Temporal Shifts in Antibiotic Resistance Elements Govern Virus-Pathogen Conflicts
Bibliographic record
Abstract
Bacteriophage predation selects for diverse anti-phage systems that frequently cluster on mobilizable defense islands in bacterial genomes. However, there remains a lack of molecular insight into the reciprocal dynamics of phage-bacterial adaptations in nature, particularly in clinical contexts where there is need to inform phage therapy efforts and understand how phages drive pathogen evolution. Here, using time-shift experiments we show that fluctuations in SXT integrative and conjugative elements (ICEs), which notoriously confer antibiotic resistance, govern Vibrio choleraes susceptibility to phages in clinical samples. We find that SXT ICEs, which are widespread in Gammaproteobacteria, invariably encode phage defense and function to protect other genera from phage attack following conjugation. We discover phage counter-adaptation to SXT-mediated restriction in clinical samples, and show that heterogeneity in SXT ICEs allows for re-emergence of phage resistance. Further, phage infection stimulates high frequency SXT ICE conjugation, leading to the concurrent dissemination of phage and antibiotic resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".