Selective Discrimination of <i>Listeria monocytogenes</i> Epidemic Strains by a Mixed-Genome DNA Microarray Compared to Discrimination by Pulsed-Field Gel Electrophoresis, Ribotyping, and Multilocus Sequence Typing
Bibliographic record
Abstract
Listeria monocytogenes can cause serious illness in humans, and subsequent epidemiological investigation requires molecular characterization to allow the identification of specific isolates. L. monocytogenes is usually characterized by serotyping and is subtyped by using pulsed-field gel electrophoresis (PFGE) or ribotyping. DNA microarrays provide an alternative means to resolve genetic differences among isolates, and unlike PFGE and ribotyping, microarrays can be used to identify specific genes associated with strains of interest. Twenty strains of L. monocytogenes representing six serovars were used to generate a shotgun library, and subsequently a 629-probe microarray was constructed by using features that included only potentially polymorphic gene probe sequences. Fifty-two strains of L. monocytogenes were genotyped by using the condensed array, including strains associated with five major listeriosis epidemics. Cluster analysis of the microarray data grouped strains according to phylogenetic lineage and serotype. Most epidemiologically linked strains were grouped together, and subtyping resolution was the same as that with PFGE (using AscI and ApaI) and better than that with multilocus sequence typing (using six housekeeping genes) and ribotyping. Additionally, a majority of epidemic strains were grouped together within phylogenetic Division I. This epidemic cluster was clearly distinct from the two other Division I clusters, which encompassed primarily sporadic and environmental strains. Discriminant function analysis allowed identification of 22 probes from the mixed-genome array that distinguish serotypes and subtypes, including several potential markers that were distinct for the epidemic cluster. Many of the subtype-specific genes encode proteins that likely confer survival advantages in the environment and/or host.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".