A comparison of two informative SNP-based strategies for typing Pseudomonas aeruginosa isolates from patients with cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: Molecular typing is integral for identifying Pseudomonas aeruginosa strains that may be shared between patients with cystic fibrosis (CF). We conducted a side-by-side comparison of two P. aeruginosa genotyping methods utilising informative-single nucleotide polymorphism (SNP) methods; one targeting 10 P. aeruginosa SNPs and using real-time polymerase chain reaction technology (HRM10SNP) and the other targeting 20 SNPs and based on the Sequenom MassARRAY platform (iPLEX20SNP). METHODS: An in-silico analysis of the 20 SNPs used for the iPLEX20SNP method was initially conducted using sequence type (ST) data on the P. aeruginosa PubMLST website. A total of 506 clinical isolates collected from patients attending 11 CF centres throughout Australia were then tested by both the HRM10SNP and iPLEX20SNP assays. Type-ability and discriminatory power of the methods, as well as their ability to identify commonly shared P. aeruginosa strains, were compared. RESULTS: The in-silico analyses showed that the 1401 STs available on the PubMLST website could be divided into 927 different 20-SNP profiles (D-value = 0.999), and that most STs of national or international importance in CF could be distinguished either individually or as belonging to closely related single- or double-locus variant groups. When applied to the 506 clinical isolates, the iPLEX20SNP provided better discrimination over the HRM10SNP method with 147 different 20-SNP and 92 different 10-SNP profiles observed, respectively. For detecting the three most commonly shared Australian P. aeruginosa strains AUST-01, AUST-02 and AUST-06, the two methods were in agreement for 80/81 (98.8%), 48/49 (97.8%) and 11/12 (91.7%) isolates, respectively. CONCLUSIONS: The iPLEX20SNP is a superior new method for broader SNP-based MLST-style investigations of P. aeruginosa. However, because of convenience and availability, the HRM10SNP method remains better suited for clinical microbiology laboratories that only utilise real-time PCR technology and where the main interest is detection of the most highly-prevalent P. aeruginosa CF strains within Australian clinics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".