Single-Cell Sequencing Reveals the Expression of Immune-Related Genes in Macrophages of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the "AUCell" function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using "CellChat." The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms.

Association between circadian physical activity patterns and cancer incidence through regulation of inflammation: A UK biobank study.

BACKGROUND: Physical activity (PA) has been linked with cancer incidence. However, the effects and mechanisms underpinning circadian PA trajectories on cancer remain elusive. This study aimed to explore the optimal PA patterns in reducing cancer incidence and the associated potential mediators. METHODS: Between 2006 and 2010, 502,400 participants were recruited from the UK Biobank. Out of these, 102,323 participants wore accelerometers, which allowed for collecting acceleration data continuously over 7 days. After excluding participants with previous cancer history, 96,687 participants were included in K-means cluster analysis to identify PA trajectories. The association between PA and cancer incidence was assessed using Cox regression analysis. Additionally, we investigated the mediating role of inflammation. RESULTS: A total of 5995 cancer cases were recorded during a median follow-up of 7.1 years. Four distinct PA trajectories (persistent low, single peak, double peak, and vigorous) were identified. The ideal PA patterns reduced the risk of 7 out of 17 site-specific cancers, with the lowest hazard ratios and 95% confidence intervals of cancer for bladder (0.59, 0.40-0.86), breast (0.73, 0.60-0.89), kidney (0.45, 0.26-0.78), lung (0.59, 0.41-0.84), myeloma (0.49, 0.27-0.88), and oral & pharynx (0.51, 0.26-0.98) in the vigorous pattern and for colorectal (0.71, 0.54-0.93) in the double peak pattern. Moreover, the mediating effects of inflammation were significant. CONCLUSION: Optimal PA trajectories reduced cancer incidence, especially in double peak and vigorous patterns. The protective effect was associated with both intensity and circadian rhythm. Crucially, this protection was mediated by inflammation regulation.