Hammerhead ribozyme-based U-insertion and deletion RNA editing assays for multiplexing in HTS applications
Bibliographic record
Abstract
Untranslatable mitochondrial transcripts in kinetoplastids are decrypted post-transcriptionally through an RNA editing process that entails uridine insertion/deletion. This unique stepwise process is mediated by the editosome, a multiprotein complex that is a validated drug target of considerable interest in addressing the unmet medical needs for kinetoplastid diseases. With that objective, several in vitro RNA editing assays have been developed, albeit with limited success in discovering potent inhibitors. This manuscript describes the development of three hammerhead ribozyme (HHR) FRET reporter-based RNA editing assays for precleaved deletion, insertion, and ligation assays that bypass the rate-limiting endonucleolytic cleavage step, providing information on U-deletion, U-insertion, and ligation activities. These assays exhibit higher editing efficiencies in shorter incubation times while requiring significantly less purified editosome and 10,000-fold less ATP than the previously published full round of in vitro RNA editing assay. Moreover, modifications in the reporter ribozyme sequence enable the feasibility of multiplexing a ribozyme-based insertion/deletion editing (RIDE) assay that simultaneously surveils U-insertion and deletion editing suitable for HTS. These assays can be used to find novel chemical compounds with chemotherapeutic applications or as probes for studying the editosome machinery.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".