Integrated multi-omics reveals the LAT-STXBP6-CD8+T cell axis in promoting chronic rhinosinusitis progression

Integrated multi-omics reveals the LAT-STXBP6-CD8+T cell axis in promoting chronic rhinosinusitis progression | 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 Integrated multi-omics reveals the LAT-STXBP6-CD8+T cell axis in promoting chronic rhinosinusitis progression YONG ZHUANG, CHEN GAO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6934487/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 Introduction: The pathogenesis of chronic rhinosinusitis (CRS) remains complex and has not yet been fully elucidated. A significant proportion of patients demonstrate limited responses to current pharmacological treatments, highlighting the need for targeted therapeutics that address the core mechanisms of the disease. The critical roles of dysregulated protein-protein interaction networks and immune microenvironment remodeling in CRS progression are increasingly recognized; however, the specific genetic, protein, and immunoregulatory networks remain to be systematically deciphered. Methods: This study employed a multi-stage integrated analysis strategy combining Mendelian randomization (MR), mediation analysis, and proteomics. We utilized pQTL data from the deCODE and UKB-PP databases alongside CRS GWAS data from the FinnGen database to perform MR, screening for proteins associated with CRS. Subsequently, a two-step mediation analysis was conducted to construct a regulatory network encompassing protein-protein interactions and immune microenvironment factors. Results: We identified the LAT-STXBP6-CD8 + T cell regulatory axis, which is significantly associated with CRS risk. Conclusion: This study was the first to systematically elucidate the promoting role of the LAT-STXBP6-CD8 + T cell axis in CRS, providing a theoretical foundation for developing combined immunotherapy strategies targeting the microenvironment. Chronic rhinosinusitis LAT-STXBP6-CD8 + T cell axis Protein-protein interaction network Figures Figure 1 Figure 2 1. Introduction Chronic rhinosinusitis (CRS) is a chronic inflammatory disease that affects the nasal and sinus mucosa, characterized by a complex pathogenesis that remains incompletely understood. While environmental factors, microbial infections, and anatomical abnormalities are recognized as significant contributors, substantial gaps persist in elucidating its core molecular mechanisms. Most patients exhibit limited responses to current pharmacological therapies, and therapeutics specifically targeting the disease etiology are lacking [ 1 ] . Recent studies indicate that dysregulation of protein interaction networks and remodeling of the immune microenvironment may drive the persistent inflammatory progression observed in CRS [ 2 ] . These regulatory imbalances can result in mucosal barrier destruction, amplification of inflammatory signaling, and tissue fibrosis; however, the precise molecular regulatory networks remain undefined. This knowledge gap directly impedes the development of precision therapeutic strategies. Protein networks play a central role in the pathogenesis of CRS. Dynamic regulatory imbalances within these networks are implicated in key pathological processes, including sustained inflammation, mucosal barrier disruption, and tissue remodeling [ 3 ] . Functioning as hubs of complex molecular interactions, protein networks profoundly influence disease progression by coordinating inflammatory signaling pathways, regulating epithelial barrier integrity, and mediating dynamics at the host-microbe interface. In CRS, the abnormal activation or suppression of innate and adaptive immunity-related protein clusters promotes the cascade release of pro-inflammatory cytokines and chemokines, thereby driving excessive activation of local immune responses [ 4 ] . Moreover, dysregulation of protein networks compromises mucosal barrier integrity by disrupting tight junction proteins, which facilitates pathogen penetration and perpetuates a vicious cycle of inflammation [ 5 ] . Furthermore, the reprogramming of tissue repair-associated protein networks promotes aberrant fibroblast proliferation and extracellular matrix deposition, exacerbating fibrotic alterations in sinonasal structures. The pathogenesis of CRS primarily involves an imbalance in the immune microenvironment [ 6 ] . The normal defense mechanisms of the sinonasal mucosa rely on a delicate equilibrium between innate and adaptive immunity. However, in patients with CRS, impaired innate immune barrier function, abnormal immune cell distribution, and persistently activated inflammatory signaling contribute to chronic inflammation, which underpins disease recurrence. Infiltrating immune cells create a self-reinforcing positive feedback loop through the release of pro-inflammatory factors, amplifying local inflammatory responses while inhibiting tissue repair. Concurrently, the exhaustion or dysfunction of regulatory immune cells further diminishes the negative regulation of inflammation. Dynamic shifts in pathogen-commensal microbiota interactions also reshape the immune microenvironment: dysregulated microbial communities activate aberrant immune responses via pattern recognition receptors, disrupting the anti-inflammatory/pro-inflammatory balance and inducing immune polarization. Moreover, chronic inflammation leads to functional distortion of mucosal epithelial cells, characterized by a breakdown in barrier integrity and abnormal secretory profiles, which not only exacerbate pathogen colonization but also amplify immune dysregulation through paracrine signaling. These multi-layered disorders of immune interactions ultimately result in persistent inflammatory damage and pathological remodeling of sinonasal tissues [ 7 ] . Mendelian randomization (MR) combined with mediation analysis offers unique advantages for dissecting protein interactions and the dynamics of the immune microenvironment. MR utilizes genetic variants as instrumental variables to establish causal relationships between specific proteins and disease outcomes, effectively addressing confounding biases and reverse causation that are inherent in observational studies [ 8 ] . Mediation analysis further disentangles causal pathways by distinguishing the direct effects of proteins on diseases from the indirect effects mediated through intermediate proteins or immune cells. This integrative approach is particularly well-suited for exploring protein interaction networks and alterations in the immune microenvironment associated with diseases. It enables the identification of both direct and indirect causal pathways while uncovering critical mediator proteins and immune cell subtypes [ 9 ][ 10 ] . Such strategies deepen mechanistic insights and hold the potential for identifying novel targets for precision therapies. Collectively, the MR-mediation framework serves as a powerful tool for decoding the "gene-protein-phenotype" cascades, providing essential insights into the complex biological mechanisms underlying diseases. In this study, we systematically dissected the genetic regulatory network connecting proteins, proteins, and the immune microenvironment in CRS through multi-stage integrated analyses. First, proteins significantly associated with CRS risk were identified by integrating proteomic quantitative trait loci (pQTL) data from the Icelandic deCODE cohort with CRS genome-wide association study (GWAS) data from the FinnGen database. Second, upstream regulatory factors were validated using pQTL data from the UK Biobank and FinnGen GWAS. Subsequent two-step mediation analysis confirmed a critical protein regulatory axis,LAT-STXBP6. Further mediation analysis revealed that STXBP6 influences CRS via naïve CCR7 + CD8 + T lymphocytes expressing the chemokine receptor CCR7. Our findings demonstrate the pivotal role of the LAT-STXBP6-CD8 + T cell axis in CRS pathogenesis. This multidimensional "gene-protein-immune phenotype" evidence chain not only elucidates the mechanistic contribution of this axis but also provides a theoretical foundation for developing targeted precision therapies. 2. Methods 2.1 Study Design Figure 1 illustrates the overall workflow of the study. Specifically, we conducted a two-sample Mendelian randomization (MR) analysis utilizing proteomic quantitative trait loci (pQTL) data from the deCODE and UKB-PPP databases as exposures, alongside chronic rhinosinusitis genome-wide association study (GWAS) data from the FinnGen database as outcomes. This approach aimed to investigate the causal relationships between proteins and CRS. Subsequently, we employed mediation analysis to identify potential molecular regulatory axes and further explored their downstream mechanisms, ultimately constructing a genetic regulatory network encompassing protein-protein interactions and the immune microenvironment. Throughout the analysis, instrumental variables (IVs) were selected based on stringent inclusion and exclusion criteria, followed by comprehensive sensitivity analyses to ensure the reliability of the MR results. Notably, all analyses adhered to rigorous ethical standards, with datasets obtained from original studies that had received ethical approval and participant informed consent. 2.2 Data Sources Proteomic data were obtained from two sources: (1) pQTL data for the Icelandic population from the deCODE database ( https://www.decode.com/summarydata/ ) [ 11 ] ; (2) plasma proteome pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP) ( https://www.synapse.org/Synapse:syn51364943/wiki/622119 ) [ 12 ] . Immunological trait data (accession numbers: GCST0001391-GCST0002121) were retrieved from the GWAS Catalog, comprising summary statistics for 731 immune features: absolute counts, median fluorescence intensity, morphological parameters, and relative counts. A complete list of immune traits is provided in Table S1 [ 13 ] . The CRS GWAS summary statistics were derived from the FinnGen consortium R12 release, comprising 22,099 cases and 371,520 controls. 2.3 Instrumental Variables (IVs) The selection of instrumental variables (IV) strictly adhered to Mendelian randomization (MR) assumptions [ 14 ] . Initially, single nucleotide polymorphisms (SNPs) that met the genome-wide significance threshold (P < 5.0×10⁻⁸) were retained. Subsequently, independent SNPs were clumped using data from the European 1000 Genomes Project, applying a linkage disequilibrium (LD) threshold of r² < 0.1 and a window size of 10,000 kb [ 15 ] . SNPs exhibiting allele mismatches between exposure and outcome were excluded; palindromic SNPs were addressed using allele frequency data or excluded if such data were unavailable. Finally, SNPs with F-statistics lower than 10 were removed to mitigate weak instrument bias [ 16 ] . 2.4 Mendelian Randomization and Sensitivity Analyses Two-sample Mendelian Randomization (MR) analyses were conducted using the TwoSampleMR package in R. Inverse-variance weighted (IVW) regression was employed as the primary method [ 17 ] , excluding exposures with unreliable IVW estimates. Sensitivity analyses included MR-Egger intercept tests for pleiotropy assessment [ 18 ] , Cochran’s Q tests for heterogeneity evaluation [ 19 ] , and Steiger tests for validating directional causality. These methods were implemented to enhance the robustness of the findings and minimize the risk of reverse causation bias [ 20 ] . 2.5 Mediation Analysis Upstream Analysis We employed a two-step Mendelian randomization (MR) mediation analysis method to explore the protein regulatory network [ 21 ][ 22 ] . First, we calculated the effect size of upstream proteins on chronic sinusitis, denoted as β_all. Second, we determined the effect size of upstream proteins on downstream proteins, denoted as β1, and the effect size of downstream proteins on chronic sinusitis, denoted as β2. Finally, we calculated the mediation effect (β12) as β1 × β2, and the mediation proportion (β12_p) as (β12 / β_all) × 100% (Fig. 2 A). Downstream Analysis We utilized the mediation analysis method to investigate potential downstream mechanisms of proteins. First, we calculated the effect size of proteins on chronic sinusitis, denoted as β_all; then, we assessed the effect size of proteins on immune cells, denoted as β1, and the effect size of immune cells on chronic sinusitis, denoted as β2. Next, we computed the mediation effect (β12) as β1 × β2, and the mediation proportion (β12_p) as (β12 / β_all) × 100% (Fig. 2 B). We employed the delta method to calculate the 95% confidence interval of the mediation effect and determined the statistical p-value of the mediation effect via the delta method to assess its statistical significance. We selected results with a p-value < 0.05 and a mediation proportion greater than 10% for further investigation [ 23 ][ 24 ] . 2.6 Statistical Methods All analyses utilized R software (v4.4.2; https://www.r-project.org/ ). Statistical significance was defined as two-sided P < 0.05. 3. Results 3.1 Identification of CRS-Associated Molecules Utilizing Mendelian randomization (MR) analysis in conjunction with proteomic quantitative trait loci (pQTLs) sourced from the deCODE database as exposures, we identified proteins that are causally associated with CRS. After conducting thorough sensitivity analyses, we retained 95 molecules that exhibited significant associations with CRS (Table S2 ). 3.2 Screening of Upstream Regulators We subsequently employed Mendelian Randomization (MR) analysis to identify upstream regulators utilizing protein quantitative trait locus (pQTL) data from the UK Biobank Pharma Proteomics Project (UKB-PPP). This analysis identified 50 molecules that were significantly associated with CRS (Table S3 ). Further MR analyses revealed additional upstream regulatory molecules associated with the CRS-related proteins identified in the deCODE cohort. After conducting sensitivity validation, we established 561 causally linked protein pairs (Table S4 ). 3.3 Mediation Analysis for Molecular Regulatory Axes To delineate the molecular regulatory axes, we conducted mediation analysis, calculating mediation effects and proportions using the delta method (Table S5 ). Among the 34 statistically significant mediator pairs identified, we prioritized those exhibiting strong mediation effects and mechanistic relevance. To enhance biological interpretability, we excluded pairs that lacked downstream mechanisms. This approach ultimately led to the identification of the LAT-STXBP6 regulatory axis (Fig. 2 and Tables 1 – 3 ). Table 1 Causal Relationships of CRS–Associated Proteins and Upstream/Downstream Regulators with CRS Identified by Mendelian Randomization Analysis. Exposure Outcome Mendelian randomization analysis method p β OR OR(95%CI) deCODE FinnGen STXBP6 CRS IVW 2.41e-08 0.126 1.13 (1.09,1.19) UKB-PPP FinnGen LAT CRS IVW 0.046 0.131 1.14 (1.01,1.29) UKB-PPP deCODE LAT STXBP6 IVW 0.002 0.141 1.15 (1.05,1.26) Table 2 Sensitivity Analysis and Directionality Test of Causal Relationships in Mendelian Randomization for CRS–Associated Proteins and Upstream/Downstream Regulators. Exposure Outcome SNP Steiger direction Steiger P value Heterogeneity Pleiotropy deCODE FinnGen STXBP6 CRS rs4672884 TRUE 2.69e-250 0.297 0.881 UKB-PPP FinnGen LAT CRS rs892090 TRUE 2.74e-19 0.103 0.790 UKB-PPP deCODE LAT STXBP6 rs6993770 TRUE 1.79e-10 7.22e-132 0.163 3.4 Downstream Mechanisms of the LAT-STXBP6 Axis To elucidate the downstream mechanisms of the LAT-STXBP6 axis, we conducted mediation analyses to assess immune cell phenotypes as intermediaries between proteins and CRS. First, Mendelian randomization (MR) analysis identified immune cell subtypes associated with CRS (Table S6 ). Next, we quantified MR-derived causal relationships between proteins and these immune phenotypes (Tables S6, Table 1 – 2 ). By integrating these results with prior protein-CRS associations, we established the LAT-STXBP6-CD8 + T cell axis and calculated the extent of its causal mediation in CRS pathogenesis (Table 3 ). Table 3 Mediation Effect of the LAT-STXBP6-CD8 + T cell Regulatory Axis in CRS. Exposure Mediator Proportion mediated P value β_all β1 β2 β12/β_all LAT STXBP6 0.131 0.141 0.126 13.5% 0.013 STXBP6 CCR7 + naive CD8 + T cells 0.126 0.158 0.139 17.4% 0.047 4. Discussion This study systematically dissected the genetic regulatory network of the LAT-STXBP6-CD8 + T cell axis in CRS through multi-stage integrative analyses. The multidimensional “gene-protein-phenotype” evidence chain elucidated the mechanistic role of this axis in CRS pathogenesis and provided a theoretical foundation for the development of precision therapies targeting immune microenvironment dysregulation. The protein-protein interaction network plays a pivotal role in the pathogenesis of CRS by regulating the dynamic balance of the immune microenvironment [ 25 ] . As a central hub for molecular signaling, this network shapes the chronic inflammatory characteristics of the disease by coordinating communication among immune cells [ 26 ] , facilitating pathogen recognition, and maintaining epithelial barrier homeostasis [ 27 ] . In CRS, aberrant protein interactions can lead to the persistent activation of pro-inflammatory cytokine networks, creating a vicious cycle of immune response imbalance. Concurrently, pathogen-derived proteins interfere with microbial clearance by targeting host immune-related proteins, inducing immune evasion and biofilm formation, which further exacerbate the increased permeability of the mucosal barrier. Dysfunction in the interaction of tight junction proteins compromises the integrity of the epithelial barrier, providing a pathway for pathogen invasion and allergen exposure, thereby amplifying the cascade of local inflammatory signals. Furthermore, the protein interaction network associated with tissue repair is intricately interwoven with the immune microenvironment. Its abnormal activation not only drives fibroblast proliferation and fibrosis but also remodels the extracellular matrix, inhibiting the normal function of immune cells [ 28 ] . Proteins regulate the immune imbalance in chronic sinusitis through multidimensional mechanisms. The dynamic equilibrium of proteins is crucial for maintaining immune responses and repair mechanisms. In summary, the protein interaction network orchestrates immune responses and the immune microenvironment through multi-level interactions. Its dynamic disorder serves as both a driving force for persistent inflammation in CRS and a potential target for therapeutic intervention [ 29 ] . Restoring the homeostasis of protein-protein interactions at critical nodes may rebuild immune balance and reverse the chronic progression of diseases. CCR7 + naive CD8 + T cells serve as a critical “inflammatory regulatory hub” in CRS, with functions encompassing immune activation, polarization, and tissue remodeling processes [ 30 ] . During the immune activation phase, CCR7 directs these cells to migrate and differentiate into effector cytotoxic T cells or memory T cells. However, persistent antigen stimulation and inflammatory factors in CRS result in CTL exhaustion, leading to the release of numerous pro-inflammatory factors and the establishment of a vicious cycle. In the immune polarization phase, Th2/Th17 cytokines alter the fate of CD8 + T cells, inducing Tc2 and Tc17 subsets, which promote eosinophil infiltration and disrupt the epithelial barrier, respectively. These effector cells can also activate B cells, thereby increasing IgE production. During the phase of immune homeostasis imbalance, abnormally activated CD8 + T cells disturb mucosal immune equilibrium through various mechanisms, including the suppression of Treg cells, promotion of Tfh expansion, induction of vascular proliferation and tissue remodeling, as well as the formation of inflammatory memory [ 31 ][ 32 ] . Deciphering the functional heterogeneity and dynamic regulatory networks of CCR7 + CD8 + T cells will yield critical breakthroughs for the development of molecular targeted therapies for CRS. The immunopathological mechanisms underlying CRS are complex, involving aberrant regulation of protein networks and the immune microenvironment. This study identified the LAT protein, STXBP6 protein, and CCR7-positive naïve CD8 + T cells (naïve CCR7 + CD8 + T cells) as crucial components forming a functional axis that collectively drives the inflammatory cascade and tissue remodeling in CRS. As a T-cell activation linker protein, LAT plays a pivotal scaffolding role in the T-cell receptor (TCR) signaling pathway, potentially enhancing the expression or function of STXBP6 by regulating the spatial aggregation of signaling complexes, remodeling membrane dynamics, and facilitating post-transcriptional modifications mediated by downstream PI3K/Akt or MAPK pathways [ 33 ] . STXBP6, as a syntaxin-binding protein, primarily focuses on vesicle transport and secretion regulation [ 34 ] . It may significantly enhance the responsiveness of naïve CD8 + T cells to chemotactic signals by increasing the membrane transport efficiency of CCR7 receptors and optimizing the release of cytotoxic granules in immune synapses, thereby accelerating their migration to draining lymph nodes and promoting clonal expansion [ 35 ][ 36 ] . In summary, the molecular mechanisms of chronic sinusitis involve a complex network of protein interactions and dysregulation of immune microenvironment control. Our study was the first to reveal the critical role of the LAT-STXBP6-CD8 + T cell axis in chronic sinusitis, a finding that has not been previously reported in this field. The breakthrough of this study lies in the organic integration of the protein interaction network with immune regulation, unveiling a novel mechanism by which LAT regulates CCR7-positive naïve CD8 + T cells through STXBP6. The LAT-STXBP6-CD8 + T cell axis and the functional regulation of CCR7-positive naïve CD8 + T cells together constitute a dynamic protein-immune-inflammation interaction system, the disruption of which may represent a breakthrough in the treatment of chronic sinusitis. These findings not only expand our understanding of the pathogenesis of chronic sinusitis but also provide a significant basis for the development of precision treatment strategies based on microenvironment regulation. The strength of this study lies in its multi-level and multi-dimensional analytical strategy. First, we adopted a multi-stage integrated approach that combines Mendelian randomization (MR), mediation analysis, and bioinformatics analysis to construct a comprehensive evidence chain linking genes, proteins, and immune phenotypes. This approach enhanced the robustness and reliability of our results. Second, we utilized data from several large databases, including the deCODE database, UKB-PPP database, GWAS Catalog database, and FinnGen database, ensuring the broad applicability and representativeness of our findings. Additionally, we conducted rigorous sensitivity analyses to minimize potential biases and further strengthen the reliability of our results. Finally, this study was the first to reveal the promoting role of the LAT-STXBP6-CD8 + T cell axis in chronic sinusitis, marking an innovative discovery. This finding provides a new theoretical basis for developing precision treatment strategies based on microenvironment regulation and holds significant clinical implications. While this study has made significant progress, it is crucial to acknowledge several limitations. First, the data utilized primarily originated from European populations. Although this ensured the reliability and representativeness of the results within this specific demographic, it may limit the generalizability of the findings to other ethnic and geographical groups. Future research should validate these findings across diverse populations to ensure their broad applicability. Second, our study primarily inferred causal relationships through the Mendelian randomization (MR) method. Given the complexity of dynamic interactions among proteins under varying biological states, heterogeneity is inevitable. Nevertheless, our MR analysis underwent rigorous sensitivity testing, indicating that the causal relationships were not significantly influenced by heterogeneity or pleiotropy. Although we implemented thorough sensitivity analyses to minimize bias, further validation through in vitro and in vivo experiments is essential to elucidate the specific molecular mechanisms. Moreover, while MR analysis provides evidence for causal inference, the results may be subject to potential confounding factors. Despite the stringent controls applied during the analysis, the potential impact of these factors cannot be entirely dismissed. Lastly, this study primarily focused on the LAT-STXBP6-CD8 + T cell axis; however, its specific mechanisms of action may be more intricate, involving interactions with other related molecules and pathways. Future research should further investigate these potential mechanisms to achieve a comprehensive understanding of the genetic regulatory network of the protein-protein-immune microenvironment in chronic sinusitis. 5. Conclusion This study was the first to elucidate the promoting role of the LAT-STXBP6-CD8 + T cell axis in chronic sinusitis through a multi-stage integrative analysis. By integrating pQTL data, GWAS data, and Mendelian randomization (MR) analysis, we constructed a comprehensive “gene-protein-immune cell phenotype” multidimensional evidence chain, which provides new insights into the pathogenesis of chronic sinusitis. This discovery not only enhances the theoretical understanding of the dysregulation of protein interaction networks and the remodeling of the immune microenvironment in chronic sinusitis but also establishes a crucial theoretical foundation for precision therapy based on microenvironment regulation. Future studies should further validate the specific molecular mechanisms of this regulatory axis and investigate its potential roles in other chronic inflammatory conditions, thereby expanding treatment possibilities for chronic inflammation. Moreover, the development of inhibitors targeting LAT and STXBP6 may pave the way for new treatment strategies for chronic sinusitis, which holds significant importance for clinical translation. Declarations The analytical approach was ethically sound, as all data utilized in this study had received prior approval and consent in their original studies. 1.Proteome data were sourced from the deCODE database (https://www.decode.com/summarydata/) for the Icelandic population as well as from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP) (https://www.synapse.org/Synapse:syn51364943/wiki/622119) 2.chronic rhinosinusitis GWAS statistics were obtained from the Finnish Database Consortium Funding Declaration There is no external funding received for this research. All costs and expenses were covered by the authors themselves or by the affiliated institution without any specific funding source. Ethics and Consent Statements The analysis in this study utilized summary statistics from a genome-wide association study. The original studies had obtained ethical approval and informed consent from participants, as confirmed by the institutional review boards. As this analysis did not involve any new data collection or require additional ethical clearance, there was no need for further ethical approval or informed consent for this study specifically. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Clinical Trial Registration Statement This study is not a clinical trial. No clinical trial registration is required as the research exclusively analyzed pre-existing genetic and proteomic datasets. Competing Interests The authors declare no competing financial or non-financial interests directly or indirectly related to this work. The corresponding author (CHEN GAO) affirms full responsibility for the integrity of this declaration. Consent to Publish declaration not applicable References Rudmik L,Soler ZM. Medical Therapies for Adult Chronic Sinusitis: A Systematic Review. 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Patients and mice with deficiency in the SNARE protein SYNTAXIN-11 have a secondary B cell defect. J Exp Med. 2024;221 (7):. doi: 10.1084/jem.20221122 Kögl T,Chang HF,Staniek J, et al. Patients and mice with deficiency in the SNARE protein SYNTAXIN-11 have a secondary B cell defect. J Exp Med. 2024;221 (7):. doi: 10.1084/jem.20221122 Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx 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. 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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-6934487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482363732,"identity":"e4626812-2976-4acb-8d54-1d4ed324d598","order_by":0,"name":"YONG ZHUANG","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"YONG","middleName":"","lastName":"ZHUANG","suffix":""},{"id":482363733,"identity":"5a079351-7952-41f5-81fc-555ade87d45d","order_by":1,"name":"CHEN GAO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLACCYaEejb25oMPEipqiNLA2ADUksDHcyzZ4MGZY0RqYQBqkZPIMZN82MJMWL28++HjDyzb0vLYGBLMKhIb2Bj427sT8GoxPJOW2CDZllPMxnAg7UbiDhkGiTNnN+DX0pBj2CC5rYKxjbHh2I3EM2wMBhK5BLT0v4FqYWZsK0hsYyasRV4CbEtOYhsbMxsDUVoMJJ4lzpD8l2bMxsPGLJFw5hgPQb/I9ycf+CxxJllOfv77jx9/VNTI8bf3ErDlAAMDswSSAA9e5WBbGoBx+YGgslEwCkbBKBjRAAAzq0rQQD8CxgAAAABJRU5ErkJggg==","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"CHEN","middleName":"","lastName":"GAO","suffix":""}],"badges":[],"createdAt":"2025-06-20 02:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6934487/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6934487/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86407420,"identity":"5e344908-d622-4e5e-a8c9-8f40a440b399","added_by":"auto","created_at":"2025-07-10 10:05:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1057845,"visible":true,"origin":"","legend":"\u003cp\u003eStudy workflow. MR, mendelian randomization; pQTL, protein quantitative trait locus.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/2e83bf96c3a25e9e83d7dae0.jpg"},{"id":86407149,"identity":"0b65201e-f1ef-4b13-b829-9e7da24aec5d","added_by":"auto","created_at":"2025-07-10 09:57:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":890685,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of mediation analysis for regulatory axis screening.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/b9a35bd090409a28d3e192b7.jpg"},{"id":91617019,"identity":"3591f463-e813-4905-847b-ecb64bad93d7","added_by":"auto","created_at":"2025-09-18 10:47:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2632128,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/e82b5d91-3247-40f4-88b6-d3db014e1a37.pdf"},{"id":86407142,"identity":"10a15338-6276-48f6-ac0d-fa1010a1d3dc","added_by":"auto","created_at":"2025-07-10 09:57:56","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":48027,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/e90972081eb6363d9d45f9be.xlsx"},{"id":86407423,"identity":"d97e054a-faaa-44c4-8eb0-ee8420595e7a","added_by":"auto","created_at":"2025-07-10 10:05:56","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":24367,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/d0da2d414dcc60f76b11a183.xlsx"},{"id":86407421,"identity":"ea0c2cbe-de0d-4acb-9ebb-8e382e09c09b","added_by":"auto","created_at":"2025-07-10 10:05:56","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":18001,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/835fbed0eb635f98aa721aa7.xlsx"},{"id":86407424,"identity":"26de809a-75a0-467b-b35b-088cc9e43e9b","added_by":"auto","created_at":"2025-07-10 10:05:56","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":97171,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/1dc306c4c3d745032240f57d.xlsx"},{"id":86407154,"identity":"cfd552da-c0fa-4877-b2cd-4ca63c8574da","added_by":"auto","created_at":"2025-07-10 09:57:56","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":120578,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/0bf96a375e500f0ebfa90cfb.xlsx"},{"id":86407156,"identity":"4e54206a-9fe9-45fa-9ea9-83031ff64592","added_by":"auto","created_at":"2025-07-10 09:57:56","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":12567,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6934487/v1/e67ddc9b292ea2893bdfb42f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated multi-omics reveals the LAT-STXBP6-CD8+T cell axis in promoting chronic rhinosinusitis progression","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChronic rhinosinusitis (CRS) is a chronic inflammatory disease that affects the nasal and sinus mucosa, characterized by a complex pathogenesis that remains incompletely understood. While environmental factors, microbial infections, and anatomical abnormalities are recognized as significant contributors, substantial gaps persist in elucidating its core molecular mechanisms. Most patients exhibit limited responses to current pharmacological therapies, and therapeutics specifically targeting the disease etiology are lacking\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Recent studies indicate that dysregulation of protein interaction networks and remodeling of the immune microenvironment may drive the persistent inflammatory progression observed in CRS\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. These regulatory imbalances can result in mucosal barrier destruction, amplification of inflammatory signaling, and tissue fibrosis; however, the precise molecular regulatory networks remain undefined. This knowledge gap directly impedes the development of precision therapeutic strategies.\u003c/p\u003e\u003cp\u003eProtein networks play a central role in the pathogenesis of CRS. Dynamic regulatory imbalances within these networks are implicated in key pathological processes, including sustained inflammation, mucosal barrier disruption, and tissue remodeling\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Functioning as hubs of complex molecular interactions, protein networks profoundly influence disease progression by coordinating inflammatory signaling pathways, regulating epithelial barrier integrity, and mediating dynamics at the host-microbe interface. In CRS, the abnormal activation or suppression of innate and adaptive immunity-related protein clusters promotes the cascade release of pro-inflammatory cytokines and chemokines, thereby driving excessive activation of local immune responses\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e4\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Moreover, dysregulation of protein networks compromises mucosal barrier integrity by disrupting tight junction proteins, which facilitates pathogen penetration and perpetuates a vicious cycle of inflammation\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Furthermore, the reprogramming of tissue repair-associated protein networks promotes aberrant fibroblast proliferation and extracellular matrix deposition, exacerbating fibrotic alterations in sinonasal structures.\u003c/p\u003e\u003cp\u003eThe pathogenesis of CRS primarily involves an imbalance in the immune microenvironment\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The normal defense mechanisms of the sinonasal mucosa rely on a delicate equilibrium between innate and adaptive immunity. However, in patients with CRS, impaired innate immune barrier function, abnormal immune cell distribution, and persistently activated inflammatory signaling contribute to chronic inflammation, which underpins disease recurrence. Infiltrating immune cells create a self-reinforcing positive feedback loop through the release of pro-inflammatory factors, amplifying local inflammatory responses while inhibiting tissue repair. Concurrently, the exhaustion or dysfunction of regulatory immune cells further diminishes the negative regulation of inflammation. Dynamic shifts in pathogen-commensal microbiota interactions also reshape the immune microenvironment: dysregulated microbial communities activate aberrant immune responses via pattern recognition receptors, disrupting the anti-inflammatory/pro-inflammatory balance and inducing immune polarization. Moreover, chronic inflammation leads to functional distortion of mucosal epithelial cells, characterized by a breakdown in barrier integrity and abnormal secretory profiles, which not only exacerbate pathogen colonization but also amplify immune dysregulation through paracrine signaling. These multi-layered disorders of immune interactions ultimately result in persistent inflammatory damage and pathological remodeling of sinonasal tissues\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR) combined with mediation analysis offers unique advantages for dissecting protein interactions and the dynamics of the immune microenvironment. MR utilizes genetic variants as instrumental variables to establish causal relationships between specific proteins and disease outcomes, effectively addressing confounding biases and reverse causation that are inherent in observational studies\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Mediation analysis further disentangles causal pathways by distinguishing the direct effects of proteins on diseases from the indirect effects mediated through intermediate proteins or immune cells. This integrative approach is particularly well-suited for exploring protein interaction networks and alterations in the immune microenvironment associated with diseases. It enables the identification of both direct and indirect causal pathways while uncovering critical mediator proteins and immune cell subtypes\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Such strategies deepen mechanistic insights and hold the potential for identifying novel targets for precision therapies. Collectively, the MR-mediation framework serves as a powerful tool for decoding the \"gene-protein-phenotype\" cascades, providing essential insights into the complex biological mechanisms underlying diseases.\u003c/p\u003e\u003cp\u003eIn this study, we systematically dissected the genetic regulatory network connecting proteins, proteins, and the immune microenvironment in CRS through multi-stage integrated analyses. First, proteins significantly associated with CRS risk were identified by integrating proteomic quantitative trait loci (pQTL) data from the Icelandic deCODE cohort with CRS genome-wide association study (GWAS) data from the FinnGen database. Second, upstream regulatory factors were validated using pQTL data from the UK Biobank and FinnGen GWAS. Subsequent two-step mediation analysis confirmed a critical protein regulatory axis,LAT-STXBP6. Further mediation analysis revealed that STXBP6 influences CRS via na\u0026iuml;ve CCR7\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T lymphocytes expressing the chemokine receptor CCR7. Our findings demonstrate the pivotal role of the LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell axis in CRS pathogenesis. This multidimensional \"gene-protein-immune phenotype\" evidence chain not only elucidates the mechanistic contribution of this axis but also provides a theoretical foundation for developing targeted precision therapies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the overall workflow of the study. Specifically, we conducted a two-sample Mendelian randomization (MR) analysis utilizing proteomic quantitative trait loci (pQTL) data from the deCODE and UKB-PPP databases as exposures, alongside chronic rhinosinusitis genome-wide association study (GWAS) data from the FinnGen database as outcomes. This approach aimed to investigate the causal relationships between proteins and CRS. Subsequently, we employed mediation analysis to identify potential molecular regulatory axes and further explored their downstream mechanisms, ultimately constructing a genetic regulatory network encompassing protein-protein interactions and the immune microenvironment. Throughout the analysis, instrumental variables (IVs) were selected based on stringent inclusion and exclusion criteria, followed by comprehensive sensitivity analyses to ensure the reliability of the MR results. Notably, all analyses adhered to rigorous ethical standards, with datasets obtained from original studies that had received ethical approval and participant informed consent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Sources\u003c/h2\u003e\u003cp\u003eProteomic data were obtained from two sources: (1) pQTL data for the Icelandic population from the deCODE database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.decode.com/summarydata/\u003c/span\u003e\u003cspan address=\"https://www.decode.com/summarydata/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e; (2) plasma proteome pQTL data from the UK Biobank Pharma Proteomics Project (UKB-PPP) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.synapse.org/Synapse:syn51364943/wiki/622119\u003c/span\u003e\u003cspan address=\"https://www.synapse.org/Synapse:syn51364943/wiki/622119\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e12\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Immunological trait data (accession numbers: GCST0001391-GCST0002121) were retrieved from the GWAS Catalog, comprising summary statistics for 731 immune features: absolute counts, median fluorescence intensity, morphological parameters, and relative counts. A complete list of immune traits is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. The CRS GWAS summary statistics were derived from the FinnGen consortium R12 release, comprising 22,099 cases and 371,520 controls.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Instrumental Variables (IVs)\u003c/h2\u003e\u003cp\u003eThe selection of instrumental variables (IV) strictly adhered to Mendelian randomization (MR) assumptions\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e14\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Initially, single nucleotide polymorphisms (SNPs) that met the genome-wide significance threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;5.0\u0026times;10⁻⁸) were retained. Subsequently, independent SNPs were clumped using data from the European 1000 Genomes Project, applying a linkage disequilibrium (LD) threshold of r\u0026sup2; \u0026lt; 0.1 and a window size of 10,000 kb\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. SNPs exhibiting allele mismatches between exposure and outcome were excluded; palindromic SNPs were addressed using allele frequency data or excluded if such data were unavailable. Finally, SNPs with F-statistics lower than 10 were removed to mitigate weak instrument bias\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Mendelian Randomization and Sensitivity Analyses\u003c/h2\u003e\u003cp\u003eTwo-sample Mendelian Randomization (MR) analyses were conducted using the TwoSampleMR package in R. Inverse-variance weighted (IVW) regression was employed as the primary method\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, excluding exposures with unreliable IVW estimates. Sensitivity analyses included MR-Egger intercept tests for pleiotropy assessment\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, Cochran\u0026rsquo;s Q tests for heterogeneity evaluation\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, and Steiger tests for validating directional causality. These methods were implemented to enhance the robustness of the findings and minimize the risk of reverse causation bias\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e20\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Mediation Analysis\u003c/h2\u003e\u003cp\u003eUpstream Analysis\u003c/p\u003e\u003cp\u003eWe employed a two-step Mendelian randomization (MR) mediation analysis method to explore the protein regulatory network\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e21\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e22\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. First, we calculated the effect size of upstream proteins on chronic sinusitis, denoted as β_all. Second, we determined the effect size of upstream proteins on downstream proteins, denoted as β1, and the effect size of downstream proteins on chronic sinusitis, denoted as β2. Finally, we calculated the mediation effect (β12) as β1\u0026thinsp;\u0026times;\u0026thinsp;β2, and the mediation proportion (β12_p) as (β12 / β_all) \u0026times; 100% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eDownstream Analysis\u003c/p\u003e\u003cp\u003eWe utilized the mediation analysis method to investigate potential downstream mechanisms of proteins. First, we calculated the effect size of proteins on chronic sinusitis, denoted as β_all; then, we assessed the effect size of proteins on immune cells, denoted as β1, and the effect size of immune cells on chronic sinusitis, denoted as β2. Next, we computed the mediation effect (β12) as β1\u0026thinsp;\u0026times;\u0026thinsp;β2, and the mediation proportion (β12_p) as (β12 / β_all) \u0026times; 100% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eWe employed the delta method to calculate the 95% confidence interval of the mediation effect and determined the statistical p-value of the mediation effect via the delta method to assess its statistical significance. We selected results with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a mediation proportion greater than 10% for further investigation\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e24\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Methods\u003c/h2\u003e\u003cp\u003eAll analyses utilized R software (v4.4.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Statistical significance was defined as two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of CRS-Associated Molecules\u003c/h2\u003e\u003cp\u003eUtilizing Mendelian randomization (MR) analysis in conjunction with proteomic quantitative trait loci (pQTLs) sourced from the deCODE database as exposures, we identified proteins that are causally associated with CRS. After conducting thorough sensitivity analyses, we retained 95 molecules that exhibited significant associations with CRS (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Screening of Upstream Regulators\u003c/h2\u003e\u003cp\u003eWe subsequently employed Mendelian Randomization (MR) analysis to identify upstream regulators utilizing protein quantitative trait locus (pQTL) data from the UK Biobank Pharma Proteomics Project (UKB-PPP). This analysis identified 50 molecules that were significantly associated with CRS (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Further MR analyses revealed additional upstream regulatory molecules associated with the CRS-related proteins identified in the deCODE cohort. After conducting sensitivity validation, we established 561 causally linked protein pairs (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Mediation Analysis for Molecular Regulatory Axes\u003c/h2\u003e\u003cp\u003eTo delineate the molecular regulatory axes, we conducted mediation analysis, calculating mediation effects and proportions using the delta method (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Among the 34 statistically significant mediator pairs identified, we prioritized those exhibiting strong mediation effects and mechanistic relevance. To enhance biological interpretability, we excluded pairs that lacked downstream mechanisms. This approach ultimately led to the identification of the LAT-STXBP6 regulatory axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eCausal Relationships of CRS\u0026ndash;Associated Proteins and Upstream/Downstream Regulators with CRS Identified by Mendelian Randomization Analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u003cp\u003eMendelian randomization analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003emethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edeCODE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinnGen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTXBP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIVW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.41e-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.09,1.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUKB-PPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinnGen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIVW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.01,1.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUKB-PPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edeCODE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTXBP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIVW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.05,1.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSensitivity Analysis and Directionality Test of Causal Relationships in Mendelian Randomization for CRS\u0026ndash;Associated Proteins and Upstream/Downstream Regulators.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSNP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSteiger direction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSteiger P value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHeterogeneity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePleiotropy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edeCODE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinnGen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTXBP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers4672884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.69e-250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUKB-PPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFinnGen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers892090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.74e-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUKB-PPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003edeCODE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTXBP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ers6993770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTRUE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.79e-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.22e-132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.163\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=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Downstream Mechanisms of the LAT-STXBP6 Axis\u003c/h2\u003e\u003cp\u003eTo elucidate the downstream mechanisms of the LAT-STXBP6 axis, we conducted mediation analyses to assess immune cell phenotypes as intermediaries between proteins and CRS. First, Mendelian randomization (MR) analysis identified immune cell subtypes associated with CRS (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Next, we quantified MR-derived causal relationships between proteins and these immune phenotypes (Tables S6, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By integrating these results with prior protein-CRS associations, we established the LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell axis and calculated the extent of its causal mediation in CRS pathogenesis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Effect of the LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell Regulatory Axis in CRS.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eProportion mediated\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ_all\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eβ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eβ12/β_all\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSTXBP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTXBP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCCR7\u0026thinsp;+\u0026thinsp;naive CD8\u0026thinsp;+\u0026thinsp;T cells\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.047\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"},{"header":"4. Discussion","content":"\u003cp\u003eThis study systematically dissected the genetic regulatory network of the LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell axis in CRS through multi-stage integrative analyses. The multidimensional \u0026ldquo;gene-protein-phenotype\u0026rdquo; evidence chain elucidated the mechanistic role of this axis in CRS pathogenesis and provided a theoretical foundation for the development of precision therapies targeting immune microenvironment dysregulation.\u003c/p\u003e\u003cp\u003eThe protein-protein interaction network plays a pivotal role in the pathogenesis of CRS by regulating the dynamic balance of the immune microenvironment\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e25\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. As a central hub for molecular signaling, this network shapes the chronic inflammatory characteristics of the disease by coordinating communication among immune cells\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, facilitating pathogen recognition, and maintaining epithelial barrier homeostasis\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In CRS, aberrant protein interactions can lead to the persistent activation of pro-inflammatory cytokine networks, creating a vicious cycle of immune response imbalance. Concurrently, pathogen-derived proteins interfere with microbial clearance by targeting host immune-related proteins, inducing immune evasion and biofilm formation, which further exacerbate the increased permeability of the mucosal barrier. Dysfunction in the interaction of tight junction proteins compromises the integrity of the epithelial barrier, providing a pathway for pathogen invasion and allergen exposure, thereby amplifying the cascade of local inflammatory signals. Furthermore, the protein interaction network associated with tissue repair is intricately interwoven with the immune microenvironment. Its abnormal activation not only drives fibroblast proliferation and fibrosis but also remodels the extracellular matrix, inhibiting the normal function of immune cells\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Proteins regulate the immune imbalance in chronic sinusitis through multidimensional mechanisms. The dynamic equilibrium of proteins is crucial for maintaining immune responses and repair mechanisms. In summary, the protein interaction network orchestrates immune responses and the immune microenvironment through multi-level interactions. Its dynamic disorder serves as both a driving force for persistent inflammation in CRS and a potential target for therapeutic intervention\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Restoring the homeostasis of protein-protein interactions at critical nodes may rebuild immune balance and reverse the chronic progression of diseases.\u003c/p\u003e\u003cp\u003eCCR7\u0026thinsp;+\u0026thinsp;naive CD8\u0026thinsp;+\u0026thinsp;T cells serve as a critical \u0026ldquo;inflammatory regulatory hub\u0026rdquo; in CRS, with functions encompassing immune activation, polarization, and tissue remodeling processes\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. During the immune activation phase, CCR7 directs these cells to migrate and differentiate into effector cytotoxic T cells or memory T cells. However, persistent antigen stimulation and inflammatory factors in CRS result in CTL exhaustion, leading to the release of numerous pro-inflammatory factors and the establishment of a vicious cycle. In the immune polarization phase, Th2/Th17 cytokines alter the fate of CD8\u0026thinsp;+\u0026thinsp;T cells, inducing Tc2 and Tc17 subsets, which promote eosinophil infiltration and disrupt the epithelial barrier, respectively. These effector cells can also activate B cells, thereby increasing IgE production. During the phase of immune homeostasis imbalance, abnormally activated CD8\u0026thinsp;+\u0026thinsp;T cells disturb mucosal immune equilibrium through various mechanisms, including the suppression of Treg cells, promotion of Tfh expansion, induction of vascular proliferation and tissue remodeling, as well as the formation of inflammatory memory\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e32\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Deciphering the functional heterogeneity and dynamic regulatory networks of CCR7\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells will yield critical breakthroughs for the development of molecular targeted therapies for CRS.\u003c/p\u003e\u003cp\u003eThe immunopathological mechanisms underlying CRS are complex, involving aberrant regulation of protein networks and the immune microenvironment. This study identified the LAT protein, STXBP6 protein, and CCR7-positive na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T cells (na\u0026iuml;ve CCR7\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells) as crucial components forming a functional axis that collectively drives the inflammatory cascade and tissue remodeling in CRS. As a T-cell activation linker protein, LAT plays a pivotal scaffolding role in the T-cell receptor (TCR) signaling pathway, potentially enhancing the expression or function of STXBP6 by regulating the spatial aggregation of signaling complexes, remodeling membrane dynamics, and facilitating post-transcriptional modifications mediated by downstream PI3K/Akt or MAPK pathways\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e33\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. STXBP6, as a syntaxin-binding protein, primarily focuses on vesicle transport and secretion regulation\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. It may significantly enhance the responsiveness of na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T cells to chemotactic signals by increasing the membrane transport efficiency of CCR7 receptors and optimizing the release of cytotoxic granules in immune synapses, thereby accelerating their migration to draining lymph nodes and promoting clonal expansion\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e35\u003c/sup\u003e\u003csup\u003e][\u003c/sup\u003e\u003csup\u003e36\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. In summary, the molecular mechanisms of chronic sinusitis involve a complex network of protein interactions and dysregulation of immune microenvironment control. Our study was the first to reveal the critical role of the LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell axis in chronic sinusitis, a finding that has not been previously reported in this field. The breakthrough of this study lies in the organic integration of the protein interaction network with immune regulation, unveiling a novel mechanism by which LAT regulates CCR7-positive na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T cells through STXBP6. The LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell axis and the functional regulation of CCR7-positive na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T cells together constitute a dynamic protein-immune-inflammation interaction system, the disruption of which may represent a breakthrough in the treatment of chronic sinusitis. These findings not only expand our understanding of the pathogenesis of chronic sinusitis but also provide a significant basis for the development of precision treatment strategies based on microenvironment regulation.\u003c/p\u003e\u003cp\u003eThe strength of this study lies in its multi-level and multi-dimensional analytical strategy. First, we adopted a multi-stage integrated approach that combines Mendelian randomization (MR), mediation analysis, and bioinformatics analysis to construct a comprehensive evidence chain linking genes, proteins, and immune phenotypes. This approach enhanced the robustness and reliability of our results. Second, we utilized data from several large databases, including the deCODE database, UKB-PPP database, GWAS Catalog database, and FinnGen database, ensuring the broad applicability and representativeness of our findings. Additionally, we conducted rigorous sensitivity analyses to minimize potential biases and further strengthen the reliability of our results. Finally, this study was the first to reveal the promoting role of the LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell axis in chronic sinusitis, marking an innovative discovery. This finding provides a new theoretical basis for developing precision treatment strategies based on microenvironment regulation and holds significant clinical implications.\u003c/p\u003e\u003cp\u003eWhile this study has made significant progress, it is crucial to acknowledge several limitations. First, the data utilized primarily originated from European populations. Although this ensured the reliability and representativeness of the results within this specific demographic, it may limit the generalizability of the findings to other ethnic and geographical groups. Future research should validate these findings across diverse populations to ensure their broad applicability. Second, our study primarily inferred causal relationships through the Mendelian randomization (MR) method. Given the complexity of dynamic interactions among proteins under varying biological states, heterogeneity is inevitable. Nevertheless, our MR analysis underwent rigorous sensitivity testing, indicating that the causal relationships were not significantly influenced by heterogeneity or pleiotropy. Although we implemented thorough sensitivity analyses to minimize bias, further validation through in vitro and in vivo experiments is essential to elucidate the specific molecular mechanisms. Moreover, while MR analysis provides evidence for causal inference, the results may be subject to potential confounding factors. Despite the stringent controls applied during the analysis, the potential impact of these factors cannot be entirely dismissed. Lastly, this study primarily focused on the LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell axis; however, its specific mechanisms of action may be more intricate, involving interactions with other related molecules and pathways. Future research should further investigate these potential mechanisms to achieve a comprehensive understanding of the genetic regulatory network of the protein-protein-immune microenvironment in chronic sinusitis.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study was the first to elucidate the promoting role of the LAT-STXBP6-CD8\u0026thinsp;+\u0026thinsp;T cell axis in chronic sinusitis through a multi-stage integrative analysis. By integrating pQTL data, GWAS data, and Mendelian randomization (MR) analysis, we constructed a comprehensive \u0026ldquo;gene-protein-immune cell phenotype\u0026rdquo; multidimensional evidence chain, which provides new insights into the pathogenesis of chronic sinusitis. This discovery not only enhances the theoretical understanding of the dysregulation of protein interaction networks and the remodeling of the immune microenvironment in chronic sinusitis but also establishes a crucial theoretical foundation for precision therapy based on microenvironment regulation. Future studies should further validate the specific molecular mechanisms of this regulatory axis and investigate its potential roles in other chronic inflammatory conditions, thereby expanding treatment possibilities for chronic inflammation. Moreover, the development of inhibitors targeting LAT and STXBP6 may pave the way for new treatment strategies for chronic sinusitis, which holds significant importance for clinical translation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe analytical approach was ethically sound, as all data utilized in this study had received prior approval and consent in their original studies.\u003c/p\u003e\n\u003cp\u003e1.Proteome data were sourced from the deCODE database (https://www.decode.com/summarydata/) for the Icelandic population as well as from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP) (https://www.synapse.org/Synapse:syn51364943/wiki/622119)\u003c/p\u003e\n\u003cp\u003e2.chronic rhinosinusitis GWAS statistics were obtained from the Finnish Database Consortium\u003c/p\u003e\n\u003cp\u003eFunding Declaration\u003c/p\u003e\n\u003cp\u003eThere is no external funding received for this research. All costs and expenses were covered by the authors themselves or by the affiliated institution without any specific funding source.\u003c/p\u003e\n\u003cp\u003eEthics and Consent Statements\u003c/p\u003e\n\u003cp\u003eThe analysis in this study utilized summary statistics from a genome-wide association study. The original studies had obtained ethical approval and informed consent from participants, as confirmed by the institutional review boards. As this analysis did not involve any new data collection or require additional ethical clearance, there was no need for further ethical approval or informed consent for this study specifically.\u003c/p\u003e\n\u003cp\u003eDeclaration of competing interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eClinical Trial Registration Statement\u003c/p\u003e\n\u003cp\u003eThis study is not a clinical trial. No clinical trial registration is required as the research exclusively analyzed pre-existing genetic and proteomic datasets.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial or non-financial interests directly or indirectly related to this work. The corresponding author (CHEN GAO) affirms full responsibility for the integrity of this declaration.\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRudmik L,Soler ZM. Medical Therapies for Adult Chronic Sinusitis: A Systematic Review. JAMA. 2015;314 (9):926\u0026thinsp;\u0026minus;\u0026thinsp;39. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2015.7544\u003c/span\u003e\u003cspan address=\"10.1001/jama.2015.7544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKato, A., Kita, H. The immunology of asthma and chronic rhinosinusitis. 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J Exp Med. 2024;221 (7):. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1084/jem.20221122\u003c/span\u003e\u003cspan address=\"10.1084/jem.20221122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026ouml;gl T,Chang HF,Staniek J, et al. Patients and mice with deficiency in the SNARE protein SYNTAXIN-11 have a secondary B cell defect. J Exp Med. 2024;221 (7):. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1084/jem.20221122\u003c/span\u003e\u003cspan address=\"10.1084/jem.20221122\" 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":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":"Chronic rhinosinusitis, LAT-STXBP6-CD8 + T cell axis, Protein-protein interaction network","lastPublishedDoi":"10.21203/rs.3.rs-6934487/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6934487/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eThe pathogenesis of chronic rhinosinusitis (CRS) remains complex and has not yet been fully elucidated. A significant proportion of patients demonstrate limited responses to current pharmacological treatments, highlighting the need for targeted therapeutics that address the core mechanisms of the disease. The critical roles of dysregulated protein-protein interaction networks and immune microenvironment remodeling in CRS progression are increasingly recognized; however, the specific genetic, protein, and immunoregulatory networks remain to be systematically deciphered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study employed a multi-stage integrated analysis strategy combining Mendelian randomization (MR), mediation analysis, and proteomics. We utilized pQTL data from the deCODE and UKB-PP databases alongside CRS GWAS data from the FinnGen database to perform MR, screening for proteins associated with CRS. Subsequently, a two-step mediation analysis was conducted to construct a regulatory network encompassing protein-protein interactions and immune microenvironment factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We identified the LAT-STXBP6-CD8 + T cell regulatory axis, which is significantly associated with CRS risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study was the first to systematically elucidate the promoting role of the LAT-STXBP6-CD8 + T cell axis in CRS, providing a theoretical foundation for developing combined immunotherapy strategies targeting the microenvironment.\u003c/p\u003e","manuscriptTitle":"Integrated multi-omics reveals the LAT-STXBP6-CD8+T cell axis in promoting chronic rhinosinusitis progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 09:57:51","doi":"10.21203/rs.3.rs-6934487/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":"cb383729-dff5-4c8c-aeaf-0a396c68e9c1","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:39:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 09:57:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6934487","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6934487","identity":"rs-6934487","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}