High-throughput screening of compounds targeting RNA editing in Trypanosoma brucei: Novel molecular scaffolds with broad trypanocidal effects
Bibliographic record
Abstract
Mitochondrial uridine insertion/deletion RNA editing, catalyzed by a multiprotein complex (editosome), is essential for gene expression in trypanosomes and Leishmania parasites. As this process is absent in the human host, a drug targeting this mechanism promises high selectivity and reduced toxicity. Here, we successfully miniaturized our FRET-based full-round RNA editing assay, which replicates the complete RNA editing process, adapting it into a 1536-well format. Leveraging this assay, we screened over 100,000 compounds against purified editosomes derived from Trypanosoma brucei, identifying seven confirmed primary hits. We sourced and evaluated various analogs to enhance the inhibitory and parasiticidal effects of these primary hits. In combination with secondary assays, our compounds marked inhibition of essential catalytic activities, including the RNA editing ligase and interactions of editosome proteins. Although the primary hits did not exhibit any growth inhibitory effect on parasites, we describe eight analog compounds capable of effectively killing T. brucei and/or Leishmania donovani parasites within a low micromolar concentration. Whether parasite killing is - at least in part - due to inhibition of RNA editing in vivo remains to be assessed. Our findings introduce novel molecular scaffolds with the potential for broad antitrypanosomal effects.
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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 | 0.000 |
| 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".