A systematic review of the molecular mechanisms related to anthelmintic resistance in Haemonchus contortus: A contemporary narrative

Abstract

Haemonchus contortus is a gastrointestinal parasite that affects ruminants (cattle, sheep, etc.), having a significant welfare impact worldwide. The rise of anthelmintic resistance poses a growing challenge to adequate control, compromising the success of treatments. This study presents a systematic review of the molecular mechanisms involved in the resistance of H. contortus to anthelmintic drugs. Following an extensive literature search (9075 total articles/excluding duplications), 61 articles were examined. From these, benzimidazoles (BZD) and macrocyclic lactone (ML) were the most reported drug classes (17 and 29, respectively). The mutations in the β-tubulin gene were the primary mechanism of BZD resistance. Important comparisons from early reports of resistance mechanisms to ML (published before 2020) mainly based on parasite-population gene expression (e.g., ligand-gated chloride channels, LGCC, and P-glycoproteins, PGP) with more recent genomic and transcriptomic data (e.g., transcription factor, cky-1 gene) are highlighted. Additionally, resistance mechanisms to levamisole (LEV) and monepantel are discussed, showing evidence of polymorphisms in genes related to the nicotinic acetylcholine receptors (nAChR). Considering the available data, it is possible to divide the reports into two technological periods, evidencing that the availability of a chromosome-level genome for H. contortus in association with experiments based on controlled genetic crosses and transcriptome-wide data allowed for the visualization of genes and polymorphisms that were previously indistinguishable from unrelated genetic variation (i.e., genetic noise). Therefore, the study of anthelmintic resistance mechanisms is facing new possibilities, reflecting the large data banks and the speed at which this information is being processed. We suggest that new publications on drug resistance should adopt the approaches and refer to this new era of scientific discoveries. Consistent data interpretation, including artificial intelligence (AI) support, will help us to suggest novel biological mechanisms involved in drug resistance and predict its evolution, allowing a more comprehensible approach toward sustainable parasite control strategies.