Uses of <i>Staphylococcus aureus</i> GeneChips in Genotyping and Genetic Composition Analysis
Bibliographic record
Abstract
Understanding the relatedness of strains within a bacterial species is essential for monitoring reservoirs of antimicrobial resistance and for epidemiological studies. Pulsed-field gel electrophoresis (PFGE), ribotyping, and multilocus sequence typing are commonly used for this purpose. However, these techniques are either nonquantitative or provide only a limited estimation of strain relatedness. Moreover, they cannot extensively define the genes that constitute an organism. In the present study, 21 oxacillin-resistant Staphylococcus aureus (ORSA) isolates, representing eight major ORSA lineages, and each of the seven strains for which the complete genomic sequence is publicly available were genotyped using a novel GeneChip-based approach. Strains were also subjected to PFGE and ribotyping analysis. GeneChip results provided a higher level of discrimination among isolates than either ribotyping or PFGE, although strain clustering was similar among the three techniques. In addition, GeneChip signal intensity cutoff values were empirically determined to provide extensive data on the genetic composition of each isolate analyzed. Using this technology it was shown that strains could be examined for each element represented on the GeneChip, including virulence factors, antimicrobial resistance determinants, and agr type. These results were validated by PCR, growth on selective media, and detailed in silico analysis of each of the sequenced genomes. Collectively, this work demonstrates that GeneChips provide extensive genotyping information for S. aureus strains and may play a major role in epidemiological studies in the future where correlating genes with particular disease phenotypes is critical.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".