Scientific ReportsGenomicsPeer-reviewed journal article
Population level mitogenomics of the long-lived Greater Mouse-eared bat, Myotis myotis, reveals dynamic heteroplasmy and challenges the Free Radical Theory of Ageing
David JebbiD, Nicole M. FoleyiD, Conor WhelaniD, Frédéric TouzaliniD, Sébastien J. PuechmailleiD, Emma C. TeelingiD
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Abstract
BackgroundBats are the only mammals capable of true, powered flight, which drives an extremely high metabolic rate. The \"Free Radical Theory of Ageing\" (FTRA) posits that a high metabolic rate leads to mitochondrial heteroplasmy and the progressive ageing phenotype. Contrary to this, bats are the longest lived order of mammals despite their small size and high metabolic rate. To investigate if bats exhibit increased mitochondrial heteroplasmy with age as predicted by the FRTA, we performed targeted, deep sequencing of mitogenomes and measured point heteroplasmy in wild, long lived Myotis myotis as they age.\n\nResultsIn total, blood was sampled from 195 individuals, aged between <1 and at 6+ years old, and whole mitochondria were sequenced, with a subset sampled over multiple years. The majority of heteroplasmies, 77.6%, were at a frequency below 5%. Oxidative mutations were not the primary source of heteroplasmies and present in only a small number of individuals, likely representing local oxidative stress events. There was a significant positive correlation between age and heteroplasmy, with a rate of increase of 0.13 sites per year. Longitudinal data from recaptured individuals show heteroplasmy is dynamic, and does not increase uniformly over time.\n\nConclusionsWe show that bats do not suffer from the predicted, inevitable increase in heteroplasmy which underscores the FRTA. Most heteroplasmies were at low frequency and are primarily transitions. Heteroplasmy increased with age, however how this contributes to ageing is unclear, as heteroplasmy was dynamic, questioning its presumed role as a primary driver of ageing.
- organism: mouse
- assay: DNA methylation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant calling
- research focus: variant effect prediction
- approach: statistical model
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