Somatostatin contributes to long-term potentiation at excitatory synapses onto hippocampal somatostatinergic interneurons
Bibliographic record
Abstract
Abstract Somatostatin-expressing interneurons (SOM-INs) are a major subpopulation of GABAergic cells in CA1 hippocampus that receive excitation from pyramidal cells (PCs), and, in turn, provide feedback inhibition onto PC dendrites. Excitatory synapses onto SOM-INs show a Hebbian long-term potentiation (LTP) mediated by type 1a metabotropic glutamate receptors (mGluR1a) that is implicated in hippocampus-dependent learning. The neuropeptide somatostatin (SST) is also critical for hippocampal long-term synaptic plasticity, as well as learning and memory. SST effects on hippocampal PCs are well documented, but its actions on inhibitory interneurons remain largely undetermined. In the present work, we investigate the involvement of SST in long-term potentiation of CA1 SOM-IN excitatory synapses using pharmacological approaches targeting the somatostatinergic system and whole cell recordings in slices from transgenic mice expressing eYFP in SOM-INs. We report that application of exogenous SST14 induces long-term potentiation of excitatory postsynaptic potentials in SOM-INs via somatostatin type 1–5 receptors (SST1-5Rs) but does not affect synapses of PC or parvalbumin-expressing interneurons. Hebbian LTP in SOM-INs was prevented by inhibition of SSTRs and by depletion of SST by cysteamine treatment, suggesting a critical role of endogenous SST in LTP. LTP of SOM-IN excitatory synapses induced by SST14 was independent of NMDAR and mGluR1a, activity-dependent, and prevented by blocking GABAA receptor function. Our results indicate that endogenous SST may contribute to Hebbian LTP at excitatory synapses of SOM-INs by controlling GABAA inhibition, uncovering a novel role for SST in regulating long-term synaptic plasticity in somatostatinergic cells that may be important for hippocampus-dependent memory processes.
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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".