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

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

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.

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.

Single-cell gene networks nominate IKZF1 as an Alzheimer’s microglial regulator

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.

Integrative single-nucleus multi-omics profiling identifies candidate regulators and signaling axes in Alzheimer’s disease lipid-processing microglia

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.

Bioinformatic analysis of differentially expressed mitochondrion-related genes, immune cell infiltration, and diagnostic value in Alzheimer's disease.

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.

Multi-level Transcriptomic and Machine-learning Analyses Identify MZT1 as a Proliferation-associated Prognostic Marker in Lung Adenocarcinoma