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Bone & Joint Research
Vol. 11, Issue 2 | Pages 121 - 133
22 Feb 2022
Hsu W Lin S Hung J Chen M Lin C Hsu W Hsu WR

Aims. 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. Methods. 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. Results. After the bioinformatic analysis, the PI3K-Akt signalling pathway and the regulation of actin cytoskeleton in particular were highlighted among the top ten pathways with the most differentially expressed genes involved in the young/MID and MID+ T/MID groups. The expression of Gng5, Atf2, and Rtor in the PI3K-Akt signalling pathway was higher in the young and MID+ T groups compared with the MID group. Similarly, Limk1, Arhgef12, and Araf in the regulation of the actin cytoskeleton pathway had a similar bias. Moreover, the protein expression profiles of Atf2, Rptor, and Ccnd3 in each group were paralleled with the results of NGS. Conclusion. Our results revealed that age-induced muscle loss might result from age-influenced genes that contribute to muscle development in SCs. After resistance training, age-impaired genes were reactivated, and age-induced genes were depressed. The change fold in these genes in the young/MID mice resembled those in the MID + T/MID group, suggesting that resistance training can rejuvenate the self-renewing ability of SCs by recovering age-influenced genes to prevent sarcopenia. Cite this article: Bone Joint Res 2022;11(2):121–133


Bone & Joint Research
Vol. 11, Issue 8 | Pages 548 - 560
17 Aug 2022
Yuan W Yang M Zhu Y

Aims

We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism.

Methods

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.