epigenetic databases in cattle and the - eaapeaap.org/annual_meeting/2015_warsaw/s16_04.pdf ·...

EAAP 30 Aug- 4th Sept 2015- Warsaw

Epigenetic databases in cattle and the

prediction of phenotypes from genotypes

Jean-Paul Renard

Hélène Jammes - Hélène Kiefer

Académie d’Agriculture de France - Paris

INRA UMR 1198- Jouy en Josas

66th EAAP Annual Meeting

30 August – 4th september 2015

Warsaw, Poland

Eve Devinoy INRA GABI- 1113- Jouy en Josas

http://www.academie-agriculture.fr/

van Driel, R. et al., 2003 Journal of Cell Science

Between genotype and phenotype…

…3 levels of genes regulation…

Sequence

Chromatin

…Controlled by epigenetic information

Histone modifications

DNA methylation

Non coding RNAs

Nuclear compartments Nuclear organization

ex: interpreting non coding genetic variation

in complex human traits at a single locus

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)

Ward and Kellis, 2012, Nat.Botech.

Involves the use of reference

epigenome maps of primary

and cultured cells

Epigenetic information in the prediction of traits from GWAS..

in human


first cell lineages establishment

of body plan

waves of epigenetic reprogramming shape transcriptomic patterns

during both embryonic, foetal and post natal periods

organogenesis,

Post-natal

development

BIR

TH

posterior pole

anterior pole

2 weeks 2 months perinatal

adult Blastocyst placenta foetus

gastrulation



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

Measuring the contribution of epigenetics

to the variability of phenotype in the bovine species


*

16

8

12

10

14

6

4

0

2 Su

ccss

ra

tes

of

recl

on

ing

Wakayama et al. 2013 Cell Stem cell

Cloning techniques do not select for somatic cells

more amenable to full reprogramming

Lif

esp

ans

in y

ears

0.5

1.0

1.5

2.0

2.5

3.0

3.5

recloning number recloning number

mouse cloning

(%)

-optimisation of in vitro conditions (empirical)

**

** **

no impact on viability no drop in efficiency

-recloning from adult cloned mouse


fibroblastes

- Ca++

In vitro culture

donor cells

enucleated recipient oocytes

in

viv

o

In

vit

ro

in

vit

ro

in

vit

ro

in

viv

o

transfer into

recipient

One-cell stage embryo blastocyste

different nuclear microenvironment from the one-cell stage

same nuclear genome

in vivo in vitro (culture) in vivo (oocyte ) - in vitro (culture)

relatively efficiency at birth 40%

9 months- long gestation

1998 2002 2006 2000 2004 2008

MOET

IVP

Cal

vin

g r

ate

(%)

40

30

20

10

0

- 40%

Watanabe and Nagai2011 and EFSA 2012

SCNT

from 482 calving

Pregancy rate

Transfer (D7)

D21 D70 D90 calving

IVF 0

30

50

70

100

D35 Clone

Bovine cloning: a way to profile epivariants in a context of high

environmental constraints applied on the genome.



10-3

CpG

Sequence

Environment

Stochastic

Active DNA methylation Dnmt1: maintenance Inheritable (mitosis) Dnmt3: de novo in response to stimuli

DNA methylation is highly dependant

on genomic sequence

DNA methylation loss Passive Active



Sonication

Denaturation

Anti-5meC

Methylated DNA analysis

Analyse by Chip hybridization or sequencing

2. Whole genome analysis

MeDIP/Chip

MeDIP/sequencing

RRBS (Reduced ReprensentativeBisulfite Sequencing)

1. Global analysis

Cytosine methylation electrophoretic assay

LUMA (Luminometric Methylation Assay)

Immunocytochemistry

3. Sequence specific approach

Bisulfite conversion

/pyrosequencing

Tools used to characterize, identify and measure DNA

methylation variations


Bovine clones are genetically identical but epigenomically variants

A

Clones Holstein

Clones Simmental

Jumelles Simmental

Vache donneuse

% d

e C

yto

sin

es m

éth

ylé

es

B

Dev

iati

on

in

div

idu

elle

du

% d

e cy

tosi

nes

mét

hylé

es

Holstein,

4 genotypes

Simmental,

5 genotypes

Twins pairs, n=13

Simmental

Variability of global

methylation rate

in lymphocytes of

adult bovine clones

De Montera et al., 2010 Cytosine methylation electrophoretic assay

BIR

TH

posterior pole

anterior pole

2 weeks 2 months perinatal adult

Blastocyst placenta foetus

gastrulation

From : http://www.epigenome-noe.net/researchtools/protocol.php?protid=33

bovine microarray targeting

21 416 genes

Methylated Unmethylated

Epigenetic signatures at perinatal stage in liver

MeDIP/Chip- Kiefer et al. submitted

Post-natal

development

BIR

TH

posterior pole

anterior pole


adult Blastocyst placenta foetus

gastrulation

-2000 pb 0 +1360 pb

(~34 probes per promoter region ) + 12 larger regions

Data analysis framework

Enriched in MeDIP « methylated » Not enriched in MeDIP

Identification of 83 Differentially

Methylated Regions (DMRs)

between clones and AI SpatStat R package

3

Identification of highly

methylated regions containing clusters of enriched probes in at least one condition

2

4 Validation by

pyrosequencin

80% regiond vamidaterd

1 Identification of

methylated probes ChIPmix R package 720 000


Identification of

methylated probes

among 720 000 probes ChIPmix R package




1

3

Identification of 15885

highly methylated

regions containing clusters of enriched probes in at least one condition

2

4 Validation by

pyrosequencing


Identification of

methylated probes ChIPmix R package




1

3



2

4 Validation by

pyrosequencing

Perinatal AI

Perinatal clones


Identification of

methylated probes ChIPmix R package




1

3



2

4 Validation by

pyrosequencing

C U T 39%

mC C C 61%

Native DNA Treated DNA PCR products Pyrogram

Bisulfite conversion

PCR amplification

Pyro-sequencing

35 40 45 50

180

220

260

Cell a

rea (

µm

2)

35 40 45 50

2

3

4

5

6

7

Nu

CV

Sh

ap

e F

acto

r (%

)

35 40 45 50

0.2

0.4

0.6

0.8

NL

DH

A/A

A

35 40 45 50

14

18

22

26

Methylation (%)

Nu

CV

are

a (

%)

35 40 45 50

0.2

0.4

0.6

0.8

1.0

Methylation (%) P

L C

14.0

(%

)

Fetus AI

Calf AI

Fetus clone

Calf clone

Perinatal

AI

Perinatal

clone

% m

eth

yla

tio

n

p=0.018

35

40

45

50

979

76

Perinatal AI

Perinatal clones

Microarray data

Pyrosequencing validation

Correlation methylation-phenotypic markers

r=-0.86

r=-0.69

r=-0.89

r=0.61

r=-0.55

Permutation test

Spearman’s rank correlation test

Metabolic parameters Cellular parameters

One example (among hundred others) of DMR

methylation rate correlated to phenotypic markers of

liver physiology

35 40 45 50

180

220

260

Cell a

rea (

µm

2) Fetus AI

Calf AI

Fetus clone

Calf clone

Correlation methylation-phenotypic markers

r=0.61

Clone with no glycogen storage (average size of hepatocytes is smaller)

Clone with normal glycogen storage (average size of hepatocytes is similar to AI)

An example of DMR methylation rate

correlated to phenotypic markers of the liver’s physiology

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)

Ongoing validation from the blood of dairy cows at early lactation.

to predict early lactation-associated

hepatosteatosis in dairy cows

BIR

TH

posterior pole

anterior pole

Intercaruncule

Caruncule


Extension to muscle epigenomic analysis:

% m

eth

yla

tio

n

DMR1 DMR2 DMR3 DMR4

AI controls, 18 months n=9

Healthy clones, 18 months n=8

Muscle biopsy at 18th months ~ 500 µg genomic DNA

*p<0.05

Permutation test

A first step towards the tracability of cloned products ? Kiefer et al. submitted

screening AI and cloned adults


Genomic data

phenotypical data

environmental data

Genome and

functions

• Genomics (QTL, SNPs…

• Epigenomics

• Metabolomics

• Ruminomics

Traits

size, growth,

Productive traits

Reproductive traits

puberty

ovulation rates

fertilisation

embryo death

fetal losses…..

Phenomes

• more data

• higher resolution

environment

Animal management

Animal health/treatment

What happened during the reproductive cycle?

•What was the outcome?

through EOL

capture more Data on an

individual basis

through ATOL

• more data

• higher resolution

and from now …

The individual animal within its environement as the reference

through FANG


Leading Data Science Platform in the Industry

Software Engineering

How can it be built?

Domain

Science

What is important?

Statistics

How can

predictions be

made?

DATA INSIGHTS

Models:

Predict the Future

In agriculture


Storing Data is Getting Cheaper In Agriculture, Environmental and Operating Data

Are Taking Shape

Low Cost Computing is Proliferating

Average Price Per Gigabyte, 1980-2013 $192,308

$200,000

$180,000

$160,000

$140,000 $119,950

$120,000

$100,000

$80,000

$60,000

$40,000

$20,000

$0

$44,950

$176 $0.70 $6.21

$0.07 $0.04

1980 1984 1989 1996 2000 2004 2009 2013

Source: Wayback Machine (Statistics Brain)

Distributed Computing Allows Multiple Computers to Use More Data and Solve a Problem Together

97% drop in price

per gigabyte over

past 10 Years

Computing Applications Make Data Relevant to Animal production.

They are already economically operational in Agriculture

ex: Hadoop platform for Big data management



Domain

Science

What is important?

Statistics

How can

predictions be

made?

DATA

Software Engineering

How can it be built?

Domain

Science

What is important?

Statistics

How can

predictions be

made?

DATA INSIGHTS

Unified

platform

Continue

capability

development

Establish

coordinated

industry

framework

to address

data privacy

Establish broad,

engaged

foundation base

experience with

product & service

offerings

Establish

anchor

producers

distribution

partnerships

Models: Predict

the Future



• Oral session

• n°37wed.17h

Poster

n°15-21249

Jean Philippe Perrier

Maxime Gasselin

Hala Al Adhami

François Piumi

Luc Jouneau

Audrey Prézelin

Evelyne Camplion

Hélène Kiefer

Hélène Jammes

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

• Marion Boutinaud, Pegase, INRA, Rennes

• Jorg Tost, CEA/CNG, Evry

Poster

n°14-21257


epigenetic databases in cattle and the - eaapeaap.org/annual_meeting/2015_warsaw/s16_04.pdf ·...

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