Quantitative proteomics reveals unique responses to antimicrobial treatments in clinical <i>Pseudomonas aeruginosa</i> isolates
Bibliographic record
Abstract
ABSTRACT Epidemic strains of Pseudomonas aeruginosa often have increased levels of antibiotic resistance, and these resistance phenotypes are difficult to predict. We used quantitative proteomics to compare the responses of laboratory strain PAO1 and two isolates of the Liverpool epidemic strain (LES) to four antibiotics and hydrogen peroxide. The majority of proteome changes were unique to either one isolate or treatment, but smaller groups of proteins were differentially abundant in more than one sample. Proteins in these shared adaptive responses represent promising avenues for further investigation to understand the contribution of these proteins to resistance and their potential as targets for novel treatments. We observed the largest overlap between the proteome profiles of PAO1 challenged with tobramycin and hydrogen peroxide, and our data support previous work suggesting a role for heat shock protein IbpA in response to tobramycin. Our proteome profiling uncovered extensive changes in LESB58 in response to carbenicillin, with more than 1,000 proteins significantly changed in abundance. This included unique changes in the abundance of proteins involved in cell wall synthesis and division. We used phase contrast microscopy to check for corresponding changes in cell morphology and show that LESB58 maintained shorter cell lengths under treatment with β-lactams. We propose that the ability to maintain the processes of cell wall synthesis and division through changes in protein abundances contributes to the high levels of β-lactam resistance in LESB58. IMPORTANCE Pseudomonas aeruginosa is an important pathogen often associated with hospital-acquired infections and chronic lung infections in people with cystic fibrosis. P. aeruginosa possesses a wide array of intrinsic and adaptive mechanisms of antibiotic resistance, and the regulation of these mechanisms is complex. Label-free quantitative proteomics is a powerful tool to compare susceptible and resistant strains of bacteria and their responses to antibiotic treatments. Here we compare the proteomes of three isolates of P. aeruginosa with different antibiotic resistance profiles in response to five challenge conditions. We uncover unique and shared proteome changes for the widely used laboratory strain PAO1 and two isolates of the Liverpool epidemic strain of P. aeruginosa , LESlike1 and LESB58. Our data set provides insight into antibiotic resistance in clinically relevant Pseudomonas isolates and highlights proteins, including those with uncharacterized functions, which can be further investigated for their role in adaptive responses to antibiotic treatments.
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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 | 0.001 |
| 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".