Exploring the bacteriome in anthropophilic ticks: To investigate the vectors for diagnosis
Exploring the bacteriome in anthropophilic ticks: To investigate the vectors for diagnosis
Ar a?nzazu PortilloID 0 1
Ana M. Palomar 0 1
Mar??a de Toro 1
Sonia Santiba? ?ez 0 1
Paula Santiba? ?ez 0 1
Jos e? A. Oteo 0 1
0 Center for Rickettsiosis and Arthropod-Borne Diseases, Infectious Diseases Department, Hospital Universitario San Pedro-Center for Biomedical Research from La Rioja (CIBIR) , Logron?o, Spain, 2 Genomics and Bioinformatics Core Facility, CIBIR, Logron?o , Spain
1 Editor: Ben J. Mans, Onderstepoort Veterinary Institute , SOUTH AFRICA
Data Availability Statement: All raw read files are
available from the NCBI Sequence Read Archive
(SRA) database (Bioproject accession number
Funding: This work was supported by: 1. Fondo de
Investigaciones Sanitarias (Accio?n Estrate?gica en
Salud 2015), ISCIII, M. Econom??a y Competitividad,
Spain (PI15/02269): AP, AMP, SS and JAO; 2.
Ministerio de Econom??a y Competitividad-FEDER
(FRSA15-EE-3502): MDT; 3. Fundacio?n Rioja Salud
(internal support): AP, AMP, MDT, PS, SS. The
The aim of this study was to characterize the bacterial microbiome of hard ticks with affinity
to bite humans in La Rioja (North of Spain).
A total of 88 adult ticks (22 Rhipicephalus sanguineus sensu lato, 27 Haemaphysalis
punctata, 30 Dermacentor marginatus and 9 Ixodes ricinus) and 120 I. ricinus nymphs (CRETAV
collection, La Rioja, Spain), representing the main anthropophilic species in our
environment, were subjected to a metagenomic analysis of the V3-V4 region of the 16S rRNA gene
using an Illumina MiSeq platform. Data obtained with Greengenes database were refined
with BLAST. Four groups of samples were defined, according to the four tick species.
Proteobacteria was the predominant phylum observed in all groups. Gammaproteobacteria
was the most abundant class, followed by Alphaproteobacteria for R. sanguineus, H.
punctata and D. marginatus but the relative abundance of reads for these classes was reversed
for I. ricinus. This tick species showed more than 46% reads corresponding to ?not assigned?
OTUs (Greengenes), and >97% of them corresponded to ?Candidatus Midichloriaceae?
using BLAST. Within Rickettsiales, ?Candidatus Midichloria?, Rickettsia, Ehrlichia,
?Candidatus Neoehrlichia? and Wolbachia were detected. I. ricinus was the most alpha-diverse
species. Regarding beta-diversity, I. ricinus and H. punctata samples grouped according to their
tick species but microbial communities of some R. sanguineus and D. marginatus
specimens clustered together.
The metagenomics approach seems useful to discover the spectrum of tick-related bacteria.
More studies are needed to identify and differentiate bacterial species, and to improve the
knowledge of tick-borne diseases in Spain.
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
Introduction / Objective
The identification of microorganisms from biological samples has been dominated by the use
of traditional culture-dependent methods and conventional molecular biology techniques
(mostly polymerase chain reaction, PCR). The isolation of most tick-borne bacteria in
synthetic media or in cell culture is difficult to obtain, and a high number of microbes remain
uncultured. For the last two decades, the identification of Rickettsia spp. and other
tick-associated pathogens has been mainly based on the use of specific PCR assays and sequence analysis
]. Until recently, most studies focused on the detection of pathogens in vectors were able to
detect a unique or a few microorganisms in a single assay. Metagenomic approaches, based on
the development of the Next Generation Sequencing (NGS) techniques, and primary focused
on the 16S rRNA study combined with bioinformatics tools, is revolutionizing the research in
the fields of epidemiology and diagnosis of infectious diseases, among others, overcoming the
limitation of detecting only one or few microorganisms at a time . Metagenomic analysis
can reveal the complexity of the microbiota of a given sample [
]. The number of pathogens
associated with ticks has increased over the last years. Currently, there is a worldwide rising
incidence of patients with a history of a tick-bite [
]. The importance of tick-borne diseases
(TBDs) as a growing threat for public health has been recently underlined, and ?what is not
sought, is not found? . As ticks are able to transmit different microorganisms at one bite, it is
necessary to be aware of possible co-infections. To investigate the microbial community
composition harbored by ticks can facilitate the knowledge about the interactions among
tick-associated microorganisms, the discovery of new uncultured microorganisms and subsequently,
their implications as human pathogens.
Up to date, reports about metagenomics to investigate bacterial diversity of tick species are
scarce. Our aim was to characterize the bacterial microbiome of hard ticks with affinity to bite
humans in La Rioja (North of Spain).
Materials and methods
A total of 280 questing ticks (130 adults and 150 nymphs) belonging to the main species with
affinity to bite humans in La Rioja (Ixodes ricinus, Rhipicephalus sanguineus sensu lato,
Dermacentor marginatus and Haemaphysalis punctata) were selected from the -80?C freezer of the
CRETAV collection (CIBIR, La Rioja, Spain) for the study of their bacterial profile. Ixodes
ricinus is the most common arthropod vector of human diseases, and particularly nymphs of this
species are the most frequent stage attacking humans in La Rioja [
]. Therefore, I. ricinus
nymphs were also included in the study, in addition to adult specimens.
Ticks had been obtained from the field in La Rioja by flagging methods or by direct capture,
either in urban habitats or in natural areas where outdoor activities are usually practiced, with
the subsequent risk of infestation for humans (S1 Table). Specimens had been classified using
taxonomic keys [
] and kept frozen at -80?C while still alive. Before DNA extraction, a half
from every adult tick (longitudinally cut) was immediately frozen again at -80?C.
For DNA extraction, ticks were manipulated under sterile conditions in a Class II biosafety
cabinet using cycles of UV light prior and between uses to prevent contamination. All the tools
were also irradiated with UV light for at least 15 min. Sterile single-use instruments were used
whenever possible. Non-disponsable material was sterilized between samples (e.g. forceps
were rinsed in 70% ethanol and flamed). Ticks were surface-sterilized by immersion and
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shaking in 70% ethanol for two min. followed by rinsing twice in sterile deionized water (one
min. each). All the solutions were sterile. Ticks were dried on autoclaved sterile filter paper,
transferred to sterile petri dishes and cut into small fragments that were collected in sterile
tubes. The DNA was extracted using DNeasy Blood and Tissue kit (Qiagen, Hilden, Germany),
following the manufacturer?s instructions except for an overnight digestion and a final elution
in 25 ?L of warm (at 56?C) elution buffer. All the kit reagents had been previously tested for
the absence of microorganisms using a pan-bacterial PCR [
]. Moreover, negative controls of
extraction corresponding to extraction tubes without tick samples were included in parallel.
DNA was quantified with a Qubit 3.0 fluorometer (Thermo Scientific) using Qubit dsDNA HS
(High Sensitivity) assay kit. The quality of DNA was assessed by capillary electrophoresis with
Fragment Analyzer (AATI) using Genomic DNA 50kb kit. DNA of enough quantity and
quality for the NGS study was obtained from 88 adult ticks (22 Rhipicephalus sanguineus s.l., 27
Haemaphysalis punctata, 30 Dermacentor marginatus and 9 Ixodes ricinus) and 120 I. ricinus
nymphs (in pools of ten individuals each) (S1 Table).
DNA extraction, preparation of PCR master mix, and amplification were performed in
separate rooms to prevent contamination.
16S rRNA gene amplification, library preparation and sequencing
A total of 12.5 ng DNA per sample were used for the amplification step. Primers targeting the
hypervariable V3 and V4 regions of 16S rRNA gene were used [
]. Amplified regions were
purified and indexed with Nextera XT Index kit (Illumina). The library quality was assessed on
a Qubit 3.0 Fluorometer (Thermo Scientific) and Fragment Analyzer (AATI) using dsDNA
reagent (35-5000bp) kit. Paired-end 300 bp sequences were obtained on an Illumina MiSeq
Sequence processing and analysis
Quality controls of raw reads were carried out with FastQC software [
], and trimmed with
the Trimmomatic software [
]. The V3-V4 amplified region (550?580 bp) was reconstructed
through paired reads according the Quantitative Insights Into Microbial Ecology (QIIME)
protocol (v1.9.1) [
]. Operational Taxonomic Units (OTUs) were defined as sequences with
at least 97% similarity versus Greengenes database [
] using UClust clustering algorithm 
and following the open-reference method described by QIIME [
]. OTUs with <0.01%
relative abundance of the total read counts on a per-sample basis were removed (spurious and
chimeric reads). Data were refined with BLAST tool against GenBank database using the
consensus sequence from each OTU .
Four groups of samples were defined, according to the four tick species. Rarefaction curves
were calculated prior to all analytical techniques in order to assess species richness from the
samples. OTU abundance was normalized by Cumulative Sum Scaling (CSS) method with
metagenomeSeq software  and barplots were constructed.
Alpha diversity and relative evenness of communities? analyses were calculated by Chao1,
Fisher, Margalef, Observed OTUs, Phylogenetic diversity (PD) whole tree, Shannon, Simpson,
and Singles indexes with QIIME. Similarity distance matrixes between species groups were
calculated following Bray-Curtis, Weighted Unifrac and Unweighted Unifrac beta-diversity
metrics. Principal Coordinate Analysis (PCoA) and Hierarchical Clustering Dendrograms
(UPGMA) for each beta-diversity metric were drawn to visualize sample groupings. The
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Kruskall-Wallis (KW) test was calculated to study significant differences between species
groups. Analysis was also performed with MicrobiomeAnalyst software [
A total of 19,977,253 read counts (average counts per sample = 201,790) and 227 OTUs were
observed. The rarefaction curves reached a plateau, demonstrating that bacterial diversity had
been satisfactorily detected for all samples (S1?S4 Figs).
Proteobacteria was the dominant phylum in all tick species (S2 Table, Fig 1). Phyla
Bacteroidetes, Actinobacteria, Acidobacteria, Tenericutes, Cyanobacteria, Verrucromicrobia and
Spirochaetes were also observed in all groups (S2 Table, Fig 1).
At class level, Gammaproteobacteria and Alphaproteobacteria represented more than 82%
of abundance of reads for the four tick species. Gammaproteobacteria was the most abundant
class, followed by Alphaproteobacteria for R. sanguineus (95.25% and 3.65%), H. punctata
(92.89% and 5.13%) and D. marginatus (80.92% and 15.90%). These percentages of relative
abundance of reads were different for I. ricinus, in which predominated Alphaproteobacteria
(70.41%) followed by Gammaproteobacteria (12.56%) (S2 Table). For Gammaproteobacteria,
statistically significant differences (False Discovery Rate, FDR<0.05, calculated by the
Kruskall-Wallis test) were found when I. ricinus was compared vs. R. sanguineus (FDR = 1.168e-10),
vs. H. punctata (FDR = 1.229e-11), and vs. D. marginatus (FDR = 1.132e-7).
Fig 1. Phyla-level relative abundance of reads for each tick species analyzed. The histograms show the portion of MiSeq 16S rRNA gene sequences assigned
to each phylum.
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Fig 2. Nucleotide alignment of ?Candidatus Midichloriaceae? partial 16S rRNA references (according to BLAST) versus closed undefined
OTUs (according to Greengenes database).
Alphaproteobacteria showed significant differences between D. marginatus and H. punctata
(FDR = 0.034), D. marginatus and I. ricinus (FDR = 0.020), D. marginatus and R. sanguineus
(FDR = 0.008), H. punctata and I. ricinus (FDR = 0.492e-3), and R. sanguineus and I. ricinus
(FDR = 0.732e-3) (S3 Table).
At least 23 orders were present (S2 Table). At family level, Coxiellaceae was the most
abundant one for D. marginatus (79.96%), H. punctata (92.76%) and R. sanguineus (94.73%) but
not for I. ricinus (0.77%). Abundance of reads for Coxiellaceae showed significant differences
between D. marginatus and I. ricinus (FDR = 2.267e-7), H. punctata and I. ricinus
(FDR = 2.049e-13), and R. sanguineus and I. ricinus (FDR = 1.635e-10) (S3 Table). At this level,
I. ricinus showed the highest percentage (46.48%) corresponding to not assigned OTUs against
Greengenes database (S2 Table). From them, 97.94% of reads (seven undefined OTUs
according to Greengenes) showed maximum similarity with ?Candidatus Midichloriaceae? using
BLAST (Fig 2).
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Other detected families (>3%) were assigned to Rickettsiaceae (14.07 and 6.14%) for D.
marginatus and I. ricinus; Pseudomonaceae (7.85%) and Oxalobacteraceae (3.55%) for I.
ricinus; and Sphingomonadaceae (11.52% and 3.87%) for I. ricinus and H. punctata, respectively
Within I. ricinus, sequences belonging to ?Candidatus Midichloriaceae? showed the highest
identity with endosymbionts such as ?Candidatus Midichloria mitochondrii? or ?Candidatus
Nicolleia massiliensis?, according to BLAST analysis. They were prevalent in female samples
(44.07?99.33%) but not in male specimens (0.57%), in which Pseudomonadaceae (60.19%)
and Nocardiaceae (13.57%) were dominant (S4 Table).
Sequences assigned to ?Candidatus Midichloriaceae? using BLAST (that corresponded to
not assigned OTUs against Greengenes) appeared in all I. ricinus nymph pools, with relative
abundance of reads that ranged from 2.01 to 52.02% depending on the sample (S5 Table).
Within order Rickettsiales (17.26% abundance of reads), genera ?Candidatus Midichloria?,
Rickettsia, Ehrlichia, Anaplasma and Wolbachia were found. However, Anaplasma sequences
corresponded to ?Candidatus Neoehrlichia mikurensis? using BLAST (GenBank accession
number KU535862). Rickettsia was the most abundant genus for D. marginatus and R.
sanguineus, showing significant differences between D. marginatus and R. sanguineus (FDR = 0.002),
D. marginatus and I. ricinus (FDR = 2.580e-5), and D. marginatus and H. punctata (FDR =
1.8694e-6). Ehrlichia was the most represented genus in H. punctata, but no significant
differences were observed when comparing tick species in pairs. ?Ca. Midichloria? was the most
abundant in I. ricinus. Wolbachia and ?Ca. Neoehrlichia? were more prevalent in I. ricinus than
in the remaining groups. Significant differences for Wolbachia were observed between I.
ricinus and D. marginatus (FDR = 0.109e-3); and for ?Ca. Neoehrlichia?, between and I. ricinus and
D. marginatus (FDR = 0.238e-3) and between I. ricinus and H. punctata (FDR = 0.007)
(Table 1; S3 Table).
Bacteria belonging to the order Borreliales were minority (0.52% abundance of reads).
Specifically, Borrelia spp. belonging to B. burgdorferi sensu lato (B. garinii) and relapsing fever
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group (B. miyamotoi) were detected (66.79% and 33.21%, respectively). B. garinii was mainly
found in I. ricinus and it was less frequently detected in R. sanguineus, whereas B. miyamotoi
showed the highest relative abundance of reads in I. ricinus followed by H. punctata. The joint
presence of Rickettsiales and Borreliales was observed in I. ricinus as an example of potential
source of human co-infections. Thus, female I. ricinus harboured Rickettsia with ?Ca.
Neoehrlichia? or with Borrelia or with both genera.
Using BLAST, Entomoplasmatales (0.53% abundance of reads) appeared in all species, with
predominance of Spiroplasma spp. (class Mollicutes) in D. marginatus (2.00%).
According to alpha-diversity measures, the mean alpha diversity was greater for I. ricinus,
followed by D. marginatus, R. sanguineus and H. punctata. Differences in alpha diversity
between I. ricinus and D. marginatus, between I. ricinus and H. punctata and between I. ricinus
and R. sanguineus were statistically significant (p<0.01) using all but Singles index. The highest
standard deviation of the mean appeared in R. sanguineus for all but Shannon, Simpson and
Singles indexes, which showed the highest standard deviation of the mean in I. ricinus
(Table 2). Group differences using Chao1 are showed in Fig 3.
Regarding beta diversity metrics (distance measure), using PCoA with Bray-Curtis or
Weighted Unifrac distance index and Analysis of Group Similarities (ANOSIM) method at
genus level, I. ricinus and H. punctata samples gathered according to their tick species. On the
contrary, microbial communities of several specimens of R. sanguineus and D. marginatus
groups clustered together, suggesting profile similarity (Fig 4).
At OTU level, the best correlation between samples and tick species was showed using
Bray-Curtis index. All samples grouped according to their tick species, except four R.
sanguineus specimens that clustered within H. punctata (n = 2) or I. ricinus (n = 2) (Fig 5).
With respect to the analysis of differential abundance of reads, 30 OTUs were significantly
present in a group and not in others (p<0.01): Rickettsia (2), Coxiella endosymbionts (18),
Spiroplasma (2), Ehrlichia (1), Pedobacter (1), Pseudomonadaceae (1), Sphingomonas wittichii
(1), Spirosoma (1) and 3 ?not assigned? OTUs (according to Greengenes) whose sequences
R. sanguineus s.l.
std: standard deviation; I.: Ixodes; H.: Haemaphysalis; R.: Rhipicephalus; s.l.: sensu lato; D.: Dermacentor.
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Fig 3. Chao1 alpha diversity index showing differences among tick species.
showed 91% of maximum identity with Coxiella endosymbionts or with Rickettsia spp.,
according to BLAST.
Many TBDs have been recognized for the first time in the last few years, and emerging
tickborne pathogens are being detected [
]. Not only the clinical observation but also the
application of new diagnostic methods (based on culture and molecular biology assays) has
contributed to this progress . Nevertheless, TBDs are dangerously expanding and they
constitute underestimated causes of human illness worldwide [
]. The implementation of NGS
platforms aimed to diagnosis is being developed, although reports about the contribution of
this technique to the clinical diagnostic of TBDs are sporadic [
]. Herein, the bacteriome of
tick species with affinity to bite humans was analysed using the 16S metagenomic approach to
investigate tick-related microorganisms and to improve the diagnosis of TBDs, particularly in
cases with unknown etiologic agents.
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Fig 4. Principal Component Analysis (PCoA) generated among groups at genus level using Weighted UniFrac metric (a measure of differences in
bacterial community structure).
Data generated with NGS studies for tick microbiome characterization allow us to delve
into microorganism interactions [
]. As reported by Estrada-Pe?a and Cabezas-Cruz (2019),
recent findings about the tick microbiome are driving to a change of paradigm: ?most bacteria
found in tick microbiome are fundamental for tick biological processes? [
]. We agree with that
statement, although the ?traditional? point of view should not be forgotten. We have learned
throughout history that microorganisms first detected in ticks, and for a long time considered
non-pathogenic to humans, have been later implicated in human diseases (e.g. Rickettsia
parkeri), even though some of them do not fulfil Koch?s Postulates (e.g. ?Ca. N mikurensis?, a
notyet-cultivated bacterium) [
]. The finding of an infectious agent in a vector could enable
its involvement in human pathology, especially if repeatedly detected. Metagenomics can
allow the identification of microorganisms carried by arthropod vectors in people with
suspected TBDs, thus contributing to the etiological diagnostics. Clinicians should consider that
infection with multiple TBDs is possible, especially in tick-endemic areas. In cases of
co-infection with more than one pathogen the clinical symptoms may be longer and more severe than
expected, and the diagnosis can be even more difficult [
]. Data analysis obtained from
NGS methods can be promising for the simultaneous detection of tick-borne pathogens in
patients suffering TBDs of unknown aetiology.
In our study, expected tick-associated bacteria (Borrelia, Rickettsia, Coxiella, Spiroplasma,
Ehrlichia, ?Ca. Neoehrlichia?, Wolbachia and ?Ca. Midichloria?) were found, as previously
reported by other authors [
]. Other bacteria genera, associated to soil, water, plants,
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Fig 5. Cluster dendrogram generated among samples at OTU level using Bray Curtis distance index.
vertebrates or arthropods, and never reported to be related with TBDs were also identified
herein: Acinetobacter, Agrobacterium, Arthrobacter, Bosea, Bradyrhizobium, Brevundimonas,
Burkholderia, Chryseobacterium, Comamonas, Devosia, Erwinia, Flavobacterium,
Hymenobacter, Janthinobacterium, Kineococcus, Luteibacter, Luteolibacter, Methylibium,
Methylobacterium, Methylopila, Mycobacterium, Mycoplana, Novosphingobium, Ochrobactrum, Paracoccus,
Patulibacter, Pedobacter, Phyllobacterium, Pseudomonas, Rathayibacter, Rhizobium,
Rhodobacter, Rhodococcus, Rhodoferax, Rubellimicrobium, Saccharothrix, Salinibacterium,
Sphingomonas, Spirosoma, Stenotrophomonas, Streptomyces, Terriglobus and Williamsia. Our findings
suggest that these bacteria may be acquired from the environment. A recent study also
recorded 15 of these genera associated to I. ricinus ticks collected from the field in France:
Arthrobacter, Bosea, Burkholderia, Devosia, Kineococcus, Luteibacter, Luteolibacter,
Mycobacterium, Patulibacter, Pedobacter, Phyllobacterium, Spirosoma, Stenotrophomonas, Terriglobus
and Williamsia [
]. In addition, non-characterized bacteria whose pathogenicity remains
unelucidated were detected based on the V3-V4 region of the 16S rRNA. More than 46% of
?not assigned? OTUs were found in I. ricinus, according to Greengenes. This database has been
the preferred one for taxonomic classification due to its discrimination power at species level
], but the gap for recently discovered bacteria is a weak point. When these OTU sequences
were analysed with BLAST (GenBank database), they showed correspondence with ?Ca. M.
mitochondrii?, an endosymbiont belonging to the order Rickettsiales [
]. In addition, within
the family Coxiellaceae (Greengenes), Coxiella endosymbionts were identified through
GenBank sequence analysis, showing again the limitation of Greengenes as a referral database for
the study of tick-associated bacteria. The same occurred with Spiroplasma spp. (order
Entomoplasmatales), symbionts associated with ticks and other arthropods, and whose potential
pathogenicity is discussed [
Bacteria corresponding to genus Wolbachia were also detected in our samples. Wolbachia
spp. are obligate intracellular endosymbionts of arthropods and nematodes. There is evidence
about the capacity of these bacteria to affect biology, physiology, immunity, ecology and
evolution and reproduction of the hosts, and to influence other infectious diseases due to viruses,
protozoa and filariae [
]. The co-occurrence of these microorganisms considered
endosymbionts can constitute a valuable research field of future studies because the viability of ticks, or
even of the pathogens that ticks are able to transmit, may depend on these endosymbionts.
According to our data, other examples of OTUs that could be better identified using
BLAST were those that matched with Anaplasma, Borreliaceae and Entomoplasmatales.
However, the identification was not possible for other ?not assigned? OTUs that showed 91%
identity (the highest) with known sequences of Rickettsia spp. or Coxiella endosymbionts. These
findings can be useful for a future targeted search of unknown bacteria associated with ticks,
and their potential implications for human health.
According to our results, the composition of the microbiota of ticks was affected by sex and
geography, as previously reported [
]. For instance, on the one hand, H. punctata males from
our study showed higher relative abundance of reads for Rickettsiales than females of the
species or other tick species. This pattern could be explained by different host preferences between
males and females and/or influence of host hormones and/or higher adaptive capacity of the
microorganism to the tick and/or relationships between tick microorganisms, among other
factors. Nevertheless, our data refer to the abundance of reads but not to prevalence, and a bias
may have occurred since females generally have much more of the endosymbiont than males.
On the other hand, D. marginatus and R. sanguineus showed overlapping PCoA plots, maybe
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because specimens of both species were collected from the same site (Villalba de Rioja). There
is preliminary evidence that ticks that are geographically close share microbes [
Of particular interest is our observation of the highest I. ricinus alpha diversity over the
other tick species analyzed herein. The generalist behavior in host choice of I. ricinus could
have played a major role in the great variability of this tick-associated microbiota. Nearly all
the life cycle of this tick species is spent in the surface layers of soil or forest litter where
environmental conditions influence its development. I. ricinus is the primary vector of a wide
variety of pathogens with considerable impact on human and animal health [
]. Contrary to
experiments that have demonstrated higher mortality rates of R. sanguineus infected with
Rickettsia conorii than non-infected when exposed at low or high temperature [
], I. ricinus is a
tick with potential to adapt to new climates as they change [
Unfortunately, the comparison of data between studies that evaluate tick microbiomes is
complex since every research team is focused on different research interests. Variations in
techniques, target regions of the 16S rRNA gene, reference taxonomic databases or source of
tick samples may hinder comparisons. As an example, relevant information about the ecology
of tick-associated microorganisms in ticks and in voles from a French area has been recently
]. However, our reads from Coxiella endosymbionts could not be accurately
compared to those obtained by Estrada-Pe?a et al. (2018) due to differences in length of reads
(V3-V4 vs. V4 region). A review of NGS strategies for the study of the microbiome of ticks
shows an updated view of the current scene [
]. As the authors conclude, further studies
aimed to assess the influences of the environments, the hosts or the ticks themselves on the
diversity of the tick microbiomes are required. According to the authors, bacteriome tick
findings must be completed with new ones focused on viruses and eukarya in ticks [
]. Herein, a
picture of bacteriome of ticks in a certain environment is showed, although ticks also harbour
viruses, protozoa, fungi, helminths, etc. [
] and plenty of questions remain unresolved. The
technique has difficulties and possible bias due to: storage of samples, DNA extraction method,
reagents contamination, amplified 16S rRNA regions, updating and maintenance of curated
sequences by reference databases or multiple repeated partial sequences of GenBank database,
among others. However, the metagenomic approach seems useful to discover the spectrum of
bacteria carried by ticks. More studies are needed to identify and differentiate bacterial species,
and to improve the knowledge of TBDs in Spain.
S1 Table. Data about tick species, sex (or stage) of ticks, sampling year, sites of sampling,
coordinates, habitats and sampling methods of the specimens included in this study.
S2 Table. Bacterial taxa found in samples. Each sheet in the file describes share of taxa for
each taxonomic level: phylum, class, order, family and genus. Taxonomic level is given on the
S3 Table. OTUs with statistically significant (p<0.05) abundance of reads at class, family
and genus. Each sheet in the file describes comparisons between tick species for each
taxonomic level: class, family and genus. Additionally, comparisons of male vs. female and nymphs
vs. adults were performed for I. ricinus samples. This analysis was performed with
MicrobiomeAnalyst <https://www.microbiomeanalyst.ca/> using a univariate analysis by the
nonparametric Kruskall-Wallis test.
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S4 Table. Family-level relative abundance of reads per sample for adult I. ricinus
speciS5 Table. Family-level relative abundance of reads per sample for I. ricinus nymph pools.
S1 Fig. Rarefaction curves of Observed OTUs richness for each sample.
S2 Fig. This is the legend for S1 Fig. Sample IDs included in this study.
S3 Fig. Rarefaction curves of Observed OTUs richness for each group (tick species).
S4 Fig. This is the legend for S3 Fig. Sample groups (tick species) included in this study.
To Mar??a Bea, laboratory technician from the Genomics and Bioinformatics Core Facility,
Partial results were presented at ESCCAR International Congress on Rickettsia and other
intracellular bacteria, Marseille-France, 2017, and at XXII SEIMC National Congress,
Conceptualization: Ara?nzazu Portillo, Jose? A. Oteo.
Data curation: Mar??a de Toro.
Formal analysis: Ara?nzazu Portillo, Ana M. Palomar, Mar??a de Toro.
Funding acquisition: Ara?nzazu Portillo, Jose? A. Oteo.
Investigation: Ana M. Palomar, Sonia Santiba??ez, Paula Santiba??ez.
Methodology: Ara?nzazu Portillo, Ana M. Palomar, Sonia Santiba??ez, Paula Santiba??ez, Jose?
Software: Mar??a de Toro.
Supervision: Ara?nzazu Portillo, Jose? A. Oteo.
Writing ? original draft: Ara?nzazu Portillo, Jose? A. Oteo.
Writing ? review & editing: Ara?nzazu Portillo, Jose? A. Oteo.
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20. metagenomeSeq. Statistical analysis for sparse high-throughput sequencing. http://cbcb.umd.edu/
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