Compressive loading of the murine tibia reveals site-specific micro-scale differences in adaptation and maturation rates of bone

STAT3 Hyperactivation Due to SOCS3 Deletion in Murine Osteocytes Accentuates Responses to Exercise- and Load-Induced Bone Formation

Cortical bone develops and changes in response to mechanical load, which is sensed by bone-embedded osteocytes. The bone formation response to load depends on STAT3 intracellular signals, which are upregulated after loading and are subject to negative feedback from Suppressor of Cytokine Signaling 3 (Socs3). Mice with Dmp1Cre-targeted knockout of Socs3 have elevated STAT3 signaling in osteocytes and display delayed cortical bone maturation characterized by impaired accrual of high-density lamellar bone. This study aimed to determine whether these mice exhibit an altered response to mechanical load. The approach used was to test both treadmill running and tibial compression in female Dmp1Cre.Socs3^f/f mice. Treadmill running for 5 days per week from 6 to 11 weeks of age did not change cortical bone mass in control mice, but further delayed cortical bone maturation in Dmp1Cre.Socs3^f/f mice; accrual of high-density bone was suppressed, and cortical thickness was less than in genetically-matched sedentary controls. When strain-matched anabolic tibial loading was tested, both control and Dmp1Cre.Socs3^f/f mice exhibited a significantly greater cortical thickness and periosteal perimeter in loaded tibia compared with the contralateral non-loaded bone. At the site of greatest compressive strain, the loaded Dmp1Cre.Socs3^f/f tibias showed a significantly greater response than controls, indicated by a greater increase in cortical thickness. This was due to a greater bone formation response on both periosteal and endocortical surfaces, including formation of abundant woven bone on the periosteum. This suggests a greater sensitivity to mechanical load in Dmp1Cre.Socs3^f/f bone. In summary, mice with targeted SOCS3 deletion and immature cortical bone have an exaggerated response to both physiological and experimental mechanical loads. We conclude that there is an optimal level of osteocytic response to mechanical load required for cortical bone maturation and that load-induced bone formation may be increased by augmenting STAT3 signaling within osteocytes. © 2021 American Society for Bone and Mineral Research (ASBMR).

Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo μCT imaging and deep learning

Throughout the process of aging, dynamic changes of bone material, micro- and macro-architecture result in a loss of strength and therefore in an increased likelihood of fragility fractures. To date, precise contributions of age-related changes in bone (re)modeling and (de)mineralization dynamics to this fragility increase are not completely understood. Here, we present an image-based deep learning approach to quantitatively describe the effects of short-term aging and adaptive response to cyclic loading applied to proximal mouse tibiae and fibulae. Our approach allowed us to perform an end-to-end age prediction based on μCT imaging to determine the dynamic biological process of aging during a two week period, therefore permitting short-term bone aging analysis with 95% accuracy in predicting time points. In a second application, our deep learning analysis reveals that two weeks of in vivo mechanical loading are associated with an underlying rejuvenating effect of 5 days. Additionally, by quantitatively analyzing the learning process, we could, for the first time, identify the localization of the age-relevant encoded information and demonstrate 89% load-induced similarity of these locations in the loaded tibia with younger control bones. These data therefore suggest that our method enables identifying a general prognostic phenotype of a certain skeletal age as well as a temporal and localized loading-treatment effect on this apparent skeletal age for the studied mouse tibia and fibula. Future translational applications of this method may provide an improved decision-support method for osteoporosis treatment at relatively low cost. STATEMENT OF SIGNIFICANCE: Bone is a highly complex and dynamic structure that undergoes changes during the course of aging as well as in response to external stimuli, such as loading. Automatic assessment of "age" and "state" of the bone may lead to early prognosis of deceases such as osteoporosis and enables evaluating the effects of certain treatments. Here, we present an artificial intelligence-based method capable of automatically predicting the skeletal age from μCT images with 95% accuracy. Additionally, we utilize it to demonstrate the rejuvenation effects of in-vivo loading treatment on bones. We further, for the first time, break down aging-related local changes in bone by quantitatively analyzing "what the age assessment model has learned" and use this information to investigate the structural details of rejuvenation process.

Bone adaptation: Safety factors and load predictability in shaping skeletal form

Much is known about skeletal adaptation in relation to the mechanical functions that bones serve. This includes how bone adapts to mechanical loading during an individual's lifetime as well as over evolutionary time. Although controlled loading in animal models allows us to observe short-term bone adaptation (epigenetic mechanobiology), examining an assemblage of extant vertebrate bones or a group of fossils' bony structures can reveal the combined effects of long-term trends in loading history and the effects of natural selection. In this survey we examine adaptations that take place over both time scales and highlight a few of the extraordinary insights first published by John Currey. First, we provide a historical perspective on bone adaptation control mechanisms, followed by a discussion of safety factors in bone. We then summarize examples of structural- and material-level adaptations and mechanotransduction, and analyze the relationship between these structural- and material-level adaptations observed in situations where loading modes are either predictable or unpredictable. We argue that load predictability is a major consideration for bone adaptation broadly across an evolutionary timescale, but that its importance can also be seen during ontogenetic growth trajectories, which are subject to natural selection as well. Furthermore, we suggest that bones with highly predictable load patterns demonstrate more precise design with lower safety factors, while bones that experience less predictable loads or those that are less capable of repair and adaptation are designed with a higher safety factor. Finally, exposure to rare loading events with high potential costs of failure leads to design of structures with very high safety factor compared to everyday loading experience. Understanding bone adaptations at the structural and material levels, which take place over an individual's lifetime or over evolutionary time has numerous applications in translational and clinical research to understand and treat musculoskeletal diseases, as well as to permit the furthering of human extraterrestrial exploration in environments with altered gravity.

Murine Axial Compression Tibial Loading Model to Study Bone Mechanobiology: Implementing the Model and Reporting Results

In vivo, tibial loading in mice is increasingly used to study bone adaptation and mechanotransduction. To achieve standardized and defined experimental conditions, loading parameters and animal-related factors must be considered when performing in vivo loading studies. In this review, we discuss these loading and animal-related experimental conditions, present methods to assess bone adaptation, and suggest reporting guidelines. This review originated from presentations by each of the authors at the workshop "Developing Best Practices for Mouse Models of In Vivo Loading" during the Preclinical Models Section at the Orthopaedic Research Society Annual Meeting, San Diego, CA, March 2017. Following the meeting, the authors engaged in detailed discussions with consideration of relevant literature. The guidelines and recommendations in this review are provided to help researchers perform in vivo loading experiments in mice, and thus further our knowledge of bone adaptation and the mechanisms involved in mechanotransduction. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:233-252, 2020.

αSMA Osteoprogenitor Cells Contribute to the Increase in Osteoblast Numbers in Response to Mechanical Loading

Bone is a dynamic tissue that site-specifically adapts to the load that it experiences. In response to increasing load, the cortical bone area is increased, mainly through enhanced periosteal bone formation. This increase in area is associated with an increase in the number of bone-forming osteoblasts; however, the origin of the cells involved remains unclear. Alpha-smooth muscle actin (αSMA) is a marker of early osteoprogenitor cells in the periosteum, and we hypothesized that the new osteoblasts that are activated by loading could originate from αSMA-expressing cells. Therefore, we used an in vivo fate-mapping approach in an established axial loading model to investigate the role of αSMA-expressing cells in the load-induced increase in osteoblasts. Histomorphometric analysis was applied to measure the number of cells of different origin on the periosteal surface in the most load-responsive region of the mouse tibia. A single loading session failed to increase the number of periosteal αSMA-expressing cells and osteoblasts. However, in response to multiple episodes of loading, the caudal, but not the cranial, periosteal surface was lined with an increased number of osteoblasts originating from αSMA-expressing cells 5 days after the initial loading session. The proportion of osteoblasts derived from αSMA-labeled progenitors increased by 70% (p < 0.05), and the proportion of αSMA-labeled cells that had differentiated into osteoblasts was doubled. We conclude that αSMA-expressing osteoprogenitors can differentiate and contribute to the increase in periosteal osteoblasts induced by mechanical loading in a site-specific manner.

Strain Distribution Evaluation of Rat Tibia under Axial Compressive Load by Combining Strain Gauge Measurement and Finite Element Analysis

This study is aimed at providing an effective method for determining strain-load relationship and at quantifying the strain distribution within the whole tibia under axial compressive load on rats. Rat tibial models with axial compressive load were designed. Strains in three directions (0°, 45°, and 90°) at the proximal shaft of the tibia were measured by using a strain gauge rosette, which was used to calculate the maximum and minimum principal strains. Moreover, the strain at the midshaft of the tibia was measured by a single-element strain gauge. The slopes of the strain-load curves with different peak loads were calculated to assess the stability of the strain gauge measurement. Mechanical environment in the whole tibia by the axial compressive load was quantified using finite element analysis (FEA) based on microcomputed tomography images. The von Mises elastic strain distributions of the whole tibiae were evaluated. Slopes of the strain-load curves showed no significant differences among different peak loads (ANOVA; P > 0.05), indicating that the strain-load relationship obtained from the strain gauge measurement was reasonable and stable. The FEA results corresponded to the experimental results with an error smaller than 15% (paired Student's t-test, P > 0.05), signifying that the FEA can simulate the experiment reasonably. FEA results showed that the von Mises elastic strain was the lowest in the middle and gradually increased to both sides along the lateral direction, with the maximal von Mises elastic strain being observed on the posterior side under the distal tibiofibular synostosis. The method of strain gauge measurements and FEA used in this study can provide a feasible way to obtain the mechanical environment of the tibiae under axial compressive load on the rats and serve as a reference for further exploring the mechanical response of the bone by axial compressive load.

Experimental mechanical strain measurement of tissues

Strain, an important biomechanical factor, occurs at different scales from molecules and cells to tissues and organs in physiological conditions. Under mechanical strain, the strength of tissues and their micro- and nanocomponents, the structure, proliferation, differentiation and apoptosis of cells and even the cytokines expressed by cells probably shift. Thus, the measurement of mechanical strain (i.e., relative displacement or deformation) is critical to understand functional changes in tissues, and to elucidate basic relationships between mechanical loading and tissue response. In the last decades, a great number of methods have been developed and applied to measure the deformations and mechanical strains in tissues comprising bone, tendon, ligament, muscle and brain as well as blood vessels. In this article, we have reviewed the mechanical strain measurement from six aspects: electro-based, light-based, ultrasound-based, magnetic resonance-based and computed tomography-based techniques, and the texture correlation-based image processing method. The review may help solving the problems of experimental and mechanical strain measurement of tissues under different measurement environments.

Vitamin A decreases the anabolic bone response to mechanical loading by suppressing bone formation

Increased vitamin A consumption is associated with decreased cortical bone mass and increased fracture risk in humans. Rodent studies have demonstrated that hypervitaminosis A increases cortical bone resorption, whereas the importance of the effects on bone formation is less well defined. We used an experimental model of increased bone formation by loading of the tibiae to investigate the effect of vitamin A on bone formation. Control [retinol activity equivalents (RAE) 4.5 µg/g chow] or vitamin A (RAE 60 µg/g chow) diets were given to female C57BL/6N mice for 4 wk, after which the tibiae were subjected to axial loading on alternate days for 2 wk, while the diets were continued. Vitamin A inhibited the loading-induced increase in trabecular and cortical bone volume. This was attributed to inhibition of loading-induced increase in osteoblast number and activity, and expression of osteoblastic genes Sp7, Alpl, and Col1a1 in cortical bone. Vitamin A, loading, and combination thereof also resulted in site-specific effects on bone composition measured by Raman spectroscopy. In summary, a clinically relevant dose of vitamin A suppresses the loading-induced gain of bone mass by decreasing bone formation. These observations may have implications for regulation of bone mass caused by physical activity and the risk of osteoporosis in humans.-Lionikaite, V., Henning, P., Drevinge, C., Shah, F. A., Palmquist, A., Wikström, P., Windahl, S. H., Lerner, U. H. Vitamin A decreases the anabolic bone response to mechanical loading by suppressing bone formation.

Treadmill running and targeted tibial loading differentially improve bone mass in mice

Treadmill running and tibial loading are two common modalities used to assess the role of mechanical stimulation on the skeleton preclinically. The primary advantage of treadmill running is its physiological relevance. However, the applied load is complex and multiaxial, with observed results influenced by cardiovascular and musculoskeletal effects. In contrast, with tibial loading, a direct uniaxial load is applied to a single bone, providing the advantage of greater control but with less physiological relevance. Despite the importance and wide-spread use of both modalities, direct comparisons are lacking. In this study, we compared effects of targeted tibial loading, treadmill running, and their combination on cancellous and cortical architecture in a murine model. We show that tibial loading and treadmill running differentially improve bone mass, with tibial loading resulting in thicker trabeculae and increased cortical mass, and exercise resulting in greater number of trabeculae and no cortical mass-based effects. Combination of the modalities resulted in an additive response. These data suggest that tibial loading and exercise may improve mass differentially.

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Tibial loading increased trabecular thickness while exercise increased number.

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Combined effects of loading and exercise were additive in cancellous bone.

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In cortical bone, loading increased cross-sectional area.

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No mass-based effects were noted due to exercise.

Moderate tibia axial loading promotes discordant response of bone composition parameters and mechanical properties in a hindlimb unloading rat model

OBJECTIVES

The purpose of the present study was to characterize the dynamic alterations of bone composition parameters and mechanical properties to disuse and mechanical intervention.

METHODS

A tail suspension hindlimb unloading model and an in vivo axial tibia loading model in rats were used. A moderate mechanical loading that was capable of engendering 800 µε tibia strain was applied to the right tibia of rats in both control and hindlimb unloading group across 28 days of the experimental period. The contralateral tibia served as control.

RESULTS

Hindlimb unloading led to bone loss in tibia from day 14. Bone mineral density, mineral content and mechanical properties responded differently with microstructure to disuse in timing course. Mechanical loading of 800 µε tibia strain failed to alter the bone of the control group, but minimized the detrimental effects of unloading by completely prohibiting the decrease of bone mineral content and main mechanical properties after 28 days. Less obvious influence of mechanical loading on bone microstructure was found.

CONCLUSIONS

The moderate mechanical loading is not able to stimulate the mechanical response of healthy tibia, but indeed lead to discordant recovery of bone composition parameters and mechanical properties.

The compositional and nano-structural basis of fracture healing in healthy and osteoporotic bone

Osteoporosis, a prevalent metabolic bone disorder, predisposes individuals to increased susceptibility to fractures. It is also, somewhat controversially, thought to delay or impair the regenerative response. Using high-resolution Fourier-transform infrared spectroscopy and small/wide-angle X-ray scattering we sought to answer the following questions: Does the molecular composition and the nano-structure in the newly regenerated bone differ between healthy and osteoporotic environments? And how do pharmacological treatments, such as bone morphogenetic protein 7 (BMP-7) alone or synergistically combined with zoledronate (ZA), alter callus composition and nano-structure in such environments? Cumulatively, on the basis of compositional and nano-structural characterizations of newly formed bone in an open-osteotomy rat model, the healing response in untreated healthy and ovariectomy-induced osteoporotic environments was fundamentally the same. However, the BMP-7 induced osteogenic response resulted in greater heterogeneity in the nano-structural crystal dimensions and this effect was more pronounced with osteoporosis. ZA mitigated the effects of the upregulated catabolism induced by both BMP-7 and an osteoporotic bone environment. The findings contribute to our understanding of how the repair processes in healthy and osteoporotic bone differ in both untreated and treated contexts and the data presented represents the most comprehensive study of fracture healing at the nanoscale undertaken to date.