Longitudinal effects of thymidine analogues on mtDNA, mtRNA and multidrug resistance (MDR-1) induction in cultured cells
Bibliographic record
Abstract
OBJECTIVES: HIV nucleoside reverse transcriptase inhibitors (NRTIs) can cause mitochondrial toxicity. In spite of several studies performed on cells, little is known about their long-term effects on mitochondrial DNA (mtDNA), mitochondrial gene expression (mtRNA) and cellular protective mechanisms that such exposure may trigger. Our aim was to investigate the longitudinal effects of two thymidine analogue NRTIs, zidovudine and stavudine, on human cells, measuring their effects on the levels of mtDNA, mtRNA and on induction of the multidrug resistance (MDR) gene MDR-1. METHODS: K562 lymphoblastoid cells were treated for 74 days with zidovudine or stavudine concentrations corresponding to approximately 1x, 20x and 400x those measured in plasma. Samples were collected longitudinally and assayed for mtDNA, mtRNA and MDR-1 mRNA levels by real-time quantitative PCR. mtDNA deletions were investigated by long PCR. RESULTS: Upon exposure to both zidovudine and stavudine, an early dose-dependent and transient increase in mtDNA content was observed. This was followed by a concurrent and transient elevation in both mtRNA and MDR-1 mRNA levels. Interestingly, the increase in mtRNA was most pronounced at low concentrations, whereas that of MDR-1 expression occurred at the highest concentrations only. No mtDNA deletions were detected under any conditions. CONCLUSIONS: Cellular response to thymidine analogue NRTI exposure showed a complex, time- and dose-dependent pattern over time. We report for the first time that NRTIs can induce MDR-1 expression; however, this effect is delayed, possibly in response to oxidative damage or mitochondrial dysfunction. Our results indicate that longitudinal experiments may refine our knowledge about NRTI toxicity.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".