Longitudinal profiling and correlations of vaginal and fecal microbiomes throughout the menstrual cycle: A pilot prospective study

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Since no longitudinal study examining both matrices together in this context exists, our study aimed to evaluate microbiome changes in three phases of the menstrual cycle and to investigate correlations between bacteriomes and mycobiomes in vaginal and fecal samples. Over the course of three consecutive menstrual cycles, vaginal and stool swabs were self-collected in the follicular, ovulatory, and luteal phases from eight parous women of reproductive age who had regular menstrual cycle. Microscopic fungi in all vaginal samples were cultured and identified by mass spectrometry. DNA from 72 vaginal and 72 fecal samples were analyzed using quantitative PCRs, 16S and ITS rRNA amplicon sequencing for bacteriome and mycobiome profiling. Metabolic pathways were analyzed with a focus on the members of the Lactobacillaceae family. Bacteriome diversity in vaginal samples remained stable across studied months and individual phases, while changes in bacteriome alpha diversity (Shannon index) across phases were observed in fecal samples, with the highest diversity observed during the luteal phase. As expected, the genus Lactobacillus (mostly L. crispatus ) predominated in the vaginal samples. Furthermore, different patterns of the predicted metabolic potential were observed in vaginal samples dominated by Lactobacillus iners or Gardnerella vaginalis with Lactobacillus jensenii , compared to the profile dominated by L. crispatus . Yeasts, such as Candida albicans , Nakaseomyces glabratus , and Pichia kudriavzevii , were found in some vaginal samples. The presence of vaginal yeasts correlated with relative abundances of L. crispatus (negatively) and L. iners (positively) in vaginal samples, and with genus Streprococcus in stool samples (positively). Relative abundances of L. jensenii in vaginal samples correlated with genera Bifidobacterium (negatively) and Dialister (positively) in stool samples. The results indicate that when investigating female fecal microbiome, the phase of menstrual cycle should be considered. Some strong correlations between the relative abundance/presence of vaginal lactobacilli and yeasts and fecal bacteria were found, suggesting a possible link between the composition of microbial communities of these two anatomical sites. Sexual & Reproductive Medicine Menstrual Cycle Ovulation Microbiota Mycobiome Lactobacillus Candida Metabolic Networks and Pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The vaginal microbiota plays a crucial role in maintaining female reproductive health. In healthy individuals, this ecosystem is typically dominated by bacteria of the family Lactobacillaceae, which creates a protective environment through the production of lactic acid, hydrogen peroxide, and antimicrobial peptides [1]. These metabolites lower vaginal pH and inhibit the growth of microorganisms with pathogenic potential, thereby reducing the risk of infections, inflammation, and fertility-related complications [2], [3]. Lactobacillus crispatus , Lactobacillus jensenii , Lactobacillus gasseri , Lacticaseibacillus rhamnosus , and Lactobacillus iners are among the most prevalent species of the family Lactobacillaceae present in the vaginal environment [4]. While L. crispatus is widely considered a marker of vaginal health due to its ability to maintain a stable acidic environment, L. iners is frequently detected in transitional or dysbiotic states, and its role remains under investigation [4], [5], [6], [7], [8]. The specific composition of vaginal microbiome, particularly lactobacilli, has been associated with a lower incidence of bacterial vaginosis (BV), vulvovaginal candidiasis (VVC), human papillomavirus (HPV) infections, and adverse pregnancy outcomes [9], [10]. Vaginal dysbiosis, defined as a disruption of the microbial balance, is typically characterized by a reduction in the abundance of Lactobacillaceae and overgrowth of anaerobic bacteria, which increases the susceptibility to infections and inflammatory conditions. Such imbalances have also been linked to gynecological disorders, including endometriosis, where the vaginal microbiome often exhibits reduced levels of protective lactobacilli and higher prevalence of opportunistic pathogens, such as Gardnerella vaginalis , species Streptococcus , and Escherichia coli [11], [12], [13], [14], [15]. Emerging evidence suggests that microbial dysregulation may contribute to disease progression, modulate immune responses, and negatively impact fertility [16]. Microscopic fungi, particularly yeasts, represent another important component of the vaginal ecosystem. Candida albicans is the most common cause of VVC, affecting up to 75% of women at least once in their lifetime [17], [18]. Its pathogenicity is driven by virulence factors such as hyphal formation, biofilm development, and phenotypic switching, which enable adaptation to changing microenvironments and immune evasion [19]. C. albicans has gained clinical relevance due to its persistence and antifungal resistance profiles, resulting in treatment strategy complications [20]. Despite growing interest in the vaginal microbiome, longitudinal studies examining its stability throughout the menstrual cycle remain limited, and even fewer have integrated fungal community analysis [21], [22], [23], [24]. Interestingly, recent evidence indicates a relationship between the gut microbiota and the menstrual cycle [25]. Several human and animal studies have suggested that cyclical hormonal fluctuations can modulate gut bacteriome composition, with associations reported between specific bacterial taxa and menstrual disorders, premenstrual symptoms, or changes in gut permeability during different phases of the menstrual cycle [26], [27], [28], [29]. Consequently, many healthy women experience cyclical changes in gastrointestinal function and symptomatology across the menstrual cycle, reflecting the broader physiological impact of sex‑hormone–microbiome interactions (Bharadwaj et al., 2015; Mohib et al., 2018). Understanding these dynamics is essential for elucidating the interplay between hormonal fluctuations, microbial resilience, and potential dysbiosis. We hypothesize that the vaginal bacteriome remains relatively stable throughout the menstrual cycle, whereas the gut bacteriome may exhibit subtle cyclical fluctuations. Furthermore, we propose that integrating fungal community analysis will reveal additional complexity and potential clinical relevance within these ecosystems. In this study, we aimed to (i) assess vaginal and fecal bacteriome diversity and composition across different phases of the menstrual cycle, (ii) explore predicted metabolic profiles of the vaginal and fecal bacteriomes, (iii) describe the presence of microscopic fungi in vaginal and fecal samples, and (iv) investigate correlations among relative abundances/presences of vaginal bacteria and yeasts with fecal bacteria. Material and Methods In this pilot prospective observational study, eight women (W1-W8) of reproductive age (25–45 years) who already had children were enrolled and their vaginal and fecal bacteriome and mycobiome were analyzed. Over the course of three consecutive menstrual cycles (C1-C3), vaginal and stool swabs were collected by self-sampling at defined time points corresponding to the three phases of the menstrual cycle: follicular (days 4–11 of the menstrual cycle), ovulatory (days 11–17), and luteal (days 18–24). The ovulatory phase was determined using over-the-counter ovulation tests. No samples were taken during the menstrual phase. Informed consent was obtained from all participants in accordance with the principles outlined in the Helsinki Declaration. The study was approved by the Ethics Committee of CELSPAC, Czech Republic (Ref. No. CELSPAC/EK/1/2018, dated 12th March 2018). Inclusion and exclusion criteria, and collection of exposure and behavioral variables Both sexually active and non–sexually active women were eligible for inclusion in the study, with a regular menstrual cycle lasting > 21 days (the median length was 26 days) being the crucial inclusion criterion. Exclusion criteria were current pregnancy, lactation, the use of hormonal contraceptives, and treatment with antimicrobials within three months prior to study initiation. Throughout the study, participants were monitored using questionnaires for additional exposure and behavioral factors potentially influencing their microbiomes, including intercurrent illnesses, use of antimicrobials, probiotic supplementation, sexual intercourse, and the use of lubricants or condoms during intercourse. Sample collection and microbiological examination of microscopic fungi Sampling was conducted at each of the nine time points; participants self‑collected vaginal and stool samples using sterile flocked swabs (FLOQSwabs, COPAN, Italy). At each sampling point, an additional vaginal swab was placed in a tube with the Amies medium (TransystemTM, COPAN, Italy) and transported at 4°C for microbiological examination of microscopic fungi as described previously [30]. The presence of microscopic fungi was monitored using conventional culture techniques; subsequently, the isolated strains were identified using matrix-assisted laser desorption ionization–time of flight mass spectrometry (Bruker, Switzerland). Preprocessing and DNA isolation Immediately after collection, swabs were transferred into sterile cryogenic tubes and stored at − 80°C until further processing. DNA isolation from all collected samples was performed using the DNA Mini Kit (QIAGEN, Hilden, Germany), following the manufacturer’s protocol optimized for microbial DNA isolation. Prior to DNA isolation, samples were homogenized in two 50-second cycles using 1.4 mm ceramic beads (VWR International, USA) and subjected to enzymatic pretreatment with lyticase (20 mg/mL) to enhance cell lysis efficiency. All procedures were conducted in a sterile handling box to minimize contamination. A bacterial internal standard for bacteriome analysis (ZymoBIOMICS Spike-In Control I, High Microbial Load; Zymo Research), as well as negative controls (NC), were also included in the isolation process. All DNA samples were eluted in a final volume of 50 µL and stored at − 20°C until further analysis. Quantification of the total content of bacterial and fungal DNA by qPCRs The total contents of bacterial and fungal DNA were quantified by qPCRs using universal primers targeting the gene for 16S rRNA (UNI 515/806) [Kurina I, et al .,2020] and the gene for ITS rRNA region (FungiQuanti_F/FungiQuanti_R) [Liu CM, et al ., 2012], respectively. Reactions were carried out with SsoAdvanced™ Universal Inhibitor-Tolerant SYBR® Green Supermix (Bio-Rad, USA) on a LightCycler® 480 Instrument II (Roche, Switzerland). For quantification of the gene for 16S rRNA, the thermal profile comprised an initial denaturation at 98°C for 3 min, followed by 40 cycles of 98°C for 10 s and 55°C for 15 s, using each primer at a final concentration of 0.5 µM [31]. For quantification of the gene for ITS rRNA, the protocol was identical except for the annealing step that was performed at 60°C for 15 s, and each primer was used at 0.4 µM. Fluorescence data were processed using LightCycler® 480 Software v1.5.1.62 (Roche, Switzerland). Technical duplicates were evaluated for concordance, and Cp differences of ≤ 0.5 were considered acceptable. Values corresponding to fewer than 50 gene copies were regarded as below the limit of detection. 16S and ITS rRNA amplicon sequencing and bioinformatic analysis DNA isolates (N = 144) and NCs (N = 12) were spiked with bacterial internal standard, and amplification of the hypervariable V3–V4 region of the gene for 16S rRNA was performed using IL primers (Supplementary Table S1) with Q5® High-Fidelity 2× Master Mix (New England BioLabs, USA). The PCR master mix (excluding template DNA) was decontaminated by the addition of 40 µM 8-methoxypsoralen (Sigma-Aldrich, USA). For the general description of vaginal and fecal mycobiomes, individual DNA isolates from all nine samples collected from each participant were pooled into a single sample and spiked with an internal fungal standard. Eight pooled vaginal, eight pooled fecal samples, and two NCs were spiked with fungal internal standard, and amplification of the ITS2 region was performed using ITC2_C primers (Supplementary Table S2) with the same polymerase and process as described above. For both bacteriome and mycobiome analysis, thermal cycling was carried out as follows: initial denaturation at 98°C for 30 s; 30 cycles of 98°C for 10 s, 55°C for 15 s, and 72°C for 30 s; a final extension at 72°C for 2 min; and a hold at 4°C. Following amplification, PCR products were purified using SPRIselect beads (Beckman Coulter, USA), quantified on a Synergy HTX fluorimeter (BioTek, USA), and combined in equimolar proportions. Indexed libraries were generated via secondary PCR using Nextera XT indices (Illumina, USA). Sequencing was conducted on a MiSeq system with the MiSeq Reagent Kit v3, in accordance with the manufacturer’s instructions (Illumina, USA). Both datasets were processed separately using nf-core/ampliseq version 2.11.0 [32] of the nf-core collection of workflows [33], utilizing reproducible software environments from the Bioconda [34] and Biocontainers [35] projects. Data quality was evaluated with FastQC and summarized with MultiQC [36]. Sequences were processed with DADA2 tool, an open-source software package that denoises and removes sequencing errors from Illumina amplicon sequence data [37]. Processing steps include elimination of PhiX contamination, trimming reads (before median quality drops below 25 and at least 85% of reads are retained; forward reads at 300 bp and reverse reads at 253 bp, reads shorter than this were discarded), discarding reads with more than 3 expected errors, correcting errors (denoising), merging read pairs, and removing PCR chimeras. Taxonomic classification was performed in DADA2 using the naïve Bayesian classifier [38] with the SILVA 138.1 prokaryotic SSU database [39] for 16S rRNA amplicon data and the UNITE database for ITS data [40], [41]. Additionally, the BLAST algorithm [42] was used to identify the species, and all taxa with the maximum identity and minimum e-value were selected for each ASV. ASV sequences, abundance table, and DADA2 taxonomic assignments were loaded into QIIME2 [43]. Within QIIME2, the final microbial community data were processed into bacteriome and mycobiome profiles separately. PICRUSt2 [44] was used to predict hypothetical abundances of KEGG orthologs in each vaginal and fecal sample of each woman and summarized into higher functional processes. Normalization of gene copy numbers that were used for determining bacterial functional profiles was performed using the MUSICC algorithm [45]. Additionally, the Predicted Relative Metabolomic Turnover (PRMT) [46] approach was applied to the normalized data to generate community-wide metabolic potential scores (PRMT scores) for each metabolite annotated in the KEGG database. To investigate the temporal dynamics of bacterial metabolic potential, we performed single-sample enrichment analysis on metabolites grouped into KEGG pathways across collected samples. Only pathways covered by at least 25% of their constituent metabolites in our dataset were included in the final presentation. Statistical analysis Only samples and NCs with a sequencing depth of at least 2000 reads (including the specific internal standard) were retained for downstream analysis. While reads mapped to internal standards were used to assess sequencing performance and subsequently removed from the dataset, NCs were retained throughout the analysis to monitor for potential contamination and are displayed in relevant figures for comparison. Alpha diversity indices, including the Shannon index and the number of distinct amplicon sequence variants (ASVs), were computed using the vegan package (v.2.6–6.1). Differences between groups were tested using the Wilcoxon test, a paired non-parametric test, for testing the differences between vaginal and fecal samples. Friedman’s ANOVA test was used to test the differences between phases of the menstrual cycle. Evaluation of the stability of individual variables in each phase within a given matrix was performed to find out if measurements across the months could be aggregated. If no significant difference was detected, the median of the three measurements was used for each phase of the menstrual cycle. Principal component analysis (PCA) was performed on CLR-transformed abundance data using the PCAtools (v.2.20.0) package to visualize differences in microbial community composition. To statistically confirm the observed differences between sample types, a PERMANOVA analysis was conducted using Euclidean distances, as appropriate for CLR-transformed data. In the correlation analysis, Spearman’s correlation coefficient was used to assess associations between microbial taxa. Spearman correlation was applied to the relative abundances of the 25 most abundant bacterial genera detected in fecal samples, as well as five selected bacterial species and one bacterial genus present in vaginal samples. In addition, information on the presence or absence of yeasts in vaginal samples at each sampling time point (binary variable obtained by conventional microbiological methods) was incorporated into the analysis. To evaluate associations between yeasts occurrence and bacterial relative abundances, the rank-biserial correlation coefficient was calculated, and statistical significance was assessed using the Wilcoxon rank-sum test. Correlations with an absolute magnitude ≥ 0.6 were considered strong. The resulting correlation network was visualized using the igraph package (v.2.2.2). P -values ≤ 0.05 were considered statistically significant. All resulting p -values were adjusted for multiple hypotheses testing using the Benjamini–Hochberg procedure. All analyses were conducted using R version 4.5.1 (2025-06-13). Results In the context of 16S and ITS rRNA amplicon sequencing, all vaginal (N = 72) and fecal samples (N = 72) passed the quality control process. Importantly, although the genera Flavobacterium , Pseudoxanthomonas , and Chryseobacterium , as well as members of the class Parcubacteria , were detected in one of the twelve NCs, none of these genera appeared in any vaginal or fecal sample included in the study. Longitudinal stability of bacteriome profiles within individual phases of the menstrual cycle To assess the consistency and potential month-to-month variation of the vaginal and fecal bacteriome within specific phases of the menstrual cycle, bacteriome diversity represented by the Shannon index (and, for the vaginal samples, the abundance of the family Lactobacillaceae) across three consecutive cycles was analyzed. In the vaginal bacteriome, no statistically significant differences were observed between months within any of the phases, indicating an overall temporal stability of the dominance of the genus Lactobacillus . Complementing these analyses, Table S3 summarizes the results of statistical testing for the top 25 most abundant taxa in both vaginal and fecal samples, comparing their relative abundances within each phase across the three consecutive menstrual cycles. This allowed us to aggregate data, where the median values of studied parameters were calculated from three measurements for each woman and menstrual cycle phase. This strategy allowed robust comparisons across phases while minimizing the influence of short-term fluctuations. Bacteriome alpha diversity across menstrual cycle phases Log‑scaled total content of bacterial DNA and Shannon diversity indices from 16S rRNA amplicon sequencing across follicular, ovulatory, and luteal phases in both vaginal and fecal samples were compared to assess the changes throughout the menstrual cycle (Fig. 1 ). No statistically significant differences in the total content of bacterial DNA were observed between phases of the menstrual cycle in either vaginal or fecal samples (Fig. 1 A,B). No significant differences in Shannon diversity were detected between phases in vaginal swabs (Fig. 1 C), while phase‑dependent change in fecal bacteriome diversity was observed (Fig. 1 D). Shannon diversity indices showed a gradual increase from the follicular through the ovulatory phase, reaching the highest values in the luteal phase; this trend resulted in a statistically significant difference between the follicular and luteal phases ( p = 0.028). Bacteriome composition across menstrual cycle phases In vaginal samples, PCA revealed substantial overlap between follicular, ovulatory, and luteal phases (Fig. 2 A), with no clear clustering by phase of the menstrual cycle. This lack of separation was confirmed by PERMANOVA analysis, which showed no significant effect of the phase on vaginal bacteriome composition (R² = 0.016, F = 0.554, p = 0.826). Similarly, PCA of fecal samples showed overlapping distributions of samples from all phases of the menstrual cycle (Fig. 2 B, PC1 = 13.2%, PC2 = 10.9% of explained variance), indicating high inter‑individual variability but no phase‑specific structuring of the fecal bacteriome. Consistently, PERMANOVA analysis revealed no significant association between the phase and fecal bacteriome composition (R² = 0.02, F = 0.694, p = 0.914). Samples from the follicular, ovulatory, and luteal phases were distributed throughout the vaginal and fecal clusters without forming any phase-specific subgroups. Within each matrix type, points tended to cluster according to individual women rather than the phase of the menstrual cycle (Figure S1). To further explore the bacteriome composition across sample types and phases of the menstrual cycle, the 25 most abundant bacterial genera across the entire dataset were selected (Figure S1). This selection predominantly included genera typical of the gut bacteriome, such as Faecalibacterium , Segatella (formerly Prevotella ), Blautia , Bacteroides , and Bifidobacterium , which were highly abundant in fecal samples. Lactobacillus and Gardnerella , which were present only in vaginal samples, were also among the top genera. Genera Streptococcus , Clostridium, Dialister, Bifidobacterium , and some others (such as Escherichia ) were found in both vaginal and fecal samples. Taxonomic structure and metabolic functional landscape of the vaginal bacteriome Upon closer examination of vaginal swab samples at the species level using BLAST, we observed that in most samples, L. crispatus was the dominant species within the family Lactobacillaceae (Fig. 3 ). One woman (W5), however, exhibited L. iners as the predominant species, and in another one (W1), in whose samples a high presence of G. vaginalis was observed, the predominant lactobacillus was identified as L. jensenii . The heatmap in Fig. 3 also presents the enrichment scores based on the predicted metabolic potential of selected bacterial metabolic pathways in vaginal samples for individual women and phases of three menstrual cycles. Notably, pathways associated with environmental adaptation and pathogenicity (such as the two-component system and metabolic pathways, carbon metabolism , and glycerolipid metabolism ) were markedly enriched in samples with reduced relative abundance of L. crispatus and increased relative abundances of G. vaginalis . Interestingly, one woman (W5) displayed a distinct metabolic profile despite the fact that at the genus level (on which the PRMT calculations are based), her bacteriome composition was comparable with women in whom L. crispatus dominated in vaginal swabs (Fig. 3 ). However, BLAST-based species identification in this woman revealed the dominance of L. iners , a species frequently associated with transitional or dysbiotic states (Fig. 3 ). This may suggest that the predicted metabolic profile in this woman was influenced by the genera in the “others” category, which might have been different from those found in women with predominance of L. crispatus in their vaginal bacteriome. A similar pattern was observed in another woman (W4), whose samples from time points dominated by L. iners exhibited a different metabolic potential than those from periods with L. crispatus dominance. The similarities between periods dominated by L. iners in women W4 and W5 are striking, showing clear differences from the metabolic profiles of L. crispatus -dominated samples across all women (Fig. 3 ). Notably, another participant (W1), characterized by a high abundance of G. vaginalis and dominance of L. jensenii , also showed a distinct metabolic potential in vaginal samples. In addition, the selected exposure and behavioral factors, such as recent sexual activity, use of lubricants or condoms, antimicrobial treatment, probiotic intake, and reported illness, were recorded. A reduction in the relative abundance of G. vaginalis in W1 following the supplementation with oral probiotics and an increase in the relative abundance of the same bacteria following infections in W7 were observed (see Fig. 3 ). Still, the low number of women in this pilot study does not allow us to perform any formal statistical analysis. Bacteriome composition and predicted metabolic potential of fecal samples Figure 4 displays a combined heatmap of the 25 most abundant bacterial genera in fecal samples alongside the predicted enrichment of key bacterial metabolic pathways across women and phases of three menstrual cycles. Analysis of fecal samples revealed a bacterial community dominated by genera commonly associated with a healthy gut microbiome, including Faecalibacterium, Segatella , Blautia , Bacteroides , Dialister , Bifidobacterium , and members of the families Lachnospiraceae and Ruminococcaceae (Fig. 4 ). Supplementary Table S4 summarizes the results of statistical testing for the top 25 most abundant taxa on the aggregated data (the approach mentioned above). The predicted metabolic potential of the fecal bacteriota was also consistent across time points and reflected a distinct compositional pattern of each participant (Fig. 4 ). Pathways belonging to taurine and hypotaurine metabolism , nitrogen metabolism , and butanoate metabolism were among the most enriched pathways across the participants. On the other hand, tryptophan metabolism and benzoate degradation showed consistently lower enrichment scores. Mycobiome profiles of vaginal and fecal samples In total, presence of yeasts was detected by conventional microbiological culture with subsequent identification by mass spectrometry in fourteen vaginal samples collected from five women (Fig. 3 ). C. albicans was found in four cases and Nakaseomyces glabratus (formerly Candida glabrata ) in one woman. The microbiological examination revealed no more than one microscopic fungal species per woman across the entire study period (nine time points). This allowed us to pool vaginal samples from individual women for mycobiome analyses, and the resulting profiles from each woman was compared with the corresponding microbiological findings. Pooling was also applied to fecal samples. No statistically significant difference between vaginal and fecal samples was observed in the total content of fungal DNA ( p = 0.19). However, Shannon diversity indices differed significantly ( p = 0.008). In agreement with results of the conventional microbiological examination, C. albicans was identified by ITS rRNA amplicon sequencing in vaginal samples from four women (W2, W4, W6, W7) and Nakaseomyces glabratus in samples from one woman (W5). Moreover, Pichia kudriavzevii (formerly Candida krusei ) not revealed by conventional methods was detected by ITS rRNA amplicon sequencing (74 reads) in one woman (W3). Fecal samples showed a broader fungal spectrum, including environmental and food-associated genera such as Saccharomyces uvarum , Penicillium spp., and Maudiozyma barnettii . Correlations among relative abundances/presence of vaginal bacteria and yeasts with fecal bacteria Presence of vaginal yeasts positively correlated with relative abundances of genus Streprococcus in fecal samples ( r = 0.76). In vaginal samples, the presence of yeasts positively correlated with the relative abundance of L. iners ( r = 0.61) and negatively correlated with the relative abundance of L. crispatus ( r = -0.69). While the relative abundance of L. jensenii in vaginal samples correlated positively with the relative abundances of the genus Bifidobacterium ( r = -0.63), a negative correlation with this lactobacillus was observed with genus Dialister ( r = 0.83). Additional strong correlations ( r ≥ 0.6) among fecal bacterial genera are also presented in Fig. 6 . Discussion Our longitudinal analysis of vaginal and fecal microbiomes across three consecutive menstrual cycles provides a comprehensive view of microbial dynamics in relation to matrix type and phase of the menstrual cycle. Importantly, this study extends current knowledge by incorporating fungal community profiling through ITS amplicon sequencing, offering a more holistic perspective on microbial ecosystems in healthy women. Vaginal and fecal bacteriome diversity and composition across phases of the menstrual cycle The findings confirm fundamental differences between the vaginal and gut microbiomes in terms of total bacterial load, bacteriome diversity, and taxonomic composition [21], [24]. Fecal samples exhibited significantly higher microbial richness and diversity, reflecting the complexity of the gut environment [47], whereas vaginal samples were dominated by Lactobacillus , consistent with the typical profile of a healthy vaginal microbiome [4], [48], [49], [50], [51], [52]. As expected, the two sample matrices separated well in PCA, while differences among phases of the menstrual cycle were minor. Significant changes in alpha diversity between follicular and luteal phases were only detected in fecal samples. This pattern supports previous findings that anatomical site and individual factors are dominant drivers of microbiome profile, whereas hormonal fluctuations exert only minor influence [10]. While the vaginal microbiome remained relatively stable across phases of the menstrual cycle, both in terms of alpha diversity represented by Shannon index and Lactobacillaceae dominance, fecal samples displayed modest but statistically significant variation in alpha diversity across phases, with the highest diversity observed during the luteal phase [53]. This pattern suggests potential hormonal modulation of gut microbial richness, possibly mediated by estrogen and progesterone-driven changes in gut motility and immune function [26], [27], [28], [29]. These findings align with previous reports indicating that the gut microbiome is predominantly shaped by long-term host‑specific factors [54]. Although the effect size was small, these findings warrant further investigation into the interplay between hormonal cycles and gut microbiome dynamics. On the other hand, the results suggest that, where the vaginal microbiome is concerned, the phase of the menstrual cycle is not a major driver of compositional changes in healthy women. These results align with previous studies reporting resilience of the vaginal microbiome across phases of the menstrual cycle studied in this paper [52], [55]. Our results indicate that the taxonomic composition and functional potential of fecal microbiome of healthy women is stable across the menstrual cycle. This sugests that inter‑individual variability is the dominant driver of fecal bacteriome composition, overshadowing any potential influence of menstrual cycle–related hormonal shifts. This is consistent with existing literature demonstrating that gut microbial communities are generally stable in healthy adults and strongly modulated by genetics, diet, immune, and lifestyle. The stability observed in our dataset thus supports the view that short‑term endocrine oscillations alone are unlikely to drive robust shifts in gut bacterial communities under healthy conditions. The taxonomic composition of fecal samples in our cohort, which was dominated by Faecalibacterium , Blautia , Bacteroides , Bifidobacterium , and members of the Lachnospiraceae and Ruminococcaceae families, corresponds to a typical gut microbiome. This composition remained largely consistent across all menstrual cycle phases. Predicted metabolic pathway profiles showed strong individual‑specific signatures with minimal temporal variation. Predicted metabolic profiles of the vaginal and fecal bacteriomes Our analysis of enrichment scores based on the predicted metabolic potential on 16S rRNA sequencing data of selected microbial metabolic pathways provides important insights into the functional dimension of vaginal dysbiosis. The enrichment of adaptive systems, such as the two-component system in samples with increased abundance of L. iners (at the expense of L. crispatus ), suggests that potential pathogens may exploit these mechanisms to persist and thrive in the vaginal environment, possibly utilizing these systems to adapt to changing conditions within the vaginal environment to enhance their survival and virulence [56]. While L. crispatus is widely considered a marker of vaginal health due to its ability to maintain a stable environment and inhibition of E. coli and Candida spp. [57], [58], L. iners is frequently associated with transitional or dysbiotic states, and its role remains under investigation [4], [59], [60]. These findings suggest that metabolic potential might serve as an early indicator of community instability even when taxonomic composition appears stable [5], [49], [61], [62], [63]. In the woman W1, who exhibited elevated levels of G. vaginalis , species-level analysis revealed L. jensenii as the dominant Lactobacillus species rather than L. crispatus . While L. jensenii is generally considered a health-associated species, its protective capacity may differ from that of L. crispatus . Previous studies suggest that L. jensenii produces lower amounts of lactic acid and hydrogen peroxide compared to L. crispatus , potentially resulting in a less acidic vaginal environment and reduced antimicrobial activity [57], [64], [65]. This could facilitate the persistence of opportunistic pathogens such as G. vaginalis , contributing to a state of suboptimal vaginal health. Microscopic fungi in vaginal and fecal samples Our study extends current knowledge by incorporating fungal community analysis through ITS sequencing. Vaginal swabs were characterized by C. albicans , N. glabratus , and P. kudriavzevii , while fecal samples exhibited a broader fungal spectrum, including environmental and food-associated taxa such as S. uvarum and Penicillium spp. C. albicans remains the leading cause of vulvovaginal candidiasis, with pathogenicity linked to hyphal formation, biofilm development, and phenotypic switching [17], [18], [66]. The results of the analysis of the vaginal microbiome in participant W5 are particularly interesting. Although the taxonomic analysis at the genus level suggested a typical Lactobacillus -dominated profile, species-level identification revealed L. iners as the predominant species, accompanied by markedly higher fungal load of N. glabratus on the mycobial analysis and a distinct metabolic signature predicted from 16S rRNA sequencing data. N. glabratus is an opportunistic yeast that has gained clinical importance due to its ability to persist in the vaginal environment and its intrinsic tolerance to azole antifungals. Unlike C. albicans , N. glabratus does not form true hyphae, which limits its invasive potential but enhances its capacity for biofilm formation and survival under stress conditions [67], [68]. Its growth is favored in environments with reduced acidity, which can occur during dysbiosis or following shifts in the Lactobacillus species composition (note that L. crispatus is known for its ability to reduce pH). The dominance of L. iners in this sample highlights the limitations of the genus-level analysis for clinical interpretation. The predicted enrichment of core metabolic pathways and pyruvate metabolism supports its association with transitional or dysbiotic states. If oxidative stress is present (e.g., inflammation or immune response), L. iners may redirect pyruvate toward acetate instead of lactate, creating a less acidic environment that can facilitate pathogen colonization. N. glabratus is a facultative anaerobic yeast and grows better in conditions with higher pH, that is, less acidic. The higher fungal load in both quantification and sequencing depth compared to other participants indicates a persistent infection with N. glabratus throughout the study period. Interestingly, the same metabolic patterns were found in another woman (W4) who showed the predominance of both L. crispatus and L. iners at different periods. The PRMT-based metabolic profiles (determined at the genus-level) in the periods dominated by these two bacteria were still distinct, with those dominated by L. iners being markedly similar to those detected in woman W5. Correlations among relative abundances/presence of vaginal bacteria and yeasts with fecal bacteria The correlations observed between vaginal yeasts and both vaginal and fecal bacterial communities suggest a tightly interconnected relationship that may be facilitated by the potential transfer of bacteria, yeasts, and their metabolites across the rectal tract. This also suggests a bidirectional relationship between the mycobiome and bacteriome, spanning both the vaginal and gut environments. The presence of vaginal yeasts was positively associated with the relative abundance of Streptococcus in fecal samples, indicating the possible existence of broader dysbiotic patterns along the gut–urogenital tract. Within the vaginal environment, yeast presence correlated positively with L. iners and negatively with L. crispatus . This pattern is consistent with the characteristics of these species, where L. iners often dominate in transitional or less stable vaginal states, whereas L. crispatus is typically linked to a stable, protective, and anti‑yeast vaginal microbiome. The associations involving L. jensenii positively correlated with Bifidobacterium and negatively with Dialister may indicate broader interactions between anti‑inflammatory and pro‑inflammatory bacterial taxa. Overall, these findings highlight the potential systemic nature of bacteria–yeast interactions and emphasize the importance of studying vaginal and gut microbial communities as interconnected ecosystems rather than isolated compartments. Limitations and strengths One of the limitations of our study was that the pooling of samples for mycobiome analysis made it impossible to track dynamics during the menstrual cycle; however, the results of microbiological examination showed that the composition of vaginal samples was very similar throughout the observation period. Our pilot study was also limited by its small sample size. However, the longitudinal design, collection of two types of matrices, and analysis of both the bacteriome and mycobiome have enabled us to expand our knowledge of the female microbiome during the menstrual cycle. The sample size was limiting especially for the analysis of the microbiome in relation to exposure and behavioral data. Our analysis of exposure and behavioral variables revealed that most factors, including recent sexual activity, use of lubricants or condoms, and probiotic intake, had minimal impact on vaginal microbial diversity and Lactobacillaceae dominance. In our study, reduced relative abundance of family Lactobacillaceae was associated with an increased presence of potential pathogens linked to BV, such as Gardnerella vaginalis and the genus Streptococcus [11], [12]. Conclusions Our findings confirm that anatomical site and interindividual variation are the primary determinants of microbiome composition in reproductively healthy women, with phases of menstrual cycle playing only a minor role. The stability of the vaginal microbiome across phases of menstrual cycle reflects its resilience and supports its potential protective role against dysbiosis and infection. The observed metabolic differences between L. crispatus, L. iners, L. jensenii , and G. vaginalis -dominated communities highlight the importance of functional profiling for early detection of instability. Finally, the inclusion of mycobiome analysis reveals an additional layer of complexity in host-microbe interactions, emphasizing the need to consider fungal communities in studies of women’s health. Conversely, the observed cyclical variation in fecal bacteriome diversity may influence metabolic and immune processes, suggesting a need for further research into systemic effects of hormonal cycles. Importantly, our correlation analyses highlight a deeper level of interconnection between body sites by revealing that the presence of vaginal yeasts is linked to shifts in both vaginal and fecal bacterial communities. These patterns, together with the possibility of microbial and metabolite transfer across the rectal tract, underscore a dynamic interplay between the bacteria and microscopic fungi that extends beyond local microbial environments. Although this is only a pilot study the results of which need to be verified on a larger sample, the results indicate that when investigating female fecal microbiome, the phase of menstrual cycle should be considered. Declarations Supplementary materials Figure S1. Heatmap of the 25 most abundant bacterial genera across all samples. The heatmap displays the log-transformed relative abundances of the top 25 bacterial genera across all vaginal and stool samples collected over three menstrual cycles. Squares highlighted by framing indicate taxa reaching a relative abundance greater than 20% in the corresponding sample. Grey squares indicate taxa that were not detected in the corresponding sample. W1, woman 1; C1, menstrual cycle 1. Table S1. Illumina primer sequences used in this study. Table S2. ITC2_C primers used in this study. Table S3. Results of statistical testing for the top 25 most abundant taxa in both vaginal and fecal samples, comparing their relative abundances within each phase across the three consecutive menstrual cycles. Table S4. Results of statistical testing for the top 25 most abundant taxa on the median value of the three measurements for each phase of the menstrual cycle. Data availability statement Sequencing data were uploaded to the European Nucleotide Archive under accession number PRJEB108971. Funding This work was carried out with the support of RECETOX Research Infrastructure (ID LM2023069). This publication was supported from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 857560. This publication reflects only the author’s view and the European Commission is not responsible for any use that may be made of the information it contains. Authors also thank to project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632) financed by the Ministry of Education, Youth and Sports for supportive background and MH CZ - DRO FNBr 65269705 financed by Czech ministry of Health and by project provided by University Hospital Brno (SUp 2/26 NS: 8734). Computational resources were provided by the e-INFRA CZ project (ID:90140), supported by the Ministry of Education, Youth and Sports of the Czech Republic. Author contribution BZ: Bioinformatic Analysis, Statistical Analysis, Visualization, Writing – Original Draft. JH: Writing – Revision. OT: Investigation. PB: Methodology. PH: Methodology. LM: Writing – Revision. MJ: Writing – Revision. FR: Methodology, Investigation, Writing – Revision. PBL: Conceptualization, Methodology, Supervision, Writing – Original Draft, Funding. Acknowledgements We would like to thank Dr. Jaroslav Janosek for his valuable comments and all participants for enrollment. We are also thankful to our colleagues from the Environmental Genomics research Group and RECETOX for their support in samples and data processing. Sequencing was carried out in the laboratories of the Institute of Applied Biotechnologies a.s. Use of AI statement The authors declare that they have used generative artificial intelligence, specifically Microsoft Copilot only to improve R scripts for handling data formatting and generation of figures. References G. Tachedjian, M. Aldunate, C. S. Bradshaw, and R. A. Cone, “The role of lactic acid production by probiotic Lactobacillus species in vaginal health,” Res. Microbiol. , vol. 168, no. 9–10, pp. 782–792, Nov. 2017, doi: 10.1016/j.resmic.2017.04.001. P. Mirmonsef et al. , “Free Glycogen in Vaginal Fluids Is Associated with Lactobacillus Colonization and Low Vaginal pH,” PLoS ONE , vol. 9, no. 7, p. e102467, Jul. 2014, doi: 10.1371/journal.pone.0102467. D. J. 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Supplementary Files TableS1.xlsx Table S1. Illumina primer sequences used in this study. TableS2.xlsx Table S2. ITC2_C primers used in this study. TableS3.xlsx Table S3. Results of statistical testing for the top 25 most abundant taxa in both vaginal and fecal samples, comparing their relative abundances within each phase across the three consecutive menstrual cycles. TableS4.xlsx Table S4. Results of statistical testing for the top 25 most abundant taxa on the median value of the three measurements for each phase of the menstrual cycle. FigureS1.pdf Figure S1. Heatmap of the 25 most abundant bacterial genera across all samples. The heatmap displays the log-transformed relative abundances of the top 25 bacterial genera across all vaginal and stool samples collected over three menstrual cycles. Squares highlighted by framing indicate taxa reaching a relative abundance greater than 20% in the corresponding sample. Grey squares indicate taxa that were not detected in the corresponding sample. W1, woman 1; C1, menstrual cycle 1. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Medicine, Masaryk University, Brno, Czech Republic","correspondingAuthor":false,"prefix":"","firstName":"Michal","middleName":"","lastName":"Jeseta","suffix":""},{"id":601922058,"identity":"34485d25-c621-4d1c-ac94-61e2fb8af926","order_by":7,"name":"Filip Ruzicka","email":"","orcid":"https://orcid.org/0000-0001-5679-0513","institution":"Department of Microbiology, St. Anne´s University Hospital, Faculty of Medicine, Masaryk University, Pekarska 664/53, Brno, Czech Republic","correspondingAuthor":false,"prefix":"","firstName":"Filip","middleName":"","lastName":"Ruzicka","suffix":""},{"id":601922059,"identity":"f367b57b-b7f6-4672-97af-6a9e3fdb3579","order_by":8,"name":"Petra Borilova Linhartova","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0953-3615","institution":"RECETOX, Faculty of Science, Masaryk University, Koltarska 2, Brno, Czech Republic","correspondingAuthor":true,"prefix":"","firstName":"Petra","middleName":"Borilova","lastName":"Linhartova","suffix":""}],"badges":[],"createdAt":"2026-03-06 13:11:06","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9050950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9050950/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104204114,"identity":"3091a8b6-c28b-4a8d-b062-3280586696cb","added_by":"auto","created_at":"2026-03-09 06:29:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":115888,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe total content of bacterial DNA and bacteriome alpha diversity in vaginal (A and B, respectively) and fecal samples (C and D, respectively) from eight women across three phases\u003c/strong\u003e \u003cstrong\u003eof the menstrual cycle. \u003c/strong\u003eData shown as medians derived from three independent measurements. Paired samples are connected by a grey line.\u003c/p\u003e","description":"","filename":"6c4ce3301.png","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/f2d6efa35d6aac964b1825c0.png"},{"id":104404741,"identity":"d0bf1b3e-665e-477f-a9c1-87d858d709b4","added_by":"auto","created_at":"2026-03-11 12:20:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal component analysis (PCA) of bacteriome composition in vaginal (A) and fecal samples (B) from eight women across three phases\u003c/strong\u003e \u003cstrong\u003eof three menstrual cycles\u003c/strong\u003e. Paired samples are connected by a grey line.\u003c/p\u003e","description":"","filename":"9291adb31.png","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/59810be4fcfb0f2a7fd31f74.png"},{"id":104404785,"identity":"63e92b8e-a38c-47df-957e-ac6975c04eae","added_by":"auto","created_at":"2026-03-11 12:21:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":235737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShannon indices, relative abundances of selected bacterial species, and predicted enrichment of bacterial metabolic pathways in the vaginal samples of eight women across three phases of three menstrual cycles. \u003c/strong\u003eSquares highlighted by framing indicate taxa reaching a relative abundance greater than 20 % in the corresponding sample. Grey squares indicate taxa that were not detected in the corresponding sample.\u003cstrong\u003e \u003c/strong\u003eThe heatmap of metabolic pathways displays z-score-transformed enrichment scores based on the predicted metabolic potential from 16S rRNA amplicon sequencing data for selected metabolic pathways. The presence of vaginal yeasts, as well as exposure and behavioral factors potentially influencing the vaginal microbiome, are also shown. W1, woman 1; C1, menstrual cycle 1.\u003c/p\u003e","description":"","filename":"384321bf1.png","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/ce8a2b067ba0bdbd38d8b27f.png"},{"id":104405126,"identity":"034cb9e6-05e6-4833-bfbe-5b6b6f6b742d","added_by":"auto","created_at":"2026-03-11 12:21:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":395229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eShannon indices, relative abundances of the 25 most abundant genera, and predicted enrichment of bacterial metabolic pathways in fecal samples from eight women across three phases of three menstrual cycles. \u003c/strong\u003eSquares highlighted by framing indicate taxa reaching a relative abundance greater than 20% in the corresponding sample. Grey squares indicate taxa that were not detected in the corresponding sample. The heatmap of metabolic pathways displays z-score-transformed enrichment scores based on the predicted metabolic potential from 16S rRNA amplicon sequencing data for selected metabolic pathways. W1, woman 1; C1, menstrual cycle 1.\u003c/p\u003e","description":"","filename":"42db86f91.png","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/93f428f43336724ce3014f5d.png"},{"id":104403787,"identity":"884905e1-abb0-41ca-a12a-4a4e77265e17","added_by":"auto","created_at":"2026-03-11 12:19:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMycobiome diversity and composition of vaginal and fecal samples from eight women. \u003c/strong\u003eThe heatmap displays the relative abundance of fungal taxa detected across pooled samples from eight participants, representing nine time points in three menstrual cycles per individual. Relative abundances are shown on a log\u003csub\u003e10\u003c/sub\u003e scale. Taxa in vaginal samples confirmed by conventional microbiological methods are marked accordingly. Squares highlighted by framing indicate taxa reaching a relative abundance greater than 20% in the corresponding sample. Grey squares indicate taxa that were not detected in the corresponding sample.\u003c/p\u003e","description":"","filename":"dd4851dd1.png","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/27e38bc86982e0274f222b33.png"},{"id":104204123,"identity":"4b4bf3d8-4efc-42be-b05c-61b22faf4676","added_by":"auto","created_at":"2026-03-09 06:29:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":58847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation among relative abundances/presence of vaginal bacteria and yeasts with fecal bacteria. \u003c/strong\u003eNote that the distances between individual bacteria correspond to the calculated correlations, even where no connecting lines are shown (worse correlation corresponds to longer distance; only the strongest correlations with absolute value of \u0026gt;0.6 are shown)\u003c/p\u003e","description":"","filename":"a096d8681.png","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/9921d3cf20385705169b6b33.png"},{"id":104779598,"identity":"8e9b49bf-e8ad-4e31-a284-687ee909352e","added_by":"auto","created_at":"2026-03-17 07:42:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3554252,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/c438125c-6484-499e-9077-c0de07372311.pdf"},{"id":104204117,"identity":"7155fddd-7a64-4dd8-bb7c-1242068ce28f","added_by":"auto","created_at":"2026-03-09 06:29:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. \u003c/strong\u003eIllumina primer sequences used in this study.\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/d538945d0b0f1a8086fafe9f.xlsx"},{"id":104405089,"identity":"aa65816a-b005-428f-92c5-c050efcf58a6","added_by":"auto","created_at":"2026-03-11 12:21:44","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2.\u003c/strong\u003e ITC2_C primers used in this study.\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/75cf4450c2adf369de006624.xlsx"},{"id":104204116,"identity":"b54c4bc2-d110-4883-8dab-4e5983bb4798","added_by":"auto","created_at":"2026-03-09 06:29:00","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3. \u003c/strong\u003eResults of statistical testing for the top 25 most abundant taxa in both vaginal and fecal samples, comparing their relative abundances within each phase across the three consecutive menstrual cycles.\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/b9a6a1dcb37a9f77d5600ccd.xlsx"},{"id":104204119,"identity":"5e5d398d-f480-4016-a001-9a888c4d0dd8","added_by":"auto","created_at":"2026-03-09 06:29:00","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":9826,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S4. \u003c/strong\u003eResults of statistical testing for the top 25 most abundant taxa on the median value of the three measurements for each phase of the menstrual cycle.\u003c/p\u003e","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/3711b3d9cfc6319f42db78e5.xlsx"},{"id":104204124,"identity":"e73cf284-6d06-403e-af2e-16cdcc1a887a","added_by":"auto","created_at":"2026-03-09 06:29:01","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":72022,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003e Heatmap of the 25 most abundant bacterial genera across all samples. The heatmap displays the log-transformed relative abundances of the top 25 bacterial genera across all vaginal and stool samples collected over three menstrual cycles. Squares highlighted by framing indicate taxa reaching a relative abundance greater than 20% in the corresponding sample. Grey squares indicate taxa that were not detected in the corresponding sample. W1, woman 1; C1, menstrual cycle 1.\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9050950/v1/93d153581355c0f14bbeed84.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eLongitudinal profiling and correlations of vaginal and fecal microbiomes throughout the menstrual cycle: A pilot prospective study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe vaginal microbiota plays a crucial role in maintaining female reproductive health. In healthy individuals, this ecosystem is typically dominated by bacteria of the family Lactobacillaceae, which creates a protective environment through the production of lactic acid, hydrogen peroxide, and antimicrobial peptides [1]. These metabolites lower vaginal pH and inhibit the growth of microorganisms with pathogenic potential, thereby reducing the risk of infections, inflammation, and fertility-related complications [2], [3].\u003c/p\u003e \u003cp\u003e \u003cem\u003eLactobacillus crispatus\u003c/em\u003e, \u003cem\u003eLactobacillus jensenii\u003c/em\u003e, \u003cem\u003eLactobacillus gasseri\u003c/em\u003e, \u003cem\u003eLacticaseibacillus rhamnosus\u003c/em\u003e, and \u003cem\u003eLactobacillus iners\u003c/em\u003e are among the most prevalent species of the family Lactobacillaceae present in the vaginal environment [4]. While \u003cem\u003eL. crispatus\u003c/em\u003e is widely considered a marker of vaginal health due to its ability to maintain a stable acidic environment, \u003cem\u003eL. iners\u003c/em\u003e is frequently detected in transitional or dysbiotic states, and its role remains under investigation [4], [5], [6], [7], [8]. The specific composition of vaginal microbiome, particularly lactobacilli, has been associated with a lower incidence of bacterial vaginosis (BV), vulvovaginal candidiasis (VVC), human papillomavirus (HPV) infections, and adverse pregnancy outcomes [9], [10].\u003c/p\u003e \u003cp\u003eVaginal dysbiosis, defined as a disruption of the microbial balance, is typically characterized by a reduction in the abundance of Lactobacillaceae and overgrowth of anaerobic bacteria, which increases the susceptibility to infections and inflammatory conditions. Such imbalances have also been linked to gynecological disorders, including endometriosis, where the vaginal microbiome often exhibits reduced levels of protective lactobacilli and higher prevalence of opportunistic pathogens, such as \u003cem\u003eGardnerella vaginalis\u003c/em\u003e, species \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e [11], [12], [13], [14], [15]. Emerging evidence suggests that microbial dysregulation may contribute to disease progression, modulate immune responses, and negatively impact fertility [16].\u003c/p\u003e \u003cp\u003eMicroscopic fungi, particularly yeasts, represent another important component of the vaginal ecosystem. \u003cem\u003eCandida albicans\u003c/em\u003e is the most common cause of VVC, affecting up to 75% of women at least once in their lifetime [17], [18]. Its pathogenicity is driven by virulence factors such as hyphal formation, biofilm development, and phenotypic switching, which enable adaptation to changing microenvironments and immune evasion [19]. \u003cem\u003eC. albicans\u003c/em\u003e has gained clinical relevance due to its persistence and antifungal resistance profiles, resulting in treatment strategy complications [20]. Despite growing interest in the vaginal microbiome, longitudinal studies examining its stability throughout the menstrual cycle remain limited, and even fewer have integrated fungal community analysis [21], [22], [23], [24].\u003c/p\u003e \u003cp\u003eInterestingly, recent evidence indicates a relationship between the gut microbiota and the menstrual cycle [25]. Several human and animal studies have suggested that cyclical hormonal fluctuations can modulate gut bacteriome composition, with associations reported between specific bacterial taxa and menstrual disorders, premenstrual symptoms, or changes in gut permeability during different phases of the menstrual cycle [26], [27], [28], [29]. Consequently, many healthy women experience cyclical changes in gastrointestinal function and symptomatology across the menstrual cycle, reflecting the broader physiological impact of sex‑hormone\u0026ndash;microbiome interactions (Bharadwaj et al., 2015; Mohib et al., 2018). Understanding these dynamics is essential for elucidating the interplay between hormonal fluctuations, microbial resilience, and potential dysbiosis.\u003c/p\u003e \u003cp\u003eWe hypothesize that the vaginal bacteriome remains relatively stable throughout the menstrual cycle, whereas the gut bacteriome may exhibit subtle cyclical fluctuations. Furthermore, we propose that integrating fungal community analysis will reveal additional complexity and potential clinical relevance within these ecosystems. In this study, we aimed to (i) assess vaginal and fecal bacteriome diversity and composition across different phases of the menstrual cycle, (ii) explore predicted metabolic profiles of the vaginal and fecal bacteriomes, (iii) describe the presence of microscopic fungi in vaginal and fecal samples, and (iv) investigate correlations among relative abundances/presences of vaginal bacteria and yeasts with fecal bacteria.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eIn this pilot prospective observational study, eight women (W1-W8) of reproductive age (25\u0026ndash;45 years) who already had children were enrolled and their vaginal and fecal bacteriome and mycobiome were analyzed. Over the course of three consecutive menstrual cycles (C1-C3), vaginal and stool swabs were collected by self-sampling at defined time points corresponding to the three phases of the menstrual cycle: follicular (days 4\u0026ndash;11 of the menstrual cycle), ovulatory (days 11\u0026ndash;17), and luteal (days 18\u0026ndash;24). The ovulatory phase was determined using over-the-counter ovulation tests. No samples were taken during the menstrual phase.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003e was obtained from all participants in accordance with the principles outlined in the Helsinki Declaration.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e The study was approved by the Ethics Committee of CELSPAC, Czech Republic (Ref. No. CELSPAC/EK/1/2018, dated 12th March 2018).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria, and collection of exposure and behavioral variables\u003c/h2\u003e \u003cp\u003eBoth sexually active and non\u0026ndash;sexually active women were eligible for inclusion in the study, with a regular menstrual cycle lasting\u0026thinsp;\u0026gt;\u0026thinsp;21 days (the median length was 26 days) being the crucial inclusion criterion. Exclusion criteria were current pregnancy, lactation, the use of hormonal contraceptives, and treatment with antimicrobials within three months prior to study initiation.\u003c/p\u003e \u003cp\u003eThroughout the study, participants were monitored using questionnaires for additional exposure and behavioral factors potentially influencing their microbiomes, including intercurrent illnesses, use of antimicrobials, probiotic supplementation, sexual intercourse, and the use of lubricants or condoms during intercourse.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection and microbiological examination of microscopic fungi\u003c/h3\u003e\n\u003cp\u003eSampling was conducted at each of the nine time points; participants self‑collected vaginal and stool samples using sterile flocked swabs (FLOQSwabs, COPAN, Italy).\u003c/p\u003e \u003cp\u003eAt each sampling point, an additional vaginal swab was placed in a tube with the Amies medium (TransystemTM, COPAN, Italy) and transported at 4\u0026deg;C for microbiological examination of microscopic fungi as described previously [30]. The presence of microscopic fungi was monitored using conventional culture techniques; subsequently, the isolated strains were identified using matrix-assisted laser desorption ionization\u0026ndash;time of flight mass spectrometry (Bruker, Switzerland).\u003c/p\u003e\n\u003ch3\u003ePreprocessing and DNA isolation\u003c/h3\u003e\n\u003cp\u003eImmediately after collection, swabs were transferred into sterile cryogenic tubes and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further processing. DNA isolation from all collected samples was performed using the DNA Mini Kit (QIAGEN, Hilden, Germany), following the manufacturer\u0026rsquo;s protocol optimized for microbial DNA isolation. Prior to DNA isolation, samples were homogenized in two 50-second cycles using 1.4 mm ceramic beads (VWR International, USA) and subjected to enzymatic pretreatment with lyticase (20 mg/mL) to enhance cell lysis efficiency. All procedures were conducted in a sterile handling box to minimize contamination. A bacterial internal standard for bacteriome analysis (ZymoBIOMICS Spike-In Control I, High Microbial Load; Zymo Research), as well as negative controls (NC), were also included in the isolation process. All DNA samples were eluted in a final volume of 50 \u0026micro;L and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until further analysis.\u003c/p\u003e\n\u003ch3\u003eQuantification of the total content of bacterial and fungal DNA by qPCRs\u003c/h3\u003e\n\u003cp\u003eThe total contents of bacterial and fungal DNA were quantified by qPCRs using universal primers targeting the gene for 16S rRNA (UNI 515/806) [Kurina I, et \u003cem\u003eal\u003c/em\u003e.,2020] and the gene for ITS rRNA region (FungiQuanti_F/FungiQuanti_R) [Liu CM, et \u003cem\u003eal\u003c/em\u003e., 2012], respectively. Reactions were carried out with SsoAdvanced\u0026trade; Universal Inhibitor-Tolerant SYBR\u0026reg; Green Supermix (Bio-Rad, USA) on a LightCycler\u0026reg; 480 Instrument II (Roche, Switzerland). For quantification of the gene for 16S rRNA, the thermal profile comprised an initial denaturation at 98\u0026deg;C for 3 min, followed by 40 cycles of 98\u0026deg;C for 10 s and 55\u0026deg;C for 15 s, using each primer at a final concentration of 0.5 \u0026micro;M [31]. For quantification of the gene for ITS rRNA, the protocol was identical except for the annealing step that was performed at 60\u0026deg;C for 15 s, and each primer was used at 0.4 \u0026micro;M. Fluorescence data were processed using LightCycler\u0026reg; 480 Software v1.5.1.62 (Roche, Switzerland). Technical duplicates were evaluated for concordance, and Cp differences of \u0026le;\u0026thinsp;0.5 were considered acceptable. Values corresponding to fewer than 50 gene copies were regarded as below the limit of detection.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e16S and ITS rRNA amplicon sequencing and bioinformatic analysis\u003c/span\u003e \u003c/p\u003e \u003cp\u003eDNA isolates (N\u0026thinsp;=\u0026thinsp;144) and NCs (N\u0026thinsp;=\u0026thinsp;12) were spiked with bacterial internal standard, and amplification of the hypervariable V3\u0026ndash;V4 region of the gene for 16S rRNA was performed using IL primers (Supplementary Table S1) with Q5\u0026reg; High-Fidelity 2\u0026times; Master Mix (New England BioLabs, USA). The PCR master mix (excluding template DNA) was decontaminated by the addition of 40 \u0026micro;M 8-methoxypsoralen (Sigma-Aldrich, USA).\u003c/p\u003e \u003cp\u003eFor the general description of vaginal and fecal mycobiomes, individual DNA isolates from all nine samples collected from each participant were pooled into a single sample and spiked with an internal fungal standard. Eight pooled vaginal, eight pooled fecal samples, and two NCs were spiked with fungal internal standard, and amplification of the ITS2 region was performed using ITC2_C primers (Supplementary Table S2) with the same polymerase and process as described above.\u003c/p\u003e \u003cp\u003eFor both bacteriome and mycobiome analysis, thermal cycling was carried out as follows: initial denaturation at 98\u0026deg;C for 30 s; 30 cycles of 98\u0026deg;C for 10 s, 55\u0026deg;C for 15 s, and 72\u0026deg;C for 30 s; a final extension at 72\u0026deg;C for 2 min; and a hold at 4\u0026deg;C. Following amplification, PCR products were purified using SPRIselect beads (Beckman Coulter, USA), quantified on a Synergy HTX fluorimeter (BioTek, USA), and combined in equimolar proportions. Indexed libraries were generated via secondary PCR using Nextera XT indices (Illumina, USA). Sequencing was conducted on a MiSeq system with the MiSeq Reagent Kit v3, in accordance with the manufacturer\u0026rsquo;s instructions (Illumina, USA).\u003c/p\u003e \u003cp\u003eBoth datasets were processed separately using nf-core/ampliseq version 2.11.0 [32] of the nf-core collection of workflows [33], utilizing reproducible software environments from the Bioconda [34] and Biocontainers [35] projects.\u003c/p\u003e \u003cp\u003eData quality was evaluated with FastQC and summarized with MultiQC [36]. Sequences were processed with DADA2 tool, an open-source software package that denoises and removes sequencing errors from Illumina amplicon sequence data [37]. Processing steps include elimination of PhiX contamination, trimming reads (before median quality drops below 25 and at least 85% of reads are retained; forward reads at 300 bp and reverse reads at 253 bp, reads shorter than this were discarded), discarding reads with more than 3 expected errors, correcting errors (denoising), merging read pairs, and removing PCR chimeras.\u003c/p\u003e \u003cp\u003eTaxonomic classification was performed in DADA2 using the na\u0026iuml;ve Bayesian classifier [38] with the SILVA 138.1 prokaryotic SSU database [39] for 16S rRNA amplicon data and the UNITE database for ITS data [40], [41]. Additionally, the BLAST algorithm [42] was used to identify the species, and all taxa with the maximum identity and minimum e-value were selected for each ASV.\u003c/p\u003e \u003cp\u003eASV sequences, abundance table, and DADA2 taxonomic assignments were loaded into QIIME2 [43]. Within QIIME2, the final microbial community data were processed into bacteriome and mycobiome profiles separately.\u003c/p\u003e \u003cp\u003ePICRUSt2 [44] was used to predict hypothetical abundances of KEGG orthologs in each vaginal and fecal sample of each woman and summarized into higher functional processes. Normalization of gene copy numbers that were used for determining bacterial functional profiles was performed using the MUSICC algorithm [45]. Additionally, the Predicted Relative Metabolomic Turnover (PRMT) [46] approach was applied to the normalized data to generate community-wide metabolic potential scores (PRMT scores) for each metabolite annotated in the KEGG database. To investigate the temporal dynamics of bacterial metabolic potential, we performed single-sample enrichment analysis on metabolites grouped into KEGG pathways across collected samples. Only pathways covered by at least 25% of their constituent metabolites in our dataset were included in the final presentation.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eOnly samples and NCs with a sequencing depth of at least 2000 reads (including the specific internal standard) were retained for downstream analysis. While reads mapped to internal standards were used to assess sequencing performance and subsequently removed from the dataset, NCs were retained throughout the analysis to monitor for potential contamination and are displayed in relevant figures for comparison.\u003c/p\u003e \u003cp\u003eAlpha diversity indices, including the Shannon index and the number of distinct amplicon sequence variants (ASVs), were computed using the vegan package (v.2.6\u0026ndash;6.1). Differences between groups were tested using the Wilcoxon test, a paired non-parametric test, for testing the differences between vaginal and fecal samples. Friedman\u0026rsquo;s ANOVA test was used to test the differences between phases of the menstrual cycle. Evaluation of the stability of individual variables in each phase within a given matrix was performed to find out if measurements across the months could be aggregated. If no significant difference was detected, the median of the three measurements was used for each phase of the menstrual cycle.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was performed on CLR-transformed abundance data using the PCAtools (v.2.20.0) package to visualize differences in microbial community composition. To statistically confirm the observed differences between sample types, a PERMANOVA analysis was conducted using Euclidean distances, as appropriate for CLR-transformed data.\u003c/p\u003e \u003cp\u003eIn the correlation analysis, Spearman\u0026rsquo;s correlation coefficient was used to assess associations between microbial taxa. Spearman correlation was applied to the relative abundances of the 25 most abundant bacterial genera detected in fecal samples, as well as five selected bacterial species and one bacterial genus present in vaginal samples. In addition, information on the presence or absence of yeasts in vaginal samples at each sampling time point (binary variable obtained by conventional microbiological methods) was incorporated into the analysis. To evaluate associations between yeasts occurrence and bacterial relative abundances, the rank-biserial correlation coefficient was calculated, and statistical significance was assessed using the Wilcoxon rank-sum test. Correlations with an absolute magnitude\u0026thinsp;\u0026ge;\u0026thinsp;0.6 were considered strong. The resulting correlation network was visualized using the igraph package (v.2.2.2).\u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered statistically significant. All resulting \u003cem\u003ep\u003c/em\u003e-values were adjusted for multiple hypotheses testing using the Benjamini\u0026ndash;Hochberg procedure. All analyses were conducted using R version 4.5.1 (2025-06-13).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn the context of 16S and ITS rRNA amplicon sequencing, all vaginal (N\u0026thinsp;=\u0026thinsp;72) and fecal samples (N\u0026thinsp;=\u0026thinsp;72) passed the quality control process. Importantly, although the genera \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003ePseudoxanthomonas\u003c/em\u003e, and \u003cem\u003eChryseobacterium\u003c/em\u003e, as well as members of the class \u003cem\u003eParcubacteria\u003c/em\u003e, were detected in one of the twelve NCs, none of these genera appeared in any vaginal or fecal sample included in the study.\u003c/p\u003e\u003cp\u003e\u003cu\u003eLongitudinal stability of bacteriome profiles within individual phases of the menstrual cycle\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the consistency and potential month-to-month variation of the vaginal and fecal bacteriome within specific phases of the menstrual cycle, bacteriome diversity represented by the Shannon index (and, for the vaginal samples, the abundance of the family Lactobacillaceae) across three consecutive cycles was analyzed. In the vaginal bacteriome, no statistically significant differences were observed between months within any of the phases, indicating an overall temporal stability of the dominance of the genus \u003cem\u003eLactobacillus\u003c/em\u003e. Complementing these analyses, Table\u0026nbsp;S3 summarizes the results of statistical testing for the top 25 most abundant taxa in both vaginal and fecal samples, comparing their relative abundances within each phase across the three consecutive menstrual cycles.\u003c/p\u003e\n\u003cp\u003eThis allowed us to aggregate data, where the median values of studied parameters were calculated from three measurements for each woman and menstrual cycle phase. This strategy allowed robust comparisons across phases while minimizing the influence of short-term fluctuations.\u003c/p\u003e\n\n\u003ch3\u003eBacteriome alpha diversity across menstrual cycle phases\u003c/h3\u003e\n\u003cp\u003eLog‑scaled total content of bacterial DNA and Shannon diversity indices from 16S rRNA amplicon sequencing across follicular, ovulatory, and luteal phases in both vaginal and fecal samples were compared to assess the changes throughout the menstrual cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo statistically significant differences in the total content of bacterial DNA were observed between phases of the menstrual cycle in either vaginal or fecal samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,B). No significant differences in Shannon diversity were detected between phases in vaginal swabs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), while phase‑dependent change in fecal bacteriome diversity was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Shannon diversity indices showed a gradual increase from the follicular through the ovulatory phase, reaching the highest values in the luteal phase; this trend resulted in a statistically significant difference between the follicular and luteal phases (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBacteriome composition across menstrual cycle phases\u003c/h2\u003e \u003cp\u003eIn vaginal samples, PCA revealed substantial overlap between follicular, ovulatory, and luteal phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), with no clear clustering by phase of the menstrual cycle. This lack of separation was confirmed by PERMANOVA analysis, which showed no significant effect of the phase on vaginal bacteriome composition (R\u0026sup2; = 0.016, F\u0026thinsp;=\u0026thinsp;0.554, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.826).\u003c/p\u003e \u003cp\u003eSimilarly, PCA of fecal samples showed overlapping distributions of samples from all phases of the menstrual cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, PC1\u0026thinsp;=\u0026thinsp;13.2%, PC2\u0026thinsp;=\u0026thinsp;10.9% of explained variance), indicating high inter‑individual variability but no phase‑specific structuring of the fecal bacteriome. Consistently, PERMANOVA analysis revealed no significant association between the phase and fecal bacteriome composition (R\u0026sup2; = 0.02, F\u0026thinsp;=\u0026thinsp;0.694, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.914).\u003c/p\u003e \u003cp\u003eSamples from the follicular, ovulatory, and luteal phases were distributed throughout the vaginal and fecal clusters without forming any phase-specific subgroups. Within each matrix type, points tended to cluster according to individual women rather than the phase of the menstrual cycle (Figure S1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the bacteriome composition across sample types and phases of the menstrual cycle, the 25 most abundant bacterial genera across the entire dataset were selected (Figure S1). This selection predominantly included genera typical of the gut bacteriome, such as \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eSegatella\u003c/em\u003e (formerly \u003cem\u003ePrevotella\u003c/em\u003e), \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e, which were highly abundant in fecal samples. \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eGardnerella\u003c/em\u003e, which were present only in vaginal samples, were also among the top genera. Genera \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eClostridium, Dialister, Bifidobacterium\u003c/em\u003e, and some others (such as \u003cem\u003eEscherichia\u003c/em\u003e) were found in both vaginal and fecal samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic structure and metabolic functional landscape of the vaginal bacteriome\u003c/h2\u003e \u003cp\u003eUpon closer examination of vaginal swab samples at the species level using BLAST, we observed that in most samples, \u003cem\u003eL. crispatus\u003c/em\u003e was the dominant species within the family Lactobacillaceae (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). One woman (W5), however, exhibited \u003cem\u003eL. iners\u003c/em\u003e as the predominant species, and in another one (W1), in whose samples a high presence of \u003cem\u003eG. vaginalis\u003c/em\u003e was observed, the predominant lactobacillus was identified as \u003cem\u003eL. jensenii\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e also presents the enrichment scores based on the predicted metabolic potential of selected bacterial metabolic pathways in vaginal samples for individual women and phases of three menstrual cycles. Notably, pathways associated with environmental adaptation and pathogenicity (such as the \u003cem\u003etwo-component system\u003c/em\u003e and \u003cem\u003emetabolic pathways, carbon metabolism\u003c/em\u003e, and \u003cem\u003eglycerolipid metabolism\u003c/em\u003e) were markedly enriched in samples with reduced relative abundance of \u003cem\u003eL. crispatus\u003c/em\u003e and increased relative abundances of \u003cem\u003eG. vaginalis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, one woman (W5) displayed a distinct metabolic profile despite the fact that at the genus level (on which the PRMT calculations are based), her bacteriome composition was comparable with women in whom \u003cem\u003eL. crispatus\u003c/em\u003e dominated in vaginal swabs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, BLAST-based species identification in this woman revealed the dominance of \u003cem\u003eL. iners\u003c/em\u003e, a species frequently associated with transitional or dysbiotic states (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This may suggest that the predicted metabolic profile in this woman was influenced by the genera in the \u0026ldquo;others\u0026rdquo; category, which might have been different from those found in women with predominance of \u003cem\u003eL. crispatus\u003c/em\u003e in their vaginal bacteriome. A similar pattern was observed in another woman (W4), whose samples from time points dominated by \u003cem\u003eL. iners\u003c/em\u003e exhibited a different metabolic potential than those from periods with \u003cem\u003eL. crispatus\u003c/em\u003e dominance. The similarities between periods dominated by \u003cem\u003eL. iners\u003c/em\u003e in women W4 and W5 are striking, showing clear differences from the metabolic profiles of \u003cem\u003eL. crispatus\u003c/em\u003e-dominated samples across all women (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, another participant (W1), characterized by a high abundance of \u003cem\u003eG. vaginalis\u003c/em\u003e and dominance of \u003cem\u003eL. jensenii\u003c/em\u003e, also showed a distinct metabolic potential in vaginal samples.\u003c/p\u003e \u003cp\u003eIn addition, the selected exposure and behavioral factors, such as recent sexual activity, use of lubricants or condoms, antimicrobial treatment, probiotic intake, and reported illness, were recorded. A reduction in the relative abundance of \u003cem\u003eG. vaginalis\u003c/em\u003e in W1 following the supplementation with oral probiotics and an increase in the relative abundance of the same bacteria following infections in W7 were observed (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Still, the low number of women in this pilot study does not allow us to perform any formal statistical analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBacteriome composition and predicted metabolic potential of fecal samples\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays a combined heatmap of the 25 most abundant bacterial genera in fecal samples alongside the predicted enrichment of key bacterial metabolic pathways across women and phases of three menstrual cycles.\u003c/p\u003e \u003cp\u003eAnalysis of fecal samples revealed a bacterial community dominated by genera commonly associated with a healthy gut microbiome, including \u003cem\u003eFaecalibacterium, Segatella\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and members of the families Lachnospiraceae and Ruminococcaceae (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Supplementary Table S4 summarizes the results of statistical testing for the top 25 most abundant taxa on the aggregated data (the approach mentioned above).\u003c/p\u003e \u003cp\u003eThe predicted metabolic potential of the fecal bacteriota was also consistent across time points and reflected a distinct compositional pattern of each participant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Pathways belonging to \u003cem\u003etaurine and hypotaurine metabolism\u003c/em\u003e, \u003cem\u003enitrogen metabolism\u003c/em\u003e, and \u003cem\u003ebutanoate metabolism\u003c/em\u003e were among the most enriched pathways across the participants. On the other hand, \u003cem\u003etryptophan metabolism\u003c/em\u003e and \u003cem\u003ebenzoate degradation\u003c/em\u003e showed consistently lower enrichment scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMycobiome profiles of vaginal and fecal samples\u003c/h2\u003e \u003cp\u003eIn total, presence of yeasts was detected by conventional microbiological culture with subsequent identification by mass spectrometry in fourteen vaginal samples collected from five women (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eC. albicans\u003c/em\u003e was found in four cases and \u003cem\u003eNakaseomyces glabratus\u003c/em\u003e (formerly \u003cem\u003eCandida glabrata\u003c/em\u003e) in one woman. The microbiological examination revealed no more than one microscopic fungal species per woman across the entire study period (nine time points). This allowed us to pool vaginal samples from individual women for mycobiome analyses, and the resulting profiles from each woman was compared with the corresponding microbiological findings. Pooling was also applied to fecal samples.\u003c/p\u003e \u003cp\u003eNo statistically significant difference between vaginal and fecal samples was observed in the total content of fungal DNA (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19). However, Shannon diversity indices differed significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). In agreement with results of the conventional microbiological examination, \u003cem\u003eC. albicans\u003c/em\u003e was identified by ITS rRNA amplicon sequencing in vaginal samples from four women (W2, W4, W6, W7) and \u003cem\u003eNakaseomyces glabratus\u003c/em\u003e in samples from one woman (W5). Moreover, \u003cem\u003ePichia kudriavzevii\u003c/em\u003e (formerly \u003cem\u003eCandida krusei\u003c/em\u003e) not revealed by conventional methods was detected by ITS rRNA amplicon sequencing (74 reads) in one woman (W3). Fecal samples showed a broader fungal spectrum, including environmental and food-associated genera such as \u003cem\u003eSaccharomyces uvarum\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e spp., and \u003cem\u003eMaudiozyma barnettii\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations among relative abundances/presence of vaginal bacteria and yeasts with fecal bacteria\u003c/h2\u003e \u003cp\u003ePresence of vaginal yeasts positively correlated with relative abundances of genus \u003cem\u003eStreprococcus\u003c/em\u003e in fecal samples (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76). In vaginal samples, the presence of yeasts positively correlated with the relative abundance of \u003cem\u003eL. iners\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.61) and negatively correlated with the relative abundance of \u003cem\u003eL. crispatus\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e = -0.69). While \u003cem\u003ethe\u003c/em\u003e relative abundance of \u003cem\u003eL. jensenii\u003c/em\u003e in vaginal samples correlated positively with the relative abundances of the genus \u003cem\u003eBifidobacterium\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e = -0.63), a negative correlation with this lactobacillus was observed with genus \u003cem\u003eDialister\u003c/em\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.83). Additional strong correlations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.6) among fecal bacterial genera are also presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur longitudinal analysis of vaginal and fecal microbiomes across three consecutive menstrual cycles provides a comprehensive view of microbial dynamics in relation to matrix type and phase of the menstrual cycle. Importantly, this study extends current knowledge by incorporating fungal community profiling through ITS amplicon sequencing, offering a more holistic perspective on microbial ecosystems in healthy women.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVaginal and fecal bacteriome diversity and composition across phases of the menstrual cycle\u003c/h2\u003e \u003cp\u003eThe findings confirm fundamental differences between the vaginal and gut microbiomes in terms of total bacterial load, bacteriome diversity, and taxonomic composition [21], [24]. Fecal samples exhibited significantly higher microbial richness and diversity, reflecting the complexity of the gut environment [47], whereas vaginal samples were dominated by \u003cem\u003eLactobacillus\u003c/em\u003e, consistent with the typical profile of a healthy vaginal microbiome [4], [48], [49], [50], [51], [52]. As expected, the two sample matrices separated well in PCA, while differences among phases of the menstrual cycle were minor. Significant changes in alpha diversity between follicular and luteal phases were only detected in fecal samples. This pattern supports previous findings that anatomical site and individual factors are dominant drivers of microbiome profile, whereas hormonal fluctuations exert only minor influence [10].\u003c/p\u003e \u003cp\u003eWhile the vaginal microbiome remained relatively stable across phases of the menstrual cycle, both in terms of alpha diversity represented by Shannon index and Lactobacillaceae dominance, fecal samples displayed modest but statistically significant variation in alpha diversity across phases, with the highest diversity observed during the luteal phase [53]. This pattern suggests potential hormonal modulation of gut microbial richness, possibly mediated by estrogen and progesterone-driven changes in gut motility and immune function [26], [27], [28], [29]. These findings align with previous reports indicating that the gut microbiome is predominantly shaped by long-term host‑specific factors [54]. Although the effect size was small, these findings warrant further investigation into the interplay between hormonal cycles and gut microbiome dynamics. On the other hand, the results suggest that, where the vaginal microbiome is concerned, the phase of the menstrual cycle is not a major driver of compositional changes in healthy women. These results align with previous studies reporting resilience of the vaginal microbiome across phases of the menstrual cycle studied in this paper [52], [55].\u003c/p\u003e \u003cp\u003eOur results indicate that the taxonomic composition and functional potential of fecal microbiome of healthy women is stable across the menstrual cycle. This sugests that inter‑individual variability is the dominant driver of fecal bacteriome composition, overshadowing any potential influence of menstrual cycle\u0026ndash;related hormonal shifts. This is consistent with existing literature demonstrating that gut microbial communities are generally stable in healthy adults and strongly modulated by genetics, diet, immune, and lifestyle. The stability observed in our dataset thus supports the view that short‑term endocrine oscillations alone are unlikely to drive robust shifts in gut bacterial communities under healthy conditions.\u003c/p\u003e \u003cp\u003eThe taxonomic composition of fecal samples in our cohort, which was dominated by \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and members of the Lachnospiraceae and Ruminococcaceae families, corresponds to a typical gut microbiome. This composition remained largely consistent across all menstrual cycle phases. Predicted metabolic pathway profiles showed strong individual‑specific signatures with minimal temporal variation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePredicted metabolic profiles of the vaginal and fecal bacteriomes\u003c/h2\u003e \u003cp\u003eOur analysis of enrichment scores based on the predicted metabolic potential on 16S rRNA sequencing data of selected microbial metabolic pathways provides important insights into the functional dimension of vaginal dysbiosis. The enrichment of adaptive systems, such as the \u003cem\u003etwo-component system\u003c/em\u003e in samples with increased abundance of \u003cem\u003eL. iners\u003c/em\u003e (at the expense of \u003cem\u003eL. crispatus\u003c/em\u003e), suggests that potential pathogens may exploit these mechanisms to persist and thrive in the vaginal environment, possibly utilizing these systems to adapt to changing conditions within the vaginal environment to enhance their survival and virulence [56]. While \u003cem\u003eL. crispatus\u003c/em\u003e is widely considered a marker of vaginal health due to its ability to maintain a stable environment and inhibition of \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eCandida\u003c/em\u003e spp. [57], [58], \u003cem\u003eL. iners\u003c/em\u003e is frequently associated with transitional or dysbiotic states, and its role remains under investigation [4], [59], [60]. These findings suggest that metabolic potential might serve as an early indicator of community instability even when taxonomic composition appears stable [5], [49], [61], [62], [63].\u003c/p\u003e \u003cp\u003eIn the woman W1, who exhibited elevated levels of \u003cem\u003eG. vaginalis\u003c/em\u003e, species-level analysis revealed \u003cem\u003eL. jensenii\u003c/em\u003e as the dominant \u003cem\u003eLactobacillus\u003c/em\u003e species rather than \u003cem\u003eL. crispatus\u003c/em\u003e. While \u003cem\u003eL. jensenii\u003c/em\u003e is generally considered a health-associated species, its protective capacity may differ from that of \u003cem\u003eL. crispatus\u003c/em\u003e. Previous studies suggest that \u003cem\u003eL. jensenii\u003c/em\u003e produces lower amounts of lactic acid and hydrogen peroxide compared to \u003cem\u003eL. crispatus\u003c/em\u003e, potentially resulting in a less acidic vaginal environment and reduced antimicrobial activity [57], [64], [65]. This could facilitate the persistence of opportunistic pathogens such as \u003cem\u003eG. vaginalis\u003c/em\u003e, contributing to a state of suboptimal vaginal health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMicroscopic fungi in vaginal and fecal samples\u003c/h2\u003e \u003cp\u003eOur study extends current knowledge by incorporating fungal community analysis through ITS sequencing. Vaginal swabs were characterized by \u003cem\u003eC. albicans\u003c/em\u003e, \u003cem\u003eN. glabratus\u003c/em\u003e, and \u003cem\u003eP. kudriavzevii\u003c/em\u003e, while fecal samples exhibited a broader fungal spectrum, including environmental and food-associated taxa such as \u003cem\u003eS. uvarum\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e spp. \u003cem\u003eC. albicans\u003c/em\u003e remains the leading cause of vulvovaginal candidiasis, with pathogenicity linked to hyphal formation, biofilm development, and phenotypic switching [17], [18], [66].\u003c/p\u003e \u003cp\u003eThe results of the analysis of the vaginal microbiome in participant W5 are particularly interesting. Although the taxonomic analysis at the genus level suggested a typical \u003cem\u003eLactobacillus\u003c/em\u003e-dominated profile, species-level identification revealed \u003cem\u003eL. iners\u003c/em\u003e as the predominant species, accompanied by markedly higher fungal load of \u003cem\u003eN. glabratus\u003c/em\u003e on the mycobial analysis and a distinct metabolic signature predicted from 16S rRNA sequencing data.\u003c/p\u003e \u003cp\u003e \u003cem\u003eN. glabratus\u003c/em\u003e is an opportunistic yeast that has gained clinical importance due to its ability to persist in the vaginal environment and its intrinsic tolerance to azole antifungals. Unlike \u003cem\u003eC. albicans\u003c/em\u003e, \u003cem\u003eN. glabratus\u003c/em\u003e does not form true hyphae, which limits its invasive potential but enhances its capacity for biofilm formation and survival under stress conditions [67], [68]. Its growth is favored in environments with reduced acidity, which can occur during dysbiosis or following shifts in the \u003cem\u003eLactobacillus\u003c/em\u003e species composition (note that \u003cem\u003eL. crispatus\u003c/em\u003e is known for its ability to reduce pH). The dominance of \u003cem\u003eL. iners\u003c/em\u003e in this sample highlights the limitations of the genus-level analysis for clinical interpretation. The predicted enrichment of core metabolic pathways and pyruvate metabolism supports its association with transitional or dysbiotic states. If oxidative stress is present (e.g., inflammation or immune response), \u003cem\u003eL. iners\u003c/em\u003e may redirect pyruvate toward acetate instead of lactate, creating a less acidic environment that can facilitate pathogen colonization. \u003cem\u003eN. glabratus\u003c/em\u003e is a facultative anaerobic yeast and grows better in conditions with higher pH, that is, less acidic. The higher fungal load in both quantification and sequencing depth compared to other participants indicates a persistent infection with \u003cem\u003eN. glabratus\u003c/em\u003e throughout the study period. Interestingly, the same metabolic patterns were found in another woman (W4) who showed the predominance of both \u003cem\u003eL. crispatus\u003c/em\u003e and \u003cem\u003eL. iners\u003c/em\u003e at different periods. The PRMT-based metabolic profiles (determined at the genus-level) in the periods dominated by these two bacteria were still distinct, with those dominated by \u003cem\u003eL. iners\u003c/em\u003e being markedly similar to those detected in woman W5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations among relative abundances/presence of vaginal bacteria and yeasts with fecal bacteria\u003c/h2\u003e \u003cp\u003eThe correlations observed between vaginal yeasts and both vaginal and fecal bacterial communities suggest a tightly interconnected relationship that may be facilitated by the potential transfer of bacteria, yeasts, and their metabolites across the rectal tract. This also suggests a bidirectional relationship between the mycobiome and bacteriome, spanning both the vaginal and gut environments.\u003c/p\u003e \u003cp\u003eThe presence of vaginal yeasts was positively associated with the relative abundance of \u003cem\u003eStreptococcus\u003c/em\u003e in fecal samples, indicating the possible existence of broader dysbiotic patterns along the gut\u0026ndash;urogenital tract. Within the vaginal environment, yeast presence correlated positively with \u003cem\u003eL. iners\u003c/em\u003e and negatively with \u003cem\u003eL. crispatus\u003c/em\u003e. This pattern is consistent with the characteristics of these species, where \u003cem\u003eL. iners\u003c/em\u003e often dominate in transitional or less stable vaginal states, whereas \u003cem\u003eL. crispatus\u003c/em\u003e is typically linked to a stable, protective, and anti‑yeast vaginal microbiome. The associations involving \u003cem\u003eL. jensenii\u003c/em\u003e positively correlated with \u003cem\u003eBifidobacterium\u003c/em\u003e and negatively with \u003cem\u003eDialister\u003c/em\u003e may indicate broader interactions between anti‑inflammatory and pro‑inflammatory bacterial taxa.\u003c/p\u003e \u003cp\u003eOverall, these findings highlight the potential systemic nature of bacteria\u0026ndash;yeast interactions and emphasize the importance of studying vaginal and gut microbial communities as interconnected ecosystems rather than isolated compartments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and strengths\u003c/h2\u003e \u003cp\u003eOne of the limitations of our study was that the pooling of samples for mycobiome analysis made it impossible to track dynamics during the menstrual cycle; however, the results of microbiological examination showed that the composition of vaginal samples was very similar throughout the observation period.\u003c/p\u003e \u003cp\u003eOur pilot study was also limited by its small sample size. However, the longitudinal design, collection of two types of matrices, and analysis of both the bacteriome and mycobiome have enabled us to expand our knowledge of the female microbiome during the menstrual cycle. The sample size was limiting especially for the analysis of the microbiome in relation to exposure and behavioral data. Our analysis of exposure and behavioral variables revealed that most factors, including recent sexual activity, use of lubricants or condoms, and probiotic intake, had minimal impact on vaginal microbial diversity and Lactobacillaceae dominance. In our study, reduced relative abundance of family Lactobacillaceae was associated with an increased presence of potential pathogens linked to BV, such as \u003cem\u003eGardnerella vaginalis\u003c/em\u003e and the genus \u003cem\u003eStreptococcus\u003c/em\u003e [11], [12].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings confirm that anatomical site and interindividual variation are the primary determinants of microbiome composition in reproductively healthy women, with phases of menstrual cycle playing only a minor role. The stability of the vaginal microbiome across phases of menstrual cycle reflects its resilience and supports its potential protective role against dysbiosis and infection. The observed metabolic differences between \u003cem\u003eL. crispatus, L. iners, L. jensenii\u003c/em\u003e, and \u003cem\u003eG. vaginalis\u003c/em\u003e-dominated communities highlight the importance of functional profiling for early detection of instability. Finally, the inclusion of mycobiome analysis reveals an additional layer of complexity in host-microbe interactions, emphasizing the need to consider fungal communities in studies of women\u0026rsquo;s health.\u003c/p\u003e \u003cp\u003eConversely, the observed cyclical variation in fecal bacteriome diversity may influence metabolic and immune processes, suggesting a need for further research into systemic effects of hormonal cycles. Importantly, our correlation analyses highlight a deeper level of interconnection between body sites by revealing that the presence of vaginal yeasts is linked to shifts in both vaginal and fecal bacterial communities. These patterns, together with the possibility of microbial and metabolite transfer across the rectal tract, underscore a dynamic interplay between the bacteria and microscopic fungi that extends beyond local microbial environments. Although this is only a pilot study the results of which need to be verified on a larger sample, the results indicate that when investigating female fecal microbiome, the phase of menstrual cycle should be considered.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003e Heatmap of the 25 most abundant bacterial genera across all samples. The heatmap displays the log-transformed relative abundances of the top 25 bacterial genera across all vaginal and stool samples collected over three menstrual cycles.\u0026nbsp;Squares highlighted by framing indicate taxa reaching a relative abundance greater than 20% in the corresponding sample. Grey squares indicate taxa that were not detected in the corresponding sample. W1, woman 1; C1, menstrual cycle 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1.\u0026nbsp;\u003c/strong\u003eIllumina primer sequences used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2.\u003c/strong\u003e ITC2_C primers used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3.\u0026nbsp;\u003c/strong\u003eResults of statistical testing for the top 25 most abundant taxa in both vaginal and fecal samples, comparing their relative abundances within each phase across the three consecutive menstrual cycles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4.\u0026nbsp;\u003c/strong\u003eResults of statistical testing for the top 25 most abundant taxa on the median value of the three measurements for each phase of the menstrual cycle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing data were uploaded to the European Nucleotide Archive under accession number PRJEB108971.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was carried out with the support of RECETOX Research Infrastructure\u0026nbsp;(ID LM2023069). This publication was supported from the European Union\u0026rsquo;s Horizon 2020 Research and Innovation Programme under grant agreement No 857560. This publication reflects only the author\u0026rsquo;s view and the European Commission is not responsible for any use that may be made of the information it contains. Authors also thank to project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632) financed by the Ministry of Education, Youth and Sports for supportive background and MH CZ - DRO FNBr 65269705 financed by Czech ministry of Health and by project provided by University Hospital Brno (SUp 2/26 NS: 8734). Computational resources were provided by the e-INFRA CZ project (ID:90140), supported by the Ministry of Education, Youth and Sports of the Czech Republic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBZ: Bioinformatic Analysis, Statistical Analysis, Visualization, Writing \u0026ndash; Original Draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJH: Writing \u0026ndash; Revision.\u003c/p\u003e\n\u003cp\u003eOT: Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePB: Methodology.\u003c/p\u003e\n\u003cp\u003ePH: Methodology.\u003c/p\u003e\n\u003cp\u003eLM: Writing \u0026ndash; Revision.\u003c/p\u003e\n\u003cp\u003eMJ: Writing \u0026ndash; Revision.\u003c/p\u003e\n\u003cp\u003eFR: Methodology, Investigation, Writing \u0026ndash; Revision.\u003c/p\u003e\n\u003cp\u003ePBL: Conceptualization, Methodology, Supervision, Writing \u0026ndash; Original Draft, Funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Dr. Jaroslav Janosek for his valuable comments and all participants for enrollment. We are also thankful to our colleagues from the Environmental Genomics research Group and RECETOX for their support in samples and data processing. Sequencing was carried out in the laboratories of the Institute of Applied Biotechnologies a.s.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eUse of AI statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have used generative artificial intelligence, specifically Microsoft Copilot only to improve R scripts for handling data formatting and generation of figures.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eG. Tachedjian, M. Aldunate, C. S. Bradshaw, and R. A. Cone, \u0026ldquo;The role of lactic acid production by probiotic Lactobacillus species in vaginal health,\u0026rdquo; \u003cem\u003eRes. Microbiol.\u003c/em\u003e, vol. 168, no. 9\u0026ndash;10, pp. 782\u0026ndash;792, Nov. 2017, doi: 10.1016/j.resmic.2017.04.001.\u003c/li\u003e\n\u003cli\u003eP. 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Silva, \u0026ldquo;Environmental pH modulates biofilm formation and matrix composition in \u003cem\u003eCandida albicans\u003c/em\u003e and \u003cem\u003eCandida glabrata\u003c/em\u003e,\u0026rdquo; \u003cem\u003eBiofouling\u003c/em\u003e, vol. 36, no. 5, pp. 621\u0026ndash;630, May 2020, doi: 10.1080/08927014.2020.1793963.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"b0a0f077-6e14-4c11-a182-cb37a273419e","identifier":"10.13039/100010661","name":"Horizon 2020 Framework Programme","awardNumber":"857560","order_by":0},{"identity":"e4ebfbb7-9a1f-4e36-997f-8df679100e23","identifier":"10.13039/501100001823","name":"Ministerstvo Školství, Mládeže a Tělovýchovy","awardNumber":"CZ.02.1.01/0.0/0.0/17_043/0009632","order_by":1},{"identity":"c69a49f4-f343-4090-b7e0-f2b46378d9dc","identifier":"10.13039/501100003243","name":"Ministerstvo Zdravotnictví Ceské Republiky","awardNumber":"DRO FNBr 65269705","order_by":2},{"identity":"189cb3d1-2ab9-4390-8e72-5df51690b413","identifier":"10.13039/501100001823","name":"Ministerstvo Školství, Mládeže a Tělovýchovy","awardNumber":"90140","order_by":3},{"identity":"b8921fb4-d3a5-4f92-b03b-532a7ab082ef","identifier":"10.13039/501100001823","name":"Ministerstvo Školství, Mládeže a Tělovýchovy","awardNumber":"LM2023069","order_by":4}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Masaryk University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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":"Menstrual Cycle, Ovulation, Microbiota, Mycobiome, Lactobacillus, Candida, Metabolic Networks and Pathways","lastPublishedDoi":"10.21203/rs.3.rs-9050950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9050950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHormonal and endometrial fluctuations across the menstrual cycle may influence both vaginal and fecal microbiomes. Since no longitudinal study examining both matrices together in this context exists, our study aimed to evaluate microbiome changes in three phases of the menstrual cycle and to investigate correlations between bacteriomes and mycobiomes in vaginal and fecal samples.\u003c/p\u003e \u003cp\u003eOver the course of three consecutive menstrual cycles, vaginal and stool swabs were self-collected in the follicular, ovulatory, and luteal phases from eight parous women of reproductive age who had regular menstrual cycle. Microscopic fungi in all vaginal samples were cultured and identified by mass spectrometry. DNA from 72 vaginal and 72 fecal samples were analyzed using quantitative PCRs, 16S and ITS rRNA amplicon sequencing for bacteriome and mycobiome profiling. Metabolic pathways were analyzed with a focus on the members of the Lactobacillaceae family.\u003c/p\u003e \u003cp\u003eBacteriome diversity in vaginal samples remained stable across studied months and individual phases, while changes in bacteriome alpha diversity (Shannon index) across phases were observed in fecal samples, with the highest diversity observed during the luteal phase. As expected, the genus \u003cem\u003eLactobacillus\u003c/em\u003e (mostly \u003cem\u003eL. crispatus\u003c/em\u003e) predominated in the vaginal samples. Furthermore, different patterns of the predicted metabolic potential were observed in vaginal samples dominated by \u003cem\u003eLactobacillus iners\u003c/em\u003e or \u003cem\u003eGardnerella vaginalis\u003c/em\u003e with \u003cem\u003eLactobacillus jensenii\u003c/em\u003e, compared to the profile dominated by \u003cem\u003eL. crispatus\u003c/em\u003e. Yeasts, such as \u003cem\u003eCandida albicans\u003c/em\u003e, \u003cem\u003eNakaseomyces glabratus\u003c/em\u003e, and \u003cem\u003ePichia kudriavzevii\u003c/em\u003e, were found in some vaginal samples. The presence of vaginal yeasts correlated with relative abundances of \u003cem\u003eL. crispatus\u003c/em\u003e (negatively) and \u003cem\u003eL. iners\u003c/em\u003e (positively) in vaginal samples, and with genus \u003cem\u003eStreprococcus\u003c/em\u003e in stool samples (positively). Relative abundances of \u003cem\u003eL. jensenii\u003c/em\u003e in vaginal samples correlated with genera \u003cem\u003eBifidobacterium\u003c/em\u003e (negatively) and \u003cem\u003eDialister\u003c/em\u003e (positively) in stool samples.\u003c/p\u003e \u003cp\u003eThe results indicate that when investigating female fecal microbiome, the phase of menstrual cycle should be considered. Some strong correlations between the relative abundance/presence of vaginal lactobacilli and yeasts and fecal bacteria were found, suggesting a possible link between the composition of microbial communities of these two anatomical sites.\u003c/p\u003e","manuscriptTitle":"Longitudinal profiling and correlations of vaginal and fecal microbiomes throughout the menstrual cycle: A pilot prospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 06:28:56","doi":"10.21203/rs.3.rs-9050950/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"2bd501ca-a465-4934-871b-0c242e28e536","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64058745,"name":"Sexual \u0026 Reproductive Medicine"}],"tags":[],"updatedAt":"2026-03-09T06:28:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 06:28:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9050950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9050950","identity":"rs-9050950","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}