A Data-Independent Methodology for the Structural Characterization of Microcystins and Anabaenopeptins Leading to the Identification of Four New Congeners
Bibliographic record
Abstract
Toxin-producing cyanobacteria are responsible for the presence of hundreds of bioactive compounds in aquatic environments undergoing increasing eutrophication. The identification of cyanotoxins is still emerging, due to the great diversity of potential congeners, yet high-resolution mass spectrometry (HRMS) has the potential to deepen this knowledge in aquatic environments. In this study, high-throughput and sensitive on-line solid-phase extraction ultra-high performance liquid chromatography (SPE-UHPLC) coupled to HRMS was applied to a data-independent acquisition (DIA) workflow for the suspect screening of cyanopeptides, including microcystin and anabaenopeptin toxin classes. The unambiguous characterization of 11 uncommon cyanopeptides was possible using a characterization workflow through extensive analysis of fragmentation patterns. This method also allowed the characterization of four unknown cyanotoxins ([Leu1, Ser7] MC-HtyR, [Asp3]MC-RHar, AP731, and AP803). The quantification of 17 common cyanotoxins along with the semi-quantification of the characterized uncommon cyanopeptides resulted with the identification of 23 different cyanotoxins in 12 lakes in Canada, United Kingdom and France. The concentrations of the compounds varied between 39 and 41,000 ng L−1. To our knowledge, this is the first DIA method applied for the suspect screening of two families of cyanopeptides simultaneously. Moreover, this study shows the great diversity of cyanotoxins in lake water cyanobacterial blooms, a growing concern in aquatic systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".