Skip to content
Frontiers
Search

Original Research

Frontiers in Veterinary Science ·

Volume 13 - 2026 | https://doi.org/10.3389/fvets.2026.1950560

Comparative transcriptomic analysis reveals the protective mechanism of ferulic acid against lipopolysaccharide-induced ferroptosis in IPEC-J2 cells

  • Jiayu Chen1,2,3
  • Hongchao Li1,2,3
  • Yaxuan Niu1,2,3
  • Tao Guo1,2,3
  • Xiangyu Wang4
  • Yue Zhang4
  • Wenwen Wang1,2,3
  • Yuanqing Xu1
  • Yuan Wang1,2,3*corresponding author
  • Mingxing Chu4*corresponding author
  1. 1College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
  2. 2Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Hohhot, China
  3. 3Dairy-Breeding and Production Research Subcenter of National Technology Innovation Center for Dairy, Hohhot, China
  4. 4State Key Laboratory of Animal Biotech Breeding, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
Article metrics0 viewsView details
PDFCiteMetrics

Abstract

Lipopolysaccharide (LPS) is a key virulence factor that induces intestinal oxidative damage in livestock and poultry, and cellular ferroptosis is one of the triggers of oxidative stress. Ferulic acid (FA) is a natural phenolic acid with antioxidant, anti-inflammatory and anti-ferroptosis properties. This study aimed to investigate the protective effects of FA against LPS-induced ferroptosis in the porcine small intestinal epithelial cell line (IPEC-J2) and to elucidate the underlying mechanisms. The experiment included four treatment groups: control group (CON), 50 μg/mL FA group (FA), 10 μg/mL LPS group (LPS), and 50 μg/mL FA combined with 10 μg/mL LPS group (FA and LPS), with all treatments lasting 24 h. The results showed that FA inhibited ferroptosis in IPEC-J2 cells. LPS successfully induced ferroptosis, as evidenced by decreased cell viability, increased levels of ROS, MDA, and Fe 2+, downregulation of GPX4 and SLC7A11, and upregulation of TFRC and ACSL4. FA effectively alleviated LPS-induced ferroptosis in IPEC-J2 cells, with an efficacy comparable to that of the ferroptosis inhibitor Fer-1. Transcriptomic analysis revealed that FA significantly reversed the gene expression changes induced by LPS, with differentially expressed genes enriched in pathways related to ferroptosis, TNF signaling, NF-κB signaling, arachidonic acid metabolism, and PI3K-Akt signaling. Protein–protein interaction (PPI) network analysis indicated that genes such as HMGCS1, CXCL8, and IL-18 may serve as key nodes mediating the protective effects of FA. In conclusion, FA alleviates LPS-induced ferroptosis in IPEC-J2 cells by regulating glutathione metabolism, maintaining iron homeostasis, and inhibiting lipid peroxidation. This effect is closely associated with the coordinated regulation of inflammatory responses, lipid metabolism, and ferroptosis-related signaling pathways.

Keywords

1 Introduction

The intestine is a key organ responsible for digestion, absorption, and immune defense in animals. Its structural integrity and functional homeostasis directly affect productive performance, health status, and product quality. It is also essential for the efficient and sustainable development of animal husbandry (1). In intensive farming systems, livestock and poultry often face environmental stress, nutritional imbalance, and pathogenic infections. These factors can induce intestinal stress and lead to intestinal injury (2). Gram-negative bacteria are common pathogens. Their cell wall component, lipopolysaccharide (LPS), is a major virulence factor that induces intestinal inflammation and damage (3). LPS can be recognized by intestinal epithelial cells. It activates downstream inflammatory signaling pathways. This process compromises epithelial barrier integrity by disrupting tight junctions, thereby increasing intestinal permeability. (4). To further investigate the molecular mechanisms of LPS-induced intestinal injury, this study used the porcine intestinal epithelial cell line IPEC-J2 as a classical model.

Previous studies have shown that LPS exposure can induce inflammatory cascades, oxidative stress, mitochondrial dysfunction, and disruption of tight junctions between cells (5). LPS binds to Toll-like receptor 4 (TLR4) on the surface of intestinal epithelial cells. This interaction activates downstream inflammatory signaling pathways, such as NF-κB. It also induces excessive production of reactive oxygen species (ROS), which leads to redox imbalance (6). Persistent oxidative stress and inflammation can further damage membrane lipids and proteins. They may also disrupt iron metabolism, which results in abnormal accumulation of intracellular ferrous ions (Fe2+). Recent studies have shown that enhanced lipid peroxidation, glutathione (GSH) depletion, and iron accumulation can trigger ferroptosis (7). Ferroptosis is a form of cell death that depends on iron and is driven by lipid peroxidation (8). In intestinal diseases, ferroptosis has been reported to participate in pathological processes such as ulcerative colitis and necrotizing enterocolitis. This suggests that it may play a key role in intestinal injury (9, 10). However, most existing studies focus on inflammatory bowel disease. The specific molecular mechanisms by which LPS induce intestinal epithelial cell injury through ferroptosis remain unclear. Therefore, it is important to systematically clarify the mechanisms of LPS-induced intestinal injury and to explore effective intervention strategies. This will help protect intestinal health in animals.

Extracts from traditional Chinese herbal medicine can reduce intracellular levels of reactive ROS and malondialdehyde (MDA). They can also increase the protein expression of ferritin and GPX4. These effects help reverse or alleviate ferroptosis in cells (11). Ferulic acid (FA) is a naturally occurring phenolic acid abundant in traditional Chinese medicinal herbs, including Angelica sinensis (Danggui) and Ligusticum chuanxiong (Chuanxiong), and exhibits a broad range of biological activities, including antioxidant, anti-inflammatory, and lipid metabolism-regulating effects. Our previous study showed that FA can alleviate LPS-induced ferroptosis in primary sheep hepatocytes (12). However, the effects of FA on ferroptosis in intestinal epithelial cells remain unclear. The protective role of FA against LPS-induced ferroptosis in IPEC-J2 cells has not been systematically studied. Therefore, this study used IPEC-J2 cells as a model to investigate the mechanism by which FA alleviates LPS-induced ferroptosis. This work provides a theoretical basis for the use of FA as a potential feed additive targeting ferroptosis to maintain intestinal health in livestock and poultry.

2 Materials and methods

2.1 Cell culture and treatments

The IPEC-J2 cell line was obtained from Beina Bio (China). This cell line originates from porcine jejunal epithelium. It is a well-established in vitro model for studying intestinal function in pigs, including barrier integrity, immune response, and nutrient absorption. Cells were cultured in DMEM/F12 medium (Gibco, USA). The medium was supplemented with 10% fetal bovine serum (FBS, Gibco), 100 U/mL penicillin, and 100 μg/mL streptomycin. Cultures were maintained at 37 °C in a humidified incubator with 5% CO₂. Cells were passaged every 2 to 3 days. When cultures reached 80–90% confluency, cells were dissociated using 0.25% trypsin–EDTA (Gibco, USA). All experiments used cells in the logarithmic growth phase under optimal conditions.

2.2 Reagent preparation and experimental groups

FA (Pengyu Biotechnology Co., China) solution: A stock solution of 10 mg/mL FA was prepared in PBS (Gibco, USA) and stored for subsequent use. LPS solution: A stock solution of 5 mg/mL LPS was prepared in PBS and stored for subsequent use. Ferrostatin-1 (Fer-1, MCE, USA) solution: A stock solution of 10 μmol/L Fer-1 was prepared in DMSO (Gibco, USA) and stored for subsequent use. All stock solutions were aliquoted and stored at −20 °C. Immediately before each experiment, working solutions were freshly prepared by diluting the stocks in PBS (FA and LPS) or in complete DMEM/F12 medium (Fer-1). IPEC-J2 cells were seeded in appropriate culture plates and allowed to adhere overnight prior to the following treatments: (1) FA treatment: Cells were treated with FA at 0, 1, 10, 100, 500, or 1,000 μg/mL for 24 h. (2) LPS treatment: Cells were treated with LPS at 0, 1, 10, 50, 100, or 200 μg/mL for 24 h. (3) Fer-1 concentration screening: Cells were treated for 24 h as follows: control (CON); LPS (10 μg/mL); or LPS (10 μg/mL) combined with Fer-1 at 2, 4, 6, 8, or 10 μmol/L. (4) FA concentration screening: Cells were treated for 24 h as follows: CON; LPS (10 μg/mL); or LPS (10 μg/mL) combined with FA at 50, 100, 200, or 400 μg/mL. (5) Assessment of FA’s protective effect: Cells were treated for 24 h in one of six groups: CON; LPS (10 μg/mL); FA (50 μg/mL); Fer-1 (4 μmol/L); LPS and FA (10 μg/mL and 50 μg/mL); or LPS and Fer-1 (10 μg/mL and 4 μmol/L). (6) RNA sequencing and validation: Cells were treated for 24 h in one of four groups: CON; FA (50 μg/mL); LPS (10 μg/mL); or FA and LPS (50 μg/mL and 10 μg/mL). For all in vitro experiments, three independent biological replicates were performed, each with three technical replicates.

2.3 Cellular assays

Cells were seeded into 96-well plates at a density of 1 × 105 cells/mL (200 μL per well) and pre-cultured for 12 h. Following treatment according to the experimental design, 10 μL of CCK-8 solution (Dojindo, Japan) was added to each well. After incubation for 2 h at 37 °C in the dark, the absorbance at 450 nm was measured using a microplate reader. Cell viability was expressed as a percentage of the control group’s OD value. The levels of reactive ROS, MDA, superoxide dismutase (SOD), total antioxidant capacity (T-AOC), catalase (CAT), Fe2+, and the ratio of reduced to Glutathione/Glutathione disulfide (GSH/GSSG) were measured using commercial assay kits according to the respective manufacturers’ protocols. Kits for MDA, SOD, T-AOC, CAT, and Fe2+ were purchased from Nanjing Jiancheng Bioengineering Institute (China). Kits for ROS, GSH, and GSSG were obtained from Beyotime Biotechnology (China).

2.4 qRT-PCR

IPEC-J2 cells were seeded into 6-cm dishes at a density of 1 × 106 cells/mL (5 mL per dish) and cultured for 12 h. Processed samples were snap-frozen immediately in liquid nitrogen and subsequently stored at −80 °C for total RNA extraction. Total RNA was extracted from IPEC-J2 cells using a total RNA extraction kit (Invitrogen, USA). RNA concentration and purity were assessed with a microvolume spectrophotometer, and A260/A280 ratios ranged between 1.8 and 2.1. RNA integrity was verified by 1.5% agarose gel electrophoresis. For cDNA synthesis, 1 μg of total RNA was subjected to genomic DNA removal and reverse transcription according to the protocol of the PrimeScript™ RT reagent Kit (TaKaRa, Japan). Reverse transcription was performed at 37 °C for 15 min, followed by 85 °C for 5 s. The resulting cDNA was stored at −20 °C until further analysis. Real-time quantitative PCR was conducted on a QuantStudio 5 Real-Time PCR System using TB Green® Premix Ex Taq™ (TaKaRa, Japan). The thermal cycling conditions were as follows: initial denaturation at 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s and 60 °C for 30 s. β-actin served as the internal reference gene, and the relative mRNA expression levels of target genes were calculated using the 2-ΔΔCt method (13). Each sample was analyzed with three technical replicates, and the entire experiment was independently repeated three times. The primer sequences are listed in Table 1.

Table 1. Primer sequence used for quantitative qRT-PCR.

GeneNucleotide Sequences 5′ to 3′Size/bp
ABCG1F: GTGTACTGGATGACGTCGCA199
R: CGAAGCTGACGAAGAACCCT
CXCL8F: ACGCATTCCACACCTTTCCAC157
R: TCTGTACAACCTTCTGCACCC
LPCAT3F: TCACTGGTACCATCGCCTCT222
R: TGTCTTGAATCAGGCTGGCA
IL6F: AGAATCACCACCGGTCTTGT95
R: ATCTGCACAGCCTCGACATT
PFKF83F: GAATGTGGAGTCGGTGAGCA225
R: CCTTTCACGTTTTGTCCGGG
SCDF: GCCACCTTTCTTCGTTACGC142
R: CCTCACCCACAGCTCCCAAT
ALOX5APF: TCATCAGCGTGGTCCAGAAC147
R: TGGGGTATGCATCGACACAG
TAGLNF: ATAGTGCAGTGTGGCCCTG200
R: GCGGCCTTTAAGAACTGAGC
ACSL4F: TTGCCAAGAGTGACCAGTCC215
R: TGGGCTTAAGCGAACCTTGA
GPX4F: TTACGGATTCTGGCCTTCCC286
R: CTATGACCTGGGGCTCTTCC
TFRCF: ATGGACCGGGCTAACTTTCC94
R: GGAATCCAGGTGTGTACGGG
SLC7A11F: CTGGGCAGGAGAAAGTTGT285
R: AGACTCGCACAAAAGCTGGT

2.5 RNA extraction, library construction, and sequencing

IPEC-J2 cells (1 × 106 cells/mL, 5 mL) were seeded into 6 cm culture dishes and incubated at 37 °C in a 5% CO₂ incubator for 12 h. Once the cells reached 60–70% confluence, they were divided into groups. Based on the optimal dosage determined in previous experiments, four treatment groups were established: control group (CON), FA group (50 μg/mL FA), LPS group (10 μg/mL LPS), and FA and LPS group (50 μg/mL FA and 10 μg/mL LPS). All treatments were performed for 24 h, with three biological replicates per group. After treatment, the cells were washed twice with PBS and collected. Total RNA was extracted using Trizol (Invitrogen, USA) following the manufacturer’s instructions. RNA integrity was assessed using an Agilent 2,100 Bioanalyzer (Agilent Technologies, USA), with a requirement for the RNA Integrity Number (RIN) to be >7.0. The RNA integrity was further verified by agarose gel electrophoresis. RNA samples that passed the integrity check were sent to Shanghai Meiji Biological Pharmaceutical Technology Co., Ltd. (China) for library construction and sequencing. Library construction followed the Illumina platform’s standard procedures: mRNA was enriched and purified using Oligo(dT) magnetic beads, while rRNA and other non-coding RNAs were removed. The mRNA was fragmented into approximately 300 bp segments using Mg2+ ion fragmentation buffer to ensure uniform fragment length. First-strand cDNA was synthesized by reverse transcription using 6 bp random primers, and second-strand cDNA was synthesized using the first-strand cDNA as a template and dNTPs. The cDNA product was purified using a PCR purification kit (Qiagen, Venlo, The Netherlands), followed by end repair, poly(A) tail addition, and Illumina sequencing adapter ligation. The final sequencing library was assessed for size using the Agilent 2,100 Bioanalyzer and proceeded to sequencing upon passing size qualification. Sequencing was carried out on the Illumina NovaSeq 6,000 platform (Illumina, USA) with a 150 bp paired-end read strategy (PE 150 bp). The sequencing process adhered strictly to the platform’s standard operating procedures, with sequencing quality controlled to ensure a Q30 value ≥85%, ensuring the accuracy of the raw sequencing data. Each sample generated at least 6 Gb of sequencing data to meet the requirements for subsequent bioinformatics analysis. The final raw sequencing data (raw reads) were used for further data analysis.

2.6 RNA sequencing data analysis

First, rigorous quality control (QC) was performed on the raw sequencing data (Raw reads). Base quality, adapter contamination, and the proportion of N bases were assessed using FastQC software (v0.11.9). Low-quality data were filtered with Trimmomatic software (v0.39). Reads containing adapter sequences, low-quality reads (with more than 50% bases having a quality score <20), and reads with more than 10% N bases were removed. High-quality clean reads were obtained. After quality control, clean reads were rechecked with FastQC software to ensure no significant adapter remnants and that the base quality met the required standards. Next, sequence alignment and gene expression quantification were performed. The clean reads were aligned to the pig reference genome (Sus scrofa 11.1) using HISAT2 software (v2.2.1). Default alignment parameters were used to ensure efficiency and accuracy, with an alignment rate of ≥85%. Gene expression was quantified by counting the number of reads mapped to each gene using HTSeq software (v0.11.2). FPKM (Fragments Per Kilobase of transcript per Million mapped reads) was used to quantify gene expression. A higher FPKM value indicates higher gene expression. Finally, differential gene expression analysis was performed. DESeq2 software (v1.34.0) was used to analyze gene expression differences between the groups. Differentially expressed genes (DEGs) were identified using a raw p-value threshold of < 0.05.

2.7 KEGG pathway enrichment analysis

To further explore the potential biological functions of the candidate DEGs identified under different treatments, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed using KOBAS software (v3.0) based on the hypergeometric test. Candidate DEGs identified using a nominal p-value < 0.05 were used as input for the enrichment analysis. Enrichment p-values were calculated for individual KEGG pathways, and p < 0.05 was considered statistically significant. In addition to statistically significant pathways, selected pathways closely related to the biological processes investigated in this study, including ferroptosis, lipid metabolism, oxidative stress, and inflammatory responses, were retained for exploratory visualization and interpretation. Pathways that did not meet the statistical significance threshold were not interpreted as significantly enriched.

2.8 PPI network construction and module analysis

To reveal the functional associations between proteins encoded by DEGs at the system level, this study further constructed a protein–protein interaction (PPI) network. The PPI network was constructed using the STRING online database (version 11.0, https://string-db.org/), with Sus scrofa as the species. Interaction relationships with a combined confidence score (combined_score) ≥ 400 were retained to control the network quality. The PPI network was then visualized using Cytoscape software (v3.6.1, http://cytoscape.org/). Topological parameters, including degree centrality and betweenness centrality, were calculated to identify hub genes in the network. These genes typically play a central regulatory role in functional modules. To screen for hub genes and significant network modules, we used the Cytoscape plugins cytoHubba and Molecular Complex Detection (MCODE). Hub genes were selected using the maximum clique centrality (MCC) method, and significant network modules were identified using the default parameters of the MCODE plugin.

2.9 Statistical analysis

The raw experimental data were organized and calculated using Excel 2010. Normality and homogeneity of variance were assessed before ANOVA. The data were then analyzed by one-way analysis of variance (ANOVA) using SAS 9.2 software. Multiple comparisons were performed using Tukey’s test. Statistical graphs were generated using GraphPad Prism 9.0 software. Differences were considered significant at p ≤ 0.05.

3 Results

3.1 Effects of FA on cell viability, antioxidant capacity and Iron metabolism in IPEC-J2 cells

To clarify the effect of FA treatment alone on IPEC-J2 cells, as shown in Figure 1, compared with the control group, supplementation with 1–500 μg/mL FA significantly increased the cell proliferation rate (p < 0.05) and significantly reduced the relative fluorescence intensity of ROS (p < 0.05). Supplementation with 1–100 μg/mL FA significantly increased CAT activity (p < 0.05) and significantly decreased MDA content (p < 0.05). Supplementation with 1 and 100 μg/mL FA significantly increased T-AOC content (p < 0.05). Supplementation with 10 and 100 μg/mL FA significantly increased the GSH/GSSG ratio (p < 0.05). Supplementation with 1–100 μg/mL FA had no significant effect on SOD activity. Supplementation with 100 and 500 μg/mL FA significantly reduced Fe2+ content (p < 0.05). Notably, compared with the control group (0 μg/mL FA), supplementation with 1,000 μg/mL FA significantly decreased CAT and SOD activities, T-AOC content, and the GSH/GSSG ratio (p < 0.05), indicating that 1,000 μg/mL FA exhibited certain cytotoxicity. In summary, FA inhibited ferroptosis in IPEC-J2 cells, and 10–500 μg/mL was identified as the safe and effective concentration range.

Effects of FA on Cell Viability, Antioxidant Capacity and Iron Metabolism in IPEC-J2 Cells. **(A)** Effects of different concentrations of FA on cell viability; **(B)** effects of different concentrations of FA on ROS; **(C)** effects of different concentrations of FA on MDA; **(D)** effects of different concentrations of FA on the GSH/GSSG ratio; **(E)** effects of different concentrations of FA on Fe<sup>2+</sup>; **(F)** effects of different concentrations of FA on CAT; **(G)** effects of different concentrations of FA on T-AOC; **(H)** effects of different concentrations of FA on SOD. Bars with the same letters or without letters indicate no significant difference (*p* > 0.05), whereas bars with different lowercase letters indicate significant differences (*p* < 0.05). The same applies to the following figures.
Figure 1. Effects of FA on Cell Viability, Antioxidant Capacity and Iron Metabolism in IPEC-J2 Cells. (A) Effects of different concentrations of FA on cell viability; (B) effects of different concentrations of FA on ROS; (C) effects of different concentrations of FA on MDA; (D) effects of different concentrations of FA on the GSH/GSSG ratio; (E) effects of different concentrations of FA on Fe2+; (F) effects of different concentrations of FA on CAT; (G) effects of different concentrations of FA on T-AOC; (H) effects of different concentrations of FA on SOD. Bars with the same letters or without letters indicate no significant difference (p > 0.05), whereas bars with different lowercase letters indicate significant differences (p < 0.05). The same applies to the following figures.

3.2 Effects of LPS on cell viability, oxidative damage and Iron metabolism in IPEC-J2 cells

To clarify the mechanism by which LPS induces injury in IPEC-J2 cells, as shown in Figure 2, compared with the control group (0 μg/mL LPS), supplementation with 1–200 μg/mL LPS significantly reduced cell viability (p < 0.05). At the same time, it significantly increased the relative fluorescence intensity of ROS, as well as MDA and Fe2+ contents (p < 0.05). These results indicate that LPS successfully induced ferroptosis in IPEC-J2 cells. Among the tested concentrations, 10 μg/mL LPS caused moderate cell damage with obvious ferroptosis-related phenotypic changes, while avoiding excessive cell death that would interfere with subsequent drug-intervention assays. Based on the extent of cell injury and the feasibility of subsequent experiments, 10 μg/mL LPS was selected as the optimal concentration to induce ferroptosis in IPEC-J2 cells.

Effects of LPS on Cell Viability, Oxidative Damage and Fe<sup>2+</sup> in IPEC-J2 Cells. **(A)** Effects of different concentrations of LPS on cell viability; **(B)** effects of different concentrations of LPS on ROS; **(C)** effects of different concentrations of LPS on MDA; **(D)** effects of different concentrations of LPS on Fe<sup>2+</sup>.
Figure 2. Effects of LPS on Cell Viability, Oxidative Damage and Fe2+ in IPEC-J2 Cells. (A) Effects of different concentrations of LPS on cell viability; (B) effects of different concentrations of LPS on ROS; (C) effects of different concentrations of LPS on MDA; (D) effects of different concentrations of LPS on Fe2+.

3.3 FA alleviates LPS-induced ferroptosis in IPEC-J2 cells

To determine the protective effect of FA on LPS-induced ferroptosis in IPEC-J2 cells, as shown in Figure 3, compared with the LPS group, the LPS and FA50 group and LPS and FA100 group significantly increased cell viability (p < 0.05). They also significantly reduced the relative fluorescence intensity of ROS, as well as MDA and Fe2+ contents (p < 0.05). These results indicate that supplementation with 50 and 100 μg/mL FA can alleviate LPS-induced ferroptosis. Based on the degree of cell recovery and the feasibility of subsequent experiments, 50 μg/mL FA was selected as the optimal concentration for further studies.

Effects of different concentrations of FA on LPS-induced ferroptosis in IPEC-J2 cells. **(A)** Effects of different concentrations of FA on cell viability under LPS stimulation; **(B)** effects of different concentrations of FA on ROS under LPS stimulation; **(C)** effects of different concentrations of FA on MDA under LPS stimulation; **(D)** effects of different concentrations of FA on Fe<sup>2+</sup> under LPS stimulation. CON group: control group; LPS group: 10 μg/mL LPS; LPS and FA50 group: 10 μg/mL LPS + 50 μg/mL FA; LPS and FA100 group: 10 μg/mL LPS + 100 μg/mL FA; LPS and FA200 group: 10 μg/mL LPS + 200 μg/mL FA; LPS and FA400 group: 10 μg/mL LPS + 400 μg/mL FA.
Figure 3. Effects of different concentrations of FA on LPS-induced ferroptosis in IPEC-J2 cells. (A) Effects of different concentrations of FA on cell viability under LPS stimulation; (B) effects of different concentrations of FA on ROS under LPS stimulation; (C) effects of different concentrations of FA on MDA under LPS stimulation; (D) effects of different concentrations of FA on Fe2+ under LPS stimulation. CON group: control group; LPS group: 10 μg/mL LPS; LPS and FA50 group: 10 μg/mL LPS + 50 μg/mL FA; LPS and FA100 group: 10 μg/mL LPS + 100 μg/mL FA; LPS and FA200 group: 10 μg/mL LPS + 200 μg/mL FA; LPS and FA400 group: 10 μg/mL LPS + 400 μg/mL FA.

To further investigate how FA protects IPEC-J2 cells by regulating ferroptosis, Fer-1 was introduced for validation. As shown in Figure 4, compared with the LPS group, both the LPS and FA group and the LPS and Fer-1 group significantly reduced MDA content, ROS relative fluorescence intensity, and Fe2+ levels (p < 0.05). Meanwhile, they significantly increased the GSH/GSSG ratio, CAT and SOD activities, and T-AOC content (p < 0.05). These findings indicate that FA alleviates LPS-induced ferroptosis in IPEC-J2 cells by enhancing antioxidant capacity, maintaining iron homeostasis, and regulating glutathione metabolism. This effect is consistent with the ferroptosis-inhibitory activity of Fer-1. As shown in Figure 5, compared with the LPS group, the FA group, Fer-1 group, and LPS and Fer-1 group significantly upregulated the relative mRNA expression level of GPX4 (p < 0.05). The FA group, Fer-1 group, LPS and Fer-1 group, and LPS and FA group significantly upregulated the relative mRNA expression level of SLC7A11 (p < 0.05). In addition, the FA group, Fer-1 group, LPS and Fer-1 group, and LPS and FA group significantly downregulated the relative mRNA expression levels of TFRC and ACSL4 (p < 0.05). These results at the gene level indicate that LPS can induce ferroptosis in cells. Using Fer-1 as a positive control, the findings further demonstrate that FA can alleviate LPS-induced ferroptosis in IPEC-J2 cells.

Effects of FA on LPS-induced ferroptosis in IPEC-J2 cells. **(A–G)** Effects of different treatments on ROS relative fluorescence intensity, MDA content, GSH/GSSG ratio, CAT content, SOD activity, T-AOC content, and Fe<sup>2+</sup> content in IPEC-J2 cells. CON group: control group; LPS group: 10 μg/mL LPS; FA group: 50 μg/mL FA; Fer-1 group: 4 μmol/L Fer-1; LPS and FA group: 10 μg/mL LPS + 50 μg/mL FA; LPS and Fer-1 group: 10 μg/mL LPS + 4 μmol/L Fer-1.
Figure 4. Effects of FA on LPS-induced ferroptosis in IPEC-J2 cells. (A–G) Effects of different treatments on ROS relative fluorescence intensity, MDA content, GSH/GSSG ratio, CAT content, SOD activity, T-AOC content, and Fe2+ content in IPEC-J2 cells. CON group: control group; LPS group: 10 μg/mL LPS; FA group: 50 μg/mL FA; Fer-1 group: 4 μmol/L Fer-1; LPS and FA group: 10 μg/mL LPS + 50 μg/mL FA; LPS and Fer-1 group: 10 μg/mL LPS + 4 μmol/L Fer-1.
Effects of FA on ferroptosis-related marker genes in LPS-induced IPEC-J2 cells. **(A–D)** Effects of different treatments on the relative mRNA expression levels of GPX4, SLC7A11, TFRC, and ACSL4 in IPEC-J2 cells. CON group: control group; LPS group: 10 μg/mL LPS; FA group: 50 μg/mL FA; Fer-1 group: 4 μmol/L Fer-1; LPS and FA group: 10 μg/mL LPS + 50 μg/mL FA; LPS and Fer-1 group: 10 μg/mL LPS + 4 μmol/L Fer-1.
Figure 5. Effects of FA on ferroptosis-related marker genes in LPS-induced IPEC-J2 cells. (A–D) Effects of different treatments on the relative mRNA expression levels of GPX4, SLC7A11, TFRC, and ACSL4 in IPEC-J2 cells. CON group: control group; LPS group: 10 μg/mL LPS; FA group: 50 μg/mL FA; Fer-1 group: 4 μmol/L Fer-1; LPS and FA group: 10 μg/mL LPS + 50 μg/mL FA; LPS and Fer-1 group: 10 μg/mL LPS + 4 μmol/L Fer-1.

3.4 Comparative transcriptomics reveals the mechanism by which FA alleviates LPS-induced ferroptosis in IPEC-J2 cells

To clarify the molecular mechanisms by which FA alleviates LPS-induced intestinal epithelial cell injury through regulation of the ferroptosis pathway, RNA sequencing was performed in four groups: CON, LPS, FA, and FA and LPS. The sequencing quality was high. Q20 (base error rate <1%) and Q30 (base error rate <0.1%) were 99.23 and 96.19%, respectively. The overall base error rate was as low as 0.0118%. After removing reads containing poly-N sequences and adapter contamination, and filtering low-quality reads, about 41.51 million clean reads (99.34%) were obtained. The rRNA contamination rate was 0.773%, and the average GC content was 51.10%. These results indicate that the data met the requirements for high-quality sequencing analysis.

An expression matrix containing 35,682 genes was obtained from RNA sequencing. Compared with the CON group, the LPS group was used to establish a ferroptosis model. A total of 402 DEGs were identified, including 193 upregulated and 209 downregulated genes. Compared with the CON group, the FA group was used to evaluate the effect of ferulic acid alone on ferroptosis, and 2,799 DEGs were identified, including 1,350 upregulated and 1,449 downregulated genes. Compared with the CON group, the FA and LPS group was used to clarify the protective effects of FA through intervention in the above injury pathways. A total of 3,126 DEGs were identified, including 1,593 upregulated and 1,533 downregulated genes. Compared with the LPS group, the FA and LPS group was used to determine whether FA could specifically antagonize LPS-induced ferroptosis in IPEC-J2 cells by targeting core ferroptosis pathways and key gene expression. A total of 2,882 DEGs were identified, including 1,449 upregulated and 1,433 downregulated genes. Figure 6A shows the upregulated and downregulated genes in the comparisons of CON vs. LPS, CON vs. FA, CON vs. FA and LPS, and LPS vs. FA and LPS. In addition, co-expressed DEGs among CON vs. LPS, CON vs. FA and LPS, LPS vs. FA and LPS, and CON vs. FA were analyzed. As shown in the Venn diagram (Figure 6B), 162 DEGs were shared between CON vs. LPS and LPS vs. FA and LPS. Notably, 29 DEGs that were downregulated in CON vs. LPS were significantly upregulated in LPS vs. FA and LPS. In addition, 41 DEGs that were upregulated in CON vs. LPS were also significantly upregulated in LPS vs. FA and LPS (Table 2).

Differentially expressed gene (DEG) profiling based on RNA-seq analysis. **(A)** Number of upregulated and downregulated DEGs among different treatment group comparisons; **(B)** Venn diagram of DEGs. CON group: control group; FA group: 50 μg/mL FA; FA and LPS group: 10 μg/mL LPS + 50 μg/mL FA; LPS group: 10 μg/mL LPS.
Figure 6. Differentially expressed gene (DEG) profiling based on RNA-seq analysis. (A) Number of upregulated and downregulated DEGs among different treatment group comparisons; (B) Venn diagram of DEGs. CON group: control group; FA group: 50 μg/mL FA; FA and LPS group: 10 μg/mL LPS + 50 μg/mL FA; LPS group: 10 μg/mL LPS.

Table 2. Summary of co-expressed differentially expressed genes between the CON vs. LPS and LPS vs. LPS + (and) FA groups.

Gene IDGene nameFull names of gensCON vs. LPSCON vs. FACON vs. LPS and FALPS vs. LPS and FA
ENSSSCG00000027745ABCG1Atp binding cassette subfamily g member 1UPDownUpDOWN
ENSSSCG00000004597AQP9Aquaporin 9UPUpDownDOWN
ENSSSCG00000016185ARPC2Actin related protein 2/3 complex subunit 2UPDownDownDOWN
ENSSSCG00000018080ATP8Atp synthase f0 subunit 8UPDownUpDOWN
ENSSSCG00000036136BHLHE40Basic helix–loop–helix family member e40UPDownUpDOWN
ENSSSCG00000040779CBSCystathionine beta-synthaseUPDownUpDOWN
ENSSSCG00000021259CDACytidine deaminaseUPDownDownDOWN
ENSSSCG00000008094CHCHD5Coiled-coil-helix-coiled-coil-helix domain containing 5UPDownDownDOWN
ENSSSCG00000014924CTSCCathepsin cUPDownDownDOWN
ENSSSCG00000008953CXCL8C-x-c motif chemokine ligand 8UPDownUpDOWN
ENSSSCG00000011628DNAJC13Dnaj heat shock protein family (hsp40) member c13UPDownDownDOWN
ENSSSCG00000013147FAM111BFam111 trypsin like peptidase bUPUpUpDOWN
ENSSSCG00000052947GSTA1Glutathione s-transferase alpha 1UPDownDownDOWN
ENSSSCG00000039882HTRA2Htra serine peptidase 2UPDownUpDOWN
ENSSSCG00000027607IER3Immediate early response 3UPDownDownDOWN
ENSSSCG00000020970IL6Interleukin 6UPUpDownDOWN
ENSSSCG00000027205LPCAT3Lysophosphatidylcholine acyltransferase 3UPDownDownDOWN
ENSSSCG00000029698LRRC42Leucine rich repeat containing 42UPDownDownDOWN
ENSSSCG00000026981MRPS6Mitochondrial ribosomal protein s6UPDownDownDOWN
ENSSSCG00000003775MSH4Rab geranylgeranyltransferase subunit betaUPDownDownDOWN
ENSSSCG00000000859PARPBPParp1 binding proteinUPUpDownDOWN
ENSSSCG00000016884PELOPelota mrna surveillance and ribosome rescue factorUPDownDownDOWN
ENSSSCG00000011133PFKFB36-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3UPDownDownDOWN
ENSSSCG00000012440PGK1Phosphoglycerate kinase 1UPDownDownDOWN
ENSSSCG00000002747PMFBP1Polyamine modulated factor 1 binding protein 1UPUpDownDOWN
ENSSSCG00000014406PRELID2Preli domain containing 2UPDownDownDOWN
ENSSSCG00000036658PSMD8Prostaglandin e synthaseUPDownDownDOWN
ENSSSCG00000005688PTGESProstaglandin e synthaseUPDownDownDOWN
ENSSSCG00000010479RBP4Retinol binding protein 4UPDownUpDOWN
ENSSSCG00000036311RPAINRpa interacting proteinUPDownDownDOWN
ENSSSCG00000010554SCDStearoyl-coa desaturaseUPDownDownDOWN
ENSSSCG00000042767SERGEFSecretion regulating guanine nucleotide exchange factorUPDownDownDOWN
ENSSSCG00000021443SGK1Serum/glucocorticoid regulated kinase 1UPUpDownDOWN
ENSSSCG00000017925SLC16A11Solute carrier family 16 member 11UPDownUpDOWN
ENSSSCG00000021968SLC19A3Solute carrier family 19 member 3UPDownDownDOWN
ENSSSCG00000031954SLC39A10Solute carrier family 39 member 10UPDownDownDOWN
ENSSSCG00000034167SLC5A3Solute carrier family 5 member 3UPDownDownDOWN
ENSSSCG00000014818STARD10Star related lipid transfer domain containing 10UPDownDownDOWN
ENSSSCG00000062098TMEM218Transmembrane protein 218UPUpUpDOWN
ENSSSCG00000026695TNFSF14Tnf superfamily member 14UPDownDownDOWN
ENSSSCG00000030388UPP1Uridine phosphorylase 1UPDownDownDOWN
ENSSSCG00000011782ABCC5Atp binding cassette subfamily c member 5DOWNUpUpUP
ENSSSCG00000001725ADGRF5Adhesion g protein-coupled receptor f5DOWNUpUpUP
ENSSSCG00000009330ALOX5APArachidonate 5-lipoxygenase activating proteinDOWNUpDownUP
ENSSSCG00000024569ANO9Anoctamin 9DOWNUpUpUP
ENSSSCG00000035565CCDC134Coiled-coil domain containing 134DOWNDownUpUP
ENSSSCG00000004484COL12A1Collagen type xii alpha 1 chainDOWNUpUpUP
ENSSSCG00000004441DSEDermatan sulfate epimeraseDOWNUpDownUP
ENSSSCG00000040166FLRT2Fibronectin leucine rich transmembrane protein 2DOWNUpUpUP
ENSSSCG00000039025FREM1Fras1 related extracellular matrix 1DOWNUpUpUP
ENSSSCG00000009364FREM2Fras1 related extracellular matrix 2DOWNUpUpUP
ENSSSCG00000014920FZD4Frizzled class receptor 4DOWNUpUpUP
ENSSSCG00000016430GALNT11Polypeptide n-acetylgalactosaminyltransferase 11DOWNUpUpUP
ENSSSCG00000032241GPNMBGlycoprotein nmbDOWNUpUpUP
ENSSSCG00000039318HS3ST3B1Heparan sulfate-glucosamine 3-sulfotransferase 3b1DOWNUpUpUP
ENSSSCG00000004475HTR1B5-hydroxytryptamine receptor 1bDOWNUpUpUP
ENSSSCG00000026449KDM4ALysine demethylase 4aDOWNUpUpUP
ENSSSCG00000015555LAMC1Laminin subunit gamma 1DOWNUpUpUP
ENSSSCG00000007430NEURL2Neuralized e3 ubiquitin protein ligase 2DOWNDownDownUP
ENSSSCG00000006236NSMAFNeutral sphingomyelinase activation associated factorDOWNDownUpUP
ENSSSCG00000022676OPHN1Oligophrenin 1DOWNDownUpUP
ENSSSCG00000005665PHYHD1Phytanoyl-coa dioxygenase domain containing 1DOWNDownDownUP
ENSSSCG00000013276PRDM11Pr/set domain 11DOWNUpUpUP
ENSSSCG00000014224SEMA6ASemaphorin 6aDOWNUpUpUP
ENSSSCG00000005177SH3GL2Sh3 domain containing grb2 like 2, endophilin a1DOWNUpUpUP
ENSSSCG00000029815SRGAP1Slit-robo rho gtpase activating protein 1DOWNUpUpUP
ENSSSCG00000015073TAGLNTransgelinDOWNUpUpUP
ENSSSCG00000016457TAS2R41Taste 2 receptor member 41DOWNDownDownUP
ENSSSCG00000030018ZNF396Zinc finger protein 396DOWNDownDownUP
ENSSSCG00000003129ZSWIM9Zinc finger swim-type containing 9DOWNUpUpUP

KEGG pathway enrichment analysis was performed for the candidate DEGs identified in the four pairwise comparisons to explore the biological processes potentially affected by different treatments (Figures 7A–H). Figures 7A–D show the selected KEGG pathways associated with the biological processes of interest, whereas Figures 7E–H integrate Sankey diagrams and bubble plots to illustrate the relationships between DEGs and representative pathways. The identified pathways were mainly distributed across five KEGG categories, including environmental information processing, metabolism, cellular processes, organismal systems, and human diseases, with distinct enrichment patterns among the four comparisons. In the CON vs. FA comparison, nitrogen metabolism showed the most prominent enrichment signal, together with pathways related to IL-17, MAPK, HIF-1, JAK–STAT, and PI3K-Akt signaling. In the CON vs. LPS comparison, the enrichment profile was mainly associated with inflammatory and metabolic responses, including cytokine–cytokine receptor interaction, IL-17 signaling, retinol metabolism, arachidonic acid metabolism, MAPK signaling, and NF-κB signaling. In the CON vs. LPS + FA comparison, TNF signaling showed the strongest enrichment signal, accompanied by NOD-like receptor, IL-17, Toll-like receptor, and other inflammation-related pathways. Ferroptosis, arachidonic acid metabolism, NF-κB signaling, and PI3K-Akt signaling were also included among the biologically relevant pathways selected for exploratory analysis. In the LPS vs. LPS + FA comparison, prominent enrichment was observed in efferocytosis, neuroactive ligand–receptor interaction, calcium signaling, and cytokine–cytokine receptor interaction, while NF-κB, MAPK, PI3K-Akt, and arachidonic acid metabolism were selected as additional pathways of biological interest. Together, these enrichment patterns suggest that FA treatment may be associated with coordinated alterations in inflammatory signaling, lipid metabolism, oxidative stress, and ferroptosis-related processes under LPS challenge.

Functional annotation and pathway enrichment analysis. **(A–D)** KEGG pathway enrichment analysis of DEGs in four different comparisons, and **(E–H)** show the sankey diagrams of five key pathways in each group.
Figure 7. Functional annotation and pathway enrichment analysis. (A–D) KEGG pathway enrichment analysis of DEGs in four different comparisons, and (E–H) show the sankey diagrams of five key pathways in each group.

For the DEGs in the four comparisons, KEGG pathway enrichment analysis was performed to identify altered biological pathways under different treatments (Figures 7A–H). Figures 7E–H employ a combination of Sankey diagrams and bubble plots to illustrate the relationships between differentially expressed genes and significantly enriched key signaling pathways across comparisons of different treatment groups. All DEGs were mainly classified into five categories: environmental information processing, metabolism, cellular processes, organismal systems, and human diseases, and showed distinct enrichment patterns. DEGs in the CON vs. FA group were enriched in Nitrogen metabolism, MAPK, and JAK–STAT signaling pathways. DEGs in the LPS vs. FA and LPS group were mainly enriched in Efferocytosis, Neuroactive ligand-receptor interaction, Calcium signaling pathway, Cytokine-cytokine receptor interaction, Endocytosis, Apoptosis, Arachidonic acid metabolism, and Nucleotide metabolism. In the CON vs. LPS and CON vs. FA and LPS groups, which involved LPS stimulation, DEGs were widely enriched in key inflammation- and stress-related pathways, including TNF, NF-κB, and HIF-1 signaling pathways. Notably, in the CON vs. FA and LPS group, DEGs were enriched in the Ferroptosis pathway. In addition, DEGs in all four comparisons were enriched in IL-17 and PI3K-Akt signaling pathways.

To further investigate the interactions among DEGs, a protein–protein interaction (PPI) network was constructed based on the DEGs between the CON and LPS groups. As shown in Figure 8A, the network contained 152 nodes and 343 interaction pairs. Among them, genes such as UBB, HMGCS1, IL-6, and CXCL8 showed high connectivity. These hub genes may coordinately regulate key processes of ferroptosis. UBB may affect the stability of proteins such as GPX4 through the ubiquitin–proteasome system. HMGCS1 is a key enzyme in the mevalonate pathway, which provides essential metabolic support for maintaining GPX4 activity. IL-6 and CXCL8 are major pro-inflammatory factors. They can amplify oxidative stress and form a vicious cycle between inflammation and oxidation, thereby promoting lipid peroxidation and ferroptosis. A total of 30 hub genes with the highest connectivity were identified using cytoHubba. The top 15 hub genes formed two independent networks. These corresponded to the top two significant modules identified by MCODE, which included 9 nodes with 32 edges and 7 nodes with 21 edges, respectively (Figure 8B). These highly interconnected modules may represent subnetworks that jointly regulate cell death under LPS stimulation. To explore how FA modulates gene interactions, a PPI network was constructed based on 232 co-expressed DEGs shared between the CON vs. LPS and LPS vs. FA and LPS groups. A core interaction network was obtained. HMGCS1 showed interactions with multiple genes, including HMGCR, CXCL8, and IL18 (Figure 8C). These results suggest that FA may reshape the network by regulating the mevalonate pathway axis (HMGCS1/HMGCR) and inhibiting key inflammatory nodes (CXCL8/IL18), thereby coordinately suppressing ferroptosis.

Construction of protein–protein interaction (PPI) networks of DEGs. **(A)** PPI network of DEGs between the CON group and LPS group; **(B)** the top two significant modules extracted from the top 30 DEGs with the highest connectivity in the PPI network (MCODE scores of 9 and 7, respectively); **(C)** PPI network constructed based on 232 co-expressed genes between the CON vs. LPS and LPS vs. FA and LPS groups. Red indicates upregulated genes in **A,B**, and blue indicates downregulated genes. Node size represents the number of interacting genes in **A,C**. The interaction score threshold was set at 0.4 in **A–C**.
Figure 8. Construction of protein–protein interaction (PPI) networks of DEGs. (A) PPI network of DEGs between the CON group and LPS group; (B) the top two significant modules extracted from the top 30 DEGs with the highest connectivity in the PPI network (MCODE scores of 9 and 7, respectively); (C) PPI network constructed based on 232 co-expressed genes between the CON vs. LPS and LPS vs. FA and LPS groups. Red indicates upregulated genes in A,B, and blue indicates downregulated genes. Node size represents the number of interacting genes in A,C. The interaction score threshold was set at 0.4 in A–C.

To verify the reliability of the transcriptomic sequencing data, eight randomly selected key genes identified from transcriptome analysis were validated via RT-qPCR. As shown in Figure 9, compared with the LPS group, the LPS and FA group significantly upregulated the relative mRNA expression levels of ABCG1, CXCL8, LPCAT3, IL-6, PFKFB3, and SCD (p < 0.05). The LPS and FA group also significantly upregulated the relative mRNA expression levels of ALOX5AP and TAGLN (p < 0.05), which was consistent with the transcriptomic results. These findings indicate that the RT-qPCR validation results are consistent with the RNA sequencing data, confirming the reliability and validity of the transcriptomic analysis.

Validation of RNA-seq results by RT-qPCR. **(A–H)** Effects of different treatments on the relative mRNA expression levels of *ABCG1*, *CXCL8*, *LPCAT3*, *IL-6*, *PFKF83*, *SCD*, *ALOX5AP*, and *TAGLN* in IPEC-J2 cells. CON group: control group; LPS group: 10 μg/mL LPS; FA group: 50 μg/mL FA; LPS and FA group: 10 μg/mL LPS + 50 μg/mL FA.
Figure 9. Validation of RNA-seq results by RT-qPCR. (A–H) Effects of different treatments on the relative mRNA expression levels of ABCG1, CXCL8, LPCAT3, IL-6, PFKF83, SCD, ALOX5AP, and TAGLN in IPEC-J2 cells. CON group: control group; LPS group: 10 μg/mL LPS; FA group: 50 μg/mL FA; LPS and FA group: 10 μg/mL LPS + 50 μg/mL FA.

4 Discussion

LPS from Gram-negative bacteria causes intestinal epithelial damage in livestock and poultry, but information regarding LPS-triggered ferroptosis in intestinal epithelial cells is scarce (14–16). FA has been reported to possess potent antioxidant and intestinal-protective properties, suggesting its potential application in improving intestinal health. (17, 18, 44) Therefore, this study employed biochemical assays and RNA sequencing to investigate whether and how FA alleviates LPS-induced ferroptosis in IPEC-J2 cells, providing a theoretical basis for the utilization of FA as a functional feed additive. The present study verified that FA inhibits LPS-induced ferroptosis in IPEC-J2 cells. LPS gave rise to canonical ferroptotic characteristics, manifested by decreased cell viability, increased ROS, MDA and Fe2+ levels, decreased GPX4/SLC7A11 expression and increased TFRC/ACSL4 expression. FA exhibited protective effects comparable to Fer-1 against LPS-evoked ferroptosis. Transcriptomic analysis suggested that FA partially reversed LPS-induced transcriptional alterations, with candidate DEGs associated with several biologically relevant pathways, including ferroptosis, TNF, NF-κB, arachidonic acid metabolism, and PI3K-Akt signaling. PPI analysis highlighted HMGCS1, CXCL8, and IL-18 as potential hub genes. In brief, FA alleviates LPS-triggered ferroptosis by regulating glutathione metabolism, iron homeostasis and lipid peroxidation, which is closely associated with the joint regulation of inflammation, lipid metabolism and ferroptosis-related signaling.

4.1 LPS induces ferroptosis in IPEC-J2 cells through iron metabolism imbalance and lipid peroxidation

MDA is a characteristic metabolite of lipid peroxidation, while Fe2+ imbalance constitutes a fundamental prerequisite for the occurrence of ferroptosis, and reactive ROS serve as the core mediators driving the ferroptotic process (19–21). Previous studies have demonstrated that LPS can induce a pronounced ferroptotic phenotype in BEAS-2B cells, characterized by Fe2+ accumulation and elevated MDA levels (22). These findings are consistent with the results of the present study, in which LPS treatment significantly reduced the viability of IPEC-J2 cells and increased ROS relative fluorescence intensity, as well as MDA and Fe2+ contents. Fer-1 is a classical inhibitor of ferroptosis (23). In this study, the addition of Fer-1 effectively reversed the pathological changes in the above indicators, suggesting that LPS successfully induced ferroptosis in IPEC-J2 cells. This is consistent with the findings of Pengfei L (24), who reported that Fer-1 can effectively alleviate LPS-induced ferroptosis in BEAS-2B cells. To elucidate the potential regulatory network underlying LPS-induced ferroptosis in IPEC-J2 cells, transcriptomic analysis was performed in this study. A total of 402 DEGs were identified between the CON and LPS groups. KEGG pathway analysis indicated that these candidate DEGs were associated with several biologically relevant pathways, including TNF, NF-κB, arachidonic acid metabolism, MAPK, and PI3K-Akt signaling pathways, consistent with previous studies (25). Activation of the TNF/NF-κB pathway drives a sustained inflammatory cascade (14). The arachidonic acid metabolism pathway generates key lipid precursors for ferroptosis. Abnormal metabolism in this pathway directly promotes lipid peroxidation and thereby regulates the progression of ferroptosis (26). The MAPK signaling pathway responds to oxidative stress signals. It disrupts intracellular iron homeostasis and ROS balance, thus mediating the initiation and progression of ferroptosis (27). The PI3K-AKT pathway regulates cellular antioxidant capacity and lipid metabolism. In this way, it bidirectionally controls cellular sensitivity to ferroptotic stimuli (28). These pathways act together to aggravate ferroptosis. Their concurrent enrichment suggests that LPS-induced ferroptosis in IPEC-J2 cells is not driven by a single factor. Instead, it results from the combined effects of multiple stress-related networks, including inflammation, hypoxia, and oxidative stress. At the transcriptomic level, this further confirms that LPS can induce ferroptosis in IPEC-J2 cells, which is consistent with the findings of Guo J. (29). To further verify this conclusion, ferroptosis-related genes were examined. GPX4 and SLC7A11 are anti-ferroptotic genes, whereas TFRC and ACSL4 are pro-ferroptotic genes (30). The results showed that LPS stimulation significantly downregulated the expression of GPX4 and SLC7A11, while significantly upregulated the expression of TFRC and ACSL4. Notably, the addition of Fer-1 effectively reversed these gene expression changes. These findings further confirm that LPS induces ferroptosis in IPEC-J2 cells.

4.2 Ferulic acid protects IPEC-J2 cells against ferroptosis through modulation of oxidative stress and iron metabolism

As a natural phenolic acid, FA is known for its antioxidant properties and has potential to maintain intestinal health (31, 45). In this study, we examined the direct effects of FA on IPEC-J2 cells. The results showed that an appropriate concentration of FA exerted clear protective effects. This protection is closely related to the regulation of key processes involved in ferroptosis. The GSH/GSSG ratio reflects the cellular redox balance. An increased ratio enhances the glutathione-mediated antioxidant system and upregulates GPX4 activity, thereby slowing ferroptosis (32). In this study, an appropriate concentration of FA not only directly scavenged ROS and reduced MDA levels, but also significantly increased the intracellular GSH/GSSG ratio and decreased Fe2+ content. SOD removes excess reactive ROS and alleviates oxidative damage, thereby inhibiting ferroptosis (33). CAT degrades toxic hydrogen peroxide and maintains redox homeostasis, effectively blocking ferroptosis progression (34). However, high concentrations of FA (500–1,000 μg/mL) showed inhibitory effects on SOD and CAT activities. This suggests that excessive FA may disrupt normal enzyme function and induce pro-oxidant effects, which is consistent with the findings of Krawczyk M (35). At the molecular level, FA upregulated the expression of GPX4 and SLC7A11, while downregulated TFRC and ACSL4. This pattern is consistent with the effect of Fer-1, suggesting that FA can systematically reverse ferroptosis. Transcriptomic analysis further supported these findings. A total of 2,799 DEGs were identified in the CON vs. FA comparison. KEGG enrichment showed that these genes were mainly involved in PI3K-AKT, MAPK, and JAK–STAT pathways. These results indicate that an appropriate concentration of FA alone does not damage IPEC-J2 cells. Instead, it enhances cellular homeostasis by regulating stress-related pathways. This is consistent with the findings of Xiangyi M et al. (36), who reported that FA alone can enhance the antioxidant capacity of IPEC-J2 cells. Therefore, the present study indicates that an appropriate concentration of FA can inhibit ferroptosis in IPEC-J2 cells.

4.3 Ferulic acid alleviates LPS-induced ferroptosis in IPEC-J2 cells through regulation of the PI3K-AKT pathway and ferroptosis-related genes

To elucidate the mechanism by which FA alleviates LPS-induced ferroptosis in IPEC-J2 cells, key related indicators were examined in this study. The results showed that FA intervention markedly reversed the key pathological changes induced by LPS. It reduced MDA levels and ROS relative fluorescence intensity, indicating effective inhibition of lipid peroxidation. It also increased the GSH/GSSG ratio and enhanced the activities of antioxidant enzymes such as CAT and SOD, suggesting an overall improvement in the cellular endogenous antioxidant defense system. In addition, FA significantly decreased intracellular Fe2+ accumulation, thereby weakening the primary driving force of ferroptosis at its source. Notably, the protective effects of FA were similar to those of Fer-1. These results confirm that FA exerts anti-ferroptotic effects by regulating redox balance and iron homeostasis. This finding is consistent with the conclusions of Tugba K and Li N X (37, 38), and further supports the view that phenolic acids can counteract ferroptosis by enhancing antioxidant capacity and modulating iron metabolism. At the gene level, FA significantly upregulated the expression of SLC7A11, thereby promoting GSH synthesis. At the same time, it downregulated the expression of TFRC and ACSL4. Notably, the effects of FA were similar to those of Fer-1, further indicating that FA can alleviate ferroptosis. Studies have shown that the antioxidant L-theanine can effectively restore gene expression imbalance in Erastin-induced HT22 cells. It reverses the upregulation of TFRC and ACSL4, as well as the downregulation of SLC7A11. This is consistent with the findings of the present study (39). Transcriptomic analysis identified the ferroptosis pathway as one of the biologically relevant pathways associated with the candidate DEGs in the CON vs. LPS + FA comparison. DEGs in the LPS vs. FA and LPS group were mainly enriched in pathways such as arachidonic acid metabolism and cytokine–cytokine receptor interaction. Arachidonic acid is a key substrate for lipid peroxidation, and its metabolic dysregulation is a critical driver of ferroptosis (40). FA intervention can reverse abnormalities in this pathway, thereby directly blocking a core step in the ferroptotic process. Although the ferroptosis pathway was significantly enriched in the CON vs. FA + LPS comparison, it was not identified as a significantly enriched pathway in the LPS vs. LPS + FA comparison, which represents the most direct comparison for evaluating the protective effect of FA against LPS-induced ferroptosis. This discrepancy may be attributed to the inherent characteristics of pathway enrichment analysis. KEGG enrichment relies on the coordinated alteration of multiple genes within a predefined pathway and is highly dependent on the number and statistical significance of differentially expressed genes. Therefore, selective regulation of several key ferroptosis-related genes by FA may not be sufficient to achieve pathway-level enrichment significance. Instead, FA may alleviate ferroptosis by precisely modulating critical molecular nodes involved in iron metabolism, lipid peroxidation, and antioxidant defense rather than broadly remodeling the entire ferroptosis pathway. Importantly, the subsequent validation experiments confirmed that FA significantly reversed LPS-induced ferroptosis-related changes, including the regulation of SLC7A11, TFRC, and ACSL4, further supporting the anti-ferroptotic role of FA. Similarly, salvianolic acid has been reported to alleviate ferroptosis in mice by inhibiting lipid peroxidation (41). A total of 70 co-expressed DEGs were identified between the CON vs. LPS and LPS vs. FA and LPS groups. Among them, 29 genes that were downregulated by LPS were significantly upregulated after FA intervention, while 41 genes that were upregulated by LPS showed the opposite trend. These results suggest that these DEGs represent a core set of target genes underlying the protective effects of FA. Moreover, the RT-qPCR validation results were consistent with the transcriptomic data, confirming the reliability and validity of the sequencing analysis. In the PPI network analysis, a core interaction network was constructed based on co-expressed DEGs. Within this network, HMGCS1 showed close interactions with genes such as HMGCR, CXCL8, and IL18. As a key gene involved in lipid metabolism, downregulating expression of HMGCS1 can exacerbate lipid peroxidation (42). FA may inhibit ferroptosis by upregulating HMGCS1 expression. CXCL8 and IL18 are important inflammatory factors. Their interaction may help alleviate the promoting effect of inflammation on ferroptosis. This finding is consistent with the results reported by Zhou H T and Liang Y et al. (43, 46). The enrichment of ferroptosis-related pathways in transcriptomic analysis, together with the involvement of the PI3K-Akt pathway, suggests that FA may regulate the expression of key genes such as HMGCS1 via the PI3K-Akt signaling pathway. On one hand, it suppresses TNF/NF-κB-mediated inflammatory responses. On the other hand, it blocks abnormalities in arachidonic acid metabolism and iron accumulation. Ultimately, these effects contribute to protection against ferroptosis.

This study integrated biochemical assays, ferroptosis-related gene validation, transcriptomic analysis, and PPI network analysis to investigate the protective effects of FA against LPS-induced ferroptosis in IPEC-J2 cells. Potential regulatory targets, including the HMGCS1/HMGCR axis and CXCL8/IL18-related inflammatory network, were identified, and eight candidate DEGs were independently validated by RT-qPCR. However, several limitations remain. DEGs were screened using a nominal p-value < 0.05 without multiple-testing correction or a predefined fold-change threshold; therefore, the transcriptomic and network results should be considered exploratory. In addition, β-actin was used as a single reference gene without stability validation under LPS/FA treatment, the identified candidate targets lack functional validation, and only a single intervention time point was examined. Future studies using more stringent transcriptomic criteria, validated reference genes, functional experiments, time-course designs, and in vivo models are warranted.

5 Conclusion

LPS induced ferroptosis-associated injury in IPEC-J2 cells, characterized by oxidative stress, lipid peroxidation, iron accumulation, impaired antioxidant capacity, and altered expression of ferroptosis-related genes. FA supplementation effectively alleviated these changes, and its protective effects were comparable to those of the ferroptosis inhibitor Fer-1. Transcriptomic and network analyses further suggested that inflammatory signaling, lipid metabolism, and ferroptosis-related processes, including NF-κB, PI3K-Akt, arachidonic acid metabolism, and potential regulatory nodes such as UBB and HMGCS1, may be involved in the protective effects of FA. These findings provide new insights into the potential molecular mechanisms by which FA protects intestinal epithelial cells against LPS-induced ferroptosis and provide a preliminary theoretical basis for its potential application in maintaining intestinal health in livestock and poultry. Further in vivo and functional studies are required to validate these proposed mechanisms.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2026.1950560/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Oluwakamisi Festus Akinmoladun, The University of the South Pacific - Samoa Campus, Samoa

Reviewed by: Yuan-Yu Lin, National Taiwan University, Taiwan

Margaret Zaitoun, Anhui Medical University, China

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the National Natural Science Foundation of China (32460842), the Agricultural Science and Technology Innovation Program of China (CAAS-ZDRW202502 and ASTIP-IAS13) and the Earmarked Fund for China Agriculture Research System of MOF and MARA (CARS-38-02).

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ncbi.nlm.nih.gov/, PRJNA1462534.

Ethics approval

Ethical approval was not required for the studies on animals in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.

Author contributions

JC: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing. HL: Investigation, Validation, Writing – review & editing. YN: Investigation, Validation, Writing – review & editing. TG: Investigation, Validation, Writing – review & editing. XW: Investigation, Validation, Writing – review & editing. YZ: Investigation, Validation, Writing – review & editing. WW: Investigation, Validation, Writing – review & editing. YX: Investigation, Validation, Writing – review & editing. YW: Conceptualization, Funding acquisition, Supervision, Validation, Writing – review & editing. MC: Funding acquisition, Supervision, Validation, Writing – review & editing.

References

  1. 1. Wanapat M, Prachumchai R, Dagaew G, Matra M, Phupaboon S, Sommai S. Potential use of seaweed as a dietary supplement to mitigate enteric methane emission in ruminants. Sci Total Environ. (2024): 931173015-173015. doi: doi:10.1016/j.scitotenv.2024.173015
  2. 2. Ding S, Yan W, Ma Y, Fang J. The impact of probiotics on gut health via alternation of immune status of monogastric animals. Anim Nutr. (2020) 7: 24 – 30. doi:, doi:10.1016/j.aninu.2020.11.004
  3. 3. Tume R, El Sherbiny S, Bono R, Gautier T, Pais de Barros JP, Meroño T. The balance between proinflammatory, “bad”, and immunomodulatory, “good”, lipopolysaccharide for understanding gut-derived systemic inflammation. Front Immunol. (2025) 16: 161588129 – 9. doi:, doi:10.3389/fimmu.2025.1588129
  4. 4. Wang Q, Wang F, Zhou Y, Li X, Xu S, Jin Q. Bacillus amyloliquefaciens SC06 relieving intestinal inflammation by modulating intestinal stem cells proliferation and differentiation via AhR/STAT3 pathway in LPS-challenged piglets. J Agric Food Chem. (2024) 72: 6096 – 109. doi:, doi:10.1021/acs.jafc.3c05956
  5. 5. Wongkrasant P, Pongkorpsakol P, Ariyadamrongkwan J, Meesomboon R, Satitsri S, Pichyangkura R. A prebiotic fructo-oligosaccharide promotes tight junction assembly in intestinal epithelial cells via an AMPK-dependent pathway. Biomed Pharmacother. (2020) 129: 110415. doi:, doi:10.1016/j.biopha.2020.110415
  6. 6. Lan J, Dou X, Li J, Yang Y, Xue C, Wang C. L-arginine ameliorates lipopolysaccharide-induced intestinal inflammation through inhibiting the TLR4/NF-κB and MAPK pathways and stimulating β-defensin expression in vivo and in vitro. J Agric Food Chem. (2020) 68: 2648 – 63. doi:, doi:10.1021/acs.jafc.9b07611
  7. 7. Hadian K, Stockwell RB. SnapShot: Ferroptosis. Cell. (2020) 181: 1188 – 1188.e1. doi:, doi:10.1016/j.cell.2020.04.039
  8. 8. Eikan M, Marcus C. Nutritional and metabolic control of ferroptosis. Annu Rev Nutr. (2022): 42275-309. doi: doi:10.1146/annurev-nutr-062320-114541
  9. 9. Luo S, Zeng Y, Chen B, Yan J, Ma F, Zhuang G. Vitamin E and GPX4 cooperatively protect treg cells from ferroptosis and alleviate intestinal inflammatory damage in necrotizing enterocolitis. Redox Biol. (2024) 75: 103303. doi:, doi:10.1016/j.redox.2024.103303
  10. 10. Chen YT, Liu CH, Lin YY. Involvement of ferroptosis in lipopolysaccharide-induced intestinal inflammation in broilers. Poult Sci. (2025) 104: 105512. doi:, doi:10.1016/j.psj.2025.105512
  11. 11. Qu Z, Zeng J, Zeng L, Li X, Zhang F. Esculetin triggers ferroptosis via inhibition of the Nrf2-xCT/GPx4 axis in hepatocellular carcinoma. Chin J Nat Med. (2025) 23: 443 – 56. doi:, doi:10.1016/S1875-5364(25)60853-3
  12. 12. Wang W, Li H, Wang Y, Yin N, Chen J, Niu Y. Ferulic acid protects against LPS-induced sheep hepatocytes oxidative damage via activating the GSH-GPX4 pathway and inhibiting lipid metabolism-mediated ferroptosis. Antioxidants. (2025) 14: 1185 – 5. doi:, doi:10.3390/antiox14101185
  13. 13. Arocho A, Chen B, Ladanyi M, Pan Q. Validation of the 2-DeltaDeltaCt calculation as an alternate method of data analysis for quantitative PCR of BCR-ABL P210 transcripts. Diagn Mol Pathol. (2006) 15: 56 – 61. doi:, doi:10.1097/00019606-200603000-00009
  14. 14. Ruan D, Wu S, Fouad AM, Zhu Y, Huang W, Chen Z. Curcumin alleviates LPS-induced intestinal homeostatic imbalance through reshaping gut microbiota structure and regulating group 3 innate lymphoid cells in chickens. Food Funct. (2022) 13: 11811 – 24. doi:, doi:10.1039/d2fo02598a
  15. 15. Tao D, Dong Y, Che D, Wang Z, Zheng Y, Han R. Acanthopanax senticosus polysaccharide alleviates LPS-induced intestinal inflammation in piglets by gut microbiota and hyodeoxycholic acid regulation. Int J Biol Macromol. (2025) 307: 141467. doi:, doi:10.1016/j.ijbiomac.2025.141467
  16. 16. Zhang F, Yue Y, Chen J, Xiao P, Ma H, Feng J. Albumen exosomes alleviate LPS-induced inflammation of intestinal epithelial cells via miR-22/ATM/p53/NF-κB axis. Int J Biol Macromol. (2024) 267: 131241. doi: doi:10.1016/j.ijbiomac.2024.131241
  17. 17. Shao XY, Wu P, Jiang WD, Liu Y, Shi HQ, Zhang RN. An emerging role of ferulic acid on sub-adult grass carp (Ctenopharyngodon idellus): increasing protein digestion and regulating amino acid and oligopeptide transporters expression. Aquaculture Reports. (2024) 39: 102438 – 8. doi:, doi:10.1016/j.aqrep.2024.102438
  18. 18. Zheng M, Liu Y, Zhang G, Yang Z, Xu W, Chen Q. The antioxidant properties, metabolism, application and mechanism of ferulic acid in medicine, food, cosmetics, livestock and poultry. Antioxidants. (2024) 13: 853 – 3. doi:, doi:10.3390/antiox13070853
  19. 19. Han WM, Hong YX, Xiao GS, Wang RY, Li G. NMDARs activation regulates endothelial ferroptosis via the PP2A-AMPK-HMGB1 axis. Cell Death Discovery. (2024) 10: 34 – 4. doi:, doi:10.1038/s41420-023-01794-3
  20. 20. Zhang B, Li L, Wang N, Zhu Z, Wang M, Tan WP. A new pathway for ferroptosis regulation: the PRMTs. Int J Biol Macromol. (2024) 285. doi:, doi:10.1016/j.ijbiomac.2024.138143
  21. 21. Zhang Z, Yang J, Zhou Q, Zhong S, Liu J, Zhang X. The cGAS−STING-mediated ROS and ferroptosis are involved in manganese neurotoxicity. J Environ Sci. (2025) 152: 71 – 86. doi:, doi:10.1016/j.jes.2024.05.003
  22. 22. Li J, Lu K, Sun F, Tan S, Zhang X, Sheng W. Panaxydol attenuates ferroptosis against LPS-induced acute lung injury in mice by Keap1-Nrf2/HO-1 pathway. J Transl Med. (2021) 19: 96 – 6. doi:, doi:10.1186/s12967-021-02745-1
  23. 23. Miotto G, Rossetto M, Di Paolo ML, Orian L, Venerando R, Roveri A. Insight into the mechanism of ferroptosis inhibition by ferrostatin-1. Redox Biol. (2020) 28: 101328. doi:, doi:10.1016/j.redox.2019.101328
  24. 24. Liu P, Feng Y, Li H, Chen X, Zhao L. Ferrostatin-1 alleviates lipopolysaccharide-induced acute lung injury via inhibiting ferroptosis. Cell Mol Biol Lett. (2020) 25: 10. doi:, doi:10.1186/s11658-020-00205-0
  25. 25. Pathak JL, Fang Y, Chen Y, Ye Z, Guo X, Yan Y. Downregulation of macrophage-specific act-1 intensifies periodontitis and alveolar bone loss possibly via TNF/NF-κB signaling. Front Cell Dev Biol. (2021) 9: 9628139 – 9. doi:, doi:10.3389/fcell.2021.628139
  26. 26. Wen W, Xu Y, Qian W, Huang L, Gong J, Li Y. PUFAs add fuel to Crohn's disease-associated AIEC-induced enteritis by exacerbating intestinal epithelial lipid peroxidation. Gut Microbes. (2023) 15: 2265578 – 8. doi:, doi:10.1080/19490976.2023.2265578
  27. 27. Xu D, Xie Y, Cheng J, He D, Liu J, Fu S. Amygdalin alleviates DSS-induced colitis by restricting cell death and inflammatory response, maintaining the intestinal barrier, and modulating intestinal flora. Cells. (2024) 13: 444. doi:, doi:10.3390/cells13050444
  28. 28. Hao J, Chen Q, Feng Y, Jiang Q, Sun H, Deng B. Combination treatment with FAAH inhibitors/URB597 and ferroptosis inducers significantly decreases the growth and metastasis of renal cell carcinoma cells via the PI3K-AKT signaling pathway. Cell Death Dis. (2023) 14: 247 – 7. doi:, doi:10.1038/s41419-023-05779-z
  29. 29. Guo J, Chen X, Zhou M, Yu X, Zhu H, Xiao K. Flaxseed oil attenuates intestinal damage by regulating ferroptosis signaling pathway following LPS challenge in piglets. Mol Nutr Food Res. (2024) 68: e2400199. doi:, doi:10.1002/mnfr.202400199
  30. 30. Zhang Y, Jiang Y, Li Y, Wu F, Chen G, Lu Z. SLC7A11 alleviates diquat-induced neurotoxicity by inhibiting ferroptosis and preserving mitochondrial function. Ecotoxicol Environ Saf. (2026) 309: 119694. doi:, doi:10.1016/J.ECOENV.2026.119694
  31. 31. Li Y, Han S, Zhao Y, Yan J, Luo K, Li F. A redox-triggered polymeric nanoparticle for disrupting redox homeostasis and enhanced ferroptosis. Small (Weinheim an der Bergstrasse, Germany). (2024) 21: e2404299. doi: doi:10.1002/smll.202404299
  32. 32. He Q, Lin Y, Chen B, Chen C, Zeng J, Dou X. Vitamin K2 ameliorates osteoarthritis by suppressing ferroptosis and extracellular matrix degradation through activation GPX4's dual functions. Biomed Pharmacother. (2024) 175: 175116697 – 7. doi:, doi:10.1016/j.biopha.2024.116697
  33. 33. Wang X, Shen T, Lian J, Deng K, Qu C, Li E. Resveratrol reduces ROS-induced ferroptosis by activating SIRT3 and compensating the GSH/GPX4 pathway. Mol Med. (2023) 29: 137 – 7. doi: doi:10.1186/s10020-023-00730-6
  34. 34. Guan Z, Jin X, Guan Z, Liu S, Tao K, Luo L. The gut microbiota metabolite capsiate regulate SLC2A1 expression by targeting HIF-1α to inhibit knee osteoarthritis-induced ferroptosis. Aging Cell. (2023) 22: e13807 – 7. doi:, doi:10.1111/acel.13807
  35. 35. Krawczyk M, Burzynska-Pedziwiatr I, Wozniak LA, Bukowiecka-Matusiak M. Impact of polyphenols on inflammatory and oxidative stress factors in diabetes mellitus: nutritional antioxidants and their application in improving antidiabetic therapy. Biomolecules. (2023) 13: 1402 – 2. doi:, doi:10.3390/biom13091402
  36. 36. Meng X, Yu W, Duan N, Wang Z, Shen Y, Wu S. Protective effects of Ferulic acid on Deoxynivalenol-induced toxicity in IPEC-J2 cells. Toxins. (2022) 14: 275 – 5. doi:, doi:10.3390/toxins14040275
  37. 37. Tugba KAPS, O G L U. Upregulation of Nrf2 signalling and the inhibition of erastin-induced ferroptosis by ferulic acid in MIN6 cells. Int J Mol Sci. (2022) 23: 15886 – 6. doi: doi:10.3390/ijms232415886
  38. 38. Li XN, Shang NY, Kang YY, Sheng N, Lan JQ, Tang JS. Caffeic acid alleviates cerebral ischemic injury in rats by resisting ferroptosis via Nrf2 signaling pathway. Acta Pharmacol Sin. (2024) 45: 248 – 67. doi:, doi:10.1038/s41401-023-01177-5
  39. 39. Huang X, Lu X, Wu Z, Wu H, Xie Z, Cao Y. L-Theanine improves learning and memory through attenuation of NOX4-mediated ferroptosis in hippocampal neurons of sleep-deprived mice. Phytother Res. (2025) 39: 3952 – 65. doi:, doi:10.1002/ptr.70048
  40. 40. Xue P, Zhang J, Wang Z, Wu L, Tang J, Yang R. Arachidonic acid induces ferroptosis in hepatocellular carcinoma via the SIRT5-ACSL4/LPCAT3/ALOX15 axis, leading to lipid peroxidation and mitochondrial dysfunction. Phytomedicine: Int J Phytother Phytopharmacol. (2025) 148: 157450. doi:, doi:10.1016/j.phymed.2025.157450
  41. 41. Shi Y, Yan D, Nan C, Sun Z, Zhuo Y, Huo H. Salvianolic acid a inhibits ferroptosis and protects against intracerebral hemorrhage. Sci Rep. (2024) 14: 12427 – 7. doi:, doi:10.1038/s41598-024-63277-4
  42. 42. Xia C, Sun X, Wang Y, Min J, Zhang W, Wei C. Synergistic targeting of FASN and HMGCS1 by cerulenin enhances tumor cell ferroptosis sensitivity through rewiring lipid metabolism and blocking GPX4 biosynthesis. Cancer Lett. (2025) 633: 217992. doi:, doi:10.1016/j.canlet.2025.217992
  43. 43. Zhou HT, Huang J, Liu YK, Wang JH, Wang J. The emerging role of ferroptosis in the pathological development and progression of sepsis. Mil Med Res. (2025) 12: 81 – 1. doi:, doi:10.1186/s40779-025-00665-5
  44. 44. Li D, Rui YX, Guo SD, Luan F, Zeng N. Ferulic acid: a review of its pharmacology, pharmacokinetics and derivatives. Life Sci. (2021) 284: 119921. doi:, doi:10.1016/j.lfs.2021.119921
  45. 45. Ghasemi-Dehnoo M, Amini-Khoei H, Lorigooini Z, AnjomShoa M, Rafieian-Kopaei M. Ferulic acid ameliorates ulcerative colitis in a rat model via the inhibition of two LPS-TLR4-NF-κB and NF-κB-INOS-NO signaling pathways and thus alleviating the inflammatory, oxidative and apoptotic conditions in the colon tissue. Inflammopharmacology. (2023) 31: 2587 – 97. doi:, doi:10.1007/s10787-023-01277-y
  46. 46. Liang Y, Qi J, Yu D, Wang Z, Li W, Long F. Ferulic acid alleviates lipid and bile acid metabolism disorders by targeting FASN and CYP7A1 in iron overload-treated mice. Antioxidants. (2024) 13: 1277 – 7. doi:, doi:10.3390/antiox13111277

Citation

Chen, J., et al. (2026). Comparative transcriptomic analysis reveals the protective mechanism of ferulic acid against lipopolysaccharide-induced ferroptosis in IPEC-J2 cells. Frontiers in Veterinary Science, Volume 13. https://doi.org/
Received
Accepted
Published
Volume
13 - 2026

Copyright

Copyright © 2026 Chen, Li, Niu, Guo, Wang, Zhang, Wang, Xu, Wang and Chu.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Yuan Wang; Mingxing Chu

Article metrics

Views
0
Downloads
0
Citations
0
Shares
0

Related articles

Original ResearchCharacteristics of cattle in chronic pens at 17 North American feedlotsEmiline R. Sundman, Renée D. Dewell, Suzanne T. Millman, Karen Schwartzkopf-Genswein, Sarah E. Erickson, Anna K. Johnson, Calvin W. Booker, Tye Perrett, Ryan Rademacher, Daniel U. Thomson, Grant A. DewellOriginal ResearchSeasonal runoff-driven transport of oxytetracycline from swine feces and its effects on soil bacteria and antimicrobial resistance riskJoaquín Mozo, Julieta María Decundo, Guadalupe Martínez, Susana Nelly Dieguez, Alejandro Luis Soraci, Denisa Soledad Pérez GaudioOriginal ResearchAn update on freshwater gastropods serving as intermediate hosts of amphistomes infecting livestock in South AfricaIgnore Nyagura, Mokgadi Pulane Malatji, Sophy Nukeri, Madeline Siyasiziwe Sibula, Philile Ignecious Ngcamphalala, Samson Mukaratirwa