The Elucidation of Planktothrix Cyanopeptide Profiles using Mass Spectrometry-Based Metabolomics
Bibliographic record
Abstract
The magnitude and frequency of cyanobacteria blooms are increasing due to environmental changes including increased temperatures, eutrophication and anthropogenic inputs.Cyanobacteria are a source of diverse natural products that can negatively impact ecosystem and human health.The most commonly studied cyanopeptides are microcystins, a group of potent liver toxins and possible human carcinogens.Together with microcystins, common bloom forming cyanobacteria co-produce other cyanopeptide groups such as aeruginosins, anabaenopeptins, cyanobactins, cyanopeptolins, microginins and microviridins.There is a lack of toxicological and environmental concentration data for these lesser studied cyanopeptide groups.Advances in analytical techniques allow further comprehension of the complex metabolomes of cyanobacteria.Mass spectrometry-based metabolomic techniques can be applied to decipher the cyanopeptide profiles of common bloom-forming cyanobacterial strains.In this study, the cyanopeptide profiles of Planktothrix rubescens CPCC 507, P. agardhii CPCC 720, P. rubescens CPCC 731, P. rubescens CPCC 732, P. rubescens CPCC 733, and Oscillatoria tenuis CPCC 735 (possibly Planktothrix sp.) isolated from Ontario and Quebec lakes, were characterized using mass spectrometry-based metabolomics.Cyanopeptide chemical classes were determined based on shared structural features identified by characteristic product ions within their tandem mass spectra.Metabolomic strategies including multivariate analysis, diagnostic fragmentation filtering and global natural product society (GNPS) molecular networking were implemented to decipher strain-specific cyanopeptide profiles from metabolomic data sets.Two-hundred and twenty-five cyanopeptides were identified from the extracts of the six investigated strains.Each strain produced different mixtures of microcystins, aeruginosins, anabaenopeptins, cyanopeptolins and microviridins.Microginin and cyanobactin groups were not identified from studied strains.The most diverse cyanopeptide groups were cyanopeptolins and anabaenopeptins with eighty and sixty-one unique congeners, respectfully.This work provides critical information for the cyanopeptide composition present in Planktothrix extracts, consequently aiding in the prioritization of new natural products for isolation and risk characterizations.Furthermore, these complex mixtures of List of abbreviations: (HR)-MS/MS -high-resolution tandem mass spectrometry A/Ala -alanine AChE -acetylcholinesterase ACN -acetonitrile ACP -acyl carrier protein Adda -3-amino-9-methoxy-10-phenyl-2,6,8-trimethyldeca-4,6-dienoic acid AEG -N-2-aminoethyglycine Ahda -3-amino-2-hydroxy decanoic acid Ahoa -3-amino-2-hydroxy octanoic acid Ahp -3-amino-6-hydroxy-2-piperidone ALS/PDC -amyotrophic lateral sclerosis/Parkinsonism dementia complex ATXa -Anatoxin-a AP -anabaenopeptin
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".