Comprehensive genomic analysis of the potential limitations of several published PCR primers targeting <i>prfA</i>-virulence gene cluster in <i>Listeria</i> species
Bibliographic record
Abstract
Polymerase chain reaction (PCR) is commonly used to detect Listeria monocytogenes, foodborne pathogen. This study conducted in silico genomic analysis to investigate the specificity and binding efficacy of four published pairs of PCR primers targeting Listeria prfA-virulence gene cluster (pVGC) based on Listeria sequences available. We first performed comprehensive genomic analyses of the pVGC, the main pathogenicity island in Listeria spp. In total, 2961 prfA, 642 plcB, 629 mpl, and 1181 hlyA gene sequences were retrieved from the NCBI database. Multiple sequence alignments and phylogenetic trees were generated using unique (non-identical or not-shared) sequences of each represented genes, targeting four pairs of PCR primers published previously, namely 202 prfA, 82 plcB, 150 mpl, and 176 hlyA unique gene sequences. Only the hlyA gene showed strong (over 94%) primer mapping results, while prfA, plcB, and mpl genes showed weak (<50%) matching results. In addition, nucleotide variations were observed at the 3' end of the primers, indicating non-binding to the targets could potentially cause false-negative results. Thus, we propose designing degenerate primers or multiple PCR primers based on as many isolates as possible to minimize the false-negative risk and reach the aim of low tolerable limits of detection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".