基于代谢学估计活性污泥的微生物组成和预测丝状积聚的预测
Jie Wang1, Feng Ju2, Pingfeng Yu3
1Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental Resource Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Water research
|June 5, 2024
概括
机器学习使用代谢物概况预测活性污泥 (AS) 微生物群落. 这种具有成本效益的方法可以提前警告治疗问题,如丝状膨胀.
科学领域:
- 环境微生物学环境微生物学
- 生物技术是生物技术.
- 污水处理 污水处理 污水处理
背景情况:
- 活性污泥 (AS) 微生物组结构对于废水处理效率至关重要.
- 高通量元基因组学提供了全面的数据,但成本高且耗时.
- 需要使用替代方法进行快速的AS微生物组分析.
研究的目的:
- 开发一个机器学习模型,整合代谢和代谢基因组数据.
- 从代谢物概况预测活性污泥中的微生物丰度.
- 为了能够主动管理废水处理过程.
主要方法:
- 从AS系统收集了大量的微生物和代谢物丰度数据.
- 采用机器学习算法,包括多重线性回归.
- 训练有素的模型预测选定的关键或核心微生物 (MAGs) 的丰度.
主要成果:
- 多重线性回归实现了10个选定的MAGs的高预测准确度 (R2 = 0.70±0.02).
- 在散装过程中准确预测的细菌 (Microtrichaceae,Thiotrichaceae) (14-17.8%的误差).
- 确定了阿斯巴达酸盐,甘氨酸和叶酸作为灌装事件的关键预测代谢物.
结论:
- 代谢物分析可以准确且具有成本效益地预测AS的微生物组成.
- 这种方法提供了早期预警的潜力 (长达一周) 丝状膨胀.
- 为主动,高效和稳定的废水处理管理开创了代谢学.
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