Spotted Fever Group rickettsiae are the Dominant Pathogens in Parasitic Rhipicephalus microplus in Yunpan and Menghun, Xishuangbanna, Yunnan Province, China

Abstract

Objective:

Distribution of the bacterium flora in ticks has an essential role in mapping and preventing local tick-borne diseases. The purpose of the current study was to map the bacterium flora and identify the main pathogens in ticks in grazing areas of Yunnan province.

Methods:

The V3-V4 region of 16S rRNA amplifier sequencing was used to analyze the tick-borne pathogens in Rh. Microplus. A prevalence survey on B. burgdorferi s.l., B. miyamotoi, E. chaffeensis, A. phagocytophilum, Coxiella burnetiid, and the spotted fever group rickettsiae was carried out using PCR. Phylogenetic analysis was used to identify and cluster the main prevalent microbe amplicons.

Results:

One hundred five genera and 117 species were detected in 50 ticks. Rickettsia, Anaplasma, Borrelia, Coxiella, and Ehrlichia were identified in tick samples by high-throughput sequencing at the genus level. Pathogen prevalence testing showed that the ticks were positive for B. burgdorferi s.l. (9/50 [18%]), A. phagocytophilum (1/50 [2%]), and the spotted fever group rickettsiae (35/50 [70%]), which were closely related to Candidatus R. jiangxinensis.

Conclusion:

The spotted fever group rickettsiae was the dominant pathogens in Xishuangbanna. Dual co-infection (1/50 [14%]) with Candidatus R. jiangxinensis and Borrelia garinii was demonstrated. V3-V4 high-throughput sequencing of the 16S rRNA gene was not sensitive to identify species for some bacteria, so more accurate and comprehensive analysis is necessary.

Main article text

INTRODUCTION

Ticks are obligate, blood-sucking ectoparasites that are commonly found worldwide. Ticks are parasitic on amphibians, birds, reptiles, and mammals. Some species can invade human beings and animals, and transmit many pathogens, including bacteria, viruses, and protozoa, which can cause many diseases and bring great harm to human health, animal husbandry production, and wildlife [1, 2]. Ticks are thought to be second only to mosquitoes as worldwide vectors of human disease [3, 4].

Of the 117 described species in the Chinese tick fauna, 60 are known to transmit one or more diseases. Moreover, 38 of these species carry multiple pathogens, indicating the potentially vast role of these vectors in transmitting pathogens [5]. The local dominant tick species was Rhipicephalus Microplus (Rh. Microplus) according to our surveillance in grazing areas in Yunnan province from 2017–2018 [6] and other reports [7–9]. Rh. Microplus is an important tick species that is ubiquitous in China. Rh. Microplus can transmit several pathogens, including Anaplasma marginale, Babesia bigemina, Ba. bovis, Theileria equi, Ehrlichia chaffeensis, tick-borne encephalitis virus (TBEV), and Coxiella burnetii [10].

Yunnan is located in southwest China. The geographic environment and climate are complex and diverse. According to the report, Rh. Microplus is widely distributed in Yunnan province [10, 11]. Rickettsia tsutsugamushi, typhus, Q fever, and Bartonella infection have been reported in the Yunnan population [12, 13]. Yunpan and Menghun is part of Jingna county in Xishuangbanna, which is covered by forests in 82.3% of the entire areas. The warm and wet climate is suitable for wildlife, including ticks. Animal husbandry is one of the most important local, profitable industries. Furthermore, Xishuangbanna is a well-known tourist area that attracts numerous tourists every year. Therefore, the prevention and control of tick-borne diseases is particularly important for local residents and travelers. However, little research has been conducted on vector ticks and their pathogens in these areas. To fully understand the status of tick-borne pathogens, identify the main tick-borne pathogens in Yunpan and Menghun and provide a scientific basis for the prevention and control of tick-borne diseases, the V3-V4 region of 16S rRNA amplifier sequencing was used to analyze and study tick-borne pathogens in Rh. Microplus in these two areas.

METHODS

Tick collection and storage

Greater than 1000 parasitic ticks were collected from cattle in Yunpan and Menghun in March 2018. Local livestock husbandry develops well in Yunpan and Menghun because of the natural wide grass and forest land conditions and with the support from government. All ticks were adults and nearly equally divided between engorged and semi-engorged ticks. These ticks were identified as Boophilus microplus. To thoroughly investigate microbes in parasitic ticks, 24 and 26 Rh. Microplus from Yunpan and Menghun, respectively, were randomly chosen, washed with 70% ethanol by gently shaking for 30 seconds, rinsed 3 times in sterile water to remove environmental contaminants [14–16], then frozen at −80°C until use.

Genomic DNA extraction

Genomic DNA was extracted from 50 individual ticks using the QIAamp DNA Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s protocols. The extracted DNA was stored at −20°C until use.

V3-V4 16S rRNA amplicon sequencing and data analysis

V3-V4 16S rRNA amplicon sequencing was performed by BGI (Shenzhen, China). The paired-end sequencing was performed on an Illumina Miseq platform (BGI) based on a standard protocol from the manufacturer. Raw data were filtered to generate high-quality clean reads follows the normal rules [17], as follows: 1. truncate reads with average phred quality values <20 over a 30 bp sliding window; 2. remove reads, the length of which are <75% of the original lengths after truncation; 3. remove reads that are contaminated by adapter sequences; 4. remove reads with an ambiguous base (N base); and 5. remove low complexity reads. Then, we made tags connections if paired-end reads overlapped under the minimum overlapping length of 15 bp and mismatching ratio of 0.1 using FLASH (v1.2.11) [18]. Tags were clustered into an operational taxonomic unit (OUT) with a 97% threshold using UPARSE [19]. Chimeras in the OTU were screened and filtered by mapping to the gold database (v20110519) by UCHIME (v4.2.40) [20]. All tags were mapped to the OUT representative sequences using USEARCH GLOBAL to calculate an OUT abundance table. The RDP Classifier [21] was used to assign each OTU to a taxonomic level against the Greengene database, including removal of the OTUs that were not annotated and mismatched with bacteria group. Additional analysis, such as rarefraction curves, the Shannon index, and Good’s coverage, were carried out with mothur (v1.31.2) [22] and QIIME (v1.80) [23].

PCR amplification and sequence analysis

The prevalence survey involving B. burgdorferi s.l., B. miyamotoi, E. chaffeensis, A. phagocytophilum, and the spotted fever group rickettsiae was carried out via PCR based on the genomic DNA extracted from individual ticks. All primer sequences and gene targets are listed in Table 1. The PCR products were separated using 1.5% agarose gel electrophoresis and visualized under UV light. PCR amplicons were sequenced by TsingKe Biological Technology Company (Beijing, China).

TABLE 1 |

Target genes and primers of five tick-borne pathogens.

Pathogens	Target genes	Primers sequence (5′-3′)	Size	References
Borrelia burgdorferi	rrf-rrl	P1: CGACCTTCTTCGCCTTAAAGC P2: TAAGCTGACTAATACTAATTACCC P3: TCCTAGGCATTCACCATA P4: GAGTTCGCGGGAGA	255 bp	[24]
Borrelia miyamotoi	glpQ	Q1: CACCATTGATCATAGCTCACAG Q2: CTGTTGGTGCTTCATTCCAGTC Q3: GCTAGTGGGTATCTTCCAGAAC Q4: CTTGTTGTTTATGCCAGAAGGGT	424 bp	[25]
A. phagocytophilum	msp2	ATGGAAGGTAGTGTTGGTTATGGTATT TTGGTCTTGAAGCGCTCGTA	77 bp	[26]
E. chaffeensis	16SrRNA	ECA: AACACATGCAAGTCGAACGGA H3: TATAGGTACCGTCATTATCTTCCCTAT H1: CAATTGCTTATAACCTTTTGGTTATAAAT H3: TATAGGTACCGTCATTATCTTCCCTAT	389 bp	[27, 28]
Coxiella burnetii	htpAB-associated repetitive element	IS111F1: TACTGGGTGTTGATATTGC IS111R1: CCGTTTCATCCGCGGTG IS111F2: GTAAAGTGATCTACACGA IS111R2: TTAACAGCGCTTGAACGT	260 bp	[29]
Spotted fever group rickettsiae	ompA	Rr190-70F: ATGGCGAATATTTCTCCAAAA Rr190-701R: GTTCCGTTAATGGCAGCATCT	632 bp	[30]

The obtained sequences were queried from the NCBI BLASTn database to find the closest counterparts. Then, phylogenetic analysis was performed with main positive amplicon sequences. Reference sequences of target genes downloaded from GenBank were aligned with the positive amplicon sequences using the ClustalW method with default parameters in MEGA11.0.13. One of the same nucleotide sequences was selected as the representative sequences. Phylogenetic analysis was performed using the neighbor-joining algorithm and bootstrap values were set for 1000 replicates.

RESULTS

Bacterial community and diversity analysis

The V3-V4 region of 16S rRNA was sequenced in 50 tick samples. A total of 3,371,929 reads and 3,326,122 tags were obtained. The average length of reads was 67,438.58 bp. There were 4844 OTUs produced by 50 tick co-polymers.

At least 19 phyla were detected in 50 ticks, among which the 4 phyla with more content (in descending order) were Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes (Fig 1A). The relative abundance of Gammaproteobacteria was the highest at the class level (Fig 1B). There were 63 families detected at the family level, among which Coxiellaceae was highest (37.03%) in all 50 tick microbiomes. The next most frequent families were Moraxellaceae, Enterobacteriaceae, Rickettsiaceae, and Aeromonadaceae (19.48%, 12.39%, 11.91%, and 7.03%, respectively). Spirochaetes was detected in three ticks (YN48, YN50, and YN72) at the phylum and class levels with a relative abundance of 0.005399%, 0.045022%, and 0.023304%, respectively.

FIGURE 1 |

Bacterial community structure variation in 50 tick samples at A, the phylum level; B, the class level, and C, the order level.

One hundred five genera were detected in 50 ticks (Fig 2A). The microorganisms of ticks had their own distribution characteristics at the genus level and the variation among individuals was large (Fig 2B). Acinetobacter was detected in all 50 tick samples; the relative abundance was 1.842026%–44.86713%. Aeromonas was detected in 27 tick samples; the relative abundance was 38.91585%–82.95751%. Anaplasma was detected in 9 tick samples, among which the relative abundance of YN30 was the highest (51.09362%). The relative abundance of YN36 and YN46 was 3.699123% and 0.010648%, respectively. The relative abundance of the other six ticks was <0.01%. Borrelia was only detected in YN72 with a relative abundance of 0.023304%. Coxiella existed in all tick samples with a relative abundance of 0.011416%–95.07642%. Ehrlichia was detected in 27 ticks with a relative abundance of 0.001763%–35.84495%. Enterococcus was detected in 38 ticks but the relative abundance was very low (0.001771%–0.280828%). Pseudomonas was detected in all tick samples; the relative abundance in YN32 and YN56 was 59.54905 and 24.9647, respectively. Rickettsia existed in 48 ticks; the relative abundance in YN35 was the highest (33.72437%). Staphylococcus existed in all ticks with a relative abundance of 0.001893%–0.208508%. Stenotrophomonas was detected in all 50 ticks but the relative abundance in 13 ticks was higher (1.045084%–4.415626%).

FIGURE 2 |

Bacterial community structure variation in 50 tick samples at the genus level. A, Relative abundance of barplot of all tick samples at the genus level; B, relative abundance of potential pathogenic bacteria in ticks and C, average abundance in Menghun County (maroon) and Yunpan County (yellow); D, heatmap of bacterial abundance at the genus level.

In addition, high-probable pathogenic microorganisms in all 50 samples were Coxielia, Acinetobacter, and Rickettsia (Fig 2B). The average abundance of the microorganisms showed a similar pattern although there were differences between the two sample areas. Specifically, ticks in Menhun contained a higher number of Coxilla and Rickettsia than ticks in Yunpan, while the ticks in Yunpan contained a higher number of Acinetobacter and Aeromonas than ticks in Menhun (Fig 2C). More differences in the tick microflora in Menghun and Yunpan existed when considering the relative abundance of Weissella, Aeromonas, and Ehrlichia (Fig 2D).

Although OTU identity can most accurately be viewed as indicating genus equivalents [31], Greengene provides the species level data to users. We listed the species level results as species level data, which might provide insight for corollary studies. However, pathogen investigation should not be limited to the following results. At the species level, 117 species were listed in 50 ticks (Fig 3). Five Acinetobacter species, including A. guillouiae, A. johnsonii, A. lwoffii, A. ursingii, and A. variabilis, were assigned in tick samples. Aeromonas hydrophila existed in 27 ticks with a relative abundance of 0.001814%–82.64443% and Aeromonas salmonicida in 13 ticks with the relative abundance 0.001856%–0.319997%. Coxiella burnetii accounted for a large proportion of the microbial composition in ticks. Rickettsia heilongjiangensis was listed in 48 ticks and was another important species. The proportion of Anaplasma phagocytophilum in YN30, YN36, and YN46 was 51.09362%, 3.699123%, and 0.010648%, respectively, and <0.01% in the other 6 ticks. Borrelia miyamotoi was only detected in YN72 with the relative abundance of 0.023304%. Four Stenotrophomonas species were listed in ticks and included S. chelatiphaga, S. maltophilia, S. rhizophila, and S. terrae.

FIGURE 3 |

Bacterial community structure variation in 50 tick samples at the species level.

Prevalence of tick-borne pathogens

To verify the prevalence of potential pathogens detected by high-throughout sequencing, B. burgdorferi s.l., B. miyamotoi, E. chaffeensis, A. phagocytophilum, Coxiella burnetiid, and the spotted fever group rickettsiae were examined in 50 ticks. Nine ticks were positive for B. burgdorferi s.l. (18% [7 in Yunpan and 2 in Menghun]), 35 were positive for the spotted fever group rickettsiae (70% [20 in Yunpan and 15 in Menghun]), and 1 was positive for A. phagocytophilum (2% [Yunpan]). However, B. miyamotoi, Coxiella burnetiid, or E. chaffeensis were not detected in these 50 ticks based on PCR (Table 2), although 16S rRNA amplifier sequencing results showed these species did in fact exist. This discrepancy suggests that species detection by 16S rRNA amplifier sequencing may lead to a misunderstanding of accuracy or read counts. Therefore, verification using PCR or other methods is necessary.

TABLE 2 |

Results of 5 tick-borne pathogens in Rh. Microplus.

Pathogens	No. of ticks	Positives (rates)	Yunpan		Menghun
			No. of ticks	Positives (rates)	No. of ticks	Positives (rates)
B.b.s.l	50	9 (18%)	24	7 (29%)	26	2 (8%)
B.m	50	0	24	0	26	0
A.P.	50	1 (2%)	24	1 (4%)	26	0
E.C.	50	0	24	0	26	0
C. burnetii	50	0	24	0	26	0
SFGR	50	35 (70%)	24	20 (83%)	26	15 (58%)
B.b.s.l & SFGR	50	7 (14%)	24	6 (25%)	26	1 (4%)
A.P. & SFGR	50	1 (2%)	24	1 (4%)	26	0

Additionally, 7 ticks (14%) were co-infected with B. burgdorferi s.l. and the spotted fever group rickettsiae, and 1 tick (2%) was co-infected with A. phagocytophilum and the spotted fever group rickettsiae.

Genospecies of B. burgdorferi s.l. and the spotted fever group rickettsiae

The results of 16S rRNA amplicon sequencing showed that the spotted fever group rickettsiae was the dominant pathogen in ticks in Yunpan or Menghun. PCR tests showed that B. burgdorferi s.l. was an important pathogen in Yunpan. B. burgdorferi s.l. and the spotted fever group rickettsiae were determined in ticks. The 5S-23S rRNA intergenic region was sequenced for positive ticks. B. burgdorferi s.l. in 9 positive ticks all belonged to B. garinii. The OmpA gene was used to identify the spotted fever group rickettsiae. Candidatus rickettsia jiangxinensis was the main subspecies in these areas. A phylogenetic tree based on ompA gene sequences of SFGR is shown in Fig 4. Of the 35 rickettsia positives, all PCR amplicon sequences were identical (PP515626 and YN-MH13) and placed in a clade with Candidatus R. jiangxinensis from China (MH932061.1).

FIGURE 4 |

Phylogenetic analysis for ompA gene of SFGR. The sequence clustered as Candidatus R. jiangxinensis is marked as “◆”.

DISCUSSION

Rh. Microplus is the dominant tick species from cattle in Xishuangbanna (Yunnan province). Using the 16S rRNA high-throughput sequencing method, a study involving the microbial community and pathogens in Rh. Microplus was conducted to provide a scientific basis for the prevention and control of tick-borne diseases in a local area. In the current study 16S rRNA high-throughput sequencing was used to identify the full community of microbes and main pathogens within 50 parasitic Rh. Microplus. PCR was used to test the pathogens in these ticks. As a result we have a comprehensive understanding of the microorganisms and pathogens in Rh. Microplus in Yungpan and Menghan.

Engorged and semi-engorged ticks included in the current study were collected from cattle. Cattle blood may affect the results of tick microbiomes. Cows have an important role in the tick life cycle because Rh. Microplus prefers to attach to cows and may not change hosts during all life stages. However, Rh. Microplus also can infect human beings. Therefore, ticks with cow blood would help us understand how the natural pathogenic spectrum is from its host. The dominant microflora of parasitic Rh. Microplus in Menghun and Yunpan were Proteobacteria, Bacteroidetes, Actinobacteria, and Firmicutes (in descending order). Proteobacteria is widely distributed in nature, including many pathogens, such as E. coli, Salmonella, Vibrio, and Helicobacter pylori. Bacteroidetes has an important role in carbohydrate fermentation, polysaccharide metabolism, and bile acid and steroid metabolism, which maintains normal physiologic function and microecological balance [32]. These roles are of great significance to ticks.

Based on an analysis of the population structure and diversity index, the species and relative abundance of microorganisms carried with Rh. Microplus varied greatly, which was related to feeding habits, males and females, and different growth stages. Studies have shown that there are some differences in the bacterial population structure of midgut contents between male and female Rh. Microplus in different stages [33].

In the current study conditional pathogens, such as Acinetobacter and aeromonas, were detected with high relative abundance in some Rh. Microplus. Acinetobacter is one of the important opportunistic pathogenic bacteria to cause nosocomial infections. Acinetobacter can cause respiratory tract infections, septicemia, meningitis, endocarditis, and urogenital tract infections. Aeromonas hydrophila also existed in 27 ticks with a relative abundance of 0.001814%–82.64443%. Aeromonas hydrophila is widely distributed in all kinds of water bodies in nature. Aeromonas hydrophila is the primary pathogen of many kinds of aquatic animals. Fish, frogs, and other cold-blooded animals are the natural hosts of bacteria, which are the main sources of human infections. Patient carriers can also cause human-to-human transmission. If infected with Aeromonas hydrophila, the patients with low immune competence are likely to have infections of endogenous blood, the abdominal cavity, biliary tract, wounds, or the urinary tract. The spotted fever group rickettsiae was detected in 48 ticks by 16S rRNA sequencing with different relative abundances, among which 35 tested positive for the OmpA gene by PCR. A previous study showed there were patients infected with Rickettsia siberia in the Yuxi area of Yunnan province [34]. The current study showed that ticks in the Xishuangbanna area were infected with Rickettsia jingxinensis. The results suggested that there may be multiple subspecies of the spotted fever group rickettsiae in Yunnan province. Coxiella burnetii is the pathogen leading to Q fever. Previous research showed that patients with Q fever exist in some areas of Yunnan province [12, 13]. In the current study Coxiella burnetii was detected in 16S rRNA sequencing at the species level but was not verified by PCR, possibly because the results of 16 sRNA V3-V4 high-throughput sequencing at the species level for Coxiella burnetii was not sufficient. Therefore, more verification is needed. The current study research showed that circulation of Coxiella burnetii between livestock and Rh. Microplus in these areas was at a low rate. The findings herein are also consistent with a previous study that to date there are no reports of Q fever in Pu’er or the Xishuangbanna area.

Because the read length for Illumina is more limited than pyrosequencing OTU identity can most accurately be viewed as indicating genus equivalents [31]. Species level results of high throughput sequencing are only presented as references. In the current study no tick infections with B. burgdorferi s.l. were detected by V3-V4 sequencing of 16S rRNA. However, the 50 ticks with B. burgdorferi s.l. were tested by PCR, which showed that 9 ticks were positive. Sequencing of the 5S-23S rRNA intergenic region showed that all the positive ticks were infected with B. garinii, which is one of the main pathogenic Borreliella genotypes in China. This finding suggested that V3-V4 sequencing of 16S rRNA is limited in the detection of species for some pathogens.

Among the 50 ticks, 7 (14%) were co-infected with B. burgdorferi s.l. and the spotted fever group rickettsiae, and 1 (2%) was co-infected with A. phagocytophilum and the spotted fever group rickettsiae, indicating an increased risk of simultaneous human infection with these pathogens in the Xishuangbanna area.

When focusing on the differences between Yunpan and Menghun, we noticed that the diversity of microorganisms in Menghun was higher than Yunpan. However, considering the detection of pathogenic agents the prevalence of B. burgdorferi s.l. and the spotted fever group rickettsiae was slightly higher in Yunpan than Menghun. Regional differences may exist because of the geographic separation of the two areas. However, these distinctions may also be due to uncertainty of sample randomness or size. This finding may require additional investigation on dominant pathogenic agents in ticks to target ticks properly and quickly in patients.

Based on the current study we not only know that the main microbial communities but also the dominant pathogen of parasitic Rh. Microplus was the spotted fever group rickettsiae in Yunpan and Menghun, as determined using the 16S rRNA high-throughput sequencing method. This finding provides basic data for local prevention and control of tick-borne disease. At the same time, it was shown that V3-V4 high-throughput sequencing of 16S rRNA gene is not sensitive to identify species for some bacteria, such as B. burgdorferi s.l. According to the report, while 16S rRNA sequencing is now widely used for microbial identification, this technique has been constrained by short read length of the most commonly used sequencing platform for the microbial community, which often targets only 1-3 variable regions in the 16S rRNA gene, such as V3-V5, V1-V3, or V4 alone. This constraint limits the taxonomic resolution [35, 36]. If more accurate and comprehensive data are needed, a combination of sequencing multiple regions in the 16S rRNA gene is warranted.

CONFLICT OF INTEREST

The authors report no relationship that could be construed as a conflict of interest.

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