Skip to main content

Long-term stability of the urogenital microbiota of asymptomatic European women



To date, information on healthy female urinary microbiota is available mostly at genus level and at one time point. However, profound species-level characterization of healthy urinary microbiome and its stability over time are essential for further correct interpretation of its role in healthy urogenital tract. In this study, we investigated female urogenital microbiome (FUM) at two timepoints (within 2.5-year interval) in young asymptomatic European women. We used culturomics with accurate isolates’ identification (MALDI-TOF MS and gene markers sequencing) to understand species stability within healthy FUM.


Extended culturomics of voided midstream urine sample pairs revealed a mean Shannon diversity index of 1.25 and mean of 19 species/sample (range 5–39 species; total of 115 species; 1830 isolates). High overall species variability between individuals was captured by beta diversity and a variety of community structure types, with the largest cluster characterized by Lactobacillus crispatus, often in combination with Gardnerella vaginalis or Gardnerella genomospecies 3. Significant FUM composition differences, related to Finegoldia magna and Streptococcus anginosus, according to smoking status were found.

A high species variability within individuals (Shannon index SD > 0.5 in 7 out of 10 sample pairs) with a mean of 29% of shared species (range 9.1–41.7%) was observed. Moreover, 4 out of 10 sample pairs clustered in the same community structure type. The stable FUM sample pairs presented high abundance of Lactobacillus crispatus, Streptococcus agalactiae or Lactobacillus paragasseri and Bifidobacterium spp.. Moreover, Gardnerella vaginalis, Gardnerella genomospecies 3 or Gardnerella swidsinskii were often maintained within individuals in high abundance.


Shift in species composition at two distant timepoints was frequently observed among urogenital microbiome of European asymptomatic women. This suggests possible interchange of particular species in healthy FUM and the existence of multiple health-associated FUM compositions in certain individuals.

Additionally, we provided additional evidence on resilience of particular bacterial communities and identified certain species more prone to persist in urogenital tract.

This study revealed important details on the FUM composition complexity relevant for studies aiming to understand microbiota role in the urogenital tract health and for identification of eubiotic and dysbiotic FUM.


In the recent years, novel high-throughput culture- and DNA-based studies revealed the existence of a microbial community inhabiting the human lower urinary tract [1,2,3,4,5,6,7,8]. The majority of available observations have been made in female urogenital microbiota (FUM) composition under disease state and, simultaneously, data originated from asymptomatic controls provided a broad overview at high taxonomic levels on healthy FUM [2, 6, 9,10,11].

To date, a set of microbiota profiles based on dominant taxa, with interpersonal differences in bacterial load, diversity and abundance of specific bacteria, has been reported. Lactobacillus, Gardnerella and Streptococcus genera have been often highlighted as most prevalent healthy FUM members, in combinations with other genera such as Staphylococcus, Corynebacterium or Escherichia [2, 12,13,14,15,16]. A few studies indicated species such as Lactobacillus crispatus, Lactobacillus jensenii, Gardnerella vaginalis or Streptococcus anginosus, identified by MALDI-TOF MS analysis, as the most prevalent within healthy FUM [2, 12, 16, 17].

Since it is widely recognized in other body niches that microbiota dysbiosis, i.e., significant change in microbiota composition, may contribute to disease development [18, 19], it is important to assess the scale of microbiota compositional shifts occurring naturally in healthy urogenital tract and evaluate resilience of urogenital microbiota. To date, three-months daily assessment of female urinary microbiota demonstrated that it can be both dynamic and resilient. Moreover, changes in urinary microbiota composition may occur daily and certain shifts are associated with particular physiological or lifestyle factors, such as increased detection of Streptococcus and Staphylococcus genus associated with vaginal intercourse, or increased detection of e.g., Corynebacterium and Actinomyces during menstruation [14].

Therefore, fundamental knowledge on urogenital microbiota compositional stability remains incomplete and needs to be enlarged by long-term studies, addressing adequately the species shifts occurring in the urogenital tract and preferably eliminating factors known to alter microbiota structure.

To evaluate compositional stability of healthy FUM at two timepoints within a long period of time (2.5-year interval), we performed a comprehensive culturomic-based analysis (extended number of characterized isolates and improved methodologies for bacterial identification) of voided midstream urine samples of ten reproductive-age asymptomatic women. To the best of our knowledge, this is the first study assessing long-term FUM compositional stability at two distant timepoints in urogenital tract of asymptomatic women.


Cohort overview

Ten asymptomatic reproductive-age women (24–40 years old) provided voided midstream urine samples (n = 20) at two time points within the 2.5-year interval. All participants were residents of Portugal, declared to have a balanced diet, and reported good or very good general health conditions according to their individual interpretation. None of the participants had symptoms or discomfort associated with their urogenital tract at either sampling time. Although some participants declared to have had UTI in the past, none of them reported to suffer from recurrent UTIs. Additionally, 2 participants acquired UTI (U7, U23) in the time between first and second sampling. One participant resigned from hormonal contraception (U4) in the interval between samplings. Three individuals reported themselves as active smokers (U9, U15, U26). Detailed demographic information about participants at the first and second sampling time is provided in Table 1.

Table 1 Demographic characteristics of participants

Culturomic analysis overview

The bacterial load varied from 10^4 to 10^8 CFU/ml with maximum difference of 10^2 CFU/ml for sample pair (4 out of 10 sample pairs). A range of 17–321 (mean = 103, median = 63) isolates per sample was characterized. Identification of 1830 bacterial isolates resulted in detection of 5 phyla, 48 genera and 115 species. Overall, identified taxa distribution at phylum level was characterized by dominance of the Firmicutes (50–51% of total species for first and second sampling, respectively) and Actinobacteria (40%; 30%), and less prevalent Proteobacteria (6%; 11%), Bacteroidetes (3%; 3%) and Fusobacteria (1%; 2%). A list of species detected in each participant during first and second sampling can be found in Additional file 1: Table S1.

Diversity of healthy FUM over time

Overall, alpha diversity represented by mean Shannon index was 1.25 (standard deviation = 0.79; SD), species richness varied within range of 5 to 39 species/sample (mean = 19, SD = 8) and species evenness (Pielou’s evenness index) varied within range of 0.0002 to 0.29 per sample (mean = 0.18, SD = 0.1). All values of species richness, evenness and Shannon index per each sample are presented in Additional file 1: Table S2. Species richness for sample pairs differed in a range of 2 to 18 species. Shannon index SD for sample pairs was ranging from 0.12 to 1.44. In 7 out of 10 sample pairs Shannon index SD was higher than 0.5. Graphic representation of alpha diversity measures is presented in Fig. 1.

Fig. 1

Alpha diversity among samples measured by observed number of species and Shannon index

Sample pairs presented a range of 1–12 (median of 10) species in common. Percentage of shared species within individual over time was in a range of 9.1–41.7% (mean = 29%), with changes in their relative abundance (Fig. 2). Species observed in both samples of at least one participant, corresponded to 38 out of 115 species detected. Those included prevalent Staphylococcus epidermidis, Micrococcus luteus, Streptococcus anginosus and Staphylococcus haemolyticus (in more than 5 sample pairs) mostly present in low relative abundance. Additionally, Lactobacillus crispatus, Gardnerella vaginalis, Gardnerella swidsinskii, Gardnerella genomospecies 3 and Streptococcus agalactiae were among shared species however usually present in high relative abundance (range of 29–44% average relative abundance).

Fig. 2

Summarized relative abundance (%) of shared species in each sample. Sample pairs are ordered next to each other

Beta diversity is presented with Bray-Curtis dissimilarity matrix (Fig. 3a) and two-dimension non-metric ordination (Fig. 3b). Most samples between individuals (6/10) were different based on Bray-Curtis dissimilarity > 0.5. In NMDS ordination (stress value 0.2) U26 sample pair was observed as the more dissimilar pair and was previously characterized by particularly low species richness. ANOSIM test revealed statistically significant differences between bacterial communities and smoking status of the individuals (R = 0.25, p = 0.03). Multilevel pattern analysis identified 2 species associated with smoking status and FUM variance, namely Finegoldia magna (p < 0.05) and Streptococcus anginosus (p < 0.05). Remaining factors tested did not show statistically significant microbiota composition differences.

Fig. 3

Beta diversity among samples. a Heatmap representing Bray-Curtis dissimilarity matrix between the samples. b Relationship between samples presented by 2-dimension NMDS ordination based on Bray-Curtis distance matrix, with 0.2 stress value

Healthy FUM community structure types over time

FUM structure types identified in asymptomatic women are presented in Fig. 4. Ten community structure types (dendrogram representing samples hierarchical clustering based on species level identification is available in Additional file 2: Fig. S1) were identified. The largest cluster (6 out of 20 samples) was characterized by Lactobacillus crispatus, often in combination with Gardnerella spp. namely, Gardnerella vaginalis or Gardnerella genomospecies 3. The other more common clusters presented abundant Streptococcus agalactiae or abundant Bifidobacterium spp. and Lactobacillus paragasseri, with other Gram-positive bacteria in lower abundance. Summary of clusters with different bacterial combinations characterizing community structure types are presented in Table 2.

Fig. 4

Heatmap based on abundance (%) of species detected at least once in abundance more than 1%. Species are ordered in decreasing prevalence. Dendrogram presents hierarchical clustering of microbiota profiles into community structure types, based on 0.8 cutoff. Colorful bar below the dendrogram stands for different community structure types

Table 2 Summary of community structure types detected within healthy FUM

Gardnerella vaginalis and the recently described Gardnerella species (Gardnerella swidsinskii or Gardnerella genomospecies 3) were observed in 5 individuals, usually with single species per individual (4/5) and Gardnerella vaginalis was the more prevalent one. Moreover, the recently described Lactobacillus mulieris, originally isolated from other cohort of our FUM study [20], was also identified in one individual (U26b). Furthermore, two putatively new species close to Limosilactobacillus vaginalis were also depicted in 2 individuals (U9a and U11a) (data not shown).

Different community structure types were observed within 6 out of 10 sample pairs, with changes related to genus or species presence and abundance [e.g., Lactobacillus jensennii, Staphylococcus haemolyticus, Staphylococcus epidermidis type (U12a) converted to Lactobacillus crispatus type (U12b) or Gardnerella swidsinskii, Atopobium vaginae and Dialister microaerophilus type (U15a) converted to Gardnerella vaginalis, Bifidobacterium spp., Cutibacterium avidum community structure type (U15b)]. Noteworthy, an individual with highly abundant Enterobacteriaceae members (U26) presented a shift in the community structure type (Citrobacter koseri to Escherichia coli) and shared just one species, the Lactobacillus jensenii.

Stable community structure types within individuals were observed in 4 (U3, U7, U11, U23) out of 10 individuals and were represented by Lactobacillus crispatus, Bifidobacterium spp. with Lactobacillus paragasseri or Streptococcus agalactiae, in combination with other Gram-positive bacteria.

Interestingly, two of those sample pairs (U7 and U23) are from individuals that acquired UTI, followed by antibiotic treatment, in the interval between samplings. Maintenance of 9 species (23.7%) including Lactobacillus paragasseri was observed in U7 sample pair, and 10 species (41.7%), including Lactobacillus crispatus and Gardnerella genomospecies 3 was observed in U23 sample pair, despite changes in their relative abundance.

Additional analysis based on genus level revealed higher stability, with 6 out of 10 individuals comprising sample pairs in the same clusters. Overall, the highest number of samples belonged to the cluster represented by Lactobacillus genus, followed by clusters characterized by abundant Gardnerella or Streptococcus genera. Heatmap and dendrogram representing hierarchical clustering at genus level is available in Additional file 2: Fig. S2 and Fig. S3, respectively.


In this study, using a comprehensive and extended culturomic approach, we enlarged the knowledge on diversity of FUM and its bacterial community structures in asymptomatic individuals. We also demonstrated FUM stability in two timepoints within long period of time.

Most of FUM studies are based on genus level classification and on most dominant taxa [12,13,14,15,16], while particular functional characteristics are often species specific, e.g., antimicrobials or metabolites production [21, 22].

In our study, due to the higher number of morphotypes characterized per sample and the higher taxonomic resolution conferred by genotypic markers used (e.g, pheS), we captured a slightly higher amount of species, however reflecting a similar diversity as previous reports [2, 12]. For instance, Gardnerella swidsinskii and Gardnerella genomospecies 3 were here identified for the first time within urogenital microbiota of asymptomatic individuals, in addition to Gardnerella vaginalis, suggesting their frequent occurrence in healthy FUM. Moreover, higher species diversity within Lactobacillaceae was captured with the recently described Lactobacillus mulieris infrequently observed among the samples tested [20].

Although alpha diversity measures (mean Shannon index < 1.5) suggest that FUM is less diverse than other human body niches [17], we observed a high overall species variability and diverse community structure types. Hierarchical community clustering based on Bray-Curtis dissimilarity matrix enabled to capture various community structure types based on bacterial species combinations. The largest cluster was characterized by the commonly described Lactobacillus crispatus, often in combination with Gardnerella vaginalis or Gardnerella genomospecies 3. Gardnerella species were also observed in other community structure types, usually with only one species present in individual FUM, confirming previously reported high occurrence of this genus in urinary microbiota [16, 17]. The remaining community structure types were characterized by many diverse species, including species commonly associated with UTI.

Of interest, FUM composition differences, related to Finegoldia magna and Streptococcus anginosus, according to smoking status were observed, requiring further validation. Those species were previously associated with urinary symptoms presence and/or severity [17, 23].

Within individuals, FUM changes were frequently detected at two distant timepoints (e.g., Gardnerella swidsinskii, Atopobium vaginae and Dialister microaerophilus community type converted to Gardnerella vaginalis, Bifidobacterium spp., Cutibacterium avidum type). This data suggests, the possibility of interchange between certain bacterial groups that might share common metabolic functions. Additionally, few communities that maintained their composition at two timepoints were detected and characterized by combinations of Lactobacillus crispatus, Bifidobacterium spp. with Lactobacillus paragasseri or Streptoccocus agalactiae. Further evidence on the resilience of those communities is the maintenance of Lactobacillus crispatus or Bifidobacterium spp. with Lactobacillus paragasseri community structure type in women after antibiotic treatment for a UTI. Studying short-term FUM dynamics, Price et al. also reported resilience of lower urinary tract microbiota in communities with dominance of e.g., Lactobacillus or Lactobacillus and Gardnerella combination [14]. Similarly to their findings, at the genus level, changes in relative abundance of Lactobacillus, Gardnerella or Streptococcus were observed in three individuals, leading to change in community structure type [14].

Moreover, maintenance of Gardnerella species, Lactobacillus gasseri and Lactobacillus jensenii was also observed in certain sample pairs, for which different FUM composition was observed at two time points tested. The protective role of particular strains belonging to Lactobacillus jensenii [24, 25], can possibly contribute to the health maintenance of an individual with high abundance of Enterobacteriaceae, including an uropathogenic ST131 Escherichia coli (UPEC) according to its gene content [26] (unpublished data). This data also highlights the relevance of strain level characterization to understand FUM role in health and disease, as previously noticed [27, 28].

It is notable that our study focused on the group of well characterized young age European women, contrary to most urinary and urogenital microbiota studies [4, 6, 9,10,11,12, 17]. Moreover, samples were collected only on the 3rd week of women’s menstrual cycle to prevent interferences (e.g., menstrual discharge), which was recently demonstrated by Price et al., [14] as factor influencing microbiota composition. Women providing samples over time were under similar lifestyle and physiological conditions.

Although our cohort included a small number of participants, we believe that detailed description of a small group of women may substantially enlarge knowledge originated from studies with large scale cohorts but less detailed analysis. Additionally, we are aware that our choice of using voided urine samples brings a risk of genital contamination, thus representing more accurately the urogenital microbiome. However, Chen et al., recently reported that prevalence of Lactobacillus and Gardnerella were detected with equal sensitivity in voided urine and urine collected by catheter [15]. Moreover, we are convinced that characterizing microbiota from samples routinely used for screening and diagnosis is highly valuable to facilitate accurate results interpretation and potentiate their use in future diagnostics. Undoubtedly, knowing also genital tract microbiota composition would be highly beneficial to enlarge our understanding of health-associated and pathogenic strains similarity between urinary and vaginal microbiota [28].

Another potential limitation could be the lack of culture-independent DNA-based data, however current diagnostic procedures for urine samples are based on culturing. Moreover, culturomic approach is necessary to assess alive bacterial communities and provide isolates for further characterization.


In this study, we characterized species level stability of the FUM of reproductive age women at two timepoints within a long period of time. We present further evidence that FUM can be dynamic over time and multiple FUM communities may be associated with urogenital tract of some asymptomatic individuals.

Additionally, at 2 sampling points with long time interval, we identified community structure types that seem to indicate persistence of certain species in healthy FUM and provides further evidence on resilient bacterial communities.

We also revealed previously unknown diverse community structure types in healthy FUM. These findings may challenge further identification of eubiotic and dysbiotic states and consequently, diagnostic and treatment strategies for urogenital and urinary tract pathologies.

Moreover, our results support that culturomic analysis with the large-scale isolates characterization is a valuable tool for microbiota diversity description and provides isolates for further analysis.

The future studies focusing on strain level characterization to discern functions contributing to health maintenance in urogenital tract are required. Furthermore, healthy FUM structures characterized by highly abundant species commonly associated with UTI, as here reported, highlight the need for a better understanding of microbiota-host interactions.


Participant information

Ten asymptomatic women (24–40 years old) were recruited to voluntarily participate in the FUM study conducted at the Faculty of Pharmacy, University of Porto, Portugal, at two time points. All women provided informed written consent for participation in the study and fulfilled a detailed questionnaire containing demographic, health-associated and lifestyle information before both sampling times. The study was developed according to the Helsinki Declaration principles and the protocol was submitted and approved by the Ethical Commission of Faculty of Pharmacy, University of Porto. Inclusion criteria at both sampling times were no pregnancy, no antibiotic treatment in the previous month and no current symptoms or diagnosis of urinary tract infection (UTI).

Sample collection

Ten women provided first morning voided midstream urine samples at two time points (total number of samples = 20; sample pairs = 10). Interval between first and second sample collection varied in a range of 11 and 28 months, depending on donors’ availability. Samples were collected in the 3rd week of the menstrual cycle. Participants also provided vaginal swab collected prior to urine sample collection (data not shown). Detailed verbal and written instructions were provided to each woman before sampling. Flyer included written and graphical information on proper wash prior to sampling and vaginal swab collection in order to minimize possible vulvo-vaginal contamination.

Sample analysis

Urine samples were subjected to an extended culturomic analysis within 2 h from sample collection. The extended culturomic protocol is a modification of the expanded quantitative urine culture (EQUC) previously described [2]. Culture included plating of 100 μl of urine into 140 mm diameter-Petri dishes. Protocol included Columbia Agar with 5% sheep blood (Biogerm, Portugal) and chromogenic agar typically used for uropathogens detection (HiCrome UTI, HiMedia, India) supplemented with previously described nutrients i.e., 2% (w/v) gelatin, 0.5% (w/v) yeast extract, 0.1% (w/v) starch, 0.1% (w/v) glucose and 0.1% (v/v) Tween 80 [29, 30]. Incubation at 37 °C for 48 h was performed under aerobic, microaerophilic, and anaerobic atmospheric conditions for Columbia Agar plates, and aerobic and microaerophilic condition for supplemented chromogenic agar (GENbox MICROAER and GENbox ANAER, bioMérieux, France). Besides culture, dipstick test (Combur-Test, Roche) and microscopy examination were performed. Additionally, when during microscopic examination a higher bacterial load was suspected, diluted volume of urine was plated and incubated following the same protocol. Each colony morphotype was quantified to obtain a most approximate number of colony forming units per milliliter (CFU/ml) and up to 5 colonies of the same morphotype were isolated, stored and subjected to identification. Multiple representatives were isolated to ensure reliable microbiota profiling. Many species belonging to genera widely present within urogenital microbiota e.g., Lactobacillus, Staphylococcus, Corynebacterium have very similar or equal colony morphology, thus their diversity may be easily underestimated.

Isolates identification

Firstly, all isolates were subjected to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) VITEK MS system (bioMérieux, France), using in-vitro diagnostic database version 3.0. In case of no identification by MALDI-TOF MS isolates were subjected to 16S rRNA gene sequencing and/or other suitable genotypic biomarkers (pheS, rpoB, recN) [31,32,33,34]. Additionally, due to recent taxonomic reclassification of genus Gardnerella, all isolates identified as Gardnerella vaginalis by MALDI-TOF MS were subjected to cpn60 gene sequencing [35, 36].

Statistical analysis

Continuous and categorical variables referring to participants’ demographic and lifestyle characteristics were interpreted based on descriptive statistics. All community analyses were based on relative abundance (%) calculated as the CFU percentage of identified species from total CFU/ml count. Alpha-diversity (within-samples diversity) represented by the number of observed species and Shannon index (increases when species richness and evenness increase), was performed and visualized using phyloseq package (version 1.30.0) [37] R version 3.6.1 [38]. Pielou’s evenness index was calculated; evenness refers to the distribution of species in terms of relative abundance. Pielou’s index comprises values between 0 and 1, where lower values stand for lower degree of evenness. Figure representing cumulative relative abundance of shared species was created with phyloseq package. Beta-diversity (between-samples diversity) was represented by Bray-Curtis dissimilarity matrix with values comprised between 0 and 1, where 0 states for high similarity and 1 for high dissimilarity, and 2-dimension Non-metric Multi-dimensional Scaling (NMDS) with samples ordination based on dissimilarity matrix. Stress value was measured using vegan (version 2.5.6) [39] package. Heatmap representing Bray-Curtis dissimilarity matrix was generated with lattice package (version 0.20.38) [40]. NMDS plot was performed using phyloseq package. Statistical significance for age, body mass index, smoking status, previous UTI, usage of anti-inflammatory drugs in a week before sampling, hormonal contraceptives usage, previous pregnancies and presence or absence of menstrual cycle was accessed with analysis of similarities (ANOSIM) performed with vegan package, based on Bray-Curtis dissimilarity matrix. ANOSIM analysis result in significance level (p value) and R value where number close to 0 stands for similarity, and close to 1 stand for dissimilarity. Multilevel pattern analysis for identification of bacterial species responsible for community divergence was accessed using indicspecies package (version 1.7.9) [41]. A heatmap including a dendrogram for hierarchical clustering with cutoff value of 0.8 to define the clusters was generated using vegan (version 2.5.6) and gplots (version [42] R packages. Hierarchical clustering was performed using unweighted pair group method with arithmetic mean (UPGMA) based on Bray-Curtis dissimilarity matrix. Additionally, ggplot2 (version 3.2.1) package [43] was used for data visualization.

Availability of data and materials

The raw datasets generated and analyzed during the current study and accession numbers for DNA sequences deposited in NCBI database are available in the GitHub repository (



Female urogenital microbiota


Urinary tract infection


Colony forming units


Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry


Non-metric Multi-dimensional Scaling


Standard deviation


Analysis of similarities


  1. 1.

    Wolfe AJ, Toh E, Shibata N, Rong R, Kenton K, Fitzgerald M, et al. Evidence of uncultivated bacteria in the adult female bladder. J Clin Microbiol. 2012;50:1376–83.

    Article  Google Scholar 

  2. 2.

    Hilt EE, McKinley K, Pearce MM, Rosenfeld AB, Zilliox MJ, Mueller ER, et al. Urine is not sterile: use of enhanced urine culture techniques to detect resident bacterial Flora in the adult female bladder. J Clin Microbiol. 2014;52:871–6.

    Article  Google Scholar 

  3. 3.

    Neugent ML, Hulyalkar NV, Nguyen VH, Zimmern PE, De Nisco NJ. Advances in understanding the human urinary microbiome and its potential role in urinary tract infection. mBio. 2020;11:e00218–20.

  4. 4.

    Karstens L, Asquith M, Davin S, Stauffer P, Fair D, Gregory WT, et al. Does the urinary microbiome play a role in urgency urinary incontinence and its severity? Front Cell Infect Microbiol. 2016;6:78.

    Article  Google Scholar 

  5. 5.

    Karstens L, Asquith M, Caruso V, Rosenbaum JT, Fair DA, Braun J, et al. Community profiling of the urinary microbiota: considerations for low-biomass samples. Nat Rev Urol. 2018;15:735–49.

    Article  Google Scholar 

  6. 6.

    Khasriya R, Sathiananthamoorthy S, Ismail S, Kelsey M, Wilson M, Rohn JL, et al. Spectrum of bacterial colonization associated with urothelial cells from patients with chronic lower urinary tract symptoms. J Clin Microbiol. 2013;51:2054–62.

    Article  Google Scholar 

  7. 7.

    Ackerman AL, Underhill DM. The mycobiome of the human urinary tract: potential roles for fungi in urology. Ann Transl Med. 2017;5:31.

    Article  Google Scholar 

  8. 8.

    Pearce MM, Zilliox MJ, Rosenfeld AB, Thomas-White KJ, Richter HE, Nager CW, et al. The female urinary microbiome in urgency urinary incontinence. Am J Obstet Gynecol. 2015;213:347.e1–11.

    Article  Google Scholar 

  9. 9.

    Wu P, Chen Y, Zhao J, Zhang G, Chen J, Wang J, et al. Urinary microbiome and psychological factors in women with overactive bladder. Front Cell Infect Microbiol. 2017;7:488.

    Article  Google Scholar 

  10. 10.

    Gill K, Kang R, Sathiananthamoorthy S, Khasriya R, Malone-Lee J. A blinded observational cohort study of the microbiological ecology associated with pyuria and overactive bladder symptoms. Int Urogynecol J. 2018;29:1493–500.

    Article  Google Scholar 

  11. 11.

    Curtiss N, Balachandran A, Krska L, Peppiatt-Wildman C, Wildman S, Duckett J. A case controlled study examining the bladder microbiome in women with overactive bladder (OAB) and healthy controls. Eur J Obstet Gynecol Reprod Biol. 2017;214:31–5.

    Article  Google Scholar 

  12. 12.

    Coorevits L, Heytens S, Boelens J, Claeys G. The resident microflora of voided midstream urine of healthy controls: standard versus expanded urine culture protocols. Eur J Clin Microbiol Infect Dis. 2017;36:635–9.

    CAS  Article  Google Scholar 

  13. 13.

    Curtiss N, Balachandran A, Krska L, Peppiatt-Wildman C, Wildman S, Duckett J. Age, menopausal status and the bladder microbiome. Eur J Obstet Gynecol Reprod Biol. 2018;228:126–9.

    Article  Google Scholar 

  14. 14.

    Price TK, Wolff B, Halverson T, Limeira R, Brubaker L, Dong Q, et al. Temporal dynamics of the adult female lower urinary tract microbiota. mBio. 2020.

  15. 15.

    Chen YB, Hochstedler B, Pham TT, Acevedo Alvarez M, Mueller ER, Wolfe AJ. The urethral microbiota - a missing link in the female urinary microbiota. J Urol. 2020.

  16. 16.

    Price TK, Hilt EE, Thomas-White K, Mueller ER, Wolfe AJ, Brubaker L. The urobiome of continent adult women: a cross-sectional study. BJOG. 2020;127:193–201.

    CAS  Article  Google Scholar 

  17. 17.

    Pearce MM, Hilt EE, Rosenfeld AB, Zilliox MJ, Thomas-White K, Fok C, et al. The female urinary microbiome: a comparison of women with and without urgency urinary incontinence. MBio. 2014;5:e01283–14.

    CAS  Article  Google Scholar 

  18. 18.

    Lloyd-Price J, Arze C, Ananthakrishnan AN, Schirmer M, Avila-Pacheco J, Poon TW, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569:655–62.

    CAS  Article  Google Scholar 

  19. 19.

    Wilkins LJ, Monga M, Miller AW. Defining Dysbiosis for a cluster of chronic diseases. Sci Rep. 2019;9:1–10.

    Article  Google Scholar 

  20. 20.

    Rocha J, Botelho J, Ksiezarek M, Perovic SU, Machado M, Carriço JA, et al. Lactobacillus mulieris sp. nov., a new species of Lactobacillus delbrueckii group. Int J Syst Evol Microbiol. 2020;70(3):1522–7.

    CAS  Article  Google Scholar 

  21. 21.

    Edelman SM, Lehti TA, Kainulainen V, Antikainen J, Kylväjä R, Baumann M, et al. Identification of a high-molecular-mass lactobacillus epithelium adhesin (LEA) of Lactobacillus crispatus ST1 that binds to stratified squamous epithelium. Microbiology. 2012;158(Pt 7):1713–22.

    CAS  Article  Google Scholar 

  22. 22.

    Ojala T, Kankainen M, Castro J, Cerca N, Edelman S, Westerlund-Wikström B, et al. Comparative genomics of Lactobacillus crispatus suggests novel mechanisms for the competitive exclusion of Gardnerella vaginalis. BMC Genomics. 2014;15:1070.

    Article  Google Scholar 

  23. 23.

    Fok CS, Gao X, Lin H, Thomas-White KJ, Mueller ER, Wolfe AJ, et al. Urinary symptoms are associated with certain urinary microbes in urogynecologic surgical patients. Int Urogynecol J. 2018;29:1765–71.

    Article  Google Scholar 

  24. 24.

    Petrova MI, Lievens E, Malik S, Imholz N, Lebeer S. Lactobacillus species as biomarkers and agents that can promote various aspects of vaginal health. Front Physiol. 2015;6:81.

    Article  Google Scholar 

  25. 25.

    Sihra N, Goodman A, Zakri R, Sahai A, Malde S. Nonantibiotic prevention and management of recurrent urinary tract infection. Nat Rev Urol. 2018;15:750–76.

    Article  Google Scholar 

  26. 26.

    Spurbeck RR, Dinh PC, Walk ST, Stapleton AE, Hooton TM, Nolan LK, et al. Escherichia coli isolates that carry vat, fyuA, chuA, and yfcV efficiently colonize the urinary tract. Infect Immun. 2012;80:4115–22.

    CAS  Article  Google Scholar 

  27. 27.

    Garretto A, Miller-Ensminger T, Ene A, Merchant Z, Shah A, Gerodias A, et al. Genomic survey of E. coli from the bladders of women with and without lower urinary tract symptoms. Front Microbiol. 2020;11.

  28. 28.

    Thomas-White K, Forster SC, Kumar N, Van Kuiken M, Putonti C, Stares MD, et al. Culturing of female bladder bacteria reveals an interconnected urogenital microbiota. Nat Commun. 2018;9.

  29. 29.

    Alves P, Castro J, Sousa C, Cereija TB, Cerca N. Gardnerella vaginalis outcompetes 29 other bacterial species isolated from patients with bacterial vaginosis, using in an in vitro biofilm formation model. J Infect Dis. 2014;210:593–6.

    Article  Google Scholar 

  30. 30.

    Man JCD, Rogosa M, Sharpe ME. A medium for the cultivation of Lactobacilli. J Appl Bacteriol. 1960;23:130–5.

    Article  Google Scholar 

  31. 31.

    Héritier C, Poirel L, Nordmann P. Genetic and biochemical characterization of a chromosome-encoded carbapenem-hydrolyzing ambler class D beta-lactamase from Shewanella algae. Antimicrob Agents Chemother. 2004;48:1670–5.

    Article  Google Scholar 

  32. 32.

    Naser SM, Thompson FL, Hoste B, Gevers D, Dawyndt P, Vancanneyt M, et al. Application of multilocus sequence analysis (MLSA) for rapid identification of Enterococcus species based on rpoA and pheS genes. Microbiology. 2005;151:2141–50.

    CAS  Article  Google Scholar 

  33. 33.

    Mellmann A, Becker K, von Eiff C, Keckevoet U, Schumann P, Harmsen D. Sequencing and staphylococci identification. Emerg Infect Dis. 2006;12:333–6.

    CAS  Article  Google Scholar 

  34. 34.

    Ribeiro TG, Novais Â, Branquinho R, Machado E, Peixe L. Phylogeny and comparative genomics unveil independent diversification trajectories of qnrB and genetic platforms within particular Citrobacter species. Antimicrob Agents Chemother. 2015;59:5951–8.

    CAS  Article  Google Scholar 

  35. 35.

    Hill JE, Albert AYK, VOGUE Research Group. Resolution and co-occurrence patterns of Gardnerella leopoldii, Gardnerella swidsinskii, Gardnerella piotii and Gardnerella vaginalis within the vaginal microbiome. Infect Immun. 2019.

  36. 36.

    Vaneechoutte M, Guschin A, Van Simaey L, Gansemans Y, Van Nieuwerburgh F, Cools P. Emended description of Gardnerella vaginalis and description of Gardnerella leopoldii sp. nov., Gardnerella piotii sp. nov. and Gardnerella swidsinskii sp. nov., with delineation of 13 genomic species within the genus Gardnerella. Int J Syst Evol Microbiol. 2019;69:679–87.

    CAS  Article  Google Scholar 

  37. 37.

    McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.

    CAS  Article  Google Scholar 

  38. 38.

    R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2018.

    Google Scholar 

  39. 39.

    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: community ecology package; 2019.

    Google Scholar 

  40. 40.

    Sarkar D. Lattice: multivariate data visualization with R. New York: Springer; 2008. ISBN 978-0-387-75968-5

    Google Scholar 

  41. 41.

    De Caceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;

  42. 42.

    Warnes GR, Bolker B, Bonebakker L, Gentleman R, Huber W, Liaw A, et al. Gplots: various R programming tools for plotting data; 2020.

    Google Scholar 

  43. 43.

    Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016. ISBN 978-3-319-24277-4.

    Google Scholar 

Download references


The authors would like to thank all women voluntarily providing the samples and participating in this study and also thank Helena Ramos and Paulo Pinto (Hospital Geral de Santo António, Porto, Portugal) for their technical support with MALDI-TOF MS-based microbial identification. The bioMérieux (Portugal, Lda) provided equipment and material for MALDI-TOF MS analysis, and had no role in the study design, data collection and analysis, decision to publish, or writing of the manuscript. The authors would also like to thank Teresa Gonçalves Ribeiro for the assistance with Gardnerella species identification.


This work received financial support from Applied Molecular Biosciences Unit - UCIBIO, which is financed by national funds from FCT/MCTES [UID/Multi/04378/2019, UIDP/04378/2020 and UIDB/04378/2020]. MK is supported by Fundação para a Ciência e Tecnologia, I.P. - FCT [SFRH/BD/132497/2017]. SUP was supported from project NORTH-01-0145-FEDER-000024, JR received support from ICETA [UID/MULTI/04378/2013] and FG is supported by national funds through FCT in the context of the transitional norm [DL57/2016/CP1346/CT0034]. The funding bodies had no role in the design of the study; collection, analysis and interpretation of data; writing the manuscript.

Author information




MK, SUP, JR, FG and LP designed the study and supervised participant recruitment. MK and SUP processed the samples and collected the raw data. MK and SUP performed isolates identification. MK performed data analysis, interpretation, and visualization. MK wrote the manuscript. All authors contributed significantly to manuscript revision and approved the final version of the manuscript.

Corresponding author

Correspondence to Luísa Peixe.

Ethics declarations

Ethics approval and consent to participate

Approval of the study was obtained from the Faculty of Pharmacy (University of Porto, Porto, Portugal) Ethics Committee. Procedures performed in the study were all in accordance with the ethical standards of the institutional and national research committee, with the 1964 Helsinki Declaration, and its later amendments. All individual participants included in the study had given written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that there are no conflicts of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Species identified in each participant during first and second sampling and their relative abundance (%). Table S2. Number of observed species, Pielou’s evenness index and Shannon index per each sample.

Additional file 2: Figure S1.

Dendrogram representing samples hierarchical clustering based on species level identification. A cutoff value of 0.8 was used to define the clusters (dashed blue line). Figure S2. Heatmap based on abundance (%) of genera detected. Dendrogram presents clustering of microbiota profiles into community structure types, based on 0.8 cutoff. Colorful bar below the dendrogram stands for different community structure types. Figure S3. Dendrogram representing samples hierarchical clustering based on genus level identification.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ksiezarek, M., Ugarcina-Perovic, S., Rocha, J. et al. Long-term stability of the urogenital microbiota of asymptomatic European women. BMC Microbiol 21, 64 (2021).

Download citation


  • Microbiome
  • Culturomics
  • Species diversity
  • Uropathogens
  • Voided midstream urine