Negative Regulation of the Type I Interferon Signaling Pathway by Synthetic Toll-Like Receptor 7 Ligands
Bibliographic record
Abstract
Ten Toll-like receptor (TLR) family members have been reported in humans. Here, the endoplasmatic receptors TLR9, TLR8, TLR7, and TLR3 respond to nucleic acids and derivatives or to small molecules (TLR7 and 8). Another cytoplasmic RNA receptor, retinoic acid inducible gene I (RIG-I), is stimulated by 5' triphosphate double-stranded RNA. We discovered that TLR7 small-molecule agonists inhibit nucleic acid-mediated TLR3, TLR7, TLR9, or RIG-I-dependent interferon-α (IFN-α) immune response. Other cytokines and chemokines stimulated by nucleic acid agonists remained unaffected. The observed blockage of TLR3, TLR7, TLR9, and RIG-I-mediated IFN-α response appears to be driven by a competitive mechanism at the type I IFN pathway. Besides type I IFN, IFN response genes such as IFIT-1, Mx1, OAS1, or IRF7 were affected, which indicates that the key element driving the inhibition is located in the type I IFN pathway. Indeed, the heterotrimeric complex formation of phosphor-signal transducer and activator of transcription factor 1 (STAT1), phosphor-STAT2, and IRF9 (called ISGF3, IFN-stimulated gene factor 3) is inhibited through the TLR7 small-molecule agonists by phosphor-STAT2 blockage. These findings provide novel insights into the use of synthetic TLR7 or TLR7/8 small molecules as ligands for immune activation and suppression.
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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".