Trophic factor BDNF inhibits GABAergic signaling by facilitating dendritic enrichment of SUMO E3 ligase PIAS3 and altering gephyrin scaffold
Bibliographic record
Abstract
Posttranslational addition of a small ubiquitin-like modifier (SUMO) moiety (SUMOylation) has been implicated in pathologies such as brain ischemia, diabetic peripheral neuropathy, and neurodegeneration. However, nuclear enrichment of SUMO pathway proteins has made it difficult to ascertain how ion channels, proteins that are typically localized to and function at the plasma membrane, and mitochondria are SUMOylated. Here, we report that the trophic factor, brain-derived neurotrophic factor (BDNF) regulates SUMO proteins both spatially and temporally in neurons. We show that BDNF signaling via the receptor tropomyosin-related kinase B facilitates nuclear exodus of SUMO proteins and subsequent enrichment within dendrites. Of the various SUMO E3 ligases, we found that PIAS-3 dendrite enrichment in response to BDNF signaling specifically modulates subsequent ERK1/2 kinase pathway signaling. In addition, we found the PIAS-3 RING and Ser/Thr domains, albeit in opposing manners, functionally inhibit GABA-mediated inhibition. Finally, using oxygen-glucose deprivation as an in vitro model for ischemia, we show that BDNF-tropomyosin-related kinase B signaling negatively impairs clustering of the main scaffolding protein at GABAergic postsynapse, gephyrin, whereby reducing GABAergic neurotransmission postischemia. SUMOylation-defective gephyrin K148R/K724R mutant transgene expression reversed these ischemia-induced changes in gephyrin cluster density. Taken together, these data suggest that BDNF signaling facilitates the temporal relocation of nuclear-enriched SUMO proteins to dendrites to influence postsynaptic protein SUMOylation.
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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".