Renal Metabolomics Study and Critical Pathway Validation of Shenkang Injection in the Treatment of Chronic Renal Failure

Abstract

To reveal the mechanism of Shenkang injection (SKI) in the treatment of chronic renal failure, and verify the key pathway. In this work, an untargeted metabolomics approach was performed by LC-MS coupled with multivariate statistical analysis to provide new insights into therapeutic mechanism of SKI. Hematoxylin–eosin (H&E) Staining and Immunohistochemistry were used to evaluate the effects of drug treatment, Western blot was used to verify the critical pathway. Then, a total of 44 potential biomarkers of chronic renal failure (CRF) were identified and reversed regulation, including 2,8-dihydroxypurine, 5-methoxytryptophan, uric acid, acetylcarnitine, taurine, etc. Mainly concerned with arginine and proline metabolism, purine metabolism, histidine metabolism, etc. Pathological examination showed that the renal interstitium of SKI group was significantly improved, with fewer inflammatory cells and thinner vascular walls compared with the model group. Immunohistochemical results showed that the expression of α-smooth muscle actin (α-SMA) was decreased, and the expression of E-cadherin was increased in CRF model group, and the two indicators were reversed regulation in SKI injection, indicating that the degree of fibrosis was relieved. Critical signaling pathway phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) and nuclear factor-kappaB (NF-κB) protein expressions were significantly inhibited. This study was the first to employ metabolomics to elucidate the underlying mechanisms of SKI in chronic renal failure. The results would provide some support for clinical application of traditional Chinese medicines in clinic.

INTRODUCTION

Chronic renal failure (CRF) refers to chronic progressive renal parenchymal injury caused by various reasons, with renal atrophy, impaired renal function and involvement of various systemic systems as the main manifestations of clinical syndrome.¹⁾ Many theories had been proposed to explain the pathogenesis of CRF, including “glomerular hyperfiltration,” “renal tubule hypermetabolism,” “lipid metabolism disorder,” etc., but none of them could explain the whole process of the occurrence and development of the disease.²⁾ Due to the complex pathogenesis of chronic renal failure, western medicine focused on treating the primary disease, avoiding and correcting the risk factors for the progression of chronic renal failure, and preventing and treating complications.³⁾ With the development of traditional Chinese medicine (TCM), the clinical application of TCM compounds had achieved significant effects in delaying the progression of CRF, improving patients’ clinical symptoms, and reducing and slowing down the occurrence of complications.⁴⁾

SKI was composed of extracts of Astragalus, Salvia miltiorrhiza, rhubarb and safflower. It had the functions of “tongfu xiezhuo, huoxue huayu, buqi lishi.”⁵⁾ In recent years, Shenkang injection (SKI) had been commonly used in clinic to delay the progression of chronic renal failure, and the effect was good. Modern pharmacology found that SKI could improve lipid metabolism, correct lymphocyte disorder, inhibit immune inflammatory response, inhibit mesangial cell proliferation, slow down renal fibrosis, delay glomerular sclerosis and so on.⁶⁾ The composition of traditional Chinese medicine preparations was complex and the mechanism of action was diverse. At present, the key mechanism of SKI in the treatment of chronic renal failure had not been reported.

Metabolomics, the latest of the “omics” sciences, referred to the systematic study of metabolites and their changes in biological samples due to physiological stimuli and/or genetic modification. Because metabolites represented the downstream expression of genome, transcriptome, and proteome, they could closely reflect the phenotype of an organism at a specific time. As an emerging field in analytical biochemistry, metabolomics had the potential to play a major role in monitoring real-time kidney function and detecting adverse renal events. Small molecule metabolites could provide mechanistic insights into novel biomarkers of kidney diseases, given the limitations of the current traditional markers. In addition, metabolomics could detect the metabolic characteristics of organisms in vivo without clarifying the chemical composition and structure of Traditional Chinese Medicine, and help to determine which components played a therapeutic effect.⁷⁾ Through the analysis of endogenous metabolites of organisms, metabolomics could find changes in metabolic pathways and metabolites after the action of internal or external factors.⁸⁾ Therefore, this study intended to use metabolomics technology to fully reveal the mechanism of SKI in the treatment of chronic renal failure, and use molecular biological methods to verify the relevant results. This study would provide scientific reference ideas for revealing the relevant mechanism of TCM treatment of complex diseases.

MATERIALS AND METHODS

Instruments and Reagents

Q Exactive mass spectrometry (Thermo, U.S.A.); Ultimate 3000 ultra performance liquid chromatography (UPLC) (Dionex, U.S.A.), Waters ACQUITY UPLC BEH hydrophilic interaction liquid chromatography (HILIC) Column (100 × 2.1 mm, 1.7 µm), (Waters, U.S.A.); KQ-SOB Ultrasonic Instrument (Kunshan Ultrasonic Instrument Co., Ltd., China); AL104 balance with 0.0001 accuracy, (Mettler Toledo, Switzerland); enzyme-linked immunosorbent assay (ELISA) (Bio-rad, U.S.A.).

Acetonitrile, methanol (HPLC grade, Fisher Scientific, U.S.A.); Formic acid (99%, J.T.Baker, the Netherlands). Distilled water was used in the experiment (China Wahaha Company, China). Lysine, Glycine, 5-methoxytryptophan, 3-Methyladenine, Proline, Triglyceride, Taurine, 5α-dihydrotestosterone, Serine, 8-hydroxy-2-deoxyguanosine, Azelaic acid, Cortisol, Arginine, Tryptophan, Inosine, Lactic acid standard (American Sigma Company, U.S.A.).

Animals Model and Sample Collection

Eighteen Sprague-Dawley (SD) rats with body weighted of 200–220 g were purchased from Henan Experimental Animal Center (China). Animal Production, License No. SCXK(Yu)2017-0001; Before the test, the animals were kept in Specific Pathogen Free (SPF) with standard rodent food and water, light and dark cycles every 12 h at constant room temperature. The experiment was divided into control group, model group, SKI group. Animal Ethics Review Approval Institutions & Number: Ethics Committee of the First Affiliated Hospital of Zhengzhou University (2023-KY-0543).

Adenine was dispersed in sodium carboxymethyl cellulose solution. Except for the normal group, the other groups were given continuous gavage of 250 mg·(kg·d)⁻¹ to establish renal failure model group. The whole process lasted 24 d (once a day for days 1 to 12 and every other day after day 12).

SKI group was injected with 9 mL·(kg·d)⁻¹ SKI through the tail vein. The control group and model group were injected with the same amount of normal saline for 7 consecutive days. At the end of the experiment, all rats were fasted for 12 h, the kidneys were rinsed with normal saline, immediately put in liquid nitrogen and stored at −80 °C for follow-up study.

Sample Processing

Frozen kidney samples were thawed at room temperature and homogenized in 50% methanol at a tissue/solution ratio of 1 : 10 (w/v). In an ice bath using a homogenizer (IKA Germany), after 10 s of each homogenization, the homogenization is paused for 10 s, and the homogenization was repeated twice until a uniform homogenization was obtained. After each sample was homogenized, the homogenizer probe was cleaned with water, methanol, and water in turn. Then 200 µL cold organic solution (acetonitrile: methanol = 1 : 1) was added to the 40 µL homogenate, violently shaken for 20 s, stored at room temperature for 10 min, centrifuged at 4 °C for 13000 × g for 15 min, and the supernatant was taken for analysis.

Preparation of quality control (QC) samples: 10 µL was absorbed from each of the samples, swirled for 5 min and mixed, and QC samples were pre-treated according to the above “sample treatment” method. In order to ensure the reliability of data, QC samples were interspersed in all sample data collection processes, and 1 QC sample was inserted in every 9 samples.

Renal Pathological Examination

After weighing, the kidney was cut lengthwise from the middle and about 1 mm thick slice was cut, fixed with 4% formaldehyde, prepared with paraffin embedding, routine hematoxylin–eosin (H&E) staining, and pathological examination was performed.

Expression of α-Smooth Muscle Actin (α-SMA) and E-Cadherin in Renal Tissue Detected by Immunohistochemistry

Kidney tissue was dephosphorized with xylene, rehydrated with gradient ethanol, inactivated endogenous peroxidase with 3% hydrogen peroxide, and heat recovery antigen with 0.1 mol/L citrate buffer. Phosphate-buffered saline (PBS) solution containing 10% goat serum was added and incubated at room temperature for 30 min to block the binding of non-specific antibodies. α-SMA and E-cadherin antibodies were added and incubated overnight at 4 °C. After rinsed with PBS solution, goat anti-rabbit antibody labeled by HRP was added. Dimethylaminoazobenzene (DAB) was used for color development, stained with hematoxylin, observed under microscope and photographed.

Data Processing

Samples were tested with UHPLC-(±) electrospray ionization (ESI) MS to obtain the total ion flow chromatogram of the sample, and the raw detected data was imported into the SIEVE 1.3 software for peak alignment, baseline correction and peak area normalization pretreatment for the sample chromatogram of each group. The pre-processed data were imported into SIMCA 14.0 software for multivariate statistical analysis, and the principal component analysis and orthogonal partial least square discriminant analysis models were established. The differences between the two groups were compared by score chart. SPSS 22.0 was used to conduct two-independent sample t-test for all data. Variables with Variable importance in projection (VIP) values greater than 1.0 and p < 0.05 were used as potential difference markers. The secondary spectra of potentially different compounds were compared with Metlin, HMDB, Kyoto Encyclopedia of Genes and Genomes (KEGG) and other network databases.

The Expression of Phosphatidylinositol 3-Kinase (PI3K)/Protein Kinase B (Akt) and Nuclear Factor-KappaB (NF-κB) Signaling Pathway Related Proteins in Renal Tissues Was Detected by Western Blotting

Kidney tissues of each group were collected and homogenized with radio immunoprecipitation assay (RIPA) lysate to extract protein. The protein samples were transferred to polyvinylidene difluoride (PVDF) membrane by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis. After sealing, p-PI3K, PI3K, p-Akt, Akt, p-NF-κBp65, NF-κBp65, p-IKBa, IKBa and β-actin antibodies were added, respectively, and incubated at 4 °C overnight. HRP labeled goat anti-rabbit antibody was added, incubated for 2 h, and the developer was added for color development. Enhanced chemiluminescence (ECL) instrument was used for development.

Statistical Analysis

SPSS 22.0 software was used for data statistics, which were expressed as x ± s and tested by one-way ANOVA. Benjamini–Hochberg false discovery rate (FDR) procedure was employed for the multiple test adjustments, and adjusted-p values <0.05 were considered statistically significant.

RESULTS

Pathological Examination

In the normal group, the structure of glomeruli, renal tubules, blood vessels and interstitial were cleared, and no obvious lesions were found. In the model group, some glomerulus atrophy disappeared. The structure of the residual glomerulus was unclear, mainly showing small balloon. Hyaline degeneration of capillaries, some only seen endothelial nucleus. Interstitial fibrosis occurred. In the treatment group, the glomeruli were mostly present and the structure was not clear. Renal tubule epithelium turbiditis, some eosinophilic change, nucleus contraction; some cells shed and form clumps in the lumen. Mild interstitial fibrous tissue hyperplasia and inflammatory cell infiltration were observed, shown in Fig. 1.

Fig. 1. Evaluation of Pathological Effect of SKI on Chronic Renal Failure

Immunohistochemical Results of Renal Tissue

There were significant differences among the groups for E-cadherin (F = 487.00, p < 0.01), Group Control vs. Group Model (MD = 0.715, p < 0.01), Group Model vs. Group SKI (MD = −0.511, p < 0.01), homogeneity of variance (L = 1.232, p = 0.320). The expression of E-cadherin was significantly decreased in the kidney of model group indicating the occurrence of renal fibrosis, the expression of E-cadherin in SKI group was increased.

There were significant differences among the groups for α-SMA (F = 725.241, p < 0.01), Group Control vs. Group Model (MD = −0.661, p < 0.01), Group Model vs. Group SKI (MD = 0.602, p < 0.01), homogeneity of variance (L = 0.704, p = 0.510). The expression of α-SMA protein was increased in the kidney of model group indicating the occurrence of renal fibrosis, the expression of α-SMA protein was decreased in SKI group.

The above results showed that the degree of fibrosis in the treatment group was relieved, shown in Fig. 2.

Fig. 2. Immunohistochemical Results of Key Protein of SKI in Treatment of Chronic Renal Failure

** p < 0.01, Compared with the control group; ## p < 0.01, Compared with the model group, n = 6.

Reproducibility and Stability Assessment of the Metabolism Analytical Strategy

To evaluate the quality of QC data, unsupervised PCA analysis was performed. Small fluctuations (2SD) in QC samples indicate the stability of analytical methods and instruments. The relative standard deviation (R.S.D.) of the peak area of each QC sample was used to evaluate its repeatability, and it was found that the R.S.D. value of more than 95% of the peak area was less than 18%. The results show that our analysis strategy achieves satisfactory reproducibility and stability, shown in Fig. 3.

Fig. 3. The Stability Results of the Instrument and Analysis Method

QC fluctuation.

Multivariate Statistical Analysis

Pattern recognition was performed on kidney samples from the model group and the control group. PCA model of the three groups in positive and negative ion mode showed samples from the same group can be well clustered together and samples from different groups can be clearly separated, indicating that the body state and metabolites were indeed very different between groups, which laid the foundation for the next exploration. Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) model could remove factors unrelated to sample classification, and the results showed that the sample points in the two groups were completely separated, and the sample points in the group tended to be concentrated, indicating that the kidney metabolic network in the model group had significant changes. In the positive ion mode, the relevant parameters of the OPLS-DA model, R²Y and Q², were 0.992 and 0.997 respectively, and in the negative ion mode, R²Y and Q² were 0.995 and 0.987 respectively, both greater than 0.5, indicating that the pattern recognition has been successfully established and has good fitting and prediction capabilities, shown in Fig. 4.

Fig. 4. Analysis Results of Pattern Recognition between Different Groups

n = 6.

Screening of Potential Biomarkers

The VIP value was set as 1.2 and S plot values was <∣0.05∣, 133 and 100 differential ions were selected for Model vs. Control in ESI⁺ and ESI⁻ mode, respectively. The volcano map shows both p values and fold change values to further screen for different metabolites. The volcano plot p values <0.05, fold change values>∣1∣. According to accurate m/z and fragmentation information, a total of 44 differential endogenous metabolites were identified, 32 metabolites of which were confirmed by standard references, shown in Fig. 5. Finally, as listed in Table 1, 36 metabolites elevated and 8 metabolites decreased in Model group. Some representative metabolite changes were shown in Fig. 6.

Fig. 5. Screening Process and Results of Different Markers for SKI in the Treatment of Chronic Renal Failure

n = 6.

Table 1. Information of Metabolites Involved in the Treatment of Chronic Renal Failure by Shenkang Injection

No.	Metabolites	Theoretical mass (m/z)	Measured mass (m/z)	Delta (ppm)	Rt (min)	Ion mode	VIP	Fold change (model/control)	Fold change (SK/model)	Molecular formula	ANOVA
											F	p
1	Linoleamide	280.26349	280.26362	0.464	16.28	P	9.08	5.78↑	0.54↓	C18H33NO	39.736	<0.01
2	Cholesterol	387.36214	387.36238	0.620	2.42	P	8.31	5.72↑	0.71↓	C27H46O	24.624	<0.01
3	2,8-Dihydroxypurine	153.04070	153.04054	−1.046	1.74	P	8.18	6.66↑	0.64↓	C5H4N4O2	36.214	<0.01
4	Arginine	175.11895	175.11887	−0.457	1.85	P	7.84	0.19↓	3.71↑	C6H14N4O2	35.763	<0.01
5	Acetylcarnitine	204.12303	204.12329	1.275	1.75	P	7.19	0.22↓	2.91↑	C9H17NO4	37.518	<0.01
6	Lysine	147.11280	147.11370	6.122	5.46	P	6.87	0.30↓	3.23↑	C6H14N2O2	31.254	<0.01
7	Glycine	76.03930	76.03904	−3.421	17.00	P	6.24	5.46↑	0.57↓	C2H5NO2	29.672	<0.01
8	5-Methoxytryptophan	235.10771	235.10716	−2.340	11.64	P	6.19	0.32↓	2.75↑	C12H14N2O3	34.254	<0.01
9	S-Adenosy-L-methioninamine	399.14451	399.14408	−1.078	12.19	P	6.13	5.32↑	0.62↓	C15H22N6O5S	24.675	<0.01
10	3-Methyladenine	150.07742	150.07793	3.400	7.09	P	4.42	5.10↑	0.71↓	C6H7N5	31.628	<0.01
11	Proline	116.07060	116.07013	−4.052	4.94	P	4.29	5.39↑	0.65↓	C5H9NO2	32.335	<0.01
12	L-Hydroxyproline	131.13254	131.13296	3.520	4.08	P	4.23	5.07↑	0.65↓	C5H9NO3	36.258	<0.01
13	Taurine	126.02194	126.02118	−6.032	5.07	P	4.07	4.92↑	0.53↓	C2H7NO3S	28.745	<0.01
14	5α-Dihydrotestosterone	291.23185	291.23113	−2.474	9.02	P	4.00	4.81↑	0.76↓	C19H30O2	24.484	<0.01
15	Serine	106.04986	106.04914	−6.792	5.99	P	3.85	4.67↑	0.62↓	C3H7NO3	29.387	<0.01
16	8-Hydroxy-2-deoxyguanosine	284.09894	284.09802	−3.239	2.24	P	3.80	4.53↑	0.49↓	C10H13N5O5	25.346	<0.01
17	LysoPC(18 : 1(9Z))	522.35541	522.35515	−0.498	20.58	P	3.75	4.37↑	0.52↓	C26H52NO7P	26.269	<0.01
18	Azelaic acid	187.09758	187.09796	2.032	5.99	N	3.66	4.40↑	0.55↓	C9H16O4	27.398	<0.01
19	Cortisol	363.21660	363.21625	−0.964	7.05	P	3.65	4.28↑	0.53↓	C21H30O5	28.332	<0.01
20	N-Acetyl-D-glucosamine-6-phosphate	302.06354	302.06397	1.424	5.52	P	3.61	0.29↓	5.00↑	C8H16NO9P	33.829	<0.01
21	Inosine	269.08804	269.08887	3.086	4.05	P	3.21	4.15↑	0.50↓	C10H12N4O5	29.923	<0.01
22	Lactic acid	89.02441	89.02483	4.719	2.39	N	3.21	3.83↑	0.43↓	C3H6O3	28.982	<0.01
23	N-Acetyl-L-glutamate	190.07099	190.07004	−5.000	3.51	P	3.16	3.70↑	0.74↓	C7H11NO5	33.793	<0.01
24	N-Acetylmethionine	192.06889	192.06816	−3.802	6.72	P	3.14	3.28↑	0.79↓	C7H13NO3S	37.681	<0.01
25	Nicotinuric acid	179.04621	179.04679	3.240	2.80	N	3.14	2.98↑	0.59↓	C8H8N2O3	29.294	<0.01
26	Ornithine	133.09715	133.09783	5.113	20.07	P	3.10	2.83↑	0.70↓	C5H12N2O2	36.949	<0.01
27	Palmitoylcarnitine	400.34213	400.34238	0.625	11.97	P	3.10	2.74↑	0.65↓	C23H45NO4	33.782	<0.01
28	Phenylacetylglycine	194.08116	194.08196	4.124	3.46	P	3.09	2.66↑	0.60↓	C10H11NO3	38.291	<0.01
29	Uric acid	167.02106	167.02101	−0.293	8.33	N	3.09	2.62↑	0.55↓	C5H4N4O3	36.807	<0.01
30	EPA	303.23185	303.23116	−2.277	13.40	P	2.97	2.57↑	0.82↓	C20H30O2	36.179	<0.01
31	Acetic acid	59.01385	59.01390	0.729	5.50	N	2.96	2.45↑	0.45↓	C2H4O2	36.225	<0.01
32	Phenylalanine	166.08625	166.08675	3.012	9.21	P	2.77	0.31↓	5.00↑	C9H11NO2	29.648	<0.01
33	Glutamic acid	146.04580	146.04480	−6.849	3.39	N	2.72	2.38↑	0.62↓	C5H9NO4	25.155	<0.01
34	DPA	331.26315	331.26378	1.903	12.66	P	2.71	2.27↑	0.78↓	C22H34O2	30.490	<0.01
35	6-Hydroxycaproic acid	131.07136	131.07167	2.366	3.24	N	2.70	2.22↑	0.66↓	C6H12O3	33.305	<0.01
36	Stearic acid	283.26425	283.26485	2.120	2.47	N	2.68	2.17↑	0.68↓	C18H36O2	36.391	<0.01
37	Indoxylglucuronide	310.09212	310.09261	1.581	0.73	P	2.67	2.12↑	0.54↓	C14H15NO7	30.292	<0.01
38	PC(18 : 0/20 : 0)	818.66333	818.66377	0.538	23.02	P	2.64	2.08↑	0.57↓	C46H92NO8P	34.272	<0.01
39	Nicotinate-D-ribonucleotide	337.05571	337.05519	−1.543	10.17	P	2.63	2.07↑	0.74↓	C11H15NO9P +	35.889	<0.01
40	LysoPC(P-18 : 0)	508.37615	508.37693	1.535	21.58	P	2.61	2.05↑	0.67↓	C26H54NO6P	29.636	<0.01
41	PS(16 : 0/18 : 0)	764.54361	764.54318	−0.563	22.91	P	2.56	2.03↑	0.80↓	C40H78NO10P	38.921	<0.01
42	Adenosine-monophosphate	348.07036	348.07097	1.753	11.78	P	2.56	0.30↑	5.50↑	C10H14N5O7P	28.163	<0.01
43	L-Histidine	156.07675	156.07634	−2.628	1.16	P	2.54	2.01↑	0.69↓	C6H9N3O2	37.224	<0.01
44	Adenosine	268.10403	268.10464	2.276	3.77	P	2.47	0.19↓	4.77↑	C10H13N5O4	25.799	<0.01

Fig. 6. Box Plots of Representative Metabolite Changes

** p < 0.01, Compared with the control group; ## p < 0.01, Compared with the model group, n = 6.

Heat Map Analysis of Metabolites

The annular heat map was used to show the metabolic changes between different groups. From the heat map, an obvious demarcation line was found between Control group and Model group, showing that the metabolites had occurred great changes in the model disease. While, most of the metabolites changed in model group had reversibly adjusted in SKI group by Shenkang injections, as a result, the drug had played a huge role in regulating the metabolic pathways, shown in Fig. 7.

Fig. 7. Heat Map Analysis of Differential Metabolite Expression in Different Groups

n = 6.

Metabolite Data Analysis

In order to explore the pathogenesis of CRF, the metabolites were submitted to MetaboAnalyst 5.0 to discover the important and significant metabolic pathways, shown in Fig. 8. We had made a further biology analysis on the pathways which had significant change, the results suggested that the most metabolic disorders induced by CRF mainly involved arginine biosynthesis, purine metabolism, histidine metabolism, arginine and proline metabolism, glutathione metabolism. After giving SKI injection, most disordered metabolites were adjusted in reverse, and may be in this way the drug played an excellent therapeutic effect.

Fig. 8. Enrichment Analysis of Differential Metabolites

n = 6.

The Protein Expressions of PI3K/Akt and NF-κB Determined by Western Blotting

WB results showed that there were significant differences among the groups for P-PI3K/PI3K (F = 999.792, p < 0.01), Group Control vs. Group Model (MD = −0.765, p < 0.01), Group Model vs. Group SKI (MD = 0.337, p < 0.01), homogeneity of variance (L = 0.995, p = 0.393). There were significant differences among the groups for P-Akt/Akt (F = 48.092, p < 0.01), Group Control vs. Group Model (MD = −0.907, p < 0.01), Group Model vs. Group SKI (MD = 0.064, p < 0.01), homogeneity of variance (L = 1.765, p = 0.205). There were significant differences among the groups for p-NF-κBp65/NF-κBp65 (F = 3318.145, p < 0.01), Group Control vs. Group Model (MD = −0.833, p < 0.01), Group Model vs. Group SKI (MD = 0.642, p < 0.01), homogeneity of variance (L = 1.013, p = 0.387). There were significant differences among the groups for p-IKBa/IKBa (F = 319.564, p < 0.01), Group Control vs. Group Model (MD=−0.223, p < 0.01), Group Model vs. Group SKI (MD = 0.185, p < 0.01), homogeneity of variance (L = 1.564, p = 0.242).

Compared with normal control group, the expression levels of P-PI3K/PI3K, P-Akt/Akt, p-NF-κBp65/NF-κBp65, p-IKBa/IKBa in model group were significantly increased, with statistical significance (** p < 0.01). Compared with model group, the expression levels of P-PI3K/PI3K, P-Akt/Akt, p-NF-κBp65/NF-κBp65, p-IKBa/IKBa in SKI group were significantly decreased, with statistical significance (^## p < 0.01). The results showed that SKI could significantly inhibit the expression levels of PI3K/Akt and NF-κB pathway in the kidney, as shown in Figs. 9A and B.

Fig. 9. Effect of SKI on Key Signal Pathway of Chronic Renal Failure

** p < 0.01, Compared with the control group; ^## p < 0.01, Compared with the model group, n = 6.

DISCUSSION

Metabolomics results showed that amino acid metabolism in the model group of rats was disturbed. Lysine and Phenylalanine were classified as essential amino acid, these amino acids showed a decreasing trend in model group; Glycine, Proline, Serine, Ornithine, Glutamic acid and L-Histidine were classified as non-essential amino acid, these amino acids showed an upward trend in model group. In chronic renal failure, non-essential amino acids increased, while essential amino acids decreased, these changes increased the progression of kidney failure. Supplementing essential amino acids could combine with non-essential amino acids in the body to synthesize protein, reducing azotemia, and maintain n-azapine balance.⁹⁾ Glycine, Proline, and HYP were the three most abundant amino acids in collagen, which were ultimately found in fibroblasts and were important components of renal fibrosis, as well as important indicators of fibrosis. When there was fibrosis in the kidney, there was a significant increase in these amino acids in the kidney. After SKI treatment, the disrupted amino acid levels were reversed in this study. It showed that correcting the disorder of amino acid metabolism was also an important way to treat diseases with SKI.

2,8-Dihydroxyadenine (2,8-DHA) was produced by adenine through xanthine oxidase.¹⁰⁾ It was deposited in the glomerulus and renal interstitium, forming foreign body granulomatous inflammation and blocking the renal tubule lumen, resulting in the corresponding renal tubule cystic dilatation.¹¹⁾ As the disease progressed, massive nephron loss led to renal failure. Purines, which were mainly present in the body in the form of purine nucleotides, provide energy, regulate metabolism and form coenzymes. Uric acid was the end product of purine metabolism.¹²⁾ When this metabolism was disturbed, uric acid was not excreted with urine, but was carried to the blood, forming hyperuricemia.¹³⁾ In this study, 2,8-dihydroxyadenin and uric acid level increased significantly in the CRF model group, and significantly declined in the SKI treatment. The results indicated that SKI could effectively promote the dissolution metabolism of 2,8-dihydroxyadeni and inhibit the overproduction of uric acid.

Adenosine bind to the adenosine A1 receptor could play a role in protecting kidney epithelial cells, promoting immune response, maintaining fluid balance, and regulating blood pressure.¹⁴⁾ When renal failure occured, the kidney was ischemic, and the concentration of adenosine was reduced, which exacerbated the ischemia. The differential metabolite 3-methylxanthine was a non-selective adenosine A1 receptor antagonist. Elevated levels of 3-methylxanthine could inhibit adenosine’s role in boosting blood flow to the kidneys, thus exacerbating the progression of kidney failure.¹⁵⁾ In this study, 3-methylxanthine level significantly increased, adenosine and adenosine-monophosphate significantly decreased in the CRF model group. After drug treatment, the levels of these metabolites were reversed, thus playing a therapeutic role. 5-Methoxytryptophan (5-MTP) could reduce kidney inflammation and fibrosis.¹⁶⁾ In this study, the level of 5-MTP in the model group rats was significantly reduced, and after SKI drug treatment, the level of 5-MTP was reversed. During the occurrence and development of renal failure disease, the level of oxidative stress in the body increases and accelerates the process of the disease. 8-Hydroxy-2-deoxyguanosine (8-OHDG) was used as a biomarker of oxidative damage to DNA by endogenous and exogenous factors.¹⁷⁾ The level of 8-OHDG was significantly increased in the renal tissue, indicating a more intense oxidative stress response in the body. It has been reported that the renal protection effect of 8-OHDG was related to the inhibition of NF-κB pathway.¹⁸⁾ NF-κB pathway played a key role in promoting the occurrence and development of kidney disease.¹⁹⁾ This study found that SKI could inhibit the NF-κB signaling pathway after treatment, which may be one of the ways of drug treatment.

Renal fibrosis was the manifestation of progression from chronic kidney disease to end-stage renal disease. Renal fibrosis was not only a marker of kidney injury, but could also be used to predict the progression of renal function loss and kidney damage, mainly including glomerular sclerosis and interstitial tubule fibrosis. L-Arginine supplementation accelerated renal fibrosis and shorten life span in experimental lupus nephritis, and was an important factor in the occurrence of renal fibrosis.²⁰⁾ Research reports showed that L-arginine could activate PI3K/AKT signaling pathway.²¹⁾ The PI3K/AKT signaling pathway was involved in gene expression, cell growth, proliferation and differentiation. Studies had shown that this signaling pathway was closely related to the pathogenesis and pathological changes of renal fibrosis, renal cysts, diabetic nephropathy, etc.^22,23) Therefore, the intervention of this signaling pathway would find a new therapeutic target for the treatment of chronic renal failure.²⁴⁾ The results showed that SKI could inhibit PI3K/AKT pathway for alleviating kidney fibrosis. The specific manifestation in renal tissue was the decrease expression of α-SMA protein, and the high expression of E-cadherin, indicating that the degree of fibrosis in the treatment group was relieved, which was consistent with the results of previous related study.²⁵⁾ However, the main differences with the above study in this research was that, previous study had focused solely on oxidative stress, it could not fully reflect the overall regulatory effect of the drug. In this study, metabonomics could comprehensively reflect the changes of metabolites after drug intervention, which was consistent with the theory of traditional Chinese medicine. The main innovation of this study was that, through metabolomics, a large number of differential metabolites were found, then the key signal pathways were enriched according to the metabolites, and the results were verified by experiments, which was more scientific.

To sum up, we described a UHPLC-Q-Orbitrap HRMS method in both positive and negative modes for metabolomic evaluation of effect of SKI in CRF. The main regulated metabolic pathways involved after drug intervention include arginine and proline metabolism, urea cycle, ammonia recycling, purine metabolism, alanine metabolism, etc. The protein expressions of the main signal pathway including PI3K/Akt and NF-κB were significantly inhibited. This study was the first to employ metabolomics to elucidate the underlying mechanisms of SKI in chronic renal failure. The results would provide some support for clinical application of traditional Chinese medicines in clinic.

Funding

This research was supported by the National Natural Science Foundation of China (82104515); Key scientific research Project of colleges and universities in Henan Province (23A360018; 23A320050); Guangzhou Basic and Applied Basic Research Program 202102020992 (R.C.), GuangDong Basic and Applied Basic Research Foundation 2021A1515110107 (R.C.).

Author Contributions

Lin Zhou wrote the first draft of the article, Xiaohui Wang and Rui Cui performed the experiment, Yi Zhang and Yan Xie reviewed the data, Jinlan Xia and Zhi Sun designed the study. All the listed authors have read and approved the submitted manuscript.

Conflict of Interest

The authors declare no conflict of interest.

Data Availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. The data storage site is: https://pan.baidu.com/s/1RNr_NDGF3Gd3r0h2nA2X2A?pwd=zh

Supplementary Materials

This article contains supplementary materials.

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