Epigenetic changes in blood leukocytes following an omega-3 fatty acid supplementation

Abstract

Background
Omega-3 polyunsaturated fatty acids (n-3 FAs) have several beneficial effects on cardiovascular (CV) disease risk factors. These effects on CV risk profile may be mediated by several factors, including epigenetic modifications. Our objective is to investigate, using genome-wide DNA methylation analyses, methylation changes following an n-3 FA supplementation in overweight and obese subjects and to identify specific biological pathways potentially altered by the supplementation.

Results
Blood leukocytes genome-wide DNA methylation profiles of 36 overweight and obese subjects before and after a 6-week supplementation with 3 g of n-3 FAs were compared using GenomeStudio software. After supplementation, 308 CpG sites, assigned to 231 genes, were differentially methylated (FDR-corrected Diffscore ≥│13│~ P ≤ 0.05). Using Ingenuity Pathway Analysis system, a total of 55 pathways were significantly overrepresented following supplementation. Among these pathways, 16 were related to inflammatory and immune response, lipid metabolism, type 2 diabetes, and cardiovascular signaling. Changes in methylation levels of CpG sites within AKT3, ATF1, HDAC4, and IGFBP5 were correlated with changes in plasma triglyceride and glucose levels as well as with changes in the ratio of total cholesterol/HDL-cholesterol following the supplementation.

Conclusions
These data provide key differences in blood leukocytes DNA methylation profiles of subjects following an n-3 FA supplementation, which brings new, potential insights on metabolic pathways underlying the effects of n-3 FAs on CV health.

Electronic supplementary material
The online version of this article (doi:10.1186/s13148-017-0345-3) contains supplementary material, which is available to authorized users.

Full text

R E S E A R C H Open Access
Epigenetic changes in blood leukocytes
following an omega-3 fatty acid
supplementation
Bénédicte L. Tremblay
1
, Frédéric Guénard
1
, Iwona Rudkowska
2
, Simone Lemieux
1
, Patrick Couture
1,2
and Marie-Claude Vohl
1,2*
Abstract
Background: Omega-3 polyunsaturated fatty acids (n-3 FAs) have several beneficial effects on cardiovascular (CV)
disease risk factors. These effects on CV risk profile may be mediated by several factors, including epigenetic
modifications. Our objective is to investigate, using genome-wide DNA methylation analyses, methylation changes
following an n-3 FA supplementation in overweight and obese subjects and to identify specific biological pathways
potentially altered by the supplementation.
Results: Blood leukocytes genome-wide DNA methylation profiles of 36 overweight and obese subjects before and
after a 6-week supplementation with 3 g of n-3 FAs were compared using GenomeStudio software. After
supplementation, 308 CpG sites, assigned to 231 genes, were differentially methylated (FDR-corrected Diffscore
≥│13│~P≤0.05). Using Ingenuity Pathway Analysis system, a total of 55 pathways were significantly
overrepresented following supplementation. Among these pathways, 16 were related to inflammatory and immune
response, lipid metabolism, type 2 diabetes, and cardiovascular signaling. Changes in methylation levels of CpG
sites within AKT3,ATF1,HDAC4, and IGFBP5 were correlated with changes in plasma triglyceride and glucose levels
as well as with changes in the ratio of total cholesterol/HDL-cholesterol following the supplementation.
Conclusions: These data provide key differences in blood leukocytes DNA methylation profiles of subjects
following an n-3 FA supplementation, which brings new, potential insights on metabolic pathways underlying the
effects of n-3 FAs on CV health.
Keywords: DNA methylation, Omega-3 fatty acids, Microarray, Metabolic pathways, Blood leukocytes
Background
Fish-oil-derived long-chain omega-3 fatty acids (n-3
FAs), including eicosapentaenoic acid (EPA, 20:5 n-3)
and docosahexaenoic acid (DHA, 22:6 n-3) have several
benefits on cardiovascular (CV) health. They exert hypo-
triglyceridemic [1, 2], anti-inflammatory [3–5], anti-
arrhythmic [6, 7], and anti-thrombotic effects [8, 9].
Many factors, including genetic and epigenetic factors,
may contribute to the observed effects of n-3 FAs on the
CV risk profile. Indeed, emerging evidence suggests that
n-3 FAs might influence global DNA methylation
patterns due to their role in one-carbon metabolism
[10]. A study in rats fed on a vitamin B
12
-deficient diet
demonstrated that DHA modify DNA methylation, indi-
cating that it plays a role in one-carbon metabolism [11].
DNA methylation is the best-characterized epigenetic
factor and consists of the methylation of cytosine residues,
mainly at cytosine-phosphate-guanine (CpG) dinucleo-
tides [12]. The modification of DNA methylation by the
environment may influence the regulation of CV risk fac-
tors, such as hypertension [13], atherosclerosis [14, 15],
and inflammation [16]. Moreover, the methylation of re-
petitive sequences in blood has also been associated with
CV diseases in epidemiological studies [17, 18].
Only a few studies have dealt with the impact of n-3
FAs on DNA methylation in human subjects. These
* Correspondence: marie-claude.vohl@fsaa.ulaval.ca
1
Institute of Nutrition and Functional Foods (INAF), Laval University, 2440
Hochelaga Blvd, Quebec, QC G1V 0A6, Canada
2
CHU de Québec Research Center –Endocrinology and Nephrology, 2705
Laurier Blvd, Quebec, QC G1V 4G2, Canada
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Tremblay et al. Clinical Epigenetics (2017) 9:43
DOI 10.1186/s13148-017-0345-3
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studies were conducted in various populations including
children and adolescents [19, 20], adults with renal im-
pairment [21], women under caloric restriction [22], and
Yup’ik Alaska Native individuals [23]. Moreover, a recent
study demonstrated that a DHA supplementation during
pregnancy was associated with changes in global methy-
lation levels of inflammatory mediated genes [24].
The aim of this study was to investigate DNA methyla-
tion changes following n-3 FA supplementation in
overweight and obese subjects and identify biological
pathways potentially altered by the n-3 FA supplementa-
tion, by using whole-genome DNA methylation analyses.
To our knowledge, this is the first study to examine the
possible effect of n-3 FA supplementation on genome-
wide DNA methylation levels in blood leukocytes of
overweight and obese adults.
Results
Effects of n-3 FA supplementation
Biochemical parameters of study subjects (n= 36) before
and after the n-3 FA supplementation are presented in
Table 1. The supplementation was associated with a de-
crease in fasting plasma triglyceride (TG) concentra-
tions, similar to results reported in full cohort [25]. In
the same manner, total cholesterol (TC) and the ratio TC/
high-density lipoprotein cholesterol (HDL-C) decreased
whereas glucose concentrations slightly increased after the
supplementation (Table 1). As expected, the supplementa-
tion was associated with a decrease in percentage and ab-
solute values of linoleic, arachidonic, and total n-6 FAs
(including all cis and trans n-6 FAs) in plasma phospho-
lipids (Pvalue <0.0001 for all, data not shown). It was also
associated with an increase in percentage and absolute
values of EPA, DHA, ratio n-3/n-6, and total n-3 FAs (in-
cluding all cis and trans n-3 FAs) in plasma phospholipids
(Pvalue <0.0001 for all, data not shown).
Genome-wide DNA methylation analyses
Globally, 484,027 of the 485,577 probes (99.7%) on the
array were detected with a detection Pvalue ≤0.05. After
n-3 FA supplementation, 308 CpG sites, assigned to 231
genes, were differentially methylated (false discovery rate
(FDR)-corrected DiffScore ≥│13│~P≤0.05). A total
of 286 CpG sites were hypermethylated (93%) and 22
were hypomethylated (7%) after supplementation as
compared to levels before supplementation (Table 2). A
total of 36.4% of significant differentially methylated
CpG sites were located in gene bodies (Table 2). The
genomic localization of CpG sites is summarized in
Table 2. Detailed information about the 308 differentially
methylated CpG sites is presented in Additional file 1.
Relationship between CpG sites and surrounding SNPs
Using results from a recent GWAS done by our group
in the same cohort [26], we tested potential relationship
between pre-supplementation methylation levels and
changes in methylation levels (Δmethylation) of the 308
differentially methylated CpG sites and surrounding
single-nucleotide polymorphisms (SNPs) (±1 kb). Single-
Table 1 Biochemical parameters of subjects before and after n-3
FA supplementation (n=36)
Before n-3 suppl. After n-3 suppl. Pvalue
Gender 18 men and 18 women
Age (years) 34.7 ± 8.8
BMI (kg/m
2
) 29.2 ± 3.65 29.2 ± 3.83 0.24
Triglycerides
a
(mmol/L) 1.42 ± 0.80 1.24 ± 0.65 0.0034
Cholesterol (mmol/L)
Total 5.24 ± 0.9 5.12 ± 0.92 0.048
LDL-C 3.18 ± 0.91 3.10 ± 0.92 0.19
HDL-C 1.40 ± 0.35 1.44 ± 0.40 0.061
Ratio TC/HDL-C 4.0 ± 1.31 3.85 ± 1.35 0.018
Glucose (mmol/L) 4.86 ± 0.51 5.06 ± 0.47 0.0085
Insulin
a
(pmol/L) 108.0 ± 143.5 91.7 ± 48.4 0.70
CRP
a
(mg/L) 3.89 ± 6.57 3.24 ± 5.32 0.18
Data are shown as mean ± SD
Abbreviations:BMI body mass index, CRP C-reactive protein, HDL-C high-
density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, suppl
supplementation, TC total cholesterol
a
Pvalue derived from log
10
transformed data
Table 2 Summary of methylation results
n
Probes 485 577
Number of probes detected (P≤0.05) 484 027
Differentially methylated (FDR-corrected DiffScore ≥ǀ13ǀ) 308
Hypermethylated after n-3 FA supplementation 286
Gene body 107
3′-UTR 34
1st exon 4
5′-UTR 19
Promoter region
a
50
Intergenic region 72
Hypomethylated after n-3 FA supplementation 22
Gene body 5
3′-UTR 0
1st exon 1
5′-UTR 5
Promoter region
a
6
Intergenic region 5
Localization according to the first annotated transcript for each CpG site and
provided for the Infinium HumanMethylation450 BeadChip
Abbreviations:FDR false discovery rate, UTR untranslated region, TSS
transcription start site
a
Promoter region includes TSS1500 and TSS200
Tremblay et al. Clinical Epigenetics (2017) 9:43 Page 2 of 9
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nucleotide polymorphisms (SNPs) had an effect on pre-
supplementation methylation levels of 15 CpG sites.
Moreover, SNPs only affected Δmethylation of two CpG
sites. Indeed, rs41286653 and rs899388 affected Δmethy-
lation of cg02296904 located in the body of the gene
GAK (chromosome 4). The SNP rs114329043 also
affected Δmethylation of cg27270362 located in an inter-
genic region on chromosome 10. We can conclude that
the potential effect of the n-3 FA supplementation on
Δmethylation of the 308 CpG sites is due in very small
proportion to surrounding SNPs.
Pathway analyses
From the 231 differentially methylated genes following
supplementation, Ingenuity Pathway Analysis (IPA)
mapped 227 genes. IPA revealed 55 pathways that were
significantly overrepresented (P≤0.05) (detailed path-
ways are presented in the Additional file 2), but we fo-
cused on the 16 pathways related to CV health. Selected
CV health-related pathways, associated Pvalues, and dif-
ferentially methylated genes identified in pathways are
presented in Table 3. These selected pathways revealed
relevant genes known to be related to inflammatory and
immune response (AKT3,ATF1,BAX,CASP6,DHRS9,
FAS,PRKAG2,PRKCZ,PRKD3,PTEN,TRIM24), lipid
metabolism (AKT3,IGFBP5,KLK6,NUDT3,PLCH1,
PPP2RE5,PTEN,PTPN12,PRKAG2,PRKCZ,PRKD3,
SLCO1B3), type 2 diabetes (T2D) (AKT3,PRKAG2,
PRKCZ,PRKD3), and CV signaling (AKT3,HDAC4,
PRKAG2,PRKCZ,PRKD3).
DNA methylation and biochemical parameters
We further investigated the possible relationship be-
tween Δmethylation of these 19 genes related to CV
health and changes in the four biochemical parameters
modified by the n-3 FA supplementation (ΔTG, ΔTC,
ΔTC/HDL-C, and ΔGlucose). As shown in Table 4,
Δmethylation of cg00011856 (IGFBP5) was positively
correlated with ΔTG (r= 0.39, p= 0.023) while Δmethy-
lation of cg05655647 (ATF1) was negatively correlated
with ΔTG (r=−0.35, p= 0.047). The Δmethylation of
cg00011856 (IGFBP5) and cg24455383 (AKT3) were
positively correlated with ΔTC/HDL-C (r= 0.36, p=
0.042 and r= 0.42, p= 0.016, respectively). The Δmethy-
lation of cg15656521 (HDAC4) was positively correlated
with ΔGlucose (r= 0.35, p= 0.043). There was no signifi-
cant correlation with ΔTC. After adjustments for age,
sex, and body mass index (BMI), the correlation between
Δmethylation of cg00011856 and ΔTG remained signifi-
cant (r= 0.42, p= 0.020) as well as the one between
Δmethylation of cg24455383 and ΔTC/HDL-C (r= 0.40,
p= 0.031).
Using previous data on gene expression in the same
cohort [27], we performed further analyses to test
whether correlations between Δmethylation and changes
in biochemical parameters may potentially be attribut-
able to changes in expression levels (Δexpression). We
first correlated Δmethylation and Δexpression for these
four genes (ATF1,AKT3,HDAC4, and IGFBP5). ATF1
and HDAC4 showed significant correlation between
Δmethylation and Δexpression after adjustments for the
Table 3 CV health-related overrepresented pathways identified from differential methylation analysis following an n-3 FA
supplementation
IPA canonical pathways Pvalue Differentially methylated genes
Tumoricidal function of hepatic natural killer cells
a
0.0017 BAX,CASP6,FAS
RAR activation
a
0.0035 AKT3,DHRS9,PRKAG2,PRKCZ,PRKD3,PTEN,TRIM24
VDR/RXR activation
b
0.0078 IGFBP5,KLK6,PRKCZ,PRKD3
Fcγreceptor-mediated phagocytosis in macrophages and monocytes
a
0.017 AKT3,PRKCZ,PRKD3,PTEN
D-myo-inositol-5-phosphate metabolism
b
0.018 NUDT3,PLCH1,PPP2R5E,PTEN,PTPN12
Nitric oxide signaling in the cardiovascular system
c
0.026 AKT3,PRKAG2,PRKCZ,PRKD3
IL-3 signaling
a
0.033 AKT3,PRKCZ,PRKD3
PXR/RXR activation
b
0.035 AKT3,SLCO1B3,PRKAG2
LPS-stimulated MAPK signaling
a
0.035 ATF1,PRKCZ,PRKD3
NF-кB activation by viruses
a
0.037 AKT3,PRKCZ,PRKD3
CCR5 signaling in nacrophages
a
0.037 FAS,PRKCZ,PRKD3
Role of NFAT in cardiac hypertrophy
c
0.038 AKT3,HDAC4,PRKAG2,PRKCZ,PRKD3
P2Y purigenic receptor signaling pathway
c
0.040 AKT3,PRKAG2,PRKCZ,PRKD3
Cytotoxic T lymphocyte-mediated apoptosis of target cells
a
0.040 FAS,CASP6
PI3K signaling in B lymphocytes
a
0.044 AKT3,ATF1,PRKCZ,PTEN
Type II diabetes mellitus signaling
d
0.049 AKT3,PRKAG2,PRKCZ,PRKD3
Pathways related to the following:
a
Inflammatory and immune response (n= 9);
b
Lipid metabolism (n= 3);
c
Cardiovascular signaling (n= 3);
d
Diabetes (n=1)
Tremblay et al. Clinical Epigenetics (2017) 9:43 Page 3 of 9
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effects of age, sex, and BMI (r=−0.44, p= 0.02 and r=
0.47, p= 0.01). We then correlated Δexpression of ATF1
and HDAC4 with ΔTG and ΔGlucose, respectively.
However, we found no significant association.
Discussion
The aim of this study was to investigate DNA methyla-
tion changes following n-3 FA supplementation and
identify potentially altered biological pathways. We first
observed that a 6-week supplementation with 3 g of n-3
FAs per day was associated with a decrease in n-6 FAs
and an increase in n-3 FAs in plasma phospholipids of
36 overweight and obese adults. The n-3 FA supplemen-
tation also decreased plasma TG, TC, and the ratio TC/
HDL-C, while increasing plasma glucose concentrations.
The slight increase in glucose concentrations was similar
to the one reported in the entire cohort [25]. Studies
have reported conflicting results on the effect of n-3 FAs
on blood glucose concentrations [28]. There is a large
variability in plasma glucose response to n-3 FAs with
effects varying from 1.61 mmol/L net reduction to
1.4 mmol/L net increase [29].
Using a genome-wide methylation analysis, we identi-
fied differences in CpG sites methylation levels following
an n-3 FA supplementation. As previously mentioned,
only few studies were conducted on the impact of n-3
FAs on DNA methylation in humans [19–24, 30]. Our
results are in agreement with these studies suggesting
that EPA and DHA can modulate DNA methylation
levels. Moreover, we identified overrepresented pathways
from differentially methylated genes. More precisely,
pathway analysis revealed 16 overrepresented pathways
related to CV health. These results are in line with pre-
viously reported effects of n-3 FAs and DNA methyla-
tion on CV health. Indeed, n-3 FAs have beneficial
effects on CV risk factors [1–9], even if they are not
associated with CV disease events [31]. Moreover, epi-
demiological studies have reported the association be-
tween global DNA methylation levels and prevalence of
CV diseases [17, 18].
Among the 16 pathways related to CV health, nine
were related to inflammatory and immune response
which may be in line with potential anti-inflammatory
effects of n-3 FAs, more particularly DHA [5, 32]. Stud-
ies have also reported the link between DNA methyla-
tion and inflammation. A recent study in the GOLDN
study and the ENCODE consortium reported that higher
erythrocyte total n-3 FAs was associated with lower
cg01770232 methylation (IL-6) and lower plasma IL-6
concentration [30]. The hypomethylation of long-
interspersed element-1 was also associated with higher
serum vascular cell adhesion molecule-1 in elderly men
[33]. Moreover, three pathways were related to lipid me-
tabolism, which is in accordance with known hypotrigly-
ceridemic effects of n-3 FAs [1]. Methylation levels of
one CpG site in APOE and one in ABCA1 were nega-
tively associated with plasma TG and HDL-C levels, re-
spectively, in the GOLDN study [34, 35]. DNA
methylation is also implicated in the regulation of ath-
erosclerosis. Indeed, genome-wide DNA methylation
changes (mainly hypermethylation) occur during the on-
set and progression of atherosclerotic lesions in humans
[36, 37]. A total of three pathways were related to car-
diovascular signaling. Indeed, reported antiplatelet ef-
fects of n-3 FAs [38] are consistent with the
overrepresentation of the nitric oxide signaling in the
cardiovascular system pathway identified herein. Finally,
one pathway was related to T2D, which may be in line
with potential, but yet controversial, effects of n-3 FAs
on glucose homeostasis [39]. Data mining analysis sug-
gests a role of epigenetic factors in the pathogenesis of
T2D [40]. Differential methylation profiles in pancreatic
islets from T2D and non-diabetic subjects were also
identified thus suggesting a role of DNA methylation in
pathogenesis of T2D [41]. Moreover, blood DNA methy-
lation of some CpG sites have been associated with
blood glucose concentrations in an epigenome-wide as-
sociation study [42]. All together, these finding suggest a
possible link between n-3 FAs, DNA methylation, and
CV risk factors.
Among differentially methylated genes, some were also
of particular interest in the field of CV health. For ex-
ample, a genetic variation in the FAS gene was associ-
ated with an increased occurrence of myocardial
Table 4 Significant correlations between changes in biochemical parameters and changes in methylation levels following an n-3 FA
supplementation
CpG site ID (gene, position
a
)ΔTriglyceride ΔTC/HDL-C ΔGlucose
Δcg00011856 (IGFBP5, Chr2:217560946) 0.39 (0.023)
b
0.36 (0.042) –
Δcg05655647 (ATF1, Chr12:51157023) -0.35 (0.047) ––
Δcg24455383 (AKT3, Chr1:243736307) –0.42 (0.016)
b
–
Δcg15656521 (HDAC4, Chr2:239970617) ––0.35 (0.043)
Results are R(Pvalue)
Abbreviations:HDL-C high-density lipoprotein cholesterol, TC total cholesterol
a
All positions are from the Genome Build 37
b
Correlation remains significant after adjustments for age, sex, and body mass index
Tremblay et al. Clinical Epigenetics (2017) 9:43 Page 4 of 9
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infarction in Japanese subjects [43]. Interestingly, FAS
methylation levels were significantly associated with n-3
FA intakes in Yup’ik Alaska Native individuals [23].
Moreover, a total of four CpG sites showed correlations
between changes in their methylation levels and changes
in biochemical parameters following the supplementa-
tion. First, Δmethylation of cg15656521 in HDAC4 gene
was positively correlated with Δglucose. HDAC4 gene
encodes for a histone deacetylase 4 that is a signal-
dependent modulator of transcription with role in
muscle differentiation and neuronal survival [44].
HDAC4 methylation was inversely associated with n-3
FAs in whole blood of men [45]. HDAC4 also downregu-
lates GLUT4 transcription in cultured adipocytes and
fasting mice [46]. Interestingly, Benton et al. reported a
robust inverse correlation between changes in fasting
glucose and changes in methylation of HDAC4
(cg26078407) in subcutaneous fat [47]. However, they
did not test for association with cg15656521 since it was
not differentially methylated in their cohort after gastric
bypass surgery [47]. Second, Δmethylation of cg00011856
in IGFBP5 gene was positively correlated with ΔTG (even
after adjustments for age, sex, and BMI) and with ΔTC/
HDL-C. IGFBP5 gene encodes for insulin-like growth fac-
tor binding protein 5 [48]. Young patients with coronary
heart disease have significantly higher serum IGFBP5 than
age- and BMI-matched controls [49]. A study in arthritic
rats demonstrated that EPA increases IGFBP5 mRNA in
the gastrocnemius muscle [50]. Another study found
higher IGFBP5 expression in the liver of beef fed with n-3
FAs compared to control diet [51]. Unfortunately, we can-
not verify if the n-3 FA supplementation increases IGFBP5
expression in our sample since it is not expressed in blood
[52], but it would be interesting to look at its expression
in the liver after an n-3 FA supplementation. Third,
Δmethylation of cg24455383 in AKT3 was positively cor-
related with ΔTC-HDL-C, even after adjustments. AKT3
gene encodes for an AKTserine/threonine kinase 3 stimu-
lated by insulin and growth factors [53, 54]. It is likely
involved in insulin-stimulated glucose transport in the hu-
man skeletal muscles [55]. AKT3 also has anti-atherogenic
properties due to its capacity to inhibit macrophages foam
cells formation by reducing lipoprotein uptake and pro-
moting ACAT-1 degradation [56, 57]. AKT signaling has
also been shown to regulate lipid metabolism through
phosphorylation and inhibition of GSK3 [58]. The associ-
ation between changes in AKT3 methylation and changes
in TC/HDL-C ratio reported herein may be plausible since
AKT3 seems to be involved in lipid metabolism. Finally,
Δmethylation of cg05655647 in ATF1 gene was negatively
correlated with ΔTG. The ATF 1 gene encodes for the acti-
vating transcription factor 1 that leads to the production
of atheroprotective macrophages [59, 60]. SNPs within
this gene were also associated with an increased risk of
essential hypertension in a case-control study [61]. Data
are insufficient to propose a mechanism of action between
methylation of ATF1 and plasma TG levels. Nevertheless,
we could hypothesize that methylation of these genes may
play a role in the effects of n-3 FAs on CV health since
they are modulated by n-3 FAs, they have been associated
with CV health in pathway analysis, and they are corre-
lated with changes in biochemical parameters. However,
we acknowledge that our study design does not allow us
to prove or investigate causality between n-3 FA supple-
mentation, DNA methylation, and CV disease risk factors.
Moreover, we were not able to investigate the potential
link between Δmethylation of AT F 1 ,AKT3,IGFBP5,and
HDAC4 and changes in biochemical parameters using
Δexpression. At this time, we cannot rule out the pos-
sibility that the small sample size or low Δexpression
values of these genes limit our statistical power to de-
tect significant associations. This possible link does
not seem to be due either to surrounding SNPs since
they affected Δmethylation of only two CpG sites
among the 308 CpG sites.
The present study has some limitations. DNA methy-
lation levels are specific to the type of cell and tissue
[62]. These methylation profiles in blood leukocytes
might not represent DNA methylation in other tissues
even though these patterns are globally conserved across
tissues [63–65]. Pathway analysis has certain methodo-
logical considerations. As part of Genetic Analysis
Workshop 18, seven research groups raised the fact that
annotation of genetic variants is inconsistent across da-
tabases, incomplete and biased toward known genes
[66]. Moreover, insufficient statistical power is an issue
in pathway analyses [66]. Thus, these results need to be
validated in larger and independent studies considering
that replication remains the gold standard to establish
validity of the findings. Finally, the sample size is rela-
tively small so these results need to be validated in lar-
ger, independent studies.
Conclusions
In conclusion, the present data provide key differences
in blood leukocytes DNA methylation levels of subjects
following n-3 FA supplementation, which provides new,
potential insights on novel genes and metabolic path-
ways underlying the effects of n-3 FAs on the CV risk
profile. Further studies in larger, independent samples
are required to unveil potential functional mechanisms
underlying metabolic improvements with n-3 FA
supplementation.
Methods
Study population and study design
The present study is based on a subsample of a larger
intervention study that aimed at studying the inter-
Tremblay et al. Clinical Epigenetics (2017) 9:43 Page 5 of 9
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individual variability in TG response to an n-3 FA sup-
plementation as previously described [2]. A total of 254
subjects from the greater Quebec City metropolitan area
(Canada) were recruited between September 2009 and
December 2011. In total, 210 subjects completed the
intervention. Non-smoker subjects, aged between 18 and
50, with a BMI between 25 and 40 kg/m
2
, and not taking
any lipid-lowering medication were included. Subjects
were excluded if they had taken n-3 FA supplementation
at least 6 months prior to the intervention or had been
diagnosed with any metabolic disorder. A subset of 36
subjects who first completed the study (equal proportion
of men and women) was selected for the purpose of this
genome-wide DNA methylation analysis.
First, subjects received dietary instructions by a trained
registered dietician to achieve recommendations from
Canada’s Food Guide during a 2-week run-in period
[67]. After this period, subjects receive a bottle contain-
ing fish oil capsules needed for the following 6 weeks.
They were invited to take five capsules per day providing
a total of 3 g of n-3 FAs (including 1.9–2.2 g of EPA and
1.1 g of DHA) per day. Compliance was assessed by the
count of remaining capsules and by measuring EPA and
DHA in plasma phospholipids. Details on the study de-
sign have been described elsewhere [2]. The experimen-
tal protocol was approved by the Ethics Committees of
Laval University Hospital Research Center and Laval
University. This trial was registered at clinicaltrials.gov
as NCT01343342.
Anthropometric and metabolic measurements
Body weight, waist girth, and height were measured ac-
cording to the procedures recommended by the Airlie
Conference [68] and were taken before the run-in period
as well as before and after the supplementation period.
Blood samples were collected from an antecubital vein
into vacutainer tubes containing EDTA after 12-h over-
night fast and 48-h alcohol abstinence before the run-in
period to identify and exclude participants with meta-
bolic disorders. Afterwards, the selected participants had
blood samples taken before and after the supplementa-
tion period. Plasma was separated by centrifugation
(2500 g for 10 min at 4 °C), and samples were aliquoted
and frozen (−80 °C) for subsequent analyses. Enzymatic
assays were used to measure plasma TC and TG concen-
trations [69, 70]. Precipitation of very-low density lipo-
protein (VLDL) and low-density lipoprotein (LDL)
particles in the infranatant with heparin manganese
chloride generated the HDL-C fraction [71]. LDL choles-
terol (LDL-C) was calculated with the Friedewald for-
mula [72]. Using a sensitive assay, plasma C-reactive
protein (CRP) was measured by nephelometry (Prospec
equipment Behring) [73].
DNA extraction and DNA methylation analysis
We extracted genomic DNA from blood leukocytes
using the GenElute Blood Genomic DNA kit (Sigma-Al-
drich, St. Louis, MO, USA) for the 72 samples: 36 sam-
ples before and 36 samples after the supplementation.
DNA was quantified using both NanoDrop Spectropho-
tometer (Thermo Scientific, Wilmington, DE, USA) and
PicoGreen DNA methods. McGill University and Gen-
ome Quebec Innovation Center (Montreal, QC, Canada)
conducted the bisulfite conversion and quantitative
DNA methylation analysis using Infinium Human-
Methylation450 array (Illumina, San Diego, CA, USA).
Three samples (one sample before and two samples after
supplementation) were excluded from microarray ana-
lysis following quality control steps (bisulfite conversion,
extension, staining, hybridization, target removal, nega-
tive and nonpolymorphic control probes).
We used Illumina GenomeStudio software version
2011.1 and the Methylation Module to analyze methyla-
tion data on 485,577 CpG sites. Methylation levels (vary-
ing from 0 to 1) were estimated as the proportion of
total signal intensity from methylated-specific probe. To
avoid false positives, probes with a detection Pvalue
>0.05 in more than 10% of samples were removed.
Probes on the X and Y chromosomes were also removed
to eliminate gender bias. Thus, 472,715 probes were
considered in differential methylation analysis. Differ-
ences in methylation levels before and after n-3 FA sup-
plementation were tested using the Illumina Custom
model in GenomeStudio software. FDR-corrected Diff-
Scores were computed to account for multiple testing
and limit false positives. We established significant dif-
ferences following n-3 FA supplementation with a FDR-
corrected DiffScore ≥│13│~P≤0.05.
Pathway analyses
We used the IPA system (Ingenuity® Systems, www.in-
genuity.com) to analyze potentially modified pathways
from differentially methylated genes following the sup-
plementation. Using a right-tailed Fisher’s exact test, IPA
measured the likelihood that pathways were overrepre-
sented among the list of significant differentially methyl-
ated genes.
Statistical analyses
We tested potential relationship between pre-
supplementation methylation levels and Δmethylation
of the 308 CpG sites and surrounding SNPs (±1 kb).
We used results from a recent GWAS done by our
group in the same cohort (n= 141) [26]. We consid-
ered only SNPs located at ±1 kb from CpG sites and
with a minor allele frequency ≥1%. We tested 652 as-
sociations using analysis of variance (general linear
model, type III sum of squares) and adjusted for the
Tremblay et al. Clinical Epigenetics (2017) 9:43 Page 6 of 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

effects of age, sex, and BMI. We used a Bonferroni
correction for multiple testing; thus, associations with
aPvalue ≤7.67 × 10
−5
(0.05/652) were considered
significant.
Biochemical parameters are expressed as means ± SD.
We used a paired Student’sttest to test differences in bio-
chemical parameters before and after the supplementa-
tion. Variables not normally distributed were log
10
transformed before analyses. Δmethylation of CpG sites
(n= 20) within genes in the 16 pathways related to CV
health were defined as post-supplementation methylation
levels minus pre-supplementation methylation levels.
Changes in the four significantly modulated biochemical
parameters (ΔTG, ΔTC, ΔTC/HDL-C, and ΔGlucose)
were defined as (post-supplementation values minus pre-
supplementation values)/ pre-supplementation values to
account for baseline values. Potential relationships
between Δmethylation, Δexpression, and changes in bio-
chemical parameters (ΔTG, ΔTC, ΔTC/HDL-C, and
ΔGlucose) were investigated using Pearson’s correlation.
We also accounted for potential confounding effects of
age, sex, and BMI in correlations. Statistical analyses were
conducted using SAS software version 9.3 (SAS Institute,
Cary, NC, USA).
Additional files
Additional file 1: Differentially methylated probes after the n-3 FA
supplementation. Description of data: List of all 308 differentially
methylated probes (FDR-corrected DiffScore ≥│13│~P≤0.05).
(XLSX 42 kb)
Additional file 2: Overrepresented pathways identified from differential
methylation analysis following an n-3 FA supplementation. Description of
data: Table describing all 55 significant overrepresented pathways identified
from methylation analysis (IPA canonical pathways, associated Pvalue, and
list of differentially methylated genes). (DOCX 21 kb)
Abbreviations
BMI: Body mass index; CpG: Cytosine-phosphate-guanine; CV: Cardiovascular;
DHA: Docosahexaenoic acid; EPA: Eicosapentaenoic acid; FDR: False
discovery rate; HDL-C: High-density lipoprotein cholesterol; IPA: Ingenuity
Pathway Analysis; n-3 FAs: Omega-3 fatty acids; SNPs: Single-nucleotide
polymorphisms; T2D: Type 2 diabetes; TC: Total cholesterol; TG: Triglyceride
Acknowledgements
We would like to thank Véronique Garneau, Ann-Marie Paradis, Élisabeth Thifault,
Karelle Dugas-Bourdage, Catherine Ouellette, and Annie Bouchard-Mercier, who
contributed to the success of this study. We also thank Catherine Raymond for
the laboratory work.
Funding
This work was supported by a grant from Canadian Institutes of Health
Research (CIHR)-(MOP-110975). IR holds a Junior 1 Research Scholar from the
Fonds de Recherche du Québec—Santé (FRQS). BLT is a recipient of a
scholarship from FRQS. MCV is Tier 1 Canada Research Chair in Genomics
Applied to Nutrition and Health.
Availability of data and materials
Methylation datasets supporting the conclusion of this article are available in
the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo
(GSE98056).
Authors’contributions
Each author contribution to work: IR, SL, and MCV designed the research; PC
was responsible for the medical follow-up; BLT and FG conducted the re-
search and performed the statistical analyses; BLT wrote the paper; and BLT
and MCV have primary responsibility for the final content. All authors read
and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
All participants signed an informed consent document. This trial was
registered at clinicaltrials.gov as NCT01343342.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Received: 15 December 2016 Accepted: 14 April 2017
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