Untargeted metabolomics to understand the basis of phenotypic differences in amphotericin B-resistant Leishmania parasites
Bibliographic record
Abstract
Background : Protozoan Leishmania parasites are responsible for a range of clinical infections that represent a substantial challenge for global health. Amphotericin B (AmB) is increasingly used to treat Leishmania infection, so understanding the potential for resistance to this drug is an important priority. Previously we described four independently-derived AmB-resistant L. mexicana lines that exhibited resistance-associated genetic lesions resulting in altered sterol content. However, substantial phenotypic variation between these lines, including differences in virulence attributes, were not fully explained by these changes. Methods: To identify alterations in cellular metabolism potentially related to phenotypic differences between wild-type and AmB-resistant lines, we extracted metabolites and performed untargeted metabolomics by liquid chromatography-mass spectrometry. Results: We observed substantial differences in metabolite abundance between lines, arising in an apparently stochastic manner. Concerted remodeling of central carbon metabolism was not observed; however, in three lines, decreased abundance of several oligohexoses was observed. Given that the oligomannose mannogen is an important virulence factor in Leishmania , this could relate to loss of virulence in these lines. Increased abundance of the reduced forms of the oxidative stress-protective thiols trypanothione and glutathione was also observed in multiple lines. Conclusions: This dataset will provide a useful resource for understanding the molecular basis of drug resistance in Leishmania , and suggests a role for metabolic changes separate from the primary mechanism of drug resistance in determining the phenotypic profile of parasite lines subjected to experimental selection of resistance.
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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 | 0.000 |
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".