bioRxivCell BiologyPreprint
Towards a Rosetta Stone to Decipher the Survival Networks of Senescence
Abstract
Cellular senescence, an irreversible state of cell cycle arrest in response to various stressors that can damage cells, is a key player in developing and progressing multiple chronic degenerative diseases associated with aging. Over the years, these senescent cells accumulate in organs and tissues, it is thought that the accumulation of these cells results from their capacity to evade programmed cell death by developing and activating Senescent Cell Anti-apoptotic Pathways (SCAPs); however, numerous aspects of the development and activation of these SCAPs are still unknown. In this study, we analyzed the variations in the expression levels and co-expression patterns of 39 SCAPs genes across 33 tissues from young and elderly individuals. Surprisingly, we did not observe a consistent increase in SCAPs gene expression with age in any tissue. Instead, we found tissue-specific variations in gene expression levels, changes in the strength of gene coordination, and the wiring and rewiring of gene co-expression networks that depend on the tissue. Our results suggest that the development and activation of SCAPs are far more complex than previously understood; merely increasing the expression of specific survival genes would not adequately explain the anti-apoptotic capabilities of senescent cells. We suggest that the formation and triggering of SCAPs entail a complex interplay of factors, encompassing distinct alterations in expression levels alongside shifts in the intensity and configurations of gene interactions, moreover, these modifications are unique to individual tissues. Our results deepen our understanding of how senescent cells evade programmed cell death and are essential for developing targeted pharmacological therapies that can selectively and safely eliminate senescent cells more effectively.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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