Mass Spectrometry based Metabolomics to Decipher Strain Specific Microcystis Cyanopeptide Profiles
Bibliographic record
Abstract
Over the last one hundred years, ecosystem changes have occurred as a result of human population growth, pollution, increased temperatures, and habitat degradation.A visible change is the increase in frequency and magnitude of toxic cyanobacterial blooms.Cyanobacterial blooms release mixtures of biologically active compounds into freshwater that negatively impact human and ecosystem health as well as having socioeconomic consequences.The factors that influence cyanobacterial growth and toxin production are broadly understood.However, cyanobacteria are a prolific source of structurally diverse and strain specific mixtures of biologically active compounds.Currently, the chemistry, toxicology, environmental concentrations, and risks posed to human and ecosystem health by most cyanobacterial secondary metabolites are unknown.Advances in mass spectrometry and metabolomic techniques can aid in comprehension of complex metabolomes.Here, the use of untargeted and semi-targeted mass spectrometry-based metabolomics is used to decipher the non-ribosomal peptide natural products (cyanopeptides) from five Microcystis strains.Cyanopeptides are grouped based on shared structural features, such as the incorporation of non-proteogenic amino acids or partial amino acid sequences that generate diagnostic product ions with the MS/MS of metabolites within the same cyanopeptide group.Global natural product society (GNPS) molecular networking and diagnostic fragmentation filtering (DFF) techniques utilize the similarity in product ion spectra to visualize all variants in the different cyanopeptide groups and the production by Microcystis strains.The semi-targeted DFF technique is applied to environmental water and bloom samples for the detection of entire cyanopeptide groups.Finally, growth medium composition effects on Microcystis chemical profiles are assessed with the established metabolomic techniques.Optimal growth conditions for cyanopeptide production have been elucidated for the Microcystis strains.This will enable the iii.Table of Contents i.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".