Retrograde regulation of synaptic function at «Drosophila» neuromuscular junctions
Bibliographic record
Abstract
Neuronal growth and synaptic function are key determinants of nervous system behaviour. Understanding the molecular mechanisms that regulate these processes is central to comprehending nervous system function, both in health and during disease states. My thesis has focused on trying to better understand the mechanisms controlling glutamatergic neuronal growth, synaptic function and synaptic plasticity using the Drosophila melanogaster larval neuromuscular junction (NMJ) as a model system. This work has concentrated on the role that protein synthesis regulators play in modulating NMJ function and plasticity, and has revealed important roles for multiple regulators of cap-dependent translation in these processes. Importantly, genetic manipulations predicted to enhance cap-dependent translation in postsynaptic muscle can induce retrograde enhancements in synaptic function. In each case, these manipulations are without effect on neuronal growth, the number of synaptic sites or glutamate receptor levels. Instead, enhanced postsynaptic cap-dependent translation leads to increased presynaptic release probability. This retrograde pathway plays a key role in the expression of homeostatic synaptic plasticity at NMJ synapses. Glutamate receptor subunit IIA (GluRIIA) mutants exhibit reduced postsynaptic receptor function which is compensated for by enhanced presynaptic release; cap-dependent translation, under control of the TOR (target of rapamycin) pathway, is essential for this retrograde homeostatic response. Furthermore, we find that Parkinson's disease related protein LRRK2 (leucine rich repeat kinase 2), and its Drosophila orthologue dLRRK, can regulate synaptic function at the NMJ via cap-dependent translation. Importantly, however, mutant forms of LRRK2 are defective in their ability to regulate synaptic strength. Together, these findings further our understanding of the molecular mechanisms regulating glutamatergic synaptic function and plasticity.
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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 | 0.001 |
| 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".