Osmotic conditions shape fitness gains and resistance mechanisms during <i>E. coli</i> and T4 phage co-evolution
Bibliographic record
Abstract
Abstract Environmental conditions strongly influence interactions between bacteria and bacterio-phages (phages). Here, we examined how osmolality (solute concentration) shapes the in vitro co-evolution of T4 phage and its host Escherichia coli during serial passage. When evolved independently, we observed substantial fitness gains in both bacteria and phages, particularly in high osmotic conditions. During co-evolution, however, fitness gains were limited, bacterial populations consistently evolved phage resistance, and several phage populations went extinct. Furthermore, the resistance mechanisms varied by osmolality. In lower osmolalities, mutations disrupted phage binding sites, conferring strong resistance. In higher osmolalities, mutations led to increased colonic acid production, producing a mucoid phenotype with weaker resistance. Because mucoidy has been associated with increased bacterial virulence, these findings suggest that gut-relevant osmotic conditions may constrain evolutionary trajectories, favoring resistance strategies that are less effective against phage but potentially more virulent, with important implications for phage therapy design. Significance Phages offer a promising alternative to antibiotics, but their safety and efficacy strongly depends on the environmental conditions where the bacteria and phages interact. In the human gut, for instance, solute concentrations can vary widely due to factors like food intolerances or laxative use. In this study, we show that such variations significantly impact how bacteria and phages co-evolve. In particular, we find that in higher osmolalities, bacteria evolve phage resistance through mucoidy – a phenotype linked with increased bacterial virulence – rather than receptor loss. This highlights the need to consider environmental factors when developing phage therapies.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".