Ageing transcriptome meta-analysis reveals similarities and differences between key mammalian tissues

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.

Prediction of drug candidates for clear cell renal cell carcinoma using a systems biology-based drug repositioning approach

“…These three measurements are commonly used to characterize the centrality of nodes from three distinct perspectives: a higher degree indicates that the node is involved in more interactions; a higher betweenness indicates that the node acts as a bridge which lies on the shortest path between other nodes; a higher closeness indicates that the node shows shorter paths to all the other nodes and is likely to be the geometric center of the module. 36 , 37 , 38 , 39 We then calculated the Spearman correlation of each centrality measurement between the two unfavorable modules (or favorable modules) identified from TCGA and Japanese datasets. As shown in Figure 3 a and 3 b, the correlation coefficients of degree, betweenness and closeness were 0.77, 0.73 and 0.78 between M11 from the TCGA cohort and M3 from the Japanese cohort, and 0.73, 0.71 and 0.72 between M1/2/3/4/18 from TCGA cohort and M1 from the Japanese cohort.…”

Age-associated differences in the cancer molecular landscape

“…Aging is associated with a decline in immune system function (immunosenescence) and chronic and persistent inflammation (inflammaging) [50,51]. Immunosenescence is linked with a decrease in immune cell ability to eliminate cancer cells, while inflammaging is associated with carcinogenesis and cancer progression [52,53].…”

Integration of segmented regression analysis with weighted gene correlation network analysis identifies genes whose expression is remodeled throughout physiological aging in mouse tissues

“…The most commonly reported age-related dysregulations involve the immune system [ 9 , 11 – 13 ] where inflammatory response genes are upregulated even in the absence of pathogen infection [ 5 , 6 , 9 , 11 , 14 – 19 ]. Energy metabolism, redox homeostasis, and mitochondrial function alterations are also frequently observed in age-related studies [ 6 , 9 , 11 , 15 – 18 , 20 ], particularly the downregulation of genes encoding mitochondrial ribosomal proteins and components of the electron transport chain [ 5 , 11 , 14 – 16 , 18 ], protein synthesis machinery [ 5 , 11 , 17 ], developmental and cell differentiation genes [ 9 , 11 , 19 ], and extracellular matrix components [ 6 , 14 – 16 ]. Up-regulated genes are associated with the stress response and DNA repair [ 5 , 6 , 9 , 11 , 14 , 16 – 18 ], RNA processing [ 11 , 12 , 17 ] and cell cycle arrest [ 5 , 16 , 19 ].…”

“…Energy metabolism, redox homeostasis, and mitochondrial function alterations are also frequently observed in age-related studies [ 6 , 9 , 11 , 15 – 18 , 20 ], particularly the downregulation of genes encoding mitochondrial ribosomal proteins and components of the electron transport chain [ 5 , 11 , 14 – 16 , 18 ], protein synthesis machinery [ 5 , 11 , 17 ], developmental and cell differentiation genes [ 9 , 11 , 19 ], and extracellular matrix components [ 6 , 14 – 16 ]. Up-regulated genes are associated with the stress response and DNA repair [ 5 , 6 , 9 , 11 , 14 , 16 – 18 ], RNA processing [ 11 , 12 , 17 ] and cell cycle arrest [ 5 , 16 , 19 ]. Despite this, the existence of specific genetic signatures of aging continue to be a matter of debate as gene regulation is mostly tissue- [ 5 – 7 , 15 , 20 – 24 ] and cell-specific [ 25 , 26 ], but also because there is focus on comparisons between young and old individuals without much consideration of the dynamics of gene expression throughout the lifespan.…”