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Record W3160850462 · doi:10.1073/pnas.2021309118

DNAM-1 regulates Foxp3 expression in regulatory T cells by interfering with TIGIT under inflammatory conditions

2021· article· en· W3160850462 on OpenAlexaff
K. Satoh, Yumi Yamashita-Kanemaru, Fumie Abe, Rikito Murata, Yuho Nakamura-Shinya, Kazumasa Kanemaru, Masafumi Muratani, André Veillette, Motohito Goto, Mamoru Ito, Akira Shibuya, Kazuko Shibuya

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsMontreal Clinical Research Institute
FundersMinistry of Education, Culture, Sports, Science and Technology
KeywordsTIGITImmune systemFOXP3ImmunologyRegulatory T cellT cellBiologyImmune toleranceInflammationCell biologyIL-2 receptor

Abstract

fetched live from OpenAlex

Regulatory T (Treg) cells that express forkhead box P3 (Foxp3) are pivotal for immune tolerance. Although inflammatory mediators cause Foxp3 instability and Treg cell dysfunction, their regulatory mechanisms remain incompletely understood. Here, we show that the transfer of Treg cells deficient in the activating immunoreceptor DNAM-1 ameliorated the development of graft-versus-host disease better than did wild-type Treg cells. We found that DNAM-1 competes with T cell immunoreceptor with Ig and ITIM domains (TIGIT) in binding to their common ligand CD155 and therefore regulates TIGIT signaling to down-regulate Treg cell function without DNAM-1-mediated intracellular signaling. DNAM-1 deficiency augments TIGIT signaling; this subsequently inhibits activation of the protein kinase B-mammalian target of rapamycin complex 1 pathway, resulting in the maintenance of Foxp3 expression and Treg cell function under inflammatory conditions. These findings demonstrate that DNAM-1 regulates Treg cell function via TIGIT signaling and thus, it is a potential molecular target for augmenting Treg function in inflammatory diseases.

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)0.000
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 score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.016
GPT teacher head0.252
Teacher spread0.236 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicImmune Cell Function and InteractionFrench-language works237,207