Label‐free quantitative proteomics identifies unique proteomes of clinical isolates of the Liverpool Epidemic Strain of <i>Pseudomonas aeruginosa</i> and laboratory strain PAO1
Bibliographic record
Abstract
PURPOSE: Comparative genomics and phenotypic assays have shown that antibiotic resistance profiles differ among clinical isolates of Pseudomonas aeruginosa and that genotype-phenotype associations are difficult to establish for resistance phenotypes based on these comparisons alone. EXPERIMENTAL DESIGN: Here, we used label-free quantitative proteomics to compare two isolates of the Liverpool Epidemic Strain (LES) of P. aeruginosa, LESlike1 and LESB58, and the common laboratory strain P. aeruginosa PAO1 to more accurately predict functional differences between strains. RESULTS: Our results show that the proteomes of the LES isolates are more similar to each other than to PAO1; however, a number of differences were observed in the abundance of proteins involved in quorum sensing, virulence, and antibiotic resistance, including in the comparison of LESlike1 and LESB58. Additionally, the proteomic data revealed a higher abundance of proteins involved in polymyxin and aminoglycoside resistance in LESlike1. Minimum inhibitory concentration assays showed that LESlike1 had up to 128-fold higher resistance to antibiotics from these classes. CONCLUSIONS: These findings provide an example of the ability of proteomic data to complement genotypic and phenotypic studies to understand resistance in clinical isolates. CLINICAL RELEVANCE: P. aeruginosa is a predominant pathogen in chronic lung infections in individuals with cystic fibrosis (CF). LES isolates are capable of transferring between CF patients and have been associated with increased hospital visits and 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 | 0.001 | 0.001 |
| 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".