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. 2026 Feb 2;16:6992. doi: 10.1038/s41598-026-38193-4

Identification and validation of a novel ferroptosis-related gene signature associated with inherited retinal degeneration in Rd10 mice

Xu Qiu 2,#, Xue-Wei Fu 2,#, Xin-Lan Lei 3,#, Wei-Jie Huang 4, Mao-Lin Tao 4, Guo-Li Zheng 4, Yong-Zhao Wei 4, Fei Chen 5, Hong-Yang Luo 6, Jian-Wei Xu 7,✉, Hao Gu 2,✉, Kun-Chao Wu 1,4,7,✉
PMCID: PMC12920653  PMID: 41629618

Abstract

Retinitis pigmentosa (RP) is among the most irreversible inherited blindness diseases. Previous evidences have demonstrated that ferroptosis plays a significant role in various neurodegenerative diseases. Thus, elucidating the relationship between ferroptosis and retinitis pigmentosa may yield valuable insights for identifying therapeutic targets for inherited retinal degeneration. In the current study, a public dataset of GSE56473 from the Gene Expression Omnibus (GEO) database was analysed to identify the differentially expressed ferroptosis related genes (DE-FRGs) within the degenerative retinas between Rd10 mice and control individuals. Gene Ontology (GO), Kyoto Encyclopedia of Genomes (KEGG) and protein-protein interaction (PPI) network analyses were performed on these DE-FRGs. Hub ferroptosis-related genes (HFRGs) were subsequently identified using multiple bioinformatic algorithms. Changes in the expression profiles of the identified HFRGs were validated through the GSE178928 dataset. Immunofluorescence staining, quantitative PCR (qPCR) and a single cell sequencing dateset analysis were performed to evaluated the expression patterns of several HFRGs in the Rd10 mice. Furthermore, potential target drugs were predicted utilizing the DGIdb database. In total, 37 ferroptosis-related genes were identified among the 2096 differentially expressed genes, which were enriched in several biological processes including the response to oxidative stress, positive regulation of neuron death, ferroptosis and the PPAR signaling. Through protein–protein interaction network and multiple bioinformatics analyses, eight HFRGs (Egr1, Cd44, Egfr, Tlr4, Timp1, Cybb, Lcn2, and Ppara) were ultimately identified, with most being upregulated in the retinas of Rd10 mice. Among these HFRGs, Egr1 expression was significantly increased in rod and cone photoreceptors, whereas Cd44 expression was markedly upregulated in Müller cells. Several potential therapeutic compounds, such as Genipin, were also predicted. Our study provides novel biomarkers and therapeutic targets for the inherited retinal degeneration.

Keywords: Retinitis pigmentosa, Ferroptosis, Photoreceptor, Egr1 and Cd44

Subject terms: Computational biology and bioinformatics, Diseases, Genetics, Molecular biology, Neuroscience

Introduction

Retinitis pigmentosa (RP) is among the leading causes of irreversible inherited blindness, with a prevalence of approximately 1 in every 3000 births and affecting more than 2 million individuals worldwide1–3. The disease is characterized by a progressively sequential loss of rod and cone photoreceptors; thus, patients with RP experience initial deterioration of night vision and ultimately progress to complete blindness4,5. Approximately 100 genes have been reported to be associated with RP, and gene therapy has demonstrated significant potential for treating this condition6–9. In practice, some financial and logistical constraints, such as inherent genetic heterogeneity of pathogenic genes and the capacity limitation of adeno-associated viruses, may influence the application of gene therapy10–12. Therefore, identifying new potential targets that are not dependent on inherited pathogenic genes is particularly important for promoting the treatment of retinitis pigmentosa.

Ferroptosis is a newly characterized form of programmed cell death mediated by the accumulation of reactive oxygen species (ROS) resulting from iron overload and lipid peroxidation13–15. Numerous studies have demonstrated that iron accumulation occurs in various animal models of retinal degeneration16–18. Moreover, intravitreal injection of Fe2+ leads to photoreceptor degeneration accompanied by elevated levels of 4-hydroxy-2-nonenal (4-HNE)19. Notably, under oxidative stress, the expression level of glutathione peroxidase 4 (GPX-4) in the retina is significantly decreased20. In our previous study, we revealed multiple key ferroptosis-related molecules within the degenerative retina induced by light exposure damage21. Recently, Sun et al. reported that overexpression of solute carrier family 7 member 11 (Slc7a11), a critical repressor of ferroptosis, can protect photoreceptors from damage through the Nrf2–Slc7a11–GPX4 signaling pathway in an animal model of age-related retinal degeneration (AMD)22. These findings strongly suggest that ferroptosis plays a significant role in neurodegenerative diseases.

Although signs suggest the involvement of ferroptosis in the pathogenesis of retinal degeneration, the underlying molecular mechanism associated with retinitis pigmentosa remains uncertain. Here, we aimed to identify and validate novel ferroptosis-related genes involved in the pathogenesis of retinal degeneration in Rd10 mice, a classical hereditary model of retinitis pigmentosa caused by a homozygous point mutation, c.1678 C > T, in the Pde6b gene. Datasets were downloaded from the Gene Expression Omnibus (GEO) database. Bioinformatics analyses were performed to screen for differentially expressed genes (DEGs). After interacting with ferroptosis-related genes, eight hub genes (Egr1, Cd44, Egfr, Tlr4, Timp1, Cybb, Lcn2, and Ppara) were ultimately identified and validated. Moreover, the distribution and expression of Egr1 and Cd44 were further assessed in the retinas of Rd10 mice at transcriptome and protein levels. Our study provides new insights into ferroptosis and inherited retinal degeneration.

Results

Differentially expressed genes and gene set enrichment analysis in the retinas of Rd10 mice

Principal component analysis of a dataset from GSE56473 revealed high reproducibility of retinal samples23 (Fig. 1A). With a threshold of fold change ≥ 2 and adjusted p value < 0.05, 2096 genes were significantly differentially expressed in the retinas of the Rd10 mice compared with those of the control mice (Fig. 1B). To compare the significantly different pathways between the two groups, gene set enrichment analysis (GSEA) was conducted. The results revealed that the janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway, transforming growth factor beta (TGF-β) signaling pathway, Wnt signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, calcium signaling pathway, axon guidance, focal adhesion and Ecm receptor interaction were significantly enriched in the retinas of Rd10 mice (Fig. 1C, D).

Fig. 1.

Fig. 1

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Identification of differentially expressed genes and gene set enrichment analysis. (A) Principal component analysis of the GSE56473 dataset. (B) Volcano plots showing significantly differentially expressed genes. Red, blue and grey nodes indicate upregulated, downregulated and unchanged genes, respectively. (C, D) GSEA revealed that several KEGG pathways are enriched in the retinas of Rd10 mice. NOM p < 0.05, FDR < 25%.

Identification and functional enrichment analysis of differentially expressed ferroptosis-related genes

To investigate differentially expressed ferroptosis-related genes (DE-FRGs) in the retinas of Rd10 mice, 484 ferroptosis-related genes were extracted from the FerrDb database24. After the DEGs and ferroptosis-related genes were intersected, 37 DE-FRGs were identified (Fig. 2A), and the expression levels of all the DE-FRGs were visualized in a heatmap (Fig. 2B). We subsequently explored the potential biological functions and pathways of the 37 identified DE-FRGs. The results of the top biological process (BP) of Gene Ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) pathway analyses suggested that the DE-FRGs were significantly enriched in related processes, such as the response to oxidative stress, positive regulation of neuron death, regulation of DNA-templated transcription in response to stress, ferroptosis and the PPAR signaling pathway (Fig. 2C, D).

Fig. 2.

Fig. 2

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Functional annotation of DE-FRGs. (A) Venn diagram displaying the DE-FRGs. The blue circle represents DEGs in the GSE56473 dataset, the pink circle indicates FRGs, and the intersection shows DE-FRGs. (B) Heatmap showing the expression profiles of the 37 DE-FRGs in the Rd10 mice retinas. Red bricks indicate genes with high expression and blue bricks represent genes with low expression. Red and blue annotation bars indicate the Rd10 and control groups, respectively. (C) Biological processes of the DE-FRGs according to the results of the GO enrichment analysis. (D) KEGG pathway enrichment analysis of the enriched DE-FRGs.

Construction of a protein–protein interaction network and identification of hub DE-FRGs

To explore the interactions between each DE-FRG, the protein-protein interaction (PPI) network for the 37 DE-FRGs was constructed and visualized using the GeneMANIA platform25 (Fig. 3A). In the PPI network, coexpression, physical interactions, colocalization and predicted interactions separately accounted for 64.97%, 2.5%, 5.17% and 22.51%, respectively. The top 10 key genes were subsequently analysed using the maximum clique centrality (MCC), maximum neighborhood component (MNC), Degree, and edge percolated component (EPC) algorithms of cytoHubba, and the 8 interacting genes of all four algorithms were selected as the hub ferroptosis-related genes (HFRGs), which included Egr1, Cd44, Egfr, Tlr4, Timp1, Cybb, Lcn2 and Ppara (Fig. 3B). The expression levels of the HFRGs in the GSE56473 dataset were displayed in Table 1. Spearman correlation analysis was used to investigate the correlations among the expression profiles of these HFRGs, which indicated that most of the HFRGs were positively correlated (Fig. 3C). The strong interactions among these HFRGs suggested that these genes may be involved in the pathogenesis of retinal degeneration in Rd10 mice.

Fig. 3.

Fig. 3

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PPI analysis of the DE-FRGs. (A) The PPI network among 37 DE-FRGs constructed by the GeneMANIA platform. (B) HFRGs calculated according to the MCC, MNC, Degree, and EPC algorithms. (C) Spearman correlation analyses of the HFRGs.

Table 1.

Hub ferroptosis-related genes identified using the MCC, MNC, Degree and EPC algorithms by cytoHubba.

Gene symbol Description Log2FC Padj Changes
Egr1 Early growth response 1 2.04 0.0129 Up
Cd44 Cd44 antigen 1.99 0.0001 Up
Egfr Epidermal growth factor receptor 1.65 0.0008 Up
Tlr4 Toll-like receptor 4 3.33 0.0081 Up
Timp1 Tissue inhibitor of metalloproteinase 1 1.92 0.0054 Up
Cybb Cytochrome b-245, beta polypeptide 1.35 0.0449 Up
Lcn2 Lipocalin 2 1.58 0.0114 Up
Ppara Peroxisome proliferator activated receptor alpha −3.76 1.21E-06 Down
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Verification of the 8 HFRGs using a dataset from the GEO database

A GSE178928 dataset, which included 3 Rd10 mouse retinas and 3 control samples, was further selected to verify the Transcripts Per Million (TPM) values of the eight HFRGs26. The R software ggplot2 package was used to construct boxplots and Student’s t test was used for statistical analyses. Consistent with our predictions, compared with those in normal controls, the mRNA expression levels of seven HFRGs in the Rd10 mice retinas were significantly increased, whereas the expression level of Ppara was significantly decreased (Fig. 4).

Fig. 4.

Fig. 4

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Verification of the eight HFRGs using the GSE178928 dataset derived from the GEO database. (A–H) Verification by the GSE178928 dataset. Compared with those in normal control samples, the expression levels of seven HFRGs were significantly upregulated in the retinas of the Rd10 mice. ***p < 0.001, **p < 0.01, *p < 0.05.

Validation of the distribution and expression characterization of Egr1 and Cd44

The expression characteristics of two of the eight identified HFRGs, Egr1 and Cd44, were subsequently further validated in the degenerating retinas of the Rd10 mice.

Sanger sequencing revealed that a homozygous point mutation, c.1678 C > T, was present within the Pde6b gene of Rd10 mice (Fig. 5A). To evaluate changes in retinal structure, we conducted hematoxylin and eosin (HE) staining of retinas at postnatal Day 21. Compared with normal individuals, the thickness of whole retina (WR) and outer nuclear layer (ONL) were significantly thinner in Rd10 mice (Fig. 5B, C). The expression level of rhodopsin was also evaluated, and the immunofluorescence staining results revealed that it was significantly decreased in the retinas of Rd10 mice (Fig. 5D). These findings suggested that the mouse model can effectively simulate RP disease in humans.

Fig. 5.

Fig. 5

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Genotyping and retinal structure changes in Rd10 mice. (A) Sanger sequencing image showing the point mutation of the Pde6b gene. (B) HE staining of mouse retinas at postnatal Day 21. The scale bar represents for 50 μm. (C) Comparison of WR and ONL thicknesses between the Rd10 and control groups, ***p < 0.001, **p < 0.01, *p < 0.05. (D) Rhodopsin expression in the retinas of Rd10 mice determined by immunofluorescence, the scale bar represents 20 μm.WR, whole retina; OS/IS, outer segment/inner segment; ONL, outer nuclear layer; OPL, outer plexiform layer; INL, inner nuclear layer; IPL, inner plexiform layer; GCL, Ganglion Cell Layer.

Retinal immunohistochemistry and real-time PCR were further conducted to evaluate the changes in the distribution and expression of two HFRGs, Egr1 and Cd44, in the retinas of the Rd10 mice. Compared with that in control individuals, the immunofluorescence staining signal of Egr1 was markedly increased and was located mainly at the ONL of retina in Rd10 mice, strongly indicating that the Egr1 was distributed mainly within photoreceptors (Fig. 6A). An anti-glutamine synthetase (GS) antibody is widely used to label retinal Müller cells. Immunofluorescence staining revealed that strong Cd44 fluorescence signal was colocalized with the GS at the outer limiting membrane of the retinas of the Rd10 mice (Fig. 6C), indicating that the expression of Cd44 may be an indicator of stimulus the status of Müller cells. The real-time PCR results also showed that the expression levels of both Egr1 and Cd44 were significantly greater in the retina of the Rd10 mice than in those of the normal control group (Fig. 6B, D).

Fig. 6.

Fig. 6

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Distribution and expression analysis of Egr1 and Cd44 genes in the retinas of Rd10 mice. (A) Retinal immunofluorescence staining of Egr1. (B) QPCR result for Egr1expression in mouse retina. (C) Retinal immunofluorescence staining of Cd44. (D) Comparison of Cd44 expression level by qPCR in the retina between Rd10 mice and normal individuals. N = 3. Error bars represent SEM. ***p < 0.001, **p < 0.01, *p < 0.05.

To further validate the changes in Egr1 expression within photoreceptors as well as Cd44 expression within Müller cells, a GSE183206 dataset of retinal single cell sequencing of Rd10 mice was deeply explored27. Using a standard single cell analysis procedure, 30 cell clusters were finally identified (Fig. 7A). All 30 cell clusters were Subsequently annotated by several widely used marker genes of retinal neurons and glial cells (Fig. 7B). Consistent with the immunofluorescence results, statistical analyses revealed that Egr1 was strongly upregulated in both the rod and cone photoreceptors, whereas Cd44 expression was significantly increased in Müller cells (Fig. 7C-E).

Fig. 7.

Fig. 7

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Verification of Egr1 expression within photoreceptors and Cd44 within Müller cells based on a single-cell sequencing dataset from GSE183206. (A) Cell cluster analysis. with a resolution of 0.8, a total of 30 cell clusters were identified in total. (B) Annotation of cell types based on all known retinal makers. (C, D) Comparison of Egr1 expression level in rod and cone photoreceptors between the Rd10 mice and control group. (E) Cd44 expression level in Müller cells from Rd10 mice and normal individuals. ***p < 0.001, **p < 0.01, *p < 0.05.

Predictions of drugs targeting Egr1 and Cd44

Potential drugs that may be useful for treating retinal degeneration through the regulation of Egr1 and Cd44 were predicted using the DGIdb database28. The predictions strongly suggested that Genipin and Brivoligide have the potential to regulate Egr129,30. Several molecular compounds, such as Cisplatin, Gentamicin and Maralixibat, were also predicted to intersect with Cd44 (Fig. 8).

Fig. 8.

Fig. 8

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Interaction network between Egr1 and Cd44, as well as their potential targeted drugs. Egr1 and Cd44 genes are coloured in red, and the candidate molecules are coloured in green.

Discussion

Ferroptosis, a newly identified form of iron-dependent programmed cell death, plays a significant role in various neurodegenerative diseases31–33. The progressive death of rod and cone photoreceptors is the primary outcome of inherited retinal degeneration4,5,34. Clarifying the relationship between ferroptosis and retinitis pigmentosa may provide novel insights into the pathogenesis and treatment of inherited retinal neurodegenerative disorders.

In this study, we identified novel hub genes related to ferroptosis that are involved in the pathogenesis of retinal degeneration in Rd10 mice, a classical and widely used animal model for mechanistic and therapeutic studies of retinitis pigmentosa. We first performed a systematic analysis of an expression profiles from the GSE56473 dataset utilizing bioinformatics analysis. Totally, 2096 DEGs were identified between the Rd10 and control individuals. By intersection of the DEGs and FRGs derived from the FerrDb database, a total of 37 DE-FRGs were identified. GO and KEGG analyses revealed that these ferroptosis-related DEGs were involved mainly in the response to oxidative stress, positive regulation of neuron death, regulation of DNA-templated transcription in response to stress, ferroptosis and the PPAR signaling pathway. Through the use of systematic bioinformatics algorithms, we ultimately identified eight hub ferroptosis-related genes, namely, Egr1, Cd44, Egfr, Tlr4, Timp1, Cybb, Lcn2, and Ppara. Furthermore, real-time PCR, immunohistochemistry, and single cell transcriptome analysis revealed that Egr1 and Cd44 expression levels were significantly upregulated in photoreceptors and Müller cells, separately. Finally, we identified several potential targeted drugs that may interact with Egr1 and Cd44.

Egr1, a zinc finger transcription factor, belongs to the early growth response (EGR) family and is essential for multiple biological processes, including cell differentiation, proliferation and apoptosis35–37. As a transcription factor that rapidly responds to oxidative stress responses and inflammation, Egr1 may participate in the pathogenesis of several retinal degeneration diseases38–41. A significant upregulation of Egr1 expression was observed in a Rs1h gene knockout mouse model, which displays typical retinal features of X-linked juvenile retinoschisis42. The inhibition of MAPK/c-Jun-EGR1 pathway can decrease photoreceptor cell death in the Rd1 mice model of retinitis pigmentosa43. Several recent studies also suggest that the Egr1 plays important role in the ferroptosis44–46. For example, Li et al.. found that the expression levels of Prmt1, Egr1, and Gls2 were increased in the acute lung injury (ALI) models, in which Egr1 could induce the transcription of Gls2, thereby promoting ferroptosis45. In our study, Egr1 was found to be a hub ferroptosis-related gene whose expression specifically increased in the retinal ONL of Rd10 mice. Hence, the Egr1 associated ferroptosis in inherited retinal degeneration is worthy of further exploration.

Cd44 is a nonkinase single transmembrane glycoprotein that functions as a principal cell surface receptor for various extracellular matrix components and is involved in numerous physiological and pathological processes47–49. A previous study demonstrated that the knockout of Cd44 improved behavioural deficits and inhibited the activation of microglia and astrocytes in a neurodegenerative animal model of Parkinson’s disease48. The gliosis of microglial and Müller cells was also reported in the animal model of RP, and this effect can be mitigated by treatment with Norgestrel50. Cd44 can promote intracellular iron accumulation, thereby accelerating iron death ferroptosis in the acute kidney injury (AKI) mice, and the deletion of Cd44 can improved mitochondrial biogenesis and fatty acid oxidation (FAO) function, further protecting against tubular cell death and kidney injury51. The cells undergoing ferroptosis secrete Galectin-13, and this protein can bind to Cd44 and inhibit the plasma membrane localization of Slc7a11 in neighboring cells, as a result, it accelerates ferroptosis in these neighboring cells52. In addition, Cd44 were highly expressed in the microvilli of Müller cells in multiple autosomal recessive and autosomal dominant RP mouse model53. In line with these studies, we also found that Cd44 expression was significantly and specifically increased in the retinal Müller cells of Rd10 mice, suggesting that Cd44 may play a critical role in pathogenesis of inherited retinal degeneration.

Egfr is a transmembrane tyrosine kinase receptor (RTK) that belongs to the ErbB family54. It can activate multiple downstream signaling pathways by binding to EGF family ligands, thereby regulating cell differentiation, migration, and apoptosis54,55. Pharmacological inhibition of Egfr effectively mitigates retinal fibrosis through stimulation of the YAP signaling pathway in diabetic retinopathy56, suggesting that abnormal expression of this gene may be involved in the pathogenesis of retinal degeneration diseases.

Timp1 is a key inhibitor of matrix metalloproteinases (MMPs)57. A previous study revealed that TIMP1 expression is significantly elevated in osteoporosis associated with type 2 diabetes, and that promotes ferroptosis in osteoblasts by regulating the expression of Tfrc58. Moreover, inhibition of Timp1 expression can alleviate the progression of osteoporosis in a mouse model of type 2 diabetes58. In this study, Timp1 was also identified as a hub ferroptosis-related gene in the retinas of Rd10 mice. Its pathogenesis associated with inherited retinal degeneration through the regulation of ferroptosis is worthy of further exploration.

Tlr4, a member of the Toll-like receptor family, is widely involved in the pathogenesis of multiple biological conditions, including inflammation, metabolic regulation and autoimmune disease59,60. Lipopolysaccharide (LPS) exposure activates retinal microglia through Tlr4 expressed in endothelial cells, which can lead to synaptic dysfunction and retinal impairment61. The inhibition of Tlr4 and Nlrp3 effectively reduced retinal pigment epithelium (RPE) cell death and inflammation in mouse model of AMD62. A previously study revealed that the expression of Tlr1 to Tlr9 was increased in the retinal myeloid cells of Rd10 mice63. We also found that the expression of Tlr4 was significantly upregulated in the retinas of Rd10 mice, and that targeting downregulation of Tlr4 may be a latent strategy to delay the progression of inherited retinal degeneration.

LCN2, a secretory protein, plays important roles in inflammatory stress and neuronal damage64. It can be stimulated through the NF-κB and STAT-1 signaling pathways in the retinas of both AMD animal model and human patients, resulting in a marked inflammatory response65. Cybb encodes Nox2 protein, which is a key component of the NADPH oxidase complex66. Nox2 induction exacerbates retinal ischemic injury67. The absence of Nox2 or its activity inhibition significantly alleviates retinal oxidative stress68. Moreover, inhibition of Nox2 can also reduce microglial activation and neuronal damage69,70. These evidences suggested that the induction of Lcn2 and Cybb may be involved in the pathogenesis of retinal degeneration. In line with these studies, Lcn2 and Cybb were also found to be hub ferroptosis-related genes in the neurodegenerative retinas of Rd10 mice, suggesting that these genes may be novel therapeutic targets for inherited retinal degeneration.

Pparα is a member of the nuclear hormone receptor family (Pparα, Pparδ, and Pparγ)71. It has been shown to play a crucial role in retinal lipid metabolism and neuronal survival by regulating fatty acid oxidation72. Deletion of Pparα can lead to retinal degeneration associated with energy deficiency72. Additionally, Pparα agonists has been demonstrated to prevent retinal neovascularization in an animal model of AMD73, indicating the important protective role of Pparα during retinopathy. Consistent with these findings, the expression of Pparα was significantly decreased in the retinas of Rd10 mice. Hence, we suggest a potential strategy for treating inherited retinal degeneration through the use of Pparα agonists.

Identifying new potential target molecules that are independent of inherited pathogenic genes may expand the therapeutic scope for retinitis pigmentosa. Recently, Xu et al. demonstrated that the application of ferrostatin-1, a small molecule inhibitor of ferroptosis, effectively protected photoreceptors from apoptosis in a rat model of retinal degeneration induced by light exposure74. Furthermore, Sun et al. reported that photoreceptors can be rescued through the overexpression of Slc7a11 in an AMD animal model22. Consequently, targeting ferroptosis has significant potential for treating retinal neurodegenerative disorders, and the HFRGs identified in our study may provide novel targets for future therapeutic research.

In this study, a strong interaction between Genipin and Egr1 was predicted. Genipin, as a representative iridoid, can prevent α-synuclein aggregation and toxicity in an animal model of Parkinson’s disease by regulating metabolism, lipid storage and endocytosis75; moreover, it has been shown to be useful for rescuing developmental and degenerative defects in familial dysautonomia models and accelerating axon regeneration76, and treatment with Genipin can ameliorate diabetic retinopathy via the HIF-1α and AGE–RAGE pathways though reducing reactive oxygen species (ROS) levels and inflammatory responses77. Given these findings, the potential role of Genipin in treating inherited retinal degeneration through targeting Egr1 warrants further investigation in the following study.

Our study inevitably has several limitations. First, the small sample size may affect the accuracy of the results. Second, besides Egr1 and Cd44, the characterization of another HFRG warrants further evaluation. Third, the functional mechanisms will be explored in detail through in vivo and in vitro experiments in future studies.

In summary, our study identified eight hub ferroptosis-related genes—Egr1, Cd44, Egfr, Tlr4, Timp1, Cybb, Lcn2, and Ppara—in a classical Rd10 inherited mouse model of retinitis pigmentosa utilizing a comprehensive bioinformatics algorithm. We also characterized the expression of Egr1 and Cd44 in degenerative retinas, as well as several potential therapeutic drugs that may interact with them. Our findings may enhance the understanding of the ferroptosis regulatory mechanisms that underlie inherited retinal degeneration.

Materials and methods

Data acquisition and processing

The raw read counts of the GSE56473 dataset, which consists of retinal samples from wild-type individuals and Rd10 mice aged postnatal day 6123, were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). We utilized the R software (version 4.2.1) and the edgeR package (version 3.38.2) to conduct differential analysis on the raw counts matrix. We filtered low-abundance molecules according to standard procedures and normalized the raw counts matrix using the log counts per million (CPM) implemented in the edgeR package (version 3.38.2)78. Principal component analysis was performed to assess reproducibility using the stats R package (version 4.2.1). GSEA was conducted to identify the relevant signaling pathways between the two groups using the clusterProfiler R package (version 4.4.4). Significant gene sets were confirmed based on a false discovery rate (FDR) of less than 0.25 and a nominal p value of less than 0.05.

Identification of differentially expressed ferroptosis-related genes

We analysed DEGs using the edgeR package (version 3.38.2). DEGs were identified based on a threshold of fold change ≥ 2 and adjusted p value < 0.05 between the control group and the Rd10 mice. The results were visualized using volcano plots generated by the ggplot2 package (version 3.4.4). A total of 484 ferroptosis-related genes that either drive, suppress, or serve as markers for ferroptosis were retrieved from the FerrDb database (http://www.zhounan.org/ferrdb)24. A Venn diagram was created to illustrate the number of DE-FRGs using the VennDiagram package (version 1.7.3). A heatmap of the DE-FRGs was subsequently generated using the ComplexHeatmap package (version 2.13.1).

Functional enrichment analysis of DE-FRGs

Biological process enrichment analysis of DE-FRGs was conducted using the clusterProfiler package (version 4.4.4), alongside pathway enrichment analysis based on the KEGG (www.kegg.jp/kegg/kegg1.html). The top results of the enrichment analysis were visualized utilizing the ggplot2 package (version 3.4.4).

Construction of a protein‒protein interaction network and identification of hub genes associated with ferroptosis

A PPI network of the DE-FRGs was constructed and visualized using the GENEMANI platform25. The CytoHubba plugin in Cytoscape software was employed to identify hub modules within the PPI network79. The top ten genes in the PPI network were determined separately through the MCC, MNC, Degree, and EPC algorithms.The genes identified by all four algorithms were designated as HFRGs. The correlations among the HFRGs were analyzed and visualized using the igraph (version 1.4.1) and ggraph (version 2.1.0) R packages. The expression profiles of all the HFRGs were validated through the GSE178928 dataset, which contained retinal transcriptome data from Rd10 mice and control individuals aged P2326. The TPM values of the HFRGs were visualized in box plots using the ggplot2 package (version 3.4.4).

Animals

Rd10 mice were purchased from Cyagen Biosciences Inc. (Suzhou, Jiangsu, China). The mice were maintained at the animal resource centre of Guizhou Medical University under a 12 h light‒dark cycle and provided ample food and water. The light intensity in the cages was approximately 20 lux. Animal care followed the Association for Research in Vision and Ophthalmology (ARVO) Statement for the Use of Animals in Ophthalmic and Vision Research. All animal experiments and procedures were approved by Guizhou Medical University (approval number 2101071). All the experiments were performed in accordance with the ARRIVE guidelines. To anaesthetize the animals, sodium pentobarbital (50 mg/kg, intraperitoneal) was used. Genomic DNA was extracted from the tail, and subsequently amplified using polymerase chain reaction (PCR) with the primers of Pde6b Fwd (5’-GGCCAGTGAGAACAAGGAAC-3’) and Pde6b Rev (5’-TGATTCATCTAGCCCATCCA-3’). Finally the genotypes of the mice were was identified through Sanger sequencing.

Retinal immunohistochemistry

Retinal immunohistochemistry was performed as previously described80. The animals were sacrificed through an intraperitoneal injection of a lethal dose of pentobarbital (100 mg/kg). The eyeballs were extracted immediately after euthanasia. Following the removal of the cornea and lens, the eyecups were fixed in 4% paraformaldehyde for 2 h, dehydrated in a 30% sucrose solution, and finally embedded in optimal cutting temperature compound. Sections with a thickness of 12 μm were cut. The slides were rinsed for 10 min in 0.01 M PBS and subsequently blocked for 1 h in a solution containing 4% BSA and 0.5% Triton X-100 in PBS. Primary antibodies were diluted in 1% BSA and 0.5% Triton X-100 in PBS. The slides were incubated with primary antibodies overnight at 4 °C and then incubated for 1 h in solutions containing the appropriate secondary antibodies. The primary antibodies and dilutions used were as follows: mouse anti-rhodopsin (1:3000; Cat No. ab5417, Abcam), rabbit anti-recoverin (1:500; Cat No. CL-488–10073, Proteintech), mouse anti-Egr1 (1:200; Cat No. sc-101033, Santa Cruz), rabbit anti-Cd44 (1:200; Cat No. A00052, Boster), anti-GS (1:2000; Cat No. Mab302, Millipore). With respect to secondary antibodies, donkey anti-rabbit-Alexa488 (1:200, Cat No. 711-545-152, Jackson ImmunoResearch) and donkey anti-mouse Alexa594 (1:200, Cat No. 715-585-151, Jackson ImmunoResearch) were used. Nuclei were stained with 4’,6-diamidino-2-phenylindole (1:3000, Invitrogen). Images of the antibody-stained retinas were acquired using a Zeiss LSM900 laser scanning confocal microscope.

Haematoxylin and Eosin staining of retinas

Paraffin sections of retinas derived from Rd10 mice and control individuals at postnatal Day 21 were conducted. Retinal sections were sequentially immersed in environmentally friendly dewaxing transparent liquid I (Servicebio, G1128-1 L) for 20 min, followed by environmentally friendly dewaxing transparent liquid II (Servicebio, G1128-1 L) for an additional 20 min, anhydrous ethanol I (SCRC, 100092683) for 5 min, anhydrous ethanol II (SCRC, 100092683) for 5 min, and 75% ethyl alcohol for another 5 min. The sections were then rinsed with tap water. The retinal sections were subsequently incubated in haematoxylin solution for 3–5 min, followed by another rinse with tap water. The sections were then treated with a haematoxylin differentiation solution and rinsed again with tap water. Afterwards, the sections were placed in a haematoxylin bluing solution and rinsed with tap water. Then, the sections were immersed in 95% ethanol for 1 min and then in eosin for 15 s. Finally, the sections were sequentially placed in absolute ethanol I for 2 min, absolute ethanol II for 2 min, absolute ethanol III for 2 min, normal butanol I for 2 min, normal butanol II for 2 min, xylene I for 2 min, and xylene II for 2 min, before being sealed with neutral gum. Retinal images were obtained using a Nikon optical microscope (NIKON DS-U3), and the thicknesses of the WR and ONL were compared between the two groups.

RNA isolation and real-time PCR

Total RNA was extracted from the whole mount retinas of mice using TRIzol reagent (G3013, Servicebio, China), and cDNA was synthesized using SweScript All-in-One RT SuperMix (G3337, Servicebio, China). For quantitative RT‒PCR analysis, gene expression was assessed using SYBR Green qPCR Master Mix (G3320, Servicebio, China) in a LightCycler® 96 real-time PCR system (Roche, Germany) following the manufacturer’s instructions. The mRNA levels of the target genes were measured and quantified using specific primers and normalized to those of β-actin. The sequences of primers are listed in Table 2.

Table 2.

Primers used in this study.

Gene symbol Forward primer Reverse primer
Egr1 AACAACCCTATGAGCACCTG GAGTCGTTTGGCTGGGATAA
Cd44 CAGTCACAGACCTACCCAATTC GTGTGTTCTATACTCGCCCTTC
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Analysis Egr1 and Cd44 expression in the retinas of Rd10 mice using a single-cell sequencing dataset

The GSE183206 dataset contains single-cell sequencing data derived from 2 Rd10 and 2 wild-type mouse retinal samples aged P2127. We employed the Seurat R package (version 4.0) to explore single-cell RNA sequencing data, focusing on the expression patterns of Egr1 in photoreceptors and Cd44 in Müller cells. For cell filtration, cells with fewer than 200 or more than 6000 genes detected or a mitochondrial gene ratio greater than 10% were excluded. We employed 1500 highly variable genes for principal component analysis dimensionality reduction. The “FindNeighbors” and “FindClusters” (resolution = 0.8) functions, along with the uniform manifold approximation and projection (UMAP) dimensional reduction algorithm, were subsequently used to choose the best clusters for display. The following cell types were assigned to each cluster according to the abundance of known marker genes: rods (Nrl), cones (Arr3), horizontals (Onecut2), bipolars (Vsx1, Vsx2), amacrines (Gad1, Slc66a9), RGCs (Rbpms), microglia (C1qa, Cx3cr1), astrocytes (Gfap), Müllers (Rlbp1, Crabp1, Slc1a3), and endothelials (Vwf).

Prediction of target drugs regulating Egr1 and Cd44

The DGIdb database integrates resources from 20 databases and consists of existing drugs, including those that are FDA approved, antineoplastic, and immunotherapies28. We used the DGIdb to predict the drugs and molecular compounds that potentially interact with Egr1 and Cd44.

Statistical analyses

All the results are presented as the mean ± SEM, and statistical significance was assessed using Student’s t test. Statistical analysis was conducted using GraphPad Prism Software; P* < 0.05, P**< 0.01, and P*** < 0.001 indicate significant differences between the Rd10 mice and the control group.

Author contributions

Designed the experiments: K.-C.W., H.G. and X.-J.W. Performed the experiments: X.Q., X.-W.F., X.-L.L., W.-J.H., M.-L.T., G.-L.Z.,Y.-Z.W.,F.C. and H.-Y.L. Analysed the data: X.Q., X.-W.F., X.-L.L., W.-J.H., M.-L.T., G.-L.Z.,Y.-Z.W.,F.C. and H.-Y.L. Contributed to the writing of the manuscript: K.-C.W., H.G. and X.-J.W., X.Q., X.-W.F., and X.-L.L. All the authors read and approved the final manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (No.82101169, No.82201202), the Guizhou Science Technology Support Project (Qiankehezhicheng[2020]4Y146), the Guizhou Province Foundation for Innovative Thousand-Level Talent (Zhukehetong-GCC[2022]015), the Academic Seed Cultivation Base Free Exploration Innovation Special Project of Guizhou Province, the Medical Scientific Research Foundation of Wuhan Municipal Health Commission (No.WG21D03), the ‌Guiyang Science and Technology Planning Project (Zhukehetong[2024]No.2–25), the Guizhou Medical University–Guizhou Double Helix Biotechnology Co., Ltd. Joint Project (No.HY2403), and the Science and Technology Fund of Guizhou Provincial Health Commission (Qianweijianhan[2024]24).

Data availability

All the data generated or analysed during this study are included in this manuscript. The Sanger sequencing data for mouse genotype identification have been deposited in Figshare and are available from the following web link: https://doi.org//10.6084/m9.figshare.30484022.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Xu Qiu, Xue-Wei Fu and Xin-Lan Lei contributed equally to this work.

Contributor Information

Jian-Wei Xu, Email: xujianwei@gmc.edu.cn.

Hao Gu, Email: 13765135577@139.com.

Kun-Chao Wu, Email: wukunchao@126.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All the data generated or analysed during this study are included in this manuscript. The Sanger sequencing data for mouse genotype identification have been deposited in Figshare and are available from the following web link: https://doi.org//10.6084/m9.figshare.30484022.

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