MétaCan
Menu
Back to cohort

A new role for Dysferlin in endothelial cell adhesion and neo‐vascularization

2010· article· en· W3169206327 on OpenAlexaff
Carol Yu, Arpeeta Sharma, Andy Trane, Cleo Leung, Pascal Bernatchez

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCaveolin-1 and cellular processes
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsDysferlinCell biologyChemistryCaveolaeEndothelial stem cellAngiogenesisBiologyMuscular dystrophySignal transductionBiochemistryCancer researchIn vitro

Abstract

fetched live from OpenAlex

Dysferlin is a member of the Ferlin family of proteins known to regulate plasma membrane integrity and are linked to muscular dystrophy. However, the peripheral roles of Dysferlin in non‐muscle cells remain virtually unknown. By performing proteomic analysis of caveolae/lipid rafts, we reported that endothelial cells (EC) express Myoferlin and that it mediates plasma membrane translocation of vascular endothelial growth factor receptor‐2 (VEGFR‐2). Herein, we show by Northern and Western blot as well as by immunohistochemistry that EC and intact blood vessels of rodent and human origin all express Dysferlin but that its presence is not required for VEGFR‐2 signaling. Instead, loss of Dysferlin in EC results in deficient adhesion, detachment and growth arrest in sub‐confluent cells whereas confluent EC monolayers are remarkably resistant to cell detachment. In Dysferlin‐null mice, delivery of VEGF, an EC‐specific angiogenic agonist, results in blunted neo‐angiogenesis compared to WT mice. Mechanistically, loss of Dysferlin causes poly‐ubiquitination and proteasomal degradation of platelet endothelial cellular adhesion molecule‐1 (PECAM‐1/CD31). Our discovery that Dysferlin is a regulator of EC adhesion and neo‐vascularization broadens the functional scope of this family of proteins and suggest that specialization exist between the different Ferlins and their respective membrane‐based cargo.

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 score0.012
Threshold uncertainty score0.226

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 head0.209
Teacher spread0.205 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicCaveolin-1 and cellular processesFrench-language works237,207