Application of Metabolomics for the Detection and Evaluation of Cyanopeptide Mixtures in Ontario and Quebec Lakes
Bibliographic record
Abstract
The magnitude and frequency of cyanobacteria harmful algae blooms (cHABs) are increasing on a global scale.Anthropogenic nutrient enrichment, and climate change are drivers of bloom formation and favour cyanobacterial dominance in most aquatic ecosystems.The release of cyanotoxin mixtures by blooms into aquatic systems pose a risk to public and ecosystem health.The majority of cyanotoxin research has focused on specific groups: microcystins (MCs), cylindrospermopsins (CYNs), BMAA, saxitoxins (STXs) (anatoxins) ATXs.Other bioactive cyanopeptides (CNPs) produced by cHABs have received much less attention.The chemistry, toxicology, and environmental concentrations of the anabaenopeptin (APs), cyanopeptolin (CPs), microginin (MGs), cyanobactin (CBs), aeruginosin (ASs) CNP groups are largely unknown despite having notable biological activities and documented co-occurrence with cyanotoxins.Untargeted, targeted, and semi-targeted mass-spectrometry based metabolomic approaches were applied to study the CNP profiles of fifty-five cyanobacteria bloom samples collected from fifteen watercourses in Eastern Ontario and Western Quebec.117 unique cyanopeptides were identified and ninety-four quantitated.MCs and ferintoic acid A (FA A) were quantified with reference materials and other CNPs were determined semi-quantitatively.CPs and APs displayed the greatest diversity in group variants and possessed similar concentrations and occurrence to that of MCs.Based on environmental concentrations and ubiquity of the CP and AP groups, they are recommended for prioritization in future toxicological and environmental research.Additionally, the apparent rise in cHABs, has drawn attention from paleolimnological researchers.Historical records of cHABs are limited, and proxies of cyanobacteria occurrence are typically restricted to pigments and DNA -both of which are sensitive to environmental degradation.However, MCs produced by freshwater cyanobacteria are stable cyclic hexapeptides.MCs in lake sediment archives can provide context to both the occurrence and toxicity of historical cHABs.To evaluate the use of MCs as a paleolimnological proxy, an MC extraction and quantitation method was iii developed and validated for four MC congeners (MR RR, MC LR, [Dha 7 ]MC LR, MC LA) in lake sediments.The method was applied to sediment cores collected from the Rideau Canal system.The MC method was combined with multiple proxies of Itrax-XRF, chlorophyll-a analyses and radioscopic dating methods to develop a chronology of historical lake conditions.The multi-proxy approach provided a clear indication of increasing biological productivity towards the surface of both cores.The apparent trend of productivity appears to be a result of climate warming and anthropogenic nutrient enrichment.I would like to extend my upmost gratitude and thanks to my supervisor, David McMullin, for the continued guidance and support, and providing the opportunity to develop and expand my chemistry skillset with natural products and cyanobacteria metabolites.I am very grateful for the opportunity to attend and present at 12 th international conference on toxic cyanobacteria (ICTC) in Toledo, OH -It was a valuable experience.Thank you to Kim McDonald (MSc.) for extracting the bloom samples analyzed here and, imparting knowledge of lab operations and all things cyanobacteria.Thank you to Dr. Justin Renaud for his analytical expertise with high-resolution mass-spectrometry and metabolomics -the knowledge of analytical chemistry I gained was monumental.Thank you to Dr. Mark Sumarah for imparting both his academic guidance and expertise with the cyanopeptide bloom project.Thank you to Dr. Jesse Vermaire for collection of Rideau sediment cores, guidance with Rideau core project, and procurement of Chl-a and gamma spectroscopy data, used here.Thank you to Dr. Frances Pick for the collection and detailed information for the cyanobacteria bloom samples collected in the National Capital Region and analyzed here.Thank you to Dr. David Miller for his guidance and providing the initial reference for this master's opportunity.Thank you to Dr. Tyler Avis for both his guidance and assistance with laboratory operations and statistics.I thank Dr. Tim Patterson for the experience and knowledge of sediment core collection and the opportunity to preform such work in New Brunswick lakes.Thank you to Dr. Nawaf Nasser for imparting his knowledge of R for 14 C agedepth modelling, the workshops were fantastic.I would like to
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".