Pilot metaproteomic profiling reveals bacterial diversity and potential medical and veterinary relevance of tick microbiomes in northern Algeria

Abstract

Ticks are major ectoparasites and vectors of pathogens affecting humans, livestock, and wildlife. They harbor diverse microbial communities that may influence tick biology and interactions with microorganisms; however, functional information on tick-associated microbiomes remains limited, particularly in North Africa. In this pilot study, we applied a metaproteomic approach based on high-resolution tandem mass spectrometry to characterize bacterial communities associated with three tick species collected in Algeria: Rhipicephalus sanguineus sensu lato, Hyalomma aegyptium, and Hyalomma dromedarii. Peptide spectra were assigned to taxa using a two-step database search strategy based on NCBInr, and bacterial composition and relative abundance were compared across tick species and sampling locations. A total of 40 bacterial genera belonging to 32 families and four phyla were identified. Microbiome composition differed significantly between tick genera and collection locations, suggesting an influence of species-specific and geographical factors on microbial community structure. Dominant genera included Streptomyces, Bacillus, Clostridium, Escherichia, Flavobacterium, Paenibacillus, and Providencia. Peptides related to Coxiella spp. were frequently detected, consistent with previous reports of Coxiella-like endosymbionts in ticks. This pilot study provides a first metaproteomic characterization of tick-associated communities in Algeria. The results reveal species- and location-associated differences in microbial composition and highlight the potential of metaproteomics for exploring tick-associated microbiomes in North Africa.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1038/s41598-026-58580-1.

Introduction

Ticks are obligate hematophagous ectoparasites, the group called “hard” ticks is belonging to the phylum Arthropoda, Class Arachnida, Order Acari, Suborder Ixodida Family Ixodidae¹. As obligate blood-feeders of a wide range of terrestrial vertebrates, including mammals, birds, reptiles, and humans, ticks are well recognized vectors of a variety of pathogens, including bacteria, parasites, and viruses, involved in the spread of zoonotic and veterinary diseases². In addition to harboring tick-borne pathogens (TBPs), ticks maintain diverse microbial communities, composed of opportunistic, commensal and mutualistic viruses, bacteria and parasites, influencing tick nutrition, development, reproduction, and vector competence^3–10. These symbiotic microbes may also influence the colonization and transmission of pathogenic microorganisms, therefore being a relevant target for tick control^11,12. Recently, studies have focused on the taxonomic and functional composition of the tick microbiome, its microbial diversity and variation under different factors including tick species, sex, and environment among others^6,13. Despite their importance, studies on tick microbiomes have primarily relied on polymerase chain reaction (PCR)-based detection methods, which focus on the presence of specific human or animal pathogens or co-infection patterns¹⁴. Advances in high-throughput sequencing techniques, such as DNA barcoding and metagenomics, have greatly enhanced our understanding of the tick microbiome, providing deeper insights into its taxonomic composition^11,15–21. Such studies have demonstrated the coexistence of TBPs and commensal and symbiotic microorganisms within ticks; however, they primarily focus on genetic material detection and lack functional insights into microbial interactions. To address this limitation, meta-omics approaches, particularly combined metagenomics and metaproteomics, have emerged as powerful tools to characterize microbial communities at phenotypical and functional levels^22–24. The quantitative aspect of the proteotyping methodology provides both a comprehensive taxonomical description and an estimation of the respective microbial communities biomasses. Furthermore, metaproteomics by quantifying the proteins of these taxa offers a functional perspective on microbial activity, metabolic pathways, and host-microbe interactions within microbiomes^25,26. This approach has already been successfully applied to ticks to understand ectoparasite microbiomes, shedding light on their complexity and functional roles^27,28.

Algeria is home to 37 tick species, classified into two families: Argasidae (soft ticks; 12 species) and Ixodidae (Hard ticks; 25 species). Among Algerian tick species, Rhipicephalus sanguineus sensu lato, Hyalomma aegyptium, and Hyalomma dromedarii are highly adapted to xeric environments and exhibit broad and diverse host ranges^29,30. H. dromedarii is a well-established vector of Coxiella burnetii^31,32, Rickettsia aeschlimannii³³, Rickettsia africae³⁴, and Anaplasma spp. closely related to A. platys³². H. aegyptium has been implicated in the transmission of R. aeschlimannii in Algeria³⁵ and is also capable of transmitting Borrelia spp., R. africae³⁶, and the Crimean–Congo hemorrhagic fever virus³⁷. Finally, R. sanguineus sensu lato acts as a competent vector for a wide array of pathogens, including C. burnetii, Theileria spp., Babesia spp., Anaplasma spp., Rickettsia spp., Borrelia spp., Bartonella spp., Candidatus Rickettsia barbariae, and Francisella-like endosymbionts^36,38–47. Recently, metagenomics studies investigated pathogen–pathogen interactions in ticks using high-throughput microfluidic PCR and network analysis^48,49, or examined seasonal variation in tick microbiomes using NGS^43,50. Additionally, in arid and semi-arid regions of North Africa and the Arabian Peninsula, the camel tick H. dromedarii has also been the focus of several microbiome studies based on 16 S rRNA gene sequencing, particularly in camels from the United Arab Emirates, Saudi Arabia and Tunisia. These works consistently report microbiota dominated by Proteobacteria and Firmicutes, with Francisella-like endosymbionts, Staphylococcus, Corynebacterium and Bacillus among the most abundant genera, and reveal marked variation in community structure between countries, farms and tick sexes. Related studies on other Hyalomma species in the wider MENA region, including H. excavatum in Algeria, further highlight strong seasonal and ecological effects on microbiome composition and recurrent associations between endosymbionts such as Francisella and pathogens like Rickettsia^43,49–51.

Despite the growing interest in tick-associated microbiomes, metaproteomic data remain extremely limited, particularly for ticks from North Africa. The logistical constraints associated with high-resolution proteomics, together with the scarcity of reference datasets for this region and for the diversity of North African ticks, currently limit large-scale investigations. Therefore, we conducted a pilot study to provide a pilot metaproteomic characterization of the bacterial communities associated with three medically and veterinary relevant tick species collected in Algeria. This exploratory approach aimed to establish a preliminary reference framework, assess interspecific and geographic variability, and identify priority taxa and regions for future targeted surveillance. Specifically, we sought to (i) describe the taxonomic composition and relative abundance of bacterial communities associated with Rhipicephalus sanguineus sensu lato, H. aegyptium, and H. dromedarii, and (ii) assess species- and locality-specific variation that may influence the composition and functional profiles of tick-associated microbial communities. Because this study is based on cross-sectional metaproteomic profiling, the results should be interpreted as a characterization of bacterial communities associated with ticks rather than evidence of transmission dynamics or vector competence.

Methods

Ethics approval and consents of animal owners

This study was conducted on live animals in compliance with the animal welfare guidelines established by the World Organization for Animal Health⁵² as outlined in the Terrestrial Animal Health Code⁵³. Verbal informed consent was obtained from animal owners to tick collection. Samples were collected under the aegis of the Pasteur Institute of Algeria. Authorization for transport was granted by the Algerian Ministry of Health, permitting their handling as homogenized material in compliance with Biosafety Level 3 laboratory requirements and following chemical inactivation for transport. Sample transfer was conducted in accordance with the Material Transfer Agreement between the Pasteur Institute of Algeria (IPA) and the French Agricultural Research Centre for International Development (CIRAD) (MTA CIRAD reference no. 2023.009).

Tick collection and identification

A total of 448 Ixodid ticks were collected during an entomological survey conducted between January and March 2023 across northern Algeria. Sampling was conducted between January and March 2023 based, collection of active ticks from several host species, ensuring the availability of sufficient biological material for metaproteomic analysis. Sampling locations included Tizi-Ouzou, Tipaza, Biskra, and Algiers (the capital city) (Fig. 1). Ticks were collected directly from animal hosts, including dogs, camels, goats, and tortoises, using tweezers to ensure the collection of the whole arthropod. The collected ticks were kept alive for subsequent identification, dissection, and then, microbiome analysis. Each tick was individually identified using previously described morphological keys^54,55.

Fig. 1.

A representative geographic map illustrating the sampling region, tick species and feeding hosts. The map was created by the authors using ArcMap version 10.8 (Esri, Redlands, CA, USA; https://www.esri.com/arcgis). Administrative boundaries were derived from the Global Administrative Areas database (GADM, version 4.1; https://gadm.org/). Coordinates of sampling sites were added by the authors.

Tick dissection and sample preparation

All dissections and sample processing were conducted in a biosafety level 3 (BSL-3) laboratory at the Pasteur Institute of Algeria. Prior to dissection for protein extraction, live ticks were surface-decontaminated by immersing them in 1% bleach for 30 s, followed by three consecutive 1-minute rinses in sterile distilled water to remove external microorganisms⁵⁶. Tick dissection was performed to recover soft tissues, including Malpighian tubules, midgut, ovaries, and salivary glands, after removing the dorsal cuticle, following a previously described method⁵⁷. The excised organs were placed in sterile 1.5 ml Eppendorf tubes, 300 µl of culture medium (Leibovitz’s L-15 and Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 2% Fetal Bovine Serum (FBS)) were added to homogenize the samples. Organs were then mechanically crushed using 3 mm steel beads (DUTSCHER, France) in a MM400 homogenizer (Retsch, Germany) at 30 Hz per second for 3 min. The homogenate was then centrifuged at 7,000 rpm for 6 min at + 4 °C, after which the supernatant was divided into three aliquots of 100 µl each and stored at − 80 °C for further analysis. Prior to being frozen, aliquots for metaproteomics analysis were mixed v: v with 100 µl of DIGE buffer (7 M urea, 2 M thiourea, 4% CHAPS, 1% Triton-X-100, 30 mM Tris, pH 8) for both inactivation and protein conservation.

Sample selection and protein extraction

A total of 56 individual specimens were selected from the 448 collected ticks for metaproteomic analysis, including: (i) H. dromedarii specimens (n = 4 collected from camels, 2 males and 2 females), (ii) H. aegyptium (n = 4 collected from tortoises, 2 males and 2 females), and (iii) Rh. sanguineus s. l. (n = 48, of which n = 40 were collected from dogs, 20 males and 20 females, and n = 8 from goats, 4 males and 4 females) (Fig. 2). These specimens were selected based on species identity, host association, geographic origin, and specimen integrity after collection and dissection. The selection aimed to represent the main tick species and host types encountered during sampling while ensuring sufficient biological material for proteomic analysis. Because protein yields from individual dissected organs were low, specimens were pooled to obtain sufficient quantities for LC–MS/MS analysis. A total of 10 pooled samples were generated from the 56 ticks, with each pool containing between 2 and 10 specimens. Pools were constructed using specimens belonging to the same tick species, sex, host type, and collection locality. Each pool was treated as an independent biological replicate during downstream analyses. A detailed list of the 10 analyzed pooled samples, including tick species, sex, collection municipality, and host species, is provided in Fig. 2.

Fig. 2.

Schematic overview of the study design, from tick collection to protein extraction and metaproteomic analysis.

Before protein extraction, the pooled homogenates were centrifuged at 500 g for 10 min at 4 °C to remove cell debris. The supernatant was carefully transferred to a new tube without disrupting the pellet. Protein precipitation was performed using the DOC-TCA protocol adapted from the method previously described by Bensadoun et al.⁵⁸ and Chevallet et al.⁵⁹. Na-deoxycholate (DOC) was added to homogenates to a final concentration of 0.1%, and after mixing trichloroacetic acid (TCA) was added to a final 10% concentration. The protein solution was precipitated at + 4 °C overnight and then centrifuged at 10,000 rpm for 15 min at 4 °C. The resulting pellet was washed with tetrahydrofuran (THF, precooled in ice) by vortexing until the pellet unstuck from the bottom of the tube and centrifuged again. The protein pellet was then resuspended in a buffer containing 50 mM Tris, 150 mM NaCl, anti-proteases (cOmplete™ Protease Inhibitor Cocktail, Roche), and 1% Triton-X100. The sample was vortexed until the pellet detached from the tube bottom, and was then fully resuspended using an agitator overnight at 4 °C.

Extraction blanks consisting of buffer-only samples were processed in parallel with the tick samples during protein extraction and digestion. These negative controls were analyzed using the same LC–MS/MS workflow to identify potential contaminant peptides. Peptides detected exclusively in the negative controls were removed from the final dataset prior to downstream analysis.

Protein quantification

Protein concentration was measured using the fluorescence-based Qubit™ quantitation assays (Invitrogen™ Qubit™ 3 Fluorometer, Thermo Fisher Scientific, Singapore) according to the manufacturer’s instructions.

Electrophoresis and sample preparation for mass spectrometry

To assess protein extraction quality, proteins were separated using PAGE (polyacrylamide gel electrophoresis). Extracted proteins (5 µl) were mixed with 5 µl of Laemmli buffer 2X (containing 5 µl of 2-Mercaptoethanol Bio-Rad^® and 95 µl Laemmli buffer 2X Bio-Rad^®), and then heated at 95 °C for 1 min before migration in NuPAGE™ 4–12% Bis-Tris protein gels (1.0 mm, 10-well) at 80 V for stacking, then at 200 V constant voltage for 7 min. After electrophoresis, the gel was washed 3 times with Milli-Q water before staining using colloïdal Coomassie blue G-250 (SafeStain SimplyBlue™, Invitrogen) for 5 min and washed twice for 10 min with Milli-Q water.

For the metaproteomic analysis, each gel lane were excised and divided into four bands, resulting in a total of 40 fractions for the 10 samples (4 fractions per sample). Each band was subjected to in-gel protein digestion using Trypsin Gold (V5280, Promega, Madison, USA) with 0.011% ProteaseMAX surfactant (V2071, Promega, Madison, USA), following the protocols of Hamitouche et al.⁶⁰ and Hartman et al.⁶¹. The resulting peptides were quantified using the Pierce Quantitative Fluorometric Peptide Assay.

Proteomics data acquisition and interpretation

A standardized quantity of peptides (200 ng) of each of the 40 fractions were analyzed via nanoLC-MS/MS using an Exploris 480 mass spectrometer incorporating an ultra-high-field Orbitrap analyzer, coupled to a reversed-phase column (ThermoFisher Scientific, llkirch-Graffenstaden Les Ulys, France), essentially as previously described⁶².

Briefly, peptides were loaded on a reverse-phase Acclaim PepMap 100 C18 precolumn (5 μm, 100 Å, 300 μm i.d. × 5 mm, Thermo) and then resolved on a EasySpray PepMap 100 Neo C18 column (2 μm, 100 Å, 75 μm i.d. × 50 cm, Thermo) at a flow rate of 250 nL per min using a 60-min gradient (5–25% B in 60 min), followed by a short 5 min gradient (25–40% B) and wash of 7 min at 76% B, with 0.1% HCOOH/100% H2O as mobile phase A and 0.1% HCOOH/100% CH3CN as mobile phase B. The mass spectrometer was operated in data dependent acquisition mode as previously described, using a Top30 strategy and a dynamic exclusion time of 20 s.

MS/MS spectra were processed using Mascot software (version 2.6.1, Matrix Science, Boston, USA) to established the list of taxa and proteins using the same strategy as previously described⁶². Taxonomic assignment was performed using the NCBInr ((NCBI, NIH, Bethesda; http://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/nr.gz and tick-associated microbial genomes incomplete representation was mitigated using a two-step search strategy. MS/MS spectra were firstsearched against the complete NCBInr and Taxon-to-Spectrum Matches (TSMs) together with genus-specific peptide markers were used for initial genus-level identification. Second, a custom database restricted to detected genera and their associated taxa was generated to refine peptide assignment to reduce database redundancy and helps limit false-positive identifications. Species-level identification was retained only when peptides uniquely matched a single species. When peptides matched multiple closely related species, taxonomic assignment was restricted to the lowest unambiguous rank (typically genus level). This conservative approach was applied to reduce potential misclassification arising from highly conserved bacterial proteins.

Statistical and data analysis

Statistical analyses were conducted using custom R (v 4.3.2)⁶³ scripts. Alpha diversity, describing the composition of a microbial community in terms of richness (the number of taxonomic groups), evenness (the distribution of their abundances), or both, was assessed using observed richness and Shannon diversity index, computed with the Vegan R package. Statistical significance of alpha diversity differences was tested using the Wilcoxon rank-sum test (for comparison between two groups) and Kruskal-Wallis test (for comparison among more than two groups). Beta diversity, assessing differences between microbial communities by considering taxon-specific sequence abundances or simply the presence and absence of sequences, was analyzed using multidimensional scaling (MDS), implemented via the “cmdscale” function available in the R Stats package. Bray-Curtis dissimilarities were calculated using the Vegan R package, and statistical significance was evaluated using the analysis of similarities (ANOSIM) test.

Relative bacterial abundance and taxonomic composition were visualized using heatmaps generated with the “ComplexHeatmap” R package, incorporating a pruned phylogenetic tree based on NCBI taxonomy. Principal Component Analysis (PCA) was performed using the “factoextra”, MASS, Vegan R packages and visualized with the R data visualisation library “ggplot2”. Finally, bar plots depicting bacterial genus richness per cohort were generated using the “tidyverse” R package.

Results

Taxonomic characterization of tick microbiomes

To provide insights into the diversity of microbiomes from the three tick species collected in Algeria, namely R. sanguineus sensu lato, H. aegyptium, and H. dromedarii, a comparative analysis was performed across tick species, sex, host species and geographic locations (Fig. 1).

The 40 resulting nanoLC-MS/MS acquisitions resulted in a global dataset comprising a total of 2,337,208 MS/MS spectra (Table 1). The reproducibility of the measurements across the ten samples was high with an average of 233,997 (± 2.7%) MS/MS spectra. The spectra from each sample were independently searched against a generic protein database to identify the peptide sequences that could then be assigned to taxa at all the different taxonomic ranks. The analysis produced 315,614 taxon-to-spectrum matches (TSMs) and 8,792 peptide sequences (detailed in Supplementary Table S1). The identified peptides indicated the presence of taxa belonging to Bacteria, Archaea, Eukaryota and Viruses. Bacterial taxa represented 4.92% of the global identified signal based on TSMs (Fig. 3).

Table 1.

Overview of the main features of the ten nanoLC-MS/MS datasets of studied tick species.

Tick species	Datasets	#MS/MS	#TSMs	#Peptides	#Phyla	#Class	#Order	#Family	#Genus	#Species
H. aegyptium	TOFT	241,545	33,535	2147	7	11	13	14	15	35
	TOMT	240,443	30,259	1763	8	12	12	13	16	20
H. dromedarii	BFD	234,005	25,324	1252	9	14	15	15	16	32
	BMD	229,305	23,034	931	9	13	15	16	17	40
R. sanguineus	AFCE	240,472	36,627	2918	10	13	15	15	17	27
	AMCE	229,150	26,313	1542	9	13	14	14	15	31
	TFCE	240,472	36,627	7668	8	12	14	14	14	34
	TMCE	231,577	34,068	6777	9	12	14	14	14	42
R. sanguineus s.l	TFCV	224,163	36,773	8512	9	13	18	19	19	31
	TMCV	228,839	33,054	6325	9	14	16	17	17	32

#: number of entities (MS/MS spectra, TSMs, Peptides) or taxa identified; TOFT & TOMT: Tizi-Ouzou, Female/Male, Tortoise; BFD & BMD: Biskra, Female/Male, Dromedary; AFCE & AMCE: Algiers, Female/Male, Dog; TFCE & TMCE: Tipaza, Female/Male, Dog; TFCV & TMCV: Tipaza, Female/Male, Goat; s.l: sensu lato.

Fig. 3.

Microbial diversity identified in tick samples using metaproteomics.

The Fig. 3 and Supplementary Tables S1 list the genus identified in the dataset and their abundances based on TSMs. Notably, 40 bacterial genera representing 143 putative species-level matches could be identified, as well as five archaeal species, 19 genera of Eukaryote (including 9 fungi) and one virus. For example, Clostridium estertheticum is detected in two samples with 16 distinctive peptides (unique peptides in the database) and relatively high abundance (108 TSMs, 0.03% of the biological biomass) in TMCV1. Other examples of fungi that could belong to the tick microbiota are Fusarium austroafricanum, which was detected in five samples, and Penicillium italicum and Aspergillus calidoustus, which were each detected in a single sample.

• A.Relative proportions of taxon–spectrum matches (TSMs) assigned to the four domains detected in tick samples.
• B.Relative proportions of TSMs assigned to the different eukaryotic genera detected.
• C.Relative proportions of TSMs assigned to the different archaeal genera detected.

In addition to common phyla, 39 bacterial genera were identified that are typically associated with environmental sources such as soil, fresh and saltwater, plants, vertebrates or arthropods. These genera include Bacillus, Chitinophaga, Clostridium, Desulfobulbus, Devosia, Escherichia, Halomonas, Lutibacter, Lysinibacillus, Massilia, Microbacterium, Mycolicibacterium, Nocardia, Paenibacillus, Paraburkholderia, Paracoccus, Providencia, Roseomonas, unclassified Lachnospiraceae, unclassified Rhodobacteraceae, and Variovorax (Fig. 3 and Supplementary Tables S1).

Several previously unreported bacterial genera were also detected in our tick samples including Amphritea, Aquimarina, Chitinophaga, Kushneria, Lutibacter, Myxococcales, Nonomuraea, Novosphingobium, Roseovarius, Ruegeria, and unclassified taxa from Desulfobacteraceae, Lachnospiraceae, Rhodobacteraceae The Fig. 3 and Supplementary Tables S1)

Alpha and beta diversity of tick-associated bacterial communities

Microbiota diversity analyses revealed variation among tick groups. Differences in alpha diversity were observed between some tick genera and collection locations, whereas no significant differences were detected according to tick sex or host species. Beta diversity analyses also indicated differences in microbial community composition between certain groups of samples.

Figure 4 presents the alpha and beta diversity measurements for tick samples stratified by tick genus, sex, host species, and collection location. No significant differences in alpha diversity were observed according to tick sex (P = 0.75 and 0.84; Fig. 4A) or host species (P = 0.74 and 0.085; Fig. 4E). In contrast, significant differences were detected the Shannon diversity index between the microbiomes in the genera Hyalomma and Rhipicephalus (P = 0.019, Fig. 4C) and among sampling locations (P = 0.047, Fig. 4G), although observed richness did not differ significantly between these groups (P = 0.83 and P = 0.46, respectively).

Fig. 4.

Overall results of alpha and beta diversity analyses of the tick-associated microbiome at the species level.

Beta diversity analysis further confirmed significant differences in microbial community composition between Hyalomma and Rhipicephalus (ANOSIM: P = 0.047; Fig. 4D) and between collection locations (ANOSIM: P = 0.004; Fig. 4H), whereas no significant differences were observed according to tick sex (ANOSIM: P = 0.631; Fig. 4B) or host species (P = 0.285; Fig. 4F).

Alpha diversity was assessed using observed richness and Shannon indices. Beta diversity was assessed using Bray–Curtis dissimilarity. P values were determined using two-sided paired Wilcoxon tests, with no adjustment for multiple comparisons. Error bars indicate the median and interquartile range.

• A.Alpha diversity comparison between female (deep pink) and male (deep sky blue) ticks shown as boxplots.
• B.Beta diversity between female and male ticks visualized using multidimensional scaling (MDS).
• C.Alpha diversity comparison between ticks of the genera Hyalomma (deep pink) and Rhipicephalus (deep sky blue).
• D.Alpha diversity among ticks collected from dogs (deep pink), dromedaries (deep sky blue), goats (dark green), and tortoises (moderate grey).
• E.Beta diversity among ticks collected from dogs, dromedaries, goats, and tortoises visualized using MDS (ANOSIM, P = 0.258).
• F.Alpha diversity among ticks collected from Algiers (strong blue), Biskra (deep sky blue), Tipaza (dark green), and Tizi Ouzou (moderate grey).
• G.Beta diversity among ticks collected from Algiers, Biskra, Tipaza, and Tizi Ouzou visualized using MDS.

Differences in microbial community composition and taxonomic relative abundance

In our tick samples, we identified 40 bacterial genera belonging to 32 families spanning four phyla (Fig. 5 and Table S1). At the phylum level, Proteobacteria was the dominant group (44.29% of the bacterial biomass as judged by TSMs), followed by Firmicutes (27.52%), Actinobacteria (20.77%), and Bacteroidetes (7.42%). Among the identified bacterial genera, four could not be named, as they are not yet taxonomically described: unclassified_Rhodobacteraceae, unclassified_Lachnospiraceae, unclassified_Myxococcales, unclassified_Desulfobacteraceae. The most abundant identified genera (%TSMs) in the ten tick microbiomes were Streptomyces (22.36%), Bacillus (15.1%), unclassified_Desulfobacteraceae (10.3%), Coxiella (8.2%), Escherichia (6.6%), Clostridium (5.6%), Flavobacterium (4.5%), Rhizobium (4.2%), Paenibacillus (4%), while other genera were accounting for less than 3% (Fig. 3). In addition to the overall diversity, 143 bacterial species were identified, including well-characterized endosymbionts like Candidatus Coxiella mudrowiae (a Coxiella-like endosymbiont), and commensal bacteria such as Escherichia coli, which has been rarely reported in ticks. It also includes environmental bacteria typically found in soil, plants and marine environment, including the genera Paenibacillus, Bacillus, Bradyrhizobium, Novosphingobium, Rhizobium, Aquimarina. Pathogenic species such as Clostridium perfringens⁶⁴ and Coxiella burnetii, a well-documented tick-borne pathogen⁶⁵, were also detected. In particular, C. burnetii was identified in sample TMCV1 with 236 distinctive peptides, and 438 TSMs, thus representing 1.3% of the global biomass in this sample (Fig. 5 and Table S1).

Fig. 5.

Relative abundance of tick-associated microbial taxa. Bar charts show the relative abundance of microbial taxa at the phylum and genus levels according to tick-associated factors: (A) phylum-level composition by tick sex; (B) genus-level composition by tick sex; (C) phylum-level composition by tick genus; (D) genus-level composition by tick genus; (E) phylum-level composition by tick feeding host; (F) genus-level composition by tick feeding host; (G) phylum-level composition by tick collection location; and (H) genus-level composition by tick collection location. The x-axis represents individual samples, and the y-axis indicates the relative abundance of microbial taxa.

Influence of tick species and sex on the tick microbiome

The relative abundance (RA) of bacterial genera varied among tick species and sexes, as illustrated in Fig. 5. Hyalomma ticks exhibited the lowest diversity in peptide sequences, resulting in the lowest microbial biomass and microbial diversity, compared to Rhipicephalusticks. While Streptomyces was the microbial genus consistently present in all tick species (mean abundance: 22.36%, prevalence 100%), whereas Escherichia and Coxiella were exclusively identified in Rhipicephalus ticks (mean abundance: 6.56% and 8.16%, respectively, prevalence 60%). Bacillus (family Bacillaceae) was detected in R. sanguineus and H. aegyptium but not in H. dromedarii (mean abundance: 15.07%, prevalence 60%). Non-Metric Multidimensional Scaling (NMDS) analysis and ordination plots indicated that tick genus appeared to be associated with differences in microbiome composition (Fig. 4D and Fig. 5, Panels C and D) whereas tick sex had a minimal influence on microbial diversity (Fig. 4B and Fig. 5, Panels A & B).

Finally, a heatmap of the total relative abundance of the top 40 bacterial species at the phylum level revealed that collection location may contribute to differences in tick microbial composition (Fig. 6).

Fig. 6.

Heatmap representation of tick-associated microbiota composition. The heatmap shows the relative abundances (log-transformed) of the 40 most prevalent prokaryotic taxa across tick samples. Microbial composition is displayed according to tick-associated factors, including tick genus, tick sex, feeding host, and collection location. Dendrograms for samples and taxa were generated using hierarchical clustering. Annotations at the top of the heatmap indicate the corresponding tick-associated factors (sex, genus, host, and geographic location).

Influence of feeding host species on the tick microbiome

The composition of the tick microbiome varied according to feeding host species (Fig. 5. Panels E and F). Proteobacteria exhibited the highest relative abundance in ticks collected from goats, whereas Bacteroidetes were more prevalent in ticks from dromedaries. Firmicutes dominated the microbiome of ticks collected from tortoises.

Additionaly, we recorded a notably higher relative abundance of Coxiella in ticks collected from goats, while, the Streptomyces genus was present across all host species.

Influence of collection location on the tick microbiome

The microbiome composition of R. sanguineus sensu lato, H. aegyptium, and H. dromedarii varied across collection localities, with distinct bacterial genera dominating in different regions (Figs. 5 and 6). In Algiers, R. sanguineus s.l. collected from dogs’ harbored microbiomes dominated by Bacillus and Paenibacillus (Firmicutes), whereas in Tipaza, Escherichia and Desulfobacteraceae (Proteobacteria) plus Streptomyces (Actinobacteria) were predominant. Interestingly, R. sanguineus s.l. collected from goats in Tipaza, males exhibited higher relative abundance of Coxiella (a tick-borne pathogen, TBP) and Escherichia (a commensal proteobacterium), especially in male specimens, while unclassified Myxococcales (Proteobacteria) and Streptomyces (Actinobacteria) were more prevalent in females.

In H. dromedarii collected from dromedaries in Biskra, Desulfobacteraceae and Rhizobium (Proteobacteria) were dominant, while Streptomyces (Actinobacteria), also abundant in H. aegyptium collected from tortoises in Algiers was mainly associated with Bacillus (Firmicutes) and Streptomyces (Actinobacteria).

Functional insights into tick-associated microbial communities

A total of 110 proteins were identified, including abundant proteins such as chaperonin GroEL, elongation factor Tu, DNA-directed RNA polymerase, and several ribosomal proteins, suggesting the presence of metabolically active bacterial cells in the sample. Among the detected proteins were LbtU family siderophore porins (WP_039669233.1 and WP_010957902.1), the opacity protein LomR (WP_005772774.1), the periplasmic OmpH chaperone for outer membrane proteins (WP_042525978.1), and the protein-export chaperone SecB (WP_005772050.1), which are commonly associated with bacterial membrane function and protein export.

Stress-related proteins including the universal stress protein UspA (WP_032074717.1), recombinase RecA (WP_005768713.1), catalase (WP_005772736.1), and superoxide dismutase (WP_005770533.1), were also detected.

The most abundant proteins originating from the R. sanguineus host were associated with muscle structure, including myosin, filamin, tropomyosin, tubulin, spectrin, and troponin. A stress-induced phosphoprotein (XP_037524567.1) was also detected at relatively high abundance.

Discussion

Although Algeria hosts a rich diversity of tick species, most studies focus on species identification, pathogen occurrence, and seasonal dynamics, rather than microbial community profiling²⁹. In contrast, tick-associated microbiomes have been increasingly characterized in other regions using omics approaches, revealing that microbial composition varies according to tick species, sex, developmental stage, environment, and can influence pathogen acquisition and transmission^11,66. This indicates that tick microbiomes are still a relatively unexplored ecosystem of high interest for improving prokaryote systematics⁶⁷ Previous recent studies used mass spectrometry to identify tick species in Algeria, whereas associated microorganisms were identified mainly by molecular methods^36,68. Here, we performed a metaproteomic approach to characterize bacterial communities associated with the soft organs of R. sanguineus s.l., H. aegyptium, and H. dromedarii, providing the first metaproteomic characterization of tick-associated bacterial communities in Algeria.

The use of pooled samples limits the assessment of individual variability and reduces statistical power. Nevertheless, the observed patterns provide a useful overview of bacterial communities associated with Algerian ticks. Recent improvements in high-resolution mass spectrometry, including the Quadrupole-Orbitrap platforms, are likely to enhance metaproteomic sensitivity and coverage in future work^68,69. Metaproteomics remains less sensitive than DNA-based tools for detecting low-abundance taxa, but it provides more accurate taxonomic resolution and a functional view of the active microbiome^70,71.

Overall, despite the small sample size, we detected significant differences in alpha and beta diversity across tick genera and collection sites, consistent with patterns observed in other regions^72–74. Rhipicephalus ticks displayed lower microbiome diversity than Hyalomma ticks, likely reflecting differences in host use and life-history traits^75–77. Ticks from Algiers showed higher diversity than those from other regions, possibly due to a higher probability of pathogen uptake or to environmental factors (climate, altitude, biodiversity, and anthropogenic activity) influencing microbial acquisition, as many detected taxa are known to be environmentally derived as previously described for several tick species^66,78. Tick sex and host species showed no significant effect on microbiome composition, in agreement with previous findings in dog-associated ticks⁷⁹. Nonetheless, other studies have demonstrated that species identity, feeding status, geography, and developmental stage can shape microbial communities^27,80,81. Together, these findings highlight the influence of ecological and biological factors on tick-associated bacterial communities and provide new insights into microbiome diversity in Mediterranean tick species.

The bacterial communities were dominated by Proteobacteria, followed by Actinobacteria, Firmicutes and Bacteroidetes. This distribution is consistent with previous metagenomics and metagenomic studies performed in other tick species, including I. ricinus (laboratory colonies), where these phyla were also predominant^27,82,83. Proteobacteria are commonly reported as the dominant bacterial phylum in various tick genera: including Rhipicephalus, Haemaphysalis Dermacentor Ambylomma Ixodes^81–84. Conversely, with the exception for Coxiella, several well-known tick-associated bacteria such as Borrelia, Rickettsia, Spiroplasma, Ehrlichia, and Wolbachia were not detected in our study. This absence may reflect the limitations of metaproteomics in detecting low-abundance taxa compared with DNA-based methods, but it may be influenced by host specificity, geographic location, and environmental conditions.

In addition to common phyla, 39 bacterial genera were identified that are typically associated with environmental sources such as soil, fresh and saltwater, plants, vertebrates or arthropods (Supplementary Table S1), suggesting a possible role in microbial symbiosis, vector competence, or environmental contamination⁶⁶. In particular, genera such as Novosphingobium and Roseovarius, despite their usual association with marine and soil environments, suggest that they may contribute to microbiome stability, biofilm formation, or antimicrobial defense mechanisms within the tick host. Grouping these taxa based on ecological function could help clarify their roles and relevance within tick biology.

Streptomyces was the dominant bacterial genus detected in all three tick species. This genus is widely distributed in soil and is well-known for producing bioactive compounds, including antimicrobial compounds⁸⁵. Streptomyces has also been identified as an endosymbiont in various arthropods, including ticks (D. nuttalli and Ornithodoros turicata), woodlice, leaf beetles, millipedes, wasps, ants, sawflies and mite (Sarcoptes scabiei)^83,85–90.

Its high abundance across all three tick species highlights a possible ecological role within the tick microbiome, potentially contributing to microbial and colonization resistance, as described in other arthropods-associated symbioses. Further functional and experimental studies are warranted to explore its possible role in shaping microbial interactions and enhancing tick fitness.

Another highly abundant bacterial genus in our study was Coxiella, particularly in R. sanguineus s.l. tick collected from goats. The Coxiellaceae family includes both symbiotic and pathogenic species that can cause important diseases in humans and animals, with Coxiella burnetii being the well-known etiological agent of Q-fever, while Coxiella-like endosymbionts (CLEs) are widely distributed in ticks and other arthropods^7,91–93. Our proteomic data identified several proteins associated with Coxiella burnetii, including GroEL, which also matched Candidatus Coxiella mudrowiae, a recently described endosymbiont identified in hard ticks from China85. This finding suggests that the detected Coxiella may represent an endosymbiotic lineage rather than a pathogenic form such as C. burnetii⁹⁴. Overall, our findings are consistent with previous studies on the genus Rhipicephalus, which have identified Coxiella as one of the most dominant bacterial taxa in these ticks, often in association with Coxiella-like endosymbionts (CLEs)^95,96. As peptide sequences may map to conserved proteins shared across Coxiella species, additional molecular and functional validation of Coxiella-related proteins through targeted proteomics, transcriptomics, or immunoassays, would be necessary to distinguish pathogenic from symbiotic strains. This distinction is critical for interpreting their role in tick physiology and vector competence. Similar challenges have been highlighted by Jourdain et al.⁹⁷ who reported that molecular assays targeting Coxiella burnetii may cross-react with Coxiella-like bacteria, complicating epidemiological interpretations.

Conclusion

This pilot metaproteomic study provides the first metaproteomic characterization of bacterial communities associated with ticks in Algeria. The microbiome was dominated by Proteobacteria, particularly taxa such as Streptomyces and Coxiella, and varied according to tick species and collection site. These findings demonstrate the ability of metaproteomics to characterize active tick-associated microbial communities and provide new insights into the diversity of bacterial taxa associated with Algerian ticks.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

For research articles Conceptualization, B.A., T.K., N.E.,D.S., P.H. Methodology, S.D., C.L., N.E., A.H., T.K., N.E.,D.S., P.H.; software, B.M.; validation, T.K., B.M., S.D.,D.S. and P.H; formal analysis, C.L., B. A., T.K., B.M., D.S. and P.H.; investigation, B.A., T.K., B.M., D.S. and P.H.; resources, D.S.; data curation, BA., B.F., T.K., D.S. and P.H.; writing—original draft preparation, B.A., T.K., B.M. P.H. and D.S.; writing—review and editing, All auteurs; visualization, T.K., S.D., D.S. and P.H.; supervision, D.S.; project administration, P.H. and D.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by LeiSHeild-RISE MATI project, Grant Agreement No 778298.

Data availability

Data available upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.