Geographics and bacterial networks differently shape the acquired and latent global sewage resistomes
Bibliographic record
Abstract
Antimicrobial resistance genes (ARGs) have rapidly emerged and spread globally, but the pathways driving their spread remain poorly understood. We analyzed 1240 sewage samples from 351 cities across 111 countries, comparing ARGs known to be mobilized with those identified through functional metagenomics (FG). FG ARGs showed stronger associations with bacterial taxa than the acquired ARGs. Network analyses further confirmed this and showed potential for source attribution of both known and novel ARGs. The FG resistome was more evenly dispersed globally, whereas the acquired resistome followed distinct geographical patterns. City-wise distance-decay analyses revealed that the FG ARGs showed significant decay within countries but not across regions or globally. In contrast, acquired ARGs showed decay at both national and regional scales. At the variant level, both ARG groups had significant national and regional distance-decay effects, but only FG ARGs at a global scale. Additionally, we observed stronger distance effects in Sub-Saharan Africa and East Asia compared to North America. Our findings suggest that differential selection and niche competition, rather than dispersal, shape the global resistome patterns. A limited number of bacterial taxa may act as reservoirs of latent FG ARGs, highlighting the need of targeted surveillance to mitigate future resistance threats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".