Pilot-Scale Compound Screening against RNA Editing Identifies Trypanocidal Agents
Bibliographic record
Abstract
Most mitochondrial messenger RNAs in trypanosomatid pathogens undergo a unique type of posttranscriptional modification involving insertion and/or deletion of uridylates. This process, RNA editing, is catalyzed by a multiprotein complex (~1.6 MDa), the editosome. Knockdown of core editosome proteins compromises mitochondrial function and, ultimately, parasite viability. Hence, because the editosome is restricted to trypanosomatids, it serves as a unique drug target in these pathogens. Currently, there is a lack of editosome inhibitors for antitrypanosomatid drug development or that could serve as unique tools for perturbing and characterizing editosome interactions or RNA editing reaction stages. Here, we screened a library of pharmacologically active compounds (LOPAC1280) using high-throughput screening to identify RNA editing inhibitors. We report that aurintricarboxylic acid, mitoxantrone, PPNDS, and NF449 are potent inhibitors of deletion RNA editing (IC50 range, 1-5 µM). However, none of these compounds could specifically inhibit the catalytic steps of RNA editing. Mitoxantrone blocked editing by inducing RNA-protein aggregates, whereas the other three compounds interfered with editosome-RNA interactions to varying extents. Furthermore, NF449, a suramin analogue, was effective at killing Trypanosoma brucei in vitro. Thus, new tools for editosome characterization and downstream RNA editing inhibitor have been identified.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".