A fluorescence-based reporter substrate for monitoring RNA editing in trypanosomatid pathogens
Bibliographic record
Abstract
RNA editing regulates mitochondrial gene expression in trypanosomatid pathogens by creating functional mRNAs. It is catalyzed by a multi-protein complex (the editosome), and is found to be essential in both insect stage and mammalian blood stream form of Trypanosoma brucei. This particular form of RNA editing is unique to trypanosomatids, and thus provides a suitable drug target in trypanosomatid pathogens. Here, we demonstrate the feasibility of a rapid and sensitive fluorescence-based reporter assay to monitor RNA editing based on ribozyme activity. We could validate our new assay using previously identified inhibitors against the essential RNA editing ligase. The principle advantages of this assay are: (i) the use of non-radioactively labeled materials, (ii) sensitivity afforded by fluorescence instrumentation applicable to high-throughput screening of chemical inhibitors against the essential editosome and (iii) a rapid and convenient 'mix and measure' type of assay in low volume with a high signal to noise ratio. This assay should enhance rapid identification and characterization of the editosome inhibitors primarily based on the overall composition of the editosomes from T. brucei. These inhibitors could also be tested against the editosomes from the closely related pathogens including T. cruzi and Leishmania species.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".