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Record W2090905561 · doi:10.1128/mcb.00687-14

Novel Functional Complexity of Polycystin-1 by GPS Cleavage <i>In Vivo</i>: Role in Polycystic Kidney Disease

2014· article· en· W2090905561 on OpenAlexafffund
Almira Kurbegovic, Hyunho Kim, Hangxue Xu, Shengqiang Yu, Julie Cruanès, Robin L. Maser, Alessandra Boletta, Marie Trudel, Feng Qian

Bibliographic record

VenueMolecular and Cellular Biology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsUniversité de MontréalMontreal Clinical Research Institute
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCanadian Institutes of Health Research
KeywordsCleavage (geology)PKD1BiologyEndoplasmic reticulumPolycystic kidney diseaseCell biologyBiogenesisIn vivoComplementationGeneticsKidneyGenePhenotype

Abstract

fetched live from OpenAlex

Polycystin-1 (Pc1) cleavage at the G protein-coupled receptor (GPCR) proteolytic site (GPS) is required for normal kidney morphology in humans and mice. We found a complex pattern of endogenous Pc1 forms by GPS cleavage. GPS cleavage generates not only the heterodimeric cleaved full-length Pc1 (Pc1(cFL)) in which the N-terminal fragment (NTF) remains noncovalently associated with the C-terminal fragment (CTF) but also a novel (Pc1) form (Pc1(deN)) in which NTF becomes detached from CTF. Uncleaved Pc1 (Pc1(U)) resides primarily in the endoplasmic reticulum (ER), whereas both Pc1(cFL) and Pc1(deN) traffic through the secretory pathway in vivo. GPS cleavage is not a prerequisite, however, for Pc1 trafficking in vivo. Importantly, Pc1(deN) is predominantly found at the plasma membrane of renal epithelial cells. By functional genetic complementation with five Pkd1 mouse models, we discovered that CTF plays a crucial role in Pc1(deN) trafficking. Our studies support GPS cleavage as a critical regulatory mechanism of Pc1 biogenesis and trafficking for proper kidney development and homeostasis.

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 score0.038
Threshold uncertainty score0.959

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 head0.206
Teacher spread0.199 · 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

Citations64
Published2014
Admission routes2
Has abstractyes

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