Potential Role of Nitrite for Abiotic Fe(II) Oxidation and Cell Encrustation during Nitrate Reduction by Denitrifying Bacteria
Bibliographic record
Abstract
Microorganisms have been observed to oxidize Fe(II) at neutral pH under anoxic and microoxic conditions. While most of the mixotrophic nitrate-reducing Fe(II)-oxidizing bacteria become encrusted with Fe(III)-rich minerals, photoautotrophic and microaerophilic Fe(II) oxidizers avoid cell encrustation. The Fe(II) oxidation mechanisms and the reasons for encrustation remain largely unresolved. Here we used cultivation-based methods and electron microscopy to compare two previously described nitrate-reducing Fe(II) oxidizers ( Acidovorax sp. strain BoFeN1 and Pseudogulbenkiania sp. strain 2002) and two heterotrophic nitrate reducers (Paracoccus denitrificans ATCC 19367 and P. denitrificans Pd 1222). All four strains oxidized ∼8 mM Fe(II) within 5 days in the presence of 5 mM acetate and accumulated nitrite (maximum concentrations of 0.8 to 1.0 mM) in the culture media. Iron(III) minerals, mainly goethite, formed and precipitated extracellularly in close proximity to the cell surface. Interestingly, mineral formation was also observed within the periplasm and cytoplasm; intracellular mineralization is expected to be physiologically disadvantageous, yet acetate consumption continued to be observed even at an advanced stage of Fe(II) oxidation. Extracellular polymeric substances (EPS) were detected by lectin staining with fluorescence microscopy, particularly in the presence of Fe(II), suggesting that EPS production is a response to Fe(II) toxicity or a strategy to decrease encrustation. Based on the data presented here, we propose a nitrite-driven, indirect mechanism of cell encrustation whereby nitrite forms during heterotrophic denitrification and abiotically oxidizes Fe(II). This work adds to the known assemblage of Fe(II)-oxidizing bacteria in nature and complicates our ability to delineate microbial Fe(II) oxidation in ancient microbes preserved as fossils in the geological record.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".