Understanding the Relationship Between Immune Cells and Sepsis Through Mendelian Randomization and Single-Cell Transcriptome Analysis

Understanding the Relationship Between Immune Cells and Sepsis Through Mendelian Randomization and Single-Cell Transcriptome Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Understanding the Relationship Between Immune Cells and Sepsis Through Mendelian Randomization and Single-Cell Transcriptome Analysis Ruiming Deng, Qizhi Liao, Yan Fang, Guiming Huang, Juan Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4022923/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Sepsis is a condition characterized by an immune system imbalance, leading to high rates of morbidity and mortality. Although immune cells have the ability to eliminate infection, they can also cause tissue damage. Therefore, understanding the role of different immune cells in sepsis is crucial for effective treatment. Purpose: The goal of this research is to examine the correlation between sepsis and immune cells, as well as their specific traits, through the utilization of Mendelian randomization (MR) analysis and single-cell transcriptome analysis. Method: To investigate the causal association between immune cell signals and the susceptibility to sepsis, we conducted a comprehensive two-sample MR analysis utilizing publicly accessible genetic data. The analysis focused on four types of immune signals: median fluorescence intensity (MFI), relative cell number (RC), absolute cell number (AC), and morphological parameters (MP). Additionally, single-cell transcriptome sequencing data analysis techniques were used to explore the characteristics of immune cells in sepsis. Result: After correcting for multiple testing, there was no statistically significant impact of sepsis on immune phenotype. However, our research findings support the notion that the FSC-A parameter on the HLA DR + natural killer immune cell phenotype has a protective effect against sepsis. Furthermore, analysis of single-cell RNA sequencing data revealed a significant increase in the S100A8+S100A9+ neutrophil subpopulation in sepsis, while the proportion of T cells was significantly lower compared to healthy controls. Conclusion: Our results suggest that HLA DR + natural killer cells have a significant protective effect on sepsis. Additionally, the S100A8+S100A9+ neutrophil subpopulation is significantly increased in sepsis. Mendelian randomization Genome-wide association studies Quantitative trait loci Immune cells Sepsis Figures Figure 1 Highlights HLA DR + natural killer cells have a protective effect against sepsis. Sepsis is not associated with other immune phenotype, except for the protective effects of one immunophenotype, implying potential therapeutic implications. Sepsis shows a significant increase in the S100A8+S100A9+ neutrophil subpopulation, providing insights into the immune response. T cell proportion is lower in sepsis patients, suggesting potential T cell dysfunction in the pathogenesis. This study unveils immune cell characteristics in sepsis, offering a potential molecular target for diagnosis and treatment. 1. Introduction Sepsis is a medical condition characterized by an imbalance in the immune system triggered by infection from pathogens, resulting in severe organ dysfunction 1 . The underlying cause of sepsis is an abnormality in the host's natural and acquired immune responses 2 . The failure and suppression of the immune system due to sepsis can ultimately lead to death 3 . Sepsis is commonly observed among patients admitted to intensive care units 4 . Severe sepsis can cause hypotension, hypoperfusion, lactic acidosis, acute kidney injury (AKI), and acute respiratory distress syndrome (ARDS) 5 , which contribute to a mortality rate of approximately 26% 6 . Currently, the treatment options for sepsis are limited to antibiotics, fluid administration, and support for cardiovascular and respiratory functions. However, despite the implementation of these interventions, mortality rates for sepsis remain high, with survivors often experiencing a reduced quality of life 7 . Extensive research conducted on immune mechanisms related to sepsis has focused on immunotherapy as a potential new approach for treating sepsis, offering hope for improving patient outcomes. Recent studies have identified a correlation between extended periods of immune system inactivity and increased mortality rates in sepsis. These periods of immune system inactivity are a result of alterations in immune cells and excessive or unregulated apoptosis 8 . Initially, sepsis is characterized by an excessively activated immune system, but in later stages, it transitions into an inflammatory response accompanied by immune suppression. Abnormal immune responses induced by pathogens, such as excessive inflammation or immune system suppression, play a significant role in the high mortality rates associated with sepsis 9 . Immune disorders, particularly immune suppression in sepsis, are attributed to pathological changes in immune cells, including abnormal activation, extensive cell death, phenotypic variations, and functional alterations 10 . The advanced stages of sepsis are characterized by increased apoptosis of immune cells and a decrease in INF-γ production, which can lead to secondary infections and even death 11 . Therefore, it is crucial to suppress the apoptosis of immune cells during sepsis in order to mitigate cell damage and prevent or reduce immune system suppression 12 . Neutrophils, macrophages, dendritic cells, and natural killer T cells are innate immune cells that mediate the inflammatory responses and organ damage caused by sepsis 13 . The precise functions of numerous immune cell types in sepsis remain unclear, limiting our understanding of effective treatment methods. As a result, this study is conducted with the objective of gaining new insights into sepsis treatment by conducting a comprehensive analysis of single-cell omics data and utilizing MR to shed light on the role of immune cells in sepsis. 2. Materials and methods 2.1. Study design We performed an extensive analysis to examine the correlation between sepsis and 731 immune cell signatures, which were classified into 7 distinct groups. Our methodology involved using a two-sample MR method that utilized genetic variations as instrumental variables (IVs) to represent possible risk factors. To ensure the validity of the IVs, three important assumptions need to be met: ( 1 ) there is a direct relationship between the genetic variations and the exposure; ( 2 ) the genetic variations being studied are not influenced by any additional factors that could potentially confound the relationship between the exposure and the outcome; and ( 3 ) the genetic variations do not impact the outcome through alternative pathways other than the exposure. Ethical approval was obtained from the institutional review boards pertinent to the studies included in our analysis. Additionally, informed consent was obtained from all participants. 2.2. Immunity-wide GWAS data sources The GWAS Catalog offers comprehensive genetic associations data for a wide range of immune traits. This includes a total of 731 immunophenotypes, with accession numbers ranging from GCST0001391 to GCST0002121 14 . These traits encompass various features such as absolute cell (AC) counts (n = 118), median fluorescence intensities (MFI) indicating surface antigen levels (n = 389), morphological parameters (MP) (n = 32), and relative cell (RC) counts (n = 192). The MFI, AC, and RC features cover B cells, CDCs, mature T cell stages, monocytes, myeloid cells, TBNK (T cells, B cells, natural killer cells) panels, and Treg panels. The MP feature consists of CDC and TBNK panels. In the initial GWAS analysis, immune traits were examined using data from 3,757 individuals of European descent, ensuring no overlap between the cohorts. Genotyping was performed using a high-density array, targeting around 22 million SNPs, which were subsequently imputed using a Sardinian sequence-based reference panel 15 . Associations were evaluated after adjusting for covariates such as sex, age, and age-squared. 2.3. Selection of IVs The significance threshold for the independent variables of each immune trait was established at 5×10 − 8 . The PLINK software (version v1.90) was used to perform a clumping procedure to prune these SNPs, with a threshold of linkage disequilibrium (LD) r2 < 0.001 within a 10,000 kb distance 16 . The LD r2 values were computed using the 1000 Genomes Project as a reference panel. A significance threshold of 5×10 − 8 was employed for the sepsis analysis. To evaluate the strength of the IVs and mitigate potential weak instrumental bias, the proportion of phenotypic variation explained (PVE) and F statistic were calculated for each IV. 2.4. Statistical analysis All statistical analyses were conducted using R 4.3.1 software ( http://www.Rproject.org ). In our study, we aimed to explore the possible relationship between 731 immunophenotypes and sepsis. To achieve this, we utilized two statistical methods, namely the inverse variance weighting (IVW) 17 and the weighted median-based 18 MR Egger methods, which were implemented using the 'Two Sample MR' package. We evaluated the heterogeneity among the chosen IVs by employing Cochran's Q statistic and associated p-values. In cases where the null hypothesis was rejected, we adopted random effects IVW instead of fixed-effects IVW 17 . To account for potential horizontal pleiotropy, we applied the MR-Egger method, which detects the presence of horizontal pleiotropy by examining the significance of the intercept term 19 . Furthermore, we utilized scatter plots and funnel plots to assess the robustness and heterogeneity of the associations. The scatter plots visually demonstrated that the outcomes were not affected by outliers, whereas the funnel plots showcased the consistency of the associations and the lack of heterogeneity. 2.5. scRNA-seq data collection and preprocess Whole-blood single-cell transcriptome data for 26 patients with all-cause sepsis and 6 age- and sex-matched healthy controls (HCs) were collected from previous study at Zenodo ( https://doi.org/10.5281/zenodo.7723202 ) 20 . The methods of quality control and cell annotation are consistent with the original study. 2.6. scRNA-seq data analysis During the analysis of processed scRNA-seq data, FindAllMarkers function in Seurat 21 was used to identify DEGs. DEG lists were filtered, only genes expressed in at least 10% of cluster cells and expression log2 fold change > 0.25 were left. And then clusterProfiler 3 was used to perform GO pathway enrichment analysis (pvalueCutoff = 0.05) 22 . All the plots were plotted by ggplot2. 3. Result 3.1. Exploration of the causal effect of immunophenotypes on sepsis After applying Bonferroni adjustment ( P < 6.84×10 − 5 = 0.05/731), we observed a protective effect of a specific immunophenotype, HLA DR + natural killer, in relation to sepsis. Using the IVW method, the odds ratio (OR) for the association between FSC-A on HLA DR + natural killer and sepsis risk was estimated to be 0.876 (95% CI = 0.824 ~ 0.932, P = 2.62 × 10 − 5 ). Similar results were obtained with four other methods: weighted mode (OR = 0.888, 95% CI = 0.821 ~ 0.959, P = 0.057), weighted median (OR = 0.881, 95% CI = 0.823 ~ 0.942, P = 2.09 × 10 − 4 ), MR Egger (OR = 0.917, 95% CI = 0.809 ~ 1.038, P = 0.305), and simple mode (OR = 0.862, 95% CI = 0.773 ~ 0.961, P = 0.075). Moreover, the presence of horizontal pleiotropy was effectively ruled out by the intercept of MR-Egger analysis, offering increased support for the robustness and reliability of the observed associations. Detailed sensitivity analysis provided additional evidence of the robustness of the causal relationships. The stability of the results was further demonstrated through scatter plots and funnel plots. 3.2. scRNA-seq analysis reveals the immunosuppressive microenvironment of sepsis patients We conducted a study using single-cell RNA sequencing data from 26 patients diagnosed with all-cause sepsis and 6 age- and sex-matched healthy controls (HCs). By annotating the cells, we identified the main cell types in the dataset, including T cells ( CD3D , CD4 , CD8A ), B cells ( CD19 ), NK cells ( NKG7 ), and neutrophils ( CSF3R ) (A-B). Notably, we observed a distinct difference in immune cell composition between sepsis patients and healthy controls. When analyzing the relative proportions of different cell types in both the healthy control group and sepsis patients, we found a significant increase in a subset of S100A8 + S100A9 + neutrophils in sepsis patients. This suggests that these cells may play a crucial role in the development of sepsis. Additionally, we performed pathway enrichment analysis on the differentially expressed genes in this cell subset between healthy and sepsis patients. The results revealed an enrichment in the type I interferon signaling pathway, highlighting a close association between this cell subtype and the excessive inflammatory response seen in sepsis. In addition to this, through the analysis of the ratios of NK cells and T cells in each participant, we were able to determine a notable decrease in the proportions of T cells in individuals with sepsis when compared to the control group of healthy individuals. This suggests that sepsis patients may exhibit immunosuppressive features. Overall, our findings shed light on the alterations in the immune system of sepsis patients and provide important insights into the immunopathological mechanisms underlying sepsis. 4. Discussion Using publicly available genetic data, this research undertook a comprehensive examination of the causal connection between sepsis and 731 immune cell traits through MR. This pioneering investigation represents the first exploration of the link between sepsis and various immune cell phenotypes using this innovative approach. Sepsis-related immune paralysis is primarily driven by dysfunctional immune cells, leading to immunosuppression and a significant cause of mortality 23 . Therefore, this study employed Mendelian randomization and an extensive analysis of single-cell transcriptomes to investigate the characteristics and interplay of immune cells in sepsis, providing unique insights into its treatment. This study marks a significant achievement as it unveils the beneficial impact of HLA DR + natural killer (NK) cells against sepsis. NK cells serve as a vital component of the innate immune system and play a crucial role in mediating the natural immune response. Unlike the reliance on antibodies and complement, NK cells directly eliminate target cells, such as infected, foreign, stressed, or transformed cells, thereby ensuring the protection of the host. Proinflammatory cytokines, namely interferon-γ and granulocyte macrophage colony-stimulating factor, are primarily produced by NK cells, and they play an essential role in combatting infections 24 . However, it is worth noting that their rapid response may also give rise to excessive and harmful inflammation 25 . In humans, NK cells express surface markers involved in differentiation, migration, and cytolysis, and different subsets of NK cells have varying immune functions 26 . Therefore, detection of changes in NK cell subsets is crucial for a more comprehensive understanding of NK cell functions in sepsis. The presence of HLA-DR, a critical antigen located on the surface of monocytes, assumes a significant role in identifying foreign antigens during the activation of specific T cells for immune response. Numerous clinical and experimental animal studies have observed a decrease in NK cells during sepsis, and it is believed that NK cells expressing HLA-DR are actively proliferating during inflammatory disorders. This explains why these cells are present in high numbers in peripheral blood 27,28 . Notably, HLA DR + NK cells possess phenotypic characteristics of both NK cells and dendritic cells, allowing them to produce pro-inflammatory cytokines 29 . The expression of HLA-DR on NK cells is an indicator of cell activation. HLA DR + NK cells are associated with a higher level of IFN-γ production, and IFN-γ stimulates the expression of HLA-DR on NK cells 30 . Moreover, these cells have the ability to uptake and direct CD4 + and CD8 + T cells towards specific antigens, thereby inducing their activation and proliferation 29,31 . Additionally, HLA-DR + NK cells play a regulatory role in both innate and adaptive immune responses, making them valuable contributors to the innate immune system 32 . Exploring immunotherapy utilizing NK cells holds promising potential for the treatment of various cancers 33 . In our study, a higher expression level of HLA-DR on NK cells is associated with a lower risk of sepsis. The discovery of the protective effect of HLA DR + NK cells in sepsis not only adds new dimensions to our understanding but also paves the way for potential immunotherapy strategies in sepsis. In order to gain further insights into the characteristics of immune cell subtypes in sepsis, we conducted an analysis on single-cell transcriptome data. Our findings revealed a noticeable increase in the subpopulations of neutrophils expressing S100A8 and S100A9 in sepsis patients, indicating their crucial role in the development of sepsis 34 . S100A8 and S100A9 are pro-inflammatory molecules that are released by myeloid cells in various acute and chronic inflammatory conditions. These molecules exist as inactive heterodimers known as S100A8/A9 or calprotectins within neutrophils, and are rapidly released upon activation. It is believed that they contribute to the recruitment of neutrophils and the progression of the disease 35,36 . Moreover, the overexpression of S100A8 and S100A9 accelerates the release of additional cytokines from neutrophils and macrophages, triggering a vicious cycle that exacerbates the inflammatory response 37 . These proteins also serve as molecular markers associated with inflammation 38–40 . To gain further insight, we conducted pathway enrichment analysis on the differentially expressed genes within the S100A8 + S100A9 + neutrophil subpopulation in both healthy individuals and sepsis patients. The results revealed a significant enrichment of the type I interferon signaling pathway, providing additional evidence of the strong link between this particular neutrophil subpopulation and the excessive inflammatory response observed in sepsis 41 . Based on our findings, it is evident that the significantly elevated level of the S100A8 + S100A9 + neutrophil subpopulation can serve as a crucial marker for assessing the severity of inflammation and predicting the prognosis of sepsis. This understanding could potentially contribute to improved diagnostic and therapeutic strategies for sepsis. It has been observed through the analysis of the ratio between natural killer cells (NK cells) and T cells in sepsis patients that the T cell ratio is significantly lower compared to healthy individuals. This finding suggests the presence of immunosuppression in sepsis patients. At the onset of sepsis, patients exhibit an extreme immune response, followed by persistent immunosuppression in later stages. Ultimately, this sepsis-induced immunosuppression can lead to fatal outcomes. The immunocompromised state in the late stages of sepsis is attributed to the upregulation of myelosuppressive cells and reduced immune activity in lymphocytes, known as lymphocyte hypofunction 42 . T cell responses, specifically those mediated by CD4 and CD8 αβ T cells, are impaired in sepsis patients 43 . Additionally, severe and transient lymphopenia induced by sepsis plays a crucial role in weakening T cell immunity. The quantity of T cells during infection is closely linked to the initiation of inflammatory responses. Research on tumors and inflammation has demonstrated that a decrease in T cells can hinder immune function in the body 44–47 . Therefore, promoting the restoration of T cell numbers and function in sepsis holds great potential as an effective strategy to reduce sepsis mortality and improve prognosis. A Mendelian randomization trial has established that natural killer cells expressing HLA-DR possess a protective effect against sepsis. Further analysis of the transcriptome of individual immune cells has revealed specific changes in subpopulations of neutrophils, characterized by the expression of S100A8 + and S100A9+ , as well as a notable decrease in the proportion of T cells. These alterations may serve as potential indicators for the development and prognosis of sepsis. Therefore, it can be deduced that sepsis is a multifaceted disorder characterized by an immune system dysfunction, where the involvement of immune cells is pivotal in both its onset and severity. Nevertheless, it is crucial to acknowledge that this study did not investigate the distinct immunomodulatory and targeting functions of various subtypes of NK cells, T cells, and neutrophils in the context of sepsis, thereby highlighting the necessity for additional scientific investigation. By focusing on immune cells as a treatment strategy for sepsis, it may be possible to restore immune balance and improve patient outcomes. 5. Conclusions In this study, we have undertaken a comprehensive bidirectional MR analysis to validate the causative associations between different immunophenotypes and sepsis. Our research sheds light on the intricate network of interactions that occur between the immune system and sepsis. The findings offer valuable insights into the complex interplay between these two factors. Additionally, our study sheds light on potential therapeutic approaches in sepsis that focus on NK cells. Moreover, our study presents valuable information regarding the occurrence and prognosis of sepsis. To further advance our understanding of sepsis, it is crucial to investigate the precise mechanism underlying the imbalance of innate immune cells, which will serve as a theoretical basis for developing effective treatment strategies. Declarations Funding None. Acknowledgement We thank Orrù V. et al. for sharing the GWAS summary data from the published manuscript. Conflict of Interest The author declares no conflict of interest. Data Availability All data can be provided as needed. Author Contributions JZ and GH conceived and designed the study. RD and YF analyzed the data. RD and QL wrote the manuscript. All authors reviewed and approved the final version of the manuscript. 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Supplementary Files HorizontalMultipleValidityTest.xlsx Sepsis.pdf exposure.xlsx forestplot.pdf funnelplot.pdf harmonisedata.xlsx heterogeneitytest.xlsx immunecellsepsis.xlsx leaveoneoutplot.pdf mrresult.xlsx outcome.xlsx scatterplot.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4022923","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276909541,"identity":"ba54f1bd-7b7f-4f21-90fc-0eb26f526261","order_by":0,"name":"Ruiming Deng","email":"","orcid":"","institution":"Ganzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruiming","middleName":"","lastName":"Deng","suffix":""},{"id":276909542,"identity":"5659a506-3924-41f8-87ea-54548f725e5e","order_by":1,"name":"Qizhi Liao","email":"","orcid":"","institution":"Ganzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qizhi","middleName":"","lastName":"Liao","suffix":""},{"id":276909543,"identity":"c8de642a-bb7e-47f4-964f-5c90885da63c","order_by":2,"name":"Yan Fang","email":"","orcid":"","institution":"Ganzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Fang","suffix":""},{"id":276909544,"identity":"1582f5b0-e440-4ee4-aedc-b38b7d8dfde3","order_by":3,"name":"Guiming Huang","email":"","orcid":"","institution":"Ganzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guiming","middleName":"","lastName":"Huang","suffix":""},{"id":276909545,"identity":"47f5df56-e90c-4244-b374-d3ccaf81add1","order_by":4,"name":"Juan Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3PMWrDMBTG8QcCZVHq9ZkG5woKgay5QA6hR6BbIVPQkEHGRRrakgP0Et3SbjYBTUpmDx0ceoJsHQotnVtsZ+ug3/z+8D2AKPqH+Lgy+0/9JeauqBqlN93JFbK8EYFlUvilbILvTjIcFHJo2VTiYpae7liPYde5xdRy2iHMNBkOibtX7cmosiiPgl6fzE1NLyPAcHhuT4AsqjWSeSt9TYGDxNseScklmZrsiizrkSAVE2PVVNbLn7xPIqr8HUKZpQ+eoQpedP4ydq7Zgy5FMtiezx96kyXusT35RVx2HkVRFP3pG01TTgoZne7rAAAAAElFTkSuQmCC","orcid":"","institution":"Ganzhou People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Juan","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-03-07 04:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4022923/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4022923/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52437848,"identity":"e0970380-5484-49db-932f-69fa986658d5","added_by":"auto","created_at":"2024-03-11 16:33:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":536190,"visible":true,"origin":"","legend":"\u003cp\u003eA, Uniform Manifold Approximation and Projection (UMAP) for annotated scRNA-seq data of sepsis patients (n = 26) and HCs (n = 6). B, Feature plot of marker genes for T cells, B cells, NK cells and neutrophils. C, Proportion of each types of cells in HCs and sepsis patients. D, Gene Ontology (GO) terms of genes significantly enriched in HCs and sepsis patients in \u003cem\u003eS100A8-9\u003c/em\u003ehi neutrophils. E, Proportion of NK cells and T cells in HCs and sepsis tissues.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4022923/v1/56cffd96a01296a6e0a5ff3b.png"},{"id":52795040,"identity":"3c1944e5-daee-47b9-af41-a71aff8160f3","added_by":"auto","created_at":"2024-03-15 21:22:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":785952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022923/v1/6855e168-3d5b-4d96-ac08-932fbb2d5e20.pdf"},{"id":52437850,"identity":"b9f2c831-29a4-4dd9-8da0-5a5059a04472","added_by":"auto","created_at":"2024-03-11 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16:41:09","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":4946,"visible":true,"origin":"","legend":"","description":"","filename":"leaveoneoutplot.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022923/v1/a8e7b2026e8021e660f069a6.pdf"},{"id":52437859,"identity":"0d065e90-18b7-4a4d-8a14-c1b855ebf037","added_by":"auto","created_at":"2024-03-11 16:33:09","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":11113,"visible":true,"origin":"","legend":"","description":"","filename":"mrresult.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4022923/v1/52669675750231857638010d.xlsx"},{"id":52438612,"identity":"3e20a6c1-2180-428d-b064-c562b95b913e","added_by":"auto","created_at":"2024-03-11 16:41:09","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":10763,"visible":true,"origin":"","legend":"","description":"","filename":"outcome.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4022923/v1/87ed1e019e122314bce3dd4a.xlsx"},{"id":52437853,"identity":"1e3ec1df-49b6-4730-a110-20a21cbd487d","added_by":"auto","created_at":"2024-03-11 16:33:09","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":5324,"visible":true,"origin":"","legend":"","description":"","filename":"scatterplot.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022923/v1/be079b3f7329147eccd45e37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding the Relationship Between Immune Cells and Sepsis Through Mendelian Randomization and Single-Cell Transcriptome Analysis","fulltext":[{"header":"Highlights","content":"\u003col\u003e\n \u003cli\u003eHLA DR\u003csup\u003e+\u003c/sup\u003e natural killer cells have a protective effect against sepsis.\u003c/li\u003e\n \u003cli\u003eSepsis is not associated with other immune phenotype, except for the protective effects of one immunophenotype, implying potential therapeutic implications.\u003c/li\u003e\n \u003cli\u003eSepsis shows a significant increase in the\u0026nbsp;\u003cem\u003eS100A8+S100A9+\u003c/em\u003e neutrophil subpopulation, providing insights into the immune response.\u003c/li\u003e\n \u003cli\u003eT cell proportion is lower in sepsis patients, suggesting potential T cell dysfunction in the pathogenesis.\u003c/li\u003e\n \u003cli\u003eThis study unveils immune cell characteristics in sepsis, offering a potential molecular target for diagnosis and treatment.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eSepsis is a medical condition characterized by an imbalance in the immune system triggered by infection from pathogens, resulting in severe organ dysfunction\u003csup\u003e1\u003c/sup\u003e. The underlying cause of sepsis is an abnormality in the host's natural and acquired immune responses\u003csup\u003e2\u003c/sup\u003e. The failure and suppression of the immune system due to sepsis can ultimately lead to death\u003csup\u003e3\u003c/sup\u003e. Sepsis is commonly observed among patients admitted to intensive care units\u003csup\u003e4\u003c/sup\u003e. Severe sepsis can cause hypotension, hypoperfusion, lactic acidosis, acute kidney injury (AKI), and acute respiratory distress syndrome (ARDS)\u003csup\u003e5\u003c/sup\u003e, which contribute to a mortality rate of approximately 26%\u003csup\u003e6\u003c/sup\u003e. Currently, the treatment options for sepsis are limited to antibiotics, fluid administration, and support for cardiovascular and respiratory functions. However, despite the implementation of these interventions, mortality rates for sepsis remain high, with survivors often experiencing a reduced quality of life\u003csup\u003e7\u003c/sup\u003e. Extensive research conducted on immune mechanisms related to sepsis has focused on immunotherapy as a potential new approach for treating sepsis, offering hope for improving patient outcomes.\u003c/p\u003e \u003cp\u003eRecent studies have identified a correlation between extended periods of immune system inactivity and increased mortality rates in sepsis. These periods of immune system inactivity are a result of alterations in immune cells and excessive or unregulated apoptosis\u003csup\u003e8\u003c/sup\u003e. Initially, sepsis is characterized by an excessively activated immune system, but in later stages, it transitions into an inflammatory response accompanied by immune suppression. Abnormal immune responses induced by pathogens, such as excessive inflammation or immune system suppression, play a significant role in the high mortality rates associated with sepsis\u003csup\u003e9\u003c/sup\u003e. Immune disorders, particularly immune suppression in sepsis, are attributed to pathological changes in immune cells, including abnormal activation, extensive cell death, phenotypic variations, and functional alterations\u003csup\u003e10\u003c/sup\u003e. The advanced stages of sepsis are characterized by increased apoptosis of immune cells and a decrease in INF-γ production, which can lead to secondary infections and even death\u003csup\u003e11\u003c/sup\u003e. Therefore, it is crucial to suppress the apoptosis of immune cells during sepsis in order to mitigate cell damage and prevent or reduce immune system suppression\u003csup\u003e12\u003c/sup\u003e. Neutrophils, macrophages, dendritic cells, and natural killer T cells are innate immune cells that mediate the inflammatory responses and organ damage caused by sepsis\u003csup\u003e13\u003c/sup\u003e. The precise functions of numerous immune cell types in sepsis remain unclear, limiting our understanding of effective treatment methods. As a result, this study is conducted with the objective of gaining new insights into sepsis treatment by conducting a comprehensive analysis of single-cell omics data and utilizing MR to shed light on the role of immune cells in sepsis.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eWe performed an extensive analysis to examine the correlation between sepsis and 731 immune cell signatures, which were classified into 7 distinct groups. Our methodology involved using a two-sample MR method that utilized genetic variations as instrumental variables (IVs) to represent possible risk factors. To ensure the validity of the IVs, three important assumptions need to be met: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) there is a direct relationship between the genetic variations and the exposure; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the genetic variations being studied are not influenced by any additional factors that could potentially confound the relationship between the exposure and the outcome; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the genetic variations do not impact the outcome through alternative pathways other than the exposure. Ethical approval was obtained from the institutional review boards pertinent to the studies included in our analysis. Additionally, informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Immunity-wide GWAS data sources\u003c/h2\u003e \u003cp\u003eThe GWAS Catalog offers comprehensive genetic associations data for a wide range of immune traits. This includes a total of 731 immunophenotypes, with accession numbers ranging from GCST0001391 to GCST0002121\u003csup\u003e14\u003c/sup\u003e. These traits encompass various features such as absolute cell (AC) counts (n\u0026thinsp;=\u0026thinsp;118), median fluorescence intensities (MFI) indicating surface antigen levels (n\u0026thinsp;=\u0026thinsp;389), morphological parameters (MP) (n\u0026thinsp;=\u0026thinsp;32), and relative cell (RC) counts (n\u0026thinsp;=\u0026thinsp;192). The MFI, AC, and RC features cover B cells, CDCs, mature T cell stages, monocytes, myeloid cells, TBNK (T cells, B cells, natural killer cells) panels, and Treg panels. The MP feature consists of CDC and TBNK panels. In the initial GWAS analysis, immune traits were examined using data from 3,757 individuals of European descent, ensuring no overlap between the cohorts. Genotyping was performed using a high-density array, targeting around 22\u0026nbsp;million SNPs, which were subsequently imputed using a Sardinian sequence-based reference panel\u003csup\u003e15\u003c/sup\u003e. Associations were evaluated after adjusting for covariates such as sex, age, and age-squared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Selection of IVs\u003c/h2\u003e \u003cp\u003eThe significance threshold for the independent variables of each immune trait was established at 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e. The PLINK software (version v1.90) was used to perform a clumping procedure to prune these SNPs, with a threshold of linkage disequilibrium (LD) r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within a 10,000 kb distance\u003csup\u003e16\u003c/sup\u003e. The LD r2 values were computed using the 1000 Genomes Project as a reference panel. A significance threshold of 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e was employed for the sepsis analysis. To evaluate the strength of the IVs and mitigate potential weak instrumental bias, the proportion of phenotypic variation explained (PVE) and F statistic were calculated for each IV.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R 4.3.1 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.Rproject.org\u003c/span\u003e\u003cspan address=\"http://www.Rproject.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In our study, we aimed to explore the possible relationship between 731 immunophenotypes and sepsis. To achieve this, we utilized two statistical methods, namely the inverse variance weighting (IVW)\u003csup\u003e17\u003c/sup\u003e and the weighted median-based\u003csup\u003e18\u003c/sup\u003e MR Egger methods, which were implemented using the 'Two Sample MR' package. We evaluated the heterogeneity among the chosen IVs by employing Cochran's Q statistic and associated p-values. In cases where the null hypothesis was rejected, we adopted random effects IVW instead of fixed-effects IVW\u003csup\u003e17\u003c/sup\u003e. To account for potential horizontal pleiotropy, we applied the MR-Egger method, which detects the presence of horizontal pleiotropy by examining the significance of the intercept term\u003csup\u003e19\u003c/sup\u003e. Furthermore, we utilized scatter plots and funnel plots to assess the robustness and heterogeneity of the associations. The scatter plots visually demonstrated that the outcomes were not affected by outliers, whereas the funnel plots showcased the consistency of the associations and the lack of heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. scRNA-seq data collection and preprocess\u003c/h2\u003e \u003cp\u003eWhole-blood single-cell transcriptome data for 26 patients with all-cause sepsis and 6 age- and sex-matched healthy controls (HCs) were collected from previous study at Zenodo (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.7723202\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.7723202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e20\u003c/sup\u003e. The methods of quality control and cell annotation are consistent with the original study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. scRNA-seq data analysis\u003c/h2\u003e \u003cp\u003eDuring the analysis of processed scRNA-seq data, \u003cem\u003eFindAllMarkers\u003c/em\u003e function in Seurat\u003csup\u003e21\u003c/sup\u003e was used to identify DEGs. DEG lists were filtered, only genes expressed in at least 10% of cluster cells and expression log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.25 were left. And then clusterProfiler\u003csup\u003e3\u003c/sup\u003e was used to perform GO pathway enrichment analysis (pvalueCutoff\u0026thinsp;=\u0026thinsp;0.05)\u003csup\u003e22\u003c/sup\u003e. All the plots were plotted by ggplot2.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":" \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Exploration of the causal effect of immunophenotypes on sepsis\u003c/h2\u003e \u003cp\u003eAfter applying Bonferroni adjustment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;6.84\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e = 0.05/731), we observed a protective effect of a specific immunophenotype, HLA DR\u003csup\u003e+\u003c/sup\u003e natural killer, in relation to sepsis. Using the IVW method, the odds ratio (OR) for the association between FSC-A on HLA DR\u003csup\u003e+\u003c/sup\u003e natural killer and sepsis risk was estimated to be 0.876 (95% CI\u0026thinsp;=\u0026thinsp;0.824\u0026thinsp;~\u0026thinsp;0.932, P\u0026thinsp;=\u0026thinsp;2.62 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Similar results were obtained with four other methods: weighted mode (OR\u0026thinsp;=\u0026thinsp;0.888, 95% CI\u0026thinsp;=\u0026thinsp;0.821\u0026thinsp;~\u0026thinsp;0.959, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057), weighted median (OR\u0026thinsp;=\u0026thinsp;0.881, 95% CI\u0026thinsp;=\u0026thinsp;0.823\u0026thinsp;~\u0026thinsp;0.942, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.09 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), MR Egger (OR\u0026thinsp;=\u0026thinsp;0.917, 95% CI\u0026thinsp;=\u0026thinsp;0.809\u0026thinsp;~\u0026thinsp;1.038, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.305), and simple mode (OR\u0026thinsp;=\u0026thinsp;0.862, 95% CI\u0026thinsp;=\u0026thinsp;0.773\u0026thinsp;~\u0026thinsp;0.961, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.075). Moreover, the presence of horizontal pleiotropy was effectively ruled out by the intercept of MR-Egger analysis, offering increased support for the robustness and reliability of the observed associations. Detailed sensitivity analysis provided additional evidence of the robustness of the causal relationships. The stability of the results was further demonstrated through scatter plots and funnel plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. scRNA-seq analysis reveals the immunosuppressive microenvironment of sepsis patients\u003c/h2\u003e \u003cp\u003eWe conducted a study using single-cell RNA sequencing data from 26 patients diagnosed with all-cause sepsis and 6 age- and sex-matched healthy controls (HCs). By annotating the cells, we identified the main cell types in the dataset, including T cells (\u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD4\u003c/em\u003e, \u003cem\u003eCD8A\u003c/em\u003e), B cells (\u003cem\u003eCD19\u003c/em\u003e), NK cells (\u003cem\u003eNKG7\u003c/em\u003e), and neutrophils (\u003cem\u003eCSF3R\u003c/em\u003e) (A-B). Notably, we observed a distinct difference in immune cell composition between sepsis patients and healthy controls. When analyzing the relative proportions of different cell types in both the healthy control group and sepsis patients, we found a significant increase in a subset of \u003cem\u003eS100A8\u0026thinsp;+\u0026thinsp;S100A9\u0026thinsp;+\u003c/em\u003e\u0026thinsp;neutrophils in sepsis patients. This suggests that these cells may play a crucial role in the development of sepsis. Additionally, we performed pathway enrichment analysis on the differentially expressed genes in this cell subset between healthy and sepsis patients. The results revealed an enrichment in the type I interferon signaling pathway, highlighting a close association between this cell subtype and the excessive inflammatory response seen in sepsis. In addition to this, through the analysis of the ratios of NK cells and T cells in each participant, we were able to determine a notable decrease in the proportions of T cells in individuals with sepsis when compared to the control group of healthy individuals. This suggests that sepsis patients may exhibit immunosuppressive features. Overall, our findings shed light on the alterations in the immune system of sepsis patients and provide important insights into the immunopathological mechanisms underlying sepsis.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUsing publicly available genetic data, this research undertook a comprehensive examination of the causal connection between sepsis and 731 immune cell traits through MR. This pioneering investigation represents the first exploration of the link between sepsis and various immune cell phenotypes using this innovative approach. Sepsis-related immune paralysis is primarily driven by dysfunctional immune cells, leading to immunosuppression and a significant cause of mortality\u003csup\u003e23\u003c/sup\u003e. Therefore, this study employed Mendelian randomization and an extensive analysis of single-cell transcriptomes to investigate the characteristics and interplay of immune cells in sepsis, providing unique insights into its treatment.\u003c/p\u003e \u003cp\u003eThis study marks a significant achievement as it unveils the beneficial impact of HLA DR\u003csup\u003e+\u003c/sup\u003e natural killer (NK) cells against sepsis. NK cells serve as a vital component of the innate immune system and play a crucial role in mediating the natural immune response. Unlike the reliance on antibodies and complement, NK cells directly eliminate target cells, such as infected, foreign, stressed, or transformed cells, thereby ensuring the protection of the host. Proinflammatory cytokines, namely interferon-γ and granulocyte macrophage colony-stimulating factor, are primarily produced by NK cells, and they play an essential role in combatting infections\u003csup\u003e24\u003c/sup\u003e. However, it is worth noting that their rapid response may also give rise to excessive and harmful inflammation\u003csup\u003e25\u003c/sup\u003e. In humans, NK cells express surface markers involved in differentiation, migration, and cytolysis, and different subsets of NK cells have varying immune functions\u003csup\u003e26\u003c/sup\u003e. Therefore, detection of changes in NK cell subsets is crucial for a more comprehensive understanding of NK cell functions in sepsis. The presence of HLA-DR, a critical antigen located on the surface of monocytes, assumes a significant role in identifying foreign antigens during the activation of specific T cells for immune response. Numerous clinical and experimental animal studies have observed a decrease in NK cells during sepsis, and it is believed that NK cells expressing HLA-DR are actively proliferating during inflammatory disorders. This explains why these cells are present in high numbers in peripheral blood\u003csup\u003e27,28\u003c/sup\u003e. Notably, HLA DR\u003csup\u003e+\u003c/sup\u003e NK cells possess phenotypic characteristics of both NK cells and dendritic cells, allowing them to produce pro-inflammatory cytokines\u003csup\u003e29\u003c/sup\u003e. The expression of HLA-DR on NK cells is an indicator of cell activation. HLA DR\u003csup\u003e+\u003c/sup\u003e NK cells are associated with a higher level of IFN-γ production, and IFN-γ stimulates the expression of HLA-DR on NK cells\u003csup\u003e30\u003c/sup\u003e. Moreover, these cells have the ability to uptake and direct CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells towards specific antigens, thereby inducing their activation and proliferation \u003csup\u003e29,31\u003c/sup\u003e. Additionally, HLA-DR\u0026thinsp;+\u0026thinsp;NK cells play a regulatory role in both innate and adaptive immune responses, making them valuable contributors to the innate immune system\u003csup\u003e32\u003c/sup\u003e. Exploring immunotherapy utilizing NK cells holds promising potential for the treatment of various cancers\u003csup\u003e33\u003c/sup\u003e. In our study, a higher expression level of HLA-DR on NK cells is associated with a lower risk of sepsis. The discovery of the protective effect of HLA DR\u003csup\u003e+\u003c/sup\u003e NK cells in sepsis not only adds new dimensions to our understanding but also paves the way for potential immunotherapy strategies in sepsis.\u003c/p\u003e \u003cp\u003eIn order to gain further insights into the characteristics of immune cell subtypes in sepsis, we conducted an analysis on single-cell transcriptome data. Our findings revealed a noticeable increase in the subpopulations of neutrophils expressing \u003cem\u003eS100A8\u003c/em\u003e and \u003cem\u003eS100A9\u003c/em\u003e in sepsis patients, indicating their crucial role in the development of sepsis\u003csup\u003e34\u003c/sup\u003e. \u003cem\u003eS100A8\u003c/em\u003e and \u003cem\u003eS100A9\u003c/em\u003e are pro-inflammatory molecules that are released by myeloid cells in various acute and chronic inflammatory conditions. These molecules exist as inactive heterodimers known as \u003cem\u003eS100A8/A9\u003c/em\u003e or calprotectins within neutrophils, and are rapidly released upon activation. It is believed that they contribute to the recruitment of neutrophils and the progression of the disease\u003csup\u003e35,36\u003c/sup\u003e. Moreover, the overexpression of \u003cem\u003eS100A8\u003c/em\u003e and \u003cem\u003eS100A9\u003c/em\u003e accelerates the release of additional cytokines from neutrophils and macrophages, triggering a vicious cycle that exacerbates the inflammatory response\u003csup\u003e37\u003c/sup\u003e. These proteins also serve as molecular markers associated with inflammation\u003csup\u003e38\u0026ndash;40\u003c/sup\u003e. To gain further insight, we conducted pathway enrichment analysis on the differentially expressed genes within the \u003cem\u003eS100A8\u0026thinsp;+\u0026thinsp;S100A9\u0026thinsp;+\u003c/em\u003e\u0026thinsp;neutrophil subpopulation in both healthy individuals and sepsis patients. The results revealed a significant enrichment of the type I interferon signaling pathway, providing additional evidence of the strong link between this particular neutrophil subpopulation and the excessive inflammatory response observed in sepsis\u003csup\u003e41\u003c/sup\u003e. Based on our findings, it is evident that the significantly elevated level of the \u003cem\u003eS100A8\u0026thinsp;+\u0026thinsp;S100A9\u0026thinsp;+\u003c/em\u003e\u0026thinsp;neutrophil subpopulation can serve as a crucial marker for assessing the severity of inflammation and predicting the prognosis of sepsis. This understanding could potentially contribute to improved diagnostic and therapeutic strategies for sepsis.\u003c/p\u003e \u003cp\u003eIt has been observed through the analysis of the ratio between natural killer cells (NK cells) and T cells in sepsis patients that the T cell ratio is significantly lower compared to healthy individuals. This finding suggests the presence of immunosuppression in sepsis patients. At the onset of sepsis, patients exhibit an extreme immune response, followed by persistent immunosuppression in later stages. Ultimately, this sepsis-induced immunosuppression can lead to fatal outcomes. The immunocompromised state in the late stages of sepsis is attributed to the upregulation of myelosuppressive cells and reduced immune activity in lymphocytes, known as lymphocyte hypofunction\u003csup\u003e42\u003c/sup\u003e. T cell responses, specifically those mediated by CD4 and CD8 αβ T cells, are impaired in sepsis patients\u003csup\u003e43\u003c/sup\u003e. Additionally, severe and transient lymphopenia induced by sepsis plays a crucial role in weakening T cell immunity. The quantity of T cells during infection is closely linked to the initiation of inflammatory responses. Research on tumors and inflammation has demonstrated that a decrease in T cells can hinder immune function in the body\u003csup\u003e44\u0026ndash;47\u003c/sup\u003e. Therefore, promoting the restoration of T cell numbers and function in sepsis holds great potential as an effective strategy to reduce sepsis mortality and improve prognosis.\u003c/p\u003e \u003cp\u003eA Mendelian randomization trial has established that natural killer cells expressing HLA-DR possess a protective effect against sepsis. Further analysis of the transcriptome of individual immune cells has revealed specific changes in subpopulations of neutrophils, characterized by the expression of \u003cem\u003eS100A8\u0026thinsp;+\u003c/em\u003e\u0026thinsp;and \u003cem\u003eS100A9+\u003c/em\u003e, as well as a notable decrease in the proportion of T cells. These alterations may serve as potential indicators for the development and prognosis of sepsis. Therefore, it can be deduced that sepsis is a multifaceted disorder characterized by an immune system dysfunction, where the involvement of immune cells is pivotal in both its onset and severity. Nevertheless, it is crucial to acknowledge that this study did not investigate the distinct immunomodulatory and targeting functions of various subtypes of NK cells, T cells, and neutrophils in the context of sepsis, thereby highlighting the necessity for additional scientific investigation. By focusing on immune cells as a treatment strategy for sepsis, it may be possible to restore immune balance and improve patient outcomes.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, we have undertaken a comprehensive bidirectional MR analysis to validate the causative associations between different immunophenotypes and sepsis. Our research sheds light on the intricate network of interactions that occur between the immune system and sepsis. The findings offer valuable insights into the complex interplay between these two factors. Additionally, our study sheds light on potential therapeutic approaches in sepsis that focus on NK cells. Moreover, our study presents valuable information regarding the occurrence and prognosis of sepsis. To further advance our understanding of sepsis, it is crucial to investigate the precise mechanism underlying the imbalance of innate immune cells, which will serve as a theoretical basis for developing effective treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Orrù V. et al. for sharing the GWAS summary data from the published manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data can be provided as needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJZ and GH conceived and designed the study. RD and YF analyzed the data. RD and QL wrote the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLarsen FF, Petersen JA. Novel biomarkers for sepsis: A narrative review. Eur J Intern Med. 2017;45:46\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang Y, Yang X, Shu H, et al. Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury. Hereditas. 2021;158(1):13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu CM, Chiu LC, Yu CC, et al. Increased Death of Peripheral Blood Mononuclear Cells after TLR4 Inhibition in Sepsis Is Not via TNF/TNF Receptor-Mediated Apoptotic Pathway. Mediat Inflamm. 2021;2021:2255017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG. 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Biomaterials. 2021;264:120405.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, Genome-wide association studies, Quantitative trait loci, Immune cells, Sepsis","lastPublishedDoi":"10.21203/rs.3.rs-4022923/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4022923/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Sepsis is a condition characterized by an immune system imbalance, leading to high rates of morbidity and mortality. Although immune cells have the ability to eliminate infection, they can also cause tissue damage. Therefore, understanding the role of different immune cells in sepsis is crucial for effective treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e The goal of this research is to examine the correlation between sepsis and immune cells, as well as their specific traits, through the utilization of Mendelian randomization (MR) analysis and single-cell transcriptome analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eTo investigate the causal association between immune cell signals and the susceptibility to sepsis, we conducted a comprehensive two-sample MR analysis utilizing publicly accessible genetic data. The analysis focused on four types of immune signals: median fluorescence intensity (MFI), relative cell number (RC), absolute cell number (AC), and morphological parameters (MP). Additionally, single-cell transcriptome sequencing data analysis techniques were used to explore the characteristics of immune cells in sepsis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eAfter correcting for multiple testing, there was no statistically significant impact of sepsis on immune phenotype. However, our research findings support the notion that the FSC-A parameter on the HLA DR\u003csup\u003e+\u003c/sup\u003e natural killer immune cell phenotype has a protective effect against sepsis. Furthermore, analysis of single-cell RNA sequencing data revealed a significant increase in the \u003cem\u003eS100A8+S100A9+\u003c/em\u003e neutrophil subpopulation in sepsis, while the proportion of T cells was significantly lower compared to healthy controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our results suggest that HLA DR\u003csup\u003e+\u003c/sup\u003e natural killer cells have a significant protective effect on sepsis. Additionally, the \u003cem\u003eS100A8+S100A9+\u003c/em\u003e neutrophil subpopulation is significantly increased in sepsis.\u003c/p\u003e","manuscriptTitle":"Understanding the Relationship Between Immune Cells and Sepsis Through Mendelian Randomization and Single-Cell Transcriptome Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 16:33:04","doi":"10.21203/rs.3.rs-4022923/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7d76672-798a-4cb0-9026-d569b0ee3e88","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-15T21:14:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 16:33:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4022923","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4022923","identity":"rs-4022923","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}