Microbial Composition of Haemaphysalis longicornis in Shaanxi Province, Determined Through Next-Generation Sequencing

Abstract

Background:

Ticks are parasitic organisms that can transmit a wide range of pathogenic microorganisms. They are the second most common vectors of diseases affecting humans and animals. The occurrence and transmission of tick-borne diseases has recently shown increasing or fluctuating trends.

Methods:

DNA was extracted after the collection of tick samples. A library targeting the V4 hypervariable region of the 16S rRNA gene was constructed. After quality control, next-generation sequencing was performed with the Illumina NextSeq platform to analyze microbial diversity within the ticks.

Results:

Samples were gathered between May 2023 and July 2023. A total of 77 ticks from five distinct regions were selected for next-generation sequencing. Molecular identification confirmed that all sequenced samples belonged to Haemaphysalis longicornis. The most abundant bacteria belonged to the phylum Proteobacteria, which was present in all samples. Variations in sample richness and evenness were observed among sampling sites (Shannon index, P = 0.019). The bacterial diversity in LT exhibited the highest value, with an average of 2.449. Rickettsia and Coxiella were the predominant bacterial species, both of which are classified as tick-borne pathogens. The linear discriminant analysis effect size revealed significant differences in microbial composition among groups, except for the PC and LY groups, and identified distinct biomarkers for each group.

Conclusions:

Our findings indicated the high relative abundance of both pathogenic bacteria and non-pathogenic endosymbionts in H. longicornis and the potential for pathogen transmission to residents. However, further validation through human case studies is necessary. Health care providers should be aware of the possibility of the occurrence of these diseases.

Main article text

BACKGROUND

Emerging infectious diseases (EIDs) pose substantial economic and health challenges, and therefore are a key focus of public health research [1,2]. In humans, 75% of EIDs are believed to have originated in animals [3,4]. Over the past seven decades, more than half of all EIDs have been attributed to viral zoonotic diseases associated with human agricultural activities. Ticks, belonging to the class Arachnida and subclass Acari, are obligate parasites that must feed on blood at all stages of their life cycle, and can transmit a wide range of pathogenic microorganisms, protozoa, and viruses [5]. Ticks are the second most common vectors for pathogens causing human disease [6]. The prevalence and transmission of tick-borne infections have shown increasing or fluctuating trends in recent decades, which have been associated with a rise in human-tick interactions [7–9].

A variety of microbial communities are found in ticks, encompassing pathogens and obligate endosymbionts [10]. Ticks have been found to carry as many as 167 pathogens, some of which have the potential to induce natural focal diseases and zoonoses [11]. The composition of microorganisms can either inhibit or facilitate the growth of pathogens [12]. Nader’s research has revealed that the sex of ticks affects pathogen transmission [13]. The microbiota of ticks can vary by sex and geographical regions [14,15]. These microbial communities can affect the colonization and spread of tick-borne pathogens (TBPs), thus affecting the ability of the vectors to successfully spread pathogens [16–18]. The microbiota is associated with host susceptibility to systemic viral infection and disease outcomes. Adegoke et al. have shown that tick-borne pathogens can establish infection by interacting with the core microbiome of tick vectors [19]. Wu-Chuang et al. have found that an anti-microbial vaccine can be used as a tool to perturb and control important vector-borne pathogens [20]. Moreover, frankenbacteriosis has been used to control tick pathogen infection [21].

The Qinling Mountains serve as an essential reservoir of biodiversity, a natural boundary, and a crucial ecological barrier [22]. In Shaanxi Province, which is known for carrying a wide range of TBPs, the predominant tick species is Haemaphysalis [23], primarily Haemaphysalis longicornis (H. longicornis), which poses substantial threats to public health and the livestock industry. H. longicornis is responsible for transmitting fever with thrombocytopenia syndrome, which has an average mortality rate of approximately 20% [24]. Consequently, additional research is imperative to enhance the prevention and treatment of diseases associated with these vectors.

In pathogen detection and surveillance, accurate and prompt detection are equally critical. Next-generation sequencing (NGS) enables the simultaneous sequencing of large quantities of nucleic acid molecules. This technology is widely used for the detection and identification of pathogens. By focusing on the hypervariable regions of bacterial 16S rRNA genes, particularly the V1-V4 region, bacterial taxa can be effectively identified [25]. The objective of this study was to investigate tick microbial diversity in Shaanxi Province.

MATERIALS AND METHODS

Tick collection

From May 2023 to July 2023, ticks collected from the environment and hosts were gathered from five designated sites in Chang’an District, Xi’an City (CA), Pucheng County, Weinan City (PC), Lantian County, Xi’an City (LT), Qishan County, Baoji City (QS), and Linyou County, Baoji City (LY) in Shaanxi Province. The tick collection methods comprised the flag laying method and animal surface-picking method. The collection process involved recording the date, location, host species, and other relevant details. Collected tick samples were transported to the laboratory in ice bags, photographed, packaged, and stored at a temperature of −80°C. Before collection, consent was obtained from all animal owners and farmers involved, after they had been informed of the study’s objectives and procedures.

Tick species identification

The collected ticks were initially immersed in 70% ethanol. After a decrease in their activity, the tick species were identified under a stereomicroscope (HIROX RH-2000, Japan). Identification of tick species was based on morphological characteristics such as the capitulum, scutum, and festoons. Tick species were identified with a polymerase chain reaction (PCR) method based on the cytochrome c oxidase I (COI) and 16S rDNA genes [26,27]. All sequences were compared against the NCBI GenBank with the Basic Local Alignment Search Tool (BLAST). The species were considered the same if their sequence similarity was 98% or higher.

Tick surface sterilization and DNA extraction

The ticks were immersed in Petri dishes containing 70% ethanol to eliminate fragments or microorganisms adhering to their surfaces. Subsequently, the ticks were air-dried on filter paper and subjected to extraction with an Ex-DNA/RNA nucleic acid extraction kit (TIANLONG Biotechnology, Xi’an, China). The concentration of nucleic acid was quantified with a Qubit 4.0 instrument (Thermo Fisher Scientific, Waltham, USA). The extracted samples were stored at −80°C until subsequent analysis.

PCR amplification of bacterial 16S rRNA

After DNA extraction, the V4 regions of the bacterial 16S rRNA gene were amplified through PCR with locus-specific primers, specifically 515F (5′ GTGYCAGCMGCCGCGGTAA 3′) and 806R (5′ GGACTACNVGGGTWTCTAAT 3′), thus producing a 292 bp fragment. This step ensured efficient amplification of the V4 regions. The amplification was performed in a 30 μL final volume containing 0.5 μL Ex Taq (Takara), 4 μL dNTP Mixture, 3 μL 10× buffer, 0.5 μL each of 10 μM forward and reverse primers, 5 μL DNA template, and 16.5 μL nuclease-free ddH₂O. The thermocycling protocol consisted of an initial denaturation step at 94°C for 5 minutes, followed by 30 cycles of denaturation at 95°C for 1 minute, annealing at 55°C for 1 minute, extension at 72°C for 1 minute, and a final extension at 72°C for 10 minutes.

Library construction of V4 regions

Construction of the bacterial 16S rRNA library for the selected V4 regions was achieved through two rounds of PCR. In the initial PCR round, the following locus-specific primers with appended adapters were used: forward overhang sequence–5′ TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG-[GTGYCAGCMGCCGCGGTAA] and reverse overhang sequence–5′ GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG-[GGACTACNVGGGTWTCTAAT]. The PCR reaction system and reaction conditions for the first round were the same as described above.

In the second PCR round, the double-ended adaptor was ligated to the amplicon PCR with a self-assembled reaction. Library quality was assessed with a QIAxcel DNA screening kit. The remaining primers and adaptors in PCR products were purified with Hieff NGS^® DNA Selection Beads (Yeasen Biotechnology, Shanghai, China). Quantification was performed with a Qubit 4.0 instrument. The constructed libraries meeting the quality control criteria were subjected to NGS.

Next-generation sequencing

After the normalization and pooling of the selected libraries according to Illumina’s suggested process, sequencing was performed with a NextSeq P2 Reagent Cartridge (Illumina, San Diego, CA, USA).

Sequence processing and data analysis

QIIME2 was used to process and evaluate the raw sequence data. To construct amplicon sequence variants (ASVs) matrix of samples, sequences were quality regulated with demultiplexing, quality filtering, denoising, and chimera removal with the Divisive Amplicon Denoising Algorithm 2 (DADA2). The reads for each sample were used to create an ASV table. By BLAST searching against the Silva reference dataset v138 for bacterial 16S rRNA genes with the default parameters plugin in QIIME2 v.2021.02, we determined the taxonomy of ASVs with RDP’s Naïve Bayesian Classifier. Samples with < 100 reads were deleted in quality control of the sequencing data. The annotated ASV relative abundance tables were exported into the R v3.6.2 program, which enabled generation of stacked bar plots and visualization of the relative abundance of bacterial taxa across samples.

Statistical analysis

The sampling point map was created with ArcGIS 10.8. The Shannon, Simpson, and Chao1 indexes were used to calculate the alpha diversity and evenness of the bacterial community analysis. Bray-Curtis pairwise distance matrices were generated to evaluate differences in beta diversity. A Bray-Curtis principal coordinate analysis (PCoA) graphic was created to demonstrate the relationships between the population and sampling points. To investigate the variations in bacterial composition across locations, and to determine whether substantial differences existed among locations, we conducted a similarity analysis (ANOSIM). The linear discriminant analysis effect size (LEfSe) was determined with ImageGP to identify the dominant strains [28]. R software version 3.6.2 was used for all statistical analyses. The results were considered statistically significant at P ≤ 0.05.

RESULTS

Tick species identification

A total of 7344 ticks were collected from five sampling points. The distribution of sampling points is shown in Fig 1. The results of morphological identification were consistent with the molecular identification. All selected ticks were H. longicornis, and showed 98.6%–99.8% similarity to reference sequences (KJ710087). A total of 77 samples were selected from five regions for sequencing. A total of 62 samples remained after removal of the samples not meeting the criteria. Table 1 shows only the samples meeting the sequencing quality standards.

FIGURE 1 |

Map of collection sites.

TABLE 1 |

Basic information on tick sampling points.

Location	GPS coordinates	Altitude	Sampling date	Host	Number
					Female	Male
Chang’an District, Xi’an City (CA)	34.026667°N, 108.967290°E	575 m	2023.05	Sheep	7	1
			2023.06	Sheep	3	1
			2023.06	-	0	7
Pucheng County, Weinan City (PC)	34.96124°N, 109.59296°E	423 m	2023.05	Cattle	3	4
			2023.06	Cattle	1	0
			2023.06	-	0	3
			2023.07	Cattle	2	0
			2023.07	-	1	0
Lantian County, Xi’an City (LT)	34.15128°N, 109.32339°E	469 m	2023.06	Sheep	13	1
Qishan County, Baoji City (QS)	34.508°N, 107.821°E	782.5 m	2023.05	Sheep	10	0
Linyou County, Baoji City (LY)	34.681°N, 107.747°E	1046.6 m	2023.05	Sheep	9	0
			2023.05	-	0	2

“-” indicates host-seeking ticks.

Bacterial community diversity

With the Illumina Nextseq platform, the sequencing of the amplicons of the V4 hypervariable regions of the bacterial 16S rRNA for the tick samples yielded 1,132,919 raw reads (average: 18,273). After quality filtering, the samples from CA, PC, LT, LY, and QS had average reads of 7,041, 6,245, 8,235, 4,570, and 8,247, respectively (Table 2) (Kruskal-Wallis H-test, H = 4.572, df = 4, P = 0.334). In comparison to male ticks, which had an average of 6,973 sequences, female ticks had an average of 6,924 filtered reads (Mann-Whitney U = 335.5, Z = 0.294, P = 0.769).

TABLE 2 |

Numbers of reads obtained from NGS and alpha diversity.

Location	Code	Sequence number		Alpha diversity
		Raw reads	Reads after quality filtering	Shannon index	Simpson index	Chao1 index
Chang’an District, Xi’an City (CA)	CA1	5,907	3,414	1.704	0.599	5
	CA2	26,731	14,102	1.692	0.472	15
	CA3	3,869	3,307	1.958	0.683	7
	CA4	37,934	27,338	2.809	0.737	22
	CA5	5,732	4,356	2.34	0.688	11
	CA6	38,768	8,532	2.448	0.657	16
	CA7	33,744	5,317	2.707	0.772	16
	CA8	23,663	3,955	2.841	0.759	22
	CA9	10,996	1,072	2.208	0.657	10
	CA10	9,957	579	2.186	0.750	6
	CA11	10,103	1,326	2.417	0.723	12
	CA12	22,787	10,604	1.415	0.431	18
	CA13	46,718	7,630	2.762	0.767	20
	x̅ ± s	21,301 ± 14,616	7,041 ± 7,268	2.268 ± 0.466	0.669 ± 0.109	13.846 ± 5.857
Pucheng County, Weinan City (PC)	PC1	12,466	4,205	2.151	0.704	9
	PC2	30,766	19,305	2.013	0.659	9
	PC3	25,822	22,386	1.495	0.387	14
	PC4	10,275	7,015	3.356	0.867	15
	PC5	6,895	5,358	1.114	0.322	7
	PC6	8,169	5,728	1.757	0.595	7
	PC7	29,830	3,052	2.813	0.762	14
	PC8	5,231	372	2.381	0.794	6
	PC9	22,448	2,259	3.165	0.833	16
	PC10	25,274	2,025	2.479	0.780	9
	PC11	7,591	2,049	1.652	0.577	6
	PC12	31,950	9,829	2.228	0.702	15
	PC13	30,643	3,745	2.458	0.689	15
	PC14	475	109	0.806	0.372	2
	x̅ ± s	17,703 ± 11,364	6,245 ± 6,735	2.133 ± 0.729	0.646 ± 0.175	10.286 ± 4.462
Lantian County, Xi’an City (LT)	LT1	18,448	12,104	3.148	0.843	17
	LT2	18,757	14,777	1.761	0.467	13
	LT3	33,988	27,316	2.740	0.756	20
	LT4	9,516	7,795	1.440	0.421	8
	LT5	29,383	6,638	2.258	0.721	10
	LT6	38,138	16,319	1.506	0.462	14
	LT7	17,682	1,233	2.411	0.761	10
	LT8	30,950	13,377	1.397	0.361	19
	LT9	14,999	3,297	3.211	0.859	15
	LT10	21,199	4,642	2.802	0.803	13
	LT11	15,743	1,752	3.521	0.890	19
	LT12	25,323	3,557	3.115	0.825	22
	LT13	11,130	1,401	1.855	0.653	9
	LT14	11,356	1,087	3.118	0.868	12
	x̅ ± s	21,187 ± 9,023	8,235 ± 7,668	2.449 ± 0.744	0.692 ± 0.186	14.357 ± 4.448
Qishan County, Baoji City (QS)	QS1	10,326	9,102	1.575	0.578	5
	QS2	9,140	4,823	2.387	0.769	8
	QS3	17,328	15,238	1.884	0.669	8
	QS4	21,483	17,746	2.11	0.692	13
	QS5	16,826	5,328	1.935	0.652	8
	QS6	25,432	5,332	2.393	0.719	13
	QS7	9,006	1,907	1.418	0.466	6
	QS8	21,289	9,969	1.124	0.350	8
	QS9	21,711	7,791	1.171	0.332	12
	QS10	18,139	5,234	0.794	0.252	5
	x̅ ± s	17,068 ± 5,806	8,247 ± 4,956	1.679 ± 0.551	0.548 ± 0.184	8.600 ± 3.062
Linyou County, Baoji City (LY)	LY1	1,273	803	1.755	0.665	5
	LY2	1,914	1,062	0.791	0.269	4
	LY3	6,056	5,038	0.863	0.243	7
	LY4	2,056	1,582	1.595	0.629	5
	LY5	21,017	14,102	1.622	0.504	10
	LY6	29,984	6,598	2.259	0.706	9
	LY7	43,149	9,772	2.557	0.677	17
	LY8	22,246	7,895	1.917	0.663	13
	LY9	6,181	919	1.540	0.624	4
	LY10	2,432	1,350	1.077	0.342	5
	LY11	4,575	1,149	2.028	0.703	9
	x̅ ± s	12,808 ± 14,157	4,570 ± 4,518	1.637 ± 0.558	0.548 ± 0.179	8.000 ± 4.147

x̅ ± s: mean ± standard deviation.

Alpha diversity

A total of 711 ASVs were used for taxonomy assignment and grouping. The Shannon, Simpson, and Chao1 indexes were used to calculate the alpha diversity. The inter-group analysis demonstrated statistically significant differences between the Chao1 index (Kruskal-Wallis H-test, H = 16.058, df = 4, P = 0.003) and Shannon index (ANOVA, F _{(4, 57)} = 4.718, P = 0.002) but not the Simpson index (Kruskal-Wallis H-test, H = 8.996, df = 4, P = 0.061) (Fig 2). Bacterial diversity, assessed via the Shannon index, was notably higher in ticks collected at LT in Xi’an City (mean = 2.449) than the other groups (Table 2). The calculated mean Shannon diversity index ranged from 0.791 (LY2) to 3.521 (LT11), with a 95% confidence interval of 1.897 to 2.246. Inter-group comparison with the Shannon index revealed statistically significant differences between LT and QS (Tukey’s multiple comparisons test, mean difference = 0.972, P = 0.003), as well as between LT and LY (Tukey’s multiple comparisons test, mean difference = 0.815, P = 0.013).

FIGURE 2 |

Alpha diversity of bacterial microbiomes in different areas. *p < 0.05, **p < 0.01.

Beta diversity

PCoA uses the Bray-Curtis species distance to find the principal coordinates. We primarily assessed variations across sampling locations, lifestyle practices, tick sexes, and host types. The results indicated no statistically significant variations among lifestyle habits (adonis R² = 0.02, P = 0.096) (Fig 3B), sexes (adonis R² = 0.02, P = 0.477) (Fig 3C), and hosts (adonis R² = 0.04, P = 0.481) (Fig 3D). The effect of microbial composition among sites was statistically significant, with axis 1 contributing 20.8% and axis 2 contributing 13.8% (adonis R² = 0.27, P < 0.001) (Fig 3A).

FIGURE 3 |

Bray-Curtis principal coordinate analysis plot: A) differences among sampling sites; B) differences among tick types; C) differences between sexes; D) differences among hosts.

ANOSIM indicated a low R-value (0.164) for the dissimilarity between bacterial compositions of ticks from diverse sampling sites, with statistical significance (P = 0.001) (Fig 4).

FIGURE 4 |

ANOSIM analysis across sampling points.

Taxonomic composition of the bacterial community

Bacterial sequences were taxonomically classified with the SILVA database. After denoising, 14 phyla were identified, including one unclassified phylum. A boxplot of taxa revealed that Proteobacteria was the dominant phylum, with an average relative abundance of 81.0%, and was present in all samples (Fig 5A). Firmicutes (16.0%) was detected in 57 tick samples, whereas an unclassified phylum was found in 41 tick samples, constituting 2.2% of the bacterial composition. In contrast, Fusobacteriota, Actinobacteriota, Cyanobacteria, Synergistota, Chloroflexi, Patescibacteria, Planctomycetota, and Verrucomicrobiota, were rarely detected in these samples. The overall relative abundance of these rarely detected bacteria is less than 1%. The CA group had the highest relative abundance, with 12 annotated bacterial phyla, whereas the LY group had the lowest, with only six identified phyla.

FIGURE 5 |

A) Relative abundance of bacterial communities at the phylum level. B) Top ten most abundant bacterial genera identified in tick samples.

After denoising, 98 genera (including one unidentified genus) were observed. A boxplot of taxa indicated Rickettsia as the dominant genus (Fig 5B), accounting for 35.6% of the average relative abundance (Table 3). Rickettsia was detected in 82.3% (51/62) of H. longicornis, and was the dominant species in 26 samples. Both Rickettsia and Coxiella are TBPs of public health importance. Coxiella was present in 58 of 62 H. longicornis samples, and was the dominant species in 19 samples, with an average relative abundance of 27.4%. Coxiella was predominant in the CA and LY groups, whereas Rickettsia was more prevalent in the PC, LT, and QS groups. Acidovorax was detected in 50 H. longicornis samples, with an average relative abundance of 10.3%; further investigation is necessary to ascertain its pathogenicity.

TABLE 3 |

Total relative abundance of common bacterial communities.

	Samples with relative abundance (%)					Average relative abundance (%)
	CA	PC	LT	LY	QS
Rickettsia	11.36	30.72	42.95	59.84	23.27	35.59
Coxiella	46.41	29.33	15.90	15.60	38.88	27.40
Acidovorax	20.71	7.09	9.25	4.77	9.93	10.26
Staphylococcus	4.05	11.46	3.39	13.88	8.29	7.93
Leuconostoc	0.00	0.91	11.01	0.00	0.00	3.10
Acinetobacter	1.17	4.84	3.84	1.10	4.43	2.95
Unassigned	1.95	6.21	0.45	0.32	3.84	2.18
Streptococcus	1.86	1.62	2.50	2.52	0.00	1.88
Others	1.70	1.68	0.94	0.50	3.23	1.44

Notably, other TBPs, such as Bartonella and Anaplasma phagocytophilum, were not identified with NGS assays. Additionally, pathogenic bacteria including Staphylococcus, Streptococcus, Helicobacter, and Yersinia were present in the samples.

ASVs corresponding to Rickettsia were extracted and compared with the reference sequence (OR228882) in the NCBI GenBank Nucleotide database through BLAST analysis, thus revealing high sequence similarity ranging from 99.0% to 99.7%. Similarly, the ASVs associated with Coxiella exhibited notable similarity (98.6%–99.3%) to the reference sequence (MG682448).

ASVs were visualized with a Venn diagram to assess taxonomic diversity across all sampling locations (Fig 6). The analysis included examination of species at the genus level, including a total of 98 genera in five groups. Notably, the population from CA exhibited the highest diversity: 28 ASVs in CA possessed unique sequences, compared with ten ASVs in LT and nine ASVs in LY. In contrast, QS displayed the lowest diversity, with only four genera endemic to this population.

FIGURE 6 |

Variations among groups, illustrated through a Venn diagram.

Analysis of community differences among groups

LEfSe was used to examine the species exhibiting notable differences in relative abundance across various groups, thus serving as biomarkers. Only species displaying statistically significant variances are shown in the chart, according to a threshold LDA score > 2 and P < 0.05. The lengths of the bars in the chart represent the significance of the respective species. Except for the PC and LY groups, all other groups exhibited significant microbial populations, each characterized by distinct biomarkers. The CA group had five significant microorganisms, the LT group had nine, and the QS group had two (Fig 7B). The biomarkers for each group were identified as Gammaproteobacteria, Lactobacillales, and Rickettsiales, respectively (Fig 7A).

FIGURE 7 |

LEfSe analysis of the microbiota. A) Bar plot showing the degree of contribution of distinct species. B) Cladistic map illustrating the evolutionary classification from phylum to genus, progressing from the inner to the outer circle.

DISCUSSION

All samples were collected between May 2023 and July 2023. All 62 ticks meeting the inclusion criteria were identified as H. longicornis, the predominant tick species in Shaanxi Province, which is known for transmitting various pathogens including Rickettsia and Anaplasma. The 16S rRNA gene diversity spectrum consisted primarily of Proteobacteria (81.0%) and Firmicutes (16.0%). The tick species findings in this study aligned with those in previous research [29–31]. The Shannon index indicated variations in relative abundance across sampling sites in terms of richness and diversity (P = 0.002), in agreement with previous research [32,33]. Notably, the LT group exhibited the highest community diversity, with a mean of 2.449.

Six bacteria of medical importance were identified in the tick samples. The primary TBPs identified were Rickettsia and Coxiella. Rickettsia was the dominant strain in the PC, LT, and QS groups, whereas Coxiella was present in the CA and LY groups.

Rickettsia, a gram-negative bacterium, belongs to a large class of strictly intracellular prokaryotic microorganisms. This species parasitizes primarily arthropods, particularly ticks, which serve as both hosts and vectors [34]. These bacteria are responsible for diseases such as epidemic typhus, endemic typhus, tsutsugamushi disease, and other infectious diseases. Rickettsia can replicate within various organs of ticks and have a particular affinity for the salivary glands, thus facilitating transmission to animal hosts during blood meals.

Coxiella belongs to the order Legionellales and encompasses species such as Coxiella burnetii and Coxiella cheraxi. Coxiella burnetii is an obligate intracellular pathogen [35,36] responsible for both acute and chronic Q fever [37]. However, most existing studies have not distinguished between Coxiella spp. and the sub class of Coxiella [38]. Growing evidence of the association between Coxiella-like bacteria and ticks with different living habits and geographical boundaries underscores the need for further investigation [39]. Coxiella symbiotic bacteria and pathogenic bacteria are associated with human and animal diseases [40]. For example, Coxiella burnetii is the pathogen responsible for Q fever. Coxiella regulates tick biology, affects blood intake, and arrests tick development [41]; Coxiella-like endosymbionts are involved in the biosynthesis of vitamins and cofactors, thereby maintaining the symbiotic relationship between the pathogen and its host [42,43]. Consequently, additional research is imperative to elucidate the pathogenic potential of Coxiella endosymbionts.

Our findings revealed that the predominant bacterial species were Rickettsia and Coxiella, which showed relative abundance distributions of 35.6% and 27.4%, respectively. These findings suggested potential co-occurrence of Rickettsia and Coxiella within tick hosts. Co-infection with Rickettsia spp. and Coxiella spp. was observed in 48 samples (77.4%), thus indicating a higher likelihood of ticks being infected with additional bacteria after a prior infection with one bacterium [44].

The tick microbial community comprises TBPs and endosymbionts. Commensal bacteria facilitate an environment conducive to the colonization of TBPs [45]. Moreover, TBPs can actively modulate the microbiome [46]. Higher counts of other bacteria can diminish the sensitivity of TBP detection. Furthermore, co-infection of ticks with multiple pathogens may aggravate the clinical complexity of diseases [47,48].

Bacterial relative abundance in ticks is influenced by various factors, including the host of the ticks [49], sampling position [50], life stage, and feeding versus questing tick populations [51]. The microorganisms found in the surrounding vegetation or soil are likely to colonize ticks when they detach from their vertebrate hosts. Tick cleaning also affects the microbial communities within ticks [52]. Determining whether these environmental microorganisms constitute a component of the tick’s internal microbiota or are associated with the microbiome present on its cuticle will be essential. For instance, Acinetobacter spp. occupy diverse environments, and some are known to be pathogenic, whereas others are considered commensal and part of the normal flora in animals [53].

Several bacterial genera remained unidentified, accounting for 2.2% of the average relative abundance. This finding underscores their potential as candidates for further investigation of previously unstudied bacteria. Moreover, the lack of further use of PCR to detect specific pathogen classifications is also a limitation of this study.

CONCLUSION

This research characterized the microbiomes of ticks collected in Shaanxi Province. The findings indicated high relative abundance of pathogenic bacteria alongside nonpathogenic endosymbionts in H. longicornis, thus suggesting a potential for pathogen transmission to residents. However, this possibility must be confirmed in human cases. Health care providers must be aware of the possibility of the occurrence of these diseases. Additional investigations are warranted to elucidate the involvement of bacteria and underlying pathogenic mechanisms, to manage ticks and TBDs in Shaanxi Province.

CONFLICTs OF INTEREST

The authors declared no conflict of interest.

REFERENCES

1. Hao R, Liu Y, Shen W, Zhao R, Jiang B, Song H, et al.. Surveillance of emerging infectious diseases for biosecurity. Sci China Life Sci. 2022. Vol. 65(8):1504–1516

2. McArthur DB. Emerging infectious diseases. Nurs Clin North Am. 2019. Vol. 54(2):297–311

3. Cui M, Shen B, Fu ZF, Chen H. Animal diseases and human future. Anim Dis. 2022. Vol. 2(1):6

4. Shaheen MNF. The concept of one health applied to the problem of zoonotic diseases. Rev Med Virol. 2022. Vol. 32(4):e2326

5. Estrada-Peña A, de la Fuente J. The ecology of ticks and epidemiology of tick-borne viral diseases. Antiviral Res. 2014. Vol. 108:104–128

6. Kernif T, Leulmi H, Raoult D, Parola P. Emerging tick-borne bacterial pathogens. Microbiol Spectr. 2016. Vol. 4(3):EI10-0012-2016

7. Tijsse-Klasen E, Koopmans MP, Sprong H. Tick-borne pathogen - reversed and conventional discovery of disease. Front Public Health. 2014. Vol. 2:73

8. Rodino KG, Theel ES, Pritt BS. Tick-borne diseases in the United States. Clin Chem. 2020. Vol. 66(4):537–548

9. Sonenshine DE. Range expansion of tick disease vectors in North America: implications for spread of tick-borne disease. Int J Environ Res Public Health. 2018. Vol. 15(3):478

10. Narasimhan S, Fikrig E. Tick microbiome: the force within. Trends Parasitol. 2015. Vol. 31(7):315–323

11. Wei W, Mengchao Z, Yaxian L, et al.. Tick-borne pathogens and public health and safety risks. Heilongjiang Med J. 2024. Vol. 48(03):371–375

12. Perveen N, Muzaffar SB, Vijayan R, Al-Deeb MA. Microbial communities associated with the camel tick, Hyalomma dromedarii: 16S rRNA gene-based analysis. Sci Rep. 2020. Vol. 10(1):17035

13. Nader J, Król N, Pfeffer M, Ohlendorf V, Marklewitz M, Drosten C, et al.. The diversity of tick-borne bacteria and parasites in ticks collected from the Strandja Nature Park in south-eastern Bulgaria. Parasit Vectors. 2018. Vol. 11(1):165

14. Batool M, Blazier JC, Rogovska YV, Wang J, Liu S, Nebogatkin IV, et al.. Metagenomic analysis of individually analyzed ticks from Eastern Europe demonstrates regional and sex-dependent differences in the microbiota of Ixodes ricinus. Ticks Tick Borne Dis. 2021. Vol. 12(5):101768

15. Lau ACC, Mohamed WMA, Nakao R, Onuma M, Qiu Y, Nakajima N, et al.. The dynamics of the microbiome in Ixodidae are shaped by tick ontogeny and pathogens in Sarawak, Malaysian Borneo. Microb Genom. 2023. Vol. 9(2):mgen000954

16. Bonnet SI, Binetruy F, Hernández-Jarguín AM, Duron O. The tick microbiome: why non-pathogenic microorganisms matter in tick biology and pathogen transmission. Front Cell Infect Microbiol. 2017. Vol. 7:236

17. Adegoke A, Kumar D, Bobo C, Rashid MI, Durrani AZ, Sajid MS, et al.. Tick-borne pathogens shape the native microbiome within tick vectors. Microorganisms. 2020. Vol. 8(9):1299

18. Lejal E, Chiquet J, Aubert J, Robin S, Estrada-Peña A, Rue O, et al.. Temporal patterns in Ixodes ricinus microbial communities: an insight into tick-borne microbe interactions. Microbiome. 2021. Vol. 9(1):153

19. Adegoke A, Kumar D, Budachetri K, Karim S. Hematophagy and tick-borne Rickettsial pathogen shape the microbial community structure and predicted functions within the tick vector, Amblyomma maculatum . Front Cell Infect Microbiol. 2022. Vol. 12:1037387

20. Wu-Chuang A, Mateos-Hernandez L, Maitre A, Rego ROM, Šíma R, Porcelli S, et al.. Microbiota perturbation by anti-microbiota vaccine reduces the colonization of Borrelia afzelii in Ixodes ricinus. Microbiome. 2023. Vol. 11(1):151

21. de la Fuente J, Mazuecos L, Contreras M. Innovative approaches for the control of ticks and tick-borne diseases. Ticks Tick Borne Dis. 2023. Vol. 14(6):102227

22. Huang M, Duan R, Wang S, Wang Z, Fan W. Species presence frequency and diversity in different patch types along an altitudinal gradient: Larix chinensis Beissn in Qinling Mountains (China). PeerJ. 2016. Vol. 4:e1803

23. Ting-ting F, Xin-ge B, Fang T, et al.. Investigation on vector tick and tick-borne pathogens in sheep in some regions of Shaanxi and Liaoning province. Chin J Vet Med. 2023. Vol. 59(06):1–8

24. Seo JW, Kim D, Yun N, Kim DM. Clinical update of severe fever with thrombocytopenia syndrome. Viruses. 2021. Vol. 13(7):1213

25. Greay TL, Gofton AW, Paparini A, Ryan UM, Oskam CL, Irwin PJ. Recent insights into the tick microbiome gained through next-generation sequencing. Parasit Vectors. 2018. Vol. 11(1):12

26. Chen Z, Li Y, Ren Q, Luo J, Liu Z, Zhou X, et al.. Dermacentor everestianus Hirst, 1926 (Acari: Ixodidae): phylogenetic status inferred from molecular characteristics. Parasitol Res. 2014. Vol. 113(10):3773–3779

27. Lv J, Wu S, Zhang Y, Zhang T, Feng C, Jia G, et al.. Development of a DNA barcoding system for the Ixodida (Acari: Ixodida). Mitochondrial DNA. 2014. Vol. 25(2):142–149

28. Chen T, Liu Y-X, Huang L. ImageGP: an easy-to-use data visualization web server for scientific researchers. Imeta. 2022. Vol. 1(1):e5

29. Carvajal-Agudelo JD, Ramírez-Chaves HE, Ossa-López PA, Rivera-Páez FA. Bacteria related to tick-borne pathogen assemblages in Ornithodoros cf. hasei (Acari: Argasidae) and blood of the wild mammal hosts in the Orinoquia region, Colombia. Exp Appl Acarol. 2022. Vol. 87(2-3):253–271

30. Grandi G, Chiappa G, Ullman K, Lindgren PE, Olivieri E, Sassera D, et al.. Characterization of the bacterial microbiome of Swedish ticks through 16S rRNA amplicon sequencing of whole ticks and of individual tick organs. Parasit Vectors. 2023. Vol. 16(1):39

31. Che Lah EF, Ahamad M, Dmitry A, Md-Zain BM, Yaakop S. Metagenomic profile of the bacterial communities associated with Ixodes granulatus (Acari: Ixodidae): a potential vector of tick-borne diseases. J Med Entomol. 2023. Vol. 60(4):753–768

32. Beard D, Stannard HJ, Old JM. Morphological identification of ticks and molecular detection of tick-borne pathogens from bare-nosed wombats (Vombatus ursinus). Parasit Vectors. 2021. Vol. 14(1):60

33. Perveen N, Muzaffar SB, Vijayan R, Al-Deeb MA. Microbial composition in Hyalomma anatolicum collected from livestock in the United Arab Emirates using next-generation sequencing. Parasit Vectors. 2022. Vol. 15(1):30

34. Merhej V, Angelakis E, Socolovschi C, Raoult D. Genotyping, evolution and epidemiological findings of Rickettsia species. Infect Genet Evol. 2014. Vol. 25:122–137

35. Shaw EI, Voth DE. Coxiella burnetii: a pathogenic intracellular acidophile. Microbiology (Reading, England). 2019. Vol. 165(1):1–3

36. Siadous FA, Cantet F, Van Schaik E, Burette M, Allombert J, Lakhani A, et al.. Coxiella effector protein CvpF subverts RAB26-dependent autophagy to promote vacuole biogenesis and virulence. Autophagy. 2021. Vol. 17(3):706–722

37. Shipman M, Lubick K, Fouchard D, Gurram R, Grieco P, Jutila M, et al.. Proteomic and systems biology analysis of the monocyte response to Coxiella burnetii infection. PLoS One. 2013. Vol. 8(8):e69558

38. Yessinou RE, Katja MS, Heinrich N, Farougou S. Prevalence of Coxiella-infections in ticks - review and meta-analysis. Ticks Tick Borne Dis. 2022. Vol. 13(3):101926

39. Khoo JJ, Lim FS, Chen F, Phoon WH, Khor CS, Pike BL, et al.. Coxiella detection in ticks from wildlife and livestock in Malaysia. Vector Borne Zoonotic Dis. 2016. Vol. 16(12):744–751

40. Guizzo MG, Tirloni L, Gonzalez SA, Farber MD, Braz G, Parizi LF, et al.. Coxiella endosymbiont of Rhipicephalus microplus modulates tick physiology with a major impact in blood feeding capacity. Front Microbiol. 2022. Vol. 13:868575

41. Brenner AE, Muñoz-Leal S, Sachan M, Labruna MB, Raghavan R. Coxiella burnetii and related tick endosymbionts evolved from pathogenic ancestors. Genome Biol Evol. 2021. Vol. 13(7):evab108

42. Hirunkanokpun S, Ahantarig A, Baimai V, Pramual P, Rakthong P, Trinachartvanit W. Correction to: spotted fever group Rickettsia, Anaplasma and Coxiella-like endosymbiont in Haemaphysalis ticks from mammals in Thailand. Vet Res Commun. 2023. Vol. 47(1):321

43. Smith TA, Driscoll T, Gillespie JJ, Raghavan R. A Coxiella-like endosymbiont is a potential vitamin source for the Lone Star tick. Genome Biol Evol. 2015. Vol. 7(3):831–838

44. Hersh MH, Ostfeld RS, McHenry DJ, Tibbetts M, Brunner JL, Killilea ME, et al.. Co-infection of blacklegged ticks with Babesia microti and Borrelia burgdorferi is higher than expected and acquired from small mammal hosts. PLoS One. 2014. Vol. 9(6):e99348

45. Mukhacheva TA, Kovalev SY. Bacteria of the family ‘Candidatus Midichloriaceae’ in sympatric zones of Ixodes ticks: genetic evidence for vertical transmission. Microb Ecol. 2017. Vol. 74(1):185–193

46. Abraham NM, Liu L, Jutras BL, Yadav AK, Narasimhan S, Gopalakrishnan V, et al.. Pathogen-mediated manipulation of arthropod microbiota to promote infection. Proc Natl Acad Sci U S A. 2017. Vol. 114(5):E781–E790

47. Wikel SK. Ticks and tick-borne infections: complex ecology, agents, and host interactions. Vet Sci. 2018. Vol. 5(2):60

48. Zhang L, Han J, Zhou Q, He Z, Sun SW, Li R, et al.. Differential microbial composition in parasitic vs. questing ticks based on 16S next-generation sequencing. Front Microbiol. 2023. Vol. 14:1264939

49. Couper LI, Kwan JY, Ma J, Swei A. Drivers and patterns of microbial community assembly in a Lyme disease vector. Ecol Evol. 2019. Vol. 9(13):7768–7779

50. Carpi G, Cagnacci F, Wittekindt NE, Zhao F, Qi J, Tomsho LP, et al.. Metagenomic profile of the bacterial communities associated with Ixodes ricinus ticks. PLoS One. 2011. Vol. 6(10):e25604

51. Thapa S, Zhang Y, Allen MS. Bacterial microbiomes of Ixodes scapularis ticks collected from Massachusetts and Texas, USA. BMC Microbiol. 2019. Vol. 19(1):138

52. Binetruy F, Dupraz M, Buysse M, Duron O. Surface sterilization methods impact measures of internal microbial diversity in ticks. Parasit Vectors. 2019. Vol. 12(1):268

53. van der Kolk JH, Endimiani A, Graubner C, Gerber V, Perreten V. Acinetobacter in veterinary medicine, with an emphasis on Acinetobacter baumannii. J Glob Antimicrob Resist. 2019. Vol. 16:59–71