Mitochondrial-Inflammatory Axis Dysregulation Triggers Disulfidptosis and the Systemic Repair Mechanism of Bisphenol A following Spinal Cord Injury

Mitochondrial-Inflammatory Axis Dysregulation Triggers Disulfidptosis and the Systemic Repair Mechanism of Bisphenol A following Spinal Cord Injury | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mitochondrial-Inflammatory Axis Dysregulation Triggers Disulfidptosis and the Systemic Repair Mechanism of Bisphenol A following Spinal Cord Injury Zixing Xu, Zhechen Li, Xinhao Huang, Chuanrong Chen, Changyi Jiang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9253233/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Spinal cord injury (SCI) often leads to significant neurological impairment and poses a substantial therapeutic challenge, with disulfidptosis recently identified as a potential mechanism exacerbating such pathologies. This study sought to elucidate the role of Bisphenol A (BPA) in modulating key genes and metabolites associated with disulfidptosis in the context of SCI. Utilizing a murine SCI model, we established three cohorts: a sham control, an SCI group, and an SCI group treated with BPA. Comprehensive assessments, including locomotor recovery via the Basso Mouse Scale (BMS), gait analysis, histopathological evaluations through H&E and Nissl staining, and integrated transcriptomic and metabolomic profiling, were conducted. Results revealed that BPA administration significantly improved locomotor recovery and mitigated histopathological alterations, with Ndufs1, Ndufa11, and Ndufb10 identified as pivotal genes, alongside leukotriene B4 (LTB4) and prostaglandin B2 (PGB2) as crucial metabolites. Notably, these genes were intricately linked to the oxidative phosphorylation (OXPHOS) pathway and exhibited positive intercorrelations, while the metabolites were enriched within the arachidonic acid (AA) metabolism pathway. As the injury progressed, key gene expression diminished, whereas metabolite concentrations increased; BPA treatment effectively reversed these trends. Collectively, these findings indicate that BPA exerts a protective effect against SCI by disrupting a harmful feedback loop involving mitochondrial dysfunction and inflammatory activation, thus countering disulfidptosis and fostering an environment conducive to neural regeneration, underscoring its potential as a therapeutic agent in SCI management. Spinal cord injury Bisphenol A Disulfidptosis Metabolomics Key genes Key metabolites Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Spinal cord injury (SCI) represents a devastating condition associated with significant disability. The difficulties in attaining effective repair arise from complex pathological cascades and the constrained regenerative potential of cells in the central nervous system (CNS) [ 1 ]. Presently, the pathophysiological regulatory mechanisms post-SCI remain poorly understood, despite researchers working to fully understand these mechanisms and develop targeted treatments to encourage axonal regrowth and rebuild neural connections, but outcomes have not yet met expectations [ 2 ]. Current clinical approaches, such as surgical decompression, drug treatments, stem cell therapy, and neurostimulation, offer limited benefits and have not led to breakthroughs [ 3 , 1 , 4 , 5 ]. The development of SCI encompasses two distinct stages: primary injury and secondary injury. The primary injury results in permanent cellular impairment and necrosis of tissues, while the secondary injury involves reversible programmed cell death (PCD) [ 6 ]. Understanding the different PCD pathways in SCI and how they are regulated is key to managing secondary injury effectively and promptly [ 7 , 8 ]. Recent studies have identified several PCD types, including apoptosis, necroptosis, autophagy, ferroptosis, and cuproptosis. A deeper understanding of the molecular mechanisms behind these cell death pathways could lead to new treatment strategies that improve the survival of neurons and glial cells and reduce neurological impairments [ 9 ]. In 2023, researchers discovered and described a new form of cell death called disulfidptosis, which involves the collapse of the cytoskeleton due to disulfide stress caused by a lack of glucose [ 10 ]. This unique cell death pathway results from the buildup of intracellular disulfides, such as cystine, mainly due to a decrease in intracellular reduced NADPH. This accumulation leads to disulfide bonds forming in the actin cytoskeleton, causing it to contract and creating cellular disulfide stress. Eventually, the cytoskeleton detaches from the cell membrane, leading to cell death [ 11 , 12 ]. As a newly identified type of PCD, disulfidptosis has been reported in research on tumors, neurodegenerative diseases, and metabolic disorders [ 13 – 15 ]. However, its role in SCI is not yet clear. SCI typically entails significant impairment to neurons and axons, with the cytoskeleton playing a vital role in preserving neuronal architecture and functionality. Actin, a crucial element of the neuronal cytoskeleton, is fundamental to neuronal plasticity and repair. Emerging research indicates that dysregulated actin dynamics may drive neurodegenerative alterations [ 7 , 16 ]. These observations imply a potential link between disulfidptosis and SCI, though the exact fundamental mechanisms require additional exploration. Several studies have explored disulfidptosis-associated genes in SCI to identify possible diagnostic indicators and therapeutic targets [ 17 – 19 ]. Due to limitations in analytical methods, gene expression data across studies are inconsistent. Therefore, a thorough and multifaceted investigation is necessary to clarify and confirm the role of disulfidptosis in SCI. Changes in metabolism may result from gene expression alterations revealed by transcriptomics. Integrated analysis can help infer potential regulatory relationships, offering a basis for deeper insights into disease development and progression [ 20 ]. Metabolomics can identify abnormal changes in lipids, amino acids, energy-related metabolites, oxidative stress, and antioxidant metabolites in plasma after SCI, while transcriptomics provides gene regulatory networks and detailed molecular insights into biological processes. Combining metabolomics with transcriptomics offers a more complete understanding of SCI pathogenesis compared to using either method alone [ 21 ]. Comprehensive multi-omics analysis demonstrates substantial dysregulation within the purine metabolic pathway, as evidenced by significant alterations at both transcriptomic and metabolomic levels. This includes the identification of 48 differentially expressed genes and 16 significantly altered metabolites. Further investigation suggests that this metabolic perturbation may critically impair energy metabolism within the injured microenvironment, aggravating oxidative stress and other harmful responses, thereby impeding neural repair and regeneration [ 20 ]. Although integrated multi-omics approaches have not been widely applied in SCI research, our previous identification of key disulfidptosis-related genes in SCI and screening of potential therapeutic agents provides a foundation for combining metabolomic and transcriptomic analyses. Such integration can elucidate differential metabolites and key genes associated with disulfidptosis in SCI, analyze their co-enriched pathways, deepen the understanding of SCI pathological mechanisms, and propose novel diagnostic and therapeutic targets. In preliminary work, we analyzed the SCI-related RNA-seq dataset GSE151371 alongside 106 disulfidptosis-associated genes, identifying eight key genes. Subsequent comparison with toxicogenomic databases revealed that all eight genes are linked to bisphenol A (BPA), suggesting that BPA may influence disulfidptosis in SCI by targeting these genes. BPA, an endocrine-disrupting chemical, interferes with hormone receptor binding and is associated with various health issues. Studies indicate that BPA, acting as an exogenous hormone, can trigger multiple cell death pathways—including necroptosis, inflammatory apoptosis, apoptosis, ferroptosis, and autophagy—across different cell types [ 22 ]. Additional studies indicate that minimal concentrations of BPA can boost the production of reduced glutathione (GSH), which helps neutralize reactive oxygen species (ROS) [ 23 ], thereby displaying antioxidant characteristics [ 24 ]. Consequently, the exact process through which BPA influences disulfidptosis in SCI is still not fully understood. This study seeks to explore the underlying mechanisms of disulfidptosis in SCI through a mouse model subjected to BPA intervention, combined with metabolomic and transcriptomic analyses. 2. Method 2.1 Data source The SCI training dataset was derived from bulk RNA sequencing data in the GSE151371 series, retrieved from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). Based on the GPL20301 platform, GSE151371 encompasses 38 blood samples from SCI patients and 10 healthy control samples. In addition, 106 disulfidptosis-associated genes were compiled from previously published literature (Supplementary Table 1). 2.2 Differential expression analysis Within the GSE151371 dataset, DEGs between SCI and control groups (SCI vs. control), designated as DEGs1, were identified using the "limma" package (v 3.54.2) with thresholds of |log 2 FC| > 0.5 and P < 0.05. Results were visualized through a volcano plot generated via the "ggplot2" package (v 3.5.1) and a heatmap constructed using the "ComplexHeatmap" package (v 2.14.0) based on log 2 FC values. 2.3 Analysis of Protein-protein interaction (PPI) networks DEGs1 were cross-referenced against disulfidptosis-associated genes using the "ggvenn" package (v 0.1.9), and the resulting overlapping genes were subjected to PPI analysis to determine hub genes. These overlapping genes were uploaded to the STRING database ( https://string-db.org/ ) to construct a PPI network with an interaction score threshold above 0.4. The top 30 genes were identified by applying three algorithms—MCC, MNC, and Degree—via the "Cytohubba" plugin in Cytoscape (v 3.7.2). Hub genes were subsequently defined by intersecting the three resulting gene sets using the "ggvenn" package (v 0.1.9). Hub genes were further subjected to machine learning analyses within GSE151371 to pinpoint key genes. The SVM-RFE method was implemented using the "e1071" package (v 1.7–13) with 5-fold cross-validation to select genes associated with optimal accuracy and minimal error. The RF algorithm was then applied using the "randomForest" package (v 4.7–11), with the ntree parameter optimized over values ranging from 10 to 100 in increments of 2 by minimizing the out-of-bag error. Importance scores were subsequently computed for all genes, and the top ten were retained. The overlap between genes selected by both algorithms was determined using the "ggvenn" package (v 0.1.9) to define the final key genes. 2.4 Expression validation and ROC analysis Within GSE151371, the Wilcoxon test was applied to compare expression levels of key genes between SCI and control samples. ROC curves were subsequently generated using the "pROC" package (v 1.18.0) to evaluate the diagnostic capacity of each key gene, with an AUC threshold set above 0.7. 2.5 Drug prediction To explore candidate therapeutics for SCI, upstream small-molecule drugs targeting the key genes were retrieved from the CTD ( https://ctdbase.org ), with a minimum interaction count threshold of greater than 2. Only drugs predicted across all key genes were retained for subsequent analysis. 2.6 Establishment of mouse models Animal procedures followed laboratory animal welfare guidelines and were approved by the Laboratory Animal Welfare & Ethics Committee of Fujian Medical University. Female C57BL/6J mice aged 6–8 weeks were procured from Henan Scibes Biotechnology Co., Ltd. (total n = 18; Production License: SCXK (Yu) 2020-0005; Use License: SYXK (Dian) K2020-0006). After a three-day acclimatization period, animals were randomly divided into three groups (n = 6 per group): a control group, an SCI model group, and an SCI model group receiving Bisphenol A (BPA; Sigma, B26926, USA) treatment, referred to as the BPA group. Mice in the SCI and BPA groups were anesthetized via intraperitoneal injection of 1% sodium pentobarbital. Following sterilization, a laminectomy was performed at the T9 vertebral level to fully expose the spinal cord, and SCI was induced using an impact force of 50 kdynes delivered via a spinal cord impactor. Successful model establishment was verified by localized hemorrhage at the lesion site, hindlimb convulsions, and tail spasms. From postoperative day one, the BPA group mice received daily intragastric BPA administration at 5 µg/kg/day. Body weight was recorded weekly, and Basso Mouse Scale (BMS) scores were evaluated every 10 days. Gait analysis and stride measurements were conducted across all groups after model establishment. The following day, animals were euthanized by overdose with 20% urethane. Serum was collected, and spinal cord tissues were divided into three portions: one fixed in 4% paraformaldehyde, one stored at − 80°C for subsequent analyses, and one preserved overnight in RNA stabilization solution at 4°C before transfer to − 80°C. 2.7 Hematoxylin-eosin (HE) staining Paraffin-embedded spinal cord sections were stained with HE to evaluate tissue pathology. Slides were baked at 64°C for 60 minutes, deparaffinized in xylene I and xylene II (10 min each), and rehydrated through graded ethanol (100% I, 100% II, 95%, 80%, 70%; 5 min each) followed by distilled water (10 min). Sections were stained with hematoxylin for 2 minutes, differentiated for 10–15 seconds, and blued under running water for ≥ 15 minutes. Eosin counterstaining was applied for 5–10 seconds. After dehydration through ascending ethanol and clearance in xylene, sections were mounted with neutral balsam and whole-slide scanned. 2.8 Nissl staining Nissl staining was performed to assess neuropathological changes in spinal cord sections. After baking at 64°C for 60 minutes, deparaffinization, and rehydration as described in Section 2.7 , sections were stained in Nissl solution for 5 minutes and washed under running water until the eluate was clear. Differentiation was performed with 0.1% glacial acetic acid, and slides were dried completely at 37°C. Sections were then cleared in xylene, mounted with neutral balsam, and scanned. 2.9 Transcriptome sequencing and data preprocessing Total RNA was extracted with TRIzol reagent (Invitrogen, Carlsbad, CA, USA), quantified using a NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA), and integrity-assessed by Bioanalyzer 2100 (Agilent, CA, USA; RIN > 7.0) with additional verification by denaturing agarose gel electrophoresis. Poly(A) RNA was enriched from 1 µg total RNA using Dynabeads Oligo(dT)25-61005 (Thermo Fisher, CA, USA) and fragmented with a Magnesium RNA Fragmentation Module (NEB, cat. e6150, USA) at 94°C for 5–7 minutes. First-strand cDNA synthesis used SuperScript™ II Reverse Transcriptase (Invitrogen, cat. 1896649, USA), and second-strand synthesis employed RNase H (NEB, cat. m0297, USA), E. coli DNA polymerase I (NEB, cat. m0209, USA), and dUTP Solution (Thermo Fisher, cat. R0133, USA) to produce U-labeled DNA. After end-repair, adapter ligation, and AMPureXP bead size selection, libraries were treated with heat-labile UDG enzyme (New England Biolabs, cat. m0280, USA) and PCR-amplified (95°C 3 min; 8 cycles of 98°C 15 s / 60°C 15 s / 72°C 30 s; 72°C 5 min). Mean insert size was 300 ± 50 bp. Paired-end sequencing (2 × 150 bp) was performed on an Illumina NovaSeq™ 6000 platform (LC-Bio Technology Co., Ltd., Hangzhou, China). Raw reads were quality-filtered with fastp ( https://github.com/OpenGene/fastp ) under default parameters. 2.10 Identification and functions of candidate genes In the transcriptome sequencing data, using the same method as described in Section 2.2 , DEGs2 between the SCI and control groups (SCI vs control), as well as DEGs3 between the BPA and SCI groups (BPA vs SCI), were obtained via “DESeq2” package (v 1.42.0) (|log 2 FC| > 0.1, P < 0.05). The findings were graphically represented through a volcano plot plotted via “ggplot2” package (v 3.5.1) and a heatmap created via “pheatmap” package (version 1.0.12). To identify disulfidptosis-related DEGs in SCI under BPA intervention, referred to as candidate genes, the following approach was implemented. First, upregulated genes from DEGs2 were intersected with downregulated genes from DEGs3 to obtain intersection gene set 1. Next, downregulated genes from DEGs2 were intersected with upregulated genes from DEGs3 to produce intersection gene set 2. Subsequently, using the "ggvenn" package (version 1.7.3), the combined set of intersection gene sets 1 and 2 was intersected with disulfidptosis-related genes, which were first converted to their mouse orthologs (Supplementary Table 2). The final intersecting genes were designated as candidate genes. Specifically, the "clusterProfiler" package (version 4.10) was utilized to conduct GO and KEGG analyses on the candidate genes, investigating their associated functions and pathways (P < 0.05). The top five results, ranked by P-values, were presented. Furthermore, to investigate the functional roles and regulatory mechanisms of the candidate genes in SCI, a PPI network was built for them using the STRING database, with an interaction score threshold set above 0.15. Besides, the expression levels of disulfidptosis-related genes, including Slc7a11, were compared between SCI and control samples, as well as between BPA and SCI samples, using the “DESeq2” package (v 1.42.0) to investigate changes in disulfidptosis-related genes during BPA intervention in SCI. 2.11 Metabolome sequencing and data preprocessing Samples were thawed at 4°C, and 100 µL aliquots were combined with 400 µL ice-cold methanol and incubated at − 20°C for 30 minutes. After centrifugation (20,000 g, 10 min, 4°C), supernatants were collected for UPLC-HRMS analysis, with a pooled QC sample prepared from equal volumes of all supernatants. Separation was performed on an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 µm, Waters) using a Waters I-CLASS UPLC system coupled to an AB SCIEX TOF 6600 mass spectrometer. Mobile phase A was aqueous 5 mmol/L ammonium acetate / 5 mmol/L acetic acid; mobile phase B was acetonitrile. Gradient: 2% B (0–0.8 min) → 70% B (2.8 min) → 90% B (5.6 min) → 100% B (6.4–8.0 min) → 2% B (8.1 min) → re-equilibration to 10.0 min. Flow rate was 0.35 mL/min, injection volume 4 µL, and column temperature 40°C. Detection used a Triple TOF 6600 instrument (AB SCIEX) in positive and negative ESI modes (source: 500°C; +5000 V / −4500 V; curtain gas 30 psi; Gas 1/2: 60 psi). Full scan (60–1200 Da, 150 ms) with IDA of the 12 most abundant ions > 100 (25–1200 Da, 30 ms; 4-s dynamic exclusion) was applied. 2.12 Assessment of metabolomic data Shapiro-Wilk tests were applied to assess metabolite normality within each group (P > 0.05 indicating normality), summarized by P-value bar charts and Q-Q plots of 10 randomly selected metabolites. PCA was performed using "factoextra" (v 1.0.7) for inter-group distribution visualization, followed by OPLS-DA with "ropls" (v 1.34.0) for finer group discrimination. 2.13 Enrichment analysis of candidate metabolites Differentially expressed metabolites (DEMs) between SCI and control groups, and between BPA-treated and SCI groups, were separately identified from positive and negative ion mode metabolomic data using a t-test, with selection criteria of Variable Importance in Projection (VIP) > 1.0, P 0. These sets were designated DEMs1 (SCI vs. control) and DEMs2 (BPA vs. SCI), respectively. Candidate metabolites—those altered in SCI and modulated by BPA—were identified by applying the same intersection strategy as described in Section 2.6 . KEGG pathway information for mice was retrieved using the "KEGGREST" package (v 1.40.1), and enrichment analysis was conducted on candidate metabolites with P < 0.05, with results ranked by ascending P-value. 2.14 Identification of Key Genes and Key Metabolites Key genes were defined as candidate genes enriched within a selected KEGG pathway from the significantly enriched pathways identified for candidate genes. Pairwise correlations between key genes and all remaining candidate genes were computed across transcriptomic samples using the cor.test function from the "stats" package (v 4.3.1), with thresholds of |cor| > 0.3 and P < 0.05. The identical methodology and thresholds were applied to identify key metabolites from the metabolomic data. 2.15 Gene Set Enrichment Analysis (GSEA) For each key gene, Pearson correlation coefficients with all other genes were computed using "psych" (v 2.2.9) and ranked in descending order to generate pre-ranked gene lists. GSEA was performed with "clusterProfiler" (v 4.7.1.003) against the "M2.all.v2025.1.Mm.symbols.gmt" gene set from MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb ; |NES| > 1, adjusted P < 0.05). The top five pathways per key gene are presented, and the top 20 pathways shared across all key genes were visualized. 2.16 GeneMANIA analysis, molecular regulatory network, and disease network Key genes were submitted to GeneMANIA ( https://genemania.org/ ) to construct a GGI network (Mus musculus). Mouse genes were mapped to human homologs; miRNAs targeting key genes were retrieved from miRTarBase ( https://mirtarbase.cuhk.edu.cn/ ); cognate lncRNAs were identified via miRNet ( https://www.mirnet.ca ); and associated TFs were predicted through CHEA3 ( https://maayanlab.cloud/chea3/ ). These elements were integrated into a TF-mRNA-miRNA-lncRNA regulatory network. Disease associations were explored via DisGeNET ( http://www.disgenet.org/ ). 2.17 The expression levels of key genes and key metabolites Expression levels of key genes and key metabolites across all transcriptomic and metabolomic samples were compared with P < 0.05 and visualized as boxplots using the "ggplot2" package (v 3.5.1). 2.18 Statistical analysis All analyses were conducted in R (v 4.2.2). Two-group comparisons used the Wilcoxon test or t-test. Significance levels: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; ns, not significant. 3. Results 3.1 BPA as a Potential Therapeutic Agent for SCI In the GSE151371 dataset, compared to the control group, 7,142 differentially expressed genes (DEGs1) were identified in the SCI group, comprising 439 upregulated and 6,703 downregulated genes (Supplementary Fig. 1A, B). By intersecting DEGs1 with genes associated with disulfidptosis, 50 overlapping genes were identified (Supplementary Fig. 1C). Notably, 27 hub genes were further selected through PPI analysis (Supplementary Fig. 1D). Among these, NDUFA10, HNRNPA3, PPIH, RPA1, SAFB2, MYL6, HNRNPH1, and PCBP2 were pinpointed as key genes for further investigation through the application of two machine learning methodologies (Supplementary Fig. 1E). Except for MYL6, which showed a notable increase in expression within the SCI group, the remaining seven key genes exhibited significant downregulation in this group (Supplementary Fig. 1F). Moreover, the AUC values derived from the ROC analysis for each gene surpassed 0.8, indicating their diagnostic potential for SCI (Supplementary Fig. 1G). All these genes were found to be associated with BPA (Supplementary Fig. 1H). Supplementary Fig. 1: BPA was a potential drug for protecting SCI. (A, B) Volcano plot and heatmap of differentially expressed genes1 (DEGs1) between the SCI and control groups in GSE151371. (C) Venn diagram of genes between DEGs1 and disulfidptosis-related genes. (D) Venn diagram of genes obtained from MCC, MNC, and Degree algorithms. (E) Venn diagram of genes obtained from Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and random forest (RF) algorithms. (F) The expression levels of core genes between SCI and control groups. ****P < 0.0001. (G) The receiver operating characteristic (ROC) curves of core genes. (H) The drug-core gene network. Orange: core gene; blue: drug. 3.2 Pathological Changes in SCI Mice Following BPA Intervention Gross examination of harvested tissues revealed marked atrophy at the SCI site in all mice of the SCI group (Fig. 1 A). Body weight data indicated that mice in the control group maintained stable body weight, whereas those in the SCI group exhibited a significant decrease after the first week, remaining at a low level in subsequent weeks. Mice in the BPA group exhibited a rise in body weight from the second week onward, yet their weight stayed below that of the control group (Fig. 1 B). Moreover, Basso Mouse Scale (BMS) scores demonstrated that the control group maintained a score of 10 throughout the experiment, indicating normal motor function. The SCI group had a BMS score of 0 at day 0, which gradually increased but remained significantly lower than that of the control group throughout the experimental period. The BPA group also had a BMS score of 0 at day 0; however, their scores increased more rapidly than those of the SCI group and approached the level of the control group in the later stages of the experiment (Fig. 1 C). Subsequent step distance measurements revealed that the control group had the longest step distance (approximately 6 cm), while the SCI group had the shortest step distance (approximately 3 cm). The step distance of mice in the BPA group was intermediate between those of the control and SCI groups (approximately 4 cm) (Fig. 1 D). Subsequently, HE staining and Nissl staining were performed on the BPA group, which exhibited superior efficacy. HE staining results revealed that the spinal cord in the control group had an intact structure with tightly arranged cells; in contrast, the SCI group presented obvious lesion areas with disorganized structure and sparse cellular distribution. The spinal cord damage in the BPA group was significantly alleviated, and its structural integrity and cell density were much closer to those of the control group, indicating that BPA intervention can improve the histopathological condition of the spinal cord following SCI (Fig. 1 E). Consistent findings were observed with Nissl staining. The control group exhibited a regular architecture and tightly arranged cells, whereas the SCI group showed disorganized tissue, sparse and fragmented cells in the injured region. The BPA intervention group displayed notably less severe injury, with tissue morphology and cellular density much more similar to the control group (Fig. 1 F). These results further confirm that BPA treatment can ameliorate the histopathological state of the spinal cord post-injury. 3.3 Functions of the 21 candidate genes A total of 5,687 differentially expressed genes (DEGs2) were identified in the SCI group compared to the control group, comprising 3,176 up-regulated and 2,511 down-regulated genes (Fig. 2 A, B). Using the same methodology, 3,965 DEGs3 were identified in the BPA group, comprising 1,612 up-regulated and 2,353 down-regulated genes (Fig. 2 C, D). The intersection between the 3,176 up-regulated DEGs2 and the 2,353 down-regulated DEGs3 yielded 1,836 overlapping genes (overlapping genes1, Fig. 2 E). Similarly, the intersection between the 2,511 down-regulated DEGs2 and the 1,612 up-regulated DEGs3 resulted in 1,156 overlapping genes (overlapping genes2, Fig. 2 F). After merging overlapping genes1 and overlapping genes2, a total of 2,992 DEGs associated with SCI under BPA intervention were identified. These were further intersected with 101 disulfidptosis-related genes converted to mouse homologs, ultimately yielding 21 candidate genes (Fig. 2 G). The candidate genes exhibited significant enrichment across 648 GO terms, including 503 terms in Biological Process (e.g., NADH dehydrogenase complex assembly), 70 terms in Cellular Component (for instance, mitochondrial respiratory chain complex I), and 75 terms in Molecular Function (e.g., actin binding), along with 22 KEGG pathways such as oxidative phosphorylation (P < 0.05, Fig. 2 H, Supplementary Table 3). These findings preliminarily suggest that the candidate genes may be involved in mitochondrial function. Subsequently, the PPI network further revealed interactions among 17 candidate genes, including Flna and Prdx1 (Fig. 2 I). Finally, the results of differential expression analyses of disulfidptosis-related genes between groups showed that the expression level of Slc7a11 was significantly higher in SCI samples than in control samples (P < 0.05, Supplementary Fig. 2A), whereas the expression of Slc7a11 was markedly decreased in BPA-treated samples after BPA intervention (P < 0.001, Supplementary Fig. 2B). These findings were highly consistent with the expected changes in disulfidptosis. Supplementary Fig. 2: The expression of disulfidptosis-related genes altered following SCI, and was reversed following BPA intervention. (A) The differential expression levels of genes between the SCI and control groups. (B) The differential expression levels of genes between the BPA and SCI groups. Note: The horizontal axis denotes the experimental groups (Control, SCI, BPA), and the vertical axis represents the FPKM gene expression levels. Each box plot displays the median, the interquartile range, and individual data points; * indicates significance markers (*p < 0.05, **p < 0.01, ***p 0.05 revealed that 94.22% of metabolites followed a normal distribution in the BPA group (Fig. 3 A), 94.41% in the SCI group (Fig. 3 B), and 95.95% in the control group (Fig. 3 C). Subsequent PCA results demonstrated clear separation of metabolites between the SCI and control groups (Fig. 3 D), as well as between the BPA and SCI groups (Fig. 3 E). OPLS-DA analysis indicated that between the SCI and control groups, the model explanatory power (R2Y) reached 0.9795, and predictive ability (Q2Y) was 0.8983 (Fig. 3 F). Between the BPA and SCI groups, R2Y reached 0.9699, and Q2Y was 0.822 (Fig. 3 G). These results indicate a significant separation trend in metabolic profiles both between the SCI and control groups and between the BPA and SCI groups. Collectively, the results indicate the excellent quality of the metabolomics dataset. 3.5 Pathways significantly enriched by the 39 candidate metabolites Compared with the control group, 142 DEMs1 were identified in the SCI group, of which 60 were up-regulated and 82 were down-regulated (Fig. 4 A, B). Relative to the SCI group, the BPA group yielded 84 DEMs2, evenly distributed between 42 up-regulated and 42 down-regulated metabolites (Fig. 4 C, D). Applying the same intersection strategy as the transcriptomic analysis, the overlap between the 60 up-regulated DEMs1 and 42 down-regulated DEMs2 produced 20 candidate metabolites (Fig. 4 E), while the intersection of the 82 down-regulated DEMs1 and 42 up-regulated DEMs2 yielded 19 candidate metabolites (Fig. 4 F). Merging these two sets resulted in 39 candidate metabolites with BPA-modulated differential expression in SCI. Pathway enrichment analysis revealed that these metabolites were significantly enriched in 22 metabolic pathways (P < 0.05), including bile secretion, arachidonic acid (AA) metabolism, and tyrosine metabolism, all potentially implicated in the modulatory effects of BPA on SCI (Fig. 4 G). 3.6 Correlations between key genes/metabolites and other genes/metabolites Candidate genes significantly enriched in the oxidative phosphorylation (OXPHOS) pathway are associated with mitochondrial oxidative stress, which can drive mitochondrial dysfunction and exacerbate SCI. We therefore focused on three OXPHOS-enriched candidate genes—Ndufs1, Ndufa11, and Ndufb10—and designated them as key genes. Ndufa11 exhibited the strongest positive correlations with Pcbp3 and Atxn10 (cor = 0.88, P < 0.01) and the most pronounced negative correlations with Inf2 and Trip6 (cor = − 0.95, P < 0.001). Ndufb10 showed the strongest positive correlation with Atxn10 (cor = 0.92, P < 0.001) and the strongest negative correlations with Flna, Actn4, and Trip6 (cor = − 0.97, P < 0.001). Ndufs1 displayed the strongest positive correlation with Atxn10 (cor = 0.83, P < 0.01) and the most significant negative correlations with Actn4 and Trip6 (cor = − 0.9, P < 0.001) (Fig. 5 A, Supplementary Table 4). All three key genes were mutually positively correlated, with the strongest intercorrelation observed between Ndufb10 and Ndufs1 (cor = 0.93, P < 0.001) (Fig. 5 B–D).Among the significantly enriched KEGG pathways of candidate metabolites, arachidonic acid (AA) metabolism warranted particular attention, as it interacts with the mitochondrial electron transport chain (ETC) by selectively inhibiting complexes I and III, thereby compromising mitochondrial respiratory function; it is also involved in post-SCI inflammatory regulation and secondary injury. Two metabolites mapping to this pathway—leukotriene B4 (LTB4) and prostaglandin B2 (PGB2)—were defined as key metabolites. LTB4 showed the strongest positive correlation with ent-7α,12β-dihydroxy-16-kauren-19,6β-olide (cor = 0.84, P < 0.001) and the most significant negative correlation with citrulline (cor = − 0.77, P < 0.001). PGB2 demonstrated the strongest positive correlation with encainide and fluanisone (cor = 0.82, P < 0.001) and the most pronounced negative correlation with cholic acid (cor = − 0.69, P < 0.01) (Fig. 5 E, Supplementary Table 5). LTB4 and PGB2 were also significantly positively correlated with each other (cor = 0.68, P 1, adjusted P < 0.05, Fig. 6 A-C), with 741 pathways commonly enriched across all three key genes (Fig. 6 D, Supplementary Table 6). Among the top 20 co-enriched pathways, these key genes showed notable enrichment in pathways associated with energy metabolism and mitochondrial function (e.g., OXPHOS), nervous system development (e.g., lein neuron markers), signal transduction and transcriptional regulation (e.g., GLIS2 targets up), as well as biosynthesis and metabolic function (e.g., cholesterol biosynthesis) (Fig. 6 E). 3.8 Comprehensive analysis of key gene-associated networks The GGI network analysis predicted that 20 genes, including Ndufs2 and Ndufv1, likely share functional similarities with the identified key genes. These genes are potentially co-involved in mitochondrial-associated biological processes, such as the assembly of the mitochondrial respirasome and the preservation of the mitochondrial inner membrane integrity (Fig. 7 A). The TF-mRNA-miRNA-lncRNA regulatory network analysis revealed that 36 miRNAs were predicted to target NDUFS1, 17 miRNAs for NDUFA11, and 3 miRNAs for NDUFB10. From the initial 56 miRNAs, only those lncRNAs predicted to be targeted by miRNAs linked to all three key genes were retained, resulting in a final selection of 9 lncRNAs. Subsequently, transcription factors (TFs) targeting these three key genes were predicted, identifying 36 TF-mRNA regulatory pairs where the TFs could simultaneously regulate all key genes, highlighting distinct molecular regulatory mechanisms (Fig. 7 B). Furthermore, the disease association network suggested potential involvement of these key genes in other mitochondrial-related pathologies, including mitochondrial complex I deficiency and broader mitochondrial disorders (Fig. 7 C). 3.9 Expression Profiles of Key Genes and Metabolites in Samples Transcriptomic analysis revealed that the expression levels of Ndufs1, Ndufa11, and Ndufb10 declined progressively as SCI advanced, whereas BPA intervention significantly restored their expression (P < 0.05, Fig. 8 A). In parallel, metabolomic profiling showed that LTB4 and PGB2 concentrations were markedly elevated in the SCI group relative to controls, but were substantially suppressed following BPA treatment (P < 0.01, Fig. 8 B). Collectively, these findings indicate that SCI progression is accompanied by a gradual reduction in key gene expression and a concurrent rise in key metabolite levels, both of which were effectively reversed by BPA intervention. 4. Discussion Disulfidptosis, a recently identified form of PCD, is distinct from previously characterized PCD pathways. It not only impairs cell viability but also exhibits resistance to inhibitors targeting other PCD mechanisms, such as the ferroptosis inhibitors Deferoxamine (DFO) and Ferrostatin-1 (Ferr-1), the apoptosis inhibitor Z-VAD-fmk, and the autophagy inhibitor Chloroquine (CQ) [ 13 , 25 , 26 ]. Furthermore, genetic ablation of key ferroptosis and apoptosis-related genes (e.g., ACSL4, BAX, BAK) fails to prevent disulfidptosis induction [ 10 ]. Research has established a strong association between disulfidptosis and CNS disorders, including neurodegenerative diseases, neuroglioma, and ischemic stroke [ 27 , 16 , 13 ]. Additionally, genes associated with disulfidptosis might be involved in modulating acute SCI [ 18 , 19 ]. Consequently, disulfidptosis is likely intricately linked to CNS injury, yet its specific impact on the pathological mechanisms underlying SCI remains largely unexplored. Secondary SCI results from a cascade of responses initiated by oxidative stress, inflammation, tissue hemorrhage, hypoxia, and cell death [ 28 ]. Mitochondrial dysfunction is a pivotal contributor, as it can precipitate impaired energy production, excessive generation of ROS, disruption of calcium homeostasis, and potentiated apoptotic signaling [ 29 – 31 ]. Specifically, post-SCI, dysfunction in the mitochondrial electron transport chain (ETC) leads to overproduction of ROS, thereby instigating oxidative stress [ 32 ]. This results in damage to mitochondrial DNA, proteins, and lipids, subsequently triggering a series of downstream effects. These include dysregulated mitophagy, exacerbated inflammatory responses, and activation of various PCD pathways, forming a vicious cycle that ultimately culminates in neuronal death and neurological deficits [ 33 ]. Some research indicates that disulfide stress represents a distinct form of oxidative stress [ 34 ]. In our prior bioinformatics investigations, we identified BPA as an intervention agent linked to eight disulfidptosis-associated genes. Building on this, the current research utilized transcriptomic and metabolomic datasets from a BPA-treated mouse SCI model to explore disulfidptosis-related mechanisms via differential expression and enrichment analyses. Our findings highlight Ndufs1, Ndufa11, and Ndufb10—genes enriched in the OXPHOS pathway—as critical molecular players, alongside LTB4 and PGB2, key metabolites enriched in AA metabolism. During SCI progression, expression levels of these key genes progressively declined, while metabolite concentrations increased. Conversely, BPA intervention reversed these trends, elevating gene expression and reducing metabolite levels. Neurons are highly dependent on mitochondrial OXPHOS for energy production and are particularly vulnerable to mitochondrial dysfunction [ 31 ]. Thus, downregulation of OXPHOS genes signifies aggravated mitochondrial impairment. AA and its metabolites contribute to neuroinflammation, pain, and functional deficits post-SCI through multiple signaling pathways [ 35 ]. These results underscore the therapeutic potential of BPA in SCI and reinforce the interconnected roles of mitochondrial damage-driven oxidative stress, AA metabolism, and disulfidptosis in SCI pathology. Integrated dual-omics analysis may offer novel insights into SCI mechanisms and reveal promising therapeutic targets. As candidate genes significantly enriched in the OXPHOS pathway, Ndufs1, Ndufa11, and Ndufb10 have been implicated in mitochondrial dysfunction, oxidative stress regulation, and disulfidptosis, potentially exacerbating SCI progression [ 36 – 39 ]. Ndufs1 is a core component of the electron transport chain (ETC); its dysfunction severely compromises ATP synthesis and markedly elevates ROS production [ 40 ]. Ndufa11, an accessory subunit, destabilizes complex I when dysregulated, impairing mitochondrial respiration and promoting oxidative stress [ 41 ]. Ndufb10, a transmembrane subunit, directly affects proton pump activity, reducing energy conversion efficiency [ 42 ]. Our correlation analysis revealed positive associations among these three genes. Specifically, Ndufs1 showed positive correlation with Atxn10 but negative correlations with Actn4 and Trip6. Ndufa11 correlated positively with Pcbp3 and Atxn10, and negatively with Inf2 and Trip6. Ndufb10 was positively associated with Atxn10 and negatively with Flna, Actn4, and Trip6. PPI network analysis indicated interactions among 17 candidate genes, including Flna and Prdx1. Pcbp3 and Atxn10 are RNA-binding proteins involved in mRNA stability, splicing, and translational control, whereas Inf2, Trip6, Flna, and Actn4 are cytoskeleton-associated proteins that participate in actin filament dynamics, cross-linking, and anchoring [ 43 – 46 ]. Prdx1 (peroxiredoxin 1) is a major antioxidant enzyme that scavenges hydrogen peroxide (H₂O₂), maintains cellular redox balance, and modulates oxidative stress signaling [ 47 ]. The execution phase of disulfidptosis involves abnormal disulfide bond cross-linking of actin. Actively downregulating proteins responsible for actin cross-linking, anchoring, and stabilization (Actn4 is a cross-linking protein, Flna is an anchoring protein, and Inf2 regulates polymerization) in the SCI microenvironment may favor the stability of the cytoskeleton and cell survival[ 16 , 10 , 48 ]. Transcriptomic analysis reveals that essential OXPHOS genes are collectively downregulated following SCI, resulting in mitochondrial impairment, ETC disruption, diminished ATP synthesis, compromised antioxidant capacity, heightened oxidative stress, GSH depletion, and destabilization of the cytoskeleton, ultimately driving disulfidptosis. BPA intervention targets the origin of mitochondrial dysfunction, inducing the coordinated upregulation of three pivotal genes and facilitating the simultaneous elevation of mitochondrial protective factors or cell survival cofactors (Pcbp3 and Atxn10). This improves cellular energy metabolism and strengthens antioxidant defenses, thereby mitigating oxidative stress and suppressing disulfidptosis. After SCI, Prdx1 functions to scavenge ROS, particularly H₂O₂, shielding cytoskeletal proteins such as Flna from oxidative damage. Metabolomic profiling identified 39 candidate metabolites exhibiting differential expression under BPA treatment. Among the KEGG pathways significantly enriched with these metabolites, AA metabolism is noteworthy due to its interaction with the mitochondrial ETC, as it selectively inhibits complexes I and III, thereby compromising mitochondrial respiratory function [ 49 ]. Furthermore, AA metabolism is implicated in modulating inflammation and secondary injury post-SCI [ 35 , 50 ]. Oxidative stress and AA metabolism engage in a detrimental bidirectional positive feedback loop: on one hand, ROS and other oxidative signals promote AA release from membrane phospholipids. Subsequent metabolism, via phospholipase A₂ (PLA₂) activation and modulation of cyclooxygenase/lipoxygenase activity, generates abundant pro-inflammatory mediators like LTB4 and PGB2. Conversely, these inflammatory mediators markedly intensify oxidative damage at cellular and tissue levels through multiple mechanisms, including direct induction of mitochondrial dysfunction, activation of immune cell "respiratory burst" to produce substantial ROS, and generation of reactive oxygen byproducts from their own metabolic enzymes [ 51 – 53 ]. In this study, two metabolites mapped to the AA metabolism pathway—LTB4 and PGB2—were identified as key mediators. LTB4, a lipid-derived inflammatory mediator synthesized from AA, plays a significant role in mediating leukocyte infiltration after SCI [ 54 ]. PGB2, a lipid mediator produced from AA via the cyclooxygenase pathway, is involved in regulating inflammatory responses, immune modulation, and cellular physiological processes [ 55 ]. LTB4 levels demonstrate a positive correlation with protective metabolites and a negative correlation with toxic metabolites. PGB2 exhibits a similar correlation pattern, suggesting that BPA not only suppresses inflammatory mediators but also reprograms the metabolic milieu toward a reparative state. Pathway enrichment analysis further indicated that, besides the OXPHOS pathway, key genes were notably concentrated in pathways linked to energy metabolism and mitochondrial function, nervous system development, signal transduction and transcriptional regulation, as well as biosynthesis and metabolic functions. These pathways collectively constitute a core network encompassing energy homeostasis, neural development, and transcriptional control. This finding implies that the key genes and the complex I function they represent serve as critical hubs in cellular processes. By upregulating these genes, BPA not only rescues cells from disulfide-induced death but may also concurrently activate genetic programs related to neuroregeneration and synaptic plasticity. Additionally, changes in the expression of key genes may be governed by a shared upstream transcriptional regulatory network (such as GLIS2 or other key transcriptional regulators). This research demonstrates that following SCI, the suppression of essential genes within the OXPHOS pathway (including Ndufs1, Ndufa11, and Ndufb10), coupled with the elevation of key metabolites in the AA metabolism pathway (specifically LTB4 and PGB2), establishes a detrimental "mitochondria-inflammation" feedback loop. Mitochondrial impairment initiates oxidative stress, which in turn promotes the overproduction of inflammatory mediators. Concurrently, the ensuing inflammatory cascade inflicts further damage upon mitochondria, collectively worsening the breakdown of intracellular redox equilibrium, as evidenced by GSH depletion. This cascade results in the failure of crucial cytoskeletal protective mechanisms, such as the interaction between Prdx1 and Flna, ultimately precipitating disulfidptosis by the creation of disulfide bonds inside the actin cytoskeleton. Intervention with BPA elicits comprehensive reparative effects by simultaneously enhancing the expression of mitochondrial function-related genes (thereby reversing their negative regulatory network with cytoskeletal proteins) and reducing levels of inflammatory mediators (thereby reorienting the overall metabolic network toward a reparative state). This dual action disrupts the vicious cycle, reinstates redox balance and cytoskeletal integrity, counteracts disulfidptosis, and establishes a microenvironment conducive to neural regeneration (as illustrated in Fig. 9 ). The principal limitations of this investigation are outlined below. Although a coherent framework implicating the "mitochondria-inflammation axis" in the induction of disulfidptosis was established through multi-omics correlation analyses, and the therapeutic efficacy of BPA intervention was substantiated, direct validation of causal mechanisms remains inadequate. Specifically, there is a paucity of direct evidence confirming that BPA modulates upstream transcription factors. Additionally, it remains uncertain whether the protective effects of BPA extend to other cell death pathways, such as apoptosis and ferroptosis. Moreover, the examination of inflammatory mechanisms primarily concentrated on AA metabolites, without delving deeply into more upstream classical inflammatory signaling pathways such as NF-κB and the NLRP3 inflammasome. Declarations Author contributions: Zixing Xu designed the experiment, wrote the manuscript, and provided funding support. Zhechen Li and Xinhao Huang were responsible for animal experimentation, specimen collection, and bioinformatics analysis. Chuanrong Chen and Changyi Jiang conducted animal experimentation, dual-omics analysis, and data collection. Weihong Xu revised the manuscript and was responsible for funding support. Funding: This study was supported by the Natural Science Foundation of Fujian Province (NO. 2024J01528), Fujian Provincial Health Technology Project (No. 2025CXB017), and Fujian Provincial Joint Funds for the Innovation of Science and Technology (No. 2023Y9089). 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Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 05 Apr, 2026 Submission checks completed at journal 05 Apr, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9253233","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625634736,"identity":"f4f65d89-4db9-47ea-8062-77c1d204acde","order_by":0,"name":"Zixing Xu","email":"","orcid":"","institution":"Department of Spinal Surgery, the First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zixing","middleName":"","lastName":"Xu","suffix":""},{"id":625634737,"identity":"4305eeeb-0be5-455f-9e29-51539045a534","order_by":1,"name":"Zhechen Li","email":"","orcid":"","institution":"Department of Spinal Surgery, the First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhechen","middleName":"","lastName":"Li","suffix":""},{"id":625634738,"identity":"23574b09-2359-43bc-987f-edae2a41c7c1","order_by":2,"name":"Xinhao Huang","email":"","orcid":"","institution":"Department of Spinal Surgery, the First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinhao","middleName":"","lastName":"Huang","suffix":""},{"id":625634739,"identity":"0ed8da7c-0215-45e5-b6d3-a63e08fcc459","order_by":3,"name":"Chuanrong Chen","email":"","orcid":"","institution":"Department of Spinal Surgery, the First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chuanrong","middleName":"","lastName":"Chen","suffix":""},{"id":625634740,"identity":"bb2e6763-f7c8-4da2-b6a9-0b93b3c74b9c","order_by":4,"name":"Changyi Jiang","email":"","orcid":"","institution":"Department of Spinal Surgery, the First Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changyi","middleName":"","lastName":"Jiang","suffix":""},{"id":625634741,"identity":"a50c532b-b728-4418-a70f-43637bdaa372","order_by":5,"name":"Weihong Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYLCCxAYgwXwg8UFCRQ0pWtgSkg0enDlGpBZGiBY2yYctzIRVG9zIMXzwcIdNnrwbw7OKxAY2Bv727gS8WiRn5BgbJJ5JKzY8xpB2I3GHDIPEmbMb8Grhl8jdJpHYdjhx4/wGoJYzbAwGErn4tbBJ5G7/AdbSxpBWkNjGTFgLyBYGkJb5bAxpDERpkex5/xnosLTEDWwMyRIJZ47xEPSLwfG0xI8/22wS57fxJH78UVEjx9/ei18LQu8BngQQzUOcchCQb2A/QLzqUTAKRsEoGFEAAEKBTXHgty8SAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Spinal Surgery, the First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-03-28 13:54:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9253233/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9253233/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107528260,"identity":"6ea19d37-2048-41b4-acd2-23b2564d3555","added_by":"auto","created_at":"2026-04-22 09:58:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4690941,"visible":true,"origin":"","legend":"\u003cp\u003ePathological changes in spinal cord injury (SCI) mice under bisphenol A (BPA) intervention. (A) Gross tissue images of mice in the control, SCI, BPA groups. (B) The weight changes of the mice in each group over each week. (C) The Basso Mouse Scale (BMS) score changes of the mice in each group over a period of 10 days. (D) Step distance measurement results of mice in each group. (E) Results of hematoxylin-eosin (HE) staining. (F) Results of nylon staining. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/c344f05665d2724350927499.png"},{"id":107528261,"identity":"a89226be-976f-465f-a44a-73c39aa6e113","added_by":"auto","created_at":"2026-04-22 09:58:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1276544,"visible":true,"origin":"","legend":"\u003cp\u003eFunctions of the 21 candidate genes in the transcriptome data. (A, B) Volcano plot and heatmap of differentially expressed genes2 (DEGs2) between the SCI and control groups. (C, D) Volcano plot and heatmap of DEGs3 between the BPA and SCI groups. (E) Venn diagram of genes between up-regulated DEGs2 and down-regulated DEGs3. (F) Venn diagram of genes between down-regulated DEGs2 and up-regulated DEGs3. (G) Venn diagram of genes between DEGs associated with SCI under BPA intervention and disulfidptosis-related genes. (H) Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of candidate genes. BP: biological process; CC: cellular component; MF: molecular function. (I) The protein-protein interaction (PPI) network among candidate genes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/9760703ff36c620a76bff1f3.png"},{"id":107528168,"identity":"f942e079-db1b-4825-b0d6-db74d65f2b44","added_by":"auto","created_at":"2026-04-22 09:58:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":869208,"visible":true,"origin":"","legend":"\u003cp\u003eThe high quality of the metabolomics data. (A-C) Bar plot of Shapiro-Wilk test P-values for metabolomic data and quantile-quantile (Q-Q) plots of 10 randomly selected metabolites in the BPA (A), SCI (B), and control (C) groups. (D-E) Principal Component Analysis (PCA) plots between the SCI and control groups (D) and between the BPA and SCI groups (E). (F-G) Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) plots between the SCI and control groups (F) and between the BPA and SCI groups (G).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/f0dda53dc8fbaad911271844.png"},{"id":107528251,"identity":"ecac209e-ce95-4bec-b93a-c37181248e21","added_by":"auto","created_at":"2026-04-22 09:58:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1612789,"visible":true,"origin":"","legend":"\u003cp\u003ePathways significantly enriched by the 39 candidate metabolites. (A, B) Volcano plot and heatmap of differentially expressed metabolites (DEMs) between the SCI and control groups. (C, D) Volcano plot and heatmap of DEMs between the BPA and SCI groups. (E) Venn diagram of metabolites between up-regulated DEMs1 and down-regulated DEMs2. (F) Venn diagram of metabolites between down-regulated DEMs1 and up-regulated DEMs2. (G) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of candidate metabolites.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/4e434e26de42b3b64039a310.png"},{"id":107528172,"identity":"11221507-16ef-4c0c-9506-91ccfd2b0ae4","added_by":"auto","created_at":"2026-04-22 09:58:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1354876,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between key genes/metabolites and other genes/metabolites. (A) The correaltion between key genes and other genes. (B-D) The correaltion between key genes. (B) Ndufa11 VS Ndufb10 (C) Ndufs1 VS Ndufa11 (D) Ndufs1 VS Ndufb10 (E) The correaltion between key metabolites and other metabolites. (F) The correlation between key metabolites.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/f17612ae5764628b365c1c2c.png"},{"id":107528272,"identity":"2e4a7bff-5ec8-45ef-9574-b91a77305f44","added_by":"auto","created_at":"2026-04-22 09:58:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1083687,"visible":true,"origin":"","legend":"\u003cp\u003ePathways significantly enriched by key genes. (A-C) The top 5 pathways that were significantly enriched by Ndufs1 (A), Ndufa11 (B), and Ndufb10 (C). (D) Venn diagram of significantly enriched pathways for key genes. (E) The top 20 pathways that were significantly enriched by the common key genes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/395ec8d622e963974f1179f5.png"},{"id":107528253,"identity":"8c422b1b-3342-47b1-80b3-519de4e34670","added_by":"auto","created_at":"2026-04-22 09:58:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3457214,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive analysis of key gene-related networks. (A) The Gene-Gene Interaction (GGI) network between key genes and functionally similar genes. (B) The TF-mRNA-miRNA-lncRNA regulatory network. From left to right, the columns represented transcription factors (TFs), key genes, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs). (C) The disease-key gene network. Left: key genes; right: diseases.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/aae3fd3be4d6638e6ebe0345.png"},{"id":107528254,"identity":"2e8b281a-a686-4ebd-ace1-d2d37e505d05","added_by":"auto","created_at":"2026-04-22 09:58:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":427353,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression levels of key genes and metabolites in the samples. (A) The expression levels of key genes in the three groups. (B) The expression levels of key metabolites in the three groups. *P \u0026lt; 0.05, **P \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/3bc152aa190c969086a18459.png"},{"id":107528244,"identity":"62857591-e585-419c-8749-2fcbe9d5065c","added_by":"auto","created_at":"2026-04-22 09:58:15","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4896614,"visible":true,"origin":"","legend":"\u003cp\u003eMitochondrial-Inflammatory axis dysregulation triggers disulfidptosis and the systemic repair mechanism of BPA following SCI.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/de5f25287e59bc036c5364aa.png"},{"id":107528388,"identity":"7755248a-0c2b-4d1a-9201-6c9d0abece3a","added_by":"auto","created_at":"2026-04-22 09:58:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18808011,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/1a372e7a-9787-4c85-934e-81f24ff3a1d8.pdf"},{"id":107528268,"identity":"17df6eaf-e725-4a60-8051-d106afede1ef","added_by":"auto","created_at":"2026-04-22 09:58:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":658721,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-9253233/v1/862d0d9b24374266e07e976c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mitochondrial-Inflammatory Axis Dysregulation Triggers Disulfidptosis and the Systemic Repair Mechanism of Bisphenol A following Spinal Cord Injury","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSpinal cord injury (SCI) represents a devastating condition associated with significant disability. The difficulties in attaining effective repair arise from complex pathological cascades and the constrained regenerative potential of cells in the central nervous system (CNS) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Presently, the pathophysiological regulatory mechanisms post-SCI remain poorly understood, despite researchers working to fully understand these mechanisms and develop targeted treatments to encourage axonal regrowth and rebuild neural connections, but outcomes have not yet met expectations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current clinical approaches, such as surgical decompression, drug treatments, stem cell therapy, and neurostimulation, offer limited benefits and have not led to breakthroughs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The development of SCI encompasses two distinct stages: primary injury and secondary injury. The primary injury results in permanent cellular impairment and necrosis of tissues, while the secondary injury involves reversible programmed cell death (PCD) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Understanding the different PCD pathways in SCI and how they are regulated is key to managing secondary injury effectively and promptly [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent studies have identified several PCD types, including apoptosis, necroptosis, autophagy, ferroptosis, and cuproptosis. A deeper understanding of the molecular mechanisms behind these cell death pathways could lead to new treatment strategies that improve the survival of neurons and glial cells and reduce neurological impairments [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 2023, researchers discovered and described a new form of cell death called disulfidptosis, which involves the collapse of the cytoskeleton due to disulfide stress caused by a lack of glucose [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This unique cell death pathway results from the buildup of intracellular disulfides, such as cystine, mainly due to a decrease in intracellular reduced NADPH. This accumulation leads to disulfide bonds forming in the actin cytoskeleton, causing it to contract and creating cellular disulfide stress. Eventually, the cytoskeleton detaches from the cell membrane, leading to cell death [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As a newly identified type of PCD, disulfidptosis has been reported in research on tumors, neurodegenerative diseases, and metabolic disorders [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, its role in SCI is not yet clear. SCI typically entails significant impairment to neurons and axons, with the cytoskeleton playing a vital role in preserving neuronal architecture and functionality. Actin, a crucial element of the neuronal cytoskeleton, is fundamental to neuronal plasticity and repair. Emerging research indicates that dysregulated actin dynamics may drive neurodegenerative alterations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These observations imply a potential link between disulfidptosis and SCI, though the exact fundamental mechanisms require additional exploration. Several studies have explored disulfidptosis-associated genes in SCI to identify possible diagnostic indicators and therapeutic targets [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Due to limitations in analytical methods, gene expression data across studies are inconsistent. Therefore, a thorough and multifaceted investigation is necessary to clarify and confirm the role of disulfidptosis in SCI.\u003c/p\u003e \u003cp\u003eChanges in metabolism may result from gene expression alterations revealed by transcriptomics. Integrated analysis can help infer potential regulatory relationships, offering a basis for deeper insights into disease development and progression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Metabolomics can identify abnormal changes in lipids, amino acids, energy-related metabolites, oxidative stress, and antioxidant metabolites in plasma after SCI, while transcriptomics provides gene regulatory networks and detailed molecular insights into biological processes. Combining metabolomics with transcriptomics offers a more complete understanding of SCI pathogenesis compared to using either method alone [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Comprehensive multi-omics analysis demonstrates substantial dysregulation within the purine metabolic pathway, as evidenced by significant alterations at both transcriptomic and metabolomic levels. This includes the identification of 48 differentially expressed genes and 16 significantly altered metabolites. Further investigation suggests that this metabolic perturbation may critically impair energy metabolism within the injured microenvironment, aggravating oxidative stress and other harmful responses, thereby impeding neural repair and regeneration [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although integrated multi-omics approaches have not been widely applied in SCI research, our previous identification of key disulfidptosis-related genes in SCI and screening of potential therapeutic agents provides a foundation for combining metabolomic and transcriptomic analyses. Such integration can elucidate differential metabolites and key genes associated with disulfidptosis in SCI, analyze their co-enriched pathways, deepen the understanding of SCI pathological mechanisms, and propose novel diagnostic and therapeutic targets.\u003c/p\u003e \u003cp\u003eIn preliminary work, we analyzed the SCI-related RNA-seq dataset GSE151371 alongside 106 disulfidptosis-associated genes, identifying eight key genes. Subsequent comparison with toxicogenomic databases revealed that all eight genes are linked to bisphenol A (BPA), suggesting that BPA may influence disulfidptosis in SCI by targeting these genes. BPA, an endocrine-disrupting chemical, interferes with hormone receptor binding and is associated with various health issues. Studies indicate that BPA, acting as an exogenous hormone, can trigger multiple cell death pathways\u0026mdash;including necroptosis, inflammatory apoptosis, apoptosis, ferroptosis, and autophagy\u0026mdash;across different cell types [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additional studies indicate that minimal concentrations of BPA can boost the production of reduced glutathione (GSH), which helps neutralize reactive oxygen species (ROS) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], thereby displaying antioxidant characteristics [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Consequently, the exact process through which BPA influences disulfidptosis in SCI is still not fully understood. This study seeks to explore the underlying mechanisms of disulfidptosis in SCI through a mouse model subjected to BPA intervention, combined with metabolomic and transcriptomic analyses.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eThe SCI training dataset was derived from bulk RNA sequencing data in the GSE151371 series, retrieved from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Based on the GPL20301 platform, GSE151371 encompasses 38 blood samples from SCI patients and 10 healthy control samples. In addition, 106 disulfidptosis-associated genes were compiled from previously published literature (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Differential expression analysis\u003c/h2\u003e \u003cp\u003eWithin the GSE151371 dataset, DEGs between SCI and control groups (SCI vs. control), designated as DEGs1, were identified using the \"limma\" package (v 3.54.2) with thresholds of |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 0.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Results were visualized through a volcano plot generated via the \"ggplot2\" package (v 3.5.1) and a heatmap constructed using the \"ComplexHeatmap\" package (v 2.14.0) based on log\u003csub\u003e2\u003c/sub\u003eFC values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of Protein-protein interaction (PPI) networks\u003c/h2\u003e \u003cp\u003eDEGs1 were cross-referenced against disulfidptosis-associated genes using the \"ggvenn\" package (v 0.1.9), and the resulting overlapping genes were subjected to PPI analysis to determine hub genes. These overlapping genes were uploaded to the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to construct a PPI network with an interaction score threshold above 0.4. The top 30 genes were identified by applying three algorithms\u0026mdash;MCC, MNC, and Degree\u0026mdash;via the \"Cytohubba\" plugin in Cytoscape (v 3.7.2). Hub genes were subsequently defined by intersecting the three resulting gene sets using the \"ggvenn\" package (v 0.1.9). Hub genes were further subjected to machine learning analyses within GSE151371 to pinpoint key genes. The SVM-RFE method was implemented using the \"e1071\" package (v 1.7\u0026ndash;13) with 5-fold cross-validation to select genes associated with optimal accuracy and minimal error. The RF algorithm was then applied using the \"randomForest\" package (v 4.7\u0026ndash;11), with the ntree parameter optimized over values ranging from 10 to 100 in increments of 2 by minimizing the out-of-bag error. Importance scores were subsequently computed for all genes, and the top ten were retained. The overlap between genes selected by both algorithms was determined using the \"ggvenn\" package (v 0.1.9) to define the final key genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Expression validation and ROC analysis\u003c/h2\u003e \u003cp\u003eWithin GSE151371, the Wilcoxon test was applied to compare expression levels of key genes between SCI and control samples. ROC curves were subsequently generated using the \"pROC\" package (v 1.18.0) to evaluate the diagnostic capacity of each key gene, with an AUC threshold set above 0.7.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Drug prediction\u003c/h2\u003e \u003cp\u003eTo explore candidate therapeutics for SCI, upstream small-molecule drugs targeting the key genes were retrieved from the CTD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctdbase.org\u003c/span\u003e\u003cspan address=\"https://ctdbase.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a minimum interaction count threshold of greater than 2. Only drugs predicted across all key genes were retained for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Establishment of mouse models\u003c/h2\u003e \u003cp\u003eAnimal procedures followed laboratory animal welfare guidelines and were approved by the Laboratory Animal Welfare \u0026amp; Ethics Committee of Fujian Medical University. Female C57BL/6J mice aged 6\u0026ndash;8 weeks were procured from Henan Scibes Biotechnology Co., Ltd. (total n\u0026thinsp;=\u0026thinsp;18; Production License: SCXK (Yu) 2020-0005; Use License: SYXK (Dian) K2020-0006). After a three-day acclimatization period, animals were randomly divided into three groups (n\u0026thinsp;=\u0026thinsp;6 per group): a control group, an SCI model group, and an SCI model group receiving Bisphenol A (BPA; Sigma, B26926, USA) treatment, referred to as the BPA group. Mice in the SCI and BPA groups were anesthetized via intraperitoneal injection of 1% sodium pentobarbital. Following sterilization, a laminectomy was performed at the T9 vertebral level to fully expose the spinal cord, and SCI was induced using an impact force of 50 kdynes delivered via a spinal cord impactor. Successful model establishment was verified by localized hemorrhage at the lesion site, hindlimb convulsions, and tail spasms. From postoperative day one, the BPA group mice received daily intragastric BPA administration at 5 \u0026micro;g/kg/day. Body weight was recorded weekly, and Basso Mouse Scale (BMS) scores were evaluated every 10 days. Gait analysis and stride measurements were conducted across all groups after model establishment. The following day, animals were euthanized by overdose with 20% urethane. Serum was collected, and spinal cord tissues were divided into three portions: one fixed in 4% paraformaldehyde, one stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent analyses, and one preserved overnight in RNA stabilization solution at 4\u0026deg;C before transfer to \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Hematoxylin-eosin (HE) staining\u003c/h2\u003e \u003cp\u003eParaffin-embedded spinal cord sections were stained with HE to evaluate tissue pathology. Slides were baked at 64\u0026deg;C for 60 minutes, deparaffinized in xylene I and xylene II (10 min each), and rehydrated through graded ethanol (100% I, 100% II, 95%, 80%, 70%; 5 min each) followed by distilled water (10 min). Sections were stained with hematoxylin for 2 minutes, differentiated for 10\u0026ndash;15 seconds, and blued under running water for \u0026ge;\u0026thinsp;15 minutes. Eosin counterstaining was applied for 5\u0026ndash;10 seconds. After dehydration through ascending ethanol and clearance in xylene, sections were mounted with neutral balsam and whole-slide scanned.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Nissl staining\u003c/h2\u003e \u003cp\u003eNissl staining was performed to assess neuropathological changes in spinal cord sections. After baking at 64\u0026deg;C for 60 minutes, deparaffinization, and rehydration as described in Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e2.7\u003c/span\u003e, sections were stained in Nissl solution for 5 minutes and washed under running water until the eluate was clear. Differentiation was performed with 0.1% glacial acetic acid, and slides were dried completely at 37\u0026deg;C. Sections were then cleared in xylene, mounted with neutral balsam, and scanned.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Transcriptome sequencing and data preprocessing\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted with TRIzol reagent (Invitrogen, Carlsbad, CA, USA), quantified using a NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA), and integrity-assessed by Bioanalyzer 2100 (Agilent, CA, USA; RIN\u0026thinsp;\u0026gt;\u0026thinsp;7.0) with additional verification by denaturing agarose gel electrophoresis. Poly(A) RNA was enriched from 1 \u0026micro;g total RNA using Dynabeads Oligo(dT)25-61005 (Thermo Fisher, CA, USA) and fragmented with a Magnesium RNA Fragmentation Module (NEB, cat. e6150, USA) at 94\u0026deg;C for 5\u0026ndash;7 minutes. First-strand cDNA synthesis used SuperScript\u0026trade; II Reverse Transcriptase (Invitrogen, cat. 1896649, USA), and second-strand synthesis employed RNase H (NEB, cat. m0297, USA), E. coli DNA polymerase I (NEB, cat. m0209, USA), and dUTP Solution (Thermo Fisher, cat. R0133, USA) to produce U-labeled DNA. After end-repair, adapter ligation, and AMPureXP bead size selection, libraries were treated with heat-labile UDG enzyme (New England Biolabs, cat. m0280, USA) and PCR-amplified (95\u0026deg;C 3 min; 8 cycles of 98\u0026deg;C 15 s / 60\u0026deg;C 15 s / 72\u0026deg;C 30 s; 72\u0026deg;C 5 min). Mean insert size was 300\u0026thinsp;\u0026plusmn;\u0026thinsp;50 bp. Paired-end sequencing (2 \u0026times; 150 bp) was performed on an Illumina NovaSeq\u0026trade; 6000 platform (LC-Bio Technology Co., Ltd., Hangzhou, China). Raw reads were quality-filtered with fastp (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) under default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Identification and functions of candidate genes\u003c/h2\u003e \u003cp\u003eIn the transcriptome sequencing data, using the same method as described in Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e, DEGs2 between the SCI and control groups (SCI vs control), as well as DEGs3 between the BPA and SCI groups (BPA vs SCI), were obtained via \u0026ldquo;DESeq2\u0026rdquo; package (v 1.42.0) (|log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 0.1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The findings were graphically represented through a volcano plot plotted via \u0026ldquo;ggplot2\u0026rdquo; package (v 3.5.1) and a heatmap created via \u0026ldquo;pheatmap\u0026rdquo; package (version 1.0.12).\u003c/p\u003e \u003cp\u003eTo identify disulfidptosis-related DEGs in SCI under BPA intervention, referred to as candidate genes, the following approach was implemented. First, upregulated genes from DEGs2 were intersected with downregulated genes from DEGs3 to obtain intersection gene set 1. Next, downregulated genes from DEGs2 were intersected with upregulated genes from DEGs3 to produce intersection gene set 2. Subsequently, using the \"ggvenn\" package (version 1.7.3), the combined set of intersection gene sets 1 and 2 was intersected with disulfidptosis-related genes, which were first converted to their mouse orthologs (Supplementary Table\u0026nbsp;2). The final intersecting genes were designated as candidate genes.\u003c/p\u003e \u003cp\u003eSpecifically, the \"clusterProfiler\" package (version 4.10) was utilized to conduct GO and KEGG analyses on the candidate genes, investigating their associated functions and pathways (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The top five results, ranked by P-values, were presented. Furthermore, to investigate the functional roles and regulatory mechanisms of the candidate genes in SCI, a PPI network was built for them using the STRING database, with an interaction score threshold set above 0.15. Besides, the expression levels of disulfidptosis-related genes, including Slc7a11, were compared between SCI and control samples, as well as between BPA and SCI samples, using the \u0026ldquo;DESeq2\u0026rdquo; package (v 1.42.0) to investigate changes in disulfidptosis-related genes during BPA intervention in SCI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Metabolome sequencing and data preprocessing\u003c/h2\u003e \u003cp\u003eSamples were thawed at 4\u0026deg;C, and 100 \u0026micro;L aliquots were combined with 400 \u0026micro;L ice-cold methanol and incubated at \u0026minus;\u0026thinsp;20\u0026deg;C for 30 minutes. After centrifugation (20,000 g, 10 min, 4\u0026deg;C), supernatants were collected for UPLC-HRMS analysis, with a pooled QC sample prepared from equal volumes of all supernatants. Separation was performed on an ACQUITY UPLC HSS T3 column (100 mm \u0026times; 2.1 mm, 1.8 \u0026micro;m, Waters) using a Waters I-CLASS UPLC system coupled to an AB SCIEX TOF 6600 mass spectrometer. Mobile phase A was aqueous 5 mmol/L ammonium acetate / 5 mmol/L acetic acid; mobile phase B was acetonitrile. Gradient: 2% B (0\u0026ndash;0.8 min) \u0026rarr; 70% B (2.8 min) \u0026rarr; 90% B (5.6 min) \u0026rarr; 100% B (6.4\u0026ndash;8.0 min) \u0026rarr; 2% B (8.1 min) \u0026rarr; re-equilibration to 10.0 min. Flow rate was 0.35 mL/min, injection volume 4 \u0026micro;L, and column temperature 40\u0026deg;C. Detection used a Triple TOF 6600 instrument (AB SCIEX) in positive and negative ESI modes (source: 500\u0026deg;C; +5000 V / \u0026minus;4500 V; curtain gas 30 psi; Gas 1/2: 60 psi). Full scan (60\u0026ndash;1200 Da, 150 ms) with IDA of the 12 most abundant ions\u0026thinsp;\u0026gt;\u0026thinsp;100 (25\u0026ndash;1200 Da, 30 ms; 4-s dynamic exclusion) was applied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Assessment of metabolomic data\u003c/h2\u003e \u003cp\u003eShapiro-Wilk tests were applied to assess metabolite normality within each group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating normality), summarized by P-value bar charts and Q-Q plots of 10 randomly selected metabolites. PCA was performed using \"factoextra\" (v 1.0.7) for inter-group distribution visualization, followed by OPLS-DA with \"ropls\" (v 1.34.0) for finer group discrimination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Enrichment analysis of candidate metabolites\u003c/h2\u003e \u003cp\u003eDifferentially expressed metabolites (DEMs) between SCI and control groups, and between BPA-treated and SCI groups, were separately identified from positive and negative ion mode metabolomic data using a t-test, with selection criteria of Variable Importance in Projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1.0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 0. These sets were designated DEMs1 (SCI vs. control) and DEMs2 (BPA vs. SCI), respectively. Candidate metabolites\u0026mdash;those altered in SCI and modulated by BPA\u0026mdash;were identified by applying the same intersection strategy as described in Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e2.6\u003c/span\u003e. KEGG pathway information for mice was retrieved using the \"KEGGREST\" package (v 1.40.1), and enrichment analysis was conducted on candidate metabolites with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, with results ranked by ascending P-value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Identification of Key Genes and Key Metabolites\u003c/h2\u003e \u003cp\u003eKey genes were defined as candidate genes enriched within a selected KEGG pathway from the significantly enriched pathways identified for candidate genes. Pairwise correlations between key genes and all remaining candidate genes were computed across transcriptomic samples using the cor.test function from the \"stats\" package (v 4.3.1), with thresholds of |cor| \u0026gt; 0.3 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The identical methodology and thresholds were applied to identify key metabolites from the metabolomic data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eFor each key gene, Pearson correlation coefficients with all other genes were computed using \"psych\" (v 2.2.9) and ranked in descending order to generate pre-ranked gene lists. GSEA was performed with \"clusterProfiler\" (v 4.7.1.003) against the \"M2.all.v2025.1.Mm.symbols.gmt\" gene set from MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; |NES| \u0026gt; 1, adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The top five pathways per key gene are presented, and the top 20 pathways shared across all key genes were visualized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.16 GeneMANIA analysis, molecular regulatory network, and disease network\u003c/h2\u003e \u003cp\u003eKey genes were submitted to GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to construct a GGI network (Mus musculus). Mouse genes were mapped to human homologs; miRNAs targeting key genes were retrieved from miRTarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirtarbase.cuhk.edu.cn/\u003c/span\u003e\u003cspan address=\"https://mirtarbase.cuhk.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); cognate lncRNAs were identified via miRNet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca\u003c/span\u003e\u003cspan address=\"https://www.mirnet.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); and associated TFs were predicted through CHEA3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/chea3/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/chea3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These elements were integrated into a TF-mRNA-miRNA-lncRNA regulatory network. Disease associations were explored via DisGeNET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.disgenet.org/\u003c/span\u003e\u003cspan address=\"http://www.disgenet.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.17 The expression levels of key genes and key metabolites\u003c/h2\u003e \u003cp\u003eExpression levels of key genes and key metabolites across all transcriptomic and metabolomic samples were compared with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and visualized as boxplots using the \"ggplot2\" package (v 3.5.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.18 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in R (v 4.2.2). Two-group comparisons used the Wilcoxon test or t-test. Significance levels: *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; ns, not significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.1 BPA as a Potential Therapeutic Agent for SCI\u003c/h2\u003e \u003cp\u003eIn the GSE151371 dataset, compared to the control group, 7,142 differentially expressed genes (DEGs1) were identified in the SCI group, comprising 439 upregulated and 6,703 downregulated genes (Supplementary Fig.\u0026nbsp;1A, B). By intersecting DEGs1 with genes associated with disulfidptosis, 50 overlapping genes were identified (Supplementary Fig.\u0026nbsp;1C). Notably, 27 hub genes were further selected through PPI analysis (Supplementary Fig.\u0026nbsp;1D). Among these, NDUFA10, HNRNPA3, PPIH, RPA1, SAFB2, MYL6, HNRNPH1, and PCBP2 were pinpointed as key genes for further investigation through the application of two machine learning methodologies (Supplementary Fig.\u0026nbsp;1E). Except for MYL6, which showed a notable increase in expression within the SCI group, the remaining seven key genes exhibited significant downregulation in this group (Supplementary Fig.\u0026nbsp;1F). Moreover, the AUC values derived from the ROC analysis for each gene surpassed 0.8, indicating their diagnostic potential for SCI (Supplementary Fig.\u0026nbsp;1G). All these genes were found to be associated with BPA (Supplementary Fig.\u0026nbsp;1H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;1: BPA was a potential drug for protecting SCI. (A, B) Volcano plot and heatmap of differentially expressed genes1 (DEGs1) between the SCI and control groups in GSE151371. (C) Venn diagram of genes between DEGs1 and disulfidptosis-related genes. (D) Venn diagram of genes obtained from MCC, MNC, and Degree algorithms. (E) Venn diagram of genes obtained from Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and random forest (RF) algorithms. (F) The expression levels of core genes between SCI and control groups. ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. (G) The receiver operating characteristic (ROC) curves of core genes. (H) The drug-core gene network. Orange: core gene; blue: drug.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pathological Changes in SCI Mice Following BPA Intervention\u003c/h2\u003e \u003cp\u003eGross examination of harvested tissues revealed marked atrophy at the SCI site in all mice of the SCI group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Body weight data indicated that mice in the control group maintained stable body weight, whereas those in the SCI group exhibited a significant decrease after the first week, remaining at a low level in subsequent weeks. Mice in the BPA group exhibited a rise in body weight from the second week onward, yet their weight stayed below that of the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Moreover, Basso Mouse Scale (BMS) scores demonstrated that the control group maintained a score of 10 throughout the experiment, indicating normal motor function. The SCI group had a BMS score of 0 at day 0, which gradually increased but remained significantly lower than that of the control group throughout the experimental period. The BPA group also had a BMS score of 0 at day 0; however, their scores increased more rapidly than those of the SCI group and approached the level of the control group in the later stages of the experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Subsequent step distance measurements revealed that the control group had the longest step distance (approximately 6 cm), while the SCI group had the shortest step distance (approximately 3 cm). The step distance of mice in the BPA group was intermediate between those of the control and SCI groups (approximately 4 cm) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eSubsequently, HE staining and Nissl staining were performed on the BPA group, which exhibited superior efficacy. HE staining results revealed that the spinal cord in the control group had an intact structure with tightly arranged cells; in contrast, the SCI group presented obvious lesion areas with disorganized structure and sparse cellular distribution. The spinal cord damage in the BPA group was significantly alleviated, and its structural integrity and cell density were much closer to those of the control group, indicating that BPA intervention can improve the histopathological condition of the spinal cord following SCI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Consistent findings were observed with Nissl staining. The control group exhibited a regular architecture and tightly arranged cells, whereas the SCI group showed disorganized tissue, sparse and fragmented cells in the injured region. The BPA intervention group displayed notably less severe injury, with tissue morphology and cellular density much more similar to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). These results further confirm that BPA treatment can ameliorate the histopathological state of the spinal cord post-injury.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Functions of the 21 candidate genes\u003c/h2\u003e \u003cp\u003eA total of 5,687 differentially expressed genes (DEGs2) were identified in the SCI group compared to the control group, comprising 3,176 up-regulated and 2,511 down-regulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Using the same methodology, 3,965 DEGs3 were identified in the BPA group, comprising 1,612 up-regulated and 2,353 down-regulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). The intersection between the 3,176 up-regulated DEGs2 and the 2,353 down-regulated DEGs3 yielded 1,836 overlapping genes (overlapping genes1, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Similarly, the intersection between the 2,511 down-regulated DEGs2 and the 1,612 up-regulated DEGs3 resulted in 1,156 overlapping genes (overlapping genes2, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). After merging overlapping genes1 and overlapping genes2, a total of 2,992 DEGs associated with SCI under BPA intervention were identified. These were further intersected with 101 disulfidptosis-related genes converted to mouse homologs, ultimately yielding 21 candidate genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). The candidate genes exhibited significant enrichment across 648 GO terms, including 503 terms in Biological Process (e.g., NADH dehydrogenase complex assembly), 70 terms in Cellular Component (for instance, mitochondrial respiratory chain complex I), and 75 terms in Molecular Function (e.g., actin binding), along with 22 KEGG pathways such as oxidative phosphorylation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, Supplementary Table\u0026nbsp;3). These findings preliminarily suggest that the candidate genes may be involved in mitochondrial function. Subsequently, the PPI network further revealed interactions among 17 candidate genes, including Flna and Prdx1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Finally, the results of differential expression analyses of disulfidptosis-related genes between groups showed that the expression level of Slc7a11 was significantly higher in SCI samples than in control samples (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Fig.\u0026nbsp;2A), whereas the expression of Slc7a11 was markedly decreased in BPA-treated samples after BPA intervention (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Fig.\u0026nbsp;2B). These findings were highly consistent with the expected changes in disulfidptosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSupplementary Fig.\u0026nbsp;2: The expression of disulfidptosis-related genes altered following SCI, and was reversed following BPA intervention. (A) The differential expression levels of genes between the SCI and control groups. (B) The differential expression levels of genes between the BPA and SCI groups. Note: The horizontal axis denotes the experimental groups (Control, SCI, BPA), and the vertical axis represents the FPKM gene expression levels. Each box plot displays the median, the interquartile range, and individual data points; * indicates significance markers (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Quality Assessment of Metabolomics Data\u003c/h2\u003e \u003cp\u003eNormality testing of the metabolomics data using a threshold of P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 revealed that 94.22% of metabolites followed a normal distribution in the BPA group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), 94.41% in the SCI group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), and 95.95% in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Subsequent PCA results demonstrated clear separation of metabolites between the SCI and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), as well as between the BPA and SCI groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). OPLS-DA analysis indicated that between the SCI and control groups, the model explanatory power (R2Y) reached 0.9795, and predictive ability (Q2Y) was 0.8983 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Between the BPA and SCI groups, R2Y reached 0.9699, and Q2Y was 0.822 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). These results indicate a significant separation trend in metabolic profiles both between the SCI and control groups and between the BPA and SCI groups. Collectively, the results indicate the excellent quality of the metabolomics dataset.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Pathways significantly enriched by the 39 candidate metabolites\u003c/h2\u003e \u003cp\u003eCompared with the control group, 142 DEMs1 were identified in the SCI group, of which 60 were up-regulated and 82 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Relative to the SCI group, the BPA group yielded 84 DEMs2, evenly distributed between 42 up-regulated and 42 down-regulated metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Applying the same intersection strategy as the transcriptomic analysis, the overlap between the 60 up-regulated DEMs1 and 42 down-regulated DEMs2 produced 20 candidate metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), while the intersection of the 82 down-regulated DEMs1 and 42 up-regulated DEMs2 yielded 19 candidate metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Merging these two sets resulted in 39 candidate metabolites with BPA-modulated differential expression in SCI. Pathway enrichment analysis revealed that these metabolites were significantly enriched in 22 metabolic pathways (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including bile secretion, arachidonic acid (AA) metabolism, and tyrosine metabolism, all potentially implicated in the modulatory effects of BPA on SCI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlations between key genes/metabolites and other genes/metabolites\u003c/h2\u003e \u003cp\u003eCandidate genes significantly enriched in the oxidative phosphorylation (OXPHOS) pathway are associated with mitochondrial oxidative stress, which can drive mitochondrial dysfunction and exacerbate SCI. We therefore focused on three OXPHOS-enriched candidate genes\u0026mdash;Ndufs1, Ndufa11, and Ndufb10\u0026mdash;and designated them as key genes. Ndufa11 exhibited the strongest positive correlations with Pcbp3 and Atxn10 (cor\u0026thinsp;=\u0026thinsp;0.88, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the most pronounced negative correlations with Inf2 and Trip6 (cor\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.95, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Ndufb10 showed the strongest positive correlation with Atxn10 (cor\u0026thinsp;=\u0026thinsp;0.92, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the strongest negative correlations with Flna, Actn4, and Trip6 (cor\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.97, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Ndufs1 displayed the strongest positive correlation with Atxn10 (cor\u0026thinsp;=\u0026thinsp;0.83, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the most significant negative correlations with Actn4 and Trip6 (cor\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.9, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Supplementary Table\u0026nbsp;4). All three key genes were mutually positively correlated, with the strongest intercorrelation observed between Ndufb10 and Ndufs1 (cor\u0026thinsp;=\u0026thinsp;0.93, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u0026ndash;D).Among the significantly enriched KEGG pathways of candidate metabolites, arachidonic acid (AA) metabolism warranted particular attention, as it interacts with the mitochondrial electron transport chain (ETC) by selectively inhibiting complexes I and III, thereby compromising mitochondrial respiratory function; it is also involved in post-SCI inflammatory regulation and secondary injury. Two metabolites mapping to this pathway\u0026mdash;leukotriene B4 (LTB4) and prostaglandin B2 (PGB2)\u0026mdash;were defined as key metabolites. LTB4 showed the strongest positive correlation with ent-7α,12β-dihydroxy-16-kauren-19,6β-olide (cor\u0026thinsp;=\u0026thinsp;0.84, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the most significant negative correlation with citrulline (cor\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.77, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). PGB2 demonstrated the strongest positive correlation with encainide and fluanisone (cor\u0026thinsp;=\u0026thinsp;0.82, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the most pronounced negative correlation with cholic acid (cor\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.69, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, Supplementary Table\u0026nbsp;5). LTB4 and PGB2 were also significantly positively correlated with each other (cor\u0026thinsp;=\u0026thinsp;0.68, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Pathways significantly enriched by key genes\u003c/h2\u003e \u003cp\u003eNdufs1, Ndufa11, and Ndufb10 were significantly enriched in 839, 805, and 899 pathways, respectively (|NES| \u0026gt; 1, adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C), with 741 pathways commonly enriched across all three key genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, Supplementary Table\u0026nbsp;6). Among the top 20 co-enriched pathways, these key genes showed notable enrichment in pathways associated with energy metabolism and mitochondrial function (e.g., OXPHOS), nervous system development (e.g., lein neuron markers), signal transduction and transcriptional regulation (e.g., GLIS2 targets up), as well as biosynthesis and metabolic function (e.g., cholesterol biosynthesis) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Comprehensive analysis of key gene-associated networks\u003c/h2\u003e \u003cp\u003eThe GGI network analysis predicted that 20 genes, including Ndufs2 and Ndufv1, likely share functional similarities with the identified key genes. These genes are potentially co-involved in mitochondrial-associated biological processes, such as the assembly of the mitochondrial respirasome and the preservation of the mitochondrial inner membrane integrity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The TF-mRNA-miRNA-lncRNA regulatory network analysis revealed that 36 miRNAs were predicted to target NDUFS1, 17 miRNAs for NDUFA11, and 3 miRNAs for NDUFB10. From the initial 56 miRNAs, only those lncRNAs predicted to be targeted by miRNAs linked to all three key genes were retained, resulting in a final selection of 9 lncRNAs. Subsequently, transcription factors (TFs) targeting these three key genes were predicted, identifying 36 TF-mRNA regulatory pairs where the TFs could simultaneously regulate all key genes, highlighting distinct molecular regulatory mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Furthermore, the disease association network suggested potential involvement of these key genes in other mitochondrial-related pathologies, including mitochondrial complex I deficiency and broader mitochondrial disorders (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Expression Profiles of Key Genes and Metabolites in Samples\u003c/h2\u003e \u003cp\u003eTranscriptomic analysis revealed that the expression levels of Ndufs1, Ndufa11, and Ndufb10 declined progressively as SCI advanced, whereas BPA intervention significantly restored their expression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). In parallel, metabolomic profiling showed that LTB4 and PGB2 concentrations were markedly elevated in the SCI group relative to controls, but were substantially suppressed following BPA treatment (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Collectively, these findings indicate that SCI progression is accompanied by a gradual reduction in key gene expression and a concurrent rise in key metabolite levels, both of which were effectively reversed by BPA intervention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eDisulfidptosis, a recently identified form of PCD, is distinct from previously characterized PCD pathways. It not only impairs cell viability but also exhibits resistance to inhibitors targeting other PCD mechanisms, such as the ferroptosis inhibitors Deferoxamine (DFO) and Ferrostatin-1 (Ferr-1), the apoptosis inhibitor Z-VAD-fmk, and the autophagy inhibitor Chloroquine (CQ) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, genetic ablation of key ferroptosis and apoptosis-related genes (e.g., ACSL4, BAX, BAK) fails to prevent disulfidptosis induction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research has established a strong association between disulfidptosis and CNS disorders, including neurodegenerative diseases, neuroglioma, and ischemic stroke [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, genes associated with disulfidptosis might be involved in modulating acute SCI [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, disulfidptosis is likely intricately linked to CNS injury, yet its specific impact on the pathological mechanisms underlying SCI remains largely unexplored.\u003c/p\u003e \u003cp\u003eSecondary SCI results from a cascade of responses initiated by oxidative stress, inflammation, tissue hemorrhage, hypoxia, and cell death [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Mitochondrial dysfunction is a pivotal contributor, as it can precipitate impaired energy production, excessive generation of ROS, disruption of calcium homeostasis, and potentiated apoptotic signaling [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Specifically, post-SCI, dysfunction in the mitochondrial electron transport chain (ETC) leads to overproduction of ROS, thereby instigating oxidative stress [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This results in damage to mitochondrial DNA, proteins, and lipids, subsequently triggering a series of downstream effects. These include dysregulated mitophagy, exacerbated inflammatory responses, and activation of various PCD pathways, forming a vicious cycle that ultimately culminates in neuronal death and neurological deficits [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Some research indicates that disulfide stress represents a distinct form of oxidative stress [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our prior bioinformatics investigations, we identified BPA as an intervention agent linked to eight disulfidptosis-associated genes. Building on this, the current research utilized transcriptomic and metabolomic datasets from a BPA-treated mouse SCI model to explore disulfidptosis-related mechanisms via differential expression and enrichment analyses. Our findings highlight Ndufs1, Ndufa11, and Ndufb10\u0026mdash;genes enriched in the OXPHOS pathway\u0026mdash;as critical molecular players, alongside LTB4 and PGB2, key metabolites enriched in AA metabolism. During SCI progression, expression levels of these key genes progressively declined, while metabolite concentrations increased. Conversely, BPA intervention reversed these trends, elevating gene expression and reducing metabolite levels. Neurons are highly dependent on mitochondrial OXPHOS for energy production and are particularly vulnerable to mitochondrial dysfunction [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Thus, downregulation of OXPHOS genes signifies aggravated mitochondrial impairment. AA and its metabolites contribute to neuroinflammation, pain, and functional deficits post-SCI through multiple signaling pathways [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These results underscore the therapeutic potential of BPA in SCI and reinforce the interconnected roles of mitochondrial damage-driven oxidative stress, AA metabolism, and disulfidptosis in SCI pathology. Integrated dual-omics analysis may offer novel insights into SCI mechanisms and reveal promising therapeutic targets.\u003c/p\u003e \u003cp\u003eAs candidate genes significantly enriched in the OXPHOS pathway, Ndufs1, Ndufa11, and Ndufb10 have been implicated in mitochondrial dysfunction, oxidative stress regulation, and disulfidptosis, potentially exacerbating SCI progression [\u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Ndufs1 is a core component of the electron transport chain (ETC); its dysfunction severely compromises ATP synthesis and markedly elevates ROS production [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Ndufa11, an accessory subunit, destabilizes complex I when dysregulated, impairing mitochondrial respiration and promoting oxidative stress [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Ndufb10, a transmembrane subunit, directly affects proton pump activity, reducing energy conversion efficiency [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our correlation analysis revealed positive associations among these three genes. Specifically, Ndufs1 showed positive correlation with Atxn10 but negative correlations with Actn4 and Trip6. Ndufa11 correlated positively with Pcbp3 and Atxn10, and negatively with Inf2 and Trip6. Ndufb10 was positively associated with Atxn10 and negatively with Flna, Actn4, and Trip6. PPI network analysis indicated interactions among 17 candidate genes, including Flna and Prdx1. Pcbp3 and Atxn10 are RNA-binding proteins involved in mRNA stability, splicing, and translational control, whereas Inf2, Trip6, Flna, and Actn4 are cytoskeleton-associated proteins that participate in actin filament dynamics, cross-linking, and anchoring [\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Prdx1 (peroxiredoxin 1) is a major antioxidant enzyme that scavenges hydrogen peroxide (H₂O₂), maintains cellular redox balance, and modulates oxidative stress signaling [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The execution phase of disulfidptosis involves abnormal disulfide bond cross-linking of actin. Actively downregulating proteins responsible for actin cross-linking, anchoring, and stabilization (Actn4 is a cross-linking protein, Flna is an anchoring protein, and Inf2 regulates polymerization) in the SCI microenvironment may favor the stability of the cytoskeleton and cell survival[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Transcriptomic analysis reveals that essential OXPHOS genes are collectively downregulated following SCI, resulting in mitochondrial impairment, ETC disruption, diminished ATP synthesis, compromised antioxidant capacity, heightened oxidative stress, GSH depletion, and destabilization of the cytoskeleton, ultimately driving disulfidptosis. BPA intervention targets the origin of mitochondrial dysfunction, inducing the coordinated upregulation of three pivotal genes and facilitating the simultaneous elevation of mitochondrial protective factors or cell survival cofactors (Pcbp3 and Atxn10). This improves cellular energy metabolism and strengthens antioxidant defenses, thereby mitigating oxidative stress and suppressing disulfidptosis. After SCI, Prdx1 functions to scavenge ROS, particularly H₂O₂, shielding cytoskeletal proteins such as Flna from oxidative damage.\u003c/p\u003e \u003cp\u003eMetabolomic profiling identified 39 candidate metabolites exhibiting differential expression under BPA treatment. Among the KEGG pathways significantly enriched with these metabolites, AA metabolism is noteworthy due to its interaction with the mitochondrial ETC, as it selectively inhibits complexes I and III, thereby compromising mitochondrial respiratory function [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Furthermore, AA metabolism is implicated in modulating inflammation and secondary injury post-SCI [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Oxidative stress and AA metabolism engage in a detrimental bidirectional positive feedback loop: on one hand, ROS and other oxidative signals promote AA release from membrane phospholipids. Subsequent metabolism, via phospholipase A₂ (PLA₂) activation and modulation of cyclooxygenase/lipoxygenase activity, generates abundant pro-inflammatory mediators like LTB4 and PGB2. Conversely, these inflammatory mediators markedly intensify oxidative damage at cellular and tissue levels through multiple mechanisms, including direct induction of mitochondrial dysfunction, activation of immune cell \"respiratory burst\" to produce substantial ROS, and generation of reactive oxygen byproducts from their own metabolic enzymes [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In this study, two metabolites mapped to the AA metabolism pathway\u0026mdash;LTB4 and PGB2\u0026mdash;were identified as key mediators. LTB4, a lipid-derived inflammatory mediator synthesized from AA, plays a significant role in mediating leukocyte infiltration after SCI [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. PGB2, a lipid mediator produced from AA via the cyclooxygenase pathway, is involved in regulating inflammatory responses, immune modulation, and cellular physiological processes [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. LTB4 levels demonstrate a positive correlation with protective metabolites and a negative correlation with toxic metabolites. PGB2 exhibits a similar correlation pattern, suggesting that BPA not only suppresses inflammatory mediators but also reprograms the metabolic milieu toward a reparative state.\u003c/p\u003e \u003cp\u003ePathway enrichment analysis further indicated that, besides the OXPHOS pathway, key genes were notably concentrated in pathways linked to energy metabolism and mitochondrial function, nervous system development, signal transduction and transcriptional regulation, as well as biosynthesis and metabolic functions. These pathways collectively constitute a core network encompassing energy homeostasis, neural development, and transcriptional control. This finding implies that the key genes and the complex I function they represent serve as critical hubs in cellular processes. By upregulating these genes, BPA not only rescues cells from disulfide-induced death but may also concurrently activate genetic programs related to neuroregeneration and synaptic plasticity. Additionally, changes in the expression of key genes may be governed by a shared upstream transcriptional regulatory network (such as GLIS2 or other key transcriptional regulators).\u003c/p\u003e \u003cp\u003eThis research demonstrates that following SCI, the suppression of essential genes within the OXPHOS pathway (including Ndufs1, Ndufa11, and Ndufb10), coupled with the elevation of key metabolites in the AA metabolism pathway (specifically LTB4 and PGB2), establishes a detrimental \"mitochondria-inflammation\" feedback loop. Mitochondrial impairment initiates oxidative stress, which in turn promotes the overproduction of inflammatory mediators. Concurrently, the ensuing inflammatory cascade inflicts further damage upon mitochondria, collectively worsening the breakdown of intracellular redox equilibrium, as evidenced by GSH depletion. This cascade results in the failure of crucial cytoskeletal protective mechanisms, such as the interaction between Prdx1 and Flna, ultimately precipitating disulfidptosis by the creation of disulfide bonds inside the actin cytoskeleton. Intervention with BPA elicits comprehensive reparative effects by simultaneously enhancing the expression of mitochondrial function-related genes (thereby reversing their negative regulatory network with cytoskeletal proteins) and reducing levels of inflammatory mediators (thereby reorienting the overall metabolic network toward a reparative state). This dual action disrupts the vicious cycle, reinstates redox balance and cytoskeletal integrity, counteracts disulfidptosis, and establishes a microenvironment conducive to neural regeneration (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe principal limitations of this investigation are outlined below. Although a coherent framework implicating the \"mitochondria-inflammation axis\" in the induction of disulfidptosis was established through multi-omics correlation analyses, and the therapeutic efficacy of BPA intervention was substantiated, direct validation of causal mechanisms remains inadequate. Specifically, there is a paucity of direct evidence confirming that BPA modulates upstream transcription factors. Additionally, it remains uncertain whether the protective effects of BPA extend to other cell death pathways, such as apoptosis and ferroptosis. Moreover, the examination of inflammatory mechanisms primarily concentrated on AA metabolites, without delving deeply into more upstream classical inflammatory signaling pathways such as NF-κB and the NLRP3 inflammasome.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZixing Xu designed the experiment, wrote the manuscript, and provided funding support. Zhechen Li and Xinhao Huang were responsible for animal experimentation, specimen collection, and bioinformatics analysis. Chuanrong Chen and Changyi Jiang conducted animal experimentation, dual-omics analysis, and data collection. Weihong Xu revised the manuscript and was responsible for funding support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Natural Science Foundation of Fujian Province (NO. 2024J01528), Fujian Provincial Health Technology Project (No. 2025CXB017), and Fujian Provincial Joint Funds for the Innovation of Science and Technology (No. 2023Y9089).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest, financial or otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGartit M, Noumairi M, Rhoul A, Mahla H, El Anbari Y, El Oumri AA (2025) Scientific Advances in Neural Regeneration After Spinal Cord Injury. 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Switzerland, p 1595059. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphar.2025.1595059\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2025.1595059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Spinal cord injury, Bisphenol A, Disulfidptosis, Metabolomics, Key genes, Key metabolites","lastPublishedDoi":"10.21203/rs.3.rs-9253233/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9253233/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpinal cord injury (SCI) often leads to significant neurological impairment and poses a substantial therapeutic challenge, with disulfidptosis recently identified as a potential mechanism exacerbating such pathologies. This study sought to elucidate the role of Bisphenol A (BPA) in modulating key genes and metabolites associated with disulfidptosis in the context of SCI. Utilizing a murine SCI model, we established three cohorts: a sham control, an SCI group, and an SCI group treated with BPA. Comprehensive assessments, including locomotor recovery via the Basso Mouse Scale (BMS), gait analysis, histopathological evaluations through H\u0026amp;E and Nissl staining, and integrated transcriptomic and metabolomic profiling, were conducted. Results revealed that BPA administration significantly improved locomotor recovery and mitigated histopathological alterations, with Ndufs1, Ndufa11, and Ndufb10 identified as pivotal genes, alongside leukotriene B4 (LTB4) and prostaglandin B2 (PGB2) as crucial metabolites. Notably, these genes were intricately linked to the oxidative phosphorylation (OXPHOS) pathway and exhibited positive intercorrelations, while the metabolites were enriched within the arachidonic acid (AA) metabolism pathway. As the injury progressed, key gene expression diminished, whereas metabolite concentrations increased; BPA treatment effectively reversed these trends. Collectively, these findings indicate that BPA exerts a protective effect against SCI by disrupting a harmful feedback loop involving mitochondrial dysfunction and inflammatory activation, thus countering disulfidptosis and fostering an environment conducive to neural regeneration, underscoring its potential as a therapeutic agent in SCI management.\u003c/p\u003e","manuscriptTitle":"Mitochondrial-Inflammatory Axis Dysregulation Triggers Disulfidptosis and the Systemic Repair Mechanism of Bisphenol A following Spinal Cord Injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 09:57:15","doi":"10.21203/rs.3.rs-9253233/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-19T07:28:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T06:55:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"40601520414865279540697779484915546873","date":"2026-04-17T03:51:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T08:49:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84954879725969264482366848123791630871","date":"2026-04-15T08:36:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T06:37:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T01:33:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T01:32:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2026-03-28T13:40:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"feec319a-edd7-45b5-abc5-0e7f07cf4035","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T17:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 09:57:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9253233","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9253233","identity":"rs-9253233","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}