Bacterial Genetic Determinants of Non‐O157 STEC Outbreaks and Hemolytic‐Uremic Syndrome after Infection
Bibliographic record
Abstract
Although O157:H7 Shiga toxin-producing Escherichia coli (STEC) are the predominant cause of hemolytic-uremic syndrome (HUS) in the world, non-O157:H7 serotypes are a medically important cause of HUS that are underdetected by current diagnostic approaches. Because Shiga toxin is necessary but not sufficient to cause HUS, identifying the virulence determinants that predict severe disease after non-O157 STEC infection is of paramount importance. Disease caused by O157:H7 STEC has been associated with a 26-gene pathogenicity island known as O island (OI) 122. To assess the public-health significance of this pathogenicity island, we examined the association between OI122 genes and outbreaks and HUS after non-O157 STEC infection. We found that a subset of OI122 genes is independently associated with outbreaks and HUS after infection with non-O157 STEC. The presence of multiple virulence genes in non-O157 serotypes strengthened this association, which suggests that the additive effects of a variable repertoire of virulence genes contribute to disease severity. In vivo, Citrobacter rodentium mutants lacking outbreak- and HUS-associated genes were deficient for virulence in mice; in particular, nleB mutant bacteria were unable to cause mortality in mice. The present study shows that virulence genes associated epidemiologically with outbreaks and HUS after non-O157 STEC infection are pivotal to the initiation, progression, and outcome of in vivo disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".