Identification of a molecular locus for normalizing dysregulated GABA release from interneurons in the Fragile X brain
Bibliographic record
Abstract
Abstract Principal neurons encode information by varying their firing rate and patterns precisely fine-tuned through GABAergic interneurons. Dysregulation of inhibition can lead to neuropsychiatric disorders, yet little is known about the molecular basis underlying inhibitory control. Here, we find that excessive GABA release from basket cells (BCs) attenuates the firing frequency of Purkinje neurons (PNs) in the cerebellum of Fragile X Mental Retardation 1 ( Fmr1 ) knockout (KO) mice, a model of Fragile X Syndrome (FXS) with abrogated expression of the Fragile X Mental Retardation Protein (FMRP). This over-inhibition originates from increased excitability and Ca 2+ transients in the presynaptic terminals, where Kv1.2 potassium channels are downregulated. By paired patch-clamp recordings, we further demonstrate that acutely introducing an N-terminal fragment of FMRP into BCs normalizes GABA release in the Fmr1 -KO synapses. Conversely, direct injection of an inhibitory FMRP antibody into BCs, or membrane depolarization of BCs, enhances GABA release in the wild type synapses, leading to abnormal inhibitory transmission comparable to the Fmr1 -KO neurons. We discover that the N-terminus of FMRP directly binds to a phosphorylated serine motif on the C-terminus of Kv1.2; and that loss of this interaction in BCs exaggerates GABA release, compromising the firing activity of PNs and thus the output from the cerebellar circuitry. An allosteric Kv1.2 agonist, docosahexaenoic acid, rectifies the dysregulated inhibition in vitro as well as acoustic startle reflex and social interaction in vivo of the Fmr1 -KO mice. Our results unravel a novel molecular locus for targeted intervention of FXS and perhaps autism.
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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) | 0.001 | 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) | 0.002 | 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".