Unexpected association of the “inhibitory” neuroligin 2 with excitatory PSD95 in neuropathic pain
Bibliographic record
Abstract
In the spinal nerve ligation (SNL) model of neuropathic pain, synaptic plasticity shifts the excitation/inhibition balance toward excitation in the spinal dorsal horn. We investigated the deregulation of the synaptogenic neuroligin (NL) molecules, whose NL1 and NL2 isoforms are primarily encountered at excitatory and inhibitory synapses, respectively. In the dorsal horn of SNL rats, NL2 was overexpressed whereas NL1 remained unchanged. In control animals, intrathecal injections of small interfering RNA (siRNA) targeting NL2 increased mechanical sensitivity, which confirmed the association of NL2 with inhibition. By contrast, siRNA application produced antinociceptive effects in SNL rats. Regarding NL partners, expression of the excitatory postsynaptic scaffolding protein PSD95 unexpectedly covaried with NL2 overexpression, and NL2/PSD95 protein interaction and colocalization increased. Expression of the inhibitory scaffolding protein gephyrin remained unchanged, indicating a partial change in NL2 postsynaptic partners in SNL rats. This phenomenon appears to be specific to the NL2(-) isoform. Our data showed unexpected upregulation and pronociceptive effects of the "inhibitory" NL2 in neuropathic pain, suggesting a functional shift of NL2 from inhibition to excitation that changed the synaptic ratio toward higher excitation.
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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".