DOI: 10.1161/CIRCGENETICS.114.000804 1 DNA Methylation of Lipid-Related Genes Affects Blood Lipid Levels Running title: Pfeiffer et al.; genome-wide DNA methylation and blood lipid levels Liliane Pfeifferm, MSc 1,2 ; Simone Wahl, MSc 1-3 ; Luke C. Pilling, MSc 4 ; Eva Reischl, PhD 1,2 ; Johanna K. Sandling, PhD 5,6 ; Sonja Kunze, PhD 1,2 ; Lesca M. Holdt, MD, PhD 7 ; Anja Kretschmer, PhD 1,2,8 ; Katharina Schramm, PhD 9,10 ; Jerzy Adamski, PhD 11 ; Norman Klopp, PhD 12 ; Thomas Illig, PhD 12 ; Åsa K. Hedman, PhD 13,14 ; Michael Roden, MD, PhD 15-17 ; Dena G. Hernandez, MSc 18 ; Andrew B. Singleton, PhD 18 ; Wolfgang E. Thasler, MD 19 ; Harald Grallert, PhD 1-3 ; Christian Gieger, PhD 20 ; Christian Herder, PhD 15,16 ; Daniel Teupser, MD 7 ; Christa Meisinger, MD 2 ; Timothy D. Spector, MD, FRCP 21 ; Florian Kronenberg, MD 22 ; Holger Prokisch, PhD 9,10 ; David Melzer, MBBCh, PhD 4 ; Annette Peters PhD 1,2,23 ; Panos Deloukas, PhD 5,24,25 ; Luigi Ferrucci, MD, PhD 26 ; Melanie Waldenberger, PhD 1,2 1 Rsrch Unit of Molecular Epidemiology, 2 Inst of Epidemiology II, 9 Inst of Human Genetics, 11 Genome Analysis Ctr, Inst of Experimental Genetics, 20 Inst of Genetic Epidemiology, Helmholtz Zentrum München, German Rsrch Ctr for Environmental Health; 3 German Ctr for Diabetes Rsrch (DZD), Neuherberg, Germany; 4 Epidemiology & Public Health Group, Univ of Exeter Medical School, Exeter; 5 Wellcome Trust Sanger Inst, Wellcome Trust Genome Campus, Hinxton, UK; 6 Present: Dept of Medical Sciences, Molecular Medicine & Science for Life Laboratory, 14 Present: Dept of Medical Sciences, Molecular Epidemiology & Science for Life Laboratory, Uppsala Univ, Uppsala, Sweden; 7 Inst of Laboratory Medicine, Univ Hospital Munich & Ludwig Maximilians Univ Munich, Munich; 8 Dept of Dermatology, Venereology & Allergy, Christian Albrechts Univ Kiel, Kiel; 10 Inst of Human Genetics, Technical Univ Munich, Munich; 12 Hannover Unified Biobank, Hannover Medical School, Hannover, Germany; 13 Wellcome Trust Ctr for Human Genetics, Univ of Oxford, Oxford, UK; 15 German Ctr for Diabetes Rsrch (DZD); 16 Inst for Clinical Diabetology, German Diabetes Ctr, Leibniz Ctr for Diabetes Rsrch at Heinrich Heine Univ; 17 Dept of Endocrinology & Diabetology, Univ Hospital, Düsseldorf, Germany; 18 Laboratory of Neurogenetics, Nat Inst on Aging, NIH, Bethesda, MD; 19 Biobank under Administration of HTCR, Dept of General, Visceral, Transplantation, Vascular & Thoracic Surgery, Hospital of the Univ of Munich, Munich, Germany; 21 Dept of Twin Rsrch & Genetic Epidemiology, King’s College London, London, UK; 22 Division of Genetic Epidemiology, Dept of Medical Genetics, Molecular & Clinical Pharmacology, Medical Univ of Innsbruck, Innsbruck, Austria; 23 German Rsrch Ctr for Cardiovascular Disease (DZHK), Partner-site Munich, Germany; 24 William Harvey Rsrch Inst, Barts & The London School of Medicine & Dentistry, Queen Mary Univ of London, London, UK; 25 Princess Al-Jawhara Al-Brahim Ctr of Excellence in Rsrch of Hereditary Disorders (PACER-HD), King Abdulaziz Univ, Jeddah, Saudi Arabia; 26 Clinical Rsrch Branch, Nat Inst on Aging, Baltimore, MD Correspondence: Melanie Waldenberger PhD Research Unit of Molecular Epidemiology & Institute of Epidemiology II Helmholtz Zentrum München, Ingolstaedter Landstraße 1 D-85764 Neuherberg, Germany Tel: +49-89-3187-1270 Fax: +49-89-3187-4567 E-mail: [email protected]Journal Subject Codes: [135] Risk factors, [142] Gene expression, [90] Lipid and lipoprotein metabolism, [8] Epidemiology hD D D D 1,2,23 ; Pa a a ano n n n s De De De Delo lo lo lou u uk u be b b b rger er er er , , , , Ph Ph Ph PhD D D D 1 1, 1, 1 2 2 2 2 of Molecular Epidemiology, Inst of Epidemiology II Inst of Human Genetics Genome Analysis C t r Inst of Experim o N s b D n e A b n e r i U k n M p c o of Molecu u cula la ar Ep id id i em mio io ology, Inst of Epidemiology II, Inst of H Human Genetics, Genome me me Analysis C t r, Inst of Experim of G G G Gen en enet eti i ic Epi pi pide d d m m miol ol olog og ogy, Helmholtz Zent rum München, Germa m man R s rch C t r for En Environm nm mental Health ; 3 German C t r for Neu u uhe e e erberg, Ger rm m m many ; 4 Epidem em em emio io iolo l gy & & & & Pu P P P blic Health Group p, U Univ of of f E E E Exe x x ter Me Me Me M dical Sc Sc c School, Ex Ex Ex Exet et eter ; 5 Well ll l lco c c me me me e Trus T Trus s st Genome Camp pus s s, Hi Hinx x xto t ton, n U U U K K K K ; 6 6 Pr r r resen e t t t t : D Dept o of Med dical Sc cie ence e es, Mo olec cular Me e Medici ine ne ne & Sc Sc cie ie ience e e fo o or r r Li Li ife fe fe Lab De p p p t o o of o Medical Scien nce e es, M Mol l lecula a ar a E E Epidemiolo o ogy y & S S Sci ience e fo or Life e e L L L Lab ab ab a o orato o ory y, Upp psa a ala Un Un Un iv , Upp p p psa al a a, Sw w wede e en ; 7 In edic ic icin n n ne, e e Univ Ho sp s s ita al M M Munich h h & L Lu Lu Ludw dw dwig M M Max a im m mi ilians s s U Univ Mu Mu Munich ch ch c , , Mu unich ; 8 8 D D Dep t of f D D Derm mat t tology, , , V Ve V V ne e ere e eolog g gy & A brecht ht ht hts s s s Un Un Un niv iv v iv Ki Ki Ki Kiel el el e , Ki Ki Kiel ; ; ; ; 10 In In Inst of of o of Hum um um uman n n G G G Ge en e etic ic c cs, T T T Tec ec ec echn n nic ic ical al al al Uni ni ni niv v Mu Mu Mu Muni ni ni nich ch ch c , Mu Mu Mu M ni n n ch ch ch ch ; ; ; ; 12 Ha Ha Ha H nn nn nn nnov v v ver er er er U U U Uni ni ni ifi fi fi fie ed B B B Bioba ba b bank nk nk k, Ha Ha Han Hannover , Germany ; 13 We W W llcome Trust C tr for Human Genetics, Univ of O Oxford, Oxford, UK ; 15 German C t r for Diabe r Clinical Diabe tology y y y, Ge Ge Germ rm rm man an an a D D Dia ia ia iabe b bete te es s s s C C C C t t t t r, r, r, r, L L L Lei ei ei eibn bn bniz iz iz iz C C C C t t t r r r fo fo for r r Di Di Diab ab ab abet et et e es e e R R R R s s s s rc c h h h at at at at H H Hei ei ei inr nr nr nric ic ic i h h h He He He H in n ne e e e Un Un Un Univ ; 17 Dep t of Endocri Univ Hospital, Düssel ldo do do dorf rf rf r , Ge Ge Ge Germ rm man an an any y y ; ; ; 18 8 8 8 La La La Labo bo bora ra ra ato to to tory ry ry o o o of f f Ne Ne Ne N ur ur ur rog og ogen en net et et etic ic ic ics s, Na a a a t t t In In Inst st st st on on on on A A Agi gi gi ging ng ng n , N N N N IH IH IH H , , Be Be Be Beth th th t esda, MD ; 19 Biobank n o o of f f HT HT HTCR CR CR, , , De De Dep p p t t t of of of G G Gen en ener er eral al al, , , Vi Vi Visc sc scer er eral al al, , , Tr Tr Tran an ansp sp spla la lant nt ntat at atio io ion, n, n, V V Vas as ascu cu cula la lar r r & & & Th Th Thor or orac ac acic ic ic S S Sur ur urge ge gery ry ry, , , Ho Ho Hosp sp spit it ital al al o o of f f th th the e e Un Un Univ iv iv of of of M M Mun un unic ic ich, h, h, M p p p t of Twin R s rc ch h h h & & & & Ge Ge e Gene ne ne neti ti tic c c c Ep E Ep E id d dem em em emio i io iolo lo o logy gy gy gy, Ki K K ng ng ng ng’s s s C C C Col ol ol olle le lege ge e ge L L L Lon on ondo do do don, , L L L Lon on on ondo do don, n, n, U U U U K K K ; 22 2 2 2 Di D Di Divi vi vi isi si si si on on n on o o of f f f Ge Ge G Gene ne ne eti ti ti tic c c c Ep E E idemiology, cs c cs, , Mo Mole le lecu cula la lar r & & Cl Cl Clin in in ic ic ical al al P Pha ha harm rmac acol ol olog ogy, y, M Med ed edic ic ical al al U Uni ni niv v of of of I Inn nnsb sb sbru ruck ck ck, , In Inns nsbr br bruc uck, k, k, A Aus ustr tria ia ia ; 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DOI: 10.1161/CIRCGENETICS.114.000804
1
DNA Methylation of Lipid-Related Genes Affects Blood Lipid Levels
Running title: Pfeiffer et al.; genome-wide DNA methylation and blood lipid levels
Liliane Pfeifferm, MSc1,2; Simone Wahl, MSc1-3; Luke C. Pilling, MSc4; Eva Reischl, PhD1,2; Johanna K. Sandling, PhD5,6; Sonja Kunze, PhD1,2; Lesca M. Holdt, MD, PhD7; Anja
Kretschmer, PhD1,2,8; Katharina Schramm, PhD9,10; Jerzy Adamski, PhD11; Norman Klopp, PhD12; Thomas Illig, PhD12; Åsa K. Hedman, PhD13,14; Michael Roden, MD, PhD15-17; Dena G. Hernandez, MSc18; Andrew B. Singleton, PhD18; Wolfgang E. Thasler, MD19; Harald Grallert,
PhD1-3; Christian Gieger, PhD20; Christian Herder, PhD15,16; Daniel Teupser, MD7; Christa Meisinger, MD2; Timothy D. Spector, MD, FRCP21; Florian Kronenberg, MD22; Holger
Prokisch, PhD9,10; David Melzer, MBBCh, PhD4; Annette Peters PhD1,2,23; Panos Deloukas, PhD5,24,25; Luigi Ferrucci, MD, PhD26; Melanie Waldenberger, PhD1,2
1Rsrch Unit of Molecular Epidemiology, 2Inst of Epidemiology II, 9Inst of Human Genetics, 11Genome Analysis Ctr, Inst of Experimental Genetics, 20Inst of Genetic Epidemiology, Helmholtz Zentrum München, German Rsrch Ctr for Environmental Health; 3German Ctr for Diabetes Rsrch (DZD), Neuherberg, Germany; 4Epidemiology & Public Health Group, Univ of Exeter Medical School, Exeter; 5Wellcome Trust Sanger Inst, Wellcome Trust Genome Campus, Hinxton, UK; 6Present: Dept of Medical Sciences, Molecular Medicine & Science for Life Laboratory,
14Present: Dept of Medical Sciences, Molecular Epidemiology & Science for Life Laboratory, Uppsala Univ, Uppsala, Sweden; 7Inst of Laboratory Medicine, Univ Hospital Munich & Ludwig Maximilians Univ Munich, Munich; 8Dept of Dermatology, Venereology & Allergy,
Christian Albrechts Univ Kiel, Kiel; 10Inst of Human Genetics, Technical Univ Munich, Munich; 12Hannover Unified Biobank, Hannover Medical School, Hannover, Germany; 13Wellcome Trust Ctr for Human Genetics, Univ of Oxford, Oxford, UK; 15German Ctr for Diabetes Rsrch (DZD); 16Inst for Clinical Diabetology, German Diabetes Ctr, Leibniz Ctr for Diabetes Rsrch at Heinrich Heine Univ; 17Dept of Endocrinology &
Diabetology, Univ Hospital, Düsseldorf, Germany; 18Laboratory of Neurogenetics, Nat Inst on Aging, NIH, Bethesda, MD; 19Biobank under Administration of HTCR, Dept of General, Visceral, Transplantation, Vascular & Thoracic Surgery, Hospital of the Univ of Munich, Munich, Germany; 21Dept of Twin Rsrch & Genetic Epidemiology, King’s College London, London, UK; 22Division of Genetic Epidemiology, Dept of
Medical Genetics, Molecular & Clinical Pharmacology, Medical Univ of Innsbruck, Innsbruck, Austria; 23German Rsrch Ctr for Cardiovascular Disease (DZHK), Partner-site Munich, Germany; 24William Harvey Rsrch Inst, Barts & The London School of Medicine & Dentistry, Queen
Mary Univ of London, London, UK; 25Princess Al-Jawhara Al-Brahim Ctr of Excellence in Rsrch of Hereditary Disorders (PACER-HD), King Abdulaziz Univ, Jeddah, Saudi Arabia; 26Clinical Rsrch Branch, Nat Inst on Aging, Baltimore, MD
Correspondence:
Melanie Waldenberger PhD
Research Unit of Molecular Epidemiology & Institute of Epidemiology II
Helmholtz Zentrum München, Ingolstaedter Landstraße 1
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DOI: 10.1161/CIRCGENETICS.114.000804
2
Abstract:
Background - Epigenetic mechanisms might be involved in the regulation of inter-individual
lipid level variability and thus may contribute to the cardiovascular risk profile. The aim of this
study was to investigate the association between genome-wide DNA methylation and blood lipid
levels HDL-C, LDL-C, triglycerides (TG) and total cholesterol (TC). Observed DNA
methylation changes were further analyzed to also examine their relationship with previous
hospitalized myocardial infarction.
Methods and Results - Genome-wide DNA methylation patterns were determined in whole
blood samples of 1776 subjects of the KORA F4 cohort using the Infinium
HumanMethylation450 BeadChip (Illumina). Ten novel lipid-related CpG sites (CpGs)
annotated to various genes including ABCG1, MIR33B/SREBF1 and TNIP1 were identified. CpG
cg06500161, located in ABCG1, was associated in opposite directions with both HDL-C
are ppprovided in thheh suppppllplementall l mate iirialll.
ll ii
DOI: 10.1161/CIRCGENETICS.114.000804
6
consumption, intake of lipid-lowering drugs, physical activity, history of myocardial infarction,
current hypertension, HbA1c levels, C-reactive protein levels and white blood cell count.
Experimental plate was included as a random effect. To correct for multiple comparisons, a
genome-wide significance level of 1.1E-07 was used, determined according to the Bonferroni
procedure. Since whole blood DNA samples were used, cell heterogeneity had to be considered
as a confounder. As no measured cell count information was available for any cohort, sample-
specific estimates of the proportion of the major white blood cell types were obtained using a
statistical method described by Houseman et al.10. The significant associations of the first model
were recalculated, additionally adjusting for the estimated white blood cell proportions (CD8 T-
cells, CD4 T-cells, natural killer cells, B-lymphocytes, monocytes and granulocytes). To get a
measure of the variance in the lipid levels explained by methylation levels, R2 statistics were
calculated according to Edwards et al.11, using the R package pbkrtest, version 0.3-7.
Replication step
Identified loci were replicated using the same statistical model in KORA F3 (N=499) as well as
in InCHIANTI (N=472). In KORA F3 an adjustment for C-reactive protein was not possible
since this variable was not available for this cohort. A fixed-effects meta-analysis of KORA F3
and InCHIANTI results was conducted with the R package metafor, version 1.9-2. Results were
corrected according to the Bonferroni procedure (level of significance=4.5E-03).
For the MuTHER cohort the Infinium HumanMethylation450 BeadChip Array signal
intensities were quantile normalized and methylation beta values were calculated using R 2.12 as
previously described12. For cg06500161 no DNA methylation data were available as it did not
pass the quality control filters. Data for N=634 adipose and N=395 skin samples were available
for the final analysis. A linear mixed effects model was fitted for blood lipid values using the
ociations of the firsrsrsst t t t m
d cell lll prpp oportitititions (C(C(C(CD
T e
e
a
n
oci ere replicated sing the same statistical model in KORA F3 (N 499) as e
T-cececellllllsss, nnnatattatuuuralalalal killer cells, B-lymphocyyytetetess, monocytes anddd granulocytes). To ge
ththhheee e variance in thee llilipiiddd d llel veeelslsls eeexxplaaainned bby memeeethththyylatttioon leevvvelslsls,,, R2 stttatiisssticsss wwe
accorrdiididingngngng to EdEdEdwawa ddrdrdss et aalll.1111111, usininini ggg ththththe R RRR papackckckaagagage ee pppbkrkrkrteteteteststt, vversioioioionnn 0.3-33 7777.
n steppp
ii lili tedd iin hth ta itistiic lal dod lel ii KOKORARA FF33 (N(N 449999)) e
DOI: 10.1161/CIRCGENETICS.114.000804
7
lme4 package in R. The model was adjusted for age, BMI, smoking, statins, technical covariates
(fixed effects) and family relationship and zygosity (random effects). A likelihood ratio test was
used to assess significance, and the p-value was calculated from the Chi2 distribution with 1df
using -2log (likelihood ratio) as the test statistic. Results were corrected according to the
Bonferroni procedure (level of significance=7.14E-03).
SNP analysis
Investigation of genetic confounding was carried out to identify whether the observed
associations between lipid and methylation levels in KORA F4 were due to single nucleotide
polymorphisms (SNPs) being associated with both lipid levels and DNA methylation. 157 lipid-
associated SNPs identified by the Global Lipids Genetics Consortium were included in the
analysis4. SNP rs9411489 was excluded because genotype data were not available for the KORA
F4 dataset. Genotype data of 156 lipid-associated SNPs as well as DNA methylation data were
available for 1710 KORA F4 participants. A pre-selection was done to reveal the lipid-associated
SNPs which were at the same time nominally associated (p<0.05) with differentially methylated
lipid-related CpG sites (CpGs; Suppl. Table 2). Next, models for each significant CpG–lipid pair
were recalculated with additional adjustment for the respective pre-selected SNPs to see if the
association was based on genetic confounding. Discovery, replication step and SNP analysis
were analyzed using the statistical package R, version 2.15.3.
Gene expression analysis
For the gene expression analysis, 724 KORA F4 subjects were included, as for these participants
both DNA methylation data and expression data were available. We tried to disentangle the
relationships between methylation at the CpGs, expression of the corresponding annotated gene
and lipid levels in an ad hoc approach based on a sequence of regression models with and
due to singlg e nucleoeoeoeotittt d
NA metttthhyhh lalll tititition. 151515157777 l
S
SNP rs9411489 was excluded because genotype data were not available for the K
w
o o
h ere at the same time nominall associated (p<0 05) ith differentiall meth
SNPsPsPs iiiidedededentntnttifiii ieeed ddd by the Global Lipids Genenenete ics Consortiumm wwwere included in the
or 1710 KORA F4F4F4F pppartiiii iicipapp nts. AAAA prp e-selelll ctiiioi n was ddod ne to reveal the lipip d-asso
hhh at thhe iti imi llll iciat ded ((p 0<0 005)5) ii hth didiffff iti lalll ethh
DOI: 10.1161/CIRCGENETICS.114.000804
8
without adjusting for the third of the three components. For each significant lipid-methylation
pair, the association between lipid level and DNA methylation was recalculated for KORA F4
(N=724). Afterwards we repeated the analysis, adjusting for the expression levels of the
annotated gene (except for cg07504977 which has no annotation to a gene according to the
UCSC genome Browser) (Suppl. Table 3). A p-value for the association was determined through
a likelihood ratio test. Similarly, the association between DNA methylation and transcript levels,
and between lipid levels and transcript levels, were determined. All models were also adjusted
for age, sex, body mass index, smoking, alcohol consumption, intake of lipid lowering drugs,
physical activity, history of myocardial infarction, current hypertension, HbA1c levels and C-
reactive protein levels, as well as for white blood cell count and estimated white blood cell
proportions. Models including expression data were additionally adjusted for the technical
variables RNA Integrity Number (RIN), sample storage time and RNA amplification batch13.
The level of significance was set to 8.3E-04.
Association of DNA methylation with prevalent myocardial infarction
To assess the association of the observed lipid-related CpGs with previous hospitalized MI in
KORA F4, generalized linear mixed effects models were fitted with adaptive Gauss-Hermite
quadrature using the R package lme4, version 1.0-4. Three models were analyzed. The first
model was adjusted for age, sex and estimated white blood cell proportions. In the second model
we additionally included body mass index, smoking, alcohol consumption, physical activity,
current hypertension, HbA1c levels, C-reactive protein levels and white blood cell count as
covariates, and in the third model the lipid variables (HDL-C, LDL-C, TG, TC) were also
included. The Bonferroni correction was used with a significance level of 6.3E-03. The same
analyses were done for KORA F3 and InCHIANTI. This statistical analysis and the gene
of lipid lowering ddddrurururug
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DOI: 10.1161/CIRCGENETICS.114.000804
9
expression analysis were performed using the statistical package R, version 3.0.2.
Results
Associations between genome-wide DNA methylation and blood lipid levels
Characteristics of the discovery cohort (KORA F4) as well as the replication cohorts (KORA F3,
InCHIANTI, MuTHER cohort) are shown in Table 1.
In KORA F4, DNA methylation levels at one, 68, 17 and 80 CpGs were associated with
HDL-C, TG, LDL-C and TC levels, respectively. When white blood cell proportions were
included as covariates, the number of significant associations (p<1.1E-07) decreased, indicating
the presence of blood cell confounding. The association of methylation level at one CpG with
HDL-C and LDL-C remained significant, as well as the association of 10 CpGs with TG levels.
There were no longer any associations with TC. P-values ranged from 1.21E-27 to 9.66E-08 with
percentage of explained lipid level variance ranging from 1.6% to 6.5% (Table 2). CpG
cg06500161, located in ABCG1, was associated in opposite directions with HDL-C ( =-0.049,
p=8.26E-17) and TG levels ( =0.070, p=1.21E-27). TG levels were associated with nine
additional CpGs located in genes including ABCG1, MIR33B, SREBF1 and CPT1A. LDL-C
showed a positive association with methylation status of one CpG located in TNIP1 ( =0.040,
p=4.27E-09).
The lipid-related CpGs were carried forward to replication in a meta-analysis of the
KORA F3 and InCHIANTI cohorts. Nine of the twelve associations were confirmed (p-values
from 9.00E-11 to 3.78E-03; Table 2).
Tissue expression of candidate genes and replication in an adipose tissue cohort
To address cell- and tissue-specificity of ABCG1, CPT1A and SREBF1 expression, we quantified
their expression in human blood cell types (peripheral blood mononuclear cells (PBMC), CD14-,
e of blood cell confounding. The association of methylation level at one CpG w
d LDL-C remained si ificant, as well as the association of 10 CpGs with TG le
0
1, located in , pp ( =-0.0
e of blblooooood cecc lll cconfounding. The associatioion of methylationonn level at one CpG w
d LLLDDDLD -C remmaaainenned dd sisisisignngnifififificicicananant,tt aaas weeelll as thhe asassoooccic atatatioon ofofofo 10 CpCpCpCpGsGsGsGs wwwitititi h h h h TGTGTGTG le
noooo lololol ngngngngererer aaaanynyny asasasa sososocicicic ata ioioioonsnsnss wwwwiith hhh TCTCTCC... PPP---vavavav lululuuesesese rrrranananangegegeed d d frfrfrfromomomo 1111.2.2. 1E1E1E1E-2-2-227777 tooo 9999.6.6.66E6E6E6 -0
of explained liiiipipipipid dd leleleleveveeel lll vavavaririririananana cececece rrranananangigigig ngngngn fffrorororom mm 1.1 6%6%6%6% tttto o 6.6.6.6 5%5%5%% (((TaTaTaTabbblb e 2). ffffff CpG
MI in the discovery cohort KORA F4 (N=1776 with N=60 cases). Three models were analyzed
and CpG cg06500161, located in the ABCG1 gene, showed an association with MI independent
of lipid levels in all three models (e.g., model 3: =0.141, p=1.30E-03) (Suppl. Table 6). The
results could not be replicated in KORA F3 and InCHIANTI, possibly due to the low number of
MI cases (N=8 in KORA F3, N=36 in InCHIANTI).
Discussion
DNA methylation of genes involved in lipid metabolism is associated with HDL-C, TG and
LDL-C levels
Our results indicated that DNA methylation of cg06500161 in ABCG1 was associated in
opposite directions with HDL-C and TG levels. Integrating gene expression data revealed an
association between cg06500161 methylation and lipid levels which might be partly mediated by
ABCG1 expression. DNA methylation at this CpG was also elevated in cases of MIs compared to
healthy individuals.
One challenge of genome-wide DNA methylation analyses in blood samples is the
difference in methylation patterns between different blood cell types10, 14. In our blood cell
expression panel of ABCG1, CPT1A and SREBF1 varying expression patterns were also
detectable (Suppl. Figure 1) which underlines the issue of cell heterogeneity. After adjustment
for estimated blood cell proportions using the method proposed by Houseman et al.10, the
number of significant CpGs decreased from 166 to 12. Therefore, in all further analyses cell
proportions were included as covariates to correct for cell heterogeneity.
We identified seven new lipid-related CpGs located in ABCG1 (HDL-C, TG),
MIR33B/SREBF1, in an intergenic region (TG) and in TNIP1 (LDL-C). In addition, we
replicated one CpG (cg00574958 in CPT1A) which was found to be associated with TG levels in
,
indicated that DNA methylation of cg06500161 in ABCG1 was associated in
r a
a
p a
ividuals.
indicaacateteted thththatt DDDNA methylation of cg0650500161 in ABCG111 was associated in
reectttit ons withhh HHHDLDLDL-C-C-CC aaandndndnd TTTG G G G leveveveels. Integrratinining geggeneee eexppprer ssssiooonnn dadadaatatatta rreveveveaeaealeleleled d a
beeetwtwtwtweeeeeeeennn n cgcgcgcg060606505050500101016161616 memememeththhhylylyly atioioioon nn anananand d d lilililipipipip d ddd lelelel vevevevellsll whwhwhw icicicch h h h mimimimighghghg ttt t bebebeb papapapartrtrtlylylyly mmmmededede iaii
pression. DNA AAA memmemeththththylylylatatatatioioion nnn atatata tttthihihih s ss CpCpCpCpG GGG wawawassss alaa soooo eeeleleeevavvvateteed ddd inininn cccasasasseseseses of MIs compa
iiivividudualals.s.
DOI: 10.1161/CIRCGENETICS.114.000804
13
CD4+ T-cells in the GOLDN study (N=991)6. Five of the associations were also found in
adipose tissue, of which the strongest associations were observed between TG levels and
MIR33B/SREBF1 as well as ABCG1 DNA methylation. Both genes are highly expressed in
adipose tissue (> 1.0E07 copies/μg RNA, Suppl. Figure 1). In skin TG levels were associated
with SREBF1 and CPT1A DNA methylation but there was no significant association with
ABCG1 methylation. These results indicate a tissue-specific association between TG levels and
MIR33B/SREBF1 and ABCG1 DNA methylation.
Additionally, we examined whether the observed associations between lipid and
methylation levels in KORA F4 were based on confounding by lipid-associated SNPs. Most
associations remained significant after additional adjustment for SNPs which were nominally
associated with DNA methylation at the respective CpG site. Only one CpG-lipid association
was found to be confounded. The association between DNA methylation of cg12556569 (located
in the promoter region of APOA5) and TG levels was confounded by rs964184, which is known
to primarily affect TG levels4. One study had previously identified this SNP as an mQTL
(cytosine modification quantitative trait loci)15. Our results indicate that the nominal associations
between trait-associated SNPs and DNA methylation of lipid-related CpCs were dependent on
lipids and that the identified lipid–DNA methylation associations were not due to genetic
confounding.
Interaction of genes of lipid-associated CpGs
Interestingly, three of the genes where the lipid-related CpGs are localized - ABCG1,
MIR33B/SREBF1 and CPT1A - and their gene products interact with one another (Figure 2).
SREBF1 and SREBF2 (sterol regulatory element-binding transcription factor 1 and 2) code for
the membrane-bound transcription factors SREBP1 and SREBP2, which activate the synthesis of
between lipip d and dd
associii tttat ddded SSSSNPNPNPNPs. MoMoMoMos
s l
w o
to be confounded. The association between DNA methylation of cg12556569 (lo
m n
affect TG le els4 One st d had pre io sl identified this SNP as an mQTL
s remememaiaiaiainenened d d d signgngnificant after additional aaadjdjdjdjuustment for SNPsPsPs which were nominal
wwwwithhh DNA methththylaatiiioi n atatata theeee rresssppectttivve CpCpG GG ssis tetetete. OnOnOnlyy onne CCpGpGpGG-lipipipipid ddd asasassoccciaatio
to be cocococonfnfnfn oundndd ddeded. ThhThThe e assososoociciciciationnn bebebebettwtweeeenn DNDNDNNAAAA mmmethththylylylylatattiioionn ofofoff ccccg1g1g11255556656565656656999 (l(( o
moter regig on of APAPAPAPOAOAOA5)))) and ddd TGTGTGG llllevelllls was confoundeddd d dd bybbyb r 99s99646464411811 4,, which is kn
ffff t TGTG ll lel 44 OOn st dd hhadd iio ll idid itififi ded thihi SNSNPP QmQTLTL
DOI: 10.1161/CIRCGENETICS.114.000804
14
fatty acid and the synthesis and uptake of cholesterol16, 17. The intronic microRNAs 33a and 33b
(MIR33a/b) are located within SREBF2 and SREBF1, respectively. Coincident with transcription
of SREBF2/1, the embedded MIR33a/b is co-transcribed18. MIR33a/b act as negative regulators,
repressing a number of genes involved in fatty acid oxidation and cholesterol transport18-23 such
as carnitine palmitoyltransferase 1A (CPT1A), which is important for the transport of fatty acids
into the mitochondria for their oxidation24. Studies also identified a role for MIR33a/b in the
repression of the ABC transporters ABCA1 and ABCG120, 25. ABCG1 encodes the ABC-
transporter G1, a cholesterol transporter which plays a role in cellular lipid homeostasis. It has
been shown that ABCG1 functions cooperatively with ABCA126. ABCA1 transports
phospholipids and cholesterol to lipid-poor HDL subclasses such as apoA-I, whereas ABCG1
has more mature HDL particles as its acceptor27, 28.
In the present study the methylation levels of these genes, MIR33B/SREBF1, ABCG1 and
CPT1A, are associated with blood TG levels, suggesting an epigenetic modulation of lipid and
fatty acid metabolism. Here, ABCG1 might play a key role since one CpG (cg06500161) located
in this gene is associated with both HDL-C and TG levels. The function of ABCG1 in HDL-C
metabolism has been recorded in several studies and reviews29-31; however no report yet exists
about a direct role of ABCG1 in connection with TG levels. One study showed that genetic
variants in the ABCG1 promoter were associated with ABCG1 expression which showed an
influence on the bioavailability of lipoprotein lipase (LPL). Accordingly, ABCG1 regulates the
bioavailability of macrophage-secreted LPL, thereby promoting lipid accumulation, primarily in
the form of TG, in primary human macrophages32.
TNIP1, the methylation of which was associated with LDL-C levels in this study,
encodes the TNF -induced protein 3 (TNFAIP3)-interacting protein 1. This protein appears to be
lipid homeostasis.... ItItII
CA1 trttt ansportttst
ids and cholesterol to lipid-poor HDL subclasses such as apoA-I, whereas ABCG
m
h G
e
metabolism Here ABCG1 might pla a ke role since one CpG (cg06500161) lo
ids ananand ddd chchchc ololololessteteterol to lipid-poor HDL suuubcbcbb lasses such as aappopoA-I, whereas ABCG
maata uuuru e HDL paarttticleesss as iiiittst accccceppptoor2777, 2228.
he prereresesesesentntntn stuudydydy tttheheh mmethyhyhylalalalatttion lllevevevelelele s ofofff ttthehehh sesesee genenenes, MIMIMIMIR3R3R333B333 /S/S/S/SREREREREBFFF111, ABABABABCCGC
e associated with hhh blblbllood ddd TGTGTG llllevelllls,,, suggggegg stiiing gg an epipiiigegg netiiic modddud lation of lipip d
et bab loliis HH ABABCGCG11 imi hght lpl kk lol isi CCpGG ((c 0g06565000016161)1) ll
DOI: 10.1161/CIRCGENETICS.114.000804
15
important in regulating multiple receptor-mediated transcriptional activity of peroxisome
proliferator activated receptors (PPAR)33 and retinoic acid receptors (RAR)34. Interestingly,
ligand activated RAR increases ABCA1 and ABCG1 expression in human macrophages
modulating ABCG1 promoter activity via LXR responsive elements-dependent mechanisms35.
Additionally, studies revealed that PPAR / -activators induce ABCA1 expression in
macrophages36 and PPAR induce ABCG1 expression37. Therefore TNIP1 might have an indirect
impact on the expression of ABCA1/G1.
Methylation of ABCG1 is associated with ABCG1 transcripts
The identified negative association between ABCG1 methylation (cg06500161, cg27243685) and
ABCG1 mRNA levels is possibly mediated by methylation-dependent transcription factor
binding, as observed in the EMSA experiments. ABCG1 mRNA levels were additionally
associated with HDL-C and TG levels in opposite directions. The negative association between
ABCG1 methylation (cg06500161) and HDL-C might be partly mediated by the expression of
ABCG1. These results demonstrate the complexity of the relationship between DNA methylation
and gene expression.
Our findings could provide the missing link between disturbed blood lipid levels and
changed expression patterns of ABCG1. Studies have shown that in patients with type 2 diabetes
mellitus the ABCG1 expression in macrophages is reduced, leading to decreased cholesterol
efflux to HDL38. Interestingly, a recent study shows an association between the methylation
status of cg06500161 (ABCG1) and fasting insulin as well as with HOMA-IR (homeostatic
model assessment), a surrogate marker of insulin resistance39. All results indicate a key role of
DNA methylation of ABCG1 in the development of complex lipid-related diseases.
6500101010161616161, cg27272727242424436363636888
R
w w
e n
hese res lts demonstrate the comple it of the relationship bet een DNA meth
RNA A A leleleevevevelslslss is popopossibly mediated by methyhyhyhylal tion-dependenttnt ttttranscription factor ff
withhhh HHHHDLDLDLD -C aa ddndnd TTTGGGG lell velslsls iiiin oppopoposisisisittete ddddiirirecectititionnonons. TTTThehee nnnnegeg ttatativiii e asaasassssociatattiioion n bbebetwtt
ethyylation ((cggg06665050505 01010101616161) )) anddd d HDHDDLL-CCCC iimighhght bbbeb papp rtlyly m ddeddiiai teddd d bybbyb the expppression
hhh llt dde trat hth lle iit ff hth lla iti hshiip bbet DDNANA ethh
DOI: 10.1161/CIRCGENETICS.114.000804
16
ABCG1 – an epigenetic link between blood lipid levels and myocardial infarction?
DNA methylation has been linked to biological processes of cardiovascular disease such as
atherosclerosis40. An association between ABCA1 methylation and HDL-C levels as well as CAD
in patients with familial hypercholesterolemia has been reported41.
We identified a positive association between cg06500161 (ABCG1) and MI in the KORA
F4 cohort: DNA methylation levels of cg06500161 are higher in subjects with previous
hospitalized MI compared to healthy people. Since the number of subjects with self-reported
hospitalized MI was low in KORA F3 and InCHIANTI, no replication was achieved. These
results need further confirmation by prospective genome-wide DNA methylation studies.
Genetic variants in ABCG1 were shown to be associated with CAD42, 43. However,
nothing is yet known about an epigenetic impact of ABCG1 on the development of MI. A human
cell culture study showed a reduction of macrophage ABCG1 expression when higher TG levels
were present in the culture media. The author suggests that hypertriglyceridemia may increase
the risk of CAD via direct actions on macrophages favoring foam cell formation, thus leading to
the development of atherosclerotic plaque44. Changes in ABCG1 DNA methylation might
mediate the development of atherosclerotic plaques in response to high TG levels. Thus, with
this study we found hints for a new perspective on the molecular background of CAD.
Conclusions
We found associations between DNA methylation and lipid levels for genes contributing to the
modulation of cholesterol and fatty acid metabolism. Epigenetic modification of ABCG1 and its
regulatory network could play a key role on the path from disturbed blood lipid levels to the
development of complex lipid-related diseases. These results indicate an epigenetic impact on
metabolic regulation in humans and give new insights into the complex picture of lipid-related
n was achieved. Theheheheses
methylyllylattttiiiion tttstuddddieeessss.
n 42 43
y h
n e
CAD ia direct actions on macrophages fa oring foam cell formation th s leadi
neticcc vavavaririririananantttts iiin n n ABCG1 were shown to bebebebe associated with CCCCAD42, 43. However,
yeete kkknown aboouttt ann eeepiggiggeene eticicici impmmpaccct of ABABCGCG1111 onn tthhee deeveeelooopppmp ennnnt offf MMMMI. AAAA h
studydyddy sssshohohoh weed dd aa reredduductc ion nn ofofofof macccrororophpphphagggee ABABABA CGCGCGCG111 eeexprprpreseesessisiionon whehehehennn highghherer TTTTGGG l
nt in the culture medididid a. ThThThe authhhhor suggggegg stsr thhhhat hhhhypypypertriigigi lylyl ceriiiddded mia may yy incre
CACCADD ii didi ct ctiio hha ffa iri ff llll ff iti hth ll ddi
DOI: 10.1161/CIRCGENETICS.114.000804
17
complex diseases.
Acknowledgments: The authors thank Nadine Lindemann, Viola Maag and Franziska Scharl for technical support and acknowledge the support of the nonprofit foundation HTCR, which holds human tissue on trust, making it broadly available for research on an ethical and legal basis.
Funding Sources: The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. This project has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 261433 (Biobank Standardisation and Harmonisation for Research Excellence in the European Union - BioSHaRE-EU) and under grant agreement: 603288. The German Diabetes Center is funded by the German Federal Ministry of Health (Berlin, Germany) and the Ministry of Innovation, Science and Research of the State of North Rhine-Westphalia (Düsseldorf, Germany). This study was supported in part by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.). The MuTHER Study was funded by the WT (081917/Z/07/Z) and core funding for the Wellcome Trust Centre for Human Genetics (090532). TwinsUK was funded by the Wellcome Trust; European Community’s Seventh Framework Programme (FP7/2007-2013). The study also receives support from the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. SNP genotyping was performed by The Wellcome Trust Sanger Institute and National Eye Institute via NIH/CIDR. PD’s work forms part of the research themes contributing to the translational research portfolio of Barts Cardiovascular Biomedical Research Unit, supported and funded by the National Institute for Health Research.
Conflict of Interest Disclosures: None
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Table 1: Characteristics of subjects of the discovery cohort and the replication cohorts
Intake of lipid-lowering drugs (excl. herbal substances) 290 (16.3%) 31 (6.2%) 61 (13.0%) 69 (8.1%)Fasting at time of blood collection# 1776 (100.0%) 47 (9.4%) 472 (100.0%) 844 (98.5%)
Continuous and categorical characteristics are given as mean (sd) or absolute numbers and relative proportions, respectively. *BMI: body mass index †NA: variable not available ‡HbA1c: hemoglobin A1c §in InCHIANTI, HbA1c levels were calculated using the formula (46.7+glucose level)/28.7, in KORA F3/F4 they were analyzed using the HPLC method ||>140/90 mmHg or medically controlled #overnight fast of at least 8 hours
*meta-analysis of results of replication in KORA F3 and InCHIANTI †Chr: chromosome ‡ §SE: standard error ||exp var: explained variance #CpG with association confirmed by replication meta-analysis; level of significance: 1.1E-07 (discovery cohort), 4.5E-03 (replication meta-analysis) **no gene annotation for this CpG according to the UCSC Genome Browser
* coef: coefficient †SE: standard error ‡no DNA methylation data available for this CpG site §no gene annotation for this CpG according to the UCSC Genome Browser. Level of significance: 7.14E-03