Sign in
Evolutionary BiologyEvolutionary Applications
Peer-reviewed journal article

Evolution of ivermectin resistance in the nematode model Caenorhabditis elegans: critical influence of population size and unexpected cross-resistance to emodepside

Jacqueline R. HellingaiD, Barbora TrubenováiD, Jessica Wagner, Marc Borchert, Jürgen KrückeniD, Roland R. RegoesiD, Hinrich Schulenburg YiD, Georg von Samson‐HimmelstjernaiD

Abstract

The emergence and spread of anthelmintic resistance represent a major challenge for treating parasitic nematodes, threatening mass-drug control programs in humans and zoonotic species. Currently, experimental evidence to understand the influence of management (e.g., treatment intensity and frequency) and parasite-associated factors (e.g., genetic variation, population size and mutation rates) is lacking. To rectify this knowledge gap, we performed controlled evolution experiments with the model nematode Caenorhabditis elegans and further evaluated the evolution dynamics with a computational model. Large population size was critical for rapid ivermectin resistance evolution in vitro and in silico. Male nematodes were favored during resistance evolution, indicating a selective advantage of sexual recombination under drug pressure in vitro. Ivermectin resistance evolution led to the expected emergence of cross-resistance to the structurally related anthelmintic moxidectin but unexpectedly also to the structurally unrelated anthelmintic emodepside that has an entirely different mode of action. In contrast, albendazole, levamisole, and monepantel efficacy were not influenced by the evolution of Ivermectin resistance. We conclude that combining computational modeling with in vitro evolution experiments to test specific aspects of evolution directly represents a promising approach to guide the development of novel treatment strategies to anticipate and mitigate resistance evolution in parasitic nematodes.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers
Citations
3
FWCI
12.53
Field percentile
top 1%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISTrubenová '25Shaver '23Shaver '24Wit '21Doyle '19Patel '25
79% semantic matchEvolutionary Biology
Barbora Trubenová, Jacqueline R. Hellinga, Jürgen Krücken+3 more·Proceedings of the Royal Society B Biological Sciences·Oct 1, 2025· 2 citations