Identification of Four Fimbria-Encoding Genomic Islands That Are Highly Specific for Verocytotoxin-Producing <i>Escherichia coli</i> Serotype O157 Strains
Bibliographic record
Abstract
Verocytotoxin-producing Escherichia coli causes zoonotic food- or waterborne infection that may be associated with massive outbreaks and with the serious complication of hemolytic uremic syndrome (HUS). Serotypes O157:H7 and O157:NM are more commonly associated with HUS and outbreaks than other serotypes, such as O26:H11. To determine whether a genetic basis exists for why serotype O157:H7/NM causes HUS and outbreaks more often than other serotypes, such as O26:H11, we conducted suppression subtractive hybridization (SSH) between the genomes of the sequenced O157:H7 strain EDL933 and CL1, a clinical serotype O26:H11 isolate. Genes from four EDL933 fimbria-encoding genomic O islands (OIs) (OI-1, -47, -141, and -154) were identified in the SSH library. OI-47 encodes several additional putative virulence factors, including secreted and signaling proteins, a hemolysin locus, a lipoprotein, an ABC transport system, and a lipid biosynthesis locus. The distribution of the OIs was investigated by PCR and Southern hybridization (when PCR was negative) with 69 VTEC strains belonging to 39 different serotypes corresponding to 5 seropathotypes that differ in their disease and epidemic potential. The four OIs described here were distributed almost exclusively in serotypes O157:H7 and O157:NM, which indicates that they may be associated with the ability of these strains to colonize human and/or animal intestinal tracts and to cause epidemic and serious disease more frequently than other serotypes. The occurrence of the four OIs in enteropathogenic E. coli O55:H7 strains is consistent with their vertical inheritance by VTEC O157:H7/NM from this clonally related ancestor.
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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".