Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay.

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.

Identification of neurotoxic compounds in cyanobacteria exudate mixtures.

Release of toxic cyanobacterial secondary metabolites threatens biosecurity, foodwebs and public health. Microcystis aeruginosa (Ma), the dominant species in global freshwater cyanobacterial blooms, produces exudates (MaE) that cause adverse outcomes including nerve damage. Previously, we identified > 300 chemicals in MaE. It is critical to investigate neurotoxicity mechanisms of active substances among this suite of Ma compounds. Here, we screened 103 neurotoxicity assays from the ToxCast database to reveal targets of action of MaE using machine learning. We then built a potential Adverse Outcome Pathway (AOP) to identify neurotoxicity mechanisms of MaE as well as key targets. Finally, we selected potential neurotoxins matched with those targets using molecular docking. We found 38 targets that were inhibited and eight targets that were activated, collectively mainly related to neurotransmission (i.e. cholinergic, dopaminergic and serotonergic neurotransmitter systems). The potential AOP of MaE neurotoxicity could be caused by blocking calcium voltage-gated channel (CACNA1A), because of antagonizing neurotransmitter receptors, or because of inhibiting solute carrier transporters. We identified nine neurotoxic MaE compounds with high affinity to those targets, including LysoPC(16:0), 2-acetyl-1-alkyl-sn-glycero-3-phosphocholine, egonol glucoside, polyoxyethylene (600) monoricinoleate, and phytosphingosine. Our study enhances understanding of neurotoxicity mechanisms and identifies neurotoxins in cyanobacterial bloom exudates, which may help identify priority compounds for cyanobacteria management.