Shuang Liu1,2†Rui Hou1†Quanfu Zhang3†
Mingna Duan1
Shaobo Tang1Weishuo Wang4Jiayao Chen4Jun Wu5
Yujuan Shen6*Wei Gu7*
Yi Sun8*
Xing Yang1,9*- 1Integrated Laboratory of Pathogenic Biology, College of Preclinical Medicine, Dali University, Dali, China
- 2School of Health Management, Lanzhou College of Foreign Studies, Lanzhou, China
- 3Department of Gastroenterology, Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China
- 4School of Public Health, Dali University, Dali, China
- 5Dali Bai Autonomous Prefecture People's Hospital Ophthalmology, Xiaguan, Dali, Yunnan, China
- 6National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Key Laboratory of Parasite and Vector Biology, Collaborating Centre for Tropical Diseases, National Institute of Parasitic Diseases at Chinese Center for Disease Control and Prevention, Chinese Center for Tropical Diseases Research, National Health Commission of the People's Republic of China, World Health Organization, Shanghai, China
- 7Department of Infection of the First Affiliated Hospital of Dali University, Yunnan Provincial Department of Education Key Laboratory of Infectious Diseases and the Clinical Medical Sub-Center of Infectious Diseases of Yunnan Province, Dali, China
- 8State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China
- 9Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from Western Yunnan, Dali, China
Ticks, recognized as the second most significant vector of pathogens after mosquitoes, are of considerable interest in medical research. Although mitochondrial genomes are commonly employed in the phylogenetic studies of insects and arthropods, investigations into tick mitochondrial genomes are relatively scarce. The evolutionary and phylogenetic relationships at the family and genus levels remain unresolved. In this study, the mitochondrial genomes of Haemaphysalis warburtoni, Ixodes crenulatus, Rhipicephalus bursa, and Rhipicephalus pumilio were first sequenced and annotated based on the Illumina NovaSeq 6,000 platform, and compared with the mitochondrial genes of 150 other hard ticks. All examined tick mitochondrial genomes exhibit a notable AT bias, with A+T content ranging significantly from 72.28% to 81.06%. They also exhibit distinct codon usage patterns, with most codons ending in either A or U. A phylogenetic analysis, based on 13 protein-coding genes (PCGs), confirms that the Ixodidae family forms a monophyletic group. Based on the phylogenetic analysis of 13 protein-coding genes, the relationships of Ixodidae family as follows: Ixodes + (Robertsicus + ((Bothriocroton + Archaeocroton + Cryptocroton + Haemaphysalis) + (Amblyomma + (Dermacentor + (Rhipicentor + (Hyalomma + Rhipicephalus)))))). This study provides in-depth insights into tick mitochondrial genomes, offering important references for research on their systematics, evolution, and species identification, while also laying the groundwork for tick-borne disease control and public health risk assessment.
Introduction
Ticks (Acari: Ixodida) are obligate hematophagous arthropods that are distributed throughout the world and parasitize almost every type of vertebrate except fish (1). As the second largest vector of pathogen transmission after mosquitoes, ticks have a serious impact on human and animal health (2). It causes reactions such as poisoning, irritation, sensitization and paralysis (which can be severe enough to lead to death) by parasitizing outside the body and biting and sucking blood. In addition, ticks can transmit many pathogens including rickettsia, spirochetes, viruses, bacteria and deadly protozoa, which can cause human disease such as Lyme disease (Borrelia burgdorferi), Rocky Mountain spotted fever (Rickettsia rickettsii), Mediterranean spotted fever (Rickettsia conorii), human granulocytic anaplasmosis (Anaplasma phagocytophilum), tularemia (Francisella tularensis), Colorado tick fever (Reoviridae, Orbivirus), and tick-borne encephalitis (Flaviviridae, Flavivirus). Additionally, in animals, they can lead to babesiosis (Babesia spp.), theileriosis (Theileria spp.), heartwater (Ehrlichia ruminantium), anaplasmosis (Anaplasma spp.), Lyme disease (Borrelia burgdorferi), and ehrlichiosis [Ehrlichia spp.; (3–7)]. All of these pathogens are detrimental to the health of humans, domestic animals, and wildlife. Factors such as global warming and increased human contact with nature have heightened the chances of tick bites and related diseases (8, 9). Furthermore, global expenditures on the management of ticks and the control and treatment of tick-borne diseases exceed $7 billion annually, imposing an additional heavy economic burden on the livestock industry (10, 11). Despite significant advances in the study of ticks and tick-borne pathogens, new tick-borne viruses have been discovered in recent years (12–14). Due to the important role that ticks play in veterinary medicine, medicine and veterinary medicine, species identification and taxonomic studies of ticks are of particular importance.
To date, ticks have been classified into 3 families, 18 genera and over 900 species according to traditional described and categorized (15). Based on the morphological description with a hard shield for the Ixodidae family and without a hard shield for the Argasidae family, the family Nuttalliellidae has its own unique characteristics and has only one species (Nuttalliella namaqua) recorded (4, 16). In recent years, scholars have discovered a new tick species (Deinocroton draculi) in Burmese amber ore that is about 99 million years old, a discovery that has allowed them to establish a new tick family-the Deinocroton family (17).
Initially, phylogenetic relationships among ticks were inferred based on their morphology, host types, and life cycles. The constructed “family tree” revealed two genera of soft ticks and five subfamilies with thirteen genera of hard ticks (18). However, it is unreliable to speculate on the phylogeny of ticks based on judgments such as tick “key morphological characters” or “host type” alone. Since the 1990s, molecular biology has been gradually applied to the study of ticks, and as a complementary method, molecular markers have been effective in making accurate identifications of parasites and clarifying the taxonomic status of some (19–22). However, most previous molecular studies have been limited to the use of relatively small datasets, such as 12S rRNA, 16S rRNA, cytochrome c oxidase subunit (cox1) (23–25). These data have helped solve some of the phylogenetic problems of ticks to some extent, but because these molecular markers are usually short and evolve rapidly, they are often not responsive to a deeper understanding of phylogenetic relationships.
Mitochondria are important organelles in eukaryotic cells. As semi-autonomous organelles, mitochondria have their own genetic material, known as the mitochondrial genome [mtDNA; (26)]. Over the past decade, the mitochondrial genome has been widely used in animal species identification, species reclassification, phylogenetic studies, and population genetics due to its structurally simplicity, strictly matrilineal inheritance, high degree of conservation, and relatively small molecular (27–31). The mitochondrial genome of ticks is structurally consistent with the number and sequence of metazoans, with a classic 37 genes (32) were the first to use complete mitochondrial genome sequences to study the phylogeny of hard tick lineages. Subsequent comparative analyses of mitochondrial genome sequences revealed that only by using the mitochondrial genome could the controversial phylogenetic relationships within soft tick lineages be effectively resolved (33). Subsequently, more scholars analyzed more mitochondrial genes of ticks, and combined with morphology, conducted more accurate inferences and analyzes on the phylogeny of families, subfamilies, genera and subgenus of lxodes ticks (31, 34–36). At the time of writing, the mitochondrial genome sequences of 184 tick species have been published in GenBank. The mitochondrial genome data provide reliable molecular for phylogeny, but there are a number of ticks for which mtDNA is still lacking, creating an important to refining the phylogenetic relationships between a wider range of tick lineages.
Ixodes crenulatus parasitizes companion animals such as cats and dogs, and can readily transmit tick-borne pathogens between humans and animals (37). Additionally, Rhipicephalus bursa transmits Babesia ovis (38), while Rhipicephalus pumilio serves as a vector for Anaplasma ovis (39). These three tick species pose threats to both human and animal health. However, current knowledge of their mitochondrial genomes, including that of the less-studied Haemaphysalis warburtoni, remains uncharacterized, with no relevant data reported in public databases. Therefore, elucidating the complete mitochondrial genomes of these tick species is of great importance for subsequent research on pathogen transmission mechanisms and tick evolution. This study reports the first complete mitochondrial genome sequences of four tick species: Haemaphysalis warburtoni, Ixodes crenulatus, Rhipicephalus bursa, and Rhipicephalus pumilio. Combined with previously published data from 150 hard tick species, a comprehensive phylogenetic analysis was conducted. Comparative genomic analyses were performed on newly sequenced and publicly available tick mitochondrial genomes to explore the diversity and evolutionary patterns within tick species. This research not only expands the molecular database for ticks but also provides foundational genetic markers for species identification, taxonomic classification of tick genera, and phylogenetic analysis.
Materials and methods
All four newly sequenced specimens were from China, and detailed information is listed in Supplementary Table 1. The collected specimens were morphologically characteristics according to key morphological features and verified using cox1 sequencing (40). The specimens were then preserved in 95% ethanol and stored at−80 °C until use. The collected specimens were divided into two copies, one for DNA extraction according to the instructions, and the other was stored as a voucher specimen in the Parasite Museum of Dali University (voucher numbers DLUP3010-DLUP3013). Total genomic DNA was extracted from individual ticks using the TIANamp Genomic DNA Kit according the manufacturer's instructions.
Construction of the genomic library and sequencing
Total DNA from each specimen was extracted and normalized to a concentration of 0.3 ng/L for genomic library construction. Libraries were constructed using a whole genome shotgun (WGS) strategy on the Illumina NovaSeq 6,000 sequencing platform. These libraries, composed of 400 bp fragments, underwent paired-end sequencing. The sequencing output was transferred to a computer workstation, where data from each sample were counted and assessed individually. AdapterRemoval v2.0 was employed to eliminate splice-contaminated sequences (41), while FastQC software (https://www.bioinformatics.babraham.ac.uk/projects/fastqc) was used for data quality control. The average quality scores were 35.966 for H. warburtoni, 35.507 for I. crenulatus, 35.576 for R. bursa, and 35.780 for R. pumilio. Overlap clusters and scaffold sequences were generated using A5 miseq v20150522 (42) and SPAdes v3.9.0 (43), resulting in high-quality, assembled sequencing data. The average sequencing depth was 1060.21 X (H. warburtoni), 5938.68 X (I. crenulatus), 114.68 X (R. bursa) and 392.35 X (R. pumilio). Based on sequencing depth, sequences were extracted, and those with high depth were subjected to BLASTN analysis against the NCBI (nt) database to identify mitochondrial sequences from each assembly. The complete mitochondrial genome sequences and annotations for the four newly sequenced tick species have been deposited in the NCBI database under the following GenBank accession numbers: H. warburtoni (NC084204), I. crenulatus (OR872332), R. bursa (OR773535), and R. pumilio (NC084205).
Annotation and bioinformatic analysis
Functional annotation of the obtained complete mitochondrial genome sequence using the MITOS web server (http://mitos.bioinf.uni-leipzig.de/index.py) (44). tRNAscan-SE Search Server v2.0 was used to predict the secondary structure of tRNAs (45). The NCBI BLAST online web page was used to search and compare sequence similarities, while protein-coding genes (PCGs) were analyzed and translated using an online open reading frame searcher. DNAStar V.5.0 software was used to calculate the AT and GC content of each gene, and the nucleotide skew values were analyzed using the formula AT-skew = (A – T)/(A + T) and GC-skew = (G – C)/(G + C) (46). The calculated values of AT-skew, GC-skew and AT% were counted and expressed in Origin Pro v9.0 using 3D scatter plots. To analyze the selection pressure, Ka and Ks values of 13 PCGs were calculated using DNASP V5.0 (47).
Phylogenetic analysis
Phylogenetic relationships were constructed for 154 mitochondrial genomes of hard ticks (including the 4 sequenced in this study), with Argas africolumbae (NC019642), Ornithodoros compactus (NC067908), Nuttalliella namaqua (NC019663) and Limulus polyphemus (NC003057) as outgroups. Taxonomic information and GenBank data accession numbers for each species analyzed are listed in Supplementary Table 2. DAMBE v.7.2.102 was used to assess substitution saturation at each of the 13 mitochondrial PCG positions. Phylogenetic relationships between species were inferred using both maximum likelihood (ML) and Bayesian inference (BI) methods. The most appropriate model was inferred to be GTR+I+G based on ModelFinder, and the phylogenetic relationships of BI were constructed using MrBayes v3.2.6 (48) with four independent Markov chain Monte Carlo (MCMC) running simultaneously for 10,000,000 generations, with samples every 1,000 generations. The first 25% of the flutter trees were aged and the remaining trees were used to compute Bayesian posterior probabilities. Phylogenetic relationships for ML are constructed using IQ-Tree (49), and 1,000 bootstrapped replicates are selected to evaluate the reliability of branch nodes (50). Finally, the phylogenetic tree was displayed using Figtree v.1.42.
Results
Mitogenome nucleotide composition and genome organization
This study is the first to sequence the mitochondrial genomes of H. warburtoni, I. crenulatus, R. bursa, and R. Pumilio, which adds to the diversity of mitogenomes reported, especially for Ixodes, Haemaphysalis and Rhipicephalus (Figure 1 and Supplementary Table 3). The mitochondrial genomes of all 154 tick species studied contain the typical 37 genes, including 13 protein-coding genes (PCGs), 2 ribosomal RNA genes (16S rRNA and 12S rRNA), and 22 transfer RNA genes (tRNAs), as well as a varying number of D-loops. Most of these genes are located in the positive strand of the mitochondrial genome, while a few are located in the negative strand. The mitochondrial complete genome sequence length varies among Ixodidae family ticks, ranging from 14,463 bp (Ixodes loricatus) to 15,307 bp (Dermacentor sp.), with an average length of 14,778 bp. In hard ticks, the A+T content shows significant variation, ranging from 72.28% (Ixodes (Pholeoixodes) sp.) to 81.06% (Haemaphysalis danieli), with an average of 77.72%. The average AT-skew and GC-skew are −0.017 and −0.171, respectively (Figure 2 and Supplementary Table 5).
Figure 1. Mitochondrial genome structure of Ixodes crenulatus (A), Haemaphysalis warburtoni (B), Rhipicephalus bursa (C), Rhipicephalus pumilio (D). With genes located on the J (forward) strand are displayed on the outer side, while the N (reverse) strand genes are indicated in the inner. The colors of gene names represent control regions (gray), tRNA genes (red), PCGs (blue) and rRNA genes (yellow), respectively.
Figure 2. Three-dimensional scatter plot displaying AT-skew, GC-skew, and AT% composition across 154 mitochondrial genomes of hard ticks, with different colors representing the various genera of the mtgenomes.
Protein-coding genes
In hard ticks (Ixodidae), the length of the mitochondrial complete protein-coding genome ranges from 10,639 bp (Amblyomma marmoreum) to 10,910 bp (Bothriocroton concolor), with an average length of 10,832 bp. The A+T content varies between 70.78% (Ixodes (Pholeoixodes) sp.) and 81.20% (Haemaphysalis danieli), with an average A+T content of 77.40%. The AT-skew is predominantly negative, and the GC-skew also mostly shows negative values.
The initiation codon usage in Ixodidae species shows similarity at the species level. Nearly all species use ATN as the initiation codon, with ATT and ATG being more common. ATA is used more frequently in a few species, while ATC is the least used. Among the 13 protein-coding genes (PCGs), atp6, cox2, cox3, cytb, nad4, and nad4l preferentially use ATG as the initiation codon. Notably, the NADH dehydrogenase subunit genes (nad1, nad2, nad3, and nad5) more frequently use ATT as the initiation codon, A few genes, namely nad5, nad3, nad2, and nad1, employ the relatively rare initiation codon GTG (Figure 3). In all species, TAA is the most common termination codon for mitochondrial protein-coding genes, while TA is used less frequently. Among the 13 PCGs, atp6, atp8, cox1, nad2, nad4l, and nad6 very frequently use TAA as the termination codon. Particularly, cox2 and cox3 tend to use the incomplete termination codons T and TA (Figure 4).
Figure 3. Analysis of start codon usage in the 13 protein-coding genes from 154 Ixodidae (hard tick) species.
Figure 4. Analysis of stop codon usage in the 13 protein-coding genes from 154 Ixodidae (hard tick) species.
By calculating the synonymous (Ks) and non-synonymous (Ka) substitution rates for the mitochondrial protein-coding genes across the Ixodidae family, we estimated their evolutionary rates. The results indicated that atp8 exhibits the highest evolutionary rate, whereas cox1 shows the lowest. Although all Ka/Ks values were less than 1, the other PCGs exhibited considerable variation in their evolutionary rates (Figure 5). The amino acid usage frequency was calculated for the 13 PCGs. The results indicated a similarity in amino acid usage across the 154 hard tick species. The most prevalent amino acids were leucine (Leu), phenylalanine (Phe), and isoleucine (Ile), while tryptophan (Trp) was the least frequent (Figure 6).
Figure 5. The evolutionary rates of the 13 protein-coding genes (PCGs) across the 154 Ixodidae mitochondrial genomes were assessed using the Ka/Ks ratio, with values interpreted as follows: Ka/Ks = 1 signifies neutral evolution; Ka/Ks < 1, purifying selection; and Ka/Ks > 1, positive selection.
Figure 6. Frequency percentage of each of the 20 coded amino acids in 154 mitochondrial genome sequences of in the family Ixodidae.
Relative Synonymous Codon Usage (RSCU) can intuitively reflect codon usage bias. As shown in Figure 7, codon clustering based on RSCU values (represented by color gradients) reveals the following characteristics: light yellow indicates high-frequency usage (RSCU > 1), orange represents relatively high-frequency usage (RSCU > 2), and blue denotes low-frequency usage (RSCU < 1). The codons AUG and UGG show consistent usage frequencies across species, both with an RSCU value of 1. The most frequently used codon is UUA, while the least frequently used is CCG. Notably, the high-frequency codons UUA, UCA, and AGA (all ending with A at the third nucleotide position), along with most preferred codons in the right cluster, predominantly terminate with A or U. This demonstrates a significant A or U-ending preference for optimal codons in hard ticks (Ixodidae; Figure 7 and Supplementary Table 4).
Figure 7. Comparative analysis of synonymous codon usage (RSCU) preferences and conservation in the mitochondrial protein-coding genes of 154 Ixodidae tick species.
D-loop, transfer and ribosomal RNA genes
The mitochondrial tRNA genes of hard ticks (Ixodidae) range in length from 50 to 76 bp (Figure 8). Most tRNA genes exhibit a complete cloverleaf secondary structure, with the presence of non-canonical base pairs (G-U, U-U, and U-G mismatches) that contribute to structural stability. The mitochondrial genome of hard ticks also includes two rRNA genes (16S rRNA and 12S rRNA). The 16S rRNA gene exhibits a length range of 982–1,381 bp and the 12S rRNA gene ranges from 649 to 1,201 bp in length (Figure 9). The mitochondrial genome typically contains 1–2 non-coding regions (NCRs), though some tick species possess 3 or more NCRs. The total length of these NCRs varies considerably from 210 to 1,412 bp, with the number of NCRs differing among genera without apparent regularity. Notably, in Amblyomma calabyi, an exceptionally short 1 bp intergenic spacer was observed between trnL1 and trnC genes, which may potentially represent a mitochondrial genome annotation artifact. It is worth noting that a unique common sequence marker, termed “Tick-box”, has been identified in the mitochondrial genomes of ticks. However, this marker was not found in the mitochondrial genomes of the 4 tick species analyzed in the present study.
Figure 8. Composition of mitochondrial tRNA gene lengths across 154 hard tick species.
Figure 9. Composition of mitochondrial rRNA genes and control region (CR) lengths in 154 hard tick species.
Mitogenomic phylogenetic analyses
None of the nucleotide databases were saturated, suggesting the suitability of constructing phylogenetic trees as a means of inferring relationships within ticks (Figure 10). We then constructed a phylogenetic tree using published data for 150 species available in NCBI to infer tick internal relationships using both ML and BI methods, and ultimately found that the use of the same sequence matrix yielded two highly similar topologies that differed slightly at the genus level.
Figure 10. Substitution saturation test of different coding sites in the 13 protein-coding genes (PCGs) of 154 tick mitogenomes. In the figure, s and v represent transition distance and transversion distance, and general time reversible (GTR) distance represents the use of the GTR model to calculate the total genetic distance. (A) PCG with all codon positions; (B) PCG with 1st codon position; (C) PCG with 2nd codon position; (D) PCG with 3rd codon position.
Within the hard tick family, both the ML and BI tree topologies exhibit the same two lineages: the Prostriata and Metastriata. Prostriata consists of only the hard tick genera and is the basis of all hard tick genera. Metastriata include Robertsicus, Bothriocroton, Archaeocroton, Haemaphysalis, Dermacentor, Amblyomma, Rhipicentor, Hyalomma, and Rhipicephalus. Most genera are monophyletic, with the (Archaeocroton + Bothriocroton + Haemaphysalis) forming a sister group relationship with the (Amblyomma + (Dermacentor + (Rhipicentor + Rhipicephalus))) branch. The relationship between Bothriocroton, Archaeocroton, and Haemaphysalis is not yet clear (Figure 11 and Supplementary Figure 1).
Figure 11. Mt-genome based phylogenetic relationships of 154 hard tick species. They are constructed based on 13 protein-coding genes dataset using Bayesian inference methods. The different colors of species name blocks represent the different genus. Red font indicates the 4 newly sequenced mt-genomes of hard ticks in this study. Species and NCBI accession numbers for mt-genomes used in the phylogenetic analysis are provided in Supplementary Table 2.
The phylogenetic analysis revealed several strongly supported relationships. I. simplex and I. vespertilionis formed a highly supported clade (100% bootstrap support, 1.00 posterior probability), which grouped with I. crenulatus (100% bootstrap support, 1.00 posterior probability); R. bursa is sister to R. evertsi (100% bootstrap support, 1.00 posterior probability); while R. pumilio was most closely related to R. sanguineus (77.72% bootstrap support, 1.00 posterior probability); H. tibetensis was sister to H. nepalensis (100% bootstrap support, 1.00 posterior probability), and this clade further grouped with H. warburtoni (100% bootstrap support, 1.00 posterior probability).
Discussion
This study reports the first sequencing and annotation of the mitochondrial genomes of Haemaphysalis warburtoni, Ixodes crenulatus, Rhipicephalus bursa, and Rhipicephalus pumilio, integrating these data with 150 publicly available hard tick mitogenomes for a comprehensive systematic analysis. The mitochondrial genomes of the 154 tick species examined in this study varied in length from 14,463 bp to 15,307 bp. All tick genomes contained 37 genes. Length variations, primarily due to D-loops, spacer regions, and overlapping regions, were consistent with previous reports on ticks (51, 52). The nucleotides of all species studied showed a pronounced AT content preference, with high levels of A+T, particularly in postnatal animals, where A+T content typically exceeds G+C content. Additionally, soft ticks exhibited significantly higher A+T content than hard ticks, correlating with variations in their habitats, life strategies, and survival environments (53–55). Base skewing is mainly attributed to asymmetric base mutations during replication and transcription, as well as to selective pressures. Most hard ticks exhibit a negative AT-skew, a notable exception to the general metazoan pattern of a positive AT-skew and a negative GC-skew in their mitochondrial genomes. Similar phenomena have also been observed in other animal groups such as Araneae and Lepidoptera (56, 57).
In the 13 protein-coding genes (PCGs) of ticks, the start codons predominantly conform to the classical ATN format, whereas the stop codons are primarily TAA. Additionally, the presence of atypical initiation codons TTG and GTG was identified. These codons are corrected during mRNA editing, thus not affecting normal protein translation (58). In some protein genomes, incomplete stop codons “T” and “TA” have been noted. These incomplete stop codons are converted to TAA through post-transcriptional polyadenylation (59). The Ka/Ks ratios of PCGs reflect the selective pressures on genes. For ticks, the Ka/Ks ratios for individual protein-coding genes are below 1, indicating that ticks have primarily undergone purifying selection during their evolution (60). The cox1 gene exhibits the lowest Ka/Ks ratio, highlighting its conservation and the strong functional constraints it has faced. Conversely, the atp8 gene displays the highest Ka/Ks ratio, suggesting it has the greatest variation and has experienced relatively weak selective pressure.
Most tRNA genes in tick mitochondrial genomes possess the typical cloverleaf structure, including four main structural regions: the amino acid acceptor arm (AA), the TΨC arm (T), the anticodon arm (AC), and the dihydrouridine arm (DHU) (61). The absence of the DHU arm in trnS1 is common among ticks, a trait shared with most insects and metazoan mitochondrial genomes (62, 63). Similarly, trnC often lacks the D-arm, a feature believed to be an ancestral trait within the Mesostigmata (64). In insects, mitochondrial non-coding region transcription termination is facilitated by the binding of transcription factors to specific sites (65). In tick mitochondrial genomes, the size, number, and position of non-coding regions (NCRs) vary, with the most frequent location being between the rrnS and trnI genes. Typically, the length of NCRs within a single genus varies little, generally containing only 1 or 2 NCRs. However, in the genus Dermacentor, multiple non-coding regions have been identified, which may contribute to gene rearrangement processes and help protect gene function (55). A distinct marker sequence known as the “Tick-box” motif has been identified in the non-coding regions of tick mitochondrial genomes, likely arising from gene degeneration during evolution (66). This “Tick-box” motif, particularly when directionally repeated at the 3′ ends of the nad1 gene and 16S rRNA gene in H. inermis and other species, may have facilitated transposition events, leading to the formation of partial gene copies (31).
The results of this study, which utilize the mitochondrial genome to infer phylogenetic relationships among genera within the Ixodidae family, are robust and align with prior morphological and molecular findings. The Ixodidae family is confirmed as a monophyletic group, with each genus forming well-defined branches (51). Specifically, the Ixodes genus is monophyletic, with Australasian Ixodes and non-Australasian Ixodes forming distinct clusters. However, the precise number of extant species within the Australasian Ixodes remains uncertain, as most hard ticks have not been thoroughly studied. In accordance with earlier research, a phylogenetic tree constructed from 13 protein-coding genes places the Ixodes genus at the basal position within the Ixodidae (29). I. crenulatus is identified as forming a sister group with I. simplex and I. Vespertilionis (100% BS, 1.00 PP). Previous research indicated that the Haemaphysalinae subfamily is closely related to the Hyalomminae and Rhipicephalinae subfamilies (18), based on morphological traits such as leg structure and palpal spurs. Current analyses based on PCGs further substantiate the phylogenetic placement of the Ixodes genus. Both the Maximum Likelihood and Bayesian Inference trees confirm that the Ixodes genus forms a monophyletic group and is sister to A. sphenodonti, R. elaphensis, B. undatum, and B. consolor, upporting current conclusions (29, 51). H. warburtoni is sister to H. nepalensis and H. tibetensis (100% BS, 1.00 PP), the genus Haemaphysalis may not be monophyletic. All Amblyomma species form a monophyletic group. Amblyomma species collectively form a monophyletic group. The Rhipicephalinae subfamily, including Hyalomma and Rhipicephalus, forms a monophyletic clade with Dermacentor and Rhipicentor (67, 68). R. bursa is sister to R. Evertsi (100% BS, 1.00 PP); while Rh. Pumilio was most closely related to R. sanguineus (77.72% BS, 1.00 PP), with all species of Rhipicephalus also forming a monophyletic group (69).
Ticks are a group of harmful pests that pose a significant threat to the health of both humans and animals. Currently, biological and chemical methods are widely used for tick control (70, 71); however, accurately identifying their species and clarifying their taxonomic status remain fundamental to effective prevention and control efforts. In this study, the mitochondrial genomes of H. warburtoni, I. crenulatus, R. bursa, and R. pumilio were sequenced for the first time, providing valuable genetic markers for species identification, population genetics, and molecular epidemiology of ticks. Previous mitochondrial genomic studies have supported the monophyly of the families Ixodidae, Argasidae, and Nuttalliellidae. The results of this study also support the monophyly of these three families. However, the phylogenetic relationship between the Nuttalliellidae family and other families remains unclear. Furthermore, more evidence is needed to verify whether the genera within the Ixodidae and Argasidae families form monophyletic or paraphyletic groups (31, 72). In recent years, mitochondrial genomes have been widely used in the systematic taxonomy and evolutionary studies of ticks due to their maternal inheritance and relatively conserved sequences. However, their high nucleotide substitution rate may lead to the accumulation of base substitutions and saturation of homologous sequences, making it difficult to accurately resolve deeper phylogenetic relationships, such as those at the subfamily level (51). Therefore, future research integrating mitochondrial genome data with nuclear genome data is expected to provide clearer insights into the phylogenetic relationships of ticks. Therefore, in future research, the integrated analysis of both mitochondrial and nuclear genomic data is expected to more clearly reveal the evolutionary relationships within tick phylogeny.
Conclusion
This study is the first to sequence the mitochondrial genomes of H. warburtoni, I. crenulatus, R. bursa, and R. Pumilio, these data provide a genomic foundation for future molecular epidemiology, species delimitation, and evolutionary studies in ticks, particularly within the Ixodidae. With the widespread application of molecular data, mitochondrial genomes will effectively compensate for the limitations of traditional morphological identification and play important roles in areas such as accurate species identification and resolution of phylogenetic relationships.
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.
Ethics statement
The manuscript presents research on animals that do not require ethical approval for their study.
Author contributions
SL: Formal analysis, Supervision, Data curation, Software, Writing – original draft, Conceptualization, Writing – review & editing, Validation. RH: Data curation, Formal analysis, Writing – review & editing, Methodology. QZ: Supervision, Data curation, Writing – review & editing, Conceptualization. MD: Formal analysis, Writing – review & editing, Data curation. ST: Writing – review & editing, Formal analysis, Data curation. WW: Validation, Visualization, Writing – review & editing. JC: Data curation, Writing – review & editing. JW: Writing – review & editing, Resources, Funding acquisition. YS: Writing – review & editing, Funding acquisition. WG: Software, Writing – review & editing, Data curation. YS: Resources, Writing – review & editing, Formal analysis. XY: Software, Writing – review & editing, Funding acquisition.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by Scientific Research Fund of Yunnan Education Department [2024J0857], National Natural Science Foundation of China (Nos. 82372283 and 82072307) and the NHC Key Laboratory of Parasite and Vector Biology (National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention) (No. NHCKFKT2022-14), the Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities' Association (Grant No. 202401BA070001-005) and Yunnan Fundamental Research Projects (Grant No. 202401AT070084). Organization name: National Key Research and Development Program of China (2024YFC2607504).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The reviewer QC declared a past co-authorship with the author YS to the handling editor at the time of review.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fvets.2025.1678349/full#supplementary-material
References
1. Nava S, Guglielmone AA, Mangold AJ. An overview of systematics and evolution of ticks. Front Biosci (Landmark Ed). (2009) 14:2857–77. doi: 10.2741/3418
2. de la Fuente J, Estrada-Pena A, Venzal JM, Kocan KM, Sonenshine DE. Overview: ticks as vectors of pathogens that cause disease in humans and animals. Front. Biosci J Virt Libr. (2008) 13: 6938–46. doi: 10.2741/3200
4. Chen Z, Yang X, Bu F, Yang X, Yang X, Liu J. Ticks (acari: ixodoidea: argasidae, ixodidae) of China. Exp Appl Acarol. (2010) 51:393–404. doi: 10.1007/s10493-010-9335-2
5. Allsopp BA. Heartwater—Ehrlichia ruminantium infection. Revue scientifique et technique (Int Off Epizoot). (2015) 34:557–68. doi: 10.20506/rst.34.2.2379
6. Araya-Anchetta A, Busch JD, Scoles GA, Wagner DM. Thirty years of tick population genetics: a comprehensive review. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis. (2015) 29:164–79. doi: 10.1016/j.meegid.2014.11.008
7. Bakken JS, Dumler JS. Human granulocytic anaplasmosis. Infect Dis Clin North Am. (2015) 29:341–55. doi: 10.1016/j.idc.2015.02.007
8. Jia N, Wang J, Shi W, Du L, Sun Y, Zhan W, et al. Large-scale comparative analyses of tick genomes elucidate their genetic diversity and vector capacities. Cell. (2020) 182:1328–40.e1313. doi: 10.1016/j.cell.2020.07.023
9. Gray JS, Dautel H, Estrada-Peña A, Kahl O, Lindgren E. Effects of climate change on ticks and tick-borne diseases in Europe. Interdiscip Pers Infect Dis. (2009) 2009:593232. doi: 10.1155/2009/593232
10. Jongejan F, Uilenberg G. The global importance of ticks. Parasitology. (2004) 129:S3–14. doi: 10.1017/S0031182004005967
11. Ahmed J, Alp H, Aksin M, Seitzer U. Current status of ticks in Asia. Parasitol Res. (2007) 101:S159–62. doi: 10.1007/s00436-007-0696-3
12. Lv X, Liu Z, Li L, Xu W, Yuan Y, Liang X, et al. Yezo virus infection in tick-bitten patient and ticks, Northeastern China. Emerg Infect Dis. (2023) 29:797–800. doi: 10.3201/eid2904.220885
13. Qin T, Shi M, Zhang M, Liu Z, Feng H, Sun Y. Diversity of RNA viruses of three dominant tick species in North China. Front Vet Sci. (2022) 9:1057977. doi: 10.3389/fvets.2022.1057977
14. Liu Z, Li L, Xu W, Yuan Y, Liang X, Zhang L, et al. Extensive diversity of RNA viruses in ticks revealed by metagenomics in northeastern China. PLoS Negl Trop Dis. (2022) 16:e0011017. doi: 10.1371/journal.pntd.0011017
15. Guglielmone AA, Nava S. Names for Ixodidae (Acari: Ixodoidea): valid, synonyms, Incertae sedis, Nomina dubia, Nomina nuda, lapsus, incorrect and suppressed names—with notes on confusions and misidentifications. Zootaxa. (2014) 3767:1–256. doi: 10.11646/zootaxa.3767.1.1
16. Horak IG, Camicas JL, Keirans JE. The Argasidae, Ixodidae and Nuttalliellidae (Acari: Ixodida): a world list of valid tick names. Exp Appl Acarol. (2002) 28:27–54. doi: 10.1023/A:1025381712339
17. Peñalver E, Arillo A, Delclòs X, Peris D, Grimaldi DA, Anderson SR, et al. Parasitised feathered dinosaurs as revealed by Cretaceous amber assemblages. Nat Commun. (2017) 8:1924. doi: 10.1038/s41467-017-01550-z
19. Klompen JS, Black WC, Keirans JE, OliverJH, Jr. Evolution of ticks. Annu Rev Entomol. (1996) 41:141–61. doi: 10.1146/annurev.en.41.010196.001041
20. Klompen JSH, Black WC, Keirans JE, Norris DE. Systematics and biogeography of hard ticks, a total evidence approach. Clad Int J Willi Hennig Soc. (2000) 16:79–102. doi: 10.1111/j.1096-0031.2000.tb00349.x
21. Zahler M, Filippova NA; Morel PC, Gothe R, Rinder H. Relationships between species of the Rhipicephalus sanguineus group: a molecular approach. J Parasitol. (1997) 83:302–6. doi: 10.2307/3284460
22. Barker SC, Murrell A. Phylogeny, evolution and historical zoogeography of ticks: a review of recent progress. Exp Appl Acarol. (2002) 28:55–68. doi: 10.1023/A:1025333830086
23. Black WC, Klompen JS, Keirans JE. Phylogenetic relationships among tick subfamilies (Ixodida: Ixodidae: Argasidae) based on the 18S nuclear rDNA gene. Mol Phylogenet Evol. (1997) 7:129–44. doi: 10.1006/mpev.1996.0382
24. Murrell A, Campbell NJ, Barker SC. Recurrent gains and losses of large (84-109 bp) repeats in the rDNA internal transcribed spacer 2 (ITS2) of rhipicephaline ticks. Insect Mol Biol. (2001) 10:587–96. doi: 10.1046/j.0962-1075.2001.00298.x
25. Song S, Shao R, Atwell R, Barker S, Vankan D. Phylogenetic and phylogeographic relationships in Ixodes holocyclus and Ixodes cornuatus (Acari: Ixodidae) inferred from COX1 and ITS2 sequences. Int J Parasitol. (2011) 41:871–80. doi: 10.1016/j.ijpara.2011.03.008
26. Boore JL. Animal mitochondrial genomes. Nucleic Acids Res. (1999) 27:1767–80. doi: 10.1093/nar/27.8.1767
27. Vico P, Bonilla S, Cremella B, Aubriot L, Iriarte A, Piccini C, et al. Biogeography of the cyanobacterium Raphidiopsis (Cylindrospermopsis) raciborskii: integrating genomics, phylogenetic and toxicity data. Mol Phylogenet Evol. (2020) 148:106824. doi: 10.1016/j.ympev.2020.106824
28. Luo T, Yan SS, Xiao N, Zhou JJ, Wang XL, Chen WC, et al. Phylogenetic analysis of combined mitochondrial genome and 32 nuclear genes provides key insights into molecular systematics and historical biogeography of Asian warty newts of the genus Paramesotriton (Caudata: Salamandridae). Zool Res. (2022) 43:787–804. doi: 10.24272/j.issn.2095-8137.2022.081
29. Liu J, Yu J, Yu X, Bi W, Yang H, Xue F, et al. Complete mitogenomes of ticks Ixodes acutitarsus and Ixodes ovatus parasitizing giant panda: deep insights into the comparative mitogenomic and phylogenetic relationship of ixodidae species. Genes. (2022) 13:2049. doi: 10.3390/genes13112049
30. Uribe JE, Kelava S, Nava S, Cotes-Perdomo AP, Castro LR, Rivera-Paéz FA, et al. New insights into the molecular phylogeny, biogeographical history, and diversification of Amblyomma ticks (Acari: Ixodidae) based on mitogenomes and nuclear sequences. Parasit Vectors. (2024) 17:139. doi: 10.1186/s13071-024-06131-w
31. Kelava S, Mans BJ, Shao R, Barker D, Teo EJM, Chatanga E, et al. Seventy-eight entire mitochondrial genomes and nuclear rRNA genes provide insight into the phylogeny of the hard ticks, particularly the Haemaphysalis species, Africaniella transversale and Robertsicus elaphensis. Ticks Tick Borne Dis. (2023) 14:102070. doi: 10.1016/j.ttbdis.2022.102070
32. Black WC, Roehrdanz RL. Mitochondrial gene order is not conserved in arthropods: prostriate and metastriate tick mitochondrial genomes. Mol Biol Evol. (1998) 15:1772–85. doi: 10.1093/oxfordjournals.molbev.a025903
33. Burger TD, Shao R, Labruna MB, Barker SC. Molecular phylogeny of soft ticks (Ixodida: Argasidae) inferred from mitochondrial genome and nuclear rRNA sequences. Ticks Tick Borne Dis. (2014) 5:195–207. doi: 10.1016/j.ttbdis.2013.10.009
34. Mans BJ, Featherston J, Kvas M, Pillay KA, de Klerk DG, Pienaar R, et al. Argasid and ixodid systematics: implications for soft tick evolution and systematics, with a new argasid species list. Ticks Tick Borne Dis. (2019) 10:219–40. doi: 10.1016/j.ttbdis.2018.09.010
35. Mans BJ, Chitimia-Dobler L, Pienaar R, de Castro M, Khan M, Almutairi MM, et al. Mitochondrial genome and nuclear ribosomal RNA analysis place Alveonasus lahorensis within the Argasinae and suggest that the genus Alveonasus is paraphyletic. Parasitology. (2024) 151:1–10. doi: 10.1017/S0031182024001537
36. Burger TD, Shao R, Barker SC. Phylogenetic analysis of the mitochondrial genomes and nuclear rRNA genes of ticks reveals a deep phylogenetic structure within the genus Haemaphysalis and further elucidates the polyphyly of the genus Amblyomma with respect to Amblyomma sphenodonti and Amblyomma elaphense. Ticks Tick Borne Dis. (2013) 4:265–74. doi: 10.1016/j.ttbdis.2013.02.002
37. Kocoń A, Nowak-Chmura M, Asman M, Kłyś M. Review of ticks attacking domestic dogs and cats, and their epidemiological role in the transmission of tick-borne pathogens in Poland. Ann Agric Environ Med: AAEM. (2023) 30:22–30. doi: 10.26444/aaem/161552
38. Firat R, Ulucesme MC, Aktas M, Ceylan O, Sevinc F, Bastos RG, et al. Role of Rhipicephalus bursa larvae in transstadial transmission and endemicity of Babesia ovis in chronically infected sheep. Front Cell Infect Microbiol. (2024) 14:1428719. doi: 10.3389/fcimb.2024.1428719
39. Yin H, Luo J. Ticks of small ruminants in China. Parasitol Res. (2007) 101:S187–9. doi: 10.1007/s00436-007-0688-3
41. Schubert M, Lindgreen S, Orlando L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res Notes. (2016) 9:88. doi: 10.1186/s13104-016-1900-2
42. Coil D, Jospin G, Darling AE. A5-miseq: an updated pipeline to assemble microbial genomes from Illumina MiSeq data. Bioinformatics (Oxford, England). (2015) 31:587–9. doi: 10.1093/bioinformatics/btu661
43. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol J Comput Mol Cell Biol. (2012) 19:455–77. doi: 10.1089/cmb.2012.0021
44. Bernt M, Donath A, Jühling F, Externbrink F, Florentz C, Fritzsch G, et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol Phylogenet Evol. (2013) 69:313–9. doi: 10.1016/j.ympev.2012.08.023
45. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. (1997) 25:955–64. doi: 10.1093/nar/25.5.955
46. Perna NT, Kocher TD. Patterns of nucleotide composition at fourfold degenerate sites of animal mitochondrial genomes. J Mol Evol. (1995) 41:353–8. doi: 10.1007/BF01215182
47. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics (Oxford, England). (2009) 25:1451–2. doi: 10.1093/bioinformatics/btp187
48. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol. (2012) 61:539–42. doi: 10.1093/sysbio/sys029
49. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. (2020) 37:1530–4. doi: 10.1093/molbev/msaa015
50. Minh BQ, Nguyen MA, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. (2013) 30:1188–95. doi: 10.1093/molbev/mst024
51. Kelava S, Mans BJ, Shao R, Moustafa MAM, Matsuno K, Takano A, et al. Phylogenies from mitochondrial genomes of 120 species of ticks: insights into the evolution of the families of ticks and of the genus Amblyomma. Ticks Tick Borne Dis. (2021) 12:101577. doi: 10.1016/j.ttbdis.2020.101577
52. Burger G, Gray MW, Lang BF. Mitochondrial genomes: anything goes. Trends Genet TIG. (2003) 19:709–16. doi: 10.1016/j.tig.2003.10.012
53. Lu XY, Zhang QF, Jiang DD, Wang T, Sun Y, Du CH, et al. Characterization of the complete mitochondrial genome of Haemaphysalis (Alloceraea) kolonini (Ixodidae) and its phylogenetic implications. Parasitol Res. (2022) 121:1951–62. doi: 10.1007/s00436-022-07535-2
54. Duan DY, Chen Z, Fu YT, Liu GH, Suleman, Cheng TY. Characterization of the complete mitochondrial genomes of two Ixodes ticks, I. nipponensis and Ixodes (Pholeoixodes) sp. Med Vet Entomol. (2021) 35:513–22. doi: 10.1111/mve.12523
55. Wang T, Zhang S, Pei T, Yu Z, Liu J. Tick mitochondrial genomes: structural characteristics and phylogenetic implications. Parasit Vectors. (2019) 12:451. doi: 10.1186/s13071-019-3705-3
56. Li M, Liu M, Hu SY, Luo FZ, Yuan ML. Comparative mitogenomic analyses provide evolutionary insights into the retrolateral tibial apophysis clade (Araneae: Entelegynae). Front Genet. (2022) 13:974084. doi: 10.3389/fgene.2022.974084
57. Chen L, Wahlberg N, Liao CQ, Wang CB, Ma FZ, Huang GH, et al. Fourteen complete mitochondrial genomes of butterflies from the genus Lethe (Lepidoptera, Nymphalidae, Satyrinae) with mitogenome-based phylogenetic analysis. Genomics. (2020) 112:4435–41. doi: 10.1016/j.ygeno.2020.07.042
58. Wilson K, Cahill V, Ballment E, Benzie J. The complete sequence of the mitochondrial genome of the crustacean Penaeus monodon: are malacostracan crustaceans more closely related to insects than to branchiopods? Mol Biol Evol. (2000) 17:863–74. doi: 10.1093/oxfordjournals.molbev.a026366
59. Ojala D, Montoya J, Attardi G. tRNA punctuation model of RNA processing in human mitochondria. Nature. (1981) 290:470–4. doi: 10.1038/290470a0
60. Nekrutenko A, Makova KD, Li WH. The K(A)/K(S) ratio test for assessing the protein-coding potential of genomic regions: an empirical and simulation study. Genome Res. (2002) 12:198–202. doi: 10.1101/gr.200901
61. Zhang M, Nie X, Cao T, Wang J, Li T, Zhang X, et al. The complete mitochondrial genome of the butterfly Apatura metis (Lepidoptera: Nymphalidae). Mol Biol Rep. (2012) 39:6529–36. doi: 10.1007/s11033-012-1481-7
62. Wolstenholme DR. Animal mitochondrial DNA: structure and evolution. Int Rev Cytol. (1992) 141:173–216. doi: 10.1016/S0074-7696(08)62066-5
63. Gissi C, Iannelli F, Pesole G. Evolution of the mitochondrial genome of Metazoa as exemplified by comparison of congeneric species. Heredity. (2008) 101:301–20. doi: 10.1038/hdy.2008.62
64. Dermauw W, Vanholme B, Tirry L, Van Leeuwen T. Mitochondrial genome analysis of the predatory mite Phytoseiulus persimilis and a revisit of the Metaseiulus occidentalis mitochondrial genome. Genome. (2010) 53:285–301. doi: 10.1139/G10-004
65. Beckenbach AT. Mitochondrial genome sequences of Nematocera (lower Diptera): evidence of rearrangement following a complete genome duplication in a winter crane fly. Genome Biol Evol. (2012) 4:89–101. doi: 10.1093/gbe/evr131
66. Montagna M, Sassera D, Griggio F, Epis S, Bandi C, Gissi C, et al. Tick-box for3′-end formation of mitochondrial transcripts in Ixodida, basal chelicerates and Drosophila. PLoS ONE. (2012) 7:e47538. doi: 10.1371/journal.pone.0047538
67. Murrell A, Campbell NJ, Barker SC. A total-evidence phylogeny of ticks provides insights into the evolution of life cycles and biogeography. Mol Phylogenet Evol. (2001) 21:244–58. doi: 10.1006/mpev.2001.1018
68. Burger TD, Shao R, Barker SC. Phylogenetic analysis of mitochondrial genome sequences indicates that the cattle tick, Rhipicephalus (Boophilus) microplus, contains a cryptic species. Mol Phylogenet Evol. (2014) 76:241–53. doi: 10.1016/j.ympev.2014.03.017
69. Barker SC, Murrell A. Systematics and evolution of ticks with a list of valid genus and species names. Parasitology. (2004) 129:S15–36. doi: 10.1017/S0031182004005207
70. Mohamed MB, Alanoud TA, Laila AAS, Ahlam FM. Efficacy and comparative toxicity of phytochemical compounds extracted from aromatic perennial trees and herbs against vector borne Culex pipiens (Diptera: Culicidae) and Hyalomma dromedarii (Acari: Ixodidae) as Green Insecticides. Pak Vet J. (2024) 44:55. doi: 10.29261/pakvetj/2024.144
71. Muhammad Y, Kamran A, Muhammad IR, Muhammad I, Muhammad S, Tahir AC, et al. Expression and purification of recombinant multi-epitope protein of Rhipicephalus microplus tick and its antigenicity in rabbits. Pak Vet J. (2023) 43:778. doi: 10.29261/pakvetj/2023.086
Keywords: characterization, tick, evolution, mitogenomes, phylogeny, genome annotation
Citation: Liu S, Hou R, Zhang Q, Duan M, Tang S, Wang W, Chen J, Wu J, Shen Y, Gu W, Sun Y and Yang X (2026) Comparative mitogenomic and phylogenetic insights from four newly sequenced tick mitochondrial genomes. Front. Vet. Sci. 12:1678349. doi: 10.3389/fvets.2025.1678349
Received: 02 August 2025; Revised: 25 November 2025;
Accepted: 22 December 2025; Published: 22 January 2026.
Edited by:
Hongbin Yan, Chinese Academy of Agricultural Sciences, ChinaReviewed by:
Mourad Ben Said, University of Manouba, TunisiaQiaocheng Chang, Shantou University, China
Warda Qamar, University of Agriculture, Pakistan
Copyright © 2026 Liu, Hou, Zhang, Duan, Tang, Wang, Chen, Wu, Shen, Gu, Sun and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yujuan Shen, c2hlbnlqQG5pcGQuY2hpbmFjZGMuY24=; Wei Gu, Z3c3NzdAMTYzLmNvbQ==; Yi Sun, c3VueWk3MzEwQHNpbmEuY29t; Xing Yang, eWFuZzA4MjIwMDEzQDE2My5jb20=
†These authors have contributed equally to this work and share first authorship