Scanlan R-L, Pease L, O'Keefe H, Martinez-Guimera A, Rasmussen L, Wordsworth J, Shanley D
Campus for Ageing and Vitality, Newcastle University, Newcastle, United Kingdom.
Center for Healthy Aging, Institute of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark.
Front Aging. 2024 Aug 29;5:1448543. doi: 10.3389/fragi.2024.1448543. eCollection 2024.
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.
细胞衰老具有多种表型，其特征是细胞周期永久性停滞以及相关的分泌表型（SASP），其中包括炎性细胞因子。通常，衰老细胞会被免疫系统清除，但随着年龄增长，这一过程会失调，导致衰老细胞积累并引发慢性炎症信号传导。由于衰老表型的异质性，识别衰老细胞具有挑战性，而衰老细胞疗法通常需要采用组合方法。在此，我们系统地收集了119个与人类成纤维细胞相关的转录组数据集，形成了一个在线数据库，该数据库描述了每项研究的相关变量，允许用户筛选感兴趣的变量和基因。我们对该数据库的分析确定，与增殖对照相比，在四种衰老类型（DNA损伤诱导的衰老（DDIS）、癌基因诱导的衰老（OIS）、复制性衰老和旁分泌诱导的衰老）中，有28个基因显著上调或下调。我们还发现，传统衰老标志物的基因表达模式对于不同的衰老诱导因素、细胞系和时间点具有高度特异性和可靠性。我们的综合数据支持了现有研究中使用单个数据集得出的若干观察结果，包括与OIS相比，DDIS中p53信号更强。然而，与一些早期观察结果相反，p16和p21 mRNA水平均会迅速升高，具体取决于衰老类型，并且至少持续8至11天。此外，几乎没有证据支持最初以转化生长因子β为中心的SASP。为了支持我们的转录组分析，我们通过计算模拟了DDIS和OIS过程中选定核心衰老蛋白的时间性蛋白质变化，并进行了基因敲低干预。我们得出结论，虽然衰老的通用生物标志物难以确定，但传统衰老标志物遵循可预测的模式，构建衰老研究框架可能会产生更具可重复性的数据，并有助于理解衰老异质性。
