eLife Assessment: Heat Stress Induced Bacterial Tolerance against Phage Facilitates the Evolution of Resistance
Bibliographic record
Abstract
Antibiotic resistance and tolerance present significant challenges in global healthcare, necessitating alternative strategies such as phage therapy. However, the rapid emergence of phage-resistant mutants poses a potential risk. Here, we investigated bacterial persistence against phages, characterized by heterogeneous survival, analogous to antibiotic persistence. We found that heat treatment enhanced persistence and increase bacterial survival under phage exposure, subsequently promoting the evolution of phage resistance. Further experiments demonstrated that heat stress leads to a reduction in envelope components, thereby inhibiting phage DNA injection. Additionally, this heat-induced reduction resulted in systematic alterations in envelope stress responses, rendering bacteria tolerant to the antibiotic polymyxin while making them hypersensitive to pH changes and immune clearance. Our findings provide novel insights into bacteria-phage interactions and highlight potential challenges in implementing phage therapy in clinical settings.This study identifies bacterial persistence against phages, showing that heat treatment enhances this persistence and promotes phage resistance evolution by altering envelope components, which affects both phage susceptibility and antibiotic tolerance, highlighting challenges for clinical phage therapy.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.040 |
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".