Development of a DNA Microarray for Enterococcal Species, Virulence, and Antibiotic Resistance Gene Determinations among Isolates from Poultry
Bibliographic record
Abstract
A DNA microarray (Enteroarray) was designed with probes targeting four species-specific taxonomic identifiers to discriminate among 18 different enterococcal species, while other probes were designed to identify 18 virulence factors and 174 antibiotic resistance genes. In total, 262 genes were utilized for rapid species identification of enterococcal isolates, while characterizing their virulence potential through the simultaneous identification of endogenous antibiotic resistance and virulence genes. Enterococcal isolates from broiler chicken farms were initially identified by using the API 20 Strep system, and the results were compared to those obtained with the taxonomic genes atpA, recA, pheS, and ddl represented on our microarray. Among the 171 isolates studied, five different enterococcal species were identified by using the API 20 Strep system: Enterococcus faecium, E. faecalis, E. durans, E. gallinarum, and E. avium. The Enteroarray detected the same species as API 20 Strep, as well as two more: E. casseliflavus and E. hirae. Species comparisons resulted in 15% (27 isolates) disagreement between the two methods among the five API 20 Strep identifiable species and 24% (42 isolates) disagreement when considering the seven Enteroarray identified species. The species specificity of key antibiotic and virulence genes identified by the Enteroarray were consistent with the literature adding further robustness to the redundant taxonomic probe data. Sequencing of the cpn60 gene further confirmed the complete accuracy of the microarray results. The new Enteroarray should prove to be a useful tool to accurately genotype strains of enterococci and assess their virulence potential.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".