Antimicrobial targets localize to the extracellular vesicle-associated proteome of Pseudomonas aeruginosa grown in a biofilm
Bibliographic record
Abstract
Microbial biofilms are particularly resistant to antimicrobial therapies. These surface-attached communities are protected against host defenses and pharmacotherapy by a self-produced matrix that surrounds and fortifies them. Recent proteomic evidence also suggests that some bacteria, including the opportunistic pathogen Pseudomonas aeruginosa, undergo modifications within a biofilm that make them uniquely resistant compared to their planktonic (free-living) counterparts. This study examines 50 proteins in the resistance subproteome of both surface-associated and free-living P. aeruginosa PAO1 over three time points. Proteins were grouped into categories based on their roles in antimicrobial: (i) binding, (ii) efflux, (iii) resistance, and (iv) susceptibility. In addition, the extracellular outer membrane vesicle-associated proteome is examined and compared between the two growth modes. We show that in whole cells between 12-24% of the proteins are present at significantly different abundance levels over time, with some proteins being unique to a specific growth mode; however, the total abundance levels in the four categories remain consistent. In contrast, marked differences are seen in the protein content of the outer membrane vesicles, which contain a greater number of drug-binding proteins in vesicles purified from late-stage biofilms. These results show how the method of analysis can impact the interpretation of proteomic data (i.e., individual proteins vs. systems), and highlight the advantage of using protein-based methods to identify potential antimicrobial resistance mechanisms in extracellular sample components. Furthermore, this information has the potential to inform the development of specific antipseudomonal therapies that quench possible drug-sequestering vesicle proteins. This strategy could serve as a novel approach for combating the high-level of antimicrobial resistance in P. aeruginosa biofilms.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".