Epigenetic databases in cattle and the prediction of phenotypes from genotypes

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1 Epigenetic databases in cattle and the prediction of phenotypes from genotypes Jean-Paul Renard Hélène Jammes - Hélène Kiefer Eve Devinoy Académie d Agriculture de France - Paris INRA UMR Jouy en Josas INRA GABI Jouy en Josas 66th EAAP Annual Meeting 30 August 4th september 2015 Warsaw, Poland EAAP 30 Aug- 4th Sept Warsaw

2 Between genotype and phenotype 3 levels of genes regulation Sequence Histone modifications DNA methylation Non coding RNAs Nuclear compartments Chromatin Nuclear organization Controlled by epigenetic information van Driel, R. et al., 2003 Journal of Cell Science

3 Epigenetic information in the prediction of traits from GWAS.. ex: interpreting non coding genetic variation in complex human traits at a single locus in human Involves the use of reference epigenome maps of primary and cultured cells Ward and Kellis, 2012, Nat.Botech. a- Genetic association with an organismal trait (GWAs) b- Genetic association with a molecular trait (eqtl) c- Genetic association allelic activity (ASE) d-molecular biomarker for an physiological trait (EpiWAs)

4 waves of epigenetic reprogramming shape transcriptomic patterns during both embryonic, foetal and post natal periods Blastocyst placenta foetus adult BIRTH anterior pole 2 weeks 2 months perinatal posterior pole gastrulation Post-natal development first cell lineages establishment of body plan organogenesis, EAAP 30 Aug- 4th Sept Warsaw

5 Measuring the contribution of epigenetics to the variability of phenotype in the bovine species First step: simplify the approach using - genetically identical animals: through cloning - DNA methylation Second step: develop a wide genome approach using key tissues: blood, liver, muscle placenta, mammary gland Third step: combine phenotypic and epigenetic data Outcome: - extension to individuals from non related genotypes - integration into a bovine data platform EAAP 30 Aug- 4th Sept Warsaw

6 Succss rates of recloning Lifespans in years Cloning techniques do not select for somatic cells more amenable to full reprogramming mouse cloning -optimisation of in vitro conditions (empirical) -recloning from adult cloned mouse (%) no drop in efficiency * ** ** ** no impact on viability recloning number recloning number Wakayama et al Cell Stem cell

7 in vitro in vivo In vitro in vitro in vivo Calving rate (%) Bovine cloning: a way to profile epivariants in a context of high environmental constraints applied on the genome. same nuclear genome 40 relatively efficiency at birth 40% from 482 calving MOET IVP - 40% SCNT Watanabe and Nagai2011 and EFSA 2012 different nuclear microenvironment from the one-cell stage In vitro culture donor cells fibroblastes - Ca++ enucleated recipient oocytes One-cell stage embryo in vitro (culture) in vivo (oocyte ) - in vitro (culture) 9 months- long gestation transfer into recipient blastocyste in vivo Pregancy rate 0 Transfer (D7) D21 D35 Clone D70 D90 calving IVF EAAP 30 Aug- 4th Sept Warsaw

8 DNA methylation is highly dependant on genomic sequence Environment Sequence Stochastic CpG 10-3 Active DNA methylation Dnmt1: maintenance Inheritable (mitosis) Dnmt3: de novo in response to stimuli DNA methylation loss Passive Active EAAP 30 Aug- 4th Sept Warsaw

9 Tools used to characterize, identify and measure DNA methylation variations 1. Global analysis Cytosine methylation electrophoretic assay LUMA (Luminometric Methylation Assay) Immunocytochemistry 2. Whole genome analysis MeDIP/Chip MeDIP/sequencing RRBS (Reduced ReprensentativeBisulfite Sequencing) Sonication Denaturation Anti-5meC Methylated DNA analysis Analyse by Chip hybridization or sequencing 3. Sequence specific approach Bisulfite conversion /pyrosequencing EAAP 30 Aug- 4th Sept Warsaw

10 % de Cytosines méthylées Deviation individuelle du % de cytosines méthylées Bovine clones are genetically identical but epigenomically variants Blastocyst placenta foetus anterior pole 2 weeks 2 months perinatal adult BIRTH posterior pole gastrulation Variability of global methylation rate in lymphocytes of adult bovine clones A Clones Holstein Clones Simmental Jumelles Simmental Vache donneuse B Holstein, 4 genotypes Cytosine methylation electrophoretic assay De Montera et al., 2010 Simmental, 5 genotypes Twins pairs, n=13 Simmental

11 Epigenetic signatures at perinatal stage in liver Blastocyst placenta foetus adult anterior pole 2 weeks 2 months perinatal posterior pole gastrulation BIRTH Post-natal development Methylated Unmethylated bovine microarray targeting genes pb pb From : (~34 probes per promoter region ) + 12 larger regions MeDIP/Chip- Kiefer et al. submitted

12 1 Identification of methylated probes ChIPmix R package Enriched in MeDIP «methylated» Not enriched in MeDIP Data analysis framework 2 Identification of highly methylated regions containing clusters of enriched probes in at least one condition 3 Identification of 83 Differentially Methylated Regions (DMRs) between clones and AI SpatStat R package 4 Validation by pyrosequencin 80% regiond vamidate

13 1 Identification of methylated probes among probes ChIPmix R package Data analysis framework 2 Identification of highly methylated regions containing clusters of enriched probes in at least one condition 3 Identification of 83 Differentially Methylated Regions (DMRs) between clones and AI SpatStat R package 4 Validation by pyrosequencing

14 1 Identification of methylated probes ChIPmix R package Perinatal AI Perinatal clones Data analysis framework 2 Identification of highly methylated regions containing clusters of enriched probes in at least one condition 3 Identification of 83 Differentially Methylated Regions (DMRs) between clones and AI SpatStat R package 4 Validation by pyrosequencing

15 1 Identification of methylated probes ChIPmix R package Bisulfite conversion Data analysis framework Identification of highly methylated regions containing clusters of enriched probes in at least one condition PCR amplification 2 Pyrosequencing Native DNA Treated DNA PCR products Pyrogram C U T 39% m C C C 61% 3 Identification of 83 Differentially Methylated Regions (DMRs) between clones and AI SpatStat R package 4 Validation by pyrosequencing

16 Nu CV area (%) % methylation PL C14.0 (%) Nu CV Shape Factor (%) NL DHA/AA Cell area (µm 2 ) One example (among hundred others) of DMR methylation rate correlated to phenotypic markers of liver physiology Microarray data Perinatal AI Perinatal clones Correlation methylation-phenotypic markers Metabolic parameters r=-0.55 Cellular parameters r=0.61 Pyrosequencing validation p= r= r= Methylation (%) Permutation test Perinatal AI 76 Perinatal clone Fetus AI Calf AI Fetus clone Calf clone Spearman s rank correlation test r= Methylation (%)

17 Cell area (µm 2 ) An example of DMR methylation rate correlated to phenotypic markers of the liver s physiology Correlation methylation-phenotypic markers Fetus AI Calf AI Fetus clone Calf clone r=0.61 to predict early lactation-associated hepatosteatosis in dairy cows Ongoing validation from the blood of dairy cows at early lactation In human, the associated gene shows differential methylation in the blood of newly diagnosed, drug naive patients with type 2 diabetes Canivell et al. 2014, PLoS one) Clone with no glycogen storage (average size of hepatocytes is smaller) Clone with normal glycogen storage (average size of hepatocytes is similar to AI)

18 % methylation Extension to muscle epigenomic analysis: screening AI and cloned adults anterior pole Intercaruncule Caruncule 2 weeks 2 months perinatal BIRTH posterior pole Muscle biopsy at 18 th months ~ 500 µg genomic DNA DMR1 DMR2 DMR3 DMR4 *p<0.05 Permutation test AI controls, 18 months n=9 Healthy clones, 18 months n=8 Kiefer et al. submitted A first step towards the tracability of cloned products?

19 and from now capture more Data Genome and functions Genomics (QTL, SNPs Epigenomics Metabolomics Ruminomics through FANG Genomic data phenotypical data environmental data Traits size, growth, Productive traits Reproductive traits puberty ovulation rates fertilisation embryo death fetal losses.. Phenomes more data higher resolution through ATOL on an individual basis environment Animal management Animal health/treatment What happened during the reproductive cycle? What was the outcome? more data higher resolution through EOL The individual animal within its environement as the reference EAAP 30 Aug- 4th Sept Warsaw

20 Models: Predict the Future In agriculture Leading Data Science Platform in the Industry Software Engineering How can it be built? DATA Domain Science What is important? Statistics How can predictions be made? INSIGHTS

21 Computing Applications Make Data Relevant to Animal production. They are already economically operational in Agriculture Storing Data is Getting Cheaper Average Price Per Gigabyte, $192,308 $200,000 $180,000 $160,000 $140,000 $120,000 $100,000 $80,000 $60,000 $40,000 $20,000 $0 $119,950 $44,950 $176 $0.70 $6.21 $0.07 $ % drop in price per gigabyte over past 10 Years In Agriculture, Environmental and Operating Data Are Taking Shape Source: Wayback Machine (Statistics Brain) Low Cost Computing is Proliferating Distributed Computing Allows Multiple Computers to Use More Data and Solve a Problem Together ex: Hadoop platform for Big data management EAAP 30 Aug- 4th Sept Warsaw

22 Models: Predict the Future Unified platform Continue capability development Software Engineering How can it be built? Establish coordinated industry framework to address data privacy DATA DATA Domain Science Establish anchor producers distribution partnerships What is important? Domain Science What is important? Statistics Statistics How can predictions be How made? can predictions be made? INSIGHTS Establish broad, engaged foundation base experience with product & service offerings EAAP 30 Aug- 4th Sept Warsaw

23 Hélène Jammes Hélène Kiefer Evelyne Camplion Audrey Prézelin Luc Jouneau Maxime Gasselin Hala Al Adhami François Piumi Poster n Oral session n 37wed.17h Poster n Coll. i Biostatistical Analysis Marie-Laure Magniette AgroParisTech Coll. Methylation Analysis (RRBS) CNRS, Strasbourg EpigRAni partners Eve Devinoy, GABI, INRA, Jouy en Josas Dominique Rocha, GABI, INRA, Jouy en Josas Jean Philippe Perrier Marion Boutinaud, Pegase, INRA, Rennes Jorg Tost, CEA/CNG, Evry EAAP 30 Aug- 4th Sept Warsaw