Adaptation of <i>Leishmania donovani</i> to Cutaneous and Visceral Environments: in Vivo Selection and Proteomic Analysis
Bibliographic record
Abstract
Leishmaniasis is a neglected tropical disease caused by Leishmania protozoa. Two main forms are found in the Old World, self-limited cutaneous leishmaniasis and potentially fatal visceral leishmaniasis, with parasite dissemination to liver, bone marrow, and spleen. The Leishmania donovani species complex is the causative agent of visceral leishmaniasis worldwide, but atypical L. donovani strains can cause cutaneous leishmaniasis. We hypothesized that L. donovani can adapt to survive in response to restrictions imposed by the host environment. To assess this, we performed in vivo selection in BALB/c mice with a cutaneous L. donovani clinical isolate to select for parasites with increased capacity to survive in visceral organs. We then performed whole cell proteomic analysis and compared this visceral-selected strain to the original cutaneous clinical isolate and to a visceral leishmaniasis clinical isolate. Overall, there were no major shifts in proteomic profiles; however, translation, biosynthetic processes, antioxidant protection, and signaling were elevated in visceral strains. Conversely, transport and trafficking were elevated in the cutaneous strain. Overall, these results provide new insight into the adaptability of Leishmania parasites to the host environment and on the factors that mediate their ability to survive in different organs.
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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".