Simultaneous Detection of 14 Microcystin Congeners from Tissue Samples Using UPLC- ESI-MS/MS and Two Different Deuterated Synthetic Microcystins as Internal Standards

International audienceCyanobacterial microcystins (MCs), potent serine/threonine-phosphatase inhibitors, pose an increasing threat to humans. Current detection methods are optimised for water matrices with only a few MC congeners simultaneously detected. However, as MC congeners are known to differ in their toxicity, methods are needed that simultaneously quantify the congeners present, thus allowing for summary hazard and risk assessment. Moreover, detection of MCs should be expanded to complex matrices, e.g., blood and tissue samples, to verify in situ MC concentrations, thus providing for improved exposure assessment and hazard interpretation. To achieve this, we applied two synthetic deuterated MC standards and optimised the tissue extraction protocol for the simultaneous detection of 14 MC congeners in a single ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) run. This procedure was validated using plasma and liver homogenates of mice (male and female) spiked with deuterated MC standards. For proof of concept, tissue and plasma samples from mice i.p. injected with MC-LR and MC-LF were analysed. While MC-LF was detected in all tissue samples of both sexes, detection of MC-LR was restricted to liver samples of male mice, suggesting different toxicokinetics in males, e.g., transport, conjugation or protein binding. Thus, deconjugation/-proteinisation steps should be employed to improve detection of bound MC. Key Contribution: The use of deuterated microcystin standards and an improved extraction procedure using UPLC-MS/MS analytics, provides for the simultaneous detection of fourteen microcystin congeners. Thus, it allows more accurate quantitation of total microcystin load of a given sample in complex matrices like blood or tissue, and therefore better hazard interpretation

Rollins Alumni Record, October 1999

Rite of Passage: That bittersweet first day at Rollin

Total Synthesis of Microcystin-LR, Microcystin-LF, and Unnatural Derivatives thereof

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Total Synthesis of Microcystin-LF and Derivatives Thereof

Microcystins (MCs) are highly toxic natural products which are produced by cyanobacteria. They can be released to the water during harmful algal blooms and are a serious threat to animals and humans. Described is the total synthesis of the cyanotoxin microcystin-LF (MC-LF, <b>1a</b>) and two derivatives thereof. Deuterated derivative <b>1b</b> is of interest as an internal standard during MC quantification in biological samples by mass spectrometry and alkyne-labeled <b>1c</b> can be employed for toxin derivatization by click chemistry with an azide-containing reporter molecule or as an activity-based probe to identify interaction partners. Application of <i>tert</i>-butyl ester protecting groups for <i>erythro</i>-β-d-methylaspartic acid and γ-d-glutamic acid were key for an isomerization-free synthesis. The analytical data of synthetic MC-LF were identical to those of an authentic sample of the natural product. All derivatives <b>1a</b>–<b>c</b> were determined to be potent inhibitors of protein phosphatase-1 with similar activity

Machine learning prediction of cyanobacterial toxin (microcystin) toxicodynamics in humans

Microcystins (MC) represent a family of cyclic peptides with approx. 250 congeners presumed harmful to human health due to their ability to inhibit ser/thr-proteinphosphatases (PPP), albeit all hazard and risk assessments (RA) are based on data of one MC-congener (MC-LR) only. MC congener structural diversity is a challenge for the risk assessment of these toxins, especially as several different PPPs have to be included in the RA. Consequently, the inhibition of PPP1, PPP2A and PPP5 was determined with 18 structurally different MC and demonstrated MC congener dependent inhibition activity and a lower susceptibility of PPP5 to inhibition than PPP1 and PPP2A. The latter data were employed to train a machine learning algorithm that should allow prediction of PPP inhibition (toxicity) based on MCs 2D chemical structure. IC50 values were classified in toxicity classes and three machine learning models were used to predict the toxicity class, resulting in 80-90% correct predictions.publishe