137 DNA nanostructure serum stability: greater than the sum of their parts
Bibliographic record
Abstract
DNA cages hold tremendous potential to encapsulate and selectively release therapeutic drugs, and can provide useful tools to probe the size and shape dependence of nucleic acid delivery (McLaughlin & Sleiman, H. F., 2011). These structures have been shown to site-specifically present ligands, small molecule drugs, or antisense/siRNA motifs, in order to increase their therapeutic efficiency (Li & Fan, C. 2012). One of the major barriers towards their in vivo applications is the susceptibility of their strands towards nuclease degradation. A number of chemical strategies have been used to block nuclease digestion of oligonucleotides and improve potency, such as the use of a phosphorothioate backbone, 2´-O-methyl, locked nucleic acids, and short hybrid gapmers. However, the synthesis of these oligonucleotides is often complicated and expensive, driving the need for simple modifications to enhance serum stability and address in vivo biodistribution. We show here a simple method to significantly enhance the nuclease stability of DNA strands, through introduction of commercially available, single-endmodifications (Conway & Sleiman 2013). We use these oligonucleotides to construct DNA cages in a single step and in quantitative yields. Even in single-stranded form, these cages stabilize their component strands towards nucleases, with mean lifetimes as long as 62 h in 10 % (v/v) fetal bovine serum (FBS). We examine the effect of other DNA-end modifications on nuclease susceptibility. Finally, we show the ligation of these single-stranded cages into topologically interesting catenane ‘necklaces,’ with mean lifetimes in serum of ∼200 h.
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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) | 0.002 | 0.001 |
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".