Integrative transcriptomic and machine learning analysis identifies S100A8 as a key hub bridging peripheral inflammation and synaptic deficits in schizophrenia.
Aug 2026· Psychiatry Research· Vol 365, pp.
117403
· 0 citations· 51 references
Medicine
Abstract
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.
Astrocytic upregulation of HSP90AA1 is associated with altered synapse-related intercellular communication patterns in the PD substantia nigra, potentially involving a predicted TP53 associated regulatory component.
Yiyuan Xu, Yanfeng Shi, Yan Li et al.· International Journal of Mol...· 0 citations
This study integrated mRNA expression profiles from five post-mortem brain tissue GEO datasets to identify ASD-associated genes and found that EIF4A1 mRNA expression was significantly elevated in ASD subjects and rescued by treatment with the antipsychotics olanzapine or risperidone.
Background Microglia drive neuroinflammation in Alzheimer’s disease (AD), yet no approved therapy targets this compartment. Human genome-wide association studies consistently implicate innate immune loci in AD risk, establishing microglial transcriptional programs as therapeutically relevant but pharmacologically underexploited targets. Objective We sought to identify transcription factors (TFs) governing microglial state transitions computationally and to nominate structurally tractable drug repurposing candidates. Methods We applied trajectory inference (PAGA), pseudobulk DESeq2, pySCENIC gene regulatory network (GRN) inference, CellChat, and virtual screening of 1,962 approved compounds to 236,002 microglial nuclei from 84 donors (SEA-AD atlas). Results IKZF1 was the sole target TF retained under cisTarget v10 motif constraints, with peak regulon activity in LateAD-DAM (pseudotime ρ = +0.309) and replication in an independent bulk cohort (GSE95587; adjusted P value =.004). CellChat identified SLIT2→ROBO2 from multiple neuron subtypes (predominantly inhibitory interneurons) as the top predicted pathway to microglia. Tafamidis (→IRF8) and diflunisal (→PPARG) were top virtual screening hits; all evaluated compounds failed the pre-specified selectivity threshold. Conclusions IKZF1 is prioritised as a candidate late-disease microglial TF, supported by six convergent evidence dimensions including independent bulk replication. Tafamidis and diflunisal are low-confidence repurposing hypotheses requiring experimental validation.
Lipid-processing microglia are among the microglial states most strongly associated with Alzheimer’s disease (AD) pathology, yet whether this association reproduces across independent cohorts, what transcriptional programs define the state, and which upstream signals and small molecules can modulate it remain unsolved. We address these questions through a cross-cohort analysis of one such substate (MG4) by integrating differential expression, transcription factor activity inference, gene set enrichment, and cell-cell communication across five independent single-nucleus RNA sequencing cohorts (ntotal = 140 donors), with paired single-nucleus ATAC sequencing in one multi-omic cohort for epigenomic corroboration. A held-out cohort (n = 150 donors) supported donor-level regression of MG4 proportion on ligand expression, and two spatial transcriptomics datasets (ntotal = 30 donors) related ligand expression to MG4 identity in neighboring spots. MG4 was reproducibly enriched in AD across all five cohorts (pooled log2 fold change = 0.90, p = 3.0 × 10−4). Expression-based inference and motif accessibility jointly nominated MITF and BACH1 as regulators of a program led by V-ATPase-driven lysosomal acidification and cholesterol efflux, a lysosomal-biogenesis signature distinct from the catabolic DAM and lipid-storage LDAM programs, with AD-specific upregulation of energy metabolism. FGF1 and TGFB2 were the most supported candidate upstream ligands, each significant in donor-level regression with further spatial evidence. Computational drug repurposing nominated ten blood-brain barrier-penetrant compounds as perturbational probes. Together, these results advance a described disease-associated microglial state into a reproducible, mechanistically framed regulatory model, providing candidate regulators, upstream ligands, and pharmacological probes for functional validation.
C. Zheng, Tianhua Zhai, Fangyuan Zhang et al.· bioRxiv· 0 citations
BACKGROUND
Mitochondrial dysfunction and neuroinflammation are critically implicated in the pathogenesis of Alzheimer's disease (AD). However, a systematic exploration of key mitochondrion-related genes (MRGs) in AD, and their specific roles in reshaping the immune microenvironment and serving as diagnostic biomarkers, remains insufficient.
METHODS
To address this, we conducted an integrative bioinformatics analysis. Differentially expressed MRGs were identified from public AD transcriptomic datasets. Their biological functions were elucidated through enrichment analyses. The correlations between core MRGs and ssGSEA-derived immune-cell signature enrichment scores were quantified using transcriptome-based computational analysis. Finally, machine learning models were constructed and validated to assess the diagnostic potential of identified MRG signatures.
RESULTS
A robust set of dysregulated MRGs was identified in AD brains, showing predominant enrichment in pathways of oxidative phosphorylation and energy metabolism. Notably, the expression of key MRGs correlated significantly with altered infiltration abundances of specific immune cell types, including neutrophil-, eosinophil-, NK CD56bright cell-, and T follicular helper cell-related signatures. A diagnostic model constructed from a refined MRG signature exhibited promising predictive accuracy, with area under the curve (AUC) values reaching approximately 0.82 in the training cohort and around 0.74 in independent validation cohorts.
CONCLUSION
Our study defines a novel landscape of MRGs in AD, deciphers their tight crosstalk with the immune microenvironment, and establishes a promising MRG-based signature for AD diagnosis. These findings provide fresh insights into the potential molecular interplay between mitochondrial dysfunction and neuroinflammation in AD and nominate candidate mitochondrion-related biomarkers and regulatory mechanisms that warrant further experimental and clinical validation.
Lingjian Tang, Ningning Wu, Yao Zhu et al.· BMC Neurology· 0 citations
Abstract Background/Aim: Lung adenocarcinoma (LUAD) exhibits substantial molecular heterogeneity and variable clinical outcomes, highlighting the need for biomarkers that reflect core tumor biological processes. Centrosome-associated proteins regulate mitotic fidelity and genome stability, yet their roles in LUAD remain incompletely defined. In this study, we systematically characterized mitotic spindle organizing protein 1 (MOZART1; MZT1) and related family members in LUAD. Materials and Methods: We performed integrated analyses combining bulk transcriptomic datasets, survival modeling, gene set enrichment, immune deconvolution, machine-learning based prognostic modeling, and single-cell RNA sequencing. Expression patterns and clinical associations of MZT family genes were evaluated across pan-cancer and LUAD cohorts. Results: MZT family genes were consistently upregulated in tumor tissues, with MZT1 showing the most robust expression pattern. Elevated MZT1 expression was significantly associated with reduced overall survival. Functional analyses revealed coordinated activation of proliferative and genome maintenance pathways, including G2/M checkpoint regulation, E2F and MYC signaling, and DNA repair. A multivariable analysis indicated that the prognostic association of MZT1 was reduced after adjusting for canonical proliferation markers, suggesting partial overlap with established proliferation signals. The LASSO-based Cox model demonstrated stable time-dependent predictive performance at 1-, 3-, and 5-year survival. Immune analyses indicated associations between MZT1 expression and tumor microenvironmental features. Single-cell analysis showed that MZT1 expression was predominantly enriched in malignant epithelial cells and associated with proliferative cellular states. Protein-level validation supported concordance with transcriptomic findings. Conclusion: MZT1 is a proliferation-associated marker that integrates clinical risk, transcriptional programs, cellular heterogeneity, and predictive modeling in LUAD, providing a potential framework for biomarker development and risk stratification.
Dahlak Daniel Solomon, Hui-Ru Lin, Yung-Kuo Lee et al.· Cancer Genomics & Proteomics· 0 citations
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