NLRC5-Mediated Epigenetic and Proteomic Regulation of Microglial Panoptosis Drives Neuroinflammation in Multiple Sclerosis

NLRC5-Mediated Epigenetic and Proteomic Regulation of Microglial Panoptosis Drives Neuroinflammation in Multiple Sclerosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article NLRC5-Mediated Epigenetic and Proteomic Regulation of Microglial Panoptosis Drives Neuroinflammation in Multiple Sclerosis Yan Wu, Jianhong Wang, Ping Gan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6759227/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Molecular Neurobiology → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Microglial dysfunction contributes to multiple sclerosis (MS) pathogenesis, yet the link between epigenetic regulation and inflammatory cell death (PANoptosis) remains unclear. This study explores NOD-like receptor family CARD domain containing 5 (NLRC5) as a regulator of microglial PANoptosis in MS. Methods: Transcriptomic data from experimental autoimmune encephalomyelitis (EAE) microglia (GSE253318) and GEO datasets (GSE78809, GSE154228) were integrated to identify panoptosis-related genes. Mendelian randomization (MR) linked NLRC5 expression to proteomic targets using UK Biobank and deCODE Iceland protein quantitative trait loci (pQTLs). Methylation quantitative trait locus (mQTL) analysis assessed MS-associated CpG sites. Lipopolysaccharide (LPS)-treated BV2 microglial models were used to validate NLRC5–PANoptosome assembly via Western blot and immunofluorescence. Results: NLRC5 was identified as a hub gene in PANoptosis-related pathways. MR revealed causal links between NLRC5 and apoptotic (GABA Type A Receptor-Associated Protein (GABARAP), BR Serine/Threonine Kinase 2 (BRSK2), TNF Superfamily Member 12 (TNFSF12)) and necroptotic (BCL2) effectors, consistent across inverse-variance weighted (iVW), Bayesian Weighted Mendelian Randomization (BWMR), and Generalized Summary-data-based Mendelian Randomization (GSMR) methods. Hypermethylation of NLRC5 (cg04097610) was associated with reduced MS risk (Odds Ratio (OR) = 0.885, p = 0.039). LPS stimulation upregulated NLRC5, Z-DNA binding protein 1 (ZBP1), apoptosis-associated speck-like protein containing a CARD (ASC), and cysteine-aspartic acid protease 8 (caspase-8), supporting PANoptosome activation. Conclusions: NLRC5 regulates microglial PANoptosis via epigenetic and proteomic mechanisms, linking inflammatory cell death to MS progression. These findings highlight NLRC5 as a potential therapeutic target in MS. NLRC5 PANoptosis Multiple sclerosis Microglia DNA methylation Apoptosis Necroptosis Pyroptosis Experimental autoimmune encephalomyelitis (EAE) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system (CNS), characterized by inflammatory demyelination, axonal damage, and progressive neurodegeneration [ 1 ] . Microglia, the resident immune cells of the CNS, play a dual role in MS pathogenesis. In early disease stages, they contribute to tissue repair by clearing myelin debris and releasing neurotrophic factors [ 2 ] . However, chronic activation shifts microglia toward a pro-inflammatory phenotype, marked by elevated cytokine production (e.g., TNF-α, IL-1β) and antigen presentation, which exacerbates neuronal injury [ 3 , 4 ] . Single-cell transcriptomic studies of chronic active MS lesions have identified disease-associated microglial subsets, notably the "microglia inflamed in MS" (MIMS) population. These cells exhibit upregulated expression of complement components (e.g., C1q) and iron metabolism-related genes (e.g., FTL, FTH1), underscoring their role in perpetuating neuroinflammation and oxidative stress [ 1 , 4 ] . Despite these insights, the molecular mechanisms underlying microglial pathogenic activation remain incompletely understood, particularly the interaction between regulated cell death pathways and epigenetic regulation [ 5 , 6 ] . Multiple forms of programmed cell death (PCD), including apoptosis, pyroptosis, necroptosis, and ferroptosis, have been implicated in MS and its animal models. Apoptosis, marked by caspase-3/7 activation, regulates microglial turnover during experimental autoimmune encephalomyelitis (EAE) [ 7 – 9 ] . Pyroptosis, driven by NLRP3 inflammasome activation and gasdermin D (GSDMD) cleavage, amplifies neuroinflammation through IL-1β release [ 10 , 11 ] . Necroptosis, mediated by RIPK1 and MLKL signaling, exerts paradoxical effects on demyelination and remyelination processes [ 12 , 13 ] . Recently, PANoptosis has emerged as a unifying mechanism integrating pyroptosis, apoptosis, and necroptosis [ 14 ] . This process is mediated by the formation of multiprotein complexes called PANoptosomes, such as Z-DNA binding protein 1 (ZBP1)- and AIM2-PANoptosomes [ 15 – 17 ] . While PANoptosis has been implicated in neurodegenerative and inflammatory diseases [ 17 , 18 ] , its relevance to MS has yet to be investigated. NOD-like receptor family CARD domain containing 5 (NLRC5) has recently gained attention as a regulator of immune responses and cell death. Traditionally known for controlling MHC class I gene expression and inflammasome activation [ 19 ] , NLRC5 has now been identified as a key component of PANoptosome complexes, where it cooperates with caspase-1, RIPK3, and Apoptosis-associated speck-like protein containing a CARD (ASC) to promote inflammatory cell death [ 19 ] . In EAE models, NLRC5 expression correlates with transactivator of MHC I of oligodendrocyte [ 20 ] , while genetic deletion of NLRC5 attenuates neuroinflammatory responses in lipopolysaccharide (LPS)-induced microglial activation and LPS-induced depressive mouse models [ 21 ] . These findings highlight NLRC5 as a potential central regulator linking microglial dysfunction to PANoptotic cell death in MS. However, its upstream regulation, particularly epigenetic mechanisms such as DNA methylation, and downstream signaling cascades in CNS autoimmunity remain largely undefined. Given the critical involvement of microglial activation in MS pathogenesis and emerging insights into PANoptosis-mediated neuroinflammation, this study explores the hypothesis that epigenetically regulated NLRC5 drives microglial PANoptosis to exacerbate MS progression. To address this, we integrated EAE microglia RNA sequencing data, genome-wide association studies (GWAS), cis-expression quantitative trait loci (cis-eQTLs), protein quantitative trait loci (pQTLs), methylation quantitative trait locus (mQTL) datasets, and mendelian randomization (MR) analyses. By pioneering the investigation of PANoptosis in MS and positioning NLRC5 as a multimodal orchestrator of microglial death, this work provides novel mechanistic insights and highlights NLRC5's potential as a therapeutic target for epigenetic interventions in neuroinflammatory disorders. Methods Study Design This multimodal investigation integrated self-generated RNA-seq analysis of EAE microglia (GSE253318) with cross-dataset validation (GSE154228, GSE78809) (detailed in Table S1) to identify PANoptosis-associated hub genes, followed by MR (exposure: NLRC5 eQTLs; outcomes: pQTLs) to map causal effectors, and methylation profiling to pinpoint regulatory loci. The study design was outlined in Fig. 1. EAE: experimental autoimmune encephalomyelitis; PPI: protein-protein interaction; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; IMSGC: International Multiple Sclerosis Genetics Consortium; LPS: lipopolysaccharide Comparative Analysis of Microglial Transcriptomes in EAE Models To investigate microglial responses in MS, we first established the EAE mouse model and performed RNA sequencing on isolated brain microglia. Female C57BL/6 mice (8 weeks old) were randomly divided into healthy control and EAE groups (n = 8/group). EAE was induced by immunization with MOG35-55 peptide in complete Freund's adjuvant, with PBS-injected controls. At peak disease (day 14 post-induction), brain tissues were collected, and CD11b+/CD45int microglia were isolated via gradient centrifugation and flow cytometry (purity > 99%). For transcriptomic analysis, RNA was extracted from microglia (n = 16 samples), ribosomal RNA-depleted (TruSeq Stranded kit), and sequenced on Illumina NovaSeq 6000 (70–80M reads/sample). Reads were processed using HISAT2 and DESeq2, with data deposited in GEO (GSE253318). All procedures followed ARRIVE guidelines and were approved by Kunming Medical University Ethics Committee. To strengthen our experimental findings, we analyzed two complementary GEO datasets examining microglial transcriptomes in EAE models. The GSE154228 dataset comprises six EAE and four control samples of spinal cord-derived microglia [22] , and the GSE78809 dataset contains three EAE and three control samples of brain-derived microglia [23] . Mendelian Randomization We utilized genome-wide summary-level statistics for single-nucleotide polymorphisms (SNPs) from publicly accessible GWAS datasets, with detailed descriptions of these datasets available in Table S1, and ethical approval was secured for all original studies. Genetic instruments were selected through genome-wide significance thresholds (P < 5×10⁻⁸) and linkage disequilibrium clumping (r² thresholds analysis-dependent). MR analyses were implemented using the TwoSampleMR R package, with multi-SNP associations evaluated through IVW and MR-Egger regression complemented by three auxiliary methods (Weighted median, Simple mode and Weighted mode). We also employed MR-PRESSO and MREGGER to test for pleiotropy and used Bayesian Weighted Mendelian Randomization (BWMR) [24] for further validation. Sensitivity analyses systematically assessed heterogeneity (Cochran's Q test) and horizontal pleiotropy (MR-Egger intercept test), maintaining P < 0.05 significance thresholds. To address multiple comparison inflation, we implemented Benjamini-Hochberg false discovery rate (FDR) correction. Associations with nominal significance (P < 0.05) underwent FDR adjustment, with FDR < 0.05 defining statistical significance. Generalized Summary-data-based Mendelian Randomization approach In our primary analyses, we utilized Generalized Summary-data-based Mendelian Randomization (GSMR), a versatile methodology that conducts MR analyses employing several nearly independent instruments to evaluate the causal relationship between NLRC5 cis-eQTL and downstream pQTL, leveraging summary-level GWAS data obtained from independent investigations [25] . To eliminate instruments exhibiting significant potential pleiotropic effects, we employed the HEIDI-outlier approach, establishing a P value threshold of 0.01 for the HEIDI-outlier filtering analysis [25] . NLRC5 Downstream Gene-Protein Causal Mapping We constructed a two-sample MR framework using NLRC5 cis-eQTLs from the eQTLGen Consortium [26] as genetic instruments (Table S2), with plasma pQTLs derived from the UK Biobank Pharma Proteomics Project (UKB-PPP v1.0, including pQTLs of 2,918 proteins) [27] (Table S3) and deCODE Iceland (including pQTLs of 4,907 proteins) [28] (Table S4). Instrument selection employed LD clumping (r² <0.001, 250 kb window), followed by single-SNP Wald ratio estimation and multi-SNP meta-analyses. NLRC5 Methylation-Disease Association Analysis Integration of MS GWAS data from International Multiple Sclerosis Genetics Consortium (IMSGC) [29] with HumanMethylation450-annotated CpG sites identified NLRC5-associated methylation quantitative trait loci (mQTLs) from MeQTL EPIC Database [30] (Table S5). Independent variants were determined through PLINK-based LD clumping (r² 1%) and F-statistics (F > 10). Causal effect estimation employed IVW regression, with FDR < 0.05 applied to sensitivity analyses. Functional Insights and Network Analyses To further our understanding, we combined our findings with protein-protein interaction (PPI) networks using the STRING database (https://cn.string-db.org/) and performed GO enrichment analysis, including Molecular Functions (MF), Biological Processes (BP), and Cellular Components (CC), alongside KEGG pathway analysis to systematically annotate functional ontologies and signaling pathways linked to NLRC5 interactions. Cell Culture and LPS Treatment BV2 microglia were maintained in MEM basal medium (Servicebio G4552) supplemented with 10% fetal bovine serum (FBS, Gibco 10270-106) and 1% penicillin-streptomycin (P/S, Gibco 15140-122) under standard culture conditions (37°C, 5% CO₂, humidified incubator, Jingqi CL-191C). Upon reaching 80–90% confluence, cells were detached using 0.25% Trypsin (Gibco 1894145), centrifuged at 300 × g for 5 min (SCILOGEX DMO-412), and resuspended in fresh medium. Cells were divided into control group (untreated) and LPS-treated group (stimulated with 100 ng/mL LPS, Solarbio L8880). For functional assays, cells were seeded into 6-well plates (1 × 10⁵ cells/well, 9 replicates per group) and 24-well plates pre-loaded with coverslips (1 × 10⁴ cells/well, 2 replicates per group). After 24 h of incubation (~ 60% confluence), the medium was replaced with serum-free MEM devoid of antibiotics. The LPS-treated group was exposed to 100 ng/mL LPS for 24 h, while the control group remained untreated. Western Blot Analysis Following treatment, cells were lysed with RIPA buffer and centrifuged (12,000 × g, 10 min, 4°C) to collect supernatants. Protein concentrations were quantified spectrophotometrically, and equal amounts (60 µg) were denatured in SDS-PAGE loading buffer. Proteins were separated on 10% SDS-polyacrylamide gels and transferred to PVDF membranes using wet transfer. Membranes were blocked with 5% non-fat milk and probed overnight at 4°C with primary antibodies against NLRC5 (1:500), ZBP1 (1:1500), ASC (1:1000), cysteine-aspartic acid protease 8 (caspase-8) (1:500), RIPK3 (1:1000), and β-actin (1:4000). HRP-conjugated secondary antibodies (1:4000) were applied for 2 h at room temperature. Signals were detected using chemiluminescent substrate and imaged with a gel documentation system. Immunofluorescence (IF) Staining Cells on coverslips (24-well plate) were fixed with 4% paraformaldehyde for 30 min at 4°C, permeabilized with 0.1% Triton X-100 for 10 min, and blocked with 5% normal goat serum (ZSGB-Bio ZLI-9021) for 30 min at room temperature. Primary antibodies against NLRC5 (1:80, Santa Cruz sc-515668), ZBP1 (1:200, Proteintech 13285-1-AP), and ASC (1:100, Proteintech 30641-1-AP) were co-incubated overnight at 4°C. After washing with PBS, Polymer-HRP secondary antibodies (AFIHC035 kit) were applied for 30 min at room temperature. Tyramide Signal Amplification (TSA) was performed using TYR-520Plus (green fluorescence, 1:200), TYR-570Plus (red fluorescence, 1:200), and TYR-620Plus (cyan fluorescence, 1:200) fluorophores diluted in TSA buffer. Nuclei were counterstained with DAPI (ZSGB-Bio ZLI-9557). Fluorescent images were acquired using an Olympus laser scanning confocal microscope, and colocalization analysis was conducted using ImageJ software. Analytical Tools and Statistical Methods Analyses were conducted in R (v4.4.0) using the TwoSampleMR (v0.6.2) and MR-PRESSO (v1.0) packages for MR and pleiotropy correction, supplemented by BWMR (v2.21.0) and GSMR2 for robustness validation. Single- and multi-SNP instrumental variables were analyzed via Wald ratio and five MR methods (IVW, MR-Egger, weighted median, simple/weighted mode), respectively. Sensitivity tests included Cochran’s Q (P < 0.05 for heterogeneity) and MR-Egger intercept (P < 0.05 for pleiotropy). Data were visualized with ggplot2 (v3.4.4). Western blot bands were quantified using ImageJ (v1.53t) after background subtraction and normalization to β-actin, with triplicate biological replicates. Statistical comparisons between experimental groups were conducted using Unpaired Student’s t-test for two-group comparisons. Results Integrated Analysis of EAE Microglial RNA-Seq and GEO Datasets Reveals Potential PANoptosis Activation Intersection analysis was conducted between differentially expressed genes (DEGs) identified in our EAE microglial RNA sequencing dataset (GSE253318) and two publicly available GEO datasets (GSE78809 and GSE154228). This comparative approach revealed six overlapping genes: BST2, CCL5, GBP3, GBP8, H2-AB1 , and NLRC5 (Fig. 2 a). To further investigate the potential involvement of regulated cell death pathways, DEGs from all three datasets were cross-referenced with canonical genes associated with apoptosis, pyroptosis, and necroptosis (Fig. 2 b). The gene lists associated with apoptosis, necroptosis, and pyroptosis were sourced from the comprehensive study by Qin et al. (2023) [ 31 ] and displayed in Table S11 . Notably, multiple key regulators of these pathways, CASP1, CASP8, NOD2, MLKL , and NLRC5 , were consistently upregulated across three datasets (Fig. 2 c-e). The co-occurrence of CASP1 (pyroptosis), CASP8 (apoptosis/necroptosis switch), NOD2 (inflammasome priming), MLKL (necroptosis), and NLRC5 (panoptosis regulation) suggests convergent immunogenic cell death signaling. NLRC5 , identified as a consensus DEG across three independent RNA-seq datasets, was found to interact with AIM2 (inflammasome sensor), CHUK (IKKα; NF-κB kinase), DDX58 (RIG-I; viral RNA sensor), IFIH1 (MDA5; dsRNA sensor), IKBKB (IKKβ; NF-κB activator), MAVS (mitochondrial antiviral signaling protein), NLRC4 (inflammasome component), and NLRP1/3/6 (canonical inflammasome sensors) in PPI network analysis (Fig. 2 f). This interactome implicates NLRC5 in multimodal regulation of cell death modalities (pyroptosis/apoptosis/necroptosis) and inflammatory responses via inflammasome activation, antiviral signaling, and NF-κB pathway engagement. These findings highlight PANoptosis as a plausible contributor to microglial dysfunction and neuroinflammation in EAE pathogenesis. Identification of NLRC5-Regulated Apoptotic and Necroptotic Mediators via Cross-Database Mendelian Randomization To investigate downstream effectors of NLRC5, we performed two-sample MR analyses using NLRC5 cis-eQTLs from the eQTLGen Consortium as genetic instruments for exposure. Outcome datasets were derived from pQTLs in the deCODE Iceland and UKB-PPP databases, respectively. MR analysis identified 1,596 proteins in the deCODE Iceland cohort (Table S6 ) and 334 proteins in the UKB-PPP cohort (Table S7 ) as potential downstream molecular targets of NLRC5 (FDR < 0.05). Intersection of these candidate proteins across both databases revealed 78 overlapping molecules (Table S8 ), demonstrating robust consistency. GO/KEGG analysis of NLRC5-associated downstream molecules across the UKB-PPP and deCODE Iceland pQTL databases revealed enrichment in pathways (cell cycle, oocyte meiosis, cellular senescence), MF (microtubule/tubulin binding, motor activity), CC (spindle, centromeric regions), and BP (mitotic nuclear division, sister chromatid segregation) (Fig. 3 a). These findings implicate NLRC5 in regulating mitotic fidelity and chromosomal dynamics, with cross-talk to PCD pathways. Collectively, NLRC5 may act as a nexus linking cell cycle control to divergent cell death mechanisms under stress or damage conditions. Among the 78 overlapping molecule, three proteins, including GABA Type A Receptor-Associated Protein (GABARAP), BR Serine/Threonine Kinase 2 (BRSK2), and TNF Superfamily Member 12 (TNFSF12), were implicated in apoptosis regulation, while B-cell Lymphoma 2 (BCL2) exhibited a known role in necroptosis (Fig. 3 b and Table S8 ). MR analysis revealed that genetically predicted NLRC5 expression modestly upregulated the levels of GABARAP (UKB-PPP: OR = 1.04, FDR = 2.7×10 − 4 ; deCODE Iceland: OR = 1.02, FDR = 0.02), BRSK2 (UKB-PPP: OR = 1.03, FDR = 7.45×10 − 4 ; deCODE Iceland: OR = 1.03, FDR = 0.02), TNFSF12 (UKB-PPP: OR = 1.02, FDR = 0.03; deCODE Iceland: OR = 1.02, FDR = 0.03), and BCL2 (UKB-PPP: OR = 1.02, FDR = 0.03; deCODE Iceland: OR = 1.02, FDR = 0.04) (Fig. 3 c and Table 1 ). All associations were analyzed using the inverse-variance weighted (iVW) method and further validated through BWMR and GSMR, revealing robust concordance in directionality and nominal significance across both cohorts, albeit with modest effect magnitudes. Scatter plots and Forest plots illustrating the SNP effects on NLRC5 eQTL and four downstream protein pQTL across the two cohorts are presented in FigS1 and FigS2. The NLRC5 eQTL-associated SNPs exhibiting significant associations with downstream pQTLs (GABARAP, BRSK2, TNFSF12, and BCL2) are comprehensively annotated in Table S9 , including chromosomal positions, effect alleles, beta coefficients (β), standard errors (SE), and iVW p values. Table 1 Mendelian Randomization Analysis of NLRC5 eQTIL and pQTL of potential downstream molecular targets from the UKB-PPP and deCODE Iceland cohorts Outcome Method Number of SNP F-statistic Odds ratio (95%CI) FDR Heterogeneity P h Egger intercept P intercept GABARAP (UKB-PPP) MR Egger 23 1.02(0.98–1.07) 0.39 14.63 0.84 4.63×10 − 3 0.40 Weighted median 23 1.03(1.01–1.06) 0.02 IVW 23 162.97 1.04(1.012–1.06) 2.70×10 − 4 15.37 0.85 PRESSO 23 1.04(1.03–1.04) 2.52×10 − 4 BWMR 23 1.04 (1.02–1.06) 4.79×10 − 4 GSMR 23 1.04 (1.02–1.06) 5.62×10 − 4 GABARAP (deCODE Iceland) MR Egger 21 1.03(0.98–1.08) 0.28 15.78 0.67 -6.87×10 − 4 0.91 Weighted median 21 1.03(1.00-1.06) 0.10 IVW 21 162.97 1.02(1.00-1.05) 0.02 15.80 0.73 PRESSO 21 1.02(1.02–1.03) 0.02 BWMR 21 1.03(1.01–1.05) 0.01 GSMR 21 1.03 (1.01–1.05) 0.01 BRSK2 (UKB-PPP) MR Egger 23 1.01(0.98–1.05) 0.70 20.54 0.49 0.01 0.22 Weighted median 23 1.03(1.00-1.06) 0.04 IVW 23 162.97 1.03(1.01–1.05) 7.45×10 − 4 22.15 0.45 PRESSO 23 1.03(1.03–1.04) 2.75×10 − 3 BWMR 23 1.03 (1.01–1.05) 0.005 GSMR 23 1.03 (1.01–1.05) 2.00×10 − 3 BRSK2 (deCODE Iceland) MR Egger 21 1.02(0.97–1.08) 0.36 22.24 0.27 8.72×10 − 4 0.89 Weighted median 21 1.03(1.00-1.06) 0.04 IVW 21 162.97 1.03(1.01–1.05) 0.02 22.27 0.33 PRESSO 21 1.03(1.02–1.03) 0.03 BWMR 21 1.03(1.01–1.05) 0.01 GSMR 21 1.03 (1.01–1.05) 0.01 TNFSF12 (UKB-PPP) MR Egger 21 1.02(0.98–1.07) 0.35 26.97 0.17 6.57×10 − 4 0.91 Weighted median 21 1.01(0.99–1.04) 0.28 IVW 21 162.97 1.03(1.005–1.05) 0.02 26.99 0.21 PRESSO 21 1.03(1.02–1.03) 0.03 BWMR 21 1.03(1.00-1.05) 0.03 GSMR 21 1.02 (1.004–1.04) 0.02 TNFSF12 (deCODE Iceland) MR Egger 21 1.00(0.95–1.05) 0.97 15.93 0.66 0.01 0.28 Weighted median 21 1.02(0.99–1.05) 0.20 IVW 21 162.97 1.02(1.00-1.05) 0.03 17.16 0.64 PRESSO 21 1.02(1.02–1.03) 0.03 BWMR 21 1.03 (1.004–1.05) 0.02 GSMR 21 1.03 (1.004–1.05) 0.02 BCL2 (UKB-PPP) MR Egger 23 1.04(0.99–1.09) 0.10 13.38 0.89 -4.43×10 − 3 0.42 Weighted median 23 1.02(1.00-1.05) 0.10 IVW 23 162.97 1.02(1.00-1.04) 0.03 14.08 0.90 PRESSO 23 1.02(1.02–1.03) 0.01 BWMR 23 1.02 (1.002–1.04) 0.03 GSMR 23 1.02 (1.001–1.04) 0.04 BCL2 (deCODE Iceland) MR Egger 21 1.02(0.97–1.07) 0.41 12.11 0.88 5.19×10 − 4 0.93 Weighted median 21 1.02(0.99–1.06) 0.11 IVW 21 162.97 1.02(1.00-1.04) 0.04 12.12 0.91 PRESSO 21 1.02(1.02–1.03) 0.02 BWMR 21 1.02 (1.003–1.05) 0.02 GSMR 21 1.02 (1.003–1.05) 1.05 Sensitivity analyses incorporating both weighted median and MR-Egger regression approaches demonstrated consistent directional estimates, confirming the robustness of our findings (Table 1 ). The lack of significant heterogeneity (P > 0.05) and absence of horizontal pleiotropy (MR-Egger intercept P > 0.05) further strengthened the validity of the causal inference (Table 1 ). Instrument strength was supported by F-statistics (Tables S9), while leave-one-out analysis revealed no influential individual SNPs (FigS3). Symmetrical funnel plots suggested minimal estimation bias (FigS4). These findings suggest that NLRC5 may orchestrate cell survival and death pathways through distinct downstream mediators, with shared regulatory mechanisms across populations. The intersectional targets provide prioritized candidates for further mechanistic validation. Mendelian Randomization Analysis of NLRC5 Methylation Sites in Multiple Sclerosis To investigate the causal relationship between NLRC5 methylation and MS susceptibility, a two-sample MR analysis was performed using mQTLs of NLRC5 as instrumental variables (exposure) and GWAS summary statistics for MS from the IMSGC database (outcome). The MR analysis revealed a statistically significant association between genetically predicted NLRC5 methylation levels at cg04097610 and reduced risk of MS (OR = 0.885, 95% CI: 0.79–0.99, p = 0.039) (Table S10 ). Sensitivity analyses, including weighted median and MR-Egger regression, yielded consistent directional estimates, supporting the robustness of the findings. No significant heterogeneity (P h > 0.05) or horizontal pleiotropy (MR-Egger intercept p > 0.05) was detected, reinforcing the validity of the causal inference. The result suggests that altered DNA methylation patterns at NLRC5 loci may play a protective role in MS pathogenesis. Western Blot and Immunofluorescence Analysis of NLRC5-PANoptosome Complex Components in LPS-Induced Microglial Neuroinflammatory Models Western blot analysis revealed that LPS stimulation in BV2 microglial cells significantly upregulated key components of the NLRC5-PANoptosome complex, including NLRC5, ZBP1, ASC, and caspase-8 (Fig. 4 a,b). Specifically, ASC and NLRC5 exhibited statistically significant increases with P < 0.05, while caspase-8 and ZBP1 showed highly significant upregulation (P < 0.001). In contrast, RIPK3 expression displayed a non-significant trend toward elevation (ns). Raw Western blot images (FigS5) and densitometry data (Table S12 ) are available in the Supplementary Materials. Immunofluorescence triple-labeling with four-color imaging, combined with confocal microscopy, further confirmed the cytoplasmic localization of NLRC5, ZBP1, and ASC in BV2 microglia. Quantitative analysis demonstrated that the fluorescence intensity of these proteins was markedly higher in the LPS-treated group compared to the untreated control (Fig. 4 c), consistent with the Western blot findings. These results collectively suggest that LPS induces the assembly of the NLRC5-PANoptosome complex in microglia, characterized by coordinated transcriptional and spatial regulation of its core components. Discussion Integrative analysis of EAE microglial RNA-seq and GEO datasets revealed a potential activation of PANoptosis, marked by the co-upregulation of core regulators (CASP1, CASP8, NOD2, MLKL, and NLRC5) across datasets, suggesting coordinated engagement of apoptosis, pyroptosis, and necroptosis in neuroinflammatory microglial dysfunction. This multimodal cell death signature aligns with the MR-driven identification of NLRC5 as a pleiotropic modulator of cell cycle and PCD pathways. MR analyses across UKB-PPP and deCODE Iceland pQTLs demonstrated that NLRC5 causally upregulates apoptosis-associated proteins (GABARAP, BRSK2, TNFSF12) and necroptosis-linked BCL2, albeit with modest effect sizes (OR = 1.02–1.04, FDR < 0.05). These findings are robustly supported by BWMR and GSMR. The convergence of NLRC5-associated pathways, including mitotic fidelity, cellular senescence, and microtubule dynamics, with PANoptosis-related mediators underscores its potential role as a molecular bridge linking cell cycle regulation to inflammatory cell death. The MR analysis provides novel evidence supporting a causal role for NLRC5 methylation in modulating susceptibility to MS. Genetically predicted hypermethylation of NLRC5 at specific CpG sites (cg04097610) was associated with a reduced risk of MS. This inverse relationship aligns with prior functional insights into NLRC5’s role in inflammatory pathways and PCD [ 20 , 32 ] . NLRC5 methylation associate with levels of pro-inflammatory mediators (IL-12 and IL-18) and regulate the major histocompatibility complex MHC class I genes through interaction with various interleukins and NFκB [ 32 , 33 ] . Furthermore, methylation of NLRC5 has been linked to both rheumatoid arthritis [ 34 ] and lupus [ 35 ] . These findings complement our MR results implicating NLRC5 in PANoptosis regulation and highlight its dual role as both a transcriptional regulator and an epigenetically tunable node in neuroinflammation. Future studies should validate the methylation site cg04097610 mediating this protective effect and delineate whether NLRC5 methylation impacts MS progression by modulating its downstream targets (e.g., BCL2, GABARAP) or inflammasome activity in immune and glial cells. Emerging evidence highlights the multifaceted role of NLRC5 in neuroinflammatory and neurodegenerative disorders. NLRC5 deficiency protects against inflammation, tissue damage, and lethality in hemolytic disease, colitis, and hemophagocytic lymphohistiocytosis (HLH) models, highlighting its central role in pathogenic inflammation. [ 19 ] . In Parkinson’s disease (PD) models, NLRC5 expression increases in the nigrostriatal pathway of MPTP-treated mice and in toxin-exposed glial and neuronal cells. NLRC5 deficiency protects against neurodegeneration and motor deficits by inhibiting glial activation and inflammatory cytokine release (IL-1β, IL-6, TNF-α, COX2). Clinically, lower blood NLRC5 mRNA levels in PD patients may indicate its role as a glial activation marker [ 36 ] . In addition, NLRC5 exacerbates ischemic retinopathy by driving microglial pyroptosis and apoptosis through inflammasome interactions (NLRP3/NLRC4), leading to GSDMD cleavage, caspase-3 activation, and IL-1β release, thereby aggravating retinal ganglion cell death and ischemic damage [ 37 ] . In relapsing-remitting RRMS, dysregulated expression of the long noncoding RNA MEG3 and NLRC5 is observed, suggesting MEG3-mediated modulation of NLRC5 in neuroinflammatory pathways [ 38 ] . Collectively, NLRC5 exhibits context-dependent roles, promoting cell death (via pyroptosis/apoptosis) and neuroinflammation either through direct inflammasome engagement, underscoring its therapeutic relevance across diverse neurological pathologies. The NLRC5-PANoptosome is recently reported to be a multiprotein complex comprising NLRC5, NLRP12, ASC, RIPK3, caspase-8, and NLRP3 [ 19 ] . It is activated by PAMP/DAMP (e.g., heme + LPS/Pam3) or DAMP/cytokine combinations via TLR2/4 signaling and NAD⁺-mediated pathways, which drive ROS production and NLRC5 upregulation [ 19 ] . NLRC5, alongside NLRP12, regulates PANoptosis execution, while NLRP3-NLRP12 mediates caspase-1 activation, though ASC-NLRP3 interaction requires NLRC5 [ 39 ] . Previous studies have demonstrated NLRC5-dependent cell death in murine bone marrow-derived macrophages (BMDMs) [ 39 ] . However, the mechanistic impact of the NLRC5-PANoptosome complex on microglial activation and function in CNS inflammatory pathologies, including MS, remains poorly characterized. Our study position NLRC5 at the nexus of PANoptotic signaling in EAE, where its subtle but consistent regulatory effects on apoptotic, pyroptotic, and necroptotic mediators may cumulatively drive microglial dysfunction. Further validation of these targets in experimental models is warranted to dissect their contributions to NLRC5-dependent neuroinflammatory pathology. This study has several limitations. First, the reliance on murine EAE models and BV2 microglial cells may not fully replicate the complexity of human MS pathology, particularly in chronic stages or across heterogeneous patient subtypes. Second, the modest effect sizes observed in MR analyses (OR = 1.02–1.04) highlight the need for further experimental validation to confirm the biological significance of NLRC5’s regulatory roles. Finally, while cg04097610 methylation was associated with MS susceptibility, the direct mechanistic link between this epigenetic modification and NLRC5 transcription or PANoptosis activation remains undefined. Future research should prioritize several key directions. Mechanistic studies using NLRC5 knockout or overexpression models in EAE could elucidate its role in PANoptosome assembly and neuroinflammation. Validating NLRC5 methylation patterns, PANoptosis markers, and downstream targets in postmortem MS brain tissues would enhance translational relevance. Additionally, exploring interactions between NLRC5-PANoptosome components (e.g., ZBP1, ASC, RIPK3) and other cell death pathways, such as ferroptosis, may provide deeper insights into microglial dysfunction. Finally, testing pharmacological inhibitors targeting NLRC5 or epigenetic modulators of cg04097610 in preclinical MS models could pave the way for novel therapeutic strategies to mitigate microglial panoptosis. Conclusion This study elucidates a pivotal role for NLRC5 in orchestrating microglial panoptosis, a coordinated interplay of pyroptosis, apoptosis, and necroptosis, during MS pathogenesis. By integrating multi-omics analyses of EAE models and human GWAS datasets, we identified NLRC5 as a hub regulator that modulates neuroinflammatory responses through epigenetic (cg04097610 methylation) and proteomic mechanisms. MR revealed causal links between NLRC5 expression and downstream apoptotic/necroptotic effectors, including GABARAP, BRSK2, TNFSF12 and BCL2, while experimental validation confirmed LPS-induced assembly of the NLRC5-PANoptosome complex in microglia. These findings position NLRC5 as a critical mediator bridging epigenetic dysregulation, inflammatory cell death, and CNS autoimmunity, offering novel therapeutic avenues for MS. Abbreviations MS Multiple sclerosis EAE Experimental autoimmune encephalomyelitis MR Mendelian randomization mQTL Methylation quantitative trait locus IVW Inverse-variance weighted BWMR Bayesian Weighted Mendelian Randomization GSMR Generalized Summary-data-based Mendelian Randomization OR Odds Ratio ZBP1 Z-DNA binding protein 1 ASC Apoptosis-associated speck-like protein containing a CARD Caspase-8 Cysteine-aspartic acid protease 8 CNS Central nervous system MIMS microglia inflamed in MS PCD programmed cell death GSDMD Gasdermin D NLRC5 NOD-like receptor family CARD domain containing 5 LPS lipopolysaccharide GWAS Genome-wide association studies cis-eQTLs Cis-expression quantitative trait loci pQTLs Protein quantitative trait loci SNPs Single-nucleotide polymorphisms FDR False discovery rate UKB-PPP UK Biobank Pharma Proteomics Project IMSGC International Multiple Sclerosis Genetics Consortium mQTLs Methylation quantitative trait loci PPI Protein-protein interaction MF Molecular Functions BP Biological Processes CC Cellular Components FBS Fetal bovine serum P/S Penicillin-streptomycin TSA Tyramide Signal Amplification DEGs Differentially expressed genes GABARAP GABA Type A Receptor-Associated Protein BRSK2 BR Serine/Threonine Kinase 2 TNFSF12 TNF Superfamily Member 12 BCL2 B-cell Lymphoma 2 β Beta coefficients SE Standard errors Ns Non-significant HLH Hemophagocytic lymphohistiocytosis PD Parkinson’s disease BMDMs Bone marrow-derived macrophages Declarations Acknowledgements The authors would like to thank the contributors to the publicly available datasets used in this study, including the GEO datasets (GSE154228, GSE78809), UK Biobank, deCODE Iceland, eQTLGen Consortium, IMSGC, and MeQTL EPIC Database. These resources provided essential data for transcriptomic, proteomic, epigenetic, and genome-wide association studies (GWAS) analyses. Data availability The RNA-seq data of EAE microglia generated in this study have been deposited in the GEO database under the accession number GSE253318. Publicly available GEO datasets (GSE154228 and GSE78809) were used for cross-dataset validation. Genome-wide summary-level statistics for SNPs from GWAS datasets, including those from the UK Biobank, deCODE Iceland, eQTLGen Consortium, and IMSGC, were utilized in the Mendelian randomization and methylation analyses. The pQTLs data were obtained from the UK Biobank Pharma Proteomics Project (UKB-PPP v1.0) and deCODE Iceland. Methylation quantitative trait locus (mQTL) data for NLRC5 were sourced from the MeQTL EPIC Database. Author contributions WY designed this study. WY and WJH integrated and analyzed the data. WY and WJH wrote the manuscript. WY, GP and WJH edited and revised the manuscript. All authors contributed to the article and approved the submitted version. Competing Interests statement No disclosures to report. The authors assert that the study was carried out without any commercial or financial affiliations that could be interpreted as a possible conflict of interest. Ethics approval and consent to participate This study did not require additional ethical approval or informed consent, as it solely utilized publicly available summary data. The original GEO datasets and GWAS, from which these data were derived, had obtained the necessary ethical approval and participant consent. Funding Wu Yan is supported by the Yunnan Clinical Medical Center for Neurocardiac Diseases and Yunnan Fundamental Research Projects (grant nos. 202201AT070291) and Priority Union Foundation of Yunnan Provincial Science and Technology Department and Kunming Medical University (grant nos. 202301AY070001-197). Consent for publication No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. References Absinta M, Maric D, Gharagozloo M, Garton T, Smith MD, Jin J et al (2021) A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis. Nature 597(7878):709–714. 10.1038/s41586-021-03892-7 Guerrero BL, Sicotte NL (2020) Microglia in Multiple Sclerosis: Friend or Foe? Front Immunol 11:374. 10.3389/fimmu.2020.00374 Voet S, Prinz M, van Loo G (2019) Microglia in Central Nervous System Inflammation and Multiple Sclerosis Pathology. Trends Mol Med 25(2):112–123. 10.1016/j.molmed.2018.11.005 Yong VW (2022) Microglia in multiple sclerosis: Protectors turn destroyers. Neuron 110(21):3534–3548. 10.1016/j.neuron.2022.06.023 Cai L, Fan Q, Pang R, Chen C, Zhang Y, Xie H et al (2025) Microglia programmed cell death in neurodegenerative diseases and CNS injury. Apoptosis 30(1–2):446–465. 10.1007/s10495-024-02041-5 Darwish SF, Elbadry AMM, Elbokhomy AS, Salama GA, Salama RM (2023) The dual face of microglia (M1/M2) as a potential target in the protective effect of nutraceuticals against neurodegenerative diseases. Front Aging 4:1231706. 10.3389/fragi.2023.1231706 West PK, McCorkindale AN, Guennewig B, Ashhurst TM, Viengkhou B, Hayashida E et al (2022) The cytokines interleukin-6 and interferon-α induce distinct microglia phenotypes. J Neuroinflammation 19(1):96. 10.1186/s12974-022-02441-x Zhu H, Pan H, Fang Y, Wang H, Chen Z, Hu W et al (2024) Apoptosis-induced decline in hippocampal microglia mediates the development of depression-like behaviors in adult mice triggered by unpredictable stress during adolescence. Eur J Pharmacol 978:176763. 10.1016/j.ejphar.2024.176763 Hu P, Wang D, Zhang Y, Cai Z, Ye T, Tong L et al (2020) Apoptosis-triggered decline in hippocampal microglia mediates adolescent intermittent alcohol exposure-induced depression-like behaviors in mice. Neuropharmacology 170:108054. 10.1016/j.neuropharm.2020.108054 McKenzie BA, Mamik MK, Saito LB, Boghozian R, Monaco MC, Major EO et al (2018) Caspase-1 inhibition prevents glial inflammasome activation and pyroptosis in models of multiple sclerosis. Proc Natl Acad Sci U S A 115(26):E6065–E6074. 10.1073/pnas.1722041115 McKenzie BA, Fernandes JP, Doan MAL, Schmitt LM, Branton WG, Power C (2020) Activation of the executioner caspases-3 and – 7 promotes microglial pyroptosis in models of multiple sclerosis. J Neuroinflammation 17(1):253. 10.1186/s12974-020-01902-5 Lloyd AF, Davies CL, Holloway RK, Labrak Y, Ireland G, Carradori D et al (2019) Central nervous system regeneration is driven by microglia necroptosis and repopulation. Nat Neurosci 22(7):1046–1052. 10.1038/s41593-019-0418-z Zelic M, Pontarelli F, Woodworth L, Zhu C, Mahan A, Ren Y et al (2021) RIPK1 activation mediates neuroinflammation and disease progression in multiple sclerosis. Cell Rep 35(6):109112 Malireddi RKS, Kesavardhana S, Kanneganti TD (2019) ZBP1 and TAK1: Master Regulators of NLRP3 Inflammasome/Pyroptosis, Apoptosis, and Necroptosis (PAN-optosis). Front Cell Infect Microbiol 9:406. 10.3389/fcimb.2019.00406 Sun X, Yang Y, Meng X, Li J, Liu X, Liu H (2024) PANoptosis: Mechanisms, biology, and role in disease. Immunol Rev 321(1):246–262. 10.1111/imr.13279 Zhu P, Ke ZR, Chen JX, Li SJ, Ma TL, Fan XL (2023) Advances in mechanism and regulation of PANoptosis: Prospects in disease treatment. Front Immunol 14:1120034. 10.3389/fimmu.2023.1120034 Qi Z, Zhu L, Wang K, Wang N, PANoptosis (2023) Emerging mechanisms and disease implications. Life Sci 333:122158. 10.1016/j.lfs.2023.122158 Xiang Q, Geng ZX, Yi X, Wei X, Zhu XH, Jiang DS (2024) PANoptosis: a novel target for cardiovascular diseases. Trends Pharmacol Sci 45(8):739–756. 10.1016/j.tips.2024.06.002 Sundaram B, Pandian N, Kim HJ, Abdelaal HM, Mall R, Indari O et al (2024) NLRC5 senses NAD + depletion, forming a PANoptosome and driving PANoptosis and inflammation. Cell 187(15):4061–4077e17. 10.1016/j.cell.2024.05.034 Zhang L, Jiao C, Liu L, Wang A, Tang L, Ren Y et al (2021) NLRC5: A Potential Target for Central Nervous System Disorders. Front Immunol 12:704989. 10.3389/fimmu.2021.704989 Sun C, Shen Y, Liu P, Shen Y, Hu Y, Li P et al (2023) NLRC5 Deficiency Reduces LPS-Induced Microglial Activation via Inhibition of NF-κB Signaling and Ameliorates Mice's Depressive-like Behavior. Int J Mol Sci 24(17):13265. 10.3390/ijms241713265 Zelic M, Pontarelli F, Woodworth L, Zhu C, Mahan A, Ren Y et al (2021) RIPK1 activation mediates neuroinflammation and disease progression in multiple sclerosis. Cell Rep 35(6):109112. 10.1016/j.celrep.2021.109112 PMID: 33979622; PMCID: PMC8917516 Wlodarczyk A, Holtman IR, Krueger M, Yogev N, Bruttger J, Khorooshi R et al (2017) A novel microglial subset plays a key role in myelinogenesis in developing brain. EMBO J 36(22):3292–3308. 10.15252/embj.201696056 Epub 2017 Sep 28. PMID: 28963396; PMCID: PMC5686552 Zhao J, Ming J, Hu X, Chen G, Liu J, Yang C (2020) Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics 36(5):1501–1508. 10.1093/bioinformatics/btz749 Zhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R et al (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun 9(1):224. 10.1038/s41467-017-02317-2 Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B et al (2021) Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 53(9):1300–1310. 10.1038/s41588-021-00913-z Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG et al (2023) Plasma proteomic associations with genetics and health in the UK Biobank. Nature 622(7982):329–338. 10.1038/s41586-023-06592-6 Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL et al (2021) Large-scale integration of the plasma proteome with genetics and disease. Nat Genet 53(12):1712–1721. 10.1038/s41588-021-00978-w Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE et al (2016) Nat Genet 48(5):481–487. 10.1038/ng.3538 Epub 2016 Mar 28. PMID: 27019110 Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets Villicaña S, Castillo-Fernandez J, Hannon E, Christiansen C, Tsai PC, Maddock J et al (2023) Genetic impacts on DNA methylation help elucidate regulatory genomic processes. Genome Biol 24(1):176. 10.1186/s13059-023-03011-x Qin H, Abulaiti A, Maimaiti A, Abulaiti Z, Fan G, Aili Y et al (2023) Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma. J Transl Med 21(1):588. 10.1186/s12967-023-04468-x Zaghlool SB, Kühnel B, Elhadad MA, Kader S, Halama A, Thareja G et al (2020) Epigenetics Meets Proteomics in an Epigenome-Wide Association Study With Circulating Blood Plasma Protein Traits. Nat Commun 11(1):15. 10.1038/s41467-019-13831-w Sun X, Watanabe T, Oda Y, Shen W, Ahmad A, Ouda R et al (2024) Targeted demethylation and activation of NLRC5 augment cancer immunogenicity through MHC class I. Proc Natl Acad Sci U S A 121(6):e2310821121. 10.1073/pnas.2310821121 Zou J, Lippert C, Heckerman D, Aryee M, Listgarten J (2014) Epigenome-Wide Association Studies Without the Need for Cell-Type Composition. Nat Methods 11(3):309–311. 10.1038/nmeth.2815 Yeung KS, Chung BH, Choufani S, Mok MY, Wong WL, Mak CC et al (2017) Genome-Wide DNA Methylation Analysis of Chinese Patients With Systemic Lupus Erythematosus Identified Hypomethylation in Genes Related to the Type I Interferon Pathway. PLoS ONE 12(1):e0169553 Liu Z, Shen C, Li H, Tong J, Wu Y, Ma Y et al (2023) NOD-like receptor NLRC5 promotes neuroinflammation and inhibits neuronal survival in Parkinson's disease models. J Neuroinflammation 20(1):96. 10.1186/s12974-023-02755-4 Deng Y, Fu Y, Sheng L, Hu Y, Su L, Luo J et al (2021) The Regulatory NOD-Like Receptor NLRC5 Promotes Ganglion Cell Death in Ischemic Retinopathy by Inducing Microglial Pyroptosis. Front Cell Dev Biol 9:669696. 10.3389/fcell.2021.669696 Torkamandi S, Bahrami S, Ghorashi T, Dehani M, Bayat H, Hoseini SM et al (2021) Dysregulation of long noncoding RNA MEG3 and NLRC5 expressions in patients with relapsing-remitting multiple sclerosis: is there any correlation? Genes Immun 22(7–8):322–326. 10.1038/s41435-021-00154-4 Guerra J, Guarda G (2024) NLRC5: back to innate immunity. Trends Immunol 45(8):571–573. 10.1016/j.it.2024.07.002 Additional Declarations No competing interests reported. Supplementary Files FigS1.docx FigS2.docx FigS3.docx FigS4.docx FigS5.docx TableS1.ListofGEOdatasetsandGWASdatausedinthisstudy.xlsx TableS2.ListofSNPsforNLRC5eQTLs.xlsx TableS3.listof2918proteinsfromUKBPPPeQTLs.xlsx TableS4.listof4907proteinsfromdeCODEIcelandpQTL.xlsx TableS5.listofDNAmethylationlocusofNLRC5.xlsx TableS6.NLRC5AssociatedDownstreamEffectorsinthedeCODEIcelandCohort.xlsx TableS7.NLRC5LinkedDownstreamSignalingMoleculesintheUKBPPPCohort.xlsx TableS8.OverlappingNLRC5downstreamproteinsbetweenUKBPPPanddeCODEIcelandpQTLdatabases.xlsx TableS9.NLRC5eQTLSNPsassociatedwithdownstreampQTLs.xlsx TableS10.DNAmethylationlocusofNLRC5correlatedtoMS.xlsx TableS11.Genelistsassociatedwithapoptosisnecroptosisandpyroptosis.xlsx TableS12.DensitometricdataofWesternblotbandsnormalizedtoloadingcontrolsactin.xlsx Supplementaryfigure.docx Cite Share Download PDF Status: Published Journal Publication published 28 Nov, 2025 Read the published version in Molecular Neurobiology → Version 1 posted Editorial decision: Revision requested 24 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviews received at journal 24 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 23 Jun, 2025 Editor assigned by journal 22 Jun, 2025 Submission checks completed at journal 22 Jun, 2025 First submitted to journal 27 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6759227","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475872373,"identity":"65166e7b-a02c-40c9-bd2c-2650bfc59977","order_by":0,"name":"Yan Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYLACxn81cgwMCSTpYTtmTLIW5sQGorUY3Mgx/FzAw5a+4XiOmQRDzR2itBhLz5CQyd1w5g1Qy7FnhLWY3cgxkOYxYMvdcANoC2PDYaK0GP/mSWBONyBFi5k0zwHmBOK12J95VmbN23DMcOaZZ8UWCceI0CLZnrz5Nm9DjTzf8eSNNz7UEKGFgYHDAEwpHOAwkUggRgMDA/sDMCXfwP74A3E6RsEoGAWjYKQBAN0wPGIWbChuAAAAAElFTkSuQmCC","orcid":"","institution":"Neurology Department of First Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wu","suffix":""},{"id":475872374,"identity":"0d69b559-799d-4e5c-a98e-b92ea0c10c4a","order_by":1,"name":"Jianhong Wang","email":"","orcid":"","institution":"Neurology Department of First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianhong","middleName":"","lastName":"Wang","suffix":""},{"id":475872375,"identity":"088fda75-5f5d-40e3-86a8-cde67e00351b","order_by":2,"name":"Ping Gan","email":"","orcid":"","institution":"Biochemistry and Molecular Department, College of Basic Medicine, Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Gan","suffix":""}],"badges":[],"createdAt":"2025-05-27 12:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6759227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6759227/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12035-025-05365-8","type":"published","date":"2025-11-28T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85514505,"identity":"845c496b-09e2-488b-ad59-ebadd2f967c7","added_by":"auto","created_at":"2025-06-26 17:31:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":374994,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOutlines the study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEAE: experimental autoimmune encephalomyelitis; PPI: protein-protein interaction; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; IMSGC: International Multiple Sclerosis Genetics Consortium; LPS: lipopolysaccharide\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/67a93a863d5e39f09e6e387b.jpg"},{"id":85513629,"identity":"56ff9370-30e4-4a2d-ba05-6daf44cfa6ba","added_by":"auto","created_at":"2025-06-26 17:15:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":661784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrative analysis of microglial transcriptomic signatures in EAE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a)Venn diagram illustrating overlapping differentially expressed genes (DEGs) across three independent EAE microglial RNA-seq datasets. (b) UpSet plot depicting intersections of DEGs associated with apoptosis, pyroptosis, and necroptosis pathways in EAE microglia. (c) Heatmap of DEGs from in-house RNA-seq data, highlighting genes linked to Panoptosis processes (e.g., apoptosis, pyroptosis, necroptosis); red and blue denote significant upregulation and downregulation, respectively (P \u0026lt; 0.05). (d, e) Volcano plots validating DEG reproducibility in the GSE154228 and GSE78809 microglial transcriptomic datasets (|log2 fold change| \u0026gt; 1, FDR \u0026lt; 0.05). (f) Protein-protein interaction (PPI) network of NLRC5 generated from the STRING database, with confidence scores \u0026gt; 0.7.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/7aeb4017cc4bf74e85a8badb.jpg"},{"id":85514506,"identity":"a5a4949b-9ae5-4465-b06d-a88b90456313","added_by":"auto","created_at":"2025-06-26 17:31:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":465169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional and Genetic Insights into NLRC5-Associated Pathways and Downstream Effectors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) GO/KEGG pathway enrichment analysis of NLRC5-associated downstream molecules from the UKB-PPP and deCODE Iceland pQTL databases highlights significant enrichment in cell cycle regulation (e.g., mitotic nuclear division, sister chromatid segregation), cellular senescence, and microtubule-related functions (microtubule binding, spindle/centromere localization).\u003c/p\u003e\n\u003cp\u003e(b) Venn diagram illustrating 78 overlapping NLRC5-associated downstream molecules identified through cross-database integration of UKB-PPP and deCODE Iceland pQTL datasets. Among these, GABARAP, BRSK2, and TNFSF12 overlapped with apoptosis-related gene sets, while BCL2 intersected with necroptosis-related gene sets, highlighting their functional divergence in distinct cell death pathways.\u003c/p\u003e\n\u003cp\u003e(c) Forest plot summarizing MR results for genetically predicted NLRC5 expression on key downstream effectors. NLRC5 significantly upregulated GABARAP (UKB-PPP: iVW OR=1.04, FDR=2.7×10\u003csup\u003e-\u003c/sup\u003e⁴; deCODE Iceland: OR=1.02, FDR=0.02), BRSK2 (UKB-PPP: OR=1.03, FDR=7.45×10\u003csup\u003e-4\u003c/sup\u003e; deCODE Iceland: OR=1.03, FDR=0.02), TNFSF12 (UKB-PPP: OR=1.02, FDR=0.03; deCODE Iceland: OR=1.02, FDR=0.03), and BCL2 (UKB-PPP: OR=1.02, FDR=0.03; deCODE Iceland: OR=1.02, FDR=0.04). Error bars represent 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eOR: Odds Ratio; FDR: False Discovery Rate-adjusted p-value; eQTL/pQTL: Expression/Protein Quantitative Trait Loci; MF/BP/CC: Molecular Functions/Biological Processes/Cellular Components (Gene Ontology categories); GABARAP: GABA Type A Receptor-Associated Protein; BRSK2: BR Serine/Threonine Kinase 2; TNFSF12: TNF Superfamily Member 12; BCL2: B-cell Lymphoma 2.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/57510505d92af45cde74d96e.jpg"},{"id":85513635,"identity":"13ca5c81-3422-4f0c-89d8-edda3908ea43","added_by":"auto","created_at":"2025-06-26 17:15:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":325869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLPS induces NLRC5-PANoptosome complex formation in BV2 microglial cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Western blot analysis of NLRC5, ZBP1, ASC, caspase-8, and RIPK3 protein expression in BV2 microglial cells treated with or without LPS (1 μg/mL, 24 hours). β-actin served as the loading control. Representative blots from three independent experiments are shown. LPS treatment upregulated the expression of NLRC5, ZBP1, ASC, and caspase-8, with distinct statistical significance across targets.\u003c/p\u003e\n\u003cp\u003e(b) Quantitative densitometric analysis of Western blot bands normalized to β-actin. Data are presented as mean ± SD (n = 3). Statistical significance was determined by unpaired Student’s t-test: *p \u0026lt; 0.05, ***p \u0026lt; 0.001; ns, not significant. LPS significantly increased the expression of NLRC5 (*p \u0026lt; 0.05), ZBP1 (***p \u0026lt; 0.001), ASC (*p \u0026lt; 0.05), and caspase-8 (***p \u0026lt; 0.001), while RIPK3 showed no significant change.\u003c/p\u003e\n\u003cp\u003e(c) Immunofluorescence (IF) staining of NLRC5 (green, TYR-520Plus), ASC (red, TYR-570Plus), and ZBP1 (cyan, TYR-620Plus) in BV2 microglia. Nuclei were counterstained with DAPI (blue). Fluorescence signals were captured using a confocal microscope under UV excitation (NLRC5: 488 nm, ASC: 561 nm, ZBP1: 640 nm; DAPI: 405 nm). LPS-treated cells exhibited markedly enhanced cytoplasmic fluorescence intensity for NLRC5, ZBP1, and ASC compared to the control group, consistent with Western blot results. Scale bar: 20 μm.\u003c/p\u003e\n\u003cp\u003eLPS: lipopolysaccharide; DAPI: 4′,6-diamidino-2-phenylindole\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/0ef0a5ff5cde0147517d371f.jpg"},{"id":97178868,"identity":"711e2695-b0b1-4b67-828e-3b80296653dd","added_by":"auto","created_at":"2025-12-01 16:13:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3258488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/0d0484be-ceed-47b4-98cf-acfe0484da39.pdf"},{"id":85513623,"identity":"7078a76e-79db-46bb-9457-05a759eec928","added_by":"auto","created_at":"2025-06-26 17:15:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":401474,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/bd18281f4e138e5363d347ea.docx"},{"id":85513624,"identity":"e842963a-00bb-4d69-b72d-1467b91f5de7","added_by":"auto","created_at":"2025-06-26 17:15:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":420381,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/66480ab063af569d693e4be3.docx"},{"id":85514197,"identity":"95b6e057-adbb-4b1d-bd5e-d8180e5e9143","added_by":"auto","created_at":"2025-06-26 17:23:35","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":447220,"visible":true,"origin":"","legend":"","description":"","filename":"FigS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/4e1c8c5f12fb89ee33c0794b.docx"},{"id":85514953,"identity":"2236e95f-ee6e-467d-a1b8-58279a439ce1","added_by":"auto","created_at":"2025-06-26 17:39:35","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":191030,"visible":true,"origin":"","legend":"","description":"","filename":"FigS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/f1f9a5fa1cdb160bc38dc6f2.docx"},{"id":85513636,"identity":"36f5d7ad-60a3-404f-8bdb-f680c9e5c533","added_by":"auto","created_at":"2025-06-26 17:15:35","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1026868,"visible":true,"origin":"","legend":"","description":"","filename":"FigS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/32a917668d0e68e296724a45.docx"},{"id":85513642,"identity":"d87e7572-8374-4fdf-ad02-ad4ac7aa375f","added_by":"auto","created_at":"2025-06-26 17:15:35","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":12017,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.ListofGEOdatasetsandGWASdatausedinthisstudy.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/a84671a6272b11e091efa4df.xlsx"},{"id":85513658,"identity":"250f7bc0-0bc2-4dd7-912a-85c2740cbcfa","added_by":"auto","created_at":"2025-06-26 17:15:36","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":12494,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.ListofSNPsforNLRC5eQTLs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/be9b10985247a07f17555830.xlsx"},{"id":85514202,"identity":"673ce434-83d1-4719-9d0b-e6e8dede4f61","added_by":"auto","created_at":"2025-06-26 17:23:35","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":74097,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.listof2918proteinsfromUKBPPPeQTLs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/dc93dd4c935a68b9cab53031.xlsx"},{"id":85514203,"identity":"dc6e9048-eae5-4958-a949-cb118a9f7161","added_by":"auto","created_at":"2025-06-26 17:23:35","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":69581,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.listof4907proteinsfromdeCODEIcelandpQTL.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/c56e763f58b269cfb5e1f4ab.xlsx"},{"id":85514507,"identity":"2d80dcfe-587e-4a5c-8856-034ecad2a6a1","added_by":"auto","created_at":"2025-06-26 17:31:35","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":9718,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.listofDNAmethylationlocusofNLRC5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/96c65d36641e7e0cbbccdd8e.xlsx"},{"id":85513662,"identity":"018d949d-3d7f-46bc-a369-ad563b188635","added_by":"auto","created_at":"2025-06-26 17:15:36","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":214213,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.NLRC5AssociatedDownstreamEffectorsinthedeCODEIcelandCohort.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/62dcc3acc2c1593c056e305f.xlsx"},{"id":85514512,"identity":"2aa61adb-3a0c-44bf-9796-0af90db546b9","added_by":"auto","created_at":"2025-06-26 17:31:37","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":50297,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.NLRC5LinkedDownstreamSignalingMoleculesintheUKBPPPCohort.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/52cc4f6b29fa76aa8b720eb2.xlsx"},{"id":85513674,"identity":"efeff702-ae06-415c-af25-653faf11253e","added_by":"auto","created_at":"2025-06-26 17:15:37","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":12086,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.OverlappingNLRC5downstreamproteinsbetweenUKBPPPanddeCODEIcelandpQTLdatabases.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/6a3fb4abb87bee8026148c85.xlsx"},{"id":85513649,"identity":"2366aeba-bce9-4114-a7ca-b81c0f9313fb","added_by":"auto","created_at":"2025-06-26 17:15:36","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":62843,"visible":true,"origin":"","legend":"","description":"","filename":"TableS9.NLRC5eQTLSNPsassociatedwithdownstreampQTLs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/5bc8126496f56cff6dca9952.xlsx"},{"id":85513665,"identity":"32d49fab-4774-4656-b539-af459b81178f","added_by":"auto","created_at":"2025-06-26 17:15:36","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":12206,"visible":true,"origin":"","legend":"","description":"","filename":"TableS10.DNAmethylationlocusofNLRC5correlatedtoMS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/188b337d160e0f2c898e2675.xlsx"},{"id":85513675,"identity":"e2d699e8-e884-4544-a525-6d0a23ec8bc6","added_by":"auto","created_at":"2025-06-26 17:15:37","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":22259,"visible":true,"origin":"","legend":"","description":"","filename":"TableS11.Genelistsassociatedwithapoptosisnecroptosisandpyroptosis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/16649c7b0001a08c6c8b7430.xlsx"},{"id":85513682,"identity":"ecd946a9-8187-4be4-b7da-e60f57c23d74","added_by":"auto","created_at":"2025-06-26 17:15:37","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":13321,"visible":true,"origin":"","legend":"","description":"","filename":"TableS12.DensitometricdataofWesternblotbandsnormalizedtoloadingcontrolsactin.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/fb237219668ba461429f9db9.xlsx"},{"id":85513672,"identity":"bff4df9e-7e3f-4003-bd47-c049da5cea97","added_by":"auto","created_at":"2025-06-26 17:15:37","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":2438119,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-6759227/v1/b803da391741415b8f6376be.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"NLRC5-Mediated Epigenetic and Proteomic Regulation of Microglial Panoptosis Drives Neuroinflammation in Multiple Sclerosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system (CNS), characterized by inflammatory demyelination, axonal damage, and progressive neurodegeneration \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Microglia, the resident immune cells of the CNS, play a dual role in MS pathogenesis. In early disease stages, they contribute to tissue repair by clearing myelin debris and releasing neurotrophic factors \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. However, chronic activation shifts microglia toward a pro-inflammatory phenotype, marked by elevated cytokine production (e.g., TNF-\u0026alpha;, IL-1\u0026beta;) and antigen presentation, which exacerbates neuronal injury \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Single-cell transcriptomic studies of chronic active MS lesions have identified disease-associated microglial subsets, notably the \u0026quot;microglia inflamed in MS\u0026quot; (MIMS) population. These cells exhibit upregulated expression of complement components (e.g., C1q) and iron metabolism-related genes (e.g., FTL, FTH1), underscoring their role in perpetuating neuroinflammation and oxidative stress \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Despite these insights, the molecular mechanisms underlying microglial pathogenic activation remain incompletely understood, particularly the interaction between regulated cell death pathways and epigenetic regulation \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMultiple forms of programmed cell death (PCD), including apoptosis, pyroptosis, necroptosis, and ferroptosis, have been implicated in MS and its animal models. Apoptosis, marked by caspase-3/7 activation, regulates microglial turnover during experimental autoimmune encephalomyelitis (EAE) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Pyroptosis, driven by NLRP3 inflammasome activation and gasdermin D (GSDMD) cleavage, amplifies neuroinflammation through IL-1\u0026beta; release \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Necroptosis, mediated by RIPK1 and MLKL signaling, exerts paradoxical effects on demyelination and remyelination processes \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Recently, PANoptosis has emerged as a unifying mechanism integrating pyroptosis, apoptosis, and necroptosis \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This process is mediated by the formation of multiprotein complexes called PANoptosomes, such as Z-DNA binding protein 1 (ZBP1)- and AIM2-PANoptosomes \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. While PANoptosis has been implicated in neurodegenerative and inflammatory diseases \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, its relevance to MS has yet to be investigated.\u003c/p\u003e\n\u003cp\u003eNOD-like receptor family CARD domain containing 5 (NLRC5) has recently gained attention as a regulator of immune responses and cell death. Traditionally known for controlling MHC class I gene expression and inflammasome activation \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, NLRC5 has now been identified as a key component of PANoptosome complexes, where it cooperates with caspase-1, RIPK3, and Apoptosis-associated speck-like protein containing a CARD (ASC) to promote inflammatory cell death \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In EAE models, NLRC5 expression correlates with transactivator of MHC I of oligodendrocyte \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, while genetic deletion of NLRC5 attenuates neuroinflammatory responses in lipopolysaccharide (LPS)-induced microglial activation and LPS-induced depressive mouse models \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. These findings highlight NLRC5 as a potential central regulator linking microglial dysfunction to PANoptotic cell death in MS. However, its upstream regulation, particularly epigenetic mechanisms such as DNA methylation, and downstream signaling cascades in CNS autoimmunity remain largely undefined.\u003c/p\u003e\n\u003cp\u003eGiven the critical involvement of microglial activation in MS pathogenesis and emerging insights into PANoptosis-mediated neuroinflammation, this study explores the hypothesis that epigenetically regulated NLRC5 drives microglial PANoptosis to exacerbate MS progression. To address this, we integrated EAE microglia RNA sequencing data, genome-wide association studies (GWAS), cis-expression quantitative trait loci (cis-eQTLs), protein quantitative trait loci (pQTLs), methylation quantitative trait locus (mQTL) datasets, and mendelian randomization (MR) analyses. By pioneering the investigation of PANoptosis in MS and positioning NLRC5 as a multimodal orchestrator of microglial death, this work provides novel mechanistic insights and highlights NLRC5\u0026apos;s potential as a therapeutic target for epigenetic interventions in neuroinflammatory disorders.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eThis multimodal investigation integrated self-generated RNA-seq analysis of EAE microglia (GSE253318) with cross-dataset validation (GSE154228, GSE78809) (detailed in Table S1) to identify PANoptosis-associated hub genes, followed by MR (exposure: NLRC5 eQTLs; outcomes: pQTLs) to map causal effectors, and methylation profiling to pinpoint regulatory loci. The study design was outlined in Fig.\u0026nbsp;1.\u003c/p\u003e\n \u003cp\u003eEAE: experimental autoimmune encephalomyelitis; PPI: protein-protein interaction; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; IMSGC: International Multiple Sclerosis Genetics Consortium; LPS: lipopolysaccharide\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eComparative Analysis of Microglial Transcriptomes in EAE Models\u003c/h3\u003e\n\u003cp\u003eTo investigate microglial responses in MS, we first established the EAE mouse model and performed RNA sequencing on isolated brain microglia. Female C57BL/6 mice (8 weeks old) were randomly divided into healthy control and EAE groups (n\u0026thinsp;=\u0026thinsp;8/group). EAE was induced by immunization with MOG35-55 peptide in complete Freund\u0026apos;s adjuvant, with PBS-injected controls. At peak disease (day 14 post-induction), brain tissues were collected, and CD11b+/CD45int microglia were isolated via gradient centrifugation and flow cytometry (purity\u0026thinsp;\u0026gt;\u0026thinsp;99%). For transcriptomic analysis, RNA was extracted from microglia (n\u0026thinsp;=\u0026thinsp;16 samples), ribosomal RNA-depleted (TruSeq Stranded kit), and sequenced on Illumina NovaSeq 6000 (70\u0026ndash;80M reads/sample). Reads were processed using HISAT2 and DESeq2, with data deposited in GEO (GSE253318). All procedures followed ARRIVE guidelines and were approved by Kunming Medical University Ethics Committee.\u003c/p\u003e\n\u003cp\u003eTo strengthen our experimental findings, we analyzed two complementary GEO datasets examining microglial transcriptomes in EAE models. The GSE154228 dataset comprises six EAE and four control samples of spinal cord-derived microglia \u003csup\u003e[22]\u003c/sup\u003e, and the GSE78809 dataset contains three EAE and three control samples of brain-derived microglia \u003csup\u003e[23]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eMendelian Randomization\u003c/h3\u003e\n\u003cp\u003eWe utilized genome-wide summary-level statistics for single-nucleotide polymorphisms (SNPs) from publicly accessible GWAS datasets, with detailed descriptions of these datasets available in Table S1, and ethical approval was secured for all original studies.\u003c/p\u003e\n\u003cp\u003eGenetic instruments were selected through genome-wide significance thresholds (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10⁻⁸) and linkage disequilibrium clumping (r\u0026sup2; thresholds analysis-dependent). MR analyses were implemented using the TwoSampleMR R package, with multi-SNP associations evaluated through IVW and MR-Egger regression complemented by three auxiliary methods (Weighted median, Simple mode and Weighted mode). We also employed MR-PRESSO and MREGGER to test for pleiotropy and used Bayesian Weighted Mendelian Randomization (BWMR) \u003csup\u003e[24]\u003c/sup\u003e for further validation. Sensitivity analyses systematically assessed heterogeneity (Cochran\u0026apos;s Q test) and horizontal pleiotropy (MR-Egger intercept test), maintaining P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance thresholds. To address multiple comparison inflation, we implemented Benjamini-Hochberg false discovery rate (FDR) correction. Associations with nominal significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) underwent FDR adjustment, with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 defining statistical significance.\u003c/p\u003e\n\u003ch3\u003eGeneralized Summary-data-based Mendelian Randomization approach\u003c/h3\u003e\n\u003cp\u003eIn our primary analyses, we utilized Generalized Summary-data-based Mendelian Randomization (GSMR), a versatile methodology that conducts MR analyses employing several nearly independent instruments to evaluate the causal relationship between NLRC5 cis-eQTL and downstream pQTL, leveraging summary-level GWAS data obtained from independent investigations \u003csup\u003e[25]\u003c/sup\u003e. To eliminate instruments exhibiting significant potential pleiotropic effects, we employed the HEIDI-outlier approach, establishing a P value threshold of 0.01 for the HEIDI-outlier filtering analysis \u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003eNLRC5 Downstream Gene-Protein Causal Mapping\u003c/h2\u003e\n \u003cp\u003eWe constructed a two-sample MR framework using NLRC5 cis-eQTLs from the eQTLGen Consortium \u003csup\u003e[26]\u003c/sup\u003e as genetic instruments (Table S2), with plasma pQTLs derived from the UK Biobank Pharma Proteomics Project (UKB-PPP v1.0, including pQTLs of 2,918 proteins) \u003csup\u003e[27]\u003c/sup\u003e (Table S3) and deCODE Iceland (including pQTLs of 4,907 proteins) \u003csup\u003e[28]\u003c/sup\u003e (Table S4). Instrument selection employed LD clumping (r\u0026sup2; \u0026lt;0.001, 250 kb window), followed by single-SNP Wald ratio estimation and multi-SNP meta-analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eNLRC5 Methylation-Disease Association Analysis\u003c/h3\u003e\n\u003cp\u003eIntegration of MS GWAS data from International Multiple Sclerosis Genetics Consortium (IMSGC) \u003csup\u003e[29]\u003c/sup\u003e with HumanMethylation450-annotated CpG sites identified NLRC5-associated methylation quantitative trait loci (mQTLs) from MeQTL EPIC Database \u003csup\u003e[30]\u003c/sup\u003e (Table S5). Independent variants were determined through PLINK-based LD clumping (r\u0026sup2; \u0026lt;0.1, 10 Mb window) and functional annotation filtering. Instrument validity was confirmed via variance explanation metrics (R\u0026sup2; \u0026gt;1%) and F-statistics (F\u0026thinsp;\u0026gt;\u0026thinsp;10). Causal effect estimation employed IVW regression, with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 applied to sensitivity analyses.\u003c/p\u003e\n\u003ch3\u003eFunctional Insights and Network Analyses\u003c/h3\u003e\n\u003cp\u003eTo further our understanding, we combined our findings with protein-protein interaction (PPI) networks using the STRING database (https://cn.string-db.org/) and performed GO enrichment analysis, including Molecular Functions (MF), Biological Processes (BP), and Cellular Components (CC), alongside KEGG pathway analysis to systematically annotate functional ontologies and signaling pathways linked to NLRC5 interactions.\u003c/p\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003eCell Culture and LPS Treatment\u003c/h2\u003e\n \u003cp\u003eBV2 microglia were maintained in MEM basal medium (Servicebio G4552) supplemented with 10% fetal bovine serum (FBS, Gibco 10270-106) and 1% penicillin-streptomycin (P/S, Gibco 15140-122) under standard culture conditions (37\u0026deg;C, 5% CO₂, humidified incubator, Jingqi CL-191C). Upon reaching 80\u0026ndash;90% confluence, cells were detached using 0.25% Trypsin (Gibco 1894145), centrifuged at 300 \u0026times; g for 5 min (SCILOGEX DMO-412), and resuspended in fresh medium. Cells were divided into control group (untreated) and LPS-treated group (stimulated with 100 ng/mL LPS, Solarbio L8880). For functional assays, cells were seeded into 6-well plates (1 \u0026times; 10⁵ cells/well, 9 replicates per group) and 24-well plates pre-loaded with coverslips (1 \u0026times; 10⁴ cells/well, 2 replicates per group). After 24 h of incubation (~\u0026thinsp;60% confluence), the medium was replaced with serum-free MEM devoid of antibiotics. The LPS-treated group was exposed to 100 ng/mL LPS for 24 h, while the control group remained untreated.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eWestern Blot Analysis\u003c/h2\u003e\n \u003cp\u003eFollowing treatment, cells were lysed with RIPA buffer and centrifuged (12,000 \u0026times; g, 10 min, 4\u0026deg;C) to collect supernatants. Protein concentrations were quantified spectrophotometrically, and equal amounts (60 \u0026micro;g) were denatured in SDS-PAGE loading buffer. Proteins were separated on 10% SDS-polyacrylamide gels and transferred to PVDF membranes using wet transfer. Membranes were blocked with 5% non-fat milk and probed overnight at 4\u0026deg;C with primary antibodies against NLRC5 (1:500), ZBP1 (1:1500), ASC (1:1000), cysteine-aspartic acid protease 8 (caspase-8) (1:500), RIPK3 (1:1000), and \u0026beta;-actin (1:4000). HRP-conjugated secondary antibodies (1:4000) were applied for 2 h at room temperature. Signals were detected using chemiluminescent substrate and imaged with a gel documentation system.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eImmunofluorescence (IF) Staining\u003c/h2\u003e\n \u003cp\u003eCells on coverslips (24-well plate) were fixed with 4% paraformaldehyde for 30 min at 4\u0026deg;C, permeabilized with 0.1% Triton X-100 for 10 min, and blocked with 5% normal goat serum (ZSGB-Bio ZLI-9021) for 30 min at room temperature. Primary antibodies against NLRC5 (1:80, Santa Cruz sc-515668), ZBP1 (1:200, Proteintech 13285-1-AP), and ASC (1:100, Proteintech 30641-1-AP) were co-incubated overnight at 4\u0026deg;C. After washing with PBS, Polymer-HRP secondary antibodies (AFIHC035 kit) were applied for 30 min at room temperature. Tyramide Signal Amplification (TSA) was performed using TYR-520Plus (green fluorescence, 1:200), TYR-570Plus (red fluorescence, 1:200), and TYR-620Plus (cyan fluorescence, 1:200) fluorophores diluted in TSA buffer. Nuclei were counterstained with DAPI (ZSGB-Bio ZLI-9557). Fluorescent images were acquired using an Olympus laser scanning confocal microscope, and colocalization analysis was conducted using ImageJ software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eAnalytical Tools and Statistical Methods\u003c/h2\u003e\n \u003cp\u003eAnalyses were conducted in R (v4.4.0) using the TwoSampleMR (v0.6.2) and MR-PRESSO (v1.0) packages for MR and pleiotropy correction, supplemented by BWMR (v2.21.0) and GSMR2 for robustness validation. Single- and multi-SNP instrumental variables were analyzed via Wald ratio and five MR methods (IVW, MR-Egger, weighted median, simple/weighted mode), respectively. Sensitivity tests included Cochran\u0026rsquo;s Q (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for heterogeneity) and MR-Egger intercept (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for pleiotropy). Data were visualized with ggplot2 (v3.4.4). Western blot bands were quantified using ImageJ (v1.53t) after background subtraction and normalization to \u0026beta;-actin, with triplicate biological replicates. Statistical comparisons between experimental groups were conducted using Unpaired Student\u0026rsquo;s t-test for two-group comparisons.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eIntegrated Analysis of EAE Microglial RNA-Seq and GEO Datasets Reveals Potential PANoptosis Activation\u003c/h2\u003e\n \u003cp\u003eIntersection analysis was conducted between differentially expressed genes (DEGs) identified in our EAE microglial RNA sequencing dataset (GSE253318) and two publicly available GEO datasets (GSE78809 and GSE154228). This comparative approach revealed six overlapping genes: \u003cem\u003eBST2, CCL5, GBP3, GBP8, H2-AB1\u003c/em\u003e, and \u003cem\u003eNLRC5\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). To further investigate the potential involvement of regulated cell death pathways, DEGs from all three datasets were cross-referenced with canonical genes associated with apoptosis, pyroptosis, and necroptosis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). The gene lists associated with apoptosis, necroptosis, and pyroptosis were sourced from the comprehensive study by Qin et al. (2023) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e and displayed in Table \u003cspan class=\"InternalRef\"\u003eS11\u003c/span\u003e. Notably, multiple key regulators of these pathways, \u003cem\u003eCASP1, CASP8, NOD2, MLKL\u003c/em\u003e, and \u003cem\u003eNLRC5\u003c/em\u003e, were consistently upregulated across three datasets (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec-e).\u003c/p\u003e\n \u003cp\u003eThe co-occurrence of \u003cem\u003eCASP1\u003c/em\u003e (pyroptosis), \u003cem\u003eCASP8\u003c/em\u003e (apoptosis/necroptosis switch), \u003cem\u003eNOD2\u003c/em\u003e (inflammasome priming), MLKL (necroptosis), and \u003cem\u003eNLRC5\u003c/em\u003e (panoptosis regulation) suggests convergent immunogenic cell death signaling. \u003cem\u003eNLRC5\u003c/em\u003e, identified as a consensus DEG across three independent RNA-seq datasets, was found to interact with AIM2 (inflammasome sensor), CHUK (IKK\u0026alpha;; NF-\u0026kappa;B kinase), DDX58 (RIG-I; viral RNA sensor), IFIH1 (MDA5; dsRNA sensor), IKBKB (IKK\u0026beta;; NF-\u0026kappa;B activator), MAVS (mitochondrial antiviral signaling protein), NLRC4 (inflammasome component), and NLRP1/3/6 (canonical inflammasome sensors) in PPI network analysis (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ef). This interactome implicates NLRC5 in multimodal regulation of cell death modalities (pyroptosis/apoptosis/necroptosis) and inflammatory responses via inflammasome activation, antiviral signaling, and NF-\u0026kappa;B pathway engagement. These findings highlight PANoptosis as a plausible contributor to microglial dysfunction and neuroinflammation in EAE pathogenesis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of NLRC5-Regulated Apoptotic and Necroptotic Mediators via Cross-Database Mendelian Randomization\u003c/h2\u003e\n \u003cp\u003eTo investigate downstream effectors of NLRC5, we performed two-sample MR analyses using NLRC5 cis-eQTLs from the eQTLGen Consortium as genetic instruments for exposure. Outcome datasets were derived from pQTLs in the deCODE Iceland and UKB-PPP databases, respectively. MR analysis identified 1,596 proteins in the deCODE Iceland cohort (Table \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e) and 334 proteins in the UKB-PPP cohort (Table \u003cspan class=\"InternalRef\"\u003eS7\u003c/span\u003e) as potential downstream molecular targets of NLRC5 (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eIntersection of these candidate proteins across both databases revealed 78 overlapping molecules (Table \u003cspan class=\"InternalRef\"\u003eS8\u003c/span\u003e), demonstrating robust consistency. GO/KEGG analysis of NLRC5-associated downstream molecules across the UKB-PPP and deCODE Iceland pQTL databases revealed enrichment in pathways (cell cycle, oocyte meiosis, cellular senescence), MF (microtubule/tubulin binding, motor activity), CC (spindle, centromeric regions), and BP (mitotic nuclear division, sister chromatid segregation) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). These findings implicate NLRC5 in regulating mitotic fidelity and chromosomal dynamics, with cross-talk to PCD pathways. Collectively, NLRC5 may act as a nexus linking cell cycle control to divergent cell death mechanisms under stress or damage conditions.\u003c/p\u003e\n \u003cp\u003eAmong the 78 overlapping molecule, three proteins, including GABA Type A Receptor-Associated Protein (GABARAP), BR Serine/Threonine Kinase 2 (BRSK2), and TNF Superfamily Member 12 (TNFSF12), were implicated in apoptosis regulation, while B-cell Lymphoma 2 (BCL2) exhibited a known role in necroptosis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb and Table \u003cspan class=\"InternalRef\"\u003eS8\u003c/span\u003e). MR analysis revealed that genetically predicted NLRC5 expression modestly upregulated the levels of GABARAP (UKB-PPP: OR\u0026thinsp;=\u0026thinsp;1.04, FDR\u0026thinsp;=\u0026thinsp;2.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e; deCODE Iceland: OR\u0026thinsp;=\u0026thinsp;1.02, FDR\u0026thinsp;=\u0026thinsp;0.02), BRSK2 (UKB-PPP: OR\u0026thinsp;=\u0026thinsp;1.03, FDR\u0026thinsp;=\u0026thinsp;7.45\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e; deCODE Iceland: OR\u0026thinsp;=\u0026thinsp;1.03, FDR\u0026thinsp;=\u0026thinsp;0.02), TNFSF12 (UKB-PPP: OR\u0026thinsp;=\u0026thinsp;1.02, FDR\u0026thinsp;=\u0026thinsp;0.03; deCODE Iceland: OR\u0026thinsp;=\u0026thinsp;1.02, FDR\u0026thinsp;=\u0026thinsp;0.03), and BCL2 (UKB-PPP: OR\u0026thinsp;=\u0026thinsp;1.02, FDR\u0026thinsp;=\u0026thinsp;0.03; deCODE Iceland: OR\u0026thinsp;=\u0026thinsp;1.02, FDR\u0026thinsp;=\u0026thinsp;0.04) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). All associations were analyzed using the inverse-variance weighted (iVW) method and further validated through BWMR and GSMR, revealing robust concordance in directionality and nominal significance across both cohorts, albeit with modest effect magnitudes. Scatter plots and Forest plots illustrating the SNP effects on NLRC5 eQTL and four downstream protein pQTL across the two cohorts are presented in FigS1 and FigS2. The \u003cem\u003eNLRC5\u003c/em\u003e eQTL-associated SNPs exhibiting significant associations with downstream pQTLs (GABARAP, BRSK2, TNFSF12, and BCL2) are comprehensively annotated in Table \u003cspan class=\"InternalRef\"\u003eS9\u003c/span\u003e, including chromosomal positions, effect alleles, beta coefficients (\u0026beta;), standard errors (SE), and iVW\u0026nbsp;\u003cem\u003ep\u003c/em\u003e values.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMendelian Randomization Analysis of NLRC5 eQTIL and pQTL of potential downstream molecular targets from the UKB-PPP and deCODE Iceland cohorts\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003cp\u003eof SNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF-statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOdds ratio (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHeterogeneity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003csub\u003eh\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEgger intercept\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003csub\u003e\u003cem\u003eintercept\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eGABARAP (UKB-PPP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(0.98\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.01\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04(1.012\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.70\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04(1.03\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.52\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04 (1.02\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.79\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04 (1.02\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.62\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eGABARAP (deCODE Iceland)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(0.98\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.87\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.00-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.00-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.02\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.01\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03 (1.01\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eBRSK2 (UKB-PPP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01(0.98\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.00-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.01\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.45\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.03\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.75\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03 (1.01\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03 (1.01\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.00\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eBRSK2 (deCODE Iceland)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(0.97\u0026ndash;1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.72\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.00-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.01\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.02\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.01\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03 (1.01\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eTNFSF12\u003c/p\u003e\n \u003cp\u003e(UKB-PPP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(0.98\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01(0.99\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.005\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.02\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03(1.00-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.004\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eTNFSF12 (deCODE Iceland)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00(0.95\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(0.99\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.00-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.02\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03 (1.004\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03 (1.004\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eBCL2 (UKB-PPP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04(0.99\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.43\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.00-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.00-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.02\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.002\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.001\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eBCL2\u003c/p\u003e\n \u003cp\u003e(deCODE Iceland)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(0.97\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.19\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(0.99\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.00-1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02(1.02\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBWMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.003\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.02 (1.003\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSensitivity analyses incorporating both weighted median and MR-Egger regression approaches demonstrated consistent directional estimates, confirming the robustness of our findings (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The lack of significant heterogeneity (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and absence of horizontal pleiotropy (MR-Egger intercept P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) further strengthened the validity of the causal inference (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Instrument strength was supported by F-statistics (Tables S9), while leave-one-out analysis revealed no influential individual SNPs (FigS3). Symmetrical funnel plots suggested minimal estimation bias (FigS4).\u003c/p\u003e\n \u003cp\u003eThese findings suggest that NLRC5 may orchestrate cell survival and death pathways through distinct downstream mediators, with shared regulatory mechanisms across populations. The intersectional targets provide prioritized candidates for further mechanistic validation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMendelian Randomization Analysis of\u003c/strong\u003e \u003cstrong\u003eNLRC5\u003c/strong\u003e \u003cstrong\u003eMethylation Sites in Multiple Sclerosis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo investigate the causal relationship between \u003cem\u003eNLRC5\u003c/em\u003e methylation and MS susceptibility, a two-sample MR analysis was performed using mQTLs of \u003cem\u003eNLRC5\u003c/em\u003e as instrumental variables (exposure) and GWAS summary statistics for MS from the IMSGC database (outcome).\u003c/p\u003e\n \u003cp\u003eThe MR analysis revealed a statistically significant association between genetically predicted \u003cem\u003eNLRC5\u003c/em\u003e methylation levels at cg04097610 and reduced risk of MS (OR\u0026thinsp;=\u0026thinsp;0.885, 95% CI: 0.79\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.039) (Table \u003cspan class=\"InternalRef\"\u003eS10\u003c/span\u003e). Sensitivity analyses, including weighted median and MR-Egger regression, yielded consistent directional estimates, supporting the robustness of the findings. No significant heterogeneity (P\u003csub\u003eh\u003c/sub\u003e \u0026gt; 0.05) or horizontal pleiotropy (MR-Egger intercept p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was detected, reinforcing the validity of the causal inference. The result suggests that altered DNA methylation patterns at \u003cem\u003eNLRC5\u003c/em\u003e loci may play a protective role in MS pathogenesis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eWestern Blot and Immunofluorescence Analysis of NLRC5-PANoptosome Complex Components in LPS-Induced Microglial Neuroinflammatory Models\u003c/h2\u003e\n \u003cp\u003eWestern blot analysis revealed that LPS stimulation in BV2 microglial cells significantly upregulated key components of the NLRC5-PANoptosome complex, including NLRC5, ZBP1, ASC, and caspase-8 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea,b). Specifically, ASC and NLRC5 exhibited statistically significant increases with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, while caspase-8 and ZBP1 showed highly significant upregulation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, RIPK3 expression displayed a non-significant trend toward elevation (ns). Raw Western blot images (FigS5) and densitometry data (Table \u003cspan class=\"InternalRef\"\u003eS12\u003c/span\u003e) are available in the Supplementary Materials.\u003c/p\u003e\n \u003cp\u003eImmunofluorescence triple-labeling with four-color imaging, combined with confocal microscopy, further confirmed the cytoplasmic localization of NLRC5, ZBP1, and ASC in BV2 microglia. Quantitative analysis demonstrated that the fluorescence intensity of these proteins was markedly higher in the LPS-treated group compared to the untreated control (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec), consistent with the Western blot findings. These results collectively suggest that LPS induces the assembly of the NLRC5-PANoptosome complex in microglia, characterized by coordinated transcriptional and spatial regulation of its core components.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIntegrative analysis of EAE microglial RNA-seq and GEO datasets revealed a potential activation of PANoptosis, marked by the co-upregulation of core regulators (CASP1, CASP8, NOD2, MLKL, and NLRC5) across datasets, suggesting coordinated engagement of apoptosis, pyroptosis, and necroptosis in neuroinflammatory microglial dysfunction. This multimodal cell death signature aligns with the MR-driven identification of \u003cem\u003eNLRC5\u003c/em\u003e as a pleiotropic modulator of cell cycle and PCD pathways. MR analyses across UKB-PPP and deCODE Iceland pQTLs demonstrated that \u003cem\u003eNLRC5\u003c/em\u003e causally upregulates apoptosis-associated proteins (GABARAP, BRSK2, TNFSF12) and necroptosis-linked BCL2, albeit with modest effect sizes (OR\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.04, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings are robustly supported by BWMR and GSMR. The convergence of NLRC5-associated pathways, including mitotic fidelity, cellular senescence, and microtubule dynamics, with PANoptosis-related mediators underscores its potential role as a molecular bridge linking cell cycle regulation to inflammatory cell death.\u003c/p\u003e\n\u003cp\u003eThe MR analysis provides novel evidence supporting a causal role for \u003cem\u003eNLRC5\u003c/em\u003e methylation in modulating susceptibility to MS. Genetically predicted hypermethylation of \u003cem\u003eNLRC5\u003c/em\u003e at specific CpG sites (cg04097610) was associated with a reduced risk of MS. This inverse relationship aligns with prior functional insights into NLRC5\u0026rsquo;s role in inflammatory pathways and PCD \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eNLRC5\u003c/em\u003e methylation associate with levels of pro-inflammatory mediators (IL-12 and IL-18) and regulate the major histocompatibility complex MHC class I genes through interaction with various interleukins and NF\u0026kappa;B \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Furthermore, methylation of \u003cem\u003eNLRC5\u003c/em\u003e has been linked to both rheumatoid arthritis \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e and lupus \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. These findings complement our MR results implicating \u003cem\u003eNLRC5\u003c/em\u003e in PANoptosis regulation and highlight its dual role as both a transcriptional regulator and an epigenetically tunable node in neuroinflammation. Future studies should validate the methylation site cg04097610 mediating this protective effect and delineate whether \u003cem\u003eNLRC5\u003c/em\u003e methylation impacts MS progression by modulating its downstream targets (e.g., BCL2, GABARAP) or inflammasome activity in immune and glial cells.\u003c/p\u003e\n\u003cp\u003eEmerging evidence highlights the multifaceted role of \u003cem\u003eNLRC5\u003c/em\u003e in neuroinflammatory and neurodegenerative disorders. \u003cem\u003eNLRC5\u003c/em\u003e deficiency protects against inflammation, tissue damage, and lethality in hemolytic disease, colitis, and hemophagocytic lymphohistiocytosis (HLH) models, highlighting its central role in pathogenic inflammation. \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In Parkinson\u0026rsquo;s disease (PD) models, NLRC5 expression increases in the nigrostriatal pathway of MPTP-treated mice and in toxin-exposed glial and neuronal cells. NLRC5 deficiency protects against neurodegeneration and motor deficits by inhibiting glial activation and inflammatory cytokine release (IL-1\u0026beta;, IL-6, TNF-\u0026alpha;, COX2). Clinically, lower blood NLRC5 mRNA levels in PD patients may indicate its role as a glial activation marker \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In addition, NLRC5 exacerbates ischemic retinopathy by driving microglial pyroptosis and apoptosis through inflammasome interactions (NLRP3/NLRC4), leading to GSDMD cleavage, caspase-3 activation, and IL-1\u0026beta; release, thereby aggravating retinal ganglion cell death and ischemic damage \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. In relapsing-remitting RRMS, dysregulated expression of the long noncoding RNA MEG3 and NLRC5 is observed, suggesting MEG3-mediated modulation of NLRC5 in neuroinflammatory pathways \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Collectively, NLRC5 exhibits context-dependent roles, promoting cell death (via pyroptosis/apoptosis) and neuroinflammation either through direct inflammasome engagement, underscoring its therapeutic relevance across diverse neurological pathologies.\u003c/p\u003e\n\u003cp\u003eThe NLRC5-PANoptosome is recently reported to be a multiprotein complex comprising NLRC5, NLRP12, ASC, RIPK3, caspase-8, and NLRP3 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. It is activated by PAMP/DAMP (e.g., heme\u0026thinsp;+\u0026thinsp;LPS/Pam3) or DAMP/cytokine combinations via TLR2/4 signaling and NAD⁺-mediated pathways, which drive ROS production and NLRC5 upregulation \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. NLRC5, alongside NLRP12, regulates PANoptosis execution, while NLRP3-NLRP12 mediates caspase-1 activation, though ASC-NLRP3 interaction requires NLRC5 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Previous studies have demonstrated NLRC5-dependent cell death in murine bone marrow-derived macrophages (BMDMs) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. However, the mechanistic impact of the NLRC5-PANoptosome complex on microglial activation and function in CNS inflammatory pathologies, including MS, remains poorly characterized. Our study position NLRC5 at the nexus of PANoptotic signaling in EAE, where its subtle but consistent regulatory effects on apoptotic, pyroptotic, and necroptotic mediators may cumulatively drive microglial dysfunction. Further validation of these targets in experimental models is warranted to dissect their contributions to NLRC5-dependent neuroinflammatory pathology.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the reliance on murine EAE models and BV2 microglial cells may not fully replicate the complexity of human MS pathology, particularly in chronic stages or across heterogeneous patient subtypes. Second, the modest effect sizes observed in MR analyses (OR\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.04) highlight the need for further experimental validation to confirm the biological significance of NLRC5\u0026rsquo;s regulatory roles. Finally, while cg04097610 methylation was associated with MS susceptibility, the direct mechanistic link between this epigenetic modification and NLRC5 transcription or PANoptosis activation remains undefined.\u003c/p\u003e\n\u003cp\u003eFuture research should prioritize several key directions. Mechanistic studies using \u003cem\u003eNLRC5\u003c/em\u003e knockout or overexpression models in EAE could elucidate its role in PANoptosome assembly and neuroinflammation. Validating \u003cem\u003eNLRC5\u003c/em\u003e methylation patterns, PANoptosis markers, and downstream targets in postmortem MS brain tissues would enhance translational relevance. Additionally, exploring interactions between NLRC5-PANoptosome components (e.g., ZBP1, ASC, RIPK3) and other cell death pathways, such as ferroptosis, may provide deeper insights into microglial dysfunction. Finally, testing pharmacological inhibitors targeting NLRC5 or epigenetic modulators of cg04097610 in preclinical MS models could pave the way for novel therapeutic strategies to mitigate microglial panoptosis.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidates a pivotal role for NLRC5 in orchestrating microglial panoptosis, a coordinated interplay of pyroptosis, apoptosis, and necroptosis, during MS pathogenesis. By integrating multi-omics analyses of EAE models and human GWAS datasets, we identified NLRC5 as a hub regulator that modulates neuroinflammatory responses through epigenetic (cg04097610 methylation) and proteomic mechanisms. MR revealed causal links between NLRC5 expression and downstream apoptotic/necroptotic effectors, including GABARAP, BRSK2, TNFSF12 and BCL2, while experimental validation confirmed LPS-induced assembly of the NLRC5-PANoptosome complex in microglia. These findings position NLRC5 as a critical mediator bridging epigenetic dysregulation, inflammatory cell death, and CNS autoimmunity, offering novel therapeutic avenues for MS.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple sclerosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEAE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExperimental autoimmune encephalomyelitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMethylation quantitative trait locus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse-variance weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBWMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Weighted Mendelian Randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Summary-data-based Mendelian Randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZBP1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZ-DNA binding protein 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApoptosis-associated speck-like protein containing a CARD\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCaspase-8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCysteine-aspartic acid protease 8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentral nervous system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emicroglia inflamed in MS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogrammed cell death\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSDMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGasdermin D\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLRC5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNOD-like receptor family CARD domain containing 5\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elipopolysaccharide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide association studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecis-eQTLs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCis-expression quantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epQTLs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein quantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle-nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUKB-PPP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUK Biobank Pharma Proteomics Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMSGC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Multiple Sclerosis Genetics Consortium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emQTLs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMethylation quantitative trait loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein-protein interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular Functions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological Processes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCellular Components\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFetal bovine serum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eP/S\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePenicillin-streptomycin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTyramide Signal Amplification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially expressed genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGABARAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGABA Type A Receptor-Associated Protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBRSK2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBR Serine/Threonine Kinase 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNFSF12\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTNF Superfamily Member 12\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCL2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eB-cell Lymphoma 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eβ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBeta coefficients\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard errors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-significant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemophagocytic lymphohistiocytosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMDMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBone marrow-derived macrophages\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the contributors to the publicly available datasets used in this study, including the GEO datasets (GSE154228, GSE78809), UK Biobank, deCODE Iceland, eQTLGen Consortium, IMSGC, and MeQTL EPIC Database. These resources provided essential data for transcriptomic, proteomic, epigenetic, and genome-wide association studies (GWAS) analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RNA-seq data of EAE microglia generated in this study have been deposited in the GEO database under the accession number GSE253318. Publicly available GEO datasets (GSE154228 and GSE78809) were used for cross-dataset validation. Genome-wide summary-level statistics for SNPs from GWAS datasets, including those from the UK Biobank, deCODE Iceland, eQTLGen Consortium, and IMSGC, were utilized in the Mendelian randomization and methylation analyses. The pQTLs data were obtained from the UK Biobank Pharma Proteomics Project (UKB-PPP v1.0) and deCODE Iceland. Methylation quantitative trait locus (mQTL) data for NLRC5 were sourced from the MeQTL EPIC Database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWY designed this study. WY and WJH integrated and analyzed the data. WY and WJH wrote the manuscript. WY, GP and WJH edited and revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo disclosures to report.\u003c/p\u003e\n\u003cp\u003eThe authors assert that the study was carried out without any commercial or financial affiliations that could be interpreted as a possible conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not require additional ethical approval or informed consent, as it solely utilized publicly available summary data. The original GEO datasets and GWAS, from which these data were derived, had obtained the necessary ethical approval and participant consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWu Yan is supported by the Yunnan Clinical Medical Center for Neurocardiac Diseases and Yunnan Fundamental Research Projects (grant nos. 202201AT070291) and Priority Union\u0026nbsp;Foundation of Yunnan Provincial Science and Technology Department and Kunming Medical University (grant nos. 202301AY070001-197).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbsinta M, Maric D, Gharagozloo M, Garton T, Smith MD, Jin J et al (2021) A lymphocyte-microglia-astrocyte axis in chronic active multiple sclerosis. Nature 597(7878):709\u0026ndash;714. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-021-03892-7\u003c/span\u003e\u003cspan address=\"10.1038/s41586-021-03892-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerrero BL, Sicotte NL (2020) Microglia in Multiple Sclerosis: Friend or Foe? Front Immunol 11:374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2020.00374\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.00374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoet S, Prinz M, van Loo G (2019) Microglia in Central Nervous System Inflammation and Multiple Sclerosis Pathology. Trends Mol Med 25(2):112\u0026ndash;123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.molmed.2018.11.005\u003c/span\u003e\u003cspan address=\"10.1016/j.molmed.2018.11.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYong VW (2022) Microglia in multiple sclerosis: Protectors turn destroyers. Neuron 110(21):3534\u0026ndash;3548. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuron.2022.06.023\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2022.06.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai L, Fan Q, Pang R, Chen C, Zhang Y, Xie H et al (2025) Microglia programmed cell death in neurodegenerative diseases and CNS injury. Apoptosis 30(1\u0026ndash;2):446\u0026ndash;465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10495-024-02041-5\u003c/span\u003e\u003cspan address=\"10.1007/s10495-024-02041-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarwish SF, Elbadry AMM, Elbokhomy AS, Salama GA, Salama RM (2023) The dual face of microglia (M1/M2) as a potential target in the protective effect of nutraceuticals against neurodegenerative diseases. Front Aging 4:1231706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fragi.2023.1231706\u003c/span\u003e\u003cspan address=\"10.3389/fragi.2023.1231706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWest PK, McCorkindale AN, Guennewig B, Ashhurst TM, Viengkhou B, Hayashida E et al (2022) The cytokines interleukin-6 and interferon-α induce distinct microglia phenotypes. J Neuroinflammation 19(1):96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-022-02441-x\u003c/span\u003e\u003cspan address=\"10.1186/s12974-022-02441-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu H, Pan H, Fang Y, Wang H, Chen Z, Hu W et al (2024) Apoptosis-induced decline in hippocampal microglia mediates the development of depression-like behaviors in adult mice triggered by unpredictable stress during adolescence. Eur J Pharmacol 978:176763. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejphar.2024.176763\u003c/span\u003e\u003cspan address=\"10.1016/j.ejphar.2024.176763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu P, Wang D, Zhang Y, Cai Z, Ye T, Tong L et al (2020) Apoptosis-triggered decline in hippocampal microglia mediates adolescent intermittent alcohol exposure-induced depression-like behaviors in mice. Neuropharmacology 170:108054. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuropharm.2020.108054\u003c/span\u003e\u003cspan address=\"10.1016/j.neuropharm.2020.108054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKenzie BA, Mamik MK, Saito LB, Boghozian R, Monaco MC, Major EO et al (2018) Caspase-1 inhibition prevents glial inflammasome activation and pyroptosis in models of multiple sclerosis. Proc Natl Acad Sci U S A 115(26):E6065\u0026ndash;E6074. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1722041115\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1722041115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKenzie BA, Fernandes JP, Doan MAL, Schmitt LM, Branton WG, Power C (2020) Activation of the executioner caspases-3 and \u0026ndash;\u0026thinsp;7 promotes microglial pyroptosis in models of multiple sclerosis. J Neuroinflammation 17(1):253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-020-01902-5\u003c/span\u003e\u003cspan address=\"10.1186/s12974-020-01902-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd AF, Davies CL, Holloway RK, Labrak Y, Ireland G, Carradori D et al (2019) Central nervous system regeneration is driven by microglia necroptosis and repopulation. Nat Neurosci 22(7):1046\u0026ndash;1052. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41593-019-0418-z\u003c/span\u003e\u003cspan address=\"10.1038/s41593-019-0418-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZelic M, Pontarelli F, Woodworth L, Zhu C, Mahan A, Ren Y et al (2021) RIPK1 activation mediates neuroinflammation and disease progression in multiple sclerosis. Cell Rep 35(6):109112\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalireddi RKS, Kesavardhana S, Kanneganti TD (2019) ZBP1 and TAK1: Master Regulators of NLRP3 Inflammasome/Pyroptosis, Apoptosis, and Necroptosis (PAN-optosis). Front Cell Infect Microbiol 9:406. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2019.00406\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2019.00406\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Yang Y, Meng X, Li J, Liu X, Liu H (2024) PANoptosis: Mechanisms, biology, and role in disease. Immunol Rev 321(1):246\u0026ndash;262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/imr.13279\u003c/span\u003e\u003cspan address=\"10.1111/imr.13279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu P, Ke ZR, Chen JX, Li SJ, Ma TL, Fan XL (2023) Advances in mechanism and regulation of PANoptosis: Prospects in disease treatment. Front Immunol 14:1120034. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1120034\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1120034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi Z, Zhu L, Wang K, Wang N, PANoptosis (2023) Emerging mechanisms and disease implications. Life Sci 333:122158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.lfs.2023.122158\u003c/span\u003e\u003cspan address=\"10.1016/j.lfs.2023.122158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang Q, Geng ZX, Yi X, Wei X, Zhu XH, Jiang DS (2024) PANoptosis: a novel target for cardiovascular diseases. Trends Pharmacol Sci 45(8):739\u0026ndash;756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tips.2024.06.002\u003c/span\u003e\u003cspan address=\"10.1016/j.tips.2024.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSundaram B, Pandian N, Kim HJ, Abdelaal HM, Mall R, Indari O et al (2024) NLRC5 senses NAD\u0026thinsp;+\u0026thinsp;depletion, forming a PANoptosome and driving PANoptosis and inflammation. Cell 187(15):4061\u0026ndash;4077e17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2024.05.034\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2024.05.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Jiao C, Liu L, Wang A, Tang L, Ren Y et al (2021) NLRC5: A Potential Target for Central Nervous System Disorders. Front Immunol 12:704989. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2021.704989\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2021.704989\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun C, Shen Y, Liu P, Shen Y, Hu Y, Li P et al (2023) NLRC5 Deficiency Reduces LPS-Induced Microglial Activation via Inhibition of NF-κB Signaling and Ameliorates Mice's Depressive-like Behavior. Int J Mol Sci 24(17):13265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms241713265\u003c/span\u003e\u003cspan address=\"10.3390/ijms241713265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZelic M, Pontarelli F, Woodworth L, Zhu C, Mahan A, Ren Y et al (2021) RIPK1 activation mediates neuroinflammation and disease progression in multiple sclerosis. Cell Rep 35(6):109112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.celrep.2021.109112\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2021.109112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 33979622; PMCID: PMC8917516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWlodarczyk A, Holtman IR, Krueger M, Yogev N, Bruttger J, Khorooshi R et al (2017) A novel microglial subset plays a key role in myelinogenesis in developing brain. EMBO J 36(22):3292\u0026ndash;3308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15252/embj.201696056\u003c/span\u003e\u003cspan address=\"10.15252/embj.201696056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2017 Sep 28. PMID: 28963396; PMCID: PMC5686552\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Ming J, Hu X, Chen G, Liu J, Yang C (2020) Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics 36(5):1501\u0026ndash;1508. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btz749\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btz749\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Zheng Z, Zhang F, Wu Y, Trzaskowski M, Maier R et al (2018) Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun 9(1):224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-017-02317-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-017-02317-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026otilde;sa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B et al (2021) Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 53(9):1300\u0026ndash;1310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-021-00913-z\u003c/span\u003e\u003cspan address=\"10.1038/s41588-021-00913-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG et al (2023) Plasma proteomic associations with genetics and health in the UK Biobank. Nature 622(7982):329\u0026ndash;338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-023-06592-6\u003c/span\u003e\u003cspan address=\"10.1038/s41586-023-06592-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL et al (2021) Large-scale integration of the plasma proteome with genetics and disease. Nat Genet 53(12):1712\u0026ndash;1721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-021-00978-w\u003c/span\u003e\u003cspan address=\"10.1038/s41588-021-00978-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE et al (2016) Nat Genet 48(5):481\u0026ndash;487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.3538\u003c/span\u003e\u003cspan address=\"10.1038/ng.3538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2016 Mar 28. PMID: 27019110 Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillica\u0026ntilde;a S, Castillo-Fernandez J, Hannon E, Christiansen C, Tsai PC, Maddock J et al (2023) Genetic impacts on DNA methylation help elucidate regulatory genomic processes. Genome Biol 24(1):176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13059-023-03011-x\u003c/span\u003e\u003cspan address=\"10.1186/s13059-023-03011-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin H, Abulaiti A, Maimaiti A, Abulaiti Z, Fan G, Aili Y et al (2023) Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma. J Transl Med 21(1):588. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-023-04468-x\u003c/span\u003e\u003cspan address=\"10.1186/s12967-023-04468-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaghlool SB, K\u0026uuml;hnel B, Elhadad MA, Kader S, Halama A, Thareja G et al (2020) Epigenetics Meets Proteomics in an Epigenome-Wide Association Study With Circulating Blood Plasma Protein Traits. Nat Commun 11(1):15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-019-13831-w\u003c/span\u003e\u003cspan address=\"10.1038/s41467-019-13831-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Watanabe T, Oda Y, Shen W, Ahmad A, Ouda R et al (2024) Targeted demethylation and activation of NLRC5 augment cancer immunogenicity through MHC class I. Proc Natl Acad Sci U S A 121(6):e2310821121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.2310821121\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2310821121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou J, Lippert C, Heckerman D, Aryee M, Listgarten J (2014) Epigenome-Wide Association Studies Without the Need for Cell-Type Composition. Nat Methods 11(3):309\u0026ndash;311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nmeth.2815\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.2815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung KS, Chung BH, Choufani S, Mok MY, Wong WL, Mak CC et al (2017) Genome-Wide DNA Methylation Analysis of Chinese Patients With Systemic Lupus Erythematosus Identified Hypomethylation in Genes Related to the Type I Interferon Pathway. PLoS ONE 12(1):e0169553\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Shen C, Li H, Tong J, Wu Y, Ma Y et al (2023) NOD-like receptor NLRC5 promotes neuroinflammation and inhibits neuronal survival in Parkinson's disease models. J Neuroinflammation 20(1):96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-023-02755-4\u003c/span\u003e\u003cspan address=\"10.1186/s12974-023-02755-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng Y, Fu Y, Sheng L, Hu Y, Su L, Luo J et al (2021) The Regulatory NOD-Like Receptor NLRC5 Promotes Ganglion Cell Death in Ischemic Retinopathy by Inducing Microglial Pyroptosis. Front Cell Dev Biol 9:669696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcell.2021.669696\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.669696\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorkamandi S, Bahrami S, Ghorashi T, Dehani M, Bayat H, Hoseini SM et al (2021) Dysregulation of long noncoding RNA MEG3 and NLRC5 expressions in patients with relapsing-remitting multiple sclerosis: is there any correlation? Genes Immun 22(7\u0026ndash;8):322\u0026ndash;326. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41435-021-00154-4\u003c/span\u003e\u003cspan address=\"10.1038/s41435-021-00154-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerra J, Guarda G (2024) NLRC5: back to innate immunity. Trends Immunol 45(8):571\u0026ndash;573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.it.2024.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.it.2024.07.002\" 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":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"NLRC5, PANoptosis, Multiple sclerosis, Microglia, DNA methylation, Apoptosis, Necroptosis, Pyroptosis, Experimental autoimmune encephalomyelitis (EAE)","lastPublishedDoi":"10.21203/rs.3.rs-6759227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6759227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMicroglial dysfunction contributes to multiple sclerosis (MS) pathogenesis, yet the link between epigenetic regulation and inflammatory cell death (PANoptosis) remains unclear. This study explores NOD-like receptor family CARD domain containing 5 (NLRC5) as a regulator of microglial PANoptosis in MS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTranscriptomic data from experimental autoimmune encephalomyelitis (EAE) microglia (GSE253318) and GEO datasets (GSE78809, GSE154228) were integrated to identify panoptosis-related genes. Mendelian randomization (MR) linked NLRC5 expression to proteomic targets using UK Biobank and deCODE Iceland protein quantitative trait loci (pQTLs). Methylation quantitative trait locus (mQTL) analysis assessed MS-associated CpG sites. Lipopolysaccharide (LPS)-treated BV2 microglial models were used to validate NLRC5–PANoptosome assembly via Western blot and immunofluorescence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eNLRC5 was identified as a hub gene in PANoptosis-related pathways. MR revealed causal links between NLRC5 and apoptotic (GABA Type A Receptor-Associated Protein (GABARAP), BR Serine/Threonine Kinase 2 (BRSK2), TNF Superfamily Member 12 (TNFSF12)) and necroptotic (BCL2) effectors, consistent across inverse-variance weighted (iVW), Bayesian Weighted Mendelian Randomization (BWMR), and Generalized Summary-data-based Mendelian Randomization (GSMR) methods. Hypermethylation of NLRC5 (cg04097610) was associated with reduced MS risk (Odds Ratio (OR) = 0.885, p = 0.039). LPS stimulation upregulated NLRC5, Z-DNA binding protein 1 (ZBP1), apoptosis-associated speck-like protein containing a CARD (ASC), and cysteine-aspartic acid protease 8 (caspase-8), supporting PANoptosome activation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eNLRC5 regulates microglial PANoptosis via epigenetic and proteomic mechanisms, linking inflammatory cell death to MS progression. These findings highlight NLRC5 as a potential therapeutic target in MS.\u003c/p\u003e","manuscriptTitle":"NLRC5-Mediated Epigenetic and Proteomic Regulation of Microglial Panoptosis Drives Neuroinflammation in Multiple Sclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 17:15:30","doi":"10.21203/rs.3.rs-6759227/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-24T07:40:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-22T07:31:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41185474906026726531439858491108967815","date":"2025-08-12T05:11:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T17:15:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311120144808602866212833889162111684786","date":"2025-07-10T12:23:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T03:03:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-22T23:53:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-22T23:52:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2025-05-27T11:54:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"901d4358-a8f1-4f57-9052-349d0ad93b88","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:07:51+00:00","versionOfRecord":{"articleIdentity":"rs-6759227","link":"https://doi.org/10.1007/s12035-025-05365-8","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2025-11-28 15:58:22","publishedOnDateReadable":"November 28th, 2025"},"versionCreatedAt":"2025-06-26 17:15:30","video":"","vorDoi":"10.1007/s12035-025-05365-8","vorDoiUrl":"https://doi.org/10.1007/s12035-025-05365-8","workflowStages":[]},"version":"v1","identity":"rs-6759227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6759227","identity":"rs-6759227","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}