NMDA Receptor Activation Increases Free Radical Production through Nitric Oxide and NOX2
Bibliographic record
Abstract
Reactive oxygen species (ROS) and nitric oxide (NO) participate in NMDA receptor signaling. However, the source(s) of the ROS and their role in the increase in cerebral blood flow (CBF) induced by NMDA receptor activation have not been firmly established. NADPH oxidase generates ROS in neurons, but there is no direct evidence that this enzyme is present in neurons containing NMDA receptors, or that is involved in NMDA receptor-dependent ROS production and CBF increase. We addressed these questions using a combination of in vivo and in vitro approaches. We found that the CBF and ROS increases elicited by topical application of NMDA to the mouse neocortex were both dependent on neuronal NO synthase (nNOS), cGMP, and the cGMP effector kinase protein kinase G (PKG). In mice lacking the NADPH oxidase subunit NOX2, the ROS increase was not observed, but the CBF increase was still present. Electron microscopy of the neocortex revealed NOX2 immunolabeling in postsynaptic somata and dendrites that also expressed the NMDA receptor NR1 subunit and nNOS. In neuronal cultures, the NMDA-induced increase in ROS was mediated by NADPH oxidase through NO, cGMP and PKG. We conclude that NADPH oxidase in postsynaptic neurons generates ROS during NMDA receptor activation. However, NMDA receptor-derived ROS do not contribute to the CBF increase. The findings establish a NOX2-containing NADPH oxidase as a major source of ROS produced by NMDA receptor activation, and identify NO as the critical link between NMDA receptor activity and NOX2-dependent ROS production.
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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 | 0.000 |
| 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".