Senescent cells are often associated with changes in gene expression that appear to occur independent of the regulated gene expression linked to aspects of the senescent phenotype such as cell cycle arrest, the secretory response and apoptosis resistance. This phenomenon has been termed promiscuous gene expression (pGE) (Burton and Krizhanovsky, 2014) and can be more specifically defined as gene expression that is uncoupled from tissue or developmental regulation.
pGE can be observed in microarray analysis by comparing the gene expression profiles of different senescent cell types and lines. Zhang et al (2003) has demonstrated that the up-regulation of genes in senescent fibroblasts was associated with gene clustering (150 of the 376 gene up-regulated), whereas the down-regulation of genes (313) was not. 48.1% of the up-regulated genes were designated as membrane-associated proteins, 10.5% related to apoptosis and 15.8% to transport, whereas 17.9% of the down-regulated genes are involved in cell cycle regulation. Gene expression changes in senescent human mammary epithelial cells (HMECs) were shown to be drastically different than that of the fibroblasts, despite both undergoing senescence induced by telomere attrition. Only five genes up-regulated and seven genes down-regulated in HMECs showed similar regulation in fibroblasts. However, like senescent fibroblasts, HMECs also demonstrated gene clustering associated with up-regulated genes only. Zhang et al postulated at the time, that if senescence is a response to DNA damage, then the observed differences in gene expression between senescent fibroblasts and HMECs imply that the effects of DNA damage must vary according to cell type and line. This study also suggested that processes occurring during senescence may lead to localized alteration in chromatin and the consequent up-regulation of groups of genes within “opened” domains.
Shelton et al (1999) also demonstrated that senescence-mediated gene expression between different cell lineages varies greatly. BJ fibroblasts, HUVECs and retinal pigment epithelial cells (RPE340) that underwent replicative senescence demonstrated substantial variation in gene expression. A genomic comparison of three different senescent fibroblasts strains also demonstrated significant differences in gene expression, but also shared trends were apparent. If indeed pGE is uncoupled from tissue or developmental regulation, then stochastic processes that alter chromatin structure could be at play and the different response between cell types and cell strains could reflect differences in cell-specific chromatin architecture important for cell-specific gene expression. Elevated levels of oxidative stress, a feature of senescent cells could be one such stochastic process.
Bahar et al demonstrated that although gene expression levels varied among cardiomyocytes taken from hearts of young mice, the heterogeneity is elevated with age (Bahar et al. 2006). This increased stochastic gene expression with age was suggested to be the result of genomic damage, as mouse embryonic fibroblasts treated with hydrogen peroxide in culture resulted in significant cell-cell variation in gene expression in conjunction with these cells showing morphological signs of cellular senescence (Bahar et al. 2006).
So how could DNA damage induced by oxidative stress result in stochastic changes in gene expression? When cells sustain DNA damage, chromatin undergoes remodeling to facilitate DNA repair (Price and D’Andrea, 2013, House et al. 2014). This remodeling or “opening” of tightly packed DNA could allow transcription factors access to previously inaccessible genes. Therefore, persistent DNA damage and consequently continuous chromatin remodeling may facilitate pGE. While the induction of DNA damage is likely a stochastic process, the sites of DNA damage may not be completely random, as certain areas of the genome may be more or less prone to genomic insults (Ma et al. 2012). The clustering phenomenon reported by Zhang et al may be the result of these DNA damage prone sites (Zhang et al. 2003). If this were indeed the case, while there may be substantial differences in gene expression at a cell-cell comparison, an overall comparison between cell cultures would likely demonstrate consistent gene alterations resulting from an average expression of all cells within a culture.
In addition to oxidative stress, a number of other possible mechanisms may exist for generating pGE. Senescent fibroblasts are known to undergo methylation changes (Cruickshanks et al. 2013) and these alterations may lead to epigenetic alterations that promote stochastic changes in gene expression. Alternatively, it has been suggested that DNA damage may modulate gene expression by altering the binding capacity of transcription factors (Rose et al. 2012).
Interestingly, the reprogramming of fibroblasts into induced pluripotent stem cells (iPSCs) via the addition of OCT4, SOX2, KLF4 and MYC (OSKM) requires a long stochastic phase of gene activation associated with changes in histone modifications at somatic genes and activation of DNA repair and RNA processing (Buganim et al. 2013). This stochastic gene expression may be the result of “promiscuous binding” by OCT4, SOX2 and KLF4, where they occupy accessible chromatin and bind to promoters of genes that are active or repressed (Buganim et al. 2013). It is possible that pGE in senescent cells partly mimics stochastic gene activation associated with cellular reprogramming. However, whether pGE in senescent cells is associated with factors that can undergo “promiscuous binding” has yet to be determined.
Whether pGE plays a functional role in cell senescence has yet to be determined. However, it can be speculated that pGE may function to generate an array of tissue-restricted proteins that can subsequently be processed into peptides by autophagic proteases for presentation on MHC molecules (Dengjel et al. 2005). Similar to the presentation of tumour-associated antigens (Reuschenbach et al. 2009), senescent cells may also present antigens that can be recognized by immune cells, thereby becoming antigen-presenting cells (APCs). Although the up-regulation of MHC molecules on senescent cells have yet to be fully evaluated, the up-regulation of MHC class I but not MHC class II in response to DNA damage in fibroblasts has been reported (Tang et al. 2014). It remains to be determined whether pGE is a component of immunogenic conversion.
Atypical senescent states: Experimental induction of cyclin-dependent kinase inhibitors (e.g. p16, p21)
For many researchers, irreversible cell cycle arrest is the canonical trait of senescent cells. Such growth arrest can be induced experimentally by the up-regulation or over-expression of cyclin dependent kinase inhibitors (CDKi). Thus valuable models are, at least potentially, available in which to study the physiological effect of growth arrest distinct from the DDR or any other upstream response. Unfortunately there has been little characterization of the phenotype of cells rendered ‘senescent’ by this means.
Blagosklonny and co-workers (Korotchkina et al. 2009) used an isopropyl-thio-galactosidase (IPTG)-inducible p21 expression construct to induce a senescence-like state in an HT1080-derived cell line (HT-p21-9). Characterisation of the phenotype of these cells does not appear to have been attempted beyond observing irreversible growth arrest and the presence of increased SA-β-Gal activity. Given that HT1080 is a highly tumorigenic fibrosarcoma carrying an activated N-ras oncogene (Benedict et al. 1984), it probably represents a poor genetic background in which to assess whether markers of immunogenic conversion or resistance to cell death can be induced by CDKi overexpression alone. However, the basic principle of using such a construct for that purpose is sound.
Tokarsky-Amiel et al (2013) showed that overexpression of p14ARF in the epidermis of the skin of mice (using a tetracyclin-inducible construct) resulted in mass apoptosis and cell cycle arrest. As measured by SA-β-Gal activity, the p14ARF transgene drove senescence in up to 8% of the surviving cells in the epithelium by a p53-dependent mechanism (demonstrated by ablation of p53 through co-expression of a specific shRNA directed against it). These senescent cells were viable within the epidermis for several weeks consistent with lack of clearance. Unfortunately, minimal analysis of their phenotype was conducted (beyond assessment of the message levels for the senescence-associated genes Pai-1 and Dcr2). Thus, the immune state of the p14ARF-senescent cells is currently unclear and the picture is complicated by the fact that senescent rodent cells do not display a senescent secretome under some conditions. However, given that alopecia and follical stem cell dysfunction were observed in the animals, it is clear that cells rendered ‘senescent’ in this manner can exert phenotypic effects. Thus, there is some evidence that cell cycle arrest alone may be sufficient to cause problems in highly mitotic tissues such as the epidermis, but large amounts of work remain to be done.
CDKi overexpression systems clearly have the potential to be valuable tools. However the extent to which these are physiologically reflective can legitimately be challenged. This can be understood in two ways (i) the mechanism by which the growth arrest is induced has not been reported in vivo and (ii) cells do not become senescent en mass but gradually as a result of tissue turnover throughout life. Thus, findings made with these systems could be considered ‘artefactual’
By way of addressing these concerns, it is worth remembering that for many years replicative senescence was dismissed as a ‘tissue culture artefact’ because senescent cells had not been observed in vivo (evidence for their existence in tissue remained severely limited until the late 1990s). By the same token, elevation of CDKi alone in cells in vivo is not impossible. Absence of evidence is never evidence of absence. Similarly, many over-expression systems model systems can be said to be non-physiological. However, valuable data is routinely gathered using them and in this instance could allow researchers to gage the maximum physiological impact that irreversible growth arrest can have on tissue function. Thus, if these limits are recognized, such models are potentially utile, especially when combined with detailed analysis of phenotypes known to exist in other ‘senescent cells’ (e.g. apoptosis resistance, immune ligand presentation and the secretory response)