Bicistronic Gene Transfer Tools for Delivery of miRNAs and Protein Coding Sequences
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a category of small RNAs that modulate levels of proteins via post-transcriptional inhibition. Currently, a standard strategy to overexpress miRNAs is as mature miRNA duplexes, although this method is cumbersome if multiple miRNAs need to be delivered. Many of these miRNAs are found within introns and processed through the RNA polymerase II pathway. We have designed a vector to exploit this naturally-occurring intronic pathway to deliver the three members of the sensory-specific miR-183 family from an artificial intron. In one version of the vector, the downstream exon encodes the reporter (GFP) while another version encodes a fusion protein created between the transcription factor Atoh1 and the hemaglutinin epitope, to distinguish it from endogenous Atoh1. In vitro analysis shows that the miRNAs contained within the artificial intron are processed and bind to their targets with specificity. The genes downstream are successfully translated into protein and identifiable through immunofluorescence. More importantly, Atoh1 is proven functional through in vitro assays. These results suggest that this cassette allows expression of miRNAs and proteins simultaneously, which provides the opportunity for joint delivery of specific translational repressors (miRNA) and possibly transcriptional activators (transcription factors). This ability is attractive for future gene therapy use.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.002 |
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".