Function and Mechanism of Mitochondrial-Associated Membranes in Acute Respiratory Distress Syndrsome: A Comprehensive Study Combining Bioinformatics and Experimental Approaches

Function and Mechanism of Mitochondrial-Associated Membranes in Acute Respiratory Distress Syndrsome: A Comprehensive Study Combining Bioinformatics and Experimental Approaches | 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 Article Function and Mechanism of Mitochondrial-Associated Membranes in Acute Respiratory Distress Syndrsome: A Comprehensive Study Combining Bioinformatics and Experimental Approaches Yanqiong Zhou, Qiuying Chen, Hui Huang, Xiaoxia Wang, Kaimin Lv, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6113941/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background : Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition characterized by severe inflammation, immune dysregulation, and oxidative stress, leading to high mortality (30–40%). Mitochondria-associated membranes (MAMs) regulate cellular metabolism and immune signaling, but their role in ARDS remains unclear. This study explores the involvement of MAM-related genes in ARDS pathogenesis through bioinformatics and experimental validation. Methods : Publicly available RNA-sequencing data from ARDS and control samples were analyzed to identify differentially expressed genes (DEGs). Functional enrichment, gene set variation analysis (GSVA), and weighted gene co-expression network analysis (WGCNA) were performed to explore pathway alterations and hub gene interactions. Immune cell infiltration analysis was conducted using CIBERSORT. Candidate MAM-related genes were validated in a Poly I:C-induced ARDS mouse model and MLE-12 murine lung epithelial cells. The mouse model was assessed for lung histopathology, wet-to-dry lung weight ratio, bronchoalveolar lavage fluid (BALF) inflammatory cytokine levels (IL-1β and TNF-α), and lung injury scores. MLE-12 cells were treated with Poly I:C, and cell viability, lactate dehydrogenase (LDH) release, and apoptosis were evaluated. Protein-protein interaction (PPI) network analysis and drug prediction were used to identify potential therapeutic targets. Results : A total of 3152 DEGs including 1549 upregulated and 1603 downregulated were identified in ARDS samples. Pathway analysis revealed autophagy suppression and immune activation, with 14 immune cell types significantly elevated in ARDS patients. Experimental validation confirmed that Poly I:C-induced ARDS mice exhibited severe lung injury and increased inflammatory reaction, while Poly I:C-treated MLE-12 cells showed increased cytotoxicity and LDH release. HBB and ZMAT2 were identified as key MAM-related hub genes, with HBB negatively correlating with lung injury severity and ZMAT2 positively associated with disease progression. Drug prediction analysis identified 29 pharmacological agents interacting with HBB, suggesting therapeutic potential. Conclusions : This study identifies HBB and ZMAT2 as key MAM-related genes contributing to ARDS pathogenesis, with potential diagnostic and therapeutic applications. The integration of bioinformatics with in vivo and in vitro validation provides novel insights into ARDS molecular mechanisms. Further clinical studies are needed to explore their translational relevance. Health sciences/Diseases/Respiratory tract diseases/Chronic obstructive pulmonary disease Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Health sciences/Biomarkers ARDS Viral Pneumonia Mitochondria-associated Membrane Bioinformatics Therapeutic Targets Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction Acute respiratory distress syndrome (ARDS) is a life-threatening clinical condition characterized by severe pulmonary insufficiency and is associated with high mortality rates ranging from 30–40% ( 1 ). The syndrome can arise from various etiologies, including pneumonia, sepsis, and trauma, and is often complicated by multiple organ dysfunction, which significantly complicates management and increases mortality risk ( 2 , 3 ). Despite advances in critical care medicine, there remains a lack of specific therapeutic interventions for ARDS, and current management primarily involves supportive care, including mechanical ventilation and fluid management ( 1 , 4 ). Recent developments in the understanding of the pathophysiology of ARDS suggest that inflammatory processes play a crucial role in its progression, often mediated by immune cell dysregulation and cytokine storms ( 3 , 5 ). However, the complexity and heterogeneity of ARDS make it difficult to establish effective treatment protocols, leading to a pressing need for further research into the underlying molecular mechanisms and potential therapeutic targets ( 6 ). One emerging area of interest in ARDS research is the role of mitochondria-associated endoplasmic reticulum membranes (MAMs), which have been implicated in various cellular processes, including apoptosis, inflammation, and stress responses ( 7 , 8 ). MAMs are specialized contact sites between the outer mitochondrial membrane and the endoplasmic reticulum membrane, playing a key role in cellular homeostasis. They regulate crucial processes such as calcium ion (Ca²⁺) transfer, lipid metabolism, and the coordination of cellular stress responses. Dysfunction of MAMs has been linked to impaired calcium homeostasis, excessive oxidative stress, and dysregulated lipid metabolism, all of which contribute to cellular injury and inflammation ( 9 ). In the context of ARDS, MAMs may act as critical regulators of immune activation, oxidative damage, and programmed cell death, thereby influencing disease severity. Given that ARDS is characterized by extensive immune cell infiltration, epithelial and endothelial injury, and uncontrolled inflammation, the disruption of MAM function may exacerbate these pathological processes, ultimately leading to worsened pulmonary dysfunction. Investigating the role of MAM-related genes in ARDS may unveil novel biomarkers and therapeutic targets that could improve clinical outcomes ( 10 ). This study aims to elucidate the relationship between MAM-associated genes and ARDS by employing a multifaceted approach that integrates bioinformatics analyses, gene expression profiling, and molecular assays. We will focus on key genes, such as ZMAT2 and HBB, which have shown differential expression in ARDS patients compared to healthy controls ( 11 ). By characterizing the biological functions of these genes and their pathways, we hope to contribute to a more comprehensive understanding of ARDS and identify potential avenues for targeted therapies. In summary, the complexity of ARDS, coupled with the need for novel therapeutic strategies, underscores the necessity for detailed investigations into its molecular mechanisms. Through this research, we aim to bridge the gap in understanding the role of MAM-associated genes in ARDS, potentially leading to improved diagnostic and therapeutic strategies that address this critical healthcare challenge. Materials and Methods Data Download The overall technical route was showed in Figure 1. All data used in this study are free and publicly available from GEO (Gene Expression Omnibus, https://www.ncbi.nlm.nih. gov/geo/). The ARDS whole-genome expression profiles were retrieved and downloaded from the GEO database using the R package "GEOquery." GSE243066 contains 49 samples, including peripheral blood samples from 34 ARDS patients and 15 healthy controls. However, the ARDS group samples GSM7778549, GSM7778550, GSM7778563, and GSM7778569 were excluded due to incomplete expression matrices, leaving 30 ARDS samples and 15 control samples included in this study. GSE89953 contains 52 samples, from which 26 peripheral blood mononuclear cell samples from ARDS patients were selected. Batch effects caused by non-biotechnological biases were corrected using the ComBat method from the R package "sva" (12). The correction effect is examined using principal component analysis (PCA). This study adhered to the data access policies of each database. Mitochondria-associated membranes (MAM) related genes were obtained from the article studied by Yi Luan, Guangyu Guo, et al. (13) (Supplementary Table 1). Differential Analysis Related to ARDS This study used the R package "limma (version 3.50.0)" (14) to identify differentially expressed genes (DEGs) between the control group (n=15) and the ARDS group (n=56), with screening criteria of |log2Fold Change| >0.5 and corrected p-value <0.05, which were used for subsequent analyses. Heatmaps were generated using the R package "pheatmap," employing Euclidean distance and hierarchical clustering methods for clustering. Gene Set Variation Analysis (GSVA) GSVA (Gene Set Variation Analysis) is an unsupervised and non-parametric gene set enrichment method that allows the assessment of associations between biological pathways and gene features using gene expression profiles. To investigate the biological functional differences between the control group and the ARDS group, the "c2.cp.kegg.v7.5.1.symbols" gene set from the MSigDB database (http://software.broadinstitute.org/gsea/msigdb) was used as the reference gene set, and GSVA was performed with the R package "GSVA (version 1.42.0)." The results were visualized using the R package "pheatmap (version 1.0.12)." Additionally, 50 hallmark gene sets (h.all.v7.4.1.symbols) were downloaded from the MSigDB database as reference gene sets, and the GSVA scores for each gene set were calculated across different samples using the ssGSEA function in the GSVA package. The GSVA score differences between the control group and the ARDS group for different gene sets were compared using the Limma package. Weighted Gene Co-expression Network Analysis (WGCNA) and Identification of Significant Modules The WGCNA algorithm was implemented using the R package WGCNA (version 1.70-3) to construct a co-expression network (15). The similarity of gene expression profiles was assessed by calculating Pearson correlation coefficients, and the correlation coefficients between genes were weighted using a power function to obtain a scale-free network. The co-expression similarity was raised to a power of β = 10 to establish a weighted adjacency matrix using the R package "PickSoftThreshold." Gene modules are groups of genes that are densely connected in co-expression. WGCNA identifies gene modules using hierarchical clustering and indicates modules with colors. The dynamic tree cutting method was used to identify different modules, converting the adjacency matrix (a measure of topological similarity) into a topological overlap matrix (TOM) during the module selection process, and modules were detected through clustering analysis. To assess the association between modules and MAM, Pearson correlation analysis was performed to calculate the correlation between module eigengenes (ME, the first principal component of the module representing the overall expression level of the module) and MAM. Modules significantly associated with MAM were obtained. The structure of co-expression modules was visualized through a heatmap of gene network topological overlap. The relationships between modules were summarized through hierarchical clustering trees of eigengenes and corresponding eigengene heatmaps. MAM-related differentially expressed genes (MAM-related DEGs) were obtained from the intersection of DEGs and genes in MAM-related modules. GO Term Enrichment Analysis Gene Ontology (GO) analysis is a common method for conducting large-scale functional enrichment studies, including biological processes (BP), molecular functions (MF), and cellular components (CC). The R package "clusterProfiler (version 4.2.2)" was applied for GO annotation analysis of MAM-related differentially expressed genes (p-value <0.05) (16). GeneMANIA The GeneMANIA website (http://genemania.org) can predict the relationships between functionally similar genes and hub genes, including protein-protein interactions, protein-DNA interactions, pathways, physiological and biochemical responses, co-expression, and co-localization (17). We constructed a protein-protein interaction (PPI) network of key genes through the GeneMANIA website. Receiver Operating Characteristic Curve (ROC) The receiver operating characteristic curve (ROC) is an effective method for evaluating the performance of diagnostic tests. The ROC curve reflects the relationship between sensitivity and specificity as continuous variables, illustrating the interplay between sensitivity and specificity through graphical representation. The most common metric is the area under the curve (AUC), obtained from the sensitivity and specificity operational characteristic plot. We used the R package "pROC" to create ROC curves to determine the area under the curve for screening feature genes and assessing their diagnostic value (18). The area value under the ROC curve generally ranges between 0.5 and 1, with an AUC closer to 1 indicating better diagnostic performance. Immune Infiltration Analysis Single sample gene set enrichment analysis (ssGSEA) is an extension of gene set enrichment analysis (GSEA) that calculates separate enrichment scores for each sample and gene set (19). Each ssGSEA enrichment score indicates the degree of coordinated upregulation and downregulation of genes in a specific gene set within a sample. ssGSEA is a variant of the GSEA algorithm that provides a score for each sample and gene set pair rather than calculating enrichment scores for sample groups (such as control and disease groups) and gene sets (such as pathways). Twenty-eight immune cell types, including Activated CD8 T cell; Central memory CD8 T cell; Effector memory CD8 T cell; Activated CD4 T cell; Central memory CD4 T cell; Effector memory CD4 T cell; T follicular helper cell; Gamma delta T cell; Type 1 T helper cell; Type 17 T helper cell; Type 2 T helper cell; Regulatory T cell; Activated B cell; Immature B cell; Memory B cell; Natural killer cell; CD56bright natural killer cell; CD56dim natural killer cell; Myeloid derived suppressor cell; Natural killer T cell; Activated dendritic cell; Plasmacytoid dendritic cell; Immature dendritic cell; Macrophage; Eosinophil; Mast cell; Monocyte; Neutrophil were downloaded from the Tumor and Immune System Interaction Database (TISIDB) (http://cis.hku.hk/TISIDB/ index.php) (20). We calculated the relative enrichment scores for each immune cell type based on the gene expression profiles of each sample. The R package "ggplot2 (version 3.3.6)" was used to plot the changes in immune cell infiltration levels between ARDS and control group samples (21). RBP-mRNA Network Construction This study utilized the widely used open-source platform StarBase (https://starbase.sysu. edu.cn/tutorialAPI.php#RBPTarget) to analyze ncRNA interactions, using CLIP-seq, degradome-seq, and RNA-RNA interaction data to investigate the association between mRNA and RBP (RNA-binding protein) expression. In the context of disease, p-value <0.05, clusterNum ≥5, and clipExpNum ≥5 were defined as cutoff criteria for identifying key mRNA-RBP pairs. Subsequently, the RBP-mRNA network was constructed using Cytoscape. ceRNA Network Construction Due to the unclear mechanism of competing endogenous RNA (ceRNA) in viral lung injury, we used the miRTarBase (https://mirtarbase.cuhk.edu.cn/~miRTarBase/ miRTarBase_2022/php/index.php) (22) , starbase2.0 (https://starbase.sysu.edu.cn/ starbase2/index.php) (23), and miRDB (https://mirdb.org/index.html) databases to perform reverse predictions of microRNAs for key genes and to predict LncRNAs for common microRNAs of key genes, ultimately constructing the ceRNA network. TF-mRNA Network Construction Transcription factors regulate the transcription process by recognizing specific DNA sequences, thus participating in various complex biological processes. The hTFtarget database (http://bioinfo.life.hust.edu.cn/hTFtarget#!) has compiled comprehensive TF target regulations from large-scale human transcription factor ChIP-Seq data (7,190 experimental samples of 659 transcription factors), covering 569 conditions (399 cell lines, 129 tissue or cell types, and 141 treatment methods), allowing for the prediction of common transcription factors for multiple genes. Detected Hub Gene Expression in Vivo and in Vitro Respiratory viral infections are common pathogenic factors of acute respiratory distress syndrome (ARDS) (24). After viral infection, the immune response of the body may become imbalanced, leading to abnormal responses of cellular immunity and humoral immunity. This abnormal immune response may exacerbate pulmonary pathological changes, accelerating the progression of ARDS (25). The study chose to construct mouse models of viral acute lung injury and mouse alveolar epithelial cell models to further verify the expression levels of the key genes ZMAT2 and HBB that were screened (26). Animal models and sample collection We required all authors to comply with the ARRIVE 2.0 guidelines and regulations. All procedures were performed according to the guidelines of the National Institute of Health for Animal Care. Wild-type C57BL/6J male mice aged from 6-8 weeks and weighing about 25±5g were obtained from the Animal Center of Guangxi Medical University (Nanning, China). Virgin (WASHU). These mice were housed in a room fitted with air-filters where they could freely access food and water. The conditions of the room were maintained at a temperature of 20-25 ºC, with 50-70% humidity levels.For the poly(I:C) mouse model, 10 mg of high-molecular-weight poly(I:C) in 10 mL PBS was administered intranasally (i. n.) to C57BL/6 mice with 5 mg/Kg under light anesthesia (27). Pentobarbital sodium (120 mg/kg) was used to euthanize all mice via intraperitoneal injection after administering poly(I:C) or PBS for 48 hours, to facilitate further downstream examinations. Cell culture and sample collection MLE-12 cells purchased from ATCC were cultured in RMPI 1640 medium containing 10% fetal bovine serum (FBS) (10091148, Gibco, New Zealand), 20 mM HEPES, and 2 mM L-glutamine.MLE-12 cells were exposed to poly(I:C) (30 µg/mL) stimulation for 48 h with or without interventions (28). Cell Death assessment assay SYTOX Green nucleic acid stain only penetrates compromised membranes characteristic of dying cells. Cell death was quantified as a percent of Sytox+ cell nuclei out of total cell nuclei in culture by measuring uptake of the cell impermeable dye Sytox Green (R37109, InvivoGen, USA). Fluorescence was measured using a fluorescence microplate reader, with excitation/emission at 504/523 nm. The release of lactate dehydrogenase (LDH) into the culture medium only occurs upon cell death. LDH activity in supernatants of cells was assessed according to the protocol of the manufacturer (Thermo Fisher Scientific, USA). Measurement of pulmonary edema, permeability, and cytokines The right upper lobe with excess water was eliminated using filter paper to ascertain its weight (W). The lung tissues were subjected to a drying process at 60 °C for 48 hours to attain their dry weight (D). The calculation of the W/D ratio was used as a measurement index for pulmonary edema. An evaluation of changes in lung permeability was conducted by assessing total BALF protein using a BCA Protein Assay Kit (23225, Thermo Fisher Scientific, Waltham, MA, USA). Additionally, a hemocytometer was used to count total cell infiltration. Interleukin 1β (IL-1β), tumor necrosis factor ɑ (TNF-ɑ), and IL-18 levels in cell culture supernatant and BALF were measured using enzyme-linked immunosorbent assay kits (CUSABIO, Wuhan, China). Measurement of mRNA expression Total mRNA of the cells or lung tissues were extracted using TRIzol reagent (Thermo Fisher Scientific) following the guidelines listed by the manufacturer. The High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, 4368814) was used to prepare the cDNA, which was then quantified using the PowerUp™ SYBR™ Green Master Mix (Applied Biosystems, A25742). The relative expression levels of mRNA were determined using the 2-△△ct cycle threshold method. The primer sequences of ZMAT2 used were as follows: forward: 5′-TCGGAGTCCAGGAGTGTGAG-3′, reverse: 5′-GCTGCTCAGAGGACATCGTG-3′. The primer sequences of HBB used were as follows: forward: 5′-GTGCACCTGACTCCTGAGGAGA-3′, reverse: 5′-CTTGATACCAACCTGCCCAG-3′. The fold change, adjusted to GAPDH normalization, was utilized to illustrate the variances between groups. Histologic study The lower lobes of the right lung were preserved using 4% paraformaldehyde (30525-89-4; Sigma-Aldrich, AR, USA), and then encapsulated within the Tissue-Tek OCT compound (4583; Sakura, Tokyo, Japan). The pathological assessment of lung damage was independently evaluated by two authors on sections stained with hematoxylin and eosin, following criteria that had been reported earlier (29). Immunoblotting The left lower lung lobes were thoroughly mixed into a uniform solution using RIPA lysis buffer (20-188, Sigma-Aldrich, AR, USA). During this process, to prevent protein degradation and dephosphorylation, both a Protease Inhibitor Tablet (product number 11836170001 from Roche, located in Basel, Switzerland) and a PhosphoSTOP Phosphatase Inhibitor Tablet (product number 4906845001, also from Roche in Basel, Switzerland) were added. This homogenization was achieved with the aid of a mechanical tissue homogenizer.The samples underwent lysis for a duration of thirty minutes at an icy temperature, followed by centrifugation at 12,000 g-force for 15 minutes. Following the measurement of protein concentrations by the bicinchoninic acid (BCA) assay, the obtained supernatants from the cell lysates were heated to 85 °C for a duration of 5 minutes with a loading buffer added. Between 50 and 75 micrograms of proteins were subjected to separation through SDS-polyacrylamide gel electrophoresis (PAGE) and subsequently transferred to polyvinylidene fluoride (PVDF) membranes. After blocking a 1-hour incubation period at 22-25℃ with 5% nonfat milk, the membranes underwent an overnight incubation with primary antibodies (Supplemental Table 2) at a temperature of 4 °C. This was followed by a 1-hour incubation at room temperature with secondary antibodies (Abcam, Cambridge, UK) conjugated with horseradish peroxidase. Band intensities corresponding to different proteins were quantified from digitized films through the employment of an Odyssey® CLX imaging system (LI-COR, USA). Statistical analysis Statistical analyses were performed using R software v4.1.2. Spearman correlation tests were used to infer the correlation between two independent parameters. Wilcoxon tests were used to compare differences between two independent groups, and Kruskal-Wallis tests were used to compare differences among three or more groups. A two-sided p-value of less than 0.05 was considered statistically significant. Results Differentially Expressed Genes Related to ARDS By comparing ARDS samples with control groups, a total of 3152 differentially expressed genes (DEGs) were identified, showing significant differences between the two groups (corrected p -value 0.5). In ARDS samples, 1549 genes were upregulated, and 1603 genes were downregulated (Supplementary Table 2). All DEGs were shown in a volcano plot (Figure 2A). Additionally, a heatmap displayed the expression of the top-ranked genes (SH3GLB1, MAP2K6, TXN, BCL2A1, ANXA3NOG, FAM102A, TCF7, ABLIM1, DGKA) in the samples (Figure 2B). GSVA To look into the functional aspects of ARDS, we performed GSVA analysis to assess the relative expression differences of pathways in the two groups. The GSVA analysis highlighted many pathways that were differentially expressed and visualized them through a heatmap. Compared to the control group, the expression of KEGG_ REGULATION_OF_AUTOPHAGY and KEGG_TAURINE_AND_ HYPOTAURINE_METABOLISM was significantly lower in the ARDS group, while the expression of pathways related to KEGG_PRIMARY_IMMUNODEFICIENCY and KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY was significantly higher (Figure 3, Supplementary Table 3). Weighted Gene Co-expression Network Construction and Module Identification We applied WGCNA to study the gene set associated with MAM. Scale independence and average connectivity analysis indicated that when the minimum soft threshold (soft threshold) β was equal to 10 (Figure 4A), the average connectivity approached 0, and scale independence was greater than 0.85. We identified twelve co-expression modules, and unrelated genes were assigned to the gray module, which we ignored in the next studies (Figure 4B). To study the relationships between modules and see how they relate to each other, we correlated the module eigengenes (MEs). A heatmap was used to plot the correlation of feature gene networks (Figure 4C). A heatmap depicting the topological overlap in the gene network was also created (Figure 4D). To understand what the genes in the modules mean for physiology, we associated the 12 MEs with MAM and sought the most significant associations. Based on the module-trait correlation heatmap (Figure 4E), the genes clustered in the yellow module (n = 302, Supplementary Table 4) exhibited the strongest positive correlation with MAM (r = 0.4067, p < 0.05). Therefore, we primarily focused on the yellow module, as it may serve as a more accurate indicator of MAM. The intersection of DEGs and the genes in the MAM-associated module yielded a total of 82 MAM-related DEGs, which were considered key genes (Supplementary Table 5, Figure 4F). GO Enrichment Analysis To investigate the biological functions linked to MAM-related differences, we conducted GO term enrichment analysis (Supplementary Table 6). The GO results showed that these genes were found in gas transport, oxygen transport, hydrogen peroxide metabolic process, hemoglobin complex, haptoglobin-hemoglobin complex, cytosolic small ribosomal subunit, haptoglobin binding, oxygen carrier activity, peroxidase activity (Figure 5A-D). Machine Learning Screening of Hub Genes We further used LASSO regression, random forest, and SVM algorithms to screen for key genes. Through LASSO regression analysis, we identified 32 key MAM-related DEGs (Figure 6A-B, Supplementary Table 7). Using the random forest algorithm, we selected the top 30 genes as key MAM-related DEGs based on feature importance MDA and MDG (Figure 6C-D, Supplementary Table 8), ultimately obtaining 22 genes. Through the SVM-RFE method, we screened 4 key MAM-related DEGs (Figure 6E, Supplementary Table 9). Finally, the intersection of MAM-related DEGs detected by each method yielded 2 most critical MAM-related DEGs as hub genes for subsequent analysis: ZMAT2, HBB (Figure 6F). Diagnostic Value of Hub Genes To validate the hub genes, we constructed a diagnostic nomogram model using the hub genes (Figure 7A) and evaluated its predictive ability using calibration curves. The calibration curve showed minimal differences between the actual ARDS risk and the predicted ARDS risk, indicating that the ARDS model is highly accurate (Figure 7B). ROC curve analysis also confirmed the correctness of the model (Figure 7C). We tested the hub gene expression differences between the two groups and found significant differences in hub genes between the two groups, with hub gene ZMAT2 being significantly upregulated in ARDS compared to the control group, while hub gene HBB was significantly downregulated in ARDS compared to the control group (Figure 7D). To examine the correlation between hub genes, we created a correlation heatmap and found that the correlation between ZMAT2 and HBB was not strong (Figure 7E). To further verify the diagnostic value of the hub genes, we used ROC curves to validate the hub genes and found that the area under the ROC curve (AUC) values for ZMAT2 (AUC=0.992) and HBB (AUC=0.868) were both greater than 0.8 (Figure 7F-G), indicating that the hub genes have discriminatory ability as potential biomarkers for ARDS. Interaction Analysis of Hub Genes We created a PPI network for the hub genes using the GeneMANIA database, where the two genes had interaction relationships (Figure 8A). To further study the functional characteristics of the feature genes, we conducted GO and KEGG analyses on a total of 22 genes including 2 hub genes and 20 genes related to hub genes. The GO enrichment results showed that these genes were significantly enriched in positive regulation of response to cytokine stimulus , regulation of epidermal cell differentiation, zymogen activation, cytosolic ribosome, ficolin-1-rich granule lumen, centriole, protein N-terminus binding, ammonium transmembrane transporter activity, heme transmembrane transporter activity, and other pathways (Figure 8B, Supplementary Table 10). The KEGG analysis results showed that the main enriched pathways were African trypanosomiasis and Malaria (Figure 8C, Supplementary Table 11). Single Gene GSEA Enrichment We analyzed the enriched pathways related to the hub genes through single gene GSEA. The results showed that HBB did not return enrichment results, butgenes with expression patterns similar to ZMAT2 were mainly enriched in pathways such as KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY, KEGG_ALZHEIMERS_DISEASE,KEGG_PARKINSONS_DISEASE,KEGG_ NITROGEN_METABOLISM, KEGG_COMPLEMENT_AND_COAGULATION_ CASCADES (Figure 9). Key Gene-Related Signaling Pathways Further GSVA analysis was conducted to study the differences between ARDS patients and control groups across 50 Hallmark signaling pathways. In ARDS patients, 33 Hallmark signaling pathways were significantly upregulated, 10 pathways were significantly downregulated (Figure 10A, Supplementary Table 12). We also analyzed the correlation between the two hub genes and the 50 Hallmark signaling pathways. ZMAT2 was associated with many pathways, including HALLMARK_ADIPOGENESIS and HALLMARK_ALLOGRAFT_REJECTION. HBB was associated with many pathways, including HALLMARK_ADIPOGENESIS and HALLMARK_ALLOGRAFT_REJECTION (Figure 10B). Immune Infiltration Immune cell infiltration may play an important role in the pathogenesis of ARDS. Therefore, we investigated the association between ARDS/control samples and infiltrating immune cells. Among 28 immune cells, 23 immune cells showed significant differences in immune infiltration abundance between the two groups ( p< 0.05) (Figure 11A, Supplementary Table 13). Among them, 14 immune cells (Central memory CD8 T cell, Activated CD4 T cell, Gamma delta T cell, Type 17 T helper cell, Regulatory T cell, Memory B cell, Natural killer cell, Myeloid derived suppressor cell, Activated dendritic cell, Plasmacytoid dendritic cell, Immature dendritic cell, Macrophage, Monocyte, Neutrophil) had significantly higher infiltration levels in the ARDS group compared to the control group (Figure 11A). As shown in Figure 11B, the overall level of immune cell infiltration showed significant differences between the ARDS and control groups. We also examined the significant correlations between the top-ranked hub genes and the corresponding immune cells. ZMAT2 was significantly correlated with Macrophage (R=0.73, p< 0.001) (Figure 11C), ZMAT2 was significantly correlated with Effector memory CD8 T cell (R=-0.688, p< 0.001) (Figure 11D), ZMAT2 was significantly correlated with Neutrophil (R=0.675, p< 0.001) (Figure 11E), ZMAT2 was significantly correlated with Gamma delta T cell (R=0.625, p< 0.001) (Figure 11F), and ZMAT2 was significantly correlated with Activated dendritic cell (R=0.615, p< 0.001) (Figure 11G). Hub mRNA Interaction Network Due to the interaction of RBPs (RNA binding proteins) with mRNA, we utilized the StarBase online database to search for the two hub mRNAs and downloaded the corresponding mRNA/RBP pairs for ZMAT2. Based on the relationships provided by the online dataset between target genes, we constructed an RBP-mRNA network consisting of 26 nodes, 25 RBPs, 1 mRNA, and 25 edges (Supplementary Table 14, Figure 12A). To understand the potential molecular mechanisms of hub genes in ARDS, we subsequently constructed an mRNA-TF interaction network. The network diagram constructed using Cytoscape included 1 mRNA and 19 transcription factors (TF) (Figure 12B). To understand the potential molecular mechanisms of hub genes in ARDS, we subsequently constructed an mRNA-miRNA interaction network. The network diagram constructed using Cytoscape included 1 mRNA and 24 miRNAs, totaling 25 nodes and 24 edges (Figure 12C). Gene HBB did not return available results. Drug Prediction DGldb was used to identify potential sensitive drugs or molecular compounds. We predicted drugs for the two hub genes using DGldb and found that hub gene HBB was detected to have correlations with drugs, while gene ZMAT2 did not return available results. As shown in the drug-gene interaction network (Figure 13A), 29 drugs or molecular compounds had varying degrees of regulatory effects on hub gene HBB (Supplementary Table 15). We performed molecular docking of hub gene HBB with drugs AES-103 and AZATHIOPRINE, and the molecular docking results are shown in Table 1. The binding energies of AZATHIOPRINE and AES-103 with hub gene HBB were both negative, indicating that the components of AZATHIOPRINE and AES-103 bind well to hub gene HBB. AZATHIOPRINE and AES-103 may exert therapeutic effects by binding to the key target gene protein HBB, and the results of molecular docking were visualized (Figure 13B-C). Table 1 Basic information on molecular docking of drugs and target proteins Molecular name Targets Protein Accession Binding energy(kcal/Mol) AZATHIOPRINE HBB P68871 -4.953 AES-103 HBB P68871 -5.766 Hub gene expression in Vivo and in Vitro To explore the potential of hub genes in clinical translation, this study employed Poly I:C to simulate viral infection in C57BL/6 mice, successfully constructing an ARDS animal model closely related to viral infection. Based on the successfully established virus infection-induced ARDS animal and cell models, the expression differences of the hub genes HBB and ZMAT2 between the ARDS group and the control group were further investigated. Through HE staining technique, we visually observed that Poly I:C treatment significantly induced damage in the lungs of mice (Figure14 A-B). Notably, the wet-to-dry weight ratio (W/D ratio) of lung tissue, as a key indicator for assessing the degree of lung tissue edema, plays a crucial role in revealing the severity of lung diseases and their pathological mechanisms. In comparative experiments, we found that the W/D ratio of lung tissue in the Poly I:C treatment group significantly increased (Figure14 C). Further analysis showed that in the bronchoalveolar lavage fluid (BALF) of mice treated with Poly I:C, cell counts, total protein content, and levels of inflammatory factors IL-1β and TNF-α were significantly elevated (Figure14 C), collectively confirming the successful construction of the virus infection-related ARDS model and revealing the intensity of the inflammatory response in the model. Additionally, to verify the impact of viral infection at the cellular level, we also treated MLE-12 cells with Poly I:C to construct the corresponding cell model. Through Sytox Green fluorescence and LDH detection, we found that Poly I:C treatment significantly promoted the death of MLE-12 cells and increased LDH release (Figure14 D-F), further confirming the direct damaging effect of viral infection on lung cells. The research results showed that compared to the control group, the mRNA and protein expression levels of the HBB gene were significantly downregulated in the ARDS group, while the mRNA and protein expression levels of the ZMAT2 gene showed a clear upward trend (Figures15 A-E). Correlation analysis between IHC score and lung injury score indicated that the expression level of HBB protein was negatively correlated with the lung injury score, while the expression level of ZMAT2 protein was positively correlated with the lung injury score (Figure15 F). These findings, combined with the bulk RNA-seq results, consistently suggest that HBB and ZMAT2 could be potential treatment targets for ARDS. Discussion Acute respiratory distress syndrome (ARDS) is a serious condition marked by severe lung inflammation, damage to the alveolar-capillary barrier, and respiratory failure ( 30 ). Despite improvements in critical care management, such as lung-protective mechanical ventilation and fluid management, ARDS is still linked to high rates of illness and death ( 31 ). The varied pathophysiology of ARDS involves immune dysregulation, oxidative stress, and mitochondrial dysfunction ( 32 ). This complexity requires further research to discover new biomarkers and treatment targets. In this study, we employed a bioinformatics-based approach to investigate the role of mitochondria-associated endoplasmic reticulum membranes (MAMs) and their related genes in ARDS. MAMs play a crucial role in cellular homeostasis by regulating calcium signaling, lipid metabolism, and oxidative stress responses ( 33 ). Considering the role of mitochondrial dysfunction in ARDS, we hypothesized that MAMs-associated genes could influence disease progression by affecting inflammatory responses and oxidative stress. Our analysis identified HBB and ZMAT2 as key MAMs-related hub genes that may serve as potential biomarkers and therapeutic targets for ARDS. Differential expression analysis revealed 3,152 genes that were significantly dysregulated in ARDS samples compared to controls, with 1,549 genes upregulated and 1,603 genes downregulated. This finding emphasizes the significant molecular changes involved in ARDS pathogenesis. GO and KEGG pathway enrichment analyses provided insights into the functional roles of MAMs-related differentially expressed genes (DEGs) in ARDS. Notably, GO enrichment analysis revealed significant involvement of gas transport, oxygen transport, and hydrogen peroxide metabolic processes, suggesting that MAMs dysfunction may impair oxidative homeostasis in ARDS. KEGG pathway analysis demonstrated significant downregulation of the regulation of autophagy and the metabolism of taurine and hypotaurine in ARDS patients, indicating that defective autophagy and dysregulated cellular metabolism may contribute to disease severity. The suppression of autophagy-related pathways is particularly significant, as autophagy plays a crucial role in clearing damaged organelles and mitigating oxidative stress. Disruption of this process may lead to excessive cell death and inflammation, exacerbating pulmonary injury in ARDS. Conversely, pathways associated with primary immunodeficiency and the T cell receptor signaling pathway were significantly upregulated, indicating a dysregulated immune response in ARDS. These findings suggest that MAMs dysfunction may serve as a key mediator linking mitochondrial stress with immune activation, thereby contributing to the exaggerated inflammatory response observed in ARDS. Immune dysregulation is a key feature of ARDS pathology. Our analysis of immune infiltration showed that 14 immune cell types were significantly increased in ARDS patients. The most notable increases were seen in activated CD4 T cells, regulatory T cells, macrophages, neutrophils, and activated dendritic cells. These findings are consistent with previous reports highlighting the roles of both innate and adaptive immunity in the development of ARDS ( 3 ). Notably, the expression of ZMAT2 was strongly correlated with macrophages, neutrophils, and activated dendritic cells. This correlation suggests that ZMAT2 may play a role in ARDS pathogenesis by promoting immune cell activation and cytokine release. Macrophage-driven cytokine storms and neutrophil-mediated oxidative damage are well-established contributors to lung injury in ARDS, further supporting the role of ZMAT2 as a pro-inflammatory regulator in this condition. Among the identified hub genes, HBB was significantly downregulated, while ZMAT2 was upregulated in ARDS samples, suggesting distinct yet complementary roles in disease progression. The downregulation of HBB in ARDS is of particular significance given its oxygen transport, antioxidant, and immunomodulatory properties. GO enrichment analysis revealed that HBB is involved in heme transmembrane transporter activity, peroxidase activity, and oxygen carrier activity, all of which are essential for maintaining pulmonary oxygenation and mitigating oxidative damage. Reduced HBB expression may contribute to worsening hypoxemia, impaired erythrocyte function, and increased susceptibility to oxidative stress, ultimately exacerbating pulmonary injury ( 34 ). ZMAT2 was significantly upregulated in ARDS and was closely linked to pathways involved in natural killer cell-mediated cytotoxicity, complement and coagulation cascades, and neurodegenerative diseases. These findings suggest that ZMAT2 may enhance immune activation, promote coagulation dysfunction, and contribute to endothelial damage in ARDS. Moreover, the enrichment of ZMAT2 in pathways related to neurodegenerative diseases suggests a potential role in endoplasmic reticulum stress and mitochondrial dysfunction, further exacerbating pulmonary injury ( 10 ). Our drug prediction analysis identified HBB as a potential therapeutic target, with AZATHIOPRINE and AES-103 showing strong binding affinities to the HBB protein. Given that AZATHIOPRINE is an immunosuppressant, it may hold promise for mitigating excessive immune activation in ARDS. Further preclinical and clinical investigations are warranted to explore the therapeutic potential of targeting HBB and ZMAT2 in ARDS management. Despite the encouraging results of this study, there are several limitations that must be addressed. First, the sample size is relatively small. Therefore, it is necessary to conduct external validation using independent cohorts to confirm the robustness of the identified biomarkers. Second, bioinformatics analyses provided valuable insights; however, functional validation using in vivo and in vitro models is essential to establish the causal relationships between HBB, ZMAT2, and the development of ARDS. Finally, clinical trials are needed to evaluate how pharmacological modulation of these genes can translate our findings into potential therapeutic strategies. Conclusion This study identifies HBB and ZMAT2 as key MAM-related hub genes involved in the pathophysiology of ARDS. It highlights their roles in immune activation, oxidative stress, and mitochondrial dysfunction. These findings provide novel insights into the molecular mechanisms underlying ARDS and suggest potential biomarkers for early diagnosis and therapeutic intervention. Future studies should focus on functional validation and clinical translation to assess the therapeutic potential of targeting MAM-associated pathways in ARDS. Declarations Acknowledgements This study acknowledges the valuable contribution of the GEO database, which provided a rich dataset for our analysis. Author contributions Bijun Luo and Jifeng Feng designed all the investigation. Bijun Luo, Yanqiong Zhou, Qiuying Chen performed the major experiments and drafted the manuscript. Hui Huang, Xiaoxia Wang, and Kaimin Lv contributed the data analysis and contributed visualization. Bijun Luo designed the overall study and determined the final version. All the authors have approved the manuscript and agreed submission to the esteemed journal. There are no conflicts of interest to declare. Funding This work was supported by the National Natural Science Foundation of China (No. 82060024), "139" Plan for High-level Medical Backbone Talents of Guangxi Zhuang Autonomous Region (No. G202002015), and Guangxi Medical and Health Appropriate Technology Developmental and Popularizational Application Project (No. S2024084). Data availability The datasets analyzed are publicly available in Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih. gov/geo/. Mitochondria-associated membranes (MAM) related genes were obtained from the article studied by Yi Luan, Guangyu Guo, et al. (13), (PMID:37221368, DOI:10.1038/s41598-023-35464-2). No datasets were generated during the current study. Clinical trial number Not applicable. Ethics approval Animal studies were reviewed and approved by the Medicine Animal Care and Use Committee of Guangxi Medical University. Consent to participate Not applicable. Competing interests The authors declare no competing interests. References C. 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Supplementary Files SupplementaryTables.zip WBZMAT2.tiff WBHBB.tiff WBGAPDH.tiff WBintegrated.tif Cite Share Download PDF Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 May, 2025 Reviews received at journal 08 May, 2025 Reviewers agreed at journal 27 Apr, 2025 Reviews received at journal 29 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 26 Mar, 2025 Editor assigned by journal 17 Mar, 2025 Editor invited by journal 12 Mar, 2025 Submission checks completed at journal 12 Mar, 2025 First submitted to journal 26 Feb, 2025 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. 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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-6113941","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":427640746,"identity":"3994854e-5f93-4f24-9bdc-ca1c71c9758e","order_by":0,"name":"Yanqiong Zhou","email":"","orcid":"","institution":"Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Yanqiong","middleName":"","lastName":"Zhou","suffix":""},{"id":427640750,"identity":"0cd3d1d0-88eb-4f50-8f1e-50dae403d4ed","order_by":1,"name":"Qiuying Chen","email":"","orcid":"","institution":"Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Qiuying","middleName":"","lastName":"Chen","suffix":""},{"id":427640751,"identity":"aa904878-c75d-4358-b65a-dc66f7bfa28c","order_by":2,"name":"Hui Huang","email":"","orcid":"","institution":"Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Huang","suffix":""},{"id":427640752,"identity":"392d9e73-4200-4655-a29b-29fcc158de94","order_by":3,"name":"Xiaoxia Wang","email":"","orcid":"","institution":"Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Wang","suffix":""},{"id":427640753,"identity":"df466bad-7c92-4eaf-801a-80f80c19b5e9","order_by":4,"name":"Kaimin Lv","email":"","orcid":"","institution":"Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Kaimin","middleName":"","lastName":"Lv","suffix":""},{"id":427640754,"identity":"d7d2fd3a-ecdd-4161-a33d-46a73c92c1c9","order_by":5,"name":"Jifeng Feng","email":"","orcid":"","institution":"Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Jifeng","middleName":"","lastName":"Feng","suffix":""},{"id":427640755,"identity":"1886b7f4-35b1-4f0f-8c93-7fc2601c79a8","order_by":6,"name":"Bijun Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3RMQrCMBSA4ZRCXIJdn6AVbxApFI+TIHSq4NhBpFCpgoir3iKjY0WIS9w7VjxBNxfFOitN3RzyzflJ8h5ChvGHsJNkp8cTXNxKjgWLZvqkDZIXBI88h8gxLZTUJy4KPUpwxPe70O9cF3aDhyEZABDgImd+xGOMnOWa1Sd2IoECeEIVQc4PXQTqIvS3MAo9cWYy5wojChNdEvqQMbBExtMpT+1GiTeMMxjsV2OMmiXVkG9WDO8h28CUJNq/9LfVKq14Xq1yW5b3aOY6y0198oH8dtwwDMP46gU0NUt6/xxcNwAAAABJRU5ErkJggg==","orcid":"","institution":"Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region","correspondingAuthor":true,"prefix":"","firstName":"Bijun","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-02-26 14:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6113941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6113941/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-10405-3","type":"published","date":"2025-07-09T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78672599,"identity":"7d085f2a-a053-44c7-8d79-cfa85bd01d7a","added_by":"auto","created_at":"2025-03-17 12:54:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127829,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/0c513d32dbc7c3d22ca70ff7.png"},{"id":78672882,"identity":"696c32b0-e535-48c5-8241-2ff39a73533a","added_by":"auto","created_at":"2025-03-17 13:02:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":286974,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Gene Expression Associated with ARDS. (A) The volcano plot describes the distribution of DEGs between ARDS and control group samples. (B) The heatmap describes the top-ranked DEGs.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/ca0d0dce0604e649e322f265.png"},{"id":78673613,"identity":"2baf64eb-c1d9-4cdf-9014-76bc138e678c","added_by":"auto","created_at":"2025-03-17 13:10:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":224308,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis results, visualizing significantly enriched pathways in ARDS through a heatmap.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/5b2d3b2e3c4d3abbbe5f2c8f.png"},{"id":78673612,"identity":"376516a0-b2f7-4bcd-aade-03cb00245b22","added_by":"auto","created_at":"2025-03-17 13:10:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":722373,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of the WGCNA co-expression network. (A) Soft threshold β=10, scale-free topology fitting index (R2). (B) Analysis of gene expression networks in ARDS identified different modules of co-expression data. (C) Relationships between modules. Correlation heatmap of feature gene networks. Each row and column in the heatmap corresponds to a module's feature genes (marked by color). In the heatmap, red indicates high adjacency, while blue indicates low adjacency. The red squares on the diagonal are the meta-modules. (D) Heatmap of topological overlap in the gene network. In the heatmap, each row and column corresponds to a gene, with lighter colors indicating low topological overlap and progressively darker reds indicating high topological overlap. Darker squares on the diagonal correspond to modules. The gene dendrogram and module assignments are displayed on the left and top. (E) Relationship between consensus module feature genes and MAM. Each row in the table corresponds to a consensus module, and each column corresponds to a feature. The numbers in the table represent the correlation between the corresponding module feature genes and traits, with p-values in parentheses printed below the correlations. Correlations are color-coded according to the color legend. (F) Correlation between module membership (MM) of all genes in the yellow module and significance (GS) of MAM-related genes, where Cor represents the absolute correlation coefficient between GS and MM.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/923333008b8bfcd0d12159c5.png"},{"id":78672877,"identity":"12ce17df-c185-4c67-8385-40fd46ab9ea8","added_by":"auto","created_at":"2025-03-17 13:02:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":387069,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis based on MAM-related differentially expressed genes.(A) GO term lollipop chart display (B) BP term bubble chart display (C) CC term bubble chart display (D) MF term bubble chart display\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/a28a95189529fdd28c8ae03b.png"},{"id":78672878,"identity":"e31396ea-0aed-4944-87f4-ab10e4e3e59d","added_by":"auto","created_at":"2025-03-17 13:02:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":511551,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of candidate diagnostic biomarkers for ARDS using machine learning methods. (A) The trajectory of changes in independent variables using LASSO regression, with the x-axis representing the logarithm of the independent variable lambda and the y-axis representing the coefficients that can be independently obtained. (B) Confirmation intervals at each lambda in LASSO regression. (C) Random forest error rate compared to the number of classification trees. (D) The top 30 MAM-related DEGs in the random forest algorithm based on two importance rankings. (E) Screening of the most suitable feature genes using the SVM-RFE algorithm. (F) Venn diagram showing the intersection of three machine learning methods.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/0baffdc1521fb136730483df.png"},{"id":78672880,"identity":"44b14eae-3784-4b18-9a30-e9afb66cce15","added_by":"auto","created_at":"2025-03-17 13:02:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":362606,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and validation of the diagnostic nomogram model for ARDS and ROC curves for hub genes.(A) Nomogram used to predict differences between disease and control groups. (B) Calibration curve assessing the predictive ability of the nomogram model. (C) ROC curve evaluating the clinical value of the nomogram model. (D) Box plot describing the expression of hub genes in ARDS and control groups. (E) Heatmap describing the correlation magnitude between hub genes. (F) ROC curve for ZMAT2. (G) ROC curve for HBB.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/4e960d5a7a87c05dab9fc32d.png"},{"id":78672628,"identity":"e291277c-b5ac-46b5-a9c4-1a421cb206e7","added_by":"auto","created_at":"2025-03-17 12:54:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2348127,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis of hub genes.(A) Gene co-expression network diagram. (B) GO analysis of co-expressed genes. (C) KEGG analysis of co-expressed genes.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/9b23d85b97a8cd789d0b91b2.png"},{"id":78672632,"identity":"3494be3d-da4a-40cc-ac4f-fe2a8cef5805","added_by":"auto","created_at":"2025-03-17 12:54:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":167327,"visible":true,"origin":"","legend":"\u003cp\u003eSingle gene GSEA enrichment analysis of hub genes, single gene GSEA enrichment analysis of ZMAT2.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/dc5f37a4315965f49f833853.png"},{"id":78672638,"identity":"ed3fdb14-3417-49b9-a0c4-7139c0b1e44e","added_by":"auto","created_at":"2025-03-17 12:54:19","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":756396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between hub genes and 50 HALLMARK signaling pathways.\u003c/strong\u003e (A) Comparison of 50 HALLMARK signaling pathways between ARDS and control groups. (B) Correlation between hub genes and 50 HALLMARK signaling pathways. ****\u003cem\u003ep \u0026lt; \u003c/em\u003e0.0001, ***\u003cem\u003ep \u0026lt; \u003c/em\u003e0.001, **\u003cem\u003ep \u0026lt; \u003c/em\u003e0.01, *\u003cem\u003ep \u0026lt; \u003c/em\u003e0.05.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/09b55c1002d38175f1b10665.png"},{"id":78672610,"identity":"cebc8f9b-6f89-4198-b34b-3397ab0fb37f","added_by":"auto","created_at":"2025-03-17 12:54:17","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":811771,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in immune infiltration between ARDS and control groups. (A) Estimated immune cell infiltration proportions between ARDS and control groups. (B) Heatmap showing changes in immune infiltration levels between ARDS and control groups. (C) Correlation scatter plot between ZMAT2 and Macrophage. (D) Correlation scatter plot between ZMAT2 and Effector memory CD8 T cell. (E) Correlation scatter plot between ZMAT2 and Neutrophil. (F) Correlation scatter plot between ZMAT2 and Gamma delta T cell. (G) Correlation scatter plot between ZMAT2 and Activated dendritic cell. Asterisks indicate p values: ****\u003cem\u003ep \u0026lt; \u003c/em\u003e0.0001, ***\u003cem\u003ep \u0026lt; \u003c/em\u003e0.001, **\u003cem\u003ep \u0026lt; \u003c/em\u003e0.01, *\u003cem\u003ep \u0026lt; \u003c/em\u003e0.05.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/60f1feb8c8adb72c8f1cf017.png"},{"id":78672639,"identity":"916f2185-09eb-45a2-98d6-1d332b237295","added_by":"auto","created_at":"2025-03-17 12:54:19","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":475153,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction analysis of hub gene-related networks. (A) RBP-mRNA regulatory network. Green represents RBP, pink represents mRNA. (B) mRNA-TF network of hub genes. Yellow represents TF, pink represents mRNA. (C) miRNA-mRNA network of hub genes. Pink circles represent mRNA, purple represents miRNA.\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/a0b2e4b5619a86e6f7ef139c.png"},{"id":78672612,"identity":"9162098c-4f98-43fd-9ed1-d8353a716fd5","added_by":"auto","created_at":"2025-03-17 12:54:17","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1961716,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking. (A) Drug prediction, with yellow representing drugs or molecular compounds and pink representing hub genes. Molecular docking: (B) Molecular docking of drug AZATHIOPRINE with target protein HBB. (C) Molecular docking of drug AES-103 with target protein HBB.\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/c777ec592dc5be42dacc51df.png"},{"id":78672641,"identity":"ed90a76f-c14e-421d-a649-aecf01a832cf","added_by":"auto","created_at":"2025-03-17 12:54:19","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1799865,"visible":true,"origin":"","legend":"\u003cp\u003eSuccessfully constructed in vivo and in vitro models mimic RNA virus infection using Poly (I:C).(A) HE staining of lung tissues from mice subjected to poly(I:C) stimulation versus control vehicle respectively. Scale bar = 50 μm. (B) Pathological scores were assessed by results of HE staining. (C) Lung edema was assessed by determining the weight ratio between wet and dry lungs, total protein concentration in BALF, infiltrated cell counts in BALF, and levels of IL-1β and TNF-α in BALF. (D) Fluorescence microscopy observation of MLE-12 cells stimulated with Poly I:C or not for different times, scale bar= 100 μM; (E) Statistical analysis of cell death percentage from (D), n = 3 per group. (F) Quantitative LDH measurement in culture supernatant for cell damage/death assessment. All data are representative as means ± s.e.m of three independent experiments. Student’s t-test for A-F; * \u003cem\u003ep \u0026lt; \u003c/em\u003e0.05, ** \u003cem\u003ep \u0026lt; \u003c/em\u003e0.01,***\u003cem\u003ep \u0026lt; \u003c/em\u003e0.001,**** \u003cem\u003ep \u0026lt; \u003c/em\u003e0.0001.\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/33e08c2370946034e5f66f11.png"},{"id":78672879,"identity":"61241c6d-3beb-47df-9f82-3ad089a85516","added_by":"auto","created_at":"2025-03-17 13:02:18","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":1045594,"visible":true,"origin":"","legend":"\u003cp\u003eDetected Hub Gene Expression in Vivo and in Vitro. (A-B) Relative expression of HBB and ZMAT2 mRNA in lung tissues from mice or MLE-12 cells treated with or without poly(I:C). (C-D) Relative expression of ZMAT2 and HBB protein in lung tissues from mice or MLE-12 cells treated with or without poly(I:C). (E) IHC representation of HBB and ZMAT2 protein expression in lung tissues from poly(I:C)-treated or untreated mice. (F) Correlation analysis of IHC score and lung injury score.All data are representative as means ± s.e.m of three independent experiments. Student’s t-test for A-F; * \u003cem\u003ep \u0026lt; \u003c/em\u003e0.05,** \u003cem\u003ep \u0026lt; \u003c/em\u003e0.01,*** \u003cem\u003ep \u0026lt; \u003c/em\u003e0.001,****\u003cem\u003ep \u0026lt; \u003c/em\u003e0.0001.\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/54a9964ad97583452c9df770.png"},{"id":86700103,"identity":"11d10112-49d9-4369-aa2e-85e4b4bde087","added_by":"auto","created_at":"2025-07-14 16:11:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12575565,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/fdc3c32b-00eb-42da-ba67-33f4ec48db27.pdf"},{"id":78673611,"identity":"53805348-a094-43f0-98dc-c064e7107d85","added_by":"auto","created_at":"2025-03-17 13:10:17","extension":"zip","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":771136,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.zip","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/ef7ba4d8ee454de536561f78.zip"},{"id":78673617,"identity":"437a69e1-9816-44fb-a60b-4b33b68c79af","added_by":"auto","created_at":"2025-03-17 13:10:19","extension":"tiff","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":2683414,"visible":true,"origin":"","legend":"","description":"","filename":"WBZMAT2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/fa8fc7a3104eb44a8418167f.tiff"},{"id":78672894,"identity":"3f6f160c-783c-4d77-bd21-5a1f052859de","added_by":"auto","created_at":"2025-03-17 13:02:19","extension":"tiff","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":2683414,"visible":true,"origin":"","legend":"","description":"","filename":"WBHBB.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/ef3fb3d6c5e969fe487ae1f5.tiff"},{"id":78673619,"identity":"89d1ef55-e0b9-4702-8cf7-26305a7610b2","added_by":"auto","created_at":"2025-03-17 13:10:20","extension":"tiff","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":2780340,"visible":true,"origin":"","legend":"","description":"","filename":"WBGAPDH.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/219930260634655ba36c7bf4.tiff"},{"id":78672643,"identity":"bc2b2166-8cd9-4f90-83eb-346026318998","added_by":"auto","created_at":"2025-03-17 12:54:19","extension":"tif","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":10789960,"visible":true,"origin":"","legend":"","description":"","filename":"WBintegrated.tif","url":"https://assets-eu.researchsquare.com/files/rs-6113941/v1/b5abb8a80db0f466cb48f91f.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Function and Mechanism of Mitochondrial-Associated Membranes in Acute Respiratory Distress Syndrsome: A Comprehensive Study Combining Bioinformatics and Experimental Approaches","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute respiratory distress syndrome (ARDS) is a life-threatening clinical condition characterized by severe pulmonary insufficiency and is associated with high mortality rates ranging from 30\u0026ndash;40% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The syndrome can arise from various etiologies, including pneumonia, sepsis, and trauma, and is often complicated by multiple organ dysfunction, which significantly complicates management and increases mortality risk (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Despite advances in critical care medicine, there remains a lack of specific therapeutic interventions for ARDS, and current management primarily involves supportive care, including mechanical ventilation and fluid management (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent developments in the understanding of the pathophysiology of ARDS suggest that inflammatory processes play a crucial role in its progression, often mediated by immune cell dysregulation and cytokine storms (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, the complexity and heterogeneity of ARDS make it difficult to establish effective treatment protocols, leading to a pressing need for further research into the underlying molecular mechanisms and potential therapeutic targets (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne emerging area of interest in ARDS research is the role of mitochondria-associated endoplasmic reticulum membranes (MAMs), which have been implicated in various cellular processes, including apoptosis, inflammation, and stress responses (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). MAMs are specialized contact sites between the outer mitochondrial membrane and the endoplasmic reticulum membrane, playing a key role in cellular homeostasis. They regulate crucial processes such as calcium ion (Ca\u0026sup2;⁺) transfer, lipid metabolism, and the coordination of cellular stress responses. Dysfunction of MAMs has been linked to impaired calcium homeostasis, excessive oxidative stress, and dysregulated lipid metabolism, all of which contribute to cellular injury and inflammation (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In the context of ARDS, MAMs may act as critical regulators of immune activation, oxidative damage, and programmed cell death, thereby influencing disease severity. Given that ARDS is characterized by extensive immune cell infiltration, epithelial and endothelial injury, and uncontrolled inflammation, the disruption of MAM function may exacerbate these pathological processes, ultimately leading to worsened pulmonary dysfunction. Investigating the role of MAM-related genes in ARDS may unveil novel biomarkers and therapeutic targets that could improve clinical outcomes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to elucidate the relationship between MAM-associated genes and ARDS by employing a multifaceted approach that integrates bioinformatics analyses, gene expression profiling, and molecular assays. We will focus on key genes, such as ZMAT2 and HBB, which have shown differential expression in ARDS patients compared to healthy controls (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). By characterizing the biological functions of these genes and their pathways, we hope to contribute to a more comprehensive understanding of ARDS and identify potential avenues for targeted therapies.\u003c/p\u003e \u003cp\u003eIn summary, the complexity of ARDS, coupled with the need for novel therapeutic strategies, underscores the necessity for detailed investigations into its molecular mechanisms. Through this research, we aim to bridge the gap in understanding the role of MAM-associated genes in ARDS, potentially leading to improved diagnostic and therapeutic strategies that address this critical healthcare challenge.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData Download \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall technical route was showed in Figure 1. All data used in this study are free and publicly available from GEO (Gene Expression Omnibus, https://www.ncbi.nlm.nih. gov/geo/). The ARDS whole-genome expression profiles were retrieved and downloaded from the GEO database using the R package \u0026quot;GEOquery.\u0026quot; GSE243066 contains 49 samples, including peripheral blood samples from 34 ARDS patients and 15 healthy controls. However, the ARDS group samples GSM7778549, GSM7778550, GSM7778563, and GSM7778569 were excluded due to incomplete expression matrices, leaving 30 ARDS samples and 15 control samples included in this study. GSE89953 contains 52 samples, from which 26 peripheral blood mononuclear cell samples from ARDS patients were selected. Batch effects caused by non-biotechnological biases were corrected using the ComBat method from the R package \u0026quot;sva\u0026quot; (12). The correction effect is examined using principal component analysis (PCA). This study adhered to the data access policies of each database. Mitochondria-associated membranes (MAM) related genes were obtained from the article studied by Yi Luan, Guangyu Guo, et al. (13) \u0026nbsp; (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Analysis Related to ARDS \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used the R package \u0026quot;limma (version 3.50.0)\u0026quot;\u0026nbsp;(14)\u0026nbsp;to identify differentially expressed genes (DEGs) between the control group (n=15) and the ARDS group (n=56), with screening criteria of |log2Fold Change| \u0026gt;0.5 and corrected p-value \u0026lt;0.05, which were used for subsequent analyses. Heatmaps were generated using the R package \u0026quot;pheatmap,\u0026quot; employing Euclidean distance and hierarchical clustering methods for clustering. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Variation Analysis (GSVA)\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGSVA (Gene Set Variation Analysis) is an unsupervised and non-parametric gene set enrichment method that allows the assessment of associations between biological pathways and gene features using gene expression profiles. To investigate the biological functional differences between the control group and the ARDS group, the \u0026quot;c2.cp.kegg.v7.5.1.symbols\u0026quot; gene set from the MSigDB database (http://software.broadinstitute.org/gsea/msigdb)\u0026nbsp;was used as the reference gene set, and GSVA was performed with the R package \u0026quot;GSVA (version 1.42.0).\u0026quot; The results were visualized using the R package \u0026quot;pheatmap (version 1.0.12).\u0026quot; Additionally, 50 hallmark gene sets (h.all.v7.4.1.symbols) were downloaded from the MSigDB database as reference gene sets, and the GSVA scores for each gene set were calculated across different samples using the ssGSEA function in the GSVA package. The GSVA score differences between the control group and the ARDS group for different gene sets were compared using the Limma package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-expression Network Analysis (WGCNA) and Identification of Significant Modules\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe WGCNA algorithm was implemented using the R package WGCNA (version 1.70-3) to construct a co-expression network\u0026nbsp;(15). The similarity of gene expression profiles was assessed by calculating Pearson correlation coefficients, and the correlation coefficients between genes were weighted using a power function to obtain a scale-free network. The co-expression similarity was raised to a power of \u0026beta; = 10 to establish a weighted adjacency matrix using the R package \u0026quot;PickSoftThreshold.\u0026quot; Gene modules are groups of genes that are densely connected in co-expression. WGCNA identifies gene modules using hierarchical clustering and indicates modules with colors. The dynamic tree cutting method was used to identify different modules, converting the adjacency matrix (a measure of topological similarity) into a topological overlap matrix (TOM) during the module selection process, and modules were detected through clustering analysis. To assess the association between modules and MAM, Pearson correlation analysis was performed to calculate the correlation between module eigengenes (ME, the first principal component of the module representing the overall expression level of the module) and MAM. Modules significantly associated with MAM were obtained. The structure of co-expression modules was visualized through a heatmap of gene network topological overlap. The relationships between modules were summarized through hierarchical clustering trees of eigengenes and corresponding eigengene heatmaps. MAM-related differentially expressed genes (MAM-related DEGs) were obtained from the intersection of DEGs and genes in MAM-related modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO Term Enrichment Analysis\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) analysis is a common method for conducting large-scale functional enrichment studies, including biological processes (BP), molecular functions (MF), and cellular components (CC). The R package \u0026quot;clusterProfiler (version 4.2.2)\u0026quot; was applied for GO annotation analysis of MAM-related differentially expressed genes (p-value \u0026lt;0.05)\u0026nbsp;(16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneMANIA\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GeneMANIA website (http://genemania.org) can predict the relationships between functionally similar genes and hub genes, including protein-protein interactions, protein-DNA interactions, pathways, physiological and biochemical responses, co-expression, and co-localization\u0026nbsp;(17). We constructed a protein-protein interaction (PPI) network of key genes through the GeneMANIA website. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic Curve (ROC)\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe receiver operating characteristic curve (ROC) is an effective method for evaluating the performance of diagnostic tests. The ROC curve reflects the relationship between sensitivity and specificity as continuous variables, illustrating the interplay between sensitivity and specificity through graphical representation. The most common metric is the area under the curve (AUC), obtained from the sensitivity and specificity operational characteristic plot. We used the R package \u0026quot;pROC\u0026quot; to create ROC curves to determine the area under the curve for screening feature genes and assessing their diagnostic value\u0026nbsp;\u0026nbsp;(18). The area value under the ROC curve generally ranges between 0.5 and 1, with an AUC closer to 1 indicating better diagnostic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Infiltration Analysis\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSingle sample gene set enrichment analysis (ssGSEA) is an extension of gene set enrichment analysis (GSEA) that calculates separate enrichment scores for each sample and gene set\u0026nbsp;(19). Each ssGSEA enrichment score indicates the degree of coordinated upregulation and downregulation of genes in a specific gene set within a sample. ssGSEA is a variant of the GSEA algorithm that provides a score for each sample and gene set pair rather than calculating enrichment scores for sample groups (such as control and disease groups) and gene sets (such as pathways). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwenty-eight immune cell types, including Activated CD8 T cell; Central memory CD8 T cell; Effector memory CD8 T cell; Activated CD4 T cell; Central memory CD4 T cell; Effector memory CD4 T cell; T follicular helper cell; Gamma delta T cell; Type 1 T helper cell; Type 17 T helper cell; Type 2 T helper cell; Regulatory T cell; Activated B cell; Immature B cell; Memory B cell; Natural killer cell; CD56bright natural killer cell; CD56dim natural killer cell; Myeloid derived suppressor cell; Natural killer T cell; Activated dendritic cell; Plasmacytoid dendritic cell; Immature dendritic cell; Macrophage; Eosinophil; Mast cell; Monocyte; Neutrophil were downloaded from the Tumor and Immune System Interaction Database (TISIDB) (http://cis.hku.hk/TISIDB/ index.php)\u0026nbsp;(20). We calculated the relative enrichment scores for each immune cell type based on the gene expression profiles of each sample. The R package \u0026quot;ggplot2 (version 3.3.6)\u0026quot; was used to plot the changes in immune cell infiltration levels between ARDS and control group samples\u0026nbsp;(21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRBP-mRNA Network Construction\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study utilized the widely used open-source platform StarBase (https://starbase.sysu. edu.cn/tutorialAPI.php#RBPTarget) to analyze ncRNA interactions, using CLIP-seq, degradome-seq, and RNA-RNA interaction data to investigate the association between mRNA and RBP (RNA-binding protein) expression. In the context of disease, p-value \u0026lt;0.05, clusterNum \u0026ge;5, and clipExpNum \u0026ge;5 were defined as cutoff criteria for identifying key mRNA-RBP pairs. Subsequently, the RBP-mRNA network was constructed using Cytoscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eceRNA Network Construction\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to the unclear mechanism of competing endogenous RNA (ceRNA) in viral lung injury, we used the miRTarBase (https://mirtarbase.cuhk.edu.cn/~miRTarBase/ miRTarBase_2022/php/index.php)\u0026nbsp;(22)\u0026nbsp;, starbase2.0 (https://starbase.sysu.edu.cn/\u0026nbsp;starbase2/index.php)\u0026nbsp;(23), and miRDB (https://mirdb.org/index.html) databases to perform reverse predictions of microRNAs for key genes and to predict LncRNAs for common microRNAs of key genes, ultimately constructing the ceRNA network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTF-mRNA Network Construction \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscription factors regulate the transcription process by recognizing specific DNA sequences, thus participating in various complex biological processes. The hTFtarget database (http://bioinfo.life.hust.edu.cn/hTFtarget#!) has compiled comprehensive TF target regulations from large-scale human transcription factor ChIP-Seq data (7,190 experimental samples of 659 transcription factors), covering 569 conditions (399 cell lines, 129 tissue or cell types, and 141 treatment methods), allowing for the prediction of common transcription factors for multiple genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetected Hub Gene Expression in Vivo and in Vitro \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRespiratory viral infections are common pathogenic factors of acute respiratory distress syndrome (ARDS)\u0026nbsp;(24). After viral infection, the immune response of the body may become imbalanced, leading to abnormal responses of cellular immunity and humoral immunity. This abnormal immune response may exacerbate pulmonary pathological changes, accelerating the progression of ARDS\u0026nbsp;(25). The study chose to construct mouse models of viral acute lung injury and mouse alveolar epithelial cell models to further verify the expression levels of the key genes ZMAT2 and HBB that were screened\u0026nbsp;(26).\u003c/p\u003e\n\u003cp\u003eAnimal models and sample collection\u003c/p\u003e\n\u003cp\u003eWe required all authors to comply with the ARRIVE 2.0 guidelines and regulations. All procedures were performed according to the guidelines of the National Institute of Health for Animal Care. Wild-type C57BL/6J male mice aged from 6-8 weeks and weighing about 25\u0026plusmn;5g were obtained from the Animal Center of Guangxi Medical University (Nanning, China). Virgin (WASHU). These mice were housed in a room fitted with air-filters where they could freely access food and water. The conditions of the room were maintained at a temperature of 20-25 \u0026ordm;C, with 50-70% humidity levels.For the poly(I:C) mouse model, 10 mg of high-molecular-weight poly(I:C) in 10 mL PBS was administered intranasally (i. n.) to C57BL/6 mice with 5 mg/Kg under light anesthesia\u0026nbsp;(27).\u0026nbsp;Pentobarbital sodium (120 mg/kg) was used to euthanize all mice via intraperitoneal injection after administering poly(I:C) or PBS for 48 hours, to facilitate further downstream examinations.\u003c/p\u003e\n\u003cp\u003eCell culture and sample collection\u003c/p\u003e\n\u003cp\u003eMLE-12 cells purchased from ATCC were cultured in RMPI 1640 medium containing 10% fetal bovine serum (FBS) (10091148, Gibco, New Zealand), 20 mM HEPES, and 2 mM L-glutamine.MLE-12 cells were exposed to poly(I:C) (30 \u0026micro;g/mL) stimulation for 48 h with or without interventions\u0026nbsp;(28).\u003c/p\u003e\n\u003cp\u003eCell Death assessment assay\u003c/p\u003e\n\u003cp\u003eSYTOX Green nucleic acid stain only penetrates compromised membranes characteristic of dying cells. Cell death was quantified as a percent of Sytox+ cell nuclei out of total cell nuclei in culture by measuring uptake of the cell impermeable dye Sytox Green (R37109, InvivoGen, USA). Fluorescence was measured using a fluorescence microplate reader, with excitation/emission at 504/523 nm. The release of lactate dehydrogenase (LDH) into the culture medium only occurs upon cell death. LDH activity in supernatants of cells was assessed according to the protocol of the manufacturer (Thermo Fisher Scientific, USA).\u003c/p\u003e\n\u003cp\u003eMeasurement of pulmonary edema, permeability, and cytokines\u003c/p\u003e\n\u003cp\u003eThe right upper lobe with excess water was eliminated using filter paper to ascertain its weight (W). The lung tissues were subjected to a drying process at 60 \u0026deg;C for 48 hours to attain their dry weight (D). The calculation of the W/D ratio was used as a measurement index for pulmonary edema. An evaluation of changes in lung permeability was conducted by assessing total BALF protein using a BCA Protein Assay Kit (23225, Thermo Fisher Scientific, Waltham, MA, USA). Additionally, a hemocytometer was used to count total cell infiltration. Interleukin 1\u0026beta; (IL-1\u0026beta;), tumor necrosis factor ɑ (TNF-ɑ), and IL-18 levels in cell culture supernatant and BALF were measured using enzyme-linked immunosorbent assay kits (CUSABIO, Wuhan, China).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeasurement of mRNA expression\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal mRNA of the cells or lung tissues were extracted using TRIzol reagent (Thermo Fisher Scientific) following the guidelines listed by the manufacturer. The High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, 4368814) was used to prepare the cDNA, which was then quantified using the PowerUp\u0026trade; SYBR\u0026trade; Green Master Mix (Applied Biosystems, A25742). The relative expression levels of mRNA were determined using the 2-△△ct cycle threshold method. The primer sequences of ZMAT2 used were as follows: forward: 5\u0026prime;-TCGGAGTCCAGGAGTGTGAG-3\u0026prime;, reverse: 5\u0026prime;-GCTGCTCAGAGGACATCGTG-3\u0026prime;. The primer sequences of HBB used were as follows: forward: 5\u0026prime;-GTGCACCTGACTCCTGAGGAGA-3\u0026prime;, reverse: 5\u0026prime;-CTTGATACCAACCTGCCCAG-3\u0026prime;. The fold change, adjusted to GAPDH normalization, was utilized to illustrate the variances between groups.\u003c/p\u003e\n\u003cp\u003eHistologic study\u003c/p\u003e\n\u003cp\u003eThe lower lobes of the right lung were preserved using 4% paraformaldehyde (30525-89-4; Sigma-Aldrich, AR, USA), and then encapsulated within the Tissue-Tek OCT compound (4583; Sakura, Tokyo, Japan). The pathological assessment of lung damage was independently evaluated by two authors on sections stained with hematoxylin and eosin, following criteria that had been reported earlier\u0026nbsp;(29).\u003c/p\u003e\n\u003cp\u003eImmunoblotting\u003c/p\u003e\n\u003cp\u003eThe left lower lung lobes were thoroughly mixed into a uniform solution using RIPA lysis buffer (20-188, Sigma-Aldrich, AR, USA). During this process, to prevent protein degradation and dephosphorylation, both a Protease Inhibitor Tablet (product number 11836170001 from Roche, located in Basel, Switzerland) and a PhosphoSTOP Phosphatase Inhibitor Tablet (product number 4906845001, also from Roche in Basel, Switzerland) were added. This homogenization was achieved with the aid of a mechanical tissue homogenizer.The samples underwent lysis for a duration of thirty minutes at an icy temperature, followed by centrifugation at 12,000 g-force for 15 minutes. Following the measurement of protein concentrations by the bicinchoninic acid (BCA) assay, the obtained supernatants from the cell lysates were heated to 85 \u0026deg;C for a duration of 5 minutes with a loading buffer added. Between 50 and 75 micrograms of proteins were subjected to separation through SDS-polyacrylamide gel electrophoresis (PAGE) and subsequently transferred to polyvinylidene fluoride (PVDF) membranes. After blocking a 1-hour incubation period at 22-25℃ with 5% nonfat milk, the membranes underwent an overnight incubation with primary antibodies (Supplemental Table 2) at a temperature of 4 \u0026deg;C. This was followed by a 1-hour incubation at room temperature with secondary antibodies (Abcam, Cambridge, UK) conjugated with horseradish peroxidase. Band intensities corresponding to different proteins were quantified from digitized films through the employment of an Odyssey\u0026reg; CLX imaging system (LI-COR, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R software v4.1.2. Spearman correlation tests were used to infer the correlation between two independent parameters. Wilcoxon tests were used to compare differences between two independent groups, and Kruskal-Wallis tests were used to compare differences among three or more groups. A two-sided p-value of less than 0.05 was considered statistically significant. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDifferentially Expressed Genes Related to ARDS \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy comparing ARDS samples with control groups, a total of 3152 differentially expressed genes (DEGs) were identified, showing significant differences between the two groups (corrected \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05, |Log2 fold change| \u0026gt; 0.5). In ARDS samples, 1549 genes were upregulated, and 1603 genes were downregulated (Supplementary Table 2). All DEGs were shown in a volcano plot (Figure 2A). Additionally, a heatmap displayed the expression of the top-ranked genes (SH3GLB1, MAP2K6, TXN, BCL2A1, ANXA3NOG, FAM102A, TCF7, ABLIM1, DGKA) in the samples (Figure 2B). \u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGSVA \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo look into the functional aspects of ARDS, we performed GSVA analysis to assess the relative expression differences of pathways in the two groups. The GSVA analysis highlighted many pathways that were differentially expressed and visualized them through a heatmap. Compared to the control group, the expression of KEGG_ REGULATION_OF_AUTOPHAGY and KEGG_TAURINE_AND_ HYPOTAURINE_METABOLISM was significantly lower in the ARDS group, while the expression of pathways related to KEGG_PRIMARY_IMMUNODEFICIENCY and KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY was significantly higher (Figure 3, Supplementary Table 3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-expression Network Construction and Module Identification \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied WGCNA to study the gene set associated with MAM. Scale independence and average connectivity analysis indicated that when the minimum soft threshold (soft threshold) \u0026beta; was equal to 10 (Figure 4A), the average connectivity approached 0, and scale independence was greater than 0.85. We identified twelve co-expression modules, and unrelated genes were assigned to the gray module, which we ignored in the next studies (Figure 4B). To study the relationships between modules and see how they relate to each other, we correlated the module eigengenes (MEs). A heatmap was used to plot the correlation of feature gene networks (Figure 4C). A heatmap depicting the topological overlap in the gene network was also created (Figure 4D). To understand what the genes in the modules mean for physiology, we associated the 12 MEs with MAM and sought the most significant associations. Based on the module-trait correlation heatmap (Figure 4E), the genes clustered in the yellow module (n = 302, Supplementary Table 4) exhibited the strongest positive correlation with MAM (r = 0.4067,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Therefore, we primarily focused on the yellow module, as it may serve as a more accurate indicator of MAM. The intersection of DEGs and the genes in the MAM-associated module yielded a total of 82 MAM-related DEGs, which were considered key genes (Supplementary Table 5, Figure 4F). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO Enrichment Analysis \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the biological functions linked to MAM-related differences, we conducted GO term enrichment analysis (Supplementary Table 6). The GO results showed that these genes were found in gas transport, oxygen transport, hydrogen peroxide metabolic process, hemoglobin complex, haptoglobin-hemoglobin complex, cytosolic small ribosomal subunit, haptoglobin binding, oxygen carrier activity, peroxidase activity (Figure 5A-D). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMachine Learning Screening of Hub Genes \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further used LASSO regression, random forest, and SVM algorithms to screen for key genes. Through LASSO regression analysis, we identified 32 key MAM-related DEGs (Figure 6A-B, Supplementary Table 7). Using the random forest algorithm, we selected the top 30 genes as key MAM-related DEGs based on feature importance MDA and MDG (Figure 6C-D, Supplementary Table 8), ultimately obtaining 22 genes. Through the SVM-RFE method, we screened 4 key MAM-related DEGs (Figure 6E, Supplementary Table 9). Finally, the intersection of MAM-related DEGs detected by each method yielded 2 most critical MAM-related DEGs as hub genes for subsequent analysis: ZMAT2, HBB (Figure 6F). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Value of Hub Genes \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the hub genes, we constructed a diagnostic nomogram model using the hub genes (Figure 7A) and evaluated its predictive ability using calibration curves. The calibration curve showed minimal differences between the actual ARDS risk and the predicted ARDS risk, indicating that the ARDS model is highly accurate (Figure 7B). ROC curve analysis also confirmed the correctness of the model (Figure 7C). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe tested the hub gene expression differences between the two groups and found significant differences in hub genes between the two groups, with hub gene ZMAT2 being significantly upregulated in ARDS compared to the control group, while hub gene HBB was significantly downregulated in ARDS compared to the control group (Figure 7D). To examine the correlation between hub genes, we created a correlation heatmap and found that the correlation between ZMAT2 and HBB was not strong (Figure 7E). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further verify the diagnostic value of the hub genes, we used ROC curves to validate the hub genes and found that the area under the ROC curve (AUC) values for ZMAT2 (AUC=0.992) and HBB (AUC=0.868) were both greater than 0.8 (Figure 7F-G), indicating that the hub genes have discriminatory ability as potential biomarkers for ARDS. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction Analysis of Hub Genes \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe created a PPI network for the hub genes using the GeneMANIA database, where the two genes had interaction relationships (Figure 8A). To further study the functional characteristics of the feature genes, we conducted GO and KEGG analyses on a total of 22 genes including 2 hub genes and 20 genes related to hub genes. The GO enrichment results showed that these genes were significantly enriched in positive regulation of response to cytokine stimulus , regulation of epidermal cell differentiation, zymogen activation, cytosolic ribosome, ficolin-1-rich granule lumen, centriole, protein N-terminus binding, ammonium transmembrane transporter activity, heme transmembrane transporter activity, and other pathways (Figure 8B, Supplementary Table 10). The KEGG analysis results showed that the main enriched pathways were African trypanosomiasis and Malaria (Figure 8C, Supplementary Table 11). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle Gene GSEA Enrichment \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the enriched pathways related to the hub genes through single gene GSEA. The results showed that HBB did not return enrichment results, butgenes with expression patterns similar to ZMAT2 were mainly enriched in pathways such as KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY, KEGG_ALZHEIMERS_DISEASE,KEGG_PARKINSONS_DISEASE,KEGG_ NITROGEN_METABOLISM, KEGG_COMPLEMENT_AND_COAGULATION_ CASCADES (Figure 9). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Gene-Related Signaling Pathways \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther GSVA analysis was conducted to study the differences between ARDS patients and control groups across 50 Hallmark signaling pathways. In ARDS patients, 33 Hallmark signaling pathways were significantly upregulated, 10 pathways were significantly downregulated (Figure 10A, Supplementary Table 12). We also analyzed the correlation between the two hub genes and the 50 Hallmark signaling pathways. ZMAT2 was associated with many pathways, including HALLMARK_ADIPOGENESIS and HALLMARK_ALLOGRAFT_REJECTION. HBB was associated with many pathways, including HALLMARK_ADIPOGENESIS and HALLMARK_ALLOGRAFT_REJECTION (Figure 10B). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Infiltration \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cell infiltration may play an important role in the pathogenesis of ARDS. Therefore, we investigated the association between ARDS/control samples and infiltrating immune cells. Among 28 immune cells, 23 immune cells showed significant differences in immune infiltration abundance between the two groups (\u003cem\u003ep\u0026lt;\u003c/em\u003e 0.05) (Figure 11A, Supplementary Table 13). Among them, 14 immune cells (Central memory CD8 T cell, Activated CD4 T cell, Gamma delta T cell, Type 17 T helper cell, Regulatory T cell, Memory B cell, Natural killer cell, Myeloid derived suppressor cell, Activated dendritic cell, Plasmacytoid dendritic cell, Immature dendritic cell, Macrophage, Monocyte, Neutrophil) had significantly higher infiltration levels in the ARDS group compared to the control group (Figure 11A). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 11B, the overall level of immune cell infiltration showed significant differences between the ARDS and control groups. We also examined the significant correlations between the top-ranked hub genes and the corresponding immune cells. ZMAT2 was significantly correlated with Macrophage (R=0.73,\u0026nbsp;\u003cem\u003ep\u0026lt;\u003c/em\u003e0.001) (Figure 11C), ZMAT2 was significantly correlated with Effector memory CD8 T cell (R=-0.688,\u0026nbsp;\u003cem\u003ep\u0026lt;\u003c/em\u003e0.001) (Figure 11D), ZMAT2 was significantly correlated with Neutrophil (R=0.675,\u0026nbsp;\u003cem\u003ep\u0026lt;\u003c/em\u003e0.001) (Figure 11E), ZMAT2 was significantly correlated with Gamma delta T cell (R=0.625,\u0026nbsp;\u003cem\u003ep\u0026lt;\u003c/em\u003e0.001) (Figure 11F), and ZMAT2 was significantly correlated with Activated dendritic cell (R=0.615,\u0026nbsp;\u003cem\u003ep\u0026lt;\u003c/em\u003e0.001) (Figure 11G). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHub mRNA Interaction Network \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the interaction of RBPs (RNA binding proteins) with mRNA, we utilized the StarBase online database to search for the two hub mRNAs and downloaded the corresponding mRNA/RBP pairs for ZMAT2. Based on the relationships provided by the online dataset between target genes, we constructed an RBP-mRNA network consisting of 26 nodes, 25 RBPs, 1 mRNA, and 25 edges (Supplementary Table 14, Figure 12A). To understand the potential molecular mechanisms of hub genes in ARDS, we subsequently constructed an mRNA-TF interaction network. The network diagram constructed using Cytoscape included 1 mRNA and 19 transcription factors (TF) (Figure 12B). To understand the potential molecular mechanisms of hub genes in ARDS, we subsequently constructed an mRNA-miRNA interaction network. The network diagram constructed using Cytoscape included 1 mRNA and 24 miRNAs, totaling 25 nodes and 24 edges (Figure 12C). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene HBB did not return available results. \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug Prediction \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDGldb was used to identify potential sensitive drugs or molecular compounds. We predicted drugs for the two hub genes using DGldb and found that hub gene HBB was detected to have correlations with drugs, while gene ZMAT2 did not return available results. As shown in the drug-gene interaction network (Figure 13A), 29 drugs or molecular compounds had varying degrees of regulatory effects on hub gene HBB (Supplementary Table 15). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe performed molecular docking of hub gene HBB with drugs AES-103 and AZATHIOPRINE, and the molecular docking results are shown in Table 1. The binding energies of AZATHIOPRINE and AES-103 with hub gene HBB were both negative, indicating that the components of AZATHIOPRINE and AES-103 bind well to hub gene HBB. AZATHIOPRINE and AES-103 may exert therapeutic effects by binding to the key target gene protein HBB, and the results of molecular docking were visualized (Figure 13B-C). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 Basic information on molecular docking of drugs and target proteins \u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eMolecular name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003eTargets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003eProtein Accession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;Binding energy(kcal/Mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eAZATHIOPRINE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eHBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eP68871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-4.953\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eAES-103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003eHBB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eP68871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-5.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eHub gene expression in Vivo and in Vitro\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the potential of hub genes in clinical translation, this study employed Poly I:C to simulate viral infection in C57BL/6 mice, successfully constructing an ARDS animal model closely related to viral infection. Based on the successfully established virus infection-induced ARDS animal and cell models, the expression differences of the hub genes HBB and ZMAT2 between the ARDS group and the control group were further investigated.\u003c/p\u003e\n\u003cp\u003eThrough HE staining technique, we visually observed that Poly I:C treatment significantly induced damage in the lungs of mice (Figure14 A-B). Notably, the wet-to-dry weight ratio (W/D ratio) of lung tissue, as a key indicator for assessing the degree of lung tissue edema, plays a crucial role in revealing the severity of lung diseases and their pathological mechanisms. In comparative experiments, we found that the W/D ratio of lung tissue in the Poly I:C treatment group significantly increased (Figure14 C). Further analysis showed that in the bronchoalveolar lavage fluid (BALF) of mice treated with Poly I:C, cell counts, total protein content, and levels of inflammatory factors IL-1\u0026beta; and TNF-\u0026alpha; were significantly elevated (Figure14 C), collectively confirming the successful construction of the virus infection-related ARDS model and revealing the intensity of the inflammatory response in the model. Additionally, to verify the impact of viral infection at the cellular level, we also treated MLE-12 cells with Poly I:C to construct the corresponding cell model. Through Sytox Green fluorescence and LDH detection, we found that Poly I:C treatment significantly promoted the death of MLE-12 cells and increased LDH release (Figure14 D-F), further confirming the direct damaging effect of viral infection on lung cells.\u003c/p\u003e\n\u003cp\u003eThe research results showed that compared to the control group, the mRNA and protein expression levels of the HBB gene were significantly downregulated in the ARDS group, while the mRNA and protein expression levels of the ZMAT2 gene showed a clear upward trend (Figures15 A-E). Correlation analysis between IHC score and lung injury score indicated that the expression level of HBB protein was negatively correlated with the lung injury score, while the expression level of ZMAT2 protein was positively correlated with the lung injury score (Figure15 F). These findings, combined with the bulk RNA-seq results, consistently suggest that HBB and ZMAT2 could be potential treatment targets for ARDS.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAcute respiratory distress syndrome (ARDS) is a serious condition marked by severe lung inflammation, damage to the alveolar-capillary barrier, and respiratory failure (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Despite improvements in critical care management, such as lung-protective mechanical ventilation and fluid management, ARDS is still linked to high rates of illness and death (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The varied pathophysiology of ARDS involves immune dysregulation, oxidative stress, and mitochondrial dysfunction (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This complexity requires further research to discover new biomarkers and treatment targets.\u003c/p\u003e \u003cp\u003eIn this study, we employed a bioinformatics-based approach to investigate the role of mitochondria-associated endoplasmic reticulum membranes (MAMs) and their related genes in ARDS. MAMs play a crucial role in cellular homeostasis by regulating calcium signaling, lipid metabolism, and oxidative stress responses (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Considering the role of mitochondrial dysfunction in ARDS, we hypothesized that MAMs-associated genes could influence disease progression by affecting inflammatory responses and oxidative stress. Our analysis identified HBB and ZMAT2 as key MAMs-related hub genes that may serve as potential biomarkers and therapeutic targets for ARDS.\u003c/p\u003e \u003cp\u003eDifferential expression analysis revealed 3,152 genes that were significantly dysregulated in ARDS samples compared to controls, with 1,549 genes upregulated and 1,603 genes downregulated. This finding emphasizes the significant molecular changes involved in ARDS pathogenesis. GO and KEGG pathway enrichment analyses provided insights into the functional roles of MAMs-related differentially expressed genes (DEGs) in ARDS. Notably, GO enrichment analysis revealed significant involvement of gas transport, oxygen transport, and hydrogen peroxide metabolic processes, suggesting that MAMs dysfunction may impair oxidative homeostasis in ARDS. KEGG pathway analysis demonstrated significant downregulation of the regulation of autophagy and the metabolism of taurine and hypotaurine in ARDS patients, indicating that defective autophagy and dysregulated cellular metabolism may contribute to disease severity. The suppression of autophagy-related pathways is particularly significant, as autophagy plays a crucial role in clearing damaged organelles and mitigating oxidative stress. Disruption of this process may lead to excessive cell death and inflammation, exacerbating pulmonary injury in ARDS. Conversely, pathways associated with primary immunodeficiency and the T cell receptor signaling pathway were significantly upregulated, indicating a dysregulated immune response in ARDS. These findings suggest that MAMs dysfunction may serve as a key mediator linking mitochondrial stress with immune activation, thereby contributing to the exaggerated inflammatory response observed in ARDS.\u003c/p\u003e \u003cp\u003eImmune dysregulation is a key feature of ARDS pathology. Our analysis of immune infiltration showed that 14 immune cell types were significantly increased in ARDS patients. The most notable increases were seen in activated CD4 T cells, regulatory T cells, macrophages, neutrophils, and activated dendritic cells. These findings are consistent with previous reports highlighting the roles of both innate and adaptive immunity in the development of ARDS (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Notably, the expression of ZMAT2 was strongly correlated with macrophages, neutrophils, and activated dendritic cells. This correlation suggests that ZMAT2 may play a role in ARDS pathogenesis by promoting immune cell activation and cytokine release. Macrophage-driven cytokine storms and neutrophil-mediated oxidative damage are well-established contributors to lung injury in ARDS, further supporting the role of ZMAT2 as a pro-inflammatory regulator in this condition.\u003c/p\u003e \u003cp\u003eAmong the identified hub genes, HBB was significantly downregulated, while ZMAT2 was upregulated in ARDS samples, suggesting distinct yet complementary roles in disease progression. The downregulation of HBB in ARDS is of particular significance given its oxygen transport, antioxidant, and immunomodulatory properties. GO enrichment analysis revealed that HBB is involved in heme transmembrane transporter activity, peroxidase activity, and oxygen carrier activity, all of which are essential for maintaining pulmonary oxygenation and mitigating oxidative damage. Reduced HBB expression may contribute to worsening hypoxemia, impaired erythrocyte function, and increased susceptibility to oxidative stress, ultimately exacerbating pulmonary injury (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). ZMAT2 was significantly upregulated in ARDS and was closely linked to pathways involved in natural killer cell-mediated cytotoxicity, complement and coagulation cascades, and neurodegenerative diseases. These findings suggest that ZMAT2 may enhance immune activation, promote coagulation dysfunction, and contribute to endothelial damage in ARDS. Moreover, the enrichment of ZMAT2 in pathways related to neurodegenerative diseases suggests a potential role in endoplasmic reticulum stress and mitochondrial dysfunction, further exacerbating pulmonary injury (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur drug prediction analysis identified HBB as a potential therapeutic target, with AZATHIOPRINE and AES-103 showing strong binding affinities to the HBB protein. Given that AZATHIOPRINE is an immunosuppressant, it may hold promise for mitigating excessive immune activation in ARDS. Further preclinical and clinical investigations are warranted to explore the therapeutic potential of targeting HBB and ZMAT2 in ARDS management.\u003c/p\u003e \u003cp\u003eDespite the encouraging results of this study, there are several limitations that must be addressed. First, the sample size is relatively small. Therefore, it is necessary to conduct external validation using independent cohorts to confirm the robustness of the identified biomarkers. Second, bioinformatics analyses provided valuable insights; however, functional validation using in vivo and in vitro models is essential to establish the causal relationships between HBB, ZMAT2, and the development of ARDS. Finally, clinical trials are needed to evaluate how pharmacological modulation of these genes can translate our findings into potential therapeutic strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies HBB and ZMAT2 as key MAM-related hub genes involved in the pathophysiology of ARDS. It highlights their roles in immune activation, oxidative stress, and mitochondrial dysfunction. These findings provide novel insights into the molecular mechanisms underlying ARDS and suggest potential biomarkers for early diagnosis and therapeutic intervention. Future studies should focus on functional validation and clinical translation to assess the therapeutic potential of targeting MAM-associated pathways in ARDS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study acknowledges the valuable contribution of the GEO database, which provided a rich dataset for our analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBijun Luo and Jifeng Feng designed all the investigation. Bijun Luo, Yanqiong Zhou, Qiuying Chen performed the major experiments and drafted the manuscript. Hui Huang, Xiaoxia Wang, and Kaimin Lv contributed the data analysis and contributed visualization. Bijun Luo designed the overall study and determined the final version. All the authors have approved the manuscript and agreed submission to the esteemed journal. There are no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 82060024), \u0026quot;139\u0026quot; Plan for High-level Medical Backbone Talents of Guangxi Zhuang Autonomous Region (No. G202002015), and Guangxi Medical and Health Appropriate Technology Developmental and Popularizational Application Project (No. S2024084).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed are publicly available in Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih. gov/geo/. Mitochondria-associated membranes (MAM) related genes were obtained from the article studied by Yi Luan, Guangyu Guo, et al. (13), (PMID:37221368, DOI:10.1038/s41598-023-35464-2). No datasets were generated during the current study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimal studies were reviewed and approved by the Medicine Animal Care and Use Committee of Guangxi Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\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\n\u003cli\u003eC. Gibbons, Acute respiratory distress syndrome. \u003cem\u003eRadiol. 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Rotwein, The zmat2 gene in non-mammalian vertebrates: organizational simplicity within a divergent locus in fish. \u003cem\u003ePlos One\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, e233081 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ARDS, Viral Pneumonia, Mitochondria-associated Membrane, Bioinformatics, Therapeutic Targets","lastPublishedDoi":"10.21203/rs.3.rs-6113941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6113941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition characterized by severe inflammation, immune dysregulation, and oxidative stress, leading to high mortality (30–40%). Mitochondria-associated membranes (MAMs) regulate cellular metabolism and immune signaling, but their role in ARDS remains unclear. This study explores the involvement of MAM-related genes in ARDS pathogenesis through bioinformatics and experimental validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Publicly available RNA-sequencing data from ARDS and control samples were analyzed to identify differentially expressed genes (DEGs). Functional enrichment, gene set variation analysis (GSVA), and weighted gene co-expression network analysis (WGCNA) were performed to explore pathway alterations and hub gene interactions. Immune cell infiltration analysis was conducted using CIBERSORT. Candidate MAM-related genes were validated in a Poly I:C-induced ARDS mouse model and MLE-12 murine lung epithelial cells. The mouse model was assessed for lung histopathology, wet-to-dry lung weight ratio, bronchoalveolar lavage fluid (BALF) inflammatory cytokine levels (IL-1β and TNF-α), and lung injury scores. MLE-12 cells were treated with Poly I:C, and cell viability, lactate dehydrogenase (LDH) release, and apoptosis were evaluated. Protein-protein interaction (PPI) network analysis and drug prediction were used to identify potential therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 3152 DEGs including 1549 upregulated and 1603 downregulated were identified in ARDS samples. Pathway analysis revealed autophagy suppression and immune activation, with 14 immune cell types significantly elevated in ARDS patients. Experimental validation confirmed that Poly I:C-induced ARDS mice exhibited severe lung injury and increased inflammatory reaction, while Poly I:C-treated MLE-12 cells showed increased cytotoxicity and LDH release. HBB and ZMAT2 were identified as key MAM-related hub genes, with HBB negatively correlating with lung injury severity and ZMAT2 positively associated with disease progression. Drug prediction analysis identified 29 pharmacological agents interacting with HBB, suggesting therapeutic potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study identifies HBB and ZMAT2 as key MAM-related genes contributing to ARDS pathogenesis, with potential diagnostic and therapeutic applications. The integration of bioinformatics with in vivo and in vitro validation provides novel insights into ARDS molecular mechanisms. Further clinical studies are needed to explore their translational relevance.\u003c/p\u003e","manuscriptTitle":"Function and Mechanism of Mitochondrial-Associated Membranes in Acute Respiratory Distress Syndrsome: A Comprehensive Study Combining Bioinformatics and Experimental Approaches","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 12:54:12","doi":"10.21203/rs.3.rs-6113941/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T11:11:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-08T05:09:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162029248627649478537446465466583188782","date":"2025-04-28T01:03:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-29T20:19:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6167981681721268421100121707053031406","date":"2025-03-28T15:35:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-26T11:38:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-17T14:37:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-12T06:51:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-12T06:48:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-02-26T14:05:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a70a839f-6747-477a-9354-eadbf82303d1","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45561845,"name":"Health sciences/Diseases/Respiratory tract diseases/Chronic obstructive pulmonary disease"},{"id":45561846,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":45561847,"name":"Biological sciences/Drug discovery"},{"id":45561848,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-07-14T16:09:32+00:00","versionOfRecord":{"articleIdentity":"rs-6113941","link":"https://doi.org/10.1038/s41598-025-10405-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-09 15:57:44","publishedOnDateReadable":"July 9th, 2025"},"versionCreatedAt":"2025-03-17 12:54:12","video":"","vorDoi":"10.1038/s41598-025-10405-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10405-3","workflowStages":[]},"version":"v1","identity":"rs-6113941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6113941","identity":"rs-6113941","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}