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Record W2582983756 · doi:10.1073/pnas.1621355114

E3 ubiquitin ligase Mule targets β-catenin under conditions of hyperactive Wnt signaling

2017· article· en· W2582983756 on OpenAlexafffund
Carmen Dominguez‐Brauer, Rahima Khatun, Andrew Elia, Kelsie L. Thu, Parameswaran Ramachandran, Shakiba P. Baniasadi, Zhenyue Hao, Lisa D. Jones, Jillian Haight, Yi Sheng, Tak W. Mak

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsUniversity Health NetworkYork UniversityOntario Institute for Cancer Research
FundersCanadian Institutes of Health ResearchGovernment of CanadaDana-Farber Cancer Institute
KeywordsWnt signaling pathwayAdenomatous polyposis coliCell biologyBiologyCarcinogenesisUbiquitin ligaseLRP6Signal transductionStem cellCateninLRP5Progenitor cellCancer researchEmbryonic stem cellCell growthBeta-cateninUbiquitinGeneGeneticsCancerColorectal cancer

Abstract

fetched live from OpenAlex

) gene, whose product is an important component of the destruction complex that regulates β-catenin levels. Stabilization and nuclear localization of β-catenin result in the expression of a panel of Wnt target genes. We previously showed that Mule/Huwe1/Arf-BP1 (Mule) controls murine intestinal stem and progenitor cell proliferation by modulating the Wnt pathway via c-Myc. Here we extend our investigation of Mule's influence on oncogenesis by showing that Mule interacts directly with β-catenin and targets it for degradation under conditions of hyperactive Wnt signaling. Our findings suggest that Mule uses various mechanisms to fine-tune the Wnt pathway and provides multiple safeguards against tumorigenesis.

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 score0.004

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 communication0.0010.000
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 head0.045
GPT teacher head0.333
Teacher spread0.288 · 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

Citations61
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicWnt/β-catenin signaling in development and cancerFrench-language works237,207