Parasites & Vectors (Nov 2019)

High resolution melting analysis of the 18S rRNA gene for the rapid diagnosis of bovine babesiosis

  • Jinming Wang,
  • Aihong Liu,
  • Shangdi Zhang,
  • Shandian Gao,
  • Muhammad Rashid,
  • Youquan Li,
  • Junlong Liu,
  • Quanying Ma,
  • Zhi Li,
  • Zhijie Liu,
  • Jianxun Luo,
  • Guiquan Guan,
  • Hong Yin

DOI
https://doi.org/10.1186/s13071-019-3781-4
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 10

Abstract

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Abstract Background Bovine babesiosis is caused by protozoan parasites of the genus Babesia and presents a wide spectrum of clinical manifestations. Disease severity depends on the type of Babesia species infection. Generally, B. bovis and B. bigemina are considered as the causative agents of bovine babesiosis; in addition, Babesia ovata and B. major are a group of benign bovine piroplasms. Therefore, species identification is important for diagnosis, epidemiological investigations and follow-up management. Methods Real-time PCR combined with high resolution melting (RT-PCR-HRM) analysis was used to detect and discriminate four Babesia species infective to cattle, including Babesia bovis, B. bigemina, B. major and B. ovata. The melting profiles and melting temperatures (Tm) of the amplicon targeting 18S rRNA revealed differences that can discriminate the four Babesia spp. Sensitivity and specificity of the analytical method were evaluated using 50 blood samples collected from experimentally infected cattle and 240 blood samples from areas where bovine babesiosis is an issue. Results RT-PCR-HRM analysis allowed to detect and discriminate four Babesia spp. (B. bovis, B. bigemina, B. major and B. ovata), which were responsible for bovine babesiosis in China. The protocol was validated with DNA samples from experimentally infected cattle and field infection in cattle. Conclusions Our results indicate that RT-PCR-HRM is a fast and robust tool for the simultaneous detection and discrimination of four Babesia species that are responsible for bovine babesiosis in China. This approach is applicable for both field and experimental samples, thus it could be useful in epidemiological investigations and diagnoses of bovine babesiosis.

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