Comparative genomics of a drug-resistant Pseudomonas aeruginosa panel and the challenges of antimicrobial resistance prediction from genomes
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is now recognized as a global threat to human health. The accessibility of microbial whole-genome sequencing offers an invaluable opportunity for resistance surveillance via the resistome, i.e. the genes and mutations underlying AMR. Unfortunately, AMR prediction from genomic data remains extremely challenging, especially for species with a large pan-genome. One such organism, for which multidrug-resistant (MDR) isolates are frequently encountered in the clinic, is Pseudomonas aeruginosa. This study focuses on a commercially available panel of seven MDR P. aeruginosa strains. The main goals were to sequence and compare these strains' genomes, attempt to predict AMR from whole genomes using two different methods and determine whether this panel could be an informative complement to the international P. aeruginosa reference panel. As expected, the results highlight the complexity of associating genotype and AMR phenotype in P. aeruginosa, mainly due to the intricate regulation of resistance mechanisms. Our results also urge caution in the interpretation of predicted resistomes regarding the occurrence of gene identity discrepancies between strains. We envision that, in addition to accounting for the genomic diversity of P. aeruginosa, future development of predictive tools will need to incorporate a transcriptomic, proteomic and/or metabolomic component.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".