Muscle tissue is both highly adaptable, remodeling to different activity patterns, and highly polymorphic, where individuals may be predisposed to endurance or strength or large or small muscle volume. Interindividual differences is a combination of genetics and training, with the gene polymorphisms influencing muscle phenotypes often called “genetic modifiers”(18). A polymorphism in the α-actinin 3 gene (ACTN3) is the best characterized and validated genetic modifier of muscle in healthy populations and athletes (7,8,37). This common nonsense (loss-of-function) polymorphism (R577X) is broadly distributed worldwide. In general, individuals with the intact ACTN3 gene are predisposed to greater strength and speed, whereas individuals with the loss-of-function allele have greater endurance (37).
Duchenne muscular dystrophy (DMD) is the most common muscle disease, causing progressive weakness and early death in approximately 1 in 3500 men worldwide. The disease results from the loss of dystrophin, a membrane cytoskeletal protein normally present at the myofiber sarcolemma (16). Dystrophin deficiency causes myofiber membrane instability during muscle contraction and chronic bouts of muscle degeneration and regeneration. Patients with DMD show variability in the severity of their disease, both with regard to age of onset and disease progression, and this led to the search for genetic modifiers of disease severity. We recently reported a study of 29 candidate genes that had been previously shown to be correlated with muscle phenotypes in healthy volunteers (including ACTN3) and gene loci associated with metabolic syndrome and cardiovascular disease phenotypes (32). We also took a disease-centric approach, where archival muscle biopsies were accessed from particularly severe or mild DMD patients and then mRNA profiled to identify potential biomarkers of later disease severity (31). In these studies, 2 of 29 polymorphisms studied showed an association with disease severity in two independent DMD cohorts: ACTN3 and osteopontin (OPN). ACTN3 had been chosen as a candidate due to its association with muscle phenotypes in healthy volunteers and athletes, and the nonsense (loss-of-function) allele was associated with less severe disease. The OPN polymorphism studied was chosen due to the observation of high mRNA levels in muscle biopsies from particularly severe DMD patients and due to the well-characterized nature of the rs28357094 single nucleotide polymorphism (SNP) on the gene promoter and mRNA expression (13,14,19). The OPN polymorphism was particularly strongly associated with disease severity of DMD in two independent cohorts, with the more common TT genotype (60%–70% of subjects) showing a milder disease progression compared with the GT/GG combined genotypes (P = 0.003 in one DMD cohort and P = 0.0003 in a second independent DMD cohort). The OPN rs28357094 SNP is the most robust genetic modifier of DMD identified to date (32), and as the rarer G allele has been shown to reduce OPN gene expression in reporter constructs in HeLa cells (13), the data suggest that less OPN protein causes greater DMD severity.
Given that the ACTN3 polymorphism is a modifier of muscle function in both health and disease, we hypothesized that the OPN polymorphism might also influence muscle traits in normal volunteers. OPN is a protein that is increasingly studied as a key inflammatory cytokine involved in tissue remodeling (28,36). OPN is actively secreted by both inflammatory cells and remodeling tissues, and the extracellular protein is extensively yet variably processed by O-linked glycosylation, phosphorylation, and transglutamination (6,20). OPN also shows multiple proteolytic cleavage sites for proteases such as thrombin and matrix metalloproteinases (MMPs) (mmp-2, mmp-7, and mmp-9), which cleave the protein into different biologically active isoforms (38). One of the key roles for OPN is to interact with at least nine integrins and CD44 at the cell surface, modulating cell–cell interactions and cell–matrix interactions. OPN has been shown to be a key factor in cancer progression and metastasis in multiple tumor types, including prostrate (12), breast (22), and esophageal cancers (21) and hepatocellular carcinoma (5).
The different products of OPN have different roles in inflammatory cell recruitment and chemotaxis. At the sites of tissue damage, thrombin cleaves OPN to create an N-terminal fragment that is chemotactic for neutrophils (38). In addition, transglutaminase 2 at sites of tissue damage acts on OPN, resulting in polymerized OPN (polyOPN), and polyOPN is also highly chemotactic for neutrophils (29). The chemotactic activity of N-terminal fragment and polyOPN is directly through interactions with α9β1 integrin on leukocytes (38). However, the role of OPN is not only restricted to recruitment of neutrophils. There are numerous studies that support a role for OPN in the accumulation of macrophages at the sites of tissue injury and pathology (23,29). OPN has been shown to be expressed in myogenic cells (35).
Given that OPN was a validated genetic modifier of muscle disease, we sought to determine whether the same OPN polymorphism might modify muscle phenotypes in healthy young adult volunteers. To study this, we carried out sex-stratified genotype/phenotype associations in two previously described cohorts: a unilateral upper arm resistance training intervention in 752 healthy Caucasian volunteers (FAMuSS cohort; 34) and a unilateral upper arm eccentric challenge in 147 young adults (8,33). The eccentric challenge was studied as OPN is a cytokine frequently associated with tissue damage.
Subjects: Unilateral Resistance Training Cohort (FAMuSS)
The study population was derived from a multicenter study funded by the National Institutes of Health designed to identify genetic factors that dictate baseline bone, muscle and fat volume, and the variability in response to exercise training. The study design protocol has been described in detail elsewhere (11,34), and we have previously published genetic associations with muscle strength and size, response to exercise, and metabolic syndrome markers (7). Briefly, 1300 men and women, average age 24 yr, were recruited by one of the eight centers (University of Massachusetts Amherst; University of Connecticut; Dublin University, Ireland; Florida Atlantic University; Hartford Hospital; University of Central Florida; West Virginia University; and Central Michigan University). The study was approved by the Children’s National Medical Center Institutional Review Board (protocol 2449) and was in compliance with the Helsinki Declaration and included written informed consent.
Exercise training program and strength assessments.
Unilateral resistant training was performed with the nondominant arm. The protocol was described elsewhere (34). Briefly, it consisted of two 45- to 60-min sessions per week for 12 wk. Each session included dumbbell biceps curls, dumbbell biceps preacher curls, incline dumbbell biceps curls, overhead dumbbell triceps extension, and dumbbell triceps kickbacks. The amount of weight was aggressively increased during the 12 wk. Strength assessments were isometric biceps strength (maximum voluntary contraction [MVC]), and one-repetition maximum (1RM) weight before and after training. Details of the strength measurement protocols have been described previously (34).
MRI assessment of both upper arms (trained and untrained), before and after the 12-wk resistance training intervention, has been previously described (34). Entry MRI was performed 24–48 h before the isometric or 1RM test. Scans posttraining MRI was performed 48–96 h after the last training session. Fifteen 16 mm contiguous axial slices from each arm were taken from each arm independently. Scans for both arms were taken by fast spoiled gradient recalled and fast spin echo (echo time = 1.8 ms, repetition time = 200 ms). All eight centers submitted the MRI data to the Center for Genetic Medicine Research at Children’s National Medical Center in Washington, DC, via e-mail or DAT disc, and all scans were integrated into the study SQL data base. The complete MRI data set with both pre- and postmeasures of high-quality images for both arms was n = 752.
For the volumetric analysis of the MRI images, we used Rapidia (INFINITT Inc., Seoul, Korea), a PC-based software that allows the semiautomatic quantification of muscle, bone, and subcutaneous fat. The software was optimized to provide automated position adjustment and to distinguish muscle, fat, and bone, with automatic edge detection, user modification of ambiguous edges, and automated propagation of defined tissue boundaries when possible. Volume measures were taken using an anatomical landmark (metaphyseal–diaphyseal junction of the humerus) as our starting point and assayed the six 1-cm slices proximal to it. Reliability of measures was determined using the untrained arm, with muscle volume measured at a 12-wk interval, showing that our quantitation of muscle volume was highly reliable and sensitive (R2 = 0.968). Measurement values were automatically written and saved in an SQL database together with anthropomorphic and genotyping data.
Subjects: Eccentric Challenge Cohort
Data for this study were derived from a larger clinical trial assessing the efficacy of a topical analgesic on delayed onset muscle soreness, and genotype associations have been previously reported for this cohort (8,17). Markers of muscle damage included muscle function, muscle soreness, serum creatine kinase (CK), and myoglobin (Mb) levels. Study protocols were approved by the Human Subjects Review Committee of the University of Massachusetts and included written informed consent.
During visit 1, subjects were screened for inclusion/exclusion criteria and signed informed consent documents. Within 1 wk after visit 1, subjects were seen by the study physician, height and weight were recorded, and subjects had blood drawn for base line analyses (visit 2). Visit 3 occurred 2 d after visit 2, during which preexercise soreness and strength were assessed, followed by the eccentric exercise bout, followed immediately by reassessment of strength. Twelve hours after visit 3, subjects returned for treatment with topical analgesic (visit 4). Strength recovery was assessed at 3 (visit 5), 4 (visit 6), 7 (visit 7), and 10 d postexercise (visit 8). Blood was drawn at 4, 7, and 10 d postexercise.
Maximal voluntary isometric strength of the elbow flexors was assessed before and immediately after the exercise. Strength was assessed on a modified preacher bench, with the elbow flexed at 90°. A strain gauge (model 32628CTL; Lafayette Instrument, Lafayette, IN) was used to measure the force produced. Three trials were averaged, with 1 min of rest between sets.
The single exercise bout consisted of 50 maximal eccentric (muscle lengthening) contractions of the elbow flexor muscles of the nondominant arm. Two sets of 25 contractions were separated by a 5-min rest period. Each contraction was 3 s long, followed by 12 s of rest.
DNA Extraction and Genotyping
DNA was extracted from blood samples obtained by phlebotomy before starting the exercise training using Qiagen Gentra PureGene kits. Genotyping was done using the TaqMan allele discrimination assay that uses the 5′ nuclease activity of Taq polymerase to detect a fluorescent reporter signal generated during polymerase chain reactions.
The Hardy–Weinberg equilibrium was determined for reach SNP using χ2 test to compare the observed genotype frequencies with those expected under the Hardy–Weinberg equilibrium.
In the FAMuSS cohort, 12 quantitative phenotypes (baseline and 12-wk change in MVC and 1RM strength, whole muscle, cortical bone, total bone volume, and whole muscle cross-sectional area) were analyzed as continuous quantitative traits. In the eccentric challenge cohort, muscle strength, soreness, serum CK, and Mb were analyzed as continuous traits. The normality of each quantitative trait was confirmed using the Shapiro–Wilk normality test. Bivariate correlation analyses of each quantitative measurement showed several significant correlations with age and baseline mass; therefore, associations between each SNP and quantitative traits were assessed using ANCOVA methods. Because of large sex differences in baseline values and the response to training, all analyses were performed separately for men and women.
Initially, codominant genetic models were tested, and all significant associations from the main ANCOVA model were subjected to pairwise statistical tests among each of the three genotype groups for each SNP. Positive associations with the codominant model were then tested with both recessive and dominant models. Linear tests were performed between each of the genotype groups to determine which genotype groups were significantly different from one another. The resulting P values from these linear tests were adjusted for multiple comparisons using the Sidak post hoc multiple comparison test. For all models, linear regression analysis, including likelihood ratio tests between full (containing genotype and covariates) and constrained (containing covariates only) models, were performed to estimate the proportion of variance in volumetric measurements attributable to each SNP’s genotype. To avoid false-positives, the P value for significance was set at 0.002 to account for multiple testing (i.e., 12 phenotypes in men and women for a total of 24 tests in the FAMuSS cohort).
OPN (Spp1) KO Mice
Six male and six female homozygous B6.129S6(Cg)-Spp1tm1Blh/J osteopontin (Spp1) knockout mice at 5–7 wk of age were purchased from and housed at the Jackson Laboratory (Bar Harbor, ME, USA; http://jaxmice.jax.org/strain/004936.html) (24). Age- and sex-matched C57BL/6J mice served as controls. The animals were housed with food and water ad libitum in a 12-h light–12-h dark cycle environment. The animals were euthanized via CO2 at 8–10 wk of age, as approved by the Jackson Laboratory Institutional Animal Care and Use Committee.
Muscle groups were isolated immediately after the animals were euthanized. Muscles isolated from the left distal leg were the soleus, plantaris, gastrocnemius (which includes both the medial and lateral gastrocnemius), tibialis anterior, and extensor digitorum longus. In the left proximal arm, the biceps bracchii longus and the triceps brachii longus were isolated. All muscles were trimmed of excess tendon and weighed, and sex-stratified comparisons of wild-type and knockout animals were observed using the Student t-test.
Murine Degeneration/Regeneration Time Series
We have previously reported a 27–time point wild-type mouse muscle degeneration/regeneration time series mRNA profiling data set (39,40). Briefly, the gastrocnemius of adult mice were caused to undergo a complete round of staged degeneration and regeneration using a 10-needle manifold and injection of cardiotoxin. The muscles were then harvested at defined time points after the injection, with two muscles analyzed in each of 27 times point. mRNA profiling data were generated using Affymetrix U74v2 microarrays from the 54 murine gastrocnemius, and the dCHIP mismatch model probe set algorithm used to normalize array and generate signal intensities. This data set is accessible for dynamic queries via a public Web site visualization tool we developed (http://pepr.cnmcresearch.org) (4), and data for OPN shown in this current publication is taken from this web site analytical and graphic tool.
OPN rs28357094 is associated with baseline muscle size in young women.
Associations of rs28357094 genotypes (TT vs GG/GT) with 12 quantitative phenotypes in sex-stratified cohort were evaluated (baseline isometric strength, absolute difference in isometric strength, percent change in isometric strength, baseline 1RM strength, difference in 1RM strength, percent change in 1RM strength, baseline whole muscle volume, difference in whole muscle volume, baseline total bone volume, difference in total bone volume, baseline cortical bone volume, and difference in cortical bone volume). Bone phenotypes were included because of the known role of OPN in regulation of bone remodeling. Muscle phenotypes were chosen because of the association of the rs28357094 genotypes with muscle strength and disease progression in DMD. The normality of each phenotype was confirmed, genotype data were in Hardy–Weinberg equilibrium, and genotype/phenotype associations were adjusted for multiple testing. MVC strength, 1RM strength, and whole muscle phenotypes were adjusted for age and weight, and bone phenotypes were adjusted for age and height.
We found a strong association between OPN genotype and baseline muscle volume in females (Fig. 1) (n = 389). The other 11 phenotypes tested showed no significant genotype–phenotype associations after adjustment for multiple testing. Given the strong association with muscle volume in females (Fig. 1), we further queried additional data fields in our FAMuSS data set. We had three measures of “baseline” muscle volume: the dominant arm at study entry, the dominant (unexercised) arm at study exit, and the nondominant arm at study entry (before exercise). However, these three measures are not independent measures, as there is a strong colinear relationship between an individual’s dominant (untrained) pre- and postintervention measures and the nondominant (trained) preintervention measure. Using a dominant inheritance model (TT vs GT/GG), all three measures of untrained (baseline) size showed a striking association of larger muscle size with the G allele (14.3%–17.0% increase in size) (Fig. 1). Considering the dominant arm at baseline, women carrying the G allele showed significantly larger muscle size (TT, N = 167, 433,910 ± 9344 mm3; TG/GG, N = 122, 507,835 ± 10,938 mm3) (F-test = 13.1; P = 5.3 × 10−7). The percentage of population variation for muscle size explained by this genotype was 5.1% (likelihood ratio test [LRT] P < 0.001), making this one of the largest effect sizes for any genetic modifier of muscle identified to date. This association was specific to females, with no association of baseline muscle volume in males. Tests of other traits such as baseline strength or response to resistance training (size or strength) showed no associations, in either males or females.
rs28357094 G allele is associated with increased serum myoglobin at baseline in women.
Young adult volunteers undertook a bout of eccentric exercise with phenotypes then measured as a function of time after the bout. Phenotypes included muscle strength loss, muscle soreness, and serum CK and Mb, tested in men (n = 74) and women (n = 78) separately. Using the same dominant inheritance model used in the muscle size data, we found an association of the GG/GT genotypes with increased serum myoglobin levels at baseline in women only (TT, N = 61, 26.96 ± 0.61 IU·L−1; GT/GG, N = 16, 29.91 ± 1.20 IU·L−1) (Table 1). However, the limit of detection of the serum myoglobin assay used was 23 ng·mL−1, and many subjects in both genotype groups were at or below the limit of detection, and these were set at 23 ng·mL−1.
There were two female homozygotes for the rare GG allele, and one of these showed unusually high increase CK postexercise (22,099, 11,257, and 1986 IU·L−1 compared with an average of 5265 ± 6687, 1930 ± 2410, and 306 ± 297 IU·L−1 in other females at 4, 7, and 10 d, respectively) (Fig. 2). This led to the significant associations of the GG genotype with both CK at 10 d and difference between day 0 and day 10 CK, but this was driven by the single subject coupled with low variance of all other subjects in day 0 and day 10 time points (Table 1).
It is important to note that the eccentric exercise association data were not corrected for multiple testing, and there were many comparisons run in both men and women (soreness, strength, myoglobin, and CK at multiple time points). Thus, myoglobin and CK associations are tentative and require validation in additional cohorts.
OPN-null mice show sexually dimorphic differences in muscle size relative to wild-type mice.
Given the female-specific association of muscle size with the OPN polymorphism in young adult females, we hypothesized that knockout mice for the OPN gene might also show sex-specific alterations in muscle weights. The weights of seven dissected muscle groups were compared between OPN-null mice and wild-type controls in both males and females (n = 6 per group; 24 mice total). Weights were normalized to body weight and sex-specific statistical comparisons of each muscle group done between the OPN and the wild-type mice (Fig. 3). OPN female mice showed significant reductions in muscle weight in five of the seven muscle groups analyzed, whereas male mice showed reductions in two of the seven groups. Murine female biceps and triceps were both significantly reduced in weight, and these correspond to the muscle groups analyzed by MRI in the young adult female volunteers (Fig. 1).
OPN expression in muscle is associated with experimental muscle degeneration and regeneration.
mRNA profiling has been used to study changes in the muscle transcriptome after different interventions and disease states, and we have developed a public access resource to query many of these data sets (http://pepr.cnmcresearch.org) (4). Using this interface, we queried the response of OPN mRNA accumulation after: experimental muscle damage in mouse (cardiotoxin-induced degeneration/regeneration; 27 time point series) (39,40) (Fig. 4).
OPN mRNA showed low baseline expression in uninjured muscle (baseline; time 0) but was then rapidly and strongly induced after cardiotoxin-induced muscle degeneration, with a 120-fold increase in levels by 12–24 h (Fig. 4). High levels of OPN were maintained in regenerating muscle through day 14, whereupon levels fell to baseline by day 16.
The ACTN3 polymorphism has been identified as a genetic modifier of muscle function in both healthy volunteers and athletes (7,8,37) as well as in DMD (30). We sought to determine whether an OPN gene promoter polymorphism we previously associated with disease severity in Duchenne dystrophy was also associated with muscle phenotypes in healthy young adult volunteers. The G allele of the rs28357094 polymorphism is a 66-bp upstream of the transcription start site, alters an SP1 transcription factor binding site, and reduces OPN gene transcription. This G allele was associated with more severe disease in DMD, suggesting that less OPN led to poor muscle remodeling in the context of continuous degeneration/regeneration (muscular dystrophy). We did not have expectations regarding the effect of the G allele in normal volunteer groups, and the effects on normal muscle (baseline, trained, or eccentric challenge) are quite different than the severe and chronic remodeling seen in muscular dystrophy.
The two normal volunteer groups studied were a resistance training intervention cohort and an eccentric bout group. The resistance training group was meant to study a 12-wk training effect, whereas the eccentric group was meant to study acute responses to a single bout of muscle damage from lengthening contractions. In the resistance training group, we found a strong association of the OPN polymorphism with baseline muscle size in women but not in men (Fig. 1). Women with the rare allele (GT and GG) showed 17% increase in muscle volume of the upper arm by MRI volume compared with TT (P = 5.3 × 10−7), and this finding was similar in both arms before the exercise intervention and in the control unexercised dominant arm after the unilateral resistance training of the nondominant arm (Fig. 1). This single genetic locus explained approximately 5% of population variance (LRT P < 0.001) in the females in FAMuSS. This was a surprisingly large proportion of variance and is quite unusual for any single genetic polymorphism in any population-based trait. A factor that may contribute to the large effect size is the highly objective and quantitative nature of the MRI phenotype studied here. As stated by Long and Langley (25), “an increase in the variance attributable to a quantitative trait locus is seen with the careful definition of the phenotype so that any variation in phenotype is less likely due to other factors.” The cohort studied was also relatively homogenous in age and sex, and phenotypes were adjusted for age, weight, and stratified by sex. Each of these would be expected to reduce variance and thus augment our sensitivity in detecting gene × phenotype (MRI) associations. Finally, our cohort was quite young, and we have previously shown that young cohorts may augment effect sizes in genetic association studies (10). This is because age-associated phenotypes (such as sarcopenia with aging) would serve as confounding (hidden) variables and contribute to greater variance in older cohorts (10). This more robust nature of the MRI phenotype may also explain the lack of association with muscle strength, as the latter has a central effort component.
A correlation of a gene polymorphism with muscle size might be expected to similarly be correlated with muscle strength. However, studies of the relationship of size and strength typically show correlation coefficients of only r = 0.7 (1,26). In addition, strength has a central component (effort) that likely contributes to increased variance, whereas MRI measures of size have no such central component. Further studies of the relationship of size and strength and the role of OPN in muscle are required.
The association with muscle size was not seen in males, and there was no association of strength or size changes upon resistance training in either males or females. It is important to validate genetic association studies in additional cohorts; however, we were unable to identify similar large cohorts of young adults with volumetric MRI of muscle. Thus, independent validation must await future studies.
In an eccentric contraction (muscle damage) group, we found weak associations with females with baseline myoglobin and serum CK at 7 d (Table 1; Fig. 2). The associations with CK were in a recessive model and were driven by one of two female homozygotes because the rare allele (GG) showed striking elevations of CK 10 d after the exercise challenge (Fig. 2). These findings were not corrected for multiple testing and are subject to false-positive associations.
The sex-related effects of OPN on muscle size were validated in our study of knockout mice (null) for OPN (Fig. 3). OPN-null female mice showed significant reductions in five of seven muscle groups examined, including the biceps and triceps muscles (the same muscle groups studied by MRI in the healthy young adult female volunteers in FAMuSS). Male mice showed significant reductions in two of the seven muscle groups. Thus, OPN expression appears related to muscle size in mice as well as humans and that the effect is also largely sex-specific to females. We also showed that OPN is strongly expressed during muscle remodeling in mice subjected to a bout of muscle degeneration and regeneration (Fig. 4). This suggests that OPN likely plays a role in repair of muscle damage, with expression elevated until the muscle is fully regenerated at approximately 14 d after injury.
Our findings indicate that the OPN polymorphism is a genetic modifier of muscle in both health (young adult volunteers) and disease (DMD). Thus, both ACTN3 and OPN polymorphisms modulate both healthy volunteer muscle as well as dystrophic muscle. That said, our study raises a question regarding the sex specificity of our findings. DMD is an X-linked disorder that affects young boys, yet our significant genetic association findings in healthy volunteers were restricted to women. OPN has gene promoter elements that have been shown to be responsive to estrogen (9,27), and other disease-related association studies with the same polymorphism have also found sex-specific effects (2,15). It is challenging to draw parallels between prepubertal DMD boys and the likely estrogen-sensitive gene effects we have observed in the postpubertal volunteer women studied. However, the sex-specific effects of gene polymorphisms in other loci have also been reported (7,19,31,32).
The association of the rare allele of the OPN promoter SNP with larger muscle size in females and more severe disease in DMD boys is challenging to reconcile into a single unifying gene–phenotype association. Our finding that mice with loss of OPN (knockout) show sexually dimorphic effects on muscle size is supportive of sex-modified effects and the apparent importance of OPN in muscle tissue but again does not aid in defining a unifying model. We also showed that mouse muscle shows very low levels of OPN at baseline, but it is rapidly and extensively induced by muscle damage, is at quite low levels in normal muscle, and is strongly induced upon muscle damage (120-fold increase in levels by 12–24 h). Taken together, our data suggest that OPN likely plays a role in response of muscle to damage (as a tissue remodeling cytokine) and may also have a role in normal development. With regard to muscle damage, it is important to note that OPN is expressed in both infiltrating macrophages and in regenerating myoblasts, and different isoforms of the protein have different effects on initiation of inflammation (e.g., transglutaminated form and neutrophil chemotaxis) (29,38) and resolution of inflammation (e.g., macrophage polarization toward a tissue repair IL-10 producing phenotype) (3,35,36). Future research should focus on the mechanistic studies of specific OPN protein isoforms and their role in muscle inflammation and remodeling in both healthy muscle and dystrophic muscle. The effect of the promoter polymorphism on OPN regulation in muscle and the modulatory effect of estrogen also require further study.
This study was funded by the National Institutes of Health (grant nos. 2RO1AR55100, 1U54HD053177, and 5R24HD050846), the U.S. Army Research Institute of Environmental Medicine, and Medivona Inc.
The authors thank Dr. Kitipong Uaesoontrachoon for helping in the revisions of the manuscript and the assistance of Amy Kearns and Monica Hubal in carrying out the eccentric exercise study.
None of the authors have any conflicts of interest related to the described studies.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Akagi R, Takai Y, Kato E, et al. Relationships between muscle strength and indices of muscle cross-sectional area determined during maximal voluntary contraction in middle-aged and elderly individuals. J Strength Cond Res
. 2009; 23 (4): 1258–62.
2. Banerjee A, Apte UM, Smith R, Ramaiah SK. Higher neutrophil infiltration mediated by osteopontin is a likely contributing factor to the increased susceptibility of females to alcoholic liver disease. J Pathol
. 2006; 208 (4): 473–85.
3. Bollyky PL, Wu RP, Falk BA, et al. ECM components guide IL-10 producing regulatory T-cell (TR1) induction from effector memory T-cell precursors. Proc Natl Acad Sci U S A
. 2011; 108: 7938–43.
4. Chen J, Zhao P, Massaro D, et al. The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface. Nucleic Acids Res
. 2004; 32: D578–81.
5. Chen RX, Xia YH, Cui JF, Xue TC, Ye SL. Osteopontin, a single marker for predicting the prognosis of patients with tumor-node-metastasis stage I hepatocellular carcinoma after surgical resection. J Gastroenterol Hepatol
. 2010; 25: 1435–42.
6. Christensen B, Nielsen MS, Haselmann KF, Petersen TE, Sorensen ES. Post-translationally modified residues of native human osteopontin are located in clusters: identification of 36 phosphorylation and five O-glycosylation sites and their biological implications. Biochem J
. 2005; 390: 285–92.
7. Clarkson PM, Devaney JM, Gordish-Dressman H, et al. ACTN3 genotype is associated with increases in muscle strength in response to resistance training in women. J Appl Physiol
. 2005a; 99: 154–63.
8. Clarkson PM, Hoffman EP, Zambraski E, et al. ACTN3 and MLCK genotype associations with exertional muscle damage. J Appl Physiol
. 2005b; 99: 564–9.
9. Craig AM, Denhardt DT. The murine gene encoding secreted phosphoprotein 1 (osteopontin): promoter structure, activity, and induction in vivo by estrogen
and progesterone. Gene
. 1991; 100: 163–71.
10. Devaney JM, Thompson PD, Visich PS, et al. The 1p13.3 LDL (C)-associated locus shows large effect sizes in young populations. Pediatr Res
. 2011a; 69: 538–43.
11. Devaney JM, Gordish-Dressman H, Harmon BT, et al. AKT1 polymorphisms are associated with risk for metabolic syndrome. Hum Genet
. 2011; 129: 129–39.
12. Ding Z, Wu CJ, Chu GC, et al. SMAD4-dependent barrier constrains prostate cancer growth and metastatic progression. Nature
. 2011; 470: 269–73.
13. Giacopelli F, Marciano R, Pistorio A, et al. Polymorphisms in the osteopontin promoter affect its transcriptional activity. Physiol Genomics
. 2004; 20: 87–96.
14. Glas J, Seiderer J, Bayrle C, et al. The role of osteopontin (OPN/SPP1) haplotypes in the susceptibility to Crohn’s disease. PLoS One
. 2011; 6: e29309.
15. Han S, Guthridge JM, Harley IT, et al. Osteopontin and systemic lupus erythematosus association: a probable gene-gender interaction. PLoS One
. 2008; 3: e0001757.
16. Hoffman EP, Brown RH, Kunkel LM. Dystrophin: the protein product of the Duchenne muscular dystrophy locus. Cell
. 1987; 51: 919–28.
17. Hubal MJ, Devaney JM, Hoffman EP, et al. CCL2 and CCR2 polymorphisms are associated with markers of exercise-induced skeletal muscle damage. J Appl Physiol
. 2010; 108: 1651–8.
18. Hubal MJ, Urso ML, Clarkson PM. Genetic aspects of muscular strength and size. In: Pescatello L, Roth SM, editors. Exercise Genomics
. New York: Humana Press; 2011. pp. 157–78.
19. Kariuki SN, Moore JG, Kirou KA, Crow MK, Utset TO, Niewold TB. Age- and gender-specific modulation of serum osteopontin and interferon-alpha by osteopontin genotype in systemic lupus erythematosus. Genes Immun
. 2009; 10: 487–94.
20. Keykhosravani M, Doherty-Kirby A, Zhang C, et al. 2005. Comprehensive identification of post-translational modifications of rat bone osteopontin by mass spectrometry. Biochemistry
. 2005; 44: 6990–7003.
21. Kim SM, Park YY, Park ES, et al. Prognostic biomarkers for esophageal adenocarcinoma identified by analysis of tumor transcriptome. PLoS One
. 2010; 5: e15074.
22. Kretschmer C, Sterner-Kock A, Siedentopf F, Schoenegg W, Schlag PM, Kemmner W. Identification of early molecular markers for breast cancer. Mol Cancer
. 2011; 10: 15.
23. Kwon HJ, Won YS, Yoon WK, Nam KH, Kim DY, Kim HC. The role of osteopontin in D-galactosamine-induced liver injury in genetically obese mice. Toxicol Appl Pharmacol
. 2010; 242: 344–51.
24. Liaw L, Birk DE, Ballas CB, Whitsitt JS, Davidson JM, Hogan BL. Altered wound healing in mice lacking a functional osteopontin gene (spp1). J Clin Invest
. 1998; 101: 1468–78.
25. Long AD, Langley CH. The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits. Genome Res
. 1999; 8: 720–31.
26. Minotti JR, Pillay P, Oka R, Wells L, Christoph I, Massie BM. Skeletal muscle size: relationship to muscle function in heart failure. J Appl Physiol
. 1993; 75 (1): 373–81.
27. Miyajima J, Hayashi T, Saito K, Iida S, Matsuoka K. The interaction between female sex hormone receptors and osteopontin in a rat hyperoxaluric model. Kurume Med J
. 2010; 57: 73–80.
28. Mori R, Shaw TJ, Martin P. Molecular mechanisms linking wound inflammation and fibrosis: knockdown of osteopontin leads to rapid repair and reduced scarring. J Exp Med
. 2008; 205: 43–51.
29. Nishimichi N, Hayashita-Kinoh H, Chen C, Matsuda H, Sheppard D, Yokosaki Y. Osteopontin undergoes polymerization in vivo and gains chemotactic activity for neutrophils mediated by integrin alpha9beta1. J Biol Chem
. 2011; 286: 11170–8.
30. Pegoraro E, Hoffman EP, Piva L, et al. Cooperative International Neuromuscular Research Group. SPP1 genotype is a determinant of disease severity in Duchenne muscular dystrophy. Neurology
. 2011; 76: 219–26.
31. Sayer AA, Syddall H, O’Dell SD, et al. Polymorphism of the IGF2 gene, birth weight and grip strength in adult men. Age Ageing
. 2002; 31: 468–70.
32. Schrager MA, Roth SM, Ferrell RE, et al. Insulin-like growth factor-2 genotype, fat-free mass, and muscle performance across the adult life span. J Appl Physiol
. 2004; 97: 2176–83.
33. Sewright KA, Hubal MJ, Kearns A, Holbrook MT, Clarkson PM. Sex differences in response to maximal eccentric exercise. Med Sci Sports Exerc
. 2008; 40 (2): 242–51.
34. Thompson PD, Moyna N, Seip R, et al. Functional polymorphisms associated with human muscle size and strength. Med Sci Sports Exerc
. 2004; 36 (7): 1132–9.
35. Uaesoontrachoon K, Yoo HJ, Tudor EM, Pike RN, Mackie EJ, Pagel CN. Osteopontin and skeletal muscle myoblasts: association with muscle regeneration and regulation of myoblast function in vitro. Int J Biochem Cell Biol
. 2008; 40: 2303–14.
36. Wang KX, Denhardt DT. Osteopontin: role in immune regulation and stress responses. Cytokine Growth Factor Rev
. 2008; 19: 333–45.
37. Yang N, MacArthur DG, Gulbin JP, et al. ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet
. 2003; 73: 627–31.
38. Yokosaki Y, Matsuura N, Sasaki T, et al. The integrin alpha(9)beta(1) binds to a novel recognition sequence (SVVYGLR) in the thrombin-cleaved amino-terminal fragment of osteopontin. J Biol Chem
. 1999; 274: 36328–34.
39. Zhao P, Iezzi S, Carver E, et al. Slug is a novel downstream target of MyoD. Temporal profiling in muscle regeneration. J Biol Chem
. 2002; 277: 30091–101.
40. Zhao P, Caretti G, Mitchell S, et al. Fgfr4 is required for effective muscle regeneration in vivo. Delineation of a MyoD-Tead2-Fgfr4 transcriptional pathway. J Biol Chem
. 2006; 281: 429–38.