Structural analysis of DNA complexation with cationic lipids
Bibliographic record
Abstract
Complexes of cationic liposomes with DNA are promising tools to deliver genetic information into cells for gene therapy and vaccines. Electrostatic interaction is thought to be the major force in lipid-DNA interaction, while lipid-base binding and the stability of cationic lipid-DNA complexes have been the subject of more debate in recent years. The aim of this study was to examine the complexation of calf-thymus DNA with cholesterol (Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), dioctadecyldimethylammoniumbromide (DDAB) and dioleoylphosphatidylethanolamine (DOPE), at physiological condition, using constant DNA concentration and various lipid contents. Fourier transform infrared (FTIR), UV-visible, circular dichroism spectroscopic methods and atomic force microscopy were used to analyse lipid-binding site, the binding constant and the effects of lipid interaction on DNA stability and conformation. Structural analysis showed a strong lipid-DNA interaction via major and minor grooves and the backbone phosphate group with overall binding constants of K(Chol) = 1.4 (+/-0.5) x 10(4) M(-1), K(DDAB) = 2.4 (+/-0.80) x 10(4) M(-1), K(DOTAP) = 3.1 (+/-0.90) x 10(4) M(-1) and K(DOPE) = 1.45 (+/- 0.60) x 10(4) M(-1). The order of stability of lipid-DNA complexation is DOTAP>DDAB>DOPE>Chol. Hydrophobic interactions between lipid aliphatic tails and DNA were observed. Chol and DOPE induced a partial B to A-DNA conformational transition, while a partial B to C-DNA alteration occurred for DDAB and DOTAP at high lipid concentrations. DNA aggregation was observed at high lipid content.
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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 | 0.001 |
| 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".