Emergence and genetic heterogeneity of STEC O113:H4: insights from whole-genome sequences of isolates across human and non-human sources
Bibliographic record
Abstract
The increased detection of Shiga toxin-producing Escherichia coli (STEC) O113:H4 among human cases in Belgium questions the importance of this serotype as an emerging pathogen. However, detailed information focusing on serotype O113:H4 from human and non-human sources remains limited. We analysed a collection of 140 STEC O113:H4 isolates and their whole genomes, originating from animal hosts (cattle, deer, goats, and sheep), food, and humans, to determine their genetic relationship and assess key virulence genes. All STEC O113:H4 genomes lacked the locus of enterocyte effacement (LEE) and belonged to Pasteur Sequence Type (pST) 367 complex, dominated by pST367 ( ehxA - , stx 2d + ) and pST1729 ( ehxA + , stx 2b + ). Compared to stx 2d + isolates, stx 2b + isolates carried on median more virulence factors, which might thus contribute to enhanced pathogenicity. Besides, humans appear to be infected with distinct subgroups of STEC O113:H4 carrying distinct stx subtypes and originating from potentially different sources: deer, goats, and sheep for STEC carrying stx 2b (alone or in combination with stx 1c ) and mainly cattle for STEC carrying stx 2d . Our results call for improved understanding and continuous surveillance of emerging STEC O113:H4.
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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 | 0.001 |
| 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 | 0.000 | 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".