Abstract
Biological aging is a complex process dependent on the interplay of cell autonomous and tissue contextual changes which occur in response to cumulative molecular stress and manifest through adaptive transcriptional reprogramming. Here we describe a transcription factor (TF) meta-analysis of gene expression datasets accrued from 18 tissue sites collected at different biological ages and from 7 different in-vitro aging models. In-vitro aging platforms included replicative senescence and an energy restriction model in quiescence (ERiQ), in which ATP was transiently reduced. TF motifs in promoter regions of trimmed sets of target genes were scanned using JASPAR and TRANSFAC. TF signatures established a global mapping of agglomerating motifs with distinct clusters when ranked hierarchically. Remarkably, the ERiQ profile was shared with the majority of in-vivo aged tissues. Fitting motifs in a minimalistic protein-protein network allowed to probe for connectivity to distinct stress sensors. The DNA damage sensors ATM and ATR linked to the subnetwork associated with senescence. By contrast, the energy sensors PTEN and AMPK connected to the nodes in the ERiQ subnetwork. These data suggest that metabolic dysfunction may be linked to transcriptional patterns characteristic of many aged tissues and distinct from cumulative DNA damage associated with senescence.
Highlights
The analysis of transcriptomes has become an important tool to study aging-associated processes, but has yet to deliver consistent datasets across tissues and experimental platforms
Our analysis revealed three distinct tissue groups, which can be aligned with transcription factor (TF) signatures of specific experimental models: classical replicative senescence in proliferative cells, senescence compared to quiescence excluding the influence of cell cycle, and an energy restriction model in quiescence (ERiQ), i.e. forced restriction of ATP supply
The resulting 150 genes were analyzed for transcription factor enrichment, using JASPAR and TRANSFAC catalogues
Summary
The analysis of transcriptomes has become an important tool to study aging-associated processes, but has yet to deliver consistent datasets across tissues and experimental platforms. Gene expression studies comparing tissues from flies, worms, mice and humans have revealed tissue- and organism-specific aging profiles [1], with commonalities in gene ontology classifications centered around metabolism, mitochondrial function [2, 3]. To what extent cellular heterogeneity, epigenetics or stochastic processes play a role in this diversity is unknown [5,6,7]. Another unresolved issue is the relevance of replicative in-vitro senescence to biologically aged tissues [8,9,10]. In-vitro replicative senescence represents a permanent post-mitotic state with a specific
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