Experience of applying the metagenomic sequencing method on fragments of the 16S rRNA gene for the detection and identification of natural focal infection pathogens

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Natural focal infections (NFI) are widespread in the world and represent an important medical and social problem, the importance of which has been steadily increasing in recent years with the identification of new pathogens, the sources and vectors of which are bloodsucking arthropods, small mammals and birds [1–3]. Continued activity and expansion of the territories of natural foci, as well as high anthropogenic load on the environment lead to a constant increase in the number of people in contact with pathogens and exposed to the risk of infection [4]. It has been proved that simultaneous infection of carriers and vectors with different NFI pathogens is a natural and widespread phenomenon, which, in turn, determines the possibility of combined pathology in humans [5–7].

Currently, molecular genetic methods, primarily polymerase chain reaction (PCR), are widely used for laboratory diagnostics of NFI, which allows detecting the presence of DNA/RNA of NFI pathogens in the material in a short period of time. Most of the developed PCR test systems for detection of pathogens are designed for detection of one or more pathogens [8]. Detection of all potential pathogens requires the use of a set of test systems, which is time-consuming and labor-intensive.

Metagenomic sequencing (MGS) methods are modern approaches that allow simultaneous detection and identification of all microorganisms, both known and new, contained in a sample, and do not require culturing [9]. The use of MGS methods for the identification of infectious agents, including NFI, seems to be especially demanded in cases when traditional laboratory tests do not allow identifying the etiologic agent in atypical course of the disease, as well as in cases of mixed infection with different pathogens [10–12]. Furthermore, MGS of field material samples (ectoparasites, organs of small mammals, birds, etc.) collected during epizootological survey of the territory can be useful for obtaining new comprehensive data on the species composition of pathogenic and endosymbiotic microorganisms associated with different types of carriers and vectors of infections [13].

There are several variants of MGS: targeted sequencing of genome regions encoding evolutionarily conserved genes (16S rRNA, etc.) and whole-genome MGS. An approach based on target sequencing of variable regions of the 16S rRNA gene has been widely used to analyze the taxonomic composition of bacteria in samples and to detect pathogenic bacterial species. The advantages of this method include the possibility of taxonomic classification of a wide range of bacteria, the presence of a stage of preliminary specific enrichment of the target region of the bacterial genome before sequencing, and the relative simplicity of bioinformatics analysis of the results compared to the method of sequencing the complete metagenome [14, 15].

The aim of the study was to perform detection and identification of pathogens in samples of field and clinical material by MGS of 16S rRNA gene fragments, analyze the taxonomic composition of endosymbiotic microorganisms in samples.

Materials and methods

Sixteen samples of field (collected during the epizootologic survey) and clinical material with different load of PCR-determined DNA of pathogens of bacterial etiology (Borrelia burgdorferi sensu lato, Anaplasma phagocytophilum, Francisella tularensis, Rickettsia spp., Coxiella burnetii) were studied. The samples contained genetic material of one and several pathogens (Table 1).

 

Table 1. Data on samples used for MGS analysis

No.	Sample type	Sample data, location of extraction	PCR-confirmed pathogen	Сt
1	Tick suspensions	Ixodes ricinus, from vegetation, Krasnodar, Sochi	B. burgdorferi s.l.	21.8
2		I. ricinus, from vegetation, Krasnodar, Sochi	B. burgdorferi s.l.	22.1
3		I. ricinus, from vegetation, Krasnodar, Sochi	B. burgdorferi s.l.	21.1
4		I. ricinus, cattle, Republic of South Ossetia	A. phagocytophilum	31.4
5		I. ricinus, cattle, Republic of South Ossetia	A. phagocytophilum	23.4
6		Dermacentor marginatus, from vegetation, Stavropol	Rickettsia spp.	17.2
7		Rhipicephalus annulatus, cattle, Republic of South Ossetia	Rickettsia spp.	23.3
8		D. marginatus, from vegetation, Stavropol	F. tularensis	26.6
9	Flush from chest cavity	Microtus arvalis, Stavropol	F. tularensis	10.1
10	Tick suspensions	Hyalomma aegyptium from Mediterranean turtle, Republic of Dagestan	B. burgdorferi s.l.	20.2
			Rickettsia spp.	16.3
11		I. ricinus from vegetation, Krasnodar, Sochi	B. burgdorferi s.l.	25.4
			Rickettsia spp.	17.0
12		I. ricinus from vegetation, Krasnodar	B. burgdorferi s.l.	25.7
			Rickettsia spp.	18.8
13		Dermacentor reticulatus from vegetation, Stavropol	F. tularensis	25.5
			Rickettsia spp.	17.2
14		D. reticulatus from vegetation, Stavropol	F. tularensis	12.6
			Rickettsia spp.	21.1
15	Blood serum	Human, Stavropol	C. burnetii	21.4
16		Human, Stavropol	C. burnetii	21.3

 

Work with clinical material was performed with voluntary informed consent of patients. The authors confirm compliance with institutional and national standards for the use of laboratory animals in accordance with the “Consensus Author Guidelines for Animal Use” (IAVES, 23.07.2010). Materials from animals used in the study was obtained according to the Plan of epizootological survey of the Stavropol Territory for NFI and particularly dangerous infections for 2024 (agreed by the Head of the Department of Rospotrebnadzor in Stavropol Krai on 21.12.2023, approved by the Chief Physician of the Center of Hygiene and Epidemiology in Stavropol Krai on 21.12.2023). The study protocol was approved by the Local ethical committee of Stavropol State Medical University (conclusion No. 112 dated 19.05.2023).

Ixodid ticks were collected from April through June from animals and vegetation (flagging), species identification of ticks was performed by morphological method [16]. The ticks were used to make pools of 10 specimens each according to Methodological Recommendations 3.1.0322-23¹. Sample preparation of clinical and field material was performed in accordance with Methodological guidelines 1.3.2569-09².

Ticks were treated with 70% ethanol and washed in phosphate-buffered saline. Homogenization parameters for the obtained samples were selected based on the genus of the ticks. The obtained suspension was centrifuged in 300 μL of sterile physiological solution.

Extraction of nucleic acids from human blood serum samples, homogenates of tick pools and flush from the thoracic cavity of the common vole was performed using the RIBO-prep reagent kit (InterLabService).

The presence of DNA of NFI pathogens in the samples was determined by PCR using the following reagent kits: AmpliSens Coxiella burnetii-FL, AmpliSens TBEV, B. burgdorferi s.l., A. phagocytophilum, E. chaffeensis/E. muris-FL (Central Research Institute of Epidemiology of Rospotrebnadzor), Francisella tularensis-RGF gene (Russian Anti-Plague Institute “Microbe”). DNA of rickettsiae in field samples was detected according to the protocol described by O. Mediannikov et al. [17].

 

Table 2. Primer sequences for amplification of fragments of the gene encoding 16S rRNA

No.	Fragment marking	Fragment length, bp	Annealing temperature, °С	Primer	Sequence 5’–3’
1	V1–V2	311	57	27F	AGAGTTTGATYMTGGCTCAG
				338R	GCTGCCTCCCGTAGGAGT
2	V1–V3	507	57	27F	AGAGTTTGATYMTGGCTCAG
				534R	ATTACCGCGGCTGCTGG
3	V3–V4	404	54	341F	CCTACGGGNGGCWGCAG
				785R	GACTACHVGGGTATCTAATCC
4	V4	293	54	515F	GTGCCAGCMGCCGCGGTAA
				806R	GGACTACHVGGGTWTCTAAT
5	V4–V5	429	54	515F	GTGCCAGCMGCCGCGGTAA
				944R	GAATTAAACCACATGCTC
6	V6–V8	439	57	939F	GAATTGACGGGGGCCCGCACAAG
				1378R	CGGTGTGTACAAGGCCCGGGAACG
7	V7–V9	377	51	1115F	CAACGAGCGCAACCCT
				1492R	TACGGYTACCTTGTTACGACTT

 

Amplification of microbial 16S rRNA gene fragments contained in the samples for MGS was performed using primers described by I. Abellan-Schneyder et al. [18] (Table 2). A separate reaction mixture was prepared for amplification of each variable fragment of the 16S rRNA gene (V1–V2, V1–V3, V3–V4, V4, V4–V5, V6–V8, V7–V9). The composition of the reaction mixture: primer F (C = 7.2 pmol/µL) — 1.25 µL, primer R (C = 7.2 pmol/µL) — 1.25 µL, BioMaster HS-Taq PCR-Color (2×) PCR mixture (Biolabmix) — 12.5 µL, sample DNA — 10 µL. PCR products were amplified according to the thermocycling program: 95°C — 5 min; 95–20 s, Ta — 30 s, 72°C — 40 s (40 cycles); 72°C — 5 min; 4 — ∞.

The size and purity of the obtained PCR products were assessed by electrophoresis in 1% agarose gel. The procedure for purification of PCR products from excess primers and components of the reaction mixture was performed using the CleanMag DNA kit (Eurogen). Equivalent amounts of amplification products of 16S rRNA V1–V9 gene fragments were taken for library preparation. The final concentration of target DNA was measured on a Qubit fluorimeter using the Qubit 1X dsDNA High Sensitivity (HS) kit (Invitrogen).

DNA fragment libraries were prepared according to the Ion Xpress Plus gDNA Fragment Library Preparation protocol (Revision K.0) using the Ion Plus Fragment Library Kit (Thermo Fisher Scientific Inc.). Sequencing of libraries prepared from amplicon mixtures was performed on the GeneStudio S5 Plus platform (Thermo Fisher Scientific Inc.).

The Fastp Qs³, Kallisto⁴, STAR [19], Bowtie2⁵ programs were used for bioinformatics analysis of MGS data on 16S rRNA gene regions. The quality of fastq-files was assessed using Fastp Qc and Kallisto programs; reads with quality index Q < 20 were excluded from the analysis. Sequence alignment and filtering were performed using STAR and Bowtie2 software.

Assembly of short de novo read sequences into longer sequences (contigs) was performed using SPAdes assembler. Taxonomic affiliation of genomic sequences was determined by comparing them with the NCBI database (RefSeq and GenBank using Rapsearch2⁶).

The results of species identification of microorganisms (Borrelia, rickettsiae) obtained by MGS using 16S rRNA gene sections were confirmed by Sanger sequencing method.

Results

MGS was performed on the 16S rRNA gene regions of 16 samples of clinical and field material containing DNA of bacterial pathogens (Table 3). Nucleotide sequences obtained by MGS were deposited in the GenBank database (BioProject PRJNA1227530; SAMN46987881-SAMN46987896).

The number of reads satisfying the Q20 parameter for the studied samples was 1127–40,969. The GC value for all samples varied in the range of 49.7–52.4%, which corresponds to the exome regions of 16S rRNA gene fragments used for MGS analysis. During the processing of the data obtained, it was found that the highest amount of reads after filtration was obtained for sample No. 13 (93,789,000 K). A reduction in the number of reads was observed for samples Nos. 9 and 14 (29,314,000 K and 28,704,000 K). The total number of reads after the filtering step for the 4 samples (Nos. 2, 5, 7 and 10) ranged between 9,172,000–16,651,000 K. The number of filtered reads for the remaining samples ranged 488–7,633,000 K. The least amount of filtered data (Q < 20) after bioinformatics processing was observed for samples Nos. 4, 8 and 11. The highest number of poor quality data (Q < 20) was obtained for samples Nos. 9, 13, and 14. The result of data quality assessment is shown in Fig. 1.

 

Fig. 1. Histogram showing the result of MGS data quality assessment by 16S rRNA gene regions. The color of the sectors in the histogram reflects the number of reads for each sample that passed quality filtering (in %, top). For a color version of the figure, see the journal website.

 

NFI pathogens identified by MGS using 16S rRNA gene variant regions

In the study of suspension samples of ixodid ticks (Nos. 1–8, Table 3) with PCR-confirmed mono-infection with Borrelia genetic complex B. burgdorferi s.l., A. phagocytophilum, rickettsiae and F. tularensis, using the MGS method of 16S rRNA gene, detected representatives of the Borrelia (samples Nos. 1–3), Francisella (sample No. 8) (microorganisms identified to genus) genera, as well as R. aeschlimannii (samples Nos. 6, 7, microorganism identified to species). The pathogen of human granulocytic anaplasmosis could not be detected by MGS (samples Nos. 4, 5). The results of species identification of R. aeschlimannii in samples Nos. 6, 7 were confirmed by Sanger sequencing of a genome fragment.

 

Table 3. Comparison of results obtained by PCR and MGS methods of 16S rRNA gene fragments

Sample No.	PCR method		MGS method of 16S rRNA gene fragments
	identified pathogens	Ct	Q20, % (number of reads)	pathogens identified (number of reads corresponding to the target pathogen, %)
Mono-infected samples
1	B. burgdorferi s.l.	21.80	91.50 (5822)	Borrelia spp. (2,90)
2	B. burgdorferi s.l.	22.10	91.90 (20,236)	Borrelia spp. (3,10)
3	B. burgdorferi s.l.	21.10	92.20 (10,627)	Borrelia spp. (3,20)
4	A. phagocytophilum	31.40	87.50 (12,922)	Unidentified
5	A. phagocytophilum	23.40	91.80 (6618)	Unidentified
6	Rickettsia spp.	17.20	91.60 (8239)	R. aeschlimannii (8,90)
7	Rickettsia spp.	23.30	92.10 (22,324)	R. aeschlimannii (0,80)
8	F. tularensis	26.60	91.00 (1127)	Francisella spp. (2,60)
9	F. tularensis	10.10	91.60 (40,161)	F. tularensis (9,90)
Mixed-infected samples
10	B. burgdorferi s.l.	20.20	90.60 (163,336)	B. turcica (27,00) B. hispanica (7,60)
	Rickettsia spp.	16.20		R. aeschlimannii (9,50)
11	B. burgdorferi s.l.	25.40	91.90 (11,506)	Borrelia spp. (2,40)
	Rickettsia spp.	17.00		Rickettsia spp. (2,60)
12	B. burgdorferi s.l.	25.70	92.40 (5952)	B. valaisiana (7,60)
	Rickettsia spp.	18.80		Rickettsia spp. (2,60)
13	F. tularensis	25.50	91.00 (11,506)	F. tularensis (9,90)
	Rickettsia spp.	17.20		R. aeschlimannii (11,30)
14	F. tularensis	12.60	91.70 (40,696)	F. tularensis (9,90)
	Rickettsia spp.	21.10		Rickettsia spp. (4,10)
Clinical material
15	C. burnetii	21.40	90.60 (8926)	C. burnetii (5,30)
16	C. burnetii	21.30	90.00 (7223)	C. burnetii (5,00)

 

Mixed-infected samples of ixodid ticks (samples Nos. 10–14, Table 3) with a combination of two tick-borne pathogens (B. burgdorferi s.l and Rickettsia spp.; F. tularensis and Rickettsia spp.) were studied. All target microorganisms were detected in the samples by the MGS method. R. aeschlimannii (samples Nos. 10, 13), B. valaisiana (sample No. 12), F. tularensis (samples Nos. 13, 14) were identified to species, also microorganisms of the Rickettsia (samples Nos. 11, 12, 14) and Borrelia (samples Nos. 11, 12, 14) genera whose species could not be identified were detected in the samples. Genetic markers (DNA) of Borrelia, pathogens of relapsing fevers (B. turcica, B. hispanica) were detected in sample No. 10 by MGS. It was not possible to confirm the results of Borrelia species identification in sample No. 10 by Sanger sequencing, which is due to the mixed-infection of the sample with H. aegyptium tick suspension. The results of species identification of the remaining microorganisms detected in the samples were confirmed by sequencing of pathogen genome fragments.

By MGS method in 3 samples containing DNA of the tularemia pathogen (Ct 10.1; 12.6; 25.5) F. tularensis was identified to species, in 1 sample (Ct 26.6) the presence of microorganisms of the Francisella spp. genus was found, species identification could not be performed.

С. burnetii was identified by MGS results using the 16S rRNA gene region in 2 obviously positive blood plasma samples from patients with Q fever (Ct values 21.3–21.4). The presence of C. burnetii DNA was detected in clinical samples 15, 16 from Q fever patients by MGS, the proportion of target reads was 5.0–5.3%. Furthermore, nucleotide sequences of Methylophilus medardicus bacteria, as well as representatives of the Acinetobacter and Shingomonas genera were detected in clinical samples, which may indicate possible contamination of samples at the stages of collection, storage and laboratory examination [20].

We compared the results of the study of field and clinical samples with different DNA load of pathogens of NFI obtained by MGS methods using 16S rRNA gene regions and PCR. It is shown that as a result of MGS of 6 samples, positive for the presence of borreliae DNA of the B. burgdorferi s.l. genetic complex, identification of borreliae to genus (Borrelia spp. Ct 21.8; 22.1; 21.1; 25.4) was carried out in 4 samples, while the identification to species (B. valaisiana Ct 25.7, B. turcica, B. hispanica St 20.2) was carried out in 2 samples.

According to MGS results, fragments of Rickettsia spp. genome were detected in all obviously positive samples, in 4 samples (Ct values 16.2; 17.2; 17.2 and 23.3) the rickettsia species (R. aeschlimannii) was identified, in 3 samples (Ct values 17.0; 18.8 and 21.1) species identification of rickettsia could not be performed. The presented results of identification of Borrelia and rickettsiae in the studied material (Table 3) are confirmed by the literature data on the difficulty of species identification by MGS of representatives of the Rickettsia and Borrelia genera [8, 15]. Accurate species identification of Rickettsia and Borrelia using MGS is difficult due to high homology of nucleotide sequences of 16S rRNA gene for these bacterial pathogens [12, 13]. In the case of detection of microorganisms of the Rickettsia and Borrelia genera by MGS of the 16S rRNA gene, further identification to species by Sanger sequencing may be necessary.

The only pathogen that could not be confirmed by MGS was A. phagocytophilum.

Taxonomic composition of the microbiome of ixodid ticks

The study of the taxonomic structure of the microbiome of ticks was carried out in accordance with their species affiliation, place and territory of collection (Fig. 2).

 

Fig. 2. Taxonomic composition of microbiomes of ixodid ticks (sample numbers are indicated by numbers). Due to the availability of a large amount of data, only the most represented taxa are marked with color markers. For a color version of the figure, see the journal’s website

 

Main taxonomic groups of the tick microbiome:

• for representatives of I. ricinus (samples Nos. 1–3): Flavobacterium spp. (57–81%), Pseudomonas spp. (7–27%), Serratia spp. (2–4%), Pedobacter spp. (2–4%);
• for representatives of I. ricinus (samples Nos. 4, 5): Candidatus Midichloria mitochondrii (31–87%), Clostridium spp. (6–61%), Sphingomonas spp. (3%), Staphylococcus spp. (1–10%), Bradyrhizobium spp. (1%);
• for representatives of I. ricinus (samples Nos. 11, 12): Pseudomonas spp. (7–49%), Serratia spp. (4–12%), Rickettsiella endosymbiont of Pandinus imperator (3–19%), Rhodobacterales spp. (3%);
• for representatives of D. reticulatus (samples Nos. 13, 14): Flavobacterium sp. Nj (25–53%), Cardinium endosymbiont of Bemisia tabaci (19%), Clostridium spp. (15%), Francisella-like endosymbiont of Dermacentor reticulatus (9–21%), Francisella persica (2%), uncultured Francisella spp. (1–6%), Bradyrhizobium spp. (1–3%), Dyadobacter spp. (1–3%);
• for representatives of R. annulatus (samples No. 7): Rickettsiella endosymbiont of Pandinus imperator (5%), uncultured Coxiella spp. (10%), Wolbachia pipientis (9%), Candidatus Coxiella mudrowiae (6%), Coxiella endosymbiont of Rhipicephalus microplus (3%), Coxiella endosymbiont of Rhipicephalus geigyi (1%), Coxiella endosymbiont of Rhipicephalus turanicus (3%), Shingomonas spp. (5%), Staphilococcus spp. (5%), Bradyrhizobium spp. (3%), Flavobacterium spp. (2%), Leptotrichia wadei (2%);
• for representatives of H. aegyptium (samples No. 10): Rickettsia endosymbiont of Bemisia tabaci (3%), Flavitalea flava (1%), uncultured Borrelia spр. (9%), Blastopirellula marina (4%), Dyadobacter alkalitolerans (2%), Bradyrhizobium (1%);
• for representatives of D. marginatus (samples Nos. 6, 8): Pseudomonas spp. (9–33%), uncultured Arsenophonus spp. (11%), uncultured Alteromonas spp. (2%), Alphaproteobacteria bacterium (2%), Coxiella endosymbiont of Dermacentor marginatus (2%).

Discussion

In this study, we applied the MGS method using 16S rRNA gene regions for detection and identification of known pathogens of bacterial etiology in samples of clinical and field material, and investigated the possibility of its use in simultaneous detection of different types of pathogens. Mixed infection with two pathogens of NFI (borreliosis, tick-borne rickettsiosis, tularemia) in several pools of ixodid ticks was determined. The negative result in the detection of A. phagocytophilum may be due to low concentration of the bacterial pathogen in the tested material, as well as insufficient quality and quantity of data obtained after bioinformatics processing.

The results of using the method of targeting MGS by 16S rRNA gene regions to detect pathogens of NFI in samples of clinical and field material are presented in a number of publications. Thus, L. Kingry et al., using the MGS method at the 16S rRNA gene region in clinical samples from febrile patients, detected tick-borne pathogens: B. burgdorferi s.l., B. mayonii, B. miyamotoi, B. hermsii, A. phagocytophilum, Ehrlichia chaffeensis, E. muris subsp. eauclarinsis, E. ewingii, and F. tularensis [8]. R. Takhampunya et al. detected microorganisms of the Anaplasma, Bartonella, Coxiella, Leptospira, Orientia genera in the blood of patients with fever of unclear genesis [15]. Furthermore, other authors have obtained the results of the study using the 16S rRNA MGS method of samples of ixodid ticks for the entire spectrum of tick-borne pathogens [20–22].

One of the demanded areas of application of the MGS method for the 16S rRNA gene region is the study of clinical samples from patients with fevers of unclear genesis in cases when traditional methods of research (PCR, enzyme immunoassay, serologic methods, etc.) failed to identify the pathogen. Detection of microorganisms belonging to genera including pathogens of NFI in the material from febrile patients will allow further in-depth molecular genetic analysis to confirm the presence of DNA of the detected pathogens in the sample.

In the literature, there are numerous reports of human cases of combined forms of NFI caused by associations of microorganisms, the clinical course of which is significantly more severe compared to mono-infections, and laboratory confirmation of the diagnosis is difficult [23, 24]. In the etiologic interpretation of such cases, the metagenomic approach acquires special relevance and clearly demonstrates its advantage.

As a result of bioinformatics processing of MGS data using variable fragments of the 16S rRNA gene, the taxonomic composition of the microbiome associated with I. ricinus, D. reicinus, R. annulatus, H. aegyptium, D. marginatus ticks collected in the southern regions of Russia (Fig. 2) was determined. The microbiome of all ticks was dominated by the following microorganisms: Flavobacterium spp., Pseudomonas spp., Serratia spp., Aeromonas spp., Pedobacter spp., Bradyrhizobium spp. and Shingomonas spp. Probably, some of these bacteria entered the organism of mites in the process of their vital activity or inhabit their chitinous exoskeleton and digestive system, while not being symbionts of arthropods [25].

Furthermore, DNA markers of microorganisms — endosymbionts of ticks, including Candidatus Midichloria mitochondrii (samples Nos. 4, 5), representatives of genera Rickettsiella, Coxiella, Candidatus Coxiella mudrowiae (sample No. 7), non-pathogenic and conditionally pathogenic for human species Francisella spp. (F. frigiditurris, F. philomiragia, F. persica) (sample No. 13).

It is interesting to note that the composition of the bacterial community of the ixodid tick pool of sample No. 10 based on the data of MGS sites of the 16S rRNA gene differed significantly from the other samples, which may be related to the peculiarities of the tick feeder and the species of the vector of tick-borne infections — the Mediterranean turtle. Bacteria of the Bradyrhizobium genus — symbiotic microorganisms of plants, Blastopirellula marina and Dyadobacter alkalitolerans, being natural inhabitants of saline water bodies and sandy soils, were detected in small amounts. The obtained results, presented in Table 3 and Fig. 2, are consistent with the literature data on bacterial pathogens carried by H. aegyptium ticks and found in the blood of reptiles (pythons, lizards and turtles) [26, 27]. Information has been published on the detection of markers of pathogens (borreliosis, tick-borne rickettsiosis) during the study of biological material from reptiles and ticks removed from reptiles: R. aeschlimannii [28], B. turcica [29, 30], B. hermsii [31], B. crocidurae [32] and B. hispanica [33]. The above data on the high occurrence of Borrelia — pathogens of relapsing fevers in animals confirm the wide distribution of these bacterial pathogens in a number of regions and have almost ubiquitous character.

The use of MGS in the study of ixodid ticks can obviously be effective in obtaining comprehensive information on the species spectrum of NFI pathogens, as well as endosymbionts associated with different species of ixodid ticks inhabiting different regions. As a consequence, new perspectives in the study of the species spectrum of pathogens, as well as the selection of microorganisms to assess the specificity of existing and developing PCR test systems for the study of field samples [34]. The information obtained in this work about the species of endosymbiotic microorganisms detected in ixodid tick pools is consistent with previously published data [34].

It is necessary to take into account the limitations of the method when determining the species affiliation of closely related microorganisms, including for a number of Borrelia and Rickettsia species based on MGS data [35, 36]. It has been shown that the results of taxonomic classification may differ depending on the variation regions used [37–39]. In this case, the use of a mixture of primers targeting different hypervariable regions of the 16S rRNA gene [40–42] contributes to increasing the discriminatory power of the method, which was applied in the present study.

Conclusion

We analyzed the taxonomic composition of microorganisms, as well as the detection and identification of pathogens in samples by MGS method using 16S rRNA gene sections, and experimentally confirmed the effectiveness of this method for the detection of pathogens in clinical and field samples. Microorganisms belonging to Rickettsia spp., Borrelia spp., Francisella spp. were detected, including human pathogenic species, as well as species identification of pathogens with different DNA load in the studied material, in particular, R. aeschlimannii (Ct at PCR up to 23.3), C. burnetii (Ct < 21.4), F. tularensis (Ct < 26.6), Borrelia spp. burgdorferi s.l. (B. valaisiana, Ct < 25.7), borreliae of the pathogens of relapsing fevers (B. turcica, B. hispanica Ct < 20.2). The taxonomic structure of the microbiome of I. ricinus, D. reticulatus, R. annulatus, H. aegyptium, D. marginatus ticks collected in the southern regions of Russia was studied. It is shown that microorganisms from genera Flavobacterium, Pseudomonas, Serratia, Aeromonas, Pedobacter, Bradyrhizobium and Shingomonas predominate. DNA markers of microorganisms — endosymbionts of ticks Candidatus Midichloria mitochondrii, representatives of genera Rickettsiella, Coxiella, non-pathogenic and conditionally pathogenic for human species of the genus Francisella — were found in the pools of ixodid ticks.

Continued work in this area will allow a more accurate assessment of the resolution of the method for the detection and identification of pathogens. The study of patterns of existence of pathogens in the structure of the tick microbiome is a promising area for further research.

The main advantage of the MGS method using the 16S rRNA gene region in the study of field and clinical samples is the possibility to perform simultaneous detection and identification of all bacteria in the sample, including known pathogens, without the necessity for several diagnostic tests. Targeted MGS can be used for etiologic interpretation in case of atypical course and abbreviated clinical picture of the disease, in case of mixed-infection with several pathogens of bacterial etiology, when there is a difficulty with the diagnosis using traditional laboratory methods of investigation. MGS can also be used to obtain information on the taxonomic composition of the bacterial microbiome in the organism of different species of carriers and vectors of NFI pathogens.

 

¹  Methodological recommendations MP 3.1.0322-23 “Collection, accounting and preparation for laboratory examination of blood-sucking arthropods in natural foci of infectious diseases” (approved by the Head of Rospotrebnadzor on 04/13/2023).

² Methodological guidelines MU 1.3.2569-09 “Organization of work of laboratories using methods of nucleic acid amplification when working with material containing microorganisms of pathogenicity groups I-IV” (approved by the Head of Rospotrebnadzor on 12/22/2009).

³ URL: https://github.com/OpenGene/fastp

⁴ URL: https://github.com/Roslin-Aquaculture/RNA-Seq-kallisto

⁵ URL: https://bowtie-bio.sourceforge.net/bowtie2/index.shtml

⁶ URL: https://github.com/zhaoyanswill/RAPSearch2

About the authors

Oksana V. Vasilieva

Stavropol Plague Control Research Institute

Author for correspondence.
Email: vasileva_ov@snipchi.ru
ORCID iD: 0000-0002-8882-6477

Cand. Sci. (Med.), Head, Laboratory for diagnostics of bacterial infections

Россия, Stavropol

Diana V. Ul’shina

Stavropol Plague Control Research Institute

Email: vladidiana@yandex.ru
ORCID iD: 0000-0001-7754-2201

Cand. Sci. (Biol.), senior researcher, Laboratory for diagnostics of bacterial infections

Россия, Stavropol

Anna S. Volynkina

Stavropol Plague Control Research Institute

Email: volyn444@mail.ru
ORCID iD: 0000-0001-5554-5882

Cand. Sci. (Biol.), Head, Laboratory for diagnostics of viral infections

 

Россия, Stavropol

Sergey V. Pisarenko

Stavropol Plague Control Research Institute

Email: pisarenko_sv@mail.ru
ORCID iD: 0000-0001-6458-6790

Cand. Sci. (Chem.), leading researcher, Laboratory of biochemistry

Россия, Stavropol

Yulia V. Siritsa

Stavropol Plague Control Research Institute

Email: merendera@mail.ru
ORCID iD: 0000-0001-9442-6966

researcher, Laboratory for diagnostics of bacterial infections

Россия, Stavropol

Olga A. Gnusareva

Stavropol Plague Control Research Institute

Email: gnusarevao@mail.ru
ORCID iD: 0000-0002-9044-1808

researcher, Laboratory for diagnostics of bacterial infections

Россия, Stavropol

Natalia A. Yatsenkо

Regional Specialized Clinical Infectious Diseases Hospital

Email: natali.yanet@yandex.ru
ORCID iD: 0000-0003-4353-4777

Chief Physician

Россия, Stavropol

Alexandr N. Kulichenko

Stavropol Plague Control Research Institute

Email: stavnipchi@mail.ru
ORCID iD: 0000-0002-9362-3949

Sci. (Med.), Professor, Аcademician of RAS, Director

 

Россия, Stavropol

References

Supplementary files