The Human Carnitine Transporter SLC22A16 Mediates High Affinity Uptake of the Anticancer Polyamine Analogue Bleomycin-A5
Bibliographic record
Abstract
Bleomycin is used in combination with other antineoplastic agents to effectively treat lymphomas, testicular carcinomas, and squamous cell carcinomas of the cervix, head, and neck. However, resistance to bleomycin remains a persistent limitation in exploiting the full therapeutic benefit of the drug with other types of cancers. Previously, we documented that the Saccharomyces cerevisiae L-carnitine transporter Agp2 is responsible for the high affinity uptake of polyamines and of the polyamine analogue bleomycin-A5. Herein, we document that the human L-carnitine transporter hCT2 encoded by the SLC22A16 gene is involved in bleomycin-A5 uptake, as well as polyamines. We show that NT2/D1 human testicular cancer cells, which highly express hCT2, are extremely sensitive to bleomycin-A5, whereas HCT116 human colon carcinoma cells devoid of detectable hCT2 expression or MCF-7 human breast cancer cells that only weakly express the permease showed striking resistance to the drug. NT2/D1 cells accumulated fluorescein-labeled bleomycin-A5 to substantially higher levels than HCT116 cells. Moreover, L-carnitine protected NT2/D1 cells from the lethal effects of bleomycin-A5 by preventing its influx, and siRNA targeted to hCT2 induced resistance to bleomycin-A5-dependent genotoxicity. Furthermore, hCT2 overexpression induced by transient transfection of a functional hCT2-GFP fusion protein sensitized HCT116 cells to bleomycin-A5. Collectively, our data strongly suggest that hCT2 can mediate bleomycin-A5 and polyamine uptake, and that the rate of bleomycin-A5 accumulation may account for the differential response to the drug in patients.
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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 | 0.001 |
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".