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Record W4385998441 · doi:10.1111/febs.16931

Metabolic characterisation of transglutaminase 2 inhibitor effects in breast cancer cell lines

2023· article· en· W4385998441 on OpenAlexaff
Mariana Gallo, Elena Ferrari, Anna Terrazzan, Federica Brugnoli, Alberto Spisni, Cristian Taccioli, Gianluca Aguiari, Alessandro Trentini, Stefano Volinia, Jeffrey W. Keillor, Carlo M. Bergamini, Nicoletta Bianchi, Thelma A. Pertinhez

Bibliographic record

VenueFEBS Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicBlood properties and coagulation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTissue transglutaminasePhosphocholineCancer cellCell cultureIntracellularMetabolomicsBiochemistryPhospholipaseCellBiologyApoptosisEnzymeCancerCell biologyChemistryCancer researchPhospholipidBioinformatics

Abstract

fetched live from OpenAlex

H-NMR metabolomics to study the effects of AA9, a novel TG2 inhibitor, on two breast cancer cell lines with distinct phenotypes, MCF-7 and MDA-MB-231. AA9 can promote apoptosis in both cell lines, but it is particularly effective in MD-MB-231, inhibiting transamidation reactions and decreasing cell migration and invasiveness. This metabolomics study provides evidence of a major effect of AA9 on MDA-MB-231 cells, impacting glutamate and aspartate metabolism, rather than on MCF-7 cells, characterised by choline and O-phosphocholine decrease. Interestingly, AA9 treatment induces myo-inositol alteration in both cell lines, indicating action on phosphatidylinositol metabolism, likely modulated by the G protein activity of TG2 on phospholipase C. Considering the metabolic deregulations that characterise various breast cancer subtypes, the existence of a metabolic pathway affected by AA9 further points to TG2 as a promising hot spot. The metabolomics approach provides a powerful tool to monitor the effectiveness of inhibitors and better understand the role of TG2 in cancer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.257
Teacher spread0.243 · 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 teacher head, 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

Citations13
Published2023
Admission routes1
Has abstractyes

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