Unexpected diversity in the mobilome of a <i>Pseudomonas aeruginosa</i> strain isolated from a dental unit waterline revealed by SMRT Sequencing
Bibliographic record
Abstract
The Gram-negative bacterium Pseudomonas aeruginosa is found in several habitats, both natural and human-made, and is particularly known for its recurrent presence as a pathogen in the lungs of patients suffering from cystic fibrosis, a genetic disease. Given its clinical importance, several major studies have investigated the genomic adaptation of P. aeruginosa in lungs and its transition as acute infections become chronic. However, our knowledge about the diversity and adaptation of the P. aeruginosa genome to non-clinical environments is still fragmentary, in part due to the lack of accurate reference genomes of strains from the numerous environments colonized by the bacterium. Here, we used PacBio long-read technology to sequence the genome of PPF-1, a strain of P. aeruginosa isolated from a dental unit waterline. Generating this closed genome was an opportunity to investigate genomic features that are difficult to accurately study in a draft genome (contigs state). It was possible to shed light on putative genomic islands, some shared with other reference genomes, new prophages, and the complete content of insertion sequences. In addition, four different group II introns were also found, including two characterized here and not listed in the specialized group II intron database.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".