Differences in Metabolism between the Biofilm and Planktonic Response to Metal Stress
Bibliographic record
Abstract
Bacterial biofilms are known to withstand the effects of toxic metals better than planktonic cultures of the same species. This phenomenon has been attributed to many features of the sessile lifestyle not present in free-swimming populations, but the contribution of intracellular metabolism has not been previously examined. Here, we use a combined GC-MS and (1)H NMR metabolomic approach to quantify whole-cell metabolism in biofilm and planktonic cultures of the multimetal resistant bacterium Pseudomonas fluorescens exposed to copper ions. Metabolic changes in response to metal exposure were found to be significantly different in biofilms compared to planktonic cultures. Planktonic metabolism indicated an oxidative stress response that was characterized by changes to the TCA cycle, glycolysis, pyruvate and nicotinate and niacotinamide metabolism. Similar metabolic changes were not observed in biofilms, which were instead dominated by shifts in exopolysaccharide related metabolism suggesting that metal stress in biofilms induces a protective response rather than the reactive changes observed for the planktonic cells. From these results, we conclude that differential metabolic shifts play a role in biofilm-specific multimetal resistance and tolerance. An altered metabolic response to metal toxicity represents a novel addition to a growing list of biofilm-specific mechanisms to resist environmental stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".