Evaluation of <i>Thiobacillus denitrificans</i>’ sustainability in nitrate-reducing Fe(II) oxidation and the potential significance of Fe(II) as a growth-supporting reductant
Bibliographic record
Abstract
The betaproteobacterium Thiobacillus denitrificans (ATCC 25259) oxidizes Fe(II) while reducing nitrate, yet its capacity for autotrophic growth as a nitrate-reducing Fe(II)-oxidizer remains uncertain. This study explored this capacity through cultivation experiments across multiple transfers, using growth medium with Fe(II) and nitrate as sole electron donor and acceptor, respectively. This setup necessitated nitrate reduction coupled to Fe(II) oxidation as the primary energy-yielding mechanism and Fe(II) as the exclusive electron donor for CO2 fixation and biomass production. Thiosulfate/nitrate pregrown T. denitrificans oxidized 42% of 10 mM Fe(II), reduced 54% of 3.5 mM nitrate, and accumulated 1.6 mM nitrite, but showed no cell growth. Subsequent transfers from this Fe(II)/nitrate culture to fresh medium with Fe(II) and nitrate showed no nitrate-reducing Fe(II) oxidation or population growth. While bacterial activity [Fe(II) oxidation, nitrate reduction] occurred in the first transfer from thiosulfate/nitrate to Fe(II)/nitrate, nitrite was produced, further reacting with Fe(II) abiotically (chemodenitrification). A kinetic model assessed enzymatic versus abiotic Fe(II) oxidation, revealing enzymatic oxidation accounted for twice as much (ca. 70%) as abiotic denitrification (ca. 30%) within 22 days. These findings suggest T. denitrificans performs the first step of denitrification with Fe(II) as an electron donor but does not achieve autotrophic growth under these 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 | 0.004 | 0.001 |
| 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 | 0.001 |
| 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".