Cholesterol up-regulates neuronal G protein-gated inwardly rectifying potassium (GIRK) channel activity in the hippocampus
Bibliographic record
Abstract
Hypercholesterolemia is a well known risk factor for the development of neurodegenerative disease. However, the underlying mechanisms are mostly unknown. In recent years, it has become increasingly evident that cholesterol-driven effects on physiology and pathophysiology derive from its ability to alter the function of a variety of membrane proteins including ion channels. Yet, the effect of cholesterol on G protein-gated inwardly rectifying potassium (GIRK) channels expressed in the brain is unknown. GIRK channels mediate the actions of inhibitory brain neurotransmitters. As a result, loss of GIRK function can enhance neuron excitability, whereas gain of GIRK function can reduce neuronal activity. Here we show that in rats on a high-cholesterol diet, cholesterol levels in hippocampal neurons are increased. We also demonstrate that cholesterol plays a critical role in modulating neuronal GIRK currents. Specifically, cholesterol enrichment of rat hippocampal neurons resulted in enhanced channel activity. In accordance, elevated currents upon cholesterol enrichment were also observed in Xenopus oocytes expressing GIRK2 channels, the primary GIRK subunit expressed in the brain. Furthermore, using planar lipid bilayers, we show that although cholesterol did not affect the unitary conductance of GIRK2, it significantly enhanced the frequency of channel openings. Last, combining computational and functional approaches, we identified two putative cholesterol-binding sites in the transmembrane domain of GIRK2. These findings establish that cholesterol plays a critical role in modulating GIRK activity in the brain. Because up-regulation of GIRK function can reduce neuronal activity, our findings may lead to novel approaches for prevention and therapy of cholesterol-driven neurodegenerative disease.
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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".