Antisense inhibition of Flk-1 by oligonucleotides composed of 2<i>'</i>-deoxy-2<i>'</i>-fluoro-β-<scp>D</scp>-arabino- and 2<i>'</i>-deoxy-nucleosides
Bibliographic record
Abstract
The design of new antisense oligomers with improved binding affinity for targeted RNA, while still activating RNase H, is a major research area in medicinal chemistry. RNase H recognizes the RNA-DNA duplex and cleaves the complementary mRNA strand, providing the main mechanism by which antisense oligomers elicit their activities. It has been shown that configuration inversion at the C2' position of the DNA sugar moiety (arabinonucleic acid, ANA), combined with the substitution of the 2'OH group by a fluorine atom (2'F-ANA) increases the oligomer's binding affinity for targeted RNA. In the present study, we evaluated the antisense activity of mixed-backbone phosphorothioate oligomers composed of 2'-deoxy-2'-fluoro-beta-D-arabinose and 2'-deoxyribose sugars (S-2'F-ANA-DNA chimeras). We determined their abilities to inhibit the protein expression and phosphorylation of Flk-1, a vascular endothelial growth factor receptor (VEGF), and VEGF biological effects on endothelial cell proliferation, migration, and platelet-activating factor synthesis. Treatment of endothelial cells with chimeric oligonucleotides reduced Flk-1 protein expression and phosphorylation more efficiently than with phosphorothioate antisenses (S-DNA). Nonetheless, these two classes of antisenses inhibited VEGF activities equally. Herein, we also demonstrated the capacity of the chimeric oligomers to elicit RNase H activity and their improved binding affinity for complementary RNA as compared with S-DNA.
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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) | 0.001 | 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".