RNA Catalyst as a Reporter for Screening Drugs against RNA Editing in Trypanosomes
Bibliographic record
Abstract
Substantial progress has been made in determining the mechanism of mitochondrial RNA editing in trypanosomes. Similarly, considerable progress has been made in identifying the components of the editosome complex that catalyze RNA editing. However, it is still not clear how those proteins work together. Chemical compounds obtained from a high-throughput screen against the editosome may block or affect one or more steps in the editing cycle. Therefore, the identification of new chemical compounds will generate valuable molecular probes for dissecting the editosome function and assembly. In previous studies, in vitro editing assays were carried out using radio-labeled RNA. These assays are time consuming, inefficient and unsuitable for high-throughput purposes. Here, a homogenous fluorescence-based "mix and measure" hammerhead ribozyme in vitro reporter assay to monitor RNA editing, is presented. Only as a consequence of RNA editing of the hammerhead ribozyme a fluorescence resonance energy transfer (FRET) oligoribonucleotide substrate undergoes cleavage. This in turn results in separation of the fluorophore from the quencher thereby producing a signal. In contrast, when the editosome function is inhibited, the fluorescence signal will be quenched. This is a highly sensitive and simple assay that should be generally applicable to monitor in vitro RNA editing or high throughput screening of chemicals that can inhibit the editosome function.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".