Metaproteomic profiling of the tick microbiome in northern Algeria: a pilot study of bacterial diversity and potential medical or veterinary relevance

Metaproteomic profiling of the tick microbiome in northern Algeria: a pilot study of bacterial diversity and potential medical or veterinary relevance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metaproteomic profiling of the tick microbiome in northern Algeria: a pilot study of bacterial diversity and potential medical or veterinary relevance Tahar kernif, Clément Lozano, Fayez Ahmed KHARDINE, Bachir MEDROUH, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9160125/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background 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. Methods 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 H. 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. Results 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. Conclusions This pilot metaproteomic study provides a first functional overview of bacterial communities associated with ticks 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. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Microbiology Ticks Tick microbiome Metaproteomics Bacterial diversity Hyalomma Rhipicephalus Algeria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Ticks are obligate hematophagous ectoparasites, the group called "hard" ticks is belonging to the phylum Arthropoda, Class Arachnida, Order Acari, Suborder Ixodida Family Ixodidae [ 1 ]. 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, that pose threats on the spread of zoonotic and veterinary diseases [ 2 ]. 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 patterns [ 14 ]. 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 ]. These studies have demonstrated the coexistence of TBPs and commensal and symbiotic microorganisms within ticks; however, they primarily focus on genetic material 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 the functional level [ 22 , 23 ]. Interestingly, through peptide sequencing by tandem mass spectrometry metaproteomics offers the possibility to infer the organisms that produced the detected proteins in a biological sample [ 24 ]. This proteotyping methodology allows providing a comprehensive taxonomical description and an estimation of their respective 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 the hard ticks, genera such as Hyalomma (10 species), Rhipicephalus (6 species), Ixodes (5 species), Haemaphysalis (3 species), and Dermacentor (1 species) have been documented across 34 host species, including 27 mammals, 4 reptiles, and 3 birds [ 29 , 30 ]. Among Algerian tick species, Rhipicephalus sanguineus sensu lato, Hyalomma aegyptium , and H. dromedarii are highly adapted to xeric environments and exhibit broad and diverse host ranges [ 29 ]. H. dromedarii is a well-established vector of Coxiella burnetii [ 31 , 32 ], Rickettsia aeschlimannii [ 33 ], Rickettsia africae [ 34 ], and Anaplasma spp. closely related to A. platys [ 32 ]. H. aegyptium has been implicated in the transmission of R. aeschlimannii in Algeria [ 35 ] and is also capable of transmitting Borrelia spp., R. africae [ 36 ], and the Crimean–Congo hemorrhagic fever virus [ 37 ]. 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 , 39 – 46 , 47 ]. Since the early 2000s, research on tick-borne pathogens (TBPs) in the Mediterranean basin, including Algeria, has increased considerably with the development of molecular detection techniques such as PCR, next-generation sequencing (NGS), and other high-throughput approaches [ 48 , 49 ]. More recently, several studies have begun to explore tick-associated microbial communities in Algeria. For instance, [ 50 ] investigated pathogen–pathogen interactions in Hyalomma excavatum ticks using high-throughput microfluidic PCR and network analysis, while [ 51 ] examined seasonal variation in tick microbiomes using NGS. Despite these advances, most investigations remain focused on DNA-based detection of specific microorganisms rather than on functional characterization of the broader microbial community. One of the few studies addressing multiple microorganisms simultaneously in Algerian ticks is that of [ 42 ] et al. (2021), who used high-throughput microfluidic real-time PCR to detect several microorganisms in ixodid cattle ticks from northeastern Algeria [ 42 ]. 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 an initial 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, Hyalomma 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, which could potentially affect interactions between ticks and microorganisms. 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 Health [52] as outlined in the Terrestrial Animal Health Code [53]. Verbal informed consent was obtained from animal owners to tick collection. Tick removal was non-invasive and did not involve any experimental manipulation or harm to the animals. Verbal informed consent was obtained from animal owners prior to tick collection. 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 primarily on the availability of live ticks during the winter season and logistical constraints related to field collection and laboratory processing. This period allowed the collection of active ticks from several host species, ensuring the availability of sufficient biological material for metaproteomic analysis. Because sampling was restricted to a single season, the present study does not aim to evaluate seasonal variation in tick microbiomes, and the results should therefore be interpreted as a snapshot of microbial communities during this specific sampling period. Sampling locations included Tizi-Ouzou, Tipaza, Biskra, and Algiers (the capital city) ( Figure 1 ). Ticks were collected directly from animal hosts, including dogs, camels, goats, and tortoises. 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]. Tick dissection and sample preparation Prior to dissection for protein extraction, live ticks were surface-decontaminated by immersing them in 1% bleach for 30 seconds, followed by three consecutive 1-minute rinses in sterile distilled water to remove external microorganisms [56]. 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 method [57]. 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. This medium was used to stabilize tissues during mechanical disruption and facilitate homogenization. Potential contamination from bovine proteins was minimized by filtering peptides matching bovine sequences and by estimating bacterial abundance using taxon–spectrum matches rather than total peptide counts. Organs were then mechanically crushed using 3mm steel beads (DUTSCHER, France) in a MM400 homogenizer (Retsch, Germany) at 30 Hz per second for 3 minutes. 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. All dissections and sample processing were conducted in a biosafety level 3 (BSL-3) laboratory at the Pasteur Institute of Algeria to ensure proper containment of potential pathogens. Before transport, 100 µl of homogenates was mixed with 100 µl of DIGE buffer (7 M urea, 2 M thiourea, 4% CHAPS, 1% Triton-X-100, 30 mM Tris, pH 8) at a 1:1 volume ratio (V/V) for inactivation according to biosafety consideration. The samples were then shipped at +4°C, to CIRAD-IRD Montpellier for further analysis, following the Category B infectious substances (UN 3373) regulations with Accord for Dangerous goods by Road (ADR) packing instruction PI 650 for road transport and International Air Transport Association (IATA) packing instruction PI 650 for air transport. Sample selection and protein extraction We selected Hyalomma dromedarii , H. aegyptium , and Rhipicephalus sanguineus sensu lato because they are among the most prevalent and epidemiologically relevant tick species in Algeria, with broad host ranges and documented associations with several pathogens. Sampling was conducted between January and March primarily based on technical feasibility, including the availability of live ticks during the winter season and logistical constraints related to field collection and laboratory processing. Thus, 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), (ii) H. aegyptium (n = 4 collected from tortoises), and (iii) Rh. sanguineus s. l. (n = 48, of which n = 40 were collected from dogs and n = 8 from goats). 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. Protein extraction was performed on pools of 2 to 10 specimens generated from the 56 ticks. Pools were constructed using specimens belonging to the same tick species, sex, and collection locality to preserve ecological coherence while ensuring sufficient protein quantity for metaproteomic analysis. Consequently, statistical comparisons were conducted at the pooled-sample level and should be interpreted as exploratory patterns rather than definitive ecological relationships. Because the analysis was conducted on pooled organs from multiple ticks, tissue-specific microbial signatures could not be evaluated. Previous studies have shown that microbiome composition may vary between tick organs such as the midgut, salivary glands, and reproductive tissues. Therefore, the present analysis should be interpreted as a global characterization of the organ-associated microbiome, and future studies focusing on individual tissues will be necessary to investigate organ-specific microbial interactions. A detailed list of the 10 analyzed pooled samples, including tick species, sex, collection municipality, and host species, is provided in Figure 2 . Before protein extraction, the pooled homogenates were centrifuged at 500 g for 10 minutes 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. (1976) and Chevallet et al. (2007) [58,59]. Na-deoxycholate (DOC) was added to homogenates to a final concentration of 0.1%, and after mixing trichloroacetic acid (TCA) was added to 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. This method provides high sensitivity and accuracy for protein quantification, making it suitable for low-concentration protein samples. Electrophoresis and sample preparation for mass spectrometry To assess the quality and reproducibility of protein extractions, proteins obtained from tick organs were separated using polyacrylamide gel electrophoresis (PAGE). 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 minute in a dry thermomixer block (ThermoMixer C, Eppendorf, Montesson, France) to obtain fully denaturated proteins before migration. Samples were loaded onto NuPAGE™ 4-12% Bis-Tris protein gels (1.0 mm, 10-well) and separated using the NuPAGE™ Bis-Tris XCell SureLock™ Mini-Cell system with MES SDS running buffer. Electrophoresis was performed at 80V for stacking, then at 200V constant voltage for 7 min. After electrophoresis, the gel was washed 3 times with Milli-Q water before protein staining. Staining was performed using colloïdal Coomassie blue G-250 (SafeStain SimplyBlue™, Invitrogen) for 5 min, which produces a blue-green tint. The gels were then washed twice for 10 min with Milli-Q water and left in Milli-Q water under gentle agitation overnight. For the metaproteomic analysis, each polyacrylamide gel lanes containing the whole soluble proteome 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., (2021) and Hartman et al., (2014) [60,61]. The resulting peptide extracts were collected in 50 μl aliquots per sample and 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 described [62]. 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 sec. MS/MS spectra were processed and interpreted 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 described [62]. The NCBInr database was used because it provides broad taxonomic coverage and is widely applied in metaproteomic studies. Although tick-associated microbial genomes remain incompletely represented in public databases, this limitation was mitigated using a two-step search strategy. Briefly, MS/MS spectra were first searched against the NCBInrS database derived from NCBInr (NCBI, NIH, Bethesda; ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/nr.gz). The resulting peptide sequences were assigned to taxonomic groups across all ranks (from superkingdom to species), and Taxon-to-Spectrum Matches (TSMs) together with genus-specific peptide markers were used for initial genus-level identification. A second search was then performed using a custom database generated from NCBInr that included only the detected genera and their associated taxa. This approach refines peptide assignment, reduces database redundancy, and helps limit false-positive identifications associated with large non-curated databases. Species-level identification was considered only when peptide sequences uniquely matched proteins from a single species within the database. 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. Although the NCBInr database provides broad taxonomic coverage and is widely used in metaproteomic analyses, it remains non-curated and may incompletely represent microorganisms associated with arthropod microbiomes. Consequently, some taxa present in tick samples may not yet be represented in public protein databases, which could limit taxonomic resolution or lead to conservative assignments at higher taxonomic ranks. To mitigate these limitations, we applied a two-step database search strategy and retained species-level identifications only when peptides uniquely matched proteins from a single species. Ambiguous matches were restricted to the lowest reliable taxonomic level, typically the genus level. This conservative approach reduces the risk of false-positive identifications and improves the robustness of taxonomic interpretation in complex environmental samples. Statistical and data analysis Statistical analyses were conducted using custom R (v 4.3.2) 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 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 microbiomes diversity of 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 ( Figure 1 ). As illustrated in Figure 2 , the experimental workflow to conduct metaproteomics on ten samples included protein extraction, trypsin digestion, protein identification, and quantification. To acquire as much as possible mass spectrometry signal, each proteome was subdivided into 4 fractions and analyzed with 4 x 1 h of high-resolution tandem mass spectrometry. 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. Table 1 summarizes the results obtained at the genus level. This analysis produced 315,614 taxon-to-spectrum matches (TSMs) and 8,792 peptide sequences. The detailed taxonomic assignments is provided in Supplementary Table S1 . The identified peptides indicated the presence of biological material belonging to the 3 major domains, Archaea and Bacteria that are constitutive of the tick microbiomes, Eukaryota that could originate from the tick itself or the mammalian host’s blood or tick environment, plus the fourth domain constituted by the Viruses (with only one nonpathogenic viral genus detected). Notably, 4.92% of the global identified signal corresponded to bacterial taxa, as determined by TSMs ( Figure 3 ). No specific host-protein depletion step was applied during sample preparation. Because tick and host proteins represent the majority of the biological material in dissected tissues, eukaryotic proteins were expected to dominate the detected spectra. Bacterial abundance was therefore estimated using taxon–spectrum matches after filtering host-derived peptides to reduce potential bias in microbial detection. The Figure 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. 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 Hyalomma aegyptium TOFT 241545 33535 2147 7 11 13 14 15 35 TOMT 240443 30259 1763 8 12 12 13 16 20 H. dromedarii BFD 234005 25324 1252 9 14 15 15 16 32 BMD 229305 23034 931 9 13 15 16 17 40 Rhipicephalus sanguineus AFCE 240472 36627 2918 10 13 15 15 17 27 AMCE 229150 26313 1542 9 13 14 14 15 31 TFCE 240472 36627 7668 8 12 14 14 14 34 TMCE 231577 34068 6777 9 12 14 14 14 42 R. sanguineus s.l TFCV 224163 36773 8512 9 13 18 19 19 31 TMCV 228839 33054 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 . Alpha and beta diversity of tick-associated bacterial communities Because each analyzed dataset corresponds to pooled samples combining several individuals and organs, diversity estimates represent aggregate microbial profiles rather than individual-level microbiomes. Consequently, the diversity comparisons presented here should be interpreted cautiously and primarily reflect global differences between pooled sample groups. 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; Figure 4A ) or host species (P = 0.74 and 0.085; Figure 4E ). In contrast, significant differences were detected between the genera Hyalomma and Rhipicephalus ( Figure 4C ) and among collection locations ( Figure 4G ). In particular, the Shannon diversity index distinguished the microbiomes of Hyalomma and Rhipicephalus (P = 0.019; Figure 4C ) and revealed differences among sampling locations (P = 0.047; Figure 4G ), although observed richness did not differ significantly between these groups (P = 0.83 and P = 0.46, respectively). Beta diversity analysis further confirmed significant differences in microbial community composition between Hyalomma and Rhipicephalus (ANOSIM: P = 0.047; Figure 4D ) and between collection locations (ANOSIM: P = 0.004; Figure 4H ), whereas no significant differences were observed according to tick sex (ANOSIM: P = 0.631; Figure 4B ) or host species (P = 0.285; Figure 4F ). These results suggest that tick genus and collection location may influence microbiota diversity in the analyzed samples. It should also be noted that several ecological variables are partially associated in the sampling design. For instance, specific tick species were collected from particular hosts and geographic locations. Therefore, the observed differences may reflect combined effects of tick species, host, and environmental conditions rather than a single isolated factor. 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 ( Figure 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. This indicates that tick microbiomes are still a relatively unexplored ecosystem of high interest for improving prokaryote systematics [63]. 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 % (Figure 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, notably, commensal bacteria such as Escherichia coli , which has been rarely, if ever, reported in ticks prior to this study. It also includes environmental bacteria typically found in soil, plants and marine environment, including the genera Paenibacillus , Bacillus , Bradyrhizobium , Novosphingobium , Rhizobium , Aquimarina, and others. Their identification raises questions about their biological role in tick physiology. Finally, pathogenic species, such as Clostridium perfringens , which is known for its impact on both humans and animal health [64]. Noteworthy, Coxiella burnetii, a well-documented tick-borne pathogen (TBP, etiologic agent of Q fever) [65] , was also detected in TMCV1 with 236 distinctive peptides, and 438 TSMs, thus representing 1.3% of the global biomass in this sample. Such identification is consistent with previous reports describing ticks as potential carriers of bacteria of medical and veterinary relevance ( Figure 5 and Table S1 ). The distinction between endosymbionts and pathogenic species within the genus Coxiella remains a subject of ongoing debate, largely due to methodological challenges in precisely identifying species. As highlighted by Jourdain et al. (2015) [66], molecular methods routinely used to detect Coxiella burnetii in ticks may cross-react with Coxiella-like bacteria, complicating epidemiological interpretations. In this context, strain-level characterization could be achieved using next-generation mass spectrometers such as the Astral mass analyzer [67], which enable extensive detection of specific peptides. The use of comprehensive peptide sets would thus allow identification approaching the strain level, providing a more accurate picture of Coxiella diversity and enabling finer-scale epidemiological studies on transmission dynamics and the distinction between endosymbiotic and pathogenic forms. Influence of tick species and sex on the tick microbiome The relative abundance (RA) of bacterial genera was analyzed across different tick species and sexes, as illustrated in Figure 5 . Among the three tick species, Hyalomma ticks exhibited the lowest diversity in peptide sequences detected by tandem mass spectrometry, resulting in the lowest microbial biomass and microbial diversity. While Streptomyces was the genus consistently present in all tick species (mean abundance: 22.36%, prevalence 100%), certain bacterial genera exhibited species-specific distributions. 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 (Figure 3, Panels B & D) indicated that tick genus appeared to be associated with differences in microbiome composition, supporting the hypothesis that arthropod-associated factors shape microbial communities. Tick sex had a minimal influence on microbial diversity, suggesting that breeding strategies are not correlated with microbiome diversity, and that other factors may play a more significant role in shaping bacterial communities in ticks. 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 microbial composition, although the limited sample size prevents robust statistical ranking of all factors ( Figure 6 ). Influence of feeding host species on the tick microbiome The bar charts in Figure 5 (Panels C and F) illustrate the influence of the feeding host species on the composition of the tick microbiome. At the phylum level, Proteobacteria exhibited the highest relative abundance in ticks collected from goats compared with other host species. (Tortoises, dromedaries or dogs). By contrast, Bacteroidetes was more prevalent in ticks from dromedaries than those from other feeding hosts. Firmicutes dominated the microbiome of ticks collected from tortoises. This indicates a host associated signature. At the genus level, we recorded a notably higher relative abundance of Coxiella in ticks collected from goats, absent or scarce in ticks from other hosts. Other genera also displayed host-specific abundance patterns, further suggesting a potential role of host species in shaping the tick microbiome. Notably, the Streptomyces genus was present across all host species, whereas the Coxiella genus showed a marked affinity for ticks sampled on goats. Other genera exhibited fluctuating relative abundances depending on the specific host species. The MDS analysis (Figure 4F) did not effectively differentiate microbiome composition among host species, likely due to the small sample size. This limitation suggests that feeding host species influence on microbiome qualitative and quantitative diversity needs to be weighted in comparison to other factors such as tick species or tick ecology influenced by the habitat/environment. These findings indicate that tick-associated factors, such as species and feeding host preference, are key determinants of microbiome composition ( Figure 6 ), but need larger sample sizes and additional studies to fully assess the hierarchical influence of each factor on microbiome diversity. 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 ( Figures 5 and 6 ). In Algiers, R. sanguineus s. l. collected from dogs’ harbored microbiomes dominated by Bacillus and Paenibacillus (Firmicutes), with a notable presence of Bradyrhizobium (Proteobacteria). In contrast, ticks from dogs in Tipaza, showed a distinct patterns: Escherichia (Proteobacteria) and Streptomyces (Actinobacteria) were prominent with additional representation of Desulfobacteraceae (Proteobacteria). 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 from dromedaries in Biskra, Proteobacteria genera such as Desulfobacteraceae and Rhizobium were dominant, while Streptomyces (Actinobacteria) also appeared prominently, particularly in males. Similarly, in H. aegyptium collected from tortoises in Algiers, Bacillus (Firmicutes) and Streptomyces (Actinobacteria) were the most abundant taxa. Overall, these findings highlight the influence of collection locality, and by extension the host species, on tick microbiome structure. The presence of potentially pathogenic bacteria such as Coxiella further investigation into how microbial communities may interact with tick-associated microorganisms. Future research with larger sample sizes is essential to unravel the ecological and environmental drivers of microbiome diversity in Algerian ticks, particularly the interactions between geographical area of collection, feeding host species, and tick physiology. Functional insights into tick-associated microbial communities Next, we focused on the TMCV1 sample in which Coxiella burnetii –related peptides were detected. 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 were also detected, including the universal stress protein UspA (WP_032074717.1), recombinase RecA (WP_005768713.1), catalase (WP_005772736.1), and superoxide dismutase (WP_005770533.1), which are involved in cellular stress responses and redox homeostasis. While these proteins may reflect bacterial responses to host immune defenses or environmental conditions within the tick, they could also arise from physiological stress associated with sample processing, storage, or environmental exposure. Consequently, the functional implications of these proteins remain speculative and require further experimental validation. The identification of multiple proteins matching Coxiella -related sequences suggests the presence of bacteria related to this genus in the analyzed sample. However, several of these proteins (e.g., GroEL, elongation factor Tu, ribosomal proteins) are highly conserved among bacteria and may not be specific to Coxiella burnetii . Therefore, these findings should be interpreted cautiously, and additional molecular or targeted proteomic analyses would be required to confirm the precise identity and activity of the detected bacteria within the tick host. As expected, the most abundant proteins originating from the Rhipicephalus 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 profiling [ 29 ], which has nevertheless more broadly matured through next-generation sequencing and revealed that tick-associated microbial communities vary by species, sex, developmental stage, environment, and can influence pathogen acquisition and transmission [ 13 ]. In recent years, increasing research on ticks has focused on their microbiomes and microbial interactions, particularly in understanding the composition, functional roles, and ecological implications of microbial communities, with an oriented focus on their potential impact in pathogen persistence, dissemination, and fitness in ticks [ 11 , 68 ]. Previous recent studies used mass spectrometry to identify tick species in Algeria but their associated microorganisms were identified by molecular biology [ 36 , 69 ]. Here, we performed the first direct proteotyping approach to characterize the microbial communities present in the soft organs (Malpighian tubules, midgut, ovaries, and salivary glands) of three tick species ( R. sanguineus s. l., H. aegyptium , and H. dromedarii ) collected from dogs, goats, tortoises and dromedaries in different geographical areas in Algeria. This study is based on a limited number of samples, which is expected for a pilot investigation designed to provide a first descriptive overview of tick species, their hosts, and associated microbiome diversity in Algeria. Because samples were generated from pools of 2–10 ticks, individual-level variability could not be assessed, and the interpretation of microbiome differences associated with tick sex, host species, or collection location should be approached with caution. However, this pooling strategy was necessary to obtain sufficient protein material for metaproteomic analysis and is appropriate for an exploratory pilot study. This pilot study also aimed at showing the potential of proteotyping and metaproteomics, recent technologies, to better identify the whole microbial communities present in ticks. Although this restricted dataset reduces statistical power for assessing factors such as host species or tick sex, it offers an essential baseline to guide future, larger-scale studies on tick microbiota, including the characterization of organs-specific microbiomes. Recent improvements in high-resolution mass spectrometry, including the Quadrupole-Orbitrap platforms, are likely to enhance metaproteomic sensitivity and coverage in future work [ 67 , 69 ]. While metaproteomics remains less sensitive than DNA-based tools for detecting rare taxa, it provides more accurate taxonomic resolution and a functional view of the active microbiome [ 70 , 71 ]. 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 [ 73 – 75 ]. Rhipicephalus ticks displayed lower microbiome diversity than Hyalomma ticks, likely reflecting differences in host use and life-history traits [ 76 – 78 ]. Ticks from Algiers showed higher diversity than those from other regions, possibly due 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 [ 68 , 79 ]. Tick sex and host species showed no significant effect on microbiome composition, in agreement with previous findings in dog-associated ticks [ 80 ]. Nonetheless, other studies have demonstrated that species identity, feeding status, geography, and developmental stage can shape microbial communities [27,81,82], which highlights that by integrating proteomic evidence with ecological and taxonomic data, our pilot study should be viewed as a first descriptive milestone supporting future hypothesis-driven research on microbiome-pathogen-vector interactions in Mediterranean tick species. The most abundant bacterial phyla identified in the soft organs of the studied tick species were Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. Proteobacteria was the most dominant phylum (25 genera), followed by Actinobacteria (6), and Firmicutes (5). These results are in agreement with previous meta-omics studies performed on I. ricinus ticks from laboratory colonies, which identified similar bacterial distributions (21 genera from Proteobacteria, 7 from Actinobacteria, and 4 from Firmicutes) [ 27 ]. Additionally, 16S rRNA-based next-generation sequencing studies on tick microbiome, have frequently reported these three phyla as dominant bacterial groups [ 83 , 84 ]. Proteobacteria is the largest and most diverse bacterial phylum, and its dominance in tick microbiomes is well documented [ 85 ]. Specifically phylum-level dominance of Proteobacteria has been recorded in various tick species independently of the feeding host species: R. sanguineus s.l. (99%) and Haemaphysalis punctata (98%) collected on vegetation in Spain [ 69 ], H. dromedarii (98%) infesting camels in Saudi Arabia [ 84 ], Dermacentor marginatus (97%) collected on vegetation in Spain [ 80 ], D. nuttalli (90.7%) sampled on vegetation in China [ 83 , 84 ], Ambylomma tuberculatum (89%) collected on Gopher tortoise in the USA [ 70 ], I. ricinus (88%) collected on vegetation in Spain [ 80 ], and D. marginatus and D. reticulatus (60%) harvested on vegetation in Slovakia. Conversely, with the exception for Coxiella , several well-known tick-associated bacterial genera and species ( Borrelia , Rickettsia , Spiroplasma , Ehrlichia , Ca . neoehrlichia, Wolbachia and Ca . midichloria ) were not detected in our study. This absence in microbiome composition has also been reported in H. dromedarii [ 86 ], whereas these bacteria were identified in I. ricinus using metatranscriptomics and metaproteomics [ 27 ]. Such absence of well-known tick-associated bacterial genera, such as Borrelia and Rickettsia , may reflect the limitations of metaproteomics in detecting low-abundance taxa, which are continually being pushed back by advances in mass spectrometry technology and digital data processing tools [ 87 ]. Another explanation that is increasingly being highlighted in the literature is that the tick microbiome may be influenced by host specificity, geographic location, and environmental conditions, as described for I. ricinus [ 88 ]. 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. Some bacterial species within these genera have been previously reported in ticks and other arthropods ( Supplementary Table S1 ), suggesting a possible role in microbial symbiosis, vector competence, or environmental contamination [ 68 ]. Additionally, these bacteria may also be acquired from external environmental sources, such as water uptake or air intake through tick spiracles [ 19 ]. The presence of bacterial genera typically associated with marine and soil environments, such as Roseovarius , Halomonas , and Novosphingobium , raises questions about their mode of acquisition and potential ecological roles. These taxa may act as passive environmental colonizers, but they could also contribute to tick physiology through nutrient cycling, resistance to desiccation, or antimicrobial defense. Their detection across different tick species suggests a potential selective retention, which warrants further experimental validation. 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 detection of Novosphingobium and Roseovarius in ticks, despite their usual association with marine and soil environments, suggests 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. The most dominant bacterial genus in our study was Streptomyces , found in all three tick species. Streptomyces is an aerobic, spore-forming actinomycete initially described by Waksman and Henrici (1943) [ 89 ]. This genus is ubiquitous in soil and is well-known for producing bioactive compounds, including antibiotics, tumor suppressors, and enzymes. Streptomyces has also been identified as an endosymbionts in various arthropods, including ticks ( D. nuttalli and Ornithodoros turicata ), woodlice, leaf beetles, millipedes, wasps, ants, sawflies and mite ( Sarcoptes scabiei ) [ 84 , 89 – 94 ]. The high abundance of Streptomyces across all three tick species highlights its potential significance in microbiome stability. Known for producing a wide range of bioactive metabolites, Streptomyces may play an important role in regulating the tick microbiota through antimicrobial activity, limiting the colonization of pathogenic bacteria, and possibly contributing to tick innate immunity. Similar functional associations have been described in other arthropods, where Streptomyces acts as a symbiotic partner conferring defense benefits. 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 goat. Coxiellaceae family includes both symbiotic and pathogenic species that can cause significant diseases in humans and animals. Coxiella burnetii is the well-known causative agent of Q-fever, whereas Coxiella -like endosymbionts (CLEs) are widely distributed in ticks and other arthropods [ 7 , 95 – 97 ]. Our proteomic data identified several proteins associated with Coxiella burnetii , including GroEL, which also matched Candidatus coxiella mudrowiae a newly identified endosymbiont discovered in hard ticks from China [ 98 ], suggesting that the detected Coxiella may represent an endosymbiont rather than a pathogenic form like C. burnetii . 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) [ 99 , 100 ]. 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 determine whether these sequences originate from pathogenic C. burnetii or symbiotic strains (e.g. Candidatus Coxiella mudrowiae ). This distinction is critical for interpreting their role in tick physiology and vector competence. Conclusion This pilot metaproteomic study provides the first functional overview of bacterial communities associated with three major tick species collected in northern Algeria. The analysis revealed a diverse microbiome dominated by taxa such as Streptomyces , Bacillus , and Coxiella , and highlighted differences in microbial composition associated with tick species and collection location. These findings are consistent with previous studies showing that tick-associated microbial communities can vary according to ecological and biological factors. Although based on a limited number of pooled samples, this study demonstrates the potential of metaproteomics to characterize active microbial communities within ticks and to complement DNA-based microbiome investigations. By establishing an initial metaproteomic reference framework for Algerian ticks, this work provides a foundation for future studies aimed at better understanding microbiome diversity and its ecological determinants in tick populations. Declarations Acknowledgments: We thank all of the people who participated in the tick collection. The authors would like to express their sincere gratitude to Ms. Julie Puget, from the Interns and Students Office at IRD, for her invaluable support and dedication. Without her assistance, Mr. Kernif Tahar would not have been able to complete his secondment at IRD. Her efficiency and commitment were instrumental to the success of this collaboration. This work benefited from a capacity-building initiative aimed at strengthening research collaborations and supporting researchers from Southern institutions. This program provides support for training and research mobility but does not fund research expenses or publication costs. Finally, Mr. Clément LOZANO & Mr. Jean ARMENGAUD gratefully acknowledge the support from the French National Agency for Research (France 2030, the French Proteomics Infrastructure INBS ProFI, grant number ANR-24-INBS-0015-05) which has contributed to the advancement of metaproteomics expertise within their research team. Supplementary Materials: Table S1. Biological and taxonomic characterization of tick-associated bacteria identified via a metaproteomic approach. Funding: This research was funded by LeiSHeild-RISE MATI project, Grant Agreement N°778298. Availability of Data and Materials: Data available upon reasonable request. 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. Ethics approval and consent to participate: Not applicable. 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Supplementary Files SupplementaryTableS1.doc Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Editor invited by journal 23 Mar, 2026 Submission checks completed at journal 20 Mar, 2026 First submitted to journal 20 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9160125","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622539623,"identity":"98cdc9fd-8ebd-4b17-98ea-7a9c5f5ecff3","order_by":0,"name":"Tahar kernif","email":"","orcid":"","institution":"Institut Pasteur d’Algérie","correspondingAuthor":false,"prefix":"","firstName":"Tahar","middleName":"","lastName":"kernif","suffix":""},{"id":622539625,"identity":"a6086152-f217-407b-a995-f2a7dfb97043","order_by":1,"name":"Clément Lozano","email":"","orcid":"","institution":"Atomic Energy and Alternative Energies 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hosts.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9160125/v1/4cfca01d1b8798c6508680df.png"},{"id":107362687,"identity":"a85cb2e1-407c-48b0-b00d-aa294a0da0a4","added_by":"auto","created_at":"2026-04-20 18:48:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":500342,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic overview of the study design, from tick collection to protein extraction and metaproteomic analysis\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9160125/v1/a2571d91005a9d8447df15ac.png"},{"id":107487441,"identity":"86163490-69d2-4212-abfc-4f3e21572f05","added_by":"auto","created_at":"2026-04-22 02:41:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":187556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial diversity identified in tick samples using metaproteomics.\u003c/strong\u003e\u003cbr\u003e\n(A) Relative proportions of taxon–spectrum matches (TSMs) assigned to the four domains detected in tick samples.\u003cbr\u003e\n(B) Relative proportions of TSMs assigned to the different eukaryotic genera detected.\u003cbr\u003e\n(C) Relative proportions of TSMs assigned to the different archaeal genera detected.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9160125/v1/8bece3a24d71f8799565b2e4.png"},{"id":107362689,"identity":"8a5c329c-5463-4bd6-b5b2-6b21953833f0","added_by":"auto","created_at":"2026-04-20 18:48:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall results of alpha and beta diversity analyses of the tick-associated microbiome at the species level.\u003c/strong\u003e\u003cbr\u003e\nAlpha 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.\u003cbr\u003e\n(A) Alpha diversity comparison between female (deeppink) and male (deepskyblue) ticks shown as boxplots.\u003cbr\u003e\n(B) Beta diversity between female and male ticks visualized using multidimensional scaling (MDS).\u003cbr\u003e\n(C) Alpha diversity comparison between ticks of the genera \u003cem\u003eHyalomma\u003c/em\u003e(deeppink) and \u003cem\u003eRhipicephalus\u003c/em\u003e (deepskyblue).\u003cbr\u003e\n(D) Alpha diversity among ticks collected from dogs (deeppink), dromedaries (deepskyblue), goats (dark green), and tortoises (moderate grey).\u003cbr\u003e\n(E) Beta diversity among ticks collected from dogs, dromedaries, goats, and tortoises visualized using MDS (ANOSIM, P = 0.258).\u003cbr\u003e\n(F) Alpha diversity among ticks collected from Algiers (strong blue), Biskra (deepskyblue), Tipaza (dark green), and Tizi Ouzou (moderate grey).\u003cbr\u003e\n(G) Beta diversity among ticks collected from Algiers, Biskra, Tipaza, and Tizi Ouzou visualized using MDS.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9160125/v1/f96782e00996b24a52c8f9c8.png"},{"id":107362690,"identity":"5cb75ee7-475b-4dc2-ae74-43105f14bdca","added_by":"auto","created_at":"2026-04-20 18:48:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":500780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative abundance of tick-associated microbial taxa. \u003c/strong\u003eBar 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.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9160125/v1/a58a887be35d2a73eb44210e.png"},{"id":107487605,"identity":"8af3d8bc-f857-4dfe-bd5a-ef7bedbe235b","added_by":"auto","created_at":"2026-04-22 02:42:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":281029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap representation of tick-associated microbiota composition.\u003c/strong\u003e 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).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9160125/v1/7606542931776487a02d480c.png"},{"id":107705415,"identity":"ebe4a300-8b25-40a4-b7fc-254a2b36415e","added_by":"auto","created_at":"2026-04-24 09:12:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2441508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9160125/v1/2f7fdc00-b9b3-4f4b-a73f-64e799cac5cf.pdf"},{"id":107362685,"identity":"2bee208e-8bfa-42ac-b9b0-54d9d381c3f8","added_by":"auto","created_at":"2026-04-20 18:48:20","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":386048,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.doc","url":"https://assets-eu.researchsquare.com/files/rs-9160125/v1/c51d3b8dea064e9ad03f0b23.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metaproteomic profiling of the tick microbiome in northern Algeria: a pilot study of bacterial diversity and potential medical or veterinary relevance","fulltext":[{"header":"Background","content":"\u003cp\u003eTicks are obligate hematophagous ectoparasites, the group called \"hard\" ticks is belonging to the phylum Arthropoda, Class Arachnida, Order Acari, Suborder Ixodida Family Ixodidae [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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, that pose threats on the spread of zoonotic and veterinary diseases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These symbiotic microbes may also influence the colonization and transmission of pathogenic microorganisms, therefore being a relevant target for tick control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecently, 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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 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 patterns [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These studies have demonstrated the coexistence of TBPs and commensal and symbiotic microorganisms within ticks; however, they primarily focus on genetic material 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 the functional level [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Interestingly, through peptide sequencing by tandem mass spectrometry metaproteomics offers the possibility to infer the organisms that produced the detected proteins in a biological sample [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This proteotyping methodology allows providing a comprehensive taxonomical description and an estimation of their respective 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 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This approach has already been successfully applied to ticks to understand ectoparasite microbiomes, shedding light on their complexity and functional roles [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlgeria is home to 37 tick species, classified into two families: Argasidae (soft ticks; 12 species) and Ixodidae (Hard ticks; 25 species). Among the hard ticks, genera such as \u003cem\u003eHyalomma\u003c/em\u003e (10 species), \u003cem\u003eRhipicephalus\u003c/em\u003e (6 species), \u003cem\u003eIxodes\u003c/em\u003e (5 species), \u003cem\u003eHaemaphysalis\u003c/em\u003e (3 species), and \u003cem\u003eDermacentor\u003c/em\u003e (1 species) have been documented across 34 host species, including 27 mammals, 4 reptiles, and 3 birds [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Among Algerian tick species, \u003cem\u003eRhipicephalus sanguineus sensu lato, Hyalomma aegyptium\u003c/em\u003e, and \u003cem\u003eH. dromedarii\u003c/em\u003e are highly adapted to xeric environments and exhibit broad and diverse host ranges [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. \u003cem\u003eH. dromedarii\u003c/em\u003e is a well-established vector of \u003cem\u003eCoxiella burnetii\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], \u003cem\u003eRickettsia aeschlimannii\u003c/em\u003e [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], \u003cem\u003eRickettsia africae\u003c/em\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and \u003cem\u003eAnaplasma\u003c/em\u003e spp. closely related to \u003cem\u003eA. platys\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003eH. aegyptium\u003c/em\u003e has been implicated in the transmission of \u003cem\u003eR. aeschlimannii\u003c/em\u003e in Algeria [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and is also capable of transmitting \u003cem\u003eBorrelia\u003c/em\u003e spp., \u003cem\u003eR. africae\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and the Crimean\u0026ndash;Congo hemorrhagic fever virus [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Finally, \u003cem\u003eR. sanguineus\u003c/em\u003e sensu lato acts as a competent vector for a wide array of pathogens, including \u003cem\u003eC. burnetii\u003c/em\u003e, \u003cem\u003eTheileria\u003c/em\u003e spp., \u003cem\u003eBabesia\u003c/em\u003e spp., \u003cem\u003eAnaplasma\u003c/em\u003e spp., \u003cem\u003eRickettsia\u003c/em\u003e spp., \u003cem\u003eBorrelia\u003c/em\u003e spp., \u003cem\u003eBartonella\u003c/em\u003e spp., \u003cem\u003eCandidatus Rickettsia barbariae\u003c/em\u003e, and \u003cem\u003eFrancisella\u003c/em\u003e-like endosymbionts [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44 CR45\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the early 2000s, research on tick-borne pathogens (TBPs) in the Mediterranean basin, including Algeria, has increased considerably with the development of molecular detection techniques such as PCR, next-generation sequencing (NGS), and other high-throughput approaches [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. More recently, several studies have begun to explore tick-associated microbial communities in Algeria. For instance, [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] investigated pathogen\u0026ndash;pathogen interactions in \u003cem\u003eHyalomma excavatum\u003c/em\u003e ticks using high-throughput microfluidic PCR and network analysis, while [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] examined seasonal variation in tick microbiomes using NGS. Despite these advances, most investigations remain focused on DNA-based detection of specific microorganisms rather than on functional characterization of the broader microbial community. One of the few studies addressing multiple microorganisms simultaneously in Algerian ticks is that of [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] et al. (2021), who used high-throughput microfluidic real-time PCR to detect several microorganisms in ixodid cattle ticks from northeastern Algeria [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite 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 an initial 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 \u003cem\u003eRhipicephalus sanguineus\u003c/em\u003e sensu lato, \u003cem\u003eHyalomma aegyptium\u003c/em\u003e, and \u003cem\u003eH. dromedarii\u003c/em\u003e, and (ii) assess species- and locality-specific variation that may influence the composition and functional profiles of tick-associated microbial communities, which could potentially affect interactions between ticks and microorganisms. 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.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consents of animal owners\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted on live animals in compliance with the animal welfare guidelines established by the World Organization for Animal Health [52] as outlined in the Terrestrial Animal Health Code [53]. Verbal informed consent was obtained from animal owners to tick collection. Tick removal was non-invasive and did not involve any experimental manipulation or harm to the animals. Verbal informed consent was obtained from animal owners prior to tick collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTick collection and identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 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 primarily on the availability of live ticks during the winter season and logistical constraints related to field collection and laboratory processing. This period allowed the collection of active ticks from several host species, ensuring the availability of sufficient biological material for metaproteomic analysis. Because sampling was restricted to a single season, the present study does not aim to evaluate seasonal variation in tick microbiomes, and the results should therefore be interpreted as a snapshot of microbial communities during this specific sampling period. Sampling locations included Tizi-Ouzou, Tipaza, Biskra, and Algiers (the capital city) (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Ticks were collected directly from animal hosts, including dogs, camels, goats, and tortoises. 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].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTick dissection and sample preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to dissection for protein extraction, live ticks were surface-decontaminated by immersing them in 1% bleach for 30 seconds, followed by three consecutive 1-minute rinses in sterile distilled water to remove external microorganisms [56]. Tick dissection was performed \u0026nbsp; to recover soft tissues, including Malpighian tubules, midgut, ovaries, and salivary glands, after removing the dorsal cuticle, following a previously described method [57]. The excised organs were placed in sterile 1.5 ml Eppendorf tubes, 300 \u0026mu;l of culture medium (Leibovitz\u0026apos;s L-15 and Dulbecco\u0026apos;s Modified Eagle\u0026apos;s Medium (DMEM) supplemented with 2% Fetal Bovine Serum (FBS)) were added to homogenize the samples. This medium was used to stabilize tissues during mechanical disruption and facilitate homogenization.\u003c/p\u003e\n\u003cp\u003ePotential contamination from bovine proteins was minimized by filtering peptides matching bovine sequences and by estimating bacterial abundance using taxon\u0026ndash;spectrum matches rather than total peptide counts. Organs were then mechanically crushed using 3mm steel beads (DUTSCHER, France) in a MM400 homogenizer (Retsch, Germany) at 30 Hz per second for 3 minutes. The homogenate was then centrifuged at 7,000 rpm for 6 min at +4\u0026deg;C, after which the supernatant was divided into three aliquots of 100 \u0026mu;l each and stored at -80\u0026deg;C for further analysis. All dissections and sample processing were conducted in a biosafety level 3 (BSL-3) laboratory at the Pasteur Institute of Algeria to ensure proper containment of potential pathogens. Before transport, 100 \u0026micro;l of homogenates was mixed with 100 \u0026micro;l of DIGE buffer (7 M urea, 2 M thiourea, 4% CHAPS, 1% Triton-X-100, 30 mM Tris, pH 8) at a 1:1 volume ratio (V/V) for inactivation according to biosafety consideration. The samples were then shipped at +4\u0026deg;C, to CIRAD-IRD Montpellier for further analysis, following the Category B infectious substances (UN 3373) regulations with Accord for Dangerous goods by Road (ADR) packing instruction PI 650 for road transport and International Air Transport Association (IATA) packing instruction PI 650 for air transport.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample selection and protein extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected \u003cem\u003eHyalomma dromedarii\u003c/em\u003e, \u003cem\u003eH. aegyptium\u003c/em\u003e, and \u003cem\u003eRhipicephalus sanguineus sensu lato\u003c/em\u003e because they are among the most prevalent and epidemiologically relevant tick species in Algeria, with broad host ranges and documented associations with several pathogens. Sampling was conducted between January and March primarily based on technical feasibility, including the availability of live ticks during the winter season and logistical constraints related to field collection and laboratory processing.\u003c/p\u003e\n\u003cp\u003eThus, a total of 56 individual specimens were selected from the 448 collected ticks for metaproteomic analysis, including: (i) \u003cem\u003eH. dromedarii\u003c/em\u003e specimens (n = 4 collected from camels), (ii) \u003cem\u003eH. aegyptium\u003c/em\u003e (n = 4 collected from tortoises), and (iii) \u003cem\u003eRh. sanguineus\u003c/em\u003e s. l. (n = 48, of which n = 40 were collected from dogs and n = 8 from goats). 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.\u003c/p\u003e\n\u003cp\u003eBecause protein yields from individual dissected organs were low, specimens were pooled to obtain sufficient quantities for LC\u0026ndash;MS/MS analysis. Protein extraction was performed on pools of 2 to 10 specimens generated from the 56 ticks. Pools were constructed using specimens belonging to the same tick species, sex, and collection locality to preserve ecological coherence while ensuring sufficient protein quantity for metaproteomic analysis. Consequently, statistical comparisons were conducted at the pooled-sample level and should be interpreted as exploratory patterns rather than definitive ecological relationships.\u003c/p\u003e\n\u003cp\u003eBecause the analysis was conducted on pooled organs from multiple ticks, tissue-specific microbial signatures could not be evaluated. Previous studies have shown that microbiome composition may vary between tick organs such as the midgut, salivary glands, and reproductive tissues. Therefore, the present analysis should be interpreted as a global characterization of the organ-associated microbiome, and future studies focusing on individual tissues will be necessary to investigate organ-specific microbial interactions. A detailed list of the 10 analyzed pooled samples, including tick species, sex, collection municipality, and host species, is provided in \u003cstrong\u003eFigure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBefore protein extraction, the pooled homogenates were centrifuged at 500 g for 10 minutes at 4\u0026deg;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. (1976) and Chevallet et al. (2007) [58,59]. Na-deoxycholate (DOC) was added to homogenates to a final concentration of 0.1%, and after mixing trichloroacetic acid (TCA) was added to to a final 10% concentration. The protein solution was precipitated at +4\u0026deg;C overnight and then centrifuged at 10,000 rpm for 15 min at 4\u0026deg;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\u0026trade; 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\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eExtraction 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\u0026ndash;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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein concentration was measured using the fluorescence-based Qubit\u0026trade; quantitation assays (Invitrogen\u0026trade; Qubit\u0026trade; 3 Fluorometer, Thermo Fisher Scientific, Singapore) according to the manufacturer\u0026rsquo;s instructions. This method provides high sensitivity and accuracy for protein quantification, making it suitable for low-concentration protein samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectrophoresis and sample preparation for mass spectrometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the quality and reproducibility of protein extractions, proteins obtained from tick organs were separated using polyacrylamide gel electrophoresis (PAGE). Extracted proteins (5 \u0026mu;l) were mixed with 5 \u0026mu;l of Laemmli buffer 2X (containing 5 \u0026micro;l of 2-Mercaptoethanol Bio-Rad\u0026reg; and 95 \u0026micro;l Laemmli buffer 2X Bio-Rad\u0026reg;), and then heated at 95\u0026deg;C for 1 minute in a dry thermomixer block (ThermoMixer C, Eppendorf, Montesson, France) to obtain fully denaturated proteins before migration. Samples were loaded onto NuPAGE\u0026trade; 4-12% Bis-Tris protein gels (1.0 mm, 10-well) and separated using the NuPAGE\u0026trade; Bis-Tris XCell SureLock\u0026trade; Mini-Cell system with MES SDS running buffer. Electrophoresis was performed at 80V for stacking, then at 200V constant voltage for 7 min. After electrophoresis, the gel was washed 3 times with Milli-Q water before protein staining. Staining was performed using collo\u0026iuml;dal Coomassie blue G-250 (SafeStain SimplyBlue\u0026trade;, Invitrogen) for 5 min, which produces a blue-green tint. The gels were then washed twice for 10 min with Milli-Q water and left in Milli-Q water under gentle agitation overnight.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the metaproteomic analysis, each polyacrylamide gel lanes containing the whole soluble proteome 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., (2021) and Hartman et al., (2014) [60,61]. The resulting peptide extracts were collected in 50 \u0026mu;l aliquots per sample and quantified using the Pierce Quantitative Fluorometric Peptide Assay.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProteomics data acquisition and interpretation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 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 described [62]. Briefly, peptides were loaded on a reverse-phase Acclaim PepMap 100 C18 precolumn (5 \u0026mu;m, 100 \u0026Aring;, 300 \u0026mu;m i.d. \u0026times; 5 mm, Thermo) and then resolved on a EasySpray PepMap 100 Neo C18 column (2 \u0026mu;m, 100 \u0026Aring;, 75 \u0026mu;m i.d. \u0026times; 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 sec. MS/MS spectra were processed and interpreted 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 described [62]. The NCBInr database was used because it provides broad taxonomic coverage and is widely applied in metaproteomic studies. Although tick-associated microbial genomes remain incompletely represented in public databases, this limitation was mitigated using a two-step search strategy. Briefly, MS/MS spectra were first searched against the NCBInrS database derived from NCBInr (NCBI, NIH, Bethesda; ftp://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/nr.gz). The resulting peptide sequences were assigned to taxonomic groups across all ranks (from superkingdom to species), and Taxon-to-Spectrum Matches (TSMs) together with genus-specific peptide markers were used for initial genus-level identification. A second search was then performed using a custom database generated from NCBInr that included only the detected genera and their associated taxa. This approach refines peptide assignment, reduces database redundancy, and helps limit false-positive identifications associated with large non-curated databases. Species-level identification was considered only when peptide sequences uniquely matched proteins from a single species within the database. 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.\u003c/p\u003e\n\u003cp\u003eAlthough the NCBInr database provides broad taxonomic coverage and is widely used in metaproteomic analyses, it remains non-curated and may incompletely represent microorganisms associated with arthropod microbiomes. Consequently, some taxa present in tick samples may not yet be represented in public protein databases, which could limit taxonomic resolution or lead to conservative assignments at higher taxonomic ranks. To mitigate these limitations, we applied a two-step database search strategy and retained species-level identifications only when peptides uniquely matched proteins from a single species. Ambiguous matches were restricted to the lowest reliable taxonomic level, typically the genus level. This conservative approach reduces the risk of false-positive identifications and improves the robustness of taxonomic interpretation in complex environmental samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical and data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using custom R (v 4.3.2) 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelative 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 ggplot2. Finally, bar plots depicting bacterial genus richness per cohort were generated using the tidyverse R package.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTaxonomic characterization of tick microbiomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo provide insights into the microbiomes diversity of three tick species collected in Algeria, namely \u003cem\u003eR. sanguineus sensu lato, H. aegyptium\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;H. dromedarii,\u003c/em\u003e a comparative analysis was performed across tick species, sex, host species and geographic locations (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs illustrated in \u003cstrong\u003eFigure 2\u003c/strong\u003e, the experimental workflow to conduct metaproteomics on ten samples included protein extraction, trypsin digestion, protein identification, and quantification. To acquire as much as possible mass spectrometry signal, each proteome was subdivided into 4 fractions and analyzed with 4 x 1 h of high-resolution tandem mass spectrometry. The 40 resulting nanoLC-MS/MS acquisitions resulted in a global dataset comprising a total of 2,337,208 MS/MS spectra (\u003cstrong\u003eTable 1\u003c/strong\u003e). The reproducibility of the measurements across the ten samples was high with an average of 233,997 (\u0026plusmn; 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. \u003cstrong\u003eTable 1\u003c/strong\u003e summarizes the results obtained at the genus level. This analysis produced 315,614 taxon-to-spectrum matches (TSMs) and 8,792 peptide sequences. The detailed taxonomic assignments is provided in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e. The identified peptides indicated the presence of biological material belonging to the 3 major domains, Archaea and Bacteria that are constitutive of the tick microbiomes, Eukaryota that could originate from the tick itself or the mammalian host\u0026rsquo;s blood or tick environment, plus the fourth domain constituted by the Viruses (with only one nonpathogenic viral genus detected). Notably, 4.92% of the global identified signal corresponded to bacterial taxa, as determined by TSMs (\u003cstrong\u003eFigure 3\u003c/strong\u003e). No specific host-protein depletion step was applied during sample preparation. Because tick and host proteins represent the majority of the biological material in dissected tissues, eukaryotic proteins were expected to dominate the detected spectra. Bacterial abundance was therefore estimated using taxon\u0026ndash;spectrum matches after filtering host-derived peptides to reduce potential bias in microbial detection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe\u003cstrong\u003e\u0026nbsp;Figure 3\u003c/strong\u003e and \u003cstrong\u003eSupplementary Tables S1\u003c/strong\u003e 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, \u003cem\u003eClostridium estertheticum\u003c/em\u003e 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 \u003cem\u003eFusarium austroafricanum\u003c/em\u003e, which was detected in five samples, and \u003cem\u003ePenicillium italicum\u003c/em\u003e and \u003cem\u003eAspergillus calidoustus\u003c/em\u003e, which were each detected in a single sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Overview of the main features of the ten nanoLC-MS/MS datasets of studied tick species.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"731\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTick species\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDatasets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#MS/MS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#TSMs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#Peptides\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#Phyla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#Order\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#Family\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#Genus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e#Species\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cem\u003eHyalomma aegyptium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTOFT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e241545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e33535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTOMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e240443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e30259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cem\u003eH. dromedarii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eBFD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e234005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e25324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eBMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e229305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e23034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cem\u003eRhipicephalus sanguineus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eAFCE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e240472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e36627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eAMCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e229150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e26313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTFCE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e240472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e36627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTMCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e231577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e34068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e6777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cem\u003eR. sanguineus s.l\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTFCV \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e224163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e36773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e8512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eTMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e228839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e33054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e6325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e#: \u0026nbsp;number of entities (MS/MS spectra, TSMs, Peptides) or taxa identified; TOFT \u0026amp; TOMT: Tizi-Ouzou, Female/Male, Tortoise; BFD \u0026amp; BMD: Biskra, Female/Male, Dromedary; AFCE \u0026amp; AMCE: Algiers, Female/Male, Dog; TFCE \u0026amp; TMCE: Tipaza, Female/Male, Dog; TFCV \u0026amp; TMCV: Tipaza, Female/Male, Goat; \u003cem\u003es.l:\u003c/em\u003e \u003cem\u003esensu lato\u003c/em\u003e. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlpha and beta diversity of tick-associated bacterial communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause each analyzed dataset corresponds to pooled samples combining several individuals and organs, diversity estimates represent aggregate microbial profiles rather than individual-level microbiomes. Consequently, the diversity comparisons presented here should be interpreted cautiously and primarily reflect global differences between pooled sample groups.\u003c/p\u003e\n\u003cp\u003eMicrobiota 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e 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; \u003cstrong\u003eFigure 4A\u003c/strong\u003e) or host species (P = 0.74 and 0.085; \u003cstrong\u003eFigure 4E\u003c/strong\u003e). In contrast, significant differences were detected between the genera \u003cem\u003eHyalomma\u003c/em\u003e and \u003cem\u003eRhipicephalus\u003c/em\u003e (\u003cstrong\u003eFigure 4C\u003c/strong\u003e) and among collection locations (\u003cstrong\u003eFigure 4G\u003c/strong\u003e). In particular, the Shannon diversity index distinguished the microbiomes of \u003cem\u003eHyalomma\u003c/em\u003e and \u003cem\u003eRhipicephalus\u003c/em\u003e (P = 0.019; \u003cstrong\u003eFigure 4C\u003c/strong\u003e) and revealed differences among sampling locations (P = 0.047; \u003cstrong\u003eFigure 4G\u003c/strong\u003e), although observed richness did not differ significantly between these groups (P = 0.83 and P = 0.46, respectively).\u003c/p\u003e\n\u003cp\u003eBeta diversity analysis further confirmed significant differences in microbial community composition between \u003cem\u003eHyalomma\u003c/em\u003e and \u003cem\u003eRhipicephalus\u003c/em\u003e (ANOSIM: P = 0.047; \u003cstrong\u003eFigure 4D\u003c/strong\u003e) and between collection locations (ANOSIM: P = 0.004; \u003cstrong\u003eFigure 4H\u003c/strong\u003e), whereas no significant differences were observed according to tick sex (ANOSIM: P = 0.631; \u003cstrong\u003eFigure 4B\u003c/strong\u003e) or host species (P = 0.285; \u003cstrong\u003eFigure 4F\u003c/strong\u003e). These results suggest that tick genus and collection location may influence microbiota diversity in the analyzed samples.\u003c/p\u003e\n\u003cp\u003eIt should also be noted that several ecological variables are partially associated in the sampling design. For instance, specific tick species were collected from particular hosts and geographic locations. Therefore, the observed differences may reflect combined effects of tick species, host, and environmental conditions rather than a single isolated factor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences in microbial community composition and taxonomic relative abundance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our tick samples, we identified 40 bacterial genera belonging to 32 families spanning four phyla (\u003cstrong\u003eFigure 5 and Table S1\u003c/strong\u003e). 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. This indicates that tick microbiomes are still a relatively unexplored ecosystem of high interest for improving prokaryote systematics [63]. The most abundant identified genera (%TSMs) in the ten tick microbiomes were \u003cem\u003eStreptomyces\u003c/em\u003e (22.36%), \u003cem\u003eBacillus\u003c/em\u003e (15.1%), unclassified_\u003cem\u003eDesulfobacteraceae\u003c/em\u003e (10.3 %), \u003cem\u003eCoxiella\u003c/em\u003e (8.2%), \u003cem\u003eEscherichia\u003c/em\u003e (6.6%), \u003cem\u003eClostridium\u003c/em\u003e (5.6%), \u003cem\u003eFlavobacterium\u0026nbsp;\u003c/em\u003e(4.5%), \u003cem\u003eRhizobium\u0026nbsp;\u003c/em\u003e(4.2%), \u003cem\u003ePaenibacillus\u003c/em\u003e (4%), while other genera were accounting for less than 3 % \u003cstrong\u003e(Figure 3)\u003c/strong\u003e. \u0026nbsp;In addition to the overall diversity, 143 bacterial species were identified, including well-characterized endosymbionts like \u003cem\u003eCandidatus Coxiella mudrowiae\u003c/em\u003e (a \u003cem\u003eCoxiella\u003c/em\u003e-like endosymbiont), and, notably, commensal bacteria such as \u003cem\u003eEscherichia coli\u003c/em\u003e, which has been rarely, if ever, reported in ticks prior to this study. It also includes environmental bacteria typically found in soil, plants and marine environment, including the genera \u003cem\u003ePaenibacillus\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eNovosphingobium\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Aquimarina,\u003c/em\u003e and others. Their identification raises questions about their biological role in tick physiology. Finally, pathogenic species, such as \u003cem\u003eClostridium perfringens\u003c/em\u003e, which is\u003cem\u003e\u0026nbsp;\u003c/em\u003eknown for its impact on both humans and animal health [64]. Noteworthy, \u003cem\u003eCoxiella\u003c/em\u003e \u003cem\u003eburnetii,\u0026nbsp;\u003c/em\u003ea well-documented tick-borne pathogen (TBP, etiologic agent of Q fever) [65] , was also detected in TMCV1 with 236 distinctive peptides, and 438 TSMs, thus representing 1.3% of the global biomass in this sample. Such identification is consistent with previous reports describing ticks as potential carriers of bacteria of medical and veterinary relevance\u0026nbsp;(\u003cstrong\u003eFigure 5 and Table S1\u003c/strong\u003e). The distinction between endosymbionts and pathogenic species within the genus \u003cem\u003eCoxiella\u003c/em\u003e remains a subject of ongoing debate, largely due to methodological challenges in precisely identifying species. As highlighted by Jourdain et al. (2015) [66], molecular methods routinely used to detect \u003cem\u003eCoxiella burnetii\u003c/em\u003e in ticks may cross-react with Coxiella-like bacteria, complicating epidemiological interpretations. In this context, strain-level characterization could be achieved using next-generation mass spectrometers such as the Astral mass analyzer [67], which enable extensive detection of specific peptides. The use of comprehensive peptide sets would thus allow identification approaching the strain level, providing a more accurate picture of \u003cem\u003eCoxiella\u003c/em\u003e diversity and enabling finer-scale epidemiological studies on transmission dynamics and the distinction between endosymbiotic and pathogenic forms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInfluence of tick species and sex on the tick microbiome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe relative abundance (RA) of bacterial genera was analyzed across different tick species and sexes, as illustrated in \u003cstrong\u003eFigure 5\u003c/strong\u003e. Among the three tick species, \u003cem\u003eHyalomma\u003c/em\u003e ticks exhibited the lowest diversity in peptide sequences detected by tandem mass spectrometry, resulting in the lowest microbial biomass and microbial diversity. While \u003cem\u003eStreptomyces\u003c/em\u003e was the genus consistently present in all tick species (mean abundance: 22.36%, prevalence 100%), certain bacterial genera exhibited species-specific distributions. \u003cem\u003eEscherichia\u003c/em\u003e and \u003cem\u003eCoxiella\u003c/em\u003e were exclusively identified in \u003cem\u003eRhipicephalus\u003c/em\u003e ticks (mean abundance: 6.56% and 8.16%, respectively, prevalence 60%). \u003cem\u003eBacillus\u003c/em\u003e (family Bacillaceae) was detected in \u003cem\u003eR. sanguineus\u003c/em\u003e and \u003cem\u003eH. aegyptium\u003c/em\u003e but not in \u003cem\u003eH. dromedarii\u0026nbsp;\u003c/em\u003e(mean abundance: 15.07%, prevalence 60%)\u003cem\u003e.\u003c/em\u003e Non-Metric Multidimensional Scaling (NMDS) analysis and ordination plots \u003cstrong\u003e(Figure 3, Panels B \u0026amp; D)\u003c/strong\u003e indicated that tick genus appeared to be associated with differences in microbiome composition, supporting the hypothesis that arthropod-associated factors shape microbial communities. Tick sex had a minimal influence on microbial diversity, suggesting that breeding strategies are not correlated with microbiome diversity, and that other factors may play a more significant role in shaping bacterial communities in ticks.\u003c/p\u003e\n\u003cp\u003eFinally, 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 microbial composition, although the limited sample size prevents robust statistical ranking of all factors (\u003cstrong\u003eFigure 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInfluence of feeding host species on the tick microbiome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe bar charts in \u003cstrong\u003eFigure 5 (Panels C and F)\u003c/strong\u003e illustrate the influence of the feeding host species on the composition of the tick microbiome. At the phylum level, Proteobacteria exhibited the highest relative abundance in ticks collected from goats compared with other host species. (Tortoises, dromedaries or dogs). By contrast, Bacteroidetes was more prevalent in ticks from dromedaries than those from other feeding hosts. Firmicutes dominated the microbiome of ticks collected from tortoises. This indicates a host associated signature. At the genus level, we recorded a notably higher relative abundance of \u003cem\u003eCoxiella\u003c/em\u003e in ticks collected from goats, absent or scarce in ticks from other hosts. Other genera also displayed host-specific abundance patterns, further suggesting a potential role of host species in shaping the tick microbiome. Notably, the\u003cem\u003e\u0026nbsp;Streptomyces\u0026nbsp;\u003c/em\u003egenus was present across all host species, whereas the \u003cem\u003eCoxiella\u003c/em\u003e genus showed a marked affinity for ticks sampled on goats. Other genera exhibited fluctuating relative abundances depending on the specific host species. The MDS analysis \u003cstrong\u003e(Figure 4F)\u003c/strong\u003e did not effectively differentiate microbiome composition among host species, likely due to the small sample size. This limitation suggests that feeding host species influence on microbiome qualitative and quantitative diversity needs to be weighted in comparison to other factors such as tick species or tick ecology influenced by the habitat/environment. These findings indicate that tick-associated factors, such as species and feeding host preference, are key determinants of microbiome composition (\u003cstrong\u003eFigure 6\u003c/strong\u003e), but need larger sample sizes and additional studies to fully assess the hierarchical influence of each factor on microbiome diversity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInfluence of collection location on the tick microbiome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe microbiome composition of \u003cem\u003eR. sanguineus sensu lato, H. aegyptium,\u003c/em\u003e and \u003cem\u003eH. dromedarii\u003c/em\u003e varied across collection localities, with distinct bacterial genera dominating in different regions (\u003cstrong\u003eFigures 5 and 6\u003c/strong\u003e). In Algiers, \u003cem\u003eR. sanguineus s. l.\u003c/em\u003e collected from dogs\u0026rsquo; harbored microbiomes dominated by \u003cem\u003eBacillus\u003c/em\u003e and \u003cem\u003ePaenibacillus\u003c/em\u003e (Firmicutes), with a notable presence of \u003cem\u003eBradyrhizobium\u003c/em\u003e (Proteobacteria). In contrast, ticks from dogs in Tipaza, showed a distinct patterns: \u0026nbsp;\u003cem\u003eEscherichia\u003c/em\u003e (Proteobacteria) and \u003cem\u003eStreptomyces\u003c/em\u003e (Actinobacteria) were prominent with additional representation of \u003cem\u003eDesulfobacteraceae\u003c/em\u003e (Proteobacteria). Interestingly, \u003cem\u003eR. sanguineus s.l.\u003c/em\u003e collected from goats in Tipaza, males exhibited higher relative abundance of \u003cem\u003eCoxiella\u003c/em\u003e (a tick-borne pathogen, TBP) and \u003cem\u003eEscherichia\u003c/em\u003e (a commensal proteobacterium), especially in male specimens, while unclassified\u003cem\u003e\u0026nbsp;Myxococcales\u003c/em\u003e (Proteobacteria) and \u003cem\u003eStreptomyces\u003c/em\u003e (Actinobacteria) were more prevalent in females. In \u003cem\u003eH. dromedarii\u003c/em\u003e from dromedaries in Biskra, Proteobacteria genera such as \u003cem\u003eDesulfobacteraceae\u003c/em\u003e and \u003cem\u003eRhizobium\u003c/em\u003e were dominant, while \u003cem\u003eStreptomyces\u003c/em\u003e (Actinobacteria) also appeared prominently, particularly in males. Similarly, in \u003cem\u003eH. aegyptium\u003c/em\u003e collected from tortoises in Algiers, \u003cem\u003eBacillus\u003c/em\u003e (Firmicutes) and \u003cem\u003eStreptomyces\u003c/em\u003e (Actinobacteria) were the most abundant taxa. Overall, these findings highlight the influence of collection locality, and by extension the host species, on tick microbiome structure. The presence of potentially pathogenic bacteria such as \u003cem\u003eCoxiella\u003c/em\u003e further investigation into how microbial communities may interact with tick-associated microorganisms. Future research with larger sample sizes is essential to unravel the ecological and environmental drivers of microbiome diversity in Algerian ticks, particularly the interactions between geographical area of collection, feeding host species, and tick physiology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional insights into tick-associated microbial communities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we focused on the TMCV1 sample in which \u003cem\u003eCoxiella burnetii\u003c/em\u003e\u0026ndash;related peptides were detected. 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.\u003c/p\u003e\n\u003cp\u003eStress-related proteins were also detected, including the universal stress protein UspA (WP_032074717.1), recombinase RecA (WP_005768713.1), catalase (WP_005772736.1), and superoxide dismutase (WP_005770533.1), which are involved in cellular stress responses and redox homeostasis. While these proteins may reflect bacterial responses to host immune defenses or environmental conditions within the tick, they could also arise from physiological stress associated with sample processing, storage, or environmental exposure. Consequently, the functional implications of these proteins remain speculative and require further experimental validation. The identification of multiple proteins matching \u003cem\u003eCoxiella\u003c/em\u003e-related sequences suggests the presence of bacteria related to this genus in the analyzed sample. However, several of these proteins (e.g., GroEL, elongation factor Tu, ribosomal proteins) are highly conserved among bacteria and may not be specific to \u003cem\u003eCoxiella burnetii\u003c/em\u003e. Therefore, these findings should be interpreted cautiously, and additional molecular or targeted proteomic analyses would be required to confirm the precise identity and activity of the detected bacteria within the tick host.\u003c/p\u003e\n\u003cp\u003eAs expected, the most abundant proteins originating from the \u003cem\u003eRhipicephalus sanguineus\u003c/em\u003e 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.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough Algeria hosts a rich diversity of tick species, most studies focus on species identification, pathogen occurrence, and seasonal dynamics, rather than microbial community profiling [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which has nevertheless more broadly matured through next-generation sequencing and revealed that tick-associated microbial communities vary by species, sex, developmental stage, environment, and can influence pathogen acquisition and transmission [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In recent years, increasing research on ticks has focused on their microbiomes and microbial interactions, particularly in understanding the composition, functional roles, and ecological implications of microbial communities, with an oriented focus on their potential impact in pathogen persistence, dissemination, and fitness in ticks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Previous recent studies used mass spectrometry to identify tick species in Algeria but their associated microorganisms were identified by molecular biology [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Here, we performed the first direct proteotyping approach to characterize the microbial communities present in the soft organs (Malpighian tubules, midgut, ovaries, and salivary glands) of three tick species (\u003cem\u003eR. sanguineus s. l., H. aegyptium\u003c/em\u003e, and \u003cem\u003eH. dromedarii\u003c/em\u003e) collected from dogs, goats, tortoises and dromedaries in different geographical areas in Algeria.\u003c/p\u003e \u003cp\u003eThis study is based on a limited number of samples, which is expected for a pilot investigation designed to provide a first descriptive overview of tick species, their hosts, and associated microbiome diversity in Algeria. Because samples were generated from pools of 2\u0026ndash;10 ticks, individual-level variability could not be assessed, and the interpretation of microbiome differences associated with tick sex, host species, or collection location should be approached with caution. However, this pooling strategy was necessary to obtain sufficient protein material for metaproteomic analysis and is appropriate for an exploratory pilot study. This pilot study also aimed at showing the potential of proteotyping and metaproteomics, recent technologies, to better identify the whole microbial communities present in ticks. Although this restricted dataset reduces statistical power for assessing factors such as host species or tick sex, it offers an essential baseline to guide future, larger-scale studies on tick microbiota, including the characterization of organs-specific microbiomes. Recent improvements in high-resolution mass spectrometry, including the Quadrupole-Orbitrap platforms, are likely to enhance metaproteomic sensitivity and coverage in future work [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. While metaproteomics remains less sensitive than DNA-based tools for detecting rare taxa, it provides more accurate taxonomic resolution and a functional view of the active microbiome [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. 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 [\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. \u003cem\u003eRhipicephalus\u003c/em\u003e ticks displayed lower microbiome diversity than \u003cem\u003eHyalomma\u003c/em\u003e ticks, likely reflecting differences in host use and life-history traits [\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Ticks from Algiers showed higher diversity than those from other regions, possibly due 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 [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Tick sex and host species showed no significant effect on microbiome composition, in agreement with previous findings in dog-associated ticks [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Nonetheless, other studies have demonstrated that species identity, feeding status, geography, and developmental stage can shape microbial communities [27,81,82], which highlights that by integrating proteomic evidence with ecological and taxonomic data, our pilot study should be viewed as a first descriptive milestone supporting future hypothesis-driven research on microbiome-pathogen-vector interactions in Mediterranean tick species.\u003c/p\u003e \u003cp\u003eThe most abundant bacterial phyla identified in the soft organs of the studied tick species were Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. Proteobacteria was the most dominant phylum (25 genera), followed by Actinobacteria (6), and Firmicutes (5). These results are in agreement with previous meta-omics studies performed on \u003cem\u003eI. ricinus\u003c/em\u003e ticks from laboratory colonies, which identified similar bacterial distributions (21 genera from Proteobacteria, 7 from Actinobacteria, and 4 from Firmicutes) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, 16S rRNA-based next-generation sequencing studies on tick microbiome, have frequently reported these three phyla as dominant bacterial groups [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Proteobacteria is the largest and most diverse bacterial phylum, and its dominance in tick microbiomes is well documented [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Specifically phylum-level dominance of Proteobacteria has been recorded in various tick species independently of the feeding host species: \u003cem\u003eR. sanguineus s.l.\u003c/em\u003e (99%) and \u003cem\u003eHaemaphysalis punctata\u003c/em\u003e (98%) collected on vegetation in Spain [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], \u003cem\u003eH. dromedarii\u003c/em\u003e (98%) infesting camels in Saudi Arabia [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], \u003cem\u003eDermacentor marginatus\u003c/em\u003e (97%) collected on vegetation in Spain [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], \u003cem\u003eD. nuttalli\u003c/em\u003e (90.7%) sampled on vegetation in China [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], \u003cem\u003eAmbylomma tuberculatum\u003c/em\u003e (89%) collected on Gopher tortoise in the USA [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], \u003cem\u003eI. ricinus\u003c/em\u003e (88%) collected on vegetation in Spain [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], and \u003cem\u003eD. marginatus\u003c/em\u003e and \u003cem\u003eD. reticulatus\u003c/em\u003e (60%) harvested on vegetation in Slovakia. Conversely, with the exception for \u003cem\u003eCoxiella\u003c/em\u003e, several well-known tick-associated bacterial genera and species (\u003cem\u003eBorrelia\u003c/em\u003e, \u003cem\u003eRickettsia\u003c/em\u003e, \u003cem\u003eSpiroplasma\u003c/em\u003e, \u003cem\u003eEhrlichia\u003c/em\u003e, \u003cem\u003eCa\u003c/em\u003e. neoehrlichia, \u003cem\u003eWolbachia\u003c/em\u003e and \u003cem\u003eCa\u003c/em\u003e. \u003cem\u003emidichloria\u003c/em\u003e) were not detected in our study. This absence in microbiome composition has also been reported in \u003cem\u003eH. dromedarii\u003c/em\u003e [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], whereas these bacteria were identified in \u003cem\u003eI. ricinus\u003c/em\u003e using metatranscriptomics and metaproteomics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Such absence of well-known tick-associated bacterial genera, such as \u003cem\u003eBorrelia\u003c/em\u003e and \u003cem\u003eRickettsia\u003c/em\u003e, may reflect the limitations of metaproteomics in detecting low-abundance taxa, which are continually being pushed back by advances in mass spectrometry technology and digital data processing tools [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Another explanation that is increasingly being highlighted in the literature is that the tick microbiome may be influenced by host specificity, geographic location, and environmental conditions, as described for \u003cem\u003eI. ricinus\u003c/em\u003e [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. 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 \u003cem\u003eBacillus, Chitinophaga, Clostridium, Desulfobulbus, Devosia, Escherichia, Halomonas, Lutibacter, Lysinibacillus, Massilia, Microbacterium, Mycolicibacterium, Nocardia, Paenibacillus, Paraburkholderia, Paracoccus, Providencia, Roseomonas, unclassified Lachnospiraceae, unclassified Rhodobacteraceae, and Variovorax.\u003c/em\u003e Some bacterial species within these genera have been previously reported in ticks and other arthropods (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), suggesting a possible role in microbial symbiosis, vector competence, or environmental contamination [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Additionally, these bacteria may also be acquired from external environmental sources, such as water uptake or air intake through tick spiracles [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The presence of bacterial genera typically associated with marine and soil environments, such as \u003cem\u003eRoseovarius\u003c/em\u003e, \u003cem\u003eHalomonas\u003c/em\u003e, and \u003cem\u003eNovosphingobium\u003c/em\u003e, raises questions about their mode of acquisition and potential ecological roles. These taxa may act as passive environmental colonizers, but they could also contribute to tick physiology through nutrient cycling, resistance to desiccation, or antimicrobial defense. Their detection across different tick species suggests a potential selective retention, which warrants further experimental validation. Several previously unreported bacterial genera were also detected in our tick samples including \u003cem\u003eAmphritea\u003c/em\u003e, \u003cem\u003eAquimarina\u003c/em\u003e, \u003cem\u003eChitinophaga\u003c/em\u003e, \u003cem\u003eKushneria\u003c/em\u003e, \u003cem\u003eLutibacter\u003c/em\u003e, \u003cem\u003eMyxococcales\u003c/em\u003e, \u003cem\u003eNonomuraea\u003c/em\u003e, \u003cem\u003eNovosphingobium\u003c/em\u003e, \u003cem\u003eRoseovarius\u003c/em\u003e, \u003cem\u003eRuegeria\u003c/em\u003e, and unclassified taxa from \u003cem\u003eDesulfobacteraceae\u003c/em\u003e, \u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eRhodobacteraceae\u003c/em\u003e. The detection of \u003cem\u003eNovosphingobium\u003c/em\u003e and \u003cem\u003eRoseovarius\u003c/em\u003e in ticks, despite their usual association with marine and soil environments, suggests 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.\u003c/p\u003e \u003cp\u003eThe most dominant bacterial genus in our study was \u003cem\u003eStreptomyces\u003c/em\u003e, found in all three tick species. \u003cem\u003eStreptomyces\u003c/em\u003e is an aerobic, spore-forming actinomycete initially described by Waksman and Henrici (1943) [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. This genus is ubiquitous in soil and is well-known for producing bioactive compounds, including antibiotics, tumor suppressors, and enzymes. \u003cem\u003eStreptomyces\u003c/em\u003e has also been identified as an endosymbionts in various arthropods, including ticks (\u003cem\u003eD. nuttalli\u003c/em\u003e and \u003cem\u003eOrnithodoros turicata\u003c/em\u003e), woodlice, leaf beetles, millipedes, wasps, ants, sawflies and mite (\u003cem\u003eSarcoptes scabiei\u003c/em\u003e) [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan additionalcitationids=\"CR90 CR91 CR92 CR93\" citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. The high abundance of \u003cem\u003eStreptomyces\u003c/em\u003e across all three tick species highlights its potential significance in microbiome stability. Known for producing a wide range of bioactive metabolites, \u003cem\u003eStreptomyces\u003c/em\u003e may play an important role in regulating the tick microbiota through antimicrobial activity, limiting the colonization of pathogenic bacteria, and possibly contributing to tick innate immunity. Similar functional associations have been described in other arthropods, where \u003cem\u003eStreptomyces\u003c/em\u003e acts as a symbiotic partner conferring defense benefits. Further functional and experimental studies are warranted to explore its possible role in shaping microbial interactions and enhancing tick fitness.\u003c/p\u003e \u003cp\u003eAnother highly abundant bacterial genus in our study was \u003cem\u003eCoxiella\u003c/em\u003e particularly in \u003cem\u003eR. sanguineus s.l.\u003c/em\u003e tick collected from goat. Coxiellaceae family includes both symbiotic and pathogenic species that can cause significant diseases in humans and animals. \u003cem\u003eCoxiella burnetii\u003c/em\u003e is the well-known causative agent of Q-fever, whereas \u003cem\u003eCoxiella\u003c/em\u003e-like endosymbionts (CLEs) are widely distributed in ticks and other arthropods [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR96\" citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Our proteomic data identified several proteins associated with \u003cem\u003eCoxiella burnetii\u003c/em\u003e, including GroEL, which also matched \u003cem\u003eCandidatus coxiella mudrowiae\u003c/em\u003e a newly identified endosymbiont discovered in hard ticks from China [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], suggesting that the detected \u003cem\u003eCoxiella\u003c/em\u003e may represent an endosymbiont rather than a pathogenic form like \u003cem\u003eC. burnetii\u003c/em\u003e. Overall, our findings are consistent with previous studies on the genus \u003cem\u003eRhipicephalus\u003c/em\u003e, which have identified \u003cem\u003eCoxiella\u003c/em\u003e as one of the most dominant bacterial taxa in these ticks, often in association with \u003cem\u003eCoxiella\u003c/em\u003e-like endosymbionts (CLEs) [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. As peptide sequences may map to conserved proteins shared across \u003cem\u003eCoxiella\u003c/em\u003e species, additional molecular and functional validation of \u003cem\u003eCoxiella\u003c/em\u003e-related proteins through targeted proteomics, transcriptomics, or immunoassays, would be necessary to determine whether these sequences originate from pathogenic \u003cem\u003eC. burnetii\u003c/em\u003e or symbiotic strains (e.g. \u003cem\u003eCandidatus Coxiella mudrowiae\u003c/em\u003e). This distinction is critical for interpreting their role in tick physiology and vector competence.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis pilot metaproteomic study provides the first functional overview of bacterial communities associated with three major tick species collected in northern Algeria. The analysis revealed a diverse microbiome dominated by taxa such as \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, and \u003cem\u003eCoxiella\u003c/em\u003e, and highlighted differences in microbial composition associated with tick species and collection location. These findings are consistent with previous studies showing that tick-associated microbial communities can vary according to ecological and biological factors.\u003c/p\u003e \u003cp\u003eAlthough based on a limited number of pooled samples, this study demonstrates the potential of metaproteomics to characterize active microbial communities within ticks and to complement DNA-based microbiome investigations. By establishing an initial metaproteomic reference framework for Algerian ticks, this work provides a foundation for future studies aimed at better understanding microbiome diversity and its ecological determinants in tick populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank all of the people who participated in the tick collection. The authors would like to express their sincere gratitude to Ms. Julie Puget, from the Interns and Students Office at IRD, for her invaluable support and dedication. Without her assistance, Mr. Kernif Tahar would not have been able to complete his secondment at IRD. Her efficiency and commitment were instrumental to the success of this collaboration. This work benefited from a capacity-building initiative aimed at strengthening research collaborations and supporting researchers from Southern institutions. This program provides support for training and research mobility but does not fund research expenses or publication costs. Finally, Mr. Cl\u0026eacute;ment LOZANO \u0026amp; Mr. Jean ARMENGAUD gratefully acknowledge the support from the French National Agency for Research (France 2030, the French Proteomics Infrastructure INBS ProFI, grant number ANR-24-INBS-0015-05) which has contributed to the advancement of metaproteomics expertise within their research team.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials:\u0026nbsp;\u003c/strong\u003eTable S1.\u0026nbsp;Biological and taxonomic characterization of tick-associated bacteria identified via a metaproteomic approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by LeiSHeild-RISE MATI project, Grant Agreement N\u0026deg;778298.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e Data available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e 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\u0026mdash;original draft preparation, B.A., T.K., B.M. P.H. and D.S.; writing\u0026mdash;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest:\u003c/strong\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMans BJ. Paradigms in tick evolution. Trends Parasitol. 2023, \u003cem\u003e39\u003c/em\u003e, 475\u0026ndash;486, doi:10.1016/j.pt.2023.03.011.\u003c/li\u003e\n\u003cli\u003eFivaz B, Petney T, Horak IG, editors. \u003cem\u003eTick vector biology: medical and veterinary aspects\u003c/em\u003e. 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Microbiol.\u003c/em\u003e \u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e78\u003c/em\u003e, 4110\u0026ndash;4116.\u003c/li\u003e\n\u003cli\u003eRen\u0026eacute;-Martellet, M.; Minard, G.; Massot, R.; Tran Van, V.; Valiente Moro, C.; Chabanne, L.; Mavingui, P. Bacterial Microbiota Associated with Rhipicephalus Sanguineus (Sl) Ticks from France, Senegal and Arizona. \u003cem\u003eParasit. Vectors\u003c/em\u003e \u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e10\u003c/em\u003e, 1\u0026ndash;10.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ticks, Tick microbiome, Metaproteomics, Bacterial diversity, Hyalomma, Rhipicephalus, Algeria","lastPublishedDoi":"10.21203/rs.3.rs-9160125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9160125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTicks 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn 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: \u003cem\u003eRhipicephalus sanguineus sensu lato\u003c/em\u003e, \u003cem\u003eHyalomma aegyptium\u003c/em\u003e, and \u003cem\u003eH. dromedarii\u003c/em\u003e. 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA 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 \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003ePaenibacillus\u003c/em\u003e, and \u003cem\u003eProvidencia\u003c/em\u003e. Peptides related to \u003cem\u003eCoxiella\u003c/em\u003e spp. were frequently detected, consistent with previous reports of \u003cem\u003eCoxiella\u003c/em\u003e-like endosymbionts in ticks.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis pilot metaproteomic study provides a first functional overview of bacterial communities associated with ticks 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.\u003c/p\u003e","manuscriptTitle":"Metaproteomic profiling of the tick microbiome in northern Algeria: a pilot study of bacterial diversity and potential medical or veterinary relevance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 18:48:16","doi":"10.21203/rs.3.rs-9160125/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T12:01:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T06:38:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131628932015673359161230622762493359627","date":"2026-04-22T05:24:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T19:56:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161051804219030792331652980273225834518","date":"2026-04-10T11:48:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-10T11:36:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T11:25:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T18:28:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-20T09:10:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-20T08:58:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36d15248-4a56-4587-92ee-1895342be9d6","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T12:01:02+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66248420,"name":"Biological sciences/Ecology"},{"id":66248425,"name":"Earth and environmental sciences/Ecology"},{"id":66248426,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-15T12:10:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 18:48:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9160125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9160125","identity":"rs-9160125","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}