Sequence-based typing of genetic targets encoded outside of the O-antigen gene cluster is indicative of Shiga toxin-producing Escherichia coli serogroup lineages
Bibliographic record
Abstract
Serogroup classifications based upon the O-somatic antigen of Shiga toxin-producing Escherichia coli (STEC) provide significant epidemiological information on clinical isolates. Each O-antigen determinant is encoded by a unique cluster of genes present between the gnd and galF chromosomal genes. Alternatively, serogroup-specific polymorphisms might be encoded in loci that are encoded outside of the O-antigen gene cluster. Segments of the core bacterial loci mdh, gnd, gcl, ppk, metA, ftsZ, relA and metG for 30 O26 STEC strains have previously been sequenced, and comparative analyses to O157 distinguished these two serogroups. To screen these loci for serogroup-specific traits within a broader range of clinically significant serogroups, DNA sequences were obtained for 19 strains of 10 additional STEC serogroups. Unique alleles were observed at the gnd locus for each examined STEC serogroup, and this correlation persisted when comparative analyses were extended to 144 gnd sequences from 26 O-serogroups (comprising 42 O : H-serotypes). These included O157, O121, O103, O26, O5 : non-motile (NM), O145 : NM, O113 : H21, O111 : NM and O117 : H7 STEC; and furthermore, non-toxin encoding O157, O26, O55, O6 and O117 strains encoded distinct gnd alleles compared to STEC strains of the same serogroup. DNA sequencing of a 643 bp region of gnd was, therefore, sufficient to minimally determine the O-antigen of STEC through molecular means, and the location of gnd next to the O-antigen gene cluster offered additional support for the co-inheritance of these determinants. The gnd DNA sequence-based serogrouping method could improve the typing capabilities for STEC in clinical laboratories, and was used successfully to characterize O121 : H19, O26 : H11 and O177 : NM clinical isolates prior to serological confirmation during outbreak investigations.
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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".