Identification of Genes to Differentiate Closely Related Salmonella Lineages
Bibliographic record
Abstract
BACKGROUND: Salmonella are important human and animal pathogens. Though highly related, the Salmonella lineages may be strictly adapted to different hosts or cause different diseases, from mild local illness like gastroenteritis to fatal systemic infections like typhoid. Therefore, rapid and accurate identification of Salmonella is essential for timely and correct diagnosis of Salmonella infections. The current identification methods such as 16S rRNA sequencing and multilocus sequence typing are expensive and time consuming. Additionally, these methods often do not have sufficient distinguishing resolution among the Salmonella lineages. METHODOLOGIES/PRINCIPAL FINDINGS: We compared 27 completely sequenced Salmonella genomes to identify possible genomic features that could be used for differentiation of individual lineages. We concatenated 2372 core genes in each of the 27 genomes and constructed a neighbor-joining tree. On the tree, strains of each serotype were clustered tightly together and different serotypes were unambiguously separated with clear genetic distances, demonstrating systematic genomic divergence among the Salmonella lineages. We made detailed comparisons among the 27 genomes and identified distinct sets of genomic differences, including nucleotide variations and genomic islands (GIs), among the Salmonella lineages. Two core genes STM4261 and entF together could unambiguously distinguish all Salmonella lineages compared in this study. Additionally, strains of a lineage have a common set of GIs and closely related lineages have similar sets of GIs. CONCLUSIONS: Salmonella lineages have accumulated distinct sets of mutations and laterally acquired DNA (e.g., GIs) in evolution. Two genes entF and STM4261 have diverged sufficiently among the Salmonella lineages to be used for their differentiation. Further investigation of the distinct sets of mutations and GIs will lead to novel insights into genomic evolution of Salmonella and greatly facilitate the elucidation of pathogeneses of Salmonella infections.
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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".