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EpiSCOPE Project C: DOMiNO cohort
CSIRO North RydeSusan van Dijk
Tim Peters
Research Questions
Does increased n-3 PUFA exposure before birth change the epigenetic state in the neonate?
Do these epigenetic changes persist at 5 years of age?
Are there epigenetic marks which correlate with measures of body fat mass and/or insulin sensitivity?
Materials and methods
Samples:• 0 yrs: DNA from Guthrie card blood punches (~150 ng DNA)• 5 yrs: DNA from venous blood (~10 μg DNA)
Global DNA methylation• 0 and 5 yrs: Methylation in repetitive elements• 5 yrs: Total methyl cytosine content
Genome wide methylation• 0 and 5 yrs (pools): Illumina 450k array ~480,000 methylation sites• 5 yrs (60 individuals): Illumina 450k array ~480,000 methylation
sites
EpiSCOPE science meeting April 2013 | Susan van Dijk3 |
Methylation repetitive elements: End specific PCR
• Only few ng of DNA needed per reaction
• Repetitive elements, such as Alu and LINE1:• Highly present throughout genome• Surrogate measure of global methylation• normally highly methylated, hypomethylated in cancer • sensitive to methylation changes after environmental exposures
• LINE1 methylation in cord blood associated with birth weight1
EpiSCOPE science meeting April 2013 | Susan van Dijk4 |
1 Michels et al., PLoS One. 2011;6(9)
• Method measures amount of a DNA fragment resulting from digestion with methylation-sensitive restriction enzymes
• Hypomethylation level for repetitive element of interest
EpiSCOPE science meeting April 2013 | Susan van Dijk5 |
Detection
Extension and amplification
Tagging of these cut elements
Cutting by methylation sensitive enzyme
Input: 2 ng DNA
End specific PCR
Rand KN Molloy PL, Biotechniques. 2010 Oct;49(4):
Global methylation: first results
EpiSCOPE science meeting April 2013 | Susan van Dijk6 |
LINE1 AluAciI0
0.2
0.4
0.6
0.8
1
1.2
1.4
MaleFemale
Rela
tive
hypo
met
hyla
tion
*
*
• Blood DNA from sixty 5 yr old children• Higher interindividual variation in LINE1 hypomethylation
compared to Alu hypomethylation• Lower hypomethylation levels LINE1 and Alu in females
• Liquid chromatography electrospray ionization tandem mass spectrometry with multiple reaction monitoring (LC–ESI–MS/MS–MRM) to sensitively and simultaneously measure levels of 5mC and 5hmC in digested genomic DNA 1
Global methylation: %5 meC and 5hmeC
Mass based detectionCalculation %meC (and %hmeC)
Measurement of the intensities of specific MH+ → fragment ion transitions for each component
Separation by chromatography
Input ~500 ng-1 ug hydrolysed DNA
1 Le T et al. Anal Biochem. (2011)
RT: 0.00 - 10.00
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0
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Inte
nsity
RT: 4.47
RT: 5.24
RT: 1.16
RT: 0.34
NL:7.70E3
TIC F: + c SRM ms2 [email protected] [ 111.95-112.25] MS Genesis DNA5
NL:1.72E3
TIC F: + c SRM ms2 [email protected] [ 126.00-126.20] MS Genesis DNA55mdC
dC
Genome wide methylation: Pooling• Illumina Infinium Human methylation 450k bead chip• 500 ng- 1 ug DNA needed → pooling necessary for 0 yr samples• 5 yr individuals→ pools and n=60 individual samples
EpiSCOPE science meeting April 2013 | Susan van Dijk8 |
Newborn
DHA
Male
BMI deciles
Female
BMI deciles
Placebo
Male
BMI deciles
Female
BMI deciles
5 years
DHA
Male
BMI deciles
Female
BMI deciles
Placebo
Male
BMI deciles
Female
BMI deciles
Genome wide methylation: Individual data
• Individual methylation data (450k array)• selection of sixty 5 yr old children
– equal number boys & girls– equal number DHA & placebo
• Study is still blinded• Analysis:
– Unsupervised clustering of individuals (PCA)– Most variable sites/regions
• Method: Kernel density estimator
EpiSCOPE science meeting April 2013 | Susan van Dijk9 |
EpiSCOPE science meeting April 2013 | Susan van Dijk10 |
Genome-wide methylation: Individual dataDensity plot with beta values for all samplesColoured by batch in 450k array scanning
Genome-wide methylation: Individual data
EpiSCOPE science meeting April 2013 | Susan van Dijk11 |
Females Males
PCA using all probes by beta value (variance explained=31.4%)Coloured by batch in 450k array scanning
Genome-wide methylation: Individual data
EpiSCOPE science meeting April 2013 | Susan van Dijk12 |
PCA using top 10% most variable autosomal probes by beta value (variance explained=29.5%)
Kernel Density Estimation (KDE)• Modelling probes on the contiguous genome as a density function
is a natural and intuitive solution• 1-dimensional substrate makes finding regions of interest (e.g.
variability or differential methylation) computationally fast (and easy to visualise)
• Model form: For the hg19 positions of the DM probes X drawn from their underlying density f:
• K(x) is the kernel function, H the bandwidth (needs to be estimated) and w(Xi) the weight vector (methylation variance)
Identifying Differentially Methylated Regions from HM450K array data| Tim J. Peters13 |
)()(ˆ1
1iH
n
iinH XxKXwnxf
Plug-in Bandwidth Selector
• Selects optimal bandwidth value input for the KDE• Uses the Approximate Mean Squared Integrated Error (AMISE1 ), a tractable
version of MISE, where H is the bandwidth “matrix” (or single value when d=1)
• is in fact for this problem• Fast rate of asymptotic convergence and good finite-sample properties for 1-
dimensional data sets2 e.g. genomic position; only a single value needed
1Chacon, J.E. & Duong, T. (2010) Multivariate plug-in bandwidth selection with unconstrained pilot matrices. Test, 19, 375-398.
2Sheather, S.J. & Jones, M.C. (1991) A reliable data-based bandwidth selection method for kernel
density estimation. Journal of the Royal Statistical Society, Series B, 53, 683-690.
Identifying Differentially Methylated Regions from HM450K array data| Tim J. Peters14 |
Presentation title | Presenter name
Bandwidth value
• However, often plug-in bandwidths will return a very large bandwidth (e.g for 104 probes across hg19, the estimate will be in the range of 107 bases), resulting in a coarse KDE, where adjacent “bumps” will overlap
• Realistically, we want regions that are localised within about a 2KB domain, so we are using a 1KB bandwidth
15 |
Most variable regions
EpiSCOPE science meeting April 2013 | Susan van Dijk16 |
• Chromosome 6: Major Histocompatibility Complex (MHC) region (~29Mb to 33Mb)
• Gene dense region, many polymorphisms • MHC, cell surface molecule, essential role in immunity
MHC region
Most variable regions: example
EpiSCOPE science meeting April 2013 | Susan van Dijk17 |
Red: gene bodyLight green: TSS 1500Dark green: TSS 200Magenta: 1st ExonDark Blue: 5’UTRAqua: 3’UTR
HLA-C
Variation in genes of interest
EpiSCOPE science meeting April 2013 | Susan van Dijk18 |
Red: gene bodyLight green: TSS 1500Dark green: TSS 200Magenta: 1st ExonDark Blue: 5’UTRAqua: 3’UTR
RXRα
Adipocyte differentiation- SGBS cells
Effect of DHA (10 uM) on adipocyte differentiation• During complete differentiation (D0-D14)• Early differentiation (D0-D4)• Late differentiation (D4-D14)
Do we see an effect of DHA on adipocyte differentiation?Is this effect mediated via epigenetic regulator EZH2?
Day 0 Day 4 Day 14Day 10
Presentation title | Presenter name
MilestonesJan 2013: At least 800 neonatal DNA samples isolatedOct 2013: Global methylation analysis of >800 neonatal DNA
samples completed; association with ω-3 fatty acids identifiedJune 2014: Blood sample collection/DNA isolation complete for at
least 800 subjectsJan 2015: Global DNA methylation levels in >800 5yr children
samples determined and related to health measures.Jan 2015: Epigenome profiles of 50 children determined• April 2015:Reduced methylome analysis of children stratified into
4 pools by gender and nutritional intervention (~960 children) and into 20 pools by gender and BMI (~480 children)
• October 2015: Epigenetic signatures in early life associated with 5 year health outcomes established
20 |
Peter MolloyBrodie FullerHilal Varinli
Dimitrios ZabarasTim Peters
Thank you
Presentation title | Presenter name21 |