Elevated baseline expression of seven virulence factor RNA transcripts in visceralizing species of <i>Leishmania</i> : a preliminary quantitative PCR study
Bibliographic record
Abstract
Introduction: Leishmaniasis is a neglected tropical disease that manifests as three major disease phenotypes: cutaneous, mucocutaneous, and visceral. In this preliminary study, we quantified virulence factor (VF) RNA transcript expression in Leishmania species, stratified by geographic origin and propensity for specific disease phenotypes. Methods: Cultured promastigotes of 19 Leishmania clinical and ATCC isolates were extracted for total cellular RNA, cDNA was reverse transcribed, and qPCR assays were performed to quantify VF RNA transcript expression for hsp23, hsp70, hsp83, hsp100, mpi, cpb, and gp63. Results: Comparison of visceralizing species (Leishmania donovani, Leishmania chagasi, and Leishmania infantum) versus non-visceralizing species [Leishmania (Viannia) spp., Leishmania tropica, Leishmania major, Leishmania mexicana, and Leishmania amazonensis] revealed a significantly greater pooled transcript expression for visceralizing species (p = 0.0032). Similarly, Old World species demonstrated significantly higher VF RNA transcript expression than New World species (p = 0.0015). On a per-gene basis, species with a propensity to visceralize ubiquitously expressed higher levels of gp63 (p = 0.005), cpb (p = 0.0032), mpi (p = 0.0032), hsp23 (p = 0.0039), hsp70 (p = 0.0032), hsp83 (p = 0.0032), and hsp100 (p = 0.0032). Conclusion: Here, we provide quantitative, preliminary evidence of elevated VF RNA transcript expression driven largely by the visceralizing causative species of Leishmania. This work highlights the extensive heterogeneity in pathogenicity mechanisms between Leishmania species, which may partly underpin the fatal progression of visceral leishmaniasis.
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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) | 0.002 | 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".