Epigenome-wide analysis in newborn blood spots from monozygotic twins discordant for cerebral palsy reveals consistent regional differences in DNA methylation
Mohandas et al. Clinical Epigenetics
Epigenome-wide analysis in newborn blood spots from monozygotic twins discordant for cerebral palsy reveals consistent regional differences in DNA methylation
Namitha Mohandas 0 1 2 4
Sebastian Bass-Stringer 0 2 4
Jovana Maksimovic 1 3
Kylie Crompton 1 6 7
Yuk J. Loke 2 4
Janet Walstab 1 7
Susan M. Reid 1 6 7
David J. Amor 1 6 7
Dinah Reddihough 1 6 7
Jeffrey M. Craig 1 2 4 5
0 Equal contributors
1 Department of Paediatrics, The University of Melbourne , Flemington Road, Parkville, Victoria 3052 , Australia
2 Environmental and Genetic Epidemiology Research, Murdoch Children's Research Institute, Royal Children's Hospital , Flemington Road, Parkville, Victoria 3052 , Australia
3 Bioinformatics Group, Murdoch Children's Research Institute, Royal Children's Hospital , Flemington Road, Parkville, Victoria 3052 , Australia
4 Environmental and Genetic Epidemiology Research, Murdoch Children's Research Institute, Royal Children's Hospital , Flemington Road, Parkville, Victoria 3052 , Australia
5 Centre for Molecular and Medical Research, School of Medicine, Deakin University , Geelong, Victoria 3220 , Australia
6 Neurodevelopment and Disability, The Royal Children's Hospital , Flemington Road, Parkville, Victoria 3052 , Australia
7 Developmental Disability and Rehabilitation Research, Murdoch Children's Research Institute , Flemington Road, Parkville, Victoria 3052 , Australia
Background: Cerebral palsy (CP) is a clinical description for a group of motor disorders that are heterogeneous with respect to causes, symptoms and severity. A diagnosis of CP cannot usually be made at birth and in some cases may be delayed until 2-3 years of age. This limits opportunities for early intervention that could otherwise improve longterm outcomes. CP has been recorded in monozygotic twins discordant for the disorder, indicating a potential role of non-genetic factors such as intrauterine infection, hypoxia-ischaemia, haemorrhage and thrombosis. The aim of this exploratory study was to utilise the discordant monozygotic twin model to understand and measure epigenetic changes associated with the development of CP. Methods: We performed a genome-wide analysis of DNA methylation using the Illumina Infinium Human Methylation 450 BeadChip array with DNA from newborn blood spots of 15 monozygotic twin pairs who later became discordant for CP. Quality control and data preprocessing were undertaken using the minfi R package. Differential methylation analysis was performed using the remove unwanted variation (RUVm) method, taking twin pairing into account in order to identify CP-specific differentially methylated probes (DMPs), and bumphunter was performed to identify differentially methylated regions (DMRs). Results: We identified 33 top-ranked DMPs based on a nominal p value cut-off of p < 1 × 10−4 and two DMRs (p < 1 × 10−3) associated with CP. The top-ranked probes related to 25 genes including HNRNPL, RASSF5, CD3D and KALRN involved in immune signalling pathways, in addition to TBC1D24, FBXO9 and VIPR2 previously linked to epileptic encephalopathy. Gene ontology and pathway analysis of top-ranked DMP-associated genes revealed enrichment of inflammatory signalling pathways, regulation of cytokine secretion and regulation of leukocyte-mediated immunity. We also identified two top-ranked DMRs including one on chromosome 6 within the promoter region of LTA gene encoding tumour necrosis factor-beta (TNF-β), an important regulator of inflammation and brain development. The second was within the transcription start site of the LIME1 gene, which plays a key role in inflammatory pathways such as MAPK signalling. CP-specific differential DNA methylation within one of our two top DMRs was validated using an independent platform, MassArray EpiTyper. (Continued on next page)
(Continued from previous page)
Conclusions: Ours is the first epigenome-wide association study of CP in disease-discordant monozygotic twin pairs
and suggests a potential role for immune dysfunction in this condition.
Cerebral palsy (CP) describes a group of motor impairment
syndromes caused by lesions or anomalies of the developing
]. It is non-progressive, but the severity of symptoms
may change over time [
]. CP is the most common
childhood physical disability [
] with a worldwide prevalence of
2.11 per 1000 live births [
]. In preterm infants (< 37 weeks
gestation), the prevalence is higher, ranging from 5 to 92
per 1000 depending on gestational age [
]. The prevalence
of CP in multiple births is almost four times that of
]. There are many factors that may be responsible for
this increased risk in multiple birth pregnancies, with the
most likely being low birth weight and preterm birth, both
known risk factors for CP.
The brain insult or anomaly resulting in CP may occur
during the prenatal, perinatal or early postnatal period [
and in many cases, the timing is unknown. Although
newborns may be recognised as being at risk of CP, less than half
of all children who are ultimately diagnosed are identified
before 1 year of age, and only three quarters are identified
before age 2 .
CP has a multifactorial pathogenesis and risk factors
including intrauterine growth restriction or infection,
placental abnormalities, inflammation, signs of fetal
distress and genetic variation [
]. Although the latter
explains a proportion of CP cases, particularly cerebral
9, 11, 12
], many non-genetic factors
likely play a role  though their respective
contributions have not been comprehensively addressed [
Genetically identical monozygotic (MZ) twin pairs
discordant for CP highlight the role of non-shared factors in
the pathogenesis of CP [
]. Non-shared factors are often
described as the differences in the intrauterine
environment that influences the development of individual
members of a twin pair [
]. Such within-pair variation can
arise from differences in the length and morphology of the
umbilical cord or placenta. This can affect growth rate
and development of the individual twins leading to
discordance in infection or inflammation, leading to disease
13, 19, 20
]. More generally, the study of
phenotypically discordant MZ twins, matched for genetic
variation, sex, gestational age and maternal factors,
provides a great opportunity to examine the role of
epigenetics in disease aetiology by allowing us to isolate the effect
of such non-shared environmental factors [
Epigenetics refers to a range of modifications and
processes that regulate the activity of DNA, including
gene expression. Epigenetic variation has emerged as a
candidate mediator of a range of health outcomes
beginning in early life as part of the ‘Developmental Origins
of Health and Disease’ (DOHaD) phenomenon. [
In DOHaD, environmental conditions in utero and
during infancy alter the developmental trajectory of an
individual, which manifests as specific chronic health
phenotypes later in life.
DNA methylation is the most widely studied epigenetic
process and is one of the mechanisms that are involved in
tissue differentiation during early development. Despite
this, previous studies have investigated the concordance in
DNA methylation state between the brain and
peripheral tissues, revealing many similarities [
epigenome-wide association studies (EWAS) have
identified DNA methylation variation in the cord blood in
association with later neurocognitive function and
]. Similarly, the whole blood has been
used to detect differential methylation patterns between
affected and unaffected individuals in brain disorders
such as schizophrenia [
], bipolar disorder [
and Alzheimer’s disease . Animal studies have also
reported that environmental factors affecting brain
processes leave biomarker signatures in the blood with
consistent methylation status across the brain and
peripheral tissues [
]. We have previously shown that
DNA methylation varies within pairs of MZ twins from
]. In this study, we hypothesised that early
life non-shared factors that play a role in the aetiology
of CP in discordant MZ twins may be reflected in
differences in DNA methylation across tissues including
neonatal blood. Furthermore, we hypothesised that a
subset of differential methylation will be located in
genes previously implicated in CP aetiology [
particular, pathways involved in inflammation,
hypoxiaischaemia and thrombosis.
Samples and DNA extraction
Participant CP-discordant twin pairs, who were suspected
to be MZ on the basis of same sex and questionnaire data
that measured concordance for hair and eye colour, were
recruited through the Victorian Cerebral Palsy Register, a
population-based registry of individuals born or receiving
medical services in the Australian state of Victoria.
Participants were excluded if either twin presented with another
known neurological disorder. Written informed consent
from the families was obtained. A single 1-cm-diameter
dried blood spot was acquired from all participants from a
neonatal newborn screening card collected 2 to 4 days
after birth and then stored within the Victorian Clinical
Genomic DNA (gDNA) was extracted from the dried
blood spot samples using the ZR DNA Card Extraction
Kit (Zymo Research, Irvine, CA, USA) with some
modifications to the manufacturer’s protocol. Briefly, eight
3-mm punches were from each 1-cm-diameter blood
spot and were transferred to a 2-mL Eppendorf Safe-Lock
microcentrifuge tube (Merck, Darmstadt, Germany)
containing ZR BashingBeads. Four hundred microliters of
PBS containing 40 μL of 20 mg/mL proteinase K
(SigmaAldrich, St. Louis, Missouri, USA) was added, and samples
were vortexed and centrifuged briefly, followed by
incubation overnight at 37 °C. Following incubation, 400 μL of
ZR lysis solution was added to each tube. Punches were
homogenised for 30 s at 4 m/s2 using Thermo Savant
FastPrep 120 Cell Disrupter System (Global Medical
Instrumentation (GMI) Incorporation, Minnesota, USA).
Tubes were centrifuged for 1 min at 10,000 rpm, and
390 μL of 2× digestion buffer and 10 μL of 20 mg/mL
proteinase K were added. Tubes were mixed by inversion
and incubated for 30 min at 55 °C, then left to cool at
ambient temperature for 3–4 min before centrifuging for
1 min at 8000 rpm. Six hundred fifty microliters of
supernatant was added to 1.3 mL of DNA isolation buffer
contained in a 5-mL Falcon tube (Thermo Fisher
Scientific, MA, USA). This mixture was passed through
the Zymo-Spin IC column by centrifuging for 1 min at
14,000 rpm, followed by the discard of flow-through
liquid. The spin column was then washed twice by adding
200 μL of DNA wash buffer and centrifuged for 1 min at
14,000 rpm. Finally, 20 μL of DNA elution buffer
(prewarmed at 55 °C) was added to the column and incubated
at ambient temperature for 15 min before final
centrifugation for 2 min at 14,000 rpm. This was repeated, resulting
in a final elution volume of 40 μL containing genomic
DNA. DNA concentration was measured by
spectrophotometry (Nanodrop, Wilmington, DE, USA) to allow
calculation of the required volume of each sample for
array analysis. The quality of the extracted gDNA samples
was visualised using agarose gel electrophoresis.
Illumina Infinium HumanMethylation450 arrays
Following bisulphite conversion of genomic DNA,
genomewide analysis of DNA methylation was assessed using
HM450 (Illumina, San Diego, CA, USA), at the Department
of Pathology, University of Melbourne. Hybridisation and
scanning were performed following the manufacturer’s
instructions. Statistical analysis was performed using
the R statistical programming language
(http://www.Rproject.org) in conjunction with Bioconductor packages
developed for the analysis of methylation arrays.
Preprocessing of Illumina Infinium 450K array data
The raw intensity data (IDAT files) were imported into R
(3.3.1; http://cran.r-project.org/). Data quality was
assessed using the minfi (v1.20.2) Bioconductor package
]. From 485,512 HM450 probes, 67,120 were
removed based on either (1) poor performance (mean
detection p value of > 0.01, n = 14,056); (2) probes
containing either a single nucleotide polymorphism
(SNP) at the target CpG site or at the single nucleotide
extension site (n = 16,307); (3) probes that map to
multiple locations in the genome (n = 27,120), [
]; and (4)
or to sex chromosomes (n = 9637). Samples were also
evaluated using a modified version of the Houseman
] implemented in minfi, to estimate the
cell type composition. The Wilcoxon signed-rank
statistical test was used to compare the difference in cell
type proportion between CP cases and controls. The
analysis was completed before a cord blood reference
panel was widely available, so cohorts used an adult
whole blood reference  to estimate the proportion
of B cells, CD8+ T-cells, CD4+ T-cells, granulocytes,
NK cells and monocytes in each sample. The data was
normalised using subset-quantile within array
normalisation (SWAN) [
]. Covariates such as birth weight, birth
order and postnatal age (in days) at which newborn
screening cards were created were assessed as potential
Differential methylation analysis
Beta (β) values (proportion of the methylated signal over
the total signal) were converted to M-values, the log2 ratio
of the intensities of the methylated signal versus the
unmethylated signal. Differential methylation analysis was
performed using remove unwanted variation (RUVm) [
implemented in the missMethyl R package [
] taking into
account the twin relationships. RUVm is a data-driven
method of controlling for unwanted technical and
biological variation in regression analyses using an empirically
determined set of negative control probes assumed not to
be associated with the biological factor of interest. p values
were adjusted to control for the false discovery rate (FDR)
using the Benjamini-Hochberg method [
methylated probes (DMPs) were considered significant if
they fell within the FDR threshold of 0.1. We also
investigated the top-ranked DMPs with an unadjusted p value
less than 1 × 10−4 [
Identification of differentially methylated regions
Differentially methylated regions (DMRs) were identified
using the bumphunter package [
]. The cut-off
value, which is a user-defined numeric value that
determines the upper and lower bounds of the genomic
profiles that will be used as candidate regions, was set to
0.02 and the number of permutations set to 1000.
Functional annotation and pathway analysis
Gene ontology and pathway analysis were performed using
the gometh function from the missMethyl package [
which appropriately takes into account the variable number
of HM450 probes associated with each gene. Gene
ontology enrichment was performed for the 1000
topranked DMP-associated genes. The KEGG option of the
gometh function in missMethyl was used to provide further
insights into relevant biological processes associated with
the top-ranked DMPs.
Validation of differentially methylated regions
Site-specific validation was performed using the Sequenom
MassArray EpiTYPER (Agena Biosciences). T7-tagged
primers were designed for two regions (Additional file 1)
using the Sequenom EpiDesigner package [
primer sequences contained a 10 base 5′ tag
(AGGAAGAGAG) and reverse primers a 31 base 5′ tag
(CAGTAATACGACTCACTATAGGGAGAAGGCT). In silico, assay
prediction was performed using the BiocLite MassArray
package. DNA used for validation was the same as that
used for the HM450 analysis. Bisulphite treatment of
genomic DNA was accomplished using the MethylEasy
Xceed Kit (Human Genetic Signatures, North Ryde,
Australia). One microliter of bisulphite-converted product
was amplified in triplicate for each sample using the
FastStart kit (Roche, Mannheim, Germany) in 15 μL of
reagents with thermal cycling conditions as follows: 95 °C
for 10 min; 5 cycles of 95 °C for 10 s, 60 for 30 s and 72 °C
for 2 min; 40 cycles of 95 °C for 10 s, 62 for 30 s and 72 °C
for 90 s; and final extension at 72 °C for 7 min. Raw data
generated from the MassArray EpiTYPER was cleaned
using a Microsoft Excel macro developed in-house [
The median value of triplicates was determined, and any
replicates > 10% from the median were removed as
previously described [
Within-twin pair analysis
To explore the top-ranked CpGs within each twin pair
and compare them across twin pair groups, probes were
ranked according to delta beta (Δβ, the difference in
DNA methylation of the CP minus non-CP twin) within
pairs, and the top-ranked 100 probes were compared
across all 15 twin pairs. The genes corresponding to all
probes with a Δβ value > 0.5 were then compared across
the 15 twin pairs. Gene ontology analysis was performed
on the top 1000 probes from each twin pair, and
common ontologies between twin pairs were identified.
The study cohort consisted of 16 CP-discordant twin
pairs (ten male and six female) for which pre-screening
suggested a high probability of monozygosity (Table 1).
All were tested for genetic zygosity using data from 65
SNPs from the Infinium arrays. The variability of SNPs
for one twin pair (pair no. 9003) was substantially larger
than the remaining samples and was therefore assigned
as dizygotic (DZ). This pair was excluded from further
analysis. Five subtypes of CP were reported: spastic
diplegia (6), spastic quadriplegia (3), spastic hemiplegia
(3), dyskinesia (2) and ataxia (1). The severity of CP
ranged from mild (independently ambulant) to severe
(wheelchair dependent), and the underlying
neuropathology included white matter (11), grey matter brain
injury (2) and both white and grey matter mixed injury
(2). Three twin pairs were born at term (37–41 weeks),
while all other twin pairs were born preterm (< 37 weeks).
Global DNA methylation profiles in CP-discordant monozygotic twins
Global DNA methylation (average β value across all
probes) within twin pairs was compared using a pairwise
Pearson correlation for the 418,392 probes remaining
after filtering and quality control (see the ‘Methods’
section) for all 15 twin pairs. Within-pair methylation
WMI white matter injury, M miscellaneous, GMI grey matter injury
*Twin pair 9003 was later confirmed not to be MZ and removed from the analysis
correlation coefficients ranged from 0.980 to 0.996
(Additional file 2) compared to 0.976 to 0.995 between
unrelated unaffected individuals.
Top-ranked CP-associated DMPs
Cleaned data was explored by principal component (PC)
analysis which revealed few (6/54) significant correlations
(p < 0.05, r < 0.6 shaded in Additional file 3) between the
top six principal components of DNA methylation and
nine technical (e.g. age at which Guthrie card was created)
and biological (e.g. sex, subtype of CP) covariates. This
suggested that none of the covariates tested was
consistently associated with DNA methylation. In addition, none
were associated with the largest principal component of
variation within the dataset, PC1. Multi dimensional
scaling (MDS) plots of the first three dimensions of the
processed methylation data also showed that chip location and
position on the 450K array were not found to affect
methylation data (Additional file 4).
Apart from the above covariates, it is known that
celltype heterogeneity within the whole blood can confound
epigenome-wide analyses. Therefore cell-type composition
within CP-discordant pairs was evaluated. The levels of
CD8+ T cells (CD8T) and CD4+ T cells (CD4T), B cells,
natural-killer (NK) cells, monocytes and granulocytes were
compared between the two groups (CP cases and normal
co-twins). There was no statistically significant difference
in the estimated cell-type proportions of CD8+ T cells, NK
cells, B cells and monocytes (p > 0.05). However, the
proportion of CD4+ T cells was lower (p = 0.002), and the
proportion of granulocytes was found higher (p = 0.021) in
CP cases relative to normal co-twins (Additional file 5).
To take into account potential sources of unwanted
variation (such as cell-type composition), the
genomewide analysis was performed using RUVm, which adjusts
for biological and technical variation using a set of
datadriven negative control probes [
46, 48, 56, 57
analysis did not identify any significant CP-associated
DMPs after adjusting for multiple testing. Nevertheless,
as this is an exploratory epigenome-wide study of CP, we
focused on the characteristics of the top-ranked DMPs
based on a nominal p-value cut-off of p < 1 × 10−4 as
used by others . This resulted in a list of 33
topranked DMPs, corresponding to 25 genes (Table 2),
most of which showed a consistent direction in the
majority of twin pairs (> 12/15). The average difference in
methylation (Δβ = CP twin minus unaffected twin)
ranged from + 0.6 to + 11.9% and from − 2.5 to − 12.4%
(Table 2). Figure 1 shows the within-pair differences in
methylation for the top ten DMPs.
The top-ranked probe cg00376816 (average Δβ = 11.6%,
p = 4.57 × 10−6) was located on chromosome 19, within
the gene body of the HNRNPL gene encoding the
heterogeneous nuclear ribonucleoprotein L. Others
included cg04242728 (in the 5′ end of the TBC1D24
gene, ranked 3) and cg19607845 (in the gene body of
FBXO9 gene, ranked 13). Probes located near the
gene body of immune and inflammatory genes, such
as Ras association domain family member 5 (RASSF5),
major histocompatibility complex DM alpha-chain
(HLA-DMA), CD3D and kalirin (KALRN) genes, were
also among the top-ranked 33 DMPs.
To identify enriched biological processes or molecular
functions, we performed gene ontology analysis on genes
associated with the top-ranked 1000 DMPs. The top 20
gene ontologies ranked by nominal p-value were ‘regulation
of immune response,’ ‘lymphocyte activation,’ ‘differentiation
and aggregation and T cell activation’ with the top two
ontologies related to cell-cell adhesion processes (Table 3;
Additional file 6). Enriched disease pathways, as reported
by KEGG analysis, included MAPK signalling (19 associated
genes from 245 genes in the KEGG pathway list; p value
3.6 × 10−10), cytokine-cytokine receptor interaction (13
associated genes from 240 genes; p value 1.3 × 10−08) and Ras
signalling (15 associated genes from 218 genes; p value:
9.8 × 10−08).
We also identified DMRs associated with CP [
(Table 4). The top-ranked DMR, spanning 434 bp and
with a p value of 5.6 × 10−4, was located on chromosome 6
(Fig. 2). This DMR spans 12 probes (average Δβ = 3.7%)
within the coding region of the LTA gene, approximately
~ 800 bp downstream of the transcription start site (TSS).
LTA codes for the lymphotoxin-alpha protein otherwise
known as tumour necrosis factor beta (TNF-β). Other top
DMRs include those within LTBP1, CD300, CHST11 and
LIME1. Gene ontology analysis of the top-ranked
DMR-associated genes revealed similar findings to
topranked DMPs (Table 5; Additional file 7). We found an
over-representation of inflammatory signalling
pathways, also similar to that of the top-ranked DMPs. The
top pathways included TNF and TGF-beta signalling
and cytokine-cytokine receptor interaction. The nuclear
factor kappa-light-chain-enhancer of activated B cell
(NF-κB) signalling pathway was also enriched.
Validation of DMRs
LIME1 and LTA DMRs were selected for validation as
they were highly ranked, had large, consistent effect sizes
across all pairs and were biologically relevant to CP.
Three CpGs from the HM450 platform contained within
three CpG units on the MassArray Epityper (consisting
of seven CpG sites in total) were tested for the LIME1
DMR, and four CpGs contained within three units on
the MassArray Epityper (four CpG sites in total) were
tested for the LTA DMR, both in regions being
approximately 200 base pairs upstream of the transcriptional
start site (TSS200) and likely to be in gene promoters.
Scatter plots were generated to assess the validity of the
Methylation difference (%) was calculated as the mean of the DNA methylation levels of the CP twin minus the unaffected twin (Δβ)
data for the three LIME1 and LTA probes within a
250base-pair region across both the HM450 and the EpiTYPER
platforms (Additional file 8). Pearson’s correlation
coefficients were determined, and the significance of the
correlation was assessed for each probe-CpG unit
comparison. Five out of six probes (cg21201401,
cg06653796, cg14597739, cg11586857, cg21999229) had
a positive correlation between the two platforms.
Among these, one probe out of three for LIME1 and
two of three for LTA had moderate correlations (r > 0.5).
All moderate correlations were also significant with p < 0.05
(r = 0.88, p = 1.6 × 10−4; r = 0.40, p = 0.197; r = 0.12, p = 0.65
for LIME1 and r = 0.69, p = 1.4 × 10−4; r = 0.59, p = 0.043;
r = 0.21, p = 0.34 for LTA.). The Δβ values were calculated
for two of the three probes within the LTA gene region,
with correlation coefficient values of r = 0.69 for
cg14597739 and r = 0.21 for cg21999229. The Δβ for probes
within the LIME1 gene region were not calculated
due to insufficient data points, resulting from limited
material remaining, for a valid within-pair analysis.
Differential methylation analysis within individual twin pairs
Since CP is a highly heterogeneous disorder [
], it is
possible that a subset of disease-associated DNA methylation
patterns may be specific to each proband. To test this
], we determined the top CP-associated CpGs for each
twin pair ranked by absolute differences in DNA
methylation (Δβ > 0.5) and looked at significant gene ontologies
common to multiple pairs (Additional file 9). Gene
ontologies corresponding to cell adhesion were found in 5/15
twin pairs (Additional file 10). Similarly, common CpGs
with a within-pair methylation difference > 50% were found
in multiple twin pairs (Table 6) in genes such as BICD2,
HLA-DPB2, RPTOR and PIK3CG (Additional file 11),
involved in neuronal cell migration, muscular atrophy or
muscle contraction pathways and immune response and
inflammatory pathways, respectively [
]. Notably, two
different CpG sites within twin pairs 4 and 8 corresponding
to the WWTR1 gene had an absolute methylation
difference of greater than 50% with the same direction of effect.
Affected CpG sites were located near the 5′ end of the gene
in both pairs (Table 6).
This exploratory study represents an initial step towards
investigating potential CP-associated epigenetic
differences, with the longer-term aim of identifying predictive
biomarkers with clinical utility. We identified DNA
methylation differences in dried blood spots from 15
CP-discordant MZ twin pairs and found differential
methylation at several gene loci associated with hypoxia
signalling, inflammation and cell adhesion. These
pathways had been previously linked to CP, consistent with
part of our hypothesis.
Pairwise global DNA methylation difference between
CP and non-CP members of each pair measured for
taking CP status within discordant MZ pairs into
account. Although no probe reached an adjusted
statistical significance at FDR < 0.1, the top-ranked
DMPs, at a nominal cut-off of p < 1 × 10−4, were
enriched for the cellular processes of inflammation,
cell adhesion and immune response. These showed
the direction of effect across most or all discordant
twin pairs. This was in accordance with EWAS of
other neurodevelopmental disorders including twins
discordant for autism spectrum disorders [
], depression [
] and aggressive behaviour [
each twin group and comparison of the size of DNA
methylation difference made between groups allowed
for an assessment of how variable the differences in
methylation may be for different cases of CP. Our
results are consistent with previous studies (e.g. [
that have indicated that neurodevelopmental disorders
such as autism spectrum disorder are not associated
with systemic within-twin pair differences in global
We tested site-specific DNA methylation patterns
across the genome for their association with CP,
fwer family-wise error rate
Latent transforming growth factor beta binding protein 1
CD300 molecule like family member b
Carbohydrate chondroitin 4 sulfotransferase 11
Solute carrier family 25 member 25
Potassium voltage-gated channel subfamily Q member 1
FGR proto-oncogene, Src family tyrosine kinase
Lck interacting transmembrane adaptor 1
Average within-pair DNA methylation differences of
up to 12.5% were observed, comparable to previous
findings in neurodevelopmental disorders, ranging
from 1.5 to 12%. Furthermore, we validated five of
the six CpG probes, with positive correlations across
platforms. Three of these five probes (one from
LIME1 and two from LTA) showed a strong and
significant cross-platform correlation, indicating the
validity of methylation values between platforms.
Genes associated with top-ranked DMPs and DMRs
were enriched for similar ontologies and pathways, namely
immune response, lymphocyte-mediated immunity,
interferongamma production and regulation of immune response.
Our study revealed top-ranked DMRs associated with
genes that play a role in inflammation, such as LTA/TNFβ
and LIME1, supporting part of our hypothesis that
inflammation plays a key role in CP aetiology. Genetic variants of
LTA have been implicated in multiple studies as being
associated with risk for CP [
]. LTA plays an important role
in inflammation and brain development, mediating preterm
birth and white matter brain injury [
]. It has been
implicated that inflammation [
] and increased levels of its
isoform TNF-α were found in children with CP compared to
healthy controls [
]. LIME1 gene links T and B cell
signalling to the activation of tyrosine and MAP kinases [
Other top DMR-associated genes such as LTBP1 and CD300
are also known mediators of inflammatory pathways such as
ERK signalling pathway, interleukin-3,-5 pathway, B cell
receptor signalling pathway and other chemokine signalling
]. Genes associated with top-ranked DMPs also
showed involvement in other key inflammatory pathways
such as Ras signalling (WDFY4), MAP kinase signalling
(CD3D) and interleukin-3,-5 signalling (KALRN). Analysis
within individual twin pairs also revealed associations to the
WWTR1 gene, which is involved in the activation of TGF-β
signalling pathway, an inflammatory pathway that regulates
neural survival and death [
]. Gene ontology analysis of
top-ranked DMPs showed enrichment of genes involved in
regulation of immune response pathways such as those
involved in signalling or T-cell activation. It is noteworthy that
intrauterine infection is a known risk factor for CP and that
many inflammatory cytokines have been shown to be critical
to the risks associated with CP [
38, 41, 60
Perinatal brain injury can be induced by a range of
insults such as hypoxic-ischaemic injury or infection [
An in utero infection such as chorioamnionitis may
trigger an innate immune response, resulting in elevated
cytokine levels. Cytokines in the fetal blood may contribute to
a systemic fetal inflammatory response with eventual
penetration across the blood-brain barrier resulting in an
inflammatory cascade in the fetal brain [
]. Brain injury
induced by neonatal hypoxia-ischaemia also involves key
components of inflammation such as immune cells,
chemokines, cytokines and cell adhesion molecules [
Therefore, we suggest that inflammation may play a role
in perinatal brain damage associated with CP.
We also observed an enrichment of CP-associated
DMPs and DMRs in gene ontologies associated with cell
adhesion, and this was also observed in individual twin
pairs. Aberrant expression of cell adhesion molecules
has been reported in muscle biopsies of both children
and adults with CP [
]. Previous whole exome and
whole genome sequencing studies have also illustrated
the potential role of cell adhesion in CP by identifying
genetic variants in novel candidate genes which function
as neural adhesion molecules essential for neurite
outgrowth and axon guidance [
]. The NF-κB
transcription factor signalling pathway was common in both
DMPs and DMRs.
Our results are consistent with a previous gene
expression study in newborn blood spot samples from children
with CP [
], which identified up-regulation of
inflammatory pathways in preterm children who later developed
the disorder. Other similarities between the two studies
include variation in genes involved in T-cell and B-cell
receptor signalling pathways and cytokine-cytokine receptor
interaction, all of which were shown to have a dysregulation
in CP cases [
]. However, we found no evidence for an
association with increased thyroid function in preterm-born
CP cases as hypothesised and reported previously [
Another top-ranked DMP was HNRNPL, which likely
plays a role in response to hypoxia via regulation of the
vascular endothelial growth factor (VEGF) gene [
is known to hinder normal development and maturation of
the brain and can cause white matter injury in preterm
born infants [
] resulting in CP [
]. This finding supports
our hypothesis that epigenetic alterations in genes involved
in hypoxic pathways play a role in the aetiology of CP.
Two high-ranking DMPs lie within the TBC1D24 and
FBOX9 genes respectively, and both have previously
been associated with epilepsy [
]. In 29% of CP
cases in Victoria, Australia, epilepsy is comorbid with
CP . These results may suggest a potentially shared
aetiology between epilepsy and CP [
Our findings agree with those of others showing a link
between early life DNA methylation state and
neurodevelopmental and cognitive outcomes [
], which would
allow for early diagnosis and facilitate timely intervention.
Currently, MRI scans, assessment tests such as the General
Movements Assessment and interventions such as
environmental enrichment, early developmental, early motor and
physiotherapy interventions are used to inform strategies
for early intervention in high-risk groups, such as preterm
born children, [
Given that CP is a highly heterogeneous condition, this
study highlights the importance of using epigenetic
biomarkers to distinguish and detect underlying
pathways across the disorder. For individuals, such an
epigenetic state at birth could be used to estimate risk for
subsequent development of overt CP.
The strength of using MZ twins is that they are
matched for parental age, age, sex, season of birth and
genetic factors. Although twins have a higher risk of CP
than singletons, the causative mechanisms, such as
thrombosis, and infection in the mother, the placenta or
the umbilical cords are likely to be similar [
twins discordant for CP allows genetic and environmental
components to be partitioned from each other and
provides a unique opportunity to evaluate the importance
of non-shared environmental factors such as umbilical
cord or placental function during early development in
isolation. It is possible that only twin of a pair may develop
an infection or inflammation of the umbilical cord or
13, 19, 20, 94
]). Such non-shared environmental
factors are known to influence the development of
individual members of a twin pair. This pilot study also
highlights the importance of analysing DNA methylation
in dried blood spots, which are collected at birth and
stored by many countries and the potential for developing
future predictive diagnostic tests [
55, 95, 96
Although each tissue has a subset of CpGs whose DNA
methylation patterns are tissue-specific, DNA methylation
changes that are concordant between the blood and brain
have been detected in previous studies [
32, 97, 98
example is where they identified parallel changes in DNA
methylation between the brain and blood in 30% of the genes
implicated in Parkinson’s disease [
]. It was also shown that
a DNA methylation module exists in key ageing-related
regulatory genes both in the brain and blood [
]. In addition,
animal studies have reported that an early environment
resulting in a brain disorder can alter DNA methylation in
the same gene across the brain and peripheral tissues [
There are some limitations to this study. While similar
in sample size to many comparable twin studies of
brain-related disorders [
58, 59, 64, 100
], we acknowledge
that larger sample sizes of 25 twin pairs or more are
preferable to detect a mean effect size of at least 8%
methylation (FDR = 0.05) . As CP is a
heterogeneous condition, the small sample size of our cohort,
with a lack of CP concordant and healthy twin pairs for
comparison, also limits our capability to understand the
biological mechanisms of brain injury that may be
specifically associated with CP subtype. The use of
peripheral tissue and blood also limits our capability to
pinpoint the mechanism of CP. Despite the fact that the
brain and blood arise from separate cell lineages, and
are thought to be epigenetically distinct, many epigenetic
studies are often conducted in the blood due to ease of
]. Previous investigations of methylomic
variation across the blood and brain tissue from different
regions of the brain have found distinct differences in
gene expression and DNA methylation patterns [
]. Studies have also shown the inconsistencies in
DNA methylation markers from the blood in predicting
brain DNA methylation status [
evidence from animal studies have also shown that blood
DNA methylation patterns may in fact reflect patterns in
the brain in a subset of genes [
], suggesting that
peripheral epigenetic marks may reflect disease
mechanisms in some cases. Examples where methylation levels
correlate between blood and brain have been reported in
Parkinson’s disease, depression, schizophrenia, bipolar
disorder and autism [
]. The blood is also
particularly useful in investigating disease biomarkers [
and is an important peripheral tissue to consider for
neurological disorders, as it is easily accessible to assist
in diagnosis. Another limitation is that our data apply to
twins only, and we cannot yet generalise our findings
more broadly, as there is evidence that risk factors and
associated mechanisms leading to CP may be different
in twins compared to singletons [
]. To overcome
this, our analysis will be repeated in further sets of twins
and singletons. Only with this information can we then
start to put together risk models for predicting CP at the
time of birth. This approach will provide a unique
opportunity to identify a biomarker to predict
neurodevelopmental outcomes such as CP.
This study provides the first evidence that
environmentmediated differential methylation in genes involved in
known processes such as hypoxia and inflammation, and
perhaps processes such as cell adhesion, may contribute
to the development of CP. Our data also pave the way
for larger studies to use DNA methylation data in risk
models to help predict CP before the onset of overt
symptoms and therefore provide a chance for timely
Additional file 1: Primer sequences used in site-specific validation using
MassArray EpiTYPER. (XLSX 8 kb)
Additional file 2: Scatter plots of genome-wide DNA methylation
discordance within twin groups. (PPTX 331 kb)
Additional file 3: Heat map of the associations between the six largest
principal components and specified covariates. The heat map provides a
score of the strength of the association between DNA methylation (using M
values) and each covariate, with positive and negative correlations ranging
according to the magnitude (red positive, blue negative). The values in
brackets for each association represent the p-value of the correlation. Of the
six significant (p < 0.05) associations, all are weak (correlation < 0.6).
Abbreviations: CP, cerebral palsy; PC, principal component; PIC, person in
charge of performing DNA extraction; GA, gestational age; GMFCS, gross
motor function classification system; Guthrie age, age in postnatal days
when Guthrie card was made. (PDF 53 kb)
Additional file 4: MDS plots for preprocessed data. Samples are coloured
based on chip location ranging from 1 to 3. The figure represents similarities
between samples’ 1000 most variable probes based on Euclidean distance
(sum of squared differences). Dimension 1 represents the largest variation in
the dataset, and 2 and 3 are the second and third largest, respectively.
(PDF 42 kb)
Additional file 5: Comparison of cell type composition of cerebral palsy
cases versus normal individuals. CD8T and CD4T: cytotoxic T cells; NK:
natural-killer cells; B cell: B cell or B lymphocytes; Mono: monocytes; Gran:
granulocytes. (PDF 87 kb)
Additional file 6: Gene ontology (GO) analysis for top-ranked 1000
DMPs ranked by p value. (XLSX 20 kb)
Additional file 7: Gene ontology (GO) analysis for top DMRs ranked by
p-value (XLSX 38 kb)
Additional file 8: Cross-platform validation of the two top DMRs, LTA
and LIME1, between HM450 and EpiTYPER platforms. Pearson’s correlation
coefficients for each probe are shown. The scale of both axes reflects a
methylation value between 0 and 1 (β). The regression lines are shown in
black. Based on the r value (correlation coefficient), correlations across
both platforms are shown. The p-value indicates the significance of the
correlation. (ZIP 126 kb)
Additional file 9: DNA methylation differences within each discordant
CP twin pair, identifying numerous loci showing large DNA methylation
differences within each discordant twin pair. (PPTX 3104 kb)
Additional file 10: Gene ontology (biological process) common to
multiple twin pairs. (XLSX 21 kb)
Additional file 11: CpG sites (probes) within each twin pair group with
an absolute methylation difference > 0.5 and their corresponding genes.
Genes are colour coded to highlight overlaps between twin pair groups.
(XLSX 30 kb)
CP: Cerebral palsy; DMP: Differentially methylated probe; DMR: Differentially
methylated region; EWAS: Epigenome-wide association study;
HM450: Human Methylation 450 BeadChip array; MZ: Monozygotic;
RUV: Remove unwanted variation; SNP: Single nucleotide polymorphism;
SWAN: Subset-quantile within array normalisation
We thank the Victorian Cerebral Palsy Register staff for their help with the
consent and recruitment of participants included in this study. The Victorian
Cerebral Palsy Register receives funding from the Victorian Department of
Health and Human Services. We thank Braydon Meyer, MCRI, for developing a
Microsoft Excel macro to clean MassArray EpiTYPER data and Eric Joo from the
Department of Pathology, University of Melbourne for the bisulphite conversion
of genomic DNA and generation of methylation profiles using the HM450 array.
We thank Alicia Oshlack from the Murdoch Children’s Research Institute for her
support and feedback with the data analysis and interpretation of the data. We
thank Richard Saffery from the Murdoch Children’s Research Institute for his
feedback and comments on the manuscript. SMR received support through an
Australian National Health and Medical Research Foundation Early Career
Fellowship. The authors are also grateful for the support from the Murdoch
Children’s Research Institute (MCRI), which is supported in part by the Victorian
Government’s Operational Infrastructure Support Program.
This work was supported by a grant from the Financial Markets Foundation
for Children (grant number 2013-207).
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on request.
JMC, KC, DR, JW and JM conceived and designed the study. KC, SMR and
DJA were involved with the ethics, patient recruitment and sample
collections. YJL and SBS performed the lab work required for this study. SBS,
NM, JM and DJA implemented the analysis and interpretation. SBS, NM and
JM wrote the code and performed the data analysis. NM, SBS and JMC wrote
the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study was approved by The Royal Children’s Hospital Human Research
Ethics Committee (project ID: 33050) and involved written informed consent
from each participating family.
Consent for publication
The authors declare that they have no competing interests.
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