| Autor: |
Justin Jaesuk Lee, Byeong Min Ahn, Nara Kim, Yuran Noh, Hee Ju Ahn, Eun Sol Hwang, Jaewon Shim, Ki Won Lee, Young Jin Jang |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Functional Foods, Vol 104, Iss , Pp 105542- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1756-4646 |
| DOI: |
10.1016/j.jff.2023.105542 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full Text from ScienceDirect