{"paper_id":"49507a0d-d86c-4d80-8f32-302ddb400fa1","body_text":"Integrative Metagenomic and Culture-Based Profiling of Vaginal Microbiome Diversity among Ethnic Groups in Armenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrative Metagenomic and Culture-Based Profiling of Vaginal Microbiome Diversity among Ethnic Groups in Armenia Aleksanyan Tigran, Simonyan Rima, Harutyunyan Ruzanna, Semerjyan Inesa, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8186315/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The vaginal microbiome (VMB) plays a crucial role in women’s reproductive health, yet its composition and variability among different ethnic groups in Armenia have not been previously investigated using metagenomic approaches. This study aimed to characterize and compare the VMB of women from three ethnic groups—Armenians, Yezidis, and Molokans—using classical microbiological methods, MALDI-TOF MS, and 16S rRNA amplicon-based metagenomic sequencing. The analyses revealed a high level of consistency between culture-based and metagenomic data. Among the studied groups, Armenian women exhibited the greatest microbial diversity at both species and phylum levels. In women with candidiasis, a marked reduction in lactobacilli abundance and an increased presence of pathogenic species were observed. Pregnant women generally displayed a balanced, lactobacilli-dominated microbiome, although opportunistic pathogens were occasionally detected, likely due to immune modulation during pregnancy. Regional and ethnic differences were most pronounced at the phylum level. These findings represent the first comprehensive metagenomic assessment of the VMB in Armenia, providing important insights into microbial diversity across ethnic groups and its implications for women’s reproductive health. Health sciences/Diseases Biological sciences/Microbiology vaginal microbiome lactic acid bacteria bacterial diversity MALDI-TOF MS metagenomics reproductive health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The human body functions as a holobiont, comprising a complex consortium of microbial communities that have co-evolved with their host over millions of years [ 1 ]. Among these, the vaginal microbiome (VMB) plays a critical role in women’s reproductive health, yet it has long received less scientific attention compared to the gut or skin microbiota [ 2 ]. In recent years, however, interest in the VMB has increased substantially due to its recognized importance in maintaining vaginal health, preventing infections, and influencing pregnancy outcomes. The vaginal ecosystem provides a moist, nutrient-rich, and thermally stable environment for resident microbes, which in turn produce antimicrobial and anti-inflammatory compounds that help sustain microbial balance [ 3 ]. Disruption of this equilibrium, known as dysbiosis, can result from hormonal fluctuations, aging, immune alterations, antibiotic use, or exposure to environmental microbes [ 3 , 4 ]. Such imbalances are associated with a range of gynecological and obstetric conditions, including bacterial vaginosis (BV), increased susceptibility to sexually transmitted infections, pelvic inflammatory disease, and pregnancy complications such as miscarriage or preterm birth [ 5 ]. BV is characterized by a significant reduction in the number of lactobacilli, accompanied by a dramatic increase—by 100 to 1,000 times—in the abundance of facultative or obligate anaerobic bacteria known as microbes associated with BV including Gardnerella , Prevotella , Atopobium , Mobiluncus , Bifidobacterium , Sneathia , Leptotrichia , and certain members of the Clostridiales order [ 7 ]. The composition of the VMB is dynamic and varies across the menstrual cycle, pregnancy, and menopause. It can vary significantly between individuals, with these differences influenced by factors such as sexual activity, chronic stress, geographic location, race, and other socio-environmental variables [ 2 , 5 ]. Vaginal fungi primarily belong to the phyla Ascomycota and Basidiomycota [ 8 ], with predominant fungal genera including Candida , Cladosporium , Pichia , Aspergillus , and Rhodotorula [ 9 ]. Although fungi such as Candida spp. are part of the normal vaginal microbiota, overgrowth can lead to vulvovaginal candidiasis (VVC) [ 10 , 11 ]. The interplay between bacterial and fungal communities is complex—bacterial dysbiosis can promote fungal proliferation, whereas lactobacilli generally help suppress fungal infections through acidification and competitive exclusion. The composition of the VMB is highly dynamic and varies depending on factors such as age, ethnicity, physiological changes (including hormonal fluctuations and immune system status), vaginal infections, medications, probiotics, lifestyle, and diet [ 12 – 16 ]. Typically, Lactobacillus species dominate the VMB of healthy women, producing lactic acid and other metabolites that maintain a low vaginal pH and inhibit pathogenic bacteria. However, the predominance and diversity of Lactobacillus species vary across populations and ethnic groups. For instance, lactobacilli are predominant in the VMB of Asian and white women, while African-American and Hispanic women are more likely to harbor a broader range of bacteria, including several species commonly associated with BV [ 2 ]. The composition of the VMB undergoes significant changes from childhood to the onset of puberty, during pregnancy, and after menopause. In childhood, the VMB is primarily dominated by Gram-positive anaerobic bacteria, including species from the genera Actinomyces , Bifidobacterium , Peptococcus , Peptostreptococcus , and Propionibacterium . It also includes Gram-negative anaerobic bacteria, such as Bacteroides , Fusobacterium , and Veillonella , as well as aerobic bacteria like Staphylococcus aureus , Staphylococcus epidermidis , Streptococcus viridans , and Enterococcus faecalis . During this period, the vaginal pH is typically acidic. However, as a girl reaches puberty, the vaginal pH shifts, becoming neutral or slightly alkaline [ 17 ]. Some studies suggest that vaginitis, in particular, is linked to a decrease in the number of lactobacilli, accompanied by an increase in the abundance of pathogens such as Gardnerella vaginalis , Atopobium vaginae , Ureaplasma parvum , Ureaplasma urealyticum , and Candida species. This shift in microbial composition can contribute to the development of reproductive issues [ 18 , 19 ]. Pregnancy is accompanied by profound hormonal and immunological changes that also shape the vaginal microbiome. Several studies have reported reduced microbial diversity and increased dominance of lactobacilli during pregnancy, which is considered beneficial for maintaining vaginal health and supporting fetal development [ 11 ]. Conversely, reduced levels of lactobacilli have been linked to adverse pregnancy outcomes, including miscarriage and preterm delivery [ 20 ]. After menopause, declining estrogen levels and increased vaginal pH lead to diminished lactobacilli populations and greater susceptibility to infection [ 20 , 21 ]. Ethnic and regional variations in the vaginal microbiome have been reported worldwide, yet no comprehensive metagenomic analysis has been conducted in Armenia. Given the country’s unique population structure, cultural diversity, and ecological heterogeneity, understanding these variations could provide valuable insights into microbial adaptation, women’s health, and potential probiotic development. Therefore, the aim of the present study was to investigate and compare the vaginal microbiomes of women of reproductive age from three ethnic groups in Armenia—Armenians, Yezidis, and Molokans—using classical microbiological methods, MALDI-TOF MS, and 16S rRNA amplicon sequencing. The study sought to identify dominant microbial taxa, reveal intergroup differences, and provide a foundation for developing microbiota-based therapeutic and preventive strategies tailored to local populations. 2. Materials and Methods 2.1 Subjects of the study. Vaginal samples were collected from 64 women residing in three different regions of the Republic of Armenia, each group reflecting distinct lifestyles shaped by their cultural traditions and ethnic backgrounds. The participants were divided into three groups: Group 1: Residents of Yerevan who self-identified as Armenians (32 subjects) Group 2: Residents of Aragatsotn Province who self-identified as Yezidis (15 subjects) Group 3: Residents of Lori Province who self-identified as Molokans (21 subjects) (See Table 2 for details.) Table 2 Division of samples according to their ethnicity Target groups Healthy Pregnant Presence of signs of dysbiosis Armenians G3, G8, G11, G12, G14, G19, G30, G42, G43 G9, G15, G18, G24, G26 G4, G5, G6, G7, G10, G13, G17, G20, G21, G22, G25, G28, G29, G31 Yazidis G32, G33, G34, G35, G36 G44, G45, G46, G47, G48 G37, G38, G39, G40, G41 Molokans G49, G50, G51, G52, G53 G16, G54, G55, G56, G57, G58 G22, G59, G60, G61, G62, G63, G64, G65, G66, G67, G68 The relatively small number of the samples from group 2 and 3 is due to the fact that these ethnic groups represent minorities in Armenia, and it was challenging for healthcare providers to collect samples that met the study's inclusion criteria. Sampling was carried out across three subgroups: healthy women, women in the first trimester of pregnancy, and women diagnosed with candidiasis. Diagnoses were made by medical professionals based on clinical symptoms and examination data from diagnostic centers. All participants were between 18 and 39 years old and provided written informed consent prior to participation. Study of the VMB using classical microbiological methods. Vaginal smear analysis was initiated within two hours of sample collection. Samples were transported from the hospital to the laboratory under sterile conditions at a controlled temperature of 30–37°C. Upon arrival, each sample was diluted in 2.5 mL of sterile saline and incubated with shaking at 37°C for 30 minutes. Subsequently, 0.5 mL of this suspension was transferred into a medium containing 10% skim milk to initiate the enrichment culture of LAB. The culture was then incubated at 37°C for 24 hours to promote LAB growth. Serial dilutions of the remaining sample suspensions were prepared up to 10⁻⁶. From each dilution, including the undiluted sample, 0.5 mL was plated on various media: Nutrient agar, Sabouraud agar, and Endo agar for surface cultivation, and De Man–Rogosa–Sharpe (MRS) agar for deep (microaerophilic) cultivation. It is important to note that chloramphenicol was not added to Sabouraud agar, as the goal was to allow the growth of all bacterial species, not just fungi. After incubation at 37°C for 2–7 days, colony-forming units (CFUs) were counted, and colonies with distinct morphologies were identified. Colonies grown within MRS agar (deep growth) were selected and transferred into 10% skim milk to initiate LAB enrichment cultures. LAB cultures were incubated at 37°C for 1–5 days. To obtain pure LAB cultures, at least three successive dilutions and single-colony transfers into skim milk were performed. The purity of each culture was verified using a phase-contrast microscope (Omax M837ZL, China). Pure LAB cultures were preserved in MRS broth supplemented with 20% glycerol and stored at − 80°C. 2.2 Identification of bacterial isolates using MALDI-TOF MS. Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) analysis was conducted on the vaginal isolates. Protein extraction was carried out using third-generation axenic isolates, and the MALDI-TOF MS procedure followed previously described methods [34]. Each isolate was analyzed in duplicate using the Bruker Microflex® LT/SH s-Smart instrument (Bruker Daltonics, Bremen, Germany). The resulting mass spectra were processed using the SPeDE algorithm with its standard parameters to group them into unique mass spectrometry-based strain types [35]. For each of these defined types, one representative (or 'reference') isolate was selected for further identification when necessary. The mass spectra generated were compared with the Bruker BDAL MSP library using MBT Compass Explorer software (Bruker Daltonics). Identifications were determined based on the manufacturer’s scoring criteria. Isolates achieving a Bruker log score of 2.2 or higher were classified as reliably identified at the species level. In contrast, isolates with scores below 2.2 were considered to have low-confidence identifications and were subjected to whole genome sequencing for more accurate identification. 2.3 Extraction of chromosomal DNA. Chromosomal DNA was extracted using a hexadecyltrimethylammonium bromide (CTAB) protocol [36]. The presence and integrity of the extracted genomic DNA were assessed by electrophoresis on a 0.8% agarose gel prepared in Tris-Acetate-EDTA (TAE) buffer. DNA bands were stained with ethidium bromide (1 µg/mL) and visualized under UV illumination using a transilluminator (Transilluminator Dlin UV 312, AC input 220V, 50 Hz, China). The extracted DNA samples were then submitted to \"Novogen\" CJSC (China) for amplicon-based metagenomic sequencing targeting the V3–V4 hypervariable regions of the 16S rRNA gene. 2.4 16S rDNA amplicon sequencing. Library Construction, Quality Control and Sequencing. Polymerase Chain Reaction (PCR) amplification of targeted regions was performed by using specific primers connecting with barcodes. The PCR products with proper size were selected by 2% agarose gel electrophoresis. The same amount of PCR products from each sample was pooled, end-repaired, A-tailed and further ligated with Illumina adapters. Libraries were sequenced on a paired-end Illumina platform to generate 250bp paired-end raw reads. The library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection. Quantified libraries will be pooled and sequenced on Illumina platforms, according to effective library concentration and data amount required according to manufacturer’s recommendations (Novogene). 2.5 Sequencing data processing . Paired-end reads were assigned to samples based on their unique barcodes and truncated by cutting off the barcode and primer sequences. Paired-end reads were merged using FLASH (V1.2.7) [37] (see details http://ccb.jhu.edu/software/FLASH/). To obtain the high-quality clean tags, quality filtering of the raw tags was performed by Qiime (V1.7.0) [38] (see details http://qiime.org/scripts/split_libraries_fastq.html) under specific filtering conditions [39]. The tags were compared with the reference database (SILVA138 database, see details http://www.arb-silva.de/) using UCHIME algorithm (UCHIME Algorithm, see details http://www.drive5.com/usearch/manual/uchime_algo.html) [40] to detect chimera sequences (see details https://drive5.com/usearch/manual/chimeras.html). And then the chimera sequences were removed [41]. Then the Effective Tags finally obtained. 2.6 Operational Taxonomic Unit (OTU) cluster and Taxonomic annotation . Sequences analysis were performed by Uparse software (Uparse v7.0.1090, see details http://drive5.com/uparse/) [42] using all the effective tags. Sequences with ≥ 97% similarity were assigned to the same OTUs. The representative sequence for each OTU was screened for further annotation. For each representative sequence, Qiime (Version 1.7.0, see details http://qiime.org/scripts/assign_taxonomy.html) [43] in Mothur method was performed against the SSUrRNA database of SILVA138 Database (see details http://www.arb-silva.de/) [44] for species annotation at each taxonomic rank (Threshold:0.8 ~ 1) [45] (kingdom, phylum, class, order, family, genus, species). To obtain the phylogenetic relationship of all OTUs representative sequences, the MUSCLE [46] (Version 3.8.31, see details http://www.drive5.com/muscle/) can compare multiple sequences rapidly. OTUs abundance information were normalized using a standard of sequence number corresponding to the sample with the least sequences. Subsequent analysis of alpha diversity and beta diversity were all performed basing on this output normalized data. 2.7 Alpha Diversity . Alpha diversity was determined using the Chao1 estimator (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.chao1.html#skbio.diversity.alpha.chao1), which measures community richness [47], and the ACE estimator (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.ace.html) [48, 49]. Community diversity was analyzed with the Shannon index (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.shannon.html) [50, 51] and the Simpson index (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.inv_simpson.html) [51, 52]. Sequencing depth was assessed by Good’s coverage index (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.goods_coverage.html) [53]. 2.8 Beta Diversity . Beta diversity analysis was used to evaluate differences of samples in species complexity, Beta diversity on both weighted and unweighted unifrac were calculated by QIIME software (Version 1.7.0). Cluster analysis was preceded by principal component analysis (PCA), which was applied to reduce the dimension of the original variables using the FactoMineR package and ggplot2 package in R software (Version 2.15.3). Principal Coordinate Analysis (PCoA) was performed to get principal coordinates and visualize from complex, multidimensional data. A distance matrix of weighted or unweighted unifrac among samples obtained before was transformed to a new set of orthogonal axes by which the maximum variation factor is demonstrated by first principal coordinate and the second maximum one by the second principal coordinate, and so on. PCoA analysis was displayed by WGCNA package, stat packages and ggplot2 package in R software (Version 2.15.3). Unweighted Pair-group Method with Arithmetic Means (UPGMA) Clustering was performed as a type of hierarchical clustering method to interpret the distance matrix using average linkage and was conducted by QIIME software (Version 1.7.0). LEfSe analysis was conducted using LEfSe software. Metastat was calculated using R software. P-value was calculated using method of permutation test while q-value was calculated using method of Benjamini and Hochberg False Discovery Rate [54]. Anosim, MRPP and Adonis were performed using R software (Vegan package: anosim function, mrpp function and adonis function). ANOVA was calculated by mothur using anova function. T_test and drawing were conducted by R software according to Novogene’s protocol. All methods were carried out in accordance with the relevant guidelines and regulations. All experimental protocols involving human participants were approved by the National Center of Bioethics in Armenia. 3. Results Vaginal samples from 64 women were analyzed, including 29 samples from Group 1, 15 from Group 2, and 20 from Group 3. A total of 190 cultures were isolated from MRS and NA media and subsequently purified. All isolates were Gram-positive and exhibited either coccal or bacillary morphology (Table 1 ). Table 1 Morphological division of isolates Target groups Cocci Bacilli Armenians Healthy 23 11 Pregnant 11 1 With dysbiosis 32 21 Molokans Healthy 5 7 Pregnant 11 8 With dysbiosis 17 10 Yezidis Healthy 7 4 Pregnant 10 3 With dysbiosis 5 4 MALDI-TOF MS analysis identified the following bacterial species in the vaginal samples: Lactobacillus delbrueckii, Lacticaseibacillus rhamnosus, Lacticaseibacillus paracasei, Enterococcus faecalis, Enterococcus hirae, Enterococcus lactis, Enterococcus faecium, Enterococcus durans , and S. epidermidis . Several patients had clinically confirmed candidiasis. Three women had undergone antifungal therapy; among them, one (G7) had fully recovered, while another (G8) still harbored a high abundance of Candida spp. In participant G5, although Candida was not detected, pronounced dysbiosis was observed. From Group 3, participant G22 showed clinical signs of vaginitis without detectable Candida. Another participant, G61, was diagnosed with vaginal wall atrophy. Serial dilutions plated on four types of cultural media revealed variation in microbial abundance and composition across the samples (Table S1 ). Metagenomic sequencing was performed in parallel for all 64 samples. In most cases, the metagenomic profiles were consistent with classical microbiological findings. Overall, 1580 microbial taxa were identified at different taxonomic levels (species, genus, order, etc.). For comparative analysis, 19 dominant taxa—each present in at ≥ 0.5% relative abundance—were selected. Phylum-level comparisons were conducted to highlight broad differences in vaginal microbiome structure among the three groups. As shown in Fig. 1 , although minor microbial groups varied among the study groups clear differences were observed at the phylum level. Group 2 was dominated by Actinobacteriota, whereas Firmicutes predominanted in both Group 1 and Group 3. As shown in Fig. 2 , the number of OTUs identified in the three subgroups of Group 1 totaled 350, compered with 187 OTUs in Group 2 and 159 OTUs in Group 3. In healthy women, OUT richness was significantly higher (1436 OTUs) relative to other two groups (367 and 359 OTUs, respectively). Figure 3 presents an analysis of vaginal samples from healthy women across the three groups. In Group 1, Lactobacillus spp., including Lactobacillus iners , predominated. While L. iners is commonly associated with a healthy VMB, sample G12 showed an unusually high abundance of G. vaginalis (18%) and a reduced level of L. iners (4.5%); this deviation may be related to postpartum changes, as the participant had given birth three months earlier. Sample G24 contained a notably high proportion of Streptococcus anginosus (26.46%), compared with 15% L. iners and 12.9% Lactobacillus spp. In healthy women from Group 2, lactobacilli were the predominant taxa. The exception was sample G40, which had a relatively high level of G. vaginalis (32.5%), although lactobacilli still represented 52.6% of the community, likely limiting the pathogenic potential of G. vaginalis . Most healthy women in Group 3 also exhibited domination by L. iners and other Lactobacillus spp. (e.g., samples G55, G57, and G58). Sample G54 showed low lactobacilli abundance (14%) but a high proportion of Bifidobacterium breve (77.5%) a species considered beneficial and possibly compensatory for reduced lactobacilli. In sample G16, the relative abundances of G. vaginalis , Peptoniphilus timonensis , and Lactobacillus spp. were 36.3%, 4.7%, and 29.8%, respectively. In sample G56, G. vaginalis (47%) and Lactobacillus spp. (46%) were present in nearly equal levels. Figure 4 illustrates pregnancy-associated changes in the VMB. In pregnant participants from Group 1, L. iners and other Lactobacillus spp. were dominant, consistent with a healthy pregnancy profile. However, samples G42 and G43 deviated from this pattern, exhibiting markedly reduced lactobacilli and high levels of G. vaginalis (62.8%) and A. vaginae (80%), both commonly linked to BV. In Group 2, Lactobacillus spp. also predominated in pregnant women. Sample G33 contained 45.5% lactobacilli alongside a substantial amount of G. vaginalis (38.8%). This co-occurrence may be associated with immunological changes during pregnancy. Sample G35 showed very low lactobacilli (8.6%) and elevated abundances of several BV-associated taxa: G. vaginalis (58.4%), Megasphaera spp. (11.4%), P. timonensis (5.1%), and A. vaginae (4.6%). In contrast, sample G36 was dominated by lactobacilli (74%) with only a modest level of G. vaginalis (12.4%). Pregnant women in Group 3 consistently exhibited a lactobacilli-dominated microbiome. Sample G53 contained a notable abundance of B. breve (47.4%), supporting a healthy microbiome profile. Phenotypic assessment of bifidobacteria associated with reproductive health revealed fermentation patterns similar to intestinal bifidobacteria. Vaginal bifidobacteria produced lactic acid and tolerated high concentrations of it, enabling survival in the characteristically low pH of the vaginal environment. Figure 5 presents VMB alterations in women with candidiasis. Samples G8, G20, and G25 exhibited typical signatures of BV. In sample G8, lactobacilli (15%) were present at low levels alongside low G. vaginalis (3.5%) and a notably high abundance of Enterococcus spp. (45.8%), possibly indicating an influence from the intestinal microbiome. Sample G20 also contained low lactobacilli (11%) and a high proportion of G. vaginalis (75.8%), characteristic of advanced BV. In Group 2, samples from women with dysbiosis consistently showed reduced lactobacilli and elevated abundances of BV-associated bacteria, including G. vaginalis , A. vaginae , and Megasphaera spp. G. vaginalis levels ranged from 42% to 46.5%, A. vaginae from 13.2% to 29.6%, and Megasphaera spp. from 10.5% to 27.3%. In sample G22, lactobacilli were low (31%) while G. vaginalis was high (49.7%). Lower levels of G. vaginalis were detected in G65 (3.6%) and G66 (10.5%). In sample G65, non- iners lactobacilli were present at very low levels. S. agalactiae was identified in samples G59 (26.6%) and G60 (3.6%). Across the groups, L. iners was the predominant species. Groups 1 and 3 displayed high levels of additional Lactobacillus spp., whereas Group 2 exhibited very low abundance of lactobacilli other than L. iners . Group 1 showed the highest overall species diversity, while Group 3 showed the highest diversity within Lactobacillus spp., though it exhibited reduced diversity at the broader species level. Notably, B. breve was more abundant in Group 3. Among pathogenic taxa, S. anginosus and Peptoniphilus disiens were found only in Group 1, while P. timonensis was identified in both Groups 1 and 2. In Group 2, pathogenicity was primarily associated with G. vaginalis , A. vaginae , and Megasphaera spp. S. agalactiae was detected only in Group 3. G. vaginalis was the most frequently identified pathogen across all groups. Overall, the vaginal microbiome of pregnant women across the three groups predominantly reflected a healthy profile, though pathogens were most frequently detected in Group 2. Some women in Group 3 exhibited signs of dysbiosis, characterized by elevated levels of one or two dominant species. 4. Discussion This study provides an integrated culture-based and metagenomic characterization of the vaginal microbiome across three regional groups of women, including healthy individuals, pregnant women, and patients with dysbiosis or candidiasis. The combined approach enabled high-resolution taxonomic profiling and revealed clear differences in microbiome composition, diversity, and pathogen distribution among the groups. Historically, the VMB has been studied using cultivation methods, with two primary techniques traditionally employed to examine changes in the VMB: the Amsel test and Nugent scoring. The Amsel test focused on detecting clinical conditions, while Nugent scoring relied on assessing bacterial shape and size. However, it is well known that the large number of uncultivated and strictly anaerobic bacteria pose significant limitations to cultivation-based methods, hindering the ability to accurately capture the full biodiversity of the VMB [ 23 ]. For instance, the media used in experiments were not strictly selective. On Sabouraud nutrient agar, in addition to yeasts, Enterococcus spp. and spore-forming fungi can also grow [ 24 ]. Similarly, on Endo nutrient agar, aside from Gram-negative bacteria from the Enterobacteriaceae family, species from the genera Bacillus and Mycobacterium can also proliferate [ 25 ]. Due to its rich nutrient content, MRS medium supports the growth of a variety of other microbes [ 26 ]. Given that the aim of current research was to explore the full diversity of the VMB, no antibiotics were added to the Sabouraud medium. Studies have confirmed that the VMB of healthy women is predominantly composed of LAB [ 20 ]. In most cases, a single species of LAB dominates the microbiome. The presence of multiple LAB species is influenced by antagonistic interactions between them, the competitive colonization of one species over another, or host-specific factors that favor the colonization of certain species [ 27 ]. The metagenomic analysis of vaginal samples also confirmed that LAB predominates in the VMB. Nonetheless, some potentially pathogenic taxa may also be present within the vaginal normobiome [ 27 ]. In samples from healthy women Candida spp., as well as G. vaginalis , A. vaginae , and Megasphaera spp., have been identified. These bacteria, although potentially pathogenic, can still contribute to lactic acid production and acidification of the vagina, creating conditions that inhibit the growth of pathogenic microorganisms and reduce the risk of BV development [ 27 ]. The prevalence of BV varies across ethnic groups, though the underlying causes remain unclear. Genetic factors have been proposed as potential contributors to VMB composition [ 12 ]. Higher BV prevalence has been reported among Afro-Caribbean and Aboriginal communities in the United Kingdom and Canada, respectively. Similar trends have been noted in Roma women in Spain and Tibetan women in China. In the United States, Black women exhibit higher microbial diversity and lower rates of Lactobacillus spp. colonization compared to White women. Studies in African populations have also reported a lower abundance of L. crispatus relative to European and Asian women, with VMBs more commonly dominated by L. iners and facultative anaerobes [ 12 ]. Special emphasis was placed on analyzing the biodiversity profiles of the three study groups. The VMB of group 1 demonstrated higher diversity than groups 2 and 3, which may reflect dietary differences that influence microbial composition. S. anginosus is the predominant microorganism in aerobic vaginitis and is known to induce vaginal epithelial cell lysis through specific virulence factors. Its increased colonization in the absence of lactobacilli is characteristic of aerobic vaginitis, whereas in lactobacilli-dominated microbiomes it typically does not behave as a pathogen [ 28 ]. In sample G26, L. iners and Lactobacillus spp. accounted for 4.8% and 5.5% of the microbial community, respectively, while P. timonensis and Alloscardovia omnicolens reached 22% and 26.8%, respectively. Prevotella spp., detected at high levels, have been associated with vaginal dysbiosis and increased susceptibility to HIV, raising additional concerns given the low abundance of lactobacilli in this sample [ 29 ]. A. omnicolens , a member of Bifidobacteriaceae, is generally associated with a healthy microbiome and plays important roles in early-life development. It may also be transferred from healthy reproductive-aged women to infants during childbirth [ 30 ]. Substantial shifts in the vaginal bacteriobiome during pregnancy and illness have been well documented [ 3 , 9 ], and similar trends were observed in this study. Notably, pregnancy in group 1 was associated with increased Actinobacteriota abundance, whereas in groups 2 and 3 this phylum decreased during pregnancy. In group 2, both illness and pregnancy promoted expansion of Bacteroidota. Pregnancy-induced alterations in immunity and hormonal balance can lead to VMB dysbiosis, increasing susceptibility to pathogens and elevating the risk of complications such as miscarriage and preterm birth. This underscores the importance of examining pregnancy-associated microbiome changes across different ethnic groups [ 3 ]. Previous research has shown that the most pronounced VMB shifts occur between the first and second trimesters, with alpha diversity highest in the first trimester [ 31 ]; therefore, only first-trimester samples were included in this study. In sample G25, P. disiens was the dominant species (35%), with few other bacteria present. P. disiens , a species associated with BV and periodontal disease, has been linked to concomitant vaginal and gum infections. Women with gingivitis exhibit higher levels of P. disiens , and BV has been correlated with a greater likelihood of gingivitis. In contrast, sample G13 exhibited extremely low diversity, with only one Lactobacillus species detected; however, G. vaginalis was also present at notable levels. Considering the clinical symptoms and co-occurrence of G. vaginalis , a mixed BV–VVC infection is likely. Xiao et al. [ 32 ] demonstrated distinct symptom profiles for BV and VVC, with L. iners and Prevotella spp. dominating in affected women, while Gardnerella and Atopobium species are also more abundant. In cases of recurrent VVC, L. crispatus and L. jensenii decrease by approximately 14%, whereas L. iners increases by over 20% [ 32 ]. Thus, the findings for sample G13 may indicate a mixed infection or possibly sampling error (e.g., improper patient preparation). Low levels of G. vaginalis were also detected in samples G21 (7.3%) and G28 (3%). Although vaginal infection with S. agalactiae has not been consistently linked to specific symptoms, genital itching and burning (common in BV) have occasionally been attributed to this species. S. agalactiae can induce local inflammation, discharge, and a reduction in lactobacilli. In most samples, S. agalactiae was absent or present at very low abundance, potentially influenced by menopausal status. However, samples G21, G30, and G59 contained substantial levels of this species, correlating with symptomatic presentations. Yu et al. demonstrated, in a rat model, that S. agalactiae inhibits C. albicans hyphal growth and modulates host immunity by decreasing TH17 T cell numbers, thereby facilitating C. albicans colonization [ 33 ]. Interestingly, in sample G30, Candida tropicalis —not C. albicans —was isolated, suggesting that these interactions may be species-specific and that different mechanisms may govern the relationship between S. agalactiae and C. tropicalis . In recent decades, non-cultivation methods have substantially advanced our understanding of human-associated microbial communities, including both their composition and functional properties. In the healthy VMB, both microbial diversity and the balance among taxonomic groups are essential for maintaining host health. Obtained results indicate that L. iners is one of the dominant species among women in Armenia and that the VMB of the Yerevan population is diverse at the species and phylum levels. In samples from women with candidiasis, lactobacilli abundance was reduced and pathogenic species, particularly G. vaginalis , were prevalent. Pregnant women generally exhibited healthy microbiomes, though occasional pathogen detection likely reflects pregnancy-related immune changes. Pathogenic taxa without clinical symptoms were also found in some healthy women. Ethnic differences in the VMB were most pronounced at the phylum level, particularly among minority groups. Overall, the combined use of cultivation and metagenomic approaches in this study provides new insights that may facilitate the development of targeted strategies to modulate VMB composition and activity, ultimately supporting the health of women of reproductive age. Further studies with larger cohorts, longitudinal sampling, and host-factor assessments (e.g., hormonal status, sexual behavior, antibiotic use) are needed to clarify the drivers of these group-specific microbiome structures and their clinical relevance. 5. Conclusion This study provides the first comprehensive characterization of the vaginal microbiome of women from different ethnic groups in the Republic of Armenia using a combination of classical microbiological, MALDI-TOF MS, and 16S rRNA metagenomic approaches. The results demonstrate that the vaginal microbiome of Armenian women is notably diverse at both species and phylum levels compared to the Yezidi and Molokan groups. In women with candidiasis, a marked reduction in Lactobacillus abundance and an increased presence of potential pathogens such as G. vaginalis and A. vaginae were observed. In contrast, pregnant women generally exhibited a Lactobacillus-dominated, stable, and healthy microbiome, although some opportunistic taxa were occasionally detected, likely reflecting immune modulation during pregnancy. Ethnic and regional variations were most pronounced at the phylum level, suggesting that lifestyle, dietary habits, or environmental factors may contribute to shaping the microbial community structure. The consistency between culture-based and metagenomic results highlights the value of integrating complementary methodologies for accurate microbiome assessment. Overall, these findings enrich our understanding of the vaginal microbiome diversity among Armenian populations and provide a foundation for developing targeted probiotic and therapeutic strategies aimed at maintaining reproductive health. Future studies with larger sample sizes and functional analyses are needed to further elucidate the relationship between microbial composition, ethnicity, and clinical outcomes in women’s health. Declarations Acknowledgments The authors thank Dr. Nelli Nahapetyan, lecturer of N 1 Chair of Obstetrics and Gynecology of Yerevan State Medical University after M. Heratsi, obstetrician-gynecologist at the maternity center of Beglaryan Medical Center, Dr. Armine Dumikyan, gynecologist at the Vanadzor Medical Center CJSC, Vanadzor City and Dr. Karine Chopikyan, obstetrician-gynecologist of Tsaghkahovit Health Center CJSC in the village Tsaghkahovit for the collection of vaginal samples from different ecological zones of Armenia and the work with patients. Funding The work was supported by the Science Committee of RA, in the frames of the research project №24WS-1F005, № 23AA-1F010. Author Contributions T.A.: Formal analysis, Investigation, Writing – original draft, Funding acquisition; R.S.; Formal analysis, Investigation, Writing – original draft; R.H.: Formal analysis, Investigation; I.S.: Data curation, Validation; E.G.: Methodology, Resources; S.D.: Software, Supervision, Visualization, Writing – review & editing software; P.V.: Methodology, Resources, Supervision, Writing – review & editing, H.P.: Data curation, Visualisation, Writing – review & editing; I.B,: Funding acquisition, Project administration, Supervision, Writing – review & editing. Data availability All data generated or analyzed during this study are included in this published article. All metagenoms were submitted in NCBI and get the following accession numbers PRJNA1164001. The data are available at the following URL https://www.ncbi.nlm.nih.gov/sra/PRJNA1164001. Conflicts of Interest: The authors declare no conflicts of interest. References Maynard, C. L., Elson, C. O., Hatton, R. D. & Weaver, C. T. Reciprocal interactions of the intestinal microbiota and immune system. Nature 489 , 231–241. 10.1038/nature11551 (2012). Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108, 4680–4687. (2011). 10.1073/pnas.1002611107 Chen, X., Lu, Y., Chen, T. & Li, R. The female vaginal microbiome in health and bacterial vaginosis. Front. Cell. Infect. Microbiol. 11 , 631972. 10.3389/fcimb.2021.631972 (2021). Shao, Y. et al. Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. Nature 574 , 117–121. 10.1038/s41586-019-1560-1 (2019). Linhares, I. M., Summers, P. R., Larsen, B., Giraldo, P. C. & Witkin, S. S. Contemporary perspectives on vaginal pH and lactobacilli. Am. J. Obstet. Gynecol. 204 , 120e1–12120. 10.1016/j.ajog.2010.07.010 (2011). Javed, A., Parvaiz, F. & Manzoor, S. Bacterial vaginosis: an insight into the prevalence, alternative treatments regimen and its associated resistance patterns. Microb. Pathog . 127 , 21–30. 10.1016/j.micpath.2018.11.046 (2019). Muzny, C. A. et al. Identification of key bacteria involved in the induction of incident bacterial vaginosis: a prospective study. J. Infect. Dis. 218 , 966–978. 10.1093/infdis/jiy243 (2018). Bradford, L. L. & Ravel, J. The vaginal mycobiome: a contemporary perspective on fungi in women’s health and diseases. Virulence 8 , 342–351. 10.1080/21505594.2016.1237332 (2017). Lai, G. C., Tan, T. G. & Pavelka, N. The mammalian mycobiome: a complex system in a dynamic relationship with the host. Wiley Interdiscip Rev. Syst. Biol. Med. 11 , e1438. 10.1002/wsbm.1438 (2019). Chee, W. J. Y., Chew, S. Y. & Than, L. T. L. Vaginal microbiota and the potential of Lactobacillus derivatives in maintaining vaginal health. Microb. Cell. Fact. 19 , 203. 10.1186/s12934-020-01464-4 (2020). Lehtoranta, L. et al. Characterization of vaginal fungal communities in healthy women and women with bacterial vaginosis (BV). Microb. Pathog . 161 , 105055. 10.1016/j.micpath.2021.105055 (2021). Lehtoranta, L., Ala-Jaakkola, R., Laitila, A. & Maukonen, J. Healthy vaginal microbiota and influence of probiotics across the female life span. Front. Microbiol. 13 , 819958. 10.3389/fmicb.2022.819958 (2022). Francis, S. C. et al. Bacterial vaginosis among women at high risk for HIV in Uganda: high rate of recurrent diagnosis despite treatment. Sex. Transm Infect. 92 , 142–148. 10.1136/sextrans-2015-052160 (2016). Tuddenham, S. et al. Associations between dietary micronutrient intake and molecular-bacterial vaginosis. Reprod. Health . 16 , 151. 10.1186/s12978-019-0814-6 (2019). Smith, S. B. & Ravel, J. The vaginal microbiota, host defence and reproductive physiology. J. Physiol. 595 , 451–463. 10.1113/JP271694 (2017). Duluc, D. et al. Functional diversity of human vaginal APC subsets in directing T-cell responses. Mucosal Immunol. 6 , 626–638. 10.1038/mi.2012.104 (2013). Farage, M. & Maibach, H. Lifetime changes in the vulva and vagina. Arch. Gynecol. Obstet. 273 , 195–202. 10.1007/s00404-005-0079-x (2006). Koedooder, R. et al. The vaginal microbiome as a predictor for outcome of in vitro fertilization with or without intracytoplasmic sperm injection: a prospective study. Hum. Reprod. 34 , 1042–1054. 10.1093/humrep/dez065 (2019). García-Velasco, J. A., Menabrito, M. & Catalán, I. B. What fertility specialists should know about the vaginal microbiome: a review. Reprod. Biomed. Online . 35 , 103–112. 10.1016/j.rbmo.2017.04.005 (2017). Al-Memar, M. et al. The association between vaginal bacterial composition and miscarriage: a nested case-control study. BJOG 127 , 264–274. 10.1111/1471-0528.15972 (2020). Shen, J. et al. Effects of low dose estrogen therapy on the vaginal microbiomes of women with atrophic vaginitis. Sci. Rep. 10.1038/srep24380 (2016). Cauci, S. et al. Prevalence of bacterial vaginosis and vaginal flora changes in peri- and postmenopausal women. J. Clin. Microbiol. 40 , 2147–2152. 10.1128/JCM.40.6.2147-2152.2002 (2002). Sharma, M. et al. An insight into vaginal microbiome techniques. Life 11 , 1229. 10.3390/life11111229 (2021). Chilambi, G. S. et al. Genomic and phenotypic diversity of Enterococcus faecalis isolated from endophthalmitis. PLoS One . 16 , e0250084. 10.1371/journal.pone.0250084 (2021). Ibrahim, H. A. H., Zahran, H. F. & Youssef, M. Microbial degradation of sodium lauryl sulfate using bacterial consortium isolated from coastline of Alexandria City. J. High. Inst. Public. Health . 42 , 185–207. 10.21608/jhiph.2012.20132 (2012). Poormontaseri, M., Ostovan, R., Berizi, E. & Hosseinzadeh, S. Growth rates of Bacillus species probiotics using various enrichment media. Int. J. Nutr. Sci. 2 , 39–42 (2017). Zhou, X. et al. Characterization of vaginal microbial communities in adult healthy women using cultivation-independent methods. Microbiology 150 , 2565–2573. 10.1099/mic.0.26905-0 (2004). Costa, N. S. et al. Anovaginal colonization by Group B Streptococcus and Streptococcus anginosus among pregnant women in Brazil and its association with clinical features. Antibiotics 13 , 85. 10.3390/antibiotics13010085 (2024). van Teijlingen, N. H. et al. Prevotella timonensis bacteria associated with vaginal dysbiosis enhance HIV-1 susceptibility of vaginal CD4 + T cells. J. Infect. Dis. 230 , e43–e47. 10.1093/infdis/jiae166 (2024). Chaban, B. et al. Characterization of the vaginal microbiota of healthy Canadian women through the menstrual cycle. Microbiome 2, 23. (2014). 10.1186/2049-2618-2-23 Gupta, P., Singh, M. P. & Goyal, K. Diversity of vaginal microbiome in pregnancy: deciphering the obscurity. Front. Public. Health . 8 , 326. 10.3389/fpubh.2020.00326 (2020). Xiao, B., Qin, A. D., Mi, H., Zhang, D. & L. & Correlation analysis of vaginal microbiome changes and bacterial vaginosis plus vulvovaginal candidiasis mixed vaginitis prognosis. Front. Cell. Infect. Microbiol. 12 , 860589. 10.3389/fcimb.2022.860589 (2022). Yu, X. Y. et al. Streptococcus agalactiae inhibits Candida albicans hyphal development and diminishes host vaginal mucosal TH17 response. Front. Microbiol. 9 , 198. 10.3389/fmicb.2018.00198 (2018). Wieme, A. D. et al. Identification of beer-spoilage bacteria using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Int. J. Food Microbiol. 185 , 41–50. 10.1016/j.ijfoodmicro.2014.05.003 (2014). Dumolin, C. et al. Introducing SPeDE: high-throughput dereplication and accurate determination of microbial diversity from matrix-assisted laser desorption–ionization time of flight mass spectrometry data. mSystems 4 , e00437–e00419 (2019). 10.1128/mSystems.00437 – 19. William, S., Feil, H. & Copeland, A. Bacterial genomic DNA isolation using CTAB. JGI, Walnut Creek, CA (2012). Available at: https://jgi.doe.gov Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27 , 2957–2963. 10.1093/bioinformatics/btr507 (2011). Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods . 7 , 335–336. 10.1038/nmeth.f.303 (2010). Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods . 10 , 57–59. 10.1038/nmeth.2276 (2013). Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27 , 2194–2200. 10.1093/bioinformatics/btr381 (2011). Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21 , 494–504. 10.1101/gr.112730.110 (2011). Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods . 10 , 996–998. 10.1038/nmeth.2604 (2013). Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215 , 403–410. 10.1016/S0022-2836(05)80360-2 (1990). Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73 , 5261–5267 (2007). 10.1128/AEM.00062 – 07. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41 , D590–D596. 10.1093/nar/gks1219 (2013). Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32 , 1792–1797. 10.1093/nar/gkh340 (2004). Chao, A. Nonparametric estimation of the number of classes in a population. Scand. J. Stat. 11 , 265–270 (1984). Chao, A. & Lee, S. M. Estimating the number of classes via sample coverage. J. Am. Stat. Assoc. 87 , 210–217. 10.2307/2290471 (1992). Chao, A. & Yang, M. C. K. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80 , 193–201. 10.2307/2336768 (1993). Magurran, A. E. Measuring Biological Diversity (Blackwell Publishing, 2004). Lemos, L. N., Fulthorpe, R. R., Triplett, E. W. & Roesch, L. F. W. Rethinking microbial diversity analysis in the high throughput sequencing era. J. Microbiol. Methods . 86 , 42–51. 10.1016/j.mimet.2011.03.014 (2011). Simpson, E. Measurement of diversity. Nature 163 , 688. 10.1038/163688a0 (1949). Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93 , 2533–2547. 10.1890/11-1952.1 (2012). White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5 , e1000352. 10.1371/journal.pcbi.1000352 (2009). Additional Declarations No competing interests reported. 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among Ethnic Groups in Armenia\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eThe human body functions as a holobiont, comprising a complex consortium of microbial communities that have co-evolved with their host over millions of years [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Among these, the vaginal microbiome (VMB) plays a critical role in women\\u0026rsquo;s reproductive health, yet it has long received less scientific attention compared to the gut or skin microbiota [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. In recent years, however, interest in the VMB has increased substantially due to its recognized importance in maintaining vaginal health, preventing infections, and influencing pregnancy outcomes.\\u003c/p\\u003e \\u003cp\\u003eThe vaginal ecosystem provides a moist, nutrient-rich, and thermally stable environment for resident microbes, which in turn produce antimicrobial and anti-inflammatory compounds that help sustain microbial balance [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Disruption of this equilibrium, known as dysbiosis, can result from hormonal fluctuations, aging, immune alterations, antibiotic use, or exposure to environmental microbes [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Such imbalances are associated with a range of gynecological and obstetric conditions, including bacterial vaginosis (BV), increased susceptibility to sexually transmitted infections, pelvic inflammatory disease, and pregnancy complications such as miscarriage or preterm birth [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. BV is characterized by a significant reduction in the number of lactobacilli, accompanied by a dramatic increase\\u0026mdash;by 100 to 1,000 times\\u0026mdash;in the abundance of facultative or obligate anaerobic bacteria known as microbes associated with BV including \\u003cem\\u003eGardnerella\\u003c/em\\u003e, \\u003cem\\u003ePrevotella\\u003c/em\\u003e, \\u003cem\\u003eAtopobium\\u003c/em\\u003e, \\u003cem\\u003eMobiluncus\\u003c/em\\u003e, \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e, \\u003cem\\u003eSneathia\\u003c/em\\u003e, \\u003cem\\u003eLeptotrichia\\u003c/em\\u003e, and certain members of the Clostridiales order [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe composition of the VMB is dynamic and varies across the menstrual cycle, pregnancy, and menopause. It can vary significantly between individuals, with these differences influenced by factors such as sexual activity, chronic stress, geographic location, race, and other socio-environmental variables [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Vaginal fungi primarily belong to the phyla Ascomycota and Basidiomycota [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e], with predominant fungal genera including \\u003cem\\u003eCandida\\u003c/em\\u003e, \\u003cem\\u003eCladosporium\\u003c/em\\u003e, \\u003cem\\u003ePichia\\u003c/em\\u003e, \\u003cem\\u003eAspergillus\\u003c/em\\u003e, and \\u003cem\\u003eRhodotorula\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Although fungi such as \\u003cem\\u003eCandida\\u003c/em\\u003e spp. are part of the normal vaginal microbiota, overgrowth can lead to vulvovaginal candidiasis (VVC) [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe interplay between bacterial and fungal communities is complex\\u0026mdash;bacterial dysbiosis can promote fungal proliferation, whereas lactobacilli generally help suppress fungal infections through acidification and competitive exclusion.\\u003c/p\\u003e \\u003cp\\u003eThe composition of the VMB is highly dynamic and varies depending on factors such as age, ethnicity, physiological changes (including hormonal fluctuations and immune system status), vaginal infections, medications, probiotics, lifestyle, and diet [\\u003cspan additionalcitationids=\\\"CR13 CR14 CR15\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTypically, \\u003cem\\u003eLactobacillus\\u003c/em\\u003e species dominate the VMB of healthy women, producing lactic acid and other metabolites that maintain a low vaginal pH and inhibit pathogenic bacteria. However, the predominance and diversity of Lactobacillus species vary across populations and ethnic groups. For instance, lactobacilli are predominant in the VMB of Asian and white women, while African-American and Hispanic women are more likely to harbor a broader range of bacteria, including several species commonly associated with BV [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe composition of the VMB undergoes significant changes from childhood to the onset of puberty, during pregnancy, and after menopause. In childhood, the VMB is primarily dominated by Gram-positive anaerobic bacteria, including species from the genera \\u003cem\\u003eActinomyces\\u003c/em\\u003e, \\u003cem\\u003eBifidobacterium\\u003c/em\\u003e, \\u003cem\\u003ePeptococcus\\u003c/em\\u003e, \\u003cem\\u003ePeptostreptococcus\\u003c/em\\u003e, and \\u003cem\\u003ePropionibacterium\\u003c/em\\u003e. It also includes Gram-negative anaerobic bacteria, such as \\u003cem\\u003eBacteroides\\u003c/em\\u003e, \\u003cem\\u003eFusobacterium\\u003c/em\\u003e, and \\u003cem\\u003eVeillonella\\u003c/em\\u003e, as well as aerobic bacteria like \\u003cem\\u003eStaphylococcus aureus\\u003c/em\\u003e, \\u003cem\\u003eStaphylococcus epidermidis\\u003c/em\\u003e, \\u003cem\\u003eStreptococcus viridans\\u003c/em\\u003e, and \\u003cem\\u003eEnterococcus faecalis\\u003c/em\\u003e. During this period, the vaginal pH is typically acidic. However, as a girl reaches puberty, the vaginal pH shifts, becoming neutral or slightly alkaline [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Some studies suggest that vaginitis, in particular, is linked to a decrease in the number of lactobacilli, accompanied by an increase in the abundance of pathogens such as \\u003cem\\u003eGardnerella vaginalis\\u003c/em\\u003e, \\u003cem\\u003eAtopobium vaginae\\u003c/em\\u003e, \\u003cem\\u003eUreaplasma parvum\\u003c/em\\u003e, \\u003cem\\u003eUreaplasma urealyticum\\u003c/em\\u003e, and \\u003cem\\u003eCandida\\u003c/em\\u003e species. This shift in microbial composition can contribute to the development of reproductive issues [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003ePregnancy is accompanied by profound hormonal and immunological changes that also shape the vaginal microbiome. Several studies have reported reduced microbial diversity and increased dominance of lactobacilli during pregnancy, which is considered beneficial for maintaining vaginal health and supporting fetal development [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Conversely, reduced levels of lactobacilli have been linked to adverse pregnancy outcomes, including miscarriage and preterm delivery [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. After menopause, declining estrogen levels and increased vaginal pH lead to diminished lactobacilli populations and greater susceptibility to infection [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eEthnic and regional variations in the vaginal microbiome have been reported worldwide, yet no comprehensive metagenomic analysis has been conducted in Armenia. Given the country\\u0026rsquo;s unique population structure, cultural diversity, and ecological heterogeneity, understanding these variations could provide valuable insights into microbial adaptation, women\\u0026rsquo;s health, and potential probiotic development. Therefore, the aim of the present study was to investigate and compare the vaginal microbiomes of women of reproductive age from three ethnic groups in Armenia\\u0026mdash;Armenians, Yezidis, and Molokans\\u0026mdash;using classical microbiological methods, MALDI-TOF MS, and 16S rRNA amplicon sequencing. The study sought to identify dominant microbial taxa, reveal intergroup differences, and provide a foundation for developing microbiota-based therapeutic and preventive strategies tailored to local populations.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e2.1 Subjects of the study.\\u003c/strong\\u003e Vaginal samples were collected from 64 women residing in three different regions of the Republic of Armenia, each group reflecting distinct lifestyles shaped by their cultural traditions and ethnic backgrounds. The participants were divided into three groups:\\u003c/p\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003eGroup 1: Residents of Yerevan who self-identified as Armenians (32 subjects)\\u003c/li\\u003e\\n \\u003cli\\u003eGroup 2: Residents of Aragatsotn Province who self-identified as Yezidis (15 subjects)\\u003c/li\\u003e\\n \\u003cli\\u003eGroup 3: Residents of Lori Province who self-identified as Molokans (21 subjects)\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003e(See Table\\u0026nbsp;2 for details.)\\u003c/p\\u003e\\n\\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eDivision of samples according to their ethnicity\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003eTarget groups\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003eHealthy\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003ePregnant\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003ePresence of signs of dysbiosis\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eArmenians\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG3, G8, G11, G12, G14, G19, G30, G42, G43\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG9, G15, G18, G24, G26\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG4, G5, G6, G7, G10, G13, G17, G20, G21, G22, G25, G28, G29, G31\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eYazidis\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG32, G33, G34, G35, G36\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG44, G45, G46, G47, G48\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG37, G38, G39, G40, G41\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eMolokans\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG49, G50, G51, G52, G53\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG16, G54, G55, G56, G57, G58\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003eG22, G59, G60, G61, G62, G63, G64, G65, G66, G67, G68\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eThe relatively small number of the samples from group 2 and 3 is due to the fact that these ethnic groups represent minorities in Armenia, and it was challenging for healthcare providers to collect samples that met the study\\u0026apos;s inclusion criteria. Sampling was carried out across three subgroups: healthy women, women in the first trimester of pregnancy, and women diagnosed with candidiasis. Diagnoses were made by medical professionals based on clinical symptoms and examination data from diagnostic centers. All participants were between 18 and 39 years old and provided written informed consent prior to participation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStudy of the VMB using classical microbiological methods.\\u003c/strong\\u003e Vaginal smear analysis was initiated within two hours of sample collection. Samples were transported from the hospital to the laboratory under sterile conditions at a controlled temperature of 30\\u0026ndash;37\\u0026deg;C. Upon arrival, each sample was diluted in 2.5 mL of sterile saline and incubated with shaking at 37\\u0026deg;C for 30 minutes. Subsequently, 0.5 mL of this suspension was transferred into a medium containing 10% skim milk to initiate the enrichment culture of LAB. The culture was then incubated at 37\\u0026deg;C for 24 hours to promote LAB growth.\\u003c/p\\u003e\\n\\u003cp\\u003eSerial dilutions of the remaining sample suspensions were prepared up to 10⁻⁶. From each dilution, including the undiluted sample, 0.5 mL was plated on various media: Nutrient agar, Sabouraud agar, and Endo agar for surface cultivation, and De Man\\u0026ndash;Rogosa\\u0026ndash;Sharpe (MRS) agar for deep (microaerophilic) cultivation. It is important to note that chloramphenicol was not added to Sabouraud agar, as the goal was to allow the growth of all bacterial species, not just fungi. After incubation at 37\\u0026deg;C for 2\\u0026ndash;7 days, colony-forming units (CFUs) were counted, and colonies with distinct morphologies were identified. Colonies grown within MRS agar (deep growth) were selected and transferred into 10% skim milk to initiate LAB enrichment cultures. LAB cultures were incubated at 37\\u0026deg;C for 1\\u0026ndash;5 days. To obtain pure LAB cultures, at least three successive dilutions and single-colony transfers into skim milk were performed. The purity of each culture was verified using a phase-contrast microscope (Omax M837ZL, China). Pure LAB cultures were preserved in MRS broth supplemented with 20% glycerol and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2 Identification of bacterial isolates using MALDI-TOF MS.\\u003c/strong\\u003e Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) analysis was conducted on the vaginal isolates. Protein extraction was carried out using third-generation axenic isolates, and the MALDI-TOF MS procedure followed previously described methods [34]. Each isolate was analyzed in duplicate using the Bruker Microflex\\u0026reg; LT/SH s-Smart instrument (Bruker Daltonics, Bremen, Germany). The resulting mass spectra were processed using the SPeDE algorithm with its standard parameters to group them into unique mass spectrometry-based strain types [35]. For each of these defined types, one representative (or \\u0026apos;reference\\u0026apos;) isolate was selected for further identification when necessary.\\u003c/p\\u003e\\n\\u003cp\\u003eThe mass spectra generated were compared with the Bruker BDAL MSP library using MBT Compass Explorer software (Bruker Daltonics). Identifications were determined based on the manufacturer\\u0026rsquo;s scoring criteria. Isolates achieving a Bruker log score of 2.2 or higher were classified as reliably identified at the species level. In contrast, isolates with scores below 2.2 were considered to have low-confidence identifications and were subjected to whole genome sequencing for more accurate identification.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3 Extraction of chromosomal DNA.\\u003c/strong\\u003e Chromosomal DNA was extracted using a hexadecyltrimethylammonium bromide (CTAB) protocol [36]. The presence and integrity of the extracted genomic DNA were assessed by electrophoresis on a 0.8% agarose gel prepared in Tris-Acetate-EDTA (TAE) buffer. DNA bands were stained with ethidium bromide (1 \\u0026micro;g/mL) and visualized under UV illumination using a transilluminator (Transilluminator Dlin UV 312, AC input 220V, 50 Hz, China). The extracted DNA samples were then submitted to \\u0026quot;Novogen\\u0026quot; CJSC (China) for amplicon-based metagenomic sequencing targeting the V3\\u0026ndash;V4 hypervariable regions of the 16S rRNA gene.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.4 16S rDNA amplicon sequencing. Library Construction, Quality Control and Sequencing.\\u003c/strong\\u003e Polymerase Chain Reaction (PCR) amplification of targeted regions was performed by using specific primers connecting with barcodes. The PCR products with proper size were selected by 2% agarose gel electrophoresis. The same amount of PCR products from each sample was pooled, end-repaired, A-tailed and further ligated with Illumina adapters. Libraries were sequenced on a paired-end Illumina platform to generate 250bp paired-end raw reads.\\u003c/p\\u003e\\n\\u003cp\\u003eThe library was checked with Qubit and real-time PCR for quantification and bioanalyzer for size distribution detection. Quantified libraries will be pooled and sequenced on Illumina platforms, according to effective library concentration and data amount required according to manufacturer\\u0026rsquo;s recommendations (Novogene).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.5 Sequencing data processing\\u003c/strong\\u003e. Paired-end reads were assigned to samples based on their unique barcodes and truncated by cutting off the barcode and primer sequences. Paired-end reads were merged using FLASH (V1.2.7) [37] (see details http://ccb.jhu.edu/software/FLASH/). To obtain the high-quality clean tags, quality filtering of the raw tags was performed by Qiime (V1.7.0) [38] (see details http://qiime.org/scripts/split_libraries_fastq.html) under specific filtering conditions [39].\\u003c/p\\u003e\\n\\u003cp\\u003eThe tags were compared with the reference database (SILVA138 database, see details http://www.arb-silva.de/) using UCHIME algorithm (UCHIME Algorithm, see details http://www.drive5.com/usearch/manual/uchime_algo.html) [40] to detect chimera sequences (see details https://drive5.com/usearch/manual/chimeras.html). And then the chimera sequences were removed [41]. Then the Effective Tags finally obtained.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.6 Operational Taxonomic Unit (OTU) cluster and Taxonomic annotation\\u003c/strong\\u003e. Sequences analysis were performed by Uparse software (Uparse v7.0.1090, see details http://drive5.com/uparse/) [42] using all the effective tags. Sequences with \\u0026ge;\\u0026thinsp;97% similarity were assigned to the same OTUs. The representative sequence for each OTU was screened for further annotation. For each representative sequence, Qiime (Version 1.7.0, see details http://qiime.org/scripts/assign_taxonomy.html) [43] in Mothur method was performed against the SSUrRNA database of SILVA138 Database (see details http://www.arb-silva.de/) [44] for species annotation at each taxonomic rank (Threshold:0.8\\u0026thinsp;~\\u0026thinsp;1) [45] (kingdom, phylum, class, order, family, genus, species). To obtain the phylogenetic relationship of all OTUs representative sequences, the MUSCLE [46] (Version 3.8.31, see details http://www.drive5.com/muscle/) can compare multiple sequences rapidly. OTUs abundance information were normalized using a standard of sequence number corresponding to the sample with the least sequences. Subsequent analysis of alpha diversity and beta diversity were all performed basing on this output normalized data.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.7 Alpha Diversity\\u003c/strong\\u003e. Alpha diversity was determined using the Chao1 estimator (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.chao1.html#skbio.diversity.alpha.chao1), which measures community richness [47], and the ACE estimator (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.ace.html) [48, 49]. Community diversity was analyzed with the Shannon index (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.shannon.html) [50, 51] and the Simpson index (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.inv_simpson.html) [51, 52]. Sequencing depth was assessed by Good\\u0026rsquo;s coverage index (https://scikit.bio/docs/latest/generated/skbio.diversity.alpha.goods_coverage.html) [53].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.8 Beta Diversity\\u003c/strong\\u003e. Beta diversity analysis was used to evaluate differences of samples in species complexity, Beta diversity on both weighted and unweighted unifrac were calculated by QIIME software (Version 1.7.0). Cluster analysis was preceded by principal component analysis (PCA), which was applied to reduce the dimension of the original variables using the FactoMineR package and ggplot2 package in R software (Version 2.15.3). Principal Coordinate Analysis (PCoA) was performed to get principal coordinates and visualize from complex, multidimensional data. A distance matrix of weighted or unweighted unifrac among samples obtained before was transformed to a new set of orthogonal axes by which the maximum variation factor is demonstrated by first principal coordinate and the second maximum one by the second principal coordinate, and so on. PCoA analysis was displayed by WGCNA package, stat packages and ggplot2 package in R software (Version 2.15.3). Unweighted Pair-group Method with Arithmetic Means (UPGMA) Clustering was performed as a type of hierarchical clustering method to interpret the distance matrix using average linkage and was conducted by QIIME software (Version 1.7.0).\\u003c/p\\u003e\\n\\u003cp\\u003eLEfSe analysis was conducted using LEfSe software. Metastat was calculated using R software. P-value was calculated using method of permutation test while q-value was calculated using method of Benjamini and Hochberg False Discovery Rate [54]. Anosim, MRPP and Adonis were performed using R software (Vegan package: anosim function, mrpp function and adonis function). ANOVA was calculated by mothur using anova function. T_test and drawing were conducted by R software according to Novogene\\u0026rsquo;s protocol.\\u003c/p\\u003e\\n\\u003cp\\u003eAll methods were carried out in accordance with the relevant guidelines and regulations. All experimental protocols involving human participants were approved by the National Center of Bioethics in Armenia.\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eVaginal samples from 64 women were analyzed, including 29 samples from Group 1, 15 from Group 2, and 20 from Group 3. A total of 190 cultures were isolated from MRS and NA media and subsequently purified. All isolates were Gram-positive and exhibited either coccal or bacillary morphology (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMorphological division of isolates\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTarget groups\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCocci\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eBacilli\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eArmenians\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHealthy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePregnant\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWith dysbiosis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eMolokans\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHealthy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePregnant\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWith dysbiosis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eYezidis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHealthy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePregnant\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWith dysbiosis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eMALDI-TOF MS analysis identified the following bacterial species in the vaginal samples: \\u003cem\\u003eLactobacillus delbrueckii, Lacticaseibacillus rhamnosus, Lacticaseibacillus paracasei, Enterococcus faecalis, Enterococcus hirae, Enterococcus lactis, Enterococcus faecium, Enterococcus durans\\u003c/em\\u003e, and \\u003cem\\u003eS. epidermidis\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eSeveral patients had clinically confirmed candidiasis. Three women had undergone antifungal therapy; among them, one (G7) had fully recovered, while another (G8) still harbored a high abundance of \\u003cem\\u003eCandida\\u003c/em\\u003e spp. In participant G5, although \\u003cem\\u003eCandida\\u003c/em\\u003e was not detected, pronounced dysbiosis was observed.\\u003c/p\\u003e \\u003cp\\u003eFrom Group 3, participant G22 showed clinical signs of vaginitis without detectable \\u003cem\\u003eCandida.\\u003c/em\\u003e Another participant, G61, was diagnosed with vaginal wall atrophy. Serial dilutions plated on four types of cultural media revealed variation in microbial abundance and composition across the samples (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMetagenomic sequencing was performed in parallel for all 64 samples. In most cases, the metagenomic profiles were consistent with classical microbiological findings. Overall, 1580 microbial taxa were identified at different taxonomic levels (species, genus, order, etc.). For comparative analysis, 19 dominant taxa\\u0026mdash;each present in at \\u0026ge;\\u0026thinsp;0.5% relative abundance\\u0026mdash;were selected. Phylum-level comparisons were conducted to highlight broad differences in vaginal microbiome structure among the three groups.\\u003c/p\\u003e \\u003cp\\u003eAs shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, although minor microbial groups varied among the study groups clear differences were observed at the phylum level. Group 2 was dominated by Actinobacteriota, whereas Firmicutes predominanted in both Group 1 and Group 3.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAs shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, the number of OTUs identified in the three subgroups of Group 1 totaled 350, compered with 187 OTUs in Group 2 and 159 OTUs in Group 3. In healthy women, OUT richness was significantly higher (1436 OTUs) relative to other two groups (367 and 359 OTUs, respectively).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e presents an analysis of vaginal samples from healthy women across the three groups. In Group 1, \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp., including \\u003cem\\u003eLactobacillus iners\\u003c/em\\u003e, predominated. While \\u003cem\\u003eL. iners\\u003c/em\\u003e is commonly associated with a healthy VMB, sample G12 showed an unusually high abundance of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (18%) and a reduced level of \\u003cem\\u003eL. iners\\u003c/em\\u003e (4.5%); this deviation may be related to postpartum changes, as the participant had given birth three months earlier.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSample G24 contained a notably high proportion of \\u003cem\\u003eStreptococcus anginosus\\u003c/em\\u003e (26.46%), compared with 15% \\u003cem\\u003eL. iners\\u003c/em\\u003e and 12.9% \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp. In healthy women from Group 2, lactobacilli were the predominant taxa. The exception was sample G40, which had a relatively high level of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (32.5%), although lactobacilli still represented 52.6% of the community, likely limiting the pathogenic potential of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eMost healthy women in Group 3 also exhibited domination by \\u003cem\\u003eL. iners\\u003c/em\\u003e and other \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp. (e.g., samples G55, G57, and G58). Sample G54 showed low lactobacilli abundance (14%) but a high proportion of \\u003cem\\u003eBifidobacterium breve\\u003c/em\\u003e (77.5%) a species considered beneficial and possibly compensatory for reduced lactobacilli.\\u003c/p\\u003e \\u003cp\\u003eIn sample G16, the relative abundances of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e, \\u003cem\\u003ePeptoniphilus timonensis\\u003c/em\\u003e, and \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp. were 36.3%, 4.7%, and 29.8%, respectively. In sample G56, \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (47%) and \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp. (46%) were present in nearly equal levels.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e illustrates pregnancy-associated changes in the VMB. In pregnant participants from Group 1, \\u003cem\\u003eL. iners\\u003c/em\\u003e and other \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp. were dominant, consistent with a healthy pregnancy profile. However, samples G42 and G43 deviated from this pattern, exhibiting markedly reduced lactobacilli and high levels of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (62.8%) and \\u003cem\\u003eA. vaginae\\u003c/em\\u003e (80%), both commonly linked to BV.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn Group 2, \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp. also predominated in pregnant women. Sample G33 contained 45.5% lactobacilli alongside a substantial amount of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (38.8%). This co-occurrence may be associated with immunological changes during pregnancy. Sample G35 showed very low lactobacilli (8.6%) and elevated abundances of several BV-associated taxa: \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (58.4%), \\u003cem\\u003eMegasphaera\\u003c/em\\u003e spp. (11.4%), \\u003cem\\u003eP. timonensis\\u003c/em\\u003e (5.1%), and \\u003cem\\u003eA. vaginae\\u003c/em\\u003e (4.6%). In contrast, sample G36 was dominated by lactobacilli (74%) with only a modest level of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (12.4%).\\u003c/p\\u003e \\u003cp\\u003ePregnant women in Group 3 consistently exhibited a lactobacilli-dominated microbiome. Sample G53 contained a notable abundance of \\u003cem\\u003eB. breve\\u003c/em\\u003e (47.4%), supporting a healthy microbiome profile. Phenotypic assessment of bifidobacteria associated with reproductive health revealed fermentation patterns similar to intestinal bifidobacteria. Vaginal bifidobacteria produced lactic acid and tolerated high concentrations of it, enabling survival in the characteristically low pH of the vaginal environment.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e presents VMB alterations in women with candidiasis. Samples G8, G20, and G25 exhibited typical signatures of BV. In sample G8, lactobacilli (15%) were present at low levels alongside low \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (3.5%) and a notably high abundance of \\u003cem\\u003eEnterococcus\\u003c/em\\u003e spp. (45.8%), possibly indicating an influence from the intestinal microbiome. Sample G20 also contained low lactobacilli (11%) and a high proportion of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e (75.8%), characteristic of advanced BV.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn Group 2, samples from women with dysbiosis consistently showed reduced lactobacilli and elevated abundances of BV-associated bacteria, including \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e, \\u003cem\\u003eA. vaginae\\u003c/em\\u003e, and \\u003cem\\u003eMegasphaera\\u003c/em\\u003e spp. \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e levels ranged from 42% to 46.5%, \\u003cem\\u003eA. vaginae\\u003c/em\\u003e from 13.2% to 29.6%, and \\u003cem\\u003eMegasphaera\\u003c/em\\u003e spp. from 10.5% to 27.3%.\\u003c/p\\u003e \\u003cp\\u003eIn sample G22, lactobacilli were low (31%) while \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e was high (49.7%). Lower levels of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e were detected in G65 (3.6%) and G66 (10.5%). In sample G65, non-\\u003cem\\u003einers\\u003c/em\\u003e lactobacilli were present at very low levels. \\u003cem\\u003eS. agalactiae\\u003c/em\\u003e was identified in samples G59 (26.6%) and G60 (3.6%).\\u003c/p\\u003e \\u003cp\\u003eAcross the groups, \\u003cem\\u003eL. iners\\u003c/em\\u003e was the predominant species. Groups 1 and 3 displayed high levels of additional \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp., whereas Group 2 exhibited very low abundance of lactobacilli other than \\u003cem\\u003eL. iners\\u003c/em\\u003e. Group 1 showed the highest overall species diversity, while Group 3 showed the highest diversity within \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp., though it exhibited reduced diversity at the broader species level. Notably, \\u003cem\\u003eB. breve\\u003c/em\\u003e was more abundant in Group 3.\\u003c/p\\u003e \\u003cp\\u003eAmong pathogenic taxa, \\u003cem\\u003eS. anginosus\\u003c/em\\u003e and \\u003cem\\u003ePeptoniphilus disiens\\u003c/em\\u003e were found only in Group 1, while \\u003cem\\u003eP. timonensis\\u003c/em\\u003e was identified in both Groups 1 and 2. In Group 2, pathogenicity was primarily associated with \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e, \\u003cem\\u003eA. vaginae\\u003c/em\\u003e, and \\u003cem\\u003eMegasphaera\\u003c/em\\u003e spp. \\u003cem\\u003eS. agalactiae\\u003c/em\\u003e was detected only in Group 3. \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e was the most frequently identified pathogen across all groups.\\u003c/p\\u003e \\u003cp\\u003eOverall, the vaginal microbiome of pregnant women across the three groups predominantly reflected a healthy profile, though pathogens were most frequently detected in Group 2. Some women in Group 3 exhibited signs of dysbiosis, characterized by elevated levels of one or two dominant species.\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThis study provides an integrated culture-based and metagenomic characterization of the vaginal microbiome across three regional groups of women, including healthy individuals, pregnant women, and patients with dysbiosis or candidiasis. The combined approach enabled high-resolution taxonomic profiling and revealed clear differences in microbiome composition, diversity, and pathogen distribution among the groups.\\u003c/p\\u003e \\u003cp\\u003eHistorically, the VMB has been studied using cultivation methods, with two primary techniques traditionally employed to examine changes in the VMB: the Amsel test and Nugent scoring. The Amsel test focused on detecting clinical conditions, while Nugent scoring relied on assessing bacterial shape and size. However, it is well known that the large number of uncultivated and strictly anaerobic bacteria pose significant limitations to cultivation-based methods, hindering the ability to accurately capture the full biodiversity of the VMB [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. For instance, the media used in experiments were not strictly selective. On Sabouraud nutrient agar, in addition to yeasts, \\u003cem\\u003eEnterococcus\\u003c/em\\u003e spp. and spore-forming fungi can also grow [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Similarly, on Endo nutrient agar, aside from Gram-negative bacteria from the \\u003cem\\u003eEnterobacteriaceae\\u003c/em\\u003e family, species from the genera \\u003cem\\u003eBacillus\\u003c/em\\u003e and \\u003cem\\u003eMycobacterium\\u003c/em\\u003e can also proliferate [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Due to its rich nutrient content, MRS medium supports the growth of a variety of other microbes [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Given that the aim of current research was to explore the full diversity of the VMB, no antibiotics were added to the Sabouraud medium.\\u003c/p\\u003e \\u003cp\\u003eStudies have confirmed that the VMB of healthy women is predominantly composed of LAB [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. In most cases, a single species of LAB dominates the microbiome. The presence of multiple LAB species is influenced by antagonistic interactions between them, the competitive colonization of one species over another, or host-specific factors that favor the colonization of certain species [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. The metagenomic analysis of vaginal samples also confirmed that LAB predominates in the VMB. Nonetheless, some potentially pathogenic taxa may also be present within the vaginal normobiome [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. In samples from healthy women \\u003cem\\u003eCandida\\u003c/em\\u003e spp., as well as \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e, \\u003cem\\u003eA. vaginae\\u003c/em\\u003e, and \\u003cem\\u003eMegasphaera\\u003c/em\\u003e spp., have been identified. These bacteria, although potentially pathogenic, can still contribute to lactic acid production and acidification of the vagina, creating conditions that inhibit the growth of pathogenic microorganisms and reduce the risk of BV development [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe prevalence of BV varies across ethnic groups, though the underlying causes remain unclear. Genetic factors have been proposed as potential contributors to VMB composition [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Higher BV prevalence has been reported among Afro-Caribbean and Aboriginal communities in the United Kingdom and Canada, respectively. Similar trends have been noted in Roma women in Spain and Tibetan women in China. In the United States, Black women exhibit higher microbial diversity and lower rates of \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp. colonization compared to White women. Studies in African populations have also reported a lower abundance of \\u003cem\\u003eL. crispatus\\u003c/em\\u003e relative to European and Asian women, with VMBs more commonly dominated by \\u003cem\\u003eL. iners\\u003c/em\\u003e and facultative anaerobes [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eSpecial emphasis was placed on analyzing the biodiversity profiles of the three study groups. The VMB of group 1 demonstrated higher diversity than groups 2 and 3, which may reflect dietary differences that influence microbial composition.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eS. anginosus\\u003c/em\\u003e is the predominant microorganism in aerobic vaginitis and is known to induce vaginal epithelial cell lysis through specific virulence factors. Its increased colonization in the absence of lactobacilli is characteristic of aerobic vaginitis, whereas in lactobacilli-dominated microbiomes it typically does not behave as a pathogen [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. In sample G26, \\u003cem\\u003eL. iners\\u003c/em\\u003e and \\u003cem\\u003eLactobacillus\\u003c/em\\u003e spp. accounted for 4.8% and 5.5% of the microbial community, respectively, while \\u003cem\\u003eP. timonensis\\u003c/em\\u003e and \\u003cem\\u003eAlloscardovia omnicolens\\u003c/em\\u003e reached 22% and 26.8%, respectively. \\u003cem\\u003ePrevotella\\u003c/em\\u003e spp., detected at high levels, have been associated with vaginal dysbiosis and increased susceptibility to HIV, raising additional concerns given the low abundance of lactobacilli in this sample [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. \\u003cem\\u003eA. omnicolens\\u003c/em\\u003e, a member of Bifidobacteriaceae, is generally associated with a healthy microbiome and plays important roles in early-life development. It may also be transferred from healthy reproductive-aged women to infants during childbirth [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eSubstantial shifts in the vaginal bacteriobiome during pregnancy and illness have been well documented [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], and similar trends were observed in this study. Notably, pregnancy in group 1 was associated with increased Actinobacteriota abundance, whereas in groups 2 and 3 this phylum decreased during pregnancy. In group 2, both illness and pregnancy promoted expansion of Bacteroidota. Pregnancy-induced alterations in immunity and hormonal balance can lead to VMB dysbiosis, increasing susceptibility to pathogens and elevating the risk of complications such as miscarriage and preterm birth. This underscores the importance of examining pregnancy-associated microbiome changes across different ethnic groups [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Previous research has shown that the most pronounced VMB shifts occur between the first and second trimesters, with alpha diversity highest in the first trimester [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]; therefore, only first-trimester samples were included in this study.\\u003c/p\\u003e \\u003cp\\u003eIn sample G25, \\u003cem\\u003eP. disiens\\u003c/em\\u003e was the dominant species (35%), with few other bacteria present. \\u003cem\\u003eP. disiens\\u003c/em\\u003e, a species associated with BV and periodontal disease, has been linked to concomitant vaginal and gum infections. Women with gingivitis exhibit higher levels of \\u003cem\\u003eP. disiens\\u003c/em\\u003e, and BV has been correlated with a greater likelihood of gingivitis. In contrast, sample G13 exhibited extremely low diversity, with only one \\u003cem\\u003eLactobacillus\\u003c/em\\u003e species detected; however, \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e was also present at notable levels. Considering the clinical symptoms and co-occurrence of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e, a mixed BV\\u0026ndash;VVC infection is likely. Xiao et al. [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e] demonstrated distinct symptom profiles for BV and VVC, with \\u003cem\\u003eL. iners\\u003c/em\\u003e and \\u003cem\\u003ePrevotella\\u003c/em\\u003e spp. dominating in affected women, while \\u003cem\\u003eGardnerella\\u003c/em\\u003e and \\u003cem\\u003eAtopobium\\u003c/em\\u003e species are also more abundant. In cases of recurrent VVC, \\u003cem\\u003eL. crispatus\\u003c/em\\u003e and \\u003cem\\u003eL. jensenii\\u003c/em\\u003e decrease by approximately 14%, whereas \\u003cem\\u003eL. iners\\u003c/em\\u003e increases by over 20% [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Thus, the findings for sample G13 may indicate a mixed infection or possibly sampling error (e.g., improper patient preparation). Low levels of \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e were also detected in samples G21 (7.3%) and G28 (3%).\\u003c/p\\u003e \\u003cp\\u003eAlthough vaginal infection with \\u003cem\\u003eS. agalactiae\\u003c/em\\u003e has not been consistently linked to specific symptoms, genital itching and burning (common in BV) have occasionally been attributed to this species. \\u003cem\\u003eS. agalactiae\\u003c/em\\u003e can induce local inflammation, discharge, and a reduction in lactobacilli. In most samples, \\u003cem\\u003eS. agalactiae\\u003c/em\\u003e was absent or present at very low abundance, potentially influenced by menopausal status. However, samples G21, G30, and G59 contained substantial levels of this species, correlating with symptomatic presentations. Yu et al. demonstrated, in a rat model, that \\u003cem\\u003eS. agalactiae\\u003c/em\\u003e inhibits \\u003cem\\u003eC. albicans\\u003c/em\\u003e hyphal growth and modulates host immunity by decreasing TH17 T cell numbers, thereby facilitating \\u003cem\\u003eC. albicans\\u003c/em\\u003e colonization [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. Interestingly, in sample G30, \\u003cem\\u003eCandida tropicalis\\u003c/em\\u003e\\u0026mdash;not \\u003cem\\u003eC. albicans\\u003c/em\\u003e\\u0026mdash;was isolated, suggesting that these interactions may be species-specific and that different mechanisms may govern the relationship between \\u003cem\\u003eS. agalactiae\\u003c/em\\u003e and \\u003cem\\u003eC. tropicalis\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003eIn recent decades, non-cultivation methods have substantially advanced our understanding of human-associated microbial communities, including both their composition and functional properties. In the healthy VMB, both microbial diversity and the balance among taxonomic groups are essential for maintaining host health. Obtained results indicate that \\u003cem\\u003eL. iners\\u003c/em\\u003e is one of the dominant species among women in Armenia and that the VMB of the Yerevan population is diverse at the species and phylum levels. In samples from women with candidiasis, lactobacilli abundance was reduced and pathogenic species, particularly \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e, were prevalent. Pregnant women generally exhibited healthy microbiomes, though occasional pathogen detection likely reflects pregnancy-related immune changes. Pathogenic taxa without clinical symptoms were also found in some healthy women. Ethnic differences in the VMB were most pronounced at the phylum level, particularly among minority groups. Overall, the combined use of cultivation and metagenomic approaches in this study provides new insights that may facilitate the development of targeted strategies to modulate VMB composition and activity, ultimately supporting the health of women of reproductive age.\\u003c/p\\u003e \\u003cp\\u003eFurther studies with larger cohorts, longitudinal sampling, and host-factor assessments (e.g., hormonal status, sexual behavior, antibiotic use) are needed to clarify the drivers of these group-specific microbiome structures and their clinical relevance.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study provides the first comprehensive characterization of the vaginal microbiome of women from different ethnic groups in the Republic of Armenia using a combination of classical microbiological, MALDI-TOF MS, and 16S rRNA metagenomic approaches. The results demonstrate that the vaginal microbiome of Armenian women is notably diverse at both species and phylum levels compared to the Yezidi and Molokan groups. In women with candidiasis, a marked reduction in Lactobacillus abundance and an increased presence of potential pathogens such as \\u003cem\\u003eG. vaginalis\\u003c/em\\u003e and \\u003cem\\u003eA. vaginae\\u003c/em\\u003e were observed. In contrast, pregnant women generally exhibited a Lactobacillus-dominated, stable, and healthy microbiome, although some opportunistic taxa were occasionally detected, likely reflecting immune modulation during pregnancy.\\u003c/p\\u003e \\u003cp\\u003eEthnic and regional variations were most pronounced at the phylum level, suggesting that lifestyle, dietary habits, or environmental factors may contribute to shaping the microbial community structure. The consistency between culture-based and metagenomic results highlights the value of integrating complementary methodologies for accurate microbiome assessment.\\u003c/p\\u003e \\u003cp\\u003eOverall, these findings enrich our understanding of the vaginal microbiome diversity among Armenian populations and provide a foundation for developing targeted probiotic and therapeutic strategies aimed at maintaining reproductive health. Future studies with larger sample sizes and functional analyses are needed to further elucidate the relationship between microbial composition, ethnicity, and clinical outcomes in women\\u0026rsquo;s health.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors thank Dr. Nelli Nahapetyan, lecturer of N 1 Chair of Obstetrics and Gynecology of Yerevan State Medical University after M. Heratsi, obstetrician-gynecologist at the maternity center of Beglaryan Medical Center, \\u0026nbsp; Dr. Armine Dumikyan, gynecologist at the Vanadzor Medical Center CJSC, Vanadzor City and Dr. Karine Chopikyan, obstetrician-gynecologist of Tsaghkahovit Health Center CJSC in the village Tsaghkahovit for the collection of vaginal samples from different ecological zones of Armenia and the work with patients.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe work was supported by the Science Committee of RA, in the frames of the research project №24WS-1F005, № 23AA-1F010.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eT.A.: Formal analysis, Investigation, Writing \\u0026ndash; original draft, Funding acquisition; R.S.; Formal analysis, Investigation, Writing \\u0026ndash; original draft; \\u0026nbsp;R.H.: Formal analysis, Investigation; I.S.: Data curation, Validation; E.G.: Methodology, Resources; S.D.: Software, Supervision, Visualization, Writing \\u0026ndash; review \\u0026amp; editing software; \\u0026nbsp; P.V.: Methodology, Resources, Supervision, Writing \\u0026ndash; review \\u0026amp; editing, H.P.: Data curation, Visualisation, \\u0026nbsp;Writing \\u0026ndash; review \\u0026amp; editing; I.B,: Funding acquisition, Project administration, Supervision, Writing \\u0026ndash; review \\u0026amp; editing.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data generated or analyzed during this study are included in this published article. All metagenoms were submitted in NCBI and get the following accession numbers PRJNA1164001.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe data are available at the following URL https://www.ncbi.nlm.nih.gov/sra/PRJNA1164001.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eConflicts of Interest:\\u003c/strong\\u003e The authors declare no conflicts of interest.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eMaynard, C. L., Elson, C. O., Hatton, R. D. \\u0026amp; Weaver, C. T. Reciprocal interactions of the intestinal microbiota and immune system. \\u003cem\\u003eNature\\u003c/em\\u003e \\u003cb\\u003e489\\u003c/b\\u003e, 231\\u0026ndash;241. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nature11551\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nature11551\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRavel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108, 4680\\u0026ndash;4687. (2011). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1073/pnas.1002611107\\u003c/span\\u003e\\u003cspan address=\\\"10.1073/pnas.1002611107\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen, X., Lu, Y., Chen, T. \\u0026amp; Li, R. The female vaginal microbiome in health and bacterial vaginosis. \\u003cem\\u003eFront. Cell. Infect. Microbiol.\\u003c/em\\u003e \\u003cb\\u003e11\\u003c/b\\u003e, 631972. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fcimb.2021.631972\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fcimb.2021.631972\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2021).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShao, Y. et al. Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. \\u003cem\\u003eNature\\u003c/em\\u003e \\u003cb\\u003e574\\u003c/b\\u003e, 117\\u0026ndash;121. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41586-019-1560-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41586-019-1560-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2019).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLinhares, I. M., Summers, P. R., Larsen, B., Giraldo, P. C. \\u0026amp; Witkin, S. S. Contemporary perspectives on vaginal pH and lactobacilli. \\u003cem\\u003eAm. J. Obstet. Gynecol.\\u003c/em\\u003e \\u003cb\\u003e204\\u003c/b\\u003e, 120e1\\u0026ndash;12120. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ajog.2010.07.010\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ajog.2010.07.010\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2011).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJaved, A., Parvaiz, F. \\u0026amp; Manzoor, S. Bacterial vaginosis: an insight into the prevalence, alternative treatments regimen and its associated resistance patterns. \\u003cem\\u003eMicrob. Pathog\\u003c/em\\u003e. \\u003cb\\u003e127\\u003c/b\\u003e, 21\\u0026ndash;30. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.micpath.2018.11.046\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.micpath.2018.11.046\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2019).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMuzny, C. A. et al. Identification of key bacteria involved in the induction of incident bacterial vaginosis: a prospective study. \\u003cem\\u003eJ. Infect. Dis.\\u003c/em\\u003e \\u003cb\\u003e218\\u003c/b\\u003e, 966\\u0026ndash;978. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/infdis/jiy243\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/infdis/jiy243\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2018).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBradford, L. L. \\u0026amp; Ravel, J. The vaginal mycobiome: a contemporary perspective on fungi in women\\u0026rsquo;s health and diseases. \\u003cem\\u003eVirulence\\u003c/em\\u003e \\u003cb\\u003e8\\u003c/b\\u003e, 342\\u0026ndash;351. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1080/21505594.2016.1237332\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/21505594.2016.1237332\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2017).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLai, G. C., Tan, T. G. \\u0026amp; Pavelka, N. The mammalian mycobiome: a complex system in a dynamic relationship with the host. \\u003cem\\u003eWiley Interdiscip Rev. Syst. Biol. Med.\\u003c/em\\u003e \\u003cb\\u003e11\\u003c/b\\u003e, e1438. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1002/wsbm.1438\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/wsbm.1438\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2019).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChee, W. J. Y., Chew, S. Y. \\u0026amp; Than, L. T. L. Vaginal microbiota and the potential of Lactobacillus derivatives in maintaining vaginal health. \\u003cem\\u003eMicrob. Cell. Fact.\\u003c/em\\u003e \\u003cb\\u003e19\\u003c/b\\u003e, 203. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12934-020-01464-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12934-020-01464-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2020).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLehtoranta, L. et al. Characterization of vaginal fungal communities in healthy women and women with bacterial vaginosis (BV). \\u003cem\\u003eMicrob. Pathog\\u003c/em\\u003e. \\u003cb\\u003e161\\u003c/b\\u003e, 105055. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.micpath.2021.105055\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.micpath.2021.105055\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2021).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLehtoranta, L., Ala-Jaakkola, R., Laitila, A. \\u0026amp; Maukonen, J. Healthy vaginal microbiota and influence of probiotics across the female life span. \\u003cem\\u003eFront. Microbiol.\\u003c/em\\u003e \\u003cb\\u003e13\\u003c/b\\u003e, 819958. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fmicb.2022.819958\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fmicb.2022.819958\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2022).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFrancis, S. C. et al. Bacterial vaginosis among women at high risk for HIV in Uganda: high rate of recurrent diagnosis despite treatment. \\u003cem\\u003eSex. Transm Infect.\\u003c/em\\u003e \\u003cb\\u003e92\\u003c/b\\u003e, 142\\u0026ndash;148. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1136/sextrans-2015-052160\\u003c/span\\u003e\\u003cspan address=\\\"10.1136/sextrans-2015-052160\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTuddenham, S. et al. Associations between dietary micronutrient intake and molecular-bacterial vaginosis. \\u003cem\\u003eReprod. Health\\u003c/em\\u003e. \\u003cb\\u003e16\\u003c/b\\u003e, 151. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12978-019-0814-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12978-019-0814-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2019).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSmith, S. B. \\u0026amp; Ravel, J. The vaginal microbiota, host defence and reproductive physiology. \\u003cem\\u003eJ. Physiol.\\u003c/em\\u003e \\u003cb\\u003e595\\u003c/b\\u003e, 451\\u0026ndash;463. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1113/JP271694\\u003c/span\\u003e\\u003cspan address=\\\"10.1113/JP271694\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2017).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDuluc, D. et al. Functional diversity of human vaginal APC subsets in directing T-cell responses. \\u003cem\\u003eMucosal Immunol.\\u003c/em\\u003e \\u003cb\\u003e6\\u003c/b\\u003e, 626\\u0026ndash;638. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/mi.2012.104\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/mi.2012.104\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2013).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFarage, M. \\u0026amp; Maibach, H. Lifetime changes in the vulva and vagina. \\u003cem\\u003eArch. Gynecol. Obstet.\\u003c/em\\u003e \\u003cb\\u003e273\\u003c/b\\u003e, 195\\u0026ndash;202. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s00404-005-0079-x\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00404-005-0079-x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2006).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKoedooder, R. et al. The vaginal microbiome as a predictor for outcome of in vitro fertilization with or without intracytoplasmic sperm injection: a prospective study. \\u003cem\\u003eHum. Reprod.\\u003c/em\\u003e \\u003cb\\u003e34\\u003c/b\\u003e, 1042\\u0026ndash;1054. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/humrep/dez065\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/humrep/dez065\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2019).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGarc\\u0026iacute;a-Velasco, J. A., Menabrito, M. \\u0026amp; Catal\\u0026aacute;n, I. B. What fertility specialists should know about the vaginal microbiome: a review. \\u003cem\\u003eReprod. Biomed. Online\\u003c/em\\u003e. \\u003cb\\u003e35\\u003c/b\\u003e, 103\\u0026ndash;112. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.rbmo.2017.04.005\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.rbmo.2017.04.005\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2017).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl-Memar, M. et al. The association between vaginal bacterial composition and miscarriage: a nested case-control study. \\u003cem\\u003eBJOG\\u003c/em\\u003e \\u003cb\\u003e127\\u003c/b\\u003e, 264\\u0026ndash;274. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1111/1471-0528.15972\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/1471-0528.15972\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2020).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShen, J. et al. Effects of low dose estrogen therapy on the vaginal microbiomes of women with atrophic vaginitis. \\u003cem\\u003eSci. Rep.\\u003c/em\\u003e \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/srep24380\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/srep24380\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCauci, S. et al. Prevalence of bacterial vaginosis and vaginal flora changes in peri- and postmenopausal women. \\u003cem\\u003eJ. Clin. Microbiol.\\u003c/em\\u003e \\u003cb\\u003e40\\u003c/b\\u003e, 2147\\u0026ndash;2152. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1128/JCM.40.6.2147-2152.2002\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/JCM.40.6.2147-2152.2002\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2002).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSharma, M. et al. An insight into vaginal microbiome techniques. \\u003cem\\u003eLife\\u003c/em\\u003e \\u003cb\\u003e11\\u003c/b\\u003e, 1229. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/life11111229\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/life11111229\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2021).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChilambi, G. S. et al. Genomic and phenotypic diversity of Enterococcus faecalis isolated from endophthalmitis. \\u003cem\\u003ePLoS One\\u003c/em\\u003e. \\u003cb\\u003e16\\u003c/b\\u003e, e0250084. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1371/journal.pone.0250084\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0250084\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2021).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIbrahim, H. A. H., Zahran, H. F. \\u0026amp; Youssef, M. Microbial degradation of sodium lauryl sulfate using bacterial consortium isolated from coastline of Alexandria City. \\u003cem\\u003eJ. High. Inst. Public. Health\\u003c/em\\u003e. \\u003cb\\u003e42\\u003c/b\\u003e, 185\\u0026ndash;207. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.21608/jhiph.2012.20132\\u003c/span\\u003e\\u003cspan address=\\\"10.21608/jhiph.2012.20132\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePoormontaseri, M., Ostovan, R., Berizi, E. \\u0026amp; Hosseinzadeh, S. Growth rates of Bacillus species probiotics using various enrichment media. \\u003cem\\u003eInt. J. Nutr. Sci.\\u003c/em\\u003e \\u003cb\\u003e2\\u003c/b\\u003e, 39\\u0026ndash;42 (2017).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhou, X. et al. Characterization of vaginal microbial communities in adult healthy women using cultivation-independent methods. \\u003cem\\u003eMicrobiology\\u003c/em\\u003e \\u003cb\\u003e150\\u003c/b\\u003e, 2565\\u0026ndash;2573. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1099/mic.0.26905-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1099/mic.0.26905-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2004).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCosta, N. S. et al. Anovaginal colonization by Group B Streptococcus and Streptococcus anginosus among pregnant women in Brazil and its association with clinical features. \\u003cem\\u003eAntibiotics\\u003c/em\\u003e \\u003cb\\u003e13\\u003c/b\\u003e, 85. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/antibiotics13010085\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/antibiotics13010085\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2024).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003evan Teijlingen, N. H. et al. Prevotella timonensis bacteria associated with vaginal dysbiosis enhance HIV-1 susceptibility of vaginal CD4\\u0026thinsp;+\\u0026thinsp;T cells. \\u003cem\\u003eJ. Infect. Dis.\\u003c/em\\u003e \\u003cb\\u003e230\\u003c/b\\u003e, e43\\u0026ndash;e47. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/infdis/jiae166\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/infdis/jiae166\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2024).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChaban, B. et al. Characterization of the vaginal microbiota of healthy Canadian women through the menstrual cycle. Microbiome 2, 23. (2014). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/2049-2618-2-23\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/2049-2618-2-23\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGupta, P., Singh, M. P. \\u0026amp; Goyal, K. Diversity of vaginal microbiome in pregnancy: deciphering the obscurity. \\u003cem\\u003eFront. Public. Health\\u003c/em\\u003e. \\u003cb\\u003e8\\u003c/b\\u003e, 326. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fpubh.2020.00326\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fpubh.2020.00326\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2020).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXiao, B., Qin, A. D., Mi, H., Zhang, D. \\u0026amp; L. \\u0026amp; Correlation analysis of vaginal microbiome changes and bacterial vaginosis plus vulvovaginal candidiasis mixed vaginitis prognosis. \\u003cem\\u003eFront. Cell. Infect. Microbiol.\\u003c/em\\u003e \\u003cb\\u003e12\\u003c/b\\u003e, 860589. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fcimb.2022.860589\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fcimb.2022.860589\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2022).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYu, X. Y. et al. Streptococcus agalactiae inhibits Candida albicans hyphal development and diminishes host vaginal mucosal TH17 response. \\u003cem\\u003eFront. Microbiol.\\u003c/em\\u003e \\u003cb\\u003e9\\u003c/b\\u003e, 198. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fmicb.2018.00198\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fmicb.2018.00198\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2018).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWieme, A. D. et al. Identification of beer-spoilage bacteria using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. \\u003cem\\u003eInt. J. Food Microbiol.\\u003c/em\\u003e \\u003cb\\u003e185\\u003c/b\\u003e, 41\\u0026ndash;50. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.ijfoodmicro.2014.05.003\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ijfoodmicro.2014.05.003\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2014).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDumolin, C. et al. Introducing SPeDE: high-throughput dereplication and accurate determination of microbial diversity from matrix-assisted laser desorption\\u0026ndash;ionization time of flight mass spectrometry data. \\u003cem\\u003emSystems\\u003c/em\\u003e \\u003cb\\u003e4\\u003c/b\\u003e, e00437\\u0026ndash;e00419 (2019). 10.1128/mSystems.00437\\u0026thinsp;\\u0026ndash;\\u0026thinsp;19.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWilliam, S., Feil, H. \\u0026amp; Copeland, A. Bacterial genomic DNA isolation using CTAB. JGI, Walnut Creek, CA (2012). Available at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://jgi.doe.gov\\u003c/span\\u003e\\u003cspan address=\\\"https://jgi.doe.gov\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMagoč, T. \\u0026amp; Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. \\u003cem\\u003eBioinformatics\\u003c/em\\u003e \\u003cb\\u003e27\\u003c/b\\u003e, 2957\\u0026ndash;2963. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/bioinformatics/btr507\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bioinformatics/btr507\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2011).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCaporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. \\u003cem\\u003eNat. Methods\\u003c/em\\u003e. \\u003cb\\u003e7\\u003c/b\\u003e, 335\\u0026ndash;336. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nmeth.f.303\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nmeth.f.303\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2010).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. \\u003cem\\u003eNat. Methods\\u003c/em\\u003e. \\u003cb\\u003e10\\u003c/b\\u003e, 57\\u0026ndash;59. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nmeth.2276\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nmeth.2276\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2013).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEdgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. \\u0026amp; Knight, R. UCHIME improves sensitivity and speed of chimera detection. \\u003cem\\u003eBioinformatics\\u003c/em\\u003e \\u003cb\\u003e27\\u003c/b\\u003e, 2194\\u0026ndash;2200. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/bioinformatics/btr381\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bioinformatics/btr381\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2011).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHaas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. \\u003cem\\u003eGenome Res.\\u003c/em\\u003e \\u003cb\\u003e21\\u003c/b\\u003e, 494\\u0026ndash;504. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1101/gr.112730.110\\u003c/span\\u003e\\u003cspan address=\\\"10.1101/gr.112730.110\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2011).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEdgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. \\u003cem\\u003eNat. Methods\\u003c/em\\u003e. \\u003cb\\u003e10\\u003c/b\\u003e, 996\\u0026ndash;998. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/nmeth.2604\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/nmeth.2604\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2013).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAltschul, S. F., Gish, W., Miller, W., Myers, E. W. \\u0026amp; Lipman, D. J. Basic local alignment search tool. \\u003cem\\u003eJ. Mol. Biol.\\u003c/em\\u003e \\u003cb\\u003e215\\u003c/b\\u003e, 403\\u0026ndash;410. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/S0022-2836(05)80360-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0022-2836(05)80360-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (1990).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang, Q., Garrity, G. M., Tiedje, J. M. \\u0026amp; Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. \\u003cem\\u003eAppl. Environ. Microbiol.\\u003c/em\\u003e \\u003cb\\u003e73\\u003c/b\\u003e, 5261\\u0026ndash;5267 (2007). 10.1128/AEM.00062\\u0026thinsp;\\u0026ndash;\\u0026thinsp;07.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eQuast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. \\u003cem\\u003eNucleic Acids Res.\\u003c/em\\u003e \\u003cb\\u003e41\\u003c/b\\u003e, D590\\u0026ndash;D596. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/nar/gks1219\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gks1219\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2013).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEdgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. \\u003cem\\u003eNucleic Acids Res.\\u003c/em\\u003e \\u003cb\\u003e32\\u003c/b\\u003e, 1792\\u0026ndash;1797. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/nar/gkh340\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkh340\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2004).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChao, A. Nonparametric estimation of the number of classes in a population. \\u003cem\\u003eScand. J. Stat.\\u003c/em\\u003e \\u003cb\\u003e11\\u003c/b\\u003e, 265\\u0026ndash;270 (1984).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChao, A. \\u0026amp; Lee, S. M. Estimating the number of classes via sample coverage. \\u003cem\\u003eJ. Am. Stat. Assoc.\\u003c/em\\u003e \\u003cb\\u003e87\\u003c/b\\u003e, 210\\u0026ndash;217. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.2307/2290471\\u003c/span\\u003e\\u003cspan address=\\\"10.2307/2290471\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (1992).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChao, A. \\u0026amp; Yang, M. C. K. Stopping rules and estimation for recapture debugging with unequal failure rates. \\u003cem\\u003eBiometrika\\u003c/em\\u003e \\u003cb\\u003e80\\u003c/b\\u003e, 193\\u0026ndash;201. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.2307/2336768\\u003c/span\\u003e\\u003cspan address=\\\"10.2307/2336768\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (1993).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMagurran, A. E. \\u003cem\\u003eMeasuring Biological Diversity\\u003c/em\\u003e (Blackwell Publishing, 2004).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLemos, L. N., Fulthorpe, R. R., Triplett, E. W. \\u0026amp; Roesch, L. F. W. Rethinking microbial diversity analysis in the high throughput sequencing era. \\u003cem\\u003eJ. Microbiol. Methods\\u003c/em\\u003e. \\u003cb\\u003e86\\u003c/b\\u003e, 42\\u0026ndash;51. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.mimet.2011.03.014\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.mimet.2011.03.014\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2011).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSimpson, E. Measurement of diversity. \\u003cem\\u003eNature\\u003c/em\\u003e \\u003cb\\u003e163\\u003c/b\\u003e, 688. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/163688a0\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/163688a0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (1949).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChao, A. \\u0026amp; Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. \\u003cem\\u003eEcology\\u003c/em\\u003e \\u003cb\\u003e93\\u003c/b\\u003e, 2533\\u0026ndash;2547. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1890/11-1952.1\\u003c/span\\u003e\\u003cspan address=\\\"10.1890/11-1952.1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWhite, J. R., Nagarajan, N. \\u0026amp; Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. \\u003cem\\u003ePLoS Comput. Biol.\\u003c/em\\u003e \\u003cb\\u003e5\\u003c/b\\u003e, e1000352. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1371/journal.pcbi.1000352\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pcbi.1000352\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (2009).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"vaginal microbiome, lactic acid bacteria, bacterial diversity, MALDI-TOF MS, metagenomics, reproductive health\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8186315/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8186315/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe vaginal microbiome (VMB) plays a crucial role in women\\u0026rsquo;s reproductive health, yet its composition and variability among different ethnic groups in Armenia have not been previously investigated using metagenomic approaches. This study aimed to characterize and compare the VMB of women from three ethnic groups\\u0026mdash;Armenians, Yezidis, and Molokans\\u0026mdash;using classical microbiological methods, MALDI-TOF MS, and 16S rRNA amplicon-based metagenomic sequencing. The analyses revealed a high level of consistency between culture-based and metagenomic data. Among the studied groups, Armenian women exhibited the greatest microbial diversity at both species and phylum levels. In women with candidiasis, a marked reduction in lactobacilli abundance and an increased presence of pathogenic species were observed. Pregnant women generally displayed a balanced, lactobacilli-dominated microbiome, although opportunistic pathogens were occasionally detected, likely due to immune modulation during pregnancy. Regional and ethnic differences were most pronounced at the phylum level. These findings represent the first comprehensive metagenomic assessment of the VMB in Armenia, providing important insights into microbial diversity across ethnic groups and its implications for women\\u0026rsquo;s reproductive health.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Integrative Metagenomic and Culture-Based Profiling of Vaginal Microbiome Diversity among Ethnic Groups in Armenia\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-29 18:00:46\",\"doi\":\"10.21203/rs.3.rs-8186315/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"015adfa6-9c65-4cc4-a90b-fa989493beac\",\"owner\":[],\"postedDate\":\"January 29th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Rejected\",\"date\":\"2026-05-15T03:46:37+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":61852072,\"name\":\"Health sciences/Diseases\"},{\"id\":61852073,\"name\":\"Biological sciences/Microbiology\"}],\"tags\":[],\"updatedAt\":\"2026-05-15T03:55:46+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-29 18:00:46\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8186315\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8186315\",\"identity\":\"rs-8186315\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}