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Record W4280503736 · doi:10.1002/1873-3468.14376

Arginyl‐tRNA‐protein transferase 1 (ATE1) promotes melanoma cell growth and migration

2022· article· en· W4280503736 on OpenAlexafffund
Ikrame Lazar, Bertrand Fabre, Yongmei Feng, Ali Khateb, Philippe Frit, Anna Kashina, Tongwu Zhang, Emily Avitan‐Hersh, Hyungsoo Kim, Kevin M. Brown, Ivan Topisirović, Ze’ev A. Ronai

Bibliographic record

VenueFEBS Letters · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNational Institute of General Medical SciencesNational Cancer InstituteFonds de Recherche du Québec - SantéFondation ARC pour la Recherche sur le Cancer
KeywordsBiologyCell growthCellCancer researchMutantBiochemistryGene

Abstract

fetched live from OpenAlex

Arginyl-tRNA-protein transferase 1 (ATE1) catalyses N-terminal protein arginylation, a post-translational modification implicated in cell migration, invasion and the cellular stress response. Herein, we report that ATE1 is overexpressed in NRAS-mutant melanomas, while it is downregulated in BRAF-mutant melanomas. ATE1 expression was higher in metastatic tumours, compared with primary tumours. Consistent with these findings, ATE1 depletion reduced melanoma cell viability, migration and colony formation. Reduced ATE1 expression also affected cell responses to mTOR and MEK inhibitors and to serum deprivation. Among putative ATE1 substrates is the tumour suppressor AXIN1, pointing to the possibility that ATE1 may fine-tune AXIN1 function in melanoma. Our findings highlight an unexpected role for ATE1 in melanoma cell aggressiveness and suggest that ATE1 constitutes a potential new therapeutic target.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.393

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 head0.005
GPT teacher head0.189
Teacher spread0.183 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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