Elevated mtDNA content in RBCs promotes oxidative stress may be responsible for faster senescence in men.

BACKGROUND: Both we and others have found that RBC counts are significantly lower in older compared to younger. However, when gender is factored in, a significant age-related decrease of RBC counts is observed only in men but not in women. METHODS: qPCR and confocal microscopy were used to detect the presence of mtDNA in RBCs. Flow cytometry and specific inhibitors were used to determine how RBCs uptake cf-mtDNA. The peripheral blood was collected from 202 young adults and 207 older adults and RBC and plasma were isolated. The levels of TLR9+RBCs and apoptotic RBCs after uptake of cf-mtDNA by RBCs were measured by flow cytometry. The kit detects changes in SOD and MDA levels after cf-mtDNA uptake by RBCs. Young RBCs (YR) and old RBCs (OR) from single individuals were separated by Percoll centrifugation. RESULTS: We found a significant decrease in RBC counts and a significant increase in the RDW with aging only in men. We also found that significantly elevated mtDNA content in RBCs was observed only in men during aging and was not found in women. Further studies demonstrated that RBCs could take up cf-mtDNA via TLR9, and the uptake of mtDNA might lead to a decrease in the RBC number and an increase in RDW due to an increase of oxidative stress. CONCLUSIONS: The RBC mtDNA content might be a potential marker of RBC aging and the elevated RBC mtDNA content might be the cause of faster senescence in males than females.

Identification of Potential Biomarkers for Group I Pulmonary Hypertension Based on Machine Learning and Bioinformatics Analysis.

A number of processes and pathways have been reported in the development of Group I pulmonary hypertension (Group I PAH); however, novel biomarkers need to be identified for a better diagnosis and management. We employed a robust rank aggregation (RRA) algorithm to shortlist the key differentially expressed genes (DEGs) between Group I PAH patients and controls. An optimal diagnostic model was obtained by comparing seven machine learning algorithms and was verified in an independent dataset. The functional roles of key DEGs and biomarkers were analyzed using various in silico methods. Finally, the biomarkers and a set of key candidates were experimentally validated using patient samples and a cell line model. A total of 48 key DEGs with preferable diagnostic value were identified. A gradient boosting decision tree algorithm was utilized to build a diagnostic model with three biomarkers, PBRM1, CA1, and TXLNG. An immune-cell infiltration analysis revealed significant differences in the relative abundances of seven immune cells between controls and PAH patients and a correlation with the biomarkers. Experimental validation confirmed the upregulation of the three biomarkers in Group I PAH patients. In conclusion, machine learning and a bioinformatics analysis along with experimental techniques identified PBRM1, CA1, and TXLNG as potential biomarkers for Group I PAH.

Identification of DNA methylation-regulated genes as potential biomarkers for coronary heart disease via machine learning in the Framingham Heart Study.

BACKGROUND: DNA methylation-regulated genes have been demonstrated as the crucial participants in the occurrence of coronary heart disease (CHD). The machine learning based on DNA methylation-regulated genes has tremendous potential for mining non-invasive predictive biomarkers and exploring underlying new mechanisms of CHD. RESULTS: First, the 2085 age-gender-matched individuals in Framingham Heart Study (FHS) were randomly divided into training set and validation set. We then integrated methylome and transcriptome data of peripheral blood leukocytes (PBLs) from the training set to probe into the methylation and expression patterns of CHD-related genes. A total of five hub DNA methylation-regulated genes were identified in CHD through dimensionality reduction, including ATG7, BACH2, CDKN1B, DHCR24 and MPO. Subsequently, methylation and expression features of the hub DNA methylation-regulated genes were used to construct machine learning models for CHD prediction by LightGBM, XGBoost and Random Forest. The optimal model established by LightGBM exhibited favorable predictive capacity, whose AUC, sensitivity, and specificity were 0.834, 0.672, 0.864 in the validation set, respectively. Furthermore, the methylation and expression statuses of the hub genes were verified in monocytes using methylation microarray and transcriptome sequencing. The methylation statuses of ATG7, DHCR24 and MPO and the expression statuses of ATG7, BACH2 and DHCR24 in monocytes of our study population were consistent with those in PBLs from FHS. CONCLUSIONS: We identified five DNA methylation-regulated genes based on a predictive model for CHD using machine learning, which may clue the new epigenetic mechanism for CHD.

DYSF promotes monocyte activation in atherosclerotic cardiovascular disease as a DNA methylation-driven gene.

Dysferlin (DYSF) has drawn much attention due to its involvement in dysferlinopathy and was reported to affect monocyte functions in recent studies. However, the role of DYSF in the pathogenesis of atherosclerotic cardiovascular diseases (ASCVD) and the regulation mechanism of DYSF expression have not been fully studied. In this study, Gene Expression Omnibus (GEO) database and epigenome-wide association study (EWAS) literatures were searched to find the DNA methylation-driven genes (including DYSF) of ASCVD. The hub genes related to DYSF were also identified through weighted correlation network analysis (WGCNA). Regulation of DYSF expression through its promoter methylation status was verified using peripheral blood leucocytes (PBLs) from ASCVD patients and normal controls, and experiments on THP1 cells and Apoe-/- mice. Similarly, the expressions of DYSF related hub genes, mainly contained SELL, STAT3 and TMX1, were also validated. DYSF functions were then evaluated by phagocytosis, transwell and adhesion assays in DYSF knock-down and overexpressed THP1 cells. The results showed that DYSF promoter hypermethylation up-regulated its expression in clinical samples, THP1 cells and Apoe-/- mice, confirming DYSF as a DNA methylation-driven gene. The combination of DYSF expression and methylation status in PBLs had a considerable prediction value for ASCVD. Besides, DYSF could enhance the phagocytosis, migration and adhesion ability of THP1 cells. Among DYSF related hub genes, SELL was proven to be the downstream target of DYSF by wet experiments. In conclusion, DYSF promoter hypermethylation upregulated its expression and promoted monocytes activation, which further participated in the pathogenesis of ASCVD.