Analysis of Clostridium botulinum Serotype E Strains by Using Multilocus Sequence Typing, Amplified Fragment Length Polymorphism, Variable-Number Tandem-Repeat Analysis, and Botulinum Neurotoxin Gene Sequencing
Bibliographic record
Abstract
A total of 41 Clostridium botulinum serotype E strains from different geographic regions, including Canada, Denmark, Finland, France, Greenland, Japan, and the United States, were compared by multilocus sequence typing (MLST), amplified fragment length polymorphism (AFLP) analysis, variable-number tandem-repeat (VNTR) analysis, and botulinum neurotoxin (bont) E gene sequencing. The strains, representing environmental, food-borne, and infant botulism samples collected from 1932 to 2007, were analyzed to compare serotype E strains from different geographic regions and types of botulism and to determine whether each of the strains contained the transposon-associated recombinase rarA, involved with bont/E insertion. MLST examination using 15 genes clustered the strains into several clades, with most members within a cluster sharing the same BoNT/E subtype (BoNT/E1, E2, E3, or E6). Sequencing of the bont/E gene identified two new variants (E7, E8) that showed regions of recombination with other E subtypes. The AFLP dendrogram clustered the 41 strains similarly to the MLST dendrogram. Strains that could not be differentiated by AFLP, MLST, or bont gene sequencing were further examined using three VNTR regions. Both intact and split rarA genes were amplified by PCR in each of the strains, and their identities were confirmed in 11 strains by amplicon sequencing. The findings suggest that (i) the C. botulinum serotype E strains result from the targeted insertion of the bont/E gene into genetically conserved bacteria and (ii) recombination events (not random mutations) within bont/E result in toxin variants or subtypes within strains.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".