TNF-α Downregulates Inhibitory Neurotransmission through Protein Phosphatase 1-Dependent Trafficking of GABA <sub>A</sub> Receptors
Bibliographic record
Abstract
Inflammation has been implicated in the progression of neurological disease, yet precisely how inflammation affects neuronal function remains unclear. Tumor necrosis factor-α (TNFα) is a proinflammatory cytokine that regulates synapse function by controlling neurotransmitter receptor trafficking and homeostatic synaptic plasticity. Here we characterize the mechanisms through which TNFα regulates inhibitory synapse function in mature rat and mouse hippocampal neurons. Acute application of TNFα induces a rapid and persistent decrease of inhibitory synaptic strength and downregulation of cell-surface levels of GABA(A)Rs containing α1, α2, β2/3, and γ2 subunits. We show that trafficking of GABA(A)Rs in response to TNFα is mediated by neuronally expressed TNF receptor 1 and requires activation of p38 MAPK, phosphatidylinositol 3-kinase, protein phosphatase 1 (PP1), and dynamin GTPase. Furthermore, TNFα enhances the association of PP1 with GABA(A)R β3 subunits and dephosphorylates a site on β3 known to regulate phospho-dependent interactions with the endocytic machinery. Conversely, we find that calcineurin and PP2A are not essential components of the signaling pathway and that clustering of the scaffolding protein gephyrin is only reduced after the initial receptor endocytosis. Together, these findings demonstrate a distinct mechanism of regulated GABA(A)R endocytosis that may contribute to the disruption of circuit homeostasis under neuroinflammatory conditions.
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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".