Online Supplementary Appendix
METHODS
Study subjects
We analysed samples from three independent case-control groups. As described
previously,24 the Shanghai group consisted of 602 patients with CHD and 660
patient-matched healthy controlswho were enrolled between August 2008 and
February 2011 from the Children’s Hospital of Fudan University (Shanghai, China).
The Shandong group consisted of 735 patients with CHD and 564 controls recruited
between August 2008 and January 2011 fromthe Cardiovascular Disease Institute,
General Hospital of Jinan Military Command (Jinan, Shandong Province, China). To
determine whether population stratification might affect our results, we performed
principle component analyses using 16 ancestry-informative markers in the Shanghai
and Shandong groups. No significant difference was observed in ancestry-informative
biomarkers among the tested cases and controls. The Jiangsu group consisted of 1,003
patients with CHD and 1,046 controls recruited from the First Hospital of Nanjing
Medical University (Nanjing, Jiangsu Province, China) between March 2006 and July
2008. All of the controls were non-CHD outpatients during the same time period and
from the same geographic area who were matched to the affected individuals by age
and sex. All of the subjects were genetically unrelated ethnic Han Chinese. Patients
with CHD who had structural malformations involving another organ system or a
positive family history of CHD in a first-degree relative (parents, siblings and
children) were excluded.
We classified the 2,340 CHD cases into seven broad classes. In particular, 386 (16.5%)
had conotruncal defects, 1,652 (70.6%) exhibited septation defects, 47 (2%) exhibited
left ventricular outflow tract obstruction (LVOTO), 75 (3.2%) exhibited right
ventricular outflow tract obstruction (RVOTO), 14 (0.6%) exhibited anomalous
pulmonary venous return (APVR), 34 (1.5%) exhibited complex CHD, and 132 (5.6%)
had other CHD defects (Table S1).
To screen for non-coding variants in the MTR gene, 32 unrelated individuals, (16
patients with CHD and 16 controls) from the Shanghai and Shandong groups were
randomly selected for resequencing. For the quantitative RT-PCR assays and promoter
methylation analysis, 28 in vivo human cardiovascular tissue samples were obtained
from patients with CHD who had undergone heart catheterisation or a cardiac
operation between January 2010 and May 2010 in the Cardiovascular Disease
Institute, General Hospital of Jinan Military Command (Jinan, Shandong Province,
China).
All of the study protocols were reviewed and approved by the local medical ethics
committee, and written consent was obtained from parents and/or patients prior to
commencing the study.
SNP identification and genotyping
Genomic DNA was isolated from venous blood using conventional reagents. The
MTR
non-coding
region
from
-2,055
to
+510
bp
(2,565
bp,
chr1:
235023287-235025851, NC_000001.9, GI: 89161185) and the fragment containing
the entire 3’UTR (3,186 bp, chr1: 235127516-235130702, NC_000001.9, GI:
89161185) were amplified using PCR from 32 unrelated individuals who were
randomly selected from both the Shanghai and Shandong groups for variant screening
using sequencing. We identified 4 common polymorphisms within the screened
regulatory region of MTR gene with the minor allele frequency (MAF) >0.1,
including rs28372871 in the promoter, rs2853522, rs1131450, and rs1804742 in the
3’UTR. We genotyped all 4 SNPs in 270 CHD cases versus 552 controls in the
Shanghai group and found that the genotype distribution of rs28372871 and
rs1131450 was statistically different between CHD and control subjects in genetic
codominant model. The minor allele at each variant was associated with an increased
risk of CHD (Table S2). Similar results were obtained in the validation study, which
compared 259 cases with 324 controls in the Shandong group (Table S2). Although
the rs28372871 was not related to CHD in codominant model (P=0.064), it showed
significant association with CHD in recessive model (P=0.022), the GG genotype
could lead to 1.56-fold increase of CHD risk (OR=1.56, 95%CI=1.07-2.29). All these
results indicated that rs28372871 and rs1131450 were related to CHD risk. To further
validate these association, we genotyped rs28372871 and rs1131450 in expanded
cohorts with 602 cases versus 660 controls in the Shanghai group and 735 cases
versus 660 controls in Shandong group. In addition, 1003 cases versus 1046 controls
sampled from Jiangsu were also added for validation. Direct dye terminator
sequencing of the PCR products was performed using the ABI Prism BigDye system
according to the manufacturer’s instructions (ABI, Foster City, CA). Selected SNPs
were genotyped using SNaPshot analysis (ABI). Sequencing and genotyping samples
were processed on an ABI 3730 automated sequencer and analysed using SeqMan and
Peakscan, respectively. All of the DNA sequences of primer pairs are listed in Table
S7.
Plasmid Construction
To construct the MTR promoter reporter plasmid, a 1,304-bp fragment from -1267 to
+37 of MTR containing the T allele of the -186T>G SNP was amplified from genomic
DNAusing PCR. The PCR products were cloned into the MluI and BglII restriction
sites of the pGL3-Basic vector (Promega, Madison, WI), in which the firefly
luciferase gene was usedas a reporter. The corresponding G allele plasmid was
generated using site-directed mutagenesis using the MutanBEST kit (Takara, Berkeley,
CA) to ensureuniform backbone sequence. We verified all of the recombinant clones
using DNA sequencing.
To construct the MTR 3’UTR reporter plasmid, we amplified an 878-bp fragment of
the 3’UTR of the MTR gene containing the G allele of the +905G>A SNP from
genomic DNA. The PCR products were subsequently digested using XhoI and BamHI
and cloned into the 3’UTR of the Renilla luciferase gene of the psiCHECK-2 vector
(Promega). The Renilla luciferase gene was used as a reporter, and its expression
could be normalised to the firefly luciferase signal. The corresponding A allele was
generated using the MutanBEST kit. The primers utilised are shown in the Table S7.
Cell Culture and Transfection
For the 5-aza-20-deoxycytidine (5-Aza) (Sigma–Aldrich, St.Louis, MO) treatment,
which causes DNA demethylation or hemidemethylation, 2×105 cells were seeded in
a six-well plate in 3 ml of medium. After 24 hours of incubation, the medium was
removed, and the cells were incubated in 3 ml of fresh medium containing 10 mM
5-Aza for 24 hours. After treatment, the medium was removed, the cells were
subjected to an additional 24 hours of incubation in 3 ml of fresh medium without
5-Aza, and total RNA was extracted. In the control well, 5-Aza was replaced with
DMSO, and the medium was changed daily.
For the MTR promoter study, 1×105 HEK-293, H9C2 or HCM cells were transfected
with 1 µg of each MTR promoter reporter plasmid and 20 ng of the pRL-TK plasmid
(Promega) as a normalizing control. Alternatively, the cells were additionally
co-transfected with 50 ng of the pcDNA3.1-USF-2 expression plasmid or equivalent
amounts of empty pcDNA3.1 vector. Lipofectamine 2000 (Invitrogen, Carlsbad, CA)
was used for all of the transfections, according to the manufacturer’s instructions.
After another 24 hoursof culture, the transfected cells were lysed, and 40 µl of
supernatant was assayed for luciferase activity using the Dual-Luciferase Reporter
Assay System (Promega). The relative reporter activity was obtained by normalisation
of the firefly activity to Renilla activity. Each assay was performed intriplicate, and
each experiment was performed at least three times.
For the MTR 3’UTR study, 1×105 HEK-293, H9C2 or HCM cells were transfected
with 1 µg of each MTR 3’UTR reporter plasmid. For candidate microRNA screening,
5×104 H9C2 cells were seeded in a 96-well culture plate in DMEM that was
supplemented with 10% FBS. The cells were co-transfected with 100 ng
psiCHECK-G/A plasmid and 300 ng microRNA expression vector using
Lipofectamine 2000, in accordance with the manufacturer’s protocol. To confirm the
interaction between the MTR 3’UTR and the candidate microRNAs, HEK-293, HCM
and H9C2 cells were co-transfected with 100 ng psiCHECK G/A plasmid and 0 ng,
150 ng, 300 ng, 450 ng or 600 ng of the candidate microRNA expression vector. In
the siRNA assays, HEK-293 cells were co-transfected with 200 ng psiCHECK G/A
plasmid and 800 ng microRNA inhibitors. At 24 hours after transfection, Firefly and
Renilla luciferase activities were measured using a dual-luciferase assay. The relative
reporter activity was obtained by normalisation to the Renilla activity. We performed
luciferase assays in triplicate each time for three independent transfection
experiments.
EMSA
Nuclear proteins were extracted from HEK-293 cells using NE-PER nuclear and
cytoplasmic extraction reagents (Pierce, Rockford, IL). Duplex oligonucleotide
probes representing the -186 T or G alleles (sequences listed in the Table S7) were
labelled with biotin. The EMSAs were performed using a Light Shift
Chemiluminescent EMSA kit (Pierce) according to the manufacturer’s protocols.
Briefly, 1 pmol of biotin-labelled duplex oligonucleotides bearing either the -186 T or
G allele was incubated with 8 μg of nuclear extracts for 20 minutes in 10 × binding
buffer supplemented with 1 µg/µl poly (dI·dC), 50% glycerol and 1% Nonidet P-40.
Unlabelled probes at 5-, 10- or 100-fold molar excesses, as indicated, were added to
the reaction for competition. The reaction mixture was then electrophoresed on a
native 6% polyacrylamide gel and transferred to a positive nylon membrane. The
detection
of
biotin-labelled
DNA
was
performed
using
stabilised
streptavidin-horseradish peroxidase conjugate (Pierce) and exposed to X-ray film.
Surface plasmon resonance (SPR) analysis
SPR analysis was performed using the ProteOn XPR36 Protein Interaction Array
System (Bio-Rad Laboratories, Hercules, CA), an SPR technology-based imaging
optical biosensor. Biotinylated duplex oligonucleotide probes representing the -186 T
or G allele (sequences listed in the Table S7) were immobilised on the
streptavidin-modified surface in different channels from DNA solutions at a fixed
concentration (400 nM) to ensure identical surface density. Nuclear extracts from
HEK-293 cells or purified USF-2 protein were diluted in PBST (10 mM
Na-phosphate, 150 mM NaCl and 0.005% Tween 20, pH 7.4) to different
concentrations and then pre-incubated with non-specific DNA for 15 minutes before
being passed across the DNA-immobilised surface. Non-biotinylated competitors at a
four-fold excess were used for competition assays. All of the binding measurements
were performed using PBST as the continuously running buffer at room temperature.
The relative binding responses were determined by measuring changes in refractive
index levels before and after the addition of nuclear proteins. After each cycle, 1 M
NaCl and 50 mM NaOH were consecutively injected to regenerate the sensor surface.
The results are presented as a sensogram and converted using BIA-evaluation
software.
SPR analysis was also utilised to quantify the binding affinity between 3’UTR RNA
and microRNAs. Cytoplasmic proteins were extracted using NE-PER nuclear and
cytoplasmic extraction reagents. The single-stranded RNA probes representing +905
G or A, miR-608 or miR-1293 (sequences listed in the Table S7) were synthesised by
GenePharma Co., Ltd. (Shanghai, China). After each reaction cycle, 1 M NaCl and 50
mM NaOH were consecutively injected to regenerate the sensor surface.
Chromatin immunoprecipitation (ChIP) assays
The ChIP assays were conducted using the EZ ChIP Kit (Upstate Biotechnology,
Lake Placid, NY). Briefly, HEK-293 cells and cardiovascular tissue samples from two
patients heterozygous for -186T>G in the MTR promoter were cross-linked with 1%
formaldehyde for 10 minutes. The DNA was fragmented to a mean length of 200 to
1000 bp using sonication. For immunoprecipitation, sheared chromatin was incubated
with antibodies against USF-1/2 or non-specific rabbit IgG (Santa Cruz
Biotechnology, Santa Cruz, CA) for 12 hours at 4°C. The identity and quantity of the
DNA fragments were determined using PCR and quantitative real-time PCR. To
further investigate the difference in binding affinity between the T and G alleles of the
SNP -186T>G, the amounts of the two alleles were quantified using SNaPshot from
the ChIP input, and the products were treated with the USF-1/2 antibody. All of the
primers that were used are listed in Table S7.
Quantitative real-time PCR
Total RNA was extracted from human cardiovascular tissue samples preserved in
RNAlater (Qiagen, Valencia, CA) using the miRNeasy Mini Kit (Qiagen) and then
converted to cDNA using random hexamers, oligo(dT) primers and Moloney murine
leukaemia virus reverse transcriptase (Takara). MTR hnRNA and mRNA levels were
measured using quantitative real-time PCR using the ABI Prism 7900 sequence
detection system (Applied Biosystems, Foster City, CA), with GAPDH as an internal
reference gene. The reaction mixture contained 0.1 M of each primer, 2× SYBR
Green PCR Master Mix (TaKaRa) and 1 µl of cDNA (1:10 dilution). The primers that
were used are listed in Table S7. The identification and quantification of miRNA were
performed using the Hairpin-it miRNA qPCR Quantitation Kit (GenePharma,
Shanghai, China). Each reaction was performed in triplicate.
Bisulfite sequencing
The treatment of genomic DNA with bisulfite was performed using the EZ DNA
Methylation-Gold kit (ZYMO Research, Los Angeles, CA) according to the
manufacturer’s instructions. The core region of the MTR promoter from -220 bp to
+25 bp from its transcriptional start site contains 22 CpG sites and was amplified in a
50 μl reaction mixture containing 20 ng of bisulfite-treated DNA, 75 pmol of primer
(sequence listed in Table S7), 2.5 mM of dNTPs and 1 unit of Hotstart Taq
polymerase (Takara). The PCR products were separated using agarose gel
electrophoresis and purified using the QIAquick Gel Extraction Kit (Qiagen). The
resulting fragment was cloned into pMD T-19 (Takara). After bacterial amplification
of the cloned PCR fragments, 10 clones from 28 different PCR assays were subjected
to direct sequencing on an ABI Prism 3770 sequencer (Applied Biosystems).
Plasma homocysteine detection
EDTA-plasma samples were obtained from fasting undergraduate volunteers in the
early morning, centrifuged immediately and stored in a -80C freezer until being
subjected
to
homocysteine
detection.
The
Axis®
Homocysteine
Enzyme
Immunoassay (EIA) Kit (Axis-Shield, Norton, MA) was used to determine plasma
homocysteine levels, according to the manufacturer’s instructions. Each test was
duplicated, and the mean level was used for further analysis.
Statistical analysis
Differences in qualitative demographic features and allelic or genotypic frequencies
between the CHD cases and the controls were compared using the χ2 test.
Hardy-Weinberg equilibrium was also tested using the χ2 test in the controls. To
evaluate the associations between genotypes and CHD risk, odds ratios (ORs) and 95%
confidence intervals (CIs) were calculated using unconditional logistic regression
analysis with adjustment for age and sex. Multiple testing of association results was
conducted by Bonferroni correction. As there are four SNPs being tested under 5
genetic models (additive, dominant, recessive, codominant and overdominant), the
significance level was adjusted to 0.05/(4×5)=0.0025. A meta-analysis of three
cohorts was performed using SAS software (version 9.1.3). The estimation of
haplotype frequency and the analysis of associations between different haplotypes and
CHD risk were performed using the SNPStats web tool
(http://bioinfo.iconcologia.net/snpstats/start.htm) with adjustment for age and sex. To
investigate gene interactions between MTRR and MTR, a logistic regression model
and multifactor dimensionality reduction analysis were performed in R language
(version 2.13.1), and epistasis analysis in PLINK (version 1.1) was used for the
statistical analysis. The linear regression analysis of the relation between methylation
level and MTR expression was performed in Microsoft Office Excel 2007. The
quantitative variables are given as the mean ± standard error (SE). The differences
between the two groups were evaluated using Student's t-test, and the differences
between three or more groups were evaluated using one-way anova test. Both
Student’s t-test and one-way anova test were performed using SPSS 15.0 software
(SPSS, Chicago, IL). All of the statistical tests were two-tailed, with P<0.05 set as the
significance level.
Table S1.Demographic characteristics in CHD cases and controls
Variable
Cases
No.
%
N=602
6.21±0.30
Controls
No.
%
N=660
5.95±0.19
P value*
Stage 1, Shanghai Group
Age, years (mean±SE)
0.46
Gender
0.10
Male
332
55.1
394
59.7
Female
270
44.9
266
40.3
Stage 2, Shandong Group
N=735
N=564
Age, years (mean±SE)
6.63±0.25
7.09±0.14
0.14
Gender
0.85
Male
365
49.7
283
50.2
Female
370
50.3
281
49.8
Stage 3, Jiangsu Group
N=1003
N=1046
Age, years (mean±SE)
6.78±0.31
6.72±0.29
0.86
Gender
0.73
Male
528
52.6
558
53.3
Female
475
47.4
488
46.7
Combined samples
N=2340
N=2270
Age, years (mean±SE)
6.59±0.17
6.59±0.15
0.99
Gender
0.16
Male
1225
52.4
1235
54.4
Female
1115
47.6
1035
45.6
CHD classification Ⅰ
Conotruncal defects
386
16.5
Septation defects
1652
70.6
LVOTO
47
2.0
RVOTO
75
3.2
APVR
14
0.6
Complex CHDs
34
1.5
Other CHDs
132
5.6
CHD classification Ⅱ
Isolated CHD
2025
86.5
Nonisolated CHD
315
13.5
Isolated CHD phenotype
VSD
1220
52.1
ASD
235
10.0
TOF
291
12.4
*The comparison of age was performed by student T test, and thecomparison of
gender was performed by 2-tailed χ2 test. Date shown in the row of age are means±SE.
TableS2. The genotype frequency of the 4 identified MTR SNPs in CHD patients and
controls
SNP
Group
Shanghai
rs28372871
Shandong
Shanghai
rs2853522
Shandong
Shanghai
rs1131450
Shandong
Shanghai
rs1804742
Shandong
Genotype
Control
Case
T/T
182 (33%)
77 (28.5%)
G/T
275 (49.8%)
116 (43%)
G/G
95 (17.2%)
77 (28.5%)
T/T
89 (27.5%)
60 (23.2%)
G/T
169 (52.2%)
125(48.3%)
G/G
66 (20.4%)
74 (28.6%)
G/G
155 (28.1%)
81 (30%)
G/T
289 (52.4%)
128(47.4%)
T/T
108 (19.6%)
61 (22.6%)
G/G
90 (27.8%)
63 (24.3%)
G/T
164 (50.6%)
145 (56%)
T/T
70 (21.6%)
51 (19.7%)
G/G
377 (68.3%)
150(55.6%)
G/A
164 (29.7%)
96 (35.6%)
A/A
11 (2%)
24 (8.9%)
G/G
206 (63.6%)
146(56.4%)
G/A
107 (33%)
93 (35.9%)
A/A
11 (3.4%)
20 (7.7%)
G/G
473 (85.7%)
228(84.4%)
G/A
76 (13.8%)
36 (13.3%)
A/A
3 (0.5%)
6 (2.2%)
G/G
269 (83%)
220(84.9%)
G/A
51 (15.7%)
35 (13.5%)
A/A
4 (1.2%)
4 (1.5%)
*Genotype frequencies in case/control were compared using χ2 test;
** P value for Hardy-Weinberg equilibrium test in the control subjects;
***P value in Recessive model.
P value*
HWP**
0.0011
0.66
0.022***
0.43
0.38
0.23
0.43
0.82
<0.0001
0.17
0.036
0.6
0.11
1
0.72
0.32
Table S3. Associations between selected folate-pathway variants of coding region and
CHD in 3 separated case-control studies.
GenoHWP*
Case
Control
OR (95% CI)* P value**
type
**
A/A
406 (67.4%) 454 (68.8%)
1.00
ShangHai
C/A
167 (27.7%) 187 (28.3%) 1.01 (0.79-1.30)
0.21
1.00
C/C
29 (4.8%)
19 (2.9%)
1.69 (0.93-3.07)
A/A
562 (76.5%) 432 (76.6%)
1.00
ShanDong
C/A
146 (19.9%) 124 (22%) 0.90 (0.69-1.19)
0.029
1.00
C/C
27 (3.7%)
8 (1.4%)
2.58 (1.16-5.74)
A/A
695 (69.3%) 707 (67.6%)
1.00
JiangSu
C/A
254 (25.3%) 308 (29.4%) 0.84 (0.69-1.02) 0.0045
0.83
C/C
54 (5.4%)
31 (3%)
1.79 (1.12-2.78)
C/C
183 (30.4%) 203 (30.8%)
1.00
ShangHai
C/T
310 (51.5%) 323 (48.9%) 1.04 (0.81-1.34)
0.55
0.81
T/T
109 (18.1%) 134 (20.3%) 0.88 (0.64-1.22)
C/C
231 (41%)
231 (41%)
1.00
ShanDong
C/T
260 (46.1%) 260 (46.1%) 1.04 (0.82-1.31)
0.56
1.00
T/T
73 (12.9%) 73 (12.9%) 1.21 (0.86-1.70)
C/C
311 (31%)
314 (30%)
1.00
JiangSu
C/T
473 (47.2%) 514 (49.1%) 0.92 (0.76-1.17)
0.67
0.8
T/T
219 (21.8%) 218 (20.8%) 1.01 (0.79-1.30)
A/A
334 (55.5%) 375 (56.8%)
1.00
ShangHai
G/A
227 (37.7%) 235 (35.6%) 1.10 (0.87-1.40)
0.61
0.12
G/G
41 (6.8%)
50 (7.6%)
0.92 (0.59-1.43)
A/A
431 (58.6%) 309 (54.8%)
1.00
ShanDong G/A
243 (33.1%) 217 (38.5%) 0.80 (0.63-1.01)
0.10
1.00
G/G
61 (8.3%)
38 (6.7%)
1.16 (0.75-1.78)
A/A
543 (54.1%) 610 (58.3%)
1.00
JiangSu
G/A
390 (38.9%) 366 (35%) 1.20 (1.00-1.45)
0.15
0.15
G/G
70 (7%)
70 (6.7%)
1.12 (0.78-1.58)
A/A
513 (85.2%) 567 (85.9%)
1.00
ShangHai
G/A
80 (13.3%) 87 (13.2%) 1.01 (0.73-1.40)
0.66
0.25
G/G
9 (1.5%)
6 (0.9%)
1.61 (0.57-4.56)
A/A
627 (85.3%) 459 (81.4%)
1.00
ShanDong G/A
103 (14%)
97 (17.2%) 0.78 (0.57-1.05)
0.11
1.00
G/G
5 (0.7%)
8 (1.4%)
0.46 (0.15-1.42)
A/A
891 (88.8%) 913 (87.3%)
1.00
JiangSu
G/A
104 (10.4%) 129 (12.3%) 0.83 (0.63-1.09)
0.077
0.25
G/G
8 (0.8%)
4 (0.4%)
2.04 (0.62-6.67)
* Adjusted for age, sex;
** Genotype frequencies in case/control were compared using χ2 test;
*** P value for Hardy-Weinberg equilibrium test in the control subjects.
MTR c.2756 A>G
MTRR c.66 A>G
MTHFR c.677 C>T
MTHFR c.1298 A>C
SNP
Group
Table S4. MTR haplotype analysis between CHDs and controls.
No
OR (95 CI)*
P value$
0.4766
1.00
-
0.2906
0.2885
1.15 (1.03-1.28)
0.01
0.0355
0.0438
0.0396
1.47 (1.14-1.89)
0.019
0.1684
0.221
0.1952
1.52 (1.35-1.72)
2.4×10-12
-186
+905
Freq
Freq
Freq
T>G
G>A
(Control)
(Case)
(Total)
1
T
G
0.5094
0.4447
2
G
G
0.2866
3
T
A
4
G
A
* Adjusted by age and gender;$ P value for differencesof haplotype distribution
between case and control subjects.
Table S5. The cumulative effect of genotypes on CHD at variant -186T>G and
+905G>A
Genotype*
Case
Control
OR (95% CI)
P value P trend
TT/GG 535(22.9%) 591(26.0%)
1.00
TG/GG 549(23.5%) 651(28.7%)
0.93 (0.79-1.1) 0.39
GG/GG 215(9.2%) 181(8.0%)
1.31 (1.04-1.65) 0.02
TT/GA
62(2.6%)
64(2.8%)
1.07 (0.74-1.54) 0.73
TG/GA 452(19.3%) 465(20.5%)
1.08 (0.90-1.28) 0.41
7.61×10-15
GG/GA 329(14.1%) 239(10.5%)
1.52 (1.24-1.86) 6.29×10-5
TT/AA
20(0.9%)
15(0.7%)
1.49 (0.75-2.93) 0.25
TG/AA
51(2.2%)
18(0.8%)
3.13 (1.81-5.43) 4.76×10-5
GG/AA 127(5.4%)
46(2.0%)
3.04 (2.13-4.35) 1.04×10-9
The controls used were the total 2270 combined controls;
*Genotype combination: -186T>G/+905G>A. Boldface indicates statistical
significance.
Table S6. Stratification analysis of MTR variants genotypes according to CHD classification
and phenotype.
Variable
Case number
P value
rs28372871
Association [OR (95% CI)]*
TG vs. TT
GG vs. TT
0.94 (0.72-1.21)
1.45 (1.08-1.94)
1.01 (0.87-1.18)
1.62 (1.36-1.93)
CHD Classification Ⅰ
Conotruncal defects
Septation defects
386
0.005
-8
1652
2.55×10
LVOTO
47
0.003
1.84 (0.78-4.36)
4.13 (1.71-9.93)
RVOTO
75
0.14
0.78 (0.45-1.35)
1.39 (0.76-2.52)
APVR
14
0.04
1.06 (0.35-3.18)
--
Complex CHDs
34
0.25
2.07 (0.83-5.15)
1.67 (0.56-5.00)
Other CHDs
132
0.46
1.06 (0.69-1.61)
1.34 (0.82-2.19)
Isolated CHD
2025
1.72×10-9
0.99 (0.86-1.15)
1.56 (1.32-1.85)
Nonisolated CHD
315
0.028
1.11 (0.83-1.49)
1.54 (1.11-2.13)
235
0.005
0.98 (0.70-1.37)
1.65 (1.14-2.39)
1220
6.79×10-8
1.01 (0.85-1.20)
1.61 (1.33-1.96)
291
0.011
0.94 (0.70-1.27)
1.48 (1.06-2.05)
GA vs. GG
AA vs. GG
CHD Classification Ⅱ
Detailed phenotype
ASD (atrial septal defect)
VSD (ventricular septal
defect)
TOF (tetralogy of Fallot)
rs1131450
CHD Classification Ⅰ
386
1.19×10-5
1.21 (0.96-1.53)
2.96 (1.93-4.52)
1652
9.48×10-12
1.20 (1.05-1.37)
2.73 (2.05-3.64)
LVOTO
47
0.16
1.37 (0.74-2.54)
3.00 (1.02-8.89)
RVOTO
75
0.66
0.90 (0.54-1.49)
1.51 (0.53-4.29)
APVR
14
0.61
1.03 (0.34-3.07)
--
Complex CHDs
34
0.86
1.21 (0.60-2.45)
0.91 (0.12-6.84)
Other CHDs
132
0.0002
1.42 (0.97-2.08)
4.10 (2.22-7.58)
Isolated CHD
2025
2.85×10-13
1.21 (1.06-1.37)
2.76 (2.09-3.64)
Nonisolated CHD
315
0.0007
1.19 (0.93-1.53)
2.63 (1.64-4.21)
ASD (atrial septal defect)
235
0.070
1.15 (0.86-1.54)
2.05 (1.13-3.71)
VSD (ventricular septal
1220
5.48×10-12
1.21 (1.04-1.41)
2.97 (2.19-4.02)
291
0.0003
1.31 (1.01-1.70)
2.75 (1.69-4.48)
Conotruncal defects
Septation defects
CHD Classification Ⅱ
Detailed phenotype
defect)
TOF (tetralogy of Fallot)
The controlswerethe total 2270 controls; *Adjusted for age, sex.
Table S7. DNA sequence of all used primer pairs
SN Primer Name
Sequence (5’-3’)
1 MTR-F1
TCACTGCCCCTTTAGGCACT
2 MTR-R1
GGAAAAGCAAATGCATCCAGA
3 MTR-F2
GGTCTTGGTAAGAATGTGATACGC
4 MTR-R2
TCTCCACACTCTACAAACAAGAATGA
5 MTR-F3
CACCCCCATATGGTAATTCAGAG
6 MTR-R3
CTTGGTGTCGGCCTAGCAG
7 MTR-F4
TAACCGCGCTCTGAAAGGTT
8 MTR-R4
TCGGACAAAGAGTGGAGCAA
9 MTR-F5
TGGCTGAGGTTGAGAAATGG
10 MTR-R5
GGGCAAATGGCTTCAGTGTT
11 MTR-F6
CGGGGAAGGTGTAGCTCTGT
12 MTR-R6
TCCTCCCTTGCTTCTTCGTC
13 MTR-F7
TGGTGGTGGCAATAGTCAGG
14 MTR-R7
CAGAATTGACTTAACCATCTTGTCC
15 MTR-F8
CCTTACCTGGTGATAAGTTCCAAA
16 MTR-R8
GACAGACATACATTTCACTTTTTCCA
17 MTR-F9
CCCACCTGTATGTCCAGCAA
18 MTR-R9
TCCTGCCCCTCACCTTTCTA
19 rs28372871-F
AACGCCTACTACAACCCTAAAA
20 rs28372871-R
AGTTCTGCGCTCAATCTATCC
21 rs2853522-F
GGGTGCCTTAAAAATAACAACAACA
22 rs2853522-R
TGGCCTCCTAGATTCCACTG
23 rs1131450-F
CATGCCATTCTCCTGCCTCA
24 rs1131450-R
TGCCCACTTGTCCAACTCC
25 rs1804742-F
GGTGGTGGCAATAGTCAGGA
26 rs1804742-R
CCCACATGAATAGCCATTGTTC
27 rs1805087-F
TTTCAGTGTTCCCAGCTGTTAGAT
28 rs1805087-R
AAACTAGGATCATAAAAAACAGTCACATT
29 rs1801394-F
GGAAACACAGATTCAAGCCCAA
30 rs1801394-R
CCCAACCAAAATTCTTCAAAGC
31 rs1801131-F
TTTGCCTCCCTAAGCCCTTC
32 rs1801131-R
GGGCCTCCAGACCAAAGAGT
33 rs1801133-F
TCAGCGAACTCAGCACTCCA
34 rs1801133-R
TCTTCATCCCTCGCCTTGAA
35 rs28372871-typing CGGCTGCGAGGAGCTCG
36 rs2853522-typing TAGTCTTCGAAAACCAGAAGCAGG
37 rs1804742-typing GCCCTTATTTTGTTCCCCTGCTCA
38 rs1131450-typing AAATTAGCCGGGTGTGGTGG
39 rs1805087-typing CACTTACCTTGAGAGACTCATAATGG
40 rs1801394-typing AAAGGCCATCGCAGAAGAAAT
41 rs1801131-typing GGGGAGGAGCTGACCAGTGAAG
42 rs1801133-typing GCTGCGTGATGATGAAATCG
Purpose
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR/Sequence
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
PCR
Genotyping
Genotyping
Genotyping
Genotyping
Genotyping
Genotyping
Genotyping
Genotyping
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
MTR-promter -F ATGGCGACGCGTCGCCATCTCATTCCTCCCTCCCTTCTTT Construct
MTR-promter -R ACTGGAAGATCTTCCAGTCCTTGGTGTCGGCCTAGCAG Construct
MTR-UTR -F
CACAACTCGAGCAGAACTCCCTTTGGCAAAAGGCAT
Construct
MTR-UTR -R
AAGGATCCCTTCATTTGTTCCTCCCTTGCTTCT
Construct
-186 T-F
GCGAGGAGCTCGTGCAGACCAATCAC
EMSA/SPR
-186 T-R
GTGATTGGTCTGCACGAGCTCCTCGC
EMSA/SPR
-186 G-F
GCGAGGAGCTCGGGCAGACCAATCAC
EMSA/SPR
-186 G-R
GTGATTGGTCTGCCCGAGCTCCTCGC
EMSA/SPR
+905 G-RNA
GGACUACAGGUGCCCGCCACCACACCCGGCU
SPR
+905 A-RNA
GGACUACAGGUGCCCACCACCACACCCGGCU
SPR
miR-485
AGAGGCUGGCCGUGAUGAAUUC
SPR
miR-608
AGGGGUGGUGUUGGGACAGCUCCGU
SPR
miR-1293
UGGGUGGUCUGGAGAUUUGUGC
SPR
MTR -186 F
ACAGCAGGTGATTGGTTGA
CHIP
MTR -186 R
AGCCCCGCAGACATTTAG
CHIP
MTR-nascent-F CGGGAGAAGCTAAACGAAGA
RT-qPCR
MTR-nascent-R GGAACCTGGGAATACTTTACCTT
RT-qPCR
MTR-mRNA-F CGCAACCCGAAGGTCTGAA
RT-qPCR
MTR-mRNA-R TTCTTCGTTTAGCTTCTCCCG
RT-qPCR
GAPDH-mRNA-F GAAGGTGAAGGTCGGAGTC
RT-qPCR
GAPDH-mRNA-R GAAGATGGTGATGGGATTTC
RT-qPCR
MTR-methy-F
GGGTTAAATAGTAGGTGATTGGTTG
Methylation
MTR-methy-R
TTAATATCGACCTAACAACCAAACA
Methylation
Figure S1. The MTR -186G allele attenuates transcription factor binding affinity. (A)
EMSAs revealed that, compared with the major T allele probe, the minor G allele
oligonucleotide probe had lower affinity for nuclear proteins of Hek-293 cells; (B)
SPR analysis comparing the binding affinity of nuclear extracts or purified
recombinant USFprotein to DNA probes containing either the -186T allele or G allele;
(C) Competition SPR assays performed in the presence of a five-fold excess of
non-biotinylated A allele or C allele probe; (D) ChIP assays using Hek-293 cells and
cardiovascular tissue samples. The amount of immunoprecipitatedMTR promoter was
measured using quantitative real-time PCR; (E) The amount of the two alleles was
quantified using SNaPshot from CHIP input and products treated with the USF-1/2
antibody.
FigureS2. USF (upstream stimulatory factor) was computationally predicted as the
possible target transcription factor at position -186T>G, and the results showed that,
when the major T was substituted with the minor G, USF would lose the original
binding site.
FigureS3. 5-Aza treatment causes increased MTR gene transcription. Hek-293 cells
were treated with medium containing 10µM 5-Aza for 72 hours. MTRhnRNA and
mRNA levels were quantified using quantitative real-time PCR. Data shown are
mean±SE of three experiments, and each experiment was performed in triplicate.
FigureS4. +905G>A variant can affect the compensatory base pair binding between
miR-608(A), miR-1293(B) and mRNA. The direct SPR assays confirmed that the
+905A allele bound to microRNA more strongly than the G allele. KD
(Affinityequilibrium constant)=KD(dissociation constant) /KA(association constant).
FigureS5. miR-485, miR-608 and miR-1293 decrease MTR expression by repressing
translation. Data shown are mean±SE.(A)The ratio of MTRmRNA/hnRNA was
compared, and we found no significant difference among different +905G>A
genotypes. This result implied that MTR was not regulated bymicroRNAs in the
post-transcription stage. (B) Hek293 cells were co-transfected with the
psiCHECK2-A construct and a microRNA expression vector, and mRNA and protein
levels of Rluc were quantified using real-time quantitative PCR and a luciferase assay,
respectively. (C) Hek293 cells were transfected with microRNA and microRNA
inhibitors. After 48 hours of culture, MTR mRNA and protein levels were determined
usingreal-time quantitative PCR and western blotting.
FigureS6.The human homocysteine concentration was significantly different among
the groups with different genotype in variants -186T>G and+905G>A of MTR,
andc.677C>T of MTHFR, and eachminor allele of the above variants statistically
contributed to the elevated homocysteineconcentrations.Data shown are mean±SE.