College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
The Institute for Advanced Studies, Shaoxing University, Shaoxing, China.
Appl Environ Microbiol. 2024 Nov 20;90(11):e0193624. doi: 10.1128/aem.01936-24. Epub 2024 Nov 4.
Cyanobacterial blooms pose environmental and health risks due to their production of toxic secondary metabolites. While current methods for assessing these risks have focused primarily on bloom frequency and intensity, the lack of comprehensive and comparable data on cyanotoxins makes it challenging to rigorously evaluate these health risks. In this study, we examined 750 metagenomic data sets collected from 103 lakes worldwide. Our analysis unveiled the diverse distributions of cyanobacterial communities and the genes responsible for cyanotoxin production across the globe. Our approach involved the integration of cyanobacterial biomass, the biosynthetic potential of cyanotoxin, and the potential effects of these toxins to establish potential cyanobacterial health risks. Our findings revealed that nearly half of the lakes assessed posed medium to high health risks associated with cyanobacteria. The regions of greatest concern were East Asia and South Asia, particularly in developing countries experiencing rapid industrialization and urbanization. Using machine learning techniques, we mapped potential cyanobacterial health risks in lakes worldwide. The model results revealed a positive correlation between potential cyanobacterial health risks and factors such as temperature, NO emissions, and the human influence index. These findings underscore the influence of these variables on the proliferation of cyanobacterial blooms and associated risks. By introducing a novel quantitative method for monitoring potential cyanobacterial health risks on a global scale, our study contributes to the assessment and management of one of the most pressing threats to both aquatic ecosystems and human health.
Our research introduces a novel and comprehensive approach to potential cyanobacterial health risk assessment, offering insights into risk from a toxicity perspective. The distinct geographical variations in cyanobacterial communities coupled with the intricate interplay of environmental factors underscore the complexity of managing cyanobacterial blooms at a global scale. Our systematic and targeted cyanobacterial surveillance enables a worldwide assessment of cyanobacteria-based potential health risks, providing an early warning system.
由于产生有毒次生代谢物，蓝藻水华对环境和健康构成风险。虽然目前评估这些风险的方法主要集中在水华的频率和强度上，但缺乏关于蓝藻毒素的全面和可比数据，使得严格评估这些健康风险具有挑战性。在这项研究中，我们检查了来自全球 103 个湖泊的 750 个宏基因组数据集。我们的分析揭示了全球范围内蓝藻群落和负责产生蓝藻毒素的基因的多样分布。我们的方法涉及蓝藻生物量、蓝藻毒素生物合成潜力以及这些毒素的潜在影响的整合，以建立潜在的蓝藻健康风险。我们的研究结果表明，评估的近一半湖泊存在与蓝藻相关的中到高健康风险。最令人担忧的地区是东亚和南亚，特别是在经历快速工业化和城市化的发展中国家。我们使用机器学习技术绘制了全球湖泊中潜在的蓝藻健康风险图。模型结果显示，潜在的蓝藻健康风险与温度、NO 排放和人类影响指数等因素呈正相关。这些发现强调了这些变量对蓝藻水华的增殖及其相关风险的影响。通过引入一种新的全球范围内监测潜在蓝藻健康风险的定量方法，我们的研究有助于评估和管理对水生生态系统和人类健康最紧迫的威胁之一。
我们的研究引入了一种新的综合方法来评估潜在的蓝藻健康风险，从毒性角度提供了有关风险的见解。蓝藻群落的明显地理差异以及环境因素的复杂相互作用突出了在全球范围内管理蓝藻水华的复杂性。我们的系统和有针对性的蓝藻监测使我们能够对全球范围内基于蓝藻的潜在健康风险进行评估，提供早期预警系统。
