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Record W7043958310

Untargeted high resolution mass spectrometry approaches for identifying novel microcystins

2018· other· en· W7043958310 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMass spectrometryMetabolomicsHigh resolutionResolution (logic)IonizationMarine toxin
DOInot available

Abstract

fetched live from OpenAlex

Microcystins (MCs) are a group of hepatotoxic heptapeptides that inhibit protein phosphatases, and are produced by many species of cyanobacteria. Most MCs contain the unusual β-amino acid “Adda5 ”, γ-linked D-Glu6, and N-methyldehydroalanine (Mdha 7) at positions 5–7, with more than 250 different microcystin congeners having been reported in the literature, and with new congeners being discovered regularly. In addition to methods capable of measuring total MCs or targeting known congeners, improved methods are needed for identifying new candidate MCs , to ensure the safety of recreational and drinking waters. Untargeted high resolution mass spectrometry (HRMS) methods allows for simultaneous analysis of a tens of thousands of known and unknown chemicals in complex biological or environmental samples. Typically, identification of novel MCs involves only LC–MS/MS analysis in positive ionization mode with detection of a characteristic fragment from Adda5 at m/z 135.0804. However, some MCs contain modified Addamoieties, or are otherwise modified to change the MS/MS conditions under which this fragment is generated, and are not readily detected in this manner. We have recently shown that thiol-derivatization of the Mdha7/Dha7 group in MCs is a highly effective method for identifying even trace amounts of novel MCs in complex matrices by LC–MS. Here, we present a new approach using metabolomics software for semi-automated detection of novel MCs based on mercaptoethanol derivatization, together with accurate mass detection of precursor and characteristic product ions in negative and positive ionization modes. Taken together, this approach targets any molecules containing Adda5, D-Glu6 or Mdha7/Dha7, one or more of which is present in every microcystin reported to date. Furthermore, it is these three residues that are in closest contact with the catalytic centre of protein phosphatases, and which appear to be most important for toxicity. HRMS methods using data independent acquisition scan modes were particularly well suited for this purpose, allowing for simultaneous acquisition of MS/MS data on all compounds detected. This type of data is also well suited to retrospective analysis of newly discovered MCs from previously acquired data. The application of microcystin immunoaffinity columns containing antibodies with broad specificity to the whole family of MCs (presentation by Samdal et al.) further enhances the utility of this metabolomics workflow. We demonstrate the power of the combination of these novel approaches with the identification of numerous novel MCs in field and culture samples as well as a blue-green algal matrix reference material.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.071
GPT teacher head0.278
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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