Modulation in aquaglyceroporin <i>AQP1</i> gene transcript levels in drug‐resistant <i>Leishmania</i>
Bibliographic record
Abstract
Antimonial-containing drugs are the first line of treatment against the parasite Leishmania. Resistance to antimonials has been correlated to its reduced accumulation. We used a dominant negative functional cloning strategy where a Leishmania mexicana expression cosmid bank was transfected in cells resistant to trivalent antimony (SbIII). Cells were selected for increased sensitivity to SbIII. One cosmid was isolated that could bestow SbIII sensitivity to resistant cells. The gene part of this cosmid that is responsible for increased SbIII sensitivity corresponds to AQP1, an aquaglyceroporin. AQP1 was recently shown to be a route by which SbIII can accumulate in Leishmania cells. Transport studies have shown that the L. mexicana AQP1 can restore SbIII transport in resistant cells. Southern blot analysis indicated that the copy number of neither the AQP1 gene nor the other AQP homologues was changed in antimony-resistant mutants of several Leishmania species. The AQP1 gene sequence was also unchanged in mutants. However, the AQP1 RNA levels were downregulated in several Leishmania promastigote species resistant to antimonials. In general, but not always, the level of AQP1 transcript levels correlated well with the accumulation of SbIII and resistance levels in Leishmania cells. AQP1 thus appears to be a key determinant of antimonials accumulation and susceptibility in Leishmania.
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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".