Biology subjects

Lu, S.

Publications and source records attributed to Lu, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Saturated fatty acid-Coenzyme A supplementation restores neuronal energy levels and protein homeostasis in hereditary spastic paraplegia

Mitochondrial ATP production is fuelled by a fatty acid flux generated by phospholipase and triglyceride lipases in metabolically demanding tissues such as heart and liver, while the brain has long been believed to use almost solely glucose for energy. Phospholipase A1 enzyme DDHD2 is a major triglyceride lipase in the brain, and the loss of DDHD2 function results in a saturated free fatty acid (sFFA) imbalance and lipid droplet (LD) accumulation in the brain. The LD accumulation in neurons has been enigmatic as LDs are mainly considered to serve as a fuel storage. Here, we demonstrate that the loss of DDHD2 results in a mitochondrial respiratory dysfunction that leads to a significant decrease in ATP production and acetyl coenzyme A levels in neurons, even when the glycolytic breakdown of glycose occurs normally. Loss of DDHD2 also leads to a presynaptic defect as well as an imbalance in the global protein homeostasis in the neurons. These defects were rescued by external supplementation of the sFFA myristic acid coupled with its cofactor coenzyme A (Myr-CoA), indicating sFFA fuelling for neuronal {beta}-oxidation. We have thus discovered that the sFFAs released by the activity of DDHD2 play a central role in providing energy to fuel synaptic function. One Sentence SummaryFree fatty acids released by DDHD2 activity play a central role in maintaining neuronal energy levels and synaptic function.

neuroscience↗

bioRxiv2023

Systematic comparison of sequencing-based spatial transcriptomic methods

Recent advancements of sequencing-based spatial transcriptomics (sST) have catalyzed significant advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare six sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly impacting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.

genomics↗

bioRxiv2023

Targeting PKC alleviates iron overload in diabetes and hemochromatosis

Diabetes is one of the most prevalent chronic diseases worldwide. Iron overload increases the incidence of diabetes and aggravates diabetic complications that cause mortality. Reciprocally, diabetes potentially promotes body iron loading, but the mechanism remains not well understood. In this study, we demonstrated systemic iron excess and the upregulation of iron exporter ferroportin (Fpn) in the enterocytes and macrophages of multiple diabetic mouse models. Increased Fpn expression and iron efflux was also seen in the enterocytes of type 2 diabetic human patients. We further showed that protein kinase C (PKC), which is activated in hyperglycemia, was responsible for the sustained membrane expression of Fpn in physiological and in diabetic settings. For the first time, we identified that PKCs were novel binding proteins and positive regulators of Fpn. Mechanistically, hyperactive PKC promoted exocytotic membrane insertion while inhibited the endocytic trafficking of Fpn in the resting state. PKC also protected Fpn from internalization and degradation by its ligand hepcidin dependent on decreased ubiquitination and increased phosphorylation of Fpn. Importantly, the loss-of-function and pharmacological inhibition of PKC alleviated systemic iron overload in diabetes and hemochromatosis. Our study thus highlights PKC as a novel target in the control of systemic iron homeostasis.

cell biology↗

bioRxiv2023

Attenuating amyloid-beta pathology in mice with in situ programmed astrocytes

Astrocytes are abundant cells in the central nervous system that provide trophic support for neurons and clear detrimental factors, such as A{beta} oligomers (A{beta}Os). However, in the brains of Alzheimers disease (AD) patients, astrocytes lose these physiological functions. Here, we genetically engineered astrocytes with an anti-A{beta}O chimeric antigen receptor (CAR), constructed by replacing the antigen-binding domain of MerTK with an A{beta}O-specific single-chain variable fragment, to direct their phagocytic activity against A{beta}Os. CAR-engineered astrocytes (CAR-As) showed significantly enhanced phagocytosis of A{beta}Os due to effective activation of Rac1, Cdc42 and RhoA and markedly decreased release of pro-inflammatory cytokines due to inhibition of the NF-{kappa}B and cytokine receptor signalling pathways. Consistently, in situ CAR-As markedly ameliorated the cognitive deficits of APP/PS1 transgenic mice possibly by clearing A{beta}Os and creating a non-inflammatory microenvironment for neuronal survival and the restoration of microglia to a healthy phenotype. Our present study is the first to introduce a CAR-A-based therapy, validate its feasibility and effectiveness, and highlight its potential application for the treatment of AD and other brain disorders.

neuroscience↗

bioRxiv2023

Biomimetic computations improve neural network robustness

Object recognition by natural and artificial sensory systems requires a combination of selectivity and invariance. Both natural and artificial neural networks achieve selectivity and invariance by propagating sensory information though layers of neurons organised in a functional hierarchy. Both employ computational units performing AND-like operations for selectivity and OR-like operations for invariance. However, while biological neurons are intrinsically capable of switching between these operations, their artificial counterparts are hard-wired to perform only one of them. We wanted to test whether the flexible mapping between neurons and computations observed in biological neural networks is compatible with, or perhaps even useful to, artificial neural networks. To answer this question, we have developed a deep learning layer in which both selectivity and invariance operations can be performed by the same neurons. As with biological neurons, the choice of which operation an artificial neuron performs on a given input can be governed by the input strength. This flexible layer successfully outputs a combination of the two operations and, surprisingly, confers additional robustness to adversarial examples, which are inputs deliberately crafted to promote misclassification. The flexible mapping also improves accuracy when the training dataset is small, as well as when data are corrupted by certain types of noise. These results narrow the gap between biological and artificial neural networks and add a new bio-inspired approach to the arsenal of defenses against adversarial examples, which are known threats to model-based optimization and network security. Author summaryThe biophysical properties of a biological neuron enable it to perform both OR-like and AND-like operations over its inputs. In contrast, artificial neural networks use units that always perform only one specific operation. We wondered whether the flexibility observed in individual biological neurons could be incorporated into artificial neural networks without breaking them. If artificial neural networks required their units to perform single operations, then this requirement would either point to a fundamental difference between biological and artificial neural networks or indicate that biological neurons are somehow constrained to perform only one type of operations during object recognition. If, on the other hand, artificial units can flexibly switch between different operations, then such a result would indicate that biological and artificial neural networks are more similar than previously thought and would be an important step towards biomimetic AI. To find out, we introduced a new computational structure we call a flexible layer, in which individual units can switch between operations according to a rule (e.g., depending on input strength, or even randomly). We found that inserting the flexible layer in one or several positions in different artificial neural networks trained on several benchmark datasets not only preserves their accuracy but also makes them more robust to various perturbations and improves learning when training data is scarce.

neuroscience↗

bioRxiv2023

KatG catalase deficiency confers bedaquiline hyper-susceptibility to isoniazid resistant Mycobacterium tuberculosis

Multidrug-resistant tuberculosis (MDR-TB) is a growing source of global mortality and threatens global control of tuberculosis (TB) disease. The diarylquinoline bedaquiline (BDQ) recently emerged as a highly efficacious drug against MDR-TB, defined as resistance to the first-line drugs isoniazid (INH) and rifampin. INH resistance is primarily caused by loss-of-function mutations in the catalase KatG, but mechanisms underlying BDQs efficacy against MDR-TB remain unknown. Here we employ a systems biology approach to investigate BDQ hyper-susceptibility in INH-resistant Mycobacterium tuberculosis. We found hyper-susceptibility to BDQ in INH-resistant cells is due to several physiological changes induced by KatG deficiency, including increased susceptibility to reactive oxygen species and DNA damage, remodeling of transcriptional programs, and metabolic repression of folate biosynthesis. We demonstrate BDQ hyper-susceptibility is common in INH-resistant clinical isolates. Collectively, these results highlight how altered bacterial physiology can impact drug efficacy in drug-resistant bacteria.

microbiology↗

bioRxiv2023

Bidirectional Generative Adversarial Representation Learning for Natural Stimulus Synthesis

Thousands of species use vocal signals to communicate with one another. Vocalisations carry rich information, yet characterising and analysing these complex, high-dimensional signals is difficult and prone to human bias. Moreover, animal vocalisations are ethologically relevant stimuli whose representation by auditory neurons is an important subject of research in sensory neuroscience. A method that can efficiently generate naturalistic vocalisation waveforms would offer an unlimited supply of stimuli with which to probe neuronal computations. While unsupervised learning methods allow for the projection of vocalisations into low-dimensional latent spaces learned from the waveforms themselves, and generative modelling allows for the synthesis of novel vocalisations for use in downstream tasks, there is currently no method that would combine these tasks to produce naturalistic vocalisation waveforms for stimulus playback. In this paper, we demonstrate BiWaveGAN: a bidirectional Generative Adversarial Network (GAN) capable of learning a latent representation of ultrasonic vocalisations (USVs) from mice. We show that BiWaveGAN can be used to generate, and interpolate between, realistic vocalisation waveforms. We then use these synthesised stimuli along with natural USVs to probe the sensory input space of mouse auditory cortical neurons. We show that stimuli generated from our method evoke neuronal responses as effectively as real vocalisations, and produce receptive fields with the same predictive power. BiWaveGAN is not restricted to mouse USVs but can be used to synthesise naturalistic vocalisations of any animal species and interpolate between vocalisations of the same or different species, which could be useful for probing categorical boundaries in representations of ethologically relevant auditory signals.

neuroscience↗

bioRxiv2023

A universal preservation protocol for multi-omic and histological analysis of kidney tissue

Biobanking of tissue from clinically obtained kidney biopsies for later use with multi-omic and imaging techniques is an inevitable step to overcome the need of disease model systems and towards translational medicine. Hence, collection protocols ensuring integration into daily clinical routines using preservation media not requiring liquid nitrogen but instantly preserving kidney tissue for clinical and scientific analyses are of paramount importance. Thus, we modified a robust single nucleus dissociation protocol for kidney tissue stored snap frozen or in the preservation media RNAlater and CellCover. Using porcine kidney tissue as surrogate for human kidney tissue, we conducted single nucleus RNA sequencing with the Chromium 10X Genomics platform. The resulting data sets from each storage condition were analyzed to identify any potential variations in transcriptomic profiles. Furthermore, we assessed the suitability of the preservation media for additional analysis techniques (proteomics, metabolomics) and the preservation of tissue architecture for histopathological examination including immunofluorescence staining. In this study, we show that in daily clinical routines the RNAlater facilitates the collection of highly preserved kidney biopsies and enables further analysis with cutting-edge techniques like single nucleus RNA sequencing, proteomics, and histopathological evaluation. Only metabolome analysis is currently restricted to snap frozen tissue. This work will contribute to build tissue biobanks with well-defined cohorts of the respective kidney disease that can be deeply molecularly characterized, opening new horizons for the identification of unique cells, pathways and biomarkers for the prevention, early identification, and targeted therapy of kidney diseases.

genomics↗

bioRxiv2023

Smooth muscle-derived adventitial progenitor cells promote key cell type transitions controlling plaque stability in atherosclerosis in a Klf4-dependent manner

We previously established that vascular smooth muscle-derived adventitial progenitor cells (AdvSca1-SM) preferentially differentiate into myofibroblasts and contribute to fibrosis in response to acute vascular injury. However, the role of these progenitor cells in chronic atherosclerosis has not been defined. Using an AdvSca1-SM lineage tracing model, scRNA-Seq, flow cytometry, and histological approaches, we confirmed that AdvSca1-SM cells localize throughout the vessel wall and atherosclerotic plaques, where they primarily differentiate into fibroblasts, SMCs, or remain in a stem-like state. Klf4 knockout specifically in AdvSca1-SM cells induced transition to a more collagen-enriched myofibroblast phenotype compared to WT mice. Additionally, Klf4 depletion drastically modified the phenotypes of non-AdvSca1-SM-derived cells, resulting in more contractile SMCs and atheroprotective macrophages. Functionally, overall plaque burden was not altered with Klf4 depletion, but multiple indices of plaque vulnerability were reduced. Collectively, these data support that modulating the AdvSca1-SM population confers increased protection from the development of unstable atherosclerotic plaques.

pathology↗

bioRxiv2023

Temporal transcriptomic dynamics in developing macaque neocortex

Despite intense research on mice, the transcriptional regulation of neocortical neurogenesis remains limited in humans and non-human primates. Cortical development in rhesus macaque is known to recapitulate multiple facets of cortical development in humans, including the complex composition of neural stem cells and the thicker supragranular layer. To characterize temporal shifts in transcriptomic programming responsible for differentiation from stem cells to neurons, we sampled parietal lobes of rhesus macaque at E40, E50, E70, E80, and E90, spanning the full period of prenatal neurogenesis. Single-cell RNA sequencing produced a transcriptomic atlas of the developing rhesus macaque neocortex parietal lobe. Identification of distinct cell types and neural stem cells emerging in different developmental stages revealed a terminally bifurcating trajectory from stem cells to neurons. Notably, deep-layer neurons appear in the early stages of neurogenesis, while upper-layer neurons appear later. While these different lineages show overlap in their differentiation program, cell fates are determined post-mitotically. Pseudotime trajectories from vRGs to oRGs revealed differences in dynamic gene expression profiles and identified divergence in their activation of BMP, FGF, and WNT signaling pathways. These results provide a comprehensive picture of the temporal patterns of gene expression leading to different fates of radial glial progenitors during neocortex layer formation.

developmental biology↗

bioRxiv2023

Using the visual cliff assay to assess binocular deficits in amblyopic mice

Amblyopia, a neurodevelopmental visual disorder characterized by impaired stereoacuity, is commonly modeled in animals using monocular deprivation (MD) during a critical period of visual development. Despite extensive research at the synaptic, cellular and circuit levels of analysis, reliable behavioral assays to study stereoscopic deficits in mice are limited. This study aimed to characterize the Visual Cliff Assay (VCA) and the Pole Descent Cliff Task (PDCT) in mice, and to evaluate their utility in detecting binocular dysfunction. Using these assays, we investigated the impact of clinically relevant manipulations of binocular vision, including monocular occlusion, pupillary dilation, and amblyopia induced by long-term MD. Our findings reveal that optimal performance in both the VCA and PDCT are dependent on balanced binocular input. However, deficits after MD in the VCA exhibited relatively small effect sizes (7-14%), requiring large sample sizes for statistical comparisons. In contrast, the PDCT demonstrated larger effect sizes (43-61%), allowing for reliable detection of binocular dysfunction with a smaller sample size. Both assays were validated using multiple monocular manipulations relevant to clinical paradigms, with the PDCT emerging as the preferred assay for detecting deficits in stereoscopic depth perception in mice. These findings provide a robust framework for using the VCA and PDCT in mechanistic and therapeutic studies in mice, offering insights into the neural mechanisms of binocular vision and potential interventions for amblyopia

neuroscience↗

bioRxiv2023

Dozer: Debiased personalized gene co-expression networks for population-scale scRNA-seq data

Population-scale single cell RNA-seq (scRNA-seq) datasets create unique opportunities for quantifying expression variation across individuals at the gene co-expression network level. Estimation of co-expression networks is well-established for bulk RNA-seq; however, single-cell measurements pose novel challenges due to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased towards zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq datasets and accurately quantify network level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the datasets. Compared to alternatives, Dozer results in fewer false positive edges in the co-expression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the datasets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Co-expression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer disease and controls uniquely reveals co-expression modules of innate immune response with markedly different co-expression levels between the diagnoses. Dozer represents an important advance in estimating personalized co-expression networks from scRNA-seq data.

genomics↗

bioRxiv2023

Loss of MC1R signaling implicates TBX3 in pheomelanogenesis and melanoma predisposition

The human Red Hair Color (RHC) trait is caused by increased pheomelanin (red-yellow) and reduced eumelanin (black-brown) pigment in skin and hair due to diminished melanocortin 1 receptor (MC1R) function. In addition, individuals harboring the RHC trait are predisposed to melanoma development. While MC1R variants have been established as causative of RHC and are a well-defined risk factor for melanoma, it remains unclear mechanistically why decreased MC1R signaling alters pigmentation and increases melanoma susceptibility. Here, we use single-cell RNA-sequencing (scRNA-seq) of melanocytes isolated from RHC mouse models to reveal a Pheomelanin Gene Signature (PGS) comprising genes implicated in melanogenesis and oncogenic transformation. We show that TBX3, a well-known anti-senescence transcription factor implicated in melanoma progression, is part of the PGS and binds both E-box and T-box elements to regulate genes associated with melanogenesis and senescence bypass. Our results provide key insights into mechanisms by which MC1R signaling regulates pigmentation and how individuals with the RHC phenotype are predisposed to melanoma.

genetics↗

bioRxiv2023

MsPBRsP: Multi-scale Protein Binding Residues Prediction Using Language Model

Accurate prediction of protein binding residues (PBRs) from sequence is important for the understanding of cellular activity and helpful for the design of novel drug. However, experimental methods are time-consuming and expensive. In recent years, a lot of computational predictors based on machine learning and deep learning models are proposed to reduce such consumption. But those methods often use MSA tools such as PSI-BLAST or NetSurfP to generate some statistical features and enter them into predictive models as necessary supplementary input. The input generation process normally takes long time, and there is no standard to specify which and how many statistic results should be provided to a prediction model. In addition, prediction of PBRs relies on residue local context, but the most appropriate scale is undetermined. Most works pre-selected certain residue features as input and a scale size based on expertise for certain type of PBRs. In this study, we propose a general tool-free end-to-end framework that can be applied to all types of PBRs, Multi-scale Protein Binding Residues Prediction using language model (MsPBRsP). We adopt a pre-trained language model ProtTrans to save the large consumption caused by MSA tools, and use protein sequence alone as input to our model. To ease scale size uncertainty, we construct multi-size windows in attention layer and multi-size kernels in convolutional layer. We test our framework on various benchmark datasets including PBRs from protein-protein, protein-nucleotide, protein-small ligand, heterodimer, homodimer and antibody-antigen interactions. Compared with existing state-of-the-art methods, MsPBRsP achieves superior performance with less running time and higher prediction rates on every PBRs prediction task. Specifically, we boost F1 score by 27.1% and AUPRC score by 7.6% on NSP448 dataset and decrease running time from over 10 minutes to under 0.1s on average. The source code and datasets are available at https://github.com/biolushuai/MsPBRsP-for-multiple-PBRs-prediction.

bioinformatics↗

bioRxiv2023

Long COVID manifests with T cell dysregulation, inflammation, and an uncoordinated adaptive immune response to SARS-CoV-2

Long COVID (LC), a type of post-acute sequelae of SARS-CoV-2 infection (PASC), occurs after at least 10% of SARS-CoV-2 infections, yet its etiology remains poorly understood. Here, we used multiple "omics" assays (CyTOF, RNAseq/scRNAseq, Olink) and serology to deeply characterize both global and SARS-CoV-2-specific immunity from blood of individuals with clear LC and non-LC clinical trajectories, 8 months following infection and prior to receipt of any SARS-CoV-2 vaccine. Our analysis focused on deep phenotyping of T cells, which play important roles in immunity against SARS-CoV-2 yet may also contribute to COVID-19 pathogenesis. Our findings demonstrate that individuals with LC exhibit systemic inflammation and immune dysregulation. This is evidenced by global differences in T cell subset distribution in ways that imply ongoing immune responses, as well as by sex-specific perturbations in cytolytic subsets. Individuals with LC harbored increased frequencies of CD4+ T cells poised to migrate to inflamed tissues, and exhausted SARS-CoV-2-specific CD8+ T cells. They also harbored significantly higher levels of SARS-CoV-2 antibodies, and in contrast to non-LC individuals, exhibited a mis-coordination between their SARS-CoV-2-specific T and B cell responses. RNAseq/scRNAseq and Olink analyses similarly revealed immune dysregulatory mechanisms, along with non-immune associated perturbations, in individuals with LC. Collectively, our data suggest that proper crosstalk between the humoral and cellular arms of adaptive immunity has broken down in LC, and that this, perhaps in the context of persistent virus, leads to the immune dysregulation, inflammation, and clinical symptoms associated with this debilitating condition.

immunology↗

bioRxiv2023

GSK-3 inhibitor elraglusib enhances tumor-infiltrating immune cell activation in tumor biopsies and synergizes with anti-PD-L1 in a murine model of colorectal cancer

Inhibition of GSK-3 using small-molecule elraglusib has shown promising preclinical antitumor activity. Using in vitro systems, we found that elraglusib promotes immune cell-mediated tumor cell killing, enhances tumor cell pyroptosis, decreases tumor cell NF-{kappa}B-regulated survival protein expression, and increases immune cell effector molecule secretion. Using in vivo systems, we observed synergy between elraglusib and anti-PD-L1 in an immunocompetent murine model of colorectal cancer. Murine responders had more tumor-infiltrating T-cells, fewer tumor-infiltrating Tregs, lower tumorigenic circulating cytokine concentrations, and higher immunostimulatory circulating cytokine concentrations. To determine the clinical significance, we utilized human plasma samples from patients treated with elraglusib and correlated cytokine profiles with survival. Using paired tumor biopsies, we found that CD45+ tumor-infiltrating immune cells had lower expression of inhibitory immune checkpoints and higher expression of T-cell activation markers in post-elraglusib patient biopsies. These results introduce several immunomodulatory mechanisms of GSK-3 inhibition using elraglusib, providing a rationale for the clinical evaluation of elraglusib in combination with immunotherapy. Statement of significancePharmacologic inhibition of GSK-3 using elraglusib sensitizes tumor cells, activates immune cells for increased anti-tumor immunity, and synergizes with anti-PD-L1 immune checkpoint blockade. These results introduce novel biomarkers for correlations with response to therapy which could provide significant clinical utility and suggest that elraglusib, and other GSK-3 inhibitors, should be evaluated in combination with immune checkpoint blockade.

cancer biology↗

bioRxiv2023

The TNF{blacktriangleup}ARE mouse as a model of intestinal fibrosis

Background & AimsCrohns disease (CD) is a highly morbid chronic inflammatory disease. The majority of CD patients also develop fibrostenosing complications. Despite this, there are no medical therapies for intestinal fibrosis. This is in part due to lack of high-fidelity biomimetic models to enhance understanding and drug development. There is a need to develop in vivo models of inflammatory bowel disease-related intestinal fibrosis. We sought to determine if the TNF{Delta}ARE mouse, a model of ileal inflammation, may also develop intestinal fibrosis. MethodsSeveral clinically relevant outcomes were studied including features of structural fibrosis, histological fibrosis, and gene expression. These include the use of a luminal casting technique we developed, traditional histological outcomes, use of second harmonic imaging, and quantitative PCR. These features were studied in aged TNF{Delta}ARE mice as well as in cohorts of numerous ages. ResultsAt ages of 24+ weeks, TNF{Delta}ARE mice develop structural, histological, and genetic changes of ileal fibrosis. Genetic expression profiles have changes as early as six weeks, followed by histological changes occurring as early as 14-15 weeks, and overt structural fibrosis delayed until after 24 weeks. DiscussionThe TNF{Delta}ARE mouse is a viable and highly tractable model of intestinal fibrosis. This model and the techniques employed can be leveraged for both mechanistic studies and therapeutic development for the treatment of intestinal fibrosis.

cell biology↗

bioRxiv2022

Neuron-astrocyte metabolic coupling facilitates spinal plasticity and maintenance of persistent pain

Long-lasting pain stimuli can trigger maladaptive changes in the spinal cord, reminiscent of plasticity associated with memory formation. Metabolic coupling between astrocytes and neurons has been implicated in neuronal plasticity and memory formation in the CNS, but neither its involvement in pathological pain nor in spinal plasticity has been tested. Here, we report a novel form of neuroglia signaling involving spinal astrocytic glycogen dynamics triggered by persistent noxious stimulation via upregulation of the metabolic signaling molecule PTG exclusively in spinal astrocytes. PTG drove glycogen build-up in astrocytes, and blunting glycogen accumulation and turnover by Ptg gene deletion reduced pain-related behaviors and promoted faster recovery by shortening pain maintenance. Furthermore, mechanistic analyses revealed that glycogen dynamics is a critically required process for maintenance of pain by facilitating neuronal plasticity in spinal lamina 1 neurons. Finally, metabolic analysis indicated that glycolysis and lactate transfer between astrocytes and neurons fuels spinal neuron hyperexcitability. Spinal glycogen-metabolic cascades therefore hold therapeutic potential to alleviate pathological pain.

neuroscience↗