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The Hace1 E3 ligase: linking misfolded protein load and oncogenesis

2009· article· en· W108879930 on OpenAlexaff
Barak Rotblat, Fan Zhang, Poul H. Sorensen

Bibliographic record

VenueThe FASEB Journal · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsCell biologyUbiquitin ligaseChemistryProteostasisProteasomeUbiquitinCarcinogenesisBiologyBiochemistryGene

Abstract

fetched live from OpenAlex

Hace1 is a new member of the HECT family of E3 protein‐ubiquitin ligases. We previously showed that the Hace1 gene is a tumor suppressor that is inactivated in human Wilms' tumor, and that Hace1‐/‐ mice develop spontaneous tumors. Moreover, various stresses dramatically increases tumor incidence in these mice. The goal of this study is to characterize the role Hace1 is playing in cellular stress and more specifically in misfolded protein handling by the cell. We now show that Hace1 decreases the half‐life of GFP‐Huntingtin‐128Q and eliminates its aggregates in a proteasome independent manner. This indicates that Hace1 may mediate mutant Huntingtin degradation via other pathways such as autophagy. Indeed, Hace1‐/‐ cells show decreased autophagy induction. Fluorescence microscopy suggests that Hace1 is localized to a recently described cellular structure known as IPOD‐ Insoluble Protein Deposit. Using fluorescence recovery after photo‐bleaching we determined that GFP‐Hace1 interacts with IPOD in a highly dynamic manner suggesting that Hace1 plays a role in the function of IPOD rather than being transported to them as aggregates. Taken together, these data indicate that the tumor suppressor Hace1 reduces accumulation of misfolded proteins by promoting autophagy, suggesting that the deregulation of non‐proteasomal processes that manage misfolded protein load may promote oncogenesis in the cell.

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)0.001
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 score0.041
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.258
Teacher spread0.248 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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