Proteomic, Microarray, and Signature-Tagged Mutagenesis Analyses of Anaerobic<i>Pseudomonas aeruginosa</i>at pH 6.5, Likely Representing Chronic, Late-Stage Cystic Fibrosis Airway Conditions
Bibliographic record
Abstract
Patients suffering from cystic fibrosis (CF) commonly harbor the important pathogen Pseudomonas aeruginosa in their airways. During chronic late-stage CF, P. aeruginosa is known to grow under reduced oxygen tension and is even capable of respiring anaerobically within the thickened airway mucus, at a pH of approximately 6.5. Therefore, proteins involved in anaerobic metabolism represent potentially important targets for therapeutic intervention. In this study, the clinically relevant "anaerobiome" or "proteogenome" of P. aeruginosa was assessed. First, two different proteomic approaches were used to identify proteins differentially expressed under anaerobic versus aerobic conditions. Microarray studies were also performed, and in general, the anaerobic transcriptome was in agreement with the proteomic results. However, we found that a major portion of the most upregulated genes in the presence of NO(3)(-) and NO(2)(-) are those encoding Pf1 bacteriophage. With anaerobic NO(2)(-), the most downregulated genes are those involved postglycolytically and include many tricarboxylic acid cycle genes and those involved in the electron transport chain, especially those encoding the NADH dehydrogenase I complex. Finally, a signature-tagged mutagenesis library of P. aeruginosa was constructed to further screen genes required for both NO(3)(-) and NO(2)(-) respiration. In addition to genes anticipated to play important roles in the anaerobiome (anr, dnr, nar, nir, and nuo), the cysG and dksA genes were found to be required for both anaerobic NO(3)(-) and NO(2)(-) respiration. This study represents a major step in unraveling the molecular machinery involved in anaerobic NO(3)(-) and NO(2)(-) respiration and offers clues as to how we might disrupt such pathways in P. aeruginosa to limit the growth of this important CF pathogen when it is either limited or completely restricted in its oxygen supply.
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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 | 0.001 |
| 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 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".