TRIF Modulates TLR5-dependent Responses by Inducing Proteolytic Degradation of TLR5
Bibliographic record
Abstract
Proteolytic modification of pattern recognition receptors and their signaling adaptor molecules has recently emerged as an essential cellular event to regulate immune and inflammatory responses. Here we show that the TIR domain containing adaptor-inducing interferon-beta (TRIF), an adaptor molecule mediating TLR3 signaling and MyD88-independent signaling of TLR4, plays an inhibitory role in TLR5-elicited responses by inducing proteolytic degradation of TLR5. TRIF overexpression in human embryonic kidney (HEK293) and human colonic epithelial (NCM460) cells abolishes the cellular protein level of TLR5, whereas it does not alter TLR5 mRNA level. Thus, TRIF overexpression dramatically suppresses flagellin/TLR5-deriven NFkappaB activation in NCM460 cells. TRIF-induced TLR5 protein degradation is completely inhibited in the presence of pan-caspase inhibitor (benzyloxycarbonyl-Val-Ala-Asp-fluoromethyl ketone), whereas several specific inhibitors against cathepsin B, reactive oxygen species, or ubiquitin-mediated proteasome activity fail to suppress this degradation. These results indicate that TRIF-induced caspase activity causes TLR5 protein degradation. In addition, we identify that the C terminus of TRIF and extracellular domain of TLR5 are required for TRIF-induced TLR5 degradation. Furthermore, TRIF-induced proteolytic degradation is extended to TLR3, TLR6, TLR7, TLR8, TLR9, and TLR10, whereas the cellular level of TLR1, TLR2, and TLR4 is not affected by TRIF overexpression. These results suggest that, in addition to mediating TLR3- or TLR4-induced signaling as an adaptor molecule, TRIF can participate in proteolytic modification of certain members of TLRs to modulate the functionality of TLRs at post-translational level. Collectively, our findings propose a potential inhibitory role of TRIF at least in regulating host-microbial communication via TLR5 in colonic epithelial cells.
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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 | 0.000 |
| 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) | 0.001 | 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 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".