Saline lands pose significant environmental and agricultural challenges due to high soil salinity, which disrupts water uptake and ionic balances, limiting conventional crop productivity. Yet, certain endemic plants thrive under these conditions and may offer untapped bioactive compounds. This study proposes a novel platform that integrates species distribution modeling (SDM) and advanced metabolomics to screen for bioactive secondary metabolites, using Buchanania siamensis, a rare native species, as a case study. An ensemble SDM model incorporating environmental and soil parameters identified salinity as a critical factor influencing the species' distribution. Leaf samples were collected from naturally growing trees at both saline (SS) and non-saline (NS) sites. LC-QTOF metabolomic analysis annotated a total of 1106 metabolites across the leaf samples, with 175 found to be significantly different between the groups. Among them, 108 metabolites exhibited higher abundance in the SS group. Additionally, antioxidant assays including DPPH, FRAP, and total phenolic content tests, were conducted. Data were further analyzed using O-PLSR models to identify key metabolites most relevant to antioxidant properties. The results indicated that afzelin was the key metabolite responsible for the antioxidant properties of B. siamensis, with significantly higher levels in SS compared to NS samples (pâ¯<â¯0.05), as determined by peak area. By leveraging this multidisciplinary approach, we propose a framework to support both bioactive compound discovery and saline land reclamation, offering potential environmental and pharmaceutical benefits. This integrated platform may support pharmaceutical research, particularly in drug discovery efforts.
Integrating spatial mapping and metabolomics: A novel platform for bioactive compound discovery and saline land reclamation.
阅读:9
作者:Andriyas Tushar, Leksungnoen Nisa, Pongchaidacha Pichaya, Uthairangsee Arashaporn, Uthairatsamee Suwimon, Doomnil Peerapat, Ku-Or Yongkriat, Ngernsaengsaruay Chatchai, Andriyas Sanyogita, Yarnvudhi Arerut, Tansawat Rossarin
| 期刊: | Computational and Structural Biotechnology Journal | 影响因子: | 4.100 |
| 时间: | 2025 | 起止号: | 2025 Apr 30; 27:1741-1753 |
| doi: | 10.1016/j.csbj.2025.04.035 | ||
特别声明
1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。
2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。
3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。
4、投稿及合作请联系:info@biocloudy.com。
