Biolistics for high-throughput transformation and RNA interference in<i>Drosophila melanogaster</i>
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".