Exploring direct and indirect targets of current antileishmanial drugs using a novel thermal proteomics profiling approach
Bibliographic record
Abstract
Visceral leishmaniasis (VL), caused by Leishmania infantum, is an oft-fatal neglected tropical disease. In the absence of an effective vaccine, the control of leishmaniasis relies exclusively on chemotherapy. Due to the lack of established molecular/genetic markers denoting parasite resistance, clinical treatment failure is often used as an indicator. Antimony-based drugs have been the standard antileishmanial treatment for more than seven decades, leading to major drug resistance in certain regions. Likewise, drug resistance to miltefosine and amphotericin B continues to spread at alarming rates. In consequence, innovative approaches are needed to accelerate the identification of antimicrobial drug targets and resistance mechanisms. To this end, we have implemented a novel approach based on thermal proteome profiling (TPP) to further characterize the mode of action of antileishmanials antimony, miltefosine and amphotericin B, as well as to better understand the mechanisms of drug resistance deployed by Leishmania. Proteins become more resistant to heat-induced denaturation when complexed with a ligand. In this way, we used multiplexed quantitative mass spectrometry-based proteomics to monitor the melting profile of thousands of expressed soluble proteins in WT, antimony-resistant, miltefosine-resistant, and amphotericin B-resistant L. infantum parasites, in the presence (or absence) of the above-mentioned drugs. Bioinformatics analyses were performed, including data normalization, melting profile fitting, and identification of proteins that underwent changes (fold change > 4) caused by complexation with a drug. With this unique approach, we were able to narrow down the regions of the L. infantum proteome that interact with antimony, miltefosine, and amphotericin B; validating previously-identified and unveiling novel drug targets. Moreover, analyses revealed candidate proteins potentially involved in drug resistance. Interestingly, we detected thermal proximity coaggregation for several proteins belonging to the same metabolic pathway (i.e., tryparedoxin peroxidase and aspartate aminotransferase in proteins exposed to antimony), highlighting the importance of these pathways. Collectively, our results could serve as a jumping-off point for the future development of innovative diagnostic tools for the detection and evaluation of antimicrobial-resistant Leishmania populations, as well as open the door for new on-target therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".