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Extraction of Saliva, Haemolymph, Salivary Glands, and Midgut from Individual Ticks (Acari: Ixodidae)
Published on: October 31, 2025
Molecular Survey of Selected Tick-Borne Pathogens and Genetic Diversity of Theileria equi in Donkeys from Croatia
Nika Konstantinović1, Daria Jurković Žilić2, Antun Kostelić3
1Faculty of Veterinary Medicine, University of Zagreb, 10000 Zagreb, Croatia.
Abstract:
Tick-borne pathogens remain insufficiently investigated in donkeys, particularly in southeastern Europe. This study investigated the occurrence of piroplasms, Anaplasmataceae, and hemotropic Mycoplasma spp. in donkeys from Croatia and assessed the genetic diversity of detected Theileria equi. Blood samples from 135 apparently healthy donkeys originating from 15 herds in six Croatian counties were examined using PCR. Positive T. equi samples were further characterized by sequencing, haplotype determination, and phylogenetic analysis. Theileria equi DNA was detected in 31/135 animals (22.96%; 95% CI: 15.99-29.94%), whereas no Anaplasmataceae or hemotropic Mycoplasma spp. DNA was detected. Infection showed marked geographical and herd-level variation. The highest prevalence was recorded in Šibensko-Kninska County (17/34; 50.0%), while all positive animals originated from three herds in the Croatian littoral region. Prevalence differed significantly among herd-size categories (p = 0.012), with no positive animals detected in small herds. Sequence analysis identified three T. equi haplotypes: D was predominant (22/31; 70.97%), followed by A (8/31; 25.81%) and E (1/31; 3.23%). Phylogenetic analysis confirmed all Croatian sequences as T. equi and indicated the presence of multiple genetic lineages within the sampled donkey population. These findings provide new molecular epidemiological data on T. equi in Croatian donkeys and demonstrate a heterogeneous distribution of infection among the investigated regions and herds. Among the selected tick-borne pathogens investigated, T. equi was the only pathogen detected. Further studies integrating larger and longitudinal datasets with detailed management, environmental, and tick-vector data are needed to better understand the factors underlying the observed epidemiological patterns.
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