Various Expressions of PIK3C2A and TXNIP Genes and Their Potential Role as Independent Risk Factors for Chronic Stable Angina and Acute Coronary Syndrome

Thioredoxin (Trx): A redox target and modulator of cellular senescence and aging-related diseases

Thioredoxin (Trx) is a compact redox-regulatory protein that modulates cellular redox state by reducing oxidized proteins. Trx exhibits dual functionality as an antioxidant and a cofactor for diverse enzymes and transcription factors, thereby exerting influence over their activity and function. Trx has emerged as a pivotal biomarker for various diseases, particularly those associated with oxidative stress, inflammation, and aging. Recent clinical investigations have underscored the significance of Trx in disease diagnosis, treatment, and mechanistic elucidation. Despite its paramount importance, the intricate interplay between Trx and cellular senescence-a condition characterized by irreversible growth arrest induced by multiple aging stimuli-remains inadequately understood. In this review, our objective is to present a comprehensive and up-to-date overview of the structure and function of Trx, its involvement in redox signaling pathways and cellular senescence, its association with aging and age-related diseases, as well as its potential as a therapeutic target. Our review aims to elucidate the novel and extensive role of Trx in senescence while highlighting its implications for aging and age-related diseases.

Development of a diagnostic and risk prediction model for Alzheimer's disease through integration of single-cell and bulk transcriptomic analysis of glutamine metabolism

Background

In this study, we present a novel system for quantifying glutamine metabolism (GM) to enhance the effectiveness of Alzheimer's disease (AD) diagnosis and risk prediction.

Methods

Single-cell RNA sequencing (scRNA-seq) analysis was utilized to comprehensively assess the expression patterns of GM. The WGCNA algorithm was applied to investigate the most significant genes related to GM. Subsequently, three machine learning algorithms (Boruta, LASSO, and SVM-RFE) were employed to identify GM-associated characteristic genes and develop a risk model. Patients were divided into high- and low-risk groups based on this model. Moreover, we explored biological properties, distinct signaling pathways, and immunological characteristics of AD patients at different risk levels. Finally, in vitro and in vivo models of AD were constructed to validate the characteristics of the feature genes.

Results

Both scRNA-seq and bulk transcriptomic analyses revealed increased GM activity in AD patients, specifically in certain cell subsets (pDC, Tem/Effector helper T cells (LTB), and plasma cells). Cells with higher GM scores demonstrated more significant numbers and strengths of interactions with other cell types. The WGCNA algorithm identified 360 genes related to GM, and a risk score was constructed based on nine characteristic genes (ATP13A4, PIK3C2A, CD164, PHF1, CES2, PDGFB, LCOR, TMEM30A, and PLXNA1) identified through multiple machine learning algorithms displayed reliable diagnostic efficacy for AD onset. Nomograms, calibration curves, and decision curve analysis (DCA) based on these characteristic genes provided significant clinical benefits for AD patients. High-risk AD patients exhibited higher levels of immune-related functions and pathways, increased immune cell infiltration, and elevated expressions of immune modulators. RT-qPCR analysis revealed that the majority of the nine characteristic genes were differentially expressed in AD-induced rat neurons. Knocking down PHF1 could protect against neurite loss and alleviate cell injury in AD neurons. In vivo, down-regulation of PHF1 in AD models decreases GM metabolism levels and modulates the immunoinflammatory response in the brain.

Conclusion

This comprehensive identification of gene expression patterns contributes to a deeper understanding of the underlying pathological mechanisms driving AD pathogenesis. Furthermore, the risk model based on the nine-gene signature offers a promising theoretical foundation for developing individualized treatments for AD patients.