Abstract
Tick-borne rickettsioses, caused by Gram-negative bacteria of the Rickettsia genus, pose a growing global threat, with various arthropod vectors contributing to their transmission. Understanding the complex interactions within tick microbiota, including the role of Rickettsia species, is crucial for elucidating the dynamics of rickettsial diseases. Here, we investigate the taxonomic profiles and co-occurrence networks of Rickettsia in Rh. sanguineus sensus lato (s.l.) and Rh. turanicus ticks, revealing significant differences in community composition and local connectivity of Rickettsia species. While the microbiota of both tick species share common taxa, distinct differences in relative abundance and network topology suggest unique ecological niches. Moreover, robustness analysis demonstrates varying resilience to perturbations, indicating different strategies for network organization. Our findings also highlight metabolic differences between tick species, suggesting potential implications for Rickettsia interactions. Overall, this study provides insights into the intricate microbial landscape within ticks, shedding light on the functional redundancy and metabolic pathways associated with Rickettsia, thus advancing our understanding of tick-borne diseases.
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Introduction
Rickettsioses are vector-borne diseases caused by obligate intracellular Gram-negative bacteria within the Rickettsia genus, presenting an emergent growing global threat1. Indeed, rickettsial bacteria have developed adaptations to diverse environmental conditions, including various arthropod vectors (i.e. ticks, fleas, lice, and mites2), and display a spectrum of mutualistic and pathogenic interactions3. These bacteria employ transmission patterns that can involve either strict vertical transmission or a combination of horizontal and vertical transmission routes2. Pathogenic Rickettsia species have adapted to their specific vectors, evaded immune responses, persisted in essential organs, and exploited arthropod molecules4. Variations in transmission mechanisms within the genus explain why some Rickettsia species are virulent with horizontal transmission, while others are harmless endosymbionts with vertical transmission2.
For decades, rickettsial pathogens have emerged and re-emerged alongside vector-borne rickettsial diseases, posing a threat to human and animal health worldwide. The significance of tick-associated rickettsial pathogens has grown substantially in the past 25 years, highlighting the evolution of these infections1. In Palestine, a study found that the rickettsial spotted-fever-group was detected in approximately 17% of the hard ticks collected from domestic animals5. The ticks Rhipicephalus sanguineus sensus lato (s.l.) and Rhipicephalus turanicus are commonly found on the same hosts (such as dogs, sheep and goats5,6) and are morphologically close7. Many tick borne-bacteria (e.g. Anaplasma platys, Anaplasma spp., Ehrlichia canis, Bartonella rochalimae, Bartonella chomelii, Bartonella koehlerae8,9), viruses (canine parvovirus10) and protozoa (e.g. Theileria ovis, Babesia ovis, Babesia vogeli, Heptazoon canis6,11) have been identified in these two tick species. Notably, these findings underscored the significant role of Rh. sanguineus s.l. as the most prevalent species, infesting a diverse range of mammals, including dogs, sheep, goats, and horses. However, Ereqat et al.5 have noted that the primary vector responsible for the transmission of SFG rickettsiae in this study was Rh. turanicus.
Numerous species of Rickettsia (i.e. R. conorii, R. felis, R. africae, R. massiliae and R. sibirica mongolitimonae5,12) were found in Rhipicephalus ticks in the region. A phenomenon of rickettsial exclusion was observed by Burgdorfer et al.13, where the infection of R. peacockii in Dermacentor andersoni ticks blocked the ovarian infection of the pathogen R. rickettsii. Similar blocking phenomena of a rickettsial pathogen by another rickettsial species have been reported in different other tick species14,15. However, the occurrence rickettsial exclusion appears to be complex, as reports of this absence have also been documented16. Further investigation is warranted to elucidate the interactions among pathogenic and non-pathogenic rickettsial species, as well as their interactions with other bacterial species within the tick microbiota.
Besides, tick microbial community is an important actor in the colonization and transmission of tick-borne diseases17. When a pathogen enters the tick’s organs through blood meals, it faces a hostile environment due to midgut immunity2,18. The newly acquired pathogen interacts with the tick’s microbial midgut and will be able to persist in the ectoparasite’s body. For successful transmission to a healthy host, the pathogen must migrate to the salivary glands and interface with distinct microbiota. The microbiota of Rh. sanguineus s.l. and Rh. turanicus is mainly composed of Coxiella and Rickettsia species7,19,20. Rickettsial endosymbionts have a major role in the tick's microbiota, potentially providing nutritional symbiosis2. Nonetheless, the presence of Rickettsia helvetica, an emerging pathogen of the spotted fever group, was reported to affect Ixodes ricinus microbiota by reducing its diversity and microbial interactions21. However, in both Hyalomma marginatum and Rhipicephalus bursa ticks originating from the same region, the presence of rickettsial pathogens appears to promote the prominence of certain bacterial taxa within the tick microbiota. This prominence plays a crucial role in maintaining community stability and may potentially facilitate the colonization of Rickettsia22.
Despite their lower abundance, certain microbial members significantly influence their environments23. Network analysis offers profound insights into these effects, particularly elucidating the complex relationships between Rickettsia pathogens and the bacterial microbiota in questing ticks and those feeding on domestic animals. It also helps identify key factors driving Rickettsia's interactions within these communities24. This analytical approach aims to reveal the dynamics and nature of bacterial interactions, enhancing our understanding of community assembly, stability, and the behavioral intricacies of such complex systems24. Specifically, by analyzing microbial networks, in this study we seek to understand the interactions of Rickettsia at both the taxonomic and functional levels, elucidating their potential implications for microbial fitness, composition, and metabolic functionalities.
Although the composition of microbiota is associated with the metabolic pathways expressed within ticks, research25 has shown that despite the lack of shared taxa in the microbiota, the majority of metabolic pathways are still expressed. Therefore, the microbiota can exhibit high diversity while contributing to the same metabolic pathways25. Furthermore, it has been demonstrated that a limited set of taxa can fulfill the majority of metabolic requirements for ticks, even in the face of environmental stress26. This study aims to investigate whether Rickettsia associated with different taxa exhibit similar functions, thereby exploring the functional redundancy of the microbiome in the presence of Rickettsia. Ultimately, we aim to elucidate the metabolic pathways through which Rickettsia interacts with the broader microbiome, thus advancing our understanding of the microbial landscape within ticks and their hosts.
Results
Taxonomic profiles of Rh. sanguineus s.l. and Rh. turanicus microbiota
The analysis of beta and alpha diversity revealed that the microbiota of Rh. sanguineus s.l. and Rh. turanicus did exhibit significant differences in community composition, by analyzing the Bray–Curtis dissimilarity index, (Permanova, p = 0.04, F = 1.79; Fig. 1a), but the species richness (Kruskal–Wallis, p > 0.05; Fig. 1b) did not have significant differences. The most prevalent bacterium found in the microbiota across both studied tick species was Coxiella burnetii, with a prevalence rate of 90% (Fig. 1c). Interestingly, six Rickettsia species are present as the most prevalent taxa of the microbiota (from highest to lowest prevalent: R. rickettsii, R. conorii, R. sibirica, R. massiliae, R. felis and R. bellii; Fig. 1c). Within the dataset of 1263 identified taxa across all samples, 280 were unique to Rh. sanguineus s.l. microbiota, 49 were unique to Rh. turanicus microbiota, and 934 were shared between both tick species (Fig. 1d). From those common taxa, 15 presented significantly different relative abundances between the two tick species, including 9 Corynebacterium and 3 Staphylococcus species (Welch’s t-test with Benjamini–Hochberg correction, p < 0.05; Fig. 1e). The 15 taxa were significantly more abundant in Rh. turanicus than in Rh. sanguineus s.l. (Fig. 1e). Notably, two Rickettsia species (R. akari and R. massiliae) exhibited significantly distinct relative abundance between the two tick microbiota (Welch’s t-test, p < 0.05; Fig. 1f). Other Rickettsia species did not exhibit significantly different relative abundance (Welch’s t-test, p > 0.05; Fig. 1f), but all Rickettsia species (i.e. R. africae, R. akari, R. bellii, R. canadensis, R. conorii, R. felis, R. massilliae, R. monacensis, R. peacockii, R. prowazekii, R. rickettsia, R. sibirica and R. typhi) were more abundant in the microbiota of Rh. turanicus compared to Rh. sanguineus s.l. microbiota (Fig. 1f).
Comparative analysis of microbial diversity at the species level in Rh. sanguineus s.l. and Rh. turanicus. (a) PCoA ordination plot showing microbial community structure variation between the two tick species along the primary axes (Permanova, p = 0.04, F = 1.79). Each point denotes a sample, distinguished by tick species, with 95% confidence intervals represented by ellipses. (b) Species richness (total microbial taxa) for each tick species, indicating the median, interquartile range, and outliers. No statistically significant differences were noted (Kruskal–Wallis, p > 0.05). (c) Core microbiome heatmap reveals the prevalence of shared taxa across all tick samples at different detection thresholds, presented as relative abundance percentages. Taxa are organized in descending order based on average prevalence. (d) Venn diagram of shared and unique microbial taxa in both tick species, with the percentage of each relative to the combined taxa detected. (e) Comparison of differential taxa abundance between the two ticks species. The color gradient represents the clr values, with each row corresponding to a microbial species, and each column to a tick sample. Only species exhibiting a statistically significant difference between the two tick species are compared (Welch's t-test with Benjamini–Hochberg correction, p < 0.05). (f) Comparison of differential Rickettsia abundance between the two ticks species. The color gradient represents the clr values, with each row corresponding to a Rickettsial species, and each column to a tick group, (pink for Rh. sanguineus s.l. and blue for Rh. turanicus).
Local connectivity of Rickettsial species in Rh. sanguineus s.l. and Rh. turanicus microbial communities
Microbial co-occurrence networks of Rh. sanguineus s.l. (Fig. 2a) and Rh. turanicus (Fig. 2b) demonstrated topological differences. Rhipicephalus sanguineus s.l. network boasted a higher number of nodes and edges compared to Rh. turanicus (1388 vs 921 nodes and 53,520 vs 7804 edges respectively; Fig. 2a,b; Table 1). Moreover, Rh. sanguineus s.l. displayed a greater proportion of negative interactions compared to Rh. turanicus (7.3% vs 0.4% respectively; Fig. 2a,b; Table 1). Additionally, Rh. sanguineus s.l. microbial network showed stronger connectivity (average degree = 77.12, average path length = 3.40; Table 1), in contrast to Rh. turanicus nodes (average degree = 16.95, average path length = 4.85; Table 1). In the two microbial networks, the local connectivity of Rickettsial species is different. In Rh. sanguineus s.l. microbiota, 9 species of Rickettsia are present and do not cluster together (Fig. 2a). In comparison, in Rh. turanicus microbiota, 12 species of Rickettsia are present and cluster together, except for R. massiliae (Fig. 2b).
Co-occurrence microbial network of Rh. sanguineus s.l. and Rh. turanicus. Global co-occurrence network of (a) Rh. sanguineus s.l. and (b) Rh. turanicus microbiota. (c) Core network (shared co-occurrence interactions) of Rh. sanguineus s.l. and Rh. turanicus microbiota. Nodes correspond to species and connecting edges correspond to a positive (blue) or negative (red) interaction between two nodes. Only nodes with at least one co-occurrence interaction are represented. Nodes colors correspond to modularity class metric. Nodes with the same color belong to the same module of co-occurring species. Size of the nodes corresponds to the eigenvector centrality of each species. The topological features of the corresponding networks are presented in Table 1 and the comparison of their most central nodes in Table 2. Venn diagram comparison of the number of shared (d) edges and (e) nodes between Rh. sanguineus s.l. (pink ellipse) and Rh. turanicus (blue ellipse) microbial networks. Numerals represent the number and percentage of nodes or edges that are Rh. sanguineus-exclusive, shared and Rh. turanicus-exclusive respectively.
A ‘Core Associated Network’ (‘CAN’) was constructed featuring the co-occurrence relations that are present in Rh. sanguineus s.l. and Rh. turanicus microbial network (Fig. 2c). The CAN comprises 1631 edges that connect 430 nodes, which represent 2.7% of the total number of edges (Fig. 2d) and 22.9% of the total number of nodes (Fig. 2e). This low number of similar edges demonstrates that the two ticks' microbiota have a distinct structure. For the Rickettsial species, three Rickettsia (R. africae, R. sibirica and R. peacockii) have common relations (two positive co-occurrence correlations) between the microbiota of the two ticks species (Fig. 2c).
To compare the hierarchical organization of the two tick species, the Jaccard index was used to measure similarity of the most central nodes between Rh. sanguineus s.l. and Rh. turanicus. For all the centrality metrics measured, the Jaccard index was below 0.4 (Table 2) which demonstrates the dissimilarity of sets of most central nodes. Moreover, a significant group difference was observed for the degree, closeness centrality, eigenvector centrality and hub taxa (P (≤ Jacc) < 0.001; Table 2). Similarly, the ‘Adjusted rand index’ (‘ARI’) (0, p-value = 0.96) indicates dissimilarity of clustering which is consistent with what is observed in Fig. 2a and b. After in silico removal of Rickettsia species in the microbial networks, no significant changes were observed in the Jaccard index (Table 3) and the ARI (− 0.007, p-value = 0.202) although the absence of Rickettsia taxa tended to further distance the hierarchical organization of the two tick’s microbiota. To compare the most central taxa of the two tick’s microbiota, keystone taxa were calculated. A total of 54 keystone taxa within Rh. sanguineus s.l. microbiota and eight within Rh. turanicus microbiota were identified, as summarized in Supp. Table 1. It is noteworthy that there were no keystone taxa shared between the microbiota of both tick species which is consistent with the previous results.
Co-occurrence networks robustness
The robustness analysis was conducted to assess the resilience of co-occurrence networks for both Rh. sanguineus s.l. and Rh. turanicus in response to both, the removal or the addition of nodes. The systematic removal of nodes demonstrated that Rh. turanicus microbiota assembly was less robust than Rh. sanguineus s.l. (Fig. 3a–d). To reach 50% of connectivity loss, the fraction of nodes removed is smaller in Rh. turanicus compared with Rh. sanguineus s.l. for all removal strategies tested, namely random (0.25 vs 0.30 respectively, Fig. 3a), betweenness (0.15 vs 0.27 respectively, Fig. 3b), degree (0.25 vs 0.3 respectively, Fig. 3c) and cascading failure (0.01 vs 0.10 respectively, Fig. 3d). The analysis of changes in the Average Path Length (APL) and Largest Connected Component (LCC) size in response to node addition confirmed that Rh. sanguineus s.l. is more robust than Rh. turanicus. In Rh. turanicus network, the APL stays higher than Rh. sanguineus s.l. network even after the addition of 1000 nodes (Fig. 3e), indicating a less connected and compact network. A low LCC value is also an indicator of less robust and stable microbiota, like it is the case for Rh. turanicus network compared to Rh. sanguineus s.l. (Fig. 3f).
Comparison of the robustness of Rh. sanguineus s.l. and Rh. turanicus microbial networks. Robustness test to assess the vulnerability of networks to node removal for the Rh. sanguineus s.l. (pink) and Rh. turanicus (blue) networks using (a) random, (b) betweenness, (c) degree and (d) cascading methods. X-axis represents the fraction of nodes removed from the network and Y-axis represents the fraction of connectivity loss. The solid line represents the fraction of nodes removed to connectivity loss-ratio and the shaded area represents the standard error. The black dashed line represent the level of connectivity loss of 0.5, colored dashed line represent the corresponding level of fraction of node removed to reach 0.5 of connectivity loss. Robustness test to assess the resiliency of networks to node addition for the Rh. sanguineus s.l. (pink) and Rh. turanicus (blue) networks. X-axis represents the number of nodes added from 0 to 1000 and Y-axis represents (e) the average path length or (f) the Largest connected component size.
Local connectivity of Rickettsia spp. in the tick microbiota
The place of Rickettsial taxa in the two tick’s microbiota was compared regarding the degree and betweenness value of all of the members of the microbiota. For the two tick’s microbiota, the majority of taxa present low degree and low betweenness centrality (45.8% for Rh. sanguineus s.l. and 56.8% for Rh. turanicus; Fig. 4a,b). Four of the ten Rickettsia present in Rh. sanguineus s.l. exhibit high betweenness centrality and low degree (i.e. R. africae, R. peackockii, R. prowazekii and R. typhi; Fig. 4a), the other six present low degree and low betweenness centrality. In Rh turanicus microbiota, R. massiliae presents high degree and high betweenness centrality value (Fig. 4b), and two Rickettsia present high betweenness centrality and low degree (R. akari and R. bellii; Fig. 4b). Those results demonstrate that Rickettsia pathogens do not have the same role in the microbiota of ticks, while R. massiliae seems to influence in the microbiota assembly of Rh. turanicus.
Comparison of place and neighbors of Rickettsial species in the microbial networks of Rh. sanguineus s.l. and Rh. turanicus. Scatterplot of the degree and betweenness centrality of taxa in (a) Rh. sanguineus s.l. and (b) Rh. turanicus microbiota. Pink and blue dots represent Rickettsia spp. in Rh. sanguineus s.l. and Rh. turanicus respectively (1: Rickettsia africae. 2: Rickettsia bellii. 3: Rickettsia conorii. 4: Rickettsia felis. 5: Rickettsia peacockii. 6: Rickettsia prowazekii. 7: Rickettsia rickettsii. 8: Rickettsia sibirica. 9: Rickettsia typhi. 10: Rickettsia akari. 11: Rickettsia canadensis. 12: Rickettsia massiliae). The dash line represents the degree and betweenness centrality average. Direct neighbors of Rickettsia spp. present in the (c) Rh. sanguineus s.l. and (d) Rh. turanicus microbial network. Nodes correspond to species and connecting edges correspond to a positive (blue) or negative (red) co-occurrence interaction between two nodes. Positive edges shared between two Rickettsial species are highlighted in dark blue. In each network, nodes with the same colors belong to the same module and the node size is correlated with the eigenvector centrality value. Abbreviations are listed in Supp. File 1.
To analyze the co-occurrence relations of Rickettsia within the genera and with the rest of the microbiota, subnetworks centered on Rickettsial associations were produced. Within the co-occurrence network of Rh. sanguineus s.l., Rickettsial nodes exhibited limited connections to each other, except for R. sibirica, which was linked to both R. peacockii and R. africae (Fig. 4c). Moreover, Rickettsial species were linked with 94 external taxa (Fig. 4c). In contrast, Rickettsial nodes within the Rh. turanicus network were intricately interlinked with each other (32 edges between Rickettsial species; Fig. 4d). Only R. bellii, R. felis, and R. massiliae shared edges with non-Rickettsial taxa, totaling 18 neighbors in all (Fig. 4d).
Metabolic profile of Rh. sanguineus s.l. and Rh. turanicus microbiota
An analysis of the metagenomics data was conducted to obtain a metabolic analysis of the genes expressed by the members of the microbiota of each tick. A total of 5080 metabolic functions were found for the two tick species, from which 3093 were shared between the two tick species, 1719 were only present in Rh. sanguineus s.l. metabolism and 267 only present in Rh. turanicus (Fig. 5a). For the functions only present in Rh. sanguineus s.l., 235 are carbohydrates, 163 are Clustering based subsystems, 123 are Metabolism of aromatic compounds and 109 are Amino acids derivatives (Supp. Table 2). The 971 other functions were shared with 23 Level 3 SEED functions (Supp. Table 2). For the functions only present in Rh. turanicus, 41 are carbohydrates, 29 are Clustering based subsystems, 19 are Amino acids derivatives, 17 are Virulence, disease and defense and 16 are Regulation and cell signaling (Supp. Table 2). The 145 other functions are shared with 19 Level 3 SEED functions (Supp. Table 2). The beta diversity analysis showed no significant difference in clustering of the samples by tick species (Permanova, p = 0.424, stress = 0.05, F = 0.89; Fig. 5b). Differential abundance analysis of the gene expression highlighted 41 genes with significant difference (Wald test, p < 0.05; Fig. 5c). Among these, thirteen genes showed significant higher abundance in Rh. sanguineus s.l., including five Amino acids and derivatives and three Carbohydrate functions whereas 28 genes were significantly more abundant in Rh. turanicus, including four DNA metabolism, three Carbohydrates, three Clustering-based subsystems, three Fatty acids, lipids, and isoprenoids, three Protein metabolism and three RNA metabolism functions (Supp. Table 3).
Diversity and differential abundance of the metabolic pathways provided by Rh. sanguineus s.l. and Rh. turanicus microbiota. (a) Venn diagram representing unique and shared pathways of Rh. sanguineus s.l. (pink) and Rh. turanicus (blue) (b) Beta-diversity comparison between the pathways present in the Rh. sanguineus s.l. (pink) and Rh. turanicus (blue) microbiota samples using the Bray–Curtis dissimilarity index. Small circles represent samples and the ellipses represent the centroid for each group (Permanova, p = 0.424, stress = 0.05, F = 0.89). (c) Comparison of differential gene expression between Rh. sanguineus s.l. and Rh. turanicus microbiota samples. The color gradient indicates the clr value for the relative expression fold change of differently expressed genes (presented in each row) for each microbiota sample (represented in each column). Only genes with a statistically significant expression difference between each tick species were presented (Wald test, p < 0.05).
Discussion
The tick microbiota's contributions to tick physiology and the pathogen life cycle are crucial for understanding vector capacity17,27,28,29. Studies have reported the taxonomic composition of midgut microbiota in various tick species, including Rhipicephalus, Ixodes, Amblyomma, Haemaphysalis, Hyalomma, and Dermacentor30. Despite this taxonomic variability, certain bacterial taxa, such as Arsenophonus, Candidatus Midichloria, Rickettsia, and Wolbachia, are frequently identified in Ixodes and Rhipicephalus tick species31. Rickettsiae have evolved to adapt to diverse environmental conditions, including various arthropod vectors and mammalian hosts, displaying varying degrees of mutualism and pathogenicity3,32. The tick microbiome consists of both pathogenic and non-pathogenic microbes, which interact synergistically within the tick vector, such as Candidatus Midichloria mitochondrii (CMM) and Rickettsia parkeri, or competitively, such as CMM and Francisella-like endosymbionts (FLE)32. Understanding the functional consequences of pathogen colonization by Rickettsial species within the tick vector and their transmission to hosts is essential, also an understanding of endosymbionts and their interactions with pathogenic microbes is necessary to decipher the molecular mechanisms involved in the colonization of tick vectors by pathogenic microbes to develop new paradigms targeting tick proteins to control ticks and tick-transmitted pathogens32.
In this study, we especially focus on the relation of Rickettsial species within the genera, and their interaction with the rest of the microbiota. Leveraging published data10 from tick microbiota metagenomics sequencing, we investigate the relationship of Rickettsial species within the genera and their interaction with the rest of the microbiota in Rh. Sanguineus s.l. and Rh. turanicus. The original study of Ravi et al.10 primarily concentrated on assembling and characterizing the genomes of tick-borne pathogens and endosymbionts within ticks from four different taxa: Rh. sanguineus s.l., its close relative Rh. turanicus, Haemaphysalis, and Hyalomma, collected from various hosts, and documented significant pathogens within these vectors. Through DNA sequencing, they unexpectedly identified canine parvovirus and an unsequenced pathogen, Candidatus Rickettsia barbariae10. Our investigation offers a comprehensive analysis of microbiota composition and network dynamics in Rh. sanguineus s.l. and Rh. turanicus, yielding key insights into the microbial ecology of these tick species. To achieve this, we used comparisons of diversity metrics, differential taxonomic abundance, and a network approach to explore the complexity of microbial communities including their organization, assembly, stability, and interactions. Additionally, we compared the robustness and topological features of the microbiota in both tick species and utilized diversity metrics to compare functional pathways involved in their microbial communities.
We found no significant differences in the alpha diversity, while the Bray–Curtis dissimilarity test was significantly different between the microbiota of Rh. sanguineus s.l. and Rh. turanicus. This finding suggests an equivalent microbial richness, but a significant dissimilarity in the relative abundance across the two conditions. This similarity of microbial richness might be due to phylogenetic closeness between Rh. sanguineus s.l. and Rh. turanicus tick species19. A similar result was in prior research that demonstrated a clustering of beta diversity in Ixodes persulcatus and Ixodes ricinus, distinct from Dermacentor reticulatus33. However, the different bacterial composition observed between the two tick microbiota is consistent with previous findings22,34, emphasizing the dependence of microbial composition on multiple factors, including tick species. Moreover, the different biological traits of the two tick’s species might explain this dissimilarity, with Rh. turanicus life cycle more dependent to environmental factor35, impacting also the ticks microbiota. Their ecological niches differ, with Rh. turanicus commonly found in arid and semi-arid regions, whereas Rh. sanguineus s.l. is more prevalent in urban and peri-urban areas. These differing habitats expose the ticks to distinct microbial communities, which likely contribute to the observed differences in their microbiota.
It is noteworthy that all pathogenic Rickettsial taxa were more abundant in Rh. turanicus compared to Rh. sanguineus s.l., consistent with previous findings in the region36. Although Rh. sanguineus s.l. harbors a greater number of microbial taxa, Rh. turanicus microbiota exhibited a higher relative abundance, particularly of Corynebacterium and Staphylococcus species. The presence of the canine parvovirus in one of the Rh. sanguineus s. l. tick sample might have an effect on the pathogenic Rickettsia presence by competing for resources or ecological niche. The effect of the severe fever with thrombocytopenia virus on Haemaphysalis tick was analysed and demonstrated a reduction of operational taxonomic units and an alteration of the microbial composition37. The dominance of Coxiella burnetii and the presence of diverse Rickettsia species across both tick species underscore the importance of these bacteria in the tick microbiome, potentially influencing tick survival, reproduction, and capacity for disease transmission7,38. Coxiella-like endosymbionts (CLEs) are predominantly associated with female ticks, colonizing their Malpighian tubules and ovaries, with maternal transmission observed19,39. While tissue tropism for Rickettsia sp. in Rh. sanguineus s.l. remains unclear, maternal transmission of these symbionts has been noted39,40,41,42. Lalzar et al. proposed a competitive colonization of the ovary between Coxiella sp. and Rickettsia sp.39. Moreover, repeated horizontal transfers in Rickettsia may have conferred novel pathogenic traits, exemplified by Coxiella burnetii, which emerged from CLEs, causing Q fever43.
The topological analysis of microbial co-occurrence networks revealed distinct patterns between the two tick species. Rh. sanguineus s.l. displayed a more intricate network structure with a higher number of nodes and edges, indicating a more interconnected and potentially robust microbiota. This complexity may confer advantages in adapting to environmental stressors or resisting pathogen invasion44. In contrast, Rh. turanicus exhibited lower links and a higher abundance of Rickettsial pathogens compared to Rh. sanguineus s.l. The association between the presence of Rickettsia and low connectivity may describe a pattern of Rickettsia colonization or persistence in ticks. Indeed, a previous study21, demonstrated that the presence of the pathogenic R. helvetica in I. ricinus ticks was linked to a loss of microbial connectivity. Further analysis is needed to understand this phenomenon, especially if the presence of pathogenic Rickettsia alters the microbiota to facilitate infection, or if a less robust microbiota is more susceptible to acquire Rickettsia. However, we observed that the 'core bacterial microbiota' was not conserved between Rh. sanguineus s.l. and Rh. turanicus. Only three Rickettsia species, R. africae, R. sibirica, and R. peacockii, were common within the core microbiota. This suggests that the microbiota of these ticks have distinct structures, contradicting some studies that have shown conservation of core microbiota bacteria among closely related species, likely due to their essential roles in maintaining microbial community function45.
The intricate microbial community residing in tick guts plays a crucial role in pathogen colonization and potentially influences tick fitness46. Network robustness, reflecting the resistance of the tick microbiome to biological disturbance, is a property observed in various animal species, including arthropods46,47. In this study, we perturbed the co-occurrence bacterial networks of Rh. sanguineus s.l. and Rh. turanicus by simulating the removal and addition of nodes in silico. We then compared the topology, connectivity, local centrality measures, robustness, and community assembly between the perturbed and original networks. Additionally, we may observe a phenomenon of colonization resistance within the ticks, where the microbiota's resistance against exogenous pathogenic microbes protects and stabilizes established communities48, which was the case of Rh. turanicus that exhibited a less robust network, highlighting different network behaviors and resilience between Rh. turanicus and Rh. sanguineus s.l. The presence of Rickettsial endosymbionts often correlates with Rickettsial pathogens2. Differences in the local connectivity of Rickettsial species within the networks underscore the nuanced roles these pathogens play within the tick microbiota, influencing community dynamics and potentially impacting disease transmission pathways. The position of Rickettsia within the microbiota varies depending on the tick species49. For instance, a study22 on Hyalomma marginatum and Rh. bursa microbiota revealed that Rickettsia inhabited contrasting communities, potentially indicating different roles within the microbiota. However, despite this variability, the presence of Rickettsia was correlated with similar central taxa, potentially facilitating pathogen acquisition22. Moreover, R. peacockii is associated with the pathogen R. rickettsii or R. buchneri with R. monacensis32. Consistent with this, in our study, we observed co-occurrence connections among Rickettsia species and a higher abundance of these bacteria in Rh. turanicus. Conversely, when Rickettsial species lack co-occurrence interactions, the abundance of Rickettsia decreases, which was the case with Rh. sanguineus s.l..
Through reductive genome evolution, Rickettsia has undergone gene loss in metabolic pathways, resulting in a limited ability to synthesize amino acids and nucleotides, resembling symbiotic bacteria50. To compensate for this loss, Rickettsia species have developed parasitic mechanisms, utilizing a diverse array of transport systems to acquire essential metabolites for survival and replication within the host cytosolic compartment50. Thus, identifying specific metabolic pathways missing in Rickettsia may provide critical knowledge in gaps in developing an axenic medium that supports rickettsial extracellular replication and novel therapeutics that target essential transport mechanisms50. Our metabolic analysis revealed distinct profiles between Rh. sanguineus s.l. and Rh. turanicus, with significant differences in genes associated with carbohydrates, amino acids, and virulence. These metabolic distinctions may reflect adaptations to different environmental conditions or host preferences51, potentially influencing tick feeding behavior, reproduction, and pathogen transmission. A previous study52 that assessed the reaction of Rh. sanguineus s.l. to stress (starvation), predicted a higher level of carbohydrates metabolism in stress condition. Consistent with this, our study observed an increase in carbohydrate metabolism pathways in the functional profile of Rh. sanguineus s.l. compared to Rh. turanicus. The regulation of the carbohydrate metabolism was also correlated with a resistance to oxidative stress53. However, another study54 demonstrated that the pathogen Anaplasma phagocytophylum was able to manipulate carbohydrate metabolism to facilitate infection in tick cells. This phenomenon was described for I. scapularis ticks55.
The findings from our study have important implications for understanding the role of tick microbiota in disease transmission. The presence of primary pathogens such as Coxiella burnetii and various Rickettsia species, coupled with the intricate microbial networks observed, suggests that the tick microbiota may play a significant role in modulating tick-borne disease dynamics. Further research is needed to elucidate the functional roles of these microbial communities in pathogen transmission and tick physiology. While our study provides valuable functional metagenomic data, future research should focus on experimental settings that integrate metagenomics with other omics approaches, such as transcriptomics and proteomics. This integrated approach is essential to disentangle the roles of microbiome assembly and metabolic interdependence in the survival, replication, and transmission of pathogens. Understanding the molecular mechanisms underlying these interactions can provide insights into how microbiota influence tick-borne disease dynamics and identify key microbial partners that support tick health and development, potentially revealing targets for disrupting tick populations and reducing disease transmission.
Conclusion
Our study elucidates the intricate relationships between Rickettsia species and the broader microbiome within Rh. sanguineus s.l. and Rh. turanicus ticks. By characterizing taxonomic profiles, co-occurrence networks, and metabolic pathways, we have gained valuable insights into the complexity of tick microbiota and its implications for rickettsial diseases. The distinct community compositions and network structures observed between tick species underscore the importance of considering ecological dynamics in understanding disease transmission. Furthermore, the differential metabolic profiles highlight potential mechanisms underlying Rickettsia interactions and their role in tick physiology. Moving forward, a deeper understanding of these interactions through experimental studies and omics approaches will be essential for developing targeted interventions to control rickettsial infections and mitigate the threat of tick-borne diseases.
Methods
Study design
The study aims to describe Rh. sanguineus s.l. and Rh. turanicus microbiota at a species level and to describe the role of Rickettsia species in the microbiota of the two tick species. A functional analysis of the metabolic genes directly provided to Rickettsial species will also be investigated. For this, Rh. sanguineus s.l. (n = 5) and Rh. turanicus (n = 6) samples were used from a previous study10. The ticks were collected in 2015 from dogs for Rh. sanguineus s.l. and from sheep for Rh. turanicus in Palestine farms, from Nablus (n = 2 Rh. sanguineus s.l. and n = 3 Rh. turanicus), from Tubas (n = 3 Rh. sanguineus s.l. and n = 2 Rh. turanicus) and from Hebron (n = 1 Rh. turanicus)10. Ticks were collected from their host and placed in 70% ethanol and then the DNA was extracted and sequenced by shotgun metagenomics sequencing10. Previous metagenomics analysis permitted to assess the presence of bacterial or viral pathogens. Coxiellaceae was found in Rh. sanguineus s.l. and Rh. turanicus, while Rickettsiaceae and Anaplasmataceae were predominantly found in Rh. turanicus10. Moreover, the canine parvovirus was found in one Rh. sanguineus s.l. tick10.
Processing of raw metagenomic sequences
Whole genome sequencing (WGS) sequences underwent preprocessing and annotation via the Metagenomic Rapid Annotations using subsystems technology (MG-RAST) web application server, utilizing pipeline version 456. Initially, raw sequences were subjected to quality control (QC) through SolexaQA software, employing the "Dynamic Trim" method for excising low-quality segments based on a minimum Phred score of 15 and allowing no more than 5 bases below this threshold57. This step was followed by the elimination of artificial replicate sequences and singletons58. Protein-coding regions were identified leveraging the M5nr database for a comprehensive, non-redundant amalgamation of multiple protein databases59. The taxonomic origins of these regions were ascertained using the RefSeq database60, while functional profiles were delineated based on the SEED subsystems. Annotation parameters adhered to the guidelines recommended by61, employing an MG-RAST: maximum e-value cut-off of 1e-5, a minimum alignment length of 15 bp, and minimal identity cut-offs of 60% and 80% for taxonomic and functional profiling, respectively. This approach balanced sensitivity and accuracy. Abundance profiles were established using the ‘Representative Hit Classification’ method, with features identified as eukaryotic and viral sequences being excluded from the analysis.
Diversity indexes and taxa abundance
Beta diversity measures the variability of samples between different conditions to assess similarity of communities. To measure the microbial beta diversity, the Bray–Curtis dissimilarity index62 was used. Bray–Curtis test the dissimilarity between two conditions, considering the relative abundance of taxa62. Bray–Curtis was measured using ‘vegan’ package63 implemented on R studio64. To statistically compare the two conditions, a Permanova test (p < 0.05) was conducted using Past 4 (v. 4.08, 2001). Alpha-diversity analysis was based on microbial species richness for the two tick species. Alpha diversity was measured using MG-RAST56, and statistically compared using Kruskal–Wallis test (p < 0.05).
To further compare the diversity of the two tick species microbiota, the core microbiome analysis, encompassing all samples, was conducted to identify taxa at varying prevalences ranging from 20 to 100%. This analysis was carried out using the web-based tool, MicrobiomeAnalyst65. Moreover, a Venn diagram was built to visualize the shared taxa and the unique taxa present in each microbiota. The Venn diagram was built using ‘VennDiagram’ package66 in R studio64. Differential abundance analysis was used to compare significantly different taxa. Abundance values were measured as centered log ratio (clr) transformed values and t-test was realized using ‘ANOVA-Like Differential Expression’ (‘ALDEx2’) package67 in R-studio64. The heatmap with only statistically different taxa abundant (Benjamini–Hochberg corrected p-value68) was constructed using ‘heatmap.2’69 package implemented in R studio64. Differential abundance was also represented to compare the abundance of Rickettsial species between the two tick species. The clr values were calculated with ‘ALDEx2’, a t-test was performed and the average clr values of each Rickettsia (significant or not) in each condition were presented in a heatmap constructed with GraphPad Prism 9.0.2 (GraphPad Software).
Inference of co-occurrence networks analysis
To further explore the intricate system that microbiota is, a network approach was used to compare the two species microbiota. Networks are composed of nodes representing bacterial taxa at the species level and links referring to positive or negative co-occurrence interactions between taxa. The networks were constructed using ‘Sparse Correlation for Compositional data’ (‘SparCC’) method70 implemented in R studio64. Links correlation coefficient magnitude threshold was > 0.75 for positive and < -0.75 for negative co-occurrence interactions, as it is commonly used for microbial networks71 to obtain statistically meaningful results. Networks were visualized and topological features (i.e. number of nodes and edges, modularity, network diameter, average degree, weighted degree, clustering coefficient, and centrality metrics) were calculated using Gephi 0.1072. Rh. sanguineus s.l. and Rh. turanicus microbial network similarity in structure was compared using CAN. CAN network represents the intersection of similar links associated with the same taxa in the two microbial networks. Positive links represent edges that were positive in the two species networks. On the opposite, negative links represent edges that were negative in the two species network. CAN was constructed using ‘anuran’ toolbox73 with the default parameters, implemented on python environment (Anaconda Software Distribution 2023). Shared proportion of links and nodes were represented in a Venn diagram using ‘VennDiagram’ package66 implemented on R studio64.
To test the similarity of the most central nodes between Rh. sanguineus s.l. and Rh. turanicus, the Jaccard index was used (p < 0.05). This index compared the similarity of nodes with a central place in the microbiota (i.e., high eigenvector, betweenness, degree, closeness centrality and hub taxa) of the two ticks’ microbial networks. Jaccard value goes from 0 (central nodes completely different) to 1 (sets of most central nodes equal). Jaccard statistically compares if the high central nodes are “less similar or equal” (p-value P(J ≤ j)), or “higher similar or equal” (p-value P(J ≥ j)) than the Jaccard value expected for two random microbial networks74. Similarly to the Jaccard index, the ARI was calculated to statistically compare if the two microbial networks cluster similarly. ARI value goes from 0 (expected for two random clustering) to 1 (perfect agreement between clustering). A statistical two-tailed test was conducted with the null hypothesis that ARI = 0. The Jaccard index and the ARI were performed using ‘Network Construction and comparison for Microbiome data’ (‘Netcomi’)75 package implemented on R studio64.
Evaluation of the network robustness
The robustness analysis explores and compares the resistance of microbial networks to perturbations such as removal or addition of a substantial number of nodes. To compare the microbiota of the two tick species resistance to node removal, the connectivity loss depending on the fraction of nodes removed was calculated for four different node removal methods (i.e. random, degree, betweenness and cascading (recalculated betweenness) removal). The robustness was measures with ‘Network Strengths and Weaknesses Analysis’ (‘NetSwan’) package76 implemented on R studio64. Because the microbiota can be affected by various factors, such as environmental changes, it is crucial to test how the addition of new microbial species can affect the microbial network stability. Node addition was measured using Network Analysis77, with igraph visualization package78,79 implemented on R studio64. We quantified network connectivity after additions of 10,000 nodes using two metrics: the largest connected component (LCC) and the average path length (APL). To analyze the significance of differences in LCC and APL after node addition, we conducted one-sample Wilcoxon signed-rank test with p-values adjusted using the Benjamini–Hochberg correction68 to minimize the false discovery rate. We also implemented bootstrapping techniques to generate confidence intervals for these metrics. A significance level was set at p < 0.05 to determine statistical relevance. GraphPad Prism 9.0.2 (GraphPad Software) was used for visualization of both metrics.
Connectivity of Rickettsial species in the microbiota
To compare the role of Rickettsial species between the two ticks microbiota, the degree and the betweenness centrality value were calculated for all of the taxa in each ticks microbiota, and the place of Rickettsia in the community assembly was observed. The degree and betweenness centrality values were collected with Gephi 0.1072 software and visualized with GraphPad Prism 9.0.2 (GraphPad Software). They were separated in four groups: i. low degree and low betweenness centrality, ii. low degree and high betweenness centrality, iii. high degree and low betweenness centrality, and iv. high degree and high betweenness centrality. The threshold was the average value (77.12 for degree and 1896.31 for betweenness centrality for Rh. sanguineus and 16.95 for degree and 1857.37 for betweenness centrality for Rh. turanicus). Furthermore, the place of Rickettsial species and their relation with members of the microbiota was investigated. Reference taxa networks were constructed, and only direct co-occurrence relations of Rickettsial species were represented. Direct co-occurrence correlations and visualization were done using Gephi 0.1072 software.
Comparison of gene diversity and abundance
To compare the metagenomics values of the two tick species, a Venn diagram was prepared to know which functions are unique to Rh. turanicus or to Rh. sanguineus s.l. and the one that are shared between the two tick species. The Venn diagram was conducted using the same method as for the taxa comparison. A beta diversity dissimilarity plot was constructed to see if the metagenomics gene population was similar between the two tick species or if they clustered separately. This analysis was conducted using the same method as for the taxa comparison. The significant (Wald test, p < 0.05) different genes were measured using DeSeq2 package80 implemented on R studio64, and visualized using heatmap.269 function implemented in R studio64.
Data availability
The data that support this study results are available on the SRA repository (Bioproject No. PRJNA495172).
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Acknowledgements
UMR BIPAR is supported by the French Government’s Investissement d’Avenir program, Laboratoire d’Excellence “Integrative Biology of Emerging Infectious Diseases” (grant no. ANR-10-LABX-62-IBEID). Apolline Maitre is supported by the ‘Collectivité de Corse’, grant: ‘Formations superieures’ (SGCE – RAPPORT N° 0300).
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Conceptualization: AC-C and DO. Supervision: AC-C. Data Curation: LM-H and DO. Formal Analysis: AM, MK, IC-G, LA-D, EP-S and DO. Methodology and Software: AM, LA-D and DO. Visualization: AM and DO. Writing – Original Draft Preparation: AM, MK and AC-C. Writing – Review & Editing: AM, MK, IC-G, LA-D, LM-H, JM, CA, MBS, EP-S, DO and AC-C. All authors wrote and discussed the results, commented on the manuscript and approved the submitted version.
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Maitre, A., Kratou, M., Corona-Guerrero, I. et al. Differential interactions of Rickettsia species with tick microbiota in Rh. sanguineus and Rh. turanicus. Sci Rep 14, 20674 (2024). https://doi.org/10.1038/s41598-024-71539-4
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DOI: https://doi.org/10.1038/s41598-024-71539-4
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