Back

Sulfasalazine promotes myogenic differentiation via increasing of fast myosin heavy chain isoforms

Cho, S.; Choi, S.; Chang, H.; Tran, N. K.; Lee, H.; Ryu, S.; Park, M.; Choi, S.; Park, J.; Park, J.

2023-08-16 biochemistry
10.1101/2023.08.15.553466 bioRxiv
Show abstract

Sarcopenia is a debilitating condition characterized by the progressive and generalized degeneration of skeletal muscle mass and function. As of now, there is no approved pharmacological treatment for sarcopenia. Previously, our research revealed that Yin Yang 1 (YY1) plays a crucial role in PHD finger protein 20 (PHF20)-mediated myogenic differentiation. A significant enhancement in YY1 transcription, mediated by PHF20, was observed in C2C12 myoblasts. Within skeletal muscle, YY1 is traditionally considered to inhibit myogenesis by directly repressing the synthesis of late-stage differentiation genes, such as skeletal muscle actin, muscle creatine kinase, and myosin heavy chain IIb. Through screening of a drug library using a PHF20/YY1 promoter reporter assay, sulfasalazine emerged as a promising candidate. Sulfasalazine is known for its anti-inflammatory and immunomodulatory properties and is commonly prescribed for autoimmune diseases. In this study, the treatment of C2C12 myoblasts with sulfasalazine accelerated the myogenic differentiation and bolstered the gene and protein expression of fast myosin heavy chain via a metabolic shift towards glycolysis. Additionally, oral administration of sulfasalazine demonstrated improvement in physical performance in aged mice, as well as in mice models with hindlimb disuse or damage. Moreover, sulfasalazine exhibited a remarkable ability to facilitate the recovery of muscle fibers damaged by Velcro immobilization. Collectively, our findings suggest that sulfasalazine could represent a novel therapeutic avenue for the amelioration of muscle weakness, including sarcopenia.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.