Drosophila Neuroligin3 Regulates Neuromuscular Junction Development and Synaptic Differentiation
Bibliographic record
Abstract
Neuroligins (Nlgs) are a family of cell adhesion molecules thought to be important for synapse maturation and function. Mammalian studies have shown that different Nlgs have different roles in synaptic maturation and function. In Drosophila melanogaster, the roles of Drosophila neuroligin1 (DNlg1), neuroligin2, and neuroligin4 have been examined. However, the roles of neuroligin3 (dnlg3) in synaptic development and function have not been determined. In this study, we used the Drosophila neuromuscular junctions (NMJs) as a model system to investigate the in vivo role of dnlg3. We showed that DNlg3 was expressed in both the CNS and NMJs where it was largely restricted to the postsynaptic site. We generated dnlg3 mutants and showed that these mutants exhibited an increased bouton number and reduced bouton size compared with the wild-type (WT) controls. Consistent with alterations in bouton properties, pre- and postsynaptic differentiations were affected in dnlg3 mutants. This included abnormal synaptic vesicle endocytosis, increased postsynaptic density length, and reduced GluRIIA recruitment. In addition to impaired synaptic development and differentiation, we found that synaptic transmission was reduced in dnlg3 mutants. Altogether, our data showed that DNlg3 was required for NMJ development, synaptic differentiation, and function.
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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) | 0.000 | 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) | 0.001 | 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".