Cytosolic proteome profiling of monocytes for male osteoporosis

CD97 inhibits osteoclast differentiation via Rap1a/ERK pathway under compression

Acceleration of tooth movement during orthodontic treatment is challenging, with osteoclast-mediated bone resorption on the compressive side being the rate-limiting step. Recent studies have demonstrated that mechanoreceptors on the surface of monocytes/macrophages, especially adhesion G protein-coupled receptors (aGPCRs), play important roles in force sensing. However, its role in the regulation of osteoclast differentiation remains unclear. Herein, through single-cell analysis, we revealed that CD97, a novel mechanosensitive aGPCR, was expressed in macrophages. Compression upregulated CD97 expression and inhibited osteoclast differentiation; while knockdown of CD97 partially rescued osteoclast differentiation. It suggests that CD97 may be an important mechanosensitive receptor during osteoclast differentiation. RNA sequencing analysis showed that the Rap1a/ERK signalling pathway mediates the effects of CD97 on osteoclast differentiation under compression. Consistently, we clarified that administration of the Rap1a inhibitor GGTI298 increased osteoclast activity, thereby accelerating tooth movement. In conclusion, our results indicate that CD97 suppresses osteoclast differentiation through the Rap1a/ERK signalling pathway under orthodontic compressive force.

Postmenopausal osteoporosis: Effect of moderate-intensity treadmill exercise on bone proteomics in ovariectomized rats

Objectives

This study aimed to identify the key proteins in the bone mass of ovariectomized (OVX) rats after a period of regular moderate-intensity treadmill exercise and to investigate their effects using tag mass spectrometry and quantitative proteomics with a view to improving the understanding and treatment of postmenopausal osteoporosis.

Methods

Sixty three-month-old female Sprague-Dawley tats of specific-pathogen-free grade were randomly and equally divided into a sham operation group, ovariectomized group (OVX) and ovariectomized combined exercise (OVX + EX) group, and the latter took moderate-intensity treadmill exercise for 17 weeks. After this period of time, body composition and bone density were measured using dual-energy x-ray absorptiometry, and serum bone metabolism indicators were measured using an enzyme immunoassay. In addition, the bone microstructure was examined using micro-computed tomography and scanning of the femur, and femur proteins were subject to proteomic analysis.

Results

Compared with the rats in the OVX group, the bone metabolism indicators in the OVX + EX group decreased significantly, femur bone density increased significantly, the number of the trabeculae increased, and continuity was higher. In the OVX + EX group, 17 proteins were significantly upregulated and 33 significantly downregulated. The main gene ontology and signaling pathways enriched by the proteins were identified as the tumor necrosis factor-mediated signaling pathways. The protein-protein interaction network identified the key proteins, and the correlation analysis of these proteins and the bone parameters found histone deacetylase 8(HDAC8) and leucine-rich transmembrane and O-methyltransferase domain containing (LRTOMT) and trimethylguanosine synthase 1(TGS1) and ankyrin repeat domain 46(ANKRD46) to be the key targets of exercise in relation to postmenopausal osteoporosis.

Conclusion

Moderate-intensity treadmill exercise significantly improved the bone mass of OVX rats, and differentially expressed proteins, such as HDAC8 and LRTOMT and TGS1 and ANKRD46, could be the target of moderate-intensity treadmill exercise.

Proteomics Profiling of Osteoporosis and Osteopenia Patients and Associated Network Analysis

Bone mass reduction due to an imbalance in osteogenesis and osteolysis is characterized by low bone mineral density (LBMD) and is clinically classified as osteopenia (ON) or osteoporosis (OP), which is more severe. Multiple biomarkers for diagnosing OP and its progression have been reported; however, most of these lack specificity. This cohort study aimed to investigate sensitive and specific LBMD-associated protein biomarkers in patients diagnosed with ON and OP. A label-free liquid chromatography-mass spectrometry (LC-MS) proteomics approach was used to analyze serum samples. Patients’ proteomics profiles were filtered for potential confounding effects, such as age, sex, chronic diseases, and medication. A distinctive proteomics profile between the control, ON, and OP groups (Q² = 0.7295, R² = 0.9180) was identified, and significant dysregulation in a panel of proteins (n = 20) was common among the three groups. A comparison of these proteins showed that the levels of eight proteins were upregulated in ON, compared to those in the control and the OP groups, while the levels of eleven proteins were downregulated in the ON group compared to those in the control group. Interestingly, only one protein, myosin heavy chain 14 (MYH14), showed a linear increase from the control to the ON group, with the highest abundance in the OP group. A significant separation in the proteomics profile between the ON and OP groups (Q² = 0.8760, R² = 0.991) was also noted. Furthermore, a total of twenty-six proteins were found to be dysregulated between the ON and the OP groups, with fourteen upregulated and twelve downregulated proteins in the OP, compared to that in the ON group. Most of the identified dysregulated proteins were immunoglobulins, complement proteins, cytoskeletal proteins, coagulation factors, and various enzymes. Of these identified proteins, the highest area under the curve (AUC) in the receiver operating characteristic (ROC) analysis was related to three proteins (immunoglobulin Lambda constant 1 (IGLC1), RNA binding protein (MEX3B), and fibulin 1 (FBLN1)). Multiple reaction monitoring (MRM), LC-MS, was used to validate some of the identified proteins. A network pathway analysis of the differentially abundant proteins demonstrated dysregulation of inflammatory signaling pathways in the LBMD patients, including the tumor necrosis factor (TNF), toll-like receptor (TL4), and interferon-γ (IFNG) signaling pathways. These results reveal the existence of potentially sensitive protein biomarkers that could be used in further investigations of bone health and OP progression.

Biomimicking design of artificial periosteum for promoting bone healing

Background

Periosteum is a vascularized tissue membrane covering the bone surface and plays a decisive role in bone reconstruction process after fracture. Various artificial periosteum has been developed to assist the allografts or bionic bone scaffolds in accelerating bone healing. Recently, the biomimicking design of artificial periosteum has attracted increasing attention due to the recapitulation of the natural extracellular microenvironment of the periosteum and has presented unique capacity to modulate the cell fates and ultimately enhance the bone formation and improve neovascularization.

Methods

A systematic literature search is performed and relevant findings in biomimicking design of artificial periosteum have been reviewed and cited.

Results

We give a systematical overview of current development of biomimicking design of artificial periosteum. We first summarize the universal strategies for designing biomimicking artificial periosteum including biochemical biomimicry and biophysical biomimicry aspects. We then discuss three types of novel versatile biomimicking artificial periosteum including physical-chemical combined artificial periosteum, heterogeneous structured biomimicking periosteum, and healing phase-targeting biomimicking periosteum. Finally, we comment on the potential implications and prospects in the future design of biomimicking artificial periosteum.

Conclusion

This review summarizes the preparation strategies of biomimicking artificial periosteum in recent years with a discussion of material selection, animal model adoption, biophysical and biochemical cues to regulate the cell fates as well as three types of latest developed versatile biomimicking artificial periosteum. In future, integration of innervation, osteochondral regeneration, and osteoimmunomodulation, should be taken into consideration when fabricating multifunctional artificial periosteum.

The Translational Potential of this Article: This study provides a holistic view on the design strategy and the therapeutic potential of biomimicking artificial periosteum to promote bone healing. It is hoped to open a new avenue of artificial periosteum design with biomimicking considerations and reposition of the current strategy for accelerated bone healing.

Quantitative proteomics and reverse engineer analysis identified plasma exosome derived protein markers related to osteoporosis

Alongside an aging population, osteoporosis has become increasingly common, representing a major public health problem. Human blood provides the predominant matrix for pathological targets underlining disease mechanisms. In the present study, the protein profiles of blood plasma exosomes from patients with osteoporosis, osteopenia, and those with normal bone mass were compared. The aim of the study was to search for potential novel diagnostic/therapeutic targets for further investigation in osteoporosis. A total of 60 participants were included from the PLAGH Hip Fracture Database. Quantitative proteomics was carried out to profile the plasma exosome derived proteins from patients diagnosed with osteoporosis, osteopenia, and normal bone mass, respectively. A Parallel reaction monitoring (PRM) analysis was further carried out to validate the identified proteins. Bio-informatics analyses including GO annotation and reverse engineering of gene regulatory networks analysis were applied in annotating the biological relevance of the identified proteins. Forty-five differentially expressed proteins were identified in the discovery dataset and four of them, PSMB9, AARS, PCBP2, and VSIR were further verified in a validation set. Based on the results, an exosomal-proteins index was constructed to classify individuals with osteoporosis from those without, an AUC of 0.805 (95% CI 0.620-0.926, p < 0.001) was achieved in classification performance assessment. Additionally, a reverse engineer of the regulatory network analysis identified and predicted the proteins which may interact with the four target proteins identified, providing references for further investigations into the pathological mechanisms of osteoporosis.

Identification of osteoporosis markers through bioinformatic functional analysis of serum proteome

Osteoporosis is a severe chronic skeletal disorder that increases the risks of disability and mortality; however, the mechanism of this disease and the protein markers for prognosis of osteoporosis have not been well characterized. This study aims to characterize the imbalanced serum proteostasis, the disturbed pathways, and potential serum markers in osteoporosis by using a set of bioinformatic analyses. In the present study, the large-scale proteomics datasets (PXD006464) were adopted from the Proteome Xchange database and processed with MaxQuant. The differentially expressed serum proteins were identified. The biological process and molecular function were analyzed. The protein-protein interactions and subnetwork modules were constructed. The signaling pathways were enriched. We identified 209 upregulated and 230 downregulated serum proteins. The bioinformatic analyses revealed a highly overlapped functional protein classification and the gene ontology terms between the upregulated and downregulated protein groups. Protein-protein interactions and pathway analyses showed a high enrichment in protein synthesis, inflammation, and immune response in the upregulated proteins, and cell adhesion and cytoskeleton regulation in the downregulated proteins. Our findings greatly expand the current view of the roles of serum proteins in osteoporosis and shed light on the understanding of its underlying mechanisms and the discovery of serum proteins as potential markers for the prognosis of osteoporosis.

Artificial intelligence, osteoporosis and fragility fractures

PURPOSE OF REVIEW

Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlying trends and patterns, otherwise impossible with previous modeling capabilities. Machine learning and deep learning models have found a role in osteoporosis, both to model the risk of fragility fracture, and to help with the identification and segmentation of images.

RECENT FINDINGS

Here we survey the latest research in the artificial intelligence application to the prediction of osteoporosis that has been published between January 2017 and March 2019. Around half of the articles that are covered here predict (by classification or regression) an indicator of osteoporosis, such as bone mass or fragility fractures; the other half of studies use tools for automatic segmentation of the images of patients with or at risk of osteoporosis. The data for these studies include diverse signal sources: acoustics, MRI, CT, and of course, X-rays.

SUMMARY

New methods for automatic image segmentation, and prediction of fracture risk show promising clinical value. Though these recent developments have had a successful initial application to osteoporosis research, their development is still under improvement, such as accounting for positive/negative class bias. We urge care when reporting accuracy metrics, and when comparing such metrics between different studies.

Evaluating the Correlations Between Osteoporosis and Lifestyle-Related Factors Using Transcriptome-Wide Association Study

Osteoporosis (OP) is a multi-factorial bone disease influenced by genetic factors, age, and lifestyles. The aim of this study is to evaluate the genetic correlations between OP and multiple lifestyle-related factors, and explore the genes underlying the detected genetic correlations. Linkage disequilibrium score regression (LDSC) analysis was applied to evaluate the genetic correlations of total body bone mineral density (TB-BMD) of different ages (including 15-30 years, 30-45 years, 45-60 years, and over 60 years) with four common lifestyle/environment-related factors (including serum 25-hydroxyvitamin D, cigarette smoking, alcohol dependence, and caffeine metabolites). Transcriptome-wide association studies (TWAS) of TB-BMD (30-45 years) and smoking were conducted in peripheral blood (PB), whole blood (WB), and adipose tissues. The identified candidate genes were also subjected to gene set enrichment analysis (GSEA). Genetic correlation was only observed between TB-BMD (30-45 years) and cigarette smoking status (P = 0.01, LD score = 0.11 ± 0.04). No significant genetic correlation was detected for other lifestyle/environmental factors, including serum 25-hydroxyvitamin D, alcohol dependence, and caffeine metabolites for TB-BMD within all of the four age groups. TWAS identified 85 genes in PB and 163 genes in WB for TB-BMD, as well as 123 genes in PB and 257 genes in WB for smoking. Multiple common candidate genes shared by both TB-BMD and smoking were detected, such as MAP1LC3B (P_TB-BMD-PB = 1.00 × 10^-3, P_smoking-PB = 9.62 × 10^-3, P_TB-BMD-WB = 2.99 × 10^-2) and SLC23A3 (P_TB-BMD-WB = 1.48 × 10^-2, P_smoking-WB = 8.76 × 10^-3). GSEA detected one GO terms for TB-BMD (cytosol) in WB, one GO term for smoking (mitochondrion) in PB, and one pathway (oocyte meiosis) for smoking in WB.

Proteomic studies of bone and skeletal health outcomes

Proteins are an essential part of essentially all biological processes, and there is enormous variation in protein forms and concentrations that is not reflected in DNA or RNA. Recently there have been rapid advances in the ability to measure protein sequence, modification and concentration, particularly with methods based in mass spectrometry. Global measures of proteins in tissues or in the circulation provide a broad assessment of the proteome that can be extremely useful for discovery, and targeted proteomic measures can yield specific and sensitive assessments of specific peptides and proteins. While most proteomic measures are directed at the detection of consensus peptide sequences, mass spectrometry based proteomic methods also allow a detailed examination of the peptide sequence differences that result from genetic variants and that may have important effects on protein function. In evaluating proteomic data, a number of analytical considerations are important, including an understanding of missing data, the challenge of multiple testing and replication, and the use of rapidly evolving methods in systems biology. While proteomics has not yet had a major impact in skeletal research, interesting recent research has used these approaches in the study of bone cell biology and the discovery of biomarkers of skeletal disorders. Proteomics can be expected to have an increasing influence in the study of bone biology and pathophysiology.

Differentially expressed proteins identified by TMT proteomics analysis in bone marrow microenvironment of osteoporotic patients

We applied tandem mass tag (TMT)-based proteomics to investigate protein changes in bone marrow microenvironment of osteoporotic patients undergoing spine fusion. Multiple bioinformatics tools were used to identify and analyze 219 differentially expressed proteins. These proteins may be associated with the pathogenesis of osteoporosis.

INTRODUCTION

Bone marrow microenvironment is indispensable for the maintenance of bone homeostasis. We speculated that alterations of some factors in the microenvironment of osteoporotic subjects might influence the homeostasis. This study aimed to investigate the changes in the expression of protein factors in the bone marrow environment of osteoporosis.

METHODS

We performed a proteomics analysis in the vertebral body-derived bone marrow supernatant fluid from 8 Chinese patients undergoing posterior lumbar interbody fusion (4 osteoporotic vs. 4 non-osteoporotic) and used micro-CT to analyze the microstructural features of spinous processes from these patients. We further performed western blotting to validate the differential expressions of some proteins.

RESULTS

There was deteriorated bone microstructure in osteoporotic patients. Based on proteomics analysis, 172 upregulated and 47 downregulated proteins were identified. These proteins had multiple biological functions associated with osteoblast differentiation, lipid metabolism, and cell migration, and formed a complex protein-protein interaction network. We identified five major regulatory mechanisms, splicing, translation, protein degradation, cytoskeletal organization, and lipid metabolism, involved in the pathogenesis of osteoporosis.

CONCLUSIONS

There are various protein factors, such as DDX5, PSMC2, CSNK1A1, PLIN1, ILK, and TPM4, differentially expressed in the bone marrow microenvironment of osteoporotic patients, providing new ideas for finding therapeutic targets for osteoporosis.

Myosins in Osteoclast Formation and Function

Skeletal quantity and quality are determined by processes of bone modeling and remodeling, which are undertaken by cells that build and resorb bone as they respond to mechanical, hormonal, and other external and internal signals. As the sole bone resorptive cell type, osteoclasts possess a remarkably dynamic actin cytoskeleton that drives their function in this enterprise. Actin rearrangements guide osteoclasts’ capacity for precursor fusion during differentiation, for migration across bone surfaces and sensing of their composition, and for generation of unique actin superstructures required for the resorptive process. In this regard, it is not surprising that myosins, the superfamily of actin-based motor proteins, play key roles in osteoclast physiology. This review briefly summarizes current knowledge of the osteoclast actin cytoskeleton and describes myosins’ roles in osteoclast differentiation, migration, and actin superstructure patterning.

Rap1b Is an Effector of Axin2 Regulating Crosstalk of Signaling Pathways During Skeletal Development

Recent identification and isolation of suture stem cells capable of long-term self-renewal, clonal expanding, and differentiating demonstrate their essential role in calvarial bone development, homeostasis, and injury repair. These bona fide stem cells express a high level of Axin2 and are able to mediate bone regeneration and repair in a cell autonomous fashion. The importance of Axin2 is further demonstrated by its genetic inactivation in mice causing skeletal deformities resembling craniosynostosis in humans. The fate determination and subsequent differentiation of Axin2+ stem cells are highly orchestrated by a variety of evolutionary conserved signaling pathways including Wnt, FGF, and BMP. These signals are often antagonistic of each other and possess differential effects on osteogenic and chondrogenic cell types. However, the mechanisms underlying the interplay of these signaling transductions remain largely elusive. Here we identify Rap1b acting downstream of Axin2 as a signaling interrogator for FGF and BMP. Genetic analysis reveals that Rap1b is essential for development of craniofacial and body skeletons. Axin2 regulates Rap1b through modulation of canonical BMP signaling. The BMP-mediated activation of Rap1b promotes chondrogenic fate and chondrogenesis. Furthermore, by inhibiting MAPK signaling, Rap1b mediates the antagonizing effect of BMP on FGF to repress osteoblast differentiation. Disruption of Rap1b in mice not only enhances osteoblast differentiation but also impairs chondrocyte differentiation during intramembranous and endochondral ossifications, respectively, leading to severe defects in craniofacial and body skeletons. Our findings reveal a dual role of Rap1b in development of the skeletogenic cell types. Rap1b is critical for balancing the signaling effects of BMP and FGF during skeletal development and disease. © 2017 American Society for Bone and Mineral Research.