Appetite and adiposity of the emu (Dromaius novaehollandiae).
Evidence that early growth influences adiposity at age 9 ...
Transcript of Evidence that early growth influences adiposity at age 9 ...
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Evidence that early growth influences adiposity at age 9-13 years
and is mediated by epigenetic regulation of gene expression
Alix Groom
Human Nutrition Research CentreInstitute for Ageing & Health
Newcastle University, UK
Note: for non-commercial purposes only
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Nutrition and preterm infants
critical window
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• Beneficial for neurodevelopment • Associated with adverse metabolic consequences in adulthood
Catch up growth
Wells, Early Human Development (2007) 83:743-748
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Mechanisms of programming
DNA
Methylation
Chromosome
Histones
NucleosomeHistone
tail
Epigenetics?
Histone modification:acetylationphosphorylationubiquitinationmethylation
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Hypothesis
• Early environmental exposures are “memorised” by the cell by epigenetic markings of the genome
• These epigenetic modifications produce a stable alteration in the expression of specific genes
• Aberrant epigenetic marking and subsequent altered expression of genes results in changes to body composition and metabolic health in childhood
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ClinicalAssessment
Born < 34 wks
RCT 36 wks - 6 mo
Breast
Termformula
Pretermformula
Preterm formula 36 - 40wkTerm formula 40wk - 6mo
Discharge ~36 wks
A
B
C
D
Newcastle Preterm Birth Growth Study
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RCT
seen biweekly
seen monthly
≤34w
born
dischargeDEXA
anthroDEXA
anthroDEXA
anthroDEXA
anthromental devt
psychomotor devt
T +12w
DEXA
~36w T 6m 12m 18m
����X
X
Cooke RJ et al Pediatr Res 49(5):719-22 2001
Newcastle Preterm Birth Growth Study
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Assessment at mean age 12 years• Anthropometry• Whole body DEXA• Bio-electrical impedance• Leptin, insulin, adiponectin• Blood pressure• Fasting glucose, 30 min glucose• Triglycerides, cholesterol, lipids• Blood samples for DNA and RNA• Saliva samples
Catch up growth:difference in z score for weight betweenterm and term plus 12 weeksslow growth -0.7rapid growth 0.7p <0.0001
p=0.017
mm
ol/L
p=0.013
p=0.049
(kg) (kg)
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Gene expressionmicroarray
slow vs rapid postnatal growth N=24
Bisulphite modification
Real time PCRverification
Blood sample
Pyrosequencinganalysis of differentially
expressed genes
CHILDREN BORN PRETERM
DNARNA
Analysis of relationship between methylation, expression and phenotype at age 12y
DNA
Saliva sample
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Gene expression microarray
12 slow vs 12 rapid postnatal growth
807 loci differentially expressed(sex analysis: 245♀, 352♂)
50 “Top hits” for each sex
Common to ♀♂APOBEC3B
ARG1CNTNAP3TACSTD2
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Exon 1
-1117 -617
promoter
CpG island
TSS +1734+1209
////
-467 -428
• encodes carcinoma-associated antigen• family includes type 1 membrane proteins• transduces an intracellular calcium signal• acts as cell surface receptor• autosomal recessive disorder gelatinous drop-like corneal dystrophy
TACSTD2Tumour associated calcium signal transducer 2
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p=0.02p=0.03
TACSTD2 expression TACSTD2 methylation
expression/methylation correlation coefficient -0.89, p<0.0001
Catch up growth is associated with differential methylation and expression
Fol
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richm
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ethy
latio
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Rapid growth Slow growth Rapid growth Slow growth
TACSTD2 expression and methylation
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TACSTD2 methylation and childhood phenotype
Cor
rela
tion
co-e
ffici
ent
(rho
)Spearman correlation test showed the following variables to be associated with TACSTD2 methylation ( p<0.05)
Weight(kg)
Headcircumference
(cm)
Waist(cm)
HDLmmol/L
Total/HDL Total fat mass (kg)
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Variable Blood Saliva
b 95% CI p-value R2 b 95% CI p-value R2
Weight (kg) -0.50 -12.35 2.27 0.174 -4.86 -8.82 -0.91 0.016 0.057
Head
circumference -1.95 -3.61 -0.29 0.022 0.061 -1.57 -2.41 -0.73 <0.001 0.129
Waist (cm) -5.35 -11.57 0.87 0.091 -4.51 -7.97 -1.05 0.011 0.064
HDL (mmol/L) 0.18 -0.04 0.39 0.103 0.00 -0.14 0.13 0.960
Total/HDL -0.31 -0.75 0.14 0.179 -0.24 -0.53 0.06 0.112
Total fat mass (kg) -5.15 -9.37 -0.93 0.017 0.061 -3.32 -5.53 1.12 0.003 0.085
Variance in trait attributed to TACSTD2 methylation
Linear regression analysis defined the level of variance (R2) in each trait
(cm)
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Summary
• Differences in catch up growth were associated with changes in gene expression at age 12y
• Investigation of the differentially expressed candidate gene TACSTD2 demonstrated differential methylation
• Differential methylation of TACSTD2 was associated with childhood phenotype
• Further work is required to establish the causal nature of the observed association
– Are changes in methylation caused by childhood phenotype?
– Are changes in methylation caused by early growth patterns?
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Principal InvestigatorCaroline Relton HNRC/Institute for Ageing and Health
Focus teamHeather Cordell Institute of Human GeneticsNick Embleton Newcastle Neonatal Unit, RVIJohn Mathers HNRC/Institute for Ageing and HealthMark Pearce Lifecourse EpidemiologyDan Swan Bioinformatics Support Unit
Lab teamHannah Elliott HNRC/Institute for Ageing and HealthJames McConnell HNRC/Institute for Ageing and Health
Clinical teamTim Cheetham Newcastle Neonatal Unit, RVINick Embleton Newcastle Neonatal Unit, RVIMurthy Korada Newcastle Neonatal Unit, RVI
Newcastle Healthcare Charity
Funding
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Epigenetics and Developmental Programming
ConferenceNewcastle upon Tyne, UK
21st -22nd March 2011
Photographs by Graeme Peacock
Topics
• The environment and the epigenome• Epigenetic variation and phenotype• Epigenetic variation and common genetic
variation• Identifying and quantifying epigenetic
variation• Bioinformatic challenges in epigenetic
analyses
Confirmed speakers
• Dr Andrea Baccarelli• Dr Graham Burdge • Prof Patrick Chinnery • Prof George Davey-Smith• Dr Daniele Fallin • Dr Bas Heijmans• Prof Tom Kirkwood
• Prof John Mathers• Dr Jonathan Mill • Dr Sue Ozanne• Dr Vardhman Rakyan• Prof Wolf Reik• Dr Caroline Relton• Prof Seif Shaheen
To register interest please email;
http://www.ncl.ac.uk/iah/epi.prog
Conference OrganisersAlix Groom, Jill McKay, John Mathers & Caroline Relton