The ecology and evolution of microbial immune systems: a look on the wild vibrio side
Bibliographic record
Abstract
, provide a powerful model for investigating the eco-evolutionary dynamics of microbial immune systems. Their genetic diversity, ecological versatility, ease of culturability and the availability of time-series data enable detailed studies of phage-host interactions in natural contexts. This review synthesizes recent advances in vibriophage research, highlighting key findings and emerging tools. High-throughput assays and genomic tools have offered new perspectives on phage specificity, host range and the evolutionary pressures shaping these interactions. Theoretical frameworks, such as arms race and fluctuating selection dynamics, are informed by empirical data from vibrio-phage systems, with time-series sampling providing crucial insights into their temporal and spatial dynamics. A major finding is the role of mobile genetic elements (MGEs) in encoding bacterial defence systems, which shape phage-host coevolution. Discoveries like the phage satellite PICMI illustrate how MGEs facilitate the transfer of antiviral systems, influencing ecological and evolutionary dynamics. The paradox of generalist vibriophages, rare despite their broad host ranges, is also explored. By integrating experimental approaches with field observations, vibriophage research advances microbial ecology and informs sustainable applications in aquaculture and phage therapy, reinforcing vibrios as a versatile model system.This article is part of the discussion meeting issue 'The ecology and evolution of bacterial immune systems'.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".