MT3-MMP Promotes Excitatory Synapse Formation by Promoting Nogo-66 Receptor Ectodomain Shedding
Bibliographic record
Abstract
Cell-surface molecules are dynamically regulated at the synapse to assemble and disassemble adhesive contacts that are important for synaptogenesis and for tuning synaptic transmission. Metalloproteinases dynamically regulate cellular behaviors through the processing of cell surface molecules. In the present study, we evaluated the role of membrane-type metalloproteinases (MT-MMPs) in excitatory synaptogenesis. We find that MT3-MMP and MT5-MMP are broadly expressed in the mouse cerebral cortex and that MT3-MMP loss-of-function interferes with excitatory synapse development in dissociated cortical neurons and invivo. We identify Nogo-66 receptor (NgR1) as an MT3-MMP substrate that is required for MT3-MMP-dependent synapse formation. Introduction of the shed ectodomain of NgR1 is sufficient to accelerate excitatory synapse formation in dissociated cortical neurons and invivo. Together, our findings support a role for MT3-MMP-dependent shedding of NgR1 in regulating excitatory synapse development. SIGNIFICANCE STATEMENTIn this study, we identify MT3-MMP, a membrane-bound zinc protease, to be necessary for the development of excitatory synapses in cortical neurons. We identify Nogo-66 receptors (NgR1) as a downstream target of MT3-MMP proteolytic activity. Furthermore, processing of surface NgR1 by MT3-MMP generates a soluble ectodomain fragment that accelerates the formation of excitatory synapses. We propose that MT3-MMP activity and NgR1 shedding could stimulate circuitry remodeling in the adult brain and enhance functional connectivity after brain injury.
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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".