P2Y6R-positive microglia sense the stress metabolite uridine and exacerbate photoreceptor degeneration in the retina

P2Y6R-positive microglia sense the stress metabolite uridine and exacerbate photoreceptor degeneration in the retina | 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 P2Y6R-positive microglia sense the stress metabolite uridine and exacerbate photoreceptor degeneration in the retina Haiwei Xu, Lingyue Mo, Zhe Cha, Lingling Ge, Ting Zou, Hui Gao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6578878/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 Retinitis pigmentosa (RP) is an incurable blinding disorder characterized by progressive photoreceptor degeneration. While metabolic stress has been implicated in RP progression, the neuroimmune mechanisms driving this process remain poorly understood. In this study, we employed time-series untargeted metabolomics to profile temporal metabolic changes during RP pathogenesis using the retinal degeneration 10 (rd10) mouse model, identifying uridine as a key metabolite dynamically associated with disease progression. Intravitreal uridine administration in wild-type C57BL/6J mice induced RP-like pathology, including photoreceptor apoptosis and visual impairment, alongside aberrant microglial activation. Microglial depletion reversed these degenerative phenotypes, implicating microglia as central mediators of uridine-driven neurodegeneration. Further analysis revealed that uridine-reactive microglia adopted a pro-inflammatory state and aberrantly phagocytosed viable photoreceptors. Single-cell RNA sequencing (scRNA-seq) of rd10 retinas uncovered a distinct P2Y6R-expressing microglial subpopulation with a dual phenotype characterized by both proinflammatory and phagocytic activity. In vitro studies confirmed that uridine activates microglia via P2Y6R signaling, triggering both inflammatory cytokine release and dysregulated phagocytosis—effects that are abolished by P2Y6R inhibition. Our findings identify the uridine-P2Y6R axis as a novel metabolic-immune checkpoint in RP, orchestrating microglia-mediated photoreceptor degeneration. Targeting this axis presents a promising therapeutic strategy for RP. Biological sciences/Immunology/Neuroimmunology Biological sciences/Cell biology/Mechanisms of disease Retinitis pigmentosa Metabolomics Uridine Microglia P2Y6R Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Retinitis pigmentosa (RP) is an inherited retinal disease affecting millions of people globally. It is characterized by progressive photoreceptor degeneration, which ultimately leads to irreversible blindness[ 1 ]. Despite extensive research over many decades, no effective treatment for RP has been established, posing a considerable medical challenge. More than 100 genes associated with RP have been identified (RetNet, https://sph.uth.edu/RetNet/ ), yet the condition exhibits a high degree of phenotypic heterogeneity and extensive genetic variation[ 1 ]. The clinical heterogeneity within genetic subtypes and the phenotypic overlap across distinct subtypes hinder the development of targeted therapies. This highlights the critical need to shift research focus toward uncovering the conserved downstream pathological mechanisms that contribute to photoreceptor loss, regardless of the genetic subtypes involved. Metabolites are the bridges between genotype and phenotype, carrying information about the effect of genetic factors under environmental conditions[ 2 ]. Metabolic dysregulation, a significant yet frequently neglected aspect of neurodegenerative processes, may represent a crucial converging pathway in the pathogenesis of RP[ 3 ]. Recent studies have revealed that genetically induced photoreceptor degeneration involves profound metabolic disturbances in the retina[ 3 – 5 ]. Consistent alterations in lipid metabolism[ 6 ], amino acid metabolism[ 7 ], nucleotide metabolism[ 8 ], and impaired energy metabolism[ 9 – 11 ] have been observed in RP. These results indicate that metabolic disruptions could act as both indicators and promoters of retinal degeneration, possibly initiating a self-reinforcing cascade that accelerates disease progression. While previous studies have identified diverse metabolic changes associated with RP disease, there is still a limited understanding of how these disturbances drive retinal degeneration. Notably, two critical issues remain unaddressed: first, the temporal evolution of metabolic dysregulation across distinct pathological stages of retinal degeneration, and second, the precise molecular mechanisms by which specific metabolic pathways lead to retinal degeneration. Simultaneously, microglia are the predominant immune cells in the retinal microenvironment and play a vital role in the onset and progression of RP[ 12 – 15 ]. In RP, aberrant microglial activation promotes the pathological phagocytosis of viable photoreceptors and amplifies inflammation by releasing cytokines, further contributing to retinal degeneration[ 14 , 15 ]. Recent studies implicate the immune-metabolic interactions in the initiation and progression of retinal degenerative diseases, where immune cells respond to metabolic perturbations by modifying their effector functions[ 16 ]. For instance, lipid metabolism imbalances and lipid peroxidation products in the retina are linked to heightened microglial activation and neuroinflammation[ 17 , 18 ]. Additionally, nucleotides play a crucial role as regulators of microglial function within the retina[ 19 ]. When cells experience stress or damage, they release significant amounts of nucleotides[ 20 ], which trigger microglial activation, migration, and neuroinflammation via the nucleotides-P2 receptor pathway, thus contributing to retinal degeneration[ 21 – 23 ]. However, the extent to which an abnormal metabolic environment in the retina mediates retinal degeneration by influencing microglial function in RP remains largely unexplored. We utilized retinal degeneration rd10 mice (Pde6β rd10 mutation) as an RP animal model to investigate these questions. A comprehensive untargeted metabolomics profiling was performed on the retinas of both rd10 and C57BL/6J mice at three critical phases of degeneration: the initial, peak, and late phases. Our analysis identified uridine as the major metabolite that exhibits significant dynamic changes throughout the progression of retinal degeneration in rd10 mice. Further mechanistic investigations focused on the uridine-P2Y6R signaling in microglia-mediated photoreceptor apoptosis. Our research demonstrated that the abnormal accumulation of uridine during the progression of RP disease triggers microglial activation by activating P2Y6R. Notably, this aberrant activation promotes inflammatory responses and enhances phagocytic activity, leading to the phagocytosis of viable photoreceptors, ultimately contributing to photoreceptor loss and exacerbating retinal degeneration. Importantly, pharmacological blockade of P2Y6R alleviated uridine- induced microglial activation, suggesting a potential therapeutic strategy to extend photoreceptor survival. Materials and Methods Animals Five-week-old female and male C57BL/6J mice were purchased from Ensiweier Biotechnology Co, Ltd. (Chongqing, China). Rd10 mice, a mouse model of retinal degeneration carrying a homozygous phosphodiesterase 6b mutation (Pde6b rd10/rd10) on a C57BL/6J background, aged 18, 25, and 45 days, were obtained from the Experimental Animal Center of Army Medical University. During the experiment, all mice were kept in pathogen-free conditions of 55% humidity at 25°C, and a 12: 12 h light/dark cycle at Laboratory Animal Center of Southwest Hospital (Chongqing, China). Mice were fed a sterile standard chow diet and water ad libitum. All animal experiments were approved by the Ethics Committee of Southwest Hospital of Army Medical University and performed in accordance with the committee’s guidelines and the Association for Research in Vision and Ophthalmology (ARVO). Uridine injection Uridine (MCE, HY-B1449) was dissolved in PBS. Five-week-old C57BL/6J mice received a single intravitreal injection of 2 µl uridine (100mM final concentration) in one eye, and the contralateral eye was injected with 2 µl of sterile PBS into the vitreous cavity to serve as the normal control group. Mice were sacrificed by cervical dislocation at 1, 4, 7, and 14 days after intravitreal injection of uridine or PBS treatment. The eyeballs were harvested for follow-up experimental analysis. PLX3397 treatment PLX3397 (Absin, Shanghai, China) was added to AIN-76A standard chow (Research Diets, Inc.). To induce microglial depletion, three-week-old C57BL/6J mice (PLX group) were fed PLX3397 (290 mg/kg) in chow for 14 consecutive days, following established protocols[ 24 ]. Age-matched control mice (WT group) received a normal AIN-76A chow. Following the 14-day dietary regimen, both groups underwent bilateral intravitreal injections: the right eyes received 2 µl uridine (100mM final concentration) while the contralateral left eyes received equal-volume PBS. All mice maintained their respective dietary protocols for an additional 7-day post-injection period. Electroretinography (ERG) recordings were performed on day 7 post-injection, after which mice were sacrificed by cervical dislocation for subsequent tissue collection. Retinal metabolome profiling of rd10 mice Sample preparations . The retinas of rd10 mice and healthy control C57BL/6J mice at postnatal days 18 (P18), 25 (P25), and 45 (P45) were harvested and underwent global untargeted metabolomics analysis performed by BioNovoGene Co., Ltd (Suzhou, China). Analysis of global untargeted metabolomics was carried out using LC/MS. In brief, the retinal tissue homogenate was solved in 80% methanol, centrifuged, filtered through a 0.22 µm membrane, and transferred to LC vials. The vials were stored at − 80°C until LC/MS analysis. QC samples were prepared by pooling aliquots of all samples. Metabolic profiling and metabolite identification in the retina . The raw data was transformed into the mzXML format by ProteoWizard software (v3.0.8789). The identification, filtration, and alignment of peaks were conducted by the R-package XCMS (R-v3.3.2). After quality control, the metabolite identification was performed by metabolome databases including Metlin ( http://metlin.scripps.edu ), MoNA ( https://mona.fiehnlab.ucdavis.edu/ ), Lipidmaps ( https://www.lipidmaps.org/ ), mzCloud ( https://www.mzcloud.org/ ) and the BioNovoGene's self-built metabolome database by Suzhou PANOMIX Biomedical Tech Co., Ltd. Multivariate statistical analyses and differential metabolites analysis. Differential metabolites that discriminated between the two classes of samples were identified using a statistically significant threshold for the VIP > 1, FC ≥ 1.5 or ≤ 0.67, and were subsequently validated through Student’s t-test analysis (P-value < 0.05). PLS-DA and Heatmaps of differential metabolites among all groups were analyzed on the BioDeep Platform ( https://www.biodeep.cn ). To further identify metabolic pathways, Metabolite set enrichment analysis (MSEA) of differential metabolites was performed using the online tool MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/ ). For the pathway enrichment analysis, the KEGG database ( http://www.genome.jp/kegg/ ) was utilized to identify enriched metabolic signaling pathways involving differential metabolites between the two groups. A univariate analysis of variance (ANOVA) was employed to assess the significance of differences in relative metabolite contents across the different groups. Bioinformatics analysis of bulk RNA-seq and single-cell RNA-seq data from the retinas Retina bulk RNA-seq Transcriptomic data of rd10 mice and C57BL/6J mice at postnatal days P23 and P40 were obtained from the public database GEO (GSE178928[ 25 ]; previous data from our laboratory[ 12 ]) and our previously reported data[ 12 ], respectively. The raw read counts were input into R (version 4.4.0), and differential expression analysis of two groups was performed using the DESeq2 R package. Differentially expressed genes (DEGs) were determined with a corrected P-value 2. The P2ry genes identified in the DEGs analysis were selected for expression analysis, and an expression heatmap was generated using the heatmap package in R. Retina single-cell RNA-seq Single-cell transcriptomics data were obtained from the public database GEO (GSE183206)[ 26 ] and analyzed in R package Seurat (version 5.1.0). For quality control, we applied the following criteria to each cell: gene number between 300 and 5000; UMI count between 1000 and 20000; and mitochondrial gene percentage below 15%. The data were then normalized and scaled using the NormalizeData and ScaleData functions, and the top 2000 highly variable Genes (HVGs) were identified and used for further analysis. Using the previously computed HVGs, we performed principal component analysis (RunPCA) to reduce dimensionality. Cell clustering was performed using the first 18 principal components (PCs) with the FindNeighbors and FindClusters functions at a resolution of 0.8, followed by UMAP visualization using the RunUMAP function. To annotate each cell cluster, we used the FindAllMarkers function to find marker genes (P-value 1.5) for each cluster, and cell clusters were identified using previously reported genes[ 26 , 27 ]. Microglia clusters were extracted and integrated for subclustering analysis using the Seurat. The DEGs of microglial subpopulations were detected by FindAllMarkers (min.pct = 0.25, logfc.threshold = 0.25) with the Wilcoxon rank sum test. AverageExpression function was used to calculate the average expression of specific genes in each microglial subpopulation, and the specific gene score of each subpopulation was calculated using the AddModuleScore function. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for each microglial subpopulation of DEGs were performed using the compareCluster function of clusterProfiler R package, and P value < 0.05 was considered significant. Outer nuclear layer thickness analysis In order to analyze the outer nuclear layer (ONL) thickness of the retina, regions delineating the retinal layers were created using the DAPI channel and captured panoramic views. For each group, five mice per time point and three sections through the optic nerve head were used for immunostaining analysis. We defined the optic nerve head as the original location (recorded as 0) and measured distances of 200, 400, 600, and 800 µm from the optic nerve head on the temporal nasal side of the retina. ImageJ-win64 (Bethesda, MD) was used to measure the vertical length of the ONL to calculate its thickness. Scotopic electroretinogram (ERG) analysis The electroretinograms (ERG) were recorded on 7 and 14 days after a single intravitreal injection of uridine or PBS in five-week-old C57BL/6J mice, following a previously established protocol[ 28 ]. Mice were dark-adapted for a minimum of 12 hours before testing to ensure maximal sensitivity of the photoreceptors. Mice were anesthetized using isoflurane (3% in pure oxygen) and maintained under anesthesia with 1.5%-2.0% isoflurane throughout the procedure. After initial anesthesia, mice were instilled with a drop of mydriatic and topical anesthetics in each eye. Body temperature was monitored and maintained at 37°C using a heating pad. A pair of active gold electrodes was placed on each cornea as a recording electrode. A reference electrode was placed inside the mouth, and a ground electrode was placed in the tail. After the dark adaptation period, ERG responses and amplitudes of a-wave and b-wave were recorded using the RETI-Port device (Roland Consult, Brandenburg, Germany) at different light stimulus intensities. The data were recorded at a flash intensity of 3.0 cd·s/m². Data and waveforms were analyzed and plotted using GraphPad Prism 9 (GraphPad Software, USA). Immunofluorescence staining Mice were euthanized and enucleated eyes were fixed in 4% paraformaldehyde (PFA) at 4°C for 30 min. Next, the cornea and lens were removed, and the optic cups were further fixed in 4% paraformaldehyde for 90 min and followed by a 30% sucrose gradient overnight, all at 4°C. After drying, the samples were embedded with OCT and then made into 10 µm frozen sections. The sections were attached to a glass slide. Intact retinal sections containing or surrounding the optic disc were preferentially selected for immunofluorescence staining. Slides were rinsed with PBS and permeabilized and blocked with PBS containing 0.5% Triton X-100, 1% BSA, and 10% goat serum for 30 min at 37°C. The sections were incubated with the primary antibodies (Table 1 ) diluted in PBS containing 0.5% Triton X-100, 1% BSA, and 3% goat serum overnight at 4°C. After washing with PBS, the sections were incubated with secondary antibodies (Table 1 ) for 1 h at 37°C. Finally, the nuclei were counterstained with DAPI. Table 1 Summary of primary and secondary antibodies. Antibody name Source Catalog number Dilution ratio Iba1 Wako 019-19741 1:500 (IF) GFAP DAKO Z033401 1:500 (IF) CD16/32 Cell Signaling Technology 80366S 1:200 (IF) CD68 Abcam ab53444 1:400 (IF) IL-1β ABclonal A22257 1:200 (IF) 568 goat-rabbit Invitrogen A11036 1:400 (IF) 488 goat-rabbit Invitrogen A11008 1:400 (IF) 488 goat-rat Invitrogen A11006 1:400 (IF) 568 goat-rat Invitrogen A11007 1:400 (IF) The BV2 cells were seeded onto cell climbing slices in the six-well plates and incubated overnight. After drug treatment, gently remove the culture medium and rinse the cells with PBS. Add 4% PFA to fix the cells for 10 min at room temperature, then wash with PBS. Cell climbing slices were removed using tweezers for immunofluorescence staining. The remaining steps of immunofluorescence staining were the same as described previously. Immunofluorescence staining images were obtained using a Zeiss LSM 880 confocal microscope (Zeiss, Germany). TUNEL Assay A One Step TUNEL Apoptosis Assay Kit (Beyotime, China) was used to evaluate cell apoptosis of retinal cryosections according to the manufacturer's protocol. For the TUNEL assay combined with immunofluorescence (IF) staining, TUNEL was performed first, followed by incubation with the primary antibody overnight at 4°C. Fluorochrome-conjugated secondary antibodies were used as recommended by the manufacturer. Nuclei were stained with DAPI. Fluorescence was detected using a Zeiss LSM 880 confocal microscope (Zeiss, Germany) at appropriate wavelengths. Cell Culture and Treatment BV2 microglial cells were kindly provided by Dr. Ma (Southwest Hospital, Army Medical University, Chongqing, China). BV2 cells were cultured in DMEM/High glucose medium (Gibco, Grand Island, NY, USA), supplemented with 10% FBS (Solaibao, Bejing, China) and 1% penicillin-streptomycin (Beyotime, Shanghai, China) at 37°C and 5% CO 2 . For Uridine administration, BV2 cells were seeded at 2 × 10 5 cells per well in 6-well plates and treated with a uridine concentration gradient of 0 mM, 2 mM, 4 mM, and 8 mM, respectively. Following 24-hour treatment, cells were collected for RNA extraction. For MRS2578 rescue, BV2 cells were seeded at 2 × 10 5 cells per well in 6-well plates. Cells were pretreated with MRS2578 (10 µM) for 4 h, followed by treatment with Uridine (8 mM) for 24 h, as the MRS + Uri group. Cells were treated with uridine (8 mM) alone for 24 h as the Uri group. Cells from the control group were treated without uridine or MRS2578. Next, cells were collected for RNA preparation. In vitro microglia phagocytosis assay The phagocytic capacity of microglia was assessed by measuring the accumulation of pHrodo TM -red (Invitrogen, USA) fluorescence within the cells. BV2 cells were seeded at 1 × 10 5 cells per well in 12-well plates. After drug treatment (Cells were processed as described above and were divided into the control group, the Uri group, and the MRS + Uri group, respectively), the medium was removed, and the cells were washed once with PBS. Subsequently, according to the manufacturer's protocol, pHrodo TM -red particles (100 µg/ml) were added to cells and incubated at 37°C for 2 h (the isotype control group was incubated without pHrodo TM -red). After removing the excess liquid, PBS was added to wash once, and cells were centrifuged, resuspended in 200 µl of PBS, and analyzed using flow cytometry (BD Accuri™ C6 Plus; BD Biosciences, USA). To determine pHrodo uptake, mean fluorescence intensity (MFI) was measured in the red channel. The results are expressed as relative pHrodo fluorescent intensity, which is calculated by dividing the MFI of each sample by the MFI of the isotype control. Relative fluorescence intensity was normalized according to the control group. Flow cytometry data was analyzed using Flow Jo software (TreeStar Inc, Wokingham, UK). Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) analysis Total RNA was extracted from BV2 cells or five retinas of five mice from different groups using TRIzol reagent (TaKaRa, Japan). cDNA was synthesized from 1 µg of total RNA using the PrimeScrip™ FAST RT reagent Kit with gDNA Eraser (Takara, Dalian, China), following the manufacturer's instructions. Subsequently, RT-qPCR reactions were performed using TB Green® Premix Ex Taq™ II Fast qPCR (Takara, Dalian, China) on a CEF96 Real-Time PCR Detection System (Bio-Rad, USA). Primer sequences are specified as in Table 2 . All reactions were performed in triplicate, and the relative expression levels of target genes were normalized to β-actin using the 2 −ΔΔCT method[ 29 ]. Data were analyzed using GraphPad Prism 9 (GraphPad Software, USA). Table 2 Primer sequences. Gene name Forward 5′-3′ Reverse 5′-3′ β-actin TGAGCTGCGTTTTACACCCT TTTGGGGGATGTTTGCTCCA Fcgr3 AATGCACACTCTGGAAGCCAA CACTCTGCCTGTCTGCAAAAG Cd68 CCATCCTTCACGATGACACCT GGCAGGGTTATGAGTGACAGTT P2ry6 TGCTGCCCTTCATAGCCTTAC AGCCATACGAGCCGCCTTGC Tnf-α AGCCCACGTCGTAGCAAACCAC AGGTACAACCCATCGGCTGGCA Il-6 TAGTCCTTCCTACCCCAATTTCC TTGGTCCTTAGCCACTCCTTC Il-1β AGAGCATCCAGCTTCAAATC ATCATCCCATGAGTCACAGA Statistical analysis Statistical analyses and graph production were performed using GraphPad Prism 9 (GraphPad Software, USA). Experimental data are expressed as the mean ± standard deviation (SD) of at least three independent biological samples. Statistical analysis was performed using Student’s unpaired t-test for two groups. One-way ANOVA or Two-way ANOVA with Bonferroni post hoc test was performed for multiple groups. The difference among experimental groups was considered significant at a P-value less than 0.05. Results Metabolic profile alterations of the retina during degeneration of rd10 mice To investigate the abnormal metabolic microenvironment of the retina in RP, we performed retinal global metabolic profiling analysis on rd10 mice compared to C57BL/6J (C57) mice at postnatal days 18 (P18), 25 (P25), and 45 (P45) (Fig. 1 A). Partial Least Squares Discriminatory Analysis (PLS-DA) revealed distinct clusters of retinal metabolites in rd10 mice when compared to age-matched C57 mice (Fig. S1 A). The robustness and predictive capability of the PLS-DA model were validated through permutation testing (n = 200). It demonstrated that all permuted points were lower than the original point on the far right, indicating reliability and confirming the absence of overfitting (Fig. S1 B). A total of 116 metabolites were identified in the retinas of rd10 and C57 mice. By utilizing the P-value and variable importance in projection (VIP) methodology, we constructed volcano plots to illustrate the fold changes (FC) in the levels of the identified metabolites in rd10 mice compared to C57 mice. As shown in Fig. S1 C, seven metabolites exhibited downregulation among the differentially expressed metabolites at P18, while 19 metabolites showed upregulation. At P25, the analysis revealed that 11 metabolites were downregulated, while 26 were upregulated. At P45, 13 metabolites were downregulated, and 30 were upregulated. These findings highlight a significant progressive alteration in retina metabolism that aligns with the progression of degeneration. Differential metabolite analysis of rd10 mice throughout retinal degeneration progression To elucidate the metabolic changes associated with retinal degeneration in RP, we conducted hierarchical clustering of retinal metabolites at various stages of disease progression in rd10 mice compared to age-matched C57 mice (Fig. 1 B-E). This analysis revealed distinct metabolic characteristics in the retina at various stages of RP progression. Notably, metabolic heterogeneity became more pronounced in the mid-to-late stages (P25-P45), accompanied by a significant increase in differential metabolites compared to the early phases (P18). This highlights the increasing metabolic stress associated with the progression of degeneration (Fig. 1 B-D). Super-pathway classification analysis revealed that amino acids, lipids, and nucleotides collectively represent over 75% of the metabolic perturbations observed at all stages, suggesting their pivotal roles in retinal degeneration (Fig. 1 F). Compared with age-matched C57 mice, rd10 mice showed a notable decrease in Fructose 1,6-bisphosphate, FAD, and AMP, alongside an increase in dihydroxyacetone phosphate and ADP (Fig. 1 B-E). This suggests a significant dysregulation of energy metabolism, particularly in glycolysis and the TCA cycle, during the process of retinal degeneration. Notably, levels of ketoleucine, acetylphosphate, dodecanoic acid, and AICAR were significantly elevated, while L-valine and L-isoleucine showed a marked decrease (Fig. 1 B-D), implying a compensatory anaplerotic flux into the TCA cycle. However, at the late stage of RP, these compensatory mechanisms progressively diminished, leading to a gradual decline in the proportion of metabolites associated with energy metabolism (Fig. 1 F). A disturbance in lipid metabolism was identified in the retina of rd10 mice (Fig. 1 B-D). Notable elevations in the levels of linoleic acid derivatives were detected, including metabolites of arachidonic acid such as 9,10-Epoxyoctadecenoic acid, Prostaglandin D2, Prostaglandin J2, 15-Deoxy-d-12,14-PGJ2, 16(R)-HETE, 11,12-DiHETrE, and 5-KETE (Fig. 1 B-D). Conversely, a reduction in the level of linoleic acid was noted (Fig. 1 D). We found an accumulation of nucleosides, specifically uridine, and cytidine, alongside a downregulation of nucleotides such as UMP, CMP, and uracil. This suggests a disruption in the pyrimidine salvage pathway and uridine catabolism (Fig. 1 B-E). Concurrently, purine metabolism showed significant changes at P25 and P45, characterized by the accumulation of guanosine/GMP and a reduction in xanthine. This effectively blocks uric acid production, which is an essential antioxidant defense mechanism (Fig. 1 C, D). Our findings indicate that the retinal metabolic profile of rd10 mice alters as the disease progresses, revealing significant metabolic disturbances within the degenerative retina. These dynamic changes in metabolic disturbances are correlated with the histological transition from localized photoreceptor loss to extensive pan-retinal damage. Uridine in pyrimidine metabolism is the critical metabolite in RP progression To better understand the functional characteristics and metabolic pathways of these differential metabolites in the progression of RP, we performed a Metabolite Set Enrichment Analysis (MSEA) alongside a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The MSEA results showed significant enrichment in the pyrimidine metabolism pathway, which consistently ranked among the top 10 pathways throughout the process of retinal degeneration (Fig. 2 A). Moreover, the KEGG pathway analysis revealed that the ABC transporters and pyrimidine metabolism pathways were the most prominently altered at all three time points (Fig. 2 B), demonstrating their critical roles within the pathway network diagram (Fig. S2). Furthermore, the network analysis highlighted a significant correlation between the ABC transporters and the pyrimidine metabolism pathway (Fig. S2). Five differential metabolites (uracil, FAD, ADP, cytidine, and uridine) were found to be enriched in the pyrimidine metabolism pathway as the disease progresses (Fig. 2 C-G). Notably, the uridine levels in the retinas of rd10 mice exhibited a significant progressive increase compared to the C57 groups throughout disease progression (Fig. 2 G). Based on these observations, we proposed that uridine plays a crucial role in the pathological process of RP, and its biological functions and underlying mechanisms deserve further investigation. Uridine causes photoreceptor damage and visual function decrease To investigate the role of uridine in retinal degeneration, we performed intravitreal injections of uridine in one eye of healthy 5-week-old C57 mice while administering PBS to the contralateral eye as the control (Fig. 3 A), aiming to simulate the abnormal metabolic microenvironment characteristic of retinal degeneration. Uridine dose-response experiments identified that a concentration of 100 mM significantly impaired visual function and retinal structure in C57 mice by day 14 post-injection (Fig. S3). Consequently, we selected 100 mM as the concentration for the subsequent in vivo experiments. Retinal function was assessed using dark-adapted scotopic electroretinography (ERG) at 7 and 14 days following uridine administration (Fig. 3 B). Compared with the control eyes, the uridine-treated eyes demonstrated reduced amplitudes of a-waves and b-waves (Fig. 3 C), indicating impaired retinal function, which gradually developed into significant structural damage to the retinal tissue later. One day after uridine injection, the thickness of the outer nuclear layer (ONL) did not significantly differ from that of the control group (Fig. 3 D). However, starting four days after the uridine injection, the ONL thickness of the retina was significantly reduced when compared to the control group and gradually declined over time (Fig. 3 E-G). TUNEL staining revealed that apoptotic cells after uridine treatment were primarily located in the ONL region. Their numbers increased rapidly, peaking on day 7, and then gradually declining over time (Fig. 3 H, I1- 5 ). These findings suggest that uridine induces dysfunction and ultimately results in visual loss. Photoreceptor apoptosis mediated by uridine-induced microglial activation Previous studies have suggested a possible link between uridine and immune responses[ 30 – 32 ]. We investigated the impact of uridine on the activation of retinal microglia. Following intravitreal injections of uridine, we observed pronounced reactivity in the retinal microglia, with their numbers peaking on day 7 before gradually declining (Fig. 4A1-5, B). This pattern corresponded to the observed apoptosis of photoreceptors following treatment (Fig. 3 H). After administering uridine, we observed microglia migrated from the inner retina to the outer retina. In the retina of the control group and one day following uridine injection, microglia were found throughout the inner and outer plexiform layers (IPL and OPL); however, their presence remained relatively sparse (Fig. 4A1-2, C-D). Conversely, a significant increase in the number of microglia in the retina was observed at four days post-uridine injection, accompanied by their migration from the inner layers to the outer layers, with these cells predominantly localized in the IPL and the inner nuclear layer (INL) (Fig. 4A3, C-D). By seven days post-uridine injection, the migration of microglia continued, and their processes extended from the OPL to the ONL (Fig. 4A4, D-E). Additionally, at 14 days after uridine injection, microglia were observed to traverse both the ONL and the subretinal space (SRS) (Fig. 4A5, E). These results suggest that microglia exhibit heightened reactivity following uridine treatment and actively infiltrate areas of retinal injury. The temporal overlap between microglial activation and photoreceptor apoptosis strongly implicates their potential role in mediating photoreceptor death. Notably, increased reactivity of GFAP, a significant marker for gliosis in RP, was observed in the retina only after 14 days of uridine injection. This finding indicates that the activation of Müller cells may occur after the microglial response (Fig. S4). To investigate the role of uridine-induced microglial activation in photoreceptor apoptosis, C57 mice were fed a chow containing the CSF1R inhibitor PLX3397 (PLX group) from three to five weeks of age to deplete retinal microglia, while the controls received normal chow (WT group). All mice then received a single intravitreal injection of uridine (Uri) in one eye and PBS (Ctrl) in the contralateral eye. They maintained their diets for seven days after the injection and underwent morphological and functional assessments at the endpoint (Fig. 4 F). While the visual function of the PLX-Ctrl group showed a slight reduction compared to the WT-Ctrl group, this difference was not statistically significant. In contrast, the PLX-Uri group exhibited a marked improvement in visual function compared to the WT-Uri group, as evidenced by significantly increased a-wave and b-wave amplitudes observed in dark-adapted ERG. Remarkably, the visual function of the PLX-Uri group demonstrated no significant differences compared to the PBS control groups (WT-Ctrl, PLX-Ctrl) (Fig. 4 G, H). Iba1 and TUNEL staining revealed pronounced thinning of the ONL, along with concurrent microglial activation and photoreceptor apoptosis in the WT-Uri group (Fig. 4 I-L). In contrast, microglial depletion significantly mitigated retinal degeneration. The PLX-Uri group preserved a normal ONL architecture comparable to that of the WT-Ctrl and PLX-Ctrl groups (Fig. 4 J), achieving over 90% microglial elimination (Fig. 4 K) and reducing apoptotic photoreceptors by 86% compared to WT-Uri group (Fig. 4 L). In summary, we confirm that microglia are the key effector cells responsible for uridine-induced retinal degeneration. Uridine drives photoreceptor apoptosis by activating retinal microglia. This suggests that targeted modulation of the interaction between uridine, microglia, and photoreceptors can serve as a potential intervention strategy for retinal degenerative diseases. Uridine induces a pro-inflammatory and phagocytic phenotype in retinal microglia To further investigate the functional phenotype of uridine-responsive microglia in regulating photoreceptor apoptosis, we performed double fluorescence staining of the classical pro-inflammatory markers CD16/CD32 in conjunction with the phagocytic marker CD68 within Iba1-positive microglial cells (Fig. 5A1-5, B1-5). In the retinas of the control group, microglia displayed minimal expression of CD16/32 and CD68, indicating a resting state. Following uridine injection, microglia exhibited irregular morphology, characterized by shorter processes, reduced branching, and enlarged cell bodies, which signify an activated state (Fig. 5A2-5, B2-5). Additionally, most microglia exhibited long axes perpendicular to the retina, suggesting active migration (Fig. 5A3-4, B3-5). This subset of activated migratory microglia demonstrated pronounced immunoreactivity for CD16/32 and CD68. The quantity and proportion of CD16/32-positive and CD68-positive microglia in the uridine-treated retina increased substantially (Fig. 5 C-F), aligning with the previously noted increase in the overall number of uridine-activated microglia (Fig. 4 B). By day 7 post-treatment, nearly all Iba1-positive microglia were co-localized with CD16/32-positive cells (82.6%) and CD68-positive cells (85.6%) (Fig. 5 D, F). The RT-qPCR analysis showed that uridine treatment significantly increased the expression levels of Fcgr3 (CD16) and Cd68 compared to the control group (Fig. 5 G, H). These findings suggest that uridine promotes microglial activation, leading to a shift towards a pro-inflammatory and phagocytic phenotype. Uridine induces microglia to aberrantly engulf living photoreceptors We conducted TUNEL and Iba1 double-labeling to determine whether apoptotic photoreceptors were the primary targets of microglial phagocytosis (Fig. 6 A). DAPI staining showed that the nuclei of microglia exhibited an oval or kidney-shaped morphology, with distinct accumulations of dense heterochromatin clumps. The chromatin in the nucleolus appeared lighter, as highlighted by the green outline (Fig. 6 A1-3). In contrast, the healthy photoreceptor nuclei displayed a smooth, round shape, characterized by a tightly packed mass of heterochromatin centrally located and surrounded by euchromatin, as indicated by the red outline (Fig. 6 A2, 3). Our study revealed that most infiltrating microglia did not target the apoptotic photoreceptors; instead, they aberrantly phagocytosed healthy photoreceptors (Fig. 6 A2-3, C). Through three-dimensional reconstruction, we observed the somatic extension of infiltrating microglia contacting photoreceptors and partially encircling them. During this process, the microglial cell membrane invaginated, encapsulating the cell membrane and part of the cytoplasm of the photoreceptors (Fig. 6B1). Subsequently, the cell body of the microglia extended further, ultimately leading to the complete engulfment of the photoreceptors, isolating them within the microglial cell (Fig. 6B2). Following uridine treatment, the infiltrating microglia in the ONL displayed strikingly similar characteristics to those found in the retinas of rd10 mice[ 13 , 14 ], exhibiting abnormal phagocytic activity and pro-inflammatory effects. It suggests that uridine treatment may induce microglia to aberrantly engulf healthy photoreceptors, which could further cause photoreceptor death. Transcriptional characteristics of P2Y6R-positive microglial subpopulations in the retina To investigate the molecular mechanisms behind uridine-induced microglial activation, we conducted transcriptomic analyses of rd10 retinas (GSE178928[ 25 ], previous data from our laboratory[ 12 ]). The analysis demonstrated a significant upregulation of P2Y receptor genes, specifically P2ry2 , P2ry6 , and P2ry13 , during retinal degeneration (Fig. 7 A, B). Given the established roles of uridine in modulating P2Y receptors[ 33 – 36 ] and the predominant expression of metabolic P2Y receptors in microglia[ 37 , 38 ], we have chosen to focus specifically on the P2Y6R. This receptor subtype is primarily expressed in microglial cells and functionally associated with their activation[ 39 ]. In line with the findings from rd10 mice, we observed a significantly increased expression level of P2ry6 in the retinas of C57 mice following uridine treatment across all time points (Fig. 7 C). The trend of change was consistent with that of microglial activation (Fig. 4 B, 5 G-H), suggesting a potential uridine-P2Y6R axis in microglial activation. To figure out the characteristics of P2Y6R-positive microglial cells that may respond to uridine, we analyzed the published single-cell RNA sequencing (scRNA-seq) results of retinas from rd10 and C57 mice at P21 (GSE183206)[ 26 ]. The annotation of cell types (Fig. S5A) using established marker genes[ 26 , 27 ], scRNA-seq revealed that P2ry6 was predominantly expressed in the microglia cluster (Fig. S5B-D). To further elucidate the role of P2Y6R-positive microglia in RP progression, we performed sub-clustering of the microglia cluster, resulting in identification of six distinct microglial subpopulations (Fig. 7 D). Notably, we found that P2ry6 was primarily expressed in microglial clusters 1, 3, and 5 (Fig. 7 F, S6A), which primarily comprised cells from the rd10 group (Fig. 7 E). This suggested that wild-type and rd10 microglia exhibited different transcriptomic profiles, indicating that P2Y6R-positive microglia may play a pivotal role in retinal degeneration of rd10 mice. We conducted a quantitative analysis of the average expression levels and gene scores of the P2ry6 gene in microglial subpopulations, ultimately identifying MiG 1 and 3 as P2Y6R-positive microglial subpopulations (Fig. S6B, C). The highly expressed genes in MiG 1 and 3 were predominantly linked to phagocytosis ( Pf4 , Msr1 ) and inflammatory responses ( Il-1α , Ccl4 , Cd38 ) (Fig. S6D). Moreover, GO and KEGG enrichment analyses of the highly expressed genes in MiG 1 and 3 indicated significant enrichment of pathways related to phagocytosis, inflammation, and immune response (Fig. 7 G-J). Blockade of P2Y6R reduces phagocytosis and pro-inflammatory function of uridine-induced microglia To further verify the activation of P2Y6R-positive microglia in response to uridine accumulation, we evaluated the changes in the phagocytic and pro-inflammatory abilities of BV2 cells treated with uridine alone with or without P2Y6R inhibitor MRS2578 in vitro. RT-qPCR analysis revealed that uridine treatment significantly increased the transcription levels of pro-inflammatory factors ( Tnf-α , Il-6 , Il-1β ), the phagocytic marker ( Cd68 ), and P2ry6 in BV2 cells in a concentration-dependent manner (Fig. 8 A-E), which was consistent with our in vivo results. To further investigate the role of uridine in mediating the inflammatory response and phagocytic activity of microglia through the activation of P2Y6R, BV2 cells were pre-treated with the selective P2Y6R antagonist MRS2578 for 4 hours, followed by treatment with uridine for 24 hours. The results indicated that pretreatment with MRS2578 significantly reduced the transcriptional levels of P2ry6 (Fig. 8 F), inflammatory factors (Fig. 8 G-I), and the phagocytic marker (Fig. 8 J) in uridine-activated BV2 cells. This finding suggests that inhibition of P2Y6R can effectively suppress the inflammatory response and phagocytic activity mediated by uridine in microglia. Moreover, immunofluorescent staining also confirmed that uridine treatment alone elevated the expression of CD68 and IL-1β in BV2 cells, while pretreatment with MRS2578 reversed the microglial activation induced by uridine treatment (Fig. S7). To evaluate the phagocytic activity of microglia, BV2 cells were incubated with pH-sensitive fluorescent dyes after treatment with uridine alone or following pre-treatment with MRS2578. The phagocytic behavior of BV2 cells was observed under a fluorescence microscope (Fig. 8 K-M), and fluorescence intensity was quantitatively analyzed using flow cytometry to assess phagocytosis ability (Fig. 8 N, O). Results indicated a significant increase in fluorescence intensity in microglia after uridine treatment. However, pre-treatment with MRS2578 reversed this increase, suggesting that inhibiting P2Y6R can suppress the uridine-induced phagocytic activity of microglia. These results suggest that uridine activates microglia through P2Y6R to mediate their inflammatory response and enhance phagocytosis. Discussion In the present study, we utilized timing analysis of global untargeted metabolomics to demonstrate for the first time that uridine accumulated continuously during the progression of RP. This accumulation promotes the activation and infiltration of microglia into the ONL via the activation of P2Y6R. The microglia exhibited a highly reactive pro-inflammatory phenotype and enhanced phagocytic function, inappropriately engulfing healthy photoreceptors instead of apoptotic ones. Notably, pharmacological blockade of P2Y6R using MRS2578 effectively prevented uridine-mediated microglial activation, thereby inhibiting the inflammatory response and phagocytic activity of microglia, which could help prevent photoreceptor death. As the final product of systemic metabolism, the analysis of metabolites can serve as a bridge between gene transcription and phenotype expression. Metabolomics research has been applied across various fields within ophthalmic studies[ 40 – 42 ]. However, current investigations into RP remain limited, and existing metabolomic studies fail to adequately reflect the dynamic changes in metabolic profiles as the disease progresses. A recent serum metabolomics study involving patients with RP found elevated levels of metabolites associated with inflammatory responses during oxidative stress, suggesting that the dysregulation of inflammatory processes may play a role in the pathogenesis of RP[ 43 ]. Our metabolomics findings indicate that alterations in metabolites, such as linoleic acid and arachidonic acid, in the retinas of rd10 mice, suggest chronic activation of COX/LOX pathways, potentially driving photoreceptor apoptosis through oxidative stress and inflammatory responses. Furthermore, previous metabolomics and proteomics studies conducted on RP mice have highlighted significant alterations in retinal pyrimidine and purine nucleotide metabolism[ 8 , 44 ]. These findings align with our observations regarding the dual risks of nucleotide pool imbalance and the amplification of oxidative damage. Notably, nucleotide metabolism is crucial in the context of retinal degenerative diseases. While uridine is known to be linked to diabetic retinopathy and glaucoma[ 45 , 46 ], its involvement in RP has not been previously documented. Our study is the first to identify uridine as a key metabolite in RP. Previous reports have indicated that uridine is a regenerative protective substance associated with age-related changes. Notably, elderly individuals have significantly reduced uridine levels compared to their younger counterparts, who exhibit higher levels of uridine[ 47 , 48 ]. In C57 mice, the relative content of uridine in the retina declines with age, which aligns with the established age-related dose-response relationship observed between uridine levels and aging. Conversely, rd10 mice exhibit persistently elevated uridine levels in their retinas as they age and the disease progresses, with these levels ultimately exceeding those found in normal C57 mice. The significance of elevated uridine levels in rd10 mice potentially acting as a compensatory protective mechanism remains controversial. Prolonged exposure to high concentrations of uridine may disrupt glucose and lipid metabolism[ 49 – 52 ], and potentially increase DNA damage, thereby raising the risk of cancer[ 53 , 54 ]. To investigate the pathological effects of elevated uridine concentrations in rd10 mouse retinas, we performed intravitreal injections of exogenous uridine in normal C57 mice. This experimental manipulation resulted in impaired visual function and increased apoptosis of photoreceptors. How does uridine lead to retinal damage? Under conditions of stress or neuronal injury, nucleotides and nucleosides are released in large quantities[ 20 , 55 ], modulating both immune responses and inflammatory processes while simultaneously activating immune cells[ 55 ]. Our metabolomics study demonstrated that retinal uridine levels in rd10 mice progressively increased, correlating with pro-inflammatory mediators, suggesting that the accumulation of uridine may intensify inflammation by triggering immune responses. Subsequent experiments revealed that uridine potentiated microglial activation, migration, and differentiation toward pro-inflammatory and phagocytic phenotype. Notably, this microglial activation exhibited temporal synchrony with the progression of photoreceptor apoptosis. A marked reversal of uridine-induced visual function deterioration and photoreceptor apoptosis was achieved through microglial depletion, demonstrating that uridine mediates secondary photoreceptor death primarily through microglial activation. In animal models of RP, microglial activation and recruitment occur simultaneously with photoreceptor degeneration[ 14 , 56 ]. Microglial activation, accompanied by the release of pro-inflammatory cytokines and enhanced phagocytic activity, contributes to photoreceptor death[ 13 , 14 , 57 , 58 ]. In rd10 mice with retinal degeneration, activated microglia infiltrate the ONL, extensively interacting with photoreceptors and ultimately engulfing non-apoptotic photoreceptors[ 14 ]. This phenomenon of microglia-mediated phagocytosis of viable photoreceptors has been consistently observed in multiple RP mouse models and confirmed inhuman retinal specimens[ 13 , 14 , 59 ]. Our findings revealed that uridine-treated microglia displayed enhanced phagocytic activity, infiltrated the ONL, and established close interactions with non-apoptotic photoreceptors to form phagosome-like structures during aberrant phagocytosis. These results suggest that uridine treatment recapitulates the pathological retinal microenvironment in RP, demonstrating that uridine accumulation is a key driver of RP disease progression. Uridine may exacerbate photoreceptor degeneration by inducing microglial activation, which triggers inflammatory factors release and promotes phagocytosis of viable photoreceptors. However, the origin of pathological uridine accumulation in rd10 mouse retinas remains unclear. Damaged photoreceptors may release uridine as a damage signal, thereby contributing to microglial activation. Developing uridine-specific visualization tools is critical for elucidating the spatiotemporal dynamics, release mechanisms and regulatory pathways of extracellular uridine in future studies. A genetically encoded fluorescent probe targeting extracellular uridine represents a promising strategy to achieve this goal. The uridine-responsive purinergic P2Y6 receptor (P2Y6R) is predominantly expressed in microglia[ 60 ] and mediates microglial phagocytosis and the neuroinflammatory process[ 60 – 62 ]. Our study indicated that uridine treatment significantly increased the expression level of P2ry6 in the retinas of normal C57 mice. These changes in P2ry6 expression were consistent with the observed alterations in microglial activation following uridine treatment, suggesting that uridine may influence microglial activation through the P2Y6R. Previous studies have reported that the P2Y6R signaling pathway is implicated in various stages of the phagocytic process and that P2Y6R can trigger the expression of pro-inflammatory cytokines in immune cells[ 60 ]. We analyzed retina single-cell RNA sequencing data from rd10 mice (with uridine accumulation) and C57 mice. We found that P2Y6R was expressed almost exclusively in the rd10 microglial cluster. P2Y6R-positive microglia highly expressed proliferation genes ( Cd34 [ 63 ]), phagocytic genes ( Pf4 [ 64 ], Msr1 [ 65 ]), and inflammatory genes ( Il-1α , Ccl4 [ 66 ], Cd38 [ 67 ]), among others. GO and KEGG enrichment analyses revealed that P2Y6R-positive microglia exhibit significant enrichment in phagocytic processes, inflammatory responses, and immune regulation pathways. These findings closely mirror the functional characteristics of retinal microglia observed following uridine treatment. Our in vitro experiments demonstrated that pharmacological inhibition of P2Y6R with MRS2578 effectively suppressed the uridine-induced enhancement of microglial phagocytic activity. This intervention concurrently attenuated microglial inflammatory responses, thereby demonstrating that uridine regulates microglial phagocytosis and inflammation through the activation of P2Y6R. Notably, this study primarily utilized single-cell sequencing data from rd10 mouse retinas, rather than data derived from the uridine-injected normal C57 mice. While significant uridine accumulation was observed in rd10 retinas, the complex metabolic microenvironment of the rd10 retina suggests that uridine may collaborate with other metabolites to activate P2Y6R. Thus, our findings do not definitively confirm that P2Y6R is solely activated by uridine. Furthermore, it has been reported that P2Y6R is primarily activated by UDP[ 68 ]. Thus, further investigation is necessary to ascertain whether extracellular uridine influences P2Y6R through its enzymatic conversion to UDP. Future studies should employ real-time uridine visualization techniques and utilize microglial P2Y6R conditional knockout mice to dissect their roles in non-apoptotic phagocytosis, neuroinflammation, and photoreceptor degeneration. Notably, the clinical application of MRS2578 is constrained by its irreversible binding, poor aqueous stability, and suboptimal pharmacokinetics. Nevertheless, recent advances in developing next-generation P2Y6R antagonists[ 69 ] show promise for overcoming these pharmacological limitations. In conclusion, our research provides valuable insight into the role of uridine in the RP process. We found that uridine exacerbates phagocytosis and inflammatory responses in microglial cells mediated by P2Y6R, and aggravates neurodegeneration during the progression of RP disease. Our findings suggest that targeting microglial activation by inhibiting P2Y6R can be a promising therapeutic strategy for RP. Declarations Acknowledgments We thank Professor Mindian Li and Professor Xingdong Liu for helpful manuscript revisions. This study was supported by funding from the National Key Research and Development Program of China (2021YFA1101203, 2021YFA1101202), and the National Natural Science Foundation of China (82271104). Author Contributions Conceptualization: Haiwei Xu, Jing Xie, Xiaotang Fan, Lingyue Mo, Ting Zou. 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Single-cell RNA sequencing reveals dysregulation of spinal cord cell types in a severe spinal muscular atrophy mouse model. PLoS Genet. 2022;18(9):e1010392. doi: 10.1371/journal.pgen.1010392 . Berglund R, Guerreiro-Cacais AO, Adzemovic MZ, Zeitelhofer M, Lund H, Ewing E, et al. Microglial autophagy-associated phagocytosis is essential for recovery from neuroinflammation. Sci Immunol. 2020;5(52). doi: 10.1126/sciimmunol.abb5077 . Sabogal-Guáqueta AM, Marmolejo-Garza A, Trombetta-Lima M, Oun A, Hunneman J, Chen T, et al. Species-specific metabolic reprogramming in human and mouse microglia during inflammatory pathway induction. Nat Commun. 2023;14(1):6454. doi: 10.1038/s41467-023-42096-7 . Zhang X, He T, Wu Z, Wang Y, Liu H, Zhang B, et al. The role of CD38 in inflammation-induced depression-like behavior and the antidepressant effect of (R)-ketamine. Brain, Behavior, and Immunity. 2024;115:64–79. doi: 10.1016/j.bbi.2023.09.026 . Koizumi S, Shigemoto-Mogami Y, Nasu-Tada K, Shinozaki Y, Ohsawa K, Tsuda M, et al. UDP acting at P2Y6 receptors is a mediator of microglial phagocytosis. Nature. 2007;446(7139):1091–5. doi: 10.1038/nature05704 . Zhu Y, Zhou M, Cheng X, Wang H, Li Y, Guo Y, et al. Discovery of Selective P2Y(6)R Antagonists with High Affinity and In Vivo Efficacy for Inflammatory Disease Therapy. J Med Chem. 2023;66(9):6315–32. doi: 10.1021/acs.jmedchem.3c00210 . Additional Declarations (Not answered) Supplementary Files Supplementarydata.docx Supplementary data Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6578878","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453294716,"identity":"2c3f887f-1f31-47dd-9151-1f60e49ad44d","order_by":0,"name":"Haiwei Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACHgaGBCDJD+UyNhCtRbKNJC0gYHCMWC0GN3IPfnjYZiNjfL87dTMPg43shgPMzx7g0yI5Iy9ZIrEtjcfsGO+22zwMacYbDrCZG+DTwi+RYyCRuO0wTMvhxA0HeNgk8Glhk8gx/pG47T+PcRtYy3/CWoC2mAFtOcBjwAbWcoCwFsmeN2YWif+SeSSO5W67Occg2XjmYTYzvFoMjucY3/xxxs6ev/nsthtvKuxk+443P8OrBd0EIGYmQf0oGAWjYBSMAuwAADPXRkHPp4r1AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8840-7918","institution":"Southwest Eye Hospital, Southwest Hospital, Third Military Medical University (Army Medical University)","correspondingAuthor":true,"prefix":"","firstName":"Haiwei","middleName":"","lastName":"Xu","suffix":""},{"id":453294717,"identity":"c25a8446-f6e9-4333-9b4c-9afec50f449a","order_by":1,"name":"Lingyue Mo","email":"","orcid":"https://orcid.org/0009-0008-9478-8447","institution":"Third Military Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotang","middleName":"","lastName":"Fan","suffix":""},{"id":453294727,"identity":"38c05b1c-325e-4eb3-8aa2-21b591d2195a","order_by":11,"name":"Jing Xie","email":"","orcid":"","institution":"Southwest Eye Hospital, Southwest Hospital, Third Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2025-05-02 13:51:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6578878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6578878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82544238,"identity":"5cc49b8a-69f5-44e9-9f9b-b5328a5a0a2e","added_by":"auto","created_at":"2025-05-12 17:38:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3794733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProgressive metabolic disruption in degenerating retinas of rd10 mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A\u003c/strong\u003e) The schematic diagram of retinal collection and metabolomics analysis of rd10 and C57 mice at P18, P25, and P45.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B-D)\u003c/strong\u003e Hierarchical clustering heatmaps of significantly differential metabolites between rd10 and C57 groups at P18 (A), P25 (B), and P45 (C), respectively. Each column represents an individual sample; each row corresponds to a metabolite. The blue-to-red scale indicates the relative concentration level of metabolites (blue: low; red: high).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E) \u003c/strong\u003eHierarchical clustering heatmap of total differential metabolites between rd10 and C57 groups across the three time points (P18, P25, P45).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F)\u003c/strong\u003eClassification analysis of differential metabolites at P18, P25, and P45.\u003c/p\u003e","description":"","filename":"Onlinefigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/edc4fb101607e311a0410e48.png"},{"id":82544504,"identity":"c7077f70-db58-40cc-8f72-7b31b3a1afe1","added_by":"auto","created_at":"2025-05-12 17:46:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2693230,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUridine is identified as the key changed metabolite during the development of retina degeneration of rd10 mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eMetabolite set enrichment analysis (MSEA) of differential metabolites between rd10 and C57 groups shows the significantly affected retinal metabolic pathways at P18 (left), P25 (center), and P45 (right). The top 10 enriched pathways are shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differential metabolites between rd10 and C57 groups at P18 (left), P25 (center), and P45 (right). The top 10 KEGG pathway enrichments are demonstrated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C-G) \u003c/strong\u003eBar charts show the normalized relative intensity of five different metabolites associated with the pyrimidine pathway in the rd10 and C57 groups (n = 5).\u003c/p\u003e\n\u003cp\u003eData represent mean ± SD. Statistics: One-way ANOVA for C-G. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ns = non-significant.\u003c/p\u003e","description":"","filename":"Onlinefigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/cc7e5d3ef90f90e48fd39ca6.png"},{"id":82544935,"identity":"ea5ff864-9694-41f6-a190-b285d60a982f","added_by":"auto","created_at":"2025-05-12 17:54:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3227977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUridine-induced apoptosis of photoreceptors and impairments of visual function in C57 mice following intravitreal injection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eSchematic illustration of C57 mice receiving intravitreal injections of uridine and PBS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eRepresentative scotopic ERG waves (Flash Intensity = 3.0 cd·s/m\u003csup\u003e2\u003c/sup\u003e) of C57 mice were recorded on days 7 and 14 after intravitreal injection.\u003c/p\u003e\n\u003cp\u003eQuantification of the amplitude of a-waves (representing the function of rod cells) and b-waves (representing the activity of bipolar cells) (Flash Intensity = 3.0 cd·s/m\u003csup\u003e2\u003c/sup\u003e) on days 7 and 14 after intravitreal injection (n = 6 and 10, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D-G)\u003c/strong\u003e Measurement of ONL thickness at different positioning points of retinas. Spidergrams showing ONL thickness in the control eyes (blue) and uridine-treated eyes (red) were generated on days 1 (D, n = 5), 4 (E, n = 5), 7 (F, n = 5), and 14(G, n = 5) after intravitreal injection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(H) \u003c/strong\u003eQuantitative analysis of TUNEL-positive cell number in ONL (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(I) \u003c/strong\u003eRepresentative images of TUNEL staining (red = TUNEL, blue = DAPI) in retinal tissues.\u003c/p\u003e\n\u003cp\u003eGCL: ganglion cell layer; INL: inner nuclear layer; ONL: outer nuclear layer. Data represent mean ± SD. Statistics: Student’s unpaired t-test for C, Two-way ANOVA with Bonferroni’s multiple comparison test for D-G and One-way ANOVA for H. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, ns = non-significant (*, ns: versus control group [D-H]). Scale bars = 50 μm.\u003c/p\u003e","description":"","filename":"Onlinefigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/a4df09004b70f94ff1ba3502.png"},{"id":82544505,"identity":"562fd45a-e613-4dee-982c-c59f889871e4","added_by":"auto","created_at":"2025-05-12 17:46:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7198086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUridine-responsive microglia mediate photoreceptor apoptosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Representative images of retinal sections from control and uridine-treated eyes immunostained for Iba1 (A1-A5, red). Nuclei were visualized with DAPI (blue).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e The total number of microglia in all retinal layers (n = 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C-E)\u003c/strong\u003e Quantitative analyses of numbers of microglia in different retinal layers (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F) \u003c/strong\u003eSchematic of the experimental design: C57BL/6J mice (3-week-old) were randomly assigned to receive either PLX3397-formulated (PLX) chow or normal (WT) chow ad libitum for 14 days (from 3 to 5 weeks of age). At 5 weeks of age, all mice received a single intravitreal injection of 100 mM uridine (Uri) in one eye and PBS (Ctrl) in the contralateral eye. After injection, dietary regimens were maintained for an additional 7 days. ERG recordings were performed 7 days post-injection. Following functional assessment, all mice were euthanized, and retinal tissues were harvested for cryosection preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G) \u003c/strong\u003eRepresentative scotopic ERG waves (Flash Intensity = 3.0 cd·s/m\u003csup\u003e2\u003c/sup\u003e) of WT and PLX mice were recorded on day 7 after intravitreal injection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(H) \u003c/strong\u003eQuantification of the amplitude of a-waves and b-waves (Flash Intensity = 3.0 cd·s/m\u003csup\u003e2\u003c/sup\u003e) in WT and PLX mice on day 7 after intravitreal injection (n = 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(I) \u003c/strong\u003eImmunofluorescence staining of Iba1 (green), TUNEL (red), and DAPI (blue) was performed on retinal sections from control eyes and uridine-treated eyes of WT and PLX mice 7 days after intravitreal injection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(J) \u003c/strong\u003eSpidergrams showing ONL thickness in the control eyes and uridine-treated eyes of WT and PLX mice were generated on day 7 after intravitreal injection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(K) \u003c/strong\u003eThe total number of microglia in all retinal layers (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(L) \u003c/strong\u003eQuantitative analysis of TUNEL-positive cell number in ONL (n = 5).\u003c/p\u003e\n\u003cp\u003eGCL: ganglion cell layer; IPL: inner plexiform layer; INL: inner nuclear layer; OPL: outer plexiform layer; ONL: outer nuclear layer; SRS: subretinal space. Data represent mean ± SD. Statistics: One-way ANOVA for B-E, H, and K-L. Two-way ANOVA with Bonferroni’s multiple comparison test for J. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, ns = non-significant (*, ns: versus control group [B-E]). Scale bars = 50 μm (A, I).\u003c/p\u003e","description":"","filename":"Onlinefigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/ccc0a4d11983d1e7b9cd98df.png"},{"id":82544936,"identity":"8dbd184d-205a-4f77-8968-013ccd914419","added_by":"auto","created_at":"2025-05-12 17:54:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24487380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUridine induces a proinflammatory response and phagocytic activity in microglia of C57 mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eRepresentative triple immunofluorescence images of mouse retinal sections showing co-localization of microglia marker Iba1 (red), inflammatory phenotype marker CD16/32 (green), and DAPI (blue) following intravitreal injection. \u003cstrong\u003e(B)\u003c/strong\u003e Triple immunofluorescence images demonstrating Iba1 (red), phagocytic phenotype marker CD68 (green), and DAPI (blue) co-staining. Column 2-3, enlargement images of areas of interest indicated in column 1 by white dashed box, along with three-channel merge and single-channel images respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C, D)\u003c/strong\u003e Quantitative analysis of Iba1\u003csup\u003e+ \u003c/sup\u003eCD16/32\u003csup\u003e+\u003c/sup\u003e microglia in the retinal tissue (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E, F)\u003c/strong\u003e Quantitative analysis of Iba1\u003csup\u003e+ \u003c/sup\u003eCD68\u003csup\u003e+\u003c/sup\u003e microglia in the retinal tissue (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G, H) \u003c/strong\u003eRetinal tissues were examined using RT-qPCR to assess the mRNA expression of \u003cem\u003eFcgr3\u003c/em\u003e (CD16) (G) and \u003cem\u003eCd68\u003c/em\u003e (H) (n = 5).\u003c/p\u003e\n\u003cp\u003eGCL: ganglion cell layer; IPL: inner plexiform layer; INL: inner nuclear layer; ONL: outer nuclear layer; SRS: subretinal space. Data represent mean ± SD. Statistics: One-way ANOVA for C-F; Two-way ANOVA for G-H. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, ns = non-significant (*, ns: versus control group). Scale bars = 50 μm (column 1, A-B), scale bars = 10 μm (column 2-3, A-B).\u003c/p\u003e","description":"","filename":"Onlinefigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/b97406301ad649e5a9fe3779.png"},{"id":82544933,"identity":"21aa08fc-4a19-4e50-9496-aba340257d0f","added_by":"auto","created_at":"2025-05-12 17:54:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8391653,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUridine-responsive microglia exhibit aberrant phagocytosis of non-apoptotic photoreceptors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eImmunofluorescence staining of Iba1 (green), TUNEL (red), and DAPI (blue) was performed on retinal sections at 4 and 7 days after intravitreal injection. Column 2-3 and column 4, enlarged and orthogonal views of areas of interest indicated in column 1 by white dashed box, respectively. Column 3 shows microglial outline (white dash box), microglial nucleus (green box), and phagocytosed photoreceptor nucleus (red box).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) \u003c/strong\u003eThree-dimensional (3D) surface rendering by IMARIS software of microglia phagocytosis of living photoreceptors at 4 (B1) and 7 (B2) days after intravitreal injection of uridine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) \u003c/strong\u003ePercentage of live PRs engulfed by microglia among all engulfed PRs in retinal tissues (n = 5).\u003c/p\u003e\n\u003cp\u003eIPL: inner plexiform layer; INL: inner nuclear layer; OPL: outer plexiform layer; ONL: outer nuclear layer; SRS: subretinal space; PR: photoreceptor. Data represent mean ± SD. Statistics: One-way ANOVA for C. ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (*: versus control group). Scale bars = 20 μm (column 1, A), scale bars = 5 μm (column 2-4, A).\u003c/p\u003e","description":"","filename":"Onlinefigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/c4ab2649492b9894da1399b8.png"},{"id":82544247,"identity":"8362fd28-a70f-423b-836c-0651ebf1c4b4","added_by":"auto","created_at":"2025-05-12 17:38:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1464003,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eP2Y6R-positive microglia display enhanced phagocytosis and pro-inflammatory signaling.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A, B) \u003c/strong\u003eThe DEGs of P2Y receptors in transcriptome analysis of rd10 and C57 mouse retinas at P23 (A) and P40 (B) are shown in the heatmaps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) \u003c/strong\u003eRT-qPCR analysis of \u003cem\u003eP2ry6\u003c/em\u003e expression in the retinas of uridine-treated eyes and control eyes at each observed time point (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D) \u003c/strong\u003eUniform manifold approximation and projection (UMAP) plot of microglia subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E) \u003c/strong\u003eDistribution and dissimilarity of cells from rd10 and C57 groups visualized in UMAP plot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F) \u003c/strong\u003eUMAP plot showing expression of \u003cem\u003eP2ry6\u003c/em\u003e gene for different subpopulations of microglia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G) \u003c/strong\u003eLollipop plot showing the top 5 significantly enriched GO analysis of marker genes in MiG 1 subcluster; the values inside the circle represent the gene number in each GO term (blue: BP, red: CC, green: MF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(H) \u003c/strong\u003eThe top 10 enrichment KEGG pathways analysis of marker genes in MiG 1 subcluster. The color represents the P-value, and the size of the dot represents the number of genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(I) \u003c/strong\u003eLollipop plot showing the top 5 significantly enriched GO analysis of marker genes in the MiG 3 subcluster. The values within the circles represent the gene number in each GO term.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(J) \u003c/strong\u003eThe top 10 enriched KEGG pathways analysis of marker genes in MiG 3 subcluster.\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology, BP: biological process, CC: cellular component, MF: molecular function, KEGG: Kyoto Encyclopedia of Genes and Genomes. Data represent mean ± SD. Statistics: Two-way ANOVA for C. **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, ns = non-significant (*, ns: versus control group).\u003c/p\u003e","description":"","filename":"Onlinefigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/dafd64b07fc89b96933a76fc.png"},{"id":82544242,"identity":"a8bc91bf-5150-4823-812a-39cf2918347a","added_by":"auto","created_at":"2025-05-12 17:38:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":7213139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUridine mediates microglial phagocytosis and inflammation through P2Y6R.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-E)\u003c/strong\u003eDose-dependent effects of uridine on gene expression in BV2 microglia. RT-qPCR assay of mRNA levels of \u003cem\u003eP2ry6\u003c/em\u003e, pro-inflammatory cytokines (\u003cem\u003eTnf-α\u003c/em\u003e, \u003cem\u003eIl-6\u003c/em\u003e, \u003cem\u003eIl-1β\u003c/em\u003e), and microglial activation marker (\u003cem\u003eCd68\u003c/em\u003e) in BV2 cells treated with increasing concentrations of uridine (0, 2, 4, 8 mM) (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(F-J)\u003c/strong\u003eP2Y6R-mediated inflammatory responses. RT-qPCR assay of \u003cem\u003eP2ry6\u003c/em\u003e, \u003cem\u003eTnf-α\u003c/em\u003e, \u003cem\u003eIl-6\u003c/em\u003e, \u003cem\u003eIl-1β,\u003c/em\u003e and \u003cem\u003eCd68\u003c/em\u003e mRNA levels in BV2 cells treated with uridine and P2Y6R inhibitor MRS2578 (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(K-M)\u003c/strong\u003e Phagocytic function assessment. Representative images of fluorescent-labeled beads (red) phagocytized in DiO (green) labeled BV2 cells following treatment with medium (K), uridine (L), or MRS2578 + uridine (M), respectively. Nuclei stained by DAPI (blue).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(N) \u003c/strong\u003eTypical flow cytometry histogram of cellular uptake of fluorescent-labeled beads in BV2 cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(O)\u003c/strong\u003eQuantification of the fold changes in mean fluorescence intensity (MFI) compared to the control (n = 5).\u003c/p\u003e\n\u003cp\u003eAll data are presented as mean ± SD. Statistics: One-way ANOVA for A-J and O. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, ns = non-significant (*, ns: versus 0 mM uridine group [A-E]). Scale bars = 50 μm (K-M).\u003c/p\u003e","description":"","filename":"Onlinefigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/b9a89e674381a58458d43ead.png"},{"id":83555854,"identity":"49097c2a-0a44-44c1-b57b-039cefebc0b8","added_by":"auto","created_at":"2025-05-28 11:39:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7801638,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/87d59ad6-efc6-48dc-8c52-67ea07e085d8.pdf"},{"id":82544508,"identity":"4c26079c-7b7e-4979-a2ff-57b9e4ff3be9","added_by":"auto","created_at":"2025-05-12 17:46:59","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4657483,"visible":true,"origin":"","legend":"Supplementary data","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6578878/v1/c6eafc57220403a4f7cb8fa2.docx"}],"financialInterests":"(Not answered)","formattedTitle":"P2Y6R-positive microglia sense the stress metabolite uridine and exacerbate photoreceptor degeneration in the retina","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRetinitis pigmentosa (RP) is an inherited retinal disease affecting millions of people globally. It is characterized by progressive photoreceptor degeneration, which ultimately leads to irreversible blindness[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite extensive research over many decades, no effective treatment for RP has been established, posing a considerable medical challenge. More than 100 genes associated with RP have been identified (RetNet, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sph.uth.edu/RetNet/\u003c/span\u003e\u003cspan address=\"https://sph.uth.edu/RetNet/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), yet the condition exhibits a high degree of phenotypic heterogeneity and extensive genetic variation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The clinical heterogeneity within genetic subtypes and the phenotypic overlap across distinct subtypes hinder the development of targeted therapies. This highlights the critical need to shift research focus toward uncovering the conserved downstream pathological mechanisms that contribute to photoreceptor loss, regardless of the genetic subtypes involved.\u003c/p\u003e \u003cp\u003eMetabolites are the bridges between genotype and phenotype, carrying information about the effect of genetic factors under environmental conditions[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Metabolic dysregulation, a significant yet frequently neglected aspect of neurodegenerative processes, may represent a crucial converging pathway in the pathogenesis of RP[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent studies have revealed that genetically induced photoreceptor degeneration involves profound metabolic disturbances in the retina[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consistent alterations in lipid metabolism[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], amino acid metabolism[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], nucleotide metabolism[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and impaired energy metabolism[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] have been observed in RP. These results indicate that metabolic disruptions could act as both indicators and promoters of retinal degeneration, possibly initiating a self-reinforcing cascade that accelerates disease progression. While previous studies have identified diverse metabolic changes associated with RP disease, there is still a limited understanding of how these disturbances drive retinal degeneration. Notably, two critical issues remain unaddressed: first, the temporal evolution of metabolic dysregulation across distinct pathological stages of retinal degeneration, and second, the precise molecular mechanisms by which specific metabolic pathways lead to retinal degeneration.\u003c/p\u003e \u003cp\u003eSimultaneously, microglia are the predominant immune cells in the retinal microenvironment and play a vital role in the onset and progression of RP[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In RP, aberrant microglial activation promotes the pathological phagocytosis of viable photoreceptors and amplifies inflammation by releasing cytokines, further contributing to retinal degeneration[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recent studies implicate the immune-metabolic interactions in the initiation and progression of retinal degenerative diseases, where immune cells respond to metabolic perturbations by modifying their effector functions[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. For instance, lipid metabolism imbalances and lipid peroxidation products in the retina are linked to heightened microglial activation and neuroinflammation[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, nucleotides play a crucial role as regulators of microglial function within the retina[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. When cells experience stress or damage, they release significant amounts of nucleotides[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which trigger microglial activation, migration, and neuroinflammation via the nucleotides-P2 receptor pathway, thus contributing to retinal degeneration[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the extent to which an abnormal metabolic environment in the retina mediates retinal degeneration by influencing microglial function in RP remains largely unexplored.\u003c/p\u003e \u003cp\u003eWe utilized retinal degeneration rd10 mice (Pde6β rd10 mutation) as an RP animal model to investigate these questions. A comprehensive untargeted metabolomics profiling was performed on the retinas of both rd10 and C57BL/6J mice at three critical phases of degeneration: the initial, peak, and late phases. Our analysis identified uridine as the major metabolite that exhibits significant dynamic changes throughout the progression of retinal degeneration in rd10 mice. Further mechanistic investigations focused on the uridine-P2Y6R signaling in microglia-mediated photoreceptor apoptosis. Our research demonstrated that the abnormal accumulation of uridine during the progression of RP disease triggers microglial activation by activating P2Y6R. Notably, this aberrant activation promotes inflammatory responses and enhances phagocytic activity, leading to the phagocytosis of viable photoreceptors, ultimately contributing to photoreceptor loss and exacerbating retinal degeneration. Importantly, pharmacological blockade of P2Y6R alleviated uridine- induced microglial activation, suggesting a potential therapeutic strategy to extend photoreceptor survival.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eFive-week-old female and male C57BL/6J mice were purchased from Ensiweier Biotechnology Co, Ltd. (Chongqing, China). Rd10 mice, a mouse model of retinal degeneration carrying a homozygous phosphodiesterase 6b mutation (Pde6b rd10/rd10) on a C57BL/6J background, aged 18, 25, and 45 days, were obtained from the Experimental Animal Center of Army Medical University. During the experiment, all mice were kept in pathogen-free conditions of 55% humidity at 25\u0026deg;C, and a 12: 12 h light/dark cycle at Laboratory Animal Center of Southwest Hospital (Chongqing, China). Mice were fed a sterile standard chow diet and water ad libitum. All animal experiments were approved by the Ethics Committee of Southwest Hospital of Army Medical University and performed in accordance with the committee\u0026rsquo;s guidelines and the Association for Research in Vision and Ophthalmology (ARVO).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUridine injection\u003c/h3\u003e\n\u003cp\u003eUridine (MCE, HY-B1449) was dissolved in PBS. Five-week-old C57BL/6J mice received a single intravitreal injection of 2 \u0026micro;l uridine (100mM final concentration) in one eye, and the contralateral eye was injected with 2 \u0026micro;l of sterile PBS into the vitreous cavity to serve as the normal control group. Mice were sacrificed by cervical dislocation at 1, 4, 7, and 14 days after intravitreal injection of uridine or PBS treatment. The eyeballs were harvested for follow-up experimental analysis.\u003c/p\u003e\n\u003ch3\u003ePLX3397 treatment\u003c/h3\u003e\n\u003cp\u003ePLX3397 (Absin, Shanghai, China) was added to AIN-76A standard chow (Research Diets, Inc.). To induce microglial depletion, three-week-old C57BL/6J mice (PLX group) were fed PLX3397 (290 mg/kg) in chow for 14 consecutive days, following established protocols[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Age-matched control mice (WT group) received a normal AIN-76A chow. Following the 14-day dietary regimen, both groups underwent bilateral intravitreal injections: the right eyes received 2 \u0026micro;l uridine (100mM final concentration) while the contralateral left eyes received equal-volume PBS. All mice maintained their respective dietary protocols for an additional 7-day post-injection period. Electroretinography (ERG) recordings were performed on day 7 post-injection, after which mice were sacrificed by cervical dislocation for subsequent tissue collection.\u003c/p\u003e\n\u003ch3\u003eRetinal metabolome profiling of rd10 mice\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eSample preparations\u003c/em\u003e. The retinas of rd10 mice and healthy control C57BL/6J mice at postnatal days 18 (P18), 25 (P25), and 45 (P45) were harvested and underwent global untargeted metabolomics analysis performed by BioNovoGene Co., Ltd (Suzhou, China). Analysis of global untargeted metabolomics was carried out using LC/MS. In brief, the retinal tissue homogenate was solved in 80% methanol, centrifuged, filtered through a 0.22 \u0026micro;m membrane, and transferred to LC vials. The vials were stored at \u0026minus;\u0026thinsp;80\u0026deg;C until LC/MS analysis. QC samples were prepared by pooling aliquots of all samples.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMetabolic profiling and metabolite identification in the retina\u003c/em\u003e. The raw data was transformed into the mzXML format by ProteoWizard software (v3.0.8789). The identification, filtration, and alignment of peaks were conducted by the R-package XCMS (R-v3.3.2). After quality control, the metabolite identification was performed by metabolome databases including Metlin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metlin.scripps.edu\u003c/span\u003e\u003cspan address=\"http://metlin.scripps.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), MoNA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mona.fiehnlab.ucdavis.edu/\u003c/span\u003e\u003cspan address=\"https://mona.fiehnlab.ucdavis.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Lipidmaps (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.lipidmaps.org/\u003c/span\u003e\u003cspan address=\"https://www.lipidmaps.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), mzCloud (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mzcloud.org/\u003c/span\u003e\u003cspan address=\"https://www.mzcloud.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the BioNovoGene's self-built metabolome database by Suzhou PANOMIX Biomedical Tech Co., Ltd.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMultivariate statistical analyses and differential metabolites analysis.\u003c/em\u003e Differential metabolites that discriminated between the two classes of samples were identified using a statistically significant threshold for the VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, FC\u0026thinsp;\u0026ge;\u0026thinsp;1.5 or \u0026le;\u0026thinsp;0.67, and were subsequently validated through Student\u0026rsquo;s t-test analysis (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PLS-DA and Heatmaps of differential metabolites among all groups were analyzed on the BioDeep Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.biodeep.cn\u003c/span\u003e\u003cspan address=\"https://www.biodeep.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To further identify metabolic pathways, Metabolite set enrichment analysis (MSEA) of differential metabolites was performed using the online tool MetaboAnalyst 5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For the pathway enrichment analysis, the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to identify enriched metabolic signaling pathways involving differential metabolites between the two groups. A univariate analysis of variance (ANOVA) was employed to assess the significance of differences in relative metabolite contents across the different groups.\u003c/p\u003e\n\u003ch3\u003eBioinformatics analysis of bulk RNA-seq and single-cell RNA-seq data from the retinas\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eRetina bulk RNA-seq\u003c/em\u003e Transcriptomic data of rd10 mice and C57BL/6J mice at postnatal days P23 and P40 were obtained from the public database GEO (GSE178928[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; previous data from our laboratory[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]) and our previously reported data[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], respectively. The raw read counts were input into R (version 4.4.0), and differential expression analysis of two groups was performed using the DESeq2 R package. Differentially expressed genes (DEGs) were determined with a corrected P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and the absolute fold change value\u0026thinsp;\u0026gt;\u0026thinsp;2. The P2ry genes identified in the DEGs analysis were selected for expression analysis, and an expression heatmap was generated using the heatmap package in R.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRetina single-cell RNA-seq\u003c/em\u003e Single-cell transcriptomics data were obtained from the public database GEO (GSE183206)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and analyzed in R package Seurat (version 5.1.0). For quality control, we applied the following criteria to each cell: gene number between 300 and 5000; UMI count between 1000 and 20000; and mitochondrial gene percentage below 15%. The data were then normalized and scaled using the NormalizeData and ScaleData functions, and the top 2000 highly variable Genes (HVGs) were identified and used for further analysis. Using the previously computed HVGs, we performed principal component analysis (RunPCA) to reduce dimensionality. Cell clustering was performed using the first 18 principal components (PCs) with the FindNeighbors and FindClusters functions at a resolution of 0.8, followed by UMAP visualization using the RunUMAP function. To annotate each cell cluster, we used the FindAllMarkers function to find marker genes (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5) for each cluster, and cell clusters were identified using previously reported genes[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Microglia clusters were extracted and integrated for subclustering analysis using the Seurat. The DEGs of microglial subpopulations were detected by FindAllMarkers (min.pct\u0026thinsp;=\u0026thinsp;0.25, logfc.threshold\u0026thinsp;=\u0026thinsp;0.25) with the Wilcoxon rank sum test. AverageExpression function was used to calculate the average expression of specific genes in each microglial subpopulation, and the specific gene score of each subpopulation was calculated using the AddModuleScore function. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for each microglial subpopulation of DEGs were performed using the compareCluster function of clusterProfiler R package, and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOuter nuclear layer thickness analysis\u003c/h2\u003e \u003cp\u003eIn order to analyze the outer nuclear layer (ONL) thickness of the retina, regions delineating the retinal layers were created using the DAPI channel and captured panoramic views. For each group, five mice per time point and three sections through the optic nerve head were used for immunostaining analysis. We defined the optic nerve head as the original location (recorded as 0) and measured distances of 200, 400, 600, and 800 \u0026micro;m from the optic nerve head on the temporal nasal side of the retina. ImageJ-win64 (Bethesda, MD) was used to measure the vertical length of the ONL to calculate its thickness.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eScotopic electroretinogram (ERG) analysis\u003c/h3\u003e\n\u003cp\u003eThe electroretinograms (ERG) were recorded on 7 and 14 days after a single intravitreal injection of uridine or PBS in five-week-old C57BL/6J mice, following a previously established protocol[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Mice were dark-adapted for a minimum of 12 hours before testing to ensure maximal sensitivity of the photoreceptors. Mice were anesthetized using isoflurane (3% in pure oxygen) and maintained under anesthesia with 1.5%-2.0% isoflurane throughout the procedure. After initial anesthesia, mice were instilled with a drop of mydriatic and topical anesthetics in each eye. Body temperature was monitored and maintained at 37\u0026deg;C using a heating pad. A pair of active gold electrodes was placed on each cornea as a recording electrode. A reference electrode was placed inside the mouth, and a ground electrode was placed in the tail. After the dark adaptation period, ERG responses and amplitudes of a-wave and b-wave were recorded using the RETI-Port device (Roland Consult, Brandenburg, Germany) at different light stimulus intensities. The data were recorded at a flash intensity of 3.0 cd\u0026middot;s/m\u0026sup2;. Data and waveforms were analyzed and plotted using GraphPad Prism 9 (GraphPad Software, USA).\u003c/p\u003e\n\u003ch3\u003eImmunofluorescence staining\u003c/h3\u003e\n\u003cp\u003eMice were euthanized and enucleated eyes were fixed in 4% paraformaldehyde (PFA) at 4\u0026deg;C for 30 min. Next, the cornea and lens were removed, and the optic cups were further fixed in 4% paraformaldehyde for 90 min and followed by a 30% sucrose gradient overnight, all at 4\u0026deg;C. After drying, the samples were embedded with OCT and then made into 10 \u0026micro;m frozen sections. The sections were attached to a glass slide. Intact retinal sections containing or surrounding the optic disc were preferentially selected for immunofluorescence staining. Slides were rinsed with PBS and permeabilized and blocked with PBS containing 0.5% Triton X-100, 1% BSA, and 10% goat serum for 30 min at 37\u0026deg;C. The sections were incubated with the primary antibodies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) diluted in PBS containing 0.5% Triton X-100, 1% BSA, and 3% goat serum overnight at 4\u0026deg;C. After washing with PBS, the sections were incubated with secondary antibodies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for 1 h at 37\u0026deg;C. Finally, the nuclei were counterstained with DAPI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of primary and secondary antibodies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibody name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCatalog number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDilution ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIba1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWako\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e019-19741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:500 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAKO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ033401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:500 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD16/32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell Signaling Technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80366S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:200 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbcam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eab53444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:400 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-1β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABclonal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA22257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:200 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e568 goat-rabbit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA11036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:400 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e488 goat-rabbit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA11008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:400 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e488 goat-rat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA11006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:400 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e568 goat-rat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA11007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:400 (IF)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe BV2 cells were seeded onto cell climbing slices in the six-well plates and incubated overnight. After drug treatment, gently remove the culture medium and rinse the cells with PBS. Add 4% PFA to fix the cells for 10 min at room temperature, then wash with PBS. Cell climbing slices were removed using tweezers for immunofluorescence staining. The remaining steps of immunofluorescence staining were the same as described previously. Immunofluorescence staining images were obtained using a Zeiss LSM 880 confocal microscope (Zeiss, Germany).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTUNEL Assay\u003c/h2\u003e \u003cp\u003eA One Step TUNEL Apoptosis Assay Kit (Beyotime, China) was used to evaluate cell apoptosis of retinal cryosections according to the manufacturer's protocol. For the TUNEL assay combined with immunofluorescence (IF) staining, TUNEL was performed first, followed by incubation with the primary antibody overnight at 4\u0026deg;C. Fluorochrome-conjugated secondary antibodies were used as recommended by the manufacturer. Nuclei were stained with DAPI. Fluorescence was detected using a Zeiss LSM 880 confocal microscope (Zeiss, Germany) at appropriate wavelengths.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell Culture and Treatment\u003c/h2\u003e \u003cp\u003eBV2 microglial cells were kindly provided by Dr. Ma (Southwest Hospital, Army Medical University, Chongqing, China). BV2 cells were cultured in DMEM/High glucose medium (Gibco, Grand Island, NY, USA), supplemented with 10% FBS (Solaibao, Bejing, China) and 1% penicillin-streptomycin (Beyotime, Shanghai, China) at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e. For Uridine administration, BV2 cells were seeded at 2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well in 6-well plates and treated with a uridine concentration gradient of 0 mM, 2 mM, 4 mM, and 8 mM, respectively. Following 24-hour treatment, cells were collected for RNA extraction. For MRS2578 rescue, BV2 cells were seeded at 2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well in 6-well plates. Cells were pretreated with MRS2578 (10 \u0026micro;M) for 4 h, followed by treatment with Uridine (8 mM) for 24 h, as the MRS\u0026thinsp;+\u0026thinsp;Uri group. Cells were treated with uridine (8 mM) alone for 24 h as the Uri group. Cells from the control group were treated without uridine or MRS2578. Next, cells were collected for RNA preparation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIn vitro microglia phagocytosis assay\u003c/h2\u003e \u003cp\u003eThe phagocytic capacity of microglia was assessed by measuring the accumulation of pHrodo\u003csup\u003eTM\u003c/sup\u003e-red (Invitrogen, USA) fluorescence within the cells. BV2 cells were seeded at 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well in 12-well plates. After drug treatment (Cells were processed as described above and were divided into the control group, the Uri group, and the MRS\u0026thinsp;+\u0026thinsp;Uri group, respectively), the medium was removed, and the cells were washed once with PBS. Subsequently, according to the manufacturer's protocol, pHrodo\u003csup\u003eTM\u003c/sup\u003e-red particles (100 \u0026micro;g/ml) were added to cells and incubated at 37\u0026deg;C for 2 h (the isotype control group was incubated without pHrodo\u003csup\u003eTM\u003c/sup\u003e-red). After removing the excess liquid, PBS was added to wash once, and cells were centrifuged, resuspended in 200 \u0026micro;l of PBS, and analyzed using flow cytometry (BD Accuri\u0026trade; C6 Plus; BD Biosciences, USA). To determine pHrodo uptake, mean fluorescence intensity (MFI) was measured in the red channel. The results are expressed as relative pHrodo fluorescent intensity, which is calculated by dividing the MFI of each sample by the MFI of the isotype control. Relative fluorescence intensity was normalized according to the control group. Flow cytometry data was analyzed using Flow Jo software (TreeStar Inc, Wokingham, UK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eReverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from BV2 cells or five retinas of five mice from different groups using TRIzol reagent (TaKaRa, Japan). cDNA was synthesized from 1 \u0026micro;g of total RNA using the PrimeScrip\u0026trade; FAST RT reagent Kit with gDNA Eraser (Takara, Dalian, China), following the manufacturer's instructions. Subsequently, RT-qPCR reactions were performed using TB Green\u0026reg; Premix Ex Taq\u0026trade; II Fast qPCR (Takara, Dalian, China) on a CEF96 Real-Time PCR Detection System (Bio-Rad, USA). Primer sequences are specified as in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All reactions were performed in triplicate, and the relative expression levels of target genes were normalized to β-actin using the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Data were analyzed using GraphPad Prism 9 (GraphPad Software, USA).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward 5\u0026prime;-3\u0026prime;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse 5\u0026prime;-3\u0026prime;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eβ-actin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGAGCTGCGTTTTACACCCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTTGGGGGATGTTTGCTCCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFcgr3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAATGCACACTCTGGAAGCCAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCACTCTGCCTGTCTGCAAAAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCd68\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCATCCTTCACGATGACACCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCAGGGTTATGAGTGACAGTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP2ry6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGCTGCCCTTCATAGCCTTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGCCATACGAGCCGCCTTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTnf-α\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCCCACGTCGTAGCAAACCAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGGTACAACCCATCGGCTGGCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIl-6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAGTCCTTCCTACCCCAATTTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTGGTCCTTAGCCACTCCTTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIl-1β\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGAGCATCCAGCTTCAAATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATCATCCCATGAGTCACAGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses and graph production were performed using GraphPad Prism 9 (GraphPad Software, USA). Experimental data are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) of at least three independent biological samples. Statistical analysis was performed using Student\u0026rsquo;s unpaired t-test for two groups. One-way ANOVA or Two-way ANOVA with Bonferroni post hoc test was performed for multiple groups. The difference among experimental groups was considered significant at a P-value less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic profile alterations of the retina during degeneration of rd10 mice\u003c/h2\u003e \u003cp\u003eTo investigate the abnormal metabolic microenvironment of the retina in RP, we performed retinal global metabolic profiling analysis on rd10 mice compared to C57BL/6J (C57) mice at postnatal days 18 (P18), 25 (P25), and 45 (P45) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Partial Least Squares Discriminatory Analysis (PLS-DA) revealed distinct clusters of retinal metabolites in rd10 mice when compared to age-matched C57 mice (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). The robustness and predictive capability of the PLS-DA model were validated through permutation testing (n\u0026thinsp;=\u0026thinsp;200). It demonstrated that all permuted points were lower than the original point on the far right, indicating reliability and confirming the absence of overfitting (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 116 metabolites were identified in the retinas of rd10 and C57 mice. By utilizing the P-value and variable importance in projection (VIP) methodology, we constructed volcano plots to illustrate the fold changes (FC) in the levels of the identified metabolites in rd10 mice compared to C57 mice. As shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC, seven metabolites exhibited downregulation among the differentially expressed metabolites at P18, while 19 metabolites showed upregulation. At P25, the analysis revealed that 11 metabolites were downregulated, while 26 were upregulated. At P45, 13 metabolites were downregulated, and 30 were upregulated. These findings highlight a significant progressive alteration in retina metabolism that aligns with the progression of degeneration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDifferential metabolite analysis of rd10 mice throughout retinal degeneration progression\u003c/h2\u003e \u003cp\u003eTo elucidate the metabolic changes associated with retinal degeneration in RP, we conducted hierarchical clustering of retinal metabolites at various stages of disease progression in rd10 mice compared to age-matched C57 mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-E). This analysis revealed distinct metabolic characteristics in the retina at various stages of RP progression. Notably, metabolic heterogeneity became more pronounced in the mid-to-late stages (P25-P45), accompanied by a significant increase in differential metabolites compared to the early phases (P18). This highlights the increasing metabolic stress associated with the progression of degeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-D). Super-pathway classification analysis revealed that amino acids, lipids, and nucleotides collectively represent over 75% of the metabolic perturbations observed at all stages, suggesting their pivotal roles in retinal degeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eCompared with age-matched C57 mice, rd10 mice showed a notable decrease in Fructose 1,6-bisphosphate, FAD, and AMP, alongside an increase in dihydroxyacetone phosphate and ADP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-E). This suggests a significant dysregulation of energy metabolism, particularly in glycolysis and the TCA cycle, during the process of retinal degeneration. Notably, levels of ketoleucine, acetylphosphate, dodecanoic acid, and AICAR were significantly elevated, while L-valine and L-isoleucine showed a marked decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-D), implying a compensatory anaplerotic flux into the TCA cycle. However, at the late stage of RP, these compensatory mechanisms progressively diminished, leading to a gradual decline in the proportion of metabolites associated with energy metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eA disturbance in lipid metabolism was identified in the retina of rd10 mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-D). Notable elevations in the levels of linoleic acid derivatives were detected, including metabolites of arachidonic acid such as 9,10-Epoxyoctadecenoic acid, Prostaglandin D2, Prostaglandin J2, 15-Deoxy-d-12,14-PGJ2, 16(R)-HETE, 11,12-DiHETrE, and 5-KETE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-D). Conversely, a reduction in the level of linoleic acid was noted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eWe found an accumulation of nucleosides, specifically uridine, and cytidine, alongside a downregulation of nucleotides such as UMP, CMP, and uracil. This suggests a disruption in the pyrimidine salvage pathway and uridine catabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-E). Concurrently, purine metabolism showed significant changes at P25 and P45, characterized by the accumulation of guanosine/GMP and a reduction in xanthine. This effectively blocks uric acid production, which is an essential antioxidant defense mechanism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003eOur findings indicate that the retinal metabolic profile of rd10 mice alters as the disease progresses, revealing significant metabolic disturbances within the degenerative retina. These dynamic changes in metabolic disturbances are correlated with the histological transition from localized photoreceptor loss to extensive pan-retinal damage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eUridine in pyrimidine metabolism is the critical metabolite in RP progression\u003c/h2\u003e \u003cp\u003eTo better understand the functional characteristics and metabolic pathways of these differential metabolites in the progression of RP, we performed a Metabolite Set Enrichment Analysis (MSEA) alongside a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The MSEA results showed significant enrichment in the pyrimidine metabolism pathway, which consistently ranked among the top 10 pathways throughout the process of retinal degeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Moreover, the KEGG pathway analysis revealed that the ABC transporters and pyrimidine metabolism pathways were the most prominently altered at all three time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), demonstrating their critical roles within the pathway network diagram (Fig. S2). Furthermore, the network analysis highlighted a significant correlation between the ABC transporters and the pyrimidine metabolism pathway (Fig. S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFive differential metabolites (uracil, FAD, ADP, cytidine, and uridine) were found to be enriched in the pyrimidine metabolism pathway as the disease progresses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-G). Notably, the uridine levels in the retinas of rd10 mice exhibited a significant progressive increase compared to the C57 groups throughout disease progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Based on these observations, we proposed that uridine plays a crucial role in the pathological process of RP, and its biological functions and underlying mechanisms deserve further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eUridine causes photoreceptor damage and visual function decrease\u003c/h2\u003e \u003cp\u003eTo investigate the role of uridine in retinal degeneration, we performed intravitreal injections of uridine in one eye of healthy 5-week-old C57 mice while administering PBS to the contralateral eye as the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), aiming to simulate the abnormal metabolic microenvironment characteristic of retinal degeneration. Uridine dose-response experiments identified that a concentration of 100 mM significantly impaired visual function and retinal structure in C57 mice by day 14 post-injection (Fig. S3). Consequently, we selected 100 mM as the concentration for the subsequent in vivo experiments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRetinal function was assessed using dark-adapted scotopic electroretinography (ERG) at 7 and 14 days following uridine administration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Compared with the control eyes, the uridine-treated eyes demonstrated reduced amplitudes of a-waves and b-waves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), indicating impaired retinal function, which gradually developed into significant structural damage to the retinal tissue later. One day after uridine injection, the thickness of the outer nuclear layer (ONL) did not significantly differ from that of the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). However, starting four days after the uridine injection, the ONL thickness of the retina was significantly reduced when compared to the control group and gradually declined over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-G). TUNEL staining revealed that apoptotic cells after uridine treatment were primarily located in the ONL region. Their numbers increased rapidly, peaking on day 7, and then gradually declining over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH, I1-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These findings suggest that uridine induces dysfunction and ultimately results in visual loss.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePhotoreceptor apoptosis mediated by uridine-induced microglial activation\u003c/h2\u003e \u003cp\u003ePrevious studies have suggested a possible link between uridine and immune responses[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We investigated the impact of uridine on the activation of retinal microglia. Following intravitreal injections of uridine, we observed pronounced reactivity in the retinal microglia, with their numbers peaking on day 7 before gradually declining (Fig.\u0026nbsp;4A1-5, B). This pattern corresponded to the observed apoptosis of photoreceptors following treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). After administering uridine, we observed microglia migrated from the inner retina to the outer retina. In the retina of the control group and one day following uridine injection, microglia were found throughout the inner and outer plexiform layers (IPL and OPL); however, their presence remained relatively sparse (Fig.\u0026nbsp;4A1-2, C-D). Conversely, a significant increase in the number of microglia in the retina was observed at four days post-uridine injection, accompanied by their migration from the inner layers to the outer layers, with these cells predominantly localized in the IPL and the inner nuclear layer (INL) (Fig.\u0026nbsp;4A3, C-D). By seven days post-uridine injection, the migration of microglia continued, and their processes extended from the OPL to the ONL (Fig.\u0026nbsp;4A4, D-E). Additionally, at 14 days after uridine injection, microglia were observed to traverse both the ONL and the subretinal space (SRS) (Fig.\u0026nbsp;4A5, E). These results suggest that microglia exhibit heightened reactivity following uridine treatment and actively infiltrate areas of retinal injury. The temporal overlap between microglial activation and photoreceptor apoptosis strongly implicates their potential role in mediating photoreceptor death.\u003c/p\u003e \u003cp\u003eNotably, increased reactivity of GFAP, a significant marker for gliosis in RP, was observed in the retina only after 14 days of uridine injection. This finding indicates that the activation of M\u0026uuml;ller cells may occur after the microglial response (Fig. S4).\u003c/p\u003e \u003cp\u003eTo investigate the role of uridine-induced microglial activation in photoreceptor apoptosis, C57 mice were fed a chow containing the CSF1R inhibitor PLX3397 (PLX group) from three to five weeks of age to deplete retinal microglia, while the controls received normal chow (WT group). All mice then received a single intravitreal injection of uridine (Uri) in one eye and PBS (Ctrl) in the contralateral eye. They maintained their diets for seven days after the injection and underwent morphological and functional assessments at the endpoint (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). While the visual function of the PLX-Ctrl group showed a slight reduction compared to the WT-Ctrl group, this difference was not statistically significant. In contrast, the PLX-Uri group exhibited a marked improvement in visual function compared to the WT-Uri group, as evidenced by significantly increased a-wave and b-wave amplitudes observed in dark-adapted ERG. Remarkably, the visual function of the PLX-Uri group demonstrated no significant differences compared to the PBS control groups (WT-Ctrl, PLX-Ctrl) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, H). Iba1 and TUNEL staining revealed pronounced thinning of the ONL, along with concurrent microglial activation and photoreceptor apoptosis in the WT-Uri group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI-L). In contrast, microglial depletion significantly mitigated retinal degeneration. The PLX-Uri group preserved a normal ONL architecture comparable to that of the WT-Ctrl and PLX-Ctrl groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ), achieving over 90% microglial elimination (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK) and reducing apoptotic photoreceptors by 86% compared to WT-Uri group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL).\u003c/p\u003e \u003cp\u003eIn summary, we confirm that microglia are the key effector cells responsible for uridine-induced retinal degeneration. Uridine drives photoreceptor apoptosis by activating retinal microglia. This suggests that targeted modulation of the interaction between uridine, microglia, and photoreceptors can serve as a potential intervention strategy for retinal degenerative diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eUridine induces a pro-inflammatory and phagocytic phenotype in retinal microglia\u003c/h2\u003e \u003cp\u003eTo further investigate the functional phenotype of uridine-responsive microglia in regulating photoreceptor apoptosis, we performed double fluorescence staining of the classical pro-inflammatory markers CD16/CD32 in conjunction with the phagocytic marker CD68 within Iba1-positive microglial cells (Fig.\u0026nbsp;5A1-5, B1-5).\u003c/p\u003e \u003cp\u003eIn the retinas of the control group, microglia displayed minimal expression of CD16/32 and CD68, indicating a resting state. Following uridine injection, microglia exhibited irregular morphology, characterized by shorter processes, reduced branching, and enlarged cell bodies, which signify an activated state (Fig.\u0026nbsp;5A2-5, B2-5). Additionally, most microglia exhibited long axes perpendicular to the retina, suggesting active migration (Fig.\u0026nbsp;5A3-4, B3-5). This subset of activated migratory microglia demonstrated pronounced immunoreactivity for CD16/32 and CD68. The quantity and proportion of CD16/32-positive and CD68-positive microglia in the uridine-treated retina increased substantially (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-F), aligning with the previously noted increase in the overall number of uridine-activated microglia (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). By day 7 post-treatment, nearly all Iba1-positive microglia were co-localized with CD16/32-positive cells (82.6%) and CD68-positive cells (85.6%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, F). The RT-qPCR analysis showed that uridine treatment significantly increased the expression levels of \u003cem\u003eFcgr3\u003c/em\u003e (CD16) and \u003cem\u003eCd68\u003c/em\u003e compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, H). These findings suggest that uridine promotes microglial activation, leading to a shift towards a pro-inflammatory and phagocytic phenotype.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eUridine induces microglia to aberrantly engulf living photoreceptors\u003c/h2\u003e \u003cp\u003eWe conducted TUNEL and Iba1 double-labeling to determine whether apoptotic photoreceptors were the primary targets of microglial phagocytosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). DAPI staining showed that the nuclei of microglia exhibited an oval or kidney-shaped morphology, with distinct accumulations of dense heterochromatin clumps. The chromatin in the nucleolus appeared lighter, as highlighted by the green outline (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA1-3). In contrast, the healthy photoreceptor nuclei displayed a smooth, round shape, characterized by a tightly packed mass of heterochromatin centrally located and surrounded by euchromatin, as indicated by the red outline (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA2, 3). Our study revealed that most infiltrating microglia did not target the apoptotic photoreceptors; instead, they aberrantly phagocytosed healthy photoreceptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA2-3, C). Through three-dimensional reconstruction, we observed the somatic extension of infiltrating microglia contacting photoreceptors and partially encircling them. During this process, the microglial cell membrane invaginated, encapsulating the cell membrane and part of the cytoplasm of the photoreceptors (Fig.\u0026nbsp;6B1). Subsequently, the cell body of the microglia extended further, ultimately leading to the complete engulfment of the photoreceptors, isolating them within the microglial cell (Fig.\u0026nbsp;6B2). Following uridine treatment, the infiltrating microglia in the ONL displayed strikingly similar characteristics to those found in the retinas of rd10 mice[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], exhibiting abnormal phagocytic activity and pro-inflammatory effects. It suggests that uridine treatment may induce microglia to aberrantly engulf healthy photoreceptors, which could further cause photoreceptor death.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptional characteristics of P2Y6R-positive microglial subpopulations in the retina\u003c/h2\u003e \u003cp\u003eTo investigate the molecular mechanisms behind uridine-induced microglial activation, we conducted transcriptomic analyses of rd10 retinas (GSE178928[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], previous data from our laboratory[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]). The analysis demonstrated a significant upregulation of P2Y receptor genes, specifically \u003cem\u003eP2ry2\u003c/em\u003e, \u003cem\u003eP2ry6\u003c/em\u003e, and \u003cem\u003eP2ry13\u003c/em\u003e, during retinal degeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Given the established roles of uridine in modulating P2Y receptors[\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and the predominant expression of metabolic P2Y receptors in microglia[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], we have chosen to focus specifically on the P2Y6R. This receptor subtype is primarily expressed in microglial cells and functionally associated with their activation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In line with the findings from rd10 mice, we observed a significantly increased expression level of \u003cem\u003eP2ry6\u003c/em\u003e in the retinas of C57 mice following uridine treatment across all time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The trend of change was consistent with that of microglial activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG-H), suggesting a potential uridine-P2Y6R axis in microglial activation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo figure out the characteristics of P2Y6R-positive microglial cells that may respond to uridine, we analyzed the published single-cell RNA sequencing (scRNA-seq) results of retinas from rd10 and C57 mice at P21 (GSE183206)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The annotation of cell types (Fig. S5A) using established marker genes[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], scRNA-seq revealed that \u003cem\u003eP2ry6\u003c/em\u003e was predominantly expressed in the microglia cluster (Fig. S5B-D). To further elucidate the role of P2Y6R-positive microglia in RP progression, we performed sub-clustering of the microglia cluster, resulting in identification of six distinct microglial subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Notably, we found that \u003cem\u003eP2ry6\u003c/em\u003e was primarily expressed in microglial clusters 1, 3, and 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, S6A), which primarily comprised cells from the rd10 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). This suggested that wild-type and rd10 microglia exhibited different transcriptomic profiles, indicating that P2Y6R-positive microglia may play a pivotal role in retinal degeneration of rd10 mice. We conducted a quantitative analysis of the average expression levels and gene scores of the \u003cem\u003eP2ry6\u003c/em\u003e gene in microglial subpopulations, ultimately identifying MiG 1 and 3 as P2Y6R-positive microglial subpopulations (Fig. S6B, C). The highly expressed genes in MiG 1 and 3 were predominantly linked to phagocytosis (\u003cem\u003ePf4\u003c/em\u003e, \u003cem\u003eMsr1\u003c/em\u003e) and inflammatory responses (\u003cem\u003eIl-1α\u003c/em\u003e, \u003cem\u003eCcl4\u003c/em\u003e, \u003cem\u003eCd38\u003c/em\u003e) (Fig. S6D). Moreover, GO and KEGG enrichment analyses of the highly expressed genes in MiG 1 and 3 indicated significant enrichment of pathways related to phagocytosis, inflammation, and immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG-J).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eBlockade of P2Y6R reduces phagocytosis and pro-inflammatory function of uridine-induced microglia\u003c/h2\u003e \u003cp\u003eTo further verify the activation of P2Y6R-positive microglia in response to uridine accumulation, we evaluated the changes in the phagocytic and pro-inflammatory abilities of BV2 cells treated with uridine alone with or without P2Y6R inhibitor MRS2578 in vitro. RT-qPCR analysis revealed that uridine treatment significantly increased the transcription levels of pro-inflammatory factors (\u003cem\u003eTnf-α\u003c/em\u003e, \u003cem\u003eIl-6\u003c/em\u003e, \u003cem\u003eIl-1β\u003c/em\u003e), the phagocytic marker (\u003cem\u003eCd68\u003c/em\u003e), and \u003cem\u003eP2ry6\u003c/em\u003e in BV2 cells in a concentration-dependent manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-E), which was consistent with our in vivo results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the role of uridine in mediating the inflammatory response and phagocytic activity of microglia through the activation of P2Y6R, BV2 cells were pre-treated with the selective P2Y6R antagonist MRS2578 for 4 hours, followed by treatment with uridine for 24 hours. The results indicated that pretreatment with MRS2578 significantly reduced the transcriptional levels of \u003cem\u003eP2ry6\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF), inflammatory factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-I), and the phagocytic marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eJ) in uridine-activated BV2 cells. This finding suggests that inhibition of P2Y6R can effectively suppress the inflammatory response and phagocytic activity mediated by uridine in microglia. Moreover, immunofluorescent staining also confirmed that uridine treatment alone elevated the expression of CD68 and IL-1β in BV2 cells, while pretreatment with MRS2578 reversed the microglial activation induced by uridine treatment (Fig. S7). To evaluate the phagocytic activity of microglia, BV2 cells were incubated with pH-sensitive fluorescent dyes after treatment with uridine alone or following pre-treatment with MRS2578. The phagocytic behavior of BV2 cells was observed under a fluorescence microscope (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eK-M), and fluorescence intensity was quantitatively analyzed using flow cytometry to assess phagocytosis ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eN, O). Results indicated a significant increase in fluorescence intensity in microglia after uridine treatment. However, pre-treatment with MRS2578 reversed this increase, suggesting that inhibiting P2Y6R can suppress the uridine-induced phagocytic activity of microglia. These results suggest that uridine activates microglia through P2Y6R to mediate their inflammatory response and enhance phagocytosis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we utilized timing analysis of global untargeted metabolomics to demonstrate for the first time that uridine accumulated continuously during the progression of RP. This accumulation promotes the activation and infiltration of microglia into the ONL via the activation of P2Y6R. The microglia exhibited a highly reactive pro-inflammatory phenotype and enhanced phagocytic function, inappropriately engulfing healthy photoreceptors instead of apoptotic ones. Notably, pharmacological blockade of P2Y6R using MRS2578 effectively prevented uridine-mediated microglial activation, thereby inhibiting the inflammatory response and phagocytic activity of microglia, which could help prevent photoreceptor death.\u003c/p\u003e \u003cp\u003eAs the final product of systemic metabolism, the analysis of metabolites can serve as a bridge between gene transcription and phenotype expression. Metabolomics research has been applied across various fields within ophthalmic studies[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, current investigations into RP remain limited, and existing metabolomic studies fail to adequately reflect the dynamic changes in metabolic profiles as the disease progresses. A recent serum metabolomics study involving patients with RP found elevated levels of metabolites associated with inflammatory responses during oxidative stress, suggesting that the dysregulation of inflammatory processes may play a role in the pathogenesis of RP[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our metabolomics findings indicate that alterations in metabolites, such as linoleic acid and arachidonic acid, in the retinas of rd10 mice, suggest chronic activation of COX/LOX pathways, potentially driving photoreceptor apoptosis through oxidative stress and inflammatory responses. Furthermore, previous metabolomics and proteomics studies conducted on RP mice have highlighted significant alterations in retinal pyrimidine and purine nucleotide metabolism[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. These findings align with our observations regarding the dual risks of nucleotide pool imbalance and the amplification of oxidative damage. Notably, nucleotide metabolism is crucial in the context of retinal degenerative diseases. While uridine is known to be linked to diabetic retinopathy and glaucoma[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], its involvement in RP has not been previously documented. Our study is the first to identify uridine as a key metabolite in RP.\u003c/p\u003e \u003cp\u003ePrevious reports have indicated that uridine is a regenerative protective substance associated with age-related changes. Notably, elderly individuals have significantly reduced uridine levels compared to their younger counterparts, who exhibit higher levels of uridine[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In C57 mice, the relative content of uridine in the retina declines with age, which aligns with the established age-related dose-response relationship observed between uridine levels and aging. Conversely, rd10 mice exhibit persistently elevated uridine levels in their retinas as they age and the disease progresses, with these levels ultimately exceeding those found in normal C57 mice. The significance of elevated uridine levels in rd10 mice potentially acting as a compensatory protective mechanism remains controversial. Prolonged exposure to high concentrations of uridine may disrupt glucose and lipid metabolism[\u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and potentially increase DNA damage, thereby raising the risk of cancer[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. To investigate the pathological effects of elevated uridine concentrations in rd10 mouse retinas, we performed intravitreal injections of exogenous uridine in normal C57 mice. This experimental manipulation resulted in impaired visual function and increased apoptosis of photoreceptors.\u003c/p\u003e \u003cp\u003eHow does uridine lead to retinal damage? Under conditions of stress or neuronal injury, nucleotides and nucleosides are released in large quantities[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], modulating both immune responses and inflammatory processes while simultaneously activating immune cells[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Our metabolomics study demonstrated that retinal uridine levels in rd10 mice progressively increased, correlating with pro-inflammatory mediators, suggesting that the accumulation of uridine may intensify inflammation by triggering immune responses. Subsequent experiments revealed that uridine potentiated microglial activation, migration, and differentiation toward pro-inflammatory and phagocytic phenotype. Notably, this microglial activation exhibited temporal synchrony with the progression of photoreceptor apoptosis. A marked reversal of uridine-induced visual function deterioration and photoreceptor apoptosis was achieved through microglial depletion, demonstrating that uridine mediates secondary photoreceptor death primarily through microglial activation. In animal models of RP, microglial activation and recruitment occur simultaneously with photoreceptor degeneration[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Microglial activation, accompanied by the release of pro-inflammatory cytokines and enhanced phagocytic activity, contributes to photoreceptor death[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In rd10 mice with retinal degeneration, activated microglia infiltrate the ONL, extensively interacting with photoreceptors and ultimately engulfing non-apoptotic photoreceptors[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This phenomenon of microglia-mediated phagocytosis of viable photoreceptors has been consistently observed in multiple RP mouse models and confirmed inhuman retinal specimens[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Our findings revealed that uridine-treated microglia displayed enhanced phagocytic activity, infiltrated the ONL, and established close interactions with non-apoptotic photoreceptors to form phagosome-like structures during aberrant phagocytosis. These results suggest that uridine treatment recapitulates the pathological retinal microenvironment in RP, demonstrating that uridine accumulation is a key driver of RP disease progression. Uridine may exacerbate photoreceptor degeneration by inducing microglial activation, which triggers inflammatory factors release and promotes phagocytosis of viable photoreceptors. However, the origin of pathological uridine accumulation in rd10 mouse retinas remains unclear. Damaged photoreceptors may release uridine as a damage signal, thereby contributing to microglial activation. Developing uridine-specific visualization tools is critical for elucidating the spatiotemporal dynamics, release mechanisms and regulatory pathways of extracellular uridine in future studies. A genetically encoded fluorescent probe targeting extracellular uridine represents a promising strategy to achieve this goal.\u003c/p\u003e \u003cp\u003eThe uridine-responsive purinergic P2Y6 receptor (P2Y6R) is predominantly expressed in microglia[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and mediates microglial phagocytosis and the neuroinflammatory process[\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Our study indicated that uridine treatment significantly increased the expression level of \u003cem\u003eP2ry6\u003c/em\u003e in the retinas of normal C57 mice. These changes in \u003cem\u003eP2ry6\u003c/em\u003e expression were consistent with the observed alterations in microglial activation following uridine treatment, suggesting that uridine may influence microglial activation through the P2Y6R. Previous studies have reported that the P2Y6R signaling pathway is implicated in various stages of the phagocytic process and that P2Y6R can trigger the expression of pro-inflammatory cytokines in immune cells[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. We analyzed retina single-cell RNA sequencing data from rd10 mice (with uridine accumulation) and C57 mice. We found that P2Y6R was expressed almost exclusively in the rd10 microglial cluster. P2Y6R-positive microglia highly expressed proliferation genes (\u003cem\u003eCd34\u003c/em\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]), phagocytic genes (\u003cem\u003ePf4\u003c/em\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], \u003cem\u003eMsr1\u003c/em\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]), and inflammatory genes (\u003cem\u003eIl-1α\u003c/em\u003e, \u003cem\u003eCcl4\u003c/em\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], \u003cem\u003eCd38\u003c/em\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]), among others. GO and KEGG enrichment analyses revealed that P2Y6R-positive microglia exhibit significant enrichment in phagocytic processes, inflammatory responses, and immune regulation pathways. These findings closely mirror the functional characteristics of retinal microglia observed following uridine treatment. Our in vitro experiments demonstrated that pharmacological inhibition of P2Y6R with MRS2578 effectively suppressed the uridine-induced enhancement of microglial phagocytic activity. This intervention concurrently attenuated microglial inflammatory responses, thereby demonstrating that uridine regulates microglial phagocytosis and inflammation through the activation of P2Y6R. Notably, this study primarily utilized single-cell sequencing data from rd10 mouse retinas, rather than data derived from the uridine-injected normal C57 mice. While significant uridine accumulation was observed in rd10 retinas, the complex metabolic microenvironment of the rd10 retina suggests that uridine may collaborate with other metabolites to activate P2Y6R. Thus, our findings do not definitively confirm that P2Y6R is solely activated by uridine. Furthermore, it has been reported that P2Y6R is primarily activated by UDP[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Thus, further investigation is necessary to ascertain whether extracellular uridine influences P2Y6R through its enzymatic conversion to UDP. Future studies should employ real-time uridine visualization techniques and utilize microglial P2Y6R conditional knockout mice to dissect their roles in non-apoptotic phagocytosis, neuroinflammation, and photoreceptor degeneration. Notably, the clinical application of MRS2578 is constrained by its irreversible binding, poor aqueous stability, and suboptimal pharmacokinetics. Nevertheless, recent advances in developing next-generation P2Y6R antagonists[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] show promise for overcoming these pharmacological limitations.\u003c/p\u003e \u003cp\u003eIn conclusion, our research provides valuable insight into the role of uridine in the RP process. We found that uridine exacerbates phagocytosis and inflammatory responses in microglial cells mediated by P2Y6R, and aggravates neurodegeneration during the progression of RP disease. Our findings suggest that targeting microglial activation by inhibiting P2Y6R can be a promising therapeutic strategy for RP.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Professor Mindian Li and Professor Xingdong Liu for helpful manuscript revisions. This study was supported by funding from the National Key Research and Development Program of China (2021YFA1101203, 2021YFA1101202), and the National Natural Science Foundation of China (82271104).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Haiwei Xu, Jing Xie, Xiaotang Fan, Lingyue Mo, Ting Zou.\u003c/p\u003e\n\u003cp\u003eMethodology: Haiwei Xu, Jing Xie, Xiaotang Fan, Lingyue Mo, Ting Zou.\u003c/p\u003e\n\u003cp\u003eInvestigation: Lingyue Mo, Lingling Ge, Zhe Cha, Lijuan Yan, Yuanxing Yang.\u003c/p\u003e\n\u003cp\u003eFormal analysis: Lingyue Mo.\u003c/p\u003e\n\u003cp\u003eVisualization: Lingyue Mo, Zhe Cha.\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; Original Draft: Lingyue Mo.\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: Haiwei Xu, Jing Xie, Xiaotang Fan, Lingyue Mo, Zhe Cha, Ting Zou, Hui Gao, Xuan Chen, Shujia Huo.\u003c/p\u003e\n\u003cp\u003eSupervision: Haiwei Xu, Jing Xie, Xiaotang Fan.\u003c/p\u003e\n\u003cp\u003eFunding acquisition: Haiwei Xu, Jing Xie, Xiaotang Fan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interest exists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary information related to this article can be found in the following files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVerbakel SK, van Huet RAC, Boon CJF, den Hollander AI, Collin RWJ, Klaver CCW, et al. 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J Med Chem. 2023;66(9):6315\u0026ndash;32. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.jmedchem.3c00210\u003c/span\u003e\u003cspan address=\"10.1021/acs.jmedchem.3c00210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Retinitis pigmentosa, Metabolomics, Uridine, Microglia, P2Y6R","lastPublishedDoi":"10.21203/rs.3.rs-6578878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6578878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRetinitis pigmentosa (RP) is an incurable blinding disorder characterized by progressive photoreceptor degeneration. While metabolic stress has been implicated in RP progression, the neuroimmune mechanisms driving this process remain poorly understood. In this study, we employed time-series untargeted metabolomics to profile temporal metabolic changes during RP pathogenesis using the retinal degeneration 10 (rd10) mouse model, identifying uridine as a key metabolite dynamically associated with disease progression. Intravitreal uridine administration in wild-type C57BL/6J mice induced RP-like pathology, including photoreceptor apoptosis and visual impairment, alongside aberrant microglial activation. Microglial depletion reversed these degenerative phenotypes, implicating microglia as central mediators of uridine-driven neurodegeneration. Further analysis revealed that uridine-reactive microglia adopted a pro-inflammatory state and aberrantly phagocytosed viable photoreceptors. Single-cell RNA sequencing (scRNA-seq) of rd10 retinas uncovered a distinct P2Y6R-expressing microglial subpopulation with a dual phenotype characterized by both proinflammatory and phagocytic activity. In vitro studies confirmed that uridine activates microglia via P2Y6R signaling, triggering both inflammatory cytokine release and dysregulated phagocytosis\u0026mdash;effects that are abolished by P2Y6R inhibition. Our findings identify the uridine-P2Y6R axis as a novel metabolic-immune checkpoint in RP, orchestrating microglia-mediated photoreceptor degeneration. Targeting this axis presents a promising therapeutic strategy for RP.\u003c/p\u003e","manuscriptTitle":"P2Y6R-positive microglia sense the stress metabolite uridine and exacerbate photoreceptor degeneration in the retina","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 17:38:54","doi":"10.21203/rs.3.rs-6578878/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":"4b568b6a-444a-4e22-ae38-633c4c93ff33","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48205816,"name":"Biological sciences/Immunology/Neuroimmunology"},{"id":48205817,"name":"Biological sciences/Cell biology/Mechanisms of disease"}],"tags":[],"updatedAt":"2025-05-28T11:31:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 17:38:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6578878","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6578878","identity":"rs-6578878","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}