Login / Signup

The first report of Cryptosporidium spp. in Microtus fuscus (Qinghai vole) and Ochotona curzoniae (wild plateau pika) in the Qinghai-Tibetan Plateau area, China.

Xueyong ZhangYingna JianXiuping LiLiqing MaGabriele KaranisPanagiotis Karanis
Published in: Parasitology research (2018)
Cryptosporidium is one of the most important genera of intestinal zoonotic pathogens, which can infect various hosts and cause diarrhoea. There is little available information about the molecular characterisation and epidemiological prevalence of Cryptosporidium spp. in Microtus fuscus (Qinghai vole) and Ochotona curzoniae (wild plateau pika) in the Qinghai-Tibetan Plateau area of Qinghai Province, Northwest China. Therefore, the aim of this study was to determine Cryptosporidium species/genotypes and epidemiological prevalence in these mammals by detecting the SSU rRNA gene by PCR amplification. The Cryptosporidium spp. infection rate was 8.9% (8/90) in Qinghai voles and 6.25% (4/64) in wild plateau pikas. Positive samples were successfully sequenced, and the following Cryptosporidium species were found: C. parvum, C. ubiquitum, C. canis and a novel genotype in Qinghai voles and C. parvum and a novel genotype in wild plateau pikas. This is the first report of Cryptosporidium infections in M. fuscus and wild O. curzoniae in Northwest China. The results suggest the possibility of Cryptosporidium species transmission among these two hosts, the environment, other animals and humans and provide useful molecular epidemiological data for the prevention and control of Cryptosporidium infections in wild animals and the surrounding environments. The results of the present study indicate the existence of Cryptosporidium species infections that have potential public health significance. This is the first report of Cryptosporidium multi-species infections in these animal hosts.
Keyphrases
  • genetic diversity
  • public health
  • risk factors
  • healthcare
  • south africa
  • genome wide
  • copy number
  • climate change
  • health information
  • big data
  • label free