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Record W2784962716 · doi:10.1016/j.celrep.2017.12.096

The E3 Ubiquitin Ligase HectD1 Suppresses EMT and Metastasis by Targeting the +TIP ACF7 for Degradation

2018· article· en· W2784962716 on OpenAlexafffund
Stéphanie Duhamel, Marie-Anne Goyette, Marie‐Pier Thibault, Dominic Filion, Louis Gaboury, Jean‐François Côté

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMcGill UniversityInstitute for Research in Immunology and CancerUniversité de MontréalMontreal Clinical Research Institute
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchCancer Research Society
KeywordsUbiquitin ligaseMetastasisEpithelial–mesenchymal transitionUbiquitinCancer researchCisplatinChemistryCancerBiologyCell biologyBiochemistryChemotherapyGenetics

Abstract

fetched live from OpenAlex

Cancer cells exploit the epithelial-to-mesenchymal transition (EMT) program to become metastatic. Cytoskeletal regulators are required in mesenchymal cells where they promote EMT and EMT-induced migration. In a search for regulators of metastasis, we conducted shRNA screens targeting the microtubule plus-end tracking proteins (+TIPs). We show that the +TIP ACF7 is essential both for the maintenance of the EMT program and to promote migration. We find that the E3 ubiquitin ligase HectD1 promotes ACF7-proteasome-mediated degradation. Depletion of HectD1 stabilized ACF7, and this enhanced EMT and migration. Decreased HectD1 expression increased metastases in mouse models and conferred increased resistance to the cytotoxic drug cisplatin. A retrospective analysis of biopsies from breast cancer patients also reveals a correlation between higher ACF7 or lower HectD1 expression with poor clinical outcomes. Together, these results suggest that the control of ACF7 levels by HectD1 modulates EMT and the efficiency of metastasis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.382

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 studies0.0000.000
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 head0.015
GPT teacher head0.274
Teacher spread0.258 · 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 designNot applicable
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

Citations51
Published2018
Admission routes2
Has abstractyes

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