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Lookup NU author(s): Rebekah Scanlan, Dr Louise Pease, Hannah O'KeefeORCiD, Dr Alvaro Martinez Guimera, Dr James Wordsworth, Dr Daryl Shanley
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Cellular senescence is a diverse phenotype characterised by permanent cell cycle arrest and an associated secretory phenotype (SASP) which includes inflammatory cytokines. Typically, senescent cells are removed by the immune system, but this process becomes dysregulated with age causing senescent cells to accumulate and induce chronic inflammatory signalling. Identifying senescent cells is challenging due to senescence phenotype heterogeneity, and senotherapy often requires a combinatorial approach. Here we systematically collected 119 transcriptomic datasets related to human fibroblasts, forming an online database describing the relevant variables for each study allowing users to filter for variables and genes of interest. Our own analysis of the database identified 28 genes significantly up- or downregulated across four senescence types (DNA damage induced senescence (DDIS), oncogene induced senescence (OIS), replicative senescence, and bystander induced senescence) compared to proliferating controls. We also found gene expression patterns of conventional senescence markers were highly specific and reliable for different senescence inducers, cell lines, and timepoints. Our comprehensive data supported several observations made in existing studies using single datasets, including stronger p53 signalling in DDIS compared to OIS. However, contrary to some early observations, both p16 and p21 mRNA levels rise quickly, depending on senescence type, and persist for at least 8–11 days. Additionally, little evidence was found to support an initial TGFβ-centric SASP. To support our transcriptomic analysis, we computationally modelled temporal protein changes of select core senescence proteins during DDIS and OIS, as well as perform knockdown interventions. We conclude that while universal biomarkers of senescence are difficult to identify, conventional senescence markers follow predictable profiles and construction of a framework for studying senescence could lead to more reproducible data and understanding of senescence heterogeneity.
Author(s): Scanlan RL, Pease L, O'Keefe H, Martinez-Guimera A, Rasmussen L, Wordsworth J, Shanley D
Publication type: Article
Publication status: Published
Journal: Frontiers in Aging
Year: 2024
Volume: 5
Online publication date: 29/08/2024
Acceptance date: 19/08/2024
Date deposited: 18/03/2025
ISSN (electronic): 2673-6217
Publisher: Frontiers Media SA
URL: https://doi.org/10.3389/fragi.2024.1448543
DOI: 10.3389/fragi.2024.1448543
Data Access Statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. https://www.frontiersin.org/journals/aging/articles/10.3389/fragi.2024.1448543/full#SM1
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