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Record W2995692792 · doi:10.1016/j.celrep.2019.11.063

LC3C-Mediated Autophagy Selectively Regulates the Met RTK and HGF-Stimulated Migration and Invasion

2019· article· en· W2995692792 on OpenAlexafffund
Emily Bell, Paula P. Coelho, Colin D.H. Ratcliffe, Charles Vincent Rajadurai, Pascal Peschard, Richard Vaillancourt, Dongmei Zuo, Morag Park

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchU.S. Department of Defense
KeywordsAutophagyCell biologyHepatocyte growth factorATG8Receptor tyrosine kinaseBiologySignal transductionInternalizationCancer cellReceptorCancer researchCancerGeneticsApoptosis

Abstract

fetched live from OpenAlex

The Met/hepatocyte growth factor (HGF) receptor tyrosine kinase (RTK) is deregulated in many cancers and is a recognized target for cancer therapies. Following HGF stimulation, the signaling output of Met is tightly controlled by receptor internalization and sorting for degradation or recycling. Here, we uncover a role for autophagy in selective degradation of Met and regulation of Met-dependent cell migration and invasion. Met engagement with the autophagic pathway is dependent on complex formation with the mammalian ATG8 family member MAP1LC3C. LC3C deletion abrogates Met entry into the autophagy-dependent degradative pathway, allowing identification of LC3C domains required for rescue. Cancer cells with low LC3C levels show enhanced Met stability, signaling, and cell invasion. These findings provide mechanistic insight into RTK recruitment to autophagosomes and establish distinct roles for ATG8 proteins in this process, supporting that differential expression of ATG8 proteins can shape the functional consequences of autophagy in cancer development and 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 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.230
Teacher spread0.222 · 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

Citations53
Published2019
Admission routes2
Has abstractyes

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