Molybdenum isotope fractionation by cyanobacterial assimilation during nitrate utilization and N2fixation
Bibliographic record
Abstract
We measured the δ⁹⁸Mo of cells and media from molybdenum (Mo) assimilation experiments with the freshwater cyanobacterium Anabaena variabilis, grown with nitrate as a nitrogen (N) source or fixing atmospheric N₂. This organism uses a Mo-based nitrate reductase during nitrate utilization and a Mo-based dinitrogenase during N₂ fixation under culture conditions here. We also demonstrate that it has a high-affinity Mo uptake system (ModABC) similar to other cyanobacteria, including marine N₂-fixing strains. Anabaena variabilis preferentially assimilated light isotopes of Mo in all experiments, resulting in fractionations of -0.2‰ to -1.0‰ ± 0.2‰ between cells and media (ε(cells-media)), extending the range of biological Mo fractionations previously reported. The fractionations were internally consistent within experiments, but varied with the N source utilized and for different growth phases sampled. During growth on nitrate, A. variabilis consistently produced fractionations of -0.3 ± 0.1‰ (mean ± standard deviation between experiments). When fixing N₂, A. variabilis produced fractionations of -0.9 ± 0.1‰ during exponential growth, and -0.5 ± 0.1‰ during stationary phase. This pattern is inconsistent with a simple kinetic isotope effect associated with Mo transport, because Mo is likely transported through the ModABC uptake system under all conditions studied. We present a reaction network model for Mo isotope fractionation that demonstrates how Mo transport and storage, coordination changes during enzymatic incorporation, and the distribution of Mo inside the cell could all contribute to the total biological fractionations. Additionally, we discuss the potential importance of biologically incorporated Mo to organic matter-bound Mo in marine sediments.
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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".