Antisense‐induced down‐regulation of thymidylate synthase and enhanced cytotoxicity of 5‐FUdR in 5‐FUdR‐resistant HeLa cells
Bibliographic record
Abstract
1. Thymidylate synthase (TS) is a target for several anticancer drugs. We previously showed that an antisense oligodeoxynucleotide (ODN) directed against TS mRNA down-regulated TS protein and enhanced cytotoxicity of TS-targeting drugs [including 5-fluorodeoxyuridine (5-FUdR)] in HeLa cells. Patient tumours with increased TS expression are resistant to TS-targeting drugs. It was hypothesized that TS mRNA and consequently TS protein could be down-regulated in 5-FUdR-resistant cells that overexpress TS, sensitizing them to 5-FUdR cytotoxicity. In this study we assessed the capacity of an anti-TS antisense ODN to circumvent resistance dependent on TS overexpression. 2. Variant HeLa clones exhibiting 2 - 20 fold resistance to 5-FUdR were selected by exposing cultured cells to drug. Clones FUdR-5a, -25b, and -50a expressed TS protein levels 10 fold, 10 fold, and 17 fold higher (respectively) than parental cells. Cells were treated with antisense ODN 83 (a 2'-methoxy-ethoxylated, phosphorothioated 20-mer, complementary to a portion of the 3'-untranslated region of TS mRNA), or ODN 32 (a control ODN with the same base composition as ODN 83, but in randomized order). Twenty-four and 48 h following transfection (50-100 nM ODN, plus polycationic liposome), TS mRNA levels (by RT-PCR) and protein levels (by radiolabelled 5-FUdR-monophosphate binding) were decreased by at least 60% in ODN 83-treated cells compared with control ODN 32-treated cells. ODN 83 enhanced the cytotoxicity of 5-FUdR by up to 85% in both parental and 5-FUdR-resistant cell lines. 3. Antisense ODN can be used to down-regulate TS and attenuate drug resistance in TS-overexpressing cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".