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Record W2981984547 · doi:10.1016/j.celrep.2019.09.043

EPLIN-α and -β Isoforms Modulate Endothelial Cell Dynamics through a Spatiotemporally Differentiated Interaction with Actin

2019· article· en· W2981984547 on OpenAlexfundno aff
Muna Taha, Mohammed Aldirawi, Sigrid März, Jochen Seebach, Maria Odenthal-Schnittler, Olga Bondareva, Vesna Bojovic, Thomas Schmandra, Benedikt Wirth, Magdalena Miętkowska, Klemens Rottner, Hans‐Joachim Schnittler

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsnot available
FundersBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftCanadian Institute of Mining, Metallurgy and Petroleum
KeywordsCell biologyGene isoformDynamics (music)ActinFocus (optics)Endothelial stem cellBiologyChemistryBiochemistryGenePhysicsIn vitro

Abstract

fetched live from OpenAlex

Actin-binding proteins are essential for linear and branched actin filament dynamics that control shape change, cell migration, and cell junction remodeling in vascular endothelium (endothelial cells [ECs]). The epithelial protein lost in neoplasm (EPLIN) is an actin-binding protein, expressed as EPLIN-α and EPLIN-β by alternative promoters; however, the isoform-specific functions are not yet understood. Aortic compared to cava vein ECs and shear stress-exposed cultured ECs express increased EPLIN-β levels that stabilize stress fibers. In contrast, EPLIN-α expression is increased in growing and migrating ECs, is targeted to membrane protrusions, and terminates their growth via interaction with the Arp2/3 complex. The data indicate that EPLIN-α controls protrusion dynamics while EPLIN-β has an actin filament stabilizing role, which is consistent with FRAP analyses demonstrating a lower EPLIN-β turnover rate compared to EPLIN-α. Together, EPLIN isoforms differentially control actin dynamics in ECs, essential in shear stress responses, cell migration, and barrier function.

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 score0.141
Threshold uncertainty score0.679

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 head0.011
GPT teacher head0.263
Teacher spread0.251 · 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

Citations46
Published2019
Admission routes1
Has abstractyes

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