Download Quantitative trait loci associated with maximal exercise endurance in

J Appl Physiol 103: 105–110, 2007.
First published April 5, 2007; doi:10.1152/japplphysiol.01328.2006.
Quantitative trait loci associated with maximal exercise endurance in mice
J. Timothy Lightfoot,1 Michael J. Turner,1 Amy Kleinfehn Knab,1 Anne E. Jedlicka,2
Tomohiro Oshimura,3 Jacqui Marzec,3 Wesley Gladwell,3 Larry J. Leamy,4 and Steven R. Kleeberger3
1
Department of Kinesiology, University of North Carolina-Charlotte, Charlotte, North Carolina; 2Malaria Research Institute,
Johns Hopkins University, Baltimore, Maryland; 3Laboratory of Respiratory Biology, National Institute of Environmental
Health Sciences, Durham; and 4Department of Biology, University of North Carolina-Charlotte, Charlotte, North Carolina
Submitted 22 November 2006; accepted in final form 2 April 2007
black population. Ways et al. (36) identified a significant QTL
linked with aerobic running capacity on rat chromosome 16
and suggestive linkage on chromosome 3. Given the health
implications of understanding the genetic regulation of a complex trait such as maximal exercise endurance, it is important
that additional research be conducted in species that will allow
the use of more powerful genetic tools. We established a
foundation for such studies with our previously described
strain distribution pattern of maximal exercise endurance in 10
different mouse inbred strains (26). Given earlier studies that
have shown a genetic contribution to the regulation of exercise
endurance, we hypothesized that several QTLs associated with
maximal exercise endurance would be identified in inbred mice
using progenitor strains earlier identified by their differential
response to treadmill exercise.
candidate genes; aerobic capacity; genotyping
We earlier identified Balb/cJ and DBA/2J mice as having high
exercise endurance (HEE) and low exercise endurance (LEE), respectively (26). Ten female Balb/cJ mice were bred with nine male
DBA/2J mice to produce BaD2 F1 mice. We chose the Balb/cJ mice
as the maternal progenitor strain because earlier studies had suggested
that exercise endurance was a maternally linked characteristic (11,
24). Additionally, the number of progenitor mice used was based on
the statistical power (power ⫽ 0.99) of an earlier comparison of five
mice per strain (26). F1 mice were weaned at 21–28 days and crossed
between 6 and 8 wk of age when the mice had reached 20 –25 g in
weight. The F2 mice (n ⫽ 99; 60 female, 39 male BaD2 F2 mice) were
weaned at 21–28 days and phenotyped for exercise endurance between 45 and 68 days of age. All mice were housed individually
without access to running wheels (i.e., in the sedentary condition) in
the same vivarium room that was maintained at 65–75°F and 20 – 40%
humidity with a 12:12-h light-dark cycle. Mice were fed standard
rodent chow (Harland Teklad 8604 Rodent Diet, Madison, WI;
protein 24.5%, fat 4.4%, fiber 3.7%, nitrogen-free extract 48.6%) and
provided water ad libitum.
have shown that the maximal exercise
endurance of an individual can impact all-cause mortality rates
(31), mortality rates from hypertension (7), smoking-related
and non-smoking-related cancers (21), and stroke (22). A
tremendous amount of research has considered the modifying
role of environmental factors on maximal aerobic exercise
(e.g., training). It is well accepted that the variation seen in
human maximal exercise endurance is due to the influence of
genetic factors (3), and current estimations of the contribution
of genetics to maximal exercise endurance range from ⬇51%
in humans (3) to 58 –90% in mice (23, 26) to 39% in rats (18).
However, the specific genes that determine an individual’s
maximal exercise endurance have not been identified.
Relatively few investigations of the quantitative trait loci
(QTLs) associated with maximal exercise endurance have been
conducted. Two separate human studies by Bouchard and
colleagues (4, 33) have identified possible genetic linkages
with sedentary aerobic capacity on chromosomes 4, 8, 11, and
14 in a white population and on chromosomes 1 and 7 in a
SEVERAL RECENT STUDIES
Address for reprint requests and other correspondence: J. T. Lightfoot, Dept.
of Kinesiology, UNC-Charlotte, 9201 Univ. City Blvd., Charlotte, NC 28223
(e-mail: [email protected]).
http://www.jap.org
METHODS
Approvals
All procedures in this protocol were reviewed and approved by the
University of North Carolina-Charlotte Institutional Animal Care and
Use Committee and conformed to the humane animal care policies of
the U.S. Department of Agriculture.
Model Used
Estimation of Exercise Endurance
The exercise endurance of the F2 mice was estimated using techniques described previously (26). In brief, after a 1-wk vivarium
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Lightfoot JT, Turner MJ, Knab AK, Jedlicka AE, Oshimura T,
Marzec J, Gladwell W, Leamy LJ, Kleeberger SR. Quantitative
trait loci associated with maximal exercise endurance in mice. J Appl
Physiol 103: 105–110, 2007. First published April 5, 2007;
doi:10.1152/japplphysiol.01328.2006.—The role of genetics in the
determination of maximal exercise endurance is unclear. Six- to
nine-week-old F2 mice (n ⫽ 99; 60 female, 39 male), derived from an
intercross of two inbred strains that had previously been phenotyped
as having high maximal exercise endurance (Balb/cJ) and low maximal exercise endurance (DBA/2J), were treadmill tested to estimate
exercise endurance. Selective genotyping of the F2 cohort (n ⫽ 12
high exercise endurance; n ⫽ 12 low exercise endurance) identified a
significant quantitative trait locus (QTL) on chromosome X (53.7 cM,
DXMit121) in the entire cohort and a suggestive QTL on chromosome
8 (36.1 cM, D8Mit359) in the female mice. Fine mapping with the
entire F2 cohort and additional informative markers confirmed and
narrowed the QTLs. The chromosome 8 QTL (EE8F) is homologous
with two suggestive human QTLs and one significant rat QTL previously linked with exercise endurance. No effect of sex (P ⫽ 0.33) or
body weight (P ⫽ 0.79) on exercise endurance was found in the F2
cohort. These data indicate that genetic factors in distinct chromosomal regions may affect maximal exercise endurance in the inbred
mouse. Whereas multiple genes are located in the identified QTL that
could functionally affect exercise endurance, this study serves as a
foundation for further investigations delineating the identity of genetic
factors influencing maximum exercise endurance.
106
QTL FOR EXERCISE ENDURANCE IN MICE
acclimation, the mice were oriented twice to running in a small cell
suspended above a human treadmill. A shock grid was mounted at the
rear of the cell (1.5 mA, 150 V) to provide stimulus for the animals to
run (35). During testing, each mouse was placed in the treadmill cell
and allowed 3 min of rest. The treadmill was then started at 22 m/min
and increased in speed every 4 min thereafter to 28.4, 36.6, and 42.4
m/min, respectively. If the mouse completed 4 min at 42.4 m/min, the
speed was held constant at 42.4 m/min, but the grade was increased
2% every 4 min thereafter. The grade of the treadmill was increased
instead of further increasing the speed because it appeared in pilot
testing that some mice could not biomechanically maintain running
speeds above 45 m/min. The treadmill test was stopped when the
mouse could not get off the shock grid within 3 s, and exercise
endurance was estimated by total time that the mouse ran. This
indirect measure of exercise endurance has been used as a phenotype
in other rodent genetic studies (1, 6, 18, 26, 36), and running speed has
been positively correlated with oxygen consumption in mice (13).
Genotyping
Statistics
Prior to the QTL analysis, a one-way ANOVA was used to test
whether sex had a significant effect on exercise endurance in the F2
mice. Simple regression also was used to test whether body weight
and exercise endurance were correlated to determine if exercise
endurance should be adjusted for the effects of body weight. Exercise
endurance for mice in the F2 population and in the two inbred
populations (DBA/2J and Balb/cJ) was compared using a one-way
ANOVA with Scheffé’s post hoc analysis. Pre-QTL analyses were
completed using JMP 5.1 (SAS, Cary, NC), ␣ values were set a priori
at P ⬍ 0.05, and results are presented as means (SD).
Methods described elsewhere (20) were used to search for potential
QTLs for maximum exercise endurance in the selective subsample of
F2 progeny and in the subsequent full sample set, and the results were
confirmed with MapManager QTX (28). In brief, the phenotypic data
were first checked for normality. If the data were not normally
distributed, the data were then log-transformed, resulting in a normal
distribution of the exercise endurance scores. These scores were
subsequently analyzed for linkage using the regression approach to
J Appl Physiol • VOL
RESULTS
Demographics and Exercise Endurance
Body weight for the F2 female mice [16.33 g (SD 3.36)] was
less (P ⬍ 0.001) than that for the F2 male mice [21.0 g (SD
3.10)]. Body weight of progenitor DBA/2J [n ⫽ 9 males; 15.44
g (SD 1.74)] and Balb/cJ mice [n ⫽ 10 females; 16.5 g (SD
1.72)] was significantly less (P ⬍ 0.05) than the F2 male mice
but did not differ (P ⬎ 0.05) from that of the F2 female mice.
Despite the difference in body weight between sexes in the F2
cohort, no significant difference was found between sexes in
exercise endurance [males ⫽ 8.02 min (SD 5.86), females ⫽
9.33 min (SD 6.90); P ⫽ 0.33], but the statistical power of this
comparison was relatively low (power ⫽ 0.16). Neither body
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After phenotyping, the mice were anesthetized using 2– 4% inhaled
isoflurane. When the animal showed no response to the paw pinch
test, the heart was exposed and removed (i.e., euthanasia by exsanguination). The kidneys were then harvested, flash frozen in liquid
nitrogen, and stored at ⫺80°C for later genotyping. Initially,
we selectively genotyped the 12 mice with the highest exercise
endurance (9 females, 3 males) and the 12 mice with the lowest
exercise endurance (8 females, 4 males). It has been noted that
genotyping the upper 10% and lower 10% of the phenotypic distribution will capture approximately 80 – 85% of the genetic variance (8,
30). Our selectively genotyped subsample represented 24% of the
total sample and the upper and lower 12% of the phenotypic strain
distribution pattern.
DNA extraction from the kidneys and genotyping of the selective
subsample were performed as previously described (16). The primers
for the polymorphic simple sequence repeats markers (i.e., microsatellites) were selected from the Mouse Genome Database (MGD) (2)
and were purchased from Research Genetics (Huntsville, AL). One
hundred eight microsatellite markers, with an average 12.6-cM (SD
8.9) distance between them, provided coverage of the mouse genome
with ⬎95% confidence (34). One primer of each pair was 32P
end-labeled. After amplification, the PCR products were resolved on
6% acrylamide gels. QTL locations within the selectively genotyped
subsample were then estimated using statistical procedures. All animals (n ⫽ 99) in the F2 cohort were then genotyped around the
identified QTL using informative microsatellites spaced as close
together as possible to increase map resolution (i.e., fine mapping).
interval mapping (15). For each chromosome, the regression analyses
generated F values with their associated probabilities. These probabilities were converted to logs of the odds favoring linkage (i.e., LOD
scores) by logarithmic transformation [LOD ⫽ log10(1/probability)].
If the highest LOD calculated for a given chromosome exceeded a
specific threshold value, a QTL was considered to be present at the
position associated with that LOD score. Threshold values were
obtained from permutation tests involving 10,000 separate runs in
which the aerobic capacity values for each individual mouse were
randomly permuted and then run through the interval mapping analysis. These permutation runs provided 5% threshold values for each
chromosome as well as a 5% experiment-wise threshold value applicable across all chromosomes (37). LOD scores exceeding the chromosomewise threshold values are suggestive QTLs, whereas LOD
scores exceeding the experimentwise level provide significant evidence for QTLs (19).
Confidence intervals for each QTL were determined using the
method of Darvasi and Soller (9). If a QTL was found on any
chromosome, a test for the presence of two QTLs was conducted (20).
The QTL analysis was repeated using program-determined linkage
distances, as well as linkage distances from the MGD (2) and the
Whitehead Institution database (10) using several different models to
determine the dominance properties of identified QTLs. The models
used were a “free” model where the additive and dominant components are allowed to have any value, an additive model where the
dominant component is set to zero, a dominant model where a single
coefficient is used for both the additive and dominant components,
and a recessive model where the additive and dominant components
are the same coefficient, but with opposite signs. Additionally, the
data were checked for sex or breeding cross-direction differences in
the phenotype to control for possible inflation of the LOD scores on
the nonautosomal X chromosome (5).
Sex-specific QTL effects were tested by first assigning a code for
each sex (males ⫽ 1, females ⫽ 2). A whole genome scan was then
run as before but with multiple regression of exercise endurance on
the interaction of the genotypic values with sex. Threshold values
again were estimated by permutation tests, and chromosomes with
significant LOD values were assumed to contain a QTL whose effects
differed in the two sexes. For those chromosomes, separate QTL runs
were made for males and for females, and new chromosomewise and
experimentwise threshold LOD values were obtained for significance
testing.
In all QTL analyses, if the P value resulting from the comparison
of the LOD score with the calculated specific threshold value was less
than 0.05, but greater than 0.01, the QTL was considered “suggestive.” If the P value was less than 0.01 but greater than 0.001, the QTL
was classified as “significant,” and if less than 0.001, considered
“highly significant.” These analyses and classification of P values
follow the standardized nomenclature from Lander and Kruglyak (19).
QTL FOR EXERCISE ENDURANCE IN MICE
107
Fig. 1. Individual mice and group averages of exercise duration by progenitors
(Balb/cJ, n ⫽ 10; DBA/2J, n ⫽ 9) and F2 offspring (n ⫽ 99). Open circles are
data from individual mice; closed squares are strain average with SD bars.
Some individual data points may be hidden where similar results were found
for multiple mice. *Significantly different from Balb/cJ (P ⬍ 0.05).
weight (r2 ⫽ 0.0007) or age when phenotyped (r2 ⫽ 0.063)
correlated with exercise endurance.
Mean exercise durations for DBA/2J and Balb/cJ mice
(Fig. 1) were similar to those noted previously (26), where
Balb/cJ mice ran significantly longer (P ⫽ 0.005) than DBA/2J
mice. Exercise duration for the BaD2 F2 [8.82 min (SD 6.51)]
animals was not significantly different from the LEE DBA/2J
mice [4.82 min (SD 3.07), P ⫽ 0.08] but was significantly less
than the HEE Balb/cJ mice [14.06 min (SD 8.15), P ⫽ 0.02,
power ⫽ 0.81]. The frequency distributions of exercise duration in the F2 mice were shifted toward the DBA/2J parental
average (Fig. 2) and exhibited significant skewness [1.67 (SD
0.24)] and kurtosis [3.101 (SD 0.49)]. The application of the
Shapiro-Wilk (W ⫽ 0.847, P ⬍ 0.0001) and the KomogorovSmirnov (D ⫽ 0.147, P ⬍ 0.01) tests for normality indicated
that the distribution was not normal; thus the exercise endurance data were log-transformed for the QTL mapping analysis.
DISCUSSION
It is now generally accepted that genetic factors account for
a significant portion of the variance in maximal exercise
endurance in humans (3), mice (23, 26), and rats (18). This
study has extended these basic studies and identified two
chromosomal regions (EE8F and EEX) linked with maximal
exercise endurance in mice. Interestingly, despite no significant
difference in maximal exercise endurance by sex in our data,
our linkage analysis identified a sex-linked QTL (EE8F) that
could indicate that exercise endurance, at least in this model, is
a hormonally influenced characteristic. Our QTL findings provide a foundation to identify the specific genes and regulating
factors that control maximal exercise endurance in mammals.
Investigators have used both human and rat models in
previous attempts to identify the chromosomal regions linked
with maximal exercise endurance. Bouchard et al. (4) phenotyped 481 Caucasian subjects using a volitional fatigue cycle
ergometer protocol. The autosomal chromosomes were then
genotyped using 289 microsatellite markers with an average
spacing of 11 cM. The investigators found four significant
QTL Mapping Results
Selective mapping. The 12 highest endurance mice [endurance ⫽ 22.8 min (SD 6.3)] and the 12 lowest endurance mice
[endurance ⫽ 1.77 min (SD 0.7)] were selectively genotyped.
The body weights of the 12 highest and 12 lowest endurance
mice were not different [HEE ⫽ 19.5 g (SD 0.89) vs. LEE ⫽
17.8 g (SD 0.89), P ⫽ 0.18]. Because there were no sex
differences in phenotype and because the maternal progenitors
all derived from Balb/cJ (i.e., no breeding cross-direction
differences in phenotype), the LOD scores from the X-chromosome were not adjusted (5). Results of the selective QTL
mapping for exercise endurance in the 12 highest and 12 lowest
endurance mice revealed two potential QTLs, one on chromosome 8 in females only and one on chromosome X in all mice.
The significant QTL on chromosome X (designated Exercise
Endurance on chromosome X; EEX) was located at 59.6 cM, 6
cM beyond the DXMit121 marker, and achieved a LOD score
J Appl Physiol • VOL
Fig. 2. Frequency distribution of exercise duration in BaD2 F2 population.
Values on the x-axis are midpoint of each interval. Interval widths were
determined by dividing total range of data by 10 intervals resulting in an
interval distance of 3.3 min. Average exercise distance is indicated for Balb/cJ
(closed square) and DBA/2J (closed circle) parental strains as well as the F2
population (closed triangle) within the distribution.
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of 2.45. A suggestive (LOD score ⫽ 2.21) QTL in females
(designated Exercise Endurance on chromosome 8 in females;
EE8f) was found on chromosome 8 at 42.1 cM, 6 cM beyond
the D8Mit359 marker.
Fine mapping. All 99 F2 animals were genotyped for fine
mapping. Thirteen microsatellite markers surrounding the two
QTLs were used for fine mapping and were spaced an average
of 2.08 cM (SD 1.51) apart. Significant segregation distortion
was present in the markers surrounding the QTL for chromosome X; thus QTL analysis methods that allow for segregation
distortion were used (27). From this analysis, our previous
selective-genotype QTLs were confirmed. Significant linkage
with exercise endurance for all mice was found at 57.9 cM
(DXMIT31) on chromosome X (EEX; Table 1). Further suggestive linkages were found for D8MIT359 (36.1 cM; EE8F) in
the female mice (using both additive and recessive models) and
in the region of the chromosome X from 53.6 to 56.8 cM
(DXMIT121, DXMIT5, DXMIT236) in all mice using all statistical models.
108
QTL FOR EXERCISE ENDURANCE IN MICE
Table 1. Strongest suggestive QTL for exercise endurance from fine mapping
Chr/Marker
Location
LRS Score
LOD Score
%Variance
P Value
CI, cM
a
d
D8MIT359F
DXMIT121
DXMIT5
DXMIT236
DXMIT31
36.1cM
53.7cM
54.6cM
56.8cM
57.9cM
5.6*
9.8*
9.8*
9.7*
10.4†
1.19
2.13
2.13
2.10
2.26
9
9
9
9
10
0.02311
0.00733
0.00733
0.00766
0.00562
107
57
57
57
54
0.28
0.31
0.31
0.31
0.32
⫺0.15
0.02
0.02
0.03
0.01
Chr/Marker, chromosome and marker designation of microsatellite; Findicates quantitative trait locus (QTL) found in female mice only; Location, linkage map
location of marker from Whitehead Institution database (10); LRS, likelihood ratio statistic; LOD, logarithmic odds; %Variance, the amount of total trait variance
explained by a QTL at this location; CI, the size of the 95% confidence interval for a QTL of this strength; a, additive genotypic value; d, dominance genotypic
value. Permutation test ⫽1 cM at 10,000 permutations. Specific threshold values: *suggestive ⫽ 3.8 LRS (0.90 LOD), †significant ⫽ 10.2 LRS (2.21 LOD).
No interactions present.
J Appl Physiol • VOL
in these animals. Otherwise, there do not appear to be explanations for the variation we observed given that the mice in this
study were tested at the same time of year as our strain screen
mice and were housed in similar circumstances and in the same
location, and the testing was conducted in a similar fashion by
the same technicians using the same equipment as was used in
the earlier study (26). Li et al. (25) have noted that although an
estimated 60% of the F2 mouse mapping experiments in the
literature used fewer than 300 individuals, smaller cohorts of
mice decrease the chances of finding any but the strongest
QTLs. Although our sample size was relatively small, our
observed effect size was large as there was a 12.9-fold difference in maximal exercise endurance between the two selectively genotyped groups. In addition, the homology of our
QTLs with those reported by Ways et al. (36) and Bouchard
and colleagues (4) further strengthens the validity of our QTLs.
As noted in the consensus statement by the Complex Trait
Consortium, “there is no single ‘gold standard’ for the identification of a QTL. . .rather, there should be a predominance of
evidence that supports its identity” (29). Thus, given that our
genotyping plan in these inbred mice theoretically captured
80 – 85% of the variability in the cohort (8, 30), we are
confident that these results significantly add to the currently
sparse literature investigating the underlying genetic regulating
mechanisms of exercise endurance and provide a further foundation for the identification of candidate genes linked to exercise endurance.
Identification of candidate genes in the past has been speculative, consisting primarily of searching for known genes in
the QTL region that are involved in the basic physiological
regulating mechanism(s) of the phenotype of interest. Due to
the multifactorial nature of exercise endurance control and
because the number of genes located in each QTL region is
large, candidate gene lists generated from any species are often
quite lengthy (see discussion in Refs. 4, 32, 36). Similarly, a
large number of candidate genes that could be involved in the
regulation of maximal exercise endurance are located within 15
cM of the QTLs identified in this study; however, discussion of
any of these “candidate genes” from the genes located within
the identified QTL without further corroborating evidence such
as microarray results would be speculative at best. Thus the
future challenge is to narrow the identified QTLs to a more
manageable size, thus enabling the pursuit of more precise
identification of the location and identity of genes involved in
the regulation of exercise endurance (17).
While not its primary function, the identification of QTLs
also provides a means for investigating the validity of potential
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QTLs linked with maximal exercise endurance (4q12, 61.658
cM; 14q21.3, 49.255 cM; 8q24.12, 128.157 cM; and 11p15.1,
21.174 cM) and an additional 21 regions that were suggestive
of QTL linkage. Rico-Sanz and colleagues (33) extended this
work and using similar methods genotyped 862 subjects of
white and black ethnicity. They reported “promising linkage”
of sedentary V̇O2 max on chromosome 11 (11p15.1) in the white
subjects and four suggestive QTLs for V̇O2 max for the black
subjects on chromosomes 1 and 7 (1p31, 7q32, 7q36, 7q36) but
no significantly linked QTLs. Ways and colleagues (36) identified three QTLs linked with exercise endurance in an F2 rat
model (n ⫽ 224) derived from Copenhagen (COP) and DA
inbred progenitors. On chromosome 16, they found significant
linkage of exercise endurance with D16Rat17, suggestive linkage with D16Rat55, a marker located 30 cM distal to
D16Rat17, and suggestive linkage with D3Rat56 on chromosome 3.
Maximal exercise endurance is a complex trait regulated by
many genetic and environmental factors. While this study has
identified two QTLs associated with exercise endurance, including one that suggests a sex influence, it is probable that no
studies have identified all of the QTLs that play a role in the
regulation of maximal exercise endurance. However, one of the
strengths of genetic/QTL studies is the conserved gene synteny
among species that allows the comparison of identified genetic
loci in multiple models. Thus the exercise endurance QTLs and
nearby markers can be compared directly to similarly identified
QTLs and markers derived from other species (e.g., humans or
rats). For example, the significant D16Rat17 exercise endurance QTL identified by Ways et al. (36) is homologous to our
mouse EE8F QTL. While none of the mouse exercise endurance QTLs identified in the present study were homologous
with the significant human QTLs reported by Bouchard’s
group, EE8F is homologous with two “potentially useful”
linkages that Bouchard et al. identified on human chromosome
13 (D13S787; 13q12.11) and chromosome 22 (D22S274,
22q13.2) (4). At this time, no comparisons between our EEX
QTL and the results of either Bouchard et al. (4) or Ways et al.
(36) are possible since neither investigation reported results
from chromosome X.
The strength of any QTL investigation is determined by
multiple factors; three of the most important are the variability
of the phenotype, the effect size, and the population size. While
the variance of the exercise endurance of the F2 population was
larger than the variances observed in our earlier inbred strain
screen study (26), we had anticipated a larger variance in the F2
animals vs. the inbred strains given the heterozygous genome
QTL FOR EXERCISE ENDURANCE IN MICE
ACKNOWLEDGMENTS
We thank J. Moser, M. Belles, J. Reavis, A. Lowe, and M. Lindley for work
in exercise testing, V. McGlosson for animal husbandry skills, and the
generous support of Dr. Laura Schrum and her laboratory.
GRANTS
This study was supported by University of North Carolina Internal Grant
(J. T. Lightfoot), American Physiological Society Research Career Enhancement Award (J. T. Lightfoot), National Institutes of Health Grants AR-050085
(J. T. Lightfoot, A. M. Kleinfehn, M. J. Turner, and L. J. Leamy) and
AG-022417 (M. J. Turner), and the National Institute of Environmental Health
Science Division of Intramural Research (S. R. Kleeberger, T. Oshimura, J.
Marzec, and W. Gladwell).
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