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Identifying Mechanisms of Enhanced NK Cell Cytotoxicity Using Permanent NK Cell Lines.

2004· article· en· W2979373204 on OpenAlexaff
Garnet Suck, Donald R. Branch, Joanna Vergidis, Soad Fahim, Armand Keating

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalCanadian Blood ServicesUniversity of TorontoOntario Institute for Cancer Research
Fundersnot available
KeywordsCytotoxicityNKG2DCD16Natural killer cellLymphokine-activated killer cellBiologyCell cultureFlow cytometryCytotoxic T cellAntibody-dependent cell-mediated cytotoxicityPerforinReceptorCell biologyInterleukin 21Molecular biologyImmunologyCD8CD3AntigenIn vitroBiochemistryGenetics

Abstract

fetched live from OpenAlex

Abstract Although NK cells are promising candidates for adoptive immunotherapy and at least one permanent cell line is in clinical trials, further studies evaluating efficacy and mechanisms of action are warranted. As a first step towards identifying the most potent effector cells, we investigated the molecular mechanisms of cytotoxicity of the three natural killer lines, KHYG-1, NK-92 and YT, and the NK-T cell line, SNT-8, under standardized culture conditions with human serum as the only serum source. We confirmed the previously established differential killing potential of the 4 cell lines against target K562 cells using a new method based on detecting Annexin V (+) target cells by flow cytometry. By labeling the NK cells with specific antibodies, the assay is designed to screen any target cell for NK cytotoxicity. In contrast to previous reports, we found KHYG-1 the most cytotoxic, followed by NK-92, SNT-8 and YT. Genotypic and transcriptional phenotypic analysis of the cell lines for killer cell Ig-like receptors (KIRs) by SSP-PCR showed that inhibitory KIRs outnumbered activating KIRs in all cases but did not explain the differential cytotoxicity. A correlation with cytotoxicity was found with expression of the activating type II C lectin-like receptor, NKG2D: KHYG-1, 99%+; NK-92, 91%+; SNT-8, 6%+ and YT, 2%+. Moreover, the ITAM-bearing adaptor molecule DAP12, involved in the alternative activation signaling pathway via NKG2C-CD94 and activating KIRs, was detected only for KHYG-1 by immunoblotting, These data suggest that the superior cytotoxicity of KHYG-1 may be due, in part, to the additional activation of this alternative pathway that is not triggered in the other lines. The downstream signaling molecules involved in NK cytotoxicity, including the tyrosine phosphatases SHP-1, SHP-2 and SHIP-1 (inhibitory), as well as SHIP-2, the tyrosine kinases ZAP-70, Syk, PI3K and the MAP kinase phospho-ERK-2 (activating) were compared among the lines by immunoblotting followed by densitometry normalized to b-actin or ERK-2 for phospho-ERK-2. We found that the activating kinase Syk was expressed only in NK-92 and KHYG-1 at even higher levels. Also, phospho-ERK-2, was hyperphosphorylated only in KHYG-1. Perforin, granzyme A and granzyme B, present in cytotoxic granules, were compared by RT-PCR and intracellular flow cytometry and/or immunoblotting. Perforin was found to be almost exclusively fully processed to the active 60 kD form only in KHYG-1, in contrast to the other lines, which displayed approximately half the levels of the active form. These data provide a further explanation for the superior cytotoxicity of KHYG-1 and demonstrate the value of comparing cell lines with diverse cytotoxic potential as a means of elucidating cell killing mechanisms. It is conceivable that targeted modifications to the signaling pathways for cytotoxicity in this model will lead to the generation of activated NK cells with even greater efficacy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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
Published2004
Admission routes1
Has abstractyes

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