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Record W2979138490 · doi:10.1073/pnas.1909283116

Caspase-7 uses RNA to enhance proteolysis of poly(ADP-ribose) polymerase 1 and other RNA-binding proteins

2019· article· en· W2979138490 on OpenAlexafffund
Alexandre Desroches, Jean‐Bernard Denault

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsProteolysisRNAPolymerasePoly ADP ribose polymeraseBiochemistryBiologyRNA-binding proteinCaspaseRNase PMolecular biologyCell biologyBinding siteChemistryDNAEnzymeApoptosisProgrammed cell deathGene

Abstract

fetched live from OpenAlex

KKK) located in the N-terminal domain of caspase-7 form such an exosite and promote the rapid proteolysis of the poly(ADP-ribose) polymerase 1 (PARP-1), but the mechanism of recognition remains mostly unknown. In this study, we show that the overall positive charge of the exosite is the critical feature of this evolutionarily conserved binding site. Additionally, interaction with the caspase-7 exosite involves both the Zn3 and BRCT domains of PARP-1 and is mediated by RNA. Indeed, PARP-1 proteolysis efficacy is sensitive to RNase A and promoted by added RNA. Moreover, using affinity chromatography and gel shift assays, we demonstrate that caspase-7, but not caspase-3 or a caspase-7 with a mutated exosite, binds nucleic acids. Finally, we show that caspase-7 prefers RNA-binding proteins (RNA-BPs) as substrates compared to caspase-3 and that RNA enhances proteolysis by caspase-7 of many of these RNA-BPs. Thus, we have uncovered an unusual way by which caspase-7 selects and cleaves specific substrates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations44
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicCell death mechanisms and regulationFrench-language works237,207