Systematic Analysis and Model of Fibroblast Senescence Transcriptome

Abstract

AbstractCell senescence is a diverse phenotype and therapies often require combinatorial approaches. Here we have systematically collected transcriptomic data related to human fibroblasts to a total of 98 studies. We formed a database describing the relevant variables for each study which we have hosted online allowing users to filter the studies to select variables and genes of interest. Our own analysis of the database revealed 13 marker genes consistently downregulated in senescent cells compared to proliferating controls; however, we also found gene expression patterns that were highly specific and reliable for different senescence inducers, cell lines, and timepoint after induction, confirming several conclusions of existing studies based on single datasets, including differences in p53 and inflammatory signals between oncogene induced senescence (OIS) and DNA damage induced senescence (DDIS). We saw little evidence of an initial TGF-β-centric SASP, but we did find evidence of a decrease in Notch signalling. Contrary to some early observations, both p16 and p21 mRNA levels appeared to rise quickly, depending on senescence type, and persist for at least 8-11 days. We concluded that while universal biomarkers of senescence are difficult to identify, the conventional senescence markers follow predictable profiles and construction of a framework for studying senescence could lead to more reproducible data.