The transaminase-ω-amidase pathway senses oxidative stress to control glutamine metabolism and α-ketoglutarate levels in endothelial cells
Bibliographic record
Abstract
Abstract Oxidative stress is a major driver of cardiovascular disease; however, the fast changes in cellular metabolism caused by short-lived reactive oxygen species (ROS) remain ill-defined. Here, we characterized changes in the endothelial cell metabolome in response to acute oxidative challenges and identified novel redox-sensitive metabolic enzymes. H 2 O 2 selectively increased the amount of α-ketoglutaramate (αKGM), a largely uncharacterized metabolite produced by glutamine transamination and an unrecognized intermediate of endothelial glutamine catabolism. In addition, H 2 O 2 impaired the catalytic activity of nitrilase-like 2 ω-amidase (NIT2), the enzyme that converts αKGM to α-ketoglutarate (αKG), by the reversible oxidation of specific cysteine residues. Moreover, a NIT2 gene variant exhibited decreased expression in humans and was associated with increased plasma αKGM concentration. Endothelial-specific knockout of NIT2 in mice increased cellular αKGM levels and impaired angiogenesis. Further, NIT2 depletion impaired endothelial cell proliferation, sprouting, and induced senescence. In conclusion, we uncover NIT2 as a redox-sensitive enzyme of the glutamine transaminase-ω-amidase pathway that acts as a metabolic switch modulating endothelial glutamine metabolism in mice and humans.
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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) | 0.000 | 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".