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Calpain activation is not required for AIF translocation in PARP‐1‐dependent cell death (parthanatos)

2009· article· en· W2000080307 on OpenAlexaff
Yingfei Wang, No S. Kim, Xiaoling Li, Peter A. Greer, Raymond C. Koehler, Valina L. Dawson, Ted M. Dawson

Bibliographic record

VenueJournal of Neurochemistry · 2009
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsQueen's University
FundersNational Institute of Neurological Disorders and Stroke
KeywordsCalpainApoptosis-inducing factorPoly ADP ribose polymeraseProgrammed cell deathCell biologyMitochondrionBiologyCalpastatinChromosomal translocationApoptosisMolecular biologyBiochemistryPolymeraseCaspaseEnzyme

Abstract

fetched live from OpenAlex

Apoptosis-inducing factor (AIF) is critical for poly(ADP-ribose) polymerase-1 (PARP-1)-dependent cell death (parthanatos). The molecular mechanism of mitochondrial AIF release to the nucleus remains obscure, although a possible role of calpain I has been suggested. Here we show that calpain is not required for mitochondrial AIF release in parthanatos. Although calpain I cleaved recombinant AIF in a cell-free system in intact cells under conditions where endogenous calpain was activated by either NMDA or N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) administration, AIF was not cleaved, and it was released from mitochondria to the nucleus in its 62-kDa uncleaved form. Moreover, NMDA administration under conditions that failed to activate calpain still robustly induced AIF nuclear translocation. Inhibition of calpain with calpastatin or genetic knockout of the regulatory subunit of calpain failed to prevent NMDA- or MNNG-induced AIF nuclear translocation and subsequent cell death, respectively, which was markedly prevented by the PARP-1 inhibitor, 3,4-dihydro-5-[4-(1-piperidinyl)butoxyl]-1(2H)-iso-quinolinone. Our study clearly shows that calpain activation is not required for AIF release during parthanatos, suggesting that other mechanisms rather than calpain are involved in mitochondrial AIF release in parthanatos.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.505

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 head0.042
GPT teacher head0.325
Teacher spread0.283 · 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

Citations102
Published2009
Admission routes1
Has abstractyes

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