Identification of RNA Sequence Motifs Stimulating Sequence-Specific TLR8-Dependent Immune Responses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it