Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia
Leavey et al. Clinical Epigenetics
Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia
Katherine Leavey 0 4
Samantha L. Wilson 2 3
Shannon A. Bainbridge 5 6
Wendy P. Robinson 2 3
Brian J. Cox 0 1 4
0 Department of Physiology, University of Toronto , 1 King's College Circle, Toronto, ON , Canada
1 Department of Obstetrics and Gynecology, University of Toronto , 23 Edward Street, Toronto, ON , Canada
2 Department of Medical Genetics, University of British Columbia , C201-4500 Oak St, Vancouver, BC , Canada
3 BC Children's Hospital Research Institute , 950 W 28th Ave, Vancouver, BC , Canada
4 Department of Physiology, University of Toronto , 1 King's College Circle, Toronto, ON , Canada
5 Department of Cellular and Molecular Medicine, University of Ottawa , 451 Smyth Rd, Ottawa, ON , Canada
6 Interdisciplinary School of Health Sciences, University of Ottawa, 25 University Private , Ottawa, ON , Canada
Background: Preeclampsia (PE) is a heterogeneous, hypertensive disorder of pregnancy, with no robust biomarkers or effective treatments. We hypothesized that this heterogeneity is due to the existence of multiple subtypes of PE and, in support of this hypothesis, we recently identified five clusters of placentas within a large gene expression microarray dataset (N = 330), of which four (clusters 1, 2, 3, and 5) contained a substantial number of PE samples. However, while transcriptional analysis of placentas can subtype patients, we propose that the addition of epigenetic information could discern gene regulatory mechanisms behind the distinct PE pathologies, as well as identify clinically useful potential biomarkers. Results: We subjected 48 of our samples from transcriptional clusters 1, 2, 3, and 5 to Infinium HumanMethylation450 arrays. Samples belonging to transcriptional clusters 1-3 still showed visible relationships to each other by methylation, but cluster 5, with known chromosomal abnormalities, no longer formed a cohesive group. Within transcriptional clusters 2 and 3, controlling for fetal sex and gestational age in the identification of differentially methylated sites, compared to the healthier cluster 1, dramatically reduced the number of significant sites, but increased the percentage that demonstrated a strong linear correlation with gene expression (from 5% and 2% to 9% and 8%, respectively). Locations exhibiting a positive relationship between methylation and gene expression were most frequently found in CpG open sea enhancer regions within the gene body, while those with a significant negative correlation were often annotated to the promoter in a CpG shore region. Integrated transcriptome and epigenome analysis revealed modifications in TGF-beta signaling, cell adhesion, oxidative phosphorylation, and metabolism pathways in cluster 2 placentas, and aberrations in antigen presentation, allograft rejection, and cytokine-cytokine receptor interaction in cluster 3 samples. Conclusions: Overall, we have established DNA methylation alterations underlying a portion of the transcriptional development of “canonical” PE in cluster 2 and “immunological” PE in cluster 3. However, a significant number of the observed methylation changes were not associated with corresponding changes in gene expression, and vice versa, indicating that alternate methods of gene regulation will need to be explored to fully comprehend these PE subtypes.
Preeclampsia; Placenta; DNA methylation; Gene expression; Clustering; Subtypes
Preeclampsia (PE) is a complex, heterogeneous disorder
of pregnancy, diagnosed by the onset of maternal
hypertension after the 20th week of gestation, with signs of
maternal multi-organ dysfunction [
]. As with many
pathologies of pregnancy, PE has no cure, robust
predictive biomarkers, or effective treatments, other than
the delivery of the infant to discontinue the pregnancy and
remove what is thought to be the causative organ, the
placenta. Repeated attempts to characterize the placental
molecular pathology and identify biomarkers of PE by
applying a binary approach (PE versus control) have not
been clinically fruitful, and we hypothesized that this is due
to the existence of multiple molecular subtypes of PE [
In support of this hypothesis, we recently published a
large unsupervised clustering analysis of microarray data
from a PE-focused placental cohort (N = 330), including
157 highly annotated samples purchased from a single
]. This revealed five clusters of placental gene
expression containing at least three clinically significant
etiological subtypes of PE: “maternal”, with term and
near-term deliveries of average-sized infants and placentas
that appear molecularly similar to normal healthy control
samples; “canonical” with high placental expression of
known PE markers, preterm deliveries, low fetal weights,
and evidence of maternal malperfusion; and
“immunological” with severe fetal growth restriction, enrichment of
immune response genes, and histological signs of maternal
anti-fetal/placental rejection [
], belonging to
transcriptional clusters 1, 2, and 3, respectively. An additional
subtype of PE placentas with chromosomal abnormalities
was also discovered within cluster 5 (and supported by
array-based comparative genomic hybridization (aCGH)
analysis), but showed no strong clinical association [
However, despite our considerable progress towards
understanding the molecular diversity observed amongst
PE patients, RNA is relatively unstable, easily affected by
technical variability [
], and rarely successful as a
therapeutic target [
], limiting its clinical utility. We,
therefore, propose that the integration of an additional
level of molecular information in these placentas, such
as DNA methylation, will compensate for these
], as well as improve our understanding of the
DNA methylation is a mitotically heritable epigenetic
mark employed by the cell to control gene expression
without altering the genetic sequence [
], although the
relationship between the two data types is exceptionally
]. Given the flexibility for modification in
the epigenome, these methylation events may also serve
to provide insight into the environmental exposures
sustained by the cell , and as potential biomarkers of
early cellular transformations [
]. In fact, many examples
exist, particularly in the cancer field, for the exploitation
of DNA methylation in the diagnosis, prognosis, and
prediction of drug response in disease [
], and as
possible therapeutic targets [
Here, we subject a subset of our highly annotated cohort
samples to DNA methylation arrays and investigate
differences in the placental methylome between our
previously identified transcriptional clusters, as well as
relationships between the two data types. Furthermore, by
assessing epigenetic changes associated with the observed
pathological gene expression, we also attempt to discover
novel therapeutic targets for the various PE subtypes.
A total of 48 (out of 157) placentas from our highly
annotated cohort purchased from the Research Centre for
Women’s and Infants’ Health (RCWIH) BioBank [
were selected for DNA methylation analysis (19 from
transcriptional cluster 1, 19 from transcriptional cluster
2, 5 from transcriptional cluster 3, and 5 from
transcriptional cluster 5), using the sample function in R 3.1.3
(Additional file 1: Figure S1). The selected number of
samples per cluster is approximately representative of
the sample distribution in the full placental dataset, with
the condition of a minimum of five samples per cluster.
Our cohort selection and tissue sampling methods have
been previously described [
]. Placentas demonstrating
signs of chorioamnionitis or belonging to the
chorioamnionitis-associated transcriptional cluster 4 [
were not included as these are a known entity,
independent of preeclampsia (Additional file 1: Figure S1).
Clinical differences between these 48 patients only were
assessed using Kruskal-Wallis rank sum, Wilcoxon rank
sum, and Fisher’s exact tests, as appropriate.
Methylation arrays and data processing
DNA was isolated from the 48 placentas by ethanol
precipitation with the Wizard® Genomic DNA Purification
Kit from Promega and quantified by a NanoDrop 1000
spectrophotometer. A total of 750 ng of DNA per
sample was bisulfite converted using the EZ Gold DNA
methylation kit (Zymo) and assessed for methylation
status with Infinium HumanMethylation450 arrays from
Illumina. This array covers CpG islands (tight clusters of
CpG sites) as well as shores (up to 2 kb from CpG
islands), shelves (2–4 kb from CpG islands) and open
sea (> 4 kb from CpG islands) [
]. Arrays were scanned
by an Illumina HiScan 2000. This methylation data was
also used as a validation cohort in [
The resulting IDAT files were loaded into R using the
champ.load function (ChAMP library) [
low quality probes with a detection p value above 0.01 in
more than one sample or a beadcount < 3 in at least 5%
of samples (N = 1940). Probes known to bind sex
chromosomes, cross-hybridize to multiple locations, or
target a single-nucleotide polymorphism (SNP) were
removed, based on previous annotation [
left 410,664 probes for DNA methylation analysis. The
samples underwent functional normalization with the
preprocessFunnorm function , which is an extension
of quantile normalization utilizing the control probes on
the array, applied separately to the methylated and
unmethylated intensities, type I and type II signals, and
the male and female samples. The data was then batch
corrected for slide and array position using the ComBat
function (sva library) [
] without accounting for any
outcome of interest or other covariates to obtain the
most unbiased results. All analysis was performed using
M values to improve the statistical calculation of
differential methylation [
], although beta values are
also included in the tables for biological interpretation.
Gene expression data processing
Our entire 157 placenta dataset was previously hybridized
against Human Gene 1.0 ST Array chips from Affymetrix
]. The resulting microarray CEL files for the 48 placentas
assessed for methylation in the current study were loaded
into R, and normalized and converted to log2 values using
the affy library [
]. Expression values annotated to the
same gene symbol were merged to a mean value, and genes
with expression in the lowest quartile were filtered out to
reduce confounding by background noise, using the
Identification of differentially methylated positions
The global relationships between the 48 samples based on
the DNA methylation information alone were visualized
using t-distributed stochastic neighbor embedding (t-SNE;
tsne library) [
] with a perplexity of 10. Samples belonging
to our previously described transcriptional clusters 2, 3,
and 5 were compared to cluster 1 placentas (with a
“healthy” transcriptional profile) to identify differentially
methylated positions, using the limma library [
entire cluster 1 was employed as the comparison group
after confirming that no significant differentially methylated
positions exist between the PE and normotensive controls
within cluster 1 by limma, and no segregation of these
phenotype groups within cluster 1 were observed by t-SNE
(Fig. 1). Linear modeling, compared to cluster 1, was
performed both with and without controlling for fetal sex
(male or female) and/or gestational age (GA) at delivery
(26–40 weeks) to investigate the impact of these variables
on each cluster. Fetal sex was still considered despite the
removal of the sex chromosomes from the analysis due to
likely persistent differences on the autosomes [
were considered differentially methylated at a false discovery
rate (FDR) corrected q value < 0.05, and groups of
significant positions were noted when at least three significant
sites were identified within 1000 base pairs of each other.
Probe annotation and epigenetic regulation of gene expression
All DNA methylation probes were assigned to enhancer
regions, CpG regions (island, shore, shelf, or open sea),
and gene-centric locations (TSS1500: 200-1500 nucleotides
upstream of the transcriptional start site (TSS); TSS200:
TSS to 200 nucleotides upstream of the TSS; 5′
untranslated region (UTR); 1st exon; gene body; 3′UTR; and
intergenic region (IGR)) based on the
IlluminaHumanMethylation450kanno.ilmn12.hg19 library. A number of
sites (N = 45,354) were linked to multiple genes or gene
aliases, and all possible associations were maintained.
Probes found in the IGR were assigned to the gene with
the closest TSS. Trends in significant probe positions were
assessed by Fisher’s exact tests.
Sites identified as significantly differentially methylated
in transcriptional cluster 2 or 3 placentas, compared to
cluster 1 samples, were investigated for linear correlations
between the M values and the corresponding log2 gene
expression values within the relevant two clusters.
Correlations were considered significant at a FDR < 0.05 and
correlation groups were compared by Fisher’s exact tests.
Significance-based modules integrating the transcriptome and epigenome (SMITE)
Differential gene expression between the current subset
of transcriptional cluster 2 and 3 samples, compared to
cluster 1 placentas, was obtained using the limma library
], controlling for fetal sex and gestational age. Using
the hg19 genome build within the SMITE library [
R 3.3.2, a framework was constructed where each gene
was associated with a promoter region (+/− 1500 bp
from the TSS) and a gene body region (TSS + 1500 bp to
TES). The fetal sex and GA-corrected gene expression
and methylation results for clusters 2 and 3 (compared
to cluster 1) were then separately integrated into the
framework, and the adjusted and combined methylation
p values in the promoter and body gene regions were
obtained using Stouffer’s method, weighted by effect
strength. The relationship between expression and
methylation was set to “bidirectional” in both gene
regions to avoid biasing the results, and genes were
scored based on a weighted significance value (0.4 for
expression, 0.4 for promoter methylation, and 0.2 for
body methylation). Gene scores were considered
significant at a nominal p value < 0.05. Functional modules of
genes in transcriptional clusters 2 and 3 were then
identified based on these gene scores, a Reactome
proteinprotein interaction graph [
], and the spin-glass network
algorithm. Significant modules (nominal p < 0.05 and
10–500 genes) were subjected to KEGG pathway
enrichment analysis within the SMITE library [
and terms with a FDR < 0.05 were held as significant.
Clinical characteristics and global methylation patterns
Within this subset of 48 cases, transcriptional cluster 1
patients (N = 19) remained the healthiest clinically, with
the latest gestational ages at delivery and the highest
rates of average-for-gestational-age (AGA) infants (95%)
(Additional file 2: Table S1 and Additional file 3: Table
S2). Of these cluster 1 patients, 32% (6/19) were associated
with a diagnosis of PE, though none had co-occurring fetal
growth restriction. Cluster 2 (N = 19) and cluster 3 (N = 5)
samples demonstrated substantially worse clinical
outcomes, with abnormal Doppler ultrasound results, early
deliveries (mean = 31 weeks), and low placental and
newborn weights (mean z-scores < − 1.4) (Additional file 2:
Table S1 and Additional file 3: Table S2). In cluster 2, 89%
(17/19) were diagnosed with PE and exhibited the highest
maternal blood pressures (average maximum systolic
pressure = 175 mmHg) and proteinuria levels (average
maximum = + 3.5). Cluster 3 (60% PE (3/5)) was more
strongly associated with poor fetal growth, with the largest
portion of small-for-gestational-age (SGA) infants (80%)
and NICU transfers after delivery (80%). Cluster 5 patients
(N = 5, 80% PE) continued to display no unique clinical
association (Additional file 2: Table S1 and Additional file 3:
Table S2). These results are consistent with our previous
observations in the full transcriptional clusters [
When the global relationships between these 48
patients were visualized using t-SNE of the DNA
methylation data only, transcriptional cluster 1, 2, and 3
samples continued to demonstrate molecular similarity
to each other (Fig. 1a), indicating that methylation plays
an important role in the development of these three
clusters. Cluster 5 samples, however, were found
dispersed across the methylation plot, no longer forming
a united group (Fig. 1a).
Differentially methylated positions between transcriptional clusters
To identify potential epigenetic markers related to our
transcriptional clusters, placentas belonging to clusters
2, 3, and 5 were independently assessed for differentially
methylated positions (CpG sites) compared to the
healthier cluster 1. When fetal sex and gestational age
were not considered, this revealed a total of 66,837 positions
(53,635 hypo- and 13,202 hyper-) with significantly divergent
methylation in transcriptional cluster 2 samples compared
to cluster 1 (FDR < 0.05; Additional file 4: Table S3). When
fetal sex (p = 0.51 between clusters 1 and 2) was integrated
into the model, this number was reduced to 64,025, whereas
when gestational age (p < 0.01 between clusters 1 and 2)
alone was incorporated, only 8711 significant positions were
observed. However, when these two covariates were
simultaneously included in the model, the number of significant
sites was 8763 (3310 hypo- and 5453 hyper-) (Table 1 and
Additional file 4: Table S3). Similar to the reference
distribution across the full set of possible probes, the majority of
these (fetal sex and gestational age controlled) significant
sites were located in a gene body or an intergenic region (all
p > 0.05; Additional file 5: Figure S2a). Conversely,
substantially fewer significant positions were annotated to a CpG
island (12% versus 34%; p < 0.01) and considerably more to
the CpG open sea (49% versus 33%; p = 0.03) than the
distribution of the array as a whole (Additional file 5: Figure S2b).
Furthermore, 8% (735/8763) of these significant cluster 2
sites were found in a group of at least three significant
positions within a span of 1000 base pairs, which were,
unsurprisingly, often associated with a CpG island or shore
region (p < 0.01) (Table 1 and Additional file 4: Table S3).
In cluster 3 placentas, 13,348 positions were differentially
methylated (9084 hypo- and 4264 hyper-) compared to
cluster 1 (FDR < 0.05) without accounting for fetal sex and
GA (Additional file 6: Table S4). The inclusion of fetal sex
(p = 0.12 between clusters 1 and 3) dropped this number to
4343, while accounting for gestational age (p = 0.02
between clusters 1 and 3) only in the model reduced the
significant positions to 1749. When differences in both
these variables were considered, the number of significantly
aTSS transcription start site, IGR intergenic region, UTR untranslated region
bIncluded in a group of at least three significantly differentially methylated positions within the span of 1000 base pairs
cAlso significantly differentially methylated in cluster 3 compared to cluster 1
altered sites in transcriptional cluster 3 further
decreased to 340 (164 hypo- and 176 hyper-) (Table 2
and Additional file 6: Table S4). The dispersion of these
probes was very similar to the results observed in
cluster 2: within the gene-based regions, the (fetal sex
and GA corrected) significant sites were randomly
distributed (all p > 0.05; Additional file 7: Figure S3a);
however, the number of probes annotated to CpG islands
was lower than random (14% versus 34%; p < 0.01) and
those located in the CpG open sea was higher (51% versus
33%; p = 0.01) (Additional file 7: Figure S3b). Additionally,
5% (16/340) of these cluster 3 sites were involved in a
group of significant positions that were again more likely
to be associated with a CpG island region (p < 0.01)
(Table 2 and Additional file 6: Table S4).
Compared to transcriptional cluster 1, only four CpG
sites were initially identified as differentially methylated
in cluster 5 placentas (one hypo- and three hyper-)
(FDR < 0.05; Additional file 8: Table S5), and this
number became zero when fetal sex and gestational age
were included. This indicates that the gene expression
changes that define this cluster are not associated with
consistent DNA methylation differences. As such,
cluster 5 samples were not investigated further for
Specific functional epigenetic modifications
In order to identify individual epigenetic changes involved
in the transcriptional formation of clusters 2 and 3,
significantly differentially methylated sites in these
samples compared to cluster 1 were assessed for correlating
changes in placental gene expression. Of the 66,837
identified significant positions in transcriptional cluster 2
(before correction for fetal sex and GA), correlative analysis
with the expression of all available associated genes revealed
only 5% with a strong linear relationship (FDR < 0.05;
Additional file 9: Table S6). When restricted to the 8763
sites that maintained a significant difference between
clusters 1 and 2 after correction for both fetal sex and GA,
9% of potential DNA methylation values exhibited a
significant linear relationship with gene expression (FDR < 0.05;
Table 3 and Additional file 9: Table S6). Positively
correlating positions were more frequently found in a CpG island
within a gene body (p < 0.01) or in the CpG open sea in a
gene body (p < 0.01) or intergenic region (p = 0.01) (Fig. 2a).
Sites with a negative relationship to gene expression were
aTSS transcription start site, IGR intergenic region
bIncluded in a group of at least three significantly differentially methylated positions within the span of 1000 base pairs
cAlso significantly differentially methylated in cluster 2 compared to cluster 1
commonly annotated to a CpG shelf region in a 5′UTR
(p = 0.02) or a CpG shore region in a 5′UTR (p < 0.01),
TSS1500 (p = 0.05), or TSS200 (p = 0.01) (Fig. 2a). Most
significantly correlating positions within the CpG open
sea of a gene body or intergenic region were also
associated with an enhancer region (72%; p < 0.01
compared to the other CpG/gene regions).
In transcriptional cluster 3, the 13,348 significant sites
compared to cluster 1 (before correction for fetal sex and
GA) showed a strong linear relationship to gene expression
only 2% of the time (Additional file 10: Table S7). This
value increased to 8% when the analysis was restricted to
the 340 positions that were significantly differentially
methylated between clusters 1 and 3 when simultaneously
controlling for fetal sex and GA (Table 4). Only three sites
demonstrated a strong positive relationship with expression:
one was in the CpG open sea of AFF3’s 1st exon (p = 0.01),
which was not annotated as an enhancer region, and the
other two were in the CpG open sea of the MGST1 gene
body (p = 0.11) in an enhancer (Fig. 2b). Negatively
correlating positions were more frequently associated with
an open sea region in a TSS200 (p = 0.02), although several
were also in gene bodies or the IGR (Fig. 2b).
Integrated functional gene modules
Lastly, in order to reveal significant functional modules of
genes within clusters 2 and 3, their differential gene
expression and differential gene promoter and body
methylation information, compared to cluster 1 and
corrected for fetal sex and GA, were subjected to
Significance-based Modules Integrating the Transcriptome and
Epigenome (SMITE) analysis [
]. Transcriptional cluster 2
contained 9 significant integrated gene modules (p < 0.05),
consisting of 18–149 genes each (Fig. 3a and
Additional file 11: Figure S4). Modules in this cluster with
unique genes (1, 4, and 6) were associated with
TGFbeta signaling, cell adhesion, endocytosis, leukocyte
transendothelial migration, and carbohydrate metabolism
(Additional file 12: Table S8). Module 3 genes were
contained within module 2, and these were involved in
focal adhesion and regulation of the actin cytoskeleton.
Modules 5 and 9 were associated with lipid metabolism,
while modules 7 and 8 were linked to oxidative
phosphorylation and the citrate cycle. The significantly deregulated
genes in cluster 2, based on the integrated epigenetic and
transcriptional scores, and their module inclusions, are
shown in Additional file 13: Table S9.
In contrast, cluster 3 consisted of 11 significant gene
modules (p < 0.05), made up of 24–293 genes each
(Fig. 3b and Additional file 14: Figure S5). Modules 1, 4,
5, 6, and 7 displayed varying degrees of gene overlap and
were all involved in TGF-beta signaling, focal adhesion,
and glycosaminoglycan biosynthesis (Additional file 12:
Table S8). Modules 2 and 3 were linked to antigen
processing/presentation and allograft rejection, while
modules 8 and 9, with ~ 74% gene overlap, were associated
with cytokine-cytokine receptor interaction and Jak-STAT
signaling. Modules in this cluster with unique genes (10
and 11) were involved in purine, amino acid, and biotin
metabolism. The significantly deregulated genes in
transcriptional cluster 3, based on both gene expression
and methylation, and their module inclusions, are also
shown in Additional file 13: Table S9.
Our previous work unbiasedly investigating the placental
heterogeneity observed in preeclampsia [
] revealed five
transcriptional clusters, including four subtypes of PE
placentas. However, while gene expression microarrays
are an invaluable tool for understanding disease, it is
also possible that, in some cases, an alternate level of
molecular information is highly involved in the
development of the pathology. Combined epigenetic and
expression analysis of the same preeclamptic placentas
has only ever been performed for a small number of
] or genes . We, therefore, predicted
that the integration of matched genome-wide DNA
methylation data would further improve our understanding
of these placentas, and allow us to investigate both the
mechanisms underlying the formation of the
transcriptional clusters and the associations between the
Overall, we found that the relationships between the
transcriptional cluster 1–3 samples were still visible within
the DNA methylation information, indicating a significant
global relationship between the two data types in these
samples. Cluster 5 placentas, on the other hand, no longer
formed a distinct group by methylation. This is unsurprising
given that this data type is known to be fairly robust to copy
number abnormalities [
], the driving force behind the
molecular formation of this cluster.
Within transcriptional clusters 2 and 3, controlling for
fetal sex and gestational age in the identification of
differentially methylated sites, compared to the healthier cluster 1,
dramatically reduced the number of significant sites (66,837
to 8763 in cluster 2; 13,348 to 340 in cluster 3). However, it
predominately corrected the observed imbalance in the
direction of change (80% hypomethylated to 38%
hypomethylated in cluster 2; 68% hypomethylated to 48%
hypomethylated in cluster 3). Since both clusters 2 and 3 are
significantly younger than cluster 1 (p < 0.01 and p = 0.02,
respectively), this fits with the knowledge that placentas
become progressively more methylated with time [
while in cluster 3, a moderate bias in fetal sex (p = 0.12)
may have also been involved. Additionally, controlling for
fetal sex and GA substantially increased the proportion of
significant sites that showed a strong linear relationship
with gene expression (5% to 9% in cluster 2; 2% to
8% in cluster 3), thereby confirming that a large
number of sites in the genome undergo DNA methylation
changes in response to differences in these two
factors that are independent of epigenetic regulation
and gene expression [
9, 36, 37
An additional result of interest was the CpG distribution
of significant positions found in transcriptional clusters 2
and 3. CpG islands are most commonly associated with
the regulation of gene expression, especially when located
in the gene’s promoter region [
]. We discovered that
substantially fewer of the significant sites were mapped
into CpG islands than anticipated, based on the reference
distribution of all potential CpG sites, although those that
were annotated to islands were, unsurprisingly, often
found in close proximity to each other. Instead, the majority
of significant positions were associated with CpG open sea
enhancer regions. This is consistent with a previous report
of enrichment of altered DNA methylation at enhancers
and low CpG density regions in early-onset preeclamptic
placentas . These open sea enhancer regions, when
significantly associated with gene expression, were generally
located in the gene body and exhibited a positive
relationship. Sites with a strong negative correlation, on the other
hand, were frequently located in the promoter region
(TSS200, TSS1500, 5′UTR), as expected, but were
annotated to CpG shore regions, not islands. Relationships
between CpG shores and gene expression are thought
to be in response to the binding of transcription factors
and changes in the chromatin structure around the
While the observed proportions of differentially
methylated sites that were associated with corresponding
changes in gene expression (2–9%) are in line with prior
9, 32, 41, 42
], this indicates that a large number
of significant sites in clusters 2 and 3, compared to
cluster 1, show no meaningful relationship to gene
expression. Some of these DNA methylation alterations
could be the consequence of changes in gene expression
or function [
29, 43, 44
], or an adaptive response to
maintain stable or rebalanced expression. They could
further be remnants of an earlier developmental process,
or the result of environmental exposures or treatments,
where the transcriptional evidence is no longer
measurable . Furthermore, methylation is involved in a range
of functions outside of direct transcriptional regulation,
such as genome stability [
], splicing [
], and cellular
development , while gene expression can be regulated
by a number of other factors, such as microRNAs [
transcription factors [
], and/or histone modifications
]. Therefore, it is expected that these two data types
would not fully agree at the individual gene level, although
altered methylation sites not associated with changes in
gene expression could still provide important information
about the overall status and gestational history of these
When the transcriptome and epigenome data was
utilized simultaneously in an integrated analysis, this
revealed modifications in TGF-beta signaling, cell
adhesion and migration, oxidative phosphorylation, and
carbohydrate and lipid metabolism pathways in cluster 2
placentas, confirming that a significant global relationship
exists between the two data types. Placental dysfunction
encompassing dysregulation of these pathways has been
extensively described in the classical paradigm of PE
pathophysiology and fits with our characterization of
cluster 2 patients as demonstrating a “canonical” early-onset
form of PE [
]. Additionally, a number of the top
significant methylation and gene expression correlations
in this cluster (cg23730027 and FLNB, cg13553455 and
COL17A1, cg11079619 and INHBA, cg19140548 and
SH3PXD2A, and cg26509870 and PHYHIP) have been
previously described in a smaller sample set of early-onset
PE placentas , thus validating these relationships. We
also identified several methylation probes in the gene body
of FLT1, one of the most frequently investigated markers
of PE, with a strong positive correlation to expression, as
well as one associated site in the IGR with a strong
negative correlation. These methylation differences could
be involved in or attempting to compensate for the
pathologically elevated expression of this gene [
are significant findings missed by prior studies that have
focused only on FLT1 promoter methylation in
earlyonset PE .
In cluster 3 samples, integrated alterations were identified
involving antigen presentation, allograft rejection,
cytokinecytokine receptor interaction, Jak-STAT and TGF-beta
signaling, glycosaminoglycan biosynthesis, and metabolism.
These are also in line with our prior transcriptional results
in this “immunological” PE group [
], in which we
characterized this cluster of patients as demonstrating evidence of
maternal anti-fetal/placental rejection. While not as widely
discussed in the literature, the primary involvement of
heightened immune activation has been described in
several previous studies of PE pathophysiology, along with
these other metabolic pathways [
]. Interestingly, one
of the most significant methylation and expression
relationships observed in this cluster involved DNMT3A (one of
the DNA methyltransferase enzymes responsible for de
novo methylation): a CpG island site (cg05544807) was
hypermethylated in the DNMT3A gene body, compared to
cluster 1, and demonstrated a negative relationship to
expression. While this likely has global implications for the
DNA methylation pattern observed in these cluster 3
placentas, decreased expression of DNMT3A has been
specifically implicated in immunological-associated
] and abnormal placentation in preeclampsia
. Therefore, this CpG site may serve as a potential
target for the epigenetic modulation of pathological gene
expression in this PE subtype.
Our study also has some inherent limitations. In our
previous gene expression analysis, we utilized a large
cohort of over 300 placentas to identify clusters and
dysregulated pathways between them. Despite our current
study being the largest to integrate methylation and
transcriptional information in PE, this analysis involved
only 48 placentas. Therefore, it is likely to still be
underpowered, thus restricting our discovery of epigenetic
changes in these samples to those with large effect sizes.
As such, a future direction will be the validation of these
findings, and perhaps the identification of new significant
sites, in a larger cohort of samples. Additionally, as with
all investigations of delivered placentas, it is impossible to
determine whether the observed molecular modifications
are part of the cause or the consequence of the disease
process. Finally, our analysis is based on the assumption
that the cell composition is the same across all samples.
This is probably not the case, as differences in cell ratios
can occur for a range of reasons [
4, 42, 70–72
placental maturation or sampling variability. Therefore,
some of the epigenetic changes that we are interpreting as
being reflective of gene regulation in all cells may instead
be due to shifts in cell numbers [
unfortunately, until individual methylation patterns for all
possible placental cell types have been established, this
limitation cannot be resolved. This investigation is
currently ongoing in our groups.
Overall, we have improved our understanding of the
portion of the divergent gene expression involved in the
development of transcriptional clusters 2 and 3 that is
associated with changes in DNA methylation, as well as
confirmed the lack of true biological cohesion in cluster 5
placentas. Differentially methylated sites in clusters 2 and
3, compared to the healthier cluster 1, may have potential
as biomarkers of these patient groups for early screening
in maternal serum, whereas specific genes and sets of
genes exhibiting a strong epigenetic and transcriptional
relationship (either linear or integrated) may serve as
therapeutic targets to modify or prevent pathological
changes in PE placental groups. However, a further
increase in sample size and an assessment of additional
modes of gene regulation will be required to fully
comprehend the mechanisms underlying these subtypes.
Additional file 1: Figure S1. Selected samples for methylation arrays.
(PDF 199 kb)
Additional file 2: Table S1. Continuous clinical characteristics of the 48
samples across the transcriptional clusters (PDF 72 kb)
Additional file 3: Table S2. Categorical clinical characteristics of the 48
samples across the transcriptional clusters (PDF 79 kb)
Additional file 4: Table S3. Significantly differentially methylated sites
in transcriptional cluster 2 placentas versus transcriptional cluster 1
placentas. (XLSX 9040 kb)
Additional file 5: Figure S2. Distribution of significantly differentially
methylated positions in transcriptional cluster 2 (versus transcriptional
cluster 1) compared to the full set of possible methylation probes. (PDF
Additional file 6: Table S4. Significantly differentially methylated sites
in transcriptional cluster 3 placentas versus transcriptional cluster 1
placentas. (XLSX 1669 kb)
Additional file 7: Figure S3. Distribution of significantly differentially
methylated positions in transcriptional cluster 3 (versus transcriptional
cluster 1) compared to the full set of possible methylation probes. (PDF
Additional file 8: Table S5. Significantly differentially methylated sites
in transcriptional cluster 5 placentas versus transcriptional cluster 1
placentas. (XLSX 38 kb)
Additional file 9: Table S6. Significant gene expression correlations
associated with the significantly differentially methylated sites in
transcriptional cluster 2 placentas versus transcriptional cluster 1
placentas. (XLSX 259 kb)
Additional file 10: Table S7. Significant gene expression correlations
associated with the significantly differentially methylated sites in
transcriptional cluster 3 placentas versus transcriptional cluster 1
placentas. (XLSX 63 kb)
Additional file 11: Figure S4. Remaining functional SMITE modules
identified in cluster 2. (PDF 2447 kb)
Additional file 12: Table S8. Significant KEGG pathways associated
with the significant SMITE modules in transcriptional clusters 2 and 3
(XLSX 58 kb)
Additional file 13: Table S9. Genes with significant integrated gene
expression and methylation scores by SMITE analysis in transcriptional
clusters 2 and 3. (XLSX 86 kb)
Additional file 14: Figure S5. Remaining functional SMITE modules
identified in cluster 3. (PDF 4125 kb)
aCGH: Array-based comparative genomic hybridization; AGA:
Average-forgestational-age; FDR: False discovery rate; GA: Gestational age; IGR: Intergenic
region; PE: Preeclampsia; RCWIH: Research Centre for Women’s and Infants’
Health; SGA: Small-for-gestational-age; SMITE: Significance-based Modules
Integrating the Transcriptome and Epigenome; SNP: Single-nucleotide
polymorphism; TES: Transcriptional end site; t-SNE: t-distributed stochastic
neighbor embedding; TSS: Transcriptional start site; UTR: Untranslated region
We thank the donors and the Research Centre for Women’s and Infants’
Health (RCWIH) BioBank for the human samples used in this study. We
would also like to thank Dr. Maria Peñaherrera for assistance in running the
This work was funded by the Canadian Institutes of Health Research (CIHR)
grant #49520 to WPR and the CIHR grant #128369 to SAB and BJC. KL is
supported by an Ontario Graduate Scholarship, SLW is funded by a
University of British Columbia Four Year Doctoral Fellowship, WPR receives
salary support from the BC Children’s Hospital Research Institute, and BJC
receives salary support from a Tier 2 Canada Research Chair in Placental
Development and Maternal-Fetal Health. The funding bodies had no role in
the design of the study, the collection, analysis, and interpretation of the
data, and the writing of the manuscript.
Availability of data and materials
The gene expression microarray data for our full highly annotated sample set
(N = 157) is available from the Gene Expression Omnibus database under the
accession number GSE75010. The matched gene expression and DNA
methylation data for the 48 placentas assessed in the current study is
available under the accession number GSE98224.
KL, BJC, and WPR conceived of the study. SAB and BJC extracted RNA for
microarray analysis. KL extracted DNA for methylation analysis. SLW and WPR
ran the DNA methylation arrays. KL analyzed the data and drafted the
manuscript. BJC, WPR, SLW, and SAB critically revised the manuscript. All
authors approved the final manuscript.
Ethics approval and consent to participate
Ethics approval was granted from the Research Ethics Boards of Mount Sinai
Hospital (#13-0211-E), the University of Toronto (#29435), and the Ottawa
Health Science Network (#2011623-01H). All women provided written
informed consent for the collection of biological specimens and medical
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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