ATP-Sensitive Potassium Channel-Mediated Lactate Effect on Orexin Neurons: Implications for Brain Energetics during Arousal
Bibliographic record
Abstract
Active neurons have a high demand for energy substrate, which is thought to be mainly supplied as lactate by astrocytes. Heavy lactate dependence of neuronal activity suggests that there may be a mechanism that detects and controls lactate levels and/or gates brain activation accordingly. Here, we demonstrate that orexin neurons can behave as such lactate sensors. Using acute brain slice preparations and patch-clamp techniques, we show that the monocarboxylate transporter blocker alpha-cyano-4-hydroxycinnamate (4-CIN) inhibits the spontaneous activity of orexin neurons despite the presence of extracellular glucose. Furthermore, fluoroacetate, a glial toxin, inhibits orexin neurons in the presence of glucose but not lactate. Thus, orexin neurons specifically use astrocyte-derived lactate. The effect of lactate on firing activity is concentration dependent, an essential characteristic of lactate sensors. Furthermore, lactate disinhibits and sensitizes these neurons for subsequent excitation. 4-CIN has no effect on the activity of some arcuate neurons, indicating that lactate dependency is not universal. Orexin neurons show an indirect concentration-dependent sensitivity to glucose below 1 mm, responding by hyperpolarization, which is mediated by ATP-sensitive potassium channels composed of Kir6.1 and SUR1 subunits. In conclusion, our study suggests that lactate is a critical energy substrate and a regulator of the orexin system. Together with the known effects of orexins in inducing arousal, food intake, and hepatic glucose production, as well as lactate release from astrocytes in response to neuronal activity, our study suggests that orexin neurons play an integral part in balancing brain activity and energy supply.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".