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Zi J, Barker J, Zi Y, MacIsaac HJ, Zhou Y, Harshaw K, Chang X. Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay. J Hazard Mater 2024; 470:134170. [PMID: 38613957 DOI: 10.1016/j.jhazmat.2024.134170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/18/2024] [Accepted: 03/28/2024] [Indexed: 04/15/2024]
Abstract
Cyanobacterial blooms, often dominated by Microcystis aeruginosa, are capable of producing estrogenic effects. It is important to identify specific estrogenic compounds produced by cyanobacteria, though this can prove challenging owing to the complexity of exudate mixtures. In this study, we used untargeted metabolomics to compare components of exudates from microcystin-producing and non-microcystin-producing M. aeruginosa strains that differed with respect to their ability to produce microcystins, and across two growth phases. We identified 416 chemicals and found that the two strains produced similar components, mainly organoheterocyclic compounds (20.2%), organic acids and derivatives (17.3%), phenylpropanoids and polyketides (12.7%), benzenoids (12.0%), lipids and lipid-like molecules (11.5%), and organic oxygen compounds (10.1%). We then predicted estrogenic compounds from this group using random forest machine learning. Six compounds (daidzin, biochanin A, phenylethylamine, rhein, o-Cresol, and arbutin) belonging to phenylpropanoids and polyketides (3), benzenoids (2), and organic oxygen compound (1) were tested and exhibited estrogenic potency based upon the E-screen assay. This study confirmed that both Microcystis strains produce exudates that contain compounds with estrogenic properties, a growing concern in cyanobacteria management.
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Affiliation(s)
- Jinmei Zi
- Yunnan Collaborative Innovation Center for Plateau Lake Ecology and Environmental Health, College of Agronomy and Life Sciences, Kunming University, Kunming 650214, China; Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada
| | - Justin Barker
- Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada; Maps, Data, and Government Information Centre, Trent University, Peterborough, Ontario K9L 0G2, Canada
| | - Yuanyan Zi
- Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada
| | - Hugh J MacIsaac
- Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada; School of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, China
| | - Yuan Zhou
- The Ecological and Environmental Monitoring Station of DEEY in Kunming, Kunming 650228, China; School of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, China
| | - Keira Harshaw
- Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada
| | - Xuexiu Chang
- Yunnan Collaborative Innovation Center for Plateau Lake Ecology and Environmental Health, College of Agronomy and Life Sciences, Kunming University, Kunming 650214, China; Great Lakes Institute for Environmental Research, University of Windsor, Windsor, Ontario N9B 3P4, Canada.
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