Delivery of gene targeting siRNAs to breast cancer cells using a multifunctional peptide complex that promotes both targeted delivery and endosomal release
Bibliographic record
Abstract
RNA interference has been used to dissect the importance of individual gene products in various human disease processes, including cancer. Small-interfering RNA, or siRNA, is one of the tools utilized in this regard, but specially-designed delivery agents are required to allow the siRNA to gain optimal access to the cell interior. Our laboratory has utilized two different siRNA-binding delivery peptides containing a polyarginine core, and modified by myristoylation and targeting motifs (iRGD or Lyp-1). A third peptide was designed to assist with endosomal release. Various ratios of the peptides and siRNA were combined and assayed for the ability to form stable complexes, and optimized ratios were determined. The complexes were found to form particles, with the majority having a diameter of 100-300 nm, as visualized by electron microscopy. These siRNA complexes have enhanced protection from nucleases present in serum, as compared to "naked" unprotected siRNA. The particles were internalized by the cells and could be detected in the cell cytoplasm by confocal fluorescence microscopy. In functional assays, peptide/siRNA complexes were shown to cause the knock down of corresponding targeted proteins. The peptide with the LyP-1 targeting motif was more effective at knockdown in MDA-MB-231 breast cancer cells than the peptide with the iRGD motif. Inclusion of the endosomal release peptide in the complexes greatly enhanced the peptide/siRNA effects. Peptide/siRNA complexes simultaneously targeting Stat3 and c-Myc caused a marked reduction in anchorage-independent growth, a property correlated with tumorigenicity. This study demonstrates the ability of a peptide-based siRNA-delivery system to deliver siRNA into breast cancer cells and cause both protein knockdown and suppression of the malignant phenotype. Such peptide complexes are likely to become highly useful siRNA-delivery vehicles for the characterization, and potentially for the treatment, of human cancer.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".