Toll-like receptor 3-mediated activation of NF-κB and IRF3 diverges at Toll-IL-1 receptor domain-containing adapter inducing IFN-β
Bibliographic record
Abstract
We have previously shown that double-stranded RNA-triggered, Toll-like receptor 3 (TLR3)-mediated signaling is independent of MyD88, IRAK4, and IRAK. Instead, TRAF6, TAK1, and TAB2 are recruited to TLR3 on poly(I.C) stimulation. TRAF6-TAK1-TAB2 are then translocated to the cytosol where TAK1 is phosphorylated and activated, leading to the activation of IkappaB kinase and NFkappaB. The present study addressed two important questions: (i) How are TRAF6, TAK1, and TAB2 recruited to TLR3? (ii) Are TRAF6, TAK1, and TAB2 also required for TLR3-mediated IRF3 activation? Recently, a novel Toll-IL-1 receptor (TIR)-containing adapter, TIR domain-containing adapter inducing IFN-beta (TRIF), was shown to play a critical role in TLR3-mediated activation of NF-kappaB and IRF3. We found that TLR3 recruits TRAF6 via adapter TRIF through a TRAF6-binding sequence in TRIF (PEEMSW, amino acids 250-255). Mutation of this TRAF6-binding sequence abolished the interaction of TRIF with TRAF6, but not with TLR3. Interestingly, mutation of the TRAF6-binding site of TRIF only abolished its ability to activate NF-kappaB but not IRF3, suggesting that TLR3-mediated activation of NF-kappaB and IRF3 might bifurcate at TRIF. In support of this finding, we showed that DN-TRAF6 and DN-TAK1 blocked poly(I.C)-induced NF-kappaB but not IRF3 activation. Furthermore, whereas poly(I.C)-induced NF-kappaB activation is completely abolished inTRAF6-/- MEFs, the signal-induced activation of IRF3 is TRAF6 independent. In conclusion, TRIF recruits TRAF6-TAK1-TAB2 to TLR3 through its TRAF6-binding site, which is required for NF-kappaB but not IRF3 activation. Therefore, double-stranded RNA-induced TLR3/TRIF-mediated NF-kappaB and IRF3 activation diverge at TRIF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".