Abstract
By combining transcriptomic data with other data sources, inferences can be made about functional changes during ageing. Thus, we conducted a meta-analysis on 127 publicly available microarray and RNA-Seq datasets from mice, rats and humans, identifying a transcriptomic signature of ageing across species and tissues. Analyses on subsets of these datasets produced transcriptomic signatures of ageing for brain, heart and muscle. We then applied enrichment analysis and machine learning to functionally describe these signatures, revealing overexpression of immune and stress response genes and underexpression of metabolic and developmental genes. Further analyses revealed little overlap between genes differentially expressed with age in different tissues, despite ageing differentially expressed genes typically being widely expressed across tissues. Additionally we show that the ageing gene expression signatures (particularly the overexpressed signatures) of the whole meta-analysis, brain and muscle tend to include genes that are central in protein-protein interaction networks. We also show that genes underexpressed with age in the brain are highly central in a co-expression network, suggesting that underexpression of these genes may have broad phenotypic consequences. In sum, we show numerous functional similarities between the ageing transcriptomes of these important tissues, along with unique network properties of genes differentially expressed with age in both a protein-protein interaction and co-expression networks.
Highlights
Knowledge of expression patterns in ageing organisms can be employed as biomarker panels that estimate a ‘transcriptomic age’ [1], in addition to giving insight into the basic processes associated with ageing [2] and serving as a starting point from which to identify drugs and other interventions that may assist with healthy ageing [3].Comparative analysis of gene expression data across species is a powerful method to determine an expression signature of ageing
We have performed a meta-analysis of ageing using the methods of de Magalhães, et al [4] on 127 microarray and RNA-Seq datasets from humans, mice and rats, and applied machine learning alongside enrichment methods www.aging-us.com to analyse the results
Other interesting genes overexpressed in muscle include EFEMP1, a gene involved in eye morphogenesis that has demonstrated involvement in premature-aging like phenotypes in mice, possibly playing a role in fascial structural integrity [6], and that has recently been shown to be overexpressed in aged mouse aorta [7] and CHRNA1 that codes for a muscle acetylcholine receptor subunit
Summary
Knowledge of expression patterns in ageing organisms can be employed as biomarker panels that estimate a ‘transcriptomic age’ [1], in addition to giving insight into the basic processes associated with ageing [2] and serving as a starting point from which to identify drugs and other interventions that may assist with healthy ageing [3].Comparative analysis of gene expression data across species is a powerful method to determine an expression signature of ageing. Meta-analysis of gene expression with age in mammals has identified changes in stress responses, metabolism and immune response genes [4] while meta-analysis of the dietary restriction expression signature has identified novel changes in retinol metabolism and copper-ion detoxification in this ageing modulating process [5]. We have performed a meta-analysis of ageing using the methods of de Magalhães, et al [4] on 127 microarray and RNA-Seq datasets from humans, mice and rats, and applied machine learning alongside enrichment methods www.aging-us.com to analyse the results. This gave an ageing signature consistent with previous analyses. We performed analyses on tissue-specific subsections of these datasets for brain, heart and muscle revealing some novel tissue specific differences in network connectivity
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