CBD identified as potential therapeutic target for muscle fatigue

Unveiling muscle fatigue: identifying key gene biomarkers and therapeutic targets.

Molecular and cellular biochemistry • • Moderately Relevant
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AI Summary

Researchers have identified six key genes that appear to play a crucial role in muscle fatigue, opening new doors for both diagnosis and treatment. Using advanced genetic analysis techniques including Mendelian Randomization and Bayesian colocalization, scientists found that genes like ISYNA1, PABPC4, ZDHHC5, KATNAL1, UBOX5, and ATP11B are significantly associated with fatigue in both skeletal muscle and blood. These discoveries were validated in rats, where all six genes showed significantly altered expression levels in fatigued muscle tissue compared to controls.

In a promising finding for cannabis research, the study identified cannabidiol (CBD) as one of several promising drug candidates that could potentially target muscle fatigue. Through computational molecular docking analysis, researchers validated how CBD could interact with these newly identified genes to address fatigue symptoms. This places CBD alongside other pharmaceutical candidates like valproic acid and hesperidin as potential therapeutic interventions for muscle fatigue-related conditions.

The identification of these specific molecular biomarkers represents a significant advance for athletes, people with chronic fatigue conditions, and those recovering from injury. Having both blood and muscle-based diagnostic markers means fatigue could eventually be detected through simple blood tests, potentially enabling earlier intervention and personalized treatment approaches using CBD or other targeted therapies."

📄 Original Abstract

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.

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