Research on the Mechanism of Gallic Acid Inhibiting Ferroptosis and Delaying IgA Nephropathy by Regulating the MAPK Signaling Pathway through DUSP1

1  Introduction

IgA nephropathy (IgAN) is a chronic glomerulonephritis caused by abnormal deposition of immunoglobulin A in the glomerular mesangial area [1]. Primary glomerular disease occurs most frequently worldwide, and it is particularly common in Asia, representing 30%–50% of cases, with prevalence continuing to rise [2,3]. Compared with European and American populations, IgAN progresses faster and has a worse prognosis in Asian populations [3]. Based on statistics, about 40% of Chinese patients will progress to end-stage renal disease within 15 years and need to rely on renal replacement therapy [4,5]. This renders IgAN a major contributor to renal failure in young and middle-aged people in China, placing a heavy burden on families and society.

At present, the mechanisms underlying IgAN are not yet fully elucidated, and no targeted therapeutic strategies have been established. At this stage, supportive treatment is mainly based on the patient’s clinical indicators and pathological changes to reduce urine protein and control blood pressure [6]. For patients who continue to face the risk of disease progression after optimized supportive therapy, immunosuppressive treatment, such as glucocorticoids, along with immunomodulatory therapy targeting the intestinal mucosa, may be considered [7]. The 2025 Kidney Disease: Improving Global Outcomes (KDIGO) guidelines have listed budesonide enteric-coated capsules as the first-line treatment for high-risk IgAN patients, which reduces the production of pathogenic IgA1 by regulating the function of intestinal mucosal B cells [8,9]. Concurrently, research into gut-kidney crosstalk has revealed that specific microbial alterations and intestinal barrier damage are integral to disease progression [10,11]. This has opened therapeutic avenues for microbiota-targeted interventions, including prebiotics like chitooligosaccharides, which have been shown in preclinical models to restore microbial balance, strengthen the intestinal barrier, and attenuate renal injury [10]. Furthermore, lifestyle interventions, including a low-salt diet, moderate protein intake, and avoidance of nephrotoxic drugs, are also increasingly valued [12]. However, the existing therapies still have obvious limitations. Traditional immunosuppressants have significant side effects and low safety for long-term use [13]. Although the new targeted drugs have good safety, the long-term efficacy still needs further observation, and the treatment cost is high, and accessibility is limited [14]. Therefore, the development of novel therapeutic compounds that are effective, safe, and economical is an urgent need in current IgAN research.

Gallic acid (GA, 3,4,5-trihydroxybenzoic acid), a polyphenol widely distributed in nature, is known for its strong antioxidant activity [15]. The phenolic hydroxyl and carboxyl groups in the GA molecular structure can effectively neutralize free radicals and block the oxidation chain reaction [16]. Moreover, previous research has demonstrated that GA possesses diverse pharmacological activities, including anti-inflammatory, antibacterial, and antitumor effects [16,17]. In the study of kidney diseases, the renal protective effect of GA and its derivatives has attracted increasing attention. In the mouse model of diabetic nephropathy, GA alleviated the renal cell injury caused by oxidative stress by inhibiting the miR-709/NFE2L2 pathway [18]. Further, in the cisplatin-induced acute kidney injury model, the GA derivative epigallocatechin gallate (EGCG) can inhibit renal tubular epithelial cell apoptosis by activating the PI3K/Akt signaling pathway [19].

Although current studies suggest that GA exerts protective effects in multiple kidney injury models, its role and underlying mechanism in IgAN remain unclear. Therefore, this study aims to screen and explore the core targets and key mechanisms of GA intervention in IgAN with the help of the combined analysis of transcriptomics and network pharmacology. And establish in vitro cell model for functional verification and mechanism discussion. By systematically elucidating the mechanism of GA in IgA nephropathy, this study will help further uncover its medicinal value while offering a scientific foundation for developing new therapeutic targets and active compounds for IgA nephropathy.

2  Materials and Methods

2.1 Data Acquisition and Processing

The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), a public repository run by the National Center for Biotechnology Information (NCBI), provided the transcriptomic data (GSE175759). There were 68 samples in the dataset, including 22 normal control samples and 46 samples from IgAN patients [20].

2.2 Network Pharmacology

The potential targets of gallic acid (GA) were systematically identified through a network pharmacology approach. Initially, the canonical Simplified Molecular Input Line Entry System (SMILES) notation for GA was retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). This SMILES string served as the primary input for subsequent target prediction. Putative protein targets of GA were forecasted using two complementary online platforms: the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https://tcmsp-e.com/; accessed on 16 May 2024) and SwissTargetPrediction (http://www.swisstargetprediction.ch/; accessed on 19 May 2024). For the TCMSP search, the standard screening criteria of oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 were applied to filter bioactive components. In the SwissTargetPrediction analysis, all predicted targets for Homo sapiens with a probability score > 0 were included. The target lists obtained from these two databases were subsequently merged, and duplicate entries were removed. Visualization was performed using Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny) to identify the overlapping genes. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, alongside Gene Set Enrichment Analysis (GSEA), were performed on the relevant differentially expressed genes (DEGs). These analyses were conducted using the Weishengxin online tool (http://www.bioinformatics.com.cn/).

2.3 Polymeric IgA1 Preparation

Polymeric IgA1 (p-IgA1) was prepared by heat-polymerizing monomeric human IgA1 antibody (ab193187, Abcam, Cambridge, MA, USA) at 63°C for 150 min using a dry bath incubator (TU-100, Shanghai Yiheng Technology). Once cooled to room temperature, the sample was centrifuged at 12,000 rpm for 5 min using a high-speed centrifuge (Sorvall Legend Micro 21R, Thermo Fisher Scientific, Waltham, MA, USA) to eliminate insoluble precipitates, and the resulting supernatant contained p-IgA1 [21].

2.4 Cell Culture and Treatment

Human mesangial cells (HMCs, CP-H067, Wuhan Procell Life Science and Technology) were cultured at 37°C under 5% CO2 in a humidified incubator in RPMI-1640 medium (#22400-089, Thermo Fisher Scientific, Waltham, MA, USA) containing 10% FBS (A5669701, Gibco, Grand Island, NY, USA) and penicillin/streptomycin (100 U/mL, #15070063, Gibco, Grand Island, NY, USA). All tests were conducted using cells that were grown at passages 3–8. In serum-free RPMI-1640 media, EL transfection reagent (FT201-01, TransIntro, Beijing, China) was combined with plasmid DNA (empty, DUSP1 overexpression, or DUSP1 silencing vector, Sangon Biotech, Shanghai, China) and incubated for 15 min. Cells were exposed to complexes for six hours at 37°C. After that, the medium was changed to full RPMI-1640 medium for a 48-h culture period.

For experimental treatments, cells were plated at 5 × 104 cells/cm2 in 6-well plates. After 24-h attachment, seven treatment groups were established: the untreated control group (Ctrl); the IgAN model group (IgA), induced by treatment with 25 μg/mL p-IgA1 for 48 h; the model groups co-treated with a low or high dose (12.5 or 25 μg/mL, respectively) of GA (IgA + GA-L and IgA + GA-H groups, respectively; G7384, Sigma, St. Louis, MO, USA); and the intervention groups where, prior to modeling and drug treatment, cells were transfected with either a non-targeting control siRNA (IgA + GA-H + siCtrl group), DUSP1-targeting siRNA (IgA + GA-H + siDUSP1 group), or a DUSP1 overexpression plasmid (IgA + OE-DUSP1 group).

2.5 Cell Viability Measurement

Using the Cell Counting Kit-8 (CCK-8; C0038, Beyotime, Shanghai, China), cell viability was assessed. Logarithmic-phase HMC cells were seeded at 10,000 cells per well in 96-well plates at 37°C with 5% CO2 and exposed to varying concentrations of GA (G7384, Sigma, St. Louis, MO, USA) (0, 12.5, 25, 50, 75, and 100 μg/mL). Each well received 10 μL of CCK-8 reagent after 48 h of treatment, and the wells were then incubated for two hours at 37°C. Absorbance was recorded at 450 nm using a microplate reader (ReadMax 1200, Shanpu, Shanghai, China).

2.6 Live/Dead Staining

Following the manufacturer’s instructions, the Calcein/PI Cell Viability/Cytotoxicity Assay Kit (C2015S, Beyotime) was utilised to photograph and compare living and dead cells. The cells underwent a staining procedure using calceinacetoxymethyl ester (calcein AM) and propidium Iodide (PI) solution (1:1) following a 48-h culture period. While PI can pass through damaged cell membranes and bind to nucleic acids to create a brilliant red fluorescence (excitation/emission roughly 535/617), live cells transform calcein AM into green fluorescent calcein. Lastly, an Olympus IX71 inverted fluorescent microscope (Tokyo, Japan) was used to view the cells.

2.7 Assessment of Metabolic and Redox Parameters

Commercialized diagnostic test kits, including glutathione/glutathione oxidized (GSH/GSSG) Assay Kit (HY-K0311, MCE, Shanghai, China), JC-1 mitochondrial membrane potential assay kit (HY-K0601, MCE), malondialdehyde (MDA) assay kit (BC1175, Solarbio, Beijing, China), were employed to assess metabolic and redox parameters in HMC. Fluorogenic probe H2DCFDA (HY-D0940, MCE, Shanghai, China) and BODIPY 581/591 C11 (HY-D1301, MCE, Shanghai, China) were used to assess the reactive oxygen species (ROS) and lipid peroxidation (LPO) of HMC, respectively. All assays were performed according to the manufacturers’ protocols with at least three technical replicates per condition and repeated in six independent biological experiments.

2.8 Quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR)

Utilising qRT-PCR, the mRNA expression levels of FTH1 and TFRC were examined [22,23]. The TRIzol reagent (15596026, Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) was used to extract total RNA from cells. The HiScript II Reverse Transcriptase (R223, Vazyme, Nanjing, China) was used for mRNA reverse transcription following the manufacturer’s protocol, and the FastKing-RT SuperMix (KR118-02, Tiangen, Beijing, China) was used for cDNA synthesis via reverse transcription. In addition, the 2−ΔΔCT method served to quantify relative gene expression, with GAPDH as the endogenous control. The primer sequences are provided in Table A1.

2.9 Intracellular Fe2+ Detection

HMCs were cultured in 10-cm dishes until reaching approximately 80%–90% confluency. The cells were gently rinsed twice with 2 mL of ice-cold phosphate-buffered saline (PBS, pH 7.4). Subsequently, 1 mL of RIPA lysis buffer (containing 1× protease inhibitor cocktail and 1 mM phenylmethylsulfonyl fluoride) was added to each dish. Cells were then scraped using a chilled cell scraper, and the resulting lysates were centrifuged at 14,800 rpm for 15 min. The supernatant was collected and retained for further analysis. The protein concentration of the supernatant was determined using a BCA assay kit (P0009, Beyotime, Shanghai, China). For the detection of Fe2+, the supernatant was processed according to the manufacturer’s instructions of the Fe2+ assay kit (A039-2-1, Jiancheng, Nanjing, China). Specifically, 1.5 mL of iron chromogenic agent was added to the sample. After thorough mixing, the reaction mixture was incubated in a boiling water bath at 100°C for 5 min. Following cooling to room temperature, the mixture was centrifuged at 3500 rpm for 10 min. Finally, 200 μL of the supernatant was transferred to a 96-well plate, and its absorbance was measured at a wavelength of 520 nm using a microplate reader (ReadMax 1200, Shanpu, Shanghai, China).

2.10 Western Blot Analysis (WB)

Protein concentration was calculated after total protein was separated from HMC. 30 μg of total protein was extracted from each sample using 10% SDS-PAGE (PG112, Epizyme, Shanghai, China) and then put onto PVDF membranes (IPVH00010, Millipore, Billerica, MA, USA). After blocking the membranes with 5% non-fat milk for one hour at room temperature, the membranes were exposed to primary antibodies overnight at 4°C. p-p38 (Thr180/Tyr 182; 1:1000; #4511, CST, Danvers, MA, USA), p-38 (1:1000; ab170099, Abcam, Cambridge, MA, USA), DUSP1 (1:1000; MA5-32480, Thermo Fisher Scientific, Waltham, MA, USA), GPX4 (1:1000; #59735, CST, Danvers, MA, USA), SLC7A11 (1:1000; #28001, CST, Danvers, MA, USA), and α-Tubulin (1:1000; #2144, CST, Danvers, MA, USA) were the primary antibodies that were employed. The membranes were treated with HRP-conjugated goat anti-rabbit IgG (1:2000; ab97051, Abcam, Cambridge, MA, USA) for one hour at room temperature following three TBST washes. Protein bands were observed using a Bio-Rad imaging system (ChemiDoc Touch, Hercules, CA, USA) and measured using ImageJ software (version 1.53e, National Institutes of Health, Bethesda, MD, USA) after more TBST washes. The integrated density value (IDV) of each target protein band was measured and normalized to the IDV of the corresponding loading control band (α-Tubulin) from the same sample lane to obtain the relative protein expression level. Every experiment was carried out three times.

2.11 Statistical Analysis

All data are shown as mean ± standard deviation (M ± SD) and were gathered from a minimum of three separate studies. For data that passed the normality test, parametric tests were used. An independent two-sample t-test compared two groups, while one-way analysis of variance (ANOVA) was applied to comparisons involving three or more groups. For all statistical studies, GraphPad Prism version 8.0.5 (GraphPad Software; La Jolla, CA, USA) was used. *p < 0.05, **p < 0.01, and ***p < 0.001 served as the thresholds for statistical significance.

3  Results

3.1 Integrative Transcriptomics and Network Pharmacology Identify that DUSP1 is a Potential Target for GA against IgAN

To clarify the potential therapeutic mechanism of GA on IgAN, we integrated transcriptomic data and network pharmacology methods for analysis. Based on the GSE175759 dataset in the GEO database, this study included 46 renal tissue samples from IgAN patients and 22 normal control samples. In total, 1141 differentially expressed genes (DEGs) were identified, with |log2FoldChange| ≥1 and corrected p value (padj) <0.05 serving as the threshold for differential expression. According to the volcano plot results, 884 genes were upregulated and 515 were downregulated in the IgAN group relative to the normal control group (Fig. 1A). The heat map of the differential gene expression pattern further showed the clustering characteristics of gene expression between samples (Fig. 1B). Subsequently, KEGG pathway enrichment analysis and GO function annotation analysis were performed on all DEGs. Analysis indicated that the differentially expressed genes were notably concentrated in the MAPK signaling pathway, implying a potential key role of this pathway in IgAN pathogenesis (Fig. 1C,D).

Figure 1: Identification of core targets of GA for IgAN. (A) Volcano plot; (B) Heatmap of DEG; (C) GO enrichment analysis of all DEGs; (D) KEGG enrichment analysis of all DEGs; (E) Venn analysis; (F) Expression difference of eight core targets between IgAN and controls. For comparisons with the IgAN group. *p < 0.05, **p < 0.01, ***p < 0.001.

To identify potential targets of GA, this study retrieved its SMILES structure from the PubChem database and predicted possible targets using the SwissTargetPrediction and TCMSP platforms. After integrating the results from both platforms and removing duplicates, 109 potential GA target genes were obtained. The intersection of the target set and the above 1141 IgAN differentially expressed genes was taken, and finally, 8 common genes were screened out as candidate targets for GA intervention in IgAN, including DUSP1, SERPINE1, ADRA2A, CA5A, ALB, FASLG, FUT7, and CNR2 (Fig. 1E). Among them, DUSP1 was significantly down-regulated in the IgAN group (log2FoldChange = −4.07, padj = 1.88 × 10−158), which was the gene with the most significant difference in expression (Fig. 1F). According to the KEGG pathway enrichment analysis and GO functional annotation of the GA target set, the 515 downregulated genes in IgAN (log2FoldChange < −1, p < 0.05) showed significant enrichment in several inflammation-related pathways, including MAPK and IL-17 signalling pathways (Fig. 2A,B). The MAPK signalling pathway was frequently found in numerous enrichment results, indicating that GA may control the MAPK signalling pathway to reduce inflammation or other associated physiological and pathological processes, hence delaying the curative effect of IgAN. The samples were divided into high- and low-expression groups according to DUSP1 expression levels, and GSEA analysis was performed to further validate the functional role of DUSP1. GO and KEGG functional enrichment analyses indicated that the MAPK signalling pathway was consistently enriched in the DUSP1 low-expression group (Fig. 2C,D). These findings imply that GA may protect against IgAN by regulating the MAPK pathway by inhibiting DUSP1.

Figure 2: Identification of core pathways of GA for IgAN. (A) GO enrichment analysis of down-regulated DEGs; (B) KEGG enrichment analysis of down-regulated DEGs; (C) GSEA of GO biological processes; (D) GSEA of KEGG pathways.

3.2 GA Attenuates p-IgA1-Induced Oxidative Stress and Ferroptosis in HMCs via DUSP1

To further investigate the physiological role of DUSP1 in IgAN, HMCs were continuously stimulated with 25 μg/mL p-IgA1 for 48 h to establish an in vitro model for functional verification experiments. First, a CCK-8 assay was performed to assess the cytotoxic effects of GA on HMCs and to determine the suitable drug concentration range. As shown in Fig. 3A, GA did not show significant cytotoxicity when treated at a concentration of 12.5 μg/mL for 48 h. However, cell viability began to decrease at 25 μg/mL (Fig. 3A). Therefore, 12.5 μg/mL (GA-L) and 25 μg/mL (GA-H) were selected for subsequent experiments to ensure therapeutic effects and minimize off-target effects.

Figure 3: The effects of GA and DUSP1 on the oxidative stress and lipid peroxidation of HMCs. (A) GA on cell viability of HMCs (n = 6); (B) Representative images of ROS assay in HMCs (scale bar = 100 μm, n = 3); (C) Quantitative results of ROS assay by ImageJ; (D) The levels of GSH/GSSG ration in HMCs (n = 6); (E) Detection of lipid peroxidation using the C11-BODIPY™ 581/591 fluorescent probe (scale bar = 100 μm, n = 3); (F) Representative images of JC-1 mitochondrial membrane potential assay in HMCs (scale bar = 10 μm, n = 3); (G) Quantitative results of JC-1 mitochondrial membrane potential assay by ImageJ; (H) Representative images of live/dead assay (scale bar = 100 μm, n = 3). Quantitative data are expressed as Mean ± SD, with at least three samples per group. For comparisons with the IgA group. ns, no significance; *p < 0.05, **p < 0.01, ***p < 0.001.

Relative to the Ctrl group, the intracellular ROS level in the IgA group rose markedly (Fig. 3B), indicating successful induction of oxidative stress. The GA treatment group, in contrast, substantially decreased ROS levels in a dose-dependent manner. To investigate whether DUSP1 is involved in this antioxidant effect, we first transfected HMC cells with siDUSP1 before p-IgA1 and GA-H treatment. The ROS level in the IgA + GA-H + siDUSP1 group was significantly increased, while transfection with siCtrl did not affect the therapeutic effect of high-dose GA. In contrast, overexpression of DUSP1 (IgA + OE-DUSP1) mimicked the protective effect of GA and inhibited p-IgA1-induced ROS accumulation even in the absence of GA treatment (Fig. 3B,C). These results together indicate that DUSP1 is essential for GA-mediated regulation of oxidative stress. The ratio of GSH/GSSG in each group of cells also showed that p-IgA1 could significantly reduce the antioxidant capacity of HMCs, and GA intervention and DUSP1 overexpression could effectively restore this ratio, while DUSP1 knockdown significantly reversed the therapeutic effect of GA (Fig. 3D).

Redox imbalance leads to the massive accumulation of lipid peroxides, a key event in ferroptosis. In this study, lipid peroxides in each group of cells were detected by the fluorescent probe C11-BODIPY™ 581/591. Lipid peroxide accumulation was significantly elevated in the model group, whereas GA treatment and DUSP1 overexpression effectively suppressed this increase (Fig. 3E). Lipid peroxidation accumulation will cause damage to organelles, eventually leading to a large number of cell deaths. JC-1 analysis revealed a decrease in the red/green fluorescence ratio in the IgA group, indicating that the reduction in membrane potential led to mitochondrial dysfunction (Fig. 3F,G). In addition, the results of live/dead cell staining confirmed that p-IgA1 induced significant cell death (Fig. 3H). Consistent with the improvement of oxidative stress, GA treatment significantly improved mitochondrial membrane potential and increased cell survival. Gain-of-function and loss-of-function experiments of DUSP1 strongly indicated that DUSP1 overexpression attenuated mitochondrial depolarization and reduced cell death. These results suggest that GA alleviates p-IgA1-induced ferroptosis in HMCs through a DUSP1-dependent mechanism, which may be achieved by regulating oxidative stress and mitochondrial integrity.

3.3 GA Alleviates Ferroptosis in IgAN by Modulating the DUSP1/p38 MAPK Signaling Axis

To clarify the mechanism of GA in regulating oxidative stress and ferroptosis in IgAN, we first detected the key markers of ferroptosis. Fig. 4A illustrates that the mRNA expression of the transferrin receptor TFRC in HMCs was markedly upregulated following p-IgA1 stimulation, whereas the mRNA level of the iron storage protein FTH1 was significantly downregulated, indicating disrupted cellular iron metabolism. The above molecular changes were accompanied by a large accumulation of lipid peroxidation product malondialdehyde (MDA), a decrease in reduced glutathione (GSH), and an increase in the level of free Fe2+, which jointly confirmed that p-IgA1 stimulation induced ferroptosis of HMCs (Fig. 4B–D). High-dose GA treatment significantly reversed the above abnormal changes, restored the expression of TFRC and FTH1, and restored redox and iron homeostasis to normal. However, in the IgA + GA-H + siDUSP1 group, the up-regulation effect of GA-H on FTH1 and GSH was partially weakened, while the levels of MDA and TFRC rebounded, aggravating the oxidative stress imbalance and ferroptosis of IgAN. In contrast, DUSP1 overexpression alone could effectively improve iron metabolism, reduce oxidative stress levels and ferroptosis damage under the p-IgA1 stimulation background, and its effect was similar to that of GA-H intervention (Fig. 4A–D). Collectively, these findings suggest that DUSP1 is crucial for GA-mediated regulation of oxidative stress and ferroptosis in IgAN.

Figure 4: GA alleviates oxidative stress and ferroptosis by modulating the DUSP1/p38 MAPK signaling axis in HMCs. (A) Relative mRNA expression levels of TFRC and FTH1 determined by qRT-PCR (n = 6); (B) The levels of MDA in HMCs (n = 6); (C)The levels of GSH in HMCs (n = 6); (D) The levels of Fe2+ in HMCs (n = 6); (E) Western blot analysis of p-p38, p-38, DUSP1, GPX4, and SLC7A11 proteins in HMCs (n = 3); (F) Quantitative analysis of western blot by ImageJ. Quantitative data are expressed as Mean ± SD, with at least three samples per group. For comparisons with the IgA group. ns, no significance; *p < 0.05, **p < 0.01, ***p < 0.001.

Combined with the previous bioinformatics analysis suggesting that the MAPK pathway is the key pathway for GA intervention in IgAN, DUSP1 is the core target, and the literature shows that DUSP1 is a negative regulator of p38 MAPK, we further studied the regulatory effect of GA on DUSP1 and downstream p38MAPK by protein immunoblotting [24,25]. The results showed that p-IgA1 stimulation markedly decreased DUSP1 protein expression, whereas the phosphorylation level of p38 MAPK (p-p38) increased accordingly, with total p38 remaining unchanged. Compared with the Ctrl group, the IgA group exhibited a marked decrease in protein levels of the key ferroptosis inhibitors—glutathione peroxidase 4 (GPX4) and solute carrier family 7 member 11 (SLC7A11) (Fig. 4E,F). The data indicated that the p38 MAPK signaling pathway was aberrantly activated in the IgAN model, with its endogenous negative regulatory mechanism impaired, leading to enhanced downstream ferroptosis. High-dose GA treatment can effectively restore the expression of DUSP1, thereby inhibiting the activation of p-p38 and significantly up-regulating the expression of GPX4 and SLC7A11. In HMC cells with DUSP1 knockdown, the inhibitory effect of GA-H on p-p38 was partially reversed, and the expression of p-p38 was increased, suggesting that the inhibition of the p38 MAPK pathway by GA depends on DUSP1 (Fig. 4E,F).

In summary, this study confirmed that GA can upregulate DUSP1 expression, inhibit excessive phosphorylation of p38 MAPK, restore the protein levels of SLC7A11 and GPX4, and finally reduce the ferroptosis of HMCs induced by p-IgA1.

4  Discussion

Dual specificity protein kinase phosphatase-1 (DUSP1), referred to as MAPK phosphatase-1 (MKP-1), is involved in the negative feedback regulation of intracellular signaling and can be rapidly induced by cell stress, growth factors, and other stimuli [26]. DUSP1 is the most important negative regulator of the MAPK signaling pathway, which can specifically promote the dephosphorylation and inactivation of MAPK family members (such as ERK, JNK, p38) [27]. This negative feedback regulation can effectively prevent the pathological consequences, such as inflammation, abnormal proliferation, or cell death, caused by excessive activation of the MAPK pathway [25,28,29]. Research has shown that DUSP1 can inhibit the activation of the JNK1/2 pathway in leukemia, inhibit the expression of pro-apoptotic proteins BIM and P53, and up-regulate the expression of anti-apoptotic protein BCL2, weakening the strong apoptotic stimulation mediated by the MAPK signal [30]. Recent research has demonstrated that DUSP1 is a key molecule linking oxidative stress and ferroptosis. In the model of cerebral ischemia-reperfusion injury, DUSP1 overexpression can significantly reduce the levels of ROS and Fe2+, increase the content of GSH, and upregulate the expression of ferroptosis-related proteins such as GPX4 and FPN by inhibiting the phosphorylation of p38 MAPK, thereby reducing neuronal ferroptosis [31]. Likewise, in the acute kidney injury model, the selenium-modified triptolide phospholipid complex resisted ferroptosis by upregulating DUSP1 and inhibiting autophagy-dependent GPX4 degradation [32]. These results collectively suggest that the DUSP1/p38 MAPK axis represents a key pathway regulating cellular oxidative stress and ferroptosis.

In the IgAN in vitro model established in this study, GA can significantly inhibit ferroptosis of HMCs. Specifically, it can reverse lipid peroxidation, increase the expression of GPX4 and SLC7A11, and improve the mitochondrial membrane potential of cells. These results strongly suggest that GA plays a renal protective role by directly inhibiting ferroptosis of renal mesangial cells. This finding echoes the study of GA regulating ferroptosis in a variety of disease models. The mitochondria-targeted gallic acid nanoparticles effectively scavenge reactive oxygen species by activating the NRF2 pathway and inhibiting ferroptosis, thereby alleviating intervertebral disc degeneration [33]. In acute kidney injury, its nano-formulation can reduce intracellular free iron accumulation and mitochondrial dysfunction, and alleviate cisplatin-induced ferroptosis of renal tubular epithelial cells [34]. Additionally, the role of GA in regulating ferroptosis in non-renal diseases such as Alzheimer’s disease, cancer, and muscle injury has also been reported, further supporting its broad regulatory function in the ferroptosis pathway [35–37]. This work provides new insights into the renal protective mechanism of GA from the perspective of ferroptosis, which is an important supplement to its traditional “intestinal flora-systemic immunity” mode of action. As a compound with multiple effects, the mechanism of action of GA is systematic. GA is metabolized by the intestinal flora, and its products enter the circulation to participate in systemic immune regulation [38]. At the same time, it can also reach the kidney through the circulation in the form of prototype or active metabolites, directly act on renal cells, and improve renal injury by activating antioxidant pathways such as Nrf2 [39]. The two complement each other and jointly constitute the multi-level protection mechanism of GA.

One of the limitations of this study is that the experimental study was only carried out at the level of in vitro cells, and there was a lack of experimental data at the animal level, so the exact mechanism of action of GA in the complex pathological mechanism of the whole organism could not be determined. Nevertheless, it should be emphasized that although the in vitro cell system cannot fully replicate the complex microenvironment and overall physiological and pathological processes in vivo, it allows us to focus on the direct protective effect of GA on renal mesangial cells under controlled conditions and to precisely analyze its molecular regulation of key ferroptosis signaling pathways such as GPX4/SLC7A11. These in vitro experimental results provide direct preliminary evidence for the mechanistic hypothesis and point out the key targets and directions for subsequent animal experiments. Future studies will use the IgA nephropathy mouse model to further verify the above findings in vivo and explore the interrelationship and contribution of the “intestinal flora-ferroptosis” axis in the overall renal protective effect of GA. In addition, this study mainly focuses on the regulation of DUSP1 on the p38 MAPK pathway, but the potential impact of other MAPK pathway members (such as JNK or ERK) on the observed phenotype cannot be completely ruled out. To more comprehensively elucidate the mechanism of action of GA, future studies can further detect the effect of DUSP1 expression changes on the activation status of JNK and ERK signaling pathways in HMCs under GA intervention.

5  Conclusion

Based on our in vitro findings, this study suggests that GA may alleviate oxidative stress and inhibit ferroptosis in HMCs, potentially through modulating the DUSP1/p38 MAPK axis. These results provide preliminary evidence supporting further investigation of ferroptosis-related pathways as a potential therapeutic strategy for IgAN.

Acknowledgement: The authors would like to express their gratitude to all the researchers and consortiums who made their data publicly available in the GEO database, which made this study possible.

Funding Statement: This study was sponsored by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2024D01C152).

Author Contributions: The authors confirm contribution to the paper as follows: study conception and design: Qiguo Wang, Qin Wang, and Ting Wang; data collection: Qin Wang and Wen Ye; analysis and interpretation of results: Qiguo Wang, Qin Wang, Wen Ye, and Qin Feng; draft manuscript preparation: Qiguo Wang and Ting Wang. All authors reviewed and approved the final version of the manuscript.

Availability of Data and Materials: The original transcriptomic dataset analyzed during the current study is available in the Gene Expression Omnibus (GEO) repository https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175759. All other data generated or analyzed during this study are included in this published article, and the raw data are available from the corresponding author upon reasonable request.

Ethics Approval: The transcriptomic data used in this study were obtained from the publicly available GEO database (https://www.ncbi.nlm.nih.gov/geo/) (accession number: GSE175759). As all data are anonymized and publicly accessible, no ethics committee approval was required for their use.

Conflicts of Interest: The authors declare no conflicts of interest.

Appendix A

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