Carrier-free Gene Silencing by Amphiphilic Nucleic Acid Conjugates in Differentiated Intestinal Cells
Bibliographic record
Abstract
Nucleic acid therapy can be beneficial for the local treatment of gastrointestinal diseases that currently lack appropriate treatments. Indeed, several oligonucleotides (ONs) are currently progressing through clinical trials as potential treatments for inflammatory bowel diseases. However, due to low uptake of carrier-free ONs by mucosal cells, strategies aimed at increasing the potency of orally administered ONs would be highly desirable. In this work, we explored the silencing properties of chemically modified and highly resistant ONs derivatized with hydrophobic alkyl chain on intestinal epithelial cells. We screened a set of lipid-ON conjugates for the silencing of model Bcl-2 mRNA and selected 2'-deoxy-2'-fluoro-arabinonucleic acid modified ON bearing docosanoyl moiety (L-FANA) as the most potent candidate with lowest toxicity. The efficacy of L-FANA conjugate was preserved in simulated intestinal fluids and in the inverted transfection setup. Importantly, L-FANA conjugate was able to downregulate target gene expression at both mRNA and protein levels in a difficult-to-transfect polarized epithelial cell monolayer in the absence of delivery devices and membrane disturbing agents. These findings indicate that lipid-ON conjugates could be promising therapeutics for the treatment of intestinal diseases as well as a valuable tool for the discovery of new therapeutic targets.
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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) | 0.000 | 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 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".