EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 31 (2024) Parasite, 31 (2024) 74 Full HTML
Open Access
Issue
Parasite
Volume 31, 2024
Article Number 74
Number of page(s) 10
DOI https://doi.org/10.1051/parasite/2024074
Published online 28 November 2024

© A. Mumtaz Abbasi et al., published by EDP Sciences, 2024

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction

Ticks are blood sucking obligate ectoparasites that belong to the family Ixodidae and infest a variety of animals and humans [4]. About 28 species of ticks act as vectors of tick-borne infections to humans [8]. Tick bites can result in diseases like Crimean-Congo Haemorrhagic Fever (CCHF), Lyme disease, tick paralysis, relapsing fever, anaplasmosis, tularemia and spotted fever rickettsiosis in humans. Hyalomma anatolicum ticks are known for spreading CCHF infection in humans and cattle [29, 36, 43]. Tick-borne bacterial infections namely Francisella, Ehrlichia, Borrelia, Rickettsia and Anaplasma to humans are a major public health concern. Infection by these pathogens can have lifelong detrimental effects on host health [22]. These ticks not only transmit viruses, bacteria, and protozoa, but also harbor endosymbionts, intracellular bacteria that live in symbiotic relationships within the ticks themselves.

Tick endosymbionts were first described by Cowdry in the early 20th century. Recent studies have proved that a diverse tick microbiome composition is crucial for its health status and reproductive potential [13, 21, 44, 45]. Endosymbionts like FLE, CLE, and Candidatus Midichloria are frequently associated with ticks and have garnered attention for their unique interactions with their tick hosts. While some of these bacteria are non-pathogenic and contribute to the tick’s biology – such as reproduction, metabolism, and survival – others, like pathogenic species of FLE, can also pose a direct threat to human health. CLE bacteria play crucial roles in tick physiology. These symbionts provide essential nutrients, like B vitamins, that ticks cannot acquire from their blood meals alone [16, 20]. Midichloria, on the other hand, represents a fascinating genus known for its association with mitochondria, although its full role in tick biology remains under investigation. It has also been found to be a dual-partner in the nutritional system [9]. Acinetobacter indicus, which is prevalent in both Hyalomma and Rhipicephalus ticks, has also been observed to produce thermostable chitinase extracellularly (Strain CCS-12), which has anti-fungal properties [3]. Endosymbionts can interact with the pathogens that ticks carry, influencing their transmission to hosts. Some symbionts may compete with pathogens for resources or space within the tick, potentially reducing the tick’s ability to transmit diseases like Lyme disease, anaplasmosis, or spotted fever. In contrast, other symbionts might enhance pathogen survival or transmission [20].

Evidence of the impactful relationship between tick-borne pathogens and the microbiome has been recorded in a previous study which states that Anaplasma phagocytophilum has the ability to modulate the composition of tick microbiome and its species richness [19]. Ticks use different mechanisms to adapt to these microbes. For example, Ixodes scapularis gut infected with B. burgdorferi and A. phagocytophilum causes changes in gene expression of the tick [1]. Similarly, these tick species when collected from the field with rich and competitive microbiome profiles resist B. burgdorferi uptake and survival [6, 14].

Tick endosymbionts have been observed to modulate tick physiology and transmit rickettsial agents from ticks to mammals. Rickettsia peacockii changes tick physiology in such a way that it develops a negative correlation for transmission of R. rickettsii from Dermacentor tick to animals [11]. These findings revealed that the relationship between endosymbionts and pathogens within a tick has an impact on tick biology and has become an important topic to understand ticks. A few samples of H. anatolicum and R. microplus ticks from Pakistan have been tested for metagenomic analysis, which has provided insight into the pathogenic infections of these ticks [2]. However, this area of research is largely unexplored for developing a tick metagenome profile of bovine ticks of Pakistan.

Complexity of tick microbiome relationship with its host has recently been explored with greater success and significant findings because of the application advantages of NGS sequencing but this aspect of scientific exploration is neglected in Pakistan. Metagenomic studies provide a picture of potential pathogens carried by these arthropods of livestock and public health importance [49]. Understanding these microbes’ relationships with tick health and their vectorial potential can help to devise strategies to counter ticks and tick-borne diseases in the future [18, 30]. About 78% of the livestock in Pakistan is affected by ticks, and H. anatolicum is the most prevalent tick species in arid areas of the country [41]. Hyalomma anatolicum and R. microplus pose significant economic problems for the livestock production in Pakistan. For example, R. microplus transmits Babesia bovis, Anaplasma marginale, and Babesia bigemina in cattle, whereas Hyalomma ticks transmit Theileria annulata in cattle and CCHF from cattle to humans [37]. In another study, R. microplus and H. anatolicum prevalence was reported throughout Pakistan. In these ticks, Candidatus Rickettsia amblyommii was seen in R. microplus and H. anatolicum ticks which cause spotted fever in humans and theileriosis in bovines. Hyalomma dromedarii ticks were found to be infected with T. annulata which cause significant losses in livestock in Pakistan [23].

Studying the symbiotic relationships between ticks and their endosymbionts provides insights into co-evolution and the ecological dynamics of tick populations. The presence of specific symbionts may indicate geographic origin or evolutionary history. This can help researchers track tick migrations, understand the spread of tick-borne diseases, and develop more targeted surveillance and control measures. Moreover, understanding these interactions may reveal vulnerabilities that can be exploited to disrupt tick development and reduce tick populations. For example, symbionts could be targeted with antimicrobials, vaccines, or genetic strategies, which could reduce tick viability or reproductive success.

In this study, comprehensive microbiome diversity profiles in different provinces of Pakistan were evaluated to assess whether there has been any change in the microbial composition of these ticks. The microbiome can affect the vectorial potential of ticks and Pakistan has diverse geographic and climatic domains spanning from cooler mountainous areas to hotter deserts, which may be the reason why one particular province has reported increasing cases of CCHF and theileriosis than the others [24, 47]. Our research highlights significant changes in the H. anatolicum microbiome and reported that, at the same time, the R. microplus microbiome was consistent with previous reports.

Materials and methods

Tick sample processing

Hyalomma anatolicum (n = 72) and Rhipicephalus microplus (n = 72) ticks were collected from Punjab (PB), Khyber Pakhtunkhwa (KPK), Balochistan (BN) and Sindh (SH) provinces, Pakistan. Tick sample pre-processing and microscopic identification of tick species were performed according to the method described elsewhere [26, 28, 50]. Eighteen engorged adult female ticks were pooled into three replicates from each province. After tick gut dissection, DNA was extracted using ZymoBIOMICS 96 DNA Kits (ZYMO Research, Irvine, CA, USA) and stored at −20 °C until use.

16S rRNA gene library preparation and sequencing

The 16S rRNA gene V3-V4 region was amplified by the PCR primers recommended by Illumina, as follows:

16S-Forward Primer = 5′ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG 3′ 16S-Reverse Primer = 5′ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC 3′. The underlined section of primer bases are overhang adaptor sequences incorporated with the V3-V4 hypervariable region-specific primers [35]. The 16S rRNA V3-V4 amplification was done by NEB Q5® Hot Start High-Fidelity 2X Mix Master Mix (New England Biolabs, Ipswich, MA, USA). PCR conditions were set as 95 °C for 3 min followed by 25 cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s and final extension of 5 min at 72 °C. PCR products were verified on a Bioanalyzer DNA 1000 chip for quality of amplicons. DNA/amplicon purifications were performed by AMPure XP magnetic beads (Agencourt AMPure, Beckman Coulter, Brea, CA, USA). The 16S Illumina Metagenome Library was prepared by following the protocol recommended by Illumina (USA) (https://support.illumina.com/downloads/16s_metagenomic_sequencing_library_preparation.html). An Illumina Nextera® XT Index Kit (FC-131-1001) was used according to protocol (Illumina, San Diego, CA, USA). Further procedures previously described in another study were followed [2]. PCR product with amplified 16S amplicon was purified with magnetic beads. NGS amplicon sequencing libraries were quantified and checked for quality and average fragment size using a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and Bioanalyzer (Agilent 2100, Agilent, Santa Clara, CA, USA), following manufacturers’ protocols. A MiSeq Reagent Kit v3 (MS-102-3003) 600 cycles was used for the sequencing run. The Illumina PhiX Control v3 Library (Illumina) was used for sequencing quality control.

Bioinformatics and statistical analyses

Demultiplexed sequence files retrieved from the MiSeq instrument were processed on high throughput Computer using Linux based software. Metagenomic analysis methods with a command line interface (CLI) of Quantitative Insights into Microbial Ecology 2 (QIIME 2) Linux Distribution v2023.8 were deployed to analyze all the pooled samples [7]. Analysis steps were also reassessed on the QIIME2 Galaxy Server (Cancer Galaxy Server, https://cancer.usegalaxy.org) to verify the process. The QIIME2 Moving Pictures Guide pipeline was used for most of the analyses (https://docs.qiime2.org/2024.2/tutorials/). Sections of Atacama Soil Microbiome and Parkinson’s Mouse metagenomic pipeline were also incorporated into the analysis, where relevant. Integrity and authenticity of tabular metadata was verified by cloud based Keemei plugin. The Divisive Amplicon Denoising Algorithm (DADA2) plugin was used to denoise, filter and rejoin forward and reverse sequences. Amplicon sequence variants (ASVs) generated via DADA2 were subsequently utilized throughout further analysis [10]. Pre-trained Naive Bayes classifier at 99% similarity (silva-138-99-seqs.qza) was used as for taxonomy assignment [30]. MAFFT and FastTree extensions of QIIME2 were deployed for sequence alignment and phylogenetic tree construction [40]. Alpha and beta diversity were analyzed by qiime diversity core-metrics-phylogenetic, qiime diversity alpha-group-significance and qiime diversity beta-group-significance functions [17]. The ASVs feature table and feature data summaries were obtained using qiime feature-table summarize and qiime feature-table tabulate-seqs functions. Plots were generated using qiime taxa barplot, qiime emperor plot and qiime diversity alpha-rarefaction plugins. Data visualization including relative abundance profile and alpha and beta diversity visualization was performed with the Qiime2View online web tool by utilizing QIIME proprietary QZV artifacts obtained during the process (https://view.qiime2.org).

Alpha and beta diversity

The samples were analyzed for the Shannon–Wiener diversity index (Shannon index), Pielou’s measure of species evenness to estimate species richness and evenness across the samples based on observed ASVs, and relative abundance. Faith’s phylogenetic diversity (Faith_PD) index was also performed to account for the phylogenetic differences among observed species. For beta diversity, Bray–Curtis dissimilarity, Jaccard distance and UniFrac Distance (unweighted UniFrac and weighted UniFrac) were performed to evaluate the distance/dissimilarity between all the tick-groups [31]. Principal coordinates analysis (PCoA) was performed for their respective beta diversity metrics to visualize the community differences between the groups.

Statistical analysis

Samples from different provinces in the context of categorical metadata were analyzed for alpha diversity using the Kruskal–Wallis test to assess the change in richness and evenness within the groups (provinces) and the permutational multivariate ANOVA (PERMANOVA) statistical test was performed to analyze the diversity between groups/provinces (beta diversity).

Results

Illumina 16S rRNA (V3–V4) sequencing and bioinformatic analyses

In the case of R. microplus, 7.6 million reads were obtained from all 12 pools with an average of 636,000 reads per sample, while H. anatolicum pools yielded 6.9 million reads with an average of 575,000 reads per sample. Overall, 93.7% of clusters passed the QC filter with 649 k/mm2 cluster density. Raw sequences data were submitted into the NCBI SRA Database under the BioProjects PRJNA1100935 and PRJNA1098064 for R. microplus and H. anatolicum, respectively. After quality filtering, the forward and reverse reads were aligned and non-chimeric reads were excluded. R. microplus and H. anatolicum sequences resulted in 1200 and 968 unique ASVs, respectively. Each sample was rarefied at 5000 sequences to facilitate the analysis computation.

Comparative analysis of bacterial communities

The Coxiella-like endosymbionts (CLE) constituted more than half of the R. microplus microbial community from pools of PB, SH and BN ticks; CLE were comparatively less abundant in KPK ticks. Acinetobacter indicus constituted around 17% of the KPK R. microplus microbiome as a major contributor (Fig. 1). In the case of H. anatolicum, the most abundant genus was Candidatus Midichloria in eastern provinces of PB and SH. However, A. indicus and FLE were observed in the majority of KPK and BN samples of H. anatolicum, respectively (Fig. 2).

thumbnail Figure 1

Relative abundance showing that the Coxiella and Acinetobacter genera are dominant in all the samples. Coxiella-like endosymbionts occupied most of the Rhipicephalus microplus microbial composition from all the provinces except KPK, where Acinetobacter indicus is the dominant bacterium. Legend represents colors coding from top to bottom of abundance bars.
thumbnail Figure 2

Hyalomma anatolicum microbial abundance showing Candidatus Midichloria and Francisella-like endosymbionts (FLE) as the dominant genera of bacteria in all the samples, but KPK. Legend represents colors coding from top to bottom of abundance bars.

Alpha diversity

Microbial communities of R. microplus ticks were significantly diverse but phylogenetically not unique to each province. Also, the R. microplus Faith’s Phylogenetic Diversity index (Faith_PD) test did not show any significant difference within groups (p-value = 0.31, H-value = 3.51). The Shannon index showed significant diverse microbial communities within the R. microplus tick groups (Shannon index H-value = 8.64 and p-value = 0.03, Pielou’s evenness H-value 8.74, p-value = 0.03) (Fig. 3). Similarly, H. anatolicum did not present any significant diversity and evenness of species distribution within the groups (Faith-PD p-value = 0.21, H-value = 4.43; Pielou’s Evenness p-value = 0.31, H-value = 5.56) (Fig. 4). Nonetheless, there was enough richness to account for a statistically significant result when compared to the Shannon index (p-value = 0.03, H-value = 8.7) (Fig. 4).

thumbnail Figure 3

Rhipicephalus microplus: Faith’s Phylogenetic Diversity index (Faith_PD) p-value = 0.31, H-value = 3.51. Shannon Index (Shannon_entropy) p-value = 0.03, H-value = 8.64.
thumbnail Figure 4

Hyalomma anatolicum: Faith’s Phylogenetic Diversity index (Faith_PD), p-value = 0.21, H-value = 4.43. Shannon Index (Shannon_entropy), p-value = 0.03, H-value = 8.7.

Beta diversity

The Bray–Curtis dissimilarity (p-value = 0.005) (Fig. 5A) and Jaccard distance (p-value = 0.024) matrices revealed significant differences in R. microplus microbial diversity and distribution across provinces and represented in PCoA plots (Fig. 5C). Differences were noted for Weighted UniFrac (p-value = 0.003) and Unweighted UniFrac (p-value = 0.04) (data not shown). Additionally, H. anatolicum ticks presented a significant Bray–Curtis (p-value = 0.001) (Fig. 5B) and Jaccard distance (p-value = 0.001) (Fig. 5D) in microbial communities across the provinces, 5D). Weighted and Unweighted UniFrac both resulted in significance with p-value = 0.003 (data not shown).

thumbnail Figure 5

Principal coordinates analysis (PCoA) plots, A; Rhipicephalus microplus showed significant diversity on Bray–Curtis dissimilarity (p-value = 0.005) PERMANOVA test; otherwise most of the samples lie on the same axis and opposed dissimilarity. B; Hyalomma anatolicum Bray–Curtis clearly indicate that KPK and BN have higher dissimilarity as compared to PB and SH. C; Rhipicephalus microplus Jaccard distance indicates differences in microbial population based on presence and absence of ASVs. D; Hyalomma anatolicum Jaccard Distance clearly separated KPK and BN microbial populations.

Discussion

In our study, we analyzed H. anatolicum and R. microplus as these are two main ticks that infest the bovine population in Pakistan. The H. anatolicum tick, despite being a vector of lethal diseases like CCHF in humans [29] and theileriosis in cattle, is relatively understudied in terms of transcriptomic, genomic and microbiomic exploration. On the other hand, R. microplus has been widely studied for its microbiome, proteomic profile and genome database [25, 48, 51]. However, we aimed to investigate whether there are any differences in the microbiome profiles of H. anatolicum and R. microplus ticks in different regions of Pakistan.

Our findings indicate substantial variation in the microbiome, particularly in the case of H. anatolicum, suggesting that Hyalomma ticks in KPK harbor distinct bacterial communities compared to those previously reported. The beta diversity PCoA plots showed clear demarcation of KPK and Balochistan on an opposing axis. Balochistan has FLE as the top contributor to microbial counts (68%) and only 11.9% Candidatus Midichloria, whereas previous studies showed that their abundance was only higher if the tick is infected with Theileria protozoa [2]. Hyalomma anatolicum ticks of KPK presented Acinetobacter (16.3%) as the top contributor, unlike Candidatus Midichloria (0.43%) or FLE (1.16%), which contradicts the findings of previous studies about microbial communities of Hyalomma ticks [2, 9, 12]. In this study, only Punjab and Sindh H. anatolicum ticks showed somewhat conventional proportions of tick microbiome, consisting of a majority of Protobacteria (Rickettsiales, Francisellaceae and Moraxellaceae, in that order) for more than 80% in either case (Fig. 2). This scenario is better presented in the Bray–Curtis Matrix PCoA plot since Bray–Curtis also accounts for dissimilarity in species composition (Fig. 5B). The KPK and Balochistan microbial communities of H. anatolicum clustered on opposing coordinates. Punjab and Sindh, having predictable microbial communities, were cornered together on an axis away from KPK and Balochistan. It has already been well established that the tick microbiome is crucial for its health, reproduction and survivability [33, 34, 39, 52], and we suspect that this microbial change in Balochistan ticks has vitalized them more. The deviation from previous findings highlights the complex and dynamic nature of tick microbiomes, suggesting that environmental and geographic factors play a significant role in shaping the bacterial communities within H. anatolicum ticks. This variation may also reflect the ecological plasticity of this tick species, potentially enabling them to adapt to different habitats and climatic conditions. However, a comprehensive and controlled study is required to support this idea.

PCR amplification bias can also play a role in the distorted microbiome picture, but looking at other samples and absence of outliers in H. anatolicum removed this suspicion. Adding to that, there is a rich number of ASVs observed in this study from both ticks, especially when compared to previous studies [2, 42, 46]. However, a more accurate approach would be the use of Shotgun Metagenomic Sequencing to much more accurately analyze these microbial communities and abundance [15, 27]. The ticks from KPK showed a markedly more evenly distributed microbial population, where the top contributor was not only unpredictable, but had unexpectedly only 16% contribution, which is why KPK has the highest Shannon index points (Fig. 3), since the Shannon Index accounts for species richness in relation to their abundance (evenness) within the same sample. Usually, H. anatolicum ticks harbor one genus covering more than 50–70% of the total microbial population [5, 38], but surprisingly in this case of KPK ticks, not a single genus was able to occupy more than 16% of the total bacterial composition.

Another reason why Punjab and Sindh have similar microbial composition and abundance could be that these two provinces have regular cattle trade to and from one another. Movement of young calves, and beef and dairy cattle have for years hindered the development of a unique microbial population in these ticks. Balochistan in particular, has no evident trade of animals with any other province and with its harsh terrain and environment may have caused the ticks to harbor a slightly different microbiome over time. Our study is understandably insufficient to establish these patterns, but it presents the contrasting and interesting picture of H. anatolicum microbial composition.

On the other hand, our results for R. microplus with higher occurrence of Coxiella-like endosymbionts were in line with expected microbial communities reported previously [32]. However, two out of three pools of KPK province ticks contrasted, with A. indicus being the dominant species resulting in a skewed axis of KPK pools/samples on the Bray–Curtis Matrix PCoA plot (Fig. 5A). In most studies, both Coxiella and Acinetobacter occupy most (about 70–90%) of the R. microplus microbiome [42, 51]. Overall, the R. microplus across all provinces shared similar microbial composition; however, slightly changed abundance was observed (Figs. 1, 5A, 5C). This consistency across geographically diverse regions suggests that R. microplus ticks may maintain a more stable microbial composition regardless of environmental or ecological variations. Compared to H. anatolicum, R. microplus ticks may feed on a narrower range of hosts, reducing the diversity of microbial taxa they encounter. A stable host range could contribute to the consistency of their microbial communities across regions.

Given the role of tick microbiomes in vector competence, our findings raise important questions regarding the implications of these microbial differences for pathogen transmission dynamics in KPK and other regions of Pakistan. The variability in the H. anatolicum microbiome raises the possibility that regional differences in microbial communities could influence the tick’s ability to propagate more effectively and hence increase transmission rates of tick-borne diseases. The current study serves as a pilot study to further the investigation of H. anatolicum dynamic microbial adaptation, their effect on vectorial potential and overall survivability of ticks, with a larger sample size.

Conclusion

This study provides new insights into the geographic variation of tick microbiomes in Pakistan. While H. anatolicum ticks from the western provinces (Balochistan and Khyber Pakhtunkhwa) exhibit substantial microbial diversity, possibly influenced by environmental and ecological factors, R. microplus ticks maintain a more stable microbial community regardless of geographic location. This significant change, in a context where these western provinces have been reporting increases in cases of CCHF and theileriosis, may play a role in this adaptability and vectorial potential. These findings suggest that tick microbiomes are shaped by a combination of species-specific factors and environmental pressures, with important implications for the transmission of tick-borne diseases. Future research should focus on understanding the functional roles of microbial taxa in shaping tick vector competence and the potential for leveraging microbial interventions in tick control strategies.

Funding

This research was funded by the Higher Education Commission of Pakistan and The Grand Challenge Fund under grant number HEC-GCF-273.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Author contribution statement

A.A. and S.N. are equal contributors of this study as co-first authors. A.A. and S.N. prepared the initial samples, performed bioinformatics analysis, generated diagrams and tables, interpreted the data and wrote the primary manuscript. M.A. and A.B. helped to interpret the data and results. H.A. and M.R. conceptualized the study and objectives, supervised the research and experiments and secured the funding by winning the research project. All authors commented on previous versions of the manuscript and contributed to the final manuscript.

a

Contributed equally to this study as co-first authors.

References

  1. Abraham NM, Liu L, Jutras BL, Yadav AK, Narasimhan S, Gopalakrishnan V, Ansari JM, Jefferson KK, Cava F, Jacobs-Wagner C. 2017. Pathogen-mediated manipulation of arthropod microbiota to promote infection. Proceedings of the National Academy of Sciences, 114(5), E781–E790. [CrossRef] [PubMed] [Google Scholar]
  2. Adegoke A, Kumar D, Bobo C, Rashid MI, Durrani AZ, Sajid MS, Karim S. 2020. Tick-borne pathogens shape the native microbiome within tick vectors. Microorganisms, 8(9), 1299. [CrossRef] [PubMed] [Google Scholar]
  3. Akram F, Haq IU, Roohi A, Akram R. 2022. Acinetobacter indicus CCS-12: a new bacterial source for the production and biochemical characterization of thermostable chitinase with promising antifungal activity. Waste and Biomass Valorization, 13(7), 3371–3388. [CrossRef] [Google Scholar]
  4. Anderson JF, Magnarelli LA. 2008. Biology of ticks. Infectious Disease Clinics of North America, 22(2), 195–215. [CrossRef] [PubMed] [Google Scholar]
  5. Benyedem H, Lekired A, Mhadhbi M, Dhibi M, Romdhane R, Chaari S, Rekik M, Ouzari H-I, Hajji T, Darghouth MA. 2022. First insights into the microbiome of Tunisian Hyalomma ticks gained through next-generation sequencing with a special focus on H. scupense. PLoS One, 17(5), e0268172. [CrossRef] [PubMed] [Google Scholar]
  6. Binetruy F, Dupraz M, Buysse M, Duron O. 2019. Surface sterilization methods impact measures of internal microbial diversity in ticks. Parasites & Vectors, 12(1), 1–10. [CrossRef] [PubMed] [Google Scholar]
  7. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852–857. [CrossRef] [PubMed] [Google Scholar]
  8. Bonnet SI, Binetruy F, Hernández-Jarguín AM, Duron O. 2017. The tick microbiome: why non-pathogenic microorganisms matter in tick biology and pathogen transmission. Frontiers in Cellular and Infection Microbiology, 7, 236. [CrossRef] [PubMed] [Google Scholar]
  9. Buysse M, Floriano AM, Gottlieb Y, Nardi T, Comandatore F, Olivieri E, Giannetto A, Palomar AM, Makepeace BL, Bazzocchi C. 2021. A dual endosymbiosis supports nutritional adaptation to hematophagy in the invasive tick Hyalomma marginatum. eLife, 10, e72747. [CrossRef] [PubMed] [Google Scholar]
  10. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583. [CrossRef] [PubMed] [Google Scholar]
  11. Childs JE, Paddock CD. 2002. Passive surveillance as an instrument to identify risk factors for fatal Rocky Mountain spotted fever: is there more to learn? American Journal of Tropical Medicine and Hygiene, 66(5), 450–457. [CrossRef] [PubMed] [Google Scholar]
  12. Choubdar N, Karimian F, Koosha M, Oshaghi MA. 2021. An integrated overview of the bacterial flora composition of Hyalomma anatolicum, the main vector of CCHF. PLoS Neglected Tropical Diseases, 15(6), e0009480. [CrossRef] [PubMed] [Google Scholar]
  13. Cowdry E. 1925. A group of microorganisms transmitted hereditarily in ticks and apparently unassociated with disease. Journal of Experimental Medicine, 41(6), 817. [CrossRef] [PubMed] [Google Scholar]
  14. Díaz-Sánchez S, Hernández-Jarguín A, Torina A, de Mera IGF, Blanda V, Caracappa S, Gortazar C, de la Fuente J. 2019. Characterization of the bacterial microbiota in wild-caught Ixodes ventalloi. Ticks and Tick-Borne Diseases, 10(2), 336–343. [CrossRef] [PubMed] [Google Scholar]
  15. Durazzi F, Sala C, Castellani G, Manfreda G, Remondini D, De Cesare A. 2021. Comparison between 16S rRNA and shotgun sequencing data for the taxonomic characterization of the gut microbiota. Scientific Reports, 11(1), 3030. [CrossRef] [PubMed] [Google Scholar]
  16. Duron O, Morel O, Noël V, Buysse M, Binetruy F, Lancelot R, Loire E, Ménard C, Bouchez O, Vavre F. 2018. Tick-bacteria mutualism depends on B vitamin synthesis pathways. Current Biology, 28(12), 1896–1902. [CrossRef] [PubMed] [Google Scholar]
  17. Estaki M, Jiang L, Bokulich NA, McDonald D, González A, Kosciolek T, Martino C, Zhu Q, Birmingham A, Vázquez‐Baeza Y. 2020. QIIME 2 enables comprehensive end‐to‐end analysis of diverse microbiome data and comparative studies with publicly available data. Current Protocols in Bioinformatics, 70(1), e100. [CrossRef] [PubMed] [Google Scholar]
  18. Estrada-Peña A, Cabezas-Cruz A, Obregón D. 2020. Resistance of tick gut microbiome to anti-tick vaccines, pathogen infection and antimicrobial peptides. Pathogens, 9(4), 309. [CrossRef] [PubMed] [Google Scholar]
  19. Gall CA, Reif KE, Scoles GA, Mason KL, Mousel M, Noh SM, Brayton KA. 2016. The bacterial microbiome of Dermacentor andersoni ticks influences pathogen susceptibility. ISME Journal, 10(8), 1846–1855. [CrossRef] [PubMed] [Google Scholar]
  20. Guizzo MG, Parizi LF, Nunes RD, Schama R, Albano RM, Tirloni L, Oldiges DP, Vieira RP, Oliveira WHC, Leite MdS. 2017. A Coxiella mutualist symbiont is essential to the development of Rhipicephalus microplus. Scientific Reports, 7(1), 17554. [CrossRef] [PubMed] [Google Scholar]
  21. Hayes S, Burgdorfer W. 1982. Reactivation of Rickettsia rickettsii in Dermacentor andersoni ticks: an ultrastructural analysis. Infection and Immunity, 37(2), 779–785. [CrossRef] [PubMed] [Google Scholar]
  22. Jutras BL, Lochhead RB, Kloos ZA, Biboy J, Strle K, Booth CJ, Govers SK, Gray J, Schumann P, Vollmer W. 2019. Borrelia burgdorferi peptidoglycan is a persistent antigen in patients with Lyme arthritis. Proceedings of the National Academy of Sciences, 116(27), 13498–13507. [CrossRef] [PubMed] [Google Scholar]
  23. Karim S, Budachetri K, Mukherjee N, Williams J, Kausar A, Hassan MJ, Adamson S, Dowd SE, Apanskevich D, Arijo A. 2017. A study of ticks and tick-borne livestock pathogens in Pakistan. PLoS Neglected Tropical Diseases, 11(6), e0005681. [CrossRef] [PubMed] [Google Scholar]
  24. Kasi KK, von Arnim F, Schulz A, Rehman A, Chudhary A, Oneeb M, Sas MA, Jamil T, Maksimov P, Sauter‐Louis C. 2020. Crimean‐Congo haemorrhagic fever virus in ticks collected from livestock in Balochistan, Pakistan. Transboundary and Emerging Diseases, 67(4), 1543–1552. [CrossRef] [PubMed] [Google Scholar]
  25. Kopp N, Diaz D, Amacker M, Odongo DO, Beier K, Nitsch C, Bishop RP, Daubenberger CA. 2009. Identification of a synthetic peptide inducing cross-reactive antibodies binding to Rhipicephalus (Boophilus) decoloratus, Rhipicephalus (Boophilus) microplus, Hyalomma anatolicum anatolicum and Rhipicephalus appendiculatus BM86 homologues. Vaccine, 28(1), 261–269. [CrossRef] [PubMed] [Google Scholar]
  26. Kumar D, Sharma SR, Adegoke A, Kennedy A, Tuten HC, Li AY, Karim S. 2022. 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. Frontiers in Cellular and Infection Microbiology, 12, 787209. [CrossRef] [PubMed] [Google Scholar]
  27. Laudadio I, Fulci V, Palone F, Stronati L, Cucchiara S, Carissimi C. 2018. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. OMICS: A Journal of Integrative Biology, 22(4), 248–254. [CrossRef] [Google Scholar]
  28. Madder M, Horak I, Stoltsz H. 2014. Tick identification, Vol. 58. Faculty of Veterinary Science University of Pretoria: Pretoria. [Google Scholar]
  29. Manjunathachar HV, Kumar B, Saravanan BC, Choudhary S, Mohanty AK, Nagar G, Chigure G, Ravi Kumar GV, de la Fuente J, Ghosh S. 2019. Identification and characterization of vaccine candidates against Hyalomma anatolicum – Vector of Crimean‐Congo haemorrhagic fever virus. Transboundary and Emerging Diseases, 66(1), 422–434. [CrossRef] [PubMed] [Google Scholar]
  30. Mateos-Hernández L, Obregón D, Maye J, Borneres J, Versille N, de la Fuente J, Estrada-Peña A, Hodžić A, Šimo L, Cabezas-Cruz A. 2020. Anti-tick microbiota vaccine impacts Ixodes ricinus performance during feeding. Vaccines, 8(4), 702. [CrossRef] [PubMed] [Google Scholar]
  31. Mateos-Hernández L, Obregón D, Wu-Chuang A, Maye J, Bornères J, Versillé N, de La Fuente J, Díaz-Sánchez S, Bermúdez-Humarán LG, Torres-Maravilla E. 2021. Anti-microbiota vaccines modulate the tick microbiome in a taxon-specific manner. Frontiers in Immunology, 12, 704621. [CrossRef] [PubMed] [Google Scholar]
  32. Mesquita E, da Costa DP, Meirelles LN, Camargo MG, Corrêa TA, Bittencourt VREP, da Silva Coelho I, Santos HA, Humber RA, Golo PS. 2023. Entomopathogenic fungus treatment changes the gut bacterial diversity of Rhipicephalus microplus ticks. Parasites & Vectors, 16(1), 185. [CrossRef] [PubMed] [Google Scholar]
  33. Narasimhan S, Fikrig E. 2015. Tick microbiome: the force within. Trends in Parasitology, 31(7), 315–323. [CrossRef] [PubMed] [Google Scholar]
  34. Narasimhan S, Rajeevan N, Liu L, Zhao YO, Heisig J, Pan J, Eppler-Epstein R, DePonte K, Fish D, Fikrig E. 2014. Gut microbiota of the tick vector Ixodes scapularis modulate colonization of the Lyme disease spirochete. Cell Host & Microbe, 15(1), 58–71. [CrossRef] [PubMed] [Google Scholar]
  35. Pacheco I, Díaz-Sánchez S, Contreras M, Villar M, Cabezas-Cruz A, Gortázar C, de la Fuente J. 2021. Probiotic bacteria with high alpha-Gal content protect zebrafish against mycobacteriosis. Pharmaceuticals, 14(7), 635. [CrossRef] [PubMed] [Google Scholar]
  36. Paules CI, Marston HD, Bloom ME, Fauci AS. 2018. Tickborne diseases – confronting a growing threat. New England Journal of Medicine, 379(8), 701–703. [CrossRef] [PubMed] [Google Scholar]
  37. Perveen F. 2011. Distribution and identification of ixodid tick species on livestock in northern Pakistan. Journal of Agriculture, Science and Technology, 1, 73–81. [Google Scholar]
  38. Perveen N, Muzaffar SB, Vijayan R, Al-Deeb MA. 2022. Microbial composition in Hyalomma anatolicum collected from livestock in the United Arab Emirates using next-generation sequencing. Parasites & Vectors, 15(1), 30. [CrossRef] [PubMed] [Google Scholar]
  39. Poudel R, Jumpponen A, Schlatter DC, Paulitz T, Gardener BM, Kinkel LL, Garrett K. 2016. Microbiome networks: a systems framework for identifying candidate microbial assemblages for disease management. Phytopathology, 106(10), 1083–1096. [CrossRef] [PubMed] [Google Scholar]
  40. Price MN, Dehal PS, Arkin AP. 2010. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One, 5(3), e9490. [CrossRef] [PubMed] [Google Scholar]
  41. Rehman A, Nijhof AM, Sauter-Louis C, Schauer B, Staubach C, Conraths FJ. 2017. Distribution of ticks infesting ruminants and risk factors associated with high tick prevalence in livestock farms in the semi-arid and arid agro-ecological zones of Pakistan. Parasites & Vectors, 10(1), 190. [CrossRef] [PubMed] [Google Scholar]
  42. Rojas-Jaimes J, Lindo-Seminario D, Correa-Núñez G, Diringer B. 2021. Characterization of the bacterial microbiome of Rhipicephalus (Boophilus) microplus collected from Pecari tajacu “Sajino” Madre de Dios, Peru. Scientific Reports, 11(1), 6661. [CrossRef] [PubMed] [Google Scholar]
  43. Rosenberg R, Lindsey NP, Fischer M, Gregory CJ, Hinckley AF, Mead PS, Paz-Bailey G, Waterman SH, Drexler NA, Kersh GJ. 2018. Vital signs: trends in reported vectorborne disease cases – United States and Territories, 2004–2016. Morbidity and Mortality Weekly Report, 67(17), 496. [CrossRef] [PubMed] [Google Scholar]
  44. Sacchi L, Bigliardi E, Corona S, Beninati T, Lo N, Franceschi A. 2004. A symbiont of the tick Ixodes ricinus invades and consumes mitochondria in a mode similar to that of the parasitic bacterium Bdellovibrio bacteriovorus. Tissue & Cell, 36(1), 43–53. [CrossRef] [PubMed] [Google Scholar]
  45. Scoles GA. 2004. Phylogenetic analysis of the Francisella-like endosymbionts of Dermacentor ticks. Journal of Medical Entomology, 41(3), 277–286. [CrossRef] [PubMed] [Google Scholar]
  46. Segura JA, Isaza JP, Botero LE, Alzate JF, Gutiérrez LA. 2020. Assessment of bacterial diversity of Rhipicephalus microplus ticks from two livestock agroecosystems in Antioquia, Colombia. PLoS One, 15(7), e0234005. [CrossRef] [PubMed] [Google Scholar]
  47. Sheikh AS, Sheikh AA, Sheikh NS, Asif M, Afridi F, Malik MT. 2005. Bi-annual surge of Crimean-Congo haemorrhagic fever (CCHF): a five-year experience. International Journal of Infectious Diseases, 9(1), 37–42. [CrossRef] [Google Scholar]
  48. Tirloni L, Braz G, Nunes RD, Gandara ACP, Vieira LR, Assumpcao TC, Sabadin GA, da Silva RM, Guizzo MG, Machado JA. 2020. A physiologic overview of the organ-specific transcriptome of the cattle tick Rhipicephalus microplus. Scientific Reports, 10(1), 18296. [CrossRef] [PubMed] [Google Scholar]
  49. Tokarz R, Tagliafierro T, Sameroff S, Cucura DM, Oleynik A, Che X, Jain K, Lipkin WI. 2019. Microbiome analysis of Ixodes scapularis ticks from New York and Connecticut. Ticks and Tick-Borne Diseases, 10(4), 894–900. [CrossRef] [PubMed] [Google Scholar]
  50. Walker A, Matthews J, Preston P. 2005. The development of electronic keys for the identification of ticks. International Journal of Tropical Insect Science, 25(1), 2–5. [CrossRef] [Google Scholar]
  51. Zhang X-L, Deng Y-P, Yang T, Li L-Y, Cheng T-Y, Liu G-H, Duan D-Y. 2022. Metagenomics of the midgut microbiome of Rhipicephalus microplus from China. Parasites & Vectors, 15(1), 48. [CrossRef] [PubMed] [Google Scholar]
  52. Zhong J, Jasinskas A, Barbour AG. 2007. Antibiotic treatment of the tick vector Amblyomma americanum reduced reproductive fitness. PLoS One, 2(5), e405. [CrossRef] [PubMed] [Google Scholar]

Cite this article as: Mumtaz Abbasi A, Nasir S, Arshad Bajwa A, Akbar H, Muddassir Ali M & Imran Rashid M. 2024. A comparative study of the microbiomes of the ticks Rhipicephalus microplus and Hyalomma anatolicum. Parasite 31, 74. https://doi.org/10.1051/parasite/2024074.

All Figures

thumbnail Figure 1

Relative abundance showing that the Coxiella and Acinetobacter genera are dominant in all the samples. Coxiella-like endosymbionts occupied most of the Rhipicephalus microplus microbial composition from all the provinces except KPK, where Acinetobacter indicus is the dominant bacterium. Legend represents colors coding from top to bottom of abundance bars.
In the text
thumbnail Figure 2

Hyalomma anatolicum microbial abundance showing Candidatus Midichloria and Francisella-like endosymbionts (FLE) as the dominant genera of bacteria in all the samples, but KPK. Legend represents colors coding from top to bottom of abundance bars.
In the text
thumbnail Figure 3

Rhipicephalus microplus: Faith’s Phylogenetic Diversity index (Faith_PD) p-value = 0.31, H-value = 3.51. Shannon Index (Shannon_entropy) p-value = 0.03, H-value = 8.64.
In the text
thumbnail Figure 4

Hyalomma anatolicum: Faith’s Phylogenetic Diversity index (Faith_PD), p-value = 0.21, H-value = 4.43. Shannon Index (Shannon_entropy), p-value = 0.03, H-value = 8.7.
In the text
thumbnail Figure 5

Principal coordinates analysis (PCoA) plots, A; Rhipicephalus microplus showed significant diversity on Bray–Curtis dissimilarity (p-value = 0.005) PERMANOVA test; otherwise most of the samples lie on the same axis and opposed dissimilarity. B; Hyalomma anatolicum Bray–Curtis clearly indicate that KPK and BN have higher dissimilarity as compared to PB and SH. C; Rhipicephalus microplus Jaccard distance indicates differences in microbial population based on presence and absence of ASVs. D; Hyalomma anatolicum Jaccard Distance clearly separated KPK and BN microbial populations.
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.