A Temporal Examination of the Planktonic and Biofilm Proteome of Whole Cell Pseudomonas aeruginosa PAO1 Using Quantitative Mass Spectrometry
Bibliographic record
Abstract
Chronic polymicrobial lung infections are the chief complication in patients with cystic fibrosis. The dominant pathogen in late-stage disease is Pseudomonas aeruginosa, which forms recalcitrant, structured communities known as biofilms. Many aspects of biofilm biology are poorly understood; consequently, effective treatment of these infections is limited, and cystic fibrosis remains fatal. Here we combined in-solution protein digestion of triplicate growth-matched samples with a high-performance mass spectrometry platform to provide the most comprehensive proteomic dataset known to date for whole cell P. aeruginosa PAO1 grown in biofilm cultures. Our analysis included protein-protein interaction networks and PseudoCAP functional information for unique and significantly modulated proteins at three different time points. Secondary analysis of a subgroup of proteins using extracted ion currents validated the spectral counting data of 1884 high-confidence proteins. In this paper we demonstrate a greater representation of proteins related to metabolism, DNA stability, and molecular activity in planktonically grown P. aeruginosa PAO1. In addition, several virulence-related proteins were increased during planktonic growth, including multiple proteins encoded by the pyoverdine locus, uncharacterized proteins with sequence similarity to mammalian cell entry protein, and a member of the hemagglutinin family of adhesins, HecA. Conversely, biofilm samples contained an uncharacterized protein with sequence similarity to an adhesion protein with self-association characteristics (AidA). Increased levels of several phenazine biosynthetic proteins, an uncharacterized protein with sequence similarity to a metallo-beta-lactamase, and lower levels of the drug target gyrA support the putative characteristics of in situ P. aeruginosa infections, including competitive fitness and antibiotic resistance. This quantitative whole cell approach advances the existing P. aeruginosa subproteomes and provides a framework for identifying and studying entire pathways critical to biofilm biology in this model pathogenic organism. The identification of novel protein targets could contribute to the development of much needed antimicrobial therapies to treat the chronic infections found in patients with cystic fibrosis.
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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 | 0.000 |
| 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) | 0.001 | 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".