Metagenomic Analysis of Tick-Borne Viruses in Hulunbuir, Inner Mongolia, China: Epidemiological Risk of Potential Novel Pathogenic Viruses Relevant to Public Health

Abstract

Objective:

Inner Mongolia, with its vast grasslands providing ideal habitats for ticks, has recently emerged as a hotspot of novel tick-borne virus (TBV) prevalence. However, the diversity of TBVs and their potential spillover risks remain to be elucidated.

Methods:

In 2021, we collected 500 Haemaphysalis longicornis, Ixodes persulcatus, and Dermacentor silvarum ticks from Hulunbuir, Inner Mongolia, and conducted virome analysis and viral surveillance to evaluate TBV exposure risk in Hulunbuir.

Results:

A total of 26 viral species belonging to nine families were identified, including four novel viruses (Yiliekede tick virus 1 [YLTV1, Rhabdoviridae], Meitian tick virus [MtTV, Phenuiviridae], Wunuer pestivirus, and Wunuer tick phlebovirus) and 11 new strains of human-pathogenic viruses (including Songling virus [SGLV], Beiji nairovirus [BJNV], and Nuomin virus [NOMV]), from three dominant tick species. Virome profiling revealed striking species-specific patterns: I. persulcatus exhibited the highest viral richness and abundance, dominated by Chuviridae (NOMV) and Phenuiviridae; D. silvarum uniquely carried Phenuiviridae (MtTV); and H. longicornis showed Nairoviridae predominance (SGLV, BJNV). Phylogenetic analyses demonstrated that MtTV clustered with human-pathogenic Tacheng tick virus 2, and NOMV-YL strains showed high similarity to clinical NOMV isolates from febrile patients. Regional surveillance demonstrated MtTV’s pan-regional circulation in D. silvarum across nine locations (MIR=4–8%), contrasting with localized Yiliekede foci for SGLV (MIR=0.6%), BJNV (0.4%), and NOMV (1.0%).

Conclusions:

This study not only mapped the landscape of TBVs in a key pastoral region, but also highlighted the potential spillover risk of TBVs, including MtTV, YLTV1, SGLV, BJNV, and NOMV. Our findings underscore the urgent need for One Health strategies for monitoring emerging TBVs at the tick-human-livestock interface in northern China.

Main article text

INTRODUCTION

The rapid emergence and spread of severe infections with viruses including Zika virus [1], dengue virus [2], yellow fever virus [3], and Crimean–Congo hemorrhagic fever virus (CCHFV) [4] have highlighted the vulnerability of global populations to the possible health and economic consequences of vector-borne viruses. Ticks are an important vector transmitting viral pathogens associated with human and animal diseases, and playing critical roles in viral dissemination. Some of the most notoriously pathogenic TBVs, including CCHFV [4], tick-borne encephalitis virus (TBEV) [5], severe fever with thrombocytopenia syndrome virus (SFTSV) [6], Nairobi sheep disease virus (NSDV) [7], and African swine fever virus (ASFV) [8], have exerted notable public health and economic effects. Tick-borne viruses (TBVs) are increasingly emerging on a global scale, and posing significant threats to both human and animal health [9].

Inner Mongolia, a vast and diverse region in China, has recently emerged as a hotspot of novel TBVs, including Alongshan virus (ALSV) [10], Songling virus (SGLV) [11], Wetland virus (WELV) [12], Tacheng tick virus 2 (TcTV2) [13], and Beiji nariovirus (BJNV) [14]. The emergence of these TBVs is a growing global public health concern, because of their pathogenicity and potential to cause severe illness in humans. ALSV, a novel segmented Flavivirus, was first detected in patients with febrile illness in Inner Mongolia and Heilongjiang, and natural infections in livestock were observed in Hulunbuir, Inner Mongolia [10,15]. Similarly, the newly identified SGLV was isolated from patients in Yakeshi, Inner Mongolia in 2017 [11]. Moreover, re-emergence of globally circulating viruses such as CCHFV and TBEV has also been reported in Inner Mongolia [10,11,16,17]. CCHFV has been identified in Hyalomma spp. from the Alashan region in Inner Mongolia [16], and positive serological evidence in camels (58.3%) and sheep (6.7%) has suggested potential risk to the human population, although no recent human cases have been reported [17]. TBEV has been identified in circulation among ticks (Ixodes persulcatus and Dermacentor spp.) and humans in Inner Mongolia [18]. Therefore, Inner Mongolia might be at significant risk of TBV spread.

Beyond these known pathogenic viruses, unclassified viruses such as Bole tick virus (BLTV4), South Bay virus, Norwegian nairovirus 1, and Gakugsa tick virus have been found in Inner Mongolia [19–21]. These newly discovered viruses have prompted further concerns regarding potential public health threats, because their pathogenicity remains unknown. These discoveries underscore the need for continued surveillance and research, and highlight the complexity of the TBV landscape in Inner Mongolia.

The distribution of tick species in Inner Mongolia, particularly in the Hulunbuir region, is significant. Species of the genus Dermacentor, including D. nuttalli and D. silvarum, are distributed mainly in arid grassland areas suitable for grazing cattle and sheep [22]. Hulunbuir, an important pastoral area in Inner Mongolia known for its vast grasslands, is a notable livestock production area and an essential habitat for Dermacentor and Ixodes genera [22]. Several tick-borne pathogens reported in the Hulunbuir region pose severe threats to human and animal health.

In this study, we collected Dermacentor silvarum, Ixodes persulcatus, and Haemaphysalis longicornis tick samples across ten towns in the Inner Mongolia Autonomous Region, with the aim of investigating the diversity and ecological characteristics of TBVs. We identified two novel viruses, Yiliekede tick virus1 (YLTV1) and Meitian tick virus (MtTV), belonging to the Rhabdoviridae and Phenuiviridae families, respectively. Given the potential of these viruses to cause human infectious diseases, we conducted a survey of SGLV, BJNV, YLTV1, and MtTV Nuomin virus (NOMV) prevalence within the tick population, to assess the exposure risks in Hulunbuir region. This research not only markedly advances understanding of TBV transmission dynamics and potential health threats in northern China, but also provides essential insights for the development of effective prevention and control strategies in this region, which is a hotspot of TBV prevalence.

MATERIALS AND METHODS

Sample collection and tick species identification

A total of 500 ticks were collected from ten towns (Yiliekede, Wunuer, Meitian, Dayan, Mianduhe, Moguai, Muyuan, Tenihe, Yakeshi, and Zhaluomude) in Inner Mongolia Province, China, in 2021 (S1 Table and Fig 1). All ticks were collected from the tips of grass blades and stored on dry ice before transport to the laboratory, to ensure preservation of viral activity. After arrival at the laboratory, tick samples were directly preserved in a −80°C freezer for long-term storage. All ticks were identified morphologically according to species and life stage, with existing taxonomic keys and a Nikon SMZ445 stereomicroscope. After morphological assessment, selected tick specimens representing diverse areas, hosts, and species were subjected to molecular analysis. Each tick was individually rinsed with PBS, then homogenized in a solution containing 180 μL ATL buffer and 20 μL Proteinase K with a Tissue Cell-destroyer (Novastar, China). DNA was extracted with a QIAamp Blood and Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Polymerase chain reaction (PCR) was performed on individual specimens of each tick species to amplify an 820 bp amplicon of the mitochondrial cytochrome c oxidase subunit I gene, with optimized conditions and primers specific as previously described [23]. The PCR products were purified with a QIAquick^® PCR Purification Kit (Qiagen, Hilden, Germany) and subsequently subjected to bidirectional Sanger sequencing (Sangon Biotech, Shanghai, China).

FIGURE 1 |

Map of tick sampling locations and tick species in the Hulunbuir area.

Map of China, showing the location of Hulunbuir in Inner Mongolia Province, where sampling occurred (colored orange). The tick sampling collection locations are marked with solid black dots on the map. Tick species collected from each sampling location are color coded as follows: blue for I. persulcatus, yellow for H. longicornis, and purple for D. silvarum.

Tick sequencing library construction and sequencing

A total of 12 tick sequencing libraries were generated according to the sampling location, tick species, and sex (S2 Table). Ticks from the same library were washed three times with sterile, RNA- and DNA-free phosphate-buffered saline (PBS, HyClone™), then roughly minced with sterile tweezers and surgical scissors, and homogenized in PBS solution with a Tissue Cell-destroyer (Novastar, China). The homogenate was incubated at 55°C for 10 minutes with Proteinase K (Qiagen) and centrifuged at 15,000 g for 30 seconds. The supernatant was collected for total RNA extraction with TRIzol (Life Invitrogen™, Carlsbad, CA, USA). For each pool, 1 μg RNA was used for library preparation and subjected to RNA-Seq with a HiSeq 3000 sequencer according to the manufacturer’s instructions (Illumina, San Diego, USA), as previously described [24]. The quantity of extracted RNA was measured with a Qubit 4.0 fluorometer, and RNA quality was assessed with an Agilent Bioanalyzer 2200 (Agilent). The RNA library underwent paired-end sequencing, with a 10 G sequence depth per library and a read length of 150 base pairs per end, with the HiSeq 3000 sequencing platform (Illumina, San Diego, CA, USA). This high-throughput sequencing was performed at Novogene Technologies.

Tick virome analysis and virus discovery

The raw sequencing data were processed to remove low-quality reads, adapter sequences, and contaminants. Trimmomatic (version 0.40) was used to trim and filter the reads according to quality scores. Furthermore, FastQC (version 0.11.9) was used to assess read quality. Bowtie2 (version 2.3.3.1) was used to extract and filter the tick-related reads according to the Ixodoidea database from the National Center for Biotechnology Information (NCBI) [25]. The remaining sequencing reads were assembled in Trinity (version 2.5.1) [24]. All assembled contigs longer than 200 base pairs were subjected to BLASTn analysis against all non-redundant nucleotide databases with the local BLAST tool [26], and BLASTx analysis against all non-redundant protein databases obtained from GenBank with DIAMOND (version 0.9.24). Any result with an e-value below 1 × 10⁻⁶ was considered a “significant hit.” All putative viral contigs exhibiting overlapping regions exceeding 100 bp and a threshold value of 95% similarity were initially merged with the SeqMan program in the Lasergene package (DNAstar version 7.1). All filtered viral contigs were annotated and classified according to the top hit from BLASTx comparisons. If a contig showed no homology in the BLASTx results, it was then classified according to the best match from BLASTn. A novel virus was proposed if the amino acid (aa) similarity with respect to the RdRp domain was below 90%, as subsequently confirmed by phylogenetic analyses [27,28].

All ribosomal reads in each library were mapped to the rRNA database (https://www.arb-silva.de/) with Bowtie2 (version 2.4.3), to avoid possible effects of unequal rRNA removal efficiency during sequencing library preparation. The non-rRNA reads in each library were then aligned to the assembled sequences with the Bowtie2 end-to-end alignment method with sensitive parameters. After the removal of rRNA reads, transcripts per million were calculated and used to standardize the relative abundance of each virus. A heatmap was constructed to visualize the profile of the tick-related virome with the heatmap package in R Studio.

Annotation of viral genomes

Viral ORFs were predicted with ORF Finder (https://www.ncbi.nlm.nih.gov/Orffinder/). All predicted ORFs were longer than 100 aa. Only the longest ORF was reported if a shorter ORF was completely nested within it. All predicted ORFs were annotated by alignment with the BLASTp non-redundant protein database and the PSI-BLAST conserved domain database (e-value threshold of 1×10⁻⁵). Read coverage and continuity of novel viral genomes were verified with Bowtie2.

Genome and phylogenetic analysis

The highly conserved RdRp gene or NS5 gene (including RdRp gene) was selected to construct the family- and order-level phylogenies. The phylogenetic clades were divided at the genus level. Novel viruses and new viral strains identified in this study were aligned with representative reference sequences belonging to the corresponding viral family downloaded from GenBank, including Flaviviridae, Phenuiviridae, Chuviridae, Rhabdoviridae, and Nairoviridae. Phylogenetic trees were constructed with the maximum likelihood method and GTR+G model in MEGA (version 7.0), with 1,000 bootstrap replicates.

Virus classification

According to the taxonomy disciplines from the International Committee on Taxonomy of Viruses, on the basis of the nt and aa sequence identities, a novel virus was defined when it had an aa sequence identity of <90% with respect to the reference virus RNA-dependent RNA polymerase (RdRp), or a genomic sequence with <80% identity with respect to the reference virus [28,29]. The presence of viruses identified from metagenomic sequencing was further validated with PCR. Primers were designed on the basis of the conserved regions identified in the metagenomic analysis (S3 Table). PCR amplification was performed in 25 μL reaction mixtures containing 12.5 μL 2×PCR Master Mix (Promega, USA), 1 μL of each primer (10 μM), 1 μL cDNA template obtained by reverse transcription from RNA libraries, and 8.5 μL nuclease-free water. The cycling conditions were as follows: 95°C for 5 minutes; 35 cycles of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 1 minute; and 72°C for 5 minutes. The PCR products were verified with gel electrophoresis and further confirmed through Sanger sequencing (Sangon Biotech, Shanghai, China).

Detection of SGLV, BJNV, YLTV1, MtTV, and NOMV among tick groups

A total of 340 D. silvarum, 80 I. persulcatus, and 80 H. longicornis tick individuals were grouped into 32 pools to investigate the prevalence of viruses, including SGLV, BJNV, YLTV1, MtTV, and NOMV, identified among tick individuals in Inner Mongolia. Specific nested reverse transcription-polymerase chain reaction (nested PCR) primers were designed on the basis of the integrity of the viral genomes. The primers used for detection are listed in S3 Table. Tick group RNA extraction and viral detection were performed, as previously described [23]. All positive amplicons detected in this study were sequenced with Sanger sequencing (Sangon Biotech, Shanghai, China). We calculated the minimum infectivity rate (MIR) to identify the prevalence of viral infections among studied tick populations. The MIR is the ratio of the number of positive pools to the total number of tick individuals tested. This metric is frequently used to estimate the proportion of infected ticks within a sample and is calculated as follows:

$$MIR=\left(\frac{\text{Number\hspace{0.17em}of\hspace{0.17em}infected\hspace{0.17em}tick\hspace{0.17em}pools}}{\text{Total\hspace{0.17em}number\hspace{0.17em}of\hspace{0.17em}ticks\hspace{0.17em}individuals\hspace{0.17em}tested}}\right)\times 100.$$

To quantify the uncertainty associated with the MIR estimates, we calculated 95% confidence intervals (CIs) as follows:

$$CI=\left[p\pm Z_{\frac{\alpha }{2}}\times \sqrt{\frac{p\left(1-p\right)}{n}}\hspace{0.17em}\right],\hspace{0.17em}$$

where p is the proportion of infected ticks, calculated as follows:

$$p=\frac{\text{Number\hspace{0.17em}of\hspace{0.17em}infected\hspace{0.17em}tick\hspace{0.17em}pools}}{\text{Total\hspace{0.17em}number\hspace{0.17em}of\hspace{0.17em}ticks\hspace{0.17em}individuals\hspace{0.17em}tested}},$$

Z _α/2 is the critical value for a 95% CI (approximately 1.96), and n is the total number of samples tested.

RESULTS

Overall virome in three dominant tick species in Hulunbuir

We constructed and sequenced 12 RNA libraries containing 40 D. silvarum ticks from Meitian and Wunuer; 80 I. persulcatus ticks from Yiliekede; and 80 H. longicornis ticks from Yiliekede in Hulunbuir during 2021 (S1 Table and Fig 1). A total of 87,775,647–150,413,092 raw sequencing reads per library were obtained after trimming and cleaning with Bowtie2 v2.4.3. After removal of tick-related reads, the remaining reads were subjected to de novo assembly and alignment with the non-redundant nt and nr databases from NCBI with BLASTn, BLASTx, and DIAMOND. A total of 2,843–297,444 reads, representing 0.0014–0.2883% of the total sequencing reads within each tick library, were mapped to virus and unassigned-virus. Details of the tick sequencing libraries are summarized in S2 Table. All virus-related reads across 12 tick libraries from Inner Mongolia were annotated into at least 26 viral species within nine families: Chuviridae, Coronaviridae, Flaviviridae, Luteoviridae, Nairoviridae, Partitiviridae, Phenuiviridae, Rhabdoviridae, and Totiviridae (Table 1).

TABLE 1 |

Number of virus-related reads identified in tick sequencing libraries and their identity with respect to the closest viral species.

Family	Genus	Closest viral species	Meitian		Wunuer		Yiliekede
			D. sil (♂)	D. sil (♀)	D. sil (♂)	D. sil (♀)	I. pers (♂)	I. pers (♂)	I. pers (♀)	I. pers (♀)	H. long (♂)	H. long (♂)	H. long (♀)	H. long (♀)
Chuviridae	Chuvirus	Nuomin virus					51,070	234,471	53,205	9
Coronaviridae	Coronavirus	Longquan R1 rat coronavirus				4
Flaviviridae	Unclassified	Bole tick virus 4	10,025	9,517	610	4,312
Luteoviridae	Unclassified	Norway luteo-like virus	5						157,165	16
Nairoviridae	Orthonairovirus	Beiji-nairovirus					21,983			533
		Songling virus									41,122		23,481	20,224
Partitiviridae	Unclassified	Lichen partiti-like RNA virus 2						8,597
Phenuiviridae	Phlebovirus	Changping tick virus 1			1,775	392
		Beiji phlebovirus					88,777		65
		Blacklegged tick phlebovirus								17
		Mukawa virus								99,610
		Norway phlebovirus								17,638
		Onega tick phlebovirus						1,422
		Sara tick phlebovirus					102,307		17,679	2,170
	Uukuvirus	Tacheng tick virus 2	2,121	8,136	2,368	750
Rhabdoviridae	Unclassified	American dog tick rhabdovirus 2		4,621
		Nayun tick rhabdovirus NY-13											1,953
		Taishuan tick virus		1,238
		Norway mononegavirus 1					33,307		16,205
		Manly virus									23,456	2,843		1,222
Totiviridae	Unclassified	Lonestar tick totivirus				561					115		14
Unclassified		Bawangfen virus									81		59
		Kwi virus									2,673		14
		Tick-borne tetravirus-like virus	25,187			4,241
		Vovk virus						2,726
		Xinjiang tick-associated virus 1		1,004	1,318
Subtotal			37,338	24,516	6,071	10,260	297,444	247,216	191,114	119,993	67,447	2,843	25,521	21,446

I. pers, I. persulcatus; D. sil, D. silvarum; H. long, H. longicornis.

Virome patterns associated with tick species in Hulunbuir

The numbers of reads mapped to viruses and proportion of viral reads among three tick species libraries are shown in Fig 2A and B and S2 Table. In the I. persulcatus tick libraries, 119,993 to 297,444 reads per sample were annotated to three to seven different viral species, whereas 2,843 to 67,447 reads in H. longicornis tick libraries were annotated to two to six viral species; 6,071 to 37,338 aligned reads in D. silvarum libraries were identified to be related to four or five viral species. Moreover, the number and proportion of viral reads was significantly higher in the I. persulcatus tick libraries than the D. silvarum and H. longicornis tick libraries (Fig 2A and B). Virome composition is presented at the virus family level among the three tick species (Fig 2C). Members of the Flaviviridae and Phenuiviridae were more frequently detected in D. silvarum ticks; Chuviridae were more frequently detected in I. persulcatus ticks; and Narioviridae were more frequently detected in H. longicornis ticks. Rarefaction curve analysis of viral species richness in each tick library showed that the sequencing depth of all pools was sufficient (Fig 2D). The alpha diversity analysis indicated that both Shannon indices were higher in D. silvarum and I. persulcatus than in H. longicornis, thus suggesting a higher viral diversity in the former than the latter, although the difference was not significant (Fig 2E). Nevertheless, beta diversity analysis (NMDS) revealed a significant difference in viral species (stress=0.007, p=0.002), whereas geographic distribution and tick sex did not significantly affect tick virome diversity (Fig 2G). Furthermore, we explored the virome pattern between D. silvarum, I. persulcatus, and H. longicornis according to the log₂ normalized abundance of 26 eukaryotic viral species (rows) across the 12 libraries (Fig 2C). The viromes from the same tick species libraries clearly clustered separately, according to hierarchical clustering based on the Euclidean distance matrix. Specifically, most I. persulcatus ticks carried NOMV, Norway nomonegavirus 1, Beiji nairovirus, Mukawa virus, Norway phlebovirus, and Sara tick phlebovirus in high abundance. However, reads associated with SGLV and Wuhan insect virus 7 were highly abundant in H. longicornis ticks. Some viruses, most related to bole tick virus 4 and Tacheng tick virus 2, were uniquely present in D. silvarum ticks. In addition, the viral distribution and abundance exhibited no significant preference for tick sex and location in this study. Similar virome distribution characteristics were observed between I. persulcatus and H. longicornis ticks from Yiliekede. Moreover, the detected D. silvarum ticks from Wunuer and Metian showed highly similar predominating virome constructs. These results indicated that tick species plays an important role in shaping the characteristics of the viral spectrum carried by ticks in Hulunbuir.

FIGURE 2 |

Virome distribution pattern among ticks in the Hulunbuir area.

(A) Mapped number of viral reads in each tick library. (B) Comparison of viral read proportions among the three tick species libraries. (C) Virome composition, determined for each tick library and tick species. (D) Rarefaction curves of viral species richness in ticks. The x-axis represents the sequencing depth (10³ transcripts per million [TPM]), and the y-axis shows the cumulative number of viral species observed. Each curve corresponds to a different sample or group of samples. (E) Alpha diversity of the virome distribution among tick species, locations, and sexes, according to the Shannon index. (F) Normalized abundance of eukaryotic viral species in each tick library, calculated according to TPM on a log₂ scale. The sampling locations, tick species, and tick sexes are shown at the bottom of the map, and each closest viral species is shown on the right of the heatmap according to the annotation results from BLASTx and DIAMOND. The hierarchical clustering model was based on the calculated Euclidean distance matrix. (G) NMDS analysis of virome distribution by tick species, location, and sex.

Diversity and evolution of novel viruses and viral strains

Chuviridae

Three NOMV-YL genomes belonging to the newly classified family Chuviridae were identified in I. persulcatus from Yiliekede, with lengths of 10,874, 11,337, and 11,321 nt. Their genomes comprised three ORFs: RdRp, glycoprotein (GP), and nucleoprotein (NP). NOMV-YL showed the highest sequence similarity to NOMV-H160 (aa identity of 99.5–100%, 99.7–100%, and 99.68–100% to RdRp, GP, and NP, respectively), and was identified from febrile patients according to the GenBank (MW029969). Phylogenetic tree analysis also indicated that NOMV-YL is most closely related to NOMV-H160 (Fig 3).

FIGURE 3 |

Phylogenetic relationships and genomic structure of Flaviviridae, Phenuiviridae, Chuviridae, Rhabdoviridae, and Nairoviridae identified in this study.

A phylogenetic tree was constructed with the RdRp (Phenuiviridae, Rhabdoviridae, Narioviridae, and Chuviridae) and NS5 (Flaviviridae) protein sequences of novel viruses or new viral strains identified from tick libraries with representative viruses. Novel viruses and new viral strains are marked with yellow and gray shading, respectively.

Flaviviridae

Flaviviridae consist of four genera: Flavivirus, Hepacivirus, Pegivirus, and Pestivirus [30]. BLTV4, an unclassified pestivirus, has recently been defined as having only one ORF that encodes a polyprotein [19]. Three complete genome sequences of a novel pestivirus-like virus were obtained from D. silvarum tick species in Meitian and Wunuer, and showed 98.23–99.51% nt identity with one another. These viruses have been tentatively named Wunuer pestivirus, according to the sampling location. The three Meitian pestivirus strains encode an ORF containing 2,207 aa, and are most closely related to BLTV4, with 86.62–87.09% and 86.62–87.09% nt and aa identity, respectively (Fig 3).

Phenuiviridae

Our study identified a novel phlebovirus, Wunuer tick virus (WnTV), and novel variants of Onega tick phlebovirus (OTPV), Sara tick phlebovirus (STPV), and Mukawa virus (MKWV), along with a new Uukuvirus, MtTV, in D. silvarum ticks. The L segment of WnTV was closely related to the Changping tick virus 1 (CPTV1) identified in Dermacentor spp. from China, with an aa identity of 83.18% to RdRp [19]. Phylogenetic analysis of RdRp demonstrated that WnTV clustered with other CPTV-1 strains and formed a clade distant from other tick-borne phenuiviruses. OTPV-YL, STPV-YL, and MKWV-YL identified in I. persulcatus from Yiliekede, showed the closest relationship to OTPV Rus/Ix_persulcatus/Karelia/3/2018, STPV Rus/Ix_persulcatus/Karelia/4/2018, and MKW73 (97.32–98.20% to NP, 97.32–99.58% to RdRp), respectively (Fig 3). These viruses have recently been identified in I. persulcatus from Russia and Japan [31]. Four S and L segments of Uukuvirus-like virus MtTV in D. silvarum from Meitian and Wunuer shared high similarity to TcTV2 (83.01–83.38% to RdRp, 74.7–78.85 % to NP), which has recently been reported in infection patients with D. marginatus exposure in China and Turkey [13]. Multiple alignments of MtTV from different collections showed nt identities of 88.67–95.78% in the S segment and 95.20–98.94% in the L segment.

Nairoviridae

The Nairovirus genus belongs to the family Nairoviridae, which includes several important highly pathogenic TBVs such as CCHFV, Nairobi sheep disease viruses, SGLV, and BJNV. We identified three SGLV strains from H. longicornis ticks from Yiliekede, which were closely associated with the SGLV strain YC585 discovered in H. longicornis ticks collected from Heilongjiang, China, with high similarities of 95.89% (L), 92.18% (M), and 99.04% (S). Moreover, we identified a complete S segment (2,643 nt) and L segment (14,811 nt) of BJNV-YL in I. persulcatus from Yiliekede, near Yakeshi, where BJNV was initially identified in febrile patients [14]. The L and S segments of BJNV-YL contained a 14,459 nt and 1,659 nt ORF encoding a 4,820 aa RdRp and 552 aa NP, respectively. The BJNV-YL strain identified from Yiliekede showed high similarity to BJNV found in febrile patients (98.67% [RdRp] and 99.09% [NP] identity). Phylogenetic analysis indicated that BJNV-YL grouped into nairo-like virus clades, together with Gakugsa tick virus, South Bay virus, Norway nairovirus 1, and Grotenhout virus from Russia, the United States, Norway, and Belgium (Fig 3) [20,21].

Rhabdoviridae

The Rhabdoviridae family, known for its wide host range, has a typical genome organization encoding five proteins: NP, P, M, G, and L [32]. We identified a high proportion (82.76%) of virus-related reads belonging to the Rhabdoviridae family from the tick virome, which were determined to represent three novel viruses: Yiliekede tick virus 1 (YlTV1) from H. longicornis, and Yiliekede tick virus 2 (YlTV2) and Yiliekede tick virus 3 (YlTV3) from I. persulcatus (Table 2 and Fig 3). YlTV1 encoded four putative proteins and showed a distant relationship with the Nayun tick rhabdovirus NY-13, with low sequence identities (34.97% to NP, 34.87% to matrix protein (M), 46.49% to GP, and 70.01% to RdRp). Meanwhile, three viral strains of YlTV3, identified as unclassified Rhabdoviridae-like viruses, also contained four ORFs for NP, M, GP, and RdRp, and showed the closest similarity to Norway mononegavirus 1. Among them, the aa sequences of YlTV3-I. persulcatus/01 and YlTV3-I. persulcatus/02 were similar to Norway mononegavirus 1 NOR/H3/Skanevik/2014 (60.65% and 61.64% to NP; 71.88% and 72.5% to M; 70.99% and 71.23% to GP; and 79.33% and 80.91% to RdRp) [33], whereas YlTV3-YlTV3-I. persulcatus/03 showed a relatively distant relationship with NOR/H3/Skanevik/2014 (42.68% to NP, 33.71% to M, 47.78% to GP, and 49.46% to RdRp). Furthermore, the three viral strains of YlTV2 encoded only three proteins, NP, GP, and RdRp, which showed low sequence identities to Manly virus (34.11–34.97% to NP, 46.49% to GP, and 69.96–70.87% to RdRp). Phylogenetic analyses also demonstrated that both YlTV2 and Manly virus constituted a discrete clade within an unclassified clade, provisionally designated the Alphanemrhavirus-like group. Notably, the difference in encoded protein and sequence divergence (34.11–34.97% to NP, 46.49% to GP, and 69.96–70.87% to RdRp) between YlTV1 and YlTV2, which were identified from the same tick species and location, indicated high diversity of mononegavirus-like viruses in Yiliekede.

TABLE 2 |

Novel viruses and viral strains identified in this study.

Events	Family	Virus name	Novel virus	Collection	Tick species	Closest relative virus	Length (nt)	Gene	AA identity (%)
1	Chuviridae	NOMV-YL01/I. persulcatus/2021	N	Yiliekede	I. persulcatus	Nuomin virus H160	10,874	NP	99.5
								GP	99.7
								RdRp	99.6
		NOMV-YL02/I. persulcatus/2021		Yiliekede	I. persulcatus		11,337	NP	99.5
								GP	99.5
								RdRp	99.5
		NOMV-YL03/I. persulcatus/2021		Yiliekede	I. persulcatus		11,321	NP	99.7
								GP	99.7
								RdRp	99.7
2	Flaviviridae	Meitian pestivirus-D. silvarum/01	Y	Meitian	D. silvarum	Bole tick virus 4	16,493	Polyprotein	87.09
		Meitian pestivirus-D. silvarum/02		Meitian	D. silvarum		16,499	Polyprotein	86.8
		Meitian pestivirus-H. longicornis		Wunuer	H. longicornis		13,593	Polyprotein	86.62
3	Nairoviridae	SGLV-YL01/H. longicornis/2021	N	Yiliekede	H. longicornis	Songling virus	1,767	NP	99.7
							4,527	GP	99.1
							12,023	RdRp	99.2
		SGLV-YL02/H. longicornis/2021		Yiliekede	H. longicornis		1,808	NP	99.7
							4,588	GP	99.1
							12,029	RdRp	99.2
		SGLV-YL03/H. longicornis/2021		Yiliekede	H. longicornis		2,417	NP	99.7
							4,526	GP	99.1
							12,570	RdRp	99.2
4	Nairoviridae	BJNV-YL/I. persulcatus/2021	N	Yiliekede	I. persulcatus	Beiji nariovirus	14,811	RdRp	98.6
							2,643	NP	99.0
5	Phenuiviridae	Wunuer tick virus	Y	Wunuer	H. longicornis	Changping tick virus 1	6,640	RdRp	83.1
6	Phenuiviridae	STPV-YL01/I. persulcatus/2021	N	Yiliekede	I. persulcatus	Sara tick phlebovirus	1,912	NP	98.3
							6,713	RdRp	99.2
		STPV-YL02/I. persulcatus/2021	N	Yiliekede	I. persulcatus		2,643	NP	99.2
							4,758	RdRp	99.5
7	Phenuiviridae	MKWV-YL/I. persulcatus/2021	N	Yiliekede	I. persulcatus	Mukawa virus	1,900	NP	93.0
							3,308	GP	98.7
							6,425	RdRp	99.0
8	Phenuiviridae	OTPV-YL/I. persulcatus/2021	N	Yiliekede	I. persulcatus	Onega tick phlebovirus	1,923	NP	97.7
							6,689	RdRp	99.5
9	Phenuiviridae	Meitian tick virus-D. silvarum/2021/01	Y	Meitian	D. silvarum	Tacheng tick virus 2-TC252	1,943	NP	75.2
							6,640	RdRp	83.0
		Meitian tick virus-D. silvarum/2021/02		Meitian	D. silvarum		1,403	NP	78.8
							6,630	RdRp	83.0
		Meitian tick virus-wu/D. silvarum/2021/01		Wunuer	D. silvarum		1,903	NP	74.7
							6,644	RdRp	83.38
		Meitian tick virus-wu/D. silvarum/2021/02		Wunuer	D. silvarum		2,042	NP	75.5
10	Rhabdoviridae	Yiliekede tick virus 1	Y	Yiliekede	H. longicornis	Nayun tick rhabdovirus NY-13	11,342	NP	34.97
								Matrix	34.87
								GP	46.49
								RdRp	70.01
11	Rhabdoviridae	Yiliekede tick virus 2-H. longicornis/01	Y	Yiliekede	H. longicornis	Manly virus	11,349	NP	34.11
								GP	46.49
								RdRp	69.96
		Yiliekede tick virus 2-H. longicornis/02	Y	Yiliekede	H. longicornis		11,343	NP	34.11
								GP	46.49
								RdRp	69.96
		Yiliekede tick virus 2-H. longicornis/03	Y	Yiliekede	H. longicornis		10,562	NP	34.97
								GP	46.49
								RdRp	70.78
12	Rhabdoviridae	Yiliekede tick virus 3-I. persulcatus/01	Y	Yiliekede	I. persulcatus	Norway mononegavirus 1 NOR/H3/Skanevik/2014	11,486	NP	61.64
								Matrix	71.88
								GP	70.99
								RdRp	79.33
		Yiliekede tick virus 3-I. persulcatus/02	Y	Yiliekede	I. persulcatus		11,488	NP	60.65
								Matrix	72.5
								GP	71.23
								RdRp	80.91
		Yiliekede tick virus 3-I. persulcatus/03	Y	Yiliekede	I. persulcatus		10,331	NP	42.68
								Matrix	33.71
								GP	47.78
								RdRp	49.46

Prevalence of SGLV, BJNV, YLTV1, MtTV, and NOMV among tick populations in Inner Mongolia

To investigate the prevalence of novel viruses (MtTV and YLTV1) and new viral strains associated with febrile illnesses (NOMV, BJNV, and SGLV) identified from tick virome analysis in Hulunbuir, we performed viral detection among all 500 ticks (340 D. silvarum, 80 I. persulcatus, and 80 H. longicornis) collected from Meitian, Wunuer, Yiliekede, Mianduhe, Dayan, Moguai, Muyuan, Tenihe, Yakeshi, and Zhaluomude. All ticks were grouped into 32 pools (Table 3 and Fig 4). Consequently, the overall MIR was 6.4% (32/500; 95% CI: 4.41–8.91). The highest MIR was 4.4% for MtTV (22/500; 95% CI: 2.77–6.58), which was followed by NOMV (1.0%; 5/500; 95% CI: 0.32–2.32), YLTV1 (0.8%; 4/500; 95% CI: 0.21–2.03), SGLV (0.6%; 3/500; 95% CI: 0.12–1.74), and BJNV (0.4%; 2/500; 95% CI: 0.04–1.4). Of these five viruses, MtTV was found in all D. silvarum ticks from all collections in Hulunbuir except for Yiliekede; the MIR ranged from 8.0% in Meitian (95% CI: 2.22–19.23) to 4.0% in Wunuer (95% CI: 0.49–13.7). The presence of YLTV1 was detected in D. silvarum and H. longicornis, with MIR infection rates of 4.0% in Wunuer and 2.5% in Yiliekede. Moreover, NOMV, BJNV, and SGLV were also detected only in ticks from Yiliekede. MONV was detected in I. persulcatus, with a MIR of 3.75% (3/80; 95% CI: 0.78–10.57), and in H. longicornis, with a MIR of 2.50% (2/80; 95% CI: 0.30–8.74). BJNV and SGLV were observed only in I. persulcatus and H. longicornis, with detection rates of 2.5% (2/80) and 3.75% (3/80), respectively.

TABLE 3 |

Prevalence of SGLV, BJNV, YLTV1, MtTV, and NOMV among tick populations in Inner Mongolia.

Collections	Tick species	Nested PCR data	MtTV	YLTV1	NOMV	BJNV	SGLV
Meitian	D. sil	Numbers/pools/+	50/4/4	50/4	50/4	50/4	50/4
		MIR (%)	8.0
		95% CI	2.22–19.23
Wunuer	D. sil	Numbers/pools/+	50/4/2	50/4/2	50/4	50/4	50/4
		MIR (%)	4.0	4.0
		95% CI	0.48–13.71	0.48–13.71
Dayan	D. sil	Numbers/pools/+	30/2/2	30/2	30/2	30/2	30/2
		MIR	6.6
		95% CI	0.08–22.07
Mianduhe	D. sil	Numbers/pools/+	30/2/2	30/2	30/2	30/2	30/2
		MIR (%)	6.6
		95% CI	0.08–22.07
Moguai	D. sil	Numbers/pools/+	60/4/4	60/6	60/6	60/6	60/6
		MIR (%)	6.6
		95% CI	0.08–22.07
Muyuan	D. sil	Numbers/pools/+	30/2/2	30/2	30/2	30/2	30/2
		MIR (%)	6.6
		95% CI	0.08–22.07
Tenihe	D. sil	Numbers/pools/+	30/2/2	30/2	30/2	30/2	30/2
		MIR (%)	6.6
		95% CI	0.08–22.07
Yakeshi	D. sil	Numbers/pools/+	30/2/2	30/2	30/2	30/2	30/2
		MIR (%)	6.6
		95% CI	0.08–22.07
Zhaluomude	D. sil	Numbers/pools/+	30/2/2	30/2	30/2	30/2	30/2
		MIR (%)	6.6
		95% CI	0.08–22.07
Yiliekede	I. pers	Numbers/pools/+	80/4	80/4	80/4/3	80/4/2	80/4
		MIR (%)			3.75	2.5
		95% CI			0.78–10.57	0.30–8.74
	H. long	Numbers/pools/+	80/4	80/4/2	80/4/2	80/4	80/4/3
		MIR (%)		2.5	2.5		3.75
		95% CI		0.30–8.74	0.30–8.74		0.78–10.57
Subtotal		Numbers/pools/+	500/32/22	500/32/4	500/32/5	500/32/2	500/32/3
		MIR (%)	4.4	0.8	1	0.4	0.6
		95% CI	2.77–6.58	0.21–2.03	0.325–2.32	0.04–1.4	0.12–1.74

I. pers, I. persulcatus; D. sil, D. silvarum; H. long, H. longicornis; +, positive samples; MIR, minimum infection rate (%); CI, confidence interval.

FIGURE 4 |

Overview of the ecological distribution characteristics of MtTV, YLTV1, NOMV, BJNV, and SGLV found in Hulunbuir, based on tick group screening results. The minimum infection rate of each virus is shown in bar plots. The detected tick species are indicated by icons as follows: blue for I. persulcatus, yellow for H. longicornis, and purple for D. silvarum.

DISCUSSION

Recently, many new TBVs associated with human diseases, such as ALSV [10], SGLV [11], BJNV [14], and WELV [12] have been identified in Inner Mongolia, thus highlighting the public health significance of TBVs in Inner Mongolia. However, understanding of the distribution and reservoir host characteristics of these newly identified viruses is limited. Moreover, the diversity of TBVs in Inner Mongolia remains to be determined. Herein, we presented a metagenomic description of the viruses harbored by D. silvarum, I. persulcatus, and H. longicornis in Hulunbuir, Inner Mongolia, China. At least 26 viral species in nine families were identified in this study, including seven novel viruses (MtTV and YLTV1) and five new viral strains of human febrile disease-related TBVs (SGLV, BJNV, and NOMV), thereby revealing high viral diversity among ticks in Inner Mongolia. Virome comparison analysis indicated that tick species, but not tick sex, plays an important role in shaping the viral spectrum carried by ticks in Hulunbuir. Furthermore, in agreement with the virome distribution findings, the surveillance of SGLV, BJNV, MtTV, and NOMV among tick groups indicated that MtTV identified from D. silvarum was detected in D. silvarum ticks from the nine counties with the highest MIR of 4–8%. These findings suggested a wide distribution of this novel MtTV Uukuvirus among D. silvarum ticks in Hulunbuir. SGLV, BJNV, and NOMV identified from I. persulcatus and H. longicornis tick libraries in Yiliekede were also detected in corresponding tick species in Yiliekede, with MIR values of 0.6%, 0.4%, and 1.0%, respectively; these findings indicated the potential spillover risk of these infectious viruses in Yiliekede. Our study expands understanding of the geographic distribution and phylogenetic diversity of novel TBVs in Inner Mongolia, and highlights their medical and public health importance.

Our sampling results regarding tick species distribution were similar to the outcomes reported throughout Mongolia [34], Russia [35], and Northern Europe [33] at similar latitudes: Dermacentor spp., Ixodes spp., and Haemaphysalis spp. were the most abundant tick species in this region. These three tick species play important roles in the spread of TBVs worldwide. We observed distinct virome characteristics among D. silvarum, I. persulcatus, and H. longicornis ticks in Inner Mongolia. The I. persulcatus tick libraries exhibited the highest number and proportion of viral reads, in agreement with previous findings that I. persulcatus is a significant vector for various pathogens, because of its wide host range and adaptability. In contrast, H. longicornis and D. silvarum ticks carry a diverse virome, and showed lower viral load and species richness than I. persulcatus. At the viral family level, Flaviviridae and Phenuiviridae were more frequently detected in D. silvarum, whereas Chuviridae predominated in I. persulcatus, and Narioviridae predominated in H. longicornis. This distribution reflected each tick species’ unique ecological niches and host interactions influencing virome composition. Alpha diversity analysis revealed higher Shannon indices in D. silvarum than I. persulcatus, thus suggesting greater viral diversity in the former. The beta diversity analysis showed significant differences in viral species composition among the tick species, and highlighted the importance of species-specific factors in shaping the virome. Furthermore, our findings indicated that tick species plays a crucial role in determining the spectrum of carried viruses in Inner Mongolia. The virome patterns from the same tick species clustered separately, with I. persulcatus ticks predominantly harboring NOMV and other specific viruses; H. longicornis ticks showing a high abundance of SGLV and Wuhan insect virus 7; and D. silvarum ticks uniquely carrying bole tick virus 4 and Tacheng tick virus 2. Interestingly, viral distribution and abundance showed no significant differences between tick sex or locations. These findings suggested that species-specific factors outweigh these variables in influencing virome composition. The observed geographic invasion has drawn attention to the H. longicornis population and its associated emerging disease threats. In China, viruses belonging to the families Nairoviridae and Phenuiviridae in the order Bunyavirales are most commonly detected with high abundance and prevalence among H. longicornis [36,37]. Almost all pools of H. longicornis presented viral sequences, including the nairo-like virus SGLV, which was first isolated from Inner Mongolia and detected in Ixodes spp. and Haemaphysalis spp. [11]. These results expand knowledge of the diversity of viruses carried by these three tick species in Inner Mongolia and provide guidance for the future surveillance of viruses carried by these ticks.

Chuviruses, first identified in 2015 through metagenomics in various arthropods, have been classified into the family Chuviridae within the order Jingchuvirales, because of their diverse genomic structures, including non-segmented, segmented, and circular forms [38]. Recent studies have indicated the widespread presence of chuviruses across various regions, including China [19], Kenya [23], Thailand [39], the United States [40], and Australia [41], thus underscoring their ecological and public health significance. NOMV, a novel chuvirus, was initially identified in patients with febrile illness in northeastern China. Between 2017 and 2019, 34 NOMV human infection cases were reported in Inner Mongolia, and NOMV was also detected in I. persulcatus in the Alongshan region, thereby suggesting a potential risk of spillover and transmission in Inner Mongolia. Our identification of the presence of NOMV in I. persulcatus and H. longicornis ticks in the Yiliekede region of Inner Mongolia extends understanding of NOMV’s prevalence and vector diversity in Inner Mongolia. Additionally, we observed NOMV prevalence rates of 3.75% in I. persulcatus and 2.0% in H. longicornis, values similar to the positivity rate range of 1.9–8.2% observed in ticks from northeastern China. Because of the MIR calculation used in our study, the substantial infection rates in Inner Mongolian ticks might potentially exceed the observed MIR values of 3.75% and 2.5%. Furthermore, different tick species from different regions might have distinct infection rates because of their unique ecological niches and host interactions. NOMV is influenced by regional factors such as climate, vegetation, and host availability, which can affect tick populations and the transmission dynamics of the virus. Our findings emphasize the need for diligent monitoring of the public health effects of NOMV in Inner Mongolia to effectively address the emerging challenges posed by this pathogen.

Most known members of the family Flaviviridae are transmitted by arthropods, and several are important zoonotic agents, which have been reported in Inner Mongolia, Heilongjiang, Jilin, and Xinjiang in China [42]. We identified three novel viruses belonging to the Flaviviridae family that were most similar to the pest-like virus BLTV4, which has been recently identified in China [19], Thailand [39], and Kenya [23]. These findings contribute to the growing body of evidence highlighting the diverse flavivirus carried by ticks. Moreover, the high similarity of pest-like virus BLTV4 variants between Meitian and Wunuer in Inner Mongolia (98.3%) indicates high conservation of this novel flavivirus among ticks in Hulunbuir. This conservation might facilitate the spread of these viruses across geographical areas, and consequently pose risks to both animal and human health.

An increasing number of emerging viruses belonging to the family Phenuiviridae have been identified within various tick species and are classified into several genera, including Bandavirus, Phlebovirus, and Uukuvirus [43]. We observed high genetic divergence of novel viruses and new viral strains viruses belonging to the family Phenuiviridae; the viruses included WnTV, STPV-YL, MKWV-YL, OTPV-YL, and MtTV, which formed a separate cluster within the tick-borne phlebovirus group. OTPV and STPV exhibit high genomic similarity to strains found in the Karelia region of Russia [44]. Therefore, these viruses maintain high genetic consistency within their respective populations. However, their significant genetic differences with respect to other known tick-borne phleboviruses suggest unique evolutionary paths and potential variations in their biological characteristics. MKWV, initially discovered in Japan and later detected in Ixodes persulcatus and Haemaphysalis concinna ticks in Northeast China, has been shown to infect Vero cells and consequently to replicate in mammalian cells [45]. This finding prompts concerns regarding its potential for zoonotic transmission, given its widespread distribution in Northeast China. Therefore, further testing of a broader range of tick species, as well as wild animals, domestic animals, and humans, is of practical importance to assess the public health implications. Additionally, MtTV was detected exclusively in D. silvarum, and exhibited the highest MIR and the broadest distribution among tested viruses. The presence of MtTV in all D. silvarum groups from nine sampling locations strongly suggested stable and widespread circulation of this virus among D. silvarum in Inner Mongolia. Although no current evidence indicates the pathogenicity of MtTV, the closest phylogenetic relationship of this virus was with Tacheng tick virus 2 (75.2–83.38%, aa identity), which is associated with human febrile illnesses. Therefore, the potential public health implications of MtTV warrant further investigation.

The genus Orthonairovirus, belonging to the family Nairoviridae, contains numerous highly pathogenic zoonotic TBVs, such as CCHFV and the Nairobi sheep disease virus, causing fatal hemorrhagic fever and hemorrhagic gastroenteritis in humans as well as animals in Asia, Africa, and Europe [46]. Recently, several newly discovered viruses have been identified in ticks and mammals. SGLV was first isolated from patients who reported being bitten by ticks in Heilongjiang Province in 2021 [11]. Subsequently, another nario-like virus, BJNV, was identified in patients from Inner Mongolia [14]. Here, we reported the presence of two viral strains from Yiliekede showing high similarity (98–99%) to SGLV and BJNV identified from H. longicornis and I. persulcatus pools, respectively. Furthermore, SGLV and BJNV were detected with nested RT-PCR in tick groups. A previous study has indicated that SGLV was prevalent in four hard ticks in Heilongjiang, and I. crenulatus might be the main tick species vector for SGLV (with a prevalence of 5.5%) [11]. However, in Yiliekede, SGLV was detected only in H. longicornis (MIR: 3.75%) in our study, probably because of the limited numbers and species of ticks collected. Moreover, the positivity rate of BJNV measured in I. persulcatus (MIR: 2.5%) was similar to findings from a survey of BJNV prevalence in ticks (0.4–2.1%) in hilly and wooded regions of Inner Mongolia [14]. Previous reports and our current study demonstrate the circulation of these infectious nairoviruses in Inner Mongolia and indicates that the potential serological exposure to SGLV and BJNV among humans and animals in Yiliekede warrants further validation.

Rhabdoviridae, a group of single-stranded negative-sense RNA viruses, infect animals and plants, and their frequent host switching during evolution has led to considerable variability in viral sequences among viral members [32]. Herein, a distinct and divergent spectrum of novel Rhabdoviruses (YLTV1, YLTV2, and YLTV3) was discovered in Yiliekede and found to have low aa similarity with the Nayun tick rhabdovirus NY-13 first identified in China [47], Manly virus in Australia [41], and Norway mononegavirus 1 [33], thus indicating that substantial diversity of rhabdoviruses has not yet been sampled. Closer examination of the genomic structures of YLTV1, YLTV2, and YLTV3 indicated distinct differences. Both YLTV1 and YLTV3 encode four proteins, whereas YLTV2 encodes only three, omitting the M protein. This variation in genomic composition is poised to influence virion assembly, morphology, and functionality. The M protein is instrumental in preserving the structural integrity and morphological attributes of virions in many viruses. Thus, the absence of the M protein in YLTV2 is likely to confer unique morphological characteristics or infective properties to virions. Moreover, a comparative analysis of YLTV1 and NY-13 revealed substantial differences in genomic coding proteins and phylogenetic relationships. Notably, the aa similarity of the NP and M proteins below 35% suggested significant evolutionary distance between YLTV1 and NY-13 in terms of the genomic sequence and coding proteins. This divergence might indicate that YLTV1 is a novel branch or species within the Rhabdoviridae family. The low similarity suggests that YLTV1 might have unique biological characteristics, such as distinct host adaptability or infection mechanisms; this possibly requires further validation through experimental research. In addition, in alignment with the tick virome findings, we detected the presence of NYTRV in I. persulcatus and D. silvarum ticks from Yiliekede and Wunuer via nested PCR, which indicated a wide distribution of NYTRV within the Hulunbuir region.

This research shed light on the variety and distribution of TBVs in Hulunbuir, Inner Mongolia, and uncovered several novel viruses and new strains associated with known infectious TBVs, such as SGLV and NOMV. Surveillance across three dominant tick species at ten collection sites in Hulunbuir also revealed varying infection rates of SGLV, BJNV, YLTV1, MtTV, and NOMV. These findings underscore not only the widespread distribution of TBVs in Hulunbuir but also the critical need for region-specific strategies in monitoring and controlling TBVD. Our study highlights the potential circulation of emergent TBVs between ticks and their reservoir hosts, and indicates a significant public health risk amplified by inter-urban interactions. Finally, our findings substantially enrich understanding of TBVs, and offer crucial insights for the prevention and control of these viruses and their associated diseases in northern China.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

SUPPLEMENTARY MATERIAL

Supplementary Material can be downloaded from https://zoonoses-journal.org/wp-content/uploads/2025/03/zoonoses20240064_Suppl.pdf.

REFERENCES

1. Pielnaa P, Al-Saadawe M, Saro A, Dama MF, Zhou M, Huang Y, et al.. Zika virus-spread, epidemiology, genome, transmission cycle, clinical manifestation, associated challenges, vaccine and antiviral drug development. Virology. 2020. Vol. 543:34–42

2. Kularatne SA, Dalugama C. Dengue infection: global importance, immunopathology and management. Clin Med (Lond). 2022. Vol. 22(1):9–13

3. Tuells J, Henao-Martínez AF, Franco-Paredes C. Yellow fever: a perennial threat. Arch Med Res. 2022. Vol. 53(7):649–657

4. Kuehnert PA, Stefan CP, Badger CV, Ricks KM. Crimean-Congo hemorrhagic fever virus (CCHFV): a silent but widespread threat. Curr Trop Med Rep. 2021. Vol. 8(2):141–147

5. Pulkkinen LIA, Butcher SJ, Anastasina M. Tick-borne encephalitis virus: a structural view. Viruses. 2018. Vol. 10(7):350

6. Li J, Li S, Yang L, Cao P, Lu J. Severe fever with thrombocytopenia syndrome virus: a highly lethal bunyavirus. Crit Rev Microbiol. 2021. Vol. 47(1):112–125

7. Krasteva S, Jara M, Frias-De-Diego A, Machado G. Nairobi Sheep Disease virus: a historical and epidemiological perspective. Front Vet Sci. 2020. Vol. 7:419

8. Gaudreault NN, Madden DW, Wilson WC, Trujillo JD, Richt JA. African swine fever virus: an emerging DNA arbovirus. Front Vet Sci. 2020. Vol. 7:215

9. Moming A, Bai Y, Wang J, Zhang Y, Tang S, Fan Z, et al.. The known and unknown of global tick-borne viruses. Viruses. 2024. Vol. 16(12):1807

10. Wang ZD, Wang B, Wei F, Han SZ, Zhang L, Yang ZT, et al.. A new segmented virus associated with human febrile illness in China. N Engl J Med. 2019. Vol. 380(22):2116–2125

11. Ma J, Lv XL, Zhang X, Han SZ, Wang ZD, Li L, et al.. Identification of a new orthonairovirus associated with human febrile illness in China. Nat Med. 2021. Vol. 27(3):434–439

12. Zhang XA, Ma YD, Zhang YF, Hu ZY, Zhang JT, Han S, et al.. A new orthonairovirus associated with human febrile illness. N Engl J Med. 2024. Vol. 391(9):821–831

13. Dong Z, Yang M, Wang Z, Zhao S, Xie S, Yang Y, et al.. Human Tacheng tick virus 2 infection, China, 2019. Emerg Infect Dis. 2021. Vol. 27(2):594–598

14. Wang YC, Wei Z, Lv X, Han S, Wang Z, Fan C, et al.. A new nairo-like virus associated with human febrile illness in China. Emerg Microbes Infect. 2021. Vol. 10(1):1200–1208

15. Wang ZD, Wang W, Wang NN, Qiu K, Zhang X, Tana G, et al.. Prevalence of the emerging novel Alongshan virus infection in sheep and cattle in Inner Mongolia, northeastern China. Parasit Vectors. 2019. Vol. 12(1):450

16. Kong Y, Yan C, Liu D, Jiang L, Zhang G, He B, et al.. Phylogenetic analysis of Crimean-Congo hemorrhagic fever virus in inner Mongolia, China. Ticks Tick Borne Dis. 2022. Vol. 13(1):101856

17. Li Y, Yan C, Liu D, He B, Tu C. Seroepidemiological Investigation of Crimean-Congo hemorrhagic fever virus in sheep and camels of Inner Mongolia of China. Vector Borne Zoonotic Dis. 2020. Vol. 20(6):461–467

18. Qin H, Xin X, Tang Q, Feng X, Yin B. The Prevalence of tick-borne encephalitis virus in the ticks and humans of China from 2000 to 2023: a systematic review and meta-analysis. Vet Sci. 2025. Vol. 12:146

19. Li CX, Shi M, Tian JH, Lin XD, Kang YJ, Chen LJ, et al.. Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. Elife. 2015. Vol. 4:e05378

20. Tokarz R, Williams SH, Sameroff S, Sanchez Leon M, Jain K, Lipkin WI. Virome analysis of Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis ticks reveals novel highly divergent vertebrate and invertebrate viruses. J Virol. 2014. Vol. 88(19):11480–11492

21. Cross ST, Kapuscinski ML, Perino J, Maertens BL, Weger-Lucarelli J, Ebel GD, et al.. Co-infection patterns in individual ixodes scapularis ticks reveal associations between viral, eukaryotic and bacterial microorganisms. Viruses. 2018. Vol. 10(7):388

22. Ma R, Li C, Tian H, Zhang Y, Feng X, Li J, et al.. The current distribution of tick species in Inner Mongolia and inferring potential suitability areas for dominant tick species based on the MaxEnt model. Parasit Vectors. 2023. Vol. 16(1):286

23. Zhang Y, Hu B, Agwanda B, Fang Y, Wang J, Kuria S, et al.. Viromes and surveys of RNA viruses in camel-derived ticks revealing transmission patterns of novel tick-borne viral pathogens in Kenya. Emerg Microbes Infect. 2021. Vol. 10(1):1975–1987

24. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al.. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011. Vol. 29(7):644–652

25. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie2. Nat Methods. 2012. Vol. 9(4):357–359

26. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al.. BLAST+: architecture and applications. BMC Bioinformatics. 2009. Vol. 10:421

27. Ni XB, Cui XM, Liu JY, Ye RZ, Wu YQ, Jiang JF, et al.. Metavirome of 31 tick species provides a compendium of 1,801 RNA virus genomes. Nat Microbiol. 2023. Vol. 8(1):162–173

28. Wille M, Harvey E, Shi M, Gonzalez-Acuña D, Holmes EC, Hurt AC. Sustained RNA virome diversity in Antarctic penguins and their ticks. ISME J. 2020. Vol. 14(7):1768–1782

29. Pettersson JH, Ellström P, Ling J, Nilsson I, Bergström S, González-Acuña D, et al.. Circumpolar diversification of the Ixodes uriae tick virome. PLoS Pathog. 2020. Vol. 16(8):e1008759

30. Simmonds P, Becher P, Bukh J, Gould EA, Meyers G, Monath T, et al.. ICTV virus taxonomy profile: Flaviviridae. J Gen Virol. 2017. Vol. 98(1):2–3

31. Matsuno K, Kajihara M, Nakao R, Nao N, Mori-Kajihara A, Muramatsu M, et al.. The unique phylogenetic position of a novel tick-borne phlebovirus ensures an ixodid origin of the genus Phlebovirus. mSphere. 2018. Vol. 3(3):e00239–18

32. Longdon B, Murray GG, Palmer WJ, Day JP, Parker DJ, Welch JJ, et al.. The evolution, diversity, and host associations of rhabdoviruses. Virus Evol. 2015. Vol. 1(1):vev014

33. Pettersson JH, Shi M, Bohlin J, Eldholm V, Brynildsrud OB, Paulsen KM, et al.. Characterizing the virome of Ixodes ricinus ticks from northern Europe. Sci Rep. 2017. Vol. 7(1):10870

34. Černý J, Buyannemekh B, Needham T, Gankhuyag G, Oyuntsetseg D. Hard ticks and tick-borne pathogens in Mongolia – a review. Ticks Tick Borne Dis. 2019. Vol. 10(6):101268

35. Kholodilov IS, Belova OA, Morozkin ES, Litov AG, Ivannikova AY, Makenov MT, et al.. Geographical and tick-dependent distribution of flavi-like Alongshan and Yanggou tick viruses in Russia. Viruses. 2021. Vol. 13(3):458

36. Ma C, Zhang R, Zhou H, Yu G, Yu L, Li J, et al.. Prevalence and genetic diversity of Dabieshan tick virus in Shandong Province, China. J Infect. 2022. Vol. 85(1):90–122

37. Ye RZ, Li YY, Xu DL, Wang BH, Wang XY, Zhang MZ, et al.. Virome diversity shaped by genetic evolution and ecological landscape of Haemaphysalis longicornis. Microbiome. 2024. Vol. 12(1):35

38. Di Paola N, Dheilly NM, Junglen S, Paraskevopoulou S, Postler TS, Shi M, et al.. Jingchuvirales: a new taxonomical framework for a rapidly expanding order of unusual monjiviricete viruses broadly distributed among Arthropod subphyla. Appl Environ Microbiol. 2022. Vol. 88(6):e0195421

39. Temmam S, Chrétien D, Bigot T, Dufour E, Petres S, Desquesnes M, et al.. Monitoring silent spillovers before emergence: a pilot study at the tick/human interface in Thailand. Front Microbiol. 2019. Vol. 10:2315

40. Tufts DM, Sameroff S, Tagliafierro T, Jain K, Oleynik A, VanAcker MC, et al.. A metagenomic examination of the pathobiome of the invasive tick species, Haemaphysalis longicornis, collected from a New York City borough, USA. Ticks Tick Borne Dis. 2020. Vol. 11(6):101516

41. Harvey E, Rose K, Eden JS, Lo N, Abeyasuriya T, Shi M, et al.. Extensive diversity of RNA viruses in Australian ticks. J Virol. 2019. Vol. 93(3):e01358–18

42. Li X, Ji H, Wang D, Che L, Zhang L, Li L, et al.. Molecular detection and phylogenetic analysis of tick-borne encephalitis virus in ticks in northeastern China. J Med Virol. 2022. Vol. 94(2):507–513

43. Sun MH, Ji YF, Li GH, Shao JW, Chen RX, Gong HY, et al.. Highly adaptive Phenuiviridae with biomedical importance in multiple fields. J Med Virol. 2022. Vol. 94(6):2388–2401

44. 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. Vol. 16(12):e0011017

45. Wang YN, Jiang RR, Ding H, Zhang XL, Wang N, Zhang YF, et al.. First detection of Mukawa Virus in Ixodes persulcatus and Haemaphysalis concinna in China. Front Microbiol. 2022. Vol. 13:791563

46. Ergönül O. Crimean-Congo haemorrhagic fever. Lancet Infect Dis. 2006. Vol. 6(4):203–214

47. Xia H, Hu C, Zhang D, Tang S, Zhang Z, Kou Z, et al.. Metagenomic profile of the viral communities in Rhipicephalus spp. ticks from Yunnan, China. PLoS One. 2015. Vol. 10(3):e0121609

Graphical abstract

Highlights

• Metagenomic analysis identified 26 viral species among three dominant tick species in Hulunbuir, China, including four novel viruses and 11 human-pathogenic strains of Songling virus (SGLV), Nuomin virus (NOMV), and Beiji nariovirus (BJNV).

• Virome analysis revealed tick-specific virome patterns: I. persulcatus exhibited the highest richness, dominated by Chuviridae and Phenuiviridae; D. silvarum carried Phenuiviridae; and H. longicornis was associated with Nairoviridae.

• Regional surveillance indicated pan-regional circulation of MtTV in D. silvarum and localized prevalence of SGLV, BJNV, and NOMV in Yiliekede.

Brief statement

This study maps the ecology and epidemiology of tick-borne viruses (TBVs) in Hulunbuir, China. I. persulcatus hosted the highest viral abundance, whereas D. silvarum showed greater viral diversity. A prevalence survey highlights the widespread circulation of the novel Meitian tick virus (MtTV) and spillover risks of human-pathogenic viruses, including Songling virus (SGLV), Nuomin virus (NOMV), and Beiji nairovirus (BJNV). These findings provide critical evidence to support TBV surveillance and further underscore the need for One Health strategies in northern China.