The STAT1–IRF1–ACSL4 axis drives peritoneal fibrosis and ferroptosis via lipid peroxidation-dependent mechanisms

Abstract

Peritoneal fibrosis, a major complication of long-term peritoneal dialysis, is driven by transforming growth factor-β1 (TGF-β1)-mediated mesothelial cell dysfunction. Emerging evidence links ferroptosis—a lipid peroxidation-dependent cell death—to fibrotic diseases, but the molecular mechanisms in peritoneal pathology remain unclear. Here, we investigate the role of the STAT1–IRF1–ACSL4 axis in TGF-β1-induced fibrosis and ferroptosis in human peritoneal mesothelial cells (HPMCs). HPMCs were stimulated with TGF-β1 to establish an in vitro fibrosis model and subjected to ACSL4 knockdown, ACSL4 overexpression, or ferroptosis inhibition with Ferrostatin-1. TGF-β1 significantly upregulated fibrosis markers (α-SMA, Collagen I), ferroptosis-related proteins (MDA, 4-HNE, PTGS2), and the STAT1–IRF1 signaling pathway, while suppressing the antioxidant system (GPX4, SLC7A11) (P < 0.001). ACSL4 knockdown markedly restored cell viability, reduced lipid peroxidation, Fe²⁺ accumulation, ferroptosis activation, and inflammatory cytokine secretion (IL-1β, IL-18) (P < 0.001). In contrast, ACSL4 overexpression exacerbated TGF-β1-induced ferroptosis, fibrosis, and inflammation. Mechanistically, ACSL4 regulated activation of the STAT1–IRF1 axis, as evidenced by changes in STAT1 phosphorylation and IRF1 nuclear translocation. Notably, Ferrostatin-1 reversed ACSL4-induced lipid peroxidation, inhibited STAT1–IRF1 activation, restored GPX4 expression, and attenuated fibrotic and inflammatory responses (P < 0.01). Collectively, these findings demonstrate that the STAT1–IRF1–ACSL4 axis integrates lipid peroxidation-dependent ferroptosis with pro-fibrotic and pro-inflammatory signaling in peritoneal mesothelial cells, identifying ACSL4-centered ferroptotic signaling as a potential therapeutic target for peritoneal fibrosis.

Clinical trial number

Not applicable.

1. Introduction

Peritoneal dialysis (PD) is a vital renal replacement therapy for end-stage renal disease patients, yet its long-term efficacy is often compromised by peritoneal fibrosis, a progressive pathological process characterized by extracellular matrix (ECM) deposition, mesothelial cell loss, and chronic inflammation [1]. Fibrosis disrupts peritoneal membrane integrity, leading to ultrafiltration failure and eventual discontinuation of PD [2]. Despite advances in understanding fibrotic mechanisms, therapeutic strategies remain limited, underscoring the need to identify novel molecular targets.

In recent years, increasing attention has been paid to the role of acyl-CoA synthetase long-chain family member 4 (ACSL4) in fibrosis, particularly its critical role in lipid peroxidation and cellular fate determination. As an important enzyme in fatty acid metabolism, ACSL4 catalyzes the binding of long-chain fatty acids to coenzyme A and preferentially enriches polyunsaturated fatty acids (PUFAs) in membrane phospholipids, thereby providing substrates for lipid peroxidation and ferroptosis, an iron-dependent form of regulated cell death driven by oxidative lipid damage [3]. Beyond acting as a mere executioner of ferroptotic death, ACSL4 has emerged as a pivotal amplifier of fibrotic remodeling in multiple organs. In kidney disease models, activation of the YAP–ACSL4 pathway in tubular epithelial cells promotes ferroptosis and accelerates calcium oxalate–induced renal interstitial fibrosis, whereas genetic silencing or pharmacological inhibition of ACSL4 significantly attenuates tubular injury, collagen deposition, and renal fibrosis progression [4]. Likewise, targeting upstream regulators of ACSL4, such as the m⁶A demethylase FTO, has been shown to relieve unilateral ureteral obstruction–induced renal fibrosis by downregulating ACSL4 expression and concomitantly reducing ferroptosis and pro-fibrotic gene activation [5].

Similar mechanisms are observed in extra-renal fibrotic disorders. In bleomycin-induced systemic sclerosis, ACSL4-dependent ferroptosis in inflammatory macrophages aggravates dermal and pulmonary fibrosis, while inhibition of ACSL4 prevents macrophage ferroptosis, diminishes inflammatory cytokine release, and alleviates tissue fibrosis [6]. In experimental pulmonary fibrosis, ferroptosis has been linked to fibroblast activation and ECM accumulation, and TFEB-mediated regulation of ACSL4 has been implicated in the anti-fibrotic effects of empagliflozin, highlighting ACSL4 as a critical node that couples iron accumulation, lipid peroxidation, and fibrogenesis in the lung [7]. In the liver, hepatocyte-specific modulation of ACSL4 or covalent pharmacological inhibition of ACSL4 reduces lipid peroxidation, limits ferroptosis, and ameliorates steatohepatitis-associated liver fibrosis, further supporting a generalizable role for ACSL4-driven ferroptosis in fibrotic disease [8]. Collectively, these studies indicate that ACSL4 integrates lipid metabolic reprogramming, ferroptotic cell death, inflammatory amplification, and ECM deposition, positioning ACSL4 as a mechanistic hub in the pathogenesis of fibrosis across different organs.

Transforming growth factor-β1 (TGF-β1) is a master regulator of fibrosis, driving myofibroblast differentiation and ECM production in human peritoneal mesothelial cells (HPMCs) [9]. Recent study suggests that TGF-β1 also modulates iron metabolism and redox homeostasis, potentially linking fibrosis to ferroptosis—a form of regulated cell death driven by lipid peroxidation [10]. Ferroptosis is marked by glutathione peroxidase 4 (GPX4) inactivation and accumulation of lipid reactive oxygen species (ROS), processes regulated by ACSL4, which enriches membranes with PUFAs to enhance peroxidation susceptibility [3]. Intriguingly, ACSL4 expression is transcriptionally controlled by the interferon regulatory factor 1 (IRF1), itself activated by signal transducer and activator of transcription 1 (STAT1) [11]. This STAT1–IRF1–ACSL4 axis has been implicated in radiation-induced intestinal injury and head and neck squamous but remains unexplored in peritoneal pathology [12,13].

Emerging evidence highlights the interplay between ferroptosis and fibrosis. In renal and pulmonary fibrosis, ferroptosis inhibitors mitigate collagen deposition and inflammation, suggesting shared pathways [14,15]. However, whether ferroptosis contributes to peritoneal fibrosis and how ACSL4 integrates with TGF-β1 signaling remains unclear. Notably, STAT1 activation is elevated in fibrotic peritoneal tissues, and its crosstalk with TGF-β1 may amplify pro-fibrotic responses [16]. Yet, the mechanistic role of STAT1–IRF1–ACSL4 in peritoneal mesothelial cell fate and fibrosis progression is undefined.

This study investigates the hypothesis that TGF-β1 drives peritoneal fibrosis by activating the STAT1–IRF1–ACSL4 axis, which promotes both fibrotic remodeling and ferroptosis in HPMCs. Using gain- and loss-of-function approaches, we demonstrate that ACSL4 knockdown attenuates fibrosis and lipid peroxidation, while its overexpression exacerbates these effects. Ferroptosis inhibition partially rescues fibrotic phenotypes, implicating lipid peroxidation as a key mediator. These findings reveal a previously unrecognized link between STAT1–IRF1 signaling, ACSL4-dependent ferroptosis, and peritoneal fibrosis, offering new therapeutic avenues for PD-related complications.

2. Materials and methods

2.1. Cell culture and treatments

HPMCs (Catalog No. CP-H180, Procell Life Science & Technology Co., Ltd., Wuhan, China) were cultured in complete medium (Procell, Catalog No. CM-H180) supplemented with 10% fetal bovine serum (Excell Bio, Catalog No. FSD500), 100 U/mL penicillin, and 50 U/mL streptomycin (Beyotime, Catalog No. C0222) under standard conditions (37 °C, 5% CO₂, 95% humidity). Cells were passaged using 0.25% trypsin (Gibco, Catalog No. R001100) when reaching 80% confluence. To establish a fibrosis model, HPMCs were treated with 5 ng/mL recombinant human TGF-β1 (HEK293-derived, MedChemExpress, Catalog No. HY-P7117) for 48 h [17].

The experimental groups were as follows: (1) Control group: Cells were cultured in standard medium without treatment. (2) TGF-β1 group: Cells were treated with TGF-β1 (5 ng/mL) for 48 h. (3) si-NC + TGF-β1 group: TGF-β1-treated cells were transfected with scrambled siRNA (50 nM). (4) si-ACSL4 + TGF-β1 group: TGF-β1-treated cells were transfected with ACSL4-specific siRNA (50 nM). (5) TGF-β1 + pcDNA3.1 vector group: TGF-β1-treated cells were transfected with pcDNA3.1 vector. (6) OE-ACSL4 + TGF-β1 group: TGF-β1-treated cells were transfected with ACSL4 overexpression plasmid (2 μg/mL). (7) OE-ACSL4 + Ferrostatin-1 + TGF-β1 group: TGF-β1-treated cells were co-treated with ACSL4 overexpression plasmid (2 μg/mL) and 5 μM Ferrostatin-1 (MedChemExpress, Catalog No. HY-100579) [18].

2.2. Cell transfection

The ACSL4 overexpression plasmid was constructed by inserting the full-length coding sequence of human ACSL4 (GenBank: NM_001318509.2, nucleotides 353–2488) into the pcDNA3.1 vector (Sangon Biotech, Shanghai, China). The ACSL4 sequence was synthesized and verified by Sangon Biotech, followed by restriction digestion using EcoRI and XhoI (Thermo Fisher Scientific). The linearized pcDNA3.1 vector and ACSL4 insert were ligated using T4 DNA ligase (Vazyme, China) and transformed into DH5α competent cells (Tiangen Biotech, China). Positive clones were selected by colony polymerase chain reaction (PCR) and confirmed through Sanger sequencing. Three ACSL4-specific siRNAs and a scrambled negative control siRNA (si-NC) were designed and synthesized by Sangon Biotech (Shanghai, China). The siRNA sequences are listed in Table 1.

Table 1.

siRNA Sequences targeting ACSL4.

siRNA	Sequence (5′ → 3′)
si-ACSL4-1	Sense: ACUGUAUAUUGUUAUUAACAA
	Antisense: UUGUUAUAACAAUAUACAGU
si-ACSL4-2	Sense: UCUUCUUGGCAUUGGUAAGAA
	Antisense: UUCUUACCAAUGCCAAGAAGA
si-ACSL4-3	Sense: AUUUAUCCAGAGUAUCUGCUC
	Antisense: GAGCAUACUCUGGAUAAAUU

For plasmid transfection, HPMCs at 80% confluence in 6-well plates were transfected with either 5 μg of the pcDNA3.1-ACSL4 overexpression plasmid or 50 nM of ACSL4-targeting siRNA. For plasmid transfection, Lipofectamine 3000 reagent was mixed with plasmid DNA in Opti-MEM medium at a 1:1 ratio, incubated for 15 min at room temperature (RT) to form transfection complexes, and then added to the cells. For siRNA delivery, RNAFit transfection reagent was combined with siRNA duplexes in serum-free medium, vortexed thoroughly, and incubated for 10 min before being applied to the cells. Cells were maintained in transfection medium for 48 h under standard culture conditions. Transfection efficiency was assessed through Western blot and quantitative real-time polymerase chain reaction (qRT-PCR) analyses of ACSL4 expression at both the protein and mRNA levels.

2.3. Cell viability assay

Cell viability was assessed using the Cell Counting Kit-8 (CCK-8; Bioss, Catalog No. BA00208). HPMC were seeded into 96-well plates at a density of 5 × 10³ cells/well and pre-cultured for 6 h under 37 °C, 5% CO₂, 95% humidity to allow adherence. After 24 h of treatment, 10 μL CCK-8 reagent was added to each well, followed by incubation for 2 h in the dark. Absorbance at 450 nm was measured using a Multiskan FC microplate reader (Thermo Scientific, USA). Data were normalized to the Control group.

2.4. Quantitative PCR

Total RNA isolation was performed using a commercial RNA extraction kit (Vazyme Biotech). RNA integrity was confirmed by spectrophotometric analysis (A260/A280 > 1.8). Reverse transcription was conducted with a first-strand cDNA synthesis kit (Vazyme Biotech) using 500 ng RNA input with oligo(dT) and random hexamer priming. Quantitative PCR (qPCR) amplification utilized SYBR Green chemistry on a Bio-Rad real-time PCR platform under standardized cycling parameters: initial denaturation (95 °C, 30 s) followed by 40 cycles of two-step amplification (95 °C/10 s; 60 °C/10 s), concluding with melt curve verification. Gene-specific primers (Sangon Biotech) are listed in Table 2.

Table 2.

Primer sequences for qRT-PCR.

Gene	Forward primer (5′ → 3′)	Reverse primer (5′ → 3′)
α-SMA	TAGCACCCAGCACCATGAAG	CTGCTGGAAGGTGGACAGAG
Collagen I	TGTGAAGTTGAGAGATGTGAATGA	TTGGAGATCACCACAACGGG
STAT1	GGAGCCAGGGAGTAACGAAG	ACGGTGTCCAAACTTGGTGAA
IRF1	GACCCTGGCTAGAGATGCAG	TCCTTGTTGATGTCCCAGCC
GPX4	ATTGCCAAGGACTTCGTCGT	TCCACTGCAGGCTCTTGATG
SLC7A11	CAAATTCCATGGCACCGTCA	GACTCCACGACGTACTCAGC
ACSL4	GGACAGTGTGGCTATGGAGA	TGGTGTTGAGGAGGAGGTTC
PTGS2	CGGTGAAACTCTGGCTAGACAG	GCAAACCGTAGATGCTCAGGGA
GAPDH	TGGTCAGAAAGCCTGTTGTGT	TGCTCCAATGATGGTGCCAA

Relative gene expression was calculated using the 2^−ΔΔCt method.

2.5. Western blot

Total proteins were extracted using radioimmunoprecipitation assay lysis buffer containing 1% phenylmethylsulfonyl fluoride. Protein concentrations were determined via BCA assay (NCM Biotech, Catalog No. WB6501). Equal amounts of protein (30 μg/lane) were separated on 12% SDS-PAGE gels and transferred to polyvinylidene fluoride membranes (Sigma-Aldrich, Catalog No. IPVH00010). Membranes were blocked with 5% nonfat milk and incubated overnight at 4 °C with primary antibodies: α-SMA (1:6,000, Proteintech, Catalog No. 14395-1-AP), Collagen I (1:1,000, Abcam, Catalog No. ab138492), GPX4 (1:2,000, Proteintech, Catalog No. 67763-1-Ig), ACSL4 (1:2,000, Abcam, Catalog No. ab155282), STAT1 (1:1,000, Abcam, Catalog No. ab234400), IRF1 (1:1,000, Abcam, Catalog No. ab230652), pho-STAT1 antibody (1:1,000, Abcam, Catalog No. ab30645), GAPDH antibody (1:1,000, Abcam, Catalog No. ab181602), and β-tubulin (1:10,000, CST, Catalog No. 2146). Horseradish peroxidase (HRP)-conjugated secondary antibodies (Bioss, Catalog No. bs-0295G/0296G) were used for detection, followed by ECL visualization (NCM Biotech, P2100). Gray values were analyzed using Image J software.

2.6. ELISA assay

Cell culture supernatants were analyzed for interleukin secretion profiles using commercial ELISA kits. Briefly, samples and standards were diluted in assay buffer (1:5) and loaded onto 96-well plates. After antigen-antibody binding at 37 °C for 60 min, the plates were sequentially washed, followed by the addition of a secondary antibody conjugated to HRP (37 °C for 30 min). Chromogenic substrate development was then performed using tetramethylbenzidine for 15 min. Optical density measurements at 450 nm were normalized against standard curves to calculate the concentrations of IL-1β and IL-18.

2.7. BODIPY staining

Lipid peroxidation was assessed using BODIPY^™ 493/503 (Thermo Fisher Scientific, Catalog No. D3922). HPMC were seeded in six-well plates (Jet BIOFIL, Catalog No. TCP011006) at a density of 2 × 10⁵ cells/well and treated according to the experimental groups (Section 2.1). After 24 h of treatment, cells were washed three times with PBS (Beyotime, Catalog No. C0221A) and incubated with 1 μM BODIPY^™ 493/503 in serum-free medium at 37 °C for 30 min in the dark. Following incubation, cells were washed three times with phosphate-buffered saline (PBS) to remove excess dye. Fluorescence imaging was performed using a Keyence BZ-H4XD confocal microscope (Keyence Corporation, Japan) with the FITC channel (excitation/emission: 493/503 nm). Green fluorescence intensity, indicative of lipid peroxidation levels, was quantified using ImageJ software (NIH, USA). Data were normalized to the Control group and expressed as fold changes relative to baseline fluorescence.

2.8. Immunofluorescence

Subconfluent HPMC monolayers (2 × 10⁵ cells/well) cultured on glass coverslips were subjected to experimental treatments as described in Section 2.1. Following 24 h of treatment, the cells were processed as follows: washed three times with PBS, fixed in 4% paraformaldehyde for 20 min at RT, and permeabilized with 0.25% Triton X-100 for 20 min at RT. After three additional PBS washes (5 min each), nonspecific binding sites were blocked with 10% goat serum for 1 h at RT. The cells were then incubated overnight at 4 °C with primary antibodies targeting α-SMA (Proteintech, Catalog No. 14395-1-AP; 1:500 dilution), ACSL4 (Abcam, Catalog No. ab155282; 1:200 dilution), GPX4 (Proteintech, Catalog No. 67763-1-Ig; 1:200 dilution), and IRF1 (Abcam, Catalog No. ab230652; 1:200 dilution) at the recommended concentrations.

After PBS rinsing, fluorophore-conjugated secondary antibodies [anti-rabbit IgG-Alexa Fluor 488, anti-mouse IgG-Alexa Fluor 568, Goat Anti-Rabbit IgG-FITC (Wuhan Seville Bio, Catalog No. GB22303)] were applied for 2 h in the dark at RT. To assess IRF1 nuclear translocation, the primary anti-IRF1 antibody was used to examine the localization of IRF1 in the nucleus. Translocation was quantified by comparing the nuclear intensity of IRF1 in the treated and control groups. Nuclei were counterstained with diamidino-2-phenylindole (DAPI) for 5 min at RT to facilitate cellular localization analysis.

For co-localization studies, dual staining was performed to assess: (1) co-localization of α-SMA and ACSL4 as markers of fibrosis and lipid peroxidation and (2) co-localization of ACSL4 and GPX4 as markers of lipid metabolism and antioxidant defense. Fluorescence intensity and cellular localization were quantified using ImageJ software (NIH, USA), with results normalized to the Control group.

Finally, coverslips were mounted onto slides using anti-fade mounting medium (Beyotime, Catalog No. P0126), and fluorescence signals were captured using a confocal laser scanning microscope (Keyence BZ-H4XD, Japan).

2.9. Flow cytometric detection

Intracellular Fe²⁺ levels were measured using a fluorescence assay kit (Elabscience, Catalog No. E-BC-F101). Briefly, all reagents were equilibrated to RT. The Reagent 1 working solution was prepared by diluting Reagent 1 with distilled water at a 1:9 ratio (serum-free medium may also be used for dilution). The Reagent 2 working solution was prepared by diluting the Reagent 1 working solution to a final concentration of 5 μmol/L and used within 2 h, protected from light. After trypsin digestion, cells were collected by centrifugation and washed twice with Reagent 1 working solution. For every 10⁶ cells, 1 mL of Reagent 2 working solution was added, and the cells were incubated at 37 °C in the dark for 30–60 min. The cells were then centrifuged at 300 g for 5 min, and the supernatant was discarded. Cells were washed two to three times with Reagent 1 working solution. Finally, cells were resuspended in 0.2–0.5 mL of Reagent 1 working solution and analyzed using a flow cytometer (BD FACSCelesta, USA).

2.10. Data analysis and statistics

Multigroup analyses were performed using one-way ANOVA followed by Tukey’s multiple comparison test. Graphical representations were generated using GraphPad Prism and ImageJ, with final figure composition completed in Adobe Illustrator. Data are presented as mean ± SD from ≥3 biological replicates, and statistical significance was determined with the following thresholds: *P < 0.05, **P < 0.01, ***P < 0.001.

3. Results

3.1. ACSL4 modulates the expression of ferroptosis- and fibrosis-related proteins

To assess the impact of ACSL4 on ferroptosis and fibrosis, transfection efficiency was confirmed through Western blot and qRT-PCR, which measured ACSL4 protein and mRNA levels, respectively. In the si-ACSL4 + TGF-β1 group, there was a significant reduction in ACSL4 expression compared with si-NC + TGF-β1 group, whereas in the OE-ACSL4 + TGF-β1 group, a marked increase in ACSL4 levels was observed compared to the empty vector control (P < 0.001) (Figure 1(A,B)). Western blot and qRT-PCR analyses further demonstrated that TGF-β1 treatment significantly upregulated the expression of fibrosis markers (α-SMA, Collagen I) and ferroptosis-related proteins (GPX4, SLC7A11) at both the protein and mRNA levels. Notably, si-ACSL4 + TGF-β1 group significantly reduced the expression of these pro-fibrotic and ferroptosis-associated proteins/genes (α-SMA, Collagen I, GPX4, SLC7A11) compared to the si-NC + TGF-β1 group (P < 0.001), indicating a decrease in both fibrosis and ferroptosis signaling. Conversely, OE-ACSL4 + TGF-β1 group exacerbated TGF-β1-induced fibrosis and ferroptosis, leading to elevated α-SMA and Collagen I levels, as well as dysregulated ferroptosis markers. Co-treatment with Ferrostatin-1 in the OE-ACSL4 + Ferrostatin-1 + TGF-β1 group reversed the effects of ACSL4 overexpression, attenuating both fibrosis (α-SMA, Collagen I) and ferroptosis markers (GPX4, SLC7A11), thus supporting the hypothesis that ferroptosis plays a crucial role in ACSL4-mediated fibrosis (P < 0.001). These findings collectively confirm that ACSL4 not only regulates the expression of key ferroptosis-related proteins but also contributes to the modulation of fibrosis, linking ferroptosis as a pivotal pathway in ACSL4-induced fibrosis progression (Figure 1(C,D)).

Figure 1.

ACSL4 regulates the expression of key fibrosis and ferroptosis markers in TGF-β1-induced HPMCs and validation of ACSL4 transfection efficiency. (A) Validation of ACSL4 transfection efficiency by WB analysis. Protein expression levels were quantified and normalized to GAPDH, with the relative protein expression. (B) Validation of ACSL4 transfection efficiency by qPCR analysis. (C) Western blot analysis of key proteins involved in fibrosis and ferroptosis, including α-SMA, Collagen I, GPX4, SLC7A11. Protein expression levels were quantified and normalized to β-tubulin, with the relative protein expression. (C) qRT-PCR analysis of fibrosis- and ferroptosis-related gene expression in HPMCs under different treatments. Statistical analysis of the relative protein levels in each experimental group. Data are presented as mean ± SD (n = 3). Statistical significance was assessed by one-way ANOVA followed by Tukey’s multiple comparison. *P < 0.05, ***P < 0.001.

3.2. Inhibition of ACSL4 or ferroptosis alleviates TGF-β1-induced lipid peroxidation, and Fe²⁺ accumulation

Compared to the Control group, TGF-β1 treatment significantly reduced HPMC cell viability (P < 0.001) (Figure 2(A)). Western blot and qRT-PCR analyses revealed that TGF-β1 treatment resulted in increased expression of ferroptosis-related proteins, including MDA, 4-HNE, and PTGS2, all of which are indicative of lipid peroxidation and ferroptosis activation (P < 0.001) (Figure 2(B,C)). Additionally, TGF-β1 treatment elevated BODIPY fluorescence intensity (P < 0.001) (Figure 2(D)), further confirming lipid peroxidation and ferroptosis activation. Flow cytometry analysis also demonstrated that TGF-β1 significantly increased Fe²⁺ levels in HPMC cells compared to the Control group (P < 0.001), providing further evidence for the occurrence of ferroptosis (Figure 2(E)). In contrast, ACSL4 knockdown (si-ACSL4 group) significantly restored cell viability (P < 0.001) and reduced lipid peroxidation, as evidenced by weaker BODIPY fluorescence signals (P < 0.001), lower MDA, 4-HNE, and PTGS2 levels, and decreased Fe²⁺ accumulation (P < 0.05), indicating a protective effect against ferroptosis. However, compared to the TGF-β1 group or the vector group, ACSL4 overexpression (OE-ACSL4 group) further reduced cell viability (P < 0.01) and intensified lipid peroxidation, as indicated by stronger BODIPY fluorescence signals (P < 0.001), increased MDA, 4-HNE levels, and PTGS2, and higher Fe²⁺ accumulation (P < 0.01), demonstrating that ACSL4 overexpression exacerbates TGF-β1-induced cellular damage. Notably, co-treatment with Ferrostatin-1 in the OE-ACSL4 + Ferrostatin-1 group significantly improved cell survival (P < 0.001) and attenuated lipid peroxidation, as evidenced by reduced BODIPY fluorescence intensity (P < 0.001), lower MDA, 4-HNE, and PTGS2 levels, and decreased Fe²⁺ accumulation (P < 0.01). These findings confirm that ferroptosis is a key mechanism underlying ACSL4-mediated cellular dysfunction.

Figure 2.

Inhibition of ACSL4 or ferroptosis alleviates TGF-β1-induced cell viability loss and lipid peroxidation. (A) CCK-8 assay. (B) qRT-PCR analysis of PTGS2 gene expression in HPMCs under different treatments. (C) Western blot analysis of key proteins involved in fibrosis and ferroptosis, including MDA and 4-HNE. Protein expression levels were quantified and normalized to β-tubulin, with the relative protein expression. (D) BODIPY staining. Scale bar: 50 μm. (E) Flow cytometric detection of intracellular Fe²⁺ levels. Data are expressed as mean ± SD (n = 3). Statistical significance was assessed by one-way ANOVA followed by Tukey’s multiple comparison. *P < 0.05, **P < 0.01, ***P < 0.001.

3.3. ACSL4 modulates ferroptosis by activating the STAT1–IRF1 signaling axis

TGF-β1 treatment significantly upregulated STAT1 and IRF1 expression at the mRNA level (P < 0.001) (Figure 3(A)) and enhanced the expression of STAT1, p-STAT1, and IRF1 at the protein level (P < 0.001) (Figure 3(B)), along with increased nuclear translocation of IRF1 (Figure 3(C)). In the si-ACSL4 + TGF-β1 group, ACSL4 knockdown reduced STAT1, p-STAT1, and IRF1 levels (P < 0.01), indicating impaired activation of the STAT1–IRF1 axis. Conversely, OE-ACSL4 + TGF-β1 group showed significantly higher STAT1, p-STAT1, and IRF1 expression (P < 0.001), with increased nuclear translocation of IRF1. Co-treatment with Ferrostatin-1 in the OE-ACSL4 + Ferrostatin-1 + TGF-β1 group reduced STAT1, p-STAT1, and IRF1 expression and blocked nuclear translocation of IRF1 (P < 0.001), suggesting that ferroptosis mediates ACSL4-induced activation of the STAT1–IRF1 pathway.

Figure 3.

ACSL4 activates the STAT1–IRF1 signaling axis. (A) qPCR analysis of STAT1 and IRF1 mRNA levels in the indicated groups. (B) Western blot analysis of STAT1, p-STAT1, and IRF1 expression in HPMC cells. (C) IF staining for IRF1 nuclear translocation. Data are expressed as mean ± SD (n = 3). Statistical significance was assessed by one-way ANOVA followed by Tukey’s multiple comparison. **P < 0.01, ***P < 0.001.

3.4. ACSL4 promotes fibrosis and ferroptosis through co-localization with α-SMA and GPX4 and drives pro-inflammatory cytokine secretion

IF staining demonstrated that compared to the Control group, TGF-β1 treatment significantly upregulated ACSL4 and α-SMA expression while suppressing GPX4 (P < 0.001) (Figure 4(A,B)). In the TGF-β1 group, ACSL4 (red) exhibited strong co-localization with α-SMA (green), particularly in the cytoplasm (P < 0.001 vs. Control), suggesting its involvement in fibrotic transformation. Conversely, compared to the si-NC group, ACSL4 knockdown (si-ACSL4 group) restored GPX4 distribution to near-Control levels (P < 0.001 vs. si-NC), indicating that ACSL4 inhibition rescues antioxidant defenses. Notably, compared to TGF-β1 groups, ACSL4 overexpression (OE-ACSL4 group) intensified cytoplasmic co-localization of ACSL4 with α-SMA (P < 0.001 vs. NC; P < 0.01 vs. TGF-β1), correlating with exacerbated fibrosis. However, compared to the OE-ACSL4 group, Ferrostatin-1 co-treatment partially reversed ACSL4 overexpression (P < 0.01) and restored GPX4 expression (P < 0.001), demonstrating that ferroptosis inhibition mitigates fibrotic progression by reactivating antioxidant pathways.

Figure 4.

IF co-staining of ACSL4 with α-SMA or GPX4 reveals its spatial correlation with fibrosis and ferroptosis markers in HPMCs. (A) Representative IF images showing co-localization of ACSL4 (red) with GPX4 (green), and (B) ACSL4 (red) with α-SMA (green), in HPMCs under different treatments. Nuclei were counterstained with DAPI (blue). Scale bar: 50 μm. (C) ELISA analysis of IL-1β secretion in HPMCs following ACSL4 modulation and ferroptosis inhibition. (D) ELISA analysis of IL-18 secretion in HPMCs following ACSL4 modulation and ferroptosis inhibition. Data are expressed as mean ± SD (n = 3). Statistical significance was assessed by one-way ANOVA followed by Tukey’s multiple comparison. ***P < 0.001.

ELISA results (Figure 4(C,D)) showed that compared to the Control group, TGF-β1 treatment significantly increased IL-1β and IL-18 secretion (P < 0.001), reflecting activated inflammatory responses. Compared to the si-NC group, ACSL4 knockdown (si-ACSL4 group) suppressed IL-1β and IL-18 levels (P < 0.001), confirming ACSL4’s role in promoting pro-inflammatory pathways. Furthermore, compared to the OE-ACSL4 group, Ferrostatin-1 co-treatment significantly reduced inflammatory cytokine secretion (P < 0.01), indicating that ferroptosis inhibition partially mitigates ACSL4-driven inflammation. Importantly, compared to the TGF-β1 group, ACSL4 overexpression (OE-ACSL4 group) further elevated IL-1β and IL-18 levels (P < 0.01), highlighting that ACSL4 amplifies TGF-β1-induced inflammatory responses. These data collectively demonstrate that ACSL4 promotes pro-inflammatory pathways, which are partially counteracted by ferroptosis inhibition.

4. Discussion

The current study explores the role of the STAT1–IRF1–ACSL4 axis in regulating peritoneal fibrosis and ferroptosis in HPMCs under TGF-β1 stimulation. By combining molecular, functional, and mechanistic analyses, the findings highlight ACSL4 as a central mediator linking lipid peroxidation, inflammatory responses, and fibrotic remodeling. These results align with previous studies but also introduce novel insights into the interplay between ferroptosis and peritoneal pathology, while raising questions about unresolved mechanistic details.

A key observation was the significant upregulation of ACSL4 in TGF-β1-treated HPMCs, which correlated with elevated fibrosis markers (α-SMA, Collagen I) and ferroptosis indicators (MDA, 4-HNE). This aligns with the established role of ACSL4 in promoting lipid peroxidation by esterifying PUFAs into membrane phospholipids, rendering cells susceptible to oxidative damage [19]. The co-localization of ACSL4 with α-SMA in immunofluorescence (IF) assays further supports its dual involvement in fibrotic transformation and lipid peroxidation, as α-SMA is a hallmark of myofibroblast activation [20]. Notably, ACSL4 knockdown or ferroptosis inhibition via Ferrostatin-1 restored antioxidant defenses (GPX4, SLC7A11) and attenuated fibrosis, suggesting that ACSL4-driven lipid peroxidation disrupts redox homeostasis, a mechanism previously implicated in renal and hepatic fibrosis [21,22]. However, the incomplete rescue of fibrosis by Ferrostatin-1 implies that non-ferroptotic pathways, such as canonical TGF-β/Smad signaling, may synergistically contribute to fibrogenesis [23].

The study further identified STAT1 and IRF1 as upstream regulators of ACSL4. TGF-β1 treatment induced nuclear translocation of STAT1 and IRF1, consistent with reports linking STAT1 activation to fibrotic diseases [24,25]. STAT1 phosphorylation is known to trigger IRF1 transcription, which binds promoter regions of target genes like ACSL4 [11]. In this study, Western blot analysis revealed significantly increased p-STAT1 levels in ACSL4-overexpressing cells and reversed by the Ferrostatin-1, which was decreased in ACSL4-knockdown cells, confirming the activation of the STAT1–IRF1 signaling axis. This phosphorylation of STAT1 likely leads to its dimerization and translocation to the nucleus, where it activates the transcription of IRF1, subsequently upregulating ACSL4 expression. These findings were consistent with previous studies that demonstrated the role of STAT1–IRF1–ACSL4 axis activation in radiation-induced intestinal injury, where activation of this pathway exacerbated ferroptosis [11]. However, the current work did not directly demonstrate the transcriptional regulation of ACSL4 by IRF1, leaving the precise mechanism of their interaction unresolved. Future studies using chromatin immunoprecipitation (ChIP) or luciferase reporter assays could clarify whether IRF1 binds the ACSL4 promoter in HPMCs.

Additionally, the feedback loop between lipid peroxidation and STAT1–IRF1 signaling remains speculative. While Ferrostatin-1 reduced STAT1 and IRF1 expression, it is unclear whether this results from diminished oxidative stress or direct inhibition of upstream kinases like JAK2, which are involved in STAT1 activation [26].

This study shows that, the inflammatory component of peritoneal fibrosis was underscored by elevated IL-1β and IL-18 levels in TGF-β1-treated cells, which were suppressed by ACSL4 knockdown or ferroptosis inhibition. These cytokines are hallmarks of NLRP3 inflammasome activation, a process often triggered by mitochondrial ROS and lipid peroxidation [27]. ACSL4-driven ferroptosis likely generates oxidized phospholipids that activate NLRP3, perpetuating inflammation and fibrosis—a phenomenon observed in diabetic nephropathy and atherosclerosis [28,29]. However, the study did not explore whether NLRP3 inflammasome inhibitors could synergize with ferroptosis blockers to enhance therapeutic efficacy, a potential avenue for future research.

This work represents the first investigation of the STAT1–IRF1–ACSL4 axis in peritoneal fibrosis, bridging the gap between ferroptosis and peritoneal pathology. Unlike prior studies focusing on hepatic or renal fibrosis, the findings highlight the unique vulnerability of peritoneal mesothelial cells to lipid peroxidation, possibly due to their prolonged exposure to high glucose and pro-inflammatory cytokines during PD [30]. The dual role of ACSL4 in both fibrosis and ferroptosis positions it as a promising therapeutic target. Pharmacological inhibitors of ACSL4 (e.g., thiazolidinediones) or STAT1–IRF1 signaling (e.g., fludarabine) could be repurposed to disrupt this axis [31,32]. Moreover, the use of Ferrostatin-1, a ferroptosis inhibitor, demonstrated partial efficacy in rescuing fibrosis, suggesting that combinatorial therapies targeting both ferroptosis and traditional fibrotic pathways may yield better outcomes.

Despite its contributions, the study has several limitations. Firstly, the experimental model relied solely on in vitro HPMCs, lacking validation in animal models or patient-derived tissues. Peritoneal fibrosis involves complex interactions between mesothelial cells, immune cells, and the ECM, which cannot be fully recapitulated in monoculture. Secondly, the mechanism by which TGF-β1 activates STAT1–IRF1 signaling remains unclear. While TGF-β1 is known to crosstalk with JAK/STAT pathways in other contexts [33], direct evidence in HPMCs is absent. Thirdly, the study did not establish a causal relationship between STAT1–IRF1 and ACSL4. For instance, siRNA-mediated STAT1 or IRF1 knockdown could confirm their regulatory roles. Fourthly, the contribution of other ferroptosis regulators, such as FSP1 or DHODH, was not explored [34]. Finally, the clinical relevance of the findings is limited by the absence of data linking ACSL4 expression to peritoneal membrane function in dialysis patients.

To address these gaps, future work should integrate in vivo models (e.g., chlorhexidine gluconate-induced peritoneal fibrosis in mice) and clinical samples to validate the STAT1–IRF1–ACSL4 axis. Mechanistic studies, including ChIP-seq and kinase activity assays, could elucidate how TGF-β1 activates STAT1 and whether IRF1 directly binds the ACSL4 promoter. Additionally, single-cell RNA sequencing could reveal heterogeneity in HPMC responses to TGF-β1, identifying subpopulations prone to ferroptosis. The therapeutic potential of ACSL4 inhibitors should be tested in combination with existing anti-fibrotic agents, such as pirfenidone or rapamycin, to evaluate synergistic effects [35,36].

5. Conclusion

In conclusion, this study demonstrates that the STAT1–IRF1–ACSL4 signaling axis is a key molecular driver of TGF-β1-induced peritoneal fibrosis through lipid peroxidation-dependent ferroptotic mechanisms. Dysregulated activation of ACSL4 promotes ferroptosis, inflammatory amplification, and fibrotic remodeling in peritoneal mesothelial cells, whereas suppression of ACSL4 activity or pharmacological inhibition of ferroptosis effectively attenuates these pathogenic processes. These findings establish ACSL4-centered ferroptotic signaling as a critical mechanistic link between oxidative stress, inflammation, and peritoneal fibrosis and highlight the STAT1–IRF1–ACSL4 axis as a promising therapeutic target for the prevention and treatment of PD-associated fibrosis. Future in vivo studies are warranted to further validate the translational potential of targeting this pathway.

Funding Statement

This study was supported by three projects: Project No. 2025JJXM070: Study on the regulatory mechanism of the STAT1–IRF1–ACSL4 axis in peritoneal mesothelial cell fibrosis and ferroptosis (Grant amount: 0.8 ten thousand RMB); Project No. 2025JXM071: Irisin alleviates high-phosphorus-mediated chronic kidney disease-related vascular calcification via inhibiting glycolysis and the HKI-NLRP3-pyroptosis pathway (Grant amount: 0.8 ten thousand RMB); Project No. CXPJJH122003-51: Changes and significance of sCD163 in the adjuvant treatment of adult SLE with Huaiqihuang Granules (Grant amount: 2 ten thousand RMB).

Ethics approval and patient consent

Our study does not involve human participants, human data, or human tissue, and thus, ethics approval or informed consent were not required.

Consent for publication

Not applicable.

Disclosure statement

The authors have no conflicts of interest to declare.

Data availability statement

The data will be made available upon reasonable request to the corresponding author.