Dynamic metabolomic responses of<i>Escherichia coli</i>to nicotine stress
Bibliographic record
Abstract
Previously, we reported the metabolic responses of Pseudomonas sp. strain HF-1, a nicotine-degrading bacterium, to nicotine stress. However, the metabolic effects of nicotine on non-nicotine-degrading bacteria that dominate the environment are still unclear. Here, we have used nuclear magnetic resonance based metabolomics in combination with multivariate data analysis methods to comprehensively analyze the metabolic changes in nicotine-treated Escherichia coli. Our results showed that nicotine caused the changes of energy-related metabolism that we believe are due to enhanced glycolysis and mixed acid fermentation as well as inhibited tricarboxylic acid cycle activity. Furthermore, nicotine resulted in the alteration of choline metabolism with a decreased synthesis of betaine but an increased production of dimethylamine. Moreover, nicotine caused a decrease in amino acid concentration and an alteration of nucleotide synthesis. We hypothesize that these changes caused the decrease in bacterial cell density observed in the experiment. These findings provide a comprehensive insight into the metabolic response of E. coli to nicotine stress. Our study highlights the value of metabolomics in elucidating the metabolic mechanisms of nicotine action.
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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".