Use of In-Biofilm Expression Technology To Identify Genes Involved in <i>Pseudomonas aeruginosa</i> Biofilm Development
Bibliographic record
Abstract
Mature Pseudomonas aeruginosa biofilms form complex three-dimensional architecture and are tolerant of antibiotics and other antimicrobial compounds. In this work, an in vivo expression technology system, originally designed to study virulence-associated genes in complex mammalian environments, was used to identify genes up-regulated in P. aeruginosa grown to a mature (5-day) biofilm. Five unique cloned promoters unable to promote in vitro growth in the absence of purines after recovery from the biofilm environment were identified. The open reading frames downstream of the cloned promoter regions were identified, and knockout mutants were generated. Insertional mutation of PA5065, a homologue of Escherichia coli ubiB, was lethal, while inactivation of PA0240 (a porin homologue), PA3710 (a putative alcohol dehydrogenase), and PA3782 (a homologue of the Streptomyces griseus developmental regulator adpA) had no effect on planktonic growth but caused defects in biofilm formation in static and flowing systems. In competition experiments, mutants demonstrated reduced fitness compared with the parent strain, comprising less than 0.0001% of total biofilm cells after 5 days. Therefore, using in-biofilm expression technology, we have identified novel genes that do not affect planktonic growth but are important for biofilm formation, development, and fitness.
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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) | 0.001 | 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 | 0.001 |
| 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".