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| Status |
Public on Mar 28, 2017 |
| Title |
Multi-tissue DNA methylation age predictor in mouse |
| Organism |
Mus musculus |
| Experiment type |
Methylation profiling by high throughput sequencing
|
| Summary |
Background: DNA-methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing programme. It has also not been shown whether this ‘epigenetic clock’ is unique to humans or conserved in other animals such as the experimentally tractable mouse. Results: We have generated a comprehensive set of whole genome base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. A large number of CpG sites show significant tissue independent correlations with age and allowed us to develop a multi-tissue predictor of age in the mouse (‘mouse epigenetic clock’). The predictor, which estimates age based on DNA methylation at 644 unique CpG sites, is highly accurate (mean absolute error of 4.09 weeks) and has similar properties to the recently described human epigenetic clock. Using publicly available datasets from various biological manipulations in mice, we found that the mouse clock also measures biological age. While females and males showed no significant differences in predicted DNA methylation age, ovariectomy resulted in significant age acceleration in females. Furthermore we found significant differences in age-acceleration dependent on the lipid content of the maternal or offspring diet. Conclusions: Here we identify and characterise a highly accurate epigenetic predictor of age in mice, the ‘mouse epigenetic clock’. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ‘ticking’ rate and resetting the clock in vivo to study the impact on biological age.
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| Overall design |
RRBS-seq of different tissues from mice at different ages
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| |
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| Contributor(s) |
Stubbs TM, von Meyenn F, Katrien-Stark A, Krueger F, Reik W |
| Citation(s) |
28399939 |
| |
| Submission date |
Jan 23, 2017 |
| Last update date |
May 15, 2019 |
| Contact name |
Felix Krueger |
| E-mail(s) |
fkrueger@altoslabs.com
|
| Organization name |
Altos Labs
|
| Department |
Bioinformatics
|
| Street address |
Granta Park
|
| City |
Cambridge |
| ZIP/Postal code |
CB21 6GP |
| Country |
United Kingdom |
| |
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| Platforms (1) |
| GPL13112 |
Illumina HiSeq 2000 (Mus musculus) |
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| Samples (62)
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| Relations |
| BioProject |
PRJNA362895 |
| SRA |
SRP097629 |
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