Zinc toxicity and ATP production in<i>Pseudomonas fluorescens</i>
Bibliographic record
Abstract
AIMS: To identify the molecular networks in Pseudomonas fluorescens that convey resistance to toxic concentrations of Zn, a common pollutant and hazard to biological systems. METHODS AND RESULTS: Pseudomonas fluorescens strain ATCC 13525 was cultured in growth medium with millimolar concentrations of Zn. Enzymatic activities and metabolite levels were monitored with the aid of in-gel activity assays and high-performance liquid chromatography, respectively. As oxidative phosphorylation was rendered ineffective, the assimilation of citric acid mediated sequentially by citrate lyase (CL), phosphoenolpyruvate carboxylase (PEPC) and pyruvate phosphate dikinase (PPDK) appeared to play a key role in ATP synthesis via substrate-level phosphorylation (SLP). Enzymes generating the antioxidant, reduced nicotinamide adenine dinucleotide phosphate (NADPH) were enhanced, while metabolic modules mediating the formation of the pro-oxidant, reduced nicotinamide adenine dinucleotide (NADH) were downregulated. CONCLUSIONS: Pseudomonas fluorescens reengineers its metabolic networks to generate ATP via SLP, a stratagem that allows the microbe to compensate for an ineffective electron transport chain provoked by excess Zn. SIGNIFICANCE AND IMPACT OF THE STUDY: The molecular insights described here are critical in devising strategies to bioremediate Zn-polluted environments.
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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) | 0.001 | 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 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".