Analysis of a Uropathogenic <i>Escherichia coli</i> Clonal Group by Multilocus Sequence Typing
Bibliographic record
Abstract
Although many strain typing methods exist for pathogenic Escherichia coli, most have drawbacks in terms of resolving power, interpretability, or scalability. For this reason, multilocus sequence typing (MLST) is an appealing alternative. However, its applicability to different pathogens in specific epidemiologic contexts is not well understood. Here, we applied a previously established MLST method based on housekeeping genes to a well-characterized collection of uropathogenic E. coli isolates to compare the discriminatory ability of this procedure with that of enterobacterial repeat intergenic consensus (ERIC2) PCR, serogrouping, and pulsed-field gel electrophoresis (PFGE). Among 45 E. coli isolates studied, 17 different multilocus sequence types (ST) were identified. One MLST group (designated ST69 complex) was comprised of 22 isolates, all belonging to uropathogenic and bacteremic E. coli strains previously defined as clonal group A (CgA) by ERIC2 PCR. The ST69 strains contained five different serogroups and 14 PFGE types. ERIC2 PCR CgA strains belonging to different MLST groups were also identified. Interestingly, one cow E. coli isolate, previously shown by PFGE to be closely related to a human uropathogenic CgA strain, was found to cluster with the ST69 strains. All of the other animal and environmental CgA isolates had different MLST profiles. The discriminatory power of this MLST method based on housekeeping genes appears to be higher than that of ERIC2 PCR but lower than that of PFGE for epidemiologic study of uropathogenic E. coli.
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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) | 0.000 | 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".