Comparative Analysis of Denitrifying Activities of Hyphomicrobium nitrativorans, Hyphomicrobium denitrificans, and Hyphomicrobium zavarzinii
Bibliographic record
Abstract
Hyphomicrobium spp. are commonly identified as major players in denitrification systems supplied with methanol as a carbon source. However, denitrifying Hyphomicrobium species are poorly characterized, and very few studies have provided information on the genetic and physiological aspects of denitrification in pure cultures of these bacteria. This is a comparative study of three denitrifying Hyphomicrobium species, H. denitrificans ATCC 51888, H. zavarzinii ZV622, and a newly described species, H. nitrativorans NL23, which was isolated from a denitrification system treating seawater. Whole-genome sequence analyses revealed that although they share numerous orthologous genes, these three species differ greatly in their nitrate reductases, with gene clusters encoding a periplasmic nitrate reductase (Nap) in H. nitrativorans, a membrane-bound nitrate reductase (Nar) in H. denitrificans, and one Nap and two Nar enzymes in H. zavarzinii. Concurrently with these differences observed at the genetic level, important differences in the denitrification capacities of these Hyphomicrobium species were determined. H. nitrativorans grew and denitrified at higher nitrate and NaCl concentrations than did the two other species, without significant nitrite accumulation. Significant increases in the relative gene expression levels of the nitrate (napA) and nitrite (nirK) reductase genes were also noted for H. nitrativorans at higher nitrate and NaCl concentrations. Oxygen was also found to be a strong regulator of denitrification gene expression in both H. nitrativorans and H. zavarzinii, although individual genes responded differently in these two species. Taken together, the results presented in this study highlight the potential of H. nitrativorans as an efficient and adaptable bacterium that is able to perform complete denitrification under various conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".