Characterizing cyanopeptides and transformation products in freshwater: integrating targeted, suspect, and non-targeted analysis with in silico modeling.

Abstract

Harmful algal blooms (HABs) pose significant risks to environmental and public health, primarily through cyanotoxin production. Influenced by anthropogenic and climatic factors, cyanobacteria require advanced methods for identifying and characterizing their secondary metabolites. This study presents a multi-step approach to investigate the most abundant cyanopeptides in freshwater samples from agricultural and urban areas, aiming to improve their characterization and understand their environmental fate. A targeted method was developed to quantify 28 cyanopeptides across seven families, being one of the most extensive quantitative analyses of cyanopeptides. Significant concentrations of 14 congeners were detected, ranging from 0.038 to 5.68 µg L^-1. A suspect screening method was developed and applied to expand detection, integrating CyanoMetDB and in silico modeling for the prediction of molecular features, increasing confidence in characterization. This approach enabled the identification of 26 uncommon cyanopeptides, including the newly characterized [DMAdda⁵, GluOMe⁶]microcystin-LHty. Additionally, a novel non-targeted analysis method was developed, combining compound class search, in silico modeling, and the enviPath UG & Co KG biotransformation prediction tool. This new strategy led to the identification of seven new transformation products and potential microcystins, including a new dopamine-modified microcystin-YR and the new linear [seco-1/7][Asp³]microcystin-LR. By integrating targeted, suspect, and non-targeted approaches, this study significantly enhanced cyanopeptide detection and characterization, providing valuable insights for environmental monitoring and public health protection.