Unveiling muscle fatigue: identifying key gene biomarkers and therapeutic targets

Muscle fatigue, a potential risk factor for athlete injuries, lacks specific therapeutic targets and diagnostic biomarkers. This study aimed to identify biomarkers or targets for muscle fatigue to develop new diagnostic and treatment approaches. We utilized skeletal muscle and blood expression Quantitative Trait Loci data, employing the methods of Summary-data-based Mendelian Randomization (SMR) and Bayesian colocalization to identify genes that exhibit significant association with fatigue. DSigDB database and molecular docking method were used to predict potential drug candidates for the identified target genes and validated their interactions. Finally, the transcription levels of candidate genes were assessed in a muscle fatigue rat model using RT-qPCR. Using SMR and Bayesian colocalization analyses, we ultimately identified 24 genes stably associated with fatigue in skeletal muscle and 24 fatigue-related genes in blood, among which 6 common genes (ISYNA1, PABPC4, ZDHHC5, KATNAL1, UBOX5, and ATP11B) were found to serve as potential intervention targets for muscle fatigue and peripheral blood gene biomarkers. Several drugs associated with fatigue symptoms, including valproic acid, hesperidin, and cannabidiol were explored through DSigDB database and validated by molecular docking. RT-qPCR results confirmed that the transcriptional levels of Isyna1, Pabpc4, Zdhhc5, Katnal1, Ubox5, and Atp11b in the skeletal muscle of fatigue model rats were significantly altered compared to the control group (p = 0.020, p = 0.028, p = 0.001, p = 0.006, p = 0.027, p = 0.041). Our findings identified potential biomarkers or therapeutic targets for the diagnosis and treatment of fatigue, particularly muscle fatigue.