Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy
Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease and lacks guaranteed pharmacological therapeutic options. In this study, we applied a network-based framework for comprehensively identifying candidate flavonoids for the prevention and/or treatment of NAFLD. F...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2022.892559/full |
| _version_ | 1811255782654607360 |
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| author | Won-Yung Lee Won-Yung Lee Choong-Yeol Lee Jin-Seok Lee Chang-Eop Kim |
| author_facet | Won-Yung Lee Won-Yung Lee Choong-Yeol Lee Jin-Seok Lee Chang-Eop Kim |
| author_sort | Won-Yung Lee |
| collection | DOAJ |
| description | Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease and lacks guaranteed pharmacological therapeutic options. In this study, we applied a network-based framework for comprehensively identifying candidate flavonoids for the prevention and/or treatment of NAFLD. Flavonoid-target interaction information was obtained from combining experimentally validated data and results obtained using a recently developed machine-learning model, AI-DTI. Flavonoids were then prioritized by calculating the network proximity between flavonoid targets and NAFLD-associated proteins. The preventive effects of the candidate flavonoids were evaluated using FFA-induced hepatic steatosis in HepG2 and AML12 cells. We reconstructed the flavonoid-target network and found that the number of re-covered compound-target interactions was significantly higher than the chance level. Proximity scores have successfully rediscovered flavonoids and their potential mechanisms that are reported to have therapeutic effects on NAFLD. Finally, we revealed that discovered candidates, particularly glycitin, significantly attenuated lipid accumulation and moderately inhibited intracellular reactive oxygen species production. We further confirmed the affinity of glycitin with the predicted target using molecular docking and found that glycitin targets are closely related to several proteins involved in lipid metabolism, inflammatory responses, and oxidative stress. The predicted network-level effects were validated at the levels of mRNA. In summary, our study offers and validates network-based methods for the identification of candidate flavonoids for NAFLD. |
| first_indexed | 2024-04-12T17:30:20Z |
| format | Article |
| id | doaj.art-7b33f0da13214446ab202d242d74179c |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-04-12T17:30:20Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj.art-7b33f0da13214446ab202d242d74179c2022-12-22T03:23:09ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122022-05-011310.3389/fphar.2022.892559892559Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based StrategyWon-Yung Lee0Won-Yung Lee1Choong-Yeol Lee2Jin-Seok Lee3Chang-Eop Kim4Department of Physiology, College of Korean Medicine, Gachon University, Seongnam, South KoreaDepartment of Herbal Formula, College of Korean Medicine, Dongguk University, Goyang-si, South KoreaDepartment of Physiology, College of Korean Medicine, Gachon University, Seongnam, South KoreaInstitute of Bioscience and Integrative Medicine, Daejeon Oriental Hospital of Daejeon University, Daejeon, South KoreaDepartment of Physiology, College of Korean Medicine, Gachon University, Seongnam, South KoreaNonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease and lacks guaranteed pharmacological therapeutic options. In this study, we applied a network-based framework for comprehensively identifying candidate flavonoids for the prevention and/or treatment of NAFLD. Flavonoid-target interaction information was obtained from combining experimentally validated data and results obtained using a recently developed machine-learning model, AI-DTI. Flavonoids were then prioritized by calculating the network proximity between flavonoid targets and NAFLD-associated proteins. The preventive effects of the candidate flavonoids were evaluated using FFA-induced hepatic steatosis in HepG2 and AML12 cells. We reconstructed the flavonoid-target network and found that the number of re-covered compound-target interactions was significantly higher than the chance level. Proximity scores have successfully rediscovered flavonoids and their potential mechanisms that are reported to have therapeutic effects on NAFLD. Finally, we revealed that discovered candidates, particularly glycitin, significantly attenuated lipid accumulation and moderately inhibited intracellular reactive oxygen species production. We further confirmed the affinity of glycitin with the predicted target using molecular docking and found that glycitin targets are closely related to several proteins involved in lipid metabolism, inflammatory responses, and oxidative stress. The predicted network-level effects were validated at the levels of mRNA. In summary, our study offers and validates network-based methods for the identification of candidate flavonoids for NAFLD.https://www.frontiersin.org/articles/10.3389/fphar.2022.892559/fullflavonoidsnon-alcoholic fatty liver diseasenetwork pharmacologynetwork medicinemachine learning |
| spellingShingle | Won-Yung Lee Won-Yung Lee Choong-Yeol Lee Jin-Seok Lee Chang-Eop Kim Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy Frontiers in Pharmacology flavonoids non-alcoholic fatty liver disease network pharmacology network medicine machine learning |
| title | Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy |
| title_full | Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy |
| title_fullStr | Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy |
| title_full_unstemmed | Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy |
| title_short | Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy |
| title_sort | identifying candidate flavonoids for non alcoholic fatty liver disease by network based strategy |
| topic | flavonoids non-alcoholic fatty liver disease network pharmacology network medicine machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2022.892559/full |
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