Astrocytic HSP90AA1 Upregulation and Altered Synaptic Signaling in Parkinson’s Disease: Transcriptomic Screening and In Vivo Validation

Transcriptomic analysis reveals and validates lipid raft–associated biomarkers and functional networks in Alzheimer’s disease

Alzheimer’s disease (AD) involves complex changes, including synaptic dysfunction, neuroinflammation, and metabolic impairment, yet the role of lipid raft–associated gene networks in these processes remains unclear. In this study, we performed an integrative transcriptomic analysis using the GSE5281 dataset and validated the findings in an independent cohort (GSE33000). By combining differential expression analysis with weighted gene co-expression network analysis, we identified 72 lipid raft–associated genes linked to mitochondrial, synaptic, and immune-related pathways. Using network analysis and machine learning approaches (LASSO and SHAP), we further identified eight key biomarkers (CBL, CD44, EZR, FAS, FYN, ITGB1, MAPK1, and TGFBR1) that showed strong diagnostic performance and consistent results across datasets. Functional analysis revealed increased inflammatory signaling, including pyroptosis and cytokine pathways, alongside reduced oxidative phosphorylation and synaptic activity. Interestingly, immune profiling showed only minor differences in immune cell infiltration, but clear activation of multiple immune pathways, such as Th2 and Treg signaling, CD8⁺ T-cell signatures, and IL-6/IL-10–mediated inflammation. These immune changes were strongly associated with lipid raft–related biomarkers. Overall, our findings suggest that lipid raft dysregulation may act as a key link between immune activation, mitochondrial dysfunction, and synaptic impairment in AD.

SLC7A11-associated astrocyte states are linked to redox imbalance and synaptic microenvironment remodeling in multiple sclerosis: translational implications

Multiple sclerosis (MS) is characterized by inflammatory demyelination, oxidative stress, and neurological dysfunction. Despite disease-modifying therapies, chronic active lesions and ongoing neurodegeneration remain largely untreated, highlighting the need for mechanistically informed translational targets. We analyzed a human single-nucleus RNA-sequencing dataset (GSE279180) including healthy controls and MS samples. Astrocyte subpopulations were examined using reclustering, pseudotime inference, co-expression network analysis, SLC7A11-high/low stratification, virtual knockout prediction, and gene set enrichment analysis. Key findings were further assessed in vivo using a cuprizone-induced demyelination mouse model. Broad cellular remodeling was observed in MS lesions, including altered neuroactive ligand-receptor interactions, calcium signaling, glutamatergic synapse, and synaptic vesicle cycle-related pathways. SLC7A11 was enriched in astrocytes and increased along later pseudotime stages. SLC7A11-high astrocytes were linked to impaired antioxidant defense (GSH/GPX4) and synaptic vesicle-related remodeling. CPZ-treated mice showed decreased SLC7A11/GPX4 colocalization and increased SYP–GFAP colocalization, along with behavioral deficits, demyelination, and inflammatory-redox imbalance. SLC7A11-associated astrocyte states are associated with redox imbalance and synaptic microenvironment remodeling in MS. These states may inform future biomarker development and therapeutic investigation. Our findings provide a glial-state framework linking neuroinflammation, oxidative stress, and synaptic dysfunction, highlighting potential avenues for translational strategies in MS.

Integrative transcriptomic and machine learning analysis identifies S100A8 as a key hub bridging peripheral inflammation and synaptic deficits in schizophrenia.

Schizophrenia (SCZ) involves immune dysregulation and synaptic deficits, yet the molecular link between peripheral inflammation and central synaptic pathology remains unclear. We integrated blood transcriptomes from four SCZ cohorts (478 samples: 245 patients, 233 controls) and validated findings in single-cell brain data and an ELNI mouse model. After batch correction, 272 genes were differentially expressed, with S100A8 as the top upregulated immune gene. WGCNA identified a disease-associated module (blue, 133 genes, r = 0.16, p = 7 × 10⁻⁴) containing S100A8; its intersection with differentially expressed genes yielded 44 key genes enriched for cytoplasmic translation, mitochondrial electron transport, and innate immunity. Machine learning ranked S100A8 as the top discriminative feature, and its upregulation was robust across four independent analytic pipelines. CIBERSORTx revealed increased neutrophils and decreased regulatory T and resting NK cells in patients, with S100A8 correlating positively with neutrophils. Single-cell analysis showed that S100A8-expressing cells were specifically expanded in microglia (2.98% → 3.94%, OR = 1.34), and S100A8⁺ microglia displayed an activated state with coordinated upregulation of complement (C1QA/B/C) and phagocytic genes and downregulation of homeostatic markers (P2RY12, CX3CR1). In ELNI mice exhibiting SCZ-like behaviors, S100A8/S100A9 were upregulated in hippocampus and frontal cortex, accompanied by CD68 induction and reduced synaptophysin, with S100A8 correlating positively with CD68 and negatively with synaptophysin. Molecular docking identified hydroxyzine as a candidate S100A8 ligand. These convergent findings establish S100A8 as a hub linking peripheral immune dysregulation to microglial activation and synaptic pathology in SCZ, highlighting it as a candidate biomarker and therapeutic target.

Transcriptomic profiling and structural characterization reveal UQCRC1 and COX4I1 as key mitochondrial regulators associated with oxidative stress in multiple sclerosis.

Identification of EIF4A1 as a candidate molecular indicator of autism spectrum disorder using integrative bioinformatic and biological methods.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and molecular mechanism. Identifying reliable molecular biomarkers remains a critical challenge. In this study, we integrated mRNA expression profiles from five post-mortem brain tissue GEO datasets to identify ASD-associated genes. Following batch effect correction, differentially expressed genes (DEGs) were analysed and Weighted Gene Co-expression Network Analysis (WGCNA) was performed to screen genes correlated with ASD. Then, five machine learning algorithms - Random Forest, LASSO, Boruta, CatBoost, and LightGBM - were applied to screen hub genes. Lastly, alterations of the hub gene(s) were investigated with a maternal immune activation (MIA) rat model using poly I:C by measuring mRNA expression of the hub genes in the rat nucleus accumbens (NAc) and caudate putamen (CPu). A total of 30 DEGs and 54 WGCNA module genes were identified, yielding 29 key candidates by intersecting these two gene sets. EIF4A1 (Eukaryotic Translation Initiation Factor 4A1) was the sole gene consistently ranked among the top five by all five machine learning algorithms. Analysis of the integrated dataset confirmed that EIF4A1 mRNA expression was significantly elevated in ASD subjects. Finally, using the MIA rat model of ASD, we found that EIF4A1 mRNA expression was significantly down-regulated in the NAc and CPu, and this deficit was rescued by treatment with the antipsychotics olanzapine or risperidone. In conclusion, the present study positions EIF4A1 as a promising candidate molecular indicator with potential implications for understanding disease mechanisms and developing targeted interventions of ASD.

Integrated proteogenomic profiling reveals coordinated differential expression signatures during neuroinflammation.

BACKGROUND Experimental autoimmune encephalomyelitis (EAE) is a key model of autoimmune neuroinflammation, yet an integrated characterization of transcriptional and proteomic dysregulation of the CNS has been missing. METHODS In this study, we performed deep proteogenomic profiling of the spinal cord from mice induced with EAE during acute disease by combining RNA-seq (GEO, GSE330115) and LC-MS/MS (PRIDE, PXD078146). RESULTS We identified extensive upregulation of innate and adaptive immune response signatures alongside concordant downregulation of neuronal, synaptic, and mitochondrial pathways. Despite expected divergence as reported in previous studies discussing neuroinflammation models, log₂ fold changes and pathway enrichment scores showed high concordance between both gene product levels (Rp = 0.867, p < 2.2 × 10⁻1⁶, 95% CI [0.859, 0.874]). Loss of synaptic and metabolic integrity was predominantly observed at the protein level, whereas transcriptomics alone underestimated these structural deficits. In addition to inflammatory changes within CNS-resident cells during pathology analysis of markers typically absent in healthy CNS suggested that immune cell infiltration, in addition to pro-inflammatory phenotypic shifts of CNS-resident glial cells, accounts for the majority of non-CNS protein level changes in EAE, rather than passive plasma leakage. CONCLUSION Together, this integrated dataset reveals coordinated multilayer molecular remodelling in neuroinflammation and refines mechanistic interpretation of biomarker origin in inflamed CNS tissue in mice.