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Record W2043767546 · doi:10.1073/pnas.0906554106

T cell receptor sharing by cytotoxic T lymphocytes facilitates efficient virus control

2009· article· en· W2043767546 on OpenAlexaff
Geeta Chaudhri, Ben Quah, Yang Wang, Abel Tan, Jie Zhou, Gunasegaran Karupiah, Christopher R. Parish

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious DiseasesHoward Hughes Medical Institute
KeywordsCytotoxic T cellVirusVirologyReceptorBiologyChemistryCell biologyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

A remarkable feature of the adaptive immune system is the speed at which small numbers of antigen-specific lymphocytes can mediate a successful immune response. Rapid expansion of T and B lymphocyte clones that have receptors specific for a particular antigen is one of the primary means by which a swift response is generated. Although much of this clonal expansion is caused by the division of antigen-specific cells, here we demonstrate an additional mechanism by which the pool of effector T cells against a viral infection can quickly enlarge. Our data show that virus-specific CD8+ cytotoxic T lymphocytes (CTL) can transfer their T cell receptors (TCR) to recipient CTL of an unrelated specificity that, as a consequence, gain the antigen specificity of the donor T cell. This process occurs within minutes via membrane exchange and results in the recipient CTL acquiring the ability to recognize and eliminate cells targeted by the donor TCR, while still retaining the antigen specificity of its own TCR. Such receptor sharing allows rapid, proliferation-independent expansion of virus-specific T cell clones of low frequency and plays a highly significant antiviral role that can protect the host from an otherwise lethal infection.

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 score0.008
Threshold uncertainty score0.262

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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

Citations53
Published2009
Admission routes1
Has abstractyes

Explore more

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