Characterizing cyanopeptides and transformation products in freshwater: integrating targeted, suspect, and non-targeted analysis with in silico modeling
Bibliographic record
Abstract
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 [DMAdda 5 , GluOMe 6 ]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][Asp 3 ]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. Graphical Abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".