Jul 2026· Parkinsonism & Related Disorders· Vol 150, pp.
108418
· 0 citations· 73 references
Medicine
TL;DR
This study transforms neuroinflammation from a correlative hallmark to a mechanistically actionable axis, providing an urgently needed roadmap for inflammation-informed precision medicine in PD.
Abstract
Background
Parkinson's disease (PD) is a rapidly growing global health concern, with aging populations driving increasing prevalence. While neuronal degeneration is a hallmark, emerging evidence implicates chronic neuroinflammation as a key contributor to disease progression. Despite its recognized importance, the cellular sources, functional heterogeneity, and actionable mechanisms of inflammation in the human substantia nigra remain poorly understood, limiting the development of precise diagnostic biomarkers and therapeutic interventions.
Methods
We integrated single-nucleus RNA sequencing (snRNA-seq) from postmortem substantia nigra with bulk transcriptomic datasets (GSE133101, GSE7621) across multiple cohorts. Using Harmony-based batch correction, cell-type annotation, microglia-specific re-clustering (resolution = 0.1), pseudotime trajectory inference, weighted gene co-expression network analysis (WGCNA), and machine learning, we mapped the neuroinflammatory landscape of PD at single-cell resolution. Diagnostic performance was assessed via receiver operating characteristic (ROC) curve analysis (AUC >0.7), and druggable targets were prioritized through molecular docking and 100-ns molecular dynamics (MD) simulations.
Results
Microglia emerged as the principal immune driver of PD-associated inflammation. Six transcriptionally distinct microglial subpopulations were identified, with Micro1 enriched for antigen presentation, complement activation, and early pseudotime states. An 8-gene microglia-preferential signature (HSPA6, SERPINH1, CHORDC1, P4HA1, HSPH1, IER5, SLC38A2, and FKBP4), associated with ER stress, protein folding, and immune activation, achieved robust diagnostic performance (AUC >0.9) across cohorts. Gene set enrichment analysis revealed convergence on proteostasis and innate immune pathways, and pan-cellular activation patterns indicated a systemic, non-cell-autonomous inflammatory environment. MD simulations confirmed the structural stability of the FKBP4-SAR260301 complex, highlighting its therapeutic potential.
Conclusions
By indicating microglial functional heterogeneity and defining a validated, biologically grounded diagnostic signature, this study advances the mechanistic understanding of PD neuroinflammation. This study transforms neuroinflammation from a correlative hallmark to a mechanistically actionable axis, providing an urgently needed roadmap for inflammation-informed precision medicine in PD.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder with a prolonged prodromal phase and complex motor symptoms. Despite improved clinical criteria, early diagnosis and longitudinal monitoring remain challenging. While cerebrospinal fluid (CSF) and plasma metabolites and proteins show biomarker potential, their utility in predictive models is insufficiently characterized. We employed a secondary computational approach to integrate proteometabolomic profiles from CSF and plasma samples of >1100 Parkinson’s Progression Markers Initiative (PPMI) participants. Using multi-omics machine learning, we identified biofluid-specific signatures and evaluated predictive performance. Twenty-one biomarker candidates were validated across three models (SVM, GLMNET, RF); SVM and GLMNET achieved the highest recall (83–86%) and AUCs of 0.84–0.89. Longitudinal mixed-effects modeling revealed eight candidates associated with progression across diagnostic stages. We identified a three-part molecular framework characterizing neurodegeneration: a diagnostic subpanel reflecting early microbiome dysregulation (secretory granins and metabolites) and synaptic breakdown; a second subpanel monitoring phenoconversion via neurogenesis precursors and extracellular matrix proteins; and a third subpanel tracking progression through chronic neuroinflammation and immune activation. This integrated multi-omics approach provides a robust framework for stage-specific PD monitoring and potential clinical deployment.
A. Galal, Ahmed Moustafa, Mohamed Salama· npj Parkinson's Disease· 0 citations
Vascular dementia (VaD) is the second most prevalent form of dementia after Alzheimer’s disease and is primarily driven by chronic cerebral hypoperfusion and neurovascular dysfunction. Growing evidence indicates that immune dysregulation and neuroglial–vascular injury play critical roles in disease progression; however, the molecular regulators underlying these processes remain insufficiently characterized. This study aimed to delineate immune heterogeneity and neuroglial–vascular alterations in VaD and to identify key regulatory targets involved in disease pathogenesis. Bulk RNA sequencing data (GSE122063) and single-cell transcriptomic data (GSE282111) were retrieved from the Gene Expression Omnibus database and integratively analyzed to characterize immune infiltration patterns and cellular dysregulation. Immune profiling was performed using CIBERSORT and single-sample gene set enrichment analysis. Disease-associated cell subsets and hub genes were identified using Seurat, Scissor, and high-dimensional weighted gene co-expression network analysis (hdWGCNA). Pseudotime trajectory analysis and molecular docking were subsequently applied to investigate gene expression dynamics and potential therapeutic relevance. Bulk transcriptomic analysis revealed a pronounced pro-inflammatory shift in VaD brain tissue, characterized by increased infiltration of M1 macrophages, neutrophils, and activated dendritic cells, alongside reduced levels of M2 macrophages, resting CD4+ memory T cells, and regulatory T cells. Single-cell analysis demonstrated marked loss of oligodendrocytes, astrocytes, and endothelial cells in VaD. Scissor integration showed positive associations between VaD and oligodendrocytes, astrocytes, and endothelial cells, whereas microglia and oligodendrocyte progenitor cells were negatively associated. hdWGCNA identified 3 co-expression modules; intersection of the blue module with bulk differentially expressed genes highlighted DOCK3, ELF2, and Sin3A associated protein 25, with ELF2 uniquely co-expressed across relevant cell types. Pseudotime analysis indicated sustained downregulation of ELF2 during VaD-related cellular differentiation. Molecular docking analysis suggested strong binding affinities between ELF2 and nicergoline (−7.23 kcal/mol), nimodipine (−6.92 kcal/mol), and donepezil (−6.29 kcal/mol). This integrative transcriptomic study reveals disrupted immune balance and neuroglial–vascular cell homeostasis in vascular dementia, and prioritizes ELF2 as a candidate regulator associated with immune heterogeneity in VaD.
Li Liang, Cui-Qin Shen, Shi-Ren Huang et al.· Medicine· 0 citations
Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder worldwide, characterized by progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the pathological accumulation of Lewy bodies composed predominantly of aggregated α-synuclein (αSyn). Despite decades of progress in genetics and neuropathology, the mechanisms driving disease initiation and progression remain incompletely understood, and no disease-modifying therapy has yet demonstrated conclusive efficacy. Neuroinflammation and metabolic dysfunction have emerged as two central and mechanistically intertwined pillars of PD pathogenesis. We propose an integrative model in which these processes function not merely in parallel, but as mutually reinforcing components of a self-amplifying pathological circuit, while acknowledging that this model remains to be fully validated and that alternative causal architectures are possible. This review systematically addresses the mechanistic coupling between neuroinflammation and metabolic dysregulation in PD, covering: (1) the molecular basis of innate immune activation via DAMPs, pattern recognition receptors, and inflammasome signaling; (2) microglial metabolic reprogramming and the NLRP3/NF-κB inflammatory axis; (3) αSyn-driven innate and adaptive immune responses; (4) mitochondrial dysfunction and oxidative stress as bidirectional amplifiers; (5) the gut-brain axis as a conduit for peripheral immunometabolic disruption; (6) the AMPK/mTOR/HIF-1α molecular network integrating metabolism and inflammation; (7) sphingolipid metabolism and the GBA-lysosomal axis; and (8) translational evidence from animal models and randomized controlled trials. A concise section integrates key fluid biomarkers as clinical surrogates of the underlying mechanisms.
Yi-Xin Fu, Jiang-Hao Yu, Lu Xu et al.· Frontiers in Immunology· 0 citations
Background Epi-transcriptomic modifications, particularly N4-acetylcytidine (ac4C), and chronic neuroinflammation have emerged as pivotal players in the pathogenesis of Parkinson’s disease (PD). However, the specific molecular co-expression patterns linking ac4C RNA modification to neuronal inflammatory responses remains largely uncharted. Objective This study aimed to decode the ac4C-neuroinflammation (AN)-associated molecular patterns in PD and to identify a neuron-specific central pathogenic and therapeutic factor. Methods We integrated multi-omics analyses by using peripheral blood bulk transcriptomes (GSE18838, GSE49126, GSE22491, GSE6613, and GSE57475) and GWAS data from PD patients for identification of AN-related risk genes. Next, consensus clustering and 3 machine learning algorithms (LASSO, RF, and SVM-RFE) were applied for patient stratification, hub gene identification, and diagnostic modeling. Single-cell transcriptomic profiling of PD patients (GSE140231) was leveraged to map the cellular distribution and mechanistic roles of the hub gene within the substantia nigra (SN). An AI-driven active learning framework and the CTD database were utilized to screen therapeutic candidates targeting the hub gene, with binding affinities validated via molecular docking. In vitro experiments finally validated the expression of hub gene. Results We pinpointed 7 AN-associated risk DEGs for PD patients, including HSP90AA1, DEK, LEF1, IRF2, BCL2L1, CFL1, and BCR, which effectively stratified PD patients into 2 distinct immune-molecular subgroups. DEK can be considered as neuron-distributed and up-regulated AN-associated central pathogenic factor for PD patients. The DrugReflector active learning framework identified BRD-K57589644 as a computationally prioritized compound warranting further investigation. Conclusion This study establishes a novel AN-associated molecular patterns in PD, identifying DEK as a computationally identified neuron-specific factor associated with ac4C modification and neuroinflammatory cascades for PD patients.
Progression in early Parkinson's disease (PD) is heterogeneous, motivating transparent biological markers of group-level progression context. We analyzed longitudinal Parkinson’s Progression Markers Initiative (PPMI) data downloaded on 31 May 2026. The neuroimmune-enriched multibiofluid proteomic index (NEMPI) used 718 quality-control-eligible cerebrospinal fluid and plasma markers, organized into four source-context modules with equal marker and module weights. Direct normalized protein quantification (NPQ) z-standardization was used for the primary reporting implementation, with the log1p implementation examined as a preprocessing sensitivity analysis. Among the 521 participants in the same-sample Cox comparison (173 first observed cognitive-status abnormalities), adding NEMPI to demographic, clinical, alpha-synuclein seed amplification assay, dopamine-transporter imaging, and apolipoprotein E (APOE) epsilon4 covariates yielded an NEMPI hazard ratio (HR) of 1.287 per 1-SD higher value (95% confidence interval (CI): 1.102–1.502; P = 0.001) and improved likelihood-based fit, but the C-index increment was small (0.0032; bootstrap 95% CI: -0.0093 to 0.0239). The consecutive-abnormality association was stronger but had fewer events, with fewer than 10 events per parameter; the persistent/irreversible estimate was weaker, and its confidence interval crossed 1. Higher NEMPI was also associated with a modest group-average Montreal Cognitive Assessment (MoCA) and motor trajectory differences, with 5-year translations below published minimal clinically important difference (MCID) ranges. Results were similar under 5-year censoring, discrete-time analysis, stabilized inverse-probability weighting, age analyses, and measured confounder or preanalytic adjustments. NEMPI is therefore interpreted as an abundance-based, neuroimmune-enriched, multibiofluid proteomic composite associated with broad cognitive-motor progression, not as a patient-level prediction tool, clinical threshold, cognition-specific biomarker, or causal mechanism.
Hao Wang, Guo-Qing Wu, Deng-Ke Zhang et al.· Frontiers in Immunology· 0 citations
BACKGROUND
Neuroinflammation, a key factor in aging and neurodegeneration, is characterized by the increased activation of microglia, the brain's resident immune cells. Microglia play a central role in maintaining brain homeostasis, and their dysregulation during aging is increasingly implicated in the onset and progression of Alzheimer's disease (AD). However, the molecular mechanisms underlying microglial state transitions across physiological and pathological aging remain poorly understood.
METHODS
To address this gap, we conducted an in silico comparative transcriptomic study using publicly available datasets from two murine bulk RNA-seq including wild-type (WT) and APP/PS1 transgenic (Tg) mice at multiple ages, one human scRNA-seq dataset with multiple ages, and data obtained from SCAD-Brain.
RESULT
Our analyses revealed that physiological microglial aging is characterized by dynamic, non-linear gene expression trajectories, whereby genes involved in mitochondrial function, lysosomal degradation, and immune response follow a mirror-like pattern across aging. This mirror-like behavior was conserved in human microglial data across ages. In contrast, this adaptive pattern was disrupted at late-stage pathological aging in Tg mice, where sustained alterations in inflammatory, mitochondrial, and lysosomal pathways became more pronounced. Consistent with these findings, genes dysregulated in Tg mice showed similar expression trends in AD patients in the SCAD-Brain database.
CONCLUSION
These results suggest that middle age may represent a critical transition stage preceding neuroinflammation and neurodegeneration, making it an attractive window to identify preventive or therapeutic targets in early AD. Collectively, this study identifies candidate pathways and genes that warrant further experimental validation in the context of AD and age-related neurodegeneration.
Andrés Muedano-Sosa, Magalli Trujillo-Pineda, Samuel Ruiz-Pérez et al.· Experimental Gerontology· 0 citations
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