRP-conjugated secondary antibodies and visualized using an ECL kit (G2014; Servicebio). Band intensity was quantified with ImageJ (NIH, USA).

2.7. Statistical Analysis

Data are expressed as mean ± SD. Differences between two groups were analyzed using unpaired Student’s t-test, while one-way ANOVA was applied for multiple-group comparisons. Analyses were conducted with GraphPad Prism 9.0, and P < 0.05 was considered significant.

3. Results

3.1. Composition Analysis of CP1HD

A total of 371 metabolites were identified in CP1HD. The total ion chromatograms (TICs) acquired in positive and negative ion modes are illustrated (Figure 1A–B). The characterized active constituents are summarized (Table 2), and they primarily consist of isoquinoline alkaloids, flavonoids and isoflavones, phenolic acids, diterpenoid quinones, and triterpenoid saponins. Notably, among the top 30 most abundant metabolites, representative bioactive compounds include tetrahydropalmatine, corydaline, salvianolic acid B, calycosin, cryptotanshinone, and glycyrrhizic acid.

Figure 1.

Identification of the chemical composition of CP1HD. (A) Total ion chromatogram (TIC) in positive mode of CP1HD. (B) Total ion chromatogram (TIC) in negative mode of CP1HD

Table 2.

LC-MS/MS Data of Characterized Compounds in CP1HD (Top 30)

No.	Molecular weight (g/mol)	Formula	Compounds	Class	Precursor ion form	Peak area
1	355.17	C21H25NO4	Tetrahydropalmatine	Protoberberine alkaloids and derivatives	[M+H] +	20,119,430,270.97
2	365.16	C22H23NO4	Dehydrocorydaline	Protoberberine alkaloids and derivatives	[M+H] +	13,323,110,460.26
3	718.15	C36H30O16	Salvianolic acid B	2-arylbenzofuran flavonoids	[M-H] -	10,719,296,116.79
4	369.19	C22H27NO4	Corydaline	Protoberberine alkaloids and derivatives	[M+H] +	9,822,581,081.07
5	494.12	C26H22O10	Salvianolic acid A	Stilbenes	[M-H] -	7,819,874,678.62
6	198.05	C9H10O5	Danshensu	Phenylpropanoic acids	[M-H] -	5,950,652,870.88
7	353.12	C20H19NO5	Protopine	Protoberberine alkaloids and derivatives	[M+H] +	5,392,518,834.69
8	312.04	C13H12O9	Caftaric acid	Cinnamic acids and derivatives	[M-H] -	4,492,172,892.86
9	256.07	C15H12O4	Isoliquiritigenin	Linear 1,3-diarylpropanoids	[M+H] +	4,351,691,575.09
10	430.12	C22H22O9	Ononin	Isoflavonoids	[M+H] +	4,093,798,593.91
11	284.06	C16 H12 O5	Calycosin	Isoflavonoids	[M+H] +	4,039,364,141.7
12	474.07	C22H18O12	Cichoric acid	Carboxylic acids and derivatives	[M-H] -	3,857,738,101.17
13	369.15	C21H23NO5	Allocryptopine	Protopine alkaloids	[M+H-H20] +	3,284,716,293.09
14	418.12	C21H22O9	Liquiritin	Flavonoids	[M-H] -	3,098,832,369.15
15	296.14	C19H20O3	Cryptotanshinone	Prenol lipids	[M+H] +	3,000,856,285.57
16	268.07	C16H12O4	Formononetin	Isoflavonoids	[M+H] +	2,905,987,654.05
17	538.1	C27H22O12	Lithospermic acid	2-arylbenzofuran flavonoids	[M+H] +	2,493,515,485.62
18	446.11	C22H22O10	Calycosin-7-O-β-D-glucoside	Isoflavonoids	[M+H] +	2,434,886,855.8
19	470.33	C30H46O4	18 β-Glycyrrhetintic Acid	Prenol lipids	[M+H-H20] +	2,353,949,789.74
20	462.07	C21H18O12	Luteolin 7-glucuronide	Flavonoids	[M+H] +	2,213,686,482.32
21	256.07	C15H12O4	Liquiritigenin	Flavonoids	[M+H] +	2,122,925,717.24
22	822.4	C42H62O16	Glycyrrhizic acid	Prenol lipids	[M+H] +	2,071,493,197.99
23	267.09	C10H13N5O4	Allocryptopine	Purine nucleosides	[M+H-H20] +	1,939,643,900.2
24	162.03	C9H6O3	7-Hydroxycoumarine	Coumarins and derivatives	[M+H] +	1,844,084,964.95
25	187.06	C11H9NO2	trans-3-Indoleacrylic acid	Indoles and derivatives	[M+H] +	1,832,475,036
26	137.04	C7H7NO2	Trigonelline HCl	Pyridines and derivatives	[M+H] +	1,814,561,010.92
27	196.07	C10H12O4	2,4,5-Trimethoxybenzaldehyde	Benzene and substituted derivatives	[M+H] +	1,722,113,211.06
28	314.15	C19H22O4	1-(3,4-dihydroxyphenyl)-7-(4-hydroxyphenyl)-(6E)-6-hepten-3-ol	Diarylheptanoids	[M-H] -	1,686,013,699.86
29	666.22	C24H42O21	Stachyose	Organooxygen compounds	[M+Cl] -	1,621,420,192.56
30	339.14	C20H21NO4	Tetrahydroberberine THB	Protoberberine alkaloids and derivatives	[M+H] +	1,593,972,322.77

3.2. CP1HD Alleviates Pathological Manifestations and General Inflammation

The prostate index is commonly used to reflect inflammation severity in chronic prostatitis (Liu et al., 2021). In this study, λ-carrageenan-induced CP rats showed increased prostate volume and index, which were partially reduced by CP1HD treatment (Figure 2A). HE staining (Figure 2B) revealed that after 28 days of modeling, the prostate exhibited stromal expansion with marked inflammatory cell infiltration and varying degrees of glandular damage. Notably, CP1HD administration alleviated these histopathological alterations.

Figure 2.

Effects of different doses of CP1HD on Carrageenan-induced prostate injury in rats. (A) Changes in prostate index. (B) H&E staining of rat prostate tissue. (magnification: 200×, 20×,scale bar: 100μm) (D) Measurement of TNF-α levels by ELISA.(D) Measurement of IL-6 levels by ELISA. Data are presented as the mean ± SD (A: n = 5, B-D: n =3). ^#P <0.05, ^##P < 0.01; ^*P < 0.05, ^**P < 0.01

Substantial evidence indicates that persistent inflammatory responses and the resulting alterations in the local tissue microenvironment play critical roles in the initiation and progression of chronic prostatitis (Song et al., 2024). Accordingly, we investigated whether CP1HD mitigates systemic inflammation in a rat model of chronic prostatitis. Serum concentrations of TNF-α and IL-6 were quantified by ELISA to evaluate inflammatory status. Compared with the model group, CP1HD administration at different doses significantly reduced serum TNF-α and IL-6 levels, suggesting that CP1HD alleviates chronic prostatitis–associated systemic inflammation (Figure 2C–D). Notably, both the low- and high-dose regimens decreased circulating pro-inflammatory cytokines, supporting the anti-inflammatory potential of CP1HD.

3.3. Transcriptomics Reveals the Regulatory Mechanisms of CP1HD in Prostate Tissue

To elucidate the potential therapeutic mechanisms of CP1HD in prostate tissue, we performed transcriptomic sequencing of samples from the S, M, and CP1HD groups. Principal component analysis (PCA) showed clear separation among the three groups, indicating that model induction markedly altered the transcriptional profile of prostate tissue. Notably, after CP1HD treatment, samples in the CP1HD group displayed a trend toward clustering closer to the S group, suggesting that the intervention partially reversed the pathological transcriptional alterations (Figure 3A).

Figure 3.

Transcriptomic analysis of prostate tissues. (A)PCA of samples from Sham, Model and CP1HD-treated groups.(B)Volcano plots of DEGs between S vs. M and M vs. CP1HD groups.(C)KEGG pathway enrichment analysis of DEGs. (D)Heatmap of key DEGs in the arachidonic acid metabolism pathway across groups

Differential expression analysis identified DEGs between groups. The volcano plot shows the overall distribution of gene expression changes (Figure 3B). In total, 4,306 DEGs were found between the S and M groups, including 1,348 upregulated and 2,958 downregulated genes. 4,036 DEGs (1,275 upregulated and 2,761 downregulated) were detected between the CP1HD and M groups. Notably, many genes altered in the M group tended to return toward baseline after CP1HD treatment.

To further characterize the affected biological functions, we performed KEGG pathway enrichment analysis of DEGs from the S vs M and M vs CP1HD comparisons. The integrated results indicated that these DEGs were predominantly enriched in multiple inflammation- and metabolism-related pathways (Figure 3C). To provide a more comprehensive view of the enrichment landscape, we present the top 14 pathways with the highest enrichment significance. and, based on our study focus and prior evidence, additionally highlight the arachidonic acid metabolism pathway. Although this pathway ranked relatively lower in the overall enrichment list, published studies have implicated it in the pathogenesis of CP and in treatment-associated improvement (Jiang et al., 2024; J. H. Wang et al., 2025a), suggesting that it may contribute to the pharmacological actions of CP1HD and warrants further validation.

Based on the KEGG enrichment results, we further extracted key DEGs involved in the arachidonic acid metabolism pathway and visualized their expression patterns using a heatmap (Figure 3D). The heatmap showed that, compared with the S group, the M group exhibited markedly increased expression of Ptgs1, Ptgs2, and Ptges. Notably, CP1HD treatment significantly attenuated these changes, with expression levels shifting toward those observed in the S group. Collectively, these findings suggest that CP1HD may ameliorate pathological alterations in prostate tissue by modulating the arachidonic acid metabolism pathway and regulating the expression of its key genes.

3.4. CP1HD Modulates the Gut Microbiota Composition

With the growing focus on the “prostate–gut axis” and inflammation-related microbiome mechanisms, increasing evidence indicates that gut microbiota dysbiosis is closely associated with the onset and progression of CP(Cao et al., 2024; Liu et al., 2024). To assess the impact of the CP model on the gut microbial ecosystem and to examine the regulatory effects CP1HD-L on microbial community structure, we performed 16S rDNA sequencing of fecal samples from rats in the S, M, CP1HD groups.

ASV-level rarefaction curves plateaued with increasing sequencing depth across samples (Figure 4A), indicating that the sequencing effort was sufficient to capture most community members and that the dataset was of adequate quality for downstream diversity and community-structure analyses. Relative-abundance bar plots at the phylum and genus levels were then generated (Figure 4B). Overall, the dominant taxa were broadly comparable among groups; however, the M group exhibited a discernible compositional shift relative to the S group. Following CP1HD intervention, the microbial community profile differed from that of the M group and showed an overall tendency to move toward the S group, with the magnitude of this shift differing between the two doses (Figure 4B).

Figure 4.

CP1HD treatment restores gut microbiota dysbiosis in chronic prostatitis model.(A) Rarefaction curves at the ASV level.(B)Relative abundance of gut microbiota at the phylum and genus levels.(C)Chao1 index for alpha-diversity analysis.(D)PCoA based on Bray-Curtis distances for beta-diversity analysis. (E-F)LEfSe analysis identifying differentially abundant taxa between groups: cladogram(E) and LDA score bar chart(F).(G)Predicted functional alterations in KEGG pathways among groups

To evaluate the within-sample diversity and microbial richness among the different groups, the Chao1 index was calculated (Figure 4C). The results indicated that there was no significant difference in the Chao1 index among the S, M, and CP1HD groups (Kruskal-Wallis, p=0.73), suggesting that neither the CP modeling nor the CP1HD treatment significantly altered the overall richness of the gut microbial community.

At the beta-diversity level, principal coordinates analysis (PCoA) was performed to evaluate the overall structural variations in the gut microbiota (Figure 4D). The ordination space revealed a distinct separation between the M and S groups, suggesting that the establishment of the CP model induced an alteration in the gut microbial community structure. Notably, following treatment, samples from the CP1HD group exhibited a clear shift away from the M group and clustered closely with those of the S group. These patterns suggest that CP1HD treatment may help restore the overall structural composition of the gut microbiota altered by CP.

To identify specific key taxa driving intergroup differences, linear discriminant analysis effect size analysis was performed, and the results were visualized using cladograms (Figure 4E) and LDA score bar charts (Figure 4F). Compared with the S group, the M group exhibited a significant enrichment of dysbiosis-related taxa, primarily within the phyla Bacteroidota and Proteobacteria, along with a depletion of Firmicutes and specific beneficial taxa such as the Eubacterium siraeum group. Notably, CP1HD intervention effectively reversed these trends. The analysis between the M and CP1HD groups revealed that CP1HD treatment significantly enriched beneficial bacteria, including the class Bacilli and genus Lactobacillus, while prominently suppressing the disease-associated expansion of Bacteroidales and Enterobacterales as shown in Figure 4F. These findings suggest that these specific taxa represent crucial microbial targets through which CP1HD modulates and ameliorates chronic prostatitis associated microbiota alterations.

In addition, functional profiling based on the 16S rDNA gene dataset revealed distinct alterations in multiple KEGG functional categories, including carbohydrate metabolism, membrane transport, and replication and repair among the groups (Figure 4G). Notably, CP1HD intervention modulated a subset of these model-associated alterations, often reversing the direction of change observed in the M group. These findings collectively suggest that CP1HD may ameliorate chronic prostatitis by modulating the gut microbiome at both the community-structure and functional levels.

3.5. Metabolomics Analysis

To characterize the impact of CP1HD on the metabolic landscape of rat intestinal contents, we performed untargeted LC-MS metabolomics in the S, M, and CP1HD groups, As depicted in Figure 5A, OPLS-DA score plots showed a clear separation between the S and M groups, as well as between the M and L groups. This indicates that disease modeling was accompanied by marked remodeling of the intestinal content metabolome, and that CP1HD treatment shifted the global metabolic phenotype toward normalization. To evaluate model robustness and minimize the risk of overfitting, we performed 200-permutation tests (Fig. S1A-B) and reported the OPLS-DA performance metrics. The model yielded R²Y = 0.975,Q² = 0.724 for M vs S, and R²Y =0.998,Q² =0.822 for M vs L, supporting acceptable goodness-of-fit and predictive ability for subsequent feature selection. Differential metabolites were then identified using the prespecified criteria of VIP >1, P<0.05, and FC >1 or <1. Volcano plots summarized the significance and magnitude of changes (Figure 5B).

Figure 5.

CP1HD reshapes the intestinal metabolome and modulates the arachidonic acid metabolism pathway.(A)OPLS-DA score plots showing metabolic profile separation among the Sham (S), Model (M), and CP1HD-treated (CP1HD) groups.(B)Volcano plots of differential metabolites identified in the S vs. M and M vs. CP1HD comparisons.(C)Butterfly plot illustrating the relative abundance of the top 20 differential metabolites across groups.(D)KEGG pathway enrichment analysis of differential metabolites from the S vs. M and M vs. CP1HD comparisons.(E)Joint KEGG pathway analysis integrating transcriptomics and metabolomics data, showing the number of enriched genes (blue) and metabolites (red) in key pathways

The butterfly plot provides an intuitive overview of the directionality of change and the treatment effect for the top 20 differential metabolites (Figure 5C). Compared with the S group, the model group exhibited a disrupted intestinal metabolic profile characterized by the accumulation of an acylcarnitine (isovaleryl-L-carnitine) and aberrant increases in membrane phospholipids (PE species). Notably, metabolites related to downstream arachidonic acid signaling, including prostaglandin derivatives such as 9,11-dideoxy-9α,11α-methanoepoxyprosta-5Z, 13E-dien-1-oic acid, were significantly decreased (Figure 5C), suggesting that disease modeling induced a perturbed luminal microenvironment marked by lipid metabolic imbalance and altered inflammatory mediator precursors. In the M vs CP1HD comparison, these metabolites that were elevated in the M group (including Isovaleryl-L-carnitine and PE lipids) again emerged as significant features, with higher levels in M than in L, indicating that CP1HD treatment effectively reversed their abnormal accumulation. Collectively, CP1HD counteracted model-associated metabolic alterations and shifted the overall metabolic signature toward the Sham phenotype, with a prominent impact on lipid metabolic patterns closely linked to inflammation and membrane integrity.

Differential metabolites from each comparison were mapped to the KEGG database for pathway enrichment analysis (Figure 5D). Compared to the S group, the enriched pathways in the M group predominantly reflected inflammation-related metabolic perturbations. Notably, arachidonic acid metabolism showed significant enrichment, consistent with a characteristic pro-inflammatory lipid metabolic disturbance in the model rats. Importantly, compared to the M group, CP1HD group markedly reshaped primary bile acid and steroid hormone biosynthesis. More critically, this treatment significantly modulated arachidonic acid metabolism and its associated lipid metabolic network, including sphingolipid metabolism (Figure 5D). These results indicate that CP1HD can reverse model-induced dysregulation of arachidonic acid metabolism. Given the central role of the arachidonic acid cascade in chronic inflammation, these metabolomics findings provide a strong rationale for subsequent mechanistic interrogation of the anti-inflammatory actions of CP1HD (Figure 5D).

To further elucidate the molecular drivers underlying the observed metabolomic shifts, we integrated transcriptomic and metabolomic datasets for joint KEGG pathway analysis (Figure 5E). This analysis summarizes the distribution of enriched pathways across the top 10 categories in metabolomics (red bars) and transcriptomics (blue bars), revealing substantial concordance, particularly within lipid metabolism-related processes. Notably, arachidonic acid metabolism emerged as a key co-regulated pathway, comprising 22 differentially expressed genes (blue bar) and 3 differential metabolites (red bar). The observation of marked transcriptional involvement accompanied by fewer changes at the metabolite level suggests that CP1HD modulates the arachidonic acid axis primarily through the upstream transcriptional reprogramming of pathway enzymes (e.g., phospholipases and cyclooxygenases), thereby reshaping the abundance of downstream lipid mediators. In addition, primary bile acid biosynthesis and steroid hormone biosynthesis also showed coordinated gene-metabolite alterations, consistent with the metabolomics-only enrichment results. Collectively, this cross-omics validation strongly supports the hypothesis that CP1HD attenuates the arachidonic acid cascade via transcriptional regulation and provides a compelling rationale for subsequent protein-level validation of key rate-limiting enzymes in this pathway.

3.6. CP1HD Suppresses Inflammatory Responses and Immune Cell Infiltration in Prostate Tissue

Macrophage polarization and inflammatory cell infiltration play critical roles in the progression of chronic prostatitis (Zhang et al., 2026). To further evaluate the inflammatory microenvironment in prostate tissue, immunohistochemical staining was performed to detect the expression of the M1 macrophage marker iNOS, the M2 macrophage marker CD206, and the neutrophil-associated marker MPO in different groups (Ruan et al., 2026) (Figure 6A). As shown in the representative images and quantitative analysis, prostate tissues from the M group exhibited markedly increased iNOS and MPO expression, accompanied by decreased CD206 expression compared with the S group (P < 0.05), indicating enhanced pro-inflammatory macrophage activation and inflammatory cell infiltration after model induction. Notably, CP1HD treatment significantly reduced the expression of iNOS and MPO while markedly increasing CD206 expression relative to the M group (P < 0.05) (Figure 6B). These findings suggest that CP1HD alleviates prostatic inflammation by suppressing M1-type macrophage activation, promoting M2 polarization, and reducing inflammatory cell infiltration in prostate tissue.

Figure 6.

CP1HD alleviates prostate inflammation by modulating macrophage polarization and arachidonic acid metabolism. (A)Representative images of immunohistochemical staining for iNOS, CD206, and MPO in prostate tissues from Sham, Model, and CP1HD-treated groups. (B)Quantitative analysis of iNOS-, CD206-, and MPO-positive area percentages from (A). (C–F)​ Concentrations of the arachidonic acid-derived mediators PGE2 (C), LTB4 (D), EETs (E), and COX-2 (F) in prostate tissue, as measured by ELISA.(G)Representative Western blot images showing protein levels of cPLA2, sEH, and LTA4H in prostate tissues. (H–J)Quantitative analysis of cPLA2 (H), sEH (I), and LTA4H (J) protein expression normalized to β-actin. Data are presented as the mean ± SD (A-K: n =3). ^#P <0.05 and ^##P < 0.01 compared to the Sham group; ^*P < 0.05 and ^**P < 0.01 compared to the Model group

To explore whether CP1HD alleviates prostatic inflammation via arachidonic acid metabolism, inflammatory lipid mediators and related enzymes in prostate tissue were assessed. As shown in Figure 6C–F, PGE2 and LTB4 levels were markedly higher in the model group than in the sham group (P < 0.01), whereas EETs were reduced (P < 0.01), indicating an enhanced inflammatory response. CP1HD treatment decreased PGE2 and LTB4 and restored EETs, with a more pronounced effect at low dose (P < 0.05 or P < 0.01). Moreover, COX-2 expression was elevated in the model group (P < 0.01) and was suppressed following CP1HD administration (P < 0.05).

To further clarify the molecular basis underlying these changes, the protein expression of key enzymes involved in arachidonic acid metabolism was detected by Western blotting. Representative immunoblot bands are shown in Figure 6G, and the quantitative analyses are presented in Figure 6H–J. Compared with the sham group, the expression levels of cPLA2, sEH, and LTA4H in the model group were markedly increased, suggesting activation of arachidonic acid metabolic pathways in CP rats. Following CP1HD treatment, the expression of these enzymes was reduced to varying degrees. Notably, CP1HD significantly downregulated cPLA2, LTA4H and sEH expression compared with the model group (P < 0.05). Collectively, these findings indicate that CP1HD suppresses prostatic inflammatory responses, at least in part, by modulating key enzymes involved in arachidonic acid metabolism and rebalancing the production of arachidonic acid-derived lipid mediators.

3.7. Multi-Omics Correlation Analysis Linking Gut Microbiota, Intestinal Metabolites, and Host Inflammatory Indices

To comprehensively evaluate the interplay between gut microbiota and host metabolism following CP1HD intervention, we established a composite metabolite profile encompassing both disease-associated signatures and therapeutic responses. Specifically, the top 20 differential metabolites (based on VIP scores) were identified from the Model vs. Sham and CP1HD vs. Model comparisons, respectively. A union strategy was employed to integrate these sets, yielding a total of 31 candidate metabolites for subsequent correlation analysis. Subsequently, Spearman correlation analysis was performed between these metabolites and differential bacterial genera identified from LEfSe analysis, and the statistical significance of the correlations is indicated in the heatmap (Figure 7A). Hierarchical clustering of the correlation matrix revealed two major microbiota-metabolite correlation clusters. The first cluster, dominated by Bacteroidetes/Proteobacteria-related genera (including Bacteroides, Alistipes, Gammaproteobacteria, and Enterobacterales/Enterobacteriaceae), exhibited a predominantly positive correlation pattern with multiple metabolites. Conversely, the second cluster, centered on Firmicutes-associated taxa (such as Lachnospiraceae members, Defluviitaleaceae_UCG-011, and specific Clostridiales), displayed predominantly negative correlations with these metabolites. These findings suggest that CP1HD may modulate host metabolic alterations in association with remodeling of the gut microbial structure.

Figure 7.

Correlation analyses linking gut microbiota with metabolites and inflammatory markers. (A)Spearman correlation heatmap between differential metabolites and significantly altered bacterial genera. R values and significance levels are indicated. (B)Spearman correlation heatmap between prostatic inflammatory indicators (cytokines, lipid mediators, prostate index) and differential bacterial genera. ***P < 0.001; **P < 0.01; *P < 0.05

Specifically, the heatmap highlights clustered correlations between various small molecules, ranging from endogenous metabolites such as hypoxanthine and folinic acid to several xenobiotic or drug-related compounds such as codeine, ethylmorphine, salmeterol, atazanavir and distinct bacterial taxa. A prominent pattern emerged where genera represented by Bacteroides, Alistipes, and Gammaproteobacteria exhibited significant positive correlations with multiple metabolites within this cluster. In contrast, Lachnospiraceae-associated taxa and Defluviitaleaceae_UCG-011 predominantly displayed significant negative correlations. These results indicate a clear clustered correlation structure between the key microbiota and metabolites reversed by CP1HD. Notably, the Bacteroidetes/Proteobacteria cluster may represent an important microbial group associated with these metabolic alterations, providing correlative evidence that CP1HD may alleviate chronic prostatitis through a microbiota-metabolism interaction axis.

To further explore the association between gut microbiota alterations and host inflammatory status, Spearman correlation analysis was conducted between inflammation-related markers measured by ELISA (TNF-α, IL-6, PGE2, LTB4, and EETs), the prostate index (PI), and differential bacterial taxa identified by LEfSe analysis (Figure 7B). Distinct correlation patterns were observed between specific microbial taxa and inflammatory indicators. Several taxa affiliated with Proteobacteria-related lineages, including Rhodospirillales, Marinifilaceae, Gammaproteobacteria, and Proteobacteria, as well as Bacteroidetes-related taxa such as Bacteroides, Bacteroidales, and Rikenellaceae-associated genera (e.g., Alistipes), showed predominantly positive correlations with PI and pro-inflammatory mediators (TNF-α, IL-6, PGE2, and LTB4), while exhibiting negative correlations with the anti-inflammatory lipid mediator EETs. In addition, Candidatus_Soleaferrea, the [Ruminococcus]_torques_group, and Atopobiaceae were also positively associated with PI and several inflammatory cytokines.

In contrast, several taxa mainly belonging to Firmicutes-related groups, including Defluviitaleaceae_UCG-011, [Eubacterium]_hallii_group, and Erysipelotrichaceae_UCG-003, displayed positive correlations with EETs and inverse correlations with PI and pro-inflammatory cytokines. Moreover, Prevotellaceae_UCG-001, Micrococcaceae, and Peptococcus also tended to show negative associations with inflammatory mediators. Collectively, these results indicate a clustered microbiota-inflammation association pattern in which Proteobacteria-related taxa and several Bacteroidetes-associated genera are positively correlated with inflammatory indicators, whereas taxa such as the [Eubacterium]_hallii_group are associated with higher EETs levels and lower inflammatory mediator levels. These findings suggest a close relationship between gut microbial composition and host inflammatory status in chronic prostatitis.

4. Discussion

CP is a prevalent urological disorder characterized by persistent pelvic pain, urinary symptoms, and reduced quality of life. Increasing evidence indicates that sustained inflammatory responses within the prostate play a pivotal role in the initiation and progression of CP(Borgert et al., 2025). The carrageenan-induced CP rat model reproduces key pathological features of human prostatitis, including inflammatory cell infiltration and tissue structural damage, and is therefore widely used for evaluating therapeutic interventions and investigating disease mechanisms (J. H. Wang et al., 2025). Currently, numerous evidence suggests that TCM can treat prostate diseases, including CP. Based on its herbal composition, CP1HD likely possesses anti-inflammatory and tissue-protective properties, suggesting its potential for CP treatment, Although its pharmacological mechanisms remain unclear. A total of 371 metabolites were identified in CP1HD by UHPLC-MS/MS analysis, Among which tetrahydropalmatine, corydaline, salvianolic acid B, calycosin, cryptotanshinone, and glycyrrhizic acid have been reported to exert anti-inflammatory and tissue-protective effects, suggesting a potential pharmacological basis for the therapeutic activity of CP1HD (Q. Chen et al., 2026; X. Guo et al., 2026; X. Y. Guo et al., 2026b; K. Liu et al., 2026; Y. B. Song et al., 2025; Zhao et al., 2026). Histological and biochemical analyses of prostate tissue indicated that CP1HD markedly improved pathological alterations in carrageenan-induced CP rats and reduced pro-inflammatory cytokines (IL-6, TNF-α). These findings agree with previous clinical evidence. Elevated IL-6 (Xiao et al., 2023) and TNF-α (Zhu, 2025)have been linked to CP progression. Notably, IL-6 is increasingly recognized as a multifunctional cytokine with context-dependent roles, acting as a beneficial myokine in physiological energy metabolism but serving as a pathological driver of chronic inflammation and tissue injury under disease states, largely mediated by the activation of pathological IL-6 trans-signaling over classical signaling (Y. Wang et al., 2026). Thus, the significant downregulation of IL-6 by CP1HD highlights its potential in suppressing pathologically activated, trans-signaling-driven inflammatory cascades within the prostatic microenvironment.

Previous research has implicated that an imbalance between classically activated M1 macrophages and alternatively activated M2 macrophages has been implicated in the persistence and exacerbation of prostatic inflammation (Hu et al., 2026). The M1 macrophages were excessively activated during CP, which stimulated a cytokine storm characterized by the release of pro-inflammatory cytokines, such as IL-6 and TNF-α(Zhou et al., 2025). In contrast, the M2 macrophages were transformed into M1 phenotype with decreased the levels of anti-inflammatory cytokines such IL-10, thereby exacerbating prostate inflammation (Hong et al., 2026). Therefore, restoring the balance of M1/M2 macrophage polarization may be a promising therapeutic strategy for CP. Mechanistically, intracellular DNA-sensing cascades, particularly the cGAS-STING signaling pathway, have recently emerged as crucial upstream regulators driving M1 macrophage activation and pro-inflammatory polarization (H. Liu et al., 2026a). While acute STING activation facilitates immune surveillance, its persistent or chronic activation promotes an immunosuppressive and chronically inflamed microenvironment (Jiang et al., 2026; H. Liu et al., 2026a). Furthermore, macrophages and neutrophils are key effector cells involved in the innate immune response, acting as major effector cells involved in the clearance of inflammatory mediators, cellular debris, and damaged cells. They are able to interact with endothelial cells via surface adhesion molecules, thereby facilitating transendothelial migration into inflamed prostate tissues and contributing to local immunomodulatory processes (Hsieh et al., 2022). Studies have shown that CP is characterized by increased infiltration of macrophages and neutrophils, along with upregulated adhesion molecules such as CXCL1 and CXCL2, indicating enhanced inflammatory cell migration and adhesion in prostate tissue (Zhang et al., 2022). Specially, neutrophils are known to contribute to inflammatory processes by regulating endothelial cell activation, leukocyte infiltration, and the production of pro-inflammatory cytokines such as IL-1β and IL-17A (Yan et al., 2017). Although their role in chronic prostatitis has not been fully elucidated, evidence from other inflammatory conditions and prostate-related diseases suggests that neutrophils may participate in shaping the inflammatory microenvironment (Ene et al., 2022). Notably, the proportion of M1 macrophages was significantly decreased in CP rats following CP1HD treatment, whereas the proportion of M2 macrophages was significantly increased, indicating that CP1HD effectively regulates macrophage polarization and promotes a shift toward an anti-inflammatory phenotype. In addition, CP1HD reduced neutrophil infiltration in prostate tissue. These findings suggest that CP1HD alleviates chronic prostatitis by modulating M1/M2 macrophage polarization, neutrophil infiltration.

Although the therapeutic efficacy of CP1HD in alleviating chronic prostatitis has been demonstrated by histological and biochemical analyses, the underlying molecular mechanisms within the prostate tissue remain to be further clarified. The RNA-seq analysis in this study identified 4,306 and 4,036 DEGs in the S vs. M groups and CP1HD vs. M groups, respectively, indicating that CP1HD exerts its therapeutic effects through the regulation of multiple genes rather than a single target. Subsequent KEGG enrichment analysis showed that the differentially expressed genes were primarily enriched in inflammation- and signal transduction–related pathways, including the PI3K-Akt signaling pathway, MAPK signaling pathway, and ECM–receptor interaction. These pathways were significantly upregulated in CP rats but this was reversed after CP1HD treatment. Among the enriched pathways, arachidonic acid metabolism attracted particular attention. Although this pathway ranked relatively lower in the enrichment list, accumulating evidence has indicated that dysregulation of arachidonic acid metabolism plays a crucial role in the pathogenesis of chronic prostatitis by promoting the generation of pro-inflammatory lipid mediators (Jiang et al., 2024; J. H. Wang et al., 2025a). Arachidonic acid is released from membrane phospholipids primarily through the action of cPLA2 and subsequently metabolized into a variety of bioactive lipid mediators through cyclooxygenase, lipoxygenase, and cytochrome P450 pathways. These metabolites, including prostaglandins and leukotrienes, can amplify inflammatory responses, recruit immune cells, and aggravate tissue injury. In addition, signaling pathways such as PI3K-Akt and MAPK are known to participate in the regulation of arachidonic acid metabolism and inflammatory mediator production, thereby forming a complex inflammatory regulatory network (Yao et al., 2025). Further analysis of the transcriptomic data confirmed that key genes involved in the arachidonic acid pathway, including Ptgs1, Ptgs2, and Ptges, were markedly upregulated in the model group but substantially downregulated following CP1HD treatment. Metabolomic pathway analysis comparing the model group and the CP1HD-treated group also revealed significant enrichment of the arachidonic acid metabolism pathway, which will be discussed in detail in a later section. The coherence of these findings provides strong rationale for our focus on this pathway. We demonstrated that CP1HD alleviates prostatic inflammation primarily by modulating AA metabolism, characterized by the suppression of pro-inflammatory lipid mediators and partial restoration of anti-inflammatory metabolites. Specifically, CP1HD reduced the levels of PGE2 and LTB4 while restoring EETs, suggesting a rebalancing of the inflammatory lipid mediator network. These findings are consistent with previous studies reporting that dysregulated arachidonic acid metabolism contributes to the progression of chronic inflammatory diseases by promoting excessive production of prostaglandins and leukotrienes (D'Orazio & Mattoscio, 2024; Guardado et al., 2025). The downregulation of COX-2 further supports the inhibitory effect of CP1HD on inflammatory amplification. At the protein level, CP1HD reduced the expression of key metabolic enzymes, including cPLA2, sEH, and LTA4H, indicating suppression of the arachidonic acid metabolic cascade. Notably, sEH, a critical regulator of EETs degradation, was significantly inhibited, which may explain the restoration of EETs and suggests a potential mechanism distinct from conventional anti-inflammatory strategies targeting COX or LOX pathways. Compared with previous studies in chronic prostatitis that have predominantly focused on individual arachidonic acid-related targets, such as COX-2 (Duan & Wang, 2022; Maihemuti et al., 2026) rather than a systematic evaluation of the entire metabolic pathway, our findings highlight a multi-target regulatory pattern of CP1HD at both upstream and downstream levels of arachidonic acid metabolism. Although AA metabolism has been extensively investigated in prostate-related diseases such as benign prostatic hyperplasia and prostate cancer (Chen et al., 2025; L. Liu et al., 2026c), its comprehensive role in chronic prostatitis remains insufficiently explored.

Accumulating evidence has proposed the concept of the “prostate-gut axis,” which emphasizes the close interaction between intestinal microbiota and prostate diseases (Galla et al., 2026; S. W. Song et al., 2025). We further explored whether modulation of the gut microbiota can mediate the therapeutic effects of CP1HD in rats with CP. CP1HD exerted a significant effect on the overall gut microbiota structure of rats, primarily by inducing specific alterations in the M group, as expected. Disturbed gut microbiota was observed in experimental autoimmune prostatitis rats, characterized by decreased abundance of Lactobacillus and increased abundance of inflammation-associated taxa such as Bacteroides and Proteobacteria-related genera (e.g., Shigella), along with alterations in Firmicutes (Y. H. Wang et al., 2025b). Another study reported that gut microbiota dysbiosis in CP/CPPS patients was associated with a notable increase in Proteobacteria and related taxa, along with a decrease in beneficial bacteria including Alistipes and Eubacterium hallii group (Wang et al., 2023). Overgrowth of Desulfovibrionaceae in the gut produces excessive LPS, which can trigger systemic inflammatory responses (D. Q. Wang et al., 2026a). Consistent with these findings, our results demonstrated a marked microbial imbalance in prostate-injured rats, with the increased abundance of inflammation- and endotoxin-associated taxa, including Proteobacteria, Desulfovibrionaceae, Bacteroides, Alistipes, and Comamonadaceae being increased, while the abundance of short-chain fatty acid–producing bacteria, including Eubacterium_siraeum_group, Prevotellaceae_UCG-001, Anaerofustis, as well as the dominant phylum Firmicutes being decreased. Notably, CP1HD increased gut microbiota diversity in prostate-injured rats and restored the relative abundance of specific taxa, including Lactobacillus, Lachnospiraceae_FCS020_group and Eubacterium_siraeum_group, while decreasing the abundance of inflammation-associated bacteria, including Escherichia_Shigella, Alistipes, Bacteroides, Enterobacteriaceae, Ruminococcus_torques_group. These gut bacteria played an important role in mediating the therapeutic effects of CP1HD. Interestingly, we observed a significant increase in the relative abundance of Alistipes in the chronic prostatitis model group, which was reversed after treatment. Although some studies have reported a decreased abundance of Alistipes in certain inflammatory conditions, accumulating evidence suggests that its role is highly context-dependent (Wang et al., 2023). In particular, Alistipes has been associated with gut barrier dysfunction, altered lipid and amino acid metabolism, and low-grade chronic inflammation (He et al., 2026). In the setting of chronic prostatitis, its enrichment may reflect a dysbiotic microbial structure that contributes to increased intestinal permeability and systemic inflammatory responses. The reduction of Alistipes following treatment may indicate an improvement in gut barrier integrity and attenuation of inflammation along the gut-prostate axis. Analogous mechanistic paradigms established in other distant organ axes, such as the “gut-lung axis,” demonstrate that gut dysbiosis exacerbates peripheral inflammation through systemic metabolic reprogramming, host lipid mediator alteration, and immune cell trafficking (Sun et al., 2025). This concept provides a strong framework for understanding the “gut-prostate axis,” suggesting that microbial dysbiosis in CP may drive systemic inflammation and local prostatic lipid alterations. Previous studies have shown that alterations in the abundance of Bacteroidota and expansion of Proteobacteria are frequently associated with inflammatory disorders and intestinal barrier dysfunction, whereas many members of Firmicutes contribute to intestinal homeostasis through the production of anti-inflammatory metabolites such as short-chain fatty acids (Tian et al., 2025). In particular, Lactobacillus species are well-recognized probiotics that exert anti-inflammatory and immunomodulatory effects by maintaining intestinal barrier integrity and regulating host immune responses (H. R. Chen et al., 2026a). The concurrent enrichment of beneficial bacteria and suppression of disease-associated taxa in our study further supports the notion that CP1HD alleviates prostatic inflammation by reshaping the intestinal microbial ecosystem. Functional prediction analysis indicated that CP-associated alterations involved multiple metabolic and signaling pathways, highlighting a potential link between microbial functional shifts and disease progression.

Recent studies have highlighted that gut microbiota-derived metabolites play essential roles in mediating host physiological and inflammatory responses (S. W. Song et al., 2025a). Disturbances in intestinal metabolite profiles may also contribute to the pathogenesis of CP(Yue et al., 2025). Untargeted metabolomics was employed to examine differences in gut metabolites between prostate-injured rats and CP1HD-treated rats, with the aim of characterizing and uncovering the associated metabolic processes. The results showed that metabolic profiles in the CP1HD-treated group were closer to normal than those in the model group. Butterfly plot analysis further showed that, relative to the S group, the M group had elevated levels of 1,2-Dioleoyl-sn-glycero-3-phosphoethanolamine, 1,2-Dioleoyl-sn-glycero-3-phosphoethanolamine N,N-dimethyl, and Isovaleryl-L-carnitine, which were markedly reduced after CP1HD treatment. The isovaleryl-L-carnitine showed the most pronounced increase and contributed prominently among the differential metabolites. Isovaleryl-L-carnitine, an acylcarnitine derivative, is an important intermediate in branched-chain amino acid and fatty acid metabolism and plays a key role in mitochondrial β-oxidation and cellular energy homeostasis (Dambrova et al., 2022). A previous study showed that plasma acylcarnitines were significantly elevated in rats with chronic prostate inflammation induced by hormone exposure, suggesting that altered lipid metabolism and mitochondrial β-oxidation are closely associated with the development of chronic prostatitis (Nakamura et al., 2020). Phosphatidylethanolamine species, including 1,2-Dioleoyl-sn-glycero-3-phosphoethanolamine and its N,N-dimethylated derivative, are key components of membrane glycerophospholipids and serve as important reservoirs for polyunsaturated fatty acids (van der Veen et al., 2017). The elevation of these phosphatidylethanolamine species in the M group suggests enhanced membrane lipid remodeling during chronic prostatitis. Notably, phosphatidylethanolamine can be hydrolyzed by phospholipase A2 to release arachidonic acid, a central precursor of pro-inflammatory eicosanoids (Qin et al., 2025). Previous studies have shown that knockout of phosphatidylethanolamine-binding protein 4 (PEBP4) promotes the development of chronic non-bacterial prostatitis. The expression of PEBP4 protein in prostate tissues from patients with benign prostatic hyperplasia (BPH) combined with chronic non-bacterial prostatitis is significantly lower than that in patients with BPH alone. Reduced PEBP4 expression is associated with a higher risk of prostatitis recurrence after a 2-year follow-up. In addition, increased phosphorylation levels of NF-κB and IκB were observed in PEBP4-knockout RWPE-1 cells and in the prostate tissues of PEBP4-/- mice, indicating activation of the NF-κB signaling pathway (He et al., 2025). CP1HD may alleviate chronic prostatitis by normalizing lipid metabolic disturbances. The reversal of elevated phosphatidylethanolamine species suggests reduced arachidonic acid availability and downstream inflammatory signaling, indicating suppression of lipid-driven inflammation. Further KEGG pathway enrichment analysis demonstrated that arachidonic acid metabolism, sphingolipid metabolism, and steroid hormone biosynthesis were among the top enriched pathways. Given that arachidonic acid metabolism is a central source of pro-inflammatory eicosanoids and is tightly linked to membrane phospholipid turnover, these findings further support that CP1HD mitigates chronic prostatitis by modulating phosphatidylethanolamine-related lipid remodeling and suppressing downstream inflammatory signaling. Arachidonic acid metabolism emerged as a key co-regulated pathway at both the transcriptional and metabolic levels, supporting the hypothesis that CP1HD may regulate inflammatory lipid mediator production through coordinated transcriptional and metabolic reprogramming.

To further clarify the potential interactions among gut microbiota, microbial metabolites, and inflammatory responses, Spearman correlation analysis was performed to integrate multi-omics datasets. The results revealed significant associations between key intestinal metabolites, microbial taxa, and inflammatory indicators, suggesting a close interplay between microbial metabolic activity and host inflammatory status. Several lipid-related metabolites that were altered in the CP model showed significant correlations with specific gut microbial taxa. For instance, metabolites such as isovaleryl-L-carnitine and phospholipid species displayed positive correlations with inflammation-associated bacterial taxa including Proteobacteria and Bacteroides, whereas negative correlations were observed with beneficial taxa belonging to Firmicutes, such as Eubacterium-related groups. These findings suggest that dysbiosis of the gut microbiota may influence host lipid metabolic patterns through microbial-derived metabolic intermediates. Furthermore, inflammatory mediators measured in this study, including TNF-α, IL-6, PGE2, LTB4, and prostate index PI, also exhibited significant correlations with several bacterial taxa. In particular, inflammation-associated taxa such as Proteobacteria and Bacteroidales showed positive correlations with pro-inflammatory indicators, whereas taxa belonging to Firmicutes and other beneficial bacterial groups were negatively associated with these inflammatory markers. These observations indicate that alterations in gut microbial composition may contribute to the systemic inflammatory milieu observed in CP. Importantly, CP1HD treatment markedly reshaped these correlation patterns. The restoration of beneficial microbial taxa was accompanied by normalization of lipid-related metabolites and attenuation of inflammatory mediators. Given that many of these metabolites are closely linked to arachidonic acid metabolism and related lipid signaling pathways, these results further support the hypothesis that gut microbiota-derived metabolites may act as functional intermediates linking microbial dysbiosis with inflammatory signaling.

Taken together, the integrated multi-omics analysis suggests a mechanistic cascade whereby CP1HD modulates gut microbial composition, thereby regulating microbial-associated metabolites and downstream lipid inflammatory mediators. Through this microbiota-metabolite-inflammation axis, CP1HD may suppress the activation of the arachidonic acid pathway and ultimately alleviate prostatic inflammation. These findings provide further evidence supporting the existence of a prostate-gut axis and highlight the importance of microbial metabolic regulation in the therapeutic effects of CP1HD. Despite these promising mechanistic insights, several limitations of the present study should be considered when interpreting the results. First, although the animal model reproduced key features of chronic prostatitis, it may not fully recapitulate the complexity of the human disease. Second, a positive control group was not included, which limits direct comparison with established therapeutic strategies. Third, the inflammatory assessment mainly focused on pro-inflammatory cytokines, while anti-inflammatory mediators such as IL-10 were not evaluated, resulting in an incomplete characterization of the immune response. In addition, although our data strongly suggest the involvement of the arachidonic acid pathway, its exact causal contribution remains to be fully elucidated by studies employing pathway-specific knockout or inhibition models. Future studies with larger cohorts, more comprehensive inflammatory profiling, and targeted validation experiments are warranted to further substantiate the mechanistic findings and therapeutic potential of CP1HD.

5. Conclusion

This study demonstrates that CP1HD alleviates chronic prostatitis in rats and reveals its underlying mechanisms through integrated multi-omics analyses. CP1HD attenuates inflammation and immune cell infiltration, at least in part, by modulating arachidonic acid metabolism and reshaping gut microbiota-associated metabolic profiles. These findings provide a mechanistic basis for the potential application of CP1HD in CP treatment.

ORCID iDs

Li-Xing Lei https://orcid.org/0009-0008-1763-6052

Hao-Ran Chang https://orcid.org/0009-0001-9274-5346

Jun-Long Feng https://orcid.org/0000-0001-6836-9793

Xiang-Fa Lin https://orcid.org/0009-0006-5971-6478

Long-Ji Sun https://orcid.org/0000-0001-9841-5156

Lu Wang https://orcid.org/0009-0007-1557-7194

Ji-Sheng Wang https://orcid.org/0000-0002-3332-076X