1. Epigenetic Mechanisms of Environmentally-mediated Cardiovascular Disease Risk in the WHI
Andrea Baccarelli, MD, PhD, MPH Lab of Environmental Epigenetics Harvard School of Public Health

2. Programming of gene expression that:
Epigenetics
Programming of gene expression that:
does not depend on the DNA code
(relatively) stable, i.e., replicated through:
cell mitosis
meiosis, i.e. transgenerational (limited evidence in humans)
Characteristics of Epigenetic Programming
Modifiable (can be reprogrammed)
Active or poised to be activated:
Potentially associated with current health states or predict future events
A. Baccarelli, Harvard School of Public Health

3. DNA Phenotype Epigenetics A Symphonic Example
A. Baccarelli, Harvard School of Public Health

4. A Symphonic Example
A. Baccarelli, Harvard School of Public Health 4

5. From the Royal Society of Chemistry website (www.rsc.org)
DNA Methylation
A. Baccarelli, Harvard School of Public Health
From the Royal Society of Chemistry website (

6. Changes during mitosis
Fidelity of “transcription” of DNA methylation varies between %
De-novo methylation: 3-5% mitosis
Much more dynamic compared to DNA sequence!
DNA methylation is known to be modified:
through aging
oxidative stress, inflammation, micronutrients
A. Baccarelli, Harvard School of Public Health

7. Triggers of cardiovascular events at the population level
Exposure to traffic and air pollution are estimated to trigger more heart attacks than:
-Heavy alcohol consumpion (2.5 fold)
-Cocaine abuse (10 fold)
Long term exposure to air pollution leads to:
-Atherosclerosis
-Cardiovascular morbidity and death
Figure from Editorial by
Baccarelli & Benjamin The Lancet 2011
A. Baccarelli, Harvard School of Public Health

8. Coding & non-coding DNA in the human genome
Protein coding genes 1-2%
Unique non-coding DNA ≈45%
Pseudogenes <1%
Repetitive sequences ≈50% LINEs (e.g., LINE-1): 21% SINEs (e.g., Alu): 11% Both LINEs & SINEs are retrotranspons
One of the fruits of the genomic revolution is the finding that the majority of most eukaryote genomes don’t code for proteins. In humans, only around 1-2% of our DNA codes for proteins. The remainder consists of genes for ribosomal RNA, small regulatory RNAs of various types, regulatory elements, broken genes (including defunct viral genes incorporated into our DNA), the spacers that exist within genes, a whole lot of intergene material and lots of repetitive sequences. The regulatory elements (stretches of DNA where proteins can bind to change gene expression, stretches of DNA that code for small RNA’s that can modify gene expression etc.) are critical for co-ordinating gene expression during development and during adulthood. We need these as much as we need protein coding genes, but overall it is broadly estimated that that only about 3-5% of the non-coding genome is involved in regulation (Mouse Genome Sequencing Consortium, 2002). So what is the rest doing?
Overall, not a lot. The general consensus is that the majority of the non-coding, non-regulatory DNA is functionless (the so called “junk DNA”). Some might have a strictly structural role (where you need x bases of DNA, any kind of DNA, to support the structure of the chromosome, this role is sequence independent), but overall the vast bulk of DNA is without function. There are a number of reasons to believe this, but I will talk about some of this evidence later. Roughly 50% of DNA is what is called repetitive DNA, a sort of molecular stutter. In general (but there are exceptions), a large proportion of this is seen as “selfish” or “parasitic” DNA. The majority of this repetitive DNA is made up of transposable elements. Long interspersed elements (LINES) make up 21% of the human genome, and some code for genes that allow them to make copies of themselves. Short interspersed elements (SINES) make up about 11% of human DNA and require active LINES in order to be copied. Now, it is almost certain that a small proportion of LINES and SINES has been co-opted to do something useful in the genome, about 0.1% of SINES may be involved in alternative splicing of genes. However, as it currently stands the vast majority of these elements don’t seem to do anything useful.
Chromosomal stability
Limits retrotransposition
Limits inflammation
Heavily methylated
A. Baccarelli, Harvard School of Public Health

9. Effects of Air Pollution on Repetitive Element DNA Methylation
LINE-1
Alu
βunadj=-0.18; P=0.04
βadj=-0.19; P=0.04
βunadj=-0.30; P=0.07
βadj=-0.34; P=0.04
USE ORIGINAL?
Per questo, si è deciso di utilizzare un modello a Misure Ripetute che considera i dati di metilazione a inizio e fine settimana come misure ripetute dello stesso effetto, fornendo una maggiore potenza statistica al nostro studio.
In questo caso osserviamo una diminuzione ↓ della metilazione globale significativamente correlata ai livelli di esposizione a PM10.
Effects of PM10 found both at the beginning and at the end of the work week
β for an increment equal to the difference between the 90th and 10th percentile of PM10
Mixed models: unadjusted or adjusted for age, BMI, smoking, cigarettes/day
Tarantini, et al. Environment Health Perspect, 2009
A. Baccarelli, Harvard School of Public Health 9

10. DNA Methylation and Incidence of non-fatal IHD or Stroke in the NAS
Adj. Hazard Ratio 3.6 (95%CI )
p<0.001
HR=2.8 (95%CI ), p=0.009 for IHD
HR=4.3 (95%CI ), p=0.11 for stroke
Baccarelli et al., Epidemiology 2010
A. Baccarelli, Harvard School of Public Health

11. DNA Methylation and Death from IHD or Stroke in the NAS
Adj. Hazard Ratio 2.9 (95%CI )
P=0.006
HR=3.5 (95%CI ), p=0.007 for IHD
HR=2.2 (95%CI ), p=0.30 for stroke
Baccarelli et al., Epidemiology 2010
A. Baccarelli, Harvard School of Public Health

12. Potential Roles of Epigenetics
Baccarelli, Rienstra & Benjamin Circulation: Cardiovascular Genetics 2010
A. Baccarelli, Harvard School of Public Health

13. Conclusions and Research Needs
DNA methylation:
Sensitive to the environment
Predicts risk of cardiovascular disease
Limitations:
Limited power
Unreplicated
Limited to repeat elements (or other candidate sequence)
environmentally homogeneous, single-city populations of white men
Research needs:
Investigating a diverse population
Studies of women
Characterizing DNA methylation changes over time and their association with CVD-related risk factors
A. Baccarelli, Harvard School of Public Health

14. R01 ES020836 MPIs, Whitsel, Hou, Baccarelli
Epigenetic mechanisms of PM-mediated cardiovascular risk
R01 ES MPIs, Whitsel, Hou, Baccarelli
Pending (Score=19; Percentile 11%)
A. Baccarelli, Harvard School of Public Health

15. A. Baccarelli, Harvard School of Public Health

16. Illumina 450K BeadChip Coverage
CpG shelves, shores & islands classification (UCSC CpGi annotation)
The 450K BeadChip covers a total of 77,537 CpG Islands and CpG Shores (N+S)
Region Type
Regions
CpG sites covered on 450K BeadChip array
Average # of CpG sites per region
CpG Island
26,153
139,265
5.08
N Shore
25,770
73,508
2.74
S Shore
25,614
71,119
2.66
N Shelf
23,896
49,093
1.97
S Shelf
23,968
48,524
1.94
Remote/Unassigned -
104,926
Total
485,553
N Shelf
5’ UTR
3’ UTR
TSS1500
TSS200
S Shore
S Shelf
N Shore
CpG Island

17. Study Population
Two-stage, longitudinal study of associations between PM air pollution, DNA methylation, and CVD risk factors:
exam site- and race-stratified, randomly selected 6% minority oversample
approximately 4,300 Women’s Health Initiative clinical trial (WHI CT) women
fasting blood draws and resting, standard, twelve-lead electrocardiograms (ECGs)
repeated at three-year intervals from 1993 to 2004.
A. Baccarelli, Harvard School of Public Health

18. Study Design Stage 1 (Discovery) Stage II (Validation) Other Features:
interrogation, discovery and ranking of >450,000 DNA methylation sites potentially sensitive to PM in blood samples from 800 participants
Stage II (Validation)
Longitudinal  validation of the 10 most PM-sensitive DNA methylation sites identified by Stage 1 in up to three blood samples collected serially from the remaining 3,500 participants ( )
Focus on the temporal relationship between PM and DNA methylation at those sites, and that between site-specific DNA methylation and CVD risk factors.
Other Features:
Phenomics framework to incorporate phenotypes
Epigenetic data analyses adjusted for both ancestral admixture and multiple comparisons
External validation with cohorts with different participants characteristics (ARIC, NAS)
A. Baccarelli, Harvard School of Public Health

19. DNA methylation in the WHI
A. Baccarelli, Harvard School of Public Health

20. The WHI team for the Environmental Epigenomics proposal
Eric Whitsel, University of North Carolina (PI)
Lifang Hou, Northwestern University (PI)
Andrea Baccarelli, Harvard University (PI)
Yun Li, University of North Carolina
Duanping Liao, Penn State
Simon Lin, Northwestern University
Lesley Tinker, Fred Hutchinson
Linda Van Horn, Northwestern University
A. Baccarelli, Harvard School of Public Health