Mechanoresponse Of Bone Research Articles

Gut microbial alterations in arginine metabolism determine bone mechanical adaptation

Although mechanical loading is essential for maintaining bone health and combating osteoporosis, its practical application is limited to a large extent by the high variability in bone mechanoresponsiveness. Here, we found that gut microbial depletion promoted a significant reduction in skeletal adaptation to mechanical loading. Among experimental mice, we observed differences between those with high and low responses to exercise with respect to the gut microbial composition, in which the differential abundance of Lachnospiraceae contributed to the differences in bone mechanoresponsiveness. Microbial production of L-citrulline and its conversion into L-arginine were identified as key regulators of bone mechanoadaptation, and administration of these metabolites enhanced bone mechanoresponsiveness in normal, aged, and ovariectomized mice. Mechanistically, L-arginine-mediated enhancement of bone mechanoadaptation was primarily attributable to the activation of a nitric-oxide-calcium positive feedback loop in osteocytes. This study identifies a promising anti-osteoporotic strategy for maximizing mechanical loading-induced skeletal benefits via the microbiota-metabolite axis.

The mechanoresponse of bone is closely related to the osteocyte lacunocanalicular network architecture

Organisms rely on mechanosensing mechanisms to adapt to changes in their mechanical environment. Fluid-filled network structures not only ensure efficient transport but can also be employed for mechanosensation. The lacunocanalicular network (LCN) is a fluid-filled network structure, which pervades our bones and accommodates a cell network of osteocytes. For the mechanism of mechanosensation, it was hypothesized that load-induced fluid flow results in forces that can be sensed by the cells. We use a controlled in vivo loading experiment on murine tibiae to test this hypothesis, whereby the mechanoresponse was quantified experimentally by in vivo micro-computed tomography (µCT) in terms of formed and resorbed bone volume. By imaging the LCN using confocal microscopy in bone volumes covering the entire cross-section of mouse tibiae and by calculating the fluid flow in the three-dimensional (3D) network, we could perform a direct comparison between predictions based on fluid flow velocity and the experimentally measured mechanoresponse. While local strain distributions estimated by finite-element analysis incorrectly predicts preferred bone formation on the periosteal surface, we demonstrate that additional consideration of the LCN architecture not only corrects this erroneous bias in the prediction but also explains observed differences in the mechanosensitivity between the three investigated mice. We also identified the presence of vascular channels as an important mechanism to locally reduce fluid flow. Flow velocities increased for a convergent network structure where all of the flow is channeled into fewer canaliculi. We conclude that, besides mechanical loading, LCN architecture should be considered as a key determinant of bone adaptation.

An algorithm for bone mechanoresponsiveness: implementation to study the effect of patient-specific cell mechanosensitivity on trabecular bone loss

The rate of bone loss is subject to considerable variation between individuals. With the ‘mechanostat’ model of Frost, genetic variations in bone mechanoresponsiveness are modelled by different mechanostat ‘setpoints’ – which may also change with age or disease. In this paper, the following setpoints are used: ϵmin (strain below which resorption is triggered); ϵmax (strain above which deposition occurs); ωcrit (microdamage-level above which damage-stimulated resorption occurs). To simulate decreased mechanosensitivity, ϵmax is increased. Analyses carried out on a simplified model of a trabecula show that ϵmax is a critical parameter: if it is higher in an individual (genetics) or increases (with age) the mass deficit each remodelling cycle increases. Furthermore, there is a value of ϵmax above which trabecular perforation occurs, leading to rapid loss of bone mass. Maintaining bone cell mechanosensitivity could therefore be a therapeutic target for the prevention of osteoporosis.