Surviving the host: Microbial metabolic genes required for growth of Pseudomonas aeruginosa in physiologically-relevant conditions
Bibliographic record
Abstract
Pseudomonas aeruginosa, like other pathogens, adapts to the limiting nutritional environment of the host by altering patterns of gene expression and utilizing alternative pathways required for survival. Understanding the genes essential for survival in the host gives insight into pathways that this organism requires during infection and has the potential to identify better ways to treat infections. Here, we used a saturated transposon insertion mutant pool of P. aeruginosa strain PAO1 and transposon insertion sequencing (Tn-Seq), to identify genes conditionally important for survival under conditions mimicking the environment of a nosocomial infection. Conditions tested included tissue culture medium with and without human serum, a murine abscess model, and a human skin organoid model. Genes known to be upregulated during infections, as well as those involved in nucleotide metabolism, and cobalamin (vitamin B12) biosynthesis, etc., were required for survival in vivo- and in host mimicking conditions, but not in nutrient rich lab medium, Mueller Hinton broth (MHB). Correspondingly, mutants in genes encoding proteins of nucleotide and cobalamin metabolism pathways were shown to have growth defects under physiologically-relevant media conditions, in vivo, and in vivo-like models, and were downregulated in expression under these conditions, when compared to MHB. This study provides evidence for the relevance of studying P. aeruginosa fitness in physiologically-relevant host mimicking conditions and identified metabolic pathways that represent potential novel targets for alternative therapies.
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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".