Misinterpreting the Therapeutic Effects of Small Interfering RNA Caused by Immune Stimulation
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
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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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 | 0.000 | 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) | 0.000 | 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