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Record W2551975978 · doi:10.5334/1750-2187-11-4

PPIP5K1 Suppresses Etoposide-triggered Apoptosis

2016· article· en· W2551975978 on OpenAlexaff
Gayane Machkalyan, Terence E. Hébert, Gregory J. Miller

Bibliographic record

VenueJournal of Molecular Signaling · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsMcGill University
Fundersnot available
KeywordsApoptosisCytotoxic T cellBiologyEtoposideKinaseCell biologyInositolProgrammed cell deathCisplatinViability assayBiochemistryIn vitroReceptorGeneticsChemotherapy

Abstract

fetched live from OpenAlex

Inositol hexakisphosphate kinase 2 (IP6K2) potentiates pro-apoptotic signalling and increases the sensitivity of mammalian cells to cytotoxic agents. Diphosphoinositol pentakisphosphate kinase (PPIP5K) generates inositol pyrophosphates (InsPPs) that are structurally distinct from those produced by IP6K2 and their possible roles in affecting cell viability remain unclear. In the present study, we tested the impact of PPIP5K1 on cellular sensitivity to various genotoxic agents to determine if PPIP5K1 and IP6K2 contribute similarly to apoptosis. We observed that PPIP5K1 overexpression decreased sensitivity of cells toward several cytotoxic agents, including etoposide, cisplatin, and sulindac. We further tested the impact of PPIP5K1 overexpression on an array of apoptosis markers and observed that PPIP5K1 decreased p53 phosphorylation on key residues, including Ser-15, -46, and -392. Overexpression of a kinase-impaired PPIP5K1 mutant failed to protect cells from apoptosis, indicating this protection is a consequence PPIP5K1 catalytic activity, in contrast with the sensitivity conferred by IP6K2, which is dependent on both catalytic and non-catalytic functions. These observations reveal distinct roles for PPIP5K1 and IP6K2 and the InsPPs they produce in controlling cell death.

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 score0.130
Threshold uncertainty score0.352

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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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