Integrative transcriptomic and metabolomic analysis explores mechanisms by which Astragalus membranaceus and Salvia miltiorrhiza ameliorates hypertensive renal damage

Abstract

To explore the mechanism by which Astragalus membranaceus and Salvia miltiorrhiza (AS) regulates the “metabolic- transcriptional” co-expression network to improve Hypertensive renal damage (HRD). Spontaneously hypertensive rats (SHRs) were used to establish the model of HRD. The structure and function of the kidney were observed following AS intervention. We identified various metabolites in the kidneys using UHPLC-MS/MS and observed renal mRNA expression through RNA sequencing. The “metabolism-transcription” coexpression network was further constructed, and the target metabolites and target genes of AS were ultimately screened and validated. AS significantly reduced blood pressure, improved renal function and alleviated renal pathological damage in SHRs. A total of 596 target mRNAs of AS were identified. Of note, 254 of these mRNAs were expressed in 25 pathways that were closely related to metabolic processes. Additionally, the target metabolites of AS were determined, predominantly enriched in 8 pathways, including linoleic acid metabolism, cholesterol metabolism, choline metabolism in cancer, and the synthesis and degradation of ketone bodies, etc. In addition, the target metabolites and target mRNAs of AS were co-enriched in 3 specific pathways of linoleic acid metabolism, cholesterol metabolism, taurine and hypotaurine metabolism, involving 7 different metabolites and 18 differentially expressed (DE) mRNAs. The 7 metabolites exhibited high AUC prediction values, and the verification and sequencing results of the 4 genes were basically consistent. Conclusion The mechanisms by which AS improves HRD may be closely related to the regulation of linoleic acid metabolism, cholesterol metabolism and taurine and hypotaurine metabolism pathways as well as the relevant target genes.

Introduction

Hypertensive renal damage (HRD) represents a prevalent form of chronic kidney disease (CKD). Prolonged hypertension instigates hemodynamic alterations and renal artery endothelial damage, leading to small artery/arteriole pathology and tubular lumen narrowing. This causes ischemic renal parenchymal harm, followed by glomerulosclerosis, tubular atrophy, and interstitial fibrosis, serving as a major risk for end-stage renal disease (ESRD)^1,2. The epidemiological survey carried out in the United States in 2023 demonstrated that the prevalence rate of hypertensive nephropathy among patients suffering from CKD reached 21.5% and is currently showing an upward tendency³. Accordingly, it is of great significance to deeply investigate the therapeutic mechanism, clarify the treatment direction, and explore effective treatment strategies.

Astragalus membranaceus (AM) and Salvia miltiorrhiza (SM), classic drug pairs renowned for their functions of invigorating Qi and promoting blood circulation, are widely used in treating cardiovascular and renal diseases. The plant names of AM and SM have been checked against “Plants of the World online” (www.worldfloraonline.org). AM, a Leguminosae plant known as Huang qi in Chinese and Astragalus membranaceus Fisch. ex Bunge in Latin. is commonly employed in Traditional Chinese Medicine (TCM) mainly for Qi- tonifying and Yang-raising. AM and its active constituents exhibit the potency to impede the progression of renal tubular epithelial-mesenchymal transition (EMT) via the suppression of the TGF-β1/Smad signaling cascade. Consequently, this leads to an alleviation of renal interstitial fibrosis and a deceleration of the progression of CKD⁴. SM, the dried root and rhizome of Salvia miltiorrhiza Bunge (a perennial Labiatae herb), is famed for promoting blood circulation and dispelling blood stasis. The application of SM and its bioactive constituents has been demonstrated to alleviate renal tubular epithelial cell necrosis, augment renal arterial perfusion, and attenuate renal inflammatory responses via the modulation of the PXR/NF-κB signaling pathway⁵. The combination of Astragalus membranaceus and Salvia miltiorrhiza (AS) can modulate the gut-renal axis, rehabilitate the intestinal barrier and the flora structure, and ameliorate diabetic nephropathy and cyclosporin A-induced renal fibrosis^6,7. Our group’s previous studies have demonstrated that AS can treat HRD by regulating blood pressure, protecting renal structure, and improving renal function⁸. However, the specific mechanism of action of AS in the treatment of HRD needs to be studied in depth.

Nowadays, the development of gene sequencing and metabolomics technologies brings new perspectives and directions for the exploration of drug action mechanisms. With the aim of probing into the mechanism underlying the treatment of HRD by AS, the present study integrated transcriptome sequencing and ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) to comparatively analyze the alterations in renal mRNA and metabolites in spontaneously hypertensive rats (SHRs) prior to and subsequent to AS treatment. It endeavors to identify the key target genes, target metabolites, as well as the associated pathways, and to construct a co-expression network of AS in the amelioration of HRD. By doing so, the intention is to reveal the mechanism at the molecular stratum, and to furnish a theoretical underpinning and pioneer novel approaches for the clinical treatment of HRD.

Materials and methods

Preparation of AS

Astragalus membranaceus (Mongolian Astragalus membranaceus) formula granules (Lot No. 2203020 C, with 1 g being equivalent to 2.5 g of tablets) and Salvia miltiorrhiza formula granules (Lot No. 2206019 S, where 1 g is equivalent to 2 g of tablets) were procured from China Resources Sanjiu Modern Traditional Chinese Medicine Pharmaceutical Co., LTD (Guangdong, China). In accordance with the prior research of the research group^6,9, the drug was compounded at a ratio of Astragalus membranaceus to Salvia miltiorrhiza of 2:1. It was then dissolved in 0.9% sodium chloride solution and concentrated to 0.59 g/mL. The principal components of AS solution were also analyzed and identified by means of UHPLC-MS/MS in the aforementioned previous study⁶.

Animals and experimental design

The experimental animals were selected from 14-week-old male wistar-kyoto (WKY) rats and SHRs (Animal License No. SCXK (Beijing) 2021-0006, purchased from Beijing Viton Lever Laboratory Animal Technology Co., Ltd.). The animals were maintained in an environment with a temperature range of 22–24 °C and a relative humidity of 50%−70%. After 1-week acclimatization, 18 male SHRs were randomly grouped into Model, AS, and Valsartan groups, with 6 rats in each group. 6 male WKY rats served as the Normal group. The AS group received AS formula granule solution (0.84 g/mL, 10mL/kg) via oral gavage. The Valsartan group was given Valsartan solution (7.38 g/kg/d) orally. The Normal and Model groups were gavaged with equal-volume,0.9% NaCl solution, once daily for 8 weeks. Blood pressure was measured pre- and 2, 4, 6, 8 weeks post-drug using the noninvasive tail artery method. The systolic and diastolic blood pressure values were recorded from the tail artery of each awake rat, with the measurements being repeated three times. 24 h after the final administration, rats were anesthetized via intraperitoneal injection of Zoletil (0.6 mg/100 g), followed by execution and collection of urine, serum, and kidney samples.

All animal experiments were conducted in accordance with the ARRIVE guidelines and approved by the Ethics Committee of Shandong University of Traditional Chinese Medicine (Approval No. SDUTCM20230221001). All methods were performed in accordance with relevant guidelines and regulations.

Analysis of biochemical indices of serum and urine samples

The assays were performed following the kit instructions. Serum cystatin C (Cys-C), angiotensin II (Ang-II), endothelial nitric oxide synthase (eNOS) and endothelin 1 (ET-1) were quantified via enzyme-linked immunosorbent assay (ELISA) kits from Rady Biotechnology (Wuhan) Co (Lot No. RE3377R, RE2891R, RE1421R, RE1036R). Urinary β-N-acetylamino-glucosidase (NAG) and serum nitric oxide (NO) were determined using ELISA kits from Nanjing Institute of Biological Engineering (NIBE) (Lot No. A031-1-1, A031-2). Urinary albumin (ALB) (Lot No. S03043) was measured with kits from Shenzhen Radu Life Science Co. and detected by a Chemray 800 automatic biochemistry analyzer from Shenzhen Radiometer Life Science Co.

HE, masson, and PAS staining of kidney tissue

Kidney tissues were embedded in a 30% sucrose solution and subsequently sectioned into 9 μm sections using a freezer microtome. The sections were removed from storage at −20 °C and allowed to equilibrate to room temperature. They were then fixed with a universal tissue fixative for 15 min, rinsed with running water and subjected to staining with HE, Masson, and PAS stains. After staining, the sections underwent a dehydration and clearing process involving sequential immersion in anhydrous ethanol I, II, and III for 5 min each, followed by xylene I and II for 5 min each. Finally, the sections were mounted using neutral tree resin. The specimens were then examined under a light microscope, and images were captured and analyzed.

mRNA sequencing and analysis of kidney tissue

Total RNA was extracted from the rat kidney tissues of the Normal, Model and AS groups (n = 3) using RNeasy Mini Kit (250) (Qiagen, Germany) and quality was assessed using Qubit^® 2.0 fluorometer (Life Technologies, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, USA). The mRNA libraries were constructed after quality control and quantification, and sequenced using an Illumina NovaSeq 6000 sequencer (Illumina, USA). Raw reads were quality controlled to obtain clean reads, sequences were filtered, compared and counted sequentially using Fastp, Hisat2 and StringTie software, and data were normalized using the Trimmed Mean M (TMM) algorithm. The fragments per kilobase million (FPKM) value and fold change (FC) value were calculated for each gene. The Q value of mRNA expression was calculated using edgeR3.30.3. The Common differentially expressed (DE) mRNAs in three groups of rats were screened based on Q < 0.05 and FC > 2 or FC < 0.5 and plotted in Volcano map. Using the Wei Sheng Xin website (https://www.bioinformatics.com.cn) to draw the cluster heatmap and enrichment bubble map. Finally, DE mRNAs were annotated by Gene Ontology (https://www.geneontology.org) and analyzed for biological pathway enrichment through the Kyoto encyclopedia of Genes and Genomics (KEGG, https://www.kegg.jp)^10–12.

UHPLC-MS/MS analysis of kidney tissue

Kidney specimens were collected from six rats in the Normal, Model and AS groups for metabolomic analysis. A 100 mg sample of kidney tissue from each group was transferred into a 2 mL centrifuge tube. Then, 1000 µL of tissue extraction solution (75% (9:1 methanol: chloroform) and 25% H₂O) was precisely dispensed. The samples were homogenized by grinding at 50 Hz for two 60-sec intervals. Ultrasonication was carried out at ambient temperature for 30 min, followed by a 30-min ice bath. After centrifugation at 12,000 rpm and 4 °C for 10 min, the supernatant was collected for further analysis. After drying, it was reconstituted with 200 µL of a 50% acetonitrile solution, filtered, and readied for LC-MS detection.

A ThermoVanquish (Thermo Fisher Scientific, USA) ultra high performance liquid chromatography (UPLC) system with a Thermo Q Exactive Focus mass spectrometry detector (Thermo Fisher Scientific, USA) was used for analysis. An ACQUITY UPLC ^® HSS T3 (2.1 × 100 mm, 1.8 μm) column (Waters, Milford, MA, USA) was used with a flow rate of 0.3 mL/min, a column temperature of 40 °C, and an injection volume of 2 µL. Chromatographic conditions: in the positive ionization mode, the mobile phase consisted of acetonitrile with 0.1% formic acid (B2) and water with 0.1% formic acid (A2). In negative ionization mode, the mobile phase was acetonitrile (B3) with 5 mM ammonium formate in water (A3). Mass spectrometry detection was carried out in both positive and negative ion modes to collect data independently. The positive ion spray voltage was configured at 3.50 kV, and the negative ion spray voltage at −2.50 kV. The sheath gas flow rate was set at 40 arb, and the auxiliary gas flow rate at 10 arb. The capillary temperature was controlled at 325 °C. The primary full-scan was conducted with a resolution of 70,000 over a mass scanning range of m/z 100–1000. Additionally, the secondary fragmentation was implemented with a collision energy of 30 eV and a resolution of 17,500, along with a dynamic exclusion for MS/MS data collection.

The raw mass spectrometry downlink files were converted into mzXML format through the MSConvert tool in the Proteowizard package (v3.0.8789). Then, peak detection, filtering and alignment operations were performed using the R XCMS software package to obtain the list of substances for quantification. Substance identification was carried out by leveraging public databases, namely HMDB, massbank, LipidMaps, mzcloud, KEGG, and the self-constructed standard library of Nomi Metabolism, with the aim of obtaining metabolite characterization results. Using the R software package Ropls, the sample data were successively processed via Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) for the purpose of dimensionality reduction. The variables exhibiting a P value < 0.05 and a Variable Importance in Projection (VIP) of OPLS-DA > 1 were designated as differential metabolites. Finally, the MetaboAnalyst (www.metaboanalyst.ca) software package was employed to conduct functional pathway enrichment and topology analysis of the screened differential metabolites. The receiver operating characteristic (ROC) analysis of key differential metabolites was performed using the Wei Sheng Xin website (https://www.bioinformatics.com.cn). The KEGG Mapper visualization tool was utilized to visualize the differential metabolites and the pathway maps of the enriched pathways.

RT-qPCR analysis of mRNAs

Kidney tissues were collected from the Normal, Model and AS groups, and total RNA was extracted using the SPARKeasy Tissue/Cell RNA kit (manufactured by Sparkjade, China). Subsequently, the concentration and purity of the extracted RNA were determined. Next, reverse transcription was carried out using SPARK script II RT Plus kit (Sparkjade, China). RT-qPCR was conducted utilizing the SYBR Green qPCR Mix (2×) (With ROX) (Sparkjade, China). The Ct value of the target gene was normalized by GAPDH, and the expression level was calculated in accordance with the 2^-△△Ct method. The primers were synthesized by Beijing Prime Biology, and the primer sequences are shown in Table 1.

Table 1.

Primer sequences of the genes.

Genes	Primer(5’→3’)
Angptl4	Forward: GGACCTTAACTGTGCCAAGAGC
	Reverse: CTGCCGTTGCCGTGGAATAG
Pla2g2a	Forward: TCTGGAGTTTGGGCAAATGATTCTG
	Reverse: CACACCACAATGGCAACCGTAG
Pla2g12a	Forward: TGCTCCTGCTCTTGCTGGTC
	Reverse: TGTCTATCTTGTGGATGCCGTTTC
Baat	Forward: GAGTCAGAGGAGGAGGAAGAAGAG
	Reverse: GGCAACCCACCAACCTTGTTC

Data analysis

GraphPad Prism 10.1 software was utilized for data processing and graph generation. The data were expressed as the mean ± standard deviation (SD). Comparisons among multiple groups were analyzed by one-way ANOVA, and p < 0.05 was considered statistically significant.

Results

AS reduces blood pressure in SHRs

As illustrated in Fig. 1 (A-E), the systolic and diastolic blood pressure of rats in the Model group was markedly elevated (p < 0.0001) relative to the Normal group. In comparison with the Model group, rats in the AS group exhibited a reduction in systolic blood pressure at the 2nd week of drug administration (p < 0.05), and subsequently, both systolic and diastolic blood pressure declined substantially by the 5th and 6th weeks of administration (p < 0.0001). Meanwhile, both systolic and diastolic blood pressure of rats in the Valsartan group presented a significant decrease after the 4th week of administration (p < 0.0001).

Fig. 1.

Effects of Astragalus membranaceus and Salvia miltiorrhiza (AS) on blood pressure and renal function in SHRs. The trends of systolic and diastolic blood pressure in rats of each group before drug administration (A) and at 2 weeks (B), 4 weeks (C), 6 weeks (D), and 8 weeks (E) after drug administration.Bar graphs of urinary ALB (F), urinary NAG (G), serum Cys-c (H), serum ET-1 (I), serum AngII (J), serum Enos (K), and serum NO (L) levels in rats of each group. Data are presented as the mean ± SD (n = 6). ^## p < 0.01, ^#### p < 0.0001 compared with the Normal group, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 compared with the Model group.

AS attenuates early kidney damage in SHRs

As depicted in Fig. 1 (F-L), compared with the Normal group, the levels of urinary ALB, urinary NAG, serum Cys-c, ET-1, and AngII in the Model group were significantly elevated (all p < 0.0001 or p < 0.001), while those of eNOS and NO were significantly lower (p < 0.0001 or p < 0.001). In contrast to the Model group, in the AS group and Valsartan group, urinary ALB, urinary NAG, serum Cys-c, ET-1, and AngII were significantly decreased (p < 0.0001 or p < 0.01), and the levels of eNOS and NO were significantly increased (p < 0.0001 or p < 0.05).

AS attenuates renal pathologic injury in SHRs

As shown in Fig. 2, HE, Masson, and PAS staining were employed to assess the pathological alterations in the renal tissue structure of rats in each group. The renal tissue sections of rats in the Normal group exhibited no conspicuous abnormality, with intact renal tubule and glomerulus morphology and an absence of renal mesenchymal stroma fibrosis. In the Model group, rats presented with glomerular basement membrane thickening, renal tubular vacuolar changes, epithelial cell necrosis and shedding, and prominent renal mesenchymal stroma fibrosis. In comparison with the Model group, following drug administration intervention, the aforementioned pathological injuries were alleviated to varying extents in the AS and Valsartan groups.

Fig. 2.

The effect of Astragalus membranaceus and Salvia miltiorrhiza (AS) on kidney pathology. (A) HE staining of kidney (bar = 50 μm, 400×). (B) MASSON staining of kidney (bar = 50 μm, 400×). (C) PAS staining of kidney (bar = 50 μm, 400×).

AS modulates the expression of metabolism-related mRNAs in the kidney of SHRs

The kidneys of rats from the Normal, Model, and AS groups underwent mRNA sequencing. A fold-change cut-off of log₂FC > 0.58 or log₂FC < −0.58 and a P-value of < 0.05 were set as the cutoffs for screening DE mRNAs. In the comparison between the Normal and Model groups, 1786 DE mRNAs were identified, with 689 up-regulated and 1097 down-regulated genes (Fig. 3A). Conversely, in the comparison of the AS group with the Model group, 596 DE mRNAs were detected, comprising 246 up-regulated and 350 down-regulated genes (Fig. 3B). Notably, 192 DE mRNAs in the Model group exhibited disordered expression, which was reversed following AS intervention (Table 2). Additionally, the clustering heatmap distinctly illustrated the expression levels of the DE mRNAs in different samples (Fig. 3C and D).

Fig. 3.

Regulation of renal mRNA expression profiles by Astragalus membranaceus and Salvia miltiorrhiza (AS). (A-B) Volcano plots of DE mRNAs in Model vs. Normal group and AS vs. Model group. Red color represents up-regulated mRNAs and blue color represents down- regulated mRNAs. (C-D) Cluster analysis diagram of DE mRNAs in Model vs. Normal group and AS vs. Model group. The greater the intensity of the red color, the higher the expression, and the greater the intensity of the blue color, the lower the expression.

Table 2.

DE mRNA reversed after AS intervention.

		M vs. N			AS vs. M
ID	Name	log2FC	P-value	Up/down	log2FC	P-value	Up/down
ENSRNOG00000000036	Klhdc8a	7.25E-01	3.38E-08	Up	−6.15E-01	3.39E-03	Down
ENSRNOG00000019481	Cyp8b1	4.58E + 00	5.68E-55	Up	−7.23E-01	4.58E-05	Down
ENSRNOG00000061844	AABR07005618.1	1.36E + 00	1.69E-22	Up	−6.73E-01	2.52E-07	Down
ENSRNOG00000061544	Spock2	2.32E + 00	7.07E-20	Up	−8.75E-01	4.87E-03	Down
ENSRNOG00000047894	Liph	1.73E + 00	7.21E-19	Up	−1.02E + 00	2.66E-03	Down
ENSRNOG00000001452	Fzd9	2.10E + 00	5.83E-18	Up	−2.08E + 00	8.07E-08	Down
ENSRNOG00000029980	Zbtb16	2.13E + 00	1.17E-15	Up	−1.35E + 00	3.23E-07	Down
ENSRNOG00000019206	Nupr1	1.06E + 00	2.03E-15	Up	−1.02E + 00	3.29E-04	Down
ENSRNOG00000057855	F5	1.31E + 00	5.43E-15	Up	−1.00E + 00	7.92E-04	Down
ENSRNOG00000005957	Slc4a7	1.33E + 00	8.17E-14	Up	−1.49E + 00	6.66E-07	Down
ENSRNOG00000013547	Slc6a12	1.63E + 00	1.58E-13	Up	−1.71E + 00	5.09E-04	Down
ENSRNOG00000008000	Syt13	1.56E + 00	3.18E-12	Up	−1.50E + 00	2.80E-05	Down
ENSRNOG00000048224	AABR07035224.1	9.19E + 00	3.64E-10	Up	−9.01E + 00	2.93E-09	Down
ENSRNOG00000019216	Il12rb1	1.57E + 00	5.38E-10	Up	−1.38E + 00	7.45E-03	Down
ENSRNOG00000053631	Tdrd9	1.21E + 00	6.66E-10	Up	−7.32E-01	5.20E-03	Down
ENSRNOG00000059789	Metazoa_SRP	Inf	7.31E-10	Up	Inf	7.91E-09	Down
ENSRNOG00000058739	Snn	7.40E-01	1.26E-09	Up	−6.70E-01	9.26E-06	Down
ENSRNOG00000030285	Epha3	1.70E + 00	1.29E-09	Up	−1.16E + 00	4.88E-04	Down
ENSRNOG00000049642	Smim5	2.00E + 00	1.88E-09	Up	−1.99E + 00	7.59E-06	Down
ENSRNOG00000008223	Cnr1	1.36E + 00	2.75E-09	Up	−1.09E + 00	4.41E-04	Down
ENSRNOG00000007687	Sema7a	1.29E + 00	3.97E-09	Up	−1.04E + 00	1.08E-04	Down
ENSRNOG00000007989	Chst1	1.15E + 00	4.24E-09	Up	−6.35E-01	3.43E-03	Down
ENSRNOG00000056955	Bicdl1	1.29E + 00	5.75E-09	Up	−1.21E + 00	1.83E-07	Down
ENSRNOG00000004341	LOC498222	8.72E-01	9.20E-09	Up	−9.78E-01	1.56E-03	Down
ENSRNOG00000005404	Rasgrp1	8.19E-01	1.35E-08	Up	−6.67E-01	3.79E-04	Down
ENSRNOG00000015418	Klhl14	7.88E-01	2.62E-08	Up	−6.04E-01	1.95E-03	Down
ENSRNOG00000000609	Ipmk	8.69E-01	3.96E-08	Up	−6.37E-01	3.24E-05	Down
ENSRNOG00000012482	Ndrg4	8.44E-01	6.00E-08	Up	−1.59E + 00	1.83E-15	Down
ENSRNOG00000026065	Slit1	9.80E-01	9.29E-08	Up	−6.85E-01	3.29E-04	Down
ENSRNOG00000003929	Pcdh19	6.93E-01	2.51E-07	Up	−6.42E-01	5.85E-03	Down
ENSRNOG00000011334	Tmem63c	1.48E + 00	3.74E-07	Up	−1.09E + 00	2.40E-04	Down
ENSRNOG00000011858	Unc5d	1.16E + 00	4.74E-07	Up	−8.97E-01	4.56E-03	Down
ENSRNOG00000048769	Nek5	1.11E + 00	5.11E-07	Up	−1.07E + 00	1.09E-03	Down
ENSRNOG00000049382	AABR07071905.1	Inf	5.84E-07	Up	Inf	1.49E-06	Down
ENSRNOG00000058664	Usp9y	Inf	5.86E-07	Up	−6.37E + 00	9.01E-06	Down
ENSRNOG00000011424	Cldn23	1.13E + 00	7.34E-07	Up	−1.45E + 00	1.24E-05	Down
ENSRNOG00000016505	Fam169a	8.56E-01	8.63E-07	Up	−7.73E-01	3.48E-04	Down
ENSRNOG00000046615	AABR07064724.1	8.17E + 00	1.04E-06	Up	−7.98E + 00	5.46E-06	Down
ENSRNOG00000054212	Pde1a	6.21E-01	3.21E-06	Up	−6.02E-01	1.56E-02	Down
ENSRNOG00000001284	Uncx	1.70E + 00	3.27E-06	Up	−1.22E + 00	2.50E-03	Down
ENSRNOG00000011815	Sgk1	1.12E + 00	3.82E-06	Up	−7.43E-01	2.82E-03	Down
ENSRNOG00000007387	Per1	1.08E + 00	3.91E-06	Up	−6.14E-01	1.43E-02	Down
ENSRNOG00000058314	AABR07022162.2	1.54E + 00	7.27E-06	Up	−8.11E-01	2.45E-02	Down
ENSRNOG00000028708	Ntsr1	Inf	1.09E-05	Up	−3.24E + 00	7.54E-04	Down
ENSRNOG00000012181	Lpl	7.95E-01	1.41E-05	Up	−8.54E-01	1.32E-03	Down
ENSRNOG00000010259	Esrrb	7.19E-01	1.70E-05	Up	−5.92E-01	4.49E-03	Down
ENSRNOG00000005307	Slc4a10	1.39E + 00	1.81E-05	Up	−1.09E + 00	3.98E-02	Down
ENSRNOG00000028404	Ppp1r1b	8.95E-01	1.97E-05	Up	−7.83E-01	1.31E-05	Down
ENSRNOG00000051920	U1	9.36E-01	2.55E-05	Up	−7.82E-01	2.84E-05	Down
ENSRNOG00000013330	Cdhr1	1.34E + 00	2.90E-05	Up	−1.37E + 00	3.97E-04	Down
ENSRNOG00000007402	AC121415.1	1.35E + 00	4.18E-05	Up	−1.05E + 00	2.74E-03	Down
ENSRNOG00000028225	Tnni3k	1.01E + 00	4.21E-05	Up	−9.95E-01	2.64E-02	Down
ENSRNOG00000014710	Prom2	6.78E-01	5.42E-05	Up	−6.00E-01	4.23E-04	Down
ENSRNOG00000014296	Syt10	3.05E + 00	7.33E-05	Up	−2.78E + 00	6.12E-04	Down
ENSRNOG00000011526	Pcsk6	9.35E-01	7.97E-05	Up	−1.20E + 00	2.72E-03	Down
ENSRNOG00000031126	Tecta	9.56E-01	1.03E-04	Up	−6.27E-01	2.06E-02	Down
ENSRNOG00000060441	U1	8.71E-01	1.04E-04	Up	−9.55E-01	3.13E-08	Down
ENSRNOG00000000515	Mapk13	7.78E-01	1.06E-04	Up	−6.03E-01	1.69E-03	Down
ENSRNOG00000025625	Rnase4	1.04E + 00	1.46E-04	Up	−6.63E-01	3.02E-02	Down
ENSRNOG00000003064	Bst1	8.37E-01	2.15E-04	Up	−1.35E + 00	7.02E-06	Down
ENSRNOG00000059225	AABR07020987.1	1.30E + 00	3.06E-04	Up	−7.45E-01	4.01E-02	Down
ENSRNOG00000036777	Wfdc16	2.45E + 00	3.09E-04	Up	−2.17E + 00	2.87E-03	Down
ENSRNOG00000017234	Olr155	2.06E + 00	3.12E-04	Up	−1.70E + 00	3.08E-03	Down
ENSRNOG00000054722	U1	8.77E-01	3.30E-04	Up	−8.22E-01	8.59E-04	Down
ENSRNOG00000005367	Slc12a1	6.30E-01	4.37E-04	Up	−6.45E-01	1.65E-03	Down
ENSRNOG00000028015	Pf4	1.23E + 00	4.53E-04	Up	−7.23E-01	4.95E-02	Down
ENSRNOG00000022419	Dok7	1.02E + 00	6.26E-04	Up	−1.03E + 00	1.19E-03	Down
ENSRNOG00000023008	Fam131c	8.53E-01	8.04E-04	Up	−1.12E + 00	8.89E-04	Down
ENSRNOG00000061082	U1	8.42E-01	8.48E-04	Up	−1.12E + 00	3.10E-03	Down
ENSRNOG00000039107	Mfrp	1.87E + 00	9.41E-04	Up	−1.97E + 00	6.55E-03	Down
ENSRNOG00000021088	Tmod4	7.80E-01	1.16E-03	Up	−6.73E-01	1.15E-02	Down
ENSRNOG00000019134	Htr4	3.11E + 00	1.23E-03	Up	Inf	6.44E-05	Down
ENSRNOG00000005335	Galnt13	2.50E + 00	2.10E-03	Up	−1.70E + 00	2.05E-02	Down
ENSRNOG00000057141	RGD1561730	1.81E + 00	2.10E-03	Up	−1.19E + 00	3.76E-02	Down
ENSRNOG00000018505	Cidea	1.15E + 00	2.19E-03	Up	−7.72E-01	1.07E-02	Down
ENSRNOG00000052128	Clec2e	3.09E + 00	2.25E-03	Up	−1.82E + 00	3.12E-02	Down
ENSRNOG00000008936	Map3k6	8.38E-01	2.73E-03	Up	−1.11E + 00	7.86E-04	Down
ENSRNOG00000019890	Folr2	1.20E + 00	3.04E-03	Up	−6.43E-01	4.84E-02	Down
ENSRNOG00000045998	Sema6b	7.58E-01	3.12E-03	Up	−6.94E-01	1.26E-02	Down
ENSRNOG00000054453	AABR07072511.1	Inf	3.29E-03	Up	−3.21E + 00	1.95E-02	Down
ENSRNOG00000054419	U1	7.80E-01	3.41E-03	Up	−6.07E-01	2.29E-02	Down
ENSRNOG00000054764	Flt3	7.85E-01	3.53E-03	Up	−6.58E-01	1.93E-02	Down
ENSRNOG00000021128	Kcnj11	7.43E-01	3.55E-03	Up	−7.35E-01	1.09E-02	Down
ENSRNOG00000012972	Alox5	1.11E + 00	3.55E-03	Up	−1.01E + 00	3.45E-02	Down
ENSRNOG00000015368	Pdilt	9.94E-01	4.20E-03	Up	−9.04E-01	2.05E-02	Down
ENSRNOG00000017456	Vstm2b	1.86E + 00	4.22E-03	Up	−2.40E + 00	6.27E-04	Down
ENSRNOG00000061304	Atp2b3	6.02E-01	5.41E-03	Up	−6.89E-01	3.80E-03	Down
ENSRNOG00000023337	Sema3a	6.21E-01	5.84E-03	Up	−8.51E-01	4.76E-03	Down
ENSRNOG00000052027	U5	1.84E + 00	5.87E-03	Up	−1.34E + 00	4.99E-03	Down
ENSRNOG00000059092	Olr154	1.37E + 00	6.06E-03	Up	−1.52E + 00	5.62E-03	Down
ENSRNOG00000061399	AABR07024955.1	2.23E + 00	6.22E-03	Up	−1.74E + 00	1.58E-02	Down
ENSRNOG00000030174	-	8.59E-01	6.86E-03	Up	−6.40E-01	3.87E-02	Down
ENSRNOG00000026891	AC093995.1	Inf	8.52E-03	Up	Inf	1.32E-02	Down
ENSRNOG00000019614	Ptpn22	8.51E-01	8.63E-03	Up	−8.50E-01	2.52E-02	Down
ENSRNOG00000054952	U1	1.60E + 00	8.73E-03	Up	−6.78E-01	3.03E-02	Down
ENSRNOG00000025453	Fam221b	Inf	9.07E-03	Up	−3.23E + 00	4.11E-02	Down
ENSRNOG00000051684	St8sia6	1.26E + 00	9.79E-03	Up	−1.37E + 00	3.63E-03	Down
ENSRNOG00000019441	LOC108348086	1.58E + 00	1.09E-02	Up	−1.40E + 00	2.86E-02	Down
ENSRNOG00000020142	Gdf1	Inf	1.17E-02	Up	Inf	1.80E-02	Down
ENSRNOG00000010440	Gnal	6.11E-01	1.25E-02	Up	−6.60E-01	1.71E-02	Down
ENSRNOG00000004502	Hal	3.51E + 00	1.31E-02	Up	Inf	2.37E-03	Down
ENSRNOG00000013869	Kcnj4	1.31E + 00	1.31E-02	Up	−2.18E + 00	2.95E-04	Down
ENSRNOG00000054989	Grhl1	9.05E-01	1.33E-02	Up	−1.47E + 00	9.77E-04	Down
ENSRNOG00000015566	Atp6v1e2	2.07E + 00	1.44E-02	Up	−2.31E + 00	1.12E-02	Down
ENSRNOG00000050998	Rn60_20_0047.3	Inf	1.60E-02	Up	Inf	1.84E-02	Down
ENSRNOG00000013279	LOC681458	Inf	1.65E-02	Up	Inf	2.11E-02	Down
ENSRNOG00000033244	LOC688473	Inf	1.65E-02	Up	Inf	2.50E-02	Down
ENSRNOG00000007294	Sntg1	Inf	1.68E-02	Up	Inf	1.76E-02	Down
ENSRNOG00000055311	U1	7.27E-01	2.10E-02	Up	−8.43E-01	3.09E-11	Down
ENSRNOG00000057687	AC141334.3	2.57E + 00	2.56E-02	Up	−2.37E + 00	2.86E-02	Down
ENSRNOG00000002057	Slc10a6	9.47E-01	2.65E-02	Up	−9.69E-01	2.57E-02	Down
ENSRNOG00000011631	Fst	8.95E-01	3.00E-02	Up	−1.72E + 00	2.18E-03	Down
ENSRNOG00000060475	AABR07001555.1	Inf	3.26E-02	Up	Inf	3.34E-02	Down
ENSRNOG00000056358	Ccdc27	8.14E-01	3.84E-02	Up	−1.01E + 00	1.61E-02	Down
ENSRNOG00000046600	AABR07015066.1	1.10E + 00	4.00E-02	Up	−2.09E + 00	3.50E-05	Down
ENSRNOG00000060896	AABR07063424.1	1.03E + 00	4.24E-02	Up	−1.86E + 00	5.44E-05	Down
ENSRNOG00000028238	Sh3bgr	9.50E-01	4.63E-02	Up	−1.09E + 00	4.24E-02	Down
ENSRNOG00000059021	7SK	1.03E + 00	4.86E-02	Up	−8.10E-01	2.92E-02	Down
ENSRNOG00000004918	Kcna4	−1.09E + 00	5.96E-05	Down	5.94E-01	4.61E-02	Up
ENSRNOG00000008915	Prima1	−2.07E + 00	2.81E-20	Down	1.47E + 00	3.31E-12	Up
ENSRNOG00000010079	Car3	−3.79E + 00	3.48E-04	Down	3.90E + 00	7.68E-08	Up
ENSRNOG00000008553	Mthfr	−1.33E + 00	1.23E-11	Down	8.18E-01	4.04E-06	Up
ENSRNOG00000027350	Tns4	−7.60E-01	2.60E-04	Down	1.02E + 00	9.76E-06	Up
ENSRNOG00000015538	Abcd2	−1.16E + 00	4.90E-02	Down	1.60E + 00	5.70E-05	Up
ENSRNOG00000046022	LOC100911453	−1.84E + 00	1.93E-05	Down	1.83E + 00	9.16E-05	Up
ENSRNOG00000054637	AC115371.3	−9.81E-01	5.13E-05	Down	9.33E-01	1.22E-04	Up
ENSRNOG00000053604	7SK	−2.53E + 00	1.26E-08	Down	1.18E + 00	1.64E-04	Up
ENSRNOG00000015075	Stc1	−6.42E-01	5.30E-05	Down	9.49E-01	1.96E-04	Up
ENSRNOG00000024651	Greb1	−6.18E-01	1.07E-04	Down	6.49E-01	2.18E-04	Up
ENSRNOG00000012674	Adrb3	Inf	7.41E-04	Down	Inf	3.45E-04	Up
ENSRNOG00000015086	Plin1	−3.30E + 00	1.46E-06	Down	2.78E + 00	4.49E-04	Up
ENSRNOG00000008529	Foxs1	−1.23E + 00	2.55E-08	Down	8.18E-01	5.50E-04	Up
ENSRNOG00000032417	Gabrp	−1.82E + 00	7.57E-21	Down	7.43E-01	1.13E-03	Up
ENSRNOG00000002937	Ren	−1.22E + 00	3.96E-11	Down	6.41E-01	1.19E-03	Up
ENSRNOG00000009790	Kcnk3	−1.13E + 00	1.61E-02	Down	1.04E + 00	1.26E-03	Up
ENSRNOG00000051347	Rn50_13_0558.2	−1.24E + 00	2.24E-02	Down	1.52E + 00	1.84E-03	Up
ENSRNOG00000013552	Scd	−4.84E + 00	3.72E-04	Down	2.55E + 00	2.05E-03	Up
ENSRNOG00000007290	Atp1a2	−1.41E + 00	1.65E-03	Down	9.63E-01	2.46E-03	Up
ENSRNOG00000042264	-	−6.88E-01	1.35E-02	Down	8.40E-01	2.50E-03	Up
ENSRNOG00000002802	Cxcl1	−1.71E + 00	3.00E-04	Down	1.31E + 00	2.93E-03	Up
ENSRNOG00000059449	Metazoa_SRP	−6.61E-01	1.75E-02	Down	7.00E-01	4.50E-03	Up
ENSRNOG00000022483	Trim50	−1.22E + 00	2.36E-05	Down	8.34E-01	4.88E-03	Up
ENSRNOG00000006263	Sh2d1a	Inf	1.16E-02	Down	Inf	4.94E-03	Up
ENSRNOG00000033564	Cfd	−2.80E + 00	3.37E-03	Down	1.98E + 00	5.08E-03	Up
ENSRNOG00000004571	RGD1563680	−7.23E-01	1.57E-04	Down	6.30E-01	5.24E-03	Up
ENSRNOG00000032997	AY172581.20	−1.30E + 00	4.80E-03	Down	1.24E + 00	6.54E-03	Up
ENSRNOG00000025637	Saxo1	−2.18E + 00	1.86E-02	Down	2.46E + 00	7.50E-03	Up
ENSRNOG00000004649	Il1b	−7.34E-01	1.61E-02	Down	8.97E-01	7.58E-03	Up
ENSRNOG00000031667	AY172581.11	−1.25E + 00	2.69E-02	Down	1.38E + 00	7.98E-03	Up
ENSRNOG00000057331	U1	−6.40E + 00	2.12E-19	Down	1.30E + 00	8.71E-03	Up
ENSRNOG00000000563	Adamts14	−1.17E + 00	3.79E-06	Down	7.26E-01	8.79E-03	Up
ENSRNOG00000001984	Kcne1	−1.70E + 00	7.87E-06	Down	9.57E-01	9.74E-03	Up
ENSRNOG00000060522	Metazoa_SRP	Inf	1.42E-05	Down	6.60E-01	9.86E-03	Up
ENSRNOG00000002984	Bmp15	−1.26E + 00	4.03E-07	Down	6.68E-01	1.07E-02	Up
ENSRNOG00000009329	Nr1d1	−8.21E-01	2.54E-03	Down	7.15E-01	1.09E-02	Up
ENSRNOG00000015850	Rbp7	Inf	5.73E-03	Down	Inf	1.10E-02	Up
ENSRNOG00000038738	LOC685203	−1.35E + 00	5.09E-12	Down	5.99E-01	1.19E-02	Up
ENSRNOG00000029055	Ttk	−2.29E + 00	8.58E-06	Down	1.52E + 00	1.24E-02	Up
ENSRNOG00000008310	Mpo	−4.31E + 00	2.25E-02	Down	4.00E + 00	1.26E-02	Up
ENSRNOG00000004698	Cd244	−2.24E + 00	3.44E-14	Down	8.90E-01	1.41E-02	Up
ENSRNOG00000001821	Adipoq	−1.07E + 00	1.18E-03	Down	6.62E-01	1.43E-02	Up
ENSRNOG00000020620	Ppp1r32	−1.30E + 00	3.73E-03	Down	1.04E + 00	1.49E-02	Up
ENSRNOG00000008475	Fut9	−9.98E-01	1.60E-06	Down	6.06E-01	1.50E-02	Up
ENSRNOG00000012286	Il20ra	−9.16E-01	8.38E-04	Down	7.34E-01	1.52E-02	Up
ENSRNOG00000015541	Gnb3	Inf	1.18E-06	Down	Inf	1.75E-02	Up
ENSRNOG00000058824	-	−2.77E + 00	1.25E-02	Down	1.08E + 00	1.83E-02	Up
ENSRNOG00000035576	Mir27b	−1.41E + 00	1.75E-04	Down	9.93E-01	1.98E-02	Up
ENSRNOG00000054846	-	−2.29E + 00	4.65E-02	Down	9.25E-01	2.00E-02	Up
ENSRNOG00000009705	Lck	−6.61E-01	1.53E-02	Down	6.71E-01	2.00E-02	Up
ENSRNOG00000034190	-	−5.98E-01	4.75E-03	Down	6.81E-01	2.17E-02	Up
ENSRNOG00000055675	SNORD94	−1.22E + 00	1.55E-04	Down	8.46E-01	2.19E-02	Up
ENSRNOG00000003925	Hhat	−1.03E + 00	1.00E-04	Down	6.35E-01	2.56E-02	Up
ENSRNOG00000042781	Ropn1l	−1.40E + 00	3.23E-04	Down	9.85E-01	2.74E-02	Up
ENSRNOG00000017716	Ucp3	Inf	1.15E-03	Down	Inf	2.74E-02	Up
ENSRNOG00000014367	Ephb6	−1.10E + 00	7.67E-06	Down	7.31E-01	2.80E-02	Up
ENSRNOG00000008135	Pla2g4f	−1.04E + 00	3.34E-02	Down	9.87E-01	2.81E-02	Up
ENSRNOG00000046377	Btnl9	−1.62E + 00	2.14E-02	Down	1.55E + 00	2.83E-02	Up
ENSRNOG00000017477	Mmp23	−5.99E-01	2.53E-02	Down	5.98E-01	2.84E-02	Up
ENSRNOG00000003580	Ucp1	Inf	1.46E-05	Down	Inf	2.96E-02	Up
ENSRNOG00000060466	U3	−5.92E-01	3.94E-02	Down	1.41E + 00	3.12E-02	Up
ENSRNOG00000061945	Rn60_16_0690.2	Inf	4.66E-02	Down	Inf	3.57E-02	Up
ENSRNOG00000025121	Pla2g3	Inf	2.50E-02	Down	Inf	3.65E-02	Up
ENSRNOG00000015283	Nt5c1a	−6.00E-01	1.36E-02	Down	6.18E-01	3.67E-02	Up
ENSRNOG00000013623	Amer2	−1.06E + 00	2.37E-05	Down	6.34E-01	3.92E-02	Up
ENSRNOG00000058726	AABR07054837.1	−8.03E-01	3.00E-02	Down	1.13E + 00	3.92E-02	Up
ENSRNOG00000026870	Clic6	−1.98E + 00	1.72E-02	Down	1.86E + 00	4.15E-02	Up
ENSRNOG00000012235	Ppp1r17	Inf	3.35E-02	Down	Inf	4.48E-02	Up
ENSRNOG00000003841	Kcnh1	−7.82E-01	1.07E-02	Down	6.42E-01	4.52E-02	Up
ENSRNOG00000005180	Vstm2a	−8.19E-01	2.30E-02	Down	8.99E-01	4.59E-02	Up
ENSRNOG00000027380	Upk1b	−9.15E-01	8.01E-03	Down	7.37E-01	4.67E-02	Up
ENSRNOG00000062199	Rn60_20_0067.1	−1.47E + 00	5.28E-04	Down	9.96E-01	4.87E-02	Up
ENSRNOG00000050629	Olr312	−2.84E + 00	3.55E-03	Down	2.52E + 00	4.92E-02	Up

GO and KEGG enrichment analyses were conducted on the DE mRNAs. The GO enrichment analysis results (Fig. 4A, B) indicated that the target mRNAs of AS treatment for HRD were predominantly enriched in 26 biological processes (BP), 16 cellular components (CC), and 13 molecular functions (MF). The significantly ranked relevant categories were visualized. Specifically, 752 DE mRNAs in the Normal and Model groups were enriched in metabolic processes, and 254 DE genes in the Model and AS groups were also enriched in metabolic processes. The KEGG enrichment analysis (Fig. 4C, D) demonstrated that the targets of AS treatment for HRD were mainly concentrated in 10 metabolic, 9 organic system, 4 genetic information processing, 3 environmental information processing, and 4 cellular process pathways, implying a close association between the target mRNAs of AS for HRD and metabolism. Further enrichment analysis of metabolism-related pathways (Fig. 4E, F; Table 3) uncovered 25 metabolic pathways, such as taurine and hypotaurine metabolism, primary bile acid biosynthesis, vitamin B6 metabolism, cholesterol metabolism, linoleic acid metabolism, and arachidonic acid metabolism.

Fig. 4.

GO and KEGG enrichment analysis of Astragalus membranaceus and Salvia miltiorrhiza (AS) target mRNAs. Histogram of GO function analysis: Model vs. Normal (A), AS vs. Model (B). Biological Process is marked in red. Cellular Component is marked in green. Molecular Function is marked in blue. KEGG Classification plot༚ Model vs. Normal (C), AS vs. Model (D). Bubble plots of DE mRNAs enrichment pathways in the Model vs. Normal group(E) and AS vs. Model group (F). Horizontal coordinates indicate enrichment factors. The color is related to the p-value, the darker the color, the smaller the p-value. The larger the circle, the larger the influence factor. The closer the pathway is to the upper right corner, the more DE mRNAs are significantly enriched in the pathway and the greater the influence on the pathway.

Table 3.

Differentially expressed mRNA enrichment pathway rich factors and P-values.

Pathway name	Rich_factor	P-value	Model vs. Normal/AS vs. Model
Taurine and hypotaurine metabolism	5.657760	0.00438948	M vs. N
Primary bile acid biosynthesis	4.125450	0.00323554	M vs. N
Vitamin B6 metabolism	3.960432	0.01528459	M vs. N
Biosynthesis of unsaturated fatty acids	3.850420	0.00124750	M vs. N
Ascorbate and aldarate metabolism	3.696403	0.00161791	M vs. N
Steroid biosynthesis	3.474063	0.00749106	M vs. N
alpha-Linolenic acid metabolism	3.143200	0.01202614	M vs. N
Nicotinate and nicotinamide metabolism	2.980970	0.00607574	M vs. N
Phenylalanine metabolism	2.514560	0.04703768	M vs. N
Metabolism of xenobiotics by cytochrome P450	2.329666	0.00554278	M vs. N
Cholesterol metabolism	2.329666	0.01349901	M vs. N
Linoleic acid metabolism	2.200240	0.04409226	M vs. N
Arginine and proline metabolism	2.200240	0.02558563	M vs. N
Ether lipid metabolism	2.149071	0.03795379	M vs. N
Arachidonic acid metabolism	2.140774	0.01100428	M vs. N
Retinol metabolism	1.885920	0.03463675	M vs. N
Steroid hormone biosynthesis	1.838175	0.04116785	M vs. N
PPAR signaling pathway	4.863504	0.00002685	AS vs. M
One carbon pool by folate	4.405762	0.02281231	AS vs. M
Steroid biosynthesis	3.941998	0.03060684	AS vs. M
Aldosterone-regulated sodium reabsorption	3.120748	0.03454257	AS vs. M
Aldosterone synthesis and secretion	2.880691	0.00791804	AS vs. M
Adipocytokine signaling pathway	2.674927	0.02603838	AS vs. M
cAMP signaling pathway	2.226696	0.01157477	AS vs. M
AMPK signaling pathway	2.221550	0.03351424	AS vs. M

AS significantly regulates renal metabolism in SHRs

Transcriptomics analysis revealed that multiple metabolic pathways were implicated in the disease progression. To further explore the role of AS in metabolic changes within the kidneys of SHRs, non-targeted metabolomics analysis was conducted. Kidney tissue samples from rats in the Normal, Model, and AS groups were analyzed via UHPLC-MS/MS technology. As depicted in Fig. 5A and B, the Total Ion Chromatogram (TIC) obtained in both positive and negative ion modes exhibited good chromatographic peak shapes and a relatively uniform distribution, and the QC samples demonstrated significant aggregation, indicating a stable systematic analytical process and high-confidence data. Moreover, the reliability of the data was further corroborated by the fact that 78.3% and 77% of the characteristic peaks had coefficients of variation less than 30% in the positive and negative ion modes, respectively (Fig. 5C-F).

Fig. 5.

Quality control and quality assurance. (A-B) Total ion chromatogram. The horizontal coordinate is the retention time, the vertical coordinate is the ion intensity. The upper right corner of the graph represents the maximum ion intensity of each sample, and different colors represent different groups. (C-D) PCA score graph of QC samples. The red dots represent the QC samples, and the green dots are the samples. (E-F) Relative standard deviation (RSD) distribution graph. The left vertical coordinate represents the proportion of the percentage of the total samples, the right vertical coordinate represents the specific number of samples. The horizontal coordinates are the range of RSD values. A, C, E are positive ion modes; B, D, F are negative ion modes.

The OPLS-DA method was employed for analysis. The results presented in Fig. 6A-D indicated that under both positive and negative ion modes, significant group separations were observed between the Model group and the Normal group, as well as between the AS group and the Model group. This implied that the renal metabolism of SHRs and that after AS intervention were abnormal. Meanwhile, the results of S-plot plots (Fig. 6E-H) demonstrated the changes in the relative content of metabolites in the samples of the two groups. Metabolites closer to the upper right and lower left corners had higher significance.

Fig. 6.

OPLS-DA analysis diagram of kidney metabolites. OPLS-DA score plots for Model vs. Normal group: (A) (positive-ion modes), (B) (negative-ion mode). OPLS-DA score plots for AS vs. Model group: (C (positive-ion modes), (D) (negative-ion mode). S-plot plots for Model vs. Normal group: (E) (positive-ion modes), (F) (negative-ion mode). S-plot plots for AS vs. Model group: (G) (positive-ion modes), (H) (negative-ion mode).

By integrating the volcano plots (Fig. 7A, B) and heat maps (Fig. 7C, D) of differential metabolites, metabolites fulfilling the criteria of p < 0.05 and VIP > 1 were selected as potential biomarkers. Subsequently, leveraging spectral databases such as HMDB, massbank, LipidMaps, mzcloud, KEGG, and the Nomimetabolism self-built metabolite standards database, we searched and compared the metabolites with secondary spectra in the quantitative list against the fragment ions and other information of each secondary spectrum in the databases. Eventually, 53 endogenous metabolites exhibiting differences were identified between the Normal and Model groups, and 28 differential metabolites between the Model and AS groups, amounting to a total of 81 potential biomarkers potentially associated with HRD (Table 4). Among them, the metabolites with KEGG metabolic pathway profiles encompassed 17 metabolites, including linoleic acid, taurine, γ-linolenic acid, cholic acid, choline, glycerophosphocholine, L-carnitine, uric acid, L-aspartic acid, and tryptophan, as presented in Table 5.

Fig. 7.

Analysis of renal differential metabolites and metabolic pathways. (A) Volcano plot of differential metabolites for the Model vs. Normal group. (B) Volcano plot of differential metabolites for the AS vs. Model group. Red represents upregulated metabolites, and blue represents downregulated metabolites. (C-D) Cluster analysis diagram of differential metabolites of the Model vs. Normal group and AS vs. Model group. The greater the intensity of the red color, the higher the expression, and the greater the intensity of the blue color, the lower the expression. (E-F) Enrichment bubble map of differential metabolites of the Model vs. Normal group and AS vs. Model group. The redder the color is, the smaller the P value, and the larger the circle is, the larger the impact factor.

Table 4.

The P-value and VIP-value of potential biomarkers.

Name	P-value	VIP	Pos/neg
3-Methylxanthine	0.00008512	2.267273	Neg
Yamogenin	0.00087881	2.243953	Pos
L-Tryptophan	0.00022688	2.198240	Neg
L-Carnitine	0.00000081	2.148890	Pos
Kynurenic acid	0.00051175	2.148145	Neg
Formononetin	0.00077512	2.125990	Neg
Sedoheptulose	0.00000000	2.096995	Neg
Glycochenodeoxycholic acid	0.00115751	2.083585	Neg
(R)−3-Hydroxybutyric acid	0.00107572	2.081936	Neg
4-Methylamino-4-de(dimethylamino)anhydrotetracycline	0.00080058	2.016345	Pos
Thymidine	0.00000576	2.003731	Neg
L-beta-Phenylalanine	0.00072988	1.999441	Pos
Enol-phenylpyruvate	0.00016019	1.989629	Pos
6,8a-Seco-6,8a-deoxy-5-oxoavermectin ‘’2b’’ aglycone	0.00018365	1.982946	Pos
Undecanoic acid	0.00016293	1.980513	Pos
Methylmalonic acid	0.01029310	1.945657	Pos
Dodecanedioic acid	0.00342096	1.942115	Neg
(-)-cis-Carveol	0.00036952	1.918711	Pos
L-Tryptophan	0.00009121	1.898022	Neg
Sequoyitol	0.01278801	1.881542	Pos
Sodium deoxycholate	0.00067591	1.878326	Pos
Myristic acid	0.01489322	1.865245	Pos
Dihydrouracil	0.00697225	1.864397	Neg
Acetylphosphate	0.00095610	1.848707	Pos
Daidzein	0.00249095	1.796746	Neg
Cytidine	0.00185162	1.780328	Pos
(S)−2-Phenyloxirane	0.02070844	1.767750	Pos
Caprylic acid	0.01267336	1.759106	Neg
Taurocholic acid	0.00337767	1.744712	Pos
Sequoyitol	0.00260742	1.744257	Pos
10-Hydroxydecanoic acid	0.01328735	1.733558	Neg
Acetylphosphate	0.02517591	1.732901	Pos
Yamogenin	0.00262085	1.731736	Pos
Se-Methylselenocysteine	0.02964155	1.683079	Pos
N-Acetylglutamic acid	0.00603969	1.676105	Pos
Se-Methylselenocysteine	0.01165225	1.661163	Pos
2-Heptanone	0.03881355	1.651883	Pos
Phthalic acid	0.02337475	1.637851	Neg
Glycochenodeoxycholic acid	0.01102449	1.633576	Neg
N-Acetylleucine	0.03555979	1.625775	Pos
Phosphorylcholine	0.04321586	1.624741	Pos
2-Amino-2-deoxy-D-gluconate	0.03964664	1.619357	Pos
3,5-Diiodo-L-tyrosine	0.00416752	1.609664	Neg
Ergothioneine	0.00903021	1.605998	Pos
Levonorgestrel	0.02890387	1.604654	Neg
5’-Oxoinosine	0.00507122	1.598469	Neg
Linoleic acid	0.04707858	1.596819	Pos
Tryptophanamide	0.02925193	1.591013	Neg
Allocystathionine	0.00921014	1.586989	Pos
Gluconic acid	0.00575400	1.577360	Neg
Glucaric acid	0.03047963	1.568532	Neg
N-Acetyl-D-glucosamine	0.01391124	1.565077	Neg
Choline	0.00881010	1.564565	Pos
21-Deoxycortisol	0.02003836	1.559110	Pos
Thymidine	0.03781367	1.524762	Neg
(S)−2-Phenyloxirane	0.01509829	1.514923	Pos
Xanthoxic acid	0.02120085	1.481047	Pos
L-Aspartic acid	0.02704626	1.476041	Pos
Isopentenyl adenosine	0.03601916	1.467478	Pos
Pentazocine	0.04843435	1.467455	Neg
Gamma-Linolenic acid	0.04012186	1.467347	Pos
Glycerophosphocholine	0.03149034	1.455200	Pos
Melezitose	0.02728131	1.450171	Neg
O-Acetylcarnitine	0.02660660	1.445039	Pos
4-(Glutamylamino) butanoate	0.02782526	1.431641	Pos
3-Methylxanthine	0.01724258	1.410688	Neg
(9E)-Octadecenoic acid	0.01753361	1.405919	Neg
12,13-DHOME	0.02185230	1.404297	Neg
Propionylcarnitine	0.03182923	1.402576	Pos
Dihydrouracil	0.01883713	1.398600	Neg
4-Guanidinobutanoic acid	0.03450210	1.389351	Pos
Punicic acid	0.03542213	1.366403	Pos
Protoporphyrin IX	0.04077342	1.358980	Pos
Stearic acid	0.04080704	1.345651	Neg
(6Z)-Octadecenoic acid	0.02572169	1.344715	Neg
Linoleic acid	0.03302006	1.344396	Pos
Bovinic acid	0.02827688	1.329978	Neg
2-Pyrocatechuic acid	0.04835798	1.326165	Neg
Shikimic acid	0.03362142	1.294942	Neg
Kynurenic acid	0.04044637	1.281428	Neg
Uric acid	0.04401695	1.260016	Neg

Table 5.

Potential biomarker information Table.

Metabolite	Formula	mz	rt(sec)	Trend(N: M/AS: M)	Related pathways
Linoleic acid	C18H32O2	280.2635	551.9	↓/↓	Linoleic acid metabolism
Bovinic acid	C18H32O2	279.2326	654.1	↑/↑	Linoleic acid metabolism
Gamma-Linolenic acid	C18H30O2	278.1852	238.2	↑/↑	Linoleic acid metabolism
12,13-DHOME	C18H34O4	295.2283	434.6	↑/↑	Linoleic acid metabolism
Taurocholic acid	C26H45NO7S	516.2966	392.1	↓/↑	Cholesterol metabolism; Bile secretion༛Taurine and hypotaurine metabolism
Glycochenodeoxycholic acid	C26H43NO5	448.3052	298	↑/↑	Cholesterol metabolism; Bile secretion
Choline	C5H14NO	104.1072	66.8	↑/↑	Choline metabolism in cancer; Bile secretion༛Glycine, serine and threonine metabolism
Glycerophosphocholine	C8H21NO6P	258.1097	51.6	↑/↑	Choline metabolism in cancer
L-Carnitine	C7H15NO3	162.1128	72.8	↑/↑	Bile secretion
Uric acid	C5H4N4O3	167.0201	50.2	↑/↓	Bile secretion
L-Aspartic acid	C4H7NO4	134.045	52	↓/↑	Glycine, serine and threonine metabolism
L-Tryptophan	C11H12N2O2	203.0819	187.8	↑/↑	Glycine, serine and threonine metabolism
Acetylphosphate	C2H5O5P	139.9888	501.5	↑/↑	Taurine and hypotaurine metabolism
Thymidine	C10H14N2O5	223.0279	598.9	↑/↓	Pyrimidine metabolism
Dihydrouracil	C4H6N2O2	112.9848	662.4	↓/↓	Pyrimidine metabolism
Methylmalonic acid	C4H6O4	101.0712	654.1	↑/↓	Pyrimidine metabolism
(R)−3-Hydroxybutyric acid	C4H8O3	103.0386	121.4	↓/↑	Synthesis and degradation of ketone bodies

The ↑ and ↓ values denote an increase and decrease, respectively.

The list of differential metabolites was further subjected to KEGG pathway enrichment analysis via MetaboAnalyst, with p < 0.05 serving as the criterion for relevant pathway identification. Eventually, a total of 8 metabolic pathways, such as linoleic acid metabolism, cholesterol metabolism, choline metabolism in cancer, and ketone body synthesis and degradation, were enriched (Table 6; Fig. 7E and F).

Table 6.

Enriched pathways for potential biomarkers.

Pathway_name	Total	Hits	P value	-Log10(Pvalue)	FDR	Impact
Linoleic acid metabolism	28	4	0.0009	3.0469	0.0395	0.2105
Cholesterol metabolism	10	2	0.0104	1.9830	0.1845	0.1111
Choline metabolism in cancer	11	2	0.0126	1.9003	0.1845	0.3258
Pyrimidine metabolism	65	3	0.0166	1.7794	0.3948	0.0386
Synthesis and degradation of ketone bodies	6	1	0.0497	1.3040	0.3948	0.1364
Bile secretion	97	5	0.0183	1.7378	0.2012	0.0459
Glycine, serine and threonine metabolism	50	3	0.0446	1.3503	0.3220	0.0093
Taurine and hypotaurine metabolism	22	2	0.0472	1.3256	0.3220	0.0882

The kidney target mRNAs and target metabolites share 3 identical pathways

Through the joint analysis of the KEGG enrichment of DE mRNAs and metabolites, 3 intersecting pathways, namely linoleic acid metabolism, cholesterol metabolism, and taurine and hypotaurine metabolism, were identified as common pathways between target mRNAs and target metabolites. Concurrently, the enrichment of differential metabolites in conjunction with DE mRNAs was accomplished, involving 7 differential metabolites and 18 DE mRNAs (Fig. 8A-C). Moreover, the Area Under roc Curve (AUC) of the 7 differential metabolites exhibited high predictive value (Fig. 8D-J). RT-qPCR results demonstrated that the expression levels of key target genes within the 3 pathways were significantly modulated by AS intervention. In comparison to the Normal group, the expression of Pla2g12a, Angptl4, and Baat was down-regulated, while Pla2g2a expression was up-regulated in the kidneys of rats in the Model group. After AS intervention in SHRs, the relative expression of Pla2g12a, Angptl4, and Baat was notably increased, and the relative expression of Pla2g2a was significantly decreased (Fig. 8K-N).

Fig. 8.

Astragalus membranaceus and Salvia miltiorrhiza (AS) target metabolites and target mRNAs enrichment pathway and related validation (A) Target metabolites of AS in the three intersection pathways. (B) Bubble map of eight metabolic pathways enriched for target metabolites. The pathways annotated with red boxes are the same as those in Fig. 4 for target mRNAs metabolism. (C) Target mRNAs of AS in the three intersection pathways. (D-J) ROC curve analysis of seven target metabolites for AS vs. Model group. (K-N) The mRNA expression of Pla2g12a, Angptl4, Baat, and Pla2g2a by RT‒qPCR analysis (n = 3). Compared with the Normal group, ^# p < 0.05, ^#### p < 0.001, ^#### p < 0.0001. Compared with the Model group, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Discussion

The experimental outcomes confirmed that AS could remarkably lower the systolic and diastolic blood pressures in SHRs. The obtained data robustly supported its ameliorative influence on early renal damage and impairment of vascular endothelial function. Abnormal levels of expression of ALB, NAG, Cys-c, ET-1, and AngII constitute key characteristics of HRD¹³. AS was shown to decrease the levels of urinary ALB, urinary NAG, serum Cys-c, ET-1, AngII, and other relevant biomarkers, thereby manifesting its protective effectiveness on glomerular filtration function and tubular function. NO, which is produced by vascular endothelial cells (ECs) through eNOS, plays a crucial role as a critical determinant in modulating blood flow and blood pressure and maintaining homeostasis¹⁴. In the experimental setting, the levels of eNOS and NO were significantly elevated in the AS group. This observation suggests that AS is likely to exert the effects of vasodilation and blood pressure reduction by promoting the release of NO from vascular ECs. Subsequently, this could potentially lead to a reduction in the renal hyperfiltration state and ultimately achieve the aim of protecting renal function.

With regard to renal histopathological alterations, following the intervention of AS, the pathological injuries manifested by SHRs, including glomerular basement membrane thickening, tubular vacuolization, epithelial cell detachment and necrosis, as well as renal interstitial fibrosis, were alleviated to varying extents. These aforementioned changes suggest that AS might exert a protective effect on the kidney against hypertension-induced damage via multiple mechanisms, such as anti-inflammatory, antioxidant, and antifibrotic activities. Thus, AS improves hypertensive renal damage through a multi-target synergistic mechanism of “rapid antihypertension-vascular endothelial protection-antifibrosis”, exhibiting unique advantages particularly in the early repair of vascular endothelial function and resistance to renal interstitial fibrosis. Valsartan, by contrast, focuses on potent antihypertension via a single target, making it more suitable for hypertensive pathological scenarios dominated by Renin-Angiotensin System (RAS) activation. The differences in their mechanisms provide a theoretical basis for clinical combination therapy or individualized treatment.

Transcriptomics analysis demonstrated that the differential genes were predominantly enriched in the metabolic process, with 25 metabolism-related pathways being identified. This finding suggests that the mechanism by which AS exerts its treatment effect on SHRs is associated with the modulation of metabolism-related target genes. To further explore the underlying mechanism of AS’s treatment, metabolomics analysis was carried out. Through OPLS-DA analysis, significant metabolic differences were detected both between the Model group and the Normal group and between the AS group and the Model group, thus indicating that the metabolic profile of SHRs was altered following AS intervention. Furthermore, differential metabolite screening and pathway analysis suggest that AS might exert its therapeutic efficacy by regulating pathways such as linoleic acid metabolism, cholesterol metabolism, choline metabolism in cancer, as well as the synthesis and degradation of ketone bodies.

Comprehensive analysis of transcriptomics and metabolomics revealed that 18 DE mRNAs in conjunction with 7 differential metabolites were implicated in 3 metabolic pathways, namely linoleic acid metabolism, cholesterol metabolism, and taurine and hypotaurine metabolism. Linoleic acid (LA), which belongs to the ω−6 fatty acid family as a polyunsaturated fatty acid¹⁵, serves as a metabolic initiator in vivo. It undergoes enzymatic reactions to generate γ-linolenic acid and arachidonic acid as downstream bioactive substances. These bioactives are engaged in the synthesis of prostaglandins and leukotrienes and exert an impact on inflammation, immunomodulation, and vascular health¹⁶. Clinical studies have demonstrated that gamma-linolenic acid triple therapy markedly ameliorates arterial stiffness in patients with atherosclerosis¹⁷. Recent investigations have revealed that linoleic acid is efficacious in the treatment of hypertension by enhancing nitric oxide bioavailability, improving vascular endothelial function, and reducing blood pressure¹⁸. Moreover, it has been established that the thinning of renal medullary capillaries as a consequence of endothelial damage constitutes a crucial element of kidney injury¹⁹. Simultaneously, AS notably regulates genes related to linoleic acid metabolism, including Pla2g12a, Pla2g2a, Pla2g5, Pla2g10, and Pla2g3 of the PLA2 superfamily. These genes trigger phospholipid release from substrates and expedite their conversion, thereby governing linoleic acid metabolism. Such regulatory action is crucial in modulating vascular function, especially concerning inflammation and atherosclerosis^20,21. The Pla2g12a gene restrains the expression of pro-inflammatory cytokines, mitigates inflammation-induced vascular endothelial damage, modulates the function of the vascular endothelial barrier to decrease leakage, and triggers the production of VEGF to suppress pathologic angiogenesis, thereby safeguarding the vascular endothelium^22–24. The Pla2g2a gene, mainly expressed in vascular fibroblasts, facilitates the progression of atherosclerotic plaques²⁵. In this experiment, the renal linoleic acid content in the AS group of rats was diminished relative to the Model group, while its downstream metabolites such as taurine, γ-linolenic acid, and 12,13-DHOME were substantially elevated compared with the Model group, and the associated genes were also markedly regulated. This indicates that AS intervenes in the linoleic acid metabolic pathway, modulates metabolism and vascular function via PLA2 superfamily genes, and ameliorates vascular endothelial injury for the treatment of HRD.

Linoleic acid impacts cholesterol metabolism in the body. A decrease in its level leads to cholesterol combining with certain saturated fatty acids, resulting in metabolic disorders, deposition on the vessel wall, and the gradual formation of atherosclerosis, which may cause cardiovascular and cerebrovascular diseases²⁶. Hypercholesterolemia, a key risk factor for atherosclerosis development, reduces nitric oxide production, inhibits the activity and expression of eNOS, impairs vascular endothelial function, and promotes the progression of atherosclerosis²⁷. DE genes LDLR, APOB, and PCSK9 are closely linked to familial hypercholesterolemia. Mutations in these genes increase the risk of atherosclerosis and cardiovascular disease^28,29. Moreover, a reduction in plasma cholesterol concentration and the total cholesterol to HDL cholesterol ratio can assist in improving endothelial function in renal vascular disease³⁰. Cholesterol functions in glomeruli via lipid rafts, influencing membrane fluidity, membrane protein transport, and signaling molecule assembly. Imbalanced cholesterol metabolism may also promote the progression of renal disease by inducing podocyte dysfunction and glomerular injury³¹. DE gene Angptl4, a secreted glycoprotein, is crucial in lipid metabolism regulation, modulating cholesterol metabolism, vascular permeability, and angiogenesis³². In this study, compared to the Model group, the levels of renal cholesterol metabolism-related products in rats were reversed following AS intervention, indicating that AS might ameliorate HRD by regulating the cholesterol metabolism pathway.

Taurocholic acid, a key metabolite in taurine and hypotaurine metabolism, is a conjugated bile acid formed by the conjugation of taurine and bile acids. The Baat gene is essential in primary bile acid synthesis, catalyzing the conjugation of bile acid and taurine to generate conjugated bile acids³³. Research has demonstrated that up-regulating Baat expression can enhance bile acid synthesis and is efficacious in alleviating liver fibrosis³⁴. Taurine participates in various renal physiological processes, exerting multiple impacts on renal blood flow and endothelial cell function, modulating renal vascular resistance and the control of arterial blood pressure by the autonomic nervous system³⁵. Additionally, taurine and its derivatives possess antioxidant properties, reducing oxidative stress through scavenging reactive oxygen species, thereby enhancing vascular endothelial function and achieving vasodilation by elevating endogenous eNOS activity³⁶. Taurine metabolites, taurocholic acid and uric acid, are significant in the bile secretion metabolic pathway. Uric acid augments vascular resistance by activating the Renin-Angiotensin System (RAS), leading to elevated blood pressure³⁷. Concurrently, the deposition of urate crystals in the vasculature may trigger a pro-inflammatory response, inducing endothelial damage³⁸. Under hypoxic conditions, uric acid further impairs endothelial function by inhibiting eNOS phosphorylation³⁹.

Moreover, the levels of (R)−3-hydroxybutyric acid, L-aspartic acid, and L-tryptophan increased following AS intervention. (R)−3-hydroxybutyric acid, a type of β-hydroxybutyric acid, plays a crucial role in ketone body metabolism. Nutritional supplementation of ketone bodies can alleviate hypertension induced by a high-salt diet and inhibit Nrp3-mediated renal inflammation⁴⁰. L-aspartic acid, a non-essential amino acid, regulates blood pressure by altering the levels of L-arginine and NO⁴¹. L-tryptophan, a kind of tryptophan, and its metabolites can activate aromatic hydrocarbon receptor (AHR) signaling, influencing the progression of renal fibrosis. In a mouse model of adriamycin (ADR)-induced renal failure, L-tryptophan pretreatment significantly improved early symptoms of renal failure^42,43. L-aspartic acid and L-tryptophan are products of the glycine, serine, and threonine metabolic pathways. It has been found that the metabolism of glycine, serine, and threonine significantly influences the progression of CKD. As CKD progresses, the level of glycine increases while that of serine decreases⁴⁴. The modulatory effect of AS on these metabolites might be involved in the improvement of HRD via anti-inflammatory, antioxidant, and vascular endothelial protection pathways.

In conclusion, AS is effective for the treatment of HRD. It can effectively lower blood pressure and protect the function and structure of the kidneys in SHR rats. Transcriptomics and metabolomics analyses demonstrated that AS exerts its therapeutic effects by regulating metabolism-related target genes and metabolic pathways, specifically those related to linoleic acid metabolism, cholesterol metabolism, and taurine metabolism. Through these regulatory mechanisms, it can alleviate renal pathological damage.

Conclusion

In this study, the efficacy of AS in treating HRD was validated through an integrated approach of multiple experimental methods, combined with transcriptomics and metabolomics analyses. AS was found to attenuate renal pathological injury by decreasing blood pressure and enhancing renal function-related indices. Via mRNA sequencing and UPLC-MS/MS, it was determined that AS predominantly impacts key metabolic pathways such as linoleic acid, cholesterol, and choline metabolism, along with associated target genes. The modulation of these pathways is tightly linked to physiological effects including improved vascular endothelial function, reduced vascular stiffness, diminished cholesterol deposition, and enhanced release of vasoactive substances, thereby conferring anti-inflammatory, antioxidant, and vasculoprotective properties. For the first time, this study comprehensively and systematically elucidated the genetic and metabolic effects of AS treatment. It also systematically and comprehensively uncovered the potential biomarkers and their mechanisms in AS-mediated HRD treatment, thereby presenting novel strategies and a robust theoretical foundation for HRD treatment.

Author contributions

W.L. conducted the experiments and wrote the manuscript. C.H. conceived and designed the study and helped conduct it. W.X. and Y.J. conducted statistical analysis and data analysis. Y.L. edited and revised the manuscript and approved the final manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Funding

This study was granted by National Natural Science Foundation of China (Youth Science Fund) (82104620) and Scientific Research Fund Project of Shandong University of Traditional Chinese Medicine (KYZK2024Q01).

Data availability

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

The animal experiments conducted in this study were ethically approved by the Ethics Committee of Shandong University of Traditional Chinese Medicine and the approval number is SDUTCM20230221001. All methods in this study were conducted and reported in accordance with the ARRIVE guidelines.