A Peptide-Based Dendrimer That Enhances the Splice-Redirecting Activity of PNA Conjugates in Cells
Bibliographic record
Abstract
The full therapeutic potential of oligonucleotide (ON)-based agents has been hampered by cellular delivery challenges. Cell-penetrating peptides (CPP) represent promising delivery vectors for nucleic acids, and their potential has recently been evaluated using a functional splicing redirection assay, which capitalizes on the nuclear delivery of splice-correcting steric-block ON analogues such as peptide nucleic acids (PNA). Despite encouraging in vitro and in vivo data with arginine-rich CPP-steric block conjugates, mechanistic studies have shown that entrapment within the endosome/lysosome compartment after endocytosis remains a limiting factor. Previous work from our group has shown that CPP oligomerization greatly improves cellular delivery and increases transfection of plasmid DNA. We now report the chemical synthesis and the evaluation of multivalent CPP-PNA constructs incorporating monomeric (p53(mono)) and dendrimer-like tetrameric (p53(tet)) forms of the p53 tetramerization domain containing peptide, a 10 arginine CPP domain (R10), and a splice redirecting PNA (PNA705). These CPP-PNA conjugates were termed R10p53(tet)-PNA705 and R10p53(mono)-PNA705, referring to their oligomerization state. The present study demonstrates that the splicing redirection efficiency of PNA705 is much greater in the context of the tetrameric R10p53(tet)-PNA705 construct than for the monomeric and occurs at nanomolar concentrations, demonstrating that multivalency is an important factor in delivering PNA into cells.
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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 | 0.000 |
| 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".