Integrating Transcriptomics and Genetics to Identify Expression Patterns of RNF144B and FYN as Potential Predictors of Bacterial Meningitis

Integrating Transcriptomics and Genetics to Identify Expression Patterns of RNF144B and FYN as Potential Predictors of Bacterial Meningitis | 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 Integrating Transcriptomics and Genetics to Identify Expression Patterns of RNF144B and FYN as Potential Predictors of Bacterial Meningitis Hexiang Jiang, Xibing Yu, Jingyan Fan, Houhui Song, Yang Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5518056/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 Bacterial meningitis (BM) requires prompt treatment, especially for neonates, the elderly, and immunocompromised individuals. Understanding the immune response is essential, as it precedes clinical symptoms. However, systematic studies have been lacking. This study identifies immune-related genes that could enhance BM diagnosis and treatment. Mendelian randomization, differential gene expression, and co-expression network analyses revealed key genes linked to BM. RNF144B was identified as a risk gene, correlating with increased neutrophil levels during the initial phase of meningitis, whereas FYN was identified as a protective gene, correlating with increased NKT cells during remission and recovery. Single-cell RNA sequencing and gene set enrichment analyses showed RNF144B expression in monocytes and neutrophils, while FYN was associated with NKT cells. During BM onset, there was an increase in neutrophil proportions and a decrease in NKT cell proportions, indicating a negative correlation. In recovery, RNF144B expression and neutrophil levels decreased, while FYN expression and NKT cell levels rose, underscoring the protective role of NKT cells. FYN may regulate T-cell receptor function in NKT cells, reducing BM risk. This study suggests that the expression patterns of these two genes exhibit significant differences at various stages of the disease, thus offering potential biomarkers for aiding in more accurate diagnoses of BM and monitoring disease progression. Bacterial Meningitis Gene Expression Immune Cell Infiltration RNF144B FYN Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Meningitis is a complex disease caused by bacterial, viral, and fungal infections, as well as non-infectious agents such as drugs. Bacteria are the most common causative agents of meningitis[ 1 ]. Bacterial meningitis (BM) is an acute central nervous system (CNS) infectious process, where pathogens like Streptococcus pneumoniae , Neisseria meningitidis , Escherichia coli , and Streptococcus agalactiae penetrate the blood-brain barrier (BBB) into meningeal compartments, triggering a secondary immune response and neuroinflammatory dysfunction[ 2 , 3 ]. BM is a medical emergency[ 2 ], causing approximately 318,000 deaths annually worldwide and resulting in an estimated 20,383 years of life lost[ 4 , 5 ]. Early diagnosis and treatment of BM are crucial, as timely intervention generally leads to better recovery outcomes. Delayed treatment, however, is associated with increased in-hospital mortality and unfavorable outcomes at discharge[ 2 , 4 , 6 , 7 ]. Clinically, treatment often begins before laboratory results are available, since early intervention can significantly improve patient outcomes[ 1 , 8 ]. Diagnosing BM based on clinical symptoms is challenging, particularly in patients with weakened immune systems, children, or the elderly, where symptoms may present atypically[ 2 , 6 , 9 , 10 ]. Additionally, viral meningitis (VM) can exhibit similar symptoms, further complicating diagnosis[ 9 ]. Therefore, a treatment strategy using both antiviral and antibiotic medications is frequently employed until the infectious agent is identified[ 4 , 7 , 11 ]. These clinical challenges underscore the importance of early and accurate identification of BM to distinguish it from VM and guide timely treatment. The risk of BM is highest in neonates, the elderly, and individuals with compromised immune function, highlighting the critical role of immune responses in disease progression[ 3 – 5 , 12 ]. Multiple studies support the view that the host immune response occurs before clinical symptoms across various pathogens and diseases, including tuberculosis, Salmonella infections, and influenza virus infections [ 13 – 15 ]. They suggest that early markers of the host immune response may be critical for early diagnosis, treatment selection, and prognosis assessment of diseases. However, no studies have systematically elucidated the immune response in patients with BM. To better understand these immune responses, we analyzed peripheral blood transcriptome data from BM patients and healthy controls. Mendelian randomization (MR) analysis identified RNF144B as a risk gene and FYN as a protective gene. These findings were validated using single-cell transcriptome data from cerebrospinal fluid (CSF) and peripheral blood across different disease stages, showing predominant expression of RNF144B in neutrophils and FYN in natural killer T (NKT) cells. Moreover, during BM progression, the expression trends of RNF144B and FYN correlated with changes in neutrophil and NKT cell proportions, underscoring their roles in disease dynamics and immune response. Lastly, transcriptome comparisons between BM and VM revealed opposite expression trends for these genes, suggesting their potential as biomarkers to differentiate between the two types of meningitis. Materials and methods 1.1 Data Download and Processing Datasets GSE40586, GSE163194, GSE163195, GSE163196, and GSE248261 were downloaded from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). The GSE40586 (GPL6244) dataset includes peripheral blood samples from 21 BM patients and 18 healthy controls, which are used for differential gene screening, weighted gene co-expression network analysis (WGCNA), and immune cell infiltration analysis[ 16 ]. The GSE163194 dataset is a single-cell sequencing dataset comprising CSF samples obtained via lumbar puncture from BM patients at different disease stages, including initial onset, remission, and recovery[ 3 ]. This dataset was employed for single-cell-related analyses. The GSE163195 dataset contains transcriptomic data from CSF cell samples of 12 BM patients at various disease stages, consistent with the patient information in the GSE163194 dataset, serving as an external dataset for validating key gene expression trends[ 3 ]. The GSE163196 dataset provides transcriptomic data from peripheral blood leukocyte samples of 12 BM patients at different disease stages, also consistent with the GSE163194 dataset, used to explore the differential expression trends of key genes across the three stages[ 3 ]. The sample conditions in the GSE163194, GSE163195, and GSE163196 datasets are described as follows: S1 represents the initial stage of BM. S2 and S3 represent non-refractory remission stages of BM, with S2 having higher levels of neutrophils compared to S3. S4 and S5 represent the recovery stages after non-refractory remission, where neutrophil levels decrease further compared to S2 and S3, with S5 showing lower levels than S4. S6 and S7 represent refractory remission stages of BM, with S6 having higher levels of neutrophils compared to S7. S8 and S9 represent the recovery stages after refractory remission, where neutrophil levels decrease further compared to S6 and S7, with S9 showing lower levels than S8[ 3 ]. Additionally, the meningitis dataset GSE248261 was used to verify the expression differences of key genes FYN and RNF144B among BM, VM, and control groups[ 17 ]. This dataset includes 15 samples, with 5 samples in each group. Mitochondria-associated genes (MCAGs) were obtained from the MsigDB database ( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). 1.2 Differential Gene Analysis and Screening Differential gene analysis was performed using limma (version 3.58.1) with the following criteria for differential gene screening: FDR 0.5[ 18 ]. A volcano plot was generated using ggplot2 (version 3.5.1) to display the differentially expressed genes (DEGs)[ 19 ]. Additionally, a heatmap was created using ComplexHeatmap (version 2.18.0) to visualize the expression patterns of the top 100 up- and down-regulated DEGs[ 20 ]. 1.3 Weighted gene co-expression network analysis Using the WGCNA package (version 1.72-5) in R, a weighted co-expression network was constructed from the gene expression matrix[ 21 ]. The sample clustering dendrogram ( Additional Fig. 1 A) indicated minimal differences between samples, with no outlier samples detected. Studies indicate that the co-expression network conforms to a scale-free network, where the logarithm of the number of nodes with connectivity k (log(k)) is negatively correlated with the logarithm of the probability of such nodes (log(P(k))), and the correlation coefficient (R^2) is greater than 0.8. To ensure the network follows a scale-free network, an optimal β value of 19 was selected ( Additional Fig. 1 B). Next, the expression matrix was transformed into an adjacency matrix, which was then converted into a topological matrix. Based on the topological overlap matrix, hierarchical clustering of genes was performed using the average linkage method. The dynamic tree cutting algorithm was employed with the minimum module size set to 200 genes, resulting in the identification of 8 gene modules ( Additional Fig. 1 C). Modules with similar expression patterns were merged by setting MEDissThres to 0.2, yielding the final merged modules. To identify gene modules associated with the disease, the occurrence of the disease was defined as the trait, and the Pearson correlation was calculated between each module and the disease, identifying modules highly associated with BM. 1.4 GO/KEGG enrichment analysis The intersection of DEGs in meningitis, WGCNA green module genes, and MCAGs was identified using the ggVennDiagram package (version 1.5.2) in R[ 22 ]. The resulting genes were termed differentially expressed MCAGs (DE-MCAGs). Enrichment analysis of the DEGs or DE-MCAGs was performed using the clusterProfiler package (version 4.10.1) in R, focusing on gene ontology (GO) terms, which include biological process (BP), molecular function (MF), and cellular component(CC) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, based on the org.Hs.eg.db package background gene set[ 23 ]. Significantly enriched GO terms and KEGG pathways were identified with a padj < 0.05. The clusterProfiler package was also used for visualization of the enrichment analysis. 1.5 Mendelian Randomization Analysis Outcome IDs for "bacterial meningitis" were obtained by searching the IEU OPEN GWAS database ( https://gwas.mrcieu.ac.uk/ ), yielding finn-b-G6_MENINGBACT (ncase: 574; ncontrol: 217,485; Sample size: 218,059). Genetic data for NKT cells were also retrieved from the IEU database, with the ID ebi-a-GCST90001621 (Sample size = 3,653; nsnp = 15,195,758). Expression Quantitative Trait Loci (eQTL) data were sourced from the eQTLGen database. Both outcome and exposure data were derived from European populations. Instrumental variables satisfied the following conditions: a. P < 5 × 10^−8; b. R^2 10,000. The harmonise_data() function from the TwoSampleMR package (version 0.6.2) in R was used to align single nucleotide polymorphisms (SNPs) for exposure and outcome[ 24 ]. The mr function, combined with four algorithms (MR Egger, Weighted Median, Inverse Variance Weighted, Simple Median), was utilized for MR analysis. Horizontal pleiotropy was tested using the mr_pleiotropy_test() function, heterogeneity was assessed using the mr_heterogeneity() function, and leave-one-out analysis was conducted using the mr_leaveoneout() function. A p-value greater than 0.05 indicates no horizontal pleiotropy or heterogeneity, while a p-value less than 0.05 indicates the presence of heterogeneity. The Steiger test was employed to determine the direction of causality between exposure and outcome using the mr_steiger() function from the TwoSampleMR (version 0.6.2) package. The Steiger test measures the proportion of variance explained by SNPs for exposure (r^2.exposure) and outcome (r^2.outcome), with the criteria steiger_pval r^2.outcome. 1.6 Calculation of Immune Cell Infiltration Proportions We utilized the single sample gene set enrichment analysis (ssGSEA) algorithm from the GSVA package (version 1.50.5) to calculate the relative proportions of immune cells in the GSE40586 samples[ 25 ]. The ssGSEA algorithm ranks all genes by their expression levels in descending order and calculates the cumulative distribution function of the higher-expressing genes within a specific gene set, referred to as the gene set enrichment score (GSE). Using immune-related gene sets, the immune activity for each sample was obtained. The gene sets for 28 types of immune cells were sourced from reference [PMID: 35836132][ 26 ]. Spearman correlation analysis between two key genes and differential immune cells was conducted using the Hmisc package (v5.1-2), and correlation heatmaps were plotted using the ggplot2 (version 3.5.1) package. Additionally, radar charts for the correlation analysis between genes and immune cells were generated using the radarchart function. The correlation analysis between differential immune cells was also performed using the Spearman method, and the correlation heatmap was generated using the ggcor package (v0.9.8.1). 1.7 single-cell RNA sequencing analysis We employed the CreateSeuratObject() function from the Seurat package (version 5.0.6) to filter the single-cell sequencing data from GSE163194, retaining genes with expression data in at least 3 cells and cells with more than 50 detected genes (min.cells = 3, min.features = 50)[ 27 ]. The PercentageFeatureSet function was used to calculate the proportion of mitochondrial genes, and cells with a mitochondrial gene proportion of less than 40% were retained, resulting in 33,735 genes and 15,580 cells. Subsequently, the NormalizeData function normalized the data, and the FindVariableFeatures function identified highly variable genes between cells for subsequent cell type identification. We used the default parameters, specifically the "vst" method, to select 2,000 highly variable genes. Next, batch effects were removed using the CCA method in Seurat, and principal component analysis (PCA) was performed for data dimensionality reduction. An elbow plot was generated to identify the usable dimensions of the data, and the principal components prior to the elbow point were selected for further analysis; in this study, the first 30 principal components were chosen for clustering analysis. After PCA dimensionality reduction, we performed unsupervised clustering analysis on the cells using the FindNeighbors and FindClusters functions in the Seurat package with a default resolution of 0.8. The HumanPrimaryCellAtlasData and BlueprintEncodeData were used as reference data for annotation with the R package "singleR" (version 2.4.1). Manual annotation of different clusters was conducted using marker genes identified from the CellMarker database and reference [PMID: 38051587][ 28 ]. The distribution of cell subpopulations was visualized using t-distributed Stochastic Neighbor Embedding (t-SNE) clustering plots. Marker genes used for cell annotation were displayed using bubble charts. The proportion of each cell type was calculated, and bar charts were generated for visualization using the ggplot2 package (version 3.5.1). The expression of key genes in immune cells was presented in the form of violin plots and dot plots. 1.8 Gene Set Enrichment Analysis Correlation analysis between the target gene and other genes in the matrix was performed using training set data to obtain correlation coefficients. Genes were ranked based on these coefficients, and the clusterProfiler package (version 4.10.1) was used to conduct gene set enrichment analysis (GSEA) to explore the pathways and functions associated with key genes[ 23 ]. Pathways significantly enriched with a padj 1 were selected, and GseaVis (version 0.0.5) was used for visualization[ 29 ]. NES stands for normalized enrichment score. 1.9 Pseudotime Analysis NKT single-cell data were extracted, and the differentialGeneTest function was used to identify genes with significant differences among NKT cells. Pseudotime analysis was conducted using the monocle package (version 2.30.1)[ 30 ]. The differentialGeneTest function was employed again to identify genes with significant differences for pseudotime trajectory construction. The top 100 most significant genes, based on p-values, were selected for heatmap visualization using the plot_pseudotime_heatmap function. GO and KEGG enrichment analyses were performed based on gene clusters identified in the pseudotime analysis using the clusterProfiler (version 4.10.1) package. 1.10 Differential Gene Expression Analysis Expression levels of key genes in external independent transcriptome datasets were analyzed using the ggpubr (version 0.6.0) package for differential gene expression boxplots and visualized as bar plots using the ggplot2 package (version 3.5.1)[ 31 ]. 1.11 Statistical Analysis All statistical analyses were performed using R software (version 4.3.2, https://www.r-project.org/ ). The Wilcoxon rank-sum test was used to compare gene expression and immune cell infiltration differences between groups. A p-value < 0.05 was considered statistically significant. Results 2.1 Differential Gene Expression and Functional Enrichment Analysis in Peripheral Blood of BM Patients A differential gene expression analysis of the GSE40586 dataset was performed to investigate the peripheral blood immune response to pathogens in BM patients. The results identified 1,764 DEGs between BM and control groups (Fig. 1 A), with 914 genes upregulated and 850 downregulated in BM samples (Fig. 1 B). GO analysis showed these genes are mainly linked to cellular components like the external side of secretory granules, neutrophil tertiary granules, specific granules, and ribosomes. These genes are involved in molecular functions such as immune receptor activity, cytokine receptor activity, MHC protein complex binding, and structural constituent of ribosome. These genes participate in biological processes including immune response, cytokine production, immune cell differentiation, and cytoplasmic translation (Fig. 1 C). KEGG analysis revealed that the DEGs are mainly involved in pathways such as ribosome, human T-cell leukemia virus infection, T-cell differentiation, T-cell receptor signaling pathway, and natural killer (NK) cell-mediated cytotoxicity (Fig. 1 D). These results suggest that neutrophil tertiary granules, specific granules, and NK cells play significant roles in the innate immune response of meningitis patients, while T cells are primarily involved in the adaptive immune response. 2.2 Identification and Functional Enrichment of Mitochondrial Gene Modules in BM Mitochondria are crucial for regulating immune cell functions. This study screened mitochondrial genes involved in BM pathology. Using WGCNA, we identified five modules. Pearson correlation analysis indicated that the MEgreen module showed the highest positive correlation with the disease (correlation coefficient = 0.68) and a significant negative correlation with the control group (p < 0.05) (Figs. 2 A and B). The intersection of DEGs from meningitis, WGCNA module genes, and MCAGs identified 110 common genes, referred to as DE-MCAGs ( Additional Fig. 2 ). GO analysis revealed these DE-MCAGs are primarily located in the mitochondrial membrane, mitochondrial matrix, cytoplasmic vesicle lumen, and secretory granule lumen. They perform molecular functions such as oxidoreductase activity, NADP binding, acid-thiol ligase activity, and coenzyme A-ligase activity. These genes are involved in processes such as oxidative stress response, purine nucleotide metabolism, purine-containing compound metabolism, and protein localization (Fig. 2 C). KEGG analysis demonstrated that the DE-MCAGs are mainly involved in pathways related to reactive oxygen species production, thermogenesis, and diabetic cardiomyopathy. Carbon and glutathione metabolism are the primary metabolic pathways regulated by mitochondrial genes, while autophagy and ferroptosis are key pathways of cell death (Fig. 2 D). 2.3 Causal Impact of Differential Mitochondrial Genes on BM and Their Expression Profiles MR analysis was used to validate the impact of DE-MCAGs on the development of BM. The results indicated that two DE-MCAGs exhibit causal relationships with the disease. Specifically, the FYN gene was identified as a protective factor for BM (p-value < 0.05, odds ratio (OR) < 1), while the RNF144B gene was identified as a risk factor for BM (p-value 1) (Fig. 3 A). Sensitivity tests for each exposure factor were presented using scatter plots, funnel plots, and forest plots ( Additional Figs. 3 and 4 ). Heterogeneity and horizontal pleiotropy tests (Additional Tables 1 and 2) indicated P-values > 0.05, suggesting their absence. The Steiger test results for the two significant MR genes are shown in Additional Table 3 , both passing the test and indicating no reverse causality. Subsequently, the expression levels of the two key genes were analyzed in the disease and control groups. Results indicated that FYN was downregulated, while RNF144B was upregulated in the BM disease group (Fig. 3 B), consistent with the MR analysis trend. Excluding two outliers (M039 and M028), RNF144B transcription levels in the peripheral blood of BM patients were higher than those of FYN (Fig. 3 C). Additionally, the RNF144B-FYN gene expression ratio was significantly higher in the peripheral blood of BM patients compared to the healthy group (Fig. 3 D). 2.4 Differential Immune Cell Proportions and Their Correlation with RNF144B and FYN in Meningitis Results from the ssGSEA algorithm indicated significant differences in the proportions of 22 types of immune cells, including activated B cells, activated CD4 T cells, activated CD8 T cells, activated dendritic cells, NK cells, NKT cells, and neutrophils, between the disease and control group. Specifically, the proportions of activated dendritic cells, macrophages, mast cells, neutrophils, regulatory T cells, and type 17 T helper cells were significantly higher in the meningitis group, whereas the proportions of NKT cells, activated CD8 T cells, central memory CD4 T cells, effector memory CD8 T cells, immature B cells, memory B cells, and activated B cells were significantly lower (Figs. 4 A and B ). Further analysis of immune cell infiltration in meningitis indicated a negative correlation between immune cells with increased and decreased proportions (Fig. 4 C). RNF144B and FYN were identified as a risk factor and a protective factor for meningitis, respectively (Fig. 3 A). Correlation analysis between these genes and the immune cells revealed that RNF144B was strongly positively correlated with immune cells that had increased proportions in the disease group, such as activated dendritic cells, macrophages, mast cells, neutrophils, regulatory T cells, and type 17 T helper cells. Conversely, FYN was strongly positively correlated with immune cells that had decreased proportions, including NKT cells, activated CD8 T cells, central memory CD4 T cells, effector memory CD8 T cells, immature B cells, memory B cells, and activated B cells (Figs. 4 D-F). 2.5 Dynamic Changes in Immune Cell Proportions and Key Gene Expression During BM Progression Single-cell data analysis identified eight cell types: pDC cell, B cell, mDC cell, NKT cell, T cell, neutrophils, monocyte cell, and apoptotic cell. Cell subset distribution was visualized with a t-SNE plot (Fig. 5 A), and marker genes used for annotation are shown (Fig. 5 B). The CSF single-cell dataset GSE163194 was used to analyze the proportions of each cell type at different disease stages. Results showed that neutrophils and monocytes decreased from the initial stage of BM to remission and further in recovery. In contrast, NKT and T cells, minimal initially, significantly increased during remission and rose further in recovery (Figs. 5 C and D ). This pattern supports the observed negative correlation between neutrophil and NKT cell infiltration levels (Fig. 4 C). Key gene expression levels in immune cells were calculated, revealing RNF144B was predominantly expressed in monocytes and neutrophils, with minimal expression in NKT and T cells (Figs. 5 E and F ). This aligns with the positive correlation between RNF144B expression and neutrophil infiltration (Fig. 4 D). Conversely, FYN was mainly expressed in NKT cells, with slight expression in neutrophils, consistent with its correlation with NKT cell infiltration (Fig. 4 D). Analysis of RNF144B and FYN expression trends across different stages in datasets GSE163194 and GSE163195 showed FYN expression increased in the CSF transcriptome during remission and recovery, notably in NKT and T cells ( Additional Fig. 5 A and B ), while RNF144B expression decreased, especially in neutrophils and monocytes ( Additional Fig. 5 C and D ). These findings align with the observed variation in cell proportions during disease progression (Figs. 5 C and D ). 2.6 NKT Cells as Protective Factors in BM and the Functional Implications of FYN Gene Expression Given that FYN is highly expressed in NKT cells, this study focused on exploring the role of NKT cells in BM through MR analysis. The results showed a causal relationship, with NKT cells acting as a protective factor against BM (p-value < 0.05, OR < 1) (Fig. 6 A). Sensitivity tests for exposure factors were illustrated using scatter, funnel, and forest plots ( Additional Fig. 6 ). This causal relationship aligns with the proportion differences observed between the BM and control groups. The heterogeneity test yielded a p-value of 0.3427, and the horizontal pleiotropy test yielded a p-value of 0.6810, indicating no significant heterogeneity or horizontal pleiotropy (P > 0.05). Reverse MR analysis results suggested no reverse causation between NKT cells and BM (Additional Table 4). To understand FYN's potential role in BM, GSEA was used to identify GO pathways and functions related to FYN. GO enrichment results revealed significant positive correlations of FYN with pathways such as immune response-activating receptor signaling, regulation of T cell activation, and somatic diversification of immune receptors. Conversely, the humoral immune response, particularly antimicrobial responses mediated by peptides, was negatively correlated with FYN (Fig. 6 B). These GO functions regulated by FYN are consistent with its positive correlation with NKT and T cell infiltration levels. KEGG enrichment results showed significant positive correlations of FYN with ATP-dependent chromatin remodeling, DNA replication, and ribosome biogenesis, while neuroactive ligand-receptor interaction and olfactory transduction were negatively correlated with FYN (Fig. 6 C). 2.7 Differential Expression and Functional Implications of FYN Gene in NKT Cell Differentiation in BM Patients To investigate the transcriptional expression differences of the FYN gene in NKT cells at various differentiation states, pseudotime analysis was conducted on NKT single-cell data. The results showed a differentiation trajectory from left to right, indicated by a color gradient from light to dark. Based on the bifurcation points of these trajectories, NKT cells were classified into nine states. Among these, states 3, 5, and 7 had significantly fewer NKT cells compared to other states. In BM patients, during both remission and recovery phases, the proportion of NKT cells in State 1/2 was significantly higher in the S3\S4 (non-refractory meningitis) group compared to the S6\S7\S8 (refractory meningitis) group (Fig. 7 A). Moreover, examining the expression of the FYN across different branches revealed an upregulation trend with increased differentiation, although a decline was observed in the state 8 (Fig. 7 B). Clustering based on gene expression trends in the pseudotime analysis identified two clusters, with FYN located in cluster 2 (Fig. 7 C). Subsequent GO and KEGG enrichment analyses highlighted significant differences between the clusters (Figs. 7 D-G). GO cellular component (CC) revealed that cluster 2 was primarily enriched in the T cell receptor complex, plasma membrane signaling receptor complex, and cytolytic granule compared to cluster 1 (Fig. 7 D). GO molecular function (MF) showed that cluster 2 was mainly involved in T cell receptor binding, lipase/phospholipase activator activity, and structural constituent of the postsynaptic actin cytoskeleton (Fig. 7 E). GO biological process (BP) showed that T cell receptor signaling pathway and α-βT cell activation were significantly more enriched in cluster 2 than in cluster 1, suggesting a higher relevance of cluster 2 to T cell-related immune response regulation (Fig. 7 F). In the KEGG enrichment results, both clusters were associated with immune response regulation, excluding disease pathways, with cluster 2 notably enriched in the T cell receptor signaling pathway, consistent with the GO enrichment results (Fig. 7 G). 2.8 Differential Expression of FYN and RNF144B Genes in Peripheral Blood During Healthy, Onset, and Recovery Stages of Meningitis The GSE163196 dataset was analyzed to assess the expression differences of FYN and RNF144B in peripheral blood leukocytes across three stages: healthy, onset, and recovery. The results indicated that FYN expression was significantly reduced during the onset phase and began to increase during recovery, whereas RNF144B expression was significantly elevated at onset and decreased during recovery (Figs. 8 A and B ). These expression trends during onset and recovery were consistent with the MR analysis results. Additionally, compared to the onset phase, the expression trends of FYN and RNF144B during recovery corresponded with changes in the proportions of NKT cells and neutrophils, respectively (Figs. 5 C and D ). The meningitis dataset GSE248261 was also utilized to validate the expression differences of FYN and RNF144B among BM, VM, and control groups. While there were no significant differences (p > 0.05), the expression trends of these genes between the BM and control groups were consistent with previous analyses. Notably, both genes showed opposite trends in VM compared to BM (Figs. 8 C and D ). In summary, increased RNF144B expression and decreased FYN expression are associated with meningitis development. Discussion The study highlights the intricate molecular landscape and immune response dynamics in BM patients, shedding light on differential gene expression, mitochondrial gene involvement, immune cell distribution, and key gene functions. Differential Gene Expression and Immune Response The identification of 1,764 DEGs in the peripheral blood of BM patients highlights significant transcriptional alterations in response to bacterial infection. Functional enrichment analyses using GO and KEGG pathways provide insights into the biological roles of these DEGs (Fig. 1 ). For example, bacterial invasion into the CSF triggers a rapid inflammatory response mediated by the innate immune system[ 16 ]. The enrichment of genes localized to secretory granules and ribosomes suggests heightened protein synthesis and secretion activities, essential for immune functions such as cytokine release and pathogen recognition. In particular, in cases of meningitis, cytokines released by peripheral immune organs into the bloodstream cross the altered blood-CSF barrier and contribute to their levels in the CSF[ 16 ]. Leukocyte influx, a hallmark of acute meningitis, contributes to neuronal damage, with elevated neutrophil counts in CSF serving as a diagnostic indicator for acute BM[ 1 , 32 ]. The neutrophil-to-lymphocyte ratio (NLR) has been shown to be a predictor of BM and even mortality in pediatric patients. Additionally, this ratio is associated with increased cerebral blood flow during acute BM[ 33 – 35 ]. Thus, the release of secretory granules may represent a potential pathogenic mechanism in meningitis. Specifically, neutrophils release neutrophil extracellular traps (NETs) during infections to entrap and eliminate bacteria; however, NETs in the CNS have been shown to hinder pneumococcal clearance, suggesting that degrading NETs may have significant therapeutic implications[ 36 ]. Moreover, reducing NETs has been shown to significantly inhibit neuroinflammation and improve neurological deficits in the treatment of traumatic brain injury and stroke[ 37 ]. Based on these findings, it is hypothesized that, during the early stages of infection, neutrophils in peripheral blood primarily clear pathogenic bacteria. However, as the disease progresses, neutrophil recruitment to the CNS may amplify the inflammatory response, potentially hindering recovery in meningitis patients. Further investigation is needed to validate this hypothesis. The involvement of pathways like NK cell-mediated cytotoxicity and T-cell receptor signaling emphasizes the dual roles of innate and adaptive immunity in combating bacterial pathogens (Fig. 1 ). Analysis of CSF immune cell subset distribution in 319 patients with inflammatory or non-inflammatory neurological diseases revealed that T cells were predominant in the CSF. Additionally, NK cells were slightly elevated in neuroinflammation, and both monocytes and NK cells were elevated in CNS malignancies[ 38 ]. These findings highlight the complex interplay of immune cells, particularly neutrophils, NK cells, and T cells, in the CSF, which may contribute to the pathophysiology and progression of various neurological conditions. Mitochondrial Dysfunction and Its Role in the Pathogenesis of BM Mitochondrial genes are crucial for cellular energy metabolism and apoptotic pathways[ 39 ]. The study identifies mitochondrial gene modules linked to BM, underscoring the mitochondria’s influence on disease progression (Fig. 2 ). Additionally, the enrichment of pathways related to oxidative stress and metabolic processes underscores the mitochondria's role in maintaining cellular homeostasis and meeting infection-induced energy demands. Bacterial toxins appear to induce programmed death in neurons and microglia by causing rapid mitochondrial damage in meningitis[ 1 ]. Streptococcus suis serotype 2 (SS2) is a zoonotic pathogen responsible for meningitis in both pigs and humans. Previous studies have shown that SS2 induces mitochondrial dysfunction in human brain microvascular endothelial cells, leading to apoptosis[ 40 ]. Consequently, mitochondrial impairment may be a potential pathogenic factor of BM. Wu and colleagues demonstrated that mitochondrial insufficiency serves as an intrinsic cellular trigger for initiating T cell functional exhaustion[ 41 ]. Thus, it is hypothesized that pathogen-induced mitochondrial dysfunction may play a critical role in the pathogenesis of BM by impairing immune cell function. Causal Relationship and Diagnostic Potential of FYN and RNF144B Diagnosing BM is challenging because bacterial and aseptic meningitis often present with similar clinical symptoms. CSF analysis and microbial culture assist clinicians in differentiation, but lumbar puncture to obtain CSF samples is not always feasible due to contraindications or uncertain clinical situations[ 33 ]. Therefore, identifying biomarkers from blood samples is crucial. For instance, beyond the NLR in blood, S100B protein levels in CSF and serum serve as biomarkers for diagnosing BM[ 42 ]. S100B protein is used in diagnosing and prognosing outcomes in patients with acute brain injuries in neurological diseases[ 43 ]. In this study, using the human peripheral blood transcriptome database, MR analysis reveals the causal roles of FYN and RNF144B in BM. FYN acts as a protective factor, likely through T cell activation and NKT cell function, crucial for immune response modulation. Conversely, RNF144B is a risk factor, correlating positively with neutrophil levels and significantly elevated at meningitis onset (Figs. 3 and 4 ). The expression patterns of these genes differ significantly at various disease stages, suggesting their potential as diagnostic biomarkers for BM. In Fig. 3 C, only the M028 and M039 groups show FYN expression levels higher than RNF144B. Interestingly, Lill's study also noted that these two samples have distinct gene expression profiles compared to others. The RNF144B to FYN expression ratio may relate to disease progression in BM. The correlation between RNF144B and increased immune cell types, like neutrophils and macrophages, aligns with its role as a risk factor, whereas FYN’s association with NKT and T cells reinforces its protective nature. Single-cell analysis provides a granular view of immune cell transitions during BM progression (Fig. 5 ). Granulocyte depletion is neuroprotective in experimental meningitis, while persistence is associated with more severe neuronal damage[ 44 ]. Compared to the onset phase, there is a significant decrease in neutrophil proportions during remission and recovery phases, while NKT and T cells increase significantly (Fig. 5 ). The increase in NKT and T cells during recovery aligns with FYN's protective role, while the decrease in neutrophils correlates with RNF144B expression patterns. Specifically, RNF144B expression is significantly reduced during remission and recovery phases (Fig. 8 and Additional Fig. 4 ). Similar to how the NLR predicts BM and mortality in pediatric patients, the correlation of RNF144B and FYN with immune cell proportions suggests that the RNF144B-to-FYN expression ratio could aid in developing new biomarkers for early diagnosis and therapeutic monitoring of the disease. FYN and Its Immune Implications in BM Notably, FYN is highly expressed in NKT cells, while RNF144B expression is low in immune cells, highlighting FYN's potential role in NKT cells in this study (Fig. 5 ). FYN is one of the most highly expressed cytoplasmic tyrosine kinases in the brain, and its widespread distribution indicates its critical role[ 45 ]. FYN inhibits excitatory and inhibitory synaptic transmission stimuli and regulates mechanisms related to learning and memory processes[ 45 ]. Numerous studies have shown that FYN promotes myelination in the CNS and mediates oligodendrocyte differentiation and maturation. Following ischemic injury in the adult brain, FYN promotes the assembly and remodeling of the postsynaptic density complex[ 46 ]. Research indicates that FYN polymorphisms are risk factors for schizophrenia and bipolar disorder[ 47 , 48 ]. In our study, FYN acted as a protective gene in BM, with expression levels decreasing at the onset of BM. Changes in the endothelial tight junction structure of the BBB have been detected in brains with multiple sclerosis, notably associated with abnormal activation of the plexin-FYN-AKT signaling pathway[ 49 ]. Therefore, the functional role of FYN in BM warrants further investigation. It is possible that FYN is associated with BBB disruption and nerve damage in the pathological process of BM. Detailed GSEA and pseudotime analyses of FYN elucidate its potential functions in enhancing immune receptor signaling and T cell regulation (Figs. 6 and 7 ). Its negative association with humoral immune responses reflects a specialized function in T cell-mediated immunity, possibly prioritizing cellular immune responses during BM. Hence, FYN is likely to influence T cell function during meningitis, particularly in the recovery and remission phases, by regulating NKT cells. These findings suggest a potential therapeutic window where enhancing NKT cell functions could alter disease trajectories favorably. Conclusion This study identifies the RNF144B gene as a risk factor associated with increased neutrophils during the initial phase of meningitis. Conversely, the FYN gene acts as a protective factor, linked to increased NKT cells during the remission and recovery periods of meningitis, highlighting its important role in immune regulation and recovery. Additionally, compared to BM, these two key genes exhibit opposite expression trends in VM. The distinct expression patterns of these genes at various disease stages suggest their potential as biomarkers for differentiating BM from VM and for monitoring disease progression, thus aiding in more accurate diagnoses and targeted treatments. Declarations Availability of data and materials The data presented in the study are deposited in the Genbank of National Center for Biotechnology Information, accession number GSE40586, GSE163194, GSE163195, GSE163196, and GSE248261. Competing Interests The authors declare that there are no competing interests associated with the manuscript. Funding This study was supported by the National Natural Science Foundation of China (32302877) and Zhejiang A&F University Research and Development Fund (2024LFR078). Authors' contributions H.J. performed and designed the research; H.J. and X.Y. analyzed the data and drafted the manuscript; J.F. polished the language; H.S. and Y.Y. supervised the whole project and revised the manuscript. All the authors read, edited, and approved the manuscript. Consent for publication All the authors consent for publication. Ethics approval and consent to participate Not applicable. A cknowledgements Not applicable. References Hoffman O, Weber RJ (2009) Pathophysiology and Treatment of Bacterial Meningitis. Therapeutic Advances in Neurological Disorders 2: 1-7. https://doi.org/10.1177/1756285609337975 Tunkel AR, Hartman BJ, Kaplan SL, Kaufman BA, Roos KL, Scheld MW, Whitley RJ (2004) Practice Guidelines for the Management of Bacterial Meningitis. 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Psychiatric Genetics 23: 39-40. https://doi.org/10.1097/YPG.0b013e328358640b Franklin RJM, Ffrench-Constant C (2008) Remyelination in the CNS: from biology to therapy. Nature Reviews Neuroscience 9: 839-855. https://doi.org/10.1038/nrn2480 Additional Declarations No competing interests reported. Supplementary Files Additionalfilelegends.docx AdditionalTable1.docx AdditionalTable2.docx AdditionalTable3.docx AdditionalTable4.docx Additionalfig1.jpg Additionalfig2.jpg Additionalfig3.jpg Additionalfig4.jpg Additionalfig5.jpg Additionalfig6.jpg 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. 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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-5518056","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383801753,"identity":"35ae25be-5aea-4cce-a58c-7bf1cc6ae005","order_by":0,"name":"Hexiang Jiang","email":"","orcid":"","institution":"Zhejiang A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Hexiang","middleName":"","lastName":"Jiang","suffix":""},{"id":383801755,"identity":"2ce0713f-f8b2-4d6d-a6c8-9dbd85818b8d","order_by":1,"name":"Xibing Yu","email":"","orcid":"","institution":"Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xibing","middleName":"","lastName":"Yu","suffix":""},{"id":383801757,"identity":"564f0de4-0c4a-4a27-8978-64bc4f3dd87f","order_by":2,"name":"Jingyan Fan","email":"","orcid":"","institution":"Zhejiang A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Jingyan","middleName":"","lastName":"Fan","suffix":""},{"id":383801759,"identity":"dbe497eb-9563-441e-89d2-8fd4caea587e","order_by":3,"name":"Houhui Song","email":"","orcid":"","institution":"Zhejiang A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Houhui","middleName":"","lastName":"Song","suffix":""},{"id":383801760,"identity":"10bbcb7e-7625-4db1-a101-9e6293c549ec","order_by":4,"name":"Yang Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYNCCCgYG9gYgzUO8ljNA1QdI0sLYRooWgxvJzx5+nVcnzyORwPjgbRuDvDlhLWnmxrLb2Ax7JBKYDee2MRjubCCoJcFMWnIbD+N+iQQ2ad42hgSDAwS1pH+TlpwjYQ+0hf03kVpyzCQ/NhgkArWwMROlRfLMmzJphmMJyT08D5sl55yTMNxASAvf8fRtkj9q6mx72JMPfnhTZiNP0BaFCwkMzJDoYGwAEhIE1AOBfP8BBsYfhNWNglEwCkbBSAYAl4A+UCG0yGUAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang A\u0026F University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-11-25 07:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5518056/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5518056/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70797445,"identity":"eeda3e18-782f-4ff4-8b49-26e0729ba0ee","added_by":"auto","created_at":"2024-12-06 22:22:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4370895,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Gene Expression and Functional Enrichment Analysis in Peripheral Blood of Bacterial Meningitis Patients. (A-B) Visualization of differentially expressed genes (DEGs) between bacterial meningitis and control groups, with (A) showing a volcano plot and (B) showing a heatmap. (C-D) Enrichment analysis for DEGs, displaying a bar plot for GO enrichment analysis (C) and a bubble plot for KEGG enrichment analysis (D). Data for panels A-D are based on the GSE40586 dataset.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/a7d49e436a66c638918a9019.jpg"},{"id":70797140,"identity":"02e8eb57-7d4b-419f-a394-588958f873a8","added_by":"auto","created_at":"2024-12-06 22:06:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2739370,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and Functional Enrichment of Mitochondrial Gene Modules in Bacterial Meningitis. (A) Identification and merging of candidate genes extracted from the GSE40586 dataset using weighted gene co-expression network analysis (WGCNA). (B) Heatmap of Pearson correlation between gene modules and clinical traits of bacterial meningitis. (C-D) Bar plot (C) showing GO enrichment analysis and bubble plot (D) showing KEGG enrichment analysis for genes intersecting among meningitis DEGs, WGCNA module green genes, and mitochondrial-associated genes.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/ba3cb7a3cbafb1a55b65e337.jpg"},{"id":70797357,"identity":"f071fb3a-bfa5-478d-8476-41f2df960357","added_by":"auto","created_at":"2024-12-06 22:14:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1812509,"visible":true,"origin":"","legend":"\u003cp\u003eCausal Impact of Differential Mitochondrial Genes on Bacterial Meningitis and Their Expression Profiles. (A) Forest plot of mendelian randomization analysis on the effect of \u003cem\u003eRNF144B\u003c/em\u003e and \u003cem\u003eFYN\u003c/em\u003e on bacterial meningitis. (B) Differential expression analysis of RNF144B and FYN between the meningitis group and the healthy control group. (C) Comparison of RNF144B and FYN gene expression levels following infection with different bacterial meningitis strains. (D) Comparison of the standardized ratio of RNF144B/FYN between the meningitis group and the healthy control group. Data for panels B-D are based on the GSE40586 dataset.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/567711af50a848b698e0e5c4.jpg"},{"id":70797151,"identity":"b3157645-56b5-46ca-a51a-20d7ecb8b7e0","added_by":"auto","created_at":"2024-12-06 22:06:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4665361,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Immune Cell Proportions and Their Correlation with RNF144B and FYN in Meningitis. (A-B) Violin plot (A) illustrating and heatmap (B) displaying immune cell scores using the ssGSEA algorithm. (C) Analysis of the correlation between infiltration levels of different immune cells in meningitis using the Spearman method. (D) Heatmap of the correlation analysis between key genes (RNF144B and FYN) and immune cells. (E-F) Radar charts presenting the correlation analysis between immune cells and FYN (E) and RNF144B (F). Data for panels A-F are based on the GSE40586 dataset.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/8d9ee2eefc6a108c4fc3b714.jpg"},{"id":70797363,"identity":"ec93c80a-b33f-462c-9f0b-5a4fb3aa2d4e","added_by":"auto","created_at":"2024-12-06 22:14:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2896539,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic Changes in Immune Cell Proportions and Key Gene Expression During Bacterial Meningitis Progression. (A) t-SNE clustering plot of immune cell subsets. (B) Bubble plot showing marker genes for each immune cell type. (C-D) Bar plot (C) and column plot (D) of immune cell proportions at various stages of bacterial meningitis progression. (E-F) Dot plot (E) and violin plot (F) of FYN and RNF144B gene expression in different immune cells. Data for panels A-F are based on the GSE163194 dataset.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/def97be4513debfd6fe3f704.jpg"},{"id":70797162,"identity":"09dc9dd5-0da2-4386-907a-58740c899350","added_by":"auto","created_at":"2024-12-06 22:06:33","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3214691,"visible":true,"origin":"","legend":"\u003cp\u003eNKT Cells as Protective Factors in Bacterial Meningitis and the Functional Implications of FYN Gene Expression. (A) Forest plot illustrating the mendelian randomization analysis of the effect of NKT cells on bacterial meningitis. (B-C) GSEA analysis of the involvement of the \u003cem\u003eFYN\u003c/em\u003e gene in GO functions (B) and KEGG pathways (C).\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/6c9e2c75f286eeb2c3afcb84.jpg"},{"id":70797170,"identity":"891bfb36-e09f-488c-ba11-1f0472b34f3d","added_by":"auto","created_at":"2024-12-06 22:06:33","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2665428,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Expression and Functional Implications of FYN in NKT Cell Differentiation in BM Patients. (A) Pseudotime analysis of NTK cells. (B) Expression of the FYN across different branches. (C) Clustering analysis based on gene expression trends in pseudotime. GO and KEGG enrichment analyses for each cluster, covering GO cellular component (CC) (D), GO molecular function (MF) (E), GO biological process (BP) (F), and KEGG pathways (G).\u003c/p\u003e","description":"","filename":"fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/585005489ff48c3305ff85fe.jpg"},{"id":70797135,"identity":"dd33d1ea-f90f-46f8-b981-8badd3b97c9e","added_by":"auto","created_at":"2024-12-06 22:06:31","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":616557,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Expression of FYN and RNF144B in Peripheral Blood During Healthy, Onset, and Recovery Stages of Meningitis. (A-B) Analysis of expression levels of the FYN (A) and the RNF144B (B) at different stages of bacterial meningitis progression, extracted from the GSE163196 dataset. (C-D) Comparative analysis of gene expression levels of FYN (C) and RNF144B (D) in bacterial versus viral meningitis. Data are based on the GSE248261 dataset.\u003c/p\u003e","description":"","filename":"fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/d6b816c61d6d1cbc8ff2b177.jpg"},{"id":71399676,"identity":"a4b78bb9-ba8f-44b4-80bc-ead5ddc756c0","added_by":"auto","created_at":"2024-12-14 09:02:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23468384,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/07c94eaa-d0b2-49aa-b105-6fa5710586a0.pdf"},{"id":70797128,"identity":"ecc41eef-3bcc-48d5-b747-46fe1958c9d3","added_by":"auto","created_at":"2024-12-06 22:06:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48039,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfilelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/d7d1c6f2bad687fa043e2f8b.docx"},{"id":70797129,"identity":"452e2604-171d-41ec-a919-b9744238366a","added_by":"auto","created_at":"2024-12-06 22:06:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16200,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/fd8cd34137c4b365648afad7.docx"},{"id":70797142,"identity":"765768a9-3619-4636-b13d-c1b7722a7ec3","added_by":"auto","created_at":"2024-12-06 22:06:31","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15782,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/7ca73b894913bcaa8e5618a2.docx"},{"id":70797148,"identity":"2dc56aeb-a4ea-4433-a5e7-075cb7298975","added_by":"auto","created_at":"2024-12-06 22:06:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16132,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/a06e88a7cb986126cafb4853.docx"},{"id":70797145,"identity":"6ff9b122-4ee8-4ced-a039-d7e28b589a64","added_by":"auto","created_at":"2024-12-06 22:06:31","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15762,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/ecfbf90031bc3e10957fe87a.docx"},{"id":70797159,"identity":"3f982ee3-91aa-46a5-93fb-c33b06c36779","added_by":"auto","created_at":"2024-12-06 22:06:32","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":652603,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/1aefedf00d7f896e263cac97.jpg"},{"id":70797360,"identity":"0dd9f8a7-6d8b-4488-9b02-c5e760ac3a15","added_by":"auto","created_at":"2024-12-06 22:14:31","extension":"jpg","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":433048,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/0197abf40bd56ca414d0964d.jpg"},{"id":70797362,"identity":"94539efb-ee03-4135-92a3-d8e77c5a161d","added_by":"auto","created_at":"2024-12-06 22:14:31","extension":"jpg","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":804308,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/cbe3a3e5bbc72521703fa4cf.jpg"},{"id":70797147,"identity":"68039def-3460-4a5c-a033-09f13229e369","added_by":"auto","created_at":"2024-12-06 22:06:32","extension":"jpg","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":797689,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/764f659f395f9b66fa878ba3.jpg"},{"id":70797368,"identity":"b02150d8-e2c0-4886-9ab4-ec8e6366fb1e","added_by":"auto","created_at":"2024-12-06 22:14:33","extension":"jpg","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1426066,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/fd664703f9243d4e8c9fe219.jpg"},{"id":70797370,"identity":"ae8e1523-e302-4fef-b574-fec71d5addc0","added_by":"auto","created_at":"2024-12-06 22:14:33","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1690078,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5518056/v1/9ccb34be14826e98060a9921.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Transcriptomics and Genetics to Identify Expression Patterns of RNF144B and FYN as Potential Predictors of Bacterial Meningitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMeningitis is a complex disease caused by bacterial, viral, and fungal infections, as well as non-infectious agents such as drugs. Bacteria are the most common causative agents of meningitis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Bacterial meningitis (BM) is an acute central nervous system (CNS) infectious process, where pathogens like \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, \u003cem\u003eNeisseria meningitidis\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, and \u003cem\u003eStreptococcus agalactiae\u003c/em\u003e penetrate the blood-brain barrier (BBB) into meningeal compartments, triggering a secondary immune response and neuroinflammatory dysfunction[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. BM is a medical emergency[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], causing approximately 318,000 deaths annually worldwide and resulting in an estimated 20,383 years of life lost[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly diagnosis and treatment of BM are crucial, as timely intervention generally leads to better recovery outcomes. Delayed treatment, however, is associated with increased in-hospital mortality and unfavorable outcomes at discharge[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Clinically, treatment often begins before laboratory results are available, since early intervention can significantly improve patient outcomes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Diagnosing BM based on clinical symptoms is challenging, particularly in patients with weakened immune systems, children, or the elderly, where symptoms may present atypically[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, viral meningitis (VM) can exhibit similar symptoms, further complicating diagnosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, a treatment strategy using both antiviral and antibiotic medications is frequently employed until the infectious agent is identified[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These clinical challenges underscore the importance of early and accurate identification of BM to distinguish it from VM and guide timely treatment.\u003c/p\u003e \u003cp\u003eThe risk of BM is highest in neonates, the elderly, and individuals with compromised immune function, highlighting the critical role of immune responses in disease progression[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Multiple studies support the view that the host immune response occurs before clinical symptoms across various pathogens and diseases, including tuberculosis, \u003cem\u003eSalmonella\u003c/em\u003e infections, and influenza virus infections [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. They suggest that early markers of the host immune response may be critical for early diagnosis, treatment selection, and prognosis assessment of diseases. However, no studies have systematically elucidated the immune response in patients with BM. To better understand these immune responses, we analyzed peripheral blood transcriptome data from BM patients and healthy controls. Mendelian randomization (MR) analysis identified \u003cem\u003eRNF144B\u003c/em\u003e as a risk gene and \u003cem\u003eFYN\u003c/em\u003e as a protective gene. These findings were validated using single-cell transcriptome data from cerebrospinal fluid (CSF) and peripheral blood across different disease stages, showing predominant expression of RNF144B in neutrophils and FYN in natural killer T (NKT) cells. Moreover, during BM progression, the expression trends of RNF144B and FYN correlated with changes in neutrophil and NKT cell proportions, underscoring their roles in disease dynamics and immune response. Lastly, transcriptome comparisons between BM and VM revealed opposite expression trends for these genes, suggesting their potential as biomarkers to differentiate between the two types of meningitis.\u003c/p\u003e"},{"header":" Materials and methods","content":"\u003cp\u003e1.1 Data Download and Processing\u003c/p\u003e \u003cp\u003eDatasets GSE40586, GSE163194, GSE163195, GSE163196, and GSE248261 were downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe GSE40586 (GPL6244) dataset includes peripheral blood samples from 21 BM patients and 18 healthy controls, which are used for differential gene screening, weighted gene co-expression network analysis (WGCNA), and immune cell infiltration analysis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The GSE163194 dataset is a single-cell sequencing dataset comprising CSF samples obtained via lumbar puncture from BM patients at different disease stages, including initial onset, remission, and recovery[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This dataset was employed for single-cell-related analyses. The GSE163195 dataset contains transcriptomic data from CSF cell samples of 12 BM patients at various disease stages, consistent with the patient information in the GSE163194 dataset, serving as an external dataset for validating key gene expression trends[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The GSE163196 dataset provides transcriptomic data from peripheral blood leukocyte samples of 12 BM patients at different disease stages, also consistent with the GSE163194 dataset, used to explore the differential expression trends of key genes across the three stages[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe sample conditions in the GSE163194, GSE163195, and GSE163196 datasets are described as follows: S1 represents the initial stage of BM. S2 and S3 represent non-refractory remission stages of BM, with S2 having higher levels of neutrophils compared to S3. S4 and S5 represent the recovery stages after non-refractory remission, where neutrophil levels decrease further compared to S2 and S3, with S5 showing lower levels than S4. S6 and S7 represent refractory remission stages of BM, with S6 having higher levels of neutrophils compared to S7. S8 and S9 represent the recovery stages after refractory remission, where neutrophil levels decrease further compared to S6 and S7, with S9 showing lower levels than S8[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, the meningitis dataset GSE248261 was used to verify the expression differences of key genes \u003cem\u003eFYN\u003c/em\u003e and \u003cem\u003eRNF144B\u003c/em\u003e among BM, VM, and control groups[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This dataset includes 15 samples, with 5 samples in each group. Mitochondria-associated genes (MCAGs) were obtained from the MsigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e1.2 Differential Gene Analysis and Screening\u003c/p\u003e \u003cp\u003eDifferential gene analysis was performed using limma (version 3.58.1) with the following criteria for differential gene screening: FDR \u0026lt; 0.05, |FoldChange| \u0026gt; 0.5[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A volcano plot was generated using ggplot2 (version 3.5.1) to display the differentially expressed genes (DEGs)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, a heatmap was created using ComplexHeatmap (version 2.18.0) to visualize the expression patterns of the top 100 up- and down-regulated DEGs[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e1.3 Weighted gene co-expression network analysis\u003c/p\u003e \u003cp\u003eUsing the WGCNA package (version 1.72-5) in R, a weighted co-expression network was constructed from the gene expression matrix[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The sample clustering dendrogram (\u003cb\u003eAdditional\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) indicated minimal differences between samples, with no outlier samples detected. Studies indicate that the co-expression network conforms to a scale-free network, where the logarithm of the number of nodes with connectivity k (log(k)) is negatively correlated with the logarithm of the probability of such nodes (log(P(k))), and the correlation coefficient (R^2) is greater than 0.8. To ensure the network follows a scale-free network, an optimal β value of 19 was selected (\u003cb\u003eAdditional\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, the expression matrix was transformed into an adjacency matrix, which was then converted into a topological matrix. Based on the topological overlap matrix, hierarchical clustering of genes was performed using the average linkage method. The dynamic tree cutting algorithm was employed with the minimum module size set to 200 genes, resulting in the identification of 8 gene modules (\u003cb\u003eAdditional\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Modules with similar expression patterns were merged by setting MEDissThres to 0.2, yielding the final merged modules. To identify gene modules associated with the disease, the occurrence of the disease was defined as the trait, and the Pearson correlation was calculated between each module and the disease, identifying modules highly associated with BM.\u003c/p\u003e \u003cp\u003e1.4 GO/KEGG enrichment analysis\u003c/p\u003e \u003cp\u003eThe intersection of DEGs in meningitis, WGCNA green module genes, and MCAGs was identified using the ggVennDiagram package (version 1.5.2) in R[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The resulting genes were termed differentially expressed MCAGs (DE-MCAGs).\u003c/p\u003e \u003cp\u003eEnrichment analysis of the DEGs or DE-MCAGs was performed using the clusterProfiler package (version 4.10.1) in R, focusing on gene ontology (GO) terms, which include biological process (BP), molecular function (MF), and cellular component(CC) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, based on the org.Hs.eg.db package background gene set[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Significantly enriched GO terms and KEGG pathways were identified with a padj \u0026lt; 0.05. The clusterProfiler package was also used for visualization of the enrichment analysis.\u003c/p\u003e \u003cp\u003e1.5 Mendelian Randomization Analysis\u003c/p\u003e \u003cp\u003eOutcome IDs for \"bacterial meningitis\" were obtained by searching the IEU OPEN GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), yielding finn-b-G6_MENINGBACT (ncase: 574; ncontrol: 217,485; Sample size: 218,059). Genetic data for NKT cells were also retrieved from the IEU database, with the ID ebi-a-GCST90001621 (Sample size = 3,653; nsnp = 15,195,758). Expression Quantitative Trait Loci (eQTL) data were sourced from the eQTLGen database. Both outcome and exposure data were derived from European populations. Instrumental variables satisfied the following conditions: a. P \u0026lt; 5 × 10^−8; b. R^2 \u0026lt; 0.001, kb \u0026gt; 10,000. The harmonise_data() function from the TwoSampleMR package (version 0.6.2) in R was used to align single nucleotide polymorphisms (SNPs) for exposure and outcome[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mr function, combined with four algorithms (MR Egger, Weighted Median, Inverse Variance Weighted, Simple Median), was utilized for MR analysis. Horizontal pleiotropy was tested using the mr_pleiotropy_test() function, heterogeneity was assessed using the mr_heterogeneity() function, and leave-one-out analysis was conducted using the mr_leaveoneout() function. A p-value greater than 0.05 indicates no horizontal pleiotropy or heterogeneity, while a p-value less than 0.05 indicates the presence of heterogeneity. The Steiger test was employed to determine the direction of causality between exposure and outcome using the mr_steiger() function from the TwoSampleMR (version 0.6.2) package. The Steiger test measures the proportion of variance explained by SNPs for exposure (r^2.exposure) and outcome (r^2.outcome), with the criteria steiger_pval \u0026lt; 0.05, correct_causal_direction = TRUE, and r^2.exposure \u0026gt; r^2.outcome.\u003c/p\u003e \u003cp\u003e1.6 Calculation of Immune Cell Infiltration Proportions\u003c/p\u003e \u003cp\u003eWe utilized the single sample gene set enrichment analysis (ssGSEA) algorithm from the GSVA package (version 1.50.5) to calculate the relative proportions of immune cells in the GSE40586 samples[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The ssGSEA algorithm ranks all genes by their expression levels in descending order and calculates the cumulative distribution function of the higher-expressing genes within a specific gene set, referred to as the gene set enrichment score (GSE). Using immune-related gene sets, the immune activity for each sample was obtained. The gene sets for 28 types of immune cells were sourced from reference [PMID: 35836132][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Spearman correlation analysis between two key genes and differential immune cells was conducted using the Hmisc package (v5.1-2), and correlation heatmaps were plotted using the ggplot2 (version 3.5.1) package. Additionally, radar charts for the correlation analysis between genes and immune cells were generated using the radarchart function. The correlation analysis between differential immune cells was also performed using the Spearman method, and the correlation heatmap was generated using the ggcor package (v0.9.8.1).\u003c/p\u003e \u003cp\u003e1.7 single-cell RNA sequencing analysis\u003c/p\u003e \u003cp\u003eWe employed the CreateSeuratObject() function from the Seurat package (version 5.0.6) to filter the single-cell sequencing data from GSE163194, retaining genes with expression data in at least 3 cells and cells with more than 50 detected genes (min.cells = 3, min.features = 50)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The PercentageFeatureSet function was used to calculate the proportion of mitochondrial genes, and cells with a mitochondrial gene proportion of less than 40% were retained, resulting in 33,735 genes and 15,580 cells. Subsequently, the NormalizeData function normalized the data, and the FindVariableFeatures function identified highly variable genes between cells for subsequent cell type identification. We used the default parameters, specifically the \"vst\" method, to select 2,000 highly variable genes. Next, batch effects were removed using the CCA method in Seurat, and principal component analysis (PCA) was performed for data dimensionality reduction. An elbow plot was generated to identify the usable dimensions of the data, and the principal components prior to the elbow point were selected for further analysis; in this study, the first 30 principal components were chosen for clustering analysis.\u003c/p\u003e \u003cp\u003eAfter PCA dimensionality reduction, we performed unsupervised clustering analysis on the cells using the FindNeighbors and FindClusters functions in the Seurat package with a default resolution of 0.8. The HumanPrimaryCellAtlasData and BlueprintEncodeData were used as reference data for annotation with the R package \"singleR\" (version 2.4.1). Manual annotation of different clusters was conducted using marker genes identified from the CellMarker database and reference [PMID: 38051587][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The distribution of cell subpopulations was visualized using t-distributed Stochastic Neighbor Embedding (t-SNE) clustering plots. Marker genes used for cell annotation were displayed using bubble charts. The proportion of each cell type was calculated, and bar charts were generated for visualization using the ggplot2 package (version 3.5.1). The expression of key genes in immune cells was presented in the form of violin plots and dot plots.\u003c/p\u003e \u003cp\u003e1.8 Gene Set Enrichment Analysis\u003c/p\u003e \u003cp\u003eCorrelation analysis between the target gene and other genes in the matrix was performed using training set data to obtain correlation coefficients. Genes were ranked based on these coefficients, and the clusterProfiler package (version 4.10.1) was used to conduct gene set enrichment analysis (GSEA) to explore the pathways and functions associated with key genes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Pathways significantly enriched with a padj \u0026lt; 0.05 and |NES| \u0026gt; 1 were selected, and GseaVis (version 0.0.5) was used for visualization[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. NES stands for normalized enrichment score.\u003c/p\u003e \u003cp\u003e1.9 Pseudotime Analysis\u003c/p\u003e \u003cp\u003eNKT single-cell data were extracted, and the differentialGeneTest function was used to identify genes with significant differences among NKT cells. Pseudotime analysis was conducted using the monocle package (version 2.30.1)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The differentialGeneTest function was employed again to identify genes with significant differences for pseudotime trajectory construction. The top 100 most significant genes, based on p-values, were selected for heatmap visualization using the plot_pseudotime_heatmap function. GO and KEGG enrichment analyses were performed based on gene clusters identified in the pseudotime analysis using the clusterProfiler (version 4.10.1) package.\u003c/p\u003e \u003cp\u003e1.10 Differential Gene Expression Analysis\u003c/p\u003e \u003cp\u003eExpression levels of key genes in external independent transcriptome datasets were analyzed using the ggpubr (version 0.6.0) package for differential gene expression boxplots and visualized as bar plots using the ggplot2 package (version 3.5.1)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e1.11 Statistical Analysis\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.3.2, \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). The Wilcoxon rank-sum test was used to compare gene expression and immune cell infiltration differences between groups. A p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e2.1 Differential Gene Expression and Functional Enrichment Analysis in Peripheral Blood of BM Patients\u003c/p\u003e\u003cp\u003eA differential gene expression analysis of the GSE40586 dataset was performed to investigate the peripheral blood immune response to pathogens in BM patients. The results identified 1,764 DEGs between BM and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), with 914 genes upregulated and 850 downregulated in BM samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). GO analysis showed these genes are mainly linked to cellular components like the external side of secretory granules, neutrophil tertiary granules, specific granules, and ribosomes. These genes are involved in molecular functions such as immune receptor activity, cytokine receptor activity, MHC protein complex binding, and structural constituent of ribosome. These genes participate in biological processes including immune response, cytokine production, immune cell differentiation, and cytoplasmic translation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). KEGG analysis revealed that the DEGs are mainly involved in pathways such as ribosome, human T-cell leukemia virus infection, T-cell differentiation, T-cell receptor signaling pathway, and natural killer (NK) cell-mediated cytotoxicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eThese results suggest that neutrophil tertiary granules, specific granules, and NK cells play significant roles in the innate immune response of meningitis patients, while T cells are primarily involved in the adaptive immune response.\u003c/p\u003e\u003cp\u003e2.2 Identification and Functional Enrichment of Mitochondrial Gene Modules in BM\u003c/p\u003e\u003cp\u003eMitochondria are crucial for regulating immune cell functions. This study screened mitochondrial genes involved in BM pathology. Using WGCNA, we identified five modules. Pearson correlation analysis indicated that the MEgreen module showed the highest positive correlation with the disease (correlation coefficient = 0.68) and a significant negative correlation with the control group (p \u0026lt; 0.05) (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). The intersection of DEGs from meningitis, WGCNA module genes, and MCAGs identified 110 common genes, referred to as DE-MCAGs (\u003cb\u003eAdditional\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). GO analysis revealed these DE-MCAGs are primarily located in the mitochondrial membrane, mitochondrial matrix, cytoplasmic vesicle lumen, and secretory granule lumen. They perform molecular functions such as oxidoreductase activity, NADP binding, acid-thiol ligase activity, and coenzyme A-ligase activity. These genes are involved in processes such as oxidative stress response, purine nucleotide metabolism, purine-containing compound metabolism, and protein localization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). KEGG analysis demonstrated that the DE-MCAGs are mainly involved in pathways related to reactive oxygen species production, thermogenesis, and diabetic cardiomyopathy. Carbon and glutathione metabolism are the primary metabolic pathways regulated by mitochondrial genes, while autophagy and ferroptosis are key pathways of cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e2.3 Causal Impact of Differential Mitochondrial Genes on BM and Their Expression Profiles\u003c/p\u003e\u003cp\u003eMR analysis was used to validate the impact of DE-MCAGs on the development of BM. The results indicated that two DE-MCAGs exhibit causal relationships with the disease. Specifically, the \u003cem\u003eFYN\u003c/em\u003e gene was identified as a protective factor for BM (p-value \u0026lt; 0.05, odds ratio (OR) \u0026lt; 1), while the \u003cem\u003eRNF144B\u003c/em\u003e gene was identified as a risk factor for BM (p-value \u0026lt; 0.05, OR \u0026gt; 1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Sensitivity tests for each exposure factor were presented using scatter plots, funnel plots, and forest plots (\u003cb\u003eAdditional\u003c/b\u003e Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Heterogeneity and horizontal pleiotropy tests (Additional Tables\u0026nbsp;1 and 2) indicated P-values \u0026gt; 0.05, suggesting their absence. The Steiger test results for the two significant MR genes are shown in \u003cb\u003eAdditional Table\u0026nbsp;3\u003c/b\u003e, both passing the test and indicating no reverse causality.\u003c/p\u003e\u003cp\u003eSubsequently, the expression levels of the two key genes were analyzed in the disease and control groups. Results indicated that FYN was downregulated, while RNF144B was upregulated in the BM disease group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), consistent with the MR analysis trend. Excluding two outliers (M039 and M028), \u003cem\u003eRNF144B\u003c/em\u003e transcription levels in the peripheral blood of BM patients were higher than those of \u003cem\u003eFYN\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Additionally, the RNF144B-FYN gene expression ratio was significantly higher in the peripheral blood of BM patients compared to the healthy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e2.4 Differential Immune Cell Proportions and Their Correlation with RNF144B and FYN in Meningitis\u003c/p\u003e\u003cp\u003eResults from the ssGSEA algorithm indicated significant differences in the proportions of 22 types of immune cells, including activated B cells, activated CD4 T cells, activated CD8 T cells, activated dendritic cells, NK cells, NKT cells, and neutrophils, between the disease and control group. Specifically, the proportions of activated dendritic cells, macrophages, mast cells, neutrophils, regulatory T cells, and type 17 T helper cells were significantly higher in the meningitis group, whereas the proportions of NKT cells, activated CD8 T cells, central memory CD4 T cells, effector memory CD8 T cells, immature B cells, memory B cells, and activated B cells were significantly lower (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cb\u003eB\u003c/b\u003e). Further analysis of immune cell infiltration in meningitis indicated a negative correlation between immune cells with increased and decreased proportions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eRNF144B and FYN were identified as a risk factor and a protective factor for meningitis, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Correlation analysis between these genes and the immune cells revealed that RNF144B was strongly positively correlated with immune cells that had increased proportions in the disease group, such as activated dendritic cells, macrophages, mast cells, neutrophils, regulatory T cells, and type 17 T helper cells. Conversely, FYN was strongly positively correlated with immune cells that had decreased proportions, including NKT cells, activated CD8 T cells, central memory CD4 T cells, effector memory CD8 T cells, immature B cells, memory B cells, and activated B cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-F).\u003c/p\u003e\u003cp\u003e2.5 Dynamic Changes in Immune Cell Proportions and Key Gene Expression During BM Progression\u003c/p\u003e\u003cp\u003eSingle-cell data analysis identified eight cell types: pDC cell, B cell, mDC cell, NKT cell, T cell, neutrophils, monocyte cell, and apoptotic cell. Cell subset distribution was visualized with a t-SNE plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), and marker genes used for annotation are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The CSF single-cell dataset GSE163194 was used to analyze the proportions of each cell type at different disease stages. Results showed that neutrophils and monocytes decreased from the initial stage of BM to remission and further in recovery. In contrast, NKT and T cells, minimal initially, significantly increased during remission and rose further in recovery (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cb\u003eD\u003c/b\u003e). This pattern supports the observed negative correlation between neutrophil and NKT cell infiltration levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eKey gene expression levels in immune cells were calculated, revealing RNF144B was predominantly expressed in monocytes and neutrophils, with minimal expression in NKT and T cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE and \u003cb\u003eF\u003c/b\u003e). This aligns with the positive correlation between RNF144B expression and neutrophil infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Conversely, FYN was mainly expressed in NKT cells, with slight expression in neutrophils, consistent with its correlation with NKT cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Analysis of RNF144B and FYN expression trends across different stages in datasets GSE163194 and GSE163195 showed FYN expression increased in the CSF transcriptome during remission and recovery, notably in NKT and T cells (\u003cb\u003eAdditional\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA \u003cb\u003eand B\u003c/b\u003e), while RNF144B expression decreased, especially in neutrophils and monocytes (\u003cb\u003eAdditional\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC \u003cb\u003eand D\u003c/b\u003e). These findings align with the observed variation in cell proportions during disease progression (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cb\u003eD\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e2.6 NKT Cells as Protective Factors in BM and the Functional Implications of FYN Gene Expression\u003c/p\u003e\u003cp\u003eGiven that FYN is highly expressed in NKT cells, this study focused on exploring the role of NKT cells in BM through MR analysis. The results showed a causal relationship, with NKT cells acting as a protective factor against BM (p-value \u0026lt; 0.05, OR \u0026lt; 1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Sensitivity tests for exposure factors were illustrated using scatter, funnel, and forest plots (\u003cb\u003eAdditional\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This causal relationship aligns with the proportion differences observed between the BM and control groups. The heterogeneity test yielded a p-value of 0.3427, and the horizontal pleiotropy test yielded a p-value of 0.6810, indicating no significant heterogeneity or horizontal pleiotropy (P \u0026gt; 0.05). Reverse MR analysis results suggested no reverse causation between NKT cells and BM (Additional Table\u0026nbsp;4).\u003c/p\u003e\u003cp\u003eTo understand FYN's potential role in BM, GSEA was used to identify GO pathways and functions related to FYN. GO enrichment results revealed significant positive correlations of FYN with pathways such as immune response-activating receptor signaling, regulation of T cell activation, and somatic diversification of immune receptors. Conversely, the humoral immune response, particularly antimicrobial responses mediated by peptides, was negatively correlated with FYN (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These GO functions regulated by FYN are consistent with its positive correlation with NKT and T cell infiltration levels. KEGG enrichment results showed significant positive correlations of FYN with ATP-dependent chromatin remodeling, DNA replication, and ribosome biogenesis, while neuroactive ligand-receptor interaction and olfactory transduction were negatively correlated with FYN (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e2.7 Differential Expression and Functional Implications of FYN Gene in NKT Cell Differentiation in BM Patients\u003c/p\u003e\u003cp\u003eTo investigate the transcriptional expression differences of the FYN gene in NKT cells at various differentiation states, pseudotime analysis was conducted on NKT single-cell data. The results showed a differentiation trajectory from left to right, indicated by a color gradient from light to dark. Based on the bifurcation points of these trajectories, NKT cells were classified into nine states. Among these, states 3, 5, and 7 had significantly fewer NKT cells compared to other states. In BM patients, during both remission and recovery phases, the proportion of NKT cells in State 1/2 was significantly higher in the S3\\S4 (non-refractory meningitis) group compared to the S6\\S7\\S8 (refractory meningitis) group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eMoreover, examining the expression of the FYN across different branches revealed an upregulation trend with increased differentiation, although a decline was observed in the state 8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Clustering based on gene expression trends in the pseudotime analysis identified two clusters, with FYN located in cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Subsequent GO and KEGG enrichment analyses highlighted significant differences between the clusters (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-G). GO cellular component (CC) revealed that cluster 2 was primarily enriched in the T cell receptor complex, plasma membrane signaling receptor complex, and cytolytic granule compared to cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). GO molecular function (MF) showed that cluster 2 was mainly involved in T cell receptor binding, lipase/phospholipase activator activity, and structural constituent of the postsynaptic actin cytoskeleton (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). GO biological process (BP) showed that T cell receptor signaling pathway and α-βT cell activation were significantly more enriched in cluster 2 than in cluster 1, suggesting a higher relevance of cluster 2 to T cell-related immune response regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). In the KEGG enrichment results, both clusters were associated with immune response regulation, excluding disease pathways, with cluster 2 notably enriched in the T cell receptor signaling pathway, consistent with the GO enrichment results (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e2.8 Differential Expression of FYN and RNF144B Genes in Peripheral Blood During Healthy, Onset, and Recovery Stages of Meningitis\u003c/p\u003e\u003cp\u003eThe GSE163196 dataset was analyzed to assess the expression differences of FYN and RNF144B in peripheral blood leukocytes across three stages: healthy, onset, and recovery. The results indicated that FYN expression was significantly reduced during the onset phase and began to increase during recovery, whereas RNF144B expression was significantly elevated at onset and decreased during recovery (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cb\u003eB\u003c/b\u003e). These expression trends during onset and recovery were consistent with the MR analysis results. Additionally, compared to the onset phase, the expression trends of FYN and RNF144B during recovery corresponded with changes in the proportions of NKT cells and neutrophils, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cb\u003eD\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe meningitis dataset GSE248261 was also utilized to validate the expression differences of FYN and RNF144B among BM, VM, and control groups. While there were no significant differences (p \u0026gt; 0.05), the expression trends of these genes between the BM and control groups were consistent with previous analyses. Notably, both genes showed opposite trends in VM compared to BM (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC and \u003cb\u003eD\u003c/b\u003e). In summary, increased RNF144B expression and decreased FYN expression are associated with meningitis development.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study highlights the intricate molecular landscape and immune response dynamics in BM patients, shedding light on differential gene expression, mitochondrial gene involvement, immune cell distribution, and key gene functions.\u003c/p\u003e \u003cp\u003eDifferential Gene Expression and Immune Response\u003c/p\u003e \u003cp\u003eThe identification of 1,764 DEGs in the peripheral blood of BM patients highlights significant transcriptional alterations in response to bacterial infection. Functional enrichment analyses using GO and KEGG pathways provide insights into the biological roles of these DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For example, bacterial invasion into the CSF triggers a rapid inflammatory response mediated by the innate immune system[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The enrichment of genes localized to secretory granules and ribosomes suggests heightened protein synthesis and secretion activities, essential for immune functions such as cytokine release and pathogen recognition. In particular, in cases of meningitis, cytokines released by peripheral immune organs into the bloodstream cross the altered blood-CSF barrier and contribute to their levels in the CSF[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Leukocyte influx, a hallmark of acute meningitis, contributes to neuronal damage, with elevated neutrophil counts in CSF serving as a diagnostic indicator for acute BM[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The neutrophil-to-lymphocyte ratio (NLR) has been shown to be a predictor of BM and even mortality in pediatric patients. Additionally, this ratio is associated with increased cerebral blood flow during acute BM[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Thus, the release of secretory granules may represent a potential pathogenic mechanism in meningitis. Specifically, neutrophils release neutrophil extracellular traps (NETs) during infections to entrap and eliminate bacteria; however, NETs in the CNS have been shown to hinder pneumococcal clearance, suggesting that degrading NETs may have significant therapeutic implications[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, reducing NETs has been shown to significantly inhibit neuroinflammation and improve neurological deficits in the treatment of traumatic brain injury and stroke[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Based on these findings, it is hypothesized that, during the early stages of infection, neutrophils in peripheral blood primarily clear pathogenic bacteria. However, as the disease progresses, neutrophil recruitment to the CNS may amplify the inflammatory response, potentially hindering recovery in meningitis patients. Further investigation is needed to validate this hypothesis.\u003c/p\u003e \u003cp\u003eThe involvement of pathways like NK cell-mediated cytotoxicity and T-cell receptor signaling emphasizes the dual roles of innate and adaptive immunity in combating bacterial pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Analysis of CSF immune cell subset distribution in 319 patients with inflammatory or non-inflammatory neurological diseases revealed that T cells were predominant in the CSF. Additionally, NK cells were slightly elevated in neuroinflammation, and both monocytes and NK cells were elevated in CNS malignancies[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings highlight the complex interplay of immune cells, particularly neutrophils, NK cells, and T cells, in the CSF, which may contribute to the pathophysiology and progression of various neurological conditions.\u003c/p\u003e \u003cp\u003eMitochondrial Dysfunction and Its Role in the Pathogenesis of BM\u003c/p\u003e \u003cp\u003eMitochondrial genes are crucial for cellular energy metabolism and apoptotic pathways[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The study identifies mitochondrial gene modules linked to BM, underscoring the mitochondria\u0026rsquo;s influence on disease progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, the enrichment of pathways related to oxidative stress and metabolic processes underscores the mitochondria's role in maintaining cellular homeostasis and meeting infection-induced energy demands. Bacterial toxins appear to induce programmed death in neurons and microglia by causing rapid mitochondrial damage in meningitis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. \u003cem\u003eStreptococcus suis serotype 2\u003c/em\u003e (SS2) is a zoonotic pathogen responsible for meningitis in both pigs and humans. Previous studies have shown that SS2 induces mitochondrial dysfunction in human brain microvascular endothelial cells, leading to apoptosis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Consequently, mitochondrial impairment may be a potential pathogenic factor of BM. Wu and colleagues demonstrated that mitochondrial insufficiency serves as an intrinsic cellular trigger for initiating T cell functional exhaustion[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Thus, it is hypothesized that pathogen-induced mitochondrial dysfunction may play a critical role in the pathogenesis of BM by impairing immune cell function.\u003c/p\u003e \u003cp\u003eCausal Relationship and Diagnostic Potential of FYN and RNF144B\u003c/p\u003e \u003cp\u003eDiagnosing BM is challenging because bacterial and aseptic meningitis often present with similar clinical symptoms. CSF analysis and microbial culture assist clinicians in differentiation, but lumbar puncture to obtain CSF samples is not always feasible due to contraindications or uncertain clinical situations[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, identifying biomarkers from blood samples is crucial. For instance, beyond the NLR in blood, S100B protein levels in CSF and serum serve as biomarkers for diagnosing BM[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. S100B protein is used in diagnosing and prognosing outcomes in patients with acute brain injuries in neurological diseases[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In this study, using the human peripheral blood transcriptome database, MR analysis reveals the causal roles of FYN and RNF144B in BM. FYN acts as a protective factor, likely through T cell activation and NKT cell function, crucial for immune response modulation. Conversely, RNF144B is a risk factor, correlating positively with neutrophil levels and significantly elevated at meningitis onset (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The expression patterns of these genes differ significantly at various disease stages, suggesting their potential as diagnostic biomarkers for BM. In Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, only the M028 and M039 groups show FYN expression levels higher than RNF144B. Interestingly, Lill's study also noted that these two samples have distinct gene expression profiles compared to others. The RNF144B to FYN expression ratio may relate to disease progression in BM.\u003c/p\u003e \u003cp\u003eThe correlation between RNF144B and increased immune cell types, like neutrophils and macrophages, aligns with its role as a risk factor, whereas FYN\u0026rsquo;s association with NKT and T cells reinforces its protective nature. Single-cell analysis provides a granular view of immune cell transitions during BM progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Granulocyte depletion is neuroprotective in experimental meningitis, while persistence is associated with more severe neuronal damage[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Compared to the onset phase, there is a significant decrease in neutrophil proportions during remission and recovery phases, while NKT and T cells increase significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The increase in NKT and T cells during recovery aligns with FYN's protective role, while the decrease in neutrophils correlates with RNF144B expression patterns. Specifically, RNF144B expression is significantly reduced during remission and recovery phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cb\u003eAdditional\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similar to how the NLR predicts BM and mortality in pediatric patients, the correlation of RNF144B and FYN with immune cell proportions suggests that the RNF144B-to-FYN expression ratio could aid in developing new biomarkers for early diagnosis and therapeutic monitoring of the disease.\u003c/p\u003e \u003cp\u003eFYN and Its Immune Implications in BM\u003c/p\u003e \u003cp\u003eNotably, FYN is highly expressed in NKT cells, while RNF144B expression is low in immune cells, highlighting FYN's potential role in NKT cells in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFYN is one of the most highly expressed cytoplasmic tyrosine kinases in the brain, and its widespread distribution indicates its critical role[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. FYN inhibits excitatory and inhibitory synaptic transmission stimuli and regulates mechanisms related to learning and memory processes[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Numerous studies have shown that FYN promotes myelination in the CNS and mediates oligodendrocyte differentiation and maturation. Following ischemic injury in the adult brain, FYN promotes the assembly and remodeling of the postsynaptic density complex[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Research indicates that \u003cem\u003eFYN\u003c/em\u003e polymorphisms are risk factors for schizophrenia and bipolar disorder[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our study, FYN acted as a protective gene in BM, with expression levels decreasing at the onset of BM. Changes in the endothelial tight junction structure of the BBB have been detected in brains with multiple sclerosis, notably associated with abnormal activation of the plexin-FYN-AKT signaling pathway[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Therefore, the functional role of FYN in BM warrants further investigation. It is possible that FYN is associated with BBB disruption and nerve damage in the pathological process of BM.\u003c/p\u003e \u003cp\u003eDetailed GSEA and pseudotime analyses of FYN elucidate its potential functions in enhancing immune receptor signaling and T cell regulation (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Its negative association with humoral immune responses reflects a specialized function in T cell-mediated immunity, possibly prioritizing cellular immune responses during BM. Hence, FYN is likely to influence T cell function during meningitis, particularly in the recovery and remission phases, by regulating NKT cells. These findings suggest a potential therapeutic window where enhancing NKT cell functions could alter disease trajectories favorably.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies the RNF144B gene as a risk factor associated with increased neutrophils during the initial phase of meningitis. Conversely, the FYN gene acts as a protective factor, linked to increased NKT cells during the remission and recovery periods of meningitis, highlighting its important role in immune regulation and recovery. Additionally, compared to BM, these two key genes exhibit opposite expression trends in VM. The distinct expression patterns of these genes at various disease stages suggest their potential as biomarkers for differentiating BM from VM and for monitoring disease progression, thus aiding in more accurate diagnoses and targeted treatments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in the study are deposited in the Genbank of National Center for Biotechnology Information, accession number GSE40586, GSE163194, GSE163195, GSE163196, and GSE248261.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests associated with the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (32302877) and Zhejiang A\u0026amp;F University Research and Development Fund (2024LFR078).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.J. performed and designed the research; H.J. and X.Y. analyzed the data and drafted the manuscript; J.F. polished the language; H.S. and Y.Y. supervised the whole project and revised the manuscript. All the authors read, edited, and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ecknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHoffman O, Weber RJ (2009) Pathophysiology and Treatment of Bacterial Meningitis. Therapeutic Advances in Neurological Disorders 2: 1-7. https://doi.org/10.1177/1756285609337975\u003c/li\u003e\n\u003cli\u003eTunkel AR, Hartman BJ, Kaplan SL, Kaufman BA, Roos KL, Scheld MW, Whitley RJ (2004) Practice Guidelines for the Management of Bacterial Meningitis. 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Nature Reviews Neuroscience 9: 839-855. https://doi.org/10.1038/nrn2480\u003c/li\u003e\n\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":"Bacterial Meningitis, Gene Expression, Immune Cell Infiltration, RNF144B, FYN","lastPublishedDoi":"10.21203/rs.3.rs-5518056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5518056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBacterial meningitis (BM) requires prompt treatment, especially for neonates, the elderly, and immunocompromised individuals. Understanding the immune response is essential, as it precedes clinical symptoms. However, systematic studies have been lacking. This study identifies immune-related genes that could enhance BM diagnosis and treatment. Mendelian randomization, differential gene expression, and co-expression network analyses revealed key genes linked to BM. RNF144B was identified as a risk gene, correlating with increased neutrophil levels during the initial phase of meningitis, whereas FYN was identified as a protective gene, correlating with increased NKT cells during remission and recovery. Single-cell RNA sequencing and gene set enrichment analyses showed RNF144B expression in monocytes and neutrophils, while FYN was associated with NKT cells. During BM onset, there was an increase in neutrophil proportions and a decrease in NKT cell proportions, indicating a negative correlation. In recovery, RNF144B expression and neutrophil levels decreased, while FYN expression and NKT cell levels rose, underscoring the protective role of NKT cells. FYN may regulate T-cell receptor function in NKT cells, reducing BM risk. This study suggests that the expression patterns of these two genes exhibit significant differences at various stages of the disease, thus offering potential biomarkers for aiding in more accurate diagnoses of BM and monitoring disease progression.\u003c/p\u003e","manuscriptTitle":"Integrating Transcriptomics and Genetics to Identify Expression Patterns of RNF144B and FYN as Potential Predictors of Bacterial Meningitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-06 22:06:25","doi":"10.21203/rs.3.rs-5518056/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":"2901de77-aca8-4553-b342-cf5666dbba17","owner":[],"postedDate":"December 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-19T13:08:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-06 22:06:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5518056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5518056","identity":"rs-5518056","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}