TRAM Is Required for TLR2 Endosomal Signaling to Type I IFN Induction
Bibliographic record
Abstract
Detection of microbes by TLRs on the plasma membrane leads to the induction of proinflammatory cytokines such as TNF-α, via activation of NF-κB. Alternatively, activation of endosomal TLRs leads to the induction of type I IFNs via IFN regulatory factors (IRFs). TLR4 signaling from the plasma membrane to NF-κB via the Toll/IL-1R (TIR) adaptor protein MyD88 requires the TIR sorting adaptor Mal, whereas endosomal TLR4 signaling to IRF3 via the TIR domain-containing adaptor-inducing IFN-β (TRIF) requires the TRIF-related adaptor molecule (TRAM). Similar to TLR4 homodimers, TLR2 heterodimers can also induce both proinflammatory cytokines and type I IFNs. TLR2 plasma membrane signaling to NF-κB is known to require MyD88 and Mal, whereas endosomal IRF activation by TLR2 requires MyD88. However, it was unclear whether TLR2 requires a sorting adaptor for endosomal signaling, like TLR4 does. In this study, we show that TLR2-dependent IRF7 activation at the endosome is both Mal- and TRAM-dependent, and that TRAM is required for the TLR2-dependent movement of MyD88 to endosomes following ligand engagement. TRAM interacted with both TLR2 and MyD88, suggesting that TRAM can act as a bridging adapter between these two molecules. Furthermore, infection of macrophages lacking TRAM with herpes viruses or the bacterium Staphylococcus aureus led to impaired induction of type I IFN, indicating a role for TRAM in TLR2-dependent responses to human pathogens. Our work reveals that TRAM acts as a sorting adaptor not only for TLR4, but also for TLR2, to facilitate signaling to IRF7 at the endosome, which explains how TLR2 is capable of causing type I IFN induction.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".