Mineral Composition is Altered by Osteoblast Expression of an Engineered Gs-Coupled Receptor

Activation of the Gs G protein–coupled receptor Rs1 in osteoblasts increases bone mineral density by 5- to 15-fold in mice and recapitulates histologic aspects of fibrous dysplasia of the bone. However, the effects of constitutive Gs signaling on bone tissue quality are not known. The goal of this study was to determine bone tissue quality in mice resulting from osteoblast-specific constitutive Gs activation, by the complementary techniques of FTIR spectroscopy and synchrotron radiation micro-computed tomography (SRμCT). Col1(2.3)-tTA/TetO-Rs1 double transgenic (DT) mice, which showed osteoblast-specific constitutive Gs signaling activity by the Rs1 receptor, were created. Femora and calvariae of DT and wild-type (WT) mice (6 and 15 weeks old) were analyzed by FTIR spectroscopy. WT and DT femora (3 and 9 weeks old) were imaged by SRμCT. Mineral-to-matrix ratio was 25% lower (P = 0.010), carbonate-to-phosphate ratio was 20% higher (P = 0.025), crystallinity was 4% lower (P = 0.004), and cross-link ratio was 11% lower (P = 0.025) in 6-week DT bone. Differences persisted in 15-week animals. Quantitative SRμCT analysis revealed substantial differences in mean values and heterogeneity of tissue mineral density (TMD). TMD values were 1,156 ± 100 and 711 ± 251 mg/cm3 (mean ± SD) in WT and DT femoral diaphyses, respectively, at 3 weeks. Similar differences were found in 9-week animals. These results demonstrate that continuous Gs activation in murine osteoblasts leads to deposition of immature bone tissue with reduced mineralization. Our findings suggest that bone tissue quality may be an important contributor to increased fracture risk in fibrous dysplasia patients

Combination of Nanoindentation and Quantitative Backscattered Electron Imaging Revealed Altered Bone Material Properties Associated with Femoral Neck Fragility

Osteoporotic fragility fractures were hypothesized to be related to changes in bone material properties and not solely to reduction in bone mass. We studied cortical bone from the superior and inferior sectors of whole femoral neck sections from five female osteoporotic hip fracture cases (74–92 years) and five nonfractured controls (75–88 years). The typical calcium content (CaPeak) and the mineral particle thickness parameter (T) were mapped in large areas of the superior and inferior regions using quantitative backscattered electron imaging (qBEI) and scanning small-angle X-ray scattering, respectively. Additionally, indentation modulus (E) and hardness (H) (determined by nanoindentation) were compared at the local level to the mineral content (CaInd) at the indent positions (obtained from qBEI). CaPeak (−2.2%, P = 0.002), CaInd (−1.8%, P = 0.048), E (−5.6%, P = 0.040), and H (−6.0%, P = 0.016) were significantly lower for the superior compared to the inferior region. Interestingly, CaPeak as well as CaInd were also lower (−2.6%, P = 0.006, and –3.7%, P = 0.002, respectively) in fracture cases compared to controls, while E and H did not show any significant reduction. T values were in the normal range, independent of region (P = 0.181) or fracture status (P = 0.551). In conclusion, it appears that the observed femoral neck fragility is associated with a reduced mineral content, which was not accompanied by a reduction in stiffness and hardness of the bone material. This pilot study suggests that a stiffening process in the organic matrix component contributes to bone fragility independently of mineral content

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Human bone material characterization: integrated imaging surface investigation of male fragility fractures

SUMMARY: The interrelation of calcium and phosphorus was evaluated as a function of bone material quality in femoral heads from male fragility fracture patients via surface analytical imaging as well as scanning microscopy techniques. A link between fragility fractures and increased calcium to phosphorus ratio was observed despite normal mineralization density distribution. INTRODUCTION: Bone fragility in men has been recently recognized as a public health issue, but little attention has been devoted to bone material quality and the possible efficacy in fracture risk prevention. Clinical routine fracture risk estimations do not consider the quality of the mineralized matrix and the critical role played by the different chemical components that are present. This study uses a combination of different imaging and analytical techniques to gain insights into both the spatial distribution and the relationship of phosphorus and calcium in bone. METHODS: X-ray photoelectron spectroscopy and time-offlight secondary ion mass spectrometry imaging techniques were used to investigate the relationship between calcium and phosphorus in un-embedded human femoral head specimens from fragility fracture patients and non-fracture age-matched controls. The inclusion of the bone mineral density distribution via backscattered scanning electron microscopy provides information about the mineralization status between the groups. RESULTS: A link between fragility fracture and increased calcium and decreased phosphorus in the femoral head was observed despite normal mineralization density distribution. Results exhibited significantly increased calcium to phosphorus ratio in the fragility fracture group, whereas the nonfracture control group ratio was in agreement with the literature value of 1.66 M ratio in mature bone. CONCLUSIONS: Our results highlight the potential importance of the relationship between calcium and phosphorus, especially in areas of new bone formation, when estimating fracture risk of the femoral head. The determination of calcium and phosphorus fractions in bone mineral density measurements may hold the key to better fracture risk assessment as well as more targeted therapies.R. Zoehrer, E. Perilli, J.S. Kuliwaba, J.G. Shapter, N.L. Fazzalari and N.H. Voelcke

Efficient and Robust Machine Learning for Real-World Systems

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e.\ the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems