N-cadherin and Neuroligins Cooperate to Regulate Synapse Formation in Hippocampal Cultures
Bibliographic record
Abstract
Cadherins and neuroligins (NLs) represent two families of cell adhesion proteins that are essential for the establishment of synaptic connections in vitro; however, it remains unclear whether these proteins act in concert to regulate synapse density. Using a combination of overexpression and knockdown analyses in primary hippocampal neurons, we demonstrate that NL1 and N-cadherin promote the formation of glutamatergic synapses through a common functional pathway. Analysis of the spatial relationship between N-cadherin and NL1 indicates that in 14-day in vitro cultures, almost half of glutamatergic synapses are associated with both proteins, whereas only a subset of these synapses are associated with N-cadherin or NL1 alone. This suggests that NL1 and N-cadherin are spatially distributed in a manner that enables cooperation at synapses. In young cultures, N-cadherin clustering and its association with synaptic markers precede the clustering of NL1. Overexpression of N-cadherin at this time point enhances NL1 clustering and increases synapse density. Although N-cadherin is not sufficient to enhance NL1 clustering and synapse density in more mature cultures, knockdown of N-cadherin at later time points significantly attenuates the density of NL1 clusters and synapses. N-cadherin overexpression can partially rescue synapse loss in NL1 knockdown cells, possibly due to the ability of N-cadherin to recruit NL2 to glutamatergic synapses in these cells. We demonstrate that cadherins and NLs can act in concert to regulate synapse formation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".