Investigation of mobile genetic elements and their association with antibiotic resistance genes in clinical pathogens worldwide
Bibliographic record
Abstract
OBJECTIVES: Antimicrobial-resistant bacteria are a major global health threat. Mobile genetic elements (MGEs) have been crucial for spreading resistance to new bacterial species, including human pathogens. Understanding how MGEs promote resistance could be essential for prevention. Here we present an investigation of MGEs and their association with resistance genes in pathogenic bacteria collected from 59 diagnostic units during 2020, representing a snapshot of clinical infections from 35 counties worldwide. METHODS: We analysed 3,095 whole-genome sequenced clinical bacterial isolates from over 100 species to study the relationship between resistance genes and MGEs. The mobiliome of Staphylococcus aureus, Enterococcus faecalis, Escherichia coli, and Klebsiella pneumoniae were further examined for geographic differences, as these species were prevalent in all countries. Genes potentially mobilized by MGEs were identified by finding DNA segments containing MGEs and ARGs preserved in multiple species. Network analysis was used to investigate potential MGE interactions, host range, and transmission pathways. RESULTS: The prevalence and diversity of MGEs and resistance genes varied among species, with E. coli and S. aureus carrying more diverse elements. MGE composition differed between bacterial lineages, indicating strong vertical inheritance. 102 MGEs associated with resistance were found in multiple species, and four of these elements seemed to be highly transmissible as they were found in different phyla. We identified 21 genomic regions containing resistance genes potentially mobilized by MGEs, highlighting their importance in transmitting genes to clinically significant bacteria. CONCLUSION: Resistance genes are spread through various MGEs, including plasmids and transposons. Our findings suggest that multiple factors influence MGE prevalence and their transposability, thereby shaping the MGE population and transmission pathways. Some MGEs have a wider host range, which could make them more important for mobilizing genes. We also identified 103 resistance genes potentially mobilised by MGEs, which could increase their transmissibility to unrelated bacteria.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".