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Keywords:

  • Chromosome substitution strain;
  • dopaminergic pathway;
  • interval-specific congenic strain;
  • QTL mapping;
  • voluntary physical activity;
  • wheel running

Abstract

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

Both human beings and animals exhibit substantial inter-individual variation in voluntary physical activity, and evidence indicates that a significant component of this variation is because of genetic factors. However, little is known of the genetic basis underlying central regulation of voluntary physical activity in mammals. In this study, using an F2 intercross population and interval-specific congenic strains (ISCS) derived from the C57BL/6J strain and a chromosome 13 substitution strain, C57BL/6J-Chr13A/J/NA/J, we identified a 3.76-Mb interval on chromosome 13 containing 25 genes with a significant impact on daily voluntary wheel running activity in mice. Brain expression and polymorphisms between the C57BL/6J and A/J strains were examined to prioritize candidate genes. As the dopaminergic pathway regulates motor movement and motivational behaviors, we tested its function by examining cocaine-induced locomotor responses in ISCS with different levels of activity. The low-activity ISCS exhibited a significantly higher response to acute cocaine administration than the high-activity ISCS. Expression analysis of key dopamine-related genes (dopamine transporter and D1, D2, D3, D4 and D5 receptors) revealed that expression of D1 receptor was higher in the low-activity ISCS than in the high-activity ISCS in both the dorsal striatum and nucleus accumbens. Pathway analysis implicated Tcfap2a, a gene found within the 3.76-Mb interval, involved in the D1 receptor pathway. Using a luciferase reporter assay, we confirmed that the transcriptional factor, Tcfap2a, regulates the promoter activity of the D1 receptor gene. Thus, Tcfap2a is proposed as a candidate genetic regulator of the level of voluntary physical activity through its influence on a dopaminergic pathway.

A growing body of literature has shown the important roles of physical activity in maintaining a healthy body weight (Vetter et al. 2010), preserving physical and emotional well-being (Duman et al. 2008), promoting neurogenesis (van Praag et al. 1999) and synaptic plasticity (Redila & Christie 2006; Stranahan et al. 2007), and improving cognition and brain function (Creer et al. 2010; Hillman et al. 2008). There is strong evidence for a genetic basis underlying individual differences in voluntary physical activity in both human beings and animals (Lightfoot et al. 2004; Seabra et al. 2008). The concordance of activity levels and patterns in monozygotic twins is higher than in dizygotic twins (Kaprio et al. 2000), while significant differences in spontaneous activity are observed in inbred strains of mice (Lightfoot et al. 2010; Umemori et al. 2009). Broad sense heritability estimated for traits related to physical activity ranges from 20 to 80% in human beings and rodents (Eriksson et al. 2006; Lightfoot et al. 2004). Although a few studies have been conducted to map broad quantitative trait loci (QTL) associated with wheel running activity (Lightfoot et al. 2008; Shimomura et al. 2001; Turner et al. 2005) and home cage activity in rodents (Kas et al. 2009), this has not been supplemented by high-resolution genetic mapping to identify specific targets. Consequently, the identity of genetic factors and pathways regulating a predisposition to engage in physical activity remains unknown.

Wheel running behavior is considered as a measurement of voluntary physical activity in rodents, which is defined as purposeful movements that expend a significant amount of energy (Knab & Lightfoot 2010). The dopaminergic pathway may regulate voluntary wheel running activity because of its central role in regulating motor movement and motivational behaviors. For example, mice selectively bred for high wheel running activity respond differently to drugs acting on the dopaminergic pathway than controls do (Rhodes & Garland 2003; Rhodes et al. 2001). Additionally, the high-activity C57L/J strain has lower expression levels of dopamine D1 receptor than the low-activity C3H/HeJ strain (Knab et al. 2009). Such results raise the possibility that specific alterations in the dopaminergic pathway may be associated with differential engagement in voluntary physical activity, although this hypothesis needs further validation using animal models with less genetic and behavioral differences to exclude confounding factors.

Using a chromosome 13 substitution strain (CSS-13) in which the individual chromosome 13 from the low-activity A/J strain was substituted into the genome of the high-activity C57BL/6J strain (Singer et al. 2004), we have previously mapped a QTL associated with daily wheel running activity to a 63-Mb region on chromosome 13 containing hundreds of genes (Yang et al. 2009). In this study, we precisely map this QTL to a 3.76-Mb interval using interval-specific congenic strains (ISCS) derived from the CSS-13. We further show a functional alteration of the dopaminergic pathway in two ISCS which differ only in this 3.76-Mb region. We propose that Tcfap2a may be a promising candidate gene, which may regulate voluntary physical activity via its interaction with a dopaminergic pathway.

Materials and methods

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

Animals

Male C57BL/6J (B6), A/J and CSS-13 mice were purchased at 4 weeks of age from the Jackson Laboratory (JAX, Bar Harbor, ME, USA) and bred in our colony at Northwestern University. (B6 × CSS-13) F2 and ISCS mice derived from the B6 and CSS-13 strains were developed in our colony. All animals were weaned at 21 days and were maintained under a 14-h light : 10-h dark cycle (LD 14:10) at a room temperature of 23 ± 2°C. All mice were provided with water and standard chow (LabDiet, PMI Nutrition International St.Louis, MO, USA; 18% protein, 6% fat, 55% carbohydrate and 5% fiber) available ad libitum throughout the studies. Protocols of all animal studies were reviewed and approved by the Animal Care and Use Committee at Northwestern University.

The B6 × CSS-13 intercrosses were generated using F1 mice from B6 females mated to CSS-13 males. These F1 mice were then intercrossed to generate over 500 male F2 animals. A total of 163 male F2 mice were found to have at least one recombination between 23 and 83 Mb on chromosome 13, which is the broad QTL region we have previously identified to be associated with the amount of voluntary wheel running activity (Yang et al. 2009). To increase the QTL mapping resolution, only these recombinant male F2 mice were selected for the behavioral test of voluntary wheel running activity.

Development of seven ISCS for high-resolution QTL mapping

(B6 × CSS-13) F2 mice were used to generate seven ISCS for high-resolution QTL mapping. (B6 × CSS-13) F2 mice were only recombinant on chromosome 13, whereas all other chromosomes were in a uniform B6 genetic background. A series of F2 mice that had recombination within specific intervals were backcrossed to B6 mice, resulting in multiple offspring, (B6 × F2) N2, with the same recombination. These N2 animals were genotyped using 15 single nucleotide polymorphism (SNP) markers and 3 simple sequence length polymorphism (SSLP) markers on chromosome 13 and the ones with the donor F2 recombinant structure were selected for breeding. Male and female N2 animals were intercrossed, and their N3 offspring were genotyped. About one fourth of the total N3 offspring were homozygous for the donor recombinant structure. Final intercrosses were performed by mating male and female N3 animals to produce donor homozygotes, which constituted the seven finished ISCS (HR-1, HR-2, HR-3, LR-1, LR-2, LR-3, LR-4) that were used for the phenotypic analysis. HR and LR designate congenic lines with high and low levels of wheel running activity, respectively.

Treatment paradigms

A total of 163 male F2 mice with at least one recombination between 23 and 83 Mb on chromosome 13 were subjected to wheel running behavioral testing beginning at 7 weeks of age. Seven ISCS (the number of animals varied in each strain) and the parental B6 (n = 9), CSS-13 (n = 9) strains of male mice were also subjected to the wheel running behavioral test at 7 weeks of age. The body weights of the mice were measured before and after the test. After 4 weeks of wheel running, brain tissues, including frontal cortex, hypothalamus, striatum and cerebellum, from the HR-3 and LR-1 mice (n = 12 for each strain) were dissected and collected for expression analysis of candidate genes within the QTL.

In addition, the caudate putamen (henceforth referred to as the dorsal striatum) and nucleus accumbens were collected from another set of naÏve HR-3 and LR-1 strains of mice (n = 8 for each strain) at 8 weeks of age, which had not been exposed to running wheels. The expression levels of six dopamine-related genes were measured in the dorsal striatum and nucleus accumbens.

A separate set of B6 (n = 8), HR-3 (n = 10) and LR-1 (n = 14) strains of mice (7 weeks old) were monitored for total cage ambulating activity, as measured by infrared (IR) beam breaks for 14 days. Immediately following the IR activity recordings, these mice were tested for the acute responses to cocaine administration.

Daily wheel running activity

To record daily running wheel behavior, animals were placed into individual cages (33.0 cm long, 14.0 cm wide and 12.7 cm high) containing running wheels (diameter 11.4 cm) for 4 weeks. Wheel running activity was continuously monitored using the Chronobiology Kit (Stanford Software Systems, Stanford, CA, USA). Each wheel revolution was recorded by a micro-switch. The running wheels were examined on a daily basis to ensure that they turned freely. Data were analyzed from 21 consecutive days of recording, beginning after 1 week of acclimation to the novel cage environment. For each day of recording, activity counts were summed for consecutive 60-min bins, as well as for the 14-h light phase, 10-h dark phase and overall 24-h period using the ClockLab software (Actimetrics, Wilmette, IL, USA). Activity counts in each of these intervals were averaged over the days of recording to obtain the mean patterns for individual animals.

Daily total cage activity

To record total movement in a home cage, animals were placed into activity recording cages (33.0 cm long, 14.0 cm wide and 12.7 cm high) containing three IR beams sensor pairs across the width of the cage, 4.5 cm above the cage floor. Movement results in an interruption of a beam, which is recorded as an activity count. After 1 week of acclimation to the novel cage environment, data were collected for 7 days using ClockLab software (Actimetrics, Wilmette, IL, USA). Activity counts during a 24-h day were averaged over the 7 days of recording to calculate the mean activity level for individual animals.

Acute locomotor response to cocaine injection

B6 (n = 8), HR-3 (n = 10) and LR-1 (n = 14) mice were tested for their acute responses to cocaine using two sessions spaced over 2 days, with the first session involving a saline injection and the second session a cocaine injection (20 mg/kg body weight). The open field apparatus was a 37.5 × 37.5 cm2 arena as previously described (Vitaterna et al. 2006). For each session, mice were acclimated to the testing room for 60 min before being placed in the perimeter of the open field, and allowed to explore the apparatus for 30 min before being injected with saline or cocaine. After injection, they were immediately returned to the apparatus for another 60 min. Total horizontal distance traveled in the whole arena was measured by the BigBrother video-based activity monitor system (Actimetrics, Wilmette, IL, USA) at 1 min resolution for each animal. Each animal's response to saline or cocaine injection was normalized by subtracting the average of baseline activity during the 30 min before the injection.

Genotypic analysis and QTL analysis

The whole genomes of B6 and A/J inbred strains have been fully sequenced (Frazer et al. 2007; Keane et al. 2011), and the high-density genotype profiles of the two strains are available in Mouse Genome Informatics (MGI) database. Genomic DNA was extracted from tail biopsy. A total of 15 SNP markers and three SSLP markers were selected across chromosome 13 based on the presence of allelic differences between the A/J and B6 strains [rs3713268 (5.01 Mb), rs3679575 (23.78 Mb), rs13481734 (27.13 Mb), rs6271445 (31.75 Mb), rs3684485 (35.45 Mb), rs3720620 (38.84 Mb), rs13481778 (40.95 Mb), rs29513620 (42.60 Mb), rs2924 1911 (47.01 Mb), rs6239400 (53.74 Mb), rs29238045 (55.51 Mb), rs29854077 (59.01 Mb), rs29546097 (69.01 Mb), rs46347547 (76.01 Mb), rs30074062 (83.00 Mb), D13MIT258 (95.64 Mb), D13MIT148 (109.62 Mb), D13MIT293 (113.54 Mb)]. Among these markers, 14 SNPs from 23.78 to 83.00 Mb were genotyped in the F2 population. Additional four markers (rs38842228, D13MIT258, D13MIT148, D13MIT293) were genotyped in the ISCS to verify that they were homozygous in both ends of the chromosome. Marker names and genomic positions are from Ensembl v60, which is based on the NCBI mouse assembly m37. Genotyping of the SNP markers was conducted using real-time PCR with fluorescent SYBR Green and allele-specific primers designed for selective amplification of each allele (Applied Biosystems, Foster City, CA, USA). Genotyping of SSLP markers was performed by standard polymerase chain reaction (PCR) techniques, with visualization of SYBR Green-stained PCR products on a 4% agarose gel.

QTL analysis and epistasis analysis in the F2 population was performed using the R/qtl software package designed for mapping QTL in experimental crosses (Broman et al. 2003). Body weight before the wheel running test was used as a covariate to control for the possible effect of body weight on running ability. Results were expressed as LOD scores, and the threshold for significant loci was estimated by 1000-time permutation tests supplied by the R/qtl package. Total variance that the QTL account for in the F2 population was calculated in the MapManager QTX (Manly et al. 2001). Fine mapping in the congenic strains was achieved by comparing the average daily wheel running activity levels between each congenic strain to the parental strains using the two-tailed t-test.

Candidate genes

We identified the known and predicted genes within the 3.76-Mb QTL interval [henceforth referred to as a voluntary running activity (VRA) QTL] using public databases, including Ensembl, Mouse Phenome Database, and MGI. The Genomics Institute of Novartis Research Foundation (GNF) Database and the Allen Brain Atlas were searched to obtain expression information for each known gene in the VRA QTL. In addition, we compiled several public SNP datasets to annotate all known SNPs between the B6 and A/J strains within the VRA QTL. This compilation includes sequence data from the Mouse Phenome Database, MGI and Wellcome Trust Sanger resequencing SNP database, the latter of which involved the sequencing of the B6 and A/J strains to an over 20X coverage.

Pair-wise sequence comparison was performed to analyze polymorphisms between the B6 and A/J strains within the VRA QTL. It is commonly assumed that nonpolymorphic (SNP-desert) regions are less likely to contain causative genes for the QTL, whereas regions with high-SNP density (SNP-rich), where two strains contain alleles from different ancestral sources, are more likely to encode the causative genes (Cervino et al. 2006).

Quantitative real-time PCR expression analysis

HR-3 and LR-1 ISCS mice (n = 12 for each strain) were euthanized by cervical dislocation in the middle of the light phase after 4 weeks of the wheel running test. Brain tissues from both hemispheres, including frontal cortex, hypothalamus, dorsal striatum and cerebellum, were freshly dissected over ice and immediately flash frozen in liquid nitrogen and stored at −80°C. The nucleus accumbens and dorsal striatum were collected in a separate set of naÏve HR-3 and LR-1 mice (n = 8 for each strain) with no access to running wheels. A 0.96-mm coronal slice was taken between Bregma 1.70 and 0.74 mm. From this slide, two 1.5 mm-diameter samples of tissue were punched from the bilateral regions containing the nucleus accumbens according to the Mouse Brain Atlas (Paxinos & Franklin 2001). The dorsal striatum was punched in two coronal slices, between Bregma 1.70 and 0.74 mm, and between Bregma 0.74 and −0.10 mm. Two 1.5 mm-diameter tissue samples were punched from the bilateral regions containing the dorsal striatum in both slices. Total mRNA in different brain tissues from individual mice was extracted using the RNeasy kit (Qiagen, Valencia, CA, USA), and treated with the RNase free DNase kit (Qiagen) to eliminate potential genomic DNA contamination. The purity and quantity of the mRNA were assessed with a NanoDrop ND-100 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Reverse transcription was performed using the Taqman Reverse Transcription kit (Applied Biosystems, Foster City, CA).

For candidate genes within the VRA QTL and the dopamine-related genes (Drd1a, Drd2, Drd3, Drd4, Drd5 and Slc6a3), PCR primers were designed using the software Primer 3, v. 0.4.0, and ordered from Integrated DNA Technologies (San Jose, CA, USA). The specificity of each pair of primers was confirmed by its dissociation curve in quantitative real-time PCR (Qrt-PCR). Crossing point (Ct) values for target gene expression levels were determined by the standard software package, SDS 2.2.2 in the 7900HT fast real-time PCR system. The relative expression levels of the 25 candidate gene in the four different brain regions (cortex, hypothalamus, striatum and cerebellum) were measured in mice (n = 6) by Qrt-PCR using fluorescent SYBR Green. To correct for run-to-run variability and differences in primer efficiency, a fivefold serial dilution (ranging from 0.64 to 80 ng) of genomic DNA were included in each experiment to generate a standard curve of each candidate gene. The Ct values were then plotted against the log of the genomic DNA concentrations, which produces standard curves with regression coefficients (R2)>0.99. This regression was used for predictions of copy number for each transcript in each brain tissue. For 13 primary candidate genes that were expressed at detectable levels in brain, genotype-dependent expression analysis was performed in the four different brain regions of the HR-3 and LR-1 mice (n = 12 for each strain) by Qrt-PCR using fluorescent SYBR Green. Finally, the relative expression levels of Drd1a, Drd2, Drd3, Drd4, Drd5 and Slc6a3 were examined in the dorsal striatum and nucleus accumbens of a separate set of naÏve HR-3 and LR-1 mice (n = 8 for each strain) without access to running wheels and normalized to two different endogenous controls (Gapdh and Actb).

Pathway analysis

Ingenuity pathway analysis (IPA) Knowledge Base 8.5 (IPA, Ingenuity Systems, Redwood City, CA, USA) was employed to explore the functional relationship between the QTL candidate genes and dopamine-related genes. The IPA Knowledge Base contains millions of findings curated from literature, and it has been used as a powerful tool to identify functional pathways and networks. The 25 candidate genes within our VRA QTL and 6 dopamine-related genes were entered into the Ingenuity database. A network containing dopamine genes and candidate genes was generated.

Luciferase reporter assay

The first intron of the Drd1a containing the AP-2 consensus binding sequence was amplified from the BAC clone RP23-55B4 (BACPAC resource center, Oakland, CA, USA) using PrimeStar HS DNA polymerase (Takara Bio, Otsu, Shiga, Japan). The restriction sites were artificially attached to the primers. The 990 bp PCR product was inserted into pGL4 luciferase reporter vector (Promega, Madison, WI, USA) using the restriction enzymes KpnI and BglII (New England BioLabs, Ipswich, MA, USA) following standard digestion and ligation processes. The pGL4 construct containing the first intron of Drd1a was transformed into chemically competent cells (MAX Efficiency DH5a™, Invitrogen, Carlsbad, CA, USA) by heat shock. Then, 250 µl of recovery medium (SOC) was added to the cells and incubated at 37°C for 1 h with agitation. The transformation mixture was plated on Luria–Bertani (LB) ampicillin agar plates and incubated overnight at 37°C. The following day, single bacterial colonies for each transformation were inoculated into individual sterile culture tubes containing 10 ml of liquid LB medium with 100 µg/ml ampicillin and incubated overnight at 37°C with agitation. Minipreparation kit (Qiagen) was used to isolate the DNA clones from the bacteria. The construct containing the first intron of Drd1a was confirmed by sequencing.

HEK293 cells were cultured at 37°C under a humidified atmosphere containing 5% CO2 in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum. Cells were seeded into 96-well poly-d-lysine coated culture plates (∼1 × 105 cells/well). After 24 h, plasmid DNAs were transiently transfected into the HEK293 cells using Lipofectamine2000 reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacture's protocol. After 48 h of transfection, the cells were harvested and a luciferase assay was performed using the Luciferase Assay System (Promega).

Statistical analysis

All statistics were analyzed by NCSS, v. 2007 (NCSS, Kaysville, UT, USA) and SPSS, v. 16.0 (SPSS, Chicago, IL, USA) statistical software. Results are reported as mean ± standard error of the mean (SEM). The normality and equal variances of all groups of phenotypic distributions were checked to make sure all groups of phenotypic comparisons satisfied the assumptions of the one-way analysis of variance (anova) or the two-tailed t-test. To determine pair-wise differences in each trait, subsequent Tukey post-hoc testing was performed after one-way anova. A significance threshold of P < 0.05 was applied for all comparisons, except for comparisons of gene expression using Qrt-PCR, where the significant level was corrected for multiple comparisons based on the number of candidate genes test (i.e., P = 0.0038 = 0.05/13).

Results

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

QTL analysis in the (B6 × CSS-13) F2 population

We first examined the average 24-h wheel running activity through 3 weeks of recording in 53 (B6 × CSS-13) F1 animals. The average wheel running activity in the F1 mice (24439 ± 960 revolutions/day) is similar to the CSS-13 parental strain (24077 ± 1459 revolutions/day) but is significantly less than the B6 parental strain (32288 ± 1163 revolutions/day, P < 0.01). To increase the resolution of genetic mapping, a total of 163 (B6 × CSS-13) F2 animals having at least one recombination between 23 and 83 Mb on chromosome 13 were selected for the QTL analysis. Single marker association analysis revealed a highly significant QTL with a peak LOD score of 8.18 at 39 Mb (rs3720620, P < 0.001), as shown in Fig. 1a. The LOD score threshold for significance (P = 0.05) was 3.50, calculated by 1000-time permutation. Descriptive analysis indicated that wheel running activity over 24 h in the F2 population was normally distributed with an average daily activity count of 26575 ± 389. Characterization of the F2 progeny indicated that animals carrying homozygous B6 alleles at the peak loci of the QTL had a significantly higher level of wheel running activity (29722 ± 695 revolutions/day) compared to the animals carrying homozygous A/J alleles (24532 ± 746 revolutions/day, P < 0.001), whereas animals with heterozygous alleles were intermediate (26148 ± 521 revolutions/day). This QTL accounts for 15% of the total variance in wheel running activity in the F2 population. In addition, an epistasis analysis in the F2 population using R/qtl did not detect any interactive pair of loci for the 24-h wheel running activity trait.

image

Figure 1. Genetic mapping of 24-h voluntary wheel running activity in a (B6 × CSS-13) F 2 population (a) and ISCS (b). (a) QTL analysis using a total of 14 SNP markers on chromosome 13 in 163 recombinant F2 animals. The peak of the QTL was mapped to 39 Mb (rs3720620) with a LOD score of 8.18. The LOD score threshold was designated by the dotted line. (b) The locations of genetic markers examined to generate the congenic interval boundaries are shown (in megabases) at the bottom. For each congenic strain, the donor B6 and A/J homozygous segment is shown in black and white, respectively, and the boundary between the B6 and A/J regions is shown in gray. The level of 24-h wheel running activity for each strain is illustrated in blue bars (high-activity strains) and red bars (low-activity strains) on the left side, with n representing the number of animals tested in each strain. Levels of activity are reported as mean ± SEM revolutions/day. The blue arrow designates the common B6 allele shared by the three high-activity congenic lines, HR-1, HR-2 and HR-3. The red arrow designates the common A/J allele shared by the four low running congenic lines, LR-1, LR-2, LR-3 and LR-4. The VRA QTL, highlighted in yellow, is narrowed down to a 3.76-Mb interval (38.84–42.60 Mb), which was identified in the overlapping region of the red and blue arrows.

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QTL fine mapping using ISCS

To further narrow down the QTL, seven ISCS (HR-1, HR-2, HR-3, LR-1, LR-2, LR-3 and LR-4) were generated carrying recombination between the 76.01–83.00, 55.51–59.01, 38.84–40.95, 40.95–42.60, 47.00–55.51, 59.01–69.00 and 69.00–76.01 Mb intervals on chromosome 13, respectively (Fig. 1b). Daily wheel running activity of the congenic lines as well as the B6 and CSS-13 parental strains are shown on the left side of Fig. 1b. The average number of revolutions per day in the HR-1 (32549 ± 1319), HR-2 (30564 ± 1271) and HR-3 (29799 ± 739) strains was similar to the B6 strain (32288 ± 1163) but was remarkably higher than the CSS-13 strain (24077 ± 1459, P≤ 0.001 for HR-1, HR-2 and HR-3). The three high-activity ISCS shared a common B6 allele from 38.84 to 59.01 Mb, designated by the blue arrow in Fig. 1b. In contrast, the LR-1 (25293 ± 682 revolutions/day), LR-2 (24066 ± 1044 revolutions/day), LR-3 (25319 ± 1491 revolutions/day) and LR-4 (24021 ± 1097 revolutions/day) strains exhibited a significant reduction in daily activity compared to the B6 strain (P < 0.001 for the LR-1, LR-2, LR-4; P = 0.005 for the LR-3), but did not differ from the CSS-13 strain. The four low-activity ISCS shared a common A/J allele from 23.78 to 42.60 Mb, which is marked by the red arrow. Thus, the critical QTL of VRA (VRA QTL), highlighted in yellow, was identified in the overlapping region of the red and blue arrows. This result narrowed the genetic interval for the VRA QTL, to a maximal 3.76-Mb interval (38.84–42.60 Mb). The fact that neither daily activity levels in the HR-1, HR-2 and HR-3 strains were significantly different from each other nor were the activity levels in the LR-1, LR-2, LR-3 and LR-4 strains indicated that no additional QTL in the 23–83 Mb region on chromosome 13 contribute to the difference in the level of wheel running activity between the B6 and CSS-13 strains.

Voluntary physical activity in the HR-3 and LR-1 strains

The HR-3 and LR-1 congenic strains were of particular interest because across the genome, they were only different in the 3.76-Mb interval of interest, yet showed a significant difference in their daily wheel running activity levels. Representative actograms of mice for the B6, CSS-13, HR-3 and LR-1 strains are shown in Fig. 2a–d, respectively. Figure 3 illustrates the hourly distributions of activity in the two congenic strains and the B6, CSS-13 parental strain over a 24-h day. We have previously reported that the CSS-13 strain showed a significantly lower level of wheel running activity and total cage activity, as well as an attenuated diurnal distribution of activity compared to the B6 strain (Yang et al. 2009). However, the total cage activity levels in the HR-3 and LR-1 congenic strains were not significantly different from the B6 strain (B6 = 11299 ± 547, HR-3 = 11109 ± 922, LR-1 = 12369 ± 618 counts/day). In addition, both HR-3 and LR-1 congenic lines had around 95% of the wheel running activity in the dark phase, similar to the B6 strain. Body weights of HR-3 and LR-1 mice were not significantly different before or after wheel running (before running: HR-3: 22.34 ± 1.31 g, LR-1: 22.70 ± 1.89 g; after running: HR-3: 24.56 ± 1.59 g, LR-1: 25.59 ± 1.95 g). Therefore, these results from our high-resolution mapping showed a separation of different aspects of physical activity at a genetic level. The VRA QTL has a large and specific effect on the amount of daily voluntary wheel running activity but not on total cage activity in the absence of a running wheel.

image

Figure 2. Representative wheel running activity records of a B6 (a), CSS-13 (b), HR-3 (c) and LR-1 (d) mouse. Activity is shown for 4 weeks under a 14:10 LD cycle. Wheel revolutions are indicated by the black tic marks, with the height of marks reflecting the level of activity. The horizontal bar above each activity recording signifies the light (white) and dark (black) periods.

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image

Figure 3. Hourly distribution of wheel running activity of the B6 (open squares) and CSS-13 (light gray diamonds), HR-3 (dark gray circles), LR-1 (black triangles) mice under a 14:10 LD cycle. The B6 and HR-3 strains exhibited significantly higher levels of activity than the LR-1 and CSS-13 strain. However, the HR-3 and LR-1 strains were similar in the diurnal distribution of wheel running activity, whereas different from the CSS-13 strain, which shows onset of activity 1–3 h before light off.

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Cocaine-induced locomotor responses

To test the hypothesis that functional alterations in the dopaminergic pathway contribute to a different propensity for voluntary wheel running activity in the ISCS, locomotor responses to acute cocaine administration were examined in the HR-3, LR-1 and B6 mice using an open field apparatus. Cocaine, a dopamine transporter blocker, increases the extracellular concentration of dopamine and induces an increase in locomotor activity. The responses of the HR-3, LR-1 and B6 strains to saline injection (Day 1) in the open field apparatus were indistinguishable from one another (data not shown). However, the LR-1 strain exhibited a significantly higher response to cocaine injection compared with the HR-3 and B6 strains, which is illustrated in Fig. 4a. A one-way anova revealed marked strain differences in normalized activity counts during 30-min postcocaine injection period (F2,31 = 6.176, P = 0.006). Furthermore, a Tukey post-hoc analysis showed a stronger response in the LR-1 mice than HR-3 (P < 0.01) or B6 (P < 0.001) mice, whereas HR-3 and B6 mice were not significantly different (Fig. 4b).

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Figure 4. The effect of acute cocaine administration (20 mg/kg) on locomotor activity in the B6, HR-3 and LR-1 strains. Activity was monitored for 60 min and normalized by the average of baseline activity of preinjection. (a) Cocaine-induced hyperactivity was observed immediately after injection and peaked in 10–20 min in all strains. The LR-1 mice (black triangles) exhibited significantly higher responses to cocaine injection compared with the HR-3 mice (gray circles) and B6 controls (open squares), whereas the responses in the latter two were indistinguishable. (b) During 30 min of postinjection, normalized locomotor activity in the LR-1 mice was substantially higher than the HR-3 and B6 mice, whereas HR-3 and B6 mice are not significantly different. Results are reported as mean ± SEM. **P < 0.01, ***P < 0.001 compared with the LR-1 strain.

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Expression of dopamine genes in the dorsal striatum and nucleus accumbens

To further investigate possible alterations in the dopaminergic pathway, mRNA levels of the key dopamine-related genes (Drd1a, Drd2, Drd3, Drd4, Drd5 and Slc6a3) were examined in the dorsal striatum and nucleus accumbens in the naÏve HR-3 and LR-1 congenic strains. Expression of Drd1a, normalized by Gapdh, was significantly higher in the LR-1 strain than the HR-3 strain in both the dorsal striatum (P < 0.001, Fig. 5a) and nucleus accumbens (P = 0.05 Fig. 5b). There were no significant differences in the expression of Drd2, Drd3, Drd4, Drd5 and Slc6a3 in any of the two tissues. The differential expression of Drd1a was confirmed by another internal housekeeping gene, Actb, in both the dorsal striatum (P < 0.001, Fig. 5c) and nucleus accumbens (P < 0.001, Fig. 5d).

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Figure 5. Expression analysis of dopamine-related genes in the dorsal striatum (a, c) and nucleus accumbens (b, d) of the naÏve HR-3 (gray) and LR-1 (black) congenic strains using Qrt-PCR. Expression levels of dopamine-related genes were normalized by two different housekeeping genes, Gapdh (a, b) and Actb (c, d). Drd1a was expressed significantly higher in the LR-1 mice than the HR-3 mice in both the striatum and nucleus accumbens. *P < 0.05, ***P < 0.001 compared with the HR-3 strain.

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Candidate genes in the QTL

On the basis of the Ensembl Gene 60 (NCBIM37) database, we identified 18 known genes and 7 predicted genes within the maximal VRA QTL interval (Fig. 6, Table S1, supporting information). Of the 25 genes, 17 are protein coding and the other 8 are known or predicted noncoding RNA genes. These candidate genes are involved in diverse cellular functions including transmembrane transport, DNA-binding transcription factor activity, regulation of signal transduction, cell adhesion etc. We used the evidence of brain expression as an initial filter in prioritizing potential candidate genes involved in the central regulation of voluntary physical activity. Using the GNF database and the Allen Brain Atlas, as well as our Qrt-PCR data in four brain tissues (frontal cortex, hypothalamus, striatum and cerebellum), we confirmed that 15 of these candidate genes, including Slc35b3, AC125223.1, U1, Tcfap2a, Gcnt2, AC133496.1, Pak1ip1, Tmem14c, Mak, Elovl2, Nedd9, AC133159.1, U6, Hivep1 and Edn1, were expressed at detectable levels in brain. The expression of the remaining 10 candidate genes was too low to be reliably detected in any of the four brain tissues by real-time PCR [i.e., lower than 1/30 000 (≅2−15) of the Gapdh expression level]. In concordance with our data, they were found to have very low levels of expression in brain in the GNF database and the Allen Brain Atlas. Among those expressed in the brain, mRNA levels of U1 and U6 was difficult to assess, as they are represented multiple times in the genome, and a unique query on chromosome 13 is not possible. Genotype-dependent expression of the 13 primary candidate genes was assessed in four brain regions of the HR-3 and LR-1 congenic strains. Two genes, Slc35b3 and Mak, showed evidence of brain regionally specific differential expression: expression of Slc35b3 was significantly higher in the frontal cortex of the LR-1 than HR-3 strain (P < 0.004), and expression of Mak was significantly higher in the hypothalamus of the HR-3 than LR-1 strain (P < 0.004). However, the magnitude of difference in expression was only 11 and 6% in Slc35b3 and Mak, respectively.

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Figure 6. SNP histogram and locations of candidate genes in the VRA QTL. Pair-wise sequence comparison was performed to identify genetic regions containing few polymorphisms between the B6 and A/J inbred strains. The names of candidate genes are shown at the top of the black bars. The black bars only illustrate the approximate locations, rather than the precise sizes, of the candidate genes. Candidate genes located in the nonpolymorphic regions are less likely to give rise to the QTL.

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Next, a pair-wise sequence comparison between the B6 and A/J strains was performed to analyze polymorphisms in the VRA QTL interval. A survey of the SNP Database in the Wellcome Trust Sanger Institute, which has sequenced common inbred strains to a 20× coverage, revealed regions with few polymorphisms between the two strains (Fig. 6). Such SNP-desert regions are less likely to contain causative polymorphisms, whereas SNP-rich regions in which two strains contain alleles from different ancestral sources are more likely to encode the causal quantitative trait gene (QTG) (Cervino et al. 2006). From this perspective, U1, Ofcc1, Gm9979, Tcfap2a, AC123834.1, Gcnt2, AC133496.1, Pak1ip1, Tmem14c, Mak, Gcm2, Sycp2l, Elovl2 and Nedd9 are located in the high-density SNP regions, thus are more likely to be the QTG. Taken together, these surveys provided a sound and unbiased evaluation of all the genes within the VRA QTL for potential causative QTG based on expression and sequence variation criteria.

As the studies of cocaine-induced locomotor activity and expression of dopamine-related genes supported our hypothesis that the dopaminergic signaling pathway, especially the Drd1a, contributes to the regulation of wheel running activity in the ISCS, we further examined possible interactions of the QTL candidate genes with the dopamine-related genes. IPA was employed to explore the functional relationship between the 25 QTL candidate genes and the 6 dopamine-related genes. As shown in Fig. 7a, the transcription factor AP-2α (Tcfap2a) was found to have a direct interaction with the Drd1a. The Tcfap2a-Drd1a interaction was previously identified using a yeast one-hybrid screen and supported by gel mobility shift assays. It was found that Tcfap2a could bind the AP2 consensus sequences located in the first intron of Drd1a, which contains a promoter activator region (Yang et al. 2000).

image

Figure 7. Identification of QTL candidate genes interacting with the dopamine-related genes. (a) Pathway analysis was employed to explore the functional relationship between the QTL candidate genes and dopamine-related genes. Proteins are depicted as nodes in different shapes representing the functional classes of the gene products, and the biological relationship between genes are depicted by edges. Solid and dashed edges indicate direct and indirect molecular interactions, respectively. Among 25 candidate genes, Tcfap2a has a direct connection with Drd1a, whereas Gcm2, Hivep1, Edn1 and Pak1ip1 have indirect connections with the dopamine-related genes. These five QTL candidate genes are highlighted by orange bars, and six dopamine-related genes are marked by green triangles. (b) Luciferase reporter assay was performed to confirm a direct interaction between Tcfap2a and Drd1a. The first intron of Drd1a containing the AP-2 consensus binding site was cloned into the pGL4 luciferase reporter vector using the restriction enzymes KpnI and BglII. Cotransfection of the pGL4 construct (40 ng/reaction) and four different amounts of the Tcfap2a cDNA clone (10, 20, 40 and 80 ng/reaction) in the HEK293 cells generated significantly higher luciferase signal compared with the single transfection of the pGL4 construct containing Drd1a intron without Tcfap2a.

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To further confirm the role of Tcfap2a in the transcriptional regulation of Drd1a, we cloned the first intron of Drd1a into the pGL4 luciferase reporter vector using the restriction enzymes KpnI and BglII (New England Biolabs, Ipswich, MA, USA). Cotransfection of the murine Tcfap2a cDNA clone (10, 20, 40 and 80 ng) and the pGL4 luciferase construct (40 ng) containing AP2 consensus sequences of Drd1a significantly stimulated luciferase expression in the HEK 293 cells (Fig. 7b), suggesting that Tcfap2a can regulate the promoter activity of Drd1a and supporting the hypothesis that Tcfap2a may regulate voluntary wheel running activity via its interaction with the dopaminergic signaling pathway through Drd1a.

As shown in Fig. 7a, Gcm2, Hivep1, Edn1 and Pak1ip1 have indirect connections with the dopamine-related genes. Among these, however, Hivep1 and Edn1 are located in the genetic region having few polymorphisms, and the expression of Gcm2 is undetectable in brain tissues, making them less likely to be the causative QTG. However, we cannot exclude the possibility that additional candidate genes in the QTL interval may also influence wheel running activity and warrant further attention in future studies.

Discussion

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

In this study, we have used an F2 intercross population and seven interval-specific congenic lines to identify a novel 3.76-Mb genetic interval (VRA QTL) on chromosome 13 with a substantial and specific effect on the amount of daily voluntary wheel running activity in mice. To prioritize QTL candidate genes, we employed a hypothesis-driven approach to investigate the function of the dopaminergic pathway in the high- and low-activity ISCS. As the crucial role of the dopaminergic pathway in mediating cocaine-induced hyperactivity has been well established (Karasinska et al. 2005; Xu et al. 1994a, 1994b), the distinct responses to acute cocaine administration in the high- and low-activity ISCS suggested functional alterations in the dopaminergic regulation, which may contribute to different levels of activity in the ISCS. In addition, Drd1a expression was significantly higher in the LR-1 mice than the HR-3 mice in the dorsal striatum and nucleus accumbens, which offers a potential explanation for why the LR-1 mice exhibited stronger responses to cocaine administration than the HR-3 mice. The evidence supporting a role of the dopaminergic pathway allowed prioritization of the genes within the QTL and led to the identification of a promising candidate, Tcfap2a, which has been proposed to regulate the promoter activity of Drd1a and may affect voluntary wheel running activity via its interaction with a dopaminergic pathway.

Voluntary physical activity, which has been described as a specific type of locomotion, is commonly defined as purposeful exercise or movements that expend a significant amount of energy (Knab et al. 2009). Voluntary physical activity appears to be a separable trait from other locomotor activity traits, such as running endurance or open field activity. For example, mouse models of ‘super endurance’ and increased muscle fiber transformation, created by either transgenic over-expression of PPARδ or its coactivator-1α (PGC-1α) in skeletal muscles or oral administration of an AMPK agonist, AICAR, do not have increased spontaneous activity, in spite of their improved running endurance when placed on a continuously moving treadmill (Calvo et al. 2008; Narkar et al. 2008; Wang et al. 2004). Additionally, locomotor activity in the open field test is widely considered as a measurement of exploratory activity, novelty responsiveness and anxiety in mice, rather than of baseline spontaneous physical activity (de Mooij-van Malsen et al. 2009). Studies have shown that short-term activity measured in an open-field or open-maze apparatus is not associated with long-term home cage activity (Bronikowski et al. 2001; Mill et al. 2002). Moreover, HR-3 and LR-1 ISCS in this study exhibited a similar amount of baseline activity before injections in the open field apparatus. Therefore, at least some of the genetic components regulating voluntary physical activity are different from the genes and pathways identified for running endurance or open field activity, although there may well be some genetic factors that are common to different types of locomotion.

An emerging effort has been made to identify genetic regions associated with traits for physical activity (Kelly et al. 2010; Leamy et al. 2008; Umemori et al. 2009). For example, QTL associated with home cage activity were mapped to chromosome 1 using a (B6 × CSS-1) F2 population (Kas et al. 2009) and to chromosome 2 and 10 using a (B6 × KJR) F2 population (Umemori et al. 2009). Moreover, QTLs linked with duration, distance or speed of wheel running activity were identified on chromosomes 9 and 13 in a (C57BL/J × C3H/HeJ) F2 population (Lightfoot et al. 2008). However, the mapping resolution of conventional F2 approach is relatively low, and it has been difficult to define the cutoff boundary of QTLs (Flint et al. 2005). Within broad QTLs, there are often hundreds of potential genes that could affect the behavioral traits in different ways. In contrast, the use of ISCS derived from a chromosome substitution strain in our study represents a much more powerful and efficient approach to precisely map QTL (Darvasi 1997; Singer et al. 2004; Yazbek et al. 2011). Moreover, our high-resolution genetic mapping allows a separation of different aspects of physical activity, i.e., VRA QTL only affects the level of wheel running activity, but not the diurnal distribution of activity or day-to-day variability of activity onset.

Identification of causative genes regulating voluntary physical activity is a challenging task. Even after a QTL is narrowed down to a small region, it is still difficult to determine which gene corresponds to the QTL, especially when neither the key components nor the pathways have been uncovered for this behavior (Flint et al. 2005; Mackay 2004). Therefore, we employed a novel approach to prioritize the candidate genes by interrogating their interactions with components of the dopaminergic pathway, which were found to function differently between the HR-3 and LR-1 ISCS. The fact that both the response to cocaine and the expression levels of dopamine-related genes were examined in the naÏve mice without access to running wheels excluded the possibility that the alteration in the dopaminergic pathway was caused by an activity-dependent plasticity. Our finding of a decreased dopaminergic function in the high-activity ISCS is consistent with previous observations. Specifically, selectively bred high-activity mice were less sensitive than controls to the behavioral effects of D1-like receptor blocker. In contrast, the selected mice and controls were equally sensitive to D2-like receptor blocker (Rhodes & Garland 2003). Additionally, the active inbred C57L/J strain of mice exhibited lower levels of Drd1a mRNA expression in the striatum compared to the inactive C3H/HeJ strain (Knab et al. 2009). Although both studies suggest a reduced function of the D1-like dopaminergic signaling pathway in the high-activity mice, the presence of substantial differences in the genetic background between the selectively bred mice and controls, as well as between the two inbred strains, limits the ability of these studies to conclusively establish a link between the dopaminergic pathway and voluntary physical activity. In contrast, our study using two congenic strains that are only different in a 3.76-Mb genetic interval provides more convincing evidence to support the role of the dopaminergic pathway in regulating VRA.

The Tcfap2a gene is proposed as a promising QTG, as it is expressed in the brain, located in a highly polymorphic chromosomal region, and may interact with the Drd1a signaling pathway. The mammalian homolog of Tcfap2a exists in human beings, chimpanzee, dog, rat and mouse. This gene is highly expressed in the central and peripheral nervous system during embryogenesis and is known to play an important role in neural tube development, embryonic cranial skeleton morphogenesis and cartilage and skeletal development (Kohlbecker et al. 2002; Schorle et al. 1996; Wenke & Bosserhoff 2010). The crucial role that Tcfap2a plays in embryogenesis and its interaction with Drd1a raise the intriguing possibility that Tcfap2a could influence the predisposition for physical activity through the dopaminergic pathway in early developmental stages. One synonymous coding SNP and multiple noncoding SNPs, deletions and insertions in the UTR and introns were identified in Tcfap2a between the B6 and A/J strains, which may affect the transcript abundance or mRNA splicing in certain brain tissues or specific stages of development. However, we cannot exclude the possibility that other candidate genes within the VRA QTL, such as Slc35b3 or Mak, may regulate voluntary physical activity through an indirect interaction with Drd1a, or other pathways. While future experiments are needed to elucidate functional polymorphisms, the results presented in this study are important steps toward understanding the genetic mechanisms, including candidate genes and underlying neurotransmitter signaling pathways, regulating voluntary physical activity.

Behavioral studies suggest that wheel running is naturally rewarding and reinforcing in rodents (Werme et al. 2002a; Brene et al. 2007). For instance, rats will press a lever for access to a running wheel and develop a conditioned place preference to an environment associated with the aftereffects of wheel running (Belke & Wagner 2005; Lett et al. 2000). Like drugs of abuse, voluntary wheel running increases levels of DeltaFosB in the nucleus accumbens, a central brain reward structure (Werme et al. 2002b). In this study, our high-running HR-3 mice with a lower level of Drd1a in the nucleus accumbens showed lower responses to acute cocaine administration, whereas they had similar levels of total cage ambulating activity compared with the LR-1 mice. The increase in wheel running activity is thought to be a compensatory mechanism for decrease in the magnitude of the reinforcer, similar to the increase in cocaine self-administration in rats when SCH23390 is injected into the nucleus accumbens (Maldonado et al. 1993).

In summary, our high-resolution mapping has led to a refined QTL on chromosome 13 with a significant impact on voluntary wheel running activity in mice. In addition, we identified a promising candidate gene that may affect the phenotype through the dopaminergic pathway. The use of interval-specific congenic lines derived from a chromosome substitution strain has established an important genetic model for the fine-scale mapping of complex traits. This study has provided new insight into the genetic regulation for an individual's propensity to engage in voluntary physical activity, information that could lead to the development of new therapeutic approaches for the treatment and prevention of diseases associated with a sedentary lifestyle.

References

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information
  • Belke, T.W. & Wagner, J.P. (2005) The reinforcing property and the rewarding aftereffect of wheel running in rats: a combination of two paradigms. Behav Processes 68, 165172.
  • Brene, S., Bjornebekk, A., Aberg, E., Mathe, A.A., Olson, L. & Werme, M. (2007) Running is rewarding and antidepressive. Physiol Behav 92, 136140.
  • Broman, K.W., Wu, H., Sen, S. & Churchill, G.A. (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19, 889890.
  • Bronikowski, A.M., Carter, P.A., Swallow, J.G., Girard, I.A., Rhodes, J.S. & Garland, T. Jr (2001) Open-field behavior of house mice selectively bred for high voluntary wheel-running. Behav Genet 31, 309316.
  • Calvo, J.A., Daniels, T.G., Wang, X., Paul, A., Lin, J., Spiegelman, B.M., Stevenson, S.C. & Rangwala, S.M. (2008) Muscle-specific expression of PPARgamma coactivator-1alpha improves exercise performance and increases peak oxygen uptake. J Appl Physiol 104, 13041312.
  • Cervino, A.C., Gosink, M., Fallahi, M., Pascal, B., Mader, C. & Tsinoremas, N.F. (2006) A comprehensive mouse IBD database for the efficient localization of quantitative trait loci. Mamm Genome 17, 565574.
  • Creer, D.J., Romberg, C., Saksida, L.M., van Praag, H. & Bussey, T.J. (2010) Running enhances spatial pattern separation in mice. Proc Natl Acad Sci U S A 107, 23672372.
  • Darvasi, A. (1997) Interval-specific congenic strains (ISCS): an experimental design for mapping a QTL into a 1-centimorgan interval. Mamm Genome 8, 163167.
  • Duman, C.H., Schlesinger, L., Russell, D.S. & Duman, R.S. (2008) Voluntary exercise produces antidepressant and anxiolytic behavioral effects in mice. Brain Res 1199, 148158.
  • Eriksson, M., Rasmussen, F. & Tynelius, P. (2006) Genetic factors in physical activity and the equal environment assumption – the Swedish young male twins study. Behav Genet 36, 238247.
  • Flint, J., Valdar, W., Shifman, S. & Mott, R. (2005) Strategies for mapping and cloning quantitative trait genes in rodents. Nat Rev Genet 6, 271286.
  • Frazer, K.A., Eskin, E., Kang, H.M., Bogue, M.A., Hinds, D.A., Beilharz, E.J., Gupta, R.V., Montgomery, J., Morenzoni, M.M., Nilsen, G.B., Pethiyagoda, C.L., Stuve, L.L., Johnson, F.M., Daly, M.J., Wade, C.M. & Cox, D.R. (2007) A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Nature 448, 10501053.
  • Hillman, C.H., Erickson, K.I. & Kramer, A.F. (2008) Be smart, exercise your heart: exercise effects on brain and cognition. Nat Rev Neurosci 9, 5865.
  • Kaprio, J., Kujala, U.M., Koskenvuo, M. & Sarna, S. (2000) Physical activity and other risk factors in male twin-pairs discordant for coronary heart disease. Atherosclerosis 150, 193200.
  • Karasinska, J.M., George, S.R., Cheng, R. & O’Dowd, B.F. (2005) Deletion of dopamine D1 and D3 receptors differentially affects spontaneous behaviour and cocaine-induced locomotor activity, reward and CREB phosphorylation. Eur J Neurosci 22, 17411750.
  • Kas, M.J., de Mooij-van Malsen, J.G., de Krom, M., van Gassen, K.L., van Lith, H.A., Olivier, B., Oppelaar, H., Hendriks, J., de Wit, M., Groot Koerkamp, M.J., Holstege, F.C., van Oost, B.A. & de Graan, P.N. (2009) High-resolution genetic mapping of mammalian motor activity levels in mice. Genes Brain Behav 8, 1322.
  • Keane, T.M., Goodstadt, L., Danecek, P., et al. (2011) Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289294.
  • Kelly, S.A., Nehrenberg, D.L., Peirce, J.L., Hua, K., Steffy, B.M., Wiltshire, T., Pardo-Manuel de Villena, F., Garland, T., Jr & Pomp, D. (2010) Genetic architecture of voluntary exercise in an advanced intercross line of mice. Physiol Genomics 42, 190200.
  • Knab, A.M., Bowen, R.S., Hamilton, A.T., Gulledge, A.A. & Lightfoot, J.T. (2009) Altered dopaminergic profiles: implications for the regulation of voluntary physical activity. Behav Brain Res 204, 147152.
  • Knab, A.M. & Lightfoot, J.T. (2010) Does the difference between physically active and couch potato lie in the dopamine system?. Int J Biol Sci 6, 133150.
  • Kohlbecker, A., Lee, A.E. & Schorle, H. (2002) Exencephaly in a subset of animals heterozygous for AP-2alpha mutation. Teratology 65, 213218.
  • Leamy, L.J., Pomp, D. & Lightfoot, J.T. (2008) An epistatic genetic basis for physical activity traits in mice. J Hered 99, 639646.
  • Lett, B.T., Grant, V.L., Byrne, M.J. & Koh, M.T. (2000) Pairings of a distinctive chamber with the aftereffect of wheel running produce conditioned place preference. Appetite 34, 8794.
  • Lightfoot, J.T., Turner, M.J., Daves, M., Vordermark, A. & Kleeberger, S.R. (2004) Genetic influence on daily wheel running activity level. Physiol Genomics 19, 270276.
  • Lightfoot, J.T., Turner, M.J., Pomp, D., Kleeberger, S.R. & Leamy, L.J. (2008) Quantitative trait loci for physical activity traits in mice. Physiol Genomics 32, 401408.
  • Lightfoot, J.T., Leamy, L., Pomp, D., Turner, M.J., Fodor, A.A., Knab, A., Bowen, R.S., Ferguson, D., Moore-Harrison, T. & Hamilton, A. (2010) Strain screen and haplotype association mapping of wheel running in inbred mouse strains. J Appl Physiol 109, 623634.
  • Mackay, T.F. (2004) Complementing complexity. Nat Genet 36, 11451147.
  • Maldonado, R., Robledo, P., Chover, A.J., Caine, S.B. & Koob, G.F. (1993) D1 dopamine receptors in the nucleus accumbens modulate cocaine self-administration in the rat. Pharmacol Biochem Behav 45, 239242.
  • Manly, K.F., Cudmore, R.H., Jr & Meer, J.M. (2001) Map manager QTX, cross-platform software for genetic mapping. Mamm Genome 12, 930932.
  • Mill, J., Galsworthy, M.J., Paya-Cano, J.L., Sluyter, F., Schalkwyk, L.C., Plomin, R. & Asherson, P. (2002) Home-cage activity in heterogeneous stock (HS) mice as a model of baseline activity. Genes Brain Behav 1, 166173.
  • de Mooij-van Malsen, J.G., van Lith, H.A., Oppelaar, H., Olivier, B. & Kas, M.J. (2009) Evidence for epigenetic interactions for loci on mouse chromosome 1 regulating open field activity. Behav Genet 39, 176182.
  • Narkar, V.A., Downes, M., Yu, R.T., Embler, E., Wang, Y.X., Banayo, E., Mihaylova, M.M., Nelson, M.C., Zou, Y., Juguilon, H., Kang, H., Shaw, R.J. & Evans, R.M. (2008) AMPK and PPARdelta agonists are exercise mimetics. Cell 134, 405415.
  • Paxinos, G. & Franklin, K.B.J. (2001) The mouse brain in stereotaxic coordinates . Academic Press, San Diego, CA.
  • van Praag, H., Kempermann, G. & Gage, F.H. (1999) Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus. Nat Neurosci 2, 266270.
  • Redila, V.A. & Christie, B.R. (2006) Exercise-induced changes in dendritic structure and complexity in the adult hippocampal dentate gyrus. Neuroscience 137, 12991307.
  • Rhodes, J.S., Hosack, G.R., Girard, I., Kelley, A.E., Mitchell, G.S. & Garland, T., Jr (2001) Differential sensitivity to acute administration of cocaine, GBR 12909, and fluoxetine in mice selectively bred for hyperactive wheel-running behavior. Psychopharmacology (Berl) 158, 120131.
  • Rhodes, J.S. & Garland, T. (2003) Differential sensitivity to acute administration of Ritalin, apomorphine, SCH 23390, but not raclopride in mice selectively bred for hyperactive wheel-running behavior. Psychopharmacology (Berl) 167, 242250.
  • Schorle, H., Meier, P., Buchert, M., Jaenisch, R. & Mitchell, P.J. (1996) Transcription factor AP-2 essential for cranial closure and craniofacial development. Nature 381, 235238.
  • Seabra, A.F., Mendonca, D.M., Goring, H.H., Thomis, M.A. & Maia, J.A. (2008) Genetic and environmental factors in familial clustering in physical activity. Eur J Epidemiol 23, 205211.
  • Shimomura, K., Low-Zeddies, S.S., King, D.P., Steeves, T.D., Whiteley, A., Kushla, J., Zemenides, P.D., Lin, A., Vitaterna, M.H., Churchill, G.A. & Takahashi, J.S. (2001) Genome-wide epistatic interaction analysis reveals complex genetic determinants of circadian behavior in mice. Genome Res 11, 959980.
  • Singer, J.B., Hill, A.E., Burrage, L.C., Olszens, K.R., Song, J., Justice, M., O’Brien, W.E., Conti, D.V., Witte, J.S., Lander, E.S. & Nadeau, J.H. (2004) Genetic dissection of complex traits with chromosome substitution strains of mice. Science 304, 445448.
  • Stranahan, A.M., Khalil, D. & Gould, E. (2007) Running induces widespread structural alterations in the hippocampus and entorhinal cortex. Hippocampus 17, 10171022.
  • Turner, M.J., Kleeberger, S.R. & Lightfoot, J.T. (2005) Influence of genetic background on daily running-wheel activity differs with aging. Physiol Genomics 22, 7685.
  • Umemori, J., Nishi, A., Lionikas, A., Sakaguchi, T., Kuriki, S., Blizard, D.A. & Koide, T. (2009) QTL analyses of temporal and intensity components of home-cage activity in KJR and C57BL/6J strains. BMC Genet 10, 40.
  • Vetter, M.L., Faulconbridge, L.F., Webb, V.L. & Wadden, T.A. (2010) Behavioral and pharmacologic therapies for obesity. Nat Rev Endocrinol 6, 578588.
  • Vitaterna, M.H., Pinto, L.H. & Takahashi, J.S. (2006) Large-scale mutagenesis and phenotypic screens for the nervous system and behavior in mice. Trends Neurosci 29, 233240.
  • Wang, Y.X., Zhang, C.L., Yu, R.T., Cho, H.K., Nelson, M.C., Bayuga-Ocampo, C.R., Ham, J., Kang, H. & Evans, R.M. (2004) Regulation of muscle fiber type and running endurance by PPARdelta. PLoS Biol 2, e294.
  • Wenke, A.K. & Bosserhoff, A.K. (2010) Roles of AP-2 transcription factors in the regulation of cartilage and skeletal development. FEBS J 277, 894902.
  • Werme, M., Lindholm, S., Thoren, P., Franck, J. & Brene, S. (2002a) Running increases ethanol preference. Behav Brain Res 133, 301308.
  • Werme, M., Messer, C., Olson, L., Gilden, L., Thoren, P., Nestler, E.J. & Brene, S. (2002b) Delta FosB regulates wheel running. J Neurosci 22, 81338138.
  • Xu, M., Hu, X.T., Cooper, D.C., Moratalla, R., Graybiel, A.M., White, F.J. & Tonegawa, S. (1994a) Elimination of cocaine-induced hyperactivity and dopamine-mediated neurophysiological effects in dopamine D1 receptor mutant mice. Cell 79, 945955.
  • Xu, M., Moratalla, R., Gold, L.H., Hiroi, N., Koob, G.F., Graybiel, A.M. & Tonegawa, S. (1994b) Dopamine D1 receptor mutant mice are deficient in striatal expression of dynorphin and in dopamine-mediated behavioral responses. Cell 79, 729742.
  • Yang, Y., Hwang, C.K., Junn, E., Lee, G. & Mouradian, M.M. (2000) ZIC2 and Sp3 repress Sp1-induced activation of the human D1A dopamine receptor gene. J Biol Chem 275, 3886338869.
  • Yang, H.S., Vitaterna, M.H., Laposky, A.D., Shimomura, K. & Turek, F.W. (2009) Genetic analysis of daily physical activity using a mouse chromosome substitution strain. Physiol Genomics 39, 4755.
  • Yazbek, S.N., Buchner, D.A., Geisinger, J.M., Burrage, L.C., Spiezio, S.H., Zentner, G.E., Hsieh, C.W., Scacheri, P.C., Croniger, C.M. & Nadeau, J.H. (2011) Deep congenic analysis identifies many strong, context-dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis. Genome Res 21, 10651073.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

We thank Christopher J. Olker for his assistance with the behavioral experiments and Rebecca M. Eiden for her assistance with the molecular experiments. We also thank Dr Satoru M. Sato, Dr Anne Marie Wissman, Dr Indira M. Raman and Keith Summa for their very helpful discussion and proof-reading of the manuscript. Finally, we are grateful to Ann Schraufnagel and Tiffany Tang for their valuable contribution to the animal work and genotyping. This work was supported by an unrestricted gift for neuroscience research from Merck and Co., Inc. (F.W.T.), and by the Army Research Office, award number DAAD19-02-1-0038 from the Defense Advance Research Projects Agency.

Supporting Information

  1. Top of page
  2. Abstract
  3. Materials and methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
  8. Supporting Information

Supporting Information

Additional Supporting Information may be found in the online version of this article:

Table S1: List of candidate genes in the VRA QTL

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