bioRxivGenomicsPreprint
Unveiling the Biochemical Mechanisms of Aging and the Implications of Oxidative Stress on Cellular Senescence through Multi-Omics Analysis of Fibroblasts
Abstract
This research investigates the complex biochemical mechanisms underlying aging by analyzing primary human fibroblasts using a longitudinal multi-omics dataset. This dataset includes cytology, DNA methylation and epigenetic clocks, bioenergetics, mitochondrial DNA sequencing, RNA sequencing, and cytokine profiling. Key findings indicate that mitochondrial efficiency declines with age, while glycolysis becomes more prevalent to compensate for energy demands. Epigenetic clocks, such as Hannum and PhenoAge, showed strong correlations with biological age ({rho} > 0.650, p < 1e-6), validating the experimental setup and confirming that the cultured fibroblasts were aging appropriately. Fibroblasts with SURF1 mutations exhibited accelerated aging, marked by bioenergetic deficits, increased cell volume, and reduced proliferative capacity, underscoring the pivotal role of mitochondrial dysfunction in cellular senescence. Novel insights were gained from analyzing cytokines like IL18 and PCSK9, some of which were linked to age-related diseases such as Alzheimers and cardiovascular disorders. Experimental treatments revealed distinct effects on cellular aging. Dexamethasone reduced inflammation but also increased DNA methylation, induced metabolic inefficiencies, and shortened cellular lifespan. Oligomycin heightened oxidative stress and RNA degradation, emphasizing how such treatments contribute to cellular stress and metabolic imbalance while shedding light on aging mechanisms. By uncovering connections between mitochondrial dysfunction, epigenetic biomarkers, and immune dysregulation, this study identifies potential therapeutic targets for age-related diseases. Future research could validate the most promising biomarkers across diverse cell types and experimental treatments to build a more comprehensive understanding of aging.
- organism: human
- assay: DNA methylation
- assay: bulk RNA-seq
- assay: metabolomics
- assay: proteomics
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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