International immunopharmacology · 2025
BOLA3 as a key protein for the treatment of diabetic skeletal muscle atrophy.
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Yang Leiming, Chen Mi, Song Yan, Sun Qiyu, Zhang Shuo, Wang Pengyu, Liu Xiufen, Huang Qi, Zhang Youzhi
Аннотация
OBJECTIVE: Skeletal muscle is crucial for glucose metabolism, but diabetes impairs this function, leading to muscle atrophy. Although the mutation of BOLA Family Member 3 (BOLA3) resulted in disease Multiple Mitochondrial Dysfunctions Syndrome, the role of which in diabetic muscle atrophy is unclear.
METHODS: Firstly, datasets were downloaded and assessed from skeletal muscle tissue with diabetic individuals in GEO database and key genes were screened using weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Subsequently, external validation datasets were applied to screen and verify specific genes. We also used qRT-PCR, Western Blot, H&E staining and MitoSOX in vitro and vivo experiments. Finally, molecular docking was employed to predict potential therapeutic drugs.
RESULTS: BOLA3 and LPIN1, were screened by WGCNA, machine learning and external datasets verification. Among these, only BOLA3 emerged positively correlated with ferroptosis marker. Notably, muscle atrophy and ferroptosis phenotypes were observed in diabetic mice with reduced BOLA3 levels. Moreover, BOLA3 knockdown in C2C12 cells confirmed its association with both ferroptosis and muscle atrophy, with a surge in mitochondrial ROS emerging as a critical factor. At last, based on the protein structure of BOLA3, handelin showed the highest binding affinity to BOLA3.
CONCLUSION: BOLA3 is a potential biomarker and therapeutic target for diabetic skeletal muscle atrophy. Handelin may represent a promising therapeutic candidate for modulating BOLA3 function in diabetes.
METHODS: Firstly, datasets were downloaded and assessed from skeletal muscle tissue with diabetic individuals in GEO database and key genes were screened using weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Subsequently, external validation datasets were applied to screen and verify specific genes. We also used qRT-PCR, Western Blot, H&E staining and MitoSOX in vitro and vivo experiments. Finally, molecular docking was employed to predict potential therapeutic drugs.
RESULTS: BOLA3 and LPIN1, were screened by WGCNA, machine learning and external datasets verification. Among these, only BOLA3 emerged positively correlated with ferroptosis marker. Notably, muscle atrophy and ferroptosis phenotypes were observed in diabetic mice with reduced BOLA3 levels. Moreover, BOLA3 knockdown in C2C12 cells confirmed its association with both ferroptosis and muscle atrophy, with a surge in mitochondrial ROS emerging as a critical factor. At last, based on the protein structure of BOLA3, handelin showed the highest binding affinity to BOLA3.
CONCLUSION: BOLA3 is a potential biomarker and therapeutic target for diabetic skeletal muscle atrophy. Handelin may represent a promising therapeutic candidate for modulating BOLA3 function in diabetes.