Toll-like receptor 3-mediated activation of NF-κB and IRF3 diverges at Toll-IL-1 receptor domain-containing adapter inducing IFN-β
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it