# Anaplasma marginale modulates the microbiota of Rhipicephalus microplus organs involved in pathogen transmission

Agustina E Pérez, Eliana C Guillemi, Lianet Abuin-Denis, Elianne

Piloto-Sardiñas, Dasiel Obregon, Natalia Pin Viso, Néstor Sarmiento,

Alejandro Cabezas-Cruz, Marisa D Farber

► To cite this version:

Agustina E Pérez, Eliana C Guillemi, Lianet Abuin-Denis, Elianne Piloto-Sardiñas, Dasiel Obregon, et al.. Anaplasma marginale modulates the microbiota of Rhipicephalus microplus organs involved in pathogen transmission. Ticks and Tick-borne Diseases, 2025, 16 (5), pp.102522. 10.1016/j.ttbdis.2025.102522. hal-05214519

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Original article

*Anaplasma marginale* modulates the microbiota of *Rhipicephalus microplus*
organs involved in pathogen transmission

Agustina E. Pérez<sup>a,†</sup>, Eliana C. Guillemi<sup>a</sup>, Lianet Abuin-Denis<sup>b,c</sup>, Elianne Piloto-Sardiñas<sup>b,d</sup>, Dasiel Obregon<sup>e,f</sup>, Natalia Pin Viso<sup>a</sup>, Néstor Sarmiento<sup>f</sup>, Alejandro Cabezas-Cruz<sup>b,*</sup>, Marisa D. Farber<sup>a,†</sup>

ARTICLE INFO

Keywords:

Anaplasma marginale

Microbiome

Ovaries

Pathogen transmission

Rhipicephalus microplus

Salivary glands

A B S T R A C T

The cattle tick *Rhipicephalus microplus* is a vector for *Anaplasma marginale* in tropical and subtropical regions worldwide. The study of pathogen-microbiome-vector interactions at the tick organ scale is a promising area of development, unraveling control strategies for tick-borne diseases. Our study focused on how *A. marginale* infection affects the bacterial community within the salivary glands and ovaries of *R. microplus*. Engorged female ticks collected from cattle in a disease-endemic area of Argentina were further classified based on whether they were infected with *A. marginale* by targeting the *msp1/β* gene through PCR diagnosis in tissue samples. We included negative control samples throughout the study. We analyzed the bacterial communities in tick tissues by sequencing the 16S rRNA V3-V4 region. Our results revealed significant differences in community composition between infected and uninfected samples. *Sphingomonadaceae* was identified as a predominant taxon in uninfected salivary glands and ovaries. Additionally, we constructed co-occurrence networks to study interactions within the microbial communities. It is noteworthy that *A. marginale* infection led to an increase in network complexity in the salivary glands, exerting the opposite effect on the ovaries. These findings reinforced the hypothesis that *A. marginale* impacts the microbiota of *R. microplus* at an organ-specific level.

# 1. Introduction

Ticks are the most important vectors of pathogens for domestic and
wild animals, and the second in importance after mosquitoes as vectors
of human diseases worldwide (De La Fuente et al., 2008). There are
about 900 different tick species, most of which belong to the *Argasidae* or
the *Ixodidae* families (Apanaskevich et al., 2010). *Rhipicephalus micro-
plus*, a one-host tick from the *Ixodidae* family, is the most prevalent and
best-adapted tick parasitizing cattle both in tropical and subtropical
world areas (Guglielmonee et al., 2014). This species is the most rele-
vant tick for livestock since it is the vector of pathogens such as *Babesia*

bovis, *B. bigemina*, and *A. marginale* (De La Fuente et al., 2008). *Rhipi-
cephalus microplus* parasitosis may result in significant economic losses
due to tick-borne diseases that decline milk and meat production, in
addition to leather depreciation because of high tick loads (Corrier et al.,
1979; Pereira et al., 2021; Riek, 1962).

Anaplasma marginale, a gram-negative alpha-proteobacteria from the order Rickettsiales, is one of the vector-borne pathogens with the most significant economic impact on livestock globally. Blood-feeding is the undisputed route of A. marginale tick acquisition/transmission. After acquisition, the bacterium is maintained along the different tick stages by transstadial transmission (Kocan et al., 2010). Additionally, a

<footnote><sup>a</sup> Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Hurlingham B1686LQF, Argentina</footnote>

<footnote><sup>b</sup> ANSES, INRAE, Ecole Nationale Vétérinaire d'Alfort, UMR BIPAR, Laboratoire de Santé Animale, Maisons-Alfort, F-94700, France</footnote>

<footnote><sup>c</sup> Animal Biotechnology Department, Center for Genetic Engineering and Biotechnology, Avenue 31 between 158 and 190, P.O. Box 6162, Havana 10600, Cuba</footnote>

<footnote><sup>d</sup> Direction of Animal Health, National Center for Animal and Plant Health, Carretera de Tapaste y Autopista Nacional, Apartado Postal 10, San José de las Lajas, Mayabeque 32700, Cuba</footnote>

<footnote><sup>e</sup> School of Environmental Sciences, University of Guelph, 50 Stone Rd E, Guelph, ON, N1H 2W1, Canada</footnote>

<footnote><sup>f</sup> Estación Experimental Agropecuaria Mercedes, Instituto Nacional de Tecnología Agropecuaria, Mercedes 3470, Argentina</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>*E-mail addresses:* perez.agustina@inta.gob.ar (A.E. Pérez), guillemi.eliana@inta.gob.ar (E.C. Guillemi), lianet.abuindenis@anses.fr (L. Abuin-Denis), dasieloa@uoguelph.ca (D. Obregon), pinviso.natalia@inta.gob.ar (N. Pin Viso), sarmiento.nestor@inta.gob.ar (N. Sarmiento), alejandro.cabezas@vet-alfort.fr (A. Cabezas-Cruz), farber.marisa@inta.gob.ar (M.D. Farber).</footnote>
growing body of evidence supports the transovarial transmission of *Anaplasma* spp. from female to their offspring (de la Fournière et al., 2023; Pérez et al., 2025; Ahn et al., 2025; Xu et al., 2023; Shimada et al., 2004). This adds to the evidence supporting the study of this overlooked transmission pathway of the pathogen.

Ticks, like other species, harbor specific microbial communities that comprise commensal, symbiotic, and pathogenic microorganisms, including viruses, protozoa, and bacteria, that inhabit niches in the arthropod body (Hooper and Gordon, 2001; Krawczyk et al., 2022). Since ticks are blood-feeding parasites, their diet contains insufficient amounts of amino acids and B-vitamins, thus requiring bacteria for their biosynthesis (Duron et al., 2018; Zhong, 2012). Understanding microbiome composition and the interactions with tick-borne pathogens may pave the way for developing novel control strategies (Krawczyk et al., 2022). In this regard, the study of tick microbiomes should be approached at the finer organ scale, especially if the goal is to elucidate pathogen dynamics and transmission (Bonnet and Pollet, 2021; Khogali et al., 2025). Although it has been demonstrated that *Coxiella* endosymbionts from *R. microplus* (CERM) are abundant in the ovaries and Malpighian tubules of fully engorged females and play an important role in the developmental process of ticks (Guizzo et al., 2017), information on the tissue-specific microbiota of *R. microplus* is still scarce (Segura et al., 2020; Zhang et al., 2023). Additionally, *A. marginale* exerted significant influence on microbial community structure, particularly at the nymphal stage through competition with CEMR, revealing a potential effect on the pathogen's transstadial transmission (Abuin-Denis et al., 2024).

Pathogen transmission may be modified by vector-pathogen-microbiota interaction (Wu-Chuang et al., 2021; Bonnet and Pollet, 2021), based on this concept the modulation of the microbiome has been proposed as a novel strategy for tick-borne disease control (Krawczyk et al., 2022). Various strategies have been investigated to alter the tick's microbiome, with the use of anti-microbiome vaccines being suggested as an effective method. (Gall et al., 2016; Mateos-Hernández et al., 2020). To understand the scope of these strategies it is crucial to identify the taxa that are highly relevant in the vector microbiome and the interactions within communities (Mateos-Hernández et al., 2021).

This study aimed to assess whether the organ microbiome can influence the pathogen transmission cycle by examining the bacterial communities of salivary glands and ovaries of *R. microplus* collected from cattle reared in an *A. marginale* endemic region.

## 2. Methods

### 2.1. Sample collection and processing

The study was conducted under the guidelines of the Institutional Committee for the Use and Care of Experimentation Animals, CICUAE committee, Corrientes, Argentina (protocol number 02/2018).

Engorged adult female ticks were collected from randomly selected bovine hosts at five beef cattle farms in Corrientes province, Argentina, between March and May of 2021. Bovine hosts were part of different adult male and female Braford breed herds with high tick infestation. Cattle farms were located in Monte Caseros (30°14'21"S 57°57'34"W), Sauce (30°04'00"S 58°46'00"W) and Mercedes (29°12'00"S 58°05'00"W). Ticks from the same bovine were kept in the same plastic tube with 70 % ethanol until further processing.

Before dissection, the ticks were identified as *R. microplus* according to taxonomic keys (Nava et al., 2017). For tick processing, the arthropod surface was disinfected with 70 % ethanol for 60 s, rinsed with sterile 1X PBS, and air-dried. Dissection was performed in a 5 cm sterile Petri dish under a stereoscopic magnifier (SMZ-2T Nikon, Sendai, Natori, Japan). Ticks were stabilized ventrally using double-sided tape attached to the sterile surface of the Petri dish, then, immobilized ticks were rinsed in 1 mL of sterile 1X PBS. To identify the contaminants potentially incorporated from the environment (Lejal et al., 2020), we systematically

collected an aliquot of 1X PBS from each Petri dish before starting the tick dissection (dissection controls). An incision was made to release the dorsal cuticle and remove the organs using sterile forceps and needles (Edwards K et al., 2009; Patton et al., 2012). The ovaries and salivary glands were identified, removed, washed with sterile 1X PBS, and placed individually in 1.5 mL tubes containing 30 µL of sterile 1X PBS.

We collected 490 *R. microplus* ticks from 48 bovine hosts and successfully dissected 75 ticks from 22 bovines.

The DNA from tick ovaries and salivary glands was extracted with Nucleospin Tissue Kit (Macherey-Nagel, GmbH & Co. KG. Düren, Germany) following the manufacturer's instructions and stored at -20 °C until further use.

### 2.2. Tick organ selection for 16S community profiling

*Anaplasma marginale* identification was carried out by amplifying a fragment of *msp1β*, a three-copy gene that encodes the outer major surface protein MSP1b (Bilgiç et al., 2013). Negative samples for *A. marginale* were further confirmed by testing for the tick 16 s rRNA gene (D'oliveira et al., 1997) to monitor the DNA extraction process. Molecular amplifications were performed in a 20 µL reaction mixture containing 0.4 M of each primer, 0.2 mM of each deoxyribonucleotide triphosphate (INBIO Highway, Buenos Aires, Argentina), 0.4 U of Q5 High-Fidelity DNA Polymerase (NEB, Whitby, ON, Canada), 4 µL of 5X PCR buffer (NEB, Whitby, ON, Canada), 4 µL of 5X PCR Enhancer (NEB, Whitby, ON, Canada) and ~100 ng of genomic DNA as previously described (Bilgiç et al., 2013; de la Fournière et al., 2021). Positive (DNA from *A. marginale* Mercedes strain) and negative (MiliQ® water-Direct-Q 5 UV, Millipore, Merk, USA) controls were included in the assay. An aliquot of each amplified product (15 µL) was analyzed by electrophoresis in 1.5 % agarose gel stained with ethidium bromide (INBIO Highway, Buenos Aires, Argentina) and a molecular size marker (1 Kb Plus DNA Ladder, NEB, Whitby, ON, Canada).

We obtained 51 salivary glands and 58 ovaries from the 75 ticks effectively dissected, 17 out of 51 salivary glands and 21 out of 58 ovaries tested positive for *A. marginale* (Table 1). Thus, we conformed 4 groups of samples: infected salivary glands (Rm-iSG) and ovaries (Rm-iOv), and uninfected salivary glands (Rm-uSG) and ovaries (Rm-uOv). To obtain the required DNA quantity and quality for metabarcoding, we pooled two DNA samples of each tissue type (ovaries and salivary glands). The pools were established according to the following criteria: DNA from the same tissue, the same infection condition with *A. marginale* (infected or uninfected) and the ticks must have been collected from the same bovine host. Finally, 24 pooled tissue samples (Table 2) and 6 dissection control pools were obtained for the metabarcode sequencing assay.

### 2.3. Library preparation and sequencing

For library construction, a 500 bp fragment of the V3-V4 region of the 16S rDNA was amplified using universal primers (341F-805R) as previously described (Herlemann et al., 2011). Libraries were prepared from 1 ng/µL purified DNA using the standard Illumina protocol and sequenced with 250-bp paired-end chemistry on a MiSeq -Illumina instrument, at the Genomic Unit (IABIMO, INTA-CONICET, Buenos Aires, Argentina.

<caption>**Table 1**</caption>

Results for the molecular detection of *A. marginale* (*msp1β*) on *R. microplus* or-gans. Rm-SG (Salivary Glands), Rm-Ov (Ovaries).

<table><thead><tr><th rowspan="2">Organ</th><th colspan="2">PCR for <i>A. marginale</i></th><th rowspan="2">Total</th></tr><tr><th>Positive</th><th>Negative</th></tr></thead><tbody><tr><td>Rm-SG</td><td>17</td><td>34</td><td>51</td></tr><tr><td>Rm-Ov</td><td>21</td><td>37</td><td>58</td></tr><tr><td>Total</td><td>38</td><td>71</td><td>109</td></tr></tbody></table>
<table><caption>**Table 2**
Samples used for metabarcoding in this study. Rm-SG (Salivary Glands), Rm-Ov (Ovaries).</caption><thead><tr><th rowspan="2">Organ</th><th colspan="2">A. marginale</th><th rowspan="2">Total</th></tr><tr><th>Positive</th><th>Negative</th></tr></thead><tbody><tr><td>Rm-SG</td><td>7 Rm-iSG</td><td>6 Rm-uSG</td><td>11</td></tr><tr><td>Rm-Ov</td><td>6 Rm-iOv</td><td>5 Rm-uOv</td><td>13</td></tr><tr><td>Total</td><td>13</td><td>11</td><td>24</td></tr></tbody></table>

All sequence data were deposited in the NCBI Sequence Read Archive database, under the BioProject PRJNA1241781

## 2.4. Processing of raw sequences

Raw sequences of the 16S rDNA were processed through Quantitative Insights Into Microbial Ecology 2 (QIIME2) pipeline version 2021.4 (Bolyen et al., 2019). Initial processing involved denoising and merging of sequences within the fastq files, utilizing the DADA2 software (Callahan et al., 2016) as integrated into QIIME2. Subsequently, amplicon sequence variants (ASVs) were aligned using q2-alignment of MAFFT (Katoh and Standley, 2013) and employed to construct a phylogeny via q2-phylogeny of FastTree 2 (Price et al., 2010). Taxonomic classification of ASVs was performed using a classify-sklearn naïve Bayes taxonomic classifier (Bokulich et al., 2018) trained on the SILVA database (release 138) (Yarza et al., 2014). Taxonomic data tables were then collapsed at the genus level and filtered to exclude taxa with fewer than ten total reads and present in fewer than three samples within each dataset.

The possible contaminants in the sample were statistically identified with “Decontam” (Davis et al., 2018) using the “prevalence” method. The method compares the prevalence of each sequence feature in true samples to the prevalence in negative controls from the DNA extraction process to identify contaminants. Then, contaminants were removed from the dataset before downstream microbiome analysis.

## 2.5. Diversity indexes and differential taxa abundance

To analyze bacterial diversity within-sample (Rm-uSG, Rm-iSG, Rm-uOv and Rm-iOv) alpha diversity indexes were assessed through the q2-diversity plugin in QIIME 2. Alpha diversity metrics: Observed features, Faith's phylogenetic diversity (Faith's PD), and the Pielou's evenness index (DeSantis et al., 2006; Faith, 1992; Pielou, 1966) were compared between the groups using the Kruskal-Wallis test ($p < 0.05$). Beta diversity was evaluated through Bray-Curtis dissimilarity index (Bray and Curtis, 1957) and compared between groups using the PERMANOVA test ($p < 0.05$) also implemented in QIIME 2. Bacterial variability within the population, known as dispersion, was computed using the 'beta-disper' function in the Vegan package (Oksanen et al., 2021) executed in RStudio version 2023.06.0 (RStudio Team, 2020). The dispersion between the species was further analyzed using the ANOVA test ($p < 0.05$). Cluster analysis was performed with the Jaccard coefficient of similarity using Vegan (Oksanen et al., 2021) in RStudio.

Visual representation of shared taxa between different conditions (Infected and uninfected) in each tissue (ovaries and salivary glands) was generated using Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Tables representing bacterial taxonomic and abundance information were exported from QIIME2 and used for building stacked bar plots of relative taxonomic abundance, using the MicrobiomeAnalyst web tool (https://www.microbiomeanalyst.ca) (Dhariwal et al., 2017). Bacterial taxonomic profiles collapsed at the family level, and rare taxa were removed (<4 count; <20 % prevalence). The Centered Log Ratio (CLR) transformation was used for the stacked bar plots. Finally, Linear Discriminant Analysis Effect Size (LEfSe) was performed to analyze the statistical significance of the bacterial abundance in infected and

uninfected tissues (Statistical comparisons between groups were executed using the Kruskal-Wallis rank test, $p < 0.05$, for significant differential abundance; followed by Linear Discriminant Analysis, LDA score filter value of 2, for effect size).

## 2.6. Inference of bacterial co-occurrence networks

Co-occurrence networks were derived for each experimental condition based on taxonomic profiles, employing the Sparse Correlations for Compositional data (SparCC) approach for correlation matrix calculation (Friedman and Alm, 2012), in RStudio. Topological parameters were computed to characterize each network. The Core Association Network (CAN) was utilized to examine common nodes and edges across different networks. The core structures of networks from ovaries and salivary glands were identified using the Anuran toolbox (Röttjers et al., 2021) with default configurations.

For the visualization of Anaplasma and Coxiella sp. direct positive and negative associations subnetworks were constructed using Gephi v0.10 (Bastian et al., 2009), and the strength of the edges was presented with the SparCC weight.

The topology of the taxa in the network was analyzed with two connectivity types: (i) within-module connectivity (Zi) and (ii) among-module connectivity (Pi), which describes the taxa connectivity with other taxa in different modules (Guimerà & Nunes Amaral, 2005). For each taxon, Zi and Pi values were calculated using only positive edges, with the R package “code-zi-pi-plot” (X. ; B.) in Rstudio and visualized with GraphPad Prism version 8.0.1 (GraphPad Software, San Diego, California, USA).

To assess clustering dissimilarity in networks, the adjusted Rand index (ARI) was calculated, with values ranging from -1 to 1. Positive or negative ARI values indicate higher or lower clustering than random, respectively, with identical clustering having an ARI value of 1 and dissimilar clustering having an ARI value of 0. (Peschel et al., 2021).

## 2.7. Network robustness analysis

To assess the network's robustness to node removal, the Network Strengths and Weaknesses Analysis (NetSwan) package was implemented in RStudio. The standard error for connectivity loss was computed, accounting for variability, using a threshold of 0.975. Network analysis and visualization were conducted using the igraph package (Csárdi and Nepusz, 2006). Furthermore, the robustness of microbial networks to node addition was evaluated by employing the network analysis and visualization igraph package. Nodes were systematically added in increments ranging from 1 to 200, and network connectivity was quantified based on the degree metric of the largest connected component (LCC) and average path length. Statistical significance was determined using a Wilcoxon signed-rank test, with $p$-values adjusted using the Benjamini-Hochberg (BH) method to control the false discovery rate. Additionally, bootstrapping was performed to derive confidence intervals for the variables, with significance established at a threshold of $p < 0.05$.

# 3. Results

## 3.1. The presence of A. marginale altered the bacterial community in an organ-specific manner

Alpha and beta diversity indexes were analyzed to assess differences in the microbiome between infected and uninfected tissues of R. microplus ticks. PCoA analysis of beta diversity based on the Bray-Curtis metric revealed that dissimilarities between tissue samples can be mainly attributed to the A. marginale infection condition (Fig 1A). Rm-iOV and Rm-iSG had a community structure that positioned them along the PCoA dimension1 separately from the uninfected tissues, which co-localize predominantly to the upper left of the zero axis.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Differential bacterial composition and abundance in salivary glands and ovaries of *Rhipicephalus microplus*. Comparison of beta-diversity with Bray Curtis dissimilarity index between Rm-uSG and Rm-iSG (PERMANOVA, *p* = 0.037, *S* = 0.1597, *F* = 1.425), Rm-iSG and Rm-iOv (PERMANOVA, *p* = 0.003, *S* = 0.1707, *F* = 1.878) and between Rm-uOv and Rm-iOv (PERMANOVA, *p* = 0.013, *S* = 0.1018, *F* = 4) (A). Comparison of alpha diversity Faith's index, Observed features and Pielou's evenness index between Rm-uSG, Rm-iSG, Rm-uOv and Rm-iOv (Kruskal-Wallis test, *p* ≥ 0.05) (B). Jaccard clusterization of Rm-uSG and Rm-iSG and Rm-uOv and Rm-iOv (C). Each samples are represented by rectangles and the conditions by colors (legend). Venn diagram displaying the comparison of taxa composition for Rm-uSG and Rm-iSG, (H) Rm-uOv and Rm-iOv. Shared and unique taxa are represented (D).</figcaption></figure>

PERMANOVA pairwise comparison revealed significant differences in beta diversity within organ groups (Rm-uSG vs. Rm-iSG: *p* = 0.037, *S* = 0.1597, pseudo-*F* = 1.425; Rm-uOv vs. Rm-iOv: *p* = 0.013, *S* = 0.1018, pseudo-*F* = 4.000), and between infected organs (Rm-iSG vs. Rm-iOv: *p* = 0.003, *S* = 0.1707, pseudo-*F* = 1.878). Additionally, significant differences in beta dispersion were found between Rm-uOv and Rm-iOv (ANOVA *p* = 0.0339, *R* = 0.24), indicating high within-group variability, and no significant alterations were observed between Rm-uSG and Rm-iSG (ANOVA *p* = 0.1785, *R* = 0.1217). We estimated the alpha diversity of infected and uninfected tissues using three diversity metrics (Faith's index, observed features, and Pielou's evenness) (Fig. 1B). Community richness was significantly higher in infected salivary glands (observed features between Rm-iOv and Rm-iSG, Kruskal-Wallis, *p* = 0.05, *H* = 3.704268), but no significant alterations were observed in diversity across the different conditions and tissues. Jaccard clustering analysis confirmed two distinct clusters, one for uninfected tissues and another for infected ones, both in Rm-SG and Rm-Ov (Fig. 1C), supporting Bray-Curtis index result. Compositional analysis of SG showed that from a total of 141 ASVs, 119 were shared (84.39 %) between Rm-uSG and Rm-iSG, and 22 were unique (15.62 %) for Rm-iSG samples and 0 for Rm-uSG (Fig. 1D, Supplementary Table A). On the other hand, there were 106 ASVs for OV, with 49 taxa (46.22 %) shared between Rm-uOv and Rm-iOv, 21 unique ASVs (19.81 %) for Rm-uOv and 36 (33.97 %) for Rm-iOv, respectively (Fig. 1D, Supplementary Table B).

The differential taxa abundance was analyzed at family level to evaluate the preponderant taxa present in uninfected and infected *R. microplus* organs. At the taxonomic level evaluated, the community in Rm-uSG was mainly composed of *Sphingomonadaceae*, and in Rm-iSG, *Nocardiaceae* became the preponderant taxa (Fig. 2A). In Rm-uOv, like

in Rm-uSG, the microbial community was mainly composed of *Sphingomonadaceae*. However, in the presence of *A. marginale* (Rm-iOv) no taxon appeared to be predominant (Fig. 2B). These results were supported by the LEfSe analysis in which *Sphingomonadaceae* was found as a statistically significant taxon in both uninfected conditions (p-value = 0.0455 LDA Score = -5.33 for Rm-uSG and p-value = 0.006 LDA Score = -5.34 for Rm-uOv). The taxa *Nocardiaceae* seems to be differentially abundant in Rm-iSG by the LDA score, although no statistical significance was found (p-value = 0.06, LDA Score = 4.2).

## 3.2. Anaplasma marginale differentially affected the assembly of salivary glands and ovarian microbiota communities

Co-occurrence networks were constructed to characterize the assembly and interaction patterns of the bacterial communities between the four groups (Fig. 3A and B; Table 3). The analysis included positive correlations (weight > 0.5) or negative correlations (weight < -0.5). These networks revealed distinct topological features between tissues of *R. microplus* under uninfected and infected conditions. In the salivary glands, the infected group (Rm-iSG), showed increased network complexity compared to the uninfected group (Rm-uSG), reflected by a higher number of nodes and edges, as well as a greater average degree (Fig. 3A; Table 3). In contrast, the infected ovarian network (Rm-iOv) displayed fewer edges and a lower average degree than its uninfected counterpart (Rm-uOv), despite a slightly higher node count (Fig. 3B; Table 3). This pattern suggests a reduction in microbial connectivity and potential loss of community cohesion in the presence of *A. marginale*.

Composition analysis of the co-occurrence networks identified 22 unique taxa in the infected salivary glands (Rm-iSG) and 38 unique taxa in the infected ovaries (Rm-iOv) that were not detected in their
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Bacterial abundance profiles in salivary glands (A) and ovaries (B). Relative abundance of Rm-iSG and Rm-uSG showing that the *Sphingomonadaceae* family is dominant in the uninfected condition and is less represented in the infected condition. In infected tissues *Nocardiaceae* is the most represented family. (A) Relative abundance of Rm-iOv and Rm-uOv showing that *Sphingomonadaceae* family is dominant in the uninfected condition and is less represented in the infected condition. In infected tissues no predominant family was found. (B) Legend represents the color coding of abundance bars.</figcaption></figure>

uninfected counterparts (Fig. 3C, Supplementary Table C). This increase in condition-specific taxa under infection would suggest a reshaping of the microbial community assembly in both tissues in the presence of *A. marginale*. Core association networks revealed 95 shared nodes between Rm-uSG and Rm-iSG and 32 nodes between Rm-uOv and Rm-iOv (Fig. 3D, Supplementary Table D). Relative to the total number of nodes in each network (Table 3), approximately 67 % shared taxa in salivary glands (95 out of 141 in Rm-iSG) and 42 % in ovaries (32 out of 77 in Rm-iOv) exposed a higher assembly stability in the salivary gland microbiota under *A. marginale* infection compared to the ovarian microbiota. To explore the role of *Anaplasma* and *Coxiella* sp. within the community, its direct relationship with the rest of the bacterial microbiome was determined. Notably, *Coxiella* was part of the core shared by Rm-uSG and Rm-iSG. In Rm-iSG, 35 taxa were connected to *Coxiella* and *Anaplasma*, with taxa positively correlated to *Anaplasma* being negatively correlated to *Coxiella*, and vice versa, suggestive of a complex and competitive microbial environment (Fig. 3E, Supplementary Table E). Moreover, a direct negative association was observed between *Anaplasma* and *Coxiella* (Fig. 3E) in Rm-iSG. This competitive dynamic was less pronounced in Rm-iOv, where *Anaplasma* and *Coxiella* were negatively correlated with 5 taxa and did not interact with each other (Fig. 3F).

Evaluation of within-module (Zi) and among-module (Pi) connectivity showed that most nodes in all networks occupied peripheral positions (low Zi and Pi), with no universal hubs identified (Fig. 3G). The presence of module hubs varied among the groups: none were found in Rm-uSG or Rm-iOv, while Rm-iSG and Rm-uOv each had one module hub, identified as *Coxiella* and *Zymomonas mobilis*, respectively (Fig. 3G). The number of connector nodes—taxa linking multiple modules—was highest in Rm-uSG (six), followed by Rm-iSG (four), Rm-iOv (three), and Rm-uOv (one). Notably, *Kocuria* functioned as a connector in both salivary gland networks (Rm-uSG and Rm-iSG) (Fig. 3G). *Coxiella* emerged as a hub node in Rm-iSG, while *Anaplasma*, when present, consistently occupied peripheral positions. The dynamic behavior of these taxa was further highlighted by their distinct and often opposing interactions with other bacterial species (Fig. 3E and F).

Furthermore, we examined whether *Anaplasma* and *Coxiella* were among the taxa with the strongest correlations within the networks (SparCC, weight ≥ 0.9 or ≤ -0.9). In Rm-uSG and Rm-iSG 8 and 13 nodes, respectively, presented the strongest correlations, with *Anaplasma* being strongly correlated in Rm-iSG (Fig. 4A). In Rm-uOv, 12 nodes exhibited strong correlations, whereas in Rm-iOv, no strong correlations were identified (Fig. 4B).
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Bacterial co-occurrence networks. Co-occurrence networks of (A) Rm-uSG and Rm-iSG, (B) Rm-uOv and Rm-iOv. Node colors are based on modularity class metric and equal color means modules of co-occurring taxa. The colors of nodes were assigned based on modularity class metric values, and the node size is proportional to the eigenvector centrality of each taxon. The colors of the edges represent strong positive correlations (blue) or negative correlations (red). The size of the nodes is proportional to the eigenvector centrality of each taxon. The colors in the edges represent strong positive (blue) or negative (red) correlations (SparCC > 0.50 or < -0.50). Venn diagram displays the comparison of nodes composition for (C) Rm-uSG and Rm-iSG networks, Rm-uOv and Rm-iOv networks. Core association networks between (D) Rm-uSG and Rm-iSG networks, Rm-uOv and Rm-iOv networks. *Anaplasma* (white circle) and *Coxiella* (black circle) local connectivity within is shown in (E) Rm-iSG network and (F) Rm-iOv network. Shared taxa are represented with a blue circle. Number inside the circle represents the amounts of shared taxa. Edges colors represent the interaction positive (blue) or negative (red). Within-module and among-module connectivities, Zi-Pi plot of the individual genera from (G) Rm-uSG, Rm-iSG, Rm-uOv and Rm-iOv networks. The taxa was divided into four categories: (i) peripherals taxa (Zi ≤ 2.5 and Pi ≤ 0.62), which contain taxa with few edges in and out of its module; (ii) connectors (Zi ≤ 2.5 and Pi > 0.62), which contain taxa connected to other modules than its own; (iii) module hubs (Zi > 2.5 and Pi ≤ 0.62), which contain taxa highly connected with members of their own module; and (iv) network hubs (Zi > 2.5 and Pi > 0.62), which contains taxa highly connected with members within and among its module.</figcaption></figure>

<table><caption>**Table 3**
Topological features of Rm-uSG, Rm-iSG, Rm-uOv and Rm-iOv co-occurrence networks.F.</caption><thead><tr><th>Topological features</th><th>Rm-uSG</th><th>Rm-iSG</th><th>Rm-uOv</th><th>Rm-iOv</th></tr></thead><tbody><tr><td>Total nodes</td><td>121</td><td>141</td><td>70</td><td>77</td></tr><tr><td>Edges</td><td>1301</td><td>1612</td><td>702</td><td>418</td></tr><tr><td>Average degree</td><td>21.54</td><td>22.86</td><td>20.057</td><td>9.835</td></tr><tr><td>Number of communities</td><td>3</td><td>4</td><td>2</td><td>17</td></tr></tbody></table>

### 3.3. Impact of *Anaplasma* and *Coxiella* on network robustness and node centrality distributions

One crucial aspect of robust networks is their ability to withstand

perturbations, such as the removal or addition of nodes. To evaluate this, we tested the co-occurrence networks for their tolerance to node removal. Various node removal strategies, including random, betweenness centrality, degree, and cascading, were executed to gauge network tolerance in terms of connectivity loss. We measured the resistance of the networks to random or directed node removal and recorded the proportion of taxa removal needed to reach a connectivity loss of 0.80 for each network. Our findings showed that cascading node removal had the most significant impact on network connectivity, while random node removal had the least impact across all networks (Fig. 5A and B).

When comparing connectivity loss after node removal using each strategy in Rm-SG and Rm-Ov, we observed that pathogen infection compromised network robustness. Rm-iSG is the least robust network,
<figure><img src="image_6.png" /><figcaption>(B) Rm-uOV</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Strong correlations between the networks. Strong correlation networks (SparCC ≥ 0.90) across (A) Rm-uSG and Rm-iSG networks (B) Rm-uOv and Rm-iOv networks. Nodes correspond to taxa (family or genera level) and connecting edges indicate significant connection between them. Node colors represent different modules in the networks. Edge colors represent strong positive (green) or negative (red) correlations.</figcaption></figure>

while the removal of 0.03 fractions of nodes is required to lose 80 % of connectivity (Fig. 5A, Table 4). We also tested the robustness of networks to the addition of nodes. After adding 200 nodes, Rm-iSG exhibits higher Largest connected component (LCC) size and Average path length (APL) than Rm-uSG (Fig. 5C). Both LCC size and APL values increased with node addition. Similarly, Rm-iOv displayed higher LCC size and APL values than Rm-uOv, with LCC size remaining constant during node additions while APL values continued to increase. The increased LCC size and APL displayed by Rm-iSG and Rm-iOv networks following the addition of nodes, indicated a more complex and interconnected microbial community.

The similarity in distribution of local centrality measures across networks was tested using the Jaccard index comparison. For degree, betweenness, closeness, eigenvector, and hub taxa, Jaccard values between the Rm-uSG and Rm-iSG networks were significantly lower than expected (P(≤Jacc), *p* < 0.1; Table 5). In contrast, for Rm-uOv vs. Rm-iOv, only degree and eigenvector showed significantly low similarity

(P(≤Jacc), *p* < 0.1; Table 5). For this analysis, we considered *p* < 0.1 to indicate statistical significance, given the exploratory nature of the comparisons. The ARI similarity index further supported these findings by showing low similarity between the clustering of central nodes in Rm-uSG and Rm-iSG networks (ARI = 0.034, *p* = 0.005) and Rm-uOv and Rm-iOv networks (ARI = −0.016, *p* = 0.242).

4. Discussion

In the present work, we found that *A. marginale* differentially mod-
ulates the bacterial communities present in salivary glands and ovaries
of adult *R. microplus* females collected from cattle reared in an endemic
area. The uninfected tissues were dominated by microbial communities
of *Sphingomonadaceae* bacteria, which responded differently to
*A. marginale* between tissues. While the infected salivary glands (Rm-
iSG) were mainly composed of *Nocardiaceae*, the infected ovaries (Rm-
iOv) showed no predominance of any taxa among the samples. This

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Robustness comparison after removal and addition of nodes. Connectivity loss measured after directed and indirect attack (A) Rm-uSG and Rm-iSG (B) Rm-uOv and Rm-iOv networks. Largest connected component (LCC) and Average path length (APL) values are represented and compared between (C) Rm-uSG and Rm-iSG (D)Rm-uOv and Rm-iOv after addition of nodes.</figcaption></figure>

<table><caption>**Table 4**
The fraction of nodes removed to reach 80 % of connectivity loss.</caption><thead><tr><th rowspan="2">Parameters</th><th colspan="4">Fraction of nodes removed</th></tr><tr><th>Rm-uSG</th><th>Rm-iSG</th><th>Rm-uOv</th><th>Rm-iOv</th></tr></thead><tbody><tr><td>Cascading</td><td>0.08</td><td>0.03</td><td>0.36</td><td>0.26</td></tr><tr><td>Betweenness</td><td>0.16</td><td>0.04</td><td>0.53</td><td>0.43</td></tr><tr><td>Degree</td><td>0.21</td><td>0.07</td><td>0.54</td><td>0.46</td></tr><tr><td>Random</td><td>0.35</td><td>0.34</td><td>0.54</td><td>0.52</td></tr></tbody></table>

result suggests that, when infected with *A. marginale*, ovaries seem to undergo a significant shift in microbiota composition. Likewise, *Sphingomonadaceae* was among the prevalent taxa in the bacterial community of other previously studied tick species such as *Haemaphysalis longicornis*, collected under natural conditions both from the environment and parasitized bovines (Ponnusamy et al., 2024), *Ixodes ricinus* (Wiesinger et al., 2023) and *Dermacentor reticulatus* ticks collected from the environment (Dunaj et al., 2021). Its recurrent detection across species and ecological contexts suggests that *Sphingomonadaceae* may play a core ecological role in tick microbiomes, potentially contributing to community stability or host-microbe interactions. Its displacement in infected tissues may therefore signal a deeper microbiota restructuring under pathogenic pressure.

Although the field of tick microbiomes has progressed in recent years through various studies, there is still a gap in knowledge regarding the identification of tissue-specific microbiota and their interactions. In this sense, one of the main contributions of our study was related to the methodology used for the processing of the ticks. We dissected the specimens and studied the bacterial communities at the organ scale on

salivary glands and ovaries, unlike other studies that focused on the whole *R. microplus* microbiome (Adegoke et al., 2020; Hernandez et al., 2023; Piloto-Sardiñas et al., 2024; Rojas-Jaimes et al., 2021), this enabled us to analyzed the interactions of *A. marginale* with other bacterial taxa and infer their different interactions at specific tissue environments. Salivary glands and saliva play a key role in the tick-borne pathogen transmission to vertebrate hosts (Hodosi et al., 2022). On the other hand, ovaries are of utmost importance for those vertically transmitted microorganisms since the tick progeny will harbor the microorganism that was originally infecting the parental ovaries.

On the other hand, based on the co-occurrence networks, we observed that the presence of *Anaplasma* species in ovaries destabilized the microbial community, making it more susceptible to disruptions, and highlighting a tissue-specific response to the pathogen (Ponnusamy et al., 2024). The *A. marginale* infection was associated with distinct structural shifts in the microbial networks, suggesting tissue-specific ecological consequences. In salivary glands, the changes observed in the infected network (Rm-iSG) may reflect a shift toward a more modular and competitive community, possibly favouring the persistence or integration of *A. marginale* through niche differentiation and reduced dominance of mutualistic hubs like *Coxiella*. In contrast, in ovaries, the infected network (Rm-iOv) showed signs of destabilization, suggesting a disruption of the microbial structure, loss of balanced associations, creating a more pathogen-permissive environment. The fact that *A. marginale* does not occupy a central role in Rm-iOv further supports the hypothesis that it may benefit from, rather than actively shape, this disrupted state. A previous study on *Ixodes ricinus* microbiome revealed that ovarian tissue harbored a stable microbiome mainly based on endosymbiotic bacteria, while the microbiome of the salivary glands and

<table><caption>**Table 5**
Jaccard index for Rm-SG and Rm-Ov networks.</caption><thead><tr><th rowspan="2">Parameters</th><th colspan="3">Rm-uSG vs Rm-iSG</th><th colspan="3">Rm-uOv vs Rm-iOv</th></tr><tr><th>Jacc</th><th>P(&lt;Jacc)</th><th>P(&ge;Jacc)</th><th>Jacc</th><th>P(&lt;Jacc)</th><th>P(&ge;Jacc)</th></tr></thead><tbody><tr><td>Degree</td><td><strong>0.133</strong></td><td><strong>0.000381</strong> ***</td><td>0.999891</td><td>0.115</td><td><strong>0.000292</strong> ***</td><td>0.999929</td></tr><tr><td>Betweenness centr.</td><td><strong>0.127</strong></td><td><strong>0.000167</strong> ***</td><td>0.999954</td><td>0.364</td><td>0.724869</td><td>0.388722</td></tr><tr><td>Closeness centr.</td><td>0.180</td><td><strong>0.006202</strong> **</td><td>0.997499</td><td>0.333</td><td>0.569498</td><td>0.555844</td></tr><tr><td>Eigenvector centr.</td><td>0.143</td><td><strong>0.000540</strong> ***</td><td>0.999833</td><td>0.224</td><td>0.068201</td><td>0.965267</td></tr><tr><td>Hub taxa</td><td>0.263</td><td><strong>0.066043</strong> .</td><td>0.956660</td><td>0.405</td><td>0.932015</td><td>0.102696</td></tr></tbody></table>

Significance codes.

*** : 0.001.

** : 0.01, * : 0.05, . : 0.1.
the digestive system was unstable and mostly comprised of pathogens (Hodosi et al., 2022). Regarding other relevant taxa identified, *Coxiella* emerged as a central node, becoming a module hub in Rm-iSG. Although previous reports of *R. microplus* identified the CREM endosymbiont as core and essential for the reproductive fitness of ticks (Guizzo et al., 2017, 2022), in the present study, *Coxiella*-like species were part of the core taxa for Rm-uSG, Rm-iSG and Rm-iOv, but were undetectable in Rm-uOv. These results may reflect methodological limitations due to variations in bacterial composition between samples (Liu et al., 2021). The raw sequence analysis detects *Coxiella*-like species with a low read count in all tissues. However, after downstream analysis, they are no longer represented in the Rm-uOv sample set.

Our findings also revealed distinct patterns of bacterial co-
occurrence networks between infected and uninfected ticks and, strik-
ingly, a negative association for *Anaplasma* and *Coxiella* in both tissues,
even though this competitive dynamic was mainly detected in salivary
glands. In Rm-iSG, the negative association was observed as a direct
negative interaction, while in ovaries (Rm-iOv) the negative interaction
was mediated by other taxa. Taken together, these findings reveal that
the microbial response to infection differs markedly between tissues:
while salivary glands exhibit a more structured and competitive network
under infection, ovaries show a sparser network and potentially
disturbed microbial community. This suggests tissue-specific ecological
dynamics and resilience within the microbiota of *R. microplus*. This
negative association between *Anaplasma* and *Coxiella* in tick tissues
could be the basis for developing control strategies for both *R. microplus*
and tick-borne *Anaplasma* diseases. After testing the co-occurrence
networks by perturbation through node removal, results suggested
that the presence of *Anaplasma* species destabilized the microbial com-
munity, making it more susceptible to disruptions; this finding rein-
forced the fact that infected tick organs seem to have different microbial
dynamics than the same organ without infection. In a previous study
analyzing whole *R. microplus* microbiome, the authors concluded that
the presence of the pathogen *Theileria* sp. reduced the microbial di-
versity, and they called this phenomenon "pathogen-induced dysbiosis"
(Adegoke et al., 2020). These results support our findings at the organ
level.

Recent reports studying *A. marginale* transmission dynamics sug-
gested that transovarial transmission in *R. microplus* may occur or not
depending on a broad range of variables associated with pathogen-
vector-host interactions (de la Fournière et al., 2023; Pérez et al.,
2025). In the present study, the co-occurrence network analysis revealed
relevant interactions between *Coxiella*-like and *Anaplasma* species,
highlighting their competitive dynamics. The microbiota plays diverse
roles in tick biology, including functions that influence vector compe-
tence (Zhong et al., 2024). Notably, *Coxiella*-like endosymbionts have
been reported to modulate pathogen transmission in other members of
the *Ixodidae* family (Klyachko et al., 2007). Our results support the hy-
pothesis that transovarial transmission of *A. marginale* could be modu-
lated by ovarian endosymbionts as *Coxiella*-like species. Further
investigation is needed to understand the effect of the interaction be-
tween *Anaplasma* and *Coxiella*-like species on transovarial pathogen
transmission.

In addition to the tissue-specific approach, another methodological
aspect of the present study is the systematic inclusion of negative con-
trols throughout the process. Previous studies have warned of contam-
inating taxa introduced into the various steps of the tick microbiota
analysis process, which can account for more than half of the total
sequence obtained in sequencing runs at the end of the process (Lejal
et al., 2020). In our sample processing algorithm, we included negative
controls from the tick dissection, DNA extraction and PCR amplification
steps; these negative controls were the input used to apply the 'Decon-
tam' package. This methodology ensured the reliability of the results,
with the confidence that the taxa identified were indeed the microbiota
present in the tick tissues.

5. Final remarks

By enabling the analysis of complex interactions, our study con-
tributes to the understanding of pathogen-vector transmission dynamics
through microbiome studies. This is also because the samples analyzed
are derived from natural conditions and are subject to the selective
pressures of the environment. One main limitation of this study was the
difficulty in obtaining the appropriate quantity and quality of DNA in
tick tissue samples for metabarcoding. This fact led to the need to pool
samples to achieve these parameters, which resulted in fewer final
numbers of samples for comparisons. Further studies involving the
analysis of larger numbers of tick samples will help to strengthen these
findings and provide new information on other variables associated with
microbial networks.

Author contributions

Agustina E. Pérez (Data curation, Formal analysis, Investigation, Methodology, Writing – original draft.), Eliana C. Guillemi (Conceptualization, Investigation, Methodology, Writing – original draft), Lianet Abuin-Denis (Formal analysis, Software, Writing – original draft), Elianne Piloto-Sardiñas (Software), Dasiel Obregon (Software, Writing – original draft), Natalia Pin Viso (Data curation, Formal analysis), Néstor Sarmiento (Investigation, Resources), Alejandro Cabezas-Cruz (Methodology, Supervision, Writing – original draft, Writing – review and editing), Marisa D. Farber (Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review and editing)

Funding sources

This work was supported by the Agencia Nacional de Promoción Científica y Tecnológica (grant PICT 2018-2871), which covered the collection, analysis and interpretation of data.

Funding sources

This work was supported by the Agencia Nacional de Promoción Científica y Tecnológica (grant PICT 2018-2871) and National Institute for Agricultural Technology (2023-PD-L01-I089)c, which covered the collection, analysis and interpretation of data.

CRediT authorship contribution statement

**Agustina E. Pérez:** Writing – original draft, Methodology, Investi-
gation, Formal analysis, Data curation. **Eliana C. Guillemi:** Writing –
original draft, Methodology, Investigation, Conceptualization. **Lianet
Abuin-Denis:** Writing – original draft, Software, Formal analysis. **Eli-
anne Piloto-Sardiñas:** Software. **Dasiel Obregon:** Writing – original
draft, Software. **Natalia Pin Viso:** Formal analysis, Data curation.
**Néstor Sarmiento:** Resources, Investigation. **Alejandro Cabezas-Cruz:**
Writing – review & editing, Writing – original draft, Supervision,
Methodology. **Marisa D. Farber:** Writing – review & editing, Writing –
original draft, Supervision, Resources, Project administration, Method-
ology, Funding acquisition, Conceptualization.

Acknowledgements

We are grateful to Celeste Sosa and Carla Pertile (Estación Experi-
mental Agropecuaria Mercedes, Instituto Nacional de Tecnología
Agropecuaria) for technical assistance during the collection of the
samples. We thank the staff of the Genomic Unit (IABIMO, INTA-
CONICET) for technical assistance in sequencing.
# Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ttbdis.2025.102522.

# Data availability

All sequence data were deposited in the NCBI Sequence Read Archive database under the BioProject PRJNA1241781

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