Effects of Trace Element Concentration on Enzyme Controlled Stable Isotope Fractionation during Aerobic Biodegradation of Toluene
Bibliographic record
Abstract
The effects of iron concentration on carbon and hydrogen isotopic fractionation during aerobic biodegradation of toluene by Pseudomonas putida mt-2 were investigated using a low iron medium and two different high iron media. Mean carbon enrichment factors (epsilonc) determined using a Rayleigh isotopic model were smaller in culture grown under high iron conditions (epsilonc = -1.7+/-0.1%) compared to low iron conditions (epsilonc = -2.5+/-0.3%). Mean hydrogen enrichment factors (epsilonH) were also significantly smaller for culture grown under high iron conditions (epsilonH = -77 +/-4%) versus low iron conditions (EpsilonH = -159+/-11%). A mechanistic model for enzyme kinetics was used to relate differences in the magnitude of isotopic fractionation for low iron versus high iron cultures to the efficiency of the enzymatic transformation. The increase of carbon and hydrogen enrichment factors at low iron concentrations suggests a slower enzyme-catalyzed substrate conversion step (k2) relative to the enzyme-substrate binding step (k-l) at low iron concentration. While the observed differences were subtle and, hence, do not significantly impact the ability to use stable isotope analysis in the field, these results demonstrated that resolvable differences in carbon and hydrogen isotopic fractionation were related to low and high iron conditions. This novel result highlights the need to further investigate the effects of other trace elements known to be key components of biodegradative enzymes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".