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
We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature.Aims
Methods
The aim of this study was to investigate the global and local impact of fat on bone in obesity by using the diet-induced obese (DIO) mouse model. In this study, we generated a diet-induced mouse model of obesity to conduct lipidomic and 3D imaging assessments of bone marrow fat, and evaluated the correlated bone adaptation indices and bone mechanical properties.Aims
Methods
The decrease in the number of satellite cells (SCs), contributing to myofibre formation and reconstitution, and their proliferative capacity, leads to muscle loss, a condition known as sarcopenia. Resistance training can prevent muscle loss; however, the underlying mechanisms of resistance training effects on SCs are not well understood. We therefore conducted a comprehensive transcriptome analysis of SCs in a mouse model. We compared the differentially expressed genes of SCs in young mice (eight weeks old), middle-aged (48-week-old) mice with resistance training intervention (MID+ T), and mice without exercise (MID) using next-generation sequencing and bioinformatics.Aims
Methods
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 miRNA and changes to their patterns of expression may be implicated in the pathogenesis of impaired fracture healing in DM. Closed transverse fractures were created in the femurs of 116 rats, with half assigned to the DM group and half assigned to the control group. Rats with DM were induced by a single intraperitoneal injection of streptozotocin. At post-fracture days five, seven, 11, 14, 21, and 28, miRNA was extracted from the newly generated tissue at the fracture site. Microarray analysis was performed with miRNA samples from each group on post-fracture days five and 11. For further analysis, real-time polymerase chain reaction (PCR) analysis was performed at each timepoint.Objectives
Methods
