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Eno1 in sepsis-induced coagulopathy: a pleiotropic mechanism hypothesis involving immunomodulation and endothelial dysfunction

Thrombosis Journal volume 23, Article number: 62 (2025) Cite this article

Abstract

Background

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Septic-induced coagulopathy (SIC) is a major cause of mortality in sepsis, closely associated with endothelial glycocalyx damage. Enolase 1 (Eno1), a key enzyme in glycolysis, plays a crucial role in sepsis-related systemic inflammation and the maintenance of glycocalyx integrity.

Objective

This study utilizes multi-omics analysis to investigate the Eno1-regulated network, providing a comprehensive understanding of its molecular mechanisms in SIC.

Methods

We used RNA-seq datasets to identify Eno1-related gene sets through weighted gene co-expression network analysis and validated their biological functions via gene set enrichment analysis.

Results

Through RNA-seq analysis, we identified gene sets associated with Eno1 involved in immune regulation, endothelial cell apoptosis, coagulation, and glycosaminoglycan metabolism. Immune infiltration analysis revealed that Eno1 modulates SIC pathogenesis by influencing T cells and macrophages, with significant associations with endothelial dysfunction and inflammatory markers. Additionally, we observed that Eno1 regulation of glycolysis is linked to endothelial glycocalyx degradation, contributing to microcirculatory and vascular impairments in SIC. Furthermore, preliminary studies suggest that melatonin treatment may alleviate glycocalyx damage by inhibiting Eno1-mediated glycolytic pathways, offering a potential new therapeutic avenue for intervening in endothelial injury associated with SIC.

Conclusions

This study underscores the critical role of Eno1 in promoting SIC and its potential as both a diagnostic marker and therapeutic target for glycocalyx repair. The multi-omics approach provides valuable insights into the molecular networks regulating SIC, offering new avenues for targeted interventions in sepsis management.

Introduction

Sepsis triggers an inflammatory response in the body, usually caused by pathogens entering the bloodstream, leading to immune system hyperactivation, microcirculatory disturbances, increased vascular permeability, and tissue hypoxia [1]. The dysregulated release of anti-inflammatory factors in sepsis further diminishes the ability to control the infection [1]. Septic-induced coagulopathy (SIC) is the most common pathology caused by sepsis, and coagulation dysfunction greatly increases the mortality rate of patients [2]. Recent studies have explored the modulation of immune and metabolic pathways (e.g., glycolysis inhibition or endothelial glycocalyx protection) to minimize sepsis-related organ damage [3,4,5].

The glycocalyx plays a critical role in protecting the endothelium, maintaining hemodynamic stability, and regulating cellular adhesion [6, 7]. In SIC, glycocalyx damage triggers endothelial dysfunction, leading to increased vascular permeability, inflammatory mediator diffusion, and microthrombosis [4, 8]. These changes contribute to a vicious cycle of inflammation and coagulopathy, which further aggravates tissue damage and organ failure. Therefore, understanding the molecular mechanisms underlying glycocalyx degradation and its role in endothelial dysfunction is critical for advancing diagnostic and therapeutic strategies. By targeting the key signaling pathways involved in SIC, novel interventions may be developed to preserve endothelial function, improve microvascular health, and reduce sepsis-related complications. Studies have indicated that glycocalyx degradation products serve as biomarkers for sepsis severity and prognosis [7]. Their disruption directly exacerbates systemic inflammation, vascular impairment, and coagulopathy—key drivers of sepsis progression. Consequently, preserving glycocalyx integrity has emerged as a therapeutic priority, with targeted interventions potentially improving endothelial function and reducing complications [9]. These studies have shown that glycocalyx integrity is closely related to sepsis onset and progression.

Enolase 1 (Eno1) is a glycolytic enzyme that plays a central role in glucose metabolism [10]. Its primary function is to convert 2-phosphoglycerate into phosphoenolpyruvate in the glycolytic pathway in endothelial cells [11], a process essential for maintaining cellular energy metabolism. During the pathogenesis of SIC, metabolic pathways, such as glycolysis, are activated, which increases lactate production, triggering acidosis and energy metabolism disorders and exacerbating oxygen and nutrient supply deficiencies [12, 13]. Lactate accumulation upregulates histone lactylation-mediated expression of heparanase, which increases acetylheparin sulfate degradation and elevates vascular permeability, impairing organ function [14,15,16]. In turn, metabolic dysregulation and inflammatory responses promote SIC in the endothelial cells [17, 18]. Recent studies have identified functional impairment of the glycocalyx in endothelial cells as a new diagnostic and therapeutic target for SIC, with attention to the role of Eno1 as a glycolytic enzyme in the glycolytic pathway [3, 19]. Beyond its primary function in energy metabolism, Eno1 is closely linked to immune regulation and inflammatory responses [19]. As an important enzyme in the glycolytic pathway, Eno1 plays a critical role in catalyzing the conversion of 2-phosphoglycerate to phosphoenolpyruvate, thereby supporting cellular energy metabolism; it also contributes to endothelial glycocalyx integrity, regulation of SIC, and heparan sulfate degradation [10, 11].

In patients with SIC, the disruption of the endothelial glycocalyx compromises endothelial function, increases vascular permeability, exacerbates microthrombosis, and consequently intensifies inflammatory responses and coagulation disorders [20, 21]. Inflammation of endothelial cells increases glycolytic pathway activation [22], and the inhibition of lipopolysaccharide-induced proinflammatory cytokines, lactate accumulation, and glycolysis effectively reduces the hazards of septic coagulation [23,24,25]. In sepsis, the degradation of the endothelial glycocalyx is particularly pronounced. This disruption exposes adhesion molecules on the endothelial surface, thereby promoting blood cell adhesion and intensifying the interplay between inflammatory and coagulation pathways, which collectively aggravate endothelial injury [26, 27]. Glycolysis has been identified as a key metabolic process involved in this pathological cascade, with Eno1, a critical glycolytic enzyme, playing a pivotal role in modulating both inflammatory responses and endothelial function. Through literature review, we identified melatonin as a potential upstream modulator of Eno1. Notably, melatonin has been reported to attenuate glycocalyx degradation by downregulating Eno1 expression and suppressing glycolytic activity [28,29,30]. Although our study did not directly investigate the therapeutic effects of melatonin, these findings suggest that targeting the Eno1-associated glycolytic pathway represents a promising strategy for protecting the endothelium in SIC.

This study aimed to systematically elucidate the regulatory mechanisms of Eno1 in SIC from a multi-omics perspective, demonstrating significant innovation compared to existing research. Breaking through the limitations of traditional single-pathway investigations, we integrated transcriptomic, protein interaction, and metabolic network analyses to construct, for the first time, a multidimensional regulatory map of Eno1-mediated glycolysis-endothelial dysfunction-coagulation abnormalities. Particular focus is given to the novel mechanism of Eno1, which may deepen understanding of vascular barrier dysfunction in sepsis. Furthermore, the Eno1-associated gene prognostic model developed in this study provides new insights for early clinical diagnosis and precision treatment of SIC.

Methods

Data resources and pre-processing

In this study, gene expression profiles from the original dataset were sourced from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) [31]. The datasets used include GSE15379 [32], GSE224299 [33], GSE230648, and GSE252755. In the obtained mouse dataset, the GSE15379 dataset, which excluded samples with genotypic Ppta knockout, included three sepsis samples and three normal control samples; the GSE224299 dataset excluded samples of the interleukin-3 gene-deficient phenotype and included five sepsis samples; the GSE230648 dataset included six sepsis samples and three normal control samples; and the GSE252755 dataset included seven sepsis samples. The data mainly comprised 21 sepsis and six control samples. After quality control and standardization of the acquired mouse data, principal component analysis [34] was used to analyze and visualize the data to identify potential sample differences or anomalies and provide a reliable basis for subsequent differential expression analysis and biological interpretation. Finally, the potential of Eno1 as a biomarker was evaluated via Receiver Operating Characteristic (ROC) analysis using the pROC software package in R [35].

Expression analysis of differentially expressed genes (DEGs)

In the combined dataset, DEGs between sepsis and healthy controls were identified using the “limma” R package [36], with a statistical significance threshold set at|FoldChange| (FC) > 1.2 and p < 0.05. Given the specificity of the dataset, a lower FC threshold was applied to capture even small gene expression variations that could be statistically significant and biologically relevant in this context. Consequently, the FC threshold was set to 1.2. Additionally, p-values were adjusted using the false discovery rate method to control for multiple testing. A Venn diagram was employed to illustrate the overlap between the DEGs and those associated with the glycolytic pathway, facilitating the identification of glycolysis-related genes within the DEGs. This step provides the foundation for constructing a module where Eno1 is significantly associated with glycolysis.

Identification of Eno1 co-expressed modular genes in SIC through weighted gene co-expression network analysis (WGCNA)

WGCNA was applied to identify genes co-expressed with Eno1 in SIC. DEGs were processed using the R package [37] and co-expression modules related to Eno1 and glycolysis were constructed based on glycolysis-related genes identified from overlapping DEG analyses and a mouse model (sham-operated: 0; induced infection: 1). Outlier samples were first removed (The Z-score threshold for inter-sample Pearson correlation coefficient analysis was set at ± 3 standard deviations, median absolute deviation > 3, interquartile range method to ensure analytical reliability (scale-free topology fit index 0.89)), followed by the construction of a gene co-expression network, ensuring that the similarity between genes was proportional to their logarithmic similarity for optimal network connectivity. After detecting the modules, hierarchical clustering was performed using the hclust function [38] in the standard R package, and outliers were checked. The resulting dendrogram corresponded to distinct gene modules. Subsequently, a heatmap was created to visualize the correlations between the modules and phenotypes, with strong correlations suggesting that genes within specific modules are closely associated with the disease state. In this study, genes from the MEbrown module, which show a significant positive correlation with Eno1 and Slc16a3, were selected as candidate modules.

Gene enrichment analysis of the functional roles and pathways of the mebrown module genes in SIC

This study employed the clusterProfiler software package 3.18.1 [39] (https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) to conduct Biological process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses on the MEbrown module genes, aiming to assess their functional roles across BPs and pathways. This approach also facilitated the identification of key signaling pathways enriched with these genes. The c5.bp.v7.0.entrez.gmt and c2.cp.kegg.v7.0.symbols.gmt gene sets from the Molecular Signatures Database (https://www.broadinstitute.org/msigdb) were used for reference [40]. Additionally, Gene Set Enrichment Analysis (GSEA) [41] was performed to analyze the data. The GSEA results revealed differential activation or repression of genes within specific pathways, with significant enrichment defined by a threshold of p < 0.05.

Exploring immune infiltration and Eno1 correlation in SIC using CIBERSORT

To investigate the relationship between relevant modules and immune infiltration, we used the CIBERSORT algorithm [42] (https://cibersortx.stanford.edu) to estimate the relative abundance of immune cell infiltration, selecting samples with p < 0.05 as statistically significant. Subsequently, we analyzed the correlation between DEGs and immune cells using R software, with results visualized in heatmaps. Furthermore, we conducted a correlation analysis to assess the association between Eno1 and immune cells. Finally, we explored the functional connection between immune and endothelial cells.

Regulatory network analysis for disease insights

Using the RNA-Inter database [43] and TRRUST v2 database [44], we constructed a transcription factor KEGG pathway and an RNA-binding protein (RBP) regulatory network. A network was constructed through a hypergeometric test, and the results were visualized and analyzed. By examining the regulatory networks of transcription factors and RBPs, we gain deeper insight into their biological significance in normal processes, environmental adaptations, and pathological conditions. This analysis highlights their potential roles in disease diagnosis and treatment, offering new targets for early diagnosis and targeted therapies.

Molecular docking

Melatonin, an active compound, can suppress inflammatory responses by regulating aerobic glycolysis and reducing endothelial glycocalyx damage, particularly in chronic inflammatory and autoimmune diseases [28, 45, 46]. To investigate the binding mode and affinity between melatonin and its target proteins, we retrieved the three-dimensional structures of key target proteins from the Protein Data Bank (http://www.rcsb.org/) [47] and obtained core active compound files from PubChem (https://pubchem.ncbi.nlm.nih.gov/) [48]. The target protein structures were optimized by removing water molecules and calculating hydrogen bonds and charge using PyMOL software [49]. Molecular docking and binding activity analyses were performed immediately using the AutoDock4.2.6 program [50]. The binding sites were visualized using the PLIP platform (https://plip-tool.biotec.tu-dresden.de) [51]. The binding conformation with the lowest binding energy indicates a more stable interaction, reflecting a higher likelihood of receptor-ligand interaction. Therefore, only docking pairs with the most favorable binding energies were retained for further analysis.

Data analysis and statistics

All biological information analyses were conducted using the Bioinforcloud platform (http://www.bioinforcloud.org.cn). All data are expressed as mean ± standard deviation and were considered statistically significant at p < 0.05.

Results

Exploring the potential role of Eno1 as a biomarker for SIC

The workflow diagram of this study is shown in Fig. 1. Additionally, the schematic illustrates the hypothesized signaling cascade involving Eno1 (Supplementary Fig. 1). We analyzed the integrated gene expression profiles from GEO datasets. Principal component analysis revealed a clear distinction between sepsis samples and healthy controls (Fig. 2A), with each gene set exhibiting high intragroup consistency and reproducibility, ensuring data reliability (Fig. 2B). Overall, 50 DEGs were identified, with 47 genes upregulated and three downregulated (Fig. 2C). Subsequently, an overlap analysis was conducted between these 50 DEGs and 685 glycolysis-related genes, identifying Eno1 and Slc16a3 (encoding monocarboxylate transporter 4) as glycolysis-related DEGs. This finding highlights the potentially critical roles of these genes in SIC (Fig. 2D). Using heat maps, we further visualized the top 50 DEGs across disease subgroups (Fig. 2E). ROC analysis was performed on Eno1 to assess the diagnostic potential, and the results show the area under the curve of ≥ 0.9, implying that it is highly effective in distinguishing between positive and negative samples in the SIC with high diagnostic accuracy (area under the curve = 0.951) (Fig. 2F). Eno1 expression was significantly higher in the SIC group than in healthy controls (Fig. 2G). These results indicate that Eno1 may contribute to the pathophysiology of SIC and serve as a potential diagnostic biomarker for sepsis.

Fig. 1
figure 1

Flowchart of the study. Eno1, enolase 1; GEO, Gene Expression Omnibus

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Fig. 2
figure 2

Potential role of enolase 1 as a biomarker for septic-induced coagulopathy (SIC). (A) Principal component analysis (PCA) scatterplot illustrating the differences between sepsis and healthy controls; (B) PCA scatterplot illustrating the high similarity within each group, demonstrating strong reproducibility of the data; (C) Volcano plot displaying differentially expressed genes (DEGs); (D) Intersection of DEGs with glycolysis-related genes; (E) Heatmap showing the expression patterns of the top 50 DEGs in the SIC and healthy control groups; (F) Receiver operating characteristic (ROC) curves assessing the diagnostic potential of Eno1 in sepsis; (G) Bar plot comparing Eno1 expression levels between the SIC and control groups, showing significantly higher expression in the SIC group. Eno1, enolase 1; SIC, septic-induced coagulopathy

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Weighted gene co-expression networks revealed a global regulatory role for Eno1 in SIC

The WGCNA shows that the constructed intergene relationship network adhered to the properties of a scale-free network and exhibited strong network connectivity, supporting the robustness of our analysis (Fig. 3A and B). Through module identification, three distinct gene modules were identified, with the MEbrown module showing a particularly significant correlation with the glycolysis-related genes Eno1 and Slc16a3 (Fig. 3C). Correlation analysis show a significant association between genes in the MEbrown module and Eno1 (p < 0.001, r = 0.556), highlighting the potential functional connection between this module and the glycolytic pathway (Fig. 3D). Based on these findings, the MEbrown module was selected for further investigation to explore its potential mechanistic role in regulating glycolysis in sepsis.

Fig. 3
figure 3

Role of the MEbrown module in sepsis glycolysis. (A) Selected numeric thresholds showing weighted gene co-expression network analysis (WGCNA) soft thresholds; (B) Module clustering tree showing gene modules represented by different branches; (C) Module correlation heatmap showing the correlation between the gene co-expression modules Eno1 and glycolytic genes; (D) Correlation scatter plot showing the correlation between Eno1 expression and co-expression modules. Eno1, enolase 1; WGCNA, weighted gene co-expression network analysis

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Functional role of Eno1 module genes in coagulation and immune pathways

We performed an enrichment analysis of the MEbrown module genes to identify the key BPs and signaling pathways activated by these genes. BP analysis indicated that these genes primarily regulated endothelial cell apoptosis and differentiation, coagulation mechanisms, and inflammatory cytokine responses (Fig. 4A). KEGG analysis further highlighted the significant enrichment of the MEbrown module genes in pathways associated with primary immunodeficiency and glycosaminoglycan degradation (Fig. 4B). GSEA demonstrated significant activation of these genes in multiple pathways, including the complement and coagulation cascade, Janus kinase-signal transducer and activator of transcription signaling, glycolysis-gluconeogenesis, primary immunodeficiency, and glycosaminoglycan biosynthesis (heparan sulfate/heparin) (p < 0.05) (Figs. 4C and D). These findings suggest a critical role of the MEbrown module genes in maintaining vascular function and mediating inflammatory responses in SIC disease, as well as revealing potential links to immunodeficiency diseases and glycosaminoglycan metabolism.

Fig. 4
figure 4

Functional role of enolase 1 module genes in coagulation and immune pathways. (A) Bar graph showing the enrichment of MEbrown module genes in biological pathways; (B) Bar graph showing enrichment of the Eno1 modular gene in the KEGG signaling pathway; (C) Gene Set Enrichment Analysis (GSEA) results showing the activation of Eno1 with MEbrown modular genes in inflammation-related signaling pathways; (D) GSEA results showing Eno1 activation with MEbrown modular genes in glycolysis-related signaling pathways. Eno1, enolase 1; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis; BP, biological process; NES; Jak-STAT, Janus kinase-signal transducer and activator of transcription

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Immunomodulation and the role of Eno1 in SIC progression and diagnosis

Immune and inflammatory responses play crucial roles in SIC onset and progression. To better understand the immunoregulatory mechanisms of SIC, we performed an immune infiltration analysis. The heatmap demonstrates the abundance of immune infiltration between the SIC and control groups (Fig. 5A). Correlation analysis shows that regulatory T cells (r = -0.487, p < 0.001) and CD8+ T cells (r = -0.511, p < 0.001) were negatively correlated with Eno1. In contrast, resting natural killer cells (r = 0.449, p < 0.001) and M2-type macrophages (r = 0.458, p < 0.001) were positively associated with Eno1 expression (Fig. 5B). The correlation between MEbrown module genes and immune cells was further explored (Fig. 5C). Correlations between immune cells and genes related to endothelial cell coagulation (Eng, Icam1, Lama4, and others) and inflammation-related genes (IL-6, IL-10, Tgfb1, and others) were also visualized (Fig. 5D). These results support Eno1 as a potential biomarker and therapeutic target for SIC. Notably, elevated Eno1 expression in SIC is linked to endothelial glycocalyx degradation, a process driven by immune cells, particularly regulatory T cells involved in vascular inflammation [52, 53].

Fig. 5
figure 5

Immunomodulation and the role of enolase 1 in SIC progression and diagnosis. (A) Abundance of immune cell infiltration in the SIC sample compared to the control sample; (B) Correlation between Eno1 and immune cells; (C) Correlation between MEbrown module genes and immune cells, illustrating how genes from the MEbrown module, particularly Eno1, correlate with immune cell infiltration in the SIC context; (D) Bubble plot showing the correlation between endothelial cell markers (highlighted in red box), inflammatory factors (highlighted in blue box), and immune cells, emphasizing the interactions among vascular function markers, inflammatory cytokines, and immune cells in SIC. Eno1, enolase 1; SIC, septic-induced coagulopathy

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Exploring the regulatory mechanisms of Eno1

This study investigated the upstream and downstream regulatory mechanisms of the Eno1 clinical marker gene, with a focus on RBPs and transcription factors. Our findings revealed that several upstream RBPs, including Fus and Mbnl2, regulated Eno1. These upstream factors affect the stability and translation efficiency of Eno1 mRNA (Fig. 6A). Fus expression was upregulated in the SIC group (Fig. 6B). Eno1 expression is modulated by various transcription factors, such as E2f1 and E2f4. Eno1 also regulates the downstream gene Egr1, which plays multiple roles in immune responses and vascular functions, and is closely associated with disease development and progression (Fig. 6C). Egr1 expression was upregulated in the SIC group compared with the control group (Fig. 6D). Next, the regulatory genes upstream and downstream of Eno1 were presented in the form of a network diagram (Fig. 6E). Finally, a review of the literature and data analysis from the TCMSP and PubChem databases revealed that melatonin downregulates Eno1 expression and effectively prevents the degradation of the endothelial glycocalyx [28, 45, 46]. To verify the potential binding interaction between melatonin and Eno1, we performed molecular docking, which shows a strong binding affinity with a calculated free energy release of -3.21 KJ/mol (Fig. 6F). Further structural analysis identified key docking residues, including alanine, serine, and glutamic acid (Fig. 6G). These findings suggest that melatonin effectively binds to Eno1, providing valuable insights into drug design and protein function studies.

Fig. 6
figure 6

Upstream mechanisms of melatonin-regulated enolase 1 (Eno1). (A) Sankey diagram showing upstream regulatory genes in the Eno1 and MEbrown modules; (B) Comparison of RNA-binding protein Fus expression in the SIC and control groups, highlighting differential expression; (C) Linkage diagram showing the downstream regulated genes of Eno1 and MEbrown module genes; (D) Transcription factor (TF) Egr1 expression in the SIC and control subgroups; (E). Global regulatory network showing interactions between Eno1 and MEbrown module genes in SIC progression; (F) Molecular docking model showing melatonin binding with Eno1, with calculated binding affinity; (G) Key docking sites involved in the interaction between melatonin and Eno1. Eno1, Enolase 1; RBP, RNA-binding protein; SIC, sepsis-induced coagulopathy; TF, Transcription factor

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Discussions

This study analyzed septic mouse model data from the GEO database to evaluate the potential of Eno1 as a diagnostic biomarker for SIC. Eno1, a critical enzyme in the glycolytic pathway, plays a role in diverse cellular processes, including inflammation regulation, cellular metabolism, signaling, and survival. Previous studies have indicated a potential role of Eno1 in SIC [54,55,56]. Our analysis identified a significant upregulation of Eno1 in the SIC mouse model. ROC curve analysis shows that Eno1 has high sensitivity and specificity in diagnosing SIC. Differential gene expression analysis identified 50 DEGs within SIC-related coagulation pathology, including Eno1, which is involved in various biological functions, such as immune response, glycolytic metabolism, transcriptional regulation, and inflammatory response. These results suggest that Eno1 not only exhibits significant changes in SIC but also closely relates to the BPs of SIC, making it a potential diagnostic biomarker for SIC.

WGCNA identified a gene module, MEbrown, associated with Eno1 and Slc16a3, which was analyzed through BP and KEGG enrichment to clarify the roles of Eno1-related genes in key SIC-related BPs, including immunomodulation, endothelial cell apoptosis, coagulation mechanisms, and glycosaminoglycan metabolism. This observation suggests that Eno1 influences endothelial cell inflammatory responses by promoting lactate production during glycolysis [11, 57]. Lactate accumulation acidifies the cellular microenvironment and upregulates histone lactylation, activating the transcription of heparanase and accelerating heparan sulfate degradation [58, 59]. The literature further indicates that Eno1 can regulate immune cell function, affecting tumor immune microenvironment alterations [60], and plays a pivotal role in immune cell metabolic remodeling, influencing their proliferation and activity [28]. These results support the immune regulatory role of Eno1 in SIC, indicating that Eno1 not only participates in metabolic reprogramming but also plays an important role in various immune functions.

The role of Eno1 in coagulation is similarly significant, particularly in patients with cancer, where it may influence tumorigenesis and progression by affecting platelet function and coagulation factor expression [53, 61]. Eno1 expression correlates with endothelial cell survival and apoptosis, regulating endothelial apoptosis and affecting angiogenesis and tumor vascularization [62]. Glycosaminoglycan metabolism plays a vital role in endothelial inflammation, and Eno1 modulates extracellular matrix composition through these metabolic pathways, helping stabilize the immune microenvironment [11, 63]. These results align with our findings, underscoring the key roles of Eno1 in immune regulation, glycolytic metabolism, endothelial apoptosis, and coagulation in SIC and demonstrating its high sensitivity and specificity in SIC diagnosis.

However, this study had some limitations. First, the study is based solely on preliminary data from a mouse model, and further in vivo validation is needed to confirm the generalizability of these results. Second, our analysis primarily focused on gene expression, without exploring the specific molecular mechanisms of Eno1 and its roles in various immune cells, particularly its involvement in immune cell metabolism and functional reprogramming. Finally, although we have proposed melatonin as a potential therapeutic intervention, this finding requires further validation in animal models and clinical studies.

This study explored the complex role of Eno1 in the pathological progression of SIC. Immune cell infiltration analysis revealed a distinct immune cell distribution in SIC, suggesting that Eno1 influences disease progression by modulating key immune cells, including T cells and macrophages. These insights provide new perspectives on the pathomechanisms of SIC, particularly regarding how metabolic remodeling may affect immune responses and vascular function. Furthermore, we mapped the upstream regulatory network of Eno1 and identified RBPs and transcription factors as key regulators. In our molecular docking study, we found that melatonin exhibits a strong binding affinity for Eno1 and may inhibit glycolysis by downregulating Eno1 expression. This finding suggests a promising direction for drug-based interventions, especially in treating sepsis and related diseases, where melatonin could play an important role in therapeutic applications [28]. Eno1 plays a key role in SIC pathology by affecting immune cell distribution and metabolic reprogramming, with melatonin potentially serving as a therapeutic intervention by inhibiting Eno1 expression and glycolysis.

Although these findings are promising, preliminary validation using in vivo mouse models is still required, along with further in vitro experiments to confirm the specific interactions and functional relevance of Eno1 in SIC. Additionally, the potential therapeutic application of melatonin in this context needs to be rigorously tested in animal models before considering clinical translation. Future research should focus on exploring the interaction mechanisms between Eno1 and melatonin, as well as evaluating their clinical efficacy.

Conclusions

This study preliminarily revealed the diagnostic value of Eno1 in SIC, with data showing significantly elevated Eno1 expression levels in the SIC group. These findings suggest Eno1 as a potential novel molecular marker for early SIC identification and indicate its possible involvement in glycocalyx injury pathogenesis. Further studies are warranted to elucidate Eno1-specific mechanistic roles in SIC progression, particularly its functional positioning within glycosaminoglycan metabolic regulation and immunomodulatory networks. Additionally, prospective clinical studies are needed to validate Eno1 diagnostic performance and investigate its expression profiles across different SIC subtypes.

Data availability

Data was download from the public database online at the GEO database under accession numbers GSE15379, GSE224299, GSE230648 and GSE252755.

Abbreviations

BP:

Biological process

DEGs:

Differentially expressed genes

Eno1:

Enolase 1

FC:

Fold change

GEO:

Gene Expression Omnibus

GSEA:

Gene Set Enrichment Analysis

KEGG:

Kyoto Encyclopedia of Genes and Genomes

RBPs:

RNA-binding protein

ROC:

Receiver operating characteristic

SIC:

Sepsis-induced coagulopathy

TF:

Transcription factor

WGCNA:

Weighted gene co-expression network analysis

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Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China (82460380), the Joint Project on Regional High-Incidence Diseases Research of the Guangxi Natural Science Foundation (2023GXNSFAA026318), and the Guangxi Medical and Health Key Discipline Construction Project.

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Author notes

  1. Ke Qin, Xiao Chen and Xiaoling Li contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Anesthesiology, People’s Hospital of Guilin, No. 12 Wenming Road, Guilin, 541002, Guangxi, China

    Ke Qin, Wenbin Zhang, Xinnan Song & Yuan Wang

  2. Department of Anesthesiology, Fifth Clinical Medical College, Guilin Medical University, Guilin, 541002, Guangxi, China

    Ke Qin

  3. Department of Anesthesiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530007, China

    Xiao Chen & Xiaowen Hu

  4. Intensive Care Unit, People’s Hospital of Guilin, Guilin, 541002, Guangxi, China

    Xiaoling Li

  5. Department of Emergency Medicine, Wuming Hospital of Guangxi Medical University, No. 26 Yongning Road, Nanning, 530199, Guangxi, China

    Jianfeng Zhang

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  1. Ke Qin
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  6. Yuan Wang
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  8. Jianfeng Zhang
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Contributions

K. Q. and J. Z. conceived and designed the study. K. Q., X. C., X. L., W. Z., X. S, Y. W., Y. W., and X. H. collected and analyzed the data. K. Q., X. C., and X. L. drafted the manuscript. K. Q. and J. Z. supervised manuscript preparation. All authors have read and approved the final manuscript for publication.

Corresponding authors

Correspondence to Ke Qin or Jianfeng Zhang.

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This study was approved by the institutional review board and ethics committee of the Wuming Hospital of Guangxi Medical University.

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The authors declare no competing interests.

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Qin, K., Chen, X., Li, X. et al. Eno1 in sepsis-induced coagulopathy: a pleiotropic mechanism hypothesis involving immunomodulation and endothelial dysfunction. Thrombosis J 23, 62 (2025). https://doi.org/10.1186/s12959-025-00750-8

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Keywords

Thrombosis Journal

ISSN: 1477-9560

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