Biolistics for high-throughput transformation and RNA interference in<i>Drosophila melanogaster</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With twelve Drosophila genomes now sequenced, there is a growing need to develop higher-throughput methods for identifying the functions of the many newly identified genes. Genetic transformation and RNA interference are two technologies that have been used extensively to facilitate gene-function studies in Drosophila melanogaster, to introduce genes or block the expression of endogenous genes, respectively. Both of these technologies typically require the delivery of nucleic acids into developing insect embryos, and virtually all studies to date have relied on microinjection as the DNA delivery method of choice. In this study, we describe the use of biolistics as a higher-throughput method of nucleic acid delivery. By bombarding dechorionated D. melanogaster embryos with 1 microm gold beads coated with P-element or piggyBac transformation vectors, we observed transformation frequencies (3-4%) that are comparable to those achieved using microinjection methods, but in only a fraction of the time required for the DNA delivery. Biolistic delivery of double-stranded RNA (dsRNA) specific to a beta-glucuronidase (gus) transgene resulted in a significant (71%) reduction in gus transcripts in embryos and the RNA interference (RNAi) persisted through two successive larval molts, albeit at reduced levels. DsRNAs specific to four essential genes were delivered to embryos and resulted in arrested development and phenotypes that closely match that of null mutations. These results suggest that biolistic delivery of dsRNA into embryos could be adapted for high throughput RNAi screens of early Drosophila developmental genes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it