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Record W2896869443 · doi:10.1002/rcm.8306

Diagnostic fragmentation filtering for the discovery of new chaetoglobosins and cytochalasins

2018· article· en· W2896869443 on OpenAlexafffund
Jacob P. Walsh, Justin B. Renaud, Shawn Hoogstra, David McMullin, Ashraf Ibrahim, Cobus M. Visagie, Joey B. Tanney, Ken K.‐C. Yeung, Mark W. Sumarah

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2018
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsMcMaster UniversityHealth PEIAgriculture and Agri-Food CanadaWestern University
FundersAgriculture and Agri-Food Canada
KeywordsChemistryFragmentation (computing)

Abstract

fetched live from OpenAlex

Rationale Microbial natural products are often biosynthesized as classes of structurally related compounds that have similar tandem mass spectrometry (MS/MS) fragmentation patterns. Mining MS/MS datasets for precursor ions that share diagnostic or common features enables entire chemical classes to be identified, including novel derivatives that have previously been unreported. Analytical data analysis tools that can facilitate a class‐targeted approach to rapidly dereplicate known compounds and identify structural variants within complex matrices would be useful for the discovery of new natural products. Methods A diagnostic fragmentation filtering (DFF) module was developed for MZmine to enable the efficient screening of MS/MS datasets for class‐specific product ions(s) and/or neutral loss(es). This approach was applied to series of the structurally related chaetoglobosin and cytochalasin classes of compounds. These were identified from the culture filtrates of three fungal genera: Chaetomium globosum , a putative new species of Penicillium (called here P . cf. discolor : closely related to P. discolor ), and Xylaria sp. Extracts were subjected to LC/MS/MS analysis under positive electrospray ionization and operating in a data‐dependent acquisition mode, performed using a Thermo Q‐Exactive mass spectrometer. All MS/MS datasets were processed using the DFF module and screened for diagnostic product ions at m / z 130.0648 and 185.0704 for chaetoglobosins, and m / z 120.0808 and 146.0598 for cytochalasins. Results Extracts of C. globosum and P . cf. discolor strains revealed different mixtures of chaetoglobosins, whereas the Xylaria sp. produced only cytochalasins; none of the strains studied produced both classes of compounds. The dominant chaetoglobosins produced by both C. globosum and P . cf. discolor were chaetoglobosins A, C, and F. Tetrahydrochaetoglobosin A was identified from P . cf. discolor extracts and is reported here for the first time as a natural product. The major cytochalasins produced by the Xylaria sp. were cytochalasin D and epoxy cytochalasin D. A larger unknown “cytochalasin‐like” molecule with the molecular formula C 38 H 47 NO 10 was detected from Xylaria sp. culture filtrate extracts and is a current target for isolation and structural characterization. Conclusions DFF is an effective LC/MS data analysis approach for rapidly identifying entire classes of compounds from complex mixtures. DFF has proved useful in the identification of new natural products and allowing for their partial characterization without the need for isolation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.001

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 head0.036
GPT teacher head0.312
Teacher spread0.276 · 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 designBench or experimental
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

Citations25
Published2018
Admission routes2
Has abstractyes

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