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Record W1651484967 · doi:10.4049/jimmunol.182.2.948

A Dual Role for Talin in NK Cell Cytotoxicity: Activation of LFA-1-Mediated Cell Adhesion and Polarization of NK Cells

2009· article· en· W1651484967 on OpenAlexaff
Emily M. Mace, David R. Critchley, Fumio Takei

Bibliographic record

VenueThe Journal of Immunology · 2009
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of British ColumbiaTerry Fox Research InstituteBC Cancer Agency
FundersJiangsu National Synergistic Innovation Center for Advanced MaterialsWellcome Trust
KeywordsCell biologyNKG2DJanus kinase 3Lymphocyte function-associated antigen 1Interleukin 21CytotoxicityCytotoxic T cellLymphokine-activated killer cellCD49bNatural killer cellCell adhesionBiologyChemistryCellCell adhesion moleculeIn vitroIntercellular Adhesion Molecule-1Biochemistry

Abstract

fetched live from OpenAlex

LFA-1 is critical for NK cell cytotoxicity because it mediates adhesion of NK cells to target cells. Talin is thought to associate with the cytoplasmic tail of LFA-1 and activates its ligand-binding function. In this study, we report that talin is also required for LFA-1-mediated outside-in signaling leading to NK cell polarization. NK cells generated from talin1-deficient murine embryonic stem cells are defective in LFA-1-mediated adhesion. Although exogenously added manganese activates LFA-1 on talin-deficient NK cells and induces conjugate formation with target cells, their LFA-1-dependent cytotoxicity is impaired. Binding of ICAM-1-coated beads to wild-type NK cells induces reorganization of the actin cytoskeleton and coligation of the activating receptor NKG2D induces polarization of cytotoxic granules, whereas talin1-deficient NK cells fail to polarize with or without NKG2D coligation. Thus, talin1 plays a dual role in NK cell cytotoxicity, first by activation of LFA-1-mediated adhesion and then via LFA-1-induced NK cell polarization.

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)0.001
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 score0.022
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.268
Teacher spread0.255 · 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

Citations76
Published2009
Admission routes1
Has abstractyes

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