Changes detected in the genome sequences of <i>Escherichia coli</i>, <i>Listeria monocytogenes</i>, <i>Vibrio parahaemolyticus</i>, and <i>Salmonella enterica</i> after serial subculturing
Bibliographic record
Abstract
Whole genome sequencing (WGS) is rapidly replacing other molecular techniques for identifying and subtyping bacterial isolates. The resolution or discrimination offered by WGS is significantly higher than that offered by other molecular techniques, and WGS readily allows infrequent differences that occur between 2 closely related strains to be found. In this investigation, WGS was used to identify the changes that occurred in the genomes of 13 strains of bacterial foodborne pathogens after 100 serial subcultures. Pure cultures of Shiga-toxin-producing Escherichia coli, Salmonella enterica, Listeria monocytogenes, and Vibrio parahaemolyticus were subcultured daily for 100 successive days. The 1st and 100th subcultures were whole-genome sequenced using short-read sequencing. Single nucleotide polymorphisms (SNPs) were identified between the 1st and final culture using 2 different approaches, and multilocus sequence typing of the whole genome was also performed to detect any changes at the allelic level. The number of observed genomic changes varied by strain, species, and the SNP caller used. This study provides insight into the genomic variation that can be detected using next-generation sequencing and analysis methods after repeated subculturing of 4 important bacterial pathogens.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".