Journey of Mineral Precursors in Bone Mineralization: Evolution and Inspiration for Biomimetic Design.

Bone mineralization is a ubiquitous process among vertebrates that involves a dynamic physical/chemical interplay between the organic and inorganic components of bone tissues. It is now well documented that carbonated apatite, an inorganic component of bone, is proceeded through transient amorphous mineral precursors that transforms into the crystalline mineral phase. Here, the evolution on mineral precursors from their sources to the terminus in the bone mineralization process is reviewed. How organisms tightly control each step of mineralization to drive the formation, stabilization, and phase transformation of amorphous mineral precursors in the right place, at the right time, and rate are highlighted. The paradigm shifts in biomineralization and biomaterial design strategies are intertwined, which promotes breakthroughs in biomineralization-inspired material. The design principles and implementation methods of mineral precursor-based biomaterials in bone graft materials such as implant coatings, bone cements, hydrogels, and nanoparticles are detailed in the present manuscript. The biologically controlled mineralization mechanisms will hold promise for overcoming the barriers to the application of biomineralization-inspired biomaterials.

BMSC-Derived Exosomal CircHIPK3 Promotes Osteogenic Differentiation of MC3T3-E1 Cells via Mitophagy.

Exosome-based therapy is emerging as a promising strategy to promote bone regeneration due to exosomal bioactive cargos, among which circular RNA (circRNA) has recently been recognized as the key effector. The role of exosomal circRNA derived from bone marrow mesenchymal stem cells (BMSCs) has not been well-defined. The present study aimed to clarify the regulatory function and molecular mechanism of BMSC-derived exosomal circRNA in osteogenesis. Exosomes derived from bone marrow mesenchymal stem cells (BMSC-Exos) were isolated and identified. BMSC-Exos' pro-osteogenic effect on MC3T3-E1 cells was validated by alkaline phosphatase (ALP) activity and Alizarin Red staining. Through bioinformatic analysis and molecular experiments, circHIPK3 was selected and verified as the key circRNA of BMSC-Exos to promote osteoblast differentiation of MC3T3-E1 cells. Mechanistically, circHIPK3 acted as an miR-29a-5p sponge and functioned in mitophagy via targeting miR-29a-5p and PINK1. Additionally, we showed that the mitophagy level of MC3T3-E1 cells were mediated by BMSC-Exos, which promoted the osteogenic differentiation. Collectively, our results revealed an important role for BMSC-derived exosomal circHIPK3 in osteogenesis. These findings provide a potentially effective therapeutic strategy for bone regeneration.

COPI Vesicle Disruption Inhibits Mineralization via mTORC1-Mediated Autophagy.

Bone mineralization is a sophisticated regulated process composed of crystalline calcium phosphate and collagen fibril. Autophagy, an evolutionarily conserved degradation system, whereby double-membrane vesicles deliver intracellular macromolecules and organelles to lysosomes for degradation, has recently been shown to play an essential role in mineralization. However, the formation of autophagosomes in mineralization remains to be determined. Here, we show that Coat Protein Complex I (COPI), responsible for Golgi-to-ER transport, plays a pivotal role in autophagosome formation in mineralization. COPI vesicles were increased after osteoinduction, and COPI vesicle disruption impaired osteogenesis. Mechanistically, COPI regulates autophagy activity via the mTOR complex 1 (mTORC1) pathway, a key regulator of autophagy. Inhibition of mTOR1 rescues the impaired osteogenesis by activating autophagy. Collectively, our study highlights the functional importance of COPI in mineralization and identifies COPI as a potential therapeutic target for treating bone-related diseases.

BMAL1-Downregulation Aggravates Porphyromonas Gingivalis-Induced Atherosclerosis by Encouraging Oxidative Stress.

RATIONALE: Atherosclerotic cardiovascular diseases are the leading cause of mortality worldwide. Atherosclerotic cardiovascular diseases are considered as chronic inflammation processes. In addition to risk factors associated with the cardiovascular system itself, pathogenic bacteria such as the periodontitis-associated Porphyromonas gingivalis (P gingivalis) are also closely correlated with the development of atherosclerosis, but the underlying mechanisms are still elusive. OBJECTIVE: To elucidate the mechanisms of P gingivalis-accelerated atherosclerosis and explore novel therapeutic strategies of atherosclerotic cardiovascular diseases. METHODS AND RESULTS: Bmal1-/- (brain and muscle Arnt-like protein 1) mice, ApoE-/- mice, Bmal1-/-ApoE-/- mice, conditional endothelial cell Bmal1 knockout mice (Bmal1fl/fl; Tek-Cre mice), and the corresponding jet-legged mouse model were used. Pgingivalis accelerates atherosclerosis progression by triggering arterial oxidative stress and inflammatory responses in ApoE-/- mice, accompanied by the perturbed circadian clock. Circadian clock disruption boosts P gingivalis-induced atherosclerosis progression. The mechanistic dissection shows that P gingivalis infection activates the TLRs-NF-κB signaling axis, which subsequently recruits DNMT-1 to methylate the BMAL1 promoter and thus suppresses BMAL1 transcription. The downregulation of BMAL1 releases CLOCK, which phosphorylates p65 and further enhances NF-κB signaling, elevating oxidative stress and inflammatory response in human aortic endothelial cells. Besides, the mouse model exhibits that joint administration of metronidazole and melatonin serves as an effective strategy for treating atherosclerotic cardiovascular diseases. CONCLUSIONS: P gingivalis accelerates atherosclerosis via the NF-κB-BMAL1-NF-κB signaling loop. Melatonin and metronidazole are promising auxiliary medications toward atherosclerotic cardiovascular diseases.

Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks.

Driving is a dynamic activity, which requires quick reflexes and decision making in order to respond to sudden changes in traffic conditions. Alcohol consumption impairs motor and cognitive skills, and causes many driving-related accidents annually. Passive methods of proactively detecting drivers who are too drunk to drive in order to notify them and prevent accidents, have recently been proposed. The effects of alcohol on a drinker's gait (walk) is a reliable indicator of their intoxication level. In this paper, we investigate detecting drinkers' intoxication levels from their gait by using neural networks to analyze sensor data gathered from their smartphone. Using data gathered from a large controlled alcohol study, we perform regression analysis using a Bi-directional Long Short Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architectures to predict a person's Blood Alcohol Concentration (BAC) from their smartphone's accelerometer and gyroscope data. We innovatively proposed a comprehensive suite of pre-processing techniques and model-specific extensions to vanilla CNN and bi-LSTM models, which are well thought out and adapted specifically for BAC estimation. Our Bi-LSTM architecture achieves an RMSE of 0.0167 and the CNN architecture achieves an RMSE of 0.0168, outperforming state-of-the-art intoxication detection models using Bayesian Regularized Multilayer Perceptrons (MLP) (RMSE of 0.017) and the Random Forest (RF), with hand-crafted features. Moreover, our models learn features from raw sensor data, obviating the need for hand-crafted features, which is time consuming. Moreover, they achieve lower variance across folds and are hence more generalizable.

Bmal1 Regulates Coagulation Factor Biosynthesis in Mouse Liver in Streptococcus oralis Infection.

Streptococcus oralis (S. oralis) has been recognized as a fatal pathogen to cause multiorgan failure by contributing to the formation of microthrombus. Coagulation and fibrinolysis systems have been found under the control of circadian clock genes. This study aimed to explore the correlation between BMAL1 and coagulation factor biosynthesis in S. oralis infection. Mice were administered S. oralis to induce sepsis, and HepG2 cells were also infected by S. oralis. The expression of BMAL1 of hepatocytes was downregulated in the S. oralis infection group, leading to the downregulation of coagulation factor VII (FVII) and the upregulation of the coagulation factor XII (FXII) in vitro and in vivo. Furthermore, we confirmed that the deficiency of BAML1 contributed to the elevation of FVII and the decline in FXII by constructing BMAL1-deficiency (Bmal1-/-) mice. The current result showed that BMAL1 regulates FVII directly. Thus, a novel insight into the coagulation abnormality in S. oralis infection was gained that may optimize the treatment of sepsis by rescuing the expression of BMAL1 in the liver.

On Smartphone Sensability of Bi-Phasic User Intoxication Levels from Diverse Walk Types in Standardized Field Sobriety Tests.

Intoxicated driving causes 10,000 deaths annually. Smartphone sensing of user gait (walk) to identify intoxicated users in order to prevent drunk driving, have recently emerged. Such systems gather motion sensor (accelerometer and gyroscope) data from the users' smartphone as they walk and classify them using machine or deep learning. Standard Field Sobriety Tests (SFSTs) involve various types of walks designed to cause an intoxicated person to lose their balance. However, SFSTs were designed to make intoxication apparent to a trained law enforcement officer who manually proctors them. No prior work has explored which types of walk yields the most accurate results when assessed autonomously by a smartphone intoxicated gait assessment system. In this paper, we compare how accurately Long Short Term Memory (LSTM), Convolution Neural Network (CNN), Random Forest, Gradient Boosted Machines (GBM) and neural network classifiers are able to detect intoxication levels of drunk subjects who performed normal, walk-and-turn and standing on one foot SFST walks. We also compared the accuracy of intoxication detection on the ascending (increasing intoxication) vs descending (decreasing intoxication) limbs of drinking sessions (bi-phasic). We found smartphone intoxication sensing more accurate on the descending limb of the drinking episode and that intoxication detection on the normal walks of subjects were just as accurate as the SFSTs.