Neuroligins Mediate Excitatory and Inhibitory Synapse Formation
Bibliographic record
Abstract
The balance between excitatory and inhibitory synapses is a tightly regulated process that requires differential recruitment of proteins that dictate the specificity of newly formed contacts. However, factors that control this process remain unidentified. Here we show that members of the neuroligin (NLG) family, including NLG1, NLG2, and NLG3, drive the formation of both excitatory and inhibitory presynaptic contacts. The enrichment of endogenous NLG1 at excitatory contacts and NLG2 at inhibitory synapses supports an important in vivo role for these proteins in the development of both types of contacts. Immunocytochemical and electrophysiological analysis showed that the effects on excitatory and inhibitory synapses can be blocked by treatment with a fusion protein containing the extracellular domain of neurexin-1beta. We also found that overexpression of PSD-95, a postsynaptic binding partner of NLGs, resulted in a shift in the distribution of NLG2 from inhibitory to excitatory synapses. These findings reveal a critical role for NLGs and their synaptic partners in controlling the number of inhibitory and excitatory synapses. Furthermore, relative levels of PSD-95 alter the ratio of excitatory to inhibitory synaptic contacts by sequestering members of the NLG family to excitatory synapses.
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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".