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Ageing transcriptome meta-analysis reveals similarities between key mammalian tissues

Palmer, Daniel, Fabris, Fabio, Doherty, Aoife, Freitas, Alex A., de Magalhaes, João Pedro (2021) Ageing transcriptome meta-analysis reveals similarities between key mammalian tissues. Aging, 13 (3). pp. 3313-3341. ISSN 1945-4589. (doi:10.18632/aging.202648) (KAR id:87092)

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http://dx.doi.org/10.18632/aging.202648

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.

Item Type: Article
DOI/Identification number: 10.18632/aging.202648
Uncontrolled keywords: ageing, machine learning, data mining, bioinformatics
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 13 Mar 2021 16:15 UTC
Last Modified: 15 Mar 2021 16:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/87092 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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