Genome-wide analysis reveals pathways important for the development and maturation of excitatory synaptic connections to GABAergic neurons
Bibliographic record
Abstract
Abstract A high degree of cell and circuit-specific regulation has presented challenges for efforts to precisely define molecular mechanisms controlling synapse formation and maturation. Here, we pursue an unbiased forward genetic approach to identify C. elegans genes involved in the formation and maturation of cholinergic synaptic connections with GABAergic motor neurons as indicated by the distribution of GFP-tagged postsynaptic acetylcholine receptors (AChR) on GABAergic dendrites. We identified mutations in 3 genes that identify key processes in synapse/circuit maturation. Mutation of the RUN domain ( R PIP8, U NC-14, and N ESCA) cargo adaptor gene unc-14 dramatically impacts overall GABAergic neuron morphology and dendritic spines. Mutation of the nicotinic acetylcholine alpha subunit gene unc-63 causes a failure in AChR assembly in GABAergic neurons but does not significantly alter dendritic spine structure or abundance. Finally, a mutation in the Liprin-α synaptic scaffold gene syd-2 severely disrupts both dendritic spines and AChR localization. The identification of these three genes from our screen highlights how mechanisms for cargo trafficking, receptor assembly, and synapse structural organization each make distinct contributions to synapse assembly and circuit connectivity.
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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 | 0.001 | 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".