Warburg-type metabolic reprogramming facilitated by astrocyte glycogenolysis mediates neuropathic pain chronification

Warburg-type metabolic reprogramming facilitated by astrocyte glycogenolysis mediates neuropathic pain chronification | 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 Warburg-type metabolic reprogramming facilitated by astrocyte glycogenolysis mediates neuropathic pain chronification Sung Joong Lee, Jun Seo Park, Kwang Hwan Kim, Hyewon Jun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7435294/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 Astrocytes are involved in modulating neuronal excitability in numerous neuropathological states, including chronic pain, which is characterized by aberrant neuronal firing and altered synaptic plasticity. Anterior cingulate cortex (ACC) astrocytes have been implicated in neuropathic pain chronification; however, the underlying intracellular mechanisms remain unclear. We integrated bulk metabolomics with astrocyte-specific RiboTag transcriptomics, where we identified a Warburg-type metabolic reprogramming in ACC astrocytes during the transition from acute to chronic pain. In addition, we demonstrated that ACC astrocytes underwent a biphasic glycogen program, characterized by an initial synthesis followed by glycogenolysis, and found that pharmacological inhibition of glycogen breakdown prevented chronic pain development. Mechanistically, glycogenolysis fueled lactate production and downstream Warburg-type metabolic pathways, driving astrocytic and neuronal hyperactivity. Blocking glycogenolysis disrupted this reprogramming, restored metabolic homeostasis, and alleviated pain chronification. These findings reveal a novel astrocyte‐centric neuropathic pain circuitry and implicate glycogen metabolism as a potential therapeutic target for chronic pain. Biological sciences/Neuroscience/Molecular neuroscience Biological sciences/Neuroscience/Diseases of the nervous system/Chronic pain Biological sciences/Biochemistry/Metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Neuropathic pain is a chronic pathological state arising from injury or dysfunction of the nervous system. Repeated or intense nociceptive stimuli drive the transition from acute to persistent pain 1 , 2 . This chronification is mediated by diverse adaptations within the central nervous system, including alterations in neuronal excitability and synaptic plasticity 2 , 3 . Astrocytes are key modulators in the brain, regulating neuronal hyperactivity during chronic pain development through metabolic mechanisms 4 , 5 . Specifically, lactate release from astrocytes in the anterior cingulate cortex (ACC), a supraspinal hub for chronic pain processing 6 , aggravated chronic pain 7 , 8 , 9 , and modulation of astrocytic glutamate release similarly influenced pain persistence 10 , 11 . However, beyond the intercellular astrocytic gliotransmission, the intracellular mechanisms in the chronic pain state remain largely unexplored. Within the central nervous system (CNS), astrocytes are the sole site of glycogen turnover 12 . Glycogen, a highly branched polymer of glucose, serves as the primary energy reserve of the CNS, enabling efficient glucose storage and rapid mobilization 12 , 13 . When degradation of glycogen (glycogenolysis) is initiated, neural circuits involved in cognition and anxiety are reinforced, presumably through enhanced lactate shuttling 12 , 13 , 14 . Notably, astrocytic glycogen metabolism has been reported as a unique signature in the spinal cord during pain chronification 15 , 16 . From a cell physiology perspective, glycogen metabolism exerts profound effects on cellular metabolic reprogramming 17 , 18 . Particularly, pathologically elevated energetic demands can induce cellular hypoxia, resulting in activation of hypoxia-inducible factors such as HIF-1α 19,20,21 . This further redirects glucose flux toward the Warburg effect, shifting metabolism from mitochondrial pyruvate oxidation to lactate production 20 , 21 . Given that astrocytes synthesize and convey lactate to neurons, we hypothesized that Warburg-type metabolism in the astrocytes is closely associated with neuron–astrocyte coupling during pain chronification. In this study, we sought to determine the astrocytic metabolic pathways in the ACC that drive the transition to chronic pain. We identified astrocytic glycogenolysis in the ACC as a critical regulatory node for pain chronification. Bulk metabolomic profiling revealed that ACC astrocytes engaged in a Warburg-type glycolytic shift during pain chronification. Finally, we demonstrated that glycogenolytic flux orchestrated astrocytic Warburg‐like metabolism, underlying the establishment of chronic neuropathic pain. Materials and Methods Animals All animal experiments were approved by the Institutional Animal Care and Use Committee of Seoul National University and were conducted in accordance with the Guide for the Care and Use of Laboratory Animals. GFAP-Cre and Rpl22HA/HA (RiboTag) mice were obtained from Jackson Laboratory (GFAP-Cre: B6.Cg-Tg.Gfap-cre.77.6Mvs/2J, Rpl22HA/HA: B6.129-Rpl22tm1.1Psam/J). We generated astrocyte-specific RiboTag mice by crossing floxed RiboTag mice with GFAP‐Cre transgenic mice. The genotypes of the offspring were determined by PCR with the following primers: GFAPcre -Fw: TCC ATA AAG GCC CTG ACA TC; GFAPcre -Rv: TGC GAA CCT CAT CAC TCG T; RiboTag -Fw: GGG AGG CTT GCT GGA TAT G; RiboTag -Rv: TTT CCA GAC ACA GGC TAA GTA CAC. Except in the RiboTag experiments, all mice used were C57BL/6. Male and female C57BL/6 mice (8–12 weeks of age) were purchased from DooYeol Biotech (Seoul, Korea). All animals were acclimatized to standard conditions with a 12-h light/dark cycle in a specific pathogen-free environment and given access to chow and water ad libitum . All protocols were performed in accordance with guidelines from the International Association for the Study of Pain. Neuropathic pain mouse model To generate a persistent pain model, right L5 spinal nerve transection (SNT) was performed as previously described 22 , 23 , 24 . Briefly, animals were anesthetized with isoflurane in an O 2 carrier (induction 2% and maintenance 1.5%), and a small incision was made to expose the L4 and L5 spinal nerves. The L5 spinal nerve was then transected. Pain behavior test (von Frey test) Mechanical sensitivity of the right hind paw was assessed using a calibrated series of von Frey hairs (0.02–6 g, Stoelting, Wood Dale, IL, USA) following the up-down method 25 , 26 . Tests were performed after at least three habituations at 24-h intervals. Assessments were made 1 day before surgery for baseline, and 1, 3, 5, and 7 days after SNT. Rapid paw withdrawal, licking, and flinching were interpreted as pain responses. All behavioral tests were performed blinded to conditions. Stereotaxic injection For stereotaxic drug injection, each C57BL/6 mouse received a unilateral injection of 1 µl of GPI-1 at 500 nM (CP-316819, CAS 186392-43-8; TOCRIS, Minneapolis, MN, USA), 1 µl of GPI-2 at 300 nM (361515-1MG, CAS 648926-15-2; Sigma-Aldrich, St. Louis, MO, USA), or 1 µl of 4-CIN (α-Cyano-4-hydroxycinnamic acid, CAS 28166-41-8; Sigma-Aldrich) in the ACC using the following coordinates: AP, − 1.0 mm; ML, 0.4 mm; DV, − 1.5 mm from the bregma. SNT surgery was performed 1 h after the drug injections. For stereotaxic virus injection, each C57BL/6 mouse received a unilateral injection of 1 µl of pAAV-flex-a-taCasp3-TEVp, AAV-GFAP-Cre-WPRE-hGH, or AAV5-gfaABC1D-tdTomato (~ 1 × 10 13 gene copies (GC)/ml) in the ACC using the following coordinates: AP, − 0.9 mm; ML, 0.4 mm; DV, − 1.5 mm from the bregma. SNT surgery was performed 4 weeks after the virus injections. The injection syringe (Hamilton, Reno, NV, USA) delivered GPI or AAV at a constant volume of 0.1 µl/min using a syringe pump (Stoelting, Wood Dale, IL, USA). Immunohistochemistry (IHC) Mice were transcardially perfused with ice-cold 0.1 M phosphate-buffered saline (PBS; pH 7.4) until all blood was removed, followed by perfusion with ice-cold 4% paraformaldehyde in 0.1 M PBS. Whole brains were post-fixed in 4% paraformaldehyde in 0.1 M PBS overnight at 4°C and cryoprotected with 30% sucrose for 3 days. Coronal 60-µm-thick sections were incubated in cryoprotectant at − 20°C until immunohistochemical staining was performed. The sections were incubated for 1 h at room temperature in a blocking solution containing 5% normal goat serum (Jackson ImmunoResearch, Bar Harbor, ME, USA), 2% BSA (Sigma-Aldrich), and 0.1% Triton X-100 (Sigma-Aldrich). Subsequently, the sections were incubated in the blocking solution with mouse anti-NeuN (MAB377B, 1:1000; Millipore, Billerica, MA, USA), rabbit anti-S100b (ab52642, 1:500; Abcam, Cambridge, MA, USA), mouse anti-phospho-CREB (#9198 87G3, 1:1000; Cell Signaling Technology, Danvers, MA, USA) rabbit anti-p-p38 MAPK (# 9211S, 1:1000; Cell Signaling Technology), or rabbit anti-c-Fos (#2250 9F6, 1:1000; Cell Signaling Technology) antibodies overnight at 4°C. After being washed with 0.1 M PBS containing 0.1% Triton X-100, the sections were incubated in blocking solution for 1 h with FITC-, Cy3- or Cy5-conjugated secondary antibodies (1:200, Jackson ImmunoResearch) at room temperature, washed three times, and then mounted on gelatin-coated glass slides using Vectashield (Vector Laboratories, Inc., Burlingame, CA, USA). Fluorescent images of the mounted sections were obtained using a confocal microscope (LSM800; Carl Zeiss, Jena, Germany). Glycogen assay Sample preparation For brain sections, the animals were killed under isoflurane, and the brain was quickly removed from the skull and immediately frozen with dry ice. ACC sections were collected based on measurements from the Allen brain atlas, and the samples were snap-frozen and kept at − 80°C until further processing. Glycogen colorimetric assay Tissue samples were homogenized on ice in 200 µL of ddH₂O using a Dounce homogenizer with 10–15 passes. Homogenates were boiled for 10 min to inactivate enzymes and subsequently centrifuged at 18,000 × g for 10 min at 4°C to remove insoluble material. The resulting supernatant was collected and used for the glycogen analysis. To perform the assay, 2–50 µL of the tissue supernatant was added to a 96-well plate, and the volume was adjusted to 50 µL per well with glycogen hydrolysis buffer provided in a glycogen colorimetric/fluorometric assay kit (ab65620, Abcam). For glycogen detection, 2 µL of hydrolysis enzyme mix was added to the wells designated for glycogen hydrolysis, and the background control wells received no enzyme. The samples were incubated at room temperature for 30 min to allow the hydrolysis of glycogen to glucose. Following hydrolysis, 50 µL of a reaction mix containing 46 µL of development buffer, 2 µL of development enzyme mix, and 2 µL of OxiRed probe was added to all wells. The plates were then incubated in the dark at room temperature for 30 min. Absorbance was measured at 570 nm using a microplate reader to quantify the glycogen content. A standard curve was generated using glycogen standards (0–2 µg/well) prepared according to the manufacturer’s instructions to determine the sample glycogen concentrations. Background absorbance from control wells was subtracted from the sample wells to account for any non-glycogen-derived signal. The glycogen concentration in each sample was normalized to the initial sample volume and adjusted based on dilution factors. $$\:{OD}_{corrected}=\:{OD}_{sample}-\:{OD}_{background}$$ where OD sample is the measured OD value for the sample well, and OD background is the measured OD value for the sample background control well (except in the hydrolysis mix, where only the background glucose constant was measured). $$\:Glycogen\:constant\:\left(\mu\:g/well\right)=\:\frac{{OD}_{corrected}-b}{m}\:$$ where m is the slope of the standard curve (glycogen constant), b is the intercept of the standard curve, and OD corrected is the corrected OD value for the sample well. Western blotting For western blotting, ACC tissues were homogenized in ice-cold RIPA buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS) supplemented with 1 mM PMSF and phosphatase inhibitor cocktail (Sigma-Aldrich, P5726). The homogenates were incubated on ice for 30 min and centrifuged at 13,000 rpm for 15 min at 4°C, and then the supernatants were collected. Protein concentration was measured using a BCA assay (Pierce, 23225), and 20 µg of each sample was mixed with 5× SDS sample buffer, boiled for 5 min at 95°C, and then resolved on 10% SDS-PAGE. Proteins were transferred to nitrocellulose membranes (LC2001; Invitrogen, Carlsbad, CA, USA) at 100 V for 1 h, blocked in 5% milk/TBST for 1 h, and probed overnight at 4°C with mouse anti-HA (ab9110, 1:2000; Abcam) and mouse anti-β-actin (A2228, 1:5000; Sigma-Aldrich) in 2.5% milk/TBST. After three TBST washes, the membranes were incubated with HRP-conjugated goat anti-mouse IgG (1:3000 in 2.5% milk/TBST) for 1 h at room temperature. Blots were developed with SuperSignal™ West Pico PLUS (Thermo Fisher, Waltham, MA, USA), and images were obtained using a Fusion FX6.0 system. Band intensities were quantified in ImageJ and normalized to β-actin. qPCR The real-time RT-PCR (qPCR) experiments were performed using a StepOnePlus real-time PCR system (Applied Biosystems, Foster City, CA, USA) following the 2−∆∆Ct method. Total RNA from contraACC tissue was extracted using TRIzol (Invitrogen) and reverse transcribed using TOPscript RT DryMIX (Enzynomics, Cat # RT200, Daejeon, Korea). All the ∆Ct values were normalized to the corresponding GAPDH values, and represent fold change induction. The following qPCR primers were used: Ppp1r3c -Fw: GGT GAC TCA TCT TTC TGC CAC A; Ppp1r3c -Rv: CAA GAC AAA ATT AGG CAC GAG A; Gys1 -Fw: ATC TAC ACT GTG CTG CAG ACG; Gys1 -Rv: CCC TTG CTG TTC ATG GAA TCC; Phka2 -Fw: TGG ATG CCA CCT CTC TCT TC; Phka2 -Rv: TAT CTC CAC GCT CCC ACA TC; Pygb -Fw: CAG CAG CAT TAC TAT GAG CGG; Pygb -Rv: CCA AGT CCA ACC CCA ACT GA; Hif-1a -Fw: GAT CCT TGA TGC TTG CTG GG; Hif-1a -Rv: CTG TCC CCA ATG TCC AGA GT; Ldha -Fw: AAA GAG GAC TAA GGG GTG GC; Ldha -Rv: CTG CAG GAA ACA ACC ACT CC; Ldhb -Fw: AAA GGC TAC ACC AAC TGG GC; Ldhb -Rv: GCC GTA CAT TCC CTT CAC CA; Mct4 -Fw: CAT TCC CAG GGA CGC AAA GAG; Mct4 -Rv: GAC ACG GCT TGG ATC TCC TC; *HK1-*Fw: CCA TCC CTC TTT GAC ACC CT; HK1 -Rv: ACT CAG ACT AAA GTG GCC CC; Pfk1 -Fw: CAG AAA GCC CAC ACT CAA CC; Pfk1 -Rv: ACA GAA GAC CTT GGC CTA CC; Pkm -Fw: CTG GGT GGG AGA AAT GGA GT; Pkm -Rv: TCA GAA GCC CAG AGA ACC AG; Pdha1 -Fw: GAT GCC GTG CTG ATT TAG GG; Pdha1 -Rv: CGT CCT AGA AAT GGC AGC AC. RNA-seq data analysis Raw RNA-seq data were processed in Python to analyze relative gene expression between the experimental and control groups. Raw counts were compared against the sham group and normalized for library size using DESeq2’s size-factor adjustment. A negative‐binomial model was then fitted to calculate the log₂ fold change and associated p‐value for each gene. Genes were ranked in descending order by their log₂FC/standard error, and a GSEA was performed against the Hallmark and Gene Ontology gene sets. Normalized enrichment scores and false discovery rates were computed, and enrichment plots were generated in GraphPad Prism. For each analysis, we selected gene sets corresponding to specific metabolic pathways to assess their degree of enrichment. GC-MS Tissue samples for the GC-MS analysis were prepared from the ACC using the same dissection and freezing protocol as for the glycogen assay. For each contraACC specimen, 400 µL of tissue supernatant was collected and subjected to GC-MS. Derivatization was performed using trimethylsilylation, and 1 µL of each derivatized sample was injected into a Thermo Scientific ISQ LT GC-MS system (Thermo Scientific, Waltham, MA, USA). Chromatographic separation and mass detection were carried out in standard operating conditions. Raw data were processed in Thermo Xcalibur Quan Browser: metabolites were identified by matching each chromatographic peak to reference spectra, and peak areas were integrated against baseline to obtain relative signal intensities. All metabolite abundances were then normalized to the sham controls and expressed as fold changes for downstream analysis. NMR Tissue for the NMR analyses of the ACC was prepared using the same dissection and freezing protocol as for the glycogen assay. For each contraACC sample, 500 µL of the tissue supernatant was collected and analyzed by NMR. Spectra were acquired on a Bruker AVANCE III HD 600 MHz high-resolution NMR spectrometer (Bruker BioSpin, Rheinstetten, Germany). Individual resonances were assigned using Chenomx NMR Suite Profiler, and metabolite concentrations were determined by integrating each peak relative to the subtraction baseline. Metabolomics analysis Absolute and relative metabolite concentrations obtained by GC-MS and NMR, including fold-change values normalized to the sham controls, were subjected to a comprehensive metabolomics analysis. For each metabolite, both the absolute abundance and fold-change relative to the sham and saline + SNT groups were calculated, and statistical significance was assessed. To identify enriched metabolic pathways within each experimental group, we performed both an over-representation analysis and a quantitative enrichment analysis. All pathway enrichment analyses were conducted using MetaboAnalyst v6.0. ( http://www.metaboanalyst.ca ). pH analysis Mice were euthanized and brains were rapidly removed on ice. ACC tissue was dissected bilaterally, weighed, and homogenized in ice-cold deionized water (tissue:water, 1:10 w/v) using a cell homogenizer. Homogenates were clarified by centrifugation (3,000 × g, 5 min, 4°C) and the supernatant was used for pH measurement 27 , 28 . pH was measured at room temperature with a Mettler-Toledo FiveEasy Plus pH meter (model FP20; Mettler-Toledo, Greifensee, Switzerland) equipped with a combination pH electrode. The electrode was calibrated before each measurement session using a three-point calibration with commercial standard buffers (pH 4.01, 7.00 and 10.01) and automatic temperature compensation was enabled 27 , 28 . Each sample was measured in triplicate (electrode rinsed with deionized water and blotted between readings) and the mean value was recorded. In silico analysis The crystal structure of PYGB (PDB ID: 5IKP) was downloaded from the RCSB Protein Data Bank ( https://www.rcsb.org/structure/5IKP ) and used as the starting model for all subsequent structural analyses. Ligand coordinates were generated in silico from the canonical SMILES string. Hydrogens were added, and the geometry was energy-minimized to convergence; the lowest-energy conformer was exported in PDB format. Protein coordinates (PDB) were parsed with a structural biology toolkit to extract atom and residue-level information. Ligand residues were recognized by residue name, and neighboring amino acids within a predefined distance cutoff were designated as pocket residues. These pocket residues were recorded for subsequent validation and docking calculations to characterize protein–ligand interactions. Protein–ligand docking simulations were performed using GalaxyDockWeb. Statistical analysis The data were analyzed in GraphPad software. Student’s t test was used for comparisons between two groups. For multiple group comparisons, two-way analysis of variance (ANOVA) was conducted, followed by Bonferroni’s post hoc test. All data are expressed as the mean ± standard error of the mean (SEM), and statistical significance was defined as a p-value < 0.05. Results ACC glycogenolysis mediates neuropathic pain chronification. To investigate glycogen metabolic dynamics in the brain during pain chronification, we harvested the ACC from mice 1, 3, 5, and 7 days after spinal nerve transection (SNT) 22 , 23 , 24 , and performed a colorimetric glycogen assay (Fig. 1 a). We confirmed that the SNT mice developed chronic mechanical allodynia (Fig. 1 b). During neuropathic pain chronification, we observed a distinct temporal pattern of glycogen dynamics in the ACC (Fig. 1 c). On the 0.5 post-SNT day, glycogen content in the contralateral ACC (contraACC) was comparable to sham controls. However, on days 1 and 3, the contraACC exhibited remarkable peaks in glycogen levels. On day 7, glycogen content had declined significantly (Fig. 1 c). Together, these data suggest that the contraACC undergoes a transient glycogen accumulation during the transition to chronic pain, followed by rapid depletion in the chronic phase. In the spinal cord, genetic ablation of the protein targeting glycogen (PTG), a key facilitator of glycogenolytic flux, has been shown to mitigate pain chronification 15 , 16 . Accordingly, we investigated how inhibition of glycogenolysis affects the distinctive glycogen metabolic program during pain chronification. We targeted a brain-specific glycogen phosphorylase isoform B (PYGB). The activity of PYGB was blocked using two chemically distinct inhibitors: GPI-1 (5-chloro-N-[(1S,2R)-2-hydroxy-3-(methoxymethylamino)-3-oxo-1-(phenylmethyl)propyl]-1H-indole-2-carboxamide) and GPI-2 (1-[3-(3-[2-chloro-4,5-difluorobenzoyl]ureido)-4-methoxyphenyl]-3-methylurea) 29 , 30 , 31 . The appropriate binding interactions of GPI-1 and GPI-2 with PYGB were confirmed using in silico docking analysis 32 , 33 (Supplementary Fig. 1a-d). To test whether PYGB inhibition influences pain chronification and glycogenolytic products, we stereotaxically injected GPI-1 or GPI-2 directly into the contraACC of SNT mice and then conducted a von Frey test and nuclear magnetic resonance (NMR) spectroscopy (Fig. 1 d). In both the GPI-1 and GPI-2 treated groups, chronic mechanical allodynia was rescued across the days tested (Fig. 1 e). However, they exhibited acute mechanical sensitivity comparable to the SNT control group on day 1 (Fig. 1 e). Seven days after SNT, we harvested the contraACC and performed NMR to quantify the glycogenolytic product glucose-1-phosphate (G1P). We found that G1P was significantly increased in the SNT control mice, and that the increase was reversed by both PYGB inhibitors (Fig. 1 f). We further tested neuronal activation levels in the contraACC by measuring immunohistochemical cell activation markers: c-Fos, pCREB, and p-p38-MAPK. The number of c-Fos-positive neurons was elevated in SNT control mice relative to the sham-operation controls, and it was significantly decreased following GPI-1 or GPI-2 treatment (Fig. 1 g-h). Likewise, although both pCREB- and p-p38-positive neurons in the contraACC were enriched in the SNT mice, treatment with either GPI-1 or GPI-2 normalized their expression to sham levels (Supplementary Fig. 2a-d). The ACC contributes to pain chronification not only through local neuronal hyperactivity but also by engaging downstream circuits 3 . Activation of ACC projections to both the nucleus accumbens (NAc) and ventral tegmental area (VTA) has been implicated in the establishment of chronic pain 34 , 35 , 36 . We therefore quantified c-Fos expression in the NAc and VTA contralateral to the injured nerve (Fig. 2 a). SNT induced robust neuronal activation in both regions, which was effectively suppressed by contraACC administration of a glycogen phosphorylase inhibitor (Fig. 2 b-e). These findings indicate that blocking glycogenolysis in the ACC after nerve injury attenuates neuronal hyperactivity locally and across critical pain-related circuits. Specific astrocyte glycogen metabolic dynamics in neuropathic pain chronification. Glycogen metabolism is largely confined to astrocytes in the CNS 12 . To further support and confirm this, we analyzed publicly available single-cell RNA-sequencing data 37 . We found that key glycogen metabolic enzyme transcripts, glycogen synthase 1 ( Gys1 ), Pygb , phosphorylase kinase regulatory subunit alpha 2 ( Phka2 ), and protein phosphatase 1 regulatory subunit 3C ( Ppp1r3c ), were highly enriched in astrocyte clusters, with minimal expression in neurons or other glia (Fig. 3 a-b). To define the temporal dynamics of glycogen metabolism in ACC astrocytes during pain chronification, we performed ribosome-associated mRNA profiling in GFAPCre::RiboTag mice 38 , 39 . After SNT, ACC tissues were microdissected for RiboTag immunoprecipitation and quantitative PCR analysis (Fig. 3 c). Cre-mediated RiboTag expression in astrocytes was confirmed by PCR genotyping and western blotting (Supplementary Fig. 3a-b). Consistent with the distinct glycogen metabolic trajectories observed in our glycogen assay analysis, astrocyte-specific transcripts exhibited unique, time‐dependent expression patterns. The glycogen synthesis regulators Ppp1r3c and Gys1 both peaked on day 3, marking the transition from acute to chronic pain. In contrast, Phka2 and Pygb reached maximal expression on day 7, corresponding to an established chronic phase (Fig. 3 d). Collectively, these findings reveal a biphasic regulation of glycogen metabolism in ACC astrocytes: an early increase in glycogen synthesis during the transition to chronic pain, followed by robust glycogenolysis in the chronic phase. ACC astrocyte activation mediates neuropathic pain chronification. Astrocytes in the ACC have been implicated in modulating pain chronification via multiple mechanisms 9 , 10 , 11 . To determine whether ACC astrocyte activation specifically drives the transition from acute to chronic neuropathic pain, we conditionally ablated ACC astrocytes by overexpressing caspase-3 and then performed SNT (Fig. 4 a) 40 , 41 . Immunohistochemical staining for the astrocyte marker S100β confirmed depletion in the contraACC (Fig. 4 b). Behavioral testing revealed that mice expressing caspase-3 showed post-SNT pain thresholds early on comparable to sham controls, but the development of chronic hypersensitivity was prevented (Fig. 4 c). Consistent with our observations following PYGB inhibition, c-Fos immunostaining showed a significant reduction in activated neurons in caspase-3-expressing mice relative to SNT controls (Fig. 4 d-f), demonstrating that ACC astrocytes are required for pain chronification. Building on this, we examined whether inhibiting glycogenolysis alters astrocyte abundance. Using the same stereotaxic paradigm, we delivered GPI-1, GPI-2, or saline to the contraACC immediately before SNT (Fig. 4 g). Quantification of S100β-positive cells and calculation of the ipsilateral-to-contralateral cell-count ratio revealed no significant differences among Saline + Sham, Saline + SNT, GPI-1, and GPI-2 groups (Fig. 4 h-i). These data indicate that neither pain chronification (SNT vs. Sham) nor glycogenolysis inhibition alters astrocyte number. Instead, both modulate pain chronification by changing astrocyte activation and intracellular signaling pathways. Warburg-type cancer-associated metabolic reprogramming mediates neuropathic pain chronification. Previous findings demonstrated that ACC astrocytes underwent unique glycogen-metabolic dynamics during pain chronification, and that selective inhibition of glycogenolysis mediated transition to chronic pain. Although other studies have linked glycogen metabolism indirectly to astrocyte function, for example, via lactate production, the specific role of glycogen and the broader metabolic adaptations in the brain throughout pain chronification remain unclear 13 , 16 , 42 . We first used bulk metabolomics to map temporal changes in ACC metabolites during neuropathic pain progression 43 . Following SNT, we harvested ACC tissue on 0.5, 1, 3, and 7 days post-injury, quantified 28 metabolites by GC-MS, and normalized contraACC abundances to both sham controls and the ipsiACC (Fig. 5 a). To compare acute (0.5–1 d) versus chronic (7 d) states, we performed principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA), both of which showed robust separation and statistical significance (Supplementary Fig. 4a-c) 44 , 45 . Acute and chronic samples segregated clearly along PC1 (Fig. 5 b-c), and k-means clustering further defined three metabolic trajectories: early (0.5–1 d), transitional (3 d), and late chronic (7 d) phases (Fig. 5 b-c, Supplementary Fig. 4d-e). Notably, lactate and several other metabolites surged, specifically in the late chronic phase (Fig. 5 d, Supplementary Fig. 4e). We next examined individual metabolite trends over time (Fig. 5 d) and conducted metabolite set enrichment analysis (MSEA) to identify pathways intensified during chronification (Fig. 5 e-i) 44 , 45 . The repertoire of enriched pathways shifted dynamically, with striking differences between early acute (0.5 d) and late chronic (7 d) phases (Fig. 5 e-h). In particular, cancer-associated reprogramming pathways, most prominently the Warburg effect, were progressively enriched over time and among the most significant in the chronic state (Fig. 5 h-i). Given that tissue acidification is a hallmark of cancer‐associated metabolic reprogramming 19 , 20 , we measured pH in ipsilateral and contralateral ACC at early (1 d) and late chronic (7 d) time points post-SNT (Fig. 5 j). While sham mice showed no bilateral pH difference, chronic‐state mice exhibited a significant acidification of the contraACC relative to the ipsilateral side (Fig. 5 k). To determine whether astrocytes are involved in Warburg-type metabolism during pain chronification, we used GFAPCre::RiboTag mice to isolate ribosome‐associated transcripts from ACC astrocytes. Following SNT-induced neuropathic pain, ACC tissues were microdissected and subjected to RiboTag immunoprecipitation for mRNA expression analysis (Fig. 6 a). Under hypoxic conditions, hypoxia‐inducible factor‐1α ( Hif-1α ) orchestrates the transcriptional upregulation of glycolytic and lactate‐export machinery, including Ppp1r3c , hexokinase 1 ( Hk1 ), phosphofructokinase 1 ( Pfk1 ), pyruvate kinase M1/M2 ( Pkm ), lactate dehydrogenase A/B ( Ldha/b ), and monocarboxylate transporter 4 ( Mct4 ). This transcriptional shift biases carbohydrate flux toward anaerobic glycolysis and lactate production, while repressing pyruvate dehydrogenase E1α ( Pdha1 ) to limit tricarboxylic acid cycle entry (Fig. 6 b) 19 , 20 , 21 . We conducted quantitative PCR to chart temporal expression changes of these “Warburg” genes on 1, 3, and 7 days post‐SNT (Fig. 6 c-j). Hif-1α peaked on day 3, marking the intermediate phase between acute and chronic pain (Fig. 6 c). Downstream targets Ldha , Ldhb , Mct4 , Hk1 , Pfk1 , and Pkm reached maximal expression on day 7, corresponding to the established chronic state (Fig. 6 d-i), whereas Pdha1 expression diminished in the chronic phase compared to the acute state (Fig. 6 j). Collectively, these data indicate that Hif-1α -mediated Warburg metabolism is most active during pain chronification. Given that astrocytic Warburg activation culminates in lactate secretion by MCT4 12,16,21 , we investigated whether inhibiting lactate export could modulate chronic pain. Therefore, we stereotaxically administered the MCT4 inhibitor α-cyano-4-hydroxycinnamate (4-CIN) into the ACC following SNT and evaluated both nociceptive behavior and neuronal activation (Fig. 6 k). In von Frey assays, 4-CIN selectively attenuated mechanical hypersensitivity during the chronic phase, replicating the effects of glycogenolysis inhibition and astrocyte depletion (Fig. 6 l-o). Neuronal hyperactivity, assessed by c-Fos immunohistochemistry, was markedly reduced in the contraACC of 4-CIN-treated mice, with c-Fos+/NeuN + cell counts and proportions reverting to sham levels and significantly lower than in SNT controls (Fig. 6 m-n). Notably, these reductions were comparable to those achieved with a pharmacological glycogen phosphorylase inhibitor (GPI) (Fig. 6 o). In summary, our data reveal dynamic, time-dependent metabolic reprogramming in the ACC during pain chronification. The progressive enrichment of the Warburg effect, alongside cancer-associated metabolic pathways, suggests that these adaptations may play a pivotal role in driving the transition to chronic pain. Our findings establish that the robust activation of the Warburg effect in ACC astrocytes drives pain chronification through enhanced lactate export. In a parallel analysis, we examined transcriptomic profiles from the contraACC in the chronic constriction injury (CCI) model 46 , which recapitulates features of SNT-induced neuropathic pain (Supplementary Fig. 5a). At 7 days post-CCI, contraACC expression data were normalized against sham controls, and gene set enrichment analysis (GSEA) was performed for each pathway of interest (Supplementary Fig. 5b-f). GSEA plots demonstrated significant upregulation of the Warburg effect and glutamate metabolism pathways under chronic pain conditions (Supplementary Fig. 5b-c). In contrast to the Warburg phenotype, aerobic glycolysis, along with the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS) pathways, were all downregulated (Supplementary Fig. 5d-f). Our transcriptomic analyses confirm that cancer-associated metabolic programs, including the Warburg effect and glutamate metabolism, are selectively reprogrammed during the chronic state of neuropathic pain. ACC glycogenolysis mediates cancer-associated metabolic reprogramming preceding neuropathic pain chronification. In cancer cells, the interplay between glycogen metabolism and the Warburg effect is well documented 47 , 48 . Ppp1r3c, which governs the flux between glycogen synthesis and breakdown, is transcriptionally regulated by HIF-1α, the key regulator of the Warburg phenotype (Fig. 6 k) 44 , 45 . Moreover, enhanced glycogenolysis has been shown to amplify the Warburg effect and to serve as a rapid reservoir for metabolic intermediates 48 , 49 , 50 . Thus, astrocytic glycogen shunting likely represents a critical control point in cancer-associated metabolic reprogramming, including the Warburg effect. In this context, to investigate the link between ACC astrocyte glycogenolysis and the Warburg effect, we stereotaxically injected GPI-1 or GPI-2 into the contraACC of SNT mice and performed 1H-NMR metabolomic profiling on day 7 post-injury (Fig. 7 a). We then applied PCA and PLS-DA to evaluate metabolic differences among groups 44 , 45 . Both methods showed high reproducibility and statistical robustness (Supplementary Fig. 6a-c) and clearly separated the SNT cohort from the GPI-treated groups (Fig. 7 b-c). K-means clustering further corroborated these distinctions, revealing characteristic shifts in key metabolites, most notably lactate (Supplementary Fig. 6d-e). Metabolite concentrations were determined by NMR analysis and compared across groups (Fig. 7 d-m, Supplementary Fig. 7a-d) 51 . In SNT controls, lactate was significantly increased relative to sham, whereas both GPI-1 and GPI-2 restored lactate to baseline (Fig. 7 d). We next examined key metabolites of the TCA cycle and glutamate metabolism. SNT animals exhibited reduced succinate alongside increased pyruvate and malate compared to sham. However, PYGB inhibition reversed the latter two elevations, lowering pyruvate and malate toward sham values (Fig. 7 e-g). Although glutamine and GABA showed no significant changes across groups, glutamate was markedly increased after SNT and significantly attenuated by GPI-1 or GPI-2 (Fig. 7 i-k). Collectively, blocking glycogenolysis normalized the levels of lactate, pyruvate, malate, alanine, and glutamate to those observed in sham controls, while succinate exhibited a modest elevation (Fig. 7 d-n). Focusing on the cancer-associated pathways highlighted by our GC-MS results (Fig. 5 ), we conducted pathway-level enrichment analysis for the Warburg effect and glutamate metabolism on the NMR data 44 , 45 . Both the enrichment p-value and expectation value for these pathways were substantially reduced in the PYGB‐inhibited groups versus SNT controls (Fig. 7 o-p). Furthermore, while SNT drove broad activation of multiple oncometabolic pathways, PYGB blockade substantially reduced both the impact scores and statistical significance of these pathways (Supplementary Fig. 7e-g). Taken together, these findings indicate that inhibiting glycogenolysis can effectively quench the enhanced Warburg effect and associated cancer-like metabolic reprogramming that arise during neuropathic pain chronification, thereby restoring metabolite profiles toward baseline. Discussion The concept of neuron–astrocyte metabolic coupling has emerged as a critical factor in neurological disorders 5,15 . In chronic neuropathic states, lactate, an astrocytic metabolite in the CNS, has largely been considered a fuel source that sustains excessive neuronal activity 8,12,52 . In this study, we sought to determine the specific metabolic signaling in ACC astrocytes that mediates neuropathic pain chronification. Here, we demonstrate that during the transition to chronic pain, ACC astrocytes exhibited a distinct biphasic glycogen dynamic—an early synthesis surge followed by glycogenolysis—which is necessary for chronic pain development. By integrating metabolomic and transcriptomic analyses, we showed that glycogenolysis drove a Warburg‐type, cancer‐associated metabolic reprogramming in astrocytes, and that inhibiting glycogen breakdown suppressed this reprogramming and prevented pain chronification. Our study demonstrates that astrocytic adaptations involve not merely an increase in lactate concentration but a comprehensive rewiring of cancer‐associated metabolic flux. In this context, the emergence of a Warburg‐type cancer‐associated metabolic program in astrocytes suggests that the role of lactate extends far beyond simple bioenergetics 7,8,9 . In hypoxic tumor microenvironments, excessive lactate production not only accelerates metabolite turnover and maintains redox balance but also drives histone lactylation and inflammatory signaling 53,54,55,56 . By analogy, astrocyte‐derived lactate in the ACC may similarly act as a versatile signaling metabolite, modulating gene expression, neuroinflammation, and circuit function, thereby contributing to pain chronification through multifaceted mechanisms. In this study, we demonstrated that astrocyte metabolic patterns dynamically change during the transition to chronic pain. Particularly, we found that the robust activation of a Warburg‐type metabolic program triggered by glycogenolysis is essential for driving pain chronification. Emerging evidence indicates that diverse astrocytic signaling factors can remodel metabolic flux, driving astrocytes toward a reactive phenotype and thereby altering their functional repertoire 5,57 . In this context, the metabolic reprogramming we observed during pain chronification sheds new light on astrocyte dynamics in chronic neuropathy. Targeting the regulation of astrocytic metabolic flux represents a promising strategy for novel neuropathic pain therapies. In our study, we identified glycogen as a key nodal point for modulating astrocytic metabolism. Although the downstream pathways governed by glycogen metabolism in astrocytes remain incompletely characterized, our findings demonstrate that inhibiting glycogenolysis suppresses the Warburg‐type program. These findings establish glycogen metabolism as a critical regulatory signal for astrocytic metabolic flux, with potential implications for therapeutic intervention. Limitations of the study In this study, we identified glycogenolysis and Warburg‐type metabolic signaling as defining features of pain chronification and demonstrated that inhibiting glycogenolysis effectively suppresses the Warburg effect. Nevertheless, further investigations are required to determine the upstream triggers and downstream consequences of astrocytic Warburg activation during chronic pain development. Moreover, translating these insights into therapies for chronic pain will demand a comprehensive characterization of glycogenolysis‐mediated pain modulation in murine models and rigorous validation of these mechanisms in clinical contexts. Declarations Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Junseo Park ( [email protected] ). Materials availability All mouse lines and materials used in this study were provided or purchased from the mentioned companies or researchers. This study did not generate any new or unique reagents. Data and code availability All data reported in this paper will be shared by the lead contact upon request. This paper does not report original code. All data associated with this study are present in the paper or the supplemental information. Acknowledgments We are grateful to all members of the Neuron-Glia Network Research Laboratory for their useful discussions and help. We also thank Kyungchul Noh, assistant professor at the Department of Pharmacology, School of Medicine, Ajou University, Suwon, Republic of Korea, for useful discussions. This research was supported by the 2024 Seoul National University undergraduate independent research in the Student-Directed Education (SDE) program and the National Research Foundation of Korea (RS-2024-00402116; RS-2025-02215169). Author Contributions J.S.P. designed the research, performed most experiments, analyzed the data, and wrote the first draft of the paper. K.H.K. contributed to data interpretation and manuscript revision. H.W.J. crossed RiboTag and GFAP-Cre mice to create astrocyte-specific RiboTag mice and assisted with experimental procedures. S.J.L. supervised the project. 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Additional Declarations There is no conflict of interest Supplementary Files GliaEMMSuppleFigure09.02.pdf Supplementary Figure Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7435294","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":517350883,"identity":"72e8a816-92f2-473e-8486-0afb31393997","order_by":0,"name":"Sung Joong 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Sham).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e Glycogen constant in ACC, compared in contralateral and ipsilateral areas. (Time point: Sham, D0.5, D1, D3, and D7) ****P \u0026lt; 0.0001 (D1 SNT contra versus ipsi), ***P = 0.0006 (D3 SNT contra versus ipsi).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e Experimental scheme of NMR and IHC after GPI stereotaxic injection in ACC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee\u003c/strong\u003e von Frey test of neuropathic pain model. **P = 0.0035 (D3 GPI-1 + SNT versus Saline + SNT), **P = 0.0018 (D3 GPI-2 + SNT versus Saline + SNT), ****P \u0026lt; 0.0001 (D5, D7 GPI-1 + SNT, GPI-2 + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef\u003c/strong\u003e NMR analysis of G1P constant in the ACC contra area. **P = 0.0020 (GPI-1 + SNT versus Saline + SNT), ***P = 0.0010 GPI-2 + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg\u003c/strong\u003e Representative confocal images of ACC after IHC. (Scale bar, 200 μm).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e IHC data of c-Fos+/NeuN+ cells in ACC. (Left): Number of c-Fos+/NeuN+ cells in ACC contra area. **P = 0.0062 (Saline + Sham versus Saline + SNT), ****P \u0026lt; 0.0001 (GPI-1 + SNT and GPI-2 + SNT versus Saline + SNT), (Right): Ratio of c-Fos+/NeuN+ cells in ACC contra area compared with ipsi area. **P = 0.0029 (Saline + Sham versus Saline + SNT), ****P \u0026lt; 0.0001 (GPI-1 + SNT and GPI-2 + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003eData are presented as the mean ± SEM; *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001; Two-way ANOVA-multiple comparisons (b, c, and e) and Student’s t test (f and g).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/2dc82bf1a1452109cdf20829.png"},{"id":92517804,"identity":"a1774089-95ef-4afd-979a-dca34ab444f5","added_by":"auto","created_at":"2025-09-30 14:25:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":495662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of ACC glycogenolysis decreases neuronal activation in the NAc and VTA in neuropathic chronic pain.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eExperimental scheme of IHC after GPI stereotaxic injection in ACC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb-c\u003c/strong\u003e IHC data of c-Fos+/NeuN+ cell in NAc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Representative confocal images of NAc. (Scale bar, 200 μm\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e IHC data of c-Fos+/NeuN+ cell in NAc. (Left): Number of c-Fos+/NeuN+ cell in NAc contra area. ***P = 0.0004 (Saline + Sham verses Saline + SNT), ***P = 0.0005 (GPI-1 + SNT verse Saline + SNT), *P = 0.0175 (GPI-2 + SNT verse Saline + SNT) (Right): Ratio of c-Fos+/NeuN+ cell in NAc contra area, compare with ipsi area. **P = 0.0004 (Saline + Sham verses Saline + SNT), ***P = 0.0005 (GPI-1 + SNT verse Saline + SNT), ***P = 0.0175 (GPI-2 + SNT verse Saline + SNT)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed-e\u003c/strong\u003e IHC data of c-Fos+/NeuN+ cell in VTA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e Representative confocal images of VTA. (Scale bar, 500 μm)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee\u003c/strong\u003e IHC data of c-Fos+/NeuN+ cell in NAc. (Left): Number of c-Fos+/NeuN+ cell in VTA contra area. ***P = 0.0006 (Saline + Sham verses Saline + SNT), *P = 0.0144 (GPI-1 + SNT verse Saline + SNT), *P = 0.0484 (GPI-2 + SNT verse Saline + SNT) (Right): Ratio of c-Fos+/NeuN+ cell in VTA contra area, compare with ipsi area. *P = 0.0256 (Saline + Sham verses Saline + SNT), **P = 0.0031 (GPI-1 + SNT verse Saline + SNT), **P = 0.0045 (GPI-2 + SNT verse Saline + SNT)\u003c/p\u003e\n\u003cp\u003eData are represented as the mean ± SEM; **P* \u0026lt; 0.05, ****P* \u0026lt; 0.001, *****P* \u0026lt; 0.0001; Student’s t test (c, e).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/e7c04fffee6b6e4401b80e54.png"},{"id":92518251,"identity":"a9c27e86-b6c4-416b-8be8-1a691047d975","added_by":"auto","created_at":"2025-09-30 14:33:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149008,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecific astrocyte glycogen metabolic dynamics in neuropathic pain chronification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Scheme of glycogen metabolism pathway. (Created in \u003ca href=\"https://www.biorender.com/\"\u003ehttps://www.biorender.com\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Single-cell RNA-sequencing of glycogen metabolism gene (\u003cem\u003ePpp1r3c, Gys1, Phka2, \u003c/em\u003eand\u003cem\u003e Pygb\u003c/em\u003e) expression in each cell type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e Schematic illustration of GFAPCre::RiboTag mice and neuropathic pain model transcriptomics experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e qPCR data of glycogen metabolism gene (\u003cem\u003ePpp1r3c, Gys1, Phka2, Pygb\u003c/em\u003e) expression. (Time points: 1, 3, and 7 d) (n = 4~6).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/d64998e6a0696c5ee7afe957.png"},{"id":92519358,"identity":"1db71ecc-3863-4ab3-aaac-525430f2e1b9","added_by":"auto","created_at":"2025-09-30 14:41:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":665190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eACC astrocytes mediate only neuropathic pain chronification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Experimental scheme of IHC after AAV-Caspase3 virus injection and SNT surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Representative confocal images of ACC anti-s100b staining.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e von Frey test of neuropathic pain model. ***P = 0.0010 (D5 Casp3 + SNT versus tdTomato + SNT), ****P \u0026lt; 0.0001 (D7, D10 and D14 Casp3 + SNT versus tdTomato + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e Representative confocal images of ACC. (Scale bar, 200 μm)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee-f\u003c/strong\u003e IHC data of c-Fos+/NeuN+ cells in ACC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee\u003c/strong\u003e tdTomato or Caspase3 injection group analysis. (Left): Number of c-Fos+/NeuN+ cells in the ACC contra area. ****P \u0026lt; 0.0001 (tdTomato + Sham and Casp3 + SNT versus tdTomato + SNT), (Right): Ratio of c-Fos+/NeuN+ cells in ACC contra area compared with ipsi area. *P = 0.0062 (tdTomato + Sham versus tdTomato + SNT), ****P \u0026lt; 0.0001 (Casp3 + SNT versus tdTomato + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef\u003c/strong\u003e Comparison of IHC data in the GPI injection experiment. (Left): Number of c-Fos+/NeuN+ cells in the ACC contra area. (Right): Ratio of c-Fos+/NeuN+ cells in ACC contra area compared with ipsi area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg\u003c/strong\u003e Experimental Scheme of IHC after GPI injection and SNT surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e Representative confocal images of ACC. (Scale bar, 200 μm)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei\u003c/strong\u003e IHC data of s100b+ cells in ACC. Ratio of s100b+ cells in ACC contra area compared with ipsi area. No significance (Saline + SNT vs Saline + Sham, GPI-1 + SNT, and GPI-2 + SNT).\u003c/p\u003e\n\u003cp\u003eData are presented as the mean ± SEM; Two-way ANOVA-multiple comparisons (C) and Student’s t test (e-f, i)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/4d10a58797f187b42c9a41c0.png"},{"id":92517807,"identity":"8feb5bce-8286-498d-bcf0-815ce24f24e8","added_by":"auto","created_at":"2025-09-30 14:25:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":357858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of ACC metabolic dynamics during neuropathic pain chronification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Experimental scheme of GC-MS metabolomics analysis in the SNT pain model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb-c\u003c/strong\u003e Cluster analysis of GC-MS data; compared by each time point (0.5, 1, 3, and 7 d).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Principal component analysis (PCA) of GC-MS fold-change profiles of each time point. Cluster separation was observed in acute time points (0.5 and 1 d) versus the chronic time point (7 d). The variance in the X-axis (PC1) is 79.1% and on the Y-axis (PC2) it is 9.2%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e Partial least squares–discriminant analysis (PLS-DA) of GC-MS fold-change profiles of each time point. Cluster separation was observed in acute time points (0.5 and 1 d) versus the chronic time point (7 d). The variance in the X-axis (PC1) is 79% and on the Y-axis (PC2) it is 8.9%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e Heatmap of 28 metabolites with constant fold-change in each time point (Time points: 0.5, 1, 3, and 7 d) (n = 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee-i\u003c/strong\u003e Metabolite set enrichment analysis (MSEA) of GC-MS data; compared by each time point (0.5, 1, 3, and 7 d). Labeling selected high-enrichment metabolic pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee\u003c/strong\u003e 0.5 d, \u003cstrong\u003ef\u003c/strong\u003e 1 d, \u003cstrong\u003eg\u003c/strong\u003e 3 d, \u003cstrong\u003eh\u003c/strong\u003e 7 d bubble plot after SNT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei\u003c/strong\u003e Analysis of the time-dependent enrichment of selected cancer-associated pathways. (Time points: 12 h, 1 d, 3 d, and 7 d), -log(p-value) (left), expectation value (right).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej\u003c/strong\u003e Experimental scheme of pH analysis of ACC in the SNT pain model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek\u003c/strong\u003e pH of ACC at each time point (Time points: 1 and 7 d), **\u003cem\u003eP\u003c/em\u003e = 0.0096 (SNT 7d contra versus Sham), (n = 4)\u003c/p\u003e\n\u003cp\u003eData are represented as the mean ± SEM; *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001; Two-way ANOVA-multiple comparisons (k). Pathway enrichment analysis of GC-MS data is performed using MetaboAnalyst v6.0. (\u003ca href=\"http://www.metaboanalyst.ca/\"\u003ehttp://www.metaboanalyst.ca\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/88ceb08896c525d0f18e08cf.png"},{"id":92518254,"identity":"393d29d3-8d0a-4093-bc4c-183afd08b94c","added_by":"auto","created_at":"2025-09-30 14:33:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":476165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWarburg-like metabolic reprogramming mediates neuropathic pain chronification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Schematic illustration of GFAPCre::RiboTag mice and neuropathic pain model transcriptomics experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Schematic model of metabolic reprogramming. Warburg-like metabolic pathway facilitated in pain chronification. (Created in \u003ca href=\"https://www.biorender.com/\"\u003ehttps://www.biorender.com\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec-j\u003c/strong\u003e qPCR data of Warburg-like metabolic reprogramming gene profiles. (Time points: 1, 3, and 7 d) (n = 4~6)\u003c/p\u003e\n\u003cp\u003eFold change of c \u003cem\u003eHif-1a\u003c/em\u003e, d \u003cem\u003eLdha\u003c/em\u003e, e \u003cem\u003eLdhb\u003c/em\u003e, f \u003cem\u003eMct4\u003c/em\u003e, g \u003cem\u003eHk1\u003c/em\u003e, h \u003cem\u003ePfk1,\u003c/em\u003e i \u003cem\u003ePkm\u003c/em\u003e. and j \u003cem\u003ePdha1\u003c/em\u003eat each time point.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek\u003c/strong\u003e Experimental Scheme of IHC after 4-CIN injection and SNT surgery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el\u003c/strong\u003e von Frey test of neuropathic pain model. **P = 0.0095 (D5 4-CIN + SNT versus Saline + SNT), **P = 0.0087 (D7 4-CIN + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003em\u003c/strong\u003e Representative confocal images of ACC. (Scale bar, 200 μm)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003en-o\u003c/strong\u003e IHC data of c-Fos+/NeuN+ cells in ACC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e Saline or 4-CIN injection group analysis. (Left): Number of c-Fos+/NeuN+ cells in the ACC contra area. **P = 0.0021 (Saline + Sham versus Saline + SNT), **P = 0.0075 (4-CIN + SNT versus Saline + SNT), (Right): Ratio of c-Fos+/NeuN+ cells in ACC contra area compared with ipsi area. *P = 0.0214 ((Saline + Sham versus Saline + SNT), *P = 0.0277 (4-CIN + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eo\u003c/strong\u003e IHC data compared with the 4-CIN injection experiment. (Left) Number of c-Fos+/NeuN+ cells in ACC contra area. (Right) Ratio of c-Fos+/NeuN+ cells in ACC contra area compared with the ipsi area.\u003c/p\u003e\n\u003cp\u003eData are presented as the mean ± SEM; *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001; Two-way ANOVA-multiple comparisons (i) and Student’s t test (n, o).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/b9f133ae88e0c24c027ded42.png"},{"id":92517811,"identity":"eb84e8c8-f796-4853-8c5d-628c27c30f16","added_by":"auto","created_at":"2025-09-30 14:25:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":318140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eACC glycogenolysis mediates cancer-associated metabolic reprogramming in neuropathic pain chronification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Experimental scheme of NMR metabolomics analysis in the SNT pain model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb-c\u003c/strong\u003e Cluster analysis of NMR data; compared by each group (GPI-1, GPI-2, SNT, Sham) (n = 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Principal component analysis (PCA) of NMR fold-change profiles of each time point. Cluster separation was observed in acute time points (0.5 and 1 d) versus the chronic time point (7 d). The variance in the X-axis (PC1) is 84.6% and on the Y-axis (PC2) it is 6.1%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e Partial least squares–discriminant analysis (PLS-DA) of NMR fold-change profiles of each time point. Cluster separation was observed in acute time points (0.5 and 1 d) versus the chronic time point (7 d). The variance in the X-axis (PC1) is 84.3% and on the Y-axis (PC2) it is 4.2%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed-m\u003c/strong\u003e Metabolite concentration data by NMR analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e Lactate concentration of each group. **P = 0.0084 (Saline + Sham versus Saline + SNT), *P = 0.0141 (GPI-1 + SNT versus Saline + SNT), **P = 0.0010 (GPI-2 + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee\u003c/strong\u003e Pyruvate concentration of each group. ****P \u0026lt; 0.0001 (Saline + Sham versus Saline + SNT), ****P \u0026lt; 0.0001 (GPI-2 + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef\u003c/strong\u003e Succinate concentration of each group. ****P \u0026lt; 0.0001 (Saline + Sham versus Saline + SNT), **P = 0.0046 (GPI-1 + SNT versus Saline + SNT), **P = 0.0010 (GPI-2 + SNT versus Saline + SNT)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg\u003c/strong\u003e Malate concentration of each group. **P = 0.0065 (GPI-1 + SNT versus Saline + SNT), ***P = 0.0001 (GPI-2 + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e Glutamate concentration of each group. **P = 0.0056 (Saline + Sham versus Saline + SNT), ****P \u0026lt; 0.0001 (GPI-2 + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei\u003c/strong\u003e Glutamine concentration of each group. *P = 0.0240 (GPI-2 + SNT versus Saline + SNT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej\u003c/strong\u003e GABA concentration of each group..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek\u003c/strong\u003e Alanine concentration of each group. *P = 0.0443 (Saline + Sham versus Saline + SNT), **P = 0.0044 (GPI-2 + SNT versus Saline + SNT)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el\u003c/strong\u003e Arginine concentration of each group. ***P = 0.0009 (Saline + Sham versus Saline + SNT)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003em\u003c/strong\u003e Taurine concentration of each group. *P = 0.0282 (Saline + Sham versus Saline + SNT), **P = 0.0024 (GPI-2 + SNT versus Saline + SNT)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e Scheme of the metabolic pathway of selected metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eo-p\u003c/strong\u003e Metabolite set enrichment analysis (MSEA) of NMR metabolomics data. Labeling selected high-enrichment metabolic pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eo\u003c/strong\u003e Warburg effect pathway.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e Glutamate metabolism pathway.\u003c/p\u003e\n\u003cp\u003eData are presented as the mean ± SEM; *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001, ****P \u0026lt; 0.0001; Student’s t test (d-m). Pathway enrichment analysis of NMR data is performed using MetaboAnalyst v6.0. (\u003ca href=\"http://www.metaboanalyst.ca/\"\u003ehttp://www.metaboanalyst.ca\u003c/a\u003e).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/a9b7550196c54a1df0e1ea17.png"},{"id":92600318,"identity":"c9bb0110-8f99-4e55-8280-e45c105d3f58","added_by":"auto","created_at":"2025-10-01 14:19:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3803877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/d7d21281-f2df-462a-9e7e-d9ebdc40c11c.pdf"},{"id":92517810,"identity":"69e41007-986f-48ff-9b56-7fe4dbefd6a1","added_by":"auto","created_at":"2025-09-30 14:25:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3559044,"visible":true,"origin":"","legend":"Supplementary Figure","description":"","filename":"GliaEMMSuppleFigure09.02.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7435294/v1/df17c872e4f7f281290b7143.pdf"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Warburg-type metabolic reprogramming facilitated by astrocyte glycogenolysis mediates neuropathic pain chronification","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNeuropathic pain is a chronic pathological state arising from injury or dysfunction of the nervous system. Repeated or intense nociceptive stimuli drive the transition from acute to persistent pain\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This chronification is mediated by diverse adaptations within the central nervous system, including alterations in neuronal excitability and synaptic plasticity\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Astrocytes are key modulators in the brain, regulating neuronal hyperactivity during chronic pain development through metabolic mechanisms\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Specifically, lactate release from astrocytes in the anterior cingulate cortex (ACC), a supraspinal hub for chronic pain processing\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, aggravated chronic pain\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and modulation of astrocytic glutamate release similarly influenced pain persistence\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. However, beyond the intercellular astrocytic gliotransmission, the intracellular mechanisms in the chronic pain state remain largely unexplored.\u003c/p\u003e\u003cp\u003eWithin the central nervous system (CNS), astrocytes are the sole site of glycogen turnover\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Glycogen, a highly branched polymer of glucose, serves as the primary energy reserve of the CNS, enabling efficient glucose storage and rapid mobilization\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. When degradation of glycogen (glycogenolysis) is initiated, neural circuits involved in cognition and anxiety are reinforced, presumably through enhanced lactate shuttling\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Notably, astrocytic glycogen metabolism has been reported as a unique signature in the spinal cord during pain chronification\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. From a cell physiology perspective, glycogen metabolism exerts profound effects on cellular metabolic reprogramming\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Particularly, pathologically elevated energetic demands can induce cellular hypoxia, resulting in activation of hypoxia-inducible factors such as HIF-1α\u003csup\u003e19,20,21\u003c/sup\u003e. This further redirects glucose flux toward the Warburg effect, shifting metabolism from mitochondrial pyruvate oxidation to lactate production\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Given that astrocytes synthesize and convey lactate to neurons, we hypothesized that Warburg-type metabolism in the astrocytes is closely associated with neuron\u0026ndash;astrocyte coupling during pain chronification.\u003c/p\u003e\u003cp\u003eIn this study, we sought to determine the astrocytic metabolic pathways in the ACC that drive the transition to chronic pain. We identified astrocytic glycogenolysis in the ACC as a critical regulatory node for pain chronification. Bulk metabolomic profiling revealed that ACC astrocytes engaged in a Warburg-type glycolytic shift during pain chronification. Finally, we demonstrated that glycogenolytic flux orchestrated astrocytic Warburg‐like metabolism, underlying the establishment of chronic neuropathic pain.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAnimals\u003c/h2\u003e\u003cp\u003e All animal experiments were approved by the Institutional Animal Care and Use Committee of Seoul National University and were conducted in accordance with the Guide for the Care and Use of Laboratory Animals. GFAP-Cre and Rpl22HA/HA (RiboTag) mice were obtained from Jackson Laboratory (GFAP-Cre: B6.Cg-Tg.Gfap-cre.77.6Mvs/2J, Rpl22HA/HA: B6.129-Rpl22tm1.1Psam/J). We generated astrocyte-specific RiboTag mice by crossing floxed RiboTag mice with GFAP‐Cre transgenic mice. The genotypes of the offspring were determined by PCR with the following primers:\u003c/p\u003e\u003cp\u003e\u003cem\u003eGFAPcre\u003c/em\u003e-Fw: TCC ATA AAG GCC CTG ACA TC; \u003cem\u003eGFAPcre\u003c/em\u003e-Rv: TGC GAA CCT CAT CAC TCG T; \u003cem\u003eRiboTag\u003c/em\u003e-Fw: GGG AGG CTT GCT GGA TAT G; \u003cem\u003eRiboTag\u003c/em\u003e-Rv: TTT CCA GAC ACA GGC TAA GTA CAC.\u003c/p\u003e\u003cp\u003eExcept in the RiboTag experiments, all mice used were C57BL/6. Male and female C57BL/6 mice (8\u0026ndash;12 weeks of age) were purchased from DooYeol Biotech (Seoul, Korea). All animals were acclimatized to standard conditions with a 12-h light/dark cycle in a specific pathogen-free environment and given access to chow and water \u003cem\u003ead libitum\u003c/em\u003e. All protocols were performed in accordance with guidelines from the International Association for the Study of Pain.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNeuropathic pain mouse model\u003c/h3\u003e\n\u003cp\u003eTo generate a persistent pain model, right L5 spinal nerve transection (SNT) was performed as previously described\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Briefly, animals were anesthetized with isoflurane in an O\u003csub\u003e2\u003c/sub\u003e carrier (induction 2% and maintenance 1.5%), and a small incision was made to expose the L4 and L5 spinal nerves. The L5 spinal nerve was then transected.\u003c/p\u003e\n\u003ch3\u003ePain behavior test (von Frey test)\u003c/h3\u003e\n\u003cp\u003eMechanical sensitivity of the right hind paw was assessed using a calibrated series of von Frey hairs (0.02\u0026ndash;6 g, Stoelting, Wood Dale, IL, USA) following the up-down method\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Tests were performed after at least three habituations at 24-h intervals. Assessments were made 1 day before surgery for baseline, and 1, 3, 5, and 7 days after SNT. Rapid paw withdrawal, licking, and flinching were interpreted as pain responses. All behavioral tests were performed blinded to conditions.\u003c/p\u003e\n\u003ch3\u003eStereotaxic injection\u003c/h3\u003e\n\u003cp\u003eFor stereotaxic drug injection, each C57BL/6 mouse received a unilateral injection of 1 \u0026micro;l of GPI-1 at 500 nM (CP-316819, CAS 186392-43-8; TOCRIS, Minneapolis, MN, USA), 1 \u0026micro;l of GPI-2 at 300 nM (361515-1MG, CAS 648926-15-2; Sigma-Aldrich, St. Louis, MO, USA), or 1 \u0026micro;l of 4-CIN (α-Cyano-4-hydroxycinnamic acid, CAS 28166-41-8; Sigma-Aldrich) in the ACC using the following coordinates: AP, \u0026minus;\u0026thinsp;1.0 mm; ML, 0.4 mm; DV, \u0026minus;\u0026thinsp;1.5 mm from the bregma. SNT surgery was performed 1 h after the drug injections.\u003c/p\u003e\u003cp\u003eFor stereotaxic virus injection, each C57BL/6 mouse received a unilateral injection of 1 \u0026micro;l of pAAV-flex-a-taCasp3-TEVp, AAV-GFAP-Cre-WPRE-hGH, or AAV5-gfaABC1D-tdTomato (~\u0026thinsp;1 \u0026times; 10\u003csup\u003e13\u003c/sup\u003e gene copies (GC)/ml) in the ACC using the following coordinates: AP, \u0026minus;\u0026thinsp;0.9 mm; ML, 0.4 mm; DV, \u0026minus;\u0026thinsp;1.5 mm from the bregma. SNT surgery was performed 4 weeks after the virus injections. The injection syringe (Hamilton, Reno, NV, USA) delivered GPI or AAV at a constant volume of 0.1 \u0026micro;l/min using a syringe pump (Stoelting, Wood Dale, IL, USA).\u003c/p\u003e\n\u003ch3\u003eImmunohistochemistry (IHC)\u003c/h3\u003e\n\u003cp\u003eMice were transcardially perfused with ice-cold 0.1 M phosphate-buffered saline (PBS; pH 7.4) until all blood was removed, followed by perfusion with ice-cold 4% paraformaldehyde in 0.1 M PBS. Whole brains were post-fixed in 4% paraformaldehyde in 0.1 M PBS overnight at 4\u0026deg;C and cryoprotected with 30% sucrose for 3 days. Coronal 60-\u0026micro;m-thick sections were incubated in cryoprotectant at \u0026minus;\u0026thinsp;20\u0026deg;C until immunohistochemical staining was performed. The sections were incubated for 1 h at room temperature in a blocking solution containing 5% normal goat serum (Jackson ImmunoResearch, Bar Harbor, ME, USA), 2% BSA (Sigma-Aldrich), and 0.1% Triton X-100 (Sigma-Aldrich).\u003c/p\u003e\u003cp\u003eSubsequently, the sections were incubated in the blocking solution with mouse anti-NeuN (MAB377B, 1:1000; Millipore, Billerica, MA, USA), rabbit anti-S100b (ab52642, 1:500; Abcam, Cambridge, MA, USA), mouse anti-phospho-CREB (#9198 87G3, 1:1000; Cell Signaling Technology, Danvers, MA, USA) rabbit anti-p-p38 MAPK (# 9211S, 1:1000; Cell Signaling Technology), or rabbit anti-c-Fos (#2250 9F6, 1:1000; Cell Signaling Technology) antibodies overnight at 4\u0026deg;C. After being washed with 0.1 M PBS containing 0.1% Triton X-100, the sections were incubated in blocking solution for 1 h with FITC-, Cy3- or Cy5-conjugated secondary antibodies (1:200, Jackson ImmunoResearch) at room temperature, washed three times, and then mounted on gelatin-coated glass slides using Vectashield (Vector Laboratories, Inc., Burlingame, CA, USA). Fluorescent images of the mounted sections were obtained using a confocal microscope (LSM800; Carl Zeiss, Jena, Germany).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGlycogen assay\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eSample preparation\u003c/h2\u003e\u003cp\u003eFor brain sections, the animals were killed under isoflurane, and the brain was quickly removed from the skull and immediately frozen with dry ice. ACC sections were collected based on measurements from the Allen brain atlas, and the samples were snap-frozen and kept at \u0026minus;\u0026thinsp;80\u0026deg;C until further processing.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eGlycogen colorimetric assay\u003c/h3\u003e\n\u003cp\u003eTissue samples were homogenized on ice in 200 \u0026micro;L of ddH₂O using a Dounce homogenizer with 10\u0026ndash;15 passes. Homogenates were boiled for 10 min to inactivate enzymes and subsequently centrifuged at 18,000 \u0026times; g for 10 min at 4\u0026deg;C to remove insoluble material. The resulting supernatant was collected and used for the glycogen analysis. To perform the assay, 2\u0026ndash;50 \u0026micro;L of the tissue supernatant was added to a 96-well plate, and the volume was adjusted to 50 \u0026micro;L per well with glycogen hydrolysis buffer provided in a glycogen colorimetric/fluorometric assay kit (ab65620, Abcam). For glycogen detection, 2 \u0026micro;L of hydrolysis enzyme mix was added to the wells designated for glycogen hydrolysis, and the background control wells received no enzyme. The samples were incubated at room temperature for 30 min to allow the hydrolysis of glycogen to glucose.\u003c/p\u003e\u003cp\u003eFollowing hydrolysis, 50 \u0026micro;L of a reaction mix containing 46 \u0026micro;L of development buffer, 2 \u0026micro;L of development enzyme mix, and 2 \u0026micro;L of OxiRed probe was added to all wells. The plates were then incubated in the dark at room temperature for 30 min. Absorbance was measured at 570 nm using a microplate reader to quantify the glycogen content. A standard curve was generated using glycogen standards (0\u0026ndash;2 \u0026micro;g/well) prepared according to the manufacturer\u0026rsquo;s instructions to determine the sample glycogen concentrations. Background absorbance from control wells was subtracted from the sample wells to account for any non-glycogen-derived signal. The glycogen concentration in each sample was normalized to the initial sample volume and adjusted based on dilution factors.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{OD}_{corrected}=\\:{OD}_{sample}-\\:{OD}_{background}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere OD sample is the measured OD value for the sample well, and OD background is the measured OD value for the sample background control well (except in the hydrolysis mix, where only the background glucose constant was measured).\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Glycogen\\:constant\\:\\left(\\mu\\:g/well\\right)=\\:\\frac{{OD}_{corrected}-b}{m}\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere m is the slope of the standard curve (glycogen constant), b is the intercept of the standard curve, and OD corrected is the corrected OD value for the sample well.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eWestern blotting\u003c/h2\u003e\u003cp\u003eFor western blotting, ACC tissues were homogenized in ice-cold RIPA buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS) supplemented with 1 mM PMSF and phosphatase inhibitor cocktail (Sigma-Aldrich, P5726). The homogenates were incubated on ice for 30 min and centrifuged at 13,000 rpm for 15 min at 4\u0026deg;C, and then the supernatants were collected. Protein concentration was measured using a BCA assay (Pierce, 23225), and 20 \u0026micro;g of each sample was mixed with 5\u0026times; SDS sample buffer, boiled for 5 min at 95\u0026deg;C, and then resolved on 10% SDS-PAGE. Proteins were transferred to nitrocellulose membranes (LC2001; Invitrogen, Carlsbad, CA, USA) at 100 V for 1 h, blocked in 5% milk/TBST for 1 h, and probed overnight at 4\u0026deg;C with mouse anti-HA (ab9110, 1:2000; Abcam) and mouse anti-β-actin (A2228, 1:5000; Sigma-Aldrich) in 2.5% milk/TBST. After three TBST washes, the membranes were incubated with HRP-conjugated goat anti-mouse IgG (1:3000 in 2.5% milk/TBST) for 1 h at room temperature. Blots were developed with SuperSignal\u0026trade; West Pico PLUS (Thermo Fisher, Waltham, MA, USA), and images were obtained using a Fusion FX6.0 system. Band intensities were quantified in ImageJ and normalized to β-actin.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eqPCR\u003c/h2\u003e\u003cp\u003eThe real-time RT-PCR (qPCR) experiments were performed using a StepOnePlus real-time PCR system (Applied Biosystems, Foster City, CA, USA) following the 2\u0026minus;∆∆Ct method. Total RNA from contraACC tissue was extracted using TRIzol (Invitrogen) and reverse transcribed using TOPscript RT DryMIX (Enzynomics, Cat # RT200, Daejeon, Korea). All the ∆Ct values were normalized to the corresponding GAPDH values, and represent fold change induction.\u003c/p\u003e\u003cp\u003eThe following qPCR primers were used:\u003c/p\u003e\u003cp\u003e\u003cem\u003ePpp1r3c\u003c/em\u003e-Fw: GGT GAC TCA TCT TTC TGC CAC A; \u003cem\u003ePpp1r3c\u003c/em\u003e-Rv: CAA GAC AAA ATT AGG CAC GAG A; \u003cem\u003eGys1\u003c/em\u003e-Fw: ATC TAC ACT GTG CTG CAG ACG; \u003cem\u003eGys1\u003c/em\u003e-Rv: CCC TTG CTG TTC ATG GAA TCC; \u003cem\u003ePhka2\u003c/em\u003e-Fw: TGG ATG CCA CCT CTC TCT TC; \u003cem\u003ePhka2\u003c/em\u003e-Rv: TAT CTC CAC GCT CCC ACA TC; \u003cem\u003ePygb\u003c/em\u003e-Fw: CAG CAG CAT TAC TAT GAG CGG; \u003cem\u003ePygb\u003c/em\u003e-Rv: CCA AGT CCA ACC CCA ACT GA; \u003cem\u003eHif-1a\u003c/em\u003e-Fw: GAT CCT TGA TGC TTG CTG GG; \u003cem\u003eHif-1a\u003c/em\u003e-Rv: CTG TCC CCA ATG TCC AGA GT; \u003cem\u003eLdha\u003c/em\u003e-Fw: AAA GAG GAC TAA GGG GTG GC; \u003cem\u003eLdha\u003c/em\u003e-Rv: CTG CAG GAA ACA ACC ACT CC; \u003cem\u003eLdhb\u003c/em\u003e-Fw: AAA GGC TAC ACC AAC TGG GC; \u003cem\u003eLdhb\u003c/em\u003e-Rv: GCC GTA CAT TCC CTT CAC CA; \u003cem\u003eMct4\u003c/em\u003e-Fw: CAT TCC CAG GGA CGC AAA GAG; \u003cem\u003eMct4\u003c/em\u003e-Rv: GAC ACG GCT TGG ATC TCC TC; *HK1-*Fw: CCA TCC CTC TTT GAC ACC CT; \u003cem\u003eHK1\u003c/em\u003e-Rv: ACT CAG ACT AAA GTG GCC CC; \u003cem\u003ePfk1\u003c/em\u003e-Fw: CAG AAA GCC CAC ACT CAA CC; \u003cem\u003ePfk1\u003c/em\u003e-Rv: ACA GAA GAC CTT GGC CTA CC; \u003cem\u003ePkm\u003c/em\u003e-Fw: CTG GGT GGG AGA AAT GGA GT; \u003cem\u003ePkm\u003c/em\u003e-Rv: TCA GAA GCC CAG AGA ACC AG; \u003cem\u003ePdha1\u003c/em\u003e-Fw: GAT GCC GTG CTG ATT TAG GG; \u003cem\u003ePdha1\u003c/em\u003e-Rv: CGT CCT AGA AAT GGC AGC AC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRNA-seq data analysis\u003c/h2\u003e\u003cp\u003eRaw RNA-seq data were processed in Python to analyze relative gene expression between the experimental and control groups. Raw counts were compared against the sham group and normalized for library size using DESeq2\u0026rsquo;s size-factor adjustment. A negative‐binomial model was then fitted to calculate the log₂ fold change and associated p‐value for each gene. Genes were ranked in descending order by their log₂FC/standard error, and a GSEA was performed against the Hallmark and Gene Ontology gene sets. Normalized enrichment scores and false discovery rates were computed, and enrichment plots were generated in GraphPad Prism. For each analysis, we selected gene sets corresponding to specific metabolic pathways to assess their degree of enrichment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGC-MS\u003c/h2\u003e\u003cp\u003eTissue samples for the GC-MS analysis were prepared from the ACC using the same dissection and freezing protocol as for the glycogen assay. For each contraACC specimen, 400 \u0026micro;L of tissue supernatant was collected and subjected to GC-MS. Derivatization was performed using trimethylsilylation, and 1 \u0026micro;L of each derivatized sample was injected into a Thermo Scientific ISQ LT GC-MS system (Thermo Scientific, Waltham, MA, USA). Chromatographic separation and mass detection were carried out in standard operating conditions. Raw data were processed in Thermo Xcalibur Quan Browser: metabolites were identified by matching each chromatographic peak to reference spectra, and peak areas were integrated against baseline to obtain relative signal intensities. All metabolite abundances were then normalized to the sham controls and expressed as fold changes for downstream analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eNMR\u003c/h2\u003e\u003cp\u003eTissue for the NMR analyses of the ACC was prepared using the same dissection and freezing protocol as for the glycogen assay. For each contraACC sample, 500 \u0026micro;L of the tissue supernatant was collected and analyzed by NMR. Spectra were acquired on a Bruker AVANCE III HD 600 MHz high-resolution NMR spectrometer (Bruker BioSpin, Rheinstetten, Germany). Individual resonances were assigned using Chenomx NMR Suite Profiler, and metabolite concentrations were determined by integrating each peak relative to the subtraction baseline.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMetabolomics analysis\u003c/h2\u003e\u003cp\u003eAbsolute and relative metabolite concentrations obtained by GC-MS and NMR, including fold-change values normalized to the sham controls, were subjected to a comprehensive metabolomics analysis. For each metabolite, both the absolute abundance and fold-change relative to the sham and saline\u0026thinsp;+\u0026thinsp;SNT groups were calculated, and statistical significance was assessed. To identify enriched metabolic pathways within each experimental group, we performed both an over-representation analysis and a quantitative enrichment analysis. All pathway enrichment analyses were conducted using MetaboAnalyst v6.0. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.metaboanalyst.ca\u003c/span\u003e\u003cspan address=\"http://www.metaboanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003epH analysis\u003c/h2\u003e\u003cp\u003eMice were euthanized and brains were rapidly removed on ice. ACC tissue was dissected bilaterally, weighed, and homogenized in ice-cold deionized water (tissue:water, 1:10 w/v) using a cell homogenizer. Homogenates were clarified by centrifugation (3,000 \u0026times; g, 5 min, 4\u0026deg;C) and the supernatant was used for pH measurement\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. pH was measured at room temperature with a Mettler-Toledo FiveEasy Plus pH meter (model FP20; Mettler-Toledo, Greifensee, Switzerland) equipped with a combination pH electrode. The electrode was calibrated before each measurement session using a three-point calibration with commercial standard buffers (pH 4.01, 7.00 and 10.01) and automatic temperature compensation was enabled\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Each sample was measured in triplicate (electrode rinsed with deionized water and blotted between readings) and the mean value was recorded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eIn silico analysis\u003c/h2\u003e\u003cp\u003eThe crystal structure of PYGB (PDB ID: 5IKP) was downloaded from the RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/structure/5IKP\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/structure/5IKP\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and used as the starting model for all subsequent structural analyses. Ligand coordinates were generated in silico from the canonical SMILES string. Hydrogens were added, and the geometry was energy-minimized to convergence; the lowest-energy conformer was exported in PDB format. Protein coordinates (PDB) were parsed with a structural biology toolkit to extract atom and residue-level information. Ligand residues were recognized by residue name, and neighboring amino acids within a predefined distance cutoff were designated as pocket residues. These pocket residues were recorded for subsequent validation and docking calculations to characterize protein\u0026ndash;ligand interactions. Protein\u0026ndash;ligand docking simulations were performed using GalaxyDockWeb.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe data were analyzed in GraphPad software. Student\u0026rsquo;s t test was used for comparisons between two groups. For multiple group comparisons, two-way analysis of variance (ANOVA) was conducted, followed by Bonferroni\u0026rsquo;s post hoc test. All data are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM), and statistical significance was defined as a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eACC glycogenolysis mediates neuropathic pain chronification.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate glycogen metabolic dynamics in the brain during pain chronification, we harvested the ACC from mice 1, 3, 5, and 7 days after spinal nerve transection (SNT)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and performed a colorimetric glycogen assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). We confirmed that the SNT mice developed chronic mechanical allodynia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). During neuropathic pain chronification, we observed a distinct temporal pattern of glycogen dynamics in the ACC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). On the 0.5 post-SNT day, glycogen content in the contralateral ACC (contraACC) was comparable to sham controls. However, on days 1 and 3, the contraACC exhibited remarkable peaks in glycogen levels. On day 7, glycogen content had declined significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Together, these data suggest that the contraACC undergoes a transient glycogen accumulation during the transition to chronic pain, followed by rapid depletion in the chronic phase.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the spinal cord, genetic ablation of the protein targeting glycogen (PTG), a key facilitator of glycogenolytic flux, has been shown to mitigate pain chronification\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Accordingly, we investigated how inhibition of glycogenolysis affects the distinctive glycogen metabolic program during pain chronification. We targeted a brain-specific glycogen phosphorylase isoform B (PYGB). The activity of PYGB was blocked using two chemically distinct inhibitors: GPI-1 (5-chloro-N-[(1S,2R)-2-hydroxy-3-(methoxymethylamino)-3-oxo-1-(phenylmethyl)propyl]-1H-indole-2-carboxamide) and GPI-2 (1-[3-(3-[2-chloro-4,5-difluorobenzoyl]ureido)-4-methoxyphenyl]-3-methylurea)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The appropriate binding interactions of GPI-1 and GPI-2 with PYGB were confirmed using in silico docking analysis\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;1a-d).\u003c/p\u003e\u003cp\u003eTo test whether PYGB inhibition influences pain chronification and glycogenolytic products, we stereotaxically injected GPI-1 or GPI-2 directly into the contraACC of SNT mice and then conducted a von Frey test and nuclear magnetic resonance (NMR) spectroscopy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). In both the GPI-1 and GPI-2 treated groups, chronic mechanical allodynia was rescued across the days tested (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). However, they exhibited acute mechanical sensitivity comparable to the SNT control group on day 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Seven days after SNT, we harvested the contraACC and performed NMR to quantify the glycogenolytic product glucose-1-phosphate (G1P). We found that G1P was significantly increased in the SNT control mice, and that the increase was reversed by both PYGB inhibitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003eWe further tested neuronal activation levels in the contraACC by measuring immunohistochemical cell activation markers: c-Fos, pCREB, and p-p38-MAPK. The number of c-Fos-positive neurons was elevated in SNT control mice relative to the sham-operation controls, and it was significantly decreased following GPI-1 or GPI-2 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg-h). Likewise, although both pCREB- and p-p38-positive neurons in the contraACC were enriched in the SNT mice, treatment with either GPI-1 or GPI-2 normalized their expression to sham levels (Supplementary Fig.\u0026nbsp;2a-d).\u003c/p\u003e\u003cp\u003eThe ACC contributes to pain chronification not only through local neuronal hyperactivity but also by engaging downstream circuits\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Activation of ACC projections to both the nucleus accumbens (NAc) and ventral tegmental area (VTA) has been implicated in the establishment of chronic pain\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. We therefore quantified c-Fos expression in the NAc and VTA contralateral to the injured nerve (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). SNT induced robust neuronal activation in both regions, which was effectively suppressed by contraACC administration of a glycogen phosphorylase inhibitor (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-e). These findings indicate that blocking glycogenolysis in the ACC after nerve injury attenuates neuronal hyperactivity locally and across critical pain-related circuits.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecific astrocyte glycogen metabolic dynamics in neuropathic pain chronification.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGlycogen metabolism is largely confined to astrocytes in the CNS\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. To further support and confirm this, we analyzed publicly available single-cell RNA-sequencing data\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. We found that key glycogen metabolic enzyme transcripts, glycogen synthase 1 (\u003cem\u003eGys1\u003c/em\u003e), \u003cem\u003ePygb\u003c/em\u003e, phosphorylase kinase regulatory subunit alpha 2 (\u003cem\u003ePhka2\u003c/em\u003e), and protein phosphatase 1 regulatory subunit 3C (\u003cem\u003ePpp1r3c\u003c/em\u003e), were highly enriched in astrocyte clusters, with minimal expression in neurons or other glia (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo define the temporal dynamics of glycogen metabolism in ACC astrocytes during pain chronification, we performed ribosome-associated mRNA profiling in GFAPCre::RiboTag mice\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. After SNT, ACC tissues were microdissected for RiboTag immunoprecipitation and quantitative PCR analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Cre-mediated RiboTag expression in astrocytes was confirmed by PCR genotyping and western blotting (Supplementary Fig.\u0026nbsp;3a-b).\u003c/p\u003e\u003cp\u003eConsistent with the distinct glycogen metabolic trajectories observed in our glycogen assay analysis, astrocyte-specific transcripts exhibited unique, time‐dependent expression patterns. The glycogen synthesis regulators \u003cem\u003ePpp1r3c\u003c/em\u003e and \u003cem\u003eGys1\u003c/em\u003e both peaked on day 3, marking the transition from acute to chronic pain. In contrast, \u003cem\u003ePhka2\u003c/em\u003e and \u003cem\u003ePygb\u003c/em\u003e reached maximal expression on day 7, corresponding to an established chronic phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eCollectively, these findings reveal a biphasic regulation of glycogen metabolism in ACC astrocytes: an early increase in glycogen synthesis during the transition to chronic pain, followed by robust glycogenolysis in the chronic phase.\u003c/p\u003e\u003cp\u003e\u003cb\u003eACC astrocyte activation mediates neuropathic pain chronification.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAstrocytes in the ACC have been implicated in modulating pain chronification via multiple mechanisms\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. To determine whether ACC astrocyte activation specifically drives the transition from acute to chronic neuropathic pain, we conditionally ablated ACC astrocytes by overexpressing caspase-3 and then performed SNT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Immunohistochemical staining for the astrocyte marker S100β confirmed depletion in the contraACC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Behavioral testing revealed that mice expressing caspase-3 showed post-SNT pain thresholds early on comparable to sham controls, but the development of chronic hypersensitivity was prevented (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Consistent with our observations following PYGB inhibition, c-Fos immunostaining showed a significant reduction in activated neurons in caspase-3-expressing mice relative to SNT controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-f), demonstrating that ACC astrocytes are required for pain chronification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBuilding on this, we examined whether inhibiting glycogenolysis alters astrocyte abundance. Using the same stereotaxic paradigm, we delivered GPI-1, GPI-2, or saline to the contraACC immediately before SNT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). Quantification of S100β-positive cells and calculation of the ipsilateral-to-contralateral cell-count ratio revealed no significant differences among Saline\u0026thinsp;+\u0026thinsp;Sham, Saline\u0026thinsp;+\u0026thinsp;SNT, GPI-1, and GPI-2 groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh-i). These data indicate that neither pain chronification (SNT vs. Sham) nor glycogenolysis inhibition alters astrocyte number. Instead, both modulate pain chronification by changing astrocyte activation and intracellular signaling pathways.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWarburg-type cancer-associated metabolic reprogramming mediates neuropathic pain chronification.\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrevious findings demonstrated that ACC astrocytes underwent unique glycogen-metabolic dynamics during pain chronification, and that selective inhibition of glycogenolysis mediated transition to chronic pain. Although other studies have linked glycogen metabolism indirectly to astrocyte function, for example, via lactate production, the specific role of glycogen and the broader metabolic adaptations in the brain throughout pain chronification remain unclear\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe first used bulk metabolomics to map temporal changes in ACC metabolites during neuropathic pain progression\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Following SNT, we harvested ACC tissue on 0.5, 1, 3, and 7 days post-injury, quantified 28 metabolites by GC-MS, and normalized contraACC abundances to both sham controls and the ipsiACC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). To compare acute (0.5\u0026ndash;1 d) versus chronic (7 d) states, we performed principal component analysis (PCA) and partial least squares\u0026ndash;discriminant analysis (PLS-DA), both of which showed robust separation and statistical significance (Supplementary Fig.\u0026nbsp;4a-c)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Acute and chronic samples segregated clearly along PC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-c), and k-means clustering further defined three metabolic trajectories: early (0.5\u0026ndash;1 d), transitional (3 d), and late chronic (7 d) phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-c, Supplementary Fig.\u0026nbsp;4d-e). Notably, lactate and several other metabolites surged, specifically in the late chronic phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, Supplementary Fig.\u0026nbsp;4e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe next examined individual metabolite trends over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed) and conducted metabolite set enrichment analysis (MSEA) to identify pathways intensified during chronification (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee-i)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The repertoire of enriched pathways shifted dynamically, with striking differences between early acute (0.5 d) and late chronic (7 d) phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee-h). In particular, cancer-associated reprogramming pathways, most prominently the Warburg effect, were progressively enriched over time and among the most significant in the chronic state (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh-i). Given that tissue acidification is a hallmark of cancer‐associated metabolic reprogramming\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, we measured pH in ipsilateral and contralateral ACC at early (1 d) and late chronic (7 d) time points post-SNT (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ej). While sham mice showed no bilateral pH difference, chronic‐state mice exhibited a significant acidification of the contraACC relative to the ipsilateral side (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ek).\u003c/p\u003e\u003cp\u003eTo determine whether astrocytes are involved in Warburg-type metabolism during pain chronification, we used GFAPCre::RiboTag mice to isolate ribosome‐associated transcripts from ACC astrocytes. Following SNT-induced neuropathic pain, ACC tissues were microdissected and subjected to RiboTag immunoprecipitation for mRNA expression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Under hypoxic conditions, hypoxia‐inducible factor‐1α (\u003cem\u003eHif-1α\u003c/em\u003e) orchestrates the transcriptional upregulation of glycolytic and lactate‐export machinery, including \u003cem\u003ePpp1r3c\u003c/em\u003e, hexokinase 1 (\u003cem\u003eHk1\u003c/em\u003e), phosphofructokinase 1 (\u003cem\u003ePfk1\u003c/em\u003e), pyruvate kinase M1/M2 (\u003cem\u003ePkm\u003c/em\u003e), lactate dehydrogenase A/B (\u003cem\u003eLdha/b\u003c/em\u003e), and monocarboxylate transporter 4 (\u003cem\u003eMct4\u003c/em\u003e). This transcriptional shift biases carbohydrate flux toward anaerobic glycolysis and lactate production, while repressing pyruvate dehydrogenase E1α (\u003cem\u003ePdha1\u003c/em\u003e) to limit tricarboxylic acid cycle entry (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. We conducted quantitative PCR to chart temporal expression changes of these \u0026ldquo;Warburg\u0026rdquo; genes on 1, 3, and 7 days post‐SNT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec-j). \u003cem\u003eHif-1α\u003c/em\u003e peaked on day 3, marking the intermediate phase between acute and chronic pain (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Downstream targets \u003cem\u003eLdha\u003c/em\u003e, \u003cem\u003eLdhb\u003c/em\u003e, \u003cem\u003eMct4\u003c/em\u003e, \u003cem\u003eHk1\u003c/em\u003e, \u003cem\u003ePfk1\u003c/em\u003e, and \u003cem\u003ePkm\u003c/em\u003e reached maximal expression on day 7, corresponding to the established chronic state (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed-i), whereas \u003cem\u003ePdha1\u003c/em\u003e expression diminished in the chronic phase compared to the acute state (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ej). Collectively, these data indicate that \u003cem\u003eHif-1α\u003c/em\u003e-mediated Warburg metabolism is most active during pain chronification.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven that astrocytic Warburg activation culminates in lactate secretion by MCT4\u003csup\u003e12,16,21\u003c/sup\u003e, we investigated whether inhibiting lactate export could modulate chronic pain. Therefore, we stereotaxically administered the MCT4 inhibitor α-cyano-4-hydroxycinnamate (4-CIN) into the ACC following SNT and evaluated both nociceptive behavior and neuronal activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ek). In von Frey assays, 4-CIN selectively attenuated mechanical hypersensitivity during the chronic phase, replicating the effects of glycogenolysis inhibition and astrocyte depletion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003el-o). Neuronal hyperactivity, assessed by c-Fos immunohistochemistry, was markedly reduced in the contraACC of 4-CIN-treated mice, with c-Fos+/NeuN\u0026thinsp;+\u0026thinsp;cell counts and proportions reverting to sham levels and significantly lower than in SNT controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003em-n). Notably, these reductions were comparable to those achieved with a pharmacological glycogen phosphorylase inhibitor (GPI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eo).\u003c/p\u003e\u003cp\u003eIn summary, our data reveal dynamic, time-dependent metabolic reprogramming in the ACC during pain chronification. The progressive enrichment of the Warburg effect, alongside cancer-associated metabolic pathways, suggests that these adaptations may play a pivotal role in driving the transition to chronic pain. Our findings establish that the robust activation of the Warburg effect in ACC astrocytes drives pain chronification through enhanced lactate export.\u003c/p\u003e\u003cp\u003eIn a parallel analysis, we examined transcriptomic profiles from the contraACC in the chronic constriction injury (CCI) model\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, which recapitulates features of SNT-induced neuropathic pain (Supplementary Fig.\u0026nbsp;5a). At 7 days post-CCI, contraACC expression data were normalized against sham controls, and gene set enrichment analysis (GSEA) was performed for each pathway of interest (Supplementary Fig.\u0026nbsp;5b-f). GSEA plots demonstrated significant upregulation of the Warburg effect and glutamate metabolism pathways under chronic pain conditions (Supplementary Fig.\u0026nbsp;5b-c). In contrast to the Warburg phenotype, aerobic glycolysis, along with the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS) pathways, were all downregulated (Supplementary Fig.\u0026nbsp;5d-f). Our transcriptomic analyses confirm that cancer-associated metabolic programs, including the Warburg effect and glutamate metabolism, are selectively reprogrammed during the chronic state of neuropathic pain.\u003c/p\u003e\u003cp\u003e\u003cb\u003eACC glycogenolysis mediates cancer-associated metabolic reprogramming preceding neuropathic pain chronification.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn cancer cells, the interplay between glycogen metabolism and the Warburg effect is well documented\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Ppp1r3c, which governs the flux between glycogen synthesis and breakdown, is transcriptionally regulated by HIF-1α, the key regulator of the Warburg phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ek)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Moreover, enhanced glycogenolysis has been shown to amplify the Warburg effect and to serve as a rapid reservoir for metabolic intermediates\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Thus, astrocytic glycogen shunting likely represents a critical control point in cancer-associated metabolic reprogramming, including the Warburg effect. In this context, to investigate the link between ACC astrocyte glycogenolysis and the Warburg effect, we stereotaxically injected GPI-1 or GPI-2 into the contraACC of SNT mice and performed 1H-NMR metabolomic profiling on day 7 post-injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe then applied PCA and PLS-DA to evaluate metabolic differences among groups\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Both methods showed high reproducibility and statistical robustness (Supplementary Fig.\u0026nbsp;6a-c) and clearly separated the SNT cohort from the GPI-treated groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb-c). K-means clustering further corroborated these distinctions, revealing characteristic shifts in key metabolites, most notably lactate (Supplementary Fig.\u0026nbsp;6d-e).\u003c/p\u003e\u003cp\u003eMetabolite concentrations were determined by NMR analysis and compared across groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed-m, Supplementary Fig.\u0026nbsp;7a-d)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In SNT controls, lactate was significantly increased relative to sham, whereas both GPI-1 and GPI-2 restored lactate to baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). We next examined key metabolites of the TCA cycle and glutamate metabolism. SNT animals exhibited reduced succinate alongside increased pyruvate and malate compared to sham. However, PYGB inhibition reversed the latter two elevations, lowering pyruvate and malate toward sham values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee-g). Although glutamine and GABA showed no significant changes across groups, glutamate was markedly increased after SNT and significantly attenuated by GPI-1 or GPI-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei-k). Collectively, blocking glycogenolysis normalized the levels of lactate, pyruvate, malate, alanine, and glutamate to those observed in sham controls, while succinate exhibited a modest elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed-n).\u003c/p\u003e\u003cp\u003eFocusing on the cancer-associated pathways highlighted by our GC-MS results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), we conducted pathway-level enrichment analysis for the Warburg effect and glutamate metabolism on the NMR data\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Both the enrichment p-value and expectation value for these pathways were substantially reduced in the PYGB‐inhibited groups versus SNT controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eo-p). Furthermore, while SNT drove broad activation of multiple oncometabolic pathways, PYGB blockade substantially reduced both the impact scores and statistical significance of these pathways (Supplementary Fig.\u0026nbsp;7e-g).\u003c/p\u003e\u003cp\u003eTaken together, these findings indicate that inhibiting glycogenolysis can effectively quench the enhanced Warburg effect and associated cancer-like metabolic reprogramming that arise during neuropathic pain chronification, thereby restoring metabolite profiles toward baseline.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe concept of neuron\u0026ndash;astrocyte metabolic coupling has emerged as a critical factor in neurological disorders\u003csup\u003e5,15\u003c/sup\u003e. In chronic neuropathic states, lactate, an astrocytic metabolite in the CNS, has largely been considered a fuel source that sustains excessive neuronal activity\u003csup\u003e8,12,52\u003c/sup\u003e. In this study, we sought to determine the specific metabolic signaling in ACC astrocytes that mediates neuropathic pain chronification. Here, we demonstrate that during the transition to chronic pain, ACC astrocytes exhibited a distinct biphasic glycogen dynamic\u0026mdash;an early synthesis surge followed by glycogenolysis\u0026mdash;which is necessary for chronic pain development. By integrating metabolomic and transcriptomic analyses, we showed that glycogenolysis drove a Warburg‐type, cancer‐associated metabolic reprogramming in astrocytes, and that inhibiting glycogen breakdown suppressed this reprogramming and prevented pain chronification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study demonstrates that astrocytic adaptations involve not merely an increase in lactate concentration but a comprehensive rewiring of cancer‐associated metabolic flux. In this context, the emergence of a Warburg‐type cancer‐associated metabolic program in astrocytes suggests that the role of lactate extends far beyond simple bioenergetics\u003csup\u003e7,8,9\u003c/sup\u003e. In hypoxic tumor microenvironments, excessive lactate production not only accelerates metabolite turnover and maintains redox balance but also drives histone lactylation and inflammatory signaling\u003csup\u003e53,54,55,56\u003c/sup\u003e. By analogy, astrocyte‐derived lactate in the ACC may similarly act as a versatile signaling metabolite, modulating gene expression, neuroinflammation, and circuit function, thereby contributing to pain chronification through multifaceted mechanisms.\u003c/p\u003e\n\u003cp\u003eIn this study, we demonstrated that astrocyte metabolic patterns dynamically change during the transition to chronic pain. Particularly, we found that the robust activation of a Warburg‐type metabolic program triggered by glycogenolysis is essential for driving pain chronification. Emerging evidence indicates that diverse astrocytic signaling factors can remodel metabolic flux, driving astrocytes toward a reactive phenotype and thereby altering their functional repertoire\u003csup\u003e5,57\u003c/sup\u003e. In this context, the metabolic reprogramming we observed during pain chronification sheds new light on astrocyte dynamics in chronic neuropathy. Targeting the regulation of astrocytic metabolic flux represents a promising strategy for novel neuropathic pain therapies.\u003c/p\u003e\n\u003cp\u003eIn our study, we identified glycogen as a key nodal point for modulating astrocytic metabolism. Although the downstream pathways governed by glycogen metabolism in astrocytes remain incompletely characterized, our findings demonstrate that inhibiting glycogenolysis suppresses the Warburg‐type program. These findings establish glycogen metabolism as a critical regulatory signal for astrocytic metabolic flux, with potential implications for therapeutic intervention.\u003c/p\u003e\n\u003ch2\u003eLimitations of the study\u003c/h2\u003e\n\u003cp\u003eIn this study, we identified glycogenolysis and Warburg‐type metabolic signaling as defining features of pain chronification and demonstrated that inhibiting glycogenolysis effectively suppresses the Warburg effect. Nevertheless, further investigations are required to determine the upstream triggers and downstream consequences of astrocytic Warburg activation during chronic pain development. Moreover, translating these insights into therapies for chronic pain will demand a comprehensive characterization of glycogenolysis‐mediated pain modulation in murine models and rigorous validation of these mechanisms in clinical contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eResource availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLead contact\u003c/p\u003e\n\u003cp\u003eFurther information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Junseo Park ([email protected]).\u003c/p\u003e\n\u003cp\u003eMaterials availability\u003c/p\u003e\n\u003cp\u003eAll mouse lines and materials used in this study were provided or purchased from the mentioned companies or researchers. This study did not generate any new or unique reagents.\u003c/p\u003e\n\u003cp\u003eData and code availability\u003c/p\u003e\n\u003cp\u003eAll data reported in this paper will be shared by the lead contact\u0026nbsp;upon request.\u003c/p\u003e\n\u003cp\u003eThis paper does not report original code.\u003c/p\u003e\n\u003cp\u003eAll data associated with this study are present in the paper or the supplemental information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all members of the Neuron-Glia Network Research Laboratory for their useful discussions and help. We also thank Kyungchul Noh, assistant professor at the Department of Pharmacology, School of Medicine, Ajou University, Suwon, Republic of Korea, for useful discussions. This research was supported by the 2024 Seoul National University undergraduate independent research in the Student-Directed Education (SDE) program and the National Research Foundation of Korea (RS-2024-00402116; RS-2025-02215169).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.S.P. designed the research, performed most experiments, analyzed the data, and wrote the first draft of the paper. K.H.K. contributed to data interpretation and manuscript revision. H.W.J. crossed RiboTag and GFAP-Cre mice to create astrocyte-specific RiboTag mice and assisted with experimental procedures. S.J.L. supervised the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhuo, M. 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Metab\u003c/em\u003e. \u003cstrong\u003e33\u003c/strong\u003e, 390\u0026ndash;400 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7435294/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7435294/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAstrocytes are involved in modulating neuronal excitability in numerous neuropathological states, including chronic pain, which is characterized by aberrant neuronal firing and altered synaptic plasticity. Anterior cingulate cortex (ACC) astrocytes have been implicated in neuropathic pain chronification; however, the underlying intracellular mechanisms remain unclear. We integrated bulk metabolomics with astrocyte-specific RiboTag transcriptomics, where we identified a Warburg-type metabolic reprogramming in ACC astrocytes during the transition from acute to chronic pain. In addition, we demonstrated that ACC astrocytes underwent a biphasic glycogen program, characterized by an initial synthesis followed by glycogenolysis, and found that pharmacological inhibition of glycogen breakdown prevented chronic pain development. Mechanistically, glycogenolysis fueled lactate production and downstream Warburg-type metabolic pathways, driving astrocytic and neuronal hyperactivity. Blocking glycogenolysis disrupted this reprogramming, restored metabolic homeostasis, and alleviated pain chronification. These findings reveal a novel astrocyte‐centric neuropathic pain circuitry and implicate glycogen metabolism as a potential therapeutic target for chronic pain.\u003c/p\u003e","manuscriptTitle":"Warburg-type metabolic reprogramming facilitated by astrocyte glycogenolysis mediates neuropathic pain chronification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 14:25:32","doi":"10.21203/rs.3.rs-7435294/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ad1bdd6-83b2-421f-b620-3e9cdaf3a413","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54977190,"name":"Biological sciences/Neuroscience/Molecular neuroscience"},{"id":54977191,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Chronic pain"},{"id":54977192,"name":"Biological sciences/Biochemistry/Metabolomics"}],"tags":[],"updatedAt":"2025-09-30T14:25:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 14:25:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7435294","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7435294","identity":"rs-7435294","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}