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Open Access

Peer-reviewed

Research Article

Genetic structure and Rickettsia infection rates in Ixodes ovatus and Haemaphysalis flava ticks across different altitudes

  • Maria Angenica F. Regilme,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

    Current address: National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido, Japan

    Affiliations Center for Marine Environmental Studies (CMES), Ehime University, Matsuyama, Ehime, Japan, Graduate School of Science and Engineering, Ehime University, Matsuyama, Ehime, Japan

    ⨯
  • Megumi Sato,

    Roles Conceptualization, Data curation, Funding acquisition, Methodology, Writing – review & editing

    Affiliation Graduate School of Health Sciences, Niigata University, Niigata, Japan

    ⨯
  • Tsutomu Tamura,

    Roles Conceptualization, Data curation, Investigation, Writing – review & editing

    Affiliation Niigata Prefectural Institute of Public Health and Environmental Sciences, Niigata, Japan

    ⨯
  • Reiko Arai,

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliation Niigata Prefectural Institute of Public Health and Environmental Sciences, Niigata, Japan

    ⨯
  • Marcello Otake Sato,

    Roles Conceptualization, Data curation, Methodology, Writing – review & editing

    Affiliation Faculty of Medical Technology, Division of Global Environment Parasitology, Niigata University of Pharmacy and Medical and Life Sciences, Niigata, Japan

    ⨯
  • Sumire Ikeda,

    Roles Conceptualization, Data curation, Methodology, Writing – review & editing

    Affiliation Research Laboratories, Research and Development Headquarters, Earth Corporation, Hyogo, Japan

    ⨯
  • Kozo Watanabe

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – review & editing

    watanabe.kozo.mj@ehime-u.ac.jp

    Affiliation Center for Marine Environmental Studies (CMES), Ehime University, Matsuyama, Ehime, Japan

    ⨯

Abstract

Ixodid ticks, such as Ixodes ovatus and Haemaphysalis flava, are important vectors of tick-borne diseases in Japan, such as Japanese spotted fever caused by Rickettsia japonica. This study describes the Rickettsia infection rates influenced by the population genetic structure of I.ovatus and H. flava along an altitudinal gradient. A total of 346 adult I. ovatus and 243 H. flava were analyzed for the presence of Rickettsia by nested PCR targeting the 17kDA, gltA, rOmpA, and rOmpB genes. The population genetic structure was analyzed utilizing the mitochondrial cytochrome oxidase 1 (cox1) marker. The Rickettsia infection rates were 13.26% in I. ovatus and 6.17% in H. flava. For I. ovatus, the global FST value revealed significant genetic differentiation among the different populations, whereas H. flava showed non-significant genetic differentiation. The cox1 I. ovatus cluster dendrogram showed two cluster groups, while the haplotype network and phylogenetic tree showed three genetic groups. A significant difference was observed in Rickettsia infection rates and mean altitude per group between the two cluster groups and the three genetic groups identified within I. ovatus. No significant differences were found in the mean altitude or Rickettsia infection rates of H. flava. Our results suggest a potential correlation between the low gene flow in I. ovatus populations and the spatially heterogeneous Rickettsia infection rates observed along the altitudinal gradient. This information can be used in understanding the relationship between the tick vector, its pathogen, and environmental factors, such as altitude, and for the control of tick-borne diseases in Japan.

Citation: Regilme MAF, Sato M, Tamura T, Arai R, Sato MO, Ikeda S, et al. (2024) Genetic structure and Rickettsia infection rates in Ixodes ovatus and Haemaphysalis flava ticks across different altitudes. PLoS ONE 19(3): e0298656. https://doi.org/10.1371/journal.pone.0298656

Editor: Maria Stefania Latrofa, University of Bari, ITALY

Received: April 25, 2023; Accepted: January 30, 2024; Published: March 13, 2024

Copyright: © 2024 Regilme et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Sequences used for analysis are available in the GenBank database under the accession numbers MW063669 to MW064124, MW065821 to MW066347, OR975837 to OR975875 and OR975876 to OR975898.

Funding: This study was supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) to a project on Joint Usage/Research Center– Leading Academia in Marine and Environment Pollution Research (LaMer).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Tick-borne diseases are a significant public health concern in Japan and are transmitted by a diverse range of tick species, such as Ixodes ovatus [1] that potentially transmit Borrelia sp. causing Lyme disease [2] and Haemaphysalis flava [3], which transmits Rickettsia japonica and is a suspected vector of severe fever with thrombocytopenia syndrome virus [4–6]. Their dispersal is linked to the mobility of their hosts, relying on them to disperse into new landscapes and potentially introduce pathogens [7, 8]. The dynamics of tick-borne pathogens are influenced by the habitat distribution and dispersal behaviors of vectors and hosts along environmental gradients [9]. Therefore, understanding the complex interaction between these factors is important in understanding the spread of tick-borne diseases in Japan.

Tick population genetic analysis provides data that help identify the dispersal pattern of ticks based on gene flow between local populations [10]. The potential of spreading pathogens might be influenced by ticks’ dispersal, which is related to the movements of their vertebrate hosts, especially in three-host Ixodidae species [7, 8]. For example, contrasting patterns in the population genetic structures of I. ovatus and H. flava in the Niigata Prefecture of Japan suggest that host mobility during the immature stages of tick development may influence the genetic structure of adult ticks by affecting survivability into their adult stages [11, 12]. Ixodes ovatus populations had greater genetic divergence possibly due to the limited dispersal of their small mammalian hosts during the immature development stage; H. flava populations showed a more homogenized structure possibly due to the larger mobility of their large mammalian hosts and avian-mediated dispersal [11]. Other studies have also revealed low gene flow in ticks with low-mobility hosts (e.g., small mammals) and higher gene flow in ticks with highly mobile hosts (e.g., large mammals and birds) [10, 13–16].

The spatial distribution and movement of the vector (i.e., ticks) may determine the spatial distribution of the pathogen’s (i.e., Rickettsia) infection rate [17]. Previous studies have shown that the pathogen infection rate can be influenced by many factors, such as the vector’s genetic diversity, gene flow, and spatial structure [5, 6, 11, 18–25]. For example, previous studies have shown that strong gene flow between local vector populations tends to reduce the spatial heterogeneity of pathogen infection rates between populations [26, 27]. Thus, the spatial distribution and movement of the vector may affect the spatial distribution of the pathogen. To our knowledge, no previous studies have examined the relationship between the spatial heterogeneity of Rickettsia infection rates and population genetic structure in ticks.

Environmental factors may relate to the population genetic structure of ticks [28, 29], with limited gene flow increasing genetic variation between populations along an altitudinal gradient, as reported in several studies on other species [30–32]. In the study by [11], no significant influence of environmental factors, including altitude, was observed in the genetic structures of I. ovatus and H. flava based on the mantel test, but the study did not use any other robust analytical methods to thoroughly examine the influence of altitude on tick genetic structure. In another study, major spotted fever group Rickettsia (SFGR) prevalence was analyzed in a total of 3,336 immature and adult ticks across the Niigata Prefecture, Japan in the following tick species: Dermacentor taiwanensis, H. flava, Haemaphysalis hystricis, Haemaphysalis longicornis, Haemaphysalis megaspinosa, Ixodes columnae, Ixodes monospinosus, Ixodes nipponensis, Ixodes ovatus, and Ixodes persulcatus [6]. Three SFGR species namely Rickettsia asiatica, R. helvetica and R. monacensis were detected in H. flava, Haemaphysalis longicornis, Ixodes monospinus, Ixodes nipponensis, and Ixodes ovatus, no spatial distribution of Rickettsia infection rates was found among the local populations. To our knowledge, no previous studies have considered the influence of environmental factors on the spatial distribution of Spotted fever group Rickettsia infection rates along an altitudinal gradient in local Ixodid tick populations such as Ixodes ovatus and Haemaphysalis flava as influenced by the tick population’s genetic structure.

In this study, we elucidate the relationship between Rickettsia infection rates as influenced by population genetic structure along an altitudinal gradient to improve public health understanding of the distribution of ticks and tick-borne diseases. Based on the isolation by environment (IBE) theory, genetic differentiation increases with environmental variation, regardless of geographic distance [33–35]. Thus based on the results of [11], we hypothesized that in I. ovatus with a strong population genetic structure, we expect to see a heterogenous Rickettsia infection rate along an altitudinal gradient. In contrast to the homogenous genetic structure of H. flava wherein we expect to observe a homogenous Rickettsia infection rate.

Materials and method

Published data of [6, 11]

In this study, we used cox1 sequence data from [11] for I. ovatus (n = 307) and H. flava (n = 220) ticks collected from April 2016 to November 2017 from 30 sites across the Niigata Prefecture, Japan. Sequences used for analysis are available in the GenBank database under the accession numbers MW063669-MW064124 and MW065821—MW066347. Rickettsia infection rate data were obtained from [6] from I. ovatus (n = 29) and H. flava (n = 2), from 38 sites across Niigata Prefecture. The 38 sites surveyed in the previous study by [6] include the 30 sites that were used in this present study (S1 Table). Please refer to [6, 11] for more information about the study sites, collection, sampling identification, DNA extraction, PCR amplification, and sequencing methods used in each respective study.

To strengthen our analysis, we also added new cox1 sequences and Rickettsia-infected/uninfected ticks from I. ovatus (n = 39) and H. flava (n = 23) individuals collected from April to October 2018, a total of (n = 62) sampled at 30 sites across the Niigata Prefecture, including two sites not previously sampled by [11]. The sequences are available in the GenBank database under the accession numbers OR975837 to OR975875 and OR975876 to OR975898. At these sites, ticks were collected 2–14 times from six core sites among the 30 sites, while ticks were collected once at the remaining sites. The altitude at each site ranged from 8 to 1402 meters above sea level (m.a.s.l.), with a mean altitude of 348 m.a.s.l.

Unpublished data from the 2018 collection

Ticks collected were stored at 4°C in microcentrifuge tubes with 70% ethanol. Each collected tick was morphologically identified using a stereo microscope following the identification keys of [36]. Genomic DNA was extracted from individual ticks using Isogenome DNA extraction kits (Nippon Gene Co. Ltd. Tokyo, Japan) following the manufacturer’s recommended protocol.

In this study, we combined previously published data from [6] with our newly collected data to calculate the Rickettsia infection rate, which is the percentage of Rickettsia-infected ticks from each obtained population. We analyzed the obtained tick DNA for spotted fever group Rickettsia (SFGR) detection and host identification and amplified the mitochondrial gene cox1 for population genetic analysis. We performed nested PCR targeting the following genes for the detection of Rickettsia sp.: 17-kDA antigen gene (17-kDA); citrate synthase gene (gltA); spotted fever group (SFG)-specific outer membrane protein A gene (rOmpA); and outer membrane protein B gene (rOmpB) as described and analyzed in [6, 37–41] (S2 Table). Briefly, we first amplified the 17-kDa protein. If the results were positive, then PCR was performed to target gltA. Samples that were positive with both 17-kDA and gltA were regarded as positive for SFGR and a nested PCR was performed to target the rOmpA and rOmpB gene, samples that are positive for 17-Kda, gltA, rOmpA, and rOmpB genes were sequenced to identify the Rickettsia species. The amplified PCR products were purified using AMPure XP (Beckman Coulter Co., Japan) and sequenced using the Big Dye Terminator Cycle Sequence Kit (Thermo Fisher Scientific).

The cox1 mitochondrial gene was amplified by PCR for cox1 (658 base pairs) using the primer pairs LCO-1490 (5′-GGTCAACAAATCATAAAGATATTGG-3’) and HCO1–2198 (5′–AAACTTCAGGGTGACCAAAAAATCA-3) for phylogenetic analysis and tick species identification [42]. The PCR amplification profile included an initial denaturation of 94°C for 2 min, followed by denaturation at 94°C for 30 s, then annealing at 38°C for 30 s, followed by an extension of 72°C for 1 min for 30 cycles, and a final extension of 72°C for 10 min. The obtained PCR products were purified using the QIAquick 96 PCR Purification Kit (Qiagen, Germany) following the manufacturer’s instructions and were sequenced by Eurofin Genomics, Inc. (Tokyo, Japan).

Each forward and reverse read was assembled using CodonCode Aligner version 1.2.4 software (https://www.codoncode.com/aligner/). Low-quality bases were removed in the aligned sequences, and no ambiguous bases were detected. We used the MAFFT alignment online program (https://mafft.cbrc.jp/alignment/server/) to perform multiple alignments using the default settings. The sequences were checked for similarities with the deposited reference sequences from GenBank for sequence quality and tick species confirmation using BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The protein-coding genes were translated to amino acids to confirm the absence of stop codons and the final aligned sequences were checked in Mesquite version 3.5 [43].

Population genetic analysis

Multiple sites that are within 80 kilometers were combined for population genetic analysis if less than eight individuals were obtained per site, which resulted in 8 populations labeled A to H (S1 Table). Three sites were excluded from the population genetic analysis because of the limited number of obtained individuals (<8) and the lack of a nearby site within 80 kilometers to combine into a single population.

The final cox1 sequences of the tick species: I. ovatus and H. flava were individually analyzed using DNASp version 6.12.03 to determine the haplotype diversity per species [44]. The level of genetic divergence between each population was quantified per species using global FST. Significance was tested using Arlequin software version 3.5.2.2 [45] with 9999 permutations.

The genetic relationship between the I. ovatus populations was visualized using the unweighted pair group with the arithmetic mean (UPGMA) cluster method using the APE package [46] for the RStudio software (R Development Core Team, 2016). A cluster dendrogram was created using pairwise FST values genetic distance matrix from GenAlEx.

Haplotype network and phylogenetic analyses

We constructed a haplotype network analysis using PopART program version 1.7 (http://popart.otago.ac.nz/index.shtml) on cox1 I. ovatus and H. flava sequences to assess haplotype relationships and the distribution of Rickettsia infected infected ticks using the median-joining network algorithm [47]. Briefly, we constructed a Bayesian phylogenetic tree of cox1 haplotypes for I. ovatus and H. flava, respectively, using Markov chain Monte Carlo (MCMC) approach implemented in the BEAST version 1.10.14 [48]. We used the Hasegawa-Kishino-Yano substitution model with estimated base frequencies. We employed a strict clock model and used the coalescent prior as the tree prior. A maximum clade credibility tree was acquired using TreeAnnotator version 1.10.14 using trees from BEAUti version1.10.14 with 90% of the trees as the burn-in. We viewed the constructed maximum clade credibility tree using FigTree version 1.4.4.

Statistical analysis

To determine whether there was a significant difference in the Rickettsia infection rate between haplotype groups for I. ovatus and H. flava, we performed a z-score test at p < 0.05. The z-score test was chosen because of the large sample size and because the population variance was known. To determine whether there were differences in the mean altitude between the haplotype groups, we used the Welch t-test at p < 0.05. Welch t-test was used when the means of the two populations were normally distributed and had equal variances.

Results

The total number of positive (pos) and negative (neg) ticks for Rickettsia infections from the ticks collected in 2018 were: I. ovatus (neg = 22, pos = 17) and H. flava (neg = 10, pos = 13). In this study, the total number of samples from the previously published data [6, 11] and the unpublished data from 2018 were: I. ovatus (n = 346) and H. flava (n = 243). The number of adult ticks whose cox1 was successfully sequenced per species were: I. ovatus (346) and H. flava (243) (Table 1).

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Table 1. Summary of the haplotype and Rickettsia infection rates among the 7 ixodid tick species obtained in the Niigata Prefecture, Japan.

https://doi.org/10.1371/journal.pone.0298656.t001

We detected SFGR in 78 (12.44%) out of 627 ixodid ticks, with the highest detected in I. ovatus (46/346; 13.29%) and in H. flava (15/243; 6.17%) as summarized in Table 1. Out of the 46 Rickettsia-infected I. ovatus ticks, 25 displayed a 100% identity match with Rickettsia asiatica in the 17kDA, gltA, and rOmpB markers [6], while an additional 19 adult I. ovatus from the 2018 collection were also positive with R. asiatica. Two haplotypes were found in the rOmpB and 17kDA markers, respectively. One haplotype was found in only one individual (17369). Two out of the 15 Rickettsia-infected H. flava ticks were found to have the same haplotypes in the 17Kda, gltA,and rOmpA markers, and were identified as Rickettsia sp. (LC461063). The remaining 13 Rickettsia-infected H. flava ticks were identified as Rickettsia sp.

Based on the population genetic analysis of the cox1 sequences, we found a significant global FST of 0.4154 at p < 0.05 for I. ovatus (Table 1). In contrast, no significant global FST values were observed in the H. flava (Table 1). There were 59 and 66 cox1 haplotypes found among the 346 I. ovatus and 243 H. flava individuals, respectively.

The cox1 haplotype network of I. ovatus (Fig 1) revealed four genetic groups, wherein three genetic groups (1, 2, and 3) were distributed along different altitudinal gradients, as shown in Fig 2. These four genetic groups were concordant with the four clusters found in the I. ovatus phylogenetic tree (S1 Fig). The habitat distribution of genetic group 3 was limited to high altitude sites only (range = 16–912 m.a.s.l.), whereas genetic groups 1 (255–471 m.a.s.l.) and 2 (84–1354 m.a.s.l.) were distributed at lower altitudes (Fig 2). The Welch t-test revealed a significant difference between the mean altitudes of genetic groups 1 and 3 at p < 0.05 (Table 2); however, no significant difference was observed between genetic groups 1 and 2 or groups 2 and 3. We found a significant difference in the Rickettsia infection rates between I. ovatus genetic groups 1 and 2 based on the z-score test, but no significant difference between groups 1 and 3 or groups 2 and 3 (Table 2). The mean altitude between the Rickettsia-infected (= 273.72 m.a.s.l.) and non-infected I. ovatus (= 369.61 m.a.s.l.) revealed a significant difference based on the Welch t-test at p < 0.05 (Fig 3). The UPGMA dendrogram of I. ovatus revealed two genetic clusters, 1 and 2 using the genetic distance among the seven populations excluding one population due to the limited number of samples (Fig 4).

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Fig 1. Median-joining network of the 59 cox1 haplotype sequences of Rickettsia positive and negative I. ovatus.

Haplotype groups are indicated by numbers (1 to 4).

https://doi.org/10.1371/journal.pone.0298656.g001

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Fig 2. The influence of altitude on the habitat distribution of I. ovatus.

The points indicate the sampling sites of the collected individuals per I. ovatus haplotype group (n = 3) in the haplotype network (Fig 1). The map shows the elevation level across the sampling area, with Niigata Prefecture Japan depicted as a color gradation. Group 4, not shown in this figure, with haplotypes 54 and 55 was found on Sado island, which is encircled in black in box 3.

https://doi.org/10.1371/journal.pone.0298656.g002

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Fig 3. The relationship between the mean altitude of Rickettsia positive (n = 46) and negative (n = 300) I. ovatus.

Welch t-tests revealed a significant difference in the mean altitude between Rickettsia-infected and non-infected I. ovatus at p < 0.05. Populations with combined sites are labeled A to H.

https://doi.org/10.1371/journal.pone.0298656.g003

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Fig 4. An unweighted pair group method with the arithmetic mean (UPGMA) dendrogram of I. ovatus based on the pairwise genetic distance (FST) of cox1 among the 7 populations across Niigata Prefecture, Japan.

We excluded one population due to the limited number of samples.

https://doi.org/10.1371/journal.pone.0298656.g004

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Table 2. The differences in Rickettsia infection rates and mean altitude in I. ovatus haplotype groups and cluster dendrogram groups.

The table shows the distribution of Rickettsia-infected and uninfected I. ovatus and the mean altitude in each of the haplotype groups as shown in Fig 1 (I. ovatus haplotype network) and the cluster dendrogram in Fig 4 (I. ovatus cluster dendrogram). The z-score test showed a significant difference at p < 0.05 between the Rickettsia detection rates in haplotype groups 1 and 2 (indicated by ab) and in cluster dendrogram groups 1 and 2. The Welch t-test at p < 0.05 revealed a significant difference in the mean altitude of haplotype groups 1 and 3 and cluster dendrogram groups 1 and 2 indicated by ab. Haplotype group 4 was not included in the analysis due to its low sample size.

https://doi.org/10.1371/journal.pone.0298656.t002

The cox1 haplotype network of H. flava displayed two genetic groups (S2 Fig) consistent with the H. flava phylogenetic tree (S3 Fig). No significant difference between the Rickettsia infection rates of H. flava genetic groups 1 and 2 was observed using the z-score test at p < 0.05 (S3 Table).

Discussion

Our findings support our hypothesis that a genetically structured tick population, such as I. ovatus is associated with the Rickettsia infection rate to be spatially heterogenous due to limited gene flow along an altitudinal gradient. Our results were consistent with our previous study [11] which suggested that the low mobility of the host species for immature I. ovatus contributed to low gene flow in the tick populations. Despite the addition of new samples of I. ovatus and H. flava, we found a similar pattern of population genetic structure from the previous study of [11] thus supporting the robustness of their population genetic structure results. The low I. ovatus gene flow along the altitudinal gradient might have caused the spatial heterogeneity of Rickettsia infection rates among these populations, which is supported by the significant difference found in Rickettsia infection rates between genetic clusters 1 and 2. A similar pattern was observed in the studies of [49–51] which found that the infection rate of Borrelia burgdorferi, the causative agent of Lyme disease, decreased in ticks along the altitudinal gradient. Low gene flow can cause infected and uninfected ticks to have limited opportunities to traverse a wider spatial area thus causing a heterogeneous Rickettsia infection rate [52, 53].

We found two genetic groups in the H. flava haplotype network, but no significant difference in the Rickettsia infection rates between the two groups. These results might be due to the high gene flow observed in the H. flava populations, which enable Rickettsia-infected and uninfected H. flava individuals to traverse between the study sites. The high mobility of the large mammalian hosts used by adult H. flava and avian-mediated dispersal during their immature stage probably contributed to their homogenized population genetic structure [11], and the resulting homogenized Rickettsia infection rates. Large mammalian hosts and birds have a wide dispersal range that enables the broader movement of Rickettsia-infected ticks, as observed in previous studies of Amblyomma Americanum [13, 14, 54], H. flava [11], and I. ricinus [55]. Birds are especially good at dispersing over large areas since they can easily traverse landscape barriers such as mountains, fences, glaciers, and oceans that would be difficult for mammals to cross [56].

The different Rickettsia infection rates and altitudinal ranges between the I. ovatus phylogenetic groups maybe caused by diverse factors such as host availability and distribution, other environmental factors such as climate and vegetation, and anthropogenic factors such as urbanization. However, the adaptive evolutionary theory, which states that organisms adjust to new or severe changes in their environment to become better suited to their habitat [57, 58] maybe the best explanation for our results. Based on the relationship between the I. ovatus phylogenetic groups and their mean attitudes, I. ovatus might be undergoing local adaptation along the altitudinal gradient due to the higher genetic differentiation between populations as supported by the significant global FST (0.4154) found in I. ovatus. Based on isolation by environment (IBE), genetic differentiation will increase with increased environmental differences independent of geographic distances [33, 34, 59]. Thus in our study, the addition of I. ovatus together with the published data of [11] collected from an altitudinal gradient have shown genetic differences and different Rickettsia infection rate. When environmental conditions differ, the success of immigration in a new habitat is reduced, which may increase the genetic fixation rate due to a lower chance of outcrossing; thereby enhancing genetic isolation [60]. Thus, the lower gene flow along the altitudinal gradient reduced the spatial homogeneity of Rickettsia infection rates among the I. ovatus tick populations, thus causing the different Rickettsia infection rates obtained.

The occurrence of local adaptation in tick populations could affect the future of the tick-borne disease landscape. Environmental factors, such as precipitation, temperature, and altitude, have been shown to drive population differentiation in insects, such as Anopheles mosquitoes and Drosophila flies [61–63], but studies on the environmental adaptation of ixodid ticks, such as I. ovatus, and its Rickettsia infection susceptibility, have not yet been performed. [7] suggested that environmental conditions that affect bird hosts can also affect the local adaptation of ticks. Few studies have assessed such local adaptation in multiple organisms with varying dispersal abilities [64–68] and is an area in need of future research.

One of the limitations of this study is the use of one mitochondrial gene cox1 which limited us to compare our results with other target genes to highly support our findings. If markers with high mutation rates or many markers were used, it might have been possible to look at even finer population genetic structure and see differences in infection rates among the subdivided populations. Despite this, we were able to determine the relationship between the tick population genetic structure and Rickettsia infection rates as influenced by the altitudinal gradient. The mitochondrial cox1 gene has been widely used for population genetic analysis of many tick species and was proven to be informative in determining the relationship from the subfamily to the population levels [69–73]. Mitochondrial genes have a mutation rate that is useful in species-level phylogenetics and can be used for wide geographic ranges however its resolution is not fine enough to study species selection [10]. In future studies, we suggest including additional mitochondrial genes and or nuclear genes.

Since ticks are blood-sucking ectoparasites, they directly influence their mammalian hosts and the pathogens they transmit [74–76]. The interaction between the vector (tick), host, and pathogen (Rickettsia) is essential in understanding and predicting the risk and transmission of tick-borne diseases [77]. Understanding the genetic structure of ticks can serve as an alternative indicator to infer the potential spread of its pathogen [78]. Our study found relationships between (1) the population genetic structure of ticks and the corresponding Rickettsia infection rates, (2) altitude and the population genetic structure of ticks, and (3) altitude and Rickettsia infection rates. Though our results can provide a useful information about the tick distribution and possible potential spread of pathogens, there are some factors that should also be considered to apply our results such as ticks can have different mammalian hosts during different life stages in the field that have varying hosts mobility and other environmental factors can also affect such as temperature, humidity etc.. can also be a factor. We found that host mobility may influence the genetic structure of ixodid ticks. This information can be used to design more effective tick-borne disease control programs that focus on screening and detecting pathogens found in ticks and their mammalian hosts. For example, patterns of disease transmission from ticks with a high genetic divergence and less mobile hosts, such as I. ovatus, are likely due to the movement of infected hosts rather than infected ticks. Thus, screening prospective tick hosts for Rickettsia infection would be more suitable in this example. We suggest screening the hosts of immature I. ovatus, such as small rodents, instead of screening ticks. The Rickettsia infection rate in tick genetic groups can predict the spread of tick-borne diseases caused by Rickettsia, such as the Japanese spotted fever. We also found that altitude may influence the Rickettsia infection rate of I. ovatus genetic groups. This information can be used to determine the high-risk areas (e.g., lowland, mountains, etc.) of tick-borne diseases along an altitudinal gradient. Genetically structured arthropod vectors, such as ticks, can have different vector competencies, and environmental factors, such as altitudinal gradients, that can influence the vector’s ability to acquire, transmit, and maintain the pathogen infection [79].

Supporting information

S1 Table. Summary of Ixodes ovatus and Haemaphysalis flava collected from the different locations of Niigata Prefecture and its corresponding sample number and number of Rickettsia infection per site.

https://doi.org/10.1371/journal.pone.0298656.s001

(XLSX)

S2 Table. Summary of PCR primers used in the detection of Spotted fever group Rickettsia.

https://doi.org/10.1371/journal.pone.0298656.s002

(DOCX)

S3 Table. The difference in Rickettsia infection rates in H. flava haplotype groups and a table showing the distribution of Rickettsia-infected and uninfected H. flava in each genetic groups as shown in S2 Fig (H. flava median-joining network).

The results of the z-score test for two populations proportions at p < 0.05 showed no significant difference between the Rickettsia detection rate in haplotype groups 1 and 2. The Welch t-test at p < 0.05 revealed no significant difference in the mean altitude of the two groups.

https://doi.org/10.1371/journal.pone.0298656.s003

(XLSX)

S1 Fig. Phylogenetic tree from the BEAST analysis of 59 haplotype cox1 sequences of I. ovatus.

The blue-labeled haplotypes indicate the presence of Rickettsia infection. The red parentheses provide the number of Rickettsia-positive individuals per haplotype. The black labeled haplotypes are negative for Rickettsia infection.

https://doi.org/10.1371/journal.pone.0298656.s004

(DOCX)

S2 Fig. Median-joining network of the 66 cox1 haplotype sequences of Rickettsia positive and negative H. flava.

Haplotype groups are indicated as 1 and 2.

https://doi.org/10.1371/journal.pone.0298656.s005

(DOCX)

S3 Fig. Phylogenetic tree from the BEAST analysis of 66 haplotype cox1 sequences of H. flava.

The blue-labeled haplotypes indicate Rickettsia infection in individual samples. The parentheses in red provide the number of Rickettsia-infected ticks. The black-labeled haplotypes are negative for Rickettsia infection.

https://doi.org/10.1371/journal.pone.0298656.s006

(DOCX)

Acknowledgments

The authors are thankful for the assistance from the Niigata Prefectural Office during the tick sampling collection. We would also like to thank the alumni of MECOH Lab Ehime University for their help in molecular analyses: Masaya Doi, Kohki Tanaka, and Mizuki Ueda. We are also grateful to Micanaldo Francisco for the construction of the map for this manuscript. We would like to thank Enago (www.enago.jp) for the English language review and proofreading of the manuscript.

References

  1. 1. Neumann D. Eine hysterische Hausepidemie. Monatsschrift für Psychiatrie und Neurologie. 1899 Jun 1;5(6):405–10.
  2. 2. Miyamoto K., Nakao M., Fujita H., Sato F., 1993. The ixodid ticks on migratory birds in Japan and the isolation of Lyme disease spirochetes from bird-feeding ticks. Med. Entomol. and zool., 44(4), 315–326.
  3. 3. Neumann H. Zur Behandlung der Pleuritis exsudativa. DMW-Deutsche Medizinische Wochenschrift. 1897 Jan;23(05):73–5.
  4. 4. Yu XJ, Liang MF, Zhang SY, Liu Y, Li JD, Sun YL, et al. Fever with thrombocytopenia associated with a novel bunyavirus in China. N. Engl. J. Med. 2011 Apr 21;364(16):1523–32. pmid:21410387
  5. 5. Yamaji K, Aonuma H, Kanuka H. Distribution of tick-borne diseases in Japan: Past patterns and implications for the future. J. Infect. Chemother.2018 Jul 1;24(7):499–504. pmid:29685854
  6. 6. Arai R, Sato M, Kato M, Aoki J, Nishida A, Watanabe K, et al. Spotted fever group rickettsiae (SFGR) detection in ticks following reported human case of Japanese spotted fever in Niigata Prefecture, Japan. Sci. Rep. 2021 Jan 28;11(1):2595. pmid:33510192
  7. 7. McCoy KD, Boulinier T, Tirard C, Michalakis Y. Host specificity of a generalist parasite: genetic evidence of sympatric host races in the seabird tick Ixodes uriae. J. Evol. Biol. 2001 May 9;14(3):395–405.
  8. 8. Oliver JD, Bennett SW, Beati L, Bartholomay LC. Range expansion and increasing Borrelia burgdorferi infection of the tick Ixodes scapularis (Acari: Ixodidae) in Iowa, 1990–2013. J. Med. Entomol. 2017 Nov 7;54(6):1727–34. pmid:28633503
  9. 9. Agany DD, Pietri JE, Gnimpieba EZ. Assessment of vector-host-pathogen relationships using data mining and machine learning. Comput. Struct. Biotechnol. J. 2020 Jan 1;18:1704–21. pmid:32670510
  10. 10. Araya-Anchetta A, Busch JD, Scoles GA, Wagner DM. Thirty years of tick population genetics: a comprehensive review. Infect. Genet. Evol. 2015 Jan 1;29:164–79. pmid:25461844
  11. 11. Regilme MA, Sato M, Tamura T, Arai R, Sato MO, Ikeda S, et al. Comparative population genetic structure of two ixodid tick species (Acari: Ixodidae)(Ixodes ovatus and Haemaphysalis flava) in Niigata prefecture, Japan. Infect. Genet. Evol. 2021 Oct 1;94:104999. pmid:34256167
  12. 12. Estrada-Peña AJ. Ticks as vectors: taxonomy, biology and ecology. Rev. Sci. Tech. 2015 Apr 1;34(1):53–65. pmid:26470449
  13. 13. Mixson TR, Lydy SL, Dasch GA, Real LA. Inferring the population structure and demographic history of the tick, Amblyomma americanum Linnaeus. J. Vector Ecol. 2006 Jun;31(1):181–92. https://doi.org/10.3376/1081-1710(2006)31[181:itpsad]2.0.co; 2.
  14. 14. Trout RT, Steelman CD, Szalanski AL. Population genetics of Amblyomma americanum (Acari: Ixodidae) collected from Arkansas. J. Med. Entomol. 2014 Dec 1;47(2):152–61. pmid:20380295
  15. 15. Guglielmone AA, Nava S, Mastropaolo M, Mangold AJ. Distribution and genetic variation of Amblyomma triste (Acari: Ixodidae) in Argentina. Ticks Tick Borne Dis. 2013 Sep 1;4(5):386–90. pmid:23647652
  16. 16. Lampo M, Rangel Y, Matat A. Population genetic structure of a three-host population genetic in Amblyommma dissimile. J. Parasitol. 2015;84:1137–42.
  17. 17. Dougherty ER, Seidel DP, Carlson CJ, Spiegel O, Getz WM. Going through the motions: incorporating movement analyses into disease research. Ecol. Lett. 2018 Apr;21(4):588–604. pmid:29446237
  18. 18. Regoes RR, Nowak MA, Bonhoeffer S. Evolution of virulence in a heterogeneous host population. Evol. 2000 Feb;54(1):64–71. pmid:10937184
  19. 19. Boots M, Mealor M. Local interactions select for lower pathogen infectivity. Sci. 2007 Mar 2;315(5816):1284–6. pmid:17332415
  20. 20. Lion S, Gandon S. Evolution of spatially structured host–parasite interactions. Journal of Evol. Biol. 2015 Jan;28(1):10–28. pmid:25439133
  21. 21. Chin KM, Wolfe MS. Selection on Erysiphe graminis in pure and mixed stands of barley. Plant Pathol. 1984;33:535–546.
  22. 22. Thrall PH, Burdon JJ. Evolution of virulence in a plant host-pathogen metapopulation. Science. 2003;299:1735–1737. pmid:12637745
  23. 23. White PS, Choi A, Pandey R, Menezes A, Penley M, Gibson AK, et al. Host heterogeneity mitigates virulence evolution. Biol Lett. 2020;16:20190744. pmid:31992149
  24. 24. Gibson AK, Baffoe‐Bonnie H, Penley MJ, Lin J, Owens R, Khalid A, et al. The evolution of parasite host range in heterogeneous host populations. J Evol Biol. 2020;33:773–782. pmid:32086852
  25. 25. Bono LM, Gensel CL, Pfennig DW, Burch CL. Competition and the origins of novelty: experimental evolution of niche-width expansion in a virus. Biol Lett. 2013;9:20120616. pmid:23075527
  26. 26. Streicker DG, Winternitz JC, Satterfield DA, Condori-Condori RE, Broos A, Tello C, et al. Host–pathogen evolutionary signatures reveal dynamics and future invasions of vampire bat rabies. Proc Natl Acad Sci U S A. 2016;113:10926–10931. pmid:27621441
  27. 27. Fountain‐Jones NM, Craft ME, Funk WC, Kozakiewicz C, Trumbo DR, Boydston EE, et al. Urban landscapes can change virus gene flow and evolution in a fragmentation‐sensitive carnivore. Mol Ecol. 2017;26:6487–6498. pmid:28987024
  28. 28. Daniel M, Malý M, Danielová V, Kříž B, Nuttall P. Abiotic predictors and annual seasonal dynamics of Ixodes ricinus, the major disease vector of Central Europe. Parasit Vectors. 2015;8:1–12. pmid:26385470
  29. 29. Eisen RJ, Eisen L, Beard CB. County-scale distribution of Ixodes scapularis and Ixodes pacificus (Acari: Ixodidae) in the continental United States. J Med Entomol. 2016;53:349–386. pmid:26783367
  30. 30. Chapman MA, Hiscock SJ, Filatov DA. Genomic divergence during speciation driven by adaptation to altitude. Mol Biol Evol. 2013;30:2553–2567. pmid:24077768
  31. 31. Osborne OG, Batstone TE, Hiscock SJ, Filatov DA. Rapid speciation with gene flow following the formation of Mt. Etna. Genome Biol Evol. 2013;5:1704–1715. pmid:23973865
  32. 32. Polato NR, Gray MM, Gill BA, Becker CG, Casner KL, Flecker AS, et al. Genetic diversity and gene flow decline with elevation in montane mayflies. Heredity. 2017 Aug;119(2):107–116. pmid:28489073
  33. 33. Wang J., Summers K., 2010. Genetic structure is correlated with phenotypic divergence rather than geographic isolation in the highly polymorphic strawberry poison-dart frog. Mol. Ecol., 19, 447–458. pmid:20025652
  34. 34. Bradburd G.S., Ralph P.L., Coop G.M., 2013. Disentangling the effects of geographic and ecological isolation on genetic differentiation. Evolution, 67, 3258–3273. pmid:24102455
  35. 35. Sexton J.P., Hangartner S.B., Hoffmann A.A., 2014. Genetic isolation by environment or distance: which pattern of gene flow is most common?. Evol., 68, 1–15. pmid:24111567
  36. 36. Yamaguti N., Tipton V. J., Keegan H. L., & Toshioka S. (1971). Ticks of Japan, Korea, and the Ryukyu islands. Brigham Young University Science Bulletin, Biological Series, 15, 1.
  37. 37. Ishikura M. et al. Phylogenetic analysis of spotted fever group rickettsiae based on gltA, 17-kDa, and rOmpA genes amplified by nested PCR from ticks in Japan. Microbiol. Immunol. 47, 823–832 (2003). pmid:14638993
  38. 38. Regnery R. L., Spruill C. L. & Plikaytis B. D. Genotypic identification of rickettsiae and estimation of intraspecies sequence divergence for portions of two rickettsial genes. J. Bacteriol. 173, 1576–1589 (1991). pmid:1671856
  39. 39. Fournier P.-E., Roux V. & Raoult D. Phylogenetic analysis of spotted fever group rickettsiae by study of the outer surface protein rOmpA. Int. J. Syst. Bacteriol. 48, 839–849 (1998). pmid:9734038
  40. 40. Paddock C. D. et al. Rickettsia parkeri: A newly recognized cause of spotted fever rickettsiosis in the United States. Clin. Infect. Dis. 38, 805–811 (2004). pmid:14999622
  41. 41. Choi Y.-J. et al. Spotted fever group and typhus group rickettsioses in humans, South Korea. Emerg. Infect. Dis. 11, 237–244 (2005). pmid:15752441
  42. 42. Vrijenhoek R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol Mar Biol Biotechnol. 1994 Oct 1;3(5):294–9. pmid:7881515.
  43. 43. Maddison W.P., Maddison D.R., 2011. Mesquite: A modular system for evolutionary analysis. Available at: http://mesquiteproject.org.
  44. 44. Rozas J, Ferrer-Mata A, Sánchez-DelBarrio JC, Guirao-Rico S, Librado P, Ramos-Onsins SE, et al. DnaSP 6: DNA sequence polymorphism analysis of large datasets. Mol Biol Evol. 2017 Dec 1;34(12):3299–3302. pmid:29029172
  45. 45. Excoffier L., & Lischer H. E. L. (2010). Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources, 10(3), 564–567. pmid:21565059
  46. 46. Paradis E., Schliep K., 2018. APE 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 35, 526–528. pmid:30016406
  47. 47. Bandelt H.J., Forster P., Röhl A., 1999. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. pmid:10331250
  48. 48. Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007 Nov 8;7:214. pmid:17996036
  49. 49. Burri C., Cadenas F.M., Douet V., Moret J., Gern L., 2007. Ixodes ricinus density and infection prevalence of Borrelia burgdorferi sensu lato along a north-facing altitudinal gradient in the Rhône Valley (Switzerland). Vector-Borne and Zoonotic Diseases, 7, 50–58. pmid:17417957
  50. 50. Jouda F., Perret J.L., Gern L., 2004. Ixodes ricinus density, and distribution and prevalence of Borrelia burgdorferi sensu lato infection along an altitudinal gradient. J. Med. Entomol. 41, 162–169. pmid:15061274
  51. 51. Moran-Cadenas F, Schneider H, Lommano E, Burri C, Moret J, Gern L. A comparison of two DNA extraction approaches in the detection of Borrelia burgdorferi sensu lato from live Ixodes ricinus ticks by PCR and reverse line blotting. Vector Borne Zoonotic Dis. 2007 Dec 1;7(4):555–62. pmid:17979538
  52. 52. Gooding R.H. Genetic variation in arthropod vectors of disease-causing organisms: Obstacles and opportunities. Clin. Microbiol. Rev. 1996; 9: 301. pmid:8809462
  53. 53. Páez-Triana L., Muñoz M., Herrera G., Moreno-Pérez D.A., Tafur-Gómez G.A., Montenegro D., et al., 2021. Genetic diversity and population structure of Rhipicephalus sanguineus sensu lato across different regions of Colombia. Parasit Vectors, 14, 1–11. pmid:34425895
  54. 54. Reichard M.V., Kocan A.A., Van Den Bussche A., Barker R.W., Wyckoff J., Ewing S.A., 2005. Sequence variation of the ribosomal DNA second internal transcribed spacer region in two spatially distinct populations of Amblyomma americanum (L.)(Acari: Ixodidae). J. Parasitol. 91, 260–264. pmid:15986597
  55. 55. Casati S., Bernasconi M.V., Gern L., Piffaretti J.C., 2008. Assessment of intraspecific mtDNA variability of European Ixodes ricinus sensu stricto (Acari: Ixodidae). Infect. Genet. Evol. 8, 152–158. pmid:18206426
  56. 56. Hasle G., 2013. Transport of ixodid ticks and tick-borne pathogens by migratory birds. Front. Cell. Infect. Microbiol., 3, 48. pmid:24058903
  57. 57. Kawecki TJ, Ebert D. Conceptual issues in local adaptation. Ecol Lett. 2004 Dec;7(12):1225–1241.
  58. 58. Gregory TR. Understanding natural selection: essential concepts and common misconceptions. Evol Educ Outreach. 2009 Jun;2(2):156–175.
  59. 59. Wang IJ, Bradburd GS. Isolation by environment. Mol. Ecol. 2014 Dec;23(23):5649–5662. pmid:25256562
  60. 60. Jiang S, Luo MX, Gao RH, Zhang W, Yang YZ, Li YJ, et al. Isolation-by-environment as a driver of genetic differentiation among populations of the only broad-leaved evergreen shrub Ammopiptanthus mongolicus in Asian temperate deserts. Sci Rep. 2019 Jul 30;9(1):1–14. pmid:31427616
  61. 61. Cheng C., White B.J., Kamdem C., Mockaitis K., Costantini C., Hahn M.W., et al., 2012. Ecological genomics of Anopheles gambiae along a latitudinal cline: a population-resequencing approach. Genetics, 190(4), 1417–1432. pmid:22491888
  62. 62. Kapun M., Barrón M.G., Staubach F., Obbard D.J., Wiberg R.A.W., Vieira J., et al. (2020). Genomic analysis of European Drosophila melanogaster populations reveals longitudinal structure, continent-wide selection, and previously unknown DNA viruses. Mol. Biol. Evol., 37, 2661–2678. pmid:32413142
  63. 63. Love RR, Steele AM, Coulibaly MB, Traore SF, Emrich SJ, Fontaine MC, et al. Chromosomal inversions and ecotypic differentiation in Anopheles gambiae: the perspective from whole-genome sequencing. Mol Ecol. 2016 Dec;25(23):5889–906. pmid:27759895
  64. 64. Exposito-Alonso M, Vasseur F, Ding W, Wang G, Burbano HA, Weigel D. Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana. Nat Ecol Evol. 2018 Feb;2(2):352–8., pmid:29255303
  65. 65. Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB, Sperone FG, et al. Adaptation to climate across the Arabidopsis thaliana genome. Science. 2011 Oct 7;334(6052):83–6. pmid:21980108
  66. 66. Zhen Y, Harrigan RJ, Ruegg KC, Anderson EC, Ng TC, Lao S, et al. Genomic divergence across ecological gradients in the Central African rainforest songbird (Andropadus virens). Mol. Ecol. 2017 Oct;26(19):4966–77. pmid:28752944
  67. 67. Ahrens CW, Byrne M, Rymer PD. Standing genomic variation within coding and regulatory regions contributes to the adaptive capacity to climate in a foundation tree species. Mol Ecol. 2019 May;28(10):2502–16. pmid:30950536
  68. 68. Miller AD, Hoffmann AA, Tan MH, Young M, Ahrens C, Cocomazzo M, et al. Local and regional scale habitat heterogeneity contribute to genetic adaptation in a commercially important marine mollusc (Haliotis rubra) from southeastern Australia. Mol. Ecol. 2019 Jun;28(12):3053–72. pmid:31077479
  69. 69. Crosbie PR, Boyce WM, Rodwell TC. DNA sequence variation in Dermacentor hunteri and estimated phylogenies of Dermacentor spp.(Acari: Ixodidae) in the New World. J. Med. Entomol. 1998 May 1;35(3):277–88. pmid:9615547
  70. 70. Norris DE, Klompen JS, Keirans JE, Black IV WC. Population genetics of Ixodes scapularis (Acari: Ixodidae) based on mitochondrial 16S and 12S genes. J. Med. Entomol.1996 Jan 1;33(1):78–89. pmid:8906909
  71. 71. Qiu WG, Dykhuizen DE, Acosta MS, Luft BJ. Geographic uniformity of the Lyme disease spirochete (Borrelia burgdorferi) and its shared history with tick vector (Ixodes scapularis) in the northeastern United States. Genetics. 2002 Mar 1;160(3):833–49. pmid:11901105
  72. 72. Rich SM, Caporale DA, Telford 3rd SR, Kocher TD, Hartl DL, Spielman A. Distribution of the Ixodes ricinus-like ticks of eastern North America. Proceedings of the National Academy of Sciences. 1995 Jul 3;92(14):6284–8. pmid:7603983
  73. 73. de la Fuente J, Almazán C, Van Den Bussche RA, Bowman J, Yoshioka JH, Kocan KM. Characterization of genetic diversity in Dermacentor andersoni (Acari: Ixodidae) with body size and weight polymorphism. Exp. Parasitol. 2005 Jan 1;109(1):16–26. pmid:15639135
  74. 74. Anderson JF, Magnarelli LA. Biology of ticks. Infectious disease clinics of North America. 2008 Jun 1;22(2):195–215. pmid:18452797
  75. 75. Hill CA, Wikel SK. The Ixodes scapularis Genome Project: an opportunity for advancing tick research. Trends Parasitol. 2005 Apr 1;21(4):151–3. pmid:15780833
  76. 76. Van Zee JP, Geraci NS, Guerrero FD, Wikel SK, Stuart JJ, Nene VM, et al. Tick genomics: the Ixodes genome project and beyond. Int. J. Parasitol. 2007 Oct 1;37(12):1297–305. pmid:17624352
  77. 77. Jongejan F, Nene V, de la Fuente J, Pain A, Willadsen P. Advances in the genomics of ticks and tick-borne pathogens. Trends Parasitol. 2007 Sep 1;23(9):391–6. pmid:17656151
  78. 78. Gray JS. Biology of Ixodes species ticks in relation to tick-borne zoonoses. Wiener Klinische Wochenschrift. 2002 Jul 1;114(13–14):473–8. pmid:12422585
  79. 79. Parola P, Paddock CD. Travel and tick-borne diseases: Lyme disease and beyond. Travel Med Infect Dis. 2018;26:1–2. pmid:30267770