Targeting discriminatory SNPs in Salmonella enterica serovar Heidelberg genomes using RNase H2-dependent PCR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We report a novel RNase H2-dependent PCR (rhPCR) genotyping assay for a small number of discriminatory single-nucleotide polymorphisms (SNPs) that identify lineages and sub-lineages of the highly clonal pathogen Salmonella Heidelberg (SH). Standard PCR primers targeting numerous SNP locations were initially designed in silico, modified to be RNase H2-compatible, and then optimized by laboratory testing. Optimization often required repeated cycling through variations in primer design, assay conditions, reagent concentrations and selection of alternative SNP targets. The final rhPCR assay uses 28 independent rhPCR reactions to target 14 DNA bases that can distinguish 15 possible lineages and sub-lineages of SH. On evaluation, the assay correctly identified the 12 lineages and sub-lineages represented in a panel of 75 diverse SH strains. Non-specific amplicons were observed in 160 (15.2%) of the 1050 reactions, but due to their low intensity did not compromise assay performance. Furthermore, in silico analysis of 500 closed genomes from 103 Salmonella serovars and laboratory rhPCR testing of five prevalent Salmonella serovars including SH indicated the assay can identify Salmonella isolates as SH, since only SH isolates generated amplicons from all 14 target SNPs. The genotyping results can be fully correlated with whole genome sequencing (WGS) data in silico. This fast and economical assay, which can identify SH isolates and classify them into related or unrelated lineages and sub-lineages, has potential applications in outbreak identification, source attribution and microbial source tracking.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it