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. 2024 Sep 17;58(37):16560-16569.
doi: 10.1021/acs.est.4c04813. Epub 2024 Aug 30.

Tracking Extensive Portfolio of Cyanotoxins in Five-Year Lake Survey and Identifying Indicator Metabolites of Cyanobacterial Taxa

Affiliations

Tracking Extensive Portfolio of Cyanotoxins in Five-Year Lake Survey and Identifying Indicator Metabolites of Cyanobacterial Taxa

Xuejian Wang et al. Environ Sci Technol. .

Abstract

Cyanobacterial blooms require monitoring, as they pose a threat to ecosystems and human health, especially by the release of toxins. Along with widely reported microcystins, cyanobacteria coproduce other bioactive metabolites; however, information about their dynamics in surface waters is sparse. We investigated dynamics across full bloom successions throughout a five-year lake monitoring campaign (Greifensee, Switzerland) spanning 150 sampling dates. We conducted extensive suspect screening of cyanobacterial metabolites using the database CyanoMetDB. Across all 850 samples, 35 metabolites regularly co-occurred. Microcystins were present in 70% of samples, with [d-Asp3,(E)-Dhb7]MC-RR reaching concentrations of 70 ng/L. Anabaenopeptins, meanwhile, were detected in 95% of all samples with concentrations of Oscillamide Y up to 100-fold higher than microcystins. Based on LC-MS response and frequency, we identified indicator metabolites exclusively produced by one of three cyanobacteria isolated from the lake, these being [d-Asp3,(E)-Dhb7]MC-RR from Planktothrix sp. G2020, Microginin 761B from Microcystis sp. G2011, and Ferintoic acid B from Microcystis sp. G2020. These indicators showed distinct temporal trends and peaking seasons that reflect the variance in either the abundance of the producing cyanobacteria or their toxin production dynamics. Our approach demonstrates that selecting high LC-MS response and frequent and species-specific indicator metabolites can be advantageous for cyanobacterial monitoring.

Keywords: cyanopepetides; harmful algal bloom; microcystin; monitoring; suspect screening.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Concentrations in ng/L of [d-Asp3,(E)-Dhb7]MC-RR (top) and Anabaenopeptin A (bottom) in Lake Greifensee from 2019 to 2023, with n = 21–36 annual sampling dates for biomass (black, triplicates) and aqueous samples (blue, triplicates). Dashed lines indicate the limit of quantification for each annual data set run on separate LC-MS sequences each year. Each data point represents one sample out of triplicate samples collected on each sampling date. Solid lines connect the average concentration of these triplicate samples on each sampling date, while white spaces between data sets reflect the dates where no sampling took place.
Figure 2
Figure 2
(a) Relative detection frequency and (b) LC-MS response (peak area) for 35 metabolites in Lake Greifensee over a 5-year sampling campaign spanning from 2019 to 2023 (color-coded), and (c) presence of representative metabolites in cyanobacteria isolated from Lake Greifensee, Microcystis G2011 (blue), Planktothrix G2020 (red), and Micorcystis G2020 (yellow ocher).
Figure 3
Figure 3
Time series of indicator metabolites: Oscillamide Y (black) produced by all three species from Lake Greifensee, [d-Asp3, (E)-Dhb7]MC-RR (red) produced by Planktothrix G2020 isolate, Microginin 761B (blue) produced by Microcystis G2011 isolate, and Ferintoic acid B (yellow ocher) produced by Microcystis G2020 isolate (see Figure S12 with y-axis in log-scale).

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