Droplet digital PCR as a tool to detect resistant isolates of Dirofilaria immitis
Bibliographic record
Abstract
Prevention of canine heartworm disease, caused by Dirofilaria immitis, relies on macrocyclic lactones for which drug resistance is now a concern. Although genetic polymorphisms have been associated with resistance in D. immitis populations, the mechanism is still not well understood. The lack of reliable in vitro assays to detect resistance is a limitation for confirming resistance. Ten single nucleotide polymorphisms (SNPs) were previously clinically validated in D. immitis resistant isolates, using the MiSeq platform. This technique although useful for research studies is expensive and does not facilitate rapid detection of these markers in small numbers of clinical samples. We developed a droplet digital PCR protocol for detecting SNPs correlating with ML resistance. Specific primers and hydrolysis probes encompassing the wildtype and mutant alleles were designed to amplify the SNP targets from genomic DNA of different D. immitis isolates. Allele frequencies were determined and the suitability of the ddPCR assay was assessed and compared with MiSeq data. The ddPCR assay accurately detected and quantified alternate nucleotides in two isolates of reference, the ML-susceptible Missouri (MO) and ML-resistant JYD-34, at the previously identified SNP positions. The presence of the SNPs was also determined in additional isolates with known or putative susceptible or resistant phenotypes. We observed SNP1 and SNP2 are more predictive markers and appear suitable for rapid detection and monitoring of drug resistance. Our results suggested that ddPCR could be employed to distinguish infection due to actual genetic resistance from infection with susceptible parasites and also for rapid detection of isolates not only with ML susceptible and resistant genotypes but also mixed genotypes that correspond to heterogeneous isolates containing a mixed population of ML susceptible and resistant parasites. DdPCR may be a useful tool for conducting surveys, or assessments of individual isolates, for genetic evidence of resistance or developing resistance.
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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) | 0.000 | 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".