1. Lecture 11 Epigenetics of Aging Andrea Baccarelli, MD, PhD, MPH Laboratory of Environmental Epigenetics Harvard School of Public Health abaccare@hsph.harvard.edu

2. Cellular Senescence and Aging Cells from old donors: – divide less often than cells from young donors Cells from short-lived species: – more sensitive to senescence-inducers (oxidative stress) Cells from donors with premature aging syndromes: – senesce more readily Senescent cells (expressing a senescence marker): – accumulate with age – Accumulate at sites of age-related pathology (including hyperproliferative diseases)

3. Cell Duplication & Global DNA Methylation human hamster mouse Wilson & Jones, Nature 1983

4. Individual Aging: the Twin Model

5. Twin Epigenetics Analysis of 80 MZ Twin Pairs between 3-73 years of age (Fraga et al. PNAS 2005) Global Methylation Histone H3 Acetylation Histone H4 Acetylation

6. Comparative Genomic Hybridization for Methylated DNA The 50-year-old twin pair shows abundant changes in the pattern of DNA methylation: – Green: hypermethylation – Red: hypomethylation 3-year-old twins have a very similar distribution of DNA methylation: – Yellow: equal amounts of the green and red dyes (Fraga et al. PNAS 2005)

7. Differences in mRNA Expression

8. Epigenetics & MZ Twin Discordance Wong, et al. Hum Mol Genet 14:R11, 2005

9. Global DNA Methylation & Aging

10. Global DNA Methylation in Healthy Subjects DNA from peripheral blood leukocytes Fuke et al., Ann Hum Gen 2004

11. Global DNA Methylation Loss across Mouse Tissues Wilson et al., J Biol Chem 1987

12. Age(ing)-related Histone Modifications Trimethylation of H4-K20 Histone Deacetylases have been associated with cellular aging and life span – Sirtuins – H4-AcK16 decrease through the cellular like span

13. An epigenetic clock? The Horvath calculator (Genome Biology 2013) Multi-tissue predictor of age that allows to estimate the DNA methylation age of most tissues and cell types. freely available online: – developed using 8,000 samples – from 82 Illumina DNA methylation array datasets – encompassing 51 healthy tissues and cell types. DNA methylation age has the following properties: – it is close to zero for embryonic and induced pluripotent stem cells – it correlates with cell passage number – it gives rise to a highly heritable measure of age acceleration – It can be characterized by 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. From Nature news: 21 October 2013 http://genomebiology.com/2013/14/10/R115

14. Accuracy across tissues and cell types (training)

15. Accuracy across test data

16. Using the clock for measuring the age of different parts of the body

17. Embrional stem cells and iPS cells are perfectly young

18. http://labs.genetics.ucla.edu/horvath/dnamage/

19. Case Study DNA methylation age of blood predicts all-cause mortality in later life

20. Background DNA methylation levels change with age. In some subjects, Steve Horvath’s methylation age is higher or lower than actual chronological age Do difference in methylation vs. chronological age result in differences in life expectancy? Andrea Baccarelli - Harvard School of Public Health

21. Approach Four cohorts with 450K blood data – the Lothian Birth Cohort of 1921 – the Lothian Birth Cohort of 1936 – the Framingham Heart Study – the Normative Aging Study Methylation age: – Horvath’s estimator – Alternative blood based estimator (Hannum’s) Andrea Baccarelli - Harvard School of Public Health

22. Summary details of the four analysis cohorts Marioni et. al. Genome Biol. 2015 Jan 30;16:25

23. Plot of predicted methylation age against chronological age and plot of Hannum versus Horvath predicted methylation age. *To prevent the potential identification of individual participants, only FHS data points with chronological ages between 45 and 85, and NAS data points between ages 56 and 100 are displayed. r = Pearson correlation coefficient. FHS: Framingham Heart Study, LBC: Lothian Birth Cohort, NAS: Normative Aging Study. Marioni et. al. Genome Biol. 2015 Jan 30;16:25

24. Meta-analysis results of Δ age versus mortality. The basic adjusted models controlled for chronological age, sex (NAS had only male participants), and laboratory batch (FHS only). The fully adjusted models controlled for chronological age, sex, smoking, education, childhood IQ (LBC1921 and LBC1936 only), social class (LBC1921 and LBC1936 only), APOE (LBC1921, LBC1936, and NAS only), cardiovascular disease, high blood pressure, and diabetes. CI: confidence interval, FHS: Framingham Heart Study, HR: hazard ratio, LBC: Lothian Birth Cohort, NAS: Normative Aging Study, W: fixed effect weight. Marioni et. al. Genome Biol. 2015 Jan 30;16:25

25. Survival probability by quartiles of Δ age in LBC1921 adjusted for sex, and chronological age. LBC: Lothian Birth Cohort. Marioni et. al. Genome Biol. 2015 Jan 30;16:25

26. Conclusions DNA methylation-based measures of the difference between epigenetic age and chronological age are significant predictors of mortality Individual genetic or environmental exposures that drive the associations are not yet known DNA methylation may represent an ‘epigenetic clock’ that measures biological age and runs alongside, but not always in parallel with chronological age, and may inform life expectancy predictions Andrea Baccarelli - Harvard School of Public Health

27. Next lecture Lecture 12 Environmental Mitochondriomics