One Health Spread of 16S Ribosomal RNA Methyltransferase-Harboring Gram-Negative Bacterial Genomes: An Overview of the Americas
Bibliographic record
Abstract
Aminoglycoside antimicrobials remain valuable therapeutic options, but their effectiveness has been threatened by the production of bacterial 16S ribosomal RNA methyltransferases (16S-RMTases). In this study, we evaluated the genomic epidemiology of 16S-RMTase genes among Gram-negative bacteria circulating in the American continent. A total of 4877 16S-RMTase sequences were identified mainly in Enterobacterales and nonfermenting Gram-negative bacilli isolated from humans, animals, foods, and the environment during 1931–2023. Most of the sequences identified were found in the United States, Brazil, Canada, and Mexico, and the prevalence of 16S-RMTase genes have increased in the last five years (2018–2022). The three species most frequently carrying 16S-RMTase genes were Acinetobacter baummannii, Klebsiella pneumoniae, and Escherichia coli. The armA gene was the most prevalent, but other 16S-RMTase genes (e.g., rmtB, rmtE, and rmtF) could be emerging backstage. More than 90% of 16S-RMTase sequences in the Americas were found in North American countries, and although the 16S-RMTase genes were less prevalent in Central and South American countries, these findings may be underestimations due to limited genomic data. Therefore, whole-genome sequence-based studies focusing on aminoglycoside resistance using a One Health approach in low- and middle-income countries should be encouraged.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".