Eight genes are highly associated with BMD variation in postmenopausal Caucasian women

Obesity and lipid metabolism in the development of osteoporosis (Review)

Osteoporosis is a common bone metabolic disease that causes a heavy social burden and seriously threatens life. Improving osteogenic capacity is necessary to correct bone mass loss in the treatment of osteoporosis. Osteoblasts are derived from the differentiation of bone marrow mesenchymal stem cells, a process that opposes adipogenic differentiation. The peroxisome proliferator‑activated receptor γ and Wnt/β‑catenin signaling pathways mediate the mutual regulation of osteogenesis and adipogenesis. Lipid substances play an important role in the occurrence and development of osteoporosis. The content and proportion of lipids modulate the activity of immunocytes, mainly macrophages, and the secretion of inflammatory factors, such as IL‑1, IL‑6 and TNF‑α. These inflammatory effectors increase the activity and promote the differentiation of osteoclasts, which leads to bone imbalance and stronger bone resorption. Obesity also decreases the activity of antioxidases and leads to oxidative stress, thereby inhibiting osteogenesis. The present review starts by examining the bidirectional differentiation of BM‑MSCs, describes in detail the mechanism by which lipids affect bone metabolism, and discusses the regulatory role of inflammation and oxidative stress in this process. The review concludes that a reasonable adjustment of the content and proportion of lipids, and the alleviation of inflammatory storms and oxidative damage induced by lipid imbalances, will improve bone mass and treat osteoporosis.

Identification of Transcripts with Shared Roles in the Pathogenesis of Postmenopausal Osteoporosis and Cardiovascular Disease

Epidemiological evidence suggests existing comorbidity between postmenopausal osteoporosis (OP) and cardiovascular disease (CVD), but identification of possible shared genes is lacking. The skeletal global transcriptomes were analyzed in trans-iliac bone biopsies (n = 84) from clinically well-characterized postmenopausal women (50 to 86 years) without clinical CVD using microchips and RNA sequencing. One thousand transcripts highly correlated with areal bone mineral density (aBMD) were further analyzed using bioinformatics, and common genes overlapping with CVD and associated biological mechanisms, pathways and functions were identified. Fifty genes (45 mRNAs, 5 miRNAs) were discovered with established roles in oxidative stress, inflammatory response, endothelial function, fibrosis, dyslipidemia and osteoblastogenesis/calcification. These pleiotropic genes with possible CVD comorbidity functions were also present in transcriptomes of microvascular endothelial cells and cardiomyocytes and were differentially expressed between healthy and osteoporotic women with fragility fractures. The results were supported by a genetic pleiotropy-informed conditional False Discovery Rate approach identifying any overlap in single nucleotide polymorphisms (SNPs) within several genes encoding aBMD- and CVD-associated transcripts. The study provides transcriptional and genomic evidence for genes of importance for both BMD regulation and CVD risk in a large collection of postmenopausal bone biopsies. Most of the transcripts identified in the CVD risk categories have no previously recognized roles in OP pathogenesis and provide novel avenues for exploring the mechanistic basis for the biological association between CVD and OP.

Genetic and Gene Expression Resources for Osteoporosis and Bone Biology Research

PURPOSE OF REVIEW

The integration of data from multiple genomic assays from humans and non-human model organisms is an effective approach to identify genes involved in skeletal fragility and fracture risk due to osteoporosis and other conditions. This review summarizes genome-wide genetic variation and gene expression data resources relevant to the discovery of genes contributing to skeletal fragility and fracture risk.

RECENT FINDINGS

Genome-wide association studies (GWAS) of osteoporosis-related traits are summarized, in addition to gene expression in bone tissues in humans and non-human organisms, with a focus on rodent models related to skeletal fragility and fracture risk. Gene discovery approaches using these genomic data resources are described. We also describe the Musculoskeletal Knowledge Portal (MSKKP) that integrates much of the available genomic data relevant to fracture risk. The available genomic resources provide a wealth of knowledge and can be analyzed to identify genes related to fracture risk. Genomic resources that would fill particular scientific gaps are discussed.

TiO2nanotubes promote osteogenic differentiation of human bone marrow stem cells via epigenetic regulation ofRMRP/DLEU2/EZH2 pathway

TiO2nanotubes (TNTs) significantly promote osteogenic differentiation and bone regeneration of cells. Nevertheless, the biological processes by which they promote osteogenesis are currently poorly understood. Long non-coding RNAs (lncRNAs) are essential for controlling osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). Epigenetic chromatin modification is one of the pathways in which lncRNAs regulate osteogenic differentiation. Here, we reported that TNTs could upregulate lncRNARMRP, and inhibition of lncRNARMRPin human BMSCs (hBMSCs) grown on TNTs could decrease runt-related transcription factor 2 (RUNX2), alkaline phosphatase, osteopontin, and osteocalcin (OCN) expression. Furthermore, we discovered that inhibiting lncRNARMRPelevated the expression of lncRNADLEU2, and lncRNADLEU2knockdown promoted osteogenic differentiation in hBMSCs. RNA immunoprecipitation experiments showed that lncRNADLEU2could interact with EZH2 to induce H3K27 methylation in the promoter regions of RUNX2 and OCN, suppressing gene expression epigenetically. According to these results, lncRNARMRPis upregulated by TNTs to promote osteogenic differentiation throughDLEU2/EZH2-mediated epigenetic modifications.

Bone mineral density loci specific to the skull portray potential pleiotropic effects on craniosynostosis

Skull bone mineral density (SK-BMD) provides a suitable trait for the discovery of key genes in bone biology, particularly to intramembranous ossification, not captured at other skeletal sites. We perform a genome-wide association meta-analysis (n ~ 43,800) of SK-BMD, identifying 59 loci, collectively explaining 12.5% of the trait variance. Association signals cluster within gene-sets involved in skeletal development and osteoporosis. Among the four novel loci (ZIC1, PRKAR1A, AZIN1/ATP6V1C1, GLRX3), there are factors implicated in intramembranous ossification and as we show, inherent to craniosynostosis processes. Functional follow-up in zebrafish confirms the importance of ZIC1 on cranial suture patterning. Likewise, we observe abnormal cranial bone initiation that culminates in ectopic sutures and reduced BMD in mosaic atp6v1c1 knockouts. Mosaic prkar1a knockouts present asymmetric bone growth and, conversely, elevated BMD. In light of this evidence linking SK-BMD loci to craniofacial abnormalities, our study provides new insight into the pathophysiology, diagnosis and treatment of skeletal diseases.

Identification and experimental validation of key m6A modification regulators as potential biomarkers of osteoporosis

Osteoporosis (OP) is a severe systemic bone metabolic disease that occurs worldwide. During the coronavirus pandemic, prioritization of urgent services and delay of elective care attenuated routine screening and monitoring of OP patients. There is an urgent need for novel and effective screening diagnostic biomarkers that require minimal technical and time investments. Several studies have indicated that N6-methyladenosine (m6A) regulators play essential roles in metabolic diseases, including OP. The aim of this study was to identify key m6A regulators as biomarkers of OP through gene expression data analysis and experimental verification. GSE56815 dataset was served as the training dataset for 40 women with high bone mineral density (BMD) and 40 women with low BMD. The expression levels of 14 major m6A regulators were analyzed to screen for differentially expressed m6A regulators in the two groups. The impact of m6A modification on bone metabolism microenvironment characteristics was explored, including osteoblast-related and osteoclast-related gene sets. Most m6A regulators and bone metabolism-related gene sets were dysregulated in the low-BMD samples, and their relationship was also tightly linked. In addition, consensus cluster analysis was performed, and two distinct m6A modification patterns were identified in the low-BMD samples. Subsequently, by univariate and multivariate logistic regression analyses, we identified four key m6A regulators, namely, METTL16, CBLL1, FTO, and YTHDF2. We built a diagnostic model based on the four m6A regulators. CBLL1 and YTHDF2 were protective factors, whereas METTL16 and FTO were risk factors, and the ROC curve and test dataset validated that this model had moderate accuracy in distinguishing high- and low-BMD samples. Furthermore, a regulatory network was constructed of the four hub m6A regulators and 26 m6A target bone metabolism-related genes, which enhanced our understanding of the regulatory mechanisms of m6A modification in OP. Finally, the expression of the four key m6A regulators was validated in vivo and in vitro, which is consistent with the bioinformatic analysis results. Our findings identified four key m6A regulators that are essential for bone metabolism and have specific diagnostic value in OP. These modules could be used as biomarkers of OP in the future.

In silico discovery of blood cell macromolecular associations

Background

Physical molecular interactions are the basis of intracellular signalling and gene regulatory networks, and comprehensive, accessible databases are needed for their discovery. Highly correlated transcripts may reflect important functional associations, but identification of such associations from primary data are cumbersome. We have constructed and adapted a user-friendly web application to discover and identify putative macromolecular associations in human peripheral blood based on significant correlations at the transcriptional level.

Methods

The blood transcriptome was characterized by quantification of 17,328 RNA species, including 341 mature microRNAs in 105 clinically well-characterized postmenopausal women. Intercorrelation of detected transcripts signal levels generated a matrix with > 150 million correlations recognizing the human blood RNA interactome. The correlations with calculated adjusted p-values were made easily accessible by a novel web application.

Results

We found that significant transcript correlations within the giant matrix reflect experimentally documented interactions involving select ubiquitous blood relevant transcription factors (CREB1, GATA1, and the glucocorticoid receptor (GR, NR3C1)). Their responsive genes recapitulated up to 91% of these as significant correlations, and were replicated in an independent cohort of 1204 individual blood samples from the Framingham Heart Study. Furthermore, experimentally documented mRNAs/miRNA associations were also reproduced in the matrix, and their predicted functional co-expression described. The blood transcript web application is available at http://app.uio.no/med/klinmed/correlation-browser/blood/index.php and works on all commonly used internet browsers.

Conclusions

Using in silico analyses and a novel web application, we found that correlated blood transcripts across 105 postmenopausal women reflected experimentally proven molecular associations. Furthermore, the associations were reproduced in a much larger and more heterogeneous cohort and should therefore be generally representative. The web application lends itself to be a useful hypothesis generating tool for identification of regulatory mechanisms in complex biological data sets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01077-3.

Isolating mineralized bone and bone marrow mRNA from transiliac bone biopsies stored in a stabilizing solution: A comparative study

The molecular mechanisms underlying metabolic bone diseases, including renal osteodystrophy, are poorly understood. Transcriptomics are increasingly used to characterize biological molecular networks and prove promising in identifying therapeutic targets and biomarkers. A reliable method for obtaining sufficient amounts of high quality RNA from human bone biopsies is a prerequisite for the implementation of molecular diagnostics in clinical research and practice.

The present study aimed to develop a simple and adequate method for isolating bone and bone marrow mRNA from transiliac bone biopsies. Several storage, separation, and extraction procedures were compared. The procedure was optimized in pig samples and subsequently validated in human samples. Appropriate amounts of mineralized bone and bone marrow mRNA of moderate to high quality were obtained from transiliac bone biopsies that were immersed in the stabilizing solution Allprotect Tissue Reagent at room temperature for up to 3 days prior to freezing. After thawing, bone marrow and mineralized bone were separated by a multistep centrifugation procedure and subsequently disrupted and homogenized by a bead crusher. Appropriate separation of mineralized bone and bone marrow was confirmed by discriminatory gene expression profiles.

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Molecular diagnostics increasingly gain interest in clinical practice.

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A bone biopsy immersed in a stabilization reagent yields moderate mRNA quality.

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Use of a stabilization reagent allows for easy separation of bone and bone marrow.

A review of epigenetics and its association with ageing of muscle and bone

Ageing is defined as the 'increasing frailty of an organism with time that reduces the ability of that organism to deal with stress'. It has been suggested that epigenetics may underlie the observation that some individuals appear to age faster than others. Epigenetics is the study of changes which occur in an organism due to changes in expression of the genetic code rather than changes to the genetic code itself; that is, epigenetic mechanisms impact upon the function of DNA without changing the DNA sequence. It is important to recognise that epigenetic changes, in contrast to genetic changes, can vary according to different cell types and therefore can demonstrate significant tissue-specificity. There are different types of epigenetic mechanisms: histone modification, non-coding RNAs and DNA methylation. Epigenetic clocks have been developed using statistical techniques to identify the optimal combination of CpG sites (from methylation arrays) to correlate with chronological age. This review considers how epigenetic factors may affect rates of ageing of muscle and bone and provides an overview of current understanding in this area. We discuss studies using first-generation epigenetic clocks, as well as the second-generation iterations, which appear to show stronger associations with the ageing muscle phenotype. We also review epigenome-wide association studies that have been performed in various tissues examining relationships with osteoporosis and fracture. It is hoped that an understanding of this area will lead to interventions that might prevent or reduce rates of musculoskeletal ageing in later life.

Heavy-load exercise in older adults activates vasculogenesis and has a stronger impact on muscle gene expression than in young adults

BACKGROUND

A striking effect of old age is the involuntary loss of muscle mass and strength leading to sarcopenia and reduced physiological functions. However, effects of heavy-load exercise in older adults on diseases and functions as predicted by changes in muscle gene expression have been inadequately studied.

METHODS

Thigh muscle global transcriptional activity (transcriptome) was analyzed in cohorts of older and younger adults before and after 12-13 weeks heavy-load strength exercise using Affymetrix microarrays. Three age groups, similarly trained, were compared: younger adults (age 24 ± 4 years), older adults of average age 70 years (Oslo cohort) and above 80 years (old BSU cohort). To increase statistical strength, one of the older cohorts was used for validation. Ingenuity Pathway analysis (IPA) was used to identify predicted biological effects of a gene set that changed expression after exercise, and Principal Component Analysis (PCA) was used to visualize differences in muscle gene expressen between cohorts and individual participants as well as overall changes upon exercise.

RESULTS

Younger adults, showed few transcriptome changes, but a marked, significant impact was observed in persons of average age 70 years and even more so in persons above 80 years. The 249 transcripts positively or negatively altered in both cohorts of older adults (q-value < 0.1) were submitted to gene set enrichment analysis using IPA. The transcripts predicted increase in several aspects of "vascularization and muscle contractions", whereas functions associated with negative health effects were reduced, e.g., "Glucose metabolism disorder" and "Disorder of blood pressure". Several genes that changed expression after intervention were confirmed at the genome level by containing single nucleotide variants associated with handgrip strength and muscle expression levels, e.g., CYP4B1 (p = 9.2E-20), NOTCH4 (p = 9.7E-8), and FZD4 (p = 5.3E-7). PCA of the 249 genes indicated a differential pattern of muscle gene expression in young and elderly. However, after exercise the expression patterns in both young and old BSU cohorts were changed in the same direction for the vast majority of participants.

CONCLUSIONS

The positive impact of heavy-load strength training on the transcriptome increased markedly with age. The identified molecular changes translate to improved vascularization and muscular strength, suggesting highly beneficial health effects for older adults.

Identification of Known and Novel Long Noncoding RNAs Potentially Responsible for the Effects of Bone Mineral Density (BMD) Genomewide Association Study (GWAS) Loci

Osteoporosis, characterized by low bone mineral density (BMD), is the most common complex disease affecting bone and constitutes a major societal health problem. Genome-wide association studies (GWASs) have identified over 1100 associations influencing BMD. It has been shown that perturbations to long noncoding RNAs (lncRNAs) influence BMD and the activities of bone cells; however, the extent to which lncRNAs are involved in the genetic regulation of BMD is unknown. Here, we combined the analysis of allelic imbalance (AI) in human acetabular bone fragments with a transcriptome-wide association study (TWAS) and expression quantitative trait loci (eQTL) colocalization analysis using data from the Genotype-Tissue Expression (GTEx) project to identify lncRNAs potentially responsible for GWAS associations. We identified 27 lncRNAs in bone that are located in proximity to a BMD GWAS association and harbor single-nucleotide polymorphisms (SNPs) demonstrating AI. Using GTEx data we identified an additional 31 lncRNAs whose expression was associated (false discovery rate [FDR] correction < 0.05) with BMD through TWAS and had a colocalizing eQTL (regional colocalization probability [RCP] > 0.1). The 58 lncRNAs are located in 43 BMD associations. To further support a causal role for the identified lncRNAs, we show that 23 of the 58 lncRNAs are differentially expressed as a function of osteoblast differentiation. Our approach identifies lncRNAs that are potentially responsible for BMD GWAS associations and suggest that lncRNAs play a role in the genetics of osteoporosis. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Mechanical-Stress-Related Epigenetic Regulation of ZIC1 Transcription Factor in the Etiology of Postmenopausal Osteoporosis

Mechanical loading exerts a profound influence on bone density and architecture, but the exact mechanism is unknown. Our study shows that expression of the neurological transcriptional factor zinc finger of the cerebellum 1 (ZIC1) is markedly increased in trabecular bone biopsies in the lumbar spine compared with the iliac crest, skeletal sites of high and low mechanical stress, respectively. Human trabecular bone transcriptome analyses revealed a strong association between ZIC1 mRNA levels and gene transcripts characteristically associated with osteoblasts, osteocytes and osteoclasts. This supposition is supported by higher ZIC1 expression in iliac bone biopsies from postmenopausal women with osteoporosis compared with age-matched control subjects, as well as strongly significant inverse correlation between ZIC1 mRNA levels and BMI-adjusted bone mineral density (BMD) (Z-score). ZIC1 promoter methylation was decreased in mechanically loaded vertebral bone compared to unloaded normal iliac bone, and its mRNA levels correlated inversely with ZIC1 promoter methylation, thus linking mechanical stress to epigenetic control of gene expression. The findings were corroborated in cultures of rat osteoblast progenitors and osteoblast-like cells. This study demonstrates for the first time how skeletal epigenetic changes that are affected by mechanical forces give rise to marked alteration in bone cell transcriptional activity and translate to human bone pathophysiology.

Epigenetic regulation of bone mass

Osteoporosis, characterised by low bone mass, poor bone structure, and an increased risk of fracture, is a major public health problem. There is increasing evidence that the influence of the environment on gene expression, through epigenetic processes, contributes to variation in BMD and fracture risk across the lifecourse. Such epigenetic processes include DNA methylation, histone and chromatin modifications and non-coding RNAs. Examples of associations with phenotype include DNA methylation in utero linked to maternal vitamin D status, and to methylation of target genes such as OPG and RANKL being associated with osteoporosis in later life. Epigenome-wide association studies and multi-omics technologies have further revealed susceptibility loci, and histone acetyltransferases, deacetylases and methylases are being considered as therapeutic targets. This review encompasses recent advances in our understanding of epigenetic mechanisms in the regulation of bone mass and osteoporosis development, and outlines possible diagnostic and prognostic biomarker applications.

Screening methods for linear errors-in-variables models in high dimensions

Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a small number of subjects. One common approach to analyzing such high-dimensional data is to use linear errors-in-variables models; however, current methods for fitting such models are computationally expensive. In this paper, we present two efficient screening procedures, namely corrected penalized marginal screening and corrected sure independence screening, to reduce the number of variables for final model building. Both screening procedures are based on fitting corrected marginal regression models relating the outcome to each contaminated covariate separately, which can be computed efficiently even with a large number of features. Under mild conditions, we show that these procedures achieve screening consistency and reduce the number of features substantially, even when the number of covariates grows exponentially with sample size. Additionally, if the true covariates are weakly correlated, we show that corrected penalized marginal screening can achieve full variable selection consistency. Through a simulation study and an analysis of gene expression data for bone mineral density of Norwegian women, we demonstrate that the two new screening procedures make estimation of linear errors-in-variables models computationally scalable in high dimensional settings, and improve finite sample estimation and selection performance compared with estimators that do not employ a screening stage. This article is protected by copyright. All rights reserved.

Changes in Serum Dickkopf-1, RANK Ligand, Osteoprotegerin, and Bone Mineral Density after Allogeneic Hematopoietic Stem Cell Transplantation Treatment

BACKGROUND

Dickkopf-1 (DKK1) regulates bone formation by inhibiting canonical Wnt/β-catenin pathway signaling, and indirectly enhances osteoclastic activity by altering the expression ratio of receptor activator of nuclear factor-κB ligand (RANKL) relative to osteoprotegerin (OPG). However, it is difficult to explain continued bone loss after allogeneic stem cell transplantation (allo-SCT) in terms of changes in only RANKL and OPG. Few studies have evaluated changes in DKK1 after allo-SCT.

METHODS

We prospectively enrolled 36 patients with hematologic malignancies who were scheduled for allo-SCT treatment. Serum DKK1, OPG, and RANKL levels were measured before (baseline), and at 1, 4, 12, 24, and 48 weeks after allo-SCT treatment. Bone mineral density (BMD) was assessed using dual-energy X-ray absorptiometry before (baseline) and 24 and 48 weeks after allo-SCT treatment.

RESULTS

After allo-SCT treatment, the DKK1 level decreased rapidly, returned to baseline during the first 4 weeks, and remained elevated for 48 weeks (P<0.0001 for changes observed over time). The serum RANKL/OPG ratio peaked at 4 weeks and then declined (P<0.001 for changes observed over time). BMD decreased relative to the baseline at all timepoints during the study period, and the lumbar spine in female patients had the largest decline (-11.3%±1.6% relative to the baseline at 48 weeks, P<0.05).

CONCLUSION

Serum DKK1 levels rapidly decreased at 1 week and then continued to increase for 48 weeks; bone mass decreased for 48 weeks following engraftment in patients treated with allo-SCT, suggesting that DKK1-mediated inhibition of osteoblast differentiation plays a role in bone loss in patients undergoing allo-SCT.

Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques

The availability of large human datasets for genome-wide association studies (GWAS) and the advancement of sequencing technologies have boosted the identification of genetic variants in complex and rare diseases in the skeletal field. Yet, interpreting results from human association studies remains a challenge. To bridge the gap between genetic association and causality, a systematic functional investigation is necessary. Multiple unknowns exist for putative causal genes, including cellular localization of the molecular function. Intermediate traits ("endophenotypes"), e.g. molecular quantitative trait loci (molQTLs), are needed to identify mechanisms of underlying associations. Furthermore, index variants often reside in non-coding regions of the genome, therefore challenging for interpretation. Knowledge of non-coding variance (e.g. ncRNAs), repetitive sequences, and regulatory interactions between enhancers and their target genes is central for understanding causal genes in skeletal conditions. Animal models with deep skeletal phenotyping and cell culture models have already facilitated fine mapping of some association signals, elucidated gene mechanisms, and revealed disease-relevant biology. However, to accelerate research towards bridging the current gap between association and causality in skeletal diseases, alternative in vivo platforms need to be used and developed in parallel with the current -omics and traditional in vivo resources. Therefore, we argue that as a field we need to establish resource-sharing standards to collectively address complex research questions. These standards will promote data integration from various -omics technologies and functional dissection of human complex traits. In this mission statement, we review the current available resources and as a group propose a consensus to facilitate resource sharing using existing and future resources. Such coordination efforts will maximize the acquisition of knowledge from different approaches and thus reduce redundancy and duplication of resources. These measures will help to understand the pathogenesis of osteoporosis and other skeletal diseases towards defining new and more efficient therapeutic targets.

Assessment of Wnt pathway selected gene expression levels in peripheral blood mononuclear cells (PBMCs) of postmenopausal patients with low bone mass

The purpose of the study was to assess the expression of selected genes of the Wnt pathway: APC, AXIN1, CTNNB1, DKK1, GSK3B, KREMEN1, SFRP1, and WNT1 in peripheral blood mononuclear cells (PBMC) of patients, selected in consideration of their bone mineral density (BMD), and the occurrence of low-energy fractures. The study involved 45 postmenopausal women, divided into four groups, according to BMD and fracture history. Measurements of laboratory parameters and RNA expression in PBMC cells were carried out in material, collected once at the inclusion visit. The densitometric examination was performed on all participants. In the analysis of the relative expression levels (RELs) of the studied genes in the entire population, we observed an overexpression for SFRP1 in 100% of samples and WNT1. In addition, the REL of DKK1, APC, and GSK3B genes were slightly elevated versus the calibrator. In contrast, CTNNB1 and AXIN1 presented with a slightly decreased RELs. Analysis did not show any significant differences among the groups in the relative gene expression levels (p < 0.05) of particular genes. However, we have observed quite numerous interesting correlations between the expression of the studied genes and BMD, the presence of fractures, and laboratory parameters, both in the whole studied population as well as in selected groups. In conclusion, the high level of CTNNB1 expression maintains normal BMD and/or protects against fractures. It also appears that the changes in expression levels of the Wnt pathway genes in PBMCs reflect the expected changes in bone tissue.

Using "-omics" Data to Inform Genome-wide Association Studies (GWASs) in the Osteoporosis Field

PURPOSE OF REVIEW

Osteoporosis constitutes a major societal health problem. Genome-wide association studies (GWASs) have identified over 1100 loci influencing bone mineral density (BMD); however, few of the causal genes have been identified. Here, we review approaches that use "-omics" data and genetic- and systems genetics-based analytical strategies to facilitate causal gene discovery.

RECENT FINDINGS

The bone field is beginning to adopt approaches that are commonplace in other disease disciplines. The slower progress has been due in part to the lack of large-scale "omics" data on bone and bone cells. This is however changing, and approaches such as eQTL colocalization, transcriptome-wide association studies (TWASs), network, and integrative approaches are beginning to provide significant insight into the genes responsible for BMD GWAS associations. The use of "-omics" data to inform BMD GWASs has increased in recent years, leading to the identification of novel regulators of BMD in humans. The ultimate goal will be to use this information to develop more effective therapies to treat and ultimately prevent osteoporosis.

Incorporating prior knowledge into regularized regression

MOTIVATION

Associated with genomic features like gene expression, methylation and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information and knowledge from previous studies, that can be used post hoc to facilitate the interpretation of a model. However, using this meta-feature information a priori rather than post hoc can yield improved prediction performance as well as enhanced model interpretation.

RESULTS

We propose a new penalized regression approach that allows a priori integration of external meta-features. The method extends LASSO regression by incorporating individualized penalty parameters for each regression coefficient. The penalty parameters are, in turn, modeled as a log-linear function of the meta-features and are estimated from the data using an approximate empirical Bayes approach. Optimization of the marginal likelihood on which the empirical Bayes estimation is performed using a fast and stable majorization-minimization procedure. Through simulations, we show that the proposed regression with individualized penalties can outperform the standard LASSO in terms of both parameters estimation and prediction performance when the external data is informative. We further demonstrate our approach with applications to gene expression studies of bone density and breast cancer.

AVAILABILITY AND IMPLEMENTATION

The methods have been implemented in the R package xtune freely available for download from https://cran.r-project.org/web/packages/xtune/index.html.

Omics Profiling of S2P Mutant Fibroblasts as a Mean to Unravel the Pathomechanism and Molecular Signatures of X-Linked MBTPS2 Osteogenesis Imperfecta

Osteogenesis imperfecta (OI) is an inherited skeletal dysplasia characterized by low bone density, bone fragility and recurrent fractures. The characterization of its heterogeneous genetic basis has allowed the identification of novel players in bone development. In 2016, we described the first X-linked recessive form of OI caused by hemizygous MBTPS2 missense variants resulting in moderate to severe phenotypes. MBTPS2 encodes site-2 protease (S2P), which activates transcription factors involved in bone (OASIS) and cartilage development (BBF2H7), ER stress response (ATF6) and lipid metabolism (SREBP) via regulated intramembrane proteolysis. In times of ER stress or sterol deficiency, the aforementioned transcription factors are sequentially cleaved by site-1 protease (S1P) and S2P. Their N-terminal fragments shuttle to the nucleus to activate gene transcription. Intriguingly, missense mutations at other positions of MBTPS2 cause the dermatological spectrum condition Ichthyosis Follicularis, Atrichia and Photophobia (IFAP) and Keratosis Follicularis Spinulosa Decalvans (KFSD) without clinical overlap with OI despite the proximity of some of the pathogenic variants. To understand how single amino acid substitutions in S2P can lead to non-overlapping phenotypes, we aimed to compare the molecular features of MBTPS2-OI and MBTPS2-IFAP/KFSD, with the ultimate goal to unravel the pathomechanisms underlying MBTPS2-OI. RNA-sequencing-based transcriptome profiling of primary skin fibroblasts from healthy controls (n = 4), MBTPS2-OI (n = 3), and MBTPS2-IFAP/KFSD (n = 2) patients was performed to identify genes that are differentially expressed in MBTPS2-OI and MBTPS2-IFAP/KFSD individuals compared to controls. We observed that SREBP-dependent genes are more downregulated in OI than in IFAP/KFSD. This is coupled to alterations in the relative abundance of fatty acids in MBTPS2-OI fibroblasts in vitro, while no consistent alterations in the sterol profile were observed. Few OASIS-dependent genes are suppressed in MBTPS2-OI, while BBF2H7- and ATF6-dependent genes are comparable between OI and IFAP/KFSD patients and control fibroblasts. Importantly, we identified genes involved in cartilage physiology that are differentially expressed in MBTPS2-OI but not in MBTPS2-IFAP/KFSD fibroblasts. In conclusion, our data provide clues to how pathogenic MBTPS2 mutations cause skeletal deformities via altered fatty acid metabolism or cartilage development that may affect bone development, mineralization and endochondral ossification.

Enhanced Identification of Novel Potential Variants for Appendicular Lean Mass by Leveraging Pleiotropy With Bone Mineral Density

Strong relationships have been found between appendicular lean mass (ALM) and bone mineral density (BMD). It may be due to a shared genetic basis, termed pleiotropy. By leveraging the pleiotropy with BMD, the aim of this study was to detect more potential genetic variants for ALM. Using the conditional false discovery rate (cFDR) methodology, a combined analysis of the summary statistics of two large independent genome wide association studies (GWAS) of ALM (n = 73,420) and BMD (n = 10,414) was conducted. Strong pleiotropic enrichment and 26 novel potential pleiotropic SNPs were found for ALM and BMD. We identified 156 SNPs for ALM (cFDR <0.05), of which 74 were replicates of previous GWASs and 82 were novel SNPs potentially-associated with ALM. Eleven genes annotated by 31 novel SNPs (13 pleiotropic and 18 ALM specific) were partially validated in a gene expression assay. Functional enrichment analysis indicated that genes corresponding to the novel potential SNPs were enriched in GO terms and/or KEGG pathways that played important roles in muscle development and/or BMD metabolism (adjP <0.05). In protein–protein interaction analysis, rich interactions were demonstrated among the proteins produced by the corresponding genes. In conclusion, the present study, as in other recent studies we have conducted, demonstrated superior efficiency and reliability of the cFDR methodology for enhanced detection of trait-associated genetic variants. Our findings shed novel insight into the genetic variability of ALM in addition to the shared genetic basis underlying ALM and BMD.

Lipocalin 2 serum levels correlate with age and bone turnover biomarkers in healthy subjects but not in postmenopausal osteoporotic women

Purpose

Lipocalin 2 (LCN2) is an adipokine involved in many physiological functions, including bone metabolism. We previously demonstrated its implication in mouse models of mechanical unloading-induced osteoporosis and in a cohort of bed rest volunteers. We therefore aimed at studying its involvement in postmenopausal osteoporosis.

Methods

We measured serum LCN2 and correlated its levels to Dickkopf WNT Signaling Pathway Inhibitor 1 (DKK1), Tartrate Resistant Acid Phosphatase 5B (TRAcP5B), sclerostin, urinary N-terminal telopeptide of type I collagen (NTX), serum C-terminal telopeptide of type I collagen (CTX), parathyroid hormone and vitamin K by ELISA performed in a cohort of younger (50–65 years) and older (66–90 years) osteoporotic women in comparison to healthy subjects. A cohort of male healthy and osteoarthritic patients was also included. Sobel mediation analysis was used to test indirect associations among age, LCN2 and DKK1 or NTX.

Results

LCN2 levels were unchanged in osteoporotic and in osteoarthritis patients when compared to healthy subjects and did not correlate with BMD. However, serum LCN2 correlated with age in healthy women (R = 0.44; P = 0.003) and men (R = 0.5; P = 0.001) and serum concentrations of DKK1 (R = 0.47; P = 0.003) and urinary NTX (R = 0.34; P = 0.04). Sobel mediation analysis showed that LCN2 mediates an indirect relationship between age and DKK1 (P = 0.02), but not with NTX, in healthy subjects.

Conclusions

Taken together, the results suggest a hitherto unknown association between LCN2, DKK1 and age in healthy individuals, but not in postmenopausal osteoporotic women.

lncRNA DLEU2 acts as a miR-181a sponge to regulate SEPP1 and inhibit skeletal muscle differentiation and regeneration

Sarcopenia is a serious public health problem associated with the loss of muscle mass and function. The purpose of this study was to identify molecular markers and construct a ceRNA pathway as a significant predictor of sarcopenia. We designed a prediction model to select important differentially expressed mRNAs (DEMs), and constructed a sarcopenia associated ceRNA network. After correlation analysis of each element in the ceRNA network based on clinical samples and GTEX database, C2C12 mouse myoblasts were used as a model to verify the identified ceRNA pathways. A new model for predicting sarcopenia based on four molecular markers SEPP1, SV2A, GOT1, and GFOD1 was developed. The model was used to construct a ceRNA network and showed high accuracy. Correlation analysis showed that the expression levels of lncDLEU2, SEPP1, and miR-181a were closely associated with a high risk of sarcopenia. lncDLEU2 inhibits muscle differentiation and regeneration by acting as a miR-181a sponge regulating SEPP1 expression. In this study, a highly accurate prediction tool was developed to improve the prediction outcomes of sarcopenia. These findings suggest that the lncDLEU2-miR-181a-SEPP1 pathway inhibits muscle differentiation and regeneration. This pathway may be a new therapeutic target for the treatment of sarcopenia.

Partially linear monotone methods with automatic variable selection and monotonicity direction discovery

In many statistical regression and prediction problems, it is reasonable to assume monotone relationships between certain predictor variables and the outcome. Genomic effects on phenotypes are, for instance, often assumed to be monotone. However, in some settings, it may be reasonable to assume a partially linear model, where some of the covariates can be assumed to have a linear effect. One example is a prediction model using both high-dimensional gene expression data, and low-dimensional clinical data, or when combining continuous and categorical covariates. We study methods for fitting the partially linear monotone model, where some covariates are assumed to have a linear effect on the response, and some are assumed to have a monotone (potentially nonlinear) effect. Most existing methods in the literature for fitting such models are subject to the limitation that they have to be provided the monotonicity directions a priori for the different monotone effects. We here present methods for fitting partially linear monotone models which perform both automatic variable selection, and monotonicity direction discovery. The proposed methods perform comparably to, or better than, existing methods, in terms of estimation, prediction, and variable selection performance, in simulation experiments in both classical and high-dimensional data settings.

Distinct Subsets of Noncoding RNAs Are Strongly Associated With BMD and Fracture, Studied in Weight-Bearing and Non-Weight-Bearing Human Bone

We investigated mechanisms resulting in low bone mineral density (BMD) and susceptibility to fracture by comparing noncoding RNAs (ncRNAs) in biopsies of non-weight-bearing (NWB) iliac (n = 84) and weight bearing (WB) femoral (n = 18) postmenopausal bone across BMDs varying from normal (T-score > -1.0) to osteoporotic (T-score ≤ -2.5). Global bone ncRNA concentrations were determined by PCR and microchip analyses. Association with BMD or fracture, adjusted by age and body mass index, were calculated using linear and logistic regression and least absolute shrinkage and selection operator (Lasso) analysis. At 10% false discovery rate (FDR), 75 iliac bone ncRNAs and 94 femoral bone ncRNAs were associated with total hip BMD. Eight of the ncRNAs were common for the two sites, but five of them (miR-484, miR-328-3p, miR-27a-5p, miR-28-3p, and miR-409-3p) correlated positively to BMD in femoral bone, but negatively in iliac bone. Of predicted pathways recognized in bone metabolism, ECM-receptor interaction and proteoglycans in cancer emerged at both sites, whereas fatty acid metabolism and focal adhesion were only identified in iliac bone. Lasso analysis and cross-validations identified sets of nine bone ncRNAs correlating strongly with adjusted total hip BMD in both femoral and iliac bone. Twenty-eight iliac ncRNAs were associated with risk of fracture (FDR < 0.1). The small nucleolar RNAs, RNU44 and RNU48, have a function in stabilization of ribosomal RNAs (rRNAs), and their association with fracture and BMD suggest that aberrant processing of rRNAs may be involved in development of osteoporosis. Cis-eQTL (expressed quantitative trait loci) analysis of the iliac bone biopsies identified two loci associated with microRNAs (miRNAs), one previously identified in a heel-BMD genomewide association study (GWAS). In this comprehensive investigation of the skeletal genetic background in postmenopausal women, we identified functional bone ncRNAs associated to fracture and BMD, representing distinct subsets in WB and NWB skeletal sites. © 2020 The Authors. Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research.

Postmenopausal osteoporosis is a musculoskeletal disease with a common genetic trait which responds to strength training: a translational intervention study

Background:

Clinical evidence suggests that body muscle mass is positively associated with bone mass, of significance for the elderly population at risk of osteoporosis (OP). Furthermore, muscle and bone interact mechanically and functionally, via local interactions as well as remotely via secreted components. Thus, it was of interest to compare muscle transcriptomes in postmenopausal OP and healthy women, and study effects of strength training on the muscle transcriptome, muscle stress proteins and bone mineral density (BMD).

Methods:

Skeletal muscle histological and genetic properties were compared in postmenopausal healthy (n = 18) and OP (n = 17) women before and after heavy-load strength training for 13–15 weeks. The cohorts were of similar age and body mass index without interfering diseases, medication or difference in lifestyle factors. Muscle biopsies obtained before and after intervention were studied histologically, and stress proteins and transcriptomes analyzed.

Results:

The OP women showed distinct muscle transcription profiles when compared with healthy women and had higher levels of the stress proteins HSP70 and α-β-crystalline. A set of 12 muscle transcripts, including ACSS3, FZD4, GNAI1 and IGF1, were differentially expressed before and after intervention (false discovery rate ⩽0.10, p ⩽0.001), and their corresponding bone transcripts were associated with BMD. Experimental data underline and describe the functionality of these genes in bone biology. OP women had 8% (p <0.01) higher proportion of type I fibres, but muscle fibre cross-sectional area did not differ. Muscle strength increased in both groups (p <0.01).

Conclusions:

Postmenopausal healthy and OP women have distinct muscle transcriptomes [messenger ribonucleic acids (mRNAs) and microRNAs] that are modulated by strength training, translating into key protein alterations and muscle fibre changes. The function of common skeletal muscle and bone genes in postmenopausal OP is suggestive of a shared disease trait.

Proteomic and Transcriptomic Approaches for Studying Bone Regeneration in Health and Systemically Compromised Conditions

Bone regeneration is a complex biological process, where the molecular mechanisms are only partially understood. In an ageing population, where the prevalence of chronic diseases with an impact on bone metabolism is increasing, it becomes crucial to identify new strategies that would improve regenerative outcomes also in medically compromised patients. In this context, omics are demonstrating a great potential, as they offer new insights on the molecular mechanisms regulating physiologic/pathologic bone healing and, at the same time, allow the identification of new diagnostic and therapeutic targets. This review provides an overview on the current evidence on the use of transcriptomic and proteomic approaches in bone regeneration research, particularly in relation to type 1 diabetes and osteoporosis, and discusses future scenarios and potential benefits and limitations on the integration of multi-omics. It is suggested that future research will leverage the synergy of omics with statistical modeling and bioinformatics to prompt the understanding of the biology underpinning bone formation in health and medically compromised conditions. With an eye toward personalized medicine, new strategies combining the mining of large datasets and bioinformatic data with a detailed characterization of relevant phenotypes will need to be pursued to further the understanding of disease mechanisms.

Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry

Hip geometry is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: (i) femoral neck length; (ii) neck-shaft angle; (iii) femoral neck width, and (iv) femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16 to 93 years. Association analyses were performed with ∼2.5 million polymorphisms under an additive model adjusted for age, body mass index, and height. Replication analyses of meta-GWAS significant loci (at adjusted genomewide significance [GWS], threshold p ≤ 2.6 × 10-8 ) were performed in seven additional cohorts in silico. We looked up SNPs associated in our analysis, for association with height, bone mineral density (BMD), and fracture. In meta-analysis (combined Discovery and Replication stages), GWS associations were found at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several hip geometry signals overlapped with BMD, including LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 × 10-5 ). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p = 0.0007), and rs6556301 (intergenic, chr.5 near FGFR4) and PDLIM7 expression (p = 0.005). In conclusion, we found associations between several genes and hip geometry measures that explained 12% to 22% of heritability at different sites. The results provide a defined set of genes related to biological pathways relevant to BMD and etiology of bone fragility. © 2019 American Society for Bone and Mineral Research.

Bone Matrix Levels of Dickkopf and Sclerostin are Positively Correlated with Bone Mass and Strength in Postmenopausal Osteoporosis

Wnt signaling plays a pivotal role in maintaining bone mass. Secreted pathway modulators such as sclerostin (SOST) and Dickkopfs (DKKs) may influence bone mass inhibiting the canonical Wnt pathway. We evaluated whether bone protein content of secreted Wnt antagonists is related to age, bone mass, and strength in postmenopausal osteoporosis. We measured cortical and trabecular bone contents of SOST and Dickkopf-1 (DKK1) in combined extracts obtained after ethylenediaminetetraacetic acid and guanidine hydrochloride extraction in 56 postmenopausal women aged 47–74 (mean, 63) yr with a previous distal forearm fracture and a hip or spine Z-score less than 0. Our findings were (i) SOST and DKK1 protein levels were higher in trabecular bone, (ii) cortical and trabecular DKK1 and trabecular SOST correlated positively with bone matrix levels of osteocalcin (r between 0.28 and 0.45, p < 0.05), (iii) cortical DKK1 correlated with lumbar spine bone mineral density (BMD) (r = 0.32, p < 0.05) and femoral neck BMD (r = 0.41, p < 0.01), and (iv) cortical DKK1 and SOST correlated with apparent bone volumetric density and compressive strength (r between 0.34 and 0.51, p < 0.01). In conclusion, cortical bone matrix levels of DKK1 and SOST were positively correlated with bone mass and bone strength in postmenopausal osteoporotic women.

Identification of hub genes associated with postmenopausal osteoporosis by Gibbs sampling method

Underlying pivotal pathways were identified to reveal potential key genes correlated with postmenopausal osteoporosis (PMOP). The pathways were enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) with genes intersection greater than 5 based on gene expression profile data, and the acquired pathways were then transformed to Markov chain (MC). Gibbs sampling was conducted to obtain a new MC. Moreover, the average probabilities of each pathway in normal and PMOP were computed via an MC Monte Carlo (MCMC) algorithm, and differential pathways were identified based on probabilities more than 0.7. In addition, frequencies of appearance of pathway genes were counted via MCMC and the hub genes were achieved with the probabilities of gene expression efficiencies in two states. Judging by the gene intersection more than 5, overall 280 pathways were determined. After Gibbs sampling, 2 differential pathways were obtained on the basis of probabilities more than 0.7. Moreover, the hub genes comprising TNNC1, MYL2, and TTN were achieved according to probabilities more than 0.7. The identified pathways and the three hub genes probably are useful for developing approaches for the diagnosis and treatment of PMOP in future preclinical and clinical applications.

Hub genes and gene functions associated with postmenopausal osteoporosis predicted by an integrated method

Postmenopausal osteoporosis (PO) imposes great burden on individuals and society. This study predicted hub genes and gene functions for PO by an integration of the convergent evidence (CE) method, rank product (RP) algorithm and the combing of P-values. Using the gene expression data, genes were ranked by the CE method, RP algorithm and combing P-values, respectively. Subsequently, the top 100 genes were selected from each of the three gene lists, and then the common genes for two or three methods were denoted as informative genes of PO. A mutual information network (MIN) was constructed for the informative genes utilizing the context likelihood of relatedness algorithm. Topological centrality (degree) analysis was conducted on the MIN to investigate hub genes. Then we performed Gene Ontology (GO) enrichment analysis dependent upon the Biological Networks Gene Ontology tool (BiNGO) plugin of Cytoscape to investigate hub gene functions for PO patients. Consequently, a total of 82 informative genes were obtained by integrating the results of the three methods. There were 82 nodes and 1,741 edges in the MIN, of which 8 hub genes were identified, such as PFN1, EEF2 and S100A9. The result of GO enrichment analysis showed that 49 GO terms with P<0.001 were detected, especially the top 5 gene sets were defined as hub gene functions of PO, for instance, translational elongation, translation and cellular macromolecule biosynthetic process. In conclusion, we have predicted 8 hub genes and 5 hub gene functions associated with PO patients. The findings might help understand the molecular mechanism underlying PO.

Mouse Models and Online Resources for Functional Analysis of Osteoporosis Genome-Wide Association Studies

Osteoporosis is a complex genetic disease in which the number of loci associated with the bone mineral density, a clinical risk factor for fracture, has increased at an exponential rate in the last decade. The identification of the causative variants and candidate genes underlying these loci has not been able to keep pace with the rate of locus discovery. A large number of tools and data resources have been built around the use of the mouse as model of human genetic disease. Herein, we describe resources available for functional validation of human Genome Wide Association Study (GWAS) loci using mouse models. We specifically focus on large-scale phenotyping efforts focused on bone relevant phenotypes and repositories of genotype-phenotype data that exist for transgenic and mutant mice, which can be readily mined as a first step toward more targeted efforts designed to deeply characterize the role of a gene in bone biology.

Gene-based GWAS analysis for consecutive studies of GEFOS

By integrating the multilevel biological evidence and bioinformatics analyses, the present study represents a systemic endeavor to identify BMD-associated genes and their roles in skeletal metabolism.

INTRODUCTION

Single-nucleotide polymorphism (SNP)-based genome-wide association studies (GWASs) have already identified about 100 loci associated with bone mineral density (BMD), but these loci only explain a small proportion of heritability to osteoporosis risk. In the present study, we performed a gene-based analysis of the largest GWASs in the bone field to identify additional BMD-associated genes.

METHODS

BMD-associated genes were identified by combining the summary statistic P values of SNPs across individual genes in the two consecutive meta-analyses of GWASs from the Genetic Factors for Osteoporosis (GEFOS) studies. The potential functionality of these genes to bone was partially assessed by differential gene expression analysis. Additionally, the consistency of the identification of potential bone mineral density (BMD)-associated variants were evaluated by estimating the correlation of the P values of the same single-nucleotide polymorphisms (SNPs)/genes between the two consecutive Genetic Factors for Osteoporosis Studies (GEFOS) with largely overlapping samples.

RESULTS

Compared to the SNP-based analysis, the gene-based strategy identified additional BMD-associated genes with genome-wide significance and increased their mutual replication between the two GEFOS datasets. Among these BMD-associated genes, three novel genes (UBTF, AAAS, and C11orf58) were partially validated at the gene expression level. The correlation analysis presented a moderately high between-study consistency of potential BMD-associated variants.

CONCLUSIONS

Gene-based analysis as a supplementary strategy to SNP-based genome-wide association studies, when applied here, is shown that it helped identify some novel BMD-associated genes. In addition to its empirically increased statistical power, gene-based analysis also provides a higher testing stability for identification of BMD genes.

Regulation of osteogenesis by long noncoding RNAs: An epigenetic mechanism contributing to bone formation

Long noncoding RNAs (lncRNAs) have recently emerged as novel regulators of lineage commitment, differentiation, development, viability, and disease progression. Few studies have examined their role in osteogenesis; however, given their critical and wide-ranging roles in other tissues, lncRNAs are most likely vital regulators of osteogenesis. In this study, we extensively characterized lncRNA expression in mesenchymal cells during commitment and differentiation to the osteoblast lineage using a whole transcriptome sequencing approach (RNA-Seq). Using mouse primary mesenchymal stromal cells (mMSC), we identified 1438 annotated lncRNAs expressed during MSC differentiation, 462 of which are differentially expressed. We performed guilt-by-association analysis using lncRNA and mRNA expression profiles to identify lncRNAs influencing MSC commitment and differentiation. These findings open novel dimensions for exploring lncRNAs in regulating normal bone formation and in skeletal disorders.

Identification of novel variants associated with osteoporosis, type 2 diabetes and potentially pleiotropic loci using pleiotropic cFDR method

AIMS

Clinical and epidemiological findings point to an association between type 2 diabetes (T2D) and osteoporosis. Genome-wide association studies (GWASs) have been fruitful in identifying some loci potentially associated with osteoporosis and T2D respectively. However, the total genetic variance for each of these two diseases and the shared genetic determination between them are largely unknown. The aim of this study was to identify novel genetic variants for osteoporosis and/or T2D.

METHODS

First, using a pleiotropic conditional false discovery rate (cFDR) method, we analyzed two GWAS summary data of femoral neck bone mineral density (FN_BMD, n = 53,236) and T2D (n = 159,208) to identify novel shared genetic loci. FN_BMD is an important risk factor for osteoporosis. Next, to explore the potential functions of the identified potential pleiotropic genes, differential expression analysis was performed for them in monocytes and peripheral blood mononuclear cells (PBMCs) as these cells are relevant to the etiology of osteoporosis and/or T2D. Further, weighted gene co-expression analysis (WGCNA) was conducted to identify functional connections between novel pleiotropic genes and known osteoporosis/T2D susceptibility genes by using transcriptomic expression datasets in bone biopsies (E-MEXP-1618) and pancreatic islets (GSE50397). Finally, multi-trait fine mapping for the detected pleiotropic risk loci were conducted to identify the SNPs that have the highest probability of being causal for both FN_BMD and T2D.

RESULTS

We identified 27 significant SNPs with cFDR<0.05 for FN_BMD and 61 SNPs for T2D respectively. Four loci, rs7068487 (PLEKHA1), rs10885421 (TCF7L2), rs944082 (GNG12-AS1 (WLS)) and rs2065929 (PIFO||PGCP1), were found to be potentially pleiotropic and shared between FN_BMD and T2D (ccFDR<0.05). PLEKHA1 was found differentially expressed in circulating monocytes between high and low BMD subjects, and PBMCs between diabetic and non-diabetic conditions. WGCNA showed that PLEKHA1 and TCF7L2 were interconnected with multiple osteoporosis and T2D associated genes in bone biopsy and pancreatic islets, such as JAG, EN1 and CPE. Fine mapping showed that rs11200594 was a potentially causal variant in the locus of PLEKHA1. rs11200594 is also an eQTL of PLEKHA1 in multiple tissue (e.g. peripheral blood cells, adipose and ovary) and is in strong LD with a number of functional variants.

CONCLUSIONS

Four potential pleiotropic loci were identified for shared genetic determination of osteoporosis and T2D. Our study highlights PLEKHA1 as an important potentially pleiotropic gene. The findings may help us gain a better understanding of the shared genetic determination between these two important disorders.

Dissecting the Genetics of Osteoporosis using Systems Approaches

Osteoporosis is a condition characterized by low bone mineral density (BMD) and an increased risk of fracture. Traits contributing to osteoporotic fracture are highly heritable, indicating that a comprehensive understanding of bone requires a thorough understanding of the genetic basis of bone traits. Towards this goal, genome-wide association studies (GWASs) have identified over 500 loci associated with bone traits. However, few of the responsible genes have been identified, and little is known of how these genes work together to influence systems-level bone function. In this review, we describe how systems genetics approaches can be used to fill these knowledge gaps.

Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women

BACKGROUND

Using high-dimensional penalized regression we studied genome-wide DNA-methylation in bone biopsies of 80 postmenopausal women in relation to their bone mineral density (BMD). The women showed BMD varying from severely osteoporotic to normal. Global gene expression data from the same individuals was available, and since DNA-methylation often affects gene expression, the overall aim of this paper was to include both of these omics data sets into an integrated analysis.

METHODS

The classical penalized regression uses one penalty, but we incorporated individual penalties for each of the DNA-methylation sites. These individual penalties were guided by the strength of association between DNA-methylations and gene transcript levels. DNA-methylations that were highly associated to one or more transcripts got lower penalties and were therefore favored compared to DNA-methylations showing less association to expression. Because of the complex pathways and interactions among genes, we investigated both the association between DNA-methylations and their corresponding cis gene, as well as the association between DNA-methylations and trans-located genes. Two integrating penalized methods were used: first, an adaptive group-regularized ridge regression, and secondly, variable selection was performed through a modified version of the weighted lasso.

RESULTS

When information from gene expressions was integrated, predictive performance was considerably improved, in terms of predictive mean square error, compared to classical penalized regression without data integration. We found a 14.7% improvement in the ridge regression case and a 17% improvement for the lasso case. Our version of the weighted lasso with data integration found a list of 22 interesting methylation sites. Several corresponded to genes that are known to be important in bone formation. Using BMD as response and these 22 methylation sites as covariates, least square regression analyses resulted in R2=0.726, comparable to an average R2=0.438 for 10000 randomly selected groups of DNA-methylations with group size 22.

CONCLUSIONS

Two recent types of penalized regression methods were adapted to integrate DNA-methylation and their association to gene expression in the analysis of bone mineral density. In both cases predictions clearly benefit from including the additional information on gene expressions.

Genetic Polymorphism of miR-196a-2 is Associated with Bone Mineral Density (BMD)

MicroRNAs (miRNAs) are small non-coding RNA molecules that post-transcriptionally regulate the translation of messenger RNAs. Given the crucial role of miRNAs in gene expression, genetic variants within miRNA-related sequences may affect miRNA function and contribute to disease risk. Osteoporosis is characterized by reduced bone mass, and bone mineral density (BMD) is a major diagnostic proxy to assess osteoporosis risk. Here, we aimed to identify miRNAs that are involved in BMD using data from recent genome-wide association studies (GWAS) on femoral neck, lumbar spine and forearm BMD. Of 242 miRNA-variants available in the GWAS data, we found rs11614913:C > T in the precursor miR-196a-2 to be significantly associated with femoral neck-BMD (p-value = 9.9 × 10⁻⁷, β = −0.038) and lumbar spine-BMD (p-value = 3.2 × 10⁻¹¹, β = −0.061). Furthermore, our sensitivity analyses using the Rotterdam study data showed a sex-specific association of rs11614913 with BMD only in women. Subsequently, we highlighted a number of miR-196a-2 target genes, expressed in bone and associated with BMD, that may mediate the miRNA function in BMD. Collectively, our results suggest that miR-196a-2 may contribute to variations in BMD level. Further biological investigations will give more insights into the mechanisms by which miR-196a-2 control expression of BMD-related genes.

Enhanced Identification of Potential Pleiotropic Genetic Variants for Bone Mineral Density and Breast Cancer

Epidemiological and clinical evidences have shown that bone mineral density (BMD) has a close relationship with breast cancer (BC). They might potentially have a shared genetic basis. By incorporating information about these pleiotropic effects, we may be able to explore more of the traits' total heritability. We applied a recently developed conditional false discovery rate (cFDR) method to the summary statistics from two independent GWASs to identify the potential pleiotropic genetic variants for BMD and BC. By jointly analyzing two large independent GWASs of BMD and BC, we found strong pleiotropic enrichment between them and identified 102 single-nucleotide polymorphisms (SNPs) in BMD and 192 SNPs in BC with cFDR < 0.05, including 230 SNPs that might have been overlooked by the standard GWAS analysis. cFDR-significant genes were enriched in GO terms and KEGG pathways which were crucial to bone metabolism and/or BC pathology (adjP < 0.05). Some cFDR-significant genes were partially validated in the gene expressional validation assay. Strong interactions were found between proteins produced by cFDR-significant genes in the context of biological mechanism of bone metabolism and/or BC etiology. Totally, we identified 7 pleiotropic SNPs that were associated with both BMD and BC (conjunction cFDR < 0.05); CCDC170, ESR1, RANKL, CPED1, and MEOX1 might play important roles in the pleiotropy of BMD and BC. Our study highlighted the significant pleiotropy between BMD and BC and shed novel insight into trait-specific as well as the potentially shared genetic architecture for both BMD and BC.

Genetic sharing with coronary artery disease identifies potential novel loci for bone mineral density

Bone mineral density (BMD) is a complex trait with high missing heritability. Numerous evidences have shown that BMD variation has a relationship with coronary artery disease (CAD). This relationship may come from a common genetic basis called pleiotropy. By leveraging the pleiotropy with CAD, we may be able to improve the detection power of genetic variants associated with BMD. Using a recently developed conditional false discovery rate (cFDR) method, we jointly analyzed summary statistics from two large independent genome wide association studies (GWAS) of lumbar spine (LS) BMD and CAD. Strong pleiotropic enrichment and 7 pleiotropic SNPs were found for the two traits. We identified 41 SNPs for LS BMD (cFDR<0.05), of which 20 were replications of previous GWASs and 21 were potential novel SNPs that were not reported before. Four genes encompassed by 9 cFDR-significant SNPs were partially validated in the gene expression assay. Further functional enrichment analysis showed that genes corresponding to the cFDR-significant LS BMD SNPs were enriched in GO terms and KEGG pathways that played crucial roles in bone metabolism (adjP<0.05). In protein-protein interaction analysis, strong interactions were found between the proteins produced by the corresponding genes. Our study demonstrated the reliability and high-efficiency of the cFDR method on the detection of trait-associated genetic variants, the present findings shed novel insights into the genetic variability of BMD as well as the shared genetic basis underlying osteoporosis and CAD.

[SOST knockdown promotes differentiation of osteoblasts MG63 and mesenchymal stem cells C3H10 in an in vitro model of bone metastasis of breast cancer]

OBJECTIVE

To investigate whether SOST is involved in breast cancer MDA-MB-231 cells-induced suppression of differentiation of osteoblast MG63 cells and mesenchymal stem C3H10 cells.

METHODS

SOST-specific small interfering RNA (siRNA) was transfected into breast cancer MDA-MB-231 cells, and the interfering efficiency was verified by RT-PCR. The supernatants were collected from MDA-MB-231 cells in routine culture, cells transfected with SOST siRNA via adenovirus, and cells transfected with empty adenoviral vectors and added in MG63 or C3H10 cell cultures. The changes in the expressions of OPG, OCN, OPN and IBSP in MG63 and C3H10 cells were detected using quantitative real-time PCR, and ALP activity was detected with ALP reading and ALP staining with the cells cultured in routine culture medium and cells in osteogenic induction medium as the negative and positive controls.

RESULTS

The adenovirus Ad-siSOST effectively knocked down the expression of SOST in MDA-MB-231 cells. MG63 cells and C3H10 cells cultured in osteogenic medium showed significantly upregulated expressions of the osteoblast markers OPG, OPN, OCN and IBSP (P<0.01), while co-culture with the supernatant of MDA-MB-231 cells obviously reduced the expressions of the osteoblast markers (P<0.01); the expression of the markers increased again in MG63 and C3H10 cells after treatment with the supernatant of MDA-MB-231 cells transfected with ad-siSOST (P<0.01). ALP activity in MG63 and C3H10 cells exhibited a similar pattern of variations in response to the treatments (P<0.01).

CONCLUSION

In the in vitro model of bone metastasis of breast cancer, the differentiation of MG63 or C3H10 cells is suppressed, which can be partly reversed by knocking down the expression of SOST in the bone metastasis microenvironment.

Omics analysis of human bone to identify genes and molecular networks regulating skeletal remodeling in health and disease

The skeleton is a metabolically active organ throughout life where specific bone cell activity and paracrine/endocrine factors regulate its morphogenesis and remodeling. In recent years, an increasing number of reports have used multi-omics technologies to characterize subsets of bone biological molecular networks. The skeleton is affected by primary and secondary disease, lifestyle and many drugs. Therefore, to obtain relevant and reliable data from well characterized patient and control cohorts are vital. Here we provide a brief overview of omics studies performed on human bone, of which our own studies performed on trans-iliacal bone biopsies from postmenopausal women with osteoporosis (OP) and healthy controls are among the first and largest. Most other studies have been performed on smaller groups of patients, undergoing hip replacement for osteoarthritis (OA) or fracture, and without healthy controls. The major findings emerging from the combined studies are: 1. Unstressed and stressed bone show profoundly different gene expression reflecting differences in bone turnover and remodeling and 2. Omics analyses comparing healthy/OP and control/OA cohorts reveal characteristic changes in transcriptomics, epigenomics (DNA methylation), proteomics and metabolomics. These studies, together with genome-wide association studies, in vitro observations and transgenic animal models have identified a number of genes and gene products that act via Wnt and other signaling systems and are highly associated to bone density and fracture. Future challenge is to understand the functional interactions between bone-related molecular networks and their significance in OP and OA pathogenesis, and also how the genomic architecture is affected in health and disease.

Bivariate genome-wide association meta-analysis of pediatric musculoskeletal traits reveals pleiotropic effects at the SREBF1/TOM1L2 locus

Bone mineral density is known to be a heritable, polygenic trait whereas genetic variants contributing to lean mass variation remain largely unknown. We estimated the shared SNP heritability and performed a bivariate GWAS meta-analysis of total-body lean mass (TB-LM) and total-body less head bone mineral density (TBLH-BMD) regions in 10,414 children. The estimated SNP heritability is 43% (95% CI: 34–52%) for TBLH-BMD, and 39% (95% CI: 30–48%) for TB-LM, with a shared genetic component of 43% (95% CI: 29–56%). We identify variants with pleiotropic effects in eight loci, including seven established bone mineral density loci: WNT4, GALNT3, MEPE, CPED1/WNT16, TNFSF11, RIN3, and PPP6R3/LRP5. Variants in the TOM1L2/SREBF1 locus exert opposing effects TB-LM and TBLH-BMD, and have a stronger association with the former trait. We show that SREBF1 is expressed in murine and human osteoblasts, as well as in human muscle tissue. This is the first bivariate GWAS meta-analysis to demonstrate genetic factors with pleiotropic effects on bone mineral density and lean mass.

Bone mineral density and lean skeletal mass are heritable traits. Here, Medina-Gomez and colleagues perform bivariate GWAS analyses of total body lean mass and bone mass density in children, and show genetic loci with pleiotropic effects on both traits.

Distinct DNA methylation profiles in bone and blood of osteoporotic and healthy postmenopausal women

DNA methylation affects expression of associated genes and may contribute to the missing genetic effects from genome-wide association studies of osteoporosis. To improve insight into the mechanisms of postmenopausal osteoporosis, we combined transcript profiling with DNA methylation analyses in bone. RNA and DNA were isolated from 84 bone biopsies of postmenopausal donors varying markedly in bone mineral density (BMD). In all, 2529 CpGs in the top 100 genes most significantly associated with BMD were analyzed. The methylation levels at 63 CpGs differed significantly between healthy and osteoporotic women at 10% false discovery rate (FDR). Five of these CpGs at 5% FDR could explain 14% of BMD variation. To test whether blood DNA methylation reflect the situation in bone (as shown for other tissues), an independent cohort was selected and BMD association was demonstrated in blood for 13 of the 63 CpGs. Four transcripts representing inhibitors of bone metabolism-MEPE, SOST, WIF1, and DKK1-showed correlation to a high number of methylated CpGs, at 5% FDR. Our results link DNA methylation to the genetic influence modifying the skeleton, and the data suggest a complex interaction between CpG methylation and gene regulation. This is the first study in the hitherto largest number of postmenopausal women to demonstrate a strong association among bone CpG methylation, transcript levels, and BMD/fracture. This new insight may have implications for evaluation of osteoporosis stage and susceptibility.

Identification of differentially expressed miRNAs associated with chronic kidney disease-mineral bone disorder

The purpose of this study is to characterize a meta-signature of differentially expressed mRNA in chronic kidney disease (CKD) to predict putative microRNA (miRNA) in CKD-mineral bone disorder (CKD-MBD) and confirm the changes in these genes and miRNA expression under uremic conditions by using a cell culture system. PubMed searches using MeSH terms and keywords related to CKD, uremia, and mRNA arrays were conducted. Through a computational analysis, a meta-signature that characterizes the significant intersection of differentially expressed mRNA and expected miRNAs associated with CKD-MBD was determined. Additionally, changes in gene and miRNA expressions under uremic conditions were confirmed with human Saos-2 osteoblast-like cells. A statistically significant mRNA meta-signature of upregulated and downregulated mRNA levels was identified. Furthermore, miRNA expression profiles were inferred, and computational analyses were performed with the imputed microRNA regulation based on weighted ranked expression and putative microRNA targets (IMRE) method to identify miRNAs associated with CKD occurrence. TLR4 and miR-146b levels were significantly associated with CKD-MBD. TLR4 levels were significantly downregulated, whereas primiR- 146b and miR-146b were upregulated in the presence of uremic toxins in human Saos-2 osteoblast-like cells. Differentially expressed miRNAs associated with CKD-MBD were identified through a computational analysis, and changes in gene and miRNA expressions were confirmed with an in vitro cell culture system.