Kinanti Rias G, Weningtyas Anditri, Ariesaka Kiky M, Puspitasari Sendhi T, Arsani Ni Lka, Liao Hung E
Department of Medicine, Faculty of Medicine, Universitas Negeri Malang, Malang, Indonesia.
Doctoral Program in Medical Science, Faculty of Medicine, Universitas Brawijaya, Malang, Indonesia.
Narra J. 2025 Apr;5(1):e1764. doi: 10.52225/narra.v5i1.1764. Epub 2025 Feb 24.
Understanding the molecular mechanisms underlying skeletal muscle adaptation to different training regimens is essential for advancing muscle health and performance interventions. The aim of this study was to investigate molecular and genetic adaptations in the resting skeletal muscle of sedentary individuals compared to strength- and endurance-trained athletes using bioinformatics and in vitro validation. Differentially expressed genes (DEG) analysis of the GSE9405 dataset was conducted. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed, followed by protein-protein interaction (PPI) network analysis and receiver operating characteristic (ROC) analysis. To validate the bioinformatics findings, the expression of two identified genes was assessed using real-time polymerase chain reaction (PCR) in professional athletes and age-matched non-athletes. Analysis of RNA expression profiles from the GSE9405 dataset identified 426 DEGs, with 165 upregulated and 261 downregulated in trained individuals. Enrichment analysis highlighted pathways related to metabolic efficiency, mitochondrial function, and muscle remodeling, all crucial for athletic performance. and were identified as key upregulated genes in trained individuals with central roles in these pathways. The area under the curve (AUC) values for and were 0.8558 and 0.8846, respectively, for differentiating the two groups. Validation in human samples confirmed that expression was significantly higher in athletes ( = 0.00i), suggesting its critical role in muscle adaptation. However, expression differences between the groups were not statistically significant ( = 0.32i). These findings provide insights into gene-level responses to long-term training, offering a basis for targeted interventions to enhance muscle health and athletic performance.
了解骨骼肌适应不同训练方案的分子机制对于推进肌肉健康和性能干预至关重要。本研究的目的是通过生物信息学和体外验证，研究久坐不动的个体与力量训练和耐力训练的运动员相比，其静息骨骼肌中的分子和基因适应性。对GSE9405数据集进行了差异表达基因（DEG）分析。进行了基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析，随后进行了蛋白质-蛋白质相互作用（PPI）网络分析和受试者工作特征（ROC）分析。为了验证生物信息学结果，在职业运动员和年龄匹配的非运动员中使用实时聚合酶链反应（PCR）评估了两个鉴定出的基因的表达。对GSE9405数据集的RNA表达谱分析确定了426个差异表达基因，训练个体中有165个上调，261个下调。富集分析突出了与代谢效率、线粒体功能和肌肉重塑相关的途径，这些对运动表现都至关重要。 和 被确定为训练个体中上调的关键基因，在这些途径中起核心作用。区分两组时， 和 的曲线下面积（AUC）值分别为0.8558和0.8846。在人类样本中的验证证实，运动员中 表达显著更高（ = 0.00i），表明其在肌肉适应中的关键作用。然而，两组之间的 表达差异无统计学意义（ = 0.32i）。这些发现为长期训练的基因水平反应提供了见解，为针对性干预以增强肌肉健康和运动表现提供了基础。
