Proteomic analysis of the response of the plant growth-promoting bacterium Pseudomonas putida UW4 to nickel stress
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Bibliographic record
Abstract
BACKGROUND: Plant growth-promoting bacteria can alleviate the inhibitory effects of various heavy metals on plant growth, via decreasing levels of stress-induced ethylene. However, little has been done to detect any mechanisms specific for heavy metal resistance of this kind of bacteria. Here, we investigate the response of the wild-type plant growth-promoting bacterium Pseudomonas putida UW4 to nickel stress using proteomic approaches. The mutant strain P. putida UW4/AcdS-, lacking a functional 1-aminocyclopropane-1-carboxylic acid deaminase gene, was also assessed for its response to nickel stress. RESULTS: Two dimensional difference in-gel electrophoresis (DIGE) was used to detect significantly up- or down- regulated proteins (p < 0.05, | ratio | > 1.5) in P. putida in response to the presence of 2 mM Ni. Out of a total number of 1,702 proteins detected on the analytical gels for P. putida UW4, the expression levels of 82 (4.82%) proteins increased significantly while the expression of 81 (4.76%) proteins decreased significantly. Of 1,575 proteins detected on the analytical gels for P. putida UW4/AcdS-, the expression levels of 74 (4.70%) proteins increased and 51 (3.24%) proteins decreased significantly. Thirty-five proteins whose expression was altered were successfully identified by mass spectrometry and sequence comparisons with related species. Nineteen of the identified proteins were detected as differentially expressed in both wild-type and mutant expression profiles. CONCLUSION: Functional assessment of proteins with significantly altered expression levels revealed several mechanisms thought to be involved in bacterial heavy metal detoxification, including general stress adaptation, anti-oxidative stress and heavy metal efflux proteins. This information may contribute to the development of plant growth-promoting bacteria mediated phytoremediation processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it