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Record W2129756028 · doi:10.1091/mbc.e12-05-0408

Radil controls neutrophil adhesion and motility through β2-integrin activation

2012· article· en· W2129756028 on OpenAlexaff
Lunhua Liu, Wulin Aerbajinai, Syed Mukhtar Ahmed, Griffin P. Rodgers, Stéphane Angers, Carole A. Parent

Bibliographic record

VenueMolecular Biology of the Cell · 2012
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of Toronto
FundersNational Cancer InstituteNational Institutes of Health
KeywordsBiologyIntegrinMotilityCell biologyAdhesionCell adhesionImmunologyBiochemistryReceptorCell

Abstract

fetched live from OpenAlex

Integrin activation is required to facilitate multiple adhesion-dependent functions of neutrophils, such as chemotaxis, which is critical for inflammatory responses to injury and pathogens. However, little is known about the mechanisms that mediate integrin activation in neutrophils. We show that Radil, a novel Rap1 effector, regulates β1- and β2-integrin activation and controls neutrophil chemotaxis. On activation and chemotactic migration of neutrophils, Radil quickly translocates from the cytoplasm to the plasma membrane in a Rap1a-GTP-dependent manner. Cells overexpressing Radil show a substantial increase in cell adhesion, as well as in integrin/focal adhesion kinase (FAK) activation, and exhibit an elongated morphology, with severe tail retraction defects. This phenotype is effectively rescued by treatment with either β2-integrin inhibitory antibodies or FAK inhibitors. Conversely, knockdown of Radil causes severe inhibition of cell adhesion, β2-integrin activation, and chemotaxis. Furthermore, we found that inhibition of Rap activity by RapGAP coexpression inhibits Radil-mediated integrin and FAK activation, decreases cell adhesion, and abrogates the long-tail phenotype of Radil cells. Overall, these studies establish that Radil regulates neutrophil adhesion and motility by linking Rap1 to β2-integrin activation.

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 score0.024
Threshold uncertainty score0.375

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 head0.020
GPT teacher head0.304
Teacher spread0.284 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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