ITGB2 and ICAM3 predict increased survival of sepsis with decreased intercellular communication in Cytotoxic CD8+ T- cells

ITGB2 and ICAM3 predict increased survival of sepsis with decreased intercellular communication in Cytotoxic CD8+ T- cells | 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 ITGB2 and ICAM3 predict increased survival of sepsis with decreased intercellular communication in Cytotoxic CD8+ T- cells Min Lei, Yaping Zhang, Yijin Yu, Gaojian Wang, Nianqiang Hu, Junran Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4802382/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Sepsis is closely linked to immunity. Our research aimed to identify key genes associated with sepsis immunity utilizing single-cell RNA sequencing (scRNA-seq) data. This study obtained the GSE167363 and GSE54514 datasets from the Gene Expression Omnibus (GEO). The GSE167363 dataset was subjected to cluster analysis, cell proportion analysis, cell interaction analysis, and gene set enrichment analysis (GSEA). The differentially expressed genes (DEGs) of CD8 + T cells were correlated with the DEGs in the GSE54514 dataset, and key genes related to immunity in sepsis patients were identified through Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, we validated the gene expression levels in a mouse model of sepsis caused by cecum ligation and puncture (CLP).Findings indicated that Intercellular communication of Cytotoxic CD8 + T cells was reduced in the sepsis survivors compared to non-survivors. The expression of 3 down-regulated key DEGs (ITGB2, SELL and ICAM3) was negatively correlated with the abundance of CD8 + T cells. Moreover, Cytotoxic CD8 + T cells with low expression of ITGB2, SELL and ICAM3 were more adverse to the survival of sepsis as compared to those with high expression of the above genes. These genes may predict increased survival in sepsis by regulating intercellular communication in cytotoxic CD8 + T cells, suggesting that they are potential therapeutic targets for improving sepsis prognosis. Biological sciences/Immunology Biological sciences/Molecular biology Health sciences/Medical research Health sciences/Pathogenesis Sepsis immunity Cytotoxic CD8 + T cells ITGB2 ICAM3 scRNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. INTRODUCTION Sepsis is a critical condition characterized by organ dysfunction resulting from an imbalanced host inflammatory response to infection 1 , which can involve multiple organs, leading to organ damage or failure. The prevalence of sepsis, a critical illness, is a substantial threat to human health. According to epidemiological reports, sepsis has been recognized as a global health burden since 2017 due to its high morbidity and mortality. To date, approximately 20% of annual deaths globally are associated with sepsis, which greatly affects quality of life 2 . Since the pathogenesis of sepsis is still unclear, an increasing number of studies have begun to explore the key genes involved in sepsis pathogenesis by using second-generation sequencing technology, providing potential possibilities for the treatment of sepsis and improving the prognosis of sepsis patients 3 . For example, transcriptome sequencing has been used to identify pivotal genes in adult patients with sepsis 4, and single-cell RNA sequencing (scRNA-seq) has been used to characterize the status of various immune cells during the development of sepsis 5 . Undoubtedly, the rapid development of bioinformatics has greatly promoted the process of exploring the pathogenesis of sepsis. In recent years, studies have shown that the immune response is crucial for the development of sepsis 6 . With in-depth research on immunity, immunotherapy has also been proven to be an effective method for treating sepsis 7 . Therefore, exploring the role of immune cells in sepsis has become a hot research topic. Studies have shown that autophagy can induce neutrophils to form neutrophil extracellular traps during sepsis, and increased neutrophil autophagy can improve the survival rate of patients with sepsis 8,9 . Moreover, studies have confirmed that Sestrin2 has the potential to improve the prognosis of sepsis patients by inhibiting the pyroapoptosis of dendritic cells 10 . Therefore, this study aimed to identify key genes related to immunity in sepsis patients from the perspective of immunity and to verify the causal associations among these key genes and sepsis through MR analysis with the aid of bioinformatics. Qiu X et al. extracted peripheral blood mononuclear cells (PBMCs) from healthy controls (HCs) and survivors and nonsurvivors of sepsis at 0 h and 6 h for single-cell RNA sequencing (scRNA-seq) (GSE167363) to explore the dynamic changes in human single-cell transcription characteristics during sepsis 11 . As a result, PBMCs have been proven to play key roles in the immune response to infection and have been widely used in the scRNA-seq of sepsis 12 . Parnell et al. extracted whole blood from HCs and sepsis survivors and nonsurvivors for RNA-seq (GSE54514) and identified key regulatory genes in the whole blood of patients with sepsis to monitor potential immune dysfunction 13 . Our study used 6 samples from the GSE167363 dataset, including 2 HCs, 2 survivors (0 h, 6 h), and 2 nonsurvivors of sepsis (0 h, 6 h). Bioinformatics analysis was performed on 163 samples from 18 HCs, 26 sepsis survivors, and 9 sepsis nonsurvivors in the GSE54514 dataset. Key genes related to immunity in sepsis patients were identified through cell proportion analysis, cell interaction analysis, gene set enrichment analysis (GSEA), immune score analysis, etc. Next, we conducted MR analysis to confirm the causal associations between the key genes and the risk of sepsis. Finally, a mouse model of cecal ligation and puncture (CLP) sepsis was constructed to detect the expression levels of these key genes through qRT‒PCR. 2. MATERIALS AND METHODS 2.1 Acquisition of scRNA-seq data The human scRNA-seq dataset GSE167363 was downloaded from the GEO database. Six scRNA-seq datasets obtained from human PBMCs were selected, including 2 healthy controls, 2 survivors (0 h and 6 h) and 2 nonsurvivors of sepsis (0 h and 6 h). The microarray dataset GSE54514 was downloaded, and 163 whole-blood RNA expression datasets were selected from 18 HCs and 26 sepsis survivors and 9 sepsis nonsurvivors. 2.2 Analyzing the scRNA-seq data We used the Seurat 3.2.2 package to analyze the GSE167363 dataset. First, the quality control of the cells was carried out based on the following criteria: 1) we excluded genes detected in < 5 cells; 2) we eliminated cells that had fewer than 200 genes; and 3) we removed cells with mitochondrial gene expression ≥ 10%. Subsequently, the gene expression of the remaining cells was standardized using a linear regression model. The samples were subjected to batch effect correction using the IntegrateData method of the Seurat package, and 6 samples were integrated. A total of 23,881 cells and 20,078 genes were included in the processed data. Principal component analysis (PCA) was employed to identify the dimensions that were statistically significant, with a P value less than 0.05. After that, the t-distributed stochastic neighbor embedding (t-SNE) algorithm was utilized to reduce the dimensionality of 30 initial principal components (PCs) while performing cluster classification analysis. Finally, the cell clusters were classified and annotated according to the marker genes of the cells 14 . 2.2.1 Cell proportion analysis We compared the proportions of different cells among HCs, sepsis survivors and nonsurvivors. 2.2.2 Analysis of intercellular interactions CellChat was used to examine cellular interactions among HCs and among sepsis survivors and nonsurvivors. The differences between survivors and nonsurvivors of sepsis were compared. 2.2.3 GSEA The Fgsea package was used for GSEA of cytotoxic CD8 + T cells in HCs, sepsis survivors and nonsurvivors, and C2 in the Msigdb was selected as the database. 2.2.4 Analysis of DEGs in Cytotoxic CD8 + T cells Using the Seurat FindMarkers function, the DEGs of cytotoxic CD8 + T cells were compared between the sepsis survivors and nonsurvivors, with the parameters logfc.threshold = 0.25 and min.pct = 0.2. 2.2.5 Analysis of DEGs in the GSE54514 dataset Limma was used to analyze the DEGs between sepsis survivors and nonsurvivors in the GSE54514 dataset, with | logFC | > 0.5 and adj. P < 0.05 was used as a parameter. 2.2.6 Immune cell score Gene set variation analysis (GSVA) was used to quantify the activation of the pathway by applying a single-sample gene set enrichment analysis (ssGSEA) score on a set of genes contained in the pathway. 2.2.7 Identification of key genes The upregulated and downregulated genes from the GSE54514 dataset and the number of cytotoxic CD8 + T cells in the GSE167363 dataset were intersected, and clusterProfiler was used for KEGG enrichment analysis of these DEGs. 2.3 Mouse model of CLP-induced sepsis Male C57BL/6 mice, aged 3–4 weeks, were routinely fed and kept at 22°C for 12 h with light and dark cycles. Mice with sepsis were subjected to CLP 1 week after adaptation. After abdominal anesthesia, CLP was performed on all the mice, and the abdominal cavity was closed before subcutaneous injection of normal saline (37°C, 50 ml/kg) for resuscitation. The mice used in this study were obtained from the Laboratory Animal Center of Zhejiang (Hangzhou,China). We used carbon dioxide (CO2) inhalation equipment to carry out euthanasia. We placed the mice in a chamber during euthanasia, gradually supplying CO2 to raise its concentration. The euthanasia personnel were required to observe the procedure and pause for a minimum of one minute once they detected no movement, visible breathing, or heartbeat. The trials received approval from the Institutional Animal Care and Use Committee, ZJCLA (No. ZJCLA-IACUC-20010069). Animal experiments were complied with the ARRIVE guidelines and all methods were performed in accordance with the relevant guidelines and regulations. 2.3.1 Determination of the mold results Twenty-four hours after CLP surgery, we collected lung and kidney tissue from the mice. The tissue samples were preserved in a solution containing 4% paraformaldehyde, embedded in paraffin, and finally sliced into 5-micrometer-thick sections. The sections were stained with H&E to examine morphological damage under a microscope. 2.3.2 Quantitative real-time polymerase chain reaction (qRT‒PCR) Twenty-four hours after CLP surgery, the mice were categorized into two different groups based on their mortality: the survivor group and the nonsurvivor group. Blood samples were collected from all mice, and PBMCs were extracted using Ficoll solution (Solarbio Life Sciences, Beijing, China). Then, RNA was extracted from the PBMCs via the TRIzol method for qRT‒PCR. Supplementary Table S1 shows the primers used. 3. RESULTS 3.1 Single-cell transcriptome profiling of PBMCs Generally, the scRNA-seq data for PBMCs were acquired from the GSE167363 microarray in the GEO database. A total of 13566, 4573 and 5547 cells were obtained from healthy controls and survivors and nonsurvivors of sepsis, respectively, for scRNA analysis. Then, the t-SNE algorithm was used to reduce the dimensionality of the 30 initial PCs, and cluster analysis was performed on all cells (Fig. 1 A-B). Based on our PCA and t-SNE results, PBMCs were classified into 17 clusters and annotated as dendritic cells, B cells, activated CD4 + T cells, natural killer (NK) cells, naive T cells, cytotoxic CD8 + T cells, platelet cells, or monocytes via marker genes 14 (Fig. 1 C). 3.1.1 Cell proportion analysis Figure 2 A shows the percentage of cells expressing characteristic markers and their scaled relative expression values in distinct cell clusters. The results were as follows: ( 1 ) monocytes characterized by high expression of S100A4 and CST3; ( 2 ) platelet cells specifically expressing the cell marker PPBP; ( 3 ) cytotoxic CD8 + T cells highly expressing CD8A; ( 4 ) naive T cells with high expression of IL7R; ( 5 ) NK cells specifically expressing the markers GNLY, NKG7, GZMA, and KLRF1; ( 6 ) activated CD4 + T cells expressing CD8B; ( 7 ) B cells specifically expressing the cell marker MS4A1; and ( 8 ) dendritic cells with high expression of LYZ. The proportions of cell subsets were significantly different among the healthy controls and survivors and nonsurvivors of sepsis (Figure_2B), indicating heterogeneity and consistency among these sepsis samples. 3.1.2 Cell‒cell communication assessment in patients with sepsis To characterize the intercellular communication networks of the normal controls and the survivors and nonsurvivors of sepsis, a dataset of human ligand‒receptor pairs was used to construct a dense intercellular communication network (Fig. 3 A-C). Our results highlighted that most cell‒cell interactions were between cytotoxic CD8 + T cells and monocytes in the healthy samples. A comparison investigation was performed to examine the intercellular interactions between the survivors and nonsurvivors of sepsis (Fig. 3 D). The red edge indicates much stronger intercellular interactions in the nonsurvivors of sepsis, while the blue edge indicates stronger cell‒cell communication in the survivors of sepsis. Figure 4 illustrates the ligand‒receptor or signaling pathways mediating intercellular communication in the normal controls and in the survivors and nonsurvivors of sepsis. These results showed that intercellular communication among cytotoxic CD8 + T cells was reduced in sepsis survivors compared to sepsis nonsurvivors. 3.1.3 Enrichment of pathways in cytotoxic CD8 + T cells To observe the effect of cytotoxic CD8 + T cells on sepsis, we used GSEA to analyze the enrichment of the KEGG pathways in the survivors and nonsurvivors of sepsis. The results showed that the ribosome, spliceosome, and RNA degradation pathways were highly enriched in cytotoxic CD8 + T cells from the survivors of sepsis. Notably, the ribosome, NK cell-mediated cytotoxicity, graft versus host disease and antigen processing and presentation pathways were highly enriched in cytotoxic CD8 + T cells in the nonsurvivors of sepsis (Fig. 5 ). 3.1.4 DEGs in the GSE167363 and GSE54514 datasets To further clarify the molecular mechanism by which cytotoxic CD8 + T cells affect the survival of patients with sepsis, we identified DEGs in the number of cytotoxic CD8 + T cells between the survivors and nonsurvivors of sepsis by FindMarker (min.pct = 0.2, logfc.threshold = 0.25), and a total of 582 DEGs were identified (Fig. 6 A). Moreover, DEGs between sepsis survivors and nonsurvivors in the GSE54514 dataset were analyzed with the limma package. With the criteria of |logFC| > 0.5 and adj.P. At Val < 0.05, 210 DEGs were obtained (110 upregulated DEGs and 100 downregulated DEGs) (Fig. 6 B). DEGs that were simultaneously upregulated or downregulated in both cytotoxic CD8 + T cells and the GSE54514 dataset are shown in the Venn diagram. Finally, 12 DEGs were obtained (3 upregulated DEGs and 9 downregulated DEGs) (Fig. 6 C-D). 3.1.5 KEGG enrichment analysis Then, these 12 overlapping DEGs were subjected to KEGG enrichment analysis. The pathway that was shown to be significantly enriched was cell adhesion molecules. Among the 12 overlapping DEGs, ITGB2, SELL and ICAM3 were enriched in the pathway of cell adhesion molecules (Fig. 7 A, Table 1 ), with downregulated expression in the nonsurvivors (Fig. 7 B). Table 1 Top 10 KEGG pathway analysis of the 12 overlapped DEGs. Pathway ID Gene count P -value Genes Cell adhesion molecules hsa04514 3 0.000475 ITGB2 SELL ICAM3 Adherens junction hsa04520 2 0.00263 WAS WASF2 Fc gamma R-mediated phagocytosis hsa04666 2 0.004854 WAS WASF2 Choline metabolish in cancer hsa05231 2 0.004952 WAS WASF2 Yersinia infection hsa05135 2 0.00949 WAS WASF2 Chemokine signaling pathway hsa04062 2 0.018092 PPBP WAS Rap1 signaling pathway hsa04015 2 0.021428 APBB1IP ITGB2 Regulation of action cytoskeleton hsa04810 2 0.022989 ITGB2 WASF2 Shigellosis hsa05131 2 0.029036 RBX1 WASF2 Circadian rhythm hsa04710 1 0.033996 RBX1 3.1.6 The correlation of the expression of ITGB2, SELL and ICAM3 with immune infiltration Twenty-nine immune-associated gene sets representing distinct types, functions, and pathways of immune cells were analyzed by ssGSEA 15 , and the Wilcoxon test was used to assess the disparity between sepsis survivors and nonsurvivors (Fig. 8 A). Then, we examined the relationships between the expression levels of ITGB2, SELL, and ICAM3 and the level of infiltration by CD8 + T cells. The abundance of CD8 + T cells was negatively correlated with the expression of ITGB2, SELL, and ICAM3 (Fig. 8 B-D). 3.1.7 ITGB2, SELL and ICAM3 were upregulated in cytotoxic CD8 + T cells from survivors of sepsis To elucidate the roles and importance of ITGB2, SELL and ICAM3, we determined the expression of ITGB2, SELL and ICAM3 in cytotoxic CD8 + T cells from survivors and nonsurvivors of sepsis. As shown in Fig. 9 , downregulated ITGB2, SELL and ICAM3 were significantly correlated with sepsis survivors and nonsurvivors. Combined with the above findings, these results suggested that cytotoxic CD8 + T cells with low expression of ITGB2, SELL and ICAM3 were more likely to have adverse effects on the survival of patients with sepsis than were those with high expression of the above genes. 3.2 Mouse model of CLP-induced sepsis 3.2.1 Determination of the mouse model of CLP-induced sepsis To verify the above results, a mouse model of CLP-induced sepsis was constructed. Twenty-four hours after CLP surgery, lung and kidney tissues were collected for H&E staining. Figure 10 A shows that the lungs of CLP mice were infiltrated with an enormous amount of inflammatory cells, and some alveolar cavities were fused into pulmonary bullae with interstitial hyperemia and edema. Figure 10 B shows a high level of leukocyte infiltration in the renal interstitium of CLP mice and hyperemia of the glomerular capillaries. These results suggested that the mouse model of CLP-induced sepsis was successfully constructed. 3.2.2 The mRNA expression of ITGB2, SELL and ICAM3 in the CLP mouse model After the mouse model of CLP-induced sepsis was successfully generated, the PBMCs of all 22 model mice were obtained, including 15 from the group of survivors and 7 from the group of nonsurvivors. Then, we detected the expression levels of ITGB2, SELL, and ICAM3 by qRT-PCR. Figure 11 shows that, in comparison to those in survivors, the expression levels of ITGB2 and ICAM3 were substantially lower in nonsurvivors, while there was no significant difference in the expression of SELL between the two groups. 4. DISCUSSION Sepsis is a systemic inflammatory syndrome caused by infectious diseases and is closely associated with the pathophysiological changes that occur in various systems and organs 16 . Although notable advancements in the fields of anti-infective therapy and organ function support technology for the treatment of sepsis have recently been achieved, the morbidity and mortality of sepsis are still high, seriously threatening people’s health and lives 17 . Therefore, finding new therapeutic targets for sepsis and improving the prognosis of sepsis patients are the focus of the current research. Multiple aspects contribute to the pathogenesis of sepsis, but its fundamental pathogenesis remains unclear 18 . Previous studies have shown that sepsis is associated with severe and sustained immunosuppression, immune dysfunction is considered to be the core mechanism of sepsis, and immune response disorders are susceptibility factors for secondary infection and increased mortality 19 . Immune cells, including neutrophils, monocytes, and lymphocytes, are the main players in the immune response 20 . PBMCs, including lymphocytes and monocytes, are commonly used for scRNA-seq to explore the functional characteristics of immune cells during the progression of sepsis 21 . Therefore, in this study, scRNA-seq data from human PBMCs were used for cluster analysis, and the results were annotated as dendritic cells, B cells, activated CD4 + T cells, NK cells, naive T cells, cytotoxic CD8 + T cells, platelet cells, and monocytes. Subsequently, we performed a cell proportion analysis, and significant changes were revealed in the proportions of immune cells among normal controls, as well as survivors and nonsurvivors of sepsis, which further confirmed the crucial role of immune cells in sepsis pathogenesis. On this basis, we constructed an intercellular communication network to investigate the correlations among these immune cells. The intercellular communication of cytotoxic CD8 + T cells was more significant in sepsis nonsurvivors than in sepsis survivors, which implies that enhanced cytotoxic CD8 + T-cell communication may be significantly associated with reduced survival in sepsis patients. As a type of specific T cell, CD8 + T cells can secrete a variety of cytokines to participate in the immune response and resist the invasion of some foreign pathogens 22 . Some studies have shown that increased interaction between platelets and CD8 + T cells is strongly linked to an unfavorable prognosis in sepsis patients 23 . However, studies on the mechanism of action of cytotoxic CD8 + T cells in sepsis and the correlation of cytotoxic CD8 + T cells with the prognosis of sepsis patients are lacking. In this study, we suggested that the intercellular interactions of cytotoxic CD8 + T cells are closely associated with the prognosis of sepsis patients. Subsequently, cytotoxic CD8 + T cells were chosen for further analysis to investigate the key genes associated with cytotoxic CD8 + T cells in sepsis. Subsequently, GSEA was performed on the cytotoxic CD8 + T cells from both the sepsis survivors and nonsurvivors. The enrichment functions and pathways were revealed to be inconsistent between these two groups, which also confirmed the heterogeneity of cytotoxic CD8 + T cells in sepsis patients with different prognoses. Next, the DEGs of cytotoxic CD8 + T cells were screened from both the sepsis survivors and nonsurvivors and intersected with the DEGs from the survivors and nonsurvivors of sepsis in the GSE54514 dataset. Finally, 12 DEGs were obtained, and KEGG enrichment results showed that cell adhesion molecules were most significantly correlated with these DEGs. Cell adhesion molecule (CAM) is a collective term referring to various molecules that facilitate cellular interactions and adhesion, either between cells or between cells and the extracellular matrix (ECM). It serves as the molecular foundation for a range of significant physiological and pathological processes 24 . It has been confirmed in the literature that CAM affects sepsis and its complications 25 . Some studies have shown that FAM46C can antagonize cardiac dysfunction caused by sepsis by downregulating CAM and inhibiting apoptosis. It has also been found that silencing or inhibiting PFKFB3 can significantly downregulate the expression of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) in sepsis, thus improving acute lung injury induced by sepsis 26 . Moreover, a large number of studies have recognized the diagnostic and predictive value of ICAM-1 in sepsis through bioinformatics analysis 27,28 . Therefore, we selected the downregulated genes of ITGB2, SELL,and ICAM3 that are enriched in the pathway of cell adhesion molecules for further study. To further verify the scientific nature of the above results, a mouse model of CLP-induced sepsis was constructed, and PBMCs were obtained from both survivors and nonsurvivors of sepsis 24 hours after surgery for qRT‒PCR. ITGB2 and ICAM3 expression levels were significantly lower in nonsurvivors than in survivors, but there was no significant difference in the SELL between the two groups. This is consistent with the findings from MR. ITGB2 has been reported to affect the progression of sepsis by participating in neutrophil recruitment, and defects in this gene can lead to defects in leukocyte adhesion in sepsis 29 . Unfortunately, the correlation between the expression of ITGB2 and the infiltration of immune cells other than neutrophils during the progression of sepsis has not been widely studied. For example, effective interactions between T cells and their targets depend on T-cell receptor-mediated ITGB2 activation 30 . Similarly, the interaction between ITGB2 and cytotoxic CD8 + T cells in the pathogenesis of sepsis has not been fully studied. In addition, ICAM3 belongs to the family of intercellular adhesion molecules and is the ligand of the leukocyte adhesion protein LFA-1 (integrin αL/β2) 31 . Some studies indicate that ICAM3 is critical for immune cell interactions and T lymphocyte activation 32,33 . Numerous studies have proposed that ICAM3 mediates inflammatory signaling, thereby promoting cancer cell stemness 34 . Dendritic cell-specific ICAM3-grabbing nonintegrin (DC-sign) inhibition plays a protective role in sepsis-associated organ injury and systemic inflammation 35 . However, the relationship between ICAM3 and cytotoxic CD8 + T cells in sepsis has not been elucidated. ITGB2, also known as integrin β2, is considered a key regulator of neonatal sepsis and is essential for the progression of this condition 36 . This study proposes that ITGB2 and ICAM3 can predict increased survival in sepsis patients with decreased intercellular communication in cytotoxic CD8 + T cells. Targeting ICAM3 and ITGB2 may affect the prognosis of sepsis patients by affecting the intercellular communication of cytotoxic CD8 + T cells. However, there are still many defects in this study. First, we only validated the expression levels of key genes in CLP mice with sepsis, not in a larger clinical sequence. Second, the mechanism of action of cytotoxic CD8 + T cells in sepsis has not been thoroughly explored, and the predictive significance of key genes for the prognosis of sepsis needs to be further verified. Last, there are few applications in clinical practice, and we may be able to use efficient clinical subtypes to recognize sepsis and achieve personalized treatment goals 37 . 5. CONCLUTION This study suggested that ITGB2 and ICAM3 could serve as biomarkers to predict survival rates in sepsis patients, primarily by decreasing intercellular communication among cytotoxic CD8 + T cells. This research demonstrated a close association between the levels of ITGB2 and ICAM3 and reduced cellular interactions, thereby contributing to improved patient survival rates. Moreover, this discovery highlights a potential therapeutic target for improving sepsis prognosis. Targeted regulation of these molecules may lead to new treatment strategies, optimizing the therapeutic outcomes for sepsis patients and ultimately boosting their survival rates and quality of life. Declarations Author contributions Min Lei and Yaping Zhang designed this study,conducted bioinformatics analysis and wrote the main manuscript ;Yijin Yu and Gaojian Wang conducted the animal experiments;Nianqiang Hu prepared R software and all figures.Junran Xie supervised and managed this research.All authors reviewed the manuscript. Declaration of Competing Interest The authors have no conflicts of interest. Funding information This research was supported by grants from the Medical Science Research Foundation of Zhejiang Province (2022515073). Ethics statement Ethical approval was granted by the Institutional Animal Care and Use Committee, ZJCLA (No. ZJCLA-IACUC-20010069) Data availability statement The datasets used and/or analyzed during the current study are publicly available from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) (accession number: GSE167363, GSE54514). References Singer M, Deutschman CS, Seymour CW, et al. 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Supplementary Files TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 12 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 Oct, 2024 Reviews received at journal 24 Sep, 2024 Reviews received at journal 03 Sep, 2024 Reviewers agreed at journal 18 Aug, 2024 Reviewers agreed at journal 17 Aug, 2024 Reviewers invited by journal 15 Aug, 2024 Editor assigned by journal 15 Aug, 2024 Editor invited by journal 06 Aug, 2024 Submission checks completed at journal 05 Aug, 2024 First submitted to journal 25 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4802382","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":346543900,"identity":"9c7d0e40-91c0-43d9-ae4a-281e8a4ae6e3","order_by":0,"name":"Min Lei","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Lei","suffix":""},{"id":346543901,"identity":"493d240c-4864-4207-8fd1-55032d93b83b","order_by":1,"name":"Yaping Zhang","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Zhang","suffix":""},{"id":346543902,"identity":"0289c5b9-b4f6-42b1-935b-55b276d61c83","order_by":2,"name":"Yijin Yu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yijin","middleName":"","lastName":"Yu","suffix":""},{"id":346543903,"identity":"09e074fd-10d4-418d-a742-41a0e04676c1","order_by":3,"name":"Gaojian Wang","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Gaojian","middleName":"","lastName":"Wang","suffix":""},{"id":346543904,"identity":"6e91b436-f658-4cdc-a76a-66c869a0d73c","order_by":4,"name":"Nianqiang Hu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nianqiang","middleName":"","lastName":"Hu","suffix":""},{"id":346543905,"identity":"e1581705-f5f5-41eb-a4d7-92dedfedaaa6","order_by":5,"name":"Junran Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3PPwrCMBiH4U8CcUlxtUPxCl8J6OKfq0QEL9DFsSB0dBf1Dhl1i3ToIrgqgnSqi4I30KSDokPq6JB3yW/IQwiAy/WPEQAsRz0uRy3+nTD1K3nVFOVRTTAjKvLW51bHv+QRg24gFSlyG/GnVHBvF4WbhRCcwZhLRTtoIw3CkHuJqMmTUJqkQ6kYbdoIJY27IQN53MaaPKqJfgUMGcoDAU1UNdF/wXCViJHcjSFc4ojPU9q2EtynBd4S0ZNZVuB10g9m2bSwEvMdfA8zScV9cyX/Hi6Xy+X66AlbvEKi6Gt1ggAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Junran","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-07-25 14:03:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4802382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4802382/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-93685-z","type":"published","date":"2025-04-12T16:05:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64193090,"identity":"89752185-c03a-40fb-a148-ea6c7e43fdee","added_by":"auto","created_at":"2024-09-09 19:10:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":516973,"visible":true,"origin":"","legend":"\u003cp\u003eJackstraw plot (A) and elbow plot (B) showing the p value distribution of each PC. (C) t-SNE diagram of the 8 main cell types in HCs and sepsis survivors and nonsurvivors.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/3eb22b9fac597f519b520b7a.jpg"},{"id":64192923,"identity":"7b5b437d-bd7d-49e8-8d94-d9c30a6b9cb3","added_by":"auto","created_at":"2024-09-09 19:02:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":282704,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Dot plot showing the expression of 20 signature genes in 8 cell clusters. The dot size represents the proportion of cells, and the color spectrum represents the average expression level of the markers. (B) The relative proportions of cell populations in HCs, sepsis survivors and nonsurvivors.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/425b33235e04102691d6db6a.jpg"},{"id":64192926,"identity":"ea1daaf0-67b1-40a3-9b87-0bc0d95af414","added_by":"auto","created_at":"2024-09-09 19:02:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":714762,"visible":true,"origin":"","legend":"\u003cp\u003eCircle network diagram illustrating important cell–cell communication pathways in HCs (A), survivors of sepsis (B) and nonsurvivors of sepsis (C). (D) Network representing the difference in cell‒cell communication between survivors and nonsurvivors of sepsis, with the red edge indicating stronger intercellular interactions in the sepsis nonsurvivors and the blue edge representing stronger cell‒cell communication in the sepsis survivors.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/fc0217ac42b79cc24da10007.jpg"},{"id":64193202,"identity":"d4de217f-391d-4be3-adf8-9af76bc8f195","added_by":"auto","created_at":"2024-09-09 19:18:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":308785,"visible":true,"origin":"","legend":"\u003cp\u003eDot plot indicating intercellular communication mediated by multiple ligand‒receptor or signaling pathways in HCs and sepsis survivors and nonsurvivors.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/4e58edccf617861b02a1b818.jpg"},{"id":64192379,"identity":"79830144-3931-4201-9def-06b4850ee38d","added_by":"auto","created_at":"2024-09-09 18:54:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":275427,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA of cytotoxic CD8+ T cells from sepsis survivors (A) and nonsurvivors (B).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/730926d5ec2ac17e9d02990c.jpg"},{"id":64192383,"identity":"17a313e8-92e4-4feb-bbec-d7622c4a08b4","added_by":"auto","created_at":"2024-09-09 18:54:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":476250,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Heatmap showing DEGs in cytotoxic CD8+ T cells between sepsis survivors and sepsis nonsurvivors. (B) Volcano plot showing DEGs between the survivors and nonsurvivors of sepsis in the GSE54514 dataset. Genes upregulated or downregulated by more than 0.5-fold are shown in red. (C, D) Venn diagram of the overlapping upregulated (C) and downregulated (D) DEGs based on the DEGs identified in the Cytotoxic CD8+ T-cell and GSE54514 datasets.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/162e7a79b4b2fd34b5993968.jpg"},{"id":64192381,"identity":"37db88a2-3e02-4e5f-bae3-d54b4fa8fc2d","added_by":"auto","created_at":"2024-09-09 18:54:00","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":318756,"visible":true,"origin":"","legend":"\u003cp\u003e(A) KEGG was used to analyze the most enriched pathways of the 12 overlapping DEGs. (B) The expression of ITGB2, SELL and ICAM3 in the GSE54514 dataset.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/944cef8477ded7f99858009b.jpg"},{"id":64192927,"identity":"2d23be90-8320-4af9-a019-9f1ccd2660e2","added_by":"auto","created_at":"2024-09-09 19:02:00","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":361649,"visible":true,"origin":"","legend":"\u003cp\u003e(A) ssGSEA of immune cells between survivors and nonsurvivors. (B-D) The correlation diagrams showing the correlation between CD8+ T cells and the expression of ITGB2, SELL and ICAM3.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/e2348b969c47b679307a7a81.jpg"},{"id":64192382,"identity":"5e22ccbb-3789-42c9-b241-564d7da58ab1","added_by":"auto","created_at":"2024-09-09 18:54:00","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":229821,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of ICAM3(A), ITGB2 (B) and SELL (C) in cytotoxic CD8+ T cells between survivors and nonsurvivors of sepsis.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/73e11060d5d53d6e746b4d9e.jpg"},{"id":64192380,"identity":"32ddcf5a-be13-4ad0-aa3a-88701008aa77","added_by":"auto","created_at":"2024-09-09 18:54:00","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":856882,"visible":true,"origin":"","legend":"\u003cp\u003e(A) H\u0026amp;E staining of lung tissues from sham and CLP mice (×400). (B) H\u0026amp;E staining of kidney tissues from Sham and CLP mice (×400).\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/fbe28a812b811c8b0279bba6.jpg"},{"id":64192377,"identity":"6e9cf45e-0b50-4679-bc13-cc870e1cb2ef","added_by":"auto","created_at":"2024-09-09 18:54:00","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":235964,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA expression of ITGB2, SELL and ICAM3 in the PBMCs of mice. (A-C) The expression of ITGB2, SELL and ICAM3 was quantified in the CLP groups of both survivors and nonsurvivors. **\u003cem\u003eP stands for \u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP stands for\u003c/em\u003e \u0026lt;0.001, ns stands for not significant.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/435b9432d299afb222a39618.jpg"},{"id":80559260,"identity":"4c9e88e6-3580-46da-ac37-5b072be134c4","added_by":"auto","created_at":"2025-04-14 16:18:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5556399,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/5bb8bd7f-037d-4cb4-bc5c-9bc823a8b45d.pdf"},{"id":64192373,"identity":"d3aca861-9863-40c9-a555-6b65bd23d1e3","added_by":"auto","created_at":"2024-09-09 18:54:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15384,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4802382/v1/50a52cda8cc20eaa48da4dca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"ITGB2 and ICAM3 predict increased survival of sepsis with decreased intercellular communication in Cytotoxic CD8+ T- cells","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSepsis is a critical condition characterized by organ dysfunction resulting from an imbalanced host inflammatory response to infection\u003csup\u003e1\u003c/sup\u003e, which can involve multiple organs, leading to organ damage or failure. The prevalence of sepsis, a critical illness, is a substantial threat to human health. According to epidemiological reports, sepsis has been recognized as a global health burden since 2017 due to its high morbidity and mortality. To date, approximately 20% of annual deaths globally are associated with sepsis, which greatly affects quality of life\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince the pathogenesis of sepsis is still unclear, an increasing number of studies have begun to explore the key genes involved in sepsis pathogenesis by using second-generation sequencing technology, providing potential possibilities for the treatment of sepsis and improving the prognosis of sepsis patients\u003csup\u003e3\u003c/sup\u003e. For example, transcriptome sequencing has been used to identify pivotal genes in adult patients with sepsis\u003csup\u003e4,\u003c/sup\u003e and single-cell RNA sequencing (scRNA-seq) has been used to characterize the status of various immune cells during the development of sepsis\u003csup\u003e5\u003c/sup\u003e. Undoubtedly, the rapid development of bioinformatics has greatly promoted the process of exploring the pathogenesis of sepsis.\u003c/p\u003e \u003cp\u003eIn recent years, studies have shown that the immune response is crucial for the development of sepsis\u003csup\u003e6\u003c/sup\u003e. With in-depth research on immunity, immunotherapy has also been proven to be an effective method for treating sepsis\u003csup\u003e7\u003c/sup\u003e. Therefore, exploring the role of immune cells in sepsis has become a hot research topic. Studies have shown that autophagy can induce neutrophils to form neutrophil extracellular traps during sepsis, and increased neutrophil autophagy can improve the survival rate of patients with sepsis\u003csup\u003e8,9\u003c/sup\u003e. Moreover, studies have confirmed that Sestrin2 has the potential to improve the prognosis of sepsis patients by inhibiting the pyroapoptosis of dendritic cells\u003csup\u003e10\u003c/sup\u003e. Therefore, this study aimed to identify key genes related to immunity in sepsis patients from the perspective of immunity and to verify the causal associations among these key genes and sepsis through MR analysis with the aid of bioinformatics.\u003c/p\u003e \u003cp\u003eQiu X et al. extracted peripheral blood mononuclear cells (PBMCs) from healthy controls (HCs) and survivors and nonsurvivors of sepsis at 0 h and 6 h for single-cell RNA sequencing (scRNA-seq) (GSE167363) to explore the dynamic changes in human single-cell transcription characteristics during sepsis\u003csup\u003e11\u003c/sup\u003e. As a result, PBMCs have been proven to play key roles in the immune response to infection and have been widely used in the scRNA-seq of sepsis\u003csup\u003e12\u003c/sup\u003e. Parnell et al. extracted whole blood from HCs and sepsis survivors and nonsurvivors for RNA-seq (GSE54514) and identified key regulatory genes in the whole blood of patients with sepsis to monitor potential immune dysfunction\u003csup\u003e13\u003c/sup\u003e. Our study used 6 samples from the GSE167363 dataset, including 2 HCs, 2 survivors (0 h, 6 h), and 2 nonsurvivors of sepsis (0 h, 6 h). Bioinformatics analysis was performed on 163 samples from 18 HCs, 26 sepsis survivors, and 9 sepsis nonsurvivors in the GSE54514 dataset. Key genes related to immunity in sepsis patients were identified through cell proportion analysis, cell interaction analysis, gene set enrichment analysis (GSEA), immune score analysis, etc. Next, we conducted MR analysis to confirm the causal associations between the key genes and the risk of sepsis. Finally, a mouse model of cecal ligation and puncture (CLP) sepsis was constructed to detect the expression levels of these key genes through qRT‒PCR.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Acquisition of scRNA-seq data\u003c/h2\u003e \u003cp\u003eThe human scRNA-seq dataset GSE167363 was downloaded from the GEO database. Six scRNA-seq datasets obtained from human PBMCs were selected, including 2 healthy controls, 2 survivors (0 h and 6 h) and 2 nonsurvivors of sepsis (0 h and 6 h). The microarray dataset GSE54514 was downloaded, and 163 whole-blood RNA expression datasets were selected from 18 HCs and 26 sepsis survivors and 9 sepsis nonsurvivors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analyzing the scRNA-seq data\u003c/h2\u003e \u003cp\u003eWe used the Seurat 3.2.2 package to analyze the GSE167363 dataset. First, the quality control of the cells was carried out based on the following criteria: 1) we excluded genes detected in \u0026lt;\u0026thinsp;5 cells; 2) we eliminated cells that had fewer than 200 genes; and 3) we removed cells with mitochondrial gene expression\u0026thinsp;\u0026ge;\u0026thinsp;10%. Subsequently, the gene expression of the remaining cells was standardized using a linear regression model. The samples were subjected to batch effect correction using the IntegrateData method of the Seurat package, and 6 samples were integrated. A total of 23,881 cells and 20,078 genes were included in the processed data. Principal component analysis (PCA) was employed to identify the dimensions that were statistically significant, with a P value less than 0.05. After that, the t-distributed stochastic neighbor embedding (t-SNE) algorithm was utilized to reduce the dimensionality of 30 initial principal components (PCs) while performing cluster classification analysis. Finally, the cell clusters were classified and annotated according to the marker genes of the cells\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Cell proportion analysis\u003c/h2\u003e \u003cp\u003eWe compared the proportions of different cells among HCs, sepsis survivors and nonsurvivors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Analysis of intercellular interactions\u003c/h2\u003e \u003cp\u003eCellChat was used to examine cellular interactions among HCs and among sepsis survivors and nonsurvivors. The differences between survivors and nonsurvivors of sepsis were compared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 GSEA\u003c/h2\u003e \u003cp\u003eThe Fgsea package was used for GSEA of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in HCs, sepsis survivors and nonsurvivors, and C2 in the Msigdb was selected as the database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Analysis of DEGs in Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells\u003c/h2\u003e \u003cp\u003eUsing the Seurat FindMarkers function, the DEGs of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells were compared between the sepsis survivors and nonsurvivors, with the parameters logfc.threshold\u0026thinsp;=\u0026thinsp;0.25 and min.pct\u0026thinsp;=\u0026thinsp;0.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Analysis of DEGs in the GSE54514 dataset\u003c/h2\u003e \u003cp\u003eLimma was used to analyze the DEGs between sepsis survivors and nonsurvivors in the GSE54514 dataset, with | logFC | \u0026gt; 0.5 and adj. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as a parameter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Immune cell score\u003c/h2\u003e \u003cp\u003eGene set variation analysis (GSVA) was used to quantify the activation of the pathway by applying a single-sample gene set enrichment analysis (ssGSEA) score on a set of genes contained in the pathway.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.7 Identification of key genes\u003c/h2\u003e \u003cp\u003eThe upregulated and downregulated genes from the GSE54514 dataset and the number of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in the GSE167363 dataset were intersected, and clusterProfiler was used for KEGG enrichment analysis of these DEGs.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mouse model of CLP-induced sepsis\u003c/h2\u003e \u003cp\u003eMale C57BL/6 mice, aged 3\u0026ndash;4 weeks, were routinely fed and kept at 22\u0026deg;C for 12 h with light and dark cycles. Mice with sepsis were subjected to CLP 1 week after adaptation. After abdominal anesthesia, CLP was performed on all the mice, and the abdominal cavity was closed before subcutaneous injection of normal saline (37\u0026deg;C, 50 ml/kg) for resuscitation.\u003c/p\u003e \u003cp\u003eThe mice used in this study were obtained from the Laboratory Animal Center of Zhejiang (Hangzhou,China). We used carbon dioxide (CO2) inhalation equipment to carry out euthanasia. We placed the mice in a chamber during euthanasia, gradually supplying CO2 to raise its concentration. The euthanasia personnel were required to observe the procedure and pause for a minimum of one minute once they detected no movement, visible breathing, or heartbeat. The trials received approval from the Institutional Animal Care and Use Committee, ZJCLA (No. ZJCLA-IACUC-20010069). Animal experiments were complied with the ARRIVE guidelines and all methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Determination of the mold results\u003c/h2\u003e \u003cp\u003eTwenty-four hours after CLP surgery, we collected lung and kidney tissue from the mice. The tissue samples were preserved in a solution containing 4% paraformaldehyde, embedded in paraffin, and finally sliced into 5-micrometer-thick sections. The sections were stained with H\u0026amp;E to examine morphological damage under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Quantitative real-time polymerase chain reaction (qRT‒PCR)\u003c/h2\u003e \u003cp\u003eTwenty-four hours after CLP surgery, the mice were categorized into two different groups based on their mortality: the survivor group and the nonsurvivor group. Blood samples were collected from all mice, and PBMCs were extracted using Ficoll solution (Solarbio Life Sciences, Beijing, China). Then, RNA was extracted from the PBMCs via the TRIzol method for qRT‒PCR. Supplementary Table S1 shows the primers used.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Single-cell transcriptome profiling of PBMCs\u003c/h2\u003e \u003cp\u003eGenerally, the scRNA-seq data for PBMCs were acquired from the GSE167363 microarray in the GEO database. A total of 13566, 4573 and 5547 cells were obtained from healthy controls and survivors and nonsurvivors of sepsis, respectively, for scRNA analysis. Then, the t-SNE algorithm was used to reduce the dimensionality of the 30 initial PCs, and cluster analysis was performed on all cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). Based on our PCA and t-SNE results, PBMCs were classified into 17 clusters and annotated as dendritic cells, B cells, activated CD4\u0026thinsp;+\u0026thinsp;T cells, natural killer (NK) cells, naive T cells, cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells, platelet cells, or monocytes via marker genes\u003csup\u003e14\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Cell proportion analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows the percentage of cells expressing characteristic markers and their scaled relative expression values in distinct cell clusters. The results were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) monocytes characterized by high expression of S100A4 and CST3; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) platelet cells specifically expressing the cell marker PPBP; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells highly expressing CD8A; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) naive T cells with high expression of IL7R; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) NK cells specifically expressing the markers GNLY, NKG7, GZMA, and KLRF1; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) activated CD4\u0026thinsp;+\u0026thinsp;T cells expressing CD8B; (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) B cells specifically expressing the cell marker MS4A1; and (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) dendritic cells with high expression of LYZ. The proportions of cell subsets were significantly different among the healthy controls and survivors and nonsurvivors of sepsis (Figure_2B), indicating heterogeneity and consistency among these sepsis samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Cell‒cell communication assessment in patients with sepsis\u003c/h2\u003e \u003cp\u003eTo characterize the intercellular communication networks of the normal controls and the survivors and nonsurvivors of sepsis, a dataset of human ligand‒receptor pairs was used to construct a dense intercellular communication network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Our results highlighted that most cell‒cell interactions were between cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells and monocytes in the healthy samples. A comparison investigation was performed to examine the intercellular interactions between the survivors and nonsurvivors of sepsis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The red edge indicates much stronger intercellular interactions in the nonsurvivors of sepsis, while the blue edge indicates stronger cell‒cell communication in the survivors of sepsis. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the ligand‒receptor or signaling pathways mediating intercellular communication in the normal controls and in the survivors and nonsurvivors of sepsis. These results showed that intercellular communication among cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells was reduced in sepsis survivors compared to sepsis nonsurvivors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Enrichment of pathways in cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells\u003c/h2\u003e \u003cp\u003eTo observe the effect of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells on sepsis, we used GSEA to analyze the enrichment of the KEGG pathways in the survivors and nonsurvivors of sepsis. The results showed that the ribosome, spliceosome, and RNA degradation pathways were highly enriched in cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells from the survivors of sepsis. Notably, the ribosome, NK cell-mediated cytotoxicity, graft versus host disease and antigen processing and presentation pathways were highly enriched in cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in the nonsurvivors of sepsis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 DEGs in the GSE167363 and GSE54514 datasets\u003c/h2\u003e \u003cp\u003eTo further clarify the molecular mechanism by which cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells affect the survival of patients with sepsis, we identified DEGs in the number of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells between the survivors and nonsurvivors of sepsis by FindMarker (min.pct\u0026thinsp;=\u0026thinsp;0.2, logfc.threshold\u0026thinsp;=\u0026thinsp;0.25), and a total of 582 DEGs were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Moreover, DEGs between sepsis survivors and nonsurvivors in the GSE54514 dataset were analyzed with the limma package. With the criteria of |logFC| \u0026gt; 0.5 and adj.P. At Val\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 210 DEGs were obtained (110 upregulated DEGs and 100 downregulated DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). DEGs that were simultaneously upregulated or downregulated in both cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells and the GSE54514 dataset are shown in the Venn diagram. Finally, 12 DEGs were obtained (3 upregulated DEGs and 9 downregulated DEGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5 KEGG enrichment analysis\u003c/h2\u003e \u003cp\u003eThen, these 12 overlapping DEGs were subjected to KEGG enrichment analysis. The pathway that was shown to be significantly enriched was cell adhesion molecules. Among the 12 overlapping DEGs, ITGB2, SELL and ICAM3 were enriched in the pathway of cell adhesion molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with downregulated expression in the nonsurvivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 KEGG pathway analysis of the 12 overlapped DEGs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell adhesion molecules\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eITGB2 SELL ICAM3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdherens junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWAS WASF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFc gamma R-mediated phagocytosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWAS WASF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholine metabolish in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWAS WASF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYersinia infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWAS WASF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemokine signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPBP WAS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRap1 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAPBB1IP ITGB2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulation of action cytoskeleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eITGB2 WASF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShigellosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa05131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRBX1 WASF2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircadian rhythm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehsa04710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRBX1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.1.6 The correlation of the expression of ITGB2, SELL and ICAM3 with immune infiltration\u003c/h2\u003e \u003cp\u003eTwenty-nine immune-associated gene sets representing distinct types, functions, and pathways of immune cells were analyzed by ssGSEA\u003csup\u003e15\u003c/sup\u003e, and the Wilcoxon test was used to assess the disparity between sepsis survivors and nonsurvivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Then, we examined the relationships between the expression levels of ITGB2, SELL, and ICAM3 and the level of infiltration by CD8\u0026thinsp;+\u0026thinsp;T cells. The abundance of CD8\u0026thinsp;+\u0026thinsp;T cells was negatively correlated with the expression of ITGB2, SELL, and ICAM3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.1.7 ITGB2, SELL and ICAM3 were upregulated in cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells from survivors of sepsis\u003c/h2\u003e \u003cp\u003eTo elucidate the roles and importance of ITGB2, SELL and ICAM3, we determined the expression of ITGB2, SELL and ICAM3 in cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells from survivors and nonsurvivors of sepsis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, downregulated ITGB2, SELL and ICAM3 were significantly correlated with sepsis survivors and nonsurvivors. Combined with the above findings, these results suggested that cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells with low expression of ITGB2, SELL and ICAM3 were more likely to have adverse effects on the survival of patients with sepsis than were those with high expression of the above genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mouse model of CLP-induced sepsis\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Determination of the mouse model of CLP-induced sepsis\u003c/h2\u003e \u003cp\u003eTo verify the above results, a mouse model of CLP-induced sepsis was constructed. Twenty-four hours after CLP surgery, lung and kidney tissues were collected for H\u0026amp;E staining. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA shows that the lungs of CLP mice were infiltrated with an enormous amount of inflammatory cells, and some alveolar cavities were fused into pulmonary bullae with interstitial hyperemia and edema. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB shows a high level of leukocyte infiltration in the renal interstitium of CLP mice and hyperemia of the glomerular capillaries. These results suggested that the mouse model of CLP-induced sepsis was successfully constructed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 The mRNA expression of ITGB2, SELL and ICAM3 in the CLP mouse model\u003c/h2\u003e \u003cp\u003eAfter the mouse model of CLP-induced sepsis was successfully generated, the PBMCs of all 22 model mice were obtained, including 15 from the group of survivors and 7 from the group of nonsurvivors. Then, we detected the expression levels of ITGB2, SELL, and ICAM3 by qRT-PCR. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows that, in comparison to those in survivors, the expression levels of ITGB2 and ICAM3 were substantially lower in nonsurvivors, while there was no significant difference in the expression of SELL between the two groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eSepsis is a systemic inflammatory syndrome caused by infectious diseases and is closely associated with the pathophysiological changes that occur in various systems and organs\u003csup\u003e16\u003c/sup\u003e. Although notable advancements in the fields of anti-infective therapy and organ function support technology for the treatment of sepsis have recently been achieved, the morbidity and mortality of sepsis are still high, seriously threatening people\u0026rsquo;s health and lives\u003csup\u003e17\u003c/sup\u003e. Therefore, finding new therapeutic targets for sepsis and improving the prognosis of sepsis patients are the focus of the current research.\u003c/p\u003e \u003cp\u003eMultiple aspects contribute to the pathogenesis of sepsis, but its fundamental pathogenesis remains unclear\u003csup\u003e18\u003c/sup\u003e. Previous studies have shown that sepsis is associated with severe and sustained immunosuppression, immune dysfunction is considered to be the core mechanism of sepsis, and immune response disorders are susceptibility factors for secondary infection and increased mortality\u003csup\u003e19\u003c/sup\u003e. Immune cells, including neutrophils, monocytes, and lymphocytes, are the main players in the immune response\u003csup\u003e20\u003c/sup\u003e. PBMCs, including lymphocytes and monocytes, are commonly used for scRNA-seq to explore the functional characteristics of immune cells during the progression of sepsis\u003csup\u003e21\u003c/sup\u003e. Therefore, in this study, scRNA-seq data from human PBMCs were used for cluster analysis, and the results were annotated as dendritic cells, B cells, activated CD4\u0026thinsp;+\u0026thinsp;T cells, NK cells, naive T cells, cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells, platelet cells, and monocytes. Subsequently, we performed a cell proportion analysis, and significant changes were revealed in the proportions of immune cells among normal controls, as well as survivors and nonsurvivors of sepsis, which further confirmed the crucial role of immune cells in sepsis pathogenesis.\u003c/p\u003e \u003cp\u003eOn this basis, we constructed an intercellular communication network to investigate the correlations among these immune cells. The intercellular communication of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells was more significant in sepsis nonsurvivors than in sepsis survivors, which implies that enhanced cytotoxic CD8\u0026thinsp;+\u0026thinsp;T-cell communication may be significantly associated with reduced survival in sepsis patients. As a type of specific T cell, CD8\u0026thinsp;+\u0026thinsp;T cells can secrete a variety of cytokines to participate in the immune response and resist the invasion of some foreign pathogens\u003csup\u003e22\u003c/sup\u003e. Some studies have shown that increased interaction between platelets and CD8\u0026thinsp;+\u0026thinsp;T cells is strongly linked to an unfavorable prognosis in sepsis patients\u003csup\u003e23\u003c/sup\u003e. However, studies on the mechanism of action of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in sepsis and the correlation of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells with the prognosis of sepsis patients are lacking. In this study, we suggested that the intercellular interactions of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells are closely associated with the prognosis of sepsis patients. Subsequently, cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells were chosen for further analysis to investigate the key genes associated with cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in sepsis.\u003c/p\u003e \u003cp\u003eSubsequently, GSEA was performed on the cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells from both the sepsis survivors and nonsurvivors. The enrichment functions and pathways were revealed to be inconsistent between these two groups, which also confirmed the heterogeneity of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in sepsis patients with different prognoses. Next, the DEGs of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells were screened from both the sepsis survivors and nonsurvivors and intersected with the DEGs from the survivors and nonsurvivors of sepsis in the GSE54514 dataset. Finally, 12 DEGs were obtained, and KEGG enrichment results showed that cell adhesion molecules were most significantly correlated with these DEGs. Cell adhesion molecule (CAM) is a collective term referring to various molecules that facilitate cellular interactions and adhesion, either between cells or between cells and the extracellular matrix (ECM). It serves as the molecular foundation for a range of significant physiological and pathological processes\u003csup\u003e24\u003c/sup\u003e. It has been confirmed in the literature that CAM affects sepsis and its complications\u003csup\u003e25\u003c/sup\u003e. Some studies have shown that FAM46C can antagonize cardiac dysfunction caused by sepsis by downregulating CAM and inhibiting apoptosis. It has also been found that silencing or inhibiting PFKFB3 can significantly downregulate the expression of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) in sepsis, thus improving acute lung injury induced by sepsis\u003csup\u003e26\u003c/sup\u003e. Moreover, a large number of studies have recognized the diagnostic and predictive value of ICAM-1 in sepsis through bioinformatics analysis\u003csup\u003e27,28\u003c/sup\u003e. Therefore, we selected the downregulated genes of ITGB2, SELL,and ICAM3 that are enriched in the pathway of cell adhesion molecules for further study.\u003c/p\u003e \u003cp\u003eTo further verify the scientific nature of the above results, a mouse model of CLP-induced sepsis was constructed, and PBMCs were obtained from both survivors and nonsurvivors of sepsis 24 hours after surgery for qRT‒PCR. ITGB2 and ICAM3 expression levels were significantly lower in nonsurvivors than in survivors, but there was no significant difference in the SELL between the two groups. This is consistent with the findings from MR.\u003c/p\u003e \u003cp\u003eITGB2 has been reported to affect the progression of sepsis by participating in neutrophil recruitment, and defects in this gene can lead to defects in leukocyte adhesion in sepsis\u003csup\u003e29\u003c/sup\u003e. Unfortunately, the correlation between the expression of ITGB2 and the infiltration of immune cells other than neutrophils during the progression of sepsis has not been widely studied. For example, effective interactions between T cells and their targets depend on T-cell receptor-mediated ITGB2 activation\u003csup\u003e30\u003c/sup\u003e. Similarly, the interaction between ITGB2 and cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in the pathogenesis of sepsis has not been fully studied. In addition, ICAM3 belongs to the family of intercellular adhesion molecules and is the ligand of the leukocyte adhesion protein LFA-1 (integrin αL/β2)\u003csup\u003e31\u003c/sup\u003e. Some studies indicate that ICAM3 is critical for immune cell interactions and T lymphocyte activation\u003csup\u003e32,33\u003c/sup\u003e. Numerous studies have proposed that ICAM3 mediates inflammatory signaling, thereby promoting cancer cell stemness\u003csup\u003e34\u003c/sup\u003e. Dendritic cell-specific ICAM3-grabbing nonintegrin (DC-sign) inhibition plays a protective role in sepsis-associated organ injury and systemic inflammation\u003csup\u003e35\u003c/sup\u003e. However, the relationship between ICAM3 and cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in sepsis has not been elucidated.\u003c/p\u003e \u003cp\u003eITGB2, also known as integrin β2, is considered a key regulator of neonatal sepsis and is essential for the progression of this condition\u003csup\u003e36\u003c/sup\u003e. This study proposes that ITGB2 and ICAM3 can predict increased survival in sepsis patients with decreased intercellular communication in cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells. Targeting ICAM3 and ITGB2 may affect the prognosis of sepsis patients by affecting the intercellular communication of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells.\u003c/p\u003e \u003cp\u003eHowever, there are still many defects in this study. First, we only validated the expression levels of key genes in CLP mice with sepsis, not in a larger clinical sequence. Second, the mechanism of action of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells in sepsis has not been thoroughly explored, and the predictive significance of key genes for the prognosis of sepsis needs to be further verified. Last, there are few applications in clinical practice, and we may be able to use efficient clinical subtypes to recognize sepsis and achieve personalized treatment goals\u003csup\u003e37\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5. CONCLUTION","content":"\u003cp\u003eThis study suggested that ITGB2 and ICAM3 could serve as biomarkers to predict survival rates in sepsis patients, primarily by decreasing intercellular communication among cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells. This research demonstrated a close association between the levels of ITGB2 and ICAM3 and reduced cellular interactions, thereby contributing to improved patient survival rates. Moreover, this discovery highlights a potential therapeutic target for improving sepsis prognosis. Targeted regulation of these molecules may lead to new treatment strategies, optimizing the therapeutic outcomes for sepsis patients and ultimately boosting their survival rates and quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMin Lei and Yaping Zhang designed this study,conducted bioinformatics analysis and wrote the main manuscript ;Yijin Yu and Gaojian Wang conducted the animal experiments;Nianqiang Hu prepared R software and all figures.Junran Xie supervised and managed this research.All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by grants from the Medical Science Research Foundation of Zhejiang Province (2022515073).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the Institutional Animal Care and Use Committee, ZJCLA (No. ZJCLA-IACUC-20010069)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are publicly available from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) (accession number: GSE167363, GSE54514).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, et al. 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Laparosc Endosc Robot Surg. 2024;7(1):16\u0026ndash;26. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lers.2024.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.lers.2024.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Sepsis, immunity, Cytotoxic CD8 + T cells, ITGB2, ICAM3, scRNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-4802382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4802382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSepsis is closely linked to immunity. Our research aimed to identify key genes associated with sepsis immunity utilizing single-cell RNA sequencing (scRNA-seq) data. This study obtained the GSE167363 and GSE54514 datasets from the Gene Expression Omnibus (GEO). The GSE167363 dataset was subjected to cluster analysis, cell proportion analysis, cell interaction analysis, and gene set enrichment analysis (GSEA). The differentially expressed genes (DEGs) of CD8\u0026thinsp;+\u0026thinsp;T cells were correlated with the DEGs in the GSE54514 dataset, and key genes related to immunity in sepsis patients were identified through Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, we validated the gene expression levels in a mouse model of sepsis caused by cecum ligation and puncture (CLP).Findings indicated that Intercellular communication of Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells was reduced in the sepsis survivors compared to non-survivors. The expression of 3 down-regulated key DEGs (ITGB2, SELL and ICAM3) was negatively correlated with the abundance of CD8\u0026thinsp;+\u0026thinsp;T cells. Moreover, Cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells with low expression of ITGB2, SELL and ICAM3 were more adverse to the survival of sepsis as compared to those with high expression of the above genes.\u003c/p\u003e \u003cp\u003eThese genes may predict increased survival in sepsis by regulating intercellular communication in cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells, suggesting that they are potential therapeutic targets for improving sepsis prognosis.\u003c/p\u003e","manuscriptTitle":"ITGB2 and ICAM3 predict increased survival of sepsis with decreased intercellular communication in Cytotoxic CD8+ T- cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 18:53:55","doi":"10.21203/rs.3.rs-4802382/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-07T13:51:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-25T01:54:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-03T07:14:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265465379724117003241291039583736385995","date":"2024-08-19T00:14:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240273398298299869116300612657846253083","date":"2024-08-17T18:14:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-15T08:12:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-15T07:41:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-06T13:32:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-05T09:07:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-25T14:00:52+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":"e4d60fee-6773-414d-9413-5e728d7c5168","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36747873,"name":"Biological sciences/Immunology"},{"id":36747874,"name":"Biological sciences/Molecular biology"},{"id":36747875,"name":"Health sciences/Medical research"},{"id":36747876,"name":"Health sciences/Pathogenesis"}],"tags":[],"updatedAt":"2025-04-14T16:16:53+00:00","versionOfRecord":{"articleIdentity":"rs-4802382","link":"https://doi.org/10.1038/s41598-025-93685-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-12 16:05:29","publishedOnDateReadable":"April 12th, 2025"},"versionCreatedAt":"2024-09-09 18:53:55","video":"","vorDoi":"10.1038/s41598-025-93685-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-93685-z","workflowStages":[]},"version":"v1","identity":"rs-4802382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4802382","identity":"rs-4802382","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}