Multilocus Genotyping Assays for Single Nucleotide Polymorphism-Based Subtyping of <i>Listeria monocytogenes</i> Isolates
Bibliographic record
Abstract
Listeria monocytogenes is responsible for serious invasive illness associated with consumption of contaminated food and places a significant burden on public health and the agricultural economy. We recently developed a multilocus genotyping (MLGT) assay for high-throughput subtype determination of L. monocytogenes lineage I isolates based on interrogation of single nucleotide polymorphisms (SNPs) via multiplexed primer extension reactions. Here we report the development and validation of two additional MLGT assays that address the need for comprehensive DNA sequence-based subtyping of L. monocytogenes. The first of these novel MLGT assays targeted variation segregating within lineage II, while the second assay combined probes for lineage III strains with probes for strains representing a recently characterized fourth evolutionary lineage (IV) of L. monocytogenes. These assays were based on nucleotide variation identified in >3.8 Mb of comparative DNA sequence and consisted of 115 total probes that differentiated 93% of the 100 haplotypes defined by the multilocus sequence data. MLGT reproducibly typed the 173 isolates used in SNP discovery, and the 10,448 genotypes derived from MLGT analysis of these isolates were consistent with DNA sequence data. Application of the MLGT assays to assess subtype prevalence among isolates from ready-to-eat foods and food-processing facilities indicated a low frequency (6.3%) of epidemic clone subtypes and a substantial population of isolates (>30%) harboring mutations in inlA associated with attenuated virulence in cell culture and animal models. These mutations were restricted to serogroup 1/2 isolates, which may explain the overrepresentation of serotype 4b isolates in human listeriosis cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".