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Record W2159042208 · doi:10.1126/scisignal.2005608

The PPFIA1-PP2A protein complex promotes trafficking of Kif7 to the ciliary tip and Hedgehog signaling

2014· article· en· W2159042208 on OpenAlexafffund
Yulu Cherry Liu, Amber L. Couzens, Ashish R. Deshwar, Linda DB McBroom-Cerajewski, Xiaoyun Zhang, Vijitha Puviindran, Ian C. Scott, Anne‐Claude Gingras, Chi‐chung Hui, Stéphane Angers

Bibliographic record

VenueScience Signaling · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsHospital for Sick ChildrenLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsCiliumProtein phosphatase 2Cell biologyHedgehog signaling pathwaySignal transductionBiologyHedgehogNeuroscienceMedicinePhosphatasePhosphorylation

Abstract

fetched live from OpenAlex

The primary cilium is required for Hedgehog (Hh) signaling in vertebrates. Hh leads to ciliary accumulation and activation of the transmembrane protein Smoothened (Smo) and affects the localization of several pathway components, including the Gli family of transcriptional regulators, within different regions of primary cilia. Genetic analysis indicates that the kinesin protein Kif7 both promotes and inhibits mouse Hh signaling. Using mass spectrometry, we identified liprin-α1 (PPFIA1) and the protein phosphatase PP2A as Kif7-interacting proteins, and we showed that they were important for the trafficking of Kif7 and Gli proteins to the tips of cilia and for the transcriptional output of Hh signaling. Our results suggested that PPFIA1 functioned with PP2A to promote the dephosphorylation of Kif7, triggering Kif7 localization to the tips of primary cilia and promoting Gli transcriptional activity.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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

Citations64
Published2014
Admission routes2
Has abstractyes

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