Mechanic evaluation of Jisheng Shenqi Wan (济生肾气丸) on calcium oxalate kidney stones: an integrated network pharmacology and metabolomics

Abstract

OBJECTIVE:

To understand the efficacy of Jisheng Shenqi Wan (JSSQW, 济生肾气丸) in treating calcium oxalate kidney stones (KS) and to investigate the mechanism of JSSQW action by combining network pharmacology with metabolomics analysis based on ultra-high performance liquid chromatography combined with tandem electrostatic field orbital trap high-resolution mass spectrometry (UHPLC-Q/Orbitrap HRMS).

METHODS:

The chemical components of JSSQW absorbed into rat blood were identified by UHPLC-Q/Orbitrap HRMS. The identified components were introduced into the Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine platform to screen for target genes, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and disease enrichment analysis. A KS rat model was generated using the oxalic acid precursor method to examine the efficacy of JSSQW for treating KS. Serum metabolomics was used to monitor changes in endogenous substances in KS rats after JSSQW intervention.

RESULTS:

Twenty-three chemicals from JSSQW were identified in the blood of JSSQW gavage-administered rats. KEGG enrichment analysis predicted the top 20 signaling pathways affected by these 23 chemicals. Disease enrichment analysis showed that the target genes of these 23 chemicals were enriched in diseases of the urinary system and endocrine system, including kidney stones. In a KS rat model, JSSQW inhibited the aggregation of calcium oxalate crystals, reduced renal tubular injury, lowered the renal index, and improved biochemical indicators (blood creatinine, blood urea nitrogen). Serum metabolomics identified 25 differential metabolites that responded to JSSQW treatment. They were mainly lipids, with phosphatidylethanolamine and phosphorylcholine and their derivatives accounting for the highest proportion. Metabolic pathway analysis showed that the changes in differential metabolites were related to multiple metabolic pathways, especially sphingolipid metabolism and sphingolipid signaling pathways.

CONCLUSIONS:

JSSQW can inhibit the aggregation of calcium oxalate crystals in the kidneys, reduce tubular injury, and improve kidney function in KS rats. Its mechanism of action may be related to regulating disordered metabolites and metabolic pathways, especially glycerol phospholipid metabolism, sphingolipid metabolism, and sphingolipid signaling.

1. INTRODUCTION

Urolithiasis, or the formation of stones in kidney, commonly known as kidney stones (KS), is a common urological occurrence. The movement and detachment of KS can lead to urinary tract obstruction, causing pain and discomfort, and in severe cases, a decline in kidney function.¹ The treatment of KS is divided into two stages: stone removal and stone prevention. Stone removal treatments include pharmacological treatments, extracorporeal shock-wave stone removal, and endoscopic stone removal. Although these methods can deal with most KS, there is a high recurrence rate,² resulting in a heavy burden on patients and public health care.³ Therefore, there is a need to reduce the incidence rate and recurrence rate of KS.

The pathogenesis of KS is complex and there is currently no consensus as to its mechanism.⁴ The composition of KS is complex, although 80% of KS contain calcium oxalate.⁵ Traditional Chinese Medicine (TCM) provides comprehensive treatment for patients with urolithiasis through differentiation of syndromes, constitution, and organs,^6,7 and has good therapeutic effects in preventing and treating KS.⁸ It is generally believed that the TCM treats KS pathogenesis by addressing “kidney deficiency.” The most representative classic formula for treating KS “kidney deficiency” is Jisheng Shenqi Wan (JSSQW, 济生肾气丸).⁹ The composition of JSSQW includes Shudihuang (Radix Rehmanniae Praeparata), Shanzhuyu (Fructus Corni), Shanyao (Rhizoma Dioscoreae Oppositae), Zexie (Rhizoma Alismatis), Fuling (Poria), Mudanpi (Cortex Moutan Radicis), Rougui (Cortex Cinnamomi Cassiae), Fuzi (Radix Aconiti Lateralis Preparata), Cheqianzi (Semen Plantaginis), Niuxi (Radix Achyranthis Bidentatae). JSSQW can alleviate renal colic, retard the progression of renal failure, and reduce the recurrence rate of KS.¹⁰ However, the specific mechanism of its effect is still unclear.

The formation of KS is a “point-to-surface” process, in which calcium oxalate crystals are deposited in the kidney and continuously aggregate and grow into stones.¹¹ Key factors related to KS in patients with chronic kidney disease include urinary oxalate excretion and urinary calcium excretion. These factors are associated with creatinine clearance, urinary protein occurrence, and renal pathological changes, indicating that kidney injury plays a role in the pathogenesis of calcium oxalate KS.¹² This pathology is consistent with the basic principle of TCM treatment of KS addressing “kidney deficiency.” The oxalate precursor induction of calcium oxalate KS in rats is a recognized animal model of KS. In this model, calcium oxalate crystals are observed under light microscopy to be deposited in rat kidney tissue.¹³ At this stage, calcium oxalate crystals begin to aggregate, but have not yet formed stones, which is an opportune stage for TCM intervention.

Like most TCMs, JSSQW consists of a complex mixture of bioactive components, which makes defining its specific and therapeutic targets very difficult. The traditional model of TCM research only focuses on the constituents contained in a Chinese medicine and on those compounds that have pharmacological activity in vitro, without truly elucidating the holistic pharmacological effects of a medicine.¹⁴ The vast majority of TCM components exert their therapeutic effects after being absorbed into the bloodstream and circulated to various tissues and organs, where they bind with target cells. Network pharmacology is based on systems biology and aims to establish the synergistic relationship between multiple components, targets, and pathways by constructing a component target pathway network, analyzing active ingredients and possible molecular mechanisms.¹⁵ Therefore, by detecting and analyzing small molecule metabolites in the serum of TCM-treated individuals, screening for active ingredients absorbed into the bloodstream, and performing network pharmacology analysis, the complex nature of TCM can be studied.¹⁶

Metabolomics is the study of small molecule metabolites in living organisms. It enables the dynamic changes of metabolite levels to be determined during normal physiology and in response to pathological states.^17,18 In recent years, metabolomics has been widely applied to study KS. The analysis of blood, urine, and tissue samples from KS animal models has identified various potential biomarkers associated with KS.^19,20 These are involved in multiple metabolic pathways, including lipid, amino acid, and caffeine metabolism.^20,21 Disrupting the accumulation of these metabolites may be an important factor in inhibiting KS.

Here, we first used ultra-high performance liquid chromatography combined with tandem electrostatic field orbital trap high-resolution mass spectrometry (UHPLC-Q/Orbitrap HRMS) to identify the JSSQW components that are absorbed into the rat bloodstream. We then constructed a KS rat model using the oxalic acid precursor method to examine the efficacy of JSSQW in treating KS. To explore the mechanism of JSSQW action on KS, we employed combined network pharmacology and metabolomics methods to monitor the changes in endogenous substances in KS rats after JSSQW intervention.

2. METHODS

2.1. Reagents and animals

JSSQW (9 g/pill, Z13021711) was provided by Beijing Yushengtang Group Shijiazhuang Pharmaceutical Co., Ltd. (Shijiazhuang, China). The above plant names have been checked with http://www.theplantlist.org. A urinary oxalic acid (OA) enzyme-linked immunosorbent assay (ELISA) kit was purchased from Renjie Biotechnology Co., Ltd. (Shanghai, China). Methanol, acetonitrile, formic acid, and isopropyl alcohol were purchased from ANPEL (Shanghai, China). All solvents were liquid chromatography-mass spectrometry (LC-MS) grade. Ultra-pure water was prepared in-house using a Milli-Q water purification system (Millipore, Bedford, MA, USA). Fifty-eight male, specific-pathogen-free, two-month-old SD rats, weighing [(220 ± 20) g] were provided by the Experimental Animal Center of Nantong University, China (No. S20231116-026). All procedures were conducted in accordance with the ethical standards of the World Medical Association and approved by the Ethics Committee of Medicine, Nantong University.

2.2. Identification JSSQW components absorbed into rat blood

2.2.1. Collection of rat blood samples

After one week of adaptation, ten rats were randomly divided into two groups: a normal control group (n = 5, Normal) and a JSSQW group (n = 5, Normal + JSSQW); JSSQW pills were pulverized and added to drinking water to make a suspension for gavage. Based on an adult human dose of 27 g/d, the equivalent dose for rats is 2.4 g/kg. The Normal + JSSQW group was given 2.4 g/kg JSSQW by gavage, while the Normal group was given an equal dose of drinking water by gavage. Two hours after gavage, three arbitrarily chosen rats from each group were euthanized, and abdominal aortic blood was collected for subsequent analysis. In addition, TCM solution will be used for gastric lavage and stored, and the TCM group (n = 3) will be labeled. The above nine liquid samples were stored at -80 ℃.

2.2.2. Sample preparation and extraction

The above samples were thawed slowly at 4 ℃. Each sample (1.0 mL) was placed in a centrifuge tube and an equal volume of extraction solution (methanol-acetonitrile, 1∶1, v/v) added. Samples were then homogenized for 60 s, extracted for 30 min twice by low-temperature ultrasonic, then placed at -20 ℃ for 1 h to allow protein to precipitate. Then, after centrifugation at 12 000 rpm and 4 ℃ for 20 min, the supernatant was dried in a vacuum and 200 μL 30% acetonitrile solution added to re-dissolve the pellet. After homogenization and centrifugation at 14 000 rpm and 4 ℃ for 15 min, the supernatant was collected for analysis.

2.2.3. UHPLC-Q/Orbitrap HRMS analysis

2.2.3.1. UHPLC conditions

The UHPLC analytical conditions were as follows: column, Waters HSS T3 (100 mm × 2.1 mm, 1.8 μm); column temperature, 40 ℃; flow rate, 0.3 mL/min; injection volume, 2 μL; solvent system, phase A was Milli-Q water (0.1% formic acid), phase B was isopropyl alcohol-acetonitrile solution (0.1% formic acid); gradient program, 0 min phase A/phase B (90∶10, v/v), 2 min phase A/phase B (90∶10, v/v), 6 min phase A/phase B (40∶60, v/v), 15 min phase A/phase B (40∶60, v/v), 15.1 min phase A/phase B (90∶10, v/v), and 17 min phase A/phase B (90∶10, v/v).

2.2.3.2. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis

HRMS data were recorded on a Q Exactive HF-X Hybrid Quadrupole Orbitrap mass spectrometer equipped with a heated electrospray ionization source (Thermo Fisher Scientific, Waltham, MA, USA) using the Full-ms-ddMS2 acquisition methods. The electrospray ionization source parameters were set as follows: sheath gas pressure, 40 arb; aux gas pressure, 10 arb; spray voltage, + 3000 v/-2800 v; temperature, 350 ℃; and ion transport tube temperature, 320 ℃. The scanning range of the primary mass spectrometry was (scan m/z range) 70-1050 Da, with a primary resolution of 70 000 and a secondary resolution of 17 500.

2.3. Network pharmacology analysis

Blood components were imported into A Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine platform for target gene prediction analysis, and to screen for blood component target genes based on the similarity score between “blood components target” and “known drugs target.” The kyoto encyclopedia of genes and genomes (KEGG) database is a commonly used resource for pathway research. KEGG pathway annotation and enrichment analysis was performed on blood component target genes, and pathways with P values < 0.05 and enriched targets were selected. Results were visualized using Cytoscape 3.9.0 (Stanford University, Palo Alto, CA, USA). Disease pathway annotation analysis can identify medicinal functions of serum metabolites. Online Mendelian Inheritance in Man (OMIM) and Therapeutic Target Database (TTD) databases were used for disease pathway annotation of target genes.

2.4. Animal experiments

2.4.1. Establishment and grouping of KS model rats

After one week of adaptation under standard feeding conditions, 48 male Sprague-Dawley (SD) rats were arbitrarily divided into six groups: normal control (Normal, n = 8), model (Model, n = 8), JSSQW low-dose (L-JSSQW, n = 8, Model + JSSQW 1.2 g/kg), JSSQW middle-dose (M-JSSQW, n = 8, Model + JSSQW 2.4 g/kg), JSSQW high-dose (H-JSSQW, n = 8, Model + JSSQW 4.8 g/kg), and potassium citrate (K₃cit, n = 8, Model + 10% K₃cit 8 mL/kg). The experimental intervention period was 4 weeks. The Normal group received normal drinking water; 2 mL was gavage-administered in the morning, and in the afternoon. For the Model group, the normal drinking water was replaced with 1% ethylene glycol solution and 2 mL of 2% ammonium chloride solution was gavage-administered in the morning, and an equivalent volume of drinking water was gavage-administered in the afternoon; The low-, medium-, and high-dose JSSQW and potassium citrate solution groups were given their corresponding drugs by gavage in the afternoon, while the other treatments were the same as those of the model group.

2.4.2. Collection of rat fluid samples and determination of kidney index

After the last drug or water administration, rats were placed in metabolic cages to collect urine for 24 h. After collection, the rats were weighed and anesthetized with 40% carbon dioxide gas inhalation. Blood was collected from the abdominal aorta and serum was isolated and stored in at -80 ℃. After blood collection, both kidneys were removed and weighed. The left kidney was fixed in a 4% paraformaldehyde solution, and the right kidney was stored at -80 ℃. The kidney index was calculated according to the following formula: kidney index (mg/g) = kidney mass (mg)/body mass (g).

2.4.3. Routine blood and urine tests

The levels of creatinine (Cr), blood urea nitrogen (BUN), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) in the blood of rats were detected using a fully automated biochemical analyzer (AS-1250 Ailex Technology, Shanghai, China). The levels of urinary calcium (Ca) phosphorus (P) and magnesium (Mg) in 24-h rat urine was detected using an electrolyte analyzer (K-Lite8B Cornely Technology, Shenzhen, China). The concentration of urinary oxalic acid (OA) in urine was measured using an ELISA kit.

2.4.4. Histopathology

Pre-fixed kidney tissue was dehydrated in high concentration ethanol and xylene, embedded in paraffin, and sectioned at 4 μm. To observe morphological changes of glomeruli and tubules under light microscopy, paraffin sections were processed for hematoxylin and eosin staining using standard procedures. To observe the deposition of calcium oxalate crystals under light microscopy, Von Kossa staining was performed with nuclear fast red staining.

2.5. Non-targeted metabolomics analysis

2.5.1. Sample preparation

Serum samples from the Normal, Model, and JSSQW intervention groups were randomly selected for testing (n = 3). The serum samples were thawed at 4 ℃ and 100 μL of serum and 400 μL of cold methanol/acetonitrile (1∶1, v/v) were mixed by vortexing for 30 min. After incubation for 1 h at -20 ℃, samples were centrifuged at 12 000 rpm at 4 ℃ for 10 min to remove protein. The supernatant was dried in a vacuum centrifuge and then re-dissolved in 100 μL 30% acetonitrile (vol/vol) and transferred to insert-equipped vials for LC-MS analysis.

2.5.2. Chromatography and mass spectrometry conditions of UHPLC-Q/Orbitrap HRMS

The analytical conditions were as follows, UHPLC: column, Waters HSS T3 (100 mm × 2.1 mm, 1.8 μm); column temperature, 40 ℃; flow rate, 0.3 mL/min; injection volume, 2 μL; solvent system, water (0.1% acetic acid): acetonitrile (0.1% acetic acid); gradient program,100∶0 (v/v) at 0-1 min, 5∶95 (v/v) at 9.0 min, 5∶ 95 (v/v) at 9.0-13.0 min, 100∶0 (v/v) at 13.1-17 min. The ESI source parameters were: spray voltage, -2.8 kv/ + 3.0 kv; sheath gas pressure, 40 arb; aux gas pressure, 10 arb; sweep gas pressure, 0 arb; capillary temperature, 320 ℃; and aux gas heater temperature, 350 ℃.

2.6. Data analysis

2.6.1. UHPLC-Q/Orbitrap HRMS analysis

The raw MS data were acquired on the Q-Exactive HF-X using Xcalibur 4.1 (Thermo Scientific, Waltham, MA, USA), and processed using Progenesis QI (Waters Corporation, Milford, MA, USA). Quantified data were outputted into excel format. Identification of blood components in JSSQW: qualitative analysis of metabolites in the sample was completed by matching with the self-built Chinese herbal secondary mass spectrometry database of Sanshu Biotechnology (Nantong, China). A secondary fragment score greater than 50 was considered reliable for the identification results. Identification of differential metabolites: peaks containing secondary mass spectrometry data were identified using commercial databases and the self-built metabolite secondary mass spectrometry database of Sanshu Biotechnology (Nantong, China). A secondary fragment score greater than 0.7 was considered reliable for the identification results. Data were analyzed using R package, and was subjected to multivariate data analysis, including principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The variable importance in the projection (VIP) value of each variable in the OPLS-DA model was calculated to indicate its contribution to the classification. Metabolites with a VIP value > 1 were further applied to Student’s t-test at univariate level to measure the significance of each metabolite. P values < 0.05 were considered statistically significant.

2.6.2. Animal experiment data analysis

Results are expressed as the mean ± standard deviation. All data were analyzed by one-way analysis of variance with Tukey’s post-test using SPSS.22.0 software (IBM Corp., Armonk, NY, USA). P values < 0.05 were considered statistically significant.

3. RESULTS

3.1. JSSQW components absorbed into blood and Network pharmacology

The metabolite results of the normal control (Normal), TCM (JSSQW), and drug containing serum (JSSQW Serum) groups are displayed in the form of a Venn diagram (supplementary Figure 1A). Using positive and negative ion modes to collect data from the samples, and by comparing their base peak chromatograms, a total of 23 JSSQW chemical components were identified in serum. They are listed in order of retention time in Figures 1A and 1B. Chemical information of the 23 components is presented in supplementary Table 1. The PCA score plot shows the three sample points to be far apart (supplementary Figure 1B). By integrating database information, target genes for the 23 JSSQW components absorbed into rat blood were obtained. KEGG enrichment analysis was performed on these target genes, resulting in 378 enriched pathways. Twenty of these signaling pathways were assessed by combining the proportion and mapping values of enrichment factors, as shown in Figure 1C. The target genes were then imported into the OMIM and TTD databases for disease enrichment analysis and the specific results are shown in Figure 1D. We generated a network interaction diagram of “components-genes-pathways-diseases” based on the KEGG enrichment analysis and disease enrichment analysis results using Cytoscape 3.9.0 software (supplementary Figure 1C).

Figure 1. UHPLC-Q/Orbitrap HRMS identification of JSSQW chemical components and network pharmacology analysis.

A: base peak chromatogram of the JSSQW serum group in positive mode; B: base peak chromatogram of the JSSQW Serum group in negative mode; C: KEGG pathway analysis; D: TTD and OMIM disease enrichment analysis. UHPLC-Q/Orbitrap HRMS: ultra-high performance liquid chromatography combined with tandem electrostatic field orbital trap high-resolution mass spectrometry; JSSQW: Jisheng Shenqi Wan; KEGG: kyoto encyclopedia of genes and genomes; TTD: Therapeutic Target Database; OMIM: Online Mendelian Inheritance in Man.

3.2. Effect of JSSQW in a KS rat model

3.2.1. Effect of JSSQW on calcium oxalate crystals in the kidneys

As shown in Figure 2A, Von Kossa staining showed no deposition of calcium oxalate crystals in the renal tubules of the normal control group. In the model group, a large amount of calcium oxalate crystal deposition with high density in the renal tubules was observed. JSSQW intervention decreased the density of calcium oxalate crystals in the renal tubules, with the degree of decrease being H-JSSQW group > M-JSSQW group > L-JSSQW group. The reduction in the calcium oxalate crystal density in the H-JSSQW group was greater than that in the K₃cit group.

Figure 2. JSSQW inhibits calcium oxalate crystal deposition and renal tubular structural damage in KS rat kidneys and improves renal function.

A: Von Kossa staining of kidney sections from the different groups; B: appearance of kidneys in the different groups; C: kidney index in rats of different groups; D: serum creatinine levels in rats of different groups; E: blood urea nitrogen levels in rats of different groups; F: HE staining of kidney sections from the different groups. Black arrows indicate damaged renal tubules containing transparent deposits of calcium oxalate crystals; A1, B1, F1: Normal group; A2, B2, F2: Model group; A3, B3, F3: low-dose JSSQW group; A4, B4, F4: middle-dose JSSQW group; A5, B5, F5: high-dose JSSQW group; A6, B6, F6: K₃cit group. Normal group: without calcium oxalate kidney stone modeling drug; Model group: without any treatment; low-dose JSSQW group: treated with 1.2 g/kg JSSQW for 4 weeks; middle-dose JSSQW group: treated with 2.4 g/kg JSSQW for 4 weeks; high-dose JSSQW group: treated with 4.8 g/kg JSSQW for 4 weeks; K₃cit group: treated with 8 mL/kg K₃cit for 4 weeks; JSSQW: Jisheng Shenqi Wan; KS: kidney stones; HE: hematoxylin and eosin staining. One-way analysis of variance was used to compare more than two groups, followed by the least significant difference test to detect differences between groups. The data are presented as the mean ± standard deviation (n = 8). Compared with Normal group, ^a P < 0.05; compared with Model group, ^b P < 0.05; compared with high-dose JSSQW group, ^c P < 0.05.

3.2.2. Effect of JSSQW on renal function

As shown in Figures 2B and 2F, the kidneys of the normal control group rats were of good size and shape, and HE staining showed that the glomeruli had regular morphology and the renal tubules were arranged tightly and neatly. The kidneys of the model group rats showed changes reminiscent of “big-white kidney,” with enlarged kidneys of a pale color and granular surface changes. Structural damage, such as tubular dilation, destruction, discontinuity, and even rupture, as well as epithelial cell detachment, appeared in the kidneys of the model group. After JSSQW intervention, the granular surface and “big white kidney”-like changes were significantly reduced compared with the model group, and damage to the renal tubules was significantly reduced. The H-JSSQW group showed particularly significant improvement, more than that in the K₃cit group. Compared with the normal control group, the kidney index, blood Cr, and blood BUN in the model group were significantly increased. After JSSQW intervention, the above indicators were significantly decreased. Among them, H-JSSQW group rats showed greater improvement in kidney index than K₃cit group rats (Figures 2C-2E).

3.3. JSSQW regulates serum metabolites in KS rats

3.3.1. PCA analysis of whole sample metabolomics

The H-JSSQW group (4.8 g/kg) showed the most significant improvement in renal calcium oxalate crystal deposition and related fluid indicators. Therefore, the JSSQW intervention dose for metabolomics was set at 4.8 g/kg (H-JSSQW). The PCA score plot (supplementary Figure 2) indicated a high degree of aggregation of Quality Control (QC) samples and showed significant separation between the normal control group, the model group, and the H-JSSQW group.

3.3.2. Multivariate statistical analysis

OPLS-DA is a supervised multivariate analysis method, and the OPLS-DA score (Figures 3A, 3B) showed good separation and significant differences between the normal control group and the model group, as well as between the model group and the H-JSSQW group. The evaluation parameters of the OPLS-DA model were: R2X = 0.860, R2Y = 0.996, Q2 = 0.935 for the normal control group and the model group, and R2X = 0.823, R2Y = 0.994, Q2 = 0.973 for the model group and H-JSSQW group. The closer the R2 value is to 1, the more stable the model is, and a Q2 value greater than 0.5 indicates better predictive ability of the model. The OPLS-DA permutation test analysis (Figures 3C, 3D) between the two groups showed that all blue Q2 values on the left were lower than the original point on the right.

Figure 3. OPLS-DA and volcano plot of serum metabolomics analysis.

A: OPLS-DA scores of Model vs Normal group; B: OPLS-DA scores of H-JSSQW vs Model group; C: OPLS-DA permutation test analysis of Model vs Normal group; D: OPLS-DA permutation test analysis of H-JSSQW vs Model group; E: volcano plot of Model vs Normal group; F: volcano plot of H-JSSQW vs Model group. H-JSSQW: high-dose JSSQW group; Normal group: without calcium oxalate kidney stone modeling drug; Model group: without any treatment; high-dose JSSQW group: treated with 4.8 g/kg JSSQW for 4 weeks. JSSQW: Jisheng Shenqi Wan; KS: kidney stones; OPLS-DA: orthogonal partial least-squares discriminant analysis.

3.3.3. Screening and identification of potential biomarkers

In this study, metabolites with a P value < 0.05 and a VIP value > 1 were identified as important differential metabolites. A volcano plot (Figures 3E, 3F) shows the up- or down-regulation of metabolites. Each dot represents a metabolite; the red dots represent significantly upregulated metabolites, the blue dots represent significantly downregulated metabolites, and the dot size represents the VIP value. Twenty-nine differential metabolites (25 down-regulated and 4 up-regulated) were identified between the model and the normal control groups, while 54 differential metabolites (9 down-regulated and 45 up-regulated) were identified between the H-JSSQW and the model groups. We used Venn diagrams to integrate each pair of overlapping metabolites (Figure 4A) and 25 overlapping differential metabolites were obtained (supplementary Table 2). We conducted statistical analysis on the classification of the 25 differential metabolites based on their chemical taxonomy attribution information, and the results are presented in a pie chart (Figure 4B). The differential metabolites were mainly lipids, with phosphatidylethanolamine (PE) and phosphorylcholine (PC) and their derivatives accounting for the highest proportion. We used the relative levels of differential metabolites to perform cluster analysis on each group of samples (Figure 4C). The cluster analysis heatmap showed that the normal control and H-JSSQW groups were well grouped together, with significant differences from the model group.

Figure 4. Classification and cluster analysis of differential metabolites.

A: venn diagrams of Model vs Normal group and H-JSSQW vs Model group; B: pie chart of classification of 25 differential metabolites; C: cluster analysis heatmap of 25 differential metabolites. JSSQW: Jisheng Shenqi Wan; H-JSSQW: high-dose JSSQW group; Normal group: without calcium oxalate kidney stone modeling drug; Model group: without any treatment; high-dose JSSQW group: treated with 4.8 g/kg JSSQW for 4 weeks.

3.3.4. Pathway enrichment analysis

We applied KEGG analysis to annotate differential metabolites and to analyze the significance level of metabolite enrichment in each pathway. An enriched bubble plot of differential metabolites in KEGG pathways (supplementary Figure 3) showed that changes in the differential metabolites between H-JSSQW group and Model group rats were related to multiple metabolic pathways, including pyrimidine metabolism, tryptophan alanine metabolism, sphingolipid metabolism, biosynthesis of unsaturated fatty acids, sphingolipid signaling, and phospholipase D signaling. Among them, the number of differential metabolites annotated in sphingolipid metabolism and sphingolipid signaling was the highest.

4. DISCUSSION

While breakthroughs have been achieved in current lithotripsy and stone expulsion strategies for KS, effective measures to prevent stone recurrence remain lacking. "kidney deficiency" is regarded as the fundamental pathogenesis of KS in TCM, and correcting "kidney deficiency" is recognized as an effective TCM-based approach for KS prevention and treatment.⁹ Combined with the "Shen-kidney" concept proposed by our team,⁹ JSSQW possesses a robust theoretical basis for KS prevention and treatment. Additionally, preliminary clinical studies have verified its clinical efficacy in this regard,¹⁰ and its underlying mechanism of action warrants further investigation.

In this study, Firstly, we identified 23 JSSQW components that are absorbed into rat blood using UHPLC-Q/Orbitrap HRMS. The PCA score plot shows the three sample points to be far apart, indicating significant differences between the groups and that JSSQW components are significantly absorbed into rat blood. KEGG enrichment analysis showed the targets corresponding to blood components to be enriched in signaling pathways, such as MAPK, RAS, and PI3K Akt pathways, which play important roles in the pathogenesis of calcium oxalate KS. In addition, other targets are enriched in various energy metabolism pathways, including lipid metabolism, amino acid metabolism, and purine metabolism. The disease enrichment analysis of OMIM and TTD showed enrichment of JSSQW blood component target genes in urinary and endocrine system diseases, including kidney stones, chronic kidney disease, obesity, diabetes, Cushing’s syndrome, abnormal lipid metabolism, and abnormal calcium and phosphorus metabolism. This is consistent with the KEGG enrichment analysis results, and indicates that JSSQW has potential for treating the above diseases. The network interaction diagram of “components-genes-pathways-diseases” showed that nine effective components are the main active substances of JSSQW.

We observed changes in renal function in KS rats through renal pathology and blood indicators, and evaluated the improvement after JSSQW intervention, and the results indicate that JSSQW can improve renal function in KS rats and reduce the damage to renal tubules caused by calcium oxalate crystals. The formation of calcium oxalate KS is closely related to renal dysfunction. High concentrations of oxalate in urine lead to the deposition of oxalate crystals in the renal tubules, causing direct damage to the renal tubular epithelium, which can lead to oxalate crystal nephropathy. These crystals further aggregate to form the initial form of calcium oxalate KS.¹¹ Disrupted renal function leads to impaired glomerular filtration and abnormal reabsorption of oxalic acid. Abnormal levels of oxalic acid in blood and urine lead to further deterioration of renal function,¹² forming a vicious cycle in the development of calcium oxalate KS. The creatinine clearance rate is an important independent predictor of the onset of calcium oxalate KS;²² This study demonstrates that JSSQW not only reduces calcium oxalate crystal deposition in the kidneys of KS rats but also improves renal function. This finding aligns with the "simultaneous treatment of kidney disorders and kidney stones" perspective proposed in the “Shen-kidney” theory, and also provides indirect evidence for the association between changes in renal function and calcium oxalate kidney stones.

Through untargeted metabolomics analysis, we identified 25 differential metabolites which were considered potential biomarkers as they were significantly regulated by JSSQW. Analysis based on the types of differential metabolites indicates that JSSQW significantly altered lipid metabolism in KS rats, which was consistent with the network pharmacology results based on JSSQW blood components. Recent studies indicate that lipid metabolism is closely related to the occurrence and development of KS. Some studies suggest that obesity, elevated blood pressure, abnormal blood glucose, and elevated triglyceride levels caused by visceral fat accumulation are risk factors for kidney stone formation, with insulin resistance as the core pathogenic driver.^23,24 In addition, some phospholipid metabolites can serve as blood biomarkers and drug targets for the prevention, diagnosis, and treatment of KS.²⁰ The changes in the types and groups of differential metabolites indicated that disturbances in glycerol phospholipid metabolism are associated with the progression of KS. These results indicate that JSSQW alleviates KS by regulating lipid metabolism, especially glycerol phospholipid metabolism disorders.

The abundance of differential metabolites was significantly different between the H-JSSQW group and Model group rats, indicating that metabolic pathways were significantly affected, which may be related to the protective effect of JSSQW on KS rats. KEGG analysis based on differential metabolites indicates that the protective mechanism of JSSQW on KS rats may be related to some metabolic pathways and signaling pathways, especially sphingolipid metabolism and sphingolipid signaling. Sphingolipids are a type of lipid molecule based on sphingosine, that are widely present in cell membranes, especially in neural tissues.²⁵ Sphingolipids are key components of cell membranes and play a crucial role in maintaining their structural stability and function.²⁶ Metabolites of sphingolipids, such as ceramides, are involved in various cellular processes.²⁷ The metabolism and signaling pathways of sphingolipids are widely dysregulated in various kidney diseases,²⁸ and their correlation with kidney stones has been confirmed.^29,30 However, our current understanding of the regulation of sphingolipid and their functions in the pathological environment of KS is still very basic.²⁵ Some drugs can treat calcium oxalate kidney stones by improving fatty acid oxidation, maintaining ceramide/complex sphingolipid circulation balance, and reducing phospholipid metabolism disorders,²⁹ which may be similar to the protective mechanism of JSSQW.

In conclusion, this study clarifies the material basis underlying the therapeutic efficacy of JSSQW in treating calcium oxalate KS, and conducts a preliminary exploration of its mechanism of action by integrating network pharmacology and metabolomics. This work demonstrates the holistic effect of JSSQW in the prevention and treatment of KS through multiple pathways and targets. There are some limitations to this study. We have not definitively identified the specific molecules, pathways or mechanism by which JSSQW affects KS. Further cellular and molecular level experiments are therefore warranted. In summary, this study combines network pharmacology and metabolomics to examine the efficacy and to explore the mechanism of JSSQW in treating KS. Our findings provide methodological references for further investigation of TCM mechanisms of action.

5. ACKNOWLEDGMENTS

The authors declare no competing interests. We thank Jeremy Allen, PhD, from Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing a draft of this manuscript.

6. SUPPORTING INFORMATION

Supporting data to this article can be found online at http://www.journaltcm.com.

Funding Statement

Supported by Grant from Jiangsu Provincial Research Hospital (No. YJXYY202204-YSA05); Young Elite Scientists Sponsorship Program by China Association of Chinese Medicine (No. 2023-QNRC2-B21); Nantong University Special Research Fund for Clinical Medicine: Exploring the Mechanism of Jisheng Shenqi Wan in Preventing and Treating Calcium Oxalate Kidney Stones Based on Oxidative Stress and Ferroptosis (No. 2024JQ049); Nantong Social Livelihood Science and Technology Plan Project: Exploring the Mechanism of Jisheng Shenqi Wan in Preventing and Treating Calcium Oxalate Kidney Stones Based on the Concept of “Shen-Kidney Theory” (No. MS2024013)