DNA-aptamer/protein interaction as a cause of apoptosis and arrest of proliferation in Ehrlich ascites adenocarcinoma cells
Bibliographic record
Abstract
New prospects for the applications of single-stranded DNA and RNA as therapeutic agents have been discovered in the recent years. Aptamers are the oligonucleotides that bind to their targets with high affinity and specificity due to the well-defined tertiary structures and spatial charge distribution. Aptamers can be selected for any molecules, virus particles, bacteria, cells, and tissues. They have a wide range of applications from target identification to drug delivery. Aptamers themselves can affect various cell functions by affecting certain proteins and receptors. Here, we present the technique for selecting aptamers with antitumor activity in cancer cell cultures and identifying their target proteins by mass spectrometry analysis. The evolved aptamers showed the following antitumor properties: AS-14 (K d = 3.8 nM) induced apoptosis (phosphatidylserine translocation determined with Annexin V Alexa Fluor 488) and AS-9 (K d = 0.75 nM) stopped proliferation (as determined with CellTrace™ Far Red DDAO-SE) in the culture of Ehrlich ascites adenocarcinoma cells. Using high performance liquid chromatography and high resolution tandem mass spectrometry, we have identified the proteins affected by the AS-14 and AS-9 aptamers. One of the most likely targets for AS-14 was filamin A, which is involved in metastasis formation, tumor development, and cell proliferation. According to mass spectrometry data, the AS-9 aptamer influences the α-subunit of mitochondrial ATP synthase, the key component of mitochondrial oxidative phosphorylation, stimulation of which leads to tumor growth suppression. Thus, mass spectrometry data confirmed the results of the experiments on cell cultures showing that the aptamer binding to specific protein targets causes apoptosis and stops proliferation of cancer cells. However, the mechanisms of action of aptamers in vitro and in vivo are not clear enough and still need to be determined. Our study opens up new possibilities for creation of non-toxic drugs based on DNA aptamers for targeted anticancer therapy.
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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".