Rapid identification of Brucella isolates to the species level by real time PCR based single nucleotide polymorphism (SNP) analysis
Bibliographic record
Abstract
BACKGROUND: Brucellosis, caused by members of the genus Brucella, remains one of the world's major zoonotic diseases. Six species have classically been recognised within the family Brucella largely based on a combination of classical microbiology and host specificity, although more recently additional isolations of novel Brucella have been reported from various marine mammals and voles. Classical identification to species level is based on a biotyping approach that is lengthy, requires extensive and hazardous culturing and can be difficult to interpret. Here we describe a simple and rapid approach to identification of Brucella isolates to the species level based on real-time PCR analysis of species-specific single nucleotide polymorphisms (SNPs) that were identified following a robust and extensive phylogenetic analysis of the genus. RESULTS: Seven pairs of short sequence Minor Groove Binding (MGB) probes were designed corresponding to SNPs shown to possess an allele specific for each of the six classical Brucella spp and the marine mammal Brucella. Assays were optimised to identical reaction parameters in order to give a multiple outcome assay that can differentiate all the classical species and Brucella isolated from marine mammals. The scope of the assay was confirmed by testing of over 300 isolates of Brucella, all of which typed as predicted when compared to other phenotypic and genotypic approaches. The assay is sensitive being capable of detecting and differentiating down to 15 genome equivalents. We further describe the design and testing of assays based on three additional SNPs located within the 16S rRNA gene that ensure positive discrimination of Brucella from close phylogenetic relatives on the same platform. CONCLUSION: The multiple-outcome assay described represents a new tool for the rapid, simple and unambiguous characterisation of Brucella to the species level. Furthermore, being based on a robust phylogenetic framework, the assay provides a platform that can readily be extended in the future to incorporate newly identified Brucella groups, to further type at the subspecies level, or to include markers for additional useful characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".