The use of ITS-2 rDNA nemabiome metabarcoding to enhance anthelmintic resistance diagnosis and surveillance of ovine gastrointestinal nematodes
Bibliographic record
Abstract
A lack of quantitative information on the species composition of parasite communities present in fecal samples is a major limiting factor for the sensitivity, accuracy and interpretation of the diagnostic tests commonly used to assess anthelmintic efficacy and resistance. In this paper, we investigate the ability of ITS-2 rDNA nemabiome metabarcoding to enhance fecal egg count reduction testing by providing information on the effect of drug treatments on individual parasite species. Application of ITS-2 rDNA nemabiome metabarcoding to fecal samples from ewes from over 90 flocks across western Canada revealed high gastrointestinal nematode infection intensities in many flocks with Haemonchus contortus being the most abundant species followed by Teladorsagia circumcincta and then Trichostrongylus colubriformis. Integration of ITS-2 rDNA nemabiome metabarcoding with pre- and post-treatment fecal egg counting revealed consistently poor efficacy of producer-applied ivermectin and benzimidazole treatments against H. contortus, but much better efficacy against T. circumcincta and T. colubriformis, except for in a small number of flocks. Integration of nemabiome ITS-2 rDNA metabarcoding with Fecal Egg Count Reduction Tests (FECRT), undertaken on farm visits, confirmed that ivermectin and fenbendazole resistance is widespread in H. contortus but is currently less common in T. circumcincta and T. colubriformis in western Canada. FECRT/nemabiome testing did not detect moxidectin resistance in any GIN species but suggested the early emergence of levamisole resistance specifically in T. circumcincta. It also revealed that although poor efficacy to closantel was relatively common, based on total fecal egg counts, this was due to its narrow spectrum of activity rather than the emergence of anthelmintic resistance. This study illustrates the value of ITS-2 rDNA nemabiome metabarcoding to improve fecal egg count resistance testing, perform large-scale anthelmintic resistance surveillance and direct more targeted rational anthelmintic use.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".