Strain-Dependent Thermoadaptation in the Fish Pathogen Aeromonas salmonicida subsp. salmonicida
Bibliographic record
Abstract
Strains of Aeromonas salmonicida subsp. salmonicida, a major pathogen of salmonids, typically do not grow at temperatures above 30 °C. The effects of thermal stress on this bacterium have been extensively studied. Recently, we demonstrated that repeated exposure to cyclical thermal stress, reaching up to 37 °C, can induce permanent thermoadaptation in certain strains of this bacterium. Many aspects of this adaptation process remain poorly understood. We generated 88 thermoadapted strains of A. salmonicida subsp. salmonicida capable of sustained growth at 33 °C or higher demonstrating that prolonged heat exposure can shift a substantial proportion of psychrophilic strains toward a more mesophilic-like behavior. Although growth at 35 °C was still weaker than in naturally mesophilic A. salmonicida strains, some thermoadapted strains were able to grow up to 37 °C. North American strains harboring the genomic island AsaGEI1a, a known biomarker, exhibited a significantly reduced capacity for thermoadaptation, suggesting a possible genetic constraint, although genomic analyses indicate that AsaGEI1a itself is not directly responsible for this limitation. Genotyping and phenotypic analyses revealed that thermoadaptation is frequently associated with the loss of Type III secretion system and the A-layer, two key virulence factors. Only 7% of the thermoadapted strains retained both features. Overall, our findings suggest that thermoadaptation may represent a potential mechanism influencing the persistence of some psychrophilic A. salmonicida subsp. salmonicida strains in warming aquatic environments under climate change.
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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 | 0.000 |
| 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 | 0.000 | 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".