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Sex effect on clinical and immunologic quantitative trait loci in a murine model of rheumatoid arthritis.

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ARTHRITIS & RHEUMATISM
Vol. 48, No. 6, June 2003, pp 1708–1720
DOI 10.1002/art.11016
© 2003, American College of Rheumatology
Sex Effect on Clinical and Immunologic Quantitative Trait Loci
in a Murine Model of Rheumatoid Arthritis
Vyacheslav A. Adarichev,1 Andrew B. Nesterovitch,1 Tamás Bárdos,2 Darci Biesczat,1
Raman Chandrasekaran,1 Csaba Vermes,2 Katalin Mikecz,1
Alison Finnegan,1 and Tibor T. Glant1
Objective. To explore the effect of sex on clinical
and immunologic traits in major histocompatibility
complex–matched (H-2d) F2 hybrid mice with proteoglycan (PG)–induced arthritis and to identify how the
quantitative trait locus (QTL) on the X chromosome
influences the onset QTL of another chromosome.
Methods. (BALB/c ⴛ DBA/2)F2 hybrid mice were
immunized with cartilage PG, and a genome-wide linkage analysis was performed using >200 simple
sequence-length polymorphic markers. The major clinical traits (susceptibility, onset, and severity) were
assessed, and PG-specific T and B cell responses, and
the production of proinflammatory and antiinflammatory cytokines (tumor necrosis factor ␣, interleukin-1
[IL-1], IL-6, interferon-␥, IL-4, IL-10, and IL-12) were
measured in 133 arthritic and 426 nonarthritic female
and male F2 hybrid mice. The major clinical and
immunologic traits were linked to genetic loci, and
potential linkages among these QTLs and the effect of
sex were analyzed.
Results. Thirteen QTLs reported in previous
studies were confirmed. Binary traits (susceptibility to
arthritis) and disease onset were female specific and
were identified on chromosomes 3, 7, 10, 11, 13, and X.
QTLs for disease severity were mostly male specific and
were located on chromosomes 1, 4, 5, 8, 14, 15, and 19.
In addition, we identified 4 new QTLs for the onset of
arthritis on chromosomes 3, 4, and 11, and 1 new QTL
for severity on chromosome 14; all showed a strong
gender association. A locus on the X chromosome
interacted with a QTL on chromosome 10, and these 2
loci together seemed to control disease incidence and
onset. Most of the clinical traits (QTLs) shared common
regions with the immunologic traits and frequently
showed a locus–locus interaction.
Conclusion. Numerous immunologic QTLs overlap with clinical QTLs, thus providing information
about possible mechanisms underlying QTL function.
Disease susceptibility and onset showed predominant
linkage with the female sex, under the control of a QTL
on the X chromosome, while the severity QTLs were
more strongly linked to the male sex.
Rheumatoid arthritis (RA) is a complex inflammatory autoimmune disease with a female prevalence
that affects ⬃1% of the population (1–4). In spite of
extensive epidemiologic, genetic, and immunologic studies, the etiology of RA remains unknown. Because of the
difficulty of examining early events in disease progression, most studies concentrate on inflammation in peripheral joints when and where the clinical symptoms
become evident. Linkage, or quantitative trait loci
(QTL), analysis is a unique method of identifying susceptibility genes, at least theoretically, irrespective of the
time point at which the gene was, or is still, active.
Linkage analysis of families with RA probands is in
progress, and several chromosomal regions (QTLs) have
already been identified (5–14). Among the diseaseassociated loci, the major histocompatibility complex
(MHC) on human chromosome 6 has the strongest
effect on RA susceptibility, as it does in many other
autoimmune diseases (5,6,11,12,14,15).
Supported by a grant from the NIH (AR-45652).
1
Vyacheslav A. Adarichev, PhD, Andrew B. Nesterovitch,
MD, Darci Biesczat, MS, Raman Chandrasekaran, PhD, Katalin
Mikecz, MD, PhD, Alison Finnegan, PhD, Tibor T. Glant, MD, PhD:
Rush University at Rush–Presbyterian–St. Luke’s Medical Center,
Chicago, Illinois; 2Tamás Bárdos, MD, Csaba Vermes, MD: University
of Pécs, Pécs, Hungary, and Rush University at Rush–Presbyterian–St.
Luke’s Medical Center, Chicago, Illinois.
Address correspondence and reprint requests to Vyacheslav
A. Adarichev, PhD, Department of Orthopedic Surgery, Rush University at Rush–Presbyterian–St. Luke’s Medical Center, 1735 West
Harrison Street, Room 708, Chicago, IL 60612. E-mail:
vadarich@rush.edu.
Submitted for publication November 20, 2002; accepted in
revised form February 14, 2003.
1708
SEX-CONTROLLED CLINICAL TRAITS IN ARTHRITIS
The importance of sex in the pathogenesis of RA
has long been recognized, and the female prevalence of
RA is well-documented (1,16,17). The relationship between arthritis and sex in different animal models of RA
is not evident, although many investigators have reported significant sex-related differences in arthritis
severity and incidence in inbred murine strains, their F2
hybrids, and in congenic strains (18–23). The X and Y
chromosomes are therefore logical targets of linkage
analysis when the sex effect of disease-associated traits is
at the center of a genetic study. Only a very few linkage
studies, however, have confirmed the involvement of the
X chromosome in humans with RA (6,14,15) or in
experimental models of arthritis (18,20,23).
QTL analysis in humans is hampered by the
genetic heterogeneity of the population, the uncontrollable environmental effects, and the limited number of
meioses. Therefore, animal models of RA are attractive
tools because use of such models not only overcomes
genetic complications, but also permits studies during
the early phases of disease development. Arthritis can be
induced in susceptible rodent strains by injection of
adjuvant (24), oil (25), collagen (26), bacterial cell wall
components (27), or cartilage proteoglycan (PG)
(28,29). To date, linkage analysis studies in animal
models of RA have identified more than 20 loci that are
either specific to the given animal model or shared with
other models or with RA in humans. A number of traits
of arthritis, including the severity, onset, and susceptibility and the production of antibodies and cytokines,
have been linked to chromosome regions that are syntenic in various species and often show overlapping
localization in several autoimmune diseases, thus suggesting shared genetic components (27,30–32).
Cartilage PG (aggrecan)–induced arthritis
(PGIA) can be induced in genetically susceptible
BALB/c or C3H/HeJCr mice (28,29,33–35). PGIA
shows many similarities to RA, as indicated by the
clinical symptoms and the findings of radiographic,
laboratory, and histopathologic examination of peripheral joints (28,29,33,36,37). In a special intercross,
using F2 hybrids of DBA/1 mice, which are susceptible to
collagen-induced arthritis (CIA) but not to PGIA, and
PGIA-susceptible mice, which are resistant to CIA, we
were able to compare the effect of the MHC in 2
different autoimmune arthritis models with shared genetic backgrounds (37,38). Based on the results of these
studies, we concluded that the MHC locus plays a critical
role in PGIA, although the effect is not as strong as that
in CIA. To date, we and other investigators have identified 18 Cia and 25 Pgia loci in mice (34,35,37–43); 14 of
1709
these QTLs were colocalized in the 2 models, and some
of these loci corresponded to QTLs identified in humans
with RA.
These genetic studies suggested that while numerous QTLs control and/or modify the clinical and
immunologic traits, the MHC plays a predominant role.
To exclude the effect of the MHC on some QTLs and to
detect possible interactions among other QTLs, we used
MHC-matched (BALB/c ⫻ DBA/2)F2 hybrid mice. This
particular genetic cross also allowed us to study the
effects of sex on the clinical and immunopathologic
features of the disease and on QTLs. In the present
study, we used a population of F2 hybrids (n ⫽ 559),
separated the arthritis trait into susceptibility and severity subtraits, and compared the immunologic/
pathophysiologic markers in all immunized animals.
MATERIALS AND METHODS
Animals, antigen, and immunization. Female BALB/c
mice and male DBA/2 mice (Charles River, Kingston, NY)
were mated, and the F1 offspring were intercrossed to generate
F2 hybrids (n ⫽ 559). At the age of 12 weeks, mice were
immunized with cartilage PG using standard immunization
protocols (33). Briefly, 100 ␮g of human cartilage PG (measured as protein) was emulsified in adjuvant (100 ␮l) and
injected intraperitoneally on days 0, 7, 28, and 49. Freund’s
complete adjuvant (Difco, Detroit, MI) was used for the first
and fourth injections; the second and third injections contained antigen in Freund’s incomplete adjuvant. Mice were
killed 7–8 weeks after the fourth injection (i.e., on days
102–104 of the experiment).
Assessment of arthritis and clinical traits. Arthritis
was assessed 3 times each week. Inflammation in each paw was
scored on a 0–4 scale, and the findings in the 4 paws were
summed to yield an arthritis score (range 0–16 for each
animal) (28,33,34,36). Paws with questionable clinical scores
and those with an arthritis score of 1 were evaluated histopathologically, as described previously (33,35). The clinical
score includes both qualitative and quantitative traits, such as
the incidence of arthritis (susceptibility) and the severity of
inflammation, but it does not reflect the time of arthritis onset
(i.e., how early the arthritis develops after immunization).
The primary clinical score in an autoimmune model is
the binary (qualitative) trait, which is the susceptibility to disease.
This trait has only 2 values: 0 for nonarthritic animals and 1 for
arthritic animals. All other components of the clinical score for
arthritis are quantitative: disease severity, disease progression,
and time to disease onset. Therefore, we separated the qualitative
(susceptibility) and quantitative (other clinical) traits, and introduced additional scores based on the arthritis index. The severity
score is the same as the arthritis score, but it applies only to
arthritic mice (range of scores 1–16). In addition, a disease onset
score (range 0–5) that reflected how quickly the animals developed arthritis was assigned. A maximum score of 5 was given for
all animals that developed PGIA on day 50 or earlier. An onset
score of 0 was given for animals that never showed any symptom
1710
of inflammation and did not develop arthritis by the end of the
experimental period (days 102–104). Intermediate scores between
0 and 5 were assigned using linear time adjustments.
Measurement of antibodies, T cell responses, and
cytokine production. Antibodies to the immunizing (human)
and mouse (self) cartilage PGs were determined by enzymelinked immunosorbent assay (ELISA) as described elsewhere
(38,44). Briefly, 96-well Maxisorp plates (Nunc International,
Hanover Park, IL) were coated with either chondroitinase
ABC–digested human (for heteroantibodies) or native mouse
(for autoantibodies) cartilage PGs (0.1 ␮g of antigen protein/
well). PG-specific antibodies were measured in serially diluted
(1:500–1:62,500) immune sera using peroxidase-conjugated
goat anti-mouse IgG, IgA, and IgM (for total antibodies) or
anti-IgG1 or anti-IgG2a (for Th2- and Th1-supported IgG
isotypes, respectively) secondary antibodies (Zymed, South
San Francisco, CA). Serum antibody levels were expressed in
arbitrary units, which were calculated in each case as a ratio of
the serum dilution of the experimental sample relative to the
dilution of the standard (pooled arthritic BALB/c serum) at
the median of the maximum and minimum absorbance levels
measured on the same plate (35).
Antigen-specific T cell responses were measured in
quadruplicate samples of spleen cells (3 ⫻ 105 cells/well)
cultured in the presence of 100 ␮g of PG protein/ml.
Interleukin-2 (IL-2) was measured in supernatants collected
on day 2, by determining the proliferation rate of the IL-2–
dependent CTLL-2 cell line. Antigen-specific T cell proliferation was assessed on day 5, by determining the incorporation of
3
H-thymidine (45,46). In both cases, the antigen-specific response was expressed as a stimulation index, which is the ratio
of the counts per minute of 3H-thymidine incorporated in
antigen-stimulated cultures relative to the cpm incorporated in
unstimulated cultures (33,45). Antigen-specific production of
interferon-␥ (IFN␥) and IL-4 by T cells was determined in
identical culture conditions as described for T cell proliferation
in 4-day conditioned media (2.5 ⫻ 106 mononuclear cells/ml)
using capture ELISAs (R&D Systems, Minneapolis, MN).
Serum IL-1 concentrations were determined by bioassay using D10S cells as described (35,47). Soluble CD44
(sCD44; used as a marker of inflammation) levels in serum
were determined by an ELISA developed in our laboratory
(48). Serum levels of tumor necrosis factor ␣ (TNF␣), IL-6,
IL-10, and IL-12 were assayed using capture ELISAs (R&D
Systems or PharMingen, San Diego, CA).
Genome screening and linkage analysis. Genomic
DNA was isolated from (BALB/c ⫻ DBA/2)F2 hybrid mice
and subjected to genotyping. Simple sequence-length polymorphic (SSLP) markers (MWG Biotech, High Point, NC) were
used for polymerase chain reaction, which was followed by gel
electrophoresis in 3.5% MetaPhore agarose (FMC Bioproducts, Rockland, ME) as described previously (34,35,37). These
markers covered all 19 autosomes and the X chromosome; the
Y chromosome was not analyzed in this study.
We initially prescreened an approximately equal number of arthritic (n ⫽ 133) and nonarthritic (n ⫽ 143) mice using
a set of markers that covered all chromosomes. Along with this
initial prescreening process, at least 3 markers were used for
each chromosome, and 4–5 markers were used for larger
chromosomes. The genomic screening was subsequently extended by increasing the number of primers and by screening
ADARICHEV ET AL
all 559 PG-immunized F2 mice. When a QTL with a logarithm
of odds (LOD) score ⱖ2.0 was found to be linked with any
arthritis (clinical) score, the chromosomal region was saturated
with additional markers, and linkage analysis was performed
for the entire F2 population. This process was repeated several
times to reach a reasonable density of markers with an average
distance of ⬍10 cM.
The main source of data for SSLP polymorphisms
between BALB/c and DBA/2 progenitor strains was the Jackson Laboratory Web site (http://www.informatics.jax.org).
Linkage map construction and traits–markers linkage/
regression analyses were performed using Map Manager QTX
version 13 software (49). The LOD threshold for suggestive
linkage was set at 2.8 and at 4.3 for significant linkage (50). A
permutation test to establish empirical LOD score thresholds
was used (49,51). The order of markers and their exact
positions on chromosomes were confirmed using genomic
maps from the Celera Discovery System (52).
Statistical analysis. Statistical analysis was performed
using SPSS statistical software (version 10.0.5; SPSS, Chicago,
IL). Since some clinical traits demonstrated nonparametric
distribution in the F2 hybrid population, we used the MannWhitney U test to examine differences between populations
and Spearman’s correlation to evaluate biases between traits.
Chi-square statistics were used to determine the significance of
locus–locus interactions, and Student’s 2-sample t-test was
used to compare the results from 2 groups, where the data
showed normal distribution. P values less than 0.05 were
considered significant.
RESULTS
Influence of sex on major clinical traits of arthritis in MHC-matched (BALB/c ⴛ DBA/2)F2 hybrid population. As expected, based on the results of previous
studies (28,29,34,53), 100% of the parental BALB/c
Figure 1. Distribution of arthritis onset scores in parental BALB/c
female mice and DBA/2 male mice and their F1 and F2 hybrid
offspring. Each shaded circle represents an arthritic animal; numbers
within boxes represent the proteoglycan-immunized nonarthritic animals (onset score 0). See Materials and Methods for an explanation of
the scoring system. Horizontal bars show the mean scores for the F2
female and male hybrids; values beside the bars show the mean ⫾ SEM
onset scores (ⴱ ⫽ P ⬍ 0.05).
SEX-CONTROLLED CLINICAL TRAITS IN ARTHRITIS
1711
Table 1. Comparison of arthritic versus nonarthritic mice and of male versus female (BALB/c ⫻ DBA/2)F2 mice immunized with cartilage PG*
Trait
T cell response, stimulation index†
Proliferation
IL-2
PG-specific antibodies in serum, arbitrary
units or ratio‡
Autoantibody
Heteroantibody
Mouse IgG1:IgG2a
Human IgG1:IgG2a
Human IgG1:mouse IgG1
Human IgG2a:mouse IgG2a
Cytokines in CM (IFN␥ and IL-4) or serum
(all others), pg/ml§
IFN␥
IL-4
IL-1
TNF␣
IL-6
IL-10
IL-12
Arthritic
(n ⫽ 133)
Nonarthritic
(n ⫽ 426)
P
Female
(n ⫽ 302)
Male
(n ⫽ 257)
P
1.98 ⫾ 0.08
1.58 ⫾ 0.10
2.15 ⫾ 0.08
1.70 ⫾ 0.04
NS
NS
2.13 ⫾ 0.05
1.72 ⫾ 0.06
2.15 ⫾ 0.13
1.63 ⫾ 0.05
NS
NS
0.75 ⫾ 0.19
4.00 ⫾ 0.40
6.10 ⫾ 0.30
8.48 ⫾ 0.54
1.96 ⫾ 0.05
1.59 ⫾ 0.06
1.62 ⫾ 0.22
6.08 ⫾ 1.12
7.96 ⫾ 0.41
10.8 ⫾ 0.64
2.02 ⫾ 0.04
1.61 ⫾ 0.04
0.036
NS
0.014
0.007
NS
NS
1.43 ⫾ 0.24
6.33 ⫾ 1.58
6.73 ⫾ 0.41
8.31 ⫾ 0.63
1.85 ⫾ 0.05
1.61 ⫾ 0.04
1.50 ⫾ 0.28
5.06 ⫾ 0.59
8.44 ⫾ 0.50
11.96 ⫾ 0.60
2.19 ⫾ 0.04
1.62 ⫾ 0.06
NS
NS
0.008
0.001
0.001
NS
456 ⫾ 55.1
168 ⫾ 17.6
9.76 ⫾ 0.65
420 ⫾ 241
77.6 ⫾ 18.4
18.0 ⫾ 2.27
723 ⫾ 185
691 ⫾ 44.7
228 ⫾ 11.5
9.20 ⫾ 0.33
136 ⫾ 22.8
90.8 ⫾ 9.74
34.6 ⫾ 3.05
457 ⫾ 66.5
0.008
0.010
NS
0.012
NS
0.019
NS
762.3 ⫾ 63.8
179.1 ⫾ 12.0
11.1 ⫾ 0.4
131.3 ⫾ 22.6
37.0 ⫾ 4.0
38.8 ⫾ 4.35
396.4 ⫾ 65.7
509.2 ⫾ 36.3
259.8 ⫾ 16.2
7.07 ⫾ 0.42
142.4 ⫾ 41.7
149.9 ⫾ 17.9
23.7 ⫾ 2.76
616.6 ⫾ 113.6
0.001
0.001
0.001
NS
0.001
0.005
NS
* Values are the mean ⫾ SEM. Two-sample t-test, assuming unequal variances, was applied for comparison of means; significance was set at P ⬍
0.05. Mann-Whitney U tests gave similar results. See Materials and Methods for a detailed description of the pathophysiologic traits. PG ⫽
proteoglycan; NS ⫽ not significant; IL-2 ⫽ interleukin-2; CM ⫽ conditioned medium; IFN␥ ⫽ interferon-␥; TNF␣ ⫽ tumor necrosis factor ␣.
† Bioassay of the IL-2–dependent CTLL-2 cell line was used to measure antigen (PG)–specific T cell stimulation, and direct 3H-thymidine
incorporation was used to measure T cell proliferation.
‡ Levels of serum autoantibodies and heteroantibodies to mouse and human PGs were measured by enzyme-linked immunosorbent assay. Ratios
of IgG1 to IgG2 antibodies against human or mouse cartilage PG are shown. Antibody levels are expressed as arbitrary units, calculated by
comparing serum levels with levels in a standard (pooled sera from arthritic animals), as described in Materials and Methods.
§ Enzyme-linked immunosorbent assays were used to measure IFN␥ and IL-4 in conditioned media from antigen-stimulated T cells (2.5 ⫻ 106
cells/ml), and IL-1, TNF␥, IL-6, IL-10, and IL-12 in serum.
female mice developed PGIA, and the F1 hybrid mice
were completely resistant. Approximately 24% of the
(BALB/c ⫻ DBA/2)F2 population (133 of the 559 immunized mice) developed the disease, with a similar
incidence in females (22.5%) and males (25.3%) (Figure
1).
The severity of arthritis was slightly higher in
males than in females, but the difference was not
statistically significant (data not shown). Since sex differences in disease onset have been reported in the
parental BALB/c strain (29), we also analyzed the onset
score in the (BALB/c ⫻ DBA/2)F2 population. We
found that F2 hybrid males developed arthritis sooner
(mean ⫾ SEM onset score 2.32 ⫾ 0.25, which corresponds to a mean day of onset 77.5) than did F2 hybrid
females (onset score 1.40 ⫾ 0.23, which corresponds to a
mean day of onset 86.5) (Figure 1).
Effect of sex on immune responses and cytokine
production in PGIA. We found significant statistical
differences in disease onset, with males developing
arthritis an average of 9 days sooner than females
(Figure 1), but no sex-related differences in either
severity or susceptibility were evident in the entire F2
hybrid population. To find an explanation for this, we
analyzed the 302 females and 257 males separately for T
cell and B cell responses and for the production of
proinflammatory and antiinflammatory cytokines.
Comparison of arthritic and nonarthritic (either
female or male) F2 mice yielded no significant differences in antigen-specific T cell responses (measured as T
cell proliferation or IL-2 production) or in the amounts
of antigen-specific antibodies or heteroantibodies in
serum (Table 1). This was not the case, however, when
Th1- and Th2-supported immunoglobulin isotypes and
antigen-specific production of IFN␥ and IL-4 were analyzed and compared (Table 1). Thus, while no significant
differences were found when the overall immune responses
(either T cell– or B cell–mediated) were compared in
immunized F2 hybrid mice, more sensitive markers revealed significant differences in PG-immunized arthritic
versus nonarthritic F2 populations (Table 1 and Figure 2).
This was even more evident when arthritic females were compared with arthritic males or when
nonarthritic females were compared with nonarthritic
1712
ADARICHEV ET AL
Figure 2. Differences in immunologic parameters between female and male arthritic (⫹) and nonarthritic (–) mice from the (BALB/c ⫻ DBA/2)F2 population
immunized with cartilage proteoglycan (PG) to induce arthritis. Among the 16
immunologic markers or calculated parameters measured in the 559 immunized
animals, only those which showed significant differences are presented. A, Serum
levels of autoantibodies (aAb) against mouse (self) PG. B, Ratio of mouse IgG1 to
IgG2a autoantibodies (aG1/G2a). Levels of C, interferon-␥ (IFN␥) and D,
interleukin-4 (IL-4) were measured in 4-day supernatants of PG-stimulated spleen
cell cultures. Levels of E, IL-10, F, IL-6, G, IL-1, and H, tumor necrosis factor ␣
(TNF␣) were measured in serum. Values are the mean ⫾ SEM. ⴱ ⫽ P ⬍ 0.05; ⴱⴱ
⫽ P ⬍ 0.005, by Student’s 2-sample t-test.
males (Figure 2). Serum IL-1 concentrations were significantly higher in female mice (either arthritic or
nonarthritic) (Figure 2G), and IL-10 concentrations
were significantly higher in nonarthritic female mice
(Figure 2E). IFN␥ production was more prominent in
the nonarthritic female group than in either of the male
groups (Figure 2C). IL-4 was significantly higher in both
the arthritic and nonarthritic male groups than in the
female groups (Figure 2D), whereas serum levels of
TNF␣ were uniformly elevated in all arthritic mice
compared with nonarthritic mice (Figure 2H).
Genome-wide linkage search for arthritis susceptibility genes. In previous studies (34,35), we used the
arthritis score as a single trait, which was applied to both
arthritic and nonarthritic mice. Using this definition of
the arthritis trait, we identified 12 Pgia loci in a previous
study (34). Although the experimental design was similar, the mapping was accomplished with 106 polymorphic markers and a different F2 population. This arthritis
score, however, contained a mixture of several clinical
traits. We therefore further divided the arthritis score
into 3 independent scores: susceptibility to PGIA (binary), onset of the disease (onset), and severity of inflammation (severity). Although separation of clinical traits
did not create biases among all traits, this step seemed to
be a necessary procedure for correct calculations and
linkage analysis of genes that might control the different
features of arthritis. Indeed, differences between the 3
clinical traits (binary, onset, and severity) and their
linkage to different Pgia loci clearly indicated the necessity of this approach.
The binary trait of disease susceptibility was
mapped to chromosomes 7, 11, and 13 (Figure 3). The
highest QTL was on chromosome 7 (LOD score 4.8),
while the QTL on chromosome 13 showed an LOD
score of 4.1 and the QTL on chromosome 11 had an
LOD score of 3.1. All binary QTLs were high in females,
but were lower or absent in males (Figure 3).
Disease onset as a trait correlated significantly
with arthritis, showing overlapping QTLs on chromo-
SEX-CONTROLLED CLINICAL TRAITS IN ARTHRITIS
1713
Figure 3. Linkage analysis of clinical traits in a population of (BALB/c ⫻ DBA/2)F2 mice immunized with
cartilage proteoglycan (PG) to induce arthritis. Quantitative trait loci (QTLs) were calculated for males,
females, and both sexes together. The “arthritis score” used in previous studies (34,35) was separated into
subtraits: susceptibility to disease (binary trait), onset of disease, and severity of inflammation. The y-axes
show logarithm of odds (LOD) scores, calculated using the free regression model of linkage for each
chromosome. Whiskers along the x-axes show the positions of genomic markers according to the Kosambi
linkage map for each chromosome. Interval mapping linkage analysis was performed using Map Manager
QTX software. Table 2 shows information on peak marker positions (additional information is available
from the authors upon request) and a summary of the QTLs for clinical and immunologic traits. New QTLs
(Pgia26–Pgia29) are indicated by asterisks. Loci with LOD scores exceeding the threshold of empirical
significance established with the permutation test are boldfaced. Pgia28* on chromosome 11 is an
immune-related QTL in F2 females without any evident correlation with clinical traits. hG1/G2a ⫽ ratio
of human IgG1 to IgG2a autoantibodies.
some 7 for disease onset (Pgia21 locus, LOD 2.5–3.0)
and susceptibility (LOD 4.8). For all other chromosomes, onset was an independent trait from the binary or
severity QTLs. The major QTL for onset was on chro-
mosome 3 for females (LOD 4.9), and 2 smaller QTLs
were found on chromosomes 10 and X (Figure 3 and
Table 2).
The third clinical trait, severity, did not show any
1714
ADARICHEV ET AL
Table 2. Summary of QTLs for PGIA in (BALB/c ⫻ DBA/2)F2 intercross mice*
QTL
Position,
cM
1
3
4
4
5
7
Pgia1
Pgia26‡§
Pgia13
Pgia27§
Pgia18
Pgia21‡
33–106
34–80
0–20
35–45
0–10
23–44
D1Mit445
D3Mit158
D4Mit97
D4Mit45
D5Mit1
D7Mit120
8
Pgia4‡
1–22
D8Mit224
Severity LOD 3.1, males
Onset LOD 4.9, females
Severity LOD 3.1, males
Severity LOD 3.1, males
Severity LOD 3.3, males
Binary trait LOD 4.8, females;
onset LOD 2.5, all F2
Severity LOD 3.9, all F2
9
10
11
11
13
14
15
15
16
19
X
Pgia5
Pgia6
Pgia28§
Pgia7
Pgia15‡
Pgia29§
Pgia8
Pgia9
Pgia10
Pgia12‡
Pgia25
30–50
30–40
0–28
42–70
0–47
40–54
0–20
30–55
40–72
11–33
67–72
D9Mit232
D10Mit40
D11Mit229
D11Mit132
D13Mit179
D14Mit92
D15Mit252
D15Mit28
D16Mit50
D19Mit30
DXMit5
–
Onset LOD 3.5, females
–
Binary trait LOD 3.1, females
Binary trait LOD 4.1, females
Severity LOD 2.8, males
Severity LOD 3.5, females
Severity LOD 3.0, males
–
Severity LOD 4.4, all F2
Onset LOD 3.0, all F2
Chromosome
Marker
Clinical traits†
Immunologic traits†
Hetero IgG1:auto IgG1 LOD 3.7, males
–
TNF␣ LOD 5.1, males
–
Hetero IgG1:IgG2a LOD 2.8, males
–
IL-2 LOD 3.5, females; hetero T cell
proliferation LOD 3.5, females
Hetero IgG1:IgG2a LOD, 3.4 males
–
Hetero IgG1:IgG2a LOD 2.8, males
–
IL-1 LOD 3.1, females
Hetero T cell proliferation LOD 3.5, females
–
–
IL-1 LOD 3.5, females
–
Hetero IgG1:auto IgG1 LOD 3,4, all F2; IL-1
LOD 3.2, all F2; IL-6 LOD 3.5, all F2
* Shown are the chromosomes that contain quantitative trait loci (QTLs) for clinical or immunologic traits of arthritis in major histocompatibility
complex–matched (BALB/c ⫻ DBA/2)F2 hybrid mice, the position of the QTL (in cM), and the peak marker position. PGIA ⫽ proteoglycaninduced arthritis; LOD ⫽ logarithm of odds; TNF␣ ⫽ tumor necrosis factor ␣; IL-2 ⫽ interleukin-2.
† Linkage for the binary trait, onset, and severity of the disease was calculated for males, females, and for the entire F2 population. The LOD score
for each QTL is shown. Immunologic traits are those described in Table 1.
‡ Loci with LOD scores exceeding the threshold of empirical significance established with the permutation test.
§ New QTL identified in the present study. See references 34, 35, 37, and 38 for QTLs previously identified in PGIA.
correlation with any of the other clinical traits. However,
we found that a number of severity QTLs mapped to
chromosomes 1, 4, 5, 8, 14, 15, and 19 (Figure 3 and
Table 2).
When linkage analysis was performed for both
sexes, all QTLs demonstrated either deviation of the
peak position or different LOD scores for males and
females. Interestingly, both binary and onset QTLs were
higher in females than in males, and a few QTLs were
fully sex-restricted, such as binary QTLs on chromosomes 11 and 13, and the onset QTL on chromosome 3
(Figure 3). In contrast, severity QTLs were more prominent in males than in females, showing several sexrestricted QTLs on chromosomes 1, 4, 14, and 15.
Comparison of clinical and immunologic traits
and QTLs. Table 2 summarizes the data from the QTL
analyses of all clinical and immunologic traits that were
scored in the entire F2 population and analyzed separately in males and females. Unexpectedly, only a few
immunologic traits showed QTLs by linkage analysis.
Seven of the immunologic traits showed overlap with
clinical QTLs (Table 2), and most were also sexrestricted. Binary (susceptibility) QTL Pgia15 overlapped with the serum IL-1 QTL on chromosome 13.
Disease onset QTL Pgia25 on the X chromosome was
colocalized with the QTL for serum IL-1 and IL-6 and
for PG-specific antibodies of IgG1 isotype (Table 2).
In general, most immunologic QTLs showed
overlap with the severity QTLs, particularly at loci Pgia1,
Pgia13, Pgia18, Pgia4, and Pgia29 on chromosomes 1, 4,
5, 8, and 14, respectively (Table 2). Some immunologic
QTLs, however, did not match any clinical QTLs. For
example, immunologic traits mapped to Pgia5 on chromosome 9, Pgia28 on chromosome 11, and Pgia10 on
chromosome 16 did not show any relationship with
clinical QTLs (Table 2).
Interaction between loci in different chromosomes. We identified 4 loci (Pgia6, Pgia21, Pgia25, and
Pgia26) that primarily contributed to disease onset (Figure 3 and Table 2), one of which (Pgia25) was on the X
chromosome. Taking advantage of the large number of
animals and the dense set of genomic markers used, we
sought to determine possible interactions between onset
QTLs (genes) on chromosomes 3, 7, 10, and X (Figure
4). We examined pairwise interactions (QTL to QTL),
assuming that disease onset is affected not only by an
allele represented by a single QTL peak marker, but also
by a gene from another chromosome. Interaction was
SEX-CONTROLLED CLINICAL TRAITS IN ARTHRITIS
1715
Figure 4. Interaction of the onset quantitative trait locus (QTL) on the X chromosome (peak marker DXMit5) with
onset QTLs on chromosomes 3, 7, and 10. The genotype of peak markers for these chromosomes was determined, and
the onset score was calculated for each subset of mice carrying certain marker–marker combinations. A, Interaction of
the X chromosome with chromosome 3 demonstrates that the source of arthritic allele is the BALB/c (B) parent and that
the DBA/2 allele (D) is recessive in the context of the same (D) allele. In the heterozygous state (H), the alleles were
codominant. B, Interaction of the X chromosome with chromosome 7 demonstrates that the arthritic allele is derived
from the DBA/2 background. C, Interaction of the X chromosome with chromosome 10 demonstrates locus–locus
interactions (i.e., the genotype at the X chromosome affects allele–allele interaction on chromosome 10). Values are the
mean ⫾ SEM. Asterisks indicate a significant difference between the 3 genotypes when the X chromosome was used as
a reference genotype, by chi-square test. D, Further analysis of the allele–allele interaction shown in C demonstrates that
the incidence of arthritis is under the influence of both chromosomes. Values are the number of arthritic mice/total
number of mice and the incidence.
tested using the peak marker on the X chromosome
(DXMit5 for QTL Pgia25) and peak markers D3Mit158
(Pgia26), D7Mit120 (Pgia21), and D10Mit40 (Pgia6).
Allele–allele interactions were verified using chi-square
statistics with 3 independent variables and a threshold P
value of 0.05.
Figure 4A demonstrates the interactions between
D3Mit158 and DXMit5 peak markers. The BALB/c allele
of D3Mit158 (Bchr3) is dominant for disease onset, while
the DBA/2 allele on chromosome 3 (Dchr3) is recessive,
as suggested by a high onset score for the B allele and a
low score for the D allele. In the heterozygous state, the
DBA/2 and BALB/c allele interaction (Hchr3) is clearly
codominant, resulting in intermediate onset scores.
Dchr3–Hchr3–Bchr3 interaction (Pgia26) is not affected by
Pgia 25 on the X chromosome; thus, allele Bchr3 is always
codominant with allele Dchr3 (Figure 4A).
The same approach was applied to chromosomes
7 (Figure 4B) and 10 (Figure 4C). As in the case of
chromosome 3, no interaction was found between chromosomes 7 and X. However, the gene locus marked with
D10Mit40 on chromosome 10 was affected by the X
chromosome (marker DXMit5), since mice heterozygous
for the D10Mit40 locus and having DBA/2 alleles of
1716
ADARICHEV ET AL
DXMit5 (Hchr10–DchrX) showed significantly lower onset
scores (Figure 4C, second column). To determine
whether the susceptibility of arthritis was also affected
by interacting loci on chromosomes X and 10, we
compared the effects of loci represented by DXMit5 and
D10Mit40 on the incidence of disease (Figure 4D).
Indeed, incidence was affected in the same manner as
onset, suggesting that both disease incidence and disease
onset are under the control of the QTLs localized at
chromosomes X (Pgia25) and 10 (Pgia6).
DISCUSSION
Immunization of BALB/c mice with human cartilage proteoglycan induces progressive autoimmune
polyarthritis, which leads to complete deterioration of
the articular cartilage and to joint deformities (28,33), as
in RA. There is strong genetic linkage between the
MHC and autoimmune/arthritic processes (37); however, having the “right” combination of MHC alleles is
not sufficient for the induction of an autoimmune disease (29,35,44,53). In order to exclude the MHC effect
on disease development and severity in a mixed (F2)
genetic background, we intercrossed PGIA-susceptible
BALB/c females with arthritis-resistant DBA/2 males,
both strains carrying the same H-2d haplotype. The F1
progeny were further mated to generate (BALB/c ⫻
DBA/2)F2 hybrid mice, which were then immunized for
PGIA. As expected, no disease-linked QTL was identified at MHC loci on chromosome 17. Since the original
MHC (H-2d) function in this particular intercross was
unchanged, the PG epitope repertoire was presented
and recognized by autoreactive T cells in a similar
manner in F2 hybrids and the PGIA-susceptible parental
BALB/c strain. Therefore, this particular genetic combination allowed us to examine interactions between
QTLs without the effect of the MHC. Among these loci,
we identified 4 new QTLs (Pgia26–Pgia29), all of which
were clearly affected by sex (Figure 3 and Table 2).
Statistical estimates for LOD score thresholds
that were modeled by Lander and Kruglyak in 1995 (50)
were based on a theoretical murine population with a
total genome size of 1,600 cM. However, since each F2
hybrid population differs by the distributions of markers
and traits and biases between them, we established
empirical LOD score thresholds using a permutation
test (49,51). Taking into consideration that permutations
require some computational power, we considered for
further calculations only traits that showed linkage
above the theoretically “suggestive” LOD score of 2.8
(50). These were binary and onset traits in females and
severity traits in males and females. To determine
empirical threshold values for mapping these traits, we
used 1,000 permutations, which were analyzed genomewide for all 20 genotyped chromosomes in 1-cM intervals. Permutation analysis allowed us to establish suggestive, significant, and highly significant levels for QTL
detection, corresponding to the 37th, 95th, and 99.9th
percentiles, respectively.
As a result of this analysis, suggestive levels for all
clinical traits were even lower than the theoretical
estimates, with an empirical LOD score of 2.3 versus a
theoretical LOD score of 2.8 (50). Significant empirical
thresholds (LOD scores of 4.1 and 3.9 for binary and
severity traits, respectively) were milder than the theoretical estimation (LOD score 4.3 [50]). A significant
threshold for disease onset in females was established at
4.9, which is noticeably higher than the theoretical level.
Therefore, as a result of permutation analysis, only 5
QTLs met the criteria for a significant empirical threshold with residual error probability of P ⬍ 0.05 genomewide. These are binary QTLs Pgia15 and Pgia21, onset
QTL Pgia26, and severity QTLs Pgia4 and Pgia12 (Table
2 and Figure 3). Thus, applying these more stringent
criteria, we found 1–2 major loci for each independent
clinical trait of the disease in this study. We did not find
any colocalization or overlapping between these major
loci/gene sets.
One of the most important findings of this study
was that disease onset, as a clinical trait, was significantly
different between F2 males and females (Figure 1).
Linkage analysis explored numerous QTLs, which were
influenced by sex and were linked to either a binary
(susceptibility) or a quantitative (onset or severity) trait.
Disease susceptibility and onset showed predominant
linkage to the female sex, while the severity QTLs were
prevalent in males. Only 1 QTL (Pgia21 on chromosome
7) was involved in both susceptibility and onset, whereas
all other loci seemed to control only a single clinical trait
in PGIA. Occasionally, QTLs were also linked to immunologic traits.
In addition to the well-documented effect of the
MHC (Pgia17) (excluded in this study), only 3 QTLs
appeared to control disease susceptibility. These results
corroborated the findings of our previous studies on the
MHC (35,37), further supporting our conclusion that
arthritis-susceptibility QTLs/genes (Pgia7, Pgia15,
Pgia17, and Pgia21) do not control the severity of
inflammation.
The MHC was “invisible” in this genetic cross
because it was intentionally excluded when H-2d
haplotype-matched parental strains were used. How-
SEX-CONTROLLED CLINICAL TRAITS IN ARTHRITIS
ever, even in the absence of MHC loci, the relationships
between sex, immunologic traits, and genetic background proved to be very complex. Nongenetic and
environmental factors occasionally play as important
roles in susceptibility and disease severity as the genetic
components. Among the nongenetic factors, the individual’s sex and age seem to have the strongest influence on
the development of disease.
In humans, the prevalence of RA is significantly
higher in women than in men, especially at younger ages.
Before the age of 39 years, the incidence of RA in
women is 19 times higher than that in age-matched men,
although this difference is only 2–3-fold higher by the
age of 60 years and older (1). This exaggerated ratio of
women to men, ⬃2.5:1, has been described in many
studies of RA patients (1,16,17). In contrast, case–
control studies have shown that erosive destruction
occurs more frequently in men than in women (72%
versus 31%; P ⬍ 0.05), although joint deformities are
more pronounced in women (17). Considering susceptibility to RA and severity of RA as 2 distinct features of
a complex disease, we can conclude from clinical studies
that both the incidence (or prevalence) and progression
(severity, flares, complications) of RA are features affected by the sex of the subject. According to a study
involving more than 500 patients, however, age-related
differences between female and male patients with RA
were not found (16).
In animal models of RA, differences in many
aspects of arthritis between males and females have
been demonstrated. Arthritis scores in (DA ⫻ ACI)F2
hybrid rats with CIA were significantly affected by sex
(20), in particular, by locus Cia5 on rat chromosome 10
(21). The Oia3 locus in rats with oil-induced arthritis,
which corresponds to Cia5, was also identified as a
sex-affected locus (22). Similarly, sex-dependent variations in QTL penetrance were demonstrated in F2
hybrid and congenic rats with CIA or with oil-induced or
pristane-induced arthritis (18,23). The MRL/lpr lupusprone mouse strain (carrying a mutated Fas gene) developed arthritis spontaneously, and QTLs on chromosomes 2
and 15 were both shown to be sex-affected (54).
The importance of nongenetic factors in disease
development should not be overlooked. Since genetic
risk factors can explain approximately one-half of the
population variability of RA, disease heritability in
animal models could be even higher. It is interesting that
in RA, twins showed a reduced value of heritability
ranging from 0.56 to 0.65 for both sexes (maximum value
is 1.0), albeit the value was estimated as 0.83 for the
male proband subsample and essentially zero (1 ⫻ 10–5)
1717
for the opposite sex (55,56). Consequently, the role of
nongenetic factors (environment, sex hormones, behavior, and age-related changes among them) in arthritis
have at least the same importance as genetic factors, and
lifestyle, age, certain working conditions, exercise, sex,
exposure to silica, obesity, and smoking are among the
well-known risk factors for RA (57–62). The importance
of nongenetic factors, such as aggressive behavior or
estrogen turnover, in several mouse models of arthritis
has been demonstrated as well (63,64).
In the PGIA model, parental BALB/c female
mice are more susceptible to arthritis and develop
arthritis faster than do males (28,29). In the genetic cross
that was used in the present study, significant differences
in arthritis scores (both severity and binary), as well as in
many of the immunologic parameters, were found between females and males. Almost one-half of the analyzed traits demonstrated differences between sexes
(Table 2). In this experimental condition, the only
source of the Y chromosome was the DBA/2 strain.
Thus, in the F2 population, technically, we compared the
effects of the X chromosome from BALB/c mice with
the effects of the Y chromosome from DBA/2 mice.
Existing linkage analysis software was usually
designed to find individual genetic determinants, which
control different traits, and then examine possible interactions between them. This approach may exclude chromosomal loci that control disease but only in cooperation with another locus. Locus–locus interactions in
arthritis have been reported for QTLs on chromosomes
1 and 2 in mice (42) and on chromosomes 4 and 10 in
rats (65). Addressing the question of how sex affects
susceptibility to PGIA and disease severity, we tested
the interaction of the QTL on the X chromosome
(Pgia25) with onset QTLs on other chromosomes. Loci
D3Mit158 and D7Mit120 (on chromosomes 3 and 7,
respectively) were clearly independent on the X chromosome (Figures 4A and B), although these loci showed
linkage to disease: F2 mice carried an arthritissusceptibility allele (allele B) on chromosome 3, and an
allele on chromosome 7 (allele D).
The relationship between alleles was peculiar
when the QTLs on chromosome 10 and on the X
chromosome were correlated (Figure 4C). The onset
score was significantly lower when heterozygosity at
D10Mit40 was paired with the DBA/2 allele DXMit5,
indicating a nonallelic interaction between onset genes
on the 2 chromosomes. This analysis was performed
separately for males and females as well as for the
combined population, and the pattern of interaction was
basically the same. Therefore, we presented data for the
1718
ADARICHEV ET AL
total population because we were able to use a 2 times
greater number of animals for determining significance
levels and because DXMit5-heterozygous mice exist in
the female subpopulation only.
Interaction between the X chromosome and
chromosome 10 indicated that these 2 arthritis-related
genes should operate in the same or related pathophysiologic processes. Considering genomic markers with
LOD scores ⬎2.0 as flanking markers for the QTL, we
were able to locate the QTL on chromosome 10 within
a 10-cM region (between 30 and 40 cM), and a QTL on
the X chromosome within a 5-cM region (between 67
and 72 cM) (Table 2). According to the Celera Discovery System (52) and public databases (66,67), each
chromosomal region contains ⬃150 genes, and the functions of at least half of these genes are not yet known.
Screening through the list of 150 transcripts in each
region, we found at least 10 known genes that are likely
to be contributors to the pathogenesis of arthritis, since
their functions are related to the regulation of cell
growth, apoptosis, cell–cell adhesion, or the production
of transcription factors that trigger inflammatory responses. Using congenic strain studies is an evident next
step toward narrowing the chromosomal region and/or
testing locus–locus interactions.
Table 2 summarizes data on the QTL analyses for
all immunologic traits that were scored separately in the
F2 population for males and females that developed
PGIA. One could consider those QTLs as putative loci
for PGIA, even if they did not show colocalization with
arthritis QTLs in each case. Numerous immunologic
QTLs overlap with clinical QTLs, thus providing information about possible mechanisms that underlie the
function of the QTLs. The value of these data can be
further increased by collecting information on positional
gene candidates, which are now available in public
databases (66,67) and the Celera Discovery System (52).
By projecting the 3 maps of positional gene candidates,
clinical QTLs, and immunologic QTLs onto the genome,
we could greatly accelerate the process of unraveling
disease-associated genes.
ACKNOWLEDGMENTS
We thank Dr. Kenneth Manly (Roswell Park Cancer
Institute, Buffalo, NY) for discussion of linkage analysis issues
and for help in clarifying critical features of the Map Manager
QTX package. We thank Dr. Jeffrey M. Otto (Genaissance
Pharmaceutical, New Haven, CT) for sharing his expertise on
linkage analysis. We greatly appreciate the expert assistance of
Sonja Velins (Rush University, Chicago, IL) for preparing the
manuscript.
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