<i>In vivo</i> functional genomics of <i>Pseudomonas aeruginosa</i> for high‐throughput screening of new virulence factors and antibacterial targets
Bibliographic record
Abstract
Pseudomonas aeruginosa is a model for studying opportunistic pathogens that are highly resistant to most classes of antibiotics and cause chronic pulmonary infections. We have developed and adapted a multiplex polymerase chain reaction-based signature-tagged mutagenesis (STM) for high-throughput screening of a collection of 7968 P. aeruginosa mutants in a rat model of chronic respiratory infection. After three rounds of screening, a total of 214 mutants, representing transposition events into 148 open reading frames, were shown to be attenuated in lung infection and were retained for further analysis. As proof of concept supporting this technology, we identified 11 insertions in typical virulence genes such as those coding for pili implicated in motility, attachment and swarming, alginate synthesis and its expression, a mucus transcription regulator, extracellular enzymes such as alkaline protease, esterase and amino peptidase, a rhamnosyl surfactant transferase and a lipopolysaccharide glycosyl transferase. Detailed analysis of the 148 STM mutants, including seven auxotrophs, revealed insertions in 21 of the 26 known gene classes used to characterize sequenced bacterial genomes. We noted that at least 46% of STM mutants identified had insertions in hypothetical proteins or proteins of unknown function and that approximately 40% of all STM mutants had insertions in surface proteins including the outer membrane, the periplasm and the inner membrane. Interestingly, 11 STM mutants attenuated for lung infection were also identified in microarray and transcriptome for quorum sensing and mucoidy production. The remaining 130 mutants were systematically analysed for their capability to express fully known virulence factors. In addition, testing the ability of these mutants to infect alternative model host Drosophila melanogaster revealed 36 STM mutants defective in protease, twitching motility, swimming and swarming. Finally, we identified many genes, the activity of which in respiratory infection was not fully appreciated.
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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 | 0.000 | 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) | 0.000 | 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".