Survival of enterohemorrhagic <i><scp>E</scp>scherichia coli</i> in the presence of <i><scp>A</scp>canthamoeba castellanii</i> and its dependence on <scp>P</scp>ho regulon
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli (EHEC) are involved in outbreaks of food-borne illness and transmitted to humans through bovine products or water contaminated by cattle feces. Microbial interaction is one of the strategies used by pathogenic bacteria to survive in the environment. Among protozoa, the free-living amoebae are known to host and protect several water-borne pathogens. In this study, the interaction between EHEC and the predacious protozoa Acanthamoeba castellanii was investigated. Using monoculture and cocultures, growth of both organisms was estimated for 3 weeks by total and viable cell counts. The numbers of EHEC were significantly higher when cultured with amoebae than without, and less EHEC shifted into a viable but nonculturable state in the presence of amoebae. Using several mutants, we observed that the Pho regulon is required for EHEC growth when cocultured with amoebae. In contrast, the Shiga toxins (Stx) were not involved in this association phenotype. Cocultures monitored by electron microscopy revealed a loss of the regular rod shape of EHEC and the secretion of multilamellar vesicles by the amoebae, which did not contain bacteria. As the interaction between A. castellanii and EHEC appears beneficial for bacterial growth, this supports a potential role for protozoa in promoting the persistence of EHEC in the environment.
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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".