1. Developmental Biology
  2. Genetics and Genomics
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Epigenetic age-predictor for mice based on three CpG sites

  1. Yang Han
  2. Monika Eipel
  3. Julia Franzen
  4. Vadim Sakk
  5. Bertien Dethmers-Ausema
  6. Laura Yndriago
  7. Ander Izeta
  8. Gerald de Haan
  9. Hartmut Geiger
  10. Wolfgang Wagner  Is a corresponding author
  1. RWTH Aachen University Medical School, Germany
  2. University Hospital RWTH Aachen, Germany
  3. Ulm University, Germany
  4. University Medical Center Groningen, Netherlands
  5. Instituto Biodonostia, Spain
  6. Tecnun-University of Navarra, Spain
  7. Cincinnati Children's Hospital Burnet Campus, United States
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Cite this article as: eLife 2018;7:e37462 doi: 10.7554/eLife.37462
4 figures and 5 additional files

Figures

Figure 1 with 1 supplement
Three CpG epigenetic age-predictor for mice.

(a–c) DNA methylation (DNAm) of three CpGs in the genes Prima1, Hsf4 and Kcns1 was analyzed by pyrosequencing in 24 C57BL/6 mice (training set). Coefficient of determination (R2) of DNAm versus chronological age is indicated. (d) Based on these age-associated DNAm changes a multivariable model for age prediction was calculated. (e–g) Subsequently, two independent validation sets were analyzed: 21 C57BL/6 mice from the University of Ulm and 19 C57BL/6 mice from the University of Groningen (validation sets 1 and 2, respectively). (h) Age predictions with the three-CpG-model revealed a high correlation with chronological age in the independent validation sets (MAD = mean absolute deviation; MAE = median absolute error).

https://doi.org/10.7554/eLife.37462.003
Figure 1—figure supplement 1
Target sequences of pyrosequencing assays.

Sequences for the three genomic regions are depicted and CpG sites (red) are numbered by the dispensation order. The relevant CpGs with highest age-correlation are highlighted in bold.

https://doi.org/10.7554/eLife.37462.004
Gender does not affect epigenetic age predictions in mice.

The deviations of predicted age by our three-CpG predictor versus chronological age did not reveal significant differences between female and male C57BL/6 mice (Mann–Whitney U test p=0.6).

https://doi.org/10.7554/eLife.37462.005
Age-associated DNA methylation at the three CpG sites in different tissues.

Different tissues were isolated of three young (9.6 weeks) and three old mice (56.9 weeks) and DNAm was analyzed at the three relevant CpGs in (a) Prima1, (b) Hsf4, and (c) Kcns1. Epigenetic age-predictions using the 3 CpG model for blood demonstrated also significant differences between young and old mice in skin, intestine, brain, and testis (mean ± standard deviation; Student t-tests: *p<0.05; **p<0.01; ***p<0.001).

https://doi.org/10.7554/eLife.37462.006
Epigenetic aging is accelerated in DBA/2 mice as compared to C57BL/6 mice.

(a–c) Age-related DNA methylation (DNAm) determined by pyrosequencing assay for three candidate CpGs on 33 of DBA/2 blood samples (14 mice from the University of Ulm and 19 mice from the University of Groningen; red). For comparison we provided measurements of the C57BL/6 mice (only from validation sets; blue). (d) Epigenetic age-predictions using the three CpG multivariable model for the C57BL/6 mice (blue; linear regression) and DBA/2 mice (red, logarithmic regression). Age-predictions in DBA/2 mice rather followed a logarithmic regression (R = Pearson correlation); (e) Based on the DNAm measurements in DBA/2 we adjusted the multivariate regression model for age-predictions of this mouse strain as described in the text (DBA/2 predictor).

https://doi.org/10.7554/eLife.37462.007

Data availability

Raw data of pyrosequencing is provided as supplemental EXCEL table (Source data 1).

Additional files

Source data 1

Pyrosequencing raw data of mouse epigenetic aging predictor.

https://doi.org/10.7554/eLife.37462.008
Supplementary file 1

Age-associated DNAm in nine genomic regions of the training set.

https://doi.org/10.7554/eLife.37462.009
Supplementary file 2

Multivariable model based on 15 CpGs

https://doi.org/10.7554/eLife.37462.010
Supplementary file 3

Primers for pyrosequencing.

https://doi.org/10.7554/eLife.37462.011
Transparent reporting form
https://doi.org/10.7554/eLife.37462.012

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