Probing the Effect of miRNA on siRNA–PEI Polyplexes
Bibliographic record
Abstract
Delivery of small interfering RNA (siRNA) for silencing of aberrantly expressed genes is a promising therapy for the treatment of various genetic disorders. Polymeric carriers have been used in the design of efficient delivery systems to generate nanoscale siRNA polyplexes. Despite the great amount of research pursued on siRNA therapeutics, the underlying mechanisms of polyplex dissociation in cytosol are still unclear. The fate of siRNA polyplexes during intracellular stages of delivery and how the endogenous molecules may affect the integrity of polyplexes remains to be explored. In this study, we have focused on miRNA-21 as a representative anionic endogenous molecule and performed gel electrophoresis mobility shift assays, particle size and zeta (ζ)-potential analyses, and a series of all-atom molecular dynamics simulations to elucidate the effect of miRNA on siRNA-PEI polyplexes. We report a slightly better binding to PEI by miRNA than that of siRNA, and speculated that miRNA may disrupt the integrity of preformed siRNA-PEI polyplexes. In contrast to our initial speculation, however, introduction of miRNA to a preformed siRNA-PEI polyplex revealed formation of a miRNA layer surrounding the polyplex through interactions with PEI. The resulting structure is a ternary siRNA-PEI-miRNA complex, where the experimentally determined ζ-potential was found to decrease as a function of miRNA added.
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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 | 0.001 |
| 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".