Download Using Mice to Dissect Genetic Factors in Atherosclerosis

ATVB in Focus
Lipoproteins, Inflammation, and Atherosclerosis
Series Editor:
James Scott
Previous Brief Reviews in this Series:
• Pennachhio LA, Rubin EM. Apolipoprotein A5, a newly identified gene that affects plasma triglyceride levels in humans and mice. 2003;23:529 –534.
• Cullen P, Baetta R, Bellosta S, Bernini F, Chinetti G, Cignarella A, von Eckardstein A, Exley A, Goddard M,
Hofker M, Hurt-Camejo E, Kanters E, Kovanen P, Lorkowski S, McPheat W, Pentikäinen M, Rauterberg J, Ritchie
A, Staels B, Weitkamp B, de Winther M for the MAFAPS Consortium. Rupture of the atherosclerotic plaque: does a
good animal model exist? 2003;23:535–542.
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Using Mice to Dissect Genetic Factors in Atherosclerosis
Hooman Allayee, Anatole Ghazalpour, Aldons J. Lusis
Abstract—The genes that contribute to common, complex forms of atherosclerosis remain largely unknown. Genetic
studies in humans have, for the most part, focused on identifying genes that predispose to the traditional risk factors,
such as lipid levels and blood pressure, but apart from rare, single-gene disorders, there have been few successes to date.
The use of mice to dissect the complex genetic etiology of atherosclerosis offers a viable alternative to human studies,
because experimental parameters, such as environment, breeding scheme, and detailed phenotyping, can be controlled.
Herein we review how mouse genetics can lead to the identification of genes, some of which would otherwise not have
been considered as candidates for atherosclerosis, and provide an overview of the prospects for successful gene
discovery in the future. (Arterioscler Thromb Vasc Biol. 2003;23:1501-1509.)
Key Words: atherosclerosis 䡲 genetic factors
G
enetic factors contribute importantly to atherosclerosis
in human populations, with a heritability of ⬇50%.
Although there has been considerable success in identifying
genes for rare disorders associated with atherosclerosis, our
understanding of genes involved in the common forms is very
incomplete. A large number of association studies with
candidate genes have been performed, but few have been
convincingly confirmed. Those that have been confirmed,
including variations of hepatic lipase and HDL levels, apolipoprotein E (apoE) and LDL levels, apoCIII/apoAV and
triglyceride levels, and paraoxonase 1 and myocardial infarction, as well as a handful of others, explain only a very small
fraction of genetic susceptibility to atherosclerosis in humans.
Clearly, a more complete understanding of the genetics of
atherosclerosis would have important implications for diag-
nosis, treatment, and risk assessment (reviewed in Lusis et al1
and Lusis2).
In principal, there are 2 ways to assign genes to physiologic
processes. The first, called “forward genetics,” uses naturally
occurring or induced mutations that affect the process.
Usually, the underlying gene is identified by mapping the
mutation with linkage analysis and then testing “positional
candidates.” Alternatively, if a known gene is suspected of
playing a role in a physiologic process, then variations of the
gene can be examined by association in cases versus controls,
the “candidate gene” approach. The second strategy, termed
“reverse genetics,” involves the transgenic modification of a
known gene, usually in a mouse or a rat, and then examining
its effect on the physiologic process. Both forward and
reverse genetics strategies have been crucially important for
Received May 8, 2003; revision accepted July 22, 2003.
From the Departments of Human Genetics (H.A., A.J.L.); Microbiology, Immunology, and Molecular Genetics (A.G., A.J.L.); and Medicine (A.J.L.),
and the Molecular Biology Institute (A.J.L.), David Geffen School of Medicine at UCLA, Los Angeles, Calif.
Reprint requests to Aldons J. Lusis, PhD, Department of Medicine, Division of Cardiology, 47-123 Center for the Health Sciences, David Geffen
School of Medicine at UCLA, Los Angeles, CA 90095. E-mail [email protected]
© 2003 American Heart Association, Inc.
Arterioscler Thromb Vasc Biol. is available at http://www.atvbaha.org
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DOI: 10.1161/01.ATV.0000090886.40027.DC
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Figure 1. The QTL approach: from
mouse to human. QTL studies begin
with a cross between 2 inbred strains
(eg, C57BL/6J [B6] and CAST/Ei
[CAST]) that differ in their susceptibility
to the trait of interest (A). Statistical
analysis of the correlation between
phenotypes of F2 progeny and the
genomic regions that they inherited
from the parental strains identifies the
QTL, in this case, coincident loci for
aortic lesions and insulin levels on the
middle of chromosome 6 (B). This is
represented in graphical form by LOD
scores, where the highest point of the
curve is the most likely location of the
underlying gene. Arrows along the x
axis depict genetic markers used to
genotype the F2 animals. A congenic
strain is then constructed by repeated
backcrossing of an F1 animal to 1 of
the parental strains (ie, B6), with selection of the chromosomal segment from
the other parent (ie, CAST) that
encompasses the QTL (C). Phenotyping the congenic strain for the trait(s)
originally identified in linkage analysis
confirms the locus (D). The underlying
gene can be identified by testing positional candidate genes in the locus
with either functional studies or transgenic models, in this example, 5-LO–
knockout mice (E). The human
ortholog of the gene (5-LO) can then
be examined in human studies with
family-based or case-control study
designs (F).
our field. In terms of forward genetics, the most important
advances have come from human geneticists who have
studied the rare mendelian disorders that perturb risk factors,
such as lipid metabolism or blood pressure. For example,
during the past few years, identification of genes for Tangier
disease (ABCA1) and sitosterolemia (ABCG5 and ABCG8)
has transformed our understanding of cholesterol transport.
The forward genetics approach can also be applied to the
dissection of complex genetic traits, which involve multiple
genes and environmental influences.3 Here, progress has been
relatively slow, and most of the successes relevant to atherosclerosis have involved previous biochemical knowledge of
the underlying gene (a “candidate gene” strategy). One
approach to the problem is to use naturally occurring variations in mice or rats, which simplifies the analysis. This is
expected to provide information about a subset of the genetic
variation that contributes to disease susceptibility in humans.
The Quantitative Trait Locus Approach
Like humans, inbred strains show genomic sequence variations every few thousand bases, which add up to hundreds of
thousands across the genome. Most of these are inconsequential, but those that do influence gene expression or function
are the basis for the variation in disease susceptibility (and all
other genetically based traits) that is observed among strains.
Similarly, such genetic variation in humans is also the basis
for the differential disease susceptibility that individuals
exhibit.
Using the quantitative trait locus (QTL) analysis approach
has allowed the mapping of those specific variations that are
responsible for differences in atherosclerosis susceptibility
and other related traits. The overall strategy for this approach
involves using either F2 or N2 mice generated from a cross
(Figure 1) or recombinant inbred (RI) strains. Various sets of
RI strains were the main source used for identifying loci that
underlie atherosclerosis and related traits before the advent of
the molecular and statistical tools required for analyzing a
cross. Each RI strain is in fact inbred and has been developed
from intercrossing 2 parental strains for 20 generations,
during which time the genomes of the progenitors are broken
into homozygous intervals of different length. By comparing
Allayee et al
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the distribution of a trait (eg, atherosclerosis) in the strains
from 1 RI set of animals with the distribution of the
polymorphic genetic markers already typed for those strains,
chromosomal loci that segregate with the trait of interest can
be identified. In general, RI mapping has had low power and
precision to detect QTLs, mainly owing to the small number
of available strains in each set. Recently, Williams et al4
published a dense map for all RI sets that share C57BL/6J as
a parental strain, which might provide a tool for RI mapping
of some complex traits in the future.
In recent years, investigators have initiated QTL studies
with crosses between inbred strains that differ in their
susceptibility to atherosclerosis. Even though the use of this
approach to dissect complex traits is still a long and laborious
undertaking, the development of large numbers of genetic
markers in mice has allowed the identification of some
important genes for atherosclerosis (described later). QTL
studies begin with construction of a cross between 2 selected
inbred mouse strains, typically differing in expression of the
primary trait of interest. The offspring of the initial mating (F1
generation) are either crossed back to 1 of the parental strains
(backcross) or are themselves mated (intercross) to create an
F2 generation (Figure 1A). Although the F1 mice are genetically identical, having 1 chromosome from each parent, the
backcross, or F2, mice are each genetically unique. This is
caused by independent segregation of chromosomes and
crossovers that occur in the F1 meiotic process, which leads to
combinations of genes from the original parental strains.
Because these recombined regions are inherited as relatively
large segments of chromosomes, the parental origins of each
portion of the genome can be determined by using polymorphic markers that distinguish the parental strains. QTL
analysis basically analyzes whether the measured trait varies
significantly across the population on the basis of the parental
genotype at any given location in the genome, in the same
manner as described for RI mapping. Polymorphic markers
are selected to cover the entire genome at regular intervals.
Specific software is available for this analysis, and statistical
standards have been established to determine the significance
of the results. When positive, the data are frequently presented as a graph with a curve representing the statistical
likelihood, represented by a log-of-the odds (LOD) score, of
a genetic effect across the length of the chromosome (Figure
1B). For an F2 intercross, peak LOD scores ⬎2.8 and 4.3 are
taken to represent suggestive and significant evidence for
linkage, respectively, although empirical significance levels
can be generated for each data set by using permutation
analysis. There will be a particular location along the chromosome where the likelihood of a genetic effect is greatest,
referred to as the peak. From the data, a region of the
chromosome on either side of the peak can be defined, within
which there is a 95% or 99% statistical likelihood that the
underlying gene will be present. QTL mapping in mice has
considerable power to map genes for multigenic traits, by at
least an order of magnitude greater than in human studies.
Typically, the regions identified are relatively large and
encompass several hundred genes across millions of base
pairs. However, once the genes have been mapped to chromosomal regions, each of those particular genetic intervals
Mice and Atherosclerotic Genetic Factors
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can be isolated on a common genetic background as a
“congenic strain,” which simplifies the genetics for further
analysis.5 This is accomplished by repeated backcrossing of
an F1 animal to 1 of the parental strains and selecting at each
generation only those individual animals that carry the QTL
region from the opposite parent for further study. After 10 or
more backcross generations, a congenic strain is created in
which the genome is composed entirely of the background
strain, with the exception of the region that encompasses the
QTL, which is derived from the donor strain (Figure 1C). By
phenotyping these animals for the trait(s) that was initially
identified in the QTL analysis, such congenic strains allow
confirmation of the mapping studies (Figure 1D) and, in
essence, “mendelize” the complex trait, making fine mapping
to ⬇1 Mb feasible. Examination of genes located in the finely
mapped region, either with transgenic/knockout animals or
through functional experiments, can then identify the underlying gene (Figure 1E). Ultimately, the human orthologs of
such genes can be examined in populations comprising either
families or patient cases and matched controls (Figure 1F).
A key aspect of this approach is that it does not require any
foreknowledge of the affected gene(s). QTL mapping therefore offers the possibility of identifying entirely novel genes
that might otherwise not have been considered. Because a
gene product is part of a biologic pathway, identifying 1
member might lead to elucidation of other, interacting genes.
Mapping Genes for Atherosclerosis in Mice
In the 1960s and 1970s, a number of investigators, including
Wissler and colleagues (Vesselinovitch and Wissler,6 Vesselinovitch et al7) developed diets capable of inducing mild
hyperlipidemia when fed to mice. When maintained on these
diets for several months, certain strains, but not others,
developed fatty streak lesions in the proximal aorta.8 In the
early 1980s, several groups characterized lipoproteins in mice
and demonstrated significant genetic variations in lipoprotein
levels and structures among inbred strains.9,10 In the mid1980s, Paigen and colleagues (Paigen et al,11,12 Nishima et
al13) modified the original Thompson diet and refined the
methods for evaluation of aortic lesions. In the early 1990s,
transgenic technologies led to the development of mouse
models that can develop large, advanced lesions, in contrast
to the fatty streak lesions observed after feeding of the
atherogenic diets to wild-type mice (reviewed in Breslow14).
As yet, robust mouse models for lesion rupture and thrombosis have not been developed, but recent observations by
several laboratories suggest that apoE-null or LDL receptor
(LDLR)–null mice can develop lesions that share many of the
features of advanced human lesions and that might be capable
of rupture.15–18 Molecular studies have also suggested potential mechanisms that could contribute to unstable plaque
formation in mice.19 –21
The first linkage studies of atherosclerosis susceptibility in
mice were carried out with RI strains derived from susceptible strains, such as C57BL/6J, and resistant strains, such as
C3H/HeJ, BALB/cJ, and A/J. These studies suggested that
lesions were controlled by a small number of major genes,
designated Ath1, Ath2, and Ath3.12,22–24 Subsequent studies
indicated a more complex picture.25–29 As discussed later, the
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TABLE 1.
September 2003
Atherosclerosis Loci Identified From QTL Studies in Mice
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Locus (Genes)
Chromosome
Cross
LOD Score
References
Ath1 (ApoA-II)
1
CXB and BXH RI strains
䡠䡠䡠
22, 30
Ath2
Unmapped
AXB and BXA RI strains
䡠䡠䡠
12
Ath3
7
AXB and BXA RI strains
Ath6
12
(B6-db/db⫻C57BLKS/J) F2
䡠䡠䡠
2.6
31, 32
Ath7
Unmapped
SWXJ RI strains
䡠䡠䡠
33
Ath8
Unmapped
NXSM RI strains and (SM⫻NZB) N2
1
(B6.129-apoE-/-⫻FVB/NJ0.129-apoE-/-) F2
䡠䡠䡠
3.3
34
Ath9
Ath11
10
(B6-apoE-/-⫻FVB/NCr-apoE-/-)F2 and
(B6.129-apoE-/-⫻FVB/NJ0.129-apoE-/-) F2
7.8, 11.9
35
Ath13
14
(B6-apoE-/-⫻FVB/NCr-apoE-/-) F2 and
(B6.129-apoE-/-⫻FVB/NJ0.129-apoE-/-) F2
3.2, 2.5
35
Ath16
19
(B6-apoE-/-⫻FVB/NCr-apoE-/-) F2
3.8
35
Artles (5-LO)
6
(B6⫻CAST/Ei)F2
6.7
36
Artles 2
10
(B6⫻DBA/2J)F2
5.2
37
Athsq1
4
MOLF/Ei⫻B6.129S7-Ldlr-/-) F1⫻B6-Ldlr-/-
6.2
38
Athsq2
6
MOLF/Ei⫻B6.129S7-Ldlr-/-) F1⫻B6-Ldlr-/-
6.7
38
None*
16
(B6.129-apoE-/-⫻FVB/NJ0.129-apoE-/-) F2
2.5
35
None*
1
(B6-apoE-/-⫻FVB/NCr-apoE-/-) F2
2.3
35
None*
7
(B6⫻DBA/2J) F2
3.4
37
24
35
Ath indicates atherosclerosis susceptibility locus; Artles, arterial lesion locus; Athsq, atherosclerosis susceptibility
QTL). Not shown in the table are Ath4, Ath5, Ath12, Ath14, and Ath15. Ath4 is a locus affecting HDL levels and was
identified in a subline of the Pera wild-type mouse. It is unknown if this locus has an effect on atherosclerosis. This
subline has subsequently been reported as extinct.33 Ath5 is the symbol reserved for the locus affecting non-HDL
cholesterol levels but no gene or locus has been identified yet. Ath10, Ath12, Ath14, and Ath15 are names of
atherosclerosis susceptibility loci that have not been associated with any identified chromosomal regions affecting
lesion development in mice.
*No published name has been designated for these loci.
HDL component of the Ath1 phenotype proved to be caused
by polymorphisms of apoA-II.
Numerous QTL studies of atherosclerosis have now been
reported from large genetic crosses between a variety of
inbred strains of mice, maintained on either atherogenic diets
or bred onto sensitized genetics backgrounds (such as apoEnull or LDLR-null; Table 112,22,24,30 –38). A number of loci
with highly significant evidence of linkage have been identified (Table 1 and Figure 2), and thus far, these studies have
led to the identification of 2 genes involved in atherosclerosis
(discussed subsequently).
Genetic studies in mice have also revealed information
about pathways that contribute to genetic susceptibility to
atherosclerosis, particularly in relation to lipid oxidation27
and inflammation.29,39 It is noteworthy that most of the loci
for atherosclerosis identified in mice do not influence plasma
lipid levels, blood pressure, or other systemic risk factors (see
references in Table 1). This suggests the existence of important genetic factors that act at the cellular level, presumably
by influencing vascular cells or leukocytes that infiltrate the
vessel wall.
Most of the loci identified in the various crosses are unique
(Table 1 and Figure 2). This is somewhat unexpected, if one
assumes that genetic variations are common to different
inbred strains of mice. One possible explanation for this
observation is that experimental designs can influence the
detection of loci; for example, the loci identified might
depend on whether atherosclerosis is induced by feeding an
atherogenic diet or by using a sensitized genetic background.
Whether a particular locus attains statistical significance will
also depend on the strength and number of loci that segregate
in that cross. In any case, it appears that among the various
inbred strains of mice, there exist many variations that
contribute to lesion development. Needless to say, the genetic
complexity observed in mice underscores the difficulties that
geneticists have had in identifying genes for atherosclerosis
in humans.
Pathologic studies in humans have revealed a great deal of
variation in advanced atherosclerotic lesions and in the
processes associated with myocardial infarction.40 Most genetic studies carried out in mice thus far have only examined
lesion size, usually by quantifying lipid staining near the
aortic root. Studies of lesion development at other sites (the
distal aorta or the innominate artery, for example) might
reveal distinct genetic influences.41 Curiously, few advanced
lesions are observed in the coronary arteries in mouse models.
In addition to lesion size, other parameters relevant to
atherosclerosis, such as cellular composition, calcification,
and lesion-related aneurysms, also vary among inbred strains.
The fact that many common variations with dramatic
effects on lesion development occur among inbred strains
also has an important implication for reverse genetics strategies. Gene targeting is frequently performed on 1 genetic
background (usually strain 129), and the mutant allele is then
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Figure 2. Loci identified for atherosclerosis in mice and corresponding syntenic
regions in humans. Loci with significant
or suggestive LOD scores are shown
with white or hatched boxes, respectively. Gray boxes correspond to loci
identified from RI strain mapping. Black
boxes to the left of each chromosome
represent syntenic regions in the mouse
of human loci that affect premature coronary heart disease (chromosomes 2 and
X; stroke (chromosome 5; and coronary
artery calcification (chromosomes 6 and
10. Information on the location of each
syntenic region was obtained from the
Mouse Genome Server at the Welcome
Trust at Sanger Institute (www.ensembl.org/Mus_musculus). The name of
each locus, along with proximal and distal flanking markers, are on either side of
the chromosome. The cM position of
each marker was obtained from the
Whitehead Institute database (wwwgenome.wi.mit.edu/). Genes identified by
the QTL approach are boxed (apoA-II;
5-LO; and LTA, lymphotoxin-␣).
bred onto a separate background (frequently, strain C57BL/
6J). During the breeding in this particular example, sequences
flanking the targeted gene from strain 129 will be retained in
the new congenic strain that is on a C57BL/6J background,
and if only 4 or 5 generations of backcrossing are used, these
flanking regions can be 10 or more centimorgans (cM) in size
(20 million or more base pairs). If this flanking region
contains a polymorphism that affects atherosclerosis, an
incorrect conclusion regarding the function of the targeted
gene might be drawn. This problem also applies to other traits
relevant to atherosclerosis, such as plasma lipid metabolism
and blood pressure, because many common variations also
influence these traits.
Although the mouse is clearly the most useful mammal for
genetic studies, the rat, the classic mammal for physiologic
studies, is also proving to be useful for examining certain
traits relevant to atherosclerosis, particularly diabetes, hypertension, and the metabolic syndrome.42 For example, with
QTL analysis, the CD36 and ACE2 genes were recently
shown to cause insulin resistance and defective heart func-
tions, respectively, in different rat models of hypertension.43,44 Rabbit models have also been used widely for
studies of atherosclerosis but are not as well developed for
genetic studies.45
From QTL to Gene to Humans
The first gene to be identified as contributing to atherosclerosis susceptibility from genetic studies in mice was Apoa2,
which encodes apoA-II, an abundant component of HDL.
Polymorphisms of the gene among different mouse strains
determine apoA-II production or turnover,25,46 and a congenic
strain that contains an allele for high levels of apoA-II
exhibited increased atherosclerosis.26 Subsequent transgenic
studies with both mouse apoA-II47 and human apoA-II48
indicated that apoA-II–rich HDL promotes atherogenesis.
This might be due to the reduced antioxidant potential that
apoA-II– enriched HDL exhibits.49,50 These observations
have also been extended to humans, in whom increased
apoA-II levels have been associated with metabolic
disorders.51
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TABLE 2.
Novel Tools for Accelerated Gene Discovery
Technology
Description
Genome-wide congenic panels
More than 150 congenic or chromosome substitution strains generated from B6⫻A/J, B6⫻DBA, or B6⫻CAST.
Each panel spans the entire mouse genome in contiguous fashion.
Mouse genomic sequence and polymorphisms
The sequence of 5 inbred strains has already been completed by the public effort and a private company.
Researchers have also begun to identify polymorphisms between several other strains in addition to those
sequenced.
Expression arrays and eQTLs
Microarray technologies allow the expression of thousands of genes from different tissues to be measured
simultaneously. These measured ⬘quantitative traits⬘ can also be analyzed to identify QTLs (eQTLs) for mRNA
transcript abundance. Identification of eQTLs that are coincident with QTLs for clinical traits (eg, cholesterol or
aortic lesions) can provide additional information and significantly bolster positional cloning efforts.
Small interfering RNA strategies (siRNA)
Injection of siRNA into mice has recently been developed which may allow the rapid screening of positional
candidate genes, provided that the phenotype and tissue specific expression of the gene being tested is
suitable.
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Ultimately, the goal of identifying genes for atherosclerosis
in mice is to extrapolate those results to studies in humans and
to develop novel diagnostic and therapeutic strategies. Studies of the Artles locus on chromosome 6 provide such an
example (Figure 1). Initially, Artles was identified in an F2
cross between inbred strains C57BL/6J and CAST/Ei and
exhibited a highly significant LOD score of 6.7 (Figure 1B).36
Interestingly, there was coincident linkage of insulin levels
with this locus, raising the possibility that the underlying gene
has pleiotropic effects on atherosclerosis and other related
metabolic traits (Figure 1B). Subsequently, a congenic strain
was developed to confirm the functional importance of the
QTL and to identify the underlying gene that causes resistance to lesion formation. Congenic animals carrying the
middle region of chromosome 6 derived from CAST/Ei
(Figure 1C) confirmed the phenotype of Artles because these
mice exhibited dramatic resistance to aortic lesion formation
and had lower insulin levels compared with control mice
(Figure 1D). Among the candidate genes in the congenic
interval was 5-lipoxygenase (5-LO), an enzyme involved in
leukotriene production. 5-LO is expressed primarily in leukocytes and has been studied mainly in the context of acute,
not chronic, inflammation. Based partly on the results of bone
marrow transplantation experiments, which implied the involvement of monocytes/macrophages or other leukocytes,
5-LO was examined as a positional candidate. To evaluate
5-LO as a candidate gene, Mehrabian et al39 crossed 5-LO–
knockout mice with LDLR-null mice and fed them a high-fat,
high-cholesterol diet. Importantly, these mice exhibited a
profound reduction in aortic lesion formation, despite having
cholesterol levels exceeding 500 mg/dL (Figure 1E). Conservative amino acid substitutions at the 3⬘ end of the protein
were also identified between CAST/Ei and C57BL/6J, but it
was not known whether these variations influenced 5-LO
function.39 By creating the same substitutions at the conserved positions in the human enzyme, Habenicht and colleagues (Kuhn et al52) have recently tested the functionality of
these amino acid changes and found that the CAST/Ei form
of 5-LO has a marked decrease in activity and expression
levels. Taken together, these studies provide strong evidence
that 5-LO is the gene that underlies Artles and demonstrate
the power of the QTL approach for identifying genes associated with atherosclerosis.
In preliminary studies of a human population, there is
evidence to suggest that genetic variation in the 5-LO gene
also affects similar phenotypes in humans. For example, a
promoter polymorphism consisting of a variable number of
Sp1 transcription factor binding sites was associated with
carotid intima-media thickness, an accepted surrogate marker
for atherosclerosis. Specifically, intima-media thickness in
those individuals homozygous for either the 3 or 4 allele (D
alleles) of the polymorphism was significantly increased, by
nearly 100 ␮m, compared with individuals with other genotypes.53 Furthermore, the same individuals also exhibited
significantly higher levels of insulin, C-reactive protein, and
interleukin-6. These results are consistent with the observations in 5-LO– deficient mice and support the concept that
5-LO has pleiotropic effects on the development of atherosclerosis, as well as known risk factors associated with it.
Pharmacologic inhibitors of 5-LO and other enzymes in the
leukotriene synthesis pathway are presently available, raising
the possibility of potentially novel therapeutic strategies.
The Road Ahead
A major impact of the mouse on our understanding of
atherosclerosis has involved its genetic manipulation (ie,
transgenic and knockout models) to examine mechanistic
questions. However, studies of naturally occurring variations
in the mouse, such as those described earlier, have tremendous potential to reveal new genes and pathways. Thus far,
many loci for atherosclerosis-related traits have been mapped,
but few genes have been identified. Recently, there has been
important progress in the development of genomics and
bioinformatics tools that will accelerate this process (Table
2). It seems likely that, as we enter the postgenome era of
elucidating gene function, combined genetic/genomic approaches will prove increasingly useful for the identification
of novel pathways relevant to atherosclerosis.44,54 –58
First and foremost, the currently available sequence of 5
mouse strains will allow rapid screening of candidate genes
within QTLs and congenic regions. Once the QTL has been
narrowed to several hundred kilobases, the entire sequence
can be downloaded and examined to search for both known
and unknown genes as candidates.
Recent studies have also identified numerous polymorphisms that differ among 8 strains, adding further to the tools
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that geneticists have at their disposal for gene identification.59,60 If the fine-mapping example described earlier were
used in a cross between C57BL/6J and DBA/2J, for example,
the available genomics resources would allow all of the
nucleotide substitutions between C57BL/6J and DBA/2J in
that region, as well as the entire sequence, to be determined.
By prioritizing those genetic variations that occur in or near
genes, researchers will be able to focus their effort on the
most likely genetic causes first.
Among the important new developments is the availability
of genome-wide congenic strains.61,62 As described earlier,
once a QTL has been identified, congenic strains are created
through repeated backcrossing, which can take 2 to 4 years,
even with marker-assisted breeding. However, 3 panels of
congenic strains that span the genome contiguously have
already been constructed between C57BL/6J and A/J,
C57BL/6J and CAST/Ei, and C57BL/6J B6 and DBA/2J.
These resources will be of immense use for atherosclerosisrelated QTLs, because rather than creating a congenic strain
for each identified locus, the desired strain(s) can be obtained
from the corresponding panel, and one can directly proceed to
confirming the QTL and fine mapping.
The feasibility of performing large-scale arrays for RNA
expression in affected tissues is also highly complementary to
QTL mapping. As demonstrated recently, traditional QTL
analysis when combined with expression array experiments is
an extremely powerful approach and yields tremendous
insight into the genetic complexity that underlies common
diseases.63 In essence, measuring transcript abundances in
specific tissues can be thought of as measuring any other
quantitative trait (eg, cholesterol), except that microarray
technology allows the simultaneous quantification of thousands of genes (ie, traits). Analogous to how QTLs for lipid
levels or atherosclerosis have been identified in mouse
crosses, QTLs have also now been identified for mRNA
expression (eQTLs).63 In the future, therefore, the criteria for
prioritizing positional candidates will also include those
genes that yield eQTLs over their own physical location, the
linkage of which is coincident with the “clinical” QTL that
was originally identified. On another level, by performing
concurrent measurement and analysis of multiple traits,
gene-gene interactions can be identified, including a possible
common genetic basis for apparently diverse traits, such as
bone density and lipid metabolism.64 This can be performed
with combinations of clinical traits and/or expression array
traits, adding further to the wealth of knowledge that this
approach is likely to provide.
Recently, the in vivo use of small, interfering RNA
molecules (siRNA) to attenuate gene expression has come to
the forefront and has the potential to allow rapid screening of
positional candidate genes. This novel technique involves the
injection of short, double-stranded RNA oligonucleotides into
mice through the tail vein under high hydrostatic pressure.65,66 The siRNA oligonucleotides, which are homologous
to a target gene, are taken up by different tissues (the liver is
a particularly good tissue) and cause the selective degradation
of that gene’s mRNA. Compared with the time and expense
it takes to create transgenic or knockout animal strains,
siRNA strategies hold tremendous promise for functional
Mice and Atherosclerotic Genetic Factors
1507
testing of candidate genes. However, a major drawback is the
short and transient inhibition of gene expression, on the order
of 24 to 48 hours. Thus, for traits such as atherosclerosis or
obesity, which can take months to develop, the use of siRNA
might not be entirely feasible.
Conclusions
Common forms of atherosclerosis have a very complex
etiology, and because of this complexity, it has proven
difficult to apply the mapping strategies that have revolutionized our understanding of mendelian disorders. QTL approaches in mice offer a viable alternative to the problem.
The main difficulty encountered in this approach has been the
challenge of fine mapping to reduce the number of positional
candidate genes. Several new tools in mouse genetics promise
to accelerate the identification of genes that underlie QTLs.
Ultimately, the identification of atherosclerosis genes (in both
mice and humans) will lead to a better understanding of the
pathophysiologic processes that are perturbed in and are
manifested as disease and to the focused discovery of novel
therapeutic agents and strategies.
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Using Mice to Dissect Genetic Factors in Atherosclerosis
Hooman Allayee, Anatole Ghazalpour and Aldons J. Lusis
Arterioscler Thromb Vasc Biol. 2003;23:1501-1509; originally published online August 14, 2003;
doi: 10.1161/01.ATV.0000090886.40027.DC
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