Tolerance to drug-induced cell death favours the acquisition of multidrug resistance in Leishmania
Bibliographic record
Abstract
The control of the protozoan parasite Leishmania relies on few drugs with unknown cellular targets and unclear mode of action. Several antileishmanials, however, were shown to induce apoptosis in Leishmania and this death mechanism was further studied in drug-sensitive and drug-resistant Leishmania infantum. In sensitive parasites, antimonials (SbIII), miltefosine (MF) and amphotericin B (AMB), but not paromomycin (PARO), triggered apoptotic cell death associated with reactive oxygen species (ROS). In contrast, Leishmania mutants resistant to SbIII, MF or AMB not only failed to undergo apoptosis following exposure to their respective drugs, but also were more tolerant towards apoptosis induced by other antileishmanials, provided that these killed Leishmania via ROS production. Such tolerance favored the rapid acquisition of multidrug resistance. PARO killed Leishmania in a non-apoptotic manner and failed to produce ROS. PARO resistance neither protected against drug-induced apoptosis nor provided an increased rate of acquisition of resistance to other antileishmanials. However, the PARO-resistant mutant, but not SbIII-, MF- or AMB-resistant mutants, became rapidly cross-resistant to methotrexate, a model drug also not producing ROS. Our results therefore link the mode of killing of drugs to tolerance to cell death and to a facilitated emergence of multidrug resistance. These findings may have fundamental implications in the field of chemotherapeutic interventions.
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".