Nephropathy II Decoction Attenuates Renal Fibrosis via Regulating TLR4 and Gut Microbiota Along the Gut-Kidney Axis

Abstract

Nephropathy II Decoction (NED) is a widely used Chinese medicinal formulation for managing chronic kidney disease (CKD). Despite its extensive application, the precise mechanisms underlying its therapeutic effects remain poorly understood. This study aims to elucidate the role of NED in attenuating renal fibrosis and to explore its impact on the gut-kidney axis. The principal constituents of NED were analyzed using ultra-performance LC-tandem mass spectrometry (UPLC-MS/MS). A bilateral renal ischemia-reperfusion injury (bIRI) model was employed to induce fibrosis. RT-qPCR was utilized to assess the expression of mRNA related to the toll-like receptor 4 (TLR4) and myeloid differentiation factor 88 (MyD88) and nuclear factor-κB (NF-κB) signaling pathway. Western blotting analysis was performed to identify changes in renal fibrosis markers, TLR4/MyD88/NF-κB pathway proteins, and the colon proteins ZO-1 and Occludin-1. Serum levels of uremic toxins were quantified using enzyme-linked immunosorbent assay (ELISA), and 16S ribosomal RNA (rRNA) gene sequencing was conducted to explore changes in the gut microbiome of the mice. Our study demonstrated that mice in the NED group exhibited reduced serum creatinine, blood urea nitrogen, and urinary protein levels, alongside improvements in kidney damage and a decrease in renal fibrosis markers. In the bIRI group, TLR4/MyD88/NF-κB protein and mRNA levels, as well as intestinal tight junction proteins and enterogenic uremic toxins, were significantly reduced. NED treatment reversed these changes and modified the gut microbiota. Furthermore, fecal microbial transplantation (FMT) alleviated kidney damage and fibrosis in bIRI mice. In summary, NED ameliorates kidney injury and fibrosis by modulating the gut microbiota and may further attenuate fibrosis through the inhibition of TLR4 expression, thereby influencing the gut-kidney axis.

INTRODUCTION

Chronic kidney disease (CKD) constitutes a substantial health issue, marked by increased mortality rates, and has become a prominent public health challenge worldwide. Projections indicate that by 2040, CKD will rank as the fifth leading cause of mortality globally. CKD is generally characterized by a decreased glomerular filtration rate, increased urinary albumin excretion, or a combination of these parameters.^1,2) Throughout the progression of CKD, irreversible renal fibrosis frequently develops, ultimately culminating in end-stage renal disease (ESRD). Renal fibrosis is primarily characterized by the excessive accumulation of extracellular matrix within the kidney. However, the absence of effective targeted pharmacological interventions has resulted in a limited array of clinical strategies for treating renal fibrosis. Current therapeutic approaches predominantly focus on managing the underlying primary disease. Among these, inhibitors of the renin-angiotensin system (RASi), mineralocorticoid receptor antagonists (MRA), and sodium-glucose cotransporter 2 (SGLT-2) inhibitors are regarded as the most promising options for decelerating the progression of CKD.³⁾ As CKD progresses to its most severe stage, termed ESRD, the kidneys experience substantial damage, necessitating dialysis or kidney transplantation to maintain their function.^4–6) Therefore, early detection of CKD, intervention in the progression of renal fibrosis, and the development of effective targeted pharmacological treatments are essential for delaying disease progression, preserving renal function, and minimizing complications.

Growing evidence indicates that alterations in the intestinal microbiome, along with impairments in the structure and function of the epithelial barrier, are associated with CKD. Furthermore, these intestinal changes have been shown to influence the progression of renal fibrosis.^7–11) Alterations in the macroecological composition of the gut microbiota may lead to increased concentrations of uremic toxins, such as indole sulfate (IS), p-cresol sulfate (PCS), and trimethylamine-N-oxide (TMAO).^12–14) Metabolites produced by the gut microbiota play a critical role in modulating the interplay between the gastrointestinal tract and the kidneys.¹⁵⁾ Consequently, modulation of the gut-kidney axis is essential for mitigating renal fibrosis.

Traditional Chinese Medicine (TCM) has been employed for the treatment of human ailments for thousands of years.^16–18) Recent research indicates that TCM is characterized by its multicomponent, multi-pathway, and multi-target characteristics. Clinically, TCM has a long-established history in the treatment of various renal diseases and plays a crucial role as a complementary and alternative therapeutic approach in the management of CKD.^19–21) Furthermore, research indicates that TCM can enhance disease treatment by modulating the intestinal microbiota.^22,23)

Preliminary research has indicated that Nephropathy 1st exhibits significant anti-renal fibrosis properties.²⁴⁾ Based on clinical observations and efficacy evaluations, we have advanced Nephropathy 1st to develop Nephropathy II Decoction (NED), a traditional Chinese medicinal formulation utilized in the clinical management of CKD. NED is esteemed for its significant therapeutic effects, which encompass the replenishment of qi, enhancement of spleen function, detoxification, and elimination of blood stasis. Research indicates that oral administration of astragalus polysaccharide can mitigate renal damage induced by adenine through modulation of the intestinal microbiome.²⁵⁾ Moreover, the combination of rhubarb and astragalus has been shown to reduce renal interstitial fibrosis by diminishing autophagy activity.²⁶⁾ Furthermore, bupleurum polysaccharide has been identified as a compound capable of inhibiting renal injury associated with toll-like receptor 4 (TLR4) signaling in diabetic mice.²⁷⁾ Additionally, total alkaloids extracted from Aconitum have demonstrated efficacy in alleviating acute kidney injury induced by cisplatin, primarily by mitigating inflammation and oxidative stress related to intestinal microbial processes.²⁸⁾ Clinical investigations have demonstrated that NED markedly reduces serum creatinine and urinary protein levels in patients with CKD, thereby mitigating renal fibrosis. However, the exact mechanisms by which NED exerts its anti-fibrotic effects, particularly its potential impact on the gut-kidney axis, remain inadequately elucidated. This study aims to investigate the underlying mechanisms by which NED attenuates renal fibrosis, with a specific emphasis on its role in modulating the gut-kidney axis.

MATERIALS AND METHODS

Animals

The animal experiments were conducted in accordance with the Zhejiang Province Experimental Animal Management Measures, and the experimental protocol was approved by the Animal Ethics Committee of Wenzhou Medical University (Approval No. wydw2023-0184). Male C57BL/6J mice, aged 7 weeks, were obtained from Zhejiang Vitong Lever Company (Zhejiang, China). The mice were housed under controlled conditions, with an ambient temperature of 23 ± 3°C, relative humidity between 40 and 70%, and a 12-h light/dark cycle.

Preparation of NED

The NED comprises 14 varieties of dried Chinese medicinal materials, including Dang Shen (Radix Codonopsis pilosulae), Huang Qi (Radix Astragali), Chai Hu (Radix Bupleuri), Jiang Ban Xia (Pinellia ternata (Thunb.) Breit), Chao Bai Zhu (Rhizoma Atractylodis macrocephalus), Fu Ling (Poria), Gui Zhi (Ramulus Cinmomi), Tu Bie Chong (Eupolyphaga seu Steleophaga), Fu Zi (Radix Aconiti Lateralis), Shu Da Huang (Radix Et Rhizoma Rhei), Lou Lu (Radix Rhapontici), Ba Qia (Smilax China), Huang Bo (Cortex Phellodendri), and Bi Hu (Gekko swinhonis). Each medicinal material has been individually authenticated by Xiaocheng Chen, the Deputy Director of the Traditional Chinese Medicine Pharmacy at the First Affiliated Hospital of Wenzhou Medical University. For further details regarding the crude drugs, please refer to Table 1. The NED decoction used in this study was prepared by the Traditional Chinese Medicine Pharmacy of the First Affiliated Hospital of Wenzhou Medical University. The preparation process is as follows: soak the crude drug in 15 times its volume of water for 30 min. Bring the mixture to a boil over high heat (120°C), then reduce the heat to low (90°C) and simmer for an additional 30 min. Subsequently, the mixture is filtered, and the residue is extracted again under the same conditions. The two decoctions are then combined and concentrated into low, medium, and high-dose liquids containing 1.456, 2.912, and 5.824 g/mL of crude drug, respectively. For repeatability of the experiment, the samples were thawed and kept at 4°C immediately before use. The relevant clinical research has been approved by the Ethics Review Committee of the First Affiliated Hospital of Wenzhou Medical University (Approval No.: YS2023 No. 425).

Table 1. Composition of Nephropathy II Decoction (NED)

Chinese name	Family	Latin name	Medicinal portions	Dose (g)	Lot No.	Origin
Dangshen	Campanulaceae	Radix Codonopsis pilosulae	Root	20	2406016	Gansu, China
Huangqi	Fabaceae	Radix Astragali	Root	30	20240602	Shanxi, China
Chaihu	Apiaceae	Radix Bupleuri	Root	12	2408143	Hubei, China
Jiangbanxia	Araceae	Pinellia ternata (Thunb.) Breit	Tuber	12	20240601	Sichuan, China
Chaobaizhu	Asteraceae	Rhizoma Atractylodis macrocephalae	Root	30	20240701	Zhejiang, China
Fuling	Polyporaceae	Poria	Sclerotium	30	2408134	Anhui, China
Guizhi	Lauraceae	Ramulus Cinmomi	Twig	12	20240801	Guangdong, China
Tubiechong	Corydiidae	Eupolyphaga seu Steleophaga	Eupolyphaga sinensis Walker.; Steleophaga plancyi Indications Edema	15	20240801	Jiangsu, China
Fuzi	Ranunculaceae	Radix Aconiti Lateralis	Daughter root	6	20240501	Sichuan, China
Shudahuang	Polygonaceae	Radix Et Rhizoma Rhei	Root	6	2304074	Gansu, China
Loulu	Asteraceae	Radix Rhapontici	Root	20	20240801	Hubei, China
Baqia	Liliaceae	Smilax China	Root	20	2309025	Guangdong, China
Huangbo	Rutaceae	Cortex Phellodendri	Bark	10	2404007	Sichuan, China
Bihu	Gekkonidae	Gekko swinhonis	Dried whole body	1	2407051	Guangxi, China

Bilateral Renal Ischemia-Reperfusion Injury (bIRI) Model

Following the administration of general anesthesia, a 1.5 cm incision was made along both the left and right sides of the mouse’s spine to expose the kidneys bilaterally. The bilateral renal pedicles were then clipped with microaneurysm clips for a duration of 30 min, after which reperfusion was reinstated. Mice in the sham-operated group were exposed to the bilateral renal pedicles but were not subjected to clamping. This sham operation group received physiological saline (Sham, 0.1 mL/10 g) once daily for 3 consecutive weeks. The experimental group was randomly divided into five subgroups: the model group (bIRI), which received the same dose of normal saline; the positive drug group receiving losartan potassium (bIRI + LOS, 10 mg/kg); the low-dose NED group (bIRI + NED-L, 14.56 mg/g); the medium-dose NED group (bIRI + NED-M, 29.12 mg/g); and the high-dose NED group (bIRI + NED-H, 58.24 mg/g). Mice in the experimental group were treated with the drug via gavage administration once daily.

Mass Spectrometry Analysis

Samples were analyzed using UPLC-ESI-MS/MS with an Agilent SB-C18 column. The mobile phase, consisting of water and acetonitrile with 0.1% formic acid, followed a gradient from 95% A/5% B to 5% A/95% B in 1 min, returning to the start in 2 min. The flow rate was 0.35 mL/min, column temperature 40°C, and injection volume 2 μL, with the effluent connected to an ESI-triple quadrupole-MS. The ESI source was set to 550°C with an ion spray voltage of 5500 V for positive ions and −4500 V for negative ions. Gas settings were 50 psi for GSI, 60 psi for GSII, and 25 psi for the curtain gas. The CAD was set to high, and QQQ scans were conducted using MRM with medium nitrogen collision gas. DP and CE for each MRM transition were further optimized. Specific MRM transitions were monitored based on the metabolites eluted in each period.

Biochemical Parameter Measurement and Histopathological Changes

Serum creatinine levels were measured using a biochemical analyzer (Thermo Fisher Scientific, Waltham, MA, U.S.A.). The concentrations of blood urea nitrogen, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and urine protein were determined using the Urea Nitrogen Kit (C011-2-1), the Alanine Aminotransferase Assay Kit (C009-2-1), the Aspartate Aminotransferase Assay Kit (C010-2-1), and the Urine Protein Quantification Test Kit (C035-2-1), all sourced from Nanjing Jiancheng Biotechnology Co., Ltd. (Nanjing, China).

Histological analysis involved the embedding of kidney and colon tissues in paraffin, followed by sectioning into slices with a thickness of 4 μm. These tissue sections were subsequently stained using a hematoxylin–eosin (H&E) staining kit (G1120), a Sirius red staining kit (G1472), and an Alcian blue staining kit (G1560), all procured from Beijing Solarbio Technology Co., Ltd. (Beijing, China). A semi-quantitative assessment was performed on each kidney and colon specimen using a 200× upright light microscope, where the percentage of positive regions within the entire field of view was evaluated and scored. Renal tubular damage was scored as follows: 0 for no damage, 1 for less than 25% damage, 2 for 25–50% damage, 3 for 50–75% damage, and 4 for >75% damage. Image J software was employed to quantify the area of collagen deposition in renal tissue and to count the number of goblet cells in the colon.

Quantitative Real-Time PCR (RT-qPCR) Analysis

Total RNA was extracted from murine kidney tissues using the TRIzol Reagent (15596-026CN, Invitrogen, Waltham, MA, U.S.A.) for mRNA isolation. Subsequently, cDNA synthesis was performed with the RNA reverse transcription reagent (RR047A, TaKaRa, Shiga, Japan) using a PCR machine. Quantitative PCR was conducted using a real-time fluorescence quantitative PCR instrument (CFX384, Bio-Rad, Hercules, CA, U.S.A.) and the ChamQ Universal SYBR qPCR Master Mix (Q711-03, Vazyme, Nanjing, China). The resulting data were quantitatively analyzed using the 2^−ΔΔCT method. Each experimental group consisted of six replicates, and the specific primer sequences are provided in Table 2.

Table 2. Primers for RT-qPCR

Gene name	Forward primer 5′–3′	Reverse primer 5′–3′
m-Gapdh	TCCCTCAAGATTGTCAGCAA	AGATCCACAACGGATACATT
m-Tnf	CTTGTTGCCTCCTCTTTTGCTTA	CTTTATTTCTCTCAATGACCCGTAG
m-Il1β	TCGCAGCAGCACATCAACAAGA	CCACGGGAAAGACACAGGTAGC
m-Tlr4	CGGAAGGTTATTGTGGTAGT	CTGCTAAGAAGGCGATACA
m-Myd88	GCCTTGTTAGACCGTGAG	TCCTGGTTCTGCTGCTTA
m-Rela p65	GAAGCACAGATACCACCAA	CAGCCTCATAGTAGCCATC

Western Blotting

Tissue lysis was performed using RIPA buffer (R0010, Solarbio), which was supplemented with protease inhibitor (P0100, Solarbio) and phosphatase inhibitor (B15001, Selleck, Houston, TX, U.S.A.). Protein concentration was assessed using the bicinchoninic acid (BCA) assay. Proteins were separated by SDS-PAGE and subsequently transferred to polyvinylidene fluoride (PVDF) membranes. The membranes underwent a blocking procedure with 5% skim milk in TBST at room temperature for 1 h. After washing with TBST, the primary antibody was incubated overnight at 4°C. Following this, the secondary antibody (SA00001-1, SA00001-2, 1:20000, Proteintech, Rosemont, IL, U.S.A.) was applied for incubation. For further details regarding the specific antibodies used, please refer to Table 3.

Table 3. The Relevant Antibody Information of Western Blotting

Antibody name	Dilution ratio	Company
α-SMA	1 : 1000	Abcam, China
TGF-β	1 : 1000	Abcam, China
Vimentin	1 : 1000	Abcam, China
TLR4	1 : 1000	CST, China
MyD88	1 : 1000	Wanleibio, China
NF-κB-p65	1 : 1000	Proteintech, China
p-NF-κB-p65	1 : 1000	Proteintech, China
ZO-1	1 : 1000	Abcam, China
Occludin-1	1 : 1000	Proteintech, China

Immunohistochemical (IHC) Staining

Kidney sections, embedded in paraffin and each with a thickness of 4 μm, underwent deparaffinization and rehydration processes. Antigen retrieval was performed by boiling the sections in a sodium citrate buffer at pH 6.0. To inhibit endogenous peroxidase activity, the sections were treated with a 3% hydrogen peroxide solution for 10 min. Subsequently, the sections were incubated at room temperature for 1 h with 10% normal goat serum to block non-specific binding. The primary antibody, alpha-smooth muscle actin (α-SMA) (ab7817, 1 : 100, Abcam, Cambridge, U.K.), was then applied and incubated overnight at 4°C. Following extensive washing with PBS, the sections were incubated with HRP-conjugated goat anti-rabbit IgG (ab6721, Abcam) for 1 h at 37°C. The tissue sections were subsequently treated with a diaminobenzidine peroxidase substrate for 5 min and counterstained with H&E. Positive staining areas were quantified using Image ProPlus software.

Immunofluorescence Staining

Immunofluorescence staining was conducted on paraffin-embedded tissue sections. The procedure commenced with deparaffinization, followed by rehydration and blocking steps. Subsequently, the sections were incubated overnight at 4°C in a light-protected, humidified chamber with the following primary antibodies: kidney injury molecule 1 (KIM1) (30948-1-AP, 1 : 200, Proteintech), neutrophil gelatinase-associated lipocalin (NGAL) (26991-1-AP, 1 : 200, Proteintech), fibronectin (FN) (ab268020, 1 : 200, Abcam), TLR4 (a5258, 1 : 100, Abclonal), zonula occludens 1 (ZO-1) (ab190085, 1 : 100, Abcam), and Claudin-1 (343203, Zen BioScience, Chengdu, China). Thereafter, Cy3-labeled goat anti-rabbit IgG (ab150080, 1 : 200, Beyotime, Shanghai, China) or FITC-labeled goat anti-rabbit IgG (ab150077, 1 : 200, Abcam) was applied and allowed to incubate at room temperature for 1 h. Following PBS washes, DAPI staining was performed on the sections for 10 min. Finally, images were acquired using a fluorescence microscope (Nikon, Tokyo, Japan).

ELISA Assay

The serum was isolated by subjecting blood samples to centrifugation at 2000 × g for 15 min at a temperature of 4°C. The serum levels of TMAO, IS, and PCS were subsequently measured using a mouse TMAO enzyme-linked immunoassay kit (JL48684-96T) and a mouse IS enzyme-linked immunoassay kit (JL44175-96T), following the manufacturer’s instructions. These kits were procured from Shanghai Jianglai Biotechnology Co., Ltd. (Shanghai, China).

16S rRNA Analysis

Fresh feces were collected from mice on day 21 of drug administration. Total genomic DNA was extracted using the CTAB method and assessed on a 1% agarose gel. The DNA was diluted to a concentration of 1 ng/μL. The 16S rRNA genes, specifically the V3–V4 regions, were amplified using specific primers (341F and 806R) that included barcodes. PCR was conducted with Phusion® High-Fidelity PCR master mix, and the resulting products were analyzed via 2% agarose gel electrophoresis. The PCR products were combined in equidensity ratios and subsequently purified. Sequencing libraries were prepared using the NEB Next® Ultra DNA Library Prep Kit and sequenced on an Illumina platform, generating 250 bp paired-end reads. Raw FASTQ files were imported into the QIIME 2 system, where sequences were quality filtered, de-noised, and merged using the dada2 plugin to produce ASV tables. Taxonomy was assigned using the feature-classifier plugin against the GREENGENES database. Diversity metrics and statistical analyses (e.g., ANCOM, ANOVA) were conducted to identify differences in microbial communities.

Fecal Microbiota Transplant (FMT)

Seven-week-old male C57BL/6J mice were divided into four groups: the sham operation group, the bIRI group, the NED group, and the FMT group, with eight mice in each group. All groups, except for the sham operation group, underwent bilateral renal ischemia-reperfusion surgery. The NED group received a high-dose NED via gavage, after which fresh feces were collected. The fecal pellets were resuspended in phosphate-buffered saline (PBS) at a ratio of one fecal pellet per milliliter of PBS to prepare a bacterial suspension. Mice in the FMT group were gavaged 100 μL per 10 g of body weight daily, while the same volume of normal saline was administered to the sham operation and bIRI groups for a duration of 3 weeks.

Statistical Analysis

Data were expressed as the mean ± standard deviation (S.D.). A one-way ANOVA was conducted to assess statistical significance, with a threshold set at p < 0.05. The 16S rRNA data were processed utilizing the Bioincloud platform (https://www.bioincloud.tech/), and statistical analyses were performed using GraphPad Prism version 9.0.

RESULTS

Identification of Chemical Constituents in NED

The sample extracts were analyzed utilizing a UPLC-ESI-MS/MS system in conjunction with a tandem mass spectrometry system, ensuring precise mass measurements for all detected mass peaks. Chromatographic peaks were notably abundant in both positive- and negative-ion modes. From the NED extract, a total of 1987 compounds were identified, comprising phenolic acids (15.35%), alkaloids (14.75%), flavonoids (10.97%), terpenoids (10.27%), amino acids and derivatives (10.22%), lignans and coumarins (7.6%), lipids (5.18%), organic acids (4.03%), nucleotides and derivatives (2.87%), quinones (2.21%), steroids (0.81%), tannins (0.35%), and other constituents (15.40%) (Fig. 1). Comprehensive details of the metabolite sequence number, Metware index, compounds, formulas, molecular weights, ionization models, class I, identification levels, and peak areas of the top 30 major chemical components of NED are available in Table 4.

Fig. 1. Chemical Identification of NED Utilizing Ultrahigh-Performance Liquid Chromatography with Tandem Mass Spectrometry (UPLC-MS/MS)

(A) Total ion chromatogram monitored in positive and negative ion modes for NED extract. (B) Structural classification of compounds present in NED.

Table 4. The First 30 Major Chemical Components of NED

No.	Compounds	Formula	Molecular weight (Da)	Ionization model	Class I
1	5-Methyl 1-propyl l-glutamate	C9H17NO4	203.1158	[M + H] +	Amino acids and derivatives
2	Corypalmine	C20H23NO4	341.1627	[M + H] +	Alkaloids
3	Gallic acid	C7H6O5	170.0215	[M − H] −	Phenolic acids
4	4-Methylazetidine-2-carboxylic acid	C5H9NO2	115.0633	[M + H] +	Alkaloids
5	Thaliporphine	C20H23NO4	341.1627	[M + H] +	Alkaloids
6	l-Pipecolic acid	C6H11NO2	129.079	[M + H] +	Alkaloids
7	O-Acetyl-l-carnitine	C9H17NO4	203.1158	[M + H] +	Alkaloids
8	Codonopsine	C14H21NO4	267.1471	[M + H] +	Alkaloids
9	Neochlorogenic acid (5-O-Caffeoylquinic acid)	C16H18O9	354.0951	[M − H] −	Phenolic acids
10	N-Methylhigenamine-7-O-glucopyranoside	C23H29NO8	447.1893	[M + H] +	Alkaloids
11	Coclaurine-glucose	C23H29NO8	447.1893	[M + H] +	Alkaloids
12	4-Feruloylquinic acid	C17H20O9	368.1107	[M − H] −	Phenolic acids
13	Cryptochlorogenic acid (4-O-Caffeoylquinic acid)	C16H18O9	354.0951	[M − H] −	Phenolic acids
14	3,4-Dihydroxybenzoic acid (Protocatechuic acid)	C7H6O4	154.0266	[M − H] −	Phenolic acids
15	Chlorogenic acid (3-O-Caffeoylquinic acid)	C16H18O9	354.0951	[M − H] −	Phenolic acids
16	Adenine	C5H5N5	135.0545	[M + H] +	Nucleotides and derivatives
17	2,3-Dihydroxybenzoic acid	C7H6O4	154.0266	[M − H] −	Phenolic acids
18	Methylmalonic acid	C4H6O4	118.0266	[M − H] −	Organic acids
19	5-O-Feruloylquinic acid	C17H20O9	368.1107	[2M − H] −	Phenolic acids
20	N-Benzylmethylene isomethylamine	C8H9N	119.0735	[M + H] +	Alkaloids
21	3-(4-Hydroxy-3-methoxyphenyl)prop-2-enoyl 1,3,4,5-tetrahydroxycyclohexane-1-carboxylate	C17H20O9	368.1107	[M − H] −	Phenolic acids
22	l-Tyrosine	C9H11NO3	181.0739	[M + H] +	Amino acids and derivatives
23	Pyran[3,4-b]indole-2-ketone	C11H9O2N	187.0633	[M + H] +	Alkaloids
24	Succinic acid	C4H6O4	118.0266	[M − H] −	Organic acids
25	Glutaric acid	C5H8O4	132.0423	[M − H] −	Organic acids
26	2-Methylsuccinic acid	C5H8O4	132.0423	[M − H] −	Organic acids
27	l-Arginine	C6H14N4O2	174.1117	[M + H] +	Amino acids and derivatives
28	3,5-Dihydro-2H-furo[3,2-C]quinolin-4-one	C11H9NO2	187.0633	[M + H] +	Alkaloids
29	Adenosine	C10H13N5O4	267.0968	[M + H] +	Nucleotides and derivatives
30	Naphthisoxazol A	C11H9NO2	187.0633	[M + H] +	Alkaloids

Effects of NED on bIRI-Induced Renal Injury

To investigate the impact of NED on renal injury in mice with renal fibrosis, bIRI mice were administered NED daily for 3 weeks (Fig. 2A). The body weight of the mice in each group was recorded (Fig. 2B), and the kidney index was subsequently analyzed. The results indicated that NED administration led to an increase in the kidney index of bIRI mice, with a more pronounced effect observed at higher doses (Fig. 2C). Assessment of serum creatinine, blood urea nitrogen, and urinary protein levels across the groups revealed that the NED group exhibited a significant reduction in serum creatinine, blood urea nitrogen, and urinary protein concentrations compared with the model group (Figs. 2D–2F). Additionally, no liver damage was noted following the administration of NED over the 3-week period (Figs. 2G, 2H). This suggests that renal function was enhanced in bIRI mice following NED intervention. H&E staining was employed to evaluate the extent of tubulointerstitial injury. The results showed no notable pathological changes in the renal tissue of mice from the Sham operation group. By contrast, the kidneys of bIRI mice demonstrated extensive inflammatory cell infiltration, cellular necrosis, detachment, tubular dilation, and loss of brush borders. Following NED treatment, these pathological changes were alleviated, leading to a reduction in kidney lesions among the mice. Moreover, immunofluorescence staining demonstrated a significant decrease in the expression of KIM1 and NGAL after NED intervention compared with the bIRI group (Figs. 2I–2L). This evidence further supports the potential of NED to prevent additional renal damage and promote renal repair to some extent, although it does not completely reverse kidney damage.

Fig. 2. NED Improves Kidney Function and Ameliorates Kidney Pathological Damage

(A) Schematic diagram of animal experiment design. (B) Body weight. (C) Kidney index. (D) Serum creatinine levels. (E) Blood urea nitrogen levels. (F) Quantification of urine protein in each mouse. (G) Alanine aminotransferase (ALT) levels. (H) Aspartate aminotransferase (AST) levels. (I) Representative images of H&E staining, KIM1 immunofluorescence staining, and NGAL immunofluorescence staining. (J) Tubular damage score. (K, L) Quantitative-immunofluorescence analysis of KIM1 and NGAL. Data are expressed as the mean ± S.D. Compared with the bIRI group, ns: Not significant, *p < 0.05, **p < 0.01, ***p < 0.001; scale bars = 100 μm as indicated.

Effects of NED on bIRI-Induced Renal Fibrosis

To further investigate the effects of NED on renal fibrosis, we evaluated the degree of renal fibrosis and its associated markers. Sirius red staining revealed a reduction in collagen fiber area in the kidneys of bIRI mice treated with NED (Figs. 3A, 3C). Furthermore, immunohistochemistry and immunofluorescence staining showed a significant decrease in the expression of α-SMA and FN in the NED-treated group compared with the Sham group (Figs. 3A, 3B, 3D, 3E). Western blotting analysis corroborated these findings, demonstrating a marked reduction in the expression levels of α-SMA, transforming growth factor beta (TGF-β), and Vimentin in the NED-treated group (Figs. 3F–3I). Collectively, these results indicate that NED may have the capacity to reverse renal fibrosis in bIRI mice.

Fig. 3. NED Attenuates Renal Fibrosis in CKD Mice

(A) Sirius red staining and immunohistochemical micrographs of α-SMA. (B) Immunofluorescence micrographs of FN in the kidney tissues in mice. (C) Quantitative analysis of Sirius red positive area; (D) IHC scores of α-SMA. (E) Quantitative-immunofluorescence analysis of FN. (F) Representative Western blotting of Vimentin, TGF-β, and α-SMA expression in mice. (G–I) Western blotting analysis of Vimentin, TGF-β, and α-SMA. Data are expressed as the mean ± S.D. Compared with the bIRI group, ns: Not significant, *p < 0.05, **p < 0.01, ***p < 0.001; scale bars = 100 μm as indicated.

Effects of NED on TLR4/Myeloid Differentiation Factor 88 (MyD88)/Nuclear Factor-κB (NF-κB) Expression in bIRI Mouse Kidneys

Research has demonstrated that TLR4 plays a crucial role in the pathogenesis of renal fibrosis. It contributes to the fibrotic process through the activation of the NF-κB signaling pathway, which leads to elevated levels of inflammatory mediators and the promotion of epithelial-mesenchymal transition (EMT).^29,30) Furthermore, data from the Genotype-Tissue Expression (GTEx) project revealed a positive correlation between TLR4 expression and kidney injury markers, such as hepatitis A virus cellular receptor 1 (HAVCR1) and lipocalin-2 (LCN2), as well as fibrosis markers, including actin alpha 2 (ACTA2), TGFB1, FN1, and VIM (Fig. 4A).

Fig. 4. NED Attenuates Renal Fibrosis by Mediating the TLR4/MyD88/NF-κB-p65 Signaling Pathway

(A) Analysis of the correlation between renal TLR4 content and the expression levels of HAVCR1, LCN2, ACTA2, TGFB1, FN1, and VIM was conducted. The above data were obtained from the renal GTEx data in the FerrDb database (http://www.zhounan.org/ferrdb/). (B–F) Real-time quantitative PCR (RT-qPCR) was conducted to assess the expression levels of Tlr4, Myd88, Rela p65, Tnf, and Il1b in renal tissue from mice. (G) Immunofluorescence micrographs of TLR4 in renal tissue. (H) Quantitative-immunofluorescence analysis of TLR4. (I) Representative Western blotting of TLR4, MyD88, NF-κB-p65, and p-NF-κB-p65 expression in mice. (J–L) Western blotting analysis of Fig. 4I. Data are expressed as the mean ± S.D. Compared with the bIRI group, ns: Not significant, *p < 0.05, **p < 0.01, ***p < 0.001; scale bars = 100 μm as indicated.

In this research, we utilized RT-qPCR to validate our results. Our findings indicated a significant increase in the renal mRNA expression levels of Tlr4, Myd88, Rela, tumor necrosis factor (Tnf), and interleukin-1β (Il1b) following renal ischemia-reperfusion injury in mice. Conversely, these expression levels were markedly reduced in all groups of mice treated with NED compared to the model group (Figs. 4B–4F). This observation suggests that NED plays a crucial role in modulating the renal TLR4/MyD88/NF-κB signaling pathway. Subsequently, immunofluorescence staining for TLR4 was performed, revealing a significant upregulation of renal TLR4 expression in the bIRI group compared with the Sham group, which was reversed following the NED intervention (Figs. 4G, 4H). To further assess the expression levels of relevant proteins, including TLR4, we conducted a Western blotting analysis. The findings indicated that the protein expressions of renal TLR4, MyD88, and p-NF-κB-p65 were significantly elevated in bIRI mice, whereas NED treatment resulted in a down-regulation of these proteins (Figs. 4I–4L). This suggests that NED may potentially reduce renal fibrosis in mice by suppressing the activation of the TLR4/MyD88/NF-κB signaling pathway.

Effect of NED on Intestinal Barrier and Uremic Toxins in Mice

Building on previous studies regarding the crosstalk between the intestine and kidney in bIRI mice,³¹⁾ we investigated the intestinal barrier and enterogenic uremic toxins in bIRI mice. Alcian blue staining demonstrated a significant increase in the number of goblet cells in the NED group compared with the bIRI group (Figs. 5A, 5B). Immunofluorescence analysis further revealed that NED intervention substantially elevated the levels of ZO-1 and Claudin-1 proteins in colon tissue, contrasting sharply with a marked reduction of these tight junction proteins observed in the bIRI group of mice (Figs. 5C–5F). Subsequently, Western blotting analysis was employed to assess the expression levels of Occludin-1 and ZO-1, revealing a notable decrease in these proteins within the bIRI group. Conversely, administration of NED resulted in an enhancement of the expression levels of both Occludin-1 and ZO-1 (Figs. 5G–5I).

Fig. 5. NED Improves Intestinal Barrier Damage and Reduces Serum Enterogenic Uremic Toxin Levels

(A, B) Alcian blue staining of the colons and quantification of goblet cell numbers per unit. (C, D) Immunofluorescence staining of ZO-1 and quantitative analysis of ZO-1. (E, F) Immunofluorescence staining of Claudin-1 and quantitative analysis of Claudin-1. (G) Representative Western blotting of the intestinal barrier-related proteins, Occludin-1 and ZO-1. (H, I) Western blotting analysis of Occludin-1 and ZO-1. (J–L) IS, PCS, and TMAO levels in animal serum. Data are expressed as the mean ± S.D. Compared with the bIRI group, ns: Not significant, *p < 0.05, **p < 0.01, ***p < 0.001; scale bars = 100 μm as indicated.

The progression of renal fibrosis generally causes a disruption of the intestinal barrier, which is subsequently followed by the movement of microbial metabolites.^32,33) Consequently, we assessed the serum concentrations of IS, PCS, and TMAO in murine models. The findings revealed that the bIRI group exhibited significantly elevated serum levels of IS, PCS, and TMAO compared with the Sham group. Post-intervention with NED, there was a notable decrease in the concentrations of IS, PCS, and TMAO (Figs. 5J–5L). These findings suggest that NED effectively alleviates intestinal barrier damage induced by renal ischemia-reperfusion injury and reduces the accumulation of uremic toxins.

Effect of NED on Intestinal Dysbiosis in bIRI Mice

Studies indicate that an imbalance in the gut microbiome significantly contributes to the progression of kidney fibrosis. Building on these insights, our research aims to investigate the effects of the optimal effective dose of NED on the modulation of gut microbiota. To evaluate alterations in the gut microbiota, we employed 16S rRNA gene sequencing. The Venn diagram illustrates that 2040 operational taxonomic units (OTUs) were identified across the three groups of fecal samples, with 1132 OTUs detected in the control group, 621 OTUs in the bIRI group, and 973 OTUs in the NED group (Fig. 6A). The abundance of OTUs was reduced in the bIRI group compared to the Sham group; however, treatment with NED resulted in a restoration of OTU abundance.

Fig. 6. NED Alters the Diversity of Intestinal Microbial Composition in CKD Mice

(A) Venn diagram depicting the overlapping operational taxonomic units (OTUs) in each group. (B–D) Alpha diversity analysis of the gut microbiota utilizing the ACE, Chao1, and Shannon indices. (E, F) Beta diversity analysis of the gut microbiota utilizing principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS). (G) Microbial composition at the phylum level. (H) Microbial composition at the genus level. (I) Phylogenetic tree cladogram generated by LEfSe analysis. (J) Linear discriminant analysis (LDA) score of differential taxa at genus level based on LEfSe. Microorganisms with an LDA score ≥3 are shown in the figure. Data are expressed as the mean ± S.D. Compared with the bIRI group, *p < 0.05, **p < 0.01.

Alpha diversity analysis, as indicated by the ACE, Chao1, and Shannon indices, demonstrated alterations in community richness and diversity within the bIRI group, whereas the NED group exhibited significant enhancements (Figs. 6B–6D). The examination of beta diversity, represented by Principal Coordinates Analysis (PCoA) and Non-metric Multidimensional Scaling (NMDS), revealed a clear differentiation among the bIRI, NED, and Sham groups (Figs. 6E, 6F). This evidence implies that NED plays a regulatory role in the diversity of gut microbiota. To enhance our understanding of how NED affects the structure of the microbiota, we evaluated the gut microbiota at both the phylum and genus levels. At the outset, we found that Bacteroidota and Firmicutes_A were the dominant bacterial groups at the phylum level. Notably, in comparison to the sham group, the bIRI mice exhibited a significant increase in the abundance of Bacteroidota, whereas the populations of Firmicutes_A and Actinobacteriota were markedly decreased. However, the intervention with NED effectively reversed these alterations in bacterial composition (Fig. 6G). At the genus level, the intestinal microbiota of each group of mice was primarily composed of CAG_485, Duncaniella, UBA7173, Muribaculum, Akkermansia, Amulumruptor, and Dubosiella. Among these, Duncaniella, Muribaculum, Dubosiella, and Amulumruptor were the predominant bacterial genera in the bIRI group. Treatment with NED led to a decrease in the abundance of these genera, while significantly increasing the prevalence of Prevotella and Akkermansia (Fig. 6H). LEfSe analysis was utilized to identify significant differential bacterial species, leading to the identification of 20 distinct genera among the Sham, bIRI, and NED groups (LDA >3) (Figs. 6I, 6J). Notably, the abundance of genera such as Fimenecus, Muribaculum, and Turicimonas was significantly elevated in the bIRI group compared with those in the sham group. Furthermore, the bacterial genera NM07_P_09, Intestinimonas, Allobaculum, Eubacterium_R, Limosilactobacillus, and Prevotella were found to be significantly enriched in the NED group. These findings indicate that the intervention with NED effectively regulates and restores the balance of intestinal microbiota.

Effect of NED on Renal Fibrosis through the Gut-Kidney Axis

An analysis of three distinct bacterial genera revealed a significant upregulation of Turicimonas, Muribaculum, and Fimenecus in the bIRI group following NED treatment (Figs. 7A–7C). Subsequently, we investigated the correlation among intestinal microbiota and renal NF-κB-p65. The results indicated a positive correlation between the three bacterial genera—Turicimonas, Muribaculum, and Fimenecus—and NF-κB-p65 (Figs. 7D–7F). Furthermore, the data suggest that NED inhibits the expression of renal TLR4. However, the potential regulatory effect of NED on intestinal TLR4 and the interrelationship between intestinal and renal TLR4 remain unclear. To explore this, we evaluated colonic TLR4 expression using immunofluorescence (Fig. 7G), which demonstrated a reduction in colonic TLR4 expression following NED treatment compared with the model group. Additionally, a positive correlation was identified between renal and colonic TLR4 levels (Figs. 7H–7J).

Fig. 7. Differential Bacterial Genera and Correlation Analysis

(A–C) Differential bacteria genus. (D–F) Correlation analysis between differential bacteria and kidney NF-κB expression levels. (G) Immunofluorescence staining of TLR4 in colon tissue. (H–J) Correlation analysis between colonic TLR4 levels and renal TLR4 levels. Data are expressed as the mean ± S.D. Compared with the bIRI group, *p < 0.05; scale bars = 100 μm as indicated.

Effect of FMT on Renal Injury and Renal Fibrosis in bIRI Mice

To further elucidate the role of gut microbiota in renal injury in bIRI mice, fecal microbiota from NED mice were transplanted into the bIRI mice. Compared with the model group, there was a notable reduction in serum creatinine, blood urea nitrogen, and urine protein levels, indicating that the mice receiving microbiota from the NED group experienced less severe renal damage (Figs. 8A–8C). This observation is corroborated by H&E staining results (Figs. 8D, 8H) and immunofluorescence staining for KIM1 (Figs. 8F, 8J). Furthermore, the expression levels of collagen and fibronectin (FN) in the FMT group were significantly lower than those in the model group (Figs. 8E, 8G, 8I, 8K). Collectively, these findings suggest that NED ameliorates renal fibrosis through the modulation of gut microbiota.

Fig. 8. Effects of FMT on Renal Injury and Renal Fibrosis in Mice

(A–C) Serum creatinine, blood urea nitrogen, and urine protein levels. (D) Hematoxylin and eosin staining. (E) Sirius red staining. (F, G) Representative images of KIM1 immunofluorescence staining and NGAL immunofluorescence staining. (H) Tubular damage score. (I) Quantitative analysis of Sirius red positive area. (J, K) Quantitative-immunofluorescence analysis of KIM1 and FN. Data are expressed as the mean ± S.D. Compared with the bIRI group, *p < 0.05, **p < 0.01, ***p < 0.001; scale bars = 100 μm as indicated.

DISCUSSION

Kidney fibrosis can ultimately lead to a deterioration in renal function, significantly impacting patients’ QOL. Numerous studies have explored the underlying mechanisms of renal fibrosis in the quest for effective therapeutic interventions. However, the current range of available treatments remains inadequate to halt the progression of renal fibrosis. Consequently, it is crucial to develop and implement effective strategies to mitigate this condition. The gut-kidney axis has attracted considerable attention in the pathogenesis of CKD. Research has established a link between alterations in the gut microbiome and barrier function and the progression of renal fibrosis, highlighting the potential therapeutic benefits of restoring gut microbiome homeostasis to slow disease progression.³⁴⁾ The present study demonstrates that NED has the potential to inhibit the activation of the renal TLR4/MyD88/NF-κB signaling pathway, which may lead to a reduction in renal injury and fibrosis through the modulation of the gut-kidney axis.

In this study, we utilized a renal bIRI model to induce renal fibrosis during the transition from acute kidney injury (AKI) to CKD. The applicability of the renal IRI model to human pathophysiological conditions is highlighted by its capacity to replicate the effects of diminished renal blood perfusion, as encountered in clinical contexts such as cardiac bypass surgery, kidney transplantation, and hypotension. Severe IRI can result in significant AKI and persistent renal insufficiency, a progression that is accompanied by pathological changes indicative of renal fibrosis.^35–37) In the bIRI group of mice, there was a significant elevation in serum creatinine and urea nitrogen levels, accompanied by a marked increase in urinary protein levels, indicating potential renal tubular damage or an augmented filtration load. Histological examination using H&E staining revealed tubular atrophy and the infiltration of inflammatory cells within the renal tissue. Furthermore, immunofluorescence staining demonstrated a substantial upregulation of kidney injury markers, such as KIM1 and NGAL, further corroborating the presence of renal damage. Additionally, the disruption of renal architecture, collagen accumulation in the renal mesenchyme, and increased expression of fibrosis-associated proteins, including α-SMA, FN, and vimentin, suggest the development of renal fibrosis in this model group. Thus, a model of renal fibrosis induced by renal ischemia-reperfusion injury has been effectively established. This research indicates that the administration of NED led to different levels of improvement in kidney damage among various groups, a decrease in renal fibrosis, and a lowered expression of proteins associated with fibrosis, implying that NED possesses potential therapeutic efficacy in alleviating renal fibrosis.

Upon activation, TLR4 forms a dimer and recruits the adapter molecule MyD88, which contains a TLR domain. MyD88 is independently activated by TRIF and TRAF3, leading to the recruitment of IKKε/TBK1. This recruitment facilitates the activation of transcription factors, including IRF7, IRF3, AP-1, and NF-κB, through these signaling pathways. These processes ultimately mediate immune responses, cytokine production, and other related immune activities.^38,39) TLR4 is expressed in renal microvascular endothelial cells and renal tubular epithelial cells, where it plays a regulatory role in CKD and fibrosis.^40–42) The present study demonstrated that NED could effectively mitigate the activation of the renal TLR4/MyD88/NF-κB signaling pathway and effectively reduce renal fibrosis.

The progression of renal fibrosis has been closely linked to disruptions in gut microbiota and compromised intestinal barrier integrity. This segment of the study aimed to elucidate the structural characteristics of gut microbiota within a CKD model and to investigate the regulatory role of NED in this context. Analysis using 16S rRNA sequencing revealed significant variations in microbial richness and diversity within the bIRI group, while the gut microbiota showed substantial improvement in the group receiving NED treatment. Notably, significant differences were observed among the control, model, and treatment groups, particularly in three identified bacterial genera: Fimenecus, Muribaculum, and Turicimonas. Recent studies suggest that Muribaculum has the capacity to produce the lipid MiCL-1, which may enhance the production of pro-inflammatory cytokines such as TNF-α, IL-6, and IL-23, potentially influencing the regulation of the host’s immune response.⁴³⁾

Research indicates that the gut microbiota and uremic toxins derived from the gut play essential roles in the gut-kidney axis.²⁰⁾ Renal fibrosis significantly compromises the integrity of the intestinal barrier, as evidenced by a decrease in tight junction proteins, including occludin, claudin-1, and ZO-1.⁴⁴⁾ This compromise, coupled with dysbiosis, leads to elevated levels of gut-derived uremic toxins such as IS, PCS, and TMAO.^45,46) The findings of this study suggest that NED enhances the expression of tight junction proteins within the intestinal epithelium, specifically occludin, claudin-1, and ZO-1, while simultaneously reducing serum concentrations of uremic toxins, including IS, PCS, and TMAO, in mice subjected to bIRI.

We performed a correlation analysis to investigate the association between different bacterial genera and renal NF-κB levels. Our results revealed a positive correlation between these bacterial genera and kidney NF-κB levels. Furthermore, immunofluorescence staining showed that NED decreased the expression of TLR4 in both renal and intestinal tissues, with a positive correlation identified between the two. These findings suggest that NED may play a role in mitigating kidney damage and alleviating renal fibrosis by modulating the gut-kidney axis.

To further explore the role of intestinal microbiota, we conducted an experiment involving fecal microbial transplantation. Our results demonstrated a significant reduction in kidney damage and renal fibrosis in mice subjected to microbial transplantation. These findings suggest that NED alleviates renal fibrosis through the modulation of gut microbiota, thereby offering additional evidence that NED influences the gut-kidney axis.

This study illustrates that NED mediates the intestinal-renal axis to mitigate renal fibrosis through the regulation of intestinal microbiota, and it identifies a positive correlation between colonic TLR4 and renal TLR4. Nevertheless, further investigation is necessary to determine whether NED influences the gut-kidney axis and alleviates renal fibrosis by modulating TLR4 levels in both the colon and kidneys. While the primary constituents of NED were identified using UPLC-MS/MS, it is important to recognize certain limitations. The inherent complexity of traditional Chinese medicine compounds presents challenges in elucidating the mechanisms by which the principal monomeric compounds in NED exert their effects. Moreover, more comprehensive studies are required to clarify the mechanism of action of NED in in vitro cell lines. Future research should aim to address these limitations to achieve a more thorough understanding of the therapeutic potential of NED.

CONCLUSION

The current study demonstrated that NED effectively inhibits the activation of the TLR4/MyD88/NF-κB signaling pathway and mitigates renal injury and fibrosis. Additionally, NED has the potential to modify gut microbiota composition and enhance intestinal barrier integrity, thereby reducing renal fibrosis through modulation of intestinal flora (Fig. 9), as further corroborated by fecal microbial transplantation. Collectively, these findings provide compelling evidence that NED can inhibit renal fibrosis by regulating the gut-kidney axis, suggesting a novel approach for the clinical management of CKD and effectively preventing its progression. However, the complex composition of herbal compounds poses challenges in identifying specific active ingredients and potential synergistic effects of NED. Ongoing research in these areas is essential to deepen our understanding of the mechanisms underlying NED’s therapeutic effects in renal fibrosis, thereby advancing the application of TCM in CKD management.

Fig. 9. Schematic Diagram of NED Regulating the Gut-Kidney Axis to Alleviate Renal Fibrosis

Funding

This work was supported by the Zhejiang Provincial Science and Technology Department Research and Development Project (No.: 2022C03160) and the Traditional Chinese Medicine of Zhejiang Provincial Science and Technology Program Project (No.: GZY-ZJ-KJ-24037).

Author Contributions

The authors have read and approved the manuscript. Data curation, investigation, methodology, and writing-original draft: CL. Methodology, software, formal analysis, and writing-review & editing: Y-JG. Data curation, investigation, and methodology: Y-RC and L-TZ. Data curation, investigation, and methodology: FR, Y-HH, Y-NZ, and RC. Methodology: F-YW. Conceptualization, project administration, supervision, and writing–review & editing: J-GC.

Data Availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Conflict of Interest

The authors declare no conflict of interest.

REFERENCES

• 1)  Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, Saran R, Wang AY, Yang CW. Chronic kidney disease: global dimension and perspectives. Lancet, 382, 260–272 (2013).
• 2)  Bikbov B, Purcell CA, Levey AS, et al. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet, 395, 709–733 (2020).
• 3)  Turner JM, Bauer C, Abramowitz MK, Melamed ML, Hostetter TH. Treatment of chronic kidney disease. Kidney Int., 81, 351–362 (2012).
• 4)  Minutolo R, Lapi F, Chiodini P, Simonetti M, Bianchini E, Pecchioli S, Cricelli I, Cricelli C, Piccinocchi G, Conte G, De Nicola L. Risk of ESRD and death in patients with CKD not referred to a nephrologist: a 7-year prospective study. Clin. J. Am. Soc. Nephrol., 9, 1586–1593 (2014).
• 5)  Levey AS, Coresh J. Chronic kidney disease. Lancet, 379, 165–180 (2012).
• 6)  Haynes R, Staplin N, Emberson J, Herrington WG, Tomson C, Agodoa L, Tesar V, Levin A, Lewis D, Reith C, Baigent C, Landray MJ. Evaluating the contribution of the cause of kidney disease to prognosis in CKD: results from the study of heart and renal protection (SHARP). Am. J. Kidney Dis., 64, 40–48 (2014).
• 7)  Vaziri ND, Wong J, Pahl M, Piceno YM, Yuan J, DeSantis TZ, Ni Z, Nguyen TH, Andersen GL. Chronic kidney disease alters intestinal microbial flora. Kidney Int., 83, 308–315 (2013).
• 8)  Vaziri ND, Zhao YY, Pahl MV. Altered intestinal microbial flora and impaired epithelial barrier structure and function in CKD: the nature, mechanisms, consequences and potential treatment. Nephrol. Dial. Transplant., 31, 737–746 (2016).
• 9)  Magnusson M, Magnusson KE, Sundqvist T, Denneberg T. Impaired intestinal barrier function measured by differently sized polyethylene glycols in patients with chronic renal failure. Gut, 32, 754–759 (1991).
• 10)  Vaziri ND, Yuan J, Norris K. Role of urea in intestinal barrier dysfunction and disruption of epithelial tight junction in chronic kidney disease. Am. J. Nephrol., 37, 1–6 (2013).
• 11)  Yang J, Lim SY, Ko YS, Lee HY, Oh SW, Kim MG, Cho WY, Jo SK. Intestinal barrier disruption and dysregulated mucosal immunity contribute to kidney fibrosis in chronic kidney disease. Nephrol. Dial. Transplant., 34, 419–428 (2019).
• 12)  Yang T, Richards EM, Pepine CJ, Raizada MK. The gut microbiota and the brain-gut-kidney axis in hypertension and chronic kidney disease. Nat. Rev. Nephrol., 14, 442–456 (2018).
• 13)  Mahmoodpoor F, Rahbar Saadat Y, Barzegari A, Ardalan M, Zununi Vahed S. The impact of gut microbiota on kidney function and pathogenesis. Biomed. Pharmacother., 93, 412–419 (2017).
• 14)  Chen YY, Chen DQ, Chen L, Liu JR, Vaziri ND, Guo Y, Zhao YY. Microbiome-metabolome reveals the contribution of gut-kidney axis on kidney disease. J. Transl. Med., 17, 5 (2019).
• 15)  Rukavina Mikusic NL, Kouyoumdzian NM, Choi MR. Gut microbiota and chronic kidney disease: evidences and mechanisms that mediate a new communication in the gastrointestinal-renal axis. Pflugers Arch., 472, 303–320 (2020).
• 16)  Zhou L, Zhang CL, Jiang K, Cheng HY, Xiong WW, Zhu JX. Therapeutic potential of danyankang capsule in high-fat diet-induced cholelithiasis and its impact on liver FXR signaling and gut microbiota. Biol. Pharm. Bull., 47, 680–691 (2024).
• 17)  Niu Y, Feng Q, Cui M, Fan C, Wang T, Yuan R, Tsering D, Huang S, Li B. Siweixizangmaoru decoction ameliorated type II collagen-induced arthritis in rats via regulating JAK2-STAT3 and NF-κB signaling pathway. Biol. Pharm. Bull., 47, 1511–1524 (2024).
• 18)  Sun CX, Li DH, Xu YP, Yang ZF, Wei LY, Gao YM, Liu Y, Yan CH, Li YZ. Hua-Zhuo-Jie-Du decoction combined with cisplatin inhibits the development of gastric cancer cells by regulating immune and autophagy signaling. Biol. Pharm. Bull., 47, 1823–1831 (2024).
• 19)  Zhou L, Wang X, Zhang Y, Xie Y, Cui R, Xia J, Sun Z. Renal metabolomics study and critical pathway validation of shenkang injection in the treatment of chronic renal failure. Biol. Pharm. Bull., 47, 499–508 (2024).
• 20)  Li X, Wang H. Chinese herbal medicine in the treatment of chronic kidney disease. Adv. Chronic Kidney Dis., 12, 276–281 (2005).
• 21)  Thongnuanjan P, Soodvilai S, Fongsupa S, Chabang N, Vivithanaporn P, Tuchinda P, Soodvilai S. Protective effect of panduratin A on cisplatin-induced apoptosis of human renal proximal tubular cells and acute kidney injury in mice. Biol. Pharm. Bull., 44, 830–837 (2021).
• 22)  Zhang HY, Tian JX, Lian FM, Li M, Liu WK, Zhen Z, Liao JQ, Tong XL. Therapeutic mechanisms of traditional Chinese medicine to improve metabolic diseases via the gut microbiota. Biomed. Pharmacother., 133, 110857 (2021).
• 23)  Yu L, Xing ZK, Mi SL, Wu X. Regulatory effect of traditional Chinese medicine on intestinal microbiota. Zhongguo Zhong Yao Za Zhi, 44, 34–39 (2019).
• 24)  Mu L, Zhu L, Feng Y, Chen N, Wang F, He L, Cheng J. Nephropathy 1st inhibits renal fibrosis by activating the PPARγ signaling pathway. Front. Pharmacol., 13, 992421 (2022).
• 25)  Liu W, Zhang Y, Hu D, Huang L, Liu X, Lu Z. Oral Astragalus polysaccharide alleviates adenine-induced kidney injury by regulating gut microbiota-short-chain fatty acids-kidney G protein-coupled receptors axis. Ren. Fail., 46, 2429693 (2024).
• 26)  Zhang Q, Ye X, Zhu L, Xu Z, Hou Y, Ke Q, Feng J, Xie X, Chen D, Piao JG, Wei Y. Spatiotemporal delivery of multiple components of rhubarb-astragalus formula for the sysnergistic treatment of renal fibrosis. Front. Pharmacol., 15, 1456721 (2024).
• 27)  Liu ZZ, Weng HB, Zhang LJ, Pan LY, Sun W, Chen HX, Chen MY, Zeng T, Zhang YY, Chen DF, Li H. Bupleurum polysaccharides ameliorated renal injury in diabetic mice associated with suppression of HMGB1-TLR4 signaling. Chin. J. Nat. Med., 17, 641–649 (2019).
• 28)  Yang X, Xin Y, Gu Y, Wang Y, Hu X, Ying G, Zhang Q, He X. Total alkaloids of Aconitum carmichaelii Debx alleviate cisplatin-induced acute renal injury by inhibiting inflammation and oxidative stress related to gut microbiota metabolism. Phytomedicine, 135, 156128 (2024).
• 29)  Zhang YH, Liu B, Meng Q, Zhang D, Yang H, Li G, Wang Y, Liu M, Liu N, Yu J, Liu S, Zhou H, Xu ZX, Wang Y. ACOX1 deficiency-induced lipid metabolic disorder facilitates chronic interstitial fibrosis development in renal allografts. Pharmacol. Res., 201, 107105 (2024).
• 30)  Fu Y, Xiang Y, Wang Y, Liu Z, Yang D, Zha J, Tang C, Cai J, Chen G, Dong Z. The STAT1/HMGB1/NF-κB pathway in chronic inflammation and kidney injury after cisplatin exposure. Theranostics, 13, 2757–2773 (2023).
• 31)  Huang P, Cao J, Chen J, Luo Y, Gong X, Wu C, Wang Y. Crosstalk between gut microbiota and renal ischemia/reperfusion injury. Front. Cell. Infect. Microbiol., 12, 1015825 (2022).
• 32)  Chen JH, Chao CT, Huang JW, Hung KY, Liu SH, Tarng DC, Chiang CK. Early elimination of uremic toxin ameliorates AKI-to-CKD transition. Clinical Science (London, England: 1979), 135, 2643–2658 (2021).
• 33)  Pastrana DV. mSphere of influence: it’s not me, it’s you—how donor factors influence kidney transplant outcomes. mSphere, 5, e00964–19 (2020).
• 34)  Tong Y, Guo S, Li T, Yang K, Gao W, Peng F, Zou X. Gut microbiota and renal fibrosis. Life Sci., 357, 123072 (2024).
• 35)  Fu Y, Tang C, Cai J, Chen G, Zhang D, Dong Z. Rodent models of AKI-CKD transition. Am. J. Physiol. Renal Physiol., 315, F1098–F1106 (2018).
• 36)  Lu X, Li N, Shushakova N, Schmitt R, Menne J, Susnik N, Meier M, Leitges M, Haller H, Gueler F, Rong S. C57BL/6 and 129/Sv mice: genetic difference to renal ischemia-reperfusion. J. Nephrol., 25, 738–743 (2012).
• 37)  Xiao L, Zhou D, Tan RJ, Fu H, Zhou L, Hou FF, Liu Y. Sustained activation of Wnt/β-catenin signaling drives AKI to CKD progression. J. Am. Soc. Nephrol., 27, 1727–1740 (2016).
• 38)  Mahmoud AM, Sayed AM, Ahmeda AF, Abd-Alhameed EK, Salem SH, Alruhaimi RS, Shukur A, Hassanein EHM. Flavonoids-mediated TLR4 inhibition as a promising therapy for renal diseases. Comb. Chem. High Throughput Screen., 26, 2124–2148 (2023).
• 39)  Akira S, Takeda K. Toll-like receptor signalling. Nat. Rev. Immunol., 4, 499–511 (2004).
• 40)  El-Achkar TM, Huang X, Plotkin Z, Sandoval RM, Rhodes GJ, Dagher PC. Sepsis induces changes in the expression and distribution of Toll-like receptor 4 in the rat kidney. Am. J. Physiol. Renal Physiol., 290, F1034–F1043 (2006).
• 41)  Wu H, Chen G, Wyburn KR, Yin J, Bertolino P, Eris JM, Alexander SI, Sharland AF, Chadban SJ. TLR4 activation mediates kidney ischemia/reperfusion injury. J. Clin. Invest., 117, 2847–2859 (2007).
• 42)  Souza AC, Tsuji T, Baranova IN, Bocharov AV, Wilkins KJ, Street JM, Alvarez-Prats A, Hu X, Eggerman T, Yuen PS, Star RA. TLR4 mutant mice are protected from renal fibrosis and chronic kidney disease progression. Physiol. Rep., 3, e12558 (2015).
• 43)  Bang S, Shin YH, Ma X, Park SM, Graham DB, Xavier RJ, Clardy J. A cardiolipin from Muribaculum intestinale induces antigen-specific cytokine responses. J. Am. Chem. Soc., 145, 23422–23426 (2023).
• 44)  Vaziri ND, Yuan J, Rahimi A, Ni Z, Said H, Subramanian VS. Disintegration of colonic epithelial tight junction in uremia: a likely cause of CKD-associated inflammation. Nephrol. Dial. Transplant., 27, 2686–2693 (2012).
• 45)  Zhang W, Miikeda A, Zuckerman J, Jia X, Charugundla S, Zhou Z, Kaczor-Urbanowicz KE, Magyar C, Guo F, Wang Z, Pellegrini M, Hazen SL, Nicholas SB, Lusis AJ, Shih DM. Inhibition of microbiota-dependent TMAO production attenuates chronic kidney disease in mice. Sci. Rep., 11, 518 (2021).
• 46)  Wang H, Ainiwaer A, Song Y, Qin L, Peng A, Bao H, Qin H. Perturbed gut microbiome and fecal and serum metabolomes are associated with chronic kidney disease severity. Microbiome, 11, 3 (2023).