Gastrointestinal nematode diversity in Kenyan cattle at wildlife–livestock interfaces: a deep-amplicon sequencing approach

Abstract

Abstract Background Gastrointestinal nematodes (GIN) have significant economic impacts on livestock production worldwide. Comprehensive information on their epidemiology is crucial for effective control. In Africa, limited data are available on the genetic diversity of GIN, as most studies rely on faecal egg counts and larvae morphology, which provide low resolution. This study aimed to assess GIN diversity and evaluate deep-amplicon-based sequencing tools in an African setting. Methods We investigated Clade V nematode infections in adult cattle and their calves at a livestock/wildlife interface, in Ol Pejeta Conservancy, Kenya. Using nemabiome metabarcoding targeting the internal transcribed spacer-2 region, we analysed faecal, first-stage larvae (L 1 ) and third-stage larvae (L 3 ) samples to determine the GIN species composition in individual animals. Results Nemabiome analysis confirmed the presence of eight GIN species in 29 cattle which were infected by Cooperia curticei , Cooperia pectinata, Cooperia punctata , Haemonchus placei , Haemonchus contortus, Teladorsagia circumcincta, Trichostrongylus axei, and Trichostrongylus colubriformis . Co-infections were common, accounting for 77.4% of all infections. Calves had higher infection rates than dams and exhibited more diverse nematode communities. In addition, faecal and L 1 samples proved reliable DNA sources, offering a practical alternative to labour-intensive L 3 culturing in tropical settings. Conclusions Nemabiome sequencing provides a powerful approach to capture and assess the diversity and variability of GIN co-infections within and between individual animals. Uncovering nematode diversity offers new opportunities to investigate inter-species interactions and complex epidemiology. Given the diverse nature of infections in livestock/wildlife, these approaches offer strong potential for unbiased disease surveillance in complex ecosystems.