Physical activity when young provides lifelong benefits to cortical bone size and strength in men.

The skeleton shows greatest plasticity to physical activity-related mechanical loads during youth but is more at risk for failure during aging. Do the skeletal benefits of physical activity during youth persist with aging? To address this question, we used a uniquely controlled cross-sectional study design in which we compared the throwing-to-nonthrowing arm differences in humeral diaphysis bone properties in professional baseball players at different stages of their careers (n = 103) with dominant-to-nondominant arm differences in controls (n = 94). Throwing-related physical activity introduced extreme loading to the humeral diaphysis and nearly doubled its strength. Once throwing activities ceased, the cortical bone mass, area, and thickness benefits of physical activity during youth were gradually lost because of greater medullary expansion and cortical trabecularization. However, half of the bone size (total cross-sectional area) and one-third of the bone strength (polar moment of inertia) benefits of throwing-related physical activity during youth were maintained lifelong. In players who continued throwing during aging, some cortical bone mass and more strength benefits of the physical activity during youth were maintained as a result of less medullary expansion and cortical trabecularization. These data indicate that the old adage of "use it or lose it" is not entirely applicable to the skeleton and that physical activity during youth should be encouraged for lifelong bone health, with the focus being optimization of bone size and strength rather than the current paradigm of increasing mass. The data also indicate that physical activity should be encouraged during aging to reduce skeletal structural decay.

Regulatory mechanisms in bone following mechanical loading.

Bone responds with increased bone formation to mechanical loading, and the time course of bone formation after initiating mechanical loading is well characterized. However, the regulatory activities governing the loading-dependent changes in gene expression are not well understood. The goal of this study was to identify the time-dependent regulatory mechanisms that governed mechanical loading-induced gene expression in bone using a predictive bioinformatics algorithm. A standard model for bone loading in rodents was employed in which the right forelimb was loaded axially for three minutes per day, while the left forearm served as a non-loaded, contralateral control. Animals were subjected to loading sessions every day, with 24 hours between sessions. Ulnas were sampled at 11 time points, from 4 hours to 32 days after beginning loading. Using a predictive bioinformatics algorithm, we created a linear model of gene expression and identified 44 transcription factor binding motifs and 29 microRNA binding sites that were predicted to regulate gene expression across the time course. Known and novel transcription factor binding motifs were identified throughout the time course, as were several novel microRNA binding sites. These time-dependent regulatory mechanisms may be important in controlling the loading-induced bone formation process.

Alternative splicing in bone following mechanical loading.

It is estimated that more than 90% of human genes express multiple mRNA transcripts due to alternative splicing. Consequently, the proteins produced by different splice variants will likely have different functions and expression levels. Several genes with splice variants are known in bone, with functions that affect osteoblast function and bone formation. The primary goal of this study was to evaluate the extent of alternative splicing in a bone subjected to mechanical loading and subsequent bone formation. We used the rat forelimb loading model, in which the right forelimb was loaded axially for 3 min, while the left forearm served as a non-loaded control. Animals were subjected to loading sessions every day, with 24 h between sessions. Ulnae were sampled at 11 time points, from 4 h to 32days after beginning loading. RNA was isolated and mRNA abundance was measured at each time point using Affymetrix exon arrays (GeneChip® Rat Exon 1.0 ST Arrays). An ANOVA model was used to identify potential alternatively spliced genes across the time course, and five alternatively spliced genes were validated with qPCR: Akap12, Fn1, Pcolce, Sfrp4, and Tpm1. The number of alternatively spliced genes varied with time, ranging from a low of 68 at 12h to a high of 992 at 16d. We identified genes across the time course that encoded proteins with known functions in bone formation, including collagens, matrix proteins, and components of the Wnt/ß-catenin and TGF-ß signaling pathways. We also identified alternatively spliced genes encoding cytokines, ion channels, muscle-related genes, and solute carriers that do not have a known function in bone formation and represent potentially novel findings. In addition, a functional characterization was performed to categorize the global functions of the alternatively spliced genes in our data set. In conclusion, mechanical loading induces alternative splicing in bone, which may play an important role in the response of bone to mechanical loading.

Gene expression patterns in bone following mechanical loading.

The advent of high-throughput measurements of gene expression and bioinformatics analysis methods offers new ways to study gene expression patterns. The primary goal of this study was to determine the time sequence for gene expression in a bone subjected to mechanical loading during key periods of the bone-formation process, including expression of matrix-related genes, the appearance of active osteoblasts, and bone desensitization. A standard model for bone loading was employed in which the right forelimb was loaded axially for 3 minutes per day, whereas the left forearm served as a nonloaded contralateral control. We evaluated loading-induced gene expression over a time course of 4 hours to 32 days after the first loading session. Six distinct time-dependent patterns of gene expression were identified over the time course and were categorized into three primary clusters: genes upregulated early in the time course, genes upregulated during matrix formation, and genes downregulated during matrix formation. Genes then were grouped based on function and/or signaling pathways. Many gene groups known to be important in loading-induced bone formation were identified within the clusters, including AP-1-related genes in the early-response cluster, matrix-related genes in the upregulated gene clusters, and Wnt/ß-catenin signaling pathway inhibitors in the downregulated gene clusters. Several novel gene groups were identified as well, including chemokine-related genes, which were upregulated early but downregulated later in the time course; solute carrier genes, which were both upregulated and downregulated; and muscle-related genes, which were primarily downregulated.