Bacterial Flavodoxins Support Nitric Oxide Production by Bacillus subtilis Nitric-oxide Synthase
Bibliographic record
Abstract
Unlike animal nitric-oxide synthases (NOSs), the bacterial NOS enzymes have no attached flavoprotein domain to reduce their heme and so must rely on unknown bacterial proteins for electrons. We tested the ability of two Bacillus subtilis flavodoxins (YkuN and YkuP) to support catalysis by purified B. subtilis NOS (bsNOS). When an NADPH-utilizing bacterial flavodoxin reductase (FLDR) was added to reduce YkuP or YkuN, both supported NO synthesis from either L-arginine or N-hydroxyarginine and supported a linear nitrite accumulation over a 30-min reaction period. Rates of nitrite production were directly dependent on the ratio of YkuN or YkuP to bsNOS. However, the V/Km value for YkuN (5.2 x 10(5)) was about 20 times greater than that of YkuP (2.6 x 10(4)), indicating YkuN is more efficient in supporting bsNOS catalysis. YkuN that was either photo-reduced or prereduced by FLDR transferred an electron to the bsNOS ferric heme at rates similar to those measured for heme reduction in the animal NOSs. YkuN supported a similar NO synthesis activity by a different bacterial NOS (Deinococcus radiodurans) but not by any of the three mammalian NOS oxygenase domains nor by an insect NOS oxygenase domain. Our results establish YkuN as a kinetically competent redox partner for bsNOS and suggest that FLDR/flavodoxin proteins could function physiologically to support catalysis by bacterial NOSs.
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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) | 0.001 | 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 | 0.001 |
| 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".