Positive Signature-Tagged Mutagenesis in Pseudomonas aeruginosa: Tracking Patho-Adaptive Mutations Promoting Airways Chronic Infection
Bibliographic record
Abstract
The opportunistic pathogen Pseudomonas aeruginosa can establish life-long chronic infections in the airways of cystic fibrosis (CF) patients. Persistent lifestyle is established with P. aeruginosa patho-adaptive variants, which are clonal with the initially-acquired strains. Several reports indicated that P. aeruginosa adapts by loss-of-function mutations which enhance fitness in CF airways and sustain its clonal expansion during chronic infection. To validate this model of P. aeruginosa adaptation to CF airways and to identify novel genes involved in this microevolution, we designed a novel approach of positive-selection screening by PCR-based signature-tagged mutagenesis (Pos-STM) in a murine model of chronic airways infection. A systematic positive-selection scheme using sequential rounds of in vivo screenings for bacterial maintenance, as opposed to elimination, generated a list of genes whose inactivation increased the colonization and persistence in chronic airways infection. The phenotypes associated to these Pos-STM mutations reflect alterations in diverse aspects of P. aeruginosa biology which include lack of swimming and twitching motility, lack of production of the virulence factors such as pyocyanin, biofilm formation, and metabolic functions. In addition, Pos-STM mutants showed altered invasion and stimulation of immune response when tested in human respiratory epithelial cells, indicating that P. aeruginosa is prone to revise the interaction with its host during persistent lifestyle. Finally, sequence analysis of Pos-STM genes in longitudinally P. aeruginosa isolates from CF patients identified signs of patho-adaptive mutations within the genome. This novel Pos-STM approach identified bacterial functions that can have important clinical implications for the persistent lifestyle and disease progression of the airway chronic infection.
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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".