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Record W2101649073 · doi:10.4049/jimmunol.1003414

CpG and Non-CpG Oligodeoxynucleotides Directly Costimulate Mouse and Human CD4+ T Cells through a TLR9- and MyD88-Independent Mechanism

2011· article· en· W2101649073 on OpenAlexfundno aff
Angela Landrigan, Michael T. Wong, Paul J. Utz

Bibliographic record

VenueThe Journal of Immunology · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchU.S. Public Health ServiceNational Institutes of HealthNational Science Foundation
KeywordsTLR9CpG OligodeoxynucleotideCD28Cell biologyT-cell receptorBiologyCytokineJurkat cellsT cellDownregulation and upregulationCpG siteSecretionPhosphorylationImmunologyImmune systemBiochemistryGeneGene expression

Abstract

fetched live from OpenAlex

TLR ligands are known to activate APCs, but direct T cell responsiveness to TLR ligands is controversial. Because of their clinical relevance, we performed in-depth studies of the effects of the TLR9-associated ligands, oligodeoxynucleotides (ODNs), on highly purified T lymphocytes. Both CpG and non-CpG ODNs directly costimulate mouse and human CD4(+) T cells, resulting in activation marker upregulation, cytokine secretion, elevated TCR phosphorylation, and proliferation. Surprisingly, ODN costimulation occurred independently of TLR9 and MyD88, as well as ICOS, CD28, and TRIF. TLR9-antagonist ODNs likewise promoted T cell activation, which has important implications for the study of these "inhibitory" ODNs in inflammatory diseases. Cytokine profiling revealed that ODNs promote polarization of distinct Th subsets, and that ODNs differentially affect human naive and memory T cells. Our studies reveal a striking and unexpected ability of ODNs to directly activate and polarize T cells, presenting an opportunity to enhance the paradigm for selection of therapeutic ODNs in humans.

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 score0.020
Threshold uncertainty score0.659

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.224
Teacher spread0.209 · 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

Citations48
Published2011
Admission routes1
Has abstractyes

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