Interspecies DNA acquisition by a naturally competent <i>Acinetobacter baumannii</i> strain
Bibliographic record
Abstract
ABSTRACT Acinetobacter baumannii is a human pathogen that frequently acquires antibiotic resistance genes leading to the emergence of multi-drug-resistant (MDR) strains. To investigate the role of transformation in the acquisition of resistance determinants by this species, the susceptible strain A118 was exposed to genomic DNA of carbapenem-resistant Klebsiella pneumoniae (CRKp). Resistant transformants were obtained and an increase in the resistance level to all β-lactam antibiotics was observed. Whole genome analysis of transformant clones demonstrated the acquisition of CRKp DNA. The most frequently acquired genes correspond to mobile elements, antibiotic resistance genes, and operons involved in metabolism. Bioinformatic analyses and in silico gene flow prediction strengthen our findings, showing that a continuing exchange of genetic material between A. baumannii and K. pneumoniae occurs when they share the same niche. Our results reinforce the idea that natural transformation may play a key role in the increasing emergence of A. baumannii MDR. IMPORTANCE Since the characterization of antibiotic resistance in the late ‘50s, antibiotic resistance propagation was classically associated with horizontal gene transfer (HGT) mediated by plasmids bearing multiple resistance genes. Here we show that, at least in the human pathogen A. baumannii , transformation also plays a major role in the acquisition of antibiotic resistance determinants. This study unravels that at least for certain pathogens the propagation of resistance genes occurs by alternative HGT mechanisms which in the past have been unappreciated.
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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 | 0.000 |
| 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) | 0.001 | 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".