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Record W2015094489 · doi:10.1038/ncomms6775

Clusterin facilitates stress-induced lipidation of LC3 and autophagosome biogenesis to enhance cancer cell survival

2014· article· en· W2015094489 on OpenAlexaff
Fan Zhang, Masafumi Kumano, Eliana Beraldi, Ladan Fazli, Caigan Du, Susan C. Moore, Poul H. Sorensen, Amina Zoubeidi, Martin Gleave

Bibliographic record

VenueNature Communications · 2014
Typearticle
Languageen
FieldMedicine
TopicClusterin in disease pathology
Canadian institutionsUniversity of British Columbia
FundersNational Cancer InstituteNational Institutes of Health
KeywordsClusterinAutophagyAutophagosomeCell biologyBiogenesisSignal transducing adaptor proteinLipid-anchored proteinProgrammed cell deathCancer cellGene silencingATG5BAG3BiologyProteostasisCancer researchApoptosisChemistryCancerSignal transductionBiochemistry

Abstract

fetched live from OpenAlex

We define stress-induced adaptive survival pathways linking autophagy with the molecular chaperone clusterin (CLU) that function to promote anticancer treatment resistance. During treatment stress, CLU co-localizes with LC3 via an LIR-binding sequence within autophagosome membranes, functioning to facilitate LC3-Atg3 heterocomplex stability and LC3 lipidation, and thereby enhance autophagosome biogenesis and autophagy activation. Stress-induced autophagy is attenuated with CLU silencing in CLU(-/-) mice and human prostate cancer cells. CLU-enhanced cell survival occurs via autophagy-dependent pathways, and is reduced following autophagy inhibition. Combining CLU inhibition with anticancer treatments attenuates autophagy activation, increases apoptosis and reduces prostate cancer growth. This study defines a novel adaptor protein function for CLU under stress conditions, and highlights how co-targeting CLU and autophagy can amplify proteotoxic stress to delay cancer progression.

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 score0.006

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.361
Teacher spread0.335 · 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

Citations124
Published2014
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicClusterin in disease pathologyFrench-language works237,207