Choosing Your Battles: Which Resistance Genes Warrant Global Action?
Bibliographic record
Abstract
Abstract The increasing accumulation of antibiotic resistance genes (ARGs) in pathogens poses a severe threat to the treatment of bacterial infections. However, not all ARGs do not pose the same threats to human health. Here, we present a framework to rank the risk of ARGs based on three factors: “anthropogenic enrichment”, “mobility”, and “host pathogenicity”. The framework is informed by all available bacterial genomes (55,000), plasmids (16,000), integrons (3,000), and 850 metagenomes covering diverse global eco-habitats. The framework prioritizes 3% of all known ARGs in Rank I (the most at risk of dissemination amongst pathogens) and 0.3% of ARGs in Rank II (high potential emergence of new resistance in pathogens). We further validated the framework using a list of 38 ARG families previously identified as high risk by the World Health Organization and published literature, and found that 36 of them were properly identified as top risk (Rank I) in our approach. Furthermore, we identified 43 unreported Rank I ARG families that should be prioritized for public health interventions. Within the same gene family, homologous genes pose different risks, host range, and ecological distributions, indicating the need for high resolution surveillance into their sequence variants. Finally, to help strategize the policy interventions, we studied the impact of industrialization on high risk ARGs in 1,120 human gut microbiome metagenomes of 36 diverse global populations. Our findings suggest that current policies on controlling the clinical antimicrobial consumptions could effectively control Rank I, while greater antibiotic stewardship in veterinary settings could help control Rank II. Overall, our framework offered a straightforward evaluation of the risk posed by ARGs, and prioritized a shortlist of current and emerging threats for global action to fight ARGs.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".