Genome-scale modeling of <i>Pseudomonas aeruginosa</i> PA14 unveils its broad metabolic capabilities and role of metabolism in drug potentiation
Bibliographic record
Abstract
Abstract P. aeruginosa is an opportunistic human pathogen that is one of the leading causes of hospital-acquired infections. We have developed an updated genome-scale model (GEM) of Pseudomonas aeruginosa PA14 for systems-study of the pathogen. We used both automated and semi-manual approaches to reconstruct and curate the model. After an extensive literature research, we added organism-specific reactions (e.g., phenazine transport and redox metabolism, cofactor metabolism, carnitine metabolism, oxalate production, etc.) to the model. This effort led to a highly curated, three-compartment, and mass-and-charge balanced BiGG model of PA14 that contains 1509 genes, 1779 metabolic reactions and 1151 unique metabolites. The model ( i SD1509) has the largest genome coverage of P. aeruginosa PA14 to date with 424 more genes than the previous model (iPau1129). It is also the most accurate with prediction accuracies as high as 92.4% (for gene essentiality) and 93.5% (for substrate utilization). The model simulates growth in both aerobic and anaerobic conditions. It predicts the biosynthesis of the virulence factor phenazine as a process for the pathogen to grow in low-oxygen environment. Further, a mechanism for the overproduction of a drug susceptibility biomarker (gluconate) can be elucidated by the principles of optimal growth. Finally, the model also simulates drug activity potentiation and protection by fumarate and glyoxylate, respectively, and provides mechanistic explanations for these processes. Overall, i SD1509 can be utilized to decipher the metabolic mechanisms associated with virulence and antibiotic susceptibility of P. aeruginosa PA14 to aid in the development of effective intervention strategies.
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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) | 0.001 | 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".