Genetic diversity analysis of Dermacentor nuttalli within Inner Mongolia, China

Abstract

Abstract Background Ticks (Arthropoda, Ixodida), after mosquitoes, are the second most prevalent vector of infectious diseases. They are responsible for spreading a multitude of pathogens and threatening the health and welfare of animals and human beings. However, given the history of tick-borne pathogen infections in the Inner Mongolia Autonomous Region of China, surprisingly, neither the genetic diversity nor the spatial distribution of haplotypes within ticks has been studied. Methods We characterized the haplotype distribution of Dermacentor nuttalli in four main pastoral areas of the Inner Mongolia Autonomous Region, by sampling 109 individuals (recovered from sheep) in April–August 2019. The 16S rRNA gene, cytochrome c oxidase subunit I (COI), and the internal transcribed spacer 2 region (ITS2) were amplified and sequenced from extracted DNA. Results Twenty-six haplotypes were identified using 16S rRNA sequences, 57 haplotypes were identified with COI sequences, and 75 haplotypes were identified with ITS2 sequences. Among the three genes, total haplotype diversity was greater than 0.7, while total nucleotide diversity was greater than 0.06. Neutrality tests revealed a significantly negative Tajima’s D result, while Fu's Fs was not significantly positive. Fixation index values (FST) indicated that the degree of genetic differentiation among some sampled populations was small, while for others it was moderate. Analysis of molecular variance (AMOVA) revealed that the variation within populations was greater than that among populations. The mismatch analysis of D. nuttalli exhibited double peaks. Conclusion The genetic diversity of D. nuttalli populations in our region can likely adapt to different geographical environments, thereby leading to genetic diversity, and creating genetic differentiation among different populations. However, genetic differentiation is cryptic and does not form a pedigree geographical structure. Graphical Abstract