Background

Rickettsiosis is an important zoonosis, which brings serious harm to the health of humans and animals [1, 2]. The species of Rickettsia belong to three monophyletic groups: spotted fever group (SFG), typhus group (TG), and transitional group (TRG). SFG is transmitted by ticks [3, 4], while TG is transmitted by lice and fleas [5]. The vast territory and diverse habitats of Inner Mongolia greatly benefit the survival of ticks. Dermacentor nuttalli is the dominant tick species and is probably the main vector carrying spotted fever group Rickettsia (SFGR) in Inner Mongolia [6,7,8]. SFGR infections occur around the world and may cause serious diseases in humans. In China, Rickettsia heilongjiangensis, Rickettsia raoultii, Rickettsia slovaca, Rickettsia sibirica, Rickettsia mongolotimonae, Rickettsia monacensis, and Candidatus Rickettsia hebeiii and Candidatus Rickettsia **gxinensis have been detected in ticks. Furthermore, Rickettsia raoultii, Rickettsia heilongjiangensis, and R. sibirica have been reported in emerging tick-borne diseases of humans. In recent years, the threat that SFGR poses to public health in China has been magnified by the increasing number of potentially novel SFGR detected in ticks [9,10,11].

Molecular biological techniques allow for the identification of Rickettsia species. 16S rRNA can accurately identify a specimen as belonging to Rickettsia. However, it is difficult to distinguish the species, because the 16S rRNA sequence is highly conserved in almost all prokaryotes. The gltA and ompA genes are used for species identification in Rickettsia [12,13,14]. The gltA gene encodes a citrate synthase, the sequence of which allows for highly reliable identification of the evolutionary distances among Rickettsia. The ompA gene is an outer membrane protein gene. It is considered the “gold standard” for species identification in Rickettsia, owing to the highly specific 5′ end [15].

Previous studies found high genetic disparity in D. nuttalli, allowing for its existence in different geographical environments [16]. However, data on the genetic diversity of Rickettsia in D. nuttalli are scarce. Therefore, in our study, we included a high number of tick specimens, and explored the percentage of Rickettsia-positive samples and the genotype distribution to identify the genetic diversity of Rickettsia. Studying the genetic diversity of Rickettsia is essential for develo** effective control strategies and predicting pathogen evolutionary trends.

Methods

Sample collection

In this study, a total of 408 ticks were collected from 1078 sheep at four sampling spots in Inner Mongolia, China: Chengchuan Town, early Banner of Etoke Banner Ordos (EEDS); Siziwang Banner, Hohhot (SZWQ); the Bayan WenduSumu area Arukorqin Banner Chifeng (CF); and **nbarhu right Banner, Hulun Buir (HLBE). The locations of the sampling areas are shown in Additional file 1: Table S1 and Fig. 1.

Fig. 1
figure 1

Collection site map. Samples of D. nuttalli were collected in four regions of Inner Mongolia. Colors indicate different collection regions in Inner Mongolia, and each graphic represents the approximate geographical coordinates of each collection site

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DNA extraction, amplification, and sequencing

Ticks were identified as D. nuttalli through morphological characteristics [17]. All D. nuttalli samples were individually extracted using a TIANamp Tissue and Blood Kit (TIANGEN, Bei**g, China) [18]. For amplification of Rickettsia DNA, the gltA and ompA genes were amplified by polymerase chain reaction (PCR), following the protocols described by Bermúdez et al. [19]. Specific primers targeting the gltA and ompA genes of Rickettsia from D. nuttalli were synthesized by Shanghai Sangon. DNA was amplified using a system of 40 μl, each including Taq PCR Master Mix (Sangon, Shanghai, China), 2 μl of DNA from each sample, and 1 μl of each reverse and forward primer, and filled to volume with double-distilled water. The PCR primers, amplification sizes (base pairs), and annealing temperatures are listed in Additional file 1: Table S2. Double-distilled water was used as the negative control in each PCR reaction. Prior to sequencing, the quality of the PCR products was checked with 1.5% agarose gel electrophoresis stained with GoldView (Sangon, Shanghai, China). If the quality of the PCR product was suboptimal, it was purified using the Gel DNA Recovery Kit (TIANGEN, Bei**g, China) and cloned using the pGEM-T Easy Vector System (Promega, Madison, WI, USA).

Data analysis

Sequences were edited in SeqMan 7.1 and identified by comparative analysis with sequences deposited in GenBank, using the National Center for Biotechnology Information (NCBI) BLAST search engine. Sequencing data are available at the NCBI Sequence Read Archive (https://submit.ncbi.nlm.nih.gov/about/bankit/), with accession numbers OK638141-OK638150, OL304270-OL304271, OL348251-OL348270. Multiple sequence alignment and sequence similarity calculations were done using DNAMAN 7.0. Phylogenetic trees were constructed with the neighbor-joining algorithm using MEGA 7 with 1000 bootstrap replicates to assess tree stability [20,21,22]. Sequences were analyzed in DNAsp 5.10 and Arlequin 3.5 for calculating polymorphic sites, nucleotide differences, the number of haplotypes, both haplotype and nucleotide diversity, the distribution pattern of DNA haplotype variation, and genetic variation parameters [23, 24]. The extent of genetic differentiation among and between Rickettsia populations was estimated by analysis of molecular variance (AMOVA) and FST values [25]. To determine whether genetic differentiation and population structures of Rickettsia varied among the four sampling localities in Inner Mongolia. Neutral tests were analyzed using Tajima’s D and Fu’s Fs tests using Arlequin 3.5 and DNAsp 5.10. PopART (Population Analysis with Reticulate Trees) version 1.7 software was used to evaluate the relationships between haplotypes by constructing TCS haplotype network maps.

Results

Detection of Rickettsia DNA

We collected and tested a total of 408 D. nuttalli in 2019 from four sites in Inner Mongolia: CF (n = 219), EEDS (n = 85), SZWQ (n = 30), and HLBE (n = 74) (Table 1). The quantity of amplification products of the gltA and ompA genes was regarded as the percentage of samples positive for Rickettsia in D. nuttalli [26]. Across the four regions, the average percentage of positive Rickettsia samples was 50.7%, with the highest value found in the HLBE region (85.1%).

Table 1 The percentage of Rickettsia-positive samples in D. nuttalli
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Rickettsia identification

We detected 10 haplotypes of gltA sequences and 22 haplotypes of ompA sequences. The sequences had the highest similarity with R. raoultii and R. sibirica, as registered in GenBank, with 98% and 99% identity, respectively. In the gltA phylogenetic trees, haplotypes G1–G7 and G9 were clustered with R. raoultii, while G8 and G10 were clustered with R. sibirica. Furthermore, we found high similarity with Candidatus Rickettsia uralica, with 98% identity, and distinctly lower similarity in Rickettsia asembonensis. In the ompA phylogenetic trees, haplotypes O1–O15, O18, and O20–O22 clustered with R. raoultii, while O16 and O19 clustered with R. sibirica. The highest similarity was found with Candidatus R. uralica, with 98% identity, and substantially lower similarity with Rickettsia montana. The phylogenetic trees for both genes showed that the haplotypes of Rickettsia clustered into one branch with the ingroup, which contained the genotypes of R. raoultii and R. sibirica (Fig. 2a, b).

Fig. 2
figure 2

a Phylogenetic tree based on tbe gltA gene of R. raoultii. b Phylogenetic tree based on the ompA gene of R. raoultii. c TCS haplotype network of R. raoultii based on the gltA gene from four different populations in Inner Mongolia. d TCS haplotype network of R. raoultii based on the ompA gene from four different populations in Inner Mongolia

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Rickettsia genetic diversity by gltA gene

In the gltA sequences, the final alignment consisted of 1318 base pairs, with 842 variable sites. Of 10 haplotypes recovered, four were shared haplotypes (G1, G2, G3, G10). Numerically, the most common haplotype was G1, with 167 sequences (80.7% of all sequences) (Additional file 1: Table S3). G1 turned out to be the dominant haplotype. It was placed in the center of the haplotype network and was found in four geographically separate populations (Fig. 2c). The average haplotype diversity was 0.335. The average nucleotide diversity was 0.04922. EEDS was the region with the highest haplotype diversity (h = 0.909). Neutrality analysis revealed nonsignificant values of Tajima's D and Fu's Fs results, confirming that the population had not experienced recent expansion (Table 2). Wright’s F index was calculated to measure the levels of genetic differentiation among the four geographical populations, indicating the allelic variation between populations, which correlated negatively with gene flow. Comparing pairwise FST indices showed that the FST value between HLBE and SZWQ was greater than 0.25, indicating high genetic differentiation among populations, probably on account of low gene flow. The FST values among the other regions were less than 0.05, indicating that the genetic differentiation among these populations was very small, with a high degree of gene flow (Additional file 1: Table S4). The AMOVA showed that the variability in Rickettsia mainly arose from within each population, and the genetic differentiation between populations was very small (Additional file 1: Table S5). The mismatch analysis presented double peaks, indicating that the four geographical populations did not experience rapid population expansion (Fig. 3a).

Table 2 Summary statistics for polymorphism and neutrality tests of the gltA gene from Rickettsia in Inner Mongolia
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Fig. 3
figure 3

a Mismatch distribution analysis for the R. raoultii groups based on gltA. b Mismatch distribution analysis for the R. raoultii groups based on ompA

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Rickettsia genetic diversity by ompA gene

In the ompA sequences, the final alignment consisted of 738 base pairs, with 466 variable sites. Of the 22 haplotypes that were found, four were shared haplotypes (O1, O2, O3, O16). Numerically, the most common haplotypes were O1, O2, and O3, with 170 sequences (82.1% of all sequences) (Additional file 1: Table S6). O2 was the dominant haplotype. It was placed in the center of the haplotype network and was found in four different populations (Fig. 2d). The total average haplotype diversity was 0.735 and the total nucleotide diversity was 0.07308. SZWQ was the locality with the highest haplotype diversity (h = 1). Neutrality results were the same as those for gltA, indicating that the population had not experienced expansion recently (Table 3). The FST value between CF and HLBE was greater than 0.25, indicating that there was high genetic differentiation between populations. The FST values between HLBE, SZWQ, and EEDS were all greater than 0.05, confirming a moderate genetic differentiation between populations, with a small extent of gene flow. The FST values between the other regions were less than 0.05, confirming a very small genetic differentiation between these populations, likely on account of high gene flow (Additional file 1: Table S7). The results of the AMOVA and mismatch analyses were consistent with the gltA results (Additional file 1: Table S8, Fig. 3b).

Table 3 Summary statistics for polymorphism and neutrality tests of the ompA gene from Rickettsia in Inner Mongolia
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Discussion

For planning effective control measures against Rickettsia infection, it is of utmost importance to continuously monitor the emergence of new species, study the population structure, and investigate the genetic diversity of the pathogen. In this study, two Rickettsia species were identified in Inner Mongolia, R. raoultii and R. sibirica, both belonging to the SFGR. In recent years, human infection with R. raoultii has led to tick-borne lymphadenitis in many countries [27,28,29,30]. To strengthen appropriate detection and treatment measures in endemic regions, public health workers and physicians should pay close attention to the high risk of human infection by R. raoultii.

This study analyzed the genetic diversity of a region within gltA and ompA genes of Rickettsia from four localities of Inner Mongolia. The aim of the investigation was to find new possibilities for controlling the transmission and reproduction of Rickettsia. Our results showed that the gltA and ompA genes have shared haplotypes in four regions. These are dominant haplotypes characterized as primitive and stable. Shared haplotypes indicate that the degree of genetic communication is high in Rickettsia populations. Furthermore, the lowest genetic diversity was found in HLBE in the gltA gene. The diversity of the ompA was higher in HLBE. The different results for the two genes indicate that the genetic diversity of species is affected by many factors, such as geographical distribution and population size [31]. Different gene markers are under different selective pressure during the evolution of species, leading to inconsistent genetic diversity [32]. Overall, the high genetic diversity of Rickettsia is in accord with different environments. The high rate of genetic communication in Rickettsia populations not only leads to a higher positive rate of Rickettsia in Inner Mongolia, but also increases the probability of Rickettsia transmission among and between humans and livestock.

Regarding the four geographical populations investigated, there is genetic differentiation between populations from AMOVA results. Further studies on FST values revealed that the degree of genetic differentiation was highest between HLBE and the other three regions. Obviously, HLBE is not only a beneficial habitat for ticks but also an important pastoral region in Inner Mongolia [33]. It also has the highest Rickettsia-positive rate and the highest genetic differentiation in Rickettsia populations in Inner Mongolia.

The neutrality test values of Tajima’s D and Fu’s Fs are used to test the historical dynamics of populations. If both are significantly negative, it indicates that the D. nuttalli population has historically experienced rapid population expansion. In this study, we did not find evidence of a recent rapid expansion of the population. Furthermore, the mismatch analysis for the two genes gltA and ompA showed genetic differentiation with no population expansion. The haplotypes of Rickettsia did not branch in relation to the clustering of geographical regions, indicating that the Rickettsia populations did not form a geographical differentiation structure. Therefore, in recent years, Rickettsia populations have not experienced excessive outbreaks, and the phenomenon of high genetic diversity may be a historical effect.

Since Rickettsia can be transmitted either vertically or horizontally, the high carry and infection rates of Rickettsia in D. nuttalli, together with the animal husbandry exchange in Inner Mongolia, gradually led to the harmonization of genetic characteristics of Rickettsia across various regions. The National Center for Disease Control and Prevention should strengthen the monitoring of tick-borne Rickettsia in Inner Mongolia and develop effective control measures.

Conclusion

The gltA and ompA genes were used to study the genetic diversity of Rickettsia from four geographical localities in Inner Mongolia. This study provides a reference for detecting new genotypes and complex genetic structures of Rickettsia populations. It also indicates that although Rickettsia species in Inner Mongolia have adapted to different environments, effective control measures of tick-borne Rickettsia transmission is crucial, especially in the HLBE region.