A Genetic Screen for Suppressors of Drosophila NSF2 Neuromuscular Junction Overgrowth
Bibliographic record
Abstract
The Drosophila larval neuromuscular system serves as a valuable model for studying the genes required for synaptic development and function. N-Ethylmaleimide sensitive factor (NSF) is a molecule known to be important in vesicular trafficking but neural expression of a dominant negative form of NSF2 induces an unexpected overgrowth of the Drosophila larval neuromuscular synapse. We have taken a genetic approach to understanding this novel phenotype by conducting a gain-of-function modifier screen to isolate genes that interact with the overgrowth phenotype. Our approach was to directly visualize the neuromuscular junction (NMJ) using a GFP transgene and screen for suppressors of NMJ overgrowth using the Gene Search collection of P-element insertions. Of the 3000 lines screened, we identified 99 lines that can partially restore the normal phenotype. Analysis of the GS element insertion sites by inverse PCR and comparison of the flanking DNA sequence to the Drosophila genome sequence revealed nearby genes for all but 10 of the 99 lines. The recovered genes, both known and predicted, include transcription factors, cytoskeletal elements, components of the ubiquitin pathway, and several signaling molecules. This collection of genes that suppress the NSF2 neuromuscular junction overgrowth phenotype is a valuable resource in our efforts to further understand the role of NSF at the synapse.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".