Mechanism of Disulfide Death-Driven Intestinal Epithelial Injury in Neonatal Necrotizing Enterocolitis and Exploration of Potential Therapeutic Targets

Mechanism of Disulfide Death-Driven Intestinal Epithelial Injury in Neonatal Necrotizing Enterocolitis and Exploration of Potential Therapeutic Targets | 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 Mechanism of Disulfide Death-Driven Intestinal Epithelial Injury in Neonatal Necrotizing Enterocolitis and Exploration of Potential Therapeutic Targets Yufeng Shi, Cong Yan, Lifan Chen, Zhanzhen Cao, Zhijie Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8994121/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Neonatal necrotizing enterocolitis (NEC) remains a devastating gastrointestinal emergency in premature infants, characterized by abrupt onset and rapid progression to transmural necrosis. Recent evidence suggests that disulfidptosis, a novel form of regulated cell death driven by disulfide stress, may play a pivotal role in various inflammatory diseases. However, its specific contribution to NEC pathogenesis remains largely unexplored. Methods We integrated multi-omic bioinformatic analyses with in vitro experimental validation. Disulfidptosis-related hub genes were identified via weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis using GEO datasets (GSE46619, GSE297483), with diagnostic efficacy evaluated by ROC curves. In vitro validation was conducted in LPS-stimulated Caco-2 cells, utilizing tris(2-chloroethyl) phosphate (TCEP) to assess disulfidptosis inhibition. Results NEC tissues exhibited significantly elevated disulfidptosis scores, which correlated positively with disease severity. Integrating WGCNA and DEGs identified 147 core genes primarily enriched in inflammatory signaling and intercellular communication. Among these core genes (TKTL1, PFKFB3, SLC2A3, and SLC2A14), TKTL1 exhibited the highest diagnostic accuracy (AUC = 0.892) and were closely associated with altered immune infiltration, supporting a 'metabolic-inflammatory axis' in NEC. In vitro , LPS-stimulated Caco-2 cells manifested definitive disulfidptosis hallmarks—NADP⁺ depletion, cystine accumulation, and F-actin collapse—synchronized with barrier failure. Pharmacological inhibition via TCEP successfully stabilized the cellular redox state, restored cytoskeletal integrity, and attenuated IL-6/TNF-α secretion, thereby preserving epithelial function. Conclusion his study identified disulfidptosis as a critical driver of intestinal epithelial injury in NEC. Targeting disulfidptosis-related pathways may offer a promising diagnostic and therapeutic strategy for neonatal intestinal injury. Neonatal necrotizing enterocolitis Disulfidptosis Intestinal epithelial barrier Metabolic-inflammatory axis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Neonatal necrotizing enterocolitis (NEC) remains one of the most devastating gastrointestinal emergencies encountered in the neonatal intensive care unit (NICU), mostly seen in premature and low birth weight newborns[ 1 , 2 ]. With its abrupt onset and resultant rapid course toward transmural necrosis, NEC is traditionally attributed to a complex interplay of intestinal immaturity, microbial dysbiosis, and an intense inflammatory response[ 3 , 4 ]. Despite significant advancements in neonatal intensive care, the mortality rate for surgical NEC remains alarmingly high, often exceeding 30%[ 5 ], while survivors frequently face long-term complications such as short bowel syndrome and neurodevelopmental impairment[ 6 ]. A major hurdle in improving clinical outcomes is the lack of highly sensitive and specific biomarkers for early risk stratification, as current diagnosis often relies on non-specific clinical signs and late-stage radiographic findings[ 7 ]. Consequently, there is an urgent need to elucidate the underlying molecular mechanisms driving intestinal cell death and to identify novel diagnostic targets that can capture the disease in its earliest phases. Recently, the involvement of regulated cell death in NEC pathogenesis has attracted increasing interests, where the roles of necroptosis, apoptosis, and ferroptosis in the epithelial injury of the intestinal barrier have been reported[ 8 , 9 ]. Nevertheless, these well-known pathways cannot explain the severe metabolic failure and explosive tissue necrosis feature of NEC exacerbations. In 2023, a novel form of regulated cell death termed disulfidptosis was discovered, characterized by the accumulation of disulfides within the cells, triggering the destruction of the actin cytoskeleton[ 10 ]. By contrast to ferroptosis, disulfidptosis requires the over-expression of SLC7A11 in the glucose-deprived state[ 11 , 12 ]. In such conditions, the depletion of NADPH—principally supplied by the pentose phosphate pathway—prevents the reduction of cystine to cysteine, yielding the cytotoxic disulfide bonds[ 13 ]. Importantly, the pathological microenvironment of NEC is defined by the concurrent presence of severe oxidative injury and metabolic failure. While the former acts as a potent stimulus for SLC7A11 expression to mitigate oxidative stress, the latter results in profound glucose depletion. This convergence of molecular events in the neonatal intestine aligns closely with the biochemical prerequisites for disulfidptosis initiation. Recent findings in Crohn's disease have identified disulfidptosis as a critical regulator of intestinal immune infiltration and barrier dysfunction. Furthermore, cross-disease analyses have identified shared disulfidptosis-associated gene signatures across ankylosing spondylitis and inflammatory bowel diseases. This study aims to systematically investigate the clinical significance and molecular mechanisms of disulfidptosis in NEC. Through integrated analysis of NEC transcriptomic data from the GEO database, core hub genes associated with disulfidptosis were identified, and their diagnostic value was evaluated in an independent validation cohort. Combined with functional enrichment analysis and immune infiltration profiling, this study further revealed the potential role of the "metabolism-inflammation axis" in disease progression. An NEC-like in vitro model was established to validate the bioinformatic predictions. By detecting disulfidptosis-related metabolic markers and changes in tight junction protein expression, the protective effect of the disulfidptosis inhibitor TCEP on intestinal epithelial barrier function was systematically evaluated. This study is expected to provide a new theoretical basis for the development of early diagnostic markers and metabolism-targeted therapeutic strategies for NEC. METHODS 2.1 Data Acquisition and Preprocessing Publicly available transcriptomic datasets associated with neonatal necrotizing enterocolitis (NEC) were acquired from the Gene Expression Omnibus (GEO) database. The study utilized GSE46619 as the discovery cohort, comprising five NEC tissues and four adjacent surgical controls, and GSE297483 (containing 10 NEC and 13 healthy controls) for external validation. For the microarray data, normalization and log2 transformation were performed using the "normalizeBetweenArrays" function within the limma R package to eliminate technical batch effects and ensure data comparability[ 14 ]. 2.2 Screening of Differentially Expressed Genes (DEGs) The limma toolkit was employed to screen for differentially expressed genes (DEGs) between the NEC and control groups.[ 14 ] Genes satisfying the criteria of adjusted P-value < 0.05 and |log₂ Fold Change (FC)| ≥ 1 were identified as significant DEGs. The expression patterns and distribution of these DEGs were visualized using volcano plots and hierarchical clustering heatmaps generated via the ggplot2 and pheatmap R packages. 2.3 Characterization of Disulfidptosis Functional Signatures To quantify the activity of disulfidptosis across intestinal samples, we implemented the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm via the GSVA R package[ 15 ]. Based on established literature, disulfidptosis-related genes (DRGs) were curated and categorized into functional "promoters" and "inhibitors.[ 13 ]" We then calculated each category's enrichment scores respectively. A comprehensive "Disulfidptosis Score" for each sample was then derived (Score = ES_promoters - ES_inhibitors), serving as a metric for disulfidptosis levels in each sample. 2.4 Weighted Gene Co-expression Network Analysis (WGCNA) The WGCNA package (v1.71) was applied to the preprocessed expression matrix to identify key gene modules. The “pickSoftThreshold" function was utilized to determine the optimal soft-thresholding power for scale-free topology. Subsequently, a topological overlap matrix (TOM) was constructed to assess pairwise gene co-expression similarity, followed by a dynamic tree-cutting algorithm to cluster genes into distinct modules, represented by different colors. 2.5 Functional Enrichment Analysis To elucidate the biological significance of the identified genes, the intersection of DEGs and WGCNA hub modules was subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis via the STRING database[ 16 ]. These analyses targeted biological processes (BP), molecular functions (MF), cellular components (CC), and signaling pathways associated with the overlapping gene set. 2.6 PPI Network Construction and Module Clustering A Protein-Protein Interaction (PPI) network was constructed by mapping the overlapping genes to the STRING database. The network topology was visualized using Cytoscape (v3.10.3). Sub-clusters within the network were identified using the k-means clustering algorithm, and the correlation between key clusters and phenotype-associated DEGs was evaluated via Pearson correlation analysis. 2.7 Immune Infiltration Landscape The immune microenvironment was characterized using the CIBERSORT deconvolution algorithm, guided by the LM22 reference signature matrix. We quantified the relative fractions of 22 infiltrating immune cell subtypes in NEC and control tissues. Only samples with a CIBERSORT P-value < 0.05 were included in subsequent analyses to ensure reliable immune cell estimation. 2.8 External Validation and Diagnostic Evaluation The predictive validity of the identified biomarkers was tested in an independent dataset (GSE297483). Receiver Operating Characteristic (ROC) curves were generated using the pROC R package. The diagnostic performance was quantified by the Area Under the Curve (AUC), along with sensitivity and specificity metrics, to assess the translational potential of the core genes. 2.9 Cell Culture and Treatment Caco-2 cells (CL-0050, Procell, China) were routinely cultured at 37°C with 5% CO₂. The culture medium was DMEM medium (11965118, Gibco, USA) with 10% fetal bovine serum (FSD500, Excell Bio, China) and 1% penicillin‒streptomycin (C0222, Beyotime, China). Cells were randomly divided into three groups: control group, LPS model group (treated with 100 µg/mL LPS), and LPS+TCEP intervention group (co-treated with 100 µg/mL LPS and 5 µM TCEP). TCEP was added 2 hours prior to LPS stimulation to investigate its inhibitory effect on the disulfidptosis pathway. LPS stimulation was used to establish an in vitro NEC cell injury model. After the corresponding treatment duration, samples were collected for subsequent analysis. 2.10 Cell Viability The procedure was carried out in accordance with the instructions of the CCK-8 kit (BA00208, Bioss, China). After treatment, 10 µL of CCK-8 solution was added to each well of cells, followed by an additional 2-hour incubation. Subsequently, the absorbance (OD value) at a wavelength of 450 nm was measured using a microplate reader, and the cell survival rate was calculated based on these readings to assess cell viability. Each experiment was independently repeated five times to ensure data reliability. 2.11 qRT-PCR Total RNA was extracted from cells using TRIzol reagent (RC112, Beyotime, China). Complementary DNA (cDNA) was then synthesized using the HiScript III First Strand cDNA Synthesis Kit (R312, Beyotime, Shanghai, China) according to the manufacturer's instructions. Quantitative real-time PCR (qRT-PCR) was performed on a 7500 Real-Time PCR System (Applied Biosystems, USA) with a Universal SYBR qPCR Master Mix (MQ101, Beyotime, China), using GAPDH as the internal reference gene. Following the recommended cycling conditions, the relative mRNA expression levels of target genes were calculated using the 2 −ΔΔCT method. 2.12 ELISA After the intervention, cell culture supernatants were collected. Strictly following the kit instructions (Coibo, China), the secretion levels of TNF-α (CB11762-Hu) and IL-6 (CB10373-Hu) in the supernatants were quantitatively analyzed using the ELISA method. The absorbance was measured with a full-wavelength microplate reader, and the specific concentrations were calculated based on the standard curve to assess the release of inflammatory cytokines. Each experiment was independently repeated five times to ensure the accuracy and reproducibility of the data. 2.13 Measurement of TEER and permeability Cells in a good logarithmic growth phase were selected, digested with trypsin to prepare a single-cell suspension, and seeded into 24-well Transwell inserts (polycarbonate membrane, pore size 0.4 µm) at a density of 1×10⁴ cells/mL. Specifically, 200 µL of cell suspension was added to the apical (AP) side, and 600 µL of complete medium was added to the basolateral (BL) side. The culture system was then incubated at 37°C with 5% CO₂ for 21 days to allow for monolayer formation. Subsequently, supernatants from different experimental groups were added to the apical compartment, followed by a 24-hour incubation. Prior to the TEER assay, the medium on both sides of the Caco-2 cell monolayer was replaced with PBS buffer. Then, 0.1 mg/mL FITC-dextran (T2935, Beyotime, China) was added to the apical side of the Transwell inserts. After 1 hour of incubation, the basolateral medium was collected. Fluorescence intensity was measured using a fluorescence microplate reader at an excitation wavelength of 480 nm and an emission wavelength of 520 nm to evaluate the permeability changes of the cell monolayer. 2.14 Immunofluorescence Staining Cells from each group were plated in chamber slides under glucose starvation conditions and cultured overnight to allow complete adhesion. The cells were then fixed with 4% paraformaldehyde at room temperature for 15 minutes, followed by permeabilization with 0.5% Triton X-100 at room temperature for 5 minutes. After washing three times with phosphate-buffered saline (PBS), the cells were stained with 100 nM Actinstain 568 fluorescent phalloidin (HY-K0903, MCE, USA) to label the F-actin cytoskeleton, with incubation for approximately 30 minutes. Following staining, the cells were washed three times with PBS to remove unbound dye. Subsequently, the nuclei were counterstained with DAPI (Thermo Fisher Scientific, USA) for 5 minutes. Finally, the slides were mounted and images were captured using a confocal microscope (Leica, Germany). 2.15 Intracellular NADP+/NADPH ratio and cystine level detection Intracellular levels of NADPH and total NADP were quantified using the NADP⁺/NADH Assay Kit (S0179, Beyotime, China) according to the manufacturer's protocol. The NADP⁺/NADPH ratio was subsequently determined based on the formula (Total NADP – NADPH)/NADPH. In parallel, intracellular cysteine levels were assessed following the procedure provided with the Mouse Cysteine ELISA Kit (YS16037B, Yaji Biological, China). 2.16 Statistical Analysis Statistical analysis and visualization were performed using R (v4.3.1) and GraphPad Prism (v9.0). Continuous variables were expressed as mean ± SD, with group comparisons conducted via two-tailed Student's t-test and Mann-Whitney U test according to data normality; categorical variables were assessed by Fisher's exact test. In the bioinformatic workflow, Wilcoxon rank-sum test was used for DEGs and ssGSEA scores. Pearson correlation was applied within WGCNA for hub module identification (MM and GS), while Spearman correlation evaluated associations between hub genes, SLC7A11, and CIBERSORT immune infiltration. Diagnostic robustness was validated via ROC and AUC metrics in dataset GSE297483. indicated statistical significance. RESULTS 3.1 Transcriptomic Alterations and Activation of Disulfidptosis in NEC For analyzing the transcriptomic changes occurring in NEC, Principal Component Analysis (PCA) was initially performed on the GSE46619 dataset. The PCA results plotted on a graph revealed a distinct separation between the NEC samples and surgery controls, indicating substantial global molecular divergence between the two cohorts (Fig. 1 a). Differential expression analysis identified 1,749 differentially expressed genes (DEGs), including 488 upregulated and 1,261 downregulated genes in NEC compared with controls (P < 0.05 and ∣log2 FC | ≥ 1; Fig. 1 b). In addition, heat map analysis of 15 disulfidptosis-related genes (DRGs) showed varied expressions, with some significant disulfidptosis-related genes being differentially expressed between the NEC samples and the normal tissues (Fig. 1 c). To understand the extent of changes, the "disulfidptosis score" profile was determined through the ssGSEA algorithm. The NEC sample exhibited high disulfidptosis activities compared to normally growing tissues (Fig. 1 d), suggesting the significance of disulfidptosis-related pathways associated with the pathogenesis of NEC. 3.2 Co-expression Network Analysis and Hub Module Identification To systematically identify gene clusters that have functional correlations with the NEC phenotype, we developed a co-expression network using the WGCNA algorithm (Fig. 2 a). The dendrogram and heatmap further demonstrated a clear differentiation in the molecular profile between the NEC and surgery control groups (Fig. 2 b). We evaluated several soft thresholding powers β and chose nine (as indicated by the R 2 threshold of 0.9), for choosing the optimum connectivity and biological significance to achieve scale free topology (Fig. 2 c). Through the implementation of a dynamic tree-cutting technique, a total of 14 co-expression modules were obtained based on TOM-based dissimilarity (Fig. 2 d). We performed a correlation analysis based on the 14 co-expression modules obtained. Two modules emerged as highly significant: the ME_Blue module (Module Membership = 0.98, Gene Significance < 3e-05) and the ME_turquoise module (Module Membership = 0.73, Gene Significance < 0.04) (Fig. 2 e). These two hub modules were prioritized for identifying the key genes and characterizing their functional roles. 3.3 Core Candidate Genes and PPI Network Architecture To enhance the screening stringency and prioritize biologically relevant candidates, we performed an intersection analysis between the 488 upregulated DEGs and the WGCNA hub modules (Blue and Turquoise). This dual-filtering strategy yielded 147 overlapping genes, which were referred to as our defined as the core candidates associated with NEC progression (Fig. 3 a). To further investigate the functional interplay among these 147 candidates, a PPI network was established. Using the k-means clustering algorithm, the network was partitioned into three distinct functional clusters (Fig. 3 b). A simplified sub-network was extracted from the critical functional unit Cluster 3 to visualize the most essential protein associations (Fig. 3 c). Subsequent correlation analysis revealed that the expression of these core genes—particularly SLC2A3, PFKFB3, SLC2A14, and TKTL1— highly correlated with SLC7A11, the master regulator of disulfidptosis (Fig. 3 d). These findings suggest a close transcriptional association between these core genes and the disulfidptosis machinery in the context of NEC. 3.4 Distinct Functional Signatures and Inflammatory Landscape of Core Genes We performed a comprehensive enrichment analysis leveraging GO and KEGG databases to characterize the biological identity of the 147 core candidate genes (Fig. 4 ). GO BP analysis revealed that the main biological processes in these genes were related to cell communication, signal transduction, and response to stimulus (Fig. 4 a). MF was significantly enriched in the binding to cells involved in signal transduction and the activity of cytokines (Fig. 4 b). Analysis using CC revealed significant enrichment in the plasma membrane and the external side of the plasma membrane (Fig. 4 c). KEGG pathway analysis also revealed the pathway architecture of the core genes (Fig. 4 d). The core genes were highly enriched in some important pro-inflammatory pathways, such as Cytokine-cytokine receptor interaction, NF-kappa B signaling pathway, and TNF signaling pathway. Some non-inflammatory pathways, such as MAPK signaling pathway and IL-17 signaling pathway, were also found to be significantly associated with the core genes. This not only simplifies the functional landscape of disulfidptosis-related genes but also emphasizes their tight link to metabolic and inflammatory signal transduction pathways, respectively, in NEC. 3.5 Landscape of Immune Infiltration and Correlation with Core Genes To explore further the effects of disulfidptosis on the immune microenvironment in NEC, we adopted the CIBERSORT algorithm for assessment of the infiltration levels of 22 types of immune cells (Fig. 5 a). Inter-cellular correlation analysis revealed a significant synergistic relationship between M1 macrophages and neutrophils(Fig. 5 b). Comparative analysis demonstrated a distinct pro-inflammatory shift in the NEC immune landscape, specifically a significant increase in the infiltration of M1 macrophages, neutrophils, and activated mast cells (P < 0.05), alongside a concomitant reduction in monocytes and resting T cells (Fig. 5 c). Furthermore, Spearman correlation analysis was performed to evaluate the association between the four disulfidptosis-related core genes and specific immune cell subsets (Figs. 5 d–g). The expression levels of TKTL1,PFKFB3,SLC2A3, and SLC2A14 correlated not only with M1 macrophages and neutrophils but also, most significantly, with activated Mast cells and activated Dendritic cells (all R > 0.8, P < 0.05). These data define a specific statistical link between disulfidptosis-related gene expression and a pro-inflammatory immune cell profile in NEC. 3.6 External Validation and Predictive Performance of Core Genes The discriminatory power of PFKFB3, SLC2A3, SLC2A14, and TKTL1 remained remarkably consistent when projected onto the independent dataset GSE297483. As seen from the ROC curve, these metabolic drivers act as reliable indicators for the NEC disease state, transcending the limitations of the discovery dataset (Fig. 6 ). Among the validated candidates, TKTL1 emerged with a predominant AUC of 0.892, defining it as a superior molecular signature for the disulfidptosis-driven necrotic process (Fig. 6 d). This predictive strength was further substantiated by PFKFB3 (AUC = 0.823) and SLC2A14 (AUC = 0.800), while SLC2A3 also maintained a significant discriminatory threshold (AUC = 0.754; Fig. 6 a-c). The robust AUC values attained across this external validation set transition these disulfidptosis-related genes from purely bioinformatic candidates to validated clinical indicators. 3.7 Disulfidptosis Inhibitor TCEP Alleviates LPS-Induced Intestinal Epithelial Cell Injury and Barrier Dysfunction To validate the findings from bioinformatics analysis in vitro, we established a NEC-like injury model by stimulating Caco-2 cells with lipopolysaccharide (LPS). The disulfidptosis inhibitor tris(2-chloroethyl) phosphate (TCEP) was applied for intervention. CCK-8 assay results showed that LPS treatment significantly reduced cell viability compared to the control group (Fig. 7 a, P < 0.001). Co-treatment with TCEP effectively reversed the LPS-induced cytotoxicity and partially restored cell viability (P < 0.05). Regarding the inflammatory response, LPS stimulation significantly increased the secretion levels of the pro-inflammatory cytokines IL-6 and TNF-α (Fig. 7 b, c, P < 0.001). In contrast, TCEP treatment significantly inhibited the production of these key inflammatory mediators (P < 0.001), suggesting that disulfidptosis may be involved in regulating NEC-related inflammation. To further assess intestinal epithelial barrier function, we monitored the establishment of a Caco-2 cell monolayer model. The transepithelial electrical resistance (TEER) value gradually increased with culture time, reaching a plateau on day 11 (approximately 1287.67 Ω·cm²). This indicated the formation of an intact epithelial barrier with well-structured tight junctions (TJs) (Fig. 7 d). Barrier permeability experiments revealed that LPS stimulation significantly increased the permeation of FITC-dextran through the cell monolayer (Fig. 7 e, P < 0.001), confirming that LPS disrupted barrier integrity. TCEP treatment significantly reduced this LPS-induced dextran leakage (P < 0.001). Mechanistically, qRT-PCR analysis showed that LPS treatment significantly downregulated the mRNA expression of the tight junction core proteins ZO-1 and Occludin (Fig. 7 f, g, P < 0.001). TCEP effectively prevented this downregulation (P < 0.05). Together, these results indicate that the disulfidptosis inhibitor TCEP can alleviate LPS-induced intestinal epithelial inflammation, improve tight junction protein expression, and protect the integrity of epithelial barrier function. 3.8 Disulfidptosis is a Key Mechanism Mediating LPS-Induced Intestinal Epithelial Cell Injury To directly confirm that LPS-induced cell injury is driven by disulfidptosis, we performed further validation under glucose starvation conditions. Immunofluorescence staining showed that, compared to the control group, LPS-treated cells exhibited typical morphological features of disulfidptosis, including intense contraction of the F-actin cytoskeleton and significant cell shrinkage (Fig. 8 a). Biochemical assays provided further evidence at the metabolic level. LPS stimulation led to substantial intracellular cystine accumulation, accompanied by a significant increase in the NADP⁺/NADPH ratio (Fig. 8 b, c, P < 0.01). This suggests the cells entered a state of severe metabolic stress and redox imbalance. TCEP intervention effectively reversed these morphological changes and metabolic abnormalities. Concurrently, we validated the expression of core genes identified through bioinformatics screening. qRT-PCR results showed that LPS treatment significantly upregulated the mRNA expression levels of four key genes: PFKFB3, SLC2A3, SLC2A14, and TKTL1 (Fig. 8 d-g, P < 0.05). This aligns with the bioinformatics finding that NEC tissues exhibit a high disulfidptosis score. Importantly, TCEP treatment effectively suppressed the LPS-induced overexpression of these genes. In summary, this study confirms from morphological, metabolomic, and transcriptomic perspectives that LPS drives intestinal epithelial cell injury by activating the disulfidptosis pathway, and the pathway inhibitor TCEP can provide a protective effect. DISCUSSION The pathogenesis of neonatal NEC involves complex interactions between intestinal immaturity, microbial dysbiosis, and an exaggerated inflammatory response[ 17 , 18 ]. In this study, we systematically explored the potential role of disulfidptosis—a newly discovered form of regulated cell death—in the progression of NEC. By integrating transcriptomic data from GSE46619 and GSE297483, we demonstrated that NEC intestinal tissues exhibit a significantly elevated disulfidptosis score, suggesting that metabolic-stress-induced cell death is a critical hallmark of this disease[ 19 ]. Through WGCNA and PPI network analysis, we identified four core hub genes (TKTL1, PFKFB3, SLC2A3, and SLC2A14) that are not only significantly upregulated in NEC but also strongly correlated with SLC7A11 expression, the primary driver of disulfidptosis[ 10 , 20 ]. Furthermore, the exceptional diagnostic performance of these genes in an independent cohort, particularly TKTL1 (AUC = 0.892), validates their potential as robust biomarkers for assessing intestinal damage and disease severity. Together, our data suggest a previously unappreciated role of metabolic vulnerability in NEC pathogenesis, extending emerging concepts of epithelial metabolic control under conditions of nutrient deprivation and oxidative stress[ 21 ]. While apoptosis and necrosis have been extensively studied in NEC, our findings suggest that disulfidptosis represents a more complex metabolic "executioner" of epithelial cell death. The molecular mechanism of disulfidptosis is fundamentally tied to the balance between cystine import and the availability of reducing equivalents[ 22 ], primarily NADPH. Our analysis revealed a significant upregulation of SLC7A11 in NEC tissues, a phenomenon traditionally viewed as a compensatory response to oxidative stress[ 23 ]. However, in the nutrient-deprived environment of the neonatal intestine, excessive SLC7A11-mediated cystine uptake becomes a metabolic liability. Such a specific biochemical reaction, initially identified for cancer cells by Liu et al. (2023), results in the intracellular build-up of disulfides, ultimately causing the irreversible disruption of the actin cytoskeleton[ 10 ]. Moreover, this discovery introduces a novel mechanism that could account for the rapid, or 'explosive,’ course of intestinal necrosis that occurs in NEC infants, for whom the barrier layer lacks not only integrity but suffers a catastrophic failure. The functional identity of our identified hub genes reinforces this metabolic-stress model. We identified TKTL1—a pivotal enzyme in the non-oxidative branch of the pentose phosphate pathway (PPP)—as a top-performing core gene with high diagnostic accuracy (AUC = 0.892). Given that the PPP is the primary source of NADPH required to reduce intracellular disulfides, the strong correlation between TKTL1 and SLC7A11 suggests that TKTL1 serves as a critical metabolic regulator attempting to maintain redox homeostasis[ 24 , 25 ]. As in other forms of ischemic injury, PPP activation is often a survival strategy, yet in the context of NEC, this backup system appears to be overwhelmed. Concurrently, the elevated expression of PFKFB3, a potent stimulator of glycolysis, and glucose transporters like SLC2A3 and SLC2A144, reflects the state of high metabolic demand and glucose starvation[ 26 , 27 ]. PFKFB3 has been documented in literature to induce pro-inflammatory responses in intestinal myofibroblasts[ 28 ], suggesting that its upregulation in NEC might inadvertently accelerate glucose depletion. In NEC, when the supply of glucose-derived NADPH fails to keep pace with the SLC7A11-driven influx of cystine, the resulting accumulation of intracellular disulfides triggers the irreversible collapse of the actin cytoskeleton, thereby executing the disulfidptosis program[ 10 , 29 ]. Beyond the cell-intrinsic metabolic collapse, our findings underscore a pivotal link between disulfidptosis and the hyper-inflammatory state characteristic of NEC. The KEGG pathway analysis highlighted a significant enrichment of genes in the TNF signaling pathway, NF-κB signaling pathway, and cytokine-cytokine receptor interactions. We propose that the massive, non-programmed cell death induced by disulfidptosis serves as a potent trigger for this "inflammatory storm[ 30 ]". As intestinal epithelial cells undergo disulfidptosis, the release of damage-associated molecular patterns (DAMPs) may activate pattern recognition receptors on resident and recruited immune cells[ 31 , 32 ]. This hypothesis is supported by our CIBERSORT analysis, which identified a shift toward a pro-inflammatory immune landscape in NEC tissues. The positive correlation observed between our core hub genes—particularly PFKFB3 and TKTL1—and the infiltration of M1 macrophages and neutrophils suggests that these metabolic drivers do not act in isolation. Notably, we observed a significant coupling between these metabolic drivers and the infiltration of activated mast cells and dendritic cells. Activated mast cells within the neonatal intestines have been well recognized to augment vascular permeability and barrier injury[ 33 , 34 ]; our results suggest that metabolic exhaustion in enterocytes may directly fuel the recruitment and activation of these inflammatory cells, ultimately creating a self-amplifying metabolic-inflammatory axis where metabolic exhaustion directly perpetuates the rapid infiltration, leading to the transmural necrosis seen in clinical NEC cases. The diagnostic ability of these disulfidptosis-related genes marks a dramatic improvement over already available medical markers. As of now, already available medical markers such as C-reactive protein (CRP) or fecal calprotectin have proven highly reactive and nonspecific in recognizing two distinct conditions - ordinary inflammation versus intestinal necrosis[ 35 ]. That such a metabolic-inflammation pathway remains operative in all different patients strengthens our results. Moreover, our in vitro validation experiment involving TCEP, which works highly effectively in cleaving and thus reducing disulfide bonds, shows that therapeutic intervention at the metabolic level of intestinal barrier integrity can reverse not only cellular viability but restore the integrity of the F-actin network. This suggests that the progression of NEC could potentially be halted by therapeutic strategies aimed at supplementing reducing equivalents or inhibiting excessive cystine uptake before the onset of irreversible disulfidptosis[ 17 ]. In summary, our findings provide the first comprehensive evidence that disulfidptosis acts as a pivotal bridge between metabolic failure and the rapid intestinal barrier disruption characteristic of NEC. By identifying a core signature of four metabolic hub genes (TKTL1, PFKFB3, SLC2A3, and SLC2A14), we have established a robust molecular link between glucose-depleted metabolic stress and the catastrophic disruption of the actin cytoskeleton. This study introduces the concept of a metabolic-inflammatory axis, wherein metabolic exhaustion in enterocytes directly perpetuates immune cell infiltration and the subsequent inflammatory storm. This approach not only refocuses the current pathogenesis of NEC from the established immune system hypothesis to the metabolic susceptibility hypothesis but also provides a high-fidelity diagnostic framework for identifying neonatal intestinal injury in its earliest, potentially reversible phases. This study has certain limitations. Retrospective analyses based on public databases cannot fully avoid the influences of sample heterogeneity and batch effects. Although in vitro cell models can simulate key pathological features, they differ from the complex in vivo microenvironment. Therefore, animal experiments are needed in the future to further validate the core mechanisms. Additionally, the specific regulatory network of disulfidptosis in NEC and its interactions with other forms of cell death have not been fully elucidated. Looking ahead, multicenter clinical samples could be used to validate the diagnostic efficacy of the core genes, and conditional gene knockout animal models could be constructed to systematically analyze the spatiotemporal dynamic regulatory mechanisms of disulfidptosis at the in vivo level. Meanwhile, developing more specific disulfidptosis-targeted inhibitors will provide new translational directions for the precise treatment of NEC. CONCLUSION This study establishes disulfidptosis as a pivotal driver of epithelial injury in NEC and identifies a metabolic-inflammatory axis triggered by SLC7A11-mediated disulfide stress. We further elucidated how metabolic exhaustion leads to the catastrophic collapse of the actin cytoskeleton by characterizing a core hub gene signature (TKTL1, PFKFB3, SLC2A3, and SLC2A14) with high diagnostic accuracy. Furthermore, the successful in vitro reversal of these pathological hallmarks via TCEP suggests that metabolic stabilization represents a more promising therapeutic paradigm than conventional anti-inflammatory interventions, providing a robust theoretical framework for the early detection and precision management of neonatal necrotizing enterocolitis. Declarations Acknowledgements Not applicable. Data availability Supporting data for this study are available from the corresponding author on reasonable request. Funding Not applicable. Author Contributions Yufeng Shi : Study design, data collection and analysis, manuscript drafting. Cong Yan: Clinical guidance, data verification, manuscript revision. Lifan Chen : Clinical data collection, nursing information sorting, data analysis assistance. Zhanzhen Cao : Clinical experiment participation, literature collation, manuscript proofreading. Zhijie Huang : Overall study supervision, manuscript finalization, correspondence. Ethics approval and consent to participate This study did not involve any human participants or animals and therefore did not require ethical approval. Consent for publication Not applicable, as this study does not involve any individual person’s data in any form. Competing interests The authors declare no competing interests. References Singh DK, et al. Necrotizing enterocolitis: Bench to bedside approaches and advancing our understanding of disease pathogenesis. Front Pediatr. 2022;10:1107404. Ishiyama A et al. Necrotizing Enterocolitis: A Comprehensive Review on Toll-like Receptor 4-Mediated Pathophysiology, Clinical, and Therapeutic Insights. Biomedicines, 2025. 13(9). Niño DF, Sodhi CP, Hackam DJ. Necrotizing enterocolitis: new insights into pathogenesis and mechanisms. Nat Rev Gastroenterol Hepatol. 2016;13(10):590–600. Duess JW, et al. Necrotizing enterocolitis, gut microbes, and sepsis. Gut Microbes. 2023;15(1):2221470. Pijpers AGH, et al. Risk Factors for 30-day Mortality in Patients with Surgically Treated Necrotizing Enterocolitis: A Multicenter Retrospective Cohort Study. Eur J Pediatr Surg. 2025;35(4):332–40. Canvasser J, et al. Long-term outcomes and life-impacts of necrotizing enterocolitis: A survey of survivors and parents. Semin Perinatol. 2023;47(1):151696. Liu A, et al. Predictive value of biomarkers in neonatal necrotizing enterocolitis. Front Pediatr. 2025;13:1661371. Yang S, et al. Programmed death of intestinal epithelial cells in neonatal necrotizing enterocolitis: a mini-review. Front Pediatr. 2023;11:1199878. Shen L, Chen J, Tou J. Inhibition of ferroptosis in inflammatory macrophages alleviates intestinal injury in neonatal necrotizing enterocolitis. Cell Death Discov. 2025;11(1):365. Liu X, et al. Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nat Cell Biol. 2023;25(3):404–14. Zhu WW, et al. SLC7A11-mediated cell death mechanism in cancer: a comparative study of disulfidptosis and ferroptosis. Front Cell Dev Biol. 2025;13:1559423. Wan S, et al. Disulfidptosis in tumor progression. Cell Death Discov. 2025;11(1):205. Liu X, Zhuang L, Gan B. Disulfidptosis: disulfide stress-induced cell death. Trends Cell Biol. 2024;34(4):327–37. Ritchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. Szklarczyk D, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–12. Hackam DJ, Sodhi CP. Bench to bedside - new insights into the pathogenesis of necrotizing enterocolitis. Nat Rev Gastroenterol Hepatol. 2022;19(7):468–79. Hu X, et al. Necrotizing enterocolitis: current understanding of the prevention and management. Pediatr Surg Int. 2024;40(1):32. O'Neill LA, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nat Rev Immunol. 2016;16(9):553–65. Liu X, et al. NADPH debt drives redox bankruptcy: SLC7A11/xCT-mediated cystine uptake as a double-edged sword in cellular redox regulation. Genes Dis. 2021;8(6):731–45. Schwärzler J, et al. Epithelial metabolism as a rheostat for intestinal inflammation and malignancy. Trends Cell Biol. 2024;34(11):913–27. Xiao F, et al. Disulfidptosis: A new type of cell death. Apoptosis. 2024;29(9–10):1309–29. Yan Y, et al. SLC7A11 expression level dictates differential responses to oxidative stress in cancer cells. Nat Commun. 2023;14(1):3673. Liu X, et al. Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer. Nat Cell Biol. 2020;22(4):476–86. TeSlaa T, et al. The pentose phosphate pathway in health and disease. Nat Metab. 2023;5(8):1275–89. Xiao M, et al. Role of PFKFB3-driven glycolysis in sepsis. Ann Med. 2023;55(1):1278–89. Pizzagalli MD, Bensimon A, Superti-Furga G. A guide to plasma membrane solute carrier proteins. Febs j. 2021;288(9):2784–835. Zhou Z, et al. Increased stromal PFKFB3-mediated glycolysis in inflammatory bowel disease contributes to intestinal inflammation. Front Immunol. 2022;13:966067. Meng Y, Chen X, Deng G. Disulfidptosis: a new form of regulated cell death for cancer treatment. Mol Biomed. 2023;4(1):18. Liu T, et al. NF-κB signaling in inflammation. Signal Transduct Target Ther. 2017;2:17023. Galluzzi L, et al. Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ. 2018;25(3):486–541. Man SM, Kanneganti TD. Innate immune sensing of cell death in disease and therapeutics. Nat Cell Biol. 2024;26(9):1420–33. Hasler WL, et al. Mast cell mediation of visceral sensation and permeability in irritable bowel syndrome. Neurogastroenterol Motil. 2022;34(7):e14339. Karhausen J, et al. Intestinal mast cells mediate gut injury and systemic inflammation in a rat model of deep hypothermic circulatory arrest. Crit Care Med. 2013;41(9):e200–10. Pimenta S, et al. Serum biomarkers in the early detection of necrotizing enterocolitis: a systematic review. J Perinat Med. 2025;53(8):966–92. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8994121","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614125632,"identity":"5729e677-a5f4-4f80-a366-87faabcd1081","order_by":0,"name":"Yufeng Shi","email":"","orcid":"","institution":"Central People's Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Shi","suffix":""},{"id":614125633,"identity":"4d414a14-7df0-4a5b-aed6-8f97cfe0c8bd","order_by":1,"name":"Cong Yan","email":"","orcid":"","institution":"Central People's Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Yan","suffix":""},{"id":614125634,"identity":"1aff1020-7474-4901-b5ba-792210985b1a","order_by":2,"name":"Lifan Chen","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lifan","middleName":"","lastName":"Chen","suffix":""},{"id":614125635,"identity":"74642953-de61-4cf0-b039-52fe4b9fa80a","order_by":3,"name":"Zhanzhen Cao","email":"","orcid":"","institution":"Central People's Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"Zhanzhen","middleName":"","lastName":"Cao","suffix":""},{"id":614125636,"identity":"af14d897-c894-405c-894b-e880db9c05e7","order_by":4,"name":"Zhijie Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYFCCAwwSDAdsePj5G0jTkiYjOeMACfYAtRy2MWhIIFK5fOPxi7d5zpznMWA4wPjhYw4RWhgbzhRb89y4zWPO3MAsOXMbEVqYGc6kSed8uM1j2XCAjZmXGC1sEC3neAwOJBCphYfh+DHpnBsHSNAiwXCG2frPmWQeyRkHm4nzi/yM4w9vzjhmZ8/P33zww0ditDBInDGAshgbiFEPBPztD4hUOQpGwSgYBSMWAAC2EDsKrE6b2QAAAABJRU5ErkJggg==","orcid":"","institution":"Central People's Hospital of Zhanjiang","correspondingAuthor":true,"prefix":"","firstName":"Zhijie","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-02-28 09:54:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8994121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8994121/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105877595,"identity":"5d0896f2-f623-4d65-bd54-208be8c26652","added_by":"auto","created_at":"2026-04-01 06:05:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142957,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic signatures of disulfidptosis in NEC. (a) Principal Component Analysis (PCA) of the GSE46619 dataset, revealing a distinct separation in global gene expression profiles between NEC samples (n=5) and surgical controls (n=5). (b) Volcano plot identifying 1,749 differentially expressed genes (DEGs), with red dots indicating upregulated genes (n=488) and blue dots indicating downregulated genes (n=1261) (c) Heatmap displaying the differential expression of 15 key disulfidptosis-related genes (DRGs) between NEC and control groups. (d) Box plot comparing the disulfidptosis ssGSEA scores, showing significant activation of the disulfidptosis pathway in NEC tissues.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/03f8c78f2d12e3892e10884e.png"},{"id":105905133,"identity":"2fe211dc-3a52-491f-a66b-652f3f41d2c1","added_by":"auto","created_at":"2026-04-01 10:11:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104889,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA and Module Correlation. (a) Sample clustering analysis for outlier detection. (b) Dendrogram and trait heatmap illustrating the molecular distinction between Normal and NEC phenotypes. (c) Analysis of network topology; a soft-thresholding power of β = 9 was selected based on the scale-free fit index reaching R2 = 0.9. (d) Gene dendrogram and module identification through dynamic tree cutting. (e) Heatmap of module-trait relationships; the Blue and Turquoise modules exhibited the strongest positive correlation with the NEC pathological state.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/870d865455e27cce3e717841.png"},{"id":105905134,"identity":"131ea2a5-6f95-4b05-8610-9aa834521b34","added_by":"auto","created_at":"2026-04-01 10:11:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":265162,"visible":true,"origin":"","legend":"\u003cp\u003ePPI Network Architecture and Correlation with Disulfidptosis Regulators. (a) Venn diagram showing 147 core candidate genes obtained from the intersection of upregulated DEGs and WGCNA hub modules. (b) Comprehensive Protein-Protein Interaction (PPI) network of the core genes. (c) A refined sub-network highlighting hub candidates, including SLC2A3, TKTL1, PFKFB3, and SLC2A14. (d) Correlation matrix and pie charts demonstrating the significant positive transcriptional association between core hub genes and SLC7A11.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/554f2d03f5275566e7d479cd.png"},{"id":105877598,"identity":"eb385196-104e-4a3f-94b3-cdb3bf360b54","added_by":"auto","created_at":"2026-04-01 06:05:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":387302,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional Enrichment and Signaling Pathway Architecture. (a–c) Dot plots for Gene Ontology (GO) enrichment analysis across Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) categories, highlighting enrichment in cell communication and cytokine activity. (d) KEGG pathway enrichment analysis revealing the activation of pro-inflammatory cascades, including the Cytokine-cytokine receptor interaction, NF-$\\kappa$B, and TNF signaling pathways.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/12cef8a76735bd5d2a6e8a42.png"},{"id":105905427,"identity":"e67c1121-e53e-4ee1-b800-08c998270f7b","added_by":"auto","created_at":"2026-04-01 10:12:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":124427,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of the Immune Infiltration Landscape. (a) Comparison of the infiltrating proportions of 22 immune cell types between NEC and Normal groups using the CIBERSORT algorithm. (b–c) Heatmap and stacked bar plot visualizing the immune cell composition across individual samples. (d–g) Correlation scatter plots between hub genes (TKTL1, PFKFB3, SLC2A3, SLC2A14) and specific immune cell types (Activated Dendritic cells and Activated Mast cells), indicating a metabolic-immune coupling.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/c3f272841c3bbaeead9043aa.png"},{"id":105877600,"identity":"747fcb89-b0c5-48b6-a260-efb50ff075d0","added_by":"auto","created_at":"2026-04-01 06:05:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":58854,"visible":true,"origin":"","legend":"\u003cp\u003eExternal Validation and Diagnostic Performance of Core Genes. (a–d) Receiver Operating Characteristic (ROC) curves evaluating the diagnostic efficacy of core hub genes in an independent dataset. The Area Under the Curve (AUC) values are: PFKFB3 (0.823), SLC2A3 (0.754), SLC2A14 (0.800), and TKTL1 (0.892).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/d64815f25cea1da9da04bc4a.png"},{"id":105877601,"identity":"7cbd8d23-bea7-406e-aa20-9dce0834e0c6","added_by":"auto","created_at":"2026-04-01 06:05:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":128819,"visible":true,"origin":"","legend":"\u003cp\u003eProtective effects of TCEP on the LPS-induced injury model in Caco-2 cells. (a) Cell viability of Caco-2 cells treated with LPS and TCEP was detected by CCK-8 assay. (b, c) Levels of the pro-inflammatory factors IL-6 and TNF-α in cell culture supernatants were measured by ELISA. (d) TEER values of the Caco-2 cell monolayer were monitored over time to assess barrier formation. (e) Permeability of the cell monolayer to macromolecular solutes was assessed by detecting the fluorescence intensity of FITC-dextran in the basolateral medium. (f, g) mRNA expression levels of the tight junction proteins ZO-1 and Occludin were detected by qRT-PCR. Data are presented as mean ± SEM, n=6. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/f9af1effc220f7ae0a3f7752.png"},{"id":105877602,"identity":"c3e04ae5-482e-45e5-a037-c694eb9c52e9","added_by":"auto","created_at":"2026-04-01 06:05:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":385998,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental validation of LPS-induced disulfidptosis in Caco-2 cells. (a) Under glucose starvation, phalloidin staining (orange) shows the F-actin cytoskeleton morphology, and DAPI (blue) stains nuclei. (b, c) Intracellular cystine accumulation and the NADP⁺/NADPH ratio were measured using a cystine ELISA kit and an NADP⁺/NADPH assay kit, respectively. (d-g) mRNA expression levels of disulfidptosis-related genes PFKFB3, SLC2A3, SLC2A14, and TKTL1 were detected by qRT-PCR. Data are presented as mean ± SEM, n=6. *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/b236a56cda2301e8ade2b6d1.png"},{"id":107483943,"identity":"8e6bb202-275a-431f-b22b-fd7a6f23707d","added_by":"auto","created_at":"2026-04-22 02:30:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1648662,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8994121/v1/ad39086c-13f2-4659-aeee-e961c0cf6e36.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mechanism of Disulfide Death-Driven Intestinal Epithelial Injury in Neonatal Necrotizing Enterocolitis and Exploration of Potential Therapeutic Targets","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eNeonatal necrotizing enterocolitis (NEC) remains one of the most devastating gastrointestinal emergencies encountered in the neonatal intensive care unit (NICU), mostly seen in premature and low birth weight newborns[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. With its abrupt onset and resultant rapid course toward transmural necrosis, NEC is traditionally attributed to a complex interplay of intestinal immaturity, microbial dysbiosis, and an intense inflammatory response[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite significant advancements in neonatal intensive care, the mortality rate for surgical NEC remains alarmingly high, often exceeding 30%[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e], while survivors frequently face long-term complications such as short bowel syndrome and neurodevelopmental impairment[\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. A major hurdle in improving clinical outcomes is the lack of highly sensitive and specific biomarkers for early risk stratification, as current diagnosis often relies on non-specific clinical signs and late-stage radiographic findings[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, there is an urgent need to elucidate the underlying molecular mechanisms driving intestinal cell death and to identify novel diagnostic targets that can capture the disease in its earliest phases.\u003c/p\u003e \u003cp\u003eRecently, the involvement of regulated cell death in NEC pathogenesis has attracted increasing interests, where the roles of necroptosis, apoptosis, and ferroptosis in the epithelial injury of the intestinal barrier have been reported[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nevertheless, these well-known pathways cannot explain the severe metabolic failure and explosive tissue necrosis feature of NEC exacerbations. In 2023, a novel form of regulated cell death termed disulfidptosis was discovered, characterized by the accumulation of disulfides within the cells, triggering the destruction of the actin cytoskeleton[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. By contrast to ferroptosis, disulfidptosis requires the over-expression of SLC7A11 in the glucose-deprived state[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. In such conditions, the depletion of NADPH—principally supplied by the pentose phosphate pathway—prevents the reduction of cystine to cysteine, yielding the cytotoxic disulfide bonds[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. Importantly, the pathological microenvironment of NEC is defined by the concurrent presence of severe oxidative injury and metabolic failure. While the former acts as a potent stimulus for SLC7A11 expression to mitigate oxidative stress, the latter results in profound glucose depletion. This convergence of molecular events in the neonatal intestine aligns closely with the biochemical prerequisites for disulfidptosis initiation. Recent findings in Crohn's disease have identified disulfidptosis as a critical regulator of intestinal immune infiltration and barrier dysfunction. Furthermore, cross-disease analyses have identified shared disulfidptosis-associated gene signatures across ankylosing spondylitis and inflammatory bowel diseases.\u003c/p\u003e \u003cp\u003eThis study aims to systematically investigate the clinical significance and molecular mechanisms of disulfidptosis in NEC. Through integrated analysis of NEC transcriptomic data from the GEO database, core hub genes associated with disulfidptosis were identified, and their diagnostic value was evaluated in an independent validation cohort. Combined with functional enrichment analysis and immune infiltration profiling, this study further revealed the potential role of the \"metabolism-inflammation axis\" in disease progression. An NEC-like in vitro model was established to validate the bioinformatic predictions. By detecting disulfidptosis-related metabolic markers and changes in tight junction protein expression, the protective effect of the disulfidptosis inhibitor TCEP on intestinal epithelial barrier function was systematically evaluated. This study is expected to provide a new theoretical basis for the development of early diagnostic markers and metabolism-targeted therapeutic strategies for NEC.\u003c/p\u003e "},{"header":"METHODS","content":"\u003ch2\u003e2.1 Data Acquisition and Preprocessing\u003c/h2\u003e\u003cp\u003ePublicly available transcriptomic datasets associated with neonatal necrotizing enterocolitis (NEC) were acquired from the Gene Expression Omnibus (GEO) database. The study utilized GSE46619 as the discovery cohort, comprising five NEC tissues and four adjacent surgical controls, and GSE297483 (containing 10 NEC and 13 healthy controls) for external validation. For the microarray data, normalization and log2 transformation were performed using the \"normalizeBetweenArrays\" function within the limma R package to eliminate technical batch effects and ensure data comparability[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003e2.2 Screening of Differentially Expressed Genes (DEGs)\u003c/h2\u003e\u003cp\u003eThe limma toolkit was employed to screen for differentially expressed genes (DEGs) between the NEC and control groups.[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] Genes satisfying the criteria of adjusted P-value \u0026lt; 0.05 and |log₂ Fold Change (FC)| ≥ 1 were identified as significant DEGs. The expression patterns and distribution of these DEGs were visualized using volcano plots and hierarchical clustering heatmaps generated via the ggplot2 and pheatmap R packages.\u003c/p\u003e\u003ch2\u003e2.3 Characterization of Disulfidptosis Functional Signatures\u003c/h2\u003e\u003cp\u003eTo quantify the activity of disulfidptosis across intestinal samples, we implemented the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm via the GSVA R package[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. Based on established literature, disulfidptosis-related genes (DRGs) were curated and categorized into functional \"promoters\" and \"inhibitors.[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\" We then calculated each category's enrichment scores respectively. A comprehensive \"Disulfidptosis Score\" for each sample was then derived (Score = ES_promoters - ES_inhibitors), serving as a metric for disulfidptosis levels in each sample.\u003c/p\u003e\u003ch2\u003e2.4 Weighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e\u003cp\u003eThe WGCNA package (v1.71) was applied to the preprocessed expression matrix to identify key gene modules. The “pickSoftThreshold\" function was utilized to determine the optimal soft-thresholding power for scale-free topology. Subsequently, a topological overlap matrix (TOM) was constructed to assess pairwise gene co-expression similarity, followed by a dynamic tree-cutting algorithm to cluster genes into distinct modules, represented by different colors.\u003c/p\u003e\u003ch2\u003e2.5 Functional Enrichment Analysis\u003c/h2\u003e\u003cp\u003eTo elucidate the biological significance of the identified genes, the intersection of DEGs and WGCNA hub modules was subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis via the STRING database[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. These analyses targeted biological processes (BP), molecular functions (MF), cellular components (CC), and signaling pathways associated with the overlapping gene set.\u003c/p\u003e\u003ch2\u003e2.6 PPI Network Construction and Module Clustering\u003c/h2\u003e\u003cp\u003eA Protein-Protein Interaction (PPI) network was constructed by mapping the overlapping genes to the STRING database. The network topology was visualized using Cytoscape (v3.10.3). Sub-clusters within the network were identified using the k-means clustering algorithm, and the correlation between key clusters and phenotype-associated DEGs was evaluated via Pearson correlation analysis.\u003c/p\u003e\u003ch2\u003e2.7 Immune Infiltration Landscape\u003c/h2\u003e\u003cp\u003eThe immune microenvironment was characterized using the CIBERSORT deconvolution algorithm, guided by the LM22 reference signature matrix. We quantified the relative fractions of 22 infiltrating immune cell subtypes in NEC and control tissues. Only samples with a CIBERSORT P-value \u0026lt; 0.05 were included in subsequent analyses to ensure reliable immune cell estimation.\u003c/p\u003e\u003ch2\u003e2.8 External Validation and Diagnostic Evaluation\u003c/h2\u003e\u003cp\u003eThe predictive validity of the identified biomarkers was tested in an independent dataset (GSE297483). Receiver Operating Characteristic (ROC) curves were generated using the pROC R package. The diagnostic performance was quantified by the Area Under the Curve (AUC), along with sensitivity and specificity metrics, to assess the translational potential of the core genes.\u003c/p\u003e\u003ch2\u003e2.9 Cell Culture and Treatment\u003c/h2\u003e\u003cp\u003eCaco-2 cells (CL-0050, Procell, China) were routinely cultured at 37°C with 5% CO₂. The culture medium was DMEM medium (11965118, Gibco, USA) with 10% fetal bovine serum (FSD500, Excell Bio, China) and 1% penicillin‒streptomycin (C0222, Beyotime, China). Cells were randomly divided into three groups: control group, LPS model group (treated with 100 µg/mL LPS), and LPS+TCEP intervention group (co-treated with 100 µg/mL LPS and 5 µM TCEP). TCEP was added 2 hours prior to LPS stimulation to investigate its inhibitory effect on the disulfidptosis pathway. LPS stimulation was used to establish an in vitro NEC cell injury model. After the corresponding treatment duration, samples were collected for subsequent analysis.\u003c/p\u003e\u003ch2\u003e2.10 Cell Viability\u003c/h2\u003e\u003cp\u003eThe procedure was carried out in accordance with the instructions of the CCK-8 kit (BA00208, Bioss, China). After treatment, 10 µL of CCK-8 solution was added to each well of cells, followed by an additional 2-hour incubation. Subsequently, the absorbance (OD value) at a wavelength of 450 nm was measured using a microplate reader, and the cell survival rate was calculated based on these readings to assess cell viability. Each experiment was independently repeated five times to ensure data reliability.\u003c/p\u003e\u003ch2\u003e2.11 qRT-PCR\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from cells using TRIzol reagent (RC112, Beyotime, China). Complementary DNA (cDNA) was then synthesized using the HiScript III First Strand cDNA Synthesis Kit (R312, Beyotime, Shanghai, China) according to the manufacturer's instructions. Quantitative real-time PCR (qRT-PCR) was performed on a 7500 Real-Time PCR System (Applied Biosystems, USA) with a Universal SYBR qPCR Master Mix (MQ101, Beyotime, China), using GAPDH as the internal reference gene. Following the recommended cycling conditions, the relative mRNA expression levels of target genes were calculated using the 2\u003csup\u003e−ΔΔCT\u003c/sup\u003e method.\u003c/p\u003e\u003ch2\u003e2.12 ELISA\u003c/h2\u003e\u003cp\u003eAfter the intervention, cell culture supernatants were collected. Strictly following the kit instructions (Coibo, China), the secretion levels of TNF-α (CB11762-Hu) and IL-6 (CB10373-Hu) in the supernatants were quantitatively analyzed using the ELISA method. The absorbance was measured with a full-wavelength microplate reader, and the specific concentrations were calculated based on the standard curve to assess the release of inflammatory cytokines. Each experiment was independently repeated five times to ensure the accuracy and reproducibility of the data.\u003c/p\u003e\u003ch2\u003e2.13 Measurement of TEER and permeability\u003c/h2\u003e\u003cp\u003eCells in a good logarithmic growth phase were selected, digested with trypsin to prepare a single-cell suspension, and seeded into 24-well Transwell inserts (polycarbonate membrane, pore size 0.4 µm) at a density of 1×10⁴ cells/mL. Specifically, 200 µL of cell suspension was added to the apical (AP) side, and 600 µL of complete medium was added to the basolateral (BL) side. The culture system was then incubated at 37°C with 5% CO₂ for 21 days to allow for monolayer formation. Subsequently, supernatants from different experimental groups were added to the apical compartment, followed by a 24-hour incubation. Prior to the TEER assay, the medium on both sides of the Caco-2 cell monolayer was replaced with PBS buffer. Then, 0.1 mg/mL FITC-dextran (T2935, Beyotime, China) was added to the apical side of the Transwell inserts. After 1 hour of incubation, the basolateral medium was collected. Fluorescence intensity was measured using a fluorescence microplate reader at an excitation wavelength of 480 nm and an emission wavelength of 520 nm to evaluate the permeability changes of the cell monolayer.\u003c/p\u003e\u003ch2\u003e2.14 Immunofluorescence Staining\u003c/h2\u003e\u003cp\u003eCells from each group were plated in chamber slides under glucose starvation conditions and cultured overnight to allow complete adhesion. The cells were then fixed with 4% paraformaldehyde at room temperature for 15 minutes, followed by permeabilization with 0.5% Triton X-100 at room temperature for 5 minutes. After washing three times with phosphate-buffered saline (PBS), the cells were stained with 100 nM Actinstain 568 fluorescent phalloidin (HY-K0903, MCE, USA) to label the F-actin cytoskeleton, with incubation for approximately 30 minutes. Following staining, the cells were washed three times with PBS to remove unbound dye. Subsequently, the nuclei were counterstained with DAPI (Thermo Fisher Scientific, USA) for 5 minutes. Finally, the slides were mounted and images were captured using a confocal microscope (Leica, Germany).\u003c/p\u003e\u003ch2\u003e2.15 Intracellular NADP+/NADPH ratio and cystine level detection\u003c/h2\u003e\u003cp\u003eIntracellular levels of NADPH and total NADP were quantified using the NADP⁺/NADH Assay Kit (S0179, Beyotime, China) according to the manufacturer's protocol. The NADP⁺/NADPH ratio was subsequently determined based on the formula (Total NADP – NADPH)/NADPH. In parallel, intracellular cysteine levels were assessed following the procedure provided with the Mouse Cysteine ELISA Kit (YS16037B, Yaji Biological, China).\u003c/p\u003e\u003ch2\u003e2.16 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis and visualization were performed using R (v4.3.1) and GraphPad Prism (v9.0). Continuous variables were expressed as mean ± SD, with group comparisons conducted via two-tailed Student's t-test and Mann-Whitney U test according to data normality; categorical variables were assessed by Fisher's exact test. In the bioinformatic workflow, Wilcoxon rank-sum test was used for DEGs and ssGSEA scores. Pearson correlation was applied within WGCNA for hub module identification (MM and GS), while Spearman correlation evaluated associations between hub genes, SLC7A11, and CIBERSORT immune infiltration. Diagnostic robustness was validated via ROC and AUC metrics in dataset GSE297483. indicated statistical significance.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Transcriptomic Alterations and Activation of Disulfidptosis in NEC\u003c/h2\u003e \u003cp\u003eFor analyzing the transcriptomic changes occurring in NEC, Principal Component Analysis (PCA) was initially performed on the GSE46619 dataset. The PCA results plotted on a graph revealed a distinct separation between the NEC samples and surgery controls, indicating substantial global molecular divergence between the two cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Differential expression analysis identified 1,749 differentially expressed genes (DEGs), including 488 upregulated and 1,261 downregulated genes in NEC compared with controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and ∣log2 FC | \u0026ge; 1; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In addition, heat map analysis of 15 disulfidptosis-related genes (DRGs) showed varied expressions, with some significant disulfidptosis-related genes being differentially expressed between the NEC samples and the normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). To understand the extent of changes, the \"disulfidptosis score\" profile was determined through the ssGSEA algorithm. The NEC sample exhibited high disulfidptosis activities compared to normally growing tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), suggesting the significance of disulfidptosis-related pathways associated with the pathogenesis of NEC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Co-expression Network Analysis and Hub Module Identification\u003c/h2\u003e \u003cp\u003eTo systematically identify gene clusters that have functional correlations with the NEC phenotype, we developed a co-expression network using the WGCNA algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The dendrogram and heatmap further demonstrated a clear differentiation in the molecular profile between the NEC and surgery control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). We evaluated several soft thresholding powers β and chose nine (as indicated by the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e threshold of 0.9), for choosing the optimum connectivity and biological significance to achieve scale free topology (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Through the implementation of a dynamic tree-cutting technique, a total of 14 co-expression modules were obtained based on TOM-based dissimilarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). We performed a correlation analysis based on the 14 co-expression modules obtained. Two modules emerged as highly significant: the ME_Blue module (Module Membership\u0026thinsp;=\u0026thinsp;0.98, Gene Significance\u0026thinsp;\u0026lt;\u0026thinsp;3e-05) and the ME_turquoise module (Module Membership\u0026thinsp;=\u0026thinsp;0.73, Gene Significance\u0026thinsp;\u0026lt;\u0026thinsp;0.04) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). These two hub modules were prioritized for identifying the key genes and characterizing their functional roles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Core Candidate Genes and PPI Network Architecture\u003c/h2\u003e \u003cp\u003eTo enhance the screening stringency and prioritize biologically relevant candidates, we performed an intersection analysis between the 488 upregulated DEGs and the WGCNA hub modules (Blue and Turquoise). This dual-filtering strategy yielded 147 overlapping genes, which were referred to as our defined as the core candidates associated with NEC progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). To further investigate the functional interplay among these 147 candidates, a PPI network was established. Using the k-means clustering algorithm, the network was partitioned into three distinct functional clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). A simplified sub-network was extracted from the critical functional unit Cluster 3 to visualize the most essential protein associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Subsequent correlation analysis revealed that the expression of these core genes\u0026mdash;particularly SLC2A3, PFKFB3, SLC2A14, and TKTL1\u0026mdash; highly correlated with SLC7A11, the master regulator of disulfidptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). These findings suggest a close transcriptional association between these core genes and the disulfidptosis machinery in the context of NEC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Distinct Functional Signatures and Inflammatory Landscape of Core Genes\u003c/h2\u003e \u003cp\u003eWe performed a comprehensive enrichment analysis leveraging GO and KEGG databases to characterize the biological identity of the 147 core candidate genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). GO BP analysis revealed that the main biological processes in these genes were related to cell communication, signal transduction, and response to stimulus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). MF was significantly enriched in the binding to cells involved in signal transduction and the activity of cytokines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Analysis using CC revealed significant enrichment in the plasma membrane and the external side of the plasma membrane (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eKEGG pathway analysis also revealed the pathway architecture of the core genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The core genes were highly enriched in some important pro-inflammatory pathways, such as Cytokine-cytokine receptor interaction, NF-kappa B signaling pathway, and TNF signaling pathway. Some non-inflammatory pathways, such as MAPK signaling pathway and IL-17 signaling pathway, were also found to be significantly associated with the core genes. This not only simplifies the functional landscape of disulfidptosis-related genes but also emphasizes their tight link to metabolic and inflammatory signal transduction pathways, respectively, in NEC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Landscape of Immune Infiltration and Correlation with Core Genes\u003c/h2\u003e \u003cp\u003eTo explore further the effects of disulfidptosis on the immune microenvironment in NEC, we adopted the CIBERSORT algorithm for assessment of the infiltration levels of 22 types of immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Inter-cellular correlation analysis revealed a significant synergistic relationship between M1 macrophages and neutrophils(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Comparative analysis demonstrated a distinct pro-inflammatory shift in the NEC immune landscape, specifically a significant increase in the infiltration of M1 macrophages, neutrophils, and activated mast cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), alongside a concomitant reduction in monocytes and resting T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Furthermore, Spearman correlation analysis was performed to evaluate the association between the four disulfidptosis-related core genes and specific immune cell subsets (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed\u0026ndash;g). The expression levels of TKTL1,PFKFB3,SLC2A3, and SLC2A14 correlated not only with M1 macrophages and neutrophils but also, most significantly, with activated Mast cells and activated Dendritic cells (all R\u0026thinsp;\u0026gt;\u0026thinsp;0.8, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These data define a specific statistical link between disulfidptosis-related gene expression and a pro-inflammatory immune cell profile in NEC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6 External Validation and Predictive Performance of Core Genes\u003c/h2\u003e \u003cp\u003eThe discriminatory power of PFKFB3, SLC2A3, SLC2A14, and TKTL1 remained remarkably consistent when projected onto the independent dataset GSE297483. As seen from the ROC curve, these metabolic drivers act as reliable indicators for the NEC disease state, transcending the limitations of the discovery dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among the validated candidates, TKTL1 emerged with a predominant AUC of 0.892, defining it as a superior molecular signature for the disulfidptosis-driven necrotic process (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). This predictive strength was further substantiated by PFKFB3 (AUC\u0026thinsp;=\u0026thinsp;0.823) and SLC2A14 (AUC\u0026thinsp;=\u0026thinsp;0.800), while SLC2A3 also maintained a significant discriminatory threshold (AUC\u0026thinsp;=\u0026thinsp;0.754; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-c). The robust AUC values attained across this external validation set transition these disulfidptosis-related genes from purely bioinformatic candidates to validated clinical indicators.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Disulfidptosis Inhibitor TCEP Alleviates LPS-Induced Intestinal Epithelial Cell Injury and Barrier Dysfunction\u003c/h2\u003e \u003cp\u003eTo validate the findings from bioinformatics analysis in vitro, we established a NEC-like injury model by stimulating Caco-2 cells with lipopolysaccharide (LPS). The disulfidptosis inhibitor tris(2-chloroethyl) phosphate (TCEP) was applied for intervention. CCK-8 assay results showed that LPS treatment significantly reduced cell viability compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Co-treatment with TCEP effectively reversed the LPS-induced cytotoxicity and partially restored cell viability (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding the inflammatory response, LPS stimulation significantly increased the secretion levels of the pro-inflammatory cytokines IL-6 and TNF-α (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb, c, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, TCEP treatment significantly inhibited the production of these key inflammatory mediators (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that disulfidptosis may be involved in regulating NEC-related inflammation.\u003c/p\u003e \u003cp\u003eTo further assess intestinal epithelial barrier function, we monitored the establishment of a Caco-2 cell monolayer model. The transepithelial electrical resistance (TEER) value gradually increased with culture time, reaching a plateau on day 11 (approximately 1287.67 Ω\u0026middot;cm\u0026sup2;). This indicated the formation of an intact epithelial barrier with well-structured tight junctions (TJs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Barrier permeability experiments revealed that LPS stimulation significantly increased the permeation of FITC-dextran through the cell monolayer (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming that LPS disrupted barrier integrity. TCEP treatment significantly reduced this LPS-induced dextran leakage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mechanistically, qRT-PCR analysis showed that LPS treatment significantly downregulated the mRNA expression of the tight junction core proteins ZO-1 and Occludin (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef, g, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). TCEP effectively prevented this downregulation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Together, these results indicate that the disulfidptosis inhibitor TCEP can alleviate LPS-induced intestinal epithelial inflammation, improve tight junction protein expression, and protect the integrity of epithelial barrier function.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Disulfidptosis is a Key Mechanism Mediating LPS-Induced Intestinal Epithelial Cell Injury\u003c/h2\u003e \u003cp\u003eTo directly confirm that LPS-induced cell injury is driven by disulfidptosis, we performed further validation under glucose starvation conditions. Immunofluorescence staining showed that, compared to the control group, LPS-treated cells exhibited typical morphological features of disulfidptosis, including intense contraction of the F-actin cytoskeleton and significant cell shrinkage (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Biochemical assays provided further evidence at the metabolic level. LPS stimulation led to substantial intracellular cystine accumulation, accompanied by a significant increase in the NADP⁺/NADPH ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, c, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This suggests the cells entered a state of severe metabolic stress and redox imbalance. TCEP intervention effectively reversed these morphological changes and metabolic abnormalities.\u003c/p\u003e \u003cp\u003eConcurrently, we validated the expression of core genes identified through bioinformatics screening. qRT-PCR results showed that LPS treatment significantly upregulated the mRNA expression levels of four key genes: PFKFB3, SLC2A3, SLC2A14, and TKTL1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed-g, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This aligns with the bioinformatics finding that NEC tissues exhibit a high disulfidptosis score. Importantly, TCEP treatment effectively suppressed the LPS-induced overexpression of these genes. In summary, this study confirms from morphological, metabolomic, and transcriptomic perspectives that LPS drives intestinal epithelial cell injury by activating the disulfidptosis pathway, and the pathway inhibitor TCEP can provide a protective effect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe pathogenesis of neonatal NEC involves complex interactions between intestinal immaturity, microbial dysbiosis, and an exaggerated inflammatory response[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, we systematically explored the potential role of disulfidptosis\u0026mdash;a newly discovered form of regulated cell death\u0026mdash;in the progression of NEC. By integrating transcriptomic data from GSE46619 and GSE297483, we demonstrated that NEC intestinal tissues exhibit a significantly elevated disulfidptosis score, suggesting that metabolic-stress-induced cell death is a critical hallmark of this disease[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Through WGCNA and PPI network analysis, we identified four core hub genes (TKTL1, PFKFB3, SLC2A3, and SLC2A14) that are not only significantly upregulated in NEC but also strongly correlated with SLC7A11 expression, the primary driver of disulfidptosis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, the exceptional diagnostic performance of these genes in an independent cohort, particularly TKTL1 (AUC\u0026thinsp;=\u0026thinsp;0.892), validates their potential as robust biomarkers for assessing intestinal damage and disease severity. Together, our data suggest a previously unappreciated role of metabolic vulnerability in NEC pathogenesis, extending emerging concepts of epithelial metabolic control under conditions of nutrient deprivation and oxidative stress[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile apoptosis and necrosis have been extensively studied in NEC, our findings suggest that disulfidptosis represents a more complex metabolic \"executioner\" of epithelial cell death. The molecular mechanism of disulfidptosis is fundamentally tied to the balance between cystine import and the availability of reducing equivalents[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], primarily NADPH. Our analysis revealed a significant upregulation of SLC7A11 in NEC tissues, a phenomenon traditionally viewed as a compensatory response to oxidative stress[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, in the nutrient-deprived environment of the neonatal intestine, excessive SLC7A11-mediated cystine uptake becomes a metabolic liability. Such a specific biochemical reaction, initially identified for cancer cells by Liu et al. (2023), results in the intracellular build-up of disulfides, ultimately causing the irreversible disruption of the actin cytoskeleton[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, this discovery introduces a novel mechanism that could account for the rapid, or 'explosive,\u0026rsquo; course of intestinal necrosis that occurs in NEC infants, for whom the barrier layer lacks not only integrity but suffers a catastrophic failure.\u003c/p\u003e \u003cp\u003eThe functional identity of our identified hub genes reinforces this metabolic-stress model. We identified TKTL1\u0026mdash;a pivotal enzyme in the non-oxidative branch of the pentose phosphate pathway (PPP)\u0026mdash;as a top-performing core gene with high diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.892). Given that the PPP is the primary source of NADPH required to reduce intracellular disulfides, the strong correlation between TKTL1 and SLC7A11 suggests that TKTL1 serves as a critical metabolic regulator attempting to maintain redox homeostasis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As in other forms of ischemic injury, PPP activation is often a survival strategy, yet in the context of NEC, this backup system appears to be overwhelmed. Concurrently, the elevated expression of PFKFB3, a potent stimulator of glycolysis, and glucose transporters like SLC2A3 and SLC2A144, reflects the state of high metabolic demand and glucose starvation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. PFKFB3 has been documented in literature to induce pro-inflammatory responses in intestinal myofibroblasts[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], suggesting that its upregulation in NEC might inadvertently accelerate glucose depletion. In NEC, when the supply of glucose-derived NADPH fails to keep pace with the SLC7A11-driven influx of cystine, the resulting accumulation of intracellular disulfides triggers the irreversible collapse of the actin cytoskeleton, thereby executing the disulfidptosis program[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond the cell-intrinsic metabolic collapse, our findings underscore a pivotal link between disulfidptosis and the hyper-inflammatory state characteristic of NEC. The KEGG pathway analysis highlighted a significant enrichment of genes in the TNF signaling pathway, NF-κB signaling pathway, and cytokine-cytokine receptor interactions. We propose that the massive, non-programmed cell death induced by disulfidptosis serves as a potent trigger for this \"inflammatory storm[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\". As intestinal epithelial cells undergo disulfidptosis, the release of damage-associated molecular patterns (DAMPs) may activate pattern recognition receptors on resident and recruited immune cells[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This hypothesis is supported by our CIBERSORT analysis, which identified a shift toward a pro-inflammatory immune landscape in NEC tissues. The positive correlation observed between our core hub genes\u0026mdash;particularly PFKFB3 and TKTL1\u0026mdash;and the infiltration of M1 macrophages and neutrophils suggests that these metabolic drivers do not act in isolation. Notably, we observed a significant coupling between these metabolic drivers and the infiltration of activated mast cells and dendritic cells. Activated mast cells within the neonatal intestines have been well recognized to augment vascular permeability and barrier injury[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]; our results suggest that metabolic exhaustion in enterocytes may directly fuel the recruitment and activation of these inflammatory cells, ultimately creating a self-amplifying metabolic-inflammatory axis where metabolic exhaustion directly perpetuates the rapid infiltration, leading to the transmural necrosis seen in clinical NEC cases.\u003c/p\u003e \u003cp\u003eThe diagnostic ability of these disulfidptosis-related genes marks a dramatic improvement over already available medical markers. As of now, already available medical markers such as C-reactive protein (CRP) or fecal calprotectin have proven highly reactive and nonspecific in recognizing two distinct conditions - ordinary inflammation versus intestinal necrosis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. That such a metabolic-inflammation pathway remains operative in all different patients strengthens our results. Moreover, our in vitro validation experiment involving TCEP, which works highly effectively in cleaving and thus reducing disulfide bonds, shows that therapeutic intervention at the metabolic level of intestinal barrier integrity can reverse not only cellular viability but restore the integrity of the F-actin network. This suggests that the progression of NEC could potentially be halted by therapeutic strategies aimed at supplementing reducing equivalents or inhibiting excessive cystine uptake before the onset of irreversible disulfidptosis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, our findings provide the first comprehensive evidence that disulfidptosis acts as a pivotal bridge between metabolic failure and the rapid intestinal barrier disruption characteristic of NEC. By identifying a core signature of four metabolic hub genes (TKTL1, PFKFB3, SLC2A3, and SLC2A14), we have established a robust molecular link between glucose-depleted metabolic stress and the catastrophic disruption of the actin cytoskeleton. This study introduces the concept of a metabolic-inflammatory axis, wherein metabolic exhaustion in enterocytes directly perpetuates immune cell infiltration and the subsequent inflammatory storm. This approach not only refocuses the current pathogenesis of NEC from the established immune system hypothesis to the metabolic susceptibility hypothesis but also provides a high-fidelity diagnostic framework for identifying neonatal intestinal injury in its earliest, potentially reversible phases.\u003c/p\u003e \u003cp\u003eThis study has certain limitations. Retrospective analyses based on public databases cannot fully avoid the influences of sample heterogeneity and batch effects. Although in vitro cell models can simulate key pathological features, they differ from the complex in vivo microenvironment. Therefore, animal experiments are needed in the future to further validate the core mechanisms. Additionally, the specific regulatory network of disulfidptosis in NEC and its interactions with other forms of cell death have not been fully elucidated. Looking ahead, multicenter clinical samples could be used to validate the diagnostic efficacy of the core genes, and conditional gene knockout animal models could be constructed to systematically analyze the spatiotemporal dynamic regulatory mechanisms of disulfidptosis at the in vivo level. Meanwhile, developing more specific disulfidptosis-targeted inhibitors will provide new translational directions for the precise treatment of NEC.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study establishes disulfidptosis as a pivotal driver of epithelial injury in NEC and identifies a metabolic-inflammatory axis triggered by SLC7A11-mediated disulfide stress. We further elucidated how metabolic exhaustion leads to the catastrophic collapse of the actin cytoskeleton by characterizing a core hub gene signature (TKTL1, PFKFB3, SLC2A3, and SLC2A14) with high diagnostic accuracy. Furthermore, the successful in vitro reversal of these pathological hallmarks via TCEP suggests that metabolic stabilization represents a more promising therapeutic paradigm than conventional anti-inflammatory interventions, providing a robust theoretical framework for the early detection and precision management of neonatal necrotizing enterocolitis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupporting data for this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYufeng Shi\u0026nbsp;\u003c/strong\u003e: Study design, data collection and analysis, manuscript drafting.\u003c/p\u003e\n\u003cp\u003eCong Yan: Clinical guidance, data verification, manuscript revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLifan Chen\u0026nbsp;\u003c/strong\u003e: Clinical data collection, nursing information sorting, data analysis assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhanzhen Cao\u0026nbsp;\u003c/strong\u003e: Clinical experiment participation, literature collation, manuscript proofreading.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhijie Huang\u0026nbsp;\u003c/strong\u003e: Overall study supervision, manuscript finalization, correspondence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve any human participants or animals and therefore did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as this study does not involve any individual person’s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSingh DK, et al. 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Trends Cell Biol. 2024;34(4):327\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHackam DJ, Sodhi CP. Bench to bedside - new insights into the pathogenesis of necrotizing enterocolitis. Nat Rev Gastroenterol Hepatol. 2022;19(7):468\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu X, et al. Necrotizing enterocolitis: current understanding of the prevention and management. Pediatr Surg Int. 2024;40(1):32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Neill LA, Kishton RJ, Rathmell J. A guide to immunometabolism for immunologists. Nat Rev Immunol. 2016;16(9):553\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, et al. NADPH debt drives redox bankruptcy: SLC7A11/xCT-mediated cystine uptake as a double-edged sword in cellular redox regulation. Genes Dis. 2021;8(6):731\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchw\u0026auml;rzler J, et al. Epithelial metabolism as a rheostat for intestinal inflammation and malignancy. Trends Cell Biol. 2024;34(11):913\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao F, et al. Disulfidptosis: A new type of cell death. Apoptosis. 2024;29(9\u0026ndash;10):1309\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, et al. SLC7A11 expression level dictates differential responses to oxidative stress in cancer cells. Nat Commun. 2023;14(1):3673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, et al. Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer. Nat Cell Biol. 2020;22(4):476\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeSlaa T, et al. The pentose phosphate pathway in health and disease. Nat Metab. 2023;5(8):1275\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao M, et al. Role of PFKFB3-driven glycolysis in sepsis. Ann Med. 2023;55(1):1278\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePizzagalli MD, Bensimon A, Superti-Furga G. A guide to plasma membrane solute carrier proteins. Febs j. 2021;288(9):2784\u0026ndash;835.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Z, et al. Increased stromal PFKFB3-mediated glycolysis in inflammatory bowel disease contributes to intestinal inflammation. Front Immunol. 2022;13:966067.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng Y, Chen X, Deng G. Disulfidptosis: a new form of regulated cell death for cancer treatment. Mol Biomed. 2023;4(1):18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu T, et al. NF-κB signaling in inflammation. Signal Transduct Target Ther. 2017;2:17023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalluzzi L, et al. Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ. 2018;25(3):486\u0026ndash;541.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMan SM, Kanneganti TD. Innate immune sensing of cell death in disease and therapeutics. Nat Cell Biol. 2024;26(9):1420\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasler WL, et al. Mast cell mediation of visceral sensation and permeability in irritable bowel syndrome. Neurogastroenterol Motil. 2022;34(7):e14339.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarhausen J, et al. Intestinal mast cells mediate gut injury and systemic inflammation in a rat model of deep hypothermic circulatory arrest. Crit Care Med. 2013;41(9):e200\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePimenta S, et al. Serum biomarkers in the early detection of necrotizing enterocolitis: a systematic review. J Perinat Med. 2025;53(8):966\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonatal necrotizing enterocolitis, Disulfidptosis, Intestinal epithelial barrier, Metabolic-inflammatory axis","lastPublishedDoi":"10.21203/rs.3.rs-8994121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8994121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNeonatal necrotizing enterocolitis (NEC) remains a devastating gastrointestinal emergency in premature infants, characterized by abrupt onset and rapid progression to transmural necrosis. Recent evidence suggests that disulfidptosis, a novel form of regulated cell death driven by disulfide stress, may play a pivotal role in various inflammatory diseases. However, its specific contribution to NEC pathogenesis remains largely unexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe integrated multi-omic bioinformatic analyses with in vitro experimental validation. Disulfidptosis-related hub genes were identified via weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis using GEO datasets (GSE46619, GSE297483), with diagnostic efficacy evaluated by ROC curves. \u003cem\u003eIn vitro\u003c/em\u003e validation was conducted in LPS-stimulated Caco-2 cells, utilizing tris(2-chloroethyl) phosphate (TCEP) to assess disulfidptosis inhibition.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNEC tissues exhibited significantly elevated disulfidptosis scores, which correlated positively with disease severity. Integrating WGCNA and DEGs identified 147 core genes primarily enriched in inflammatory signaling and intercellular communication. Among these core genes (TKTL1, PFKFB3, SLC2A3, and SLC2A14), TKTL1 exhibited the highest diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.892) and were closely associated with altered immune infiltration, supporting a 'metabolic-inflammatory axis' in NEC. \u003cem\u003eIn vitro\u003c/em\u003e, LPS-stimulated Caco-2 cells manifested definitive disulfidptosis hallmarks\u0026mdash;NADP⁺ depletion, cystine accumulation, and F-actin collapse\u0026mdash;synchronized with barrier failure. Pharmacological inhibition via TCEP successfully stabilized the cellular redox state, restored cytoskeletal integrity, and attenuated IL-6/TNF-α secretion, thereby preserving epithelial function.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ehis study identified disulfidptosis as a critical driver of intestinal epithelial injury in NEC. Targeting disulfidptosis-related pathways may offer a promising diagnostic and therapeutic strategy for neonatal intestinal injury.\u003c/p\u003e","manuscriptTitle":"Mechanism of Disulfide Death-Driven Intestinal Epithelial Injury in Neonatal Necrotizing Enterocolitis and Exploration of Potential Therapeutic Targets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 06:05:24","doi":"10.21203/rs.3.rs-8994121/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5212885d-62d8-428e-b86b-62ec6baf048e","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-18T12:09:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 06:05:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8994121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8994121","identity":"rs-8994121","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}