Therapeutic inhibition of glycolysis preferentially targets pathogenic monocyte subsets and attenuates CNS inflammation in flavivirus encephalitis

Therapeutic inhibition of glycolysis preferentially targets pathogenic monocyte subsets and attenuates CNS inflammation in flavivirus encephalitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Therapeutic inhibition of glycolysis preferentially targets pathogenic monocyte subsets and attenuates CNS inflammation in flavivirus encephalitis Nicholas King, Claire Wishart, Alanna Spiteri, Jian Tan, Laurence Macia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4018869/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 Infiltrating monocytes play a dual role in central nervous system (CNS) diseases, both driving and attenuating inflammation. However, it is unclear how metabolic pathways preferentially fuel protective or pathogenic processes and whether these can be therapeutically targeted to enhance or inhibit these opposing functions. Here, we employed single-cell RNA-sequencing and metabolic protein flow analysis of brain and bone marrow (BM) to map the metabolic signatures of monocyte-derived cells (MCs) to their functions during lethal West Nile virus encephalitis. Using trajectory analysis, we showed progression of BM monocytes through 3 metabolic profiles before their migration to the brain where they differentiated into metabolically distinct MC populations. These included a single pro-inflammatory HIF1-α MC cluster that diverged into two disparate populations: an inducible nitric oxide synthase-positive (iNOS + ) M1-like MC, with high glycolysis and amino acid metabolic scores, and a glycolytically quiescent, MHC-II + antigen-presenting MC. Daily in vivo glycolysis inhibition with 2-deoxy-D-glucose significantly reduced CNS leukocyte numbers, reducing neuroinflammation and disease signs without increasing viral load. Reduced leukocyte numbers were not due to decreased myelopoiesis, but a preferential decrease in iNOS + , compared to antigen-presenting MC, highlighting different glycolytic dependencies between these subsets. Importantly, HIF1-a was independent of glycolysis, enabling continued antigen-presenting MC differentiation, while glycolysis inhibition did not impair generation of an effective antiviral response by cervical node T cells. Together, this integrative approach unveils the tight coupling of MC function and metabolism in viral CNS disease, highlighting novel metabolic therapeutic intervention points, potentially with anti-viral therapy, during severe or uncontrolled inflammation. Biological sciences/Immunology/Innate immune cells/Monocytes and macrophages Biological sciences/Immunology/Inflammation/Acute inflammation Biological sciences/Immunology/Infectious diseases/Viral infection Biological sciences/Immunology/Neuroimmunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Monocytes play a pivotal role in both inflammation and maintenance of tissue homeostasis. In the central nervous system (CNS), they replenish a small proportion of tissue-resident macrophages in the dura mater and choroid plexus [1, 2], and while typically constrained by the blood-brain barrier, readily infiltrate the CNS during inflammation [3, 4]. Monocyte-derived effector cells (MCs), such as dendritic cells and macrophages, play crucial roles in CNS disorders. Their diverse functions include innate host defence, (e,g., phagocytosis of pathogens and tissue debris), initiation of adaptive defences (antigen presentation) and tissue repair. However, the precise role of cellular metabolism in shaping these functions in the context of CNS disease remains poorly defined. MC metabolism tends to be dichotomized simplistically into inflammatory (M1) and regulatory (M2) profiles, which are predominantly studied in vitro [5]. In this classification, M2 MCs have regulatory functions that rely on an oxygen-dependent pathway supporting slower, but more efficient, ATP generation, while M1 MCs exert pro-inflammatory functions and rely on glycolysis to meet their high energy demands [6]. Various M1 stimuli, such as interferon (IFN)-γ, lipopolysaccharide (LPS), tumour necrosis factor, viruses, and granulocyte-macrophage colony-stimulating factor, are characterized by their proinflammatory effects, yet they induce significantly different phenotypes and metabolic responses. This suggests a more complex and nuanced metabolic framework underpinning inflammatory functions. Supporting this, disease environments comprised of diverse inflammatory stimuli give rise to metabolic states tailored to cellular functions [7, 8]. Such functions include nitric oxide (NO) production, type I interferon responses [9-11], cytokine synthesis [12, 13], antigen presentation [14], phagocytosis [13, 15, 16], and migration into inflamed tissues [17, 18]. West Nile virus (WNV) is a mosquito-borne, neurotropic flavivirus and one of the most important causative agents of human viral encephalitis worldwide [19]. In its neuroinvasive phase, WNV can cause severe encephalitis, the pathogenesis of which is driven by infiltration of M1-like MCs into the CNS. Murine models clearly show that monocytes recruited from the bone marrow (BM) migrate to the brain in a CC motif chemokine ligand 2 (CCL2)-dependent manner, diapedesing into the CNS via Ly6C and activated very late antigen-4 (VLA-4) expressed on the monocyte surface. Once in the CNS they mediate immune pathology by the sustained production of NO [20-22]. Strategies that block the entry of inflammatory monocytes into the brain, such as by CCL2, VLA-4 or Ly6C antibody blockade or immune modifying particles, or that attenuate their inflammatory response, such as inhibition of NO production using aminoguanidine hemisulphate, have been shown to improve survival of infected mice markedly without affecting viral load [20-24], thus emphasizing the role of MCs in causing the inflammatory damage observed in WNV infection. Notably, these cells also adopt an antigen-presenting phenotype [24] and may indirectly aid in viral clearance by promoting an effective T cell response. However, it is unclear whether these differential functions are reflected in and effected by distinct metabolic profiles. Numerous tools exist for the easy assessment of metabolism ex vivo. However, traditional methods that measure metabolic respiration in bulk or across whole populations fail to capture the increasingly recognized metabolic diversity of MC populations in the context of disease. To address this, recent technological advancements have enabled single-cell analysis of metabolism through two primary methods. The first uses single-cell RNA sequencing (scRNA-seq) for broad analysis of gene expression related to metabolic changes. The second combines cytometric techniques with the detection of key metabolic enzymes, transporters, and transcription factors as a proxy measurement of the metabolic profile [25-28]. An additional cytometric approach, SCENITH, combines metabolic inhibitors and protein synthesis quantification to assess the metabolic dependencies of immune cells at the single-cell level [29]. The latter approaches overcome the limitations of inferring function from gene expression alone. Nevertheless, comprehensive integration of gene and protein data at the single-cell level is ultimately required to fully elucidate and map the cellular metabolic processes that lead to disease resolution or exacerbation. In this study, we identify tissue- and time-specific metabolic changes in response to severe flaviviral infection in the CNS, using both scRNA-seq and flow-based metabolic analysis. In particular, we show that MCs with a high glycolytic phenotype exhibited an inflammatory M1-like phenotype that distinguished them from antigen-presenting and other MC populations. Targeting glycolysis as an anti-inflammatory strategy preferentially affected M1-like cells while sparing antigen-presenting populations, resulting in amelioration of disease without increasing viral burden. This selective targeting emphasizes the potential of metabolic interventions in managing the intricate temporal pathogenesis of complex CNS disorders. Materials and Methods WNV infection Female 9 to 10-week-old C57BL/6 mice obtained from Animal BioResources (NSW, Australia) or the Animal Resource Centre (WA, Australia) were anesthetized with isoflurane and infected intranasally with a LD100 dose (3x10 4 plaque-forming units) of WNV (Sarafend), as described previously [30]. All experiments involving mice were approved by the University of Sydney Animal Ethics Committee (Protocol 1696). Animal treatments 2-deoxy-d-glucose (2-DG) treatment 2-DG (Sigma-Aldrich, USA) was injected intraperitoneally once daily from 4-7 days post-infection (dpi) at a dose of 2g/kg body weight in 200 μL of sterile PBS. In some experiments, 2-DG (2g/kg) was administered once at 7 dpi at either 4.5 hours or 5.5 hours before euthanasia. Detection of proliferating cells with BrdU Bromodeoxyuridine (BrdU) (Sigma-Aldrich, USA) was injected intraperitoneally 3 hours before sacrifice at a dose of 1 mg prepared in 200 μL of sterile PBS. Tracking recently infiltrating cells into the CNS with PKH26 PKH26 cell linker was combined with diluent C (Sigma-Aldrich, USA) at a 10-fold higher concentration than the manufacturer’s recommendation and injected intravenously via the lateral tail vein 2 hrs before euthanasia, as previously described [31]. Quantification of viral titre using a plaque assay Baby Hamster Kidney (BHK) fibroblasts were used to perform a plaque assay, as previously described [30]. Briefly, BHK cells were inoculated with brain tissue homogenates in a series of ten-fold dilutions for 1 hr, then subsequently overlaid with agarose. After a 3-day incubation, cells were fixed with 10% formalin (Sigma-Aldrich, USA), then stained with 1% crystal violet solution. Viral load, expressed as plaque-forming units (PFU) per gram of brain tissue, was calculated by quantifying the number of visible plaques and accounting for inoculum volume and dilutions used. Tissue processing The brain, BM and cervical lymph nodes (LN) were collected from mice anesthetized with an intraperitoneal injection of avertin and perfused transcardially with PBS. Cells from the BM were isolated by flushing the femur with PBS using a 30-gauge needle. Red blood cells were then lysed using 1x Pharm Lyse Buffer (Invitrogen). Brains were digested enzymatically with DNase I (0.1 mg/mL, DN25, Sigma-Aldrich, USA) and collagenase type IV (1 mg/mL, C5138, Sigma-Aldrich, USA) using the gentleMACS dissociator (Miltenyi Biotec, DE). A 30/80% Percoll gradient was then used to isolate leukocytes from brain homogenates. Cervical lymph nodes were gently mashed through a 70 μm nylon mesh sieve using a syringe plunger. Live cells were counted with trypan blue (0.4%) on a hemocytometer. Cell culture T cell proliferation assay Five million LN cells were stained with 1.5 μM of Carboxyfluorescein succinimidyl ester (CFSE) (Life Technologies) in 1 mL of PBS, as previously described [32]. CFSE +‑ cells (5 x 10 5 cells/well with two technical replicates) were then cultured for 1 hr at 37°C and 5% CO 2 in media or 50 μL of WNV solution (1 PFU/cell) with agitation every 10 minutes. Media was composed of RPMI (Lonza Biosciences, USA) supplemented with 1 U of HEPES (Sigma-Aldrich, USA), 5% FCS, 0.1 M of b-mercaptoethanol (Gibco), and 1x penicillin streptomycin (Thermofisher Scientific). Unbound virus was then washed off and cells were cultured for a further 72 hours in media alone or media supplemented with 1 ug/mL of Concanavalin A (Sigma), as a positive control for T cell proliferation. BM stimulation BM cells (1.2 x 10 6 cells/well with two technical replicates) were stimulated with 100 ng/mL IFN-γ (Biolegend, USA) for four hours and 100 ng/mL of lipopolysaccharide (LPS) (Invitrogen) overnight in a non-adherent 96-well plate (Corning). In some experiments (Figure 3D, E), 10 mM of 2-DG (Sigma-Aldrich, USA) was added to BM cells 1 hour prior to and during the IFN-γ and LPS stimulations. Spectral flow cytometry Single-cell suspensions were stained with anti-CD16/32 (fluorescently conjugated or unconjugated, Biolegend, USA) and Zombie UV Fixable Viability kit (Biologend, USA) for 30 minutes on ice, washed twice, and subsequently stained with MitoSOX in PBS for 30 minutes at 37°C. Cells were then washed twice and stained with a cocktail of fluorescently-labelled surface-stain antibodies in FACS on ice for 30 minutes. Cells were washed twice and permeabilized with True-Nuclear 1x Fix Concentrate (Biolegend, USA) for one hour at room temperature prior to staining with an intracellular/intranuclear antibody cocktail for 1 hour on ice, washed twice, and then stained with secondary antibodies targeting ACAC and CPT1α for 1 hr on ice. The 5-Laser Aurora spectral cytometer (Cytek Biosciences, USA) was used to measure fluorescently tagged antibodies. Unstained controls for each specific condition were used as reference controls for spectral unmixing in each experiment. Acquired data was analysed using FlowJo (v10.8, BD Biosciences, USA). Quality control measures such as time, single cells, non-debris, and Live/Dead staining were applied to exclude debris, doublets, and dead cells. The FCS files were compensated and gated in FlowJo (BD Biosceicnes, USA). Monocytes (for BM samples) or MCs and microglia (for brain samples) were then exported and downsampled to a total of 3x10 5 cells (1.5x10 5 per organ). FCS files were formatted with the CyCombine package [33] and Arcsinh transformed (cofactor = 6000) before being converted into a Seurat object using a custom R script using Seurat (v4.0) [34]. Two independent experiments were then batched integrated using rPCA in Seurat using all markers as integration anchors. All subsequent analysis was performed on data as a Seurat object using the Seurat pipeline [34]. Uniform manifold approximation and projection (UMAP) and clustering analysis was performed using only metabolic features. Analysis of autofluorescence signatures on unstained samples was conducted using the OMIQ software from Dotmatics (www.omiq.aiwww.omiq.ai, www.dotmatics.comwww.dotmatics.com). Off-scale or debris events were excluded from analysis using scatter gating. Samples were arcsinh transformed (cofactor = 6000) and Opt-SNE was performed using the following OMIQ settings: maximum iterations = 1000, opt-sne End = 5000, perplexity = 30, Theta = 0.5, components = 2, verbosity = 25, and random seed values on 5x10 4 cells (non-debris and non-erythrocyte single cells). Data was exported and analysed for media fluorescent intensity (MFI) values in FlowJo (BD Biosciences, USA). Statistical analysis The FCS files were compensated and gated down to individual cell populations prior to exporting cell proportions and MFI in FlowJo (BD Biosciences, USA). Cell proportions and live cell counts were used to quantify cell numbers. MFIs from positive populations, as determined by the isotype or fluorescence minus one (FMO) control, were used to quantify protein expression of metabolic targets. Statistical analyses were carried out in GraphPad Prism (version 10.2.0 for Mac, GraphPad Software, Boston, Massachusetts USA, www.graphpad.com) and the statistical tests used are indicated in each figure legend. Single-cell RNA-sequencing Monocytes were isolated from WNV-infected murine brains and BM at the designated dpi. To ensure ample cells for sorting, tissues from two animals were combined per sample. Single cell suspensions underwent CD16/32 blocking and Zombie UV viability staining (Biolegend, USA) before incubation with a cocktail of fluorescently-conjugated surface stain antibodies [24]. Sorting was performed on a 10-laser Influx Cell Sorter (BD Bioscences, USA) using the FACS Diva Program (BD Biosciences, USA) followed by sample barcoding with a mouse multiplexing kit using anti-CD45 (BD Biosciences, USA), as previously described [24]. Cells were then pooled, stained for viability with Calcein AM and Draq7, and quantified using a BD Rhapsody Scanner. Quality control for cell sorting included metrics such as doublet rate and viability percentages. Cell capture was performed according to manufacturer’s instructions. Briefly, single-cell transcriptomics was conducted using the BD Rhapsody Express System, and lysed cells were processed for reverse transcription and exonuclease I treatment. Libraries were created using microbead-captured single cell transcriptomes as per the manufacturer’s protocol using the BD Rhapsody cDNA Kit (BD Biosciences, USA) and the BD Rhapsody Targeted mRNA Amplification Kit (BD Biosciences, USA). The BD Rhapsody Immune Response Panel (Cat. # 633753), consisting of 397 genes, and an additional 67 custom genes were used. For sequencing, libraries were quantified using a Qubit Fluorometer (Thermo Fisher Scientific) and KAPA Library Quantification Kit (Roche), adjusted to 2nM, and pooled in a mRNA:sample tag ratio of 12.5:1. Sequencing was performed on an Illumina NextSeq1000, with stringent QC metrics such as 93.28 %Q30 and 81.73 %PF achieved. The run yielded 131 million reads with a high loading efficiency (98.4%). Single-cell RNA-sequencing analysis SevenBridges (Seven Bridges Genomics Inc., USA) was used to process counts (ie., distribution-based error correction molecules per cell) from single-cell RNA-seq data. Seurat (v4.0) was used for pre-processing and removal of doublets/multiplets and cells with high mitochondrial reads (+3 median absolute deviations from the mean). After stringent quality control measures the final dataset contained 6,405 cells (Figure S1). Normalization and variance stabilization was then performed using sctransform() prior to UMAP and clustering. Cell types were annotated by sample tags and marker expression (Figure S1). MCs, separated from microglia and T cells, were re-clustered using 9 PCs at a resolution of 0.25. The R package Slingshot (v1.8) [35] was used to define computationally imputed pseudotime trajectories from brain and BM MCs/monocytes at 7 dpi. For module scores, genes for all functional and metabolic pathways were downloaded from the Mouse Genome Informatics database [36]. Genes associated with the negative regulation of the module of interest were manually filtered out prior to filtering genes available in the dataset. Module scores for a particular functional or metabolic module were derived by running the function AddModuleScore in Seurat, with 16 control genes and 24 bins. Results MCs adopt distinct metabolic and functional profiles in CNS infection Since BM-derived MCs infiltrating the CNS contribute significantly to immunopathology in WNV encephalitis, we sought to identify the specific metabolic pathways associated with the differentiation and development of these pathogenic responses. Monocyte-derived cells were sorted flow cytometrically from the brain at 5 and 7 days post-infection (dpi) and identified as Ly6G - , CD49 hi, P2RY12 lo , NK1.1 - , CD3e - , CD11b + , CD64 + and CX3CR1 + , capturing the entire population of Ly6C hi and Ly6C lo MCs at 5 and 7 dpi [37]. Mature monocytes from the BM at 7 dpi were identified as CD45.2 + , Ly6G - , CD48 hi , NK1.1 - , CD3e - , B220 - , CD11b + , CD117 - , CD115 hi , as previously described [24]. We then used a targeted scRNA-seq panel (n = 397 genes and 67 custom genes) to link the metabolic state and functional transcriptional profiles of monocyte subsets in acute viral infection. Clustering on scRNA-seq data from 3498 MCs and BM monocytes revealed four distinct states in the brain and three in the BM, respectively (Figure 1A). Of the brain subsets, we identified 1) an “antigen-presenting cell ( APC )” population highly expressing genes involved in antigen presentation, including H2-Aa , H2-Eb1 , H2-Ab1 , Cd74, and Cd86 (Figure 1B), 2) a “Microglia (Mg)-like MC” subset resembling microglia, due to its relative expression of microglia-specific markers Cd81 , Sparc , Hexb , and Tmem119 [37, 38] (Figure 1B), 3) a Hif1a- expressing population, and 4) a Nos2 -expressing population, which likely represented the nitric oxide (NO)-producing population causing inflammatory damage in this model (Figure 1B). The functional status of these cell clusters was supported by manually querying several M1 functional programs, such as antigen presentation , inflammatory response , and viral response against gene modules from the Mouse Genome database [39] (Figure 1C). A high module score represents the average expression of the genes in the module relative to a set number of randomly extracted control genes from the dataset. The APC MC cluster had a high antigen-presenting score and viral response score, suggesting this population may be involved in indirectly contributing to viral clearance (Figure 1C). By contrast, Hif1a + and Nos2 + MCs exhibited a higher inflammatory response score, supporting a potential inflammatory phenotype that contributes to inflammatory damage in WNV encephalitis [21] (Figure 1C). All BM monocyte clusters ( Mo1 - Mo3 ) displayed low M1 functional scores, supporting the notion that these cells are an undifferentiated monocyte state (Figure 1C). As metabolism is coupled with the phenotype and functionality of MCs, we next scored genes involved in several metabolic pathways known to be important in M1- or M2-like functions, including glycolysis, adenosine triphosphate (ATP) biosynthesis, fatty acid synthesis, electron transport chain (ETC), amino acid metabolism, and the tricarboxylic acid (TCA) cycle (Figure 1D). BM monocytes ( Mo1 , Mo2 , and Mo3 ) and Mg-like MC displayed higher expression of metabolic pathways related to homeostatic functions, including the TCA cycle, ATP biosynthesis, and ETC, but also displayed high amino acid metabolism scores (Figure 1D and 1E). Inflammatory Hif1a + and Nos2 + MCs displayed higher glycolysis and amino acid metabolism scores with low TCA cycle scores (Figure 1D). Expression of these metabolic pathways clustered with typical inflammatory functions, including the inflammatory response, reactive oxygen species production , and phagocytosis (Figure 1E), supporting the notion that these Hif1a + and Nos2 + cells adopt a typical M1-like phenotype in WNV encephalitis. On the other hand, APC MCs were evidently less reliant on glycolysis and amino acid metabolism pathways, but exhibited higher fatty acid synthesis scores (Figure 1D and 1E). This suggests that there exists wide metabolic heterogeneity in typical M1-like functions in CNS infection, including the inflammatory response and antigen presentation, which is likely obscured in bulk in vitro systems. To understand the differentiation pathway of BM monocytes into specific metabolic states in the CNS, we employed trajectory analysis. This showed that BM monocytes progressed through 3 different metabolic profiles before they migrated to the brain, where they underwent a transition towards either APC or Nos2 + MC populations in the brain at disease endpoint (Figure 1G). Both lineages passed through the Hif1a + MC population, implying that this population is a transitional state. The Mg-like MC population, however, did not align with these pathways, indicating that it is a unique MC population (Figure 1F). This distinctness of this 4 th population raises the question of contamination by microglial cells. However, we have established that monocytes circulating in the bloodstream can assume a microglial-like phenotype within the WNV-infected brain, a change attributable to prolonged interaction with the CNS milieu [37]. During their differentiation into brain MCs, BM monocytes consistently downregulated genes related to ATP synthesis, the TCA cycle, ETC and pentose phosphate pathway, while upregulating genes related to glycolysis (Supplementary Figure 2). This indicates a significant metabolic shift as MCs transition from the BM to the infected brain, possibly through a shared Hif1a + intermediate, before diverging into functionally distinct pathways towards antigen-presenting ( APC ) or NO-producing ( Nos2 + ) phenotypes. Application of MetFlow to CNS infection reveals distinct metabolic changes in CNS disease Given the limitations in existing tools to study metabolism by gene expression alone, we adapted MetFlow [27] to murine cells and used metabolic marker proteins with high relevance to myeloid cells during inflammation. This comprised 13 immune cell identification markers and 9 metabolic marker proteins, including rate-limiting enzymes, signalling molecules, and transcription factors that have roles in glycolysis, the tricarboxylic acid cycle, hypoxia-induced inflammation, amino acid transport, fatty acid synthesis and oxidation, the kynurenine pathway, NO production, and mitochondrial ROS production (Figure 2A, Table 1). To identify metabolic signatures independently of cell lineage and origin, we performed dimensionality reduction on whole brain and BM cell isolates and clustered on metabolic markers (Figure 2B). Resident microglia (Ly6G - , SSCA lo , NK1.1 - , CD3e - , B220 - , CD45 low-int , CX3CR1 + ), brain-infiltrating MCs (defined as Ly6G - , SSCA lo , NK1.1 - , CD3e - , B220 - , CD45 hi , CX3CR1 low ) and BM monocytes (defined as Ly6G - , SSCA lo , NK1.1 - , CD3e - , B220 - , CD11c lo , MHC-II lo , CD11b + , Ly6C hi/lo , CX3CR1 hi/lo ) clustered into 8 distinct metabolic states which varied in proportion across timepoints and were distinctly grouped by organ and lineage/differentiation status (Figure 2C-H), demonstrating a significant metabolic adaptation over the course of infection. Within the brain, four main MC populations were detected (clusters 2, 4, 5, & 6) (Figure 2F, H), while three predominant monocyte populations were found in the BM (Figure 2F). This distribution mirrors the population diversity revealed by single-cell RNA sequencing (Figure 1), corroborating our findings across both techniques. In the brain, cluster 0 was associated with microglia and was found in the highest proportions in the mock-infected brain (Figure 2F, 2G). By 7 dpi, microglia predominantly transitioned into cluster 6 (Figure 2G). However, the overall proportion of microglia was significantly reduced due to the massive influx of infiltrating MCs in the brain, which exceed microglia by approximately 10-fold at this timepoint (Figure 2F). Thus, cell numbers in clusters 1, 2, 4, 5, 6, and 7, predominantly comprising infiltrating MCs, were markedly increased in the brain, compared to mock-infected mice (Figure 2I), with numbers in cluster 7 some 8-fold greater than the next largest, cluster 6 (Figure 2K). In the BM, clusters 0, 1, 2, 3, 6 and 7 were numerically increased at 7 dpi, relative to mock-infected mice (Figure 2J), however, clusters 1, 2, and 3 comprised the majority of BM monocytes at 7 dpi, both numerically and by proportion (Figure 2F, L). These findings demonstrate clear metabolic remodelling both at peripheral sites of monocyte myelopoiesis and inflammatory foci in response to infection. Supporting our scRNA-seq findings, GAPDH was upregulated in the brain, compared to the BM (Figure 2M), emphasizing the importance of glycolysis in the differentiation of MCs. This was most obvious in cluster 7 expressing high levels of iNOS (Figure 2N), likely denoting the pathogenic NO-producing MCs implicated in immunopathology. Along with increased iNOS expression, this cluster exhibited elevated GAPDH, HIF1-α, and CD98 levels (Figure 2N), reflecting the Nos2 + and Hif1a + profiles identified via scRNA-seq (Figure 1) and constituted the majority of myeloid cells in the brain, as mentioned above (Figure 2K). In contrast to cluster 7, clusters 6, 4, and 2, the next-largest remaining clusters, displayed lower expression of iNOS, HIF1-α and GAPDH (Figure 2N), suggesting a deviation from typical pro-inflammatory metabolic pathways. Interestingly, however, cluster 2, which expresses higher HIF1-α, MitoSOX, and fatty acid metabolism markers (Figure 2N), is initially present in the brain by 5 dpi, coinciding with significant monocyte infiltration (Figure 2F, H). While remaining significantly elevated compared to controls (Figure 2L), cluster 2 had declined in the brain by 7 dpi (Figure 2H), coinciding with the appearance of iNOS + cluster 7, suggesting it may be a precursor to this subset in the brain at 5 dpi. The metabolic diversity of monocytes in the BM additionally reflects an adaptation to infection. BM cluster 3 is the only BM cluster with high GAPDH expression (Figure 2N) and it expands at 7 dpi in the BM (Figure 2J, L), suggesting this cluster is a possible BM precursor for the pathogenic iNOS + state observed in the brain at 7 dpi. This notion is further supported by the close clustering of these populations by expression of their metabolic proteins (Figure 2N). While the developmental trajectory of metabolic clusters in the BM is unclear, it is possible that the metabolically quiescent cluster 1 (Figure 2F), which expanded its proportion early in infection but was reduced by dpi 7, transits progressively towards more metabolically active subsets, such as cluster 2 and 3, which may give rise to distinct MC subsets in the brain. These findings suggest that the metabolic conditioning of BM cells may prime MCs for distinct trajectories of differentiation prior to their infiltration into the inflamed brain. MHC-II + and iNOS + MCs have distinct metabolic profiles As MCs in WNV may adopt both NO-producing and antigen-presenting phenotypes in the brain, we next aimed to determine if these functional profiles were reflected by metabolic differences. Cluster 7 and 4 were determined to be an NO-producing and antigen-presenting cell (APC) subset, respectively, based on their differential expression of MHC-II and iNOS (Figure 3A). Intriguingly, compared to the NO-producing cluster 7, the APC cluster 4 expressed all metabolic proteins at lower levels (Figure 3B). This observation suggests that the metabolic activity of cluster 7 might require synergism from multiple metabolic pathways to sustain this heightened inflammatory state, compared to that required for antigen presentation. Interestingly, inflammatory iNOS + cells displayed an increased expression of glycolysis-related markers, such as GAPDH and HIF1-α, compared to APC MCs (Figures 3B and 3C). These markers are known to be closely linked with glycolytic activity, suggesting that an APC MC phenotype is less reliant on glycolysis than the classical M1-like phenotype associated with cluster 7. To substantiate the link between glycolysis and the inflammatory M1-like phenotype, BM-derived cells were stimulated to express a classical inflammatory M1 phenotype using IFN-γ and LPS. Treatment with the glycolysis inhibitor 2-deoxy-D-glucose (2-DG) significantly reduced both and the percentage of F4/80 hi MCs expressing iNOS and the level of iNOS expressed (Figure 3D and 3E). Additionally, 2-DG treatment led to a reduction in GAPDH (Figure 3F), further supporting the relationship between NO production and glycolysis under M1 stimulation. Notably, 2-DG did not alter the expression of HIF1-α, indicating that while HIF1-α may be associated with glycolytic processes, its activity can operate independently of them (Figure 3G). Collectively, these findings reinforce the notion that pathogenic, NO-producing MCs utilize glycolysis differently from other MC subsets, such as antigen-presenting cells, particularly in the context of virus-induced neuroinflammation. Glycolysis inhibition is protective in West Nile virus encephalitis To determine whether pro-inflammatory monocyte metabolism could be therapeutically targeted in lethal infection, we next treated mice with a glycolysis inhibitor, 2-deoxy-D-glucose (2-DG) (Figure 4A), a nonmetabolizing glucose analogue and competitive inhibitor of hexokinase 1. 2-DG was administered at a dose of 2g/kg daily from 4 dpi to the disease endpoint at 7 dpi (Figure 4B). Remarkably, 2-DG treatment led to a discernible clinical improvement, as indicated by lower disease severity scores at 7 dpi (Figure 4C) and reduced weight loss (Figure 4D), with a modest, but significant increase in the mean time to death (Figure 4E). These improvements were not due to a reduction in viral load, as both 2-DG and PBS-treated mice exhibited comparable viral burdens in the brain at 7 dpi (Figure 4F), emphasizing that the effects of 2-DG are likely mediated through modulation of the pathological immune response within the brain. Notably, 2-DG also reduced the overall neuroinflammatory infiltrate to approximately 35% of that seen in control-treated mice on day 7 post-infection (Figure 4G). A significant reduction in cell numbers was observed in infiltrating neutrophils, natural killer cells, CD4 + and CD8 + T cells, and MCs in the brain, but not microglia (Figure 4H). Furthermore, all these cells, including microglia, showed a decrease in GAPDH expression (Figure S3). This reduction in immune cell infiltration may be due to a requirement of glycolysis for immune cell migration across endothelial barriers [40], and/or the reduction in MC numbers, which contribute to the accumulation of other immune cells [24, 41]. Irrespective, these findings collectively highlight the potential of glycolysis inhibition as a therapeutic approach to attenuate immune cell infiltration during severe CNS inflammation. Glycolysis inhibition reduces monocyte infiltration into the CNS, but does not reduce BM myelopoiesis Glycolysis has been shown to be important for both myelopoiesis and cellular migration. We have previously shown that diminished myelopoiesis correlates with reduced brain MC numbers and better clinical outcomes in WNV infection [31]. Thus, to investigate whether the protective effect of 2-DG was due to a reduction in myelopoiesis and/or subsequent CNS infiltration, we first measured changes in monocyte proliferation using BrdU, which incorporates detectably into synthesising DNA (Figure 5A, B). Our data revealed no significant changes in BrdU incorporation following 2-DG treatment (Figures 5C-D), with proliferating cell proportions consistent across all monocyte differentiation phases and other myeloid cell types (Figure 5D, S4). Correspondingly, total BM monocyte counts were comparable between PBS- and 2-DG-treated mice (Figure 5E). We then assessed the effect of 2-DG on cellular migration into the brain. WNV-infected mice were administered a single dose of 2-DG at 7 dpi followed by intravenous injection of the fluorescent dye, PKH26 (Figures 5F, G). This labels blood and BM cells in the vasculature in vivo (Figures 5F, G), thereby enabling the discrete identification of cells that have recently infiltrated into the brain from the periphery [31]. Post-treatment analysis showed no significant change in total brain MC numbers, compared to untreated WNV-infected mice (Figure 5H). However, there was a significant decrease in the proportion of PKH26 + MCs (Figure 5I), with a notable reduction in the infiltration rate of PKH26 + MCs into the brain, but no reduction in the rate of infiltration of other dye-positive leukocytes (Figure 5J). This corresponded to a significant decrease in the total number of PKH26 + MCs in the brain not evident in other infiltrating cells (Figure 5K). This indicates an acute MC-specific effect of 2-DG on MC migration into the brain. It also strongly suggests that the reduced presence of other immigrating leukocytes after longer term treatment with 2-DG from 4-7 dpi (Figure 4G, H) is a consequence of reduced recruitment occasioned by the accumulation of fewer MCs in the brain, rather than the dependence on glycolysis per se for diapedesis by non-MCs. Supporting this, 2-DG treatment did not affect T cell proliferation or the absolute number of effector and memory T cells nor their GAPDH levels in the cervical lymph nodes draining the brain following daily 2-DG treatment from 4-7 dpi (Figure S5), indicating that the reduced T cell numbers in the brain is not due to systemic effects of 2-DG on T cell expansion in the lymph nodes. Taken together, this is consistent with findings that inhibiting monocyte brain accumulation e.g., via Ly6C blockade or clodronate liposome administration results in significantly reduced T cell and NK cell infiltration [24, 41]. Additionally, we found that 2-DG preferentially reduced the number of dye-positive iNOS + MCs compared to MHC-II + MCs in the brain (Figure 5 L, M), suggesting that glycolysis inhibition may preferentially impede the differentiation of iNOS + MCs once in the brain, likely due to their particular reliance on glycolysis. Glycolysis inhibition differentially affects NO-producing and antigen-presenting capacity of myeloid cells We next aimed to determine whether systemic glycolysis inhibition differentially affects MC subsets. To do this, all iNOS + and MHC-II + MCs were manually gated for analysis (Figures 6A, 6E and Figure S6). Metabolic profiling revealed a >80% decrease in numbers of iNOS + MCs within the inflamed brain post 2-DG treatment (Figure 6B), mirroring a similar decline in the proportion of MCs expressing iNOS (Figure 6C). Importantly, 2-DG treatment only affected the expression of markers related to glycolysis (GAPDH) and nitric oxide production (iNOS) (Figure 6D), demonstrating that 2-DG treatment selectively reduced glycolysis without impacting other metabolic pathways. We also observed a significant increase in HIF1-α with 2-DG treatment (Figure 6D), supporting our in vitro work suggesting that glycolysis-dependent NO production is independent of HIF1-α signalling in M1-like cells (Figure 3G). This pattern emphasizes the specificity of 2-DG on glycolysis, with negligible effects on other examined metabolic processes. Despite the substantial decrease in iNOS + MCs, the number of MHC-II + MCs present in the brain was not reduced following 2-DG treatment (Figure 6F). This led to a significant proportional increase of 3-4-fold in MHC-II + MCs (Figure 6G), emphasising the disproportionate impact of glycolysis inhibition on iNOS + cells. Interestingly, in the MHC-II + cell population, we noted a significant reduction solely in the GAPDH and iNOS expression, similar to the iNOS + cells (Figure 6H), indicating that pathways already reduced in this population may be inhibited still further by 2-DG in these cells. To examine the functional implications of glycolysis inhibition in more detail, we stimulated BM cells, isolated from 2-DG- and PBS-treated WNV-infected mice, with classical M1 activation stimuli (IFN-γ + LPS) in vitro (Figure 6I). Monocytes from 2-DG-treated mice exhibited significantly reduced expression of iNOS relative to vehicle-treated WNV-infected mice, suggesting that BM monocytes from 2-DG-treated mice have reduced capacity for NO production in response to inflammatory stimuli. Interestingly, although these cells significantly reduced iNOS expression, BM monocytes from 2-DG-treated mice showed significantly increased MHC-II expression in response to inflammatory stimulation relative to control-treated mice, presumably due to IFN-γ exposure (Figure 6K). This further suggests that systemic 2-DG treatment may affect the inflammatory potential of monocytes prior to their differentiation into effector MCs, without affecting their capacity for antigen presentation. To confirm that antigen-presenting functions are not impacted by 2-DG, we isolated draining cervical lymph nodes from 4-7 dpi 2-DG- and vehicle-treated WNV-infected mice and vehicle-treated mock-infected mice at 7 dpi and stimulated them with WNV, which contains both replicating virus and free viral antigen, for 72 hours (Figure 6L). Effector CD4 + T cell differentiation (Figure 6M) was significantly increased following in vitro viral restimulation of T cells isolated from WNV-infected mice treated with vehicle or 2-DG, compared to those from mock-infected mice (Figure 6N). Importantly, 2-DG-treated and vehicle-treated WNV-infected mice showed comparable numbers of effector CD4 + T cells, suggesting that the capacity of antigen-presenting cells to stimulate an effector T cell response is unaffected by 2-DG treatment. Supporting this, the proportions of proliferating effector CD4 + T cells (Figure 6O and 6P) and IFN-γ-producing effector T cells (Figure 6Q) in response to viral re-stimulation were also unaffected by 2-DG treatment. Together, this demonstrates that systemic 2-DG treatment specifically targets NO-producing MCs in cluster 7 to reduce NO, without impacting antigen-presenting function of MCs in cluster 4. Discussion This study represents the first dual approach using both scRNA-seq and spectral cytometry to inspect the metabolic profiles of MCs at a gene and protein level, uncovering for the first time the intricate metabolic diversity of these cells within the context of CNS disease. In doing this, we identified a pathogenic NO-producing MC population in the WNV-infected brain which expressed glycolytic markers. Targeting this population with 2-DG specifically reduced their migration into the brain and impaired their ability to produce NO. Strikingly, this corresponded with a significant reduction in clinical and neuroinflammatory signs, highlighting the therapeutic potential of modulating immunometabolism to resolve disease. Our findings indicate that MCs adopt multiple functional roles, each defined by a unique metabolic phenotype. While the NO-producing MCs described in this report exhibited a metabolic profile suggestive of the traditional M1 phenotype, this varied significantly from the conventional M1 or M2 categorization. The local tissue environment was a decisive factor influencing these metabolic patterns, as BM monocytes and brain MCs demonstrated distinct metabolic signatures. This observation is consonant with recent research highlighting the significance of tissue origin in shaping the metabolic characteristics of resident macrophages [8], in which the varying nutritional and cellular contexts provided by different tissues likely drive cells towards specific metabolic pathways. Furthermore, the divergent developmental origins of microglia (arising from the yolk sac) and MCs (originating from hematopoietic stem cells in the BM) likely contributed to their metabolic differences during infection. Notably, microglia tended to maintain a more homeostatic metabolic state, while MCs infiltrating the brain shifted towards more active metabolic profiles. Overall, this distinction emphasizes the importance of both origin and environment in the metabolic identity of immune cells in the CNS during infection. The metabolic programming of monocytes—whether established during development in the BM or upon entry into the virus-infected CNS—remains unclear. Our findings suggest metabolic cues in each organ play an important role, with trajectory analysis revealing a clear metabolic progression of monocytes migrating from the BM to the CNS. This transition is marked by a decreased expression of genes involved in ATP production, the TCA cycle, and the ETC, coupled with an increased reliance on glycolysis. While the developmental trajectory of metabolic clusters in BM remains to be fully elucidated, our observations indicate that cluster 1, initially expanding in the early stages of infection but diminishing by dpi 7, may evolve into more metabolically active clusters 2 and 3. This initial metabolic state of BM cells could set the stage for varied differentiation pathways of MCs, effectively 'priming' them before they migrate to the inflamed brain environment. Once in the brain, BM monocytes evidently assume a HIF1-α + intermediate state, which may act as the branching point for divergence into iNOS + or APC MC subsets. Such a metabolic shift might be an adaptive strategy to ensure survival in the hypoxic conditions of the infected CNS [42]. Indeed, the transcription factor HIF1α, known to drive the expression of glycolytic enzymes, is crucial for myeloid cell motility during mild hypoxia such as inflammation [17, 18]. Importantly, this marker was significantly upregulated in glycolytic MCs in our study and its expression was retained in infiltrating MCs following 2-DG treatment, presumably enabling diapedesis of APC MCs, but not iNOS + MCs. Our scRNA-seq analysis further supports this hypothesis, showing a close association between the upregulation of glycolysis and macrophage migration in Nos2 + and Hif1a + MCs. Additionally, 2-DG treatment significantly reduced physical monocyte migration into the inflamed brain without impacting myelopoiesis, suggesting that glycolytic inhibition mediates its protective effect by preferentially preventing inflammatory cellular transmigration across the blood-brain barrier. This specific targeting of inflammatory MCs may be due to their entry at distinct anatomical locations in the CNS or their exposure to different regions of the brain that have different rates of infection and unique cytokine profiles [43]. This link is substantiated by observations in experimental autoimmune encephalomyelitis and human multiple sclerosis, where heightened glycolytic activity is associated with the transmigration of inflammatory macrophages into the brain [44]. Enhanced glycolysis observed in immune cells during WNV encephalitis and other diseases strongly suggests that targeting metabolic reprogramming within inflammatory cells could be a promising approach for immune therapy. In this study, 2-DG treatment preferentially impacted pathogenic, NO-producing MCs. Furthermore, the ability of 2-DG to cross the blood-brain barrier [45] may contribute to ongoing targeting of these cells at the site of inflammation. This is supported by data showing the 2-DG reduces the expression of inflammatory genes and interleukins in the CNS during WNV infection [46], suggesting a broad reduction in brain inflammation. Additionally, in experimental autoimmune encephalomyelitis, 2-DG treatment skewed monocytes/macrophages towards an anti-inflammatory state in the CNS, providing protection and overall clinical improvement [45]. Despite the evident anti-inflammatory effect of glycolysis inhibition at the sites of inflammation, we additionally observed that BM monocytes derived from 2-DG treated mice showed a decreased NO response to inflammatory stimuli outside the brain. This suggests an early modulation of their inflammatory potential, likely due to the dependence of NO synthesis on glycolysis, with the capacity to produce NO not fully restored upon their entry into the brain. Interestingly, we show that antigen-presenting MCs have a metabolic profile distinct from those producing NO, primarily attributed to reduced glycolytic activity. MHC-II hi macrophages also displayed lower reliance on glycolysis than MHC-II lo macrophages [47], and monocytes diminish glycolytic activity as they develop into an antigen-presenting phenotype in vitro [48], suggesting a negative association between glycolysis and antigen presentation. Supporting this, elevated glucose levels have been shown to hinder antigen presentation and disrupt CD4 + T cell activation [49]. Conversely, glucose limitation appears to enhance MC-mediated T cell responses, as demonstrated by the increased expression by glucose-deprived MCs of co-stimulatory molecules and interleukin-12 essential for T cell proliferation and function [50]. Thus, APC may strategically reduce glycolysis to adapt to the glucose-scarce environment generated by metabolically-active T cells, thereby prolonging the T cell response [50]. In this study, MCs may transition to an antigen-presenting role as their glycolytic activity declines to optimize their capacity to present antigens effectively. Supporting this, APCs from the draining lymph nodes of 2-DG treated mice maintained their ability to elicit an anti-viral T cell response after antigen rechallenge ex vivo , suggesting that (1) APCs from the 2-DG-treated mice preserve their antigen presenting efficacy, and (2) the formation of a memory T cell response is not compromised by systemic 2-DG treatment. The unaffected memory T cell response during glycolytic inhibition may be attributed to their reliance on fatty acid oxidation, a metabolic pathway essential for memory development [51, 52]. Importantly, while 2-DG treatment significantly supressed glycolytic markers, it did not alter other metabolic pathways, including fatty acid oxidation. Consequently, despite a reduction in T cell numbers in the brain over three days of 2-DG treatment, T cell proliferation and memory formation evidently remained intact. Furthermore, the absence of acute inhibition of T cell immigration by 2-DG, in contrast to MCs, strongly suggests that reduced T cell infiltration into the brain by longer term 2-DG treatment was more likely a consequence of their reduced recruitment by low MC numbers than direct migration inhibition of these cells by 2-DG. In summary, our findings suggest that glycolytic inhibition selectively hinders the infiltration of hyperinflammatory cells without affecting the functional development of a robust T cell response during WNV infection. This research highlights that modulating the metabolic pathways active in pathogenic monocytes can mitigate disease severity by specifically tempering uncontrolled inflammation, a key contributor to disease exacerbation and progression. This nuanced approach preserves essential immune processes, including pathogen clearance and memory formation, and may be most effective when combined with anti-viral therapies. Unlike broad-acting immunosuppressants like corticosteroids, which indiscriminately suppress both detrimental and beneficial immune responses, metabolic modulation offers more targeted control of the inflammatory response. Although the effectiveness of this strategy in humans requires further study, these findings reinforce the potential of metabolic targeting as a component of combination therapy for immune regulation in diseases with severe or uncontrolled inflammation. Declarations Acknowledgements This work was supported by the Merridew Foundation, National Health and Medical Research Council (1088242), and the Charles Perkins Centre Early to Mid-Career Researcher Seed Funding Grant (University of Sydney). Our appreciation goes to Dr. Carol Ford, Dr. Frank Kao, and Dr. Andy Law from BD Bioscience, as well as Dr. Thomas Ashhurst, Moumita Paul and Associate Professor Alex Sharland for their support in aiding with our Rhapsody data sequencing. We would also like to thank Kate Pilkington for her expertise on autofluorescence extraction and analysis of spectral flow cytometry data, and Jemma Taitz, Camille Potier-Villette, and Dr. Duan Ni for assistance with experiments. We also wish to acknowledge the support of the University of Sydney’s Laboratory Animal Services and the Sydney Cytometry facilities. Author Contributions CLW: conceptualization; data curation; formal analysis; investigation; methodology; visualisation; writing – original draft; writing – reviewing and editing. AGS and JT: data curation; methodology; writing – reviewing and editing. LM: supervision; writing – reviewing and editing. 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Metabolic targets included in panel Target Name Pathway Function in pathway GAPDH Glyceraldehyde 3-phosphate dehydrogenase Glycolysis & fermentation Rate limiting glycolytic enzyme IDH1 Isocitrate dehydrogenase TCA cycle Rate limiting enzyme in TCA cycle CPT1A Carnitine Palmitoyltransferase 1A Fatty acid oxidation Fatty acid shuttling into mitochondria ACAC Acetyl-CoA carboxylase Fatty acid synthesis Acetyl-CoA carboxylase/ fatty acid synthesis CD98 CD98 Amino acid metabolism Essential amino acid transporter HIF1-α Hypoxia-inducible factor 1-alpha Metabolic regulation/signaling Hypoxia and inflammation-induced transcription factor iNOS Inducible nitric oxide synthase Oxidative stress, amino acid metabolism Nitric oxide production, initial rate-limiting enzyme involved in arginine degradation MitoSOX N/A Free radical superoxide generation Stains for free radical superoxides, produced in the electron transport chain IDO1 Indoleamine 2,3-dioxygenase Amino acid metabolism (kynurenine pathway) Initial and rate-limiting enzyme for tryptophan degradation to N -formylkynurenine in the kynurenine pathway Additional Declarations (Not answered) Supplementary Files SupplementaryFigures.pdf Supplementary Material Figure S1. Quality control metrics and cell type classification for scRNA-seq data in the WNV infected brain and BM. (A) Analysis workflow. (B) Quality control metrics, including nFeature_RNA, nCount RNA, and percent mitochondrial gene expression before and after filtering for each of the sample tags. The unfiltered data set contained 8,238 cells and the filtered data set contained 6,305 cells. (D, E) UMAPs showing clustering of filtered data set prior to subsetting MCs from microglia and T cells, pseudocoloured by sample name (D) and cell type (E). (F) Dot plot heatmap showing the expression of select genes in identified cell types. Data is from one independent experiment with four mice per group. Figure S2. Pseudotemporal ordering of Nos2 + and APC lineages with Slingshot. (A) Expression of select genes in Nos2 + and APC lineages along pseudotime. (B) Top differentially expressed markers between the lineage root ( Mo3 ) to lineage end-point ( Nos2 + MC or APC MC ) for each lineage. Data is represented as log 2 fold-change. Data is from one independent experiment with four mice per group. Figure S3. 2-DG treatment uniformly downregulates glycolytic marker GAPDH across cell types. (A) Median fluorescence intensity of GAPDH across the indicated cell types. (B) Histograms depicting the median fluorescence intensity of GAPDH in 2-DG- and PBS-treated mice at 7 dpi, relative to the fluorescence minus one (FMO) control. Data is from one independent experiments with eight mice per group. Statistics were calculated using an unpaired t-test. * p<0.05, ** p<0.01, ***p<0.001. Error bars are representative of mean ± SD. Figure S4. Gating strategy for bone marrow myeloid populations. Figure S5. 2-DG treatment from 4-7 dpi does not affect T cell proliferation in draining cervical lymph nodes. (A) Contour plot showing gating of naïve, central memory (CM) and effector (eff) CD4 + T cells based on CD62L and CD44 expression. (B) Absolute cell numbers of indicated T cell populations per cervical lymph node. (C, D) Percentage of BrdU + T cells (C) and their expression of CD69 (D) and GAPDH (E) in 2-DG- and PBS-treated mice at 7 dpi. Data is from one independent experiment with eight mice per group. Statistics were calculated using an unpaired t-test (B, E) or multiple t-tests with two-stage step up method of Bejamini, Krieger, and Yekutieli tests for multiple comparisons (C, D). Error bars are representative of mean ± SD. CM, central memory; Eff, effector T cell; Treg, regulatory T cell. Figure S6. Gating strategy for iNOS + and MHC-II + MC populations in the brain. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4018869","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":277818026,"identity":"75245cc1-758e-4280-8605-ed4197f6d9dd","order_by":0,"name":"Nicholas King","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-3877-9772","institution":"Charles Perkins Centre, University of Sydney","correspondingAuthor":true,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"King","suffix":""},{"id":277818027,"identity":"4cbc03a4-eb5a-4833-a634-099869f54123","order_by":1,"name":"Claire Wishart","email":"","orcid":"","institution":"Charles Perkins Centre, University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Wishart","suffix":""},{"id":277818028,"identity":"12b634aa-bbf6-48f5-9eef-d652c1e97e4b","order_by":2,"name":"Alanna Spiteri","email":"","orcid":"","institution":"Charles Perkins Centre, University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Alanna","middleName":"","lastName":"Spiteri","suffix":""},{"id":277818029,"identity":"770446de-81e0-4029-bc35-b508f4f260b2","order_by":3,"name":"Jian Tan","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Tan","suffix":""},{"id":277818030,"identity":"f23d8b0a-b49d-4633-8b52-121c68853481","order_by":4,"name":"Laurence Macia","email":"","orcid":"https://orcid.org/0000-0003-0835-851X","institution":"The University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Laurence","middleName":"","lastName":"Macia","suffix":""}],"badges":[],"createdAt":"2024-03-06 01:00:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4018869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4018869/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52568788,"identity":"4f089d10-6b4a-4619-8c40-4864c25113b0","added_by":"auto","created_at":"2024-03-13 05:09:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1501025,"visible":true,"origin":"","legend":"\u003cp\u003eMCs adopt distinct metabolic and functional transcriptomic profiles in CNS infection. (\u003cstrong\u003eA\u003c/strong\u003e) UMAPs of monocyte-derived cells identified in WNV infection across the BM at 7 dpi and brain at 5 and 7 dpi. \u003cstrong\u003e(B\u003c/strong\u003e) Top differentially expressed genes per cluster. Differentially expressed genes were defined as genes enriched in a cluster versus all other clusters. (\u003cstrong\u003eC, D\u003c/strong\u003e). Module scores for functional (\u003cstrong\u003eC\u003c/strong\u003e) and metabolic (\u003cstrong\u003eD\u003c/strong\u003e) pathways across each of the identified subsets. (\u003cstrong\u003eE\u003c/strong\u003e) Heatmap depicting the scaled average module scores for \u003cem\u003eAPC\u003c/em\u003e, \u003cem\u003eMg-like\u003c/em\u003e, \u003cem\u003eHif1a\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e, and \u003cem\u003eNos2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e MCs in the brain across selected metabolic and functional modules. (\u003cstrong\u003eF\u003c/strong\u003e) Pseudotime trajectory analysis on BM and brain myeloid cells using Slingshot with \u003cem\u003eMo3\u003c/em\u003e set as the root (starting) subset. UMAP is colour-coded by monocyte subsets shown in Figure 4B. (\u003cstrong\u003eG\u003c/strong\u003e) Lineage trajectories from Slingshot analysis, 1) \u003cem\u003eNos2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e \u003cem\u003eMC\u003c/em\u003e and 2) \u003cem\u003eAPC MC.\u003c/em\u003e UMAPs are coloured by pseudotime. Data is from one experiment with four mice per group.\u003c/p\u003e","description":"","filename":"MainFiguresPage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4018869/v1/bff390817019a637b33ae43a.jpg"},{"id":52568474,"identity":"31451e00-e18a-4a04-95f6-742066ca14c4","added_by":"auto","created_at":"2024-03-13 05:01:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1338079,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic heterogeneity of MCs in viral infection by spectral flow cytometry.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Schematic of metabolic targets for flow-based analysis. (\u003cstrong\u003eB\u003c/strong\u003e) Experimental workflow. The bone marrow (BM) and brain were isolated from mock-infected and WNV-infected mice at 3, 5, or 7 dpi and stained with a metabolic panel. UMAP and k-nearest neighbour clustering was performed on BM monocytes, brain microglia and brain MCs across time points based on arcsinh transformed metabolic protein MFI. (\u003cstrong\u003eC-E\u003c/strong\u003e) UMAPs pseudocoloured by metabolic cluster ID (\u003cstrong\u003eC, D\u003c/strong\u003e) and cell types (\u003cstrong\u003eE\u003c/strong\u003e) in the BM and brain. (\u003cstrong\u003eF-H\u003c/strong\u003e) Proportion of each metabolic cluster comprising the total myeloid pool across organs and time points (\u003cstrong\u003eF\u003c/strong\u003e), or the total microglia (\u003cstrong\u003eG\u003c/strong\u003e), or the MC (\u003cstrong\u003eH\u003c/strong\u003e) population in the brain. (\u003cstrong\u003eI, J\u003c/strong\u003e) Number of myeloid cells per brain (\u003cstrong\u003eI\u003c/strong\u003e) and monocytes per femur (\u003cstrong\u003eJ\u003c/strong\u003e) in each cluster in mock-infected and WNV-infected mice at 7 dpi. (\u003cstrong\u003eK, L\u003c/strong\u003e) Number of myeloid cells per brain (\u003cstrong\u003eK\u003c/strong\u003e) and monocytes per femur (\u003cstrong\u003eL\u003c/strong\u003e) in each cluster at 7 dpi. (\u003cstrong\u003eM\u003c/strong\u003e) Arcsinh transformed median fluorescence intensity (MFI) of GAPDH in the BM and brain at 7 dpi. (\u003cstrong\u003eN\u003c/strong\u003e) Dot plot heatmap depicting the MFI of each metabolic marker across the identified metabolic cluster at 7 dpi. Size of dot represents the percentage of cells in each cluster expressing the metabolic marker. Data is pooled from two independent experiments with 3-4 mice per group. Statistics done using Mann-Whitney test (\u003cstrong\u003eI, J, M\u003c/strong\u003e) or Kruskal-Wallis test with Dunn’s test for multiple comparisons (\u003cstrong\u003eK, L\u003c/strong\u003e). * p\u0026lt;0.05, ** p \u0026lt;0.01, *** p\u0026lt;0.001, **** p \u0026lt;0.0001. Error bars are representative of mean ± SD.\u003c/p\u003e","description":"","filename":"MainFiguresPage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4018869/v1/9e8be7bc5c53772fb65e7949.jpg"},{"id":52568789,"identity":"330621f7-49c0-417a-a071-358cf4aa8d5a","added_by":"auto","created_at":"2024-03-13 05:09:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":638589,"visible":true,"origin":"","legend":"\u003cp\u003eMHC-II\u003csup\u003e+\u003c/sup\u003e and iNOS\u003csup\u003e+\u003c/sup\u003e MCs have distinct metabolic profiles in CNS viral infection. (\u003cstrong\u003eA\u003c/strong\u003e) Contour plot depicting MHC-II and iNOS expression of metabolic cluster 4 (blue) and 7 (pink). (\u003cstrong\u003eB\u003c/strong\u003e) Arcsinh transformed MFI of selected functional and metabolic markers. (\u003cstrong\u003eC\u003c/strong\u003e) UMAP showing the expression of GAPDH, HIF1-α and iNOS in the brain in mock-infected and WNV-infected mice at 3, 5 and 7 dpi. (\u003cstrong\u003eD\u003c/strong\u003e) Frequency of cultured iNOS\u003csup\u003e+ \u003c/sup\u003eBM-derived macrophages (BMDMs) following LPS + IFN-γ stimulation in the presence or absence of 2-DG or vehicle (PBS) control. (\u003cstrong\u003eE-G\u003c/strong\u003e) Change in MFI of iNOS (\u003cstrong\u003eE\u003c/strong\u003e), GAPDH (\u003cstrong\u003eF\u003c/strong\u003e), and HIF1-α (\u003cstrong\u003eG\u003c/strong\u003e) in cultured BMDMs post stimulation. Data is from two (\u003cstrong\u003eA\u003c/strong\u003e, \u003cstrong\u003eB\u003c/strong\u003e, \u003cstrong\u003eC\u003c/strong\u003e) or one (\u003cstrong\u003eD\u003c/strong\u003e-\u003cstrong\u003eG\u003c/strong\u003e) independent experiment(s) with 3-4 mice per group. Statistics calculated with paired t-test (\u003cstrong\u003eB\u003c/strong\u003e) or RM one-way ANOVA with Tukey’s test for multiple comparisons (\u003cstrong\u003eE\u003c/strong\u003e). * p\u0026lt;0.05, ** p \u0026lt;0.01, *** p\u0026lt;0.001, **** p \u0026lt;0.0001. Error bars are representative of mean ± SD.\u003c/p\u003e","description":"","filename":"MainFiguresPage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4018869/v1/e7a6e468e56952e0232c4180.jpg"},{"id":52568477,"identity":"b004358a-9368-4736-af98-9687d7515d26","added_by":"auto","created_at":"2024-03-13 05:01:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":366190,"visible":true,"origin":"","legend":"\u003cp\u003eGlycolysis inhibition is protective in West Nile virus encephalitis. (\u003cstrong\u003eA\u003c/strong\u003e) Schematic depicting 2-DG mechanism of action. (\u003cstrong\u003eB\u003c/strong\u003e) Schematic experimental design. WNV-infected mice were treated with 2 g/kg of 2-DG delivered i.p. daily from 4-7 dpi. (\u003cstrong\u003eC\u003c/strong\u003e) Clinical scores of 2-DG and PBS-treated mice at 7 dpi. (\u003cstrong\u003eD\u003c/strong\u003e) Weights of 2-DG and PBS-treated animals over the duration of infection shown as percentage of initial weight. (\u003cstrong\u003eE\u003c/strong\u003e) Mean time to death of WNV-infected mice treated with 2-DG or PBS (vehicle control). (\u003cstrong\u003eF\u003c/strong\u003e) WNV PFU in brains of 2-DG- and PBS-treated mice at 7 dpi. (\u003cstrong\u003eG\u003c/strong\u003e) Stacked bar graph showing numbers of indicated CD45\u003csup\u003e+\u003c/sup\u003e cell subsets in the brains of 2-DG- and PBS-treated mice at 7 dpi. (\u003cstrong\u003eH\u003c/strong\u003e) Numbers of infiltrating and resident immune cell populations in the brain at 7 dpi. Data in (\u003cstrong\u003eD\u003c/strong\u003e) are pooled and representative of mean ± SD. Data is from one (\u003cstrong\u003eE\u003c/strong\u003e, \u003cstrong\u003eF\u003c/strong\u003e) or two (\u003cstrong\u003eD\u003c/strong\u003e, \u003cstrong\u003eG\u003c/strong\u003e, \u003cstrong\u003eH\u003c/strong\u003e) independent experiment(s) with at least five mice per group. Statistics calculated with Mann-Whitney test (\u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eG\u003c/strong\u003e, \u003cstrong\u003eH\u003c/strong\u003e) or unpaired t-test (\u003cstrong\u003eD\u003c/strong\u003e,\u003cstrong\u003e E, F\u003c/strong\u003e). * p\u0026lt;0.05, ** p \u0026lt;0.01, *** p\u0026lt;0.001, **** p \u0026lt;0.0001. Error bars are representative of mean ± SD.\u003c/p\u003e","description":"","filename":"MainFiguresPage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4018869/v1/febc74c9905a0dcf4ffd65f2.jpg"},{"id":52568479,"identity":"1f0c64a6-6c03-4bec-8ce8-88dc9427dee4","added_by":"auto","created_at":"2024-03-13 05:01:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":684410,"visible":true,"origin":"","legend":"\u003cp\u003eGlycolytic inhibition with 2-DG reduces CNS infiltration without impacting BM myelopoiesis.\u0026nbsp; (\u003cstrong\u003eA\u003c/strong\u003e) Schematic showing treatment regimen. Mice infected with WNV received 2-DG or PBS treatment once daily from 4-6 dpi and 5.5 hours prior to euthanasia at 7 dpi. BrdU was administered to both groups 2.5 hours after 2-DG treatment at 7 dpi to assess differential rates of cellular proliferation. (\u003cstrong\u003eB)\u003c/strong\u003e Contour plots showing BrdU incorporation in BM-derived monocytes compared to a fluorescence minus one (FMO) control. (\u003cstrong\u003eC\u003c/strong\u003e) Histogram depicting BrdU expression in mature monocytes from 2-DG-treated versus PBS-treated mice.\u0026nbsp; (\u003cstrong\u003eD\u003c/strong\u003e) Proportion of BM myeloid cells incorporating BrdU. (\u003cstrong\u003eE)\u003c/strong\u003e Absolute numbers of indicated myeloid cell subsets in the femur. (\u003cstrong\u003eF\u003c/strong\u003e) Schematic showing the experimental workflow for tracking CNS infiltration. Mice were treated with 2-DG once 4.5 hours before euthanasia at 7 dpi, followed by an injection with PKH26 2.5 hours later for \u003cem\u003ein vivo\u003c/em\u003e labelling of blood and BM cells. (\u003cstrong\u003eG\u003c/strong\u003e) Contour Plot showing PKH26 staining in brain MCs compared to the FMO control (\u003cem\u003ei.e. \u003c/em\u003evehicle-injected mice). (\u003cstrong\u003eH\u003c/strong\u003e) Total number of MCs present in the brain at 7 dpi. (\u003cstrong\u003eI\u003c/strong\u003e) Percentage of PKH26\u003csup\u003e+\u003c/sup\u003e MCs in the brain (\u003cem\u003ei.e., \u003c/em\u003erecently infiltrating cells). (\u003cstrong\u003eJ\u003c/strong\u003e) Absolute numbers of indicated CD45\u003csup\u003e+\u003c/sup\u003e, PKH26\u003csup\u003e+\u003c/sup\u003e leukocyte subsets infiltrating the brain per minute. (\u003cstrong\u003eK-M\u003c/strong\u003e) Absolute numbers of indicated CD45\u003csup\u003e+\u003c/sup\u003e, PKH26\u003csup\u003e+\u003c/sup\u003e leukocyte subsets (\u003cstrong\u003eK\u003c/strong\u003e), PKH26\u003csup\u003e+\u003c/sup\u003e iNOS\u003csup\u003e+ \u003c/sup\u003eMCs (\u003cstrong\u003eL\u003c/strong\u003e), and PKH26\u003csup\u003e+\u003c/sup\u003e MHC-II\u003csup\u003e+\u003c/sup\u003e MCs (\u003cstrong\u003eM\u003c/strong\u003e) within the brain. Data is representative of one independent experiment with 4-8 mice per group. Dotted red line in J, K is indicative of threshold for background PKH26\u003csup\u003e+\u003c/sup\u003e staining as determined by positive staining of microglia. Statistics calculated by unpaired t-test (\u003cstrong\u003eH, I, L, M\u003c/strong\u003e) or multiple t-tests with two-stage step up method of Bejamini, Krieger, and Yekutieli tests for multiple comparisons (\u003cstrong\u003eD, E, J, K\u003c/strong\u003e). * p\u0026lt;0.05, *** p\u0026lt;0.001. Error bars are representative of mean ± SD.\u003c/p\u003e","description":"","filename":"MainFiguresPage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4018869/v1/d25cc665f398f350b6f539f7.jpg"},{"id":52568476,"identity":"e73a4d1e-07b8-4c53-adab-2348aa0124c3","added_by":"auto","created_at":"2024-03-13 05:01:31","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":786747,"visible":true,"origin":"","legend":"\u003cp\u003eGlycolysis inhibition differentially affects iNOS\u003csup\u003e+\u003c/sup\u003e and MHC-II\u003csup\u003e+\u003c/sup\u003e MCs. (\u003cstrong\u003eA\u003c/strong\u003e) Dot plot showing iNOS\u003csup\u003e+\u003c/sup\u003e MCs in the brain at 7 dpi. (\u003cstrong\u003eB, C\u003c/strong\u003e) Number (\u003cstrong\u003eB\u003c/strong\u003e) and percent (\u003cstrong\u003eC\u003c/strong\u003e) of iNOS\u003csup\u003e+\u003c/sup\u003e MCs in the brain at 7 dpi. (\u003cstrong\u003eD\u003c/strong\u003e) Fold-change in MFI of selected metabolic markers in 2-DG-treated mice relative to PBS-treated mice. (\u003cstrong\u003eE\u003c/strong\u003e) Dot plot showing MHC-II\u003csup\u003e+\u003c/sup\u003e MCs in the brain at 7 dpi. (\u003cstrong\u003eF, G\u003c/strong\u003e) Number (\u003cstrong\u003eF\u003c/strong\u003e) and percent \u003cstrong\u003e(G) \u003c/strong\u003eof MHC-II\u003csup\u003e+\u003c/sup\u003e MCs in the brain at 7 dpi. (\u003cstrong\u003eH\u003c/strong\u003e) Fold-change in median fluorescence intensity of selected metabolic markers in 2-DG-treated mice relative to PBS-treated mice. (\u003cstrong\u003eI\u003c/strong\u003e) Schematic outlining experimental design for BM stimulation. Whole BM cells were isolated from WNV-infected mice treated with 2-DG or PBS daily from day 4-7, and then stimulated with LPS and IFN-γ for 24 hours. (\u003cstrong\u003eJ, K\u003c/strong\u003e) MFI of iNOS and MHC-II in cultured monocytes. (\u003cstrong\u003eL\u003c/strong\u003e) Schematic showing experimental design for assessment of T cell proliferation. Whole cervical draining lymph nodes (dLN) were stimulated with WNV or PBS for 1 hour, prior to being cultured with either PBS or Con A for 72 hours. (\u003cstrong\u003eM\u003c/strong\u003e) Contour plots showing gating of naïve, central memory (CM) and effector (eff) CD4\u003csup\u003e+ \u003c/sup\u003eT cells based on CD62L and CD44 expression across groups stimulated with WNV. (\u003cstrong\u003eN\u003c/strong\u003e) Number of effector CD4\u003csup\u003e+\u003c/sup\u003e T cells per well of cultured lymph nodes. (\u003cstrong\u003eO\u003c/strong\u003e) Histogram depicting CFSE staining relative to fluorescence-minus-one control (FMO). The dotted box indicates proliferating cells. (\u003cstrong\u003eP\u003c/strong\u003e, \u003cstrong\u003eQ\u003c/strong\u003e) Percent of proliferating (\u003cstrong\u003eP\u003c/strong\u003e) and IFN-γ\u003csup\u003e+\u003c/sup\u003e (\u003cstrong\u003eQ\u003c/strong\u003e) effector CD4\u003csup\u003e+\u003c/sup\u003e T cells. Data is from one independent experiment with eight mice per group. Statistics calculated ordinary one-way ANOVA with Tukey’s test for multiple comparisons (\u003cstrong\u003eD\u003c/strong\u003e, \u003cstrong\u003eH\u003c/strong\u003e), Mann-Whitney test (\u003cstrong\u003eB\u003c/strong\u003e, \u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eF\u003c/strong\u003e, \u003cstrong\u003eG\u003c/strong\u003e, \u003cstrong\u003eJ\u003c/strong\u003e, \u003cstrong\u003eK\u003c/strong\u003e), RM one-way ANOVA with Tukey’s test for multiple comparisons (\u003cstrong\u003eP\u003c/strong\u003e, \u003cstrong\u003eN\u003c/strong\u003e, \u003cstrong\u003eQ\u003c/strong\u003e) and paired t-test (\u003cstrong\u003eN\u003c/strong\u003e, \u003cstrong\u003eQ\u003c/strong\u003e). * p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001, **** p\u0026lt;0.0001. Error bars are representative of mean ± SD.\u003c/p\u003e","description":"","filename":"MainFiguresPage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4018869/v1/cc3103b336c33b7e535ae8db.jpg"},{"id":53288593,"identity":"4d4a252c-c75a-4d4b-83ef-3e60354bd51c","added_by":"auto","created_at":"2024-03-23 02:08:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1303188,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4018869/v1/e2999ec4-4b50-4581-a373-64c39be99713.pdf"},{"id":52568481,"identity":"9c1a2204-3217-44d1-9425-df48950221d1","added_by":"auto","created_at":"2024-03-13 05:01:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4519422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1. \u003c/strong\u003eQuality control metrics and cell type classification for scRNA-seq data in the WNV infected brain and BM. (\u003cstrong\u003eA\u003c/strong\u003e) Analysis workflow. (\u003cstrong\u003eB\u003c/strong\u003e) Quality control metrics, including nFeature_RNA, nCount RNA, and percent mitochondrial gene expression before and after filtering for each of the sample tags. The unfiltered data set contained 8,238 cells and the filtered data set contained 6,305 cells. (\u003cstrong\u003eD, E\u003c/strong\u003e) UMAPs showing clustering of filtered data set prior to subsetting MCs from microglia and T cells, pseudocoloured by sample name \u003cstrong\u003e(D) \u003c/strong\u003eand cell type \u003cstrong\u003e(E)\u003c/strong\u003e. (\u003cstrong\u003eF\u003c/strong\u003e) Dot plot heatmap showing the expression of select genes in identified cell types. Data is from one independent experiment with four mice per group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2. \u003c/strong\u003ePseudotemporal ordering of \u003cem\u003eNos2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand \u003cem\u003eAPC\u003c/em\u003e lineages with Slingshot. (\u003cstrong\u003eA\u003c/strong\u003e) Expression of select genes in \u003cem\u003eNos2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003eand \u003cem\u003eAPC\u003c/em\u003e lineages along pseudotime. (\u003cstrong\u003eB\u003c/strong\u003e) Top differentially expressed markers between the lineage root (\u003cem\u003eMo3\u003c/em\u003e) to lineage end-point (\u003cem\u003eNos2\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e MC\u003c/em\u003e or \u003cem\u003eAPC MC\u003c/em\u003e) for each lineage. Data is represented as log\u003csub\u003e2\u003c/sub\u003efold-change. Data is from one independent experiment with four mice per group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3. \u003c/strong\u003e2-DG treatment uniformly downregulates glycolytic marker GAPDH across cell types. (\u003cstrong\u003eA\u003c/strong\u003e) Median fluorescence intensity of GAPDH across the indicated cell types. (\u003cstrong\u003eB\u003c/strong\u003e) Histograms depicting the median fluorescence intensity of GAPDH in 2-DG- and PBS-treated mice at 7 dpi, relative to the fluorescence minus one (FMO) control. Data is from one independent experiments with eight mice per group. Statistics were calculated using an unpaired t-test. * p\u0026lt;0.05, ** p\u0026lt;0.01, ***p\u0026lt;0.001. Error bars are representative of mean ± SD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S4\u003c/strong\u003e. Gating strategy for bone marrow myeloid populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S5. \u003c/strong\u003e2-DG treatment from 4-7 dpi does not affect T cell proliferation in draining cervical lymph nodes. (\u003cstrong\u003eA\u003c/strong\u003e) Contour plot showing gating of naïve, central memory (CM) and effector (eff) CD4\u003csup\u003e+ \u003c/sup\u003eT cells based on CD62L and CD44 expression. (\u003cstrong\u003eB\u003c/strong\u003e) Absolute cell numbers of indicated T cell populations per cervical lymph node. (\u003cstrong\u003eC, D\u003c/strong\u003e) Percentage of BrdU\u003csup\u003e+\u003c/sup\u003e T cells (\u003cstrong\u003eC\u003c/strong\u003e) and their expression of CD69\u003cstrong\u003e (D) \u003c/strong\u003eand GAPDH (\u003cstrong\u003eE\u003c/strong\u003e) in 2-DG- and PBS-treated mice\u003cstrong\u003e \u003c/strong\u003eat 7 dpi. Data is from one independent experiment with eight mice per group. Statistics were calculated using an unpaired t-test (\u003cstrong\u003eB, E\u003c/strong\u003e) or multiple t-tests with two-stage step up method of Bejamini, Krieger, and Yekutieli tests for multiple comparisons (\u003cstrong\u003eC, D\u003c/strong\u003e). Error bars are representative of mean ± SD. CM, central memory; Eff, effector T cell; Treg, regulatory T cell.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S6\u003c/strong\u003e. Gating strategy for iNOS\u003csup\u003e+\u003c/sup\u003e and MHC-II\u003csup\u003e+\u003c/sup\u003e MC populations in the brain.\u003c/p\u003e","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4018869/v1/18291b8a0ebf5b346781977f.pdf"}],"financialInterests":"(Not answered)","formattedTitle":"Therapeutic inhibition of glycolysis preferentially targets pathogenic monocyte subsets and attenuates CNS inflammation in flavivirus encephalitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMonocytes play a pivotal role in both inflammation and maintenance of tissue homeostasis. In the central nervous system (CNS), they replenish a small proportion of tissue-resident macrophages in the dura mater and choroid plexus\u0026nbsp;[1, 2], and while typically constrained by the blood-brain barrier, readily infiltrate the CNS during inflammation\u0026nbsp;[3, 4]. Monocyte-derived effector cells (MCs), such as dendritic cells and macrophages, play crucial roles in CNS disorders. Their diverse functions include innate host defence, (e,g., phagocytosis of pathogens and tissue debris), initiation of adaptive defences (antigen presentation) and tissue repair. However, the precise role of cellular metabolism in shaping these functions in the context of CNS disease remains poorly defined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMC metabolism tends to be dichotomized simplistically into inflammatory (M1) and regulatory (M2) profiles, which are predominantly studied \u003cem\u003ein vitro\u003c/em\u003e [5]. In this classification, M2 MCs have regulatory functions that rely on an oxygen-dependent pathway supporting slower, but more efficient, ATP generation, while M1 MCs exert pro-inflammatory functions and rely on glycolysis to meet their high energy demands\u0026nbsp;[6]. Various M1 stimuli, such as interferon (IFN)-\u0026gamma;, lipopolysaccharide (LPS), tumour necrosis factor, viruses, and granulocyte-macrophage colony-stimulating factor, are characterized by their proinflammatory effects, yet they induce significantly different phenotypes and metabolic responses. This suggests a more complex and nuanced metabolic framework underpinning inflammatory functions. Supporting this, disease environments comprised of diverse inflammatory stimuli give rise to metabolic states tailored to cellular functions\u0026nbsp;[7, 8]. Such functions include nitric oxide (NO) production, type I interferon responses\u0026nbsp;[9-11], cytokine synthesis\u0026nbsp;[12, 13], antigen presentation\u0026nbsp;[14], phagocytosis\u0026nbsp;[13, 15, 16], and migration into inflamed tissues\u0026nbsp;[17, 18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWest Nile virus (WNV) is a mosquito-borne, neurotropic flavivirus and\u0026nbsp;one of the most important causative agents of human viral encephalitis worldwide\u0026nbsp;[19]. In its neuroinvasive phase, WNV can cause severe encephalitis, the pathogenesis of which is driven by infiltration of M1-like MCs into the CNS. Murine models clearly show that monocytes recruited from the bone marrow (BM) migrate to the brain in a CC motif chemokine ligand 2 (CCL2)-dependent manner, diapedesing into the CNS via Ly6C and activated very late antigen-4 (VLA-4) expressed on the monocyte surface. Once in the CNS they mediate immune pathology by the sustained production of NO\u0026nbsp;[20-22]. Strategies that block the entry of inflammatory monocytes into the brain, such as by CCL2, VLA-4 or Ly6C antibody blockade or immune modifying particles, or that attenuate their inflammatory response, such as inhibition of NO production using aminoguanidine hemisulphate, have been shown to improve survival of infected mice markedly without affecting viral load\u0026nbsp;[20-24], thus emphasizing the role of MCs in causing the inflammatory damage observed in WNV infection. Notably, these cells also adopt an antigen-presenting phenotype\u0026nbsp;[24]\u0026nbsp;and may indirectly aid in viral clearance by promoting an effective T cell response. However, it is unclear whether these differential functions are reflected in and effected by distinct metabolic profiles.\u003c/p\u003e\n\u003cp\u003eNumerous tools exist for the easy assessment of metabolism \u003cem\u003eex vivo.\u0026nbsp;\u003c/em\u003eHowever, traditional methods that measure metabolic respiration in bulk or across whole populations fail to capture the increasingly recognized metabolic diversity of MC populations in the context of disease. To address this, recent technological advancements have enabled single-cell analysis of metabolism through two primary methods. The first uses single-cell RNA sequencing (scRNA-seq) for broad analysis of gene expression related to metabolic changes. The second combines cytometric techniques with the detection of key metabolic enzymes, transporters, and transcription factors as a proxy measurement of the metabolic profile\u0026nbsp;[25-28]. An additional cytometric approach, SCENITH, combines metabolic inhibitors and protein synthesis quantification to assess the metabolic dependencies of immune cells at the single-cell level\u0026nbsp;[29]. The latter approaches overcome the limitations of inferring function from gene expression alone. Nevertheless, comprehensive integration of gene and protein data at the single-cell level is ultimately required to fully elucidate and map the cellular metabolic processes that lead to disease resolution or exacerbation.\u003c/p\u003e\n\u003cp\u003eIn this study, we identify tissue- and time-specific metabolic changes in response to severe flaviviral infection in the CNS, using both scRNA-seq and flow-based metabolic analysis. In particular, we show that MCs with a high glycolytic phenotype exhibited an inflammatory M1-like phenotype that distinguished them from antigen-presenting and other MC populations. Targeting glycolysis as an anti-inflammatory strategy preferentially affected M1-like cells while sparing antigen-presenting populations, resulting in amelioration of disease without increasing viral burden. This selective targeting emphasizes the potential of metabolic interventions in managing the intricate temporal pathogenesis of complex CNS disorders.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch2\u003eWNV infection\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFemale 9 to 10-week-old C57BL/6 mice obtained from Animal BioResources (NSW, Australia) or the Animal Resource Centre (WA, Australia) were anesthetized with isoflurane and infected intranasally with a LD100 dose (3x10\u003csup\u003e4\u003c/sup\u003e plaque-forming units) of WNV (Sarafend), as described previously\u0026nbsp;[30]. All experiments involving mice were approved by the University of Sydney Animal Ethics Committee (Protocol 1696).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAnimal treatments\u003c/h2\u003e\n\u003ch3\u003e2-deoxy-d-glucose (2-DG) treatment\u003c/h3\u003e\n\u003cp\u003e2-DG (Sigma-Aldrich, USA) was injected intraperitoneally once daily from 4-7 days post-infection (dpi) at a dose of 2g/kg body weight in 200 \u0026mu;L of sterile PBS. In some experiments, 2-DG (2g/kg) was administered once at 7 dpi at either 4.5 hours or 5.5 hours before euthanasia.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eDetection of proliferating cells with BrdU\u003c/h3\u003e\n\u003cp\u003eBromodeoxyuridine (BrdU) (Sigma-Aldrich, USA) was injected intraperitoneally 3 hours before sacrifice at a dose of 1 mg prepared in 200 \u0026mu;L of sterile PBS.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTracking recently infiltrating cells into the CNS with PKH26\u003c/h3\u003e\n\u003cp\u003ePKH26 cell linker was combined with diluent C (Sigma-Aldrich, USA) at a 10-fold higher concentration than the manufacturer\u0026rsquo;s recommendation and injected intravenously via the lateral tail vein 2 hrs before euthanasia, as previously described\u0026nbsp;[31].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eQuantification of viral titre using a plaque assay\u003c/h2\u003e\n\u003cp\u003eBaby Hamster Kidney (BHK) fibroblasts were used to perform a plaque assay, as previously described\u0026nbsp;[30]. Briefly, BHK cells were inoculated with brain tissue homogenates in a series of ten-fold dilutions for 1 hr, then subsequently overlaid with agarose. After a 3-day incubation, cells were fixed with 10% formalin (Sigma-Aldrich, USA), then stained with 1% crystal violet solution. Viral load, expressed as plaque-forming units (PFU) per gram of brain tissue, was calculated by quantifying the number of visible plaques and accounting for inoculum volume and dilutions used.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eTissue processing\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe brain, BM and cervical lymph nodes (LN) were collected from mice anesthetized with an intraperitoneal injection of avertin and perfused transcardially with PBS. Cells from the BM were isolated by flushing the femur with PBS using a 30-gauge needle. Red blood cells were then lysed using 1x Pharm Lyse Buffer (Invitrogen). Brains were digested enzymatically with DNase I (0.1 mg/mL, DN25, Sigma-Aldrich, USA) and collagenase type IV (1 mg/mL, C5138, Sigma-Aldrich, USA) using the gentleMACS dissociator (Miltenyi Biotec, DE). A 30/80% Percoll gradient was then used to isolate leukocytes from brain homogenates. Cervical lymph nodes were gently mashed through a 70 \u0026mu;m nylon mesh sieve using a syringe plunger. Live cells were counted with trypan blue (0.4%) on a hemocytometer.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCell culture\u003c/h2\u003e\n\u003ch3\u003eT cell proliferation assay\u003c/h3\u003e\n\u003cp\u003eFive million LN cells were stained with 1.5 \u0026mu;M of Carboxyfluorescein succinimidyl ester (CFSE) (Life Technologies) in 1 mL of PBS, as previously described\u0026nbsp;[32]. CFSE\u003csup\u003e+‑\u003c/sup\u003ecells (5 x 10\u003csup\u003e5\u003c/sup\u003e cells/well with two technical replicates) were then cultured for 1 hr at 37\u0026deg;C and 5% CO\u003csup\u003e2\u003c/sup\u003e in media or 50 \u0026mu;L of WNV solution (1 PFU/cell) with agitation every 10 minutes. Media was composed of RPMI (Lonza Biosciences, USA) supplemented with 1 U of HEPES (Sigma-Aldrich, USA), 5% FCS, 0.1 M of\u0026nbsp;b-mercaptoethanol (Gibco), and 1x penicillin streptomycin (Thermofisher Scientific). Unbound virus was then washed off and cells were cultured for a further 72 hours in media alone or media supplemented with 1 ug/mL of Concanavalin A (Sigma), as a positive control for T cell proliferation.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eBM stimulation\u003c/h3\u003e\n\u003cp\u003eBM cells (1.2 x 10\u003csup\u003e6\u0026nbsp;\u003c/sup\u003ecells/well with two technical replicates) were stimulated with 100 ng/mL IFN-\u0026gamma; (Biolegend, USA) for four hours and 100 ng/mL of lipopolysaccharide (LPS) (Invitrogen) overnight in a non-adherent 96-well plate (Corning). In some experiments (Figure 3D, E), 10 mM of 2-DG (Sigma-Aldrich, USA) was added to BM cells 1 hour prior to and during the IFN-\u0026gamma; and LPS stimulations.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSpectral flow cytometry\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSingle-cell suspensions were stained with anti-CD16/32 (fluorescently conjugated or unconjugated, Biolegend, USA) and Zombie UV Fixable Viability kit (Biologend, USA) for 30 minutes on ice, washed twice, and subsequently stained with MitoSOX in PBS for 30 minutes at 37\u0026deg;C. Cells were then washed twice and stained with a cocktail of fluorescently-labelled surface-stain antibodies in FACS on ice for 30 minutes. Cells were washed twice and permeabilized with True-Nuclear 1x Fix Concentrate (Biolegend, USA) for one hour at room temperature prior to staining with an intracellular/intranuclear antibody cocktail for 1 hour on ice, washed twice, and then stained with secondary antibodies targeting ACAC and CPT1\u0026alpha; for 1 hr on ice. The 5-Laser Aurora spectral cytometer (Cytek Biosciences, USA) was used to measure fluorescently tagged antibodies. Unstained controls for each specific condition were used as reference controls for spectral unmixing in each experiment. Acquired data was analysed using FlowJo (v10.8, BD Biosciences, USA). Quality control measures such as time, single cells, non-debris, and Live/Dead staining were applied to exclude debris, doublets, and dead cells.\u003c/p\u003e\n\u003cp\u003eThe FCS files were compensated and gated in FlowJo (BD Biosceicnes, USA). Monocytes (for BM samples) or MCs and microglia (for brain samples) were then exported and downsampled to a total of 3x10\u003csup\u003e5\u003c/sup\u003e cells (1.5x10\u003csup\u003e5\u003c/sup\u003e per organ). FCS files were formatted with the CyCombine package\u0026nbsp;[33]\u0026nbsp;and Arcsinh transformed (cofactor = 6000) before being converted into a Seurat object using a custom R script using Seurat (v4.0)\u0026nbsp;[34]. Two independent experiments were then batched integrated using rPCA in Seurat using all markers as integration anchors. \u0026nbsp;All subsequent analysis was performed on data as a Seurat object using the Seurat pipeline\u0026nbsp;[34]. Uniform manifold approximation and projection (UMAP) and clustering analysis was performed using only metabolic features.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of autofluorescence signatures on unstained samples was conducted using the OMIQ software from Dotmatics (www.omiq.aiwww.omiq.ai, www.dotmatics.comwww.dotmatics.com). Off-scale or debris events were excluded from analysis using scatter gating. Samples were arcsinh transformed (cofactor = 6000) and Opt-SNE was performed using the following OMIQ settings: maximum iterations = 1000, opt-sne End = 5000, perplexity = 30, Theta = 0.5, components = 2, verbosity = 25, and random seed values on 5x10\u003csup\u003e4\u0026nbsp;\u003c/sup\u003ecells (non-debris and non-erythrocyte single cells). Data was exported and analysed for media fluorescent intensity (MFI) values in FlowJo (BD Biosciences, USA).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe FCS files were compensated and gated down to individual cell populations prior to exporting cell proportions and MFI in FlowJo (BD Biosciences, USA). Cell proportions and live cell counts were used to quantify cell numbers. MFIs from positive populations, as determined by the isotype or fluorescence minus one (FMO) control, were used to quantify protein expression of metabolic targets. Statistical analyses were carried out in GraphPad Prism (version 10.2.0 for Mac, GraphPad Software, Boston, Massachusetts USA, www.graphpad.com) and the statistical tests used are indicated in each figure legend. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSingle-cell RNA-sequencing\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eMonocytes were isolated from WNV-infected murine brains and BM at the designated dpi. To ensure ample cells for sorting, tissues from two animals were combined per sample. Single cell suspensions underwent CD16/32 blocking and Zombie UV viability staining (Biolegend, USA) before incubation with a cocktail of fluorescently-conjugated surface stain antibodies\u0026nbsp;[24]. Sorting was performed on a 10-laser Influx Cell Sorter (BD Bioscences, USA) using the FACS Diva Program (BD Biosciences, USA) followed by sample barcoding with a mouse multiplexing kit using anti-CD45 (BD Biosciences, USA), as previously described\u0026nbsp;[24]. Cells were then pooled, stained for viability with Calcein AM and Draq7, and quantified using a BD Rhapsody Scanner. Quality control for cell sorting included metrics such as doublet rate and viability percentages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCell capture was performed according to manufacturer\u0026rsquo;s instructions. Briefly, single-cell transcriptomics was conducted using the BD Rhapsody Express System, and lysed cells were processed for reverse transcription and exonuclease I treatment. Libraries were created using microbead-captured single cell transcriptomes as per the manufacturer\u0026rsquo;s protocol using the BD Rhapsody cDNA Kit (BD Biosciences, USA) and the BD Rhapsody Targeted mRNA Amplification Kit (BD Biosciences, USA). The BD Rhapsody Immune Response Panel (Cat. # 633753), consisting of 397 genes, and an additional 67 custom genes were used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor sequencing, libraries were quantified using a Qubit Fluorometer (Thermo Fisher Scientific) and KAPA Library Quantification Kit (Roche), adjusted to 2nM, and pooled in a mRNA:sample tag ratio of 12.5:1. Sequencing was performed on an Illumina NextSeq1000, with stringent QC metrics such as 93.28 %Q30 and 81.73 %PF achieved. The run yielded 131 million reads with a high loading efficiency (98.4%).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSingle-cell RNA-sequencing analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eSevenBridges (Seven Bridges Genomics Inc., USA) was used to process counts (ie., distribution-based error correction molecules per cell)\u0026nbsp;from single-cell RNA-seq data. Seurat (v4.0) was used for pre-processing and removal of doublets/multiplets and cells with high mitochondrial reads (+3 median absolute deviations from the mean). After stringent quality control measures the final dataset contained 6,405 cells (Figure S1). Normalization and variance stabilization was then performed using \u003cem\u003esctransform()\u003c/em\u003e prior to UMAP and clustering. Cell types were annotated by sample tags and marker expression (Figure S1). MCs, separated from microglia and T cells, were re-clustered using 9 PCs at a resolution of 0.25.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe R package Slingshot (v1.8)\u0026nbsp;[35]\u0026nbsp;was used to define computationally imputed pseudotime trajectories from brain and BM MCs/monocytes at 7 dpi.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor module scores, genes for all functional and metabolic pathways were downloaded from the Mouse Genome Informatics database\u0026nbsp;[36]. Genes associated with the negative regulation of the module of interest were manually filtered out prior to filtering genes available in the dataset. Module scores for a particular functional or metabolic module were derived by running the function \u003cem\u003eAddModuleScore\u003c/em\u003e in Seurat, with 16 control genes and 24 bins.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eMCs adopt distinct metabolic and functional profiles in CNS infection\u003c/h2\u003e\n\u003cp\u003eSince BM-derived MCs infiltrating the CNS contribute significantly to immunopathology in WNV encephalitis, we sought to identify the specific metabolic pathways associated with the differentiation and development of these pathogenic responses. Monocyte-derived cells were sorted flow cytometrically from the brain at 5 and 7 days post-infection (dpi) and identified as Ly6G\u003csup\u003e-\u003c/sup\u003e, CD49\u003csup\u003ehi,\u0026nbsp;\u003c/sup\u003eP2RY12\u003csup\u003elo\u003c/sup\u003e, NK1.1\u003csup\u003e-\u003c/sup\u003e, CD3e\u003csup\u003e-\u003c/sup\u003e, CD11b\u003csup\u003e+\u003c/sup\u003e, CD64\u003csup\u003e+\u003c/sup\u003e and CX3CR1\u003csup\u003e+\u003c/sup\u003e, capturing the entire population of Ly6C\u003csup\u003ehi\u0026nbsp;\u003c/sup\u003eand Ly6C\u003csup\u003elo\u0026nbsp;\u003c/sup\u003eMCs at 5 and 7 dpi\u0026nbsp;[37]. Mature monocytes from the BM at 7 dpi were identified as CD45.2\u003csup\u003e+\u003c/sup\u003e, Ly6G\u003csup\u003e-\u003c/sup\u003e, CD48\u003csup\u003ehi\u003c/sup\u003e, NK1.1\u003csup\u003e-\u003c/sup\u003e, CD3e\u003csup\u003e-\u003c/sup\u003e, B220\u003csup\u003e-\u003c/sup\u003e, CD11b\u003csup\u003e+\u003c/sup\u003e, CD117\u003csup\u003e-\u003c/sup\u003e, CD115\u003csup\u003ehi\u003c/sup\u003e, as previously described\u0026nbsp;[24]. We then used a targeted scRNA-seq panel (n = 397 genes and 67 custom genes) to link the metabolic state and functional transcriptional profiles of monocyte subsets in acute viral infection.\u003c/p\u003e\n\u003cp\u003eClustering on scRNA-seq data from 3498 MCs and BM monocytes revealed four distinct states in the brain and three in the BM, respectively (Figure 1A). Of the brain subsets, we identified 1) an \u0026ldquo;antigen-presenting cell (\u003cem\u003eAPC\u003c/em\u003e)\u0026rdquo; population highly expressing genes involved in antigen presentation, including \u003cem\u003eH2-Aa\u003c/em\u003e, \u003cem\u003eH2-Eb1\u003c/em\u003e, \u003cem\u003eH2-Ab1\u003c/em\u003e, \u003cem\u003eCd74,\u003c/em\u003e and \u003cem\u003eCd86\u003c/em\u003e (Figure 1B), 2) a \u003cem\u003e\u0026ldquo;Microglia (Mg)-like MC\u0026rdquo;\u003c/em\u003e subset resembling microglia, due to its relative expression of microglia-specific markers \u003cem\u003eCd81\u003c/em\u003e, \u003cem\u003eSparc\u003c/em\u003e, \u003cem\u003eHexb\u003c/em\u003e, and \u003cem\u003eTmem119\u003c/em\u003e [37, 38]\u0026nbsp;(Figure 1B), 3) a \u003cem\u003eHif1a-\u003c/em\u003eexpressing population, and 4) a \u003cem\u003eNos2\u003c/em\u003e-expressing population, which likely represented the nitric oxide (NO)-producing population causing inflammatory damage in this model (Figure 1B). The functional status of these cell clusters was supported by manually querying several M1 functional programs, such as \u003cem\u003eantigen presentation\u003c/em\u003e, \u003cem\u003einflammatory response\u003c/em\u003e, and \u003cem\u003eviral response\u003c/em\u003e against gene modules from the Mouse Genome database\u0026nbsp;[39]\u0026nbsp;(Figure 1C). A high module score represents the average expression of the genes in the module relative to a set number of randomly extracted control genes from the dataset. The \u003cem\u003eAPC MC\u0026nbsp;\u003c/em\u003ecluster had a high antigen-presenting score and viral response score, suggesting this population may be involved in indirectly contributing to viral clearance (Figure 1C). By contrast, \u003cem\u003eHif1a\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e\u003c/em\u003eand \u003cem\u003eNos2\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e\u003c/em\u003eMCs exhibited a higher inflammatory response score, supporting a potential inflammatory phenotype that contributes to inflammatory damage in WNV encephalitis\u0026nbsp;[21]\u0026nbsp;(Figure 1C). All BM monocyte clusters (\u003cem\u003eMo1\u003c/em\u003e-\u003cem\u003eMo3\u003c/em\u003e) displayed low M1 functional scores, supporting the notion that these cells are an undifferentiated monocyte state (Figure 1C).\u003c/p\u003e\n\u003cp\u003eAs metabolism is coupled with the phenotype and functionality of MCs, we next scored genes involved in several metabolic pathways known to be important in M1- or M2-like functions, including glycolysis, adenosine triphosphate (ATP) biosynthesis, fatty acid synthesis, electron transport chain (ETC), amino acid metabolism, and the tricarboxylic acid (TCA) cycle (Figure 1D). BM monocytes (\u003cem\u003eMo1\u003c/em\u003e, \u003cem\u003eMo2\u003c/em\u003e, and \u003cem\u003eMo3\u003c/em\u003e) and \u003cem\u003eMg-like\u003c/em\u003e \u003cem\u003eMC\u003c/em\u003e displayed higher expression of metabolic pathways related to homeostatic functions, including the TCA cycle, ATP biosynthesis, and ETC, but also displayed high amino acid metabolism scores (Figure 1D and 1E). Inflammatory \u003cem\u003eHif1a\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e and \u003cem\u003eNos2\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;\u003c/em\u003eMCs displayed higher glycolysis and amino acid metabolism scores with low TCA cycle scores (Figure 1D). Expression of these metabolic pathways clustered with typical inflammatory functions, including the \u003cem\u003einflammatory response, reactive oxygen species production\u003c/em\u003e, and \u003cem\u003ephagocytosis\u003c/em\u003e (Figure 1E), supporting the notion that these \u003cem\u003eHif1a\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e and \u003cem\u003eNos2\u003csup\u003e+\u003c/sup\u003e\u0026nbsp;\u003c/em\u003ecells adopt a typical M1-like phenotype in WNV encephalitis. On the other hand, \u003cem\u003eAPC MCs\u003c/em\u003e were evidently less reliant on glycolysis and amino acid metabolism pathways, but exhibited higher fatty acid synthesis scores (Figure 1D and 1E). This suggests that there exists wide metabolic heterogeneity in typical M1-like functions in CNS infection, including the inflammatory response and antigen presentation, which is likely obscured in bulk \u003cem\u003ein vitro\u003c/em\u003e systems.\u003c/p\u003e\n\u003cp\u003eTo understand the differentiation pathway of BM monocytes into specific metabolic states in the CNS, we employed trajectory analysis. This showed that BM monocytes progressed through 3 different metabolic profiles before they migrated to the brain, where they underwent a transition towards either \u003cem\u003eAPC\u003c/em\u003e or \u003cem\u003eNos2\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eMC\u003c/em\u003e populations in the brain at disease endpoint (Figure 1G). Both lineages passed through the \u003cem\u003eHif1a\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e \u003cem\u003eMC\u003c/em\u003e population, implying that this population is a transitional state. The \u003cem\u003eMg-like MC\u003c/em\u003e population, however, did not align with these pathways, indicating that it is a unique MC population (Figure 1F). This distinctness of this 4\u003csup\u003eth\u003c/sup\u003e population raises the question of contamination by microglial cells. However, we have established that monocytes circulating in the bloodstream can assume a microglial-like phenotype within the WNV-infected brain, a change attributable to prolonged interaction with the CNS milieu\u0026nbsp;[37].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring their differentiation into brain MCs, BM monocytes consistently downregulated genes related to ATP synthesis, the TCA cycle, ETC and pentose phosphate pathway, while upregulating genes related to glycolysis (Supplementary Figure 2). This indicates a significant metabolic shift as MCs transition from the BM to the infected brain, possibly through a shared \u003cem\u003eHif1a\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e intermediate, before diverging into functionally distinct pathways towards antigen-presenting (\u003cem\u003eAPC\u003c/em\u003e) or NO-producing (\u003cem\u003eNos2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) phenotypes.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eApplication of MetFlow to CNS infection reveals distinct metabolic changes in CNS disease\u003c/h2\u003e\n\u003cp\u003eGiven the limitations in existing tools to study metabolism by gene expression alone, we adapted MetFlow\u0026nbsp;[27]\u0026nbsp;to murine cells and used metabolic marker proteins with high relevance to myeloid cells during inflammation. This comprised 13 immune cell identification markers and 9 metabolic marker proteins, including rate-limiting enzymes, signalling molecules, and transcription factors that have roles in glycolysis, the tricarboxylic acid cycle, hypoxia-induced inflammation, amino acid transport, fatty acid synthesis and oxidation, the kynurenine pathway, NO production, and mitochondrial ROS production (Figure 2A, Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify metabolic signatures independently of cell lineage and origin, we performed dimensionality reduction on whole brain and BM cell isolates and clustered on metabolic markers (Figure 2B). Resident microglia (Ly6G\u003csup\u003e-\u003c/sup\u003e, SSCA\u003csup\u003elo\u003c/sup\u003e, NK1.1\u003csup\u003e-\u003c/sup\u003e, CD3e\u003csup\u003e-\u003c/sup\u003e, B220\u003csup\u003e-\u003c/sup\u003e, CD45\u003csup\u003elow-int\u003c/sup\u003e, CX3CR1\u003csup\u003e+\u003c/sup\u003e), brain-infiltrating MCs (defined as Ly6G\u003csup\u003e-\u003c/sup\u003e, SSCA\u003csup\u003elo\u003c/sup\u003e, NK1.1\u003csup\u003e-\u003c/sup\u003e, CD3e\u003csup\u003e-\u003c/sup\u003e, B220\u003csup\u003e-\u003c/sup\u003e, CD45\u003csup\u003ehi\u003c/sup\u003e, CX3CR1\u003csup\u003elow\u003c/sup\u003e) and BM monocytes (defined as Ly6G\u003csup\u003e-\u003c/sup\u003e, SSCA\u003csup\u003elo\u003c/sup\u003e, NK1.1\u003csup\u003e-\u003c/sup\u003e, CD3e\u003csup\u003e-\u003c/sup\u003e, B220\u003csup\u003e-\u003c/sup\u003e, CD11c\u003csup\u003elo\u003c/sup\u003e, MHC-II\u003csup\u003elo\u003c/sup\u003e, CD11b\u003csup\u003e+\u003c/sup\u003e, Ly6C\u003csup\u003ehi/lo\u003c/sup\u003e, CX3CR1\u003csup\u003ehi/lo\u003c/sup\u003e) clustered into 8 distinct metabolic states which varied in proportion across timepoints and were distinctly grouped by organ and lineage/differentiation status (Figure 2C-H), demonstrating a significant metabolic adaptation over the course of infection. Within the brain, four main MC populations were detected (clusters 2, 4, 5, \u0026amp; 6) (Figure 2F, H), while three predominant monocyte populations were found in the BM (Figure 2F). This distribution mirrors the population diversity revealed by single-cell RNA sequencing (Figure 1), corroborating our findings across both techniques.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the brain, cluster 0 was associated with microglia and was found in the highest proportions in the mock-infected brain (Figure 2F, 2G). By 7 dpi, microglia predominantly transitioned into cluster 6 (Figure 2G). However, the overall proportion of microglia was significantly reduced due to the massive influx of infiltrating MCs in the brain, which exceed microglia by approximately 10-fold at this timepoint (Figure 2F). Thus, cell numbers in clusters 1, 2, 4, 5, 6, and 7, predominantly comprising infiltrating MCs, were markedly increased in the brain, compared to mock-infected mice (Figure 2I), with numbers in cluster 7 some 8-fold greater than the next largest, cluster 6 (Figure 2K). In the BM, clusters 0, 1, 2, 3, 6 and 7 were numerically increased at 7 dpi, relative to mock-infected mice (Figure 2J), however, clusters 1, 2, and 3 comprised the majority of BM monocytes at 7 dpi, both numerically and by proportion (Figure 2F, L). These findings demonstrate clear metabolic remodelling both at peripheral sites of monocyte myelopoiesis and inflammatory foci in response to infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupporting our scRNA-seq findings, GAPDH was upregulated in the brain, compared to the BM (Figure 2M), emphasizing the importance of glycolysis in the differentiation of MCs. This was most obvious in cluster 7 expressing high levels of iNOS (Figure 2N), likely denoting the pathogenic NO-producing MCs implicated in immunopathology. Along with increased iNOS expression, this cluster exhibited elevated GAPDH, HIF1-\u0026alpha;, and CD98 levels (Figure 2N), reflecting the \u003cem\u003eNos2\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e and \u003cem\u003eHif1a\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eprofiles identified via scRNA-seq (Figure 1) and constituted the majority of myeloid cells in the brain, as mentioned above (Figure 2K). In contrast to cluster 7, clusters 6, 4, and 2, the next-largest remaining clusters, displayed lower expression of iNOS, HIF1-\u0026alpha; and GAPDH (Figure 2N), suggesting a deviation from typical pro-inflammatory metabolic pathways. Interestingly, however, cluster 2, which expresses higher HIF1-\u0026alpha;, MitoSOX, and fatty acid metabolism markers (Figure 2N), is initially present in the brain by 5 dpi, coinciding with significant monocyte infiltration (Figure 2F, H). While remaining significantly elevated compared to controls (Figure 2L), cluster 2 had declined in the brain by 7 dpi (Figure 2H), coinciding with the appearance of iNOS\u003csup\u003e+\u003c/sup\u003e cluster 7, suggesting it may be a precursor to this subset in the brain at 5 dpi.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe metabolic diversity of monocytes in the BM additionally reflects an adaptation to infection. BM cluster 3 is the only BM cluster with high GAPDH expression (Figure 2N) and it expands at 7 dpi in the BM (Figure 2J, L), suggesting this cluster is a possible BM precursor for the pathogenic iNOS\u003csup\u003e+\u003c/sup\u003e state observed in the brain at 7 dpi. This notion is further supported by the close clustering of these populations by expression of their metabolic proteins (Figure 2N). While the developmental trajectory of metabolic clusters in the BM is unclear, it is possible that the metabolically quiescent cluster 1 (Figure 2F), which expanded its proportion early in infection but was reduced by dpi 7, transits progressively towards more metabolically active subsets, such as cluster 2 and 3, which may give rise to distinct MC subsets in the brain. These findings suggest that the metabolic conditioning of BM cells may prime MCs for distinct trajectories of differentiation prior to their infiltration into the inflamed brain.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMHC-II\u003csup\u003e+\u003c/sup\u003e and iNOS\u003csup\u003e+\u003c/sup\u003e MCs have distinct metabolic profiles\u003c/h2\u003e\n\u003cp\u003eAs MCs in WNV may adopt both NO-producing and antigen-presenting phenotypes in the brain, we next aimed to determine if these functional profiles were reflected by metabolic differences. Cluster 7 and 4 were determined to be an NO-producing and antigen-presenting cell (APC) subset, respectively, based on their differential expression of MHC-II and iNOS (Figure 3A). Intriguingly, compared to the NO-producing cluster 7, the APC cluster 4 expressed all metabolic proteins at lower levels (Figure 3B). This observation suggests that the metabolic activity of cluster 7 might require synergism from multiple metabolic pathways to sustain this heightened inflammatory state, compared to that required for antigen presentation. Interestingly, inflammatory iNOS\u003csup\u003e+\u003c/sup\u003e cells displayed an increased expression of glycolysis-related markers, such as GAPDH and HIF1-\u0026alpha;, compared to APC MCs (Figures 3B and 3C). These markers are known to be closely linked with glycolytic activity, suggesting that an APC MC phenotype is less reliant on glycolysis than the classical M1-like phenotype associated with cluster 7.\u003c/p\u003e\n\u003cp\u003eTo substantiate the link between glycolysis and the inflammatory M1-like phenotype, BM-derived cells were stimulated to express a classical inflammatory M1 phenotype using IFN-\u0026gamma; and LPS. Treatment with the glycolysis inhibitor 2-deoxy-D-glucose (2-DG) significantly reduced both and the percentage of F4/80\u003csup\u003ehi\u003c/sup\u003e MCs expressing iNOS and the level of iNOS expressed (Figure 3D and 3E). Additionally, 2-DG treatment led to a reduction in GAPDH (Figure 3F), further supporting the relationship between NO production and glycolysis under M1 stimulation. Notably, 2-DG did not alter the expression of HIF1-\u0026alpha;, indicating that while HIF1-\u0026alpha; may be associated with glycolytic processes, its activity can operate independently of them (Figure 3G). Collectively, these findings reinforce the notion that pathogenic, NO-producing MCs utilize glycolysis differently from other MC subsets, such as antigen-presenting cells, particularly in the context of virus-induced neuroinflammation.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eGlycolysis inhibition is protective in West Nile virus encephalitis\u003c/h2\u003e\n\u003cp\u003eTo determine whether pro-inflammatory monocyte metabolism could be therapeutically targeted in lethal infection, we next treated mice with a glycolysis inhibitor, 2-deoxy-D-glucose (2-DG) (Figure 4A), a nonmetabolizing glucose analogue and competitive inhibitor of hexokinase 1. 2-DG was administered at a dose of 2g/kg daily from 4 dpi to the disease endpoint at 7 dpi (Figure 4B). Remarkably, 2-DG treatment led to a discernible clinical improvement, as indicated by lower disease severity scores at 7 dpi (Figure 4C) and reduced weight loss (Figure 4D), with a modest, but significant increase in the mean time to death (Figure 4E).\u0026nbsp;These improvements were not due to a reduction in viral load, as both 2-DG and PBS-treated mice exhibited comparable viral burdens in the brain at 7 dpi (Figure 4F), emphasizing that the effects of 2-DG are likely mediated through modulation of the pathological immune response within the brain.\u003c/p\u003e\n\u003cp\u003eNotably, 2-DG also reduced the overall neuroinflammatory infiltrate to approximately 35% of that seen in control-treated mice on day 7 post-infection (Figure 4G). A significant reduction in cell numbers was observed in infiltrating neutrophils, natural killer cells, CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells, and MCs in the brain, but not microglia (Figure 4H). Furthermore, all these cells, including microglia, showed a decrease in GAPDH expression (Figure S3). This reduction in immune cell infiltration may be due to a requirement of glycolysis for immune cell migration across endothelial barriers\u0026nbsp;[40], and/or the reduction in MC numbers, which contribute to the accumulation of other immune cells\u0026nbsp;[24, 41]. Irrespective, these findings collectively highlight the potential of glycolysis inhibition as a therapeutic approach to attenuate immune cell infiltration during severe CNS inflammation.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eGlycolysis inhibition reduces monocyte infiltration into the CNS, but does not reduce BM myelopoiesis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eGlycolysis has been shown to be important for both myelopoiesis and cellular migration. We have previously shown that diminished myelopoiesis correlates with reduced brain MC numbers and better clinical outcomes in WNV infection\u0026nbsp;[31]. Thus, to investigate whether the protective effect of 2-DG was due to a reduction in myelopoiesis and/or subsequent CNS infiltration, we first measured changes in monocyte proliferation using BrdU, which incorporates detectably into synthesising DNA (Figure 5A, B). Our data revealed no significant changes in BrdU incorporation following 2-DG treatment (Figures 5C-D), with proliferating cell proportions consistent across all monocyte differentiation phases and other myeloid cell types (Figure 5D, S4). Correspondingly, total BM monocyte counts were comparable between PBS- and 2-DG-treated mice (Figure 5E).\u003c/p\u003e\n\u003cp\u003eWe then assessed the effect of 2-DG on cellular migration into the brain. WNV-infected mice were administered a single dose of 2-DG at 7 dpi followed by intravenous\u0026nbsp;injection of the\u0026nbsp;fluorescent\u0026nbsp;dye,\u0026nbsp;PKH26\u0026nbsp;(Figures 5F, G).\u0026nbsp;This labels blood and BM cells in the vasculature \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003e(Figures 5F, G), thereby enabling the discrete identification of cells that have recently infiltrated into the brain from the periphery\u0026nbsp;[31]. Post-treatment analysis showed no significant change in total brain MC numbers, compared to untreated WNV-infected mice (Figure 5H). However, there was a significant decrease in the proportion of PKH26\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e MCs (Figure 5I), with a notable reduction in the infiltration rate of PKH26\u003csup\u003e+\u003c/sup\u003e MCs into the brain, but no reduction in the rate of infiltration of other dye-positive leukocytes (Figure 5J). This corresponded to a significant decrease in the total number of PKH26\u003csup\u003e+\u003c/sup\u003e MCs in the brain not evident in other infiltrating cells (Figure 5K). This indicates an acute MC-specific effect of 2-DG on MC migration into the brain. It also strongly suggests that the reduced presence of other\u003cem\u003e\u0026nbsp;\u003c/em\u003eimmigrating leukocytes after longer term treatment with 2-DG from 4-7 dpi (Figure 4G, H) is a consequence of reduced recruitment occasioned by the accumulation of fewer MCs in the brain, rather than the dependence on glycolysis \u003cem\u003eper se\u0026nbsp;\u003c/em\u003efor diapedesis by non-MCs. Supporting this, 2-DG treatment did not affect T cell proliferation or the absolute number of effector and memory T cells nor their GAPDH levels in the cervical lymph nodes draining the brain following daily 2-DG treatment from 4-7 dpi (Figure S5), indicating that the reduced T cell numbers in the brain is not due to systemic effects of 2-DG on T cell expansion in the lymph nodes. Taken together, this is consistent with findings that inhibiting monocyte brain accumulation \u003cem\u003ee.g.,\u0026nbsp;\u003c/em\u003evia Ly6C blockade or clodronate liposome administration results in significantly reduced T cell and NK cell infiltration\u0026nbsp;[24, 41]. Additionally, we found that 2-DG preferentially reduced the number of dye-positive iNOS\u003csup\u003e+\u003c/sup\u003e MCs compared to MHC-II\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eMCs in the brain (Figure 5 L, M), suggesting that glycolysis inhibition may preferentially impede the differentiation of iNOS\u003csup\u003e+\u003c/sup\u003e MCs once in the brain, likely due to their particular reliance on glycolysis.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eGlycolysis inhibition differentially affects NO-producing and antigen-presenting capacity of myeloid cells\u003c/h2\u003e\n\u003cp\u003eWe next aimed to determine whether systemic glycolysis inhibition differentially affects MC subsets. To do this, all iNOS\u003csup\u003e+\u003c/sup\u003e and MHC-II\u003csup\u003e+\u003c/sup\u003e MCs were manually gated for analysis (Figures 6A, 6E and Figure S6). Metabolic profiling revealed a \u0026gt;80% decrease in numbers of iNOS\u003csup\u003e+\u003c/sup\u003e MCs within the inflamed brain post 2-DG treatment (Figure 6B), mirroring a similar decline in the proportion of MCs expressing iNOS (Figure 6C). Importantly, 2-DG treatment only affected the expression of markers related to glycolysis (GAPDH) and nitric oxide production (iNOS) (Figure 6D), demonstrating that 2-DG treatment selectively reduced glycolysis without impacting other metabolic pathways. We also observed a significant increase in HIF1-\u0026alpha; with 2-DG treatment (Figure 6D), supporting our \u003cem\u003ein vitro\u003c/em\u003e work suggesting that glycolysis-dependent NO production is independent of HIF1-\u0026alpha; signalling in M1-like cells (Figure 3G). This pattern emphasizes the specificity of 2-DG on glycolysis, with negligible effects on other examined metabolic processes.\u003c/p\u003e\n\u003cp\u003eDespite the substantial decrease in iNOS\u003csup\u003e+\u003c/sup\u003e MCs, the number of MHC-II\u003csup\u003e+\u003c/sup\u003e MCs present in the brain was not reduced following 2-DG treatment (Figure 6F). This led to a significant proportional increase of 3-4-fold in MHC-II\u003csup\u003e+\u003c/sup\u003e MCs (Figure 6G), emphasising the disproportionate impact of glycolysis inhibition on iNOS\u003csup\u003e+\u003c/sup\u003e cells. Interestingly, in the MHC-II\u003csup\u003e+\u003c/sup\u003e cell population, we noted a significant reduction solely in the GAPDH and iNOS expression, similar to the iNOS\u003csup\u003e+\u003c/sup\u003e cells (Figure 6H), indicating that pathways already reduced in this population may be inhibited still further by 2-DG in these cells.\u003c/p\u003e\n\u003cp\u003eTo examine the functional implications of glycolysis inhibition in more detail, we stimulated BM cells, isolated from 2-DG- and PBS-treated WNV-infected mice, with classical M1 activation stimuli (IFN-\u0026gamma; + LPS) \u003cem\u003ein vitro\u003c/em\u003e (Figure 6I). Monocytes from 2-DG-treated mice exhibited significantly reduced expression of iNOS relative to vehicle-treated WNV-infected mice, suggesting that BM monocytes from 2-DG-treated mice have reduced capacity for NO production in response to inflammatory stimuli. Interestingly, although these cells significantly reduced iNOS expression, BM monocytes from 2-DG-treated mice showed significantly increased MHC-II expression in response to inflammatory stimulation relative to control-treated mice, presumably due to IFN-\u0026gamma; exposure (Figure 6K). This further suggests that systemic 2-DG treatment may affect the inflammatory potential of monocytes prior to their differentiation into effector MCs, without affecting their capacity for antigen presentation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo confirm that antigen-presenting functions are not impacted by 2-DG, we isolated draining cervical lymph nodes from 4-7 dpi 2-DG- and vehicle-treated WNV-infected mice and vehicle-treated mock-infected mice at 7 dpi and stimulated them with WNV, which contains both replicating virus and free viral antigen, for 72 hours (Figure 6L). Effector CD4\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cell differentiation (Figure 6M) was significantly increased following \u003cem\u003ein vitro\u003c/em\u003e viral restimulation of T cells isolated from WNV-infected mice treated with vehicle or 2-DG, compared to those from mock-infected mice (Figure 6N). Importantly, 2-DG-treated and vehicle-treated WNV-infected mice showed comparable numbers of effector CD4\u003csup\u003e+\u003c/sup\u003e T cells, suggesting that the capacity of antigen-presenting cells to stimulate an effector T cell response is unaffected by 2-DG treatment. Supporting this, the proportions of proliferating effector CD4\u003csup\u003e+\u003c/sup\u003e T cells (Figure 6O and 6P) and IFN-\u0026gamma;-producing effector T cells (Figure 6Q) in response to viral re-stimulation were also unaffected by 2-DG treatment. Together, this demonstrates that systemic 2-DG treatment specifically targets NO-producing MCs in cluster 7 to reduce NO, without impacting antigen-presenting function of MCs in cluster 4.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents the first dual approach using both\u0026nbsp;scRNA-seq and spectral cytometry to inspect the metabolic profiles of MCs at a gene and protein level, uncovering for the first time the intricate metabolic diversity of these cells within the context of CNS disease. In doing this, we identified a pathogenic NO-producing MC population in the WNV-infected brain which expressed glycolytic markers. Targeting this population with 2-DG specifically reduced their migration into the brain and impaired their ability to produce NO. Strikingly, this corresponded with a significant reduction in clinical and neuroinflammatory signs, highlighting the therapeutic potential of modulating immunometabolism to resolve disease.\u003c/p\u003e\n\u003cp\u003eOur findings indicate that MCs adopt multiple functional roles, each defined by a unique metabolic phenotype.\u0026nbsp;While the NO-producing MCs described in this report exhibited a metabolic profile suggestive of the traditional M1 phenotype, this varied significantly from the conventional M1 or M2 categorization.\u0026nbsp;The local tissue environment was a decisive factor influencing these metabolic patterns, as BM monocytes and brain MCs demonstrated distinct metabolic signatures. This observation\u0026nbsp;is consonant\u0026nbsp;with recent research highlighting the significance of tissue origin in shaping the metabolic characteristics of resident macrophages\u0026nbsp;[8], in which the varying nutritional and cellular contexts provided by different tissues likely drive cells towards specific metabolic pathways. Furthermore, the divergent developmental origins of microglia (arising from the yolk sac) and MCs (originating from hematopoietic stem cells in the BM) likely contributed to their metabolic differences during infection. Notably, microglia tended to maintain a more homeostatic metabolic state, while MCs infiltrating the brain shifted towards more active metabolic profiles. Overall, this distinction emphasizes the importance of both origin and environment in the metabolic identity of immune cells in the CNS during infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;metabolic programming of monocytes\u0026mdash;whether established during development in the BM or upon entry into the virus-infected CNS\u0026mdash;remains unclear. Our findings suggest metabolic cues in each organ play an important role, with\u0026nbsp;trajectory analysis\u0026nbsp;revealing a clear metabolic progression of monocytes migrating from the BM to the CNS.\u0026nbsp;This transition is marked by a decreased expression of genes involved in ATP production, the TCA cycle, and the ETC, coupled with an increased reliance on glycolysis.\u0026nbsp;While the developmental trajectory of metabolic clusters in BM remains to be fully elucidated, our observations indicate that cluster 1, initially expanding in the early stages of infection but diminishing by dpi 7, may evolve into more metabolically active clusters 2 and 3. This initial metabolic state of BM cells could set the stage for varied differentiation pathways of MCs, effectively \u0026apos;priming\u0026apos; them before they migrate to the inflamed brain environment. Once in the brain, BM monocytes evidently assume a HIF1-\u0026alpha;\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eintermediate state, which may act as the branching point for divergence into iNOS\u003csup\u003e+\u003c/sup\u003e or APC MC subsets.\u0026nbsp;Such a metabolic shift might be an adaptive strategy to ensure survival in the hypoxic conditions of the infected CNS\u0026nbsp;[42]. Indeed, the transcription factor HIF1\u0026alpha;, known to drive the expression of glycolytic enzymes, is crucial for myeloid cell motility during mild hypoxia such as inflammation\u0026nbsp;[17, 18]. Importantly, this marker was significantly upregulated in glycolytic MCs in our study and its expression was retained in infiltrating MCs following 2-DG treatment, presumably enabling diapedesis of\u0026nbsp;APC MCs, but not iNOS\u003csup\u003e+\u003c/sup\u003e MCs. Our scRNA-seq analysis further supports\u0026nbsp;this hypothesis, showing a close association between the upregulation of glycolysis and macrophage migration in \u003cem\u003eNos2\u003csup\u003e+\u003c/sup\u003e\u003c/em\u003e and \u003cem\u003eHif1a\u003csup\u003e+\u0026nbsp;\u003c/sup\u003e\u003c/em\u003eMCs.\u0026nbsp;Additionally,\u0026nbsp;2-DG treatment significantly reduced physical monocyte migration into the inflamed brain without impacting myelopoiesis, suggesting that glycolytic inhibition mediates\u0026nbsp;its\u0026nbsp;protective effect by preferentially preventing inflammatory cellular transmigration across the blood-brain barrier. This specific targeting of inflammatory MCs may be due to their entry at distinct anatomical locations in the CNS or their exposure to different regions of the brain that have different rates of infection and unique cytokine profiles\u0026nbsp;[43]. This link is substantiated by observations in experimental autoimmune encephalomyelitis and human multiple sclerosis, where heightened glycolytic activity is associated with the transmigration of inflammatory macrophages into the brain\u0026nbsp;[44].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnhanced glycolysis observed in immune cells during WNV encephalitis and other diseases strongly suggests that targeting metabolic reprogramming within inflammatory cells could be a promising approach for immune therapy. In this study, 2-DG treatment preferentially impacted pathogenic, NO-producing MCs. Furthermore, the ability of 2-DG to cross the blood-brain barrier\u0026nbsp;[45]\u0026nbsp;may contribute to ongoing targeting of these cells at the site of inflammation. This is supported by data showing the 2-DG reduces the expression of inflammatory genes and interleukins in the CNS during WNV infection\u0026nbsp;[46], suggesting a broad reduction in brain inflammation. Additionally, in experimental autoimmune encephalomyelitis, 2-DG treatment skewed monocytes/macrophages towards an anti-inflammatory state in the CNS, providing protection and overall clinical improvement\u0026nbsp;[45]. Despite the evident anti-inflammatory effect of glycolysis inhibition at the sites of inflammation, we additionally observed that BM monocytes derived from 2-DG treated mice showed a decreased NO response to inflammatory stimuli outside the brain. This suggests an early modulation of their inflammatory potential, likely due to the dependence of NO synthesis on glycolysis, with the capacity to produce NO not fully restored upon their entry into the brain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, we show that antigen-presenting MCs have a metabolic profile distinct from those producing NO, primarily attributed to reduced glycolytic activity. MHC-II\u003csup\u003ehi\u0026nbsp;\u003c/sup\u003emacrophages also displayed lower reliance on glycolysis than MHC-II\u003csup\u003elo\u0026nbsp;\u003c/sup\u003emacrophages\u0026nbsp;[47], and monocytes diminish glycolytic activity as they develop into an antigen-presenting phenotype \u003cem\u003ein vitro\u003c/em\u003e [48], suggesting a negative association between glycolysis and antigen presentation. Supporting this, elevated glucose levels have been shown to hinder antigen presentation and disrupt CD4\u003csup\u003e+\u003c/sup\u003e T cell activation\u0026nbsp;[49]. Conversely, glucose limitation appears to enhance MC-mediated T cell responses, as demonstrated by the increased expression by glucose-deprived MCs of co-stimulatory molecules and interleukin-12 essential for T cell proliferation and function\u0026nbsp;[50]. Thus, APC may strategically reduce glycolysis to adapt to the glucose-scarce environment generated by metabolically-active T cells, thereby prolonging the T cell response\u0026nbsp;[50]. In this study, MCs may transition to an antigen-presenting role as their glycolytic activity declines to optimize their capacity to present antigens effectively. Supporting this, APCs from the draining lymph nodes of 2-DG treated mice maintained their ability to elicit an anti-viral T cell response after antigen rechallenge \u003cem\u003eex vivo\u003c/em\u003e, suggesting that (1) APCs from the 2-DG-treated mice preserve their antigen presenting efficacy, and (2) the formation of a memory T cell response is not compromised by systemic 2-DG treatment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe unaffected memory T cell response during glycolytic inhibition may be attributed to their reliance on fatty acid oxidation, a metabolic pathway essential for memory development\u0026nbsp;[51, 52]. Importantly, while 2-DG treatment significantly supressed glycolytic markers, it did not alter other metabolic pathways, including fatty acid oxidation. Consequently, despite a reduction in T cell numbers in the brain over three days of 2-DG treatment, T cell proliferation and memory formation evidently remained intact. Furthermore, the absence of acute inhibition of T cell immigration by 2-DG, in contrast to MCs, strongly suggests that reduced T cell infiltration into the brain by longer term 2-DG treatment was more likely a consequence of their reduced recruitment by low MC numbers than direct migration inhibition of these cells by 2-DG. In summary, our findings suggest that glycolytic inhibition selectively hinders the infiltration of hyperinflammatory cells without affecting the functional development of a robust T cell response during WNV infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research highlights that modulating the metabolic pathways active in pathogenic monocytes can mitigate disease severity by specifically tempering uncontrolled inflammation, a key contributor to disease exacerbation and progression. This nuanced approach preserves essential immune processes, including pathogen clearance and memory formation, and may be most effective when combined with anti-viral therapies. Unlike broad-acting immunosuppressants like corticosteroids, which indiscriminately suppress both detrimental and beneficial immune responses, metabolic modulation offers more targeted control of the inflammatory response. Although the effectiveness of this strategy in humans requires further study, these findings reinforce the potential of metabolic targeting as a component of combination therapy for immune regulation in diseases with severe or uncontrolled inflammation. \u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch1\u003eAcknowledgements\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eThis work was supported by the Merridew Foundation, National Health and Medical Research Council (1088242), and the Charles Perkins Centre Early to Mid-Career Researcher Seed Funding Grant (University of Sydney). Our appreciation goes to Dr. Carol Ford, Dr. Frank Kao, and Dr. Andy Law from BD Bioscience, as well as Dr. Thomas Ashhurst, Moumita Paul and Associate Professor Alex Sharland for their support in aiding with our Rhapsody data sequencing. We would also like to thank Kate Pilkington for her expertise on autofluorescence extraction and analysis of spectral flow cytometry data, and Jemma Taitz, Camille Potier-Villette, and Dr. Duan Ni for assistance with experiments. We also wish to acknowledge the support of the University of Sydney’s Laboratory Animal Services and the Sydney Cytometry facilities.\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003eAuthor Contributions\u0026nbsp;\u003c/h1\u003e\n\u003cp\u003eCLW: conceptualization; data curation; formal analysis; investigation; methodology; visualisation; writing – original draft; writing – reviewing and editing. AGS and JT: data curation; methodology; writing – reviewing and editing. LM: supervision; writing – reviewing and editing. NJCK: conceptualization; funding acquisition; project administration; resources; supervision; writing – reviewing and editing.\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003eConflict of Interest\u003c/h1\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eT. Goldmann, P. Wieghofer, M.J.C. Jord\u0026atilde;o, F. Prutek, N. Hagemeyer, K. Frenzel, L. Amann, O. Staszewski, K. Kierdorf, M. Krueger, Origin, fate and dynamics of macrophages at central nervous system interfaces, Nat Immunol, 17 (2016) 797.\u003c/li\u003e\n\u003cli\u003eH. Van Hove, L. Martens, I. Scheyltjens, K. De Vlaminck, A.R.P. Antunes, S. De Prijck, N. Vandamme, S. De Schepper, G. Van Isterdael, C.L. 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Yong, Enhanced glycolytic metabolism supports transmigration of brain-infiltrating macrophages in multiple sclerosis, The Journal of clinical investigation, 129 (2019) 3277-3292.\u003c/li\u003e\n\u003cli\u003eI. Zahoor, H. Suhail, I. Datta, M.E. Ahmed, L.M. Poisson, J. Waters, F. Rashid, R. Bin, J. Singh, M. Cerghet, Blood-based untargeted metabolomics in relapsing-remitting multiple sclerosis revealed the testable therapeutic target, Proceedings of the National Academy of Sciences, 119 (2022) e2123265119.\u003c/li\u003e\n\u003cli\u003eP. Mingo-Casas, A.-B. Bl\u0026aacute;zquez, M. G\u0026oacute;mez de Cedr\u0026oacute;n, A. San-F\u0026eacute;lix, S. Molina, E. Escribano-Romero, E. Calvo-Pinilla, N. Jim\u0026eacute;nez de Oya, A. Ram\u0026iacute;rez de Molina, J.-C. Saiz, Glycolytic shift during West Nile virus infection provides new therapeutic opportunities, Journal of Neuroinflammation, 20 (2023) 217.\u003c/li\u003e\n\u003cli\u003eX. Geeraerts, J. Fernandez-Garcia, F.J. Hartmann, K.E. de Goede, L. Martens, Y. Elkrim, A. Debraekeleer, B. Stijlemans, A. Vandekeere, G. Rinaldi, Macrophages are metabolically heterogeneous within the tumor microenvironment, Cell reports, 37 (2021).\u003c/li\u003e\n\u003cli\u003eJ. Adamik, P.V. Munson, F.J. Hartmann, A.J. Combes, P. Pierre, M.F. Krummel, S.C. Bendall, R.J. Arg\u0026uuml;ello, L.H. Butterfield, Distinct metabolic states guide maturation of inflammatory and tolerogenic dendritic cells, Nature communications, 13 (2022) 5184.\u003c/li\u003e\n\u003cli\u003eG. Monroy-M\u0026eacute;rida, S. Guzm\u0026aacute;n-Beltr\u0026aacute;n, F. Hern\u0026aacute;ndez, T. Santos-Mendoza, K. Bobadilla, High glucose concentrations impair the processing and presentation of Mycobacterium tuberculosis antigens in vitro, Biomolecules, 11 (2021) 1763.\u003c/li\u003e\n\u003cli\u003eS.J. Lawless, N. Kedia-Mehta, J.F. Walls, R. McGarrigle, O. Convery, L.V. Sinclair, M.N. Navarro, J. Murray, D.K. Finlay, Glucose represses dendritic cell-induced T cell responses, Nature communications, 8 (2017) 15620.\u003c/li\u003e\n\u003cli\u003eS.S. Gupta, R. Sharp, C. Hofferek, L. Kuai, G.W. Dorn, J. Wang, M. Chen, NIX-mediated mitophagy promotes effector memory formation in antigen-specific CD8+ T cells, Cell reports, 29 (2019) 1862-1877. e1867.\u003c/li\u003e\n\u003cli\u003eD. O\u0026rsquo;Sullivan, G.J. van der Windt, S.C.-C. Huang, J.D. Curtis, C.-H. Chang, M.D. Buck, J. Qiu, A.M. Smith, W.Y. Lam, L.M. DiPlato, Memory CD8+ T cells use cell-intrinsic lipolysis to support the metabolic programming necessary for development, Immunity, 41 (2014) 75-88.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Metabolic targets included in panel\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"108%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunction in pathway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"NaN%\" height=\"28\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eGlyceraldehyde 3-phosphate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eGlycolysis \u0026amp; fermentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eRate limiting glycolytic enzyme\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eIDH1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eIsocitrate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eTCA cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eRate limiting enzyme in TCA cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCPT1A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eCarnitine Palmitoyltransferase 1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eFatty acid oxidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eFatty acid shuttling into mitochondria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eACAC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eAcetyl-CoA carboxylase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eFatty acid synthesis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eAcetyl-CoA carboxylase/ fatty acid synthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCD98\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eCD98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eAmino acid metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eEssential amino acid transporter\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHIF1-\u0026alpha;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHypoxia-inducible factor 1-alpha\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eMetabolic regulation/signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eHypoxia and inflammation-induced transcription factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eiNOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eInducible nitric oxide synthase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eOxidative stress, amino acid metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eNitric oxide production, initial rate-limiting enzyme involved in arginine degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eMitoSOX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eFree radical superoxide generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eStains for free radical superoxides, produced in the electron transport chain\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eIDO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003eIndoleamine 2,3-dioxygenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eAmino acid metabolism (kynurenine pathway)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" valign=\"top\"\u003e\n \u003cp\u003eInitial and rate-limiting enzyme for tryptophan degradation to \u003cem\u003eN\u003c/em\u003e-formylkynurenine in the kynurenine pathway\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"8\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"","lastPublishedDoi":"10.21203/rs.3.rs-4018869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4018869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInfiltrating monocytes play a dual role in central nervous system (CNS) diseases, both driving and attenuating inflammation. However, it is unclear how metabolic pathways preferentially fuel protective or pathogenic processes and whether these can be therapeutically targeted to enhance or inhibit these opposing functions. Here, we employed single-cell RNA-sequencing and metabolic protein flow analysis of brain and bone marrow (BM) to map the metabolic signatures of monocyte-derived cells (MCs) to their functions during lethal West Nile virus encephalitis. Using trajectory analysis, we showed progression of BM monocytes through 3 metabolic profiles before their migration to the brain where they differentiated into metabolically distinct MC populations. These included a single pro-inflammatory HIF1-α MC cluster that diverged into two disparate populations: an inducible nitric oxide synthase-positive (iNOS\u003csup\u003e+\u003c/sup\u003e) M1-like MC, with high glycolysis and amino acid metabolic scores, and a glycolytically quiescent, MHC-II\u003csup\u003e+\u003c/sup\u003e antigen-presenting MC. Daily \u003cem\u003ein vivo\u003c/em\u003e glycolysis inhibition with 2-deoxy-D-glucose significantly reduced CNS leukocyte numbers, reducing neuroinflammation and disease signs without increasing viral load. Reduced leukocyte numbers were not due to decreased myelopoiesis, but a preferential decrease in iNOS\u003csup\u003e+\u003c/sup\u003e, compared to antigen-presenting MC, highlighting different glycolytic dependencies between these subsets. Importantly, HIF1-a was independent of glycolysis, enabling continued antigen-presenting MC differentiation, while glycolysis inhibition did not impair generation of an effective antiviral response by cervical node T cells. Together, this integrative approach unveils the tight coupling of MC function and metabolism in viral CNS disease, highlighting novel metabolic therapeutic intervention points, potentially with anti-viral therapy, during severe or uncontrolled inflammation.\u003c/p\u003e","manuscriptTitle":"Therapeutic inhibition of glycolysis preferentially targets pathogenic monocyte subsets and attenuates CNS inflammation in flavivirus encephalitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 05:01:27","doi":"10.21203/rs.3.rs-4018869/v1","editorialEvents":[],"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":"c013e491-a4b0-430e-ad42-40f2f6372953","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29268833,"name":"Biological sciences/Immunology/Innate immune cells/Monocytes and macrophages"},{"id":29268834,"name":"Biological sciences/Immunology/Inflammation/Acute inflammation"},{"id":29268835,"name":"Biological sciences/Immunology/Infectious diseases/Viral infection"},{"id":29268836,"name":"Biological sciences/Immunology/Neuroimmunology"}],"tags":[],"updatedAt":"2024-04-15T11:50:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 05:01:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4018869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4018869","identity":"rs-4018869","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}