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Record W2414204484 · doi:10.1038/srep13730

Methylene Blue Inhibits Caspases by Oxidation of the Catalytic Cysteine

2015· article· en· W2414204484 on OpenAlexafffund
Prateep Pakavathkumar, Gyanesh Sharma, Vikas Kaushal, Bénédicte Foveau, Andréa C. LeBlanc

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health ResearchJournal of Gastroenterology and Hepatology FoundationAlzheimer SocietyMcGill University
KeywordsMethylene blueCysteineCaspaseRecombinant DNAChemistryApoptosisBiochemistryTransfectionMolecular biologyBiologyProgrammed cell deathEnzymeCatalysis

Abstract

fetched live from OpenAlex

Methylene blue, currently in phase 3 clinical trials against Alzheimer Disease, disaggregates the Tau protein of neurofibrillary tangles by oxidizing specific cysteine residues. Here, we investigated if methylene blue can inhibit caspases via the oxidation of their active site cysteine. Methylene blue, and derivatives, azure A and azure B competitively inhibited recombinant Caspase-6 (Casp6), and inhibited Casp6 activity in transfected human colon carcinoma cells and in serum-deprived primary human neuron cultures. Methylene blue also inhibited recombinant Casp1 and Casp3. Furthermore, methylene blue inhibited Casp3 activity in an acute mouse model of liver toxicity. Mass spectrometry confirmed methylene blue and azure B oxidation of the catalytic Cys163 cysteine of Casp6. Together, these results show a novel inhibitory mechanism of caspases via sulfenation of the active site cysteine. These results indicate that methylene blue or its derivatives could (1) have an additional effect against Alzheimer Disease by inhibiting brain caspase activity, (2) be used as a drug to prevent caspase activation in other conditions, and (3) predispose chronically treated individuals to cancer via the inhibition of caspases.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.270
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.299
Teacher spread0.268 · 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 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

Citations38
Published2015
Admission routes2
Has abstractyes

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