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. 2025 Sep 3;47(1):2552913. doi: 10.1080/0886022X.2025.2552913

Triptolide improves microbial dysbiosis and metabolite disorder in db/db mice

Lingfei Lu a,*, Yixin Li b,*, Yanyan Zhou b, Jiwei Chen c, Tian Fu d, Jiamei Zhuang d, Hongcheng Peng d, Fang Liu e, Linlin Sun a, Jiandong Lu a,✉, Guoliang Xiong b,✉
PMCID: PMC12409871  PMID: 40898827

Abstract

Background

Diabetic kidney disease (DKD) is an increasing global public health problem. Triptolide (TP) has a good therapeutic effect on DKD and is widely used in China. However, the mechanism of TP is still unclear.

Methods

Db/db mice models were subjected to TP for 12 weeks. UHPLC-QE-MS and 16S rRNA amplicon sequencing were used to investigate the correlations between the metabolome, microbiome, and DKD-related indicators under DKD condition.

Results

TP demonstrated significant nephroprotective effects in db/db mice, ameliorated renal functional impairment and structural damage while attenuated inflammatory responses associated with DKD. Notably, TP administration effectively restored gut microbiota dysbiosis in db/db mice. Comparative analysis identified ten altered microbial taxa across groups, including Bifidobacterium, Erysipelotrichaceae_U-CG003, Herminiimonas, Domibacillus, Methylobacterium-Methylorubrum, Phascolarctobacterium, Dorea, Ralstonia, UCG-002, and Dubosiella, suggesting their potential utility as discriminative biomarkers for DKD progression and therapeutic response. Metabolomic profiling revealed 11 significantly perturbed metabolites, with small molecule pathway database (SMPDB) enrichment analysis highlighting three critical metabolic pathways: vitamin K metabolism, propionate metabolism, and steroid biosynthesis. Mechanistic investigations suggest that TP may reduce the inflammatory response through the JNK/STAT/P53 pathway, regulate the changes of intestinal flora, and correct renal metabolic disorders to exert renal protection.

Conclusion

TP may play a renal protective role by regulating the changes of intestinal microflora and correcting renal metabolic disorders, which may be related to the JNK/STAT/P53 pathway involved in reducing the inflammatory response. In addition, Vitamin K2 has a synergistic anti-inflammatory effect with TP.

Keywords: Diabetic kidney disease, Traditional Chinese Medicine, Triptolide, Metabolome, Microbiome

HIGHLIGHTS

A healthy and balanced relationship between the gut and the host is crucial for maintaining the health of the host, and dysbiosis of this bidirectional crosstalk is implicated in the pathogenesis of DKD.

Bacterial genera and metabolites were significantly altered and restored to normal levels after Triptolide treatment.

Triptolide may play a renal protective role by regulating the changes of intestinal microflora and correcting renal metabolic disorders, which may be related to the JNK/STAT/P53 pathway involved in reducing the inflammatory response.

Introduction

Diabetic kidney disease (DKD) is a progressive disorder defined by reduced renal function due to hyperglycemia, often co-occurring with albuminuria [1]. Nearly half of patients with type 2 diabetes mellitus (T2DM) will have DKD disease, which is one of the most common and destructive complications of diabetes mellitus [2]. Although the treatment methods including sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide-1receptor agonists are recommended, people with type 2 diabetes and chronic kidney disease are still at high risk of developing end-stage renal disease and cardiovascular complications, especially when high-concentration proteinuria persists [3,4].

Traditional Chinese Medicine has been recognized as a potentially effective therapy for DKD [5]. Tripterygium wilfordii Hook F. (TwHF), a traditional Chinese herb known for its anti-inflammatory and immunosuppressive properties, has been used to treat DKD for years. Triptolide (TP), is one of the main active components of TwHF, has been reported to dramatically attenuate renal injury and regulates immune-inflammatory responses in DKD animal models [6,7]. However, the mechanism of TP in the treatment of DKD is still unclear.

Recent studies show that normal intestinal flora and metabolites have been shown to play an integral role in the protection of the kidney, while dysbiosis and dysfunction might account for the pathogenesis of DKD [8–10]. Metabolomics can be defined as a method for quantitative analysis of all metabolites in organisms to find the relationship between metabolites, pathophysiological changes, and the mechanism of therapeutic drugs [11]. Metabolites are regarded as the downstream products of intestinal flora and may influence the metabolism of the host [12]. Recent studies collectively highlight the critical role of gut microbiota and microbial-derived metabolites in kidney diseases, particularly chronic kidney disease. Specific Lactobacillus species, such as L. johnsonii, and their metabolites play a protective role by modulating the aryl hydrocarbon receptor signaling pathway, which is implicated in renal fibrosis and inflammation. Additionally, metabolomics analyses reveal significant alterations in amino acid, nucleotide, and steroid hormone metabolism in kidney disease patients, with potential biomarkers like dehydroepiandrosterone offering diagnostic and therapeutic promise. These findings underscore the importance of the gut-kidney axis and the potential for microbiota-targeted interventions in managing kidney diseases [13–17]. The latest research reports that compounds isolated from natural products can improve metabolic disorders and microbial ecological disorders [18,19]. In this study, we combined a metabolomics analysis and gut flora analysis to investigate the potential biomarkers in db/db mice after TP treatment.

Materials and methods

Materials and reagents

TP (purity 98.23%, molecular weight 360.4) was bought from Chengdu Herbpurify Co., Ltd (Chengdu, China). Vitamin K2 (purity 99.96%, molecular weight 444.65) was bought from MedChemExpress (Monmouth Junction, NJ, USA). QuantiChromTM Creatinine Assay Kit (BioAssay Systems, Hayward, CA, USA). Mouse albumin ELISA Kit (Bethyl Laboratories, Montgomery, TX, USA). Creatinine (serum) Colorimetric Assay Kit (Cayman Chemical, Ann Arbor, Michigan, USA). Blood Urea Nitrogen Detection Kit purchased from StressMarq Biosciences (British Columbia, Canada). Cell Counting Kit-8 (CCK-8) was bought from Glpbio (Montclair, NJ, USA).

Animals

Specific pathogen-free (SPF) male diabetic db/db mice and non-diabetic m/m mice (age 8 weeks) on a C57BLKS/J background were purchased from Changzhou Cavens laboratory animal Co., Ltd. (Changzhou, China). All mice were kept in an SPF-grade facility. The facility was kept at a fixed temperature (22–24 °C) and relative humidity (50 ∼ 70%) with the 12 h light/dark cycle. The m/m mice were used as a control group (m/m, n = 7). Fourteen db/db mice were randomly divided into two groups: the model group (db/db, n = 7) and the TP treatment group (db/db + TP, n = 7). The db/db + TP group was administered TP (50 μg/kg/day) by gastric irrigation for 12 weeks. At the end of the study, urine samples were collected in metabolic cages. Blood was collected from anesthetized mice. Kidney samples were collected for further analysis. Samples are stored at −80 °C for further use.

Biochemical analysis

Levels of serum creatinine (Scr) and blood urea nitrogen (BUN) were measured by Creatinine Serum Detection Kit and Blood Urea Nitrogen Detection Kit. Urine creatinine and urinary albumin were measured by a QuantiChromTM Creatinine Assay Kit and mouse albumin ELISA Kit, respectively. Urinary albumin to creatinine ratio (ACR) was calculated by dividing urinary albumin by urine creatinine.

Histological analysis

Kidneys were isolated and fixed in 4% paraformaldehyde for the paraffin-embedded section. Then, Periodic-acid-Schiff (PAS) staining was performed to detect the structures of the paraffin-embedded kidney sections. The mesangial matrix expansion was defined as a PAS positive and nuclei-free area in the mesangium, and was measured using ImageJ software. Masson staining was used to estimate renal fibrosis. We used ImageJ software to evaluate tubular interstitial fibrosis in Masson staining. Collagen volume fraction was calculated as follows: collagen area/total area × 100%.

Electron microscope observation

Cortex of kidneys were collected and fixed with electron microscope fixing solution and post-fixed in 1% OsO4, followed by washing in distilled water and en bloc staining in 3% uranyl acetate. The subcellular constituents and structures were observed using a transmission electron microscope (Hitachi, HT7800).

Data processing of 16S rRNA gene sequences

Fecal samples were collected under sterile conditions and stored at −80 °C before analysis. After splicing, quality control and chimera sequence filtering of the original data, sequence denoising was performed using the division amplicon denoising algorithm 2 (DADA2) to generate a representative sequence and amplicon sequence variants (ASVs) table. Alpha diversity was analyzed by Chao-1 index, Simpson index and Shannon index. Beta analysis used principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS).

Kidney metabolites were performed using an untargeted metabolomics UHPLC-QE-MS

The metabolomics process includes sample preparation, metabolite separation and detection, data preprocessing, and multivariate data analysis for metabolite identification, with slight modifications to our previous protocol [20].

Western blot analysis

The renal cortical tissue lysate was placed in 8 or 10% SDS-PAGE gel for electrophoresis and transmembrane. After blocking with 5% skimmed milk powder, the membrane was incubated with the primary antibodies overnight at 4 °C. Primary antibodies are shown in Supplementary Table 1. ECL luminescence solution was used to observe the protein bands after the membrane was incubated with HRP-conjugated secondary antibody at room temperature for 1 h.

Quantitative real-time qPCR

The RNA Isolation Kit V2 (Vazyme, Nanjing, China) was used for the isolation of total RNA from the kidneys or cells. Primer sequences are shown in Supplementary Table 2. β-actin (2−△△Ct) was used as a reference gene for determining the relative expression of genes.

Cell culture and treatment

HK-2 cells were obtained from Wuhan Pricella Biotechnology Co., Ltd. (Wuhan, China). Cells were cultured in MEM medium with 10% FBS. Cells were cultured at 37 °C in a humidified incubator with an atmosphere of 5% CO2 and 95% air. The control group (Control) was cultured in MEM medium containing 5.5 mM glucose. The high glucose group (HG) was cultured in MEM medium containing 30 mM glucose. TP and Vitamin K2 (Vitk2) was dissolved in DMSO and the final concentration of DMSO was below 0.1% in the cell culture media.

Cell counting kit-8 (CCK-8) assay

The cells in the logarithmic growth stage were selected and seeded in a 96-well plate at a rate of 1 × 104 cells/well, and each sample was set up with 4 subsidiary wells while cultured in the incubator. The Cell Counting Kit-8 (CCK-8) solution was diluted according to the ratio of medium: CCK-8 = 1:10, and then 100 μl of above mixed medium was added to each well, meanwhile, the absorbance at 450 nm was measured after 2 h of incubation at 37 °C.

Immunofluorescence

HK-2 cells were seeded into 35 mm confocal dishes at a density of about 1 × 105 cells per well and incubated overnight at 37 °C in a 5% CO2 incubator. After stimulation, the cells were washed three times with sterile PBS and then fixed using 4% PFA fix solution for 15 min. The cells were then permeabilized with PBS solution of 0.1% triton X-100 for 30 min and then blocked with immunostaining blocking buffer for 30 min, and then incubated with the primary antibody overnight at 4 °C. The cells were then washed with PBS and incubated with secondary antibodies with Alexa Fluor® 488 (Abcam, ab150077) in the next day.

Statistical analysis

All quantitative data were presented as the mean ± standard error of mean (SEM). GraphPad Prism version 8.0.0 for Windows (GraphPad Software, San Diego, California, USA) was used for all statistical analyses. One-way ANOVA was used to determine statistically significant differences and Tukey’s multiple comparison test was used to determine the significance of the differences. Brown-Forsythe and Welch ANOVA tests when variances are unequal. Kruskal-Wallis tests should be used if the data do not follow a normal distribution. We used non-parametric tests to determine the relative abundance of genera and metabolites, including Wilcoxon rank sum tests and Mann–Whitney U tests. A Spearman rank correlation was then conducted to determine whether kidney metabolite intensities were associated with 16S levels. p < 0.05 was considered statistically significant.

Result

TP ameliorated renal injury in the db/db mice

To explore the protective mechanism of TP on DKD mice model was established. As shown in Figure 1A and 1B, the levels of body weight and blood sugar were significantly increased in the DKD group, compared to the control group (p < 0.01). There was no significant change in body weight and blood sugar after TP treatment. As shown in Figure 1C and 1D, the levels of BUN and Scr were significantly increased in the DKD group, compared to the control group (p < 0.05). TP administration reduced BUN levels but with no statistical difference (p > 0.05). There was no significant change in Scr after TP treatment. Urinary albumin-to-creatinine ratio (ACR) was significantly increased in db/db mice relative to m/m mice at the end of 12 weeks. This increase was reversed by TP treatment of the db/db + TP mice (Figure 1E). Histopathological analysis in the DKD group revealed glomerular hypertrophy by PAS staining (Figure 1F). In addition, there was a clear accumulation of collagen fibrils (blue staining) by Masson staining. TP significantly alleviated glomerular hypertrophy and reduced collagen deposition in db/db mice (Figure 1G). Moreover, we also observed podocyte ultra-structures by TEM. The foot processes of the podocytes fused and the number of foot processes decreased in the DKD group, TP treatment reduced podocyte damage (Figure 1H). These data demonstrate that TP presents a renoprotective effect on DKD in vivo.

Figure 1.

Figure 1.

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Effects of TP in the db/db mice. (A) The level of body weight (n = 7). (B) The level of blood sugar (n = 7). (C) The level of BUN (n = 7). (D) The level of Scr (n = 7). (E) The level of ACR (n = 7). (F) PAS staining of kidney and semi-quantitative analysis of mesangial matrix expansion (n = 7, scale bar = 100μm). (G) Masson staining of kidney and collagen volume fraction (n = 7, scale bar =100μm). (H) TEM of podocyte and foot process width (n = 4, scale bar = 500 nm). Data are presented as the means ± SEM. (*p < 0.05, **p < 0.01, ns: no statistical differences).

TP alleviated renal fibrosis in db/db mice

By measuring the expression of fibrotic markers, we investigated the effect of TP on fibrosis. As shown in Figure 2, the protein expressions of Fibronectin (FN), Collagen IV (Col-IV), and alpha smooth muscle actin (α-SMA) were all significantly upregulated in the db/db mice (p < 0.01). By contrast, TP treatment markedly decreased these proteins’ expression. These results provide further evidence that TP alleviates renal fibrosis in db/db mice.

Figure 2.

Figure 2.

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Effects of TP on renal fibrosis in db/db mice. (A) Representative Western blot images of FN, Col-IV, and α-SMA expression in the kidney of mice. (B) Densitometric analysis of FN, Col-IV, and α-SMA normalized to vinculin content (n = 4). Data are presented as the means ± SEM. (*p < 0.05, **p < 0.01).

Regulatory effects of TP on fecal microbiota profiles of db/db mice

To explore the protective mechanism of TP, we further performed fecal microbiota profiles of db/db mice. In the Alpha diversity analysis, Chao1 index, Shannon index, and Simpson index were used to assess bacterial community diversity and abundance, respectively. Rarefaction curves exhibited a tendency toward saturation, indicating that current sequencing depths are sufficient to study gut microbiome diversity (Supplementary Figure 1). Beta diversity analysis focuses on comparing species differences between microbial communities in different samples. Principal coordinate analysis (PCoA) based on weighted unifrac distances of the three groups (PCoA1 = 19.3%, PCoA2 = 8.8%) showed a significant separation between three groups (Figure 3A). NMDS analysis showed that there were some differences in the composition of intestinal flora among db/db, m/m and db/db + TP groups (Figure 3B). The db/db + TP group were close to the m/m group, pointing out that they could callback the disorders of gut microbiome of mice from DKD status to normal status. We further evaluated groupsimilarities at the Genus level via hierarchical clustering of the three groups (Figure 3C). The Mann-Whitney U test was used to analyze the microbial community composition of the two groups (m/m vs. db/db, db/db vs. db/db + LP), and the common difference species were selected to draw the histogram for visualization (Figure 3D). Bifidobacterium, Erysipelotrichaceae_UCG 003, Herminiimonas, Domibacillus, Methylobacterium-Methylorubrum, Phascolarctobacterium, Dorea, Ralstonia, UCG-002, and Dubosiella were common difference species, suggesting the microbial flora composition of these genera could be used as biomarkers with significant differences between groups. To evaluate the direct effect of TP on Bifidobacterium, we conducted in vitro experiments (Supplementary Figure 2). The results showed that compared with the control, DMSO (vector control) and TP (low and high doses) significantly inhibited the proliferation of Bifidobacterium. This suggests that TP does not clearly promote the growth of Bifidobacterium directly.

Figure 3.

Figure 3.

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Effects of TP on fecal microbiota. (A) PCoA plot generated by using microbiota OTU metrics based on the jaccard similarity for the m/m, db/db, and db/db + TP groups (n = 7). (B) NMDS analysis by using microbiota OTU metrics based on the jaccard similarity for the m/m, db/db, and db/db + TP groups (n = 7). (C) The hierarchical cluster based on the Bray-Curtis similarity of the samples from the m/m, db/db, and db/db + TP groups. The bar plot shows the abundance of each phylum or genus in each sample. (D) The 10 most abundant genera in the m/m, db/db, and db/db + TP groups. Data are expressed as mean ± SEM. Group differences were assessed by using the Kruskal-Wallis test.

Regulatory effects of TP on metabolic profiles of db/db mice

To explore the protective mechanism of TP, we performed metabolic profiles of db/db mice. Using the mixed solution as quality control (QC) samples, a QC sample was inserted every 6 assay analysis samples during the instrument analysis. The total ion current (TIC) of QC samples are shown in Supplementary Figure 3. By overlapping display analysis of TIC of mass spectrometry detection and QC sample analysis during the test, we can assess the stability of the instrument. OPLS-DA, a more reliable pattern recognition method, was carried out to sharpen the separation among groups (Figure 4A and 4D). R2 and Q2 are used to represent the fitting and the prediction abilities of the OPLS-DA model (Figure 4B and 4E). OPLS-DA was used to screen differential metabolites that associated to db/db mice and regulatory effects of TP on metabolic profiles of db/db mice (Figure 4C and 4F). A total of 119 differential metabolites were screened by combining VIP value (VIP > 1), and p-value of T-test (p < 0.05) (Figure 4G). A total of 11 differential metabolites were screened (Supplementary Table 3), and the SMPDB enrichment of these 11 differential metabolites was mainly concentrated in vitamin K metabolism, propanoate metabolism, and steroid biosynthesis (Figure 4H).

Figure 4.

Figure 4.

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Effects of TP on the kidney metabolome. (A, D) The PCA plot of metabolites of m/m (n = 7), db/db (n = 7), and db/db + TP (n = 7). (B, E) The OPLS-DA plot of metabolites of m/m (n = 7), db/db (n = 7), and db/db + TP (n = 7). (C, F) Volcano plots of metabolites with p < 0.05 and VIP of > 1 when comparing db/db vs. m/m or comparing db/db + TP vs. db/db. (G) Venn analysis for metabolites with p < 0.05 and VIP of > 1 when comparing db/db vs. m/m or comparing db/db + TP vs. db/db. (H) Enrichment of activated pathways induced by alterations of metabolites in m/m, db/db, and db/db + TP groups.

Correlations of DKD-related indicators, intestinal flora and differential metabolites

Based on Spearman’s relevant data, we constructed a heat map of correlation analysis (Figure 5). Based on the correlation coefficient, the different flora of Top5 are Bifidobacterium, Erysipelotrichaceae_UCG-003, Methylobacterium-Methylorubrum, Phascolarctobacterium, and Dubosiella, all of which are negatively correlated with ACR. Simulansamide, 5-Hydroxy-N-formylkynurenine, Methylmalonic acid, γ-Glutamylglutamate, and Isobutyrylglycation are the different metabolites with correlation coefficient Top5, among which Isobutyrylglycation has a negative correlation with ACR, while the others have a positive correlation.

Figure 5.

Figure 5.

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Correlations of DKD-related indicators, intestinal flora and differential metabolites. Pearman correlations of the expression of DKD-related indicators, the relative abundance of fecal microbial genus or species, and the abundance of metabolites in kidney. The r values are represented by gradient colors, with red cells indicating positive correlations and blue cells indicating negative correlations.

Effects of TP on JNK/STAT/P53 pathway in db/db mice

It has been found that Bifidobacterium and vitamin K are closely related to cellular inflammatory response [21]. Therefore, we investigated the effect of TP on inflammation. We investigated the relative expression of inflammatory factors. As shown in Figure 6A, the relative expression of IL-1β, IL-6, and TNFα was significantly up-regulated in db/db mice (p < 0.05). On the contrary, TP treatment significantly reduced the expression of inflammatory cytokines. The study found that c-Jun N-terminal kinase (JNK)/signal transducer and activator of transcription (STAT)/P53 pathway is closely related to inflammation [22]. Therefore, we examined the expression of proteins associated with the JNK/STAT/P53 pathway. As shown in Figure 6B and C, the protein expression of p-JNK, p-STAT1, p-STAT3 and p-P53 was significantly up-regulated in db/db mice (p < 0.01). On the contrary, TP treatment significantly reduced the expression of these proteins. These results suggest that TP may play a renal protective role by regulating the JNK/STAT/P53 pathway to reduce the inflammatory response.

Figure 6.

Figure 6.

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Effects of TP on inflammation and JNK/STAT/P53 pathway in db/db mice. (A) Expression of IL-1β, IL-6, and TNF-α in the kidneys of mice (n = 4). (B) Representative Western blot images of p-JNK, JNK, p-STAT1, STAT1, p-STAT3, STAT3, p-P53, and P53 expression in the kidneys of mice. (C) Densitometric analysis of p-JNK, JNK, p-STAT1, STAT1, p-STAT3, STAT3, p-P53, and P53 normalized to vinculin content (n = 3). Values are expressed as mean ± SEM. (*p < 0.05 **p < 0.01).

Effects of drug concentrations on high glucose-induced viability of HK-2 cells

Recent studies collectively highlight the critical role of microbial-derived metabolites in kidney diseases, we found that Bifidobacterium is a significant vitamin K2 (VitK2) producer [23], in order to further verify the anti-inflammatory effect of TP and whether it has a synergistic effect with VitK2 in anti-inflammatory effects, we used high glucose (HG)-induced HK-2 cells for in vitro experiments. The CCK-8 assay was employed to evaluate the effects of varying concentrations of TP and VitK2 on the viability of HG-induced HK-2 cells, aiming to determine optimal drug intervention concentrations. Results demonstrated that 50 nM TP exhibited significant cytotoxicity toward HG-induced HK-2 cells, markedly suppressing cell viability (p < 0.01). In contrast, lower TP concentrations (0.04, 0.2, 1, 5, and 25 nM) were showed no apparent cytotoxicity and even enhanced cell viability compared to the HG group (p < 0.05). Among these, 1 nM TP exerted the most pronounced effect and was selected as the optimal intervention concentration (Figure 7A). Similarly, all tested concentrations of VitK2 (5–50 μM) displayed no cytotoxicity and improved cell viability under high glucose conditions, with 10 μM VitK2 showing the most significant enhancement (p < 0.05). Consequently, 10 μM VitK2 was chosen for subsequent experiments (Figure 7B).

Figure 7.

Figure 7.

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Effects of TP on inflammation. (A) HK-2 cell viability after TP intervention. (B) HK-2 cell viability after Vitk2 intervention. (C) Expression of IL-1β in the HG-induced HK-2 cells were evaluated by RT-qPCR. (D) Expression of IL-6 in the HG-induced HK-2 cells were evaluated by RT-qPCR. (E) Expression of TNF-α in the HG-induced HK-2 cells were evaluated by RT-qPCR. (F) Expression of IL-1β in the HG-induced HK-2 cells were evaluated by immunofluorescence (scale bar = 25 μm). (G) Expression of TNF-α in the HG-induced HK-2 cells were evaluated by immunofluorescence (scale bar = 25 μm). Values are expressed as mean ± SEM. (*represents a comparison with the control group, #represents a comparison with the HG group. **p < 0.01, ##p < 0.01).

Synergistic effects of TP and vitamin K2 on inflammatory cytokine in high glucose-induced HK-2 cells

RT-qPCR and immunofluorescence analyses were performed to assess inflammatory cytokine levels in HK-2 cells. Results revealed a significant upregulation of inflammatory cytokines (IL-1β, IL-6, and TNFα) in the HG group compared to the control (p < 0.01). Both TP monotherapy (1 nM), VitK2 monotherapy (10 μM), and their combination effectively attenuated HG-induced inflammatory response (p < 0.01). Notably, the combination therapy exhibited the most substantial reduction in inflammatory cytokines, suggesting a synergistic anti-inflammatory interaction between TP and VitK2 in mitigating HG-induced cellular injury (Figure 7C–G). Collectively, our research suggested that TP may play a role in renal protection by reducing inflammatory response, thereby regulating intestinal flora changes and correcting renal metabolic disorders (Figure 8).

Figure 8.

Figure 8.

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The mechanism map of TP reducing inflammatory response and regulating intestinal flora changes to correct renal metabolic disorders and play a role in renal protection. Draw a chart with figdraw.

Discussion

Accumulated evidence has demonstrated that TCM improved the metabolite dysregulation and microbial dysbiosis [24,25]. In this study, we used 16S rRNA sequencing technology and tissue metabolomics of kidney samples to study the possible mechanism of its renal protective effect on DKD. The results showed that TP could protect the renal function and structure of db/db mice. In addition, the occurrence and progression of DKD may be related to the changes of host intestinal microflora and renal metabolic disorders. In order to evaluate the direct effect of TP on Bifidobacterium, we conducted in vitro experiments, and the results suggested that TP does not clearly promote the growth of Bifidobacterium directly. Adding TP powder directly to the culture medium may affect the uptake and utilization of bacteria. When we dissolved TP in a solvent, TP was only soluble in DMSO. However, we recognize that DMSO itself may exhibit antibacterial properties, which may confuse the experimental results. In vitro experiment assessing the direct effect of TP on Bifidobacterium is valuable; however, the results showed only a comparable level of growth suppression to the DMSO control. This finding suggests that the TP-induced alterations in the gut microbial community likely represent secondary or indirect consequences. Taken together with our observations that TP reduces inflammatory cytokines and inhibits the activation of the JNK/STAT/p53 signaling pathway, we hypothesize that the renoprotective effects of TP may be primarily mediated through the modulation of inflammatory responses. This anti-inflammatory action could subsequently influence metabolite profiles and gut microbiota composition, ultimately contributing to renal protection.

As a complex ecosystem containing trillions of bacteria belonging to thousands of species, the large intestinal microbiota plays an important role in both health and disease. A healthy and balanced relationship between the gut and the host is crucial for maintaining the health of the host, and dysbiosis of this bidirectional crosstalk is often implicated in the pathogenesis of metabolic diseases, including diabetes [26]. Toxic uremic solutes accumulating in the circulation trigger fibrosis, apoptosis, and inflammatory pathways, resulting in intestinal ecological disturbances, permeability deterioration, and renal failure in DKD [27]. In this study, we found that db/db mice showed a decreased abundance of Bifidobacterium compared to m/m mice. Bifidobacterium is a gram-positive microorganism, which participates in the recovery of the intestinal mucosal barrier [28]. As commonly used probiotics, Bifidobacterium can produce SCFAs to inhibit the NF-κB pro-inflammatory signaling pathway, further reduce inflammatory response, and protect the intestinal barrier for immune protection. It was negatively correlated with inflammation, hyperglycemia and insulin resistance [29]. Bifidobacterium has been proved to produce bacteriocin, prevent adhesion to mucosa and maintain intestinal barrier function [30]. Diabetes mellitus patients presented a significantly lower abundance of Bifidobacterium compared to healthy subjects [31], which was consistent with our results.

Bifidobacterium is a key player in intestinal microbiology and gut immunology. The abundance of Bifidobacteria was significantly increased in CKD patients after probiotic administration, while serum TNF-α, IL-6, IL-18 and endotoxin levels were significantly decreased. These results highlight the potential of probiotics to improve systemic innate immunity and pro-inflammatory cytokines in kidney disease [32]. Bifidobacteria abundance decreased in db/db mice, whereas inflammatory cytokines increased in these mice. TP is believed to have a crucial role in healthcare and disease treatment, with notable anti-obesity and anti-diabetic effects [33]. In China, TP has been widely used in the treatment of diabetes mellitus and its complications for a long time. Previous studies suggest that TP has antioxidative, immunosuppressive, anti-inflammatory, and podocyte-protective effects [34]. We found TP was significantly inhibit inflammation cytokines such as IL-6, IL-1β, and TNFα in db/db mice and HG-induced HK-2 cells. DKD is considered to be a chronic inflammatory disease, in which the structure of glomeruli and renal tubules is changed due to chronic microinflammation, leading to proteinuria [35]. Therefore, specific blockade of inflammatory cytokines may alleviate renal inflammatory response and restore kidney structure, and serve therapeutically in DKD.

In this study, we found the SMPDB enrichment of differential metabolites was mainly concentrated in vitamin K metabolism. Vitamin K is classified into three types: K1 (phylloquinone), K2 (menaquinone), and K3 (menadione). Vegetables contain phylloquinone, especially the dark green portions. In contrast, menaquinone is synthesized by specific bacteria species in the human gut and during bacterial fermentation of certain food products. Studies have shown that both anaerobic and aerobic bacteria can produce Vitk2. However, the performance of anaerobic bacteria and micro-aerobic bacteria was significantly better than that of aerobic bacteria. The most significant Vitk2 producers were Lactobacillus, Bifidobacterium and Bacillus, and found Vitk2 can inhibit the cytokine storm, mainly through significant pro-inflammatory cytokines inhibition [23]. It has been found that Bifidobacterium and vitamin K are closely related to cellular inflammatory response. In our study, the abundance of Bifidobacteria decreased and inflammatory cytokines increased in the DKD model. After TP treatment, the abundance of Bifidobacteria increased and inflammatory cytokines decreased. At the same time, TP and vitamin K2 have a synergistic effect in anti-inflammatory was verified in vitro. It has been reported that TP can reduce the infiltration of inflammatory cells in the colon and reduce the destruction of colonic epithelial cells [36]. Therefore, we speculate that TP may play a renal protective role by reducing the inflammatory infiltration of intestinal epithelial cells, thus affecting the changes of metabolites and intestinal flora.

Therefore, we speculate that TP may play a renal protective role by regulating the changes of intestinal microflora and correcting renal metabolic disorders, which may be related to reducing the inflammatory response. It is reported that vitamin K is closely related to JNK pathway and P53 [37,38]. The JNK pathway is very important in the regulation of cellular inflammatory response. It is found that JNK/STAT/P53 pathway is closely related to inflammation. In this study, we found the protein expressions of p-JNK, p-STAT1, p-STAT3, and p-P53 were significantly upregulated in the db/db mice. After TP treatment markedly decreased these proteins. TP may play a protective role in DKD through JNK/STAT/P53 pathway.

Conclusions

In summary, we found TP may play a renal protective role by regulating the changes of intestinal microflora and correcting renal metabolic disorders, which may be related to the JNK/STAT/P53 pathway involved in reducing the inflammatory response. In addition, Vitk2 has a synergistic anti-inflammatory effect with TP. The limitation of this study is that there is no experimental microbiota-depleted model to further verify the role of TP. Based on the clues obtained in this study, it is necessary to further study the use of antibiotics to clear mice or co-housing and fecal bacteria transplantation experiments to prove the mechanism that mediates or affects drug effects.

Supplementary Material

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IRNF_A_2552913_SM8695.docx (15.7KB, docx)

Funding Statement

This study was supported by Shenzhen Science and Technology Program (JCYJ20220531092201003 and JCYJ20240813152409012).

Author contributions

Material preparation, data collection and analysis were performed by Lingfei Lu, Yixin Li. The first draft of the manuscript was written by Lingfei Lu. Animal and cell experiments were performed by Yanyan Zhou, Yixin Li, Jiwei Chen, Tian Fu, Jiamei Zhuang, Hongcheng Peng, Fang Liu, Linlin Sun, and Lingfei Lu. Jiandong Lu and Guoliang Xiong designed the experiments and provided revisions and comments to the manuscript. All authors reviewed and approved the final manuscript.

Disclosure statement

The authors declare that there are no conflicts of interest regarding the publication of this article.

Data availability statement

The metabolomics data of this study was deposited in NCBI sequence read archive under accession PRJNA1044925. The 16S rRNA data of this study was deposited in Metabolights under accession MTBLS9151.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Material
IRNF_A_2552913_SM8705.docx (1.1MB, docx)
Supplemental Material
IRNF_A_2552913_SM8700.docx (724.8KB, docx)
Supplemental Material
IRNF_A_2552913_SM8699.docx (539.2KB, docx)
Supplemental Material
IRNF_A_2552913_SM8698.jpg (850KB, jpg)
Supplemental Material
IRNF_A_2552913_SM8697.docx (16.5KB, docx)
Supplemental Material
IRNF_A_2552913_SM8696.docx (15.3KB, docx)
Supplemental Material
IRNF_A_2552913_SM8695.docx (15.7KB, docx)

Data Availability Statement

The metabolomics data of this study was deposited in NCBI sequence read archive under accession PRJNA1044925. The 16S rRNA data of this study was deposited in Metabolights under accession MTBLS9151.

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