Identification of RNA Sequence Motifs Stimulating Sequence-Specific TLR8-Dependent Immune Responses
Bibliographic record
Abstract
The TLRs 7, 8, and 9 stimulate innate immune responses upon recognizing pathogen nucleic acids. U-rich RNA sequences were recently discovered that stimulate human TLR7/8-mediated or murine TLR7-mediated immune effects. In this study we identified single-stranded RNA sequences containing defined sequence motifs that either preferentially activate human TLR8-mediated as opposed to TLR7- or TLR7/8-mediated immune responses. The identified TLR8 RNA motifs signal via TLR8 and fail to induce IFN-alpha from TLR7-expressing plasmacytoid dendritic cells but induce the secretion of Th1-like and proinflammatory cytokines from TLR8-expressing immune cells such as monocytes or myeloid dendritic cells. In contrast, RNA sequences containing the TLR7/8 motif signal via TLR7 and TLR8 and stimulate cytokine secretion from both TLR7- and TLR8-positive immunocytes. The TLR8-specific RNA sequences are able to trigger cytokine responses from human and bovine but not from mouse, rat, and porcine immune cells, suggesting that these species lack the capability to respond properly to TLR8 RNA ligands. In summary, we describe two classes of single-stranded TLR7/8 and TLR8 RNA agonists with diverse target cell and species specificities and immune response profiles.
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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) | 0.002 | 0.001 |
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".