Backgrounds: As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass and bone microarchitectural damage. The global incidences of OP are high.Methods: Data were retrieved from databases like Gene Expression Omnibus (GEO), GeneCards, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene Expression Profiling Interactive Analysis (GEPIA2), and other databases. R software (version 4.1.1) was used to identify differentially expressed genes (DEGs) and perform functional analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and random forest algorithm were combined and used for screening diagnostic markers for OP. The diagnostic value was assessed by the receiver operating characteristic (ROC) curve. Molecular signature subtypes were identified using a consensus clustering approach, and prognostic analysis was performed. The level of immune cell infiltration was assessed by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The hub gene was identified using the CytoHubba algorithm. Real-time fluorescence quantitative PCR (RT-qPCR) was performed on the plasma of osteoporosis patients and control samples. The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.Results: A total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. Enrichment analysis showed these genes were mainly related to MAPK signaling pathways, TNF signaling pathway, apoptosis, and Salmonella infection. RT-qPCR also revealed that the MAPK signaling pathway (p38, TRAF6) and NF-kappa B signaling pathway (c-FLIP, MIP1β) were significantly different between osteoporosis patients and control samples. The analysis of immune cell infiltration revealed that monocytes, activated CD4 memory T cells, and memory and naïve B cells may be related to the occurrence and development of OP.Conclusions: We identified six novel OP diagnostic marker genes and ten OP-hub genes. These genes can be used to improve the prognostic of OP and to identify potential relationships between the immune microenvironment and OP. Our research will provide insights into the potential therapeutic targets and pathogenesis of osteoporosis.

The role of gut bacteria in preventing and delaying osteoporosis has been studied. However, the causal relationship between gut bacteria, plasma proteins, circulating metabolites and osteoporosis (OP) risk has not been fully revealed. In this study, a two-sample Mendelian randomization study (MR) approach was used to assess the causal associations between gut bacteria, plasma proteins and circulating metabolites, and osteoporosis risk using Genome Wide Association Study (GWAS) data from gut bacteria(n=8208), plasma proteins(n=2263), circulating metabolites (n=123), and osteoporosis (3203 cases and 16380452 controls). Inverse-variance weighted (IVW) was used as the main analytical method to estimate the MR causal effect and to perform directional sensitivity analysis of causality. Finally, the mediating effect values for the influence of gut flora on OP pathogenesis through circulating metabolites were calculated by univariate MR analysis, and multivariate MR analysis. Next, we evaluated the effect of Phosphatidylcholine on the osteogenic function of bone marrow mesenchymal stem cells (BMSCs) through relevant experiments, including Edu detection of cell proliferation, alkaline phosphatase (ALP) staining, Alizarin red staining to evaluate osteogenic function, qPCR and WB detection of osteogenic differentiation related gene expression. A total of 9 gut microbial taxa, 15 plasma proteins and eight circulating metabolites were analysed for significant causal associations with the development of OP. Significant causal effects of 7 on gut bacteria, plasma proteins and circulating metabolites were analysed by univariate MR analysis and these results were used as exposure factors for subsequent multivariate MR. Multivariate MR analyses yielded a significant effect of circulating metabolites Phosphatidylcholine and other cholines on OP (P<0.05). Further mediation effect analysis showed that the mediation effect of Bifidobacteriaceae affecting OP through the circulating metabolite Phosphatidylcholine and other cholines was -0.0224, with a 95 % confidence interval for the mediation effect that did not include 0, and the complete mediation effect was significant. Phosphatidylcholine can promote BMSCs proliferation and osteogenesis. Our study demonstrated significant causal associations of gut bacteria, plasma proteins and circulating metabolites on OP, and that Bifidobacteriaceae affect OP through the circulating metabolites Phosphatidylcholine and other cholines. Phosphatidylcholine affects the osteogenic ability of BMSCs. Further exploration of potential microbiota-associated mechanisms of bone metabolism may offer new avenues for osteoporosis prevention and treatment of osteoporosis.

Objective: Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in‐depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatment of osteoporosis. This study aims to explore novel bone immune markers related to osteoporosis based on single‐cell and transcriptome data, utilizing bioinformatics and machine learning methods, in order to provide novel strategies for the diagnosis and treatment of the disease.Methods: Single cell and transcriptome data sets were acquired from Gene Expression Omnibus (GEO). The data was then subjected to cell communication analysis, pseudotime analysis, and high dimensional WGCNA (hdWGCNA) analysis to identify key immune cell subpopulations and module genes. Subsequently, ConsensusClusterPlus analysis was performed on the key module genes to identify different diseased subgroups in the osteoporosis (OP) training set samples. The immune characteristics between subgroups were evaluated using Cibersort, EPIC, and MCP counter algorithms. OP's hub genes were screened using 10 machine learning algorithms and 113 algorithm combinations. The relationship between hub genes and immunity and pathways was established by evaluating the immune and pathway scores of the training set samples through the ESTIMATE, MCP‐counter, and ssGSEA algorithms. Real‐time fluorescence quantitative PCR (RT‐qPCR) testing was conducted on serum samples collected from osteoporosis patients and healthy adults.Results: In OP samples, the proportions of bone marrow‐derived mesenchymal stem cells (BM‐MSCs) and neutrophils increased significantly by 6.73% (from 24.01% to 30.74%) and 6.36% (from 26.82% to 33.18%), respectively. We found 16 intersection genes and four hub genes (DND1, HIRA, SH3GLB2, and F7). RT‐qPCR results showed reduced expression levels of DND1, HIRA, and SH3GLB2 in clinical blood samples of OP patients. Moreover, the four hub genes showed positive correlations with neutrophils (0.65–0.90), immature B cells (0.76–0.92), and endothelial cells (0.79–0.87), while showing negative correlations with myeloid‐derived suppressor cells (negative 0.54–0.73), T follicular helper cells (negative 0.71–0.86), and natural killer T cells (negative 0.75–0.85).Conclusion: Neutrophils play a crucial role in the occurrence and development of osteoporosis. The four hub genes potentially inhibit metabolic activities and trigger inflammation by interacting with other immune cells, thereby significantly contributing to the onset and diagnosis of OP.

The mechanism by which chondrocytes respond to mechanical stress in joints significantly affects the balance and function of cartilage. This study aims to characterize osteoarthritis-associated chondrocyte subpopulations and key gene targets for regulatory drugs. To begin, single-cell and transcriptome datasets were obtained from the Gene Expression Omnibus (GEO) database. Cell communication and pseudo-temporal analysis, as well as High-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA), were conducted on the single-cell data to identify key chondrocyte subtypes and module genes. Subsequently, Consensus Cluster Plus analysis was utilized to identify distinct disease subgroups within the osteoarthritis (OA) training dataset based on the key module genes. Furthermore, differential gene expression analysis and GO/KEGG pathway enrichment analysis were performed on the identified subgroups. To screen for hub genes associated with OA, a combination of 10 machine learning algorithms and 113 algorithm compositions was integrated. Additionally, the immune and pathway scores of the training dataset samples were evaluated using the ESTIMATE, MCP-counter, and ssGSEA algorithms to establish the relationship between the hub genes and immune and pathways. Following this, a network depicting the interaction between the hub genes and transcription factors was constructed based on the Network Analyst database. Moreover, the hub genes were subjected to drug prediction and molecular docking using the RNAactDrug database and AutoDockTools. Finally, real-time fluorescence quantitative PCR (RT-qPCR) was employed to detect the expression of hub genes in the plasma samples collected from osteoarthritis patients and healthy adults. In the OA sample, there is a significant increase in the proportion of prehypertrophic chondrocytes (preHTC), particularly in subgroups 6, 7, and 9. We defined these subgroups as OA_PreHTC subgroups. The OA_PreHTC subgroup exhibits a higher communication intensity with proliferative-related pathways such as ANGPTL and TGF-β. Furthermore, two OA disease subgroups were identified in the training set samples. This led to the identification of 411 differentially expressed genes (DEGs) related to osteoarthritis, 2485 DEGs among subgroups, as well as 238 intersecting genes and 5 hub genes (MMP13, FAM26F, CHI3L1, TAC1, and CKS2). RT-qPCR results indicate significant differences in the expression levels of five hub genes and their related TFs in the clinical blood samples of OA patients compared to the healthy control group (NC). Moreover, these five hub genes are positively associated with inflammatory pathways such as TNF-α, JAK-STAT3, and inflammatory response, while being negatively associated with proliferation pathways like WNT and KRAS. Additionally, the five hub genes are positively associated with neutrophils, activated CD4 T cell, gamma delta T cell, and regulatory T cell, while being negatively associated with CD56dim natural killer cell and Type 17T helper cell. Molecular docking results reveal that CAY10603, Tenulin, T0901317, and Nonactin exhibit high binding activity to CHI3L1, suggesting their potential as therapeutic drugs for OA. The OA_PreHTC subgroups plays a crucial role in the occurrence and development of osteoarthritis (OA). Five hub genes may exert their effects on OA through interactions with PreHTC cells, other chondrocytes, and immune cells, playing a role in inhibiting cell proliferation and stimulating inflammation, thus having high diagnostic value for OA. Additionally, CAY10603, Tenulin, T0901317, and Nonactin have potential therapeutic effects for OA patients.

Targeting G6PD via the hypoxia-responsive prodrugs.

Background: Osteoporosis is one of the most common bone metabolic diseases that affects mainly the health of elderly people. It is a kind of prevalent chronic disease, and the conventional treatment methods have some limitations or side effects. Targeting nanoparticles represent a novel technology that has garnered extensive attention in recent years. They can selectively enhance the drug concentration at the targeted site, offering a novel treatment method. Methods: The review is carried out according to the Preferred Reporting Items for Systematic Reviews (PRISMA 2020) guidelines. Results: This article comprehensively summarizes recent research progress on the status of existing anti-osteoporosis drugs and bone-targeting nanoparticles for the treatment of osteoporosis, including their carrier materials, modification techniques, preparation methods, and function mechanisms. It also discusses their applications in RNA interference (RNAi) therapy and other related areas. Furthermore, given the limitations of bone-targeting nanoparticles, solutions and viewpoints have been proposed. This review summarizes that bone-targeting nanoparticles are useful for osteoporosis therapy and provide a novel perspective for new drug discovery. Conclusions: Bone-targeting nanoparticles overcome the limitations of traditional treatment methods and enhance therapeutic efficacy. However, the clinical translation of bone-targeted nanoparticles remains challenging and requires further investigation.