Identification of immune-associated genes in vascular dementia by integrated bioinformatics and inflammatory infiltrates

Molecular Hydrogen Modulates T Cell Differentiation and Enhances Neuro-Regeneration in a Vascular Dementia Mouse Model

This study explores whether molecular hydrogen (H2) administration can alleviate cognitive and immunological disturbances in a mouse model of vascular dementia (VaD). Adult male C57BL/6 mice underwent bilateral common carotid artery stenosis to induce VaD and were subsequently assigned to three groups: VaD, VaD with hydrogen-rich water treatment (VaD + H2), and Sham controls. Behavioral assessments using open field and novel object recognition tests revealed that VaD mice exhibited anxiety-deficient behavior and memory impairment, both of which were reversed by H2 treatment. Histological examinations showed pyknotic neuronal morphologies and elevated reactive oxygen species (ROS) in the VaD hippocampus, whereas H2 administration mitigated these alterations. Furthermore, VaD-induced downregulation of BCL2 was reversed in the VaD + H2 group, in parallel with increased IL-4 expression. Flow cytometric analyses revealed that VaD disrupted T regulatory cell homeostasis by significantly increasing their proportion, an effect reversed by H2 treatment, thereby restoring immunological balance. Transcriptomic evaluations confirmed that VaD suppressed key neuroprotective and anti-inflammatory genes, while H2 treatment restored or enhanced their expression. Collectively, these findings highlight the neuroprotective and immuno-modulatory potential of molecular hydrogen, suggesting that H2 supplementation may promote neuronal resilience, modulate T cell differentiation, and support cognitive recovery in vascular dementia.

Comprehensive bioinformatics analysis identifies hub genes associated with immune cell infiltration in early-onset schizophrenia

BACKGROUND

Early-onset schizophrenia (EOS) occurs between the ages of 13 and 17 years, and neurobiological factors leading to cognitive deficits and psychotic symptoms with varying degrees of positive and negative symptoms. Numerous studies have demonstrated a broad link between immune dysregulation and the central nervous system in EOS, and its pathogenesis involves immune dysfunction, but the exact biological mechanisms have not been elucidated. This study employs immune infiltration analysis and bioinformatics to unveil the pathogenic mechanisms of EOS and identify potential diagnostic biomarkers, aiming for more precise clinical interventions.

METHODS

In this study, we recruited 26 EOS patients and 27 healthy controls (HCs), and microarray data were collected. Crossover genes were identified using weighted gene co-expression network analysis (WGCNA) and differential expression genes (DEGs) analysis. These genes were subjected to genome enrichment analysis (GSEA) and gene ontology (GO) analysis. Hub genes were identified through protein-protein interactions (PPIs) and the GeneMANIA database. The diagnostic potential of immune-associated hub genes was evaluated using ROC analysis. Immune infiltration in EOS was analyzed with CIBERSORT. Regulatory miRNAs for the hub genes were predicted using miRNet, and the correlation between mRNAs and miRNAs was analyzed and validated in clinical samples.

RESULTS

By WGCNA and DEGs analysis, 330 relevant genes were screened in EOS patients compared to HCs. Functional enrichment analysis using Metascape showed significant enrichment in immune system pathways. Subsequently, a PPI network was constructed to select the top 10 potential hub genes, and functional analysis was performed by GeneMANIA, resulting in the identification of four immune-related genes. In addition, significant differences were observed among the four immune cell types in the two groups of samples. ROC analysis showed clinical relevance of the immune-related hub genes, and the AUC of all genes was greater than 0.7. A miRNA-mRNA regulatory network was constructed from miRNA data, and three miRNAs were found to be significantly associated with the immune-related hub genes.

CONCLUSION

Our findings demonstrated that CCL3, IL1B, CXCL8, CXCL10 and miR-34a-5p may be biomarkers that play crucial roles in the underlying mechanisms of EOS immune-related pathways. These findings contribute to the understanding of EOS pathophysiology and may help identify new diagnostic and therapeutic targets.

Exploring the shared gene signatures and mechanism among three autoimmune diseases by bulk RNA sequencing integrated with single-cell RNA sequencing analysis

Background

Emerging evidence underscores the comorbidity mechanisms among autoimmune diseases (AIDs), with innovative technologies such as single-cell RNA sequencing (scRNA-seq) significantly advancing the explorations in this field. This study aimed to investigate the shared genes among three AIDs-Multiple Sclerosis (MS), Systemic Lupus Erythematosus (SLE), and Rheumatoid Arthritis (RA) using bioinformatics databases, and to identify potential biomarkers for early diagnosis.

Methods

We retrieved transcriptomic data of MS, SLE, and RA patients from public databases. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to construct gene co-expression networks and identify disease-associated modules. Functional enrichment analyses and Protein-Protein Interaction (PPI) network was constructed. We used machine learning algorithms to select candidate biomarkers and evaluate their diagnostic value. The Cibersort algorithm was and scRNA-seq analysis was performed to identify key gene expression patterns and assess the infiltration of immune cells in MS patients. Finally, the biomarkers' expression was validated in human and mice experiments.

Results

Several shared genes among MS, SLE, and RA were identified, which play crucial roles in immune responses and inflammation regulation. PPI network analysis highlighted key hub genes, some of which were selected as candidate biomarkers through machine learning algorithms. Receiver Operating Characteristic (ROC) curve analysis indicated that some genes had high diagnostic value (Area Under the Curve, AUC >0.7). Immune cell infiltration pattern analysis showed significant differences in the expression of various immune cells in MS patients. scRNA-seq analysis revealed clusters of genes that were significantly upregulated in the single cells of cerebrospinal fluid in MS patients. The expression of shared genes was validated in the EAE mose model. Validation using clinical samples confirmed the expression of potential diagnostic biomarkers.

Conclusion

This study identified shared genes among MS, SLE, and RA and proposed potential early diagnostic biomarkers. These genes are pivotal in regulating immune responses, providing new targets and theoretical basis for the early diagnosis and treatment of autoimmune diseases.