The antibiotic resistance “mobilome”: searching for the link between environment and clinic
Bibliographic record
Abstract
Antibiotic resistance is an ancient problem, owing to the co-evolution of antibiotic-producing and target organisms in the soil and other environments over millennia. The environmental "resistome" is the collection of all genes that directly or indirectly contribute to antibiotic resistance. Many of these resistance determinants originate in antibiotic-producing organisms (where they serve to mediate self-immunity), while others become resistance determinants only when mobilized and over-expressed in non-native hosts (like plasmid-encoded β-lactamases). The modern environmental resistome is under selective pressure from human activities such as agriculture, which may influence the composition of the local resistome and lead to gene transfer events. Beyond the environment, we are challenged in the clinic by the rise in both frequency and diversity of antibiotic resistant pathogens. We assume that clinical resistance originated in the environment, but few examples of direct gene exchange between the environmental resistome and the clinical resistome have been documented. Strong evidence exists to suggest that clinical aminoglycoside and vancomycin resistance enzymes, the extended-spectrum β-lactamase CTX-M and the quinolone resistance gene qnr have direct links to the environmental resistome. In this review, we highlight recent advances in our understanding of horizontal gene transfer of antibiotic resistance genes from the environment to the clinic. Improvements in sequencing technologies coupled with functional metagenomic studies have revealed previously underappreciated diversity in the environmental resistome, and also established novel genetic links to the clinic. Understanding mechanisms of gene exchange becomes vital in controlling the future dissemination of antibiotic resistance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".