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EpiSCOPE Project C: DOMiNO cohort

EpiSCOPE Project C: DOMiNO cohort . CSIRO North Ryde Susan 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?

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EpiSCOPE Project C: DOMiNO cohort

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

  2. 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?

  3. 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 Dijk

  4. Methylation repetitive elements: End specific PCR 1Michels et al., PLoS One. 2011;6(9) • 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 Dijk

  5. End specific PCR Rand KN Molloy PL, Biotechniques. 2010 Oct;49(4): 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 Dijk

  6. Global methylation: first results • Blood DNA from sixty 5 yr old children • Higher interindividual variation in LINE1 hypomethylation compared to Aluhypomethylation • Lower hypomethylation levels LINE1 and Alu in females * * EpiSCOPE science meeting April 2013 | Susan van Dijk

  7. Global methylation: %5 meC and 5hmeC dC 5mdC 1Le T et al. Anal Biochem. (2011) 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

  8. Genome wide methylation: Pooling • IlluminaInfinium 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 Dijk

  9. 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 Dijk

  10. Genome-wide methylation: Individual data • Density plot with beta values for all samples • Coloured by batch in 450k array scanning EpiSCOPE science meeting April 2013 | Susan van Dijk

  11. Genome-wide methylation: Individual data • PCA using all probes by beta value (variance explained=31.4%) • Coloured by batch in 450k array scanning Females Males EpiSCOPE science meeting April 2013 | Susan van Dijk

  12. Genome-wide methylation: Individual data • PCA using top 10% most variable autosomal probes by beta value (variance explained=29.5%) EpiSCOPE science meeting April 2013 | Susan van Dijk

  13. 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, Hthe bandwidth (needs to be estimated) and w(Xi)the weight vector (methylation variance) Identifying Differentially Methylated Regions from HM450K array data| Tim J. Peters

  14. 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 His 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. Peters

  15. 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 Presentation title | Presenter name

  16. Most variable regions MHC region Chromosome 6: Major Histocompatibility Complex (MHC) region (~29Mb to 33Mb) Gene dense region, many polymorphisms MHC, cell surface molecule, essential role in immunity EpiSCOPE science meeting April 2013 | Susan van Dijk

  17. Most variable regions: example HLA-C Red: gene body Light green: TSS 1500 Dark green: TSS 200 Magenta: 1stExon Dark Blue: 5’UTR Aqua: 3’UTR EpiSCOPE science meeting April 2013 | Susan van Dijk

  18. Variation in genes of interest RXRα Red: gene body Light green: TSS 1500 Dark green: TSS 200 Magenta: 1stExon Dark Blue: 5’UTR Aqua: 3’UTR EpiSCOPE science meeting April 2013 | Susan van Dijk

  19. Adipocyte differentiation- SGBS cells Day 0 Day 4 Day 10 Day 14 • 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?

  20. Milestones • Jan 2013: At least 800 neonatal DNA samples isolated • Oct 2013: Global methylation analysis of >800 neonatal DNA samples completed; association with ω-3 fatty acids identified • June 2014: Blood sample collection/DNA isolation complete for at least 800 subjects • Jan 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 Presentation title | Presenter name

  21. Thank you Peter Molloy Brodie Fuller Hilal Varinli Dimitrios Zabaras Tim Peters Presentation title | Presenter name

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