Diagnostic Fragmentation Filtering for Cyanopeptolin Detection
Bibliographic record
Abstract
Abstract Cyanobacteria are ubiquitous photosynthetic prokaryotes that produce structurally diverse bioactive metabolites. Although microcystins are extensively studied, other cyanopeptides produced by common bloom-forming species have received little attention. Cyanopeptolins are a large cyanopeptide group that contain a characteristic 3-amino-6-hydroxy-2-piperidone (Ahp) moiety. In the present study we used diagnostic fragmentation filtering (DFF), a semitargeted liquid chromatography-tandem mass spectrometry (MS/MS) product ion filtering approach, to investigate cyanopeptolin diversity from 5 Microcystis strains and 4 bloom samples collected from lakes in Ontario and Quebec, Canada. Data processing by DFF was used to search MS/MS data sets for pairs of diagnostic product ions corresponding to cyanopeptolin partial sequences. For example, diagnostic product ions at m/z 150.0912 and 215.1183 identified cyanopeptolins with the NMe-Tyr–Phe–Ahp partial sequence. Forty-eight different cyanopeptolins, including 35 new variants, were detected from studied strains and bloom samples. Different cyanopeptolin profiles were identified from each sample. We detected a new compound, cyanopeptolin 1143, from a bloom and elucidated its planar structure from subsequent targeted MS/MS experiments. Diagnostic fragmentation filtering is a rapid, easy-to-perform postacquisition metabolomics strategy for inferring structural features and prioritizing new compounds for further study and dereplication. More work on cyanopeptolin occurrence and toxicity is needed because their concentrations in freshwater lakes after blooms can be similar to those of microcystins. Environ Toxicol Chem 2021;40:1087–1097. © 2020 SETAC Abstract Diagnostic fragmentation filtering metabolomic data processing was applied to search tandem mass spectometry data sets for product ions corresponding to specific cyanopeptolin partial sequences. Diverse cyanopeptolin profiles were identified from bloom samples and cyanobacteria cultures. DFF = diagnostic fragmentation filtering; LC-MS/MS = liquid chromatography tandem mass spectrometry.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".