Home > Medical Research Archives > Issue 149 > The RNA Genome Sequencing Demonstrates Increased Production of Metabolic Genes in Late Ages in Highly Differentiated Tissues
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Published in the Medical Research Archives
Jan 2024 Issue
The RNA Genome Sequencing Demonstrates Increased Production of Metabolic Genes in Late Ages in Highly Differentiated Tissues
Published on Jan 30, 2024
DOI
Abstract
In this paper, we continue statistical analysis of RNA-Seq results of the whole genome of Mus musculus during their lifetime. We propose that the implementation of the developmental program by cells and their transition to the active performance of functions is the main mechanism of aging. The data obtained confirm the basis of our ideas that the triggering of aging processes is embedded in the very "design" of multicellulars. Previously, we noted a gradual decrease in RNA production in the part of the genome responsible for cellular infrastructure. At the same time, we noted a rise in the level of production of genes of this part in late ages. We hypothesized that this is associated with the increased demand of cells for energy production to maintain the weakening functions of the organism. We identified a block of 24 most productive genes responsible for metabolic activity and energy production in the cell. As shown by data analysis, it was these genes that appeared to be responsible for the rise in the overall activity of infrastructural genes in the late period. We also hypothesized that the rise in demand for cellular energy structures in the aging organism is most pronounced in highly differentiated tissues. For this purpose, we distinguished two groups of tissues, according to the level of their mitotic index. The results show that the rise in the production of the infrastructural part of the genome found in late ages is due to RNA synthesis of metabolic genes and is expressed only in the group of tissues with low mitotic index. We plan to further investigate the age-related dynamics of the proteome, comparing our results with other databases to identify similar patterns of RNA production dynamics in them.
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