Identification of neurotoxic compounds in cyanobacteria exudate mixtures

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.

Adverse effects of Microcystis aeruginosa exudates on the filtration, digestion, and reproduction organs of benthic bivalve Corbicula fluminea

Cyanobacteria bloom and the secondary metabolites released by the microorganism are extremely harmful to aquatic animals, yet study on their adverse effects in zoobenthos is rare. Corbicula fluminea widely distributed in freshwater environment with algal blooms. It is a typical filter feeding zoobenthos that may be affected by the secondary metabolites of cyanobacteria due to its high filtering rate. In this study, C. fluminea was exposed to Microcystis aeruginosa exudates (MaE) for 96 h, which was obtained from 5 × 105 cells/mL and 2.5 × 106 cells/mL exponential stage M. aeruginosa culture solution that represented cyanobacteria cell density needs environmental risk precaution control and emergent control, respectively. The responses of C. fluminea critical organs to MaE were analyzed and evaluated based on histopathological sections, antitoxicity biomarkers, and organ function biomarkers. The results showed that all the organs underwent structural disorders, cell vacuolization, apoptosis, and necrosis, and the damage levels increased as MaE concentration increased. The detoxification and antioxidant defense systems biomarkers in each organ response to MaE exposure differently and the level of reaction improved when MaE concentration increased. The siphon rate and acetylcholinesterase activity showed that the filtration function decreased significantly as the MaE concentration increased. Increased activity of glutathione S-transferase and amylase in the digestive gland indicate that it is the major detoxification organ of C. fluminea. Increased vitellogenin concentration and enlarged oocytes in the gonad indicate that MaE may have an estrogenic effect on C. fluminea. This study demonstrates that cyanobacteria threat benthic bivalves by inducing oxidative stress, inhibiting filtering feeding system, and disturbing digestion system and reproduction potential of C. fluminea.

Predicting chemicals' toxicity pathway of female reproductive disorders using AOP7 and deep neural networks

Experimental evidence shows that certain chemicals, particularly endocrine disrupting chemicals, may negatively affect the female reproductive system, thereby lowering women's fertility. However, humans are constantly exposed to a number of different chemicals with limited or no experimental data regarding their effect and the mechanism of action in the female reproductive system. To predict chemical hazards to the female reproductive system, we used a previously defined adverse outcome pathway (AOP) that links activation of the peroxisome proliferator-activated receptor γ to the reproductive toxicity in adult females (AOP7) and the Convolutional Deep Neural Network models that produce meaningful predictions when trained on a significant amount of data. The models trained using CompTox assays with intended molecular and biological targets corresponding to AOP7 achieved high performance (over 90% validation accuracy). The integration of AOP7 and Deep Neural Network identified chemicals that could negatively affect female reproduction through the mechanism described in AOP7. We provide a solution to quickly analyze the data and produce machine learning models to identify potentially active chemicals in the female reproductive system. Although we focused on the female reproductive system, this approach could be valid for a number of other chemicals and AOPs if the right data exist.

Comparative metabolomic analysis of exudates of microcystin-producing and microcystin-free Microcystis aeruginosa strains

Cyanobacterial harmful algal blooms (cHABs) dominated by Microcystis aeruginosa threaten the ecological integrity and beneficial uses of lakes globally. In addition to producing hepatotoxic microcystins (MC), M. aeruginosa exudates (MaE) contain various compounds with demonstrated toxicity to aquatic biota. Previously, we found that the ecotoxicity of MaE differed between MC-producing and MC-free strains at exponential (E-phase) and stationary (S-phase) growth phases. However, the components in these exudates and their specific harmful effects were unclear. In this study, we performed untargeted metabolomics based on liquid chromatography-mass spectrometry to reveal the constituents in MaE of a MC-producing and a MC-free strain at both E-phase and S-phase. A total of 409 metabolites were identified and quantified based on their relative abundance. These compounds included lipids, organoheterocyclic compounds, organic acid, benzenoids and organic oxygen compounds. Multivariate analysis revealed that strains and growth phases significantly influenced the metabolite profile. The MC-producing strain had greater total metabolites abundance than the MC-free strain at S-phase, whereas the MC-free strain released higher concentrations of benzenoids, lipids, organic oxygen, organic nitrogen and organoheterocyclic compounds than the MC-producing strain at E-phase. Total metabolites had higher abundance in S-phase than in E- phase in both strains. Analysis of differential metabolites (DMs) and pathways suggest that lipids metabolism and biosynthesis of secondary metabolites were more tightly coupled to growth phases than to strains. Abundance of some toxic lipids and benzenoids DMs were significantly higher in the MC-free strain than the MC-producing one. This study builds on the understanding of MaE chemicals and their biotoxicity, and adds to evidence that non-MC-producing strains of cyanobacteria may also pose a threat to ecosystem health.