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Article

Diversity of the Bacterial and Viral Communities in the Tropical Horse Tick, Dermacentor nitens, in Colombia

by
Andres F. Holguin-Rocha
1,
Arley Calle-Tobon
2,
Gissella M. Vásquez
3,
Helvio Astete
3,
Michael L. Fisher
3,†,
Alberto Tobon-Castano
4,
Gabriel Velez-Tobon
4,
L. Paulina Maldonado-Ruiz
1,
Kristopher Silver
1,
Yoonseong Park
1,* and
Berlin Londono-Renteria
5,*
1
Department of Entomology, College of Agriculture, Kansas State University, Manhattan, KS 66506, USA
2
Grupo Entomologia Medica, Facultad de Medicina, Universidad de Antioquia, Medellin 050010, Colombia
3
U.S. Naval Medical Research Unit No. 6 (NAMRU-6), Bellavista, Lima 15001, Peru
4
Grupo Malaria, Facultad de Medicina, Universidad de Antioquia, Medellin 050010, Colombia
5
School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA
*
Authors to whom correspondence should be addressed.
†
Current Address: Navy Warfare Development Center, Norfolk, VA 23511, USA.
Pathogens 2023, 12(7), 942; https://doi.org/10.3390/pathogens12070942
Submission received: 24 May 2023 / Revised: 26 June 2023 / Accepted: 5 July 2023 / Published: 16 July 2023
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Abstract

:
Ticks are obligatory hematophagous ectoparasites that transmit pathogens among various vertebrates, including humans. The microbial and viral communities of ticks, including pathogenic microorganisms, are known to be highly diverse. However, the factors driving this diversity are not well understood. The tropical horse tick, Dermacentor nitens, is distributed throughout the Americas and it is recognized as a natural vector of Babesia caballi and Theileria equi, the causal agents of equine piroplasmosis. In this study, we characterized the bacterial and viral communities associated with partially fed Dermacentor nitens females collected using a passive survey on horses from field sites representing three distinct geographical areas in the country of Colombia (Bolivar, Antioquia, and Cordoba). RNA-seq and sequencing of the V3 and V4 hypervariable regions of the 16S rRNA gene were performed using the Illumina-Miseq platform (Illumina, San Diego, CA, USA). A total of 356 operational taxonomic units (OTUs) were identified, in which the presumed endosymbiont, Francisellaceae/Francisella spp., was predominantly found. Nine contigs corresponding to six different viruses were identified in three viral families: Chuviridae, Rhabdoviridae, and Flaviviridae. Differences in the relative abundance of the microbial composition among the geographical regions were found to be independent of the presence of Francisella-like endosymbiont (FLE). The most prevalent bacteria found in each region were Corynebacterium in Bolivar, Staphylococcus in Antioquia, and Pseudomonas in Cordoba. Rickettsia-like endosymbionts, mainly recognized as the etiological agent of rickettsioses in Colombia, were detected in the Cordoba samples. Metatranscriptomics revealed 13 contigs containing FLE genes, suggesting a trend of regional differences. These findings suggest regional distinctions among the ticks and their bacterial compositions.

1. Introduction

Ticks are important vectors of pathogens that cause livestock and human diseases, such as ehrlichiosis, borreliosis (Lyme disease), human and cattle babesiosis, and theileriosis. Tick-borne encephalitis virus, Powassan virus, and Crimean–Congo hemorrhagic fever virus are some of the most prevalent tick-borne viral infections [1,2]. The risk of emerging and re-emerging tick-borne diseases remains a continuing threat since prevention and management are hampered by suboptimal diagnostics, lack of treatment options for emerging pathogens, and a scarcity of vaccines [3,4]. Further, the increased movement of ticks due to human activities and globalization has been described as a direct factor driving the migration and colonization of human and animal hosts by ticks and their associated pathogens [5]. In addition, global climate change caused by human activities has increased the incidence and diversity of pathogens in various habitats [6].
Ticks harbor diverse microorganisms, including symbionts, in addition to pathogenic organisms, which may have direct positive or negative effects on the tick or other members of their microbial communities [1,7,8]. The interaction between the different bacterial members of the tick’s microbial community is considered an important factor in the transmission of human and animal pathogenic organisms [9,10]. Among non-pathogenic communities, common bacterial endosymbionts found in ticks are mainly associated with Rickettsia, Coxiella, and Francisella genera [1,11,12]. These microorganisms act as primary endosymbionts providing essential nutrients involved in survival, development, and tick fitness, such as the biosynthesis of B vitamins and cofactors such as riboflavin, folic acid, and biotin [13]. Tick endosymbionts are generally tissue specific, with microbial guilds well established in salivary glands, gut, ovaries, and other tissues [14]. Some of these microorganisms, including pathogenic and non-pathogenic bacteria, can be transovarially transmitted to tick offspring [15]. Given the importance of ticks as vectors of many pathogens, understanding ticks and their symbiont compositions in different ecological systems has arisen as an important area of study [2].
The tick microbiome includes communities of viruses, bacteria, protozoa, and fungi [8,14]. Recent experimental approaches to characterize the bacterial diversity in various species of ticks used next-generation sequencing (NGS) of the 16S rRNA gene sequence amplicons [16,17,18]. These studies revealed tick bacterial communities, including mammalian pathogens, that are dependent on the tick species, type of host, and geographic location [4,11,19]. Characterizing tick populations by microbial diversity, may give us a better understanding of the potential intra- and interspecific microbial interactions occurring within the tick host and their involvement in the tick–vector competence of important human and animal diseases [4,7,20].
Viruses are present in all domains of life, and are particularly rich in the phylum Arthropoda, which includes ticks [21]. Metatranscriptomics are a specific set of widely used tools to investigate RNA viruses in ticks. Despite considerable insights into bacterial diversity, our understanding of tick-associated viruses is still limited, and largely unexplored compared with bacterial diversity [22]. Virome studies of ticks collected in Asia, Europe, and North America have revealed the emergence of novel pathogenic tick-borne viruses as well as a dearth of data on tick viromes which point to a strong need for increased viral surveillance and discovery in this group of arthropods [23,24,25]. Progress in sequencing technology and metagenomics data has allowed for an approximation of the viral community composition present in an increasing, but still few, number of tick species [22,24,26,27,28,29,30]. More information from different species may be an efficient strategy to mitigate the increasing threats of tick-borne diseases to human and animal health [2,3,25,30].
The tropical horse tick, Dermacentor nitens, is distributed throughout the Americas and it is recognized as a natural vector of Babesia caballi and Theileria equi, the causal agents of equine piroplasmosis [31,32]. Dermacentor nitens is a one-host tick, with three to four generations per year [33]. Severe infestation in vertebrate animals can cause lesions, especially in the ears, and predispose the host to secondary bacterial infections [34]. Although equines are the primary host, natural infestations have been reported in other domestic and companion animals, as well as wild animals [35,36,37]. Dermacentor nitens is considered a sporadic ectoparasite of humans, where tick infestations are probably a consequence of humans continually entering or working in infested livestock environments, resulting in a transference of ticks from the host animals to persons [38]. Accidental infestations by Dermacentor nitens in humans, related to agricultural activities, may represent a potential danger, although the vectorial capacity of Dermacentor nitens for pathogens related to public health is unknown. There are a few studies that document the occurrence of human pathogenic agents in this tick species [39,40].
This study aimed to survey the diversity of microbial communities of D. nitens in three distinct geographical regions in Colombia where equine production is common and identify the main bacterial and viral community members present in the ticks using 16S rRNA gene sequences combined with metatranscriptomic. These results will provide a better understanding of an important equine disease vector through the contribution of large numbers of sequences annotated as tick viruses and operons of Francisella-like endosymbionts (FLE) and will help to reveal a trend of differences among the three key geographical regions in Colombia.

2. Materials and Methods

2.1. Sample Collection and Nucleic Acid Extraction

Tick collection was carried out using a passive survey at “La Rinconada” slaughterhouse (06°11’26.0” N; 75°22’43.4” W) in the municipality of Rionegro, Antioquia, Colombia in July and September 2019. A total of 45 blood-fed D. nitens adults were obtained from three horses native to each region, Bolivar, Antioquia, and Cordoba (Supplementary Figure S1). The three departments are located in the northwest of Colombia and share borders with the department of Antioquia. Live ticks were transported to the Universidad de Antioquia facilities, where taxonomical identification was carried out using morphological keys [41], and specimens were subsequently stored at −0 or −80 °C until they could be shipped to Kansas State University. Blood-fed female D. nitens collected from horses were pooled and processed based on the host (individual animal) and region (Bolivar, Antioquia, and Cordoba). A total of three horses per region (5 ticks per horse × 3 horses × 3 regions = 45 ticks) were chosen by using a random selection method. The 5 ticks collected from each horse were pooled for DNA and RNA extraction, for a total of 9 pools. Genomic DNA and RNA were extracted independently following manufacturer instructions using Zymo™ DNA and RNA extraction kits (Zymo Research, Irvine, CA, USA) from the pools previously separated from the tick exoskeleton.

2.2. NGS Library Preparations and Data Processing

The genomic DNA extracted from each of the 9 pools of ticks was sent to the Genome Sequencing Core at the University of Kansas, Lawrence, KS. Amplicon libraries were prepared by Illumina Miseq targeting the V3-V4 region with the primers 16S-F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′) and 16S-R (5′- GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′) of the 16S rRNA, with an expected length of ~465 basepair (bp) for the DNA analysis [16].
The 16S rRNA sequences were analyzed with Mothur v.1.45, according to the MiSeq Standard Operating Procedure [42]. Operational Taxonomic Units (OTUs) with 97% of identity were clustered and classified using the database SILVA v.138. Raw reads were filtered to a maximum length of 465 base pair without ambiguous bases [43]. Another filtering step was carried out in Excel to remove low-count OTUs with a prevalence in samples of less than 0.005% [44]. Bacterial relative abundance was analyzed in R studio (vegan package), and GraphPad Prism 9.2.0 software [45,46,47]. We also compared the differences in the proportion of the bacterial composition of the regions through a Non-Metric Multidimensional Scaling (NMDS) ordination plot. It is important to note that there is the potential for low-frequency background noise in this dataset due to the absence of blank extraction control during the nucleic acid extraction and bioinformatics workflow [44].
RNA-seq library preparation was carried out with the NEB Next Stranded RNA library kit without PolyA selection of the mRNA. The nine extracted pools of RNA were sent to the Genome Sequencing Core at the University of Kansas, Lawrence, Kansas. For the metatranscriptomics analysis, the RNA-seq reads were processed for removal of Illumina adaptor sequences, trimmed, and quality-based filtered using Fastp software v.0.20.0 [48,49]. The high-quality reads (Phred-score > 30) were removed by mapping onto the reference genome of Dermacentor silvarum (assembly ASM1333974v1) and Equus caballus (assembly EquCab3.0) using STAR v.2.7 [50]. The unmapped reads (Supplementary Table S1) were used to perform the assembly and annotation of the transcriptome by using Trinity and Blast2GO suite in OmicsBox v.2.0.36 software [51,52,53]. Contigs annotated in Blast2GO were reexamined manually by BLASTn and BLASTx (https://blast.ncbi.nlm.nih.gov/Blast.cgi, Accessed on 25 August 2022) to confirm the results and eliminate potential false positives. Empirical Bayes estimation and Fisher’s exact tests (α = 0.05) by pairwise comparison based on the negative binomial distribution analysis were carried out with edgeR by using the Galaxy platform to test statistically significant differences in abundance between the bacterial and viral sequences annotated with the geographic location for the blood-fed D. nitens.

2.3. Phylogenetic Analyses of Viral, Rickettsia spp., and Francisella spp. Contigs

Phylogenetic analyses by comparison of Bayesian inference, maximum likelihood, minimum evolution, and neighbor-joining methods were performed as an initial assessment for the identification and classification of the bacterial protein sequences and the OTUs detected in this study compared to the reference sequences retrieved from the NCBI GenBank database by conducting homology-based taxonomic assignment and gene function via BLAST. Bacterial protein sequences, partial 16s rRNA nucleotide sequences of Rickettsia-like endosymbiont (RLE), FLE, and viral protein sequences were retrieved from the GenBank database as indicated with the GenBank accession numbers in Figures 2–4. Sequences were aligned by using Muscle in MEGA-X software [54]. Bayesian inference analysis was carried out using BEAST v1.10.4 software [55]. Phylogenetic trees for the analysis of the 16s rRNA nucleotide sequences were constructed based on the neighbor-joining method with a pairwise deletion. The trees for the V3–V4 regions sequenced in this study were constructed with 500 bootstrap replicates [56,57,58] unless otherwise specified. For the metatranscriptomic analyses of the FLE and viral protein sequences, the cladograms were constructed using annotated and concatenated genes for each contig by using the maximum likelihood method with the Tamura–Nei model and 500 bootstrap replicates [59].

2.4. Ethical Approval

This study was approved by the Bioethics Committee of the Universidad de Antioquia (Approval record No. 15-32-436 of June 2015). It was also granted an environmental license by the Colombian government through the National Environmental Licensing Authority (Autoridad Nacional de Licencias Ambientales-ANLA, Resolution ANLA 00908 of 27 May 2017).

3. Results

3.1. Bacterial Diversity Investigated Using V3–V4 Regions of the 16S rRNA Sequences

A total of 372,493 sequences after filtering 392,819 raw reads were assembled into 6686 contigs and assigned to 356 OTUs with a threshold of 97% of sequence identity (Table 1). Notably, the sequences consisted of three main OTUs, all identified as FLE (>80%) in all nine samples (Figure 1A). Among the remaining <20% OTUs, the most prevalent bacteria in different regions were Corynebacterium in Bolivar, Staphylococcus in Antioquia, and Pseudomonas in Cordoba (Figure 1B). We also compared the differences in bacterial compositions of the regions through Non-Metric Multidimensional Scaling (NMDS) in the datasets before and after excluding FLE (Figure 1C,D). Our NMDS plots suggest that regional bacterial composition is unique and independent of the presence of FLE and can be useful to differentiate the bacterial composition from different geographical regions (Figure 1).
The FLEs categorized by a 97% identity threshold were three different OTUs (OTU001, 002, and 010 in Figure 2A and Table S2). These sequences are significantly different from each other with 20 nucleotide (nt) mismatches between OTU001 and OTU002, 21 nt mismatches between OTU002 and OTU010, and 8 nt mismatches between OTU001 and OTU010. High frequencies of the reads for each FLE OTUs, which are in independent libraries, suggest that the three different FLE OTUs are not sequencing artifacts. The cladogram of the FLE sequences showed these three OTU clustered in a branch with the bootstrapping value of 100 (Figure 2A). A single OTU, OTU184, was categorized into Rickettsia-like endosymbiont (RLE) in one pool of the Cordoba region. Phylogenetic analysis supports the position of this sequence in the tree clustered with RLE of Amblyomma latepunctatum and a clear separation from the pathogenic Rickettsia, although the bootstrapping value was 68 (Figure 2B).

3.2. Metatranscriptome Containing Viral and Francisella spp. RNA

A total of 152.2 million raw reads were obtained from the nine pools representing the three different regions. After quality trimming and filtering out against E. caballus and D. silvarum sequences, 92.18 million reads were used for downstream analysis (Supplementary Table S1). De novo assembly was conducted using the TRINITY pipeline built in OmicsBox software. After cleaning and filtering, 16.8 million reads were assembled into 81 contigs. Homology-based taxonomic assignment and gene function for each contig was carried out in Blast2Go and using manual BLAST searches.
Thirteen contigs were categorized as FLE, containing presumed independent operons with an average length of 4794 bp. Table 2 represents the length and coverage information, the sequence name, the gene encoded, and the putative gene size for each contig (Figure S2). The highest coverage of the FLE contigs was Contig_ORF_FLE_of_D. nitens_13, which partially encodes the Mechanosensitive ion channel protein MscS with a length of 596 and 1892.14 TPM (transcripts per million reads) (Figure S3 and Table S3). FLE putative operon sequences were submitted to GenBank with the accession numbers contained in the BioProject PRJNA953638.
Six different putative viruses covered by nine viral contigs with an average length of 1749 bp were identified in BLAST searches for the non-redundant protein database of NCBI and the Viral Genomes database. The sequences were manually inspected and annotated for the coding regions. Table 3 shows the viral contigs with the length and coverage information. The highest coverage for the viral contigs was the D. nitens_Colombia_Flaviviridae_Polyprotein_6 contig with a total of 2346.25 TPM with the coverage predominantly higher in the region of Cordoba (Supplementary Figure S4 and Supplementary Table S4). The D. nitens virus contig sequences were submitted to GenBank with the accession numbers contained in the BioProject PRJNA953638.

3.3. Phylogenetic Analyses of Viral and Francisella spp Contigs

Thirteen FLE groups and nine viral contigs identified by metatranscriptomics were further analyzed for their phylogenetic positions. All 13 FLE contigs clustered with other FLEs identified in tick species when rooted in the pathogenic and opportunistic Francisella group. The sequences had a 100% bootstrapping value for the tick endosymbiont clade represented by Amblyomma maculatum and Ornithodoros moubata [60], Figure 3 showing the phylogeny of the concatenated sequences of 13 contigs. The overall similarity was 90% with the FLE of the Ixodidae family represented by Aamblyomma maculatum. The green branched clade, containing Francisella persica, Francisella opportunistica, and Francisella hispaniensis, represents the opportunistic pathogens that have been linked as potential causative agents of illness episodes in humans [12,60,61]. The red-branched clusters, shown as the outgroup, are the pathogenic strains of Francisella tularensis sl. To show the relationship of the contigs identified with the FLE clade, the sequence named Contig_ORF_FLE_of_D.nitens_1 was used as a representative sequence for the phylogenetic analysis, mainly because all 13 contigs grouped with the tick-endosymbiont clade. The total coverage found for the 13 contigs classified as FLE was 12,515, with contigs 13 and 1 being the most predominant among all pools of samples (Supplementary Table S3).
Phylogenetic analysis of nine viral contigs found three different families for the viral species. The genes were capsid protein, glycoprotein, nucleocapsid, polyprotein, and RNA-dependent RNA polymerase (RdRp) (Table 3). Most of the putative viruses were found by identifying genes encoding RdRp with five annotated sequences and classified into two viral families, Chuviridae and Rhabdoviridae. Two different contigs, D. nitens_Colombia_Chuviridae_Glycoprotein_2 and D. nitens_Colombia_Chuviridae_RdRp_5, were grouped into the same family Chuviridae. Based on the sequence similarities and the tree pattern (Figure 4A.B), these contigs are likely presenting two different viruses, although the name of the closely related virus is the same as Changping Tick Virus 2, a virus that has been reported in China and Turkey infecting Dermacentor spp. and Hyalomma asiaticum ticks [23,24]. These two viruses were found to be more abundant in the region of Antioquia (Supplementary Table S4). The Family Rhabdoviridae is represented by five sequences clustered into two putative viruses (Figure 4C,D). Four of them targeting RdRp were grouped in a clade with Blanchseco virus. The remaining sequence was found encoding a nucleocapsid protein and clustered with the American dog tick Rhabdovirus-2. The contig D. nitens_Colombia_Unclassified_Capsid_Protein_1 showed a close relationship with the capsid protein of Xinjiang tick-associated virus-2, a virus sequence that was presumably reported for the first time in the province of Xinjiang in China. This virus remains as unclassified for the family, and it is grouped with other tick viruses found in Ixodes scapularis and Dermacentor variabilis (Figure 4E). The family Flaviviridae was found to be represented by one contig named D. nitens_Colombia_Flaviviridae_Polyprotein_6 (Figure 4F). This name was assigned due to the high similarity found with a portion of a Flaviviridae polyprotein from Haemaphysalis longicornis and Rhipicephalus microplus infesting goats [30].

4. Discussion

Hard ticks harbor a considerable diversity of bacteria and viruses, of which there are pathogens of concern for humans and domestic animals [2,4,6,8,62,63,64]. A comprehensive survey of tick microorganisms may allow us to uncover the vectorial capacity of ticks for known pathogens and allow for the early identification of emerging pathogenic microorganisms. In addition, these surveys may provide a better understanding of the interactions among microorganisms under different environmental conditions and across geographic regions. Thus, identifying symbiotic microorganisms and their effects on the vectorial capacity of ticks is critical for predicting future outbreaks caused by febrile diseases of unknown etiology [3].
In this study, metatranscriptomics and bacterial 16S rRNA sequencing enriched the sequence database with newly uncovered Francisella-like endosymbionts (FLE) and virus genes in the blood-fed D. nitens originating from three different geographical areas in Colombia. Differences in the bacterial microbiome composition of ticks collected from animals coming from Bolivar, Antioquia, and Cordoba populations were found in either inclusion or exclusion of the FLE sequences (Figure 1C,D). The NMDS plot for 16S sequences revealed clusters for the tick geographical origin with a unique bacterial assortment. Geographically separated populations of ticks have previously been shown to have distinctive microbial communities in a number of tick species [17,39,40,65,66]. Microbial community composition could be influenced by other factors, such as the degree of tick engorgement, which has been reported previously [67,68,69]. The capacity of ticks to acquire and spread pathogens may also be significantly impacted by these variations in the microbial community composition.
We found that the most abundant bacterium was FLE (80% of classified reads), which is phylogenetically distantly related to the pathogenic bacteria F. tularensis, and causes tularemia in humans [9]. While Dermacentor variabilis and Dermacentor andersoni are known to carry this pathogen but are distributed in the northern hemisphere where F. tularensis is not commonly found, the effect of FLE interactions with pathogens and their role in disease transmission remains unknown [1,11,17,70,71]. Previous results have shown a positive association of vertically transmitted FLE against pathogenic Francisella novicida artificial infection in Dermacentor andersoni, however, Francisella novicida is not considered a tick-borne pathogen, which means this interaction is unlikely to happen under natural conditions [7].
Our results show that the microbial community composition of D. nitens appears to vary depending on the geographic location of the species’ population. We observed a higher proportion of FLE in these communities compared to data previously reported for D. variabilis (62%), and D. occidentalis (41%) in the Americas [17,72]. The highly abundant FLE found in D. nitens was a similar finding when compared with previous 16S rRNA sequencing studies on whole-body samples obtained from partially or fully engorged adult Dermacentor spp. females as D. variabilis, D. marginatus, D. reticulatus, D. silvarum, and D. albipictus [72,73,74,75]. Metatranscriptomic analysis suggested high levels of FLE coverage (i.e., transcript per million reads TPM) for Cordoba samples, but without statistical significance in all pairwise comparisons by Student’s t-test. The 16S rRNA analysis, showing the relative abundance, also suggested that the Cordoba population is richer in FLE.
Bolivar, Cordoba, and Antioquia have tropical climates, but with variations. Bolivar is warm and humid, and is known for its coastal areas. Cordoba has distinct wet and dry seasons, with challenges such as heavy rainfall and water availability. Antioquia has diverse climates, with milder conditions in lower areas and cooler temperatures in the mountains. These differences affect agricultural practices, and sustainable land and water management are crucial for addressing environmental challenges [28,29,76,77]. The department of Cordoba, an agricultural stronghold in northern Colombia, has a constant flow and exchange of animals. Thus, the associated ticks may be exposed to a more diverse bacterial environment, which may explain the increased detection frequency of main endosymbiont and transient bacteria, through mechanisms such as horizontal transfer [1,65].
These tendencies of small differences in the communities of endosymbionts related to the geographical origin of the ticks have also been reported for Dermacentor occidentalis [17]. In other tick species, such as Ixodes scapularis, the endosymbiont population has been shown to impact pathogen infection processes. An unaltered intestinal microbiota favored colonization by Borrelia burgdorferi s.l., while an induced microbial dysbiosis environment showed a negative effect by blocking the colonization of Anaplasma phagocytophilum [1,19]. In Dermacentor nitens, the transmission of human pathogens is still unknown. However, D. nitens ticks collected from equines in Brazil were found positive for B. burgdorferi s.l., the complex known as the causal agent of Lyme disease in the Americas [78]. While D. nitens’ potential as a Lyme disease vector, and the roles of FLE populations have not been documented, the initial characterization of these FLE populations may provide insights into their involvement in tick–vector competence.
Our FLE sequence analysis revealed three different D. nitens FLE variants, OTU001, 002, and 010, with relatively large variations (8 to 21 bp or 1.7 to 4.5% difference) in the V3–V4 region. The sources of these variants are likely from different strains that occur in all three geographical locations. While the genus Francisella contains three 16S rRNA copies, we exclude the possibility of intra-genomic variation from these copies based on a study that described a 99.65% minimum similarity average in 1374 Proteobacteria genomic sequences of 16S rRNA [79]. These results are comparable to our previously reported study in Amblyomma americanun, where at least two different strains of Coxiella-like endosymbionts were found, at the individual tick level [44]. Three D. nitens FLE OTUs were monophyletic and also grouped with the FLE of other Dermacentor FLEs (Figure 2). We found the FLEs of R. microplus and I. scapularis were also grouped in this clade [80], indicating, first that endosymbionts are more diverse than previously thought, and second that relatively recent independent invasions or transfers of FLEs frequently occur. This could be due to the fact that the FLE initially evolved from the pathogenic Francisella species [1,12,13,60,61,63,72,80,81].
Metatranscriptomics revealed several contigs highly similar to viral families. The Rhabdoviridae family was found as the most abundant and common in the pools of all sequences. This group of Rhabdoviridae viruses (Figure 4D) was also reported for different Ixodidae species such as Rhipicephalus annulatus, R. sanguineus, Hyalomma marginatum, H. asiaticum, and D. variabilis in the United States [23,24,26]. Blanchseco virus (Rhabdoviridae family) was found in one pool of Amblyomma ovale ticks infesting cattle and dogs in Trinidad and Tobago [27]. Similarly, we identified Chuviridae-related sequences in the D. nitens RNA pools as the second predominant viral family (Figure 4A). Chuviridae is a newly proposed viral family, that constitutes a large monophyletic group, clustering in an intermediate phylogenetic branch between segmented and unsegmented negative-sense RNA viruses identified in ticks, true flies, mosquitoes, cockroaches, and crabs [23]. The most closely related virus to the D. nitens virus, found in this study, was previously identified in China (Figure 4A) with a 90.2% (11,275 out of 12,500 bp) nucleotide sequence identity match. This finding of similar viruses in different continents may originate from the historical commerce of animals between nations such as Colombia and China.
We found geographical differences in the Rhabdoviridae family, according to the contig Rhabdoviridae_RdRp, between the Antioquia and Cordoba regions (p = 0.02). Interestingly, we found that the sequence coverage for Rhabdoviridae_Nucleocapsid in the third region, Bolivar, is high when compared with those in the other two regions (p = 0.03). This frequency data support the discovery of unique viral community compositions for the three different regions (Table S4). The coverage of the viral gene composition among the ticks in the three different populations showed statistical differences in transcripts classified into the Rhabdoviridae family (Table S5). A previous study with R. microplus, D. nitens, and R. sanguineus s.l. in the Magdalena Valley and Magdalena/Urabá ecoregions in Colombia reported the presence of Flaviviridae, Rhabdoviridae, Chuviridae, and unclassified viruses [29]. We conclude that the core RNA virome composition appears to be poor compared with the bacterial endosymbiotic communities.
However, we are aware of the limitations of our study; a small number of horses per region were sampled, so we recommend future studies in the same regions should consider including a larger sample size. Another limitation we had is that identifying viruses by using the few preexisting viral sequences in the GenBank may limit the ability to properly identify novel viruses. This sequence-based survey needs further investigation to understand whether those viruses are transiently acquired with the mammalian blood or established and vertically transmitted.
Finally, this study offers a description of the diversity of bacterial and viral communities of partially fed D. nitens female ticks collected in animals originating from three Colombian regions based on our 16S rRNA sequences and transcriptomic analysis. In addition to the differentiated geographical populations in the bacterial and viral composition, we also found multiple co-existing strains of FLE and six different viruses in D. nitens, which offers the foundation for future studies. A deeper understanding of the microbial and viral communities hosted by ticks can be utilized to develop future measures to mitigate tick pathogen transmission.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pathogens12070942/s1, Figure S1: Locations of origin of horses from which ticks were sampled in this study; Figure S2: Graphical representation of the genes annotated for each contig identified as Francisella-Like Endosymbiont (FLE) obtained from the partially-fed D. nitens female pools; Figure S3: Abundance of FLE contigs in the metatranscriptome of D. nitens; Figure S4: Abundance of virus contigs in the metatranscriptome of D. nitens; Table S1: List of library sequences obtained in the metatranscriptomics study; Table S2: Relative abundances of Francisella-Like Endosymbiont (FLE) captured in 16S sequencing; Table S3: Coverages of contigs for Francisella-Like Endosymbiont (FLE) genes; Table S4: Coverages of viral contigs shown by log10(transcript per million) for the metatransciptome of D. nitens; Table S5: Pairwise comparisons of the frequencies (transcript per million, TPM) in virus contigs among the ticks collected in different locations.

Author Contributions

Conceptualization, B.L.-R. and Y.P.; experimental design—A.F.H.-R., L.P.M.-R., B.L.-R. and Y.P.; sample collection—A.F.H.-R., G.M.V., H.A., A.T.-C. and G.V.-T.; sample processing—A.F.H.-R.; data analysis—A.F.H.-R., A.C.-T. and Y.P.; writing—original draft preparation, A.F.H.-R. and Y.P.; writing—review and editing, A.F.H.-R., L.P.M.-R., K.S., G.M.V., M.L.F., Y.P. and B.L.-R.; funding acquisition, G.M.V., M.L.F., Y.P. and B.L.-R. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Armed Forces Health Surveillance Division (AFHSD), Global Emerging Infections Surveillance (GEIS) Branch, ProMIS ID P0143_19_N6_04 and PROMIS ID P0144_20_N6_04 to MLF and G.M.V., USDA-multistate fund KS17MS1443-NE1443 to B.L.-R., NIH-NIAID R21 AI163423 and USDA-NIFA GRANT13066347 to Y.P..

Institutional Review Board Statement

This study was approved by the Bioethics Committee of the Universidad de Antioquia (Approval record No. 15-32-436 of June 2015).

Informed Consent Statement

Not applicable.

Disclaimer

The views expressed in this article reflect the results of research conducted by the authors and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, nor the U.S. Government.

Copyright Statement

Some authors of this manuscript are employees of the U.S. Government. This work was prepared as part of their official duties. Title 17 U.S.C. §105 provides that “Copyright protection under this title is not available for any work of the United States Government”. Title 17 U.S.C. §101 defines U.S. Government work as work prepared by a military service member or employee of the U.S. Government as part of that person’s official duties.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank the employees of “La Rinconada” slaughterhouse, the collaborators from Universidad de Antioquia, the Kansas State Department of Entomology, and the College of Agriculture.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Bonnet, S.I.; Binetruy, F.; Hernández-Jarguín, A.M.; Duron, O. The Tick Microbiome: Why Non-pathogenic Microorganisms Matter in Tick Biology and Pathogen Transmission. Front. Cell. Infect. Microbiol. 2017, 7, 236. Available online: https://www.frontiersin.org/article/10.3389/fcimb.2017.00236 (accessed on 8 April 2022). [CrossRef] [PubMed]
  2. Madison-Antenucci, S.; Kramer, L.D.; Gebhardt, L.L.; Kauffman, E. Emerging Tick-Borne Diseases. Clin. Microbiol. Rev. 2020, 33, e00083-18. [Google Scholar] [CrossRef] [PubMed]
  3. Prasad, N.; Murdoch, D.R.; Reyburn, H.; Crump, J.A. Etiology of Severe Febrile Illness in Low- and Middle-Income Countries: A Systematic Review. PLoS ONE 2015, 10, e0127962. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Cabezas-Cruz, A.; Vayssier-Taussat, M.; Greub, G. Tick-borne pathogen detection: What’s new? Microbes Infect. 2018, 20, 441–444. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Bouchard, C.; Dibernardo, A.; Koffi, J.; Wood, H.; Leighton, P.; Lindsay, L. N Increased risk of tick-borne diseases with climate and environmental changes. Can. Commun. Dis. Rep. 2019, 45, 83–89. [Google Scholar] [CrossRef] [PubMed]
  6. Dantas-Torres, F.; Chomel, B.B.; Otranto, D. Ticks and tick-borne diseases: A One Health perspective. Trends Parasitol. 2012, 28, 437–446. [Google Scholar] [CrossRef]
  7. Gall, C.A.; Reif, K.E.; Scoles, G.A.; Mason, K.L.; Mousel, M.; Noh, S.M.; Brayton, K.A. The bacterial microbiome of Dermacentor andersoni ticks influences pathogen susceptibility. ISME J. 2016, 10, 1846–1855. [Google Scholar] [CrossRef] [Green Version]
  8. Narasimhan, S.; Swei, A.; Abouneameh, S.; Pal, U.; Pedra, J.H.F.; Fikrig, E. Grappling with the tick microbiome. Trends Parasitol. 2021, 37, 722–733. [Google Scholar] [CrossRef]
  9. Park, J.M.; Oliva Chávez, A.S.; Shaw, D.K. Ticks: More Than Just a Pathogen Delivery Service. Front. Cell. Infect. Microbiol. 2021, 11, 808. Available online: https://www.frontiersin.org/article/10.3389/fcimb.2021.739419 (accessed on 25 April 2022). [CrossRef]
  10. Wu-Chuang, A.; Obregon, D.; Mateos-Hernández, L.; Cabezas-Cruz, A. Anti-tick microbiota vaccines: How can this actually work? Biologia 2022, 77, 1555–1562. [Google Scholar] [CrossRef]
  11. Cabezas-Cruz, A.; Pollet, T.; Estrada-Peña, A.; Allain, E.; Bonnet, S.I.; Moutailler, S. Handling the Microbial Complexity Associated to Ticks. In Ticks and Tick-Borne Pathogens; Abubakar, M., Perera, P.K., Eds.; IntechOpen: London, UK, 2019; Available online: https://www.intechopen.com/books/ticks-and-tick-borne-pathogens/handling-the-microbial-complexity-associated-to-ticks (accessed on 23 April 2022).
  12. Díaz-Sánchez, S.; Estrada-Peña, A.; Cabezas-Cruz, A.; de la Fuente, J. Evolutionary Insights into the Tick Hologenome. Trends Parasitol. 2019, 35, 725–737. [Google Scholar] [CrossRef]
  13. Duron, O.; Morel, O.; Noël, V.; Buysse, M.; Binetruy, F.; Lancelot, R.; Loire, E.; Ménard, C.; Bouchez, O.; Vavre, F.; et al. Tick-Bacteria Mutualism Depends on B Vitamin Synthesis Pathways. Curr. Biol. 2018, 28, 1896–1902.e5. [Google Scholar] [CrossRef] [Green Version]
  14. Narasimhan, S.; Fikrig, E. Tick microbiome: The force within. Trends Parasitol. 2015, 31, 315–323. [Google Scholar] [CrossRef] [Green Version]
  15. Sumrandee, C.; Hirunkanokpun, S.; Grubhoffer, L.; Baimai, V.; Trinachartvanit, W.; Ahantarig, A. Phylogenetic relationships of Francisella-like endosymbionts detected in two species of Amblyomma from snakes in Thailand. Ticks Tick-Borne Dis. 2014, 5, 29–32. [Google Scholar] [CrossRef]
  16. Klindworth, A.; Pruesse, E.; Schweer, T.; Peplies, J.; Quast, C.; Horn, M.; Glöckner, F.O. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013, 41, e1. [Google Scholar] [CrossRef]
  17. Gurfield, N.; Grewal, S.; Cua, L.S.; Torres, P.J.; Kelley, S.T. Endosymbiont interference and microbial diversity of the Pacific coast tick, Dermacentor occidentalis, in San Diego County, California. PeerJ 2017, 5, e3202. [Google Scholar] [CrossRef] [Green Version]
  18. Xiang, L.; Poźniak, B.; Cheng, T.-Y. Bacteriological analysis of saliva from partially or fully engorged female adult Rhipicephalus microplus by next-generation sequencing. Antonie Van Leeuwenhoek 2017, 110, 105–113. [Google Scholar] [CrossRef]
  19. Bonnet, S.I.; Pollet, T. Update on the intricate tango between tick microbiomes and tick-borne pathogens. Parasite Immunol. 2021, 43, e12813. [Google Scholar] [CrossRef]
  20. Wu-Chuang, A.; Hodžić, A.; Mateos-Hernández, L.; Estrada-Peña, A.; Obregon, D.; Cabezas-Cruz, A. Current debates and advances in tick microbiome research. Curr. Res. Parasitol. Vector-Borne Dis. 2021, 1, 100036. [Google Scholar] [CrossRef]
  21. Pettersson, J.H.-O.; Shi, M.; Bohlin, J.; Eldholm, V.; Brynildsrud, O.B.; Paulsen, K.M.; Andreassen, Å.; Holmes, E.C. Characterizing the virome of Ixodes ricinus ticks from northern Europe. Sci. Rep. 2017, 7, 10870. [Google Scholar] [CrossRef] [Green Version]
  22. Tokarz, R.; Williams, S.H.; Sameroff, S.; Sanchez Leon, M.; Jain, K.; Lipkin, W.I. Virome Analysis of Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis Ticks Reveals Novel Highly Divergent Vertebrate and Invertebrate Viruses. J. Virol. 2014, 88, 11480–11492. [Google Scholar] [CrossRef] [Green Version]
  23. Li, C.-X.; Shi, M.; Tian, J.-H.; Lin, X.-D.; Kang, Y.-J.; Chen, L.-J.; Qin, X.-C.; Xu, J.; Holmes, E.C.; Zhang, Y.-Z. Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. eLife 2015, 4, e05378. [Google Scholar] [CrossRef] [PubMed]
  24. Brinkmann, A.; Dinçer, E.; Polat, C.; Hekimoğlu, O.; Hacıoğlu, S.; Földes, K.; Özkul, A.; Öktem, İ.M.A.; Nitsche, A.; Ergünay, K. A metagenomic survey identifies Tamdy orthonairovirus as well as divergent phlebo-, rhabdo-, chu- and flavi-like viruses in Anatolia, Turkey. Ticks Tick-Borne Dis. 2018, 9, 1173–1183. [Google Scholar] [CrossRef] [PubMed]
  25. Shi, J.; Hu, Z.; Deng, F.; Shen, S. Tick-Borne Viruses. Virol. Sin. 2018, 33, 21–43. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Tokarz, R.; Sameroff, S.; Tagliafierro, T.; Jain, K.; Williams, S.H.; Cucura, D.M.; Rochlin, I.; Monzon, J.; Carpi, G.; Tufts, D.; et al. Identification of Novel Viruses in Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis Ticks. mSphere 2018, 3, e00614-17. [Google Scholar] [CrossRef] [Green Version]
  27. Sameroff, S.; Tokarz, R.; Charles, R.A.; Jain, K.; Oleynik, A.; Che, X.; Georges, K.; Carrington, C.V.; Lipkin, W.I.; Oura, C. Viral Diversity of Tick Species Parasitizing Cattle and Dogs in Trinidad and Tobago. Sci. Rep. 2019, 9, 10421. [Google Scholar] [CrossRef] [Green Version]
  28. Gómez, G.F.; Isaza, J.P.; Segura, J.A.; Alzate, J.F.; Gutiérrez, L.A. Metatranscriptomic virome assessment of Rhipicephalus microplus from Colombia. Ticks Tick-Borne Dis. 2020, 11, 101426. [Google Scholar] [CrossRef]
  29. Orozco Orozco, M.; Gómez, G.F.; Alzate, J.F.; Isaza, J.P.; Gutiérrez, L.A. Virome analysis of three Ixodidae ticks species from Colombia: A potential strategy for discovering and surveying tick-borne viruses. Infect. Genet. Evol. 2021, 96, 105103. [Google Scholar] [CrossRef]
  30. Xu, L.; Guo, M.; Hu, B.; Zhou, H.; Yang, W.; Hui, L.; Huang, R.; Zhan, J.; Shi, W.; Wu, Y. Tick virome diversity in Hubei Province, China, and the influence of host ecology. Virus Evol. 2021, 7, veab089. [Google Scholar] [CrossRef]
  31. Schwint, O.N.; Knowles, D.P.; Ueti, M.W.; Kappmeyer, L.S.; Scoles, G.A. Transmission of Babesia caballi by Dermacentor nitens (Acari: Ixodidae) Is Restricted to One Generation in the Absence of Alimentary Reinfection on a Susceptible Equine Host. J. Med. Entomol. 2008, 45, 1152–1155. [Google Scholar] [CrossRef] [Green Version]
  32. da Silva Rodrigues, V.; Garcia, M.V.; Cruz, B.C.; Maciel, W.G.; Zimmermann, N.P.; Koller, W.W.; Barros, J.C.; Andreotti, R. Life cycle and parasitic competence of Dermacentor nitens Neumann, 1897 (Acari: Ixodidae) on different animal species. Ticks Tick-Borne Dis. 2017, 8, 379–384. [Google Scholar] [CrossRef] [Green Version]
  33. Labruna, M.B.; Kasai, N.; Ferreira, F.; Faccini, J.L.H.; Gennari, S.M. Seasonal dynamics of ticks (Acari: Ixodidae) on horses in the state of São Paulo, Brazil. Vet. Parasitol. 2002, 105, 65–77. [Google Scholar] [CrossRef]
  34. Borges, L.M.F.; Oliveira, P.R.; Ribeiro, M.F.B. Seasonal dynamics of Anocentor nitens on horses in Brazil. Vet. Parasitol. 2000, 89, 165–171. [Google Scholar] [CrossRef]
  35. Borges, L.M.F.; da Silva, C.R.F. Ixodídeos parasitos de bovinos e equinos da microrregiáo de goiânia, goiás. Rev. Patol. Trop./J. Trop. Pathol. 1994, 23, 69–74. Available online: https://revistas.ufg.br/iptsp/article/view/20035 (accessed on 4 August 2022).
  36. Martins, T.F.; Teixeira, R.H.F.; Labruna, M.B. Ocorrência de carrapatos em animais silvestres recebidos e atendidos pelo Parque Zoológico Municipal Quinzinho de Barros, Sorocaba, São Paulo, Brasil. Braz. J. Vet. Res. Anim. Sci. 2015, 52, 319–324. [Google Scholar] [CrossRef] [Green Version]
  37. Nelson, S.L.; Durden, L.A.; Reuter, J.D. Rhipicephalus microplus and Dermacentor nitens (Acari: Ixodidae) Coparasitize White-Tailed Deer on St. John, U.S. Virgin Islands. J. Med. Entomol. 2017, 54, 1440–1443. [Google Scholar] [CrossRef]
  38. Guglielmone, A.A.; Robbins, R.G.; Apanaskevich, D.A.; Petney, T.N.; Estrada-Peña, A.; Horak, I.G. The Hard Ticks of the World; Springer: Dordrecht, The Netherlands, 2014; Available online: http://link.springer.com/10.1007/978-94-007-7497-1 (accessed on 29 July 2022).
  39. Santodomingo, A.; Sierra-Orozco, K.; Cotes-Perdomo, A.; Castro, L.R. Molecular detection of Rickettsia spp., Anaplasma platys and Theileria equi in ticks collected from horses in Tayrona National Park, Colombia. Exp. Appl. Acarol. 2019, 77, 411–423. [Google Scholar] [CrossRef]
  40. Cotes-Perdomo, A.P.; Oviedo, Á.; Castro, L.R. Molecular detection of pathogens in ticks associated with domestic animals from the Colombian Caribbean region. Exp. Appl. Acarol. 2020, 82, 137–150. [Google Scholar] [CrossRef]
  41. Barros-Battesti, D.M.; Arzua, M.; Bechara, G.H. Carrapatos de Importância Médico-Veterinária da Região Neotropical: Um Guia Ilustrado Para Identificação de Espécies; ICTTD-3; Instituto Butantan: São Paulo, Brazil, 2006. Available online: https://repositorio.butantan.gov.br/handle/butantan/3153 (accessed on 4 August 2022).
  42. Schloss, P.D.; Westcott, S.L.; Ryabin, T.; Hall, J.R.; Hartmann, M.; Hollister, E.B.; Lesniewski, R.A.; Oakley, B.B.; Parks, D.H.; Robinson, C.J.; et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl. Environ. Microbiol. 2009, 75, 7537–7541. [Google Scholar] [CrossRef] [Green Version]
  43. Kozich, J.J.; Westcott, S.L.; Baxter, N.T.; Highlander, S.K.; Schloss, P.D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 2013, 79, 5112–5120. [Google Scholar] [CrossRef] [Green Version]
  44. Maldonado-Ruiz, L.P.; Neupane, S.; Park, Y.; Zurek, L. The bacterial community of the lone star tick (Amblyomma americanum). Parasites Vectors 2021, 14, 49. [Google Scholar] [CrossRef] [PubMed]
  45. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 2003, 14, 927–930. [Google Scholar] [CrossRef]
  46. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013, 41, D590–D596. [Google Scholar] [CrossRef] [PubMed]
  47. RStudio Team. RStudio: Integrated Development for R; RStudio, PBC: Boston, MA, USA, 2020; Available online: http://www.rstudio.com/ (accessed on 20 April 2021).
  48. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef]
  49. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403–410. [Google Scholar] [CrossRef]
  50. Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef]
  51. Götz, S.; García-Gómez, J.M.; Terol, J.; Williams, T.D.; Nagaraj, S.H.; Nueda, M.J.; Robles, M.; Talón, M.; Dopazo, J.; Conesa, A. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008, 36, 3420–3435. [Google Scholar] [CrossRef]
  52. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; Adiconis, X.; Fan, L.; Raychowdhury, R.; Zeng, Q.; et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011, 29, 644–652. [Google Scholar] [CrossRef] [Green Version]
  53. Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef] [Green Version]
  54. Kumar, S.; Stecher, G.; Li, M.; Knyaz, C.; Tamura, K. MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol. Biol. Evol. 2018, 35, 1547–1549. [Google Scholar] [CrossRef]
  55. Suchard, M.A.; Lemey, P.; Baele, G.; Ayres, D.L.; Drummond, A.J.; Rambaut, A. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 2018, 4, vey016. [Google Scholar] [CrossRef] [Green Version]
  56. Felsenstein, J. Confidence Limits on Phylogenies: An Approach Using the Bootstrap. Evolution 1985, 39, 783–791. [Google Scholar] [CrossRef]
  57. Saitou, N.; Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 1987, 4, 406–425. [Google Scholar] [CrossRef]
  58. Tamura, K.; Nei, M.; Kumar, S. Prospects for inferring very large phylogenies by using the neighbor-joining method. Proc. Natl. Acad. Sci. USA 2004, 101, 11030–11035. [Google Scholar] [CrossRef]
  59. Tamura, K.; Nei, M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 1993, 10, 512–526. [Google Scholar] [CrossRef] [Green Version]
  60. Gerhart, J.G.; Moses, A.S.; Raghavan, R. A Francisella-like endosymbiont in the Gulf Coast tick evolved from a mammalian pathogen. Sci. Rep. 2016, 6, 33670. [Google Scholar] [CrossRef] [Green Version]
  61. Gerhart, J.G.; Auguste Dutcher, H.; Brenner, A.E.; Moses, A.S.; Grubhoffer, L.; Raghavan, R. Multiple Acquisitions of Pathogen-Derived Francisella Endosymbionts in Soft Ticks. Genome Biol. Evol. 2018, 10, 607–615. [Google Scholar] [CrossRef] [Green Version]
  62. Jongejan, F.; Uilenberg, G. The global importance of ticks. Parasitology 2004, 129, S3–S14. [Google Scholar] [CrossRef]
  63. Duron, O.; Binetruy, F.; Noël, V.; Cremaschi, J.; McCoy, K.D.; Arnathau, C.; Plantard, O.; Goolsby, J.; Pérez de León, A.A.; Heylen, D.J.A.; et al. Evolutionary changes in symbiont community structure in ticks. Mol. Ecol. 2017, 26, 2905–2921. [Google Scholar] [CrossRef] [Green Version]
  64. CDC. Tickborne Diseases of the United States; CDC: Atlanta, GA, USA, 2022. Available online: https://www.cdc.gov/ticks/tickbornediseases/index.html (accessed on 27 October 2022).
  65. Van Treuren, W.; Ponnusamy, L.; Brinkerhoff, R.J.; Gonzalez, A.; Parobek, C.M.; Juliano, J.J.; Andreadis, T.G.; Falco, R.C.; Ziegler, L.B.; Hathaway, N.; et al. Variation in the Microbiota of Ixodes Ticks with Regard to Geography, Species, and Sex. Appl. Environ. Microbiol. 2015, 81, 6200–6209. [Google Scholar] [CrossRef] [Green Version]
  66. Kumar, D.; Downs, L.P.; Adegoke, A.; Machtinger, E.; Oggenfuss, K.; Ostfeld, R.S.; Embers, M.; Karim, S. An Exploratory Study on the Microbiome of Northern and Southern Populations of Ixodes scapularis Ticks Predicts Changes and Unique Bacterial Interactions. Pathogens 2022, 11, 130. [Google Scholar] [CrossRef]
  67. Moreno, C.X.; Moy, F.; Daniels, T.J.; Godfrey, H.P.; Cabello, F.C. Molecular analysis of microbial communities identified in different developmental stages of Ixodes scapularis ticks from Westchester and Dutchess Counties, New York. Environ. Microbiol. 2006, 8, 761–772. [Google Scholar] [CrossRef] [PubMed]
  68. Clay, K.; Klyachko, O.; Grindle, N.; Civitello, D.; Oleske, D.; Fuqua, C. Microbial communities and interactions in the lone star tick, Amblyomma americanum. Mol. Ecol. 2008, 17, 4371–4381. [Google Scholar] [CrossRef] [PubMed]
  69. Clow, K.M.; Weese, J.S.; Rousseau, J.; Jardine, C.M. Microbiota of field-collected Ixodes scapularis and Dermacentor variabilis from eastern and southern Ontario, Canada. Ticks Tick-Borne Dis. 2018, 9, 235–244. [Google Scholar] [CrossRef] [PubMed]
  70. Ahantarig, A.; Trinachartvanit, W.; Baimai, V.; Grubhoffer, L. Hard ticks and their bacterial endosymbionts (or would be pathogens). Folia Microbiol. 2013, 58, 419–428. [Google Scholar] [CrossRef]
  71. Yeni, D.K.; Büyük, F.; Ashraf, A.; Shah, M.S.u.D. Tularemia: A re-emerging tick-borne infectious disease. Folia Microbiol. 2021, 66, 1–14. [Google Scholar] [CrossRef]
  72. Travanty, N.V.; Ponnusamy, L.; Kakumanu, M.L.; Nicholson, W.L.; Apperson, C.S. Diversity and structure of the bacterial microbiome of the American dog tick, Dermacentor variabilis, is dominated by the endosymbiont Francisella. Symbiosis 2019, 79, 239–250. [Google Scholar] [CrossRef]
  73. Zhang, Y.-K.; Yu, Z.-J.; Wang, D.; Bronislava, V.; Branislav, P.; Liu, J.-Z. The bacterial microbiome of field-collected Dermacentor marginatus and Dermacentor reticulatus from Slovakia. Parasites Vectors 2019, 12, 325. [Google Scholar] [CrossRef]
  74. Duan, D.-Y.; Liu, G.-H.; Cheng, T.-Y. Microbiome analysis of the saliva and midgut from partially or fully engorged female adult Dermacentor silvarum ticks in China. Exp. Appl. Acarol. 2020, 80, 543–558. [Google Scholar] [CrossRef]
  75. Sperling, J.; MacDonald, Z.; Normandeau, J.; Merrill, E.; Sperling, F.; Magor, K. Within-population diversity of bacterial microbiomes in winter ticks (Dermacentor albipictus). Ticks Tick-Borne Dis. 2020, 11, 101535. [Google Scholar] [CrossRef]
  76. Segura, J.A.; Isaza, J.P.; Botero, L.E.; Alzate, J.F.; Gutiérrez, L.A. Assessment of bacterial diversity of Rhipicephalus microplus ticks from two livestock agroecosystems in Antioquia, Colombia. PLoS ONE 2020, 15, e0234005. [Google Scholar] [CrossRef]
  77. Instituto Geográfico Agustín Codazzi. Límites Generales Del Departamento de Antioquia. 2022. Available online: https://www.antioquiadatos.gov.co/index.php/1-2-4-limites-generales-del-departamento-de-antioquia#:~:text=Antioquia%20limita%20al%20norte%20con,gran%20parte%20de%20su%20recorrido (accessed on 30 August 2022).
  78. Gonçalves, D.D.; Carreira, T.; Nunes, M.; Benitez, A.; Lopes-Mori, F.M.R.; Vidotto, O.; de Freitas, J.C.; Vieira, M.L. First record of Borrelia burgdorferi B31 strain in Dermacentor nitens ticks in the northern region of Parana (Brazil). Braz. J. Microbiol. 2013, 44, 883–887. [Google Scholar] [CrossRef] [Green Version]
  79. Ibal, J.C.; Pham, H.Q.; Park, C.E.; Shin, J.-H. Information about variations in multiple copies of bacterial 16S rRNA genes may aid in species identification. PLoS ONE 2019, 14, e0212090. [Google Scholar] [CrossRef] [Green Version]
  80. Scoles, G.A. Phylogenetic Analysis of the Francisella-like Endosymbionts of Dermacentor Ticks. J. Med. Entomol. 2004, 41, 277–286. [Google Scholar] [CrossRef] [Green Version]
  81. Kumar, D.; Sharma, S.R.; Adegoke, A.; Kennedy, A.; Tuten, H.C.; Li, A.Y.; Karim, S. Recently Evolved Francisella-Like Endosymbiont Outcompetes an Ancient and Evolutionarily Associated Coxiella-Like Endosymbiont in the Lone Star Tick (Amblyomma americanum) Linked to the Alpha-Gal Syndrome. Front. Cell. Infect. Microbiol. 2022, 12, 425. Available online: https://www.frontiersin.org/article/10.3389/fcimb.2022.787209 (accessed on 23 April 2022). [CrossRef]
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Figure 1. Bacterial diversity shown by genera in 16S rDNA sequences from Dermacentor nitens samples collected from three different regions of Colombia. (A) Relative abundance is shown by bacterial genera. (B) Relative abundance after excluding the sequences of endosymbionts Francisellaceae/Francisella spp. (C) Non-metric multidimensional scaling plot (NMDS) plot showing the differences among tick samples from different regions. (D) NMDS plot showing the differences among tick samples after excluding the endosymbionts.
Figure 1. Bacterial diversity shown by genera in 16S rDNA sequences from Dermacentor nitens samples collected from three different regions of Colombia. (A) Relative abundance is shown by bacterial genera. (B) Relative abundance after excluding the sequences of endosymbionts Francisellaceae/Francisella spp. (C) Non-metric multidimensional scaling plot (NMDS) plot showing the differences among tick samples from different regions. (D) NMDS plot showing the differences among tick samples after excluding the endosymbionts.
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Figure 2. Phylogenetic analyses for the Francisella-like endosymbionts (FLE, A) and Rickettsia-like endosymbionts (RLE, B) identified in this study for Dermacentor nitens samples. (A) Neighbor-joining cladogram rooted to Francisella tularensis strains representing the phylogenetic relationship of 16S rDNA sequence (465 bp) OTUs classified as Francisella spp. in Dermacentor nitens. The tree was built using the pairwise deletion method. Blue branches represent the FLE clade, green branches represent opportunistic pathogenic Francisella species, and red branches represent the pathogenic Francisella tularensis strains as an outgroup. (B) Neighbor-joining cladogram rooted to pathogenic Rickettsia strains to represent the phylogenetic relationship of rickettsial 16S rDNA sequences (465 bp) with the OTU184 classified as Rickettsia spp. in the D. nitens sample. Red branches represent pathogenic Rickettsia spp., blue branches represent the sequences of RLE, and dark branches represent candidate–human pathogenic Rickettsia. The OTUs were determined by a 97% identity threshold. Bootstrapping percentages in 500 replications are shown on the nodes with a 60% cut-off. The GenBank accession numbers for each sequence are shown at the beginning of names of taxa.
Figure 2. Phylogenetic analyses for the Francisella-like endosymbionts (FLE, A) and Rickettsia-like endosymbionts (RLE, B) identified in this study for Dermacentor nitens samples. (A) Neighbor-joining cladogram rooted to Francisella tularensis strains representing the phylogenetic relationship of 16S rDNA sequence (465 bp) OTUs classified as Francisella spp. in Dermacentor nitens. The tree was built using the pairwise deletion method. Blue branches represent the FLE clade, green branches represent opportunistic pathogenic Francisella species, and red branches represent the pathogenic Francisella tularensis strains as an outgroup. (B) Neighbor-joining cladogram rooted to pathogenic Rickettsia strains to represent the phylogenetic relationship of rickettsial 16S rDNA sequences (465 bp) with the OTU184 classified as Rickettsia spp. in the D. nitens sample. Red branches represent pathogenic Rickettsia spp., blue branches represent the sequences of RLE, and dark branches represent candidate–human pathogenic Rickettsia. The OTUs were determined by a 97% identity threshold. Bootstrapping percentages in 500 replications are shown on the nodes with a 60% cut-off. The GenBank accession numbers for each sequence are shown at the beginning of names of taxa.
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Figure 3. Phylogenetic relationship of the Francisella-like endosymbiont in the D. nitens samples in this study. The sequence is the translated sequence for the concatenated open reading frames. The selected contig contains nine genes (Table 2) annotated with a total length for the concatenated contig of 3323 amino acids (9969 bp). and 1892 transcript per million (TPM) in the pooled metatranscriptome. The tree is for maximum likelihood cladogram built using the complete deletion method. Bootstrapping percentage values are based on 500 replications and are shown at the nodes. The outgroup is for the sequences of pathogenic F. tularensis strains. The blue lines correspond to tick FLE, the green lines correspond to opportunistic pathogens, and the red lines correspond to pathogenic strains of F. tularensis. The GenBank accession numbers are shown at the beginning of each label.
Figure 3. Phylogenetic relationship of the Francisella-like endosymbiont in the D. nitens samples in this study. The sequence is the translated sequence for the concatenated open reading frames. The selected contig contains nine genes (Table 2) annotated with a total length for the concatenated contig of 3323 amino acids (9969 bp). and 1892 transcript per million (TPM) in the pooled metatranscriptome. The tree is for maximum likelihood cladogram built using the complete deletion method. Bootstrapping percentage values are based on 500 replications and are shown at the nodes. The outgroup is for the sequences of pathogenic F. tularensis strains. The blue lines correspond to tick FLE, the green lines correspond to opportunistic pathogens, and the red lines correspond to pathogenic strains of F. tularensis. The GenBank accession numbers are shown at the beginning of each label.
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Figure 4. Phylogenetic relationship of the contigs for RNA viruses captured in the D. nitens samples in this study. The maximum likelihood cladograms were constructed with complete deletion of assembly gaps. Bootstrapping percentages in 500 replications are shown at the nodes. The contig D. nitens Colombia Chuviridae Glycoprotein 2 encodes a glycoprotein gene with a length of 668 bp (A), D.nitens_Colombia_Chuviridae_Polymerase_5 encodes an RNA-dependent RNA polymerase with a length of 2156 (B), Rhabdoviridae_Dermacentor_nitens_Colombia_Polymerase_1 encodes an RNA-dependent RNA polymerase with a length of 7061 bp (C), Rhabdoviridae_Dermacentor_nitens_Colombia_Nucleocapsid_3 encodes a nucleocapsid with a length of 524 bp (D), Unclassified_Dermacentor_nitens_Capsid_Protein_1 encodes a capsid protein with a length of 168 bp (E), Flaviviridae_Dermacentor_nitens_Colombia_Polyprotein_6 encodes a polyprotein with a length of 5140 bp (F). Names in blue correspond to the viral contigs found in this study, and red names correspond to the closest viral protein sequence in the GenBank database. The GenBank accession numbers are shown at the beginning of the names of taxa.
Figure 4. Phylogenetic relationship of the contigs for RNA viruses captured in the D. nitens samples in this study. The maximum likelihood cladograms were constructed with complete deletion of assembly gaps. Bootstrapping percentages in 500 replications are shown at the nodes. The contig D. nitens Colombia Chuviridae Glycoprotein 2 encodes a glycoprotein gene with a length of 668 bp (A), D.nitens_Colombia_Chuviridae_Polymerase_5 encodes an RNA-dependent RNA polymerase with a length of 2156 (B), Rhabdoviridae_Dermacentor_nitens_Colombia_Polymerase_1 encodes an RNA-dependent RNA polymerase with a length of 7061 bp (C), Rhabdoviridae_Dermacentor_nitens_Colombia_Nucleocapsid_3 encodes a nucleocapsid with a length of 524 bp (D), Unclassified_Dermacentor_nitens_Capsid_Protein_1 encodes a capsid protein with a length of 168 bp (E), Flaviviridae_Dermacentor_nitens_Colombia_Polyprotein_6 encodes a polyprotein with a length of 5140 bp (F). Names in blue correspond to the viral contigs found in this study, and red names correspond to the closest viral protein sequence in the GenBank database. The GenBank accession numbers are shown at the beginning of the names of taxa.
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Table
Table 1. Nine sequencing libraries for the pools for D. nitens, targeting V3–V4 regions of the 16 rRNA gene.
Table 1. Nine sequencing libraries for the pools for D. nitens, targeting V3–V4 regions of the 16 rRNA gene.
Library (Paired Reads)RegionRaw readsMapped ReadsContigs
DNA_Pool_1Bolivar4885246109706
DNA_Pool_2Bolivar4143039512508
DNA_Pool_3Bolivar3784636438503
DNA_Pool_4Antioquia4514142948842
DNA_Pool_5Antioquia39380378471044
DNA_Pool_6Antioquia4377841116886
DNA_Pool_7Cordoba4787845604665
DNA_Pool_8Cordoba4124438268583
DNA_Pool_9Cordoba4727044651949
Total3928193724936686
Table
Table 2. Annotations of bacterial contigs captured in the metatranscriptome of Dermacentor nitens.
Table 2. Annotations of bacterial contigs captured in the metatranscriptome of Dermacentor nitens.
Sequence IDGene NameOpen Reading Frame (bp)
Contig_FLE_D.nitens_1, length = 9969 bp, Coverage = 1628
TRINITY_DN179725_c0_g1_Gene13-Oxoacyl-ACP synthase CDS972
TRINITY_DN179725_c0_g1_Gene2Phosphate acyltransferase CDS1047
TRINITY_DN179725_c0_g1_Gene3rpmF CDS183
TRINITY_DN179725_c0_g1_Gene4Hypothetical protein CDS504
TRINITY_DN179725_c0_g1_Gene5Transketolase CDS1992
TRINITY_DN179725_c0_g1_Gene6Glyceraldehyde-3-phospate dehydrogenase CDS1002
TRINITY_DN179725_c0_g1_Gene7Phosphoglycerate kinase CDS1179
TRINITY_DN179725_c0_g1_Gene8Pyruvate kinase CDS1437
TRINITY_DN179725_c0_g1_Gene9Fructose-1,6-bisphosphate aldolase CDS1065
Contig_FLE_D.nitens_2, length = 5250 bp, Coverage = 696
TRINITY_DN15830_c0_g2_Gene1Nucleotide exchange factor GrpE CDS588
TRINITY_DN15830_c0_g2_Gene2Molecular chaperone DnaK CDS1929
TRINITY_DN15830_c0_g2_Gene3Molecular chaperone DnaJ CDS1122
TRINITY_DN15830_c0_g2_Gene4LysR family transcriptional regulator CDS906
TRINITY_DN15830_c0_g2_Gene5Hypothetical protein CDS705
Contig_FLE_D.nitens_3, length = 8089 bp, Coverage = 675
TRINITY_DN25174_c0_g1_Gene1Hypothetical protein CDS1444
TRINITY_DN25174_c0_g1_Gene2Hypothetical protein CDS620
TRINITY_DN25174_c0_g1_Gene3Hypothetical protein CDS1006
TRINITY_DN25174_c0_g1_Gene4Hypothetical protein CDS1003
TRINITY_DN25174_c0_g1_Gene5Membrane protein CDS478
TRINITY_DN25174_c0_g1_Gene6Hypothetical protein CDS934
TRINITY_DN25174_c0_g1_Gene7moxR CDS962
TRINITY_DN25174_c0_g1_Gene8Hypothetical protein CDS444
TRINITY_DN25174_c0_g1_Gene9pdcY CDS853
TRINITY_DN25174_c0_g1_Gene10Hypothetical protein CDS345
Contig_FLE_D.nitens_4, length = 5373 bp, Coverage = 660
TRINITY_DN3539_c0_g1_Gene1Carbamoyl phosphate synthase small subunit CDS1167
TRINITY_DN3539_c0_g1_Gene2Carbamoyl phosphate synthase large subunit CDS3285
TRINITY_DN3539_c0_g1_Gene3Aspartate carbamoyltransferase CDS921
Contig_FLE_D.nitens_5, length = 5215 bp, Coverage = 617
TRINITY_DN112697_c0_g1_Gene1Coproporphyrinogen III oxidase CDS1143
TRINITY_DN112697_c0_g1_Gene2Polysacccharide biosynthesis protein GtrA CDS378
TRINITY_DN112697_c0_g1_Gene3Peroxidase CDS882
TRINITY_DN112697_c0_g1_Gene4Aconitate hydratase CDS2812
Contig_FLE_D.nitens_6, length = 1350 bp, Coverage = 787
TRINITY_DN1678_c0_g1_Gene1Glutamate dehydrogenase CDS1350
Contig_FLE_D.nitens_7, length = 2846 bp, Coverage = 942
TRINITY_DN396500_c0_g1_Gene1Glycine dehydrogenase CDS1381
TRINITY_DN396500_c0_g1_Gene2Glycine dehydrogenase CDS1465
Contig_FLE_D.nitens_8, length = 4254 bp, Coverage = 880
TRINITY_DN1569_c0_g1_Gene1ATP synthase subunit alpha CDS1542
TRINITY_DN1569_c0_g1_Gene2ATP F0F1 synthase subunit gamma CDS897
TRINITY_DN1569_c0_g1_Gene3ATP synthase subunit beta CDS1377
TRINITY_DN1569_c0_g1_Gene4atpC CDS438
Contig_FLE_D.nitens_9, length = 7945 bp, Coverage = 1393
TRINITY_DN253568_c0_g1_Gene1Leucyl aminopeptidase CDS1440
TRINITY_DN253568_c0_g1_Gene2lptF CDS1087
TRINITY_DN253568_c0_g1_Gene3lptG CDS1063
TRINITY_DN253568_c0_g1_Gene4Insulinase family protein CDS1254
TRINITY_DN253568_c0_g1_Gene5Insulinase family protein CDS1254
TRINITY_DN253568_c0_g1_Gene6rsmD CDS579
TRINITY_DN253568_c0_g1_Gene7Trimeric intracellular cation channel family protein CDS654
TRINITY_DN253568_c0_g1_Gene8tRNA-(ms [2]io [6]A)-hydrolase CDS614
Contig_FLE_D.nitens_10, length = 3170 bp, Coverage = 221
TRINITY_DN182378_c0_g1_Gene1Amino acid transporter CDS705
TRINITY_DN182378_c0_g1_Gene2Oxidoreductase, short chain dehydrogenase/reductase family CDS827
TRINITY_DN182378_c0_g1_Gene3Hypothetical protein CDS471
TRINITY_DN182378_c0_g1_Gene4NAD(FAD)-utilizing dehydrogenase CDS1167
Contig_FLE_D.nitens_11, length = 4745 bp, Coverage = 306
TRINITY_DN15837_c0_g1_Gene1Hypothetical protein CDS653
TRINITY_DN15837_c0_g1_Gene2Hypothetical protein CDS417
TRINITY_DN15837_c0_g1_Gene3Alanine--tRNA ligase CDS2598
TRINITY_DN15837_c0_g1_Gene4Transporter CDS1077
Contig_FLE_D.nitens_12, length = 3517 bp, Coverage = 491
TRINITY_DN182530_c0_g1_Gene1Hypothetical protein CDS537
TRINITY_DN182530_c0_g1_Gene2rpIT CDS357
TRINITY_DN182530_c0_g1_Gene350S ribosomal protein L35 CDS199
TRINITY_DN182530_c0_g1_Gene4Translation initiation factor IF-3 CDS519
TRINITY_DN182530_c0_g1_Gene5Threonine--tRNA ligase CDS1905
Contig_FLE_D.nitens_13 length = 596 bp, Coverage = 3219
TRINITY_DN15777_c0_g1_Gene1Mechanosensitive ion channel protein MscS-Partial596
Total coverage12,515
Table
Table 3. Viral contigs captured in the metatranscriptome of D. nitens, shown for the lengths, coverages, and Blast results.
Table 3. Viral contigs captured in the metatranscriptome of D. nitens, shown for the lengths, coverages, and Blast results.
Contig IDLengthCoverageSequence NameBlast Result
GenBank IDe-ValueName of Virus
Unclassified_Capsid_Protein_11981TRINITY_DN36539_c0_g1QBQ65105.14.00 × 10−140Xinjiang Tick associated virus 2
Chuviridae_Glycoprotein_2668168TRINITY_DN179920_c0_g1YP_00917 7705.10Changping Tick Virus 2
Chuviridae_Polymerase_52156355TRINITY_DN180002_c0_g1YP_009177704.10Changping Tick Virus 2
Rhabdoviridae_Nucleocapsid_35244TRINITY_DN327528_c0_g1AUX13127.10American dog tick rhabdovirus 2
Rhabdoviridae_Polymerase_17061218TRINITY_DN16706_c0_g1QDW81034.10Blanchseco virus
TRINITY_DN399801_c0_g1QDW81033.10Blanchseco virus
TRINITY_DN405583_c0_g1QDW81033.10Blanchseco virus
TRINITY_DN31349_c0_g1QDW81033.10Blanchseco virus
Flaviviridae_Polyprotein_651403374TRINITY_DN544_c0_g1UGM45976.10Flaviviridae sp.
Total coverage4120
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Holguin-Rocha, A.F.; Calle-Tobon, A.; Vásquez, G.M.; Astete, H.; Fisher, M.L.; Tobon-Castano, A.; Velez-Tobon, G.; Maldonado-Ruiz, L.P.; Silver, K.; Park, Y.; et al. Diversity of the Bacterial and Viral Communities in the Tropical Horse Tick, Dermacentor nitens, in Colombia. Pathogens 2023, 12, 942. https://doi.org/10.3390/pathogens12070942

AMA Style

Holguin-Rocha AF, Calle-Tobon A, Vásquez GM, Astete H, Fisher ML, Tobon-Castano A, Velez-Tobon G, Maldonado-Ruiz LP, Silver K, Park Y, et al. Diversity of the Bacterial and Viral Communities in the Tropical Horse Tick, Dermacentor nitens, in Colombia. Pathogens. 2023; 12(7):942. https://doi.org/10.3390/pathogens12070942

Chicago/Turabian Style

Holguin-Rocha, Andres F., Arley Calle-Tobon, Gissella M. Vásquez, Helvio Astete, Michael L. Fisher, Alberto Tobon-Castano, Gabriel Velez-Tobon, L. Paulina Maldonado-Ruiz, Kristopher Silver, Yoonseong Park, and et al. 2023. "Diversity of the Bacterial and Viral Communities in the Tropical Horse Tick, Dermacentor nitens, in Colombia" Pathogens 12, no. 7: 942. https://doi.org/10.3390/pathogens12070942

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