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The tumor necrosis factor superfamily member RANKL suppresses effector cytokine production in group 3 innate lymphoid cells

2019· article· en· W4313383302 on OpenAlexaff
Jennifer K. Bando, Susan Gilfillan, Christina Song, Keely G. McDonald, Stanley C-C Huang, Rodney D. Newberry, Yasuhiro Kobayashi, David Allan, James R. Carlyle, Marina Cella, Marco Colonna

Bibliographic record

VenueThe Journal of Immunology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRANKLInnate lymphoid cellCCL20C-C chemokine receptor type 6CytokineTumor necrosis factor alphaBiologyImmunologyCell biologyCancer researchReceptorChemokineInnate immune systemActivator (genetics)Chemokine receptorInflammationImmune systemBiochemistry

Abstract

fetched live from OpenAlex

Abstract While signals that activate group 3 innate lymphoid cells (ILC3s) have been described, the factors that negatively regulate these cells are less well understood. Here we found that the tumor necrosis factor (TNF) superfamily member receptor activator of nuclear factor kB ligand (RANKL) suppressed ILC3 activity in the intestine. Deletion of RANKL in ILC3s and T cells increased C-C motif chemokine receptor 6 (CCR6)+ ILC3 abundance and enhanced production of interleukin-17A (IL-17A) and IL-22 in response to IL-23 and during infection with the enteric murine pathogen Citrobacter rodentium. Additionally, CCR6+ ILC3s produced higher amounts of the master transcriptional regulator RORγt at steady state in the absence of RANKL. RANKL-mediated suppression was independent of T cells, and instead occurred via interactions between CCR6+ ILC3s that expressed both RANKL and its receptor, RANK. Thus, RANK-RANKL interactions between ILC3s regulate ILC3 abundance and activation, suggesting that cell clustering may control ILC3 activity.

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 score0.100
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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