Comparison of Small Molecule-Responsive RNA Aptazymes for Applications in Gene Control
Bibliographic record
Abstract
Modelling how genes act in both space and time is critical to understand animal development, which can potentially drive intervention in gene expression. Gene regulation is examined using many techniques; however, challenges such as cell delivery, invasiveness, toxicity, and efficacy limit our ability to fully probe gene networks. Recent advances have led to the development of tunable, titratable, and reversible tools that can be genetically-encoded into animal model systems to modulate genes with temporal and spatial control. This study compares such tools, testing several aptazyme-based switches that can be expressed inside cells and controlled through the addition of non-toxic small molecules. Three switches responsive to different small molecules were compared for switching activity in mammalian cells. The most efficient switches in terms of activity gauged by their modulation of gene expression were then further assayed. Finally, the specificity of the hypoxanthine switch was tested based on chemical structure and classification. The comparisons revealed the importance of both timing and small molecule concentrations on switch activity, while the specificity testing demonstrated switch activity inside the cell correlated to the aptamer binding properties that were measured biochemically. This work demonstrates the suitability of aptazyme-based switches for application in diverse genetic environments, and in controlling and studying gene networks in animals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".