A <i>Drosophila</i> CRISPR/Cas9 Toolkit for Conditionally Manipulating Gene Expression in the Prothoracic Gland as a Test Case for Polytene Tissues
Bibliographic record
Abstract
Abstract Targeting gene function with spatial or temporal specificity is a key goal in molecular genetics. CRISPR-Cas9 has greatly facilitated this strategy, but some standard approaches are problematic. For instance, simple tissue-specific or global overexpression of Cas9 can cause significant lethality or developmental delays even in the absence of gRNAs. In particular, we found that Gal4-mediated expression of UAS-Cas9 in the Drosophila prothoracic gland (PG) was not a suitable strategy to disrupt gene expression, since Cas9 alone caused widespread lethality. The PG is widely used for studying endocrine gland function during animal development, but tools validating PG-specific RNAi phenotypes are lacking. Here, we present a collection of modular gateway-compatible CRISPR-Cas9 tools that allow precise modulation of target gene activity with temporal and spatial specificity. We also demonstrate that Cas9 fused to the progesterone ligand-binding domain can be used to activate gene expression via RU486. Using these approaches, we were able to avoid the lethality associated with simple GAL4-mediated overexpression of Cas9 in the PG. Given that the PG is a polytene tissue, we conclude that these tools work effectively in endoreplicating cells where Cas9 has to target multiple copies of the same locus. Our toolkit can be easily adapted for other tissues and can be used both for gain- and loss-of-function studies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".