Abstract
Objective: To investigate the role of the ferroptosis pathway in ulcerative colitis (UC) and conduct molecular validation. Methods: The UC dataset GSE3365 was downloaded from the GEO database, and differentially expressed genes were identified after data correction. Ferroptosis-related genes were obtained from the FerrDb database and intersected with the differentially expressed genes. Gene ontology (GO) and KEGG enrichment analyses were performed. A protein-protein interaction (PPI) network was constructed, leading to the identification of four hub genes. Thirty mice were randomly assigned to control, UC, and Ferrostatin-1, (Fer-1) groups (10 mice per group). UC was induced in the UC and Fer-1 groups using sodium dextran sulfate, and Fer-1, an iron death inhibitor, was injected into the Fer-1 group. Pathologic changes in colonic tissues were observed by H&E staining, serum TNF-α levels were measured with ELISA, and RT-qPCR was used to detect the expression of hub genes. Results: A total of 24 ferroptosis-related genes were identified in UC. GO enrichment analysis showed that these genes were involved in oxidative stress response, glucocorticoid response, and reactive oxygen species metabolism. PPI network analysis identified HSP90AA1, SNCA, TLR4, and PTGS2 as hub genes. Pathologic changes were alleviated in the Fer-1 group compared with the UC group. Serum TNF-α levels were significantly higher in the UC and Fer-1 groups than in the control group, with the UC group showing higher levels than the Fer-1 group (both P<0.05). The expression of HSP90AA1, SNCA, TLR4, and PTGS2 mRNA was significantly higher in UC and Fer-1 groups compared to the control group, with lower levels in the Fer-1 group than in the UC group (both P<0.05). Conclusions: HSP90AA1, SNCA, TLR4, and PTGS2 are ferroptosis-related genes that may play a crucial role in the pathogenesis of UC by regulating the ferroptosis pathway.
Keywords: Ferroptosis pathway, ulcerative colitis, gene expression analysis, validation experiments
Introduction
Ulcerative colitis (UC) is a chronic intestinal disease characterized by persistent inflammation of the colonic mucosa. The global incidence of UC is increasing annually, and it is listed by the World Health Organization among the refractory diseases [1,2]. The pathogenesis of UC is complex, involving multiple factors such as genetic susceptibility, intestinal dysbiosis, immune system activation, and dysfunction of the intestinal mucosal barrier [3,4]. Although the current treatment regimen, which h includes aminosalicylic acid preparations, glucocorticoids, and biological agents, can partially relieve clinical symptoms, approximately 30% of patients still exhibit poor responses to traditional treatments. Moreover, long-term use of these medications often leads to complications such as infections and metabolic disorders [5,6]. Therefore, exploring the molecular mechanisms underlying UC and identifying new therapeutic targets has become a key area of research.
In recent years, there has been increasing interest in the role of programmed cell death in inflammatory diseases. Ferroptosis, a novel iron-dependent mode of cell death, is characterized by the accumulation of lipid peroxidation products and inactivation of the glutathione peroxidase system [7,8]. Research has shown that ferroptosis is involved in the pathology of cancers and neurodegenerative diseases, and it is also closely linked to damage to the intestinal epithelial barrier in inflammatory bowel diseases [9,10]. Intestinal epithelial cells serve as the first line of defense against intestinal pathogens and are crucial for maintaining immune homeostasis. Abnormal cell death in these epithelial cells can lead to increased intestinal permeability and persistent inflammation. The oxidative stress mechanisms associated with ferroptosis may influence UC by regulating arachidonic acid metabolism, inflammatory factor release, and other signaling pathways [11,12]. Studies have observed abnormal mitochondrial morphology and dysregulated expression of iron metabolism-related proteins in the colon tissues of UC patients, suggesting that ferroptosis may play a role in its pathogenesis. However, there is still a gap in research regarding the systematic identification of key regulatory genes involved in ferroptosis in UC and their molecular validation [13]. Building on previous findings, this study aims to characterize the expression patterns of ferroptosis-related genes in UC through bioinformatics and experimental validation, thereby elucidating the molecular regulatory network of ferroptosis in UC and providing a foundation for developing therapy targeting this pathway.
Materials and methods
Key genes in UC analyzed by GEO database
Data acquisition
Gene expression data from the GSE3365 dataset were downloaded from the GEO database (http://www.ncbi.nlm), including 85 enteritis samples and 42 control samples.
Data processing
Differentially expressed genes in the GSE3365 dataset were analyzed using the online tool (http://www.ncbi.nlm.nih.gov/geo/geo2r). The screening criteria were an adjusted P-value (adj. P) <0.05 and |logFC|≥1.5. A positive logFC indicates gene overexpression, while a negative logFC indicates underexpression.
UC and ferroptosis related genes
Ferroptosis-related genes were searched in the FerrDb database (http://zhounan.org/ferrdb/), including “Driver” and “Suppressor” genes. Differentially expressed genes from the previous analysis were intersected with ferroptosis-related genes. Intersections and Venn plots were generated using OmicStudio (http://www.omistudio.cn/tool).
Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analysis
Filter conditions were set with P-value <0.05 and Q-value <0.05. GO and KEGG enrichment analyses of the intersecting genes were performed using the R package “clusterProfiler”, with visualization of significant results using ggplot2.
Construction and module analysis of protein-protein interaction (PPI) network
A web-based PPI network for the intersecting genes was constructed using the STRING database (http://cn.string-db.org/cgi/input.pl) to visualize the data.
Consensus clustering analysis
The clustering analysis was conducted on the 24 intersecting genes in intestinal inflammation tissues using the ConsensusClusterPlus software package. The number of unsupervised clustering categories was quantitatively estimated with 50 iterations and an 80% resampling rate [14]. Principal component analysis was used to assess subtype differences.
Screening of hub genes
Cytoscape software, with the CytoHubba plugin, was used to analyze the topology of the PPI network. The top five node genes were identified and intersected as hub genes using three methods: “Degree”, “MNC”, and “EPC”.
Verification by animal experiments
Experimental animals
Thirty healthy SPF-grade C57BL/6J mice, weighing 20 ± 2 g, were purchased from the Animal Experiment Center of Chongqing Medical University. The mice underwent a one-week fitness training period. Rearing conditions included a temperature of 22-26°C, humidity of 40-70%, a 12-hour light-dark cycle, and free access to water and feed. The study was approved by the Weifang Hospital of Traditional Chinese Medicine animal ethics committee.
Animal model
The mice were randomly divided into three groups (control, UC, and Fer-1) with 10 mice per group. UC modeling was performed with 3% dextran sulfate sodium (DSS, Sigma Corporation, USA) [15]. Fresh DSS solution was replaced daily. Successful modeling was indicated by the appearance of blood in stool on days 4 to 5. In the Fer-1 group, Fer-1 (Sigma, USA), an iron death inhibitor, was administered intraperitoneally at 10 mg/kg per day for 7 days starting from the first day of model establishment. Mice were euthanized by cervical dislocation after anesthesia with 4%-5% isoflurane.
H&E staining
H&E stain was performed to observe pathologic changes in colon tissues. Colonic mucosal tissues were fixed in 10% neutral formaldehyde, embedded, dehydrated, and sectioned. Stained slides were observed under a high-power microscope. Tissue inflammation was scored using the Gebobes system, with criteria ranging from 0 (no structural changes) to 5 (erosion or ulceration).
Detection of serum TNF-α, IL-1β, and IL-6 content
Whole blood (0.5 ml) was collected from each group using anticoagulation tubes, centrifuged and analyzed using ELISA kit (Abcam, USA) for TNF-α, IL-1β, and IL-6 levels.
Detection of oxidative stress markers in mouse serum
Blood samples (0.5 ml) were collected, centrifuged, and the serum analyzed for glutathione (GSH), malondialdehyde (MDA), and reactive oxygen species (ROS) levels using ELISA kits (Abcam, USA).
Identification of hub genes in colon tissues
Total RNA from colon tissues was extracted using TRIzol reagent (Invitrogen, USA) and reverse transcribed into cDNA. GAPDH was used as an internal reference gene. RT-qPCR was conducted with the following conditions: 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. Relative gene expression was calculated using the 2-∆∆Ct method. Primer sequences are listed in Table 1.
Table 1.
Primer sequences
| Primer | Sequence |
|---|---|
| HSP90AA1 | 5’-GACGCTCTCTGGATAAAATCCGTT-3’ |
| 5’-TGGGAATGAGATTGATGTGCAG-3’ | |
| SNCA | 5’-GGGAGTCCTCTATGTAGGTTCC-3’ |
| 5’-TCCAACATTTGTCACTTGCTCT-3’ | |
| TLR4 | 5’-ATGGCATGGCTTACACCACC-3’ |
| 5’-GAGGCCAATTTTGTCTCCACA-3’ | |
| PTG2 | 5’-AGCGTCAGGTCCCGTTTTC-3’ |
| 5’-CTGGTGCATTGTGTTGGGT-3’ | |
| GAPDH | 5’-AGCGTCAGGTCCCGTTTTC-3’ |
| 5’-AGCGTCAGGTCCCGTTTTC-3’ |
Western blot detection of protein expression in mouse colon tissues
Proteins from colon tissues were extracted using a protein extraction kit (Shanghai Beyotime Biotechnology, China), and concentrations were determined using a BCA assay kit. A total of 20 μg of protein was separated by SDS-PAGE and transferred to PVDF membranes. After blocking with 5% skim milk, membranes were incubated overnight at 4°C with primary antibodies against HSP90AA1, SNCA, TLR4, and PTGS2 (Abcam, USA), followed by secondary antibody incubation. Images were developed using ECL chemiluminescence and analyzed with ImageJ software. β-actin served as the internal reference protein.
Statistical analysis
Statistical analysis was performed using SPSS 27.0. Continuous data were expressed as mean ± standard deviation (x̅ ± s), and categorical data as counts and percentages. The t-test was used for comparisons of continuous data, and the χ2 test for categorical data. One-way or two-way ANOVA followed by LSD testing was used for comparisons among multiple groups. A P-value <0.05 was considered significant.
Results
Differential gene expression in GSE3365 dataset
Analysis of the GSE3365 dataset revealed 571 differentially expressed genes, with 338 up-regulated and 233 down-regulated. Functional enrichment analysis was then performed on these genes, as shown in Figure 1.
Figure 1.
Differentially expressed genes and functional enrichment. A: Volcano plot of differentially expressed genes; B: Heat map of differentially expressed genes; C: GO term enrichment analysis and KEGG enrichment analysis of differentially expressed genes.
Construction of intestinal inflammation tissue subtypes and screening of hub genes
The intersection of the differentially expressed genes and ferroptosis-related genes was analyzed, yielding 24 intersecting genes (Figure 2). A PPI network was constructed, and as shown in Figure 3, the optimal number of subtypes was determined to be 2. Principal component analysis results indicated complete separation between the two subtypes, with each being well-defined. Therefore, we classified the intestinal inflammation tissues into C1 and C2 subtypes based on the 24 intersecting genes. Using Cytoscape software, four hub genes were identified: HSP90AA1, SNCA, TLR4, and PTGS2.
Figure 2.
Hub gene screening. A: Venn diagram of intersection of the above differentially expressed genes and ferroptosis-related genes; B: Protein-protein interaction network diagram; C: Selected Hub genes; D: GOKEGG enrichment analysis of Hub genes.
Figure 3.
Construction of subtypes of enteritis tissues. A: Consensus matrix heatmap at k=2. B: CDF from k=2 to k=9. C: Relative changes in the area under the CDF curve. D: Sample classification tracking diagram from k=2 to k=9. CDF: Cumulative distribution function.
Pathology of mice
Pathologic examination revealed that the colonic mucosa of control mice was smooth, with a well-organized glandular structure and normal crypts, without neutrophil infiltration, thickening, congestion, edema, or ulceration of the intestinal wall. In contrast, the UC and Fer-1 groups showed disrupted colonic mucosal tissue, disorganized glandular structures, crypt disappearance, obvious ulceration, and inflammatory cell infiltration. The UC group showed destruction of colonic mucosal tissue, disorganization of glandular structure, disappearance of crypts, obvious ulceration, and inflammatory cell infiltration. These pathologic changes were reduced in the Fer-1 group compared to the UC group. The Gebobes scores of the UC and Fer-1 groups were significantly higher than that of the control group (P<0.05), and the Gebobes score in the Fer-1 group was significantly lower than that in the UC group (P<0.05), as shown in Figure 4.
Figure 4.
H&E staining and Gebobes score of colon in each group of mice. Note: (A) H&E stain of the colon in each group of mice (×400); (B) Gebobes score of each group of mice. Compared to the control group, *P<0.05; Compared to UC group, #P<0.05.
Comparison of Serum TNF-α, IL-1β, and IL-6 levels
Serum levels of TNF-α, IL-1β, and IL-6 were significantly higher in the UC and Fer-1 groups compared to the control group (all P<0.05). Additionally, the UC group exhibited significantly higher levels of these markers than the Fer-1 group (all P<0.05) (Figure 5).
Figure 5.
Comparison of serum TNF-α, IL-1β and IL-6 of mice in each group. Note: (A) TNF-α. (B) IL-1β. (C) IL-6. Compared to the control group, *P<0.05; Compared to UC group, #P<0.05. TNF-α: Tumor necrosis factor-α. IL-1β: Interleukin-1β. IL-6: Interleukin-6.
Comparison of serum GSH, MDA, and ROS levels among mice groups
Serum GSH levels in the UC and Fer-1 groups were significantly lower than those of the control group (P<0.05), while serum MDA and ROS levels were significantly higher (both P<0.05). GSH levels in the UC group were significantly lower than those in the Fer-1 group, and MDA and ROS levels were significantly higher in the UC group than in the Fer-1 group (all P<0.05, Figure 6).
Figure 6.
Serum GSH, MDA, and ROS levels in each group of mice. A: GSH. B: MDA. C: ROS. Compared to the control group, *P<0.05; Compared to UC group, #P<0.05. GSH: glutathione. MDA: Malondialdehyde. ROS: Reactive Oxygen Species.
Hub gene and protein expression in colonic tissues of each group
The relative mRNA expression levels of HSP90AA1, SNCA, TLR4, and PTGS2, as well as their protein expressions in the colon tissues of the UC and Fer-1 groups, were significantly higher than those of the control group (all P<0.05). Notably, both the mRNA and protein levels of these hub genes in the Fer-1 group were lower than those of the UC group (all P<0.05, Figure 7).
Figure 7.
Hub gene expression in colonic tissue of mice in each group. Notes: (A) relative expression of HSP90AA1 mRNA; (B) SNCA mRNA relative expression; (C) TLR4 mRNA relative expression; (D) relative expression of PTG2 mRNA; (E) Western blotting was used to detect the protein expression in the colon tissues of mice in each group. Compared to the control group, *P<0.05; Compared to UC group, #P<0.05.
Discussion
UC is a chronic inflammatory bowel disease characterized by complex interactions between genetic predisposition, immune dysfunction, intestinal microbiota imbalance, and oxidative stress [16-18]. In recent years, ferroptosis, a novel form of programmed cell death, has gained attention in inflammatory disease research due to its close association with lipid peroxidation and iron metabolism [19-21]. Key features of ferroptosis include inhibition of glutathione peroxidase, accumulation of ROS, and lipid peroxidation. These processes can exacerbate damage to the intestinal epithelial barrier and amplify inflammatory cascades [22,23]. It has been proposed that ferroptosis in inflammatory bowel disease may accelerate disease progression by activating immune cells and modulating epithelial cell death. However, the specific ferroptosis network and key regulatory genes in UC remain poorly understood [24,25]. This study integrated bioinformatics and experimental validation to investigate the expression patterns and molecular mechanisms of the ferroptosis pathway in UC.
In this study, 24 UC-related ferroptosis genes were identified through intersection analysis of the GSE3365 dataset and ferroptosis-related genes, with a focus on four hub genes: HSP90AA1, SNCA, TLR4, and PTGS2. The up-regulation of these genes suggests that ferroptosis may contribute to UC pathogenesis through various biologic processes. HSP90AA1, a member of the heat shock protein family, may regulate the stability of ferroptosis-related proteins under stress, and its overexpression may exacerbate oxidative stress-induced epithelial damage [26-28]. SNCA (α-synuclein), typically associated with neurodegenerative diseases, may participate in ferroptosis in UC by regulating mitochondrial iron metabolism or lipid peroxidation [29,30]. TLR4, a key receptor of innate immunity, can amplify the inflammatory response through NF-κB and other pathways, creating a positive feedback loop with ferroptosis signaling to promote mucosal barrier disruption [31,32]. PTGS2 may connect ferroptosis and the inflammatory microenvironment by mediating the synthesis of inflammatory mediators such as prostaglandin E2 [33-35]. Notably, the ferroptosis inhibitor Fer-1 significantly reduced the expression of these hub genes and TNF-α levels, alleviating pathologic damage. These results suggest that ferroptosis may drive inflammation and tissue damage in UC by regulating these hub genes, indicating that targeting the ferroptosis pathway could block the vicious cycle of oxidative stress and inflammation. This offers a novel therapeutic strategy for UC.
However, several limitations should be acknowledged. First, the bioinformatic analysis was based on the public GSE3365 dataset, which has a limited sample size and lacks clinical multicenter validation, potentially affecting the generalizability of the findings. Second, the animal experiments used a DSS-induced UC model, which, although commonly used to simulate human UC, primarily represents acute inflammation, differing from the chronic inflammation observed in patients. Therefore, further multi-level verification using organoid models or clinical samples is necessary.
In conclusion, this study is the first to reveal that HSP90AA1, SNCA, TLR4, and PTGS2 are key regulatory genes in the ferroptosis pathway in UC, and their abnormal expression may contribute to UC progression through oxidative stress, inflammatory signaling, and lipid metabolism dysregulation. The therapeutic potential of Fer-1 further supports the value of targeting the ferroptosis pathway. However, larger clinical studies, functional experiments, and comprehensive mechanistic investigations are required to clarify the specific targets and regulatory networks of these hub genes, providing a theoretical foundation for developing targeted treatments for UC based on ferroptosis modulation.
Disclosure of conflict of interest
None.
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