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Record W2078283762 · doi:10.1139/o06-083

Poly(ADP-ribosyl)ation regulates heat shock factor-1 activity and the heat shock response in murine fibroblasts

2006· article· en· W2078283762 on OpenAlexvenueno aff
Silvia Fossati, Laura Formentini, Zhao‐Qi Wang, Flavio Moroni, Alberto Chiarugi

Bibliographic record

VenueBiochemistry and Cell Biology · 2006
Typearticle
Languageen
FieldMedicine
TopicPARP inhibition in cancer therapy
Canadian institutionsnot available
FundersEnte Cassa di Risparmio di Firenze
KeywordsPoly ADP ribose polymeraseHeat shock factorHeat shockHeat shock proteinBiologyTranscription factorMolecular biologyPARP inhibitorPolymeraseHsp70Cell biologyChemistryDNABiochemistryGene

Abstract

fetched live from OpenAlex

Poly(ADP-ribose) polymerase-1 (PARP-1)-dependent poly(ADP-ribose) formation is emerging as a key regulator of transcriptional regulation, even though the targets and underlying molecular mechanisms have not yet been clearly identified. In this study, we gathered information on the role of PARP-1 activity in the heat shock response of mouse fibroblasts. We show that DNA binding of heat shock factor (HSF)-1 was impaired by PARP-1 activity in cellular extracts, and was higher in PARP-1(-/-) than in PARP-1+/+ cells. No evidence for HSF-1 poly(ADP-ribosyl)ation or PARP-1 interaction was found, but a poly(ADP-ribose) binding motif was identified in the transcription factor amino acid sequence. Consistent with data on HSF-1, the expression of heat-shock protein (HSP)-70 and HSP-27 was facilitated in cells lacking PARP-1. Thermosensitivity, however, was higher in PARP-1(-/-) than in PARP-1+/+ cells. Accordingly, we report that heat-shocked PARP-1 null fibroblasts showed an increased activation of proapoptotic JNK and decreased transcriptional efficiency of prosurvival NF-kappaB compared with wild-type counterparts. The data indicate that poly(ADP-ribosyl)ation finely regulates HSF-1 activity, and emphasize the complex role of PARP-1 in the heat-shock response of mammalian cells.

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 score0.031
Threshold uncertainty score0.536

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 head0.009
GPT teacher head0.255
Teacher spread0.246 · 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

Citations27
Published2006
Admission routes1
Has abstractyes

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