Unraveling the mechanism of assimilatory nitrate reduction and methane oxidation by Methylobacter sp. YHQ through dual N-O isotope analysis and kinetic modeling
Bibliographic record
Abstract
Abstract Assimilatory nitrate reduction and methane (CH4) oxidation by bacteria play important roles in carbon (C) and nitrogen (N) biogeochemical cycles. Here, an investigation of enzymatic assimilatory nitrate reduction and CH4 oxidation by Methylobacter sp. YHQ from the wetlands is presented, specifically concentrating on N and oxygen (O) isotope fractionation with various initial nitrate and oxygen concentrations. The N enrichment factors (15εassimilation) increased from 4.2 ± 0.7‰ to 6.9 ±1.3‰ and the O isotope enrichment factors (18εassimilation) increased from 2.7 ± 0.9‰ to 4.7 ± 0.8‰ during nitrate assimilation when initial nitrate concentrations increased from 0.9 mM to 2 mM. Similar 18ε and 15ε values were observed at different oxygen concentrations. The values of 18ε and 15ε provided vital parameters for the assessment of assimilatory nitrate reduction via the Rayleigh equation approach. The ratios of O and N isotope enrichment factors (18ε:15ε)assimilation ranged from 0.64 ± 0.15 to 0.74 ± 0.18 during nitrate assimilation by Methylobacter sp. YHQ with Nas, which were different from (18ε:15ε)assimilation for assimilatory eukaryotic nitrate reductase (eukNR) from literature data. Thus, N and O isotope fractionation could be useful tools to distinguish eukNR from Nas during nitrate assimilation. Additionally, the rates of CH4 oxidation and nitrate reduction were evaluated with a reaction-based kinetic model, and it quantitatively described the enzymatic reactions of nitrate assimilation. Combining dual N-O isotope analysis with kinetic modeling provides new insights into the microbially driven C-N interactions. Graphical Abstract
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".