Exploring the microbiomes of camel ticks to infer vector competence: Insights from tissue level symbiont-pathogen relationships

doi:10.21203/rs.3.rs-5224940/v1

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Exploring the microbiomes of camel ticks to infer vector competence: Insights from tissue level symbiont-pathogen relationships

https://doi.org/10.21203/rs.3.rs-5224940/v1

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Background

Ticks are blood-feeding ectoparasites that harbor diverse pathogens and endosymbionts. Their microbial communities vary based on tick species, stage, sex, geographical location, surrounding environment, and tissue type. Understanding tick microbiota at the tissue level is crucial for unraveling how microbiomes are distributed in tick tissues and influence pathogen transmission. We used 16S rRNA gene sequencing to analyze tissue-specific bacterial compositions (hemolymph, saliva, salivary glands, and midgut) of Amblyomma gemma, Rhipicephalus pulchellus, Hyalomma dromedarii, and Hyalomma rufipes ticks collected from camels in Marsabit County, northern Kenya.

Results

The V1-V2 region of the 16S rRNA gene effectively differentiated Rickettsia africae and Rickettsia aeschlimannii from other rickettsial species, as well as Coxiella endosymbionts from Coxiella burnetii. In contrast, the V3-V4 region sequences of these species could not be clearly distinguished. Coxiella endosymbionts were most common in Am. gemma and Rh. pulchellus, while Francisella endosymbionts predominated in Hyalomma ticks; both were primarily localized in the salivary glands. High abundances of Coxiella endosymbionts, as well as Pseudomonas, were associated with the absence or low abundance of Rickettsia pathogens in both Am. gemma and Rh. pulchellus, suggesting competitive interactions between these microbes. Additionally, Proteus mirabilis, an opportunistic pathogen of the urinary tract in humans, was found predominantly in Hyalomma ticks, except for the salivary glands, which were most abundant with Francisella endosymbionts. Furthermore, we detected the Acinetobacter, Pseudomonas, and Corynebacterium genera in all the tick tissues, supporting the hypothesis that these bacteria might circulate between camel blood and ticks. Saliva and hemolymph generally harbored more extracellular bacteria than the salivary glands and midgut.

Conclusions

This study provides a new approach to unravel tick-endosymbiont-pathogen interactions by examining the tissue localization of tick-borne pathogens and symbionts in Am. gemma, Rh. pulchellus, Hy. dromedarii, and Hy. rufipes from camels in northern Kenya. Our findings establish a baseline for developing an understanding of the functional capacities of symbionts and for designing symbiont-based control strategies.

Tick tissues

Amblyomma gemma

Rhipicephalus pulchellus

Hyalomma dromedarii

Hyalomma rufipes

Endosymbionts

Rickettsia

Proteus mirabilis

Tick-borne pathogens

Ticks are obligate blood-sucking ectoparasites widely known to transmit pathogens to animals and humans¹. In addition to tick-borne pathogens (TBPs), ticks host a diverse array of microorganisms, both commensal and symbiotic, constituting microflora known as the microbiome². The tick microbiome comprises bacteria, Archaea, viruses, protozoa, nematodes, and fungi³. The functional properties of bacterial microbiomes are still being investigated^4–6. Recent studies have provided evidence that the bacterial microbiome contributes significantly to the overall fitness and survival of ticks^3,4,7,8. Infection can also influence the likelihood of ticks becoming infected with pathogens by modulating the tick immune response^3,7,8. Characterizing tick microbiota is crucial for obtaining a better understanding of tick-microbiome interactions^4,9–11. Most related studies have focused on identifying the microbiomes of whole ticks¹². However, there is limited understanding of how different microbial communities are distributed across tick tissues¹³. Although microbial communities are found not only in the mucosal organs of ticks¹⁴, only one study has investigated tick saliva microbiomes¹⁵, and no studies have examined the microbiomes of tick hemolymph. Saliva and hemolymph are crucial for the spread of pathogens to mammalian hosts during the process of feeding and are also involved in modulating tick immune responses^16,17. Investigating microbial communities at the organ level is essential for unraveling pathogen transmission mechanisms and understanding the microbiome's influence on this process¹⁸. Additionally, this approach can elucidate the potential impact of antimicrobial vaccines aimed at blocking pathogen transmission by blocking their transfer between tick tissues¹⁹.

Understanding how microbiomes interact with TBPs is particularly important for devising strategies to reduce tick and TBP burdens in regions heavily reliant on livestock, such as camels in northern Kenya, which play a crucial role in regional economies. Kenya’s camel population was 4.7 million in 2020, approximately 6% of the African camel population^20,21. Annual camel meat and milk production in Kenya is estimated to be approximately US$ 11 million²¹. In northern Kenya, camels are infested mainly by four tick species, Rhipicephalus pulchellus, Amblyomma gemma, Hyalomma dromedarii, and Hyalomma rufipes ²². Camels in Kenya are associated with several bacterial and viral TBPs that can significantly impact both human and veterinary health. These include bacteria, such of the genera Rickettsia, Ehrlichia, and Coxiella ^23–25, and viruses such as the Iftin tick virus, Mbalambala tick virus, Bangali torovirus, Bole tick virus 4, Liman tick virus ²⁶, and Crimean-Congo hemorrhagic fever virus²⁷. Knowledge on the bacterial microbiomes of ticks infesting livestock in Africa is limited at present. However, some studies have begun to shed light on tick microbiomes. For instance, the bacterial community structures of Amblyomma variegatum, A. hebraeum, and Hyalomma truncatum ticks infesting cattle in South Africa have been characterized^28,29. Furthermore, Lee et al. (2019)³⁰ identified the microbial community of Am. gemma collected from black rhinos, Diceros bicornis, in Tanzania. Moreover, the bacterial microbiota of Rhipicephalus sanguineus (sensu lato) in Senegal was identified by René-Martellet et al.³¹. In Kenya, Coxiella endosymbionts, which play a pivotal role in tick physiology and reproduction, have been reported for various tick species, including Am. gemma, Am. variegatum, Rh. evertsi, and Rh. appendiculatus ^{24,27,32–34}. Additionally, Francisella endosymbionts, known for their involvement in tick nutrition³⁵, have been associated with the camel ticks Hy. dromedarii in Egypt³⁶ and Hy. rufipes in Ethiopia³⁷. These studies underscore the necessity of further research to elucidate symbiotic relationships and their impact on tick vector competence.

Next-generation sequencing techniques enable the identification of vector microbiomes through various platforms, such as Illumina MiSeq, which is known for its cost-effectiveness and accuracy^38,39. Amplicon sequencing targeting the 16S rRNA gene is widely used, due to its conserved and variable regions, to analyze microbiota by next-generation sequencing⁴⁰. Recently, 16S rRNA metabarcoding techniques have provided new insights into the diversity of dissected organs from engorged Ixodes ricinus ticks⁷. The bacterial community sizes of tick midguts are overall reduced in comparison to those of other hematophagous arthropods⁴¹. Evidence indicates that different tick tissues represent different microbial environments, yet the impact of such variation remains unclear^13,42. The microbiota in tick tissues can influence their immunity and pathogen transmission competency⁴³. A broader understanding of microbial diversity will be required for the manipulation of these microbes as a new strategy for blocking pathogen transmission. Here, we employed metabarcode sequencing to characterize the bacterial communities within tick tissues (saliva, hemloymph, salivary glands and midgut) of tick species infesting camels in northern Kenya.

2.1. Study area

This study was conducted in November 2022 in Marsabit County, northern Kenya. Marsabit County is the largest county in Kenya, with an area of ~ 66,923 km², and is located between longitudes 37°57' and 39°21'E and latitudes 02°45' and 04°27' N. It borders Wajir and Isiolo counties to the east⁴⁴. Marsabit County is located approximately 550 km north of Nairobi⁴⁵. Marsabit County is home to nomadic pastoralists who mainly keep camels, cattle, sheep, and goats and rely on mobile livestock production for their livelihoods. In Marsabit, the camel population is estimated to be 132,215⁴⁶. We collected ticks from dromedary camels (Camelus dromedarius) in Laisamis and from Saku in Marsabit County (Fig. 1).

2.2. Ethical approval

We obtained ethical approval from the Pwani University Ethics Review Committee (Ref: ERC/EXT/002/2020E) and research permit from the National Commission for Science Technology and Innovation (NACOSTI) under (Ref: NACOSTI/P/22/16467). Verbal consent was obtained from the camel farmers before sampling, as most of them could not read or write. We carried out the sampling process with the assistance of veterinarians and field assistants from each community. The field assistants explained the purpose of the study and assisted with translations.

2.3. Tick collection, transportation, identification, and dissection:

Out of 1778 adult ticks collected from 278 one-humped camels (Camelus dromedarius), during a larger, ongoing cross-sectional study on ticks and tick-borne pathogens, we selected 70 tick specimens from 18 camels to be representative of the four tick species infesting the camels. We transported the ticks alive to the Martin Lüscher Emerging Infectious Diseases (ML-EID) laboratory at the International Centre of Insect Physiology and Ecology (icipe) in Nairobi, where tissue collection and tick dissection were performed using a Stemi 2000-C microscope (Zeiss, Oberkochen, Germany) according to the methods described by Khogali et al.²². Within the constraints of efficient dissection and storage of tick tissues, we aseptically harvested 70 samples corresponding to four tick tissue sample types, viz. saliva (SL), hemolymph (HL), salivary glands (SGs), and midgut (MG). For each of the four tick species, the following tissue samples were obtained: Am. gemma (n = 19; HL = 5, SL = 4, SGs = 6, and MG = 4; ten females and nine males), Rh. pulchellus (n = 20; HL = 5, SL = 4, SGs = 6, and MG = 5; 11 females and 9 males), Hy. dromedarii (n = 18; HL = 5, SL = 4, SGs = 5, and MG = 4; 11 females and 7 males), and Hy. rufipes (n = 13; HL = 3, SL = 3, SGs = 4, and MG = 3; nine females and four males). All ticks were identified to the species level using taxonomic keys^47,48. We collected SL and HL from ticks according to Patton et al.⁴⁹ and Khogali et al.²². Subsequently, the ticks were dissected to isolate the SGs and MGs following the methods of Edwards et al.⁵⁰. Before dissection, the ticks were sterilized by three immersions in 1% bleach solution, followed by a ﬁnal rinse with nuclease-free water⁵¹. To avoid contamination by the HL, we washed the SGs and MGs with ﬁve sequential droplets of phosphate-buffered saline. Thereafter, the SGs and MGs were homogenized in sterile 1.5-mL Eppendorf tubes containing 750 mg of 2.0-mm stabilized zirconium oxide beads (Biospec, USA) using a Mini-Beadbeater-16 (BioSpec, Bartlesville, OK) to mechanically disrupt the tissue for 1 min.

2.4. DNA extraction

The SGs and MG homogenates as well as the SL and HL were processed for extraction. Total DNA was extracted from the tick samples using HighPrep™ Viral DNA/RNA kits (Magbio, Gaithersburg, USA) according to the manufacturer’s instructions.

2.5. PCR, library preparation and sequencing

To determine the bacterial microbiome profiles, we amplified the hypervariable regions, V1-V2 and V3-V4, of the 16S ribosomal RNA (rRNA) gene from tick tissues (SL, HL, SGs and MG). This was achieved using primers targeting the 16S rRNA V1-V2 (27F: 5'-GAGTTTGATCMTGGCTCAG-3'; 388R: 5'-GCTGCCTCCCGTAGGAGT-3') and V3-V4 (341F: 5'-CCTACGGGNGGCWGCAG-3'; 805R: 5'-GACTACHVGGGTATCTAATCC-3') regions. DNA amplicons were shipped to Macrogen Europe (The Netherlands) for sequencing, and the library was prepared according to the Illumina standard protocol for MiSeq. Overhang adapters were incorporated into the primers using 2x KAPA HiFi HotStartReadyMix. AMPure XP beads were utilized to purify the amplicons from primers and primer dimers. Dual indices and Illumina sequencing adapters were attached using the Herculase II Fusion DNA Polymerase Nextera XT Index V2 Kit. Sequencing was performed with 300-bp paired-end reads in the V3 chemistry MiSeq instrument. Quality control was performed using FastQC, and the primers were identified and trimmed using Cutadapt (v4.1).

2.6. Bioinformatics and phylogenetic analysis

The raw amplicon sequences obtained from the Illumina MiSeq platform were preprocessed using the nf-core/ampliseq (v2.6.1) pipeline. This pipeline was implemented using Nextflow (v21.10.3)⁵² and Singularity (v3.6.3) for optimal processing efficiency. The Divisive Amplicon Denoising Algorithm 2 (DADA2; v1.26.0)⁵³ analysis workflow generated amplicon sequence variant (ASV) abundance and taxonomic classification tables. The DADA2 preprocessing parameters used were Filter and Trim, Denoise, Chimera removal, Dereplication, Read Merge, ASV inference, and Taxonomy Assignment. Taxonomic assignment utilized the “Silva=138” database⁵⁴ and the Basic Rapid Ribosomal RNA Predictor (Barrnap, v0.9). Subsequently, the filtered ASV abundance matrix, taxonomy table, and metadata were merged into a phyloseq R object to determine differential microbiota abundance among tick species using the phyloseq (v1.41) package⁵⁵. For the heatmap, data normalization was performed through log10 transformation using GraphPad Prism version 0.8⁵⁶. The metagMisc (v.0.04) package facilitated the manipulation and visualization of the phyloseq object, offering insights into microbiome abundances and percentages across various factors, such as tick tissues (SL, HL, SGs, and MG), and tick species (Am. gemma, Hy. dromedarii, Hy. rufipes, and Rh. pulchellus). The Microbiota Process function (v1.9.3) was used to assess prokaryotic species richness and diversity based on ASV read counts. Alpha diversity metrics, including the Chao1, Pielou evenness, and Shannon indices, were calculated using the phyloseq (v1.42.0) and vegan (v2.6-4) packages to gauge microbial diversity. A principal coordinate analysis (PCoA) was conducted on the phyloseq R object using Bray-Curtis dissimilarity to identify factors contributing to bacterial diversity variation. Permutational multivariate analysis of variance (PERMANOVA) was used to compare bacterial populations across tick tissues (SL, HL, SGs, and MG) and tick species (Am. gemma, Hy. dromedarii, Hy. rufipes, and Rh. pulchellus). These analyses collectively provided a solid understanding of the intricate microbial dynamics within the tick microbiome. We extracted, edited, trimmed, and aligned the sequences of the V1-V2 and V3-V4 16S rRNA genes using Geneious Prime software v. 2020.2.2 (created by Biomatters, Auckland, New Zealand)⁵⁷. We obtained the sequences of Rickettsia spp. and Coxiella endosymbionts from the four ticks, and aligned them using the MAFFT plugin within the Geneious software. Nucleotide sequences were queried against known annotated sequences in the GenBank nr database (http://www.ncbi.nlm.nih.gov) using BLAST nucleotide search⁵⁸ to confirm their identity and relation to homologous reference sequences. Maximum-likelihood phylogenies were constructed using PhyML version 3.0, using the Akaike information criterion for automatic model selection⁵⁹. Tree visualization was performed with FigTree version 1.4.4⁶⁰. All V1-V2 and V3-V4 region sequences were submitted to the SRA database of NCBI (http://www.ncbi.nlm.nih.gov) under the BioProject accession: PRJNA1134173 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1134173); V1-V2 16S rRNA gene sequences (SRA accessions: SRX25265166 to SRX25265249); and V3-V4 16S rRNA gene sequences (SRA accessions: SRX25266559 to SRX2526669).

3.1. Sequence analyses

After preprocessing 4,944,524 16S V1-V2 rRNA amplicon reads, we obtained 4,060,361 quality reads from the four tick species, which generated a total of 3,742 ASVs. A total of 18 phyla, 29 classes, 161 families, and 450 genera were recovered from the 16S rRNA ASVs. Similarly, from 4,584,849 raw reads of the V3-V4 16S rRNA variable region, we obtained 3,317,439 filtered reads after quality control, filtering, denoising, and chimera removal, with 9,241 ASVs generated. A total of 11 phyla, 34 classes, 150 families, and 546 genera were obtained from the V3-V4 region. Unlike those of the V3-V4 sequences, the V1-V2 sequences were effective at differentiating Rickettsia africae and Rickettsia aeschlimannii pathogens from the other rickettsial species as well as Coxiella endosymbionts from Coxiella burnetii (Supp. Figure 1A). Therefore, we henceforth only report the V1-V2 16S rRNA microbial diversity of the tick samples.

3.2. Bacterial relative abundance

3.2.1. Tick species

Taxonomic assignment based on the V1-V2 16S rRNA regions revealed notable similarities in the abundances of some bacteria between Am. gemma and Rh. pulchellus, and between Hy. dromedarii and Hy. Rufipes (Figure 2). Coxiella endosymbionts were abundant in Rh. pulchellus (16.3%) and Am. gemma (11.6%). In contrast, Francisella was the predominant endosymbiont, especially in the salivary glands, in Hy. rufipes (42.5%) and Hy. dromedarii (33.5%), whereas Proteus was predominant the other tissues. Acinetobacter was the most abundant bacterial genus in Rh. pulchellus, especially in the hemolymph and saliva. Remarkably, Rickettsia was the most abundant genus in Am. gemma (36.6%). Candidatus Midichloria, though less abundant, was found mainly in the midgut and saliva of Hy. rufipes but absent in Am. gemma (Supp. Table 1; Figure 2). BLAST analysis confirmed the ASV identities of Coxiella endosymbionts, Francisella endosymbionts, Proteus mirabilis and R. africae in all four tick species. In addition, we found Ehrlichia ruminantium in Am. gemma and R. aeschlimannii in Rh. pulchellus, Hy. rufipes, and Hy. dromedarii.

3.2.2. Bacterial compositions of tick tissues

We observed that low abundances of Coxiella endosymbionts correlated with high levels of Rickettsia in the HL, SGs, and MG of Am. gemma. In contrast, there were high abundances of Coxiella endosymbionts in the SGs and MG of Rh. pulchellus, coinciding with low levels of Rickettsia. Ehrlichia ruminantium was detected in extracts from only two different Am. gemma ticks (one from an MG and one from SGs). We observed that Acinetobacter was more abundant in the HL and SL of Rh. pulchellus and Am. gemma, while Pseudomonas was more abundant in the SGs and MGs of Rh. pulchellus. Notably, there was also a negative correlation between Pseudomonas and Rickettsia in both Am. gemma and Rh. pulchellus (Supp. Table 2, Figure 3A & 3B). Francisella endosymbionts were predominantly found in the SGs of both Hy. rufipes and Hy. dromedarii. Intriguingly, a negative correlation was observed between the abundance of Francisella endosymbionts and that of Proteus. Among Hyalomma spp., Proteus was highly prevalent in the HL, MG, and SL samples, whereas the abundance of Francisella endosymbionts was low. Cutibacterium, Staphylococcus, Pseudomonas and other bacteria were highly concentrated in the HL and SL and less so in the SGs and MG of Hylomma spp. (Supp. Table 2, Figure 3C & 3D).

3.3. Alpha diversities

3.3.1. Tick species

We assessed the alpha diversities of the four tick species, Am. gemma, Rh. pulchellus, Hy. dromedarii, and Hy. rufipes, using three diversity metrics (Chao1, Shannon, and Evenness) (Figure 4). The Shannon index revealed that Rh. pulchellus had the most bacterial diversity and richness. The Rh. pulchellus bacterial diversity was significantly greater than that of Hy. dromedarii (P < 0.01), and the bacterial richness index indicated that Rh. pulchellus also had higher bacterial species richness than Hy. rufipes and Hy. dromedarii (P < 0.01) (Supp. Table 3).

3.3.2. Comparing the alpha diversities of tick tissues within tick species

In Am. gemma, we observed that the bacterial richness and diversity were significantly greater in the HL and SL than in the SGs (P < 0.01). These bacteria were more evenly distributed in the HL than in the SGs (P < 0.05). In contrast, the bacterial diversity was greater in the MG than in the HL (P < 0.05) of Rh. pulchellus. Furthermore, in Hy. dromedarii, the bacterial diversity and richness in the SL was greater than those in the SGs and MG (P > 0.01), and these bacteria were more evenly distributed in the SL than in the SGs (P > 0.05) and MG (P > 0.01). The bacterial richness in the SL of Hy. rufipes was significantly greater than that in the SGs and MG (P < 0.01). These bacteria were significantly more diverse in the SL than in the SGs (P < 0.01), and the bacteria were more evenly distributed in the SL than in the SGs (P < 0.05) and in the MG (P < 0.01) (Figure 5 and Supp. Table 4).

3.3.3. Comparing the alpha diversities of the different tick tissues between tick species

There were no significant differences in the alpha diversities of the HL and SL samples among the four tick species. However, the alpha diversity of the MG of Rh. pulchellus was greater than that of the MG of Hy. dromedarii (P < 0.01), Hy. rufipes (P < 0.05), and Am. gemma (P < 0.05), and its bacterial richness was also greater than that in Hy. rufipes and Hy. dromedarii (P < 0.01). Additionally, the SGs of Rh. pulchellus showed greater bacterial richness than those of Hy. dromedarii and Hy. rufipes (P < 0.05) (Figure 6 and Supp. Table 5).

3.4. Beta diversity

We assessed beta diversity using PCoA to determine the dissimilarity and diversity of bacterial communities between the tick species and tick tissues using Venn diagrams of core taxa to show the percentages of the shared and unique bacteria. We found that the bacterial composition was significantly different among the four tick species (PERMANOVA, P < 0.001); Hy. dromedarii and Hy. rufipes samples were clustered together, and Am. gemma and Rh. pulchellus formed discrete clusters (Figure 7A). The Venn diagram shows that Hy. dromedarii and Hy. rufipes harbored fewer unique bacterial species than Rh. pulchellus and Am. gemma, which exhibited greater diversities of unique bacteria (Figure 7B). Additionally, the PCoA showed that the SL samples clustered significantly with the HL samples and that the SG samples clustered significantly with the MG samples; this was more apparent in Rh. pulchellus (P < 0.001) (Figure 8B) than in Am. gemma (Figure 8A), Hy. dromedarii (Figure 8C), and Hy. rufipes (P < 0.05) (Figure 8D). Interestingly, the Venn diagram shows that, among the four tick species, the SL and HL exhibited greater numbers of unique bacterial species and shared more bacterial species than did the SGs and MG. (Figure 8).

Using 16S rRNA metabarcode sequencing, we characterized the bacterial composition of the tissues (SL, HL, SGs, and MG) of four tick species (Am. gemma, Rh. pulchellus, Hy. dromedarii, and Hy. rufipes) collected from camels in northern Kenya. The phylogenetic analysis of ASV sequences with reference sequences showed that, unlike the V3-V4 region, the V1-V2 16S rRNA sequences could clearly distinguish R. africae from the other rickettsial species, as well as Coxiella endosymbionts from the C. burnetii pathogen.

We identified Coxiella endosymbionts (CEs) predominantly in Rh. pulchellus and Am. gemma, while Francisella endosymbionts (FEs) were primarily found in Hyalomma ticks. These endosymbionts produce B vitamins, which are crucial for cell growth and energy metabolism and compensate for nutrient deficiencies in blood meals³⁵. Similarly, CEs were previously identified as the predominant endosymbionts associated with Rh. sanguineus, Rh. turanicus and Rh. Microplus^61,62. We found that CEs and FEs were most highly concentrated in the SGs, followed by the MG, and less abundant in HL and SL samples.

Our findings are congruent with the fact that CEs are predominantly concentrated in SGs of Am. americanum, suggesting that they play a significant role in blood-sucking during which they interact with other microbes⁶³. Other observations suggest that CEs may aid tick feeding and infestation by promoting the expression of DA-P36 proteins, which help inhibit blood-meal host immune responses. These proteins, identified in the SGs of Rh. microplus in the presence of CEs⁶⁴, play a crucial role in inhibiting T-lymphocyte proliferation, disrupting the host immune response to tick infestation^65-67. Moreover, a high prevalence of R. africae linked with a low prevalence of CEs was previously reported in Am. gemma³⁴, and in Rh. sanguineus, Dermacentor andersoni, and Dermacentor variabilis^61,68,69. Buysse et al.⁷⁰ hypothesized that CEs might protect ticks against TBPs. However, the ability of CEs to block R. africae transmission needs further investigation.

The presence of CEs in the HL is likely due to their role in recycling metabolites to synthesize essential compounds like B vitamins⁷¹. Rickettsia africae was highly abundant in the HL of Am. gemma. The ability of Amblyomma species to transmit R. africae is well known, and its high abundance in the HL supports the competency of Am. gemma to transmit this pathogen²². The transmissibility of other bacteria from ticks to camels, which were highly concentrated in the HL, needs to be investigated. To date, most studies have focused on whole-tick microbiome analysis. However, our study provides a comprehensive approach to better understand the interactions between TBPs and endosymbionts within specific tissue environments.

Francisella endosymbionts (FEs) were found in high abundance in Hyalomma ticks, with lower levels in Am. gemma and Rh. pulchellus. The obligatory symbiotic relationship between FEs and Hyalomma spp. was previously shown by Azagi et al.⁷². Consistent with the findings of Ravi et al.⁷³, Elbir et al.⁷⁴, and Perveen et al.³⁹, this study reports the highest prevalence of FEs in Hy. dromedarii. We observed the highest concentrations of FEs in the SGs of the two Hyalomma species. These results concur with those from previous studies^72,75, which showed that FEs exhibited predominant localization in the SGs of Hyalomma spp. The high occurrence of FEs in the SGs suggests that their potential role in supplying B vitamins could be important for cell growth and potentially saliva production^35,76. The colonization of SGs by FEs also suggests that FEs might play a key role in horizontal transmission of microbes to vertebrate hosts. Furthermore, FEs may interact with various pathogens inside the SGs, potentially facilitating pathogen transmission⁷⁷. We hypothesize that FEs may play a key role in modulating microbiomes and optimizing conditions for effective saliva production during blood-feeding. However, additional studies are needed to understand the interactions between FEs and pathogens transmitted by Hyalomma spp.

We identified Proteus mirabilis in all tick species and tissues; it was most abundant in Hyalomma spp., particularly in Hy. dromedarii. This bacterium, recognized as an opportunistic pathogen, was previously identified in Dermacentor andersoni⁷⁸. Proteus mirabilis has also been identified in Amblyomma spp. and Rhipicephalus spp. in Kenya⁷⁹. Proteus mirabilis has been associated with urinary tract infection in humans and animals^80,81 and detected in camels with conjunctivitis infections⁸². Our findings revealed high abundances of P. mirabilis across all tick tissues, particularly in the HL, SL, and MG, with the lowest abundances in the SGs. The high abundance in the MG could be due to the symbiotic relationship between the ticks and P. mirabilis, which is reflected by hemolytic activity, contributing to efficient blood digestion in the MG⁸³. Given the circulation of P. mirabilis in all tissues and the high concentration in the HL, further investigations are needed to confirm the transmission of this bacterium by Hyalomma species.

We identified Acinetobacter spp., Pseudomonas spp., and Corynebacterium spp. among all the tick species. While some studies suggest that these bacteria might be contaminants⁸⁴, others indicate that they could be environmental bacteria acquired by ticks and maintained throughout the tick life cycle^85-87. Notably, Mohmed et al.⁸⁸ reported the presence of these bacteria in camel blood. Along with our finding of these bacteria in different tick tissues, we suggest that these bacteria circulate between camel blood and ticks. These three bacterial genera were also identified at tick bite sites⁸⁹. They are likely to influence pathogen transmission by modulating inflammation and the host’s response to tick bites⁹⁰. Interestingly, we noticed an inverse correlation between the abundance of R. africae and Pseudomonas spp. in Am. gemma, which requires further investigation.

According to the alpha diversity analysis, Rh. pulchellus had a greater bacterial composition and diversity than did the other tick species, and its MGs had more diverse bacteria than did the other organs. This could be due to its diverse range of host animals²², including humans⁹¹; Rh. pulchellus questing ticks opportunistically attach to hosts as they pass by. Generally, as we observed for the other tick species, the bacterial diversity of their MGs were lowest. In ixodid ticks in particular, the bacterial diversity of MG was reduced after a blood meal^41,92. This bacterial reduction could be due to bacterial digestion within tick digestive cells through endocytosis⁹⁴ or the influence of tick immunity on bacterial populations in tick MG, facilitated by antimicrobial peptides and reactive oxygen species^95,97. Remarkably, we identified higher abundances of Pseudomonas in the MG of Rh. pulchellus than in other organs. Pseudomonas species have been found to detoxify phytotoxins such as terpene. For instance, Pseudomonas in the mountain pine beetle (Dendroctonus ponderosae) was found to be rich in genes encoding terpene-degrading enzymes, which are frequently detected in the gut metagenome^97-99. The high abundance of Pseudomonas in Rh. pulchellus may suggest a detoxification mechanism for the tick while it is questing on plants.

In general, we found greater bacterial diversity and richness in the SL and HL than in the MG and SGs in the Am. gemma, Hy. dromedarii, and Hy. rufipes. Similarly, greater diversity was observed in the SL than in the MG of Dermacentor silvarum¹⁵. According to Itoh et al.⁹⁹, symbiotic bacteria in insects generally exhibit tissue tropism and are localized in symbiotic organs. These bacteria can range from extracellular, within the body cavity, to intracellular, within specialized cells. Beta diversity analysis (PCoA) indicated that the SL and HL shared similar bacterial compositions, as did the SGs and MG. This is largely explained by the fact that we identified many extracellular genera concentrated in the SL and HL, while intracellular bacteria such as Coxiella, Francisella, and Rickettsia were found mainly in the SGs and MG. Nonetheless, extracellular bacteria, including Acinetobacter, Pseudomonas, Staphylococcus, Corynebacterium, and Sphingobacterium, were found mainly in the MG of Rh. pulchellus, indicating that if the gut lumen harbors such extracellular bacteria, the bacteria must be capable of surviving the heme-filled lumen, the toxic reactive oxygen species from neutrophils and macrophages, and proteases in blood meals⁴. Narasimhan et al.¹⁰⁰ identified a similar complement of extracellular bacteria in the gut of questing Ixodes scapularis tick nymphs. The most detoxifying symbiosis, referred to as 'gut symbiosis', involves bacteria that are extracellularly localized in the lumen of the gastrointestinal tract in various insects, such as Acinetobacter and Pseudomonas⁹⁹. Understanding the mechanisms by which endosymbionts are distributed in different tick tissues could reveal new strategies for tick control.

Our study provides new insights into the tissue-specific localization of symbionts in four tick species infesting camels in northern Kenya, thus enhancing our understanding of their functional roles and interactions in different tissue environments. This study represents the first bacterial microbiome characterization of four tick tissues (hemolymph, saliva, salivary glands, and midgut) in Am. gemma, Rh. pulchellus, Hy. dromedarii, and Hy. rufipes. This work contributes to the understanding of the interactions between TBPs and endosymbionts in specific tissues, thereby enhancing our knowledge of vector competence. Additionally, we highlight the advantage of using the V1-V2 region, rather than the more commonly used V3-V4 region, for 16S rRNA metabarcoding of tick microbiomes. The ASVs generated from the V1-V2 16S sequences clearly differentiated Coxiella endosymbionts from the C. burnetii pathogen and R. africae and R. aeschlimannii from other rickettsial species. We show that both tick species and their specific tissues are determining factors for the colonization of microbial communities. For instance, the HL and SL tend to select for extracellular bacteria, while SGs and MG contain most intracellular bacteria. Further investigations are needed to understand how the distinct tick tissue microbiomes affect tick immunity and pathogen transmission. The interactions between Coxiella endosymbionts, Pseudomonas, and R. africae warrant further investigations. Understanding the relationships between ticks and their bacterial endosymbionts is a crucial step toward developing symbiont-based tick and TBP control strategies.

Acknowledgments

We thank the Marsabit County Veterinary Services Department staff and all camel owners and herdsmen who consented to our request to collect ticks from their camels. We are also grateful to David Wainaina at icipe for providing technical support.

Funding information

The authors gratefully acknowledge the financial support for this research by the following organizations and agencies: European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Program under grant agreement No. 101000365 (PREPARE4VBD); the Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); the Australian Centre for International Agricultural Research (ACIAR); the Government of Norway; the German Federal Ministry for Economic Cooperation and Development (BMZ); and the Government of the Republic of Kenya. RK was supported by a German Academic Exchange Service (DAAD) through an icipe ARPPIS-DAAD scholarship and through a UP post-graduate bursary. The views expressed herein do not necessarily reflect the official opinion of the donors.

Author contributions

RK: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing original draft, Writing – review & editing. AB: Conceptualization, Supervision, Writing – review & editing. DG: Data curation, Investigation, Methodology, Writing – review& editing. JB: Investigation, Writing review & editing, SK: Supervision, Writing – review & editing. NO: Data curation, Bioinformatic analysis. JTPV: Data curation, Bioinformatic analysis. JK: Investigation, Methodology, Writing review & editing. JN: Investigation and Methodology. DM: Conceptualization, Supervision, Writing review & editing. JV: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

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 potential conﬂicts of interest.

Data availability statement

The datasets utilized in this study are available in online repositories. The names of the repositories and their respective accession numbers are provided as follows: https://www.ncbi.nlm.nih.gov/genbank under the BioProject accession number: PRJNA1134173 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1134173); SRX25265166-SRX25265249 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1134173); and SRX25266559-SRX2526669 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1134173).

Ethics statement

The animal studies were approved by the Pwani University Ethics Review Committee (Ref: ERC/EXT/002/2020E), a license from the National Commission for Science Technology and Innovation (NACOSTI) under (Ref: NACOSTI/P/22/16467). The studies were conducted in accordance with local legislation and institutional requirements. Written informed consent was not obtained from the owners for the participation of their animals in this study prior to livestock sampling. However, verbal and non-written consent was obtained from the livestock keepers, as most of the participants were unable to read or write. Field assistants from the community assisted in translating the language from English to the local language spoken by the community members to ensure that they understood the purpose of the study and how it would beneﬁt them.

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Exploring the microbiomes of camel ticks to infer vector competence: Insights from tissue level symbiont-pathogen relationships

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