Attenuated monocyte apoptosis, a new mechanism for osteoporosis suggested by a transcriptome-wide expression study of monocytes

Investigating the impact of Wnt pathway-related genes on biomarker and diagnostic model development for osteoporosis in postmenopausal females

The Wnt signaling pathway is essential for bone development and maintaining skeletal homeostasis, making it particularly relevant in osteoporosis patients. Our study aimed to identify distinct molecular clusters associated with the Wnt pathway and develop a diagnostic model for osteoporosis in postmenopausal Caucasian women. We downloaded three datasets (GSE56814, GSE56815 and GSE2208) related to osteoporosis from the GEO database. Our analysis identified a total of 371 differentially expressed genes (DEGs) between low and high bone mineral density (BMD) groups, with 12 genes associated with the Wnt signaling pathway, referred to as osteoporosis-associated Wnt pathway-related genes. Employing four independent machine learning models, we established a diagnostic model using the 12 osteoporosis-associated Wnt pathway-related genes in the training set. The XGB model showed the most promising discriminative potential. We further validate the predictive capability of our diagnostic model by applying it to three external datasets specifically related to osteoporosis. Subsequently, we constructed a diagnostic nomogram based on the five crucial genes identified from the XGB model. In addition, through the utilization of DGIdb, we identified a total of 30 molecular compounds or medications that exhibit potential as promising therapeutic targets for osteoporosis. In summary, our comprehensive analysis provides valuable insights into the relationship between the osteoporosis and Wnt signaling pathway.

Transcriptional reprogramming during human osteoclast differentiation identifies regulators of osteoclast activity

Enhanced osteoclastogenesis and osteoclast activity contribute to the development of osteoporosis, which is characterized by increased bone resorption and inadequate bone formation. As novel antiosteoporotic therapeutics are needed, understanding the genetic regulation of human osteoclastogenesis could help identify potential treatment targets. This study aimed to provide an overview of transcriptional reprogramming during human osteoclast differentiation. Osteoclasts were differentiated from CD14+ monocytes from eight female donors. RNA sequencing during differentiation revealed 8 980 differentially expressed genes grouped into eight temporal patterns conserved across donors. These patterns revealed distinct molecular functions associated with postmenopausal osteoporosis susceptibility genes based on RNA from iliac crest biopsies and bone mineral density SNPs. Network analyses revealed mutual dependencies between temporal expression patterns and provided insight into subtype-specific transcriptional networks. The donor-specific expression patterns revealed genes at the monocyte stage, such as filamin B (FLNB) and oxidized low-density lipoprotein receptor 1 (OLR1, encoding LOX-1), that are predictive of the resorptive activity of mature osteoclasts. The expression of differentially expressed G-protein coupled receptors was strong during osteoclast differentiation, and these receptors are associated with bone mineral density SNPs, suggesting that they play a pivotal role in osteoclast differentiation and activity. The regulatory effects of three differentially expressed G-protein coupled receptors were exemplified by in vitro pharmacological modulation of complement 5 A receptor 1 (C5AR1), somatostatin receptor 2 (SSTR2), and free fatty acid receptor 4 (FFAR4/GPR120). Activating C5AR1 enhanced osteoclast formation, while activating SSTR2 decreased the resorptive activity of mature osteoclasts, and activating FFAR4 decreased both the number and resorptive activity of mature osteoclasts. In conclusion, we report the occurrence of transcriptional reprogramming during human osteoclast differentiation and identified SSTR2 and FFAR4 as antiresorptive G-protein coupled receptors and FLNB and LOX-1 as potential molecular markers of osteoclast activity. These data can help future investigations identify molecular regulators of osteoclast differentiation and activity and provide the basis for novel antiosteoporotic targets.

Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model

Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, including GO and KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = -0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP.

Comparisons of gene expression between peripheral blood mononuclear cells and bone tissue in osteoporosis

Osteoporosis (OP) is one of the major public health problems in the world. However, the biomarkers between the peripheral blood mononuclear cells (PBMs) and bone tissue for prognosis of OP have not been well characterized. This study aimed to explore the similarities and differences of the gene expression profiles between the PBMs and bone tissue and identify potential genes, transcription factors (TFs) and hub proteins involved in OP. The patients were enrolled as an experimental group, and healthy subjects served as normal controls. Human whole-genome expression chips were used to analyze gene expression profiles from PBMs and bone tissue. And the differentially expressed genes (DEGs) were subsequently studied using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. The above DEGs were constructed into protein-protein interaction network. Finally, TF-DEGs regulation networks were constructed. Microarray analysis revealed that 226 DEGs were identified between OP and normal controls in the PBMs, while 2295 DEGs were identified in the bone tissue. And 13 common DEGs were obtained by comparing the 2 tissues. The Gene Ontology analysis indicated that DEGs in the PBMs were more involved in immune response, while DEGs in bone were more involved in renal response and urea transmembrane transport. And the Kyoto Encyclopedia of Genes and Genomes analysis indicated almost all of the pathways in the PBMs were overlapped with those in the bone tissue. Furthermore, protein-protein interaction network presented 6 hub proteins: PI3K1, APP, GNB5, FPR2, GNG13, and PLCG1. APP has been found to be associated with OP. Finally, 5 key TFs were identified by TF-DEGs regulation networks analysis (CREB1, RUNX1, STAT3, CREBBP, and GLI1) and were supposed to be associated with OP. This study enhanced our understanding of the pathogenesis of OP. PI3K1, GNB5, FPR2, GNG13, and PLCG1 might be the potential targets of OP.

Immunoporosis: Role of Innate Immune Cells in Osteoporosis

Osteoporosis or porous bone disorder is the result of an imbalance in an otherwise highly balanced physiological process known as 'bone remodeling'. The immune system is intricately involved in bone physiology as well as pathologies. Inflammatory diseases are often correlated with osteoporosis. Inflammatory mediators such as reactive oxygen species (ROS), and pro-inflammatory cytokines and chemokines directly or indirectly act on the bone cells and play a role in the pathogenesis of osteoporosis. Recently, Srivastava et al. (Srivastava RK, Dar HY, Mishra PK. Immunoporosis: Immunology of Osteoporosis-Role of T Cells. Frontiers in immunology. 2018;9:657) have coined the term "immunoporosis" to emphasize the role of immune cells in the pathology of osteoporosis. Accumulated pieces of evidence suggest both innate and adaptive immune cells contribute to osteoporosis. However, innate cells are the major effectors of inflammation. They sense various triggers to inflammation such as pathogen-associated molecular patterns (PAMPs), damage-associated molecular patterns (DAMPs), cellular stress, etc., thus producing pro-inflammatory mediators that play a critical role in the pathogenesis of osteoporosis. In this review, we have discussed the role of the innate immune cells in great detail and divided these cells into different sections in a systemic manner. In the beginning, we talked about cells of the myeloid lineage, including macrophages, monocytes, and dendritic cells. This group of cells explicitly influences the skeletal system by the action of production of pro-inflammatory cytokines and can transdifferentiate into osteoclast. Other cells of the myeloid lineage, such as neutrophils, eosinophils, and mast cells, largely impact osteoporosis via the production of pro-inflammatory cytokines. Further, we talked about the cells of the lymphoid lineage, including natural killer cells and innate lymphoid cells, which share innate-like properties and play a role in osteoporosis. In addition to various innate immune cells, we also discussed the impact of classical pro-inflammatory cytokines on osteoporosis. We also highlighted the studies regarding the impact of physiological and metabolic changes in the body, which results in chronic inflammatory conditions such as ageing, ultimately triggering osteoporosis.

Identification of Critical Functional Modules and Signaling Pathways in Osteoporosis

Background:

Osteoporosis is the most common bone metabolic disease. Abnormal osteoclast formation and resorption play a fundamental role in osteoporosis pathogenesis. Recent researches have greatly broadened our understanding of molecular mechanisms of osteoporosis. However, the molecular mechanisms leading to osteoporosis are still not entirely clear.

Objective:

The purpose of this work is to study the critical regulatory genes, functional modules, and signaling pathways.

Methods:

Differential expression analysis, network topology-based analysis, and overrepresentation enrichment analysis (ORA) were used to identify differentially expressed genes (DEGs), gene subnetworks, and signaling pathways related to osteoporosis, respectively.

Results:

Differential expression analysis identified DEGs, such as POGLUT1, DAPK3 and NFKBIA, associated with osteoclastogenesis, which highlighted Notch, apoptosis and NF-kB signaling pathways. Network topology-based analysis identified the upregulated subnetwork characterized by EXOSC8 and DIS3L from the RNA exosome complex, and the downregulated subnetwork composed of histone deacetylases and the cofactors, MORF4L1 and JDP2. Furthermore, the overrepresentation enrichment analysis highlighted that corticotrophin-releasing hormone signaling pathway might affect osteoclastogenesis through its component NR4A1, and suppressing osteoclast differentiation and osteoclast bone resorption with urocortin (UCN).

Conclusion:

Our systematic analysis not only discovered novel molecular mechanisms but also proposed potential drug targets for osteoporosis.

Integrative Analysis of Genomics and Transcriptome Data to Identify Regulation Networks in Female Osteoporosis

Background: Osteoporosis is a highly heritable skeletal muscle disease. However, the genetic mechanisms mediating the pathogenesis of osteoporosis remain unclear. Accordingly, in this study, we aimed to clarify the transcriptional regulation and heritability underlying the onset of osteoporosis.

Methods: Transcriptome gene expression data were obtained from the Gene Expression Omnibus database. Microarray data from peripheral blood monocytes of 73 Caucasian women with high and low bone mineral density (BMD) were analyzed. Differentially expressed messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) were identified. Differences in BMD were then attributed to several gene modules using weighted gene co-expression network analysis (WGCNA). LncRNA/mRNA regulatory networks were constructed based on the WGCNA and subjected to functional enrichment analysis.

Results: In total, 3,355 mRNAs and 999 lncRNAs were identified as differentially expressed genes between patients with high and low BMD. The WGCNA yielded three gene modules, including 26 lncRNAs and 55 mRNAs as hub genes in the blue module, 36 lncRNAs and 31 mRNAs as hub genes in the turquoise module, and 56 mRNAs and 30 lncRNAs as hub genes in the brown module. JUN and ACSL5 were subsequently identified in the modular gene network. After functional pathway enrichment, 40 lncRNAs and 16 mRNAs were found to be related to differences in BMD. All three modules were enriched in metabolic pathways. Finally, mRNA/lncRNA/pathway networks were constructed using the identified regulatory networks of lncRNAs/mRNAs and pathway enrichment relationships.

Conclusion: The mRNAs and lncRNAs identified in this WGCNA could be novel clinical targets in the diagnosis and management of osteoporosis. Our findings may help elucidate the complex interactions between transcripts and non-coding RNAs and provide novel perspectives on the regulatory mechanisms of osteoporosis.

Network-based Transcriptome-wide Expression Study for Postmenopausal Osteoporosis

PURPOSE

Menopause is a crucial physiological transition during a woman's life, and it occurs with growing risks of health issues like osteoporosis. To identify postmenopausal osteoporosis-related genes, we performed transcriptome-wide expression analyses for human peripheral blood monocytes (PBMs) using Affymetrix 1.0 ST arrays in 40 Caucasian postmenopausal women with discordant bone mineral density (BMD) levels.

METHODS

We performed multiscale embedded gene coexpression network analysis (MEGENA) to study functionally orchestrating clusters of differentially expressed genes in the form of functional networks. Gene sets net correlations analysis (GSNCA) was applied to assess how the coexpression structure of a predefined gene set differs in high and low BMD groups. Bayesian network (BN) analysis was used to identify important regulation patterns between potential risk genes for osteoporosis. A small interfering ribonucleic acid (siRNA)-based gene silencing in vitro experiment was performed to validate the findings from BN analysis.

RESULT

MEGENA showed that the "T cell receptor signaling pathway" and the "osteoclast differentiation pathway" were significantly enriched in the identified compact network, which is significantly correlated with BMD variation. GSNCA revealed that the coexpression structure of the "Signaling by TGF-beta receptor complex pathway" is significantly different between the 2 BMD discordant groups; the hub genes in the postmenopausal low and high BMD group are FURIN and SMAD3 respectively. With siRNA in vitro experiments, we confirmed the regulation relationship of TGFBR2-SMAD7 and TGFBR1-SMURF2.

MAIN CONCLUSION

The present study suggests that biological signals involved in monocyte recruitment, monocyte/macrophage lineage development, osteoclast formation, and osteoclast differentiation might function together in PBMs that contribute to the pathogenesis of postmenopausal osteoporosis.

Modulation of Transient Receptor Potential Channels 3 and 6 Regulates Osteoclast Function with Impact on Trabecular Bone Loss

Enhanced osteoclast formation and function is a fundamental cause of alterations to bone structure and plays an important role in several diseases impairing bone quality. Recent work revealed that TRP calcium channels 3 and 6 might play a special role in this context. By analyzing the bone phenotype of TRPC6-deficient mice we detected a regulatory effect of TRPC3 on osteoclast function. These mice exhibit a significant decrease in bone volume per tissue volume, trabecular thickness and -number together with an increased number of osteoclasts found on the surface of trabecular bone. Primary bone marrow mononuclear cells from TRPC6-deficient mice showed enhanced osteoclastic differentiation and resorptive activity. This was confirmed in vitro by using TRPC6-deficient RAW 264.7 cells. TRPC6 deficiency led to an increase of TRPC3 in osteoclasts, suggesting that TRPC3 overcompensates for the loss of TRPC6. Raised intracellular calcium levels led to enhanced NFAT-luciferase reporter gene activity in the absence of TRPC6. In line with these findings inhibition of TRPC3 using the specific inhibitor Pyr3 significantly reduced intracellular calcium concentrations and normalized osteoclastic differentiation and resorptive activity of TRPC6-deficient cells. Interestingly, an up-regulation of TRPC3 could be detected in a cohort of patients with low bone mineral density by comparing micro array data sets of circulating human osteoclast precursor cells to those from patients with high bone mineral density, suggesting a noticeable contribution of TRP calcium channels on bone quality. These observations demonstrate a novel regulatory function of TRPC channels in the process of osteoclastic differentiation and bone loss.

Integrative Analyses of Genes Associated With Osteoporosis in CD16+ Monocyte

BACKGROUND

Osteoporosis is a metabolic bone disease characterized by decreased bone mineral density and abnormal bone quality. Monocytes can secret cytokines for bone resorption, resulting in bone mass loss. However, the mechanism by which monocytes subpopulations lead to osteoporosis remains unclear. The aim of this study was to identify genes associated with osteoporosis in monocytes subsets.

METHODS

Three microarray datasets including GSE7158 (transcription of low/high-peak bone mass), GSE101489 (transcription of CD16+/CD16- monocyte) and GSE93883 (miRNA expression profile of primary osteoporosis) were derived from the Gene Expression Omnibus (GEO) database and analyzed with GEO2R tool to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using Metascape database and GSEA software. STRING was utilized for the Protein-Protein Interaction Network construct. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in miRWalk, miRDB, and TargetScan databases.

RESULTS

Total 368 DEGs from GSE7158 were screened out, which were mostly enriched in signaling, positive regulation of biological process and immune system process. The hub genes were clustered into two modules by PPI network analysis. We identified 15 overlapping DGEs between GSE101489 and GSE7158 microarray datasets. Moreover, all of them were up-regulated genes in both datasets. Then, nine key genes were screened out from above 15 overlapping DEGs using Cytoscape software. It is a remarkable fact that the nine genes were all in one hub gene module of GSE7158. Additionally, 183 target miRNAs were predicted according to the above nine DEGs. After cross-verification with miRNA express profile dataset for osteoporosis (GSE93883), 12 DEmiRNAs were selected. Finally, a miRNA-mRNA network was constructed with the nine key genes and 12 miRNAs, which were involved in osteoporosis.

CONCLUSION

Our analysis results constructed a gene expression framework in monocyte subsets for osteoporosis. This approach could provide a novel insight into osteoporosis.

Long Noncoding RNA Analyses for Osteoporosis Risk in Caucasian Women

INTRODUCTION

Osteoporosis is a prevalent bone metabolic disease characterized by bone fragility. As a key pathophysiological mechanism, the disease is caused by excessive bone resorption (by osteoclasts) over bone formation (by osteoblasts). Peripheral blood monocytes (PBMs) is a major systemic cell model for bone metabolism by serving as progenitors of osteoclasts and producing cytokines important for osteoclastogenesis. Protein-coding genes for osteoporosis have been widely studied by mRNA analyses of PBMs in high versus low hip bone mineral density (BMD) subjects. However, long noncoding RNAs (lncRNAs), which account for a large proportion of human transcriptome, have seldom been studied.

METHODS

In this study, microarray analyses of monocytes were performed using Affymetrix exon 1.0 ST arrays in 73 Caucasian females (age: 47-56). LncRNA profile was generated by re-annotating exon array for lncRNAs detection, which yielded 12,007 lncRNAs mapped to the human genome.

RESULTS

575 lncRNAs were differentially expressed between the two groups. In the high BMD subjects, 309 lncRNAs were upregulated and 266 lncRNAs were downregulated (nominally significant, raw p-value < 0.05). To investigate the relationship between mRNAs and lncRNAs, we used two approaches to predict the target genes of lncRNAs and found that 26 candidate lncRNAs might regulate mRNA expression. The majority of these lncRNAs were further validated to be potentially correlated with BMD by GWAS analysis.

CONCLUSION

Overall, our findings for the first time reported the lncRNAs profiles for osteoporosis and suggested the potential regulatory mechanism of lncRNAs on protein-coding genes in bone metabolism.

Transcriptomic Data Identified Key Transcription Factors for Osteoporosis in Caucasian Women

Osteoporosis is a prevalent bone metabolic disease, mainly caused by excessive bone resorption (by osteoclasts) over bone formation (by osteoblasts). Identifying the key transcription factors and understanding the regulatory network influencing osteoclastogenesis will be helpful to explore the potential biological mechanism for osteoporosis. In our study, peripheral blood monocyte (PBM) was used as a cell model for bone mineral density (BMD) research. PBMs serve as progenitors of osteoclasts and produce important cytokines for osteoclastogenesis. In our study, via exon arrays, gene expression profiles of PBMs were analyzed between high versus low hip BMD groups. Transcription factors for differentially expressed genes were then predicted based on the enrichment analysis. We found that 591 genes were differentially expressed between the two BMD groups (nominally significant, raw p value < 0.05). For high BMD subjects, 482 genes were up-regulated and 109 genes were down-regulated. We then found 29 potential transcription factors for up-regulated genes and nine transcription factors for down-regulated genes. Among these transcription factors, HMGA1 and NFKB2 were differentially expressed between high versus low BMD groups. In addition, their regulation types with their target genes were consistent with the information from public databases. Our findings of key transcription factors and their target genes for osteoporosis were further validated by GWAS analysis. Overall, we predicted important transcription factors for osteoporosis. We were also able to infer the regulatory mechanism that exists between transcription factors and target genes in bone metabolism.

Integration of summary data from GWAS and eQTL studies identified novel causal BMD genes with functional predictions

PURPOSE

Osteoporosis is a common global health problem characterized by low bone mineral density (BMD) and increased risk of fracture. Genome-wide association studies (GWAS) have identified >100 genetic loci associated with BMD. However, the functional genes responsible for most associations remain largely unknown. We conducted an innovative summary statistic data-based Mendelian randomization (SMR) analysis to identify novel causal genes associated with BMD and explored their potential functional significance.

METHODS

After quality control of the largest GWAS meta-analysis data of BMD and the largest expression quantitative trait loci (eQTL) meta-analysis data from peripheral blood samples, 5967 genes were tested using the SMR method. Another eQTL data was used to verify the results. Next we performed a fine-mapping association analysis to investigate the functional SNP in the identified loci. Weighted gene co-expression network analysis (WGCNA) was used to explore functional relationships for the identified novel genes with known putative osteoporosis genes. Further, we assessed functions of the identified genes through in vitro cellular study or previous functional studies.

RESULTS

We identified two potentially causal genes (ASB16-AS1 and SYN2) associated with BMD. SYN2 was a novel osteoporosis candidate gene and ASB16-AS1 locus was known to be associated with BMD but was not the nearest gene to the top GWAS SNP. Fine-mapping association analysis showed that rs184478 and rs795000 was predicted to be possible causal SNPs in ASB16-AS1 and SYN2, respectively. ASB16-AS1 co-expressed with several known putative osteoporosis risk genes. In vitro cellular study showed that over-expressed ASB16-AS1 increased the expression of osteoblastogenesis related genes (BMP2 and ALPL), indicating its functional significance.

CONCLUSION

Our findings support that ASB16-AS1 and SYN2 may represent two novel functional genes underlying BMD variation. The findings provide a basis for further functional mechanistic studies.

Integrative computational approach to evaluate risk genes for postmenopausal osteoporosis

In recent years, numerous studies reported over a hundred of genes playing roles in the etiology of postmenopausal osteoporosis (PO). However, many of these candidate genes were lack of replication and results were not always consistent. Here, the authors proposed a computational workflow to curate and evaluate PO related genes. They integrate large-scale literature knowledge data and gene expression data (PO case/control: 10/10) for the marker evaluation. Pathway enrichment, sub-network enrichment, and gene-gene interaction analysis were conducted to study the pathogenic profile of the candidate genes, with four metrics proposed and validated for each gene. By using the authors' approach, a scalable PO genetic database was developed; including PO related genes, diseases, pathways, and the supporting references. The PO case/control classification supported the effectiveness of the four proposed metrics, which successfully identified eight well-studied top PO genes (e.g. TGFB1, IL6, IL1B, TNF, ESR2, IGF1, HIF1A, and COL1A1) and highlighted one recently reported PO genes (e.g. IFNG). The computational biology approach and the PO database developed in this study provide a valuable resource which may facilitate understanding the genetic profile of PO.

"Omics" Signatures in Peripheral Monocytes from Women with Low BMD Condition

Postmenopausal osteoporosis (PMO) is a result of increased bone resorption compared to formation. Osteoclasts are responsible for bone resorption, which are derived from circulating monocytes that undertake a journey from the blood to the bone for the process of osteoclastogenesis. In recent times, the use of high throughput technologies to explore monocytes from women with low versus high bone density has led to the identification of candidate molecules that may be deregulated in PMO. This review provides a list of molecules in monocytes relevant to bone density which have been identified by "omics" studies in the last decade or so. The molecules in monocytes that are deregulated in low BMD condition may contribute to processes such as monocyte survival, migration/chemotaxis, adhesion, transendothelial migration, and differentiation into the osteoclast lineage. Each of these processes may be crucial to the overall route of osteoclastogenesis and an increase in any/all of these processes can lead to increased bone resorption and subsequently low bone density. Whether these molecules are indeed the cause or effect is an arena currently unexplored.

A novel approach for correction of crosstalk effects in pathway analysis and its application in osteoporosis research

Osteoporosis is a prevalent bone metabolic disease and peripheral blood monocytes represent a major systemic cell type for bone metabolism. To identify the key dysfunctional pathways in osteoporosis, we performed pathway analyses on microarray data of monocytes from subjects with extremely high/low hip bone mineral density. We first performed a traditional pathway analysis for which different pathways were treated as independent. However, genes overlap among pathways will lead to “crosstalk” phenomenon, which may lead to false positive/negative results. Therefore, we applied correction techniques including a novel approach that considers the correlation among genes to adjust the crosstalk effects in the analysis. In traditional analysis, 10 pathways were found to be significantly associated with BMD variation. After correction for crosstalk effects, three of them remained significant. Moreover, the MAPK signaling pathway, which has been shown to be important for osteoclastogenesis, became significant only after the correction for crosstalk effects. We also identified a new module mainly consisting of genes present in mitochondria to be significant. In summary, we describe a novel method to correct the crosstalk effect in pathway analysis and found five key independent pathways involved in BMD regulation, which may provide a better understanding of biological functional networks in osteoporosis.

Network based subcellular proteomics in monocyte membrane revealed novel candidate genes involved in osteoporosis

In this study, label-free-based quantitative subcellular proteomics integrated with network analysis highlighted several candidate genes including P4HB, ITGB1, CD36, and ACTN1 that may be involved in osteoporosis. All of them are predicted as significant membrane proteins with high confidence and enriched in bone-related biological process. The results were further verified in transcriptomic and genomic levels.

INTRODUCTION

Osteoporosis is a metabolic bone disease mainly characterized by low bone mineral density (BMD). As the precursors of osteoclasts, peripheral blood monocytes (PBMs) are supported to be important candidates for identifying genes related to osteoporosis. We performed subcellular proteomics study to identify significant membrane proteins that involved in osteoporosis.

METHODS

To investigate the association between monocytes, membrane proteins, and osteoporosis, we performed label-free quantitative subcellular proteomics in 59 male subjects with discordant BMD levels, with 30 high vs. 29 low BMD subjects. Subsequently, we performed integrated gene enrichment analysis, functional annotation, and pathway and network analysis based on multiple bioinformatics tools.

RESULTS

A total of 1070 membrane proteins were identified and quantified. By comparing the proteins' expression level, we found 36 proteins that were differentially expressed between high and low BMD groups. Protein localization prediction supported the notion that the differentially expressed proteins, P4HB (p = 0.0021), CD36 (p = 0.0104), ACTN1 (p = 0.0381), and ITGB1 (p = 0.0385), are significant membrane proteins. Functional annotation and pathway and network analysis highlighted that P4HB, ITGB1, CD36, and ACTN1 are enriched in osteoporosis-related pathways and terms including "ECM-receptor interaction," "calcium ion binding," "leukocyte transendothelial migration," and "reduction of cytosolic calcium levels." Results from transcriptomic and genomic levels provided additional supporting evidences.

CONCLUSION

Our study strongly supports the significance of the genes P4HB, ITGB1, CD36, and ACTN1 to the etiology of osteoporosis risk.

The Role of Monocyte Percentage in Osteoporosis in Male Rheumatic Diseases

Osteoporosis is easily overlooked in male patients, especially in the field of rheumatic diseases mostly prevalent with female patients, and its link to pathogenesis is still lacking. Attenuated monocyte apoptosis from a transcriptome-wide expression study illustrates the role of monocytes in osteoporosis. This study tested the hypothesis that the monocyte percentage among leukocytes could be a biomarker of osteoporosis in rheumatic diseases. Eighty-seven males with rheumatic diseases were evaluated in rheumatology outpatient clinics for bone mineral density (BMD) and surrogate markers, such as routine peripheral blood parameters and autoantibodies. From the total number of 87 patients included in this study, only 15 met the criteria for diagnosis of osteoporosis. Both age and monocyte percentage remained independently associated with the presence of osteoporosis. Steroid dose (equivalent prednisolone dose) was negatively associated with BMD of the hip area and platelet counts were negatively associated with BMD and T score of the spine area. Besides age, monocyte percentage meets the major requirements for osteoporosis in male rheumatic diseases. A higher monocyte percentage in male rheumatic disease patients, aged over 50 years in this study, and BMD study should be considered in order to reduce the risk of osteoporosis-related fractures.

Cytosolic proteome profiling of monocytes for male osteoporosis

In male Caucasians with discordant hip bone mineral density (BMD), we applied the subcellular separation and proteome profiling to investigate the monocytic cytosol. Three BMD-associated proteins (ALDOA, MYH14, and Rap1B) were identified based on multiple omics evidence, and they may influence the pathogenic mechanisms of osteoporosis by regulating the activities of monocytes.

INTRODUCTION

Osteoporosis is a serious public health problem, leading to significant mortality not only in aging females but also in males. Peripheral blood monocytes (PBMs) play important roles in bone metabolism by acting as precursors of osteoclasts and producing cytokines important for osteoclast development. The first cytosolic sub-proteome profiling analysis was performed in male PBMs to identify differentially expressed proteins (DEPs) that are associated with BMDs and risk of osteoporosis.

METHODS

Here, we conducted a comparative proteomics analysis in PBMs from Caucasian male subjects with discordant hip BMD (29 low BMD vs. 30 high BMD). To decrease the proteome complexity and expand the coverage range of the cellular proteome, we separated the PBM proteome into several subcellular compartments and focused on the cytosolic fractions, which are involved in a wide range of fundamental biochemical processes.

RESULTS

Of the total of 3796 detected cytosolic proteins, we identified 16 significant (P < 0.05) and an additional 22 suggestive (P < 0.1) DEPs between samples with low vs. high hip BMDs. Some of the genes for DEPs, including ALDOA, MYH14, and Rap1B, showed an association with BMD in multiple omics studies (proteomic, transcriptomic, and genomic). Further bioinformatics analysis revealed the enrichment of DEPs in functional terms for monocyte proliferation, differentiation, and migration.

CONCLUSIONS

The combination strategy of subcellular separation and proteome profiling allows an in-depth and refined investigation into the composition and functions of cytosolic proteome, which may shed light on the monocyte-mediated pathogenic mechanisms of osteoporosis.

P4 medicine and osteoporosis: a systematic review

BACKGROUND

Osteoporosis is the most frequent bone metabolic disease. In order to improve early detection, prediction, prevention, diagnosis, and treatment of the disease, a new model of P4 medicine (personalized, predictive, preventive, and participatory medicine) could be applied. The aim of this work was to systematically review the publications of four different types of "omics" studies related to osteoporosis, in order to discover novel predictive, preventive, diagnostic, and therapeutic targets for better management of the geriatric population.

METHODS

To systematically search the PubMed database, we created specific groups of criteria for four different types of "omics" information on osteoporosis: genomic, transcriptomic, proteomic, and metabolomic. We then analyzed the intersections between them in order to find correlations and common pathways or molecules with important roles in osteoporosis, and with a potential application in disease prediction, prevention, diagnosis, or treatment.

RESULTS

Altogether, 180 publications of "omics" studies in the field of osteoporosis were found and reviewed at first selection. After introducing the inclusion and exclusion criteria (the secondary selection), 46 papers were included in the systematic review.

CONCLUSIONS

The intersection of reviewed papers identified five genes (ESR1, IBSP, CTNNB1, SOX4, and IDUA) and processes like the Wnt pathway, JAK/STAT signaling, and ERK/MAPK, which should be further validated for their predictive, diagnostic, or other clinical value in osteoporosis. Such molecular insights will enable us to fit osteoporosis into the P4 strategy and could increase the effectiveness of disease prediction and prevention, with a decrease in morbidity in the geriatric population.

Quantitative proteomics and integrative network analysis identified novel genes and pathways related to osteoporosis

Osteoporosis is mainly characterized by low bone mineral density (BMD), and can be attributed to excessive bone resorption by osteoclasts. Migration of circulating monocytes from blood to bone is important for subsequent osteoclast differentiation and bone resorption. Identification of those genes and pathways related to osteoclastogenesis and BMD will contribute to a better understanding of the pathophysiological mechanisms of osteoporosis. In this study, we applied the LC-nano-ESI-MS(E) (Liquid Chromatograph-nano-Electrospray Ionization-Mass Spectrometry) for quantitative proteomic profiling in 33 female Caucasians with discordant BMD levels, with 16 high vs. 17 low BMD subjects. Protein quantitation was accomplished by label-free measurement of total ion currents collected from MS(E) data. Comparison of protein expression in high vs. low BMD subjects showed that ITGA2B (p=0.0063) and GSN (p=0.019) were up-regulated in the high BMD group. Additionally, our protein-RNA integrative analysis showed that RHOA (p=0.00062) differentially expressed between high vs. low BMD groups. Network analysis based on multiple tools revealed two pathways: "regulation of actin cytoskeleton" (p=1.13E-5, FDR=3.34E-4) and "leukocyte transendothelial migration" (p=2.76E-4, FDR=4.71E-3) that are functionally relevant to osteoporosis. Consistently, ITGA2B, GSN and RHOA played crucial roles in these two pathways respectively. All together, our study strongly supported the contribution of the genes ITGA2B, GSN and RHOA and the two pathways to osteoporosis risk.

BIOLOGICAL SIGNIFICANCE

Mass spectrometry based quantitative proteomics study integrated with network analysis identified novel genes and pathways related to osteoporosis. The results were further verified in multiple level studies including protein-RNA integrative analysis and genome wide association studies.

Integrative Analysis of Genomics and Transcriptome Data to Identify Potential Functional Genes of BMDs in Females

Osteoporosis is known to be highly heritable. However, to date, the findings from more than 20 genome-wide association studies (GWASs) have explained less than 6% of genetic risks. Studies suggest that the missing heritability data may be because of joint effects among genes. To identify novel heritability for osteoporosis, we performed a system-level study on bone mineral density (BMD) by weighted gene coexpression network analysis (WGCNA), using the largest GWAS data set for BMD in the field, Genetic Factors for Osteoporosis Consortium (GEFOS-2), and a transcriptomic gene expression data set generated from transiliac bone biopsies in women. A weighted gene coexpression network was generated for 1574 genes with GWAS nominal evidence of association (p ≤ 0.05) based on dissimilarity measurement on the expression data. Twelve distinct gene modules were identified, and four modules showed nominally significant associations with BMD (p ≤ 0.05), but only one module, the yellow module, demonstrated a good correlation between module membership (MM) and gene significance (GS), suggesting that the yellow module serves an important biological role in bone regulation. Interestingly, through characterization of module content and topology, the yellow module was found to be significantly enriched with contractile fiber part (GO:044449), which is widely recognized as having a close relationship between muscle and bone. Furthermore, detailed submodule analyses of important candidate genes (HOMER1, SPTBN1) by all edges within the yellow module implied significant enrichment of functional connections between bone and cytoskeletal protein binding. Our study yielded novel information from system genetics analyses of GWAS data jointly with transcriptomic data. The findings highlighted a module and several genes in the model as playing important roles in the regulation of bone mass in females, which may yield novel insights into the genetic basis of osteoporosis. © 2016 American Society for Bone and Mineral Research.