Inhibition of phototrophic iron oxidation by nitric oxide in ferruginous environments
Bibliographic record
Abstract
Anoxygenic phototrophic Fe(II) oxidizers (photoferrotrophs) are thought to have thrived in Earth's ancient ferruginous oceans and played a primary role in the precipitation of Archaean and Palaeoproterozoic (3.8-1.85-billion-year-old) banded iron formations (BIFs). The end of BIF deposition by photoferrotrophs has been interpreted as the result of a deepening of water-column oxygenation below the photic zone, concomitant with the proliferation of cyanobacteria. However, photoferrotrophs may have experienced competition from other anaerobic Fe(II)-oxidizing microorganisms, altering the formation mechanism of BIFs. Here we utilize microbial incubations to show that nitrate-reducing Fe(II) oxidizers metabolically outcompete photoferrotrophs for dissolved Fe(II). Moreover, both experiments and numerical modelling show that the nitrate-reducing Fe(II) oxidizers inhibit photoferrotrophy via the production of toxic intermediates. Four different photoferrotrophs, representing both green sulfur and purple non-sulfur bacteria, are susceptible to this toxic effect despite having genomic capabilities for nitric oxide detoxification. Indeed, despite nitric oxide detoxification mechanisms being ubiquitous in some groups of phototrophs at the genomic level (for example, Chlorobi and Cyanobacteria) it is likely that they would still be affected. We suggest that the production of reactive nitrogen species during nitrate-reducing Fe(II) oxidation in ferruginous environments may have inhibited the activity of photoferrotrophs in the ancient oceans and thus impeded their role in the precipitation of BIFs.
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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".