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Record W2749338464 · doi:10.1080/15548627.2017.1339004

Hsp83 loss suppresses proteasomal activity resulting in an upregulation of caspase-dependent compensatory autophagy

2017· article· en· W2749338464 on OpenAlexafffund
Courtney Choutka, Lindsay DeVorkin, Nancy E. Go, Ying‐Chen Claire Hou, Annie Moradian, Gregg B. Morin, Sharon M. Gorski

Bibliographic record

VenueAutophagy · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of British ColumbiaCanada's Michael Smith Genome Sciences CentreSimon Fraser UniversityBC Cancer Agency
FundersCanadian Institutes of Health Research
KeywordsBiologyAutophagyCell biologyEffectorBAG3ProteostasisProteasomeHSF1UbiquitinContext (archaeology)Programmed cell deathCaspaseHeat shock proteinApoptosisHsp70BiochemistryGene

Abstract

fetched live from OpenAlex

The 2 main degradative pathways that contribute to proteostasis are the ubiquitin-proteasome system and autophagy but how they are molecularly coordinated is not well understood. Here, we demonstrate an essential role for an effector caspase in the activation of compensatory autophagy when proteasomal activity is compromised. Functional loss of Hsp83, the Drosophila ortholog of human HSP90 (heat shock protein 90), resulted in reduced proteasomal activity and elevated levels of the effector caspase Dcp-1. Surprisingly, genetic analyses showed that the caspase was not required for cell death in this context, but instead was essential for the ensuing compensatory autophagy, female fertility, and organism viability. The zymogen pro-Dcp-1 was found to interact with Hsp83 and undergo proteasomal regulation in an Hsp83-dependent manner. Our work not only reveals unappreciated roles for Hsp83 in proteasomal activity and regulation of Dcp-1, but identifies an effector caspase as a key regulatory factor for sustaining adaptation to cell stress in vivo.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.340
Teacher spread0.311 · 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

Citations20
Published2017
Admission routes2
Has abstractyes

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