Abstract
Genome-wide Illumina InfiniumMethylation 450 K DNA methylation analysis
was performed on blood samples from clinical atherosclerosis patients
(n = 8) and healthy donors (n = 8) in the LVAD study ([50]NCT02174133,
[51]NCT01799005). Multiple differentially methylated regions (DMR)
could be identified in atherosclerosis patients, related to epigenetic
control of cell adhesion, chemotaxis, cytoskeletal reorganisations,
cell proliferation, cell death, estrogen receptor pathways and
phagocytic immune responses. Furthermore, a subset of 34 DMRs related
to impaired oxidative stress, DNA repair, and inflammatory pathways
could be replicated in an independent cohort study of donor-matched
healthy and atherosclerotic human aorta tissue (n = 15) and human
carotid plaque samples (n = 19). Upon integrated network analysis,
BRCA1 and CRISP2 DMRs were identified as most central
disease-associated DNA methylation biomarkers. Differentially
methylated BRCA1 and CRISP2 regions were verified by MassARRAY Epityper
and pyrosequencing assays and could be further replicated in blood,
aorta tissue and carotid plaque material of atherosclerosis patients.
Moreover, methylation changes at BRCA1 and CRISP2 specific CpG sites
were consistently associated with subclinical atherosclerosis measures
(coronary calcium score and carotid intima media thickness) in an
independent sample cohort of middle-aged men with subclinical
cardiovascular disease in the Aragon Workers’ Health Study (n = 24).
Altogether, BRCA1 and CRISP2 DMRs hold promise as novel blood surrogate
markers for early risk stratification and CVD prevention.
Introduction
According to the World Health Organization (WHO), cardiovascular
diseases (CVD) account for the highest mortality numbers with
approximately 30% of all deaths worldwide
([52]http://www.who.int/gho/ncd/en/). Atherosclerosis is the major
principle underlying CVDs. At predisposed areas of the vascular tree,
including the branching points of coronary and carotid arteries,
localized accumulation of fatty deposits and inflammation reactions
contribute to plaque development and progression eventually leading to
impaired blood flow resulting in CVDs i.e. coronary artery disease and
cerebrovascular disease^[53]1.
The development of an atherosclerotic lesion is a slow and silent
process making early stage diagnosis difficult^[54]2. Early detection
of individuals in the process of developing atherosclerosis might be
essential for cardiovascular prevention. Approximately 60% of
individuals categorized as at low risk for cardiovascular disease based
on traditional risk factors prediction equations had subclinical
atherosclerosis^[55]3, [56]4. Thus, other factors not traditionally
included in risk scales are likely to be involved in atherogenesis.
Preclinical evidence supports that aberrant monocyte-macrophage
differentiation contributes to vascular wall inflammation in patients
at high risk for atherosclerosis^[57]5, [58]6. CpG DNA methylation is
involved in the epigenetic differentiation and regulation of leukocyte
specific gene expression profiles, including the expression of soluble
mediators and surface molecules that direct margination, adhesion, and
migration of blood leukocytes in vascular tissues^[59]7. While very
little is known about the human leukocyte DNA methylome and its
potential causal role in cardiovascular disease, blood DNA methylation
markers may contribute to the diagnosis of atherosclerosis patients.
Recent studies have illustrated the feasibility of DNA methylation
profiling using peripheral blood to identify CVD specific surrogate
biomarkers^[60]8–[61]15. Candidate-gene approaches identified
significant associations between leukocyte DNA methylation and
atherosclerosis, whereas the results for the association between global
DNA methylation and atherosclerosis were not always
consistent^[62]9–[63]12. Previous studies however, evaluated
differentially methylated sites using samples from individuals with
clinical cardiovascular disease, but did not examine the potential role
of DNA methylation regions as a marker of subclinical disease.
Therefore, we characterized genome-wide DNA methylation profiles of
blood samples of atherosclerosis patients versus healthy individuals
from the LVAD study (Impact of Left Ventricular Assist Devices
Implantation on Micro- and Macrovascular Function, ([64]NCT02174133)
and identified promising atherosclerosis-related epigenetic biomarkers.
For the selected regions, we validated these whole blood DNA
methylation profiles using publicly available Illumina
InfiniumMethylation 450 K data from carotid normal and atherosclerotic
plaque samples. Additionally, we compared CVD associated DNA
methylation changes with aging and/or immune cell epigenotypes.
Finally, we explored the role of promising regions as potential
predictors of subclinical atherosclerosis in a subsample of 24
individuals that participated in the Aragon Workers Health Study
(AWHS). The AWHS is a prospective cohort that aims to characterize the
factors associated with metabolic abnormalities and imaging-based
subclinical atherosclerosis measures in a middle-aged population free
of clinical cardiovascular disease^[65]3, [66]16.
Results
Peripheral blood of atherosclerosis patients reveals no statistically
significant changes in global DNA methylation in comparison to healthy
individuals
Significant differences in clinical parameters were observed between
the two study groups. C-reactive protein, haemoglobin concentration and
triglycerides were significantly lower in the healthy individuals as
compared to atherosclerosis patients (Table [67]1). The atherosclerosis
patients were generally older than the healthy individuals
(Table [68]1).
Table 1.
Volunteer characteristics.
Clinical parameters Healthy Atherosclerosis P value
Mean ± SD Mean ± SD
Coronary artery disease No Yes
Arterial hypertension No Yes
Arrhythmia No Yes
Age 49.7 ± 6.6 78.0 ± 8.4 < 0.0001
BMI (kg/m^2) 26.2 ± 2.3 27.0 ± 3.2 0.61
LDL cholesterol (mg/dl) 147.4 ± 37.2 121.3 ± 14.5 0.13
HDL cholesterol (mg/dl) 52.3 ± 11.2 46.8 ± 10.1 0.41
Total cholesterol (mg/dl) 212.7 ± 38.0 179.0 ± 32.5 0.09
Fasting plasma glucose (mg/dl) 90.0 ± 8.0 86.1 ± 14.3 0.54
HbA1c (%) 5.7 ± 0.3 5.8 ± 0.9 0.78
CRP (mg/dl) 0.6 ± 0.5 0.7 ± 0.5 0.04
Leukocytes (1000/ul) 6.6 ± 2.3 6.4 ± 1.4 0.84
Hb (mg/dl) 12.7 ± 1.7 14.9 ± 0.6 0.007
Creatinine (mg/dl) 1.0 ± 0.2 1.3 ± 0.5 0.31
Triglycerides (mg/dl) 95.9 ± 44.2 159.5 ± 48.0 0.03
[69]Open in a new tab
Statistical analysis performed by means of the student T test.
DNA methylation profiles covering >450,000 CpG dinucleotides of
peripheral blood of eight atherosclerosis and eight healthy individuals
were generated by Illumina 450k BeadChip arrays. CpG probes were
sub-grouped in relation to gene regions and to CpG islands to obtain
the most comprehensive view of the DNA methylation distribution in both
groups. Global DNA methylation was assessed in each individual by
calculating the median beta-value of all CpG probes. The mean median
values were calculated per sample group (healthy and atherosclerosis).
Overall, no statistical significant difference (P-value = 0.9159) was
observed in mean global DNA methylation between atherosclerosis
patients (0.6613, SEM: 0.0067) versus healthy controls (0.6624, SEM:
0.0071). In addition, no global DNA methylation shifts were found
between the groups when mapping cg probes to different genomic
locations (e.g. CpG islands, shores, shelves) (Supplementary
Fig. [70]1).
Peripheral blood of atherosclerosis patients reveals DNA methylation
signature of impaired regulation of cell adhesion, chemotaxis and estrogen
hormone responses
Criteria for the identification of sig-DMPs are summarized in
Fig. [71]1. Normalization, quality control and probe filtering for SNPs
resulted in an output of 477,044 CpG probes. Differentially methylated
CpG probes were filtered for a Benjamini-Hochberg adjusted p-value
smaller than 0.15 and a difference in β-values between atherosclerosis
patients and healthy controls of at least 0.05 (i.e. 5% difference in
DNA methylation). 712 CpG probes met the selection criteria comprising
465 hypomethylated CpG sites (hypo-DMPs) and 247 hypermethylated CpG
sites (hyper-DMPs) (Fig. [72]2A, Supplementary Table [73]2). We
observed a maximum of 20% difference in DNA methylation between
atherosclerosis patients and healthy controls. Based on the methylation
values of these CpG sites, principle component analysis reveals a clear
separation of DNA methylation profiles of atherosclerosis and healthy
individuals (Fig. [74]2B). Analysis of functional genomic locations
using Epi-explorer^[75]17 revealed that both hyper- and hypo-DMPs were
depleted in promoter regions and CpG islands and enriched in
intergenic, CpG-poor and enhancer regions (Fig. [76]3).
Figure 1.
Figure 1
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Flowchart of DMP and DMR selection.
Figure 2.
Figure 2
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Differentially methylated CpG probes between healthy and
atherosclerotic individuals. Sig-DMPs were selected based on FDR < 0.15
and Δ beta >5%. (A) Volcano plot showing 465 hypo- and 247
hypermethylated probes meeting the selection criteria. (B) PCA plot
demonstrating the separation of atherosclerosis patients for CVD (grey)
and healthy individuals (black) based on the DNA methylation values of
the 712 DMPs.
Figure 3.
Figure 3
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Genomic distribution of sig-DMPs based on (A) gene regions, (B)
CpG-island regions and (C) chromatin segmentation states (based on
GM12878 cell type data). Significant enrichment or depletion of
sig-DMPs (P-value < 0.05), determined by the Fisher’s exact test, are
marked by an asterisk.
Since median age of atherosclerosis patients (78 ± 8 years old) and
healthy controls (47 ± 8 years old) was significantly different
(Table [80]1), we next overlapped our sig-DMP list with the list of
age-responsive CpG probes, identified by Steengenga et al.^[81]18, to
discriminate between aging- and atherosclerosis-specific DNA
methylation changes. Of the 7,477 CpG probes with age-dependent
methylation changes, 196 probes overlapped with our sig-DMP list,
resulting in 516 age-independent DMPs, including 287 hypo- and 229
hyper-DMPs (Supplementary Table [82]2).
To further reduce biological complexity of DNA methylation changes, we
next determined consecutive sig-DMPs using the R-package DMRcate. In
total 236 sig-DMRs were identified (P[mean]-value < 0.001) containing
at least 5 consecutive CpG probes with a minimal DNA methylation
difference of 5% (Δβ-value >0.05) (Fig. [83]1 and Supplementary
Table [84]3). After overlap with known age-dependent CpG probes^[85]18,
75 DMRs were removed leaving a total of 161 DMRs (sig-DMRs), containing
1,424 CpG probes, for further analysis. Interestingly, further pathway
enrichment analysis of the various gene associated DMRs (Supplementary
Table [86]3), identified significant (P < 0.05) enrichment of cadherin
and integrin dependent cell adhesion, chemotaxis, cytoskeletal
reorganisations, cell cycle and cell death responses, nongenomic
estrogen receptor pathways, immune phagocytosis, all of which are
critically involved in atherosclerosis (Fig. [87]4 and Supplementary
Table [88]4).
Figure 4.
Figure 4
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Metacore based pathway enrichment analysis of gene associated DMRs in
atherosclerosis reveals a significant (P-value < 0.05;
−log(P-value) < 1.3) enrichment of CVD related pathways related to
leukocyte chemotaxis and adhesion.
Blood samples of atherosclerosis patients reveal a different immune cell type
composition in comparison to healthy individuals
Since blood is a heterogeneous collection of different cell types, each
characterized by unique DNA methylation profile, as recently
demonstrated by Jaffe and colleagues^[90]19, we next wanted to evaluate
whether identified DNA methylation changes did not simply reflect
variation in blood cell composition between both studied populations.
Using the algorithm developed by Houseman and colleagues^[91]20 for
mathematical deconvolution of relative immune cell type composition of
blood samples based on Illumina 450k data^[92]21, we observed that the
fraction of granulocytes was slightly but statistical significantly
(P < 0.05) increased in patients at high cardiovascular disease risk in
comparison to control individuals (Fig. [93]5 and Supplementary
Table [94]5). In contrast, the CD8^+ T-cell population was
significantly reduced in atherosclerosis patients. Although
statistically not significant, a fraction of CD4^+ T- and NK-cells
tended to decrease in atherosclerosis patient. Because of the relative
low sample size analysed, we were not able to correct for this in the
linear model analysis.
Figure 5.
Figure 5
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Deconvolution of immune cell type blood composition. The approach
described by Houseman et al. was applied to determine the relative
immune cell type fraction (Y-axis) in healthy and atherosclerosis blood
samples. Statistical significant differences in cell type contribution
between healthy and atherosclerosis blood samples were calculated by a
student t-test.
Pathway enrichment analysis of common gene associated DMRs in blood, aorta
and carotid plaque material reveals impaired NRF2 oxidative stress, DNA
repair, thioredoxin and inflammatory pathways
In total, 51 cell type-associated DMRs were excluded using the houseman
method leaving 110 sig-DMRs for further analysis. To replicate the
potential role for the 110 sig-DMRs as blood-based surrogate biomarkers
for plaque biopsy material in atherosclerosis, we compared our findings
with publicly available data from Zaina et al. ([96]GSE46401)^[97]22
which contains DNA methylation profiles from donor-matched healthy and
atherosclerotic human aorta tissue and from human carotid plaque
samples in a larger independent cohort^[98]22. We performed a
two-tailed student t-test for the 497 CpG probes located in the 110
sig-DMRs comparing methylation values between donor-matched healthy and
aorta plaque tissue and between healthy aorta and carotid plaque
tissue. We found that 69 CpG probes located in 34 unique DMRs were both
consistently differentially methylated in our blood-based dataset, in
aorta plaque tissue and carotid plaque tissue (Supplementary
Table [99]6). Interestingly, pathway enrichment analysis of the
34-common gene associated DMRs revealed Nuclear factor
(erythroid-derived 2)-like 2 (NRF2), oxidative stress (SOD2), DNA
repair (BRCA1), thioredoxin (TXNRD1) and inflammatory pathways (MIF,
PLA2G4D) (Supplementary Table [100]7). To find out the relationship
between the different sig-DMRs, we mapped each sig-DMR to the nearest
gene and constructed a network using the GeneMANIA Cytoscape plugin
(Fig. [101]6). Interestingly the breast cancer 1 gene (BRCA1) stands
out as one of the most highly connected node in our network, physically
interacting with nine other proteins (Fig. [102]6). In addition,
cysteine rich secretory protein 2 (CRISP2), was the gene with the
highest node degree. Due to their high network interconnectivity (high
node degree), BRCA1 and CRISP2 genes were selected for further
DNAmethylation biomarker validation studies.
Figure 6.
Figure 6
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GeneMANIA network of the 34 sig-DMRs not affected by age or cell type
composition and consistently differentially methylated in both blood
and plaque tissue.
Verification of Illumina 450 K DNA methylation intensities of BRCA1 and
CRISP2 DMRs by Epityper MassARRAY
Differentially methylated regions of BRCA1 and CRISP2 genes determined
by Illumina 450 array were selected for further technical validation by
Epityper MassARRAY. These regions were found to be significant using
the DMRcate package, having multiple CpG sites with more than 10%
difference in DNA methylation and showed optimal amplicon fragmentation
patterns in Epityper MassARRAY analysis. BRCA1 and CRISP2 had the best
assay score for MassARRAY analysis, whereas other genes of interest
(EID3, SDHAP3, TSKS and AURKC) showed limitations in their
fragmentation patterns. For BRCA1, we decided to focus on 14
consecutive hypermethylated CpG probes located in a CGI
(chr17:41,277,974–41,278,445, Supplementary Fig. [104]2). Nine CpG
probes in this region showed more than 10% DNA hypermethylation in the
atherosclerosis patients (cg26370022, cg15065591, cg02286533,
cg18372208, cg14947218, cg16006004, cg06001716, cg25288140 and
cg24900425). The CGI is located in an active promoter region (Roadmap
Epigenomics Project^[105]23). Finally, the promoter region of the
CRISP2 was selected as a second amplicon for validation. Seven
neighboring CpG sites (cg26715042, cg14997592, cg04595372, cg01706515,
cg25390787, cg08942800 and cg01076129) were found to be more than 10%
hypermethylated in this region for the atherosclerosis patients. The
amplicon covers the entire promoter region of the gene (Supplementary
Fig. [106]2). Altogether, the Epityper MassARRAY and Illumina DNA
methylation levels revealed strongly significant correlations for BRCA1
(ρ = 0.711–0.932) and CRISP2 (ρ = 0.680) (Supplementary Fig. [107]3A).
Moreover, BRCA1 and CRISP2 target genes demonstrate significant
hypermethylation in atherosclerosis blood samples, as compared to blood
samples derived from healthy individuals (Fig. [108]7). Similar DNA
atherosclerosis associated BRCA1 DNA hypermethylation results were also
obtained by pyrosequencing of the BRCA1 DMR chr17:41278125–41278228
(including cg26279233, cg cg06001716 and cg cg14947218), whereas no
valid pyrosequencing assay could be designed for the CRISP2 DMR
(Supplementary Fig. [109]3B and data not shown).
Figure 7.
Figure 7
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MassARRAY EpiTYPER validation of BRCA1 and CRISP2. The mean methylation
values of each measured region are represented in boxplots. The student
t-test was used to calculate the significance of the methylation
difference between healthy and atherosclerotic blood samples.
We further compared the methylation status of the blood cells types in
the two validated regions using the reference methylation data set of
Reinius et al.^[111]21. Four CpG probes in the BRCA1 DMR (cg26370022,
cg11529738, 14947218 and cg06001716) (Supplementary Fig. [112]4) showed
significant DNA methylation differences between immune cell types
(p < 0.05, not corrected for multiple testing). However, after
Bonferroni correction hypermethylation of only one CpG probe remained
significantly correlated with immune cell type (cg26370022). In the
CRISP2 DMR significant immune cell type specific DNA methylation
changes were observed for two CpG probes (cg01706515 and cg21710255)
(Supplementary Fig. [113]4). However, observed atherosclerosis related
DNA hypermethylation trend for most BRCA1 and CRISP2 CpG probes does
not follow the expected methylation change in blood samples enriched
for granulocyte and reduced CD8^+ T immune cell subpopulations. As
such, our results show that DNA hypermethylation of most BRCA1 and
CRISP2 CpG probes occurs independently of sample variation in blood
cell type composition and is associated with atherosclerosis pathology.
As shown in Fig. [114]8, the same hypermethylation trend of the BRCA1
and CRISP2 regions could be replicated in atherosclerotic tissues from
the study by Zaina et al., both in the carotid plaques and the
donor-matched aortic tissues^[115]22. Altogether, our data suggest that
BRCA1 and CRISP2 DNA methylation patterns are potential epigenetic
biomarkers related to atherosclerosis in blood and atherosclerotic
plaque tissue matrix.
Figure 8.
Figure 8
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DMRs associated with BRCA1 and CRISP2 in an independent publicly
available cohort (Zaina et al.^[117]22). The Zaina et al. cohort
comprises of 30 donor-matched atherosclerotic plaque tissue samples and
19 carotid plaque samples.
Association of blood DNA methylation changes in BRCA1 and CRISP2 with
subclinical atherosclerosis in healthy middle age men of the AWHS cohort
To explore the role of validated regions as predictors of subclinical
atherosclerosis in a population with a low burden of disease, we
evaluated the association of DNA methylation levels from fresh frozen
whole blood samples collected at the baseline visit (2009–2010) and
subclinical atherosclerosis measured in 2011–2013 from a subsample of
24 Aragon Workers Health Study (AWHS) participants with available
baseline InfiniumMethylation 450 K data. We looked for associations
between blood DNA methylation and the subclinical atherosclerosis
measures, coronary calcium score and carotid intima media thickness.
Associations were found for three CpG probes located in CRISP2
(cg12440062, cg25390787, cg01076129) and 1 CpG probe in BRCA1
(cg16630982). The strongest statistically significant CpG probe was
cg12440062 (located 70 bases upstream of the promotor) for CRISP2,
consistently, for both coronary calcium score and carotid intima media
thickness measures (Table [118]2). The multi-adjusted difference in
coronary calcium score comparing the 75^th to the 25^th percentiles of
DNA methylation was −46.62 score points (−86.87, −6.36; p-value = 0.03)
for cg12440062 in CRISP2. The corresponding difference in carotid
intima media thickness was −0.20 millimeters (−0.33, −0.06;
p-value = 0.009) for cg12440062 in CRIPS2. For CRISP2, DNA methylation
in cg01076129 (located in promoter region) was associated with carotid
intima-media thickness, but not coronary calcium score. The association
with cg25390787 (located in promotor region) was only borderline
significant (p = 0.06) consistently for both atherosclerosis measures.
With respect to the BRCA1 DMR, the strongest association with both
coronary calcium score (p-value = 0.018) and carotid intima media
thickness (p-value = 0.0019) was found for cg16630982 (located in the
promotor region), whereas other cg probes did not reach significance
within the limited sample series tested.
Table 2.
Significant associations of BRCA1 and CRISP2 CpG probes with coronary
calcium score and carotid intima thickness.
CpG probe Gene Difference P75vsP25 (95% CI) P-value
Coronary calcium score
cg16630982 BRCA1 −36.59 (−64.53–−8.64) 0.018
cg12440062 CRISP2 −46.65 (−86.87–−6.36) 0.03
cg25390787 CRISP2 34.98 (−69.35–−0.61) 0.06
Carotid intima thickness
cg16630982 BRCA1 −0.16 (−0.25–−0.07) 0.0019
cg12440062 CRISP2 −0.20 (−0.33–−0.06) 0.009
cg01076129 CRISP2 −0.14 (−0.26–−0.02) 0.036
cg25390787 CRISP2 −0.12 (−0.24−0.00) 0.06
[119]Open in a new tab
Discussion
In this study, we identified genomic regions in BRCA1 and CRISP2 which
were consistently differentially methylated in blood DNA of
atherosclerosis patients compared to healthy individuals, and in aortic
and carotid plaque samples compared to aorta samples without plaque.
Furthermore, methylation in BRCA1 and CRISP2 DMR was also associated
with subclinical atherosclerosis measures in an independent sample of
middle age men. Our results thus support a potential role of blood DNA
surrogate markers for early CVD detection.
Epigenetics may provide the missing mechanism linking environment,
genome and atherosclerotic phenotype. Identifying epigenomic biomarkers
that parallel the development of subclinical atherosclerosis might open
new paths for risk stratification and prevention, and may help to
further understand the pathophysiology of atherosclerosis. In
particular, changes in DNA methylation patterns have been linked to
several cardiovascular-related biomarkers, including homocysteine and
C-reactive protein^[120]9. Furthermore, an increasing number of studies
report DNA methylation alterations in atherosclerosis^[121]24–[122]26.
For example, a recent genome-wide study showed DNA methylation
differences between healthy donor-matched aortic healthy and plaque
tissue, indicated by epigenetic drift of DNA methylation in aortic
plaques with atherosclerotic progression^[123]22, [124]27. Furthermore,
known cardiovascular risk factors, including homocysteine levels,
smoking and age have been described to induce DNA methylation
changes^[125]28–[126]30. Currently, only few DNA methylation studies
have been performed with blood samples of CAD patients. Sharma et al.
identified 72 hypermethylated DMRs associated with CAD and
hyperhomocysteinemia using a 12k human CpG island microarray^[127]31.
Guay et al. performed a study on subjects with familial
hypercholesterolemia with or without CAD using the Infinium 27k
methylation array^[128]32. Since blood leukocytes have major
contributions to the initiation, progression and maintenance of
atherosclerosis, we determined genome-wide DNA methylation profiles in
blood samples of atherosclerosis patients, in comparison to healthy
individuals to identify CVD related epigenetic biomarkers. Although
Illumina 450 K profiling did not reveal significant global DNA
methylation differences between atherosclerosis patients and healthy
individuals, HPLC based methods demonstrated global DNA
hypermethylation in blood leukocytes^[129]9, [130]33 whereas both
global DNA hypermethylation and hypomethylation have been reported in
atherosclerotic vascular tissue^[131]22, [132]34, [133]35.
Upon further mapping of DNA methylation changes at specific CpG motifs
or regions, 161 DMRs were identified to be differentially methylated
based on specific selection criteria. Of particular interest, pathway
enrichment analysis of gene associated DMRs revealed DNA impaired
epigenetic regulation of integrin and cadherin dependent cell adhesion,
cell cycle, cell death, chemotaxis, immune phagocytosis and estrogen
hormone pathways which are all critically involved in atherosclerosis.
In accordance with other studies examining DNA methylation in metabolic
diseases, the observed methylation changes were relatively small (max
20%), as compared to cancer specific DNA methylation changes. More
specifically, whereas promoter regions and CpG islands were found to be
depleted of atherosclerosis associated DMPs, gene bodies, intergenic
and open sea regions show enrichment of various DMPs. Interestingly, a
strong enrichment was also observed in enhancer regions suggesting that
impaired control of distal regulatory regions may contribute to
atherosclerosis.
Remarkably, mathematical deconvolution (Houseman correction) of the
blood DNA methylation profiles revealed significant changes in the
granulocyte and CD8^+ T immune cell populations in atherosclerosis
patients as compared to healthy individuals, which could be highly
relevant for atherosclerotic plaque formation. More particularly,
important regulatory roles for granulocyte and CD4^+/CD8^+ T cell
populations have recently been identified in atherosclerotic lesions
and coronary thrombus evolution^[134]36, [135]37. As expected, a large
fraction of the sig-DMRs were also differentially methylated between
blood cell types, suggesting that their change in DNA methylation in
atherosclerosis patients could be due to a difference in blood cell
type composition between atherosclerosis and healthy controls.
Heterogeneity of blood samples could be prevented using cell count and
sorting methods (FACS) to analyze specific immune cell subpopulations.
However, these methods are difficult and costly to apply in large
epidemiological cohort studies. CpG sites associated with blood cell
type were excluded for further analysis, leaving 110 sig-DMRs not
affected by blood cell types.
Interestingly, of the 110 remaining DMRs (comprising 497 CpG sites), 34
DMRs (69 CpG sites) were also found to be differentially methylated in
plaque tissues, which suggests that blood-associated epigenetic
biomarkers can be valid surrogate markers for methylation changes in
plaque material. Of particular interest, pathway enrichment analysis of
the 34 common gene associated DMRs revealed epigenetic impairment of
NRF2 oxidative stress (SOD2), DNA repair (BRCA1), thioredoxin (TXNRD1)
and inflammatory pathways (MIF, PLA2G4D) in atherosclerosis conditions.
Upon further network analysis of each sig-DMR, mapped to the nearest
gene, we identified a highly interconnective network with central roles
of BRCA1 and CRISP2 DMRs, which prompted us to focus on these genes for
further validation. Of special note, the differentially methylated
regions in BRCA1 and CRISP2 appeared to be largely independent of age
and/or immune cell type composition, and both hold promise as valuable
atherosclerosis related biomarkers in routine blood analysis.
Interestingly, DNA methylation at the BRCA1 locus was already present
in healthy controls, which is contrary to other studies where a lack of
DNA methylation was observed^[136]38, [137]39. Nevertheless, it must be
emphasized that the methylated CpGs in our data set are located in a
CpG island approximately 600 bp upstream of the BRCA1 gene, whereas
previous studies^[138]40 detected hypomethylation around the
transcription start site of the gene, which can also be appreciated in
our data (Supplementary Fig. [139]2).
DNA methylation changes of the BRCA1 and CRISP2 DMRs could also be
replicated in an Illumina 450 K dataset of paired atherosclerotic
plaque and normal aorta samples from 24 middle aged men with
subclinical atherosclerosis of the Aragon Workers Health Study (AWHS).
Moreover, we also observed statistically significant association
between DNA methylation in several CpG sites in these regions and
coronary calcium score and carotid intima-media thickness when using
data from this well-established population-based cohort^[140]3,
[141]16, thus adding robustness to our findings. Surprisingly, the
associations with subclinical atherosclerosis measures were not always
directionally consistent compared to the associations comparing blood
DNA methylation of cardiovascular disease versus healthy individuals or
the atherosclerosis versus normal aorta samples. The explanation for
this inconsistency still remains unclear, although we cannot exclude
cell type specific variations in blood sample composition or complex
genotype SNP, microRNA or lncRNA dependent heterogenic epigenetic
regulation of different BRCA1 or CRISP2 variants^[142]41–[143]47. In
addition, changes in lifestyle (diet, smoking, pollution, exercise) and
pharmacological treatments (statins, aspirin, PARP inhibitors) could
further obscure DNA methylation changes associated with
atherosclerosis^[144]47, [145]48.
However, the known biological role of BRCA1 and CRISP2 in
cardiometabolic risk and inflammation pathways adds further
significance to our findings. Besides the well described tumor
suppressor function of BRCA1 in breast and ovarian cancers, more recent
research also demonstrates an important role for BRCA1 in suppression
of endothelial dysfunction and atherosclerosis^[146]49. In the latter
study, BRCA1-overexpressing ApoE knock-out mice developed significantly
less atherosclerotic plaque lesions together with reduced macrophage
infiltration and diminished ROS production^[147]49. In another study,
women lacking functional BRCA1/BRCA2 at increased breast cancer risk
also show greater risk for heart disease and metabolic
diseases^[148]50–[149]53. BRCA1 is involved in multiple cellular
processes and genome stability maintenance like DNA repair,
transcriptional regulation, ubiquitination and cell-cycle
control^[150]54. Excessive production of reactive oxygen species, in
part via upregulation of DNA damage pathways, is a central mechanism
governing pathologic activation of vascular smooth muscle cells.
Remarkably, BRCA1 was found to shield vascular smooth muscle cells
(VSMCs) from oxidative stress by inhibiting NADPH Nox1-dependent
reactive oxygen species production^[151]55. More recently, BRCA1 was
found to regulate lipogenesis through its interaction with acetyl
coenzyme A carboxylase^[152]56. Along the same line, BRCA1 plays a
critical role in the regulation of metabolic function in the skeletal
muscle where it is involved in lipid storage and insulin
resistance^[153]57. In analogy to BRCA1 dependent suppression of cell
motility and epithelial-mesenchymal transition in cancer^[154]58, BRCA1
may also prevent endothelial-mesenchymal transition involved in
atherosclerosis progression and other CVDs (myocardial infarction,
vascular calcification)^[155]59–[156]61. As our data suggests,
silencing of the BRCA1 gene in atherosclerosis patients may mean that
this gene not only acts a tumor suppressor but also as a vascular
protector against oxidative cell damage.
While no CRISP2 functions have so far been reported in relation to
CVDs, CRISP2 gene activities were recently associated with oxidative
stress responses and decline of lung function upon smoke or particular
matter exposure^[157]62. In another study, CRISP expression was found
to abolish the neovascularization process induced by exogenous growth
factors (bFGF, vpVEGF)^[158]63. Decreased CRISP2 expression correlated
with Th2-like eosinophilic inflammation in chronic nasal asthmatic
chronic rhinosinusitis^[159]64. As such, the potential involvement of
CRISP2 in CVD pathologies and angiogenesis via oxidative stress and
inflammatory responses warrants further investigation.
An important limitation in our study is reflected by the small sample
size of the studied samples, which renders our analysis clearly
underpowered. Future prospective studies in larger and distinct cohorts
could further enable the validation of BRCA1 and CRISP2 to predict
early cardiovascular disease. Additionally, the atherosclerosis
patients were older than the healthy controls. Even though, a
correction for age-specific methylation was performed, we cannot
exclude that age may affect the results and is therefore a confounding
factor. In addition, as atherosclerosis is an age-dependent disease,
probably some of the excluded age-dependent CpG sites overlap with CDV
related CpGs. Nonetheless, in the post-hoc analysis with the Aragon
Workers Health Study, a study population composed of cardiovascular
disease free middle age men, the main findings were consistent even
after adjustment of age, BMI, smoking and houseman cell composition.
While we cannot discard a potential lack of generalizability, which is
typical of observational studies, an important strength of our study
includes the availability of DNA-methylation data from three
independent set of samples covering the whole spectrum from blood
samples from individuals with subclinical atherosclerosis measures and
individuals at high and low cardiovascular risk to carotid and aorta
samples.
In conclusion, we identified promising novel epigenetic biomarkers of
clinical and subclinical atherosclerotic disease, in genes involved in
impaired leukocyte-endothelium functions during atherosclerosis
progression. These regions deserve further consideration in
experimental studies and prospective population-based cohort to confirm
their potential role in cardiovascular risk. If confirmed, the reported
markers could become potential tools to support early identification of
individuals at high CVD risk who could benefit from preventive
interventions for cardiovascular disease prevention and control.
Methods
We fist conducted a discovery phase analysis of DNA methylation data
from 8 healthy voluntaries and 8 patients with atherosclerosis from the
Study “Impact of Left Ventricular Assist Devices Implantation on Micro-
and Macrovascular Function” (LVAD study, clinicaltrials.gov:
[160]NCT02174133). Subsequently we validated findings from the
discovery phase by analyzing DNA methylation data from plaque material
related to GEO dataset [161]GSE46401 published by Zaina et al. Finally,
as a post-hoc analysis, we explored the potential role of the
identified markers as early detection biomarkers by evaluating the
association of DNA methylation and subclinical atherosclerosis
endpoints in whole blood DNA from 24 Aragon Workers Study participants
free of clinical cardiovascular disease.
Experimental set-up and sample collection in the discovery stage
In the LVAD study, eight healthy volunteers were recruited based on
following inclusion criteria: age (35–60 years), BMI (23–27 kg/m²),
average physical activity and normal western diet (clinicaltrials.gov:
[162]NCT01799005). The exclusion criteria for the healthy volunteers
were CVD, diabetes mellitus, acute inflammation and arrhythmia. We
additionally selected eight patients with confirmed clinical diagnosis
of atherosclerosis (clinicaltrials.gov: [163]NCT02174133). The
characteristics of the study population are described in Table [164]1.
Whole blood (0.5 ml) was collected from all individuals, following
informed consent. The study was conducted according to the guidelines
laid down in the Declaration of Helsinki and all procedures involving
human subjects were approved by the University of Düsseldorf Research
Ethics Committee (ref: 3870 and ref: 4565R).
Infinium HumanMethylation450 BeadChip Array processing and data analysis for
whole blood DNA from atherosclerosis patients and healthy donors
Genomic DNA (gDNA) isolated from 0.5 ml whole blood (EDTA), was
isolated with DNeasy Blood & Tissue kit (Qiagen Hilden, Germany) and
quantified by Nanodrop™ spectrophotometry. 1000 ng of gDNA was
bisulfite converted using the EZ DNA methylation kit (Zymo Research,
Orange, CA, USA) according to manufacturer’s instructions. Genome-wide
DNA methylation was analyzed on Infinium HumanMethylation450 BeadChip
platform (Illumina, San Diego, CA, USA) at the DKFZ Genomics and
Proteomics Core Facility. 4 µl of bisulfite-converted whole blood DNA
(~150 ng) was used for the whole genome amplification (WGA) reaction,
enzymatic fragmentation, precipitation and re-suspended in
hybridization buffer. Subsequent steps of DNA methylation analysis were
carried out according to the standard Infinium HD Assay Methylation
Protocol Guide (Part #15019519, Illumina). The BeadChip images were
captured using the Illumina iScan. Pre-processing and analysis of the
Infinium 450k data was performed using the R package RnBeads^[165]65.
CpG probes containing a SNP at least 3 bp from the 3’ query site,
having a detection p-value higher than 0.01, having empty values in at
least one sample or measuring methylation in a non-CpG context were
removed. In total 8,533 CpG probes (1.75%) were filtered. Intra-array
normalization was done using the Beta Mixture Quantile
Normalization^[166]66. Methylation values were represented as β-values
ranging from 0 to 1. β-alues were converted into M-values
[MATH: (M=log2(β(1−β))) :MATH]
before doing the statistical analysis. Limma R package was used to
identify differentially methylated positions. Raw p-values were
corrected for multiple testing using the Benjamini-Hochberg method. CpG
probes with an adjusted p-value below 0.15 and having a difference in
β-values of at least 0.05 (i.e. 5% difference in DNA methylation)
between atherosclerosis patients and healthy controls were denoted as
significant, and named sig-DMPs. Differentially methylated regions were
identified using the DMRcate R package^[167]67. A region was called
significant when Pmean-value was below 0.001 with a maximum methylation
difference of at least 5% and containing at least five CpGs. Sig-DMPs
were annotated using the HumanMethylation450 v1.2 manifest file. The
freely available EpiExplorer tool was used to add further annotation
including chromatin state segmentation and histone
modifications^[168]17. Enrichment or depletion of sig-DMPs in a
particular genomic region was determined using the Fisher’s exact test.
Commercial Metacore ([169]https://portal.genego.com/)and Ingenuity
([170]www.ingenuity.com/) software packages werre used to identify
significant pathway enrichment of gene associated DMRs.
The method of Houseman et al.^[171]20 incorporated in the RnBeads
package was used to estimate the cell type composition in blood.
Reference cell types for granulocytes, CD4+ T-cells, CD8+ T-cells,
B-cells, monocytes and NK-cells were obtained from the study of Reinius
et al.^[172]21 using the FlowSorted.Blood.450k R package. The
methylation profiles were processed (filtering and normalization)
together with the atherosclerosis methylation dataset in the same way
as described above. In total 50,000 CpG probes with the highest
variance were used to identify the top 500 CpG probes associated with
the cell types. The relative cell type contributions were compared
between healthy individuals and atherosclerosis using a normal student
t-test. One-way ANOVA and Bonferroni Post-hoc test was used to detect
methylation differences between the blood cell types using the data
from Reinius et al.
Replication in atherosclerotic plaque material methylation dataset
[173]GSE46401
Normalized Infinium 450k DNA methylation data of atherosclerotic plaque
material were obtained from GEO dataset [174]GSE46401. The dataset
contains data from 15 donor-matched aorta healthy and plaque tissue and
from 19 carotid plaque material. A paired two-tailed student t-test was
performed to find DNA methylation differences in the 15 donor-matched
samples and an unpaired two-tailed student t-test was performed to find
DNA methylation differences between the carotid plaque tissue samples
and the healthy aorta samples.
Epityper Sequenom MassARRAY
In silico cleavage was done by means of the RSeqMeth script in R to aid
selection of an optimal primer set for the genomic region of
interest^[175]68. MassARRAY primers for regions in the BRCA1
(chr17:41,277,701–41,278,776) and CRISP2 (chr6:49,680,757–49,682,289)
genes (Supplementary Table [176]1 and Supplementary Fig. [177]2) were
designed using the Sequenom EpiDesigner online tool
([178]www.epidesigner.com). Bisulfite converted DNA was used for the
methylation analysis. PCR reactions were performed using the following
reagents: 10x buffer (Qiagen®), 10 mM dNTP, 10 µM primer mix, 5 U/µl
HotStarTaq^TM polymerase (Qiagen®) and deionized water. Methylation
percentages were calculated based on the ratio of the unmethylated
versus methylated peaks. In addition, DNA methylation standards (0, 20,
40, 60, 80 and 100%) were used to control for amplification bias. The R
computing environment was used for the correction of the obtained
methylation data according to standard procedures^[179]69. Linear
regression was performed to fit the obtained data points according to
the predicted standard methylation values. The student T-test was used
to calculate the significance of the methylation difference between
healthy and atherosclerotic blood samples.
Pyrosequencing
1 µg gDNA from each sample was bisulfite converted using the EpiTect
Fast bisulfite conversion kit (Qiagen, Hilden, Germany) according to
manufacturer’s instructions. 15 ng of bisulfite treated DNA was
subsequently used in PCR amplification using the PyroMark PCR kit
(Qiagen, Hilden, Germany). Reverse primers were biotinylated to get
biotin-labelled PCR products. Finally, DNA sequences were pyrosequenced
using the PyroMark Q24 Advanced instrument (Qiagen, Hilden, Germany).
First, streptavidin-coated Sepharose beads (High Performance, GE
Healthcare, Uppsala, Sweden) were used to immobilize the
biotin-labelled PCR products. Subsequently, PCR products were captured
by the PyroMark vacuum Q24 workstation, washed and denaturated. The
single stranded PCR products were mixed and annealed with their
corresponding sequencing primer. After the pyrosequencing run was
finished, the results were analysed using the PyroMark Q24 Advanced
software (Qiagen, Hilden, Germany). Biotinylated-reverse, forward and
sequencing primers were designed using the PyroMark assay design 2.0
software (Qiagen, Hilden, Germany) (Supplementary Table [180]1).
Post-hoc analysis of human blood DNA methylation in BRCA1 and CRISP2 and
subclinical atherosclerosis in middle-age healthy men
The Aragon Workers Health Study (AWHS) is a study designed to assess
cardiovascular risk and subclinical atherosclerosis in a cohort of
middle-aged healthy men from Spain. The AWHS design and baseline
characteristics have been reported elsewhere^[181]3, [182]16. In brief,
in the baseline examination (2009–2010), the average (SD) age, body
mass index, and waist circumference were 49.3 (8.7) years, 27.7 (3.6)
kg/m2 and 97.2 (9.9) cm, respectively, The prevalence of overweight,
obesity, current smoking, hypertension, hypercholesterolemia, and
diabetes were 55.0, 23.1, 37.1, 40.3, 75.0, and 7.4%,
respectively^[183]70. The adherence of the AWHS participants to the
Mediterranean diet has been extensively studied^[184]71. 21.7% of
participants in the AWHS reported being physical active (e,g,
>150 min/week or 30 min/d of jogging, walking quickly, dance, aerobics,
gardening)^[185]71. The levels of physical activity were positively
associated with the adherence to the Mediterrean lifestyle^[186]71. In
2011–2013, calcium coronary scoring was performed using non-contrast
ECG gated prospective acquisition by a 16 multidetector computed
tomography scanner (Mx 8000 IDT 16, Philips Medical Systems, Best, the
Netherlands). During a single breath hold, images were acquired from
the tracheal bifurcation to below the base of the heart. Scan
parameters were 8 × 3 mm collimation, 220-mm field of view, 120 kVp,
55 mA, and 3-mm section thickness. Coronary calcium was quantified with
calcium scoring software (Workspace CT viewer, Philips Medical Systems)
that follows the Agatston method^[187]72. Carotid intima-media
thickness was determined using the Philips IU22 ultrasound system
(Philips Healthcare, Bothell, Washington). Ultrasound images were
acquired with linear high-frequency 2-dimensional probes (Philips
Transducer L9–3, Philips Healthcare), following the Bioimage Study
protocol^[188]73. Examination of the carotid territory included the
terminal portion (10 mm) of the common carotid, the bulb, and the
initial portion (10 mm) of the internal and external carotid arteries.
The given value for carotid artery intima-media thickness is the mean
value from all sites at both sides. The AWHS study was approved by the
Ethics Committee for Clinical Research at the Institutional Review
Board of Aragón (CEICA)^[189]3, [190]16. All study participants
provided written informed consent. The methods for DNA isolation and
bisulfite conversion were similar to the methods implemented in the
LVAD samples, which are standard manufacturer procedures. DNA
methylation was measured using the platform Illumina Infinium
Methylation 450 K in a subsample of 23 individuals with available
measures of subclinical atherosclerosis. Preprocessing and analysis of
the Infinium 450k data was performed using the R package minfi^[191]74.
CpG probes with a detection p-value higher than 0.01 were removed.
Intra-array normalization was done using the Quantile Normalization. In
exploratory analysis, we detected a potential batch effect by slide.
Methylation proportion values were represented as β-values ranging from
0 to 1. β-values were converted into M-values before doing the
statistical analysis. For analysis of site-specific DNA methylation
(independent variable) and subclinical atherosclerosis measures
(dependent variable) in the AWHS, we estimated the differences in
coronary artery score and carotid intima media thickness comparing
75^th versus 25^th percentiles of DNA methylation distribution at a
given CpG site by linear regression with the following adjustment
variables: age, smoking status (never, former and current smoking),
body mass index, and houseman cell estimates (B cell, CD4^+ and CD8^+ T
cells, granulocytes, monocytes and natural killer cells). Due to the
small sample size we tried to avoid non-parsimonious regression
parameters by performing two-stage regression for adjustment. First we
adjusted DNA methylation M-values for potential confounders using
combat^[192]75 to correct for batch effect by slide. Subsequently, we
adjusted intima media thickness and coronary artery calcium score
levels for the same set of potential confounders. Second, we ran the
final regression models using the residuals resulting from the first
step recalibrated to the corresponding marginal mean. Since this was
post-hoc analysis we evaluated the association of DNA methylation and
subclinical atherosclerosis in CpG sites from regions validated in
previous analysis (i.e. BRCA1 and CRISP2). Thus, we considered the
non-Bonferroni corrected p-values < 0.05 as statistically significant.
Availability of data
Data are available on request. DNA methylation data will be deposited
in the GEO profile database.
Ethics approval and consent to participate
The study was conducted according to the guidelines laid down in the
Declaration of Helsinki and all procedures involving human subjects
were approved by the University of Düsseldorf Research Ethics Committee
(ref: 3870 and ref: 4565 R). The Aragon Workers Health Study
participants (AWHS) study was approved by the Ethics Committee for
Clinical Research at the Institutional Review Board of Aragón
(CEICA)^[193]3, [194]16. All study participants provided written
informed consent.
Electronic supplementary material
[195]Supplementary Figures and Tables^ (1MB, pdf)
Acknowledgements