MedElevator Войти в исследования
← Поиск публикаций

DOI 10.3389/fpls.2026.1774981

Медицинская библиотека · Frontiers in plant science · 2026

Integrated metabolomic, proteomic, and transcriptomic analyses reveal the production of bioactive metabolites and antidiabetic effects in mature Solanum lasiocarpum fruit.

Li Fangbo, Jiang Zixiao, Chaveerach Arunrat, Konkobo Frédéric Anderson, Ansari Mohd Kafeel Ahmad, Ban Gwendolyn Felocity, Konare Mamadou Abdoulaye, Manjang Lamin, Rochelin Zachary, Yang Yang, Syameera Nurul Aduka, Sudmoon Runglawan, Liu Yangyang, Lee Shiou Yih

СтатьяПеревод пока не подготовлен

Аннотация

Diabetes mellitus is a chronic metabolic disorder that affects millions of people globally. Among three types of diabetes, Type 2 diabetes mellitus (T2DM) is a rapidly growing global health challenge. Despite available modern antidiabetic drugs, patients still struggle with side effects and treatment failure, as an alternative to this, there is a crucial requirement to develop a potential and traditional plant-based medicine which could be a safer sources and multi-target therapies to treat chronic disease like diabetes. Solanum lasiocarpum (S. lasiocarpum) is a sour fruit-vegetable being widely used in Southeast Asia as both food and traditional medicine, including for the management of diabetes. However, its active components and antidiabetic mechanisms have not been systematically explored. In this study, we combined metabolomics, proteomics, and transcriptomics to investigate the bioactive pathways and potential molecular targets of S. lasiocarpum. Untargeted UHPLC-QTOF-MS profiling identified 45 candidate bioactive compounds with good predicted gastrointestinal absorption, and the network pharmacology analysis linked these compounds to 43 diabetes-related human targets. Protein-protein interaction analysis highlighted several core nodes, including TNF, PPARG, IL6, AKT1, and STAT3, and functional enrichment suggested roles in hormone regulation, inflammation, glucose and lipid metabolism, and vascular function. De novo transcriptome assembly and data-independent acquisition-based proteomics of mature S. lasiocarpum fruit showed that central carbon metabolism is highly active and that the shikimate, phenylpropanoid, and flavonoid pathways are strongly expressed at both gene and protein levels. Key enzymes such as EPSPS, PAL, C4H, 4CL, CHS, CHI, F3H, and FLS formed a coherent biosynthetic network supporting sustained production of phenolic and flavonoid metabolites. Integrating these omics layers with target prediction suggests that S. lasiocarpum may exert antidiabetic effects by modulating a TNF-PPARG axis, reducing pro-inflammatory signaling while supporting insulin-sensitizing pathways. Overall, these results support the traditional use of S. lasiocarpum and provide a multi-omics resource to prioritise candidate metabolites, enzymes and targets for follow-up studies. As the pathway links were inferred computationally, the proposed TNF-PPARG-centred mechanism should be regarded as hypothesis-generating and will require validation in experimental models and, ultimately, well-designed human intervention trials.

Для цитирования: Li Fangbo, Jiang Zixiao, Chaveerach Arunrat, Konkobo Frédéric Anderson, Ansari Mohd Kafeel Ahmad, Ban Gwendolyn Felocity, Konare Mamadou Abdoulaye, Manjang Lamin, Rochelin Zachary, Yang Yang, Syameera Nurul Aduka, Sudmoon Runglawan, Liu Yangyang, Lee Shiou Yih. Integrated metabolomic, proteomic, and transcriptomic analyses reveal the production of bioactive metabolites and antidiabetic effects in mature Solanum lasiocarpum fruit.. Frontiers in plant science. 2026. DOI: 10.3389/fpls.2026.1774981.
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…

История · было → стало

Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.

Тип публикации указан по данным источника. Уровень доказательности автоматически не присваивается.