Length of dsRNA (poly I:C) drives distinct innate immune responses, depending on the cell type
Bibliographic record
Abstract
Poly I:C, a synthetic dsRNA analogue, has been used extensively for decades to study innate responses in vivo and in different cell types. We have found substantial variability while using poly I:C from different sources. In this study we found that poly I:C from 2 commercial sources induced sharply opposite responses in myeloid and fibroblasts, depending on the length of the poly I:C. Although short poly I:C (≈ 1-1.5 kb) induced greater amounts of TNF-α, IL-8, and IFN-β and a stronger antiviral response in myeloid cells, it was a poor inducer in fibroblasts. By contrast, long poly I:C (>5 kb) preferentially elicited higher cytokine and antiviral responses in fibroblasts and showed diminished responses in myeloid cells. Poly I:C activated NF-κB and STAT-1 signaling in a length- and cell-type-dependent fashion. Mechanistically, short poly I:C was better internalized in the myeloid cells and long poly I:C in the fibroblasts. Finally, long poly I:C required SR-A, whereas short poly I:C required RIG-I and Raftlin. We provide evidence that the length of dsRNA drives distinct innate responses in different cell lineages. These findings may augment in selecting the appropriate poly I:C type to design cell-type-specific potent adjuvants for vaccines against infectious diseases or cancers.
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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 | 0.001 |
| 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".