Introgression of Ivermectin Resistance Genes into a Susceptible Haemonchus contortus Strain by Multiple Backcrossing
Bibliographic record
Abstract
Anthelmintic drug resistance in livestock parasites is already widespread and in recent years there has been an increasing level of anthelmintic drug selection pressure applied to parasitic nematode populations in humans leading to concerns regarding the emergence of resistance. However, most parasitic nematodes, particularly those of humans, are difficult experimental subjects making mechanistic studies of drug resistance extremely difficult. The small ruminant parasitic nematode Haemonchus contortus is a more amenable model system to study many aspects of parasite biology and investigate the basic mechanisms and genetics of anthelmintic drug resistance. Here we report the successful introgression of ivermectin resistance genes from two independent ivermectin resistant strains, MHco4(WRS) and MHco10(CAVR), into the susceptible genome reference strain MHco3(ISE) using a backcrossing approach. A panel of microsatellite markers were used to monitor the procedure. We demonstrated that after four rounds of backcrossing, worms that were phenotypically resistant to ivermectin had a similar genetic background to the susceptible reference strain based on the bulk genotyping with 18 microsatellite loci and individual genotyping with a sub-panel of 9 microsatellite loci. In addition, a single marker, Hcms8a20, showed evidence of genetic linkage to an ivermectin resistance-conferring locus providing a starting point for more detailed studies of this genomic region to identify the causal mutation(s). This work presents a novel genetic approach to study anthelmintic resistance and provides a "proof-of-concept" of the use of forward genetics in an important model strongylid parasite of relevance to human hookworms. The resulting strains provide valuable resources for candidate gene studies, whole genome approaches and for further genetic analysis to identify ivermectin resistance loci.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".