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Endometrial microbiota composition is associated with reproductive outcome in infertile patients

Microbiome volume 10, Article number: 1 (2022) Cite this article

Abstract

Background

Previous evidence indicates associations between the female reproductive tract microbiome composition and reproductive outcome in infertile patients undergoing assisted reproduction. We aimed to determine whether the endometrial microbiota composition is associated with reproductive outcomes of live birth, biochemical pregnancy, clinical miscarriage or no pregnancy.

Methods

Here, we present a multicentre prospective observational study using 16S rRNA gene sequencing to analyse endometrial fluid and biopsy samples before embryo transfer in a cohort of 342 infertile patients asymptomatic for infection undergoing assisted reproductive treatments.

Results

A dysbiotic endometrial microbiota profile composed of Atopobium, Bifidobacterium, Chryseobacterium, Gardnerella, Haemophilus, Klebsiella, Neisseria, Staphylococcus and Streptococcus was associated with unsuccessful outcomes. In contrast, Lactobacillus was consistently enriched in patients with live birth outcomes.

Conclusions

Our findings indicate that endometrial microbiota composition before embryo transfer is a useful biomarker to predict reproductive outcome, offering an opportunity to further improve diagnosis and treatment strategies.

Video Abstract.

Introduction

Humans have co-evolved as holobionts with microbial companions including bacteria, viruses, fungi, yeast and archaea [59]. These microbes and their genetic information (the microbiome) are now well-characterised; the Human Microbiome Project has revealed that approximately 9% of the total human microbiome is found in the female reproductive tract [51]. Historically, all microbes were believed to inhabit only the lower portion of the tract; the cervix was considered a perfect barrier between the vagina and the upper genital tract that maintained sterility of the uterine cavity [64]. However, accumulating evidence demonstrates that this tract is an open system with a microbiota continuum that gradually changes from the outer to the inner organs, with decreasing bacterial abundance and increasing bacterial diversity from the vagina to the ovaries [9, 31, 40, 50, 67]. There are reportedly 102–104 fewer bacteria in the uterine cavity than in the vaginal microbiota [9, 41]. Thus, the uterine cavity contains a low-abundance bacterial community, also known as a low-biomass microbiota.

The presence of different microorganisms in the female and male reproductive tracts might influence reproductive function [19, 31, 52]. Recent studies indicate that the chance of becoming pregnant before the start of an in vitro fertilisation (IVF) treatment is stratified based on the vaginal microbiota composition, as women with a low percentage of Lactobacillus in their vaginal sample are less likely to have a successful embryo implantation [32]. Analyses by 16S ribosomal RNA (16S rRNA) gene sequencing of endometrial samples suggest that this microbiome impacts reproductive outcomes in infertile patients. Having a Lactobacillus-dominated microbiota, defined as ≥ 90% or ≥ 80% Lactobacillus spp., is associated with significantly increased implantation, pregnancy, ongoing pregnancy and live birth rates [33, 44]. Lately, 16S rRNA and whole metagenomics sequencing have been used to investigate the endometrial microbiome (EM) in cases of spontaneous clinical miscarriage [45], recurrent miscarriage [22] and at 4 weeks gestation in a pregnancy resulting in a live birth [45]. Interestingly, the EM during early successful pregnancy exhibited no bacterial diversity and higher Lactobacillus abundance [45].

In this prospective multicentre observational study, we investigated the EM composition in 342 infertile patients undergoing assisted reproductive technology (ART) in 13 different centres on 3 continents by analysing endometrial fluid (EF) and endometrial biopsy (EB) samples using 16S rRNA sequencing. The objective of the study was to determine whether EM composition is associated with reproductive outcomes: live birth (LB), biochemical pregnancy (BP), clinical miscarriage (CM) or no pregnancy (NP). Lactobacillus was consistently more abundant than dysbiotic or pathogenic bacteria in women that achieved a pregnancy with a LB. Lactobacillus spp. depletion and increased abundance of specific taxa including Gardnerella, Haemophilus, Klebsiella, Neisseria, Staphylococcus, Streptococcus, Atopobium, Bifidobacterium and Chryseobacterium were associated with NP or CM. The microbiota composition in EF did not fully reflect that in EB, although their association with clinical outcome was consistent.

Methods

Study design and sample size calculation

This was a multicentre prospective observational study analysing the EM of infertile patients with maternal age ≤ 40 years undergoing IVF or ≤ 50 years undergoing ovum donation. The EM was analysed in the same endometrial sample in which endometrial receptivity analysis (ERA) was conducted in a cycle prior to embryo transfer of frozen blastocyst stage embryos (day 5/6). Patients were all assessed in a hormone replacement therapy cycle after 120 h of progesterone administration. The EF sample was aspirated before EB collection for EM analysis by 16S rRNA sequencing. The EB was divided into two pieces for the study of endometrial receptivity and EM analysis. Patients were treated following the standard protocols in each clinic and the embryo was transferred on the day recommended by the ERA test result using the same hormonal protocol as for the diagnostic cycle.

The main objectives of this prospective study were to validate our pilot study of the endometrial microbiome and its impact on assisted reproduction in a larger number of patients [44], and to study the topologic effect of the endometrial microbiome by comparing the microbial populations found in EF versus EB. To achieve these objectives, we relied on the formula described for estimating the difference between two proportions (hypothesis of contrasts in unilateral sense), with implantation, pregnancy and ongoing pregnancy rates expected to be about 20% higher in patients with a normal microbiome than in patients with an altered microbiome (95% CI and 80% of statistical power). To detect this difference, we calculated that we would need to assess 146 patients. To validate and compare the two endometrial sample types, we used the endometrial tissue as the gold standard and the most known and standardised test (ERA). We aimed to assess at least 100 non-receptive patients and 234 receptive patients to obtain a sensitivity and specificity of 90% (estimated using 10 cases in each marginal box of a 2 × 24 table). Because this last calculation requires the greatest number of patients, we added 30% for possible losses, bringing the total target number of patients to 434.

Study population—inclusion and exclusion criteria

From August 2017 to February 2019, 452 participants were recruited from 13 reproductive clinics in Europe, America and Asia. As this was a competitive study, each centre recruited patients and sent their samples to Igenomix Foundation until the target sample size of the study was reached. Inclusion criteria were patients undergoing IVF aged ≤ 40 years or patients undertaking ovum donations aged ≤ 50 years; BMI of 18.5–30 kg/m2 (both inclusive); negative serological tests for human immunodeficiency virus, hepatitis B and C viruses and syphilis; regular menstrual formula (3–4/26–35 days); and > 2 million sperm/mL. Exclusion criteria included carriers of intrauterine devices or patients who took antibiotics in the last 3 months before sample collection (except mandatory prophylactic antibiotic treatment before egg retrieval, which occurred 1 month before sample collection); presence of uncorrected adnexal or uterine pathologies as uterine malformations; patients with severe or uncontrolled bacterial, fungal or viral infections or any illness or medical condition that risks the patient’s safety.

Sample collection

EF and EB were collected following the previously described procedures [60, 65]. Briefly, with the patient in the lithotomy position, the vagina and cervix were cleaned with a dry cotton swab to eliminate mucus or debris. After introduction of the disinfected speculum, a sterile and flexible catheter (the same one used for embryo transfer) (Gynétics, Lommel, Belgium) was introduced into the uterine cavity to aspirate EF, obtaining a volume between 20 and 80 μL. To prevent contamination, any contact with vaginal walls was avoided and suction was stopped at the entrance of the internal cervical os (the opening between the uterus and the cervix) during catheter removal. Then, an EB sample (about 50–70 mg tissue) was obtained with a cannula of Cornier (CCD Laboratories; Paris, France) by scraping the endometrium. Once the EF and EB samples were obtained, the external surfaces of the catheters were cleaned with a sterile gauze and their contents were transferred to cryotubes containing 50 μL and 1.5 mL of RNAlater solution (Qiagen, Hilden, Germany), respectively. Both samples were stored at 4 °C for 4 h, subsequently sent to Igenomix at room temperature and stored at −80 °C until use.

Endometrial receptivity diagnosis

The ERA test (https://www.igenomix.com/genetic-solutions/era-endometrial-receptivity-analysis/) was performed on the human RNA in each obtained EB sample. This test is used to assess the personalised window of implantation of a given patient and determine the optimal time frame for embryo transfer [13, 54, 60].

DNA isolation

Total DNA was isolated from EF samples by performing a pre-digestion step at 37 °C for 30 min with 25 ug/μL lysozyme, 0.12 U/μL lysostaphin, 0.4 U/μL mutanolysin and 1.8% Triton X-100 to degrade the bacterial cell walls. DNA was then extracted with the QIAamp DNA Blood Mini kit (Qiagen) following the manufacturer’s instructions. Finally, the DNA was eluted with 35 μL of nuclease free water and quantified using a photometric technology (Nanodrop, Waltham, MA, USA).

Total DNA was isolated from the EB samples by performing a pre-digestion step for difficult-to-lyse bacteria. For this digestion, 25 mg of tissue was cut into small pieces and treated with proteinase K at 56 °C for 3 h under agitation. Samples were then mixed with ATL buffer and disrupted mechanically in a TissueLyser LT for 5 min at 50 Hz using stainless-steel beads (all acquired from Qiagen). After these pretreatments, bacterial nucleic acids were purified using the DNA tissue programme V7-200-LC of the QIAsymphony (Qiagen) following the manufacturer’s instructions. Finally, the DNA was eluted with 50 μL of nuclease free water and quantified using MultiskanGO (Thermo Scientific, Waltham, MA, USA).

16S ribosomal RNA gene sequencing

The EM profiles were obtained by next-generation sequencing using the Ion 16S metagenomics kit (Thermo Fisher Scientific), which selectively amplifies 7 of the 9 hypervariable regions of the bacterial gene encoding the 16S ribosomal subunit (V2-4-8 and V3-6, 7-9). After amplification of the hypervariable regions with 10 μL of sample (per set of primers) and 30 PCR cycles, the library was prepared starting from 50 ng of the pooled short amplicons using the Ion Plus Fragment Library kit and Ion Xpress Barcode Adaptors following the manufacturer’s instructions. The library concentration was adjusted using the Ion Universal Library Quantitation Kit and QuantStudio 5 Real-Time PCR System. The diluted individual libraries were then pooled for amplification by emersion PCR in the Ion OneTouch 2 System (10 pM) or Ion Chef System (30 pM). Finally, libraries were sequenced on the Ion S5 XL system using the Ion 530 Chip (all acquired from Thermo Fisher Scientific).

To detect eventual contamination, each sequencing run included between two and four blank samples as well as negative and positive PCR controls. The blank samples consisted of an aliquot of the sample preservation buffer (RNA later; Qiagen), whilst positive and negative PCR controls were pure microbial DNA from E. coli (3 ng) and nuclease-free water, respectively (Thermo Fisher Scientific).

Sequencing-based microbiome analysis

The 16S rRNA sequencing results were analysed using the QIIME 2.0 package (https://qiime2.org) and RDP classifier 2.2 for taxonomic assignment along with the greengenes database version 13.8 (http://greengenes.second.genome.com), an update of version 13.5 released to address missing genus and species names. For all processed samples, the RDP classifier was run with the default minimum confidence estimate of 0.5 to record an assignment (i.e. an operational taxonomic unit [OTU]). The results were generated using the default parameters in the original QIIME 2 [7] and RDP [68] methods. Identification and removal of contaminant sequences was conducted using the decontam R Bioconductor package [12], using bacterial prevalence in blank samples for statistical identification of contaminant taxa. Data were transformed to log-ratios using the clr transformation [1] making them symmetric and linearly related, and thus potentially avoiding spurious correlations and sub-compositional incoherencies [8, 24, 49].

Taking into account the quality parameters such as the percentage of empty reads, the dispersion index and the ratio between filtered and mapped reads, samples were classified as detectable or not detectable biomass based on the filtered versus mapped reads ratio threshold of 0.65 in EF and 0.7 in EB samples. For further analysis, only the detectable samples (i.e. samples with filtered versus mapped reads ratios greater than 0.65 and 0.7 in EF and EB, respectively) were included. In each dataset of endometrial 16S rRNA profiles, the abundance of each taxa was analysed, and low-abundance species were removed from further consideration. We retained taxa that either (1) exhibited an abundance of at least 1% in 5% of the samples or (2) exhibited an abundance of at least 0.1% in at least 15% of samples [18]. Taxa that failed to meet both criteria were removed. Genera not colonising humans or associated with kitome contaminants were also removed from the analysis. Specifically, taxa consistently reported to be contaminants in more than 5 of the 11 published references were excluded [4, 23, 25, 26, 29, 34,35,36, 61, 63, 69]. After applying these criteria, 15 and 19 OTUs were evaluated in EF and EB microbiota respectively, with 12 OTUs shared between sample types (Supplementary Table 4).

Correlation network analysis

Pearson matrices for network analysis were generated using the Scipy Stats package (version 1.4.1) and visualised with Networkx (version 2.4), considering taxa with significant Pearson correlation coefficient [37]. To account for both the correlation and significance, each edge was assigned a weight w computed as: abs(corr)—pval, where abs(corr) was the absolute correlation value between each pair of taxa and pval was the corresponding p value. Negative correlations were represented as red edges. Network analysis was performed on each data type (i.e. EF and EB) and for each outcome (i.e. LB, NP, BP and CM).

Taxa reference ranges

The log-ratio-transformed 16S rRNA profiles from LB samples were used to define the reference range for each taxon. A confidence interval of 95% was calculated to establish reference ranges for the Student’s t distribution taxa distribution model [2]. The Scipy Stats package (version 1.4.1) was used for calculations [66].

Taxa contributions to different outcomes were statistically inferred using a two-sided Mann-Whitney U test. The difference between samples outside the range and upper/lower bound reference values was used to assess the role of the taxa in each outcome. Taxa were reported when a significant difference was observed between upper and lower difference distributions. The Scipy Stats package (version 1.4.1) [66] was used for statistical methods and Plotly was used to visualise significant taxa distributions ([27]).

Bayesian inference for reproductive outcome differences

Differences between Lactobacillus and other reproductive tract taxa were modelled using Bayesian inference following a normal distribution with mean μoutcome and dispersion σoutcomefor each reproductive outcome,

$${Y}_{ij}\sim N\left({\mu}_{outcome},{\sigma}_{outcome}\right).$$

Priors selected to estimate parameters were normal distribution for μoutcome centred on the mean difference between Lactobacillus and other taxa in the dataset and a dispersion of 10 (\({\mu}_{otucome}\sim N\left(\overline{x},10\right)\)) and, for σoutcome, vague continuous distribution from 0 to 10 (σoutcome ∼ Uniform(0, 10). Markov chain Monte Carlo sampling for posterior distribution parameter inference and predictive probability analysis were implemented using the PyMC3 Python library [56].

Statistical analysis

All data were analysed on a per-protocol basis. One-way analysis of variance was used to compare non-categorical variables amongst groups by reproductive outcomes. Mean differences and standard deviation or median and interquartile ranges were used when the variables were not homogeneous, as well as the mean differences with 95% CI values. Categorical variables were described by counts (n) and percentages (%), and the Chi-square test and two-sided Fisher’s exact test were used to compare groups by reproductive outcomes with respect to percentages. Multiple-comparison post-hoc correction (Bonferroni) was applied for all pairwise comparisons.

Multinomial logistic regression analysis was conducted to control possible confounding factors, effect modifiers, to demonstrate the homogeneity of the key variables and the absence of bias towards our final endpoint. A multinomial endpoint was included as a dependent variable (LB/NP/BP/CM) and predictors shown in Supplementary Tables 1 and 2. P < 0.05 was considered to be statistically significant. All analyses were conducted using the SPSS 25 software (IBM, MD, USA) and R version 3.6.3 (The CRAN project).

Results

Patient cohort, characteristics and outcomes

A total of 452 patients with infertility undergoing IVF were assessed for eligibility in 13 reproductive clinics in Europe, America and Asia between August 2017 and February 2019 (Fig. 1). Forty-four patients were excluded from the study because they did not meet the inclusion criteria (n = 42) or declined to participate (n = 2). The remaining 408 women were recruited and their EB and EF microbiota composition was assessed by 16S rRNA sequencing. However, 66 patients were lost to follow-up. Of the 342 remaining patients, 198 (57.9%) became pregnant [141 (41.2%) had a LB, 27 (7.9%) had a BP and 28 (8.2%) a CM], whilst 144 (42.1%) did not become pregnant (Supplementary Table 1). Moreover, 2 patients experienced an ectopic pregnancy, but their results were not considered for further comparisons due to the small sample size (Fig. 1). Analysed patients had a mean age of 36 years (range 21–49), and a mean body mass index (BMI) of 23.3 (range 18.5–30.0). The ethnic distribution was Caucasian (57.3%), East Asian (14.0%), Hispanic (11.4%) and others (17.3%). The indications for IVF were advanced maternal age, male factor infertility, unexplained infertility and ovarian pathology. The assessed clinical and embryological variables displayed homogeneity and no bias towards the clinical outcome categories was observed (Supplementary Table 2).

Fig. 1
figure 1

Flowchart of the study population. Of all patients assessed for eligibility (n = 452), 44 were excluded from the analysis and 66 were lost to follow-up. Thus, 342 patients were ultimately included in our assessment of the impact of the endometrial microbiome on pregnancy outcomes. Abbreviations: BMI, body mass index; ERA, endometrial receptivity analysis; ET, embryo transfer; HRT, hormonal replacement therapy

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Endometrial microbiota composition in EF and EB

The EM was profiled in EF from 336 patients and EB for 296 patients, with paired EF–EB results in 290 (84.8%) participants. The mean total sequencing reads per sample was 302,299 (range, 110,050–394,659) in EF samples, and 335,659 (range, 237,889–430,675) in EB samples, with an average of 89,883 (range, 27,960–137,956) and 103,539 filtered reads (range, 61,650–162,653), respectively (Supplementary Table 3).

Because the endometrium presents a low-abundance microbiota, stringent analysis was performed to ensure that contaminating reads did not interfere with downstream analysis. Samples were classified as detectable and not-detectable by comparing them to blank samples included in each run and assessing certain quality parameters (see criteria in Materials and Methods). Despite starting with equivalent amounts of extracted DNA, detectable samples showed a different clustering behaviour as compared with not-detectable/low-biomass samples (with a higher 16S amplicon concentration), which clustered together with blank controls (Fig. 2). After applying these criteria, 208 EF samples and 190 EB samples were classified as detectable and included in the analysis (Fig. 2).

Fig. 2
figure 2

Distribution of sequencing data. PCA showing the clustering of the (A) endometrial fluid samples (n = 336) and (B) endometrial biopsy samples (n = 296) and their corresponding blank controls, based on quality parameters such as percentage of empty reads, dispersion index for each sample and the ratio between the filtered and mapped reads. Samples are coloured using a filtered/mapped reads threshold of 0.65 for EF and 0.7 for EB. Abbreviations: BP, biochemical pregnancy; EP, ectopic pregnancy; LB, live birth; CM, clinical miscarriage; NP, no pregnancy

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Lactobacillus was the major genus in both EF and EB samples. Bacterial genera such as Anaerococcus, Atopobium, Bifidobacterium, Corynebacterium, Gardnerella, Haemophilus, Microbacterium, Prevotella, Propionibacterium, Staphylococcus and Streptococcus were also commonly identified in both sample types (Supplementary Fig. 1). Streptomyces, Clostridium and Chryseobacterium were detected in EF but not EB, whereas Cupriavidus, Escherichia, Klebsiella, Bacillus, Finegoldia, Micrococcus and Tepidimonas were detected in EB but not EF (Supplementary Fig. 1).

The co-occurring EM bacterial networks showed several differences between the sample types: (i) the EF microbiota had two linked communities, whilst the EB microbiota had four linked communities and two isolated nodes; (ii) the EF microbiota network was more strongly connected than the EB community and (iii) Lactobacillus had positive and negative connected neighbours in the EF microbiota, but only negative relations in the EB microbiota (Fig. 3). In the EB microbiota, Lactobacillus was negatively correlated with pathogenic bacteria Gardnerella, Bifidobacterium and Atopobium, whereas in EF, Lactobacillus was negatively correlated with Gardnerella, Bifidobacterium, Atopobium, Staphylococcus, Streptococcus and Chryseobacterium, and positively correlated to commensal bacteria (Clostridium and Streptomyces).

Fig. 3
figure 3

Co-occurrence bacterial networks in endometrial fluid (A) and endometrial biopsy (B) samples. Each network was created by computing the co-occurring bacteria with significant Pearson correlation coefficients. Samples from all reproductive outcomes are represented. Node properties: (i) circle size, proportional to the normalised and standardised bacterial relative abundances; (ii) colour, communities as retrieved by the Louvain algorithm. Edge properties: (i) thickness, proportional to p value of Pearson correlation coefficient, from the most significant (thicker) to the less significant (thinner); (ii) colour, red for negative and grey for positive Pearson correlation coefficients

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Endometrial microbiota composition and reproductive outcome

To analyse the association between EM composition in EF and EB and reproductive outcome, we built microbiota networks for each reproductive outcome. We found that the LB category was denser and had a higher node degree distribution than co-occurrence networks of unsuccessful reproductive outcomes. Additionally, we found potential interactions that only occurred in patients with LB, reflecting the relevance of these relationships to successful pregnancy and how their disruption may lead to ecosystem instability. We also noted that in the EF microbiota of patients who had a LB, Lactobacillus was negatively related to dysbiotic bacteria such as Chryseobacterium, Staphylococcus and Haemophilus, and positively correlated to Streptomyces, which in turn was part of a dense community mainly composed of commensal bacteria such as Corynebacterium, Microbacterium, Propionibacterium and Clostridium. In patients with NP, we identified a similar behaviour, with Lactobacillus negatively correlated with Gardnerella, Bifidobacterium, Atopobium, Staphylococcus, Streptococcus and Chryseobacterium, and positively related to Streptomyces. Interestingly, in the group of patients with BP and CM, these interactions disappeared, and the resulting networks were disconnected and formed sparse communities (Fig. 4A). Finally, the EB microbiota networks were more dispersed than the EF ones, with fewer interactions between Lactobacillus and other taxa. Thus, in this case, the eventual beneficial/deleterious connections amongst taxa were less evident (Fig. 4B).

Fig. 4
figure 4

Co-occurrence bacterial networks associated with reproductive outcomes. Co-occurrence bacterial networks in A endometrial fluid samples and B endometrial biopsy samples for each ART outcome. Each network was created by computing the co-occurring bacterial communities with significant Pearson correlation coefficients. Node properties: (i) circle size, proportional to the normalised and standardised bacterial relative abundances; (ii) colour, communities as retrieved by the Louvain algorithm. Edge properties: (i) thickness, proportional to p value of the Pearson correlation coefficient, from the most significant (thicker) to the less significant (thinner); (ii) colour, red for negative and grey for positive Pearson correlation coefficients. For association graphs, the same criteria were applied, with the thickness of the circle and colour intensity being proportional to the corresponding Pearson correlation coefficients. Pairs of bacteria without a circle have no significant Pearson correlation coefficient. BP, biochemical pregnancy; CM, clinical miscarriage; LB, live birth; NP, no pregnancy

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To avoid potential bias when comparing samples analysed in different runs, bacterial profiles were transformed into centred log ratio (clr) data, and the bacterial communities were analysed according to the difference between Lactobacillus and other reproductive tract taxa using z-score-normalised values. Using these conditions, higher abundance of Lactobacillus was observed in both EF and EB in patients with LB compared to patients with negative reproductive outcomes (Fig. 5A). Taxa with a higher average abundance in unsuccessful outcomes than in LB included Streptococcus, Chryseobacterium, Corynebacterium, Haemophilus, Bifidobacterium, Staphylococcus, Atopobium, Gardnerella and Propionibacterium in the EF microbiota and Gardnerella, Klebsiella, Atopobiumi Finegoldia, Escherichia, Propionibacterium, Haemophilus, Anaerococcus and Bacillus in the EB microbiota (Supplementary Fig. 2). Predictive probability analysis using a Bayesian inference model showed a different highest posterior density (HPD) interval of the difference ‘Lactobacillus – other taxa’ for each ART outcome: LB (−0.12–1.51), NP (−1.05–0.65), BP (−3.45–0.81) and CM (−2.81–0.55). Patients with a LB were more likely to have a higher abundance of Lactobacillus (Fig. 5B). This increased probability of higher Lactobacillus abundance in LB was especially distinct in EF samples, where the HPD for LB samples showed less overlap with unsuccessful outcome intervals.

Fig. 5
figure 5

Lactobacillus is more abundant than other taxa in reproductive success vs failure. A Difference between Lactobacillus and other reproductive tract taxa using z-score-normalised values in endometrial fluid (left panel) and endometrial biopsy (right panel) samples. B Predictive model showing the probability of each reproductive outcome based on the EM profile. Posterior predictive distribution density plot of z-score differences between Lactobacillus and other reproductive tract taxa by reproductive outcome. BP, biochemical pregnancy; CM, clinical miscarriage; LB, live birth; NP, no pregnancy

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Finally, we compared the EM bacterial profiles in patients who achieved a successful pregnancy with LB versus those with unsuccessful outcomes (BP, CM and NP). Our hypothesis was that the microbiota composition in patients with LB is the physiological scenario and does not interfere with functional reproductive potential. Therefore, we evaluated the distance between the abundance of each bacterial taxon and reproductive outcome in the upper and lower 95% confidence intervals established for patients with LB (Supplementary Fig. 3). In patients with NP, the EF taxa with significantly higher abundance exceeding the established upper confidence interval were Atopobium, Bifidobacterium, Chryseobacterium, Gardnerella and Streptococcus, and in those with CM, Haemophilus and Staphylococcus exceeded the physiological levels. By contrast, taxa with a significantly higher distance to the lower established confidence interval were Lactobacillus and Microbacterium in NP patients, and Lactobacillus in patients with CM (Fig. 6A). In the EB microbiota of NP patients, Bifidobacterium, Gardnerella and Klebsiella were significantly more abundant, and the abundance of Cupriavidus, Finegoldia, Lactobacillus and Tepidomonas was significantly below the established confidence interval (Fig. 6B). The remaining comparisons did not reach statistical significance, possibly due to the small number of patients with BP and CM.

Fig. 6
figure 6

Pathogenic bacterial profiles significantly associated with reproductive outcome. Box plots showing taxa with significant differential abundance in no pregnancy (NP), biochemical pregnancy (BP) and clinical miscarriage (CM) compared to live birth (LB). Differential abundance was calculated using the distance of each value to the upper (U) or lower (L) bounds for the 95% CI in LB (Supplementary Fig. 3) in A endometrial fluid and B endometrial biopsy samples. Only taxa with significant differential abundance, calculated with a two-sided Mann-Whitney U test, are represented in the graphs. BP, biochemical pregnancy; LB, live birth; CM, clinical miscarriage; NP, no pregnancy

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Endometrial microbiota composition in chronic endometritis and reproductive outcome

We also evaluated the abundance of the main pathogenic bacteria reported to cause chronic endometritis (CE): Enterococcus, Escherichia, Klebsiella, Streptococcus, Staphylococcus, Gardnerella, Mycoplasma, Ureaplasma, Chlamydia and Neisseria. These bacteria are considered to be a potential cause of infertility as well as obstetric and neonatal complications [28, 30]. We compared the abundance of these bacteria with the confidence interval generated for infertile patients that achieved a LB.

Of the CE pathogens, Gardnerella, Klebsiella and Streptococcus were significantly increased in the EF microbiota of NP patients, whereas Enterococcus was increased in patients that experienced BPs, and Klebsiella and Staphylococcus were increased in CM (Fig. 7A). In the EB microbiota, Gardnerella, Neisseria and Klebsiella were significantly enriched in women with NP compared to those that achieved LB, whilst Enterococcus abundance was below the confidence interval (Fig. 7B). In the remaining unsuccessful reproductive categories (BP and CM), no significant taxa were detected. Interestingly, Gardnerella and Klebsiella were the only common pathogens significantly enriched in both EF and EB from patients with NP.

Fig. 7
figure 7

Chronic endometritis profile associated with reproductive outcomes. Box plots showing differential abundance in chronic endometritis-causing bacteria in no pregnancy (NP), biochemical pregnancy (BP) and clinical miscarriage (CM) compared to live birth (LB). Differential abundance was calculated using the distance of each value to the upper (U) or lower (L) bounds for the 95% CI in LB (Supplementary Fig. 3) in A endometrial fluid and B endometrial biopsy samples. Only taxa with significant differential abundance, calculated with a two-sided Mann-Whitney U test, are represented in the graphs. BP, biochemical pregnancy; LB, live birth; CM, clinical miscarriage; NP, no pregnancy

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Endometrial microbiota composition fingerprinting is associated with reproductive outcome

In summary, the pathogenic profile associated with reproductive failure in our cohort of infertile patients consisted of Atopobium, Bifidobacterium, Chryseobacterium, Gardnerella, Haemophilus, Klebsiella, Neisseria, Staphylococcus and Streptococcus. In contrast, Lactobacillus was consistently enriched in the EM (both in EF and EB) from patients that achieved LB. Therefore, patients with a higher abundance of lactobacilli are more likely to achieve reproductive success. Also, some commensal bacteria such as Cupriavidus, Finegoldia, Microbacterium and Tepidimonas were positively associated with LB.

Discussion

Some diseases or organ malfunctions are marked by the presence of potentially pathogenic microbes, whereas others are characterised by depletion of health-associated bacteria. The vaginal microbiota composition is associated with obstetrical outcome [18, 32]. However, no consensus has been reached on the profile of endometrial bacterial pathogens and the mechanisms by which they could interfere with embryo implantation [3, 5]. Moreover, no consensus has been reached on the required presence or abundance of lactobacilli in the uterine cavity at the time of embryo implantation. Some studies have observed an association between the abundance of Lactobacillus spp. with reproductive outcomes [22, 34, 44, 45], whilst others have detected non-significant associations between EM composition and ART success [20, 26, 53].

In this study, we investigated the relationship between EM composition and the reproductive fate of a cohort of 342 ethnically diverse infertile patients from three different continents. Our results suggest that the composition of the EM at the time of conception is associated with the reproductive outcome. The co-occurrence bacterial association plots built separately for each ART outcome showed that the LB networks were denser and had a higher node degree distribution than the networks of failed outcomes. Moreover, Lactobacillus was generally negatively correlated to pathogenic bacteria and positively correlated to commensal bacteria, which may be important for stability in the ecosystem. Lactobacillus depletion and the presence of specific pathogenic bacteria such as Atopobium, Bifidobacterium, Chryseobacterium, Gardnerella, Streptococcus and Klebsiella in EF, and/or Bifidobacterium, Gardnerella, Klebsiella and Neisseria in EB were associated with unsuccessful reproductive outcomes.

Atopobium vaginae and Gardnerella vaginalis are the major bacterial vaginosis-associated pathogens; they stimulate an innate immune response from vaginal epithelial cells [38]. However, the deleterious impact of these pathogens is not restricted to the vagina. Women with bacterial vaginosis subjected to laparoscopic salpingectomy or curettage present significantly increased risk of developing endometrial polymicrobial biofilms with G. vaginalis and other bacteria [62]. Streptococcus agalactiae, Klebsiella pneumoniae, Enterococcus faecalis, Neisseria gonorrhoeae and, in some studies, G. vaginalis, are the major pathogens of CE [11, 30, 43]. This condition impairs reproductive outcomes in natural conception and after ART, further contributing to obstetric and neonatal complications [28]. A case series study of three women with recurrent implantation failure and CE showed the elimination of CE-associated pathogens and subsequent restoration of fertility after intrauterine antibiotic administration [58]. Moreover, bacteria such as S. agalactiae are known to be one of the leading causes of neonatal infections by vertical transmission [48]. Finally, in the absence of Lactobacillus, Bifidobacterium are hypothesised to be able to maintain healthy vaginal balance by the production of lactic acid [21, 33]. However, our results show a negative association of Bifidobacterium with LB, in agreement with other reports demonstrating Bifidobacterium species to be pathogenic in various infectious conditions [6, 10, 47].

Current evidence suggests that what might really interfere with fertility is the presence of pathogens in the uterine cavity, and not the requirement of a specific commensal taxon. This is supported by our results showing that the absence of bacteria (not detectable samples) may also be found in association to good reproductive outcomes. Our study showed that in both EF and EB samples the LB rates in not detectable samples were 50.00% and 49.06% compared to 35.58% and 40.53% in detectable samples. These data suggest that the absence of bacteria, including Lactobacillus, does not impede implantation and reinforces the evidence supporting the role of pathogenic bacteria as a risk factor in reproduction. This is consistent with results reported by others showing that the isolation of bacterial pathogens from the embryo transfer catheter tip associates with poor IVF outcomes [14, 15, 17, 42, 55, 57]. Then, we could hypothesise that the main role of Lactobacillus spp. in reproduction consists of avoiding the colonisation of the uterine cavity by pathogenic bacteria. Hence, the role of members colonising the upper reproductive tract should be further investigated.

We observed a close relationship between EF and EB microbiota, although there were some differences between the sample types. Specifically, 12 of the 15 most abundant taxa detected in EF overlapped with the taxa identified in the EB samples, but three taxonomies were unique for EF (Streptomyces, Clostridium and Chryseobacterium) and seven were detected only in EB (Cupriavidus, Escherichia, Klebsiella, Bacillus, Finegoldia, Micrococcus and Tepidimonas). Despite the small taxonomic differences associated to ART outcome in EF and EB, both sample types show compatible results from the clinical point of view when predicting pregnancy, as in both types of samples, the abundance of pathogens was associated to worse reproductive outcomes after embryo transfer. However, the Z-score value of (Lactobacillus-other taxa) presented higher predictive indexes for LB in EF that in EB.

A possible explanation for the difference in bacterial taxa between the sample sources is that bacteria present on the surface of the luminal epithelium may be different than those found deeper, close to the glandular epithelium and stroma. However, these differences may also be due to the different processing and DNA extraction protocols used for the different sample types. The occurrence bacterial networks also revealed differences between the sample types. The EB microbiota networks were more dispersed than the EF networks, suggesting that the microbiota found in the EF could be more stable [46]. Only one previous study compared the EM of EF and EB, indicating that the microbiota composition in EF does not fully reflect that in the EB [39].

To our knowledge, this is the first observational multicentre study to prospectively analyse EM composition in two types of endometrial samples (EF and EB) obtained simultaneously from the same patient and assess the association of the uterine microbial environment with pregnancy outcomes in infertile patients undergoing IVF. The data presented here are robust because both sample types were sequenced from the same bacterial 16S rRNA hypervariable regions and analysed using the same bioinformatics pipeline. However, the high sensitivity of this technology might detect DNA contamination, which can confound the interpretation of microbiome data [16], specifically in low-biomass sites such as the uterus. Therefore, we ensured that contaminating reads, but not sample-related reads, were removed from downstream analyses. For this purpose, we first classified samples as detectable or non-detectable, excluding samples that clustered with and had a roughly equivalent amplicon concentration to the blanks. Another strength of our work is that endometrial receptivity was analysed, and personalised embryo transfer was performed to synchronise endometrial receptivity with embryo development, avoiding displacement of the implantation window. Additionally, all samples were collected in a hormone replacement therapy cycle, preventing the potential bias introduced by different hormonal status. Hormonal therapy may have influenced the endometrial microbiome, but the hormonal regimen and sampling day were consistent between the sample collection analysis and embryo transfer cycles.

The main limitation of our work was the small number of BP and CM outcomes. Moreover, transcervical collection of the samples may have influenced the endometrial microbial results. Caution was taken to avoid contact of the catheter with the vaginal walls during sample collection, and although cervical contamination cannot be fully discarded, there is no other way to clinically access the endometrium. Moreover, it is now accepted that the female reproductive tract presents a continuum of microbiota [9], so even if some contamination from the cervix was carried over into the endometrial samples, the resulting microbial profiles would be consistent with the microbial environment of the uterine cavity. Another factor to be considered is that whilst sample collection and microbial analysis were performed in a cycle before embryo transfer, the microbiome may change over time.

Conclusions

We conclude that the presence of pathogenic bacteria such as Atopobium, Bifidobacterium, Chryseobacterium, Gardnerella, Haemophilus, Klebsiella, Neisseria, Staphylococcus and Streptococcus in the endometrium together with depletion of Lactobacillus spp. is associated with impaired reproductive function. These data indicate that the EM should be considered as a possible emerging cause of implantation failure and/or pregnancy loss. More studies are needed to analyse the mechanism of how pathogenic bacteria might affect embryo implantation.

Availability of data and materials

All raw data from this study can be found at the Sequence Read Archive (accession code PRJNA691300).

References

  1. Aitchison J. The statistical analysis of compositional data. London: Chapman & Hall, Ltd; 1986.

  2. Almonacid DE, Kraal L, Ossandon FJ, Budovskaya YV, Cardenas JP, Bik EM, et al. 16S rRNA gene sequencing and healthy reference ranges for 28 clinically relevant microbial taxa from the human gut microbiome. PLoS One. 2017;12(5):e0176555. https://doi.org/10.1371/journal.pone.0176555https://www.ncbi.nlm.nih.gov/pubmed/28467461.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Baker JM, Chase DM, Herbst-Kralovetz MM. Uterine microbiota: residents, tourists, or invaders? Front Immunol. 2018;9:208. https://doi.org/10.3389/fimmu.2018.00208https://www.ncbi.nlm.nih.gov/pubmed/29552006.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Barton HA, Taylor NM, Lubbers BR, Pemberton AC. DNA extraction from low-biomass carbonate rock: an improved method with reduced contamination and the low-biomass contaminant database. J Microbiol Methods. 2006;66(1):21–31. https://doi.org/10.1016/j.mimet.2005.10.005https://www.ncbi.nlm.nih.gov/pubmed/16305811.

    CAS  Article  PubMed  Google Scholar 

  5. Benner M, Ferwerda G, Joosten I, van der Molen RG. How uterine microbiota might be responsible for a receptive, fertile endometrium. Hum Reprod Update. 2018. https://doi.org/10.1093/humupd/dmy012https://www.ncbi.nlm.nih.gov/pubmed/29668899.

  6. Bhaskar MM, Sistla S, Kumaravel S. A case of pyometrocolpos with Bifidobacterium species. Anaerobe. 2017;44:48–50. https://doi.org/10.1016/j.anaerobe.2017.01.007https://www.ncbi.nlm.nih.gov/pubmed/28108392.

    Article  PubMed  Google Scholar 

  7. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–7. https://doi.org/10.1038/s41587-019-0209-9https://www.ncbi.nlm.nih.gov/pubmed/31341288.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Calle ML. Statistical analysis of metagenomics data. Genomics Inform. 2019;17(1):e6. https://doi.org/10.5808/GI.2019.17.1.e6https://www.ncbi.nlm.nih.gov/pubmed/30929407.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chen C, Song X, Wei W, Zhong H, Dai J, Lan Z, et al. The microbiota continuum along the female reproductive tract and its relation to uterine-related diseases. Nat Commun. 2017;8(1):875. https://doi.org/10.1038/s41467-017-00901-0https://www.ncbi.nlm.nih.gov/pubmed/29042534.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Chen Y, Hong Z, Wang W, Gu L, Gao H, Qiu L, et al. Association between the vaginal microbiome and high-risk human papillomavirus infection in pregnant Chinese women. BMC Infect Dis. 2019;19(1):677. https://doi.org/10.1186/s12879-019-4279-6https://www.ncbi.nlm.nih.gov/pubmed/31370796.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Cicinelli E, De Ziegler D, Nicoletti R, Colafiglio G, Saliani N, Resta L, et al. Chronic endometritis: correlation among hysteroscopic, histologic, and bacteriologic findings in a prospective trial with 2190 consecutive office hysteroscopies. Fertil Steril. 2008;89(3):677–84. https://doi.org/10.1016/j.fertnstert.2007.03.074https://www.ncbi.nlm.nih.gov/pubmed/17531993.

    Article  PubMed  Google Scholar 

  12. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6(1):226. https://doi.org/10.1186/s40168-018-0605-2https://www.ncbi.nlm.nih.gov/pubmed/30558668.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Díaz-Gimeno P, Horcajadas JA, Martínez-Conejero JA, Esteban FJ, Alamá P, Pellicer A, et al. A genomic diagnostic tool for human endometrial receptivity based on the transcriptomic signature. Fertil Steril. 2011;95(1):50–60, 60.e1-15. https://doi.org/10.1016/j.fertnstert.2010.04.063https://www.ncbi.nlm.nih.gov/pubmed/20619403.

    CAS  Article  Google Scholar 

  14. Egbase PE, Al-Sharhan M, Al-Othman S, Al-Mutawa M, Udo EE, Grudzinskas JG. Incidence of microbial growth from the tip of the embryo transfer catheter after embryo transfer in relation to clinical pregnancy rate following in-vitro fertilization and embryo transfer. Hum Reprod. 1996;11(8):1687–9 https://www.ncbi.nlm.nih.gov/pubmed/8921117.

    CAS  Article  Google Scholar 

  15. Egbase PE, Udo EE, Al-Sharhan M, Grudzinskas JG. Prophylactic antibiotics and endocervical microbial inoculation of the endometrium at embryo transfer. Lancet. 1999;354(9179):651–2 https://www.ncbi.nlm.nih.gov/pubmed/10466674.

    CAS  Article  Google Scholar 

  16. Eisenhofer R, Minich JJ, Marotz C, Cooper A, Knight R, Weyrich LS. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 2019;27(2):105–17. https://doi.org/10.1016/j.tim.2018.11.003 https://www.ncbi.nlm.nih.gov/pubmed/30497919.

    CAS  Article  PubMed  Google Scholar 

  17. Fanchin R, Harmas A, Benaoudia F, Lundkvist U, Olivennes F, Frydman R. Microbial flora of the cervix assessed at the time of embryo transfer adversely affects in vitro fertilization outcome. Fertil Steril. 1998;70(5):866–70 https://www.ncbi.nlm.nih.gov/pubmed/9806568.

    CAS  Article  Google Scholar 

  18. Fettweis JM, Serrano MG, Brooks JP, Edwards DJ, Girerd PH, Parikh HI, et al. The vaginal microbiome and preterm birth. Nat Med. 2019;25(6):1012–21. https://doi.org/10.1038/s41591-019-0450-2https://www.ncbi.nlm.nih.gov/pubmed/31142849.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Franasiak JM, Scott RT. Introduction: microbiome in human reproduction. Fertil Steril. 2015;104(6):1341–3. https://doi.org/10.1016/j.fertnstert.2015.10.021https://www.ncbi.nlm.nih.gov/pubmed/26515381.

    Article  PubMed  Google Scholar 

  20. Franasiak JM, Werner MD, Juneau CR, Tao X, Landis J, Zhan Y, et al. Endometrial microbiome at the time of embryo transfer: next-generation sequencing of the 16S ribosomal subunit. J Assist Reprod Genet. 2016;33(1):129–36. https://doi.org/10.1007/s10815-015-0614-zhttps://www.ncbi.nlm.nih.gov/pubmed/26547201.

    CAS  Article  PubMed  Google Scholar 

  21. Freitas AC, Hill JE. Quantification, isolation and characterization of Bifidobacterium from the vaginal microbiomes of reproductive aged women. Anaerobe. 2017;47:145–56. https://doi.org/10.1016/j.anaerobe.2017.05.012https://www.ncbi.nlm.nih.gov/pubmed/28552417.

    Article  PubMed  Google Scholar 

  22. Garcia-Grau I, Perez-Villaroya D, Bau D, Gonzalez-Monfort M, Vilella F, Moreno I, et al. Taxonomical and functional assessment of the endometrial microbiota in a context of recurrent reproductive failure: a case report. Pathogens. 2019;8(4). https://doi.org/10.3390/pathogens8040205https://www.ncbi.nlm.nih.gov/pubmed/31653041.

  23. Glassing A, Dowd SE, Galandiuk S, Davis B, Chiodini RJ. Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut Pathog. 2016;8:24. https://doi.org/10.1186/s13099-016-0103-7https://www.ncbi.nlm.nih.gov/pubmed/27239228.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Front Microbiol. 2017;8:2224. https://doi.org/10.3389/fmicb.2017.02224https://www.ncbi.nlm.nih.gov/pubmed/29187837.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Grahn N, Olofsson M, Ellnebo-Svedlund K, Monstein HJ, Jonasson J. Identification of mixed bacterial DNA contamination in broad-range PCR amplification of 16S rDNA V1 and V3 variable regions by pyrosequencing of cloned amplicons. FEMS Microbiol Lett. 2003;219(1):87–91. https://doi.org/10.1016/S0378-1097(02)01190-4https://www.ncbi.nlm.nih.gov/pubmed/12594028.

    CAS  Article  PubMed  Google Scholar 

  26. Hashimoto T, Kyono K. Does dysbiotic endometrium affect blastocyst implantation in IVF patients? J Assist Reprod Genet. 2019;36(12):2471–9. https://doi.org/10.1007/s10815-019-01630-7https://www.ncbi.nlm.nih.gov/pubmed/31741256.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Inc., Plotly Technologies. Collaborative data science. Montréal: QC Plotly Technologies Inc; 2015.

    Google Scholar 

  28. Kitaya K, Matsubayashi H, Yamaguchi K, Nishiyama R, Takaya Y, Ishikawa T, et al. Chronic endometritis: potential cause of infertility and obstetric and neonatal complications. Am J Reprod Immunol. 2016;75(1):13–22. https://doi.org/10.1111/aji.12438https://www.ncbi.nlm.nih.gov/pubmed/26478517.

    Article  PubMed  Google Scholar 

  29. Kitaya K, Nagai Y, Arai W, Sakuraba Y, Ishikawa T. Characterization of microbiota in endometrial fluid and vaginal secretions in infertile women with repeated implantation failure. Mediators Inflamm. 2019;2019:4893437. https://doi.org/10.1155/2019/4893437https://www.ncbi.nlm.nih.gov/pubmed/31249472.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Kitaya K, Takeuchi T, Mizuta S, Matsubayashi H, Ishikawa T. Endometritis: new time, new concepts. Fertil Steril. 2018;110(3):344–50. https://doi.org/10.1016/j.fertnstert.2018.04.012https://www.ncbi.nlm.nih.gov/pubmed/29960704.

    Article  PubMed  Google Scholar 

  31. Koedooder R, Mackens S, Budding A, Fares D, Blockeel C, Laven J, et al. Identification and evaluation of the microbiome in the female and male reproductive tracts. Hum Reprod Update. 2019a;25(3):298–325. https://doi.org/10.1093/humupd/dmy048https://www.ncbi.nlm.nih.gov/pubmed/30938752.

    Article  PubMed  Google Scholar 

  32. Koedooder R, Singer M, Schoenmakers S, Savelkoul PHM, Morré SA, de Jonge JD, et al. The vaginal microbiome as a predictor for outcome of in vitro fertilization with or without intracytoplasmic sperm injection: a prospective study. Hum Reprod. 2019b;34(6):1042–54. https://doi.org/10.1093/humrep/dez065https://www.ncbi.nlm.nih.gov/pubmed/31119299.

    CAS  Article  PubMed  Google Scholar 

  33. Kyono K, Hashimoto T, Kikuchi S, Nagai Y, Sakuraba Y. A pilot study and case reports on endometrial microbiota and pregnancy outcome: an analysis using 16S rRNA gene sequencing among IVF patients, and trial therapeutic intervention for dysbiotic endometrium. Reprod Med Biol. 2019;18(1):72–82. https://doi.org/10.1002/rmb2.12250https://www.ncbi.nlm.nih.gov/pubmed/30655724.

    CAS  Article  PubMed  Google Scholar 

  34. Kyono K, Hashimoto T, Nagai Y, Sakuraba Y. Analysis of endometrial microbiota by 16S ribosomal RNA gene sequencing among infertile patients: a single-center pilot study. Reprod Med Biol. 2018;17(3):297–306. https://doi.org/10.1002/rmb2.12105https://www.ncbi.nlm.nih.gov/pubmed/30013432.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Lauder AP, Roche AM, Sherrill-Mix S, Bailey A, Laughlin AL, Bittinger K, et al. Comparison of placenta samples with contamination controls does not provide evidence for a distinct placenta microbiota. Microbiome. 2016;4(1):29. https://doi.org/10.1186/s40168-016-0172-3https://www.ncbi.nlm.nih.gov/pubmed/27338728.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Laurence M, Hatzis C, Brash DE. Common contaminants in next-generation sequencing that hinder discovery of low-abundance microbes. PLoS One. 2014;9(5):e97876. https://doi.org/10.1371/journal.pone.0097876 https://www.ncbi.nlm.nih.gov/pubmed/24837716.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Li M, Wang B, Zhang M, Rantalainen M, Wang S, Zhou H, et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci U S A. 2008;105(6):2117–22. https://doi.org/10.1073/pnas.0712038105https://www.ncbi.nlm.nih.gov/pubmed/18252821.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Libby EK, Pascal KE, Mordechai E, Adelson ME, Trama JP. Atopobium vaginae triggers an innate immune response in an in vitro model of bacterial vaginosis. Microbes Infect. 2008;10(4):439–46. https://doi.org/10.1016/j.micinf.2008.01.004https://www.ncbi.nlm.nih.gov/pubmed/18403235.

    CAS  Article  PubMed  Google Scholar 

  39. Liu Y, Wong KK, Ko EY, Chen X, Huang J, Tsui SK, et al. Systematic comparison of bacterial colonization of endometrial tissue and fluid samples in recurrent miscarriage patients: implications for future endometrial microbiome studies. Clin Chem. 2018;64(12):1743–52. https://doi.org/10.1373/clinchem.2018.289306https://www.ncbi.nlm.nih.gov/pubmed/30237148.

    CAS  Article  PubMed  Google Scholar 

  40. Miles SM, Hardy BL, Merrell DS. Investigation of the microbiota of the reproductive tract in women undergoing a total hysterectomy and bilateral salpingo-oopherectomy. Fertil Steril. 2017;107(3):813–820.e1. https://doi.org/10.1016/j.fertnstert.2016.11.028https://www.ncbi.nlm.nih.gov/pubmed/28069180.

    Article  PubMed  Google Scholar 

  41. Mitchell CM, Haick A, Nkwopara E, Garcia R, Rendi M, Agnew K, et al. Colonization of the upper genital tract by vaginal bacterial species in nonpregnant women. Am J Obstet Gynecol. 2015;212(5):611.e1–9. https://doi.org/10.1016/j.ajog.2014.11.043https://www.ncbi.nlm.nih.gov/pubmed/25524398.

    Article  Google Scholar 

  42. Moore DE, Soules MR, Klein NA, Fujimoto VY, Agnew KJ, Eschenbach DA. Bacteria in the transfer catheter tip influence the live-birth rate after in vitro fertilization. Fertil Steril. 2000;74(6):1118–24 https://www.ncbi.nlm.nih.gov/pubmed/11119737.

    CAS  Article  Google Scholar 

  43. Moreno I, Cicinelli E, Garcia-Grau I, Gonzalez-Monfort M, Bau D, Vilella F, et al. The diagnosis of chronic endometritis in infertile asymptomatic women: a comparative study of histology, microbial cultures, hysteroscopy, and molecular microbiology. Am J Obstet Gynecol. 2018;218(6):602.e1–602.e16. https://doi.org/10.1016/j.ajog.2018.02.012https://www.ncbi.nlm.nih.gov/pubmed/29477653.

    Article  Google Scholar 

  44. Moreno I, Codoñer FM, Vilella F, Valbuena D, Martinez-Blanch JF, Jimenez-Almazán J, et al. Evidence that the endometrial microbiota has an effect on implantation success or failure. Am J Obstet Gynecol. 2016;215(6):684–703. https://doi.org/10.1016/j.ajog.2016.09.075https://www.ncbi.nlm.nih.gov/pubmed/27717732.

    Article  Google Scholar 

  45. Moreno I, Garcia-Grau I, Bau D, Perez-Villaroya D, Gonzalez-Monfort M, Vilella F, et al. The first glimpse of the endometrial microbiota in early pregnancy. Am J Obstet Gynecol. 2020;222(4):296–305. https://doi.org/10.1016/j.ajog.2020.01.031https://www.ncbi.nlm.nih.gov/pubmed/32057732.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Naqvi A, Rangwala H, Keshavarzian A, Gillevet P. Network-based modeling of the human gut microbiome. Chem Biodivers. 2010;7(5):1040–50. https://doi.org/10.1002/cbdv.200900324https://www.ncbi.nlm.nih.gov/pubmed/20491063.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. Pathak P, Trilligan C, Rapose A. Bifidobacterium--friend or foe? A case of urinary tract infection with Bifidobacterium species. BMJ Case Rep. 2014;2014. https://doi.org/10.1136/bcr-2014-205122https://www.ncbi.nlm.nih.gov/pubmed/25253483.

  48. Patras KA, Nizet V. Group B streptococcal maternal colonization and neonatal disease: molecular mechanisms and preventative approaches. Front Pediatr. 2018;6:27. https://doi.org/10.3389/fped.2018.00027https://www.ncbi.nlm.nih.gov/pubmed/29520354.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Pawlowsky-Glahn V, Egozcue JJ, Tolosana-Delgado R. Frontmatter. In: Modelling and Analysis of Compositional Data; 2015. p. i–xx.

    Google Scholar 

  50. Peric A, Weiss J, Vulliemoz N, Baud D, Stojanov M. Bacterial colonization of the female upper genital tract. Int J Mol Sci. 2019;20(14). https://doi.org/10.3390/ijms20143405https://www.ncbi.nlm.nih.gov/pubmed/31373310.

  51. Peterson J, Garges S, Giovanni M, McInnes P, Wang L, Schloss JA, et al. The NIH Human Microbiome Project. Genome Res. 2009;19(12):2317–23. https://doi.org/10.1101/gr.096651.109https://www.ncbi.nlm.nih.gov/pubmed/19819907.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Ravel J, Moreno I, Simón C. Bacterial vaginosis and its association with infertility, endometritis, and pelvic inflammatory disease. Am J Obstet Gynecol. 2020. https://doi.org/10.1016/j.ajog.2020.10.019https://www.ncbi.nlm.nih.gov/pubmed/33091407.

  53. Riganelli L, Iebba V, Piccioni M, Illuminati I, Bonfiglio G, Neroni B, et al. Structural variations of vaginal and endometrial microbiota: hints on female infertility. Front Cell Infect Microbiol. 2020;10:350. https://doi.org/10.3389/fcimb.2020.00350https://www.ncbi.nlm.nih.gov/pubmed/32760681.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Ruiz-Alonso M, Blesa D, Díaz-Gimeno P, Gómez E, Fernández-Sánchez M, Carranza F, et al. The endometrial receptivity array for diagnosis and personalized embryo transfer as a treatment for patients with repeated implantation failure. Fertil Steril. 2013;100(3):818–24. https://doi.org/10.1016/j.fertnstert.2013.05.004https://www.ncbi.nlm.nih.gov/pubmed/23756099.

    Article  Google Scholar 

  55. Salim R, Ben-Shlomo I, Colodner R, Keness Y, Shalev E. Bacterial colonization of the uterine cervix and success rate in assisted reproduction: results of a prospective survey. Hum Reprod. 2002;17(2):337–40 https://www.ncbi.nlm.nih.gov/pubmed/11821274.

    Article  Google Scholar 

  56. Salvatier J, Wiecki TV, Fonnesbeck C. Probabilistic programming in Python using PyMC3. PeerJ Comput Sci. 2016;2:e55.

  57. Selman H, Mariani M, Barnocchi N, Mencacci A, Bistoni F, Arena S, et al. Examination of bacterial contamination at the time of embryo transfer, and its impact on the IVF/pregnancy outcome. J Assist Reprod Genet. 2007;24(9):395–9. https://doi.org/10.1007/s10815-007-9146-5https://www.ncbi.nlm.nih.gov/pubmed/17636439.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Sfakianoudis K, Simopoulou M, Nikas Y, Rapani A, Nitsos N, Pierouli K, et al. Efficient treatment of chronic endometritis through a novel approach of intrauterine antibiotic infusion: a case series. BMC Womens Health. 2018;18(1):197. https://doi.org/10.1186/s12905-018-0688-8https://www.ncbi.nlm.nih.gov/pubmed/30518370.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. Simon JC, Marchesi JR, Mougel C, Selosse MA. Host-microbiota interactions: from holobiont theory to analysis. Microbiome. 2019;7(1):5. https://doi.org/10.1186/s40168-019-0619-4https://www.ncbi.nlm.nih.gov/pubmed/30635058.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Simón C, Gómez C, Cabanillas S, Vladimirov I, Castillón G, Giles J, et al. A 5-year multicentre randomized controlled trial comparing personalized, frozen and fresh blastocyst transfer in IVF. Reprod Biomed Online. 2020;41(3):402–15. https://doi.org/10.1016/j.rbmo.2020.06.002https://www.ncbi.nlm.nih.gov/pubmed/32723696.

    CAS  Article  PubMed  Google Scholar 

  61. Stinson LF, Keelan JA, Payne MS. Identification and removal of contaminating microbial DNA from PCR reagents: impact on low-biomass microbiome analyses. Lett Appl Microbiol. 2019;68(1):2–8. https://doi.org/10.1111/lam.13091https://www.ncbi.nlm.nih.gov/pubmed/30383890.

    CAS  Article  PubMed  Google Scholar 

  62. Swidsinski A, Verstraelen H, Loening-Baucke V, Swidsinski S, Mendling W, Halwani Z. Presence of a polymicrobial endometrial biofilm in patients with bacterial vaginosis. PLoS One. 2013;8(1):e53997. https://doi.org/10.1371/journal.pone.0053997https://www.ncbi.nlm.nih.gov/pubmed/23320114.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. Tanner MA, Goebel BM, Dojka MA, Pace NR. Specific ribosomal DNA sequences from diverse environmental settings correlate with experimental contaminants. Appl Environ Microbiol. 1998;64(8):3110–3. https://doi.org/10.1128/AEM.64.8.3110-3113.1998https://www.ncbi.nlm.nih.gov/pubmed/9687486.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. Tissier, Henry. 1900. “Recherches sur la flore Intestinale des Nourrissons.” Thesis no 529, Paris.

    Google Scholar 

  65. Vilella F, Ramirez L, Berlanga O, Martínez S, Alamá P, Meseguer M, et al. PGE2 and PGF2α concentrations in human endometrial fluid as biomarkers for embryonic implantation. J Clin Endocrinol Metab. 2013;98(10):4123–32. https://doi.org/10.1210/jc.2013-2205https://www.ncbi.nlm.nih.gov/pubmed/23979956.

    CAS  Article  PubMed  Google Scholar 

  66. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–72. https://doi.org/10.1038/s41592-019-0686-2 https://www.ncbi.nlm.nih.gov/pubmed/32015543.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. Walther-António MR, Chen J, Multinu F, Hokenstad A, Distad TJ, Cheek EH, et al. Potential contribution of the uterine microbiome in the development of endometrial cancer. Genome Med. 2016;8(1):122. https://doi.org/10.1186/s13073-016-0368-yhttps://www.ncbi.nlm.nih.gov/pubmed/27884207.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73(16):5261–7. https://doi.org/10.1128/AEM.00062-07https://www.ncbi.nlm.nih.gov/pubmed/17586664.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. Weyrich LS, Farrer AG, Eisenhofer R, Arriola LA, Young J, Selway CA, et al. Laboratory contamination over time during low-biomass sample analysis. Mol Ecol Resour. 2019;19(4):982–96. https://doi.org/10.1111/1755-0998.13011https://www.ncbi.nlm.nih.gov/pubmed/30887686.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Sheila M. Cherry, PhD, ELS, President and Senior Editor from Fresh Eyes Editing LLC, for her excellent work editing this manuscript.

Funding

This study was supported by the Igenomix Foundation. IGG was supported by a Formación de Profesorado Universitario grant (FPU15/01923) from the Spanish Ministry of Education. DB is supported by Torres-Quevedo grant (PTQ-16-08454) from the Spanish Ministry of Economy and Competitiveness. FV was supported by the Miguel Servet Programme Type II of ISCIII (CPII18/00020) and the FIS project (PI18/00957). CS was supported by the Spanish Government MINECO/FEDER (grant RTI2018-094946-B-I00), the Valencian Innovation Council (ref. PROMETEO/2018/161) and the European Union’s Horizon 2020 Framework Programme for Research and Innovation under grant agreement number 874867.

Author information

Author notes

  1. Inmaculada Moreno and Iolanda Garcia-Grau contributed equally to this work.

Authors and Affiliations

  1. Igenomix Foundation, INCLIVA Health Research Institute, Valencia, Spain

    Inmaculada Moreno, Iolanda Garcia-Grau, Marta Gonzalez-Monfort, Felipe Vilella & Carlos Simon

  2. Igenomix R&D, Valencia, Spain

    Inmaculada Moreno, David Perez-Villaroya, Marta Gonzalez-Monfort, Davide Bau, Carlos Gomez, Diana Valbuena & Carlos Simon

  3. Department of Obstetrics and Gynecology, University of Valencia, Valencia, Spain

    Iolanda Garcia-Grau & Carlos Simon

  4. Bahçeci Group, Istanbul, Turkey

    Mustafa Bahçeci

  5. IVF Florida, Margate, FL, USA

    Marcelo J. Barrionuevo

  6. Oak Clinic Sumiyoshi, Osaka, Osaka Prefecture, Japan

    Sagiri Taguchi

  7. Clinica Fertia, Fuengirola, Malaga, Spain

    Elena Puente

  8. Dominion Fertility, Arlington, WA, USA

    Michael Dimattina

  9. Alpha IVF and Women’s Specialists Centre, Petaling Jaya, Selangor, Malaysia

    Mei Wei Lim

  10. Gestanza Medicina Reproductiva, Rosario, Santa Fe, Argentina

    Georgina Meneghini

  11. Missouri Center for Reproductive Medicine, Chesterfield, MO, USA

    Mira Aubuchon

  12. RMA Connecticut, Norwalk, CT, USA

    Mark Leondires

  13. ProcreaTec, Madrid, Spain

    Alexandra Izquierdo

  14. Present Address: Médipôle Lyon-Villeurbanne, Villeurbanne, France

    Alexandra Izquierdo

  15. Pregna Medicina Reproductiva, Buenos Aires, Argentina

    Martina Perez-Olgiati

  16. New Hope Fertility Center, Mexico City, Mexico

    Alejandro Chavez

  17. Pacific Centre for Reproductive Medicine, Burnaby, British Columbia, Canada

    Ken Seethram

  18. Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Carlos Simon

  19. Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX, USA

    Carlos Simon

Authors
  1. Inmaculada Moreno
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  2. Iolanda Garcia-Grau
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  3. David Perez-Villaroya
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  4. Marta Gonzalez-Monfort
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  6. Marcelo J. Barrionuevo
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  7. Sagiri Taguchi
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  8. Elena Puente
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  9. Michael Dimattina
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  18. Davide Bau
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  19. Carlos Gomez
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  20. Diana Valbuena
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  22. Carlos Simon
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Contributions

IM and CS contributed to the conception, design, analysis and interpretation of the data. IGG and DPV contributed to the acquisition, analysis and interpretation of the data. MGM participated in the acquisition and analysis of the data. MB, MJB, ST, EP, MD, MWL, GM, MA, ML, AI, MPO, AC and KS contributed to sample collection and acquisition of data. DB contributed to acquisition, analysis and interpretation of the data. CG contributed to design and acquisition of data. DV contributed to the design and data analysis. FV contributed to the conception and interpretation of the data. IM, IGG and CS drafted the work. All authors have substantively revised the manuscript and approved the submitted version.

Corresponding author

Correspondence to Carlos Simon.

Ethics declarations

Ethics approval and consent to participate

Ethical approval was given by the corresponding local Ethics Committees to the protocol with reference IGX1-MIC-CS-17-05 as follows: Western Institutional Review Board (IVF-Florida, USA, study code 1176556, July 17, 2017; RMA Connecticut, USA, study code 1177331, July 31, 2017; Dominion Fertility, USA, study code 1176555, July 22, 2017; Missouri Center for Reproductive Medicine, USA, study code 1179405, October 10, 2017; Pacific Centre for Reproductive Medicine, Canada, study code 1179667, December 13, 2017); Comite de Etica de la Investigación con Medicamentos del Hospital Universitario de la Princesa (ProcreaTec, Spain, register number 3133, July 13, 2017); Comite de Etica de la Investigacion Costa del Sol (Clinica Fertia, Spain, code 006_jun17_PI-ERA-Microbioma, June 29, 2017); Comite de Bioetica del Instituto de Investigaciones Clinicas Rosario (Gestanza Medicina Reproductiva, Argentina, March 27, 2018); Comite de Etica en Investigación Centro de Educacion Medica e Investigaciones Clinicas “Norberto Quirno” (Pregna Medicina Reproductiva, Argentina, October 13, 2017); Human Medical Research Ethics Committee (HMREC) for Private Healthcare and Research Centres Malaysia (Alpha IVF & Women’s Specialists Centre, Malaysia, October 3, 2017); Oak Clinic Group’s Ethics Committee (Oak Clinic Sumiyoshi, Japan, May 29, 2017); Comite de Etica en Investigación New Hope Fertility Center (New Hope Fertility Center, Mexico, Register number RA-2017-04, June 9, 2017); Uskudar Universitesi Girisimsel Olamayan Arastirmalar Etik Kurulum (Bahceci Group, Turkey, January 25, 2018). All participants provided written informed consent. Data were monitored by clinical research associates through on-site and/or remote visits, and the database was properly curated by the data manager. All queries were duly solved and closed.

Consent for publication

Not applicable.

Competing interests

IM, DPV, MGM, DB, CG, DV and CS are partially employed by Igenomix R&D. The rest of the authors declare that they have no competing interests.

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Supplementary Information

Additional file 1

: Supplementary Figure 1. Taxa found in endometrial fluid (A) and endometrial biopsy (B) samples.

Additional file 2

: Supplementary Figure 2. Taxa enriched in patients with different reproductive outcomes.

Additional file 3

: Supplementary Figure 3. Confidence intervals and reference ranges of bacterial taxa detected in patients with live birth. The endometrial microbiome from patients with a live birth was analysed in (A) endometrial fluid and (B) endometrial biopsy samples to determine the reference ranges for each evaluated taxon. The confidence interval displays the relative abundance for all assessed patients, revealing the healthy ranges of abundance for the taxa in the tested panel. The healthy distribution was used to define the 95% confidence interval (red line) and taxa abundance in patients with poor reproductive outcomes—no pregnancy, biochemical pregnancy, and clinical miscarriage—were comparatively represented. A taxa abundance of 0 was assigned when the bacteria was not detected in a given sample. BP, biochemical pregnancy; CM, clinical miscarriage; LB, live birth; NP, no pregnancy.

Additional file 4

: Supplementary Table 1. Sociodemographic and clinical characteristics of study participants.

Additional file 5

: Supplementary Table 2. Clinical variables in patients with different reproductive outcomes.

Additional file 6

: Supplementary Table 3. Sequencing reads obtained from endometrial fluid and endometrial biopsy samples.

Additional file 7

: Supplementary Table 4. Bacteria detected in endometrial fluid and endometrial biopsy samples after filtering for low abundance taxa and potential contaminants.

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Moreno, I., Garcia-Grau, I., Perez-Villaroya, D. et al. Endometrial microbiota composition is associated with reproductive outcome in infertile patients. Microbiome 10, 1 (2022). https://doi.org/10.1186/s40168-021-01184-w

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Microbiome

ISSN: 2049-2618

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