SHAPE Society

1. Genetic Marker (s) Of MI – One, One Hundred,
or One Thousand?!
Provided by:
M. Bidya Sagar, Ph.D.
Structural Biology and Drug Discovery Laboratory
Center for Vulnerable Plaque Research
Texas Heart Institute
Editorial Slides
VP Watch – December 26, 2002 - Volume 2, Issue 51

2.  Rupture prone vulnerable plaques are
the predominant underlying process in the
pathogenesis of acute coronary syndromes.
 Although the morphology of these vulnerable
plaques has been studied extensively, the
underlying genetic markers of these plaques
and patients in whom vulnerable plaque
develop, suffer a fatal cardiovascular event, are
largely unknown.

3. • Genomic and Proteomic research may shed light
on the way to discover these markers of
plaque/patient vulnerability.
• Human Genome studies are underway to identify:
– differential expression of up- and down-regulated genes
in plaque vs normal arterial walls, and comparison of
the mRNA expression at different stages of plaque
development.
– single nucleotide polymorphism (SNP) among
cardiovascular disease patients vs. normal subjects.
– loci on chromosomes through linkage studies

4. Several studies compared the gene
expression of :
• activated human umbilical vein endothelial
cells
• vascular smooth muscle cells
• cholesterol-loaded macrophages with
that of nonactivated cells.1-3
1-Gene Expression Studies in Plaque

5.  Studies in cell lines revealed differential
regulation of genes involved in leukocyte
trafficking, cell-cycle control, and
apoptosis.
 Differences in gene expression have
been observed among fatty streaks and
advanced lesions 4
and intima and media
of human atherosclerotic plaques. 5
1- Gene Expression Studies in Plaque

6.  Tyson et al studied heterogeneity of gene
expression in human atheroma using cDNA
analysis. 6
 Faber et al have suggested perilipine, as
well as other genes potentially involved in
rupture of human atherosclerotic plaques.7-8
1- Gene Expression Studies in Plaque

7.  Genetic-linkage studies 11
and candidate
gene analyses 12-16
have implicated a
locus and several genes related to
predisposition to myocardial infraction.
 A linkage analysis for myocardial
infarction and its related risk factors was
carried out by Broecke et al. 11
2- Genetic Linkage Studies

8.  They have scanned the whole genome in
513 families to identify chromosomal regions
linked to MI
 They showed the risk of MI maps to a single
region on chromosome 14 with a significant
lod score of 3.9 (pointwise P=0.00015,
genome-wide P<0.05).
2- Genetic Linkage Studies

9.  Human Genome mapping has led to a
massive search for clinically relevant
genomic information: including (SNPs),
which consist of substitutions of one
nucleotide for another in a DNA sequence.
 Human genomes are 99.9% identical with
only 0.1% of the genome showing
polymorphisms.9-10
3- Gene Polymorphism (SNP) Studies

10.  About 2-3 million SNPs have been found in
exonic, intronic, regulatory, and intergenic
regions.9-10
 Though almost all genes contain SNPs, only
a minority of SNPs result in amino acid
variation in proteins. 9-10
3- Gene Polymorphism (SNP) Studies

11.  Myocardial infarction is a complex
multifactorial and polygenic disorder; and
therefore, it may not be possible to find an
specific genetic make-up predicting the
disease.
 Genetic polymorphisms have been associated
with myocardial infarction and angina pectoris. 20
3- Gene Polymorphism (SNP) Studies

12.  Fu L et al., investigated the association
between gene polymorphism of the
plasminogen activator inhibitor-1 (PAI-1) and
MI in a Chinese population.17
 The 5A/6A polymorphism in the promoter of
the stromelysin gene has been identified as a
novel pathogenetic risk factor for MI. 18
 Polymorphisms of the P-selectin gene and
risk of MI in men and women has been
reported.19
Gene Polymorphism (SNP) Studies

13.  As reported in VP Watch of
this week, Yamada et al., published in
the New England Journal of Medicine,
discovery of new polymorphisms in
candidate genes that confer susceptibility
to myocardial infarction. 20

14. Method:
 A fluorescence- or colorimetry-based allele-
specific DNA-primer-probe assay
 To determine Genotypes of 112
polymorphisms of 71 candidate genes in
2819 unrelated Japanese patients with MI
(2003 men and 816 women) and 2242
unrelated Japanese controls (1306 men and
936 women).

15. Results:
 Screening of the 112 polymorphisms for an
association with MI in 909 subjects.
 19 polymorphisms were selected in men and
18 in women by means of logistic-regression
analysis, after adjustment for age, BMI, the
prevalence of smoking, etc.

16. Polymorphisms Related to MI in the Screening Study (Men)
Gene Polymorphism Genetic Model P value
Platelet-activating factor acetylhydrolase G994T Additive < 0.001
p22phex
C242T Dominant 0.006
Connexin 37 C1019T Additive 0.007
Thrombospondin 4 G1186C Dominant 0.013
Angiotensinogen G-6A Recessive 0.019
Tumor necrosis factor α C-863A Dominant 0.045
Transforming growth factor β1 T869C Additive 0.049
G protein β3 subunit C825T Additive 0.051
Apolipoprotein C-III C-482T Recessive 0.057
Interleukin-10 T-819C Recessive 0.061
Thrombomodulin C2136T Additive 0.065
Apolipoprotein E C4070T Additive 0.074
Glycoprotein 1a A1648G Recessive 0.080
Interleukin-10 A-592C Recessive 0.088
Apolipoprotein G-219T Recessive 0.092
Thrombopoietin A59713G Recessive 0.094
Apolipoprotein C-III C1100T Recessive 0.095
CC Chemokine receptor 2 G190A Recessive 0.097
Endothelial Nitric Oxide Synthase T-786C Dominant 0.098
Adopted from: Yamada et al., Prediction of the Risk of Myocardial Infarction from Polymorphisms in Candidate
Genes. N Engl J Med 2002; 347: 1916-23.

17. Paraoxonase G584A Dominant 0.009
Interleukin-6 C-634G Additive 0.009
Connexin 37 C1019T Dominant 0.013
ATP-Binding Cassette Transporter I G1051A Additive 0.014
Tumor Necrosis factor α C-850T Additive 0.015
Endothelin-I G5665T Recessive 0.028
Apolipoprotein E C4070T Recessive 0.038
Apolipoprotein C-III C-482T Recessive 0.044
Apolipoprotein E T3932C Dominant 0.047
CD14 Receptor C-260T Additive 0.050
Tumor necrosis factor α G-238A Dominant 0.052
Plasminogen-activator inhibitor type I 4G-668/5G Recessive 0.055
Fatty-acid-binding protein 2 G2445A Additive 0.057
Insulin receptor substrate-I G3494A Dominant 0.058
Stromelysin-I 5A-1171/6A Additive 0.072
Glycoprotein Ibα C1018T Additive 0.072
E-selection A561C Dominant 0.074
Polymorphisms Related to MI in the Screening Study (Women)
Gene Polymorphism Genetic Model P value
Adopted from: Yamada et al., Prediction of the Risk of Myocardial Infarction from Polymorphisms in Candidate
Genes. N Engl J Med 2002; 347: 1916-23.

18. Distributions of Polymorphisms Associated with MI among
among the 4152 Subjects in the Large-Scale Study
Gene and polymorphism Controls Patents with MI
in %
Men (n=2858)
Connexin 37, C1019T at lp35.1
C/C 71.7 66.2
C/T 25.4 29.6
T/T 2.9 4.2
p22phex
, C242T at 16q24
C/C 75.9 80.7
C/T 22.5 17.9
T/T 1.5 1.4
Women (n=1294)
Plasminogen-activator inhibitor type 1
4G-668/5G at 7q21.3-q22
4G/4G 44.8 36.5
4G/5G 44.9 50.9
5G/5G 10.3 12.6
Stromelysin-1, 5A-1171/6A at 11q23
5A/5A 5.7 1.2
5A/6A 31.3 28.1
6A/6A 63.0 70.8
Adopted from: Yamada et al., Prediction of the Risk of Myocardial Infarction from Polymorphisms in Candidate
Genes. N Engl J Med 2002; 347: 1916-23.

19.  In men, MI was significantly associated with
the
C1019T polymorphism in the connexin 37 gene
(P<0.001).
 In women, the 4G-668/5G polymorphism in the
plasminogen-activator inhibitor type 1 gene
P<0.001) and the 5A-1171/6A polymorphism

20. Conclusions:
Determination of the genotypes of connexin 37,
plasminogen-activator inhibitor type 1,
and stromelysin-1 genes may prove reliable
in predicting the genetic risk of MI.

21. Questions:
• Knowing that 2-3 million gene polymorphisms
(SNP) exist, how many different SNPs do you
expect to be identified as risk markers of
arteriosclerosis and MI: >10 or >100 or >1000?
• Could there be certain polymorphisms
that protect people against atherosclerosis? Or
protect atherosclerotic subjects from
developing
myocardial infraction?

22. • Is it likely to find major gene polymorphisms
that could be responsible for 50-70% of MI
(like the 5 major traditional risk factors)?
• Is it likely that there are numerous
polymorphisms, each statistically significant,
in different populations?
• How closely do these genetic polymophisms
translate to functional proteomic variations?
Questions:

23. References
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