Comparison of whole genome sequences from human and non-human Escherichia coli O26 strains
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) O26 is the second leading E. coli serogroup responsible for human illness outbreaks behind E. coli O157:H7. Recent outbreaks have been linked to emerging pathogenic O26:H11 strains harboring stx 2 only. Cattle have been recognized as an important reservoir of O26 strains harboring stx 1; however the reservoir of these emerging stx 2 strains is unknown. The objective of this study was to identify nucleotide polymorphisms in human and cattle-derived strains in order to compare differences in polymorphism derived genotypes and virulence gene profiles between the two host species. Whole genome sequencing was performed on 182 epidemiologically unrelated O26 strains, including 109 human-derived strains and 73 non-human-derived strains. A panel of 289 O26 strains (241 STEC and 48 non-STEC) was subsequently genotyped using a set of 283 polymorphisms identified by whole genome sequencing, resulting in 64 unique genotypes. Phylogenetic analyses identified seven clusters within the O26 strains. The seven clusters did not distinguish between isolates originating from humans or cattle; however, clusters did correspond with particular virulence gene profiles. Human and non-human-derived strains harboring stx 1 clustered separately from strains harboring stx 2, strains harboring eae, and non-STEC strains. Strains harboring stx 2 were more closely related to non-STEC strains and strains harboring eae than to strains harboring stx 1. The finding of human and cattle-derived strains with the same polymorphism derived genotypes and similar virulence gene profiles, provides evidence that similar strains are found in cattle and humans and transmission between the two species may occur.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".