Metagenomic profiles of Dermacentor tick pathogens from across Mongolia, using next generation sequencing

Abstract

Tick-borne diseases are a major public health concern in Mongolia. Nomadic pastoralists, which make up ~ 26% of Mongolia's population, are at an increased risk of both tick bite exposure and economic loss associated with clinical disease in herds. This study sought to further characterize tick-borne pathogens present in Dermacentor ticks (n = 1,773) sampled in 2019 from 15 of Mongolia's 21 aimags (provinces). The ticks were morphologically identified and sorted into 377 pools which were then screened using Next-Generation Sequencing paired with confirmatory PCR and DNA sequence analysis. Rickettsia spp. were detected in 88.33% of pools, while Anaplasma spp. and Bartonella spp. were detected in 3.18 and 0.79% of pools, respectively. Khentii had the highest infection rate for Rickettsia spp. (76.61%; CI: 34.65-94.79%), while Arkhangai had the highest infection rate for Anaplasma spp. (7.79%; CI:4.04-13.72%). The exclusive detection of Anaplasma spp. in tick pools collected from livestock supports previous work in this area that suggests livestock play a significant role in disease maintenance. The detection of Anaplasma, Bartonella, and Rickettsia demonstrates a heightened risk for infection throughout Mongolia, with this study, to our knowledge, documenting the first detection of Bartonella melophagi in ticks collected in Mongolia. Further research deploying NGS methods is needed to characterize tick-borne pathogens in other endemic tick species found in Mongolia, including Hyalomma asiaticum and Ixodes persulcatus.

Keywords: Anaplasma; Bartonella; Dermacentor; Mongolia; Rickettsia; next generation sequencing; surveillance; tick-bome disease.

Figure 1

Distribution of tick collection events symbolized according to collection source. A map of Mongolia representing the location of tick pools that were chosen for further analysis. Tick pools are symbolized according to their collection source off animals (brown circle) or from the environment (green square). Individual aimags are colored according to the number of pools analyzed per aimag to demonstrate sampling intensity.

Figure 2

Pool MLE for Rickettsia spp. A map showing the distribution of identified Rickettsia species, with an aimag color gradient representing the Rickettsia spp. MLE of sampled pools within the aimag. MLE calculation for Tuv is N/A because detection rate was 100%.

Figure 3

Maximum likelihood (ML) tree was constructed from gltA gene (A) and ompA gene (B) of Rickettsia spp. using T92 + G model with 1,000 bootstrap replicates (> 50% are shown on each node). Sequences of tick samples in this study are shown in red letters.

Figure 4

ML tree was constructed from 16S rRNA (A) and groEL genes using K9 + G (B) and TN93 + G + I (C) models, respectively, with 1,000 bootstrap and value over 50% are indicated on each node.

Figure 5

ML tree constructed from gltA gene of Bartonella spp. using T92 + G model with 1,000 bootstrap replicates (> 50% value are shown on each node). Sequence of tick samples in this study are indicated in red letters.

Figure 6

Distribution of identified microbial species and pool positivity rate by aimag. A map demonstrating the geographic distribution of microbial species identified within the sampled tick pools. Each identified microbial species has a different symbol. The aimags are colored according to the proportion of pools that tested positive for Anaplasma, Bartonella, or Rickettsia species.