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.
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 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".