Objectives. Diabetes mellitus (DM) is known to impair fracture healing. Increasing evidence suggests that some microRNA (miRNA) is involved in the pathophysiology of diabetes and its complications. We hypothesized that the functions of
Aims. To investigate the effects of senescent osteocytes on bone homeostasis in the progress of age-related osteoporosis and explore the underlying mechanism. Methods. In a series of in vitro experiments, we used tert-Butyl hydroperoxide (TBHP) to induce senescence of MLO-Y4 cells successfully, and collected conditioned medium (CM) and senescent MLO-Y4 cell-derived exosomes, which were then applied to MC3T3-E1 cells, separately, to evaluate their effects on osteogenic differentiation. Furthermore, we identified differentially expressed microRNAs (miRNAs) between exosomes from senescent and normal MLO-Y4 cells by high-throughput RNA sequencing. Based on the key miRNAs that were discovered, the underlying mechanism by which senescent osteocytes regulate osteogenic differentiation was explored. Lastly, in the in vivo experiments, the effects of senescent MLO-Y4 cell-derived exosomes on age-related bone loss were evaluated in male SAMP6 mice, which excluded the effects of oestrogen, and the underlying mechanism was confirmed. Results. The CM and exosomes collected from senescent MLO-Y4 cells inhibited osteogenic differentiation of MC3T3-E1 cells. RNA sequencing detected significantly lower expression of miR-494-3p in senescent MLO-Y4 cell-derived exosomes compared with normal exosomes. The upregulation of exosomal miR-494-3p by
Long non-coding RNAs (lncRNAs) act as crucial regulators in osteoporosis (OP). Nonetheless, the effects and potential molecular mechanism of lncRNA PCBP1 Antisense RNA 1 (PCBP1-AS1) on OP remain largely unclear. The aim of this study was to explore the role of lncRNA PCBP1-AS1 in the pathogenesis of OP. Using quantitative real-time polymerase chain reaction (qRT-PCR), osteogenesis-related genes (alkaline phosphatase (ALP), osteocalcin (OCN), osteopontin (OPN), and Runt-related transcription factor 2 (RUNX2)), PCBP1-AS1, microRNA (miR)-126-5p, group I Pak family member p21-activated kinase 2 (PAK2), and their relative expression levels were determined. Western blotting was used to examine the expression of PAK2 protein. Cell Counting Kit-8 (CCK-8) assay was used to measure cell proliferation. To examine the osteogenic differentiation, Alizarin red along with ALP staining was used. RNA immunoprecipitation assay and bioinformatics analysis, as well as a dual-luciferase reporter, were used to study the association between PCBP1-AS1, PAK2, and miR-126-5p.Aims
Methods
Astragalus polysaccharide (APS) participates in various processes, such as the enhancement of immunity and inhibition of tumours. APS can affect osteoporosis (OP) by regulating the osteogenic differentiation of human bone mesenchymal stem cells (hBMSCs). This study was designed to elucidate the mechanism of APS in hBMSC proliferation and osteoblast differentiation. Reverse transcriptase polymerase chain reaction (RT-PCR) and Western blotting were performed to determine the expression of microRNA (miR)-760 and ankyrin repeat and FYVE domain containing 1 (ANKFY1) in OP tissues and hBMSCs. Cell viability was measured using the Cell Counting Kit-8 assay. The expression of cyclin D1 and osteogenic marker genes (osteocalcin (OCN), alkaline phosphatase (ALP), and runt-related transcription factor 2 (RUNX2)) was evaluated using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Mineral deposits were detected through Alizarin Red S staining. In addition, Western blotting was performed to detect the ANKFY1 protein levels following the regulation of miR-760. The relationship between miR-760 and ANKFY1 was determined using a luciferase reporter assay.Aims
Methods
This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously. Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes.Aims
Methods