Ensemble Machine learning model identified citrusinol as functional food candidate for improving myotube differentiation and controlling CT26-Induced myotube atrophy

Abstract

Skeletal muscle loss leads to decreased quality of life, increased incidence of chronic disease and mortality. To identify functional food materials to alleviate muscle atrophy, we built a multitarget-based machine learning system to identify novel phytochemicals that can inhibit TGF-β, which induce muscle weakness, and increase PGC-1α, a target of exercise mimetics. The multitarget-based machine learning system is built as an ensemble model of four algorithms with each optimal input representation. Citrusinol was identified by our model, and its anti-atrophy effects were validated using C2C12 cells. Citrusinol enhanced protein synthesis via AKT/mTORC1 pathway, increased myogenic differentiation, and increased PGC-1α and its downstream regulators, MEF2A and TFAM. Citrusinol attenuated CT26–induced myotube atrophy by blocking TGF-β, p-SMAD3, MAFbx, and TGF-β-induced MuRF1 and p-SMAD3. These results suggest that the proposed model can effectively identify functional foods to manage muscle atrophy; additionally, citrusinol was demonstrated as a promising candidate for future animal experiments.

Keywords

• Functional food
• Muscle atrophy
• Machine learning
• Citrusinol
• TGF-β pathway
• PGC-1α pathway