Transcript Presentation Slides

Etiology of Autism: A Role for
Epigenetics?
Rosanna Weksberg
NeuroDevNet
September 21, 2012
University of
Toronto
Complex Etiology of Autism Spectrum
Disorders
Genetics in ASD etiology
• Until recently ASD was considered one of the most heritable of
neurodevelopmental disorders (~90% heritability)
• Rare genetic variants :gene mutations/CNVs/chromosome
abnormalities or genetic syndromes account for ~10-20% of
ASD cases
• Each individual rare variant is found not in more than ~1-2% of
ASD cases
– 15q11-13 maternal duplication, 16p11-12 deletion
– SHANK3, NRXN1, NLGN3&4, PTCHD1
• Common variants: SNPs have been identified through several
genome-wide association studies
– MACROD2, CDH9, PITX1
– Lack of replication among studies points to high genetic heterogeneity
and small effect size of the risk factor alleles
Environment in ASD etiology
• Recent twin study - estimated that heritability of ASD is only 38%,
whereas shared environment contributes to 58% of the liability
(Hallmayer et al. 2011)
• Environmental risk factors:
• Sub-fertility/assisted reproduction?
• In utero exposure to antiepileptic drugs
– Exposure to VPA increases ASD risk ~10 times
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Pregnancy complications?
Viral infections?
Ecology?
Nutrition?
• Exposures can occur at different stages of development
– Gametes
– Prenatal development
– Postnatal development
Epigenetics
• Epigenetic modifications
can change gene expression
patterns without changing
primary nucleotide
sequence (i.e. no mutation)
• Epigenetic Mechanisms
include: DNA methylation,
chromatin conformation,
histone modifications, RNA
silencing
Weksberg R, S.P., Smith AC, Tycko B. Epigenetics. in Emery and Rimoin's Principles and Practice of Medical Genetic
Epigenetic Modifications
• CAN BE STABLE: transmitted through mitotic cell division
• CAN BE DYNAMIC: sensitive to environmental stimuli (internal and
external)
 Cell differentiation
 Memory
 Circadian cycle
 Nutrition (e.g. folic acid supplementation)
 Medications (e.g. valproate)
• CAN BE CELL TYPE-SPECIFIC and DEVELOPMENTALLY-SPECIFIC
Complexity of Histone Modifications
Site-specific
H3K4
H3K9
H3K36……
Modification-specific
Acetylation
Methylation
Phosphorylation
Ubiquitination ………
Regulators of Epigenetic Modifications
Borrelli et al. Neuron 2008
Epigenetics in ASD etiology
• Evidence for role of epigenetics
in ASD comes from both genetic
and environmental risk factors
Genetic syndromes co-morbid with ASD and
idiopathic ASD are caused by mutations in genes
involved in epigenetic regulation
Gene
Function
Locus
Disorder
OMIM
CHD7
ATPase/HelicaseChromatin remodeler
8q12
CHARGE
syndrome
214800
CHD8
ATPase/HelicaseChromatin remodeler
14q11.2
ASD
610528
NSD1
H3K36 methyltransferase
5q35
Sotos syndrome
117550
CREBBP, EP300
Histone acetyltransferase
16p13
Rubinstein-Taybi
syndrome
180849
MECP2
Methyl binding protein
Xq28
Rett syndrome
300672
MLL2
H3K4 methyltransferase
12q13.12
Kabuki syndrome
147920
EHMT1
H3K9 methyltransferase
9q34
KDM5C
H4K4 demethylase
Xp11
9q subtelomeric
deletion syndrome
Intellectual
disability
Autism/autistic features
15-50% risk of ASD (Hartshorne et al.
2005; Smith et al. 2005; Johansson et al.
2006)
One of the most frequent recurrent de
novo mutations found in idiopathic ASD
by exome sequencing: 5 mutations/1144
cases (Neale et al. 2012; O'Roak et al.
2012)
Autistic features (Rutter and Cole 1991;
Mouridsen and Hansen 2002; Sarimski
2003; Ball et al. 2005) +Case reports of
ASD (Morrow et al. 1990; Zapella 1990;
Trad et al. 1991; Mouridsen and Hansen
2002)
Autistic features (Schorry et al. 2008)
Overlap in phenotype between Rett and
ASD (White et al. ; Weaving et al. 2004;
Russo et al. 2009)
Autistic features (Ho and Eaves 1997;
Oksanen et al. 2004) and case report of
ASD (Akin Sari et al. 2008)
610253
Case report of ASD (Kleefstra et al. 2009)
300534
Case report of ASD (Adegbola et al. 2008)
Environmental Factors Associated with
Epigenetic Regulation: Valproate and Autism
• The risk of ASD in children exposed in utero to valporate
during pregnancy is estimated to be ~9% vs 0.9% in general
population
(Moore et al. 2000, J Med Genet ; Rasalam et al. 2005 Dev Med Child Neurol)
• Possible mechanisms of valproate teratogenic action:
o oxidative stress and cell toxicity caused by metabolites of
valproate
o interference with folate metabolism
o inhibition of histone deacetylases
Folate Metabolism: Combination of genetics,
environment and epigenetics
• Dietary folate and vitamin B12 are important components of
the S-adenosylmethionine (SAM) synthesis pathway, which is
the main donor of methyl group for DNA and histone
methylation
• Multiple studies have reported association of functional
polymorphisms in the SAM synthesis pathway with ASD (Boris
et al 2004; Pasca et al. 2008; Adams et al. 2007; Goin-Kochel et al. 2009;
Mohammad et al. 2009)
• Increased risk of ASD in children
– If mothers did not take periconceptional vitamin supplementation
– If mothers were carriers of a functional polymorphism in one of the
SAM pathway genes (Schmidt et al. 2011)
Epigenetic alterations in ASD
• Epigenetic alterations in ASD could occur due to:
– Genetic alterations in genes involved in epigenetic
regulation
– Environmental exposures causing epigenetic
alterations
– Unknown/stochastic factors
• Challenges of identifying epigenetic alterations in
ASD:
– Etiological heterogeneity
– Tissue specificity of epigenetic marks
Interplay of genetic and
epigenetic factors: lessons from
KDM5C mutation
KDM5C
• Mutations in X-linked gene KDM5C cause intellectual
disability (mild to severe)
• More than 20 mutations are identified to date
(Rujirabanjerd et al. 2010)
• KDM5C encodes H3 lysine4 (K4) demethylase specific
for demethylating H3K4me3/2 (Iwase et al. 2007)
• KDM5C escapes X-inactivation, and has a Y-linked
functional homologue KDM5D
• All forms of H3K4 methylation protect DNA from de
novo methylation in embryonic development by
blocking DNMT3A/L binding (Ooi et al. 2007)
Hypothesis
• KDM5C loss of function
mutations result in loss of DNA
methylation
at
specific
genomic targets
– Advantage of studying DNA
methylation – accessibility in
clinical samples
• Identification of dysregulated
epigenetic targets of KDM5C
will elucidate the molecular
pathophysiology of intellectual
disability
Study Design
• Illumina Methylation27 array containing 27,578 CpG sites
covering >14,000 genes was used for genome-wide
comparison of CpG methylation patterns in blood samples of
10 patients with KDM5C mutations vs 19 male controls
• Mann-Whitney U test with permutation analysis was used for
group comparisons
• Targeted validation by Sodium bisulfite pyrosequencing
• DNA methylation analysis of top candidate CpG sites using
publically available control datasets
• Comparison of DNA methylation in KDM5C targets between
normal XY males (KDM5C/KDM5D) and XX females
(KDM5C/KDM5C) in blood and brain using published dataset
DNA methylation
•DNA methylation level = C/C+T
•DNAm at CpG site is a quantitative
variable ranging from fully
methylated (100%) to completely
unmethylated (0%), representing
the mixture of methylated and
unmethylated cells and alleles
Number of CpG sites detected by multivariate
permutation analysis for different levels of
confidence (1-α) and false discovery proportion
limit (γ)
1-α
0.995
γ=0
53 loss/0 gain
γ = 0.005
γ = 0.01
53 loss/0 gain 125 loss/11 gain
γ = 0.05
362 loss/44 gain
0.99
98 loss/5 gain
98 loss/5 gain 207 loss/23 gain
625 loss/103 gain
0.95
207 loss/23 gain 362 loss/44 gain 568 loss/89 gain 1098 loss/207 gain
Targeted Validation:
Loss of DNA methylation observed in several
CpGs in cis
Array results for 3 top candidate genes
implicated in ubiquitin-mediated protein
degradation
C- controls
K- individuals with ID and KDM5C mutations
Loss of DNA methylation in top three candidate
genes was not found in >900 population
controls
N=
93 398 9 257 99 21 19 10
N=
93 398 9 257 99 21 19 10
N=
93 398 9 257 99 21 19 10
GEO datasets AF (Aging in females, GSE20236), AP1 (aging pediatric 1,GSE27097),
AP2 (aging pediatric 2, GSE36064), CO (cancer ovarian, GSE19711), DB (diabetes,
GSE20067), DS (Down syndrome, GSE25395).
K-C are controls from our study (), K-M are KDM5C mutation cases.
For CO and DS only control samples were included.
FBXL5 DNA methylation depends on
KDM5C/D dosage in brain and blood
p=0.029
p=0.00026
p=0.01
Direction of differences is consistent with KDM5C/D dosage:
2 copies of KDM5C (females) have higher H3K demethylating activity than
KDM5C/KDM5D (males) resulting in higher DNA methylation
Frontal and Temporal Cortex data is a published dataset of 150 neurologically normal individuals (Gibbs
et al. 2010)
Blood - pyrosequencing 13 males and 13 females
Conclusions: KDM5C
• Loss of DNA methylation at specific genes was found to be
associated with KDM5C mutation:
– Large degree of change 20-50% similar to changes seen in imprinting
disorders
– Supports interplay between H3K4 methylation and DNA methylation in
humans
– Supports the feasibility of studying DNA methylation in
neurodevelopmental disorders, including ASD
• Dependence of FBXL5 and CACYBP DNA methylation on
KDM5C/D dosage in normal males and females
– Suggest that loss of DNA methylation at FBXL5 and CACYBP promoters
in blood of patients with KDM5C mutations could be a biomarker of
similar changes occurring in brain
Interplay of environmental and
epigenetic factors in ASD etiology
DOES ART/SUBFERTILITY INCREASE
RISK OF ASD?
•
Cohort study:
– California, University of California, San Francisco: 4 fold increase of ASD in
children born following assisted reproduction (Croughan et al. American
Society for Reproductive Medicine conference 2006, 1699 naturally
conceived/1008 conceived using ART or FT)
•
Case control studies:
– Denmark: 2.3 decrease of ART in ASD(N=461) compared to controls (n-461)
(Maimburg and Vaeth, Human Reproduction, 2007)
– California: 2.2 increase of history of infertility in ASD twins (N=21) compared
to twin controls (N=54), no association in singletons (349 cases vs 1.847
controls) (Grether et al. 2012, J Autism Dev Disord)
– Boston: 1.7 increased rated of fertility treatments in ASD born to advanced
age mothers>35 (N=164) compared to controls born to advanced age mothers
(N=857) )(Lyall, et al., Paediatric and Perinatal Epidemiology
•
Comparison to general population:
– Israel: 10.7% rate of ART in 507 ASD cases vs 3% rate of ART in Israeli
population (Zachor and Itzchak, Research in Developmental Disabilities, 2011)
– Japan: 4.5% rate of ART in in 466 ASD cases vs 2,5% of ART in Japan population
(Shimada et al. Research in Autism Spectrum Disorders, 2012)
Sources of inconsistency
• Parental age
• Twinning (number of transferred embryos)
• Variability in ART/FT procedures: type/dose of
medications and embryo culture
• ASD diagnosis
EPIGENETIC DYSREGULATION and ASSISTED
REPRODUCTION IN IMPRINTING DISORDERS
General Population
ART
BWS
Enrichment of epigenetic
defects in BeckwithWiedemann (BWS) and
Angelman (AS) Syndromes
ART populations
19/20
AS
5/19
DeBaun et al., (2003)
Sutcliffe et al. (2006)
Ludwig et al. (2005)
Fertility Treatments Can Change DNA Methylation
Patterns
• Ovulation stimulation (FSH/clomid):
– Maturation and ovulation of oocytes with
incomplete/aberrant DNA methylation
• In vitro fertilization (IVF):
– In vitro embryo culture disrupts proper imprint
maintenance during global genome demethylation
• Intracytoplasmic sperm injection (ICSI):
– Sperm with incomplete/aberrant methylation bypass
natural selection
HYPOTHESIS
 Epigenetic alterations, specifically DNA
methylation, play an important role in ASD
etiology
 Subfertility/fertility treatments are associated
with an increased rate of epigenetic errors that
contribute to the ASD phenotype
Research Subjects
Ethnicity:
Subfertility/
Caucasian/Asian/Africa
Fertility treatments
n
Group
Age
(Mean ±SD)
Female/Male
ASD-FT
7±4
3/9
7/3/1
3 Subf, 7 OI, 2 IVF
ASD
7±4
3/9
8/2/2
None
Controls
11±4
5/7
8/1/3
None
Subf. is sub-fertility, defined as time to conception >2 years, OI is ovulation
induction, and IVF is in vitro fertilization
DNA was extracted from white blood cells
Experimental Outline
Illumina 27K
array
Common
variant
analysis
Individual
cases analysis
Comparison
to brain
dataset
Global
analysis
Illumina HumanMethylation27
dataset of frontal and temporal
cortex of 150 neurologically normal
individuals
GEO Accession Number: GSE15745
(Gibbs et al., 2010)
DNA methylation
•DNA methylation level = C/C+T
•DNAm at CpG site is a quantitative
variable ranging from fully
methylated (100%) to completely
unmethylated (0%), representing
the mixture of methylated and
unmethylated cells and alleles
Global Methylation Analysis
ASD –FT
ASD
Controls
A: Small, but significant reduction in average DNA methylation of 26, 486 autosomal CpGs
in ASD-FT group compared to naturally conceived ASD and controls
B: Loss results from differences between CpG sites with 10-20% and 0-10% DNA
methylation levels
Microarray Analysis
• Common variant analysis:
– Mann-Whitney test with correction for multiple testing and difference
in DNA methylation ≥ 10% did not reveal any significant changes
• Individual Analysis:
– Selection of CpG sites with at least one sample with methylation level
10% lower or higher than the minimum and maximum values in
controls:
• ASD-FT Group: 13 CpG sites with gain and 36 CpG sites with loss of
DNA methylation
• ASD Group: 2 CpG sites with gain and 6 CpG sites with loss of DNA
methylation
Loss of DNA methylation at imprinted gene DIRAS3 in
ASD-FT cases
DIRAS family, GTP-binding RAS-like 3
ASD
ASD-FT C
FC
TC
ASD: ASD blood samples (N=12)
ASD-FT: ASD-FT blood samples (N=12)
C: control blood samples (N=12)
FC: frontal cortex control samples (N=150)
TC: temporal cortex control samples (N=150)
Conclusions
Studying cases with
1. neurodevelopmental phenotypes and
genetic alterations in epigenetic regulators or
2. certain environmental exposures
can identify epigenetic dysregulation
– Likely to play important role in molecular
pathophysiology of disorder
– Could be further studied in idiopathic cases
Epigenetics plays at least as important a role in
ASD etiology as genetics
Acknowledgments
Weksberg Lab
Autism project:
Daria Grafodatskaya
Darci Butcher
Brian Chung
Rageen Rajendram
Sarah Goodman
Sanaa Choufani
Chunhua Zhao
Youliang Lou
Jonathan Shapiro
Yi-an Chen
Tanya Guha
Hailey Jin
Liis Uuskula
Michal Feigenberg
Khadine Wiltshire
Cinical Genetics
Cheryl Cytrynbaum
Cheryl Shuman
Steve Scherer , The Centre for Applied Genomics
Wendy Roberts, Autism Research Unit
Evdokia Anagnostou , Bloorview
Andrei Turinsky, Centre for Computational Biology
KDM5C project
•
•
•
C.E. Schwartz
F.E. Abidi
C. Skinn
–
Greenwood Genetic Center, South Carolina, USA