Impact of growth environment and physiological state on metal immobilization by<i>Pseudomonas aeruginosa</i>PAO1
Bibliographic record
Abstract
Environmental growth conditions and cell physiology have the potential to influence bacterial surface-metal interactions in both planktonic and biofilm systems. Here, Pseudomonas aeruginosa was studied to determine the influence of these factors (pH, redox potential, and active respiration) on surface electrostatics and metal immobilization. Acid-base titrations revealed a decrease in ionizable ligands at pKa 5 (putative carboxyls) in cells grown below pH 6.2 and in cells grown anaerobically relative to cells grown under oxic and circumneutral pH conditions. This observation correlates with Western immunoblotting assays that revealed a reduction in carboxylated B-band lipopolysaccharide in these cells. Furthermore, spectrophotometric analysis revealed a decrease in zinc, copper, and iron immobilization in these cells, suggesting that lipopolysaccharide modification in response to environmental stimuli influences metal binding. The effect of active versus inactive metabolism on metal adsorption was also examined using respiration inhibitors carbonyl cyanide m-chlorophenylhydrazone and sodium azide. Cells treated with these compounds bound more zinc, copper, and iron than untreated controls, suggesting proton extrusion through respiration competes with metal cations for reactive groups on the cell surface. Accumulation of gold did not show the same trend, and transmission electron microscopy studies confirmed it was not a surface-mediated process. These results suggest that variations in growth environment and cell physiology influence metal accumulation by bacterial cell surfaces and may help to explain discontinuous accumulation of metal observed throughout microbial communities.
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 | 0.001 | 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".