Characterisation of a new megaplasmid family associated with the spread of multidrug resistance in Pseudomonas aeruginosa
Bibliographic record
Abstract
Unlike in other important pathogens, the role of plasmids in the emergence of antimicrobial resistance (AMR) in Pseudomonas aeruginosa (Pa) has remained largely unaddressed. Previous work on AMR in Pa has mostly used genome sequencing methods that are limited because of the difficulty of using short-read data to detect and reconstruct complex plasmids. Here, using superior long-read sequencing, comprehensive bioinformatics analyses, and experimental characterization, we uncover an emerging family of important Pseudomonas megaplasmids and report its contribution to dissemination of multidrug resistance (MDR) on a global scale. Firstly, we identified large plasmids with a key role in the spread of MDR in a hospital in Thailand, and characterised their resistance regions revealing evidence of duplication, recombination and shared repeats, indicative of dynamic adaptation. Applying phylogenomics and pangenomics approaches we linked related megaplasmids and defined a core and pangenome for the family, exposing wide variations in AMR genes carriage. We then surveyed thousands of publicly available genomes, leading to discovery of dozens of megaplasmid relatives overlooked in multiple datasets, including already published studies. By integrating all this information and looking beyond the pathogenic species we gained valuable insights into the evolution of the megaplasmid family and revealed its widespread and multispecies distribution. We also showed that members of this family are stable in the absence of antibiotic pressure, bear no fitness cost to their host, and can be readily transferred between different Pseudomonas species. Our findings expand the bacterial plasmidome and provide insights on how MDR plasmids emerge from environmental reservoirs.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".