The dark matter of bacterial genomic surveillance—antimicrobial resistance plasmid transmissions in the hospital setting
Bibliographic record
Abstract
Dissemination of antimicrobial resistance (AMR) is a growing global public health burden. The aim of this study was to characterize AMR plasmid transmissions within a tertiary care hospital and identify relevant AMR plasmid transmission pathways. During an 18-month observation period, 540 clinical gram-negative multidrug-resistant bacterial (MDRB) isolates were collected during routine hospital surveillance and subjected to Pacific Biosciences long-read whole genome sequencing. Potential clonal transmissions were determined based on core genome multilocus sequence typing (cgMLST), and plasmid transmissions were detected using a novel real-time applicable tool for plasmid transmission detection. Potential transmissions were validated using epidemiological data. Among the 471 eligible MDRB isolates, we detected 1,539 plasmids; 84.41% of these were circularized. We identified 38 potential clonal transmissions in 24 clusters based on cgMLST and 121 potential plasmid transmissions in 24 clusters containing genetically related AMR plasmids. Among the latter clusters, 10 contained different multilocus sequence types (involving 2-38 isolates, median: 3 isolates), and nine contained multiple species (2-18 isolates, median: 4). Epidemiological data confirmed 19 clonal transmissions (in seven clusters) and an additional 12 plasmid transmissions (within eight plasmid clusters). Among these, we identified seven cases of intra-host and five patient-to-patient plasmid transmissions. We demonstrate that intra-host and patient-to-patient transmissions of AMR plasmids can be identified by combining long-read sequencing with real-time applicable tools during routine molecular surveillance. In addition, our study highlights that more than a decade of bacterial genomic surveillance missed at least one-third of all AMR transmission events due to plasmids. IMPORTANCE: Antimicrobial resistance (AMR) poses a significant threat to human health. Most AMR determinants are encoded extra-chromosomally on plasmids. Although current infection control strategies primarily focus on clonal transmission of multidrug-resistant bacteria, until today, AMR plasmid transmission routes are neither understood nor analyzed in the hospital setting. In our study, we simultaneously determined both clonal, that is, based on chromosomes, and AMR plasmid transmissions during routine molecular surveillance by combining long-read sequencing with a novel real-time applicable software tool and validated all potential transmission events with epidemiological data. Our analysis determined not only the yet unknown plasmid transmissions within healthcare facilities or within the community but also resulted, in addition to the clonal transmissions, in at least a third more transmissions due to AMR plasmids.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".