The Polymorphism at PLCB4 Promoter (rs6086746) Changes the Binding Affinity of RUNX2 and Affects Osteoporosis Susceptibility: An Analysis of Bioinformatics-Based Case-Control Study and Functional Validation

Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations

PURPOSE OF REVIEW

Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underrepresentation of non-European populations, limiting the applicability of polygenic risk scores (PRS). This review examines recent advancements in osteoporosis genetics, highlights existing disparities, and explores strategies for more inclusive research.

RECENT FINDINGS

European-focused GWAS have identified key loci for osteoporosis, including WNT signaling (SOST, LRP5) and RUNX2 transcriptional regulation. However, fewer than 40% of these variants can be replicated in Asian and African populations. Emerging studies in non-European groups reveal population-specific loci, sex-specific associations, and gene-environment interactions. Advances in machine learning (ML)-assisted GWAS and multi-omics integration are improving genetic discovery. Expanding GWAS in diverse populations, integrating multi-omics data, refining ML-based risk models, and standardizing biobank data are essential for equitable osteoporosis research. Future efforts must prioritize clinical translation to enhance personalized osteoporosis prevention and treatment.

Develop a prognostic and drug therapy efficacy prediction model for hepatocellular carcinoma based on telomere maintenance-associated genes

Background

Hepatocellular carcinoma (HCC) poses a substantial global health challenge because of its grim prognosis and limited therapeutic options. Telomere maintenance mechanisms (TMM) significantly influence cancer progression, yet their prognostic value in HCC remains largely unexamined. This research aims to establish a telomere maintenance-associated genes(TMGs)-based prognostic model using transcriptomic and clinical data to evaluate its effectiveness in predicting patient outcomes in HCC.

Methods

The identified differentially expressed genes (DEGs) were derived from the analysis of transcriptomic and clinical information sourced from the database of the Cancer Genome Atlas (TCGA) and were cross-referenced with TMGs. Candidate risk factors were initially assessed using univariate Cox regression, subsequently followed by LASSO, and then refined through multivariate Cox regression to establish a risk prediction model. This model's predictive accuracy was validated through Kaplan-Meier(K-M) survival analysis, with external validation in the Gene Expression Omnibus (GEO) dataset. Additionally, a nomogram incorporating age and tumor stage was developed. Tumor mutation burden (TMB), immune profile, and drug sensitivity in HCC were also analyzed. Furthermore, we employed RT-PCR to confirm the expression levels of the genes related to TMGs in HepG2 cell lines.

Results

A prognostic model comprising 3 core genes was constructed, with high-risk individuals showing significantly lower overall survival (OS). The association between elevated TMB and diminished survival in high-risk patients was uncovered through TMB analysis. Immune profiling indicated notable disparities in immune infiltration among these groups, with high-risk patients displaying elevated Tumor Immune Dysfunction and Exclusion (TIDE) scores, suggesting potential immune evasion.

Conclusion

In short, our prognosis model based on TMGs effectively categorized HCC patients using risk scores, enabling dependable prognostic forecasts and identification of potential therapeutic targets for personalized treatment in HCC management. Future studies should explore integrating this model into clinical practice to improve patient outcomes.

Bu-Sui-Dan Enhances Osteoblast Differentiation by Upregulating VGLL4 to Counteract TEAD4-Mediated RUNX2 Transcription Suppression in Ovariectomized Rats

ETHNOPHARMACOLOGICAL RELEVANCE

Postmenopausal osteoporosis (PMOP) has been considered as a major causative factor for bone-joint pain and inducing pathologic fractures. Bu-Sui-Dan (BSD), a classic ancient herbal formula, has been shown to exhibit osteoprotective effects by promoting bone marrow development and bone growth. However, the exact mechanism of BSD are still unexplored.

AIM OF STUDY

The study aimed to investigate the protective effect of BSD against osteoporotic injury, and to explore whether BSD regulated BMSCs' osteogenic differentiation by targeting VGLL4, which in turn improved PMOP.

MATERIALS AND METHODS

The anti-osteoporotic effect of BSD was studied in ovariectomized (OVX) rats and bone marrow mesenchymal stem cells (BMSCs). Micro-CT imaging and HE staining were performed, and the levels of osteogenic protein RUNX2 and osteogenesis-related factor VGLL4 were determined. Co-immunoprecipitation (Co-IP) was further employed to delve into the effects of BSD on the interactions between TEAD4 and RUNX2. The key osteogenic factors 1ALP, COLl1A1, and Osterix expression were detected by RT-qPCR. Co-IP and proximity ligation assay (PLA) were employed to scrutinize the influence of BSD on TEAD4 and RUNX2 inter-binding. Moreover, VGLL4 knockdown in BMSCs was conducted to confirm the role of VGLL4 in the therapeutic mechanism of BSD.

RESULTS

BSD showed a dose-dependent protective effect against osteoporotic injury, as evidenced by improvement in bone volume, bone microarchitecture, and histomorphometry. Additionally, BSD treatment increased the levels of RUNX2 and its downstream target genes including ALP, COL1A1, and Osterix. Moreover, BSD upregulated VGLL4 expression and lessened TEAD4-RUNX2 interactions. In BMSCs experiment, BSD-containing serum could promote osteogenic differentiation of BMSCs, boosted the expression of osteogenesis-related factors and VGLL4 level. The knockdown of VGLL4 in BMSCs diminished the promotion effect of BSD in osteoblast differentiation, suggesting that VGLL4 play a vital role in the therapeutic effects exerted by BSD.

CONCLUSION

BSD ameliorated osteoporosis injury and promoted osteoblast differentiation through upregulation of VGLL4 levels, which in turn antagonized TEAD4-mediated RUNX2 transcriptional repression. Our study implied that BSD may be an osteoporosis therapeutic agent.

Interaction between MARK3 (rs11623869), PLCB4 (rs6086746) and GEMIN2 (rs2277458) variants with bone mineral density and serum 25-hidroxivitamin D levels in Mexican Mestizo women

Introduction

Understanding the genetic factors contributing to variations in bone mineral density (BMD) and vitamin D could provide valuable insights into the pathogenesis of osteoporosis. This study aimed to evaluate the association of single nucleotide variants in MARK3 (rs11623869), PLCB4 (rs6086746), and GEMIN2 (rs2277458) with BMD in Mexican women.

Methods

The gene-gene interaction was evaluated in these variants in serum 25(OH)D levels and BMD. A genetic risk score (GRS) was created on the basis of the three genetic variants. Genotyping was performed using predesigned TaqMan assays.

Results

A significant association was found between the rs6086746-A variant and BMD at the total hip, femoral neck, and lumbar spine, in women aged 45 years or older. However, no association was observed between the variants rs11623869 and rs2277458. The rs11623869 × rs2277458 interaction was associated with total hip (p=0.002) and femoral neck BMD (p=0.013). Similarly, for vitamin D levels, we observed an interaction between the variants rs6086746 × rs2277458 (p=0.021). GRS revealed a significant association with total hip BMD (p trend=0.003) and femoral neck BMD (p trend=0.006), as well as increased vitamin D levels (p trend=0.0003). These findings provide evidence of the individual and joint effect of the MARK3, PLCB4, and GEMIN2 variants on BMD and serum vitamin D levels in Mexican women.

Discussion

This knowledge could help to elucidate the interaction mechanism between BMD-related genetic variants and 25OHD, contributing to the determination of the pathogenesis of osteoporosis and its potential implications during early interventions.

Integrated transcriptomic and metabolomic analyses reveal potential regulatory pathways regulating bone metabolism pre- and postsexual maturity in hens

At the onset of sexual maturity, the increasing circulating estrogen stimulates the formation of medullary bone, which provides available calcium for eggshell formation. The bone loss of laying hens is caused by the continuous dynamic changes of structure bone leading to bone fragility and susceptibility. The degree of medullary bone mineralization in sexual maturity is positively correlated with bone quality in the late laying stage. This study aimed to explore the molecular regulation mechanism of bone metabolism pre- and postsexual maturity in hens based on the joint analysis of transcriptome and metabolome. A total of 50 Hy-line Sonia pullets with comparable body weight at 13 wk were selected. Eight pullets were killed at 15 wk (juvenile hens, JH) and 19 wk (laying hens, LH), and LHs were killed within 3 h after oviposition. Differentially expressed genes and metabolites in tibia were analyzed based on transcriptome and metabolome, and then combined to construct the relevant metabolisms and hub genes. In the LH hens, plasma levels of estrogen and tartrate-resistant acid phosphatase were significantly elevated by 1.7 and 1.3 times. In addition, the midpoint diameter, bone mineral density and bone mineral content of the tibia and femur were higher at 19 wk of age. A total of 580 differentially expressed genes were found between the JH and LH group in the tibia, including 280 up-regulated, and 300 down-regulated genes in the LH group. Gene set enrichment analysis (GSEA) showed that the intracellular biosynthesis and secretion of matrix vesicles were significantly enrichment in the LH hens. A total of 21 differential metabolites were identified between JH and LH group. Estradiol valerate positively correlated with L-theanine, tryptophan betaine, dopamine, and perindopril. Joint analysis showed that the top 20 hub genes were enriched in cholesterol biosynthesis and phospholipid metabolism, which played a key regulatory role in bone metabolism during pre- and postsexual maturity. These results provide a theoretical foundation for maintaining efficient egg production and reducing bone health problems in laying hens.

Association between ADAMTS14_rs4747096 gene polymorphism and bone mineral density of Chinese Han population residing in fluorine exposed areas in ShanXi Province, China

This study aimed to investigate the effects of fluorine and ADAMTS14_rs4747096 on bone mineral density (BMD). The survey was explored in a cross-sectional case-control study conducted in Shanxi, China. The BMD was measured by an ultrasonic bone mineral density instrument. The urine fluoride concentration was detected using the fluoride ion electrode. ADAMTS14_rs4747096 polymorphism was examined by multiplex polymerase chain reaction (PCR) and sequencing. The multinomial logistic regressions found that the urine fluoride was a risk factor for osteopenia (OR = 1.379, 95% CI: 1.127-1.687, P = 0.0018), osteoporosis (OR = 1.480, 95% CI: 1.1138-1.926, P = 0.0035), and rs4747096 AG + GG genotype increased the risk of osteoporosis (OR = 2.017, 95% CI: 1.208-3.369, P = 0.0073). In addition, the interaction between urine fluoride and rs4747096 polymorphism on the risk of decreased BMD also was observed. The study suggests that fluoride exposure and mutation G allele in ADAMTS14_rs4747096 may be risk factors for the decrease of BMD. And there is an interaction between the two influencing factors.

A Prediction Model for Osteoporosis Risk Using a Machine-Learning Approach and Its Validation in a Large Cohort

BACKGROUND

Osteoporosis develops in the elderly due to decreased bone mineral density (BMD), potentially increasing bone fracture risk. However, the BMD is not regularly measured in a clinical setting. This study aimed to develop a good prediction model for the osteoporosis risk using a machine learning (ML) approach in adults over 40 years in the Ansan/Anseong cohort and the association of predicted osteoporosis risk with a fracture in the Health Examinees (HEXA) cohort.

METHODS

The 109 demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8,842 participants were manually selected in an Ansan/Anseong cohort and included in the ML algorithm. The polygenic risk score (PRS) of osteoporosis was generated with a genome-wide association study and added for the genetic impact of osteoporosis. Osteoporosis was defined with < -2.5 T scores of the tibia or radius compared to people in their 20s-30s. They were divided randomly into the training (n = 7,074) and test (n = 1,768) sets-Pearson's correlation between the predicted osteoporosis risk and fracture in the HEXA cohort.

RESULTS

XGBoost, deep neural network, and random forest generated the prediction model with a high area under the curve (AUC, 0.86) of the receiver operating characteristic (ROC) with 10, 15, and 20 features; the prediction model by XGBoost had the highest AUC of ROC, high accuracy and k-fold values (> 0.85) in 15 features among seven ML approaches. The model included the genetic factor, genders, number of children and breastfed children, age, residence area, education, seasons to measure, height, smoking status, hormone replacement therapy, serum albumin, hip circumferences, vitamin B6 intake, and body weight. The prediction models for women alone were similar to those for both genders, with lower accuracy. When the prediction model was applied to the HEXA study, the correlation between the fracture incidence and predicted osteoporosis risk was significant but weak (r = 0.173, P < 0.001).

CONCLUSION

The prediction model for osteoporosis risk generated by XGBoost can be applied to estimate osteoporosis risk. The biomarkers can be considered for enhancing the prevention, detection, and early therapy of osteoporosis risk in Asians.

Combined Therapy of Yishen Zhuanggu Decoction and Caltrate D600 Alleviates Postmenopausal Osteoporosis by Targeting FoxO3a and Activating the Wnt/β-Catenin Pathway

Background

Postmenopausal osteoporosis (PMO) is the most prevalent metabolic bone disease in women. Yishen Zhuanggu (YSZG) decoction and Caltrate D600 reportedly affects bone formation. This study aimed to investigate the efficacy and mechanism of YSZG decoction combined with Caltrate D600 in PMO treatment.

Methods

Ovariectomy-induced PMO rat model was treated with YSZG or/and Caltrate D600 for 12 weeks. Femur bone mineral density (BMD), osteoporosis-related protein expression, and serum parameters were measured. Pathological features of femur bone tissues were observed using hematoxylin and eosin staining. Serum levels of oxidative stress parameters were measured using corresponding commercial kits. The mRNA and protein expression of FoxO3a, Wnt, and β-catenin was detected using qRT-PCR and western blotting.

Results

The BMD and ultimate load of PMO rats were increased after treatment with YSZG. YSZG treatment promoted the bone trabeculae formation of PMO rats. YSZG treatment also induced bone differentiation and suppress oxidative stress in PMO rats, evidenced by the increased BALP, Runx2, OPG, SOD, and CAT levels, as well as the decreased TRACP 5b, RANKL, ROS, and MDA levels. Additionally, YSZG treatment downregulated the FoxO3a expression and upregulated the levels of Wnt and β-catenin in PMO rats. Caltrate D600 addition showed an auxiliary effect for YSZG.

Conclusion

YSZG decoction exerts the antiosteoporotic effect on PMO by restraining the FoxO3a expression and activating the Wnt/β-catenin pathway, which has an impressive synergistic effect with Caltrate D600.