Visualization of the Delivery and Release of Small RNAs Using Genetic Code Expansion and Unnatural RNA-Binding Proteins
Bibliographic record
Abstract
Endogenously expressed noncoding RNAs are regulators of mRNA translation and affect diverse biological pathways spanning embryogenesis to cholesterol and fatty acid metabolism. Recently, microRNAs have become an important therapeutic target with strategies that employ oligonucleotides as both mimics and inhibitors of target microRNAs, successfully altering gene expression and cellular pathways in relevant contexts. However, delivery of these exogenous effectors remains a major challenge. Here, we present a method for evaluating noncoding RNA delivery using the viral suppressor of RNA silencing (VSRS) protein p19, optimized for cellular delivery of small RNAs. Using genetic code expansion technology, p-azidophenylalanine (AzF) was incorporated into a recombinant p19 protein and used to develop a fluorescence resonance energy transfer (FRET) sensor. AzF was used to attach FRET acceptor moieties using bioorthogonal chemistry. We show that this strategy not only gives rise to FRET signals that report on small RNA binding, but also allows for fluorescence quenching as well, convenient for measuring RNA release. We demonstrate the successful use of a modified version of the probe to track the delivery and release of small RNAs into mammalian cells. The results provide a basis for a further development of vehicles for small RNA delivery and release for intervening in noncoding RNA biology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".