Download Use of Recombinant Congenic Strains in Mapping Disease

Use of Recombinant Congenic Strains in Mapping
Disease-Modifying Genes
Jana Müllerová1,2 and Pavel Hozák1,2
1
Department of Cell Ultrastructure and Molecular Biology, Institute of Experimental Medicine, Academy of Sciences of the Czech Republic, 142 20 Prague;
and 2Department of Cellular and Molecular Biology, Third Faculty of Medicine, Charles University, 100 00 Prague, Czech Republic
Previous research studies have established much information about single-gene diseases. However,
other genes also influencing the outcome of a disease and genes involved in complex disease
remain largely unknown. Here we report on recombinant congenic strains of mice, a powerful
tool for genetic dissection of a complex trait.
T
he latest research results regarding the human genome
open the possibility of suggesting an optimal therapy for
each individual on the basis of knowledge of the patient’s
genotype (12). Many genes whose mutations cause inborn single-gene diseases or cancer have been described in detail.
More importantly, additional genes involved in multigenic diseases have been identified. However, it is a particular combination of their alleles that increases the risk and modifies the
outcome and efficiency of therapy (2).
It is generally difficult to study these genes. The reasons are
in their multiplicity, high number of mutual combinations of
their alleles, low penetrance, and extrafamilial occurrence of
the diseases. Recombinant congenic strains (RCS) of mice are
a very efficient genetic tool, allowing the genetic dissection of
genes involved in a complex trait (6). Another possibility,
recombinant inbred strains (a cross between two inbred strains
of mice) is the most used model to study complex traits. However, recombinant inbred strains are more effective for mapping the genes with stronger penetrance. (Various models are
reviewed in Refs. 2, 4, and 7.) Because of the high homology
between human and mouse genome, it is possible to expect
that the same gene will relate to the same disease in humans
and mice; however, the particular polymorphism can be different.
Identification of those genes modifying risk for and course
of a disease could lead to an explanation of molecular mechanisms and to detection of new targets for therapy. Although
the initial aim of such research is not prevention, it is also possible to assess risk factors for a disease in a healthy person by
knowing his/her genotype.
Since cancer is probably the most investigated disease and
it perfectly illustrates a complex trait, we will explain the principles of genetic analysis of this trait using RCS. After that, a
short review of results obtained so far will be given.
Cancer and related genes
Cancer is caused by sequential mutations in one cell, which
give this cell and its clones selective advantages such as fast
and unregulated division, ability to induce vascularization, or
ability to invade other tissue (13). Mutations causing cancer
mainly affect well-known protooncogenes and tumor sup0886-1714/04 5.00 © 2004 Int. Union Physiol. Sci./Am. Physiol. Soc.
www.nips.org
pressor genes participating in regulation of cell division. In the
development of this disease, a large number of nonmutated
genes also play a role (1). They can influence the susceptibility to cancer after carcinogen exposition, tumor number and
size, the rate of tumor growth, the site of tumor formation, histological type, and progress toward malignancy (6). These
genes are called tumor susceptibility genes (6) or tumor modifier genes (TMGs) (2). Similarly, the alleles are called susceptible or resistant, depending on whether they support or
inhibit tumorigenesis. Besides carcinogen metabolism, TMGs
are associated with tissue’s ability to grow, to invade, to differentiate, to induce appropriate vasculature, to stimulate an
immune response, to control genome stability, or to repair
damaged DNA. All of them obviously can be critical to tumor
progression (2).
With respect to complexity of the disease, the influence of
both single gene effects as well as gene interactions can be
expected. It even appears that gene interactions are much
more frequent than predicted. The high frequency of these
interactions suggests the existence of synergic pathways and
molecular interactions (e.g., protein-protein interactions) as
well as the existence of complex interactions (23). Except for
reciprocal interactions, or epistasis, counteracting interactions
are very common. In the case where two loci are involved in
counteracting interaction, their alleles confer either susceptibility or resistance depending on a specific genetic combination. The same allele can have opposite effects in a different
background. The opposite effects of the same allele in combination with the partner locus allele mask each other. Consequently, in spite of the relatively large effects of these loci
together, there is no effect if each of them is considered alone.
At present, this type of interaction has been detected in many
loci and with many biological traits (24).
Originally, it was suggested that most TMGs influence the
cellular processes related to neoplastic development, rather
than systemic factors (6). However, as the number of identified
candidate loci increased, it was found that their chromosomal
positions overlap less randomly than expected. This implies
that multiple TMGs influence several types of cancer by functioning systematically or by functioning in one type of tissue
present in different organs, e.g., in the epithelial tissue of the
lung and of the colon (9).
News Physiol Sci 19: 105109, 2004;
10.1152/nips.01512.2003
105
The most difficult problems with mapping TMGs are their
low penetrance, phenotypic variability due to the influence of
environment, and especially their complex interactions. Frequently, the individual alleles are not intrinsically susceptible
or resistant, but their effect is influenced by the genotype at the
interacting locus. Since every individual has a unique combination of alleles of TMGs, we could not resolve their interactions and effect without decreasing genetic variability. For that
reason, we cannot use any method of genetic mapping such
as linkage analysis in affected families, allele-sharing methods, or association studies in human populations. We have to
use the fourth method: animal crosses.
The most suitable model is a mouse, because it is possible
to cross it to obtain a high number of individuals with a
defined genome. The entire experimental group can be bred
under identical conditions to eliminate environmental influence on phenotype, and, conveniently, mice have a short generation period.
RCS
The RCS can be used to analyze molecular mechanisms of
any biological process, such as atherosclerosis or infectious
and metabolic diseases, especially those complex traits for
which the two parental strains differ (19). Recently, many new
series of RCS have been founded especially to study particular complex traits (11, 15).
RCS were first developed in the Netherlands, and they
were designed to analyze specifically the traits under multigenic control (8). RCS were created by crossing two inbred
strains that strongly differ in reaction to transplacental carcinogen treatment. The former, e.g., strain A, is susceptible
because it develops many large tumors with malignant
appearance. The latter, strain B, develops few small tumors,
and that is why it is called resistant. F1 mice were backcrossed to strain A, and the N1 generation was also backcrossed to strain A. Mice from the following generation had
12.5% of their genome from parent B (donor strain) and the
remaining 87.5% of the genome from parent A (background
strain). On average, 20 litters were randomly selected, and
brother-sister mating over 20 generations followed, so that all
genes were in a homozygous state. In this way, a series of 20
inbred strains were founded, each carrying a different random subset of ~12.5% genes from the donor strain and the
remaining 87.5% genes from the background strain. In other
words, genome of the donor strain was dissected in 20 RCS
in 12.5% parts and that part is different in each of 20 RCS
(Fig. 1). Genes of a multigenic trait were also dissected into
oligo or single-gene traits (6, 19).
The only weakness of the RCS model is paradoxically connected with its greatest advantage: the very decreased genetic
variability. In fact, each set of RCS is derived from two parental
strains, and therefore the study is limited to only two alleles of
a gene. The candidate allele may also have significant effect in
one genetic background but none in another. However,
because TMG mapping in humans is difficult and TMGs are
still virtually unknown to date, their identification on the basis
of murine TMGs is of crucial importance (6).
106
News Physiol Sci • Vol. 19 • June 2004 • www.nips.org
FIGURE 1. Development of recombinant congenic strains (RCS) and a resulting group of RCS. A: development of RCS when donor and background
parental strains are crossed to obtain the F1 generation. This generation is
then backcrossed to the background parental strain, and the N1 generation is
also backcrossed to the background parental strain. Mice from the next generation are brother-sister mated over 20 generations to obtain the inbred
strain. B: RCS (Ocb/Dem as an example) as a group of inbred strains, each
carrying a different random subset of ~12.5% genes from the donor parental
strain and the remaining 87.5% genes from the background parental strain.
Mapping with RCS
The first step of course is to identify which RCS inherited the
part of the donor’s genome where TMGs are. For example,
when studying TMGs of lung cancer, RCS as well as parent
strains have to undergo transplacental carcinogen treatment.
The resulting phenotype is determined by histological analysis
of tumor tissue.
More specifically, RCS OcB/Dem are used to study lung
cancer. The background susceptible strain is O20/A (O20),
and the resistant donor strain is B10.O20/Dem (B10.O20).
O20 develops higher number of larger tumors than
B10.O20. Moreover, the O20 tumors are mainly of the solid,
alveolar type with frequent secondary papillary structures in
the central part. Nuclear pleiomorphy may be very extensive, and lymphocyte infiltration is found rather frequently,
in contrast to the B10.O20 tumors. B10.O20 mice develop
almost exclusively papillary adenomas with homogenous
populations of tumor cells with no nuclear pleiomorphy. The
majority of the 20 OcB/Dem strains develop tumors, which
are for a greater part similar to those of the O20 strain,
except two of them, OcB-4 and OcB-6. In strain OcB-4,
about half of the lung tumors are of alveolar type without
secondary papillary structures and they exhibit little or no
nuclear plesiomorphism. Tumors in the strain OcB-6 are pre-
TABLE 1. Histological appearance of tumors of different strains
Strain
O20
B10.O20
Ocb-4*
Ocb-6*
Histological type
mixed**
papillary
alveolar
papillary
Nuclear pleiomorphism
yes
no
no
yes
Lymphocytic infiltration
yes
no
no
yes
*Ocb-4 and Ocb-6 are individual strains from the group of 20 Ocb/Dem RCS. B10.O20 serves as a donor strain, and O20 serves as a background strain.
Therefore, the genome of Ocb-4 and Ocb-6 consists of 12.5% B10.O20 genome and of 87.5% O20 genome. **Alveolar with secondary papillary structures. Data are based on Refs. 9 and 21.
dominantly papillary, including the periphery of tumor, in
this respect resembling the strain B10.O20; but in contrast to
this parent, they have considerable nuclear pleiomorphy (9).
Histological appearance of tumors of different strains is summarized in Table 1.
There are so many genes involved in tumorigenesis that one
cannot assign the individual effect of each gene to a phenotype absolutely. Since parent strains serve as reference values,
phenotype variance is also decreased in RCS. Therefore, due
to parental strain we are able to recognize when an active
allele is replaced with a nonactive one or when an interaction
arises or perishes.
After phenotyping, genotyping allows us to find which part
of the donor genome is in which OcB/Dem strain. Variable
numbers of tandem repeats (called markers) are used. Individual alleles can be differentiated by PCR followed by
agarose gel electrophoresis for determination of fragment
length. Besides these molecular markers, the strains were
typed for a number of biochemical, serological, and restriction fragment length polymorphisms. The typing data for individual RCS are deposited in the Mouse Genome Database at
The Jackson Laboratory that serves as a comprehensive
resource available online (http://www.informatics.jax.org).
For example, RCS OcB/Dem have already been typed for 611
markers (19).
With the methods described briefly above, it is possible to
select an individual strain from RCS, which differs from parent
strains, and to determine which part of the donor genome it
inherited and that this part is responsible for the phenotype
difference. How to find then the particular gene in this region?
The methods are again phenotyping and genotyping. Obviously, we cannot use the homozygous RCS; we have to generate different genotypes in loci of donor origin. Therefore, we
have to cross this strain with a background strain to obtain the
F2 generation.
The major advantage of RCS is that only ~12.5% of the
genome segregates in a cross between RCS and its parental
background strain, in contrast with crosses in which the whole
genome is segregating. The effect on phenotype of each
marker, sex, and interactions between pairs are tested by
analysis of variance. The ultimate aim of this analysis is to find
a marker whose one parent allele is connected with a certain
tumor feature while the allele of the second parent origin is
not (or even causes an opposite effect). Of course, the marker
is not responsible for the phenotype difference. It is a gene and
its alleles located close to that marker. Then this candidate
locus is shortened as much as possible by detailed mapping
and submitted to positional cloning.
Major achievements obtained by using RCS
So far, two candidate genes have been identified with RCS.
The first is the combined hyperlipidemia gene Hyplip 1, and
the second is Ptprj (receptor-type protein tyrosine phosphatase), a candidate gene for the colon cancer susceptibility
locus Scc1.
Familial combined hyperlipidemia represents the most
common genetic dyslipidemia, with a prevalence of 1.0
2.0%. HcB-19, a strain from RCS HcB/Dem, also has symptoms of this disease, although parent strains are normal. This
finding suggests the development of spontaneous mutation de
novo. After fine mapping and positional cloning, the sequencing diagnosed the presence of a nonsense mutation in gene
called Hyplip 1. It codes Txnip (thioredoxin-interacting protein). Txnip binds reduced thioredoxin, a major regulator of
cellular redox state, and inhibits its reducing activity. It is suggested that altered redox status downregulates the citric acid
cycle, sparing fatty acids for triglyceride and ketone body production, major symptoms of combined hyperlipidemia. This is
a new pathway of plasma lipid metabolism with potential
clinical significance (3).
Ptprj was identified by positional cloning. It encodes a
receptor-type protein tyrosine phosphatase. The coding
sequences of resistant and susceptible alleles show 16 singlenucleotide differences. Furthermore, frequent deletions of
Prptj, allelic imbalance in loss of heterozygosity, and missense
mutations occur in human colon, lung, and breast cancer (16).
Recently, Czarnomska et al. (5) confirmed that a host
genome influences a number of intestinal and mammary
tumors as well as the histological type of mammary tumors.
Furthermore, they found that susceptibility to both types of
cancer had opposite distribution within RCS. The strains most
susceptible to intestinal tumor were the most resistant to mammary tumors and vice versa. They used OcB/Dem mice carrying multiple intestinal neoplasia (Min) mutation in the adenomatous polyposis coli (Apc) gene. Other important results are
summarized in Table 2.
Conclusions
The RCS are a very efficient tool capable of genetic dissection of complex traits and even of identification of gene interactions. The results from lung cancer research on RCS have
shown that one locus can be found by testing ~29 F2 mice
and one interaction per 35 F2 mice (23). Mapping on RCS is
very sensitive; it is not rare for a susceptible allele to be found
in a resistant strain and vice versa (24).
News Physiol Sci • Vol. 19 • June 2004 • www.nips.org
107
TABLE 2. Summary of other results obtained on RCS
Complex Trait
RCS
Loci
Studied Features
Ref.
Note
Lung cancer
OcB/Dem
OcB/Dem
OcB/Dem
Sluc130
Ltsd 18
Kras-2
Tumor number and size
3-Dimensional shape of a tumor
Nuclear cytoplasmatic invaginations
or inclusions, lymphocytic infitration
23
22
21
Colon cancer
CcS/Dem
CcS/Dem
Scc19
2 loci
Tumor number and size
Radiation-induced apoptosis
24
14
Lymphomas
CcS/Dem
1 locus
Radiation-induced tumors
20
CcS/Dem
Alan 1
Alloantigen response
24
OcB/Dem
CcS/Dem
CcS/Dem
CcS/Dem
CcS/Dem
Alan 2
Cinda12
Tria 15
Spol 1
Cypr 1
Alloantigen response
T cell activation by IL-2
T cell activation by anti-CD3
T cell spontaneus proliferation
Production of IL-4
24
24
24
24
24
CcS/Dem
Cypr 23
Production of IL-10
24
CcS/Dem
cora 1
Relationship between levels of IL-4 and IL-10
24
Leishmaniasis
CcS/Dem
Lmr 315
Susceptibility/resistance to infection
24, 25
Malaria
AcB
Char 4
Blood parasitemia at peak of infection
10
Type 1
NOR
Idd 13
Insulin-dependent diabes resistance
18
d
Type 2
Specific RCS are
developed
15
e
Oncology
Immune response
Infectious diseases
a
a
a
b
c
Diabetes mellitus
RCS, recombinant congenic strains. aCinda 1, Cinda 2, Tria 4, Tria 5, Cypr 2, and Cypr 3 are limited only to a certain concentration of activator. Genes
that control response at specific concentrations might participate in pathways, which are preferentially activated under certain conditions; those which
operate in a wide range of concentrations participate in some essential pathways (24). bAlthough infectious disease is initially dependent on random meeting pathogen and not on inherited dispositions of an individual, the course of the developing disease can be strongly influenced by interactions between
host and pathogen genome. For genetic analysis of susceptibility to infectious diseases, the mouse models are especially suitable because they allow not
only control of environmental factors such as pathogen status, dose, and route of infection but also enable generation of crosses between any individuals
(10). cTo dissect immune response to pathogen in vivo, Leishmania major infection was analyzed (24). dIdd13 contains at least two genes: E2-microglobulin is a candidate gene (17). eThe importance of these RCS is their new way of being constructed. Some strains were produced by interval-specific selection as inbreeding of these lines progressed. The mice were selectively bred to fix diabetogenic or nondiabetogenic alleles into different RCS. As a result,
the RCS allow testing of interactions predicted by previous crosses (15).
The reliability of identification of responsible genes in RCS
mapping increases when the same candidate locus is present
in more than one strain. In addition, the candidate locus can
be further shortened: the recombinant event can occur in a
different locus in each strain, but the candidate loci are present only in a segment from the donor parent.
One can expect that the importance of RCS will significantly increase in future. So far, mostly quantitative traits (e.g.,
size and number of tumors) have been analyzed. However,
quantitative aspects (e.g., tumor type, localization) might
prove to be of even more importance. Tripodis and Demant
(22) and Czarnomska et al. (5) have already published the first
studies about qualitative traits.
Identification of all genes involved in the progression of a
disease and especially their functions will enable us to understand the molecular mechanisms of these processes and to
108
News Physiol Sci • Vol. 19 • June 2004 • www.nips.org
discover new targets for therapy. This progress will be the basis
for future individualized treatment and prevention based on
knowledge of a patient’s genotype and for reliable prognosis
when the most efficient way of therapy can be determined.
We thank Dr. Peter Demant for his very helpful comments on the manuscript.
This work was supported by a grant from the Institute of Experimental
Medicine, Academy of Sciences of the Czech Republic (AVOZ5039906).
References
1. Balmain A, Gray J, and Ponder B. The genetics and genomics of cancer.
Nat Genet 33, Suppl: 238244, 2003.
2. Balmain A and Nagase H. Cancer resistance genes in mice: models for
the study of tumor modifiers. Trends Genet 14: 139144, 1998.
3. Bodnar JS, Chatterjee A, Castellani LW, Ross DA, Ohmen J, Cavalcoli J,
Wu C, Dains KM, Catanese J, Chu M, Sheth SS, Charugundla K, Demant
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
P, West DB, de Jong P, and Lusis AJ. Positional cloning of the combined
hyperlipidemia gene Hyplip1. Nat Genet 30: 110116, 2002.
Brockmann GA and Bevova MR. Using mouse models to dissect the
genetics of obesity. Trends Genet 7: 367376, 2002.
Czarnomska A, Krysiak E, Piskorowska J, Sitarz M, Pysniak K, Pilcik T, and
Demant P. Opposite effects of modifiers of the ApcMin mutation in intestine and mammary gland. Cancer Res 63: 45334537, 2003.
Demant P. Genetic resolution of susceptibility to cancernew perspectives. Semin Cancer Biol 3: 159166, 1992.
Demant P. Cancer susceptibility in the mouse: genetics, biology and
implications for human cancer. Nat Rev Genet 4: 721734, 2003.
Demant P and Hart AA. Recombinant congenic strainsa new tool for
analyzing genetic traits determined by more than one gene. Immunogenetics 24: 416422, 1986.
Fijneman RJ, van der Valk MA, and Demant P. Genetics of quantitative
and qualitative aspects of lung tumorigenesis in the mouse: multiple interacting susceptibility to lung cancer (Sluc) genes with large effects. Exp
Lung Res 24: 419436, 1998.
Fortin A, Cardon LR, Tam M, Skamene E, Stevenson MM, and Gros P. Identification of a new malaria susceptibility locus (Char4) in recombinant congenic strains of mice. Proc Natl Acad Sci USA 98: 1079310798, 2001.
Fortin A, Diez E, Rochefort D, Laroche L, Malo D, Rouleau GA, Gros P,
and Skamene E. Recombinant congenic strains derived from A/J and
C57BL/6J: a tool for genetic dissection of complex traits. Genomics 74:
2135, 2001.
Hamet P. Genes and hypertension: where we are and where we should
go. Clin Exp Hypertens 21: 947960, 1999.
Hanahan D and Weinberg RA. The hallmarks of cancer. Cell 100: 5770,
2000.
Mori N, van Wezel T, van der Valk M, Yamate J, Sakuma S, Okumoto M,
and Demant P. Genetics of susceptibility to radiation-induced apoptosis
in colon: two loci on chromosomes 9 and 16. Mamm Genome 9: 377
380.
Reifsnyder PC and Leiter EH. Deconstructing and reconstructing obesityinduced diabetes (diabesity) in mice. Diabetes 51: 825832, 2002.
Ruivenkamp CA, van Wezel T, Zanon C, Stassen AP, Vlcek C, Csikos T,
17.
18.
19.
20.
21.
22.
23.
24.
25.
Klous AM, Tripodis N, Perrakis A, Boerrigter L, Groot PC, Lindeman J,
Mooi WJ, Meijjer GA, Scholten G, Dauwerse H, Paces V, van Zandwijk
N, van Ommen GJ, and Demant P. Ptprj is a candidate for the mouse
colon-cancer susceptibility locus Scc1 and is frequently deleted in human
cancers. Nat Genet 31: 295300, 2002.
Serreze DV, Bridgett M, Chapman HD, Chen E, Richard SD, and Leiter
EH. Subcongenic analysis of the Idd13 locus in NOD/Lt mice: evidence
for several susceptibility genes including a possible diabetogenic role for
beta 2-microglobulin. J Immunol 160: 14721478, 1998.
Serreze DV, Prochazka M, Reifsnyder PC, Bridgett MM, and Leiter EH.
Use of recombinant congenic and congenic strains of NOD mice to identify a new insulin-dependent diabetes resistance gene. J Exp Med 180:
15531558, 1994.
Stassen AP, Groot PC, Eppig JT, and Demant P. Genetic composition of the
recombinant congenic strains. Mamm Genome 7: 5558, 1996.
Szymanska H, Sitarz M, Krysiak E, Piskorowska J, Czarnomska A, Skurzak
H, Hart AA, de Jong D, and Demant P. Genetics of susceptibility to radiation-induced lymphomas, leukemias and lung tumors studied in recombinant congenic strains. Int J Cancer 83: 674678, 1999.
Tripodis N and Demant P. Genetic linkage of nuclear morphology of
mouse lung tumors to the Kras-2 locus. Exp Lung Res 27: 185196, 2001.
Tripodis N and Demant P. Genetic analysis of three-dimensional shape of
mouse lung tumors reveals eight lung tumor shape-determining (Ltsd) loci
that are associated with tumor heterogeneity and symmetry. Cancer Res
63: 125131, 2003.
Tripodis N, Hart AA, Fijneman RJ, and Demant P. Complexity of lung cancer modifiers: mapping of thirty genes and twenty-five interactions in half
of the mouse genome. J Natl Cancer Inst 93: 14841491, 2001.
Van Wezel T, Lipoldova M, and Demant P. Identification of disease susceptibility genes (modifiers) in mouse models: cancer and infectious diseases. In: Genotype to Phenotype (2nd ed.). Oxford, UK: BIOS Scientific,
2001, p. 107129.
Vladimirov V, Badalova J, Svobodova M, Havelkova H, Hart AA, Blazkova
H, Demant P, and Lipoldova M. Different genetic control of cutaneous
and visceral disease after Leishmania major infection in mice. Infect
Immun 71: 20412046, 2003.
News Physiol Sci • Vol. 19 • June 2004 • www.nips.org
109