Coinfections reported in the literature in the last 4 years.
Abstract
Ticks and the pathogens they transmit constitute a growing burden for human and animal health worldwide. In the last years, high-throughput detection and sequencing technologies (HTT) have revealed that individual ticks carry a high diversity of microorganisms, including pathogenic and non-pathogenic bacteria. Despite several studies have contributed to the availability of a catalog of microorganisms associated to different tick species, major limitations and challenges remain ahead HTT studies to acquire further insights on the microbial complexity associated to ticks. Currently, using next generation sequencing (NGS), bacteria genera (or higher taxonomic levels) can be recorded; however, species identification remains problematic which in turn affects pathogen detection using NGS. Microfluidic PCR, a high-throughput detection technology, can detect up to 96 different pathogen species, and its combination with NGS might render interesting insights into pathogen-microbiota co-occurrence patterns. Microfluidic PCR, however, is also limited because detection of pathogen strains has not been implemented, and therefore, putative associations among bacterial genotypes are currently unknown. Combining NGS and microfluidic PCR data may prove challenging. Here, we review the impact of some HTT applied to tick microbiology research and propose network analysis as an integrative data analysis benchmark to unravel the structure and significance of microbial communities associated to ticks in different ecosystems.
Keywords
- high-throughput technologies
- network analysis
- ticks
- tick-borne pathogens
- microbiota
1. Introduction
Ticks are hematophagous ectoparasites of vertebrates that derive nutrition through blood feeding and are efficient vectors of major pathogens. Feeding habits and the process of blood digestion in ticks greatly differ from that in hematophagous insects (e.g. mosquitoes) and may influence pathogen acquisition and transmission. In ticks, digestion is a slow intracellular process [1, 2]. Argasidae, or “soft ticks,” feed quickly and several times during their lifetime (approximately 40–60 minutes per feeding in most species). In adult soft ticks, full digestion only proceeds once mating occurs. In contrast to soft ticks, Ixodidae, or “hard ticks,” feed for longer periods of time. Adult virgin females of Ixodidae Metastriate ticks attach to the host and take only a small quantity of blood before mating [3]. Mating induces females to fast feeding, increasing their weight approximately 100 times within few days [3]. Thus, feeding times in female hard ticks can last from few days to weeks depending on the stage and the availability of males. After hatching from the eggs, the three following developmental stages (i.e. larvae, nymphs and adults) of Prostriate
Despite tick biology favors the acquisition and transmission of a great diversity of pathogens, most studies on TBPs prevalence in ticks focused in single infections. This was probably influenced by technical limitations to detect multiple pathogens and, possibly, by the fact that initial discoveries on the role of ticks as vectors linked “one-pathogen” to “one-tick-species.” After the first demonstration of pathogen transmission by ticks, when Smith and Kilbourne [12] demonstrated that
Pathogen coinfection in ticks can be studied by standard PCR using primers that detect known pathogens suspected to occur in a given tick species of a particular geographic region. This approach is the most frequently used; however, it is strongly biased and makes pathogen detection to be strongly influenced by particular research interests [5]. This may be the reason why one of the most studied coinfection is that between two of the most prominent TBPs,
A major challenge of high-throughput data is data analysis, and therefore, integrative analytical tools are needed to improve our current understanding of tick-pathogen-microbiota interactions. Network analysis, a branch of graph theory, is a mathematical tool for the analysis of complex systems composed of many components which may interact with each other. Network analysis has been used to unravel complex microbial communities such as those present in soil [33], water [34] and human [35, 36] and tick microbiota [37]. This chapter focuses on the impact of high-throughput technologies in the current understanding of the microbial complexity associated to ticks. In addition, we propose to combine high-throughput data with network analysis to gain new insights into the structure of microbial communities associated to ticks and their impact on pathogen circulation. Throughout this review, we will use the term “microbiota” as “the microbial taxa associated with a given host” and “microbiome” as “the catalog of these microbes and their genes.” A distinction can be established between these terms, while the microbiome includes information about the microbiota composition, the latest term does not necessarily includes information about gene composition.
2. New technologies and the microbial universe of ticks
2.1 Microfluidic PCR
2.1.1 General background on the technology
Frequently, studies on TBPs prevalence in ticks focused mainly on bacteria and parasites and only few species or genera are targeted in each study. Detection assays (e.g. PCR, nested PCR or real-time PCR) are designed to detect a restricted number of pathogens that are known or suspected to be transmitted by particular tick species collected at a particular location. In addition to the “
A brief workflow of the microfluidic PCR is provided Figure 1. Firstly, ticks are homogenized in cell culture medium (i.e. D-MEM) completed with 10% of fetal calf serum to preserve viral particles and separated into three aliquots: one dedicated to total DNA extraction, one to total RNA extraction and one conserved at −80°C for back-up. Secondly, RNAs are reverse transcribed into cDNA using random primers (only 1 μL of RNA is used per reaction), and then cDNA and DNA are preamplified with a pool of primers/probe targeting TBPs to increase the signal of TBPs relative to the signal of tick RNA/DNAs. Remarkably, only 1.25 μL per sample are needed to test all the pathogens simultaneously. Two different chips were run in the BioMark™ dynamic array system: one to detect RNA viruses using the preamplified cDNAs and the other to detect DNAs from bacteria/parasites using the preamplified DNAs. In the chip, samples and primers/probes are added into the right and left wells, respectively. Pressure and oil allow the distribution of each sample and primers/probe sets into the microfluidic PCR chambers in the middle of the chip. Each sample will be mixed with all the primers/probes sets and each primers/probe set will be mixed with all samples, allowing 2304 individual real-time PCRs at a final volume of six nanoliters per reaction. For further details, we refer the reader to [18].
2.1.2 Tick-borne pathogen coinfections revealed by microfluidic PCR
The first application of microfluidic PCR targeted 37 pathogens including
This new high-throughput technology has been used mainly during epidemiological studies of TBPs in specific countries with different tick species screened as
Moreover, these high-throughput screenings of TBPs in individual ticks have highlighted the co-occurrence of several pathogens in one tick, known as tick coinfections. Before the use of this novel technique, tick coinfections were evaluated by classical PCR, nested PCR or real-time PCR, and related publications focused in few pathogens, less than 10 different genera screened per publication [43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]. After the year 2016, two publications have demonstrated the presence of up to five and four different pathogen species in
2.1.3 Challenges and perspectives
Unfortunately, only few publications are available regarding coinfection by bacteria and parasites or bacteria and viruses or parasites and viruses in ticks [49, 50, 52, 54, 60]. To solve this gap of information regarding inter-taxa coinfections, a system to detect simultaneously bacteria, parasites and viruses will be, without any doubt, an improvement of available tools. Nevertheless, even if this high-throughput system allows a rapid detection of numerous pathogens present in a high number of samples, confirmation of doubtful results or presence of unexpected pathogens should be confirmed by classical or nested PCR. Knowing the fact that for each pathogen different genotypes/strains could exist, this confirmation step could allow us to sequence different genes per pathogen leading to a better characterization of the epidemiological history of TBPs present in the targeted region/ecosystem.
High-throughput identification of pathogen strains would be also a significant improvement to current microfluidic PCR protocols. Genetic diversity of bacteria species resulting in novel strains can be associated to changes in pathogenicity, virulence and host specificity. A classic example of this is that different strains of the bacterium
An additional challenge to high-throughput detection is how to detect novel strains or species. The emergence of novel pathogens is a dynamic process. For example, a novel species of
Finally, high-throughput quantification of TBPs in tick organs could be a useful approach to assess some components of tick vector competence, for example, vector colonization by pathogens. It is known that the simple detection of pathogen DNA in a tick does not demonstrate the vector competence of this tick species for this pathogen. Vector competence depends effectively on genetic factors determining the ability of a vector to transmit a pathogen and has to be demonstrated under controlled conditions [10]. A typical TBP colonizes tick midgut and migrates to salivary glands to be transmitted with tick saliva to the host. The detection and quantification of the pathogen in different organs including midgut and salivary glands could be a step forward from pathogen detection to tick vector competence assessment. As an example, Berggoetz et al. [73] detected different pathogens (i.e.
2.2 Next-generation sequencing
2.2.1 General background on the technology
During the past decade, NGS technologies have provided new insights into microbial community dynamics and ecology. These tools allow high-throughput analysis of complex and diverse microbial communities in multiple ecosystems such as soils and aquatic systems or in the microbiota of host organisms such as plant, animals and humans. With the development of these new sequencing approaches, it has definitively become faster and more economical to comprehensively evaluate the complexity of microbial species and strains in various ecosystems. Three main sequencing strategies are commonly used to study microbial communities: (i) marker gene approaches (i.e. SSU rRNA genes) with amplicon sequencing to identify microbiota composition (the 16S rRNA gene being the most used), (ii) shotgun metagenomics to characterize the functional potential of the microbiome and (iii) shotgun metatranscriptomics to determine actively expressed genes [76]. For further details on these different sequencing approaches, the reader is referred to [77, 78].
2.2.2 Tick microbial communities revealed by NGS
While ticks are known to be one of the main vectors of various pathogenic agents [4, 9, 10, 20, 73, 79, 80], it is now recognized that TBPs in ticks coexist with microorganisms considered non-pathogenic for humans. Studies using NGS have shown that specific TBPs are frequently found together with other pathogens, symbionts and commensals [81]. This tick microbial complex, recently named “pathobiome” [82, 83], is influenced by the environment, and the interactions between its different components might influence pathogen acquisition by ticks and transmission to the host. In this context, the identification and characterization of tick microbiota has become essential to understand tick-pathogen interactions [84, 85]. While at the beginning of the twenty-first century, some studies started to characterize microbial communities associated to ticks using fingerprinting approaches (e.g. [86, 87]), the development of NGS technologies allowed higher resolution in the identification of tick microbiota bacteria and revealed an unexpected microbial diversity in these arthropods [88, 89, 90]. The general workflow commonly used to study tick microbiota using NGS is presented in Figure 1.
Since the first study using NGS to describe the bacterial diversity in the cattle tick
2.2.3 Challenges and perspectives
NGS methods have improved increasing in sequencing depth (i.e. a higher number of sequences obtained per sample) and thus a better estimation of the microbial diversity. However, the read length of the most widely used sequencing platforms today is very short (few hundreds base pairs) and requires the researchers to choose a region of the 16S rRNA gene to sequence. For NGS purposes, the 16S rRNA gene is divided into nine regions (i.e. V1–V9). Most of the previous studies that used the 454 pyrosequencing approach amplified the V1–V3 region (Table A2). Studies that used the MiSeq approach mainly amplify the second part of the 16S rRNA gene with the V3–V4/V3–V5 or V5–V6 regions (Table A2). In this context, many bacteria genera may share the same amplified region, and the taxonomic resolution of profiling is inherently limited with incomplete information on tick microbial composition at the species level. There is a need for a simple 16S rRNA gene-based profiling approach that avoid the short read length to provide a much larger coverage of the gene to obtain higher taxonomic resolution in tick microbiota identification. The limitation of 16S rRNA gene sequencing (DNA-based) for microbial community analyses is the inability to differentiate between active and non-active cells. In comparison, 16S rRNA sequencing (RNA-based) can target metabolically active cells which produce rRNA. It is thus essential to include RNA and metatranscriptomic approaches to characterize the tick microbiota [92, 93, 94]. In addition, limitations linked to the 16S rRNA gene sequencing include polymerase chain reaction (PCR) bias, resulting, as previously mentioned, in low taxonomic resolution (typically genus-level) and limited functional insight into the microorganisms. These limitations hamper our ability to investigate how the non-pathogenic members of the tick microbiota interact with the pathogens and influence their presence and transmission. One way to avoid these biases is to use whole genome sequencing (WGS) to sequence thousands of genes from hundreds of microorganisms in a given sample. By gaining access and annotating the whole genome, it would become possible to reconstruct the putative metabolism of individual microbial species and gain insight into their potential role in tick-borne pathogens and diseases.
Using NGS techniques, many studies described tick microbial community composition and diversity and reported lists of microorganisms associated to several tick species. However, as underlined by Shade [95], diversity and composition without context provide limited insights into the mechanisms underpinning community patterns. Measurement of microbial diversity should be the starting point for further inquiry of ecological mechanisms rather than the “answer” to community outcomes [95]. Studying microbial communities associated to ticks needs thus contextual data, and it appears crucial to know the dynamics in space and time of these communities and the influence of environmental factors on their dynamics. In addition to factors associated with tick biology, the composition of tick microbial communities can be highly variable due to environmental factors such as biogeography, temperature, light-dark cycles, hygrometry, and vegetation [87, 88, 89, 96, 97]. Future studies on tick microbiota will have to consider these different variables and define more deeply their role in the dynamics of microbial communities associated to ticks. Biotic interactions are also important drivers of diversity, and the nature and strength of interactions can result in complex multimember interactions. Considering the pathobiome concept, one additional challenge for the understanding and control of tick-borne diseases is to increase the measurements of microbial diversity and calls for identifying potential associations/interactions between pathogens and other tick microbes. Finally, after identifying the tick microbiota including symbionts, it becomes crucial to determine the relationships between ticks and these bacteria. Ticks are strict hematophagous arthropods, and this specific diet is limited in B vitamins. Duron et al. [98] have recently demonstrated that the exploit of this unbalanced diet is possible because an intracellular bacterial symbiont of the genus
3. Network analysis
3.1 General background on network analysis methodology
Networks are formed by components, known as nodes, and the relationships between these components are named links (Figure 4). The network may be undirected (there is not directionality in the link) or directed (there is directionality in the link). In microbial networks, each node represents a species and each link, representing co-occurring bacteria, resulting in undirected networks. Directed networks would be those resulting from, for example, parasites “on” vectors or microbes “in” a reservoir. The complete set of records can be then weighted according to the number of times one node is linked to another node (Figure 4). Several indices can be used to measure network properties from which the relationships among the co-occurring bacteria are derived. The degree centrality (DC, i.e. number of links connecting a given node to other nodes) is the most basic measure of a network and is calculated after weighting the total number of records containing this interaction. The DC provides an estimation of the strength of the association but does not evaluate the importance of each node in the context of the network. The node betweenness centrality (NBC) indicates how often a node is found on the shortest path between two nodes in the network [99, 100]. The implicit meaning of the NBC in microbial networks is the importance of a node in the flow of other components of the network and is considered a basic index defining the relative importance of a node in an ecological network. The PageRank (PR) is an index of centrality that assigns a universal rank to nodes based on the importance of the other nodes to which it is linked. Therefore, the NBC and PR are complementary measures for capturing the importance of each node in the linkage of other nodes throughout the network. These three indexes capture the ecological relationships between the interacting partners.
Real-world networks have been shown to separate into logical clusters in which nodes are tightly connected to each other but only loosely connected to nodes outside of their module [101]. They thus represent sets of organisms that interact more among them than with the others. This modularity separates the complete network into compartments that can be observed as naturally segregated niches in which a subset of taxa has a statistically higher affinity among them than with other species in the network.
3.2 Network analysis to disentangle the microbial complexity associated with ticks
The important value of the tick microbiota is the ecological interpretation of the associations or co-occurrence rates of the microorganisms detected in a collection of ticks. Whether these ticks were collected in different ecosystems, or associated to different hosts, or surveyed at different time intervals, the most important purpose is capturing the ecological meaning of these associations among the detected bacteria. Therefore, it is necessary to determine the relationships among the microorganisms, identify ‘dominant’ taxa in the microbiota and to study how they interact.
It is logical to assume that microorganisms that co-occur in the network are those that “overlap in the habitat” provided by the carrier of a given microbiota. This high co-occurrence likely ensures cohesiveness and persistence of the network improving the circulation of the microorganisms. Most important, a phylogenetic tree of the detected bacteria can be built, and the indexes of centrality can be tracked over the branches of the resulting tree (Figure 4). This is commonly known as “tracking the phylogenetic signal of quantitative traits” [102]. A common empirical observation for organisms is that continuous traits (i.e. morphological features, or the occupancy of ranges of the variables shaping its environmental niche) of closely related species in a phylogeny are often similar, meaning that these traits are under selection pressure. The link between phylogeny and continuous trait values is commonly referred in the literature as the phylogenetic signal. Therefore, it is possible to test the phylogenetic signal of the network indexes, which are actually quantitative traits, over the branches of the tree. Several indexes and dedicated computer packages are available to measure the phylogenetic signal [102]. Tracking these indexes on the phylogenetic tree explains the relative importance of the taxa of the microbiota and how it is organized in a population of ticks. The phylogenetic distance of the microorganisms detected in ticks can be calculated. This could be used to evaluate the phylogenetic diversity carried by ticks according to the habitat, the season of the year or the environmental conditions driving the tick phenology and survival. It is necessary to stress that an index of phylogenetic distance, together with the centrality indexes of the realized network, provides ecological or possibly physiological information of the microbiota composition. This cannot be achieved by listing bacterial taxa.
Most of the guidelines expressed above have been addressed in a recent study on the microbiota of
The current impossibility to obtain germ-free ticks is a gap in this field of study. Colonization of ticks with single species of bacteria could help to understand the contribution of individual bacteria to tick physiology. However, accumulating evidence demonstrated that most of these bacteria are fundamental for tick physiological processes and survival in the environment. Therefore, the information about the ecological and physiological relationships between the tick and the microbiome must be obtained from field surveys and subjected to big data analysis as proposed before. We firmly believe that the next step forward in the field of tick microbiome must be a change of paradigm from ‘taxonomical listing’ to the functional characterization of tick microbiome in the environment. Classic statistics can be of little help in such task.
4. Conclusions
High-throughput technologies have improved our current understanding of the microbial complexity associated to ticks. These technologies allowed us to move from the “one-tick-one-pathogen” paradigm to the “one-tick-many-microorganisms” paradigm. This new concept can be summarized: ticks are associated with complex microbial communities, including pathogenic and non-pathogenic microorganisms, which interact between them and with the vector and are together under the influence of the environment. Future developments may be related with the characterization of tick microbiome at the species level and with inclusion of strain diversity analysis in high-throughput pathogen detection. Finally, high-throughput data analysis could benefit from tools assessing the relevance and contribution of individual nodes of the microbial network. Network analysis can be used to calculate co-occurrence patterns and centrality indexes that may assist in the identification of highly important members of tick microbiota.
Acknowledgments
The authors thank the members of their laboratories for fruitful discussions.
Tick species | Tick stage | Microorganism detected | % of co-infection | Technique/s of detection and targeted genes | Feeding status of ticks, Engorged (E) and non-engorged (NE) | Country | Reference | ||
---|---|---|---|---|---|---|---|---|---|
Bacteria | Parasites | Viruses | |||||||
Nymphs/adults | NT | NT | 7.3 | Realtime PCR (5S and 23S rRNA genes of Intergenic Spacer region) | NE | Germany | [44] | ||
NT | NT | 0.3 | |||||||
NT | NT | 0.1 | |||||||
Adults | NT | 42 | Nested PCR (conservative regions of the flagelline gene) | E | Poland | [45] | |||
NT | 32 | NE | |||||||
Adults | NT | NT | 12.7 | Realtime PCR (5S and 23S rRNA genes of Intergenic Spacer region) | NE | Germany | [58] | ||
Nymphs | NT | NT | 12.7 | ||||||
Nymphs/adults | NT | NT | 3.6 | ||||||
Nymphs | NT | NT | 3.3 | Realtime PCR (16S rRNA and | NE | Norway | [59] | ||
Nymphs/adults | NT | NT | 2.1 | Realtime PCR ( | NE | Poland | [46] | ||
Nymphs/adults | NT | NT | 3.7 | Realtime PCR ( | NE | The Netherlands | [57] | ||
Adults | NT | NT | 0.4 | Realtime Microfluidic PCR (16S rRNA encoding | NE | France | [9] | ||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.7 | |||||||
NT | NT | 3.0 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.7 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.7 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.7 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.7 | |||||||
NT | NT | 0.7 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 1.1 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | 0.4 | ||||||||
NT | NT | 0.7 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.7 | |||||||
NT | NT | 0.4 | |||||||
NT | NT | 0.4 | |||||||
Nymphs/adults | NT | NT | 0.1 | Realtime PCR | NE | Slovakia | [48] | ||
Nymphs/adults | NT | NT | 0.29 | Realtime PCR ( | NE | Slovakia | [51] | ||
NT | NT | 0.12 | |||||||
Larvae | NT | NT | 4.5 | Realtime PCR ( | E | The Netherlands | [103] | ||
NT | NT | 0.7 | |||||||
NT | NT | 0.7 | |||||||
Nymphs | NT | NT | 9.6 | ||||||
NT | NT | 3.5 | |||||||
NT | NT | 3.5 | |||||||
NT | NT | 1.9 | |||||||
NT | NT | 1.5 | |||||||
NT | NT | 0.2 | |||||||
NT | NT | 0.2 | |||||||
NT | NT | 1.3 | |||||||
NT | NT | 0.6 | |||||||
Nymphs/adults | NT | NT | 4.3 | Realtime Microfluidic PCR + PCR [ | NE | Romania | [20] | ||
Nymphs/adults | NT | NT | 3.0 | ||||||
Nymphs | NT | NT | 0.7 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.9 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Adults | NT | NT | 1.3 | ||||||
Adults | NT | NT | 1.3 | ||||||
Nymphs | NT | NT | 0.4 | ||||||
Nymphs/adults | NT | NT | 0.4 | ||||||
Nymphs | NT | NT | 0.4 | ||||||
Nymphs | NT | NT | 0.4 | ||||||
Adults | NT | NT | 1.3 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs/adults | NT | NT | 0.6 | ||||||
Nymphs/adults | NT | NT | 0.4 | ||||||
Nymphs | NT | NT | 0.4 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.2 | ||||||
Nymphs | NT | NT | 0.4 | Realtime PCR ( | NE | The Netherlands | [55] | ||
Adults | NT | NT | 18.2 | Realtime PCR ( | The Netherlands | [103] | |||
Larvae | NT | NT | 2.5 | Nested PCR ( | E | Spain | [56] | ||
Adults | — | NT | 27,3 and 12,2 | NGS (18SrRNA) | E | Australia | [53] | ||
Nymphs/adults | NT | NT | 1.8 | Realtime PCR [23S ( | E | USA | [49] | ||
NT | 1 | ||||||||
NT | 0.4 | ||||||||
NT | 0.3 | ||||||||
Nymphs/adults | NT | NT | 16.2 | Nested PCR | NE | China | [47] | ||
NT | NT | 4.9 | |||||||
SFG | NT | NT | 2.9 | ||||||
NT | NT | 2.5 | |||||||
Adults | NT | TBEV | 1.6 | Realtime PCR ( | E | Russia | [52] | ||
NT | NT | 1.6 | |||||||
NT | NT | 1.6 | |||||||
Adults | NT | TBEV | 4.2 | ||||||
Adults | NT | TBEV | 0.32 | PCR [ | NE | Poland | [54] | ||
NT | TBEV | 4.26 | |||||||
NT | TBEV | 0.16 | |||||||
NT | NT | 0.63 | |||||||
NT | NT | 1.1 | |||||||
NT | 0.47 | ||||||||
NT | 0.95 | ||||||||
NT | 0.16 | ||||||||
TBEV | 0.45 | ||||||||
Nymphs/adults | NT | SFTSV | PCR ( | NE | China | [50] | |||
Adults | NT | 28.6 | PCR [16S rRNA ( | E | Brazil | [43] |
Tick species | Tick stage | Bacteria detected | Technique of detection | Country | References |
---|---|---|---|---|---|
Adults | Ion torrent [16S [V1–V2]) | Australia | [104] | ||
Nymphs | 454 pyrosequencing [16S (V6)] | Italy | [88] | ||
Adults | |||||
Nymphs | Hiseq (bacteria) | France | [92] | ||
Adults | |||||
Adults | RNA seq (bacteria) | Czech Republic | [94] | ||
Adults | 454 pyrosequencing (Bacteria and Archaea) | Japan | [32] | ||
Adults | MiSeq [16S (V4)] | China | [50] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | Japan | [106] | ||
Adults | MiSeq [16S (V3–V5)] | Russia | [107] | ||
Adults | Hiseq (bacteria) | China | [93] | ||
Adults | 454 pyrosequencing (Bacteria and Archaea) | Japan | [32] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | America | [108] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | America | [109] | ||
Nymphs | MiSeq [16S (V3–V4)] | America | [90] | ||
Adults | |||||
Nymphs | 454 pyrosequencing [16S (V2)] | America | [84] | ||
Adults | |||||
Nymphs | MiSeq [16S (V4)] | America | [85] | ||
Adults | |||||
Adults | MiSeq-454 pyrosequencing [16S (V1–V3; V4)] | America | [97] | ||
Adults | Ion Torrent [16S [V1–V2]) | Australia | [104] | ||
Nymphs | MiSeq [16S (V1–V2)] | [105] | |||
Adults | |||||
Adults | 454 pyrosequencing [16S (V1–V3)] | Japan | [106] | ||
Adults | 454 pyrosequencing (Bacteria and Archaea) | Japan | [32] | ||
Nymphs | MiSeq (16S) | America | [110] | ||
Adults | |||||
Adults | MiSeq [16S (V3–V5)] | Russia | [107] | ||
Nymphs | 454 pyrosequencing [16S (V1–V3)] | America | [111] | ||
Adults | |||||
Nymphs | [112] | ||||
Adults | |||||
Adults | MiSeq [16S(V3–V4)] | [113] | |||
Adults | MiSeq [16S(V1–V4)] | [114] | |||
Adults | ?? [16S (V1–V9)] | [96] | |||
Adults | 454 pyrosequencing [16S (V1–V3)] | America | [115] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | America | [74] | ||
Nymphs | 454 pyrosequencing (Bacteria and Archaea) | Japan | [32] | ||
Adults | |||||
Adults | MiSeq [16S (V1–V2)] | Australia | [105] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | America | [116] | ||
Adults | Pacific Bioscience (PacBio, Menlo Park, USA) [16S (V1-V9)] | America | [117] | ||
Adults | 454 pyrosequencing [16S (V4)] | America | [118] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | Turkey | [119] | ||
Adults | MiSeq [16S (V4)] | America | [120] | ||
Adults | MiSeq [16S (V3–V5)] | Russia | [107] | ||
Adults | 454 pyrosequencing [16S (V3–V4)] | China | [121] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | America | [108] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | America | [109] | ||
Nymphs | MiSeq [16S (V1–V2)] | Australia | [105] | ||
Adults | |||||
Nymphs | Ion Torrent [16S [V6]) | Malaysia | [122] | ||
Adults | |||||
Adults | 454 pyrosequencing [16S (V1–V3)] | Japan | [106] | ||
Nymphs | 454 pyrosequencing (Bacteria and Archaea) | Japan | [32] | ||
Adults | |||||
Nymphs | Ion Torrent [16S [V6]) | Malaysia | [122] | ||
Adults | |||||
Nymphs | MiSeq [16S (V1–V2)] | Australia | [105] | ||
Adults | |||||
Nymphs | 454 pyrosequencing (Bacteria and Archaea) | Japan | [32] | ||
Adults | |||||
Nymphs | Ion Torrent [16S (V6)] | Malaysia | [122] | ||
Adults | |||||
Adults | 454 pyrosequencing [16S (V1–V3)] | Turkey | [119] | ||
Adults | 454 pyrosequencing [16S (V1–V3)] | America | [91] | ||
Nymphs | MiSeq [16S (V5–V6)] | France | [123] | ||
Adults | |||||
Adults | |||||
Adults | Russia | ||||
Adults | 454 pyrosequencing [16S (V4–V6)] | Israel | [89] |
References
- 1.
Arthur DR. Feeding in ectoparasitic Acari, with special reference to ticks. Advances in Parasitology. 1965; 3 :249-298. DOI: 10.1016/S0065-308X(08)60367-X - 2.
Balashov YS. Bloodsucking ticks (Ixodoidea)—Vectors of disease of man and animals (English translation). Miscellaneous Publications of the Entomological Society of America. 1972; 8 :163-376 - 3.
Sonenshine DE, Michael Roe R, editors. Biology of Ticks. Vol. 1. 1st ed. New York: Oxford University Press; 1991. p. 447. ISBN: 9780199744053 - 4.
Dantas-Torres F, Chomel BB, Otranto D. Ticks and tick-borne diseases: A one health perspective. Trends in Parasitology. 2012; 28 (10):437-446. DOI: 10.1016/j.pt.2012.07.003 - 5.
Cabezas-Cruz A, Vayssier-Taussat M, Greub G. Tick-borne pathogen detection: What’s new? Microbes and Infection. 2018. pii: S1286-4579(18)30004-2. DOI: 10.1016/j.micinf.2017.12.015 - 6.
Reis C, Cote M, Paul RE, Bonnet S. Questing ticks in suburban forest are infected by at least six tick-borne pathogens. Vector Borne and Zoonotic Diseases. 2011; 11 (7):907-916. DOI: 10.1089/vbz.2010.0103 - 7.
Eshoo MW, Crowder CD, Carolan HE, Rounds MA, Ecker DJ, Haag H, et al. Broad-range survey of tick-borne pathogens in Southern Germany reveals a high prevalence of Babesia microti and a diversity of other tick-borne pathogens. Vector Borne and Zoonotic Diseases. 2014;14 (8):584-591. DOI: 10.1089/vbz.2013.1498 - 8.
Prusinski MA, Kokas JE, Hukey KT, Kogut SJ, Lee J, Backenson PB. Prevalence of Borrelia burgdorferi (Spirochaetales: Spirochaetaceae),Anaplasma phagocytophilum (Rickettsiales: Anaplasmataceae), andBabesia microti (Piroplasmida: Babesiidae) inIxodes scapularis (Acari: Ixodidae) collected from recreational lands in the Hudson Valley Region, New York State. Journal of Medical Entomology. 2014;51 (1):226-236 - 9.
Moutailler S, Valiente Moro C, Vaumourin E, Michelet L, Tran FH, Devillers E, et al. Co-infection of ticks: The rule rather than the exception. PLoS Neglected Tropical Diseases. 2016; 10 (3):e0004539. DOI: 10.1371/journal.pntd.0004539 - 10.
de la Fuente J, Antunes S, Bonnet S, Cabezas-Cruz A, Domingos AG, Estrada-Peña A, et al. Tick-pathogen interactions and vector competence: Identification of molecular drivers for tick-borne diseases. Frontiers in Cellular and Infection Microbiology. 2017; 7 :114. DOI: 10.3389/fcimb.2017.00114 - 11.
Wikel SK. Ticks and tick-borne infections: Complex ecology, agents, and host interactions. Veterinary Sciences. 2018; 5 (2). pii: E60. DOI: 10.3390/vetsci5020060 - 12.
Smith T, Kilbourne FL. Investigators into the nature, causation, and prevention of Texas or southern cattle fever. Bull Bur Anim Ind, US Dept Agric. 1893; 1 :301 - 13.
Dutton JE, Todd JL. The nature of tick fever in the eastern part of the Congo Free State, with notes on the distribution and bionomics of the tick. British Medical Journal. 1905; 2 :1259-1260 - 14.
Ricketts HT. Some aspects of Rocky Mountain spotted fever as shown by recent investigations. Medical Record. 1909; 16 :843-855 - 15.
Brumpt E. Longévité du virus de la fièvre boutonneuse ( Rickettsia conorii , n. sp.) chez la tiqueRhipicephalus sanguineus . Compte Rendu des Seances de la Societe de Biologie. 1932;110 :1197-1199 - 16.
Burgdorfer W, Barbour AG, Benach JL, Grunwaldt E, Davis JP. Lyme disease-a tick-borne spirochetosis? Science. 1982; 216 (4552):1317-1319. DOI: 10.1126/science.7043737 - 17.
Johnson RC, Schmid GP, Hyde FW, Steigerwalt AG, Brenner DJ. Borrelia burgdorferi sp. nov.: Etiological agent of Lyme disease. International Journal of Systematic Bacteriology. 1984;34 (4):496-497. DOI: 10.1099/00207713-34-4-496 - 18.
Michelet L, Delannoy S, Devillers E, Umhang G, Aspan A, Juremalm M, et al. High-throughput screening of tick-borne pathogens in Europe. Frontiers in Cellular and Infection Microbiology. 2014; 4 :103. DOI: 10.3389/fcimb.2014.00103 - 19.
Diuk-Wasser MA, Vannier E, Krause PJ. Coinfection by Ixodes tick-borne pathogens: Ecological, epidemiological, and clinical consequences. Trends in Parasitology. 2016;32 (1):30-42. DOI: 10.1016/j.pt.2015.09.008 - 20.
Raileanu C, Moutailler S, Pavel I, Porea D, Mihalca AD, Savuta G, et al. Borrelia diversity and co-infections with other tick-borne pathogens in ticks. Frontiers in Cellular and Infection Microbiology. 2017;7 :36. DOI: 10.3389/fcimb.2017.00036 - 21.
Thomas V, Anguita J, Barthold SW, Fikrig E. Coinfection with Borrelia burgdorferi and the agent of human granulocytic ehrlichiosis alters murine immune responses, pathogen burden, and severity of Lyme arthritis. Infection and Immunity. 2001;69 (5):3359-3371. DOI: 10.1128/IAI.69.5.3359-3371.2001 - 22.
Telfer S, Lambin X, Birtles R, Beldomenico P, Burthe S, Paterson S, et al. Species interactions in a parasite community drive infection risk in a wildlife population. Science. 2010; 330 (6001):243-246. DOI: 10.1126/science.1190333 - 23.
Singer M. Pathogen-pathogen interaction: A syndemic model of complex biosocial processes in disease. Virulence. 2010; 1 (1):10-18. DOI: 10.4161/viru.1.1.9933 - 24.
Johnson PT, Preston DL, Hoverman JT, LaFonte BE. Host and parasite diversity jointly control disease risk in complex communities. Proceedings of the National Academy of Sciences of the United States of America. 2013; 110 (42):16916-16921. DOI: 10.1073/pnas.1310557110 - 25.
Cattadori IM, Boag B, Hudson PJ. Parasite co-infection and interaction as drivers of host heterogeneity. International Journal for Parasitology. 2008; 38 (3–4):371-380. DOI: 10.1016/j.ijpara.2007.08.004 - 26.
Levin ML, Fish D. Acquisition of coinfection and simultaneous transmission of Borrelia burgdorferi andEhrlichia phagocytophila byIxodes scapularis ticks. Infection and Immunity. 2000;68 (4):2183-2186 - 27.
des Vignes F, Piesman J, Heffernan R, Schulze TL, Stafford KC 3rd, Fish D. Effect of tick removal on transmission of Borrelia burgdorferi andEhrlichia phagocytophila byIxodes scapularis nymphs. The Journal of Infectious Diseases. 2001;183 (5):773-778. DOI: 10.1086/318818 - 28.
Broderick NA, Lemaitre B. Gut-associated microbes of Drosophila melanogaster . Gut Microbes. 2012;3 (4):307-321. DOI: 10.4161/gmic.19896 - 29.
Ursell LK, Metcalf JL, Parfrey LW, Knight R. Defining the human microbiome. Nutrition Reviews. 2012; 70 (Supp. 1):S38-S44. DOI: 10.1111/j.1753-4887.2012.00493.x - 30.
Kroemer G, Zitvogel L. Cancer immunotherapy in 2017: The breakthrough of the microbiota. Nature Reviews. Immunology. 2018; 18 (2):87-88. DOI: 10.1038/nri.2018.4 - 31.
Narasimhan S, Fikrig E. Tick microbiome: The force within. Trends in Parasitology. 2015; 31 (7):315-323. DOI: 10.1016/j.pt.2015.03.010 - 32.
Nakao R, Abe T, Nijhof AM, Yamamoto S, Jongejan F, Ikemura T, et al. A novel approach, based on BLSOMs (batch learning self-organizing maps), to the microbiome analysis of ticks. The ISME Journal. 2013; 7 :1003-1015. DOI: 10.1038/ismej.2012.171 - 33.
Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. The ISME Journal. 2012; 6 :343-351. DOI: 10.1038/ismej.2011.119 - 34.
Zancarini A, Echenique-Subiabre I, Debroas D, Taïb N, Quiblier C, Humbert JF. Deciphering biodiversity and interactions between bacteria and microeukaryotes within epilithic biofilms from the Loue River, France. Scientific Reports. 2017; 7 :4344. DOI: 10.1038/s41598-017-04016-w - 35.
Faust K, Sathirapongsasuti JF, Izard J, Segata N, Gevers D, Raes J, et al. Microbial co-occurrence relationships in the human microbiome. PLoS Computational Biology. 2012; 8 :e1002606. DOI: 10.1371/journal.pcbi.1002606 - 36.
Sung J, Kim S, Cabatbat JJT, Jang S, Jin YS, Jung GY, et al. Global metabolic interaction network of the human gut microbiota for context-specific community scale analysis. Nature Communications. 2017; 8 :15393. DOI: 10.1038/ncomms15393 - 37.
Estrada-Peña A, Cabezas-Cruz A, Pollet T, Vayssier-Taussat M, Cosson JF. High throughput sequencing and network analysis disentangle the microbial communities of ticks and hosts within and between ecosystems. Frontiers in Cellular and Infection Microbiology. 2018; 8 :236. DOI: 10.3389/fcimb.2018.00236 - 38.
Zintl A, Moutailler S, Stuart P, Paredis L, Dutraive J, Gonzalez E, et al. Ticks and Tick-borne diseases in Ireland. Irish Veterinary Journal. 2017; 70 :4. DOI: 10.1186/s13620-017-0084-y - 39.
Jensen PM, Christoffersen CS, Moutailler S, Michelet L, Klitgaard K, Bodker R. Transmission differentials for multiple pathogens as inferred from their prevalence in larva, nymph and adult of Ixodes ricinus (Acari: Ixodidae). Experimental & Applied Acarology. 2017;71 :171-182. DOI: 10.1007/s10493-017-0110-5 - 40.
Dupraz M, Toty C, Devillers E, Blanchon T, Elguero E, Vittecoq M, et al. Population structure of the soft tick Ornithodoros maritimus and its associated infectious agents within a colony of its seabird hostLarus michahellis . International Journal for Parasitology: Parasites and Wildlife. 2017;6 :122-130. DOI: 10.1016/j.ijppaw.2017.05.001 - 41.
Gioia GV, Vinueza RL, Marsot M, Devillers E, Cruz M, Petit E, et al. Bovine anaplasmosis and tick-borne pathogens in cattle of the Galapagos Islands. Transboundary and Emerging Diseases. 2018. DOI: 10.1111/tbed.12866. [Epub ahead of print] - 42.
Hoffman T, Lindeborg M, Barboutis C, Erciyas-Yavuz K, Evander M, Fransson T, et al. Alkhurma hemorrhagic fever virus RNA in Hyalomma rufipes ticks infesting migratory birds, Europe and Asia Minor. Emerging Infectious Diseases. 2018;24 :879-882. DOI: 10.3201/eid2405.171369 - 43.
Gonçalves LR, Filgueira KD, Ahid SM, Pereira JS, Vale AM, Machado RZ, et al. Study on coinfecting vector-borne pathogens in dogs and ticks in Rio Grande do Norte, Brazil. Revista Brasileira de Parasitologia Veterinária. 2014; 23 :407-412. DOI: 10.1590/S1984-29612014071 - 44.
Tappe J, Jordan D, Janecek E, Fingerle V, Strube C. Revisited: Borrelia burgdorferi sensu lato infections in hard ticks (Ixodes ricinus ) in the city of Hanover (Germany). Parasites & Vectors. 2014;7 :441. DOI: 10.1186/1756-3305-7-441 - 45.
Asman M, Solarz K, Cuber P, Gasior T, Szilman P, Szilman E, et al. Detection of protozoans Babesia microti andToxoplasma gondii and their co-existence in ticks (Acari: Ixodida) collected in Tarnogorski district (Upper Silesia, Poland). Annals of Agricultural and Environmental Medicine. 2015;22 :80-83. DOI: 10.5604/12321966.1141373 - 46.
Sytykiewicz H, Karbowiak G, Chorostowska-Wynimko J, Szpechcinski A, Supergan-Marwicz M, Horbowicz M, et al. Coexistence of Borrelia burgdorferi s.l. genospecies withinIxodes ricinus ticks from central and eastern Poland. Acta Parasitologica. 2015;60 :654-661. DOI: 10.1515/ap-2015-0093 - 47.
Liu X, Zhang G, Liu R, Sun X, Zheng Z, Qiu E, et al. Study on co-infection of tick-borne pathogens in Ixodes persulcatus in Charles Hilary, Xinjiang Uygur autonomous region. Zhonghua Liu Xing Bing Xue Za Zhi. 2015;36 :1153-1157 - 48.
Svitalkova ZH, Harustiakova D, Mahrikova L, Mojsova M, Berthova L, Slovak M, et al. Candidatus Neoehrlichia mikurensis in ticks and rodents from urban and natural habitats of South-Western Slovakia. Parasites & Vectors. 2016;9 :2. DOI: 10.1186/s13071-015-1287-2 - 49.
Xu G, Mather TN, Hollingsworth CS, Rich SM. Passive surveillance of Ixodes scapularis (Say), their biting activity, and associated pathogens in Massachusetts. Vector Borne and Zoonotic Diseases. 2016;16 :520-527. DOI: 10.1089/vbz.2015.1912 - 50.
Zhang H, Sun Y, Jiang H, Huo X. Prevalence of severe febrile and thrombocytopenic syndrome virus, Anaplasma spp. andBabesia microti in hard ticks (Acari: Ixodidae) from Jiaodong Peninsula, Shandong Province. Vector Borne and Zoonotic Diseases. 2017;17 (2):134-140. DOI: 10.1089/vbz.2016.1978 - 51.
Hamsikova Z, Coipan C, Mahrikova L, Minichova L, Sprong H, Kazimirova M. Borrelia miyamotoi and co-infection withBorrelia afzelii inIxodes ricinus ticks and rodents from Slovakia. Microbial Ecology. 2017;73 :1000-1008. DOI: 10.1007/s00248-016-0918-2 - 52.
Dedkov VG, Simonova EG, Beshlebova OV, Safonova MV, Stukolova OA, Verigina EV, et al. The burden of tick-borne diseases in the Altai region of Russia. Ticks and Tick-borne Diseases. 2017; 8 :787-794. DOI: 10.1016/j.ttbdis.2017.06.004 - 53.
Paduraru OA, Buffet JP, Cote M, Bonnet S, Moutailler S, Paduraru V, et al. Zoonotic transmission of pathogens by Ixodes ricinus ticks, Romania. Emerging Infectious Diseases. 2012; 18 (12):2089-2090. DOI: 10.3201/eid1812.120711 - 54.
Zajac V, Wojcik-Fatla A, Sawczyn A, Cisak E, Sroka J, Kloc A, et al. Prevalence of infections and co-infections with 6 pathogens in Dermacentor reticulatus ticks collected in eastern Poland. Annals of Agricultural and Environmental Medicine. 2017;24 :26-32. DOI: 10.5604/12321966.1233893 - 55.
Wagemakers A, Jahfari S, de Wever B, Spanjaard L, Starink MV, de Vries HJC, et al. Borrelia miyamotoi in vectors and hosts in The Netherlands. Ticks and Tick-borne Diseases. 2017;8 :370-374. DOI: 10.1016/j.ttbdis.2016.12.012 - 56.
Palomar AM, Portillo A, Santibanez P, Mazuelas D, Roncero L, Gutierrez O, et al. Presence of Borrelia turdi andBorrelia valaisiana (Spirochaetales: Spirochaetaceae) in ticks removed from birds in the North of Spain, 2009-2011. Journal of Medical Entomology. 2017;54 :243-246. DOI: 10.1093/jme/tjw158 - 57.
Koetsveld J, Tijsse-Klasen E, Herremans T, Hovius JW, Sprong H. Serological and molecular evidence for spotted fever group Rickettsia and Borrelia burgdorferi sensu lato co-infections in The Netherlands. Ticks and Tick-borne Diseases. 2016;7 :371-377. DOI: 10.1016/j.ttbdis.2015.12.010 - 58.
Raulf MK, Jordan D, Fingerle V, Strube C. Association of Borrelia andRickettsia spp. and bacterial loads inIxodes ricinus ticks. Ticks and Tick-borne Diseases. 2018;9 :18-24. DOI: 10.1016/j.ttbdis.2017.10.014 - 59.
Kjelland V, Paulsen KM, Rollum R, Jenkins A, Stuen S, Soleng A, et al. Tick-borne encephalitis virus, Borrelia burgdorferi sensu lato,Borrelia miyamotoi ,Anaplasma phagocytophilum andCandidatus Neoehrlichia mikurensis inIxodes ricinus ticks collected from recreational islands in southern Norway. Ticks and Tick-borne Diseases. 2018;9 (5):1098-1102. DOI: 10.1016/j.ttbdis.2018.04.005 - 60.
Moutailler S, Popovici I, Devillers E, Vayssier-Taussat M, Eloit M. Diversity of viruses in Ixodes ricinus , and characterization of a neurotropic strain of Eyach virus. New Microbes and New Infections. 2016;11 :71-81. DOI: 10.1016/j.nmni.2016.02.012 - 61.
Scaldaferri F, Gerardi V, Mangiola F, Lopetuso LR, Pizzoferrato M, Petito V, et al. Role and mechanisms of action of Escherichia coli Nissle 1917 in the maintenance of remission in ulcerative colitis patients: An update. World Journal of Gastroenterology. 2016;22 (24):5505-5511. DOI: 10.3748/wjg.v22.i24.5505 - 62.
Gally DL, Stevens MP. Microbe profile: Escherichia coli O157 : H7—Notorious relative of the microbiologist's workhorse. Microbiology. 2017;163 (1):1-3. DOI: 10.1099/mic.0.000387 - 63.
Aardema ML, von Loewenich FD. Varying influences of selection and demography in host-adapted populations of the tick-transmitted bacterium, Anaplasma phagocytophilum . BMC Evolutionary Biology. 2015;15 :58. DOI: 10.1186/s12862-015-0335-z - 64.
Bown KJ, Lambin X, Ogden NH, Begon M, Telford G, Woldehiwet Z, et al. Delineating Anaplasma phagocytophilum ecotypes in coexisting, discrete enzootic cycles. Emerging Infectious Diseases. 2009;15 :1948-1954. DOI: 10.3201/eid1512.090178 - 65.
Huhn C, Winter C, Wolfsperger T, Wüppenhorst N, Strašek Smrdel K, Skuballa J, et al. Analysis of the population structure of Anaplasma phagocytophilum using multilocus sequence typing. PLoS One. 2014;9 :e93725. DOI: 10.1371/journal.pone.0093725 - 66.
Van Der Giessen J, Takken W, Van Wieren SE, Takumi K, Sprong H. Circulation of four Anaplasma phagocytophilum ecotypes in Europe. Parasites & Vectors. 2014;7 :365. DOI: 10.1186/1756-3305-7-365 - 67.
Cabezas-Cruz A, Zweygarth E, Vancová M, Broniszewska M, Grubhoffer L, Passos LMF, et al. Ehrlichia minasensis sp. nov., isolated from the tickRhipicephalus microplus . International Journal of Systematic and Evolutionary Microbiology. 2016;66 (3):1426-1430. DOI: 10.1099/ijsem.0.000895 - 68.
Cabezas-Cruz A, Valdés JJ, de la Fuente J. The glycoprotein TRP36 of Ehrlichia sp. UFMG-EV and related cattle pathogenEhrlichia sp. UFMT-BV evolved from a highly variable clade ofE. canis under adaptive diversifying selection. Parasites & Vectors. 2014;7 :584. DOI: 10.1186/s13071-014-0584-5 - 69.
Bremer WG, Schaefer JJ, Wagner ER, Ewing SA, Rikihisa Y, Needham GR, et al. Transstadial and intrastadial experimental transmission of Ehrlichia canis by maleRhipicephalus sanguineus . Veterinary Parasitology. 2005;131 :95-105. DOI: 10.1016/j.vetpar.2005.04.030 - 70.
Cabezas-Cruz A, Zweygarth E, Ribeiro M, da Silveira J, de la Fuente J, Grubhoffer L, et al. New species of Ehrlichia isolated fromRhipicephalus (Boophilus )microplus shows an ortholog of theE. canis major immunogenic glycoprotein gp36 with a new sequence of tandem repeats. Parasites & Vectors. 2012;5 :291. DOI: 10.1186/1756-3305-5-291 - 71.
Zweygarth E, Cabezas-Cruz A, Josemans AI, Oosthuizen MC, Matjila PT, Lis K, et al. In vitro culture and structural differences in the major immunoreactive protein gp36 of geographically distantEhrlichia canis isolates. Ticks and Tick-borne Diseases. 2014;5 :423-431. DOI: 10.1016/j.ttbdis.2014.01.011 - 72.
Aguiar DM, Ziliani TF, Zhang X, Melo AL, Braga IA, Witter R, et al. A novel Ehrlichia genotype strain distinguished by the TRP36 gene naturally infects cattle in Brazil and causes clinical manifestations associated with ehrlichiosis. Ticks and Tick-borne Diseases. 2014;5 :537-544. DOI: 10.1016/j.ttbdis.2014.03.010 - 73.
Berggoetz M, Schmid M, Ston D, Wyss V, Chevillon C, Pretorius AM, et al. Protozoan and bacterial pathogens in tick salivary glands in wild and domestic animal environments in South Africa. Ticks and Tick-borne Diseases. 2014; 5 :176-185. DOI: 10.1016/j.ttbdis.2013.10.003 - 74.
Budachetri K, Browning RE, Adamson SW, Dowd SE, Chao C-C, Ching W-M, et al. An insight into the microbiome of the Amblyomma maculatum (Acari: Ixodidae). Journal of Medical Entomology. 2014;51 :119-129 - 75.
Coudray-Meunier C, Fraisse A, Martin-Latil S, Delannoy S, Fach P, Perelle S. A novel high-throughput method for molecular detection of human pathogenic viruses using a nanofluidic real-time PCR system. PLoS One. 2016; 11 (1):e0147832. DOI: 10.1371/journal.pone.0147832 - 76.
Dhariwal A, Chong J, Habib S, King IL, Agellon LB, Xia J. Microbiome analyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Research. 2017; 45 (W1):W180-W188. DOI: 10.1093/nar/gkx295 - 77.
Boughner LA, Singh P. Microbial ecology: Where are we now? Postdoc Journal: A Journal of Postdoctoral Research and Postdoctoral Affairs. 2016; 4 :3-17. DOI: 10.14304/SURYA.JPR.V4N11.2 - 78.
Greay TL, Gofton AW, Paparini A, Ryan UM, Oskam CL, Irwin PJ. Recent insights into the tick microbiome gained through next-generation sequencing. Parasites & Vectors. 2018; 11 (1):12. DOI: 10.1186/s13071-017-2550-5 - 79.
Halos L, Jamal T, Maillard R, Beugnet F, Le Menach A, Boulouis HJ, et al. Evidence of Bartonella sp. in questing adult and nymphalIxodes ricinus ticks from France and co-infection withBorrelia burgdorferi sensu lato andBabesia sp. Veterinary Research. 2005;36 :79-87. DOI: 10.1051/vetres:2004052 - 80.
Andersson M, Bartkova S, Lindestad O, Raberg L. Co-infection with 'Candidatus Neoehrlichia Mikurensis ' andBorrelia afzelii inIxodes ricinus ticks in southern Sweden. Vectorborne and Zoonotic Diseases. 2013;13 :438-442. DOI: 10.1089/vbz.2012.1118 - 81.
Bonnet SI, Binetruy F, Hernández-Jarguín AM, Duron O. The tick microbiome: Why non-pathogenic microorganisms matter in tick biology and pathogen transmission. Frontiers in Cellular and Infection Microbiology. 2017; 7 :236. DOI: 10.3389/fcimb.2017.00236 - 82.
Vayssier-Taussat M, Albina E, Citti C, Cosson JF, Jacques MA, Lebrun MH, et al. Shifting the paradigm from pathogens to pathobiome: New concepts in the light of meta-omics. Frontiers in Cellular and Infection Microbiology. 2014; 4 :1. DOI: 10.3389/fcimb.2014.00029 - 83.
Vayssier-Taussat M, Kazimirova M, Hubalek Z, Hornok S, Farkas R, Cosson JF, et al. Emerging horizons for tick-borne pathogens: From the one pathogen-one disease vision to the pathobiome paradigm. Future Microbiology. 2015; 10 (12):2033-2043. DOI: 10.2217/fmb.15.114 - 84.
Narasimhan S, Rajeevan N, Liu L, Zhao YO, Heisig J, Pan J, et al. Gut microbiota of the tick vector Ixodes scapularis modulate colonization of the Lyme disease spirochete. Cell Host & Microbe. 2014;15 :58-71. DOI: 10.1016/j.chom.2013.12.001 - 85.
Abraham NM, Liu L, Jutras BL, Yadav AK, Narasimhan S, Gopalakrishnan V, et al. Pathogen-mediated manipulation of arthropod microbiota to promote infection. Proceedings of the National Academy of Sciences of the United States of America. 2017; 114 :781-790. DOI: 10.1073/pnas.1613422114 - 86.
Moreno CX, Moy F, Daniels TJ, Godfrey HP, Cabello FC. Molecular analysis of microbial communities identified in different developmental stages of Ixodes scapularis ticks from Westchester and Dutchess Counties, New York. Environmental Microbiology. 2006;8 :761-772. DOI: 10.1111/j.1462-2920.2005.00955.x - 87.
Van Overbeek L, Gassner F, Lombaers van der Plas C, Kastelein P, Nunes-da Rocha U, Takken W. Diversity of Ixodes ricinus tick-associated bacterial communities from different forests. FEMS Microbiology Ecology. 2008;66 :72-84. DOI: 0.1111/j.1574-6941.2008.00468.x - 88.
Carpi G, Cagnacci F, Wittekindt NE, Zhao F, Qi J, Tomsho LP, et al. Metagenomic profile of the bacterial communities associated with Ixodes ricinus ticks. PLoS One. 2011;6 (10):e25604. DOI: 10.1371/journal.pone.0025604 - 89.
Lalzar I, Harrus S, Mumcuoglu KY, Gottlieb Y. Composition and seasonal variation of Rhipicephalus turanicus andRhipicephalus sanguineus bacterial communities. Applied and Environmental Microbiology. 2012;78 :4110-4116. DOI: 10.1128/AEM.00323-12 - 90.
Zolnik CP, Prill RJ, Falco RC, Daniels TJ, Kolokotronis S-O. Microbiome changes through ontogeny of a tick pathogen vector. Molecular Ecology. 2016; 25 :4963-4977. DOI: 10.1111/mec.13832 - 91.
Andreotti R, Pérez de León AA, Dowd SE, Guerrero FD, Bendele KG, Scoles GA. Assessment of bacterial diversity in the cattle tick Rhipicephalus (Boophilus )microplus through tag-encoded pyrosequencing. BMC Microbiology. 2011;11 :6. DOI: 10.1186/1471-2180-11-6 - 92.
Vayssier-Taussat M, Moutailler S, Michelet L, Devillers E, Bonnet S, Cheval J, et al. Next generation sequencing uncovers unexpected bacterial pathogens in ticks in Western Europe. PLoS One. 2013; 8 :e81439. DOI: 10.1371/journal.pone.0081439 - 93.
Sui S, Yang Y, Sun Y, Wang X, Wang G, Shan G, et al. On the core bacterial flora of Ixodes persulcatus (Taiga tick). PLoS One. 2017;12 (7):e0180150. DOI: 10.1371/journal.pone.0180150 - 94.
Hernández-Jarguín A, Díaz-Sánchez S, Villar M, de la Fuente J. Integrated metatranscriptomics and metaproteomics for the characterization of bacterial microbiota in unfed Ixodes ricinus . Ticks and Tick-borne Diseases. 2018;9 (5):1241-1251. pii: S1877-959X(18)30034-7. DOI: 10.1016/j.ttbdis.2018.04.020 - 95.
Shade A. Diversity is the question, not the answer. The ISME Journal. 2017; 11 :1-6. DOI: 10.1038/ismej.2016.118 - 96.
Clay K, Klyachko O, Grindle N, Civitello D, Oleske D, Fuqua C. Microbial communities and interactions in the lone star tick, Amblyomma americanum . Molecular Ecology. 2008;17 :4371-4381 - 97.
Van Treuren W, Ponnusamy L, Brinkerhoff RJ, Gonzalez A, Parobek CM, Juliano JJ, et al. Variation in the microbiota of Ixodes ticks with regard to geography, species, and sex. Applied and Environmental Microbiology. 2015;81 :6200-6209. DOI: 10.1128/AEM.01562-15 - 98.
Duron O, Morel O, Noel V, Buysse M, Binetruy F, Lancelot R, et al. Tick-bacteria mutualism depends on B vitamin synthesis pathways. Current Biology. 2018; 28 :1-7. DOI: 10.1016/j.cub.2018.04.038 - 99.
Vázquez DP, Aizen MA. Asymmetric specialization: A pervasive feature of plant-pollinator interactions. Ecology. 2004; 85 (5):1251-1257. DOI: 10.1890/03-3112 - 100.
Streicker DG, Fenton A, Pedersen AB. Differential sources of host species heterogeneity influence the transmission and control of multihost parasites. Ecology Letters. 2013; 16 :975-984. DOI: 10.1111/ele.12122 - 101.
Bastolla U, Fortuna MA, Pascual-García A, Ferrera A, Luque B, Bascompte J. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature. 2009; 458 :1018-1020. DOI: 10.1038/nature07950 - 102.
Keck F, Rimet F, Bouchez A, Franc A. Phylosignal: An R package to measure, test, and explore the phylogenetic signal. Ecology and Evolution. 2016; 6 (9):2774-2780. DOI: 10.1002/ece3.2051 - 103.
Heylen D, Fonville M, Docters van Leeuwen A, Stroo A, Duisterwinkel M, van Wieren S, et al. Pathogen communities of songbird-derived ticks in Europe's low countries. Parasites & Vectors. 2017; 10 :497. DOI: 10.1186/s13071-017-2423-y - 104.
Gofton AW, Oskam CL, Lo N, Beninati T, Wei H, McCarl V, et al. Inhibition of the endosymbiont “ Candidatus Midichloria mitochondrii” during 16S rRNA gene profiling reveals potential pathogens inIxodes ticks from Australia. Parasites & Vectors. 2015;8 :345. DOI: 10.1186/s13071-015-0958-3 - 105.
Gofton AW, Doggett S, Ratchford A, Oskam CL, Paparini A, Ryan U, et al. Bacterial profiling reveals novel " Ca. Neoehrlichia ",Ehrlichia , andAnaplasma species in Australian human-biting ticks. PLoS One. 2015;10 :e0145449. DOI: 10.1371/journal.pone.0145449 - 106.
Qiu Y, Nakao R, Ohnuma A, Kawamori F, Sugimoto C. Microbial population analysis of the salivary glands of ticks; a possible strategy for the surveillance of bacterial pathogens. PLoS One. 2014; 9 :e103961. DOI: 10.1371/journal.pone.0103961 - 107.
Kurilshikov A, Livanova NN, Fomenko NV, Tupikin AE, Rar VA, Kabilov MR, et al. Comparative metagenomic profiling of symbiotic bacterial communities associated with Ixodes persulcatus ,Ixodes pavlovskyi andDermacentor reticulatus ticks. PLoS One. 2015;10 :e0131413. DOI: 10.1371/journal.pone.0131413 - 108.
Hawlena H, Rynkiewicz E, Toh E, Alfred A, Durden LA, Hastriter MW, et al. The arthropod, but not the vertebrate host or its environment, dictates bacterial community composition of fleas and ticks. The ISME Journal. 2013; 7 :221-223. DOI: 10.1038/ismej.2012.71 - 109.
Rynkiewicz EC, Hemmerich C, Rusch DB, Fuqua C, Clay K. Concordance of bacterial communities of two tick species and blood of their shared rodent host. Molecular Ecology. 2015; 24 :2566-2579. DOI: 10.1111/mec.13187 - 110.
Swei A, Kwan JY. Tick microbiome and pathogen acquisition altered by host blood meal. The ISME Journal. 2017; 11 :813-816. DOI: 10.1038/ismej.2016.152 - 111.
Williams-Newkirk AJ, Rowe LA, Mixson-Hayden TR, Dasch GA. Characterization of the bacterial communities of life stages of free living lone star ticks ( Amblyomma americanum ). PLoS One. 2014;9 :e102130. DOI: 10.1371/journal.pone.0102130 - 112.
Ponnusamy L, Gonzalez A, Van Treuren W, Weiss S, Parobek CM, Juliano JJ, et al. Diversity of R ickettsiales in the microbiome of the lone star tick,Amblyomma americanum . Applied and Environmental Microbiology. 2014;80 :354-359. DOI: 10.1128/AEM.02987-13 - 113.
Smith TA, Driscoll T, Gillespie JJ, Raghavan RA. Coxiella -like endosymbiont is a potential vitamin source for the lone star tick. Genome Biology and Evolution. 2015;7 :831-838. DOI: 10.1093/gbe/evv016 - 114.
Fryxell RT, DeBruyn JM. The microbiome of Ehrlichia infected and uninfected lone star ticks (Amblyomma americanum ). PLoS One. 2016;11 :e0146651. DOI: 10.1371/journal.pone.0155559 - 115.
Budachetri K, Williams J, Mukherjee N, Sellers M, Moore F, Karim S. The microbiome of neotropical ticks parasitizing on passerine migratory birds. Ticks and Tick-borne Diseases. 2017; 8 :170-173. DOI: 10.1016/j.ttbdis.2016.10.014 - 116.
Budachetri K, Gaillard D, Williams J, Mukherjee N, Karim SA. Snapshot of the microbiome of Amblyomma tuberculatum ticks infesting the gopher tortoise, an endangered species. Ticks and Tick-borne Diseases. 2016;7 :1225-1229. DOI: 10.1016/j.ttbdis.2016.07.010 - 117.
Gall CA, Reif KE, Scoles GA, Mason KL, Mousel M, Noh SM, et al. The bacterial microbiome of Dermacentor andersoni ticks influences pathogen susceptibility. The ISME Journal. 2016;10 (8):1846-1855. DOI: 10.1038/ismej.2015.266 - 118.
Clayton KA, Gall CA, Mason KL, Scoles GA, Brayton KA. The characterization and manipulation of the bacterial microbiome of the Rocky Mountain wood tick, Dermacentor andersoni . Parasites and Vectors. 2015;8 :632. DOI: 10.1186/s13071-015-1245-z - 119.
Tekin S, Dowd SE, Davinic M, Bursali A, Keskin A. Pyrosequencing based assessment of bacterial diversity in Turkish Rhipicephalus annulatus andDermacentor marginatus ticks (Acari: Ixodidae). Journal of Parasitology Research. 2017;116 :1055-1061. DOI: 10.1007/s00436-017-5387-0 - 120.
Gurfield N, Grewal S, Cua LS, Torres PJ, Kelley ST. Endosymbiont interference and microbial diversity of the Pacific coast tick, Dermacentor occidentalis , in San Diego County, California. PeerJ. 2017;5 :e3202. DOI: 10.7717/peerj.3202 - 121.
Wang M, Zhu D, Dai J, Zhong Z, Zhang Y, Wang J. Tissue localization and variation of major symbionts in Haemaphysalis longicornis ,Rhipicephalus haemaphysaloides andDermacentor silvarum in China. Applied and Environmental Microbiology. 2018;84 (10). pii: e00029-18. DOI: 10.1128/AEM.00029-18 - 122.
Khoo JJ, Chen F, Kho KL, Ahmad Shanizza AI, Lim FS, Tan KK, et al. Bacterial community in Haemaphysalis ticks of domesticated animals from the orang Asli communities in Malaysia. Ticks and Tick-borne Diseases. 2016;7 :929-937. DOI: 10.1016/j.ttbdis.2016.04.013 - 123.
Rene-Martellet M, Minard G, Massot R, Van Tran V, Valeinte-Moro C, Chabanne L, et al. Bacterial microbiota associated with Rhipicephalus sanguineus ticks from France, Senegal and Arizona. Parasites & Vectors. 2017;10 :416. DOI: 10.1186/s13071-017-2352-9