High-Throughput Genotyping of Single Nucleotide Polymorphisms in the <i>Plasmodium falciparum dhfr</i> Gene by Asymmetric PCR and Melt-Curve Analysis
Bibliographic record
Abstract
Mutations within the Plasmodium falciparum dihydrofolate reductase gene (Pfdhfr) contribute to resistance to antimalarials such as sulfadoxine-pyrimethamine (SP). Of particular importance are the single nucleotide polymorphisms (SNPs) within codons 51, 59, 108, and 164 in the Pfdhfr gene that are associated with SP treatment failure. Given that traditional genotyping methods are time-consuming and laborious, we developed an assay that provides the rapid, high-throughput analysis of parasite DNA isolated from clinical samples. This assay is based on asymmetric real-time PCR and melt-curve analysis (MCA) performed on the LightCycler platform. Unlabeled probes specific to each SNP are included in the reaction mixture and hybridize differentially to the mutant and wild-type sequences within the amplicon, generating distinct melting curves. Since the probe is present throughout PCR and MCA, the assay proceeds seamlessly with no further addition of reagents. This assay was validated for analytical sensitivity and specificity using plasmids, purified genomic DNA from reference strains, and parasite cultures. For all four SNPs, correct genotypes were identified with 100 copies of the template. The performance of the assay was evaluated with a blind panel of clinical isolates from travelers with low-level parasitemia. The concordance between our assay and DNA sequencing ranged from 84 to 100% depending on the SNP. We also directly compared our MCA assay to a published TaqMan real-time PCR assay and identified major issues with the specificity of the TaqMan probes. Our assay provides a number of technical improvements that facilitate the high-throughput screening of patient samples to identify SP-resistant malaria.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".