The Toll for Trafficking: Toll-Like Receptor 7 Delivery to the Endosome
Bibliographic record
Abstract
Toll-like receptor (TLR)-7 is an endosomal innate immune sensor capable of detecting single-stranded ribonucleic acid (ssRNA). TLR7-mediated induction of type I interferon (IFN) and other inflammatory cytokine production is important in anti-viral immune responses. Furthermore, altered TLR7 expression levels are implicated in various autoimmune disorders, indicating a key role for this receptor in modulating inflammation. This review is focused on the regulation of TLR7 expression and localization compared to that of the other endosomal TLRs: TLR3, 8, and 9. Endosomal TLR localization is a tightly controlled and intricate process with some shared components among various TLRs. However, TLR-specific mechanisms must also be in place in order to regulate the induction of pathogen- and cell-specific responses. It is known that TLR7 is shuttled from the endoplasmic reticulum (ER) to the endosome via vesicles from the Golgi. Several chaperone proteins are required for this process, most notably UNC93B1, recently identified to also be involved in the localization of the other endosomal TLRs. Acidification of the endosome and proteolytic cleavage of TLR7 are essential for TLR7 signaling in response to ligand binding. Cleavage of TLR7 has been demonstrated to be accomplished by furin peptidases in addition to cathepsins and asparagine endopeptidases (AEP). Moreover, triggering receptor expressed on myeloid cells like 4 (TREML4), a protein associated with antigen presentation and apoptosis in immune cells, has been implicated in the amplification of TLR7 signaling. Understanding these and other molecular mechanisms controlling TLR7 expression and trafficking will give insight into the specific control of TLR7 activity compared to the other endosomal TLRs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.044 |
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".