bioRxivEvolutionary Biology
Mutation accumulation differentially impacts ageing in mammalian tissues
Z. G. Turan, P. Parvizi, H. M. Donertas, J. Tung, P. Khaitovich, M. Somel
Abstract
Medawars mutation accumulation (MA) hypothesis explains ageing by the declining force of natural selection with age: slightly deleterious germline mutations that are functional in old age are not effectively eliminated by selection and therefore lead to ageing-related phenotypes. Although widely cited, empirical support for the MA hypothesis, particularly molecular evidence, has remained limited. Here we test one of its predictions, that genes relatively highly expressed in old adults vs. young adults should be under weaker purifying selection than those relatively highly expressed in young adults. To do so, we combine 23 RNA-sequencing and 35 microarray gene expression datasets (including 9 tissues from 5 mammalian species) with protein and regulatory sequence conservation estimates across mammals. We identify age-related decrease in transcriptome conservation (ADICT) in four tissues, brain, liver, lung, and artery, but not in other tissues, most notably muscle and heart. ADICT is driven both by decreased expression of highly conserved genes and up-regulation of poorly conserved genes during ageing, in line with the MA hypothesis. Lowly conserved and up-regulated genes in ADICT-associated tissues have overlapping functional properties, particularly involving apoptosis and inflammation, with no evidence for a history of positive selection. Our results suggest that tissues vary in how evolution has shaped their ageing patterns. We find that in some tissues, genes up-regulated during ageing, possibly in response to accumulating cellular and histological damage, are under weaker purifying selection than other genes. We propose that accumulation of slightly deleterious substitutions in these genes may underlie their suboptimal regulation and activity during ageing, shaping senescent phenotypes such as inflammaging.
- organism: human
- organism: mouse
- organism: rat
- assay: bulk RNA-seq
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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