Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water.
Decoding the metabolic response of Escherichia coli for sensing trace heavy metals in water.
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DOI：
10.1073/pnas.2210061120
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发表时间：
2023-02-14
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
作者：
当与光学传感器集成时，大肠杆菌的生化网络应激反应报告了水中重金属污染物的存在。对铬和砷暴露后释放的代谢物的振动光谱进行机器学习分析，检测到的浓度比导致细胞死亡的浓度低108倍。确定重金属类型和浓度的准确率超过92%，有望用于水质变化的纵向监测。进一步证明，训练算法的转移学习可以推广到看不见的自来水和废水样本，这些样本的数据采集需要不到10分钟的时间来评价水质。工业和农业废物造成的重金属污染对供水构成越来越大的威胁。对饮用水和农业水源中的有毒金属进行频繁和广泛的监测是必要的，以防止它们在人类、植物和动物体内积累，从而导致疾病和环境破坏。在这里，细菌的代谢应激反应被用来报告水中是否存在重金属离子，方法是将离子转换为化学信号，然后使用振动光谱的机器学习分析来提取指纹。表面增强拉曼散射表面放大细菌裂解物中的化学信号，并快速生成机器学习算法解码复杂光谱数据所需的大型、可重复的数据集。分类和回归算法实现了As3+的0.5 PM和Cr6+的6.8 PM的检测限值，比世界卫生组织推荐的限值低100,000倍，并准确地量化了六个数量级的分析物浓度，从而能够对污染物水平上升进行早期预警。训练后的算法对不同杂质的水样具有通用性；对自来水和废水的水质评价准确率为92%。
The biochemical network stress response of Escherichia coli reports the presence of heavy metal contaminants in water when integrated with optical sensors. Machine learning analysis of the vibrational spectra of metabolites released in response to chromium and arsenic exposure detects concentrations 108 times lower than those leading to cell death. Heavy metal type and concentration are determined with accuracy exceeding 92%, which is promising for longitudinally monitoring changes in water quality. Transfer learning of trained algorithms is further demonstrated to be generalizable to unseen tap water and wastewater samples where data acquisition requires less than 10 min for evaluation of water quality. Heavy metal contamination due to industrial and agricultural waste represents a growing threat to water supplies. Frequent and widespread monitoring for toxic metals in drinking and agricultural water sources is necessary to prevent their accumulation in humans, plants, and animals, which results in disease and environmental damage. Here, the metabolic stress response of bacteria is used to report the presence of heavy metal ions in water by transducing ions into chemical signals that can be fingerprinted using machine learning analysis of vibrational spectra. Surface-enhanced Raman scattering surfaces amplify chemical signals from bacterial lysate and rapidly generate large, reproducible datasets needed for machine learning algorithms to decode the complex spectral data. Classification and regression algorithms achieve limits of detection of 0.5 pM for As3+ and 6.8 pM for Cr6+, 100,000 times lower than the World Health Organization recommended limits, and accurately quantify concentrations of analytes across six orders of magnitude, enabling early warning of rising contaminant levels. Trained algorithms are generalizable across water samples with different impurities; water quality of tap water and wastewater was evaluated with 92% accuracy.
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影响因子：
4.5
作者：
Lambert G;Kussell E
通讯作者：
Kussell E
影响因子：
16.6
作者：
Ganesh, Swarna;Venkatakrishnan, Krishnan;Tan, Bo
通讯作者：
Tan, Bo
影响因子：
10.5
作者：
Battesti A;Majdalani N;Gottesman S
通讯作者：
Gottesman S
影响因子：
7.4
作者：
Fang M;Ivanisevic J;Benton HP;Johnson CH;Patti GJ;Hoang LT;Uritboonthai W;Kurczy ME;Siuzdak G
通讯作者：
Siuzdak G
影响因子：
4.2
作者：
Geissler, Matthias;Brassard, Daniel;Veres, Teodor
通讯作者：
Veres, Teodor