| Autor: |
Qu JH; Laboratory of Cardiovascular Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA., Tarasov KV; Laboratory of Cardiovascular Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA., Tarasova YS; Laboratory of Cardiovascular Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA., Chakir K; Laboratory of Cardiovascular Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA., Lakatta EG; Laboratory of Cardiovascular Science, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA. |
| Abstrakt: |
Advancing age is the most important risk factor for cardiovascular diseases (CVDs). Two types of cells, within the heart pacemaker, sinoatrial node (SAN), and within the left ventricle (LV), control two crucial characteristics of heart function, heart beat rate and contraction strength. As age advances, the heart's structure becomes remodeled, and SAN and LV cell functions deteriorate, thus increasing the risk for CVDs. However, the different molecular features of age-associated changes in SAN and LV cells have never been compared in omics scale in the context of aging. We applied deep RNA sequencing to four groups of samples, young LV, old LV, young SAN and old SAN, followed by numerous bioinformatic analyses. In addition to profiling the differences in gene expression patterns between the two heart chambers (LV vs. SAN), we also identified the chamber-specific concordant or discordant age-associated changes in: (1) genes linked to energy production related to cardiomyocyte contraction, (2) genes related to post-transcriptional processing, (3) genes involved in KEGG longevity regulating pathway, (4) prolongevity and antilongevity genes recorded and curated in the GenAge database, and (5) CVD marker genes. Our bioinformatic analysis also predicted the regulation activities and mapped the expression of upstream regulators including transcription regulators and post-transcriptional regulator miRNAs. This comprehensive analysis promotes our understanding of regulation of heart functions and will enable discovery of gene-specific therapeutic targets of CVDs in advanced age. |
