The global β-lactam resistome revealed by comprehensive sequence analysis
Bibliographic record
Abstract
Abstract Most antibiotic-resistance genes (ARGs) evolved in environmental microbes long before humanity’s antibiotic breakthrough, and widespread antibiotic use expedited the dissemination of ARGs among clinical pathogens. While widely discussed, the investigation of environmental ARG distributions lacks the scalability and taxonomic information necessary for a comprehensive analysis. Here, we present a global distribution of all five classes of β-lactamases among microbes and environments. We generated a β-lactamase taxonomy-environment map by identifying >113,000 β-lactamases across diverse bacterial phyla and environmental ecosystems. Remarkably abundant, their occurrence is only ∼2.6-fold lower than the essential recA gene in various environmental ecosystems, with particularly strong enrichment in wastewater and plant samples. The enrichment in plant samples implies an environment where the arms race of β-lactam producers and resistant bacteria occurred over millions of years. We uncover the origins of clinically relevant β-lactamases (mainly in ɣ-Proteobacteria) and expand beyond the previously suggested wastewater samples in plant, terrestrial, and other aquatic settings.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".