Macrophage metabolic regulation by phosphoglucomutase 1 shapes the host immune response in pneumococcal meningitis

Macrophage metabolic regulation by phosphoglucomutase 1 shapes the host immune response in pneumococcal 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 Macrophage metabolic regulation by phosphoglucomutase 1 shapes the host immune response in pneumococcal meningitis Rutger Koning, Dixie Bakker, Federica Conte, Marian A. van Roon, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8319354/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 Pneumococcal meningitis remains a life-threatening disease despite antibiotic and anti-inflammatory therapy, with unfavourable outcome driven by excessive inflammation and impaired bacterial clearance. Host genetic variation influences disease outcome in pneumococcal meningitis, though the underlaying mechanisms remain unclear. In a genome-wide association study, we identified the single nucleotide polymorphism (SNP) rs12081070 as a risk factor for unfavourable outcome in bacterial meningitis (Minor allele frequency (MAF) = 0.43; odds ratio (OR) = 1.63; p = 2.0 × 10 − 8 ). Chromatin conformation capture analysis linked this variant to phosphoglucomutase 1 ( PGM1) , which encodes a key enzyme in glucose metabolism and glycosylation in immune cells. In a nationwide cohort study of 1200 patients with bacterial meningitis, we show that individuals carrying the rs12081070 risk allele exhibited elevated proinflammatory cytokine responses to Streptococcus pneumoniae ( S. pneumoniae ) and an increased risk of unfavourable functional outcome. Using a murine model of pneumococcal meningitis, we show that myeloid-specific Pgm1 deletion amplifies inflammation, impairs bacterial clearance, and exacerbates brain injury. Mechanistically, Pgm1 -deficient macrophages exhibit disrupted glycolysis and mitochondrial respiration, and enhanced cytokine and nitrogen oxygen production. Our findings identify Pgm1 as a regulator of macrophage metabolism and inflammation in pneumococcal meningitis, highlighting a potential target for immune-modulation in bacterial disease. Pneumococcal meningitis neuroinflammation macrophages immune metabolism. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND Pneumococcal meningitis, caused by S. pneumoniae , remains a leading cause of mortality and long-term neurological sequelae, despite advances in vaccination, anti-inflammatory treatments and supportive care (1-5). While clinical risk factors for disease susceptibility and outcome are well recognized, host genetic variation accounts for nearly half of the variability in disease severity and outcomes (6). Previous studies have implicated immune responses as key modulators of meningitis severity, yet the impact of specific genetic variants remains poorly understood (7). Genome-wide association studies (GWAS) provide an opportunity to uncover genetic determinants that shape host-pathogen interactions and immune responses in bacterial infections (6). In a previous GWAS, we identified SNP rs12081070 as being associated with unfavourable outcome in bacterial meningitis ((MAF) = 0.43; (OR) = 1.63; p = 2.0 × 10–8) (6). This variant resides within an intronic region of UBE2U and exhibits chromatin interaction with PGM1 , a metabolic geneessential for protein glycosylation and glucose metabolism, in a different range of immune cells, including macrophages. PGM1 deficiency is known to cause congenital disorder of glycosylation syndrome type 1t (CDG1T), characterized by diverse symptoms, including hepatopathy, cardiomyopathy, and thrombosis (8). Previous studies linked PGM1 expression to proinflammatory gene expression in macrophages (9, 10), highlighting a potential role for this enzyme in macrophage polarization and immune responses. However, the role of PGM1 in the context of infections has not been studied. We leveraged our nationwide cohort study on community-acquired bacterial meningitis to study the effect of rs12081070 on modulating immune responses and clinical outcomes in patients with pneumococcal meningitis. In addition, we used a murine model of pneumococcal meningitis with a myeloid-specific Pgm1 knockdown to investigate the impact of Pgm1 on inflammation and bacterial clearance. METHODS Nationwide clinical cohort The MeninGene study is a nationwide prospective cohort study on community‐acquired bacterial meningitis in the Netherlands. Methods of patient identification and inclusion have been published previously (1). In short, we included patients aged 16 years or older who had pneumococcal meningitis between March 2006 and July 2021. Patients were identified by the treating physician or the Netherlands Reference Laboratory for Bacterial Meningitis (NRLBM). Extensive clinical data was prospectively collected through online case record forms. We included all patients with confirmed pneumococcal meningitis, identified either by the presence of S. pneumoniae in the cerebrospinal fluid (CSF) or by a combination of a positive PCR, blood culture, or CSF antigen test along with at least one predictor of bacterial meningitis, as outlined by Spanos et al. (11). These predictors include a CSF glucose concentration < 340 mg/L (1.9 mmol/L), a CSF:blood glucose ratio 2200 mg/L, white blood cell count > 2000 per microliter, or > 1180 polymorphonuclear leukocytes per microliter. We excluded patients who developed bacterial meningitis in the hospital, within one week of discharge, following head trauma or neurosurgery within the prior month, or those with a neurosurgical device in place. Patients with an altered immune status owing to asplenia, diabetes mellitus, cancer, alcoholism, or the use of immunosuppressive drugs were considered immunocompromised, as were patients infected with human immunodeficiency virus. Neurological examination was performed at discharge. Outcome was scored according to the Glasgow Outcome Scale (GOS), with scores varying from 1 (death) to 5 (mild or no disability), and then dichotomized in favourable (GOS 5) and unfavourable outcome (GOS 1-4) (12). MeninGene Recall cohort For the Recall study, selected participants were included in the MeninGene study between October 2011 and March 2015 (13). On the informed consent form of the MeninGene study, the patient was asked whether they allowed the researchers to approach them for follow-up studies on long-term neurological sequelae. Patients eligible for the current follow-up study provided this consent and had been admitted with pneumococcal meningitis 1–5 years prior the follow-up study. Before participation patients were questioned about their medical history, medication use, and ongoing illness. If patients had ongoing infections or felt ill they could not participate in the study. Patients who gave permission to participate in this follow-up study were recalled to the Amsterdam UMC for a blood withdrawal and neuropsychological examination. Serial blood sampling cohort Serial blood samples were available for a subset of pneumococcal patients from the MeninGene study, because they were also included in the Serial Meningitis Sampling (SMS) study (13). The SMS study includes patients from 12 participating centres in the Netherlands. Inclusion criteria were a clinical suspicion on bacterial meningitis and one of the following CSF characteristics: pleocytosis > 1000 cells per 3 mm3, glucose 2.20 g/L or a positive Gram stain. Pneumococcal meningitis needed to be confirmed with either a positive CSF culture or positive blood culture. Plasma for cytokine measurements was available for day 0 (n=32), 1 (n=53), 2 (n=55) and 7 (n=48) of admission and 3 months after discharge (n=30). Blood samples were immediately processed in the participating hospitals and stored at −70 or −80 ºC. RNA isolated from whole blood was available for day 0 (n=32), 1 (n=41), 2 (n=49) and 7 (n=45) of admission and 3 months after discharge (n=29). Patients were included during the acute phase of the illness and provided written informed consent for participation in the SMS study. DNA extraction and genotyping Methods on DNA extraction and genotyping for this study have been previously published (6). In short, DNA was isolated from patients’ blood collected in sodium or (ethylenediaminetetraacetic acid) EDTA tubes using the Gentra Puregene Isolation Kit (Qiagen). Genotyping was performed on the Illumina Omni array (Illumina). RNA sequencing Whole blood was collected directly into PAXgene RNA tubes (Qiagen) and stored at -80 °C until analysis. RNA was isolated using QIAcube automated RNA isolation (Qiagen) and quality was tested with RNA ScreenTape (Agilent). Samples with a RIN value > 7 and a concentration > 50 ng/µL were sequenced using the NovaSeq sequencing system (Illumina). RNA sequencing reads were initially obtained and subjected to quality control to exclude suboptimal samples. Following this, adapter sequences were removed, and quality trimming was performed using Trimmomatic (version 0.39)(14). The processed samples were subsequently aligned to the human reference genome hg38 from UCSC using the splice-aware STAR aligner (version 2.7.10b) (15). Alignment output was then sorted using Samtools (version 1.14). Gene-level read counts were quantified with featureCounts (version 2.0.6), utilizing the GENCODE v44 annotation (16). For normalization of RNA-seq read counts, fragments per million (FPM) were applied in R (version 4.2.1) using the DESeq2 package (17). Differential expression of the PGM1 gene were compared between time points and genotypes using t tests. Bacterial strain and culture All procedures related to pathogenic bacteria were conducted under biosafety level 2 protocols and guidelines. The clinical isolate S. pneumoniae D39 (serotype 2) from the Veening lab was used in this study. S. pneumoniae was grown in CY medium at 37 °C to mid-log phase, evaluated by monitoring at an optical density of 600 nm. Frozen stocks of bacteria in 20% glycerol (Sigma) were prepared and kept at −80 °C until use. Peripheral blood monocyte (PBMC) stimulation experiments PBMCs were available for pneumococcal patients included in the Recall study (13). To isolate peripheral blood mononuclear cells (PBMCs), whole blood was 1:1 diluted with Dulbecco's phosphate-buffered saline (DPBS) and centrifuged with Ficoll. Isolated PBMCs were then washed three times with DPBS before dilution in Roswell Park Memorial Institute (RPMI) medium. PBMCs were stimulated at 37 ºC with LPS 10 ng/ml, ultraviolet (UV)-killed S. pneumoniae strain D39 (serotype 2) at multiplicity of infection (MOI) 10 and RPMI (unstimulated). After 24 hours, samples were centrifuged for 10 minutes at 400 x g and the supernatant was collected and stored at −80 ºC for later analysis. Mice C57BL/6J LysMcre -/- Pgm1 fl/fl (controls) and LysMcre +/- Pgm1 fl/fl (Pgm1 M-KO ) mice were derived by crossing Pgm1 fl/fl mice with Lyz2-Cre transgenic mice. Pgm1 fl/fl mice were constructed, characterized and shared by dr. Kent Lai (University of Utah, USA) (18). LysMcre crossbreeding was performed in our mouse facility. Mice were housed at the Animal Research Institute Amsterdam UMC (ARIA). Knockdown of Pgm1 in Pgm1 M-KO mice was validated on both gene and protein level in BMDMs (isolation and cultured as described below) by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blot, respectively (Additional File 1. Figure 1). Animals (maximum of 6 mice/cage) were housed in individually ventilated cages (Tecniplast 1284L Eurostandard Type II L) with corncob bedding in a ventilated cabinet with a controlled temperature of 20–24 °C, relative humidity 45-65% and 12 hours light/dark (07:00-19:00) cycles. Mice were given food and water (acidified; pH 2,3-2,8) ad libitum . The study was conducted in accordance with the ARRIVE guidelines for reporting animal experiments. Mouse meningitis model A well-characterized mouse model of pneumococcal meningitis was used (19). Mice were acclimatized for one week prior to experiments and housed in individually ventilated cages with corncob bedding under controlled conditions. Researchers were blinded for genotype during experiments and data analysis. On day 0 (start of the experiment) mice were weighed and clinically scored. Clinical scoring included weight loss (0–4 points), activity (0–3 points), time to return to upright position (0–6 points), state of skin/fur (0–2 points), posture (0–2 points), eye discharge or protrusion (0–4 points), irregular/laboured breathing (0–4 points), presence of seizures, limb paresis or ataxia (0–6 points; Additional File 1. Table 1). Animals reaching humane endpoint (HEP) criteria (clinical scoring ≥14, weight loss ≥25%, coma, paralysis, seizure longer than 5 min. or two seizures in 15 minutes) were terminated. Subsequently, mice were inoculated with 1 µl bacterial suspension containing 5.0 * 10 ^4 CFU S. pneumoniae (infection group) or PBS (uninfected controls) into the cisterna magna under isoflurane anaesthesia. Immediately after intracisternal inoculation mice were assessed for neurologic damage as a result of the puncture using the Foot Fault Grid test. Mice suffering from neurological damage as a result of the puncture were excluded. Treatment with antibiotics was started 20 hours post inoculation (hpi) with bacteria by intraperitoneal injection of ceftriaxone (100 mg/kg body weight) and repeated every 24 hours. Clinical scoring was performed every 4 hours from 16 hpi onwards until the end of the experiment or until a HEP was reached. Mice reaching a HEP were scored as 15 points for the remainder of the study. At the experimental endpoint of the study mice were anesthetized by intraperitoneal injection of 190 mg/kg ketamine + 0,3 mg/kg dexmedetomidine in a total volume of 300 µl, followed by cardiac puncture for blood collection and perfusion of organs with sterile PBS via the left ventricle. The right hemisphere of the brain was placed in formalin. Left hemisphere of the brain and spleen were placed in ice-cold 0.9% NaCl solution, processed and stored as described previously (20). Bacterial titres were determined by plating serial tenfold dilutions of blood, CSF, brain and spleen homogenates on sheep-blood agar plates and incubated overnight at 37 °C. EDTA blood was centrifuged at 2000 g for 15 minutes. Plasma was stored at -80°C for further analysis. Outcome measures included clinical scores, number of colony forming units (CFU), brain histology scores, cytokine concentrations and gene expression in brain, spleen, and plasma. Experimental groups in the mouse model The following experimental groups were used each time point: uninfected control mice (N = 12), uninfected Pgm1 M-KO mice (N = 12), infected control mice (N = 24) and infected Pgm1 M-KO mice (N = 24). Half of the mice were terminated at 20 hpi (N = 36), the other half at 44 hpi (N = 36). The study was powered to detect a difference in inflammatory parameters between groups with an effect size (δ=|µ₁-µ₂|/σ) of 1.3. Using an 80 % power, two-sided testing, and significance level of p < 0.05, we needed 12 mice per group. Experiments were divided over two session, using 36 mice with equal representation of experimental groups in both sessions. Immunohistochemistry and histopathology Histopathology was performed on the right hemisphere of the brain. Brain was fixed in 4% paraformaldehyde and paraffin-embedded in seven coronal plaques which were cut in sections of 5 μm. Samples were stained with haematoxylin-eosin (HE) with the Ventana BenchMark ULTRA system (Roche). Immunostaining was performed with antibodies against CD45 (Biolegend) to detect leukocytes followed by a haematoxylin counterstain. Histopathology was scored (blinded) for six categories by two researchers separately as previously described (21). Discrepancies in scoring were resolved through consultation with a dedicated neuropathologist. A detailed description of the histological scoring method can be found in Additional File 1. Table 2. Bone marrow isolation and culture of BMDMs Bone marrow cells were isolated from femurs and tibias from uninfected control and Pgm1 M-KO mice by flushing the bones with ice cold DPBS (-/-). Collected cells were cultured at 37ºC and 5% CO 2 for 8 days in Dulbecco’s Modified Eagle Medium (DMEM; Low glucose, ThermoFisher) supplemented with 10% foetal calf serum (FCS), 1% penicillin/streptomycin, 2 mM L-glutamine and 15% L929-conditioned medium (LCM) for differentiation into bone marrow-derived macrophages (BMDMs). Fresh medium was added on day 3 and day 7. On day 8, differentiated cells (>95% of cells CD11b + F4/80 + determined by flow cytometry) were harvested and seeded in DMEM supplemented with 10% FCS and 2 mM L-glutamine at a concentration of 0.75 * 10 6 cells/ml for extracellular flux analysis and 1.25 * 10 6 cells/ml for all other experiments. Cells were left to attach for 24 hours before performing experiments. BMDMs were stimulated for 4 or 24 hours with LPS (10 ng/ml), LPS (10 ng/ml) + interferon (IFN)γ (10 ng/ml), S. pneumoniae D39V (MOI 1) or left unstimulated by adding fresh DMEM. For experiments lasting longer than 4 hours, fresh medium was added containing penicillin-streptomycin (final concentration 1%) after 4 hours. Western blotting To confirm knockdown of PGM1 protein expression in Pgm1 M-KO BMDMs (Additional File 1. Figure 1), cells were lysed using RIPA buffer (ThermoFisher) in combination with phosphatase and protease inhibitors (Sigma). Protein concentrations were determined with BCA Protein Assay (ThermoFisher, Cat). SDS-polyacrylamide gels (Bio-Rad) were used to separate 10 ug of protein, followed by transfer to PVDF membranes (ThermoFisher). Membranes were blocked in 5% goat serum (Biowest) for 1 hour at room temperature and subsequently incubated with primary antibodies against PGM1 (1:500, Abcam, ab192876) and GAPDH (1:2500, Bio-Connect, M00227-1). Overnight incubation was performed at 4°C. Next, membranes were washed and incubated with a HRP-conjugated secondary antibody (1:5000, ThermoFisher, G-21234), followed by visualization with an ECL detection system (ThermoFisher, Cat: 32106X4). Images were acquired using the Platinum V10 (Uvitech). Nitric oxide assay For the nitric oxide (NO) assay, 50 µl of undiluted cell supernatant was transferred to a flat-bottom 96-wells plate. Next, 50 µl of Griess reagent (Sigma) was added to the samples and absorbance was immediately measured on a microplate reader at a wavelength of 550 nm. To calculate exact concentrations of NO, a standard curve was made with NaNO 2 in culture medium. Phagocytosis assay BMDMs were incubated with pHrodo BioParticles (1 mg/mL, ThermoFisher) for 60 minutes at 37 °C, according to the manufacturer’s instructions. As a negative control, cells were incubated in presence of 10 ug/mL cytochalasin D (ThermoFisher), an inhibitor of phagocytosis. Cells were analysed by flow cytometry (BD Symphony A1). Cytokine measurements Human C-C Motif Chemokine Ligand 3 (CCL3), interleukin 1 beta (IL-1β), interleukin 6 (IL-6) and tumour necrosis factor (TNF-α) levels in the blood samples of the serial sampling cohort and in the supernatants of the PBMC stimulation experiments were measured with the Luminex technology with a Bio-Techne assay. Measurements were done according to the manufacturer's protocol. In mice, cytokine concentrations of chemokine (C-X-C motif) ligand 2 (CXCL2), IL-1β, IL-6, or TNF-α were determined in the supernatant or organ homogenates using ELISA (Duoset, R&D Systems), according to manufacturer’s instructions. Gene expression Total RNA was extracted from the mouse brain using the Nucleospin RNA kit (Macherey-Nagel), according to the manufacturer’s protocol. Quality of the samples and RNA concentrations were determined with a Nanodrop spectrophotometer (ThermoFisher). Following RNA extraction, cDNA was synthesized using the iScript cDNA Synthesis Kit (Bio-Rad). The RT-PCR measurement of individual cDNAs was performed on a Bio-Rad MyiQ Single-Color RT-PCR Detection System using the Bio-Rad iQ SYBR Green Supermix (Bio-Rad Laboratories). A list of primers that were used in this study can be found in Additional File 1. Table 3. RNA-sequencing and data analysis of murine macrophages Macrophages were cultured as described in ‘Bone Marrow isolation and culture of BMDMs’. Cells were incubated with or without S. pneumoniae (1 * 1 06 CFU) for 4 hours. After incubation, supernatant was removed and cells were harvested in RA1 buffer and stored at -80°C until RNA isolation, as described in the section ‘Gene expression’. RNA-sequencing library preparation (KAPA mRNA Hyperprep, Roche) and subsequent sequencing (NovaSeq S4 2x 150 bp, Illumina) was conducted by the Genomics Core Facility at Amsterdam UMC. Analyses of RNA-seq datasets were performed using Galaxy (version 24.2.4.dev0). After quality control with Falco, FastQ files were trimmed to remove primer adapters using Trimmomatic. Trimmed sequences were aligned to the mouse reference genome (mm10) with RNA STAR. Following alignment, gene counts were generated using featureCounts and differential gene expression analysis was performed with DESeq2. Genes were considered differentially expressed when the adjusted p value was lower than 0.05. Volcano plots and Venn diagrams and were generated using the lists of differentially expressed genes. For pathway enrichment analysis, genes that were differentially upregulated in Pgm1 M-KO macrophages following stimulation with S. pneumoniae , but not in wild-type macrophages were used using ShinyGO (version 0.82). Gene ontology (GO) terms in the Biological Process category with a p value lower than 0.05 were considered significant. The top 10 statistically significant, non-redundant GO-enriched terms were plotted. Extracellular flux analysis Extracellular flux analysis was performed according to a previously published protocol (22). In short, the XF-96 Flux Analyzer (Agilent) was used to assess the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in BMDMs. BMDMs (75 * 10 3 cells/well) were plated on XF-96-cell culture plates (Agilent), treated with LPS (10 ng/ml) or S. pneumoniae (MOI 1) as described above, or left untreated. One hour before measurement, cells were washed and replaced with DMEM (Sigma-Aldrich) without glucose, phenol red, and sodium bicarbonate. The run consisted of 2 min mixing, 3 min measuring before and after four injections: glucose (final assay concentration 25 mM), oligomycin A (OM, final assay concentration 1.5 µM), Carbonylcyanide-p-trifluoromethoxyphenylhydrazone (FCCP) (final assay concentration 1.5 µM) with sodium pyruvate (final assay concentration 1 mM), and antimycin A (AA, final assay concentration 2.5 µM) with rotenone (final assay concentration 1.25 µM). Data were analysed using Wave software as detailed before (22). Polar metabolite extraction from adherent macrophages Macrophages were seeded in 24-well plates with a density of 0.3 * 10^6 cells/well, in three replicates, and cultured as described in “Bone marrow isolation and culture of BMDMs”. Cells were treated with S. pneumoniae D39 (MOI 1), LPS (10 ng/mL) or culture medium, as a control, for 4 hours. After treatment, the cell metabolism was quenched and the polar metabolites were extracted using a modified version of the protocol previously described in Conte et al. (23), to accommodate the higher number of wells per plate. In brief, after washing the wells twice with 200 ul/well of 75 mM ammonium carbonate solution (pH 7.3±0.05, Honeywell, Fluka), the plate was snap-frozen through direct contact with liquid nitrogen, and stored at -80°C until extraction. The polar metabolite extraction was performed using -20°C cold extraction buffer composed of 40:40:20 v/v Acetonitrile (Biosolve), Methanol (Honeywell), and MS-compatible ultrapure water (HPLC-super high gradient, VWR Chemicals). While keeping the plates on ice, each well was incubated for 6 minutes with 400 ul of extraction solution and, after collection, the extracts were centrifuged at 13.000 rpm for 3 minutes at 4°C to remove larger cell debris. Each supernatant was transferred to a separate tube, and dried overnight (16 hours) in vacuum using a Savant SC100 (RVT 100) vacuum centrifuge. The extracts were stored at -80°C till MS analysis. Quantification of nucleotide sugars and phosphate sugars via ion-pairing LC-MS The extracts were dissolved in 100 μl ultra-pure water prior to analysis and spun down for 15 minutes at 13’000 rpm. For the phosphate sugar quantification, 20 ul of each sample were loaded in a 96-well plate for analysis. The analytes were separated by ion-pairing ultra-high perfomance liquid chromatography (UHPLC) on an Agilent 1290 Infinity System, equipped with Acquity HSS T3 column (Waters). The chromatographic separation was based on tributylamine (TBA) ion-pairing buffer, according to the method and separation gradient previously reported in van Scherpenzeel et al. (23). The MS analysis was performed on an Agilent 6490A QqQ UHPLC-MS/MS, with high-flow iFunnel ionization source, and controlled by Agilent MassHunter Workstation Software (version B.08.02). Data acquisition was performed in multiple-reaction monitoring (MRM), using the transition list reported in Additional File 2, sheet 5. For the nucleotide sugar quantification, 15 ul of each extract were transferred to a conical-bottom vial for analysis. The analytes were separated by liquid chromatography performed on an Agilent 1290 infinity II System equipped with Atlantis Premier BEH C18 AX Column (Waters), according to the method and gradient described in Rahm et al. (24). The MS analysis was performed on a Sciex 6500+ QTRAP with electrospray ionization source, and controlled by ScieX Analyst (version 1.7.2). Data acquisition was performed in MRM, using the transition list reported in Additional File 2, sheet 6 . After acquisition, peak integration was performed using Skyline (version 24.1) for both methods. MS data processing and analysis After peak integration, the peak area for the peak of each compound normalized according to the ‘total peak area’ method, as previously described (23). Data analysis and visualization were performed in PRISM GraphPad (version 10.4.1). Log10-transformed metabolite measurements were tested for normality. Normally distributed metabolites were analysed using a two-tailed t-test and non-normally distributed metabolites were analysed using the Mann-Whitney U test. Benjamini-Hochberg procedure was performed to correct for multiple testing. Statistics Patient data Categorical variables were presented as counts with percentages, while continuous variables were expressed as medians with interquartile ranges (IQR). Comparisons of categorical variables were performed using Fisher’s exact test, whereas differences in continuous variables were assessed using either the t-test or the Mann-Whitney U test when the data did not follow a normal distribution. Logistic regression was used to evaluate the association between predictors and unfavourable outcome. Predictors to include in this analysis were chosen based on prior research and clinical relevance (1, 5). ORs were reported with 95% confidence intervals (CIs). The linearity of the association between continuous predictors and outcome was assessed using the Hosmer-Lemeshow goodness of fit test. When no linear relationship was found, the continuous variable was categorized for further analysis. Missing data was imputed using the Mice package (version 3.16) in RStudio, generating 50 imputations over 10 iterations to produce final estimates for the multivariable model. Combined variables were imputed with passive imputation. Statistical tests were two-tailed and p-values below 0.05 were considered statistically significant. Statistical analyses were conducted in R version 4.3.2 or Graphpad version 10.2.0. Mouse data In the murine meningitis model, groups were tested for normality with the Shapiro-Wilk normality test. Normally distributed groups were compared using a t-test, while not normally distributed data was compared using a Mann-Whitney U test. Histology scores between groups were compared with the Fisher’s exact test. Experiments using BMDMs were compared using a t-test. For statistical analysis of metabolite data, see `MS data processing and analysis`. In all statistical tests, p-values below 0.05 were considered statistically significant. Statistical analyses were conducted in R version 4.3.2 or Graphpad version 10.2.0. RESULTS rs12081070 is independently associated with unfavourable outcomes in pneumococcal meningitis In a prospective cohort study of 1,200 patients with pneumococcal meningitis, we analysed rs12081070 genotype distributions and their associations with clinical outcomes (Fig. 1a); 367 (31%) were homozygous for the G allele, 616 (51%) were heterozygous, and 217 (18%) were homozygous for the A allele (Table 1). Baseline characteristics were similar across different genotypes. Unfavourable outcome (Glasgow Outcome Scale 1-4) occurred in 97 of 367 (26%) patients with the rs12081070 GG genotype, 192 of 616 (31%) with the AG genotype, and 85 of 217 (39%) with the AA genotype (p = 0.006). Multivariate regression analysis, including all established risk factors for unfavourable outcome (1, 5), showed that rs12081070 is an independent predictive value of unfavourable outcome (OR AA versus GG of 1.84; 95% CI, 1.25-2.72; p < 0.001, Table 2). rs12081070 influences cytokine response in peripheral blood mononuclear cells To explore immune responses, PBMCs from 71 recovered patients were stimulated ex vivo with S. pneumoniae (Fig. 1a, clinical data in Additional File 1. Table 4) (13). Among these, 19 (27%) carried the GG genotype, 38 (54%) the AG genotype, and 14 (20%) the AA genotype. Carriers of the rs12081070 risk allele A exhibited higher levels of IL-6 (Fig. 1b; median levels 32.7 ng/ml [IQR 24.1-40.9] for GG, 42.8 ng/ml [IQR 29.1-63.6] for AG [p = 0.030), and 45.1 ng/ml [IQR 36.6-58.6] for AA [p = 0.010]) and CCL3 (median levels 33.4 ng/ml [IQR 27.6-50.7] for GG, 44.3 ng/ml [IQR 28.2-69.2] for AG [p = 0.188], and 60.6 ng/ml [IQR 39.0-80.7] for AA [p = 0.015]) following 24-hour stimulation with UV-killed S. pneumoniae . This effect was specific to S. pneumoniae , as responses to LPS were unaffected (Additional File 1. Figure 2). We also assessed cytokine levels and gene expression during acute pneumococcal meningitis using serial blood samples from 65 patients (Fig. 1a, clinical data in Additional File 1. Table 5) (13). Although genotype was not associated with cytokine levels during acute infection, risk allele carriers exhibited elevated CCL3 levels at day 90, suggesting prolonged immune activation. (Fig. 1c; p = 0.02). PGM1 mRNA expression in whole blood was increased during the acute phase of the disease (Fig. 1d, Additional File 1. Figure 3). Myeloid-specific Pgm1 knockdown exacerbates pneumococcal meningitis in mice To dissect the role of Pgm1 in host defence we generated LysMcre +/- Pgm1 fl/fl (Pgm1 M-KO ) mice carrying a myeloid specific knockdown of Pgm1 and compared them to LysMcre -/- Pgm1 fl/fl controls in a murine model of pneumococcal meningitis (Fig. 2a) (19, 25). Mice were injected intracisternally with either S. pneumoniae or PBS as uninfected controls and sacrificed at 20 hours post injection (hpi) or 44 hpi (Fig. 2a). All infected mice developed meningitis, with no significant differences in clinical scores or overall survival (Fig. 2b). However, three Pgm1 M-KO mice reached a humane endpoint before the end of the experiment, suggesting a more severe disease course. At 20 hpi, Pgm1 M-KO mice displayed increased bacterial outgrowth in the spleen compared to control mice (Fig. 2c; 50 CFU vs. 6,715 CFU, 134 fold change, p = 0.03). Similar trends of increased bacterial counts were observed in the brain and plasma of Pgm1 M-KO mice (Fig. 2c; p = 0.07 and p = 0.14). This higher bacterial burden was accompanied by elevated levels of proinflammatory chemokine CXCL2 in Pgm1 M-KO mice (Fig. 2d; 443.9 pg/mL vs. 226.7 pg/mL, p = 0.04). Gene expression levels of proinflammatory cytokines in the brain remained unchanged (Additional File 1. Figure 4). Histological examination revealed more severe cerebral haemorrhaging (Fig. 2e and 2f; 67% of Pgm1 M-KO mice scoring ≥ 2 on haemorrhage severity compared to 33% in control mice, p = 0.04) in Pgm1 M-KO mice. Overall, these findings highlight an important role for Pgm1 in controlling inflammation and bacterial clearance during pneumococcal meningitis. Pgm1 deficiency disrupts macrophage metabolism and immune responses Because PGM1 is linked with rs12081070 in macrophages, a key immune cell type contributing to pathogenesis of pneumococcal meningitis, and has previously been shown to regulate inflammatory responses in macrophages, we investigated the role of Pgm1 in macrophage immune function, using BMDMs from Pgm1 M-KO and genotype controls. Upon ex vivo stimulation with S. pneumoniae or LPS, Pgm1 M-KO BMDMs exhibited elevated IL-6 (Fig. 3a; p = 0.003) and NO production following 24-hour stimulation with S. pneumoniae (Fig. 3b; p = 0.012). Gene expression of proinflammatory cytokines remained unchanged (Additional File 1. Figure 5). Phagocytic activity, assessed by uptake of pHrodo™-labeled Escherichia coli bioparticles, showed no significant differences between Pgm1 M-KO and control BMDMs (Fig 3c; p = 0.61), indicating that Pgm1 deficiency primarily amplifies the inflammatory response without affecting phagocytosis. RNA sequencing of macrophages after 4-hours of S. pneumoniae stimulation revealed three differentially expressed genes in Pgm1 M-KO macrophages compared to control macrophages, consisting of Pgm1 and Lyz2 (under whose promoter the LysMcre allele is located) and Erdr1 (Fig. 4a). qPCR analysis confirmed that Erdr1 , a gene involved in haemoglobin synthesis, was downregulated in both unstimulated and stimulated Pgm1 M-KO macrophages (Fig. 4b). Most gene expression changes upon pneumococcal stimulation were shared between genotypes (Fig. 4c and 4d). Yet, Pgm1 M-KO macrophages uniquely upregulated 172 genes associated with pathways linked to granulocyte migration and immune activation while 246 uniquely downregulated genes included those involved in DNA repair processes (Fig. 4e). PGM1 is a highly conserved metabolic enzyme that catalyses the interconversion of glucose 1- to glucose 6-phosphate, playing a pivotal role in energy homeostasis, glycogen storage and glycosylation (26). In dermal fibroblasts from PGM1 -deficient patients, reduction of UDP-glucose (UDP-Glc) and UDP-galactose (UDP-Gal) has been identified as the cause of defective glycosylation, leading to morbidity (26). To assess the impact of Pgm1 knockdown on murine macrophages, we first measured the levels of nucleotide sugars, including UDP-Glc and UDP-Gal, under both unstimulated and stimulated conditions (Additional file 2, sheet 1-2). Our analysis revealed that Pgm1 deficiency does not affect nucleotide sugar levels in macrophages, suggesting a cell type-specific mechanism of metabolic regulation (Fig. 5a and 5b). Further analysis of phosphate sugars revealed an accumulation of glucose 1-phosphate and mannose 1-phosphate in Pgm1 -knockdown macrophages (Fig. 5c, Additional file 2, sheet 3-4). The effect on glucose 1-phosphate was most pronounced following LPS stimulation, with levels increasing 9.3-fold in knockdown macrophages (Fig. 5c; 0.016 vs. 0.0017 normArea, adj. p = 0.009). The accumulation of glucose 1-phosphate is likely due to impaired conversion to glucose 6-phosphate, a reaction predominantly catalysed by PGM1, and could potentially compromise both glycolysis and oxidative phosphorylation. We therefore, assessed the role of Pgm1 in energy metabolism, a process that can influence macrophage polarization and inflammatory responses (22), using real-time extracellular flux analysis. Following LPS stimulation, knockdown of Pgm1 impaired glycolysis and mitochondrial respiration (Fig. 5d), reduced basal respiration (Fig. 5e; normalized oxygen consumption rate [nOCR] of 3.1 vs 4.0 pmol/min, p = 0.01), reduced ATP production (Fig. 5e; nOCR of 2.1 vs 2.8 pmol/min, p = 0.03) and resulted in a decreased proton leak (Fig. 5e; nOCR of 0.9 vs 1.2 pmol/min, p = 0.002). On the glycolytic side, Pgm1 knockout cells showed decreased glycolysis (Fig. 5e; normalized extracellular acidification rate [nECAR] of 1.3 vs 1.7 pmol/min, p = 0.007) and a lower maximum glycolytic capacity (Fig. 5e; nECAR of 2.0 vs 2.6 pmol/min, p = 0.02). Similar trends were observed after pneumococcal stimulation, suggesting that Pgm1 knockdown impairs both respiratory and glycolytic metabolism, leading to an altered inflammatory response. Collectively, these findings indicate that Pgm1 deficiency in macrophages leads to heightened IL-6 and NO production, altered gene expression profiles, and impaired metabolic functions, underscoring the enzyme’s role in modulating inflammatory responses and energy metabolism in macrophages. DISCUSSION Our findings establish Pgm1 as a regulator of macrophage immune responses via glucose metabolism in pneumococcal meningitis. SNP rs12081070 was associated with heightened inflammatory responses, marked by increased production of pro-inflammatory cytokines upon S. pneumoniae stimulation. Chromatin interaction data linked rs12081070 with PGM1 , though its effect on PGM1 expression remains indirect. PGM1 is a key metabolic enzyme involved in glucose homeostasis and protein glycosylation. Myeloid-specific Pgm1 knockdown led to exaggerated inflammation and increased bacterial burden, resulting in increased brain damage in our murine model of pneumococcal meningitis. Pgm1 -deficient macrophages exhibited an increased inflammatory phenotype, marked by elevated IL-6 and nitric oxide levels, withdisrupted glycolysis and mitochondrial respiration, supporting its role in linking immune cell metabolism with inflammatory responses. The enhanced inflammatory phenotype we observed is in contrast with a previous study reporting decreased gene expression of pro-inflammatory cytokines in Pgm1 knockdown BMDMs (9). This discrepancy may reflect the stronger (100 ng/mL LPS and 20 ng/mL IFNγ) and prolonged (24 hours) stimulation of BMDMs, inducing a chronic hyperinflammatory phenotype rather than our focus on the role of Pgm1 during the early immune response in macrophages. The ability of Pgm1 to influence glycolysis and mitochondrial respiration in macrophages is consistent with previous findings in murine Pgm1 -knockout myocytes (27). These metabolic changes might well underlie the effects of Pgm1 knockout on macrophage polarization and function. Inflammatory macrophages rely primarily on glycolysis, while alternatively activated macrophages depend more on mitochondrial respiration (28). Glycogen serves as an energy reservoir for both macrophage subsets, requiring its breakdown into glucose-1-phosphate, which is then converted into glucose-6-phosphate by PGM1 to fuel glycolysis and respiration (29). We observed elevated glucose 1-phosphate levels in Pgm1 -knockdown macrophages, suggesting a metabolic bottleneck at the glucose 1-phosphate to glucose 6-phosphate conversion step. It is likely that this bottleneck restricts flux through glycolysis and downstream mitochondrial respiration, resulting in metabolic stress, which can result in enhanced inflammatory signalling in macrophages (30, 31). Furthermore, accumulation of phosphate sugars like glucose 1-phosphate, might in itself be toxic and contribute to macrophage dysfunction (32, 33). Together, these findings indicate that Pgm1 deficiency disrupts glycogen utilization, preventing macrophages from efficiently mobilizing stored glucose leading to metabolic stress, and in turn, dysregulated innate immune responses. Changes in macrophage function and inflammatory responses may also be linked to altered glycosylation (34). In PGM1-deficient patients, symptoms have been attributed to reduced levels of the nucleotide sugars UDP-glc and UDP-gal, leading to defective protein N-glycosylation, for which galactose supplementation is beneficial (8). While it is well-documented that PGM1 deficiency leads to abnormal glycosylation in serum transferrin and fibroblasts (18, 26, 35), its impact on immune cells such as macrophages remains unexplored. Although we did not detect changes in nucleotide sugar levels , mannose 1-phosphate, an important intermediate in the N/O-glycosylation pathway (36), was strongly increased in Pgm1 -deficient macrophages. Therefore, even though we did not directly measure alterations in glycosylation, aberrant glycosylation might contribute to the observed changes in macrophage function. While our study provides important insights, certain limitations should be acknowledged. First, while we identified a chromatin interaction between rs12081070 and PGM1 , the association remains indirect. We did not observe significant changes in PGM1 expression levels among the different genotypes (Additional File 1. Figure. 2), suggesting that rs12081070 may regulate PGM1 function through alternative mechanisms, such as post-transcriptional modifications or chromatin remodelling. Future studies are needed to clarify how this SNP influences PGM1-related pathways in immune cells. Second, potential selection bias may have influenced our findings, as patients enrolled in our cohort may not fully represent the broader population of individuals with pneumococcal meningitis. Only patients who underwent lumbar puncture could be included in our study. Patients with space-occupying lesions on cranial CT do not undergo lumbar puncture, and patients with meningitis and a florid rash or septic shock also may not undergo lumbar puncture initially. Thus, an unknown number of such patients were excluded from the cohort, which may have influenced the mortality rate and the impact of rs12081070. Third, while our study focuses on the role of Pgm1 in macrophages, the LysM-Cre line also affects neutrophils, which play an important role in the host immune response to pneumococcal meningitis. Consequently, Pgm1 deletion in neutrophils is likely to have contributed to the in vivo outcomes observed in the experimental model. Further research is necessary to investigate the role of Pgm1 in neutrophils. Fourth, RNA sequencing analysis of murine macrophages following Pgm1 knockdown revealed only minor changes in gene expression. This suggests that PGM1 primarily exerts post-transcriptional effects rather than acting as a transcriptional regulator. Alternatively, the minimal impact on gene expression may be attributed to our focus on early immune responses (4 hours post stimulation), whereas Pgm1 may exert more pronounced effects at later stages of infection or during chronic inflammation. Among the genes that were differentially expressed, Erdr1 emerged as a potential modulator of the early inflammatory response. Erdr1 is highly conserved between humans and mice and has been primarily studied for its anti-inflammatory effects in skin inflammatory diseases (37). It encodes a stress-induced autocrine factor that inhibits immune cell migration and infiltration (38), findings that align with our data, which showed enrichment of migration and chemotaxis pathways in Pgm1 -deficient macrophages following pneumococcal stimulation. ln addition, Erdr1 has been implicated in macrophage polarization and IL-1β production (39), which may contribute to the heightened proinflammatory phenotype observed in Pgm1 M-KO macrophages. Despite these limitations, our findings highlight PGM1 as a metabolic regulator in macrophage-mediated immunity. The disruption of Pgm1 -dependent metabolic processes appears to drive dysregulated immune activation and promotes an inflammatory phenotype in macrophages. Therefore, targeting PGM1 activity or its downstream metabolic pathways may represent a promising therapeutic strategy for modulating macrophage polarization, immune responses and to improve outcome in pneumococcal meningitis and other bacterial infections. CONCLUSIONS The role of PGM1 in immune regulation has broader implications beyond pneumococcal meningitis and infectious diseases, offering potential therapeutic insights into other diseases where immune cell metabolism is dysregulated. Conditions like rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis are characterized by immune cells with enhanced metabolic activity, leading to increased cytokine production, chronic inflammation and increased tissue damage (40-42). Targeting metabolic enzymes such as PGM1 in these contexts could help modulate inflammation and improve outcomes by recalibrating the immune response. This approach is also applicable in acute conditions like sepsis and acute respiratory distress syndrome (ARDS), where hypermetabolic immune cells drive excessive inflammation, contributing to tissue damage (43). Abbreviations SNP: Single nucleotide polymorphism MAF: Minor allele frequency OR: Odds ratio PGM1: Phosphoglucomutase 1 S. pneumoniae: Streptococcus pneumoniae GWAS: Genome-wide association study CSF: Cerebrospinal fluid GOS: Glasgow outcome scale EDTA: Ethylenediaminetetraacetic acid FPM: Fragments per million (RT-q)PCR: Reverse transcription quantitative polymerase chain rection PBMCs: Peripheral blood mononuclear cells (D)PBS: Dulbecco’s phosphate-buffered saline UV: Ultraviolet RPMI: Roswell Park Memorial Institute LPS: Lipopolysaccharide MOI: Multiplicity of infection HEP: Humane endpoint Hpi: Hours post inoculation CFU: Colony forming units HE: Haematoxylin-eosin BMDMs: Bone marrow-derived macrophages DMEM: Dulbecco’s Modified Eagle Medium FCS: foetal calf serum LCM: L929-conditioned medium IFN: Interferon NO: Nitric oxide CCL3: C-C motif ligand 3 IL: Interleukin TNF: Tumour necrosis factor CXCL: C-X-C motif ligand FCCP: Carbonylcyanide-p-trifluoromethoxyphenylhydrazone AA: Antimycin A UHPLC: Ultra-high perfomance liquid chromatography TBA: Tributylamine MRM: Multiple-reaction monitoring IQR: Interquartile range CI: Confidence interval OCR: Oxygen consumption rate ECAR: Extracellular acidification rate Erdr1: Erythroid differentiation regulator 1 UDP-Glc: UDP-glucose UDP-Gal: UDP-galactose nOCR: Normalized oxygen consumption rate nECAR: Normalized extracellular acidification rate ARDS: Acute respiratory distress syndrome Declarations Study approval Patients or their legal representatives gave written informed consent for participation. The Medical Ethics Committee of the Amsterdam University Medical Center, location AMC, University of Amsterdam, the Netherlands, approved the human studies (approval number NL43784.018.13). Animal experiments were authorized by the Centrale Commissie Dierproeven Netherlands (document no. AVD11800202013797) and the local Animal Wellfare Body (AWB;document no. NEU20-13797-1-04 and -05), and were conducted according to institutional guidelines Consent for publication Not applicable. Data availability RNA sequencing data generated during the current study is available under accession number …. (in progress of submitting to Genome Sequence Archive (GSA)). The datasets supporting metabolomic data in this article are included within the article and its additional files. Other data is available upon reasonable request. Additional files Additional file 1. (.pdf). Supplementary materials. Supporting figures and tables. Additional file 2. (.xls). Supporting metabolomics data. TBA and TEAA raw and normalized data and transitions list. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO)-Vici-Grant (918.19.627, to DvdB). Authors’contributions All authors read and approved the final version of the manuscript. RK: data curation, formal analysis, investigation, methodology, validation, visualisation, writing – original draft DB: data curation, formal analysis, investigation, methodology, validation, visualisation, writing – original draft FC: investigation, methodology, validation, writing – review & editing MvR: investigation, writing -review & editing VJ: investigation BF: investigation, methodology BB: resources, writing - review & editing KL: resources, writing - review & editing MB: investigation DJL: resources, writing - review & editing MCB: conceptualisation, project administration, resources, supervision, writing– review & editing DvB: conceptualisation, funding acquisition, project administration, resources, supervision, writing – review & editing Acknowledgements Not applicable. References Bijlsma MW, Brouwer MC, Kasanmoentalib ES, Kloek AT, Lucas MJ, Tanck MW, et al. Community-acquired bacterial meningitis in adults in the Netherlands, 2006-14: a prospective cohort study. 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Nature Reviews Immunology. 2020;20(6):355–62. Tables Table 1. Baseline and clinical characteristics of pneumococcal meningitis patients per rs12081070 genotype GG N = 367 (31%) AG N = 616 (51%) AA N = 217 (18%) p-value Age – years A 61 (49-68) 61 (51-69) 62 (54-70) 0.064 Female sex 191/367 (52%) 310/616 (50%) 112/217 (52%) 0.86 Immunocompromised state B 90/367 (25%) 158/616 (26%) 65/217 (30%) 0.33 Symptoms on admission Temperature - °C C 39.0 (38.0-39.6) 39.0 (38.0-39.7) 39.0 (38.0-39.7) 0.71 GCS score D 11 (9-13) 11 (9-13) 10 (9-13) 0.85 Indices of CSF inflammation Leukocytes - cells/µL E 2,811 (818-6,791) 2,730 (667-7,043) 3,000 (708-7,201) 0.54 CSF protein - g/L F 4.09 (2.45-6.40) 4.24 (2.50-6.21) 3.86 (2.36-6.00) 0.59 CSF:blood glucose ratio G 0.03 (0.01-0.25) 0.03 (0.01-0.25) 0.04 (0.01-0.26) 0.82 Clinical course Cerebrovascular accident 32/350 (9%) 72/585 (12%) 22/211 (10%) 0.33 Outcome GOS 1 23/367 (6.3%) 52/616 (8.4%) 12/217 (5.5%) 0.28 2 0/367 (0%) 1/616 (0.2%) 1/217 (0.5%) 0.42 3 14/367 (3.8%) 30/616 (4.9%) 12/217 (5.5%) 0.59 4 60/367 (16%) 109/616 (18%) 60/217 (28%) 0.002 5 270/367 (74%) 424/616 (69%) 132/217 (61%) 0.006 Unfavourable outcome 97/367 (26%) 192/616 (31%) 85/217 (39%) 0.006 Cranial nerve palsy 12/305 (4%) 28/508 (6%) 15/184 (8%) 0.15 Focal cerebral deficits H 23/309 (7%) 39/515 (8%) 24/189 (13%) 0.085 Data presented as n/N (%) or median (IQR). Group differences were tested with a Chi-squared test for categorical variables and Kruskal-Wallis rank sum test for continuous variables. Abbreviations: CSF, cerebrospinal fluid; GCS, Glasgow Coma Scale; GOS, Glasgow Outcome Scale. A Age is known for all patients. B Immunocompromised state is defined as active cancer, diabetes, alcoholism, immunosuppressive treatment, splenectomy or HIV. C Temperature is known in 359 episodes with GG genotype, 603 with AG and 213 with AA. D GCS is known in 366 episodes with GG genotype, 616 with AG and 216 with AA. E Leukocyte count in CSF is known in 360 episodes with GG genotype, 599 with AG and 214 with AA. F Protein concentration in CSF is known in 354 episodes with GG genotype, 582 with AG and 210 with AA. G CSF:blood glucose ratio is known in 340 episodes with GG genotype, 575 with AG and 204 with AA. H Focal cerebral deficits are defined as aphasia or mono-or hemiparesis. Table 2. Multivariate logistic regression of rs12081070 genotype for unfavourable outcome in pneumococcal meningitis Favorable outcome N = 826 Unfavorable outcome N = 374 Multivariable odds ratio for unfavorable outcome p value of multivariable analysis rs12081070 genotype GG 270/826 (33%) 97/374 (26%) Reference AG 424/826 (51%) 192/374 (51%) 1.27 (0.93-1.75) 0.13 AA 132/826 (16%) 85/374 (23%) 1.84 (1.25-2.72) 70 121/826 (15%) 117/374 (31%) 3.37 (1.91-5.92) <0.001 Immunocompromised 197/826 (24%) 116/374 (31%) 1.24 (0.93-1.67) 0.15 Cranial nerve palsy 40/723 (6%) 37/305 (12%) 2.48 (1.44-4.25) 100 440/791 (56%) 189/361 (52%) 1.03 (0.78-1.36) 0.83 <100 351/791 (44%) 172/361 (48%) Reference GCS score a 11 (9-14) 10 (8-12) 0.88 (0.84-0.92) <0.001 CRP 200 314/806 (39%) 220/368 (60%) 2.22 (1.57-3.14) <0.001 Thrombocyte count (cells/µl) <150 149/798 (19%) 112/354 (32%) 1.48 (1.08-2.04) 0.02 150-450 628/798 (79%) 235/354 (66%) Reference <450 21/798 (3%) 7/354 (2%) 0.78 (0.3-2) 0.60 CSF WBC count (cells/µl) <100 47 (5.8%) 66 (18%) 2.84 (1.78-4.54) <0.001 100-999 144 (18%) 91 (25%) 1.7 (1.2-2.41) 10 000 141 (17%) 51 (14%) 1.07 (0.72-1.6) 0.73 Positive blood culture 591/727 (81%) 286/327 (87%) 1.25 (0.83-1.89) 0.29 Data presented as n/N (%), median (IQR) or odds ratio (95% confidence interval). The multivariate analysis used an imputed dataset with 50 rounds of imputation across 10 iterations. All variables in the table were entered in the multivariate regression model simultaneously. Abbreviations: CRP, C-reactive protein; CSF, cerebrospinal fluid; GCS, Glasgow Coma Scale; OR, odds ratio; WBC, white blood cell. A GCS is known in 366 episodes with GG genotype, 616 with AG and 216 with AA. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.Supplementarymaterials.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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(\u003cstrong\u003eb\u003c/strong\u003e) Cytokine concentrations in supernatants of PBMCs after ex vivo stimulation with UV-killed \u003cem\u003eS. pneumoniae\u003c/em\u003e. PBMCs were harvested from patients with the GG (n = 19), AG (n = 38) or AA (n = 14) genotype, one to five years after they were hospitalized for pneumococcal meningitis. (\u003cstrong\u003ec\u003c/strong\u003e) CCL3 concentration in the plasma of pneumococcal meningitis patients drawn at day 0 (n = 32), day 1 (n = 53), day 2 (n = 55), day 7 (n = 48) and day 90 (n = 30) after hospital admission. (\u003cstrong\u003ed\u003c/strong\u003e) Normalized \u003cem\u003ePGM1\u003c/em\u003e mRNA levels (fragments per million) in whole blood of pneumococcal meningitis patients drawn at day 0 (n = 32), day 1 (n = 41), day 2 (n = 49), day 7 (n = 45) and day 90 (n = 29) after hospital admission. Data are presented as median with interquartile range. Group differences are calculated with a Kruskal-Wallis test followed by a post-hoc two-sided Dunn’s test. Ns, non-significant; *, p-value \u0026lt; 0.05; **, p-value \u0026lt; 0.01; ***, p-value \u0026lt;0.001. PBMCs, peripheral blood mononuclear cells.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8319354/v1/d42160a4402e92d4f3893bce.jpg"},{"id":98778868,"identity":"66c72f4a-09d5-4f60-8926-4bd2fcf945b7","added_by":"auto","created_at":"2025-12-22 12:29:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":273316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePgm1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e controls inflammation and bacterial outgrowth in murine pneumococcal meningitis.\u003c/strong\u003e Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice and control mice (WT) were intracisternally injected with \u003cem\u003eS. pneumoniae \u003c/em\u003eor PBS\u003cem\u003e. \u003c/em\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Graphical overview of the murine meningitis model. (\u003cstrong\u003eb\u003c/strong\u003e) Clinical scores of WT mice compared to Pgm1\u003csup\u003eM-KO \u003c/sup\u003emice up to 44 hours after infection. (\u003cstrong\u003ec\u003c/strong\u003e) Colony-forming units in the brain, plasma and spleen 20 hours after infection. (N = 12 mice per group) (\u003cstrong\u003ed\u003c/strong\u003e) IL-1β, IL-6 and CXCL2 concentrations in the spleen 20 hours after infection. (N = 12 mice per group). (\u003cstrong\u003ee\u003c/strong\u003e) Histological scores of mice, represented as percentage of mice per score; for this analysis the 20 and 44 hours after infection time-point were combined (Pgm1\u003csup\u003eM-KO \u003c/sup\u003emice (N = 21), WT mice (N = 24). 0 =\u0026nbsp; not present, 1 = focally and mild, 2 = multifocal mild or focally severe, 3 = multifocal severe. (\u003cstrong\u003ef\u003c/strong\u003e) Representative images of histological brain slides stained with haematoxylin-eosin (HE) (top) or anti-CD45 and haematoxylin (bottom). Images are taken at a magnification of 200x. Data are presented as median ± SEM. Group differences are calculated with the Mann-Whitney U test. Ns, non-significant; *, p-value \u0026lt; 0.05. CXCL2, chemokine (C-X-C motif) ligand 2; IL, interleukin.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8319354/v1/b70e5d8346a178de15671a08.jpg"},{"id":98780448,"identity":"611d7923-ee1b-46d5-899e-d0ff3184495c","added_by":"auto","created_at":"2025-12-22 12:31:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterleukin-6 and nitric oxide production are increased in Pgm1-knockdown macrophages. \u003c/strong\u003eBMDMs were isolated from Pgm1\u003csup\u003eM-KO \u003c/sup\u003eand control mice (WT) and left untreated (CTRL) or stimulated with LPS (10 ng/mL) or \u003cem\u003eS. pneumoniae\u003c/em\u003e (MOI 1) for 24 hours. (\u003cstrong\u003ea\u003c/strong\u003e) CXCL2, IL-6 and TNF-α concentrations and (\u003cstrong\u003eb\u003c/strong\u003e) nitric oxide production in BMDM supernatants. (\u003cstrong\u003ec\u003c/strong\u003e) Phagocytosis of pHrodo labelled E. coli bioparticles after 60 minutes of incubation. Controls were incubated without bioparticles or with bioparticles in the presence of cytochalasin D, an inhibitor of phagocytosis. Data are presented as median ± SEM. N = 3 mice per genotype for all analyses. Group differences were calculated using a t-test. Ns, non-significant; *, p-value \u0026lt; 0.05. BMDMs, bone marrow-derived macrophages; CXCL2, chemokine (C-X-C motif) ligand 2; IL, interleukin; TNF, Tumor Necrosis Factor; LPS, lipopolysaccharide; MOI, multiplicity of infection.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8319354/v1/26e8027a0d2b1f147b05c583.jpg"},{"id":98752431,"identity":"0f268f85-47a6-41c1-83c6-1f8c9e76bcb3","added_by":"auto","created_at":"2025-12-22 09:16:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":185743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePgm1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eM-KO \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003emacrophages show limited transcriptional changes compared to wild types.\u003c/strong\u003e RNA-sequencing was performed on control and Pgm1\u003csup\u003eM-KO \u003c/sup\u003eBMDMs that were untreated or infected with \u003cem\u003eS. pneumoniae\u003c/em\u003e (MOI 1) for 4 hours. (\u003cstrong\u003ea\u003c/strong\u003e) Volcano plots of untreated (left) and \u003cem\u003eS. pneumoniae\u003c/em\u003e infected (right) Pgm1\u003csup\u003eM-KO \u003c/sup\u003ecells compared to control cells. \u003cem\u003ePgm1\u003c/em\u003e and \u003cem\u003eLys2\u003c/em\u003e are omitted for better visualization. (\u003cstrong\u003eb\u003c/strong\u003e) mRNA expression of \u003cem\u003eErdr1\u003c/em\u003e in unstimulated (CTRL) control (WT) and Pgm1\u003csup\u003eM-KO \u003c/sup\u003eBMDMs. (\u003cstrong\u003ec\u003c/strong\u003e) Volcano plots of stimulated BMDMs compared to control for both control (left) and Pgm1\u003csup\u003eM-KO \u003c/sup\u003e(right). (\u003cstrong\u003ed\u003c/strong\u003e) Venn diagram of shared and unique genes between control and Pgm1\u003csup\u003eM-KO \u003c/sup\u003eBMDMs after stimulation. (\u003cstrong\u003ee\u003c/strong\u003e) Pathway analysis of genes uniquely upregulated and downregulated in stimulated Pgm1\u003csup\u003eM-KO \u003c/sup\u003eBMDMs. For \u003cstrong\u003eb\u003c/strong\u003e, data are shown as median ± SEM and a t-test was performed to determine group differences. N = 3 mice per genotype for all analyses. *, p \u0026lt; 0.05. BMDMs, bone marrow-derived macrophages; MOI, multiplicity of infection.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8319354/v1/f80a3fc9ec70cdf1718a47fe.jpg"},{"id":98752435,"identity":"194cb8de-4862-4adc-9a2e-464cb05fa3d4","added_by":"auto","created_at":"2025-12-22 09:16:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":392652,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePgm1 knockdown in macrophages alters glucose metabolism.\u003c/strong\u003e Metabolomic analysis was performed in BMDMs from control (WT) and Pgm1\u003csup\u003eM-KO \u003c/sup\u003emice. Cells were untreated (CTRL) or stimulated with LPS (10 ng/mL) or \u003cem\u003eS. pneumoniae \u003c/em\u003e(MOI 1) for 24 hours. (\u003cstrong\u003ea\u003c/strong\u003e) Schematic representation of the PGM1-mediated reaction and associated pathways. Continuous lines indicate direct reactions, dashed lines represent pathways involving multiple intermediary reactions. (\u003cstrong\u003eb-c\u003c/strong\u003e) Metabolomic analysis of nucleotide sugars (\u003cstrong\u003eb\u003c/strong\u003e) and phosphate sugars (\u003cstrong\u003ec\u003c/strong\u003e). For metabolite analysis, data are normalized by log transformation and group differences are calculated using a t test with a Benjamini-Hochberg correction. Non-log transformed values are shown in the graphs. (\u003cstrong\u003ed-e\u003c/strong\u003e) Extracellular flux assay measuring the oxygen consumption rate (OCR; top) and extracellular acidification rate (ECAR; below) over time. The colored panels show how the various parameters are calculated from this assay (\u003cstrong\u003ed\u003c/strong\u003e). Parameters calculated from the OCR (top row) and ECAR (below) (\u003cstrong\u003ee\u003c/strong\u003e). For extracellular flux assay N = 6 mice per group for unstimulated samples, and N = 3 mice per group for stimulated samples. Group differences are calculated with a Welch t-test. Ns, non-significant; *, p-value \u0026lt; 0.05; **, p-value \u0026lt; 0.01. BMDMs, bone marrow-derived macrophages; LPS, lipopolysaccharide\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8319354/v1/164ae06e9c0c64e4fb0572de.jpg"},{"id":100593463,"identity":"7875d168-0190-4672-962e-a265ea094bb6","added_by":"auto","created_at":"2026-01-19 13:18:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2771979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8319354/v1/c28d90b3-c569-4f82-b943-e8d679e9519a.pdf"},{"id":98752432,"identity":"eb7f9e19-4c9b-4472-af4d-4d88ed3fbb3f","added_by":"auto","created_at":"2025-12-22 09:16:14","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1674503,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.Supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8319354/v1/31aa5f4b7da85eedd0753678.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Macrophage metabolic regulation by phosphoglucomutase 1 shapes the host immune response in pneumococcal meningitis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003ePneumococcal meningitis, caused by \u003cem\u003eS. pneumoniae\u003c/em\u003e, remains a leading cause of mortality and long-term neurological sequelae, despite advances in vaccination, anti-inflammatory treatments and supportive care (1-5).\u0026nbsp;\u0026nbsp;While clinical risk factors for disease susceptibility and outcome are well recognized,\u0026nbsp;host genetic variation accounts for nearly half of the variability in disease severity and outcomes\u0026nbsp;(6). Previous studies have implicated immune responses as key modulators of meningitis severity, yet the impact of specific genetic variants remains poorly understood (7).\u0026nbsp;Genome-wide association studies (GWAS) provide an opportunity to uncover genetic determinants that shape host-pathogen interactions and immune responses in bacterial infections\u0026nbsp;(6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a previous GWAS, we identified SNP rs12081070 as being associated with unfavourable outcome in bacterial meningitis ((MAF) = 0.43; (OR) = 1.63; p = 2.0 × 10–8) (6). This variant resides within an intronic region of \u003cem\u003eUBE2U\u003c/em\u003e and exhibits chromatin interaction with \u003cem\u003ePGM1\u003c/em\u003e, a metabolic geneessential for protein glycosylation and glucose metabolism, in a different range of immune cells, including macrophages. PGM1 deficiency is known to cause congenital disorder of glycosylation syndrome type 1t (CDG1T), characterized by diverse symptoms, including hepatopathy, cardiomyopathy, and thrombosis (8). Previous studies linked \u003cem\u003ePGM1\u003c/em\u003e expression to proinflammatory gene expression in macrophages (9, 10), highlighting a potential role for this enzyme in macrophage polarization and immune responses. However, the role of \u003cem\u003ePGM1\u003c/em\u003e\u0026nbsp; in the context of infections has not been studied.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe leveraged our nationwide cohort study on community-acquired bacterial meningitis to study the effect of rs12081070 on modulating immune responses and clinical outcomes in patients with pneumococcal meningitis. In addition, we used a murine model of pneumococcal meningitis with a myeloid-specific \u003cem\u003ePgm1\u003c/em\u003e knockdown to investigate the impact of \u003cem\u003ePgm1\u003c/em\u003e on inflammation and bacterial clearance.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eNationwide clinical cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MeninGene study is a nationwide prospective cohort study on community‐acquired bacterial meningitis in the Netherlands. Methods of patient identification and inclusion have been published previously (1). In short, we included patients aged 16 years or older who had pneumococcal meningitis between March 2006 and July 2021. Patients were identified by the treating physician or the Netherlands Reference Laboratory for Bacterial Meningitis (NRLBM). Extensive clinical data was prospectively collected through online case record forms. We included all patients with confirmed pneumococcal meningitis, identified either by the presence of \u003cem\u003eS. pneumoniae\u0026nbsp;\u003c/em\u003ein the cerebrospinal fluid (CSF) or by a combination of a positive PCR, blood culture, or CSF antigen test along with at least one predictor of bacterial meningitis, as outlined by Spanos et al. (11). These predictors include a CSF glucose concentration \u0026lt; 340 mg/L (1.9 mmol/L), a CSF:blood glucose ratio \u0026lt; 0.23, protein concentration \u0026gt; 2200 mg/L, white blood cell count \u0026gt; 2000 per microliter, or \u0026gt; 1180 polymorphonuclear leukocytes per microliter. We excluded patients who developed bacterial meningitis in the hospital, within one week of discharge, following head trauma or neurosurgery within the prior month, or those with a neurosurgical device in place. Patients with an altered immune status owing to asplenia, diabetes mellitus, cancer, alcoholism, or the use of immunosuppressive drugs were considered immunocompromised, as were patients infected with human immunodeficiency virus. Neurological examination was performed at discharge. Outcome was scored according to the Glasgow Outcome Scale (GOS), with scores varying from 1 (death) to 5 (mild or no disability), and then dichotomized in favourable (GOS 5) and unfavourable outcome (GOS 1-4) (12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeninGene Recall cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the Recall study, selected participants were included in the MeninGene study between October 2011 and March 2015 (13). On the informed consent form of the MeninGene study, the patient was asked whether they allowed the researchers to approach them for follow-up studies on long-term neurological sequelae. Patients eligible for the current follow-up study provided this consent and had been admitted with pneumococcal meningitis 1–5 years prior the follow-up study. Before participation patients were questioned about their medical history, medication use, and ongoing illness. If patients had ongoing infections or felt ill they could not participate in the study. Patients who gave permission to participate in this follow-up study were recalled to the Amsterdam UMC for a blood withdrawal and neuropsychological examination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSerial blood sampling cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSerial blood samples were available for a subset of pneumococcal patients from the MeninGene study, because they were also included in the Serial Meningitis Sampling (SMS) study (13). The SMS study includes patients from 12 participating centres in the Netherlands. Inclusion criteria were a clinical suspicion on bacterial meningitis and one of the following CSF characteristics: pleocytosis \u0026gt; 1000 cells per 3 mm3, glucose \u0026lt; 1.9 mmol/L, protein \u0026gt; 2.20 g/L or a positive Gram stain. Pneumococcal meningitis needed to be confirmed with either a positive CSF culture or positive blood culture. Plasma for cytokine measurements was available for\u0026nbsp;day 0 (n=32), 1 (n=53), 2 (n=55) and 7 (n=48) of admission and 3 months after discharge (n=30). Blood samples were immediately processed in the participating hospitals and stored at −70 or −80 ºC. RNA isolated from whole blood was available for day 0 (n=32), 1 (n=41), 2 (n=49) and 7 (n=45) of admission and 3 months after discharge (n=29). Patients were included during the acute phase of the illness and provided written informed consent for participation in the SMS study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and genotyping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethods on DNA extraction and genotyping for this study have been previously published (6). In short, DNA was isolated from patients’ blood collected in sodium or (ethylenediaminetetraacetic acid) EDTA tubes using the Gentra Puregene Isolation Kit (Qiagen). Genotyping was performed on the Illumina Omni array (Illumina).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole blood was collected directly into PAXgene RNA tubes (Qiagen) and stored at -80 °C until analysis. RNA was isolated using QIAcube automated RNA isolation (Qiagen) and quality was tested with RNA ScreenTape (Agilent). Samples with a RIN value \u0026gt; 7 and a concentration \u0026gt; 50 ng/µL were sequenced using the NovaSeq sequencing system (Illumina). RNA sequencing reads were initially obtained and subjected to quality control to exclude suboptimal samples. Following this, adapter sequences were removed, and quality trimming was performed using Trimmomatic (version 0.39)(14). The processed samples were subsequently aligned to the human reference genome hg38 from UCSC using the splice-aware STAR aligner (version 2.7.10b) (15). Alignment output was then sorted using Samtools (version 1.14). Gene-level read counts were quantified with featureCounts (version 2.0.6), utilizing the GENCODE v44 annotation (16). For normalization of RNA-seq read counts, fragments per million (FPM) were applied in R (version 4.2.1) using the DESeq2 package (17).\u0026nbsp;Differential expression of the PGM1 gene were compared between time points and genotypes using t tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBacterial strain and culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures related to pathogenic bacteria were conducted under biosafety level 2 protocols and guidelines. The clinical isolate \u003cem\u003eS. pneumoniae\u003c/em\u003e D39 (serotype 2) from the Veening lab was used in this study. \u003cem\u003eS. pneumoniae\u003c/em\u003e was grown in CY medium at 37 °C to mid-log phase, evaluated by monitoring at an optical density of 600 nm. Frozen stocks of bacteria in 20% glycerol (Sigma) were prepared and kept at −80 °C until use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePeripheral blood monocyte (PBMC) stimulation experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePBMCs were available for pneumococcal patients included in the Recall study (13). To isolate peripheral blood mononuclear cells (PBMCs), whole blood was 1:1 diluted with Dulbecco's phosphate-buffered saline (DPBS) and centrifuged with Ficoll. Isolated PBMCs were then washed three times with DPBS before dilution in Roswell Park Memorial Institute (RPMI) medium. PBMCs were stimulated at 37 ºC with LPS 10 ng/ml, ultraviolet (UV)-killed \u003cem\u003eS. pneumoniae\u003c/em\u003e strain D39 (serotype 2) at multiplicity of infection (MOI) 10 and RPMI (unstimulated). After 24 hours, samples were centrifuged for 10 minutes at 400 x g and the supernatant was collected and stored at −80 ºC for later analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC57BL/6J LysMcre\u003csup\u003e-/-\u003c/sup\u003ePgm1\u003csup\u003efl/fl\u003c/sup\u003e (controls) and LysMcre\u003csup\u003e+/-\u003c/sup\u003ePgm1\u003csup\u003efl/fl\u003c/sup\u003e (Pgm1\u003csup\u003eM-KO\u003c/sup\u003e) mice were derived by crossing Pgm1\u003csup\u003efl/fl\u003c/sup\u003e mice with Lyz2-Cre transgenic mice. Pgm1\u003csup\u003efl/fl\u003c/sup\u003e mice were constructed, characterized and shared by dr. Kent Lai (University of Utah, USA) (18). LysMcre crossbreeding was performed in our mouse facility. Mice were housed at the Animal Research Institute Amsterdam UMC (ARIA). Knockdown of \u003cem\u003ePgm1\u003c/em\u003e in Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice was validated on both gene and protein level in BMDMs (isolation and cultured as described below) by reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blot, respectively (Additional File 1. Figure 1). Animals (maximum of 6 mice/cage) were housed in individually ventilated cages (Tecniplast 1284L Eurostandard Type II L) with corncob bedding in a ventilated cabinet with a controlled temperature of 20–24 °C, relative humidity 45-65% and 12 hours light/dark (07:00-19:00) cycles. Mice were given food and water (acidified; pH 2,3-2,8)\u003cem\u003e\u0026nbsp;ad libitum\u003c/em\u003e. The study was conducted in accordance with the ARRIVE guidelines for reporting animal experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse meningitis model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA well-characterized mouse model of pneumococcal meningitis was used (19).\u0026nbsp;Mice were acclimatized for one week prior to experiments and housed in individually ventilated cages with corncob bedding under controlled conditions. \u0026nbsp;Researchers were blinded for genotype during experiments and data analysis.\u0026nbsp;On day 0 (start of the experiment) mice were weighed and clinically scored. Clinical scoring included weight loss (0–4 points), activity (0–3 points), time to return to upright position (0–6 points), state of skin/fur (0–2 points), posture (0–2 points), eye discharge or protrusion (0–4 points), irregular/laboured breathing (0–4 points), presence of seizures, limb paresis or ataxia (0–6 points; Additional File 1. Table 1). Animals reaching humane endpoint (HEP) criteria (clinical scoring ≥14, weight loss ≥25%, coma, paralysis, seizure longer than 5 min. or two seizures in 15 minutes) were terminated. Subsequently, mice were inoculated with 1 µl bacterial suspension containing 5.0 * 10\u003csup\u003e^4\u003c/sup\u003e CFU \u003cem\u003eS. pneumoniae\u003c/em\u003e (infection group) or PBS (uninfected controls) into the cisterna magna under isoflurane anaesthesia. Immediately after intracisternal inoculation mice were assessed for neurologic damage as a result of the puncture using the Foot Fault Grid test. Mice suffering from neurological damage as a result of the puncture were excluded. Treatment with antibiotics was started 20 hours post inoculation (hpi) with bacteria by intraperitoneal injection of ceftriaxone (100 mg/kg body weight) and repeated every 24 hours. Clinical scoring was performed every 4 hours from 16 hpi onwards until the end of the experiment or until a HEP was reached. Mice reaching a HEP were scored as 15 points for the remainder of the study. At the experimental endpoint of the study mice were anesthetized by intraperitoneal injection of 190 mg/kg ketamine + 0,3 mg/kg dexmedetomidine in a total volume of 300 µl, followed by cardiac puncture for blood collection and perfusion of organs with sterile PBS via the left ventricle. The right hemisphere of the brain was placed in formalin. Left hemisphere of the brain and spleen were placed in ice-cold 0.9% NaCl solution, processed and stored as described previously\u0026nbsp;(20). Bacterial titres were determined by plating serial tenfold dilutions of blood, CSF, brain and spleen homogenates on sheep-blood agar plates and incubated overnight at 37 °C. EDTA blood was centrifuged at 2000 g for 15 minutes. Plasma was stored at -80°C for further analysis.\u0026nbsp;Outcome measures included clinical scores, number of colony forming units (CFU), brain histology scores, cytokine concentrations and gene expression in brain, spleen, and plasma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental groups in the mouse model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following experimental groups were used each time point: uninfected control mice (N = 12), uninfected Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice (N = 12), infected control mice (N = 24) and infected Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice (N = 24). Half of the mice were terminated at 20 hpi (N = 36), the other half at 44 hpi (N = 36). The study was powered to detect a difference in inflammatory parameters between groups with an effect size\u0026nbsp;(δ=|µ₁-µ₂|/σ) of 1.3. Using an 80 % power, two-sided testing, and significance level of p \u0026lt; 0.05, we needed 12 mice per group.\u0026nbsp;Experiments were divided over two session, using 36 mice with equal representation of experimental groups in both sessions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry and histopathology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHistopathology was performed on the right hemisphere of the brain. Brain was fixed in 4% paraformaldehyde and paraffin-embedded in seven coronal plaques which were cut in sections of 5 μm. Samples were stained with haematoxylin-eosin (HE) with the Ventana BenchMark ULTRA system (Roche). Immunostaining was performed with antibodies against CD45 (Biolegend) to detect leukocytes followed by a haematoxylin counterstain. Histopathology was scored (blinded) for six categories by two researchers separately as previously described (21). Discrepancies in scoring were resolved through consultation with a dedicated neuropathologist. A detailed description of the histological scoring method can be found in Additional File 1. Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBone marrow isolation and culture of BMDMs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBone marrow cells were isolated from femurs and tibias from uninfected control and Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice by flushing the bones with ice cold DPBS (-/-). Collected cells were cultured at 37ºC and 5% CO\u003csub\u003e2\u003c/sub\u003e for 8 days in Dulbecco’s Modified Eagle Medium (DMEM; Low glucose, ThermoFisher) supplemented with 10% foetal calf serum (FCS), 1% penicillin/streptomycin, 2 mM L-glutamine and 15% L929-conditioned medium (LCM) for differentiation into bone marrow-derived macrophages (BMDMs). Fresh medium was added on day 3 and day 7. On day 8, differentiated cells (\u0026gt;95% of cells CD11b\u003csup\u003e+\u003c/sup\u003eF4/80\u003csup\u003e+\u003c/sup\u003e determined by flow cytometry) were harvested and seeded in DMEM supplemented with 10% FCS and 2 mM L-glutamine at a concentration of 0.75 * 10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003ecells/ml for extracellular flux analysis and 1.25 * 10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003ecells/ml for all other experiments. Cells were left to attach for 24 hours before performing experiments. BMDMs were stimulated for 4 or 24 hours with LPS (10 ng/ml), LPS (10 ng/ml) + interferon (IFN)γ (10 ng/ml), \u003cem\u003eS. pneumoniae\u003c/em\u003e D39V (MOI 1) or left unstimulated by adding fresh DMEM. For experiments lasting longer than 4 hours, fresh medium was added containing penicillin-streptomycin (final concentration 1%) after 4 hours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo confirm knockdown of PGM1 protein expression in Pgm1\u003csup\u003eM-KO\u0026nbsp;\u003c/sup\u003eBMDMs (Additional File 1. Figure 1), cells were lysed using RIPA buffer (ThermoFisher) in combination with phosphatase and protease inhibitors (Sigma). Protein concentrations were determined with BCA Protein Assay (ThermoFisher, Cat). SDS-polyacrylamide gels (Bio-Rad) were used to separate 10 ug of protein, followed by transfer to PVDF membranes (ThermoFisher). Membranes were blocked in 5% goat serum (Biowest) for 1 hour at room temperature and subsequently incubated with primary antibodies against PGM1 (1:500, Abcam, ab192876) and GAPDH (1:2500, Bio-Connect, M00227-1). Overnight incubation was performed at 4°C. Next, membranes were washed and incubated with a HRP-conjugated secondary antibody (1:5000, ThermoFisher, G-21234), followed by visualization with an ECL detection system (ThermoFisher, Cat: 32106X4). Images were acquired using the Platinum V10 (Uvitech).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNitric oxide assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the nitric oxide (NO) assay, 50 µl of undiluted cell supernatant was transferred to a flat-bottom 96-wells plate. Next, 50 µl of Griess reagent (Sigma) was added to the samples and absorbance was immediately measured on a microplate reader at a wavelength of 550 nm. To calculate exact concentrations of NO, a standard curve was made with NaNO\u003csub\u003e2\u003c/sub\u003e in culture medium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhagocytosis assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBMDMs were incubated with pHrodo BioParticles (1 mg/mL, ThermoFisher) for 60 minutes at 37 °C, according to the manufacturer’s instructions. As a negative control, cells were incubated in presence of 10 ug/mL cytochalasin D (ThermoFisher), an inhibitor of phagocytosis. Cells were analysed by flow cytometry (BD Symphony A1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCytokine measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman C-C Motif Chemokine Ligand 3 (CCL3), interleukin 1 beta (IL-1β), interleukin 6 (IL-6) and tumour necrosis factor (TNF-α)\u0026nbsp;levels in the blood samples of the serial sampling cohort and in the supernatants of the PBMC stimulation experiments were measured with the Luminex technology with a Bio-Techne assay. Measurements were done according to the manufacturer's protocol. In mice, cytokine concentrations of chemokine (C-X-C motif) ligand 2 (CXCL2), IL-1β, IL-6, or TNF-α\u0026nbsp;were determined in the supernatant or organ homogenates using ELISA (Duoset, R\u0026amp;D Systems), according to manufacturer’s instructions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from the mouse brain using the Nucleospin RNA kit (Macherey-Nagel), according to the manufacturer’s protocol. Quality of the samples and RNA concentrations were determined with a Nanodrop spectrophotometer (ThermoFisher). Following RNA extraction, cDNA was synthesized using the iScript cDNA Synthesis Kit (Bio-Rad). The RT-PCR measurement of individual cDNAs was performed on a Bio-Rad MyiQ Single-Color RT-PCR Detection System using the Bio-Rad iQ SYBR Green Supermix (Bio-Rad Laboratories). A list of primers that were used in this study can be found in Additional File 1. Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-sequencing and data analysis of murine macrophages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMacrophages were cultured as described in ‘Bone Marrow isolation and culture of BMDMs’. Cells were incubated with or without \u003cem\u003eS. pneumoniae\u003c/em\u003e (1 * 1\u003csup\u003e06\u003c/sup\u003e CFU) for 4 hours. After incubation, supernatant was removed and cells were harvested in RA1 buffer and stored at -80°C until RNA isolation, as described in the section ‘Gene expression’. RNA-sequencing library preparation (KAPA mRNA Hyperprep, Roche) and subsequent sequencing (NovaSeq S4 2x 150 bp, Illumina) was conducted by the Genomics Core Facility at Amsterdam UMC. \u0026nbsp; Analyses of RNA-seq datasets were performed using Galaxy (version 24.2.4.dev0). After quality control with Falco, FastQ files were trimmed to remove primer adapters using Trimmomatic. Trimmed sequences were aligned to the mouse reference genome (mm10) with RNA STAR. Following alignment, gene counts were generated using featureCounts and differential gene expression analysis was performed with DESeq2. Genes were considered differentially expressed when the adjusted p value was lower than 0.05. Volcano plots and Venn diagrams and were generated using the lists of differentially expressed genes. For pathway enrichment analysis, genes that were differentially upregulated in Pgm1\u003csup\u003eM-KO\u0026nbsp;\u003c/sup\u003emacrophages following stimulation with \u003cem\u003eS. pneumoniae\u003c/em\u003e, but not in wild-type macrophages were used using ShinyGO (version 0.82). Gene ontology (GO) terms in the Biological Process category with a p value lower than 0.05 were considered significant. The top 10 statistically significant, non-redundant GO-enriched terms were plotted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtracellular flux analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtracellular flux analysis was performed according to a previously published protocol (22). In short, the XF-96 Flux Analyzer (Agilent) was used to assess the oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in BMDMs. BMDMs (75 * 10\u003csup\u003e3\u003c/sup\u003e cells/well) were plated on XF-96-cell culture plates (Agilent), treated with LPS (10 ng/ml) or \u003cem\u003eS. pneumoniae\u0026nbsp;\u003c/em\u003e(MOI 1) as described above, or left untreated. One hour before measurement, cells were washed and replaced with DMEM (Sigma-Aldrich) without glucose, phenol red, and sodium bicarbonate. The run consisted of 2 min mixing, 3 min measuring before and after four injections: glucose (final assay concentration 25 mM), oligomycin A (OM, final assay concentration 1.5 µM), Carbonylcyanide-p-trifluoromethoxyphenylhydrazone (FCCP) (final assay concentration 1.5 µM) with sodium pyruvate (final assay concentration 1 mM), and antimycin A (AA, final assay concentration 2.5 µM) with rotenone (final assay concentration 1.25 µM). Data were analysed using Wave software as detailed before (22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolar metabolite extraction from adherent macrophages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMacrophages were seeded in 24-well plates with a density of 0.3 * 10^6 cells/well, in three replicates, and cultured as described in “Bone marrow isolation and culture of BMDMs”. Cells were treated with \u003cem\u003eS. pneumoniae\u003c/em\u003e D39 (MOI 1), LPS (10 ng/mL) or culture medium, as a control, for 4 hours.\u003c/p\u003e\n\u003cp\u003eAfter treatment, the cell metabolism was quenched and the polar metabolites were extracted using a modified version of the protocol previously described in Conte \u003cem\u003eet al.\u003c/em\u003e(23), to accommodate the higher number of wells per plate. In brief, after washing the wells twice with 200 ul/well \u0026nbsp;of 75 mM ammonium carbonate solution (pH 7.3±0.05, Honeywell, Fluka), the plate was snap-frozen through direct contact with liquid nitrogen, and stored at -80°C until extraction. The polar metabolite extraction was performed using -20°C cold extraction buffer composed of 40:40:20 v/v Acetonitrile (Biosolve), Methanol (Honeywell), and MS-compatible ultrapure water (HPLC-super high gradient, VWR Chemicals). While keeping the plates on ice, each well was incubated for 6 minutes with 400 ul \u0026nbsp;of extraction solution and, after collection, the extracts were centrifuged at 13.000 rpm for 3 minutes at 4°C to remove larger cell debris. Each supernatant was transferred to a separate tube, and dried overnight (16 hours) in vacuum using a Savant SC100 (RVT 100) vacuum centrifuge. The extracts were stored at -80°C till MS analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of nucleotide sugars and phosphate sugars via ion-pairing LC-MS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe extracts were dissolved in 100 μl ultra-pure water prior to analysis and spun down for 15 minutes at 13’000 rpm. For the phosphate sugar quantification, 20 ul of each sample were loaded in a 96-well plate for analysis. The analytes were separated by ion-pairing ultra-high perfomance liquid chromatography (UHPLC) on an Agilent 1290 Infinity System, equipped with Acquity HSS T3 column (Waters). The chromatographic separation was based on tributylamine (TBA) ion-pairing buffer, according to the method and separation gradient previously reported in van Scherpenzeel et al. (23). The MS analysis was performed on an Agilent 6490A QqQ UHPLC-MS/MS, with high-flow iFunnel ionization source, and controlled by Agilent MassHunter Workstation Software (version B.08.02). Data acquisition was performed in multiple-reaction monitoring (MRM), using the transition list reported in Additional File 2, sheet 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the nucleotide sugar quantification, 15 ul of each extract were transferred to a conical-bottom vial for analysis. The analytes were separated by liquid chromatography performed on an Agilent 1290 infinity II System equipped with Atlantis Premier BEH C18 AX Column (Waters), according to the method and gradient described in Rahm et al. (24). \u0026nbsp;The MS analysis was performed on a Sciex 6500+ QTRAP with electrospray ionization source, and controlled by ScieX Analyst (version 1.7.2). Data acquisition was performed in MRM, using the transition list reported in Additional File 2, sheet 6 .\u003c/p\u003e\n\u003cp\u003eAfter acquisition, peak integration was performed using Skyline (version 24.1) for both methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMS data processing and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter peak integration, the peak area for the peak of each compound normalized according to the ‘total peak area’ method, as previously described (23). Data analysis and visualization were performed in PRISM GraphPad (version 10.4.1). Log10-transformed metabolite measurements were tested for normality. Normally distributed metabolites were analysed using a two-tailed t-test and non-normally distributed metabolites were analysed using the Mann-Whitney U test. Benjamini-Hochberg procedure was performed to correct for multiple testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatient data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables were presented as counts with percentages, while continuous variables were expressed as medians with interquartile ranges (IQR). Comparisons of categorical variables were performed using Fisher’s exact test, whereas differences in continuous variables were assessed using either the t-test or the Mann-Whitney U test when the data did not follow a normal distribution. Logistic regression was used to evaluate the association between predictors and unfavourable outcome. Predictors to include in this analysis were chosen based on prior research and clinical relevance\u0026nbsp;(1, 5). ORs were reported with 95% \u0026nbsp;confidence intervals (CIs). The linearity of the association between continuous predictors and outcome was assessed using the Hosmer-Lemeshow goodness of fit test. When no linear relationship was found, the continuous variable was categorized for further analysis. Missing data was imputed using the Mice package (version 3.16) in RStudio, generating 50 imputations over 10 iterations to produce final estimates for the multivariable model. Combined variables were imputed with passive imputation. Statistical tests were two-tailed and p-values below 0.05 were considered statistically significant. Statistical analyses were conducted in R version 4.3.2 or Graphpad version 10.2.0.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMouse data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the murine meningitis model, groups were tested for normality with the Shapiro-Wilk normality test. Normally distributed groups were compared using a t-test, while not normally distributed data was compared using a Mann-Whitney U test. Histology scores between groups were compared with the Fisher’s exact test.\u003c/p\u003e\n\u003cp\u003eExperiments using BMDMs were compared using a t-test. For statistical analysis of metabolite data, see `MS data processing and analysis`. In all statistical tests, p-values below 0.05 were considered statistically significant. Statistical analyses were conducted in R version 4.3.2 or Graphpad version 10.2.0.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003ers12081070 is independently associated with unfavourable outcomes in pneumococcal meningitis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a prospective cohort study of 1,200 patients with pneumococcal meningitis, we analysed rs12081070 genotype distributions and their associations with clinical outcomes (Fig. 1a); 367 (31%) were homozygous for the G allele, 616 (51%) were heterozygous, and 217 (18%) were homozygous for the A allele (Table 1). Baseline characteristics were similar across different genotypes. Unfavourable outcome (Glasgow Outcome Scale 1-4) occurred in 97 of 367 (26%) patients with the rs12081070 GG genotype, 192 of 616 (31%) with the AG genotype, and 85 of 217 (39%) with the AA genotype (p = 0.006). Multivariate regression analysis, including all established risk factors for unfavourable outcome (1, 5), showed that rs12081070 is an independent predictive value of unfavourable outcome (OR AA versus GG of 1.84; 95% CI, 1.25-2.72; p \u0026lt; 0.001, Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ers12081070 influences cytokine response in peripheral blood mononuclear cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore immune responses, PBMCs from 71 recovered patients were stimulated\u003cem\u003e\u0026nbsp;ex vivo\u003c/em\u003e with \u003cem\u003eS. pneumoniae\u0026nbsp;\u003c/em\u003e(Fig. 1a, clinical data in Additional File 1. Table 4) (13). Among these, 19 (27%) carried the GG genotype, 38 (54%) the AG genotype, and 14 (20%) the AA genotype. Carriers of the rs12081070 risk allele A exhibited higher levels of IL-6 (Fig. 1b; median levels 32.7 ng/ml [IQR 24.1-40.9] for GG, 42.8 ng/ml [IQR 29.1-63.6] for AG [p = 0.030), and 45.1 ng/ml [IQR 36.6-58.6] for AA [p = 0.010]) and CCL3 (median levels 33.4 ng/ml [IQR 27.6-50.7] for GG, 44.3 ng/ml [IQR 28.2-69.2] for AG [p = 0.188], and 60.6 ng/ml [IQR 39.0-80.7] for AA [p = 0.015]) following 24-hour stimulation with UV-killed \u003cem\u003eS. pneumoniae\u003c/em\u003e. This effect was specific to \u003cem\u003eS. pneumoniae\u003c/em\u003e, as responses to LPS were unaffected (Additional File 1. Figure 2). We also assessed cytokine levels and gene expression during acute pneumococcal meningitis using serial blood samples from 65 patients (Fig. 1a, clinical data in Additional File 1. Table 5) (13). Although genotype was not associated with cytokine levels during acute infection, risk allele carriers exhibited elevated CCL3 levels at day 90, suggesting prolonged immune activation. (Fig. 1c; p = 0.02). \u003cem\u003ePGM1\u003c/em\u003e mRNA expression in whole blood was increased during the acute phase of the disease (Fig. 1d, Additional File 1. Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMyeloid-specific \u003cem\u003ePgm1\u003c/em\u003e knockdown exacerbates pneumococcal meningitis in mice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo dissect the role of \u003cem\u003ePgm1\u0026nbsp;\u003c/em\u003ein host defence we generated LysMcre\u003csup\u003e+/-\u003c/sup\u003ePgm1\u003csup\u003efl/fl\u003c/sup\u003e (Pgm1\u003csup\u003eM-KO\u003c/sup\u003e) mice carrying a myeloid specific knockdown of \u003cem\u003ePgm1\u003c/em\u003e and compared them to LysMcre\u003csup\u003e-/-\u003c/sup\u003ePgm1\u003csup\u003efl/fl\u003c/sup\u003e controls in a murine model of pneumococcal meningitis (Fig. 2a) (19, 25). Mice were injected intracisternally with either \u003cem\u003eS. pneumoniae\u0026nbsp;\u003c/em\u003eor PBS as uninfected controls and sacrificed at 20 hours post injection (hpi) or 44 hpi (Fig. 2a). All infected mice developed meningitis, with no significant differences in clinical scores or overall survival (Fig. 2b). However, three Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice reached a humane endpoint before the end of the experiment, suggesting a more severe disease course. At 20 hpi, Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice displayed increased bacterial outgrowth in the spleen compared to control mice (Fig. 2c; 50 CFU vs. 6,715 CFU, 134 fold change, p = 0.03). Similar trends of increased bacterial counts were observed in the brain and plasma of Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice (Fig. 2c; p = 0.07 and p = 0.14). This higher bacterial burden was accompanied by elevated levels of proinflammatory chemokine CXCL2 in Pgm1\u003csup\u003e\u0026nbsp;M-KO\u003c/sup\u003e mice (Fig. 2d; 443.9 pg/mL vs. 226.7 pg/mL, p = 0.04). Gene expression levels of proinflammatory cytokines in the brain remained unchanged (Additional File 1. Figure 4). Histological examination revealed more severe cerebral haemorrhaging (Fig. 2e and 2f; 67% of Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice scoring \u0026ge; 2 on haemorrhage severity compared to 33% in control mice, p = 0.04) in Pgm1\u003csup\u003eM-KO\u003c/sup\u003e mice. Overall, these findings highlight an important role for \u003cem\u003ePgm1\u0026nbsp;\u003c/em\u003ein controlling inflammation and bacterial clearance during pneumococcal meningitis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePgm1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;deficiency disrupts macrophage metabolism and immune responses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Because \u003cem\u003ePGM1\u003c/em\u003e is linked with rs12081070 in macrophages, a key immune cell type contributing to pathogenesis of pneumococcal meningitis, and has previously been shown to regulate inflammatory responses in macrophages, we investigated the role of \u003cem\u003ePgm1\u003c/em\u003e in macrophage immune function, using BMDMs from Pgm1\u003csup\u003eM-KO\u003c/sup\u003e and genotype controls. Upon \u003cem\u003eex vivo\u003c/em\u003e stimulation with \u003cem\u003eS. pneumoniae\u003c/em\u003e\u0026nbsp; or LPS, \u0026nbsp;Pgm1\u003csup\u003eM-KO\u003c/sup\u003e\u0026nbsp; BMDMs exhibited elevated IL-6 (Fig. 3a; p = 0.003) and NO production following 24-hour stimulation with \u003cem\u003eS. pneumoniae\u003c/em\u003e (Fig. 3b; p = 0.012). Gene expression of proinflammatory cytokines remained unchanged (Additional File 1. Figure 5). Phagocytic activity, assessed by uptake of pHrodo\u0026trade;-labeled \u003cem\u003eEscherichia coli\u003c/em\u003e bioparticles, showed no significant differences between Pgm1\u003csup\u003eM-KO\u003c/sup\u003e and control BMDMs (Fig 3c; p = 0.61), indicating that \u003cem\u003ePgm1\u003c/em\u003e deficiency primarily amplifies the inflammatory response without affecting phagocytosis.\u003c/p\u003e\n\u003cp\u003eRNA sequencing of macrophages after 4-hours of \u003cem\u003eS. pneumoniae\u003c/em\u003e stimulation revealed three differentially expressed genes in Pgm1\u003csup\u003eM-KO\u003c/sup\u003e macrophages compared to control macrophages, consisting of \u003cem\u003ePgm1\u003c/em\u003e and \u003cem\u003eLyz2\u0026nbsp;\u003c/em\u003e(under whose promoter the LysMcre allele is located) and \u003cem\u003eErdr1\u003c/em\u003e (Fig. 4a). qPCR analysis confirmed that \u003cem\u003eErdr1\u003c/em\u003e, a gene involved in haemoglobin synthesis, was downregulated in both unstimulated and stimulated Pgm1\u003csup\u003eM-KO\u003c/sup\u003e macrophages (Fig. 4b). Most gene expression changes upon pneumococcal stimulation were shared between genotypes (Fig. 4c and 4d). Yet, Pgm1\u003csup\u003eM-KO\u003c/sup\u003e macrophages uniquely upregulated 172 genes associated with pathways linked to granulocyte migration and immune activation while 246 uniquely downregulated genes included those involved in DNA repair processes (Fig. 4e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePGM1 is a highly conserved metabolic enzyme that catalyses the interconversion of glucose 1- to glucose 6-phosphate, playing a pivotal role in energy homeostasis, glycogen storage and glycosylation (26). In dermal fibroblasts from \u003cem\u003ePGM1\u003c/em\u003e-deficient patients, reduction of UDP-glucose (UDP-Glc) and UDP-galactose (UDP-Gal) has been \u0026nbsp;identified as the cause of defective glycosylation, leading to morbidity (26). To assess the impact of \u003cem\u003ePgm1\u003c/em\u003e knockdown on murine macrophages, we first measured the levels of nucleotide sugars, including UDP-Glc and UDP-Gal, under both unstimulated and stimulated conditions (Additional file 2, sheet 1-2). Our analysis revealed that \u003cem\u003ePgm1\u003c/em\u003e deficiency does not affect nucleotide sugar levels in macrophages, suggesting a cell type-specific mechanism of metabolic regulation (Fig. 5a and 5b). Further analysis of phosphate sugars revealed an accumulation of glucose 1-phosphate and mannose 1-phosphate in \u003cem\u003ePgm1\u003c/em\u003e-knockdown macrophages (Fig. 5c, Additional file 2, sheet 3-4). The effect on glucose 1-phosphate was most pronounced following LPS stimulation, with levels increasing 9.3-fold in knockdown macrophages (Fig. 5c; 0.016 vs. 0.0017 normArea, adj. p = 0.009). The accumulation of glucose 1-phosphate is likely due to impaired conversion to glucose 6-phosphate, a reaction predominantly catalysed by PGM1, and could potentially compromise both glycolysis and oxidative phosphorylation. We therefore, assessed the role of \u003cem\u003ePgm1\u0026nbsp;\u003c/em\u003ein energy metabolism, a process that can influence macrophage polarization and inflammatory responses (22), using real-time extracellular flux analysis. Following LPS stimulation, knockdown of \u003cem\u003ePgm1\u003c/em\u003e impaired glycolysis and mitochondrial respiration (Fig. 5d), reduced basal respiration (Fig. 5e; normalized oxygen consumption rate [nOCR] of 3.1 vs 4.0 pmol/min, p = 0.01), reduced ATP production (Fig. 5e; nOCR of 2.1 vs 2.8 pmol/min, p = 0.03) and resulted in a decreased proton leak (Fig. 5e; nOCR of 0.9 vs 1.2 pmol/min, p = 0.002). On the glycolytic side, \u003cem\u003ePgm1\u003c/em\u003e knockout cells showed decreased glycolysis (Fig. 5e; normalized extracellular acidification rate [nECAR] of 1.3 vs 1.7 pmol/min, p = 0.007) and a lower maximum glycolytic capacity (Fig. 5e; nECAR of 2.0 vs 2.6 pmol/min, p = 0.02). Similar trends were observed after pneumococcal stimulation, suggesting that \u003cem\u003ePgm1\u003c/em\u003e knockdown impairs both respiratory and glycolytic metabolism, leading to an altered inflammatory response. Collectively, these findings indicate that \u003cem\u003ePgm1\u003c/em\u003e deficiency in macrophages leads to heightened IL-6 and NO production, altered gene expression profiles, and impaired metabolic functions, underscoring the enzyme\u0026rsquo;s role in modulating inflammatory responses and energy metabolism in macrophages.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings establish \u003cem\u003ePgm1\u003c/em\u003e as a regulator of macrophage immune responses via glucose metabolism in pneumococcal meningitis.\u0026nbsp;SNP rs12081070 was associated with heightened inflammatory responses, marked by increased production of pro-inflammatory cytokines upon \u003cem\u003eS. pneumoniae\u003c/em\u003e stimulation. Chromatin interaction data linked rs12081070 with \u003cem\u003ePGM1\u003c/em\u003e, though its effect on PGM1 expression remains indirect. PGM1 is a key metabolic enzyme involved in glucose homeostasis and protein glycosylation. Myeloid-specific \u003cem\u003ePgm1\u003c/em\u003e knockdown led to exaggerated inflammation and increased bacterial burden, resulting in increased brain damage in our murine model of pneumococcal meningitis. \u003cem\u003ePgm1\u003c/em\u003e-deficient macrophages exhibited an increased inflammatory phenotype, marked by elevated IL-6 and nitric oxide levels, withdisrupted glycolysis and mitochondrial respiration, supporting its role in linking immune cell metabolism with inflammatory responses. The enhanced inflammatory phenotype we observed is in contrast with a previous study reporting decreased gene expression of pro-inflammatory cytokines in\u003cem\u003e\u0026nbsp;Pgm1\u003c/em\u003e knockdown BMDMs (9). This discrepancy may reflect the stronger (100 ng/mL LPS and 20 ng/mL IFNγ) and prolonged (24 hours) stimulation of BMDMs, inducing a chronic hyperinflammatory phenotype rather than our focus on the role of \u003cem\u003ePgm1\u003c/em\u003e during the early immune response in macrophages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ability of \u003cem\u003ePgm1\u0026nbsp;\u003c/em\u003eto influence glycolysis and mitochondrial respiration in macrophages is consistent with previous findings in murine \u003cem\u003ePgm1\u003c/em\u003e-knockout myocytes (27). These metabolic changes might well underlie the effects of \u003cem\u003ePgm1\u003c/em\u003e knockout on macrophage polarization and function. Inflammatory macrophages rely primarily on glycolysis, while alternatively activated macrophages depend more on mitochondrial respiration (28). Glycogen serves as an energy reservoir for both macrophage subsets, requiring its breakdown into glucose-1-phosphate, which is then converted into glucose-6-phosphate by PGM1 to fuel glycolysis and respiration (29). We observed elevated glucose 1-phosphate levels in \u003cem\u003ePgm1\u003c/em\u003e-knockdown macrophages, suggesting a metabolic bottleneck at the glucose 1-phosphate to glucose 6-phosphate conversion step. It is likely that this bottleneck restricts flux through glycolysis and downstream mitochondrial respiration, resulting in metabolic stress, which can result in enhanced inflammatory signalling in macrophages (30, 31). Furthermore, accumulation of phosphate sugars like glucose 1-phosphate, might in itself be toxic and contribute to macrophage dysfunction (32, 33). Together, these findings indicate that \u003cem\u003ePgm1\u003c/em\u003e deficiency disrupts glycogen utilization, preventing macrophages from efficiently mobilizing stored glucose leading to metabolic stress, and in turn, dysregulated innate immune responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChanges in macrophage function and inflammatory responses may also be linked to altered glycosylation (34). In PGM1-deficient patients, symptoms have been attributed to reduced levels of the nucleotide sugars UDP-glc and UDP-gal, leading to defective protein N-glycosylation, for which galactose supplementation is beneficial (8). While it is well-documented that PGM1 deficiency leads to abnormal glycosylation in serum transferrin and fibroblasts (18, 26, 35), its impact on immune cells such as macrophages remains unexplored. Although we did not detect changes in nucleotide sugar levels , mannose 1-phosphate, an important intermediate in the N/O-glycosylation pathway (36), was strongly increased in \u003cem\u003ePgm1\u003c/em\u003e-deficient macrophages. Therefore, even though we did not directly measure alterations in glycosylation, aberrant glycosylation might contribute to the observed changes in macrophage function.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile our study provides important insights, certain limitations should be acknowledged. First, while we identified a chromatin interaction between rs12081070 and \u003cem\u003ePGM1\u003c/em\u003e, the association remains indirect. We did not observe significant changes in \u003cem\u003ePGM1\u003c/em\u003e expression levels among the different genotypes (Additional File 1. Figure. 2), suggesting that rs12081070 may regulate PGM1 function through alternative mechanisms, such as post-transcriptional modifications or chromatin remodelling. Future studies are needed to clarify how this SNP influences PGM1-related pathways in immune cells.\u003c/p\u003e\n\u003cp\u003eSecond, potential selection bias may have influenced our findings, as patients enrolled in our cohort may not fully represent the broader population of individuals with pneumococcal meningitis. Only patients who underwent lumbar puncture could be included in our study. Patients with space-occupying lesions on cranial CT do not undergo lumbar puncture, and patients with meningitis and a florid rash or septic shock also may not undergo lumbar puncture initially. Thus, an unknown number of such patients were excluded from the cohort, which may have influenced the mortality rate and the impact of rs12081070.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThird, while our study focuses on the role of \u003cem\u003ePgm1\u003c/em\u003e in macrophages, the LysM-Cre line also affects neutrophils, which play an important role in the host immune response to pneumococcal meningitis. Consequently, \u003cem\u003ePgm1\u003c/em\u003e deletion in neutrophils is likely to have contributed to the \u003cem\u003ein vivo\u003c/em\u003e outcomes observed in the experimental model. Further research is necessary to investigate the role of \u003cem\u003ePgm1\u003c/em\u003e in neutrophils. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourth, RNA sequencing analysis of murine macrophages following \u003cem\u003ePgm1\u003c/em\u003e knockdown revealed only minor changes in gene expression. This suggests that PGM1 primarily exerts post-transcriptional effects rather than acting as a transcriptional regulator. Alternatively, the minimal impact on gene expression may be attributed to our focus on early immune responses (4 hours post stimulation), whereas \u003cem\u003ePgm1\u003c/em\u003e may exert more pronounced effects at later stages of infection or during chronic inflammation. Among the genes that were differentially expressed, \u003cem\u003eErdr1\u0026nbsp;\u003c/em\u003eemerged as a potential modulator of the early inflammatory response. \u003cem\u003eErdr1\u003c/em\u003e is highly conserved between humans and mice and has been primarily studied for its anti-inflammatory effects in skin inflammatory diseases (37). It encodes a stress-induced autocrine factor that inhibits immune cell migration and infiltration (38), findings that align with our data, which showed enrichment of migration and chemotaxis pathways in \u003cem\u003ePgm1\u003c/em\u003e-deficient macrophages following pneumococcal stimulation. ln addition, \u003cem\u003eErdr1\u003c/em\u003e has been implicated in macrophage polarization and IL-1β production (39), which may contribute to the heightened proinflammatory phenotype observed in Pgm1\u003csup\u003eM-KO\u0026nbsp;\u003c/sup\u003emacrophages.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, our findings highlight PGM1 as a metabolic regulator in macrophage-mediated immunity. The disruption of \u003cem\u003ePgm1\u003c/em\u003e-dependent metabolic processes appears to drive dysregulated immune activation and promotes an inflammatory phenotype in macrophages. Therefore, targeting PGM1 activity or its downstream metabolic pathways may represent a promising therapeutic strategy for modulating macrophage polarization, immune responses and to improve outcome in pneumococcal meningitis and other bacterial infections.\u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe role of \u003cem\u003ePGM1\u003c/em\u003e in immune regulation has broader implications beyond pneumococcal meningitis and infectious diseases, offering potential therapeutic insights into other diseases where immune cell metabolism is dysregulated. Conditions like rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis are characterized by immune cells with enhanced metabolic activity, leading to increased cytokine production, chronic inflammation and increased tissue damage (40-42). Targeting metabolic enzymes such as PGM1 in these contexts could help modulate inflammation and improve outcomes by recalibrating the immune response. This approach is also applicable in acute conditions like sepsis and acute respiratory distress syndrome (ARDS), where hypermetabolic immune cells drive excessive inflammation, contributing to tissue damage (43).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSNP:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eSingle nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMAF:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eMinor allele frequency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eOdds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePGM1:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ePhosphoglucomutase 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eS. pneumoniae:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGWAS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eGenome-wide association study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCSF:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eCerebrospinal fluid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGOS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eGlasgow outcome scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEDTA:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eEthylenediaminetetraacetic acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFPM:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eFragments per million\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(RT-q)PCR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eReverse transcription quantitative polymerase chain rection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePBMCs:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ePeripheral blood mononuclear cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e(D)PBS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eDulbecco’s phosphate-buffered saline\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUV:\u003c/em\u003e\u003c/strong\u003e Ultraviolet\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRPMI:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eRoswell Park Memorial Institute\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLPS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eLipopolysaccharide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMOI:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eMultiplicity of infection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHEP:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eHumane endpoint\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHpi:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eHours post inoculation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCFU:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eColony forming units\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHE:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eHaematoxylin-eosin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBMDMs:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eBone marrow-derived macrophages\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDMEM:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eDulbecco’s Modified Eagle Medium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFCS:\u003c/em\u003e\u003c/strong\u003e foetal calf serum\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLCM:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eL929-conditioned medium\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIFN:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eInterferon\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNO:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNitric oxide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCCL3:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eC-C motif ligand 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIL:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eInterleukin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTNF:\u003c/em\u003e\u003c/strong\u003e Tumour necrosis factor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCXCL:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eC-X-C motif ligand\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFCCP:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eCarbonylcyanide-p-trifluoromethoxyphenylhydrazone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAA:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eAntimycin A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUHPLC:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eUltra-high perfomance liquid chromatography\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTBA:\u003c/em\u003e\u003c/strong\u003e Tributylamine\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMRM:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eMultiple-reaction monitoring\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIQR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eInterquartile range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCI:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eConfidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOCR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eOxygen consumption rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eECAR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eExtracellular acidification rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eErdr1:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eErythroid differentiation regulator 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUDP-Glc:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eUDP-glucose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUDP-Gal:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eUDP-galactose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003enOCR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNormalized oxygen consumption rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003enECAR:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eNormalized extracellular acidification rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eARDS:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eAcute respiratory distress syndrome\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStudy approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients or their legal representatives gave written informed consent for participation. The Medical Ethics Committee of the Amsterdam University Medical Center, location AMC, University of Amsterdam, the Netherlands, approved the human studies (approval number NL43784.018.13).\u003c/p\u003e\n\u003cp\u003eAnimal experiments were authorized by the Centrale Commissie Dierproeven Netherlands (document no. AVD11800202013797) and the local Animal Wellfare Body (AWB;document no. NEU20-13797-1-04 and -05), and were conducted according to institutional guidelines\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA sequencing data generated during the current study is available under accession number …. (in progress of submitting to Genome Sequence Archive (GSA)). The datasets supporting metabolomic data in this article are included within the article and its additional files. Other data is available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1. (.pdf). Supplementary materials. Supporting figures and tables.\u003c/p\u003e\n\u003cp\u003eAdditional file 2. (.xls). Supporting metabolomics data. TBA and TEAA raw and normalized data and transitions list.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO)-Vici-Grant\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(918.19.627, to DvdB).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eRK: data curation, formal analysis, investigation, methodology, validation, visualisation, writing – original draft\u003c/p\u003e\n\u003cp\u003eDB: data curation, formal analysis, investigation, methodology, validation, visualisation, writing – original draft\u003c/p\u003e\n\u003cp\u003eFC: investigation, methodology, validation, writing – review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eMvR: investigation, writing -review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eVJ: investigation\u003c/p\u003e\n\u003cp\u003eBF: investigation, methodology\u003c/p\u003e\n\u003cp\u003eBB:\u0026nbsp;resources, writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eKL: resources, writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eMB: investigation\u003c/p\u003e\n\u003cp\u003eDJL: resources, writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eMCB: conceptualisation, project administration, resources, supervision, writing– review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eDvB: conceptualisation, funding acquisition, project administration, resources, supervision, writing – review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBijlsma MW, Brouwer MC, Kasanmoentalib ES, Kloek AT, Lucas MJ, Tanck MW, et al. 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Cells. 2023;12(13).\u003c/li\u003e\n\u003cli\u003eRahm M, Kwast H, Wessels H, Noga MJ, Lefeber DJ. Mixed-phase weak anion-exchange/reversed-phase LC-MS/MS for analysis of nucleotide sugars in human fibroblasts. Anal Bioanal Chem. 2024;416(15):3595–604.\u003c/li\u003e\n\u003cli\u003eKoning R, van Roon MA, Brouwer MC, van de Beek D. Adjunctive treatments for pneumococcal meningitis: a systematic review of experimental animal models. Brain Communications. 2024;6(3).\u003c/li\u003e\n\u003cli\u003eRadenkovic S, Bird MJ, Emmerzaal TL, Wong SY, Felgueira C, Stiers KM, et al. The Metabolic Map into the Pathomechanism and Treatment of PGM1-CDG. The American Journal of Human Genetics. 2019;104(5):835–46.\u003c/li\u003e\n\u003cli\u003eConte F, Ashikov A, Mijdam R, van de Ven EGP, van Scherpenzeel M, Veizaj R, et al. In Vitro Skeletal Muscle Model of PGM1 Deficiency Reveals Altered Energy Homeostasis. Int J Mol Sci. 2023;24(9).\u003c/li\u003e\n\u003cli\u003eVan den Bossche J, O’Neill LA, Menon D. 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Liposome-encapsulated mannose-1-phosphate therapy improves global N-glycosylation in different congenital disorders of glycosylation. Mol Genet Metab. 2024;142(2):108487.\u003c/li\u003e\n\u003cli\u003eKim M, Kim K-E, Jung HY, Jo H, Jeong S-w, Lee J, et al. Recombinant erythroid differentiation regulator 1 inhibits both inflammation and angiogenesis in a mouse model of rosacea. Experimental Dermatology. 2015;24(9):680–5.\u003c/li\u003e\n\u003cli\u003eLee BC, Song J, Lee A, Cho D, Kim TS. Erythroid differentiation regulator 1 promotes wound healing by inducing the production of C‑C motif chemokine ligand 2 via the activation of MAP kinases in vitro and in vivo. Int J Mol Med. 2020;46(6):2185–93.\u003c/li\u003e\n\u003cli\u003eWang Y. Erdr1 Drives Macrophage Programming via Dynamic Interplay with YAP1 and Mid1. Immunohorizons. 2024;8(2):198–213.\u003c/li\u003e\n\u003cli\u003eMorel L. Immunometabolism in systemic lupus erythematosus. 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Nature Reviews Immunology. 2020;20(6):355–62.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline and clinical characteristics of pneumococcal meningitis patients per rs12081070 genotype\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u0026nbsp;\u003cbr\u003eN = 367\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003cbr\u003eN = 616\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u0026nbsp;\u003cbr\u003eN = 217\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eAge \u0026ndash; years\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e61 (49-68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e61 (51-69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e62 (54-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e191/367 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e310/616 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e112/217 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eImmunocompromised state\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e90/367 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e158/616 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e65/217 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eSymptoms on admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Temperature - \u0026deg;C\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e39.0 (38.0-39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e39.0 (38.0-39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e39.0 (38.0-39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; GCS score\u003csup\u003eD\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e11 (9-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e11 (9-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e10 (9-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eIndices of CSF inflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Leukocytes - cells/\u0026micro;L\u003csup\u003eE\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e2,811 (818-6,791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e2,730 (667-7,043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3,000 (708-7,201)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CSF protein - g/L\u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e4.09 (2.45-6.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e4.24 (2.50-6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3.86 (2.36-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; CSF:blood glucose ratio\u003csup\u003eG\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e0.03 (0.01-0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e0.03 (0.01-0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e0.04 (0.01-0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eClinical course\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cerebrovascular accident\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e32/350 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e72/585 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e22/211 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; GOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e23/367 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e52/616 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e12/217 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e0/367 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1/616 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1/217 \u0026nbsp;(0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e14/367 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e30/616 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e12/217 \u0026nbsp;(5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e60/367 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e109/616 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e60/217 \u0026nbsp;(28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e270/367 (74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e424/616 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e132/217 \u0026nbsp;(61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Unfavourable outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e97/367 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e192/616 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e85/217 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cranial nerve palsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e12/305 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e28/508 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e15/184 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6531%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Focal cerebral deficits\u003csup\u003eH\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e23/309 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e39/515 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e24/189 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.18367%;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData presented as n/N (%) or median (IQR). Group differences were tested with a Chi-squared test for categorical variables and Kruskal-Wallis rank sum test for continuous variables. Abbreviations: CSF, cerebrospinal fluid; GCS, Glasgow Coma Scale; GOS, Glasgow Outcome Scale. \u003csup\u003eA\u003c/sup\u003eAge is known for all patients. \u003csup\u003eB\u003c/sup\u003eImmunocompromised state is defined as active cancer, diabetes, alcoholism, immunosuppressive treatment, splenectomy or HIV. \u003csup\u003eC\u003c/sup\u003eTemperature is known in 359 episodes with GG genotype, 603 with AG and 213 with AA. \u003csup\u003eD\u003c/sup\u003eGCS is known in 366 episodes with GG genotype, 616 with AG and 216 with AA. \u003csup\u003eE\u003c/sup\u003eLeukocyte count in CSF is known in 360 episodes with GG genotype, 599 with AG and 214 with AA. \u003csup\u003eF\u003c/sup\u003eProtein concentration in CSF is known in 354 episodes with GG genotype, 582 with AG and 210 with AA. \u003csup\u003eG\u003c/sup\u003eCSF:blood glucose ratio is known in 340 episodes with GG genotype, \u0026nbsp;575 with AG and 204 with AA. \u003csup\u003eH\u003c/sup\u003eFocal cerebral deficits are defined as aphasia or mono-or hemiparesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Multivariate logistic regression of rs12081070 genotype for unfavourable outcome in pneumococcal meningitis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFavorable outcome\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnfavorable outcome\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eN = 374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable odds ratio for unfavorable outcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value of multivariable analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ers12081070 genotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;GG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e270/826 (33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e97/374 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e424/826 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e192/374 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.27 (0.93-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e132/826 (16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e85/374 (23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.84 (1.25-2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;16-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e107/826 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e22/374 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;40-70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e598/826 (72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e235/374 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.45 (0.87-2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt; 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e121/826 (15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e117/374 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e3.37 (1.91-5.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eImmunocompromised\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e197/826 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e116/374 (31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.24 (0.93-1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCranial nerve palsy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e40/723 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e37/305 (12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e2.48 (1.44-4.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eHeart rate (b/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e440/791 (56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e189/361 (52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.03 (0.78-1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e351/791 (44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e172/361 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eGCS score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e11 (9-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e10 (8-12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.88 (0.84-0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e277/806 (34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e73/368 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e215/806 (27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e75/368 (20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.16 (0.78-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e314/806 (39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e220/368 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e2.22 (1.57-3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eThrombocyte count (cells/\u0026micro;l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e149/798 (19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e112/354 (32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.48 (1.08-2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;150-450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e628/798 (79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e235/354 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e21/798 (3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e7/354 (2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.78 (0.3-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003eCSF WBC count (cells/\u0026micro;l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e47 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e66 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e2.84 (1.78-4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;100-999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e144 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e91 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.7 (1.2-2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1000-9999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e478 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e155 (43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gt;10 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e141 (17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e51 (14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.07 (0.72-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25%;\"\u003e\n \u003cp\u003ePositive blood culture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e591/727 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e286/327 (87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e1.25 (0.83-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.75%;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData presented as n/N (%), median (IQR) or odds ratio (95% confidence interval). The multivariate analysis used an imputed dataset with 50 rounds of imputation across 10 iterations. All variables in the table were entered in the multivariate regression model simultaneously. Abbreviations: CRP, C-reactive protein; CSF, cerebrospinal fluid; GCS, Glasgow Coma Scale; OR, odds ratio; WBC, white blood cell. \u003csup\u003eA\u003c/sup\u003eGCS is known in 366 episodes with GG genotype, 616 with AG and 216 with AA.\u003c/p\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":"Pneumococcal meningitis, neuroinflammation, macrophages, immune metabolism.","lastPublishedDoi":"10.21203/rs.3.rs-8319354/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8319354/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePneumococcal meningitis remains a life-threatening disease despite antibiotic and anti-inflammatory therapy, with unfavourable outcome driven by excessive inflammation and impaired bacterial clearance. Host genetic variation influences disease outcome in pneumococcal meningitis, though the underlaying mechanisms remain unclear. In a genome-wide association study, we identified the single nucleotide polymorphism (SNP) rs12081070 as a risk factor for unfavourable outcome in bacterial meningitis (Minor allele frequency (MAF)\u0026thinsp;=\u0026thinsp;0.43; odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.63; p\u0026thinsp;=\u0026thinsp;2.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e). Chromatin conformation capture analysis linked this variant to phosphoglucomutase 1 (\u003cem\u003ePGM1)\u003c/em\u003e, which encodes a key enzyme in glucose metabolism and glycosylation in immune cells. In a nationwide cohort study of 1200 patients with bacterial meningitis, we show that individuals carrying the rs12081070 risk allele exhibited elevated proinflammatory cytokine responses to \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (\u003cem\u003eS. pneumoniae\u003c/em\u003e) and an increased risk of unfavourable functional outcome. Using a murine model of pneumococcal meningitis, we show that myeloid-specific \u003cem\u003ePgm1\u003c/em\u003e deletion amplifies inflammation, impairs bacterial clearance, and exacerbates brain injury. Mechanistically, \u003cem\u003ePgm1\u003c/em\u003e-deficient macrophages exhibit disrupted glycolysis and mitochondrial respiration, and enhanced cytokine and nitrogen oxygen production. Our findings identify \u003cem\u003ePgm1\u003c/em\u003e as a regulator of macrophage metabolism and inflammation in pneumococcal meningitis, highlighting a potential target for immune-modulation in bacterial disease.\u003c/p\u003e","manuscriptTitle":"Macrophage metabolic regulation by phosphoglucomutase 1 shapes the host immune response in pneumococcal meningitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:16:07","doi":"10.21203/rs.3.rs-8319354/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":"0f96a937-621f-4182-9f00-40074982c19b","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T13:17:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 09:16:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8319354","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8319354","identity":"rs-8319354","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}