Genetic diversity and pathogenic potential of Shiga toxin-producing Escherichia coli (STEC) derived from German flour
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) can cause severe human illness, which are frequently linked to the consumption of contaminated beef or dairy products. However, recent outbreaks associated with contaminated flour and undercooked dough in the United States and Canada, highlight the potential of plant based food as transmission routes for STEC. In Germany STEC has been isolated from flour, but no cases of illness have been linked to flour. In this study, we characterized 123 STEC strains isolated from flour and flour products collected between 2015 and 2019 across Germany. In addition to determination of serotype and Shiga toxin subtype, whole genome sequencing (WGS) was used for isolates collected in 2018 to determine phylogenetic relationships, sequence type (ST), and virulence-associated genes (VAGs). We found a high diversity of serotypes including those frequently associated with human illness and outbreaks, such as O157:H7 (stx2c/d, eae), O145:H28 (stx2a, eae), O146:H28 (stx2b), and O103:H2 (stx1a, eae). Serotypes O187:H28 (ST200, stx2g) and O154:H31 (ST1892, stx1d) were most prevalent, but are rarely linked to human cases. However, WGS analysis revealed that these strains, as well as, O156:H25 (ST300, stx1a) harbour high numbers of VAGs, including eae, nleB and est1a/sta1. Although STEC-contaminated flour products have yet not been epidemiologically linked to human clinical cases in Germany, this study revealed that flour can serve as a vector for STEC strains with a high pathogenic potential. Further investigation is needed to determine the sources of STEC contamination in flour and flour products particularly in regards to these rare serotypes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".