A one health study on phylogenetics and risk of pathogenic intestinal parasites at a ranch in Inner Mongolia

Abstract

Cryptosporidium, Giardia duodenalis, and Enterocytozoon bieneusi are widespread zoonotic pathogens causing gastrointestinal diseases in humans and various animal species. Inner Mongolia, a major beef production region in China, there is a notable absence of comprehensive research on intestinal parasitism. Thus, timely and comprehensive diagnosis is essential to mitigate disease spread and minimize economic losses in the livestock industry. In this study, we collected fecal samples from cattle and humans, as well as soil and water samples, and all samples were tested for pathogenic intestinal protozoa at the Simmental cattle ranch in Wengniute, Chifeng City, Inner Mongolia. Among the 393 samples tested, 76/371 (20.5 %) cattle, 6/11 (54.5 %) ranch workers, 1/7 (14.3 %) water, and 2/4 (50 %) soil samples were positive. Factors affecting the infection rate of intestinal protozoa were examined. Results showed that the infection rate was higher in June than in January, higher in calves than in adults, and higher in diarrheal calves than in healthy calves. Additionally, the infection rate of intestinal protozoa was higher in pathogen-contaminated water source sheds than in uncontaminated sheds. Genetic and evolutionary analyses revealed that the prevalent E. bieneusi subtypes are predominantly J, I, and BEB4, while the G. duodenalis subtypes are assemblages B and E. The Cryptosporidium species identified were C. bovis, C. andersoni, C. parvum, C. ryanae, and C. suis, with C. parvum being a notable zoonotic pathogen. The pathogen sequences from humans, cattle, water, and soil showed 99-100 % similarity, suggesting possible transmission or contamination between animals and the environment. This study contributes to the One Health approach by addressing the gap in research on intestinal protozoa in Inner Mongolia. It provides important data for other ranches in the region to understand the prevalence of such pathogens and develop effective control measures. Using the concept of One Health to analyze the spatiotemporal distribution of intestinal protozoa in pastures is of great significance for maintaining public health.

Keywords: Epidemiology; Genotype; Haplotype; Intestinal protozoan; One health.

Fig. 1

Phylogenetic tree constructed using neighbor-joining (NJ) method and haplotype analysis network of E. bieneusi based on ∼243 bp sequence of the internal transcribed spacer (ITS) region. (A) The phylogenetic tree was constructed using the Kimura two-parameter model with 1000 bootstrap replicates. (B) All the 19 sequences identified in this study and 23 previously published sequences were used to generate haplotype data. The TCS network algorithm was used to construct the network. The network was constructed using TCS Network algorithm. Various colors represent different groups and the sizes of the circle represent the numbers of haplotypes. One-Seven, cattle shed; Ref, reference published sequences.

Fig. 2

Phylogenetic tree constructed using neighbor-joining (NJ) method and haplotype analysis network of G. duodenalis β-giardin (bg) gene. (A) The phylogenetic analysis tree was constructed using the Kimura two-parameter model with 1000 bootstrap replications. (B), All the 26 assemblag B sequences in this study and 12 published assemblag B sequences were used to generate haplotype data. The network was constructed using TCS Network algorithm. Various colors represent different groups and the sizes of the circle represent the numbers of haplotypes. One-Eight, cattle shed; Ref, reference published sequences.

Fig. 3

Phylogenetic tree constructed using neighbor-joining (NJ) method and haplotype analysis network of Cryptosporidium SSU rRNA gene. (A) The phylogenetic analysis tree was constructed using the Kimura two-parameter model with 1000 bootstrap replications. (B) All the 14C. bovis sequences in this study and 28 published C. bovis sequences were used to generate haplotype data. The network was constructed using TCS Network algorithm. Various colors represent different groups and the sizes of the circle represent the numbers of haplotypes. One-Eight, cattle shed; People, workers; Ref, reference published sequences. (C) All the 19C. andersoni sequences in this study and 26 published C. andersoni sequences were used to generate haplotype data. The network was constructed using TCS Network algorithm. Various colors represent different groups and the sizes of the circle represent the numbers of haplotypes. One-Seven, cattle shed; Ref, reference published sequences.