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Détails bibliographiques
Auteurs principaux: Wang, Chao, Xia, Mengsheng, Su, Yixi, Shu, Yuexuan, Meng, Weizhao, Ma, Jiahao, Chen, Jiwei, Wang, Pengbin, Seymour, Mathew, Fu, Weiqi
Format: Artículo científico
Langue:en
Publié: Environmental science & technology 2025
Sujets:
Accès en ligne:https://pubmed.ncbi.nlm.nih.gov/40539973/
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  • Production of the Neurotoxin BMAA by Marine Diatoms Drives Its Widespread Occurrence in Estuarine and Coastal Ecosystems. Wang, Chao Xia, Mengsheng Su, Yixi Shu, Yuexuan Meng, Weizhao Ma, Jiahao Chen, Jiwei Wang, Pengbin Seymour, Mathew Fu, Weiqi Diatoms Amino Acids, Diamino Neurotoxins Ecosystem Cyanobacteria Toxins Estuaries Nitrogen Seawater Phytoplankton Phytoplankton are the primary producers of marine neurotoxins such as β--methylamino-l-alanine (BMAA), which cause seafood poisoning outbreaks in estuarine and coastal regions. BMAA has gained much attention for its pathogenic link to Alzheimer's and Parkinson's disease. However, whether BMAA accumulates in diatoms under fluctuations in estuarine and coastal areas remains largely unknown. In this study, we investigated BMAA biosynthesis in the model diatom under simulated conditions of fluctuating nitrogen and salinity in estuarine regions. BMAA was detectable in under all simulated seawater or hyper-eutrophic conditions, although nitrogen source and salinity interactively impact BMAA accumulation. In addition, high-nitrogen wastewater conditions promoted carbon metabolism, including the Calvin cycle, glycolysis, fatty acid biosynthesis, and decreased nitrogen metabolism and BMAA production. Finally, we proposed and elucidated a complete biosynthetic pathway of BMAA originating from glutamate, incorporating catalysis by -adenosylmethionine (SAM)-dependent methyltransferases in diatoms, as inferred through artificial intelligence-based pathway analysis. This study marks the first attempt to decipher the BMAA metabolic pathway in diatoms, which provides a paradigm shift to identify conditions that trigger toxin production and also provides valuable metrics to address the complexity and unpredictability of monitoring and forecasting algal blooms.