Misinterpreting the Therapeutic Effects of Small Interfering RNA Caused by Immune Stimulation
Bibliographic record
Abstract
Activation of innate immunity has direct effects in modulating viral replication, tumor growth, angiogenesis, and inflammatory and other immunological processes. It is now established that unmodified siRNA can activate this innate immune response and therefore there is real potential for siRNA to elicit nonspecific therapeutic effects in a wide range of disease models. Here we demonstrate that in a murine model of influenza infection, the antiviral activity of siRNA is due primarily to immune stimulation elicited by the active siRNA duplexes and is not the result of therapeutic RNA interference (RNAi) as previously reported. We show that the misinterpretation stems from the use of a particular control green fluorescent protein (GFP) siRNA that we identify as having unusually low immunostimulatory activity compared with the active anti-influenza siRNA. Curiously, this GFP siRNA has served as a negative control for a surprising number of groups reporting therapeutic effects of siRNA. The inert immunologic profile of the GFP sequence was unique among a broad panel of published siRNAs, all of which could elicit significant interferon induction from primary immune cells. This panel included eight active siRNAs against viral, angiogenic, and oncologic targets, the reported therapeutic efficacy of which was based on comparison with the nonimmunostimulatory GFP siRNA. These results emphasize the need for researchers to anticipate, monitor, and adequately control for siRNA-mediated immune stimulation and calls into question the interpretation of numerous published reports of therapeutic RNAi in vivo. The use of chemically modified siRNA with minimal immunostimulatory capacity will help to delineate more accurately the mechanism of action underlying such studies.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".