A Consistent Transcriptomic Signature of Cellular Senescence

As numerous senolytic therapies to clear senescent cells continue their progress towards the clinic, the research and development communities find themselves in ever greater need of better biomarkers for cellular senescence. Those that presently exist, such as staining tissue samples for senescence-associated β-galactosidase, are good enough for much of the present scope of research use, but not a suitable basis for either clinical assays or more sophisticated investigation of the mechanisms of senescence. As (a) there are numerous paths by which cells become senescent, prompted by different circumstances, and (b) senescence may vary in other ways between tissue types, and (c) different senolytics have different degrees of effectiveness across these varied classes and cells, it is the case that better and more consistent biomarkers would help to speed progress in this field.

Senescence is a state of indefinite growth arrest. It can be induced by various sublethal stresses, including telomere shortening, genomic injury, epigenomic damage and signaling from oncoproteins. Senescence is also characterized by a senescence-associated secretory phenotype (SASP) whereby cells produce and secrete pro-inflammatory cytokines. Senescence is beneficial for tissue remodeling, embryonic development, wound healing, and tumor suppression in young individuals. However, in old individuals it promotes aging-associated declines and diseases.

Progress to identify senescent cells in order to exploit them therapeutically has been hampered by a lack of robust and universal measurable traits. Thus far, senescence has been studied in a range of cell types induced by diverse triggers such as replicative exhaustion, DNA damage, oxidation and other stress conditions like signaling through oncoproteins. Due to this heterogeneity, finding broad biomarkers of senescence has been challenging and senescent cells are currently found through the combined detection of multiple biochemical markers such as p16, p53, p21 and SA-βGal, despite the fact that they are not exclusively nor consistently induced in senescence.

In this study, we sought to identify universally expressed transcripts across various senescent cell models. We performed RNA sequencing (RNA-seq) analysis after triggering senescence in human WI-38 and IMR-90 fibroblasts, human umbilical vein endothelial cells (HUVECs) and human alveolar endothelial cells (HAECs) through replicative exhaustion (WI-38, IMR-90), exposure to ionizing radiation (WI-38, IMR-90, HUVEC, HAEC) or doxorubicin (WI-38) or expression of an oncogene (oncogene-induced senescence, OIS) (WI-38). Comparisons of all the patterns of expressed transcripts revealed 68 RNAs that were increased (50 RNAs) or decreased (18 RNAs) across all senescence models, although a mimimum of 5 RNAs were sufficient to identify senescent cells bioinformatically. Most RNAs altered during senescence were protein-coding transcripts, but the long non-coding RNA PURPL (p53-upregulated regulator of p53 levels) was one of the most strikingly elevated transcripts.

Link: https://doi.org/10.1093/nar/gkz555

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