Establishment of transgenic <i>Drosophila suzukii</i> lines that express <scp>phiC31</scp> integrase and carry the <i>sepia</i> gene as a marker for transformation
Bibliographic record
Abstract
Many eye colour mutants have been identified in Drosophila melanogaster. Mutations in the sepia gene result in brown eyes due to a lack of PDA synthase, which is essential for production of the red drosopterin eye pigment. We previously used CRISPR/Cas9 to target the PDA synthase gene to establish sepia mutant strains for Drosophila suzukii (Matsumura) (Diptera: Drosophilidae), an invasive global pest of soft skinned fruits. The fecundity and fertility of some of the sepia mutant strains were similar to wild-type. The goal of this study was to determine if the sepia gene could be used as a marker to identify transgenic D. suzukii. By using the sepia gene as a marker, we successfully developed lines expressing Streptomyces phage phiC31 integrase in the germline. For most of these lines, hemizygotes exhibited complete rescue of the sepia eye colour and relatively high levels of phiC31 RNA in ovaries. In contrast, lines with partial rescue showed low levels of sepia RNA in heads and phiC31 RNA in ovaries. These findings suggest that the sepia gene is an effective marker for D. suzukii transgenesis and its relatively small size (1.8 kb) makes it advantageous when assembling large gene constructs. The phiC31 integrase lines established in this study should serve as a valuable resource for future genetic research in D. suzukii, including the further development of strains for genetic biocontrol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".