Pattern-Recognition Receptors and Cervical Microbiome in Patients with Early Miscarriages

Abstract

Early miscarriages are one of the main causes of reproductive losses. The objective of this study was to estimate Toll- and NOD-like receptors mRNA expression levels and their correlation with microbiome composition in the endocervix of patients with 1st trimester miscarriages (n = 15) compared to those with healthy ongoing pregnancies (n = 15). Samples of the cervical epithelium were collected using a cytobrush. The expression of Toll-like receptors (TLR1, TLR2, TLR4, and TLR6) and NOD-like receptors (NOD1, NOD2, NLRP1, and NLRC4) was estimated by quantitative reverse-transcriptase PCR. Cervical microbiome composition was assessed by 16S rRNA next-generation sequencing. We found no differences in community state types (CSTs) and alpha- and beta-diversity between the groups. The dominant microorganisms in both groups were Lactobacillus iners. Bifidobacterium breve and Aeromonas popoffii, whose abundance was significantly higher in patients with miscarriages compared to those with ongoing pregnancies. Corynebacterium callunae and Turicibacter sanguinis, on the contrary, were significantly higher in women of the control group. Only decreased expression of TLR2 was observed in patients with miscarriages. The expression of other Toll- and NOD-like receptors showed no significant differences between the groups. NOD1 mRNA expression had a strong positive correlation with the abundance of L. iners, L. delbrueckii, and Aerococcus christensenii; TLR6 mRNA expression had a strong positive correlation with L. iners. The findings suggest that variations in Toll- and NOD-like receptors mRNA expression and specific shifts in the cervical microbiome might play a role in the immunological landscape, potentially contributing to the pathogenesis of early miscarriages.

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1. Introduction

One of the main causes of early miscarriages, accounting for approximately 15% of clinically recognised pregnancies [1, 2], are infections and dysbiotic conditions of the female reproductive tract [3–5].

Culture-independent studies, such as 16S ribosomal RNA (16S rRNA) high throughput sequencing, offer significant insight into the pathogenesis of early miscarriages by enabling the detection of the full spectrum of bacteria within the human body, including those in the female reproductive tract [6, 7].

Pattern-recognition receptors are located predominantly on innate immune cells [8] and can recognize conserved molecular structures known as pathogen-associated molecular patterns (PAMPs) of bacteria, fungi, viruses, and protozoa and induce and regulate the intensity of inflammation [9]. The main pattern-recognition receptors are Toll-like receptors (TLRs) and NOD-like receptors (NLRs). Toll-like receptors 1, 2, 5, and 6 are located on the surface of innate immune cells [8]. Heterodimers of TLR2 with TLR1 can recognize bacterial triacyl lipopeptides, while heterodimers of TLR2 with TLR6 recognize diacyl lipopeptides and lipoteichoic acid of bacteria and zymosan of fungi. TLR2 without the formation of heterodimers can recognize peptidoglycan of Gram-positive bacteria, lipoarabinomannan of Mycobacterium tuberculosis, glycosylphosphatidylinositol-anchored mucin of Trypanosoma cruzi, proteins MALP-2 and MALP-404 of Mycoplasma spp. TLR5 recognizes bacterial flagellin. These receptors activate the production of proinflammatory cytokines via the MyD88-dependent pathway [8, 10, 11]. TLR4 is located on the cell surface, but it can be internalized upon activation. The main ligands for TLR4 are lipopolysaccharides of Gram-negative bacteria. If located on the cell surface, it activates proinflammatory cytokines’ production via the MyD88-dependent pathway. The intracellular location of TLR4 switches signaling to the TRIF-dependent pathway, which promotes the secretion of type I interferons [12].

NOD-like receptors are cytosolic receptors of innate immune cells recognizing intracellular pathogens [13]. NOD1 recognizes diaminopimelic acid (iE-DAP), which is the component of the peptidoglycan of some Gram-positive and all Gram-negative bacteria, which leads to activation of proinflammatory cytokines’ production via NF-kB [14]. NOD2 recognizes intracellular muramyl dipeptide, which is the major component of peptidoglycan of both Gram-negative and Gram-positive bacteria. Its activation leads to the production of antimicrobial peptides and proinflammatory cytokines [14]. NOD2 is also able to recognize viral RNA, which leads to the production of beta-interferons and the enzyme RNase L leading to the degradation of viral RNA [15]. NOD2 activates autophagy, providing the elimination of infected cells [16]. NLRP1 and NLRC4 activation leads to the formation of inflammasomes and cell death via pyroptosis. Ligands for NLRP1 are muramyl dipeptide and anthrax lethal toxin [17], as well as viral proteases [18]. The ligands for NLRC4 are bacterial flagellin and proteins of the bacterial type III secretion system [19, 20].

Although the cervical canal is lined with columnar epithelium, the same as the uterine epithelium, the cervical microbiome is still poorly understood. The same applies to the mechanisms of induction of the inflammatory response, in particular the influence of microbiome components on the expression of innate immune signaling receptors. The lack of data on the cervical microbiome and the expression of signaling receptors, and their correlation in the cervical canal during normal early pregnancy and in patients with early miscarriage, dictated the need for this study. The study aimed to assess the cervical microbiome composition in patients with ongoing pregnancy and early miscarriages and its correlation with mRNA expression of Toll- and NOD-like receptors in the cervical canal.

2. Materials and Methods

2.1. Study Design

The study was designed according to the ethical standards of the Declaration of Helsinki by the World Medical Association (1964) and its subsequent amendments and approved by the Ethical Committee of Voronezh State University (protocol No. 42-05 of 27/12/2021). The patient’s written consent for the collection of samples, data of anamnesis and clinical examination, and publication of anonymized data was obtained.

In a prospective longitudinal cohort study, pregnant women (n = 30) were recruited at Yakovlevo Central District Hospital (Belgorod region, Russia) at the beginning of their antenatal care (7–11 weeks of gestation) from January 2022 till January 2023. Patients were divided into two groups: women with ongoing pregnancy (group I, n = 15), who further were delivered at term (37–42 weeks of gestation), and patients with miscarriages (group II, n = 15). The diagnosis of ongoing pregnancy and miscarriage was made according to clinical examination and ultrasound examination and then confirmed by histopathological examination.

Exclusion criteria for all patients were vaginal intercourse during the last 3 days, vaginal bleeding, the usage of any antibiotics, prebiotics, probiotics, or synbiotics during the previous month, presence of severe nonobstetrical conditions, including primary and secondary immune deficiency, and uterine malformations. The clinical characteristics of study groups are shown in Table 1.

Table 1

Clinical characteristics of study groups (n = 30) (M ± SEM).

Data	Group I (ongoing pregnancy, n = 15)	Group II (miscarriages, n = 15)	$p$ value (ongoing pregnancy vs. miscarriages)
Age (years)	28.73 ± 1.65	30.93 ± 1.77	0.50
Gravidity	2.2 ± 0.37	2.93 ± 0.34	0.12
Parity	0.60 ± 0.21	1.13 ± 0.22	0.08
Number of miscarriages in anamnesis	0.33 ± 0.13	0.40 ± 0.16	0.99
Number of artificial abortions in anamnesis	0.33 ± 0.16	0.47 ± 0.19	0.63
Weight (kg)	64.60 ± 4.43	65.13 ± 3.61	0.52
Height (m)	167.0 ± 1.58	163.8 ± 1.03	0.21
Body mass index (BMI)	23.17 ± 1.54	23.58 ± 1.21	0.41

Two samples were taken from the cervix of each patient. One sample was used for further sequencing and microbiome studies, and another was used for gene expression studies. The total number of samples included in the study was 60. The samples for microbiome and messenger RNA (mRNA) expression studies were collected by cytobrush. The biomaterial was collected in two Eppendorf tubes with RNAlater™ stabilization solution (Thermo Fisher Scientific, Madison, WI, USA), delivered to the laboratory, and stored at +4°C for 24 hours according to the instruction. After 24 hours, the biomaterial was stored at −80°C until the start of the investigation.

2.2. RNA Extraction

Total RNA was isolated using the RNA-Extran kit (Syntol, Moscow, Russia) according to the manufacturer’s instructions. After that, the RNA sample was processed with DNase (Syntol, Moscow, Russia) to remove possible DNA contamination. To determine the content and purity of total RNA, a Nano-500 spectrophotometer (Allsheng, Hangzhou, China) was used, measuring the absorbance ratio of all samples at 230 and 260 nm, with 260/230 values ranging from 2.0 to 2.2. First-strand complementary DNA (cDNA) was synthesized using the MMLV RT kit (Evrogen, Moscow, Russia) according to the instructions.

2.3. Gene Expression Assay and Analysis

Specific primers were selected using the Blast database (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome, accessed on 26 December 2023). All obtained primer pairs were tested for the possibility of the formation of hairpins and dimers using Beacon Designer Free Edition software (https://www.premierbiosoft.com/qOligo/Oligo.jsp?PID=1, accessed on 26 December 2023). Actin beta (ACTB) was selected as a reference gene. Primers were synthesized by Evrogen (Evrogen, Moscow, Russia). The amplification efficiency of the primer sequences was evaluated before quantification. The sequences of primers are presented in Table 2.

Table 2

Primer sequence.

Gene	Primer	Sequence, 5′-3′	Accession number	Amplicon length	Annealing temperature (°C)
TLR1	Forward	AAG​CAG​GTT​GTC​TTG​TGT​TAA​AG	NM_003263.4	188	57
	Reverse	GAT​TCC​TTT​TGT​AGG​GGT​GCC
TLR2	Forward	ATC​CTG​CTC​ACG​GGG​GTC​CTG	XM_011532215.3	106	57
	Reverse	TGC​TGG​GAG​CTT​TCC​TGG​GC
TLR4	Forward	GGA​GCC​CTG​CGT​GGA​GGT​GGT​T	NM_138554.5	90	57
	Reverse	GTT​GAG​AAG​GGG​AGG​TTG​TCG​GGG​A
TLR5	Forward	TGC​TAC​TGA​CAA​CGT​GGC​TT	NM_003268.6	403	58
	Reverse	TGG​TCT​CCC​ATG​ATC​CTC​GT
TLR6	Forward	ACC​CTT​TAG​GAT​AGC​CAC​TGC	XM_011513613.4	234	59
	Reverse	GAC​CTG​AAG​CTC​AGC​GAT​GT
NOD1	Forward	CCT​GGT​GGC​CAA​GTG​ATT​GT	NM_006092.4	821	55
	Reverse	ACC​GAA​GGA​AAT​TGC​CAT​CAA​AG
NOD2	Forward	CTA​ATG​GGC​TTT​GAT​GGG​GGA​A	NM_022162.2	240	55
	Reverse	AGG​TGG​AAG​CCC​TCG​TAG​T
NLRP1	Forward	TAC​CGG​TGG​AAC​TCT​TGT​GC	NM_033004.4	942	55
	Reverse	GGG​CTG​GAG​GGA​TCA​GAG​TA
NLRC4	Forward	CCT​GCT​GAC​TGA​GAG​AAC​ACA	NM_021209.4	949	55
	Reverse	GGC​AGT​TCT​GGG​GCT​TGA​AT
ACTB	Forward	CAG​GCA​CCA​GGG​CGT​GAT​GG	NM_001101.5	994	64
	Reverse	GAT​GGA​GGG​GCC​GGA​CTC​GT

Thermal cycling conditions consisted of total denaturation—95°C for 5 min; 40 cycles of denaturation—95°C for 30 s; primer annealing—from 55 to 64°C for 30 s; and elongation—68°C for 30 s on a Bio-Rad CFX96 real-time polymerase chain reaction (qPCR) system (Bio-Rad Laboratories, Inc., CA, USA). qPCR was performed using a 5X qPCRmix-HS SYBR kit (Evrogen, Moscow, Russia). The 2^−ΔΔCt analysis method was used to calculate relative expression levels that were normalized to the reference gene.

2.4. DNA Extraction and 16S rRNA Sequencing

DNA was isolated using the Hi Pure DNA Microbiome kit (Magen, Hangzhou, China) according to the protocol. At this stage, a sample was added containing Milli-Q water used in the laboratory and a sterile cytobrush. This control sample underwent the same steps as the tested samples to exclude contamination during the data processing stage. The isolated DNA was monitored in a 1.5% agarose gel and also on a Qubit 2.0 fluorometer (Thermo Fisher Scientific, Madison, WI, USA).

The hypervariable V3 region of the 16S rRNA gene was selected for sequencing. For targeted amplification, primers 337F (5′-GACTCCTACGGGGGAGGCWGCAG-3′) and 518R (5′-GTA​TTA​CCG​CGG​CTG​CTG​CTG​G-3′) and the 5X Screen Mix-HS Master Mix Kit (Evrogen, Moscow, Russia) were used. Amplification was performed on a Bio-Rad CFX96 Touch™ Real-Time PCR Detection System (Bio-Rad Laboratories, Inc., CA, USA) using the protocol: total denaturation—94°C for 4 min; 25 cycles of denaturation—94°C for 30 s; primer annealing—53°C for 30 s; elongation—72°C for 30 s; and final elongation—72°C for 5 min.

Amplicons were purified using AMPureXP beads (Beckman Coulter, Brea, CA, USA) following the protocol. For library preparation for Ion Torrent PGM (Thermo Fisher Scientific, Madison, WI, USA), we used the NEBNext Fast DNA kit (New England Biolabs, Ipswich, MA, USA) according to the manufacturer’s instructions. Barcoding was performed using the NEXTflex adapter kit (Ion Torrent; 64 adapters; PerkinElmer, Inc., Waltham, MA, USA). Libraries were quantified using the QIAseq Library Quant System kit (QIAGEN, Hilden, Germany). The Ion PGM Hi-Q View OT2 Kit and the Ion PGM Hi-Q View Sequencing Kit were used for emulsion PCR, chip preparation and loading, and setting up the Ion Torrent Sequencer according to protocols (Thermo Fisher Scientific, Madison, WI, USA). Ion 318 Chip v2 BC was used for sequencing.

2.5. Sequencing Data Processing and Statistical Analysis

Preprocessing was performed on the Torrent Suite Software server. Reads in BAM format were converted to FASTQ format using the File Exporter plugin. A maximum expected error threshold of 1.0 (DADA2 package in R) was used to filter low-quality reads. In the next step, filtered reads of suitable quality were unified by length and demultiplexed. The UNOISE2 algorithm was used to generate operational taxonomic units (OTUs). Taxonomic identification was performed using the SILVA database version 138 (https://www.arb-silva.de/, accessed on 20 November 2023). A limit of 97% relative match with sequence variants was used to determine taxonomic status. Exclusion of contaminants from samples was performed using the Decontam R package. Decontam was run using the “prevalence” option, using algorithms based on the presence/absence of each sequence feature in true positive samples compared to their abundance in the negative control to identify contaminants. The threshold was set at 0.4. Control samples were then removed from the resulting file. A total of eight microorganisms were identified as contaminants (Erysipelotrichaceae UCG-003, Cellvibrio fibrivorans, Megamonas funiformis, Corynebacterium pyruviciproducens, Odoribacter splanchnicus, Capnocytophaga sputigena, Brochothrix thermosphacta, and Sutterella wadsworthensis).

Differential analyses of bacterial species abundance were performed using the comprehensive MaAsLin2 package for R, which is designed to efficiently identify multivariate relationships between phenotypes, environment, exposure, covariates, and microbial metacommunity traits. Expression analysis in the study groups was performed using the Mann–Whitney test implemented in GraphPad Prism 9 (GraphPad, San Diego, CA, USA). Data were considered statistically significant when $p$ values were lower than 0.05. Correlation analysis was performed by Spearman’s correlation criteria. The article presents all correlations found; those for which $R\ge 0.5$ were considered moderate correlations; those for which R ≥ |0.8| were considered strong correlations; values for which $p\le 0.05$ were considered significant. Results are presented as mean values ± the standard error of the mean (SEM).

3. Results

We did not find significant differences in the clinical characteristics of study groups (Table 1). Targeted sequencing of the V3 region of the 16S rRNA gene resulted in the identification of 70 bacterial species inhabiting the cervical canal of patients from the study groups. However, 12 bacterial species were the most abundant in the study groups, and their abundance exceeded 0.005; the remaining species whose abundance was below this threshold were combined into the “Other” group (Figure 1).

[figure(s) omitted; refer to PDF]

Lactobacillus iners was predominant in both the groups with ongoing pregnancy (0.530 ± 0.09) and the group with miscarriage (0.573 ± 0.1), $p=0.88$. Gardnerella vaginalis was the next most abundant bacteria in both groups. Its abundance was higher in the group of patients with miscarriage (0.237 ± 0.085) compared to those with ongoing pregnancy (0.192 ± 0.071), but this difference was not statistically significant ($p=0.98$). In the group with ongoing pregnancy after Gardnerella vaginalis, the bacterial abundance was divided as follows: Turicibacter sanguinis—0.173 ± 0.051, Atopobium vaginae—0.036 ± 0.012, Mycoplasma hominis—0.011 ± 0.003, and Bifidobacterium longum—0.01 ± 0.006, Lactobacillus delbrueckii—0.008 ± 0.003, Porphyromonas asaccharolytica—0.008 ± 0.002, Prevotella copri—0.002 ± 0.001, Porphyromonas somerae—0.001 ± 0.001, Bifidobacterium dentium—0.001 ± 0.001, and Sphingobacterium mizutaii—0.001 ± 0.001. In the group with miscarriages, we observed a different picture. The next most abundant bacteria after Gardnerella vaginalis were Porphyromonas asaccharolytica—0.049 ± 0.048, Turicibacter sanguinis—0.036 ± 0.033, Atopobium vaginae—0.022 ± 0.012, Porphyromonas somerae—0.015 ± 0.015, Bifidobacterium dentium—0.009 ± 0.005, Sphingobacterium mizutaii—0.006 ± 0.004, Prevotella copri—0.005 ± 0.003, Mycoplasma hominis—0.004 ± 0.002, Lactobacillus delbrueckii—0.003 ± 0.002, and Bifidobacterium longum—0.001 ± 0.0003.

In the majority of patients with both miscarriage and ongoing pregnancy, Lactobacillus iners predominated in the cervical microbiome, which is typical of CST III. We did not find any statistically significant differences in CSTs between the two groups. Gardnerella vaginalis predominated in the cervical microbiome in two patients with ongoing pregnancy and three patients with miscarriage, consistent with the CST IV-B subtype. In four patients with ongoing pregnancy and one patient with miscarriage, the cervical canal microbiome was predominated by Turicibacter sanguinis, and in one patient with miscarriage by Porphyromonas asaccharolytica, we define these cases as unclassified types of bacterial communities, as they cannot be associated with any known CST.

We also assessed the intergroup and intragroup diversity of the microbiome. We used the Shannon index to assess alpha diversity, and beta diversity was assessed using the Bray–Curtis dissimilarity metric. For both of these diversity metrics, there were no significant differences between the study groups.

Analysis of the differential abundance of identified microorganisms showed statistically significant differences for the content of four bacteria (Figure 2).

[figure(s) omitted; refer to PDF]

Thus, we found that the bacterial abundance of Turicibacter sanguinis (0.173 ± 0.051 vs. 0.036 ± 0.032, $p=0.006$) and Corynebacterium callunae (0.002 ± 0.001 vs. 1.349E − 05 ± 1.349E − 05, $p=0.022$) was statistically significantly higher in the group with ongoing pregnancy than in the miscarriage group. Moreover, the bacteria Bifidobacterium breve (0.000 vs. 0.004 ± 0.001, $p=0.001$) and Aeromonas popoffii (0.000 vs. 0.004 ± 0.002, $p=0.041$) were more abundant in the group of patients with miscarriages compared to the group of ongoing pregnancy.

RT-qPCR data showed that the relative mRNA expression levels of TLR1, TLR4, TLR5, TLR6, NOD1, NOD2, NLRP1, and NLRC4 were increased in the group of patients with miscarriage, but in none of the cases were these differences statistically significant. However, the downregulation of TLR2 mRNA expression in the miscarriage group compared with patients with ongoing pregnancy was statistically significant (2.600 ± 2.585 vs. 1.913 ± 1.255, $p=0.006$) (Figure 3).

[figure(s) omitted; refer to PDF]

We also evaluated the presence of a correlation between the expression levels of each mRNA examined and the abundance of identified microorganisms. 14 statistically significant correlations were found between the abundance of bacteria and the expression level of the mRNA studied in the group of patients with ongoing pregnancy (Figure 4).

[figure(s) omitted; refer to PDF]

Thus, in the group of patients with ongoing pregnancy, we observed a moderate positive correlation between the abundance of Actinomyces naeslundii and the expression levels of TLR1 (R = 0.563, $p=0.033$), TLR2 (R = 0.592, $p=0.009$), TLR5 (R = 0.592, $p=0.009$), and NLRP1 (R = 0.650, $p=0.015$). The abundance of Gemella asaccharolytica was negatively correlated with TLR5 expression (R = −0.636, $p=0.013$), while the abundance of Muribaculum intestinale was positively correlated with TLR5 expression (R = 0.507, $p=0.048$). The abundance of Prevotella timonensis showed a moderate positive correlation with NOD1 (R = 0., $p=0.0$). Streptococcus gordonii also had a moderate positive correlation with the expression of TLR1 (R = 0.563, $p=0.033$), TLR2 (R = 0.592, $p=0.009$), TLR5 (R = 0.592, $p=0.009$), and NLRP1 (R = 0.650, $p=0.015$). The level of NOD1 expression was positive and strongly correlated with the abundance of L. iners (R = 0.857, $p=0.011$), Lactobacillus delbrueckii (R = 0.874, $p=0.008$), and Aerococcus christensenii (R = 0.898, $p=0.005$). The expression levels of TLR4, TLR6, NOD2, and NLRC4 had no statistically significant correlation with the abundance of any bacteria.

19 statistically significant correlations were found between the abundance of bacteria and the expression levels of the mRNA studied in the group of patients with miscarriages (Figure 5).

[figure(s) omitted; refer to PDF]

In the group of patients with miscarriages, we observed a moderate positive correlation between the abundance of Aerosphaera taetra (R = 0.602, $p=0.022$) and a moderate negative correlation between the abundance of Atopobium minutum (R = −0.609, $p=0.011$) and Porphyromonas somerae (R = −0.609, $p=0.011$) and the expression level of TLR2. The abundance of Atopobium vaginae was negatively correlated (R = −0.561, $p=0.040$) and Bifidobacterium breve (R = 0.590, $p=0.029$) and Sphingobacterium mizutaii (R = 0.589, $p=0.030$) were positively correlated with TLR4 expression. Holdemania filiformis showed a moderate negative correlation with TLR1 (R = −0.752, $p=0.010$), TLR2 (R = −0.694, $p=0.005$), and TLR4 (R = −0.719, $p=0.001$) expression. Turicibacter sanguinis also showed a moderate negative correlation with TLR1 (R = −0.752, $p=0.010$), TLR2 (R = −0.694, $p=0.005$), and TLR4 (R = −0.719, $p=0.001$) expression. The abundance of Mycoplasma hominis was moderately negatively correlated with TLR1 (R = −0.663, $p=0.031$) and TLR4 (R = −0.740, $p=0.003$) expression. Also, Porphyromonas asaccharolytica was moderately negatively correlated with TLR1 (R = −0.674, $p=0.018$) and TLR4 (R = −0.548, $p=0.044$) expression.

A strong positive correlation was identified between the TLR6 expression level and L. iners (R = 0.943, $p=0.017$) in miscarriage patients. The abundance of L. iners also had a moderate positive correlation with TLR2 (R = 0.591, $p=0.029$) and TLR4 (R = 0.741, $p=0.003$) expression levels. The expression levels of TLR5, NOD1, NOD2, NLRP1, and NLRC4 had no statistically significant correlation with the abundance of any bacteria.

4. Discussion

In our study, we assessed the microbiome and mRNA expression of Toll- and NOD-like receptors in the cervical canal of patients with ongoing pregnancy and miscarriages in the 1st trimester of pregnancy.

The alpha- and beta-diversity did not have significant differences in study groups. L. iners was the predominant microorganism in both groups. This means that most of the patients who participated in the study independent of the course of pregnancy were characterized by CST III according to the classification of France et al. [21]. The data concerning L. iners predominance in the vaginal microbiome are contradictory. In some populations, for example, in Eastern Europe, China, USA, and Caribbean Islands, CST III is the most common type of the microbiome in ongoing pregnancy, which can be considered as normal condition [22–27]. In British and Canadian populations, the most common type of the microbiome is CST I with the dominance of L. crispatus [28, 29]. Meanwhile, there are some data that L. iners can contribute to the pathogenesis of early and late miscarriages [30, 31]. Gardnerella vaginalis predominance (CST IV-B microbiome) was found in two patients with ongoing pregnancy and three patients with miscarriage. It is known that Gardnerella vaginalis is involved in pathogenesis of early miscarriages [32–34]. Further research in larger cohort is needed to define presence of statistical significance between groups.

We also found differences in the abundance of some of the bacteria between patients with ongoing pregnancies and the miscarriage group. So, despite the absence of Bifidobacterium breve in the group with normal pregnancy and a small amount of this microorganism in patients with miscarriage, it was shown that the difference in the amount of this bacterium in our study groups is statistically significant. However, the available data on Bifidobacterium breve are contradictory. For example, a study by Mori et al. [35] showed an increase in the abundance of this bacterium in the cervical canal of women with normal pregnancies compared to a group of patients with recurrent pregnancy loss. A study by Jorge Lopez-Tello et al. [36] also shows the important role of this bacterium in newborns. In general, bifidobacteria are thought to have the same protective potential as lactobacilli according to the current understanding of a healthy vaginal microbiome [37–40]. However, Lee and colleagues observed that the relative abundance of B. breve was significantly increased during pregnancy in women with bacterial vaginosis compared to normal vaginal microbiota [41]. Severgnini et al. (2022) also identified the presence of associated vaginal patterns such as Prevotella and Dialister and Bifidobacterium spp. characterized by several genera associated with bacterial vaginosis [42]. Our study only confirms the ambiguous role of this microorganism and indicates its possible role in the pathogenesis of miscarriages.

Aeromonas popoffii was another bacterium that was not detected in patients with ongoing pregnancies but was present in small amounts in the miscarriage group, which was statistically significant in our study. Recently, members of the genus Aeromonas have been increasingly recognised as important human pathogens. They are known to produce several virulence factors, such as lipases, proteases, hemolysins, aerolysins, cytotoxins, and enterotoxins, and are also resistant to beta-lactam antibiotics [43, 44]. Aeromonas cause various but infrequent human infections such as soft tissue infections, diarrhoea, bacteraemia, peritonitis, septicaemia, osteomyelitis, and others [43]. Several cases of urinary tract infections, including in pregnant women, caused by Aeromonas spp. are also known, but the pathogenesis is not clarified in any available literature [45–47]. There have also been reports of Aeromonas vaginal colonisation in normal pregnant women [48] and women with preterm labour [46, 49]. Our work shows for the first time a possible association of this bacterium with the pathogenesis of miscarriage.

We found that the abundance of Turicibacter sanguinis was significantly higher in the group of patients with ongoing pregnancy compared with the miscarriage group. Turicibacter sanguinis was first isolated from the serum of a critically ill patient with fever and acute appendicitis [50]. It has been suggested that this bacterium may be involved in the development of inflammatory bowel disease, but exactly what role it plays is unknown [51, 52]. Gumenyuk et al. [53] found a decrease in the number of Turicibacter sanguinis in the intestines of patients with external genital endometriosis and found a significant inverse correlation between IL6 levels and the abundance of these bacteria. They describe a possible beneficial effect of Turicibacter on the intestinal epithelium and reproductive system, which is related to their ability to produce metabolites with protective effects, particularly short-chain fatty acids such as acetic, valerian, and butyric acids. The results of our study are consistent with these findings, but further investigation of the role of Turicibacter localized in the female genital tract is needed.

The numbers of Corynebacterium callunae were also higher in the group of patients with ongoing pregnancy compared with miscarriages. However, we were unable to find information on the effect of this bacterium on human health, including the course of pregnancy. It is known that this species is not pathogenic and is used for the production of amino acids [54].

We also estimated the relative mRNA expression of pattern-recognition receptors, which can detect bacterial ligands (TLR1, TLR2, TLR4, TLR5, TLR6, NOD1, NOD2, NLRP1, and NLRC4). We found downregulation of TLR2 mRNA expression in the miscarriage group compared with the group of patients with ongoing pregnancy, which was statistically significant. TLR2 can recognize a broad range of pathogens, such as proteins MALP-2 and MALP-404 of Mycoplasma spp., peptidoglycan of Gram-positive bacteria, lipoarabinomannan of Mycobacterium tuberculosis, and glycosylphosphatidylinositol-anchored mucin of Trypanosoma cruzi. Heterodimers of TLR2 with TLR1 can recognize bacterial triacyl lipopeptides, while heterodimers of TLR2 with TLR6 recognize diacyl lipopeptides and lipoteichoic acid of bacteria and zymosan of fungi. It is known that TLR2 expression is observed along the genital tract, including epithelium and smooth muscles of the cervix [55]. It is known that TLR2 expression normally is significantly decreased in the lower genital tract compared to the upper genital tract. The reason for it is the presence of proper autochthonous microflora in the vagina, which normally should not induce an inflammatory response. The upper genital tract is normally almost sterile or contains a few bacteria, and therefore, its innate immune cells can induce a sufficient immune response in the presence of pathogens [56]. In the mouse model, it was shown that TLR2 is expressed in trophoblast cells and induces apoptosis of trophoblastic cells if stimulated by peptidoglycan together with TLR1. Stimulation of TLR6 can shift the TLR1/TLR2 immune response to the production of proinflammatory cytokines [57]. Genetic single nucleotide polymorphism (SNP) of TLR2 is positively associated with miscarriages [58, 59]. The authors suggest that SNPs are associated with increased maternal sensitivity to infections due to decreased levels of TLR2 protein. The data on TLR2 mRNA expression in decidua of patients with miscarriages are controversial. In our previous research, we did not find a significant difference in TLR2 expression in the decidual tissue between miscarriage and ongoing pregnancy groups [60]. In the research of Xu et al., significantly higher expression of TLR2 mRNA and protein was found in the decidua of patients with miscarriages [61]. The limitation of the study was that the miscarriage group contained only patients with unexplained recurrent miscarriages. Patients with known causes of miscarriages, including infectious causes, were excluded.

In a present study, we found decreased TLR2 mRNA expression in the epithelium of the cervical canal of patients with early miscarriages compared to the control group. It can be suggested that decreased TLR2 expression does not provide sufficient recognition of bacteria, containing peptidoglycans, and does not maintain adequate immune response, including proinflammatory cytokines’ production.

In patients with ongoing pregnancy, we observed a moderate positive correlation of Actinomyces naeslundii and Streptococcus gordonii with TLR1, TLR2, TLR5, and NLRP1. It is known that Actinomyces is a Gram-positive microorganism that can induce an inflammatory response and piroptosis via TLR2 through lipoteichoic acid in its membrane [62, 63]. There are no data on the influence of Actinomyces on TLR1 and TLR5, but it can be suggested that it may involve TLR1 in its recognition as a TLR1/TLR2 heterodimer complex. Streptococcus gordonii can induce inflammation though the TLR1/TLR2 heterodimer, leading to the production of proinflammatory cytokines [64, 65]. NLRP1 can be activated by its muramyl dipeptide, which is part of the peptidoglycan of these Gram-positive bacteria.

Prevotella timonensis, a Gram-negative bacterium, whose peptidoglycan contains diaminopimelic acid, which is a ligand for NOD1, had a moderate positive correlation with NOD1 expression. However, no data were found in the literature on the ability of Prevotella to stimulate NOD1. We also did not find any data on the causes of the correlation between Gemella asaccharolytica, Muribaculum intestinale, and TLR5 mRNA expression.

We found a strong correlation between NOD1 mRNA expression and the abundance of L. iners, L. delbrueckii, and Aerococcus christensenii. We did not find any previous research on the relationship between NOD1 expression and the presence of these microorganisms in the human body.

In the miscarriage group, we observed a moderate positive correlation of TLR2 with Aerosphaera taetra and a moderate negative correlation with Atopobium minutum and Porphyromonas somerae. Aerosphaera taetra is a Gram-positive microorganism that contains peptidoglycan, which is a ligand for TLR2. Atopobium can be increased in patients with some variants of the TLR2 gene, which may contribute to susceptibility to this bacterium [66]. There are no data on the role of Porphyromonas somerae in activating or suppressing TLR2. Meanwhile, the data on other members of the same genus are contradictory. There are some data that they can stimulate TLR2 [67, 68], but in another study, they did not show any effect on TLR2 [69].

TLR4 expression was positively correlated with the abundance of Bifidobacterium breve and Sphingobacterium mizutaii. Bifidobacterium breve has previously been shown to downregulate TLR4 signaling in the gut [70, 71]. There are no data on the role of Sphingobacterium mizutaii in TLR4 activation. We also found a moderate negative correlation of TLR4 with Atopobium vaginae. It is known that TLR4 deficiency is associated with higher concentrations of Atopobium vaginae [72].

Gram-positive bacteria Holdemania filiformis and Turicibacter sanguinis showed a moderate negative correlation with TLR1, TLR2, and TLR4 expression, which was discovered for the first time.

Mycoplasma hominis abundance was moderately negatively correlated with TLR1 and TLR4 expression. Mycoplasma has previously been shown to activate TLR2 in macrophages and prostate epithelial cell lines [73, 74]. Triacetilated lipopeptides from Mycoplasma can activate TLR1/TLR2 heterodimers [75]. The influence of Mycoplasma hominis on TLR4 expression is contradictory. Some studies showed an increase in TLR4 expression in the presence of this microorganism [76, 77], while others did not show any significant changes in its expression [78] or even found an inhibition of the basal TLR4 expression in the presence of Mycoplasma [79].

The abundance of the Gram-negative bacteria Porphyromonas asaccharolytica also had a moderate negative correlation with TLR1 and TLR4 expression, which was found for the first time. Previously, lipopolysaccharides from another species of the same genus, Porphyromonas gingivalis, were shown to act as an antagonist for TLR4 [80].

The abundance of L. iners had a strong positive correlation with TLR6 mRNA expression and a moderate positive correlation with TLR2 and TLR4 mRNA expression in patients with miscarriages. It is known that TLR2/TLR6 heterodimers promote the Treg (regulatory T-lymphocytes) type of immune response, which protects the ongoing pregnancy, whereas TLR2/TLR1 heterodimers, on the contrary, stimulate the Th17 immune response, which is not beneficial for pregnancy [81]. Lactobacillus spp. are recognised by TLR2/TLR6 heterodimers [82]. It can be suggested that a decrease in L. iners may shift the immune response from Treg to Th17, which may contribute to the pathogenesis of miscarriage. It is also known that other species of the genus Lactobacillus can inhibit TLR4 signaling and the proinflammatory response [83, 84]. It can be assumed that L. iners may not have such anti-inflammatory properties.

The results of this study should be considered with some limitations, in particular the small sample size (n = 30), which included 15 patients with ongoing pregnancies and 15 patients with early miscarriages. The lack of karyotyping of miscarriage tissue is another limitation.

5. Conclusion

In our study, we have found no significant differences in CSTs and alpha- and beta-diversity between the cervical microbiomes of patients with ongoing pregnancies and patients with miscarriages. L. iners was the predominant organism in both groups, and although some studies have associated CST III with an increased likelihood of negative pregnancy outcomes, our study does not confirm these views.

Differences have been found in the microbiome composition of the cervical canal in patients with miscarriage compared to patients with ongoing pregnancy, highlighting the important role of the cervical microbiome in early pregnancy. The results also indicate the need for similar studies to identify bacterial markers characteristic of early miscarriage for the subsequent development of preventive protocols.

We have found that TLR2 mRNA expression was reduced in patients with miscarriage. The reduced expression of TLR2 mRNA does not allow for a sufficient immune response to bacteria in patients with early miscarriage. The study is the first to show a correlation between Toll- and NOD-like receptor mRNA expression and the abundance of several bacterial species. These observations extend our understanding of the links between microorganisms and immune response mechanisms.

Thus, this study shows that changes in the lower genital tract microbiome may modulate the immune response and contribute to the pathogenesis of early miscarriage.

Ethical Approval

The research protocol was approved by the Ethics Committee of Voronezh State University (No. 42-05, 27 December 2021).

Authors’ Contributions

O.L. conceptualized the study; M.G., M.S., and O.L. developed the methodology; M.G. handled the software; M.G. validated the study; M.G. and O.L. conducted formal analysis; M.G., O.K., V.I., O.A., and N.S. performed sample collection and database creation; M.G., V.I., Y.S., and I.B. investigated the study; O.L. and M.S. collected resources; M.G. and O.L. wrote the original draft; M.S. and A.M. conducted writing, review, and editing; M.G. visualized the study; O.L. supervised the study; O.L. administered the project; O.L. conducted funding acquisition. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

The study is dedicated to the memory of the head of the laboratory of metagenomics and food biotechnology of the Voronezh State University of Engineering Technologies Popov V.N. who always helped the laboratory staff in the implementation of their ideas. The research was supported by the Russian Science Foundation, project number 22-24-00802.

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