EpiSCOPE Project C: DOMiNO cohort CSIRO North Ryde Susan van Dijk Tim Peters.

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


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


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


LINE1 AluAciI0

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MaleFemale

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tive

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met

hyla

tion

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• 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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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


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



Genome-wide methylation: Individual dataDensity plot with beta values for all samplesColoured by batch in 450k array scanning

Genome-wide methylation: Individual data


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


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

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Most variable regions


• 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


Red: gene bodyLight green: TSS 1500Dark green: TSS 200Magenta: 1st ExonDark Blue: 5’UTRAqua: 3’UTR

HLA-C

Variation in genes of interest


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 |

EpiSCOPE Project C: DOMiNO cohort CSIRO North Ryde Susan van Dijk Tim Peters.

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