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Record W2516975515 · doi:10.4049/jimmunol.1600828

Endocytosis of Cytotoxic Granules Is Essential for Multiple Killing of Target Cells by T Lymphocytes

2016· article· en· W2516975515 on OpenAlexaff
Hsin‐Fang Chang, Hawraa Bzeih, Claudia Schirra, Praneeth Chitirala, Mahantappa Halimani, Emmanuelle Cordat, Elmar Krause, Jens Rettig, Varsha Pattu

Bibliographic record

VenueThe Journal of Immunology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCytotoxic T cellEndocytosisCell biologyChemistryBiologyCellIn vitroBiochemistry

Abstract

fetched live from OpenAlex

CTLs are serial killers that kill multiple target cells via exocytosis of cytotoxic granules (CGs). CG exocytosis is tightly regulated and has been investigated in great detail; however, whether CG proteins are endocytosed following exocytosis and contribute to serial killing remains unknown. By using primary CTLs derived from a knock-in mouse of the CG membrane protein Synaptobrevin2, we show that CGs are endocytosed in a clathrin- and dynamin-dependent manner. Following acidification, endocytosed CGs are recycled through early and late, but not recycling endosomes. CGs are refilled with granzyme B at the late endosome stage and polarize to subsequent synapses formed between the CTL and new target cells. Importantly, inhibiting CG endocytosis in CTLs results in a significant reduction of their cytotoxic activity. Thus, our data demonstrate that continuous endocytosis of CG membrane proteins is a prerequisite for efficient serial killing of CTLs and identify key events in this process.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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