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Record W2121874550 · doi:10.4049/jimmunol.0802971

Differential Role for c-Rel and C/EBPβ/δ in TLR-Mediated Induction of Proinflammatory Cytokines

2009· article· en· W2121874550 on OpenAlexafffund
Yong‐Chen Lu, Ira Kim, Elizabeth Lye, Fang Shen, Nobutaka Suzuki, Shinobu Suzuki, Steve Gerondakis, Shizuo Akira, Sarah L. Gaffen, Wen‐Chen Yeh, Pamela S. Ohashi

Bibliographic record

VenueThe Journal of Immunology · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of TorontoOntario Institute for Cancer Research
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCanadian Institutes of Health ResearchNational Institutes of HealthUniversity of Pennsylvania
KeywordsProinflammatory cytokineTranscription factorBiologyCell biologyCytokineSignal transductionKinaseStimulationImmunologyChemistryInflammationEndocrinologyBiochemistryGene

Abstract

fetched live from OpenAlex

TLR stimulation triggers a signaling pathway via MyD88 and IL-1R-associated kinase 4 that is essential for proinflammatory cytokine induction. Although NF-kappaB has been shown to be one of the key transcriptional regulators of these cytokines, evidence suggests that other factors may also be important. In this study, we showed that MyD88-deficient macrophages have defective c-Rel activation, which has been linked to IL-12p40 induction, but not IL-6 or TNF-alpha. We also investigated other transcription factors and showed that C/EBPbeta and C/EBPdelta expression was limited in MyD88- or IL-1R-associated kinase 4-deficient macrophages treated with LPS. Importantly, the absence of both C/EBPbeta and C/EBPdelta resulted in the impaired induction of proinflammatory cytokines stimulated by several TLR ligands. Our results identify c-Rel and C/EBPbeta/delta as important transcription factors in a MyD88-dependent pathway that regulate the induction of proinflammatory cytokines.

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 score0.161
Threshold uncertainty score0.289

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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations107
Published2009
Admission routes2
Has abstractyes

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