Priming of hypoxia-inducible factor by neuronal nitric oxide synthase is essential for adaptive responses to severe anemia
Bibliographic record
Abstract
Cells sense and respond to changes in oxygen concentration through gene regulatory processes that are fundamental to survival. Surprisingly, little is known about how anemia affects hypoxia signaling. Because nitric oxide synthases (NOSs) figure prominently in the cellular responses to acute hypoxia, we defined the effects of NOS deficiency in acute anemia. In contrast to endothelial NOS or inducible NOS deficiency, neuronal NOS (nNOS)(-/-) mice demonstrated increased mortality during anemia. Unlike wild-type (WT) animals, anemia did not increase cardiac output (CO) or reduce systemic vascular resistance (SVR) in nNOS(-/-) mice. At the cellular level, anemia increased expression of HIF-1α protein and HIF-responsive mRNA levels (EPO, VEGF, GLUT1, PDK1) in the brain of WT, but not nNOS(-/-) mice, despite comparable reductions in tissue PO(2). Paradoxically, nNOS(-/-) mice survived longer during hypoxia, retained the ability to regulate CO and SVR, and increased brain HIF-α protein levels and HIF-responsive mRNA transcripts. Real-time imaging of transgenic animals expressing a reporter HIF-α(ODD)-luciferase chimeric protein confirmed that nNOS was essential for anemia-mediated increases in HIF-α protein stability in vivo. S-nitrosylation effects the functional interaction between HIF and pVHL. We found that anemia led to nNOS-dependent S-nitrosylation of pVHL in vivo and, of interest, led to decreased expression of GSNO reductase. These findings identify nNOS effects on the HIF/pVHL signaling pathway as critically important in the physiological responses to anemia in vivo and provide essential mechanistic insight into the differences between anemia and hypoxia.
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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".