Chloride transporters controlling neuronal excitability
Bibliographic record
Abstract
Synaptic inhibition plays a crucial role in regulating neuronal excitability, which is the foundation of nervous system function. This inhibition is largely mediated by the neurotransmitters GABA and glycine that activate Cl − -permeable ion channels, which means that the strength of inhibition depends on the Cl − gradient across the membrane. In neurons, the Cl − gradient is primarily mediated by two secondarily active cation-chloride cotransporters (CCCs), NKCC1 and KCC2. CCC-mediated regulation of the neuronal Cl − gradient is critical for healthy brain function, as dysregulation of CCCs has emerged as a key mechanism underlying neurological disorders including epilepsy, neuropathic pain, and autism spectrum disorder. This review begins with an overview of neuronal chloride transporters before explaining the dependent relationship between these CCCs, Cl − regulation, and inhibitory synaptic transmission. We then discuss the evidence for how CCCs can be regulated, including by activity and their protein interactions, which underlie inhibitory synaptic plasticity. For readers who may be interested in conducting experiments on CCCs and neuronal excitability, we have included a section on techniques for estimating and recording intracellular Cl − , including their advantages and limitations. Although the focus of this review is on neurons, we also examine how Cl − is regulated in glial cells, which in turn regulate neuronal excitability through the tight relationship between this nonneuronal cell type and synapses. Finally, we discuss the relatively extensive and growing literature on how CCC-mediated neuronal excitability contributes to neurological disorders.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".